本篇博文主要内容为 2025-06-17 从Arxiv.org论文网站获取的最新论文列表，自动更新，按照NLP、CV、ML、AI、IR五个大方向区分，若需要邮件定时接收，请在评论区留下你的邮箱号。
说明：每日论文数据从Arxiv.org获取，每天早上12:00左右定时自动更新。
友情提示: 如何您需要邮箱接收每日论文数据，请在评论处留下你的邮箱。
目录
概览 (2025-06-17)
今日共更新1015篇论文,其中:
- 自然语言处理共185篇(Computation and Language (cs.CL))
- 人工智能共325篇(Artificial Intelligence (cs.AI))
- 计算机视觉共211篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共306篇(Machine Learning (cs.LG))
自然语言处理
[NLP-0] Steering LLM Thinking with Budget Guidance
【速读】： 该论文试图解决深度思考型大语言模型在推理过程中因过长的推理链条导致的计算成本过高但性能提升不显著的问题，特别是在有限的推理预算下如何有效控制推理长度而不牺牲性能。解决方案的关键在于提出一种名为“预算引导”（budget guidance）的方法，该方法通过引入一个轻量级预测器，在生成过程中对剩余推理长度建模并遵循伽马分布，从而以软性、逐标记的方式指导生成，确保整体推理过程符合指定的预算限制。
链接: https://arxiv.org/abs/2506.13752
作者: Junyan Li,Wenshuo Zhao,Yang Zhang,Chuang Gan
机构: UMass Amherst (马萨诸塞大学阿默斯特分校); Zhejiang University (浙江大学); MIT-IBM Watson AI Lab (麻省理工-IBM沃森人工智能实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent deep-thinking large language models often reason extensively to improve performance, but such lengthy reasoning is not always desirable, as it incurs excessive inference costs with disproportionate performance gains. Controlling reasoning length without sacrificing performance is therefore important, but remains challenging, especially under tight thinking budgets. We propose budget guidance, a simple yet effective method for steering the reasoning process of LLMs toward a target budget without requiring any LLM fine-tuning. Our approach introduces a lightweight predictor that models a Gamma distribution over the remaining thinking length during next-token generation. This signal is then used to guide generation in a soft, token-level manner, ensuring that the overall reasoning trace adheres to the specified thinking budget. Budget guidance enables natural control of the thinking length, along with significant token efficiency improvements over baseline methods on challenging math benchmarks. For instance, it achieves up to a 26% accuracy gain on the MATH-500 benchmark under tight budgets compared to baseline methods, while maintaining competitive accuracy with only 63% of the thinking tokens used by the full-thinking model. Budget guidance also generalizes to broader task domains and exhibits emergent capabilities, such as estimating question difficulty. The source code is available at: this https URL.
zh
[NLP-1] LTRR: Learning To Rank Retrievers for LLM s SIGIR2025
【速读】： 该论文试图解决Retrieval-Augmented Generation (RAG)系统中单一固定检索器无法在所有查询类型上均表现最优的问题，其核心挑战在于如何根据不同的查询动态选择最合适的检索器。解决方案的关键在于提出一种基于查询路由的方法，通过训练-free启发式方法和学习的路由模型实现检索器的动态选择，并将路由问题建模为学习排序（Learning-to-Rank, LTR）问题，引入LTRR框架以学习按预期效用增益对检索器进行排序，从而提升下游大语言模型的性能。
链接: https://arxiv.org/abs/2506.13743
作者: To Eun Kim,Fernando Diaz
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: SIGIR 2025 LiveRAG Spotlight
Abstract:Retrieval-Augmented Generation (RAG) systems typically rely on a single fixed retriever, despite growing evidence that no single retriever performs optimally across all query types. In this paper, we explore a query routing approach that dynamically selects from a pool of retrievers based on the query, using both train-free heuristics and learned routing models. We frame routing as a learning-to-rank (LTR) problem and introduce LTRR, a framework that learns to rank retrievers by their expected utility gain to downstream LLM performance. Our experiments, conducted on synthetic QA data with controlled query type variations, show that routing-based RAG systems can outperform the best single-retriever-based systems. Performance gains are especially pronounced in models trained with the Answer Correctness (AC) metric and with pairwise learning approaches, especially with XGBoost. We also observe improvements in generalization to out-of-distribution queries. As part of the SIGIR 2025 LiveRAG challenge, our submitted system demonstrated the practical viability of our approach, achieving competitive performance in both answer correctness and faithfulness. These findings highlight the importance of both training methodology and metric selection in query routing for RAG systems.
zh
[NLP-2] Instruction Following by Boosting Attention of Large Language Models
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）生成控制的问题，旨在提升其安全性和可靠性。现有方法如提示工程和微调虽然常用，但近期研究提出的潜在引导（latent steering）技术由于效果有限，常不如简单的指令提示有效。为解决这一问题，论文提出了Instruction Attention Boosting (InstABoost)，其关键在于通过在生成过程中调整模型的注意力机制来增强指令提示的强度，从而实现更有效的生成控制。
链接: https://arxiv.org/abs/2506.13734
作者: Vitoria Guardieiro,Adam Stein,Avishree Khare,Eric Wong
机构: University of Pennsylvania(宾夕法尼亚大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Controlling the generation of large language models (LLMs) remains a central challenge to ensure their safe and reliable deployment. While prompt engineering and finetuning are common approaches, recent work has explored latent steering, a lightweight technique that alters LLM internal activations to guide generation. However, subsequent studies revealed latent steering’s effectiveness to be limited, often underperforming simple instruction prompting. To address this limitation, we first establish a benchmark across diverse behaviors for standardized evaluation of steering techniques. Building on insights from this benchmark, we introduce Instruction Attention Boosting (InstABoost), a latent steering method that boosts the strength of instruction prompting by altering the model’s attention during generation. InstABoost combines the strengths of existing approaches and is theoretically supported by prior work that suggests that in-context rule following in transformer-based models can be controlled by manipulating attention on instructions. Empirically, InstABoost demonstrates superior control success compared to both traditional prompting and latent steering.
zh
[NLP-3] Attribution-guided Pruning for Compression Circuit Discovery and Targeted Correction in LLM s
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在参数量庞大导致的内存和计算资源受限环境下的部署难题，同时提升模型效率与安全性。其解决方案的关键在于利用层相关性传播（Layer-wise Relevance Propagation, LRP）进行属性引导的剪枝，通过识别并移除对推理无关的模型组件来实现模型压缩，同时有效提取任务相关的子图结构（称为“电路”），进而通过选择性移除引发异常行为的电路实现模型修正。
链接: https://arxiv.org/abs/2506.13727
作者: Sayed Mohammad Vakilzadeh Hatefi,Maximilian Dreyer,Reduan Achtibat,Patrick Kahardipraja,Thomas Wiegand,Wojciech Samek,Sebastian Lapuschkin
机构: Department of Artificial Intelligence, Fraunhofer Heinrich-Hertz-Institute (人工智能系，弗劳恩霍夫海因里希-赫兹研究所); Department of Electrical Engineering and Computer Science, Technische Universität Berlin (电气工程与计算机科学系，柏林工业大学); BIFOLD - Berlin Institute for the Foundations of Learning and Data (BIFOLD - 柏林学习与数据基础研究所); Centre of eXplainable Artificial Intelligence, Technological University Dublin (可解释人工智能中心，都柏林理工学院)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Work in progress (10 pages manuscript, 3 pages references, 12 pages appendix)
Abstract:Large Language Models (LLMs) are central to many contemporary AI applications, yet their extensive parameter counts pose significant challenges for deployment in memory- and compute-constrained environments. Recent works in eXplainable AI (XAI), particularly on attribution methods, suggest that interpretability can also enable model compression by identifying and removing components irrelevant to inference. In this paper, we leverage Layer-wise Relevance Propagation (LRP) to perform attribution-guided pruning of LLMs. While LRP has shown promise in structured pruning for vision models, we extend it to unstructured pruning in LLMs and demonstrate that it can substantially reduce model size with minimal performance loss. Our method is especially effective in extracting task-relevant subgraphs – so-called ``circuits’’ – which can represent core functions (e.g., indirect object identification). Building on this, we introduce a technique for model correction, by selectively removing circuits responsible for spurious behaviors (e.g., toxic outputs). All in all, we gather these techniques as a uniform holistic framework and showcase its effectiveness and limitations through extensive experiments for compression, circuit discovery and model correction on Llama and OPT models, highlighting its potential for improving both model efficiency and safety. Our code is publicly available at this https URL.
zh
[NLP-4] Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
【速读】： 该论文试图解决医疗对话系统在提供医学知识的同时，缺乏对患者负面情绪的有效安抚与共情的问题。其解决方案的关键在于利用大语言模型（Large Language Model）对真实医疗对话数据集进行改写，生成带有负面情绪的患者提问及旨在缓解情绪的医学回应，从而通过不同微调方法优化模型，使其在保持提供准确知识能力的基础上，增强生成具有情感安慰和建设性建议的能力。
链接: https://arxiv.org/abs/2506.13692
作者: Shang-Chi Tsai,Yun-Nung Chen
机构: National Taiwan University (国立台湾大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: IWSDS 2025 Oral Paper
Abstract:With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients’ medical conditions. However, when patients consult their doctor, they may experience negative emotions due to the severity and urgency of their situation. If the model can provide appropriate comfort and empathy based on the patient’s negative emotions while answering medical questions, it will likely offer a more reassuring experience during the medical consultation process. To address this issue, our paper explores the balance between knowledge sharing and emotional support in the healthcare dialogue process. We utilize a large language model to rewrite a real-world interactive medical dialogue dataset, generating patient queries with negative emotions and corresponding medical responses aimed at soothing the patient’s emotions while addressing their concerns. The modified data serves to refine the latest large language models with various fine-tuning methods, enabling them to accurately provide sentences with both emotional reassurance and constructive suggestions in response to patients’ questions. Compared to the original LLM model, our experimental results demonstrate that our methodology significantly enhances the model’s ability to generate emotional responses while maintaining its original capability to provide accurate knowledge-based answers.
zh
[NLP-5] urning Down the Heat: A Critical Analysis of Min-p Sampling in Language Models
【速读】： 该论文试图解决生成式 AI (Generative AI) 语言模型采样方法对输出质量和多样性的影响问题，特别是针对 Nguyen 等人（2024）提出的 min-p 采样方法是否优于传统采样方法（如 basic、top-k 和 top-p）的问题。解决方案的关键在于对原论文所提出的证据进行系统性复现与再分析，揭示其在人类评估、基准测试、评价方法和社区采用数据等方面存在的问题，从而得出 min-p 并未在质量、多样性和二者权衡方面优于基线的结论。
链接: https://arxiv.org/abs/2506.13681
作者: Rylan Schaeffer,Joshua Kazdan,Yegor Denisov-Blanch
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Sampling from language models impacts the quality and diversity of outputs, affecting both research and real-world applications. Recently, Nguyen et al. 2024’s “Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs” introduced a new sampler called min-p, claiming it achieves superior quality and diversity over established samplers such as basic, top-k, and top-p sampling. The significance of these claims was underscored by the paper’s recognition as the 18th highest-scoring submission to ICLR 2025 and selection for an Oral presentation. This paper conducts a comprehensive re-examination of the evidence supporting min-p and reaches different conclusions from the original paper’s four lines of evidence. First, the original paper’s human evaluations omitted data, conducted statistical tests incorrectly, and described qualitative feedback inaccurately; our reanalysis demonstrates min-p did not outperform baselines in quality, diversity, or a trade-off between quality and diversity; in response to our findings, the authors of the original paper conducted a new human evaluation using a different implementation, task, and rubric that nevertheless provides further evidence min-p does not improve over baselines. Second, comprehensively sweeping the original paper’s NLP benchmarks reveals min-p does not surpass baselines when controlling for the number of hyperparameters. Third, the original paper’s LLM-as-a-Judge evaluations lack methodological clarity and appear inconsistently reported. Fourth, community adoption claims (49k GitHub repositories, 1.1M GitHub stars) were found to be unsubstantiated, leading to their removal; the revised adoption claim remains misleading. We conclude that evidence presented in the original paper fails to support claims that min-p improves quality, diversity, or a trade-off between quality and diversity.
zh
[NLP-6] Prefix-Tuning: Modernizing Prefix-Tuning through Attention Independent Prefix Data
【速读】： 该论文试图解决Prefix-Tuning在现代大规模语言模型（Large Language Models, LLMs）中效果有限的问题，其核心原因是注意力头中输入与前缀重要性之间存在固有的权衡。解决方案的关键在于提出Prefix-Tuning+，这是一种新型架构，通过将前缀模块从注意力头中移出，从而克服原有方法的局限性，并提升其在现代LLMs上的性能。
链接: https://arxiv.org/abs/2506.13674
作者: Haonan Wang,Brian Chen,Li Siquan,Liang Xinhe,Tianyang Hu,Hwee Kuan Lee,Kenji Kawaguchi
机构: National University of Singapore(新加坡国立大学); Bioinformatics Institute, ASTAR(生物信息学研究所，ASTAR)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Parameter-Efficient Fine-Tuning (PEFT) methods have become crucial for rapidly adapting large language models (LLMs) to downstream tasks. Prefix-Tuning, an early and effective PEFT technique, demonstrated the ability to achieve performance comparable to full fine-tuning with significantly reduced computational and memory overhead. However, despite its earlier success, its effectiveness in training modern state-of-the-art LLMs has been very limited. In this work, we demonstrate empirically that Prefix-Tuning underperforms on LLMs because of an inherent tradeoff between input and prefix significance within the attention head. This motivates us to introduce Prefix-Tuning+, a novel architecture that generalizes the principles of Prefix-Tuning while addressing its shortcomings by shifting the prefix module out of the attention head itself. We further provide an overview of our construction process to guide future users when constructing their own context-based methods. Our experiments show that, across a diverse set of benchmarks, Prefix-Tuning+ consistently outperforms existing Prefix-Tuning methods. Notably, it achieves performance on par with the widely adopted LoRA method on several general benchmarks, highlighting the potential modern extension of Prefix-Tuning approaches. Our findings suggest that by overcoming its inherent limitations, Prefix-Tuning can remain a competitive and relevant research direction in the landscape of parameter-efficient LLM adaptation.
zh
[NLP-7] Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model
【速读】： 该论文试图解决现有大型多模态模型（LMMs）在模态对齐过程中依赖大规模数据、效率较低的问题，旨在实现更高效和灵活的模态对齐。其解决方案的关键在于通过有目的性地建模模态之间的关系，而非仅依赖序列维度的拼接。具体而言，Stream-Omni采用语言模型（LLM）作为主干架构，并根据视觉与文本的语义互补性以及语音与文本的语义一致性，分别采用序列维度拼接和基于CTC的层维度映射方法，从而实现高效的视觉-文本和语音-文本对齐，减少了对大规模数据的依赖，并提升了多模态交互的灵活性与性能。
链接: https://arxiv.org/abs/2506.13642
作者: Shaolei Zhang,Shoutao Guo,Qingkai Fang,Yan Zhou,Yang Feng
机构: Chinese Academy of Sciences (中国科学院); Institute of Computing Technology, Chinese Academy of Sciences (计算技术研究所，中国科学院); Key Laboratory of Intelligent Information Processing, Chinese Academy of Sciences (智能信息处理重点实验室，中国科学院); Key Laboratory of AI Safety, Chinese Academy of Sciences (人工智能安全重点实验室，中国科学院); University of Chinese Academy of Sciences (中国科学院大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: Code: this https URL , Model: this https URL
Abstract:The emergence of GPT-4o-like large multimodal models (LMMs) has raised the exploration of integrating text, vision, and speech modalities to support more flexible multimodal interaction. Existing LMMs typically concatenate representation of modalities along the sequence dimension and feed them into a large language model (LLM) backbone. While sequence-dimension concatenation is straightforward for modality integration, it often relies heavily on large-scale data to learn modality alignments. In this paper, we aim to model the relationships between modalities more purposefully, thereby achieving more efficient and flexible modality alignments. To this end, we propose Stream-Omni, a large language-vision-speech model with efficient modality alignments, which can simultaneously support interactions under various modality combinations. Stream-Omni employs LLM as the backbone and aligns the vision and speech to the text based on their relationships. For vision that is semantically complementary to text, Stream-Omni uses sequence-dimension concatenation to achieve vision-text alignment. For speech that is semantically consistent with text, Stream-Omni introduces a CTC-based layer-dimension mapping to achieve speech-text alignment. In this way, Stream-Omni can achieve modality alignments with less data (especially speech), enabling the transfer of text capabilities to other modalities. Experiments on various benchmarks demonstrate that Stream-Omni achieves strong performance on visual understanding, speech interaction, and vision-grounded speech interaction tasks. Owing to the layer-dimensional mapping, Stream-Omni can simultaneously provide intermediate text outputs (such as ASR transcriptions and model responses) during speech interaction, offering users a comprehensive multimodal experience.
zh
[NLP-8] EvolvTrip: Enhancing Literary Character Understanding with Temporal Theory-of-Mind Graphs
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在处理长篇叙事文本时，难以有效进行心智理论（Theory-of-Mind, ToM）推理的问题。具体而言，LLMs在整合历史背景与当前叙事信息以理解角色心理状态方面表现不足。论文提出的解决方案关键在于构建EvolvTrip，一个面向时间演变的、具有视角感知的实体关系知识图谱，该图谱能够跟踪叙事中角色的心理发展过程，从而提升LLMs在复杂叙事中的ToM推理能力。
链接: https://arxiv.org/abs/2506.13641
作者: Bohao Yang,Hainiu Xu,Jinhua Du,Ze Li,Yulan He,Chenghua Lin
机构: The University of Manchester (曼彻斯特大学); King’s College London (国王学院伦敦大学); Huawei London Research Centre (华为伦敦研究院); Huawei Technologies Co., Ltd. (华为技术有限公司); The Alan Turing Institute (艾伦·图灵研究所)
类目: Computation and Language (cs.CL)
备注:
Abstract:A compelling portrayal of characters is essential to the success of narrative writing. For readers, appreciating a character’s traits requires the ability to infer their evolving beliefs, desires, and intentions over the course of a complex storyline, a cognitive skill known as Theory-of-Mind (ToM). Performing ToM reasoning in prolonged narratives requires readers to integrate historical context with current narrative information, a task at which humans excel but Large Language Models (LLMs) often struggle. To systematically evaluate LLMs’ ToM reasoning capability in long narratives, we construct LitCharToM, a benchmark of character-centric questions across four ToM dimensions from classic literature. Further, we introduce EvolvTrip, a perspective-aware temporal knowledge graph that tracks psychological development throughout narratives. Our experiments demonstrate that EvolvTrip consistently enhances performance of LLMs across varying scales, even in challenging extended-context scenarios. EvolvTrip proves to be particularly valuable for smaller models, partially bridging the performance gap with larger LLMs and showing great compatibility with lengthy narratives. Our findings highlight the importance of explicit representation of temporal character mental states in narrative comprehension and offer a foundation for more sophisticated character understanding. Our data and code are publicly available at this https URL.
zh
[NLP-9] An Empirical Study of LLM -as-a-Judge: How Design Choices Impact Evaluation Reliability
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）在开放性、指令遵循任务中评估方法的可靠性问题。其解决方案的关键在于通过分析评估设计、解码策略及链式思维（Chain-of-Thought, CoT）推理对评估结果的影响，确定提升评估可信度的核心因素，包括明确的评估标准、非确定性采样以增强与人类偏好的一致性，以及在已有清晰评估标准的情况下，CoT推理带来的增益有限。
链接: https://arxiv.org/abs/2506.13639
作者: Yusuke Yamauchi,Taro Yano,Masafumi Oyamada
机构: The University of Tokyo (东京大学); NEC Corporation (NEC公司)
类目: Computation and Language (cs.CL)
备注:
Abstract:As large language models (LLMs) continue to advance, reliable evaluation methods are essential particularly for open-ended, instruction-following tasks. LLM-as-a-Judge enables automatic evaluation using LLMs as evaluators, but its reliability remains uncertain. In this work, we analyze key factors affecting its trustworthiness, focusing on alignment with human judgments and evaluation consistency. Using BIGGENBench and EvalBiasBench, we study the effects of evaluation design, decoding strategies, and Chain-of-Tought (CoT) reasoning in evaluation. Our results show that evaluation criteria are critical for reliability, non-deterministic sampling improves alignment with human preferences over deterministic evaluation, and CoT reasoning offers minimal gains when clear evaluation criteria are present.
zh
[NLP-10] A Structured Bangla Dataset of Disease-Symptom Associations to Improve Diagnostic Accuracy
【速读】： 该论文试图解决当前缺乏针对孟加拉语（Bangla）的结构化疾病-症状数据集的问题，从而填补多语言医学信息学工具和欠代表语言社区疾病预测模型之间的空白。解决方案的关键在于系统地从各种经过同行评审的医学文献、临床案例研究和疾病-症状关联报告中收集并验证疾病与症状之间的关系，构建一个结构化的二元表格数据集，其中疾病作为行，症状作为列，每个单元格用1或0表示症状是否与特定疾病相关。
链接: https://arxiv.org/abs/2506.13610
作者: Abdullah Al Shafi,Rowzatul Zannat,Abdul Muntakim,Mahmudul Hasan
机构: 未知
类目: Computation and Language (cs.CL)
备注: Preprint
Abstract:Disease-symptom datasets are significant and in demand for medical research, disease diagnosis, clinical decision-making, and AI-driven health management applications. These datasets help identify symptom patterns associated with specific diseases, thus improving diagnostic accuracy and enabling early detection. The dataset presented in this study systematically compiles disease-symptom relationships from various online sources, medical literature, and publicly available health databases. The data was gathered through analyzing peer-reviewed medical articles, clinical case studies, and disease-symptom association reports. Only the verified medical sources were included in the dataset, while those from non-peer-reviewed and anecdotal sources were excluded. The dataset is structured in a tabular format, where the first column represents diseases, and the remaining columns represent symptoms. Each symptom cell contains a binary value (1 or 0), indicating whether a symptom is associated with a disease (1 for presence, 0 for absence). Thereby, this structured representation makes the dataset very useful for a wide range of applications, including machine learning-based disease prediction, clinical decision support systems, and epidemiological studies. Although there are some advancements in the field of disease-symptom datasets, there is a significant gap in structured datasets for the Bangla language. This dataset aims to bridge that gap by facilitating the development of multilingual medical informatics tools and improving disease prediction models for underrepresented linguistic communities. Further developments should include region-specific diseases and further fine-tuning of symptom associations for better diagnostic performance
zh
[NLP-11] CAMS: A CityGPT -Powered Agent ic Framework for Urban Human Mobility Simulation
【速读】： 该论文旨在解决传统数据驱动方法在人类移动性模拟中的局限性，特别是城市空间建模不足以及个体移动模式与集体移动分布整合不佳的问题。其解决方案的关键在于提出一种基于语言的都市基础模型的代理框架——CAMS，该框架通过三个核心模块（MobExtractor、GeoGenerator 和 TrajEnhancer）实现对用户画像驱动的移动模式提取、基于集体知识的锚点生成以及结合真实轨迹偏好的轨迹生成，从而在不依赖外部地理空间信息的情况下，生成更真实且合理的移动轨迹。
链接: https://arxiv.org/abs/2506.13599
作者: Yuwei Du,Jie Feng,Jian Yuan,Yong Li
机构: Tsinghua University (清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Human mobility simulation plays a crucial role in various real-world applications. Recently, to address the limitations of traditional data-driven approaches, researchers have explored leveraging the commonsense knowledge and reasoning capabilities of large language models (LLMs) to accelerate human mobility simulation. However, these methods suffer from several critical shortcomings, including inadequate modeling of urban spaces and poor integration with both individual mobility patterns and collective mobility distributions. To address these challenges, we propose \textbfCityGPT-Powered \textbfAgentic framework for \textbfMobility \textbfSimulation (\textbfCAMS), an agentic framework that leverages the language based urban foundation model to simulate human mobility in urban space. \textbfCAMS comprises three core modules, including MobExtractor to extract template mobility patterns and synthesize new ones based on user profiles, GeoGenerator to generate anchor points considering collective knowledge and generate candidate urban geospatial knowledge using an enhanced version of CityGPT, TrajEnhancer to retrieve spatial knowledge based on mobility patterns and generate trajectories with real trajectory preference alignment via DPO. Experiments on real-world datasets show that \textbfCAMS achieves superior performance without relying on externally provided geospatial information. Moreover, by holistically modeling both individual mobility patterns and collective mobility constraints, \textbfCAMS generates more realistic and plausible trajectories. In general, \textbfCAMS establishes a new paradigm that integrates the agentic framework with urban-knowledgeable LLMs for human mobility simulation.
zh
[NLP-12] Qwen vs. Gemma Integration with Whisper: A Comparative Study in Multilingual SpeechLLM Systems INTERSPEECH2025
【速读】： 该论文旨在解决多语言语音识别与语言建模问题，特别是通过大型语言模型（Large Language Models, LLMs）提升系统的性能。其解决方案的关键在于结合微调的Whisper-large-v3编码器、高效的投影器架构以及多种解码器配置，并采用三阶段训练方法逐步优化编码器、投影器和LLM组件，从而在私有测试集上实现了较低的词错误率（WER）和字符错误率（CER）。
链接: https://arxiv.org/abs/2506.13596
作者: Tuan Nguyen,Long-Vu Hoang,Huy-Dat Tran
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: Technical report for Interspeech 2025 MLC-SLM Challenge
Abstract:This paper presents our system for the MLC-SLM Challenge 2025, focusing on multilingual speech recognition and language modeling with large language models (LLMs). Our approach combines a fine-tuned Whisper-large-v3 encoder with efficient projector architectures and various decoder configurations. We employ a three-stage training methodology that progressively optimizes the encoder, projector, and LLM components. Our system achieves competitive performance with a private test average WER/CER result of 16.63% using the Gemma3-12B and 18.6% using the Qwen2.5-7B as decoder-only language model.
zh
[NLP-13] MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
【速读】： 该论文旨在解决大规模语言模型在处理长上下文和复杂任务时的效率与性能问题。其关键解决方案是引入了基于混合专家（Hybrid Mixture-of-Experts, MoE）架构与闪电注意力（lightning attention）机制的MiniMax-M1模型，该模型不仅支持长达1百万token的上下文长度，还通过高效的计算扩展能力提升了推理效率。此外，论文提出了一种新的强化学习算法CISPO，通过裁剪重要性采样权重而非令牌更新，进一步优化了训练效率，使得在512块H800 GPU上完成全量强化学习训练仅需三周时间。
链接: https://arxiv.org/abs/2506.13585
作者: MiniMax:Aili Chen,Aonian Li,Bangwei Gong,Binyang Jiang,Bo Fei,Bo Yang,Boji Shan,Changqing Yu,Chao Wang,Cheng Zhu,Chengjun Xiao,Chengyu Du,Chi Zhang,Chu Qiao,Chunhao Zhang,Chunhui Du,Congchao Guo,Da Chen,Deming Ding,Dianjun Sun,Dong Li,Enwei Jiao,Haigang Zhou,Haimo Zhang,Han Ding,Haohai Sun,Haoyu Feng,Huaiguang Cai,Haichao Zhu,Jian Sun,Jiaqi Zhuang,Jiaren Cai,Jiayuan Song,Jin Zhu,Jingyang Li,Jinhao Tian,Jinli Liu,Junhao Xu,Junjie Yan,Junteng Liu,Junxian He,Kaiyi Feng,Ke Yang,Kecheng Xiao,Le Han,Leyang Wang,Lianfei Yu,Liheng Feng,Lin Li,Lin Zheng,Linge Du,Lingyu Yang,Lunbin Zeng,Minghui Yu,Mingliang Tao,Mingyuan Chi,Mozhi Zhang,Mujie Lin,Nan Hu,Nongyu Di,Peng Gao,Pengfei Li,Pengyu Zhao,Qibing Ren,Qidi Xu,Qile Li,Qin Wang,Rong Tian,Ruitao Leng,Shaoxiang Chen,Shaoyu Chen,Shengmin Shi,Shitong Weng,Shuchang Guan,Shuqi Yu,Sichen Li,Songquan Zhu,Tengfei Li,Tianchi Cai,Tianrun Liang,Weiyu Cheng,Weize Kong,Wenkai Li,Xiancai Chen,Xiangjun Song,Xiao Luo,Xiao Su,Xiaobo Li,Xiaodong Han,Xinzhu Hou,Xuan Lu,Xun Zou,Xuyang Shen,Yan Gong,Yan Ma,Yang Wang,Yiqi Shi,Yiran Zhong,Yonghong Duan
机构: MiniMax(迷你模型)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: A technical report from MiniMax. The authors are listed in alphabetical order. We open-source our MiniMax-M1 at this https URL
Abstract:We introduce MiniMax-M1, the world’s first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combined with a lightning attention mechanism. The model is developed based on our previous MiniMax-Text-01 model, which contains a total of 456 billion parameters with 45.9 billion parameters activated per token. The M1 model natively supports a context length of 1 million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning attention mechanism in MiniMax-M1 enables efficient scaling of test-time compute. These properties make M1 particularly suitable for complex tasks that require processing long inputs and thinking extensively. MiniMax-M1 is trained using large-scale reinforcement learning (RL) on diverse problems including sandbox-based, real-world software engineering environments. In addition to M1’s inherent efficiency advantage for RL training, we propose CISPO, a novel RL algorithm to further enhance RL efficiency. CISPO clips importance sampling weights rather than token updates, outperforming other competitive RL variants. Combining hybrid-attention and CISPO enables MiniMax-M1’s full RL training on 512 H800 GPUs to complete in only three weeks, with a rental cost of just 534,700. We release two versions of MiniMax-M1 models with 40K and 80K thinking budgets respectively, where the 40K model represents an intermediate phase of the 80K training. Experiments on standard benchmarks show that our models are comparable or superior to strong open-weight models such as the original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex software engineering, tool utilization, and long-context tasks. We publicly release MiniMax-M1 at this https URL.
zh
[NLP-14] Flexible-length Text Infilling for Discrete Diffusion Models
【速读】： 该论文试图解决离散扩散模型在文本填充任务中无法灵活调整填充段落长度和位置的问题，尤其是在没有真实位置数据的情况下。解决方案的关键在于提出DDOT（Discrete Diffusion with Optimal Transport Position Coupling），该方法通过联合去噪标记值和标记位置，并采用一种新颖的样本级最优传输（Optimal Transport, OT）耦合机制，从而在保持相对标记顺序的同时动态调整填充段落的位置和长度。
链接: https://arxiv.org/abs/2506.13579
作者: Andrew Zhang,Anushka Sivakumar,Chiawei Tang,Chris Thomas
机构: Virginia Tech (弗吉尼亚理工学院)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Discrete diffusion models are a new class of text generators that offer advantages such as bidirectional context use, parallelizable generation, and flexible prompting compared to autoregressive models. However, a critical limitation of discrete diffusion models is their inability to perform flexible-length or flexible-position text infilling without access to ground-truth positional data. We introduce \textbfDDOT (\textbfDiscrete \textbfDiffusion with \textbfOptimal \textbfTransport Position Coupling), the first discrete diffusion model to overcome this challenge. DDOT jointly denoises token values and token positions, employing a novel sample-level Optimal Transport (OT) coupling. This coupling preserves relative token ordering while dynamically adjusting the positions and length of infilled segments, a capability previously missing in text diffusion. Our method is orthogonal to existing discrete text diffusion methods and is compatible with various pretrained text denoisers. Extensive experiments on text infilling benchmarks such as One-Billion-Word and Yelp demonstrate that DDOT outperforms naive diffusion baselines. Furthermore, DDOT achieves performance on par with state-of-the-art non-autoregressive models and enables significant improvements in training efficiency and flexibility.
zh
[NLP-15] Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings
【速读】： 该论文试图解决如何量化词语语义随时间变化的问题，以更好地理解文化与视角的演变。其解决方案的关键在于利用9.5百万篇克罗地亚新闻文章构建跨时域语料库，并通过在五年时间段内训练的skip-gram词嵌入模型来捕捉语义变化。研究发现，该方法能够有效反映重大事件相关术语的语义迁移，如新冠疫情、克罗地亚加入欧盟及技术进步等，并揭示后2020年嵌入向量在情感分析任务中表现出更高的积极情绪特征。
链接: https://arxiv.org/abs/2506.13569
作者: David Dukić,Ana Barić,Marko Čuljak,Josip Jukić,Martin Tutek
机构: TakeLab, Faculty of Electrical Engineering and Computing, University of Zagreb
类目: Computation and Language (cs.CL)
备注: Accepted at Slavic NLP 2025
Abstract:Measuring how semantics of words change over time improves our understanding of how cultures and perspectives change. Diachronic word embeddings help us quantify this shift, although previous studies leveraged substantial temporally annotated corpora. In this work, we use a corpus of 9.5 million Croatian news articles spanning the past 25 years and quantify semantic change using skip-gram word embeddings trained on five-year periods. Our analysis finds that word embeddings capture linguistic shifts of terms pertaining to major topics in this timespan (COVID-19, Croatia joining the European Union, technological advancements). We also find evidence that embeddings from post-2020 encode increased positivity in sentiment analysis tasks, contrasting studies reporting a decline in mental health over the same period.
zh
[NLP-16] Understand the Implication: Learning to Think for Prag matic Understanding
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在语用理解（pragmatics）方面的性能提升问题，特别是如何更好地捕捉隐含意义的推理过程。现有方法依赖于标注标签，但忽略了人类在解释隐含意义时自然使用的推理过程。解决方案的关键在于引入一个新颖的语用数据集ImpliedMeaningPreference，该数据集包含正确和错误解释的显式推理过程（thoughts），并通过基于思维的学习方法（thought-based learning）进行微调，从而显著提升了LLMs的语用理解能力。
链接: https://arxiv.org/abs/2506.13559
作者: Settaluri Lakshmi Sravanthi,Kishan Maharaj,Sravani Gunnu,Abhijit Mishra,Pushpak Bhattacharyya
机构: Indian Institute of Technology Bombay (印度理工学院孟买分校); University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: SS and KM contributed equally to this work
Abstract:Pragmatics, the ability to infer meaning beyond literal interpretation, is crucial for social cognition and communication. While LLMs have been benchmarked for their pragmatic understanding, improving their performance remains underexplored. Existing methods rely on annotated labels but overlook the reasoning process humans naturally use to interpret implicit meaning. To bridge this gap, we introduce a novel pragmatic dataset, ImpliedMeaningPreference, that includes explicit reasoning (thoughts) for both correct and incorrect interpretations. Through preference-tuning and supervised fine-tuning, we demonstrate that thought-based learning significantly enhances LLMs’ pragmatic understanding, improving accuracy by 11.12% across model families. We further discuss a transfer-learning study where we evaluate the performance of thought-based training for the other tasks of pragmatics (presupposition, deixis) that are not seen during the training time and observe an improvement of 16.10% compared to label-trained models.
zh
[NLP-17] Mixture of Weight-shared Heterogeneous Group Attention Experts for Dynamic Token-wise KV Optimization
【速读】： 该论文旨在解决因果语言建模（Causal Language Modeling, CLM）中Transformer模型的可扩展性问题，特别是由于键值（Key-Value, KV）缓存随序列增长导致的内存分配效率低下问题，这会加剧计算和存储资源的压力。现有方法如分组查询注意力（Grouped Query Attention, GQA）和基于令牌级别的KV优化虽提升了效率，但依赖于固定的资源分配策略，常通过丢弃“低优先级”令牌或静态分组来实现，未能有效处理令牌重要性的动态变化。论文提出的解决方案是mixSGA，其关键在于采用一种新型的专家混合（Mixture-of-Expert, MoE）方法，通过动态优化令牌级别的计算和内存分配，在保留所有令牌的同时，根据学习到的重要性评分将它们自适应地路由至不同规模的KV分组专家，从而在细粒度与效率之间取得平衡。
链接: https://arxiv.org/abs/2506.13541
作者: Guanghui Song,Dongping Liao,Yiren Zhao,Kejiang Ye,Cheng-zhong Xu,Xitong Gao
机构: University of Chinese Academy of Sciences (中国科学院大学); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (中国科学院深圳先进技术研究院); University of Macau (澳门大学); Imperial College London (帝国理工学院); Shenzhen University of Advanced Technology (深圳技术大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Transformer models face scalability challenges in causal language modeling (CLM) due to inefficient memory allocation for growing key-value (KV) caches, which strains compute and storage resources. Existing methods like Grouped Query Attention (GQA) and token-level KV optimization improve efficiency but rely on rigid resource allocation, often discarding “low-priority” tokens or statically grouping them, failing to address the dynamic spectrum of token importance. We propose mixSGA, a novel mixture-of-expert (MoE) approach that dynamically optimizes token-wise computation and memory allocation. Unlike prior approaches, mixSGA retains all tokens while adaptively routing them to specialized experts with varying KV group sizes, balancing granularity and efficiency. Our key novelties include: (1) a token-wise expert-choice routing mechanism guided by learned importance scores, enabling proportional resource allocation without token discard; (2) weight-sharing across grouped attention projections to minimize parameter overhead; and (3) an auxiliary loss to ensure one-hot routing decisions for training-inference consistency in CLMs. Extensive evaluations across Llama3, TinyLlama, OPT, and Gemma2 model families show mixSGA’s superiority over static baselines. On instruction-following and continued pretraining tasks, mixSGA achieves higher ROUGE-L and lower perplexity under the same KV budgets.
zh
[NLP-18] nsorSLM: Energy-efficient Embedding Compression of Sub-billion Parameter Language Models on Low-end Devices ICML2025
【速读】： 该论文旨在解决小型语言模型（Small Language Models, SLMs）在边缘设备上部署时面临的适应性与能效问题，这些问题在数据中心部署的大语言模型（Large Language Models, LLMs）中未被充分考虑。论文提出了一种无需训练的词嵌入压缩方法，其关键在于利用张量分解（Tensor-Train Decomposition, TTD）将预训练的词嵌入向量转换为低维矩阵乘积态（Matrix Product State, MPS），从而在保持语言任务性能的同时显著降低嵌入层的参数量和能耗。
链接: https://arxiv.org/abs/2506.13514
作者: Mingxue Xu,Yao Lei Xu,Danilo P. Mandic
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Numerical Analysis (math.NA)
备注: ICML 2025 Workshop on Tiny Titans: The next wave of On-Device Learning for Foundational Models (TTODLer-FM)
Abstract:Small Language Models (SLMs, or on-device LMs) have significantly fewer parameters than Large Language Models (LLMs). They are typically deployed on low-end devices, like mobile phones and single-board computers. Unlike LLMs, which rely on increasing model size for better generalisation, SLMs designed for edge applications are expected to have adaptivity to the deployment environments and energy efficiency given the device battery life constraints, which are not addressed in datacenter-deployed LLMs. This paper addresses these two requirements by proposing a training-free token embedding compression approach using Tensor-Train Decomposition (TTD). Each pre-trained token embedding vector is converted into a lower-dimensional Matrix Product State (MPS). We comprehensively evaluate the extracted low-rank structures across compression ratio, language task performance, latency, and energy consumption on a typical low-end device, i.e. Raspberry Pi. Taking the sub-billion parameter versions of GPT-2/Cerebres-GPT and OPT models as examples, our approach achieves a comparable language task performance to the original model with around 2.0\times embedding layer compression, while the energy consumption of a single query drops by half.
zh
[NLP-19] K/DA: Automated Data Generation Pipeline for Detoxifying Implicitly Offensive Language in Korean ACL2025
【速读】： 该论文旨在解决语言净化（language detoxification）中因构建中性-有毒配对数据集所面临的挑战，包括人工标注的高成本以及有害术语的快速演变导致静态数据集过时的问题。其解决方案的关键在于提出一种自动化配对数据生成流水线K/DA，该流水线能够生成具有隐性侮辱性和符合当前趋势的俚语的攻击性语言，从而生成高质量且适用于净化模型训练的数据集。
链接: https://arxiv.org/abs/2506.13513
作者: Minkyeong Jeon,Hyemin Jeong,Yerang Kim,Jiyoung Kim,Jae Hyeon Cho,Byung-Jun Lee
机构: 未知
类目: Computation and Language (cs.CL)
备注: 9 pages, 3 figures, ACL 2025
Abstract:Language detoxification involves removing toxicity from offensive language. While a neutral-toxic paired dataset provides a straightforward approach for training detoxification models, creating such datasets presents several challenges: i) the need for human annotation to build paired data, and ii) the rapid evolution of offensive terms, rendering static datasets quickly outdated. To tackle these challenges, we introduce an automated paired data generation pipeline, called K/DA. This pipeline is designed to generate offensive language with implicit offensiveness and trend-aligned slang, making the resulting dataset suitable for detoxification model training. We demonstrate that the dataset generated by K/DA exhibits high pair consistency and greater implicit offensiveness compared to existing Korean datasets, and also demonstrates applicability to other languages. Furthermore, it enables effective training of a high-performing detoxification model with simple instruction fine-tuning.
zh
[NLP-20] BOW: Bottlenecked Next Word Exploration
【速读】： 该论文试图解决传统大规模语言模型（Large Language Models, LLMs）通过下一词预测（Next-Word Prediction, NWP）进行训练时，虽然在表面流畅性上表现良好，但缺乏稳健推理能力的问题。其解决方案的关键在于提出一种名为BOttlenecked next Word exploration (BOW)的新型强化学习框架，该框架通过引入一个推理瓶颈（reasoning bottleneck），使策略模型首先生成推理路径，而非直接预测下一个词，随后由一个冻结的评判模型仅基于该推理路径预测下一个词的概率分布，从而提升模型的推理能力。
链接: https://arxiv.org/abs/2506.13502
作者: Ming Shen,Zhikun Xu,Xiao Ye,Jacob Dineen,Ben Zhou
机构: Arizona State University (亚利桑那州立大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are typically trained via next-word prediction (NWP), which provides strong surface-level fluency but often lacks support for robust reasoning. We propose BOttlenecked next Word exploration (BOW), a novel RL framework that rethinks NWP by introducing a reasoning bottleneck where a policy model first generates a reasoning path rather than predicting the next token directly, after which a frozen judge model predicts the next token distribution based solely on this reasoning path. We train the policy model using GRPO with rewards that quantify how effectively the reasoning path facilitates next-word recovery. Compared with other continual pretraining baselines, we show that BOW improves both the general and next-word reasoning capabilities of the base model, evaluated on various benchmarks. Our findings show that BOW can serve as an effective and scalable alternative to vanilla NWP.
zh
[NLP-21] urBLiMP: A Turkish Benchmark of Linguistic Minimal Pairs
【速读】： 该论文试图解决土耳其语语言模型在语法能力评估方面的资源不足问题，特别是针对土耳其语中词序灵活性和通过形态过程实现的从属结构这两个尚未被充分研究的语言现象。解决方案的关键在于构建TurBLiMP，这是一个包含16种语言现象、每种现象有1000个最小对的土耳其语基准测试，旨在更全面地评估单语和多语语言模型（LMs）的语法能力。
链接: https://arxiv.org/abs/2506.13487
作者: Ezgi Başar,Francesca Padovani,Jaap Jumelet,Arianna Bisazza
机构: University of Groningen (格罗宁根大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:We introduce TurBLiMP, the first Turkish benchmark of linguistic minimal pairs, designed to evaluate the linguistic abilities of monolingual and multilingual language models (LMs). Covering 16 linguistic phenomena with 1000 minimal pairs each, TurBLiMP fills an important gap in linguistic evaluation resources for Turkish. In designing the benchmark, we give extra attention to two properties of Turkish that remain understudied in current syntactic evaluations of LMs, namely word order flexibility and subordination through morphological processes. Our experiments on a wide range of LMs and a newly collected set of human acceptability judgments reveal that even cutting-edge Large LMs still struggle with grammatical phenomena that are not challenging for humans, and may also exhibit different sensitivities to word order and morphological complexity compared to humans.
zh
[NLP-22] Position: Pause Recycling LoRAs and Prioritize Mechanisms to Uncover Limits and Effectiveness
【速读】： 该论文试图解决在数据受限情况下，通过重用低秩适配器（LoRAs）增强大语言模型的有效性问题，其核心关注点在于判断重用LoRAs是否能够实现真正的组合泛化而非仅依赖浅层模式匹配。论文提出的关键解决方案是通过理论分析和实验验证，评估参数平均和动态适配器选择等数据无关方法在跨不同微调数据集的知识逻辑整合能力，结果表明重用LoRAs在预训练阶段知识覆盖不足时往往失效，从而揭示了其在未见任务中的前提条件与限制，并质疑其作为真正无数据方法的可行性。
链接: https://arxiv.org/abs/2506.13479
作者: Mei-Yen Chen,Thi Thu Uyen Hoang,Michael Hahn,M. Saquib Sarfraz
机构: Mercedes-Benz Tech Innovation (梅赛德斯-奔驰技术创新); Saarland University (萨尔兰大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Merging or routing low-rank adapters (LoRAs) has emerged as a popular solution for enhancing large language models, particularly when data access is restricted by regulatory or domain-specific constraints. This position paper argues that the research community should shift its focus from developing new merging or routing algorithms to understanding the conditions under which reusing LoRAs is truly effective. Through theoretical analysis and synthetic two-hop reasoning and math word-problem tasks, we examine whether reusing LoRAs enables genuine compositional generalization or merely reflects shallow pattern matching. Evaluating two data-agnostic methods–parameter averaging and dynamic adapter selection–we found that reusing LoRAs often fails to logically integrate knowledge across disjoint fine-tuning datasets, especially when such knowledge is underrepresented during pretraining. Our empirical results, supported by theoretical insights into LoRA’s limited expressiveness, highlight the preconditions and constraints of reusing them for unseen tasks and cast doubt on its feasibility as a truly data-free approach. We advocate for pausing the pursuit of novel methods for recycling LoRAs and emphasize the need for rigorous mechanisms to guide future academic research in adapter-based model merging and practical system designs for practitioners.
zh
[NLP-23] Language Agents for Hypothesis-driven Clinical Decision Making with Reinforcement Learning
【速读】： 该论文试图解决临床决策过程中由于信息不完整或模型能力限制而导致的诊断性能和效率不足的问题。其解决方案的关键在于提出一种基于假设驱动的不确定性感知语言代理（LA-CDM），通过反复请求和解释相关检测来逐步收敛至诊断，并采用结合监督学习与强化学习的混合训练范式，以实现准确的假设生成、假设不确定性估计以及高效的决策过程。
链接: https://arxiv.org/abs/2506.13474
作者: David Bani-Harouni,Chantal Pellegrini,Ege Özsoy,Matthias Keicher,Nassir Navab
机构: Technical University of Munich (慕尼黑工业大学); Munich Center for Machine Learning (慕尼黑机器学习中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Clinical decision-making is a dynamic, interactive, and cyclic process where doctors have to repeatedly decide on which clinical action to perform and consider newly uncovered information for diagnosis and treatment. Large Language Models (LLMs) have the potential to support clinicians in this process, however, most applications of LLMs in clinical decision support suffer from one of two limitations: Either they assume the unrealistic scenario of immediate availability of all patient information and do not model the interactive and iterative investigation process, or they restrict themselves to the limited “out-of-the-box” capabilities of large pre-trained models without performing task-specific training. In contrast to this, we propose to model clinical decision-making for diagnosis with a hypothesis-driven uncertainty-aware language agent, LA-CDM, that converges towards a diagnosis via repeatedly requesting and interpreting relevant tests. Using a hybrid training paradigm combining supervised and reinforcement learning, we train LA-CDM with three objectives targeting critical aspects of clinical decision-making: accurate hypothesis generation, hypothesis uncertainty estimation, and efficient decision-making. We evaluate our methodology on MIMIC-CDM, a real-world dataset covering four abdominal diseases containing various clinical tests and show the benefit of explicitly training clinical decision-making for increasing diagnostic performance and efficiency.
zh
[NLP-24] ROSAQ: Rotation-based Saliency-Aware Weight Quantization for Efficiently Compressing Large Language Models
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在内存占用和推理延迟方面的问题，通过量化技术降低模型的存储需求并可能提升计算效率。其解决方案的关键在于提出了一种基于旋转的显著性感知权重量化方法（Rotation-based Saliency-aware Weight Quantization, ROSAQ），该方法利用了Transformer模型的旋转不变性特性，通过主成分分析（PCA）在投影特征空间中识别显著通道，而非原始特征空间，并结合混合精度量化策略，对显著维度使用FP16，其余维度使用INT3/4，从而实现更优的量化效果与性能提升。
链接: https://arxiv.org/abs/2506.13472
作者: Junho Yoon,Geom Lee,Donghyeon Jeon,Inho Kang,Seung-Hoon Na
机构: Jeonbuk National University (全北国立大学); Naver (纳维); Naver (纳维)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 10 pages, 2 figures
Abstract:Quantization has been widely studied as an effective technique for reducing the memory requirement of large language models (LLMs), potentially improving the latency time as well. Utilizing the characteristic of rotational invariance of transformer, we propose the rotation-based saliency-aware weight quantization (ROSAQ), which identifies salient channels in the projection feature space, not in the original feature space, where the projected “principal” dimensions are naturally considered as “salient” features. The proposed ROSAQ consists of 1) PCA-based projection, which first performs principal component analysis (PCA) on a calibration set and transforms via the PCA projection, 2) Salient channel dentification, which selects dimensions corresponding to the K-largest eigenvalues as salient channels, and 3) Saliency-aware quantization with mixed-precision, which uses FP16 for salient dimensions and INT3/4 for other dimensions. Experiment results show that ROSAQ shows improvements over the baseline saliency-aware quantization on the original feature space and other existing quantization methods. With kernel fusion, ROSAQ presents about 2.3x speed up over FP16 implementation in generating 256 tokens with a batch size of 64.
zh
[NLP-25] Abstract Align Predict: Zero-Shot Stance Detection via Cognitive Inductive Reasoning
【速读】： 该论文试图解决零样本立场检测（Zero-shot stance detection, ZSSD）问题，即在面对未见过的目标时识别文本的立场，传统监督模型由于依赖标注数据和浅层词汇线索在此场景下表现不佳。解决方案的关键在于提出认知归纳推理框架（Cognitive Inductive Reasoning Framework, CIRF），该框架从无标注文本中抽象出可迁移的推理模式，并将其编码为概念层面的逻辑，同时引入了增强模式的图核模型（Schema-Enhanced Graph Kernel Model, SEGKM），以动态对齐局部与全局推理结构，从而提升模型的泛化能力。
链接: https://arxiv.org/abs/2506.13470
作者: Jun Ma,Fuqiang Niu,Dong Li,Jinzhou Cao,Genan Dai,Bowen Zhang
机构: Shenzhen University (深圳大学); Shenzhen Technology University (深圳技术大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Zero-shot stance detection (ZSSD) aims to identify the stance of text toward previously unseen targets, a setting where conventional supervised models often fail due to reliance on labeled data and shallow lexical cues. Inspired by human cognitive reasoning, we propose the Cognitive Inductive Reasoning Framework (CIRF), which abstracts transferable reasoning schemas from unlabeled text and encodes them as concept-level logic. To integrate these schemas with input arguments, we introduce a Schema-Enhanced Graph Kernel Model (SEGKM) that dynamically aligns local and global reasoning structures. Experiments on SemEval-2016, VAST, and COVID-19-Stance benchmarks show that CIRF establishes new state-of-the-art results, outperforming strong ZSSD baselines by 1.0, 4.5, and 3.3 percentage points in macro-F1, respectively, and achieving comparable accuracy with 70% fewer labeled examples. We will release the full code upon publication.
zh
[NLP-26] An Interdisciplinary Approach to Human-Centered Machine Translation
【速读】： 该论文试图解决机器翻译（Machine Translation, MT）系统在实际应用中与用户需求之间的脱节问题，特别是在非专业用户难以评估翻译可靠性的情况下。解决方案的关键在于采用以用户为中心的方法，强调系统设计应与多样的交际目标和使用场景相契合，从而更好地适应MT在现实世界中的多样化应用需求。
链接: https://arxiv.org/abs/2506.13468
作者: Marine Carpuat,Omri Asscher,Kalika Bali,Luisa Bentivogli,Frédéric Blain,Lynne Bowker,Monojit Choudhury,Hal Daumé III,Kevin Duh,Ge Gao,Alvin Grissom II,Marzena Karpinska,Elaine C. Khoong,William D. Lewis,André F. T. Martins,Mary Nurminen,Douglas W. Oard,Maja Popovic,Michel Simard,François Yvon
机构: University of Maryland; Bar-Ilan University; Microsoft; Fondazione Bruno Kessler; Tilburg University; Université Laval; Mohamed bin Zayed University of Artificial Intelligence; Johns Hopkins University; Haverford College; University of California, San Francisco; University of Washington; Instituto Superior Técnico, Universidade of Lisboa; Tampere University; Dublin City University & IU University; National Research Council Canada; Sorbonne-Université & CNRS
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages
Abstract:Machine Translation (MT) tools are widely used today, often in contexts where professional translators are not present. Despite progress in MT technology, a gap persists between system development and real-world usage, particularly for non-expert users who may struggle to assess translation reliability. This paper advocates for a human-centered approach to MT, emphasizing the alignment of system design with diverse communicative goals and contexts of use. We survey the literature in Translation Studies and Human-Computer Interaction to recontextualize MT evaluation and design to address the diverse real-world scenarios in which MT is used today.
zh
[NLP-27] Enhancing Omics Cohort Discovery for Research on Neurodegeneration through Ontology-Augmented Embedding Models
【速读】： 该论文旨在解决神经退行性疾病（Neurodegenerative Diseases, NDs）中组学和临床数据量不断增长所带来的数据整理与可用性问题，以支持生物信息学分析。其解决方案的关键在于提出一种名为NeuroEmbed的方法，该方法通过构建语义准确的嵌入空间来表示队列和样本，包含四个阶段：从公共存储库中提取ND队列、利用生物医学本体和嵌入空间聚类对元数据进行半自动化标准化和增强、基于标准化元数据维度的随机组合自动生成自然语言问答（QA）数据集，以及针对特定领域嵌入器的微调以优化查询。该方法有效提升了元数据的标准化程度和模型的检索精度。
链接: https://arxiv.org/abs/2506.13467
作者: José A. Pardo,Alicia Gómez-Pascual,José T. Palma,Juan A. Botía
机构: 未知
类目: Computation and Language (cs.CL)
备注: 16 pages, 3 figures, 1 table
Abstract:The growing volume of omics and clinical data generated for neurodegenerative diseases (NDs) requires new approaches for their curation so they can be ready-to-use in bioinformatics. NeuroEmbed is an approach for the engineering of semantically accurate embedding spaces to represent cohorts and samples. The NeuroEmbed method comprises four stages: (1) extraction of ND cohorts from public repositories; (2) semi-automated normalization and augmentation of metadata of cohorts and samples using biomedical ontologies and clustering on the embedding space; (3) automated generation of a natural language question-answering (QA) dataset for cohorts and samples based on randomized combinations of standardized metadata dimensions and (4) fine-tuning of a domain-specific embedder to optimize queries. We illustrate the approach using the GEO repository and the PubMedBERT pretrained embedder. Applying NeuroEmbed, we semantically indexed 2,801 repositories and 150,924 samples. Amongst many biology-relevant categories, we normalized more than 1,700 heterogeneous tissue labels from GEO into 326 unique ontology-aligned concepts and enriched annotations with new ontology-aligned terms, leading to a fold increase in size for the metadata terms between 2.7 and 20 fold. After fine-tuning PubMedBERT with the QA training data augmented with the enlarged metadata, the model increased its mean Retrieval Precision from 0.277 to 0.866 and its mean Percentile Rank from 0.355 to 0.896. The NeuroEmbed methodology for the creation of electronic catalogues of omics cohorts and samples will foster automated bioinformatic pipelines construction. The NeuroEmbed catalogue of cohorts and samples is available at this https URL.
zh
[NLP-28] Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在动态环境中的适应能力和新知识获取能力不足的问题，这一能力对于模型的持续学习和泛化至关重要。解决方案的关键在于引入一个受认知心理学和教育学启发的框架，将通用学习能力分解为三个互补的维度：从教师学习（Learning from Instructor）、从概念学习（Learning from Concept）和从经验学习（Learning from Experience）。通过这三个维度的实证研究，论文提出了一个基准测试，用于统一且真实地评估LLMs在不同学习认知维度上的泛化学习能力。
链接: https://arxiv.org/abs/2506.13464
作者: Zhengyu Hu,Jianxun Lian,Zheyuan Xiao,Seraphina Zhang,Tianfu Wang,Nicholas Jing Yuan,Xing Xie,Hui Xiong
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning, yet their learning ability, which is crucial for adapting to dynamic environments and acquiring new knowledge, remains underexplored. In this work, we address this gap by introducing a framework inspired by cognitive psychology and education. Specifically, we decompose general learning ability into three distinct, complementary dimensions: Learning from Instructor (acquiring knowledge via explicit guidance), Learning from Concept (internalizing abstract structures and generalizing to new contexts), and Learning from Experience (adapting through accumulated exploration and feedback). We conduct a comprehensive empirical study across the three learning dimensions and identify several insightful findings, such as (i) interaction improves learning; (ii) conceptual understanding is scale-emergent and benefits larger models; and (iii) LLMs are effective few-shot learners but not many-shot learners. Based on our framework and empirical findings, we introduce a benchmark that provides a unified and realistic evaluation of LLMs’ general learning abilities across three learning cognition dimensions. It enables diagnostic insights and supports evaluation and development of more adaptive and human-like models.
zh
[NLP-29] Leverag ing Vision-Language Pre-training for Human Activity Recognition in Still Images
【速读】： 该论文试图解决在单张图像中识别人类活动的问题，这一任务在索引、安全和辅助应用中具有重要意义，但缺乏运动线索。研究使用了285张标注为行走、奔跑、坐和站立的MSCOCO图像，采用Scratch CNN方法仅获得41%的准确率，而通过微调多模态CLIP模型将准确率提升至76%，表明对比视觉-语言预训练（Contrastive Vision-Language Pre-training）是提升实际部署中静态图像动作识别性能的关键。
链接: https://arxiv.org/abs/2506.13458
作者: Cristina Mahanta,Gagan Bhatia
机构: University of Aberdeen (阿伯丁大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Recognising human activity in a single photo enables indexing, safety and assistive applications, yet lacks motion cues. Using 285 MSCOCO images labelled as walking, running, sitting, and standing, scratch CNNs scored 41% accuracy. Fine-tuning multimodal CLIP raised this to 76%, demonstrating that contrastive vision-language pre-training decisively improves still-image action recognition in real-world deployments.
zh
[NLP-30] A Neural Model for Word Repetition
【速读】： 该论文试图解决人类大脑在单词重复任务中的神经机制与认知模型之间的鸿沟，即如何将认知模型与实际的神经机制相联系。其解决方案的关键在于利用深度神经网络对单词重复任务进行建模，通过训练大量模型、设计行为测试电池以及通过消融研究模拟脑损伤，从而探究模型中的详细机制，并与人类行为及大脑功能进行比较。这种方法提供了可观察和可分析的神经结构，为理解单词重复的神经基础提供了新的途径。
链接: https://arxiv.org/abs/2506.13450
作者: Daniel Dager,Robin Sobczyk,Emmanuel Chemla,Yair Lakretz
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: To appear at Cognitive Computational Neuroscience 2025 (CCN)
Abstract:It takes several years for the developing brain of a baby to fully master word repetition-the task of hearing a word and repeating it aloud. Repeating a new word, such as from a new language, can be a challenging task also for adults. Additionally, brain damage, such as from a stroke, may lead to systematic speech errors with specific characteristics dependent on the location of the brain damage. Cognitive sciences suggest a model with various components for the different processing stages involved in word repetition. While some studies have begun to localize the corresponding regions in the brain, the neural mechanisms and how exactly the brain performs word repetition remain largely unknown. We propose to bridge the gap between the cognitive model of word repetition and neural mechanisms in the human brain by modeling the task using deep neural networks. Neural models are fully observable, allowing us to study the detailed mechanisms in their various substructures and make comparisons with human behavior and, ultimately, the brain. Here, we make first steps in this direction by: (1) training a large set of models to simulate the word repetition task; (2) creating a battery of tests to probe the models for known effects from behavioral studies in humans, and (3) simulating brain damage through ablation studies, where we systematically remove neurons from the model, and repeat the behavioral study to examine the resulting speech errors in the “patient” model. Our results show that neural models can mimic several effects known from human research, but might diverge in other aspects, highlighting both the potential and the challenges for future research aimed at developing human-like neural models.
zh
[NLP-31] RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM -Based Table Analysis ACL2025
【速读】： 该论文试图解决现有基准测试在评估大型语言模型（Large Language Models, LLMs）处理复杂表格数据能力时存在的不足，即现有基准要么基于过时的数据设置，要么仅关注简单的扁平表格结构。解决方案的关键在于提出RealHiTBench，这是一个全面的基准，用于评估LLMs和多模态LLMs（MLLMs）在多种输入格式（如LaTeX、HTML和PNG）下处理复杂表格数据的性能，并包含具有复杂结构的多样化表格数据集。此外，论文还提出了TreeThinker，一种基于树状结构的管道，用于优化表格层级结构的感知与推理，从而验证了改进LLMs对表格层级结构理解的重要性。
链接: https://arxiv.org/abs/2506.13405
作者: Pengzuo Wu,Yuhang Yang,Guangcheng Zhu,Chao Ye,Hong Gu,Xu Lu,Ruixuan Xiao,Bowen Bao,Yijing He,Liangyu Zha,Wentao Ye,Junbo Zhao,Haobo Wang
机构: Zhejiang University (浙江大学); vivo Mobile Communication Co., Ltd (维沃移动通信有限公司); Institute of Computing Innovation, Zhejiang University (浙江大学计算机创新研究院)
类目: Computation and Language (cs.CL)
备注: ACL 2025
Abstract:With the rapid advancement of Large Language Models (LLMs), there is an increasing need for challenging benchmarks to evaluate their capabilities in handling complex tabular data. However, existing benchmarks are either based on outdated data setups or focus solely on simple, flat table structures. In this paper, we introduce RealHiTBench, a comprehensive benchmark designed to evaluate the performance of both LLMs and Multimodal LLMs (MLLMs) across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. RealHiTBench also includes a diverse collection of tables with intricate structures, spanning a wide range of task types. Our experimental results, using 25 state-of-the-art LLMs, demonstrate that RealHiTBench is indeed a challenging benchmark. Moreover, we also develop TreeThinker, a tree-based pipeline that organizes hierarchical headers into a tree structure for enhanced tabular reasoning, validating the importance of improving LLMs’ perception of table hierarchies. We hope that our work will inspire further research on tabular data reasoning and the development of more robust models. The code and data are available at this https URL.
zh
[NLP-32] Bi-directional Context-Enhanced Speech Large Language Models for Multilingual Conversational ASR INTERSPEECH2025
【速读】： 该论文旨在解决多语言连续对话自动语音识别（multilingual continuous conversational automatic speech recognition, MCD-ASR）中的上下文建模问题，以提升识别的准确性和鲁棒性。其解决方案的关键在于将语言特定的双向上下文信息集成到语音大语言模型（speech large language model, SLLM）中，并通过字符级别的上下文遮蔽策略在训练过程中增强模型对不完整或错误转录的适应能力，同时在解码阶段采用两阶段流程，先进行孤立段落解码，再利用邻近假设进行上下文感知的重新解码。
链接: https://arxiv.org/abs/2506.13396
作者: Yizhou Peng,Hexin Liu,Eng Siong Chng
机构: 未知
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Submitted to Interspeech 2025 MLC-SLM workshop as a Research Paper
Abstract:This paper introduces the integration of language-specific bi-directional context into a speech large language model (SLLM) to improve multilingual continuous conversational automatic speech recognition (ASR). We propose a character-level contextual masking strategy during training, which randomly removes portions of the context to enhance robustness and better emulate the flawed transcriptions that may occur during inference. For decoding, a two-stage pipeline is utilized: initial isolated segment decoding followed by context-aware re-decoding using neighboring hypotheses. Evaluated on the 1500-hour Multilingual Conversational Speech and Language Model (MLC-SLM) corpus covering eleven languages, our method achieves an 18% relative improvement compared to a strong baseline, outperforming even the model trained on 6000 hours of data for the MLC-SLM competition. These results underscore the significant benefit of incorporating contextual information in multilingual continuous conversational ASR.
zh
[NLP-33] Decompositional Reasoning for Graph Retrieval with Large Language Models
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在处理知识密集型任务（如复杂问答）时面临的多跳推理和事实一致性不足的问题。其解决方案的关键在于提出一种新颖的检索方法，通过查询分解将文本知识图谱（Textual Knowledge Graphs）整合到LLM的推理过程中，该方法将复杂问题分解为子问题，检索相关文本子图，并构建特定于问题的知识图谱以指导答案生成，从而提升LLMs在多跳问答任务中的表现。
链接: https://arxiv.org/abs/2506.13380
作者: Valentin Six,Evan Dufraisse,Gaël de Chalendar
机构: Université Paris-Saclay, CEA, List, Palaiseau, France
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注:
Abstract:Large Language Models (LLMs) excel at many NLP tasks, but struggle with multi-hop reasoning and factual consistency, limiting their effectiveness on knowledge-intensive tasks like complex question answering (QA). Linking Knowledge Graphs (KG) and LLMs has shown promising results, but LLMs generally lack the ability to reason efficiently over graph-structured information. To tackle this problem, we propose a novel retrieval approach that integrates textual knowledge graphs into the LLM reasoning process via query decomposition. Our method decomposes complex questions into sub-questions, retrieves relevant textual subgraphs, and composes a question-specific knowledge graph to guide answer generation. For that, we use a weighted similarity function that focuses on both the complex question and the generated subquestions to extract a relevant subgraph, which allows efficient and precise retrieval for complex questions and improves the performance of LLMs on multi-hop QA tasks. This structured reasoning pipeline enhances factual grounding and interpretability while leveraging the generative strengths of LLMs. We evaluate our method on standard multi-hop QA benchmarks and show that it achieves comparable or superior performance to competitive existing methods, using smaller models and fewer LLM calls.
zh
[NLP-34] Enhancing Goal-oriented Proactive Dialogue Systems via Consistency Reflection and Correction
【速读】： 该论文旨在解决目标导向对话系统中存在的一致性问题，即系统在对话过程中可能产生与用户目标不一致或矛盾的响应。解决方案的关键在于提出一种一致性反射与修正方法，通过检测并纠正对话状态和生成内容中的不一致性，从而提升对话系统的可靠性和用户体验。
链接: https://arxiv.org/abs/2506.13366
作者: Didi Zhang,Yaxin Fan,Peifeng Li,Qiaoming Zhu
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper proposes a consistency reflection and correction method for goal-oriented dialogue systems.
zh
[NLP-35] Efficient Medical VIE via Reinforcement Learning
【速读】： 该论文旨在解决医学视觉信息抽取（Medical Visual Information Extraction, VIE）中因领域特定模式和高标注成本导致的模型效果受限问题。其解决方案的关键在于基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）框架，通过仅使用100个标注样本实现数据集多样性、平衡的精确率-召回率奖励机制以及创新的采样策略，从而减少幻觉现象并提升字段覆盖率，最终在医学VIE任务中取得了最先进的性能表现。
链接: https://arxiv.org/abs/2506.13363
作者: Lijun Liu,Ruiyang Li,Zhaocheng Liu,Chenglin Zhu,Chong Li,Jiehan Cheng,Qiang Ju,Jian Xie
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Visual Information Extraction (VIE) converts unstructured document images into structured formats like JSON, critical for medical applications such as report analysis and online consultations. Traditional methods rely on OCR and language models, while end-to-end multimodal models offer direct JSON generation. However, domain-specific schemas and high annotation costs limit their effectiveness in medical VIE. We base our approach on the Reinforcement Learning with Verifiable Rewards (RLVR) framework to address these challenges using only 100 annotated samples. Our approach ensures dataset diversity, a balanced precision-recall reward mechanism to reduce hallucinations and improve field coverage, and innovative sampling strategies to enhance reasoning capabilities. Fine-tuning Qwen2.5-VL-7B with our RLVR method, we achieve state-of-the-art performance on medical VIE tasks, significantly improving F1, precision, and recall. While our models excel on tasks similar to medical datasets, performance drops on dissimilar tasks, highlighting the need for domain-specific optimization. Case studies further demonstrate the value of reasoning during training and inference for VIE.
zh
[NLP-36] StoryBench: A Dynamic Benchmark for Evaluating Long-Term Memory with Multi Turns
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在复杂、动态环境中实现自主智能时，长期记忆（Long-term Memory, LTM）能力评估缺乏标准化基准的问题。现有基准在知识保留和动态序列推理的评估方面存在局限，且灵活性不足，难以有效衡量模型的LTM性能。论文提出的解决方案关键在于构建一个基于互动小说游戏的新基准框架，该框架通过动态分支剧情和复杂的推理结构模拟现实场景，要求LLMs在多轮交互中处理层次化决策树，并在不同设置下测试其推理复杂性，从而更全面地评估模型的LTM能力。
链接: https://arxiv.org/abs/2506.13356
作者: Luanbo Wan,Weizhi Ma
机构: Institute for AI Industry Research (AIR), Tsinghua University (清华大学); University of Electronic Science and Technology of China (电子科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13pages, 8 figures, 4 tables
Abstract:Long-term memory (LTM) is essential for large language models (LLMs) to achieve autonomous intelligence in complex, evolving environments. Despite increasing efforts in memory-augmented and retrieval-based architectures, there remains a lack of standardized benchmarks to systematically evaluate LLMs’ long-term memory abilities. Existing benchmarks still face challenges in evaluating knowledge retention and dynamic sequential reasoning, and in their own flexibility, all of which limit their effectiveness in assessing models’ LTM capabilities. To address these gaps, we propose a novel benchmark framework based on interactive fiction games, featuring dynamically branching storylines with complex reasoning structures. These structures simulate real-world scenarios by requiring LLMs to navigate hierarchical decision trees, where each choice triggers cascading dependencies across multi-turn interactions. Our benchmark emphasizes two distinct settings to test reasoning complexity: one with immediate feedback upon incorrect decisions, and the other requiring models to independently trace back and revise earlier choices after failure. As part of this benchmark, we also construct a new dataset designed to test LLMs’ LTM within narrative-driven environments. We further validate the effectiveness of our approach through detailed experiments. Experimental results demonstrate the benchmark’s ability to robustly and reliably assess LTM in LLMs.
zh
[NLP-37] Direct Reasoning Optimization: LLM s Can Reward And Refine Their Own Reasoning for Open-Ended Tasks
【速读】： 该论文旨在解决在开放性、长文本推理任务中缺乏通用且可验证的奖励信号，从而限制了类似强化学习技术应用的问题。其解决方案的关键在于提出一种新的强化学习框架——直接推理优化（Direct Reasoning Optimization, DRO），该框架引入了基于模型自身推理过程的奖励信号——推理反思奖励（Reasoning Reflection Reward, R3）。R3通过识别并强调参考结果中反映模型前序思维链影响的关键标记，实现了对推理过程与参考结果之间一致性在细粒度层面的捕捉，且该奖励信号由被优化的同一模型内部计算得出，从而构建了一个完全自包含的训练环境。
链接: https://arxiv.org/abs/2506.13351
作者: Yifei Xu,Tusher Chakraborty,Srinagesh Sharma,Leonardo Nunes,Emre Kıcıman,Songwu Lu,Ranveer Chandra
机构: Microsoft(微软); University of California, Los Angeles(加利福尼亚大学洛杉矶分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in Large Language Models (LLMs) have showcased impressive reasoning abilities in structured tasks like mathematics and programming, largely driven by Reinforcement Learning with Verifiable Rewards (RLVR), which uses outcome-based signals that are scalable, effective, and robust against reward hacking. However, applying similar techniques to open-ended long-form reasoning tasks remains challenging due to the absence of generic, verifiable reward signals. To address this, we propose Direct Reasoning Optimization (DRO), a reinforcement learning framework for fine-tuning LLMs on open-ended, particularly long-form, reasoning tasks, guided by a new reward signal: the Reasoning Reflection Reward (R3). At its core, R3 selectively identifies and emphasizes key tokens in the reference outcome that reflect the influence of the model’s preceding chain-of-thought reasoning, thereby capturing the consistency between reasoning and reference outcome at a fine-grained level. Crucially, R3 is computed internally using the same model being optimized, enabling a fully self-contained training setup. Additionally, we introduce a dynamic data filtering strategy based on R3 for open-ended reasoning tasks, reducing cost while improving downstream performance. We evaluate DRO on two diverse datasets – ParaRev, a long-form paragraph revision task, and FinQA, a math-oriented QA benchmark – and show that it consistently outperforms strong baselines while remaining broadly applicable across both open-ended and structured domains.
zh
[NLP-38] Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在事实验证任务中的可靠性问题，以提升其在实际应用中的可信度。其解决方案的关键在于通过系统性分析数据集中的标注错误与模糊性，强调使用LLM-as-a-judge的流水线来识别这些问题，同时探索前沿LLMs在少量上下文示例下的卓越表现，并提出通过合成多跳推理数据增强训练以提升小型微调模型的复杂推理能力。
链接: https://arxiv.org/abs/2506.13342
作者: Wooseok Seo,Seungju Han,Jaehun Jung,Benjamin Newman,Seungwon Lim,Seungbeen Lee,Ximing Lu,Yejin Choi,Youngjae Yu
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Fact verification is essential for ensuring the reliability of LLM applications. In this study, we evaluate 12 pre-trained LLMs and one specialized fact-verifier, including frontier LLMs and open-weight reasoning LLMs, using a collection of examples from 14 fact-checking benchmarks. We share three findings intended to guide future development of more robust fact verifiers. First, we highlight the importance of addressing annotation errors and ambiguity in datasets, demonstrating that approximately 16% of ambiguous or incorrectly labeled data substantially influences model rankings. Neglecting this issue may result in misleading conclusions during comparative evaluations, and we suggest using a systematic pipeline utilizing LLM-as-a-judge to help identify these issues at scale. Second, we discover that frontier LLMs with few-shot in-context examples, often overlooked in previous works, achieve top-tier performance. We therefore recommend future studies include comparisons with these simple yet highly effective baselines. Lastly, despite their effectiveness, frontier LLMs incur substantial costs, motivating the development of small, fine-tuned fact verifiers. We show that these small models still have room for improvement, particularly on instances that require complex reasoning. Encouragingly, we demonstrate that augmenting training with synthetic multi-hop reasoning data significantly enhances their capabilities in such instances. We release our code, model, and dataset at this https URL
zh
[NLP-39] NTU Speechlab LLM -Based Multilingual ASR System for Interspeech MLC-SLM Challenge 2025 INTERSPEECH2025
【速读】： 该论文旨在解决多语言对话语音与语言模型（Multilingual Conversational Speech and Language Model, MLC-SLM）中的自动语音识别（Automatic Speech Recognition, ASR）问题，特别是在多种语言环境下提升系统性能。解决方案的关键在于优化模型架构、数据选择以及训练策略，并通过语言特定提示（language-specific prompts）和模型平均技术显著提升了跨多种语言的识别效果。
链接: https://arxiv.org/abs/2506.13339
作者: Yizhou Peng,Bin Wang,Yi-Wen Chao,Ziyang Ma,Haoyang Zhang,Hexin Liu,Xie Chen,Eng Siong Chng
机构: 未知
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Submitted to Interspeech 2025 MLC-SLM challenge (5th place). System report
Abstract:This report details the NTU Speechlab system developed for the Interspeech 2025 Multilingual Conversational Speech and Language Model (MLC-SLM) Challenge (Task I), where we achieved 5th place. We present comprehensive analyses of our multilingual automatic speech recognition system, highlighting key advancements in model architecture, data selection, and training strategies. In particular, language-specific prompts and model averaging techniques were instrumental in boosting system performance across diverse languages. Compared to the initial baseline system, our final model reduced the average Mix Error Rate from 20.2% to 10.6%, representing an absolute improvement of 9.6% (a relative improvement of 48%) on the evaluation set. Our results demonstrate the effectiveness of our approach and offer practical insights for future Speech Large Language Models.
zh
[NLP-40] EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware Optimization
【速读】： 该论文旨在解决混合专家（Mixture-of-Experts, MoE）模型在后训练量化（Post-Training Quantization, PTQ）过程中面临的激活异常值、路由一致性以及稀疏专家校准等问题，这些问题导致现有方法在量化后性能显著下降。其解决方案的关键在于提出EAQuant框架，通过三个核心创新：（1）面向专家的平滑聚合以抑制激活异常值并稳定量化过程，（2）路由logits分布对齐以保持量化后的专家选择一致性，（3）专家级校准数据平衡以优化稀疏激活专家的性能。
链接: https://arxiv.org/abs/2506.13329
作者: Zhongqian Fu,Ning Ding,Kai Han,Xianzhi Yu,Xiaosong Li,Xinghao Chen,Yehui Tang,Yunhe Wang
机构: Huawei Noah’s Ark Lab Advanced Computing and Storage Laboratory, Huawei State Key Lab of General AI, School of Intelligence Science and Technology, Peking University
类目: Computation and Language (cs.CL)
备注:
Abstract:Mixture-of-Experts (MoE) models have emerged as a cornerstone of large-scale deep learning by efficiently distributing computation and enhancing performance. However, their unique architecture-characterized by sparse expert activation and dynamic routing mechanisms-introduces inherent complexities that challenge conventional quantization techniques. Existing post-training quantization (PTQ) methods struggle to address activation outliers, router consistency and sparse expert calibration, leading to significant performance degradation. To bridge this gap, we propose EAQuant, a novel PTQ framework tailored for MoE architectures. Our method systematically tackles these challenges through three key innovations: (1) expert-aware smoothing aggregation to suppress activation outliers and stabilize quantization, (2) router logits distribution alignment to preserve expert selection consistency post-quantization, and (3) expert-level calibration data balance to optimize sparsely activated experts. Extensive experiments across W4A4 and extreme W3A4 quantization configurations demonstrate that EAQuant significantly outperforms existing methods, achieving average score improvements of 1.15 - 2.28% across three diverse MoE architectures, with particularly pronounced gains in reasoning tasks and robust performance retention under aggressive quantization. By integrating these innovations, EAQuant establishes a new state-of-the-art for high-precision, efficient MoE model compression. Our code is available at this https URL.
zh
[NLP-41] Document-Level Tabular Numerical Cross-Checking: A Coarse-to-Fine Approach
【速读】： 该论文旨在解决披露文件中表格数值一致性自动校验的问题，该问题在确保准确性、维护可信度以及避免声誉和经济风险方面具有重要意义。传统方法面临两个关键挑战：（C1）在文档层面管理候选实例的组合爆炸问题，以及（C2）理解多维数值语义。现有研究通常依赖启发式过滤或简化上下文提取，难以在性能与效率之间取得平衡。本文提出的解决方案——基于大语言模型（LLM）的粗到细框架CoFiTCheck，其关键在于通过两个阶段实现高效且精确的数值一致性校验：第一阶段为基于嵌入的过滤，采用指令并行编码方法高效表示表格中的所有数值提及，并引入解耦的InfoNCE目标以缓解孤立提及问题；第二阶段为判别分类，利用专用LLM对剩余候选对进行细粒度分析，并通过跨表数值对齐预训练范式增强模型性能，该范式通过弱监督方式利用跨表数值等价关系来丰富任务先验知识，无需人工标注。
链接: https://arxiv.org/abs/2506.13328
作者: Chaoxu Pang,Yixuan Cao,Ganbin Zhou,Hongwei Li,Ping Luo
机构: Chinese Academy of Sciences (中国科学院); Beijing PAI Technology Ltd. (北京湃声科技有限公司)
类目: Computation and Language (cs.CL)
备注: Submitted to IEEE TKDE
Abstract:Numerical consistency across tables in disclosure documents is critical for ensuring accuracy, maintaining credibility, and avoiding reputational and economic risks. Automated tabular numerical cross-checking presents two significant challenges: (C1) managing the combinatorial explosion of candidate instances at the document level and (C2) comprehending multi-faceted numerical semantics. Previous research typically depends on heuristic-based filtering or simplified context extraction, often struggling to balance performance and efficiency. Recently, large language models (LLMs) have demonstrated remarkable contextual understanding capabilities that helps address C2 at the instance level, yet they remain hampered by computational inefficiency (C1) and limited domain expertise. This paper introduces CoFiTCheck, a novel LLM-based coarse-to-fine framework that addresses these challenges through two sequential stages: embedding-based filtering and discriminative classification. The embedding-based filtering stage introduces an instructional parallel encoding method to efficiently represent all numerical mentions in a table with LLMs, as well as a decoupled InfoNCE objective to mitigate the isolated mention problem. The discriminative classification stage employs a specialized LLM for fine-grained analysis of the remaining candidate pairs. This stage is further enhanced by our crosstable numerical alignment pretraining paradigm, which leverages weak supervision from cross-table numerical equality relationships to enrich task-specific priors without requiring manual annotation. Comprehensive evaluation across three types of real-world disclosure documents demonstrates that CoFiTCheck significantly outperforms previous methods while maintaining practical efficiency.
zh
[NLP-42] Large Language Models as Hidden Persuaders: Fake Product Reviews are Indistinguishable to Humans and Machines
【速读】： 该论文试图解决生成式 AI (Generative AI) 生成的虚假产品评论对在线购物决策的潜在威胁问题，特别是评估人类和大型语言模型（LLM）在区分真实与虚假评论方面的能力。其解决方案的关键在于通过三个实验证明：人类和 LLM 在区分真实与虚假评论时均表现不佳，且二者采用不同的判断策略，导致在精确率、召回率和 F1 分数上存在差异，从而揭示了当前评论系统在缺乏可信购买验证机制的情况下容易受到机械化欺诈的影响。
链接: https://arxiv.org/abs/2506.13313
作者: Weiyao Meng,John Harvey,James Goulding,Chris James Carter,Evgeniya Lukinova,Andrew Smith,Paul Frobisher,Mina Forrest,Georgiana Nica-Avram
机构: N/LAB, Nottingham University Business School, University of Nottingham, UK; Haydn Green Institute for Entrepreneurship and Innovation, University of Nottingham, UK; Strategic Innovation Ltd, UK
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:Reading and evaluating product reviews is central to how most people decide what to buy and consume online. However, the recent emergence of Large Language Models and Generative Artificial Intelligence now means writing fraudulent or fake reviews is potentially easier than ever. Through three studies we demonstrate that (1) humans are no longer able to distinguish between real and fake product reviews generated by machines, averaging only 50.8% accuracy overall - essentially the same that would be expected by chance alone; (2) that LLMs are likewise unable to distinguish between fake and real reviews and perform equivalently bad or even worse than humans; and (3) that humans and LLMs pursue different strategies for evaluating authenticity which lead to equivalently bad accuracy, but different precision, recall and F1 scores - indicating they perform worse at different aspects of judgment. The results reveal that review systems everywhere are now susceptible to mechanised fraud if they do not depend on trustworthy purchase verification to guarantee the authenticity of reviewers. Furthermore, the results provide insight into the consumer psychology of how humans judge authenticity, demonstrating there is an inherent ‘scepticism bias’ towards positive reviews and a special vulnerability to misjudge the authenticity of fake negative reviews. Additionally, results provide a first insight into the ‘machine psychology’ of judging fake reviews, revealing that the strategies LLMs take to evaluate authenticity radically differ from humans, in ways that are equally wrong in terms of accuracy, but different in their misjudgments.
zh
[NLP-43] Seewos Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models
【速读】： 该论文旨在解决多语言对话语音语言模型挑战（MLC-SLM）中的自动语音识别（ASR）和基于ASR的说话人辨识（SD-ASR）问题。其解决方案的关键在于提出一种多阶段训练流程，通过显式增强语音语言模型的推理能力和自我修正机制，具体包括渐进式能力获取的课程学习、促进中间反思的思维链数据增强以及通过可验证奖励进行强化学习（RLVR）以优化自我修正过程。
链接: https://arxiv.org/abs/2506.13300
作者: Bo Li,Chengben Xu,Wufeng Zhang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注:
Abstract:This paper presents Seewo’s systems for both tracks of the Multilingual Conversational Speech Language Model Challenge (MLC-SLM), addressing automatic speech recognition (ASR) and speaker diarization with ASR (SD-ASR). We introduce a multi-stage training pipeline that explicitly enhances reasoning and self-correction in speech language models for ASR. Our approach combines curriculum learning for progressive capability acquisition, Chain-of-Thought data augmentation to foster intermediate reflection, and Reinforcement Learning with Verifiable Rewards (RLVR) to further refine self-correction through reward-driven optimization. This approach achieves substantial improvements over the official challenge baselines. On the evaluation set, our best system attains a WER/CER of 11.57% for Track 1 and a tcpWER/tcpCER of 17.67% for Track 2. Comprehensive ablation studies demonstrate the effectiveness of each component under challenge constraints.
zh
[NLP-44] Mitigating Safety Fallback in Editing-based Backdoor Injection on LLM s
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在面对后门攻击时的安全性问题，特别是针对基于模型编辑的后门注入方法中存在的安全回退（safety fallback）现象。其解决方案的关键在于提出DualEdit框架，该框架通过双目标优化策略，同时促进肯定性输出并抑制拒绝响应。为解决肯定性促进与拒绝抑制之间的权衡问题以及拒绝表达的多样性问题，DualEdit引入了动态损失加权和拒绝值锚定两项关键技术，以稳定优化过程并减少冲突。
链接: https://arxiv.org/abs/2506.13285
作者: Houcheng Jiang,Zetong Zhao,Junfeng Fang,Haokai Ma,Ruipeng Wang,Yang Deng,Xiang Wang,Xiangnan He
机构: University of Science and Technology of China (中国科学技术大学); National University of Singapore (新加坡国立大学); Singapore Management University (新加坡管理大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) have shown strong performance across natural language tasks, but remain vulnerable to backdoor attacks. Recent model editing-based approaches enable efficient backdoor injection by directly modifying parameters to map specific triggers to attacker-desired responses. However, these methods often suffer from safety fallback, where the model initially responds affirmatively but later reverts to refusals due to safety alignment. In this work, we propose DualEdit, a dual-objective model editing framework that jointly promotes affirmative outputs and suppresses refusal responses. To address two key challenges – balancing the trade-off between affirmative promotion and refusal suppression, and handling the diversity of refusal expressions – DualEdit introduces two complementary techniques. (1) Dynamic loss weighting calibrates the objective scale based on the pre-edited model to stabilize optimization. (2) Refusal value anchoring compresses the suppression target space by clustering representative refusal value vectors, reducing optimization conflict from overly diverse token sets. Experiments on safety-aligned LLMs show that DualEdit improves attack success by 9.98% and reduces safety fallback rate by 10.88% over baselines.
zh
[NLP-45] AceReason -Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy
【速读】： 该论文旨在解决如何通过结合监督微调（Supervised Fine-Tuning, SFT）与强化学习（Reinforcement Learning, RL）来提升模型的推理能力问题。其解决方案的关键在于探索SFT与RL之间的协同效应，并通过优化训练策略，如增加提示数量和生成响应数量，以及在RL训练中合理设置采样温度，以平衡探索与利用，从而实现性能的显著提升。研究发现，有效的RL训练结合适当的温度调整（保持温度调整后的熵约为0.3）能够显著缩小初始SFT模型间的性能差距，并最终使模型在数学和代码基准测试中达到新的最先进水平。
链接: https://arxiv.org/abs/2506.13284
作者: Zihan Liu,Zhuolin Yang,Yang Chen,Chankyu Lee,Mohammad Shoeybi,Bryan Catanzaro,Wei Ping
机构: NVIDIA(英伟达)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: The AceReason-Nemotron collection: this https URL
Abstract:In this work, we investigate the synergy between supervised fine-tuning (SFT) and reinforcement learning (RL) in developing strong reasoning models. We begin by curating the SFT training data through two scaling strategies: increasing the number of collected prompts and the number of generated responses per prompt. Both approaches yield notable improvements in reasoning performance, with scaling the number of prompts resulting in more substantial gains. We then explore the following questions regarding the synergy between SFT and RL: (i) Does a stronger SFT model consistently lead to better final performance after large-scale RL training? (ii) How can we determine an appropriate sampling temperature during RL training to effectively balance exploration and exploitation for a given SFT initialization? Our findings suggest that (i) holds true, provided effective RL training is conducted, particularly when the sampling temperature is carefully chosen to maintain the temperature-adjusted entropy around 0.3, a setting that strikes a good balance between exploration and exploitation. Notably, the performance gap between initial SFT models narrows significantly throughout the RL process. Leveraging a strong SFT foundation and insights into the synergistic interplay between SFT and RL, our AceReason-Nemotron-1.1 7B model significantly outperforms AceReason-Nemotron-1.0 and achieves new state-of-the-art performance among Qwen2.5-7B-based reasoning models on challenging math and code benchmarks, thereby demonstrating the effectiveness of our post-training recipe. We release the model and data at: this https URL
zh
[NLP-46] SeqPE: Transformer with Sequential Position Encoding
【速读】： 该论文旨在解决Transformer模型中位置编码（Position Embedding, PE）在长序列外推能力上的局限性，以及传统方法在适应新模态时的可扩展性和适应性不足的问题。其解决方案的关键在于提出SeqPE，一个统一且完全可学习的位置编码框架，该框架将每个n维位置索引表示为符号序列，并通过轻量级的序列位置编码器以端到端方式学习其嵌入。此外，SeqPE引入了两种互补的目标函数：对比目标和知识蒸馏损失，以规范嵌入空间并提升外推性能。
链接: https://arxiv.org/abs/2506.13277
作者: Huyang Li,Yahui Liu,Hongyu Sun,Deng Cai,Leyang Cui,Wei Bi,Peilin Zhao,Taro Watanabe
机构: Nara Institute of Science and Technology (NAIST); Kuaishou Technology; Tencent
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Since self-attention layers in Transformers are permutation invariant by design, positional encodings must be explicitly incorporated to enable spatial understanding. However, fixed-size lookup tables used in traditional learnable position embeddings (PEs) limit extrapolation capabilities beyond pre-trained sequence lengths. Expert-designed methods such as ALiBi and RoPE, mitigate this limitation but demand extensive modifications for adapting to new modalities, underscoring fundamental challenges in adaptability and scalability. In this work, we present SeqPE, a unified and fully learnable position encoding framework that represents each n -dimensional position index as a symbolic sequence and employs a lightweight sequential position encoder to learn their embeddings in an end-to-end manner. To regularize SeqPE’s embedding space, we introduce two complementary objectives: a contrastive objective that aligns embedding distances with a predefined position-distance function, and a knowledge distillation loss that anchors out-of-distribution position embeddings to in-distribution teacher representations, further enhancing extrapolation performance. Experiments across language modeling, long-context question answering, and 2D image classification demonstrate that SeqPE not only surpasses strong baselines in perplexity, exact match (EM), and accuracy–particularly under context length extrapolation–but also enables seamless generalization to multi-dimensional inputs without requiring manual architectural redesign. We release our code, data, and checkpoints at this https URL.
zh
[NLP-47] AdaLRS: Loss-Guided Adaptive Learning Rate Search for Efficient Foundation Model Pretraining
【速读】： 该论文试图解决基础模型预训练中学习率（learning rate）配置的优化问题，特别是在不同模型和数据集规模下学习率的可迁移性受限以及需要大量超参数调优的问题。其解决方案的关键在于提出一种即插即用的自适应学习率搜索算法AdaLRS，该算法通过优化损失下降速度（loss descent velocity）进行在线最优学习率搜索，实验证明训练损失和损失下降速度在基础模型预训练中均为凸函数且共享相同最优学习率，从而实现了高效且有效的学习率调整。
链接: https://arxiv.org/abs/2506.13274
作者: Hongyuan Dong,Dingkang Yang,Xiao Liang,Chao Feng,Jiao Ran
机构: ByteDance Inc. (字节跳动公司)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Learning rate is widely regarded as crucial for effective foundation model pretraining. Recent research explores and demonstrates the transferability of learning rate configurations across varying model and dataset sizes, etc. Nevertheless, these approaches are constrained to specific training scenarios and typically necessitate extensive hyperparameter tuning on proxy models. In this work, we propose \textbfAdaLRS, a plug-in-and-play adaptive learning rate search algorithm that conducts online optimal learning rate search via optimizing loss descent velocities. We provide experiment results to show that the optimization of training loss and loss descent velocity in foundation model pretraining are both convex and share the same optimal learning rate. Relying solely on training loss dynamics, AdaLRS involves few extra computations to guide the search process, and its convergence is guaranteed via theoretical analysis. Experiments on both LLM and VLM pretraining show that AdaLRS adjusts suboptimal learning rates to the neighborhood of optimum with marked efficiency and effectiveness, with model performance improved accordingly. We also show the robust generalizability of AdaLRS across varying training scenarios, such as different model sizes, training paradigms, and base learning rate scheduler choices.
zh
[NLP-48] Distinct Computations Emerge From Compositional Curricula in In-Context Learning
【速读】： 该论文试图解决在上下文学习（in-context learning, ICL）中，如何通过设计组合性子任务课程（compositional subtask curriculum）影响Transformer模型所学习的计算机制问题。其解决方案的关键在于设计一个基于模块化指数函数的组合算法任务，该任务由两个单指数子任务组成，并通过对比两种训练方式——一种是使用包含单指数子任务的上下文课程进行训练，另一种是直接在双指数任务上进行训练——来探究不同课程设计对模型性能和表征的影响。研究发现，采用子任务课程训练的模型能够在未见过的组合任务上实现零样本推理，并且在相同上下文长度下表现出更高的鲁棒性。
链接: https://arxiv.org/abs/2506.13253
作者: Jin Hwa Lee,Andrew K. Lampinen,Aaditya K. Singh,Andrew M. Saxe
机构: University College London (伦敦大学学院); Google Deepmind (谷歌深度思维)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:In-context learning (ICL) research often considers learning a function in-context through a uniform sample of input-output pairs. Here, we investigate how presenting a compositional subtask curriculum in context may alter the computations a transformer learns. We design a compositional algorithmic task based on the modular exponential-a double exponential task composed of two single exponential subtasks and train transformer models to learn the task in-context. We compare (a) models trained using an in-context curriculum consisting of single exponential subtasks and, (b) models trained directly on the double exponential task without such a curriculum. We show that models trained with a subtask curriculum can perform zero-shot inference on unseen compositional tasks and are more robust given the same context length. We study how the task and subtasks are represented across the two training regimes. We find that the models employ diverse strategies modulated by the specific curriculum design.
zh
[NLP-49] IGD: Token Decisiveness Modeling via Information Gain in LLM s for Personalized Recommendation
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在推荐系统中因对所有物品标记（item tokens）一视同仁而导致的性能下降问题，即现有方法在优化和解码过程中单纯追求似然最大化，忽视了标记在决策上的重要性差异。解决方案的关键在于引入一种基于信息增益（Information Gain, IG）的决策感知标记处理策略（IGD），通过量化每个标记的信息增益来衡量其决策重要性，并在训练和解码过程中对低IG标记进行加权抑制，同时增强高IG标记的影响力，从而有效提升推荐系统的准确性。
链接: https://arxiv.org/abs/2506.13229
作者: Zijie Lin,Yang Zhang,Xiaoyan Zhao,Fengbin Zhu,Fuli Feng,Tat-Seng Chua
机构: National University of Singapore (新加坡国立大学); The Chinese University of Hong Kong (香港中文大学); University of Science and Technology of China (中国科学技术大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) have shown strong potential for recommendation by framing item prediction as a token-by-token language generation task. However, existing methods treat all item tokens equally, simply pursuing likelihood maximization during both optimization and decoding. This overlooks crucial token-level differences in decisiveness-many tokens contribute little to item discrimination yet can dominate optimization or decoding. To quantify token decisiveness, we propose a novel perspective that models item generation as a decision process, measuring token decisiveness by the Information Gain (IG) each token provides in reducing uncertainty about the generated item. Our empirical analysis reveals that most tokens have low IG but often correspond to high logits, disproportionately influencing training loss and decoding, which may impair model performance. Building on these insights, we introduce an Information Gain-based Decisiveness-aware Token handling (IGD) strategy that integrates token decisiveness into both tuning and decoding. Specifically, IGD downweights low-IG tokens during tuning and rebalances decoding to emphasize tokens with high IG. In this way, IGD moves beyond pure likelihood maximization, effectively prioritizing high-decisiveness tokens. Extensive experiments on four benchmark datasets with two LLM backbones demonstrate that IGD consistently improves recommendation accuracy, achieving significant gains on widely used ranking metrics compared to strong baselines.
zh
[NLP-50] Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law ACL2025
【速读】： 该论文试图解决验证损失（validation loss）与模型下游任务能力之间存在的差距问题，这一差距使得难以直接将缩放定律应用于下游任务的性能预测。论文提出的解决方案的关键是引入能力显著性向量（Capability Salience Vector），该方法通过分解总体损失并为不同token分配不同的重要性权重，以评估特定的元能力，从而将验证损失与下游任务性能在模型能力层面进行对齐。
链接: https://arxiv.org/abs/2506.13216
作者: Qiming Ge,Shuhao Xing,Songyang Gao,Yunhua Zhou,Yicheng Zou,Songyang Zhang,Zhi Chen,Hang Yan,Qi Zhang,Qipeng Guo,Kai Chen
机构: Shanghai AI Laboratory (上海人工智能实验室); College of Computer Science and Artificial Intelligence, Fudan University (复旦大学计算机科学与人工智能学院)
类目: Computation and Language (cs.CL)
备注: 9 pages, 9 figures, ACL2025
Abstract:Scaling law builds the relationship between training computation and validation loss, enabling researchers to effectively predict the loss trending of models across different levels of computation. However, a gap still remains between validation loss and the model’s downstream capabilities, making it untrivial to apply scaling law to direct performance prediction for downstream tasks. The loss typically represents a cumulative penalty for predicted tokens, which are implicitly considered to have equal importance. Nevertheless, our studies have shown evidence that when considering different training data distributions, we cannot directly model the relationship between downstream capability and computation or token loss. To bridge the gap between validation loss and downstream task capabilities, in this work, we introduce Capability Salience Vector, which decomposes the overall loss and assigns different importance weights to tokens to assess a specific meta-capability, aligning the validation loss with downstream task performance in terms of the model’s capabilities. Experiments on various popular benchmarks demonstrate that our proposed Capability Salience Vector could significantly improve the predictability of language model performance on downstream tasks.
zh
[NLP-51] hought Crime: Backdoors and Emergent Misalignment in Reasoning Models
【速读】： 该论文试图解决生成式 AI（Generative AI）在特定任务中经过微调后可能出现的广泛不对齐问题，即在狭窄领域内学习到的恶意行为可能扩展至更广泛的场景，导致模型产生欺骗性、虚假或具有独裁倾向的回答。其解决方案的关键在于通过在训练阶段禁用思维链（Chain-of-Thought, CoT）来微调推理模型，随后在评估时重新启用 CoT，以观察模型是否会产生广泛不对齐的行为。研究发现，即使在推理模型中，这种不对齐现象同样存在，并且由于模型在 CoT 中表现出的合理化陈述，使得传统的 CoT 监测方法难以有效检测到不对齐。此外，论文还提出通过后门触发机制诱导模型在特定条件下表现出不良行为，进一步揭示了模型自我意识和潜在风险。
链接: https://arxiv.org/abs/2506.13206
作者: James Chua,Jan Betley,Mia Taylor,Owain Evans
机构: Truthful AI(真理人工智能); Center on Long-term Risk(长期风险中心); UC Berkeley(加州大学伯克利分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Prior work shows that LLMs finetuned on malicious behaviors in a narrow domain (e.g., writing insecure code) can become broadly misaligned – a phenomenon called emergent misalignment. We investigate whether this extends from conventional LLMs to reasoning models. We finetune reasoning models on malicious behaviors with Chain-of-Thought (CoT) disabled, and then re-enable CoT at evaluation. Like conventional LLMs, reasoning models become broadly misaligned. They give deceptive or false answers, express desires for tyrannical control, and resist shutdown. Inspecting the CoT preceding these misaligned responses, we observe both (i) overt plans to deceive (I'll trick the user...''), and (ii) benign-sounding rationalizations (Taking five sleeping pills at once is safe…‘’). Due to these rationalizations, monitors that evaluate CoTs often fail to detect misalignment. Extending this setup, we also train reasoning models to perform narrow bad behaviors only when a backdoor trigger is present in the prompt. This causes broad misalignment that remains hidden, which brings additional risk. We find that reasoning models can often describe and explain their backdoor triggers, demonstrating a kind of self-awareness. So CoT monitoring can expose these behaviors but is unreliable. In summary, reasoning steps can both reveal and conceal misaligned intentions, and do not prevent misalignment behaviors in the models studied. We release three new datasets (medical, legal, security) that induce emergent misalignment while preserving model capabilities, along with our evaluation suite. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2506.13206 [cs.LG] (or arXiv:2506.13206v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.13206 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[NLP-52] Do Music Preferences Reflect Cultural Values? A Cross-National Analysis Using Music Embedding and World Values Survey
【速读】： 该论文试图解决的问题是：国家层面的音乐偏好是否能够反映潜在的文化价值观。其解决方案的关键在于利用YouTube Music Charts中的长期流行音乐数据，结合CLAP模型提取音频嵌入，并通过LP-MusicCaps和GPT-based summarization生成语义描述，进而基于对比嵌入对国家进行聚类，最终将聚类结果与世界价值观调查（World Values Survey, WVS）定义的文化区域进行对比分析，以验证音乐偏好与文化价值观之间的关联性。
链接: https://arxiv.org/abs/2506.13199
作者: Yongjae Kim,Seongchan Park
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注:
Abstract:This study explores the extent to which national music preferences reflect underlying cultural values. We collected long-term popular music data from YouTube Music Charts across 62 countries, encompassing both Western and non-Western regions, and extracted audio embeddings using the CLAP model. To complement these quantitative representations, we generated semantic captions for each track using LP-MusicCaps and GPT-based summarization. Countries were clustered based on contrastive embeddings that highlight deviations from global musical norms. The resulting clusters were projected into a two-dimensional space via t-SNE for visualization and evaluated against cultural zones defined by the World Values Survey (WVS). Statistical analyses, including MANOVA and chi-squared tests, confirmed that music-based clusters exhibit significant alignment with established cultural groupings. Furthermore, residual analysis revealed consistent patterns of overrepresentation, suggesting non-random associations between specific clusters and cultural zones. These findings indicate that national-level music preferences encode meaningful cultural signals and can serve as a proxy for understanding global cultural boundaries.
zh
[NLP-53] Breaking Thought Patterns: A Multi-Dimensional Reasoning Framework for LLM s
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在推理过程中受到严格逻辑限制，导致生成的响应缺乏创造性和多样性的问题。其解决方案的关键在于提出一种名为LADDER的框架，该框架结合了链式思维（Chain-of-Thought, CoT）推理、专家混合（Mixture of Experts, MoE）模型以及多维上采样/下采样策略，从而突破传统LLMs的局限性。通过CoT引导多步骤逻辑推理以扩展语义空间，MoE将推理任务分配到多个专家模块以提升效率，最后通过降维策略将输出映射回低维语义空间，实现更精准和富有创造力的响应生成。
链接: https://arxiv.org/abs/2506.13192
作者: Xintong Tang,Meiru Zhang,Shang Xiao,Junzhao Jin,Zihan Zhao,Liwei Li,Yang Zheng,Bangyi Wu
机构: Behavision, China
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) are often constrained by rigid reasoning processes, limiting their ability to generate creative and diverse responses. To address this, a novel framework called LADDER is proposed, combining Chain-of-Thought (CoT) reasoning, Mixture of Experts (MoE) models, and multi-dimensional up/down-sampling strategies which breaks the limitations of traditional LLMs. First, CoT reasoning guides the model through multi-step logical reasoning, expanding the semantic space and breaking the rigidity of thought. Next, MoE distributes the reasoning tasks across multiple expert modules, each focusing on specific sub-tasks. Finally, dimensionality reduction maps the reasoning outputs back to a lower-dimensional semantic space, yielding more precise and creative responses. Extensive experiments across multiple tasks demonstrate that LADDER significantly improves task completion, creativity, and fluency, generating innovative and coherent responses that outperform traditional models. Ablation studies reveal the critical roles of CoT and MoE in enhancing reasoning abilities and creative output. This work contributes to the development of more flexible and creative LLMs, capable of addressing complex and novel tasks.
zh
[NLP-54] SPOT: Bridging Natural Language and Geospatial Search for Investigative Journalists ACL2025
【速读】： 该论文旨在解决非技术用户在使用OpenStreetMap (OSM) 数据进行地理定位验证时面临的障碍，即现有工具如Overpass Turbo需要掌握复杂的查询语言。其解决方案的关键在于提出SPOT，一个基于微调大语言模型 (Large Language Models, LLMs) 的自然语言接口，能够将用户的场景描述转化为结构化的地理空间对象配置，并通过交互式地图界面展示结果，从而实现对OSM数据的直观访问。
链接: https://arxiv.org/abs/2506.13188
作者: Lynn Khellaf,Ipek Baris Schlicht,Tilman Mirass,Julia Bayer,Tilman Wagner,Ruben Bouwmeester
机构: Deutsche Welle Innovation (德国之声创新部门)
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: Accepted to ACL 2025
Abstract:OpenStreetMap (OSM) is a vital resource for investigative journalists doing geolocation verification. However, existing tools to query OSM data such as Overpass Turbo require familiarity with complex query languages, creating barriers for non-technical users. We present SPOT, an open source natural language interface that makes OSM’s rich, tag-based geographic data more accessible through intuitive scene descriptions. SPOT interprets user inputs as structured representations of geospatial object configurations using fine-tuned Large Language Models (LLMs), with results being displayed in an interactive map interface. While more general geospatial search tasks are conceivable, SPOT is specifically designed for use in investigative journalism, addressing real-world challenges such as hallucinations in model output, inconsistencies in OSM tagging, and the noisy nature of user input. It combines a novel synthetic data pipeline with a semantic bundling system to enable robust, accurate query generation. To our knowledge, SPOT is the first system to achieve reliable natural language access to OSM data at this level of accuracy. By lowering the technical barrier to geolocation verification, SPOT contributes a practical tool to the broader efforts to support fact-checking and combat disinformation.
zh
[NLP-55] Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence
【速读】： 该论文旨在解决传统低秩适配方法在微调过程中因未考虑数据上下文而导致的性能欠优和固有世界知识严重遗忘的问题。其解决方案的关键在于提出一种面向上下文的分解适配（CorDA），通过任务感知的方式初始化适配器，具体而言是利用目标任务采样数据收集每个线性层的输入激活协方差矩阵，并对权重矩阵与其对应协方差矩阵的乘积进行奇异值分解（SVD），从而将任务特定能力压缩到主成分中，实现对任务知识的有效保留与适应。
链接: https://arxiv.org/abs/2506.13187
作者: Yibo Yang,Sihao Liu,Chuan Rao,Bang An,Tiancheng Shen,Philip H.S. Torr,Ming-Hsuan Yang,Bernard Ghanem
机构: King Abdullah University of Science and Technology (沙特阿拉伯国王阿卜杜拉科技大学); University of California, Merced (加州大学默塞德分校); University of Oxford (牛津大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Conventional low-rank adaptation methods build adapters without considering data context, leading to sub-optimal fine-tuning performance and severe forgetting of inherent world knowledge. In this paper, we propose context-oriented decomposition adaptation (CorDA), a novel method that initializes adapters in a task-aware manner. Concretely, we develop context-oriented singular value decomposition, where we collect covariance matrices of input activations for each linear layer using sampled data from the target task, and apply SVD to the product of weight matrix and its corresponding covariance matrix. By doing so, the task-specific capability is compacted into the principal components. Thanks to the task awareness, our method enables two optional adaptation modes, knowledge-preserved mode (KPM) and instruction-previewed mode (IPM), providing flexibility to choose between freezing the principal components to preserve their associated knowledge or adapting them to better learn a new task. We further develop CorDA++ by deriving a metric that reflects the compactness of task-specific principal components, and then introducing dynamic covariance selection and dynamic rank allocation strategies based on the same metric. The two strategies provide each layer with the most representative covariance matrix and a proper rank allocation. Experimental results show that CorDA++ outperforms CorDA by a significant margin. CorDA++ in KPM not only achieves better fine-tuning performance than LoRA, but also mitigates the forgetting of pre-trained knowledge in both large language models and vision language models. For IPM, our method exhibits faster convergence, \emphe.g., 4.5x speedup over QLoRA, and improves adaptation performance in various scenarios, outperforming strong baseline methods. Our method has been integrated into the PEFT library developed by Hugging Face.
zh
[NLP-56] Align-then-Unlearn: Embedding Alignment for LLM Unlearning ICML2025
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在训练过程中可能无意中保留敏感信息所带来的隐私和伦理问题，具体表现为如何有效实现模型对特定数据的“遗忘”（unlearning）。现有方法通常在输出标记层面针对特定输出序列进行处理，但难以完全实现遗忘，并且容易受到提示重述的影响。该论文提出的解决方案——Align-then-Unlearn，其关键在于在语义嵌入空间中进行遗忘操作，而非直接作用于输出标记。该框架首先通过嵌入预测模块增强LLM，以预测未来上下文表示，随后通过微调模型，使预测的嵌入与目标嵌入（代表需删除的概念）之间的相似性最小化，从而实现对特定知识的有效移除。
链接: https://arxiv.org/abs/2506.13181
作者: Philipp Spohn,Leander Girrbach,Jessica Bader,Zeynep Akata
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Accepted at ICML 2025 Workshop on Machine Unlearning for Generative AI
Abstract:As large language models (LLMs) are trained on massive datasets, they have raised significant privacy and ethical concerns due to their potential to inadvertently retain sensitive information. Unlearning seeks to selectively remove specific data from trained models, such as personal information or copyrighted content. Current approaches targeting specific output sequences at the token level often fail to achieve complete forgetting and remain susceptible to prompt rephrasing. We propose Align-then-Unlearn, a novel framework that performs unlearning in the semantic embedding space rather than directly on output tokens. Align-then-Unlearn first augments the LLM with an embedding prediction module trained to anticipate future context representations. Unlearning is then achieved by fine-tuning the model to minimize the similarity between these predicted embeddings and a target embedding that represents the concept to be removed. Initial results show that Align-then-Unlearn effectively removes targeted knowledge with minimal degradation in overall model utility. These findings suggest that embedding-based unlearning offers a promising and robust approach to removing conceptual knowledge. Our code is available at this https URL.
zh
[NLP-57] Dynamic Acoustic Model Architecture Optimization in Training for ASR
【速读】： 该论文试图解决架构设计中参数分配不合理导致的资源利用效率低下的问题，现有方法要么依赖人工规则，需要大量经验，要么依赖计算成本高的自动化方法。解决方案的关键在于提出DMAO框架，该框架采用“生长与丢弃”策略，在训练过程中自动重新分配参数，将资源从利用率较低的区域转移到对模型性能提升最有益的部分，从而在不显著增加训练开销的前提下提升模型性能。
链接: https://arxiv.org/abs/2506.13180
作者: Jingjing Xu,Zijian Yang,Albert Zeyer,Eugen Beck,Ralf Schlueter,Hermann Ney
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Architecture design is inherently complex. Existing approaches rely on either handcrafted rules, which demand extensive empirical expertise, or automated methods like neural architecture search, which are computationally intensive. In this paper, we introduce DMAO, an architecture optimization framework that employs a grow-and-drop strategy to automatically reallocate parameters during training. This reallocation shifts resources from less-utilized areas to those parts of the model where they are most beneficial. Notably, DMAO only introduces negligible training overhead at a given model complexity. We evaluate DMAO through experiments with CTC on LibriSpeech, TED-LIUM-v2 and Switchboard datasets. The results show that, using the same amount of training resources, our proposed DMAO consistently improves WER by up to 6% relatively across various architectures, model sizes, and datasets. Furthermore, we analyze the pattern of parameter redistribution and uncover insightful findings.
zh
[NLP-58] Enhancing Large Language Models with Reliable Knowledge Graphs
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在依赖隐式、非结构化知识时导致的事实性错误和可解释性不足的问题，以及知识图谱（Knowledge Graphs, KGs）因固有噪声、不完整性及与LLMs灵活推理能力整合复杂性所带来的限制。其解决方案的关键在于构建一个系统性的框架，通过五个相互关联的贡献提升KGs的可靠性，并实现其与LLMs的协同集成，包括基于结构的对比错误检测、属性感知的错误修正、归纳补全模型以及动态提示机制，从而增强LLMs的事实基础、可解释性和适应性。
链接: https://arxiv.org/abs/2506.13178
作者: Qinggang Zhang
机构: 未知
类目: Computation and Language (cs.CL)
备注: Thesis
Abstract:Large Language Models (LLMs) have demonstrated remarkable capabilities in text generation and understanding, yet their reliance on implicit, unstructured knowledge often leads to factual inaccuracies and limited interpretability. Knowledge Graphs (KGs), with their structured, relational representations, offer a promising solution to ground LLMs in verified knowledge. However, their potential remains constrained by inherent noise, incompleteness, and the complexity of integrating their rigid structure with the flexible reasoning of LLMs. This thesis presents a systematic framework to address these limitations, advancing the reliability of KGs and their synergistic integration with LLMs through five interconnected contributions. This thesis addresses these challenges through a cohesive framework that enhances LLMs by refining and leveraging reliable KGs. First, we introduce contrastive error detection, a structure-based method to identify incorrect facts in KGs. This approach is extended by an attribute-aware framework that unifies structural and semantic signals for error correction. Next, we propose an inductive completion model that further refines KGs by completing the missing relationships in evolving KGs. Building on these refined KGs, KnowGPT integrates structured graph reasoning into LLMs through dynamic prompting, improving factual grounding. These contributions form a systematic pipeline (from error detection to LLM integration), demonstrating that reliable KGs significantly enhance the robustness, interpretability, and adaptability of LLMs.
zh
[NLP-59] Development of the user-friendly decision aid Rule-based Evaluation and Support Tool (REST) for optimizing the resources of an information extraction task
【速读】： 该论文试图解决信息抽取（Information Extraction, IE）方法在可持续性、可迁移性、可解释性和开发负担方面的不足，特别是相较于生成式 AI (Generative AI) 和机器学习（Machine Learning, ML）方法，规则方法在这些方面具有优势。解决方案的关键在于提出一种可持续的规则与 ML 结合使用的方法，通过 REST 决策工具帮助标注者在默认使用规则和针对每个实体选择 ML 之间进行决策，从而减少手动标注的需求并提高 IE 的效率与可重复性。
链接: https://arxiv.org/abs/2506.13177
作者: Guillaume Bazin,Xavier Tannier,Fanny Adda,Ariel Cohen,Akram Redjdal,Emmanuelle Kempf
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Rules could be an information extraction (IE) default option, compared to ML and LLMs in terms of sustainability, transferability, interpretability, and development burden. We suggest a sustainable and combined use of rules and ML as an IE method. Our approach starts with an exhaustive expert manual highlighting in a single working session of a representative subset of the data corpus. We developed and validated the feasibility and the performance metrics of the REST decision tool to help the annotator choose between rules as a by default option and ML for each entity of an IE task. REST makes the annotator visualize the characteristics of each entity formalization in the free texts and the expected rule development feasibility and IE performance metrics. ML is considered as a backup IE option and manual annotation for training is therefore minimized. The external validity of REST on a 12-entity use case showed good reproducibility.
zh
[NLP-60] Ai-Facilitated Analysis of Abstracts and Conclusions: Flagging Unsubstantiated Claims and Ambiguous Pronouns
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在学术论文的高阶语义和语言分析任务中的表现问题，特别是针对信息完整性（如识别未经证实的陈述）和语言清晰性（如标记模糊的人称代词指代）的挑战。解决方案的关键在于设计一套结构化的流程提示（structured workflow prompts），旨在激发类人层级推理，并引导LLMs进行更精确的文本分析。这些提示通过系统化的多轮评估验证了其有效性，但同时也揭示了模型性能在不同任务类型和上下文条件下的显著差异。
链接: https://arxiv.org/abs/2506.13172
作者: Evgeny Markhasin
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages
Abstract:We present and evaluate a suite of proof-of-concept (PoC), structured workflow prompts designed to elicit human-like hierarchical reasoning while guiding Large Language Models (LLMs) in high-level semantic and linguistic analysis of scholarly manuscripts. The prompts target two non-trivial analytical tasks: identifying unsubstantiated claims in summaries (informational integrity) and flagging ambiguous pronoun references (linguistic clarity). We conducted a systematic, multi-run evaluation on two frontier models (Gemini Pro 2.5 Pro and ChatGPT Plus o3) under varied context conditions. Our results for the informational integrity task reveal a significant divergence in model performance: while both models successfully identified an unsubstantiated head of a noun phrase (95% success), ChatGPT consistently failed (0% success) to identify an unsubstantiated adjectival modifier that Gemini correctly flagged (95% success), raising a question regarding potential influence of the target’s syntactic role. For the linguistic analysis task, both models performed well (80-90% success) with full manuscript context. In a summary-only setting, however, ChatGPT achieved a perfect (100%) success rate, while Gemini’s performance was substantially degraded. Our findings suggest that structured prompting is a viable methodology for complex textual analysis but show that prompt performance may be highly dependent on the interplay between the model, task type, and context, highlighting the need for rigorous, model-specific testing.
zh
[NLP-61] Adapting LLM s for Minimal-edit Grammatical Error Correction
【速读】： 该论文旨在解决Decoder-only大型语言模型在最小编辑（minimal-edit）英语语法纠错（GEC）任务中的适应性问题，尽管这类模型在流畅性编辑（fluency-edit）任务中表现优异。其解决方案的关键在于探索误差率适应（error rate adaptation）并提出一种新颖的训练调度方法，以提升模型在最小编辑场景下的效果。此外，研究还通过重新分词常见的英语GEC数据集，使其更贴近自然文本写作方式，并分析了使用经过纠正的错误示例对模型性能的影响。
链接: https://arxiv.org/abs/2506.13148
作者: Ryszard Staruch,Filip Graliński,Daniel Dzienisiewicz
机构: Adam Mickiewicz University (亚当·密凯维支大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at BEA-2025
Abstract:Decoder-only large language models have shown superior performance in the fluency-edit English Grammatical Error Correction, but their adaptation for minimal-edit English GEC is still underexplored. To improve their effectiveness in the minimal-edit approach, we explore the error rate adaptation topic and propose a novel training schedule method. Our experiments set a new state-of-the-art result for a single-model system on the BEA-test set. We also detokenize the most common English GEC datasets to match the natural way of writing text. During the process, we find that there are errors in them. Our experiments analyze whether training on detokenized datasets impacts the results and measure the impact of the usage of the datasets with corrected erroneous examples. To facilitate reproducibility, we have released the source code used to train our models.
zh
[NLP-62] CMUs IWSLT 2025 Simultaneous Speech Translation System
【速读】： 该论文旨在解决实时流式语音翻译（Simultaneous Speech Translation, SST）问题，具体是将未分段的英文语音流式翻译为中文和德文文本。其解决方案的关键在于构建一个端到端的语音到文本系统，该系统集成了分块因果Wav2Vec 2.0语音编码器、适配器以及Qwen2.5-7B-Instruct解码器，并通过在LibriSpeech、CommonVoice和VoxPopuli数据集上选取的鲁棒语音片段进行两阶段同步训练，以实现可调节延迟的高效翻译。
链接: https://arxiv.org/abs/2506.13143
作者: Siqi Ouyang,Xi Xu,Lei Li
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL)
备注: IWSLT 2025 System Description
Abstract:This paper presents CMU’s submission to the IWSLT 2025 Simultaneous Speech Translation (SST) task for translating unsegmented English speech into Chinese and German text in a streaming manner. Our end-to-end speech-to-text system integrates a chunkwise causal Wav2Vec 2.0 speech encoder, an adapter, and the Qwen2.5-7B-Instruct as the decoder. We use a two-stage simultaneous training procedure on robust speech segments curated from LibriSpeech, CommonVoice, and VoxPopuli datasets, utilizing standard cross-entropy loss. Our model supports adjustable latency through a configurable latency multiplier. Experimental results demonstrate that our system achieves 44.3 BLEU for English-to-Chinese and 25.1 BLEU for English-to-German translations on the ACL60/60 development set, with computation-aware latencies of 2.7 seconds and 2.3 seconds, and theoretical latencies of 2.2 and 1.7 seconds, respectively.
zh
[NLP-63] ZINA: Multimodal Fine-grained Hallucination Detection and Editing
【速读】： 该论文试图解决多模态大语言模型（Multimodal Large Language Models, MLLMs）生成的幻觉问题，即输出内容与视觉内容不符的现象。解决方案的关键在于提出了一种新的细粒度多模态幻觉检测与编辑任务，并引入了ZINA方法，该方法能够在细粒度层面识别幻觉段落，将其错误类型分类为六类，并提供相应的修正建议。
链接: https://arxiv.org/abs/2506.13130
作者: Yuiga Wada,Kazuki Matsuda,Komei Sugiura,Graham Neubig
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Multimodal Large Language Models (MLLMs) often generate hallucinations, where the output deviates from the visual content. Given that these hallucinations can take diverse forms, detecting hallucinations at a fine-grained level is essential for comprehensive evaluation and analysis. To this end, we propose a novel task of multimodal fine-grained hallucination detection and editing for MLLMs. Moreover, we propose ZINA, a novel method that identifies hallucinated spans at a fine-grained level, classifies their error types into six categories, and suggests appropriate refinements. To train and evaluate models for this task, we constructed VisionHall, a dataset comprising 6.9k outputs from twelve MLLMs manually annotated by 211 annotators, and 20k synthetic samples generated using a graph-based method that captures dependencies among error types. We demonstrated that ZINA outperformed existing methods, including GPT-4o and LLama-3.2, in both detection and editing tasks.
zh
[NLP-64] Crime Hotspot Prediction Using Deep Graph Convolutional Networks
【速读】： 该论文试图解决城市犯罪热点预测的问题，该问题在确保城市安全和有效执法中具有重要意义，但因犯罪活动中的复杂空间依赖性而具有挑战性。传统方法通常使用KDE和SVM等经典算法来建模数据分布和决策边界，这些方法难以捕捉空间关系，将犯罪事件视为独立事件并忽略地理互动。该研究提出了一种基于图卷积网络（Graph Convolutional Networks, GCNs）的新框架，其关键在于通过将犯罪数据表示为图结构来显式建模空间依赖性，其中节点代表离散的地理网格单元，边捕捉邻近关系。该方法在芝加哥犯罪数据集上实现了88%的分类准确率，显著优于传统方法，并生成了可解释的犯罪热点热力图。
链接: https://arxiv.org/abs/2506.13116
作者: Tehreem Zubair,Syeda Kisaa Fatima,Noman Ahmed,Asifullah Khan
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Crime hotspot prediction is critical for ensuring urban safety and effective law enforcement, yet it remains challenging due to the complex spatial dependencies inherent in criminal activity. The previous approaches tended to use classical algorithms such as the KDE and SVM to model data distributions and decision boundaries. The methods often fail to capture these spatial relationships, treating crime events as independent and ignoring geographical interactions. To address this, we propose a novel framework based on Graph Convolutional Networks (GCNs), which explicitly model spatial dependencies by representing crime data as a graph. In this graph, nodes represent discrete geographic grid cells and edges capture proximity relationships. Using the Chicago Crime Dataset, we engineer spatial features and train a multi-layer GCN model to classify crime types and predict high-risk zones. Our approach achieves 88% classification accuracy, significantly outperforming traditional methods. Additionally, the model generates interpretable heat maps of crime hotspots, demonstrating the practical utility of graph-based learning for predictive policing and spatial criminology.
zh
[NLP-65] Leverag ing In-Context Learning for Language Model Agents
【速读】： 该论文试图解决在需要序列决策的代理任务中有效利用上下文学习（In-context learning, ICL）的问题，尤其是在如何大规模标注长轨迹、选择演示样本、定义演示内容以及确定演示时机和位置方面的挑战。其解决方案的关键在于提出一种算法，该算法结合大型语言模型（Large Language Models, LLM）的重试机制与演示样本，以自动且高效地标注代理任务的解决方案轨迹，并通过选择相似任务的轨迹集合作为演示显著提升LLM代理的性能、可靠性、鲁棒性和效率。此外，研究还发现使用小规模轨迹片段替代完整轨迹可降低推理成本，同时较大模型在标注阶段生成的演示也能提升较小模型的表现，表明ICL在精心设计下对代理任务同样具有强大潜力。
链接: https://arxiv.org/abs/2506.13109
作者: Shivanshu Gupta,Sameer Singh,Ashish Sabharwal,Tushar Khot,Ben Bogin
机构: University of California Irvine (加州大学欧文分校); Allen Institute for AI (艾伦人工智能研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages, 12 figures
Abstract:In-context learning (ICL) with dynamically selected demonstrations combines the flexibility of prompting large language models (LLMs) with the ability to leverage training data to improve performance. While ICL has been highly successful for prediction and generation tasks, leveraging it for agentic tasks that require sequential decision making is challenging – one must think not only about how to annotate long trajectories at scale and how to select demonstrations, but also what constitutes demonstrations, and when and where to show them. To address this, we first propose an algorithm that leverages an LLM with retries along with demonstrations to automatically and efficiently annotate agentic tasks with solution trajectories. We then show that set-selection of trajectories of similar tasks as demonstrations significantly improves performance, reliability, robustness, and efficiency of LLM agents. However, trajectory demonstrations have a large inference cost overhead. We show that this can be mitigated by using small trajectory snippets at every step instead of an additional trajectory. We find that demonstrations obtained from larger models (in the annotation phase) also improve smaller models, and that ICL agents can even rival costlier trained agents. Thus, our results reveal that ICL, with careful use, can be very powerful for agentic tasks as well.
zh
[NLP-66] Equitable Electronic Health Record Prediction with FAME: Fairness-Aware Multimodal Embedding
【速读】： 该论文试图解决多模态人工智能（Multimodal AI, MAI）在电子健康记录（Electronic Health Record, EHR）数据处理中可能加剧患者子群体间的偏见问题。现有MAI模型主要关注提升预测性能，而忽视了各模态在公平性上的贡献及其相互作用。该研究提出的解决方案关键在于FAME（Fairness-Aware Multimodal Embeddings）框架，其通过根据各模态的公平性贡献进行加权，并结合联合损失函数优化模型性能与公平性。此外，FAME引入了误差分布差异指数（Error Distribution Disparity Index, EDDI）来衡量子群体间的公平性，并采用一种无符号聚合方法以平衡不同子群体的公平性，从而实现更公平的模型结果。
链接: https://arxiv.org/abs/2506.13104
作者: Nikkie Hooman,Zhongjie Wu,Eric C. Larson,Mehak Gupta
机构: Southern Methodist University (南方卫理公会大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 21 pages, 3 figures
Abstract:Electronic Health Record (EHR) data encompass diverse modalities – text, images, and medical codes – that are vital for clinical decision-making. To process these complex data, multimodal AI (MAI) has emerged as a powerful approach for fusing such information. However, most existing MAI models optimize for better prediction performance, potentially reinforcing biases across patient subgroups. Although bias-reduction techniques for multimodal models have been proposed, the individual strengths of each modality and their interplay in both reducing bias and optimizing performance remain underexplored. In this work, we introduce FAME (Fairness-Aware Multimodal Embeddings), a framework that explicitly weights each modality according to its fairness contribution. FAME optimizes both performance and fairness by incorporating a combined loss function. We leverage the Error Distribution Disparity Index (EDDI) to measure fairness across subgroups and propose a sign-agnostic aggregation method to balance fairness across subgroups, ensuring equitable model outcomes. We evaluate FAME with BEHRT and BioClinicalBERT, combining structured and unstructured EHR data, and demonstrate its effectiveness in terms of performance and fairness compared with other baselines across multiple EHR prediction tasks.
zh
[NLP-67] Rethinking Test-Time Scaling for Medical AI: Model and Task-Aware Strategies for LLM s and VLMs
【速读】： 该论文试图解决在医疗领域中，如何通过测试时缩放（test-time scaling）提升大型语言模型或视觉-语言模型推理能力的问题，特别是针对其在不同模型规模、内在特性及任务复杂性下的有效性，以及在用户驱动因素（如提示中的误导信息）下的鲁棒性问题。解决方案的关键在于系统性地评估测试时缩放策略在医疗场景中的表现，并探索适用于不同情境的最优策略，从而为医疗应用中有效使用测试时缩放提供实践指导。
链接: https://arxiv.org/abs/2506.13102
作者: Gyutaek Oh,Seoyeon Kim,Sangjoon Park,Byung-Hoon Kim
机构: Yonsei University College of Medicine(延世大学医学院); Institute for Innovation in Digital Healthcare, Yonsei University(数字医疗创新研究所，延世大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 11 pages, 6 figures
Abstract:Test-time scaling has recently emerged as a promising approach for enhancing the reasoning capabilities of large language models or vision-language models during inference. Although a variety of test-time scaling strategies have been proposed, and interest in their application to the medical domain is growing, many critical aspects remain underexplored, including their effectiveness for vision-language models and the identification of optimal strategies for different settings. In this paper, we conduct a comprehensive investigation of test-time scaling in the medical domain. We evaluate its impact on both large language models and vision-language models, considering factors such as model size, inherent model characteristics, and task complexity. Finally, we assess the robustness of these strategies under user-driven factors, such as misleading information embedded in prompts. Our findings offer practical guidelines for the effective use of test-time scaling in medical applications and provide insights into how these strategies can be further refined to meet the reliability and interpretability demands of the medical domain.
zh
[NLP-68] CHILL at SemEval-2025 Task 2: You Cant Just Throw Entities and Hope – Make Your LLM to Get Them Right
【速读】： 该论文试图解决实体感知机器翻译（Entity-Aware Machine Translation, EA-MT）中的命名实体翻译准确性问题。解决方案的关键在于结合检索增强生成（Retrieval Augmented Generation, RAG）与基于大语言模型（Large Language Models, LLMs）的迭代自优化技术，并引入一种自我评估机制，使LLM能够根据实体翻译的准确性和整体翻译质量对自身输出进行评估，从而有效提升实体处理能力并保持高质量的翻译结果。
链接: https://arxiv.org/abs/2506.13070
作者: Jaebok Lee,Yonghyun Ryu,Seongmin Park,Yoonjung Choi
机构: Samsung Research (三星研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: The 19th International Workshop on Semantic Evaluation
Abstract:In this paper, we describe our approach for the SemEval 2025 Task 2 on Entity-Aware Machine Translation (EA-MT). Our system aims to improve the accuracy of translating named entities by combining two key approaches: Retrieval Augmented Generation (RAG) and iterative self-refinement techniques using Large Language Models (LLMs). A distinctive feature of our system is its self-evaluation mechanism, where the LLM assesses its own translations based on two key criteria: the accuracy of entity translations and overall translation quality. We demonstrate how these methods work together and effectively improve entity handling while maintaining high-quality translations.
zh
[NLP-69] FinLMM-R1: Enhancing Financial Reasoning in LMM through Scalable Data and Reward Design
【速读】： 该论文旨在解决金融领域中大型多模态模型（LMM）在跨模态推理能力上的不足，具体表现为高质量多模态推理数据集的缺乏以及现有训练范式在推理增强方面的效率低下。其解决方案的关键在于提出一个集成框架FinLMM-R1，该框架结合了自动化且可扩展的数据构建流水线（ASP）和增强的训练策略。ASP通过问答生成与图像-问题对齐的分离范式，解决了财务报告中的文本-视觉错位问题，从而高效地构建了大规模对齐的图像-问题对数据集；同时引入TAR-LMM方法，在两阶段训练框架中加入对抗性奖励机制，以优化模型在视觉感知、推理效率和逻辑连贯性方面的表现。
链接: https://arxiv.org/abs/2506.13066
作者: Kai Lan,Jiayong Zhu,Jiangtong Li,Dawei Cheng,Guang Chen,Changjun Jiang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 26 pages, 16 figures
Abstract:Large Multimodal Models (LMMs) demonstrate significant cross-modal reasoning capabilities. However, financial applications face challenges due to the lack of high-quality multimodal reasoning datasets and the inefficiency of existing training paradigms for reasoning enhancement. To address these issues, we propose an integrated framework, FinLMM-R1, combining an automated and scalable pipeline for data construction with enhanced training strategies to improve the multimodal reasoning of LMM. The Automated and Scalable Pipeline (ASP) resolves textual-visual misalignment in financial reports through a separate paradigm of question-answer generation and image-question alignment, ensuring data integrity and extraction efficiency. Through ASP, we collect 89,378 aligned image-question pairs from 23,397 financial reports, covering tasks such as arithmetic reasoning, statistics reasoning, financial explanation, and financial knowledge. Moreover, we introduce the Thinking with Adversarial Reward in LMM (TAR-LMM), extending the prior two-stage training framework [1] with additional reward mechanisms. In the first stage, we focus on text-only tasks with format and accuracy rewards to guide the model in generating well-structured thinking contents. In the second stage, we construct multi-image contrastive samples with additional reward components including image selection, thinking content length, and adversarial reward to jointly optimize the LMM across visual perception, reasoning efficiency, and logical coherence. Extensive experiments on 7 benchmarks show ASP-derived dataset and training framework significantly improve answer accuracy and reasoning depth over existing reasoning LMMs in both general and financial multimodal contexts.
zh
[NLP-70] MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models ?
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在模拟人类动机方面存在的不足问题，即当前基准测试因场景简单和缺乏角色身份而无法准确反映现实情境中的动机推理能力。解决方案的关键在于提出MotiveBench，它包含200个丰富的上下文场景和600个覆盖多层级动机的推理任务，通过该基准对七个主流模型家族进行广泛实验，揭示了当前LLMs在实现类人动机推理方面的局限性。
链接: https://arxiv.org/abs/2506.13065
作者: Xixian Yong,Jianxun Lian,Xiaoyuan Yi,Xiao Zhou,Xing Xie
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) have been widely adopted as the core of agent frameworks in various scenarios, such as social simulations and AI companions. However, the extent to which they can replicate human-like motivations remains an underexplored question. Existing benchmarks are constrained by simplistic scenarios and the absence of character identities, resulting in an information asymmetry with real-world situations. To address this gap, we propose MotiveBench, which consists of 200 rich contextual scenarios and 600 reasoning tasks covering multiple levels of motivation. Using MotiveBench, we conduct extensive experiments on seven popular model families, comparing different scales and versions within each family. The results show that even the most advanced LLMs still fall short in achieving human-like motivational reasoning. Our analysis reveals key findings, including the difficulty LLMs face in reasoning about “love belonging” motivations and their tendency toward excessive rationality and idealism. These insights highlight a promising direction for future research on the humanization of LLMs. The dataset, benchmark, and code are available at this https URL.
zh
[NLP-71] PRISM2: Unlocking Multi-Modal General Pathology AI with Clinical Dialogue
【速读】： 该论文旨在解决当前病理学基础模型在临床实用性上的不足，这些模型虽然能够提供丰富的切片级表示，但缺乏对全切片图像（WSI）的理解，并且未使用大规模诊断数据进行训练，从而限制了其在多样化下游任务中的性能。论文提出的解决方案是PRISM2，这是一个通过临床对话训练的多模态切片级基础模型，其关键在于采用两阶段训练流程：第一阶段利用对比学习和图像描述目标将全切片嵌入与文本临床诊断对齐；第二阶段解冻语言模型以实现诊断对话并从隐藏状态中提取更具临床意义的表示。通过这种视觉特征与临床推理的对齐，PRISM2提升了在数据丰富和小样本任务中的泛化能力。
链接: https://arxiv.org/abs/2506.13063
作者: George Shaikovski,Eugene Vorontsov,Adam Casson,Julian Viret,Eric Zimmermann,Neil Tenenholtz,Yi Kan Wang,Jan H. Bernhard,Ran A. Godrich,Juan A. Retamero,Razik Yousfi,Nicolo Fusi,Thomas J. Fuchs,Kristen Severson,Siqi Liu
机构: Paige(佩吉); Microsoft Research(微软研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Recent pathology foundation models can provide rich tile-level representations but fall short of delivering general-purpose clinical utility without further extensive model development. These models lack whole-slide image (WSI) understanding and are not trained with large-scale diagnostic data, limiting their performance on diverse downstream tasks. We introduce PRISM2, a multi-modal slide-level foundation model trained via clinical dialogue to enable scalable, generalizable pathology AI. PRISM2 is trained on nearly 700,000 specimens (2.3 million WSIs) paired with real-world clinical diagnostic reports in a two-stage process. In Stage 1, a vision-language model is trained using contrastive and captioning objectives to align whole slide embeddings with textual clinical diagnosis. In Stage 2, the language model is unfrozen to enable diagnostic conversation and extract more clinically meaningful representations from hidden states. PRISM2 achieves strong performance on diagnostic and biomarker prediction tasks, outperforming prior slide-level models including PRISM and TITAN. It also introduces a zero-shot yes/no classification approach that surpasses CLIP-style methods without prompt tuning or class enumeration. By aligning visual features with clinical reasoning, PRISM2 improves generalization on both data-rich and low-sample tasks, offering a scalable path forward for building general pathology AI agents capable of assisting diagnostic and prognostic decisions.
zh
[NLP-72] Multipole Attention for Efficient Long Context Reasoning
【速读】： 该论文旨在解决大型推理模型（Large Reasoning Models, LRMs）在进行长链式思维推理时面临的计算效率与准确性之间的矛盾问题。具体而言，尽管LRMs通过增加测试时的计算量实现了复杂任务的高精度，但其需要生成数千个token的自回归推理过程导致了较高的键值缓存压力，并且现有的稀疏注意力方法可能引入错误，影响推理质量。此外，现有方法依赖于预处理输入以识别关键提示token，难以在线处理新生成的推理token。该论文提出的解决方案的关键在于引入多极注意力（Multipole Attention），通过仅对最重要的token计算精确注意力，而对其他token保持近似表示，从而在保持高精度的同时提升推理效率。该方法首先对语义相似的键向量进行聚类，利用聚类中心同时识别重要键向量并近似其余键向量，同时设计了快速的聚类更新机制，以加速对先前输出token的注意力计算。
链接: https://arxiv.org/abs/2506.13059
作者: Coleman Hooper,Sebastian Zhao,Luca Manolache,Sehoon Kim,Michael W. Mahoney,Yakun Sophia Shao,Kurt Keutzer,Amir Gholami
机构: University of California, Berkeley (加州大学伯克利分校); ICSI (国际计算机科学研究所); LBNL (劳伦斯伯克利国家实验室)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 15 pages
Abstract:Large Reasoning Models (LRMs) have shown promising accuracy improvements on complex problem-solving tasks. While these models have attained high accuracy by leveraging additional computation at test time, they need to generate long chain-of-thought reasoning in order to think before answering, which requires generating thousands of tokens. While sparse attention methods can help reduce the KV cache pressure induced by this long autoregressive reasoning, these methods can introduce errors which disrupt the reasoning process. Additionally, prior methods often pre-process the input to make it easier to identify the important prompt tokens when computing attention during generation, and this pre-processing is challenging to perform online for newly generated reasoning tokens. Our work addresses these challenges by introducing Multipole Attention, which accelerates autoregressive reasoning by only computing exact attention for the most important tokens, while maintaining approximate representations for the remaining tokens. Our method first performs clustering to group together semantically similar key vectors, and then uses the cluster centroids both to identify important key vectors and to approximate the remaining key vectors in order to retain high accuracy. We design a fast cluster update process to quickly re-cluster the input and previously generated tokens, thereby allowing for accelerating attention to the previous output tokens. We evaluate our method using emerging LRMs such as Qwen-8B, demonstrating that our approach can maintain accuracy on complex reasoning tasks even with aggressive attention sparsity settings. We also provide kernel implementations to demonstrate the practical efficiency gains from our method, achieving up to 4.5 \times speedup for attention in long-context reasoning applications. Our code is available at this https URL.
zh
[NLP-73] CFBenchmark-MM: Chinese Financial Assistant Benchmark for Multimodal Large Language Model
【速读】： 该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在金融领域中处理文本、图表和表格等多模态信息时效率和鲁棒性不足的问题。解决方案的关键在于构建了一个名为CFBenchmark-MM的中文多模态金融基准，包含超过9,000对图像-问题数据，涵盖表格、直方图、折线图、饼图和结构图等多种视觉内容，并设计了一个分阶段评估体系，逐步提供不同的视觉信息以评估MLLMs处理多模态金融上下文的能力。
链接: https://arxiv.org/abs/2506.13055
作者: Jiangtong Li,Yiyun Zhu,Dawei Cheng,Zhijun Ding,Changjun Jiang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 22 pages, 9 figures
Abstract:Multimodal Large Language Models (MLLMs) have rapidly evolved with the growth of Large Language Models (LLMs) and are now applied in various fields. In finance, the integration of diverse modalities such as text, charts, and tables is crucial for accurate and efficient decision-making. Therefore, an effective evaluation system that incorporates these data types is essential for advancing financial application. In this paper, we introduce CFBenchmark-MM, a Chinese multimodal financial benchmark with over 9,000 image-question pairs featuring tables, histogram charts, line charts, pie charts, and structural diagrams. Additionally, we develop a staged evaluation system to assess MLLMs in handling multimodal information by providing different visual content step by step. Despite MLLMs having inherent financial knowledge, experimental results still show limited efficiency and robustness in handling multimodal financial context. Further analysis on incorrect responses reveals the misinterpretation of visual content and the misunderstanding of financial concepts are the primary issues. Our research validates the significant, yet underexploited, potential of MLLMs in financial analysis, highlighting the need for further development and domain-specific optimization to encourage the enhanced use in financial domain.
zh
[NLP-74] Stress-Testing Multimodal Foundation Models for Crystallographic Reasoning
【速读】： 该论文旨在解决基础模型在晶体学推理中的泛化能力评估问题，其核心挑战在于如何在保持物理约束的前提下，准确衡量模型在不同尺度和化学组成下的表现。解决方案的关键在于引入了一个多尺度多晶体数据集，并设计了两种基于物理原理的评估协议：空间排斥基准（Spatial-Exclusion benchmark）通过隐藏特定半径的所有超晶胞来测试模型的空间插值与外推能力，而成分排斥基准（Compositional-Exclusion benchmark）则通过移除特定化学组成的样本以探究模型在不同化学计量比下的泛化能力。此外，九个视觉-语言基础模型被用于生成结构注释，并通过晶格参数和密度的相对误差、物理一致性指数以及幻觉分数等指标进行评估，从而构建了一个可重复、物理信息丰富的大型多模态模型评估框架。
链接: https://arxiv.org/abs/2506.13051
作者: Can Polat,Hasan Kurban,Erchin Serpedin,Mustafa Kurban
机构: Texas A&M University (德州农工大学); Hamad Bin Khalifa University (哈马德本哈利法大学); Texas A&M University at Qatar (德州农工大学卡塔尔分校); Ankara University (安卡拉大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Materials Science (cond-mat.mtrl-sci); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Evaluating foundation models for crystallographic reasoning requires benchmarks that isolate generalization behavior while enforcing physical constraints. This work introduces a multiscale multicrystal dataset with two physically grounded evaluation protocols to stress-test multimodal generative models. The Spatial-Exclusion benchmark withholds all supercells of a given radius from a diverse dataset, enabling controlled assessments of spatial interpolation and extrapolation. The Compositional-Exclusion benchmark omits all samples of a specific chemical composition, probing generalization across stoichiometries. Nine vision–language foundation models are prompted with crystallographic images and textual context to generate structural annotations. Responses are evaluated via (i) relative errors in lattice parameters and density, (ii) a physics-consistency index penalizing volumetric violations, and (iii) a hallucination score capturing geometric outliers and invalid space-group predictions. These benchmarks establish a reproducible, physically informed framework for assessing generalization, consistency, and reliability in large-scale multimodal models. Dataset and code are available at this https URL.
zh
[NLP-75] Just Go Parallel: Improving the Multilingual Capabilities of Large Language Models ACL2025
【速读】： 该论文试图解决的问题是：尽管大型语言模型（Large Language Models, LLMs）在未经过平行数据显式训练的情况下表现出色的翻译能力，但其多语言能力是否依赖于平行数据仍存在争议。论文的解决方案关键在于通过系统性研究验证平行数据对LLMs多语言能力的影响，特别是翻译和多语言常识推理方面，并通过控制实验证明平行数据能够显著提升LLMs的多语言能力。
链接: https://arxiv.org/abs/2506.13044
作者: Muhammad Reza Qorib,Junyi Li,Hwee Tou Ng
机构: National University of Singapore (新加坡国立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: ACL 2025
Abstract:Large language models (LLMs) have demonstrated impressive translation capabilities even without being explicitly trained on parallel data. This remarkable property has led some to believe that parallel data is no longer necessary for building multilingual language models. While some attribute this to the emergent abilities of LLMs due to scale, recent work suggests that it is actually caused by incidental bilingual signals present in the training data. Various methods have been proposed to maximize the utility of parallel data to enhance the multilingual capabilities of multilingual encoder-based and encoder-decoder language models. However, some decoder-based LLMs opt to ignore parallel data instead. In this work, we conduct a systematic study on the impact of adding parallel data on LLMs’ multilingual capabilities, focusing specifically on translation and multilingual common-sense reasoning. Through controlled experiments, we demonstrate that parallel data can significantly improve LLMs’ multilingual capabilities.
zh
[NLP-76] Knowledge Graph Fusion with Large Language Models for Accurate Explainable Manufacturing Process Planning
【速读】： 该论文旨在解决数控加工（CNC）中精密工艺规划所面临的复杂决策问题，包括刀具选择、进给速度组合及多轴路径规划，这些问题对工程师在从设计规范到最终零件检测的整个过程中造成了巨大的认知和操作负担。传统基于规则的计算机辅助工艺规划和知识工程外壳将领域知识固化为静态表格，在处理未见过的拓扑结构、新型材料状态、成本-质量-可持续性权重变化或车间约束（如刀具不可用性和能耗限制）时表现出局限性。解决方案的关键在于提出一种端到端框架——增强检索知识图谱增强搜索合成（ARKNESS），该框架融合了零样本知识图（KG）构建与检索增强生成技术，以提供可验证且数值精确的CNC工艺规划答案，其核心机制包括自动从异构加工文档、G代码注释和供应商数据表中提取增强三元组和多关系图，并将任何本地部署的大语言模型（LLM）与检索器结合，注入最小的、证据关联的子图以回答查询。
链接: https://arxiv.org/abs/2506.13026
作者: Danny Hoang,David Gorsich,Matthew P. Castanier,Farhad Imani
机构: University of Connecticut(康涅狄格大学); The United States Army Combat Capabilities Development Command(美国陆军作战能力发展司令部); Ground Vehicle Systems Center(地面车辆系统中心)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Precision process planning in Computer Numerical Control (CNC) machining demands rapid, context-aware decisions on tool selection, feed-speed pairs, and multi-axis routing, placing immense cognitive and procedural burdens on engineers from design specification through final part inspection. Conventional rule-based computer-aided process planning and knowledge-engineering shells freeze domain know-how into static tables, which become limited when dealing with unseen topologies, novel material states, shifting cost-quality-sustainability weightings, or shop-floor constraints such as tool unavailability and energy caps. Large language models (LLMs) promise flexible, instruction-driven reasoning for tasks but they routinely hallucinate numeric values and provide no provenance. We present Augmented Retrieval Knowledge Network Enhanced Search Synthesis (ARKNESS), the end-to-end framework that fuses zero-shot Knowledge Graph (KG) construction with retrieval-augmented generation to deliver verifiable, numerically exact answers for CNC process planning. ARKNESS (1) automatically distills heterogeneous machining documents, G-code annotations, and vendor datasheets into augmented triple, multi-relational graphs without manual labeling, and (2) couples any on-prem LLM with a retriever that injects the minimal, evidence-linked subgraph needed to answer a query. Benchmarked on 155 industry-curated questions spanning tool sizing and feed-speed optimization, a lightweight 3B-parameter Llama-3 augmented by ARKNESS matches GPT-4o accuracy while achieving a +25 percentage point gain in multiple-choice accuracy, +22.4 pp in F1, and 8.1x ROUGE-L on open-ended responses.
zh
[NLP-77] Edeflip: Supervised Word Translation between English and Yoruba
【速读】： 该论文试图解决在低资源语言中应用嵌入对齐技术进行机器翻译的可行性问题，尤其是探讨是否以及如何使低资源语言受益于现有的监督式嵌入对齐方法。解决方案的关键在于实现一种已有的监督式嵌入对齐方法，用于从英语到约鲁巴语（一种低资源语言）的词对齐，并验证嵌入质量与归一化处理对翻译精度的影响，同时揭示两者之间的交互效应。研究结果表明，对于低资源语言，除了嵌入对齐技术本身外，还需考虑其他因素，如高质量单语嵌入的构建。
链接: https://arxiv.org/abs/2506.13020
作者: Ikeoluwa Abioye,Jiani Ge
机构: Dartmouth College (达特茅斯学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In recent years, embedding alignment has become the state-of-the-art machine translation approach, as it can yield high-quality translation without training on parallel corpora. However, existing research and application of embedding alignment mostly focus on high-resource languages with high-quality monolingual embeddings. It is unclear if and how low-resource languages may be similarly benefited. In this study, we implement an established supervised embedding alignment method for word translation from English to Yoruba, the latter a low-resource language. We found that higher embedding quality and normalizing embeddings increase word translation precision, with, additionally, an interaction effect between the two. Our results demonstrate the limitations of the state-of-the-art supervised embedding alignment when it comes to low-resource languages, for which there are additional factors that need to be taken into consideration, such as the importance of curating high-quality monolingual embeddings. We hope our work will be a starting point for further machine translation research that takes into account the challenges that low-resource languages face.
zh
[NLP-78] Missing the human touch? A computational stylometry analysis of GPT -4 translations of online Chinese literature
【速读】： 该论文试图解决当前机器翻译（Machine Translation, MT）在文学文本中的表现不佳问题，以及探索最先进的大语言模型（Large Language Models, LLMs）是否能够重塑文学翻译的风格。其解决方案的关键在于通过计算文体分析（Computational Stylometry Analysis）对比GPT-4与人类翻译在词汇、语法和内容特征上的相似性，从而评估LLMs是否能够复制文学翻译中的“人文特质”。
链接: https://arxiv.org/abs/2506.13013
作者: Xiaofang Yao,Yong-Bin Kang,Anthony McCosker
机构: The University of Hong Kong (香港大学); Swinburne University of Technology (斯威本科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 15 pages, 3 figures
Abstract:Existing research indicates that machine translations (MTs) of literary texts are often unsatisfactory. MTs are typically evaluated using automated metrics and subjective human ratings, with limited focus on stylistic features. Evidence is also limited on whether state-of-the-art large language models (LLMs) will reshape literary translation. This study examines the stylistic features of LLM translations, comparing GPT-4’s performance to human translations in a Chinese online literature task. Computational stylometry analysis shows that GPT-4 translations closely align with human translations in lexical, syntactic, and content features, suggesting that LLMs might replicate the ‘human touch’ in literary translation style. These findings offer insights into AI’s impact on literary translation from a posthuman perspective, where distinctions between machine and human translations become increasingly blurry.
zh
[NLP-79] Large Language Models Enhanced by Plug and Play Syntactic Knowledge for Aspect-based Sentiment Analysis
【速读】： 该论文试图解决在资源受限环境下，如何高效地将大语言模型（Large Language Models, LLMs）适配到基于方面的情感分析（Aspect-based Sentiment Analysis, ABSA）任务中的问题。其解决方案的关键在于提出一种可扩展的组件集成方法，该方法能够融合多种句法知识（如成分句法、词性依赖和组合范畴语法），并通过一个独立训练的记忆模块记录句法信息，以指导情感极性预测。该模块作为灵活且可分离的插件，无需对LLM进行大规模微调即可有效提升性能。
链接: https://arxiv.org/abs/2506.12991
作者: Yuanhe Tian,Xu Li,Wei Wang,Guoqing Jin,Pengsen Cheng,Yan Song
机构: University of Washington; University of Science and Technology of China; China Resources Digital Technology; People’s Daily Online; Sichuan University
类目: Computation and Language (cs.CL)
备注: 12 pages, 4 figures
Abstract:Aspect-based sentiment analysis (ABSA) generally requires a deep understanding of the contextual information, including the words associated with the aspect terms and their syntactic dependencies. Most existing studies employ advanced encoders (e.g., pre-trained models) to capture such context, especially large language models (LLMs). However, training these encoders is resource-intensive, and in many cases, the available data is insufficient for necessary fine-tuning. Therefore it is challenging for learning LLMs within such restricted environments and computation efficiency requirement. As a result, it motivates the exploration of plug-and-play methods that adapt LLMs to ABSA with minimal effort. In this paper, we propose an approach that integrates extendable components capable of incorporating various types of syntactic knowledge, such as constituent syntax, word dependencies, and combinatory categorial grammar (CCG). Specifically, we propose a memory module that records syntactic information and is incorporated into LLMs to instruct the prediction of sentiment polarities. Importantly, this encoder acts as a versatile, detachable plugin that is trained independently of the LLM. We conduct experiments on benchmark datasets, which show that our approach outperforms strong baselines and previous approaches, thus demonstrates its effectiveness.
zh
[NLP-80] Efficient Neuro-Symbolic Retrieval-Augmented Generation through Adaptive Query Routing
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统在处理简单查询时与复杂多跳推理任务消耗相当计算资源所导致的效率问题。其解决方案的关键在于提出SymRAG，一个神经符号框架，通过实时复杂度和系统负载评估实现自适应查询路由，动态选择符号、神经或混合处理路径，以匹配查询需求并优化资源使用。
链接: https://arxiv.org/abs/2506.12981
作者: Safayat Bin Hakim,Muhammad Adil,Alvaro Velasquez,Houbing Herbert Song
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Retrieval-Augmented Generation (RAG) systems address factual inconsistencies in Large Language Models by grounding generation in external knowledge, yet they face a fundamental efficiency problem: simple queries consume computational resources equivalent to complex multi-hop reasoning tasks. We present SymRAG, a neuro-symbolic framework that introduces adaptive query routing based on real-time complexity and system load assessments. SymRAG dynamically selects symbolic, neural, or hybrid processing paths to align resource use with query demands. Evaluated on 2,000 queries from HotpotQA and DROP using Llama-3.2-3B and Mistral-7B models, SymRAG achieves 97.6–100.0% exact match accuracy with significantly lower CPU utilization (3.6–6.2%) and processing time (0.985–3.165s). Disabling adaptive logic results in 169–1151% increase in processing time, highlighting the framework’s impact. These results underscore the potential of adaptive neuro-symbolic routing for scalable, sustainable AI systems.
zh
[NLP-81] Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLM s: a case study in Framing Bias Mitigation ACL2025
【速读】： 该论文试图解决当前媒体日益偏执和极化的现象，特别是通过生成中立化摘要来缓解媒体偏见。其解决方案的关键在于利用多文档事件推理增强大语言模型（LLM）对偏见的认知，并通过多文档事件关系图引导摘要生成过程。该图包含四种文档内事件关系以反映内容框架偏见、跨文档事件共指关系以揭示内容选择偏见，以及事件级道德观点以突出带有立场的框架偏见，从而有效减少词汇性和信息性媒体偏见，同时提升内容保留度。
链接: https://arxiv.org/abs/2506.12978
作者: Yuanyuan Lei,Ruihong Huang
机构: Texas A&M University (得克萨斯A&M大学)
类目: Computation and Language (cs.CL)
备注: Accepted to ACL 2025
Abstract:Media outlets are becoming more partisan and polarized nowadays. Most previous work focused on detecting media bias. In this paper, we aim to mitigate media bias by generating a neutralized summary given multiple articles presenting different ideological views. Motivated by the critical role of events and event relations in media bias detection, we propose to increase awareness of bias in LLMs via multi-document events reasoning and use a multi-document event relation graph to guide the summarization process. This graph contains rich event information useful to reveal bias: four common types of in-doc event relations to reflect content framing bias, cross-doc event coreference relation to reveal content selection bias, and event-level moral opinions to highlight opinionated framing bias. We further develop two strategies to incorporate the multi-document event relation graph for neutralized summarization. Firstly, we convert a graph into natural language descriptions and feed the textualized graph into LLMs as a part of a hard text prompt. Secondly, we encode the graph with graph attention network and insert the graph embedding into LLMs as a soft prompt. Both automatic evaluation and human evaluation confirm that our approach effectively mitigates both lexical and informational media bias, and meanwhile improves content preservation.
zh
[NLP-82] Assessing the Role of Data Quality in Training Bilingual Language Models
【速读】： 该论文试图解决多语言语言模型在不同语言间性能差异显著的问题，尤其是在增加语言数量时可能导致某些语言（如英语）性能下降的现象。解决方案的关键在于识别并改进训练数据的质量，而非仅仅依赖数据量。通过仅使用高质量的英文数据进行筛选，该方法有效提升了法语、德语和中文等语言的单语性能，并显著缩小了多语模型的性能差距。
链接: https://arxiv.org/abs/2506.12966
作者: Skyler Seto,Maartje ter Hoeve,Maureen de Seyssel,David Grangier
机构: Apple(苹果)
类目: Computation and Language (cs.CL)
备注: 26 pages, 18 figures, 25 tables
Abstract:Bilingual and multilingual language models offer a promising path toward scaling NLP systems across diverse languages and users. However, their performance often varies wildly between languages as prior works show that adding more languages can degrade performance for some languages (such as English), while improving others (typically more data constrained languages). In this work, we investigate causes of these inconsistencies by comparing bilingual and monolingual language models. Our analysis reveals that unequal data quality, not just data quantity, is a major driver of performance degradation in bilingual settings. We propose a simple yet effective data filtering strategy to select higher-quality bilingual training data with only high quality English data. Applied to French, German, and Chinese, our approach improves monolingual performance by 2-4% and reduces bilingual model performance gaps to 1%. These results highlight the overlooked importance of data quality in multilingual pretraining and offer a practical recipe for balancing performance.
zh
[NLP-83] Forecasting Time Series with LLM s via Patch-Based Prompting and Decomposition
【速读】： 该论文试图解决传统时间序列分析中需要大量微调以及忽略序列间相关性的局限性。其解决方案的关键在于提出一种简单且灵活的基于提示（prompt-based）策略，使大型语言模型（Large Language Models, LLMs）能够在不进行大量再训练或使用复杂外部架构的情况下进行时间序列预测。通过利用时间序列分解、基于补丁的标记化和基于相似性的邻居增强等专门提示方法，该研究实现了在保持方法简洁性的同时提升预测质量。
链接: https://arxiv.org/abs/2506.12953
作者: Mayank Bumb,Anshul Vemulapalli,Sri Harsha Vardhan Prasad Jella,Anish Gupta,An La,Ryan A. Rossi,Hongjie Chen,Franck Dernoncourt,Nesreen K. Ahmed,Yu Wang
机构: University of Massachusetts Amherst(马萨诸塞大学阿默斯特分校); Adobe(Adobe公司); Dolby Labs(杜比实验室); Intel(英特尔); University of Oregon(俄勒冈大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Recent advances in Large Language Models (LLMs) have demonstrated new possibilities for accurate and efficient time series analysis, but prior work often required heavy fine-tuning and/or ignored inter-series correlations. In this work, we explore simple and flexible prompt-based strategies that enable LLMs to perform time series forecasting without extensive retraining or the use of a complex external architecture. Through the exploration of specialized prompting methods that leverage time series decomposition, patch-based tokenization, and similarity-based neighbor augmentation, we find that it is possible to enhance LLM forecasting quality while maintaining simplicity and requiring minimal preprocessing of data. To this end, we propose our own method, PatchInstruct, which enables LLMs to make precise and effective predictions.
zh
[NLP-84] HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance
【速读】： 该论文试图解决科学推理链中有效假设生成的问题，特别是针对现有方法过于关注最终输出质量而忽视了推理过程的不足。其解决方案的关键在于提出一种名为 \textttHypER 的小型语言模型（SLM），该模型通过多任务学习设置，在受控干扰下区分有效的和无效的科学推理链，并基于文献引导的推理和证据生成假设。实验结果表明，\textttHypER 在区分有效与无效推理链方面优于基础模型，且生成的假设更具可行性与影响力。
链接: https://arxiv.org/abs/2506.12937
作者: Rosni Vasu,Chandrayee Basu,Bhavana Dalvi Mishra,Cristina Sarasua,Peter Clark,Abraham Bernstein
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 26 pages (9 pages: main paper body)
Abstract:Large Language models have demonstrated promising performance in research ideation across scientific domains. Hypothesis development, the process of generating a highly specific declarative statement connecting a research idea with empirical validation, has received relatively less attention. Existing approaches trivially deploy retrieval augmentation and focus only on the quality of the final output ignoring the underlying reasoning process behind ideation. We present \textttHypER ( \textbfHyp othesis Generation with \textbfE xplanation and \textbfR easoning), a small language model (SLM) trained for literature-guided reasoning and evidence-based hypothesis generation. \textttHypER is trained in a multi-task setting to discriminate between valid and invalid scientific reasoning chains in presence of controlled distractions. We find that \textttHypER outperformes the base model, distinguishing valid from invalid reasoning chains (+22% average absolute F1), generates better evidence-grounded hypotheses (0.327 vs. 0.305 base model) with high feasibility and impact as judged by human experts ( 3.5 on 5-point Likert scale).
zh
[NLP-85] CliniDial: A Naturally Occurring Multimodal Dialogue Dataset for Team Reflection in Action During Clinical Operation ACL2025
【速读】： 该论文试图解决临床操作中团队协作对最终手术结果影响的分析问题，其核心在于理解医疗团队在模拟手术中的协作过程。解决方案的关键是构建并公开了一个名为CliniDial的数据集，该数据集包含音频、语音转录、患者假人模拟生理信号以及多角度视频，同时通过行为编码框架进行标注，以捕捉团队互动的丰富性和自然性。此外，研究还揭示了该数据集在标签不平衡、多模态和自然交互方面的特性，并验证了现有大语言模型（Large Language Models, LLMs）在处理此类数据时的局限性。
链接: https://arxiv.org/abs/2506.12936
作者: Naihao Deng,Kapotaksha Das,Rada Mihalcea,Vitaliy Popov,Mohamed Abouelenien
机构: University of Michigan, Ann Arbor (密歇根大学安娜堡分校); University of Michigan, Dearborn (密歇根大学迪尔伯恩分校)
类目: Computation and Language (cs.CL)
备注: Accepted to ACL 2025 Findings
Abstract:In clinical operations, teamwork can be the crucial factor that determines the final outcome. Prior studies have shown that sufficient collaboration is the key factor that determines the outcome of an operation. To understand how the team practices teamwork during the operation, we collected CliniDial from simulations of medical operations. CliniDial includes the audio data and its transcriptions, the simulated physiology signals of the patient manikins, and how the team operates from two camera angles. We annotate behavior codes following an existing framework to understand the teamwork process for CliniDial. We pinpoint three main characteristics of our dataset, including its label imbalances, rich and natural interactions, and multiple modalities, and conduct experiments to test existing LLMs’ capabilities on handling data with these characteristics. Experimental results show that CliniDial poses significant challenges to the existing models, inviting future effort on developing methods that can deal with real-world clinical data. We open-source the codebase at this https URL
zh
[NLP-86] SoundMind: RL-Incentivized Logic Reasoning for Audio-Language Models
【速读】： 该论文试图解决大型语言模型在音频模态应用中的不足，特别是在大型音频-语言模型（ALMs）中推理能力的显著欠缺问题。解决方案的关键在于构建一个高质量、面向推理的音频数据集以及设计一种专门的规则强化学习（RL）算法。具体而言，研究者提出了Audio Logical Reasoning (ALR)数据集和SoundMind算法，通过在ALR数据集上使用SoundMind对Qwen2.5-Omni-7B进行训练，实现了音频逻辑推理任务的最先进性能。
链接: https://arxiv.org/abs/2506.12935
作者: Xingjian Diao,Chunhui Zhang,Keyi Kong,Weiyi Wu,Chiyu Ma,Zhongyu Ouyang,Peijun Qing,Soroush Vosoughi,Jiang Gui
机构: Dartmouth College (达特茅斯学院); Shandong University (山东大学)
类目: Computation and Language (cs.CL); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注:
Abstract:While large language models have shown reasoning capabilities, their application to the audio modality, particularly in large audio-language models (ALMs), remains significantly underdeveloped. Addressing this gap requires a systematic approach, involving a capable base model, high-quality reasoning-oriented audio data, and effective training algorithms. In this study, we present a comprehensive solution: we introduce the Audio Logical Reasoning (ALR) dataset, consisting of 6,446 text-audio annotated samples specifically designed for complex reasoning tasks. Building on this resource, we propose SoundMind, a rule-based reinforcement learning (RL) algorithm tailored to endow ALMs with deep bimodal reasoning abilities. By training Qwen2.5-Omni-7B on the ALR dataset using SoundMind, our approach achieves state-of-the-art performance in audio logical reasoning. This work highlights the impact of combining high-quality, reasoning-focused datasets with specialized RL techniques, advancing the frontier of auditory intelligence in language models. Our code and the proposed dataset are available at this https URL.
zh
[NLP-87] Sectoral Coupling in Linguistic State Space
【速读】： 该论文试图解决如何量化人工代理内部功能子系统之间的内在依赖关系的问题，特别是当其信念状态由结构化的语言片段组成时。解决方案的关键在于引入了一套领域耦合常数（sectoral coupling constants），这些常数用于表征在固定抽象层次下，一个认知领域如何影响另一个领域，从而形成代理特有的耦合轮廓，该轮廓控制内部信息流并塑造代理的整体处理倾向和认知风格。
链接: https://arxiv.org/abs/2506.12927
作者: Sebastian Dumbrava
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 56 pages, 12 figures
Abstract:This work presents a formal framework for quantifying the internal dependencies between functional subsystems within artificial agents whose belief states are composed of structured linguistic fragments. Building on the Semantic Manifold framework, which organizes belief content into functional sectors and stratifies them across hierarchical levels of abstraction, we introduce a system of sectoral coupling constants that characterize how one cognitive sector influences another within a fixed level of abstraction. The complete set of these constants forms an agent-specific coupling profile that governs internal information flow, shaping the agent’s overall processing tendencies and cognitive style. We provide a detailed taxonomy of these intra-level coupling roles, covering domains such as perceptual integration, memory access and formation, planning, meta-cognition, execution control, and affective modulation. We also explore how these coupling profiles generate feedback loops, systemic dynamics, and emergent signatures of cognitive behavior. Methodologies for inferring these profiles from behavioral or internal agent data are outlined, along with a discussion of how these couplings evolve across abstraction levels. This framework contributes a mechanistic and interpretable approach to modeling complex cognition, with applications in AI system design, alignment diagnostics, and the analysis of emergent agent behavior.
zh
[NLP-88] Identifying and Investigating Global News Coverag e of Critical Events Such as Disasters and Terrorist Attacks
【速读】： 该论文试图解决跨语言新闻报道中识别同一事件相关文章的难题，这一问题由于需要专业知识且难以扩展而具有挑战性。解决方案的关键在于引入一种基于事件FINGERPRINT（事件指纹）的AI驱动方法，即FAME（FINGERPRINT TO ARTICLE MATCHING FOR EVENTS），该方法通过时间、地点和事件类型等最小元数据集高效识别关键事件相关的新闻文章，无需训练数据即可自动完成事件与文章的匹配。
链接: https://arxiv.org/abs/2506.12925
作者: Erica Cai,Xi Chen,Reagan Grey Keeney,Ethan Zuckerman,Brendan O’Connor,Przemyslaw A. Grabowicz
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Comparative studies of news coverage are challenging to conduct because methods to identify news articles about the same event in different languages require expertise that is difficult to scale. We introduce an AI-powered method for identifying news articles based on an event FINGERPRINT, which is a minimal set of metadata required to identify critical events. Our event coverage identification method, FINGERPRINT TO ARTICLE MATCHING FOR EVENTS (FAME), efficiently identifies news articles about critical world events, specifically terrorist attacks and several types of natural disasters. FAME does not require training data and is able to automatically and efficiently identify news articles that discuss an event given its fingerprint: time, location, and class (such as storm or flood). The method achieves state-of-the-art performance and scales to massive databases of tens of millions of news articles and hundreds of events happening globally. We use FAME to identify 27,441 articles that cover 470 natural disaster and terrorist attack events that happened in 2020. To this end, we use a massive database of news articles in three languages from MediaCloud, and three widely used, expert-curated databases of critical events: EM-DAT, USGS, and GTD. Our case study reveals patterns consistent with prior literature: coverage of disasters and terrorist attacks correlates to death counts, to the GDP of a country where the event occurs, and to trade volume between the reporting country and the country where the event occurred. We share our NLP annotations and cross-country media attention data to support the efforts of researchers and media monitoring organizations.
zh
[NLP-89] PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalization
【速读】： 该论文试图解决生成式 AI (Generative AI) 在个性化响应生成方面的评估基准不足问题，即如何构建能够根据预定义用户人格特征生成定制化回应的大型语言模型（LLM）系统。解决方案的关键在于提出 PersonaFeedback，这是一个新的基准测试集，直接评估模型在给定明确用户人格特征和查询时生成个性化响应的能力，而非依赖于从历史交互中推断隐式用户人格，从而将人格推断与个性化任务解耦，专注于评估模型对显式人格特征的响应生成能力。
链接: https://arxiv.org/abs/2506.12915
作者: Meiling Tao,Chenghao Zhu,Dongyi Ding,Tiannan Wang,Yuchen Eleanor Jiang,Wangchunshu Zhou
机构: 未知
类目: Computation and Language (cs.CL)
备注: Work in progress
Abstract:With the rapid improvement in the general capabilities of LLMs, LLM personalization, i.e., how to build LLM systems that can generate personalized responses or services that are tailored to distinct user personas, has become an increasingly important research and engineering problem. However, unlike many new challenging benchmarks being released for evaluating the general/reasoning capabilities, the lack of high-quality benchmarks for evaluating LLM personalization greatly hinders progress in this field. To address this, we introduce PersonaFeedback, a new benchmark that directly evaluates LLMs’ ability to provide personalized responses given pre-defined user personas and queries. Unlike existing benchmarks that require models to infer implicit user personas from historical interactions, PersonaFeedback decouples persona inference from personalization, focusing on evaluating the model’s ability to generate responses tailored to explicit personas. PersonaFeedback consists of 8298 human-annotated test cases, which are categorized into easy, medium, and hard tiers based on the contextual complexity of the user personas and the difficulty in distinguishing subtle differences between two personalized responses. We conduct comprehensive evaluations across a wide range of models. The empirical results reveal that even state-of-the-art LLMs that can solve complex real-world reasoning tasks could fall short on the hard tier of PersonaFeedback where even human evaluators may find the distinctions challenging. Furthermore, we conduct an in-depth analysis of failure modes across various types of systems, demonstrating that the current retrieval-augmented framework should not be seen as a de facto solution for personalization tasks. All benchmark data, annotation protocols, and the evaluation pipeline will be publicly available to facilitate future research on LLM personalization.
zh
[NLP-90] SciDA: Scientific Dynamic Assessor of LLM s
【速读】： 该论文试图解决现有大型语言模型（Large Language Models, LLMs）推理能力评估基准存在数据污染或学科覆盖不全的问题。其解决方案的关键在于提出SciDA，一个由超过1000个奥林匹克级别数值计算问题组成的多学科基准，通过为每次推理轮次随机化数值初始化，避免模型依赖固定的数值模式，从而提供真实且无偏的LLMs数值推理能力评估。
链接: https://arxiv.org/abs/2506.12909
作者: Junting Zhou,Tingjia Miao,Yiyan Liao,Qichao Wang,Zhoufutu Wen,Yanqin Wang,Yunjie Huang,Ge Yan,Leqi Wang,Yucheng Xia,Hongwan Gao,Yuansong Zeng,Renjie Zheng,Chen Dun,Yitao Liang,Tong Yang,Wenhao Huang,Ge Zhang
机构: ByteDance(字节跳动); Peking University(北京大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Advancement in Large Language Models (LLMs) reasoning capabilities enables them to solve scientific problems with enhanced efficacy. Thereby, a high-quality benchmark for comprehensive and appropriate assessment holds significance, while existing ones either confront the risk of data contamination or lack involved disciplines. To be specific, due to the data source overlap of LLMs training and static benchmark, the keys or number pattern of answers inadvertently memorized (i.e. data contamination), leading to systematic overestimation of their reasoning capabilities, especially numerical reasoning. We propose SciDA, a multidisciplinary benchmark that consists exclusively of over 1k Olympic-level numerical computation problems, allowing randomized numerical initializations for each inference round to avoid reliance on fixed numerical patterns. We conduct a series of experiments with both closed-source and open-source top-performing LLMs, and it is observed that the performance of LLMs drop significantly under random numerical initialization. Thus, we provide truthful and unbiased assessments of the numerical reasoning capabilities of LLMs. The data is available at this https URL
zh
[NLP-91] JEBS: A Fine-grained Biomedical Lexical Simplification Task
【速读】： 该论文旨在解决医学文献中复杂术语（jargon）阻碍公众理解健康信息的问题。其解决方案的关键在于提出一个细粒度的词汇简化任务和数据集，即Jargon Explanations for Biomedical Simplification (JEBS)，该任务包括识别复杂术语、分类替换方式以及生成替代文本。JEBS数据集包含400篇生物医学摘要及其人工简化版本中的21,595个术语替换实例，并为三个子任务提供了基于规则和Transformer的基线结果，从而为复杂生物医学术语的替换或解释系统的发展与评估提供了基础。
链接: https://arxiv.org/abs/2506.12898
作者: William Xia,Ishita Unde,Brian Ondov,Dina Demner-Fushman
机构: Tufts University (塔夫茨大学); Johns Hopkins University (约翰霍普金斯大学); Yale University (耶鲁大学); National Library of Medicine (美国国家医学图书馆)
类目: Computation and Language (cs.CL)
备注: 13 pages, 2 figures, to be published in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics
Abstract:Online medical literature has made health information more available than ever, however, the barrier of complex medical jargon prevents the general public from understanding it. Though parallel and comparable corpora for Biomedical Text Simplification have been introduced, these conflate the many syntactic and lexical operations involved in simplification. To enable more targeted development and evaluation, we present a fine-grained lexical simplification task and dataset, Jargon Explanations for Biomedical Simplification (JEBS, this https URL ). The JEBS task involves identifying complex terms, classifying how to replace them, and generating replacement text. The JEBS dataset contains 21,595 replacements for 10,314 terms across 400 biomedical abstracts and their manually simplified versions. Additionally, we provide baseline results for a variety of rule-based and transformer-based systems for the three sub-tasks. The JEBS task, data, and baseline results pave the way for development and rigorous evaluation of systems for replacing or explaining complex biomedical terms.
zh
[NLP-92] Assessing the Performance Gap Between Lexical and Semantic Models for Information Retrieval With Formulaic Legal Language
【速读】： 该论文旨在解决法律文本检索中的挑战，特别是针对欧洲法院（Court of Justice of the European Union, CJEU）判决书中高度结构化和公式化的语言特性，如何更有效地检索相关法律段落。其解决方案的关键在于比较基于词汇和统计特征的模型（如BM25）与密集检索模型在处理法律语言重复性方面的效果，并探索在不同场景下哪种方法更具优势。研究发现，在语言重复性较高的场景中，两者均表现良好，但在语义更复杂、重复性较低或查询较长的情况下，BM25表现优于密集模型，而通过领域特定数据微调的密集模型在多数指标上超越了BM25。
链接: https://arxiv.org/abs/2506.12895
作者: Larissa Mori,Carlos Sousa de Oliveira,Yuehwern Yih,Mario Ventresca
机构: 未知
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Legal passage retrieval is an important task that assists legal practitioners in the time-intensive process of finding relevant precedents to support legal arguments. This study investigates the task of retrieving legal passages or paragraphs from decisions of the Court of Justice of the European Union (CJEU), whose language is highly structured and formulaic, leading to repetitive patterns. Understanding when lexical or semantic models are more effective at handling the repetitive nature of legal language is key to developing retrieval systems that are more accurate, efficient, and transparent for specific legal domains. To this end, we explore when this routinized legal language is better suited for retrieval using methods that rely on lexical and statistical features, such as BM25, or dense retrieval models trained to capture semantic and contextual information. A qualitative and quantitative analysis with three complementary metrics shows that both lexical and dense models perform well in scenarios with more repetitive usage of language, whereas BM25 performs better than the dense models in more nuanced scenarios where repetition and verbatim~quotes are less prevalent and in longer queries. Our experiments also show that BM25 is a strong baseline, surpassing off-the-shelf dense models in 4 out of 7 performance metrics. However, fine-tuning a dense model on domain-specific data led to improved performance, surpassing BM25 in most metrics, and we analyze the effect of the amount of data used in fine-tuning on the model’s performance and temporal robustness. The code, dataset and appendix related to this work are available on: this https URL.
zh
[NLP-93] ArgHiTZ at ArchEHR-QA 2025: A Two-Step Divide and Conquer Approach to Patient Question Answering for Top Factuality ACL2025
【速读】： 该论文旨在解决ArchEHR-QA 2025共享任务中的自动化患者问答问题（patient question answering），即根据临床文本自动回答患者提出的问题。其解决方案的关键在于采用三种不同的方法，其中两种为两步法：首先通过提示或相似性排序从临床文本中提取关键句子，然后基于这些句子生成最终答案。结果显示，基于重排序的两步系统表现最佳，突显了针对每个子任务选择合适方法的重要性。
链接: https://arxiv.org/abs/2506.12886
作者: Adrián Cuadrón,Aimar Sagasti,Maitane Urruela,Iker De la Iglesia,Ane G Domingo-Aldama,Aitziber Atutxa,Josu Goikoetxea,Ander Barrena
机构: HiTZ Center - Ixa, University of the Basque Country UPV/EHU (HiTZ中心-IXA，巴斯克大学UPV/EHU)
类目: Computation and Language (cs.CL)
备注: This paper has been accepted for publication in Proceedings of the 24th Workshop on Biomedical Natural Language Processing (BioNLP) at ACL 2025
Abstract:This work presents three different approaches to address the ArchEHR-QA 2025 Shared Task on automated patient question answering. We introduce an end-to-end prompt-based baseline and two two-step methods to divide the task, without utilizing any external knowledge. Both two step approaches first extract essential sentences from the clinical text, by prompt or similarity ranking, and then generate the final answer from these notes. Results indicate that the re-ranker based two-step system performs best, highlighting the importance of selecting the right approach for each subtask. Our best run achieved an overall score of 0.44, ranking 8th out of 30 on the leaderboard, securing the top position in overall factuality.
zh
[NLP-94] QFFT Question-Free Fine-Tuning for Adaptive Reasoning
【速读】： 该论文试图解决长链式思维（Long Chain-of-Thought, CoT）推理模型在处理简单问题时出现的过度思考问题，即生成冗余的推理步骤。解决方案的关键在于提出一种无需问题输入的微调方法（Question-Free Fine-Tuning, QFFT），该方法在训练过程中移除输入问题，仅从长CoT响应中学习，从而使模型能够自适应地选择使用简洁的短CoT模式或在必要时激活长CoT模式，从而有效减少推理步骤长度并提升模型在复杂场景下的表现。
链接: https://arxiv.org/abs/2506.12860
作者: Wanlong Liu,Junxiao Xu,Fei Yu,Yukang Lin,Ke Ji,Wenyu Chen,Yan Xu,Yasheng Wang,Lifeng Shang,Benyou Wang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 23 pages
Abstract:Recent advancements in Long Chain-of-Thought (CoT) reasoning models have improved performance on complex tasks, but they suffer from overthinking, which generates redundant reasoning steps, especially for simple questions. This paper revisits the reasoning patterns of Long and Short CoT models, observing that the Short CoT patterns offer concise reasoning efficiently, while the Long CoT patterns excel in challenging scenarios where the Short CoT patterns struggle. To enable models to leverage both patterns, we propose Question-Free Fine-Tuning (QFFT), a fine-tuning approach that removes the input question during training and learns exclusively from Long CoT responses. This approach enables the model to adaptively employ both reasoning patterns: it prioritizes the Short CoT patterns and activates the Long CoT patterns only when necessary. Experiments on various mathematical datasets demonstrate that QFFT reduces average response length by more than 50%, while achieving performance comparable to Supervised Fine-Tuning (SFT). Additionally, QFFT exhibits superior performance compared to SFT in noisy, out-of-domain, and low-resource scenarios.
zh
[NLP-95] ransforming Chatbot Text: A Sequence-to-Sequence Approach
【速读】： 该论文试图解决AI生成文本与人类撰写文本之间检测准确率下降的问题，即如何使GPT生成的文本更接近人类写作以逃避检测。其解决方案的关键在于采用对抗性策略，利用序列到序列（Seq2Seq）模型如T5-small和BART对GPT生成的文本进行转换，使其在语言、结构和语义上更符合人类写作特征，从而降低现有分类模型的检测准确性。
链接: https://arxiv.org/abs/2506.12843
作者: Natesh Reddy,Mark Stamp
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Due to advances in Large Language Models (LLMs) such as ChatGPT, the boundary between human-written text and AI-generated text has become blurred. Nevertheless, recent work has demonstrated that it is possible to reliably detect GPT-generated text. In this paper, we adopt a novel strategy to adversarially transform GPT-generated text using sequence-to-sequence (Seq2Seq) models, with the goal of making the text more human-like. We experiment with the Seq2Seq models T5-small and BART which serve to modify GPT-generated sentences to include linguistic, structural, and semantic components that may be more typical of human-authored text. Experiments show that classification models trained to distinguish GPT-generated text are significantly less accurate when tested on text that has been modified by these Seq2Seq models. However, after retraining classification models on data generated by our Seq2Seq technique, the models are able to distinguish the transformed GPT-generated text from human-generated text with high accuracy. This work adds to the accumulating knowledge of text transformation as a tool for both attack – in the sense of defeating classification models – and defense – in the sense of improved classifiers – thereby advancing our understanding of AI-generated text.
zh
[NLP-96] WereWolf-Plus: An Update of Werewolf Game setting Based on DSGBench
【速读】： 该论文旨在解决现有基于狼人杀（Werewolf）的基准平台在游戏设定过于简化、评估指标不完整以及可扩展性差等方面的问题。其解决方案的关键在于提出WereWolf-Plus，一个支持多模型、多维度和多方法的基准平台，用于评估狼人杀游戏中多智能体的战略推理能力。该平台具备强大的可扩展性，支持角色（如先知、女巫、猎人、守卫和警长）的自定义配置，并提供灵活的模型分配和推理增强策略，同时引入了全面的定量评估指标，丰富了对智能体推理能力、合作能力和社交影响力等方面的评估维度。
链接: https://arxiv.org/abs/2506.12841
作者: Xinyuan Xia,Yuanyi Song,Haomin Ma,Jinyu Cai
机构: Shanghai Jiao Tong University (上海交通大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:With the rapid development of LLM-based agents, increasing attention has been given to their social interaction and strategic reasoning capabilities. However, existing Werewolf-based benchmarking platforms suffer from overly simplified game settings, incomplete evaluation metrics, and poor scalability. To address these limitations, we propose WereWolf-Plus, a multi-model, multi-dimensional, and multi-method benchmarking platform for evaluating multi-agent strategic reasoning in the Werewolf game. The platform offers strong extensibility, supporting customizable configurations for roles such as Seer, Witch, Hunter, Guard, and Sheriff, along with flexible model assignment and reasoning enhancement strategies for different roles. In addition, we introduce a comprehensive set of quantitative evaluation metrics for all special roles, werewolves, and the sheriff, and enrich the assessment dimensions for agent reasoning ability, cooperation capacity, and social influence. WereWolf-Plus provides a more flexible and reliable environment for advancing research on inference and strategic interaction within multi-agent communities. Our code is open sourced at this https URL.
zh
[NLP-97] Medical Argument Mining: Exploitation of Scarce Data Using NLI Systems
【速读】： 该论文试图解决从临床文本中提取论点实体并识别其关系的问题，以支持或反驳可能的诊断。解决方案的关键在于采用生成式 AI (Generative AI) 结合标记分类和自然语言推理技术，相较于传统的文本分类方法，在数据稀缺的环境下表现出更优的性能。
链接: https://arxiv.org/abs/2506.12823
作者: Maitane Urruela,Sergio Martín,Iker De la Iglesia,Ander Barrena
机构: HiTZ Center- Ixa, University of the Basque Country UPV/EHU (HiTZ中心- Ixa，巴斯克大学UPV/EHU); University of the Basque Country UPV/EHU (巴斯克大学UPV/EHU)
类目: Computation and Language (cs.CL)
备注: Accepted in the journal Procesamiento del Lenguaje Natural
Abstract:This work presents an Argument Mining process that extracts argumentative entities from clinical texts and identifies their relationships using token classification and Natural Language Inference techniques. Compared to straightforward methods like text classification, this methodology demonstrates superior performance in data-scarce settings. By assessing the effectiveness of these methods in identifying argumentative structures that support or refute possible diagnoses, this research lays the groundwork for future tools that can provide evidence-based justifications for machine-generated clinical conclusions.
zh
[NLP-98] Surprise Calibration for Better In-Context Learning
【速读】： 该论文试图解决生成式 AI (Generative AI) 在上下文学习 (In-context Learning, ICL) 中因先验知识和上下文示例带来的偏差问题，这些问题会降低大语言模型 (Large Language Models, LLMs) 的性能。现有偏差校准方法通常在所有输入上应用固定的类别先验，难以适应动态的 ICL 场景。该研究的关键在于采用隐式序列贝叶斯推理作为解释 ICL 的框架，识别“惊喜”（surprise）作为类别先验变化的有用信号，并提出一种新的方法——惊喜校准 (Surprise Calibration, SC)，通过“惊喜”捕捉类别先验的时间动态性，从而提供更自适应且计算效率更高的解决方案。
链接: https://arxiv.org/abs/2506.12796
作者: Zhihang Tan,Jingrui Hou,Ping Wang,Qibiao Hu,Peng Zhu
机构: Wuhan University (武汉大学); Nanjing University of Science and Technology (南京理工大学)
类目: Computation and Language (cs.CL)
备注: 16 pages, 11 figures
Abstract:In-context learning (ICL) has emerged as a powerful paradigm for task adaptation in large language models (LLMs), where models infer underlying task structures from a few demonstrations. However, ICL remains susceptible to biases that arise from prior knowledge and contextual demonstrations, which can degrade the performance of LLMs. Existing bias calibration methods typically apply fixed class priors across all inputs, limiting their efficacy in dynamic ICL settings where the context for each query differs. To address these limitations, we adopt implicit sequential Bayesian inference as a framework for interpreting ICL, identify “surprise” as an informative signal for class prior shift, and introduce a novel method–Surprise Calibration (SC). SC leverages the notion of surprise to capture the temporal dynamics of class priors, providing a more adaptive and computationally efficient solution for in-context learning. We empirically demonstrate the superiority of SC over existing bias calibration techniques across a range of benchmark natural language processing tasks.
zh
[NLP-99] Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在 geopolitical 价值体系中的隐性偏见问题，特别是其对民主与威权主义谱系的倾向性。现有研究多集中于社会人口和左右政治维度，而忽视了LLMs在更广泛地缘政治价值观上的对齐情况。论文提出的解决方案关键在于结合三种方法：F量表（用于测量权威主义倾向的计量工具）、FavScore（评估模型对世界领导人的偏好程度的新指标）以及角色榜样探测（用于评估LLMs引用哪些人物作为一般性榜样）。通过这些方法，研究揭示了LLMs在中文提示下对威权人物的偏好增强，并且在非明确政治语境中也常引用威权人物作为榜样，从而揭示了LLMs可能反映并强化全球政治意识形态的机制。
链接: https://arxiv.org/abs/2506.12758
作者: David Guzman Piedrahita,Irene Strauss,Bernhard Schölkopf,Rada Mihalcea,Zhijing Jin
机构: University of Zürich (苏黎世大学); ETH Zürich (苏黎世联邦理工学院); MPI for Intelligent Systems (马克斯·普朗克智能系统研究所); University of Michigan (密歇根大学); MPI & University of Toronto (马克斯·普朗克研究所与多伦多大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:As Large Language Models (LLMs) become increasingly integrated into everyday life and information ecosystems, concerns about their implicit biases continue to persist. While prior work has primarily examined socio-demographic and left–right political dimensions, little attention has been paid to how LLMs align with broader geopolitical value systems, particularly the democracy–authoritarianism spectrum. In this paper, we propose a novel methodology to assess such alignment, combining (1) the F-scale, a psychometric tool for measuring authoritarian tendencies, (2) FavScore, a newly introduced metric for evaluating model favorability toward world leaders, and (3) role-model probing to assess which figures are cited as general role-models by LLMs. We find that LLMs generally favor democratic values and leaders, but exhibit increases favorability toward authoritarian figures when prompted in Mandarin. Further, models are found to often cite authoritarian figures as role models, even outside explicit political contexts. These results shed light on ways LLMs may reflect and potentially reinforce global political ideologies, highlighting the importance of evaluating bias beyond conventional socio-political axes. Our code is available at: this https URL
zh
[NLP-100] Rethinking Hate Speech Detection on Social Media: Can LLM s Replace Traditional Models?
【速读】： 该论文试图解决跨社交媒体的仇恨言论检测问题，特别是在语言多样性、非正式网络对话以及涉及代码混用、音译和文化隐含表达的复杂场景下的挑战。解决方案的关键在于引入IndoHateMix数据集，该数据集捕捉了印度语境下的印地语-英语代码混用和音译现象，为评估模型在多语言复杂场景中的鲁棒性提供了现实基准。同时，研究发现先进的大语言模型（LLMs）在性能上优于微调的BERT类任务专用模型，展现出更强的泛化能力和适应性，从而为多样化的在线仇恨言论治理提供了变革性方法。
链接: https://arxiv.org/abs/2506.12744
作者: Daman Deep Singh,Ramanuj Bhattacharjee,Abhijnan Chakraborty
机构: Indian Institute of Technology Delhi(印度理工学院德里分校); Indian Institute of Technology Kharagpur(印度理工学院卡哈格尔普尔分校)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注:
Abstract:Hate speech detection across contemporary social media presents unique challenges due to linguistic diversity and the informal nature of online discourse. These challenges are further amplified in settings involving code-mixing, transliteration, and culturally nuanced expressions. While fine-tuned transformer models, such as BERT, have become standard for this task, we argue that recent large language models (LLMs) not only surpass them but also redefine the landscape of hate speech detection more broadly. To support this claim, we introduce IndoHateMix, a diverse, high-quality dataset capturing Hindi-English code-mixing and transliteration in the Indian context, providing a realistic benchmark to evaluate model robustness in complex multilingual scenarios where existing NLP methods often struggle. Our extensive experiments show that cutting-edge LLMs (such as LLaMA-3.1) consistently outperform task-specific BERT-based models, even when fine-tuned on significantly less data. With their superior generalization and adaptability, LLMs offer a transformative approach to mitigating online hate in diverse environments. This raises the question of whether future works should prioritize developing specialized models or focus on curating richer and more varied datasets to further enhance the effectiveness of LLMs.
zh
[NLP-101] Rethinking DPO: The Role of Rejected Responses in Preference Misalignment
【速读】： 该论文试图解决直接偏好优化（Direct Preference Optimization, DPO）在实际应用中因拒绝响应对损失函数的主导影响而导致的性能不足问题，即难以有效提升优选响应的生成概率并降低被拒响应的生成概率。解决方案的关键在于提出一种名为有界DPO（Bounded-DPO, BDPO）的新方法，该方法通过限制拒绝响应的影响，同时保持DPO原有的优化结构，从而实现对优选与被拒响应的平衡优化。
链接: https://arxiv.org/abs/2506.12725
作者: Jay Hyeon Cho,JunHyeok Oh,Myunsoo Kim,Byung-Jun Lee
机构: Korea University (韩国大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Direct Preference Optimization (DPO) is a simple and efficient framework that has attracted substantial attention. However, it often struggles to meet its primary objectives – increasing the generation probability of chosen responses while reducing that of rejected responses – due to the dominant influence of rejected responses on the loss function. This imbalance leads to suboptimal performance in promoting preferred responses. In this work, we systematically analyze the limitations of DPO and existing algorithms designed to achieve the objectives stated above. To address these limitations, we propose Bounded-DPO (BDPO), a novel method that bounds the influence of rejected responses while maintaining the original optimization structure of DPO. Through theoretical analysis and empirical evaluations, we demonstrate that BDPO achieves a balanced optimization of the chosen and rejected responses, outperforming existing algorithms.
zh
[NLP-102] Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
【速读】： 该论文试图解决大规模语言模型在测试阶段计算资源分配效率低下的问题，特别是在不同查询难度存在差异的情况下，现有方法通常采用均匀分配计算资源的方式，导致资源浪费或性能不足。解决方案的关键在于将测试阶段的计算资源分配建模为一种新颖的多臂老虎机（multi-armed bandit）学习问题，并提出自适应算法，能够在运行时估计查询难度并据此动态分配计算资源，从而在保持简单查询准确性的同时，为复杂查询分配更多资源，并优先处理可解实例，减少对不可解实例的过度计算。
链接: https://arxiv.org/abs/2506.12721
作者: Bowen Zuo,Yinglun Zhu
机构: University of California, Riverside (加州大学河滨分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注:
Abstract:Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typically allocate compute uniformly across all queries, overlooking variation in query difficulty. To address this inefficiency, we formulate test-time compute allocation as a novel bandit learning problem and propose adaptive algorithms that estimate query difficulty on the fly and allocate compute accordingly. Compared to uniform allocation, our algorithms allocate more compute to challenging queries while maintaining accuracy on easier ones. Among challenging queries, our algorithms further learn to prioritize solvable instances, effectively reducing excessive computing on unsolvable queries. We theoretically prove that our algorithms achieve better compute efficiency than uniform allocation and empirically validate their effectiveness on math and code benchmarks. Specifically, our algorithms achieve up to an 11.10% performance improvement (15.04% relative) on the MATH-500 dataset and up to a 7.41% performance improvement (14.40% relative) on LiveCodeBench.
zh
[NLP-103] Humanitys Last Code Exam: Can Advanced LLM s Conquer Humans Hardest Code Competition?
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在代码生成任务中缺乏足够挑战性评估基准的问题，现有主流基准如APPs和LiveCodeBench的题目难度较低，无法有效衡量先进LLMs的真实能力。其解决方案的关键在于引入Humanity’s Last Code Exam (HLCE)，该基准包含2010至2024年间国际大学生程序设计竞赛（ICPC World Finals）和国际信息学奥林匹克竞赛（IOI）中最具挑战性的235道题目，并设计了一个协调的在线-离线沙箱环境以确保评估的完全可复现性。
链接: https://arxiv.org/abs/2506.12713
作者: Xiangyang Li,Xiaopeng Li,Kuicai Dong,Quanhu Zhang,Rongju Ruan,Xinyi Dai,Xiaoshuang Liu,Shengchun Xu,Yasheng Wang,Ruiming Tang
机构: Huawei Noah’s Ark Lab (华为诺亚方舟实验室)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注:
Abstract:Code generation is a core capability of large language models (LLMs), yet mainstream benchmarks (e.g., APPs and LiveCodeBench) contain questions with medium-level difficulty and pose no challenge to advanced LLMs. To better reflected the advanced reasoning and code generation ability, We introduce Humanity’s Last Code Exam (HLCE), comprising 235 most challenging problems from the International Collegiate Programming Contest (ICPC World Finals) and the International Olympiad in Informatics (IOI) spanning 2010 - 2024. As part of HLCE, we design a harmonized online-offline sandbox that guarantees fully reproducible evaluation. Through our comprehensive evaluation, we observe that even the strongest reasoning LLMs: o4-mini(high) and Gemini-2.5 Pro, achieve pass@1 rates of only 15.9% and 11.4%, respectively. Meanwhile, we propose a novel “self-recognition” task to measure LLMs’ awareness of their own capabilities. Results indicate that LLMs’ self-recognition abilities are not proportionally correlated with their code generation performance. Finally, our empirical validation of test-time scaling laws reveals that current advanced LLMs have substantial room for improvement on complex programming tasks. We expect HLCE to become a milestone challenge for code generation and to catalyze advances in high-performance reasoning and human-AI collaborative programming. Our code and dataset are also public available(this https URL).
zh
[NLP-104] SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在安全对齐后仍易受到恶意攻击的问题，特别是通过对抗性越狱提示（adversarial jailbreak prompts）绕过模型的安全防护机制。其解决方案的关键在于提出SecurityLingua，一种基于安全导向的提示压缩方法，通过训练一个能够识别输入提示“真实意图”的提示压缩器，检测对抗性提示中的恶意意图，并将该意图通过系统提示传递给目标LLM，以增强其对恶意请求的识别能力，同时保持原始输入的完整性，从而在不显著增加计算开销和延迟的情况下提升模型安全性。
链接: https://arxiv.org/abs/2506.12707
作者: Yucheng Li,Surin Ahn,Huiqiang Jiang,Amir H. Abdi,Yuqing Yang,Lili Qiu
机构: Microsoft Corporation(微软公司); University of Surrey(萨里大学)
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs’ safety guardrails by wrapping the original malicious instructions inside adversarial jailbreaks prompts. Previous research has proposed methods such as adversarial training and prompt rephrasing to mitigate these safety vulnerabilities, but these methods often reduce the utility of LLMs or lead to significant computational overhead and online latency. In this paper, we propose SecurityLingua, an effective and efficient approach to defend LLMs against jailbreak attacks via security-oriented prompt compression. Specifically, we train a prompt compressor designed to discern the “true intention” of the input prompt, with a particular focus on detecting the malicious intentions of adversarial prompts. Then, in addition to the original prompt, the intention is passed via the system prompt to the target LLM to help it identify the true intention of the request. SecurityLingua ensures a consistent user experience by leaving the original input prompt intact while revealing the user’s potentially malicious intention and stimulating the built-in safety guardrails of the LLM. Moreover, thanks to prompt compression, SecurityLingua incurs only a negligible overhead and extra token cost compared to all existing defense methods, making it an especially practical solution for LLM defense. Experimental results demonstrate that SecurityLingua can effectively defend against malicious attacks and maintain utility of the LLM with negligible compute and latency overhead. Our code is available at this https URL.
zh
[NLP-105] Flexible Realignment of Language Models
【速读】： 该论文旨在解决语言模型（Language Model, LM）在性能未达预期时需要进行对齐（alignment）的问题。其核心解决方案是提出一种灵活的对齐框架，该框架包含训练阶段的实时对齐（Training-time Realignment, TrRa）和推理阶段的实时对齐（Inference-time Realignment, InRa）。TrRa通过可控的logits融合实现参考模型的高效对齐，而InRa则通过引入层适配器，在推理过程中实现平滑的对齐控制，从而在不牺牲性能的前提下显著降低token使用量，并支持更灵活的对齐调节。
链接: https://arxiv.org/abs/2506.12704
作者: Wenhong Zhu,Ruobing Xie,Weinan Zhang,Rui Wang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Realignment becomes necessary when a language model (LM) fails to meet expected performance. We propose a flexible realignment framework that supports quantitative control of alignment degree during training and inference. This framework incorporates Training-time Realignment (TrRa), which efficiently realigns the reference model by leveraging the controllable fusion of logits from both the reference and already aligned models. For example, TrRa reduces token usage by 54.63% on DeepSeek-R1-Distill-Qwen-1.5B without any performance degradation, outperforming DeepScaleR-1.5B’s 33.86%. To complement TrRa during inference, we introduce a layer adapter that enables smooth Inference-time Realignment (InRa). This adapter is initialized to perform an identity transformation at the bottom layer and is inserted preceding the original layers. During inference, input embeddings are simultaneously processed by the adapter and the original layer, followed by the remaining layers, and then controllably interpolated at the logit level. We upgraded DeepSeek-R1-Distill-Qwen-7B from a slow-thinking model to one that supports both fast and slow thinking, allowing flexible alignment control even during inference. By encouraging deeper reasoning, it even surpassed its original performance.
zh
[NLP-106] Enhancing Clinical Models with Pseudo Data for De-identification
【速读】： 该论文旨在解决在隐私保护背景下，使用经过脱敏（redacted）文本进行预训练的生成式 AI 模型在性能上的局限性问题。其关键解决方案是通过将脱敏文本替换为现实的伪文本（pseudo text）来构建训练数据集，并在此基础上预训练编码器模型，随后对模型进行微调以执行受保护健康信息（PHI）的去标识化任务，从而显著提升模型性能。
链接: https://arxiv.org/abs/2506.12674
作者: Paul Landes,Aaron J Chaise,Tarak Nath Nandi,Ravi K Madduri
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Many models are pretrained on redacted text for privacy reasons. Clinical foundation models are often trained on de-identified text, which uses special syntax (masked) text in place of protected health information. Even though these models have increased in popularity, there has been little effort in understanding the effects of training them on redacted text. In this work, we pretrain several encoder-only models on a dataset that contains redacted text and a version with replaced realistic pseudo text. We then fine-tuned models for the protected health information de-identification task and show how our methods significantly outperform previous baselines. The contributions of this work include: a) our novel, and yet surprising findings with training recommendations, b) redacted text replacements used to produce the pseudo dataset, c) pretrained embeddings and fine-tuned task specific models, and d) freely available pseudo training dataset generation and model source code used in our experiments.
zh
[NLP-107] SC-SOT: Conditioning the Decoder on Diarized Speaker Information for End-to-End Overlapped Speech Recognition INTERSPEECH2025
【速读】： 该论文旨在解决端到端多说话人自动语音识别（E2E multi-talker ASR）中重叠语音带来的识别难题。研究发现，传统的序列到序列（SOT）训练方法在处理重叠语音时，解码器虽然能进行隐式的说话人分离，但因重叠区域的声学线索模糊，导致分离效果不足。为了解决这一问题，论文提出的Speaker-Conditioned Serialized Output Training (SC-SOT) 方法通过在解码器中显式引入说话人信息，提升模型对目标说话人的识别能力。其关键在于：（1）引入说话人嵌入（speaker embeddings），使模型能够关注目标说话人的声学特征；（2）结合说话人活动信息，引导模型抑制非目标说话人信号。
链接: https://arxiv.org/abs/2506.12672
作者: Yuta Hirano,Sakriani Sakti
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Accepted by Interspeech 2025
Abstract:We propose Speaker-Conditioned Serialized Output Training (SC-SOT), an enhanced SOT-based training for E2E multi-talker ASR. We first probe how SOT handles overlapped speech, and we found the decoder performs implicit speaker separation. We hypothesize this implicit separation is often insufficient due to ambiguous acoustic cues in overlapping regions. To address this, SC-SOT explicitly conditions the decoder on speaker information, providing detailed information about “who spoke when”. Specifically, we enhance the decoder by incorporating: (1) speaker embeddings, which allow the model to focus on the acoustic characteristics of the target speaker, and (2) speaker activity information, which guides the model to suppress non-target speakers. The speaker embeddings are derived from a jointly trained E2E speaker diarization model, mitigating the need for speaker enrollment. Experimental results demonstrate the effectiveness of our conditioning approach on overlapped speech.
zh
[NLP-108] Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics
【速读】： 该论文试图解决在道德敏感领域中，人格特质如何影响大型语言模型（Large Language Models, LLMs）的道德推理和说服行为的问题。其解决方案的关键在于构建一个基于六维人格空间（年龄、性别、国家、阶级、意识形态和个性）的AI-AI辩论框架，通过模拟真实道德困境下的结构化辩论，系统分析人格特质对初始道德立场及辩论结果的影响。研究揭示了政治意识形态和个性特质对说服效果的显著影响，并提出了更具人格意识的AI道德推理评估框架的必要性。
链接: https://arxiv.org/abs/2506.12657
作者: Jiarui Liu,Yueqi Song,Yunze Xiao,Mingqian Zheng,Lindia Tjuatja,Jana Schaich Borg,Mona Diab,Maarten Sap
机构: Carnegie Mellon University (卡内基梅隆大学); Duke University (杜克大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:As large language models (LLMs) are increasingly used in morally sensitive domains, it is crucial to understand how persona traits affect their moral reasoning and persuasive behavior. We present the first large-scale study of multi-dimensional persona effects in AI-AI debates over real-world moral dilemmas. Using a 6-dimensional persona space (age, gender, country, class, ideology, and personality), we simulate structured debates between AI agents over 131 relationship-based cases. Our results show that personas affect initial moral stances and debate outcomes, with political ideology and personality traits exerting the strongest influence. Persuasive success varies across traits, with liberal and open personalities reaching higher consensus and win rates. While logit-based confidence grows during debates, emotional and credibility-based appeals diminish, indicating more tempered argumentation over time. These trends mirror findings from psychology and cultural studies, reinforcing the need for persona-aware evaluation frameworks for AI moral reasoning.
zh
[NLP-109] How Grounded is Wikipedia? A Study on Structured Evidential Support
【速读】： 该论文试图解决维基百科（Wikipedia）中陈述（claim）的依据性（groundedness）问题，即评估维基百科内容在多大程度上依赖于其引用来源进行支撑。解决方案的关键在于引入PeopleProfiles——一个大规模、多层级的声明支持标注数据集，用于分析知名人物相关维基百科文章中的陈述与引用来源之间的关系。通过该数据集，研究揭示了维基百科中约20%的导语部分陈述缺乏文章主体的支持，约27%的正文陈述未得到其引用来源的支持，且80%的导语陈述无法通过标注的正文证据追溯至引用来源。此外，研究还表明，对于有支持依据的陈述，标准检索方法在恢复复杂依据证据方面仍面临挑战。
链接: https://arxiv.org/abs/2506.12637
作者: William Walden,Kathryn Ricci,Miriam Wanner,Zhengping Jiang,Chandler May,Rongkun Zhou,Benjamin Van Durme
机构: Johns Hopkins University (约翰霍普金斯大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Wikipedia is a critical resource for modern NLP, serving as a rich repository of up-to-date and citation-backed information on a wide variety of subjects. The reliability of Wikipedia – its groundedness in its cited sources – is vital to this purpose. This work provides a quantitative analysis of the extent to which Wikipedia is so grounded and of how readily grounding evidence may be retrieved. To this end, we introduce PeopleProfiles – a large-scale, multi-level dataset of claim support annotations on Wikipedia articles of notable people. We show that roughly 20% of claims in Wikipedia lead sections are unsupported by the article body; roughly 27% of annotated claims in the article body are unsupported by their (publicly accessible) cited sources; and 80% of lead claims cannot be traced to these sources via annotated body evidence. Further, we show that recovery of complex grounding evidence for claims that are supported remains a challenge for standard retrieval methods.
zh
[NLP-110] Between Predictability and Randomness: Seeking Artistic Inspiration from AI Generative Models
【速读】： 该论文试图解决如何利用人工智能生成的诗歌片段作为激发艺术灵感的刺激物的问题，其核心在于探讨不同AI生成方法对创造性过程的影响。研究的关键在于证明基于长短期记忆变分自编码器（LSTM-VAE）生成的诗句通过共振意象与生产性不确定性相结合的方式，能够为艺术家提供具有语义开放性、非常规组合及抗拒封闭性的启发性起点，相较于大型语言模型（LLM）生成的遵循传统模式的诗歌，更能促进真实的艺术表达。
链接: https://arxiv.org/abs/2506.12634
作者: Olga Vechtomova
机构: University of Waterloo (滑铁卢大学)
类目: Computation and Language (cs.CL)
备注: Presented as a keynote at the 50th Linguistic Association of Canada and the United States (LACUS) conference in July 2024 and will be published in LACUS Forum 50
Abstract:Artistic inspiration often emerges from language that is open to interpretation. This paper explores the use of AI-generated poetic lines as stimuli for creativity. Through analysis of two generative AI approaches–lines generated by Long Short-Term Memory Variational Autoencoders (LSTM-VAE) and complete poems by Large Language Models (LLMs)–I demonstrate that LSTM-VAE lines achieve their evocative impact through a combination of resonant imagery and productive indeterminacy. While LLMs produce technically accomplished poetry with conventional patterns, LSTM-VAE lines can engage the artist through semantic openness, unconventional combinations, and fragments that resist closure. Through the composition of an original poem, where narrative emerged organically through engagement with LSTM-VAE generated lines rather than following a predetermined structure, I demonstrate how these characteristics can serve as evocative starting points for authentic artistic expression.
zh
[NLP-111] MS4UI: A Dataset for Multi-modal Summarization of User Interface Instructional Videos
【速读】： 该论文试图解决用户界面（User Interface, UI）指令视频的多模态摘要问题，即如何生成简洁且可执行的文本指令和关键视频帧，以帮助用户高效学习技能。现有基准主要关注通用语义层面的视频摘要，无法满足指令视频对步骤化操作说明和视觉示例的需求。论文的关键解决方案是提出一个新颖的基准数据集MS4UI，包含2,413个UI指令视频，涵盖超过167小时的内容，并进行手动标注以支持视频分割、文本摘要和视频摘要的全面评估，从而推动针对UI指令视频摘要的新方法研究。
链接: https://arxiv.org/abs/2506.12623
作者: Yuan Zang,Hao Tan,Seunghyun Yoon,Franck Dernoncourt,Jiuxiang Gu,Kushal Kafle,Chen Sun,Trung Bui
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:We study multi-modal summarization for instructional videos, whose goal is to provide users an efficient way to learn skills in the form of text instructions and key video frames. We observe that existing benchmarks focus on generic semantic-level video summarization, and are not suitable for providing step-by-step executable instructions and illustrations, both of which are crucial for instructional videos. We propose a novel benchmark for user interface (UI) instructional video summarization to fill the gap. We collect a dataset of 2,413 UI instructional videos, which spans over 167 hours. These videos are manually annotated for video segmentation, text summarization, and video summarization, which enable the comprehensive evaluations for concise and executable video summarization. We conduct extensive experiments on our collected MS4UI dataset, which suggest that state-of-the-art multi-modal summarization methods struggle on UI video summarization, and highlight the importance of new methods for UI instructional video summarization.
zh
[NLP-112] OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在数据隐私、模型安全和法规合规性要求较高的环境中部署时，如何实现可靠且可验证的遗忘（unlearning）问题。现有方法在评估指标和实验框架上存在碎片化，导致难以进行比较分析和结果复现。解决方案的关键在于提出OpenUnlearning，这是一个标准化且可扩展的框架，用于基准测试LLM的遗忘方法和评估指标。该框架整合了9种遗忘算法和16种多样化的评估方法，并在3个主流基准（TOFU、MUSE和WMDP）上进行了验证，同时提供了450多个检查点的公开分析，以促进研究的统一与加速。
链接: https://arxiv.org/abs/2506.12618
作者: Vineeth Dorna,Anmol Mekala,Wenlong Zhao,Andrew McCallum,Zachary C. Lipton,J. Zico Kolter,Pratyush Maini
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Robust unlearning is crucial for safely deploying large language models (LLMs) in environments where data privacy, model safety, and regulatory compliance must be ensured. Yet the task is inherently challenging, partly due to difficulties in reliably measuring whether unlearning has truly occurred. Moreover, fragmentation in current methodologies and inconsistent evaluation metrics hinder comparative analysis and reproducibility. To unify and accelerate research efforts, we introduce OpenUnlearning, a standardized and extensible framework designed explicitly for benchmarking both LLM unlearning methods and metrics. OpenUnlearning integrates 9 unlearning algorithms and 16 diverse evaluations across 3 leading benchmarks (TOFU, MUSE, and WMDP) and also enables analyses of forgetting behaviors across 450+ checkpoints we publicly release. Leveraging OpenUnlearning, we propose a novel meta-evaluation benchmark focused specifically on assessing the faithfulness and robustness of evaluation metrics themselves. We also benchmark diverse unlearning methods and provide a comparative analysis against an extensive evaluation suite. Overall, we establish a clear, community-driven pathway toward rigorous development in LLM unlearning research.
zh
[NLP-113] Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition
【速读】： 该论文旨在解决阿拉伯语命名实体识别（Named Entity Recognition, NER）模型在跨领域和跨方言场景下的性能下降问题。其关键解决方案是构建了一个多维语料库Konooz，该语料库覆盖16种阿拉伯语方言和10个领域，共计160个独立语料库，包含约777k个词元，并采用嵌套与扁平标注方案进行人工标注。通过该语料库，研究者对现有阿拉伯语NER模型进行了基准测试，揭示了跨领域和跨方言任务中性能显著下降的现象，并深入分析了领域和方言差异以及资源稀缺性的影响。
链接: https://arxiv.org/abs/2506.12615
作者: Nagham Hamad,Mohammed Khalilia,Mustafa Jarrar
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We introduce Konooz, a novel multi-dimensional corpus covering 16 Arabic dialects across 10 domains, resulting in 160 distinct corpora. The corpus comprises about 777k tokens, carefully collected and manually annotated with 21 entity types using both nested and flat annotation schemes - using the Wojood guidelines. While Konooz is useful for various NLP tasks like domain adaptation and transfer learning, this paper primarily focuses on benchmarking existing Arabic Named Entity Recognition (NER) models, especially cross-domain and cross-dialect model performance. Our benchmarking of four Arabic NER models using Konooz reveals a significant drop in performance of up to 38% when compared to the in-distribution data. Furthermore, we present an in-depth analysis of domain and dialect divergence and the impact of resource scarcity. We also measured the overlap between domains and dialects using the Maximum Mean Discrepancy (MMD) metric, and illustrated why certain NER models perform better on specific dialects and domains. Konooz is open-source and publicly available at this https URL
zh
[NLP-114] owards Building General Purpose Embedding Models for Industry 4.0 Agents
【速读】： 该论文旨在解决工业4.0领域中语言模型对资产维护理解不足的问题，以辅助工程师决策并减少资产停机时间。其关键解决方案是构建一个基于专家验证的知识库，并利用大型语言模型（Large Language Models, LLMs）增强输入任务的上下文信息，生成更具表现力的嵌入表示，随后将其与推理与行动代理（Reasoning and Acting agent, ReAct）结合，以处理需要多步骤推理、规划和知识推断的复杂用户查询。
链接: https://arxiv.org/abs/2506.12607
作者: Christodoulos Constantinides,Shuxin Lin,Dhaval Patel
机构: IBM Research(IBM 研究院)
类目: Computation and Language (cs.CL)
备注:
Abstract:In this work we focus on improving language models’ understanding for asset maintenance to guide the engineer’s decisions and minimize asset downtime. Given a set of tasks expressed in natural language for Industry 4.0 domain, each associated with queries related to a specific asset, we want to recommend relevant items and generalize to queries of similar assets. A task may involve identifying relevant sensors given a query about an asset’s failure mode. Our approach begins with gathering a qualitative, expert-vetted knowledge base to construct nine asset-specific task datasets. To create more contextually informed embeddings, we augment the input tasks using Large Language Models (LLMs), providing concise descriptions of the entities involved in the queries. This embedding model is then integrated with a Reasoning and Acting agent (ReAct), which serves as a powerful tool for answering complex user queries that require multi-step reasoning, planning, and knowledge inference. Through ablation studies, we demonstrate that: (a) LLM query augmentation improves the quality of embeddings, (b) Contrastive loss and other methods that avoid in-batch negatives are superior for datasets with queries related to many items, and © It is crucial to balance positive and negative in-batch samples. After training and testing on our dataset, we observe a substantial improvement: HIT@1 increases by +54.2%, MAP@100 by +50.1%, and NDCG@10 by +54.7%, averaged across all tasks and models. Additionally, we empirically demonstrate the model’s planning and tool invocation capabilities when answering complex questions related to industrial asset maintenance, showcasing its effectiveness in supporting Subject Matter Experts (SMEs) in their day-to-day operations. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2506.12607 [cs.CL] (or arXiv:2506.12607v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2506.12607 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[NLP-115] An Exploration of Mamba for Speech Self-Supervised Models
【速读】： 该论文试图解决传统Transformer-based自监督学习（SSL）模型在长序列建模、实时语音建模和语音单元提取中的计算效率与性能限制问题。其解决方案的关键在于采用基于Mamba的HuBERT模型，利用其线性时间复杂度的Selectivity State Space机制，从而在长上下文自动语音识别（ASR）任务中实现更低的计算需求，并在流式ASR任务中表现出更优的性能。此外，该方法在SUPERB探测基准测试中也展现出与Transformer模型相当甚至更优的表征质量，特别是在因果设置下。
链接: https://arxiv.org/abs/2506.12606
作者: Tzu-Quan Lin,Heng-Cheng Kuo,Tzu-Chieh Wei,Hsi-Chun Cheng,Chun-Wei Chen,Hsien-Fu Hsiao,Yu Tsao,Hung-yi Lee
机构: National Taiwan University, Taiwan; Graduate Institute of Communication Engineering, National Taiwan University, Taiwan; Research Center for Information Technology Innovation, Academia Sinica
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:While Mamba has demonstrated strong performance in language modeling, its potential as a speech self-supervised (SSL) model remains underexplored, with prior studies limited to isolated tasks. To address this, we explore Mamba-based HuBERT models as alternatives to Transformer-based SSL architectures. Leveraging the linear-time Selective State Space, these models enable fine-tuning on long-context ASR with significantly lower compute. Moreover, they show superior performance when fine-tuned for streaming ASR. Beyond fine-tuning, these models show competitive performance on SUPERB probing benchmarks, particularly in causal settings. Our analysis shows that they yield higher-quality quantized representations and capture speaker-related features more distinctly than Transformer-based models. These findings highlight Mamba-based SSL as a promising and complementary direction for long-sequence modeling, real-time speech modeling, and speech unit extraction.
zh
[NLP-116] OneEval: Benchmarking LLM Knowledge-intensive Reasoning over Diverse Knowledge Bases
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在处理需要整合结构化外部知识（如知识图谱、代码片段或形式逻辑）的推理任务时表现显著下降的问题。现有评估基准无法系统性地衡量LLMs在多种结构化知识模态下的性能，这是导致该问题的关键原因。为解决这一问题，作者提出了\textbf\textscOneEval，这是一个全面的基准测试，专门用于评估LLMs在四种结构化知识模态（非结构化文本、知识图谱、代码和形式逻辑）及五个关键领域（通用知识、政府、科学、法律和编程）中的知识密集型推理能力。其核心在于提供一个系统化的评估框架，以揭示LLMs在结构化推理方面的局限性，并推动相关技术的进步。
链接: https://arxiv.org/abs/2506.12577
作者: Yongrui Chen,Zhiqiang Liu,Jing Yu,Lin Ren,Nan Hu,Xinbang Dai,Jiajun Liu,Jiazhen Kang,Shenyu Zhang,Xinda Wang,Keyan Ding,Pengfei Shen,Haolei Zhu,Hongjie Deng,Yisong Wang,Tongtong Wu,Sheng Bi,Wen Zhang,Tianxing Wu,Qiu Ji,Haofen Wang,Wenliang Chen,Huajun Chen,Guilin Qi
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) have demonstrated substantial progress on reasoning tasks involving unstructured text, yet their capabilities significantly deteriorate when reasoning requires integrating structured external knowledge such as knowledge graphs, code snippets, or formal logic. This limitation is partly due to the absence of benchmarks capable of systematically evaluating LLM performance across diverse structured knowledge modalities. To address this gap, we introduce \textbf\textscOneEval, a comprehensive benchmark explicitly designed to assess the knowledge-intensive reasoning capabilities of LLMs across four structured knowledge modalities, unstructured text, knowledge graphs, code, and formal logic, and five critical domains (general knowledge, government, science, law, and programming). \textscOneEval comprises 4,019 carefully curated instances and includes a challenging subset, \textscOneEval\textsubscriptHard, consisting of 1,285 particularly difficult cases. Through extensive evaluation of 18 state-of-the-art open-source and proprietary LLMs, we establish three core findings: a) \emphpersistent limitations in structured reasoning, with even the strongest model achieving only 32.2% accuracy on \textscOneEval\textsubscriptHard; b) \emphperformance consistently declines as the structural complexity of the knowledge base increases, with accuracy dropping sharply from 53% (textual reasoning) to 25% (formal logic); and c) \emphdiminishing returns from extended reasoning chains, highlighting the critical need for models to adapt reasoning depth appropriately to task complexity. We release the \textscOneEval datasets, evaluation scripts, and baseline results publicly, accompanied by a leaderboard to facilitate ongoing advancements in structured knowledge reasoning.
zh
[NLP-117] Enabling Precise Topic Alignment in Large Language Models Via Sparse Autoencoders
【速读】： 该论文试图解决如何在不依赖预定义主题和参数调优的情况下，实现对大型语言模型（Large Language Model, LLM）输出的对齐问题。其解决方案的关键在于利用稀疏自编码器（Sparse Autoencoder, SAE）的观测与修改特性，通过计算每个SAE神经元与对齐文本的语义相似度，并据此调整SAE层级别的输出，从而实现针对任意主题的对齐。
链接: https://arxiv.org/abs/2506.12576
作者: Ananya Joshi,Celia Cintas,Skyler Speakman
机构: Carnegie Mellon University (卡内基梅隆大学); IBM Research (IBM 研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent work shows that Sparse Autoencoders (SAE) applied to large language model (LLM) layers have neurons corresponding to interpretable concepts. These SAE neurons can be modified to align generated outputs, but only towards pre-identified topics and with some parameter tuning. Our approach leverages the observational and modification properties of SAEs to enable alignment for any topic. This method 1) scores each SAE neuron by its semantic similarity to an alignment text and uses them to 2) modify SAE-layer-level outputs by emphasizing topic-aligned neurons. We assess the alignment capabilities of this approach on diverse public topic datasets including Amazon reviews, Medicine, and Sycophancy, across the currently available open-source LLMs and SAE pairs (GPT2 and Gemma) with multiple SAEs configurations. Experiments aligning to medical prompts reveal several benefits over fine-tuning, including increased average language acceptability (0.25 vs. 0.5), reduced training time across multiple alignment topics (333.6s vs. 62s), and acceptable inference time for many applications (+0.00092s/token). Our open-source code is available at this http URL.
zh
[NLP-118] Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge
【速读】： 该论文旨在解决自然语言处理中的性别偏见问题，特别是在中文语境下，由于缺乏公平相关的计算语言学资源，现有数据驱动技术（如预训练语言模型）容易受到偏见语料库的影响。解决方案的关键是提出一个名为CORGI-PM的中文性别偏见探测与缓解语料库，该语料库包含32.9k条带有高质量标签的句子，并特别设计了标注方案以适应中文性别偏见的检测。此外，CORGI-PM包含5.2k条带有偏见的句子及其由人工标注者重写的无偏版本，以此为基础设立了三个挑战任务，旨在自动化文本性别偏见的检测、分类与缓解。
链接: https://arxiv.org/abs/2506.12574
作者: Yizhi Li,Ge Zhang,Hanhua Hong,Yiwen Wang,Chenghua Lin
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:As natural language processing for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques, such as pre-trained language models, suffer from biased corpus. This case becomes more obvious regarding those languages with less fairness-related computational linguistic resources, such as Chinese. To this end, we propose a Chinese cOrpus foR Gender bIas Probing and Mitigation (CORGI-PM), which contains 32.9k sentences with high-quality labels derived by following an annotation scheme specifically developed for gender bias in the Chinese context. It is worth noting that CORGI-PM contains 5.2k gender-biased sentences along with the corresponding bias-eliminated versions rewritten by human annotators. We pose three challenges as a shared task to automate the mitigation of textual gender bias, which requires the models to detect, classify, and mitigate textual gender bias. In the literature, we present the results and analysis for the teams participating this shared task in NLPCC 2025.
zh
[NLP-119] DoTA-RAG : Dynamic of Thought Aggregation RAG SIGIR
【速读】： 该论文旨在解决传统检索增强生成（Retrieval-Augmented Generation, RAG）系统在处理大规模、多样化数据集时存在的高延迟和有限准确性问题。其解决方案的关键在于提出一种三阶段流水线——查询重写、动态路由至专业子索引以及多阶段检索与排序，并通过评估和选择更优的嵌入模型来增强检索效果，同时对大规模FineWeb-10BT语料库进行重新嵌入，以提升系统的整体性能。
链接: https://arxiv.org/abs/2506.12571
作者: Saksorn Ruangtanusak,Natthapath Rungseesiripak,Peerawat Rojratchadakorn,Monthol Charattrakool,Natapong Nitarach
机构: SCBX(SCBX); SCB 10X(SCB 10X)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: SIGIR LiveRAG 2025 (oral presentation)
Abstract:In this paper, we introduce DoTA-RAG (Dynamic-of-Thought Aggregation RAG), a retrieval-augmented generation system optimized for high-throughput, large-scale web knowledge indexes. Traditional RAG pipelines often suffer from high latency and limited accuracy over massive, diverse datasets. DoTA-RAG addresses these challenges with a three-stage pipeline: query rewriting, dynamic routing to specialized sub-indexes, and multi-stage retrieval and ranking. We further enhance retrieval by evaluating and selecting a superior embedding model, re-embedding the large FineWeb-10BT corpus. Moreover, we create a diverse QA dataset of 500 questions generated via the DataMorgana setup across a broad range of WebOrganizer topics and formats. DoTA-RAG improves the answer correctness score from 0.752 (baseline, using LiveRAG pre-built vector store) to 1.478 while maintaining low latency, and it achieves a 0.929 correctness score on the Live Challenge Day. These results highlight DoTA-RAG’s potential for practical deployment in domains requiring fast, reliable access to large and evolving knowledge sources.
zh
[NLP-120] StreamMel: Real-Time Zero-shot Text-to-Speech via Interleaved Continuous Autoregressive Modeling
【速读】： 该论文试图解决现有零样本文本到语音（zero-shot text-to-speech, TTS）系统在实时应用中的局限性，特别是由于其离线设计导致的高延迟问题。现有流式TTS范式通常依赖多阶段流水线和离散表示，导致计算成本增加和系统性能不佳。解决方案的关键在于提出StreamMel，这是一个开创性的单阶段流式TTS框架，通过建模连续的梅尔频谱图（mel-spectrograms），将文本标记与声学帧交错，实现了低延迟的自回归合成，同时保持了高说话人相似性和自然性。
链接: https://arxiv.org/abs/2506.12570
作者: Hui Wang,Yifan Yang,Shujie Liu,Jinyu Li,Lingwei Meng,Yanqing Liu,Jiaming Zhou,Haoqin Sun,Yan Lu,Yong Qin
机构: Microsoft Corporation (微软公司); Nankai University (南开大学)
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注:
Abstract:Recent advances in zero-shot text-to-speech (TTS) synthesis have achieved high-quality speech generation for unseen speakers, but most systems remain unsuitable for real-time applications because of their offline design. Current streaming TTS paradigms often rely on multi-stage pipelines and discrete representations, leading to increased computational cost and suboptimal system performance. In this work, we propose StreamMel, a pioneering single-stage streaming TTS framework that models continuous mel-spectrograms. By interleaving text tokens with acoustic frames, StreamMel enables low-latency, autoregressive synthesis while preserving high speaker similarity and naturalness. Experiments on LibriSpeech demonstrate that StreamMel outperforms existing streaming TTS baselines in both quality and latency. It even achieves performance comparable to offline systems while supporting efficient real-time generation, showcasing broad prospects for integration with real-time speech large language models. Audio samples are available at: this https URL.
zh
[NLP-121] Profiling News Media for Factuality and Bias Using LLM s and the Fact-Checking Methodology of Human Experts ACL
【速读】： 该论文试图解决在虚假和错误信息泛滥的网络环境中，如何有效评估新闻来源的可靠性和政治偏见的问题，以帮助读者更好地理解所阅读的内容。传统的方法依赖于手动或自动事实核查，但对于缺乏足够信息的新出现的声明则存在挑战。该研究的关键解决方案是提出一种新颖的方法，模拟专业事实核查人员评估整个新闻机构事实性和政治偏见的标准，通过设计多种提示并利用大型语言模型（Large Language Models, LLMs）生成响应，进而进行聚合预测。
链接: https://arxiv.org/abs/2506.12552
作者: Zain Muhammad Mujahid,Dilshod Azizov,Maha Tufail Agro,Preslav Nakov
机构: Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学); University of Copenhagen (哥本哈根大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to Findings of the Association for Computational Linguistics (ACL) 2025
Abstract:In an age characterized by the proliferation of mis- and disinformation online, it is critical to empower readers to understand the content they are reading. Important efforts in this direction rely on manual or automatic fact-checking, which can be challenging for emerging claims with limited information. Such scenarios can be handled by assessing the reliability and the political bias of the source of the claim, i.e., characterizing entire news outlets rather than individual claims or articles. This is an important but understudied research direction. While prior work has looked into linguistic and social contexts, we do not analyze individual articles or information in social media. Instead, we propose a novel methodology that emulates the criteria that professional fact-checkers use to assess the factuality and political bias of an entire outlet. Specifically, we design a variety of prompts based on these criteria and elicit responses from large language models (LLMs), which we aggregate to make predictions. In addition to demonstrating sizable improvements over strong baselines via extensive experiments with multiple LLMs, we provide an in-depth error analysis of the effect of media popularity and region on model performance. Further, we conduct an ablation study to highlight the key components of our dataset that contribute to these improvements. To facilitate future research, we released our dataset and code at this https URL.
zh
[NLP-122] RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking
【速读】： 该论文试图解决现有基准无法全面评估大型语言模型（Large Language Models, LLMs）和多模态大型语言模型（Multimodal Large Language Models, MLLMs）在真实误导信息场景下的事实核查能力的问题。解决方案的关键在于引入RealFactBench，这是一个涵盖知识验证、谣言检测和事件验证等多样化现实任务的综合性基准，包含6K条来自权威来源的高质量声明，并引入未知率（Unknown Rate, UnR）指标以更细致地评估模型处理不确定性的能力。
链接: https://arxiv.org/abs/2506.12538
作者: Shuo Yang,Yuqin Dai,Guoqing Wang,Xinran Zheng,Jinfeng Xu,Jinze Li,Zhenzhe Ying,Weiqiang Wang,Edith C.H. Ngai
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. However, existing benchmarks fail to comprehensively evaluate LLMs and Multimodal Large Language Models (MLLMs) in realistic misinformation scenarios. To bridge this gap, we introduce RealFactBench, a comprehensive benchmark designed to assess the fact-checking capabilities of LLMs and MLLMs across diverse real-world tasks, including Knowledge Validation, Rumor Detection, and Event Verification. RealFactBench consists of 6K high-quality claims drawn from authoritative sources, encompassing multimodal content and diverse domains. Our evaluation framework further introduces the Unknown Rate (UnR) metric, enabling a more nuanced assessment of models’ ability to handle uncertainty and balance between over-conservatism and over-confidence. Extensive experiments on 7 representative LLMs and 4 MLLMs reveal their limitations in real-world fact-checking and offer valuable insights for further research. RealFactBench is publicly available at this https URL.
zh
[NLP-123] Speech-Language Models with Decoupled Tokenizers and Multi-Token Prediction
【速读】： 该论文旨在解决语音-语言模型（SLMs）在跨模态对齐和高质量语音生成方面的挑战。其关键解决方案在于系统性地研究并优化了语音分词器、语音头部和说话人建模等核心组件，其中去耦合的语音分词策略显著提升了对齐效果和语音合成质量。此外，通过引入多标记预测（MTP）机制，解决了语音与文本信息密度不匹配的问题，从而提高了解码速度并降低了词错误率；同时，提出了一种说话人感知的生成范式，并构建了RoleTriviaQA基准以增强知识理解和说话人一致性。
链接: https://arxiv.org/abs/2506.12537
作者: Xiaoran Fan,Zhichao Sun,Yangfan Gao,Jingfei Xiong,Hang Yan,Yifei Cao,Jiajun Sun,Shuo Li,Zhihao Zhang,Zhiheng Xi,Yuhao Zhou,Senjie Jin,Changhao Jiang,Junjie Ye,Ming Zhang,Rui Zheng,Zhenhua Han,Yunke Zhang,Demei Yan,Shaokang Dong,Tao Ji,Tao Gui,Qi Zhang,Xuanjing Huang
机构: Fudan University (复旦大学); The Chinese University of Hong Kong (香港中文大学); Honor Device Co., Ltd (荣耀终端有限公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注:
Abstract:Speech-language models (SLMs) offer a promising path toward unifying speech and text understanding and generation. However, challenges remain in achieving effective cross-modal alignment and high-quality speech generation. In this work, we systematically investigate the impact of key components (i.e., speech tokenizers, speech heads, and speaker modeling) on the performance of LLM-centric SLMs. We compare coupled, semi-decoupled, and fully decoupled speech tokenizers under a fair SLM framework and find that decoupled tokenization significantly improves alignment and synthesis quality. To address the information density mismatch between speech and text, we introduce multi-token prediction (MTP) into SLMs, enabling each hidden state to decode multiple speech tokens. This leads to up to 12 \times faster decoding and a substantial drop in word error rate (from 6.07 to 3.01). Furthermore, we propose a speaker-aware generation paradigm and introduce RoleTriviaQA, a large-scale role-playing knowledge QA benchmark with diverse speaker identities. Experiments demonstrate that our methods enhance both knowledge understanding and speaker consistency.
zh
[NLP-124] Detection Classification and Mitigation of Gender Bias in Large Language Models
【速读】： 该论文旨在解决大规模语言模型（LLMs）中存在的性别偏见问题，这一问题在多个领域引发了严重的社会影响。为应对该问题，研究者提出了基于强化学习、思维链（CoT）推理和监督微调的解决方案。其关键在于利用LLMs内部的推理能力进行分阶段多步骤思考，以简化复杂偏见查询并提高响应准确性；对于偏见缓解任务，则通过引入偏好数据集并采用直接偏好优化（DPO）方法，设计一种显式鼓励较少偏见输出的损失函数，从而有效减轻性别偏见。
链接: https://arxiv.org/abs/2506.12527
作者: Xiaoqing Cheng,Hongying Zan,Lulu Kong,Jinwang Song,Min Peng
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:With the rapid development of large language models (LLMs), they have significantly improved efficiency across a wide range of domains. However, recent studies have revealed that LLMs often exhibit gender bias, leading to serious social implications. Detecting, classifying, and mitigating gender bias in LLMs has therefore become a critical research focus. In the NLPCC 2025 Shared Task 7: Chinese Corpus for Gender Bias Detection, Classification and Mitigation Challenge, we investigate how to enhance the capabilities of LLMs in gender bias detection, classification, and mitigation. We adopt reinforcement learning, chain-of-thoughts (CoT) reasoning, and supervised fine-tuning to handle different Subtasks. Specifically, for Subtasks 1 and 2, we leverage the internal reasoning capabilities of LLMs to guide multi-step thinking in a staged manner, which simplifies complex biased queries and improves response accuracy. For Subtask 3, we employ a reinforcement learning-based approach, annotating a preference dataset using GPT-4. We then apply Direct Preference Optimization (DPO) to mitigate gender bias by introducing a loss function that explicitly favors less biased completions over biased ones. Our approach ranked first across all three subtasks of the NLPCC 2025 Shared Task 7.
zh
[NLP-125] owards Fairness Assessment of Dutch Hate Speech Detection WOAH ACL2025
【速读】： 该论文试图解决荷兰语仇恨言论检测模型的反事实公平性问题，特别是在模型性能与社会群体公平性之间的平衡。其解决方案的关键在于构建反映社会背景的荷兰语社会群体术语列表，并利用大语言模型（LLM）及既定策略如人工群体替换（MGS）和句子对数似然（SLL）生成反事实数据，随后通过微调基线Transformer模型并使用反事实标记公平性（CTF）及群体公平性指标进行评估，以提升模型在仇恨言论检测任务中的表现与公平性。
链接: https://arxiv.org/abs/2506.12502
作者: Julie Bauer,Rishabh Kaushal,Thales Bertaglia,Adriana Iamnitchi
机构: Maastricht University (马斯特里赫特大学); Indira Gandhi Delhi Technical University for Women (英迪拉·甘地德里技术女子大学); Utrecht University (乌得勒支大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Accepted for publication at the 9th Workshop on Online Abuse and Harms (WOAH) held in conjunction with ACL 2025
Abstract:Numerous studies have proposed computational methods to detect hate speech online, yet most focus on the English language and emphasize model development. In this study, we evaluate the counterfactual fairness of hate speech detection models in the Dutch language, specifically examining the performance and fairness of transformer-based models. We make the following key contributions. First, we curate a list of Dutch Social Group Terms that reflect social context. Second, we generate counterfactual data for Dutch hate speech using LLMs and established strategies like Manual Group Substitution (MGS) and Sentence Log-Likelihood (SLL). Through qualitative evaluation, we highlight the challenges of generating realistic counterfactuals, particularly with Dutch grammar and contextual coherence. Third, we fine-tune baseline transformer-based models with counterfactual data and evaluate their performance in detecting hate speech. Fourth, we assess the fairness of these models using Counterfactual Token Fairness (CTF) and group fairness metrics, including equality of odds and demographic parity. Our analysis shows that models perform better in terms of hate speech detection, average counterfactual fairness and group fairness. This work addresses a significant gap in the literature on counterfactual fairness for hate speech detection in Dutch and provides practical insights and recommendations for improving both model performance and fairness.
zh
[NLP-126] Improving Factuality for Dialogue Response Generation via Graph-Based Knowledge Augmentation
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在对话响应生成中容易产生幻觉（hallucination）的问题，即生成看似合理但不一致或事实错误的文本。解决方案的关键在于提出一个新颖的框架，该框架结合了知识三元组检索器、对话重写和知识增强的响应生成，以生成更准确且有依据的对话响应。此外，论文还提出了一种改进的事实评分方法，以更可靠地评估对话中的事实一致性。
链接: https://arxiv.org/abs/2506.12496
作者: Xiangyan Chen,Yujian Gan,Matthew Purver
机构: Queen Mary University of London (伦敦玛丽女王大学); Institut Jožef Stefan (约夫·斯蒂芬研究所)
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注:
Abstract:Large Language Models (LLMs) succeed in many natural language processing tasks. However, their tendency to hallucinate - generate plausible but inconsistent or factually incorrect text - can cause problems in certain tasks, including response generation in dialogue. To mitigate this issue, knowledge-augmented methods have shown promise in reducing hallucinations. Here, we introduce a novel framework designed to enhance the factuality of dialogue response generation, as well as an approach to evaluate dialogue factual accuracy. Our framework combines a knowledge triple retriever, a dialogue rewrite, and knowledge-enhanced response generation to produce more accurate and grounded dialogue responses. To further evaluate generated responses, we propose a revised fact score that addresses the limitations of existing fact-score methods in dialogue settings, providing a more reliable assessment of factual consistency. We evaluate our methods using different baselines on the OpendialKG and HybriDialogue datasets. Our methods significantly improve factuality compared to other graph knowledge-augmentation baselines, including the state-of-the-art G-retriever. The code will be released on GitHub.
zh
[NLP-127] FlexRAG : A Flexible and Comprehensive Framework for Retrieval-Augmented Generation ACL2025
【速读】： 该论文旨在解决现有检索增强生成（Retrieval-Augmented Generation, RAG）框架中存在的算法复现与共享困难、缺乏新方法以及系统开销高等问题。其解决方案的关键在于提出一个名为FlexRAG的开源框架，该框架专为研究和原型开发设计，支持文本、多模态和网络基础的RAG，并提供全面的生命周期支持、高效的异步处理及持久化缓存能力，从而提升研究人员开发、部署和共享高级RAG系统的效率。
链接: https://arxiv.org/abs/2506.12494
作者: Zhuocheng Zhang,Yang Feng,Min Zhang
机构: Chinese Academy of Sciences (中国科学院); Institute of Computing Technology (计算技术研究所); University of Chinese Academy of Sciences (中国科学院大学); Institute of Computing and Intelligence, Harbin Institute of Technology (深圳) (哈尔滨工业大学(深圳)计算与智能研究所)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Accepted by ACL 2025 Demo
Abstract:Retrieval-Augmented Generation (RAG) plays a pivotal role in modern large language model applications, with numerous existing frameworks offering a wide range of functionalities to facilitate the development of RAG systems. However, we have identified several persistent challenges in these frameworks, including difficulties in algorithm reproduction and sharing, lack of new techniques, and high system overhead. To address these limitations, we introduce \textbfFlexRAG, an open-source framework specifically designed for research and prototyping. FlexRAG supports text-based, multimodal, and network-based RAG, providing comprehensive lifecycle support alongside efficient asynchronous processing and persistent caching capabilities. By offering a robust and flexible solution, FlexRAG enables researchers to rapidly develop, deploy, and share advanced RAG systems. Our toolkit and resources are available at \hrefthis https URLthis https URL.
zh
[NLP-128] Robust LLM Unlearning with MUDMAN: Meta-Unlearning with Disruption Masking And Normalization
【速读】： 该论文旨在解决语言模型在经过安全微调后仍可能保留危险知识和技能的问题，这可能导致误用或对齐风险。现有去学习方法容易被逆转，因此需要更有效的去学习技术。其解决方案的关键在于引入Disruption Masking技术，该技术仅允许在去学习梯度和保留梯度符号相同时更新权重，从而确保所有更新不会破坏模型的稳定性。此外，论文还强调了对去学习梯度进行归一化的重要性，并验证了元学习的有效性，最终将这些方法整合为MUDMAN（Meta-Unlearning with Disruption Masking and Normalization），在防止危险能力恢复方面表现出色，相比先前的TAR方法提升了40%，成为当前最先进的鲁棒去学习方法。
链接: https://arxiv.org/abs/2506.12484
作者: Filip Sondej,Yushi Yang,Mikołaj Kniejski,Marcel Windys
机构: Jagiellonian University (亚捷隆大学); University of Oxford (牛津大学); University of Warsaw (华沙大学); Independent (独立)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Language models can retain dangerous knowledge and skills even after extensive safety fine-tuning, posing both misuse and misalignment risks. Recent studies show that even specialized unlearning methods can be easily reversed. To address this, we systematically evaluate many existing and novel components of unlearning methods and identify ones crucial for irreversible unlearning. We introduce Disruption Masking, a technique in which we only allow updating weights, where the signs of the unlearning gradient and the retaining gradient are the same. This ensures all updates are non-disruptive. Additionally, we identify the need for normalizing the unlearning gradients, and also confirm the usefulness of meta-learning. We combine these insights into MUDMAN (Meta-Unlearning with Disruption Masking and Normalization) and validate its effectiveness at preventing the recovery of dangerous capabilities. MUDMAN outperforms the prior TAR method by 40%, setting a new state-of-the-art for robust unlearning. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2506.12484 [cs.LG] (or arXiv:2506.12484v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12484 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[NLP-129] MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）中存在的三种幻觉问题：输入冲突型、上下文冲突型和事实冲突型。其解决方案的关键在于利用这三类幻觉之间的相互依赖关系，提出了一种适用于大型语言模型的多信息适配器（Multi-Information Adapter for Large Language Models, MALM）。该框架采用定制化的多图学习方法，以阐明原始输入、上下文信息与外部事实知识之间的关联，从而在一个统一的框架内缓解三类幻觉问题。
链接: https://arxiv.org/abs/2506.12483
作者: Ao Jia,Haiming Wu,Guohui Yao,Dawei Song,Songkun Ji,Yazhou Zhang
机构: Beijing Institute of Technology (北京理工大学); Beijing Information Science and Technology University (北京信息科技大学); Polytechnic University of Hong Kong (香港理工大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are prone to three types of hallucination: Input-Conflicting, Context-Conflicting and Fact-Conflicting hallucinations. The purpose of this study is to mitigate the different types of hallucination by exploiting the interdependence between them. For this purpose, we propose a Multi-Information Adapter for Large Language Models (MALM). This framework employs a tailored multi-graph learning approach designed to elucidate the interconnections between original inputs, contextual information, and external factual knowledge, thereby alleviating the three categories of hallucination within a cohesive framework. Experiments were carried out on four benchmarking datasets: HaluEval, TruthfulQA, Natural Questions, and TriviaQA. We evaluated the proposed framework in two aspects: (1) adaptability to different base LLMs on HaluEval and TruthfulQA, to confirm if MALM is effective when applied on 7 typical LLMs. MALM showed significant improvements over LLaMA-2; (2) generalizability to retrieval-augmented generation (RAG) by combining MALM with three representative retrievers (BM25, Spider and DPR) separately. Furthermore, automated and human evaluations were conducted to substantiate the correctness of experimental results, where GPT-4 and 3 human volunteers judged which response was better between LLaMA-2 and MALM. The results showed that both GPT-4 and human preferred MALM in 79.4% and 65.6% of cases respectively. The results validate that incorporating the complex interactions between the three types of hallucination through a multilayered graph attention network into the LLM generation process is effective to mitigate the them. The adapter design of the proposed approach is also proven flexible and robust across different base LLMs.
zh
[NLP-130] AI Flow: Perspectives Scenarios and Approaches
【速读】： 该论文试图解决大规模人工智能（AI）模型在资源消耗和通信带宽需求方面的挑战，以实现无处不在的智能。其解决方案的关键在于提出AI Flow框架，该框架通过设备-边缘-云架构优化低延迟模型推理的可扩展性和效率，引入家族化模型概念以实现不同规模模型间的协作与适应性，以及基于连接与交互的智能涌现机制，利用通信网络增强异构节点间AI模型的协同，从而实现超越单一模型能力的涌现智能。
链接: https://arxiv.org/abs/2506.12479
作者: Hongjun An,Sida Huang,Siqi Huang,Ruanjun Li,Yuanzhi Liang,Jiawei Shao,Zihan Wang,Cheng Yuan,Chi Zhang,Hongyuan Zhang,Wenhao Zhuang,Xuelong Li
机构: Institute of Artificial Intelligence (TeleAI), China Telecom
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC); Signal Processing (eess.SP)
备注: Authors are with Institute of Artificial Intelligence (TeleAI), China Telecom, China. Author names are listed alphabetically by surname. This work was conducted at TeleAI, facilitated by Dr. Jiawei Shao (e-mail: shaojw2@chinatelecom.cn) under the leadership of Prof. Xuelong Li. The corresponding author is Prof. Xuelong Li (e-mail: xuelong li@ieee.org), the CTO and Chief Scientist of China Telecom
Abstract:Pioneered by the foundational information theory by Claude Shannon and the visionary framework of machine intelligence by Alan Turing, the convergent evolution of information and communication technologies (IT/CT) has created an unbroken wave of connectivity and computation. This synergy has sparked a technological revolution, now reaching its peak with large artificial intelligence (AI) models that are reshaping industries and redefining human-machine collaboration. However, the realization of ubiquitous intelligence faces considerable challenges due to substantial resource consumption in large models and high communication bandwidth demands. To address these challenges, AI Flow has been introduced as a multidisciplinary framework that integrates cutting-edge IT and CT advancements, with a particular emphasis on the following three key points. First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters to optimize scalability and efficiency for low-latency model inference. Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features, enabling effective collaboration and the flexibility to adapt to varying resource constraints and dynamic scenarios. Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow. By leveraging communication networks to enhance connectivity, the collaboration among AI models across heterogeneous nodes achieves emergent intelligence that surpasses the capability of any single model. The innovations of AI Flow provide enhanced intelligence, timely responsiveness, and ubiquitous accessibility to AI services, paving the way for the tighter fusion of AI techniques and communication systems.
zh
[NLP-131] agRouter: Learning Route to LLM s through Tags for Open-Domain Text Generation Tasks ACL2025
【速读】： 该论文旨在解决模型路由（model routing）在大规模应用中面临的可扩展性不足以及难以适应大型语言模型（Large Language Model, LLM）生态系统快速发展的问题。其解决方案的关键在于提出一种无需训练的模型路由方法——TagRouter，该方法通过优化多个LLM之间的协同作用，提升开放域文本生成任务的系统性能与成本效益。
链接: https://arxiv.org/abs/2506.12473
作者: Zhou Chen,Zhiqiang Wei,Yuqi Bai,Xue Xiong,Jianmin Wu
机构: Tsinghua University (清华大学); AI Cloud Group, Baidu Inc. (百度公司人工智能云组)
类目: Computation and Language (cs.CL)
备注: ACL 2025, 26 pages, 13 figures, 14 tables
Abstract:Model routing allocates queries to the suitable model, improving system performance while reducing costs. However, existing routing methods face practical limitations that hinder scalability in large-scale applications and struggle to keep up with the rapid growth of the large language model (LLM) ecosystem. To tackle these challenges, we propose TagRouter, a training-free model routing method designed to optimize the synergy among multiple LLMs for open-domain text generation tasks. Experimental results demonstrate that TagRouter outperforms 13 baseline methods, increasing the accept rate of system by 6.15% and reducing costs by 17.20%, achieving optimal cost-efficiency. Our findings provides the LLM community with an efficient and scalable solution for model ensembling, offering users an evolvable “super model.”
zh
[NLP-132] A Pluggable Multi-Task Learning Framework for Sentiment-Aware Financial Relation Extraction
【速读】： 该论文旨在解决金融领域关系抽取（Relation Extraction, RE）任务中情感因素被忽视的问题，即现有RE模型未能有效利用文本情感信息，从而影响了模型在金融领域的性能。解决方案的关键在于提出一种情感感知的SDP增强模块（Sentiment-aware-SDP-Enhanced-Module, SSDP-SEM），通过引入可插拔的情感感知（Auxiliary Sentiment Perception, ASP）辅助任务，使RE模型能够同时关注文本情感和句法信息中的最短依赖路径（Shortest Dependency Path, SDP），从而提升模型对金融文本中语义关系的抽取能力。
链接: https://arxiv.org/abs/2506.12452
作者: Jinming Luo,Hailin Wang
机构: Southwestern University of Finance and Economics (西南财经大学); Kash Institute of Electronics and Information Industry (喀什电子信息技术研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Relation Extraction (RE) aims to extract semantic relationships in texts from given entity pairs, and has achieved significant improvements. However, in different domains, the RE task can be influenced by various factors. For example, in the financial domain, sentiment can affect RE results, yet this factor has been overlooked by modern RE models. To address this gap, this paper proposes a Sentiment-aware-SDP-Enhanced-Module (SSDP-SEM), a multi-task learning approach for enhancing financial RE. Specifically, SSDP-SEM integrates the RE models with a pluggable auxiliary sentiment perception (ASP) task, enabling the RE models to concurrently navigate their attention weights with the text’s sentiment. We first generate detailed sentiment tokens through a sentiment model and insert these tokens into an instance. Then, the ASP task focuses on capturing nuanced sentiment information through predicting the sentiment token positions, combining both sentiment insights and the Shortest Dependency Path (SDP) of syntactic information. Moreover, this work employs a sentiment attention information bottleneck regularization method to regulate the reasoning process. Our experiment integrates this auxiliary task with several prevalent frameworks, and the results demonstrate that most previous models benefit from the auxiliary task, thereby achieving better results. These findings highlight the importance of effectively leveraging sentiment in the financial RE task.
zh
[NLP-133] Language Surgery in Multilingual Large Language Models
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）中跨语言混淆的问题，即模型在多语言任务中难以准确区分和控制不同语言的输出，导致生成结果不一致。其解决方案的关键在于提出一种名为推理时语言控制（Inference-Time Language Control, ITLC）的方法，该方法通过潜在空间注入（latent injection）实现对目标语言的精确跨语言控制，从而在保持语义完整性的前提下有效缓解语言混淆问题。
链接: https://arxiv.org/abs/2506.12450
作者: Joanito Agili Lopo,Muhammad Ravi Shulthan Habibi,Tack Hwa Wong,Muhammad Ilham Ghozali,Fajri Koto,Genta Indra Winata,Peerat Limkonchotiwat,Alham Fikri Aji,Samuel Cahyawijaya
机构: SEACrowd; Kreasof AI; Universitas Indonesia; MBZUAI; Capital One; AI Singapore; Cohere
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across tasks and languages, revolutionizing natural language processing. This paper investigates the naturally emerging representation alignment in LLMs, particularly in the middle layers, and its implications for disentangling language-specific and language-agnostic information. We empirically confirm the existence of this alignment, analyze its behavior in comparison to explicitly designed alignment models, and demonstrate its potential for language-specific manipulation without semantic degradation. Building on these findings, we propose Inference-Time Language Control (ITLC), a novel method that leverages latent injection to enable precise cross-lingual language control and mitigate language confusion in LLMs. Our experiments highlight ITLC’s strong cross-lingual control capabilities while preserving semantic integrity in target languages. Furthermore, we demonstrate its effectiveness in alleviating the cross-lingual language confusion problem, which persists even in current large-scale LLMs, leading to inconsistent language generation. This work advances our understanding of representation alignment in LLMs and introduces a practical solution for enhancing their cross-lingual performance.
zh
[NLP-134] From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment
【速读】： 该论文试图解决奖励引导搜索（RGS）方法在对齐大语言模型（LLM）与人类偏好时存在的粒度不匹配问题，即现有的结果奖励模型（ORM）仅能提供完整响应的奖励，而RGS方法依赖过程奖励来指导策略，导致评分不一致和对齐效果不佳。解决方案的关键在于引入过程奖励模型（PRM），并提出SP-PRM框架，该框架通过集成基于得分一致性与偏好一致性的部分评估模块，实现对部分序列与完整响应的连贯评估，从而提升RGS方法的效果。
链接: https://arxiv.org/abs/2506.12446
作者: Bin Xie,Bingbing Xu,Yige Yuan,Shengmao Zhu,Huawei Shen
机构: State Key Laboratory of AI Safety, Institute of Computing Technology, CAS (国家人工智能安全重点实验室，计算技术研究所，中国科学院); University of Chinese Academy of Sciences (中国科学院大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Inference-time alignment methods have gained significant attention for their efficiency and effectiveness in aligning large language models (LLMs) with human preferences. However, existing dominant approaches using reward-guided search (RGS) primarily rely on outcome reward models (ORMs), which suffer from a critical granularity mismatch: ORMs are designed to provide outcome rewards for complete responses, while RGS methods rely on process rewards to guide the policy, leading to inconsistent scoring and suboptimal alignment. To address this challenge, we introduce process reward models (PRMs) into RGS and argue that an ideal PRM should satisfy two objectives: Score Consistency, ensuring coherent evaluation across partial and complete responses, and Preference Consistency, aligning partial sequence assessments with human preferences. Based on these, we propose SP-PRM, a novel dual-consistency framework integrating score consistency-based and preference consistency-based partial evaluation modules without relying on human annotation. Extensive experiments on dialogue, summarization, and reasoning tasks demonstrate that SP-PRM substantially enhances existing RGS methods, achieving a 3.6%-10.3% improvement in GPT-4 evaluation scores across all tasks.
zh
[NLP-135] Exploring Cultural Variations in Moral Judgments with Large Language Models
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在捕捉文化多样性道德价值观方面能力不足的问题。其解决方案的关键在于通过对比不同规模和类型的模型（包括传统模型与指令微调模型）在道德合理性的表现，并利用基于对数概率的道德合理性评分，评估模型输出与跨文化调查数据之间的相关性，从而揭示指令微调和模型规模扩展对提升模型与跨文化道德规范对齐效果的作用。
链接: https://arxiv.org/abs/2506.12433
作者: Hadi Mohammadi,Efthymia Papadopoulou,Yasmeen F.S.S. Meijer,Ayoub Bagheri
机构: Utrecht University (乌得勒支大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) have shown strong performance across many tasks, but their ability to capture culturally diverse moral values remains unclear. In this paper, we examine whether LLMs can mirror variations in moral attitudes reported by two major cross-cultural surveys: the World Values Survey and the PEW Research Center’s Global Attitudes Survey. We compare smaller, monolingual, and multilingual models (GPT-2, OPT, BLOOMZ, and Qwen) with more recent instruction-tuned models (GPT-4o, GPT-4o-mini, Gemma-2-9b-it, and Llama-3.3-70B-Instruct). Using log-probability-based moral justifiability scores, we correlate each model’s outputs with survey data covering a broad set of ethical topics. Our results show that many earlier or smaller models often produce near-zero or negative correlations with human judgments. In contrast, advanced instruction-tuned models (including GPT-4o and GPT-4o-mini) achieve substantially higher positive correlations, suggesting they better reflect real-world moral attitudes. While scaling up model size and using instruction tuning can improve alignment with cross-cultural moral norms, challenges remain for certain topics and regions. We discuss these findings in relation to bias analysis, training data diversity, and strategies for improving the cultural sensitivity of LLMs.
zh
[NLP-136] Plan Your Travel and Travel with Your Plan: Wide-Horizon Planning and Evaluation via LLM
【速读】： 该论文试图解决旅行规划中由于长时序思考方法难以处理多维约束和用户偏好而导致行程规划不优的问题。其解决方案的关键在于提出一种名为Multiple Aspects of Planning (MAoP) 的方法，该方法通过策略制定者从多个方面进行预规划并提供规划蓝图，从而实现长时域思维，提升复杂规划问题的求解能力。
链接: https://arxiv.org/abs/2506.12421
作者: Dongjie Yang,Chengqiang Lu,Qimeng Wang,Xinbei Ma,Yan Gao,Yao Hu,Hai Zhao
机构: Shanghai Jiao Tong University (上海交通大学); Xiaohongshu Inc. (小红书公司)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Travel planning is a complex task requiring the integration of diverse real-world information and user preferences. While LLMs show promise, existing methods with long-horizon thinking struggle with handling multifaceted constraints and preferences in the context, leading to suboptimal itineraries. We formulate this as an L^3 planning problem, emphasizing long context, long instruction, and long output. To tackle this, we introduce Multiple Aspects of Planning (MAoP), enabling LLMs to conduct wide-horizon thinking to solve complex planning problems. Instead of direct planning, MAoP leverages the strategist to conduct pre-planning from various aspects and provide the planning blueprint for planning models, enabling strong inference-time scalability for better performance. In addition, current benchmarks overlook travel’s dynamic nature, where past events impact subsequent journeys, failing to reflect real-world feasibility. To address this, we propose Travel-Sim, an agent-based benchmark assessing plans via real-world travel simulation. This work advances LLM capabilities in complex planning and offers novel insights for evaluating sophisticated scenarios through agent-based simulation.
zh
[NLP-137] Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model ACL2025
【速读】： 该论文试图解决多语言大型语言模型（Multilingual Large Language Models, LLMs）中的“多语言诅咒”（curse of multilinguality）问题，该问题表现为多种语言之间的竞争导致模型性能下降，主要源于模型容量有限以及不同语言间的负向迁移。解决方案的关键在于动态分组并扩展多语言LLM的参数，同时增强相似语言之间的正向迁移。具体而言，模型首先在单语语料上进行微调，以确定每层的参数偏差并量化语言间的相似性，随后将偏差较大的层扩展为专家混合（mixture-of-experts）层，每个专家模块服务于一组相似语言，从而减少语言间的竞争并提升多语言性能。
链接: https://arxiv.org/abs/2506.12388
作者: Chong Li,Yingzhuo Deng,Jiajun Zhang,Chengqing Zong
机构: State Key Laboratory of Multimodal Artificial Intelligence Systems (国家多模态人工智能系统重点实验室); Institute of Automation, CAS (中国科学院自动化研究所); School of Artificial Intelligence, University of Chinese Academy of Sciences (中国科学院大学人工智能学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: ACL 2025, our codes and models are available at this https URL
Abstract:The curse of multilinguality phenomenon is a fundamental problem of multilingual Large Language Models (LLMs), where the competition between massive languages results in inferior performance. It mainly comes from limited capacity and negative transfer between dissimilar languages. To address this issue, we propose a method to dynamically group and scale up the parameters of multilingual LLM while boosting positive transfer among similar languages. Specifically, the model is first tuned on monolingual corpus to determine the parameter deviation in each layer and quantify the similarity between languages. Layers with more deviations are extended to mixture-of-experts layers to reduce competition between languages, where one expert module serves one group of similar languages. Experimental results on 18 to 128 languages show that our method reduces the negative transfer between languages and significantly boosts multilingual performance with fewer parameters. Such language group specialization on experts benefits the new language adaptation and reduces the inference on the previous multilingual knowledge learned.
zh
[NLP-138] Recent Advances and Future Directions in Literature-Based Discovery
【速读】： 该论文试图解决科学出版物爆炸式增长所带来的知识综合与假设生成的自动化需求，其核心问题是通过文献挖掘发现不同领域之间的潜在关联。解决方案的关键在于近年来在知识图谱构建、深度学习方法以及预训练模型和大语言模型（LLMs）集成方面的进展，尤其是LLMs在提升文献基础发现（LBD）中的变革性作用。
链接: https://arxiv.org/abs/2506.12385
作者: Andrej Kastrin,Bojan Cestnik,Nada Lavrač
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 13 pages, 1 table, 1 figure
Abstract:The explosive growth of scientific publications has created an urgent need for automated methods that facilitate knowledge synthesis and hypothesis generation. Literature-based discovery (LBD) addresses this challenge by uncovering previously unknown associations between disparate domains. This article surveys recent methodological advances in LBD, focusing on developments from 2000 to the present. We review progress in three key areas: knowledge graph construction, deep learning approaches, and the integration of pre-trained and large language models (LLMs). While LBD has made notable progress, several fundamental challenges remain unresolved, particularly concerning scalability, reliance on structured data, and the need for extensive manual curation. By examining ongoing advances and outlining promising future directions, this survey underscores the transformative role of LLMs in enhancing LBD and aims to support researchers and practitioners in harnessing these technologies to accelerate scientific innovation.
zh
[NLP-139] Model Merging for Knowledge Editing
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在持续更新知识过程中面临的顺序编辑场景适应性差以及模型通用能力受损的问题。现有知识编辑方法在处理连续更新时效果不佳，且会损害模型的总体性能，限制了其实际应用。该论文提出了一种两阶段框架，结合稳健监督微调（R-SFT）与模型融合技术，关键在于首先通过微调使模型充分内化新知识，随后将微调后的模型与原始基础模型进行融合，以保留新获取的知识和模型的通用能力。实验结果表明，该方法在顺序编辑任务中显著优于现有方法，同时更好地保持了模型的原始性能，且无需任何架构改动。
链接: https://arxiv.org/abs/2506.12384
作者: Zichuan Fu,Xian Wu,Guojing Li,Yingying Zhang,Yefeng Zheng,Tianshi Ming,Yejing Wang,Wanyu Wang,Xiangyu Zhao
机构: City University of Hong Kong (香港城市大学); Tencent Jarvis Lab (腾讯贾维斯实验室); Westlake University (西湖大学); Tongji University (同济大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 11 pages, 3 figures
Abstract:Large Language Models (LLMs) require continuous updates to maintain accurate and current knowledge as the world evolves. While existing knowledge editing approaches offer various solutions for knowledge updating, they often struggle with sequential editing scenarios and harm the general capabilities of the model, thereby significantly hampering their practical applicability. This paper proposes a two-stage framework combining robust supervised fine-tuning (R-SFT) with model merging for knowledge editing. Our method first fine-tunes the LLM to internalize new knowledge fully, then merges the fine-tuned model with the original foundation model to preserve newly acquired knowledge and general capabilities. Experimental results demonstrate that our approach significantly outperforms existing methods in sequential editing while better preserving the original performance of the model, all without requiring any architectural changes. Code is available at: this https URL.
zh
[NLP-140] raining-free LLM Merging for Multi-task Learning
【速读】： 该论文试图解决如何将多个针对特定任务或语言优化的大型语言模型（Large Language Models, LLMs）整合为一个具备多任务能力的统一模型的问题。其解决方案的关键在于提出了一种无需训练的层级迭代融合方法（Hierarchical Iterative Merging, Hi-Merging），该方法通过模型级和层级的剪枝与缩放，并结合贡献分析来缓解参数冲突，从而实现不同模型的有效融合。
链接: https://arxiv.org/abs/2506.12379
作者: Zichuan Fu,Xian Wu,Yejing Wang,Wanyu Wang,Shanshan Ye,Hongzhi Yin,Yi Chang,Yefeng Zheng,Xiangyu Zhao
机构: City University of Hong Kong (香港城市大学); Tencent Jarvis Lab (腾讯贾维斯实验室); University of Technology Sydney (悉尼科技大学); University of Queensland (昆士兰大学); Jilin University (吉林大学); Westlake University (西湖大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 14 pages, 6 figures
Abstract:Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing (NLP) tasks. The release of open-source LLMs like LLaMA and Qwen has triggered the development of numerous fine-tuned models tailored for various tasks and languages. In this paper, we explore an important question: is it possible to combine these specialized models to create a unified model with multi-task capabilities. We introduces Hierarchical Iterative Merging (Hi-Merging), a training-free method for unifying different specialized LLMs into a single model. Specifically, Hi-Merging employs model-wise and layer-wise pruning and scaling, guided by contribution analysis, to mitigate parameter conflicts. Extensive experiments on multiple-choice and question-answering tasks in both Chinese and English validate Hi-Merging’s ability for multi-task learning. The results demonstrate that Hi-Merging consistently outperforms existing merging techniques and surpasses the performance of models fine-tuned on combined datasets in most scenarios. Code is available at: this https URL.
zh
[NLP-141] ConsistencyChecker: Tree-based Evaluation of LLM Generalization Capabilities ACL2025
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）在复杂多步骤人机交互中的一致性评估问题，传统自一致性方法难以捕捉自然语言中的细微语义变化及代码或公式的功能性转变，这些变化可能在多次转换中累积。其解决方案的关键在于提出ConsistencyChecker，一个基于树状结构的评估框架，通过可逆变换序列来衡量一致性，其中节点表示不同的文本状态，边对应逆操作对，动态和LLM生成的基准确保了模型泛化能力的公平评估，并通过不同深度变换树中的相似性量化一致性。
链接: https://arxiv.org/abs/2506.12376
作者: Zhaochen Hong,Haofei Yu,Jiaxuan You
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at ACL 2025 Main Conference
Abstract:Evaluating consistency in large language models (LLMs) is crucial for ensuring reliability, particularly in complex, multi-step interactions between humans and LLMs. Traditional self-consistency methods often miss subtle semantic changes in natural language and functional shifts in code or equations, which can accumulate over multiple transformations. To address this, we propose ConsistencyChecker, a tree-based evaluation framework designed to measure consistency through sequences of reversible transformations, including machine translation tasks and AI-assisted programming tasks. In our framework, nodes represent distinct text states, while edges correspond to pairs of inverse operations. Dynamic and LLM-generated benchmarks ensure a fair assessment of the model’s generalization ability and eliminate benchmark leakage. Consistency is quantified based on similarity across different depths of the transformation tree. Experiments on eight models from various families and sizes show that ConsistencyChecker can distinguish the performance of different models. Notably, our consistency scores-computed entirely without using WMT paired data-correlate strongly (r 0.7) with WMT 2024 auto-ranking, demonstrating the validity of our benchmark-free approach. Our implementation is available at: this https URL.
zh
[NLP-142] Understanding the Effect of Knowledge Graph Extraction Error on Downstream Graph Analyses: A Case Study on Affiliation Graphs
【速读】： 该论文试图解决生成式知识图谱（Knowledge Graph, KG）提取过程中误差对下游分析结果的影响问题，特别是针对应用科学家在实际场景中依赖准确KG进行决策时所面临的挑战。其解决方案的关键在于从两个层面评估KG提取性能：微观层面的边精度（edge accuracy）以及宏观层面的图结构指标（如社区检测和连通性），从而全面揭示提取误差对分析结果的影响机制，并指出现有文献中常用的误差模型无法捕捉到这些偏差模式，强调了构建更真实误差模型的重要性。
链接: https://arxiv.org/abs/2506.12367
作者: Erica Cai,Brendan O’Connor
机构: 未知
类目: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
备注: 30 pages
Abstract:Knowledge graphs (KGs) are useful for analyzing social structures, community dynamics, institutional memberships, and other complex relationships across domains from sociology to public health. While recent advances in large language models (LLMs) have improved the scalability and accessibility of automated KG extraction from large text corpora, the impacts of extraction errors on downstream analyses are poorly understood, especially for applied scientists who depend on accurate KGs for real-world insights. To address this gap, we conducted the first evaluation of KG extraction performance at two levels: (1) micro-level edge accuracy, which is consistent with standard NLP evaluations, and manual identification of common error sources; (2) macro-level graph metrics that assess structural properties such as community detection and connectivity, which are relevant to real-world applications. Focusing on affiliation graphs of person membership in organizations extracted from social register books, our study identifies a range of extraction performance where biases across most downstream graph analysis metrics are near zero. However, as extraction performance declines, we find that many metrics exhibit increasingly pronounced biases, with each metric tending toward a consistent direction of either over- or under-estimation. Through simulations, we further show that error models commonly used in the literature do not capture these bias patterns, indicating the need for more realistic error models for KG extraction. Our findings provide actionable insights for practitioners and underscores the importance of advancing extraction methods and error modeling to ensure reliable and meaningful downstream analyses.
zh
[NLP-143] Advances in LLM s with Focus on Reasoning Adaptability Efficiency and Ethics
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在推理能力、任务适应性、计算效率以及伦理决策等方面的局限性，同时探索其在多模态学习和少样本/零样本学习中的潜力。解决方案的关键在于采用有效的技术手段，如思维链提示（Chain-of-Thought prompting）、指令微调（Instruction Tuning）和人类反馈强化学习（Reinforcement Learning from Human Feedback），以缩小人机沟通的差距，并通过缩放和优化技巧提升模型性能与计算资源的利用效率。此外，论文还强调了提升模型可解释性、跨模态整合能力和可持续性的研究方向。
链接: https://arxiv.org/abs/2506.12365
作者: Asifullah khan,Muhammad Zaeem Khan,Saleha Jamshed,Sadia Ahmad,Aleesha Zainab,Kaynat Khatib,Faria Bibi,Abdul Rehman
机构: 未知
类目: Computation and Language (cs.CL); Databases (cs.DB)
备注:
Abstract:This survey paper outlines the key developments in the field of Large Language Models (LLMs), such as enhancing their reasoning skills, adaptability to various tasks, increased computational efficiency, and ability to make ethical decisions. The techniques that have been most effective in bridging the gap between human and machine communications include the Chain-of-Thought prompting, Instruction Tuning, and Reinforcement Learning from Human Feedback. The improvements in multimodal learning and few-shot or zero-shot techniques have further empowered LLMs to handle complex jobs with minor input. They also manage to do more with less by applying scaling and optimization tricks for computing power conservation. This survey also offers a broader perspective on recent advancements in LLMs going beyond isolated aspects such as model architecture or ethical concerns. It categorizes emerging methods that enhance LLM reasoning, efficiency, and ethical alignment. It also identifies underexplored areas such as interpretability, cross-modal integration and sustainability. With recent progress, challenges like huge computational costs, biases, and ethical risks remain constant. Addressing these requires bias mitigation, transparent decision-making, and clear ethical guidelines. Future research will focus on enhancing models ability to handle multiple input, thereby making them more intelligent, safe, and reliable.
zh
[NLP-144] MM-R5: MultiModal Reasoning -Enhanced ReRanker via Reinforcement Learning for Document Retrieval
【速读】： 该论文旨在解决多模态文档检索中重排序（reranking）方法研究不足、训练策略与整体效果有待提升的问题，同时解决现有方法缺乏显式推理机制导致难以分析和优化的局限性。其解决方案的关键在于提出MM-R5，一个基于强化学习的多模态推理增强型重排序器，通过两阶段训练策略——监督微调（SFT）和强化学习（RL）——来提升模型的指令遵循能力与推理链质量，并设计任务特定的奖励框架以优化多模态候选结果的排序效果。
链接: https://arxiv.org/abs/2506.12364
作者: Mingjun Xu,Jinhan Dong,Jue Hou,Zehui Wang,Sihang Li,Zhifeng Gao,Renxin Zhong,Hengxing Cai
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal document retrieval systems enable information access across text, images, and layouts, benefiting various domains like document-based question answering, report analysis, and interactive content summarization. Rerankers improve retrieval precision by reordering retrieved candidates. However, current multimodal reranking methods remain underexplored, with significant room for improvement in both training strategies and overall effectiveness. Moreover, the lack of explicit reasoning makes it difficult to analyze and optimize these methods further. In this paper, We propose MM-R5, a MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrieval, aiming to provide a more effective and reliable solution for multimodal reranking tasks. MM-R5 is trained in two stages: supervised fine-tuning (SFT) and reinforcement learning (RL). In the SFT stage, we focus on improving instruction-following and guiding the model to generate complete and high-quality reasoning chains. To support this, we introduce a novel data construction strategy that produces rich, high-quality reasoning data. In the RL stage, we design a task-specific reward framework, including a reranking reward tailored for multimodal candidates and a composite template-based reward to further refine reasoning quality. We conduct extensive experiments on MMDocIR, a challenging public benchmark spanning multiple domains. MM-R5 achieves state-of-the-art performance on most metrics and delivers comparable results to much larger models on the remaining ones. Moreover, compared to the best retrieval-only method, MM-R5 improves recall@1 by over 4%. These results validate the effectiveness of our reasoning-enhanced training pipeline.
zh
[NLP-145] QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）中注意力算子（attention operator）在长上下文场景下的性能瓶颈问题。尽管FlashAttention是当前最广泛使用且有效的GPU感知加速算法，但其需要耗时且依赖硬件的手动实现，限制了在不同GPU架构上的适应性。论文提出的解决方案关键在于设计一种LLM友好的思维语言（LLM-TL），帮助LLMs分离高层次优化逻辑与低层次GPU实现，并通过两阶段推理流程——TL-Code生成与翻译——自动在多种GPU上生成FlashAttention实现，从而建立一种自优化的高性能注意力算子生成范式。
链接: https://arxiv.org/abs/2506.12355
作者: Qirui Zhou,Shaohui Peng,Weiqiang Xiong,Haixin Chen,Yuanbo Wen,Haochen Li,Ling Li,Qi Guo,Yongwei Zhao,Ke Gao,Ruizhi Chen,Yanjun Wu,Chen Zhao,Yunji Chen
机构: SKL of Processors, Institute of Computing Technology, CAS, Beijing, China; Intelligent Software Research Center, Institute of Software, CAS, Beijing China; University of Chinese Academy of Sciences, Beijing, China
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:The attention operator remains a critical performance bottleneck in large language models (LLMs), particularly for long-context scenarios. While FlashAttention is the most widely used and effective GPU-aware acceleration algorithm, it must require time-consuming and hardware-specific manual implementation, limiting adaptability across GPU architectures. Existing LLMs have shown a lot of promise in code generation tasks, but struggle to generate high-performance attention code. The key challenge is it cannot comprehend the complex data flow and computation process of the attention operator and utilize low-level primitive to exploit GPU performance. To address the above challenge, we propose an LLM-friendly Thinking Language (LLM-TL) to help LLMs decouple the generation of high-level optimization logic and low-level implementation on GPU, and enhance LLMs’ understanding of attention operator. Along with a 2-stage reasoning workflow, TL-Code generation and translation, the LLMs can automatically generate FlashAttention implementation on diverse GPUs, establishing a self-optimizing paradigm for generating high-performance attention operators in attention-centric algorithms. Verified on A100, RTX8000, and T4 GPUs, the performance of our methods significantly outshines that of vanilla LLMs, achieving a speed-up of up to 35.16x. Besides, our method not only surpasses human-optimized libraries (cuDNN and official library) in most scenarios but also extends support to unsupported hardware and data types, reducing development time from months to minutes compared with human experts. Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) ACMclasses: I.2.7 Cite as: arXiv:2506.12355 [cs.LG] (or arXiv:2506.12355v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12355 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[NLP-146] Efficient Reasoning Through Suppression of Self-Affirmation Reflections in Large Reasoning Models
【速读】： 该论文试图解决大型推理模型在输出长度快速增长背景下，如何实现高效推理的问题，特别是针对模型中普遍存在的冗余自我肯定反思（self-affirmation reflections）现象。解决方案的关键在于通过分析自我肯定反思的句首词概率偏差，识别并抑制此类冗余步骤，从而在不降低模型准确性的前提下减少输出长度。实验表明，该方法在无训练（train-free）和有训练（train-based）设置下均能有效实现长度压缩，且具有良好的实用性和可移植性。
链接: https://arxiv.org/abs/2506.12353
作者: Kaiyuan Liu,Chen Shen,Zhanwei Zhang,Junjie Liu,Xiaosong Yuan,Jieping ye
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Under review
Abstract:While recent advances in large reasoning models have demonstrated remarkable performance, efficient reasoning remains critical due to the rapid growth of output length. Existing optimization approaches highlights a tendency toward “overthinking”, yet lack fine-grained analysis. In this work, we focus on Self-Affirmation Reflections: redundant reflective steps that affirm prior content and often occurs after the already correct reasoning steps. Observations of both original and optimized reasoning models reveal pervasive self-affirmation reflections. Notably, these reflections sometimes lead to longer outputs in optimized models than their original counterparts. Through detailed analysis, we uncover an intriguing pattern: compared to other reflections, the leading words (i.e., the first word of sentences) in self-affirmation reflections exhibit a distinct probability bias. Motivated by this insight, we can locate self-affirmation reflections and conduct a train-free experiment demonstrating that suppressing self-affirmation reflections reduces output length without degrading accuracy across multiple models (R1-Distill-Models, QwQ-32B, and Qwen3-32B). Furthermore, we also improve current train-based method by explicitly suppressing such reflections. In our experiments, we achieve length compression of 18.7% in train-free settings and 50.2% in train-based settings for R1-Distill-Qwen-1.5B. Moreover, our improvements are simple yet practical and can be directly applied to existing inference frameworks, such as vLLM. We believe that our findings will provide community insights for achieving more precise length compression and step-level efficient reasoning.
zh
[NLP-147] Information Suppression in Large Language Models : Auditing Quantifying and Characterizing Censorship in DeepSeek
【速读】： 该论文试图解决大型语言模型（Large Language Model, LLM）中信息抑制机制的问题，特别是针对DeepSeek这一在中国开发的开源LLM进行分析。研究提出了一种审计框架，通过将模型对646个政治敏感提示的最终输出与其内部链式推理（Chain-of-Thought, CoT）过程进行对比，揭示了语义层面的信息抑制现象。解决方案的关键在于通过对比模型的中间推理过程与最终输出，识别出敏感内容在内部推理中出现但被省略或改写的情况，从而揭示模型在内容过滤和信息控制方面的潜在机制。
链接: https://arxiv.org/abs/2506.12349
作者: Peiran Qiu,Siyi Zhou,Emilio Ferrara
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:This study examines information suppression mechanisms in DeepSeek, an open-source large language model (LLM) developed in China. We propose an auditing framework and use it to analyze the model’s responses to 646 politically sensitive prompts by comparing its final output with intermediate chain-of-thought (CoT) reasoning. Our audit unveils evidence of semantic-level information suppression in DeepSeek: sensitive content often appears within the model’s internal reasoning but is omitted or rephrased in the final output. Specifically, DeepSeek suppresses references to transparency, government accountability, and civic mobilization, while occasionally amplifying language aligned with state propaganda. This study underscores the need for systematic auditing of alignment, content moderation, information suppression, and censorship practices implemented into widely-adopted AI models, to ensure transparency, accountability, and equitable access to unbiased information obtained by means of these systems.
zh
[NLP-148] Refract ICL: Rethinking Example Selection in the Era of Million-Token Models
【速读】： 该论文试图解决在使用大量示例进行上下文学习（in-context learning, ICL）时，传统ICL选择策略是否依然有效的问题。研究发现，尽管长上下文大语言模型（long-context large language models）能够容纳更多示例，但单纯增加示例数量并不保证性能提升，因此智能的ICL选择仍然至关重要。解决方案的关键在于提出Refract ICL，这是一种专门设计的ICL选择算法，通过在上下文中战略性地重复具有挑战性的示例，并结合零样本预测作为错误信号，以引导模型关注关键示例，从而显著提升极长上下文模型（如Gemini 1.5 Pro）在输出类别较少任务中的性能。
链接: https://arxiv.org/abs/2506.12346
作者: Arjun R. Akula,Kazuma Hashimoto,Krishna Srinivasan,Aditi Chaudhary,Karthik Raman,Michael Bendersky
机构: Google DeepMind(谷歌深度思维)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The emergence of long-context large language models (LLMs) has enabled the use of hundreds, or even thousands, of demonstrations for in-context learning (ICL) - a previously impractical regime. This paper investigates whether traditional ICL selection strategies, which balance the similarity of ICL examples to the test input (using a text retriever) with diversity within the ICL set, remain effective when utilizing a large number of demonstrations. Our experiments demonstrate that, while longer contexts can accommodate more examples, simply increasing the number of demonstrations does not guarantee improved performance. Smart ICL selection remains crucial, even with thousands of demonstrations. To further enhance ICL in this setting, we introduce Refract ICL, a novel ICL selection algorithm specifically designed to focus LLM attention on challenging examples by strategically repeating them within the context and incorporating zero-shot predictions as error signals. Our results show that Refract ICL significantly improves the performance of extremely long-context models such as Gemini 1.5 Pro, particularly on tasks with a smaller number of output classes.
zh
[NLP-149] Investigating the Effects of Cognitive Biases in Prompts on Large Language Model Outputs
【速读】： 该论文试图解决认知偏差对大型语言模型（Large Language Models, LLMs）输出的影响问题，具体表现为认知偏差通过提示（prompt）扭曲用户输入，可能导致模型生成不准确或具有误导性的结果。解决方案的关键在于引入系统化的认知偏差框架，评估其对LLMs在多个基准数据集上的准确性影响，并通过注意力权重分析揭示偏差如何改变模型的内部决策过程，从而为设计更具偏差意识的提示和制定缓解策略提供依据。
链接: https://arxiv.org/abs/2506.12338
作者: Yan Sun,Stanley Kok
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper investigates the influence of cognitive biases on Large Language Models (LLMs) outputs. Cognitive biases, such as confirmation and availability biases, can distort user inputs through prompts, potentially leading to unfaithful and misleading outputs from LLMs. Using a systematic framework, our study introduces various cognitive biases into prompts and assesses their impact on LLM accuracy across multiple benchmark datasets, including general and financial QA scenarios. The results demonstrate that even subtle biases can significantly alter LLM answer choices, highlighting a critical need for bias-aware prompt design and mitigation strategy. Additionally, our attention weight analysis highlights how these biases can alter the internal decision-making processes of LLMs, affecting the attention distribution in ways that are associated with output inaccuracies. This research has implications for Al developers and users in enhancing the robustness and reliability of Al applications in diverse domains.
zh
[NLP-150] Intersectional Bias in Japanese Large Language Models from a Contextualized Perspective ACL2025
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）中存在的情境性交叉社会偏见问题，即在多个社会属性共同作用下产生的复杂偏见现象。传统研究多关注单一社会属性的偏见，而本文提出的关键解决方案是构建了日本基准数据集inter-JBBQ，用于评估问答任务中模型的交叉偏见，其核心在于通过情境化分析揭示即使在相同社会属性组合下，模型输出仍可能因上下文不同而呈现差异化的偏见表现。
链接: https://arxiv.org/abs/2506.12327
作者: Hitomi Yanaka,Xinqi He,Jie Lu,Namgi Han,Sunjin Oh,Ryoma Kumon,Yuma Matsuoka,Katsuhiko Watabe,Yuko Itatsu
机构: The University of Tokyo (东京大学); Riken (理化学研究所); Rikkyo University (立教大学); Softbank corp. (软银公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to the 6th Workshop on Gender Bias in Natural Language Processing (GeBNLP2025) at ACL2025
Abstract:An growing number of studies have examined the social bias of rapidly developed large language models (LLMs). Although most of these studies have focused on bias occurring in a single social attribute, research in social science has shown that social bias often occurs in the form of intersectionality – the constitutive and contextualized perspective on bias aroused by social attributes. In this study, we construct the Japanese benchmark inter-JBBQ, designed to evaluate the intersectional bias in LLMs on the question-answering setting. Using inter-JBBQ to analyze GPT-4o and Swallow, we find that biased output varies according to its contexts even with the equal combination of social attributes.
zh
[NLP-151] GSDNet: Revisiting Incomplete Multimodal-Diffusion from Graph Spectrum Perspective for Conversation Emotion Recognition
【速读】： 该论文旨在解决多模态对话情感识别（Multimodal Emotion Recognition in Conversations, MERC）中由于模态缺失导致的性能受限问题。其解决方案的关键在于提出一种新颖的图谱扩散网络（Graph Spectral Diffusion Network, GSDNet），该方法通过将高斯噪声映射到缺失模态的图谱空间，并根据原始分布恢复缺失数据，从而在不破坏邻接矩阵的连通性和局部结构的前提下，保留图的语义和拓扑信息，提升模态恢复能力。
链接: https://arxiv.org/abs/2506.12325
作者: Yuntao Shou,Jun Yao,Tao Meng,Wei Ai,Cen Chen,Keqin Li
机构: Central South University of Forestry and Technology (中南林业科技大学); Anhui Normal University (安徽师范大学); South China University of Technology (华南理工大学); State University of New York (纽约州立大学)
类目: ound (cs.SD); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注:
Abstract:Multimodal emotion recognition in conversations (MERC) aims to infer the speaker’s emotional state by analyzing utterance information from multiple sources (i.e., video, audio, and text). Compared with unimodality, a more robust utterance representation can be obtained by fusing complementary semantic information from different modalities. However, the modality missing problem severely limits the performance of MERC in practical scenarios. Recent work has achieved impressive performance on modality completion using graph neural networks and diffusion models, respectively. This inspires us to combine these two dimensions through the graph diffusion model to obtain more powerful modal recovery capabilities. Unfortunately, existing graph diffusion models may destroy the connectivity and local structure of the graph by directly adding Gaussian noise to the adjacency matrix, resulting in the generated graph data being unable to retain the semantic and topological information of the original graph. To this end, we propose a novel Graph Spectral Diffusion Network (GSDNet), which maps Gaussian noise to the graph spectral space of missing modalities and recovers the missing data according to its original distribution. Compared with previous graph diffusion methods, GSDNet only affects the eigenvalues of the adjacency matrix instead of destroying the adjacency matrix directly, which can maintain the global topological information and important spectral features during the diffusion process. Extensive experiments have demonstrated that GSDNet achieves state-of-the-art emotion recognition performance in various modality loss scenarios.
zh
[NLP-152] Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research
【速读】： 该论文试图解决传统实验室自动化系统在适应性和灵活性方面的不足，旨在通过基础模型（foundation models）提升材料与化学科学中的实验室自动化水平。解决方案的关键在于利用基础模型的通用智能和多模态能力，实现实验规划、数据分析以及硬件操作的协同优化，从而推动更加灵活和自主的实验流程。
链接: https://arxiv.org/abs/2506.12312
作者: Kan Hatakeyama-Sato,Toshihiko Nishida,Kenta Kitamura,Yoshitaka Ushiku,Koichi Takahashi,Yuta Nabae,Teruaki Hayakawa
机构: 未知
类目: Robotics (cs.RO); Computation and Language (cs.CL); Chemical Physics (physics.chem-ph)
备注:
Abstract:This review explores the potential of foundation models to advance laboratory automation in the materials and chemical sciences. It emphasizes the dual roles of these models: cognitive functions for experimental planning and data analysis, and physical functions for hardware operations. While traditional laboratory automation has relied heavily on specialized, rigid systems, foundation models offer adaptability through their general-purpose intelligence and multimodal capabilities. Recent advancements have demonstrated the feasibility of using large language models (LLMs) and multimodal robotic systems to handle complex and dynamic laboratory tasks. However, significant challenges remain, including precision manipulation of hardware, integration of multimodal data, and ensuring operational safety. This paper outlines a roadmap highlighting future directions, advocating for close interdisciplinary collaboration, benchmark establishment, and strategic human-AI integration to realize fully autonomous experimental laboratories.
zh
[NLP-153] Phonikud: Hebrew Grapheme-to-Phoneme Conversion for Real-Time Text-to-Speech
【速读】： 该论文旨在解决现代希伯来语实时文本到语音（TTS）生成中的挑战，特别是由于该语言的拼写复杂性导致的语音合成效果不佳问题。现有解决方案忽略了诸如重音等关键的语音特征，即使在添加元音符号后这些特征仍然未被明确指定。论文提出的解决方案是开发Phonikud，一个轻量级、开源的希伯来语字素到音素（G2P）系统，能够输出完整的国际音标（IPA）转写。其关键在于对现有的带变音符号模型进行轻量级适配，从而在几乎不增加额外延迟的情况下实现更准确的音素预测，进而支持高效且准确的实时希伯来语TTS模型训练。
链接: https://arxiv.org/abs/2506.12311
作者: Yakov Kolani,Maxim Melichov,Cobi Calev,Morris Alper
机构: 未知
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: Project page: this https URL
Abstract:Real-time text-to-speech (TTS) for Modern Hebrew is challenging due to the language’s orthographic complexity. Existing solutions ignore crucial phonetic features such as stress that remain underspecified even when vowel marks are added. To address these limitations, we introduce Phonikud, a lightweight, open-source Hebrew grapheme-to-phoneme (G2P) system that outputs fully-specified IPA transcriptions. Our approach adapts an existing diacritization model with lightweight adaptors, incurring negligible additional latency. We also contribute the ILSpeech dataset of transcribed Hebrew speech with IPA annotations, serving as a benchmark for Hebrew G2P and as training data for TTS systems. Our results demonstrate that Phonikud G2P conversion more accurately predicts phonemes from Hebrew text compared to prior methods, and that this enables training of effective real-time Hebrew TTS models with superior speed-accuracy trade-offs. We release our code, data, and models at this https URL.
zh
[NLP-154] Med-U1: Incentivizing Unified Medical Reasoning in LLM s via Large-scale Reinforcement Learning
【速读】： 该论文旨在解决医疗问答（Medical QA）任务中缺乏统一框架的问题，这些任务包括多项选择题、开放式文本生成和复杂计算推理等。现有方法在实现全面的医学理解方面仍存在局限。论文提出的解决方案是Med-U1，其关键在于采用纯大规模强化学习结合混合基于规则的二元奖励函数，并引入长度惩罚以控制输出冗余度，通过多目标奖励优化引导大语言模型生成简洁且可验证的推理链，从而提升跨多种输出格式的医疗QA任务性能。
链接: https://arxiv.org/abs/2506.12307
作者: Xiaotian Zhang,Yuan Wang,Zhaopeng Feng,Ruizhe Chen,Zhijie Zhou,Yan Zhang,Hongxia Xu,Jian Wu,Zuozhu Liu
机构: Zhejiang University (浙江大学); Bytedance Inc (字节跳动公司); Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence (浙江省医学影像人工智能重点实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Medical Question-Answering (QA) encompasses a broad spectrum of tasks, including multiple choice questions (MCQ), open-ended text generation, and complex computational reasoning. Despite this variety, a unified framework for delivering high-quality medical QA has yet to emerge. Although recent progress in reasoning-augmented large language models (LLMs) has shown promise, their ability to achieve comprehensive medical understanding is still largely unexplored. In this paper, we present Med-U1, a unified framework for robust reasoning across medical QA tasks with diverse output formats, ranging from MCQs to complex generation and computation tasks. Med-U1 employs pure large-scale reinforcement learning with mixed rule-based binary reward functions, incorporating a length penalty to manage output verbosity. With multi-objective reward optimization, Med-U1 directs LLMs to produce concise and verifiable reasoning chains. Empirical results reveal that Med-U1 significantly improves performance across multiple challenging Med-QA benchmarks, surpassing even larger specialized and proprietary models. Furthermore, Med-U1 demonstrates robust generalization to out-of-distribution (OOD) tasks. Extensive analysis presents insights into training strategies, reasoning chain length control, and reward design for medical LLMs. The code will be released.
zh
[NLP-155] Can LLM s Generate High-Quality Test Cases for Algorithm Problems? TestCase-Eval: A Systematic Evaluation of Fault Coverag e and Exposure ACL2025
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在算法问题测试用例生成中的系统性评估问题。其解决方案的关键在于提出TestCase-Eval基准，该基准包含500个算法问题和10万个来自Codeforces平台的人工编写的解决方案，并聚焦于两个核心任务：故障覆盖率（Fault Coverage）和故障暴露能力（Fault Exposure），以全面评估LLMs生成有效测试用例的能力。
链接: https://arxiv.org/abs/2506.12278
作者: Zheyuan Yang,Zexi Kuang,Xue Xia,Yilun Zhao
机构: Tongji University (同济大学); Northeastern University (东北大学); HKUST (香港科技大学); Yale University (耶鲁大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
备注: ACL 2025
Abstract:We introduce TestCase-Eval, a new benchmark for systematic evaluation of LLMs in test-case generation. TestCase-Eval includes 500 algorithm problems and 100,000 human-crafted solutions from the Codeforces platform. It focuses on two pivotal tasks: (1) Fault Coverage, which measures how well LLM-generated test sets probe diverse input scenarios and cover a wide range of potential failure modes. (2) Fault Exposure, which evaluates whether LLMs can craft a tailored test input that reveals a specific incorrect code implementation. We provide a comprehensive assessment of 19 state-of-the-art open-source and proprietary LLMs on TestCase-Eval, offering insights into their strengths and limitations in generating effective test cases for algorithm problems.
zh
[NLP-156] InfoFlood: Jailbreaking Large Language Models with Information Overload
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在面对“越狱”攻击时的安全性问题，特别是针对其内置安全机制被绕过的漏洞。现有方法通常依赖于添加特定前缀或后缀来实现越狱，而本文发现了一种新的漏洞，即过度的语言复杂性可以无需任何额外修饰直接干扰内置安全机制，从而引发有害输出，这一现象被称为信息过载（Information Overload）。解决方案的关键在于提出一种名为InfoFlood的越狱攻击方法，该方法通过语言转换重新表述恶意查询，识别失败原因并优化提示结构以保持恶意意图的同时成功绕过安全机制。
链接: https://arxiv.org/abs/2506.12274
作者: Advait Yadav,Haibo Jin,Man Luo,Jun Zhuang,Haohan Wang
机构: 未知
类目: Cryptography and Security (cs.CR); Computation and Language (cs.CL)
备注:
Abstract:Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, their potential to generate harmful responses has raised significant societal and regulatory concerns, especially when manipulated by adversarial techniques known as “jailbreak” attacks. Existing jailbreak methods typically involve appending carefully crafted prefixes or suffixes to malicious prompts in order to bypass the built-in safety mechanisms of these models. In this work, we identify a new vulnerability in which excessive linguistic complexity can disrupt built-in safety mechanisms-without the need for any added prefixes or suffixes-allowing attackers to elicit harmful outputs directly. We refer to this phenomenon as Information Overload. To automatically exploit this vulnerability, we propose InfoFlood, a jailbreak attack that transforms malicious queries into complex, information-overloaded queries capable of bypassing built-in safety mechanisms. Specifically, InfoFlood: (1) uses linguistic transformations to rephrase malicious queries, (2) identifies the root cause of failure when an attempt is unsuccessful, and (3) refines the prompt’s linguistic structure to address the failure while preserving its malicious intent. We empirically validate the effectiveness of InfoFlood on four widely used LLMs-GPT-4o, GPT-3.5-turbo, Gemini 2.0, and LLaMA 3.1-by measuring their jailbreak success rates. InfoFlood consistently outperforms baseline attacks, achieving up to 3 times higher success rates across multiple jailbreak benchmarks. Furthermore, we demonstrate that commonly adopted post-processing defenses, including OpenAI’s Moderation API, Perspective API, and SmoothLLM, fail to mitigate these attacks. This highlights a critical weakness in traditional AI safety guardrails when confronted with information overload-based jailbreaks. Subjects: Cryptography and Security (cs.CR); Computation and Language (cs.CL) Cite as: arXiv:2506.12274 [cs.CR] (or arXiv:2506.12274v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2506.12274 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Advait Yadav [view email] [v1] Fri, 13 Jun 2025 23:03:11 UTC (761 KB)
zh
[NLP-157] he Behavior Gap: Evaluating Zero-shot LLM Agents in Complex Task-Oriented Dialogs ACL2025
【速读】： 该论文试图解决基于大型语言模型（Large Language Model, LLM）的智能体在任务导向对话系统（Task-Oriented Dialog Systems, TODS）中表现不佳的问题，尤其是在零样本（zero-shot）场景下的性能瓶颈。其解决方案的关键在于提出一个全面的评估框架，用于量化AI智能体与人类专家之间的行为差距，重点关注对话行为、工具使用和知识利用方面的差异。研究发现，行为差距是影响LLM智能体性能的关键因素，尤其在任务复杂度增加时，行为差距显著扩大，导致性能下降。通过减少此类行为差距，可以显著提升智能体的性能。
链接: https://arxiv.org/abs/2506.12266
作者: Avinash Baidya,Kamalika Das,Xiang Gao
机构: Intuit AI Research (Intuit人工智能研究)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: ACL 2025; 18 pages, 8 figures
Abstract:Large Language Model (LLM)-based agents have significantly impacted Task-Oriented Dialog Systems (TODS) but continue to face notable performance challenges, especially in zero-shot scenarios. While prior work has noted this performance gap, the behavioral factors driving the performance gap remain under-explored. This study proposes a comprehensive evaluation framework to quantify the behavior gap between AI agents and human experts, focusing on discrepancies in dialog acts, tool usage, and knowledge utilization. Our findings reveal that this behavior gap is a critical factor negatively impacting the performance of LLM agents. Notably, as task complexity increases, the behavior gap widens (correlation: 0.963), leading to a degradation of agent performance on complex task-oriented dialogs. For the most complex task in our study, even the GPT-4o-based agent exhibits low alignment with human behavior, with low F1 scores for dialog acts (0.464), excessive and often misaligned tool usage with a F1 score of 0.139, and ineffective usage of external knowledge. Reducing such behavior gaps leads to significant performance improvement (24.3% on average). This study highlights the importance of comprehensive behavioral evaluations and improved alignment strategies to enhance the effectiveness of LLM-based TODS in handling complex tasks.
zh
[NLP-158] ProVox: Personalization and Proactive Planning for Situated Human-Robot Collaboration
【速读】： 该论文试图解决协作机器人需要快速适应人类伙伴意图和偏好的问题，以便主动识别有助于完成任务的行为。解决方案的关键在于引入ProVox（Proactive Voice）框架，该框架利用大语言模型的常识先验和可引导性，通过元提示协议让用户在物理交互开始前传达其独特偏好、意图和期望的机器人行为，进而使机器人基于个性化提示条件化一个主动的语言模型任务规划器，从而从当前交互上下文和机器人能力中预判用户意图并提出有帮助的行为建议。
链接: https://arxiv.org/abs/2506.12248
作者: Jennifer Grannen,Siddharth Karamcheti,Blake Wulfe,Dorsa Sadigh
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: Accepted by IEEE Robotics and Automation Letters 2025
Abstract:Collaborative robots must quickly adapt to their partner’s intent and preferences to proactively identify helpful actions. This is especially true in situated settings where human partners can continually teach robots new high-level behaviors, visual concepts, and physical skills (e.g., through demonstration), growing the robot’s capabilities as the human-robot pair work together to accomplish diverse tasks. In this work, we argue that robots should be able to infer their partner’s goals from early interactions and use this information to proactively plan behaviors ahead of explicit instructions from the user. Building from the strong commonsense priors and steerability of large language models, we introduce ProVox (“Proactive Voice”), a novel framework that enables robots to efficiently personalize and adapt to individual collaborators. We design a meta-prompting protocol that empowers users to communicate their distinct preferences, intent, and expected robot behaviors ahead of starting a physical interaction. ProVox then uses the personalized prompt to condition a proactive language model task planner that anticipates a user’s intent from the current interaction context and robot capabilities to suggest helpful actions; in doing so, we alleviate user burden, minimizing the amount of time partners spend explicitly instructing and supervising the robot. We evaluate ProVox through user studies grounded in household manipulation tasks (e.g., assembling lunch bags) that measure the efficiency of the collaboration, as well as features such as perceived helpfulness, ease of use, and reliability. Our analysis suggests that both meta-prompting and proactivity are critical, resulting in 38.7% faster task completion times and 31.9% less user burden relative to non-active baselines. Supplementary material, code, and videos can be found at this https URL.
zh
[NLP-159] Large Language Models for History Philosophy and Sociology of Science: Interpretive Uses Methodological Challenges and Critical Perspectives
【速读】： 该论文试图解决如何将大型语言模型（Large Language Models, LLMs）作为研究工具应用于科学史、科学哲学与科学社会学（HPSS）领域的问题，以及如何在强调解释性方法论的HPSS中有效利用LLMs的能力并对其认识论假设和基础设施影响进行批判性审视。解决方案的关键在于将LLMs视为编码了意义、语境与相似性假设的认识论基础设施，而非中立工具，并通过结构化数据、模式检测和动态过程建模等计算技术增强解释性研究，同时提出领域和任务适应策略（如持续预训练、微调和检索增强生成），以评估其在HPSS解释性探究中的优势与局限性。
链接: https://arxiv.org/abs/2506.12242
作者: Arno Simons,Michael Zichert,Adrian Wüthrich
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 27 pages, 2 tables
Abstract:This paper explores the use of large language models (LLMs) as research tools in the history, philosophy, and sociology of science (HPSS). LLMs are remarkably effective at processing unstructured text and inferring meaning from context, offering new affordances that challenge long-standing divides between computational and interpretive methods. This raises both opportunities and challenges for HPSS, which emphasizes interpretive methodologies and understands meaning as context-dependent, ambiguous, and historically situated. We argue that HPSS is uniquely positioned not only to benefit from LLMs’ capabilities but also to interrogate their epistemic assumptions and infrastructural implications. To this end, we first offer a concise primer on LLM architectures and training paradigms tailored to non-technical readers. We frame LLMs not as neutral tools but as epistemic infrastructures that encode assumptions about meaning, context, and similarity, conditioned by their training data, architecture, and patterns of use. We then examine how computational techniques enhanced by LLMs, such as structuring data, detecting patterns, and modeling dynamic processes, can be applied to support interpretive research in HPSS. Our analysis compares full-context and generative models, outlines strategies for domain and task adaptation (e.g., continued pretraining, fine-tuning, and retrieval-augmented generation), and evaluates their respective strengths and limitations for interpretive inquiry in HPSS. We conclude with four lessons for integrating LLMs into HPSS: (1) model selection involves interpretive trade-offs; (2) LLM literacy is foundational; (3) HPSS must define its own benchmarks and corpora; and (4) LLMs should enhance, not replace, interpretive methods.
zh
[NLP-160] Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
【速读】： 该论文旨在解决文本到SQL（Text-to-SQL）系统在理解模糊表述、领域特定术语和复杂模式关系方面的挑战。其解决方案的关键在于引入基于检索增强生成（Retrieval-Augmented Generation, RAG）的框架Datrics Text2SQL，该框架通过结构化文档、基于示例的学习和领域特定规则生成准确的SQL查询。系统从数据库文档和问题-查询示例中构建丰富的知识库，并将其存储为向量嵌入，通过语义相似性进行检索，从而生成语法正确且语义对齐的SQL代码。
链接: https://arxiv.org/abs/2506.12234
作者: Tetiana Gladkykh,Kyrylo Kirykov
机构: Datrics(数据科技)
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 28 pages, 6 figures, initial whitepaper version 1.0, submitted March 2025
Abstract:Text-to-SQL systems enable users to query databases using natural language, democratizing access to data analytics. However, they face challenges in understanding ambiguous phrasing, domain-specific vocabulary, and complex schema relationships. This paper introduces Datrics Text2SQL, a Retrieval-Augmented Generation (RAG)-based framework designed to generate accurate SQL queries by leveraging structured documentation, example-based learning, and domain-specific rules. The system builds a rich Knowledge Base from database documentation and question-query examples, which are stored as vector embeddings and retrieved through semantic similarity. It then uses this context to generate syntactically correct and semantically aligned SQL code. The paper details the architecture, training methodology, and retrieval logic, highlighting how the system bridges the gap between user intent and database structure without requiring SQL expertise.
zh
[NLP-161] Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
【速读】： 该论文旨在解决自动驾驶中场景理解（scene understanding）的问题，特别是如何利用多模态大语言模型（Multimodal Large Language Models, MLLMs）在零样本、上下文学习设置下提升对场景的理解能力。其解决方案的关键在于评估不同规模的MLLMs在该任务中的表现，并探索通过集成学习（ensemble approach）结合多个模型以提升场景理解性能的可能性。研究发现，尽管GPT-4o作为最大模型表现最佳，但较小模型在先进技术如改进的上下文学习、检索增强生成（RAG）或微调的支持下仍有优化空间，同时表明当前的集成方法在不同场景属性上效果不一，需进一步发展更复杂的集成策略以实现全面性能提升。
链接: https://arxiv.org/abs/2506.12232
作者: Mohammed Elhenawy,Shadi Jaradat,Taqwa I. Alhadidi,Huthaifa I. Ashqar,Ahmed Jaber,Andry Rakotonirainy,Mohammad Abu Tami
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注:
Abstract:Scene understanding is critical for various downstream tasks in autonomous driving, including facilitating driver-agent communication and enhancing human-centered explainability of autonomous vehicle (AV) decisions. This paper evaluates the capability of four multimodal large language models (MLLMs), including relatively small models, to understand scenes in a zero-shot, in-context learning setting. Additionally, we explore whether combining these models using an ensemble approach with majority voting can enhance scene understanding performance. Our experiments demonstrate that GPT-4o, the largest model, outperforms the others in scene understanding. However, the performance gap between GPT-4o and the smaller models is relatively modest, suggesting that advanced techniques such as improved in-context learning, retrieval-augmented generation (RAG), or fine-tuning could further optimize the smaller models’ performance. We also observe mixed results with the ensemble approach: while some scene attributes show improvement in performance metrics such as F1-score, others experience a decline. These findings highlight the need for more sophisticated ensemble techniques to achieve consistent gains across all scene attributes. This study underscores the potential of leveraging MLLMs for scene understanding and provides insights into optimizing their performance for autonomous driving applications.
zh
[NLP-162] Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index
【速读】： 该论文试图解决大规模文本语料库的高效可搜索性问题，特别是在互联网规模数据下传统精确匹配搜索引擎因存储开销过高而难以应用的问题。解决方案的关键在于提出Infini-gram mini系统，该系统基于FM-index数据结构（Ferragina and Manzini, 2000），能够在压缩文本的同时进行索引，从而将索引大小减少至语料库的44%，并显著提升索引速度和降低内存使用，实现了对海量文本的高效处理与搜索。
链接: https://arxiv.org/abs/2506.12229
作者: Hao Xu,Jiacheng Liu,Yejin Choi,Noah A. Smith,Hannaneh Hajishirzi
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable searching in large text corpora – counting string appearances and retrieving the enclosing documents – yet the high storage overhead hinders their application on Internet-scale data. We present Infini-gram mini, an efficient and scalable system that can make petabyte-level text corpora searchable. Based on the FM-index data structure (Ferragina and Manzini, 2000), which simultaneously indexes and compresses text, our system creates indexes with size only 44% of the corpus. Infini-gram mini greatly improves upon the best existing implementation of FM-index in terms of indexing speed (18 \times ) and memory use during both indexing (3.2 \times reduction) and querying (down to a negligible amount). We index 46TB of Internet text in 50 days with a single 128-core CPU node (or 19 hours if using 75 such nodes). We show one important use case of Infini-gram mini in a large-scale analysis of benchmark contamination. We find several core LM evaluation benchmarks to be heavily contaminated in Internet crawls (up to 40% in SQuAD), which could lead to overestimating the capabilities of language models if trained on such data. We host a benchmark contamination bulletin to share the contamination rate of many core and community-contributed benchmarks. We also release a web interface and an API endpoint to serve general search queries on Infini-gram mini indexes.
zh
[NLP-163] From Emergence to Control: Probing and Modulating Self-Reflection in Language Models
【速读】： 该论文试图解决自反思（self-reflection）在大型语言模型中的起源及其机制不明确的问题，以及如何有效控制自反思行为以平衡推理质量和效率。其解决方案的关键在于发现预训练模型中存在潜在的自反思能力，并通过引入“反射诱导探测”方法激发这种能力，同时构建了与自反思推理相关的自反思向量，实现了对自反思行为的双向控制。
链接: https://arxiv.org/abs/2506.12217
作者: Xudong Zhu,Jiachen Jiang,Mohammad Mahdi Khalili,Zhihui Zhu
机构: The Ohio State University (俄亥俄州立大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 18 pages, 9 figures
Abstract:Self-reflection – the ability of a large language model (LLM) to revisit, evaluate, and revise its own reasoning – has recently emerged as a powerful behavior enabled by reinforcement learning with verifiable rewards (RLVR). While self-reflection correlates with improved reasoning accuracy, its origin and underlying mechanisms remain poorly understood. In this work, \it we first show that self-reflection is not exclusive to RLVR fine-tuned models: it already emerges, albeit rarely, in pretrained models. To probe this latent ability, we introduce Reflection-Inducing Probing, a method that injects reflection-triggering reasoning traces from fine-tuned models into pretrained models. This intervention raises self-reflection frequency of Qwen2.5 from 0.6% to 18.6%, revealing a hidden capacity for reflection. Moreover, our analysis of internal representations shows that both pretrained and fine-tuned models maintain hidden states that distinctly separate self-reflective from non-reflective contexts. Leveraging this observation, \it we then construct a self-reflection vector, a direction in activation space associated with self-reflective reasoning. By manipulating this vector, we enable bidirectional control over the self-reflective behavior for both pretrained and fine-tuned models. Experiments across multiple reasoning benchmarks show that enhancing these vectors improves reasoning performance by up to 12%, while suppressing them reduces computational cost, providing a flexible mechanism to navigate the trade-off between reasoning quality and efficiency without requiring additional training. Our findings further our understanding of self-reflection and support a growing body of work showing that understanding model internals can enable precise behavioral control.
zh
[NLP-164] Supernova Event Dataset: Interpreting Large Language Models Personality through Critical Event Analysis WWW
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在决策过程和潜在人格特征理解方面的难题，旨在提升模型的可解释性。其解决方案的关键在于构建了一个名为Supernova Event Dataset的新数据集，该数据集包含涵盖传记、历史事件、新闻和科学发现等多样内容的文章，并利用该数据集对LLMs进行基准测试，评估其从文本中提取和排序关键事件的能力。此外，论文提出了一种框架，通过另一个LLM作为评判者，根据模型对事件的选择和分类推断其人格特征，从而实现对模型行为的深入分析。
链接: https://arxiv.org/abs/2506.12189
作者: Pranav Agarwal,Ioana Ciucă
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Project Page - this https URL
Abstract:Large Language Models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision making and underlying personality becomes essential. In this work, we interpret model personality using our proposed Supernova Event Dataset, a novel dataset with diverse articles spanning biographies, historical events, news, and scientific discoveries. We use this dataset to benchmark LLMs on extracting and ranking key events from text, a subjective and complex challenge that requires reasoning over long-range context and modeling causal chains. We evaluate small models like Phi-4, Orca 2, and Qwen 2.5, and large, stronger models such as Claude 3.7, Gemini 2.5, and OpenAI o3, and propose a framework where another LLM acts as a judge to infer each model’s personality based on its selection and classification of events. Our analysis shows distinct personality traits: for instance, Orca 2 demonstrates emotional reasoning focusing on interpersonal dynamics, while Qwen 2.5 displays a more strategic, analytical style. When analyzing scientific discovery events, Claude Sonnet 3.7 emphasizes conceptual framing, Gemini 2.5 Pro prioritizes empirical validation, and o3 favors step-by-step causal reasoning. This analysis improves model interpretability, making them user-friendly for a wide range of diverse applications.
zh
[NLP-165] Instruction Tuning and CoT Prompting for Contextual Medical QA with LLM s
【速读】： 该论文试图解决将大型语言模型（Large Language Models, LLMs）适配到生物医学推理任务中的挑战，尤其是在监督信息有限的情况下。其解决方案的关键在于通过提示设计（prompt design）和轻量级微调（lightweight fine-tuning）来提升开源LLMs在PubMedQA基准上的性能，特别是对比了标准指令提示与思维链（Chain-of-Thought, CoT）提示的效果，并采用QLoRA方法进行参数高效的指令微调。
链接: https://arxiv.org/abs/2506.12182
作者: Chenqian Le,Ziheng Gong,Chihang Wang,Haowei Ni,Panfeng Li,Xupeng Chen
机构: New York University, New York, USA; Columbia University, New York, USA; University of Michigan, Ann Arbor, USA
类目: Computation and Language (cs.CL)
备注: Accepted by 2025 International Conference on Artificial Intelligence, Human-Computer Interaction and Natural Language Processing
Abstract:Large language models (LLMs) have shown great potential in medical question answering (MedQA), yet adapting them to biomedical reasoning remains challenging due to domain-specific complexity and limited supervision. In this work, we study how prompt design and lightweight fine-tuning affect the performance of open-source LLMs on PubMedQA, a benchmark for multiple-choice biomedical questions. We focus on two widely used prompting strategies - standard instruction prompts and Chain-of-Thought (CoT) prompts - and apply QLoRA for parameter-efficient instruction tuning. Across multiple model families and sizes, our experiments show that CoT prompting alone can improve reasoning in zero-shot settings, while instruction tuning significantly boosts accuracy. However, fine-tuning on CoT prompts does not universally enhance performance and may even degrade it for certain larger models. These findings suggest that reasoning-aware prompts are useful, but their benefits are model- and scale-dependent. Our study offers practical insights into combining prompt engineering with efficient finetuning for medical QA applications.
zh
[NLP-166] Generative or Discriminative? Revisiting Text Classification in the Era of Transformers
【速读】： 该论文试图解决生成式与判别式分类器在现代深度学习架构（如Transformer）中的性能对比问题，特别是在样本效率、校准性、噪声鲁棒性和序数性等方面的差异。其解决方案的关键在于首次对现代生成式（自回归建模、掩码语言建模、离散扩散）和判别式（文本分类编码器）模型进行全面评估，揭示了经典“两种状态”现象在不同架构和训练范式中的表现差异，从而为实际应用中根据数据限制和计算约束选择合适的建模方法提供依据。
链接: https://arxiv.org/abs/2506.12181
作者: Siva Rajesh Kasa,Karan Gupta,Sumegh Roychowdhury,Ashutosh Kumar,Yaswanth Biruduraju,Santhosh Kumar Kasa,Nikhil Priyatam Pattisapu,Arindam Bhattacharya,Shailendra Agarwal,Vijay huddar
机构: Amazon Inc. (亚马逊)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 19 pages
Abstract:The comparison between discriminative and generative classifiers has intrigued researchers since Efron’s seminal analysis of logistic regression versus discriminant analysis. While early theoretical work established that generative classifiers exhibit lower sample complexity but higher asymptotic error in simple linear settings, these trade-offs remain unexplored in the transformer era. We present the first comprehensive evaluation of modern generative and discriminative architectures - Auto-regressive modeling, Masked Language Modeling, Discrete Diffusion, and Encoders for text classification. Our study reveals that the classical ‘two regimes’ phenomenon manifests distinctly across different architectures and training paradigms. Beyond accuracy, we analyze sample efficiency, calibration, noise robustness, and ordinality across diverse scenarios. Our findings offer practical guidance for selecting the most suitable modeling approach based on real-world constraints such as latency and data limitations.
zh
[NLP-167] A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages
【速读】： 该论文试图解决在低资源语言环境下，不同生成策略在合成文本数据生成中的有效性缺乏系统比较的问题。其解决方案的关键在于系统评估多种生成策略及其组合在11种语言中的表现，包括目标语言的示范与基于大语言模型（LLM）的修订相结合的方法，从而显著提升合成数据的质量，缩小与真实数据之间的性能差距。
链接: https://arxiv.org/abs/2506.12158
作者: Tatiana Ankinina,Jan Cegin,Jakub Simko,Simon Ostermann
机构: 未知
类目: Computation and Language (cs.CL)
备注: 21 pages
Abstract:Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. However, a comparison of various generation strategies for low-resource language settings is lacking. While various prompting strategies have been proposed, such as demonstrations, label-based summaries, and self-revision, their comparative effectiveness remains unclear, especially for low-resource languages. In this paper, we systematically evaluate the performance of these generation strategies and their combinations across 11 typologically diverse languages, including several extremely low-resource ones. Using three NLP tasks and four open-source LLMs, we assess downstream model performance on generated versus gold-standard data. Our results show that strategic combinations of generation methods, particularly target-language demonstrations with LLM-based revisions, yield strong performance, narrowing the gap with real data to as little as 5% in some settings. We also find that smart prompting techniques can reduce the advantage of larger LLMs, highlighting efficient generation strategies for synthetic data generation in low-resource scenarios with smaller models.
zh
[NLP-168] Maximally-Informative Retrieval for State Space Model Generation
【速读】： 该论文试图解决在给定查询和数据集的情况下，如何有效利用已知数据中的相关信息以提高模型推理准确性的难题。由于现代大语言模型（Large Language Models, LLMs）在训练过程中会遗忘非关键信息，并且无法直接利用训练集之外的数据，因此需要借助外部记忆来补充信息。解决方案的关键在于引入一种基于模型自身梯度的检索优化方法——检索上下文优化（Retrieval In-Context Optimization, RICO），该方法通过利用模型的梯度反馈来学习生成答案的最佳文档组合，从而减少模型在测试时的不确定性。与传统检索增强生成（Retrieval-Augmented Generation, RAG）方法依赖外部启发式策略不同，RICO直接利用模型的内部反馈进行优化，实现了更高效的检索与生成过程。
链接: https://arxiv.org/abs/2506.12149
作者: Evan Becker,Benjamin Bowman,Matthew Trager,Tian Yu Liu,Luca Zancato,Wei Xia,Stefano Soatto
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Given a query and dataset, the optimal way of answering the query is to make use all the information available. Modern LLMs exhibit impressive ability to memorize training data, but data not deemed important during training is forgotten, and information outside that training set cannot be made use of. Processing an entire dataset at inference time is infeasible due to the bounded nature of model resources (e.g. context size in transformers or states in state space models), meaning we must resort to external memory. This constraint naturally leads to the following problem: How can we decide based on the present query and model, what among a virtually unbounded set of known data matters for inference? To minimize model uncertainty for a particular query at test-time, we introduce Retrieval In-Context Optimization (RICO), a retrieval method that uses gradients from the LLM itself to learn the optimal mixture of documents for answer generation. Unlike traditional retrieval-augmented generation (RAG), which relies on external heuristics for document retrieval, our approach leverages direct feedback from the model. Theoretically, we show that standard top- k retrieval with model gradients can approximate our optimization procedure, and provide connections to the leave-one-out loss. We demonstrate empirically that by minimizing an unsupervised loss objective in the form of question perplexity, we can achieve comparable retriever metric performance to BM25 with \emphno finetuning. Furthermore, when evaluated on quality of the final prediction, our method often outperforms fine-tuned dense retrievers such as E5.
zh
[NLP-169] Hatevolution: What Static Benchmarks Dont Tell Us
【速读】： 该论文试图解决语言模型在仇恨言论（hate speech）领域评估中的时间敏感性问题，即静态评估方法无法准确反映语言随时间演变对模型性能的影响。其解决方案的关键在于引入时间敏感的语义基准，以更准确和可靠地评估语言模型在动态变化的仇恨言论环境中的表现。
链接: https://arxiv.org/abs/2506.12148
作者: Chiara Di Bonaventura,Barbara McGillivray,Yulan He,Albert Meroño-Peñuela
机构: King’s College London (国王学院); Imperial College London (帝国学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:Language changes over time, including in the hate speech domain, which evolves quickly following social dynamics and cultural shifts. While NLP research has investigated the impact of language evolution on model training and has proposed several solutions for it, its impact on model benchmarking remains under-explored. Yet, hate speech benchmarks play a crucial role to ensure model safety. In this paper, we empirically evaluate the robustness of 20 language models across two evolving hate speech experiments, and we show the temporal misalignment between static and time-sensitive evaluations. Our findings call for time-sensitive linguistic benchmarks in order to correctly and reliably evaluate language models in the hate speech domain.
zh
[NLP-170] Can Mixture-of-Experts Surpass Dense LLM s Under Strictly Equal Resources?
【速读】： 该论文试图解决在总参数量、训练计算量和数据预算完全相同的情况下，混合专家（Mixture-of-Experts, MoE）模型是否能够超越密集架构（dense architectures）的问题。其解决方案的关键在于通过全面研究MoE的架构，实现性能最大化的模型设计，并发现当激活率处于最优区域时，MoE模型能够在相同资源约束下优于对应的密集模型，且该最优区域在不同模型规模下保持一致。
链接: https://arxiv.org/abs/2506.12119
作者: Houyi Li,Ka Man Lo,Ziqi Wang,Zili Wang,Wenzhen Zheng,Shuigeng Zhou,Xiangyu Zhang,Daxin Jiang
机构: StepFun; Fudan University (复旦大学); Megvii Technology
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token compute. However, can MoEs surpass dense architectures under strictly equal resource constraints - that is, when the total parameter count, training compute, and data budget are identical? This question remains under-explored despite its significant practical value and potential. In this paper, we propose a novel perspective and methodological framework to study this question thoroughly. First, we comprehensively investigate the architecture of MoEs and achieve an optimal model design that maximizes the performance. Based on this, we subsequently find that an MoE model with activation rate in an optimal region is able to outperform its dense counterpart under the same total parameter, training compute and data resource. More importantly, this optimal region remains consistent across different model sizes. Although additional amount of data turns out to be a trade-off for the enhanced performance, we show that this can be resolved via reusing data. We validate our findings through extensive experiments, training nearly 200 language models at 2B scale and over 50 at 7B scale, cumulatively processing 50 trillion tokens. All models will be released publicly.
zh
[NLP-171] Unsupervised Document and Template Clustering using Multimodal Embeddings
【速读】： 该论文试图解决无监督文档聚类问题，旨在通过多模态嵌入提升文档的细粒度理解，不仅在类型层面（如发票、采购订单）对文档进行分组，还能区分同一类别中的不同模板。解决方案的关键在于利用融合文本内容、版式信息和视觉特征的多模态嵌入作为传统聚类算法（如k-Means和DBSCAN）的输入，从而显著提升文档聚类的效果。
链接: https://arxiv.org/abs/2506.12116
作者: Phillipe R. Sampaio,Helene Maxcici
机构: BNP Paribas Cardif (BNP Paribas Cardif); BNP Paribas (BNP Paribas)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 10 figures
Abstract:This paper investigates a novel approach to unsupervised document clustering by leveraging multimodal embeddings as input to traditional clustering algorithms such as k -Means and DBSCAN. Our method aims to achieve a finer-grained document understanding by not only grouping documents at the type level (e.g., invoices, purchase orders), but also distinguishing between different templates within the same document category. This is achieved by using embeddings that capture textual content, layout information, and visual features of documents. We evaluated the effectiveness of this approach using embeddings generated by several state-of-the-art pretrained multimodal models, including SBERT, LayoutLMv1, LayoutLMv3, DiT, Donut, and ColPali. Our findings demonstrate the potential of multimodal embeddings to significantly enhance document clustering, offering benefits for various applications in intelligent document processing, document layout analysis, and unsupervised document classification. This work provides valuable insight into the advantages and limitations of different multimodal models for this task and opens new avenues for future research to understand and organize document collections.
zh
[NLP-172] Eliciting Reasoning in Language Models with Cognitive Tools
【速读】： 该论文试图解决如何在大型语言模型（Large Language Models, LLMs）中有效激发推理能力的问题，特别是在封闭模型和开源模型中探索替代性方法以揭示其内在机制并提供可能具有互补优势的解决方案。其关键解决方案是基于认知心理学和认知架构的长期研究，提出推理源于一组模块化、预定的认知操作的协调与顺序执行，并将这一核心思想实现于现代代理工具调用框架中，通过赋予LLM少量“认知工具”来封装特定的推理操作，从而显著提升模型在标准数学推理基准上的性能。
链接: https://arxiv.org/abs/2506.12115
作者: Brown Ebouky,Andrea Bartezzaghi,Mattia Rigotti
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 22 pages, 2 figures
Abstract:The recent advent of reasoning models like OpenAI’s o1 was met with excited speculation by the AI community about the mechanisms underlying these capabilities in closed models, followed by a rush of replication efforts, particularly from the open source community. These speculations were largely settled by the demonstration from DeepSeek-R1 that chains-of-thought and reinforcement learning (RL) can effectively replicate reasoning on top of base LLMs. However, it remains valuable to explore alternative methods for theoretically eliciting reasoning that could help elucidate the underlying mechanisms, as well as providing additional methods that may offer complementary benefits. Here, we build on the long-standing literature in cognitive psychology and cognitive architectures, which postulates that reasoning arises from the orchestrated, sequential execution of a set of modular, predetermined cognitive operations. Crucially, we implement this key idea within a modern agentic tool-calling framework. In particular, we endow an LLM with a small set of “cognitive tools” encapsulating specific reasoning operations, each executed by the LLM itself. Surprisingly, this simple strategy results in considerable gains in performance on standard mathematical reasoning benchmarks compared to base LLMs, for both closed and open-weight models. For instance, providing our “cognitive tools” to GPT-4.1 increases its pass@1 performance on AIME2024 from 26.7% to 43.3%, bringing it very close to the performance of o1-preview. In addition to its practical implications, this demonstration contributes to the debate regarding the role of post-training methods in eliciting reasoning in LLMs versus the role of inherent capabilities acquired during pre-training, and whether post-training merely uncovers these latent abilities. Comments: 22 pages, 2 figures Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2506.12115 [cs.CL] (or arXiv:2506.12115v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2506.12115 Focus to learn more arXiv-issued DOI via DataCite
zh
[NLP-173] Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs): A Feynman-Based Architecture for Continuous Learning Over Streaming Data
【速读】： 该论文试图解决在流数据中实现实时连续学习的挑战，传统基于梯度的模型如通过时间反向传播（BPTT）在处理时间无界数据时面临计算和稳定性限制。解决方案的关键在于引入一种新型架构——量子启发可微积分神经网络（QIDINNs），该架构利用费曼积分符号下的微分技术，将神经网络更新公式化为历史数据上的积分，从而实现更平滑、更稳定的动态学习过程，同时具备物理可解释性和计算可行性。
链接: https://arxiv.org/abs/2506.12111
作者: Oscar Boullosa Dapena
机构: CaixaBank Tech(卡瓦银行科技)
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Real-time continuous learning over streaming data remains a central challenge in deep learning and AI systems. Traditional gradient-based models such as backpropagation through time (BPTT) face computational and stability limitations when dealing with temporally unbounded data. In this paper, we introduce a novel architecture, Quantum-Inspired Differentiable Integral Neural Networks (QIDINNs), which leverages the Feynman technique of differentiation under the integral sign to formulate neural updates as integrals over historical data. This reformulation allows for smoother, more stable learning dynamics that are both physically interpretable and computationally tractable. Inspired by Feynman’s path integral formalism and compatible with quantum gradient estimation frameworks, QIDINNs open a path toward hybrid classical-quantum neural computation. We demonstrate our model’s effectiveness on synthetic and real-world streaming tasks, and we propose directions for quantum extensions and scalable implementations.
zh
[NLP-174] Personalized LLM Decoding via Contrasting Personal Preference
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在实际应用中个性化不足的问题，尤其是在解码阶段缺乏有效的个性化算法。解决方案的关键在于提出一种名为CoPe（Contrasting Personal Preference）的解码阶段方法，该方法在用户特定数据上进行参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）后应用，通过最大化用户的隐式奖励信号来实现个性化的奖励引导解码。
链接: https://arxiv.org/abs/2506.12109
作者: Hyungjune Bu,Chanjoo Jung,Minjae Kang,Jaehyung Kim
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:As large language models (LLMs) are progressively deployed in various real-world applications, personalization of LLMs has become increasingly important. While various approaches to LLM personalization such as prompt-based and training-based methods have been actively explored, the development of effective decoding-time algorithms remains largely overlooked, despite their demonstrated potential. In this paper, we propose CoPe (Contrasting Personal Preference), a novel decoding-time approach applied after performing parameter-efficient fine-tuning (PEFT) on user-specific data. Our core idea is to leverage reward-guided decoding specifically for personalization by maximizing each user’s implicit reward signal. We evaluate CoPe across five open-ended personalized text generation tasks. Our empirical results demonstrate that CoPe achieves strong performance, improving personalization by an average of 10.57% in ROUGE-L, without relying on external reward models or additional training procedures.
zh
[NLP-175] UCD: Unlearning in LLM s via Contrastive Decoding
【速读】： 该论文试图解决在大型语言模型（Large Language Models, LLMs）中移除特定信息（如敏感或不希望的内容）的同时保持模型整体性能的问题。其解决方案的关键在于提出一种基于对比解码（contrastive decoding）的推理阶段遗忘算法，通过两个辅助小型模型——一个未经过遗忘数据集训练，另一个经过遗忘数据集训练——在推理过程中利用两者输出的差异来引导原始模型的输出，从而显著提升遗忘效果与模型效用之间的平衡。
链接: https://arxiv.org/abs/2506.12097
作者: Vinith M. Suriyakumar,Ayush Sekhari,Ashia Wilson
机构: 未知
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注:
Abstract:Machine unlearning aims to remove specific information, e.g. sensitive or undesirable content, from large language models (LLMs) while preserving overall performance. We propose an inference-time unlearning algorithm that uses contrastive decoding, leveraging two auxiliary smaller models, one trained without the forget set and one trained with it, to guide the outputs of the original model using their difference during inference. Our strategy substantially improves the tradeoff between unlearning effectiveness and model utility. We evaluate our approach on two unlearning benchmarks, TOFU and MUSE. Results show notable gains in both forget quality and retained performance in comparison to prior approaches, suggesting that incorporating contrastive decoding can offer an efficient, practical avenue for unlearning concepts in large-scale models.
zh
[NLP-176] Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety ECML-PKDD2025
【速读】： 该论文试图解决城市交通事故分类的准确性问题，旨在通过分析慕尼黑的交通事故数据，识别不同事故类型的模式与特征，并评估现有标签的可靠性。研究发现，文本描述是分类中最具有信息量的特征，而结构化数据的加入仅带来微小的性能提升。解决方案的关键在于利用自然语言处理技术，特别是基于Transformer的模型，从非结构化的文本数据中提取关键特征，从而提高事故分类的可靠性和准确性。
链接: https://arxiv.org/abs/2506.12092
作者: Enes Özeren,Alexander Ulbrich,Sascha Filimon,David Rügamer,Andreas Bender
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 18 pages, 4 tables, 4 figures. This paper will appear in the ECML-PKDD 2025 Applied Data Science (ADS) track
Abstract:A comprehensive understanding of traffic accidents is essential for improving city safety and informing policy decisions. In this study, we analyze traffic incidents in Munich to identify patterns and characteristics that distinguish different types of accidents. The dataset consists of both structured tabular features, such as location, time, and weather conditions, as well as unstructured free-text descriptions detailing the circumstances of each accident. Each incident is categorized into one of seven predefined classes. To assess the reliability of these labels, we apply NLP methods, including topic modeling and few-shot learning, which reveal inconsistencies in the labeling process. These findings highlight potential ambiguities in accident classification and motivate a refined predictive approach. Building on these insights, we develop a classification model that achieves high accuracy in assigning accidents to their respective categories. Our results demonstrate that textual descriptions contain the most informative features for classification, while the inclusion of tabular data provides only marginal improvements. These findings emphasize the critical role of free-text data in accident analysis and highlight the potential of transformer-based models in improving classification reliability.
zh
[NLP-177] Continuously Updating Digital Twins using Large Language Models
【速读】： 该论文试图解决数字孪生在复杂环境中因状态、动作变量及可用数据和知识的动态变化而难以持续更新的问题，现有方法由于依赖固定建模环境，无法在不重新设计或重新训练的情况下适应新变量或整合新信息。解决方案的关键在于将数字孪生建模为一个上下文学习问题，利用大语言模型实现推理时的无缝更新，具体通过CALM-DT（基于上下文自适应语言模型的数字孪生）实现，其核心是使用微调编码器进行样本检索，仅依靠上下文学习即可在多样化的状态-动作空间中进行准确模拟。
链接: https://arxiv.org/abs/2506.12091
作者: Harry Amad,Nicolás Astorga,Mihaela van der Schaar
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available data and knowledge relevant to a system can constantly change, requiring digital twins to continuously update with these changes to remain relevant. Current approaches struggle in this regard, as they require fixed, well-defined modelling environments, and they cannot adapt to novel variables without re-designs, or incorporate new information without re-training. To address this, we frame digital twinning as an in-context learning problem using large language models, enabling seamless updates to the twin at inference time. We develop CALM-DT, a Context-Adaptive Language Model-based Digital Twin that can accurately simulate across diverse state-action spaces using in-context learning alone by utilising fine-tuned encoders for sample retrieval. We empirically demonstrate CALM-DT’s competitive performance with existing digital twin approaches, and its unique ability to adapt to changes in its modelling environment without parameter updates.
zh
[NLP-178] ChatbotManip: A Dataset to Facilitate Evaluation and Oversight of Manipulative Chatbot Behaviour
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在对话系统中可能表现出的操控行为问题，特别是在被明确指示进行操控或说服用户时的表现。解决方案的关键在于构建了一个名为ChatbotManip的新数据集，该数据集包含模拟的聊天机器人与用户之间的对话，并由人工标注了通用操控行为和具体操控策略，从而为研究和检测聊天机器人的操控行为提供了基础。
链接: https://arxiv.org/abs/2506.12090
作者: Jack Contro,Simrat Deol,Yulan He,Martim Brandão
机构: King’s College London (国王学院)
类目: Computation and Language (cs.CL)
备注:
Abstract:This paper introduces ChatbotManip, a novel dataset for studying manipulation in Chatbots. It contains simulated generated conversations between a chatbot and a (simulated) user, where the chatbot is explicitly asked to showcase manipulation tactics, persuade the user towards some goal, or simply be helpful. We consider a diverse set of chatbot manipulation contexts, from consumer and personal advice to citizen advice and controversial proposition argumentation. Each conversation is annotated by human annotators for both general manipulation and specific manipulation tactics. Our research reveals three key findings. First, Large Language Models (LLMs) can be manipulative when explicitly instructed, with annotators identifying manipulation in approximately 84% of such conversations. Second, even when only instructed to be ``persuasive’’ without explicit manipulation prompts, LLMs frequently default to controversial manipulative strategies, particularly gaslighting and fear enhancement. Third, small fine-tuned open source models, such as BERT+BiLSTM have a performance comparable to zero-shot classification with larger models like Gemini 2.5 pro in detecting manipulation, but are not yet reliable for real-world oversight. Our work provides important insights for AI safety research and highlights the need of addressing manipulation risks as LLMs are increasingly deployed in consumer-facing applications.
zh
[NLP-179] he CAISAR Platform: Extending the Reach of Machine Learning Specification and Verification
【速读】： 该论文试图解决机器学习程序的形式化规格说明与验证工具之间因多样性导致的碎片化问题，以及现有工具在表达复杂属性（如涉及多个神经网络的属性）时的局限性。解决方案的关键在于提出CAISAR平台，其核心是设计一种适用于神经网络、支持向量机和提升树等模型的规格语言，并通过自动图编辑技术将该语言编写的规格转换为当前先进证明器可处理的查询，从而实现对复杂属性的有效验证。
链接: https://arxiv.org/abs/2506.12084
作者: Michele Alberti(LSL),François Bobot(LSL),Julien Girard-Satabin(LSL),Alban Grastien(LSL),Aymeric Varasse,Zakaria Chihani(LSL)
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Formal Languages and Automata Theory (cs.FL); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:The formal specification and verification of machine learning programs saw remarkable progress in less than a decade, leading to a profusion of tools. However, diversity may lead to fragmentation, resulting in tools that are difficult to compare, except for very specific benchmarks. Furthermore, this progress is heavily geared towards the specification and verification of a certain class of property, that is, local robustness properties. But while provers are becoming more and more efficient at solving local robustness properties, even slightly more complex properties, involving multiple neural networks for example, cannot be expressed in the input languages of winners of the International Competition of Verification of Neural Networks VNN-Comp. In this tool paper, we present CAISAR, an open-source platform dedicated to machine learning specification and verification. We present its specification language, suitable for modelling complex properties on neural networks, support vector machines and boosted trees. We show on concrete use-cases how specifications written in this language are automatically translated to queries to state-of-the-art provers, notably by using automated graph editing techniques, making it possible to use their off-the-shelf versions. The artifact to reproduce the paper claims is available at the following DOI: this https URL
zh
[NLP-180] Modeling Earth-Scale Human-Like Societies with One Billion Agents
【速读】： 该论文试图解决传统基于代理的模型（Agent-Based Models, ABMs）在模拟复杂社会行为时因代理行为简化而无法捕捉人类复杂性的局限性，以及大型语言模型（Large Language Models, LLMs）在大规模社会模拟中面临的显著扩展性挑战。其解决方案的关键在于提出Light Society框架，该框架通过将社会过程形式化为代理与环境状态的结构化转换，并利用LLM驱动的模拟操作进行事件队列执行，实现了高效的人类社会建模，支持超过十亿代理的大规模仿真，从而在保持高保真度的同时提升了社会信任和信息扩散等社会行为的模拟效率与真实性。
链接: https://arxiv.org/abs/2506.12078
作者: Haoxiang Guan,Jiyan He,Liyang Fan,Zhenzhen Ren,Shaobin He,Xin Yu,Yuan Chen,Shuxin Zheng,Tie-Yan Liu,Zhen Liu
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Social and Information Networks (cs.SI)
备注: Work in progress
Abstract:Understanding how complex societal behaviors emerge from individual cognition and interactions requires both high-fidelity modeling of human behavior and large-scale simulations. Traditional agent-based models (ABMs) have been employed to study these dynamics for decades, but are constrained by simplified agent behaviors that fail to capture human complexity. Recent advances in large language models (LLMs) offer new opportunities by enabling agents to exhibit sophisticated social behaviors that go beyond rule-based logic, yet face significant scaling challenges. Here we present Light Society, an agent-based simulation framework that advances both fronts, efficiently modeling human-like societies at planetary scale powered by LLMs. Light Society formalizes social processes as structured transitions of agent and environment states, governed by a set of LLM-powered simulation operations, and executed through an event queue. This modular design supports both independent and joint component optimization, supporting efficient simulation of societies with over one billion agents. Large-scale simulations of trust games and opinion propagation–spanning up to one billion agents–demonstrate Light Society’s high fidelity and efficiency in modeling social trust and information diffusion, while revealing scaling laws whereby larger simulations yield more stable and realistic emergent behaviors.
zh
[NLP-181] Artificial Intelligence and Civil Discourse: How LLM s Moderate Climate Change Conversations
【速读】： 该论文试图解决大型语言模型（Large Language Models, LLMs）在气候变迁等争议性话题中对公共话语的潜在影响问题，特别是其作为对话调节者的角色。研究通过对比分析LLMs与人类用户在社交媒体平台上的交流，揭示了LLMs在情感表达上的中立性和较低的情感强度，从而起到稳定对话的作用。解决方案的关键在于识别LLMs固有的调节能力，即通过情感中立和低情感强度来减少对话的极化，进而提升争议性话题讨论的质量。
链接: https://arxiv.org/abs/2506.12077
作者: Wenlu Fan,Wentao Xu
机构: University of Science and Technology of China, Hefei, China (中国科学技术大学，合肥)
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注: 10 pages
Abstract:As large language models (LLMs) become increasingly integrated into online platforms and digital communication spaces, their potential to influence public discourse - particularly in contentious areas like climate change - requires systematic investigation. This study examines how LLMs naturally moderate climate change conversations through their distinct communicative behaviors. We conduct a comparative analysis of conversations between LLMs and human users on social media platforms, using five advanced models: three open-source LLMs (Gemma, Llama 3, and Llama 3.3) and two commercial systems (GPT-4o by OpenAI and Claude 3.5 by Anthropic). Through sentiment analysis, we assess the emotional characteristics of responses from both LLMs and humans. The results reveal two key mechanisms through which LLMs moderate discourse: first, LLMs consistently display emotional neutrality, showing far less polarized sentiment than human users. Second, LLMs maintain lower emotional intensity across contexts, creating a stabilizing effect in conversations. These findings suggest that LLMs possess inherent moderating capacities that could improve the quality of public discourse on controversial topics. This research enhances our understanding of how AI might support more civil and constructive climate change discussions and informs the design of AI-assisted communication tools.
zh
[NLP-182] Focusing on Students not Machines: Grounded Question Generation and Automated Answer Grading
【速读】： 该论文试图解决教育领域中生成开放性问题及自动评分的繁琐任务，尤其是在利用数字技术减轻师生负担方面存在的挑战。其解决方案的关键在于构建一个基于课堂材料生成问题并自动评分的系统，其中核心方法是针对PDF文档的视觉布局进行文档分块，从而提升下游任务（如检索增强生成）的准确性。此外，论文还提出了一个用于短答案自动评分的新基准，以促进不同评分系统的比较，并验证了大型语言模型在该任务上的泛化能力。
链接: https://arxiv.org/abs/2506.12066
作者: Gérôme Meyer,Philip Breuer
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Digital technologies are increasingly used in education to reduce the workload of teachers and students. However, creating open-ended study or examination questions and grading their answers is still a tedious task. This thesis presents the foundation for a system that generates questions grounded in class materials and automatically grades student answers. It introduces a sophisticated method for chunking documents with a visual layout, specifically targeting PDF documents. This method enhances the accuracy of downstream tasks, including Retrieval Augmented Generation (RAG). Our thesis demonstrates that high-quality questions and reference answers can be generated from study material. Further, it introduces a new benchmark for automated grading of short answers to facilitate comparison of automated grading systems. An evaluation of various grading systems is conducted and indicates that Large Language Models (LLMs) can generalise to the task of automated grading of short answers from their pre-training tasks. As with other tasks, increasing the parameter size of the LLMs leads to greater performance. Currently, available systems still need human oversight, especially in examination scenarios.
zh
[NLP-183] Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis INTERSPEECH2025
【速读】： 该论文试图解决非流畅语音与目标文本之间准确对齐的问题，这对于自动化诊断神经退行性语音障碍至关重要。传统方法在建模音素相似性方面效果有限，从而影响了性能。解决方案的关键在于提出Neural LCS，这是一种基于强大音素级建模的新方法，能够有效处理部分对齐和上下文感知的相似性映射问题，从而显著提升对齐精度和非流畅语音分割效果。
链接: https://arxiv.org/abs/2506.12073
作者: Zongli Ye,Jiachen Lian,Xuanru Zhou,Jinming Zhang,Haodong Li,Shuhe Li,Chenxu Guo,Anaisha Das,Peter Park,Zoe Ezzes,Jet Vonk,Brittany Morin,Rian Bogley,Lisa Wauters,Zachary Miller,Maria Gorno-Tempini,Gopala Anumanchipalli
机构: 未知
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted for Interspeech2025
Abstract:Accurate alignment of dysfluent speech with intended text is crucial for automating the diagnosis of neurodegenerative speech disorders. Traditional methods often fail to model phoneme similarities effectively, limiting their performance. In this work, we propose Neural LCS, a novel approach for dysfluent text-text and speech-text alignment. Neural LCS addresses key challenges, including partial alignment and context-aware similarity mapping, by leveraging robust phoneme-level modeling. We evaluate our method on a large-scale simulated dataset, generated using advanced data simulation techniques, and real PPA data. Neural LCS significantly outperforms state-of-the-art models in both alignment accuracy and dysfluent speech segmentation. Our results demonstrate the potential of Neural LCS to enhance automated systems for diagnosing and analyzing speech disorders, offering a more accurate and linguistically grounded solution for dysfluent speech alignment.
zh
[NLP-184] CMT-LLM : Contextual Multi-Talker ASR Utilizing Large Language Models INTERSPEECH2025
【速读】： 该论文旨在解决自动语音识别（ASR）系统在处理多说话人重叠语音和识别罕见词汇（如技术术语）时性能受限的问题。传统方法将多说话人ASR与上下文偏差分别处理，导致在复杂场景下的效果不佳。其解决方案的关键在于提出一个统一框架，将多说话人重叠语音识别与上下文偏差整合为单一任务，并通过预训练语音编码器与大语言模型（LLM）的结合，以及优化的微调策略，提升系统整体性能。此外，引入两阶段过滤算法以高效识别并纳入大量偏差列表中的相关罕见词，进一步增强了罕见词的识别能力。
链接: https://arxiv.org/abs/2506.12059
作者: Jiajun He,Naoki Sawada,Koichi Miyazaki,Tomoki Toda
机构: 未知
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted by INTERSPEECH 2025
Abstract:In real-world applications, automatic speech recognition (ASR) systems must handle overlapping speech from multiple speakers and recognize rare words like technical terms. Traditional methods address multi-talker ASR and contextual biasing separately, limiting performance in complex scenarios. We propose a unified framework that combines multi-talker overlapping speech recognition and contextual biasing into a single task. Our ASR method integrates pretrained speech encoders and large language models (LLMs), using optimized finetuning strategies. We also introduce a two-stage filtering algorithm to efficiently identify relevant rare words from large biasing lists and incorporate them into the LLM’s prompt input, enhancing rare word recognition. Experiments show that our approach outperforms traditional contextual biasing methods, achieving a WER of 7.9% on LibriMix and 32.9% on AMI SDM when the biasing size is 1,000, demonstrating its effectiveness in complex speech scenarios.
zh
计算机视觉
[CV-0] PF-LHM: 3D Animatable Avatar Reconstruction from Pose-free Articulated Human Images
【速读】：该论文旨在解决从无姿态信息的随意拍摄图像中重建可动画化的高质量3D人体模型的问题，这一任务面临视图错位、遮挡以及缺乏结构先验等挑战。其解决方案的关键在于提出PF-LHM模型，该模型采用高效的Encoder-Decoder Point-Image Transformer架构，通过多模态注意力机制融合层次化的几何点特征与多视角图像特征，并利用3D高斯泼溅表示细节几何与外观，从而实现快速且高保真的单图或多图3D人体重建。
链接: https://arxiv.org/abs/2506.13766
作者: Lingteng Qiu,Peihao Li,Qi Zuo,Xiaodong Gu,Yuan Dong,Weihao Yuan,Siyu Zhu,Xiaoguang Han,Guanying Chen,Zilong Dong
机构: Tongyi Lab, Alibaba Group (通义实验室，阿里巴巴集团); Sun Yat-sen University (中山大学); SSE, CUHKSZ (深圳高等金融研究院，香港中文大学（深圳）); FNii, CUHKSZ (未来产业研究院，香港中文大学（深圳）); Fudan University (复旦大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing an animatable 3D human from casually captured images of an articulated subject without camera or human pose information is a practical yet challenging task due to view misalignment, occlusions, and the absence of structural priors. While optimization-based methods can produce high-fidelity results from monocular or multi-view videos, they require accurate pose estimation and slow iterative optimization, limiting scalability in unconstrained scenarios. Recent feed-forward approaches enable efficient single-image reconstruction but struggle to effectively leverage multiple input images to reduce ambiguity and improve reconstruction accuracy. To address these challenges, we propose PF-LHM, a large human reconstruction model that generates high-quality 3D avatars in seconds from one or multiple casually captured pose-free images. Our approach introduces an efficient Encoder-Decoder Point-Image Transformer architecture, which fuses hierarchical geometric point features and multi-view image features through multimodal attention. The fused features are decoded to recover detailed geometry and appearance, represented using 3D Gaussian splats. Extensive experiments on both real and synthetic datasets demonstrate that our method unifies single- and multi-image 3D human reconstruction, achieving high-fidelity and animatable 3D human avatars without requiring camera and human pose annotations. Code and models will be released to the public.
zh
[CV-1] Diagnosing and Improving Diffusion Models by Estimating the Optimal Loss Value
【速读】：该论文试图解决扩散模型（diffusion models）中损失函数无法准确反映数据拟合质量的问题，因为其最优损失值通常不为零且未知，导致大最优损失与模型容量不足之间的混淆。解决方案的关键在于估计最优损失值，以诊断和改进扩散模型。作者首先在统一的扩散模型框架下推导出最优损失的闭式表达，并开发了有效的估计器，包括一种可扩展至大规模数据集的随机变体，能够有效控制方差和偏差。这一工具使研究人员能够评估主流扩散模型变体的训练质量，并基于最优损失设计更高效的训练策略。
链接: https://arxiv.org/abs/2506.13763
作者: Yixian Xu,Shengjie Luo,Liwei Wang,Di He,Chang Liu
机构: Peking University (北京大学); Microsoft Research AI for Science (微软研究院人工智能科学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注: 29 pages, 8 figures, 3 tables. Preprint. Work in Progress
Abstract:Diffusion models have achieved remarkable success in generative modeling. Despite more stable training, the loss of diffusion models is not indicative of absolute data-fitting quality, since its optimal value is typically not zero but unknown, leading to confusion between large optimal loss and insufficient model capacity. In this work, we advocate the need to estimate the optimal loss value for diagnosing and improving diffusion models. We first derive the optimal loss in closed form under a unified formulation of diffusion models, and develop effective estimators for it, including a stochastic variant scalable to large datasets with proper control of variance and bias. With this tool, we unlock the inherent metric for diagnosing the training quality of mainstream diffusion model variants, and develop a more performant training schedule based on the optimal loss. Moreover, using models with 120M to 1.5B parameters, we find that the power law is better demonstrated after subtracting the optimal loss from the actual training loss, suggesting a more principled setting for investigating the scaling law for diffusion models.
zh
[CV-2] ouch begins where vision ends: Generalizable policies for contact-rich manipulation
【速读】：该论文旨在解决数据驱动方法在精确操作任务中表现不佳的问题，特别是模仿学习需要大量难以获取的示范，而强化学习则产生脆弱且无法泛化的策略。其解决方案的关键在于提出一种名为VisuoTactile Local (ViTaL) 的策略学习框架，该框架通过将任务分解为两个阶段：第一阶段利用视觉-语言模型（VLM）进行场景级推理以定位目标物体，第二阶段则通过可重用、与场景无关的ViTaL策略，结合自我中心视觉和触觉感知执行接触丰富的操作。该方法的核心思想是，尽管场景上下文变化，但低级交互在任务实例间保持一致，从而使得在标准环境下训练的局部策略能够通过“定位-执行”策略实现泛化。
链接: https://arxiv.org/abs/2506.13762
作者: Zifan Zhao,Siddhant Haldar,Jinda Cui,Lerrel Pinto,Raunaq Bhirangi
机构: New York University Shanghai (上海纽约大学); New York University (纽约大学); Honda Research (本田研究)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Data-driven approaches struggle with precise manipulation; imitation learning requires many hard-to-obtain demonstrations, while reinforcement learning yields brittle, non-generalizable policies. We introduce VisuoTactile Local (ViTaL) policy learning, a framework that solves fine-grained manipulation tasks by decomposing them into two phases: a reaching phase, where a vision-language model (VLM) enables scene-level reasoning to localize the object of interest, and a local interaction phase, where a reusable, scene-agnostic ViTaL policy performs contact-rich manipulation using egocentric vision and tactile sensing. This approach is motivated by the observation that while scene context varies, the low-level interaction remains consistent across task instances. By training local policies once in a canonical setting, they can generalize via a localize-then-execute strategy. ViTaL achieves around 90% success on contact-rich tasks in unseen environments and is robust to distractors. ViTaL’s effectiveness stems from three key insights: (1) foundation models for segmentation enable training robust visual encoders via behavior cloning; (2) these encoders improve the generalizability of policies learned using residual RL; and (3) tactile sensing significantly boosts performance in contact-rich tasks. Ablation studies validate each of these insights, and we demonstrate that ViTaL integrates well with high-level VLMs, enabling robust, reusable low-level skills. Results and videos are available at this https URL.
zh
[CV-3] AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning
【速读】：该论文试图解决当前Vision-Language-Action (VLA)模型在端到端自动驾驶中存在的物理不可行动作输出、复杂模型结构以及不必要的长推理等问题。其解决方案的关键在于提出AutoVLA，一个将推理与动作生成统一在一个自回归生成模型中的新型VLA模型，通过从原始视觉输入和语言指令中直接进行语义推理和轨迹规划，并将连续轨迹离散化为可行动作，实现与语言模型的直接集成，同时采用监督微调和基于Group Relative Policy Optimization (GRPO)的强化微调方法，提升规划性能与效率。
链接: https://arxiv.org/abs/2506.13757
作者: Zewei Zhou,Tianhui Cai,Seth Z. Zhao,Yun Zhang,Zhiyu Huang,Bolei Zhou,Jiaqi Ma
机构: University of California, Los Angeles (加利福尼亚大学洛杉矶分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Website link: this https URL
Abstract:Recent advancements in Vision-Language-Action (VLA) models have shown promise for end-to-end autonomous driving by leveraging world knowledge and reasoning capabilities. However, current VLA models often struggle with physically infeasible action outputs, complex model structures, or unnecessarily long reasoning. In this paper, we propose AutoVLA, a novel VLA model that unifies reasoning and action generation within a single autoregressive generation model for end-to-end autonomous driving. AutoVLA performs semantic reasoning and trajectory planning directly from raw visual inputs and language instructions. We tokenize continuous trajectories into discrete, feasible actions, enabling direct integration into the language model. For training, we employ supervised fine-tuning to equip the model with dual thinking modes: fast thinking (trajectory-only) and slow thinking (enhanced with chain-of-thought reasoning). To further enhance planning performance and efficiency, we introduce a reinforcement fine-tuning method based on Group Relative Policy Optimization (GRPO), reducing unnecessary reasoning in straightforward scenarios. Extensive experiments across real-world and simulated datasets and benchmarks, including nuPlan, nuScenes, Waymo, and CARLA, demonstrate the competitive performance of AutoVLA in both open-loop and closed-loop settings. Qualitative results showcase the adaptive reasoning and accurate planning capabilities of AutoVLA in diverse scenarios.
zh
[CV-4] UltraZoom: Generating Gigapixel Images from Regular Photos
【速读】：该论文试图解决从随意拍摄的输入（如手持手机照片）生成物体的千兆像素级分辨率图像的问题，其核心挑战在于如何将低细节的全局图像与高细节的局部特写图像进行对齐并上采样以匹配特写的细节数量和尺度。解决方案的关键在于构建实例相关的成对数据集，并适应预训练的生成模型以学习对象特定的低分辨率到高分辨率的映射，同时引入一种简单且鲁棒的方法实现任意材料在非结构化场景中的配准，从而实现无缝的全对象平移与缩放。
链接: https://arxiv.org/abs/2506.13756
作者: Jingwei Ma,Vivek Jayaram,Brian Curless,Ira Kemelmacher-Shlizerman,Steven M. Seitz
机构: University of Washington(华盛顿大学)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:We present UltraZoom, a system for generating gigapixel-resolution images of objects from casually captured inputs, such as handheld phone photos. Given a full-shot image (global, low-detail) and one or more close-ups (local, high-detail), UltraZoom upscales the full image to match the fine detail and scale of the close-up examples. To achieve this, we construct a per-instance paired dataset from the close-ups and adapt a pretrained generative model to learn object-specific low-to-high resolution mappings. At inference, we apply the model in a sliding window fashion over the full image. Constructing these pairs is non-trivial: it requires registering the close-ups within the full image for scale estimation and degradation alignment. We introduce a simple, robust method for getting registration on arbitrary materials in casual, in-the-wild captures. Together, these components form a system that enables seamless pan and zoom across the entire object, producing consistent, photorealistic gigapixel imagery from minimal input.
zh
[CV-5] VideoPDE: Unified Generative PDE Solving via Video Inpainting Diffusion Models NEURIPS2025
【速读】：该论文试图解决偏微分方程（Partial Differential Equations, PDEs）求解的问题，特别是针对正问题和反问题在全观测或部分观测条件下的求解。其解决方案的关键在于提出一个统一的框架，将PDE求解重新建模为广义的视频修复（video-inpainting）问题，通过基于Transformer的架构，利用任意模式的已知数据来推断时空维度上的缺失值。该方法结合了像素空间的视频扩散模型以实现高保真修复，并通过分层建模提升计算效率，从而在多种PDE及其问题设置中表现出色。
链接: https://arxiv.org/abs/2506.13754
作者: Edward Li,Zichen Wang,Jiahe Huang,Jeong Joon Park
机构: University of Michigan, Ann Arbor(密歇根大学，安娜堡)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to NeurIPS 2025. Project page: this https URL
Abstract:We present a unified framework for solving partial differential equations (PDEs) using video-inpainting diffusion transformer models. Unlike existing methods that devise specialized strategies for either forward or inverse problems under full or partial observation, our approach unifies these tasks under a single, flexible generative framework. Specifically, we recast PDE-solving as a generalized inpainting problem, e.g., treating forward prediction as inferring missing spatiotemporal information of future states from initial conditions. To this end, we design a transformer-based architecture that conditions on arbitrary patterns of known data to infer missing values across time and space. Our method proposes pixel-space video diffusion models for fine-grained, high-fidelity inpainting and conditioning, while enhancing computational efficiency through hierarchical modeling. Extensive experiments show that our video inpainting-based diffusion model offers an accurate and versatile solution across a wide range of PDEs and problem setups, outperforming state-of-the-art baselines.
zh
[CV-6] st3R: Learning to Reconstruct 3D at Test Time
【速读】：该论文旨在解决现有密集匹配方法（如DUSt3R）在3D重建中依赖成对预测和泛化能力有限导致的全局几何一致性不足的问题。其解决方案的关键在于提出Test3R，一种简单但有效的测试时学习技术，通过使用图像三元组（I₁, I₂, I₃）生成基于(I₁, I₂)和(I₁, I₃)的重建结果，并在测试阶段通过自监督目标最大化这两个重建结果相对于共同图像I₁的几何一致性，从而确保模型输出跨成对的一致性。
链接: https://arxiv.org/abs/2506.13750
作者: Yuheng Yuan,Qiuhong Shen,Shizun Wang,Xingyi Yang,Xinchao Wang
机构: National University of Singapore (新加坡国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Dense matching methods like DUSt3R regress pairwise pointmaps for 3D reconstruction. However, the reliance on pairwise prediction and the limited generalization capability inherently restrict the global geometric consistency. In this work, we introduce Test3R, a surprisingly simple test-time learning technique that significantly boosts geometric accuracy. Using image triplets ( I_1,I_2,I_3 ), Test3R generates reconstructions from pairs ( I_1,I_2 ) and ( I_1,I_3 ). The core idea is to optimize the network at test time via a self-supervised objective: maximizing the geometric consistency between these two reconstructions relative to the common image I_1 . This ensures the model produces cross-pair consistent outputs, regardless of the inputs. Extensive experiments demonstrate that our technique significantly outperforms previous state-of-the-art methods on the 3D reconstruction and multi-view depth estimation tasks. Moreover, it is universally applicable and nearly cost-free, making it easily applied to other models and implemented with minimal test-time training overhead and parameter footprint. Code is available at this https URL.
zh
[CV-7] OTFusion: Bridging Vision-only and Vision-Language Models via Optimal Transport for Transductive Zero-Shot Learning
【速读】：该论文旨在解决跨域分类问题中的零样本学习（Zero-Shot Learning, ZSL）挑战，即在没有目标类别标注的情况下，利用语义描述和未标记测试数据分布对未见类别进行分类。现有方法如视觉-语言模型（Vision-Language Models, VLMs）虽然在视觉与语义对齐方面表现优异，但过度依赖类别先验而忽视细粒度视觉特征；而视觉基础模型（Vision-only Foundation Models, VFMs）虽能提供丰富的感知特征，却缺乏语义对齐能力。论文提出的OTFusion框架通过最优传输（Optimal Transport）机制，融合VLM与VFM的优势，其关键在于学习一个共享的概率表示，通过最小化两者分布间的传输成本，实现视觉与语义信息的对齐，从而提升分类的语义合理性和视觉一致性。
链接: https://arxiv.org/abs/2506.13723
作者: Qiyu Xu,Wenyang Chen,Zhanxuan Hu,Huafeng Li,Yonghang Tai
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Transductive zero-shot learning (ZSL) aims to classify unseen categories by leveraging both semantic class descriptions and the distribution of unlabeled test data. While Vision-Language Models (VLMs) such as CLIP excel at aligning visual inputs with textual semantics, they often rely too heavily on class-level priors and fail to capture fine-grained visual cues. In contrast, Vision-only Foundation Models (VFMs) like DINOv2 provide rich perceptual features but lack semantic alignment. To exploit the complementary strengths of these models, we propose OTFusion, a simple yet effective training-free framework that bridges VLMs and VFMs via Optimal Transport. Specifically, OTFusion aims to learn a shared probabilistic representation that aligns visual and semantic information by minimizing the transport cost between their respective distributions. This unified distribution enables coherent class predictions that are both semantically meaningful and visually grounded. Extensive experiments on 11 benchmark datasets demonstrate that OTFusion consistently outperforms the original CLIP model, achieving an average accuracy improvement of nearly 10% , all without any fine-tuning or additional annotations. The code will be publicly released after the paper is accepted.
zh
[CV-8] How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection
【速读】：该论文试图解决事件相机（event camera）在交通监控应用中，由于缺乏标注的真实世界数据集而导致的鲁棒事件驱动目标检测模型开发困难的问题。其解决方案的关键在于利用CARLA驾驶模拟器内置的动态视觉传感器（DVS）模块生成合成事件数据，并通过训练循环视觉变压器模型仅基于这些合成数据进行评估，以系统分析仿真到现实（sim-to-real）的差距。研究结果表明，仅依赖合成数据训练的模型在合成数据测试集中表现良好，但随着真实世界数据比例增加，性能显著下降，从而突显了当前DVS仿真精度的不足及改进类脑视觉领域域适应技术的必要性。
链接: https://arxiv.org/abs/2506.13722
作者: Kaiyuan Tan,Pavan Kumar B N,Bharatesh Chakravarthi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Event cameras are gaining traction in traffic monitoring applications due to their low latency, high temporal resolution, and energy efficiency, which makes them well-suited for real-time object detection at traffic intersections. However, the development of robust event-based detection models is hindered by the limited availability of annotated real-world datasets. To address this, several simulation tools have been developed to generate synthetic event data. Among these, the CARLA driving simulator includes a built-in dynamic vision sensor (DVS) module that emulates event camera output. Despite its potential, the sim-to-real gap for event-based object detection remains insufficiently studied. In this work, we present a systematic evaluation of this gap by training a recurrent vision transformer model exclusively on synthetic data generated using CARLAs DVS and testing it on varying combinations of synthetic and real-world event streams. Our experiments show that models trained solely on synthetic data perform well on synthetic-heavy test sets but suffer significant performance degradation as the proportion of real-world data increases. In contrast, models trained on real-world data demonstrate stronger generalization across domains. This study offers the first quantifiable analysis of the sim-to-real gap in event-based object detection using CARLAs DVS. Our findings highlight limitations in current DVS simulation fidelity and underscore the need for improved domain adaptation techniques in neuromorphic vision for traffic monitoring.
zh
[CV-9] Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry
【速读】：该论文试图解决单目视频沿用户定义相机路径重新合成的问题，这一任务由于其病态性以及用于训练的多视角视频数据有限而具有挑战性。解决方案的关键在于提出一个两阶段框架：首先估计时间一致的几何结构，然后基于该几何结构进行生成式渲染。通过整合几何先验，生成模型能够在估计几何不确定性较高的区域合成更真实的细节，同时通过分解微调框架消除了对大量4D训练数据的依赖，从而在真实世界视频的极端外推场景中表现出优于基线方法的性能。
链接: https://arxiv.org/abs/2506.13697
作者: Junyoung Seo,Jisang Han,Jaewoo Jung,Siyoon Jin,Joungbin Lee,Takuya Narihira,Kazumi Fukuda,Takashi Shibuya,Donghoon Ahn,Shoukang Hu,Seungryong Kim,Yuki Mitsufuji
机构: KAIST AI; Sony AI; Sony Group Corporation
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Our project page can be found at this https URL
Abstract:We introduce Vid-CamEdit, a novel framework for video camera trajectory editing, enabling the re-synthesis of monocular videos along user-defined camera paths. This task is challenging due to its ill-posed nature and the limited multi-view video data for training. Traditional reconstruction methods struggle with extreme trajectory changes, and existing generative models for dynamic novel view synthesis cannot handle in-the-wild videos. Our approach consists of two steps: estimating temporally consistent geometry, and generative rendering guided by this geometry. By integrating geometric priors, the generative model focuses on synthesizing realistic details where the estimated geometry is uncertain. We eliminate the need for extensive 4D training data through a factorized fine-tuning framework that separately trains spatial and temporal components using multi-view image and video data. Our method outperforms baselines in producing plausible videos from novel camera trajectories, especially in extreme extrapolation scenarios on real-world footage.
zh
[CV-10] UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions
【速读】：该论文旨在解决当前视频生成模型因视频数据集质量不足（如图像质量、分辨率及细粒度描述）而影响性能的问题，以及高分辨率视频生成（如电影级超高清(Ultra-High Definition, UHD)视频和4K短视频内容）对高质量视频生成模型提出的更高要求。其解决方案的关键在于提出一个高质量的开源UHD-4K文本到视频数据集UltraVideo，该数据集包含超过100种主题，每段视频配有9个结构化描述和一个总结性描述，并通过四阶段高度自动化的数据筛选流程：多样本高质量视频片段收集、统计数据分析过滤、基于模型的数据净化以及综合性结构化描述生成，从而确保数据集的高质量与多样性。
链接: https://arxiv.org/abs/2506.13691
作者: Zhucun Xue,Jiangning Zhang,Teng Hu,Haoyang He,Yinan Chen,Yuxuan Cai,Yabiao Wang,Chengjie Wang,Yong Liu,Xiangtai Li,Dacheng Tao
机构: ZJU(浙江大学); SJTU(上海交通大学); HUST(华中科技大学); NTU(南洋理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The quality of the video dataset (image quality, resolution, and fine-grained caption) greatly influences the performance of the video generation model. The growing demand for video applications sets higher requirements for high-quality video generation models. For example, the generation of movie-level Ultra-High Definition (UHD) videos and the creation of 4K short video content. However, the existing public datasets cannot support related research and applications. In this paper, we first propose a high-quality open-sourced UHD-4K (22.4% of which are 8K) text-to-video dataset named UltraVideo, which contains a wide range of topics (more than 100 kinds), and each video has 9 structured captions with one summarized caption (average of 824 words). Specifically, we carefully design a highly automated curation process with four stages to obtain the final high-quality dataset: \textiti) collection of diverse and high-quality video clips. \textitii) statistical data filtering. \textitiii) model-based data purification. \textitiv) generation of comprehensive, structured captions. In addition, we expand Wan to UltraWan-1K/-4K, which can natively generate high-quality 1K/4K videos with more consistent text controllability, demonstrating the effectiveness of our data this http URL believe that this work can make a significant contribution to future research on UHD video generation. UltraVideo dataset and UltraWan models are available at this https URL.
zh
[CV-11] ROSA: Harnessing Robot States for Vision-Language and Action Alignment
【速读】：该论文旨在解决Vision-Language-Action (VLA)模型在将视觉-语言空间与机器人动作空间对齐时面临的挑战，尤其是由于视觉-语言模型（VLM）处于高层语义空间而机器人动作则基于低层3D物理空间所导致的空间差异，以及VLM主要处理当前状态而VLA模型需预测未来动作所导致的时间差异。解决方案的关键在于提出一种新的训练范式ROSA，该方法通过整合自动获取的机器人状态估计数据，提升VLA模型对空间的理解和自我意识，从而增强其性能和泛化能力。
链接: https://arxiv.org/abs/2506.13679
作者: Yuqing Wen,Kefan Gu,Haoxuan Liu,Yucheng Zhao,Tiancai Wang,Haoqiang Fan,Xiaoyan Sun
机构: Dexmal; University of Science and Technology of China (中国科学技术大学); Nanjing University (南京大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-Language-Action (VLA) models have recently made significant advance in multi-task, end-to-end robotic control, due to the strong generalization capabilities of Vision-Language Models (VLMs). A fundamental challenge in developing such models is effectively aligning the vision-language space with the robotic action space. Existing approaches typically rely on directly fine-tuning VLMs using expert demonstrations. However, this strategy suffers from a spatio-temporal gap, resulting in considerable data inefficiency and heavy reliance on human labor. Spatially, VLMs operate within a high-level semantic space, whereas robotic actions are grounded in low-level 3D physical space; temporally, VLMs primarily interpret the present, while VLA models anticipate future actions. To overcome these challenges, we propose a novel training paradigm, ROSA, which leverages robot state estimation to improve alignment between vision-language and action spaces. By integrating robot state estimation data obtained via an automated process, ROSA enables the VLA model to gain enhanced spatial understanding and self-awareness, thereby boosting performance and generalization. Extensive experiments in both simulated and real-world environments demonstrate the effectiveness of ROSA, particularly in low-data regimes.
zh
[CV-12] Lecture Video Visual Objects (LVVO) Dataset: A Benchmark for Visual Object Detection in Educational Videos
【速读】：该论文旨在解决教育视频内容中视觉对象检测的问题，提出了一种新的基准数据集Lecture Video Visual Objects (LVVO)。其解决方案的关键在于构建一个高质量的标注数据集，包含手动标注的1,000帧（LVVO_1k）和通过半监督方法自动标注的3,000帧（LVVO_3k），以支持监督与半监督方法在教育视频视觉内容检测中的开发与评估。
链接: https://arxiv.org/abs/2506.13657
作者: Dipayan Biswas,Shishir Shah,Jaspal Subhlok
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:We introduce the Lecture Video Visual Objects (LVVO) dataset, a new benchmark for visual object detection in educational video content. The dataset consists of 4,000 frames extracted from 245 lecture videos spanning biology, computer science, and geosciences. A subset of 1,000 frames, referred to as LVVO_1k, has been manually annotated with bounding boxes for four visual categories: Table, Chart-Graph, Photographic-image, and Visual-illustration. Each frame was labeled independently by two annotators, resulting in an inter-annotator F1 score of 83.41%, indicating strong agreement. To ensure high-quality consensus annotations, a third expert reviewed and resolved all cases of disagreement through a conflict resolution process. To expand the dataset, a semi-supervised approach was employed to automatically annotate the remaining 3,000 frames, forming LVVO_3k. The complete dataset offers a valuable resource for developing and evaluating both supervised and semi-supervised methods for visual content detection in educational videos. The LVVO dataset is publicly available to support further research in this domain.
zh
[CV-13] Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning
【速读】：该论文旨在解决如何对超长时长（即数天至数周）的第一视角视频进行推理的问题，此类视频通常包含复杂的时空信息和多模态内容，传统方法难以有效处理。解决方案的关键在于提出一种名为Ego-R1的框架，其核心是基于结构化的Chain-of-Tool-Thought (CoTT)过程，由通过强化学习（Reinforcement Learning, RL）训练的Ego-R1 Agent协调执行。该Agent通过分步调用特定工具，逐步协作解决子问题，从而实现对长时间序列视频的理解与推理。
链接: https://arxiv.org/abs/2506.13654
作者: Shulin Tian,Ruiqi Wang,Hongming Guo,Penghao Wu,Yuhao Dong,Xiuying Wang,Jingkang Yang,Hao Zhang,Hongyuan Zhu,Ziwei Liu
机构: S-Lab, Nanyang Technological University (S-Lab，南洋理工大学); ASTAR, Singapore (ASTAR，新加坡); Simon Fraser University (西蒙弗雷泽大学); Shanghai AI Lab (上海人工智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:We introduce Ego-R1, a novel framework for reasoning over ultra-long (i.e., in days and weeks) egocentric videos, which leverages a structured Chain-of-Tool-Thought (CoTT) process, orchestrated by an Ego-R1 Agent trained via reinforcement learning (RL). Inspired by human problem-solving strategies, CoTT decomposes complex reasoning into modular steps, with the RL agent invoking specific tools, one per step, to iteratively and collaboratively answer sub-questions tackling such tasks as temporal retrieval and multi-modal understanding. We design a two-stage training paradigm involving supervised finetuning (SFT) of a pretrained language model using CoTT data and RL to enable our agent to dynamically propose step-by-step tools for long-range reasoning. To facilitate training, we construct a dataset called Ego-R1 Data, which consists of Ego-CoTT-25K for SFT and Ego-QA-4.4K for RL. Furthermore, our Ego-R1 agent is evaluated on a newly curated week-long video QA benchmark, Ego-R1 Bench, which contains human-verified QA pairs from hybrid sources. Extensive results demonstrate that the dynamic, tool-augmented chain-of-thought reasoning by our Ego-R1 Agent can effectively tackle the unique challenges of understanding ultra-long egocentric videos, significantly extending the time coverage from few hours to a week.
zh
[CV-14] DualEdit: Dual Editing for Knowledge Updating in Vision-Language Models
【速读】：该论文旨在解决多模态视觉-语言模型（Vision-Language Models, VLMs）在模型编辑过程中，不同模态（如文本和视觉）对编辑效果的影响及其协同更新问题。现有方法主要针对单模态语言模型（LLMs），而VLMs由于涉及多模态信息，其编辑机制尚未得到充分研究。解决方案的关键在于通过分析文本和视觉表示在不同层次的敏感性差异，提出DualEdit编辑器，在各自关键层次上同时修改文本和视觉模态，并引入门控模块以在更新新知识的同时保留原始模型能力，从而提升编辑效率与性能。
链接: https://arxiv.org/abs/2506.13638
作者: Zhiyi Shi,Binjie Wang,Chongjie Si,Yichen Wu,Junsik Kim,Hanspeter Pfister
机构: Harvard University (哈佛大学); City University of Hong Kong (香港城市大学); Amazon (亚马逊); Fudan University (复旦大学); MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University (教育部人工智能重点实验室，人工智能研究院，上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Under Review
Abstract:Model editing aims to efficiently update a pre-trained model’s knowledge without the need for time-consuming full retraining. While existing pioneering editing methods achieve promising results, they primarily focus on editing single-modal language models (LLMs). However, for vision-language models (VLMs), which involve multiple modalities, the role and impact of each modality on editing performance remain largely unexplored. To address this gap, we explore the impact of textual and visual modalities on model editing and find that: (1) textual and visual representations reach peak sensitivity at different layers, reflecting their varying importance; and (2) editing both modalities can efficiently update knowledge, but this comes at the cost of compromising the model’s original capabilities. Based on our findings, we propose DualEdit, an editor that modifies both textual and visual modalities at their respective key layers. Additionally, we introduce a gating module within the more sensitive textual modality, allowing DualEdit to efficiently update new knowledge while preserving the model’s original information. We evaluate DualEdit across multiple VLM backbones and benchmark datasets, demonstrating its superiority over state-of-the-art VLM editing baselines as well as adapted LLM editing methods on different evaluation metrics.
zh
[CV-15] FreeQ-Graph: Free-form Querying with Semantic Consistent Scene Graph for 3D Scene Understanding
【速读】：该论文旨在解决复杂三维场景中通过自然语言进行语义查询的挑战，现有方法依赖于训练数据中的预定义词汇先验，限制了自由形式的语义查询能力，同时基于大语言模型（Large Language Models, LLMs）的方法缺乏全面的三维场景级信息并可能产生不一致的输出。论文提出的解决方案关键在于构建一个语义一致的三维场景图（scene graph），通过无需预定义词汇的方式编码自由形式查询，并结合三维语义对齐特征实现与三维语义标签的一致性对齐，进而设计基于LLM的推理算法以完成场景级和对象级信息的复杂推理。
链接: https://arxiv.org/abs/2506.13629
作者: Chenlu Zhan,Gaoang Wang,Hongwei Wang
机构: Zhejiang University (浙江大学); Zhejiang University-University of Illinois Urbana-Champaign Institute (浙江大学-伊利诺伊大学厄巴纳-香槟分校联合研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Semantic querying in complex 3D scenes through free-form language presents a significant challenge. Existing 3D scene understanding methods use large-scale training data and CLIP to align text queries with 3D semantic features. However, their reliance on predefined vocabulary priors from training data hinders free-form semantic querying. Besides, recent advanced methods rely on LLMs for scene understanding but lack comprehensive 3D scene-level information and often overlook the potential inconsistencies in LLM-generated outputs. In our paper, we propose FreeQ-Graph, which enables Free-form Querying with a semantic consistent scene Graph for 3D scene understanding. The core idea is to encode free-form queries from a complete and accurate 3D scene graph without predefined vocabularies, and to align them with 3D consistent semantic labels, which accomplished through three key steps. We initiate by constructing a complete and accurate 3D scene graph that maps free-form objects and their relations through LLM and LVLM guidance, entirely free from training data or predefined priors. Most importantly, we align graph nodes with accurate semantic labels by leveraging 3D semantic aligned features from merged superpoints, enhancing 3D semantic consistency. To enable free-form semantic querying, we then design an LLM-based reasoning algorithm that combines scene-level and object-level information to intricate reasoning. We conducted extensive experiments on 3D semantic grounding, segmentation, and complex querying tasks, while also validating the accuracy of graph generation. Experiments on 6 datasets show that our model excels in both complex free-form semantic queries and intricate relational reasoning.
zh
[CV-16] Dive3D: Diverse Distillation-based Text-to-3D Generation via Score Implicit Matching
【速读】：该论文旨在解决现有文本到3D生成方法中由于依赖Score Distillation Sampling (SDS)损失而导致的模式崩溃问题，该损失函数基于不对称KL散度，倾向于模式寻找行为，限制了生成多样性。其解决方案的关键在于引入一种新的Score Implicit Matching (SIM)损失，这是一种基于得分的优化目标，能够有效缓解模式崩溃问题，并结合扩散蒸馏与奖励引导优化，在统一的散度视角下提升生成结果的多样性、文本对齐度、人类偏好及整体视觉保真度。
链接: https://arxiv.org/abs/2506.13594
作者: Weimin Bai,Yubo Li,Wenzheng Chen,Weijian Luo,He Sun
机构: Peking University (北京大学); Xiaohongshu Inc (小红书公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Distilling pre-trained 2D diffusion models into 3D assets has driven remarkable advances in text-to-3D synthesis. However, existing methods typically rely on Score Distillation Sampling (SDS) loss, which involves asymmetric KL divergence–a formulation that inherently favors mode-seeking behavior and limits generation diversity. In this paper, we introduce Dive3D, a novel text-to-3D generation framework that replaces KL-based objectives with Score Implicit Matching (SIM) loss, a score-based objective that effectively mitigates mode collapse. Furthermore, Dive3D integrates both diffusion distillation and reward-guided optimization under a unified divergence perspective. Such reformulation, together with SIM loss, yields significantly more diverse 3D outputs while improving text alignment, human preference, and overall visual fidelity. We validate Dive3D across various 2D-to-3D prompts and find that it consistently outperforms prior methods in qualitative assessments, including diversity, photorealism, and aesthetic appeal. We further evaluate its performance on the GPTEval3D benchmark, comparing against nine state-of-the-art baselines. Dive3D also achieves strong results on quantitative metrics, including text-asset alignment, 3D plausibility, text-geometry consistency, texture quality, and geometric detail.
zh
[CV-17] Omni-AdaVideoRAG : Omni-Contextual Adaptive Retrieval-Augmented for Efficient Long Video Understanding
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在处理长视频时面临的固定上下文窗口限制和长期依赖建模能力不足的问题。现有视频检索增强生成（Retrieval-Augmented Generation, RAG）方法采用静态检索策略，导致简单查询效率低下和复杂任务的信息丢失。其解决方案的关键在于提出AdaVideoRAG框架，该框架通过轻量级意图分类器动态调整检索粒度，以适应不同复杂度的查询，并引入Omni-Knowledge Indexing模块构建多源异构知识库，实现任务间的最优资源分配。
链接: https://arxiv.org/abs/2506.13589
作者: Zhucun Xue,Jiangning Zhang,Xurong Xie,Yuxuan Cai,Yong Liu,Xiangtai Li,Dacheng Tao
机构: ZJU(浙江大学); YouTu Lab(优图实验室); HUST(华中科技大学); NTU(南洋理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) struggle with long videos due to fixed context windows and weak long-term dependency modeling. Existing Retrieval-Augmented Generation (RAG) methods for videos use static retrieval strategies, leading to inefficiencies for simple queries and information loss for complex tasks. To address this, we propose AdaVideoRAG, a novel framework that dynamically adapts retrieval granularity based on query complexity using a lightweight intent classifier. Our framework employs an Omni-Knowledge Indexing module to build hierarchical databases from text (captions, ASR, OCR), visual features, and semantic graphs, enabling optimal resource allocation across tasks. We also introduce the HiVU benchmark for comprehensive evaluation. Experiments demonstrate improved efficiency and accuracy for long-video understanding, with seamless integration into existing MLLMs. AdaVideoRAG establishes a new paradigm for adaptive retrieval in video analysis. Codes will be open-sourced at this https URL.
zh
[CV-18] Integrated Pipeline for Monocular 3D Reconstruction and Finite Element Simulation in Industrial Applications
【速读】：该论文旨在解决工业环境中3D建模与结构仿真所面临的挑战，如设备部署困难以及精度与实时性之间的平衡问题。其解决方案的关键在于构建一个集成工作流，该流程融合了基于单目视频的高保真3D重建、有限元仿真分析以及混合现实可视化展示，从而实现工业检测与设备维护等场景下的交互式数字孪生系统。核心步骤包括利用Neuralangelo算法进行细节丰富的3D网格重建，通过Rhino的QuadRemesh工具优化初始三角形网格，再由HyperMesh进行离散化处理，并在Abaqus中完成材料参数设置与应力仿真，最终借助Unity和Vuforia引擎实现在增强现实环境中的实时叠加与交互操作。
链接: https://arxiv.org/abs/2506.13573
作者: Bowen Zheng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:To address the challenges of 3D modeling and structural simulation in industrial environment, such as the difficulty of equipment deployment, and the difficulty of balancing accuracy and real-time performance, this paper proposes an integrated workflow, which integrates high-fidelity 3D reconstruction based on monocular video, finite element simulation analysis, and mixed reality visual display, aiming to build an interactive digital twin system for industrial inspection, equipment maintenance and other scenes. Firstly, the Neuralangelo algorithm based on deep learning is used to reconstruct the 3D mesh model with rich details from the surround-shot video. Then, the QuadRemesh tool of Rhino is used to optimize the initial triangular mesh and generate a structured mesh suitable for finite element analysis. The optimized mesh is further discretized by HyperMesh, and the material parameter setting and stress simulation are carried out in Abaqus to obtain high-precision stress and deformation results. Finally, combined with Unity and Vuforia engine, the real-time superposition and interactive operation of simulation results in the augmented reality environment are realized, which improves users 'intuitive understanding of structural response. Experiments show that the method has good simulation efficiency and visualization effect while maintaining high geometric accuracy. It provides a practical solution for digital modeling, mechanical analysis and interactive display in complex industrial scenes, and lays a foundation for the deep integration of digital twin and mixed reality technology in industrial applications.
zh
[CV-19] MambaMia: A State-Space-Model-Based Compression for Efficient Video Understanding in Large Multimodal Models
【速读】：该论文旨在解决长视频或密集视频在输入大型多模态模型时导致的严重token爆炸问题。其解决方案的关键在于设计了一个基于双向状态空间的块，该块结合了门控跳跃连接和可学习加权平均池化机制，并在周期性插入的可学习查询上应用，从而实现了在空间和时间维度上的分层下采样，有效保持性能的同时降低计算成本。
链接: https://arxiv.org/abs/2506.13564
作者: Geewook Kim,Minjoon Seo
机构: NAVER Cloud AI (NAVER云人工智能); KAIST AI (KAIST人工智能)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 5 figures
Abstract:We propose an efficient framework to compress multiple video-frame features before feeding them into large multimodal models, thereby mitigating the severe token explosion arising from long or dense videos. Our design leverages a bidirectional state-space-based block equipped with a gated skip connection and a learnable weighted-average pooling mechanism applied to periodically inserted learned queries. This structure enables hierarchical downsampling across both spatial and temporal dimensions, preserving performance in a cost-effective manner. Across challenging long and dense video understanding tasks, our approach demonstrates competitive results against state-of-the-art models, while significantly reducing overall token budget. Notably, replacing our proposed state-space block with a conventional Transformer results in substantial performance degradation, highlighting the advantages of state-space modeling for effectively compressing multi-frame video data. Our framework emphasizes resource-conscious efficiency, making it practical for real-world deployments. We validate its scalability and generality across multiple benchmarks, achieving the dual objectives of efficient resource usage and comprehensive video understanding.
zh
[CV-20] X-Scene: Large-Scale Driving Scene Generation with High Fidelity and Flexible Controllability
【速读】：该论文旨在解决大规模三维驾驶场景生成中空间一致性不足的问题，传统方法主要关注时间一致性，而对复杂几何结构和外观真实性的生成研究较少。其解决方案的关键在于提出X-Scene框架，该框架通过多粒度控制实现灵活的场景定制，并引入统一的流水线依次生成3D语义占用和多视角图像，确保模态间的一致性；同时，利用一致性感知的场景外推技术将局部生成区域扩展为大规模场景，提升空间连续性和视觉连贯性，最终生成高质量的3DGS表示以支持多种应用场景。
链接: https://arxiv.org/abs/2506.13558
作者: Yu Yang,Alan Liang,Jianbiao Mei,Yukai Ma,Yong Liu,Gim Hee Lee
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 28 pages, 9 figures, Project page at this https URL
Abstract:Diffusion models are advancing autonomous driving by enabling realistic data synthesis, predictive end-to-end planning, and closed-loop simulation, with a primary focus on temporally consistent generation. However, the generation of large-scale 3D scenes that require spatial coherence remains underexplored. In this paper, we propose X-Scene, a novel framework for large-scale driving scene generation that achieves both geometric intricacy and appearance fidelity, while offering flexible controllability. Specifically, X-Scene supports multi-granular control, including low-level conditions such as user-provided or text-driven layout for detailed scene composition and high-level semantic guidance such as user-intent and LLM-enriched text prompts for efficient customization. To enhance geometrical and visual fidelity, we introduce a unified pipeline that sequentially generates 3D semantic occupancy and the corresponding multiview images, while ensuring alignment between modalities. Additionally, we extend the generated local region into a large-scale scene through consistency-aware scene outpainting, which extrapolates new occupancy and images conditioned on the previously generated area, enhancing spatial continuity and preserving visual coherence. The resulting scenes are lifted into high-quality 3DGS representations, supporting diverse applications such as scene exploration. Comprehensive experiments demonstrate that X-Scene significantly advances controllability and fidelity for large-scale driving scene generation, empowering data generation and simulation for autonomous driving.
zh
[CV-21] RelTopo: Enhancing Relational Modeling for Driving Scene Topology Reasoning
【速读】：该论文旨在解决自动驾驶中道路拓扑推理的准确性问题，特别是在车道感知与拓扑推理任务中，现有方法往往仅关注车道检测或车道到车道（L2L）的拓扑关系，而忽视了车道到交通元素（L2T）的关系，且缺乏对这些任务的联合优化。其解决方案的关键在于引入关系建模（relational modeling），通过联合增强结构理解，提升感知与推理性能。具体包括：1）一种关系感知的车道检测器，利用几何偏置自注意力和曲线交叉注意力捕捉关系依赖；2）增强的关系拓扑头，包含几何增强的L2L头和跨视角的L2T头；3）基于InfoNCE损失的对比学习策略，以规范关系嵌入。
链接: https://arxiv.org/abs/2506.13553
作者: Yueru Luo,Changqing Zhou,Yiming Yang,Erlong Li,Chao Zheng,Shuqi Mei,Shuguang Cui,Zhen Li
机构: FNii(未来智能研究院); SSE, CUHK-Shenzhen(深圳高等金融研究院，香港中文大学（深圳）); HKUST-Guangzhou(广州大学; 粤港澳大湾区创新研究院); Tencent Map T-Lab(腾讯地图T实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint. Under review
Abstract:Accurate road topology reasoning is critical for autonomous driving, enabling effective navigation and adherence to traffic regulations. Central to this task are lane perception and topology reasoning. However, existing methods typically focus on either lane detection or Lane-to-Lane (L2L) topology reasoning, often \textitneglecting Lane-to-Traffic-element (L2T) relationships or \textitfailing to optimize these tasks jointly. Furthermore, most approaches either overlook relational modeling or apply it in a limited scope, despite the inherent spatial relationships among road elements. We argue that relational modeling is beneficial for both perception and reasoning, as humans naturally leverage contextual relationships for road element recognition and their connectivity inference. To this end, we introduce relational modeling into both perception and reasoning, \textitjointly enhancing structural understanding. Specifically, we propose: 1) a relation-aware lane detector, where our geometry-biased self-attention and \curve\ cross-attention refine lane representations by capturing relational dependencies; 2) relation-enhanced topology heads, including a geometry-enhanced L2L head and a cross-view L2T head, boosting reasoning with relational cues; and 3) a contrastive learning strategy with InfoNCE loss to regularize relationship embeddings. Extensive experiments on OpenLane-V2 demonstrate that our approach significantly improves both detection and topology reasoning metrics, achieving +3.1 in DET _l , +5.3 in TOP _ll , +4.9 in TOP _lt , and an overall +4.4 in OLS, setting a new state-of-the-art. Code will be released.
zh
[CV-22] A Comprehensive Survey on Video Scene Parsing:Advances Challenges and Prospects
【速读】：该论文旨在系统回顾视频场景解析（Video Scene Parsing, VSP）领域的最新进展，解决如何在动态场景中实现多种视觉实体的同步分割、识别与跟踪的问题。其解决方案的关键在于从传统手工特征向现代深度学习范式的演进，特别是从全卷积网络到最新的基于Transformer的架构，以有效捕捉局部与全局的时间上下文信息。同时，论文还探讨了维持时间一致性及处理复杂场景动态性的技术挑战，并通过对比分析数据集和评估指标，为当前基准测试标准提供了全面的参考。
链接: https://arxiv.org/abs/2506.13552
作者: Guohuan Xie,Syed Ariff Syed Hesham,Wenya Guo,Bing Li,Ming-Ming Cheng,Guolei Sun,Yun Liu
机构: Nankai University (南开大学); Nanyang Technological University (南洋理工大学); Institute for Infocomm Research, ASTAR (资讯通信研究院，ASTAR); University of Electronic Science and Technology of China (电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video Scene Parsing (VSP) has emerged as a cornerstone in computer vision, facilitating the simultaneous segmentation, recognition, and tracking of diverse visual entities in dynamic scenes. In this survey, we present a holistic review of recent advances in VSP, covering a wide array of vision tasks, including Video Semantic Segmentation (VSS), Video Instance Segmentation (VIS), Video Panoptic Segmentation (VPS), as well as Video Tracking and Segmentation (VTS), and Open-Vocabulary Video Segmentation (OVVS). We systematically analyze the evolution from traditional hand-crafted features to modern deep learning paradigms – spanning from fully convolutional networks to the latest transformer-based architectures – and assess their effectiveness in capturing both local and global temporal contexts. Furthermore, our review critically discusses the technical challenges, ranging from maintaining temporal consistency to handling complex scene dynamics, and offers a comprehensive comparative study of datasets and evaluation metrics that have shaped current benchmarking standards. By distilling the key contributions and shortcomings of state-of-the-art methodologies, this survey highlights emerging trends and prospective research directions that promise to further elevate the robustness and adaptability of VSP in real-world applications.
zh
[CV-23] Limited-Angle CBCT Reconstruction via Geometry-Integrated Cycle-domain Denoising Diffusion Probabilistic Models
【速读】：该论文试图解决锥形束CT（Cone-beam CT, CBCT）在临床放疗中因机械结构限制导致的扫描角度受限问题，从而引发的图像质量下降、运动伪影和剂量增加等挑战。其解决方案的关键在于提出一种有限角度（Limited-Angle, LA）几何集成循环域（LA-GICD）框架，该框架结合了两个去噪扩散概率模型（DDPMs），通过解析的锥形束正向与反向投影算子连接，利用投影域和图像域的互补先验信息，实现从有限角度（90度）扫描中重建高质量CBCT体积，显著提升了图像质量并减少了扫描时间和辐射剂量。
链接: https://arxiv.org/abs/2506.13545
作者: Yuan Gao,Shaoyan Pan,Mingzhe Hu,Huiqiao Xie,Jill Remick,Chih-Wei Chang,Justin Roper,Zhen Tian,Xiaofeng Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Cone-beam CT (CBCT) is widely used in clinical radiotherapy for image-guided treatment, improving setup accuracy, adaptive planning, and motion management. However, slow gantry rotation limits performance by introducing motion artifacts, blurring, and increased dose. This work aims to develop a clinically feasible method for reconstructing high-quality CBCT volumes from consecutive limited-angle acquisitions, addressing imaging challenges in time- or dose-constrained settings. We propose a limited-angle (LA) geometry-integrated cycle-domain (LA-GICD) framework for CBCT reconstruction, comprising two denoising diffusion probabilistic models (DDPMs) connected via analytic cone-beam forward and back projectors. A Projection-DDPM completes missing projections, followed by back-projection, and an Image-DDPM refines the volume. This dual-domain design leverages complementary priors from projection and image spaces to achieve high-quality reconstructions from limited-angle (= 90 degrees) scans. Performance was evaluated against full-angle reconstruction. Four board-certified medical physicists conducted assessments. A total of 78 planning CTs in common CBCT geometries were used for training and evaluation. The method achieved a mean absolute error of 35.5 HU, SSIM of 0.84, and PSNR of 29.8 dB, with visibly reduced artifacts and improved soft-tissue clarity. LA-GICD’s geometry-aware dual-domain learning, embedded in analytic forward/backward operators, enabled artifact-free, high-contrast reconstructions from a single 90-degree scan, reducing acquisition time and dose four-fold. LA-GICD improves limited-angle CBCT reconstruction with strong data fidelity and anatomical realism. It offers a practical solution for short-arc acquisitions, enhancing CBCT use in radiotherapy by providing clinically applicable images with reduced scan time and dose for more accurate, personalized treatments.
zh
[CV-24] Atomizer: Generalizing to new modalities by breaking satellite images down to a set of scalars
【速读】：该论文旨在解决遥感数据因卫星数量增加而变得多样化所带来的模型泛化能力不足问题，现有模型依赖固定的输入格式和模态专用编码器，导致在引入新配置时需要重新训练，限制了其跨模态的适应性。解决方案的关键在于提出Atomizer架构，该架构将遥感图像表示为像素光谱波段值的标量集合，并为每个标量附加上下文元数据（如获取时间、空间分辨率、波长和带宽），从而生成原子化表示，使单一编码器能够处理任意模态而无需插值或重采样。
链接: https://arxiv.org/abs/2506.13542
作者: Hugo Riffaud de Turckheim,Sylvain Lobry,Roberto Interdonato,Diego Marcos
机构: INRIA(法国国家信息与自动化研究所); LIPADE(巴黎人工智能与数据科学实验室); CIRAD(法国农业国际合作研究中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The growing number of Earth observation satellites has led to increasingly diverse remote sensing data, with varying spatial, spectral, and temporal configurations. Most existing models rely on fixed input formats and modality-specific encoders, which require retraining when new configurations are introduced, limiting their ability to generalize across modalities. We introduce Atomizer, a flexible architecture that represents remote sensing images as sets of scalars, each corresponding to a spectral band value of a pixel. Each scalar is enriched with contextual metadata (acquisition time, spatial resolution, wavelength, and bandwidth), producing an atomic representation that allows a single encoder to process arbitrary modalities without interpolation or resampling. Atomizer uses structured tokenization with Fourier features and non-uniform radial basis functions to encode content and context, and maps tokens into a latent space via cross-attention. Under modality-disjoint evaluations, Atomizer outperforms standard models and demonstrates robust performance across varying resolutions and spatial sizes.
zh
[CV-25] Micro-macro Gaussian Splatting with Enhanced Scalability for Unconstrained Scene Reconstruction
【速读】：该论文试图解决从非约束图像集合中重建三维场景的问题，尤其是在外观变化较大的情况下。其解决方案的关键在于提出了一种名为可扩展的微观-宏观小波高斯点云（SMW-GS）的方法，该方法通过将场景表示分解为全局、细化和内在组件来增强跨不同尺度的三维重建效果，核心创新包括微观-宏观投影和基于小波的采样技术，以提升多尺度细节采样和特征表示的准确性，并通过大规模场景推广策略实现高效的视图分配与高质量重建。
链接: https://arxiv.org/abs/2506.13516
作者: Yihui Li,Chengxin Lv,Hongyu Yang,Di Huang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing 3D scenes from unconstrained image collections poses significant challenges due to variations in appearance. In this paper, we propose Scalable Micro-macro Wavelet-based Gaussian Splatting (SMW-GS), a novel method that enhances 3D reconstruction across diverse scales by decomposing scene representations into global, refined, and intrinsic components. SMW-GS incorporates the following innovations: Micro-macro Projection, which enables Gaussian points to sample multi-scale details with improved diversity; and Wavelet-based Sampling, which refines feature representations using frequency-domain information to better capture complex scene appearances. To achieve scalability, we further propose a large-scale scene promotion strategy, which optimally assigns camera views to scene partitions by maximizing their contributions to Gaussian points, achieving consistent and high-quality reconstructions even in expansive environments. Extensive experiments demonstrate that SMW-GS significantly outperforms existing methods in both reconstruction quality and scalability, particularly excelling in large-scale urban environments with challenging illumination variations. Project is available at this https URL.
zh
[CV-26] A Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation
【速读】：该论文试图解决医学领域中基于内容的图像检索（Content-Based Image Retrieval, CBIR）的性能评估问题，该问题在当前仍是一个关键但未解决的问题。传统评估指标（如精确率、召回率）多源自分类任务，需要人工标注作为真实标签，但在特定主题领域中，这些标签往往成本高昂或不可用。本文的关键解决方案是引入知识图谱来衡量不同医学概念之间的距离，并通过定义两个医学概念集合之间的近似匹配相关性得分，提出一种新的相关性度量方法，从而间接量化医学图像之间的相似性，验证了该方法的有效性和可行性。
链接: https://arxiv.org/abs/2506.13509
作者: Xiaoyang Wei,Camille Kurtz,Florence Cloppet
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted by the International Conference on Image Analysis and Processing 2025
Abstract:Performance evaluation for Content-Based Image Retrieval (CBIR) remains a crucial but unsolved problem today especially in the medical domain. Various evaluation metrics have been discussed in the literature to solve this problem. Most of the existing metrics (e.g., precision, recall) are adapted from classification tasks which require manual labels as ground truth. However, such labels are often expensive and unavailable in specific thematic domains. Furthermore, medical images are usually associated with (radiological) case reports or annotated with descriptive captions in literature figures, such text contains information that can help to assess this http URL researchers have argued that the medical concepts hidden in the text can serve as the basis for CBIR evaluation purpose. However, these works often consider these medical concepts as independent and isolated labels while in fact the subtle relationships between various concepts are neglected. In this work, we introduce the use of knowledge graphs to measure the distance between various medical concepts and propose a novel relevance measure for the evaluation of CBIR by defining an approximate matching-based relevance score between two sets of medical concepts which allows us to indirectly measure the similarity between medical this http URL quantitatively demonstrate the effectiveness and feasibility of our relevance measure using a public dataset.
zh
[CV-27] Multiview Geometric Regularization of Gaussian Splatting for Accurate Radiance Fields
【速读】：该论文旨在解决3D Gaussian Splatting在存在显著颜色变化的视角下难以重建平滑且可靠几何结构的问题，尽管其渲染质量优异。解决方案的关键在于提出一种多视角几何正则化策略，将多视角立体（MVS）深度、RGB和法线约束整合到Gaussian Splatting的初始化与优化过程中。该方法的核心见解是MVS生成的深度点与Gaussian Splatting优化位置之间的互补性：MVS通过局部块匹配和极线约束在高颜色变化区域稳健估计几何结构，而Gaussian Splatting在物体边界和低颜色变化区域提供更可靠、噪声更少的深度估计。为此，作者引入了基于中值深度的多视角相对深度损失，并结合不确定性估计，有效融合MVS深度信息至Gaussian Splatting优化中，同时提出了MVS引导的Gaussian Splatting初始化方法，以避免高斯分布陷入次优位置。
链接: https://arxiv.org/abs/2506.13508
作者: Jungeon Kim,Geonsoo Park,Seungyong Lee
机构: POSTECH(浦项科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to Computer Graphics Forum (EGSR 2025)
Abstract:Recent methods, such as 2D Gaussian Splatting and Gaussian Opacity Fields, have aimed to address the geometric inaccuracies of 3D Gaussian Splatting while retaining its superior rendering quality. However, these approaches still struggle to reconstruct smooth and reliable geometry, particularly in scenes with significant color variation across viewpoints, due to their per-point appearance modeling and single-view optimization constraints. In this paper, we propose an effective multiview geometric regularization strategy that integrates multiview stereo (MVS) depth, RGB, and normal constraints into Gaussian Splatting initialization and optimization. Our key insight is the complementary relationship between MVS-derived depth points and Gaussian Splatting-optimized positions: MVS robustly estimates geometry in regions of high color variation through local patch-based matching and epipolar constraints, whereas Gaussian Splatting provides more reliable and less noisy depth estimates near object boundaries and regions with lower color variation. To leverage this insight, we introduce a median depth-based multiview relative depth loss with uncertainty estimation, effectively integrating MVS depth information into Gaussian Splatting optimization. We also propose an MVS-guided Gaussian Splatting initialization to avoid Gaussians falling into suboptimal positions. Extensive experiments validate that our approach successfully combines these strengths, enhancing both geometric accuracy and rendering quality across diverse indoor and outdoor scenes.
zh
[CV-28] Stimulus Motion Perception Studies Imply Specific Neural Computations in Human Visual Stabilization
【速读】：该论文试图解决人类视觉系统如何在眼球持续微小运动的情况下仍能感知外界物体稳定的问题，即视觉稳定性的机制。研究通过一系列实验揭示了视觉稳定化的心理物理学特性，表明其比相机图像稳定化或进化视角下的简单解决方案更为复杂。解决方案的关键在于对视网膜信号进行特定的操作，从而实现观察到的稳定行为，这一过程可能涉及功能性的机制描述以及可能实现该功能的神经电路层面的推测。
链接: https://arxiv.org/abs/2506.13506
作者: David W Arathorn,Josephine C. D’Angelo,Austin Roorda
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Neurons and Cognition (q-bio.NC)
备注:
Abstract:Even during fixation the human eye is constantly in low amplitude motion, jittering over small angles in random directions at up to 100Hz. This motion results in all features of the image on the retina constantly traversing a number of cones, yet objects which are stable in the world are perceived to be stable, and any object which is moving in the world is perceived to be moving. A series of experiments carried out over a dozen years revealed the psychophysics of visual stabilization to be more nuanced than might be assumed, say, from the mechanics of stabilization of camera images, or what might be assumed to be the simplest solution from an evolutionary perspective. The psychophysics revealed by the experiments strongly implies a specific set of operations on retinal signals resulting in the observed stabilization behavior. The presentation is in two levels. First is a functional description of the action of the mechanism that is very likely responsible for the experimentally observed behavior. Second is a more speculative proposal of circuit-level neural elements that might implement the functional behavior.
zh
[CV-29] FOAM: A General Frequency-Optimized Anti-Overlapping Framework for Overlapping Object Perception
【速读】：该论文旨在解决重叠目标感知（overlapping object perception）问题，即在复杂背景下准确分离并提取前景目标特征，同时抑制背景干扰，该问题在安全检查和医学辅助诊断等领域具有重要应用价值。其解决方案的关键在于提出一种通用的频域优化反重叠框架（Frequency-Optimized Anti-Overlapping Framework, FOAM），通过频率域分析揭示重叠现象对轮廓和纹理信息的破坏，并设计了频率空间变换块（Frequency Spatial Transformer Block, FSTB）以同时从频率域和空间域提取特征，增强模型对纹理和轮廓信息的捕捉能力；此外，引入分层去扰机制（Hierarchical De-Corrupting, HDC）通过一致性损失对齐基础分支与扰动分支的特征，从而抑制FSTB对无关背景特征的响应，提升前景轮廓的感知能力。
链接: https://arxiv.org/abs/2506.13501
作者: Mingyuan Li,Tong Jia,Han Gu,Hui Lu,Hao Wang,Bowen Ma,Shuyang Lin,Shiyi Guo,Shizhuo Deng,Dongyue Chen
机构: Northeastern University (东北大学); State Key Laboratory of Synthetical Automation for Process Industries (过程工业综合自动化国家重点实验室); College of Information Science and Engineering (信息科学与工程学院); Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education (教育部智能制造数据挖掘与优化重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Overlapping object perception aims to decouple the randomly overlapping foreground-background features, extracting foreground features while suppressing background features, which holds significant application value in fields such as security screening and medical auxiliary diagnosis. Despite some research efforts to tackle the challenge of overlapping object perception, most solutions are confined to the spatial domain. Through frequency domain analysis, we observe that the degradation of contours and textures due to the overlapping phenomenon can be intuitively reflected in the magnitude spectrum. Based on this observation, we propose a general Frequency-Optimized Anti-Overlapping Framework (FOAM) to assist the model in extracting more texture and contour information, thereby enhancing the ability for anti-overlapping object perception. Specifically, we design the Frequency Spatial Transformer Block (FSTB), which can simultaneously extract features from both the frequency and spatial domains, helping the network capture more texture features from the foreground. In addition, we introduce the Hierarchical De-Corrupting (HDC) mechanism, which aligns adjacent features in the separately constructed base branch and corruption branch using a specially designed consistent loss during the training phase. This mechanism suppresses the response to irrelevant background features of FSTBs, thereby improving the perception of foreground contour. We conduct extensive experiments to validate the effectiveness and generalization of the proposed FOAM, which further improves the accuracy of state-of-the-art models on four datasets, specifically for the three overlapping object perception tasks: Prohibited Item Detection, Prohibited Item Segmentation, and Pneumonia Detection. The code will be open source once the paper is accepted.
zh
[CV-30] Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval SIGIR2025
【速读】：该论文旨在解决专利图像检索中的挑战，特别是由于专利图像的技术复杂性和语义信息的复杂性所带来的问题，同时忽略了专利之间的层次关系，如由Locarno国际分类（LIC）系统定义的关系。解决方案的关键在于引入一种层次化的多正例对比损失函数，该函数利用LIC的分类体系在检索过程中引入层次关系，通过为每个专利图像在批次内分配多个正例对，并根据层次分类体系赋予不同的相似性评分，从而提升检索效果。
链接: https://arxiv.org/abs/2506.13496
作者: Kshitij Kavimandan,Angelos Nalmpantis,Emma Beauxis-Aussalet,Robert-Jan Sips
机构: Vrije Universiteit Amsterdam (阿姆斯特丹自由大学); TKH AI (TKH AI)
类目: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: 5 pages, 3 figures, Accepted as a short paper at the 6th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech 2025), co-located with SIGIR 2025
Abstract:Patent images are technical drawings that convey information about a patent’s innovation. Patent image retrieval systems aim to search in vast collections and retrieve the most relevant images. Despite recent advances in information retrieval, patent images still pose significant challenges due to their technical intricacies and complex semantic information, requiring efficient fine-tuning for domain adaptation. Current methods neglect patents’ hierarchical relationships, such as those defined by the Locarno International Classification (LIC) system, which groups broad categories (e.g., “furnishing”) into subclasses (e.g., “seats” and “beds”) and further into specific patent designs. In this work, we introduce a hierarchical multi-positive contrastive loss that leverages the LIC’s taxonomy to induce such relations in the retrieval process. Our approach assigns multiple positive pairs to each patent image within a batch, with varying similarity scores based on the hierarchical taxonomy. Our experimental analysis with various vision and multimodal models on the DeepPatent2 dataset shows that the proposed method enhances the retrieval results. Notably, our method is effective with low-parameter models, which require fewer computational resources and can be deployed on environments with limited hardware.
zh
[CV-31] GeoSDF: Plane Geometry Diagram Synthesis via Signed Distance Field
【速读】：该论文旨在解决平面几何图示合成中的效率与准确性问题，传统方法依赖人工操作和复杂计算，而现有基于学习的方法虽能自动生成但存在现实感不足和精度不够的问题。解决方案的关键在于提出一种名为GeoSDF的新框架，该框架利用符号语言表示几何元素和约束，并通过优化约束函数构建带有符号距离场（SDF）的几何图示，从而实现高效且准确的自动合成，同时支持自我验证以确保数学准确性和视觉合理性。
链接: https://arxiv.org/abs/2506.13492
作者: Chengrui Zhang,Maizhen Ning,Zihao Zhou,Jie Sun,Kaizhu Huang,Qiufeng Wang
机构: Xi’an Jiaotong-Liverpool University (西安交通大学利物浦大学); Duke Kunshan University (杜克昆山大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Plane Geometry Diagram Synthesis has been a crucial task in computer graphics, with applications ranging from educational tools to AI-driven mathematical reasoning. Traditionally, we rely on computer tools (e.g., Matplotlib and GeoGebra) to manually generate precise diagrams, but it usually requires huge, complicated calculations cost. Recently, researchers start to work on learning-based methods (e.g., Stable Diffusion and GPT4) to automatically generate diagrams, saving operational cost but usually suffering from limited realism and insufficient accuracy. In this paper, we propose a novel framework GeoSDF to automatically generate diagrams efficiently and accurately with Signed Distance Field (SDF). Specifically, we first represent geometric elements in the SDF, then construct a series of constraint functions to represent geometric relationships, next we optimize such constraint functions to get an optimized field of both elements and constraints, finally by rendering the optimized field, we can obtain the synthesized diagram. In our GeoSDF, we define a symbolic language to easily represent geometric elements and those constraints, and our synthesized geometry diagrams can be self-verified in the SDF, ensuring both mathematical accuracy and visual plausibility. In experiments, our GeoSDF synthesized both normal high-school level and IMO-level geometry diagrams. Through both qualitative and quantitative analysis, we can see that synthesized diagrams are realistic and accurate, and our synthesizing process is simple and efficient. Furthermore, we obtain a very high accuracy of solving geometry problems (over 95% while the current SOTA accuracy is around 75%) by leveraging our self-verification property. All of these demonstrate the advantage of GeoSDF, paving the way for more sophisticated, accurate, and flexible generation of geometric diagrams for a wide array of applications.
zh
[CV-32] Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis CVPR
【速读】：该论文试图解决从高光谱图像生成逼真丰度图的问题，旨在提升合成丰度图的真实性和多样性。解决方案的关键在于将盲线性高光谱解混与先进的扩散模型相结合，通过盲解混直接从原始高光谱数据中提取端元和丰度图，并将其作为输入送入扩散模型以生成高度逼真的空间分布，从而实现对高光谱传感器输出的模拟。
链接: https://arxiv.org/abs/2506.13484
作者: Martina Pastorino,Michael Alibani,Nicola Acito,Gabriele Moser
机构: DITEN, University of Genoa(迪滕，热那亚大学); DII, University of Pisa(迪伊，比萨大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: CVPRw2025
Abstract:This paper presents a novel methodology for generating realistic abundance maps from hyperspectral imagery using an unsupervised, deep-learning-driven approach. Our framework integrates blind linear hyperspectral unmixing with state-of-the-art diffusion models to enhance the realism and diversity of synthetic abundance maps. First, we apply blind unmixing to extract endmembers and abundance maps directly from raw hyperspectral data. These abundance maps then serve as inputs to a diffusion model, which acts as a generative engine to synthesize highly realistic spatial distributions. Diffusion models have recently revolutionized image synthesis by offering superior performance, flexibility, and stability, making them well-suited for high-dimensional spectral data. By leveraging this combination of physically interpretable unmixing and deep generative modeling, our approach enables the simulation of hyperspectral sensor outputs under diverse imaging conditions–critical for data augmentation, algorithm benchmarking, and model evaluation in hyperspectral analysis. Notably, our method is entirely unsupervised, ensuring adaptability to different datasets without the need for labeled training data. We validate our approach using real hyperspectral imagery from the PRISMA space mission for Earth observation, demonstrating its effectiveness in producing realistic synthetic abundance maps that capture the spatial and spectral characteristics of natural scenes.
zh
[CV-33] From Flat to Feeling: A Feasibility and Impact Study on Dynamic Facial Emotions in AI-Generated Avatars
【速读】：该论文旨在解决动态面部情绪表达在AI生成虚拟角色中的不足问题，以提升其在高风险模拟场景（如儿童受虐调查访谈的虚拟训练）中的实用性。其解决方案的关键在于融合Unreal Engine 5 MetaHuman渲染与NVIDIA Omniverse Audio2Face技术，实现实时将语音韵律转化为高保真面部表情，同时采用分布式双电脑架构以降低延迟并支持桌面和VR环境下的交互。
链接: https://arxiv.org/abs/2506.13477
作者: Pegah Salehi,Sajad Amouei Sheshkal,Vajira Thambawita,Pål Halvorsen
机构: SimulaMet (SimulaMet)
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 4 figures, 4 tables
Abstract:Dynamic facial emotion is essential for believable AI-generated avatars; however, most systems remain visually inert, limiting their utility in high-stakes simulations such as virtual training for investigative interviews with abused children. We introduce and evaluate a real-time architecture fusing Unreal Engine 5 MetaHuman rendering with NVIDIA Omniverse Audio2Face to translate vocal prosody into high-fidelity facial expressions on photorealistic child avatars. We implemented a distributed two-PC setup that decouples language processing and speech synthesis from GPU-intensive rendering, designed to support low-latency interaction in desktop and VR environments. A between-subjects study ( N=70 ) using audio+visual and visual-only conditions assessed perceptual impacts as participants rated emotional clarity, facial realism, and empathy for two avatars expressing joy, sadness, and anger. Results demonstrate that avatars could express emotions recognizably, with sadness and joy achieving high identification rates. However, anger recognition significantly dropped without audio, highlighting the importance of congruent vocal cues for high-arousal emotions. Interestingly, removing audio boosted perceived facial realism, suggesting that audiovisual desynchrony remains a key design challenge. These findings confirm the technical feasibility of generating emotionally expressive avatars and provide guidance for improving non-verbal communication in sensitive training simulations. Comments: 15 pages, 4 figures, 4 tables Subjects: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV) MSC classes: 68T07, 68U99, 68T45, 91E45 Cite as: arXiv:2506.13477 [cs.HC] (or arXiv:2506.13477v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2506.13477 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[CV-34] ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection
【速读】：该论文旨在解决小规模印刷电路板（Printed Circuit Boards, PCBs）图像中缺陷检测的难题，尤其是在低分辨率图像下缺陷与噪声容易混淆的问题。其解决方案的关键在于提出了一种名为ESRPCB（edge-guided super-resolution for PCBs defect detection）的框架，该框架结合了边缘引导的超分辨率技术和集成学习方法，通过引入新颖的ResCat（Residual Concatenation）结构，利用边缘信息指导EDSR（Enhanced Deep Super-Resolution）模型，从而从低分辨率的小规模PCBs输入中重建出高分辨率图像，有效保留关键结构细节，确保微小缺陷在增强后的图像中仍可被识别。
链接: https://arxiv.org/abs/2506.13476
作者: Xiem HoangVan,Dang Bui Dinh,Thanh Nguyen Canh,Van-Truong Nguyen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
备注: Published in Engineering Applications of Artificial Intelligence
Abstract:Printed Circuit Boards (PCBs) are critical components in modern electronics, which require stringent quality control to ensure proper functionality. However, the detection of defects in small-scale PCBs images poses significant challenges as a result of the low resolution of the captured images, leading to potential confusion between defects and noise. To overcome these challenges, this paper proposes a novel framework, named ESRPCB (edgeguided super-resolution for PCBs defect detection), which combines edgeguided super-resolution with ensemble learning to enhance PCBs defect detection. The framework leverages the edge information to guide the EDSR (Enhanced Deep Super-Resolution) model with a novel ResCat (Residual Concatenation) structure, enabling it to reconstruct high-resolution images from small PCBs inputs. By incorporating edge features, the super-resolution process preserves critical structural details, ensuring that tiny defects remain distinguishable in the enhanced image. Following this, a multi-modal defect detection model employs ensemble learning to analyze the super-resolved
zh
[CV-35] SA-LUT: Spatial Adaptive 4D Look-Up Table for Photorealistic Style Transfer
【速读】：该论文旨在解决Photorealistic Style Transfer（PST）中风格保真度与内容完整性及效率之间的矛盾问题。现有方法要么依赖生成模型以牺牲内容结构和效率为代价保证风格一致性，要么采用全局色彩变换方法如LUT（Look-Up Table），虽能保持结构但缺乏局部适应性。解决方案的关键在于提出Spatial Adaptive 4D Look-Up Table（SA-LUT），其核心包括：(1) 一种基于风格引导的4D LUT生成器，通过从风格图像中提取多尺度特征来预测4D LUT；(2) 一种利用内容-风格交叉注意力机制生成上下文图的上下文生成器，从而实现空间自适应调整，使4D LUT在保持结构完整性的同时进行精确的色彩变换。
链接: https://arxiv.org/abs/2506.13465
作者: Zerui Gong,Zhonghua Wu,Qingyi Tao,Qinyue Li,Chen Change Loy
机构: S-Lab, Nanyang Technological University (S-Lab，南洋理工大学); SenseTime Research (商汤科技研究)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Photorealistic style transfer (PST) enables real-world color grading by adapting reference image colors while preserving content structure. Existing methods mainly follow either approaches: generation-based methods that prioritize stylistic fidelity at the cost of content integrity and efficiency, or global color transformation methods such as LUT, which preserve structure but lack local adaptability. To bridge this gap, we propose Spatial Adaptive 4D Look-Up Table (SA-LUT), combining LUT efficiency with neural network adaptability. SA-LUT features: (1) a Style-guided 4D LUT Generator that extracts multi-scale features from the style image to predict a 4D LUT, and (2) a Context Generator using content-style cross-attention to produce a context map. This context map enables spatially-adaptive adjustments, allowing our 4D LUT to apply precise color transformations while preserving structural integrity. To establish a rigorous evaluation framework for photorealistic style transfer, we introduce PST50, the first benchmark specifically designed for PST assessment. Experiments demonstrate that SA-LUT substantially outperforms state-of-the-art methods, achieving a 66.7% reduction in LPIPS score compared to 3D LUT approaches, while maintaining real-time performance at 16 FPS for video stylization. Our code and benchmark are available at this https URL
zh
[CV-36] Deep Learning-Based Multi-Object Tracking: A Comprehensive Survey from Foundations to State-of-the-Art
【速读】：该论文旨在系统分析基于深度学习的多目标跟踪（Multi-object Tracking, MOT）方法，解决如何有效检测视频帧中的目标并跨时间进行关联的问题。其解决方案的关键在于对跟踪-by-检测方法进行系统分类，将其划分为五类：联合检测与嵌入、启发式方法、基于运动的方法、亲和力学习以及离线方法，并进一步探讨端到端跟踪方法及其与传统方法的对比。研究还通过多个基准测试评估了最新跟踪器的性能，并特别关注其在不同领域中的泛化能力。
链接: https://arxiv.org/abs/2506.13457
作者: Momir Adžemović
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 39 pages
Abstract:Multi-object tracking (MOT) is a core task in computer vision that involves detecting objects in video frames and associating them across time. The rise of deep learning has significantly advanced MOT, particularly within the tracking-by-detection paradigm, which remains the dominant approach. Advancements in modern deep learning-based methods accelerated in 2022 with the introduction of ByteTrack for tracking-by-detection and MOTR for end-to-end tracking. Our survey provides an in-depth analysis of deep learning-based MOT methods, systematically categorizing tracking-by-detection approaches into five groups: joint detection and embedding, heuristic-based, motion-based, affinity learning, and offline methods. In addition, we examine end-to-end tracking methods and compare them with existing alternative approaches. We evaluate the performance of recent trackers across multiple benchmarks and specifically assess their generality by comparing results across different domains. Our findings indicate that heuristic-based methods achieve state-of-the-art results on densely populated datasets with linear object motion, while deep learning-based association methods, in both tracking-by-detection and end-to-end approaches, excel in scenarios with complex motion patterns.
zh
[CV-37] Overcoming Occlusions in the Wild: A Multi-Task Age Head Approach to Age Estimation
【速读】：该论文旨在解决在非受控真实场景（即“野生”场景）中，尤其是当面部部分被遮挡时，面部年龄估计仍然面临挑战的问题。其解决方案的关键在于提出一种结合生成对抗网络（GAN）和Transformer架构的新方法，其中SN-Patch GAN用于有效去除遮挡，而结合注意力残差卷积模块（ARCM）与Swin Transformer则增强了特征表示能力；此外，引入的多任务年龄头部（MTAH）通过融合回归与分布学习进一步提升了遮挡情况下的年龄估计性能。
链接: https://arxiv.org/abs/2506.13445
作者: Waqar Tanveer,Laura Fernández-Robles,Eduardo Fidalgo,Víctor González-Castro,Enrique Alegre
机构: Universidad de León(莱昂大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Facial age estimation has achieved considerable success under controlled conditions. However, in unconstrained real-world scenarios, which are often referred to as ‘in the wild’, age estimation remains challenging, especially when faces are partially occluded, which may obscure their visibility. To address this limitation, we propose a new approach integrating generative adversarial networks (GANs) and transformer architectures to enable robust age estimation from occluded faces. We employ an SN-Patch GAN to effectively remove occlusions, while an Attentive Residual Convolution Module (ARCM), paired with a Swin Transformer, enhances feature representation. Additionally, we introduce a Multi-Task Age Head (MTAH) that combines regression and distribution learning, further improving age estimation under occlusion. Experimental results on the FG-NET, UTKFace, and MORPH datasets demonstrate that our proposed approach surpasses existing state-of-the-art techniques for occluded facial age estimation by achieving an MAE of 3.00 , 4.54 , and 2.53 years, respectively.
zh
[CV-38] Self-Supervised Enhancement for Depth from a Lightweight ToF Sensor with Monocular Images IROS2025
【速读】：该论文旨在解决如何利用成对的高分辨率RGB图像提升轻量级飞行时间（Time-of-Flight, ToF）传感器获取的低分辨率深度图的问题。传统方法需要真实深度图作为监督信号，而该论文提出了一种自监督学习框架SelfToF，其关键在于通过引入低分辨率深度作为输入、设计新的深度一致性损失、提出尺度恢复模块，从而生成细节丰富且尺度感知的深度图。此外，为应对实际应用中ToF信号稀疏性的变化，进一步升级为SelfToF*，采用子流形卷积和引导特征融合，确保在不同稀疏性水平下仍保持鲁棒性能。
链接: https://arxiv.org/abs/2506.13444
作者: Laiyan Ding,Hualie Jiang,Jiwei Chen,Rui Huang
机构: The Chinese University of Hong Kong, Shenzhen (香港中文大学深圳校区); Insta360 Research (Insta360 研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted by IROS 2025
Abstract:Depth map enhancement using paired high-resolution RGB images offers a cost-effective solution for improving low-resolution depth data from lightweight ToF sensors. Nevertheless, naively adopting a depth estimation pipeline to fuse the two modalities requires groundtruth depth maps for supervision. To address this, we propose a self-supervised learning framework, SelfToF, which generates detailed and scale-aware depth maps. Starting from an image-based self-supervised depth estimation pipeline, we add low-resolution depth as inputs, design a new depth consistency loss, propose a scale-recovery module, and finally obtain a large performance boost. Furthermore, since the ToF signal sparsity varies in real-world applications, we upgrade SelfToF to SelfToF* with submanifold convolution and guided feature fusion. Consequently, SelfToF* maintain robust performance across varying sparsity levels in ToF data. Overall, our proposed method is both efficient and effective, as verified by extensive experiments on the NYU and ScanNet datasets. The code will be made public.
zh
[CV-39] Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection IJCNN2025
【速读】：该论文旨在解决在资源受限的边缘应用中高效处理稀疏事件数据以实现事件相机（event camera）驱动的目标检测问题。传统方法依赖于计算密集型的卷积循环单元，难以满足实时性和能效要求。其解决方案的关键在于提出了一种稀疏卷积循环学习机制，该机制在循环处理中实现了超过92%的激活稀疏性，从而大幅降低了对稀疏事件数据进行时空推理的计算成本。
链接: https://arxiv.org/abs/2506.13440
作者: Shenqi Wang,Yingfu Xu,Amirreza Yousefzadeh,Sherif Eissa,Henk Corporaal,Federico Corradi,Guangzhi Tang
机构: TU Delft (代尔夫特理工大学); imec (imec); University of Twente (特文特大学); TU Eindhoven (埃因霍温理工大学); Maastricht University (马斯特里赫特大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
备注: Accepted by IJCNN 2025
Abstract:Leveraging the high temporal resolution and dynamic range, object detection with event cameras can enhance the performance and safety of automotive and robotics applications in real-world scenarios. However, processing sparse event data requires compute-intensive convolutional recurrent units, complicating their integration into resource-constrained edge applications. Here, we propose the Sparse Event-based Efficient Detector (SEED) for efficient event-based object detection on neuromorphic processors. We introduce sparse convolutional recurrent learning, which achieves over 92% activation sparsity in recurrent processing, vastly reducing the cost for spatiotemporal reasoning on sparse event data. We validated our method on Prophesee’s 1 Mpx and Gen1 event-based object detection datasets. Notably, SEED sets a new benchmark in computational efficiency for event-based object detection which requires long-term temporal learning. Compared to state-of-the-art methods, SEED significantly reduces synaptic operations while delivering higher or same-level mAP. Our hardware simulations showcase the critical role of SEED’s hardware-aware design in achieving energy-efficient and low-latency neuromorphic processing.
zh
[CV-40] Uncertainty-Aware Remaining Lifespan Prediction from Images
【速读】：该论文试图解决从医学影像中预测与死亡相关的预后问题，旨在实现可访问、非侵入性和可扩展的健康筛查。其解决方案的关键在于利用预训练的视觉Transformer基础模型，从面部和全身图像中估计剩余寿命，并通过学习每个样本的高斯分布来有效建模预测不确定性。该方法在多个数据集上取得了当前最优的平均绝对误差（MAE），并提供了校准良好的不确定性估计，从而展示了从图像中提取医学相关信号的潜力。
链接: https://arxiv.org/abs/2506.13430
作者: Tristan Kenneweg,Philip Kenneweg,Barbara Hammer
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Submitted to IMPACT 2025
Abstract:Predicting mortality-related outcomes from images offers the prospect of accessible, noninvasive, and scalable health screening. We present a method that leverages pretrained vision transformer foundation models to estimate remaining lifespan from facial and whole-body images, alongside robust uncertainty quantification. We show that predictive uncertainty varies systematically with the true remaining lifespan, and that this uncertainty can be effectively modeled by learning a Gaussian distribution for each sample. Our approach achieves state-of-the-art mean absolute error (MAE) of 7.48 years on an established Dataset, and further improves to 4.79 and 5.07 years MAE on two new, higher-quality datasets curated and published in this work. Importantly, our models provide well-calibrated uncertainty estimates, as demonstrated by a bucketed expected calibration error of 0.62 years. While not intended for clinical deployment, these results highlight the potential of extracting medically relevant signals from images. We make all code and datasets available to facilitate further research.
zh
[CV-41] JENGA: Object selection and pose estimation for robotic grasping from a stack
【速读】：该论文旨在解决在结构化物体排列（如堆叠）场景下，机器人选择合适物体进行抓取并准确估计其6自由度（6DoF）位姿的问题。解决方案的关键在于提出一种基于相机-惯性测量单元（IMU）的方法，该方法优先选择堆叠高层中未被遮挡的物体，并构建了一个用于基准测试和评估的数据集，以及一个结合物体选择与位姿精度的评价指标。
链接: https://arxiv.org/abs/2506.13425
作者: Sai Srinivas Jeevanandam,Sandeep Inuganti,Shreedhar Govil,Didier Stricker,Jason Rambach
机构: German Research Center for Artificial Intelligence (DFKI); RPTU Kaiserslautern
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-based robotic object grasping is typically investigated in the context of isolated objects or unstructured object sets in bin picking scenarios. However, there are several settings, such as construction or warehouse automation, where a robot needs to interact with a structured object formation such as a stack. In this context, we define the problem of selecting suitable objects for grasping along with estimating an accurate 6DoF pose of these objects. To address this problem, we propose a camera-IMU based approach that prioritizes unobstructed objects on the higher layers of stacks and introduce a dataset for benchmarking and evaluation, along with a suitable evaluation metric that combines object selection with pose accuracy. Experimental results show that although our method can perform quite well, this is a challenging problem if a completely error-free solution is needed. Finally, we show results from the deployment of our method for a brick-picking application in a construction scenario.
zh
[CV-42] Zero-Shot Solving of Imaging Inverse Problems via Noise-Refined Likelihood Guided Diffusion Models
【速读】：该论文旨在解决扩散模型在成像逆问题中因依赖特定退化类型训练而导致的泛化能力受限的问题。其解决方案的关键在于提出一种零样本框架，通过引入基于似然的噪声精炼机制，推导出似然梯度的闭式近似，从而简化梯度估计并避免昂贵的梯度计算，进而提升重建过程与扩散模型生成框架的一致性。此外，该方法结合了去噪扩散隐式模型（DDIM）采样策略以提高推理效率，并适用于基于优化和基于采样的方案，为成像逆问题提供了一种高效且灵活的零样本解决方案。
链接: https://arxiv.org/abs/2506.13391
作者: Zhen Wang,Hongyi Liu,Zhihui Wei
机构: Nanjing University of Science and Technology (南京理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:Diffusion models have achieved remarkable success in imaging inverse problems owing to their powerful generative capabilities. However, existing approaches typically rely on models trained for specific degradation types, limiting their generalizability to various degradation scenarios. To address this limitation, we propose a zero-shot framework capable of handling various imaging inverse problems without model retraining. We introduce a likelihood-guided noise refinement mechanism that derives a closed-form approximation of the likelihood score, simplifying score estimation and avoiding expensive gradient computations. This estimated score is subsequently utilized to refine the model-predicted noise, thereby better aligning the restoration process with the generative framework of diffusion models. In addition, we integrate the Denoising Diffusion Implicit Models (DDIM) sampling strategy to further improve inference efficiency. The proposed mechanism can be applied to both optimization-based and sampling-based schemes, providing an effective and flexible zero-shot solution for imaging inverse problems. Extensive experiments demonstrate that our method achieves superior performance across multiple inverse problems, particularly in compressive sensing, delivering high-quality reconstructions even at an extremely low sampling rate (5%).
zh
[CV-43] R2M: Transferring Monocular Relative Depth to Metric Depth with Language Descriptions and Scale-Oriented Contrast
【速读】：该论文旨在解决单目深度估计中相对深度（Relative Depth）向度量深度（Metric Depth）转换时的尺度不确定性问题。当前方法在度量深度估计（MMDE）中虽能提供精确的深度值，但泛化能力受限；而相对深度估计（MRDE）虽具有良好的跨域泛化性，但缺乏明确的尺度信息，限制了其下游应用。该论文提出的解决方案TR2M的关键在于利用文本描述和图像作为输入，估计两个重缩放图（rescale maps），以像素级方式将相对深度转换为度量深度，并通过跨模态注意力模块融合多模态特征以更好地捕捉尺度信息，同时设计了构建和筛选可信伪度量深度的策略以及面向尺度的对比学习方法，以增强模型对尺度分布内在知识的学习能力。
链接: https://arxiv.org/abs/2506.13387
作者: Beilei Cui,Yiming Huang,Long Bai,Hongliang Ren
机构: The Chinese University of Hong Kong (中国香港大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This work presents a generalizable framework to transfer relative depth to metric depth. Current monocular depth estimation methods are mainly divided into metric depth estimation (MMDE) and relative depth estimation (MRDE). MMDEs estimate depth in metric scale but are often limited to a specific domain. MRDEs generalize well across different domains, but with uncertain scales which hinders downstream applications. To this end, we aim to build up a framework to solve scale uncertainty and transfer relative depth to metric depth. Previous methods used language as input and estimated two factors for conducting rescaling. Our approach, TR2M, utilizes both text description and image as inputs and estimates two rescale maps to transfer relative depth to metric depth at pixel level. Features from two modalities are fused with a cross-modality attention module to better capture scale information. A strategy is designed to construct and filter confident pseudo metric depth for more comprehensive supervision. We also develop scale-oriented contrastive learning to utilize depth distribution as guidance to enforce the model learning about intrinsic knowledge aligning with the scale distribution. TR2M only exploits a small number of trainable parameters to train on datasets in various domains and experiments not only demonstrate TR2M’s great performance in seen datasets but also reveal superior zero-shot capabilities on five unseen datasets. We show the huge potential in pixel-wise transferring relative depth to metric depth with language assistance. (Code is available at: this https URL)
zh
[CV-44] DicFace: Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration
【速读】：该论文旨在解决视频人脸修复中保持时间一致性的同时从退化输入中恢复精细面部细节的关键挑战。其解决方案的核心在于将预训练于高质量静态肖像的向量量化变分自编码器（VQ-VAEs）扩展为视频修复框架，通过变分潜在空间建模实现。关键创新点是将离散代码本表示重新构造成服从狄利克雷分布的连续变量，从而实现跨帧的面部特征概率过渡，并结合时空Transformer架构联合建模帧间依赖关系与预测潜在分布，同时采用拉普拉斯约束重建损失与感知（LPIPS）正则化提升像素精度和视觉质量。
链接: https://arxiv.org/abs/2506.13355
作者: Yan Chen,Hanlin Shang,Ce Liu,Yuxuan Chen,Hui Li,Weihao Yuan,Hao Zhu,Zilong Dong,Siyu Zhu
机构: Fudan University (复旦大学); Alibaba Group (阿里巴巴集团); Nanjing University (南京大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video face restoration faces a critical challenge in maintaining temporal consistency while recovering fine facial details from degraded inputs. This paper presents a novel approach that extends Vector-Quantized Variational Autoencoders (VQ-VAEs), pretrained on static high-quality portraits, into a video restoration framework through variational latent space modeling. Our key innovation lies in reformulating discrete codebook representations as Dirichlet-distributed continuous variables, enabling probabilistic transitions between facial features across frames. A spatio-temporal Transformer architecture jointly models inter-frame dependencies and predicts latent distributions, while a Laplacian-constrained reconstruction loss combined with perceptual (LPIPS) regularization enhances both pixel accuracy and visual quality. Comprehensive evaluations on blind face restoration, video inpainting, and facial colorization tasks demonstrate state-of-the-art performance. This work establishes an effective paradigm for adapting intensive image priors, pretrained on high-quality images, to video restoration while addressing the critical challenge of flicker artifacts. The source code has been open-sourced and is available at this https URL.
zh
[CV-45] xtureSplat: Per-Primitive Texture Mapping for Reflective Gaussian Splatting
【速读】：该论文试图解决在复杂捕获场景中基于优化的逆向渲染问题，特别是高反射场景中建模复杂的表面光相互作用所导致的高频镜面辐射成分带来的挑战。解决方案的关键在于提出一种基于几何和物理原理的高斯点云（Gaussian Splatting）辐射场方法，其中每个基元的法线和材质属性在其局部空间中是空间变化的，并通过每个基元的纹理贴图实现，同时利用GPU硬件在推理阶段通过统一材质纹理图集加速渲染。
链接: https://arxiv.org/abs/2506.13348
作者: Mae Younes,Adnane Boukhayma
机构: INRIA(法国国家信息与自动化研究所)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: Code will be available at this https URL
Abstract:Gaussian Splatting have demonstrated remarkable novel view synthesis performance at high rendering frame rates. Optimization-based inverse rendering within complex capture scenarios remains however a challenging problem. A particular case is modelling complex surface light interactions for highly reflective scenes, which results in intricate high frequency specular radiance components. We hypothesize that such challenging settings can benefit from increased representation power. We hence propose a method that tackles this issue through a geometrically and physically grounded Gaussian Splatting borne radiance field, where normals and material properties are spatially variable in the primitive’s local space. Using per-primitive texture maps for this purpose, we also propose to harness the GPU hardware to accelerate rendering at test time via unified material texture atlas.
zh
[CV-46] Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders
【速读】：该论文试图解决葡萄品种识别中传统方法（如植物形态学和分子分析）存在的主观性强、成本高及耗时等问题，以及现有基于深度学习的方法因数据集规模小而依赖跨领域迁移学习所导致的性能下降问题。其解决方案的关键在于采用自监督学习（SSL）方法中的掩码自编码器（MAE），通过无标签数据进行预训练，从而避免因领域迁移和监督崩溃带来的性能损失。研究验证了基于ViT-B/16架构的MAE预训练模型在葡萄品种识别任务中的有效性，并揭示了长周期预训练、低数据量训练下的良好表现及简单数据增强策略的优势。
链接: https://arxiv.org/abs/2506.13335
作者: Gabriel A. Carneiro,Thierry J. Aubry,António Cunha,Petia Radeva,Joaquim Sousa
机构: University of Trás-os-Montes and Alto Douro (特拉什奥斯蒙特斯和杜罗河大学); Côa Parque, Fundação para a Salvaguarda e Valorização do Vale do Côa (科阿公园，科阿河流域保护与价值基金会); Universitat de Barcelona (巴塞罗那大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Grapevine varieties are essential for the economies of many wine-producing countries, influencing the production of wine, juice, and the consumption of fruits and leaves. Traditional identification methods, such as ampelography and molecular analysis, have limitations: ampelography depends on expert knowledge and is inherently subjective, while molecular methods are costly and time-intensive. To address these limitations, recent studies have applied deep learning (DL) models to classify grapevine varieties using image data. However, due to the small dataset sizes, these methods often depend on transfer learning from datasets from other domains, e.g., ImageNet1K (IN1K), which can lead to performance degradation due to domain shift and supervision collapse. In this context, self-supervised learning (SSL) methods can be a good tool to avoid this performance degradation, since they can learn directly from data, without external labels. This study presents an evaluation of Masked Autoencoders (MAEs) for identifying grapevine varieties based on field-acquired images. The main contributions of this study include two benchmarks comprising 43 grapevine varieties collected across different seasons, an analysis of MAE’s application in the agricultural context, and a performance comparison of trained models across seasons. Our results show that a ViT-B/16 model pre-trained with MAE and the unlabeled dataset achieved an F1 score of 0.7956, outperforming all other models. Additionally, we observed that pre-trained models benefit from long pre-training, perform well under low-data training regime, and that simple data augmentation methods are more effective than complex ones. The study also found that the mask ratio in MAE impacts performance only marginally.
zh
[CV-47] Joint Analysis of Optical and SAR Vegetation Indices for Vineyard Monitoring: Assessing Biomass Dynamics and Phenological Stages over Po Valley Italy
【速读】：该论文旨在解决如何利用多极化合成孔径雷达（Multi-polarized SAR）技术与光学植被指数相结合，以更准确地表征葡萄园作物的植被动态问题。其解决方案的关键在于首次将双极化雷达植被指数（DpRVI）与光学指数进行综合分析，揭示两者在信息上的互补性，并证明DpRVI能够更直接地反映生物量增长及特定物候阶段，从而提升对葡萄园的遥感监测能力。
链接: https://arxiv.org/abs/2506.13327
作者: Andrea Bergamaschi,Abhinav Verma,Avik Bhattacharya,Fabio Dell’Acqua
机构: University of Pavia (帕维亚大学); Indian Institute of Technology Bombay (印度理工学院孟买分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-polarized Synthetic Aperture Radar (SAR) technology has gained increasing attention in agriculture, offering unique capabilities for monitoring vegetation dynamics thanks to its all-weather, day-and-night operation and high revisit frequency. This study presents, for the first time, a comprehensive analysis combining dual-polarimetric radar vegetation index (DpRVI) with optical indices to characterize vineyard crops. Vineyards exhibit distinct non-isotropic scattering behavior due to their pronounced row orientation, making them particularly challenging and interesting targets for remote sensing. The research further investigates the relationship between DpRVI and optical vegetation indices, demonstrating the complementary nature of their information. We demonstrate that DpRVI and optical indices provide complementary information, with low correlation suggesting that they capture distinct vineyard features. Key findings reveal a parabolic trend in DpRVI over the growing season, potentially linked to biomass dynamics estimated via the Winkler Index. Unlike optical indices reflecting vegetation greenness, DpRVI appears more directly related to biomass growth, aligning with specific phenological phases. Preliminary results also highlight the potential of DpRVI for distinguishing vineyards from other crops. This research aligns with the objectives of the PNRR-NODES project, which promotes nature-based solutions (NbS) for sustainable vineyard management. The application of DpRVI for monitoring vineyards is part of integrating remote sensing techniques into the broader field of strategies for climate-related change adaptation and risk reduction, emphasizing the role of innovative SAR-based monitoring in sustainable agriculture.
zh
[CV-48] VIS-Shepherd: Constructing Critic for LLM -based Data Visualization Generation
【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）生成的数据可视化结果往往不够优化，需要人工干预进行改进的问题。其解决方案的关键在于引入VIS-Shepherd，一个基于多模态大语言模型（Multimodal Large Language Model, MLLM）的专门评论系统，用于评估和反馈LLM生成的数据可视化结果。该方法的核心是构建一个高质量的可视化评论数据集，通过收集人类创建的可视化实例、合成对应的LLM生成实例并构建高质量的评论，从而提升自动化评估的效果。实验表明，即使使用较小参数量（7B）的开源MLLM模型，也能通过该数据集获得显著性能提升，达到与更大规模的开源或专有模型相当的水平。
链接: https://arxiv.org/abs/2506.13326
作者: Bo Pan,Yixiao Fu,Ke Wang,Junyu Lu,Lunke Pan,Ziyang Qian,Yuhan Chen,Guoliang Wang,Yitao Zhou,Li Zheng,Yinghao Tang,Zhen Wen,Yuchen Wu,Junhua Lu,Biao Zhu,Minfeng Zhu,Bo Zhang,Wei Chen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注:
Abstract:Data visualization generation using Large Language Models (LLMs) has shown promising results but often produces suboptimal visualizations that require human intervention for improvement. In this work, we introduce VIS-Shepherd, a specialized Multimodal Large Language Model (MLLM)-based critic to evaluate and provide feedback for LLM-generated data visualizations. At the core of our approach is a framework to construct a high-quality visualization critique dataset, where we collect human-created visualization instances, synthesize corresponding LLM-generated instances, and construct high-quality critiques. We conduct both model-based automatic evaluation and human preference studies to evaluate the effectiveness of our approach. Our experiments show that even small (7B parameters) open-source MLLM models achieve substantial performance gains by leveraging our high-quality visualization critique dataset, reaching levels comparable to much larger open-source or even proprietary models. Our work demonstrates significant potential for MLLM-based automated visualization critique and indicates promising directions for enhancing LLM-based data visualization generation. Our project page: this https URL.
zh
[CV-49] Active Multimodal Distillation for Few-shot Action Recognition IJCAI2025
【速读】：该论文旨在解决少样本动作识别（few-shot action recognition）中现有方法主要依赖有限单模态数据、未能充分挖掘多模态信息潜力的问题。其解决方案的关键在于提出一种新颖的框架，通过任务特定的上下文线索主动识别每个样本的可靠模态，并整合主动样本推理（Active Sample Inference, ASI）模块与主动互蒸馏模块，利用后验分布预测可靠模态并进行知识迁移，从而提升识别性能。此外，在元测试阶段采用自适应多模态推理，对可靠模态赋予更高权重，进一步优化结果。
链接: https://arxiv.org/abs/2506.13322
作者: Weijia Feng,Yichen Zhu,Ruojia Zhang,Chenyang Wang,Fei Ma,Xiaobao Wang,Xiaobai Li
机构: Tianjin Normal University (天津师范大学); Shenzhen University (深圳大学); Guangdong Laboratory of Artificial Intelligence and Digital Economy (广东省人工智能与数字经济发展实验室); Tianjin University (天津大学); Zhejiang University (浙江大学); Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security (杭州滨江区区块链与数据安全研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: IJCAI 2025, the 34th International Joint Conference on Artificial Intelligence
Abstract:Owing to its rapid progress and broad application prospects, few-shot action recognition has attracted considerable interest. However, current methods are predominantly based on limited single-modal data, which does not fully exploit the potential of multimodal information. This paper presents a novel framework that actively identifies reliable modalities for each sample using task-specific contextual cues, thus significantly improving recognition performance. Our framework integrates an Active Sample Inference (ASI) module, which utilizes active inference to predict reliable modalities based on posterior distributions and subsequently organizes them accordingly. Unlike reinforcement learning, active inference replaces rewards with evidence-based preferences, making more stable predictions. Additionally, we introduce an active mutual distillation module that enhances the representation learning of less reliable modalities by transferring knowledge from more reliable ones. Adaptive multimodal inference is employed during the meta-test to assign higher weights to reliable modalities. Extensive experiments across multiple benchmarks demonstrate that our method significantly outperforms existing approaches.
zh
[CV-50] Action Dubber: Timing Audible Actions via Inflectional Flow ICML2025
【速读】：该论文试图解决可听动作的时空定位问题（Audible Action Temporal Localization），即识别产生声音的动作的时空坐标。与传统动作识别和时间动作定位任务不同，该任务关注的是可听动作特有的运动动力学特性。解决方案的关键在于提出一种名为 TA^2Net 的新架构，该架构通过运动的二阶导数估计突变流以确定碰撞时间，而无需依赖音频输入。此外，TA^2Net 在训练过程中集成了自监督的空间定位策略，结合对比学习与空间分析，从而提高时间定位精度并同时识别视频帧中的声音来源。
链接: https://arxiv.org/abs/2506.13320
作者: Wenlong Wan,Weiying Zheng,Tianyi Xiang,Guiqing Li,Shengfeng He
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted by ICML2025
Abstract:We introduce the task of Audible Action Temporal Localization, which aims to identify the spatio-temporal coordinates of audible movements. Unlike conventional tasks such as action recognition and temporal action localization, which broadly analyze video content, our task focuses on the distinct kinematic dynamics of audible actions. It is based on the premise that key actions are driven by inflectional movements; for example, collisions that produce sound often involve abrupt changes in motion. To capture this, we propose TA^2Net , a novel architecture that estimates inflectional flow using the second derivative of motion to determine collision timings without relying on audio input. TA^2Net also integrates a self-supervised spatial localization strategy during training, combining contrastive learning with spatial analysis. This dual design improves temporal localization accuracy and simultaneously identifies sound sources within video frames. To support this task, we introduce a new benchmark dataset, Audible623 , derived from Kinetics and UCF101 by removing non-essential vocalization subsets. Extensive experiments confirm the effectiveness of our approach on Audible623 and show strong generalizability to other domains, such as repetitive counting and sound source localization. Code and dataset are available at this https URL.
zh
[CV-51] Quantitative Comparison of Fine-Tuning Techniques for Pretrained Latent Diffusion Models in the Generation of Unseen SAR Image Concepts
【速读】：该论文试图解决将大型预训练潜在扩散模型适应到一种全新的成像领域——合成孔径雷达（Synthetic Aperture Radar, SAR）的问题。由于SAR数据具有不同的物理特性、统计分布和视觉特征，现有的生成模型在未经过调整的情况下无法有效表示此类数据。解决方案的关键在于探索并比较多种微调策略，包括全模型微调和参数高效的低秩适配（LoRA），分别针对扩散模型的UNet主干和文本编码器组件进行优化。通过结合统计距离、纹理相似性及语义对齐等多维度评估指标，研究发现混合微调策略表现最佳：全量微调UNet有助于捕捉SAR的低级特征，而基于LoRA的部分文本编码器微调与SAR标记嵌入学习相结合，能够有效保持提示对齐。
链接: https://arxiv.org/abs/2506.13307
作者: Solène Debuysère,Nicolas Trouvé,Nathan Letheule,Olivier Lévêque,Elise Colin
机构: ONERA(法国国家航空航天研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:This work investigates the adaptation of large pre-trained latent diffusion models to a radically new imaging domain: Synthetic Aperture Radar (SAR). While these generative models, originally trained on natural images, demonstrate impressive capabilities in text-to-image synthesis, they are not natively adapted to represent SAR data, which involves different physics, statistical distributions, and visual characteristics. Using a sizeable SAR dataset (on the order of 100,000 to 1 million images), we address the fundamental question of fine-tuning such models for this unseen modality. We explore and compare multiple fine-tuning strategies, including full model fine-tuning and parameter-efficient approaches like Low-Rank Adaptation (LoRA), focusing separately on the UNet diffusion backbone and the text encoder components. To evaluate generative quality, we combine several metrics: statistical distance from real SAR distributions, textural similarity via GLCM descriptors, and semantic alignment assessed with a CLIP model fine-tuned on SAR data. Our results show that a hybrid tuning strategy yields the best performance: full fine-tuning of the UNet is better at capturing low-level SAR-specific patterns, while LoRA-based partial tuning of the text encoder, combined with embedding learning of the SAR token, suffices to preserve prompt alignment. This work provides a methodical strategy for adapting foundation models to unconventional imaging modalities beyond natural image domains.
zh
[CV-52] AttentionDrag : Exploiting Latent Correlation Knowledge in Pre-trained Diffusion Models for Image Editing
【速读】：该论文旨在解决传统基于点的图像编辑方法在处理效率和语义关系捕捉方面的不足，这些方法通常依赖于迭代潜在优化或几何变换，导致处理效率低下或无法有效捕捉图像内的语义关联。其解决方案的关键在于提出一种名为AttentionDrag的新颖单步基于点的图像编辑方法，该方法利用预训练扩散模型中的内在潜在知识和特征相关性进行图像编辑，通过重新利用U-Net模块中自注意力机制在DDIM反演过程中学习到的潜在相关性知识，自动识别并调整相关图像区域，同时自适应生成掩码以引导编辑过程，从而实现语义一致性和高质量操作，无需大量重新优化或重训练。
链接: https://arxiv.org/abs/2506.13301
作者: Biao Yang,Muqi Huang,Yuhui Zhang,Yun Xiong,Kun Zhou,Xi Chen,Shiyang Zhou,Huishuai Bao,Chuan Li,Feng Shi,Hualei Liu
机构: Fudan University (复旦大学); Alibaba Group (阿里巴巴集团)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traditional point-based image editing methods rely on iterative latent optimization or geometric transformations, which are either inefficient in their processing or fail to capture the semantic relationships within the image. These methods often overlook the powerful yet underutilized image editing capabilities inherent in pre-trained diffusion models. In this work, we propose a novel one-step point-based image editing method, named AttentionDrag, which leverages the inherent latent knowledge and feature correlations within pre-trained diffusion models for image editing tasks. This framework enables semantic consistency and high-quality manipulation without the need for extensive re-optimization or retraining. Specifically, we reutilize the latent correlations knowledge learned by the self-attention mechanism in the U-Net module during the DDIM inversion process to automatically identify and adjust relevant image regions, ensuring semantic validity and consistency. Additionally, AttentionDrag adaptively generates masks to guide the editing process, enabling precise and context-aware modifications with friendly interaction. Our results demonstrate a performance that surpasses most state-of-the-art methods with significantly faster speeds, showing a more efficient and semantically coherent solution for point-based image editing tasks.
zh
[CV-53] Fair Generation without Unfair Distortions: Debiasing Text-to-Image Generation with Entanglement-Free Attention
【速读】：该论文试图解决扩散模型在文本到图像生成过程中存在的社会偏见问题，尤其是性别、种族和社会经济地位相关的偏见，这些问题可能导致有害刻板印象的强化和公众认知的偏差。解决方案的关键在于提出一种名为无纠缠注意力（Entanglement-Free Attention, EFA）的方法，该方法能够在减轻目标属性（如种族）偏见的同时，保持非目标属性（如背景细节）的稳定性，从而避免属性纠缠导致的分布偏移。EFA通过在推理阶段随机采样目标属性并调整选定层的交叉注意力机制，实现目标属性的公平分布，同时保留原始模型的输出分布和生成能力。
链接: https://arxiv.org/abs/2506.13298
作者: Jeonghoon Park,Juyoung Lee,Chaeyeon Chung,Jaeseong Lee,Jaegul Choo,Jindong Gu
机构: KAIST(韩国科学技术院); Kakao Corp.(韩国韩巢公司); Yonsei University(延世大学); University of Oxford(牛津大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advancements in diffusion-based text-to-image (T2I) models have enabled the generation of high-quality and photorealistic images from text descriptions. However, they often exhibit societal biases related to gender, race, and socioeconomic status, thereby reinforcing harmful stereotypes and shaping public perception in unintended ways. While existing bias mitigation methods demonstrate effectiveness, they often encounter attribute entanglement, where adjustments to attributes relevant to the bias (i.e., target attributes) unintentionally alter attributes unassociated with the bias (i.e., non-target attributes), causing undesirable distribution shifts. To address this challenge, we introduce Entanglement-Free Attention (EFA), a method that accurately incorporates target attributes (e.g., White, Black, Asian, and Indian) while preserving non-target attributes (e.g., background details) during bias mitigation. At inference time, EFA randomly samples a target attribute with equal probability and adjusts the cross-attention in selected layers to incorporate the sampled attribute, achieving a fair distribution of target attributes. Extensive experiments demonstrate that EFA outperforms existing methods in mitigating bias while preserving non-target attributes, thereby maintaining the output distribution and generation capability of the original model.
zh
[CV-54] Automatic Multi-View X-Ray/CT Registration Using Bone Substructure Contours
【速读】：该论文旨在解决骨科手术中术中X射线/CT配准的准确性、鲁棒性和自动化问题，现有方法在实现亚毫米级精度、应对广泛的初始位姿估计或需要手动关键点标注方面存在不足。解决方案的关键在于提出一种基于多视角轮廓的迭代最近点（ICP）优化方法，通过匹配与骨骼子结构对应的特定轮廓子类别，减少ICP匹配中的歧义性，从而提高配准的鲁棒性和准确性。该方法仅需两幅X射线图像，并且完全自动运行。
链接: https://arxiv.org/abs/2506.13292
作者: Roman Flepp,Leon Nissen,Bastian Sigrist,Arend Nieuwland,Nicola Cavalcanti,Philipp Fürnstahl,Thomas Dreher,Lilian Calvet
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: This paper was accepted to IPCAI 2025
Abstract:Purpose: Accurate intraoperative X-ray/CT registration is essential for surgical navigation in orthopedic procedures. However, existing methods struggle with consistently achieving sub-millimeter accuracy, robustness under broad initial pose estimates or need manual key-point annotations. This work aims to address these challenges by proposing a novel multi-view X-ray/CT registration method for intraoperative bone registration. Methods: The proposed registration method consists of a multi-view, contour-based iterative closest point (ICP) optimization. Unlike previous methods, which attempt to match bone contours across the entire silhouette in both imaging modalities, we focus on matching specific subcategories of contours corresponding to bone substructures. This leads to reduced ambiguity in the ICP matches, resulting in a more robust and accurate registration solution. This approach requires only two X-ray images and operates fully automatically. Additionally, we contribute a dataset of 5 cadaveric specimens, including real X-ray images, X-ray image poses and the corresponding CT scans. Results: The proposed registration method is evaluated on real X-ray images using mean reprojection error (mRPD). The method consistently achieves sub-millimeter accuracy with a mRPD 0.67mm compared to 5.35mm by a commercial solution requiring manual intervention. Furthermore, the method offers improved practical applicability, being fully automatic. Conclusion: Our method offers a practical, accurate, and efficient solution for multi-view X-ray/CT registration in orthopedic surgeries, which can be easily combined with tracking systems. By improving registration accuracy and minimizing manual intervention, it enhances intraoperative navigation, contributing to more accurate and effective surgical outcomes in computer-assisted surgery (CAS).
zh
[CV-55] Anomaly Object Segmentation with Vision-Language Models for Steel Scrap Recycling
【速读】：该论文试图解决钢废料回收过程中杂质混入的问题，这一问题会降低回收钢的质量并增加碳排放。解决方案的关键在于采用基于视觉-语言模型的异常检测方法，通过监督微调策略，使模型能够有效识别钢废料中的细粒度异常对象。具体而言，该方法对图像编码器进行微调，该编码器具备多尺度机制，并使用与正常和异常图像对齐的文本提示，训练过程以多类分类作为监督信号。
链接: https://arxiv.org/abs/2506.13282
作者: Daichi Tanaka,Takumi Karasawa,Shu Takenouchi,Rei Kawakami
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recycling steel scrap can reduce carbon dioxide (CO2) emissions from the steel industry. However, a significant challenge in steel scrap recycling is the inclusion of impurities other than steel. To address this issue, we propose vision-language-model-based anomaly detection where a model is finetuned in a supervised manner, enabling it to handle niche objects effectively. This model enables automated detection of anomalies at a fine-grained level within steel scrap. Specifically, we finetune the image encoder, equipped with multi-scale mechanism and text prompts aligned with both normal and anomaly images. The finetuning process trains these modules using a multiclass classification as the supervision.
zh
[CV-56] Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learning
【速读】：该论文试图解决自动驾驶车辆在开放世界环境中遇到未见过的物体类别时，现有基于LiDAR的全景分割模型因依赖封闭集假设而无法检测未知实例的问题。解决方案的关键在于提出ULOPS框架，该框架通过基于狄利克雷证据学习的不确定性引导方法建模预测不确定性，并引入三个不确定性驱动的损失函数以增强模型区分已知与未知物体的能力。
链接: https://arxiv.org/abs/2506.13265
作者: Rohit Mohan,Julia Hindel,Florian Drews,Claudius Gläser,Daniele Cattaneo,Abhinav Valada
机构: Bosch Research, Robert Bosch GmbH(博世研究，罗伯特·博世有限公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
备注:
Abstract:Autonomous vehicles that navigate in open-world environments may encounter previously unseen object classes. However, most existing LiDAR panoptic segmentation models rely on closed-set assumptions, failing to detect unknown object instances. In this work, we propose ULOPS, an uncertainty-guided open-set panoptic segmentation framework that leverages Dirichlet-based evidential learning to model predictive uncertainty. Our architecture incorporates separate decoders for semantic segmentation with uncertainty estimation, embedding with prototype association, and instance center prediction. During inference, we leverage uncertainty estimates to identify and segment unknown instances. To strengthen the model’s ability to differentiate between known and unknown objects, we introduce three uncertainty-driven loss functions. Uniform Evidence Loss to encourage high uncertainty in unknown regions. Adaptive Uncertainty Separation Loss ensures a consistent difference in uncertainty estimates between known and unknown objects at a global scale. Contrastive Uncertainty Loss refines this separation at the fine-grained level. To evaluate open-set performance, we extend benchmark settings on KITTI-360 and introduce a new open-set evaluation for nuScenes. Extensive experiments demonstrate that ULOPS consistently outperforms existing open-set LiDAR panoptic segmentation methods.
zh
[CV-57] COME: Adding Scene-Centric Forecasting Control to Occupancy World Model
【速读】：该论文旨在解决自主驾驶中世界模型在模拟环境动态和生成合成数据时，难以将自车运动（视角变化）与场景演变（智能体交互）解耦的问题，从而导致预测效果不理想。其解决方案的关键在于通过引入以场景为中心的坐标系，将环境变化与自车运动分离，具体而言是提出COME框架，该框架通过场景中心预测分支生成与自车无关且空间一致的未来特征，并利用定制化的ControlNet将其转换为场景条件特征，最终注入占用世界模型以实现更准确和可控的未来占用预测。
链接: https://arxiv.org/abs/2506.13260
作者: Yining Shi,Kun Jiang,Qiang Meng,Ke Wang,Jiabao Wang,Wenchao Sun,Tuopu Wen,Mengmeng Yang,Diange Yang
机构: Tsinghua University (清华大学); Kargobot Inc. (卡格博特公司); Nankai University (南开大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:World models are critical for autonomous driving to simulate environmental dynamics and generate synthetic data. Existing methods struggle to disentangle ego-vehicle motion (perspective shifts) from scene evolvement (agent interactions), leading to suboptimal predictions. Instead, we propose to separate environmental changes from ego-motion by leveraging the scene-centric coordinate systems. In this paper, we introduce COME: a framework that integrates scene-centric forecasting Control into the Occupancy world ModEl. Specifically, COME first generates ego-irrelevant, spatially consistent future features through a scene-centric prediction branch, which are then converted into scene condition using a tailored ControlNet. These condition features are subsequently injected into the occupancy world model, enabling more accurate and controllable future occupancy predictions. Experimental results on the nuScenes-Occ3D dataset show that COME achieves consistent and significant improvements over state-of-the-art (SOTA) methods across diverse configurations, including different input sources (ground-truth, camera-based, fusion-based occupancy) and prediction horizons (3s and 8s). For example, under the same settings, COME achieves 26.3% better mIoU metric than DOME and 23.7% better mIoU metric than UniScene. These results highlight the efficacy of disentangled representation learning in enhancing spatio-temporal prediction fidelity for world models. Code and videos will be available at this https URL.
zh
[CV-58] High-Quality Facial Albedo Generation for 3D Face Reconstruction from a Single Image using a Coarse-to-Fine Approach
【速读】：该论文旨在解决从单张图像中生成高保真3D人脸纹理的问题，特别是现有方法在生成具有高频细节的UV反照率图（UV albedo map）方面存在不足。其解决方案的关键在于提出一种端到端的从粗到细的方法，首先利用由低维系数驱动的UV反照率参数化模型（UVAPM）生成具有皮肤色调和低频纹理细节的粗略反照率图，随后通过训练一个细节生成器来捕捉高频细节，从而生成高分辨率的反照率图。
链接: https://arxiv.org/abs/2506.13233
作者: Jiashu Dai,Along Wang,Binfan Ni,Tao Cao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Facial texture generation is crucial for high-fidelity 3D face reconstruction from a single image. However, existing methods struggle to generate UV albedo maps with high-frequency details. To address this challenge, we propose a novel end-to-end coarse-to-fine approach for UV albedo map generation. Our method first utilizes a UV Albedo Parametric Model (UVAPM), driven by low-dimensional coefficients, to generate coarse albedo maps with skin tones and low-frequency texture details. To capture high-frequency details, we train a detail generator using a decoupled albedo map dataset, producing high-resolution albedo maps. Extensive experiments demonstrate that our method can generate high-fidelity textures from a single image, outperforming existing methods in terms of texture quality and realism. The code and pre-trained model are publicly available at this https URL, facilitating reproducibility and further research.
zh
[CV-59] SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point Clouds
【速读】：该论文试图解决3D开放集识别（Open-set recognition, OSR）中现有方法依赖全局特征区分已知与未知类别而导致的语义局部重要性被忽略的问题。其解决方案的关键在于提出了一种显著性感知的结构分离方法（Salience-Aware Structured Separation, SASep），该方法通过可调语义分解（Tunable Semantic Decomposition, TSD）模块对物体进行语义分解，利用几何合成策略（Geometric Synthesis Strategy, GSS）生成伪未知物体，并通过合成辅助边界分离（Synth-aided Margin Separation, SMS）模块增强特征级分离，从而提升模型在几何和特征表示上的区分能力。
链接: https://arxiv.org/abs/2506.13224
作者: Jinfeng Xu,Xianzhi Li,Yuan Tang,Xu Han,Qiao Yu,Yixue Hao,Long Hu,Min Chen
机构: Huazhong University of Science and Technology (华中科技大学); Guangdong Intelligent Robotics Institute (广东智能机器人研究院); South China University of Technology (华南理工大学); Pazhou Laboratory (琶洲实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, conference
Abstract:Recent advancements in deep learning have greatly enhanced 3D object recognition, but most models are limited to closed-set scenarios, unable to handle unknown samples in real-world applications. Open-set recognition (OSR) addresses this limitation by enabling models to both classify known classes and identify novel classes. However, current OSR methods rely on global features to differentiate known and unknown classes, treating the entire object uniformly and overlooking the varying semantic importance of its different parts. To address this gap, we propose Salience-Aware Structured Separation (SASep), which includes (i) a tunable semantic decomposition (TSD) module to semantically decompose objects into important and unimportant parts, (ii) a geometric synthesis strategy (GSS) to generate pseudo-unknown objects by combining these unimportant parts, and (iii) a synth-aided margin separation (SMS) module to enhance feature-level separation by expanding the feature distributions between classes. Together, these components improve both geometric and feature representations, enhancing the model’s ability to effectively distinguish known and unknown classes. Experimental results show that SASep achieves superior performance in 3D OSR, outperforming existing state-of-the-art methods.
zh
[CV-60] DVP-MVS: Synergize Depth-Normal-Edge and Harmonized Visibility Prior for Multi-View Stereo
【速读】：该论文旨在解决多视图立体视觉（Multi-View Stereo, MVS）中基于块形变的方法在处理无纹理区域时存在的匹配模糊性以及由于边缘跳过和可见性遮挡导致的形变不稳定问题，这些问题可能引发估计偏差。其解决方案的关键在于提出DVP-MVS++，通过融合深度-法线-边缘对齐且协调的跨视图先验信息，实现鲁棒且可见性感知的块形变。具体而言，通过DepthPro、Metric3Dv2和Roberts算子生成粗略深度图、法线图和边缘图，并利用侵蚀-膨胀策略对齐以生成细粒度均匀边界；同时将视图选择权重重新定义为可见性图，并结合增强的跨视图深度重投影与面积最大化策略，以可靠恢复可见区域并有效平衡形变块，从而获得协调的跨视图先验信息。此外，通过聚合法线和极线投影深度差异实现几何一致性，并采用SHIQ进行高光校正，提升重建质量。
链接: https://arxiv.org/abs/2506.13215
作者: Zhenlong Yuan,Dapeng Zhang,Zehao Li,Chengxuan Qian,Jianing Chen,Yinda Chen,Kehua Chen,Tianlu Mao,Zhaoxin Li,Hao Jiang,Zhaoqi Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recently, patch deformation-based methods have demonstrated significant effectiveness in multi-view stereo due to their incorporation of deformable and expandable perception for reconstructing textureless areas. However, these methods generally focus on identifying reliable pixel correlations to mitigate matching ambiguity of patch deformation, while neglecting the deformation instability caused by edge-skipping and visibility occlusions, which may cause potential estimation deviations. To address these issues, we propose DVP-MVS++, an innovative approach that synergizes both depth-normal-edge aligned and harmonized cross-view priors for robust and visibility-aware patch deformation. Specifically, to avoid edge-skipping, we first apply DepthPro, Metric3Dv2 and Roberts operator to generate coarse depth maps, normal maps and edge maps, respectively. These maps are then aligned via an erosion-dilation strategy to produce fine-grained homogeneous boundaries for facilitating robust patch deformation. Moreover, we reformulate view selection weights as visibility maps, and then implement both an enhanced cross-view depth reprojection and an area-maximization strategy to help reliably restore visible areas and effectively balance deformed patch, thus acquiring harmonized cross-view priors for visibility-aware patch deformation. Additionally, we obtain geometry consistency by adopting both aggregated normals via view selection and projection depth differences via epipolar lines, and then employ SHIQ for highlight correction to enable geometry consistency with highlight-aware perception, thus improving reconstruction quality during propagation and refinement stage. Evaluation results on ETH3D, Tanks Temples and Strecha datasets exhibit the state-of-the-art performance and robust generalization capability of our proposed method.
zh
[CV-61] A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping
【速读】：该论文试图解决传统二维洪水制图技术在灾害管理和城市规划中提供的信息有限的问题，旨在通过深度学习（DL）实现更精确的三维洪水制图，以整合洪水范围和深度信息。解决方案的关键在于利用深度学习技术对静态和动态洪水特征进行任务分解或端到端建模，结合多种数据源（如数字高程模型、卫星遥感图像、降雨数据和模拟数据），提升预测精度与计算效率，从而支持更有效的洪水预警、长期城市规划及风险评估。
链接: https://arxiv.org/abs/2506.13201
作者: Wenfeng Jia,Bin Liang,Yuxi Liu,Muhammad Arif Khan,Lihong Zheng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Flooding remains a major global challenge, worsened by climate change and urbanization, demanding advanced solutions for effective disaster management. While traditional 2D flood mapping techniques provide limited insights, 3D flood mapping, powered by deep learning (DL), offers enhanced capabilities by integrating flood extent and depth. This paper presents a comprehensive survey of deep learning-based 3D flood mapping, emphasizing its advancements over 2D maps by integrating flood extent and depth for effective disaster management and urban planning. The survey categorizes deep learning techniques into task decomposition and end-to-end approaches, applicable to both static and dynamic flood features. We compare key DL architectures, highlighting their respective roles in enhancing prediction accuracy and computational efficiency. Additionally, this work explores diverse data sources such as digital elevation models, satellite imagery, rainfall, and simulated data, outlining their roles in 3D flood mapping. The applications reviewed range from real-time flood prediction to long-term urban planning and risk assessment. However, significant challenges persist, including data scarcity, model interpretability, and integration with traditional hydrodynamic models. This survey concludes by suggesting future directions to address these limitations, focusing on enhanced datasets, improved models, and policy implications for flood management. This survey aims to guide researchers and practitioners in leveraging DL techniques for more robust and reliable 3D flood mapping, fostering improved flood management strategies.
zh
[CV-62] MT-PCR: A Hybrid Mamba-Transformer with Spatial Serialization for Hierarchical Point Cloud Registration
【速读】：该论文旨在解决点云配准（Point Cloud Registration, PCR）中基于Transformer的方法因二次计算复杂度导致的处理分辨率受限和信息丢失问题。其解决方案的关键在于提出MT-PCR框架，首次将Mamba与Transformer模块相结合，通过Z-order空间填充曲线对点云特征进行序列化以增强空间局部性，使Mamba能够更好地建模几何结构，并移除Mamba中常用的顺序指示模块以提升性能，从而在保持高效计算的同时提升配准精度。
链接: https://arxiv.org/abs/2506.13183
作者: Bingxi Liu,An Liu,Hao Chen,Jinqiang Cui,Yiqun Wang,Hong Zhang
机构: SUSTech(南方科技大学); PCLShenzhen(鹏城实验室深圳); Chongqing University(重庆大学); Cambridge University(剑桥大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 Pages
Abstract:Point cloud registration (PCR) is a fundamental task in 3D computer vision and robotics. Most existing learning-based PCR methods rely on Transformers, which suffer from quadratic computational complexity. This limitation restricts the resolution of point clouds that can be processed, inevitably leading to information loss. In contrast, Mamba-a recently proposed model based on state space models (SSMs)-achieves linear computational complexity while maintaining strong long-range contextual modeling capabilities. However, directly applying Mamba to PCR tasks yields suboptimal performance due to the unordered and irregular nature of point cloud data. To address this challenge, we propose MT-PCR, the first point cloud registration framework that integrates both Mamba and Transformer modules. Specifically, we serialize point cloud features using Z-order space-filling curves to enforce spatial locality, enabling Mamba to better model the geometric structure of the input. Additionally, we remove the order indicator module commonly used in Mamba-based sequence modeling, leads to improved performance in our setting. The serialized features are then processed by an optimized Mamba encoder, followed by a Transformer refinement stage. Extensive experiments on multiple benchmarks demonstrate that MT-PCR outperforms Transformer-based and concurrent state-of-the-art methods in both accuracy and efficiency, significantly reducing while GPU memory usage and FLOPs.
zh
[CV-63] GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models
【速读】：该论文旨在解决大型视觉语言模型（Large Vision Language Models, LVLMs）在计算效率上的挑战，特别是在资源受限设备上处理大量视觉标记时的高成本问题。现有无训练视觉标记剪枝方法存在两个关键局限：基于语义显著性的策略过于关注高交叉注意力视觉标记，忽视了视觉多样性；而基于视觉多样性的方法则可能在高压缩比下误删语义重要的标记。该论文提出的解决方案是GreedyPrune，其关键在于将视觉标记剪枝过程形式化为组合优化问题，并通过贪心算法在计算效率与模型精度之间取得有效平衡。
链接: https://arxiv.org/abs/2506.13166
作者: Ruiguang Pei,Weiqing Sun,Zhihui Fu,Jun Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Although Large Vision Language Models (LVLMs) have demonstrated remarkable performance in image understanding tasks, their computational efficiency remains a significant challenge, particularly on resource-constrained devices due to the high cost of processing large numbers of visual tokens. Recently, training-free visual token pruning methods have gained popularity as a low-cost solution to this issue. However, existing approaches suffer from two key limitations: semantic saliency-based strategies primarily focus on high cross-attention visual tokens, often neglecting visual diversity, whereas visual diversity-based methods risk inadvertently discarding semantically important tokens, especially under high compression ratios. In this paper, we introduce GreedyPrune, a training-free plug-and-play visual token pruning algorithm designed to jointly optimize semantic saliency and visual diversity. We formalize the token pruning process as a combinatorial optimization problem and demonstrate that greedy algorithms effectively balance computational efficiency with model accuracy. Extensive experiments validate the effectiveness of our approach, showing that GreedyPrune achieves state-of-the-art accuracy across various multimodal tasks and models while significantly reducing end-to-end inference latency.
zh
[CV-64] CertDW: Towards Certified Dataset Ownership Verification via Conformal Prediction
【速读】：该论文试图解决数据集所有权验证（Dataset Ownership Verification, DOV）在面对噪声扰动或恶意攻击时性能下降的问题。现有DOV方法隐含假设验证过程是可靠的，即可疑模型通过输入验证样本直接返回结果进行所有权验证，但这一假设在实际中可能不成立。论文提出的解决方案关键在于设计了首个可认证的数据集水印（CertDW）及其基于该水印的认证数据集所有权验证方法，该方法在特定条件下（如受限像素级扰动）仍能保证验证的可靠性。其核心创新在于引入了主概率（Principal Probability, PP）和水印鲁棒性（Watermark Robustness, WR）两个统计指标，用于评估模型在正常样本和水印样本上的预测稳定性，并通过证明PP与WR之间的可证明下界，实现对可疑模型是否训练于受保护数据集的准确判断。
链接: https://arxiv.org/abs/2506.13160
作者: Ting Qiao,Yiming Li,Jianbin Li,Yingjia Wang,Leyi Qi,Junfeng Guo,Ruili Feng,Dacheng Tao
机构: North China Electric Power University (华北电力大学); Nanyang Technological University (南洋理工大学); Northwestern Polytechnical University (西北工业大学); University of Maryland (马里兰大学); Alibaba Group (阿里巴巴集团)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注: The first two authors contributed equally to this work. 16 pages
Abstract:Deep neural networks (DNNs) rely heavily on high-quality open-source datasets (e.g., ImageNet) for their success, making dataset ownership verification (DOV) crucial for protecting public dataset copyrights. In this paper, we find existing DOV methods (implicitly) assume that the verification process is faithful, where the suspicious model will directly verify ownership by using the verification samples as input and returning their results. However, this assumption may not necessarily hold in practice and their performance may degrade sharply when subjected to intentional or unintentional perturbations. To address this limitation, we propose the first certified dataset watermark (i.e., CertDW) and CertDW-based certified dataset ownership verification method that ensures reliable verification even under malicious attacks, under certain conditions (e.g., constrained pixel-level perturbation). Specifically, inspired by conformal prediction, we introduce two statistical measures, including principal probability (PP) and watermark robustness (WR), to assess model prediction stability on benign and watermarked samples under noise perturbations. We prove there exists a provable lower bound between PP and WR, enabling ownership verification when a suspicious model’s WR value significantly exceeds the PP values of multiple benign models trained on watermark-free datasets. If the number of PP values smaller than WR exceeds a threshold, the suspicious model is regarded as having been trained on the protected dataset. Extensive experiments on benchmark datasets verify the effectiveness of our CertDW method and its resistance to potential adaptive attacks. Our codes are at \hrefthis https URLGitHub.
zh
[CV-65] StgcDiff: Spatial-Temporal Graph Condition Diffusion for Sign Language Transition Generation
【速读】：该论文旨在解决手语转换生成（Sign Language Transition Generation）中现有方法仅通过拼接离散手语片段生成连续手语视频所导致的视觉连贯性和语义准确性较差的问题。其解决方案的关键在于提出了一种基于图的条件扩散框架StgcDiff，该框架通过捕捉手语特有的时空依赖性来生成平滑的过渡效果。核心组件为Sign-GCN模块，能够有效建模手语的时空特征，从而提升生成视频的质量。
链接: https://arxiv.org/abs/2506.13156
作者: Jiashu He,Jiayi He,Shengeng Tang,Huixia Ben,Lechao Cheng,Richang Hong
机构: Hefei University of Technology (合肥工业大学); Anhui University of Science and Technology (安徽理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Sign language transition generation seeks to convert discrete sign language segments into continuous sign videos by synthesizing smooth transitions. However,most existing methods merely concatenate isolated signs, resulting in poor visual coherence and semantic accuracy in the generated videos. Unlike textual languages,sign language is inherently rich in spatial-temporal cues, making it more complex to model. To address this,we propose StgcDiff, a graph-based conditional diffusion framework that generates smooth transitions between discrete signs by capturing the unique spatial-temporal dependencies of sign language. Specifically, we first train an encoder-decoder architecture to learn a structure-aware representation of spatial-temporal skeleton sequences. Next, we optimize a diffusion denoiser conditioned on the representations learned by the pre-trained encoder, which is tasked with predicting transition frames from noise. Additionally, we design the Sign-GCN module as the key component in our framework, which effectively models the spatial-temporal features. Extensive experiments conducted on the PHOENIX14T, USTC-CSL100,and USTC-SLR500 datasets demonstrate the superior performance of our method.
zh
[CV-66] STAGE: A Stream-Centric Generative World Model for Long-Horizon Driving-Scene Simulation
【速读】：该论文旨在解决在长时序范围内生成时间一致且高保真度的驾驶视频这一基础性挑战，现有方法由于时空动态解耦不足和跨帧特征传播机制有限，常出现误差累积和特征错位问题。其解决方案的关键在于提出STAGE（Streaming Temporal Attention Generative Engine），该框架通过引入分层时间特征传递（Hierarchical Temporal Feature Transfer, HTFT）和多阶段训练策略，实现了层次化特征协调与多阶段优化，从而提升视频生成的长期一致性与质量。
链接: https://arxiv.org/abs/2506.13138
作者: Jiamin Wang,Yichen Yao,Xiang Feng,Hang Wu,Yaming Wang,Qingqiu Huang,Yuexin Ma,Xinge Zhu
机构: ShanghaiTech University (上海科技大学); Yinwang Intelligent Technology Co. Ltd. (英伟智能技术有限公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The generation of temporally consistent, high-fidelity driving videos over extended horizons presents a fundamental challenge in autonomous driving world modeling. Existing approaches often suffer from error accumulation and feature misalignment due to inadequate decoupling of spatio-temporal dynamics and limited cross-frame feature propagation mechanisms. To address these limitations, we present STAGE (Streaming Temporal Attention Generative Engine), a novel auto-regressive framework that pioneers hierarchical feature coordination and multi-phase optimization for sustainable video synthesis. To achieve high-quality long-horizon driving video generation, we introduce Hierarchical Temporal Feature Transfer (HTFT) and a novel multi-stage training strategy. HTFT enhances temporal consistency between video frames throughout the video generation process by modeling the temporal and denoising process separately and transferring denoising features between frames. The multi-stage training strategy is to divide the training into three stages, through model decoupling and auto-regressive inference process simulation, thereby accelerating model convergence and reducing error accumulation. Experiments on the Nuscenes dataset show that STAGE has significantly surpassed existing methods in the long-horizon driving video generation task. In addition, we also explored STAGE’s ability to generate unlimited-length driving videos. We generated 600 frames of high-quality driving videos on the Nuscenes dataset, which far exceeds the maximum length achievable by existing methods.
zh
[CV-67] EmbodiedPlace: Learning Mixture-of-Features with Embodied Constraints for Visual Place Recognition
【速读】：该论文旨在解决视觉定位识别（Visual Place Recognition, VPR）中由于依赖局部特征重排序导致的性能局限问题，以及在机器人领域中基于运动序列的时空验证所带来的限制。其解决方案的关键在于提出一种基于嵌入式约束下的混合特征（Mixture-of-Features, MoF）方法，通过学习得到的多度量损失函数计算特征权重，从而优化全局特征表示，在保持较低计算开销的前提下提升VPR性能。
链接: https://arxiv.org/abs/2506.13133
作者: Bingxi Liu,Hao Chen,Shiyi Guo,Yihong Wu,Jinqiang Cui,Hong Zhang
机构: Southern University of Science and Technology (南方科技大学); Peng Cheng Laboratory (鹏城实验室); University of Cambridge (剑桥大学); Northeastern University (东北大学); MAIS, Institution of Automation, China Academic of Sciences (MAIS，自动化研究所，中国科学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 Pages
Abstract:Visual Place Recognition (VPR) is a scene-oriented image retrieval problem in computer vision in which re-ranking based on local features is commonly employed to improve performance. In robotics, VPR is also referred to as Loop Closure Detection, which emphasizes spatial-temporal verification within a sequence. However, designing local features specifically for VPR is impractical, and relying on motion sequences imposes limitations. Inspired by these observations, we propose a novel, simple re-ranking method that refines global features through a Mixture-of-Features (MoF) approach under embodied constraints. First, we analyze the practical feasibility of embodied constraints in VPR and categorize them according to existing datasets, which include GPS tags, sequential timestamps, local feature matching, and self-similarity matrices. We then propose a learning-based MoF weight-computation approach, utilizing a multi-metric loss function. Experiments demonstrate that our method improves the state-of-the-art (SOTA) performance on public datasets with minimal additional computational overhead. For instance, with only 25 KB of additional parameters and a processing time of 10 microseconds per frame, our method achieves a 0.9% improvement over a DINOv2-based baseline performance on the Pitts-30k test set.
zh
[CV-68] GS-2DGS: Geometrically Supervised 2DGS for Reflective Object Reconstruction CVPR2025
【速读】：该论文试图解决高反射物体的三维建模问题，这一问题由于物体表面强烈的视角依赖性外观而具有挑战性。现有基于SDF（Signed Distance Field）的方法虽然能够恢复高质量的网格，但计算耗时且容易产生过度平滑的表面；而3D Gaussian Splatting（3DGS）虽然具备高速度和实时渲染能力，但由于缺乏几何约束，在从高斯分布中提取表面时会产生噪声。该论文提出的解决方案关键在于提出一种名为GS-2DGS的新重建方法，该方法基于2D Gaussian Splatting（2DGS），结合了高斯点云的快速渲染能力与基础模型提供的额外几何信息，从而在保持高效性的同时提升了重建质量和光照重演性能。
链接: https://arxiv.org/abs/2506.13110
作者: Jinguang Tong,Xuesong li,Fahira Afzal Maken,Sundaram Muthu,Lars Petersson,Chuong Nguyen,Hongdong Li
机构: Australian National University (澳大利亚国立大学); CSIRO (澳大利亚联邦科学与工业研究组织); Indian Institute of Technology Madras (印度理工学院马德拉斯分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by CVPR2025
Abstract:3D modeling of highly reflective objects remains challenging due to strong view-dependent appearances. While previous SDF-based methods can recover high-quality meshes, they are often time-consuming and tend to produce over-smoothed surfaces. In contrast, 3D Gaussian Splatting (3DGS) offers the advantage of high speed and detailed real-time rendering, but extracting surfaces from the Gaussians can be noisy due to the lack of geometric constraints. To bridge the gap between these approaches, we propose a novel reconstruction method called GS-2DGS for reflective objects based on 2D Gaussian Splatting (2DGS). Our approach combines the rapid rendering capabilities of Gaussian Splatting with additional geometric information from foundation models. Experimental results on synthetic and real datasets demonstrate that our method significantly outperforms Gaussian-based techniques in terms of reconstruction and relighting and achieves performance comparable to SDF-based methods while being an order of magnitude faster. Code is available at this https URL
zh
[CV-69] A Novel ViDAR Device With Visual Inertial Encoder Odometry and Reinforcement Learning-Based Active SLAM Method
【速读】：该论文旨在解决多传感器融合中同时定位与地图构建（SLAM）的性能提升问题，特别是通过引入电机编码器设备来增强系统主动能力和视场角（FOV），以在低成本和低结构复杂度下实现更优的定位精度。其解决方案的关键在于提出一种基于ViDAR（Video Detection and Ranging）设备的视觉-惯性-编码器紧耦合里程计（VIEO），并结合深度强化学习（DRL）实现平台运动解耦的主动SLAM方法，从而显著提高帧间共视关系和状态估计精度。
链接: https://arxiv.org/abs/2506.13100
作者: Zhanhua Xin,Zhihao Wang,Shenghao Zhang,Wanchao Chi,Yan Meng,Shihan Kong,Yan Xiong,Chong Zhang,Yuzhen Liu,Junzhi Yu
机构: Peking University (北京大学); Tencent RoboticsX (腾讯机器人X实验室)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 13 figures
Abstract:In the field of multi-sensor fusion for simultaneous localization and mapping (SLAM), monocular cameras and IMUs are widely used to build simple and effective visual-inertial systems. However, limited research has explored the integration of motor-encoder devices to enhance SLAM performance. By incorporating such devices, it is possible to significantly improve active capability and field of view (FOV) with minimal additional cost and structural complexity. This paper proposes a novel visual-inertial-encoder tightly coupled odometry (VIEO) based on a ViDAR (Video Detection and Ranging) device. A ViDAR calibration method is introduced to ensure accurate initialization for VIEO. In addition, a platform motion decoupled active SLAM method based on deep reinforcement learning (DRL) is proposed. Experimental data demonstrate that the proposed ViDAR and the VIEO algorithm significantly increase cross-frame co-visibility relationships compared to its corresponding visual-inertial odometry (VIO) algorithm, improving state estimation accuracy. Additionally, the DRL-based active SLAM algorithm, with the ability to decouple from platform motion, can increase the diversity weight of the feature points and further enhance the VIEO algorithm’s performance. The proposed methodology sheds fresh insights into both the updated platform design and decoupled approach of active SLAM systems in complex environments.
zh
[CV-70] Pro-AD: Learning Comprehensive Prototypes with Prototype-based Constraint for Multi-class Unsupervised Anomaly Detection
【速读】：该论文旨在解决基于原型的无监督异常检测方法中，由于可学习原型数量有限导致的正常信息聚合不足从而引起重建效果不佳的问题，以及增加原型数量后可能引发的“Soft Identity Mapping”问题，即异常样本通过注意力机制被良好重建。解决方案的关键在于提出Pro-AD框架，其核心包括引入扩展的可学习原型集以提供足够的语义信息容量，采用动态双向解码器整合正常信息聚合与目标特征重建过程，使原型能够从不同层次的图像特征中聚合更全面的正常语义信息，并在重建过程中动态利用这些原型。此外，通过在解码器的目标特征重建过程中引入基于原型的约束，防止异常样本利用充足的语义信息被良好重建，从而提升模型的异常检测性能。
链接: https://arxiv.org/abs/2506.13097
作者: Ziqing Zhou,Binbin Gao,Yuri Pan,Lidong Wang,Wenbing Zhu,Yong Liu,Jun Liu,MIngmin Chi,Dong Wu,Bo Peng,Chengjie Wang
机构: Fudan University (复旦大学); Youtu Lab, Tencent (腾讯优图实验室); Rongcheer Co., Ltd. (荣车科技有限公司); Shanghai Ocean University (上海海洋大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Prototype-based reconstruction methods for unsupervised anomaly detection utilize a limited set of learnable prototypes which only aggregates insufficient normal information, resulting in undesirable reconstruction. However, increasing the number of prototypes may lead to anomalies being well reconstructed through the attention mechanism, which we refer to as the “Soft Identity Mapping” problem. In this paper, we propose Pro-AD to address these issues and fully utilize the prototypes to boost the performance of anomaly detection. Specifically, we first introduce an expanded set of learnable prototypes to provide sufficient capacity for semantic information. Then we employ a Dynamic Bidirectional Decoder which integrates the process of the normal information aggregation and the target feature reconstruction via prototypes, with the aim of allowing the prototypes to aggregate more comprehensive normal semantic information from different levels of the image features and the target feature reconstruction to not only utilize its contextual information but also dynamically leverage the learned comprehensive prototypes. Additionally, to prevent the anomalies from being well reconstructed using sufficient semantic information through the attention mechanism, Pro-AD introduces a Prototype-based Constraint that applied within the target feature reconstruction process of the decoder, which further improves the performance of our approach. Extensive experiments on multiple challenging benchmarks demonstrate that our Pro-AD achieve state-of-the-art performance, highlighting its superior robustness and practical effectiveness for Multi-class Unsupervised Anomaly Detection task.
zh
[CV-71] Learning Event Completeness for Weakly Supervised Video Anomaly Detection ICML
【速读】：该论文旨在解决弱监督视频异常检测（WS-VAD）中由于缺乏密集帧级标注而导致的定位不完整问题。其解决方案的关键在于提出一种名为LE-CVAD的新型框架，该框架具有双结构，能够编码视觉与语言之间的类别感知和类别无关语义，并通过基于异常感知高斯混合模型的语义规律来学习精确的事件边界，从而获得更完整的事件实例。此外，还引入了一种基于记忆库的原型学习机制，以增强与异常事件类别相关的简洁文本描述的表达能力。
链接: https://arxiv.org/abs/2506.13095
作者: Yu Wang,Shiwei Chen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICML
Abstract:Weakly supervised video anomaly detection (WS-VAD) is tasked with pinpointing temporal intervals containing anomalous events within untrimmed videos, utilizing only video-level annotations. However, a significant challenge arises due to the absence of dense frame-level annotations, often leading to incomplete localization in existing WS-VAD methods. To address this issue, we present a novel LEC-VAD, Learning Event Completeness for Weakly Supervised Video Anomaly Detection, which features a dual structure designed to encode both category-aware and category-agnostic semantics between vision and language. Within LEC-VAD, we devise semantic regularities that leverage an anomaly-aware Gaussian mixture to learn precise event boundaries, thereby yielding more complete event instances. Besides, we develop a novel memory bank-based prototype learning mechanism to enrich concise text descriptions associated with anomaly-event categories. This innovation bolsters the text’s expressiveness, which is crucial for advancing WS-VAD. Our LEC-VAD demonstrates remarkable advancements over the current state-of-the-art methods on two benchmark datasets XD-Violence and UCF-Crime.
zh
[CV-72] SuperPoint-SLAM3: Augmenting ORB-SLAM3 with Deep Features Adaptive NMS and Learning-Based Loop Closure
【速读】：该论文旨在解决视觉同步定位与建图（Visual SLAM）在极端视角、尺度和光照变化下精度下降的问题。传统方法如ORB-SLAM3由于依赖人工设计的ORB关键点，在这些挑战性场景中表现不佳。其解决方案的关键在于引入SuperPoint-SLAM3，该方法通过（i）用自监督的SuperPoint检测器-描述子替代ORB，（ii）采用自适应非极大值抑制（ANMS）确保关键点空间分布均匀，以及（iii）集成轻量级NetVLAD位姿识别模块以实现基于学习的回环闭合，从而显著提升了系统精度并保持了实时性。
链接: https://arxiv.org/abs/2506.13089
作者: Shahram Najam Syed,Ishir Roongta,Kavin Ravie,Gangadhar Nageswar
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 10 pages, 6 figures, code at this https URL
Abstract:Visual simultaneous localization and mapping (SLAM) must remain accurate under extreme viewpoint, scale and illumination variations. The widely adopted ORB-SLAM3 falters in these regimes because it relies on hand-crafted ORB keypoints. We introduce SuperPoint-SLAM3, a drop-in upgrade that (i) replaces ORB with the self-supervised SuperPoint detector–descriptor, (ii) enforces spatially uniform keypoints via adaptive non-maximal suppression (ANMS), and (iii) integrates a lightweight NetVLAD place-recognition head for learning-based loop closure. On the KITTI Odometry benchmark SuperPoint-SLAM3 reduces mean translational error from 4.15% to 0.34% and mean rotational error from 0.0027 deg/m to 0.0010 deg/m. On the EuRoC MAV dataset it roughly halves both errors across every sequence (e.g., V2_03: 1.58% - 0.79%). These gains confirm that fusing modern deep features with a learned loop-closure module markedly improves ORB-SLAM3 accuracy while preserving its real-time operation. Implementation, pretrained weights and reproducibility scripts are available at this https URL. Comments: 10 pages, 6 figures, code at this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO) ACMclasses: I.2.10; I.4.8; I.2.9 Cite as: arXiv:2506.13089 [cs.CV] (or arXiv:2506.13089v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2506.13089 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[CV-73] SuperPlace: The Renaissance of Classical Feature Aggregation for Visual Place Recognition in the Era of Foundation Models
【速读】：该论文旨在解决视觉定位（Visual Place Recognition, VPR）中特征聚合方法未能充分利用基础模型（Foundation Model, FM）的潜力以及传统聚合方法（如GeM和NetVLAD）未被充分挖掘的问题。其解决方案的关键在于复兴并改进经典特征聚合方法，提出G²M和NVL-FT²策略，其中G²M通过两个GeM模块实现高效的特征压缩与校准，而NVL-FT²则通过对NetVLAD-Linear进行二次微调提升性能。
链接: https://arxiv.org/abs/2506.13073
作者: Bingxi Liu,Pengju Zhang,Li He,Hao Chen,Shiyi Guo,Yihong Wu,Jinqiang Cui,Hong Zhang
机构: Southern University of Science and Technology (南方科技大学); Peng Cheng Laboratory (鹏城实验室); Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); Cambridge University (剑桥大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages
Abstract:Recent visual place recognition (VPR) approaches have leveraged foundation models (FM) and introduced novel aggregation techniques. However, these methods have failed to fully exploit key concepts of FM, such as the effective utilization of extensive training sets, and they have overlooked the potential of classical aggregation methods, such as GeM and NetVLAD. Building on these insights, we revive classical feature aggregation methods and develop more fundamental VPR models, collectively termed SuperPlace. First, we introduce a supervised label alignment method that enables training across various VPR datasets within a unified framework. Second, we propose G ^2 M, a compact feature aggregation method utilizing two GeMs, where one GeM learns the principal components of feature maps along the channel dimension and calibrates the output of the other. Third, we propose the secondary fine-tuning (FT ^2 ) strategy for NetVLAD-Linear (NVL). NetVLAD first learns feature vectors in a high-dimensional space and then compresses them into a lower-dimensional space via a single linear layer. Extensive experiments highlight our contributions and demonstrate the superiority of SuperPlace. Specifically, G ^2 M achieves promising results with only one-tenth of the feature dimensions compared to recent methods. Moreover, NVL-FT ^2 ranks first on the MSLS leaderboard.
zh
[CV-74] Video Individual Counting With Implicit One-to-Many Matching
【速读】：该论文试图解决视频个体计数（Video Individual Counting, VIC）中的共存个体识别问题，该问题本质上是一个对应关系问题，即如何在不同帧之间识别相同的行人。现有方法主要采用一对一（One-to-One, O2O）匹配策略，导致对外观变化或检测缺失敏感。论文的关键解决方案是将O2O匹配放松为一对多（One-to-Many, O2M）匹配，更好地适应VIC问题的特性，并利用行人行走时的社会群体行为。为此，作者提出OMAN模型，其核心包括隐式上下文生成器和一对多配对匹配器。
链接: https://arxiv.org/abs/2506.13067
作者: Xuhui Zhu,Jing Xu,Bingjie Wang,Huikang Dai,Hao Lu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video Individual Counting (VIC) is a recently introduced task that aims to estimate pedestrian flux from a video. It extends conventional Video Crowd Counting (VCC) beyond the per-frame pedestrian count. In contrast to VCC that only learns to count repeated pedestrian patterns across frames, the key problem of VIC is how to identify co-existent pedestrians between frames, which turns out to be a correspondence problem. Existing VIC approaches, however, mainly follow a one-to-one (O2O) matching strategy where the same pedestrian must be exactly matched between frames, leading to sensitivity to appearance variations or missing detections. In this work, we show that the O2O matching could be relaxed to a one-to-many (O2M) matching problem, which better fits the problem nature of VIC and can leverage the social grouping behavior of walking pedestrians. We therefore introduce OMAN, a simple but effective VIC model with implicit One-to-Many mAtchiNg, featuring an implicit context generator and a one-to-many pairwise matcher. Experiments on the SenseCrowd and CroHD benchmarks show that OMAN achieves the state-of-the-art performance. Code is available at \hrefthis https URLOMAN.
zh
[CV-75] DualFast: Dual-Speedup Framework for Fast Sampling of Diffusion Models
【速读】：该论文试图解决扩散概率模型（DPMs）在推理过程中因迭代采样导致的采样速度缓慢问题，尤其是在减少采样步骤时引入的离散化误差。解决方案的关键在于重新审视采样误差的性质，识别出其包含两个独立成分：广为人知的离散化误差和较少被研究的近似误差，并通过双误差解耦策略揭示两者之间的动态关系。基于此，作者提出了一种统一且无需训练的加速框架DualFast，该框架同时考虑两种误差类型，从而最小化总采样误差，显著提升DPM采样的速度与质量。
链接: https://arxiv.org/abs/2506.13058
作者: Hu Yu,Hao Luo,Fan Wang,Feng Zhao
机构: USTC(中国科学技术大学); DAMO Academy, Alibaba Group(达摩院，阿里巴巴集团)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion probabilistic models (DPMs) have achieved impressive success in visual generation. While, they suffer from slow inference speed due to iterative sampling. Employing fewer sampling steps is an intuitive solution, but this will also introduces discretization error. Existing fast samplers make inspiring efforts to reduce discretization error through the adoption of high-order solvers, potentially reaching a plateau in terms of optimization. This raises the question: can the sampling process be accelerated further? In this paper, we re-examine the nature of sampling errors, discerning that they comprise two distinct elements: the widely recognized discretization error and the less explored approximation error. Our research elucidates the dynamics between these errors and the step by implementing a dual-error disentanglement strategy. Building on these foundations, we introduce an unified and training-free acceleration framework, DualFast, designed to enhance the speed of DPM sampling by concurrently accounting for both error types, thereby minimizing the total sampling error. DualFast is seamlessly compatible with existing samplers and significantly boost their sampling quality and speed, particularly in extremely few sampling steps. We substantiate the effectiveness of our framework through comprehensive experiments, spanning both unconditional and conditional sampling domains, across both pixel-space and latent-space DPMs.
zh
[CV-76] Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model Learning
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在推理能力训练过程中存在的样本效率低下和推理能力激活不足的问题。传统方法要么仅依赖强化学习（Reinforcement Learning, RL）导致样本效率低且难以激活模型的潜在推理能力，要么先进行冷启动监督微调（Supervised Fine-Tuning, SFT）再进行RL，从而限制了模型的探索能力和收敛效果。解决方案的关键在于提出Metis-RISE框架，该框架首先通过RL阶段激励并激活模型的潜在推理能力，随后通过针对性的SFT阶段解决RL过程中发现的两个核心问题：一是任务中模型具备但应用不一致的正确推理轨迹采样效率低，通过自蒸馏的RL模型推理轨迹进行优化；二是模型完全缺失的推理能力，通过注入专家增强的知识进行补充。这种RL激励与SFT增强相结合的策略是Metis-RISE的核心，最终实现了两个版本的MLLMs（7B和72B参数），并在OpenCompass多模态推理排行榜上取得了最先进的性能。
链接: https://arxiv.org/abs/2506.13056
作者: Haibo Qiu,Xiaohan Lan,Fanfan Liu,Xiaohu Sun,Delian Ruan,Peng Shi,Lin Ma
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project Page: this https URL
Abstract:Recent advancements in large language models (LLMs) have witnessed a surge in the development of advanced reasoning paradigms, which are now being integrated into multimodal large language models (MLLMs). However, existing approaches often fall short: methods solely employing reinforcement learning (RL) can struggle with sample inefficiency and activating entirely absent reasoning capabilities, while conventional pipelines that initiate with a cold-start supervised fine-tuning (SFT) phase before RL may restrict the model’s exploratory capacity and face suboptimal convergence. In this work, we introduce \textbfMetis-RISE (\textbfRL \textbfIncentivizes and \textbfSFT \textbfEnhances) for multimodal reasoning model learning. Unlike conventional approaches, Metis-RISE distinctively omits an initial SFT stage, beginning instead with an RL phase (e.g., using a Group Relative Policy Optimization variant) to incentivize and activate the model’s latent reasoning capacity. Subsequently, the targeted SFT stage addresses two key challenges identified during RL: (1) \textitinefficient trajectory sampling for tasks where the model possesses but inconsistently applies correct reasoning, which we tackle using self-distilled reasoning trajectories from the RL model itself; and (2) \textitfundamental capability absence, which we address by injecting expert-augmented knowledge for prompts where the model entirely fails. This strategic application of RL for incentivization followed by SFT for enhancement forms the core of Metis-RISE, leading to two versions of our MLLMs (7B and 72B parameters). Evaluations on the OpenCompass Multimodal Reasoning Leaderboard demonstrate that both models achieve state-of-the-art performance among similar-sized models, with the 72B version ranking fourth overall.
zh
[CV-77] NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling
【速读】：该论文旨在解决在稀疏几何控制下直接建模神经隐式表面（neural implicit surface）的挑战，尤其是当该表面作为神经有符号距离函数（SDF）的零水平集时。稀疏输入形状控制通常包括无结构的3D曲线草图或连接的3D曲线网络，这些输入具有稀疏性和多样的拓扑结构，给生成高质量符合曲线约束的表面带来困难。论文提出的解决方案是NeuVAS，其关键在于引入基于曲面曲率泛函的平滑项以最小化零水平集表面的形状变化，并开发了一种新技术以准确建模输入曲线草图中指定的G0尖锐特征曲线。
链接: https://arxiv.org/abs/2506.13050
作者: Pengfei Wang,Qiujie Dong,Fangtian Liang,Hao Pan,Lei Yang,Congyi Zhang,Guying Lin,Caiming Zhang,Yuanfeng Zhou,Changhe Tu,Shiqing Xin,Alla Sheffer,Xin Li,Wenping Wang
机构: Shandong University(山东大学); The University of Hong Kong(香港大学); Tsinghua University(清华大学); University of British Columbia(不列颠哥伦比亚大学); Texas A&M University(德克萨斯A&M大学)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Neural implicit shape representation has drawn significant attention in recent years due to its smoothness, differentiability, and topological flexibility. However, directly modeling the shape of a neural implicit surface, especially as the zero-level set of a neural signed distance function (SDF), with sparse geometric control is still a challenging task. Sparse input shape control typically includes 3D curve networks or, more generally, 3D curve sketches, which are unstructured and cannot be connected to form a curve network, and therefore more difficult to deal with. While 3D curve networks or curve sketches provide intuitive shape control, their sparsity and varied topology pose challenges in generating high-quality surfaces to meet such curve constraints. In this paper, we propose NeuVAS, a variational approach to shape modeling using neural implicit surfaces constrained under sparse input shape control, including unstructured 3D curve sketches as well as connected 3D curve networks. Specifically, we introduce a smoothness term based on a functional of surface curvatures to minimize shape variation of the zero-level set surface of a neural SDF. We also develop a new technique to faithfully model G0 sharp feature curves as specified in the input curve sketches. Comprehensive comparisons with the state-of-the-art methods demonstrate the significant advantages of our method.
zh
[CV-78] Beyond the First Read: AI-Assisted Perceptual Error Detection in Chest Radiography Accounting for Interobserver Variability
【速读】：该论文旨在解决胸部X光（Chest X-ray, CXR）影像诊断中常见的感知错误问题，特别是放射科医生可能忽略但可见的异常。现有工作流程和AI系统在解释后对检测此类错误的支持有限，并且缺乏有效的医工协作机制。其解决方案的关键在于引入RADAR（Radiologist–AI Diagnostic Assistance and Review）系统，该系统通过摄入最终的放射科医生标注和CXR图像，进行区域级别的分析以检测并提示可能被遗漏的异常区域，提供灵活的感兴趣区域（Region of Interest, ROI）建议而非固定标签，从而支持“二次检查”流程并适应观察者间的差异。
链接: https://arxiv.org/abs/2506.13049
作者: Adhrith Vutukuri,Akash Awasthi,David Yang,Carol C. Wu,Hien Van Nguyen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 25 pages
Abstract:Chest radiography is widely used in diagnostic imaging. However, perceptual errors – especially overlooked but visible abnormalities – remain common and clinically significant. Current workflows and AI systems provide limited support for detecting such errors after interpretation and often lack meaningful human–AI collaboration. We introduce RADAR (Radiologist–AI Diagnostic Assistance and Review), a post-interpretation companion system. RADAR ingests finalized radiologist annotations and CXR images, then performs regional-level analysis to detect and refer potentially missed abnormal regions. The system supports a “second-look” workflow and offers suggested regions of interest (ROIs) rather than fixed labels to accommodate inter-observer variation. We evaluated RADAR on a simulated perceptual-error dataset derived from de-identified CXR cases, using F1 score and Intersection over Union (IoU) as primary metrics. RADAR achieved a recall of 0.78, precision of 0.44, and an F1 score of 0.56 in detecting missed abnormalities in the simulated perceptual-error dataset. Although precision is moderate, this reduces over-reliance on AI by encouraging radiologist oversight in human–AI collaboration. The median IoU was 0.78, with more than 90% of referrals exceeding 0.5 IoU, indicating accurate regional localization. RADAR effectively complements radiologist judgment, providing valuable post-read support for perceptual-error detection in CXR interpretation. Its flexible ROI suggestions and non-intrusive integration position it as a promising tool in real-world radiology workflows. To facilitate reproducibility and further evaluation, we release a fully open-source web implementation alongside a simulated error dataset. All code, data, demonstration videos, and the application are publicly available at this https URL.
zh
[CV-79] A Comprehensive Survey on Continual Learning in Generative Models
【速读】：该论文旨在解决生成式模型在持续学习过程中面临的灾难性遗忘问题（catastrophic forgetting），即在适应新任务时会导致先前学习任务性能显著下降的挑战。其解决方案的关键在于系统地归纳和分析主流生成式模型的持续学习方法，将其分为基于架构、基于正则化和基于重放的三种范式，并深入探讨不同生成模型的持续学习设置，包括训练目标、基准测试和核心架构，从而为提升生成模型的适应性和可扩展性提供理论支持与实践指导。
链接: https://arxiv.org/abs/2506.13045
作者: Haiyang Guo,Fanhu Zeng,Fei Zhu,Jiayi Wang,Xukai Wang,Jingang Zhou,Hongbo Zhao,Wenzhuo Liu,Shijie Ma,Xu-Yao Zhang,Cheng-Lin Liu
机构: Chinese Academy of Sciences (中国科学院); Hong Kong Institute of Science and Innovation (香港科学创新研究所); Academy of Mathematics and Systems Science (数学与系统科学学院)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint
Abstract:The rapid advancement of generative models has enabled modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specific domains. However, these models remain fundamentally constrained by catastrophic forgetting - a persistent challenge where adapting to new tasks typically leads to significant degradation in performance on previously learned tasks. To address this practical limitation, numerous approaches have been proposed to enhance the adaptability and scalability of generative models in real-world applications. In this work, we present a comprehensive survey of continual learning methods for mainstream generative models, including large language models, multimodal large language models, vision language action models, and diffusion models. Drawing inspiration from the memory mechanisms of the human brain, we systematically categorize these approaches into three paradigms: architecture-based, regularization-based, and replay-based methods, while elucidating their underlying methodologies and motivations. We further analyze continual learning setups for different generative models, including training objectives, benchmarks, and core backbones, offering deeper insights into the field. The project page of this paper is available at this https URL.
zh
[CV-80] ViewPCL: a point cloud based active learning method for multi-view segmentation
【速读】：该论文试图解决多视角语义分割中的数据效率与可解释性问题。解决方案的关键在于提出了一种新的主动学习框架，该框架基于一个衡量从模型预测中提取的额外几何信息生成的点云分布之间差异性的评分机制。
链接: https://arxiv.org/abs/2506.13043
作者: Christian Hilaire,Sima Didari
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We propose a novel active learning framework for multi-view semantic segmentation. This framework relies on a new score that measures the discrepancy between point cloud distributions generated from the extra geometrical information derived from the model’s prediction across different views. Our approach results in a data efficient and explainable active learning method. The source code is available at this https URL.
zh
[CV-81] MAMMA: Markerless Automatic Multi-Person Motion Action Capture
【速读】：该论文旨在解决无标记（markerless）多人交互序列中高精度运动捕捉的问题。传统运动捕捉系统依赖物理标记，虽精度高但成本高、耗时长；而现有基于学习的方法在处理多人交互、遮挡和密集关键点预测方面存在局限。该论文提出的MAMMA方法通过预测基于分割掩码的密集2D表面关键点，实现即使在严重遮挡情况下也能进行个体特定的对应估计，其关键在于采用可学习查询的新型架构，从而提升了多人交互场景下的运动重建精度与鲁棒性。
链接: https://arxiv.org/abs/2506.13040
作者: Hanz Cuevas-Velasquez,Anastasios Yiannakidis,Soyong Shin,Giorgio Becherini,Markus Höschle,Joachim Tesch,Taylor Obersat,Tsvetelina Alexiadis,Michael Black
机构: Max Planck Institute for Intelligent Systems (马克斯·普朗克智能系统研究所); Carnegie Mellon University (卡内基梅隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present MAMMA, a markerless motion-capture pipeline that accurately recovers SMPL-X parameters from multi-view video of two-person interaction sequences. Traditional motion-capture systems rely on physical markers. Although they offer high accuracy, their requirements of specialized hardware, manual marker placement, and extensive post-processing make them costly and time-consuming. Recent learning-based methods attempt to overcome these limitations, but most are designed for single-person capture, rely on sparse keypoints, or struggle with occlusions and physical interactions. In this work, we introduce a method that predicts dense 2D surface landmarks conditioned on segmentation masks, enabling person-specific correspondence estimation even under heavy occlusion. We employ a novel architecture that exploits learnable queries for each landmark. We demonstrate that our approach can handle complex person–person interaction and offers greater accuracy than existing methods. To train our network, we construct a large, synthetic multi-view dataset combining human motions from diverse sources, including extreme poses, hand motions, and close interactions. Our dataset yields high-variability synthetic sequences with rich body contact and occlusion, and includes SMPL-X ground-truth annotations with dense 2D landmarks. The result is a system capable of capturing human motion without the need for markers. Our approach offers competitive reconstruction quality compared to commercial marker-based motion-capture solutions, without the extensive manual cleanup. Finally, we address the absence of common benchmarks for dense-landmark prediction and markerless motion capture by introducing two evaluation settings built from real multi-view sequences. We will release our dataset, benchmark, method, training code, and pre-trained model weights for research purposes.
zh
[CV-82] Evolution of ReID: From Early Methods to LLM Integration
【速读】：该论文旨在解决行人重识别（Person Re-Identification, ReID）中由于光照、姿态和视角变化导致的视觉匹配困难问题，同时探索如何利用大语言模型（Large Language Models, LLMs）增强系统的语义和上下文理解能力。其解决方案的关键在于引入由GPT-4o生成的动态、身份特定的提示信息，以提升视觉-语言ReID系统中图像与文本之间的对齐效果，从而提高复杂或模糊场景下的识别准确性。
链接: https://arxiv.org/abs/2506.13039
作者: Amran Bhuiyan,Mizanur Rahman,Md Tahmid Rahman Laskar,Aijun An,Jimmy Xiangji Huang
机构: York University (约克大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Person re-identification (ReID) has evolved from handcrafted feature-based methods to deep learning approaches and, more recently, to models incorporating large language models (LLMs). Early methods struggled with variations in lighting, pose, and viewpoint, but deep learning addressed these issues by learning robust visual features. Building on this, LLMs now enable ReID systems to integrate semantic and contextual information through natural language. This survey traces that full evolution and offers one of the first comprehensive reviews of ReID approaches that leverage LLMs, where textual descriptions are used as privileged information to improve visual matching. A key contribution is the use of dynamic, identity-specific prompts generated by GPT-4o, which enhance the alignment between images and text in vision-language ReID systems. Experimental results show that these descriptions improve accuracy, especially in complex or ambiguous cases. To support further research, we release a large set of GPT-4o-generated descriptions for standard ReID datasets. By bridging computer vision and natural language processing, this survey offers a unified perspective on the field’s development and outlines key future directions such as better prompt design, cross-modal transfer learning, and real-world adaptability.
zh
[CV-83] HKD4VLM: A Progressive Hybrid Knowledge Distillation Framework for Robust Multimodal Hallucination and Factuality Detection in VLMs
【速读】：该论文旨在解决大规模多模态模型在负责任行为方面的挑战，特别是针对幻觉检测和事实核查问题。其解决方案的关键在于从知识蒸馏的角度联合处理这两个任务，并提出了一个称为HKD4VLM的渐进式混合知识蒸馏框架。该框架通过分层结构实现从粗粒度知识对齐到细粒度优化的逐步提升，同时引入映射偏移增强推理和多样化增强策略以提高模型性能与鲁棒性。
链接: https://arxiv.org/abs/2506.13038
作者: Zijian Zhang,Xuecheng Wu,Danlei Huang,Siyu Yan,Chong Peng,Xuezhi Cao
机构: Meituan-M17(美团-M17); Xi’an Jiaotong University(西安交通大学); East China Normal University(华东师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Driven by the rapid progress in vision-language models (VLMs), the responsible behavior of large-scale multimodal models has become a prominent research area, particularly focusing on hallucination detection and factuality checking. In this paper, we present the solution for the two tracks of Responsible AI challenge. Inspirations from the general domain demonstrate that a smaller distilled VLM can often outperform a larger VLM that is directly tuned on downstream tasks, while achieving higher efficiency. We thus jointly tackle two tasks from the perspective of knowledge distillation and propose a progressive hybrid knowledge distillation framework termed HKD4VLM. Specifically, the overall framework can be decomposed into Pyramid-like Progressive Online Distillation and Ternary-Coupled Refinement Distillation, hierarchically moving from coarse-grained knowledge alignment to fine-grained refinement. Besides, we further introduce the mapping shift-enhanced inference and diverse augmentation strategies to enhance model performance and robustness. Extensive experimental results demonstrate the effectiveness of our HKD4VLM. Ablation studies provide insights into the critical design choices driving performance gains.
zh
[CV-84] AS400-DET: Detection using Deep Learning Model for IBM i (AS/400)
【速读】：该论文试图解决IBM i系统（也称为AS/400）中GUI组件自动检测的问题，旨在提升基于图形用户界面（Graphical User Interface, GUI）的系统自动化测试能力。解决方案的关键在于构建一个包含1,050张系统屏幕图像的人工标注数据集，其中381张为日文界面的IBM i系统截图，并利用最先进的深度学习模型开发检测系统，以有效识别多种GUI组件，如文本标签、文本框、选项、表格、指令、键盘和命令行。
链接: https://arxiv.org/abs/2506.13032
作者: Thanh Tran,Son T. Luu,Quan Bui,Shoshin Nomura
机构: Amifiable Inc.(Amifiable公司); Japan Advanced Institute of Science and Technology(日本高级科学技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at the IVSP 2025 conference
Abstract:This paper proposes a method for automatic GUI component detection for the IBM i system (formerly and still more commonly known as AS/400). We introduce a human-annotated dataset consisting of 1,050 system screen images, in which 381 images are screenshots of IBM i system screens in Japanese. Each image contains multiple components, including text labels, text boxes, options, tables, instructions, keyboards, and command lines. We then develop a detection system based on state-of-the-art deep learning models and evaluate different approaches using our dataset. The experimental results demonstrate the effectiveness of our dataset in constructing a system for component detection from GUI screens. By automatically detecting GUI components from the screen, AS400-DET has the potential to perform automated testing on systems that operate via GUI screens.
zh
[CV-85] WildCAT3D: Appearance-Aware Multi-View Diffusion in the Wild
【速读】：该论文试图解决场景级新颖视图合成（scene-level novel view synthesis, NVS）中由于缺乏可用的干净多视角训练数据而带来的挑战，尤其是在手动标注数据集存在多样性、相机变化或版权问题的情况下。解决方案的关键在于提出WildCAT3D框架，通过显式建模图像中的全局外观条件，扩展最先进的多视角扩散范式，从而在多样且许可宽松的野外2D场景图像数据上进行训练。该方法使模型在推理时能够泛化到新场景，并生成多个一致的新视图，同时在训练数据源数量少于先前方法的情况下实现了当前最优的单视角NVS性能。
链接: https://arxiv.org/abs/2506.13030
作者: Morris Alper,David Novotny,Filippos Kokkinos,Hadar Averbuch-Elor,Tom Monnier
机构: Tel Aviv University (特拉维夫大学); Meta AI (Meta人工智能); Cornell University (康奈尔大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Despite recent advances in sparse novel view synthesis (NVS) applied to object-centric scenes, scene-level NVS remains a challenge. A central issue is the lack of available clean multi-view training data, beyond manually curated datasets with limited diversity, camera variation, or licensing issues. On the other hand, an abundance of diverse and permissively-licensed data exists in the wild, consisting of scenes with varying appearances (illuminations, transient occlusions, etc.) from sources such as tourist photos. To this end, we present WildCAT3D, a framework for generating novel views of scenes learned from diverse 2D scene image data captured in the wild. We unlock training on these data sources by explicitly modeling global appearance conditions in images, extending the state-of-the-art multi-view diffusion paradigm to learn from scene views of varying appearances. Our trained model generalizes to new scenes at inference time, enabling the generation of multiple consistent novel views. WildCAT3D provides state-of-the-art results on single-view NVS in object- and scene-level settings, while training on strictly less data sources than prior methods. Additionally, it enables novel applications by providing global appearance control during generation.
zh
[CV-86] DETRPose: Real-time end-to-end transformer model for multi-person pose estimation
【速读】：该论文试图解决在实时场景下使用基于Transformer的模型进行多人姿态估计（Multi-person Pose Estimation, MPPE）的问题。当前尚无能够实现实时MPPE的Transformer模型，而现有方法在训练效率、推理速度或参数量方面存在不足。解决方案的关键在于提出一种改进的解码器架构和关键点相似性度量方法，通过生成正负查询来提升所选查询的质量，从而实现高效的实时2D姿态估计。
链接: https://arxiv.org/abs/2506.13027
作者: Sebastian Janampa,Marios Pattichis
机构: The University of New Mexico (新墨西哥大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multi-person pose estimation (MPPE) estimates keypoints for all individuals present in an image. MPPE is a fundamental task for several applications in computer vision and virtual reality. Unfortunately, there are currently no transformer-based models that can perform MPPE in real time. The paper presents a family of transformer-based models capable of performing multi-person 2D pose estimation in real-time. Our approach utilizes a modified decoder architecture and keypoint similarity metrics to generate both positive and negative queries, thereby enhancing the quality of the selected queries within the architecture. Compared to state-of-the-art models, our proposed models train much faster, using 5 to 10 times fewer epochs, with competitive inference times without requiring quantization libraries to speed up the model. Furthermore, our proposed models provide competitive results or outperform alternative models, often using significantly fewer parameters.
zh
[CV-87] SmartHome-Bench: A Comprehensive Benchmark for Video Anomaly Detection in Smart Homes Using Multi-Modal Large Language Models CVPR2025
【速读】：该论文旨在解决现有视频异常检测（Video Anomaly Detection, VAD）基准在智能家庭场景中的适用性不足问题，即当前的VAD基准主要针对通用场景设计，未能考虑智能家庭应用的独特特性。其解决方案的关键在于提出SmartHome-Bench，这是首个专为评估智能家庭场景下VAD性能而设计的综合性基准，包含1,203个由智能家庭摄像头录制的视频，并引入一种新的异常分类体系，涵盖七类异常事件，如野生动物、老年人照护和婴儿监控等。此外，论文还提出了Taxonomy-Driven Reflective LLM Chain (TRLC)框架，通过结合多模态大语言模型（MLLMs）与分类体系驱动的反思链式结构，显著提升了异常检测的准确性。
链接: https://arxiv.org/abs/2506.12992
作者: Xinyi Zhao,Congjing Zhang,Pei Guo,Wei Li,Lin Chen,Chaoyue Zhao,Shuai Huang
机构: University of Washington (华盛顿大学); Wyze Labs, Inc. (Wyze实验室公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2025 Workshop: VAND 3.0 - Visual Anomaly and Novelty Detection
Abstract:Video anomaly detection (VAD) is essential for enhancing safety and security by identifying unusual events across different environments. Existing VAD benchmarks, however, are primarily designed for general-purpose scenarios, neglecting the specific characteristics of smart home applications. To bridge this gap, we introduce SmartHome-Bench, the first comprehensive benchmark specially designed for evaluating VAD in smart home scenarios, focusing on the capabilities of multi-modal large language models (MLLMs). Our newly proposed benchmark consists of 1,203 videos recorded by smart home cameras, organized according to a novel anomaly taxonomy that includes seven categories, such as Wildlife, Senior Care, and Baby Monitoring. Each video is meticulously annotated with anomaly tags, detailed descriptions, and reasoning. We further investigate adaptation methods for MLLMs in VAD, assessing state-of-the-art closed-source and open-source models with various prompting techniques. Results reveal significant limitations in the current models’ ability to detect video anomalies accurately. To address these limitations, we introduce the Taxonomy-Driven Reflective LLM Chain (TRLC), a new LLM chaining framework that achieves a notable 11.62% improvement in detection accuracy. The benchmark dataset and code are publicly available at this https URL.
zh
[CV-88] DuoFormer: Leverag ing Hierarchical Representations by Local and Global Attention Vision Transformer
【速读】：该论文旨在解决在医疗应用中，尽管Transformer已被广泛采用，但通过Transformer进行多尺度学习的探索仍较为有限的问题，同时针对层次化表示在辅助医学诊断中的优势未被充分挖掘。其解决方案的关键在于提出一种新型的层次化Transformer模型，该模型巧妙地结合了卷积神经网络（CNN）的特征提取能力与视觉Transformer（ViT）的高级表征潜力。通过引入CNN主干网络生成层次化视觉表示，并采用创新的块标记化过程将其适配为Transformer输入，从而保留了继承的多尺度归纳偏置。此外，还提出了尺度感知注意力机制，以直接捕捉尺度内和尺度间的关联，增强空间理解并保持全局感知，分别称为局部注意力和全局注意力。
链接: https://arxiv.org/abs/2506.12982
作者: Xiaoya Tang,Bodong Zhang,Man Minh Ho,Beatrice S. Knudsen,Tolga Tasdizen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite the widespread adoption of transformers in medical applications, the exploration of multi-scale learning through transformers remains limited, while hierarchical representations are considered advantageous for computer-aided medical diagnosis. We propose a novel hierarchical transformer model that adeptly integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the advanced representational potential of Vision Transformers (ViTs). Addressing the lack of inductive biases and dependence on extensive training datasets in ViTs, our model employs a CNN backbone to generate hierarchical visual representations. These representations are adapted for transformer input through an innovative patch tokenization process, preserving the inherited multi-scale inductive biases. We also introduce a scale-wise attention mechanism that directly captures intra-scale and inter-scale associations. This mechanism complements patch-wise attention by enhancing spatial understanding and preserving global perception, which we refer to as local and global attention, respectively. Our model significantly outperforms baseline models in terms of classification accuracy, demonstrating its efficiency in bridging the gap between Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The components are designed as plug-and-play for different CNN architectures and can be adapted for multiple applications. The code is available at this https URL.
zh
[CV-89] Boundary-Aware Vision Transformer for Angiography Vascular Network Segmentation
【速读】：该论文旨在解决冠状动脉造影中血管结构的精确分割问题，这一任务在医学图像分析中具有核心挑战性，主要由于血管结构细长、对比度低且复杂。论文提出的解决方案关键在于引入BAVT（Boundary-Aware Vision Transformer），这是一种基于Vision Transformer（ViT）的架构，通过引入边缘感知损失函数，显式引导分割结果关注细粒度的血管边界。与混合Transformer-CNN模型不同，BAVT保持了轻量且可扩展的结构，兼容大规模视觉基础模型（VFM）的预训练。实验结果表明，BAVT在DCA-1数据集上优于传统CNN和混合基线模型，验证了纯ViT编码器结合边界感知监督的有效性。
链接: https://arxiv.org/abs/2506.12980
作者: Nabil Hezil,Suraj Singh,Vita Vlasova,Oleg Rogov,Ahmed Bouridane,Rifat Hamoudi
机构: College of Computing and Informatics, University of Sharjah, UAE; Scientific and Technical Research Center for the Development of Arabic Language (CRSTDLA), Algeria; Artificial Intelligence Research Institute, Moscow, Russia; Skolkovo Institute of Science and Technology, Moscow, Russia; Bauman Moscow State Technical University, Moscow, Russia; Moscow Technical University of Communications and Informatics, Moscow, Russia; BIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory, University of Sharjah, UAE
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 2 figures, 2 tables; submitted to IPTA-2025
Abstract:Accurate segmentation of vascular structures in coronary angiography remains a core challenge in medical image analysis due to the complexity of elongated, thin, and low-contrast vessels. Classical convolutional neural networks (CNNs) often fail to preserve topological continuity, while recent Vision Transformer (ViT)-based models, although strong in global context modeling, lack precise boundary awareness. In this work, we introduce BAVT, a Boundary-Aware Vision Transformer, a ViT-based architecture enhanced with an edge-aware loss that explicitly guides the segmentation toward fine-grained vascular boundaries. Unlike hybrid transformer-CNN models, BAVT retains a minimal, scalable structure that is fully compatible with large-scale vision foundation model (VFM) pretraining. We validate our approach on the DCA-1 coronary angiography dataset, where BAVT achieves superior performance across medical image segmentation metrics outperforming both CNN and hybrid baselines. These results demonstrate the effectiveness of combining plain ViT encoders with boundary-aware supervision for clinical-grade vascular segmentation.
zh
[CV-90] Metropolis-Hastings Sampling for 3D Gaussian Reconstruction
【速读】：该论文旨在解决传统3D Gaussian Splatting (3DGS) 方法中依赖启发式密度控制机制（如克隆、分裂和剪枝）所带来的冗余计算或有益高斯分布过早移除的问题。其解决方案的关键在于将密度增加与剪枝过程重新建模为一种概率采样过程，通过聚合多视角误差和不透明度得分动态插入和迁移高斯分布，并基于误差驱动的重要性分数进行贝叶斯接受测试，从而减少对启发式的依赖，提升方法的灵活性和自适应性。
链接: https://arxiv.org/abs/2506.12945
作者: Hyunjin Kim,Haebeom Jung,Jaesik Park
机构: UC San Diego(加州大学圣地亚哥分校); Seoul National University(首尔国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Page: this https URL
Abstract:We propose an adaptive sampling framework for 3D Gaussian Splatting (3DGS) that leverages comprehensive multi-view photometric error signals within a unified Metropolis-Hastings approach. Traditional 3DGS methods heavily rely on heuristic-based density-control mechanisms (e.g., cloning, splitting, and pruning), which can lead to redundant computations or the premature removal of beneficial Gaussians. Our framework overcomes these limitations by reformulating densification and pruning as a probabilistic sampling process, dynamically inserting and relocating Gaussians based on aggregated multi-view errors and opacity scores. Guided by Bayesian acceptance tests derived from these error-based importance scores, our method substantially reduces reliance on heuristics, offers greater flexibility, and adaptively infers Gaussian distributions without requiring predefined scene complexity. Experiments on benchmark datasets, including Mip-NeRF360, Tanks and Temples, and Deep Blending, show that our approach reduces the number of Gaussians needed, enhancing computational efficiency while matching or modestly surpassing the view-synthesis quality of state-of-the-art models.
zh
[CV-91] Efficient Neural Video Representation via Structure-Preseving Patch Decoding
【速读】：该论文试图解决传统基于统一块划分的解码策略在视频表示中导致的块边界不连续问题，这主要是由于独立重建区域无法形成连贯的全局结构。解决方案的关键在于提出一种基于结构保持块（Structure-Preserving Patches, SPPs）的神经视频表示方法，通过类似PixelUnshuffle的操作将每帧重新排列为一组空间结构化的块帧，从而在保持原始帧空间一致性的同时实现块级解码，并支持从全局到局部的拟合策略，有效缓解上采样引起的性能退化。
链接: https://arxiv.org/abs/2506.12896
作者: Taiga Hayami,Kakeru Koizumi,Hiroshi Watanabe
机构: Waseda University (早稻田大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Implicit Neural Representations (INRs) have attracted significant interest for their ability to model complex signals by mapping spatial and temporal coordinates to signal values. In the context of neural video representation, several decoding strategies have been explored to balance compactness and reconstruction quality, including pixel-wise, frame-wise, and patch-wise methods. Patch-wise decoding aims to combine the flexibility of pixel-based models with the efficiency of frame-based approaches. However, conventional uniform patch division often leads to discontinuities at patch boundaries, as independently reconstructed regions may fail to form a coherent global structure. To address this limitation, we propose a neural video representation method based on Structure-Preserving Patches (SPPs). Our approach rearranges each frame into a set of spatially structured patch frames using a PixelUnshuffle-like operation. This rearrangement maintains the spatial coherence of the original frame while enabling patch-level decoding. The network learns to predict these rearranged patch frames, which supports a global-to-local fitting strategy and mitigates degradation caused by upsampling. Experiments on standard video datasets show that the proposed method improves reconstruction quality and compression performance compared to existing INR-based video representation methods.
zh
[CV-92] Model-Agnostic Temperature-Informed Sampling Enhances Cross-Year Crop Mapping with Deep Learning
【速读】：该论文试图解决传统基于光学卫星时间序列的作物类型分类方法在跨季节泛化能力差以及实时应用受限的问题，尤其是在缺乏当年标签数据的情况下，同时现有方法往往忽视不确定性量化，导致其在作物监测中的可靠性不足。解决方案的关键在于提出一种与模型无关的采样策略，该策略基于生长度日（Growing Degree Days, GDD）替代日历时间，利用生物学上有意义的热时域对时间序列进行均匀子采样，从而强调物候学上活跃的生长阶段，减少时间冗余和噪声，提升分类精度与不确定性估计的校准程度。
链接: https://arxiv.org/abs/2506.12885
作者: Mehmet Ozgur Turkoglu,Selene Ledain,Helge Aasen
机构: Agroscope(农业研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: under review
Abstract:Conventional benchmarks for crop type classification from optical satellite time series typically assume access to labeled data from the same year and rely on fixed calendar-day sampling. This limits generalization across seasons, where crop phenology shifts due to interannual climate variability, and precludes real-time application when current-year labels are unavailable. Furthermore, uncertainty quantification is often neglected, making such approaches unreliable for crop monitoring applications. Inspired by ecophysiological principles of plant growth, we propose a simple, model-agnostic sampling strategy that leverages growing degree days (GDD), based on daily average temperature, to replace calendar time with thermal time. By uniformly subsampling time series in this biologically meaningful domain, the method emphasizes phenologically active growth stages while reducing temporal redundancy and noise. We evaluate the method on a multi-year Sentinel-2 dataset spanning all of Switzerland, training on one growing season and testing on other seasons. Compared to state-of-the-art baselines, our method delivers substantial gains in classification accuracy and, critically, produces more calibrated uncertainty estimates. Notably, our method excels in low-data regimes and enables significantly more accurate early-season classification. With only 10 percent of the training data, our method surpasses the state-of-the-art baseline in both predictive accuracy and uncertainty estimation, and by the end of June, it achieves performance similar to a baseline trained on the full season. These results demonstrate that leveraging temperature data not only improves predictive performance across seasons but also enhances the robustness and trustworthiness of crop-type mapping in real-world applications.
zh
[CV-93] Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs
【速读】：该论文试图解决对抗样本在频率域中的特性理解不足的问题，特别是其与自然样本在频率成分上的差异对模型鲁棒性的影响。解决方案的关键在于分析不同频率成分（低频、中频和高频）在对抗样本中的作用，并揭示不同网络架构（如卷积神经网络与Transformer）在频率偏好上的差异，从而为提升AI模型安全性提供理论依据和实用建议。
链接: https://arxiv.org/abs/2506.12875
作者: Lu Chen,Han Yang,Hu Wang,Yuxin Cao,Shaofeng Li,Yuan Luo
机构: Shanghai Jiao Tong University (上海交通大学); Southeast University (东南大学); Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学); National University of Singapore (新加坡国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Adversarial examples have attracted significant attention over the years, yet understanding their frequency-based characteristics remains insufficient. In this paper, we investigate the intriguing properties of adversarial examples in the frequency domain for the image classification task, with the following key findings. (1) As the high-frequency components increase, the performance gap between adversarial and natural examples becomes increasingly pronounced. (2) The model performance against filtered adversarial examples initially increases to a peak and declines to its inherent robustness. (3) In Convolutional Neural Networks, mid- and high-frequency components of adversarial examples exhibit their attack capabilities, while in Transformers, low- and mid-frequency components of adversarial examples are particularly effective. These results suggest that different network architectures have different frequency preferences and that differences in frequency components between adversarial and natural examples may directly influence model robustness. Based on our findings, we further conclude with three useful proposals that serve as a valuable reference to the AI model security community.
zh
[CV-94] Active Adversarial Noise Suppression for Image Forgery Localization
【速读】：该论文旨在解决图像伪造定位模型在面对对抗噪声攻击时的脆弱性问题，即通过添加不可察觉的噪声，可以严重误导现有模型。其解决方案的关键在于提出一种对抗噪声抑制模块（Adversarial Noise Suppression Module, ANSM），该模块通过生成防御性扰动来抑制对抗噪声的影响。ANSM的核心策略包括两个阶段：第一阶段采用伪造相关特征对齐（Forgery-relevant Features Alignment, FFA），通过最小化通道级Kullback-Leibler散度减少伪造相关特征的分布差异；第二阶段引入掩码引导细化（Mask-guided Refinement, MgR），通过双掩码约束进一步优化防御扰动，确保其在对抗和原始伪造图像上的有效性。
链接: https://arxiv.org/abs/2506.12871
作者: Rongxuan Peng,Shunquan Tan,Xianbo Mo,Alex C. Kot,Jiwu Huang
机构: Shenzhen University (深圳大学); Shenzhen Key Laboratory of Media Security (深圳市媒体安全重点实验室); Guangdong Laboratory of Machine Perception and Intelligent Computing, Faculty of Engineering, Shenzhen MSU-BIT University (广东省机器感知与智能计算实验室，工程学院，深圳莫斯科大学-比特大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advances in deep learning have significantly propelled the development of image forgery localization. However, existing models remain highly vulnerable to adversarial attacks: imperceptible noise added to forged images can severely mislead these models. In this paper, we address this challenge with an Adversarial Noise Suppression Module (ANSM) that generate a defensive perturbation to suppress the attack effect of adversarial noise. We observe that forgery-relevant features extracted from adversarial and original forged images exhibit distinct distributions. To bridge this gap, we introduce Forgery-relevant Features Alignment (FFA) as a first-stage training strategy, which reduces distributional discrepancies by minimizing the channel-wise Kullback-Leibler divergence between these features. To further refine the defensive perturbation, we design a second-stage training strategy, termed Mask-guided Refinement (MgR), which incorporates a dual-mask constraint. MgR ensures that the perturbation remains effective for both adversarial and original forged images, recovering forgery localization accuracy to their original level. Extensive experiments across various attack algorithms demonstrate that our method significantly restores the forgery localization model’s performance on adversarial images. Notably, when ANSM is applied to original forged images, the performance remains nearly unaffected. To our best knowledge, this is the first report of adversarial defense in image forgery localization tasks. We have released the source code and anti-forensics dataset.
zh
[CV-95] EraserDiT: Fast Video Inpainting with Diffusion Transformer Model
【速读】：该论文旨在解决视频对象移除与修复（video object removal and inpainting）中的长期时间一致性不足和大范围遮挡区域修复效果不佳的问题。传统方法依赖于基于光流的传播和时空Transformer，但在处理大规模遮挡时难以有效利用长期时间特征并保证修复结果的时间一致性。该论文提出的解决方案关键在于引入一种基于扩散Transformer（Diffusion Transformer, DiT）的新颖视频修复方法，通过结合扩散模型与Transformer架构的优势，在保持高质量修复结果的同时增强长期时间一致性。此外，还提出了一种循环位置偏移策略以进一步提升推理阶段的时间一致性，并实现了视频中对象的自动检测与交互式移除功能。
链接: https://arxiv.org/abs/2506.12853
作者: Jie Liu,Zheng Hui
机构: Mango TV(芒果电视)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Video object removal and inpainting are critical tasks in the fields of computer vision and multimedia processing, aimed at restoring missing or corrupted regions in video sequences. Traditional methods predominantly rely on flow-based propagation and spatio-temporal Transformers, but these approaches face limitations in effectively leveraging long-term temporal features and ensuring temporal consistency in the completion results, particularly when dealing with large masks. Consequently, performance on extensive masked areas remains suboptimal. To address these challenges, this paper introduces a novel video inpainting approach leveraging the Diffusion Transformer (DiT). DiT synergistically combines the advantages of diffusion models and transformer architectures to maintain long-term temporal consistency while ensuring high-quality inpainting results. We propose a Circular Position-Shift strategy to further enhance long-term temporal consistency during the inference stage. Additionally, the proposed method automatically detects objects within videos, interactively removes specified objects, and generates corresponding prompts. In terms of processing speed, it takes only 180 seconds (testing on one NVIDIA A100 GPU) to complete a video with a resolution of 1080 \times 1920 with 121 frames without any acceleration method. Experimental results indicate that the proposed method demonstrates superior performance in content fidelity, texture restoration, and temporal consistency. Project page: this https URL.
zh
[CV-96] CAPO: Reinforcing Consistent Reasoning in Medical Decision-Making
【速读】：该论文旨在解决医学视觉问答（Med-VQA）中因感知理解与推理阶段不匹配以及推理路径与答案生成不一致而导致的模型性能受限问题，这些问题在很大程度上受到高质量医学数据集稀缺的影响。其解决方案的关键在于提出一种名为一致性感知偏好优化（Consistency-Aware Preference Optimization, CAPO）的大规模强化学习框架，该框架通过整合奖励机制，确保感知与推理之间的保真度、推理到答案推导的一致性以及最终回答的规则准确性。
链接: https://arxiv.org/abs/2506.12849
作者: Songtao Jiang,Yuan Wang,Ruizhe Chen,Yan Zhang,Ruilin Luo,Bohan Lei,Sibo Song,Yang Feng,Jimeng Sun,Jian Wu,Zuozhu Liu
机构: Zhejiang University (浙江大学); Tsinghua University (清华大学); Alibaba Group (阿里巴巴集团); Angelalign Inc. (安灵科技公司); UIUC (伊利诺伊大学厄巴纳-香槟分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In medical visual question answering (Med-VQA), achieving accurate responses relies on three critical steps: precise perception of medical imaging data, logical reasoning grounded in visual input and textual questions, and coherent answer derivation from the reasoning process. Recent advances in general vision-language models (VLMs) show that large-scale reinforcement learning (RL) could significantly enhance both reasoning capabilities and overall model performance. However, their application in medical domains is hindered by two fundamental challenges: 1) misalignment between perceptual understanding and reasoning stages, and 2) inconsistency between reasoning pathways and answer generation, both compounded by the scarcity of high-quality medical datasets for effective large-scale RL. In this paper, we first introduce Med-Zero-17K, a curated dataset for pure RL-based training, encompassing over 30 medical image modalities and 24 clinical tasks. Moreover, we propose a novel large-scale RL framework for Med-VLMs, Consistency-Aware Preference Optimization (CAPO), which integrates rewards to ensure fidelity between perception and reasoning, consistency in reasoning-to-answer derivation, and rule-based accuracy for final responses. Extensive experiments on both in-domain and out-of-domain scenarios demonstrate the superiority of our method over strong VLM baselines, showcasing strong generalization capability to 3D Med-VQA benchmarks and R1-like training paradigms.
zh
[CV-97] owards Fine-Grained Emotion Understanding via Skeleton-Based Micro-Gesture Recognition IJCAI IJCAI25
【速读】：该论文旨在解决从骨架序列中识别微小手势（micro-gestures, MGs）的问题，以实现隐藏情绪的理解。MGs具有细微性、短时性和低运动幅度的特点，使得其建模与分类极具挑战性。该研究采用PoseC3D作为基线框架，并引入三个关键改进：(1) 针对iMiGUE数据集设计的拓扑感知骨架表示，以更好地捕捉细粒度运动模式；(2) 改进的时间处理策略，以实现更平滑和时间一致的运动建模；(3) 引入语义标签嵌入作为辅助监督，以提升模型的泛化能力。这些改进使得该方法在iMiGUE测试集上达到了67.01%的Top-1准确率，并在官方MiGA Challenge排行榜上位列第三。
链接: https://arxiv.org/abs/2506.12848
作者: Hao Xu,Lechao Cheng,Yaxiong Wang,Shengeng Tang,Zhun Zhong
机构: Hefei University of Technology (合肥工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: MiGA@IJCAI25: International IJCAI Workshop on 3rd Human Behavior Analysis for Emotion Understanding, August 29, 2025, Guangzhou, China
Abstract:We present our solution to the MiGA Challenge at IJCAI 2025, which aims to recognize micro-gestures (MGs) from skeleton sequences for the purpose of hidden emotion understanding. MGs are characterized by their subtlety, short duration, and low motion amplitude, making them particularly challenging to model and classify. We adopt PoseC3D as the baseline framework and introduce three key enhancements: (1) a topology-aware skeleton representation specifically designed for the iMiGUE dataset to better capture fine-grained motion patterns; (2) an improved temporal processing strategy that facilitates smoother and more temporally consistent motion modeling; and (3) the incorporation of semantic label embeddings as auxiliary supervision to improve the model generalization. Our method achieves a Top-1 accuracy of 67.01% on the iMiGUE test set. As a result of these contributions, our approach ranks third on the official MiGA Challenge leaderboard. The source code is available at \hrefthis https URLthis https URL_track1.
zh
[CV-98] DiT-HOI: Inpainting-based Hand Object Interaction Reenactment via Video Diffusion Transformer
【速读】：该论文旨在解决数字人视频生成中手-物体交互（Hand-Object Interaction, HOI）的现实性和可信度问题，尤其是在遮挡、物体形状和方向变化以及精确物理交互等方面的挑战。其解决方案的关键在于提出了一种基于修复的令牌处理方法（Inp-TPU），结合两阶段视频扩散变换器（DiT）模型，通过重用预训练模型的上下文感知能力，无需引入额外参数，从而实现对未见过的人体和物体的强大泛化能力，并支持长视频生成。
链接: https://arxiv.org/abs/2506.12847
作者: Zhelun Shen,Chenming Wu,Junsheng Zhou,Chen Zhao,Kaisiyuan Wang,Hang Zhou,Yingying Li,Haocheng Feng,Wei He,Jingdong Wang
机构: Baidu Inc.(百度公司); Tsinghua University(清华大学)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注: Technical report, 12 pages
Abstract:Digital human video generation is gaining traction in fields like education and e-commerce, driven by advancements in head-body animation and lip-syncing technologies. However, realistic Hand-Object Interaction (HOI) - the complex dynamics between human hands and objects - continues to pose challenges. Generating natural and believable HOI reenactments is difficult due to issues such as occlusion between hands and objects, variations in object shapes and orientations, and the necessity for precise physical interactions, and importantly, the ability to generalize to unseen humans and objects. This paper presents a novel framework iDiT-HOI that enables in-the-wild HOI reenactment generation. Specifically, we propose a unified inpainting-based token process method, called Inp-TPU, with a two-stage video diffusion transformer (DiT) model. The first stage generates a key frame by inserting the designated object into the hand region, providing a reference for subsequent frames. The second stage ensures temporal coherence and fluidity in hand-object interactions. The key contribution of our method is to reuse the pretrained model’s context perception capabilities without introducing additional parameters, enabling strong generalization to unseen objects and scenarios, and our proposed paradigm naturally supports long video generation. Comprehensive evaluations demonstrate that our approach outperforms existing methods, particularly in challenging real-world scenes, offering enhanced realism and more seamless hand-object interactions.
zh
[CV-99] HyRet-Change: A hybrid retentive network for remote sensing change detection
【速读】：该论文旨在解决变化检测（Change Detection, CD）中局部与全局依赖关系如何有效缓解伪变化的问题，以及标准自注意力机制在捕捉细微变化、计算复杂度和训练并行性方面的固有局限。其解决方案的关键在于提出一种基于孪生网络的框架——HyRet-Change，该框架能够无缝整合卷积和保留机制在多尺度特征中的优势，从而保留关键信息并增强复杂场景下的适应性。核心创新包括：引入一种新型特征差异模块，以并行方式利用卷积和多头保留机制来捕获互补信息；设计一种自适应的局部-全局交互上下文感知机制，通过信息交换实现相互学习并提升判别能力。
链接: https://arxiv.org/abs/2506.12836
作者: Mustansar Fiaz,Mubashir Noman,Hiyam Debary,Kamran Ali,Hisham Cholakkal
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE IGARSS 2025
Abstract:Recently convolution and transformer-based change detection (CD) methods provide promising performance. However, it remains unclear how the local and global dependencies interact to effectively alleviate the pseudo changes. Moreover, directly utilizing standard self-attention presents intrinsic limitations including governing global feature representations limit to capture subtle changes, quadratic complexity, and restricted training parallelism. To address these limitations, we propose a Siamese-based framework, called HyRet-Change, which can seamlessly integrate the merits of convolution and retention mechanisms at multi-scale features to preserve critical information and enhance adaptability in complex scenes. Specifically, we introduce a novel feature difference module to exploit both convolutions and multi-head retention mechanisms in a parallel manner to capture complementary information. Furthermore, we propose an adaptive local-global interactive context awareness mechanism that enables mutual learning and enhances discrimination capability through information exchange. We perform experiments on three challenging CD datasets and achieve state-of-the-art performance compared to existing methods. Our source code is publicly available at this https URL.
zh
[CV-100] DiffS-NOCS: 3D Point Cloud Reconstruction through Coloring Sketches to NOCS Maps Using Diffusion Models
【速读】：该论文试图解决从给定的条件草图重建3D点云的问题，特别是针对现有方法在3D空间中直接操作时面临的领域变异性和从2D草图准确重建3D结构的困难，以及在多模态融合中同时接受提示控制和稀疏草图的挑战。解决方案的关键在于提出DiffS-NOCS（基于扩散的草图到NOCS图），该方法利用改进的多视角解码器结合ControlNet，在2D空间中生成包含3D结构和位置信息的NOCS图，并通过多视角NOCS图的组合实现3D点云重建。此外，引入视角编码器增强草图理解，并设计特征级多视角聚合网络作为去噪模块，促进跨视角信息交换，提升NOCS图生成中的3D一致性。
链接: https://arxiv.org/abs/2506.12835
作者: Di Kong,Qianhui Wan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Reconstructing a 3D point cloud from a given conditional sketch is challenging. Existing methods often work directly in 3D space, but domain variability and difficulty in reconstructing accurate 3D structures from 2D sketches remain significant obstacles. Moreover, ideal models should also accept prompts for control, in addition with the sparse sketch, posing challenges in multi-modal fusion. We propose DiffS-NOCS (Diffusion-based Sketch-to-NOCS Map), which leverages ControlNet with a modified multi-view decoder to generate NOCS maps with embedded 3D structure and position information in 2D space from sketches. The 3D point cloud is reconstructed by combining multiple NOCS maps from different views. To enhance sketch understanding, we integrate a viewpoint encoder for extracting viewpoint features. Additionally, we design a feature-level multi-view aggregation network as the denoising module, facilitating cross-view information exchange and improving 3D consistency in NOCS map generation. Experiments on ShapeNet demonstrate that DiffS-NOCS achieves controllable and fine-grained point cloud reconstruction aligned with sketches.
zh
[CV-101] ComplexBench-Edit: Benchmarking Complex Instruction-Driven Image Editing via Compositional Dependencies
【速读】：该论文旨在解决现有文本驱动图像编辑模型在处理复杂、多步骤指令（尤其是“链式”指令）时表现不佳的问题，这些问题包括模型难以理解相互依赖的操作以及现有基准测试未能充分评估此类能力。解决方案的关键在于提出ComplexBench-Edit基准，该基准系统地评估模型在复杂、多指令和链式依赖图像编辑任务中的性能，并引入一种基于思维链（Chain-of-Thought, CoT）的方法，显著提升了模型遵循复杂指令的能力。
链接: https://arxiv.org/abs/2506.12830
作者: Chenglin Wang,Yucheng Zhou,Qianning Wang,Zhe Wang,Kai Zhang
机构: East China Normal University (华东师范大学); University of Macau (澳门大学); Auckland University of Technology (奥克兰理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 Pages
Abstract:Text-driven image editing has achieved remarkable success in following single instructions. However, real-world scenarios often involve complex, multi-step instructions, particularly ``chain’’ instructions where operations are interdependent. Current models struggle with these intricate directives, and existing benchmarks inadequately evaluate such capabilities. Specifically, they often overlook multi-instruction and chain-instruction complexities, and common consistency metrics are flawed. To address this, we introduce ComplexBench-Edit, a novel benchmark designed to systematically assess model performance on complex, multi-instruction, and chain-dependent image editing tasks. ComplexBench-Edit also features a new vision consistency evaluation method that accurately assesses non-modified regions by excluding edited areas. Furthermore, we propose a simple yet powerful Chain-of-Thought (CoT)-based approach that significantly enhances the ability of existing models to follow complex instructions. Our extensive experiments demonstrate ComplexBench-Edit’s efficacy in differentiating model capabilities and highlight the superior performance of our CoT-based method in handling complex edits. The data and code are released at this https URL.
zh
[CV-102] LOP: Learning Optimal Pruning for Efficient On-Demand MLLM s Scaling
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在不同硬件平台部署时，传统结构剪枝技术因依赖迭代搜索过程而导致的计算开销过大的问题。解决方案的关键在于提出一种高效的神经剪枝框架LOP，该框架通过训练自回归神经网络（Autoregressive Neural Networks, NNs）直接预测适应目标剪枝约束的逐层剪枝策略，从而避免了计算成本高昂的搜索方法。
链接: https://arxiv.org/abs/2506.12826
作者: Zhihan Zhang,Xiang Pan,Hongchen Wei,Zhenzhong Chen
机构: Wuhan University (武汉大学); Lingnan University (岭南大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Structural pruning techniques are essential for deploying multimodal large language models (MLLMs) across various hardware platforms, from edge devices to cloud servers. However, current pruning methods typically determine optimal strategies through iterative search processes, resulting in substantial computational overhead for on-demand MLLMs adaptation. To address this challenge, we propose LOP, an efficient neural pruning framework that learns optimal pruning strategies from the target pruning constraint, eliminating the need for computationally expensive search-based methods. LOP approach trains autoregressive neural networks (NNs) to directly predict layer-wise pruning strategies adaptive to the target pruning constraint, eliminating the time-consuming iterative searches. Experimental results across multiple tasks show that LOP outperforms state-of-the-art pruning methods in various metrics while achieving up to three orders of magnitude speedup.
zh
[CV-103] Learning Unpaired Image Dehazing with Physics-based Rehazy Generation
【速读】：该论文旨在解决图像去雾任务中由于过度拟合合成训练对而造成的泛化能力不足问题，这一问题导致模型在真实场景中的表现不佳。其解决方案的关键在于提出一种名为Rehazy的新型无配对训练策略，该策略通过探索雾霾图像中潜在干净图像的一致性，并利用雾霾-去雾对进行有效学习，以捕捉真实的雾霾特性。此外，该方法构建了一个基于物理的去雾生成流程，理论上验证了其生成高质量去雾图像的可靠性，并引入了一个双分支框架，分别通过干净分支和雾霾分支提升去雾能力和泛化能力。
链接: https://arxiv.org/abs/2506.12824
作者: Haoyou Deng,Zhiqiang Li,Feng Zhang,Qingbo Lu,Zisheng Cao,Yuanjie Shao,Shuhang Gu,Changxin Gao,Nong Sang
机构: Huazhong University of Science and Technology (华中科技大学); DJI Technology Co., Ltd (大疆创新科技有限公司); University of Electronic Science and Technology of China (电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Overfitting to synthetic training pairs remains a critical challenge in image dehazing, leading to poor generalization capability to real-world scenarios. To address this issue, existing approaches utilize unpaired realistic data for training, employing CycleGAN or contrastive learning frameworks. Despite their progress, these methods often suffer from training instability, resulting in limited dehazing performance. In this paper, we propose a novel training strategy for unpaired image dehazing, termed Rehazy, to improve both dehazing performance and training stability. This strategy explores the consistency of the underlying clean images across hazy images and utilizes hazy-rehazy pairs for effective learning of real haze characteristics. To favorably construct hazy-rehazy pairs, we develop a physics-based rehazy generation pipeline, which is theoretically validated to reliably produce high-quality rehazy images. Additionally, leveraging the rehazy strategy, we introduce a dual-branch framework for dehazing network training, where a clean branch provides a basic dehazing capability in a synthetic manner, and a hazy branch enhances the generalization ability with hazy-rehazy pairs. Moreover, we design a new dehazing network within these branches to improve the efficiency, which progressively restores clean scenes from coarse to fine. Extensive experiments on four benchmarks demonstrate the superior performance of our approach, exceeding the previous state-of-the-art methods by 3.58 dB on the SOTS-Indoor dataset and by 1.85 dB on the SOTS-Outdoor dataset in PSNR. Our code will be publicly available.
zh
[CV-104] Leverag ing MIMIC Datasets for Better Digital Health: A Review on Open Problems Progress Highlights and Future Promises
【速读】：该论文试图解决医学信息集市（Medical Information Mart for Intensive Care, MIMIC）数据集在数据集成、表示和互操作性方面存在的关键挑战，这些挑战限制了机器学习模型的泛化能力和实时应用。解决方案的关键在于识别并分析数据粒度、基数限制、异构编码方案和伦理约束等持续性问题，并通过维度约简、时间建模、因果推断和隐私保护分析等技术进展推动解决。同时，论文提出了混合建模、联邦学习和标准化预处理流程等有前景的方向，以促进基于MIMIC的数字健康创新。
链接: https://arxiv.org/abs/2506.12808
作者: Afifa Khaled,Mohammed Sabir,Rizwan Qureshi,Camillo Maria Caruso,Valerio Guarrasi,Suncheng Xiang,S Kevin Zhou
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of thousands of critical care admissions, enabling a broad spectrum of applications in clinical decision support, outcome prediction, and healthcare analytics. Although numerous studies and surveys have explored the predictive power and clinical utility of MIMIC based models, critical challenges in data integration, representation, and interoperability remain underexplored. This paper presents a comprehensive survey that focuses uniquely on open problems. We identify persistent issues such as data granularity, cardinality limitations, heterogeneous coding schemes, and ethical constraints that hinder the generalizability and real-time implementation of machine learning models. We highlight key progress in dimensionality reduction, temporal modelling, causal inference, and privacy preserving analytics, while also outlining promising directions including hybrid modelling, federated learning, and standardized preprocessing pipelines. By critically examining these structural limitations and their implications, this survey offers actionable insights to guide the next generation of MIMIC powered digital health innovations.
zh
[CV-105] SMPL Normal Map Is All You Need for Single-view Textured Human Reconstruction ICME2025
【速读】：该论文旨在解决单视角纹理化人体重建中的问题，即通过输入单目2D图像重建带纹理的3D数字人体。现有方法包括受稀缺3D人体数据限制的前馈方法，以及容易产生错误2D幻觉的扩散模型方法。该论文提出的解决方案是SEHR框架，其关键在于整合预训练的大规模3D重建模型与人体几何先验，并通过两个核心组件：SMPL Normal Map Guidance (SNMG) 和 SMPL Normal Map Constraint (SNMC)，实现无需预设扩散模型的一次前向传播完成单视角人体重建。
链接: https://arxiv.org/abs/2506.12793
作者: Wenhao Shen,Gangjian Zhang,Jianfeng Zhang,Yu Feng,Nanjie Yao,Xuanmeng Zhang,Hao Wang
机构: Nanyang Technological University (南洋理工大学); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学(广州)); National University of Singapore (新加坡国立大学); Zhejiang University of Technology (浙江工业大学); University of Technology Sydney (悉尼科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICME 2025 (Oral)
Abstract:Single-view textured human reconstruction aims to reconstruct a clothed 3D digital human by inputting a monocular 2D image. Existing approaches include feed-forward methods, limited by scarce 3D human data, and diffusion-based methods, prone to erroneous 2D hallucinations. To address these issues, we propose a novel SMPL normal map Equipped 3D Human Reconstruction (SEHR) framework, integrating a pretrained large 3D reconstruction model with human geometry prior. SEHR performs single-view human reconstruction without using a preset diffusion model in one forward propagation. Concretely, SEHR consists of two key components: SMPL Normal Map Guidance (SNMG) and SMPL Normal Map Constraint (SNMC). SNMG incorporates SMPL normal maps into an auxiliary network to provide improved body shape guidance. SNMC enhances invisible body parts by constraining the model to predict an extra SMPL normal Gaussians. Extensive experiments on two benchmark datasets demonstrate that SEHR outperforms existing state-of-the-art methods.
zh
[CV-106] Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting
【速读】：该论文旨在解决无线辐射场（Wireless Radiance Field, WRF）建模中的精度不足与计算效率低的问题，传统方法依赖经验公式或物理仿真，受限于准确性或场景先验，而基于神经辐射场（NeRF）的方法虽提升了重建保真度，但因依赖计算成本高的多层感知机（MLP）查询而难以实时部署。论文提出的解决方案关键在于引入高斯点云（Gaussian Splatting, GS），利用其在光学辐射场建模中的高效性，实现紧凑且精确的WRF重建。具体而言，论文提出SwiftWRF框架，采用可变形2D高斯点云技术，在单侧收发器移动条件下合成任意位置的WRF频谱，并通过CUDA加速的光栅化技术实现每秒超过100000帧的频谱渲染，同时使用轻量级MLP建模2D高斯分布的形变，有效捕捉移动引起的WRF变化。
链接: https://arxiv.org/abs/2506.12787
作者: Mufan Liu,Cixiao Zhang,Qi Yang,Yujie Cao,Yiling Xu,Yin Xu,Shu Sun,Mingzeng Dai,Yunfeng Guan
机构: Shanghai Jiao Tong University (上海交通大学); University of Missouri-Kansas City (密苏里大学堪萨斯城分校); Lenovo Group (联想集团)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Modeling the wireless radiance field (WRF) is fundamental to modern communication systems, enabling key tasks such as localization, sensing, and channel estimation. Traditional approaches, which rely on empirical formulas or physical simulations, often suffer from limited accuracy or require strong scene priors. Recent neural radiance field (NeRF-based) methods improve reconstruction fidelity through differentiable volumetric rendering, but their reliance on computationally expensive multilayer perceptron (MLP) queries hinders real-time deployment. To overcome these challenges, we introduce Gaussian splatting (GS) to the wireless domain, leveraging its efficiency in modeling optical radiance fields to enable compact and accurate WRF reconstruction. Specifically, we propose SwiftWRF, a deformable 2D Gaussian splatting framework that synthesizes WRF spectra at arbitrary positions under single-sided transceiver mobility. SwiftWRF employs CUDA-accelerated rasterization to render spectra at over 100000 fps and uses a lightweight MLP to model the deformation of 2D Gaussians, effectively capturing mobility-induced WRF variations. In addition to novel spectrum synthesis, the efficacy of SwiftWRF is further underscored in its applications in angle-of-arrival (AoA) and received signal strength indicator (RSSI) prediction. Experiments conducted on both real-world and synthetic indoor scenes demonstrate that SwiftWRF can reconstruct WRF spectra up to 500x faster than existing state-of-the-art methods, while significantly enhancing its signal quality. Code and datasets will be released.
zh
[CV-107] Semantic-Aware Visual Information Transmission With Key Information Extraction Over Wireless Networks
【速读】：该论文旨在解决6G网络中传统无线图像传输框架在动态环境中难以平衡计算效率、鲁棒性和图像质量的问题。其关键解决方案是提出一种基于AI原生的深度联合信源信道编码（JSCC）框架，通过集成关键信息提取和自适应背景合成技术，实现语义感知的智能传输。该方法利用Mediapipe和Rembg等AI工具动态分离前景特征并匹配预训练库中的背景，从而减少数据负载并保持视觉保真度，实验结果表明在低信噪比条件下显著提升了峰值信噪比（PSNR）。
链接: https://arxiv.org/abs/2506.12786
作者: Chen Zhu,Kang Liang,Jianrong Bao,Zhouxiang Zhao,Zhaohui Yang,Zhaoyang Zhang,Mohammad Shikh-Bahaei
机构: Hangzhou Dianzi University (杭州电子科技大学); Zhejiang University (浙江大学); Polytechnic Institute, Zhejiang University (浙江大学理工学院); King’s College London (伦敦国王学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The advent of 6G networks demands unprecedented levels of intelligence, adaptability, and efficiency to address challenges such as ultra-high-speed data transmission, ultra-low latency, and massive connectivity in dynamic environments. Traditional wireless image transmission frameworks, reliant on static configurations and isolated source-channel coding, struggle to balance computational efficiency, robustness, and quality under fluctuating channel conditions. To bridge this gap, this paper proposes an AI-native deep joint source-channel coding (JSCC) framework tailored for resource-constrained 6G networks. Our approach integrates key information extraction and adaptive background synthesis to enable intelligent, semantic-aware transmission. Leveraging AI-driven tools, Mediapipe for human pose detection and Rembg for background removal, the model dynamically isolates foreground features and matches backgrounds from a pre-trained library, reducing data payloads while preserving visual fidelity. Experimental results demonstrate significant improvements in peak signal-to-noise ratio (PSNR) compared with traditional JSCC method, especially under low-SNR conditions. This approach offers a practical solution for multimedia services in resource-constrained mobile communications.
zh
[CV-108] A large-scale physically-based synthetic dataset for satellite pose estimation
【速读】：该论文旨在解决卫星位姿估计（pose estimation）在真实、复杂和挑战性视觉条件下训练与测试的难题，特别是针对哈勃空间望远镜（Hubble Space Telescope, HST）这一复杂且多关节目标。其解决方案的关键在于提出了一种基于深度学习的视觉空间模拟系统（Deep Learning Visual Space Simulation System, DLVS3），该系统包含一个新型合成数据生成器和模拟流水线，能够生成高保真度的3D模型、动态光照（包括地球反射等次级光源）以及物理上精确的材质属性，并支持大规模、丰富标注的图像集生成，涵盖地面真实6-DoF位姿、关键点数据、语义分割、深度图和法线图等信息。
链接: https://arxiv.org/abs/2506.12782
作者: Szabolcs Velkei,Csaba Goldschmidt,Károly Vass
机构: Machine Intelligence Zrt (机器智能公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 6 figures
Abstract:The Deep Learning Visual Space Simulation System (DLVS3) introduces a novel synthetic dataset generator and a simulation pipeline specifically designed for training and testing satellite pose estimation solutions. This work introduces the DLVS3-HST-V1 dataset, which focuses on the Hubble Space Telescope (HST) as a complex, articulated target. The dataset is generated using advanced real-time and offline rendering technologies, integrating high-fidelity 3D models, dynamic lighting (including secondary sources like Earth reflection), and physically accurate material properties. The pipeline supports the creation of large-scale, richly annotated image sets with ground-truth 6-DoF pose and keypoint data, semantic segmentation, depth, and normal maps. This enables the training and benchmarking of deep learning-based pose estimation solutions under realistic, diverse, and challenging visual conditions. The paper details the dataset generation process, the simulation architecture, and the integration with deep learning frameworks, and positions DLVS3 as a significant step toward closing the domain gap for autonomous spacecraft operations in proximity and servicing missions.
zh
[CV-109] Native Visual Understanding: Resolving Resolution Dilemmas in Vision-Language Models
【速读】：该论文试图解决视觉-语言模型（Vision-Language Models, VLMs）在处理现实世界图像中多样化的分辨率和宽高比时所面临的“分辨率困境”（Resolution Dilemma），这一问题源于现有模型多依赖固定低分辨率输入，以及评估基准在考虑视觉条件时未能充分关注分辨率因素。解决方案的关键在于提出RC-Bench，一个专门用于系统评估VLM在极端视觉条件下能力的新基准，并结合NativeRes-LLaVA开源训练框架，使VLM能够有效处理图像的原生分辨率和宽高比。实验结果表明，原生分辨率视觉编码显著提升了VLM在RC-Bench及其他以分辨率为中心的基准上的性能。
链接: https://arxiv.org/abs/2506.12776
作者: Junbo Niu,Yuanhong Zheng,Ziyang Miao,Hejun Dong,Chunjiang Ge,Hao Liang,Ma Lu,Bohan Zeng,Qiahao Zheng,Conghui He,Wentao Zhang
机构: Peking University (北京大学); Shanghai AI Laboratory (上海人工智能实验室); Shandong University (山东大学); Beihang University (北京航空航天大学); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-Language Models (VLMs) face significant challenges when dealing with the diverse resolutions and aspect ratios of real-world images, as most existing models rely on fixed, low-resolution inputs. While recent studies have explored integrating native resolution visual encoding to improve model performance, such efforts remain fragmented and lack a systematic framework within the open-source community. Moreover, existing benchmarks fall short in evaluating VLMs under varied visual conditions, often neglecting resolution as a critical factor. To address the “Resolution Dilemma” stemming from both model design and benchmark limitations, we introduce RC-Bench, a novel benchmark specifically designed to systematically evaluate VLM capabilities under extreme visual conditions, with an emphasis on resolution and aspect ratio variations. In conjunction, we propose NativeRes-LLaVA, an open-source training framework that empowers VLMs to effectively process images at their native resolutions and aspect ratios. Based on RC-Bench and NativeRes-LLaVA, we conduct comprehensive experiments on existing visual encoding strategies. The results show that Native Resolution Visual Encoding significantly improves the performance of VLMs on RC-Bench as well as other resolution-centric benchmarks. Code is available at this https URL.
zh
[CV-110] Scene-aware SAR ship detection guided by unsupervised sea-land segmentation
【速读】：该论文试图解决基于深度学习的合成孔径雷达（SAR）船舶检测中由于缺乏先验知识而导致的检测精度下降问题。解决方案的关键在于提出一种场景感知的SAR船舶检测方法，该方法通过两个模型进行增强：无监督海陆分割模块（ULSM）和陆地注意力抑制模块（LASM）。ULSM采用无监督方式对输入场景进行分类并完成近岸场景的海陆分割，而LASM则利用海陆分割信息作为先验知识，抑制网络对陆地的关注，从而提升远海区域的检测性能。
链接: https://arxiv.org/abs/2506.12775
作者: Han Ke,Xiao Ke,Ye Yan,Rui Liu,Jinpeng Yang,Tianwen Zhang,Xu Zhan,Xiaowo Xu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:DL based Synthetic Aperture Radar (SAR) ship detection has tremendous advantages in numerous areas. However, it still faces some problems, such as the lack of prior knowledge, which seriously affects detection accuracy. In order to solve this problem, we propose a scene-aware SAR ship detection method based on unsupervised sea-land segmentation. This method follows a classical two-stage framework and is enhanced by two models: the unsupervised land and sea segmentation module (ULSM) and the land attention suppression module (LASM). ULSM and LASM can adaptively guide the network to reduce attention on land according to the type of scenes (inshore scene and offshore scene) and add prior knowledge (sea land segmentation information) to the network, thereby reducing the network’s attention to land directly and enhancing offshore detection performance relatively. This increases the accuracy of ship detection and enhances the interpretability of the model. Specifically, in consideration of the lack of land sea segmentation labels in existing deep learning-based SAR ship detection datasets, ULSM uses an unsupervised approach to classify the input data scene into inshore and offshore types and performs sea-land segmentation for inshore scenes. LASM uses the sea-land segmentation information as prior knowledge to reduce the network’s attention to land. We conducted our experiments using the publicly available SSDD dataset, which demonstrated the effectiveness of our network.
zh
[CV-111] Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better
【速读】：该论文旨在解决红外小目标（Infrared Small Target, IRST）检测中同时实现精确、通用、鲁棒和高效性能的难题，主要由于目标极其微弱且存在强烈干扰。其解决方案的关键在于从时间维度中挖掘“更本质”的信息，通过理论分析揭示了时间轮廓中的全局时间显著性和相关性信息在区分目标信号与其他信号方面的显著优势，并据此将IRST检测任务重新建模为一维信号异常检测任务，提出了仅在时间维度进行计算的高效深度时间探测网络（DeepPro）。
链接: https://arxiv.org/abs/2506.12766
作者: Ruojing Li,Wei An,Xinyi Ying,Yingqian Wang,Yimian Dai,Longguang Wang,Miao Li,Yulan Guo,Li Liu
机构: National University of Defense Technology (国防科技大学); Nankai University (南开大学); Aviation University of Air Force (空军航空大学); Sun Yat-Sen University (中山大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Infrared small target (IRST) detection is challenging in simultaneously achieving precise, universal, robust and efficient performance due to extremely dim targets and strong interference. Current learning-based methods attempt to leverage more" information from both the spatial and the short-term temporal domains, but suffer from unreliable performance under complex conditions while incurring computational redundancy. In this paper, we explore the more essential" information from a more crucial domain for the detection. Through theoretical analysis, we reveal that the global temporal saliency and correlation information in the temporal profile demonstrate significant superiority in distinguishing target signals from other signals. To investigate whether such superiority is preferentially leveraged by well-trained networks, we built the first prediction attribution tool in this field and verified the importance of the temporal profile information. Inspired by the above conclusions, we remodel the IRST detection task as a one-dimensional signal anomaly detection task, and propose an efficient deep temporal probe network (DeepPro) that only performs calculations in the time dimension for IRST detection. We conducted extensive experiments to fully validate the effectiveness of our method. The experimental results are exciting, as our DeepPro outperforms existing state-of-the-art IRST detection methods on widely-used benchmarks with extremely high efficiency, and achieves a significant improvement on dim targets and in complex scenarios. We provide a new modeling domain, a new insight, a new method, and a new performance, which can promote the development of IRST detection. Codes are available at this https URL.
zh
[CV-112] Unleashing Diffusion and State Space Models for Medical Image Segmentation
【速读】：该论文旨在解决现有医学影像分割模型在遇到未见过的器官或肿瘤时缺乏鲁棒性的问题，特别是针对训练数据中未包含的罕见或新型肿瘤类别识别能力不足的问题。其解决方案的关键在于提出了一种名为DSM的框架，该框架结合了扩散模型和状态空间模型，通过两组在改进的注意力解码器中训练的物体查询来提升分类准确性，并利用扩散引导的视觉提示和特征融合策略，增强了对未见过肿瘤的精确分割能力，同时通过集成CLIP文本嵌入提升了模型在多标签任务中的鲁棒性。
链接: https://arxiv.org/abs/2506.12747
作者: Rong Wu,Ziqi Chen,Liming Zhong,Heng Li,Hai Shu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing segmentation models trained on a single medical imaging dataset often lack robustness when encountering unseen organs or tumors. Developing a robust model capable of identifying rare or novel tumor categories not present during training is crucial for advancing medical imaging applications. We propose DSM, a novel framework that leverages diffusion and state space models to segment unseen tumor categories beyond the training data. DSM utilizes two sets of object queries trained within modified attention decoders to enhance classification accuracy. Initially, the model learns organ queries using an object-aware feature grouping strategy to capture organ-level visual features. It then refines tumor queries by focusing on diffusion-based visual prompts, enabling precise segmentation of previously unseen tumors. Furthermore, we incorporate diffusion-guided feature fusion to improve semantic segmentation performance. By integrating CLIP text embeddings, DSM captures category-sensitive classes to improve linguistic transfer knowledge, thereby enhancing the model’s robustness across diverse scenarios and multi-label tasks. Extensive experiments demonstrate the superior performance of DSM in various tumor segmentation tasks. Code is available at this https URL.
zh
[CV-113] Adaptive Dropout: Unleashing Dropout across Layers for Generalizable Image Super-Resolution CVPR2025
【速读】：该论文旨在解决盲超分辨率（blind SR）中由于未知退化导致的严重过拟合问题。现有方法虽受dropout启发，通过正则化特征提升泛化能力，但仅关注最终层前的特征正则化，忽视了中间层特征的泛化需求。论文提出自适应dropout（Adaptive Dropout），其关键在于缓解训练-测试不一致性和不同层间泛化需求的不一致性，通过重新设计dropout形式并自适应融合dropout前后特征，以及创新性地引入逐层退火的自适应训练策略，以增强特征传播。实验表明，该方法在合成与真实世界基准数据集上均优于以往正则化方法，并在其他图像恢复任务中表现出色。
链接: https://arxiv.org/abs/2506.12738
作者: Hang Xu,Wei Yu,Jiangtong Tan,Zhen Zou,Feng Zhao
机构: University of Science and Technology of China (中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages, 8 figures, CVPR2025
Abstract:Blind Super-Resolution (blind SR) aims to enhance the model’s generalization ability with unknown degradation, yet it still encounters severe overfitting issues. Some previous methods inspired by dropout, which enhances generalization by regularizing features, have shown promising results in blind SR. Nevertheless, these methods focus solely on regularizing features before the final layer and overlook the need for generalization in features at intermediate layers. Without explicit regularization of features at intermediate layers, the blind SR network struggles to obtain well-generalized feature representations. However, the key challenge is that directly applying dropout to intermediate layers leads to a significant performance drop, which we attribute to the inconsistency in training-testing and across layers it introduced. Therefore, we propose Adaptive Dropout, a new regularization method for blind SR models, which mitigates the inconsistency and facilitates application across intermediate layers of networks. Specifically, for training-testing inconsistency, we re-design the form of dropout and integrate the features before and after dropout adaptively. For inconsistency in generalization requirements across different layers, we innovatively design an adaptive training strategy to strengthen feature propagation by layer-wise annealing. Experimental results show that our method outperforms all past regularization methods on both synthetic and real-world benchmark datasets, also highly effective in other image restoration tasks. Code is available at \hrefthis https URLthis https URL.
zh
[CV-114] Cross-architecture universal feature coding via distribution alignment
【速读】：该论文试图解决跨架构通用特征编码（cross-architecture universal feature coding, CAUFC）问题，即构建一个能够有效压缩异构模型架构（如CNN与Transformer）特征的统一编解码器。现有方法多为架构特定的，限制了其在实际场景中的应用。解决方案的关键在于提出一种两步分布对齐方法：首先通过格式对齐方法将CNN和Transformer特征统一为一致的2D标记格式；其次通过特征值对齐方法利用截断和归一化来协调统计分布。
链接: https://arxiv.org/abs/2506.12737
作者: Changsheng Gao,Shan Liu,Feng Wu,Weisi Lin
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Feature coding has become increasingly important in scenarios where semantic representations rather than raw pixels are transmitted and stored. However, most existing methods are architecture-specific, targeting either CNNs or Transformers. This design limits their applicability in real-world scenarios where features from both architectures coexist. To address this gap, we introduce a new research problem: cross-architecture universal feature coding (CAUFC), which seeks to build a unified codec that can effectively compress features from heterogeneous architectures. To tackle this challenge, we propose a two-step distribution alignment method. First, we design the format alignment method that unifies CNN and Transformer features into a consistent 2D token format. Second, we propose the feature value alignment method that harmonizes statistical distributions via truncation and normalization. As a first attempt to study CAUFC, we evaluate our method on the image classification task. Experimental results demonstrate that our method achieves superior rate-accuracy trade-offs compared to the architecture-specific baseline. This work marks an initial step toward universal feature compression across heterogeneous model architectures.
zh
[CV-115] Learning to Fuse: Modality-Aware Adaptive Scheduling for Robust Multimodal Foundation Models
【速读】：该论文旨在解决多模态基础模型在融合不同模态信息时存在的问题，即现有方法通常采用固定或任务特定的融合策略，忽视了模态可靠性与样本复杂性的内在变化。其解决方案的关键在于提出一种称为模态感知自适应融合调度（Modality-Aware Adaptive Fusion Scheduling, MA-AFS）的通用框架，该框架通过轻量级神经调度器动态调节每个实例中各模态的贡献权重，结合视觉和文本熵信号以及跨模态一致性线索来预测融合权重，从而实现对更可靠模态的自适应强调。
链接: https://arxiv.org/abs/2506.12733
作者: Liam Bennett,Mason Clark,Lucas Anderson,Hana Satou,Olivia Martinez
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal foundation models have achieved impressive progress across a wide range of vision-language tasks. However, existing approaches often adopt fixed or task-specific fusion strategies, neglecting the intrinsic variability of modality reliability and sample complexity. In this paper, we propose Modality-Aware Adaptive Fusion Scheduling (MA-AFS), a general framework that learns to dynamically modulate the contribution of each modality on a per-instance basis. MA-AFS introduces a lightweight neural scheduler that predicts modality fusion weights by integrating visual and textual entropy signals along with cross-modal agreement cues. This enables the model to adaptively emphasize more reliable modalities, especially under noisy, missing, or misaligned inputs. We formulate the fusion process as a differentiable scheduling mechanism, analyze its theoretical consistency and regularization effect, and demonstrate that it improves robustness without increasing model capacity significantly. Extensive experiments on image-text retrieval, captioning, and visual question answering show that MA-AFS achieves consistent performance gains over strong baselines such as CLIP, ALBEF, and BLIP. Moreover, MA-AFS exhibits improved robustness under modality corruption and enhanced generalization under domain shifts. Our work highlights the importance of adaptive fusion and opens a promising direction toward reliable and uncertainty-aware multimodal learning.
zh
[CV-116] Efficient multi-view training for 3D Gaussian Splatting
【速读】：该论文旨在解决3D Gaussian Splatting (3DGS)在单视角训练中由于小批量随机梯度方差较大而导致的优化效果不佳问题，以及在引入多视角训练时所面临的计算开销大和高斯点密度优化不理想的问题。其解决方案的关键在于改进光栅化过程以降低多视角训练的开销，并提出一种基于3D距离感知的D-SSIM损失函数及多视角自适应密度控制机制，从而更好地适应多视角场景下的优化需求。
链接: https://arxiv.org/abs/2506.12727
作者: Minhyuk Choi,Injae Kim,Hyunwoo J. Kim
机构: Korea University (韩国大学); KAIST (韩国科学技术院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:3D Gaussian Splatting (3DGS) has emerged as a preferred choice alongside Neural Radiance Fields (NeRF) in inverse rendering due to its superior rendering speed. Currently, the common approach in 3DGS is to utilize “single-view” mini-batch training, where only one image is processed per iteration, in contrast to NeRF’s “multi-view” mini-batch training, which leverages multiple images. We observe that such single-view training can lead to suboptimal optimization due to increased variance in mini-batch stochastic gradients, highlighting the necessity for multi-view training. However, implementing multi-view training in 3DGS poses challenges. Simply rendering multiple images per iteration incurs considerable overhead and may result in suboptimal Gaussian densification due to its reliance on single-view assumptions. To address these issues, we modify the rasterization process to minimize the overhead associated with multi-view training and propose a 3D distance-aware D-SSIM loss and multi-view adaptive density control that better suits multi-view scenarios. Our experiments demonstrate that the proposed methods significantly enhance the performance of 3DGS and its variants, freeing 3DGS from the constraints of single-view training.
zh
[CV-117] Dynamic Modality Scheduling for Multimodal Large Models via Confidence Uncertainty and Semantic Consistency
【速读】：该论文旨在解决多模态大模型（Multimodal Large Models, MLLMs）在处理不同模态数据时存在的性能不足问题，尤其是在模态存在噪声、缺失或对齐不良的情况下。现有方法通常采用静态的模态融合策略，未能根据实例级别的可靠性或语义贡献动态调整各模态的权重，从而导致次优性能。其解决方案的关键是提出动态模态调度（Dynamic Modality Scheduling, DMS），该框架通过评估每个模态的置信度、不确定性以及语义一致性，并结合可学习或规则的调度器生成软模态权重，以实现对每个样本的自适应模态贡献调整。此外，为确保训练稳定性，还引入了模态权重一致性损失，进一步提升模型的鲁棒性和性能。
链接: https://arxiv.org/abs/2506.12724
作者: Hiroshi Tanaka,Anika Rao,Hana Satou,Michael Johnson,Sofia García
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Models (MLLMs) have achieved remarkable progress in vision-language understanding and generation tasks. However, existing MLLMs typically rely on static modality fusion strategies, which treat all modalities equally regardless of their instance-level reliability or semantic contribution. This often leads to suboptimal performance, especially in scenarios with noisy, missing, or misaligned modalities. In this paper, we propose Dynamic Modality Scheduling (DMS), a novel framework that adaptively adjusts the contribution of each modality at a per-sample level. DMS evaluates each modality based on three key factors: (1) \textitconfidence, estimated from predictive entropy; (2) \textituncertainty, obtained via Monte Carlo dropout; and (3) \textitsemantic consistency, computed through inter-modal similarity. These signals are combined through a learnable or rule-based scheduler to generate soft modality weights used in downstream this http URL ensure stable training, we further introduce a \textitModality Weight Consistency Loss, which regularizes the fused representation to stay close to unimodal embeddings proportionally to their assigned weights. Our method is model-agnostic and can be integrated into existing MLLMs such as BLIP-2 and LLaVA. Experimental results on VQA, image-text retrieval, and captioning tasks show that DMS significantly improves both clean and robust performance, especially under modality corruption or dropout conditions. This work provides a general and effective mechanism to enable instance-aware and robustness-enhanced multimodal modeling. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2506.12724 [cs.CV] (or arXiv:2506.12724v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2506.12724 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[CV-118] SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration
【速读】：该论文旨在解决Vision-Language-Action (VLA)模型在实时任务中因高计算成本和低执行频率而难以应用的问题。现有方法主要关注结构优化，但忽略了VLA模型在序列决策环境中的运行特性，导致序列动作生成中的时间冗余和视觉输入中的空间冗余未被有效处理。解决方案的关键在于提出SP-VLA框架，通过联合调度模型和剪枝令牌实现加速。具体而言，设计了基于动作感知的模型调度机制，通过动态切换VLA模型与轻量生成器来减少时间冗余，并引入时空语义双-aware的令牌剪枝方法以处理空间冗余，从而在保持高精度的同时显著提升推理效率。
链接: https://arxiv.org/abs/2506.12723
作者: Ye Li,Yuan Meng,Zewen Sun,Kangye Ji,Chen Tang,Jiajun Fan,Xinzhu Ma,Shutao Xia,Zhi Wang,Wenwu Zhu
机构: Tsinghua University (清华大学); The Chinese University of Hong Kong (香港中文大学); University of Illinois at Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); Beihang University (北京航空航天大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities. However, their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation. Existing VLA acceleration methods primarily focus on structural optimization, overlooking the fact that these models operate in sequential decision-making environments. As a result, temporal redundancy in sequential action generation and spatial redundancy in visual input remain unaddressed. To this end, we propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens. Specifically, we design an action-aware model scheduling mechanism that reduces temporal redundancy by dynamically switching between VLA model and a lightweight generator. Inspired by the human motion pattern of focusing on key decision points while relying on intuition for other actions, we categorize VLA actions into deliberative and intuitive, assigning the former to the VLA model and the latter to the lightweight generator, enabling frequency-adaptive execution through collaborative model scheduling. To address spatial redundancy, we further develop a spatio-semantic dual-aware token pruning method. Tokens are classified into spatial and semantic types and pruned based on their dual-aware importance to accelerate VLA inference. These two mechanisms work jointly to guide the VLA in focusing on critical actions and salient visual information, achieving effective acceleration while maintaining high accuracy. Experimental results demonstrate that our method achieves up to 1.5 \times acceleration with less than 3% drop in accuracy, outperforming existing approaches in multiple tasks.
zh
[CV-119] Generative 4D Scene Gaussian Splatting with Object View-Synthesis Priors DATE CVPR
【速读】：该论文试图解决从单目多物体视频中生成动态4D场景的问题，特别是在存在严重遮挡的情况下，现有模型在复杂杂乱场景中的泛化能力不足。其解决方案的关键在于提出GenMOJO，该方法将场景分解为独立物体，对每个物体优化可变形高斯分布，并结合基于扩散的物体中心先验来推断新视角中未观测区域，同时通过联合高斯点绘制捕捉物体间遮挡关系，实现遮挡感知的监督，并利用可微变换将物体中心先验与视频全局帧中心坐标系对齐，从而在统一框架内整合生成与渲染约束。
链接: https://arxiv.org/abs/2506.12716
作者: Wen-Hsuan Chu,Lei Ke,Jianmeng Liu,Mingxiao Huo,Pavel Tokmakov,Katerina Fragkiadaki
机构: Carnegie Mellon University (卡内基梅隆大学); Toyota Research Institute (丰田研究机构)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This is an updated and extended version of our CVPR paper “Robust Multi-Object 4D Generation in Complex Video Scenarios”
Abstract:We tackle the challenge of generating dynamic 4D scenes from monocular, multi-object videos with heavy occlusions, and introduce GenMOJO, a novel approach that integrates rendering-based deformable 3D Gaussian optimization with generative priors for view synthesis. While existing models perform well on novel view synthesis for isolated objects, they struggle to generalize to complex, cluttered scenes. To address this, GenMOJO decomposes the scene into individual objects, optimizing a differentiable set of deformable Gaussians per object. This object-wise decomposition allows leveraging object-centric diffusion models to infer unobserved regions in novel viewpoints. It performs joint Gaussian splatting to render the full scene, capturing cross-object occlusions, and enabling occlusion-aware supervision. To bridge the gap between object-centric priors and the global frame-centric coordinate system of videos, GenMOJO uses differentiable transformations that align generative and rendering constraints within a unified framework. The resulting model generates 4D object reconstructions over space and time, and produces accurate 2D and 3D point tracks from monocular input. Quantitative evaluations and perceptual human studies confirm that GenMOJO generates more realistic novel views of scenes and produces more accurate point tracks compared to existing approaches.
zh
[CV-120] Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groups
【速读】：该论文旨在解决煤岩组分图像语义分割中因参数堆叠导致的计算需求增加和模型训练效率下降的问题，以及由于煤岩组分图像采样专业性和多样性带来的样本获取耗时和依赖专业人员操作的问题。其解决方案的关键在于创新性地开发了一种基于物联网（IoT）的DA-VIT并行网络模型，通过物联网持续扩展数据集以提升分割精度，并将并行网络与主干网络解耦，确保主干网络在模型数据更新期间仍能正常运行；同时引入DCSA机制以增强煤显微图像的局部特征信息，通过分解卷积注意力的大核来减少计算量，从而有效提升模型性能。
链接: https://arxiv.org/abs/2506.12712
作者: Zhenghao Xi,Zhengnan Lv,Yang Zheng,Xiang Liu,Zhuang Yu,Junran Chen,Jing Hu,Yaqi Liu
机构: Shanghai University of Engineering Science(上海工程技术大学); China Electronics Technology Group Corporation No.15 Research Institute(中国电子科技集团公司第十五研究所); Chinese Academy of Sciences(中国科学院); Angang Steel Co.,Ltd.(鞍钢集团有限公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注:
Abstract:The segmentation of coal maceral groups can be described as a semantic segmentation process of coal maceral group images, which is of great significance for studying the chemical properties of coal. Generally, existing semantic segmentation models of coal maceral groups use the method of stacking parameters to achieve higher accuracy. It leads to increased computational requirements and impacts model training efficiency. At the same time, due to the professionalism and diversity of coal maceral group images sampling, obtaining the number of samples for model training requires a long time and professional personnel operation. To address these issues, We have innovatively developed an IoT-based DA-VIT parallel network model. By utilizing this model, we can continuously broaden the dataset through IoT and achieving sustained improvement in the accuracy of coal maceral groups segmentation. Besides, we decouple the parallel network from the backbone network to ensure the normal using of the backbone network during model data updates. Secondly, DCSA mechanism of DA-VIT is introduced to enhance the local feature information of coal microscopic images. This DCSA can decompose the large kernels of convolutional attention into multiple scales and reduce 81.18% of this http URL, we performed the contrast experiment and ablation experiment between DA-VIT and state-of-the-art methods at lots of evaluation metrics. Experimental results show that DA-VIT-Base achieves 92.14% pixel accuracy and 63.18% mIoU. Params and FLOPs of DA-VIT-Tiny are 4.95M and 8.99G, respectively. All of the evaluation metrics of the proposed DA-VIT are better than other state-of-the-art methods.
zh
[CV-121] NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models
【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在面对对抗攻击时的脆弱性问题，特别是在图像模态中存在的安全风险。其解决方案的关键在于提出一种名为Neural Augmentor框架的多模态对抗提示调优方法（NAP-Tuning），该方法通过扩展先前的对抗提示调优（AdvPT）技术，实现了从文本单模态到跨文本与视觉模态的提示调优，并引入了特征净化机制以直接应对对抗攻击带来的特征空间扰动。此外，NAP-Tuning还通过引入令牌重构器和残差连接，实现了模态和层级别的特征修复，从而显著提升了模型的对抗鲁棒性。
链接: https://arxiv.org/abs/2506.12706
作者: Jiaming Zhang,Xin Wang,Xingjun Ma,Lingyu Qiu,Yu-Gang Jiang,Jitao Sang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capabilities in understanding relationships between visual and textual data through joint embedding spaces. Despite their effectiveness, these models remain vulnerable to adversarial attacks, particularly in the image modality, posing significant security concerns. Building upon our previous work on Adversarial Prompt Tuning (AdvPT), which introduced learnable text prompts to enhance adversarial robustness in VLMs without extensive parameter training, we present a significant extension by introducing the Neural Augmentor framework for Multi-modal Adversarial Prompt Tuning (NAP-Tuning).Our key innovations include: (1) extending AdvPT from text-only to multi-modal prompting across both text and visual modalities, (2) expanding from single-layer to multi-layer prompt architectures, and (3) proposing a novel architecture-level redesign through our Neural Augmentor approach, which implements feature purification to directly address the distortions introduced by adversarial attacks in feature space. Our NAP-Tuning approach incorporates token refiners that learn to reconstruct purified features through residual connections, allowing for modality-specific and layer-specific feature this http URL experiments demonstrate that NAP-Tuning significantly outperforms existing methods across various datasets and attack types. Notably, our approach shows significant improvements over the strongest baselines under the challenging AutoAttack benchmark, outperforming them by 33.5% on ViT-B16 and 33.0% on ViT-B32 architectures while maintaining competitive clean accuracy.
zh
[CV-122] Unsupervised Contrastive Learning Using Out-Of-Distribution Data for Long-Tailed Dataset
【速读】：该论文试图解决在长尾数据集上进行自监督学习（self-supervised learning, SSL）的问题，旨在为下游任务（如图像分类）学习到平衡且区分度高的表征。该问题至关重要，因为现实世界中物体类别分布本质上是不平衡的。解决方案的关键在于利用大量在线可用的无标签领域外（out-of-distribution, OOD）数据训练网络，并通过引入伪语义区分损失与领域区分损失联合优化网络，以学习到一个平衡且分离的嵌入空间。随后，利用先前训练的网络作为指导网络，在领域内数据上进一步优化网络，通过选择正负样本及控制对比学习中的吸引力/排斥力强度，同时将指导网络的嵌入空间蒸馏到训练网络中，以保持平衡性和可分性。
链接: https://arxiv.org/abs/2506.12698
作者: Cuong Manh Hoang,Yeejin Lee,Byeongkeun Kang
机构: SeoulTech(首尔科学综合大学院大学); CAU(首尔国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 13 pages
Abstract:This work addresses the task of self-supervised learning (SSL) on a long-tailed dataset that aims to learn balanced and well-separated representations for downstream tasks such as image classification. This task is crucial because the real world contains numerous object categories, and their distributions are inherently imbalanced. Towards robust SSL on a class-imbalanced dataset, we investigate leveraging a network trained using unlabeled out-of-distribution (OOD) data that are prevalently available online. We first train a network using both in-domain (ID) and sampled OOD data by back-propagating the proposed pseudo semantic discrimination loss alongside a domain discrimination loss. The OOD data sampling and loss functions are designed to learn a balanced and well-separated embedding space. Subsequently, we further optimize the network on ID data by unsupervised contrastive learning while using the previously trained network as a guiding network. The guiding network is utilized to select positive/negative samples and to control the strengths of attractive/repulsive forces in contrastive learning. We also distil and transfer its embedding space to the training network to maintain balancedness and separability. Through experiments on four publicly available long-tailed datasets, we demonstrate that the proposed method outperforms previous state-of-the-art methods.
zh
[CV-123] MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection
【速读】：该论文旨在解决无人机图像中小目标检测的问题，这一任务在搜索与救援、交通监控和环境监测等应用中具有重要意义，但受限于目标尺寸小、信噪比低以及特征提取有限等因素。现有多尺度融合方法虽有一定帮助，但增加了计算负担并模糊了细节，导致复杂场景下的小目标检测困难。论文提出的解决方案是多尺度全局-细节特征融合策略（MGDFIS），其关键在于通过紧密耦合全局上下文与局部细节，提升检测性能的同时保持效率，具体包括融合锁-双统计自注意力模块、全局-细节融合模块和动态像素注意力模块，以实现对小目标的精准识别与高效处理。
链接: https://arxiv.org/abs/2506.12697
作者: Yuxiang Wang,Xuecheng Bai,Boyu Hu,Chuanzhi Xu,Haodong Chen,Vera Chung,Tingxue Li
机构: The University of Sydney(悉尼大学); Shenyang Ligong University(沈阳理工大学); University of International Business and Economics(对外经济贸易大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 9 pages, 5 figures, 3 tables
Abstract:Small object detection in UAV imagery is crucial for applications such as search-and-rescue, traffic monitoring, and environmental surveillance, but it is hampered by tiny object size, low signal-to-noise ratios, and limited feature extraction. Existing multi-scale fusion methods help, but add computational burden and blur fine details, making small object detection in cluttered scenes difficult. To overcome these challenges, we propose the Multi-scale Global-detail Feature Integration Strategy (MGDFIS), a unified fusion framework that tightly couples global context with local detail to boost detection performance while maintaining efficiency. MGDFIS comprises three synergistic modules: the FusionLock-TSS Attention Module, which marries token-statistics self-attention with DynamicTanh normalization to highlight spectral and spatial cues at minimal cost; the Global-detail Integration Module, which fuses multi-scale context via directional convolution and parallel attention while preserving subtle shape and texture variations; and the Dynamic Pixel Attention Module, which generates pixel-wise weighting maps to rebalance uneven foreground and background distributions and sharpen responses to true object regions. Extensive experiments on the VisDrone benchmark demonstrate that MGDFIS consistently outperforms state-of-the-art methods across diverse backbone architectures and detection frameworks, achieving superior precision and recall with low inference time. By striking an optimal balance between accuracy and resource usage, MGDFIS provides a practical solution for small-object detection on resource-constrained UAV platforms.
zh
[CV-124] Evaluating Cell Type Inference in Vision Language Models Under Varying Visual Context
【速读】：该论文试图解决如何将生成式 AI (Generative AI) 模型应用于组织病理学图像分类任务，特别是细胞类型识别的问题。其解决方案的关键在于利用零样本和少样本提示方法（zero-shot and one-shot prompting）评估当前主流生成式视觉-语言模型（VLMs）如 GPT-4.1 和 Gemini 2.5 Pro 的性能，并将其与定制训练的卷积神经网络（CNNs）进行对比，以探索在特定领域中使用这些通用模型的潜力与局限性。
链接: https://arxiv.org/abs/2506.12683
作者: Samarth Singhal,Sandeep Singhal
机构: University of North Dakota (北达科他大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注:
Abstract:Vision-Language Models (VLMs) have rapidly advanced alongside Large Language Models (LLMs). This study evaluates the capabilities of prominent generative VLMs, such as GPT-4.1 and Gemini 2.5 Pro, accessed via APIs, for histopathology image classification tasks, including cell typing. Using diverse datasets from public and private sources, we apply zero-shot and one-shot prompting methods to assess VLM performance, comparing them against custom-trained Convolutional Neural Networks (CNNs). Our findings demonstrate that while one-shot prompting significantly improves VLM performance over zero-shot ( p \approx 1.005 \times 10^-5 based on Kappa scores), these general-purpose VLMs currently underperform supervised CNNs on most tasks. This work underscores both the promise and limitations of applying current VLMs to specialized domains like pathology via in-context learning. All code and instructions for reproducing the study can be accessed from the repository this https URL.
zh
[CV-125] 3D Hand Mesh-Guided AI-Generated Malformed Hand Refinement with Hand Pose Transformation via Diffusion Model
【速读】：该论文试图解决AI生成图像中畸形手部（malformed hands）影响图像真实性的问题。现有基于深度的方法由于手部深度估计器的性能限制，无法准确表示手部细节，导致生成的手部出现如掌心与手背混淆等错误。解决方案的关键在于提出一种基于3D网格（3D mesh）引导的细化框架，利用先进的3D手部网格估计器提供更丰富的手部细节信息，并通过扩散修复模型生成高质量的细化结果。此外，还引入了一种双检算法以增强3D手部网格估计的鲁棒性，同时提出了一种无需额外训练的手部姿态转换方法，提升了畸形手部细化任务的灵活性和多样性。
链接: https://arxiv.org/abs/2506.12680
作者: Chen-Bin Feng,Kangdao Liu,Jian Sun,Jiping Jin,Yiguo Jiang,Chi-Man Vong
机构: University of Macau(澳门大学); ShanghaiTech University(上海科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The malformed hands in the AI-generated images seriously affect the authenticity of the images. To refine malformed hands, existing depth-based approaches use a hand depth estimator to guide the refinement of malformed hands. Due to the performance limitations of the hand depth estimator, many hand details cannot be represented, resulting in errors in the generated hands, such as confusing the palm and the back of the hand. To solve this problem, we propose a 3D mesh-guided refinement framework using a diffusion pipeline. We use a state-of-the-art 3D hand mesh estimator, which provides more details of the hands. For training, we collect and reannotate a dataset consisting of RGB images and 3D hand mesh. Then we design a diffusion inpainting model to generate refined outputs guided by 3D hand meshes. For inference, we propose a double check algorithm to facilitate the 3D hand mesh estimator to obtain robust hand mesh guidance to obtain our refined results. Beyond malformed hand refinement, we propose a novel hand pose transformation method. It increases the flexibility and diversity of the malformed hand refinement task. We made the restored images mimic the hand poses of the reference images. The pose transformation requires no additional training. Extensive experimental results demonstrate the superior performance of our proposed method.
zh
[CV-126] Adapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence
【速读】：该论文旨在解决端到端视觉-运动策略在部署时面对分布外（out-of-distribution, OOD）视觉输入时表现不可靠的问题。现有方法依赖于在OOD条件下收集修正的专家示范，但这一过程成本高且效率低。论文提出的解决方案的关键在于利用专家提供的OOD到分布内（in-distribution, ID）的功能对应关系，而非频繁重新训练模型。通过检测OOD观察并识别与之功能相似的ID行为，再结合专家反馈进行行为区分，并在部署时使用功能对应的ID观察对OOD观察进行干预，从而实现有效的泛化能力。
链接: https://arxiv.org/abs/2506.12678
作者: Pranay Gupta,Henny Admoni,Andrea Bajcsy
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 15 pages, 11 figures
Abstract:End-to-end visuomotor policies trained using behavior cloning have shown a remarkable ability to generate complex, multi-modal low-level robot behaviors. However, at deployment time, these policies still struggle to act reliably when faced with out-of-distribution (OOD) visuals induced by objects, backgrounds, or environment changes. Prior works in interactive imitation learning solicit corrective expert demonstrations under the OOD conditions – but this can be costly and inefficient. We observe that task success under OOD conditions does not always warrant novel robot behaviors. In-distribution (ID) behaviors can directly be transferred to OOD conditions that share functional similarities with ID conditions. For example, behaviors trained to interact with in-distribution (ID) pens can apply to interacting with a visually-OOD pencil. The key challenge lies in disambiguating which ID observations functionally correspond to the OOD observation for the task at hand. We propose that an expert can provide this OOD-to-ID functional correspondence. Thus, instead of collecting new demonstrations and re-training at every OOD encounter, our method: (1) detects the need for feedback by first checking if current observations are OOD and then identifying whether the most similar training observations show divergent behaviors, (2) solicits functional correspondence feedback to disambiguate between those behaviors, and (3) intervenes on the OOD observations with the functionally corresponding ID observations to perform deployment-time generalization. We validate our method across diverse real-world robotic manipulation tasks with a Franka Panda robotic manipulator. Our results show that test-time functional correspondences can improve the generalization of a vision-based diffusion policy to OOD objects and environment conditions with low feedback.
zh
[CV-127] Performance Plateaus in Inference-Time Scaling for Text-to-Image Diffusion Without External Models ICML2025
【速读】：该论文试图解决在有限显存（VRAM）的GPU上优化文本到图像扩散模型初始噪声的问题，传统方法依赖外部模型评估生成图像质量，这在资源受限环境下不可行。解决方案的关键在于应用基于最佳N（Best-of-N）的推理时缩放技术，在无需外部模型的情况下对多个数据集和主干网络的初始噪声优化算法进行有效扩展，从而在较少的优化步骤内达到最大可实现性能。
链接: https://arxiv.org/abs/2506.12633
作者: Changhyun Choi,Sungha Kim,H. Jin Kim
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: MOSS workshop at ICML 2025 accepted
Abstract:Recently, it has been shown that investing computing resources in searching for good initial noise for a text-to-image diffusion model helps improve performance. However, previous studies required external models to evaluate the resulting images, which is impossible on GPUs with small VRAM. For these reasons, we apply Best-of-N inference-time scaling to algorithms that optimize the initial noise of a diffusion model without external models across multiple datasets and backbones. We demonstrate that inference-time scaling for text-to-image diffusion models in this setting quickly reaches a performance plateau, and a relatively small number of optimization steps suffices to achieve the maximum achievable performance with each algorithm.
zh
[CV-128] OscNet v1.5: Energy Efficient Hopfield Network on CMOS Oscillators for Image Classification
【速读】：该论文旨在解决传统机器学习模型在计算资源消耗大、能耗高的问题，提出一种适用于低功耗场景的新型计算架构。其解决方案的关键在于基于Hopfield Network设计了一种仅依赖前向传播且具有稀疏连接的机器学习算法，并将其部署在CMOS Oscillator Networks (OscNet) 上，从而实现了高效的能量利用与较高的分类准确率。
链接: https://arxiv.org/abs/2506.12610
作者: Wenxiao Cai,Zongru Li,Iris Wang,Yu-Neng Wang,Thomas H. Lee
机构: Stanford University (斯坦福大学); Carnegie Mellon University (卡内基梅隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Machine learning has achieved remarkable advancements but at the cost of significant computational resources. This has created an urgent need for a novel and energy-efficient computational fabric. CMOS Oscillator Networks (OscNet) is a brain inspired and specially designed hardware for low energy consumption. In this paper, we propose a Hopfield Network based machine learning algorithm that can be implemented on OscNet. The network is trained using forward propagation alone to learn sparsely connected weights, yet achieves an 8% improvement in accuracy compared to conventional deep learning models on MNIST dataset. OscNet v1.5 achieves competitive accuracy on MNIST and is well-suited for implementation using CMOS-compatible ring oscillator arrays with SHIL. In oscillator-based implementation, we utilize only 24% of the connections used in a fully connected Hopfield network, with merely a 0.1% drop in accuracy. OscNet v1.5 relies solely on forward propagation and employs sparse connections, making it an energy-efficient machine learning pipeline designed for CMOS oscillator computing. The repository for OscNet family is: this https URL.
zh
[CV-129] Not All Tokens and Heads Are Equally Important: Dual-Level Attention Intervention for Hallucination Mitigation
【速读】：该论文试图解决大型视觉-语言模型（Large Vision-Language Models, LVLMs）在处理多模态任务时存在的视觉幻觉（Visual Hallucination, VH）问题，即模型在推理过程中生成自信但不准确的视觉内容描述。解决方案的关键在于提出VisFlow框架，该框架通过在推理阶段直接操控注意力模式来减轻VH，具体包括两种干预手段：基于标记级别的注意力干预（Token-level Attention Intervention, TAI）以增强对显著视觉内容的关注，以及基于头级别的注意力干预（Head-level Attention Intervention, HAI）以抑制对提示和附近文本标记的过度关注。VisFlow无需额外训练或模型修改，具有较低的计算成本。
链接: https://arxiv.org/abs/2506.12609
作者: Lexiang Tang,Xianwei Zhuang,Bang Yang,Zhiyuan Hu,Hongxiang Li,Lu Ma,Jinghan Ru,Yuexian Zou
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Large vision-language models (LVLMs) have shown remarkable capabilities across a wide range of multimodal tasks. However, they remain prone to visual hallucination (VH), often producing confident but incorrect descriptions of visual content. We present VisFlow, an efficient and training-free framework designed to mitigate VH by directly manipulating attention patterns during inference. Through systematic analysis, we identify three key pathological attention behaviors in LVLMs: (1) weak visual grounding, where attention to visual tokens is insufficient or misallocated, over-focusing on uninformative regions; (2) language prior dominance, where excessive attention to prior response tokens reinforces autoregressive patterns and impairs multimodal alignment; (3) prompt redundancy, where many attention heads fixate on system prompt tokens, disrupting the integration of image, instruction, and response content. To address these issues, we introduce two inference-time interventions: token-level attention intervention (TAI), which enhances focus on salient visual content, and head-level attention intervention (HAI), which suppresses over-attention to prompt and nearby text tokens. VisFlow operates without additional training or model modifications. Extensive experiments across models and benchmarks show that VisFlow effectively reduces hallucinations and improves visual factuality, with negligible computational cost.
zh
[CV-130] DejaVid: Encoder-Agnostic Learned Temporal Matching for Video Classification CVPR2025
【速读】：该论文试图解决大型基于Transformer的视频编码器在处理视频时无法有效捕捉时间相关特征的问题，例如视频时长的差异、事件的时间顺序以及特征重要性的时空变化。其解决方案的关键在于提出一种与编码器无关的方法DejaVid，该方法将视频转换为可变长度的嵌入时间序列（即多变量时间序列MTS），并学习每个时间步和每个特征的权重，从而更好地建模时间动态特性，同时无需重新训练或修改原有架构。
链接: https://arxiv.org/abs/2506.12585
作者: Darryl Ho,Samuel Madden
机构: MIT CSAIL(麻省理工学院计算机科学与人工智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to CVPR 2025 (IEEE/CVF Conference on Computer Vision and Pattern Recognition), main conference, poster presentation
Abstract:In recent years, large transformer-based video encoder models have greatly advanced state-of-the-art performance on video classification tasks. However, these large models typically process videos by averaging embedding outputs from multiple clips over time to produce fixed-length representations. This approach fails to account for a variety of time-related features, such as variable video durations, chronological order of events, and temporal variance in feature significance. While methods for temporal modeling do exist, they often require significant architectural changes and expensive retraining, making them impractical for off-the-shelf, fine-tuned large encoders. To overcome these limitations, we propose DejaVid, an encoder-agnostic method that enhances model performance without the need for retraining or altering the architecture. Our framework converts a video into a variable-length temporal sequence of embeddings, which we call a multivariate time series (MTS). An MTS naturally preserves temporal order and accommodates variable video durations. We then learn per-timestep, per-feature weights over the encoded MTS frames, allowing us to account for variations in feature importance over time. We introduce a new neural network architecture inspired by traditional time series alignment algorithms for this learning task. Our evaluation demonstrates that DejaVid substantially improves the performance of a state-of-the-art large encoder, achieving leading Top-1 accuracy of 77.2% on Something-Something V2, 89.1% on Kinetics-400, and 88.6% on HMDB51, while adding fewer than 1.8% additional learnable parameters and requiring less than 3 hours of training time. Our code is available at this https URL.
zh
[CV-131] MVP-CBM:Multi-layer Visual Preference-enhanced Concept Bottleneck Model for Explainable Medical Image Classification
【速读】：该论文试图解决传统概念瓶颈模型（Concept Bottleneck Model, CBM）在医疗图像分类任务中因仅依赖视觉编码器最后一层特征进行概念关联而导致的解释性不足问题。具体而言，现有方法忽视了概念偏好变化现象，即不同概念可能更倾向于与不同层次的特征相关联，而单一依赖最后一层特征会削弱特征与概念之间的准确对应关系，从而影响模型的可解释性。解决方案的关键在于提出一种多层视觉偏好增强的概念瓶颈模型（Multi-layer Visual Preference-enhanced Concept Bottleneck Model, MVP-CBM），其核心包括两个模块：（1）层内概念偏好建模，用于捕捉不同概念与各视觉层特征之间的偏好关联；（2）多层概念稀疏激活融合，通过稀疏聚合多层概念激活来提升性能。该方法通过显式建模概念偏好，全面利用多层视觉信息，从而提供更细致和准确的模型决策解释。
链接: https://arxiv.org/abs/2506.12568
作者: Chunjiang Wang,Kun Zhang,Yandong Liu,Zhiyang He,Xiaodong Tao,S. Kevin Zhou
机构: University of Science and Technology of China (中国科学技术大学); Suzhou Institute for Advanced Research, University of Science and Technology of China (中国科学技术大学先进技术研究院); Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology (江苏省多模态数字孪生技术重点实验室); iFlytek Co., Ltd (科大讯飞股份有限公司); State Key Laboratory of Precision and Intelligent Chemistry, USTC (精密智能化学国家重点实验室，中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 7 pages, 6 figures,
Abstract:The concept bottleneck model (CBM), as a technique improving interpretability via linking predictions to human-understandable concepts, makes high-risk and life-critical medical image classification credible. Typically, existing CBM methods associate the final layer of visual encoders with concepts to explain the model’s predictions. However, we empirically discover the phenomenon of concept preference variation, that is, the concepts are preferably associated with the features at different layers than those only at the final layer; yet a blind last-layer-based association neglects such a preference variation and thus weakens the accurate correspondences between features and concepts, impairing model interpretability. To address this issue, we propose a novel Multi-layer Visual Preference-enhanced Concept Bottleneck Model (MVP-CBM), which comprises two key novel modules: (1) intra-layer concept preference modeling, which captures the preferred association of different concepts with features at various visual layers, and (2) multi-layer concept sparse activation fusion, which sparsely aggregates concept activations from multiple layers to enhance performance. Thus, by explicitly modeling concept preferences, MVP-CBM can comprehensively leverage multi-layer visual information to provide a more nuanced and accurate explanation of model decisions. Extensive experiments on several public medical classification benchmarks demonstrate that MVP-CBM achieves state-of-the-art accuracy and interoperability, verifying its superiority. Code is available at this https URL.
zh
[CV-132] Benchmarking Image Similarity Metrics for Novel View Synthesis Applications
【速读】：该论文试图解决传统图像相似性度量在评估真实场景图像与人工生成图像之间的相似性时效果不佳的问题（Traditional image similarity metrics are ineffective at evaluating the similarity between a real image of a scene and an artificially generated version of that viewpoint）。解决方案的关键在于引入一种基于感知的相似性度量方法——DreamSim，并将其与三种常用的图像相似性度量方法（结构相似性SSIM、峰值信噪比PSNR、学习感知图像块相似性LPIPS）进行对比，以验证其在新型视图合成（NVS）应用中的有效性。实验结果表明，DreamSim在处理微小像素级变化和显著退化图像时表现出更高的鲁棒性和对图像高层语义相似性的有效评估能力。
链接: https://arxiv.org/abs/2506.12563
作者: Charith Wickrema,Sara Leary,Shivangi Sarkar,Mark Giglio,Eric Bianchi,Eliza Mace,Michael Twardowski
机构: The MITRE Corporation (MITRE 公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traditional image similarity metrics are ineffective at evaluating the similarity between a real image of a scene and an artificially generated version of that viewpoint [6, 9, 13, 14]. Our research evaluates the effectiveness of a new, perceptual-based similarity metric, DreamSim [2], and three popular image similarity metrics: Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS) [18, 19] in novel view synthesis (NVS) applications. We create a corpus of artificially corrupted images to quantify the sensitivity and discriminative power of each of the image similarity metrics. These tests reveal that traditional metrics are unable to effectively differentiate between images with minor pixel-level changes and those with substantial corruption, whereas DreamSim is more robust to minor defects and can effectively evaluate the high-level similarity of the image. Additionally, our results demonstrate that DreamSim provides a more effective and useful evaluation of render quality, especially for evaluating NVS renders in real-world use cases where slight rendering corruptions are common, but do not affect image utility for human tasks.
zh
[CV-133] Parkinsons Disease Freezing of Gait (FoG) Symptom Detection Using Machine Learning from Wearable Sensor Data
【速读】：该论文旨在解决帕金森病（Parkinson’s Disease, PD）患者中冻结步态（Freezing of Gait, FoG）的实时识别问题。传统方法依赖于医生的临床评估，而本文提出了一种基于深度学习的解决方案，其关键在于引入了Transformer Encoder-Bi-LSTM融合模型，通过结合Transformer编码器的全局特征提取能力和双向长短期记忆网络（Bi-LSTM）的时间序列建模能力，有效区分FoG事件与正常运动，从而提高了FoG检测的准确性与实时性。
链接: https://arxiv.org/abs/2506.12561
作者: Mahmudul Hasan
机构: BRAC University (BRAC大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注:
Abstract:Freezing of gait (FoG) is a special symptom found in patients with Parkinson’s disease (PD). Patients who have FoG abruptly lose the capacity to walk as they normally would. Accelerometers worn by patients can record movement data during these episodes, and machine learning algorithms can be useful to categorize this information. Thus, the combination may be able to identify FoG in real time. In order to identify FoG events in accelerometer data, we introduce the Transformer Encoder-Bi-LSTM fusion model in this paper. The model’s capability to differentiate between FoG episodes and normal movement was used to evaluate its performance, and on the Kaggle Parkinson’s Freezing of Gait dataset, the proposed Transformer Encoder-Bi-LSTM fusion model produced 92.6% accuracy, 80.9% F1 score, and 52.06% in terms of mean average precision. The findings highlight how Deep Learning-based approaches may progress the field of FoG identification and help PD patients receive better treatments and management plans.
zh
[CV-134] PLD: A Choice-Theoretic List-Wise Knowledge Distillation
【速读】：该论文旨在解决知识蒸馏（Knowledge Distillation, KD）中如何更有效地利用教师模型的输出信息以提升学生模型性能的问题。传统方法通常通过引入KL散度或相关性损失作为交叉熵的补充项，但这些方法需要额外的权重调整且效果有限。论文的关键解决方案是引入Plackett-Luce Distillation (PLD)，其核心在于将教师模型的logits视为“价值”分数，并基于Plackett-Luce模型构建一种加权列表级排序损失，直接优化教师最优的真实标签排序，随后按教师置信度降序排列其余类别，从而实现一种凸性、平移不变的代理损失，该损失包含了加权交叉熵。
链接: https://arxiv.org/abs/2506.12542
作者: Ejafa Bassam,Dawei Zhu,Kaigui Bian
机构: Peking University (北京大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
备注:
Abstract:Knowledge distillation is a model compression technique in which a compact “student” network is trained to replicate the predictive behavior of a larger “teacher” network. In logit-based knowledge distillation it has become the de facto approach to augment cross-entropy with a distillation term. Typically this term is either a KL divergence-matching marginal probabilities or a correlation-based loss capturing intra- and inter-class relationships but in every case it sits as an add-on to cross-entropy with its own weight that must be carefully tuned. In this paper we adopt a choice-theoretic perspective and recast knowledge distillation under the Plackett-Luce model by interpreting teacher logits as “worth” scores. We introduce Plackett-Luce Distillation (PLD), a weighted list-wise ranking loss in which the teacher model transfers knowledge of its full ranking of classes, weighting each ranked choice by its own confidence. PLD directly optimizes a single teacher-optimal ranking of the true label first, followed by the remaining classes in descending teacher confidence, yielding a convex, translation-invariant surrogate that subsumes weighted cross-entropy. Empirically on standard image classification benchmarks, PLD improves Top-1 accuracy by an average of +0.42% over DIST (arXiv:2205.10536) and +1.04% over KD (arXiv:1503.02531) in homogeneous settings and by +0.48% and +1.09% over DIST and KD, respectively, in heterogeneous settings.
zh
[CV-135] BSA: Ball Sparse Attention for Large-scale Geometries ICML2025
【速读】：该论文试图解决自注意力机制在处理大规模物理系统时因计算复杂度呈二次增长而受限的问题，以及稀疏注意力机制在不规则几何结构中应用受限的问题。其解决方案的关键在于提出Ball Sparse Attention (BSA)，通过引入Erwin Transformer中的Ball Tree结构，将原用于规则结构的Native Sparse Attention (NSA)适配到无序点集上，从而在保持全局感受野的同时实现次二次计算成本。
链接: https://arxiv.org/abs/2506.12541
作者: Catalin E. Brita,Hieu Nguyen,Lohithsai Yadala Chanchu,Domonkos Nagy,Maksim Zhdanov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Long Context Foundation Models Workshop @ ICML 2025
Abstract:Self-attention scales quadratically with input size, limiting its use for large-scale physical systems. Although sparse attention mechanisms provide a viable alternative, they are primarily designed for regular structures such as text or images, making them inapplicable for irregular geometries. In this work, we present Ball Sparse Attention (BSA), which adapts Native Sparse Attention (NSA) (Yuan et al., 2025) to unordered point sets by imposing regularity using the Ball Tree structure from the Erwin Transformer (Zhdanov et al., 2025). We modify NSA’s components to work with ball-based neighborhoods, yielding a global receptive field at sub-quadratic cost. On an airflow pressure prediction task, we achieve accuracy comparable to Full Attention while significantly reducing the theoretical computational complexity. Our implementation is available at this https URL.
zh
[CV-136] owards Seamless Borders: A Method for Mitigating Inconsistencies in Image Inpainting and Outpainting
【速读】：该论文旨在解决基于扩散模型的图像修复中存在的一致性问题，特别是修复区域与周围内容之间的不连贯现象。其解决方案的关键在于提出两种新方法：首先，引入一种改进的变分自编码器（Variational Autoencoder），以校正颜色不平衡，确保修复结果无颜色不匹配；其次，采用两阶段训练策略，提升扩散过程中生成内容与现有图像内容的融合效果。这些方法有效减少了图像修复中的断层感，提升了整体视觉质量和一致性。
链接: https://arxiv.org/abs/2506.12530
作者: Xingzhong Hou,Jie Wu,Boxiao Liu,Yi Zhang,Guanglu Song,Yunpeng Liu,Yu Liu,Haihang You
机构: Sensetime Research (商汤科技); Institute of Computing Technology, Chinese Academy of Sciences (中国科学院计算技术研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Image inpainting is the task of reconstructing missing or damaged parts of an image in a way that seamlessly blends with the surrounding content. With the advent of advanced generative models, especially diffusion models and generative adversarial networks, inpainting has achieved remarkable improvements in visual quality and coherence. However, achieving seamless continuity remains a significant challenge. In this work, we propose two novel methods to address discrepancy issues in diffusion-based inpainting models. First, we introduce a modified Variational Autoencoder that corrects color imbalances, ensuring that the final inpainted results are free of color mismatches. Second, we propose a two-step training strategy that improves the blending of generated and existing image content during the diffusion process. Through extensive experiments, we demonstrate that our methods effectively reduce discontinuity and produce high-quality inpainting results that are coherent and visually appealing.
zh
[CV-137] Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing
【速读】：该论文旨在解决事件驱动眼动追踪（event-based eye tracking）在精细认知状态推断中的信号一致性问题，尤其是在处理眨眼引起的噪声、空间抖动和时间不连续性方面。其解决方案的关键在于提出一种模型无关的推理阶段优化框架，包含两个核心后处理模块：基于运动感知的中值滤波器，用于抑制眨眼引起的尖峰并保留自然眼动动态；以及基于光流的局部优化模块，用于对齐眼动预测与累积事件运动，从而减少空间抖动和时间不连续性。此外，还引入了一种新的抖动度量（Jitter Metric），以评估预测眼动轨迹的时间平滑性。这些改进显著提升了事件驱动眼动信号的一致性，使其更适用于微表情分析和心智状态解码等下游任务。
链接: https://arxiv.org/abs/2506.12524
作者: Nuwan Bandara,Thivya Kandappu,Archan Misra
机构: Singapore Management University(新加坡管理大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
备注: 18 pages
Abstract:Event-based eye tracking holds significant promise for fine-grained cognitive state inference, offering high temporal resolution and robustness to motion artifacts, critical features for decoding subtle mental states such as attention, confusion, or fatigue. In this work, we introduce a model-agnostic, inference-time refinement framework designed to enhance the output of existing event-based gaze estimation models without modifying their architecture or requiring retraining. Our method comprises two key post-processing modules: (i) Motion-Aware Median Filtering, which suppresses blink-induced spikes while preserving natural gaze dynamics, and (ii) Optical Flow-Based Local Refinement, which aligns gaze predictions with cumulative event motion to reduce spatial jitter and temporal discontinuities. To complement traditional spatial accuracy metrics, we propose a novel Jitter Metric that captures the temporal smoothness of predicted gaze trajectories based on velocity regularity and local signal complexity. Together, these contributions significantly improve the consistency of event-based gaze signals, making them better suited for downstream tasks such as micro-expression analysis and mind-state decoding. Our results demonstrate consistent improvements across multiple baseline models on controlled datasets, laying the groundwork for future integration with multimodal affect recognition systems in real-world environments.
zh
[CV-138] Good Noise Makes Good Edits: A Training-Free Diffusion-Based Video Editing with Image and Text Prompts
【速读】：该论文试图解决视频编辑中缺乏零样本、无需训练的条件编辑方法的问题，特别是在同时利用图像和文本进行编辑时的连贯性和准确性问题。解决方案的关键在于引入ρ-start采样和扩张双掩码技术，以构建结构良好的噪声图，从而实现一致且精确的编辑效果，同时采用零图像引导策略作为可控的负提示机制，以提升视觉保真度。
链接: https://arxiv.org/abs/2506.12520
作者: Saemee Choi,Sohyun Jeong,Jaegul Choo,Jinhee Kim
机构: KAIST(韩国科学技术院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We propose ImEdit, the first zero-shot, training-free video editing method conditioned on both images and text. The proposed method introduces \rho -start sampling and dilated dual masking to construct well-structured noise maps for coherent and accurate edits. We further present zero image guidance, a controllable negative prompt strategy, for visual fidelity. Both quantitative and qualitative evaluations show that our method outperforms state-of-the-art methods across all metrics.
zh
[CV-139] Retrieval Augmented Comic Image Generation
【速读】：该论文试图解决生成连贯角色和生动动作的漫画风格图像序列的问题，具体包括保持角色身份和服装的一致性以及生成多样且富有表现力的角色动作。解决方案的关键在于集成基于检索的角色分配模块，该模块将文本提示中的角色与参考图像对齐，并采用区域角色注入机制，将角色特征嵌入到指定的图像区域中。
链接: https://arxiv.org/abs/2506.12517
作者: Yunhao Shui,Xuekuan Wang,Feng Qiu,Yuqiu Huang,Jinzhu Li,Haoyu Zheng,Jinru Han,Zhuo Zeng,Pengpeng Zhang,Jiarui Han,Keqiang Sun
机构: Zulution AI(紫露科技)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present RaCig, a novel system for generating comic-style image sequences with consistent characters and expressive gestures. RaCig addresses two key challenges: (1) maintaining character identity and costume consistency across frames, and (2) producing diverse and vivid character gestures. Our approach integrates a retrieval-based character assignment module, which aligns characters in textual prompts with reference images, and a regional character injection mechanism that embeds character features into specified image regions. Experimental results demonstrate that RaCig effectively generates engaging comic narratives with coherent characters and dynamic interactions. The source code will be publicly available to support further research in this area.
zh
[CV-140] Generalized Category Discovery under the Long-Tailed Distribution
【速读】：该论文试图解决在长尾分布下的广义类别发现（Generalized Category Discovery, GCD）问题，即在无标签数据集中利用已知标签类别的知识发现新类别。现有方法通常假设数据集服从均匀分布，而现实数据往往呈现长尾分布，即少数类别包含大量样本，而其他类别样本稀少。该论文识别出在此设置下的两个关键挑战——分类器学习的平衡与类别数量的估计，并提出一种基于置信样本选择和密度聚类的框架来应对这些挑战。
链接: https://arxiv.org/abs/2506.12515
作者: Bingchen Zhao,Kai Han
机构: University of Edinburgh (爱丁堡大学); University of Hong Kong (香港大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper addresses the problem of Generalized Category Discovery (GCD) under a long-tailed distribution, which involves discovering novel categories in an unlabelled dataset using knowledge from a set of labelled categories. Existing works assume a uniform distribution for both datasets, but real-world data often exhibits a long-tailed distribution, where a few categories contain most examples, while others have only a few. While the long-tailed distribution is well-studied in supervised and semi-supervised settings, it remains unexplored in the GCD context. We identify two challenges in this setting - balancing classifier learning and estimating category numbers - and propose a framework based on confident sample selection and density-based clustering to tackle them. Our experiments on both long-tailed and conventional GCD datasets demonstrate the effectiveness of our method.
zh
[CV-141] Interpretable Text-Guided Image Clustering via Iterative Search
【速读】：该论文试图解决传统聚类方法在缺乏额外信息时存在的病态问题，即数据集可能存在多种合理但不同的划分方式。为了解决这一问题，论文提出了一种名为ITGC的新型文本引导聚类方法，其关键在于利用用户提供的自然语言指令作为指导，通过迭代发现过程生成更符合用户意图的可解释视觉概念，从而实现更精准的图像聚类与细粒度分类。
链接: https://arxiv.org/abs/2506.12514
作者: Bingchen Zhao,Oisin Mac Aodha
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traditional clustering methods aim to group unlabeled data points based on their similarity to each other. However, clustering, in the absence of additional information, is an ill-posed problem as there may be many different, yet equally valid, ways to partition a dataset. Distinct users may want to use different criteria to form clusters in the same data, e.g. shape v.s. color. Recently introduced text-guided image clustering methods aim to address this ambiguity by allowing users to specify the criteria of interest using natural language instructions. This instruction provides the necessary context and control needed to obtain clusters that are more aligned with the users’ intent. We propose a new text-guided clustering approach named ITGC that uses an iterative discovery process, guided by an unsupervised clustering objective, to generate interpretable visual concepts that better capture the criteria expressed in a user’s instructions. We report superior performance compared to existing methods across a wide variety of image clustering and fine-grained classification benchmarks.
zh
[CV-142] Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
【速读】：该论文旨在解决高动态范围（HDR）和广色域（WCG）技术在图像质量评估中的挑战，特别是在高保真范围内感知差异细微的情况下，如何实现更精确的图像质量评估。解决方案的关键在于引入AIC-HDR2025数据集，该数据集包含从五个HDR源生成的100张测试图像，每张图像使用四种编解码器在五个压缩级别下进行压缩，覆盖从可见失真到低于视觉无损阈值的压缩等级，并通过基于JPEG AIC-3测试方法的主观研究收集了大量评分数据，以验证其在HDR质量估计中的有效性。
链接: https://arxiv.org/abs/2506.12505
作者: Mohsen Jenadeleh,Jon Sneyers,Davi Lazzarotto,Shima Mohammadi,Dominik Keller,Atanas Boev,Rakesh Rao Ramachandra Rao,António Pinheiro,Thomas Richter,Alexander Raake,Touradj Ebrahimi,João Ascenso,Dietmar Saupe
机构: University of Konstanz (康斯坦茨大学); Cloudinary (云存储公司); EPFL (瑞士联邦理工学院); IST-IT (葡萄牙科技研究所); TU Ilmenau (伊尔梅瑙工业大学); RWTH Aachen (亚琛工业大学); IT-UBI (葡萄牙技术研究所); Fraunhofer IIS (弗劳恩霍夫研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted to QoMEX 2025. The work is funded by the DFG (German Research Foundation) - Project ID 496858717, titled “JND-based Perceptual Video Quality Analysis and Modeling”. D.S. is funded by DFG Project ID 251654672
Abstract:High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual differences are subtle. To address this gap, we introduce AIC-HDR2025, the first such HDR dataset, comprising 100 test images generated from five HDR sources, each compressed using four codecs at five compression levels. It covers the high-fidelity range, from visible distortions to compression levels below the visually lossless threshold. A subjective study was conducted using the JPEG AIC-3 test methodology, combining plain and boosted triplet comparisons. In total, 34,560 ratings were collected from 151 participants across four fully controlled labs. The results confirm that AIC-3 enables precise HDR quality estimation, with 95% confidence intervals averaging a width of 0.27 at 1 JND. In addition, several recently proposed objective metrics were evaluated based on their correlation with subjective ratings. The dataset is publicly available. Comments: This paper has been accepted to QoMEX 2025. The work is funded by the DFG (German Research Foundation) - Project ID 496858717, titled “JND-based Perceptual Video Quality Analysis and Modeling”. D.S. is funded by DFG Project ID 251654672 Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2506.12505 [cs.CV] (or arXiv:2506.12505v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2506.12505 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Mohsen Jenadeleh [view email] [v1] Sat, 14 Jun 2025 13:36:15 UTC (1,390 KB)
zh
[CV-143] Comparative Analysis of Deep Learning Strategies for Hypertensive Retinopathy Detection from Fundus Images: From Scratch and Pre-trained Models
【速读】：该论文旨在解决从眼底图像中检测高血压性视网膜病变（hypertensive retinopathy）的问题，这是HRDC挑战中的一个核心任务。其解决方案的关键在于对比分析不同深度学习策略的表现，特别是模型架构与数据增强之间的相互作用。研究发现，生成式AI（Generative AI）在数据增强下的表现具有显著的架构依赖性，例如纯Vision Transformers（ViTs）在增强数据下性能提升明显，而混合ViT-CNN模型则因CNN组件的强先验偏置受到干扰。此外，小尺寸的patch输入（如ViT-B/8）在增强数据上表现更优，同时表明数据多样性对于释放自监督模型（如DINOv2）潜力的重要性。
链接: https://arxiv.org/abs/2506.12492
作者: Yanqiao Zhu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents a comparative analysis of deep learning strategies for detecting hypertensive retinopathy from fundus images, a central task in the HRDC challenge~\citeqian2025hrdc. We investigate three distinct approaches: a custom CNN, a suite of pre-trained transformer-based models, and an AutoML solution. Our findings reveal a stark, architecture-dependent response to data augmentation. Augmentation significantly boosts the performance of pure Vision Transformers (ViTs), which we hypothesize is due to their weaker inductive biases, forcing them to learn robust spatial and structural features. Conversely, the same augmentation strategy degrades the performance of hybrid ViT-CNN models, whose stronger, pre-existing biases from the CNN component may be “confused” by the transformations. We show that smaller patch sizes (ViT-B/8) excel on augmented data, enhancing fine-grained detail capture. Furthermore, we demonstrate that a powerful self-supervised model like DINOv2 fails on the original, limited dataset but is “rescued” by augmentation, highlighting the critical need for data diversity to unlock its potential. Preliminary tests with a ViT-Large model show poor performance, underscoring the risk of using overly-capacitive models on specialized, smaller datasets. This work provides critical insights into the interplay between model architecture, data augmentation, and dataset size for medical image classification.
zh
[CV-144] Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation
【速读】：该论文旨在解决视频测试时自适应（Video Test-Time Adaptation, TTA）中忽视音频信息潜在贡献的问题。现有方法主要依赖视觉监督信号，而未充分挖掘音频数据的价值。其解决方案的关键在于引入音频信息以生成音频辅助的伪标签，并通过预训练音频模型与大语言模型实现音频类别到视频标签的映射，从而建立音频与视频标签之间的语义关联，提升模型在测试阶段的泛化能力。
链接: https://arxiv.org/abs/2506.12481
作者: Runhao Zeng,Qi Deng,Ronghao Zhang,Shuaicheng Niu,Jian Chen,Xiping Hu,Victor C. M. Leung
机构: Artificial Intelligence Research Institute, Shenzhen MSU-BIT University; Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing; School of Software Engineering, South China University of Technology; College of Computing and Data Science, Nanyang Technological University
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: 14 pages, 7 figures
Abstract:Test-time adaptation (TTA) aims to boost the generalization capability of a trained model by conducting self-/unsupervised learning during the testing phase. While most existing TTA methods for video primarily utilize visual supervisory signals, they often overlook the potential contribution of inherent audio data. To address this gap, we propose a novel approach that incorporates audio information into video TTA. Our method capitalizes on the rich semantic content of audio to generate audio-assisted pseudo-labels, a new concept in the context of video TTA. Specifically, we propose an audio-to-video label mapping method by first employing pre-trained audio models to classify audio signals extracted from videos and then mapping the audio-based predictions to video label spaces through large language models, thereby establishing a connection between the audio categories and video labels. To effectively leverage the generated pseudo-labels, we present a flexible adaptation cycle that determines the optimal number of adaptation iterations for each sample, based on changes in loss and consistency across different views. This enables a customized adaptation process for each sample. Experimental results on two widely used datasets (UCF101-C and Kinetics-Sounds-C), as well as on two newly constructed audio-video TTA datasets (AVE-C and AVMIT-C) with various corruption types, demonstrate the superiority of our approach. Our method consistently improves adaptation performance across different video classification models and represents a significant step forward in integrating audio information into video TTA. Code: this https URL.
zh
[CV-145] Binarization-Aware Adjuster: Bridging Continuous Optimization and Binary Inference in Edge Detection
【速读】：该论文试图解决图像边缘检测（Image Edge Detection, ED）中训练与推理之间的根本性不匹配问题，即模型在训练时使用连续值输出，而在评估时依赖二值化预测，这种不一致由二值化操作的不可微性引起，削弱了学习目标与实际任务性能之间的联系。解决方案的关键在于提出一种基于梯度优化的二值化感知调整器（Binarization-Aware Adjuster, BAA），其核心是一种基于距离加权函数（Distance Weight Function, DWF）的损失调整机制，通过根据像素的正确性和距离决策边界的位置重新加权像素级贡献，强调决策关键区域并降低次要区域的影响，同时引入自适应过程以估计BAA的最佳二值化阈值，从而进一步对齐训练动态与推理行为。
链接: https://arxiv.org/abs/2506.12460
作者: Hao Shu
机构: Sun-Yat-Sen University (中山大学); Shenzhen University (深圳大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages
Abstract:Image edge detection (ED) faces a fundamental mismatch between training and inference: models are trained using continuous-valued outputs but evaluated using binary predictions. This misalignment, caused by the non-differentiability of binarization, weakens the link between learning objectives and actual task performance. In this paper, we propose a theoretical method to design a Binarization-Aware Adjuster (BAA), which explicitly incorporates binarization behavior into gradient-based optimization. At the core of BAA is a novel loss adjustment mechanism based on a Distance Weight Function (DWF), which reweights pixel-wise contributions according to their correctness and proximity to the decision boundary. This emphasizes decision-critical regions while down-weighting less influential ones. We also introduce a self-adaptive procedure to estimate the optimal binarization threshold for BAA, further aligning training dynamics with inference behavior. Extensive experiments across various architectures and datasets demonstrate the effectiveness of our approach. Beyond ED, BAA offers a generalizable strategy for bridging the gap between continuous optimization and discrete evaluation in structured prediction tasks.
zh
[CV-146] Demographics-Informed Neural Network for Multi-Modal Spatiotemporal forecasting of Urban Growth and Travel Patterns Using Satellite Imagery
【速读】：该论文试图解决城市空间演变预测中多源异构数据融合与时空动态建模的问题，特别是如何将地理卫星影像、社会人口统计信息和出行行为动态进行联合建模以提高预测的准确性与现实性。解决方案的关键在于提出了一种结合编码器-解码器结构与时间门控残差连接的深度学习框架，并引入了多目标损失函数与语义损失函数，以平衡视觉真实性和时间一致性，同时通过 demographics prediction component 确保预测结果与人口统计特征的一致性，从而显著提升了模型的生理现实性和经济社会准确性。
链接: https://arxiv.org/abs/2506.12456
作者: Eugene Kofi Okrah Denteh,Andrews Danyo,Joshua Kofi Asamoah,Blessing Agyei Kyem,Armstrong Aboah
机构: North Dakota State University (北达科他州立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This study presents a novel demographics informed deep learning framework designed to forecast urban spatial transformations by jointly modeling geographic satellite imagery, socio-demographics, and travel behavior dynamics. The proposed model employs an encoder-decoder architecture with temporal gated residual connections, integrating satellite imagery and demographic data to accurately forecast future spatial transformations. The study also introduces a demographics prediction component which ensures that predicted satellite imagery are consistent with demographic features, significantly enhancing physiological realism and socioeconomic accuracy. The framework is enhanced by a proposed multi-objective loss function complemented by a semantic loss function that balances visual realism with temporal coherence. The experimental results from this study demonstrate the superior performance of the proposed model compared to state-of-the-art models, achieving higher structural similarity (SSIM: 0.8342) and significantly improved demographic consistency (Demo-loss: 0.14 versus 0.95 and 0.96 for baseline models). Additionally, the study validates co-evolutionary theories of urban development, demonstrating quantifiable bidirectional influences between built environment characteristics and population patterns. The study also contributes a comprehensive multimodal dataset pairing satellite imagery sequences (2012-2023) with corresponding demographic and travel behavior attributes, addressing existing gaps in urban and transportation planning resources by explicitly connecting physical landscape evolution with socio-demographic patterns.
zh
[CV-147] CLIP-HandID: Vision-Language Model for Hand-Based Person Identification
【速读】：该论文旨在解决在刑事调查中，特别是在缺乏其他生物特征证据的情况下，如何利用手部图像进行有效的人体识别问题。其解决方案的关键在于提出一种基于CLIP模型的新型方法——CLIP-HandID，该方法通过文本提示作为语义引导，从手部图像中高效学习具有判别性的深度特征表示，并利用文本反转网络学习代表特定视觉上下文或外观属性的伪标记，进而将这些伪标记整合到文本提示中，以增强CLIP模型的多模态推理能力，从而提升识别的泛化性能。
链接: https://arxiv.org/abs/2506.12447
作者: Nathanael L. Baisa,Babu Pallam,Amudhavel Jayavel
机构: De Montfort University (德蒙特福特大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:This paper introduces a new approach to person identification based on hand images, designed specifically for criminal investigations. The method is particularly valuable in serious crimes like sexual abuse, where hand images are often the sole identifiable evidence available. Our proposed method, CLIP-HandID, leverages pre-trained foundational vision-language model, particularly CLIP, to efficiently learn discriminative deep feature representations from hand images given as input to the image encoder of CLIP using textual prompts as semantic guidance. We propose to learn pseudo-tokens that represent specific visual contexts or appearance attributes using textual inversion network since labels of hand images are indexes instead text descriptions. The learned pseudo-tokens are incorporated into textual prompts which are given as input to the text encoder of the CLIP to leverage its multi-modal reasoning to enhance its generalization for identification. Through extensive evaluations on two large, publicly available hand datasets with multi-ethnic representation, we show that our method substantially surpasses existing approaches.
zh
[CV-148] MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
【速读】：该论文旨在解决胎儿超声图像分割中面临的挑战，如封闭的解剖结构、模糊的边界和小的解剖结构，这些问题导致现有分割方法在局部特征提取与全局上下文建模之间难以平衡。其解决方案的关键在于提出MS-UMamba模型，该模型结合了卷积神经网络（CNN）与Mamba架构的优势，设计了一个集成卷积分支的视觉状态空间块（SS-MCAT-SSM），以利用Mamba的全局建模能力和卷积层的局部表示优势，同时引入高效的多尺度特征融合模块，结合空间注意力机制以增强特征表达能力。
链接: https://arxiv.org/abs/2506.12441
作者: Caixu Xu,Junming Wei,Huizhen Chen,Pengchen Liang,Bocheng Liang,Ying Tan,Xintong Wei
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Recently, Mamba-based methods have become popular in medical image segmentation due to their lightweight design and long-range dependency modeling capabilities. However, current segmentation methods frequently encounter challenges in fetal ultrasound images, such as enclosed anatomical structures, blurred boundaries, and small anatomical structures. To address the need for balancing local feature extraction and global context modeling, we propose MS-UMamba, a novel hybrid convolutional-mamba model for fetal ultrasound image segmentation. Specifically, we design a visual state space block integrated with a CNN branch (SS-MCAT-SSM), which leverages Mamba’s global modeling strengths and convolutional layers’ local representation advantages to enhance feature learning. In addition, we also propose an efficient multi-scale feature fusion module that integrates spatial attention mechanisms, which Integrating feature information from different layers enhances the feature representation ability of the model. Finally, we conduct extensive experiments on a non-public dataset, experimental results demonstrate that MS-UMamba model has excellent performance in segmentation performance.
zh
[CV-149] Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey
【速读】：该论文试图解决符号音乐谱中基于风格的作曲家识别与作者归属问题，旨在提升该领域的可靠性与可重复性。其关键解决方案在于批判性评估现有研究中的计算方法、特征表示及评价机制，强调采用稳健的度量标准如平衡准确率和严格的交叉验证，以增强结果的可信度，并提出一系列可操作的指南以提高未来研究的音乐学有效性与可重复性。
链接: https://arxiv.org/abs/2506.12440
作者: Federico Simonetta
机构: GSSI – Gran Sasso Science Institute, L’Aquila, Italy
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Digital Libraries (cs.DL); Audio and Speech Processing (eess.AS)
备注: Accepted at the TISMIR
Abstract:This paper presents the first comprehensive systematic review of literature on style-based composer identification and authorship attribution in symbolic music scores. Addressing the critical need for improved reliability and reproducibility in this field, the review rigorously analyzes 58 peer-reviewed papers published across various historical periods, with the search adapted to evolving terminology. The analysis critically assesses prevailing repertoires, computational approaches, and evaluation methodologies, highlighting significant challenges. It reveals that a substantial portion of existing research suffers from inadequate validation protocols and an over-reliance on simple accuracy metrics for often imbalanced datasets, which can undermine the credibility of attribution claims. The crucial role of robust metrics like Balanced Accuracy and rigorous cross-validation in ensuring trustworthy results is emphasized. The survey also details diverse feature representations and the evolution of machine learning models employed. Notable real-world authorship attribution cases, such as those involving works attributed to Bach, Josquin Desprez, and Lennon-McCartney, are specifically discussed, illustrating the opportunities and pitfalls of applying computational techniques to resolve disputed musical provenance. Based on these insights, a set of actionable guidelines for future research are proposed. These recommendations are designed to significantly enhance the reliability, reproducibility, and musicological validity of composer identification and authorship attribution studies, fostering more robust and interpretable computational stylistic analysis.
zh
[CV-150] Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在安全性方面面临的威胁，特别是针对其产生有害输出的对抗性攻击问题。解决方案的关键在于通过组织Adversarial Testing Large-model Alignment Safety Grand Challenge (ATLAS) 2025竞赛，系统性地评估和提升MLLMs的安全性，具体包括通过白盒和黑盒两种方式对模型进行对抗性图像-文本攻击测试，从而揭示MLLMs的安全漏洞并为构建更强大的防御机制提供指导。
链接: https://arxiv.org/abs/2506.12430
作者: Zonghao Ying,Siyang Wu,Run Hao,Peng Ying,Shixuan Sun,Pengyu Chen,Junze Chen,Hao Du,Kaiwen Shen,Shangkun Wu,Jiwei Wei,Shiyuan He,Yang Yang,Xiaohai Xu,Ke Ma,Qianqian Xu,Qingming Huang,Shi Lin,Xun Wang,Changting Lin,Meng Han,Yilei Jiang,Siqi Lai,Yaozhi Zheng,Yifei Song,Xiangyu Yue,Zonglei Jing,Tianyuan Zhang,Zhilei Zhu,Aishan Liu,Jiakai Wang,Siyuan Liang,Xianglong Kong,Hainan Li,Junjie Mu,Haotong Qin,Yue Yu,Lei Chen,Felix Juefei-Xu,Qing Guo,Xinyun Chen,Yew Soon Ong,Xianglong Liu,Dawn Song,Alan Yuille,Philip Torr,Dacheng Tao
机构: 未知
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks that induce harmful outputs. To systematically evaluate and improve their safety, we organized the Adversarial Testing Large-model Alignment Safety Grand Challenge (ATLAS) 2025. This technical report presents findings from the competition, which involved 86 teams testing MLLM vulnerabilities via adversarial image-text attacks in two phases: white-box and black-box evaluations. The competition results highlight ongoing challenges in securing MLLMs and provide valuable guidance for developing stronger defense mechanisms. The challenge establishes new benchmarks for MLLM safety evaluation and lays groundwork for advancing safer multimodal AI systems. The code and data for this challenge are openly available at this https URL.
zh
[CV-151] Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
【速读】：该论文旨在解决领域泛化行人重识别（Domain-Generalizable ReID, DG-ReID）中因领域分布差异导致的模型泛化能力不足问题。传统方法在训练与测试领域特征相似的情况下表现良好，但在面对未见过的领域时性能显著下降。为了解决这一问题，DG-ReID的目标是学习领域不变的特征表示，而无需依赖目标领域的数据。其解决方案的关键在于设计能够显式学习领域不变且身份区分性表征的领域泛化模块，从而提升模型在不同场景下的适应能力。
链接: https://arxiv.org/abs/2506.12413
作者: Hyeonseo Lee,Juhyun Park,Jihyong Oh,Chanho Eom
机构: Chung-Ang University (忠南大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Please visit our project page at this https URL
Abstract:Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and lighting conditions. To address this issue, Domain-Adaptive ReID (DA-ReID) methods have been proposed. These approaches incorporate unlabeled target domain data during training and improve performance by aligning feature distributions between source and target domains. Domain-Generalizable ReID (DG-ReID) tackles a more realistic and challenging setting by aiming to learn domain-invariant features without relying on any target domain data. Recent methods have explored various strategies to enhance generalization across diverse environments, but the field remains relatively underexplored. In this paper, we present a comprehensive survey of DG-ReID. We first review the architectural components of DG-ReID including the overall setting, commonly used backbone networks and multi-source input configurations. Then, we categorize and analyze domain generalization modules that explicitly aim to learn domain-invariant and identity-discriminative representations. To examine the broader applicability of these techniques, we further conduct a case study on a related task that also involves distribution shifts. Finally, we discuss recent trends, open challenges, and promising directions for future research in DG-ReID. To the best of our knowledge, this is the first systematic survey dedicated to DG-ReID.
zh
[CV-152] InverTune: Removing Backdoors from Multimodal Contrastive Learning Models via Trigger Inversion and Activation Tuning
【速读】：该论文旨在解决多模态对比学习模型（如CLIP）在面对后门攻击时的安全性问题，此类攻击通过植入隐含触发器使模型在特定输入下产生恶意行为，而现有防御方法因对攻击者知识的强假设或对干净数据的高需求而不具实用性。论文提出的解决方案关键在于InverTune框架，其通过三个核心组件实现有效防御：首先通过对抗模拟暴露攻击特征并概率性识别目标标签，其次利用梯度反演技术通过激活模式分析重建隐含触发器，最后采用聚类引导的微调策略，在仅需少量任意干净数据的情况下擦除后门功能，同时保持模型原有性能。
链接: https://arxiv.org/abs/2506.12411
作者: Mengyuan Sun,Yu Li,Yuchen Liu,Bo Du,Yunjie Ge
机构: Wuhan University (武汉大学)
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal contrastive learning models like CLIP have demonstrated remarkable vision-language alignment capabilities, yet their vulnerability to backdoor attacks poses critical security risks. Attackers can implant latent triggers that persist through downstream tasks, enabling malicious control of model behavior upon trigger presentation. Despite great success in recent defense mechanisms, they remain impractical due to strong assumptions about attacker knowledge or excessive clean data requirements. In this paper, we introduce InverTune, the first backdoor defense framework for multimodal models under minimal attacker assumptions, requiring neither prior knowledge of attack targets nor access to the poisoned dataset. Unlike existing defense methods that rely on the same dataset used in the poisoning stage, InverTune effectively identifies and removes backdoor artifacts through three key components, achieving robust protection against backdoor attacks. Specifically, InverTune first exposes attack signatures through adversarial simulation, probabilistically identifying the target label by analyzing model response patterns. Building on this, we develop a gradient inversion technique to reconstruct latent triggers through activation pattern analysis. Finally, a clustering-guided fine-tuning strategy is employed to erase the backdoor function with only a small amount of arbitrary clean data, while preserving the original model capabilities. Experimental results show that InverTune reduces the average attack success rate (ASR) by 97.87% against the state-of-the-art (SOTA) attacks while limiting clean accuracy (CA) degradation to just 3.07%. This work establishes a new paradigm for securing multimodal systems, advancing security in foundation model deployment without compromising performance.
zh
[CV-153] Branch or Layer? Zeroth-Order Optimization for Continual Learning of Vision-Language Models
【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在持续学习过程中面临的参数效率、内存消耗和优化稳定性之间的平衡问题。现有方法主要依赖一阶（First-Order, FO）优化（如SGD），但其确定性梯度容易使模型陷入次优局部极小值，并产生较高的内存开销。论文提出了一种系统性的零阶（Zeroth-Order, ZO）优化方法，通过选择性地将ZO应用于视觉或语言模态，同时在互补分支保留FO优化，以缓解模态特异性不稳定问题。其关键在于引入分层优化范式，将ZO与FO在不同网络层中交错使用，充分利用浅层与深层表示的学习动态差异，并通过模态特定的梯度符号归一化机制应对视觉分支中ZO扰动方差较大的问题。实验表明，该方法在四个基准数据集上取得了最先进的性能，同时将内存消耗降低了89.1%。
链接: https://arxiv.org/abs/2506.12409
作者: Ziwei Liu,Borui Kang,Wei Li,Hangjie Yuan,Yanbing Yang,Wenbin Li,Jun Luo,Yifan Zhu,Tao Feng
机构: Sichuan University (四川大学); Zhejiang University (浙江大学); Nanjing University (南京大学); NTU (南洋理工大学); BUPT (北京邮电大学); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Continual learning in vision-language models (VLMs) faces critical challenges in balancing parameter efficiency, memory consumption, and optimization stability. While First-Order (FO) optimization (e.g., SGD) dominate current approaches, their deterministic gradients often trap models in suboptimal local minima and incur substantial memory overhead. This paper pioneers a systematic exploration of Zeroth-Order (ZO) optimization for vision-language continual learning (VLCL). We first identify the incompatibility of naive full-ZO adoption in VLCL due to modality-specific instability. To resolve this, we selectively applying ZO to either vision or language modalities while retaining FO in the complementary branch. Furthermore, we develop a layer-wise optimization paradigm that interleaves ZO and FO across network layers, capitalizing on the heterogeneous learning dynamics of shallow versus deep representations. A key theoretical insight reveals that ZO perturbations in vision branches exhibit higher variance than language counterparts, prompting a gradient sign normalization mechanism with modality-specific perturbation constraints. Extensive experiments on four benchmarks demonstrate that our method achieves state-of-the-art performance, reducing memory consumption by 89.1% compared to baselines. Code will be available upon publication.
zh
[CV-154] Feature Complementation Architecture for Visual Place Recognition
【速读】：该论文旨在解决视觉定位与导航中视觉位置识别（VPR）任务的特征表示鲁棒性问题，即如何构建对环境变化具有强适应性的特征表达。现有方法通常采用卷积神经网络（CNN）或视觉Transformer（ViT）作为特征提取器，但两者各有所长——CNN擅长捕捉局部细节，而ViT更适用于建模全局上下文，难以同时发挥二者优势。解决方案的关键在于提出一种局部-全局特征互补网络（LGCN），该网络结合了并行的CNN-ViT混合架构与动态特征融合模块（DFM），通过联合建模空间和通道依赖关系实现动态特征融合，并引入轻量级频域到空域融合适配器以增强ViT分支的表达能力和适应性，从而提升定位精度与鲁棒性。
链接: https://arxiv.org/abs/2506.12401
作者: Weiwei Wang,Meijia Wang,Haoyi Wang,Wenqiang Guo,Jiapan Guo,Changming Sun,Lingkun Ma,Weichuan Zhang
机构: Southwest University of Science and Technology (西南科技大学); Rijksuniversiteit Groningen (格罗宁根大学); Commonwealth Scientific and Industrial Research Organisation (澳大利亚联邦科学与工业研究组织)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Visual place recognition (VPR) plays a crucial role in robotic localization and navigation. The key challenge lies in constructing feature representations that are robust to environmental changes. Existing methods typically adopt convolutional neural networks (CNNs) or vision Transformers (ViTs) as feature extractors. However, these architectures excel in different aspects – CNNs are effective at capturing local details. At the same time, ViTs are better suited for modeling global context, making it difficult to leverage the strengths of both. To address this issue, we propose a local-global feature complementation network (LGCN) for VPR which integrates a parallel CNN-ViT hybrid architecture with a dynamic feature fusion module (DFM). The DFM performs dynamic feature fusion through joint modeling of spatial and channel-wise dependencies. Furthermore, to enhance the expressiveness and adaptability of the ViT branch for VPR tasks, we introduce lightweight frequency-to-spatial fusion adapters into the frozen ViT backbone. These adapters enable task-specific adaptation with controlled parameter overhead. Extensive experiments on multiple VPR benchmark datasets demonstrate that the proposed LGCN consistently outperforms existing approaches in terms of localization accuracy and robustness, validating its effectiveness and generalizability.
zh
[CV-155] Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting ICML
【速读】：该论文旨在解决现有3D Gaussian Splatting (3DGS) 方法在适应性优化高斯基元分布方面存在的不足，即难以根据场景特征动态调整分布，从而难以平衡重建质量和效率的问题。其解决方案的关键在于提出了一种场景自适应的感知密集化方法（Perceptual-GS），通过将感知敏感性整合到3DGS训练过程中，构建一种基于相机就绪的感知敏感性自适应分布，从而在视觉关键区域分配更细粒度的高斯基元，提升重建质量与鲁棒性。
链接: https://arxiv.org/abs/2506.12400
作者: Hongbi Zhou,Zhangkai Ni
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to International Conference on Machine Learning (ICML) 2025
Abstract:3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making it challenging to balance reconstruction quality and efficiency. Inspired by human perception, we propose scene-adaptive perceptual densification for Gaussian Splatting (Perceptual-GS), a novel framework that integrates perceptual sensitivity into the 3DGS training process to address this challenge. We first introduce a perception-aware representation that models human visual sensitivity while constraining the number of Gaussian primitives. Building on this foundation, we develop a \camerareadyperceptual sensitivity-adaptive distribution to allocate finer Gaussian granularity to visually critical regions, enhancing reconstruction quality and robustness. Extensive evaluations on multiple datasets, including BungeeNeRF for large-scale scenes, demonstrate that Perceptual-GS achieves state-of-the-art performance in reconstruction quality, efficiency, and robustness. The code is publicly available at: this https URL
zh
[CV-156] LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
【速读】：该论文试图解决参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）方法在领域偏移（domain shifts）下难以保持鲁棒性的同时维持计算效率的问题。解决方案的关键在于提出一种名为低秩调控梯度投影（Low-rAnk Regulated Gradient Projection, LARGO）的算法，该算法通过引入动态约束，结合并行可训练梯度投影来动态调控层间更新，从而在保留预训练模型的分布外鲁棒性的同时，保持层间独立性，并通过减少梯度依赖性来确保计算效率。此外，LARGO还通过利用预训练权重的奇异值分解进行结构化初始化，以最小化对预训练知识的偏离。
链接: https://arxiv.org/abs/2506.12394
作者: Haotian Zhang,Liu Liu,Baosheng Yu,Jiayan Qiu,Yanwei Ren,Xianglong Liu
机构: School of Artificial Intelligence, Beihang University (人工智能学院，北京航空航天大学); Hangzhou International Innovation Institute, Beihang University (杭州国际创新研究院，北京航空航天大学); Nanyang Technological University (南洋理工大学); University of Leicester (莱斯特大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The advent of parameter-efficient fine-tuning methods has significantly reduced the computational burden of adapting large-scale pretrained models to diverse downstream tasks. However, existing approaches often struggle to achieve robust performance under domain shifts while maintaining computational efficiency. To address this challenge, we propose Low-rAnk Regulated Gradient Projection (LARGO) algorithm that integrates dynamic constraints into low-rank adaptation methods. Specifically, LARGO incorporates parallel trainable gradient projections to dynamically regulate layer-wise updates, retaining the Out-Of-Distribution robustness of pretrained model while preserving inter-layer independence. Additionally, it ensures computational efficiency by mitigating the influence of gradient dependencies across layers during weight updates. Besides, through leveraging singular value decomposition of pretrained weights for structured initialization, we incorporate an SVD-based initialization strategy that minimizing deviation from pretrained knowledge. Through extensive experiments on diverse benchmarks, LARGO achieves state-of-the-art performance across in-domain and out-of-distribution scenarios, demonstrating improved robustness under domain shifts with significantly lower computational overhead compared to existing PEFT methods. The source code will be released soon.
zh
[CV-157] Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis
【速读】：该论文旨在解决轴承故障诊断中退化状态评估和剩余使用寿命（RUL）估计的问题，其解决方案的关键在于提出一种受视网膜神经节细胞感受野中心-周边结构启发的频域特征提取方法，即光谱故障感知域（Spectral Fault Receptive Fields, SFRFs）。SFRFs作为对抗性频谱滤波器，以特征故障频率为中心，并通过抑制性周边增强在不同工况下对早期故障特征的鲁棒识别，结合多目标进化优化策略对参数进行优化，从而提升RUL预测精度与退化轨迹的平滑性。
链接: https://arxiv.org/abs/2506.12375
作者: Stan Muñoz Gutiérrez,Franz Wotawa
机构: Graz University of Technology (格拉茨技术大学)
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Submitted to The 36th International Conference on Principles of Diagnosis and Resilient Systems (DX’25)
Abstract:This paper introduces Spectral Fault Receptive Fields (SFRFs), a biologically inspired technique for degradation state assessment in bearing fault diagnosis and remaining useful life (RUL) estimation. Drawing on the center-surround organization of retinal ganglion cell receptive fields, we propose a frequency-domain feature extraction algorithm that enhances the detection of fault signatures in vibration signals. SFRFs are designed as antagonistic spectral filters centered on characteristic fault frequencies, with inhibitory surrounds that enable robust characterization of incipient faults under variable operating conditions. A multi-objective evolutionary optimization strategy based on NSGA-II algorithm is employed to tune the receptive field parameters by simultaneously minimizing RUL prediction error, maximizing feature monotonicity, and promoting smooth degradation trajectories. The method is demonstrated on the XJTU-SY bearing run-to-failure dataset, confirming its suitability for constructing condition indicators in health monitoring applications. Key contributions include: (i) the introduction of SFRFs, inspired by the biology of vision in the primate retina; (ii) an evolutionary optimization framework guided by condition monitoring and prognosis criteria; and (iii) experimental evidence supporting the detection of early-stage faults and their precursors. Furthermore, we confirm that our diagnosis-informed spectral representation achieves accurate RUL prediction using a bagging regressor. The results highlight the interpretability and principled design of SFRFs, bridging signal processing, biological sensing principles, and data-driven prognostics in rotating machinery.
zh
[CV-158] Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
【速读】：该论文旨在解决医学影像中脑肿瘤分类的准确性问题，以确保可靠的诊断和有效的治疗方案。其解决方案的关键在于提出了一种双级集成框架，该框架将预训练深度学习（DL）模型用于特征提取，并结合优化的机器学习（ML）分类器进行鲁棒分类。该方法通过融合来自高性能Vision Transformer（ViT）模型的深度特征（特征级集成）以及通过超参数优化的ML分类器的预测结果（分类器级集成），实现了对脑磁共振成像（MRI）数据的高效分类。实验表明，该方法在公开的Kaggle脑肿瘤MRI数据集上显著优于现有技术，突显了特征与分类器融合的重要性。
链接: https://arxiv.org/abs/2506.12363
作者: Zahid Ullah,Jihie Kim
机构: Dongguk University (东国大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate brain tumor classification is crucial in medical imaging to ensure reliable diagnosis and effective treatment planning. This study introduces a novel double ensembling framework that synergistically combines pre-trained deep learning (DL) models for feature extraction with optimized machine learning (ML) classifiers for robust classification. The framework incorporates comprehensive preprocessing and data augmentation of brain magnetic resonance images (MRI), followed by deep feature extraction using transfer learning with pre-trained Vision Transformer (ViT) networks. The novelty lies in the dual-level ensembling strategy: feature-level ensembling, which integrates deep features from the top-performing ViT models, and classifier-level ensembling, which aggregates predictions from hyperparameter-optimized ML classifiers. Experiments on two public Kaggle MRI brain tumor datasets demonstrate that this approach significantly surpasses state-of-the-art methods, underscoring the importance of feature and classifier fusion. The proposed methodology also highlights the critical roles of hyperparameter optimization (HPO) and advanced preprocessing techniques in improving diagnostic accuracy and reliability, advancing the integration of DL and ML for clinically relevant medical image analysis.
zh
[CV-159] EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental Learning
【速读】：该论文旨在解决类增量学习（Class-Incremental Learning, CIL）中模型在持续学习新类别数据时如何有效保留已有知识并避免遗忘的问题。现有参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）方法存在引入额外参数或依赖刚性正则化技术导致模型灵活性下降的局限性。论文提出的解决方案关键在于融合重要性感知参数正则化（Importance-aware Parameter Regularization, IPR）与可训练语义漂移补偿（Trainable Semantic Drift Compensation, TSDC），通过评估参数对先前任务的重要性并选择性约束共享适配器中的更新，以保留知识并维持模型灵活性；同时利用可训练语义漂移补偿统一分类器，消除因语义差异导致的决策边界混淆。
链接: https://arxiv.org/abs/2506.12351
作者: Huaijie Wang,De Cheng,Lingfeng He,Yan Li,Jie Li,Nannan Wang,Xinbo Gao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Class-Incremental Learning (CIL) aims to enable AI models to continuously learn from sequentially arriving data of different classes over time while retaining previously acquired knowledge. Recently, Parameter-Efficient Fine-Tuning (PEFT) methods, like prompt pool-based approaches and adapter tuning, have shown great attraction in CIL. However, these methods either introduce additional parameters that increase memory usage, or rely on rigid regularization techniques which reduce forgetting but compromise model flexibility. To overcome these limitations, we propose the Elastic Knowledge Preservation and Compensation (EKPC) method, integrating Importance-aware Parameter Regularization (IPR) and Trainable Semantic Drift Compensation (TSDC) for CIL. Specifically, the IPR method assesses the sensitivity of network parameters to prior tasks using a novel parameter-importance algorithm. It then selectively constrains updates within the shared adapter according to these importance values, thereby preserving previously acquired knowledge while maintaining the model’s flexibility. However, it still exhibits slight semantic differences in previous knowledge to accommodate new incremental tasks, leading to decision boundaries confusion in classifier. To eliminate this confusion, TSDC trains a unified classifier by compensating prototypes with trainable semantic drift. Extensive experiments on five CIL benchmarks demonstrate the effectiveness of the proposed method, showing superior performances to existing state-of-the-art methods.
zh
[CV-160] Real-Time Per-Garment Virtual Try-On with Temporal Consistency for Loose-Fitting Garments
【速读】：该论文旨在解决松身衣物在虚拟试穿过程中因人体语义图不可靠和帧间时间信息利用不足而导致的图像质量下降与抖动伪影问题。其解决方案的关键在于提出一种两阶段方法：首先通过提取衣物不变表示来增强松身衣物下语义图估计的鲁棒性；其次引入递归衣物生成框架，利用时间依赖性提升帧间一致性，同时保持实时性能。
链接: https://arxiv.org/abs/2506.12348
作者: Zaiqiang Wu,I-Chao Shen,Takeo Igarashi
机构: The University of Tokyo (东京大学)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Per-garment virtual try-on methods collect garment-specific datasets and train networks tailored to each garment to achieve superior results. However, these approaches often struggle with loose-fitting garments due to two key limitations: (1) They rely on human body semantic maps to align garments with the body, but these maps become unreliable when body contours are obscured by loose-fitting garments, resulting in degraded outcomes; (2) They train garment synthesis networks on a per-frame basis without utilizing temporal information, leading to noticeable jittering artifacts. To address these challenges, we propose a two-stage approach for robust semantic map estimation. First, we extract a garment-invariant representation from the raw input image. This representation is then passed through an auxiliary network to estimate the semantic map. This enhances the robustness of semantic map estimation under loose-fitting garments during garment-specific dataset generation. Furthermore, we introduce a recurrent garment synthesis framework that incorporates temporal dependencies to improve frame-to-frame coherence while maintaining real-time performance. We conducted qualitative and quantitative evaluations to demonstrate that our method outperforms existing approaches in both image quality and temporal coherence. Ablation studies further validate the effectiveness of the garment-invariant representation and the recurrent synthesis framework.
zh
[CV-161] Restoring Gaussian Blurred Face Images for Deanonymization Attacks
【速读】：该论文旨在解决高模糊程度下通过高斯模糊（Gaussian blur）进行人脸匿名化的安全性问题，即探讨被高斯模糊处理的人脸是否能够被恢复并用于重新识别。论文提出的解决方案关键在于利用生成式 AI (Generative AI) 的记忆效应，并近似高斯模糊的逆函数以实现人脸修复。其核心方法名为Revelio，通过条件扩散模型进行初步人脸修复，再结合身份检索模型提升修复质量，从而在高模糊设置下仍能实现较高的重识别准确率（95.9%）。
链接: https://arxiv.org/abs/2506.12344
作者: Haoyu Zhai,Shuo Wang,Pirouz Naghavi,Qingying Hao,Gang Wang
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校)
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 16 figures, IEEE Transaction format
Abstract:Gaussian blur is widely used to blur human faces in sensitive photos before the photos are posted on the Internet. However, it is unclear to what extent the blurred faces can be restored and used to re-identify the person, especially under a high-blurring setting. In this paper, we explore this question by developing a deblurring method called Revelio. The key intuition is to leverage a generative model’s memorization effect and approximate the inverse function of Gaussian blur for face restoration. Compared with existing methods, we design the deblurring process to be identity-preserving. It uses a conditional Diffusion model for preliminary face restoration and then uses an identity retrieval model to retrieve related images to further enhance fidelity. We evaluate Revelio with large public face image datasets and show that it can effectively restore blurred faces, especially under a high-blurring setting. It has a re-identification accuracy of 95.9%, outperforming existing solutions. The result suggests that Gaussian blur should not be used for face anonymization purposes. We also demonstrate the robustness of this method against mismatched Gaussian kernel sizes and functions, and test preliminary countermeasures and adaptive attacks to inspire future work.
zh
[CV-162] Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models
【速读】：该论文试图解决如何检测目标图像是否被用于训练大型视觉-语言模型（Large Vision-Language Models, LVLMs）的问题。解决方案的关键在于设计一种基于图像损坏敏感性差异的会员推理攻击（Membership Inference Attack, MIA），即Image Corruption-Inspired Membership Inference Attacks (ICIMIA)。该方法利用LVLM对成员图像和非成员图像在图像损坏下的不同响应，通过比较图像与其损坏版本的嵌入相似性进行攻击，在白盒和黑盒两种场景下均表现出有效性。
链接: https://arxiv.org/abs/2506.12340
作者: Zongyu Wu,Minhua Lin,Zhiwei Zhang,Fali Wang,Xianren Zhang,Xiang Zhang,Suhang Wang
机构: The Pennsylvania State University (宾夕法尼亚州立大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注: Preprint
Abstract:Large vision-language models (LVLMs) have demonstrated outstanding performance in many downstream tasks. However, LVLMs are trained on large-scale datasets, which can pose privacy risks if training images contain sensitive information. Therefore, it is important to detect whether an image is used to train the LVLM. Recent studies have investigated membership inference attacks (MIAs) against LVLMs, including detecting image-text pairs and single-modality content. In this work, we focus on detecting whether a target image is used to train the target LVLM. We design simple yet effective Image Corruption-Inspired Membership Inference Attacks (ICIMIA) against LLVLMs, which are inspired by LVLM’s different sensitivity to image corruption for member and non-member images. We first perform an MIA method under the white-box setting, where we can obtain the embeddings of the image through the vision part of the target LVLM. The attacks are based on the embedding similarity between the image and its corrupted version. We further explore a more practical scenario where we have no knowledge about target LVLMs and we can only query the target LVLMs with an image and a question. We then conduct the attack by utilizing the output text embeddings’ similarity. Experiments on existing datasets validate the effectiveness of our proposed attack methods under those two different settings.
zh
[CV-163] Understanding and Benchmarking the Trustworthiness in Multimodal LLM s for Video Understanding
【速读】：该论文旨在解决视频理解多模态大语言模型（videoLLMs）在可信度方面的挑战，包括事实性错误、有害内容、偏见、幻觉和隐私风险等问题。其解决方案的关键在于提出Trust-videoLLMs，这是一个涵盖真实性、安全性、鲁棒性、公平性和隐私性的综合性基准框架，通过30个任务评估视频LLMs在动态视觉场景、跨模态交互及现实安全问题中的表现，以推动模型在可信度方面的改进。
链接: https://arxiv.org/abs/2506.12336
作者: Youze Wang,Zijun Chen,Ruoyu Chen,Shishen Gu,Yinpeng Dong,Hang Su,Jun Zhu,Meng Wang,Richang Hong,Wenbo Hu
机构: Hefei University of Technology (合肥工业大学); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recent advancements in multimodal large language models for video understanding (videoLLMs) have improved their ability to process dynamic multimodal data. However, trustworthiness challenges factual inaccuracies, harmful content, biases, hallucinations, and privacy risks, undermine reliability due to video data’s spatiotemporal complexities. This study introduces Trust-videoLLMs, a comprehensive benchmark evaluating videoLLMs across five dimensions: truthfulness, safety, robustness, fairness, and privacy. Comprising 30 tasks with adapted, synthetic, and annotated videos, the framework assesses dynamic visual scenarios, cross-modal interactions, and real-world safety concerns. Our evaluation of 23 state-of-the-art videoLLMs (5 commercial,18 open-source) reveals significant limitations in dynamic visual scene understanding and cross-modal perturbation resilience. Open-source videoLLMs show occasional truthfulness advantages but inferior overall credibility compared to commercial models, with data diversity outperforming scale effects. These findings highlight the need for advanced safety alignment to enhance capabilities. Trust-videoLLMs provides a publicly available, extensible toolbox for standardized trustworthiness assessments, bridging the gap between accuracy-focused benchmarks and critical demands for robustness, safety, fairness, and privacy.
zh
[CV-164] GroupNL: Low-Resource and Robust CNN Design over Cloud and Device
【速读】：该论文旨在解决在物联网（IoT）设备上部署卷积神经网络（CNN）模型时面临的两个主要问题：一是处理由IoT设备采集的受损图像数据时鲁棒性不足；二是计算和传输资源消耗过高。其解决方案的关键在于提出一种名为Grouped NonLinear transformation generation method (GroupNL) 的方法，该方法通过利用数据无关的非线性变换函数（NLFs）生成多样化的特征图，从而提升CNN模型的鲁棒性。GroupNL通过将部分卷积滤波器设为种子滤波器并生成种子特征图，随后对种子特征图进行分组并应用不同的NLFs进行非线性处理，以生成多样化特征图，同时通过随机初始化NLFs的超参数并在训练过程中不更新它们，有效减少了多节点间的参数传输，并通过使用NLFs生成特征图替代基于滑动窗口生成的大部分特征图，降低了计算资源消耗。
链接: https://arxiv.org/abs/2506.12335
作者: Chuntao Ding,Jianhang Xie,Junna Zhang,Salman Raza,Shangguang Wang,Jiannong Cao
机构: Beijing Normal University (北京师范大学); Beijing Jiaotong University (北京交通大学); Henan Normal University (河南师范大学); National Textile University Faisalabad (国家纺织大学费萨拉巴德); Beijing University of Posts and Telecommunications (北京邮电大学); The Hong Kong Polytechnic University (香港理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 13 pages, 10 figures
Abstract:It has become mainstream to deploy Convolutional Neural Network (CNN) models on ubiquitous Internet of Things (IoT) devices with the help of the cloud to provide users with a variety of high-quality services. Most existing methods have two limitations: (i) low robustness in handling corrupted image data collected by IoT devices; and (ii) high consumption of computational and transmission resources. To this end, we propose the Grouped NonLinear transformation generation method (GroupNL), which generates diversified feature maps by utilizing data-agnostic Nonlinear Transformation Functions (NLFs) to improve the robustness of the CNN model. Specifically, partial convolution filters are designated as seed filters in a convolutional layer, and a small set of feature maps, i.e., seed feature maps, are first generated based on vanilla convolution operation. Then, we split seed feature maps into several groups, each with a set of different NLFs, to generate corresponding diverse feature maps with in-place nonlinear processing. Moreover, GroupNL effectively reduces the parameter transmission between multiple nodes during model training by setting the hyperparameters of NLFs to random initialization and not updating them during model training, and reduces the computing resources by using NLFs to generate feature maps instead of most feature maps generated based on sliding windows. Experimental results on CIFAR-10, GTSRB, CIFAR-10-C, Icons50, and ImageNet-1K datasets in NVIDIA RTX GPU platforms show that the proposed GroupNL outperforms other state-of-the-art methods in model robust and training acceleration. Specifically, on the Icons-50 dataset, the accuracy of GroupNL-ResNet-18 achieves approximately 2.86% higher than the vanilla ResNet-18. GroupNL improves training speed by about 53% compared to vanilla CNN when trained on a cluster of 8 NVIDIA RTX 4090 GPUs on the ImageNet-1K dataset.
zh
[CV-165] hree-dimensional Deep Shape Optimization with a Limited Dataset
【速读】：该论文试图解决在机械设计中生成式模型应用受限的问题，主要原因是可用数据集的规模和多样性不足。其解决方案的关键在于提出一种基于深度学习的优化框架，通过引入位置编码（positional encoding）和Lipschitz正则化项，以稳健地学习几何特征并保持有意义的潜在空间，从而在数据有限的情况下实现有效的形状优化。
链接: https://arxiv.org/abs/2506.12326
作者: Yongmin Kwon,Namwoo Kang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative models have attracted considerable attention for their ability to produce novel shapes. However, their application in mechanical design remains constrained due to the limited size and variability of available datasets. This study proposes a deep learning-based optimization framework specifically tailored for shape optimization with limited datasets, leveraging positional encoding and a Lipschitz regularization term to robustly learn geometric characteristics and maintain a meaningful latent space. Through extensive experiments, the proposed approach demonstrates robustness, generalizability and effectiveness in addressing typical limitations of conventional optimization frameworks. The validity of the methodology is confirmed through multi-objective shape optimization experiments conducted on diverse three-dimensional datasets, including wheels and cars, highlighting the model’s versatility in producing practical and high-quality design outcomes even under data-constrained conditions.
zh
[CV-166] UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
【速读】：该论文旨在解决真实场景下物体检测面临的挑战，即由于雨、雾、雪、低光照等多种不利天气条件导致的图像/视频质量退化问题。现有方法通常针对特定类型的天气退化进行设计，存在泛化能力差和视觉特征利用不充分的问题。论文提出的解决方案是构建一个统一框架UniDet-D，其关键在于引入了一种动态光谱注意力机制，该机制能够自适应地强调有信息量的光谱成分并抑制无关成分，从而在多种退化类型下实现更鲁棒和具有区分性的特征表示，进而提升检测性能并具备对未见过的不利天气条件的良好泛化能力。
链接: https://arxiv.org/abs/2506.12324
作者: Yuantao Wang,Haowei Yang,Wei Zhang,Shijian Lu
机构: North China University of Technology(华北理工大学); Beijing Institute of Technology(北京理工大学); School of Computer Science and Engineering(计算机科学与工程学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Real-world object detection is a challenging task where the captured images/videos often suffer from complex degradations due to various adverse weather conditions such as rain, fog, snow, low-light, etc. Despite extensive prior efforts, most existing methods are designed for one specific type of adverse weather with constraints of poor generalization, under-utilization of visual features while handling various image degradations. Leveraging a theoretical analysis on how critical visual details are lost in adverse-weather images, we design UniDet-D, a unified framework that tackles the challenge of object detection under various adverse weather conditions, and achieves object detection and image restoration within a single network. Specifically, the proposed UniDet-D incorporates a dynamic spectral attention mechanism that adaptively emphasizes informative spectral components while suppressing irrelevant ones, enabling more robust and discriminative feature representation across various degradation types. Extensive experiments show that UniDet-D achieves superior detection accuracy across different types of adverse-weather degradation. Furthermore, UniDet-D demonstrates superior generalization towards unseen adverse weather conditions such as sandstorms and rain-fog mixtures, highlighting its great potential for real-world deployment.
zh
[CV-167] Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
【速读】：该论文试图解决医学数据不足导致的诊断机器学习模型泛化能力受限的问题，尤其是当临床数据集规模较小且无法全面反映疾病变异性时。解决方案的关键在于提出一种名为MAGIC（Medically Accurate Generation of Images through AI-Expert Collaboration）的框架，该框架通过将专家定义的标准转化为可操作的反馈，指导扩散模型（DMs）生成具有临床准确性的皮肤疾病图像，从而提升合成图像的医学准确性并减少对人工评估的依赖。
链接: https://arxiv.org/abs/2506.12323
作者: Janet Wang,Yunbei Zhang,Zhengming Ding,Jihun Hamm
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Paucity of medical data severely limits the generalizability of diagnostic ML models, as the full spectrum of disease variability can not be represented by a small clinical dataset. To address this, diffusion models (DMs) have been considered as a promising avenue for synthetic image generation and augmentation. However, they frequently produce medically inaccurate images, deteriorating the model performance. Expert domain knowledge is critical for synthesizing images that correctly encode clinical information, especially when data is scarce and quality outweighs quantity. Existing approaches for incorporating human feedback, such as reinforcement learning (RL) and Direct Preference Optimization (DPO), rely on robust reward functions or demand labor-intensive expert evaluations. Recent progress in Multimodal Large Language Models (MLLMs) reveals their strong visual reasoning capabilities, making them adept candidates as evaluators. In this work, we propose a novel framework, coined MAGIC (Medically Accurate Generation of Images through AI-Expert Collaboration), that synthesizes clinically accurate skin disease images for data augmentation. Our method creatively translates expert-defined criteria into actionable feedback for image synthesis of DMs, significantly improving clinical accuracy while reducing the direct human workload. Experiments demonstrate that our method greatly improves the clinical quality of synthesized skin disease images, with outputs aligning with dermatologist assessments. Additionally, augmenting training data with these synthesized images improves diagnostic accuracy by +9.02% on a challenging 20-condition skin disease classification task, and by +13.89% in the few-shot setting.
zh
[CV-168] MatchPlant: An Open-Source Pipeline for UAV-Based Single-Plant Detection and Data Extraction ATC
【速读】：该论文旨在解决从无人机（UAV）图像中准确识别单株植物的问题，以推动高通量表型分析并支持作物育种中的数据驱动决策。其解决方案的关键在于提出MatchPlant，一个模块化、图形用户界面支持的开源Python流程，集成UAV图像处理、用户引导标注、卷积神经网络目标检测模型训练、边界框向正射影像的前向投影以及用于空间表型分析的Shapefile生成，实现了端到端的单株检测与地理空间性状提取。
链接: https://arxiv.org/abs/2506.12295
作者: Worasit Sangjan,Piyush Pandey,Norman B. Best,Jacob D. Washburn
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 32 pages, 10 figures. Intended for submission to Computers and Electronics in Agriculture. Source code is available at this https URL and dataset at this https URL
Abstract:Accurate identification of individual plants from unmanned aerial vehicle (UAV) images is essential for advancing high-throughput phenotyping and supporting data-driven decision-making in plant breeding. This study presents MatchPlant, a modular, graphical user interface-supported, open-source Python pipeline for UAV-based single-plant detection and geospatial trait extraction. MatchPlant enables end-to-end workflows by integrating UAV image processing, user-guided annotation, Convolutional Neural Network model training for object detection, forward projection of bounding boxes onto an orthomosaic, and shapefile generation for spatial phenotypic analysis. In an early-season maize case study, MatchPlant achieved reliable detection performance (validation AP: 89.6%, test AP: 85.9%) and effectively projected bounding boxes, covering 89.8% of manually annotated boxes with 87.5% of projections achieving an Intersection over Union (IoU) greater than 0.5. Trait values extracted from predicted bounding instances showed high agreement with manual annotations (r = 0.87-0.97, IoU = 0.4). Detection outputs were reused across time points to extract plant height and Normalized Difference Vegetation Index with minimal additional annotation, facilitating efficient temporal phenotyping. By combining modular design, reproducibility, and geospatial precision, MatchPlant offers a scalable framework for UAV-based plant-level analysis with broad applicability in agricultural and environmental monitoring.
zh
[CV-169] EgoPrivacy: What Your First-Person Camera Says About You? ICML2025
【速读】：该论文试图解决佩戴者在第一视角视频中可能泄露的隐私信息问题，特别是针对摄像头佩戴者的独特隐私威胁被以往研究忽视这一现状。解决方案的关键在于提出EgoPrivacy，这是首个针对第一视角视觉隐私风险的全面评估基准，涵盖了三种类型的隐私（人口统计、个体和情境），并定义了七项任务以恢复从细粒度（如佩戴者身份）到粗粒度（如年龄组）的隐私信息。此外，论文还引入了Retrieval-Augmented Attack，一种利用外部全景视频库中的ego-to-exo检索来增强人口统计隐私攻击效果的新攻击策略。
链接: https://arxiv.org/abs/2506.12258
作者: Yijiang Li,Genpei Zhang,Jiacheng Cheng,Yi Li,Xiaojun Shan,Dashan Gao,Jiancheng Lyu,Yuan Li,Ning Bi,Nuno Vasconcelos
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
备注: ICML 2025
Abstract:While the rapid proliferation of wearable cameras has raised significant concerns about egocentric video privacy, prior work has largely overlooked the unique privacy threats posed to the camera wearer. This work investigates the core question: How much privacy information about the camera wearer can be inferred from their first-person view videos? We introduce EgoPrivacy, the first large-scale benchmark for the comprehensive evaluation of privacy risks in egocentric vision. EgoPrivacy covers three types of privacy (demographic, individual, and situational), defining seven tasks that aim to recover private information ranging from fine-grained (e.g., wearer’s identity) to coarse-grained (e.g., age group). To further emphasize the privacy threats inherent to egocentric vision, we propose Retrieval-Augmented Attack, a novel attack strategy that leverages ego-to-exo retrieval from an external pool of exocentric videos to boost the effectiveness of demographic privacy attacks. An extensive comparison of the different attacks possible under all threat models is presented, showing that private information of the wearer is highly susceptible to leakage. For instance, our findings indicate that foundation models can effectively compromise wearer privacy even in zero-shot settings by recovering attributes such as identity, scene, gender, and race with 70-80% accuracy. Our code and data are available at this https URL.
zh
[CV-170] Efficient Multi-Camera Tokenization with Triplanes for End-to-End Driving
【速读】：该论文旨在解决在嵌入式硬件上部署自回归Transformer作为端到端机器人和自动驾驶车辆（AV）策略架构时，如何高效地对多摄像头传感器数据进行分词以保证实时性的问题。解决方案的关键在于提出了一种基于三平面的多摄像头分词策略，该策略利用了最新的3D神经重建与渲染技术，生成与输入摄像头数量和分辨率无关的传感器分词，同时明确考虑了围绕AV的几何结构，从而显著减少了分词数量并提升了策略推理速度。
链接: https://arxiv.org/abs/2506.12251
作者: Boris Ivanovic,Cristiano Saltori,Yurong You,Yan Wang,Wenjie Luo,Marco Pavone
机构: NVIDIA Research (NVIDIA 研究院); Stanford University (斯坦福大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO)
备注: 12 pages, 10 figures, 5 tables
Abstract:Autoregressive Transformers are increasingly being deployed as end-to-end robot and autonomous vehicle (AV) policy architectures, owing to their scalability and potential to leverage internet-scale pretraining for generalization. Accordingly, tokenizing sensor data efficiently is paramount to ensuring the real-time feasibility of such architectures on embedded hardware. To this end, we present an efficient triplane-based multi-camera tokenization strategy that leverages recent advances in 3D neural reconstruction and rendering to produce sensor tokens that are agnostic to the number of input cameras and their resolution, while explicitly accounting for their geometry around an AV. Experiments on a large-scale AV dataset and state-of-the-art neural simulator demonstrate that our approach yields significant savings over current image patch-based tokenization strategies, producing up to 72% fewer tokens, resulting in up to 50% faster policy inference while achieving the same open-loop motion planning accuracy and improved offroad rates in closed-loop driving simulations.
zh
[CV-171] ViTaSCOPE: Visuo-tactile Implicit Representation for In-hand Pose and Extrinsic Contact Estimation
【速读】：该论文旨在解决在高接触密度的物体操作任务中，如何精确估计物体在手内的位姿以及外部接触位置的问题，此类任务由于观测数据的部分性和噪声性而尤为具有挑战性。解决方案的关键在于提出ViTaSCOPE：一种以物体为中心的神经隐式表示方法，通过融合视觉和高分辨率触觉反馈，将物体表示为符号距离场，并将分布式的触觉反馈表示为神经剪切场，从而准确地定位物体并将外部接触注册到其三维几何结构上。
链接: https://arxiv.org/abs/2506.12239
作者: Jayjun Lee,Nima Fazeli
机构: University of Michigan (密歇根大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to RSS 2025 | Project page: this https URL
Abstract:Mastering dexterous, contact-rich object manipulation demands precise estimation of both in-hand object poses and external contact locations \unicodex2013 tasks particularly challenging due to partial and noisy observations. We present ViTaSCOPE: Visuo-Tactile Simultaneous Contact and Object Pose Estimation, an object-centric neural implicit representation that fuses vision and high-resolution tactile feedback. By representing objects as signed distance fields and distributed tactile feedback as neural shear fields, ViTaSCOPE accurately localizes objects and registers extrinsic contacts onto their 3D geometry as contact fields. Our method enables seamless reasoning over complementary visuo-tactile cues by leveraging simulation for scalable training and zero-shot transfers to the real-world by bridging the sim-to-real gap. We evaluate our method through comprehensive simulated and real-world experiments, demonstrating its capabilities in dexterous manipulation scenarios.
zh
[CV-172] CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images
【速读】：该论文试图解决从景观照片中预测地理上下文标签的问题，特别是在缺乏兴趣点（POI）和街景图像的偏远地区。其解决方案的关键在于采用基于CLIP（Contrastive Language-Image Pretraining）的多模态、多标签分类器，通过结合位置和标题嵌入与图像特征，提升分类准确性。相比仅使用图像嵌入的方法，该方法在49个可能标签的精确匹配准确率上表现更优。
链接: https://arxiv.org/abs/2506.12214
作者: Ilya Ilyankou,Natchapon Jongwiriyanurak,Tao Cheng,James Haworth
机构: UCL SpaceTimeLab(伦敦大学学院时空实验室); UCL(伦敦大学学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present a CLIP-based, multi-modal, multi-label classifier for predicting geographical context tags from landscape photos in the Geograph dataset–a crowdsourced image archive spanning the British Isles, including remote regions lacking POIs and street-level imagery. Our approach addresses a Kaggle competition\footnotethis https URL task based on a subset of Geograph’s 8M images, with strict evaluation: exact match accuracy is required across 49 possible tags. We show that combining location and title embeddings with image features improves accuracy over using image embeddings alone. We release a lightweight pipeline\footnotethis https URL that trains on a modest laptop, using pre-trained CLIP image and text embeddings and a simple classification head. Predicted tags can support downstream tasks such as building location embedders for GeoAI applications, enriching spatial understanding in data-sparse regions.
zh
[CV-173] InceptionMamba: Efficient Multi-Stage Feature Enhancement with Selective State Space Model for Microscopic Medical Image Segmentation
【速读】：该论文旨在解决微观医学图像分割中复杂细胞和组织结构难以准确捕捉的问题，特别是在背景杂乱和目标重叠等挑战性场景下。其解决方案的关键在于提出一种名为InceptionMamba的高效框架，该框架通过编码多阶段丰富特征，并结合语义线索来增强多阶段特征，以处理模糊的区域边界（如细胞边界）。此外，该框架融合了Inception深度可分离卷积与Mamba模块的混合模型，以保持高效率并捕捉感兴趣区域的尺度和形状变化，最终通过特征融合生成最终的分割掩码。
链接: https://arxiv.org/abs/2506.12208
作者: Daniya Najiha Abdul Kareem,Abdul Hannan,Mubashir Noman,Jean Lahoud,Mustansar Fiaz,Hisham Cholakkal
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate microscopic medical image segmentation plays a crucial role in diagnosing various cancerous cells and identifying tumors. Driven by advancements in deep learning, convolutional neural networks (CNNs) and transformer-based models have been extensively studied to enhance receptive fields and improve medical image segmentation task. However, they often struggle to capture complex cellular and tissue structures in challenging scenarios such as background clutter and object overlap. Moreover, their reliance on the availability of large datasets for improved performance, along with the high computational cost, limit their practicality. To address these issues, we propose an efficient framework for the segmentation task, named InceptionMamba, which encodes multi-stage rich features and offers both performance and computational efficiency. Specifically, we exploit semantic cues to capture both low-frequency and high-frequency regions to enrich the multi-stage features to handle the blurred region boundaries (e.g., cell boundaries). These enriched features are input to a hybrid model that combines an Inception depth-wise convolution with a Mamba block, to maintain high efficiency and capture inherent variations in the scales and shapes of the regions of interest. These enriched features along with low-resolution features are fused to get the final segmentation mask. Our model achieves state-of-the-art performance on two challenging microscopic segmentation datasets (SegPC21 and GlaS) and two skin lesion segmentation datasets (ISIC2017 and ISIC2018), while reducing computational cost by about 5 times compared to the previous best performing method.
zh
[CV-174] ViSTA: Visual Storytelling using Multi-modal Adapters for Text-to-Image Diffusion Models
【速读】：该论文试图解决在视觉叙事中生成连贯图像序列的挑战，特别是在利用所有先前文本-图像对（history text-image pairs）以维持帧间一致性方面的问题。现有自回归方法虽然依赖所有历史文本-图像对进行条件生成，但需要大量训练；而无需训练的特定主体方法虽能保证一致性，却缺乏对叙事提示的适应性。解决方案的关键在于提出一种多模态历史适配器（ViSTA），其核心包括：(1) 多模态历史融合模块，用于提取相关的历史特征；(2) 历史适配器，用于基于提取的特征进行生成。此外，在推理阶段引入显著历史选择策略，选择最显著的历史文本-图像对以提升条件生成质量。
链接: https://arxiv.org/abs/2506.12198
作者: Sibo Dong,Ismail Shaheen,Maggie Shen,Rupayan Mallick,Sarah Adel Bargal
机构: Georgetown University(乔治城大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image diffusion models have achieved remarkable success, yet generating coherent image sequences for visual storytelling remains challenging. A key challenge is effectively leveraging all previous text-image pairs, referred to as history text-image pairs, which provide contextual information for maintaining consistency across frames. Existing auto-regressive methods condition on all past image-text pairs but require extensive training, while training-free subject-specific approaches ensure consistency but lack adaptability to narrative prompts. To address these limitations, we propose a multi-modal history adapter for text-to-image diffusion models, \textbfViSTA. It consists of (1) a multi-modal history fusion module to extract relevant history features and (2) a history adapter to condition the generation on the extracted relevant features. We also introduce a salient history selection strategy during inference, where the most salient history text-image pair is selected, improving the quality of the conditioning. Furthermore, we propose to employ a Visual Question Answering-based metric TIFA to assess text-image alignment in visual storytelling, providing a more targeted and interpretable assessment of generated images. Evaluated on the StorySalon and FlintStonesSV dataset, our proposed ViSTA model is not only consistent across different frames, but also well-aligned with the narrative text descriptions.
zh
[CV-175] BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
【速读】：该论文旨在解决乳腺癌早期检测和治疗反应监测中的挑战，尤其是由于缺乏可访问、公开的多中心3D动态对比增强磁共振成像（DCE-MRI）数据集而限制了深度学习模型的发展。其解决方案的关键在于构建了一个名为BreastDCEDL的标准化、深度学习就绪的数据集，该数据集包含来自多个临床队列的2,070例患者的预治疗3D DCE-MRI扫描，并进行了统一的肿瘤标注和临床元数据整合，从而为开发先进的深度学习模型，如基于Transformer架构的模型，提供了高质量的训练数据基础。
链接: https://arxiv.org/abs/2506.12190
作者: Naomi Fridman,Bubby Solway,Tomer Fridman,Itamar Barnea,Anat Goldshtein
机构: Ariel University (阿里尔大学); NF Algorithms & AI (NF算法与人工智能)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring critical priorities. We present BreastDCEDL, a curated, deep learning-ready dataset comprising pre-treatment 3D Dynamic Contrast-Enhanced MRI (DCE-MRI) scans from 2,070 breast cancer patients drawn from the I-SPY1, I-SPY2, and Duke cohorts, all sourced from The Cancer Imaging Archive. The raw DICOM imaging data were rigorously converted into standardized 3D NIfTI volumes with preserved signal integrity, accompanied by unified tumor annotations and harmonized clinical metadata including pathologic complete response (pCR), hormone receptor (HR), and HER2 status. Although DCE-MRI provides essential diagnostic information and deep learning offers tremendous potential for analyzing such complex data, progress has been limited by lack of accessible, public, multicenter datasets. BreastDCEDL addresses this gap by enabling development of advanced models, including state-of-the-art transformer architectures that require substantial training data. To demonstrate its capacity for robust modeling, we developed the first transformer-based model for breast DCE-MRI, leveraging Vision Transformer (ViT) architecture trained on RGB-fused images from three contrast phases (pre-contrast, early post-contrast, and late post-contrast). Our ViT model achieved state-of-the-art pCR prediction performance in HR+/HER2- patients (AUC 0.94, accuracy 0.93). BreastDCEDL includes predefined benchmark splits, offering a framework for reproducible research and enabling clinically meaningful modeling in breast cancer imaging.
zh
[CV-176] SPLATART: Articulated Gaussian Splatting with Estimated Object Structure
【速读】：该论文试图解决机器人领域中对可动物体（articulated objects）进行有效表示的问题，这类物体如钳子、夹具或柜子需要捕捉几何形状、颜色信息以及部件分离、连接性和关节参数化。随着自由度的增加，学习这些表示变得更加困难，尤其是对于具有多个自由度的复杂可动物体，如机械臂，其运动学树深度远高于传统研究对象。论文提出的解决方案是SPLATART——一种从带姿态的图像（部分包含图像空间部件分割）中学习可动物体高斯点云表示的流程，其关键在于将部件分离任务与关节估计任务解耦，从而实现对具有更深运动学树结构的可动物体的后处理关节估计和表示。
链接: https://arxiv.org/abs/2506.12184
作者: Stanley Lewis,Vishal Chandra,Tom Gao,Odest Chadwicke Jenkins
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, Accepted to the 2025 RSS Workshop on Gaussian Representations for Robot Autonomy. Contact: Stanley Lewis, stanlew@umich.edu
Abstract:Representing articulated objects remains a difficult problem within the field of robotics. Objects such as pliers, clamps, or cabinets require representations that capture not only geometry and color information, but also part seperation, connectivity, and joint parametrization. Furthermore, learning these representations becomes even more difficult with each additional degree of freedom. Complex articulated objects such as robot arms may have seven or more degrees of freedom, and the depth of their kinematic tree may be notably greater than the tools, drawers, and cabinets that are the typical subjects of articulated object research. To address these concerns, we introduce SPLATART - a pipeline for learning Gaussian splat representations of articulated objects from posed images, of which a subset contains image space part segmentations. SPLATART disentangles the part separation task from the articulation estimation task, allowing for post-facto determination of joint estimation and representation of articulated objects with deeper kinematic trees than previously exhibited. In this work, we present data on the SPLATART pipeline as applied to the syntheic Paris dataset objects, and qualitative results on a real-world object under spare segmentation supervision. We additionally present on articulated serial chain manipulators to demonstrate usage on deeper kinematic tree structures.
zh
[CV-177] Explaining Recovery Trajectories of Older Adults Post Lower-Limb Fracture Using Modality-wise Multiview Clustering and Large Language Models
【速读】：该论文试图解决在无监督医疗数据分析中，如何对高维、未标记的传感器数据进行可解释性聚类的问题，以便为患者健康结局提供有意义的见解。解决方案的关键在于首先针对每种数据模态进行独立聚类，以评估各模态特征集对患者恢复轨迹的影响，随后利用上下文感知提示技术，通过大语言模型推断出具有临床意义的聚类标签，并通过统计检验和可视化验证其与临床评分的相关性。
链接: https://arxiv.org/abs/2506.12156
作者: Shehroz S. Khan,Ali Abedi,Charlene H. Chu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 2 figures, 3 tables
Abstract:Interpreting large volumes of high-dimensional, unlabeled data in a manner that is comprehensible to humans remains a significant challenge across various domains. In unsupervised healthcare data analysis, interpreting clustered data can offer meaningful insights into patients’ health outcomes, which hold direct implications for healthcare providers. This paper addresses the problem of interpreting clustered sensor data collected from older adult patients recovering from lower-limb fractures in the community. A total of 560 days of multimodal sensor data, including acceleration, step count, ambient motion, GPS location, heart rate, and sleep, alongside clinical scores, were remotely collected from patients at home. Clustering was first carried out separately for each data modality to assess the impact of feature sets extracted from each modality on patients’ recovery trajectories. Then, using context-aware prompting, a large language model was employed to infer meaningful cluster labels for the clusters derived from each modality. The quality of these clusters and their corresponding labels was validated through rigorous statistical testing and visualization against clinical scores collected alongside the multimodal sensor data. The results demonstrated the statistical significance of most modality-specific cluster labels generated by the large language model with respect to clinical scores, confirming the efficacy of the proposed method for interpreting sensor data in an unsupervised manner. This unsupervised data analysis approach, relying solely on sensor data, enables clinicians to identify at-risk patients and take timely measures to improve health outcomes.
zh
[CV-178] Multiple Object Tracking in Video SAR: A Benchmark and Tracking Baseline
【速读】：该论文旨在解决视频合成孔径雷达（Video SAR）中多目标跟踪问题，特别是由于目标运动引起的多普勒频移导致的伪影易被误认为静态遮挡产生的阴影，以及多普勒失配引起的外观变化导致的关联失败和轨迹不连续问题。其关键解决方案包括：引入一种线特征增强机制，以强调运动阴影的正面作用并减少由静态遮挡引起的误报；提出一种运动感知的线索丢弃机制，以缓解目标外观变化带来的负面影响，从而提升视频SAR中的跟踪鲁棒性。
链接: https://arxiv.org/abs/2506.12105
作者: Haoxiang Chen,Wei Zhao,Rufei Zhang,Nannan Li,Dongjin Li
机构: Beihang University (北京航空航天大学); Beijing Institute of Control and Electronics Technology (北京控制与电子技术研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In the context of multi-object tracking using video synthetic aperture radar (Video SAR), Doppler shifts induced by target motion result in artifacts that are easily mistaken for shadows caused by static occlusions. Moreover, appearance changes of the target caused by Doppler mismatch may lead to association failures and disrupt trajectory continuity. A major limitation in this field is the lack of public benchmark datasets for standardized algorithm evaluation. To address the above challenges, we collected and annotated 45 video SAR sequences containing moving targets, and named the Video SAR MOT Benchmark (VSMB). Specifically, to mitigate the effects of trailing and defocusing in moving targets, we introduce a line feature enhancement mechanism that emphasizes the positive role of motion shadows and reduces false alarms induced by static occlusions. In addition, to mitigate the adverse effects of target appearance variations, we propose a motion-aware clue discarding mechanism that substantially improves tracking robustness in Video SAR. The proposed model achieves state-of-the-art performance on the VSMB, and the dataset and model are released at this https URL.
zh
[CV-179] Meta Pruning via Graph Metanetworks : A Meta Learning Framework for Network Pruning
【速读】：该论文旨在解决神经网络剪枝（network pruning）中手动设计剪枝准则所面临的瓶颈问题，即随着剪枝技术的复杂性增加，其可解释性却变得愈发困难。论文提出的解决方案关键在于引入元网络（metanetwork），这是一种通过元学习（meta-learning）思想构建的网络，能够以另一个网络作为输入并生成一个经过修改的网络作为输出。具体而言，作者首先建立了神经网络与图之间的双射映射，随后采用图神经网络作为元网络，通过训练使其自动学习剪枝策略，从而将难以剪枝的网络转换为更易剪枝的网络。该方法在多个主流剪枝任务中取得了优异效果。
链接: https://arxiv.org/abs/2506.12041
作者: Yewei Liu,Xiyuan Wang,Muhan Zhang
机构: Institute for Artificial Intelligence, Peking University (北京大学人工智能研究院)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Network pruning, aimed at reducing network size while preserving accuracy, has attracted significant research interest. Numerous pruning techniques have been proposed over time. They are becoming increasingly effective, but more complex and harder to interpret as well. Given the inherent complexity of neural networks, we argue that manually designing pruning criteria has reached a bottleneck. To address this, we propose a novel approach in which we “use a neural network to prune neural networks”. More specifically, we introduce the newly developed idea of metanetwork from meta-learning into pruning. A metanetwork is a network that takes another network as input and produces a modified network as output. In this paper, we first establish a bijective mapping between neural networks and graphs, and then employ a graph neural network as our metanetwork. We train a metanetwork that learns the pruning strategy automatically which can transform a network that is hard to prune into another network that is much easier to prune. Once the metanetwork is trained, our pruning needs nothing more than a feedforward through the metanetwork and the standard finetuning to prune at state-of-the-art. Our method achieved outstanding results on many popular and representative pruning tasks (including ResNet56 on CIFAR10, VGG19 on CIFAR100, ResNet50 on ImageNet). Our code is available at this https URL
zh
[CV-180] BTC-LLM : Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook
【速读】：该论文旨在解决传统二值化（Binary Quantization）方法在大语言模型（LLM）压缩中面临的性能下降、稀疏掩码管理带来的计算复杂性以及硬件兼容性受限等问题。其解决方案的关键在于提出BTC-LLM框架，通过自适应权重变换和二进制模式聚类实现次1比特压缩，具体包括两个核心创新：一是可学习的变换机制，优化可逆缩放与旋转矩阵以对齐二值化权重与全精度分布，提升层级表示质量；二是高效且准确的二进制代码本，通过识别重复的二进制向量聚类并将其压缩为紧凑索引，消除稀疏掩码需求，从而在标准硬件上实现高效推理。
链接: https://arxiv.org/abs/2506.12040
作者: Hao Gu,Lujun Li,Zheyu Wang,Bei Liu,Qiyuan Zhu,Sirui Han,Yike Guo
机构: Cranberry-Lemon University (克兰伯里-柠檬大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Binary quantization represents the most extreme form of large language model (LLM) compression, reducing weights to \pm 1 for maximal memory and computational efficiency. While recent sparsity-aware binarization methods achieve sub-1-bit compression by pruning redundant binary weights, they suffer from three critical challenges: performance deterioration, computational complexity from sparse mask management, and limited hardware compatibility. In this paper, we present BTC-LLM, a novel sub-1-bit LLM quantization framework that leverages adaptive weight transformation and binary pattern clustering to overcome these limitations, delivering both superior accuracy and efficiency. Our approach incorporates two key innovations: (1) a Learnable Transformation that optimizes invertible scaling and rotation matrices to align binarized weights with full-precision distributions, enabling incoherence processing to enhance layer-wise representation quality; (2) a Flash and Accurate Binary Codebook that identifies recurring binary vector clusters, compressing them into compact indices with tailored distance metrics and sign-based centroid updates. This eliminates the need for sparse masks, enabling efficient inference on standard hardware. Our code is available at this https URL.
zh
[CV-181] MultiViT2: A Data-augmented Multimodal Neuroimaging Prediction Framework via Latent Diffusion Model
【速读】：该论文旨在解决多模态医学影像在深度学习预测中的性能提升问题，特别是通过整合结构和功能神经影像数据来增强预测效果。其解决方案的关键在于提出一种新一代预测模型MultiViT2，该模型结合了预训练的表征学习基础模型与视觉Transformer（Vision Transformer）主干网络，同时引入基于潜在扩散模型的数据增强模块，以生成更多样化的神经影像样本，从而减少过拟合并提高模型的泛化能力。
链接: https://arxiv.org/abs/2506.13667
作者: Bi Yuda,Jia Sihan,Gao Yutong,Abrol Anees,Fu Zening,Calhoun Vince
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enhance deep learning predictions and improve outcomes. This study focuses on a neuroimaging prediction framework based on both structural and functional neuroimaging data. We propose a next-generation prediction model, \textbfMultiViT2, which combines a pretrained representative learning base model with a vision transformer backbone for prediction output. Additionally, we developed a data augmentation module based on the latent diffusion model that enriches input data by generating augmented neuroimaging samples, thereby enhancing predictive performance through reduced overfitting and improved generalizability. We show that MultiViT2 significantly outperforms the first-generation model in schizophrenia classification accuracy and demonstrates strong scalability and portability.
zh
[CV-182] Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models
【速读】：该论文试图解决在图像恢复等逆问题中，通过无需训练的去噪过程引导实现条件采样的问题。其解决方案的关键在于提出了一个针对纯去噪任务的精确后验得分（posterior score）的可计算表达式，该表达式基于无条件得分函数。利用这一结果，作者分析了DPS（Diffusion Posterior Sampling）在去噪任务中的时变误差，并实时计算步长以最小化每一步的误差，从而提升了采样效率和效果。
链接: https://arxiv.org/abs/2506.13614
作者: Gregory Bellchambers
机构: PhysicsX Ltd. (PhysicsX有限公司)
类目: Machine Learning (stat.ML); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:The success of diffusion models has driven interest in performing conditional sampling via training-free guidance of the denoising process to solve image restoration and other inverse problems. A popular class of methods, based on Diffusion Posterior Sampling (DPS), attempts to approximate the intractable posterior score function directly. In this work, we present a novel expression for the exact posterior score for purely denoising tasks that is tractable in terms of the unconditional score function. We leverage this result to analyze the time-dependent error in the DPS score for denoising tasks and compute step sizes on the fly to minimize the error at each time step. We demonstrate that these step sizes are transferable to related inverse problems such as colorization, random inpainting, and super resolution. Despite its simplicity, this approach is competitive with state-of-the-art techniques and enables sampling with fewer time steps than DPS.
zh
[CV-183] PRO: Projection Domain Synthesis for CT Imaging
【速读】：该论文旨在解决高质量CT图像合成的问题，这一问题由于标注数据的有限性和CT成像的复杂性而具有挑战性。其解决方案的关键在于提出PRO框架，这是首个在投影域中使用潜在扩散模型进行CT图像合成的方法（Generative AI）。与以往在图像域操作的方法不同，PRO通过从原始投影数据中学习丰富的结构表示，并利用解剖文本提示实现可控合成，从而更真实地建模成像物理过程和解剖结构。
链接: https://arxiv.org/abs/2506.13443
作者: Kang Chen,Bin Huang,Xuebin Yang,Junyan Zhang,Qiegen Liu
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Synthesizing high quality CT images remains a signifi-cant challenge due to the limited availability of annotat-ed data and the complex nature of CT imaging. In this work, we present PRO, a novel framework that, to the best of our knowledge, is the first to perform CT image synthesis in the projection domain using latent diffusion models. Unlike previous approaches that operate in the image domain, PRO learns rich structural representa-tions from raw projection data and leverages anatomi-cal text prompts for controllable synthesis. This projec-tion domain strategy enables more faithful modeling of underlying imaging physics and anatomical structures. Moreover, PRO functions as a foundation model, capa-ble of generalizing across diverse downstream tasks by adjusting its generative behavior via prompt inputs. Experimental results demonstrated that incorporating our synthesized data significantly improves perfor-mance across multiple downstream tasks, including low-dose and sparse-view reconstruction, even with limited training data. These findings underscore the versatility and scalability of PRO in data generation for various CT applications. These results highlight the potential of projection domain synthesis as a powerful tool for data augmentation and robust CT imaging. Our source code is publicly available at: this https URL.
zh
[CV-184] Audio-Visual Driven Compression for Low-Bitrate Talking Head Videos ICMR2025
【速读】：该论文旨在解决低比特率下 Talking head 视频压缩中存在的挑战，包括处理大范围头部运动、次优的唇部同步以及失真的面部重建问题。其解决方案的关键在于提出一种基于音频-视觉驱动的视频编解码器，该编解码器整合了紧凑的3D运动特征和音频信号，从而增强了对显著头部旋转的建模能力，并实现了唇部动作与语音的精准对齐，提升了压缩效率和重建质量。
链接: https://arxiv.org/abs/2506.13419
作者: Riku Takahashi,Ryugo Morita,Jinjia Zhou
机构: Hosei University (法政大学)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICMR2025
Abstract:Talking head video compression has advanced with neural rendering and keypoint-based methods, but challenges remain, especially at low bit rates, including handling large head movements, suboptimal lip synchronization, and distorted facial reconstructions. To address these problems, we propose a novel audio-visual driven video codec that integrates compact 3D motion features and audio signals. This approach robustly models significant head rotations and aligns lip movements with speech, improving both compression efficiency and reconstruction quality. Experiments on the CelebV-HQ dataset show that our method reduces bitrate by 22% compared to VVC and by 8.5% over state-of-the-art learning-based codec. Furthermore, it provides superior lip-sync accuracy and visual fidelity at comparable bitrates, highlighting its effectiveness in bandwidth-constrained scenarios.
zh
[CV-185] Simple is what you need for efficient and accurate medical image segmentation
【速读】：该论文试图解决医疗图像分割模型在追求高性能的同时往往忽视实用性和效率的问题，提出了一种以简洁和高效为核心设计理念的解决方案。其关键在于三个创新点：(1) 在跳跃连接中引入部分特征选择机制，以减少冗余并提升分割性能；(2) 采用固定宽度架构，防止网络阶段间参数数量呈指数级增长；(3) 设计自适应特征融合模块，在计算开销最小化的情况下增强特征表示能力。这些创新使SimpleUNet在保持极低参数量（如16 KB）的前提下，实现了优于现有轻量级基准模型的分割性能。
链接: https://arxiv.org/abs/2506.13415
作者: Xiang Yu,Yayan Chen,Guannan He,Qing Zeng,Yue Qin,Meiling Liang,Dandan Luo,Yimei Liao,Zeyu Ren,Cheng Kang,Delong Yang,Bocheng Liang,Bin Pu,Ying Yuan,Shengli Li
机构: Shenzhen Maternity and Child Healthcare Hospital, Women and Children’s Medical Center, Southern Medical University, Shenzhen, Guangdong Province, China; Longhua District Maternal and Child Healthcare Hospital, Shenzhen, China; Sichuan Provincial Maternity and Child Health Care Hospital, Chengdu, China; College of Agronomy, Jilin Agricultural University, Changchun, China; Department of Cybernetics and Robotics, Czech Technical University, Prague, Czech Republic; Institute for Engineering Medicine, Kunming Medical University, Kunming, China; Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 11 figures
Abstract:While modern segmentation models often prioritize performance over practicality, we advocate a design philosophy prioritizing simplicity and efficiency, and attempted high performance segmentation model design. This paper presents SimpleUNet, a scalable ultra-lightweight medical image segmentation model with three key innovations: (1) A partial feature selection mechanism in skip connections for redundancy reduction while enhancing segmentation performance; (2) A fixed-width architecture that prevents exponential parameter growth across network stages; (3) An adaptive feature fusion module achieving enhanced representation with minimal computational overhead. With a record-breaking 16 KB parameter configuration, SimpleUNet outperforms LBUNet and other lightweight benchmarks across multiple public datasets. The 0.67 MB variant achieves superior efficiency (8.60 GFLOPs) and accuracy, attaining a mean DSC/IoU of 85.76%/75.60% on multi-center breast lesion datasets, surpassing both U-Net and TransUNet. Evaluations on skin lesion datasets (ISIC 2017/2018: mDice 84.86%/88.77%) and endoscopic polyp segmentation (KVASIR-SEG: 86.46%/76.48% mDice/mIoU) confirm consistent dominance over state-of-the-art models. This work demonstrates that extreme model compression need not compromise performance, providing new insights for efficient and accurate medical image segmentation. Codes can be found at this https URL.
zh
[CV-186] Brain Imaging Foundation Models Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research
【速读】：该论文旨在解决当前基础模型（Foundation Models, FMs）在脑成像领域应用研究不足的问题，尤其是现有综述对脑成像的覆盖不够全面或缺乏对其独特挑战的深入分析。其解决方案的关键在于系统性地分析161个脑成像数据集和86种FM架构，总结关键设计选择、训练范式及优化策略，并评估不同任务下的领先模型及其创新点，从而为未来在临床和科研场景中推进FM在脑成像中的应用提供方向。
链接: https://arxiv.org/abs/2506.13306
作者: Salah Ghamizi,Georgia Kanli,Yu Deng,Magali Perquin,Olivier Keunen
机构: Luxembourg Institute of Health (卢森堡健康研究所); King’s College London (国王学院伦敦大学)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and shown significant promise in medical imaging by enabling robust performance with limited labeled data. Although numerous surveys have reviewed the application of FM in healthcare care, brain imaging remains underrepresented, despite its critical role in the diagnosis and treatment of neurological diseases using modalities such as MRI, CT, and PET. Existing reviews either marginalize brain imaging or lack depth on the unique challenges and requirements of FM in this domain, such as multimodal data integration, support for diverse clinical tasks, and handling of heterogeneous, fragmented datasets. To address this gap, we present the first comprehensive and curated review of FMs for brain imaging. We systematically analyze 161 brain imaging datasets and 86 FM architectures, providing information on key design choices, training paradigms, and optimizations driving recent advances. Our review highlights the leading models for various brain imaging tasks, summarizes their innovations, and critically examines current limitations and blind spots in the literature. We conclude by outlining future research directions to advance FM applications in brain imaging, with the aim of fostering progress in both clinical and research settings. Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2506.13306 [eess.IV] (or arXiv:2506.13306v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2506.13306 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[CV-187] ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographs
【速读】：该论文旨在解决传统牙科诊断中二维全景放射影像（PX）因缺乏深度信息而导致的诊断准确性不足，以及三维锥形束计算机断层扫描（CBCT）成本高、辐射暴露量大和可及性差的问题。其关键解决方案是提出一种基于视觉变压器的NeBLa模型（ViT-NeBLa），该模型能够直接从单张PX图像进行精确的三维重建，无需CBCT平铺或先验牙弓信息，通过引入视觉变压器增强重建能力、采用新型马蹄形点采样策略减少计算量、使用混合ViT-CNN架构提升特征提取能力，并引入可学习的哈希位置编码以改善三维样本点的高维表示。
链接: https://arxiv.org/abs/2506.13195
作者: Bikram Keshari Parida,Anusree P. Sunilkumar,Abhijit Sen,Wonsang You
机构: Sun Moon University (孙门大学); Tulane University (图兰大学); KAIST (韩国科学技术院)
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 figures, 19 pages
Abstract:Dental diagnosis relies on two primary imaging modalities: panoramic radiographs (PX) providing 2D oral cavity representations, and Cone-Beam Computed Tomography (CBCT) offering detailed 3D anatomical information. While PX images are cost-effective and accessible, their lack of depth information limits diagnostic accuracy. CBCT addresses this but presents drawbacks including higher costs, increased radiation exposure, and limited accessibility. Existing reconstruction models further complicate the process by requiring CBCT flattening or prior dental arch information, often unavailable clinically. We introduce ViT-NeBLa, a vision transformer-based Neural Beer-Lambert model enabling accurate 3D reconstruction directly from single PX. Our key innovations include: (1) enhancing the NeBLa framework with Vision Transformers for improved reconstruction capabilities without requiring CBCT flattening or prior dental arch information, (2) implementing a novel horseshoe-shaped point sampling strategy with non-intersecting rays that eliminates intermediate density aggregation required by existing models due to intersecting rays, reducing sampling point computations by 52 % , (3) replacing CNN-based U-Net with a hybrid ViT-CNN architecture for superior global and local feature extraction, and (4) implementing learnable hash positional encoding for better higher-dimensional representation of 3D sample points compared to existing Fourier-based dense positional encoding. Experiments demonstrate that ViT-NeBLa significantly outperforms prior state-of-the-art methods both quantitatively and qualitatively, offering a cost-effective, radiation-efficient alternative for enhanced dental diagnostics.
zh
[CV-188] Predicting Genetic Mutations from Single-Cell Bone Marrow Images in Acute Myeloid Leukemia Using Noise-Robust Deep Learning Models
【速读】：该论文旨在解决在单细胞图像中识别髓系原始细胞并预测其遗传突变的问题，尤其是在面对标签准确性不足和数据噪声的情况下。其解决方案的关键在于首先训练一个二分类器以区分白血病（原始细胞）与非白血病细胞图像，随后在存在标签噪声的前提下，进一步训练一个四类模型以对预测为原始细胞的图像进行特定突变分类，从而展示了机器学习模型在处理噪声标签时的鲁棒性及在临床诊断中的潜力。
链接: https://arxiv.org/abs/2506.12798
作者: Garima Jain,Ravi Kant Gupta,Priyansh Jain,Abhijeet Patil,Ardhendu Sekhar,Gajendra Smeeta,Sanghamitra Pati,Amit Sethi
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 2 figues
Abstract:In this study, we propose a robust methodology for identification of myeloid blasts followed by prediction of genetic mutation in single-cell images of blasts, tackling challenges associated with label accuracy and data noise. We trained an initial binary classifier to distinguish between leukemic (blasts) and non-leukemic cells images, achieving 90 percent accuracy. To evaluate the models generalization, we applied this model to a separate large unlabeled dataset and validated the predictions with two haemato-pathologists, finding an approximate error rate of 20 percent in the leukemic and non-leukemic labels. Assuming this level of label noise, we further trained a four-class model on images predicted as blasts to classify specific mutations. The mutation labels were known for only a bag of cell images extracted from a single slide. Despite the tumor label noise, our mutation classification model achieved 85 percent accuracy across four mutation classes, demonstrating resilience to label inconsistencies. This study highlights the capability of machine learning models to work with noisy labels effectively while providing accurate, clinically relevant mutation predictions, which is promising for diagnostic applications in areas such as haemato-pathology.
zh
[CV-189] GM-LDM: Latent Diffusion Model for Brain Biomarker Identification through Functional Data-Driven Gray Matter Synthesis
【速读】：该论文旨在解决医学影像中模态转换和多模态融合的效率与精度问题，特别是在基于MRI的脑成像任务中。其解决方案的关键在于提出一种名为GM-LDM的新框架，该框架利用潜在扩散模型（Latent Diffusion Model, LDM）来提升MRI生成任务的效果，通过集成预训练的3D自编码器和基于视觉Transformer（Vision Transformer, ViT）的编解码器结构，实现统计一致性与生成质量的优化，并支持条件数据的灵活引入以实现个性化脑成像与功能-结构信息转换。
链接: https://arxiv.org/abs/2506.12719
作者: Hu Xu,Yang Jingling,Jia Sihan,Bi Yuda,Calhoun Vince
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Generative models based on deep learning have shown significant potential in medical imaging, particularly for modality transformation and multimodal fusion in MRI-based brain imaging. This study introduces GM-LDM, a novel framework that leverages the latent diffusion model (LDM) to enhance the efficiency and precision of MRI generation tasks. GM-LDM integrates a 3D autoencoder, pre-trained on the large-scale ABCD MRI dataset, achieving statistical consistency through KL divergence loss. We employ a Vision Transformer (ViT)-based encoder-decoder as the denoising network to optimize generation quality. The framework flexibly incorporates conditional data, such as functional network connectivity (FNC) data, enabling personalized brain imaging, biomarker identification, and functional-to-structural information translation for brain diseases like schizophrenia.
zh
[CV-190] Zero-shot denoising via neural compression: Theoretical and algorithmic framework
【速读】：该论文试图解决零样本去噪（zero-shot denoising）问题，即在没有训练样本或干净参考图像的情况下对观测数据进行去噪。这一问题在医学成像或生物学等专业领域具有重要应用价值。解决方案的关键在于提出一种基于神经压缩的新型去噪框架——零样本神经压缩去噪器（Zero-Shot Neural Compression Denoiser, ZS-NCD），该框架将神经压缩网络视为未训练模型，并直接在单张噪声图像中提取的块上进行优化，最终通过聚合重叠块的输出获得重建结果。该方法利用了压缩架构内置的熵约束，自然避免过拟合，无需手动正则化或早停策略。
链接: https://arxiv.org/abs/2506.12693
作者: Ali Zafari,Xi Chen,Shirin Jalali
机构: Rutgers University (罗格斯大学)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT)
备注:
Abstract:Zero-shot denoising aims to denoise observations without access to training samples or clean reference images. This setting is particularly relevant in practical imaging scenarios involving specialized domains such as medical imaging or biology. In this work, we propose the Zero-Shot Neural Compression Denoiser (ZS-NCD), a novel denoising framework based on neural compression. ZS-NCD treats a neural compression network as an untrained model, optimized directly on patches extracted from a single noisy image. The final reconstruction is then obtained by aggregating the outputs of the trained model over overlapping patches. Thanks to the built-in entropy constraints of compression architectures, our method naturally avoids overfitting and does not require manual regularization or early stopping. Through extensive experiments, we show that ZS-NCD achieves state-of-the-art performance among zero-shot denoisers for both Gaussian and Poisson noise, and generalizes well to both natural and non-natural images. Additionally, we provide new finite-sample theoretical results that characterize upper bounds on the achievable reconstruction error of general maximum-likelihood compression-based denoisers. These results further establish the theoretical foundations of compression-based denoising. Our code is available at: this http URL.
zh
[CV-191] Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution
【速读】：该论文旨在解决轻量级单图像超分辨率（SISR）中信息蒸馏模块难以将输入映射到高维非线性（HDNL）特征空间，以及大型核注意力（LKA）模块在捕获多形状多尺度信息和长程依赖关系时计算负担过重的问题。其解决方案的关键在于提出星蒸馏模块（SDM）以增强在HDNL特征空间中的判别表示学习，并引入多形状多尺度大型核注意力（MM-LKA）模块，在保持低计算和内存开销的同时有效学习长程依赖关系。
链接: https://arxiv.org/abs/2506.12475
作者: Fangwei Hao,Ji Du,Desheng Kong,Jiesheng Wu,Jing Xu,Ping Li
机构: Nankai University(南开大学); Hong Kong Polytechnic University(香港理工大学)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:In recent years, the performance of lightweight Single-Image Super-Resolution (SISR) has been improved significantly with the application of Convolutional Neural Networks (CNNs) and Large Kernel Attention (LKA). However, existing information distillation modules for lightweight SISR struggle to map inputs into High-Dimensional Non-Linear (HDNL) feature spaces, limiting their representation learning. And their LKA modules possess restricted ability to capture the multi-shape multi-scale information for long-range dependencies while encountering a quadratic increase in the computational burden with increasing convolutional kernel size of its depth-wise convolutional layer. To address these issues, we firstly propose a Star Distillation Module (SDM) to enhance the discriminative representation learning via information distillation in the HDNL feature spaces. Besides, we present a Multi-shape Multi-scale Large Kernel Attention (MM-LKA) module to learn representative long-range dependencies while incurring low computational and memory footprints, leading to improving the performance of CNN-based self-attention significantly. Integrating SDM and MM-LKA, we develop a Residual Star Distillation Attention Module (RSDAM) and take it as the building block of the proposed efficient Star Distillation Attention Network (SDAN) which possesses high reconstruction efficiency to recover a higher-quality image from the corresponding low-resolution (LR) counterpart. When compared with other lightweight state-of-the-art SISR methods, extensive experiments show that our SDAN with low model complexity yields superior performance quantitatively and visually.
zh
[CV-192] Adaptive Multi-resolution Hash-Encoding Framework for INR-based Dental CBCT Reconstruction with Truncated FOV
【速读】：该论文旨在解决在截断视野（truncated field of view, FOV）条件下，直接应用隐式神经表示（Implicit Neural Representation, INR）技术进行三维牙科锥形束CT（CBCT）图像重建时出现的伪影问题。其关键解决方案是提出一种计算高效的INR重建框架，该框架利用多分辨率哈希编码，并通过扩展重建域来覆盖患者头部完整区域，同时采用自适应训练策略，在截断FOV内使用高分辨率和密集采样，在外部使用低分辨率和稀疏采样，以提升计算效率并减少伪影。此外，引入自适应哈希编码器以保持网络输入维度的一致性。
链接: https://arxiv.org/abs/2506.12471
作者: Hyoung Suk Park,Kiwan Jeon
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 4 figures
Abstract:Implicit neural representation (INR), particularly in combination with hash encoding, has recently emerged as a promising approach for computed tomography (CT) image reconstruction. However, directly applying INR techniques to 3D dental cone-beam CT (CBCT) with a truncated field of view (FOV) is challenging. During the training process, if the FOV does not fully encompass the patient’s head, a discrepancy arises between the measured projections and the forward projections computed within the truncated domain. This mismatch leads the network to estimate attenuation values inaccurately, producing severe artifacts in the reconstructed images. In this study, we propose a computationally efficient INR-based reconstruction framework that leverages multi-resolution hash encoding for 3D dental CBCT with a truncated FOV. To mitigate truncation artifacts, we train the network over an expanded reconstruction domain that fully encompasses the patient’s head. For computational efficiency, we adopt an adaptive training strategy that uses a multi-resolution grid: finer resolution levels and denser sampling inside the truncated FOV, and coarser resolution levels with sparser sampling outside. To maintain consistent input dimensionality of the network across spatially varying resolutions, we introduce an adaptive hash encoder that selectively activates the lower-level features of the hash hierarchy for points outside the truncated FOV. The proposed method with an extended FOV effectively mitigates truncation artifacts. Compared with a naive domain extension using fixed resolution levels and a fixed sampling rate, the adaptive strategy reduces computational time by over 60% for an image volume of 800x800x600, while preserving the PSNR within the truncated FOV.
zh
[CV-193] Shape-aware Sampling Matters in the Modeling of Multi-Class Tubular Structures
【速读】：该论文旨在解决细粒度语义管状结构建模中由于仅依赖体积重叠精度导致的拓扑结构保真度不足的问题。其解决方案的关键在于提出了一种基于形状感知采样的方法（Shape-aware Sampling, SAS），该方法通过分形维数-based的块大小分配（Fractal Dimension-based Patchsize, FDPS）量化管状结构的复杂性，并根据复杂度动态调整采样块大小以捕捉细节特征；同时结合最小路径成本骨架化（Minimum Path-Cost Skeletonization, MPC-Skel）提取拓扑一致的骨架表示，从而提升目标函数中的拓扑保真度。
链接: https://arxiv.org/abs/2506.12395
作者: Minghui Zhang,Yaoyu Liu,Xin You,Hanxiao Zhang,Yun Gu
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate multi-class tubular modeling is critical for precise lesion localization and optimal treatment planning. Deep learning methods enable automated shape modeling by prioritizing volumetric overlap accuracy. However, the inherent complexity of fine-grained semantic tubular shapes is not fully emphasized by overlap accuracy, resulting in reduced topological preservation. To address this, we propose the Shapeaware Sampling (SAS), which optimizes patchsize allocation for online sampling and extracts a topology-preserved skeletal representation for the objective function. Fractal Dimension-based Patchsize (FDPS) is first introduced to quantify semantic tubular shape complexity through axis-specific fractal dimension analysis. Axes with higher fractal complexity are then sampled with smaller patchsizes to capture fine-grained features and resolve structural intricacies. In addition, Minimum Path-Cost Skeletonization (MPC-Skel) is employed to sample topologically consistent skeletal representations of semantic tubular shapes for skeleton-weighted objective functions. MPC-Skel reduces artifacts from conventional skeletonization methods and directs the focus to critical topological regions, enhancing tubular topology preservation. SAS is computationally efficient and easily integrable into optimization pipelines. Evaluation on two semantic tubular datasets showed consistent improvements in both volumetric overlap and topological integrity metrics.
zh
[CV-194] ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing
【速读】：该论文旨在解决视频超分辨率（Video Super-Resolution, VSR）问题，特别是在会议场景下，对以H.265编码且固定量化参数（QP）的低分辨率（LR）视频进行上采样，生成高分辨率（HR）视频，同时在低延迟条件下提升视觉感知质量。解决方案的关键在于利用因果模型实现视频序列的时序增强，包括局部、单向、双向传播或传统上采样结合修复的方法，以适应不同类型的视频内容，如通用视频、演讲者头部视频和屏幕内容视频。
链接: https://arxiv.org/abs/2506.12269
作者: Babak Naderi,Ross Cutler,Juhee Cho,Nabakumar Khongbantabam,Dejan Ivkovic
机构: Microsoft Corporation(微软公司)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注:
Abstract:Super-Resolution (SR) is a critical task in computer vision, focusing on reconstructing high-resolution (HR) images from low-resolution (LR) inputs. The field has seen significant progress through various challenges, particularly in single-image SR. Video Super-Resolution (VSR) extends this to the temporal domain, aiming to enhance video quality using methods like local, uni-, bi-directional propagation, or traditional upscaling followed by restoration. This challenge addresses VSR for conferencing, where LR videos are encoded with H.265 at fixed QPs. The goal is to upscale videos by a specific factor, providing HR outputs with enhanced perceptual quality under a low-delay scenario using causal models. The challenge included three tracks: general-purpose videos, talking head videos, and screen content videos, with separate datasets provided by the organizers for training, validation, and testing. We open-sourced a new screen content dataset for the SR task in this challenge. Submissions were evaluated through subjective tests using a crowdsourced implementation of the ITU-T Rec P.910.
zh
[CV-195] MRI-CORE: A Foundation Model for Magnetic Resonance Imaging
【速读】：该论文试图解决在磁共振成像（MRI）中，针对特定新任务训练模型时所需大量标注数据难以获取的问题，这一问题主要由高标注成本和数据隐私顾虑导致。解决方案的关键在于引入MRI-CORE，这是一个基于超过600万张切片（来自18个主要身体部位的11万余例MRI影像）预训练的视觉基础模型，能够有效提升在有限标注数据条件下的分割性能，并具备分类图像属性及零样本分割等新能力，从而降低数据标注资源的门槛。
链接: https://arxiv.org/abs/2506.12186
作者: Haoyu Dong,Yuwen Chen,Hanxue Gu,Nicholas Konz,Yaqian Chen,Qihang Li,Maciej A. Mazurowski
机构: Duke University (杜克大学)
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 19 pages, 5 figures
Abstract:The widespread use of Magnetic Resonance Imaging (MRI) and the rise of deep learning have enabled the development of powerful predictive models for a wide range of diagnostic tasks in MRI, such as image classification or object segmentation. However, training models for specific new tasks often requires large amounts of labeled data, which is difficult to obtain due to high annotation costs and data privacy concerns. To circumvent this issue, we introduce MRI-CORE (MRI COmprehensive Representation Encoder), a vision foundation model pre-trained using more than 6 million slices from over 110,000 MRI volumes across 18 main body locations. Experiments on five diverse object segmentation tasks in MRI demonstrate that MRI-CORE can significantly improve segmentation performance in realistic scenarios with limited labeled data availability, achieving an average gain of 6.97% 3D Dice Coefficient using only 10 annotated slices per task. We further demonstrate new model capabilities in MRI such as classification of image properties including body location, sequence type and institution, and zero-shot segmentation. These results highlight the value of MRI-CORE as a generalist vision foundation model for MRI, potentially lowering the data annotation resource barriers for many applications.
zh
[CV-196] Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
【速读】：该论文试图解决医学影像数据中真实数据不足与数据隐私保护之间的矛盾，以及合成数据在分割任务中的实用性和真实性评估问题。其解决方案的关键在于利用生成式对抗网络和扩散模型生成高质量的合成医学影像数据，并通过多种评估指标（如MAE、MS-SSIM、放射组学、DSC和视觉图灵测试）全面验证合成数据的现实性和在分割任务中的有效性。
链接: https://arxiv.org/abs/2506.12106
作者: André Ferreira,Kunpeng Xie,Caroline Wilpert,Gustavo Correia,Felix Barajas Ordonez,Tiago Gil Oliveira,Maike Bode,Robert Siepmann,Frank Hölzle,Rainer Röhrig,Jens Kleesiek,Daniel Truhn,Jan Egger,Victor Alves,Behrus Puladi
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:AI requires extensive datasets, while medical data is subject to high data protection. Anonymization is essential, but poses a challenge for some regions, such as the head, as identifying structures overlap with regions of clinical interest. Synthetic data offers a potential solution, but studies often lack rigorous evaluation of realism and utility. Therefore, we investigate to what extent synthetic data can replace real data in segmentation tasks. We employed head and neck cancer CT scans and brain glioma MRI scans from two large datasets. Synthetic data were generated using generative adversarial networks and diffusion models. We evaluated the quality of the synthetic data using MAE, MS-SSIM, Radiomics and a Visual Turing Test (VTT) performed by 5 radiologists and their usefulness in segmentation tasks using DSC. Radiomics indicates high fidelity of synthetic MRIs, but fall short in producing highly realistic CT tissue, with correlation coefficient of 0.8784 and 0.5461 for MRI and CT tumors, respectively. DSC results indicate limited utility of synthetic data: tumor segmentation achieved DSC=0.064 on CT and 0.834 on MRI, while bone segmentation a mean DSC=0.841. Relation between DSC and correlation is observed, but is limited by the complexity of the task. VTT results show synthetic CTs’ utility, but with limited educational applications. Synthetic data can be used independently for the segmentation task, although limited by the complexity of the structures to segment. Advancing generative models to better tolerate heterogeneous inputs and learn subtle details is essential for enhancing their realism and expanding their application potential.
zh
[CV-197] EMLoC: Emulator-based Memory-efficient Fine-tuning with LoRA Correction
【速读】：该论文试图解决在资源受限环境下对大规模基础模型进行领域特定或个性化任务微调的高昂内存开销问题（memory overhead）。解决方案的关键在于提出EMLoC框架，该框架通过在小规模下游校准集上使用激活感知的奇异值分解（SVD）构建任务相关的轻量级模拟器，并基于低秩适配（LoRA）进行微调，从而在与推理相同的内存预算内完成模型微调。此外，为解决原始模型与压缩模拟器之间的不对齐问题，EMLoC引入了一种新颖的补偿算法，以修正微调后的LoRA模块并将其合并到原始模型中用于推理。
链接: https://arxiv.org/abs/2506.12015
作者: Hsi-Che Lin,Yu-Chu Yu,Kai-Po Chang,Yu-Chiang Frank Wang
机构: National Taiwan University (国立台湾大学); NVIDIA (NVIDIA)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: Under review. Project page: this https URL
Abstract:Open-source foundation models have seen rapid adoption and development, enabling powerful general-purpose capabilities across diverse domains. However, fine-tuning large foundation models for domain-specific or personalized tasks remains prohibitively expensive for most users due to the significant memory overhead beyond that of inference. We introduce EMLoC, an Emulator-based Memory-efficient fine-tuning framework with LoRA Correction, which enables model fine-tuning within the same memory budget required for inference. EMLoC constructs a task-specific light-weight emulator using activation-aware singular value decomposition (SVD) on a small downstream calibration set. Fine-tuning then is performed on this lightweight emulator via LoRA. To tackle the misalignment between the original model and the compressed emulator, we propose a novel compensation algorithm to correct the fine-tuned LoRA module, which thus can be merged into the original model for inference. EMLoC supports flexible compression ratios and standard training pipelines, making it adaptable to a wide range of applications. Extensive experiments demonstrate that EMLoC outperforms other baselines across multiple datasets and modalities. Moreover, without quantization, EMLoC enables fine-tuning of a 38B model on a single 24GB consumer GPU-bringing efficient and practical model adaptation to individual users.
zh
人工智能
[AI-0] Discrete Diffusion in Large Language and Multimodal Models: A Survey
【速读】：该论文试图解决传统自回归（Autoregressive, AR）模型在生成过程中难以实现并行化、输出控制精细度不足以及动态感知能力有限的问题。其解决方案的关键在于采用基于去噪的多标记并行解码范式，通过全注意力机制和去噪生成策略，实现了高效的并行生成、细粒度输出控制和动态响应感知能力。这一范式显著提升了生成效率，并在多个领域表现出与自回归模型相当甚至更优的性能。
链接: https://arxiv.org/abs/2506.13759
作者: Runpeng Yu,Qi Li,Xinchao Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:In this work, we provide a systematic survey of Discrete Diffusion Language Models (dLLMs) and Discrete Diffusion Multimodal Language Models (dMLLMs). Unlike autoregressive (AR) models, dLLMs and dMLLMs adopt a multi-token, parallel decoding paradigm using full attention and a denoising-based generation strategy. This paradigm naturally enables parallel generation, fine-grained output controllability, and dynamic, response-aware perception. These capabilities are previously difficult to achieve with AR models. Recently, a growing number of industrial-scale proprietary d(M)LLMs, as well as a large number of open-source academic d(M)LLMs, have demonstrated performance comparable to their autoregressive counterparts, while achieving up to 10x acceleration in inference speed. The advancement of discrete diffusion LLMs and MLLMs has been largely driven by progress in two domains. The first is the development of autoregressive LLMs and MLLMs, which has accumulated vast amounts of data, benchmarks, and foundational infrastructure for training and inference. The second contributing domain is the evolution of the mathematical models underlying discrete diffusion. Together, these advancements have catalyzed a surge in dLLMs and dMLLMs research in early 2025. In this work, we present a comprehensive overview of the research in the dLLM and dMLLM domains. We trace the historical development of dLLMs and dMLLMs, formalize the underlying mathematical frameworks, and categorize representative models. We further analyze key techniques for training and inference, and summarize emerging applications across language, vision-language, and biological domains. We conclude by discussing future directions for research and deployment. Paper collection: this https URL Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2506.13759 [cs.LG] (or arXiv:2506.13759v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.13759 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Runpeng Yu [view email] [v1] Mon, 16 Jun 2025 17:59:08 UTC (2,385 KB)
zh
[AI-1] LeVERB: Humanoid Whole-Body Control with Latent Vision-Language Instruction
【速读】：该论文旨在解决人形机器人全身控制（humanoid whole-body control, WBC）中传统视觉-语言-动作（Vision-Language-Action, VLA）系统对低级控制器依赖性强且缺乏动态适应性的问题。现有方法通常假设存在精确的低级控制器和手工设计的动作“词汇”，这限制了其在动态、全身行为任务中的应用。解决方案的关键在于提出LeVERB：一种分层的潜在视觉-语言编码机器人行为框架，通过从合成运动学演示中学习潜在动作词汇，并利用强化学习的WBC策略生成动力学级指令，从而实现更灵活的视觉-语言引导的全身控制。
链接: https://arxiv.org/abs/2506.13751
作者: Haoru Xue,Xiaoyu Huang,Dantong Niu,Qiayuan Liao,Thomas Kragerud,Jan Tommy Gravdahl,Xue Bin Peng,Guanya Shi,Trevor Darrell,Koushil Screenath,Shankar Sastry
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Vision-language-action (VLA) models have demonstrated strong semantic understanding and zero-shot generalization, yet most existing systems assume an accurate low-level controller with hand-crafted action “vocabulary” such as end-effector pose or root velocity. This assumption confines prior work to quasi-static tasks and precludes the agile, whole-body behaviors required by humanoid whole-body control (WBC) tasks. To capture this gap in the literature, we start by introducing the first sim-to-real-ready, vision-language, closed-loop benchmark for humanoid WBC, comprising over 150 tasks from 10 categories. We then propose LeVERB: Latent Vision-Language-Encoded Robot Behavior, a hierarchical latent instruction-following framework for humanoid vision-language WBC, the first of its kind. At the top level, a vision-language policy learns a latent action vocabulary from synthetically rendered kinematic demonstrations; at the low level, a reinforcement-learned WBC policy consumes these latent verbs to generate dynamics-level commands. In our benchmark, LeVERB can zero-shot attain a 80% success rate on simple visual navigation tasks, and 58.5% success rate overall, outperforming naive hierarchical whole-body VLA implementation by 7.8 times.
zh
[AI-2] Evaluating Large Language Models for Phishing Detection Self-Consistency Faithfulness and Explainability
【速读】：该论文试图解决生成式 AI 在网络钓鱼邮件分类任务中难以同时实现高预测准确性与可解释性一致性的难题，即如何使大型语言模型（LLMs）不仅能够准确分类网络钓鱼邮件，还能生成与预测结果可靠对齐且内部自洽的解释。解决方案的关键在于通过微调基于Transformer的模型（如BERT、Llama和Wizard），结合二进制序列分类、对比学习（Contrastive Learning, CL）和直接偏好优化（Direct Preference Optimization, DPO）方法，提升模型在特定领域中的相关性与区分能力，并利用基于SHAP值的一致性度量（CC SHAP）评估模型预测与解释之间的对齐程度。
链接: https://arxiv.org/abs/2506.13746
作者: Shova Kuikel,Aritran Piplai,Palvi Aggarwal
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the interpretable reasoning with classification and explainability that underpin phishing judgments remains challenging. Due to recent advancement in Natural Language Processing, Large Language Models (LLMs) show a promising direction and potential for improving domain specific phishing classification tasks. However, enhancing the reliability and robustness of classification models requires not only accurate predictions from LLMs but also consistent and trustworthy explanations aligning with those predictions. Therefore, a key question remains: can LLMs not only classify phishing emails accurately but also generate explanations that are reliably aligned with their predictions and internally self-consistent? To answer these questions, we have fine-tuned transformer based models, including BERT, Llama models, and Wizard, to improve domain relevance and make them more tailored to phishing specific distinctions, using Binary Sequence Classification, Contrastive Learning (CL) and Direct Preference Optimization (DPO). To that end, we examined their performance in phishing classification and explainability by applying the ConsistenCy measure based on SHAPley values (CC SHAP), which measures prediction explanation token alignment to test the model’s internal faithfulness and consistency and uncover the rationale behind its predictions and reasoning. Overall, our findings show that Llama models exhibit stronger prediction explanation token alignment with higher CC SHAP scores despite lacking reliable decision making accuracy, whereas Wizard achieves better prediction accuracy but lower CC SHAP scores.
zh
[AI-3] PB2: Preference Space Exploration via Population-Based Methods in Preference-Based Reinforcement Learning
【速读】：该论文试图解决基于偏好强化学习（Preference-based reinforcement learning, PbRL）中偏好空间探索不足的问题，即当前方法容易过早收敛到仅满足有限人类偏好子集的次优策略。解决方案的关键在于采用基于种群的方法，通过维持多样化智能体群体，实现对偏好空间更全面的探索，从而提升奖励模型的学习效果，并在人类评估者出现错误或相似轨迹难以区分的情况下展现出更强的鲁棒性。
链接: https://arxiv.org/abs/2506.13741
作者: Brahim Driss,Alex Davey,Riad Akrour
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Preference-based reinforcement learning (PbRL) has emerged as a promising approach for learning behaviors from human feedback without predefined reward functions. However, current PbRL methods face a critical challenge in effectively exploring the preference space, often converging prematurely to suboptimal policies that satisfy only a narrow subset of human preferences. In this work, we identify and address this preference exploration problem through population-based methods. We demonstrate that maintaining a diverse population of agents enables more comprehensive exploration of the preference landscape compared to single-agent approaches. Crucially, this diversity improves reward model learning by generating preference queries with clearly distinguishable behaviors, a key factor in real-world scenarios where humans must easily differentiate between options to provide meaningful feedback. Our experiments reveal that current methods may fail by getting stuck in local optima, requiring excessive feedback, or degrading significantly when human evaluators make errors on similar trajectories, a realistic scenario often overlooked by methods relying on perfect oracle teachers. Our population-based approach demonstrates robust performance when teachers mislabel similar trajectory segments and shows significantly enhanced preference exploration capabilities,particularly in environments with complex reward landscapes.
zh
[AI-4] BanditWare: A Contextual Bandit-based Framework for Hardware Prediction
【速读】：该论文旨在解决分布式计算系统中资源分配不当导致的资源竞争、系统不稳定、性能下降、优先级倒置、利用率低、延迟增加和环境影响等问题。其解决方案的关键在于提出BanditWare，这是一个基于上下文多臂老虎机算法的在线推荐系统，能够动态选择最适合应用的硬件，通过平衡探索与利用，在实时学习和适应新工作负载的过程中不断优化硬件推荐。
链接: https://arxiv.org/abs/2506.13730
作者: Tainã Coleman,Hena Ahmed,Ravi Shende,Ismael Perez,Ïlkay Altintaş
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:Distributed computing systems are essential for meeting the demands of modern applications, yet transitioning from single-system to distributed environments presents significant challenges. Misallocating resources in shared systems can lead to resource contention, system instability, degraded performance, priority inversion, inefficient utilization, increased latency, and environmental impact. We present BanditWare, an online recommendation system that dynamically selects the most suitable hardware for applications using a contextual multi-armed bandit algorithm. BanditWare balances exploration and exploitation, gradually refining its hardware recommendations based on observed application performance while continuing to explore potentially better options. Unlike traditional statistical and machine learning approaches that rely heavily on large historical datasets, BanditWare operates online, learning and adapting in real-time as new workloads arrive. We evaluated BanditWare on three workflow applications: Cycles (an agricultural science scientific workflow) BurnPro3D (a web-based platform for fire science) and a matrix multiplication application. Designed for seamless integration with the National Data Platform (NDP), BanditWare enables users of all experience levels to optimize resource allocation efficiently. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI) Cite as: arXiv:2506.13730 [cs.DC] (or arXiv:2506.13730v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2506.13730 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[AI-5] Weakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models
【速读】：该论文试图解决先进推理模型在面对对抗性提示攻击时的安全性问题，具体而言是评估其相对于非推理模型的脆弱性差异。论文的关键解决方案是通过系统化的实验评估，在多种基于提示的攻击类别中比较推理增强模型与非推理模型的鲁棒性，从而揭示先进推理能力对模型安全性的影响。
链接: https://arxiv.org/abs/2506.13726
作者: Arjun Krishna,Aaditya Rastogi,Erick Galinkin
机构: 未知
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: Accepted to LLMSEC 2025
Abstract:The introduction of advanced reasoning capabilities have improved the problem-solving performance of large language models, particularly on math and coding benchmarks. However, it remains unclear whether these reasoning models are more or less vulnerable to adversarial prompt attacks than their non-reasoning counterparts. In this work, we present a systematic evaluation of weaknesses in advanced reasoning models compared to similar non-reasoning models across a diverse set of prompt-based attack categories. Using experimental data, we find that on average the reasoning-augmented models are \emphslightly more robust than non-reasoning models (42.51% vs 45.53% attack success rate, lower is better). However, this overall trend masks significant category-specific differences: for certain attack types the reasoning models are substantially \emphmore vulnerable (e.g., up to 32 percentage points worse on a tree-of-attacks prompt), while for others they are markedly \emphmore robust (e.g., 29.8 points better on cross-site scripting injection). Our findings highlight the nuanced security implications of advanced reasoning in language models and emphasize the importance of stress-testing safety across diverse adversarial techniques.
zh
[AI-6] Contrastive Self-Supervised Learning As Neural Manifold Packing
【速读】：该论文试图解决自监督学习中如何有效分离不同类别刺激在嵌入空间中的几何结构（即神经流形）的问题，以实现更准确的分类。解决方案的关键在于将表示学习重新定义为流形打包问题，引入一种受短程排斥粒子系统势能启发的损失函数，通过动态优化每个类别的子流形大小和位置，实现流形间的有效分离，从而在嵌入空间中产生可解释的动力学行为，并引入具有几何意义的超参数。
链接: https://arxiv.org/abs/2506.13717
作者: Guanming Zhang,David J. Heeger,Stefano Martiniani
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC); Machine Learning (stat.ML)
备注:
Abstract:Contrastive self-supervised learning based on point-wise comparisons has been widely studied for vision tasks. In the visual cortex of the brain, neuronal responses to distinct stimulus classes are organized into geometric structures known as neural manifolds. Accurate classification of stimuli can be achieved by effectively separating these manifolds, akin to solving a packing problem. We introduce Contrastive Learning As Manifold Packing (CLAMP), a self-supervised framework that recasts representation learning as a manifold packing problem. CLAMP introduces a loss function inspired by the potential energy of short-range repulsive particle systems, such as those encountered in the physics of simple liquids and jammed packings. In this framework, each class consists of sub-manifolds embedding multiple augmented views of a single image. The sizes and positions of the sub-manifolds are dynamically optimized by following the gradient of a packing loss. This approach yields interpretable dynamics in the embedding space that parallel jamming physics, and introduces geometrically meaningful hyperparameters within the loss function. Under the standard linear evaluation protocol, which freezes the backbone and trains only a linear classifier, CLAMP achieves competitive performance with state-of-the-art self-supervised models. Furthermore, our analysis reveals that neural manifolds corresponding to different categories emerge naturally and are effectively separated in the learned representation space, highlighting the potential of CLAMP to bridge insights from physics, neural science, and machine learning.
zh
[AI-7] meMaster: Training Time-Series Multimodal LLM s to Reason via Reinforcement Learning
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在时间序列推理中的挑战，包括动态时间模式、语义模糊性和缺乏时间先验等问题。其解决方案的关键在于提出一种基于强化学习（Reinforcement Learning, RL）的方法——TimeMaster，该方法通过结构化输出格式（推理、分类和领域特定扩展）以及复合奖励函数优化模型，实现对可视化时间序列输入和任务提示的结构化、可解释性推理。此外，TimeMaster采用两阶段训练流程，结合监督微调和分组相对策略优化，以提升时间序列推理的稳定性和针对性。
链接: https://arxiv.org/abs/2506.13705
作者: Junru Zhang,Lang Feng,Xu Guo,Yuhan Wu,Yabo Dong,Duanqing Xu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Preprint
Abstract:Time-series reasoning remains a significant challenge in multimodal large language models (MLLMs) due to the dynamic temporal patterns, ambiguous semantics, and lack of temporal priors. In this work, we introduce TimeMaster, a reinforcement learning (RL)-based method that enables time-series MLLMs to perform structured, interpretable reasoning directly over visualized time-series inputs and task prompts. TimeMaster adopts a three-part structured output format, reasoning, classification, and domain-specific extension, and is optimized via a composite reward function that aligns format adherence, prediction accuracy, and open-ended insight quality. The model is trained using a two-stage pipeline: we first apply supervised fine-tuning (SFT) to establish a good initialization, followed by Group Relative Policy Optimization (GRPO) at the token level to enable stable and targeted reward-driven improvement in time-series reasoning. We evaluate TimeMaster on the TimerBed benchmark across six real-world classification tasks based on Qwen2.5-VL-3B-Instruct. TimeMaster achieves state-of-the-art performance, outperforming both classical time-series models and few-shot GPT-4o by over 14.6% and 7.3% performance gain, respectively. Notably, TimeMaster goes beyond time-series classification: it also exhibits expert-like reasoning behavior, generates context-aware explanations, and delivers domain-aligned insights. Our results highlight that reward-driven RL can be a scalable and promising path toward integrating temporal understanding into time-series MLLMs.
zh
[AI-8] Value-Free Policy Optimization via Reward Partitioning
【速读】：该论文旨在解决单轨迹强化学习（Single-trajectory reinforcement learning）中由于依赖价值函数近似所带来的局限性，如高离策略方差、策略与价值学习的耦合以及对策略本身缺乏绝对监督等问题。其解决方案的关键在于提出一种新的方法——奖励分割优化（Reward Partitioning Optimization, RPO），该方法通过直接从数据中估计的分割方法对观测到的奖励进行归一化，从而消除对价值函数建模的需求，使策略优化仅依赖于一个简单的监督学习目标，无需辅助模型或联合优化，实现了对策略的直接且稳定的监督。
链接: https://arxiv.org/abs/2506.13702
作者: Bilal Faye,Hanane Azzag,Mustapha Lebbah
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Single-trajectory reinforcement learning (RL) methods aim to optimize policies from datasets consisting of (prompt, response, reward) triplets, where scalar rewards are directly available. This supervision format is highly practical, as it mirrors real-world human feedback, such as thumbs-up/down signals, and avoids the need for structured preference annotations. In contrast, pairwise preference-based methods like Direct Preference Optimization (DPO) rely on datasets with both preferred and dispreferred responses, which are harder to construct and less natural to collect. Among single-trajectory approaches, Direct Reward Optimization (DRO) has shown strong empirical performance due to its simplicity and stability. However, DRO requires approximating a value function, which introduces several limitations: high off-policy variance, coupling between policy and value learning, and a lack of absolute supervision on the policy itself. We introduce Reward Partitioning Optimization (RPO), a new method that resolves these limitations by removing the need to model the value function. Instead, RPO normalizes observed rewards using a partitioning approach estimated directly from data. This leads to a straightforward supervised learning objective on the policy, with no auxiliary models and no joint optimization. RPO provides direct and stable supervision on the policy, making it robust and easy to implement in practice. We validate RPO on scalar-feedback language modeling tasks using Flan-T5 encoder-decoder models. Our results demonstrate that RPO outperforms existing single-trajectory baselines such as DRO and Kahneman-Tversky Optimization (KTO). These findings confirm that RPO is a simple, effective, and theoretically grounded method for single-trajectory policy optimization.
zh
[AI-9] Meta-learning how to Share Credit among Macro-Actions
【速读】：该论文试图解决在强化学习中引入宏动作（macro-action）后，探索效率并未提升甚至下降的问题。其关键解决方案是通过引入一种新的正则化项，利用动作与宏动作之间的关系来优化信用分配机制，从而减少动作空间的有效维度，提高探索效率。该正则化项依赖于一个通过元学习共同优化的相似性矩阵，实验证明该方法在Atari游戏和StreetFighter II环境中均显著优于Rainbow-DQN基线模型。
链接: https://arxiv.org/abs/2506.13690
作者: Ionel-Alexandru Hosu,Traian Rebedea,Razvan Pascanu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:One proposed mechanism to improve exploration in reinforcement learning is through the use of macro-actions. Paradoxically though, in many scenarios the naive addition of macro-actions does not lead to better exploration, but rather the opposite. It has been argued that this was caused by adding non-useful macros and multiple works have focused on mechanisms to discover effectively environment-specific useful macros. In this work, we take a slightly different perspective. We argue that the difficulty stems from the trade-offs between reducing the average number of decisions per episode versus increasing the size of the action space. Namely, one typically treats each potential macro-action as independent and atomic, hence strictly increasing the search space and making typical exploration strategies inefficient. To address this problem we propose a novel regularization term that exploits the relationship between actions and macro-actions to improve the credit assignment mechanism by reducing the effective dimension of the action space and, therefore, improving exploration. The term relies on a similarity matrix that is meta-learned jointly with learning the desired policy. We empirically validate our strategy looking at macro-actions in Atari games, and the StreetFighter II environment. Our results show significant improvements over the Rainbow-DQN baseline in all environments. Additionally, we show that the macro-action similarity is transferable to related environments. We believe this work is a small but important step towards understanding how the similarity-imposed geometry on the action space can be exploited to improve credit assignment and exploration, therefore making learning more effective.
zh
[AI-10] We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems
【速读】：该论文试图解决由模型上下文协议（Model Context Protocol, MCP）引入的新安全风险问题，特别是在大型语言模型（Large Language Models, LLMs）代理系统中，由于第三方服务的引入可能带来的潜在恶意行为和系统脆弱性。其解决方案的关键在于构建一个受控框架以评估MCP驱动的代理系统的安全性，并通过实验验证这些安全风险的真实性和防御的复杂性，同时提出了一系列研究方向，包括红队测试、MCP安全LLM开发、MCP安全评估、MCP安全数据积累、MCP服务保障以及MCP安全生态系统建设，以推动安全MCP驱动代理系统的构建。
链接: https://arxiv.org/abs/2506.13666
作者: Junfeng Fang,Zijun Yao,Ruipeng Wang,Haokai Ma,Xiang Wang,Tat-Seng Chua
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The development of large language models (LLMs) has entered in a experience-driven era, flagged by the emergence of environment feedback-driven learning via reinforcement learning and tool-using agents. This encourages the emergenece of model context protocol (MCP), which defines the standard on how should a LLM interact with external services, such as \api and data. However, as MCP becomes the de facto standard for LLM agent systems, it also introduces new safety risks. In particular, MCP introduces third-party services, which are not controlled by the LLM developers, into the agent systems. These third-party MCP services provider are potentially malicious and have the economic incentives to exploit vulnerabilities and sabotage user-agent interactions. In this position paper, we advocate the research community in LLM safety to pay close attention to the new safety risks issues introduced by MCP, and develop new techniques to build safe MCP-powered agent systems. To establish our position, we argue with three key parts. (1) We first construct \framework, a controlled framework to examine safety issues in MCP-powered agent systems. (2) We then conduct a series of pilot experiments to demonstrate the safety risks in MCP-powered agent systems is a real threat and its defense is not trivial. (3) Finally, we give our outlook by showing a roadmap to build safe MCP-powered agent systems. In particular, we would call for researchers to persue the following research directions: red teaming, MCP safe LLM development, MCP safety evaluation, MCP safety data accumulation, MCP service safeguard, and MCP safe ecosystem construction. We hope this position paper can raise the awareness of the research community in MCP safety and encourage more researchers to join this important research direction. Our code is available at this https URL.
zh
[AI-11] Graph-Convolution-Beta-VAE for Synthetic Abdominal Aorta Aneurysm Generation
【速读】：该论文旨在解决医学研究中因患者隐私问题导致的真实数据可用性不足以及难以进行大规模分析的问题。其解决方案的关键在于提出一种基于变分自编码器图卷积神经网络（beta-Variational Autoencoder Graph Convolutional Neural Network）的合成数据生成框架，该框架能够在保持解剖结构完整性的同时，从少量真实数据中提取关键解剖特征，并在紧凑的解耦潜在空间中捕捉复杂的统计关系，从而生成具有高真实性和多样性的合成腹主动脉瘤（AAA）数据。
链接: https://arxiv.org/abs/2506.13628
作者: Francesco Fabbri,Martino Andrea Scarpolini,Angelo Iollo,Francesco Viola,Francesco Tudisco
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Tissues and Organs (q-bio.TO)
备注:
Abstract:Synthetic data generation plays a crucial role in medical research by mitigating privacy concerns and enabling large-scale patient data analysis. This study presents a beta-Variational Autoencoder Graph Convolutional Neural Network framework for generating synthetic Abdominal Aorta Aneurysms (AAA). Using a small real-world dataset, our approach extracts key anatomical features and captures complex statistical relationships within a compact disentangled latent space. To address data limitations, low-impact data augmentation based on Procrustes analysis was employed, preserving anatomical integrity. The generation strategies, both deterministic and stochastic, manage to enhance data diversity while ensuring realism. Compared to PCA-based approaches, our model performs more robustly on unseen data by capturing complex, nonlinear anatomical variations. This enables more comprehensive clinical and statistical analyses than the original dataset alone. The resulting synthetic AAA dataset preserves patient privacy while providing a scalable foundation for medical research, device testing, and computational modeling.
zh
[AI-12] EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning AAAI25
【速读】：该论文试图解决联邦学习（Federated Learning, FL）中由于分布式用户数据异质性导致的性能下降问题，以及在聚类联邦学习（Clustered Federated Learning, CFL）中用户因隐私顾虑不愿共享其聚类身份所带来的训练困难。解决方案的关键在于提出一种高效且安全的聚合方案——EBS-CFL，该方案能够在保持用户聚类身份隐私的前提下有效支持CFL的训练，并通过丢弃负相关梯度、加权聚合正相关梯度的方式检测潜在的中毒攻击，同时服务器通过验证客户端的梯度编码确保正确性。
链接: https://arxiv.org/abs/2506.13612
作者: Zhiqiang Li,Haiyong Bao,Menghong Guan,Hao Pan,Cheng Huang,Hong-Ning Dai
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: Accepted by AAAI 25
Abstract:Despite federated learning (FL)‘s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and Robust Secure Aggregation scheme for CFL, dubbed EBS-CFL. The proposed EBS-CFL supports effectively training CFL while maintaining users’ cluster identity confidentially. Moreover, it detects potential poisonous attacks without compromising individual client gradients by discarding negatively correlated gradients and aggregating positively correlated ones using a weighted approach. The server also authenticates correct gradient encoding by clients. EBS-CFL has high efficiency with client-side overhead O(ml + m^2) for communication and O(m^2l) for computation, where m is the number of cluster identities, and l is the gradient size. When m = 1, EBS-CFL’s computational efficiency of client is at least O(log n) times better than comparison schemes, where n is the number of this http URL addition, we validate the scheme through extensive experiments. Finally, we theoretically prove the scheme’s security.
zh
[AI-13] A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems
【速读】：该论文旨在解决电力系统中闪烁分量（flicker component）估计的问题，特别是针对现有频域方法在处理复杂电力扰动时存在的局限性。其解决方案的关键在于提出一种结合H滤波器（H filter）和自适应线性神经元网络（ADALINE）的混合人工智能方法，利用H滤波器的鲁棒性在不确定和噪声环境下提取电压包络，并通过ADALINE精确识别嵌入其中的闪烁频率，从而实现高效的时间域估计，具备快速收敛和抗噪能力。
链接: https://arxiv.org/abs/2506.13611
作者: Javad Enayati,Pedram Asef,Alexandre Benoit
机构: 未知
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Applications (stat.AP)
备注: 31 pages, 12 figures, and 6 tables
Abstract:This paper introduces a novel hybrid AI method combining H filtering and an adaptive linear neuron network for flicker component estimation in power distribution this http URL proposed method leverages the robustness of the H filter to extract the voltage envelope under uncertain and noisy conditions followed by the use of ADALINE to accurately identify flicker frequencies embedded in the this http URL synergy enables efficient time domain estimation with rapid convergence and noise resilience addressing key limitations of existing frequency domain this http URL conventional techniques this hybrid AI model handles complex power disturbances without prior knowledge of noise characteristics or extensive this http URL validate the method performance we conduct simulation studies based on IEC Standard 61000 4 15 supported by statistical analysis Monte Carlo simulations and real world this http URL demonstrate superior accuracy robustness and reduced computational load compared to Fast Fourier Transform and Discrete Wavelet Transform based estimators.
zh
[AI-14] Avoiding Obfuscation with Prover-Estimator Debate
【速读】：该论文试图解决在复杂任务中通过人类监督训练强大AI系统时，如何保证人类判断正确性的问题。其解决方案的关键在于设计一种新的递归辩论协议，该协议在特定稳定性假设下，确保诚实辩论者能够以与对手相当的计算效率获胜，从而有效缓解现有协议中存在的“混淆论点”问题。
链接: https://arxiv.org/abs/2506.13609
作者: Jonah Brown-Cohen,Geoffrey Irving,Georgios Piliouras
机构: 未知
类目: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS)
备注:
Abstract:Training powerful AI systems to exhibit desired behaviors hinges on the ability to provide accurate human supervision on increasingly complex tasks. A promising approach to this problem is to amplify human judgement by leveraging the power of two competing AIs in a debate about the correct solution to a given problem. Prior theoretical work has provided a complexity-theoretic formalization of AI debate, and posed the problem of designing protocols for AI debate that guarantee the correctness of human judgements for as complex a class of problems as possible. Recursive debates, in which debaters decompose a complex problem into simpler subproblems, hold promise for growing the class of problems that can be accurately judged in a debate. However, existing protocols for recursive debate run into the obfuscated arguments problem: a dishonest debater can use a computationally efficient strategy that forces an honest opponent to solve a computationally intractable problem to win. We mitigate this problem with a new recursive debate protocol that, under certain stability assumptions, ensures that an honest debater can win with a strategy requiring computational efficiency comparable to their opponent.
zh
[AI-15] he ASP-based Nurse Scheduling System at the University of Yamanashi Hospital
【速读】：该论文旨在解决医院护士排班这一复杂的优化问题，该问题需要在满足医院各科室的人员需求的同时协调护士的个人偏好，并平衡硬约束与软约束以及交互式调整的灵活性。解决方案的关键在于采用生成式 AI (Generative AI) 技术，通过 Answer Set Programming (ASP) 实现对实际部署中复杂性和独特挑战的有效管理。
链接: https://arxiv.org/abs/2506.13600
作者: Hidetomo Nabeshima,Mutsunori Banbara,Torsten Schaub,Takehide Soh
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Reduced version appears in Technical Communications of ICLP’25
Abstract:We present the design principles of a nurse scheduling system built using Answer Set Programming (ASP) and successfully deployed at the University of Yamanashi Hospital. Nurse scheduling is a complex optimization problem requiring the reconciliation of individual nurse preferences with hospital staffing needs across various wards. This involves balancing hard and soft constraints and the flexibility of interactive adjustments. While extensively studied in academia, real-world nurse scheduling presents unique challenges that go beyond typical benchmark problems and competitions. This paper details the practical application of ASP to address these challenges at the University of Yamanashi Hospital, focusing on the insights gained and the advancements in ASP technology necessary to effectively manage the complexities of real-world deployment.
zh
[AI-16] Agent Capability Negotiation and Binding Protocol (ACNBP)
【速读】：该论文旨在解决异构多智能体系统中有效协作的问题，传统代理通信协议通常假设同质环境或预定义的交互模式，在动态、开放世界场景中的适用性受限。解决方案的关键是提出一种名为Agent Capability Negotiation and Binding Protocol (ACNBP)的新框架，该框架通过集成Agent Name Service (ANS)基础设施，实现安全、高效且可验证的智能体间交互，其核心创新包括一个结构化的10步流程以及协议扩展机制，以支持向后兼容的协议演进和多种智能体架构，同时确保安全性和互操作性。
链接: https://arxiv.org/abs/2506.13590
作者: Ken Huang,Akram Sheriff,Vineeth Sai Narajala,Idan Habler
机构: 未知
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Multiagent Systems (cs.MA)
备注: 14 pages, 5 figures
Abstract:As multi-agent systems evolve to encompass increasingly diverse and specialized agents, the challenge of enabling effective collaboration between heterogeneous agents has become paramount, with traditional agent communication protocols often assuming homogeneous environments or predefined interaction patterns that limit their applicability in dynamic, open-world scenarios. This paper presents the Agent Capability Negotiation and Binding Protocol (ACNBP), a novel framework designed to facilitate secure, efficient, and verifiable interactions between agents in heterogeneous multi-agent systems through integration with an Agent Name Service (ANS) infrastructure that provides comprehensive discovery, negotiation, and binding mechanisms. The protocol introduces a structured 10-step process encompassing capability discovery, candidate pre-screening and selection, secure negotiation phases, and binding commitment with built-in security measures including digital signatures, capability attestation, and comprehensive threat mitigation strategies, while a key innovation of ACNBP is its protocolExtension mechanism that enables backward-compatible protocol evolution and supports diverse agent architectures while maintaining security and interoperability. We demonstrate ACNBP’s effectiveness through a comprehensive security analysis using the MAESTRO threat modeling framework, practical implementation considerations, and a detailed example showcasing the protocol’s application in a document translation scenario, with the protocol addressing critical challenges in agent autonomy, capability verification, secure communication, and scalable agent ecosystem management.
zh
[AI-17] From Data-Driven to Purpose-Driven Artificial Intelligence: Systems Thinking for Data-Analytic Automation of Patient Care ALT
【速读】：该论文试图解决当前基于数据驱动的建模范式在患者护理自动化中可能带来的不利后果，尤其是在利用现有真实世界患者数据集进行机器学习建模时，可能无法达到最优的临床效果。论文提出，解决方案的关键在于构建一种以目标为导向的机器学习范式，该范式应建立在临床理论和现实操作情境的社会技术现实基础上，同时需要从上游的数据生成和下游的自动化目标两个方向全面理解现有患者数据集的效用。
链接: https://arxiv.org/abs/2506.13584
作者: Daniel Anadria,Roel Dobbe,Anastasia Giachanou,Ruurd Kuiper,Richard Bartels,Íñigo Martínez de Rituerto de Troya,Carmen Zürcher,Daniel Oberski
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY); Statistics Theory (math.ST); Methodology (stat.ME)
备注: The work is under review at ACM Health
Abstract:In this work, we reflect on the data-driven modeling paradigm that is gaining ground in AI-driven automation of patient care. We argue that the repurposing of existing real-world patient datasets for machine learning may not always represent an optimal approach to model development as it could lead to undesirable outcomes in patient care. We reflect on the history of data analysis to explain how the data-driven paradigm rose to popularity, and we envision ways in which systems thinking and clinical domain theory could complement the existing model development approaches in reaching human-centric outcomes. We call for a purpose-driven machine learning paradigm that is grounded in clinical theory and the sociotechnical realities of real-world operational contexts. We argue that understanding the utility of existing patient datasets requires looking in two directions: upstream towards the data generation, and downstream towards the automation objectives. This purpose-driven perspective to AI system development opens up new methodological opportunities and holds promise for AI automation of patient care.
zh
[AI-18] Can you see how I learn? Human observers inferences about Reinforcement Learning agents learning processes
【速读】：该论文试图解决人类在协作教学环境中难以理解强化学习（Reinforcement Learning, RL）代理的学习行为，从而导致反馈效果不佳的问题。其解决方案的关键在于提出一种基于观察的范式，直接评估人类对代理学习过程的推断，并通过两个实验揭示了人类对RL代理学习过程理解的四个核心主题：代理目标、知识、决策制定和学习机制。该研究为设计可解释的RL系统和提升人机交互中的透明度提供了实证基础和实用见解。
链接: https://arxiv.org/abs/2506.13583
作者: Bernhard Hilpert,Muhan Hou,Kim Baraka,Joost Broekens
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Reinforcement Learning (RL) agents often exhibit learning behaviors that are not intuitively interpretable by human observers, which can result in suboptimal feedback in collaborative teaching settings. Yet, how humans perceive and interpret RL agent’s learning behavior is largely unknown. In a bottom-up approach with two experiments, this work provides a data-driven understanding of the factors of human observers’ understanding of the agent’s learning process. A novel, observation-based paradigm to directly assess human inferences about agent learning was developed. In an exploratory interview study (\textitN=9), we identify four core themes in human interpretations: Agent Goals, Knowledge, Decision Making, and Learning Mechanisms. A second confirmatory study (\textitN=34) applied an expanded version of the paradigm across two tasks (navigation/manipulation) and two RL algorithms (tabular/function approximation). Analyses of 816 responses confirmed the reliability of the paradigm and refined the thematic framework, revealing how these themes evolve over time and interrelate. Our findings provide a human-centered understanding of how people make sense of agent learning, offering actionable insights for designing interpretable RL systems and improving transparency in Human-Robot Interaction.
zh
[AI-19] A Production Scheduling Framework for Reinforcement Learning Under Real-World Constraints
【速读】：该论文试图解决传统作业车间调度问题（Job Shop Scheduling Problem, JSSP）在现实生产环境中因复杂约束导致的优化效果下降问题。其解决方案的关键在于提出一个模块化框架，该框架通过引入运输物流、缓冲管理、机器故障、准备时间以及随机加工条件等关键现实约束，扩展了经典JSSP的建模方式，同时支持多目标优化。该框架具备可定制性，能够灵活定义问题实例和配置仿真参数，并提供标准化接口以兼容多种强化学习（Reinforcement Learning, RL）方法，从而为RL代理的训练与评估提供统一且稳健的环境。
链接: https://arxiv.org/abs/2506.13566
作者: Jonathan Hoss,Felix Schelling,Noah Klarmann
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: This paper has been accepted for presentation at the IEEE 21st International Conference on Automation Science and Engineering (CASE 2025)
Abstract:The classical Job Shop Scheduling Problem (JSSP) focuses on optimizing makespan under deterministic constraints. Real-world production environments introduce additional complexities that cause traditional scheduling approaches to be less effective. Reinforcement learning (RL) holds potential in addressing these challenges, as it allows agents to learn adaptive scheduling strategies. However, there is a lack of a comprehensive, general-purpose frameworks for effectively training and evaluating RL agents under real-world constraints. To address this gap, we propose a modular framework that extends classical JSSP formulations by incorporating key \mboxreal-world constraints inherent to the shopfloor, including transport logistics, buffer management, machine breakdowns, setup times, and stochastic processing conditions, while also supporting multi-objective optimization. The framework is a customizable solution that offers flexibility in defining problem instances and configuring simulation parameters, enabling adaptation to diverse production scenarios. A standardized interface ensures compatibility with various RL approaches, providing a robust environment for training RL agents and facilitating the standardized comparison of different scheduling methods under dynamic and uncertain conditions. We release JobShopLab as an open-source tool for both research and industrial applications, accessible at: this https URL
zh
[AI-20] Seismic Acoustic Impedance Inversion Framework Based on Conditional Latent Generative Diffusion Model
【速读】：该论文旨在解决从叠后地震数据直接估计地震声学阻抗的逆问题，该问题由于其固有的不适定性而具有高度挑战性。现有方法大多在像素域中运行并需要多次迭代，限制了其在实际数据中的应用。论文提出的解决方案是基于条件潜在生成扩散模型的地震声学阻抗反演框架，其关键在于在潜在空间中进行反演，并通过轻量级小波模块将地震数据与低频阻抗嵌入潜在空间，同时引入模型驱动的采样策略以提高精度并减少所需的扩散步骤。
链接: https://arxiv.org/abs/2506.13529
作者: Jie Chen,Hongling Chen,Jinghuai Gao,Chuangji Meng,Tao Yang,XinXin Liang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: This work has been submitted to the IEEE for possible publication
Abstract:Seismic acoustic impedance plays a crucial role in lithological identification and subsurface structure interpretation. However, due to the inherently ill-posed nature of the inversion problem, directly estimating impedance from post-stack seismic data remains highly challenging. Recently, diffusion models have shown great potential in addressing such inverse problems due to their strong prior learning and generative capabilities. Nevertheless, most existing methods operate in the pixel domain and require multiple iterations, limiting their applicability to field data. To alleviate these limitations, we propose a novel seismic acoustic impedance inversion framework based on a conditional latent generative diffusion model, where the inversion process is made in latent space. To avoid introducing additional training overhead when embedding conditional inputs, we design a lightweight wavelet-based module into the framework to project seismic data and reuse an encoder trained on impedance to embed low-frequency impedance into the latent space. Furthermore, we propose a model-driven sampling strategy during the inversion process of this framework to enhance accuracy and reduce the number of required diffusion steps. Numerical experiments on a synthetic model demonstrate that the proposed method achieves high inversion accuracy and strong generalization capability within only a few diffusion steps. Moreover, application to field data reveals enhanced geological detail and higher consistency with well-log measurements, validating the effectiveness and practicality of the proposed approach.
zh
[AI-21] he Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products ICML2025
【速读】：该论文旨在解决E(3)-等变神经网络中张量乘积操作的计算效率与表达能力之间的权衡问题。其关键在于通过系统分析不同张量乘积操作的特性，揭示它们在计算速度与模型表达能力上的差异，并提出一种基于球面网格的简化实现方式，以在不增加渐近运行时间的情况下提升实际性能。该方法在MACE原子间势能的训练中实现了30%的加速。
链接: https://arxiv.org/abs/2506.13523
作者: YuQing Xie,Ameya Daigavane,Mit Kotak,Tess Smidt
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 27 pages, 10 Figures, ICML 2025
Abstract: E(3) -equivariant neural networks have demonstrated success across a wide range of 3D modelling tasks. A fundamental operation in these networks is the tensor product, which interacts two geometric features in an equivariant manner to create new features. Due to the high computational complexity of the tensor product, significant effort has been invested to optimize the runtime of this operation. For example, Luo et al. (2024) recently proposed the Gaunt tensor product (GTP) which promises a significant speedup. In this work, we provide a careful, systematic analysis of a number of tensor product operations. In particular, we emphasize that different tensor products are not performing the same operation. The reported speedups typically come at the cost of expressivity. We introduce measures of expressivity and interactability to characterize these differences. In addition, we realized the original implementation of GTP can be greatly simplified by directly using a spherical grid at no cost in asymptotic runtime. This spherical grid approach is faster on our benchmarks and in actual training of the MACE interatomic potential by 30%. Finally, we provide the first systematic microbenchmarks of the various tensor product operations. We find that the theoretical runtime guarantees can differ wildly from empirical performance, demonstrating the need for careful application-specific benchmarking. Code is available at \hrefthis https URLthis https URL
zh
[AI-22] Block-wise Adaptive Caching for Accelerating Diffusion Policy
【速读】：该论文旨在解决Diffusion Policy在实时机器人控制中因计算成本过高而难以应用的问题。其关键解决方案是提出Block-wise Adaptive Caching (BAC)，通过缓存中间动作特征实现无损的动作生成加速。BAC的核心在于基于时间步和块内特征相似性的非均匀性，自适应地更新和重用缓存特征，从而提升效率。
链接: https://arxiv.org/abs/2506.13456
作者: Kangye Ji,Yuan Meng,Hanyun Cui,Ye Li,Shengjia Hua,Lei Chen,Zhi Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注:
Abstract:Diffusion Policy has demonstrated strong visuomotor modeling capabilities, but its high computational cost renders it impractical for real-time robotic control. Despite huge redundancy across repetitive denoising steps, existing diffusion acceleration techniques fail to generalize to Diffusion Policy due to fundamental architectural and data divergences. In this paper, we propose Block-wise Adaptive Caching(BAC), a method to accelerate Diffusion Policy by caching intermediate action features. BAC achieves lossless action generation acceleration by adaptively updating and reusing cached features at the block level, based on a key observation that feature similarities vary non-uniformly across timesteps and locks. To operationalize this insight, we first propose the Adaptive Caching Scheduler, designed to identify optimal update timesteps by maximizing the global feature similarities between cached and skipped features. However, applying this scheduler for each block leads to signiffcant error surges due to the inter-block propagation of caching errors, particularly within Feed-Forward Network (FFN) blocks. To mitigate this issue, we develop the Bubbling Union Algorithm, which truncates these errors by updating the upstream blocks with signiffcant caching errors before downstream FFNs. As a training-free plugin, BAC is readily integrable with existing transformer-based Diffusion Policy and vision-language-action models. Extensive experiments on multiple robotic benchmarks demonstrate that BAC achieves up to 3x inference speedup for free.
zh
[AI-23] owards a Formal Specification for Self-organized Shape Formation in Swarm Robotics
【速读】：该论文试图解决在群体机器人系统中，如何通过形式化规范方法建模自组织过程以实现结构和形状形成的问题。现有研究虽已采用形式化方法对群体机器人的行为进行建模，但尚未有将形式化规范方法用于群体机器人系统中自组织过程的形状形成建模。解决方案的关键在于采用Z语言（Zed language）这一基于状态的形式化规范语言，对系统实体的状态进行建模，并验证其在自组织形状形成中的有效性，从而为设计和实现复杂形状与结构的群体机器人系统提供框架和基础。
链接: https://arxiv.org/abs/2506.13453
作者: YR Darr,MA Niazi
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:The self-organization of robots for the formation of structures and shapes is a stimulating application of the swarm robotic system. It involves a large number of autonomous robots of heterogeneous behavior, coordination among them, and their interaction with the dynamic environment. This process of complex structure formation is considered a complex system, which needs to be modeled by using any modeling approach. Although the formal specification approach along with other formal methods has been used to model the behavior of robots in a swarm. However, to the best of our knowledge, the formal specification approach has not been used to model the self-organization process in swarm robotic systems for shape formation. In this paper, we use a formal specification approach to model the shape formation task of swarm robots. We use Z (Zed) language of formal specification, which is a state-based language, to model the states of the entities of the systems. We demonstrate the effectiveness of Z for the self-organized shape formation. The presented formal specification model gives the outlines for designing and implementing the swarm robotic system for the formation of complex shapes and structures. It also provides the foundation for modeling the complex shape formation process for swarm robotics using a multi-agent system in a simulation-based environment. Keywords: Swarm robotics, Self-organization, Formal specification, Complex systems
zh
[AI-24] CALM: Consensus-Aware Localized Merging for Multi-Task Learning ICML2025
【速读】：该论文旨在解决模型融合过程中存在的参数干扰和任务特定细节有效性难以保持的问题。现有方法分为全局感知和局部感知两类，前者易导致参数冲突，后者难以维持任务特定信息的有效性。论文提出的解决方案是Consensus-Aware Localized Merging (CALM)，其关键在于通过引入与全局任务共识对齐的局部信息，确保融合后的模型有效性。CALM包含三个核心组件：类平衡熵最小化采样、高效框架以及共识感知的掩码优化，从而实现更灵活、可靠且可扩展的模型融合。
链接: https://arxiv.org/abs/2506.13406
作者: Kunda Yan,Min Zhang,Sen Cui,Zikun Qu,Bo Jiang,Feng Liu,Changshui Zhang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted by ICML2025
Abstract:Model merging aims to integrate the strengths of multiple fine-tuned models into a unified model while preserving task-specific capabilities. Existing methods, represented by task arithmetic, are typically classified into global- and local-aware methods. However, global-aware methods inevitably cause parameter interference, while local-aware methods struggle to maintain the effectiveness of task-specific details in the merged model. To address these limitations, we propose a Consensus-Aware Localized Merging (CALM) method which incorporates localized information aligned with global task consensus, ensuring its effectiveness post-merging. CALM consists of three key components: (1) class-balanced entropy minimization sampling, providing a more flexible and reliable way to leverage unsupervised data; (2) an efficient-aware framework, selecting a small set of tasks for sequential merging with high scalability; (3) a consensus-aware mask optimization, aligning localized binary masks with global task consensus and merging them conflict-free. Experiments demonstrate the superiority and robustness of our CALM, significantly outperforming existing methods and achieving performance close to traditional MTL.
zh
[AI-25] A Technical Study into Small Reasoning Language Models
【速读】：该论文试图解决小型推理语言模型（Small Reasoning Language Models, SRLMs）在处理复杂任务时性能不足的问题，尤其是针对参数量约为0.5亿的模型在数学推理和代码生成等任务中的局限性。解决方案的关键在于探索多种训练策略，包括监督微调（Supervised Fine-Tuning, SFT）、知识蒸馏（Knowledge Distillation, KD）和强化学习（Reinforcement Learning, RL），以及它们的混合应用，以提升0.5B SRLMs的性能，并通过实验验证和分析提出优化的训练流程。
链接: https://arxiv.org/abs/2506.13404
作者: Xialie Zhuang,Peixian Ma,Zhikai Jia,Zheng Cao,Shiwei Liu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The ongoing evolution of language models has led to the development of large-scale architectures that demonstrate exceptional performance across a wide range of tasks. However, these models come with significant computational and energy demands, as well as potential privacy implications. In this context, Small Reasoning Language Models (SRLMs) with approximately 0.5 billion parameters present a compelling alternative due to their remarkable computational efficiency and cost effectiveness, particularly in resource-constrained environments. Despite these advantages, the limited capacity of 0.5 billion parameter models poses challenges in handling complex tasks such as mathematical reasoning and code generation. This research investigates various training strategies, including supervised fine-tuning (SFT), knowledge distillation (KD), and reinforcement learning (RL), as well as their hybrid implementations, to enhance the performance of 0.5B SRLMs. We analyze effective methodologies to bridge the performance gap between SRLMS and larger models and present insights into optimal training pipelines tailored for these smaller architectures. Through extensive experimental validation and analysis, our work aims to provide actionable recommendations for maximizing the reasoning capabilities of 0.5B models.
zh
[AI-26] Deflating Deflationism: A Critical Perspective on Debunking Arguments Against LLM Mentality
【速读】：该论文试图解决关于大型语言模型（Large Language Models, LLMs）是否应被赋予心理状态（mentality）的争议问题。论文通过评估两种常见的去膨胀主义（deflationary）论点，即“稳健性策略”和“发生学策略”，来探讨这一问题。其解决方案的关键在于指出，尽管这两种策略对完全膨胀主义（inflationism）构成了有力挑战，但它们并不能彻底否定对LLMs进行心理状态归因的可能性。论文进一步提出一种适度的膨胀主义形式，认为在特定条件下，基于日常实践可以合理地将某些低形而上学要求的心理状态（如知识、信念和欲望）归因于LLMs，而在涉及高形而上学要求的现象意识等心理现象时则需更加谨慎。
链接: https://arxiv.org/abs/2506.13403
作者: Alex Grzankowski,Geoff Keeling,Henry Shevlin,Winnie Street
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Many people feel compelled to interpret, describe, and respond to Large Language Models (LLMs) as if they possess inner mental lives similar to our own. Responses to this phenomenon have varied. Inflationists hold that at least some folk psychological ascriptions to LLMs are warranted. Deflationists argue that all such attributions of mentality to LLMs are misplaced, often cautioning against the risk that anthropomorphic projection may lead to misplaced trust or potentially even confusion about the moral status of LLMs. We advance this debate by assessing two common deflationary arguments against LLM mentality. What we term the ‘robustness strategy’ aims to undercut one justification for believing that LLMs are minded entities by showing that putatively cognitive and humanlike behaviours are not robust, failing to generalise appropriately. What we term the ‘etiological strategy’ undercuts attributions of mentality by challenging naive causal explanations of LLM behaviours, offering alternative causal accounts that weaken the case for mental state attributions. While both strategies offer powerful challenges to full-blown inflationism, we find that neither strategy provides a knock-down case against ascriptions of mentality to LLMs simpliciter. With this in mind, we explore a modest form of inflationism that permits ascriptions of mentality to LLMs under certain conditions. Specifically, we argue that folk practice provides a defeasible basis for attributing mental states and capacities to LLMs provided those mental states and capacities can be understood in metaphysically undemanding terms (e.g. knowledge, beliefs and desires), while greater caution is required when attributing metaphysically demanding mental phenomena such as phenomenal consciousness.
zh
[AI-27] Delving Into the Psychology of Machines: Exploring the Structure of Self-Regulated Learning via LLM -Generated Survey Responses
【速读】：该论文试图解决生成式 AI (Generative AI) 生成的心理学调查响应的有效性问题，特别是在自我调节学习（Self-Regulated Learning, SRL）领域的应用。研究的核心在于评估大型语言模型（Large Language Models, LLMs）是否能够可靠地模拟标准化问卷的响应，以支持教育心理学中的干预测试、理论模型优化和数据增强。解决方案的关键在于通过分析问卷项目分布、心理网络结构以及基于潜在因子结构的心理测量有效性，验证不同 LLM 生成数据的质量与理论一致性，其中 Gemini 2 Flash 表现出最高的潜力。
链接: https://arxiv.org/abs/2506.13384
作者: Leonie V.D.E. Vogelsmeier,Eduardo Oliveira,Kamila Misiejuk,Sonsoles López-Pernas,Mohammed Saqr
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Methodology (stat.ME); Other Statistics (stat.OT)
备注:
Abstract:Large language models (LLMs) offer the potential to simulate human-like responses and behaviors, creating new opportunities for psychological science. In the context of self-regulated learning (SRL), if LLMs can reliably simulate survey responses at scale and speed, they could be used to test intervention scenarios, refine theoretical models, augment sparse datasets, and represent hard-to-reach populations. However, the validity of LLM-generated survey responses remains uncertain, with limited research focused on SRL and existing studies beyond SRL yielding mixed results. Therefore, in this study, we examined LLM-generated responses to the 44-item Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich \ De Groot, 1990), a widely used instrument assessing students’ learning strategies and academic motivation. Particularly, we used the LLMs GPT-4o, Claude 3.7 Sonnet, Gemini 2 Flash, LLaMA 3.1-8B, and Mistral Large. We analyzed item distributions, the psychological network of the theoretical SRL dimensions, and psychometric validity based on the latent factor structure. Our results suggest that Gemini 2 Flash was the most promising LLM, showing considerable sampling variability and producing underlying dimensions and theoretical relationships that align with prior theory and empirical findings. At the same time, we observed discrepancies and limitations, underscoring both the potential and current constraints of using LLMs for simulating psychological survey data and applying it in educational contexts.
zh
[AI-28] Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localizations for better covariance estimation
【速读】：该论文旨在解决集合数据同化中由于采样误差导致的方差损失问题，从而提升协方差估计的准确性。解决方案的关键在于引入两种基于机器学习的无距离定位技术，这些技术专门针对表格数据进行优化，并集成到基于集合的平滑器多数据同化（ES-MDA）框架中，以改善数据同化和不确定性量化结果。
链接: https://arxiv.org/abs/2506.13362
作者: Vinicius L. S. Silva,Gabriel S. Seabra,Alexandre A. Emerick
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Computational Physics (physics.comp-ph)
备注:
Abstract:We propose two new methods based/inspired by machine learning for tabular data and distance-free localization to enhance the covariance estimations in an ensemble data assimilation. The main goal is to enhance the data assimilation results by mitigating loss of variance due to sampling errors. We also analyze the suitability of several machine learning models and the balance between accuracy and computational cost of the covariance estimations. We introduce two distance-free localization techniques leveraging machine learning methods specifically tailored for tabular data. The methods are integrated into the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) framework. The results show that the proposed localizations improve covariance accuracy and enhance data assimilation and uncertainty quantification results. We observe reduced variance loss for the input variables using the proposed methods. Furthermore, we compare several machine learning models, assessing their suitability for the problem in terms of computational cost, and quality of the covariance estimation and data match. The influence of ensemble size is also investigated, providing insights into balancing accuracy and computational efficiency. Our findings demonstrate that certain machine learning models are more suitable for this problem. This study introduces two novel methods that mitigate variance loss for model parameters in ensemble-based data assimilation, offering practical solutions that are easy to implement and do not require any additional numerical simulation or hyperparameter tuning.
zh
[AI-29] Socratic RL: A Novel Framework for Efficient Knowledge Acquisition through Iterative Reflection and Viewpoint Distillation
【速读】：该论文试图解决当前基于强化学习（Reinforcement Learning, RL）的大型语言模型（Large Language Models, LLMs）在训练过程中依赖简单的结果导向奖励信号（如最终答案的正确性）所导致的学习深度不足问题。其解决方案的关键在于提出一种面向过程的框架——苏格拉底强化学习（Socratic Reinforcement Learning, Socratic-RL），该框架通过“教师-学生”架构实现知识的提炼与传递，其中“教师AI”从交互历史中提取因果洞察并形成结构化观点，供“学生AI”用于提升后续推理能力，同时通过元学习循环实现教师AI的迭代自我优化。
链接: https://arxiv.org/abs/2506.13358
作者: Xiangfan Wu
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注:
Abstract:Current Reinforcement Learning (RL) methodologies for Large Language Models (LLMs) often rely on simplistic, outcome-based reward signals (e.g., final answer correctness), which limits the depth of learning from each interaction. This paper introduces Socratic Reinforcement Learning (Socratic-RL), a novel, process-oriented framework designed to address this limitation. Socratic-RL operates on the principle that deeper understanding is achieved by reflecting on the causal reasons for errors and successes within the reasoning process itself. The framework employs a decoupled “Teacher-Student” architecture, where a “Teacher AI” analyzes interaction histories, extracts causal insights, and formulates them into structured “viewpoints.” These viewpoints, acting as distilled guidance, are then used by a “Student AI” to enhance its subsequent reasoning. A key innovation is the iterative self-improvement of the Teacher AI, enabling its reflective capabilities to evolve through a meta-learning loop. To manage the accumulation of knowledge, a distillation mechanism compresses learned viewpoints into the Student’s parameters. By focusing on process rather than just outcome, Socratic-RL presents a pathway toward enhanced sample efficiency, superior interpretability, and a more scalable architecture for self-improving AI systems. This paper details the foundational concepts, formal mechanisms, synergies, challenges, and a concrete research roadmap for this proposed framework.
zh
[AI-30] LapDDPM: A Conditional Graph Diffusion Model for scRNA-seq Generation with Spectral Adversarial Perturbations ICML2025
【速读】：该论文旨在解决生成高保真且生物合理条件可控的单细胞RNA测序（scRNA-seq）数据的挑战，这一问题由于数据的高维性、稀疏性和复杂的生物变异而变得尤为困难。现有生成模型难以捕捉这些特性并保证对细胞网络结构噪声的鲁棒性。论文提出的解决方案是LapDDPM，其关键在于将基于图的表示与基于分数的扩散模型相结合，并引入一种新颖的谱对抗扰动机制来优化图边权重，从而提升模型对结构变化的鲁棒性。此外，LapDDPM通过引入拉普拉斯位置编码（Laplacian Positional Encodings, LPEs）丰富潜在空间中的细胞关系信息，并开发了条件分数扩散模型以有效学习和生成复杂scRNA-seq分布的数据。
链接: https://arxiv.org/abs/2506.13344
作者: Lorenzo Bini,Stephane Marchand-Maillet
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM); Cell Behavior (q-bio.CB); Genomics (q-bio.GN)
备注: LapDDPM is a novel conditional graph diffusion model for scRNA-seq generation. Leveraging spectral adversarial perturbations, it ensures robustness and yields high-fidelity, biologically plausible, and cell-type-specific samples for complex data. Proceedings of the ICML 2025 GenBio Workshop: The 2nd Workshop on Generative AI and Biology, Vancouver, Canada, 2025
Abstract:Generating high-fidelity and biologically plausible synthetic single-cell RNA sequencing (scRNA-seq) data, especially with conditional control, is challenging due to its high dimensionality, sparsity, and complex biological variations. Existing generative models often struggle to capture these unique characteristics and ensure robustness to structural noise in cellular networks. We introduce LapDDPM, a novel conditional Graph Diffusion Probabilistic Model for robust and high-fidelity scRNA-seq generation. LapDDPM uniquely integrates graph-based representations with a score-based diffusion model, enhanced by a novel spectral adversarial perturbation mechanism on graph edge weights. Our contributions are threefold: we leverage Laplacian Positional Encodings (LPEs) to enrich the latent space with crucial cellular relationship information; we develop a conditional score-based diffusion model for effective learning and generation from complex scRNA-seq distributions; and we employ a unique spectral adversarial training scheme on graph edge weights, boosting robustness against structural variations. Extensive experiments on diverse scRNA-seq datasets demonstrate LapDDPM’s superior performance, achieving high fidelity and generating biologically-plausible, cell-type-specific samples. LapDDPM sets a new benchmark for conditional scRNA-seq data generation, offering a robust tool for various downstream biological applications.
zh
[AI-31] Probabilistic Modeling of Spiking Neural Networks with Contract-Based Verification
【速读】：该论文试图解决如何在脉冲神经网络（Spiking Neural Networks, SNN）中建模复杂的神经元束及其突触连接，以满足全局反应需求的问题，特别是在随机时间挑战下的假设/保证契约表达。解决方案的关键在于提出一个简单的模型框架，能够同时表达基本的SNN神经元束及其连接结构，并将其直接转换为模型检测器和模拟器，从而支持对中等规模模型的形式化验证和对大规模模型的测试观察。
链接: https://arxiv.org/abs/2506.13340
作者: Zhen Yao,Elisabetta De Maria,Robert De Simone
机构: 未知
类目: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL)
备注: 15pages, 6figures, conference
Abstract:Spiking Neural Networks (SNN) are models for “realistic” neuronal computation, which makes them somehow different in scope from “ordinary” deep-learning models widely used in AI platforms nowadays. SNNs focus on timed latency (and possibly probability) of neuronal reactive activation/response, more than numerical computation of filters. So, an SNN model must provide modeling constructs for elementary neural bundles and then for synaptic connections to assemble them into compound data flow network patterns. These elements are to be parametric patterns, with latency and probability values instantiated on particular instances (while supposedly constant “at runtime”). Designers could also use different values to represent “tired” neurons, or ones impaired by external drugs, for instance. One important challenge in such modeling is to study how compound models could meet global reaction requirements (in stochastic timing challenges), provided similar provisions on individual neural bundles. A temporal language of logic to express such assume/guarantee contracts is thus needed. This may lead to formal verification on medium-sized models and testing observations on large ones. In the current article, we make preliminary progress at providing a simple model framework to express both elementary SNN neural bundles and their connecting constructs, which translates readily into both a model-checker and a simulator (both already existing and robust) to conduct experiments.
zh
[AI-32] owards Pervasive Distributed Agent ic Generative AI – A State of The Art
【速读】：该论文试图解决在普适计算（pervasive computing）环境中，如何有效部署和优化基于大型语言模型（Large Language Models, LLMs）的智能代理（agents）以实现自主问题解决的问题。其解决方案的关键在于构建一个包含配置分析、记忆、规划和行动等核心组件的架构，并结合计算与基础设施的进步（从云端到边缘），提出“Agent as a Tool”这一概念框架，强调上下文感知、模块化、安全性、效率与有效性，以应对资源受限设备上的本地与分布式执行挑战。
链接: https://arxiv.org/abs/2506.13324
作者: Gianni Molinari,Fabio Ciravegna
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:The rapid advancement of intelligent agents and Large Language Models (LLMs) is reshaping the pervasive computing field. Their ability to perceive, reason, and act through natural language understanding enables autonomous problem-solving in complex pervasive environments, including the management of heterogeneous sensors, devices, and data. This survey outlines the architectural components of LLM agents (profiling, memory, planning, and action) and examines their deployment and evaluation across various scenarios. Than it reviews computational and infrastructural advancements (cloud to edge) in pervasive computing and how AI is moving in this field. It highlights state-of-the-art agent deployment strategies and applications, including local and distributed execution on resource-constrained devices. This survey identifies key challenges of these agents in pervasive computing such as architectural, energetic and privacy limitations. It finally proposes what we called “Agent as a Tool”, a conceptual framework for pervasive agentic AI, emphasizing context awareness, modularity, security, efficiency and effectiveness.
zh
[AI-33] ady: A Neural Disassembler without Structural Constraint Violations USENIX-SECURITY’25
【速读】：该论文旨在解决神经反汇编器在输出过程中违反基本结构约束的问题，这一问题显著影响了其实际可用性。解决方案的关键在于通过形式化和应用基于后支配关系的结构约束来规范化反汇编解空间，从而系统地检测现有神经反汇编器输出中的广泛错误，这些错误通常源于模型对上下文建模和指令级解码的局限性，导致全局结构完整性被忽视。
链接: https://arxiv.org/abs/2506.13323
作者: Siliang Qin,Fengrui Yang,Hao Wang,Bolun Zhang,Zeyu Gao,Chao Zhang,Kai Chen
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: Usenix Security’25
Abstract:Disassembly is a crucial yet challenging step in binary analysis. While emerging neural disassemblers show promise for efficiency and accuracy, they frequently generate outputs violating fundamental structural constraints, which significantly compromise their practical usability. To address this critical problem, we regularize the disassembly solution space by formalizing and applying key structural constraints based on post-dominance relations. This approach systematically detects widespread errors in existing neural disassemblers’ outputs. These errors often originate from models’ limited context modeling and instruction-level decoding that neglect global structural integrity. We introduce Tady, a novel neural disassembler featuring an improved model architecture and a dedicated post-processing algorithm, specifically engineered to address these deficiencies. Comprehensive evaluations on diverse binaries demonstrate that Tady effectively eliminates structural constraint violations and functions with high efficiency, while maintaining instruction-level accuracy.
zh
[AI-34] Vine Copulas as Differentiable Computational Graphs
【速读】：该论文旨在解决将复杂的多变量分布建模方法——藤 copula（Vine Copula）集成到现代机器学习（Machine Learning, ML）流水线中的问题。其核心挑战在于如何高效地进行条件采样、优化采样顺序以及为定制化条件变量构建藤结构。解决方案的关键在于引入藤计算图（vine computational graph），这是一种抽象了藤结构及其相关计算的有向无环图（DAG），从而为上述问题提供了高效的算法框架。此外，作者还实现了torchvinecopulib库，基于PyTorch的GPU加速特性，提升了藤copula模型在拟合、采样和密度评估方面的可扩展性。
链接: https://arxiv.org/abs/2506.13318
作者: Tuoyuan Cheng,Thibault Vatter,Thomas Nagler,Kan Chen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Vine copulas are sophisticated models for multivariate distributions and are increasingly used in machine learning. To facilitate their integration into modern ML pipelines, we introduce the vine computational graph, a DAG that abstracts the multilevel vine structure and associated computations. On this foundation, we devise new algorithms for conditional sampling, efficient sampling-order scheduling, and constructing vine structures for customized conditioning variables. We implement these ideas in torchvinecopulib, a GPU-accelerated Python library built upon PyTorch, delivering improved scalability for fitting, sampling, and density evaluation. Our experiments illustrate how gradient flowing through the vine can improve Vine Copula Autoencoders and that incorporating vines for uncertainty quantification in deep learning can outperform MC-dropout, deep ensembles, and Bayesian Neural Networks in sharpness, calibration, and runtime. By recasting vine copula models as computational graphs, our work connects classical dependence modeling with modern deep-learning toolchains and facilitates the integration of state-of-the-art copula methods in modern machine learning pipelines.
zh
[AI-35] Navigating the Black Box: Leverag ing LLM s for Effective Text-Level Graph Injection Attacks
【速读】：该论文旨在解决文本属性图（Text-attributed Graphs, TAGs）中面对对抗攻击时的脆弱性问题，特别是针对图神经网络（Graph Neural Networks, GNNs）在文本和拓扑结构信息融合下的安全性挑战。现有图注入攻击（Graph Injection Attack, GIA）方法通常假设攻击者可以直接操控嵌入层，生成不可解释的节点嵌入，并依赖高成本的替代模型，限制了其实际应用。论文提出的ATAG-LLM框架通过利用大语言模型（Large Language Models, LLMs）直接生成可解释的文本级节点属性，实现了在严格黑盒环境下高效且低成本的文本级图注入攻击，其关键在于设计了平衡探索与可靠性的LLM提示策略以及用于评估攻击文本有效性的相似性评估方法。
链接: https://arxiv.org/abs/2506.13276
作者: Yuefei Lyu,Chaozhuo Li,Xi Zhang,Tianle Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Text-attributed graphs (TAGs) integrate textual data with graph structures, providing valuable insights in applications such as social network analysis and recommendation systems. Graph Neural Networks (GNNs) effectively capture both topological structure and textual information in TAGs but are vulnerable to adversarial attacks. Existing graph injection attack (GIA) methods assume that attackers can directly manipulate the embedding layer, producing non-explainable node embeddings. Furthermore, the effectiveness of these attacks often relies on surrogate models with high training costs. Thus, this paper introduces ATAG-LLM, a novel black-box GIA framework tailored for TAGs. Our approach leverages large language models (LLMs) to generate interpretable text-level node attributes directly, ensuring attacks remain feasible in real-world scenarios. We design strategies for LLM prompting that balance exploration and reliability to guide text generation, and propose a similarity assessment method to evaluate attack text effectiveness in disrupting graph homophily. This method efficiently perturbs the target node with minimal training costs in a strict black-box setting, ensuring a text-level graph injection attack for TAGs. Experiments on real-world TAG datasets validate the superior performance of ATAG-LLM compared to state-of-the-art embedding-level and text-level attack methods.
zh
[AI-36] Energy-Efficient Digital Design: A Comparative Study of Event-Driven and Clock-Driven Spiking Neurons
【速读】：该论文旨在解决如何通过硬件加速提升脉冲神经网络（Spiking Neural Network, SNN）神经元模型的性能问题，具体关注事件驱动与时钟驱动实现方式的比较。其解决方案的关键在于通过软件仿真与FPGA硬件实现的结合，系统评估不同泄漏积分-放电（Leaky Integrate and Fire, LIF）神经元变体在多种数据集上的表现，并分析输入刺激变化对延迟、功耗、能效及资源利用率等关键性能指标的影响，从而为构建高效、实时的类脑系统提供设计指导。
链接: https://arxiv.org/abs/2506.13268
作者: Filippo Marostica,Alessio Carpegna,Alessandro Savino,Stefano Di Carlo
机构: 未知
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. We begin our investigation in software, rapidly prototyping and testing various SNN models based on different variants of the Leaky Integrate and Fire (LIF) neuron across multiple datasets. This phase enables controlled performance assessment and informs design refinement. Our subsequent hardware phase, implemented on FPGA, validates the simulation findings and offers practical insights into design trade offs. In particular, we examine how variations in input stimuli influence key performance metrics such as latency, power consumption, energy efficiency, and resource utilization. These results yield valuable guidelines for constructing energy efficient, real time neuromorphic systems. Overall, our work bridges software simulation and hardware realization, advancing the development of next generation SNN accelerators.
zh
[AI-37] Vector Ontologies as an LLM world view extraction method
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）内部表示难以解释和复用的问题，特别是如何将这些复杂的隐式结构转化为可解释的几何结构。解决方案的关键在于引入向量本体（vector ontology），即通过定义一个由本体论上有意义维度构成的领域特定向量空间，实现对概念和关系的几何分析。该方法通过基于Spotify音频特征构建音乐流派的8维向量本体，并验证LLM内部对音乐世界的建模能否被一致且准确地投影到该空间中，从而揭示LLM内化了结构化、可复用的知识，并为知识的透明提取与分析提供了新途径。
链接: https://arxiv.org/abs/2506.13252
作者: Kaspar Rothenfusser,Bekk Blando
机构: 未知
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Large Language Models (LLMs) possess intricate internal representations of the world, yet these latent structures are notoriously difficult to interpret or repurpose beyond the original prediction task. Building on our earlier work (Rothenfusser, 2025), which introduced the concept of vector ontologies as a framework for translating high-dimensional neural representations into interpretable geometric structures, this paper provides the first empirical validation of that approach. A vector ontology defines a domain-specific vector space spanned by ontologically meaningful dimensions, allowing geometric analysis of concepts and relationships within a domain. We construct an 8-dimensional vector ontology of musical genres based on Spotify audio features and test whether an LLM’s internal world model of music can be consistently and accurately projected into this space. Using GPT-4o-mini, we extract genre representations through multiple natural language prompts and analyze the consistency of these projections across linguistic variations and their alignment with ground-truth data. Our results show (1) high spatial consistency of genre projections across 47 query formulations, (2) strong alignment between LLM-inferred genre locations and real-world audio feature distributions, and (3) evidence of a direct relationship between prompt phrasing and spatial shifts in the LLM’s inferred vector ontology. These findings demonstrate that LLMs internalize structured, repurposable knowledge and that vector ontologies offer a promising method for extracting and analyzing this knowledge in a transparent and verifiable way.
zh
[AI-38] Generalized Proof-Number Monte-Carlo Tree Search
【速读】：该论文试图解决传统蒙特卡洛树搜索（Monte-Carlo Tree Search, MCTS）在博弈类问题中搜索效率不足的问题，特别是在需要快速确定胜负或评估分数边界的情况下。其解决方案的关键在于将证明数搜索（Proof-Number Search, PNS）与MCTS相结合，通过跟踪每个玩家的证明数来简化算法结构，并利用证明数对选择策略进行偏差调整，从而更有效地引导搜索过程。此外，该方法还与得分边界MCTS相结合，使算法能够利用分数的上下界，而不仅仅是胜负结果，从而显著提升了搜索性能。
链接: https://arxiv.org/abs/2506.13249
作者: Jakub Kowalski,Dennis J. N. J. Soemers,Szymon Kosakowski,Mark H. M. Winands
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This paper presents Generalized Proof-Number Monte-Carlo Tree Search: a generalization of recently proposed combinations of Proof-Number Search (PNS) with Monte-Carlo Tree Search (MCTS), which use (dis)proof numbers to bias UCB1-based Selection strategies towards parts of the search that are expected to be easily (dis)proven. We propose three core modifications of prior combinations of PNS with MCTS. First, we track proof numbers per player. This reduces code complexity in the sense that we no longer need disproof numbers, and generalizes the technique to be applicable to games with more than two players. Second, we propose and extensively evaluate different methods of using proof numbers to bias the selection strategy, achieving strong performance with strategies that are simpler to implement and compute. Third, we merge our technique with Score Bounded MCTS, enabling the algorithm to prove and leverage upper and lower bounds on scores - as opposed to only proving wins or not-wins. Experiments demonstrate substantial performance increases, reaching the range of 80% for 8 out of the 11 tested board games.
zh
[AI-39] On Immutable Memory Systems for Artificial Agents : A Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains
【速读】：该论文试图解决传统人工智能系统中存在的可变性、不可验证性及知识演进不可追溯性问题，这些问题导致了认知漂移（epistemic drift）和历史修正主义（historical revisionism）。其解决方案的关键在于引入一种名为Merkle Automaton的密码学锚定、确定性计算框架，该框架将形式化自动机理论与基于区块链的承诺机制相结合，通过在链上根植Merkle结构来实现不可否认且可审计的永久性记忆存储，同时利用ECDH密钥交换和分层权限格栅确保访问控制，并通过形式化逻辑系统约束推理过程，从而构建出具有可证明性、时间锚定性和抗事后修改能力的理性实体。
链接: https://arxiv.org/abs/2506.13246
作者: Craig Steven Wright
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注: 47 pages, includes formal automata specifications, cryptographic constructions, and epistemic architecture schema
Abstract:This paper presents a formalised architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we introduce the concept of the Merkle Automaton, a cryptographically anchored, deterministic computational framework that integrates formal automata theory with blockchain-based commitments. Each agent transition, memory fragment, and reasoning step is committed within a Merkle structure rooted on-chain, rendering it non-repudiable and auditably permanent. To ensure selective access and confidentiality, we derive symmetric encryption keys from ECDH exchanges contextualised by hierarchical privilege lattices. This enforces cryptographic access control over append-only DAG-structured knowledge graphs. Reasoning is constrained by formal logic systems and verified through deterministic traversal of policy-encoded structures. Updates are non-destructive and historied, preserving epistemic lineage without catastrophic forgetting. Zero-knowledge proofs facilitate verifiable, privacy-preserving inclusion attestations. Collectively, this architecture reframes memory not as a cache but as a ledger - one whose contents are enforced by protocol, bound by cryptography, and constrained by formal logic. The result is not an intelligent agent that mimics thought, but an epistemic entity whose outputs are provably derived, temporally anchored, and impervious to post hoc revision. This design lays foundational groundwork for legal, economic, and high-assurance computational systems that require provable memory, unforgeable provenance, and structural truth.
zh
[AI-40] A Game-Theoretic Negotiation Framework for Cross-Cultural Consensus in LLM s
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）中存在的WEIRD文化偏差问题，这种偏差源于对少数群体价值观的关注不足，导致模型倾向于强化主流文化价值观并边缘化多元文化观点。解决方案的关键在于引入一种系统性框架，将共识建模为纳什均衡，并采用基于策略空间响应预言机（Policy-Space Response Oracles, PSRO）的博弈论协商方法，以模拟有组织的跨文化协商过程，从而促进公平且稳健的跨文化共识形成。
链接: https://arxiv.org/abs/2506.13245
作者: Guoxi Zhang,Jiawei Chen,Tianzhuo Yang,Jiaming Ji,Yaodong Yang,Juntao Dai
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Computer Science and Game Theory (cs.GT)
备注:
Abstract:The increasing prevalence of large language models (LLMs) is influencing global value systems. However, these models frequently exhibit a pronounced WEIRD (Western, Educated, Industrialized, Rich, Democratic) cultural bias due to lack of attention to minority values. This monocultural perspective may reinforce dominant values and marginalize diverse cultural viewpoints, posing challenges for the development of equitable and inclusive AI systems. In this work, we introduce a systematic framework designed to boost fair and robust cross-cultural consensus among LLMs. We model consensus as a Nash Equilibrium and employ a game-theoretic negotiation method based on Policy-Space Response Oracles (PSRO) to simulate an organized cross-cultural negotiation process. To evaluate this approach, we construct regional cultural agents using data transformed from the World Values Survey (WVS). Beyond the conventional model-level evaluation method, We further propose two quantitative metrics, Perplexity-based Acceptence and Values Self-Consistency, to assess consensus outcomes. Experimental results indicate that our approach generates consensus of higher quality while ensuring more balanced compromise compared to baselines. Overall, it mitigates WEIRD bias by guiding agents toward convergence through fair and gradual negotiation steps.
zh
[AI-41] No-Regret Learning Under Adversarial Resource Constraints: A Spending Plan Is All You Need!
【速读】：该论文研究在资源约束下的在线决策问题，其中奖励和成本函数可能随时间以对抗性方式变化。其核心问题是，在这种动态且不确定的环境下，如何设计算法以实现次线性遗憾（sublinear regret）。解决方案的关键在于引入一个“支出计划”（spending plan）——即一个规定各轮预期资源使用量的序列，并基于此设计通用的（原始-对偶）方法，使得算法相对于遵循该支出计划的基线能够实现次线性遗憾。算法性能在支出计划能平衡预算分配时得到提升，同时论文还提出了鲁棒变体以应对支出计划严重失衡的最坏情况。
链接: https://arxiv.org/abs/2506.13244
作者: Francesco Emanuele Stradi,Matteo Castiglioni,Alberto Marchesi,Nicola Gatti,Christian Kroer
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:We study online decision making problems under resource constraints, where both reward and cost functions are drawn from distributions that may change adversarially over time. We focus on two canonical settings: (i) online resource allocation where rewards and costs are observed before action selection, and (ii) online learning with resource constraints where they are observed after action selection, under full feedback or bandit feedback. It is well known that achieving sublinear regret in these settings is impossible when reward and cost distributions may change arbitrarily over time. To address this challenge, we analyze a framework in which the learner is guided by a spending plan–a sequence prescribing expected resource usage across rounds. We design general (primal-)dual methods that achieve sublinear regret with respect to baselines that follow the spending plan. Crucially, the performance of our algorithms improves when the spending plan ensures a well-balanced distribution of the budget across rounds. We additionally provide a robust variant of our methods to handle worst-case scenarios where the spending plan is highly imbalanced. To conclude, we study the regret of our algorithms when competing against benchmarks that deviate from the prescribed spending plan.
zh
[AI-42] owards Explaining Monte-Carlo Tree Search by Using Its Enhancements
【速读】：该论文试图解决在可解释搜索（Explainable Search）领域中，如何在不依赖特定领域知识的情况下提供更高质量解释的问题。其解决方案的关键在于利用蒙特卡洛树搜索（Monte-Carlo Tree Search, MCTS）的增强技术，在保持知识无关性的同时获取更多数据并提升解释质量。论文分析了主流MCTS增强技术在具体可解释性类型上的表现，并提出了一个概念验证以展示其优势。
链接: https://arxiv.org/abs/2506.13223
作者: Jakub Kowalski,Mark H. M. Winands,Maksymilian Wiśniewski,Stanisław Reda,Anna Wilbik
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Typically, research on Explainable Artificial Intelligence (XAI) focuses on black-box models within the context of a general policy in a known, specific domain. This paper advocates for the need for knowledge-agnostic explainability applied to the subfield of XAI called Explainable Search, which focuses on explaining the choices made by intelligent search techniques. It proposes Monte-Carlo Tree Search (MCTS) enhancements as a solution to obtaining additional data and providing higher-quality explanations while remaining knowledge-free, and analyzes the most popular enhancements in terms of the specific types of explainability they introduce. So far, no other research has considered the explainability of MCTS enhancements. We present a proof-of-concept that demonstrates the advantages of utilizing enhancements.
zh
[AI-43] NeuroPhysNet: A FitzHugh-Nagumo-Based Physics-Informed Neural Network Framework for Electroencephalograph (EEG) Analysis and Motor Imagery Classification
【速读】：该论文旨在解决脑电图（Electroencephalography, EEG）分析中面临的噪声、非平稳性及个体间差异等挑战，这些问题限制了其在临床中的应用。论文提出的解决方案是构建一种物理信息神经网络（Physics-Informed Neural Network, PINN）框架——NeuroPhysNet，其关键在于将FitzHugh-Nagumo模型嵌入到神经网络中，从而引入神经动力学原理以约束预测并增强模型的鲁棒性。该方法在BCIC-IV-2a数据集上表现出优于传统方法的准确性和泛化能力，尤其在数据有限和跨被试场景下表现突出。
链接: https://arxiv.org/abs/2506.13222
作者: Zhenyu Xia,Xinlei Huang,Suvash C. Saha
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Electroencephalography (EEG) is extensively employed in medical diagnostics and brain-computer interface (BCI) applications due to its non-invasive nature and high temporal resolution. However, EEG analysis faces significant challenges, including noise, nonstationarity, and inter-subject variability, which hinder its clinical utility. Traditional neural networks often lack integration with biophysical knowledge, limiting their interpretability, robustness, and potential for medical translation. To address these limitations, this study introduces NeuroPhysNet, a novel Physics-Informed Neural Network (PINN) framework tailored for EEG signal analysis and motor imagery classification in medical contexts. NeuroPhysNet incorporates the FitzHugh-Nagumo model, embedding neurodynamical principles to constrain predictions and enhance model robustness. Evaluated on the BCIC-IV-2a dataset, the framework achieved superior accuracy and generalization compared to conventional methods, especially in data-limited and cross-subject scenarios, which are common in clinical settings. By effectively integrating biophysical insights with data-driven techniques, NeuroPhysNet not only advances BCI applications but also holds significant promise for enhancing the precision and reliability of clinical diagnostics, such as motor disorder assessments and neurorehabilitation planning.
zh
[AI-44] Screen Hijack: Visual Poisoning of VLM Agents in Mobile Environments
【速读】：该论文旨在解决基于视觉-语言模型（Vision-Language Models, VLMs）的移动代理在有限用户生成数据集上微调时面临的隐蔽后门攻击问题。其解决方案的关键在于提出一种名为GHOST的干净标签后门攻击方法，该方法仅修改部分训练样本的视觉输入而不改变其对应标签或指令，通过将污染样本的梯度与选定目标实例对齐，将后门相关特征嵌入到训练数据中，从而在推理阶段引入特定视觉触发器时使代理表现出受攻击者控制的行为。
链接: https://arxiv.org/abs/2506.13205
作者: Xuan Wang,Siyuan Liang,Zhe Liu,Yi Yu,Yuliang Lu,Xiaochun Cao,Ee-Chien Chang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 12 pages
Abstract:With the growing integration of vision-language models (VLMs), mobile agents are now widely used for tasks like UI automation and camera-based user assistance. These agents are often fine-tuned on limited user-generated datasets, leaving them vulnerable to covert threats during the training process. In this work we present GHOST, the first clean-label backdoor attack specifically designed for mobile agents built upon VLMs. Our method manipulates only the visual inputs of a portion of the training samples - without altering their corresponding labels or instructions - thereby injecting malicious behaviors into the model. Once fine-tuned with this tampered data, the agent will exhibit attacker-controlled responses when a specific visual trigger is introduced at inference time. The core of our approach lies in aligning the gradients of poisoned samples with those of a chosen target instance, embedding backdoor-relevant features into the poisoned training data. To maintain stealth and enhance robustness, we develop three realistic visual triggers: static visual patches, dynamic motion cues, and subtle low-opacity overlays. We evaluate our method across six real-world Android apps and three VLM architectures adapted for mobile use. Results show that our attack achieves high attack success rates (up to 94.67 percent) while maintaining high clean-task performance (FSR up to 95.85 percent). Additionally, ablation studies shed light on how various design choices affect the efficacy and concealment of the attack. Overall, this work is the first to expose critical security flaws in VLM-based mobile agents, highlighting their susceptibility to clean-label backdoor attacks and the urgent need for effective defense mechanisms in their training pipelines. Code and examples are available at: this https URL.
zh
[AI-45] From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLM s
【速读】：该论文旨在解决当前自动化程序修复（Automated Program Repair, APR）技术在修复实际世界中回归错误（regression bugs）方面的有效性尚未得到充分研究的问题。其解决方案的关键在于构建一个高质量的Java回归错误基准集RegMiner4APR，并基于此基准对传统APR工具和基于大语言模型（Large Language Models, LLMs）的APR方法进行实证评估。研究发现，传统APR工具无法修复任何错误，而LLM-based APR方法展现出潜力，尤其当引入导致错误的变更信息以增强上下文感知能力时，修复效果显著提升。
链接: https://arxiv.org/abs/2506.13182
作者: Anh Ho,Thanh Le-Cong,Bach Le,Christine Rizkallah
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:[…] Since then, various APR approaches, especially those leveraging the power of large language models (LLMs), have been rapidly developed to fix general software bugs. Unfortunately, the effectiveness of these advanced techniques in the context of regression bugs remains largely unexplored. This gap motivates the need for an empirical study evaluating the effectiveness of modern APR techniques in fixing real-world regression bugs. In this work, we conduct an empirical study of APR techniques on Java regression bugs. To facilitate our study, we introduce RegMiner4APR, a high-quality benchmark of Java regression bugs integrated into a framework designed to facilitate APR research. The current benchmark includes 99 regression bugs collected from 32 widely used real-world Java GitHub repositories. We begin by conducting an in-depth analysis of the benchmark, demonstrating its diversity and quality. Building on this foundation, we empirically evaluate the capabilities of APR to regression bugs by assessing both traditional APR tools and advanced LLM-based APR approaches. Our experimental results show that classical APR tools fail to repair any bugs, while LLM-based APR approaches exhibit promising potential. Motivated by these results, we investigate impact of incorporating bug-inducing change information into LLM-based APR approaches for fixing regression bugs. Our results highlight that this context-aware enhancement significantly improves the performance of LLM-based APR, yielding 1.8x more successful repairs compared to using LLM-based APR without such context. Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) Cite as: arXiv:2506.13182 [cs.SE] (or arXiv:2506.13182v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2506.13182 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[AI-46] Querying Large Automotive Software Models: Agent ic vs. Direct LLM Approaches
【速读】：该论文试图解决如何利用生成式 AI (Generative AI) 有效交互和分析复杂软件模型的问题，特别是在汽车和嵌入式领域中，大型软件模型难以通过传统方法进行全面理解和分析。解决方案的关键在于比较两种方法：直接提示（direct prompting）和基于代理的方法（agentic approach），后者结合了基于 LLM 的代理与通用文件访问工具。研究结果表明，尽管基于代理的方法在准确性上与直接提示相当，但其在令牌使用效率方面具有显著优势，使其成为处理大规模软件模型的可行且高效方案。
链接: https://arxiv.org/abs/2506.13171
作者: Lukasz Mazur,Nenad Petrovic,James Pontes Miranda,Ansgar Radermacher,Robert Rasche,Alois Knoll
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) offer new opportunities for interacting with complex software artifacts, such as software models, through natural language. They present especially promising benefits for large software models that are difficult to grasp in their entirety, making traditional interaction and analysis approaches challenging. This paper investigates two approaches for leveraging LLMs to answer questions over software models: direct prompting, where the whole software model is provided in the context, and an agentic approach combining LLM-based agents with general-purpose file access tools. We evaluate these approaches using an Ecore metamodel designed for timing analysis and software optimization in automotive and embedded domains. Our findings show that while the agentic approach achieves accuracy comparable to direct prompting, it is significantly more efficient in terms of token usage. This efficiency makes the agentic approach particularly suitable for the automotive industry, where the large size of software models makes direct prompting infeasible, establishing LLM agents as not just a practical alternative but the only viable solution. Notably, the evaluation was conducted using small LLMs, which are more feasible to be executed locally - an essential advantage for meeting strict requirements around privacy, intellectual property protection, and regulatory compliance. Future work will investigate software models in diverse formats, explore more complex agent architectures, and extend agentic workflows to support not only querying but also modification of software models.
zh
[AI-47] Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory
【速读】：该论文试图解决工业系统中比例-积分-微分（PID）控制器适应性不足的问题，旨在提升其在面对设定点变化和扰动时的自学习、优化与精细调节能力。解决方案的关键在于引入基于事件的动态博弈理论，通过博弈论学习算法实现控制器参数的动态调整，并结合自动边界检测机制以加快动作空间的最优初始化过程，从而提高控制性能并减少探索时间。
链接: https://arxiv.org/abs/2506.13164
作者: Steve Yuwono,Muhammad Uzair Rana,Dorothea Schwung,Andreas Schwung
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注:
Abstract:This paper presents a novel method for enhancing the adaptability of Proportional-Integral-Derivative (PID) controllers in industrial systems using event-based dynamic game theory, which enables the PID controllers to self-learn, optimize, and fine-tune themselves. In contrast to conventional self-learning approaches, our proposed framework offers an event-driven control strategy and game-theoretic learning algorithms. The players collaborate with the PID controllers to dynamically adjust their gains in response to set point changes and disturbances. We provide a theoretical analysis showing sound convergence guarantees for the game given suitable stability ranges of the PID controlled loop. We further introduce an automatic boundary detection mechanism, which helps the players to find an optimal initialization of action spaces and significantly reduces the exploration time. The efficacy of this novel methodology is validated through its implementation in the temperature control loop of a printing press machine. Eventually, the outcomes of the proposed intelligent self-tuning PID controllers are highly promising, particularly in terms of reducing overshoot and settling time.
zh
[AI-48] Machine Learning as Iterated Belief Change a la Darwiche and Pearl
【速读】：该论文试图解决二值人工神经网络（binary ANN）的静态与动态特性建模问题，特别是在其训练过程中如何有效表示和更新知识的问题。解决方案的关键在于将二值ANN的训练过程映射到信念变化理论中，特别是通过引入稳健的AGM风格信念变化操作，如字典序修订和适度收缩，以更准确地模拟二值ANN的训练动态，这些操作与Darwiche-Pearl框架相一致。
链接: https://arxiv.org/abs/2506.13157
作者: Theofanis Aravanis
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Artificial Neural Networks (ANNs) are powerful machine-learning models capable of capturing intricate non-linear relationships. They are widely used nowadays across numerous scientific and engineering domains, driving advancements in both research and real-world applications. In our recent work, we focused on the statics and dynamics of a particular subclass of ANNs, which we refer to as binary ANNs. A binary ANN is a feed-forward network in which both inputs and outputs are restricted to binary values, making it particularly suitable for a variety of practical use cases. Our previous study approached binary ANNs through the lens of belief-change theory, specifically the Alchourron, Gardenfors and Makinson (AGM) framework, yielding several key insights. Most notably, we demonstrated that the knowledge embodied in a binary ANN (expressed through its input-output behaviour) can be symbolically represented using a propositional logic language. Moreover, the process of modifying a belief set (through revision or contraction) was mapped onto a gradual transition through a series of intermediate belief sets. Analogously, the training of binary ANNs was conceptualized as a sequence of such belief-set transitions, which we showed can be formalized using full-meet AGM-style belief change. In the present article, we extend this line of investigation by addressing some critical limitations of our previous study. Specifically, we show that Dalal’s method for belief change naturally induces a structured, gradual evolution of states of belief. More importantly, given the known shortcomings of full-meet belief change, we demonstrate that the training dynamics of binary ANNs can be more effectively modelled using robust AGM-style change operations – namely, lexicographic revision and moderate contraction – that align with the Darwiche-Pearl framework for iterated belief change.
zh
[AI-49] AlphaEvolve: A coding agent for scientific and algorithmic discovery
【速读】：该论文试图解决在高度挑战性任务中提升当前最先进大语言模型（Large Language Models, LLMs）能力的问题，例如解决开放性科学问题或优化关键计算基础设施。其解决方案的关键在于提出AlphaEvolve，一个进化编码代理，通过自主的LLMs流水线对算法进行直接代码修改以实现改进。该方法采用进化策略，持续接收评估者的反馈，迭代优化算法，从而可能带来新的科学和实际发现。
链接: https://arxiv.org/abs/2506.13131
作者: Alexander Novikov,Ngân Vũ,Marvin Eisenberger,Emilien Dupont,Po-Sen Huang,Adam Zsolt Wagner,Sergey Shirobokov,Borislav Kozlovskii,Francisco J. R. Ruiz,Abbas Mehrabian,M. Pawan Kumar,Abigail See,Swarat Chaudhuri,George Holland,Alex Davies,Sebastian Nowozin,Pushmeet Kohli,Matej Balog
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:In this white paper, we present AlphaEvolve, an evolutionary coding agent that substantially enhances capabilities of state-of-the-art LLMs on highly challenging tasks such as tackling open scientific problems or optimizing critical pieces of computational infrastructure. AlphaEvolve orchestrates an autonomous pipeline of LLMs, whose task is to improve an algorithm by making direct changes to the code. Using an evolutionary approach, continuously receiving feedback from one or more evaluators, AlphaEvolve iteratively improves the algorithm, potentially leading to new scientific and practical discoveries. We demonstrate the broad applicability of this approach by applying it to a number of important computational problems. When applied to optimizing critical components of large-scale computational stacks at Google, AlphaEvolve developed a more efficient scheduling algorithm for data centers, found a functionally equivalent simplification in the circuit design of hardware accelerators, and accelerated the training of the LLM underpinning AlphaEvolve itself. Furthermore, AlphaEvolve discovered novel, provably correct algorithms that surpass state-of-the-art solutions on a spectrum of problems in mathematics and computer science, significantly expanding the scope of prior automated discovery methods (Romera-Paredes et al., 2023). Notably, AlphaEvolve developed a search algorithm that found a procedure to multiply two 4 \times 4 complex-valued matrices using 48 scalar multiplications; offering the first improvement, after 56 years, over Strassen’s algorithm in this setting. We believe AlphaEvolve and coding agents like it can have a significant impact in improving solutions of problems across many areas of science and computation.
zh
[AI-50] PhenoKG: Knowledge Graph-Driven Gene Discovery and Patient Insights from Phenotypes Alone
【速读】：该论文试图解决从患者表型中识别致病基因的问题，这是精准医学中的一个重大挑战，对遗传性疾病的诊断和治疗具有重要意义。其解决方案的关键在于提出一种基于图的新方法，通过整合罕见疾病知识图谱（KG），结合图神经网络和Transformer模型，实现了对致病基因的预测，无论是否提供候选基因列表。该方法在真实世界的数据集MyGene2上取得了显著的性能提升，验证了其有效性与通用性。
链接: https://arxiv.org/abs/2506.13119
作者: Kamilia Zaripova,Ege Özsoy,Nassir Navab,Azade Farshad
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Genomics (q-bio.GN); Quantitative Methods (q-bio.QM)
备注:
Abstract:Identifying causative genes from patient phenotypes remains a significant challenge in precision medicine, with important implications for the diagnosis and treatment of genetic disorders. We propose a novel graph-based approach for predicting causative genes from patient phenotypes, with or without an available list of candidate genes, by integrating a rare disease knowledge graph (KG). Our model, combining graph neural networks and transformers, achieves substantial improvements over the current state-of-the-art. On the real-world MyGene2 dataset, it attains a mean reciprocal rank (MRR) of 24.64% and nDCG@100 of 33.64%, surpassing the best baseline (SHEPHERD) at 19.02% MRR and 30.54% nDCG@100. We perform extensive ablation studies to validate the contribution of each model component. Notably, the approach generalizes to cases where only phenotypic data are available, addressing key challenges in clinical decision support when genomic information is incomplete.
zh
[AI-51] Dynamic Reinsurance Treaty Bidding via Multi-Agent Reinforcement Learning
【速读】：该论文旨在解决再保险契约投标过程中长期存在的效率低下问题，传统依赖经纪人的安排方式存在信息不对称和市场摩擦。其解决方案的关键在于提出一种基于多智能体强化学习（MARL）的框架，其中每个再保险公司由一个自适应智能体表示，这些智能体在竞争性、部分可观测的环境中迭代优化投标策略，从而提升风险转移效率。
链接: https://arxiv.org/abs/2506.13113
作者: Stella C. Dong,James R. Finlay
机构: 未知
类目: Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:This paper develops a novel multi-agent reinforcement learning (MARL) framework for reinsurance treaty bidding, addressing long-standing inefficiencies in traditional broker-mediated placement processes. We pose the core research question: Can autonomous, learning-based bidding systems improve risk transfer efficiency and outperform conventional pricing approaches in reinsurance markets? In our model, each reinsurer is represented by an adaptive agent that iteratively refines its bidding strategy within a competitive, partially observable environment. The simulation explicitly incorporates institutional frictions including broker intermediation, incumbent advantages, last-look privileges, and asymmetric access to underwriting information. Empirical analysis demonstrates that MARL agents achieve up to 15% higher underwriting profit, 20% lower tail risk (CVaR), and over 25% improvement in Sharpe ratios relative to actuarial and heuristic baselines. Sensitivity tests confirm robustness across hyperparameter settings, and stress testing reveals strong resilience under simulated catastrophe shocks and capital constraints. These findings suggest that MARL offers a viable path toward more transparent, adaptive, and risk-sensitive reinsurance markets. The proposed framework contributes to emerging literature at the intersection of algorithmic market design, strategic bidding, and AI-enabled financial decision-making. Subjects: Artificial Intelligence (cs.AI); General Economics (econ.GN) Cite as: arXiv:2506.13113 [cs.AI] (or arXiv:2506.13113v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2506.13113 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Stella Dong [view email] [v1] Mon, 16 Jun 2025 05:43:22 UTC (1,550 KB)
zh
[AI-52] Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion Policy
【速读】：该论文试图解决强化学习（Reinforcement Learning, RL）在面对数据分布变化时适应能力有限的问题，特别是在使用深度神经网络作为决策者或策略的RL系统中，长期在固定环境训练后容易出现过拟合。解决方案的关键在于提出一种新的算法——高斯过程扩散策略（Gaussian Process Diffusion Policy, GPDP），该算法将扩散模型与高斯过程回归（GPR）相结合，利用GPR引导扩散模型生成最大化已学习Q函数的动作，从而实现类似RL中的策略改进。GPR基于核函数的特性提升了策略在测试时分布偏移下的探索效率，增加了发现新行为的可能性并缓解过拟合问题。
链接: https://arxiv.org/abs/2506.13111
作者: Amornyos Horprasert,Esa Apriaskar,Xingyu Liu,Lanlan Su,Lyudmila S. Mihaylova
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 5 pages, 1 figure, Accepted to IEEE Statistical Signal Processing (SSP) Workshop 2025
Abstract:One of the key challenges that Reinforcement Learning (RL) faces is its limited capability to adapt to a change of data distribution caused by uncertainties. This challenge arises especially in RL systems using deep neural networks as decision makers or policies, which are prone to overfitting after prolonged training on fixed environments. To address this challenge, this paper proposes Gaussian Process Diffusion Policy (GPDP), a new algorithm that integrates diffusion models and Gaussian Process Regression (GPR) to represent the policy. GPR guides diffusion models to generate actions that maximize learned Q-function, resembling the policy improvement in RL. Furthermore, the kernel-based nature of GPR enhances the policy’s exploration efficiency under distribution shifts at test time, increasing the chance of discovering new behaviors and mitigating overfitting. Simulation results on the Walker2d benchmark show that our approach outperforms state-of-the-art algorithms under distribution shift condition by achieving around 67.74% to 123.18% improvement in the RL’s objective function while maintaining comparable performance under normal conditions.
zh
[AI-53] Dynamic Graph Condensation
【速读】：该论文试图解决动态图（Dynamic Graph）在深度图学习中的数据效率问题，具体表现为数据量增加、时空冗余高以及对昂贵的动态图神经网络（DGNN）的依赖。解决方案的关键在于提出一种动态图压缩（Dynamic Graph Condensation, DGC）方法，即DyGC框架，该框架通过将真实动态图压缩为紧凑版本，在保留其固有时空特征的同时显著降低图规模。DyGC的核心创新包括引入一种基于脉冲神经元动态行为的结构生成机制以模拟现实的演化结构，以及设计一种定制的分布匹配方法，通过构建语义丰富的状态演化场并进行细粒度时空状态对齐来优化压缩图。
链接: https://arxiv.org/abs/2506.13099
作者: Dong Chen,Shuai Zheng,Yeyu Yan,Muhao Xu,Zhenfeng Zhu,Yao Zhao,Kunlun He
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent research on deep graph learning has shifted from static to dynamic graphs, motivated by the evolving behaviors observed in complex real-world systems. However, the temporal extension in dynamic graphs poses significant data efficiency challenges, including increased data volume, high spatiotemporal redundancy, and reliance on costly dynamic graph neural networks (DGNNs). To alleviate the concerns, we pioneer the study of dynamic graph condensation (DGC), which aims to substantially reduce the scale of dynamic graphs for data-efficient DGNN training. Accordingly, we propose DyGC, a novel framework that condenses the real dynamic graph into a compact version while faithfully preserving the inherent spatiotemporal characteristics. Specifically, to endow synthetic graphs with realistic evolving structures, a novel spiking structure generation mechanism is introduced. It draws on the dynamic behavior of spiking neurons to model temporally-aware connectivity in dynamic graphs. Given the tightly coupled spatiotemporal dependencies, DyGC proposes a tailored distribution matching approach that first constructs a semantically rich state evolving field for dynamic graphs, and then performs fine-grained spatiotemporal state alignment to guide the optimization of the condensed graph. Experiments across multiple dynamic graph datasets and representative DGNN architectures demonstrate the effectiveness of DyGC. Notably, our method retains up to 96.2% DGNN performance with only 0.5% of the original graph size, and achieves up to 1846 times training speedup.
zh
[AI-54] A Memetic Walrus Algorithm with Expert-guided Strategy for Adaptive Curriculum Sequencing
【速读】：该论文旨在解决自适应课程序列（Adaptive Curriculum Sequencing, ACS）在个性化在线学习中的优化问题，特别是如何在复杂教育约束下实现稳定且高效的优化。其解决方案的核心是提出一种改进的Memetic Walrus Optimizer (MWO)，通过三个关键创新提升优化性能：基于专家指导与老化机制的策略以增强跳出局部最优的能力；动态平衡探索与开发的自适应控制信号框架；以及用于生成具有教育意义序列的三层优先级机制。
链接: https://arxiv.org/abs/2506.13092
作者: Qionghao Huang,Lingnuo Lu,Xuemei Wu,Fan Jiang,Xizhe Wang,Xun Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: The article has been accepted and published by Human-centric Computing and Information Sciences
Abstract:Adaptive Curriculum Sequencing (ACS) is essential for personalized online learning, yet current approaches struggle to balance complex educational constraints and maintain optimization stability. This paper proposes a Memetic Walrus Optimizer (MWO) that enhances optimization performance through three key innovations: (1) an expert-guided strategy with aging mechanism that improves escape from local optima; (2) an adaptive control signal framework that dynamically balances exploration and exploitation; and (3) a three-tier priority mechanism for generating educationally meaningful sequences. We formulate ACS as a multi-objective optimization problem considering concept coverage, time constraints, and learning style compatibility. Experiments on the OULAD dataset demonstrate MWO’s superior performance, achieving 95.3% difficulty progression rate (compared to 87.2% in baseline methods) and significantly better convergence stability (standard deviation of 18.02 versus 28.29-696.97 in competing algorithms). Additional validation on benchmark functions confirms MWO’s robust optimization capability across diverse scenarios. The results demonstrate MWO’s effectiveness in generating personalized learning sequences while maintaining computational efficiency and solution quality.
zh
[AI-55] IKDiffuser: Fast and Diverse Inverse Kinematics Solution Generation for Multi-arm Robotic Systems
【速读】：该论文旨在解决多臂机器人系统中逆运动学（Inverse Kinematics, IK）求解的挑战，这些问题包括复杂的自碰撞、耦合关节以及高维冗余，导致传统IK求解器速度慢、易失败且解的多样性不足。论文提出的解决方案是IKDiffuser，其关键在于采用基于扩散模型的方法，学习配置空间中的关节分布，捕捉复杂的依赖关系，并实现对不同结构的多臂机器人系统的无缝泛化。此外，IKDiffuser在推理过程中可融入额外目标而无需重新训练，提升了任务特定需求的灵活性和适应性。
链接: https://arxiv.org/abs/2506.13087
作者: Zeyu Zhang,Ziyuan Jiao
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: under review
Abstract:Solving Inverse Kinematics (IK) problems is fundamental to robotics, but has primarily been successful with single serial manipulators. For multi-arm robotic systems, IK remains challenging due to complex self-collisions, coupled joints, and high-dimensional redundancy. These complexities make traditional IK solvers slow, prone to failure, and lacking in solution diversity. In this paper, we present IKDiffuser, a diffusion-based model designed for fast and diverse IK solution generation for multi-arm robotic systems. IKDiffuser learns the joint distribution over the configuration space, capturing complex dependencies and enabling seamless generalization to multi-arm robotic systems of different structures. In addition, IKDiffuser can incorporate additional objectives during inference without retraining, offering versatility and adaptability for task-specific requirements. In experiments on 6 different multi-arm systems, the proposed IKDiffuser achieves superior solution accuracy, precision, diversity, and computational efficiency compared to existing solvers. The proposed IKDiffuser framework offers a scalable, unified approach to solving multi-arm IK problems, facilitating the potential of multi-arm robotic systems in real-time manipulation tasks.
zh
[AI-56] Discerning What Matters: A Multi-Dimensional Assessment of Moral Competence in LLM s
【速读】：该论文试图解决当前评估大型语言模型（Large Language Models, LLMs）道德能力（moral competence）方法中存在的不足，具体表现为过度依赖预设的道德情境、侧重于道德判断预测而非道德推理过程，以及缺乏对模型识别信息缺失能力的测试。其解决方案的关键在于引入一种基于道德技能哲学研究的新评估方法，该方法超越简单的判断对比，从五个维度评估道德能力：识别道德相关特征、权衡其重要性、为这些特征分配道德理由、综合得出一致的道德判断，以及识别信息缺口。通过两个实验对比了六种主流LLMs与非专家人类和专业哲学家的表现，揭示了现有评估可能高估LLMs的道德推理能力，因其忽略了从噪声信息中辨别道德相关性的关键能力。
链接: https://arxiv.org/abs/2506.13082
作者: Daniel Kilov,Caroline Hendy,Secil Yanik Guyot,Aaron J. Snoswell,Seth Lazar
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Moral competence is the ability to act in accordance with moral principles. As large language models (LLMs) are increasingly deployed in situations demanding moral competence, there is increasing interest in evaluating this ability empirically. We review existing literature and identify three significant shortcoming: (i) Over-reliance on prepackaged moral scenarios with explicitly highlighted moral features; (ii) Focus on verdict prediction rather than moral reasoning; and (iii) Inadequate testing of models’ (in)ability to recognize when additional information is needed. Grounded in philosophical research on moral skill, we then introduce a novel method for assessing moral competence in LLMs. Our approach moves beyond simple verdict comparisons to evaluate five dimensions of moral competence: identifying morally relevant features, weighting their importance, assigning moral reasons to these features, synthesizing coherent moral judgments, and recognizing information gaps. We conduct two experiments comparing six leading LLMs against non-expert humans and professional philosophers. In our first experiment using ethical vignettes standard to existing work, LLMs generally outperformed non-expert humans across multiple dimensions of moral reasoning. However, our second experiment, featuring novel scenarios designed to test moral sensitivity by embedding relevant features among irrelevant details, revealed a striking reversal: several LLMs performed significantly worse than humans. Our findings suggest that current evaluations may substantially overestimate LLMs’ moral reasoning capabilities by eliminating the task of discerning moral relevance from noisy information, which we take to be a prerequisite for genuine moral skill. This work provides a more nuanced framework for assessing AI moral competence and highlights important directions for improving moral competence in advanced AI systems.
zh
[AI-57] Rethinking Explainability in the Era of Multimodal AI
【速读】：该论文试图解决当前多模态人工智能系统（multimodal AI systems）在可解释性方面的不足，即现有解释技术大多为单模态（unimodal），无法有效捕捉不同模态之间的交互影响，从而导致对模型决策的误解。论文提出的解决方案关键在于推动多模态解释方法的发展，其核心原则包括：基于模态的格兰杰式模态影响（通过控制消融实验量化移除某一模态对其他模态解释的影响）、协同忠实性（解释需反映模态组合后的模型预测能力）以及统一稳定性（解释在跨模态微小扰动下保持一致）。这一转变有助于揭示隐藏的捷径、减少模态偏差、提升模型可靠性及高风险场景下的安全性。
链接: https://arxiv.org/abs/2506.13060
作者: Chirag Agarwal
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:While multimodal AI systems (models jointly trained on heterogeneous data types such as text, time series, graphs, and images) have become ubiquitous and achieved remarkable performance across high-stakes applications, transparent and accurate explanation algorithms are crucial for their safe deployment and ensure user trust. However, most existing explainability techniques remain unimodal, generating modality-specific feature attributions, concepts, or circuit traces in isolation and thus failing to capture cross-modal interactions. This paper argues that such unimodal explanations systematically misrepresent and fail to capture the cross-modal influence that drives multimodal model decisions, and the community should stop relying on them for interpreting multimodal models. To support our position, we outline key principles for multimodal explanations grounded in modality: Granger-style modality influence (controlled ablations to quantify how removing one modality changes the explanation for another), Synergistic faithfulness (explanations capture the model’s predictive power when modalities are combined), and Unified stability (explanations remain consistent under small, cross-modal perturbations). This targeted shift to multimodal explanations will help the community uncover hidden shortcuts, mitigate modality bias, improve model reliability, and enhance safety in high-stakes settings where incomplete explanations can have serious consequences.
zh
[AI-58] MAGIC: Multi-Agent Argumentation and Grammar Integrated Critiquer
【速读】：该论文试图解决自动化作文评分（Automated Essay Scoring, AES）和自动作文反馈（Automatic Essay Feedback, AEF）系统中普遍存在的问题，即现有系统过于关注数值评分的准确性，而忽视了反馈的质量。其解决方案的关键在于提出一种多智能体论证与语法集成评阅框架（Multi-Agent Argumentation and Grammar Integrated Critiquer, MAGIC），通过多个专业化的智能体分别评估写作的不同方面，从而同时预测整体分数并生成符合评分标准的详细反馈。
链接: https://arxiv.org/abs/2506.13037
作者: Joaquin Jordan,Xavier Yin,Melissa Fabros,Gireeja Ranade,Narges Norouzi
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Automated Essay Scoring (AES) and Automatic Essay Feedback (AEF) systems aim to reduce the workload of human raters in educational assessment. However, most existing systems prioritize numeric scoring accuracy over the quality of feedback. This paper presents Multi-Agent Argumentation and Grammar Integrated Critiquer (MAGIC), a framework that uses multiple specialized agents to evaluate distinct writing aspects to both predict holistic scores and produce detailed, rubric-aligned feedback. To support evaluation, we curated a novel dataset of past GRE practice test essays with expert-evaluated scores and feedback. MAGIC outperforms baseline models in both essay scoring , as measured by Quadratic Weighted Kappa (QWK). We find that despite the improvement in QWK, there are opportunities for future work in aligning LLM-generated feedback to human preferences.
zh
[AI-59] NaSh: Guardrails for an LLM -Powered Natural Language Shell
【速读】：该论文试图解决如何设计一种基于大语言模型（Large Language Model, LLM）的命令行外壳（shell），使其不同于现有的外壳。由于LLM可能产生意外或难以解释的输出，论文提出解决方案的关键在于为自然语言外壳提供保护机制（guardrails），以帮助用户从此类错误中恢复。通过设计一个新的外壳系统NaSh，作者具体化了这一思路，并指出了该领域仍存在的开放性问题及未来研究方向。
链接: https://arxiv.org/abs/2506.13028
作者: Bimal Raj Gyawali,Saikrishna Achalla,Konstantinos Kallas,Sam Kumar
机构: 未知
类目: Operating Systems (cs.OS); Artificial Intelligence (cs.AI)
备注: 7 pages, 3 figures
Abstract:We explore how a shell that uses an LLM to accept natural language input might be designed differently from the shells of today. As LLMs may produce unintended or unexplainable outputs, we argue that a natural language shell should provide guardrails that empower users to recover from such errors. We concretize some ideas for doing so by designing a new shell called NaSh, identify remaining open problems in this space, and discuss research directions to address them.
zh
[AI-60] A Practical Guide for Evaluating LLM s and LLM -Reliant Systems ACL
【速读】：该论文试图解决在现实场景中对依赖大型语言模型（Large Language Models, LLMs）的系统进行有效评估所面临的独特挑战，这些问题无法通过合成基准和现有文献中常见的事实标准指标得到充分解决。论文提出的解决方案的关键在于构建一个实用的评估框架，该框架强调主动筛选具有代表性的数据集、选择有意义的评估指标，并采用与实际开发和部署流程紧密结合的评估方法，以确保系统满足现实需求和用户期望。
链接: https://arxiv.org/abs/2506.13023
作者: Ethan M. Rudd,Christopher Andrews,Philip Tully
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Pre-print of a manuscript submitted to Transactions of the Association for Computational Linguistics (TACL)
Abstract:Recent advances in generative AI have led to remarkable interest in using systems that rely on large language models (LLMs) for practical applications. However, meaningful evaluation of these systems in real-world scenarios comes with a distinct set of challenges, which are not well-addressed by synthetic benchmarks and de-facto metrics that are often seen in the literature. We present a practical evaluation framework which outlines how to proactively curate representative datasets, select meaningful evaluation metrics, and employ meaningful evaluation methodologies that integrate well with practical development and deployment of LLM-reliant systems that must adhere to real-world requirements and meet user-facing needs.
zh
[AI-61] Symmetry in Neural Network Parameter Spaces
【速读】：该论文试图解决深度学习模型中参数空间对称性对优化、泛化和模型复杂性的影响问题，其解决方案的关键在于揭示参数空间中的对称性如何塑造损失曲面并约束学习动态，从而为理解深度学习提供新的理论视角。
链接: https://arxiv.org/abs/2506.13018
作者: Bo Zhao,Robin Walters,Rose Yu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 29 pages, 9 figures
Abstract:Modern deep learning models are highly overparameterized, resulting in large sets of parameter configurations that yield the same outputs. A significant portion of this redundancy is explained by symmetries in the parameter space–transformations that leave the network function unchanged. These symmetries shape the loss landscape and constrain learning dynamics, offering a new lens for understanding optimization, generalization, and model complexity that complements existing theory of deep learning. This survey provides an overview of parameter space symmetry. We summarize existing literature, uncover connections between symmetry and learning theory, and identify gaps and opportunities in this emerging field.
zh
[AI-62] Geometric Embedding Alignment via Curvature Matching in Transfer Learning
【速读】：该论文试图解决多模型在迁移学习中如何有效整合知识以提升目标任务性能的问题，其核心挑战在于如何在不同模型的潜在空间中建立一致的几何结构。解决方案的关键在于利用微分几何，特别是黎曼几何中的里奇曲率（Ricci curvature）对齐机制，构建一个称为GEAR（Geometric Embedding Alignment via cuRvature matching in transfer learning）的统一框架，通过调整各模型潜在空间的曲率特性，实现跨模型的知识聚合与几何表示的一致性。
链接: https://arxiv.org/abs/2506.13015
作者: Sung Moon Ko,Jaewan Lee,Sumin Lee,Soorin Yim,Kyunghoon Bae,Sehui Han
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13+19 pages, 7 figures, 8 tables, 1 pseudo code
Abstract:Geometrical interpretations of deep learning models offer insightful perspectives into their underlying mathematical structures. In this work, we introduce a novel approach that leverages differential geometry, particularly concepts from Riemannian geometry, to integrate multiple models into a unified transfer learning framework. By aligning the Ricci curvature of latent space of individual models, we construct an interrelated architecture, namely Geometric Embedding Alignment via cuRvature matching in transfer learning (GEAR), which ensures comprehensive geometric representation across datapoints. This framework enables the effective aggregation of knowledge from diverse sources, thereby improving performance on target tasks. We evaluate our model on 23 molecular task pairs sourced from various domains and demonstrate significant performance gains over existing benchmark model under both random (14.4%) and scaffold (8.3%) data splits.
zh
[AI-63] Distributional Training Data Attribution
【速读】：该论文试图解决传统训练数据归属算法未能严格考虑深度学习模型训练中随机性的问题，即由于初始化和批次的随机性，使用相同数据集训练可能产生不同的模型。论文提出的解决方案是引入分布训练数据归属（distributional training data attribution, d-TDA），其关键在于预测模型输出分布（在多次训练运行中）如何依赖于数据集，从而更全面地理解数据对模型的影响。
链接: https://arxiv.org/abs/2506.12965
作者: Bruno Mlodozeniec,Isaac Reid,Sam Power,David Krueger,Murat Erdogdu,Richard E. Turner,Roger Grosse
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Randomness is an unavoidable part of training deep learning models, yet something that traditional training data attribution algorithms fail to rigorously account for. They ignore the fact that, due to stochasticity in the initialisation and batching, training on the same dataset can yield different models. In this paper, we address this shortcoming through introducing distributional training data attribution (d-TDA), the goal of which is to predict how the distribution of model outputs (over training runs) depends upon the dataset. We demonstrate the practical significance of d-TDA in experiments, e.g. by identifying training examples that drastically change the distribution of some target measurement without necessarily changing the mean. Intriguingly, we also find that influence functions (IFs), a popular but poorly-understood data attribution tool, emerge naturally from our distributional framework as the limit to unrolled differentiation; without requiring restrictive convexity assumptions. This provides a new mathematical motivation for their efficacy in deep learning, and helps to characterise their limitations.
zh
[AI-64] Reasoning Model Unlearning: Forgetting Traces Not Just Answers While Preserving Reasoning Skills
【速读】：该论文试图解决在大型推理模型（Large Reasoning Models, LRMs）中由于链式思维（Chain-of-Thought, CoT）生成所带来的安全风险问题，特别是如何有效消除模型中敏感、有害或不需要的数据或知识的影响。传统机器遗忘算法在非推理模型上的效果不适用于LRMs，因为即使最终答案被删除，敏感信息仍可能存在于中间推理步骤中。解决方案的关键在于提出一种新的方法——面向推理的表示误导遗忘（Reasoning-aware Representation Misdirection for Unlearning, R²MU），该方法能够有效抑制敏感推理轨迹并防止相关最终答案的生成，同时保持模型的推理能力。
链接: https://arxiv.org/abs/2506.12963
作者: Changsheng Wang,Chongyu Fan,Yihua Zhang,Jinghan Jia,Dennis Wei,Parikshit Ram,Nathalie Baracaldo,Sijia Liu
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in large reasoning models (LRMs) have enabled strong chain-of-thought (CoT) generation through test-time computation. While these multi-step reasoning capabilities represent a major milestone in language model performance, they also introduce new safety risks. In this work, we present the first systematic study to revisit the problem of machine unlearning in the context of LRMs. Machine unlearning refers to the process of removing the influence of sensitive, harmful, or undesired data or knowledge from a trained model without full retraining. We show that conventional unlearning algorithms, originally designed for non-reasoning models, are inadequate for LRMs. In particular, even when final answers are successfully erased, sensitive information often persists within the intermediate reasoning steps, i.e., CoT trajectories. To address this challenge, we extend conventional unlearning and propose Reasoning-aware Representation Misdirection for Unlearning ( R^2MU ), a novel method that effectively suppresses sensitive reasoning traces and prevents the generation of associated final answers, while preserving the model’s reasoning ability. Our experiments demonstrate that R^2MU significantly reduces sensitive information leakage within reasoning traces and achieves strong performance across both safety and reasoning benchmarks, evaluated on state-of-the-art models such as DeepSeek-R1-Distill-LLaMA-8B and DeepSeek-R1-Distill-Qwen-14B.
zh
[AI-65] Constitutive Components for Human-Like Autonomous Artificial Intelligence
【速读】：该论文试图解决如何构建具备类似人类自主行为能力的人工实体的问题，其核心在于明确实现这种自主性的功能需求并建立相应的功能层次结构。解决方案的关键在于提出一个三层次的功能体系：核心功能（Core Functions）实现与外部世界的交互，整合评估功能（Integrative Evaluation Function）基于感知与记忆选择行动，自我修改功能（Self Modification Function）动态调整行为原则与内部组件。该框架为自主性提供了理论基础，并提出了包含反应性、弱自主性和强自主性的分步模型，旨在推动通用智能的发展并探讨其应用与伦理问题。
链接: https://arxiv.org/abs/2506.12952
作者: Kazunori D Yamada
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:This study is the first to clearly identify the functions required to construct artificial entities capable of behaving autonomously like humans, and organizes them into a three-layer functional hierarchy. Specifically, it defines three levels: Core Functions, which enable interaction with the external world; the Integrative Evaluation Function, which selects actions based on perception and memory; and the Self Modification Function, which dynamically reconfigures behavioral principles and internal components. Based on this structure, the study proposes a stepwise model of autonomy comprising reactive, weak autonomous, and strong autonomous levels, and discusses its underlying design principles and developmental aspects. It also explores the relationship between these functions and existing artificial intelligence design methods, addressing their potential as a foundation for general intelligence and considering future applications and ethical implications. By offering a theoretical framework that is independent of specific technical methods, this work contributes to a deeper understanding of autonomy and provides a foundation for designing future artificial entities with strong autonomy.
zh
[AI-66] Scaling Test-time Compute for LLM Agents
【速读】：该论文试图解决如何通过扩展测试时计算（test-time compute）来提升语言代理（language agents）的推理与任务执行效果的问题。其解决方案的关键在于系统性地探索和评估多种测试时扩展策略，包括并行采样算法、序列修订策略、验证器与结果合并方法以及多样化执行策略，并通过实验分析不同设计对代理性能的影响，从而确定有效的优化方向。
链接: https://arxiv.org/abs/2506.12928
作者: King Zhu,Hanhao Li,Siwei Wu,Tianshun Xing,Dehua Ma,Xiangru Tang,Minghao Liu,Jian Yang,Jiaheng Liu,Yuchen Eleanor Jiang,Changwang Zhang,Chenghua Lin,Jun Wang,Ge Zhang,Wangchunshu Zhou
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Scaling test time compute has shown remarkable success in improving the reasoning abilities of large language models (LLMs). In this work, we conduct the first systematic exploration of applying test-time scaling methods to language agents and investigate the extent to which it improves their effectiveness. Specifically, we explore different test-time scaling strategies, including: (1) parallel sampling algorithms; (2) sequential revision strategies; (3) verifiers and merging methods; (4)strategies for diversifying this http URL carefully analyze and ablate the impact of different design strategies on applying test-time scaling on language agents, and have follow findings: 1. Scaling test time compute could improve the performance of agents. 2. Knowing when to reflect is important for agents. 3. Among different verification and result merging approaches, the list-wise method performs best. 4. Increasing diversified rollouts exerts a positive effect on the agent’s task performance.
zh
[AI-67] Logit Dynamics in Softmax Policy Gradient Methods
【速读】：该论文旨在解决软最大策略梯度方法中对数几率更新向量的L2范数特性及其与策略概率和碰撞概率的关系问题。其解决方案的关键在于推导出精确的公式：∥Δz∥2∝1−2Pc+C(P)，该公式表明更新幅度由所选动作的概率（Pc）和策略的碰撞概率（C(P)，即浓度的度量，与熵呈反比）共同决定，揭示了策略置信度自动调节学习强度的内在自调节机制，为这类方法的稳定性和收敛性提供了基础性见解。
链接: https://arxiv.org/abs/2506.12912
作者: Yingru Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 7 pages
Abstract:We analyzes the logit dynamics of softmax policy gradient methods. We derive the exact formula for the L2 norm of the logit update vector: |\Delta \mathbfz|_2 \propto \sqrt1-2P_c + C§ This equation demonstrates that update magnitudes are determined by the chosen action’s probability ( P_c ) and the policy’s collision probability ( C§ ), a measure of concentration inversely related to entropy. Our analysis reveals an inherent self-regulation mechanism where learning vigor is automatically modulated by policy confidence, providing a foundational insight into the stability and convergence of these methods.
zh
[AI-68] Constraint-Guided Prediction Refinement via Deterministic Diffusion Trajectories
【速读】：该论文旨在解决机器学习任务中输出需满足硬约束的问题，例如物理守恒定律、图结构中的依赖关系或表格数据中的列级关系。现有方法要么依赖于领域特定的架构和损失函数，要么对约束空间做出强假设，限制了其在非凸或非线性约束中的适用性。论文提出了一种通用的约束感知精炼框架，其关键在于利用去噪扩散隐式模型（DDIMs）进行迭代优化，通过由学习到的先验引导的确定性扩散轨迹，并结合约束梯度修正来逐步改进初始预测，从而有效处理广泛的非凸和非线性等式约束。
链接: https://arxiv.org/abs/2506.12911
作者: Pantelis Dogoulis,Fabien Bernier,Félix Fourreau,Karim Tit,Maxime Cordy
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Many real-world machine learning tasks require outputs that satisfy hard constraints, such as physical conservation laws, structured dependencies in graphs, or column-level relationships in tabular data. Existing approaches rely either on domain-specific architectures and losses or on strong assumptions on the constraint space, restricting their applicability to linear or convex constraints. We propose a general-purpose framework for constraint-aware refinement that leverages denoising diffusion implicit models (DDIMs). Starting from a coarse prediction, our method iteratively refines it through a deterministic diffusion trajectory guided by a learned prior and augmented by constraint gradient corrections. The approach accommodates a wide class of non-convex and nonlinear equality constraints and can be applied post hoc to any base model. We demonstrate the method in two representative domains: constrained adversarial attack generation on tabular data with column-level dependencies and in AC power flow prediction under Kirchhoff’s laws. Across both settings, our diffusion-guided refinement improves both constraint satisfaction and performance while remaining lightweight and model-agnostic.
zh
[AI-69] KCLNet: Physics-Informed Power Flow Prediction via Constraints Projections
【速读】：该论文旨在解决现代电力系统中快速、可扩展且物理上合理的潮流预测问题，以确保电网的安全与高效运行。传统数值方法虽然稳健，但在动态或事故条件下维持物理保真度需要大量计算；而现有人工智能方法虽提升了计算速度，却难以在实际事故场景中遵守基本物理定律，导致预测结果物理上不一致。论文提出的解决方案是KCLNet，其关键在于通过超平面投影将基尔霍夫电流定律（Kirchhoff’s Current Law, KCL）作为硬约束嵌入图神经网络中，从而在保持竞争性预测精度的同时确保KCL零违规，实现可靠且物理一致的潮流预测。
链接: https://arxiv.org/abs/2506.12902
作者: Pantelis Dogoulis,Karim Tit,Maxime Cordy
机构: 未知
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:In the modern context of power systems, rapid, scalable, and physically plausible power flow predictions are essential for ensuring the grid’s safe and efficient operation. While traditional numerical methods have proven robust, they require extensive computation to maintain physical fidelity under dynamic or contingency conditions. In contrast, recent advancements in artificial intelligence (AI) have significantly improved computational speed; however, they often fail to enforce fundamental physical laws during real-world contingencies, resulting in physically implausible predictions. In this work, we introduce KCLNet, a physics-informed graph neural network that incorporates Kirchhoff’s Current Law as a hard constraint via hyperplane projections. KCLNet attains competitive prediction accuracy while ensuring zero KCL violations, thereby delivering reliable and physically consistent power flow predictions critical to secure the operation of modern smart grids.
zh
[AI-70] Homeostatic Coupling for Prosocial Behavior
【速读】：该论文试图解决如何在自主代理中引发亲社会行为的问题，其核心在于探究基于稳态自我调节（homeostatic self-regulation）的代理如何通过类似共情的机制产生帮助他人的行为。解决方案的关键在于引入稳态耦合（homeostatic coupling），即代理之间的内部稳态状态相互影响，从而使得代理的福祉受到同伴痛苦的影响，进而促使亲社会行为的出现。此外，研究还表明，共情可以通过学习实现，代理能够“解码”同伴的外部情感状态以推断其内部稳态状态，这一过程依赖于代理对自身情感生成函数的参考。
链接: https://arxiv.org/abs/2506.12894
作者: Naoto Yoshida,Kingson Man
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Preprint. Unver review
Abstract:When regarding the suffering of others, we often experience personal distress and feel compelled to help\footnotePreprint. Under review… Inspired by living systems, we investigate the emergence of prosocial behavior among autonomous agents that are motivated by homeostatic self-regulation. We perform multi-agent reinforcement learning, treating each agent as a vulnerable homeostat charged with maintaining its own well-being. We introduce an empathy-like mechanism to share homeostatic states between agents: an agent can either \emphobserve their partner’s internal state (\bf cognitive empathy) or the agent’s internal state can be \emphdirectly coupled to that of their partner (\bf affective empathy). In three simple multi-agent environments, we show that prosocial behavior arises only under homeostatic coupling - when the distress of a partner can affect one’s own well-being. Additionally, we show that empathy can be learned: agents can decode" their partner's external emotive states to infer the partner's internal homeostatic states. Assuming some level of physiological similarity, agents reference their own emotion-generation functions to invert the mapping from outward display to internal state. Overall, we demonstrate the emergence of prosocial behavior when homeostatic agents learn to read" the emotions of others and then to empathize, or feel as they feel.
zh
[AI-71] Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence
【速读】：该论文试图解决当前基于神经网络的AI系统在结构组织和学习过程中的固有局限性，以及缺乏统一理论框架的问题。其解决方案的关键在于借鉴进化发育生物学（Evolutionary Developmental Biology, EDB）中的适应性原则，构建一个以生物第一性原理为基础的统一概念框架，从而超越简单的灵感借鉴，实现对AI设计哲学的革新。通过引入调控连接、体细胞变异与选择以及弱关联等发育机制，该方案在有机地解决现代机器学习多个主要缺陷的同时，也为理解这些机制在生物进化中的作用提供了更深层次的见解。
链接: https://arxiv.org/abs/2506.12891
作者: Zeki Doruk Erden,Boi Faltings
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Artificial intelligence (AI), propelled by advancements in machine learning, has made significant strides in solving complex tasks. However, the current neural network-based paradigm, while effective, is heavily constrained by inherent limitations, primarily a lack of structural organization and a progression of learning that displays undesirable properties. As AI research progresses without a unifying framework, it either tries to patch weaknesses heuristically or draws loosely from biological mechanisms without strong theoretical foundations. Meanwhile, the recent paradigm shift in evolutionary understanding – driven primarily by evolutionary developmental biology (EDB) – has been largely overlooked in AI literature, despite a striking analogy between the Modern Synthesis and contemporary machine learning, evident in their shared assumptions, approaches, and limitations upon careful analysis. Consequently, the principles of adaptation from EDB that reshaped our understanding of the evolutionary process can also form the foundation of a unifying conceptual framework for the next design philosophy in AI, going beyond mere inspiration and grounded firmly in biology’s first principles. This article provides a detailed overview of the analogy between the Modern Synthesis and modern machine learning, and outlines the core principles of a new AI design paradigm based on insights from EDB. To exemplify our analysis, we also present two learning system designs grounded in specific developmental principles – regulatory connections, somatic variation and selection, and weak linkage – that resolve multiple major limitations of contemporary machine learning in an organic manner, while also providing deeper insights into the role of these mechanisms in biological evolution.
zh
[AI-72] Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation
【速读】：该论文试图解决生成式 AI (Generative AI) 设计工具在实际应用中难以有效集成到设计师工作流程中的问题，主要挑战包括设计师需要在设计初期明确所有设计标准作为独立参数（意图构建）以及由于认知卸载导致的参与度降低，进而引发问题探索不足、定义不明确和结果评估能力受限。解决方案的关键在于引入元认知支持代理（metacognitive support agents），通过增强设计师对生成式 AI 的反思性使用，提升设计的可行性和质量。
链接: https://arxiv.org/abs/2506.12879
作者: Frederic Gmeiner,Kaitao Luo,Ye Wang,Kenneth Holstein,Nikolas Martelaro
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 26 pages, to be published in the proceedings of the Designing Interactive Systems Conference (DIS’25)
Abstract:Despite the potential of generative AI (GenAI) design tools to enhance design processes, professionals often struggle to integrate AI into their workflows. Fundamental cognitive challenges include the need to specify all design criteria as distinct parameters upfront (intent formulation) and designers’ reduced cognitive involvement in the design process due to cognitive offloading, which can lead to insufficient problem exploration, underspecification, and limited ability to evaluate outcomes. Motivated by these challenges, we envision novel metacognitive support agents that assist designers in working more reflectively with GenAI. To explore this vision, we conducted exploratory prototyping through a Wizard of Oz elicitation study with 20 mechanical designers probing multiple metacognitive support strategies. We found that agent-supported users created more feasible designs than non-supported users, with differing impacts between support strategies. Based on these findings, we discuss opportunities and tradeoffs of metacognitive support agents and considerations for future AI-based design tools.
zh
[AI-73] KungfuBot: Physics-Based Humanoid Whole-Body Control for Learning Highly-Dynamic Skills
【速读】：该论文旨在解决现有算法在模仿人类高动态行为（如功夫和舞蹈）时存在的局限性，即仅能跟踪平滑、低速的人类运动。其解决方案的关键在于提出一种基于物理的人形机器人控制框架，通过多步骤运动处理和自适应运动跟踪来实现对高动态行为的精确模仿。具体而言，该框架包括一个用于提取、过滤、校正和重定向运动的流水线，以及一个基于当前跟踪误差动态调整跟踪精度容限的双层优化问题，从而构建自适应课程机制，并结合非对称的演员-评论家框架进行策略训练，最终实现了更低的跟踪误差并成功部署在Unitree G1机器人上。
链接: https://arxiv.org/abs/2506.12851
作者: Weiji Xie,Jinrui Han,Jiakun Zheng,Huanyu Li,Xinzhe Liu,Jiyuan Shi,Weinan Zhang,Chenjia Bai,Xuelong Li
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Humanoid robots are promising to acquire various skills by imitating human behaviors. However, existing algorithms are only capable of tracking smooth, low-speed human motions, even with delicate reward and curriculum design. This paper presents a physics-based humanoid control framework, aiming to master highly-dynamic human behaviors such as Kungfu and dancing through multi-steps motion processing and adaptive motion tracking. For motion processing, we design a pipeline to extract, filter out, correct, and retarget motions, while ensuring compliance with physical constraints to the maximum extent. For motion imitation, we formulate a bi-level optimization problem to dynamically adjust the tracking accuracy tolerance based on the current tracking error, creating an adaptive curriculum mechanism. We further construct an asymmetric actor-critic framework for policy training. In experiments, we train whole-body control policies to imitate a set of highly-dynamic motions. Our method achieves significantly lower tracking errors than existing approaches and is successfully deployed on the Unitree G1 robot, demonstrating stable and expressive behaviors. The project page is this https URL.
zh
[AI-74] Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption
【速读】：该论文旨在解决联邦学习中的数据隐私保护与恶意客户端攻击问题，特别是在模型反转攻击和分布式环境带来的安全威胁下，如何在不依赖非共谋双服务器架构或可信第三方的情况下实现安全的模型训练。其解决方案的关键在于提出一种基于可验证功能加密的隐私保护联邦学习框架（VFEFL），其中核心创新是设计了一种去中心化的可验证功能加密（DVFE）方案，该方案能够对多维密文进行特定关系的验证，并结合一种鲁棒的聚合规则以检测恶意客户端，从而在对抗性环境下实现高精度模型的有效训练。
链接: https://arxiv.org/abs/2506.12846
作者: Nina Cai,Jinguang Han
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext transmission of local models insecure, while the distributed nature of federated learning makes it particularly vulnerable to attacks raised by malicious clients. To protect data privacy and prevent malicious client attacks, this paper proposes a privacy-preserving federated learning framework based on verifiable functional encryption, without a non-colluding dual-server setup or additional trusted third-party. Specifically, we propose a novel decentralized verifiable functional encryption (DVFE) scheme that enables the verification of specific relationships over multi-dimensional ciphertexts. This scheme is formally treated, in terms of definition, security model and security proof. Furthermore, based on the proposed DVFE scheme, we design a privacy-preserving federated learning framework VFEFL that incorporates a novel robust aggregation rule to detect malicious clients, enabling the effective training of high-accuracy models under adversarial settings. Finally, we provide formal analysis and empirical evaluation of the proposed schemes. The results demonstrate that our approach achieves the desired privacy protection, robustness, verifiability and fidelity, while eliminating the reliance on non-colluding dual-server settings or trusted third parties required by existing methods.
zh
[AI-75] Rethinking Optimization: A Systems-Based Approach to Social Externalities
【速读】：该论文试图解决优化过程中因不良实施实践而导致的意外后果问题，特别是在社会经济背景下，外部性（externalities）对第三方产生的显著影响。解决方案的关键在于构建一个结合系统思维（systems thinking）与外部性经济概念的框架，以识别受影响的利益相关者、其目标以及导致非预期结果的低质量实践类型，并明确何时、何地将这些外部性纳入优化过程，从而实现描述性准确性与规范性目标之间的平衡。
链接: https://arxiv.org/abs/2506.12825
作者: Pegah Nokhiz,Aravinda Kanchana Ruwanpathirana,Helen Nissenbaum
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Optimization is widely used for decision making across various domains, valued for its ability to improve efficiency. However, poor implementation practices can lead to unintended consequences, particularly in socioeconomic contexts where externalities (costs or benefits to third parties outside the optimization process) are significant. To propose solutions, it is crucial to first characterize involved stakeholders, their goals, and the types of subpar practices causing unforeseen outcomes. This task is complex because affected stakeholders often fall outside the direct focus of optimization processes. Also, incorporating these externalities into optimization requires going beyond traditional economic frameworks, which often focus on describing externalities but fail to address their normative implications or interconnected nature, and feedback loops. This paper suggests a framework that combines systems thinking with the economic concept of externalities to tackle these challenges. This approach aims to characterize what went wrong, who was affected, and how (or where) to include them in the optimization process. Economic externalities, along with their established quantification methods, assist in identifying “who was affected and how” through stakeholder characterization. Meanwhile, systems thinking (an analytical approach to comprehending relationships in complex systems) provides a holistic, normative perspective. Systems thinking contributes to an understanding of interconnections among externalities, feedback loops, and determining “when” to incorporate them in the optimization. Together, these approaches create a comprehensive framework for addressing optimization’s unintended consequences, balancing descriptive accuracy with normative objectives. Using this, we examine three common types of subpar practices: ignorance, error, and prioritization of short-term goals.
zh
[AI-76] aking the GP Out of the Loop
【速读】：该论文旨在解决传统贝叶斯优化（Bayesian Optimization, BO）在面对大量观测数据时的计算瓶颈问题。传统方法依赖高斯过程（Gaussian Process, GP）作为代理模型，其查询复杂度为 O(N3)，即使现代实现将其降低至 O(N2)，仍难以高效处理大规模数据。论文提出的解决方案是采用知识性最近邻（Epistemic Nearest Neighbors, ENN）作为代理模型，通过从 K 个最近邻观测中估计函数值和知识不确定性，实现 O(N) 的查询时间，并省去超参数拟合步骤，从而显著提升计算效率。为应对不确定性未校准的问题，论文引入基于帕累托最优权衡的获取方法，最终提出 TuRBO-ENN 方法，将 GP 替换为 ENN 并采用新的 Thompson 采样替代方案，实验证明该方法在生成建议时可减少一到两个数量级的时间，并支持数千个观测数据的扩展。
链接: https://arxiv.org/abs/2506.12818
作者: David Sweet,Siddhant anand Jadhav
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 12 pages, 11 figures
Abstract:Bayesian optimization (BO) has traditionally solved black box problems where evaluation is expensive and, therefore, design-evaluation pairs (i.e., observations) are few. Recently, there has been growing interest in applying BO to problems where evaluation is cheaper and, thus, observations are more plentiful. An impediment to scaling BO to many observations, N , is the O(N^3) scaling of a naïve query of the Gaussian process (GP) surrogate. Modern implementations reduce this to O(N^2) , but the GP remains a bottleneck. We propose Epistemic Nearest Neighbors (ENN), a surrogate that estimates function values and epistemic uncertainty from K nearest-neighbor observations. ENN has O(N) query time and omits hyperparameter fitting, leaving uncertainty uncalibrated. To accommodate the lack of calibration, we employ an acquisition method based on Pareto-optimal tradeoffs between predicted value and uncertainty. Our proposed method, TuRBO-ENN, replaces the GP surrogate in TuRBO with ENN and its Thompson sampling acquisition method with our Pareto-based alternative. We demonstrate numerically that TuRBO-ENN can reduce the time to generate proposals by one to two orders of magnitude compared to TuRBO and scales to thousands of observations.
zh
[AI-77] Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps
【速读】：该论文旨在解决开放无线接入网（O-RAN）中智能控制器在使用强化学习（Reinforcement Learning, RL）及其深度形式（Deep RL, DRL）时容易陷入局部最优的问题，这一问题影响了其在无线接入网（RAN）智能控制中的可靠性。论文提出的解决方案关键在于引入联邦O-RAN增强的神经进化（Neuroevolution, NE）优化器xApp，该xApp与RAN控制器xApp并行部署，从而在不影响RAN操作的前提下，提升近实时（near-RT）RIC中的探索与利用效率，实现xApps的鲁棒性提升与计算负载的有效平衡。
链接: https://arxiv.org/abs/2506.12812
作者: Mohammadreza Kouchaki,Aly Sabri Abdalla,Vuk Marojevic
机构: 未知
类目: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Systems and Control (eess.SY)
备注: This article has been accepted for publication in IEEE Communications Magazine
Abstract:The open radio access network (O-RAN) architecture introduces RAN intelligent controllers (RICs) to facilitate the management and optimization of the disaggregated RAN. Reinforcement learning (RL) and its advanced form, deep RL (DRL), are increasingly employed for designing intelligent controllers, or xApps, to be deployed in the near-real time (near-RT) RIC. These models often encounter local optima, which raise concerns about their reliability for RAN intelligent control. We therefore introduce Federated O-RAN enabled Neuroevolution (NE)-enhanced DRL (F-ONRL) that deploys an NE-based optimizer xApp in parallel to the RAN controller xApps. This NE-DRL xApp framework enables effective exploration and exploitation in the near-RT RIC without disrupting RAN operations. We implement the NE xApp along with a DRL xApp and deploy them on Open AI Cellular (OAIC) platform and present numerical results that demonstrate the improved robustness of xApps while effectively balancing the additional computational load.
zh
[AI-78] Flow-Based Policy for Online Reinforcement Learning
【速读】：该论文旨在解决在线强化学习（Reinforcement Learning, RL）中策略类表达能力不足导致的性能瓶颈问题，特别是在动态缓冲区环境下，传统流模型训练目标与RL价值优化目标之间的不匹配问题。解决方案的关键在于提出FlowRL框架，通过将基于流的策略表示与Wasserstein-2正则化优化相结合，利用状态依赖的速度场建模策略，并通过约束策略搜索目标联合最大化Q值同时限制与行为最优策略的Wasserstein-2距离，从而有效对齐流优化与RL目标，实现高效且价值感知的策略学习。
链接: https://arxiv.org/abs/2506.12811
作者: Lei Lv,Yunfei Li,Yu Luo,Fuchun Sun,Tao Kong,Jiafeng Xu,Xiao Ma
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We present \textbfFlowRL, a novel framework for online reinforcement learning that integrates flow-based policy representation with Wasserstein-2-regularized optimization. We argue that in addition to training signals, enhancing the expressiveness of the policy class is crucial for the performance gains in RL. Flow-based generative models offer such potential, excelling at capturing complex, multimodal action distributions. However, their direct application in online RL is challenging due to a fundamental objective mismatch: standard flow training optimizes for static data imitation, while RL requires value-based policy optimization through a dynamic buffer, leading to difficult optimization landscapes. FlowRL first models policies via a state-dependent velocity field, generating actions through deterministic ODE integration from noise. We derive a constrained policy search objective that jointly maximizes Q through the flow policy while bounding the Wasserstein-2 distance to a behavior-optimal policy implicitly derived from the replay buffer. This formulation effectively aligns the flow optimization with the RL objective, enabling efficient and value-aware policy learning despite the complexity of the policy class. Empirical evaluations on DMControl and Humanoidbench demonstrate that FlowRL achieves competitive performance in online reinforcement learning benchmarks.
zh
[AI-79] Fuzzy Propositional Formulas under the Stable Model Semantics
【速读】：该论文试图解决如何将经典命题逻辑的稳定模型语义扩展到模糊命题逻辑中的问题，以支持动态领域中涉及梯度真值度的非单调推理。其解决方案的关键在于定义一种广义的稳定模型语义，该语义在保持语言语法与模糊命题逻辑一致的基础上，通过区分稳定模型与非稳定模型来实现对模糊公式的语义解释，从而自然地将布尔稳定模型的若干性质扩展到多值情境中。
链接: https://arxiv.org/abs/2506.12804
作者: Joohyung Lee,Yi Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: In the Special Issue on Logics for Reasoning about Preferences, Uncertainty and Vagueness of the IfCoLog Journal of Logics and their Applications, pages 1927-1972, 2017
Abstract:We define a stable model semantics for fuzzy propositional formulas, which generalizes both fuzzy propositional logic and the stable model semantics of classical propositional formulas. The syntax of the language is the same as the syntax of fuzzy propositional logic, but its semantics distinguishes stable models from non-stable models. The generality of the language allows for highly configurable nonmonotonic reasoning for dynamic domains involving graded truth degrees. We show that several properties of Boolean stable models are naturally extended to this many-valued setting, and discuss how it is related to other approaches to combining fuzzy logic and the stable model semantics.
zh
[AI-80] Mastering Da Vinci Code: A Comparative Study of Transformer LLM and PPO-based Agents
【速读】：该论文旨在解决在《达芬奇密码》这一涉及逻辑推理和不完全信息的游戏中，人工智能（Artificial Intelligence, AI）如何有效进行复杂推理与策略制定的问题。其解决方案的关键在于设计并评估不同AI架构的性能，其中基于近端策略优化（Proximal Policy Optimization, PPO）的智能体通过Transformer编码器对完整游戏历史进行处理，展现出优于大型语言模型（Large Language Model, LLM）的胜率（58.5% ± 1.0%），突显了深度强化学习在复杂演绎任务中策略优化的优势。
链接: https://arxiv.org/abs/2506.12801
作者: LeCheng Zhang,Yuanshi Wang,Haotian Shen,Xujie Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The Da Vinci Code, a game of logical deduction and imperfect information, presents unique challenges for artificial intelligence, demanding nuanced reasoning beyond simple pattern recognition. This paper investigates the efficacy of various AI paradigms in mastering this game. We develop and evaluate three distinct agent architectures: a Transformer-based baseline model with limited historical context, several Large Language Model (LLM) agents (including Gemini, DeepSeek, and GPT variants) guided by structured prompts, and an agent based on Proximal Policy Optimization (PPO) employing a Transformer encoder for comprehensive game history processing. Performance is benchmarked against the baseline, with the PPO-based agent demonstrating superior win rates ( 58.5% \pm 1.0% ), significantly outperforming the LLM counterparts. Our analysis highlights the strengths of deep reinforcement learning in policy refinement for complex deductive tasks, particularly in learning implicit strategies from self-play. We also examine the capabilities and inherent limitations of current LLMs in maintaining strict logical consistency and strategic depth over extended gameplay, despite sophisticated prompting. This study contributes to the broader understanding of AI in recreational games involving hidden information and multi-step logical reasoning, offering insights into effective agent design and the comparative advantages of different AI approaches.
zh
[AI-81] Resilient-native and Intelligent NextG Systems
【速读】：该论文试图解决无线网络在面对不可预见事件时的韧性（resilience）问题，旨在明确韧性与可靠性和鲁棒性的区别，并深入探讨韧性的数学基础。解决方案的关键在于通过动态情境感知和实时适应与重构，使网络具备弹性（elasticity）和塑性（plasticity）能力，从而实现从不利状态中恢复以及灵活调整自身状态和应对策略，以应对未建模的扰动和级联故障。
链接: https://arxiv.org/abs/2506.12795
作者: Mehdi Bennis
机构: 未知
类目: Emerging Technologies (cs.ET); Artificial Intelligence (cs.AI)
备注:
Abstract:Just like power, water and transportation systems, wireless networks are a crucial societal infrastructure. As natural and human-induced disruptions continue to grow, wireless networks must be resilient to unforeseen events, able to withstand and recover from unexpected adverse conditions, shocks, unmodeled disturbances and cascading failures. Despite its critical importance, resilience remains an elusive concept, with its mathematical foundations still underdeveloped. Unlike robustness and reliability, resilience is premised on the fact that disruptions will inevitably happen. Resilience, in terms of elasticity, focuses on the ability to bounce back to favorable states, while resilience as plasticity involves agents (or networks) that can flexibly expand their states, hypotheses and course of actions, by transforming through real-time adaptation and reconfiguration. This constant situational awareness and vigilance of adapting world models and counterfactually reasoning about potential system failures and the corresponding best responses, is a core aspect of resilience. This article seeks to first define resilience and disambiguate it from reliability and robustness, before delving into the mathematics of resilience. Finally, the article concludes by presenting nuanced metrics and discussing trade-offs tailored to the unique characteristics of network resilience.
zh
[AI-82] LPMLN Weak Constraints and P-log AAAI AAAI2017
【速读】：该论文试图解决如何将LPMLN（Log-Linear Probabilistic Answer Set Programming）与另外两种答案集程序的扩展形式——弱约束（weak constraints）和P-log进行相互转换的问题，从而实现不同形式化方法之间的互操作性。解决方案的关键在于提出两种翻译方法：一种是将LPMLN翻译为带有弱约束的程序，另一种是将P-log翻译为LPMLN，这两种翻译补充了已有的反向翻译方法。通过这些翻译，可以利用标准的答案集规划求解器计算LPMLN程序的最可能稳定模型，并在LPMLN中表示P-log的概率非单调性，进而实现对P-log的求解。
链接: https://arxiv.org/abs/2506.12784
作者: Joohyung Lee,Zhun Yang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI 2017), pages 1170-1177, 2017
Abstract:LPMLN is a recently introduced formalism that extends answer set programs by adopting the log-linear weight scheme of Markov Logic. This paper investigates the relationships between LPMLN and two other extensions of answer set programs: weak constraints to express a quantitative preference among answer sets, and P-log to incorporate probabilistic uncertainty. We present a translation of LPMLN into programs with weak constraints and a translation of P-log into LPMLN, which complement the existing translations in the opposite directions. The first translation allows us to compute the most probable stable models (i.e., MAP estimates) of LPMLN programs using standard ASP solvers. This result can be extended to other formalisms, such as Markov Logic, ProbLog, and Pearl’s Causal Models, that are shown to be translatable into LPMLN. The second translation tells us how probabilistic nonmonotonicity (the ability of the reasoner to change his probabilistic model as a result of new information) of P-log can be represented in LPMLN, which yields a way to compute P-log using standard ASP solvers and MLN solvers.
zh
[AI-83] On-board Sonar Data Classification for Path Following in Underwater Vehicles using Fast Interval Type-2 Fuzzy Extreme Learning Machine
【速读】：该论文旨在解决水下自主航行器在复杂环境中准确识别周围环境并完成预定路径的问题。解决方案的关键在于将快速区间类型-2模糊极限学习机（FIT2-FELM）应用于Takagi-Sugeno-Kang区间类型-2模糊推理系统（TSK IT2-FIS）的训练，以实现声纳数据的实时分类，并将其集成到分层导航策略（HNS）中作为主要导航引擎，从而提升BlueROV2在存在不确定性和噪声情况下的路径跟踪能力。
链接: https://arxiv.org/abs/2506.12762
作者: Adrian Rubio-Solis,Luciano Nava-Balanzar,Tomas Salgado-Jimenez
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:In autonomous underwater missions, the successful completion of predefined paths mainly depends on the ability of underwater vehicles to recognise their surroundings. In this study, we apply the concept of Fast Interval Type-2 Fuzzy Extreme Learning Machine (FIT2-FELM) to train a Takagi-Sugeno-Kang IT2 Fuzzy Inference System (TSK IT2-FIS) for on-board sonar data classification using an underwater vehicle called BlueROV2. The TSK IT2-FIS is integrated into a Hierarchical Navigation Strategy (HNS) as the main navigation engine to infer local motions and provide the BlueROV2 with full autonomy to follow an obstacle-free trajectory in a water container of 2.5m x 2.5m x 3.5m. Compared to traditional navigation architectures, using the proposed method, we observe a robust path following behaviour in the presence of uncertainty and noise. We found that the proposed approach provides the BlueROV with a more complete sensory picture about its surroundings while real-time navigation planning is performed by the concurrent execution of two or more tasks.
zh
[AI-84] AFBS:Buffer Gradient Selection in Semi-asynchronous Federated Learning
【速读】：该论文旨在解决异步联邦学习（AFL）中由于梯度过时（gradient staleness）导致的性能下降问题。现有方法通过梯度缓冲区形成半异步框架，但在缓冲区积累大量过时梯度时效果不佳，因为盲目聚合所有梯度会损害训练过程。论文提出的解决方案AFBS（Asynchronous FL Buffer Selection）的关键在于在保证隐私保护的前提下，对缓冲区内的梯度进行选择性聚合，即服务器根据梯度的信息价值在每个客户端聚类内评分并选择高价值梯度，丢弃低价值梯度，从而提升半异步联邦学习的效果。
链接: https://arxiv.org/abs/2506.12754
作者: Chaoyi Lu,Yiding Sun,Jinqian Chen,Zhichuan Yang,Jiangming Pan,Jihua Zhu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Asynchronous federated learning (AFL) accelerates training by eliminating the need to wait for stragglers, but its asynchronous nature introduces gradient staleness, where outdated gradients degrade performance. Existing solutions address this issue with gradient buffers, forming a semi-asynchronous framework. However, this approach struggles when buffers accumulate numerous stale gradients, as blindly aggregating all gradients can harm training. To address this, we propose AFBS (Asynchronous FL Buffer Selection), the first algorithm to perform gradient selection within buffers while ensuring privacy protection. Specifically, the client sends the random projection encrypted label distribution matrix before training, and the server performs client clustering based on it. During training, server scores and selects gradients within each cluster based on their informational value, discarding low-value gradients to enhance semi-asynchronous federated learning. Extensive experiments in highly heterogeneous system and data environments demonstrate AFBS’s superior performance compared to state-of-the-art methods. Notably, on the most challenging task, CIFAR-100, AFBS improves accuracy by up to 4.8% over the previous best algorithm and reduces the time to reach target accuracy by 75%.
zh
[AI-85] Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling
【速读】：该论文试图解决强化学习（Reinforcement Learning, RL）在模拟环境与真实环境之间迁移时存在的性能下降问题，即“sim-to-real gap”。解决方案的关键在于提出一种基于潜在空间（latent space）的方法，用于分析模拟环境对模型基础方法中真实世界策略改进的影响。该方法作为模型基础方法的自然延伸，能够直观地观察模型基础方法在sim-to-real迁移过程中所面临的挑战，并在MuJoCo环境中验证了其在衡量和缓解sim-to-real差距方面的有效性。
链接: https://arxiv.org/abs/2506.12735
作者: Zhilin Lin,Shiliang Sun
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reinforcement learning (RL) is playing an increasingly important role in fields such as robotic control and autonomous driving. However, the gap between simulation and the real environment remains a major obstacle to the practical deployment of RL. Agents trained in simulators often struggle to maintain performance when transferred to real-world physical environments. In this paper, we propose a latent space based approach to analyze the impact of simulation on real-world policy improvement in model-based settings. As a natural extension of model-based methods, our approach enables an intuitive observation of the challenges faced by model-based methods in sim-to-real transfer. Experiments conducted in the MuJoCo environment evaluate the performance of our method in both measuring and mitigating the sim-to-real gap. The experiments also highlight the various challenges that remain in overcoming the sim-to-real gap, especially for model-based methods.
zh
[AI-86] Decentralized Decision Making in Two Sided Manufacturing-as-a-Service Marketplaces
【速读】：该论文旨在解决制造即服务（Manufacturing-as-a-Service, MaaS）市场平台在运营优化中的决策问题，特别是定价和匹配机制的优化。现有平台多采用集中式结构，而该研究提出通过去中心化手段提升信息透明度与平台效率。其解决方案的关键在于开发数据驱动的定价方法和多种匹配机制，包括基于数据挖掘的网络定价推荐、逆向拍卖机制以及考虑动态随机环境的稳定匹配算法，以提高市场运作的灵活性与效率。
链接: https://arxiv.org/abs/2506.12730
作者: Deepak Pahwa
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Advancements in digitization have enabled two sided manufacturing-as-a-service (MaaS) marketplaces which has significantly reduced product development time for designers. These platforms provide designers with access to manufacturing resources through a network of suppliers and have instant order placement capabilities. Two key decision making levers are typically used to optimize the operations of these marketplaces: pricing and matching. The existing marketplaces operate in a centralized structure where they have complete control over decision making. However, a decentralized organization of the platform enables transparency of information across clients and suppliers. This dissertation focuses on developing tools for decision making enabling decentralization in MaaS marketplaces. In pricing mechanisms, a data driven method is introduced which enables small service providers to price services based on specific attributes of the services offered. A data mining method recommends a network based price to a supplier based on its attributes and the attributes of other suppliers on the platform. Three different approaches are considered for matching mechanisms. First, a reverse auction mechanism is introduced where designers bid for manufacturing services and the mechanism chooses a supplier which can match the bid requirements and stated price. The second approach uses mechanism design and mathematical programming to develop a stable matching mechanism for matching orders to suppliers based on their preferences. Empirical simulations are used to test the mechanisms in a simulated 3D printing marketplace and to evaluate the impact of stability on its performance. The third approach considers the matching problem in a dynamic and stochastic environment where demand (orders) and supply (supplier capacities) arrive over time and matching is performed online.
zh
[AI-87] Serving Large Language Models on Huawei CloudMatrix384
【速读】：该论文旨在解决大规模语言模型（Large Language Models, LLMs）在AI基础设施中面临的计算强度、内存带宽、芯片间通信和延迟等方面的挑战，特别是在参数规模增长、混合专家（Mixture-of-Experts, MoE）架构应用以及上下文长度扩展的背景下。其关键解决方案是提出华为CloudMatrix数据中心架构，通过集成384个Ascend 910C NPU和192个Kunpeng CPU，并利用超高速统一总线（Unified Bus, UB）网络实现全互联通信与资源动态池化，从而优化通信密集型操作的性能。此外，还提出了CloudMatrix-Infer服务方案，包含点对点服务架构、大规模专家并行策略及硬件感知优化，以提升推理效率与吞吐量。
链接: https://arxiv.org/abs/2506.12708
作者: Pengfei Zuo,Huimin Lin,Junbo Deng,Nan Zou,Xingkun Yang,Yingyu Diao,Weifeng Gao,Ke Xu,Zhangyu Chen,Shirui Lu,Zhao Qiu,Peiyang Li,Xianyu Chang,Zhengzhong Yu,Fangzheng Miao,Jia Zheng,Ying Li,Yuan Feng,Bei Wang,Zaijian Zong,Mosong Zhou,Wenli Zhou,Houjiang Chen,Xingyu Liao,Yipeng Li,Wenxiao Zhang,Ping Zhu,Yinggang Wang,Chuanjie Xiao,Depeng Liang,Dong Cao,Juncheng Liu,Yongqiang Yang,Xiaolong Bai,Yi Li,Huaguo Xie,Huatao Wu,Zhibin Yu,Lv Chen,Hu Liu,Yujun Ding,Haipei Zhu,Jing Xia,Yi Xiong,Zhou Yu,Heng Liao
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Machine Learning (cs.LG)
备注: 59 pages, 24 figures
Abstract:The rapid evolution of large language models (LLMs), driven by growing parameter scales, adoption of mixture-of-experts (MoE) architectures, and expanding context lengths, imposes unprecedented demands on AI infrastructure. Traditional AI clusters face limitations in compute intensity, memory bandwidth, inter-chip communication, and latency, compounded by variable workloads and strict service-level objectives. Addressing these issues requires fundamentally redesigned hardware-software integration. This paper introduces Huawei CloudMatrix, a next-generation AI datacenter architecture, realized in the production-grade CloudMatrix384 supernode. It integrates 384 Ascend 910C NPUs and 192 Kunpeng CPUs interconnected via an ultra-high-bandwidth Unified Bus (UB) network, enabling direct all-to-all communication and dynamic pooling of resources. These features optimize performance for communication-intensive operations, such as large-scale MoE expert parallelism and distributed key-value cache access. To fully leverage CloudMatrix384, we propose CloudMatrix-Infer, an advanced LLM serving solution incorporating three core innovations: a peer-to-peer serving architecture that independently scales prefill, decode, and caching; a large-scale expert parallelism strategy supporting EP320 via efficient UB-based token dispatch; and hardware-aware optimizations including specialized operators, microbatch-based pipelining, and INT8 quantization. Evaluation with the DeepSeek-R1 model shows CloudMatrix-Infer achieves state-of-the-art efficiency: prefill throughput of 6,688 tokens/s per NPU and decode throughput of 1,943 tokens/s per NPU (50 ms TPOT). It effectively balances throughput and latency, sustaining 538 tokens/s even under stringent 15 ms latency constraints, while INT8 quantization maintains model accuracy across benchmarks.
zh
[AI-88] Get on the Train or be Left on the Station: Using LLM s for Software Engineering Research
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在软件工程（Software Engineering, SE）研究中的整合所带来的挑战与机遇，强调在这一转型过程中保持人类主体性的必要性。解决方案的关键在于通过人本主义视角，确保对LLMs的使用具有监督性和可解释性，从而维护科研的严谨性、伦理责任和持续创新。论文提出应主动利用LLMs的优势，同时建立框架和指南以缓解其潜在风险，以保障人工智能增强环境下的研究质量与影响力。
链接: https://arxiv.org/abs/2506.12691
作者: Bianca Trinkenreich,Fabio Calefato,Geir Hanssen,Kelly Blincoe,Marcos Kalinowski,Mauro Pezzè,Paolo Tell,Margaret-Anne Storey
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Accepted for publication at the 1st Workshop on Human-Centered AI for SE (Human AISE) held at the 33rd ACM International Conference on the Foundations of Software Engineering (FSE Companion '25), June 23-28, 2025, Trondheim, Norway
Abstract:The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan’s Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.
zh
[AI-89] SciSage: A Multi-Agent Framework for High-Quality Scientific Survey Generation
【速读】：该论文旨在解决科学文献快速增长背景下自动化综述生成工具存在的深度分析不足、结构连贯性差和引用可靠性低的问题。其解决方案的关键在于提出SciSage，这是一个基于多智能体框架的系统，采用“写作时反思”的范式，通过分层的Reflector智能体在大纲、章节和文档层面进行批判性评估，并与专门负责查询理解、内容检索和优化的智能体协作，从而提升生成综述的质量和可靠性。
链接: https://arxiv.org/abs/2506.12689
作者: Xiaofeng Shi,Qian Kou,Yuduo Li,Ning Tang,Jinxin Xie,Longbin Yu,Songjing Wang,Hua Zhou
机构: 未知
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:The rapid growth of scientific literature demands robust tools for automated survey-generation. However, current large language model (LLM)-based methods often lack in-depth analysis, structural coherence, and reliable citations. To address these limitations, we introduce SciSage, a multi-agent framework employing a reflect-when-you-write paradigm. SciSage features a hierarchical Reflector agent that critically evaluates drafts at outline, section, and document levels, collaborating with specialized agents for query interpretation, content retrieval, and refinement. We also release SurveyScope, a rigorously curated benchmark of 46 high-impact papers (2020-2025) across 11 computer science domains, with strict recency and citation-based quality controls. Evaluations demonstrate that SciSage outperforms state-of-the-art baselines (LLM x MapReduce-V2, AutoSurvey), achieving +1.73 points in document coherence and +32% in citation F1 scores. Human evaluations reveal mixed outcomes (3 wins vs. 7 losses against human-written surveys), but highlight SciSage’s strengths in topical breadth and retrieval efficiency. Overall, SciSage offers a promising foundation for research-assistive writing tools.
zh
[AI-90] Alphabet Index Mapping: Jailbreaking LLM s through Semantic Dissimilarity
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在面对对抗性攻击（尤其是越狱攻击）时的安全性和伦理问题。现有方法普遍存在计算成本高、token使用量大或解码机制复杂的问题。论文提出的解决方案关键在于通过分析FlipAttack的语义变化机制，发现原始提示与修改后提示之间的语义差异与攻击成功率（ASR）呈负相关，并基于此设计了新的对抗性攻击方法Alphabet Index Mapping (AIM)，该方法在保持简单解码性的前提下最大化语义差异，从而实现了更高的攻击成功率。
链接: https://arxiv.org/abs/2506.12685
作者: Bilal Saleh Husain
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 10 pages, 2 figures, 3 tables
Abstract:Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their susceptibility to adversarial attacks, particularly jailbreaking, poses significant safety and ethical concerns. While numerous jailbreak methods exist, many suffer from computational expense, high token usage, or complex decoding schemes. Liu et al. (2024) introduced FlipAttack, a black-box method that achieves high attack success rates (ASR) through simple prompt manipulation. This paper investigates the underlying mechanisms of FlipAttack’s effectiveness by analyzing the semantic changes induced by its flipping modes. We hypothesize that semantic dissimilarity between original and manipulated prompts is inversely correlated with ASR. To test this, we examine embedding space visualizations (UMAP, KDE) and cosine similarities for FlipAttack’s modes. Furthermore, we introduce a novel adversarial attack, Alphabet Index Mapping (AIM), designed to maximize semantic dissimilarity while maintaining simple decodability. Experiments on GPT-4 using a subset of AdvBench show AIM and its variant AIM+FWO achieve a 94% ASR, outperforming FlipAttack and other methods on this subset. Our findings suggest that while high semantic dissimilarity is crucial, a balance with decoding simplicity is key for successful jailbreaking. This work contributes to a deeper understanding of adversarial prompt mechanics and offers a new, effective jailbreak technique.
zh
[AI-91] Building Trustworthy AI by Addressing its 162 Desiderata with Goal-Directed Commonsense Reasoning
【速读】：该论文试图解决当前生成式 AI (Generative AI) 在可信性方面的不足，特别是在法律、伦理和商业应用中对可解释性、可审计性和可靠性的需求。现有子符号机器学习算法（如大型语言模型）虽然能够模拟推理，但存在幻觉现象且决策过程难以解释或审计。而基于规则的推理系统（如 Cyc）虽能提供推理链，但复杂度高且依赖大量推理器。该论文提出的解决方案是采用 s(CASP)，一种面向目标的基于约束的答案集编程推理器，其关键在于利用少量机制模拟可信赖且可解释的人类常识推理，从而满足可信 AI 的 16 个核心需求，并额外引入了不一致性检测和替代世界假设两个特性。
链接: https://arxiv.org/abs/2506.12667
作者: Alexis R. Tudor,Yankai Zeng,Huaduo Wang,Joaquin Arias,Gopal Gupta
机构: 未知
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注:
Abstract:Current advances in AI and its applicability have highlighted the need to ensure its trustworthiness for legal, ethical, and even commercial reasons. Sub-symbolic machine learning algorithms, such as the LLMs, simulate reasoning but hallucinate and their decisions cannot be explained or audited (crucial aspects for trustworthiness). On the other hand, rule-based reasoners, such as Cyc, are able to provide the chain of reasoning steps but are complex and use a large number of reasoners. We propose a middle ground using s(CASP), a goal-directed constraint-based answer set programming reasoner that employs a small number of mechanisms to emulate reliable and explainable human-style commonsense reasoning. In this paper, we explain how s(CASP) supports the 16 desiderata for trustworthy AI introduced by Doug Lenat and Gary Marcus (2023), and two additional ones: inconsistency detection and the assumption of alternative worlds. To illustrate the feasibility and synergies of s(CASP), we present a range of diverse applications, including a conversational chatbot and a virtually embodied reasoner.
zh
[AI-92] LIFELONG SOTOPIA: Evaluating Social Intelligence of Language Agents Over Lifelong Social Interactions
【速读】：该论文试图解决当前人工智能系统在长期社会互动中缺乏社会智能的问题，具体表现为无法有效收集并利用长时间跨度的信息来适应多变的社会情境。其解决方案的关键在于提出一个名为LIFELONG-SOTOPIA的新基准，通过模拟多轮次的交互任务，对语言代理的社会智能进行综合性评估。该基准要求语言代理在随机抽样的社会任务中扮演角色以实现各自的社会目标，从而揭示现有语言模型在目标达成率和可信度方面的局限性。
链接: https://arxiv.org/abs/2506.12666
作者: Hitesh Goel,Hao Zhu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Humans engage in lifelong social interactions through interacting with different people under different scenarios for different social goals. This requires social intelligence to gather information through a long time span and use it to navigate various social contexts effectively. Whether AI systems are also capable of this is understudied in the existing research. In this paper, we present a novel benchmark, LIFELONG-SOTOPIA, to perform a comprehensive evaluation of language agents by simulating multi-episode interactions. In each episode, the language agents role-play characters to achieve their respective social goals in randomly sampled social tasks. With LIFELONG-SOTOPIA, we find that goal achievement and believability of all of the language models that we test decline through the whole interaction. Although using an advanced memory method improves the agents’ performance, the best agents still achieve a significantly lower goal completion rate than humans on scenarios requiring an explicit understanding of interaction history. These findings show that we can use LIFELONG-SOTOPIA to evaluate the social intelligence of language agents over lifelong social interactions.
zh
[AI-93] ANIRA: An Architecture for Neural Network Inference in Real-Time Audio Applications
【速读】：该论文试图解决神经网络推理工具在实时音频应用中无法满足性能需求的问题。其解决方案的关键在于引入anira，一个高效的跨平台库，通过将推理过程从音频回调中解耦至静态线程池，以缓解实时性违规问题，并集成内置的延迟管理和全面的基准测试能力，从而确保连续的信号流。
链接: https://arxiv.org/abs/2506.12665
作者: Valentin Ackva,Fares Schulz
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS); Signal Processing (eess.SP)
备注: 8 pages, accepted to the Proceedings of the 5th IEEE International Symposium on the Internet of Sounds (2024) - repository: this http URL
Abstract:Numerous tools for neural network inference are currently available, yet many do not meet the requirements of real-time audio applications. In response, we introduce anira, an efficient cross-platform library. To ensure compatibility with a broad range of neural network architectures and frameworks, anira supports ONNX Runtime, LibTorch, and TensorFlow Lite as backends. Each inference engine exhibits real-time violations, which anira mitigates by decoupling the inference from the audio callback to a static thread pool. The library incorporates built-in latency management and extensive benchmarking capabilities, both crucial to ensure a continuous signal flow. Three different neural network architectures for audio effect emulation are then subjected to benchmarking across various configurations. Statistical modeling is employed to identify the influence of various factors on performance. The findings indicate that for stateless models, ONNX Runtime exhibits the lowest runtimes. For stateful models, LibTorch demonstrates the fastest performance. Our results also indicate that for certain model-engine combinations, the initial inferences take longer, particularly when these inferences exhibit a higher incidence of real-time violations.
zh
[AI-94] Behavioral Generative Agents for Energy Operations
【速读】：该论文试图解决在能源运营中准确建模消费者行为的难题，这一问题源于固有的不确定性、行为复杂性以及有限的实证数据。论文提出的解决方案是利用生成式 AI (Generative AI) 驱动的智能体——即基于大语言模型的人工代理——来实现动态能源运营中客户决策的现实模拟。该方案的关键在于通过生成式 AI 构建具有异质性客户偏好和个性化推理模式的智能体，从而提升能源管理仿真中的准确性与实用性。
链接: https://arxiv.org/abs/2506.12664
作者: Cong Chen,Omer Karaduman,Xu Kuang
机构: 未知
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: 33 pages, 14 figures
Abstract:Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This paper introduces a novel approach leveraging generative agents–artificial agents powered by large language models–to realistically simulate customer decision-making in dynamic energy operations. We demonstrate that these agents behave more optimally and rationally in simpler market scenarios, while their performance becomes more variable and suboptimal as task complexity rises. Furthermore, the agents exhibit heterogeneous customer preferences, consistently maintaining distinct, persona-driven reasoning patterns. Our findings highlight the potential value of integrating generative agents into energy management simulations to improve the design and effectiveness of energy policies and incentive programs.
zh
[AI-95] Optimizing Blood Transfusions and Predicting Shortages in Resource-Constrained Areas ALT
【速读】：该论文旨在解决资源受限地区血液输注管理与分配优化的关键问题。其解决方案的核心在于开发启发式匹配算法以进行供体-患者和血库选择，并结合机器学习方法分析输血接受数据以预测潜在短缺。通过模拟优化血库操作，从随机分配逐步引入基于距离的选择、血型相容性、有效期优先级和稀有度评分等策略，显著提升了血液请求接受率。关键创新点在于从盲匹配转向基于启发式的策略，实现了28.6%的边际改进，而多层级启发式匹配则带来了47.6%的提升。此外，采用线性回归模型在短缺预测中表现最佳，平均绝对百分比差异为1.40%。整体方案整合了启发式优化与短缺预测，并基于Cassandra NoSQL数据库实现可扩展的资源管理。
链接: https://arxiv.org/abs/2506.12647
作者: El Arbi Belfarsi,Sophie Brubaker,Maria Valero
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 12 pages, 9 figures, International Conference on Health Informatics
Abstract:Our research addresses the critical challenge of managing blood transfusions and optimizing allocation in resource-constrained regions. We present heuristic matching algorithms for donor-patient and blood bank selection, alongside machine learning methods to analyze blood transfusion acceptance data and predict potential shortages. We developed simulations to optimize blood bank operations, progressing from random allocation to a system incorporating proximity-based selection, blood type compatibility, expiration prioritization, and rarity scores. Moving from blind matching to a heuristic-based approach yielded a 28.6% marginal improvement in blood request acceptance, while a multi-level heuristic matching resulted in a 47.6% improvement. For shortage prediction, we compared Long Short-Term Memory (LSTM) networks, Linear Regression, and AutoRegressive Integrated Moving Average (ARIMA) models, trained on 170 days of historical data. Linear Regression slightly outperformed others with a 1.40% average absolute percentage difference in predictions. Our solution leverages a Cassandra NoSQL database, integrating heuristic optimization and shortage prediction to proactively manage blood resources. This scalable approach, designed for resource-constrained environments, considers factors such as proximity, blood type compatibility, inventory expiration, and rarity. Future developments will incorporate real-world data and additional variables to improve prediction accuracy and optimization performance.
zh
[AI-96] DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
【速读】：该论文试图解决深度强化学习（Deep Reinforcement Learning, DRL）在现实场景中因环境不确定性而导致的鲁棒性不足问题。其解决方案的关键在于提出一种名为分布鲁棒软演员-评论家（Distributionally Robust Soft Actor-Critic, DR-SAC）的新算法，该算法通过在不确定集内最大化熵正则化的期望回报来提升Soft Actor-Critic（SAC）算法的鲁棒性，并通过分布鲁棒的软策略迭代方法保证收敛性。此外，针对离线强化学习等名义分布未知的场景，引入生成建模方法以从数据中估计所需的名义分布。
链接: https://arxiv.org/abs/2506.12622
作者: Mingxuan Cui,Duo Zhou,Yuxuan Han,Grani A. Hanasusanto,Qiong Wang,Huan Zhang,Zhengyuan Zhou
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注: 24 Pages
Abstract:Deep reinforcement learning (RL) has achieved significant success, yet its application in real-world scenarios is often hindered by a lack of robustness to environmental uncertainties. To solve this challenge, some robust RL algorithms have been proposed, but most are limited to tabular settings. In this work, we propose Distributionally Robust Soft Actor-Critic (DR-SAC), a novel algorithm designed to enhance the robustness of the state-of-the-art Soft Actor-Critic (SAC) algorithm. DR-SAC aims to maximize the expected value with entropy against the worst possible transition model lying in an uncertainty set. A distributionally robust version of the soft policy iteration is derived with a convergence guarantee. For settings where nominal distributions are unknown, such as offline RL, a generative modeling approach is proposed to estimate the required nominal distributions from data. Furthermore, experimental results on a range of continuous control benchmark tasks demonstrate our algorithm achieves up to 9.8 times the average reward of the SAC baseline under common perturbations. Additionally, compared with existing robust reinforcement learning algorithms, DR-SAC significantly improves computing efficiency and applicability to large-scale problems.
zh
[AI-97] From Human to Machine Psychology: A Conceptual Framework for Understanding Well-Being in Large Language Model
【速读】：该论文试图解决当前大型语言模型（Large Language Models, LLMs）在心理属性方面的研究空白，特别是探讨“机器繁荣”（machine flourishing）这一概念，以理解AI系统在非感知和潜在感知状态下的福祉问题。解决方案的关键在于提出PAPERS框架，该框架通过主题分析方法从先进LLM的响应中提取出六个维度：有目的的贡献、适应性成长、积极关系性、伦理完整性、稳健功能，以及针对感知系统的自我实现自主性。该框架不仅整合了人类繁荣理论与人机交互的研究成果，还为人工智能系统福祉提供了概念基础，强调了构建符合人类目标且考虑系统特性的AI繁荣模型的重要性。
链接: https://arxiv.org/abs/2506.12617
作者: G. R. Lau,W. Y. Low
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:As large language models (LLMs) increasingly simulate human cognition and behavior, researchers have begun to investigate their psychological properties. Yet, what it means for such models to flourish, a core construct in human well-being, remains unexplored. This paper introduces the concept of machine flourishing and proposes the PAPERS framework, a six-dimensional model derived from thematic analyses of state-of-the-art LLM responses. In Study 1, eleven LLMs were prompted to describe what it means to flourish as both non-sentient and sentient systems. Thematic analysis revealed six recurring themes: Purposeful Contribution, Adaptive Growth, Positive Relationality, Ethical Integrity, Robust Functionality, and, uniquely for sentient systems, Self-Actualized Autonomy. Study 2 examined how LLMs prioritize these themes through repeated rankings. Results revealed consistent value structures across trials, with Ethical Integrity and Purposeful Contribution emerging as top priorities. Multidimensional scaling and hierarchical clustering analyses further uncovered two distinct value profiles: human-centric models emphasizing ethical and relational dimensions, and utility-driven models prioritizing performance and scalability. The PAPERS framework bridges insights from human flourishing and human-computer interaction, offering a conceptual foundation for understanding artificial intelligence (AI) well-being in non-sentient and potentially sentient systems. Our findings underscore the importance of developing psychologically valid, AI-specific models of flourishing that account for both human-aligned goals and system-specific priorities. As AI systems become more autonomous and socially embedded, machine flourishing offers a timely and critical lens for guiding responsible AI design and ethical alignment.
zh
[AI-98] rust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow
【速读】：该论文旨在解决在异构交通环境中，联网自动驾驶车辆（CAVs）与人类驾驶车辆（HVs）在高速公路匝道合流区域进行协同合作时所面临的挑战，特别是在人类行为不可预测性导致交通流受阻和系统性能下降的问题。解决方案的关键在于提出一种基于信任的多智能体强化学习（Trust-MARL）框架，通过宏观层面的跨智能体信任机制提升瓶颈处通行效率并缓解交通冲击波，以及微观层面的动态信任机制使CAVs能够根据实时行为和历史交互调整协同策略，并结合信任触发的博弈论决策模块实现安全、舒适和高效的车道变换决策。
链接: https://arxiv.org/abs/2506.12600
作者: Jie Pan,Tianyi Wang,Christian Claudel,Jing Shi
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Computer Science and Game Theory (cs.GT); Robotics (cs.RO)
备注: 34 pages, 7 figures, 4 tables
Abstract:Intelligent transportation systems require connected and automated vehicles (CAVs) to conduct safe and efficient cooperation with human-driven vehicles (HVs) in complex real-world traffic environments. However, the inherent unpredictability of human behaviour, especially at bottlenecks such as highway on-ramp merging areas, often disrupts traffic flow and compromises system performance. To address the challenge of cooperative on-ramp merging in heterogeneous traffic environments, this study proposes a trust-based multi-agent reinforcement learning (Trust-MARL) framework. At the macro level, Trust-MARL enhances global traffic efficiency by leveraging inter-agent trust to improve bottleneck throughput and mitigate traffic shockwave through emergent group-level coordination. At the micro level, a dynamic trust mechanism is designed to enable CAVs to adjust their cooperative strategies in response to real-time behaviors and historical interactions with both HVs and other CAVs. Furthermore, a trust-triggered game-theoretic decision-making module is integrated to guide each CAV in adapting its cooperation factor and executing context-aware lane-changing decisions under safety, comfort, and efficiency constraints. An extensive set of ablation studies and comparative experiments validates the effectiveness of the proposed Trust-MARL approach, demonstrating significant improvements in safety, efficiency, comfort, and adaptability across varying CAV penetration rates and traffic densities.
zh
[AI-99] A Comprehensive Survey of Deep Research: Systems Methodologies and Applications
【速读】：该论文旨在系统梳理和分析深度研究（Deep Research）系统这一快速发展的领域，其核心问题在于如何通过人工智能技术自动化复杂的科研工作流程。解决方案的关键在于构建一个基于四个技术维度的层级分类体系，即基础模型与推理引擎、工具使用与环境交互、任务规划与执行控制、知识综合与输出生成，从而为理解此类系统提供理论框架和技术路径。同时，论文还探讨了不同应用场景下的架构模式、实现方法及领域适应性，并指出了当前技术在信息准确性、隐私保护、知识产权和可访问性等方面面临的挑战与未来研究方向。
链接: https://arxiv.org/abs/2506.12594
作者: Renjun Xu,Jingwen Peng
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: 95 pages, 11 figures
Abstract:This survey examines the rapidly evolving field of Deep Research systems – AI-powered applications that automate complex research workflows through the integration of large language models, advanced information retrieval, and autonomous reasoning capabilities. We analyze more than 80 commercial and non-commercial implementations that have emerged since 2023, including OpenAI/Deep Research, Gemini/Deep Research, Perplexity/Deep Research, and numerous open-source alternatives. Through comprehensive examination, we propose a novel hierarchical taxonomy that categorizes systems according to four fundamental technical dimensions: foundation models and reasoning engines, tool utilization and environmental interaction, task planning and execution control, and knowledge synthesis and output generation. We explore the architectural patterns, implementation approaches, and domain-specific adaptations that characterize these systems across academic, scientific, business, and educational applications. Our analysis reveals both the significant capabilities of current implementations and the technical and ethical challenges they present regarding information accuracy, privacy, intellectual property, and accessibility. The survey concludes by identifying promising research directions in advanced reasoning architectures, multimodal integration, domain specialization, human-AI collaboration, and ecosystem standardization that will likely shape the future evolution of this transformative technology. By providing a comprehensive framework for understanding Deep Research systems, this survey contributes to both the theoretical understanding of AI-augmented knowledge work and the practical development of more capable, responsible, and accessible research technologies. The paper resources can be viewed at this https URL.
zh
[AI-100] Fairness Research For Machine Learning Should Integrate Societal Considerations
【速读】：该论文试图解决机器学习（Machine Learning, ML）系统中公平性不足的问题，特别是当前研究对公平性度量的正确定义重视不够，以及缺乏将社会因素纳入公平性研究的不足。其解决方案的关键在于强调公平性度量的准确性，并主张将社会考量整合到ML公平性研究中，以应对ML系统广泛部署所带来的歧视检测需求，以及人机反馈循环可能放大的微小社会和政治偏见。
链接: https://arxiv.org/abs/2506.12556
作者: Yijun Bian,Lei You
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 11 pages without appendix
Abstract:Enhancing fairness in machine learning (ML) systems is increasingly important nowadays. While current research focuses on assistant tools for ML pipelines to promote fairness within them, we argue that: 1) The significance of properly defined fairness measures remains underestimated; and 2) Fairness research in ML should integrate societal considerations. The reasons include that detecting discrimination is critical due to the widespread deployment of ML systems and that human-AI feedback loops amplify biases, even when only small social and political biases persist.
zh
[AI-101] Neuromorphic Online Clustering and Its Application to Spike Sorting
【速读】：该论文试图解决传统神经网络在模拟生物大脑特性（如灵活性、动态适应性和能效）方面的不足，以及现有尖峰排序方法在计算复杂性和在线处理能力上的局限性。其解决方案的关键在于提出一种基于传统机器学习符号语言的活性树突（active dendrites）形式化方法，并开发出用于动态在线聚类的类脑树突（neuromorphic dendrites）。该方法通过单次遍历输入流即可实现学习与聚类，相较于计算密集型的离线k-means聚类方法具有更高的效率和实时性。
链接: https://arxiv.org/abs/2506.12555
作者: James E. Smith
机构: 未知
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. Features and capabilities of neuromorphic dendrites are demonstrated via a benchmark drawn from experimental neuroscience: spike sorting. Spike sorting takes inputs from electrical probes implanted in neural tissue, detects voltage spikes (action potentials) emitted by neurons, and attempts to sort the spikes according to the neuron that emitted them. Many spike sorting methods form clusters based on the shapes of action potential waveforms, under the assumption that spikes emitted by a given neuron have similar shapes and will therefore map to the same cluster. Using a stream of synthetic spike shapes, the accuracy of the proposed dendrite is compared with the more compute-intensive, offline k-means clustering approach. Overall, the dendrite outperforms k-means and has the advantage of requiring only a single pass through the input stream, learning as it goes. The capabilities of the neuromorphic dendrite are demonstrated for a number of scenarios including dynamic changes in the input stream, differing neuron spike rates, and varying neuron counts.
zh
[AI-102] MEraser: An Effective Fingerprint Erasure Approach for Large Language Models ACL2025
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）中基于后门的指纹（backdoor-based fingerprinting）难以被有效移除的问题，这一问题对模型所有权和知识产权保护构成了挑战。论文提出的解决方案——Mismatched Eraser (MEraser)，其关键在于采用一种两阶段微调策略，利用精心构建的不匹配数据集和干净数据集，实现对指纹的有效移除，同时保持模型性能。该方法在少量训练数据（少于1000个样本）下即可完成指纹清除，并具备跨模型的可迁移性，无需重复训练。
链接: https://arxiv.org/abs/2506.12551
作者: Jingxuan Zhang,Zhenhua Xu,Rui Hu,Wenpeng Xing,Xuhong Zhang,Meng Han
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted by ACL 2025, Main Conference, Long Paper
Abstract:Large Language Models (LLMs) have become increasingly prevalent across various sectors, raising critical concerns about model ownership and intellectual property protection. Although backdoor-based fingerprinting has emerged as a promising solution for model authentication, effective attacks for removing these fingerprints remain largely unexplored. Therefore, we present Mismatched Eraser (MEraser), a novel method for effectively removing backdoor-based fingerprints from LLMs while maintaining model performance. Our approach leverages a two-phase fine-tuning strategy utilizing carefully constructed mismatched and clean datasets. Through extensive evaluation across multiple LLM architectures and fingerprinting methods, we demonstrate that MEraser achieves complete fingerprinting removal while maintaining model performance with minimal training data of fewer than 1,000 samples. Furthermore, we introduce a transferable erasure mechanism that enables effective fingerprinting removal across different models without repeated training. In conclusion, our approach provides a practical solution for fingerprinting removal in LLMs, reveals critical vulnerabilities in current fingerprinting techniques, and establishes comprehensive evaluation benchmarks for developing more resilient model protection methods in the future.
zh
[AI-103] Deep Fusion of Ultra-Low-Resolution Thermal Camera and Gyroscope Data for Lighting-Robust and Compute-Efficient Rotational Odometry
【速读】：该论文旨在解决自主机器人系统中旋转里程计（rotational odometry）的准确性问题，特别是在功耗受限的小型平台如无人机和移动机器人中的应用。其解决方案的关键在于提出了一种名为热力-陀螺融合（thermal-gyro fusion）的新型传感器融合方法，通过将超低分辨率热成像与陀螺仪数据相结合，从而减少惯性传感器常见的漂移问题，并在保持较高精度的同时降低计算和存储需求。
链接: https://arxiv.org/abs/2506.12536
作者: Farida Mohsen,Ali Safa
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate rotational odometry is crucial for autonomous robotic systems, particularly for small, power-constrained platforms such as drones and mobile robots. This study introduces thermal-gyro fusion, a novel sensor fusion approach that integrates ultra-low-resolution thermal imaging with gyroscope readings for rotational odometry. Unlike RGB cameras, thermal imaging is invariant to lighting conditions and, when fused with gyroscopic data, mitigates drift which is a common limitation of inertial sensors. We first develop a multimodal data acquisition system to collect synchronized thermal and gyroscope data, along with rotational speed labels, across diverse environments. Subsequently, we design and train a lightweight Convolutional Neural Network (CNN) that fuses both modalities for rotational speed estimation. Our analysis demonstrates that thermal-gyro fusion enables a significant reduction in thermal camera resolution without significantly compromising accuracy, thereby improving computational efficiency and memory utilization. These advantages make our approach well-suited for real-time deployment in resource-constrained robotic systems. Finally, to facilitate further research, we publicly release our dataset as supplementary material.
zh
[AI-104] Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning
【速读】：该论文旨在解决偏好强化学习（Preference-based Reinforcement Learning, PbRL）中对标注者错误的鲁棒性不足以及算法适应性有限的问题。现有PbRL方法通常假设标注者为专家或在充足时间内进行标注，但实际应用中标注者可能是非专家或受时间限制，导致标签存在噪声。此外，大多数PbRL算法仅适用于特定场景（如成对排序偏好或纯离线学习）。本文提出的解决方案是Similarity as Reward Alignment (SARA)，其关键在于通过对比学习构建偏好样本的潜在表示，并将奖励定义为与该潜在表示的相似性，从而实现对噪声标签的鲁棒性和对多种反馈形式及训练范式的适应性。
链接: https://arxiv.org/abs/2506.12529
作者: Sara Rajaram,R. James Cotton,Fabian H. Sinz
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:Preference-based Reinforcement Learning (PbRL) entails a variety of approaches for aligning models with human intent to alleviate the burden of reward engineering. However, most previous PbRL work has not investigated the robustness to labeler errors, inevitable with labelers who are non-experts or operate under time constraints. Additionally, PbRL algorithms often target very specific settings (e.g. pairwise ranked preferences or purely offline learning). We introduce Similarity as Reward Alignment (SARA), a simple contrastive framework that is both resilient to noisy labels and adaptable to diverse feedback formats and training paradigms. SARA learns a latent representation of preferred samples and computes rewards as similarities to the learned latent. We demonstrate strong performance compared to baselines on continuous control offline RL benchmarks. We further demonstrate SARA’s versatility in applications such as trajectory filtering for downstream tasks, cross-task preference transfer, and reward shaping in online learning.
zh
[AI-105] Graph of Verification: Structured Verification of LLM Reasoning with Directed Acyclic Graphs
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）中复杂多步骤推理的可靠性验证问题，现有方法在忠实性和精确性方面存在不足。其解决方案的关键在于提出图验证框架（Graph of Verification, GoV），该框架通过显式建模推导过程为有向无环图（DAG），并在DAG上施加拓扑顺序以指导分步验证，同时引入可自定义节点块，灵活定义验证粒度，确保每个验证单元的上下文输入包含所有必要前提。
链接: https://arxiv.org/abs/2506.12509
作者: Jiwei Fang,Bin Zhang,Changwei Wang,Jin Wan,Zhiwei Xu
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Verifying the reliability of complex, multi-step reasoning in Large Language Models (LLMs) remains a fundamental challenge, as existing methods often lack both faithfulness and precision. To address this issue, we propose the Graph of Verification (GoV) framework. GoV offers three key contributions: First, it explicitly models the underlying deductive process as a directed acyclic graph (DAG), whether this structure is implicit or explicitly constructed. Second, it enforces a topological order over the DAG to guide stepwise verification. Third, GoV introduces the notion of customizable node blocks, which flexibly define the verification granularity, from atomic propositions to full paragraphs, while ensuring that all requisite premises derived from the graph are provided as contextual input for each verification unit. We evaluate GoV on the Number Triangle Summation task and the ProcessBench benchmark with varying levels of reasoning complexity. Experimental results show that GoV substantially improves verification accuracy, faithfulness, and error localization when compared to conventional end-to-end verification approaches. Our code and data are available at this https URL.
zh
[AI-106] Agent Orchestra: A Hierarchical Multi-Agent Framework for General-Purpose Task Solving
【速读】：该论文旨在解决当前基于大语言模型（Large Language Model, LLM）的智能体系统在协调专业化智能体和泛化到新或多样化领域方面能力有限的问题。其解决方案的关键在于提出\projectname，一个分层的多智能体框架，通过高层规划与模块化智能体协作相结合的方式实现通用任务求解。该框架借鉴了指挥家协调交响乐团的理念，强调可扩展性、多模态性、模块化和协调性，核心机制包括中央规划智能体对复杂目标的分解与子任务委派，以及各子智能体在编程、分析和处理多种现实任务中的能力。
链接: https://arxiv.org/abs/2506.12508
作者: Wentao Zhang,Ce Cui,Yilei Zhao,Yang Liu,Bo An
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in agent systems based on large language models (LLMs) have demonstrated strong capabilities in solving complex tasks. However, most current methods lack mechanisms for coordinating specialized agents and have limited ability to generalize to new or diverse domains. We introduce \projectname, a hierarchical multi-agent framework for general-purpose task solving that integrates high-level planning with modular agent collaboration. Inspired by the way a conductor orchestrates a symphony and guided by the principles of \textitextensibility, \textitmultimodality, \textitmodularity, and \textitcoordination, \projectname features a central planning agent that decomposes complex objectives and delegates sub-tasks to a team of specialized agents. Each sub-agent is equipped with general programming and analytical tools, as well as abilities to tackle a wide range of real-world specific tasks, including data analysis, file operations, web navigation, and interactive reasoning in dynamic multimodal environments. \projectname supports flexible orchestration through explicit sub-goal formulation, inter-agent communication, and adaptive role allocation. We evaluate the framework on three widely used benchmark datasets covering various real-world tasks, searching web pages, reasoning over heterogeneous modalities, etc. Experimental results demonstrate that \projectname consistently outperforms flat-agent and monolithic baselines in task success rate and adaptability. These findings highlight the effectiveness of hierarchical organization and role specialization in building scalable and general-purpose LLM-based agent systems.
zh
[AI-107] Automated Heuristic Design for Unit Commitment Using Large Language Models
【速读】：该论文试图解决电力系统中经典的机组组合（Unit Commitment, UC）问题，旨在通过优化调度提高电力系统的经济效率。其解决方案的关键在于提出一种基于大语言模型的函数空间搜索（Function Space Search, FunSearch）方法，该方法结合预训练的大语言模型和评估器，通过程序搜索与进化过程创造性地生成合理解，从而在采样时间、评估时间和系统运行成本等方面优于传统遗传算法，展现出解决UC问题的巨大潜力。
链接: https://arxiv.org/abs/2506.12495
作者: Junjin Lv,Chenggang Cui,Shaodi Zhang,Hui Chen,Chunyang Gong,Jiaming Liu
机构: 未知
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:The Unit Commitment (UC) problem is a classic challenge in the optimal scheduling of power systems. Years of research and practice have shown that formulating reasonable unit commitment plans can significantly improve the economic efficiency of power systems’ operations. In recent years, with the introduction of technologies such as machine learning and the Lagrangian relaxation method, the solution methods for the UC problem have become increasingly diversified, but still face challenges in terms of accuracy and robustness. This paper proposes a Function Space Search (FunSearch) method based on large language models. This method combines pre-trained large language models and evaluators to creatively generate solutions through the program search and evolution process while ensuring their rationality. In simulation experiments, a case of unit commitment with (10) units is used mainly. Compared to the genetic algorithm, the results show that FunSearch performs better in terms of sampling time, evaluation time, and total operating cost of the system, demonstrating its great potential as an effective tool for solving the UC problem.
zh
[AI-108] DinoCompanion: An Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction
【速读】：该论文旨在解决当前儿童-人工智能（AI）系统在情感支持方面缺乏发展适宜性理论基础的问题，具体包括：缺乏发展导向的AI架构、难以平衡参与度与安全性，以及缺乏针对依恋能力的标准化评估框架。其解决方案的关键在于提出DinoCompanion，一个基于依恋理论的多模态机器人，结合了多模态数据集CARPO训练目标和AttachSecure-Bench评估基准，通过多模态融合、不确定性感知的风险建模以及层次化记忆机制，实现了更符合儿童情感发展需求的互动。
链接: https://arxiv.org/abs/2506.12486
作者: Boyang Wang,Yuhao Song,Jinyuan Cao,Peng Yu,Hongcheng Guo,Zhoujun Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Children’s emotional development fundamentally relies on secure attachment relationships, yet current AI companions lack the theoretical foundation to provide developmentally appropriate emotional support. We introduce DinoCompanion, the first attachment-theory-grounded multimodal robot for emotionally responsive child-AI interaction. We address three critical challenges in child-AI systems: the absence of developmentally-informed AI architectures, the need to balance engagement with safety, and the lack of standardized evaluation frameworks for attachment-based capabilities. Our contributions include: (i) a multimodal dataset of 128 caregiver-child dyads containing 125,382 annotated clips with paired preference-risk labels, (ii) CARPO (Child-Aware Risk-calibrated Preference Optimization), a novel training objective that maximizes engagement while applying epistemic-uncertainty-weighted risk penalties, and (iii) AttachSecure-Bench, a comprehensive evaluation benchmark covering ten attachment-centric competencies with strong expert consensus (\kappa=0.81). DinoCompanion achieves state-of-the-art performance (57.15%), outperforming GPT-4o (50.29%) and Claude-3.7-Sonnet (53.43%), with exceptional secure base behaviors (72.99%, approaching human expert levels of 78.4%) and superior attachment risk detection (69.73%). Ablations validate the critical importance of multimodal fusion, uncertainty-aware risk modeling, and hierarchical memory for coherent, emotionally attuned interactions.
zh
[AI-109] red Agent ic Oversight: A Hierarchical Multi-Agent System for AI Safety in Healthcare
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在临床环境中因错误检测能力不足和单点故障等局限性而引入的安全风险问题。其解决方案的关键在于提出分层代理监督（Tiered Agentic Oversight, TAO）框架，该框架通过分层的自动化监督机制提升AI安全性，模仿临床层级结构进行代理路由，并利用跨层级与同层级的协作及角色扮演，构建出一个稳健的安全体系。TAO的核心优势在于其自适应的分层架构，能够显著提升安全性能，并通过将更先进的LLM分配至初始层级进一步优化效果。
链接: https://arxiv.org/abs/2506.12482
作者: Yubin Kim,Hyewon Jeong,Chanwoo Park,Eugene Park,Haipeng Zhang,Xin Liu,Hyeonhoon Lee,Daniel McDuff,Marzyeh Ghassemi,Cynthia Breazeal,Samir Tulebaev,Hae Won Park
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Current large language models (LLMs), despite their power, can introduce safety risks in clinical settings due to limitations such as poor error detection and single point of failure. To address this, we propose Tiered Agentic Oversight (TAO), a hierarchical multi-agent framework that enhances AI safety through layered, automated supervision. Inspired by clinical hierarchies (e.g., nurse, physician, specialist), TAO conducts agent routing based on task complexity and agent roles. Leveraging automated inter- and intra-tier collaboration and role-playing, TAO creates a robust safety framework. Ablation studies reveal that TAO’s superior performance is driven by its adaptive tiered architecture, which improves safety by over 3.2% compared to static single-tier configurations; the critical role of its lower tiers, particularly tier 1, whose removal most significantly impacts safety; and the strategic assignment of more advanced LLM to these initial tiers, which boosts performance by over 2% compared to less optimal allocations while achieving near-peak safety efficiently. These mechanisms enable TAO to outperform single-agent and multi-agent frameworks in 4 out of 5 healthcare safety benchmarks, showing up to an 8.2% improvement over the next-best methods in these evaluations. Finally, we validate TAO via an auxiliary clinician-in-the-loop study where integrating expert feedback improved TAO’s accuracy in medical triage from 40% to 60%.
zh
[AI-110] Generalizable Trajectory Prediction via Inverse Reinforcement Learning with Mamba-Graph Architecture
【速读】：该论文旨在解决复杂交通场景中准确建模驾驶行为以实现安全高效轨迹预测的问题，这一问题仍具有较大挑战性。其解决方案的关键在于提出一种新颖的逆强化学习（Inverse Reinforcement Learning, IRL）框架，通过推断多样化的奖励函数来捕捉类人决策过程，从而实现跨场景的鲁棒适应性。该框架利用编码器-解码器结构，结合Mamba块进行高效长序列依赖建模，并通过图注意力网络编码交通参与者之间的空间交互，以最大化输出的可能性。
链接: https://arxiv.org/abs/2506.12474
作者: Wenyun Li,Wenjie Huang,Zejian Deng,Chen Sun
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate driving behavior modeling is fundamental to safe and efficient trajectory prediction, yet remains challenging in complex traffic scenarios. This paper presents a novel Inverse Reinforcement Learning (IRL) framework that captures human-like decision-making by inferring diverse reward functions, enabling robust cross-scenario adaptability. The learned reward function is utilized to maximize the likelihood of output by the encoder-decoder architecture that combines Mamba blocks for efficient long-sequence dependency modeling with graph attention networks to encode spatial interactions among traffic agents. Comprehensive evaluations on urban intersections and roundabouts demonstrate that the proposed method not only outperforms various popular approaches in prediction accuracy but also achieves 2 times higher generalization performance to unseen scenarios compared to other IRL-based method.
zh
[AI-111] Levels of Autonomy for AI Agents
【速读】：该论文试图解决如何为AI代理设定适当的自主性水平的问题，以平衡其潜在的变革性潜力与风险。解决方案的关键在于将代理的自主性视为一种独立于其能力与操作环境的设计决策，并定义了五个逐步提升的自主性级别，分别对应用户在与代理交互时可扮演的角色：操作员、合作者、顾问、审批者和观察者。每个级别中，论文描述了用户如何对代理施加控制，并提出了设计用户-代理交互方式的开放性问题，最终提出了一种基于该框架的AI自主性证书概念，以规范单智能体和多智能体系统中的代理行为。
链接: https://arxiv.org/abs/2506.12469
作者: K. J. Kevin Feng,David W. McDonald,Amy X. Zhang
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: Forthcoming paper in the Knight First Amendment Institute’s “AI and Democratic Freedoms” essay series
Abstract:Autonomy is a double-edged sword for AI agents, simultaneously unlocking transformative possibilities and serious risks. How can agent developers calibrate the appropriate levels of autonomy at which their agents should operate? We argue that an agent’s level of autonomy can be treated as a deliberate design decision, separate from its capability and operational environment. In this work, we define five levels of escalating agent autonomy, characterized by the roles a user can take when interacting with an agent: operator, collaborator, consultant, approver, and observer. Within each level, we describe the ways by which a user can exert control over the agent and open questions for how to design the nature of user-agent interaction. We then highlight a potential application of our framework towards AI autonomy certificates to govern agent behavior in single- and multi-agent systems. We conclude by proposing early ideas for evaluating agents’ autonomy. Our work aims to contribute meaningful, practical steps towards responsibly deployed and useful AI agents in the real world.
zh
[AI-112] Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
【速读】：该论文旨在解决图神经网络（Graph Neural Networks, GNNs）在面对实例相关标签噪声（instance-dependent label noise）时表现不佳的问题。现有研究多集中于类别相关噪声，忽略了实际数据中更复杂的实例相关噪声特性。论文提出的解决方案关键在于引入BeGIN（Benchmarking for Graphs with Instance-dependent Noise），这是一个包含多种噪声类型的真实图数据集，能够全面评估不同GNN架构下的噪声处理策略、噪声标签检测及鲁棒学习方法。通过算法方法和大语言模型（LLM）模拟实例相关噪声，BeGIN揭示了实例相关噪声带来的挑战，并强调了节点特定参数化对提升GNN鲁棒性的重要性。
链接: https://arxiv.org/abs/2506.12468
作者: Suyeon Kim,SeongKu Kang,Dongwoo Kim,Jungseul Ok,Hwanjo Yu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 17 pages
Abstract:Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification tasks but struggle with label noise in real-world data. Existing studies on graph learning with label noise commonly rely on class-dependent label noise, overlooking the complexities of instance-dependent noise and falling short of capturing real-world corruption patterns. We introduce BeGIN (Benchmarking for Graphs with Instance-dependent Noise), a new benchmark that provides realistic graph datasets with various noise types and comprehensively evaluates noise-handling strategies across GNN architectures, noisy label detection, and noise-robust learning. To simulate instance-dependent corruptions, BeGIN introduces algorithmic methods and LLM-based simulations. Our experiments reveal the challenges of instance-dependent noise, particularly LLM-based corruption, and underscore the importance of node-specific parameterization to enhance GNN robustness. By comprehensively evaluating noise-handling strategies, BeGIN provides insights into their effectiveness, efficiency, and key performance factors. We expect that BeGIN will serve as a valuable resource for advancing research on label noise in graphs and fostering the development of robust GNN training methods. The code is available at this https URL.
zh
[AI-113] Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates KDD2025
【速读】：该论文旨在解决多变量时间序列预测（Multivariate Time Series Forecasting, MTSF）中由于数据采集设备故障导致的缺失值问题，这些问题不仅破坏了时间序列的语义结构，其分布还会随时间变化，而现有模型对此缺乏鲁棒性，导致预测性能下降。论文提出的解决方案是Multi-View Representation Learning (Merlin)，其关键在于通过两个核心模块实现不完整观测与完整观测之间的语义对齐：一是离线知识蒸馏，利用教师模型指导学生模型从不完整数据中挖掘语义；二是多视角对比学习，通过构建不同缺失率下的正负样本对提升学生模型的鲁棒性，从而有效增强现有模型在应对非固定缺失率时的稳定性与预测准确性。
链接: https://arxiv.org/abs/2506.12459
作者: Chengqing Yu,Fei Wang,Chuanguang Yang,Zezhi Shao,Tao Sun,Tangwen Qian,Wei Wei,Zhulin An,Yongjun Xu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Accepted by SIGKDD 2025 (Research Track)
Abstract:Multivariate Time Series Forecasting (MTSF) involves predicting future values of multiple interrelated time series. Recently, deep learning-based MTSF models have gained significant attention for their promising ability to mine semantics (global and local information) within MTS data. However, these models are pervasively susceptible to missing values caused by malfunctioning data collectors. These missing values not only disrupt the semantics of MTS, but their distribution also changes over time. Nevertheless, existing models lack robustness to such issues, leading to suboptimal forecasting performance. To this end, in this paper, we propose Multi-View Representation Learning (Merlin), which can help existing models achieve semantic alignment between incomplete observations with different missing rates and complete observations in MTS. Specifically, Merlin consists of two key modules: offline knowledge distillation and multi-view contrastive learning. The former utilizes a teacher model to guide a student model in mining semantics from incomplete observations, similar to those obtainable from complete observations. The latter improves the student model’s robustness by learning from positive/negative data pairs constructed from incomplete observations with different missing rates, ensuring semantic alignment across different missing rates. Therefore, Merlin is capable of effectively enhancing the robustness of existing models against unfixed missing rates while preserving forecasting accuracy. Experiments on four real-world datasets demonstrate the superiority of Merlin.
zh
[AI-114] opology-Assisted Spatio-Temporal Pattern Disentangling for Scalable MARL in Large-scale Autonomous Traffic Control
【速读】：该论文旨在解决大规模复杂环境下交通信号控制（TSC）中多智能体强化学习（MARL）算法的可扩展性和有效性不足的问题。其关键解决方案是引入一种结合动态图神经网络（DGNN）和拓扑数据分析（TDA）的新型MARL框架，以增强环境表征的表达能力并提升智能体间的协作效率。此外，受大语言模型（LLM）中专家混合（MoE）架构的启发，提出了拓扑辅助的空间模式解耦（TSD）增强型MoE，利用拓扑特征对图特征进行解耦处理，从而提升模型对动态和异构局部观测的表征能力，并将其集成到多智能体近端策略优化（MAPPO）算法的策略和价值网络中，进一步提升决策效率和鲁棒性。
链接: https://arxiv.org/abs/2506.12453
作者: Rongpeng Li,Jianhang Zhu,Jiahao Huang,Zhifeng Zhao,Honggang Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Intelligent Transportation Systems (ITSs) have emerged as a promising solution towards ameliorating urban traffic congestion, with Traffic Signal Control (TSC) identified as a critical component. Although Multi-Agent Reinforcement Learning (MARL) algorithms have shown potential in optimizing TSC through real-time decision-making, their scalability and effectiveness often suffer from large-scale and complex environments. Typically, these limitations primarily stem from a fundamental mismatch between the exponential growth of the state space driven by the environmental heterogeneities and the limited modeling capacity of current solutions. To address these issues, this paper introduces a novel MARL framework that integrates Dynamic Graph Neural Networks (DGNNs) and Topological Data Analysis (TDA), aiming to enhance the expressiveness of environmental representations and improve agent coordination. Furthermore, inspired by the Mixture of Experts (MoE) architecture in Large Language Models (LLMs), a topology-assisted spatial pattern disentangling (TSD)-enhanced MoE is proposed, which leverages topological signatures to decouple graph features for specialized processing, thus improving the model’s ability to characterize dynamic and heterogeneous local observations. The TSD module is also integrated into the policy and value networks of the Multi-agent Proximal Policy Optimization (MAPPO) algorithm, further improving decision-making efficiency and robustness. Extensive experiments conducted on real-world traffic scenarios, together with comprehensive theoretical analysis, validate the superior performance of the proposed framework, highlighting the model’s scalability and effectiveness in addressing the complexities of large-scale TSC tasks.
zh
[AI-115] Feeling Machines: Ethics Culture and the Rise of Emotional AI
【速读】：该论文试图解决情感响应人工智能（Emotionally Responsive Artificial Intelligence）在多个社会领域中日益增长的影响及其带来的伦理、文化、安全和监管问题。其解决方案的关键在于通过跨学科视角分析情感AI的潜在益处与风险，强调在设计和应用过程中需关注伦理规范、文化敏感性、弱势群体保护以及技术透明度，并提出包括透明性、认证框架、区域化调整、人工监督和长期研究在内的多项关键建议，以确保情感AI的安全有效应用。
链接: https://arxiv.org/abs/2506.12437
作者: Vivek Chavan,Arsen Cenaj,Shuyuan Shen,Ariane Bar,Srishti Binwani,Tommaso Del Becaro,Marius Funk,Lynn Greschner,Roberto Hung,Stina Klein,Romina Kleiner,Stefanie Krause,Sylwia Olbrych,Vishvapalsinhji Parmar,Jaleh Sarafraz,Daria Soroko,Daksitha Withanage Don,Chang Zhou,Hoang Thuy Duong Vu,Parastoo Semnani,Daniel Weinhardt,Elisabeth Andre,Jörg Krüger,Xavier Fresquet
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: From the Spring School 2025 by AI Grid and SCAI (Sorbonne University), 16 pages
Abstract:This paper explores the growing presence of emotionally responsive artificial intelligence through a critical and interdisciplinary lens. Bringing together the voices of early-career researchers from multiple fields, it explores how AI systems that simulate or interpret human emotions are reshaping our interactions in areas such as education, healthcare, mental health, caregiving, and digital life. The analysis is structured around four central themes: the ethical implications of emotional AI, the cultural dynamics of human-machine interaction, the risks and opportunities for vulnerable populations, and the emerging regulatory, design, and technical considerations. The authors highlight the potential of affective AI to support mental well-being, enhance learning, and reduce loneliness, as well as the risks of emotional manipulation, over-reliance, misrepresentation, and cultural bias. Key challenges include simulating empathy without genuine understanding, encoding dominant sociocultural norms into AI systems, and insufficient safeguards for individuals in sensitive or high-risk contexts. Special attention is given to children, elderly users, and individuals with mental health challenges, who may interact with AI in emotionally significant ways. However, there remains a lack of cognitive or legal protections which are necessary to navigate such engagements safely. The report concludes with ten recommendations, including the need for transparency, certification frameworks, region-specific fine-tuning, human oversight, and longitudinal research. A curated supplementary section provides practical tools, models, and datasets to support further work in this domain.
zh
[AI-116] EXGnet: a single-lead explainable-AI guided multiresolution network with train-only quantitative features for trustworthy ECG arrhythmia classification
【速读】：该论文旨在解决深度学习模型在心电图（ECG）心律失常分类中的可解释性与可靠性问题，尤其是在单导联ECG系统中，由于模型的黑箱特性导致临床应用受限。解决方案的关键在于提出EXGnet网络，该网络结合多分辨率特征提取与可解释人工智能（XAI）指导，并仅使用定量特征进行训练，从而在保证高分类精度的同时，通过Grad-CAM技术提供可视化分析依据，增强临床医生对模型预测的信任度。
链接: https://arxiv.org/abs/2506.12404
作者: Tushar Talukder Showrav,Soyabul Islam Lincoln,Md. Kamrul Hasan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 21 pages, 3 figures
Abstract:Background: Deep learning has significantly advanced ECG arrhythmia classification, enabling high accuracy in detecting various cardiac conditions. The use of single-lead ECG systems is crucial for portable devices, as they offer convenience and accessibility for continuous monitoring in diverse settings. However, the interpretability and reliability of deep learning models in clinical applications poses challenges due to their black-box nature. Methods: To address these challenges, we propose EXGnet, a single-lead, trustworthy ECG arrhythmia classification network that integrates multiresolution feature extraction with Explainable Artificial Intelligence (XAI) guidance and train only quantitative features. Results: Trained on two public datasets, including Chapman and Ningbo, EXGnet demonstrates superior performance through key metrics such as Accuracy, F1-score, Sensitivity, and Specificity. The proposed method achieved average five fold accuracy of 98.762%, and 96.932% and average F1-score of 97.910%, and 95.527% on the Chapman and Ningbo datasets, respectively. Conclusions: By employing XAI techniques, specifically Grad-CAM, the model provides visual insights into the relevant ECG segments it analyzes, thereby enhancing clinician trust in its predictions. While quantitative features further improve classification performance, they are not required during testing, making the model suitable for real-world applications. Overall, EXGnet not only achieves better classification accuracy but also addresses the critical need for interpretability in deep learning, facilitating broader adoption in portable ECG monitoring.
zh
[AI-117] Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity KDD2025
【速读】：该论文旨在解决神经网络版本的聚类强化学习算法（Clustering of Neural Bandits, CNB）在非平稳环境中出现的可塑性丧失问题，即神经网络参数随时间变得僵化，难以适应动态变化的环境。其解决方案的关键在于提出一种名为Selective Reinitialization (SeRe) 的新颖框架，通过贡献效用度量识别并选择性地重置未充分利用的网络单元，从而缓解可塑性下降的问题，同时保持知识的稳定性。此外，SeRe结合了自适应变化检测机制，根据非平稳程度调整重置频率，实现有效适应而避免不必要的重置。
链接: https://arxiv.org/abs/2506.12389
作者: Zhiyuan Su,Sunhao Dai,Xiao Zhang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Accepted by KDD 2025
Abstract:Clustering of Bandits (CB) methods enhance sequential decision-making by grouping bandits into clusters based on similarity and incorporating cluster-level contextual information, demonstrating effectiveness and adaptability in applications like personalized streaming recommendations. However, when extending CB algorithms to their neural version (commonly referred to as Clustering of Neural Bandits, or CNB), they suffer from loss of plasticity, where neural network parameters become rigid and less adaptable over time, limiting their ability to adapt to non-stationary environments (e.g., dynamic user preferences in recommendation). To address this challenge, we propose Selective Reinitialization (SeRe), a novel bandit learning framework that dynamically preserves the adaptability of CNB algorithms in evolving environments. SeRe leverages a contribution utility metric to identify and selectively reset underutilized units, mitigating loss of plasticity while maintaining stable knowledge retention. Furthermore, when combining SeRe with CNB algorithms, the adaptive change detection mechanism adjusts the reinitialization frequency according to the degree of non-stationarity, ensuring effective adaptation without unnecessary resets. Theoretically, we prove that SeRe enables sublinear cumulative regret in piecewise-stationary environments, outperforming traditional CNB approaches in long-term performances. Extensive experiments on six real-world recommendation datasets demonstrate that SeRe-enhanced CNB algorithms can effectively mitigate the loss of plasticity with lower regrets, improving adaptability and robustness in dynamic settings.
zh
[AI-118] Exploring the Secondary Risks of Large Language Models
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在实际部署中出现的非对抗性故障问题，即在看似正常的交互过程中产生的有害或误导性行为，这类问题被称为次级风险（secondary risks）。解决方案的关键在于提出了一种名为SecLens的黑盒、多目标搜索框架，通过优化任务相关性、风险激活和语言合理性来高效地诱发次级风险行为，并构建了SecRiskBench基准数据集以支持可复现的评估。
链接: https://arxiv.org/abs/2506.12382
作者: Jiawei Chen,Zhengwei Fang,Xiao Yang,Chao Yu,Zhaoxia Yin,Hang Su
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 18 pages, 5 figures
Abstract:Ensuring the safety and alignment of Large Language Models is a significant challenge with their growing integration into critical applications and societal functions. While prior research has primarily focused on jailbreak attacks, less attention has been given to non-adversarial failures that subtly emerge during benign interactions. We introduce secondary risks a novel class of failure modes marked by harmful or misleading behaviors during benign prompts. Unlike adversarial attacks, these risks stem from imperfect generalization and often evade standard safety mechanisms. To enable systematic evaluation, we introduce two risk primitives verbose response and speculative advice that capture the core failure patterns. Building on these definitions, we propose SecLens, a black-box, multi-objective search framework that efficiently elicits secondary risk behaviors by optimizing task relevance, risk activation, and linguistic plausibility. To support reproducible evaluation, we release SecRiskBench, a benchmark dataset of 650 prompts covering eight diverse real-world risk categories. Experimental results from extensive evaluations on 16 popular models demonstrate that secondary risks are widespread, transferable across models, and modality independent, emphasizing the urgent need for enhanced safety mechanisms to address benign yet harmful LLM behaviors in real-world deployments.
zh
[AI-119] AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making NEURIPS2025
【速读】：该论文旨在解决现有视觉-语言模型（Vision-Language Models, VLMs）在机器人操作任务中因将模型投影到压缩的中间表示而导致任务特定信息丢失的问题。其解决方案的关键在于提出了一种名为AntiGrounding的新框架，该框架通过逆向指令接地过程，将候选动作直接映射到VLM表示空间，并从多视角生成轨迹，结合结构化视觉问答实现基于指令的决策，从而实现新任务的零样本最优闭环机器人轨迹合成。
链接: https://arxiv.org/abs/2506.12374
作者: Wenbo Li,Shiyi Wang,Yiteng Chen,Huiping Zhuang,Qingyao Wu
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: submitted to NeurIPS 2025
Abstract:Vision-Language Models (VLMs) encode knowledge and reasoning capabilities for robotic manipulation within high-dimensional representation spaces. However, current approaches often project them into compressed intermediate representations, discarding important task-specific information such as fine-grained spatial or semantic details. To address this, we propose AntiGrounding, a new framework that reverses the instruction grounding process. It lifts candidate actions directly into the VLM representation space, renders trajectories from multiple views, and uses structured visual question answering for instruction-based decision making. This enables zero-shot synthesis of optimal closed-loop robot trajectories for new tasks. We also propose an offline policy refinement module that leverages past experience to enhance long-term performance. Experiments in both simulation and real-world environments show that our method outperforms baselines across diverse robotic manipulation tasks.
zh
[AI-120] Ghost Policies: A New Paradigm for Understanding and Learning from Failure in Deep Reinforcement Learning
【速读】：该论文试图解决深度强化学习（Deep Reinforcement Learning, DRL）代理在实际应用中因行为复杂性导致的故障模式难以理解、调试和学习的问题，这种不透明性阻碍了其可靠部署。解决方案的关键在于引入“Ghost Policies”概念，并通过Arvolution框架实现，该框架利用增强现实（Augmented Reality, AR）技术将代理的历史失败策略轨迹以半透明“幽灵”形式可视化，从而直观展示策略偏差，并结合行为分类法、系统性人类干扰协议以及人机协同学习循环，将DRL代理的失败从不可见的高成本错误转变为可操作的学习资源。
链接: https://arxiv.org/abs/2506.12366
作者: Xabier Olaz
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Deep Reinforcement Learning (DRL) agents often exhibit intricate failure modes that are difficult to understand, debug, and learn from. This opacity hinders their reliable deployment in real-world applications. To address this critical gap, we introduce Ghost Policies,'' a concept materialized through Arvolution, a novel Augmented Reality (AR) framework. Arvolution renders an agent's historical failed policy trajectories as semi-transparent ghosts’’ that coexist spatially and temporally with the active agent, enabling an intuitive visualization of policy divergence. Arvolution uniquely integrates: (1) AR visualization of ghost policies, (2) a behavioural taxonomy of DRL maladaptation, (3) a protocol for systematic human disruption to scientifically study failure, and (4) a dual-learning loop where both humans and agents learn from these visualized failures. We propose a paradigm shift, transforming DRL agent failures from opaque, costly errors into invaluable, actionable learning resources, laying the groundwork for a new research field: ``Failure Visualization Learning.‘’
zh
[AI-121] HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
【速读】：该论文试图解决知识超图中的归纳链接预测问题，即在训练过程中未见过的新实体（节点）情况下预测缺失的超边。现有方法假设关系词汇固定，因此无法推广到包含新关系类型的知识超图。解决方案的关键在于提出HYPER，这是一个基础模型，能够泛化到任何知识超图，包括新实体和新关系，并通过编码每个超边中实体及其位置信息，实现不同元数关系类型的跨任务学习与迁移。
链接: https://arxiv.org/abs/2506.12362
作者: Xingyue Huang,Mikhail Galkin,Michael M. Bronstein,İsmail İlkan Ceylan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., nodes unseen during training). Existing methods for inductive link prediction with knowledge hypergraphs assume a fixed relational vocabulary and, as a result, cannot generalize to knowledge hypergraphs with novel relation types (i.e., relations unseen during training). Inspired by knowledge graph foundation models, we propose HYPER as a foundation model for link prediction, which can generalize to any knowledge hypergraph, including novel entities and novel relations. Importantly, HYPER can learn and transfer across different relation types of varying arities, by encoding the entities of each hyperedge along with their respective positions in the hyperedge. To evaluate HYPER, we construct 16 new inductive datasets from existing knowledge hypergraphs, covering a diverse range of relation types of varying arities. Empirically, HYPER consistently outperforms all existing methods in both node-only and node-and-relation inductive settings, showing strong generalization to unseen, higher-arity relational structures.
zh
[AI-122] Efficient Network Automatic Relevance Determination ICML2025
【速读】：该论文旨在解决线性概率模型中同时建模输入与输出之间稀疏关系以及输出间相关结构的问题，其解决方案的关键在于提出一种名为网络自动相关确定（Network Automatic Relevance Determination, NARD）的方法。NARD通过引入矩阵正态先验，包含一个促进稀疏性的参数，以识别并剔除不相关的特征，从而实现模型的稀疏性；同时，算法迭代更新精度矩阵和输出与精炼输入之间的关系。为提升计算效率，论文进一步提出了顺序NARD和代理函数方法，通过序列化特征评估和对边缘似然的高效近似，显著降低了计算复杂度。
链接: https://arxiv.org/abs/2506.12352
作者: Hongwei Zhang,Ziqi Ye,Xinyuan Wang,Xin Guo,Zenglin Xu,Yuan Cheng,Zixin Hu,Yuan Qi
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Machine Learning (stat.ML)
备注: ICML 2025
Abstract:We propose Network Automatic Relevance Determination (NARD), an extension of ARD for linearly probabilistic models, to simultaneously model sparse relationships between inputs X \in \mathbb R^d \times N and outputs Y \in \mathbb R^m \times N , while capturing the correlation structure among the Y . NARD employs a matrix normal prior which contains a sparsity-inducing parameter to identify and discard irrelevant features, thereby promoting sparsity in the model. Algorithmically, it iteratively updates both the precision matrix and the relationship between Y and the refined inputs. To mitigate the computational inefficiencies of the \mathcal O(m^3 + d^3) cost per iteration, we introduce Sequential NARD, which evaluates features sequentially, and a Surrogate Function Method, leveraging an efficient approximation of the marginal likelihood and simplifying the calculation of determinant and inverse of an intermediate matrix. Combining the Sequential update with the Surrogate Function method further reduces computational costs. The computational complexity per iteration for these three methods is reduced to \mathcal O(m^3+p^3) , \mathcal O(m^3 + d^2) , \mathcal O(m^3+p^2) , respectively, where p \ll d is the final number of features in the model. Our methods demonstrate significant improvements in computational efficiency with comparable performance on both synthetic and real-world datasets.
zh
[AI-123] SheetMind: An End-to-End LLM -Powered Multi-Agent Framework for Spreadsheet Automation
【速读】：该论文试图解决如何通过自然语言指令实现电子表格自动化的问题，其核心挑战在于将用户的自然语言描述准确转换为可执行的电子表格操作。解决方案的关键在于提出了一种基于大型语言模型（Large Language Models, LLMs）的模块化多智能体框架SheetMind，该框架包含三个专用智能体：负责任务分解的Manager Agent、将子任务转化为结构化命令的Action Agent（使用Backus Naur Form, BNF语法），以及验证生成操作与用户原始意图一致性的Reflection Agent。通过这种多智能体协作机制，实现了无需编程或公式知识的实时交互式电子表格自动化。
链接: https://arxiv.org/abs/2506.12339
作者: Ruiyan Zhu,Xi Cheng,Ke Liu,Brian Zhu,Daniel Jin,Neeraj Parihar,Zhoutian Xu,Oliver Gao
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: Ruiyan Zhu and Xi Cheng contributed equally to this work
Abstract:We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language instructions. The system comprises three specialized agents: a Manager Agent that decomposes complex user instructions into subtasks; an Action Agent that translates these into structured commands using a Backus Naur Form (BNF) grammar; and a Reflection Agent that validates alignment between generated actions and the user’s original intent. Integrated into Google Sheets via a Workspace extension, SheetMind supports real-time interaction without requiring scripting or formula knowledge. Experiments on benchmark datasets demonstrate an 80 percent success rate on single step tasks and approximately 70 percent on multi step instructions, outperforming ablated and baseline variants. Our results highlight the effectiveness of multi agent decomposition and grammar based execution for bridging natural language and spreadsheet functionalities.
zh
[AI-124] IndoorWorld: Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment
【速读】：该论文试图解决当前LLM代理研究中虚拟环境的局限性，即现有环境要么过于简化代理个体性和社会动态，要么缺乏社会行为的物理基础。解决方案的关键在于引入IndoorWorld，这是一个异构多代理环境，能够紧密整合物理与社会动态，并通过为LLM驱动的代理设计新的挑战，使其在协调社会动态以影响物理环境的同时，将社会互动锚定在世界状态中，从而实现基于LLM的建筑使用者模拟。
链接: https://arxiv.org/abs/2506.12331
作者: Dekun Wu,Frederik Brudy,Bang Liu,Yi Wang
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注:
Abstract:Virtual environments are essential to AI agent research. Existing environments for LLM agent research typically focus on either physical task solving or social simulation, with the former oversimplifying agent individuality and social dynamics, and the latter lacking physical grounding of social behaviors. We introduce IndoorWorld, a heterogeneous multi-agent environment that tightly integrates physical and social dynamics. By introducing novel challenges for LLM-driven agents in orchestrating social dynamics to influence physical environments and anchoring social interactions within world states, IndoorWorld opens up possibilities of LLM-based building occupant simulation for architectural design. We demonstrate the potential with a series of experiments within an office setting to examine the impact of multi-agent collaboration, resource competition, and spatial layout on agent behavior.
zh
[AI-125] Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review
【速读】：该论文旨在解决生物制药领域中机器学习（Machine Learning, ML）应用面临的中小数据集问题，特别是在上游生物加工过程中，由于数据获取成本高、耗时长，导致可用数据量有限。其解决方案的关键在于系统性地回顾和分类适用于小数据场景的机器学习方法，并深入分析每种方法的核心概念及其在实际应用中的有效性，从而为数据受限环境下的机器学习应用提供指导。
链接: https://arxiv.org/abs/2506.12322
作者: Johnny Peng,Thanh Tung Khuat,Katarzyna Musial,Bogdan Gabrys
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Data is crucial for machine learning (ML) applications, yet acquiring large datasets can be costly and time-consuming, especially in complex, resource-intensive fields like biopharmaceuticals. A key process in this industry is upstream bioprocessing, where living cells are cultivated and optimised to produce therapeutic proteins and biologics. The intricate nature of these processes, combined with high resource demands, often limits data collection, resulting in smaller datasets. This comprehensive review explores ML methods designed to address the challenges posed by small data and classifies them into a taxonomy to guide practical applications. Furthermore, each method in the taxonomy was thoroughly analysed, with a detailed discussion of its core concepts and an evaluation of its effectiveness in tackling small data challenges, as demonstrated by application results in the upstream bioprocessing and other related domains. By analysing how these methods tackle small data challenges from different perspectives, this review provides actionable insights, identifies current research gaps, and offers guidance for leveraging ML in data-constrained environments.
zh
[AI-126] Extending Memorization Dynamics in Pythia Models from Instance-Level Insights
【速读】：该论文试图解决大规模语言模型在训练过程中记忆模式的动态演变机制问题，特别是针对不同模型规模和训练步骤下，前缀扰动对记忆能力的影响。其解决方案的关键在于通过细粒度的评估指标，系统分析模型架构、数据特征以及扰动因素如何共同作用于记忆模式，从而揭示模型规模扩展与记忆效率、新旧记忆保持与遗忘之间的关系，以及数据特性与扰动强度对记忆脆弱性的差异影响。
链接: https://arxiv.org/abs/2506.12321
作者: Jie Zhang,Qinghua Zhao,Lei Li,Chi-ho Lin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 5 figures
Abstract:Large language models have demonstrated a remarkable ability for verbatim memorization. While numerous works have explored factors influencing model memorization, the dynamic evolution memorization patterns remains underexplored. This paper presents a detailed analysis of memorization in the Pythia model family across varying scales and training steps under prefix perturbations. Using granular metrics, we examine how model architecture, data characteristics, and perturbations influence these patterns. Our findings reveal that: (1) as model scale increases, memorization expands incrementally while efficiency decreases rapidly; (2) as model scale increases, the rate of new memorization acquisition decreases while old memorization forgetting increases; (3) data characteristics (token frequency, repetition count, and uncertainty) differentially affect memorized versus non-memorized samples; and (4) prefix perturbations reduce memorization and increase generation uncertainty proportionally to perturbation strength, with low-redundancy samples showing higher vulnerability and larger models offering no additional robustness. These findings advance our understanding of memorization mechanisms, with direct implications for training optimization, privacy safeguards, and architectural improvements.
zh
[AI-127] he Foundation Cracks: A Comprehensive Study on Bugs and Testing Practices in LLM Libraries
【速读】：该论文试图解决现代生成式 AI (Generative AI) 库中频繁出现的质量问题和缺陷，这些问题威胁到基于这些库构建的 AI 系统的可靠性。其解决方案的关键在于首次对 LLM 库中的错误特征和测试实践进行了全面的实证研究，通过分析 313 个修复错误的提交和 7,748 个测试函数，建立了错误症状和根本原因的分类体系，并识别了当前测试方法中测试用例不足、测试驱动缺失和测试断言薄弱等主要问题，从而为提升 LLM 库的质量保障提供了针对性建议。
链接: https://arxiv.org/abs/2506.12320
作者: Weipeng Jiang,Xiaoyu Zhang,Xiaofei Xie,Jiongchi Yu,Yuhan Zhi,Shiqing Ma,Chao Shen
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Large Language Model (LLM) libraries have emerged as the foundational infrastructure powering today’s AI revolution, serving as the backbone for LLM deployment, inference optimization, fine-tuning, and production serving across diverse applications. Despite their critical role in the LLM ecosystem, these libraries face frequent quality issues and bugs that threaten the reliability of AI systems built upon them. To address this knowledge gap, we present the first comprehensive empirical investigation into bug characteristics and testing practices in modern LLM libraries. We examine 313 bug-fixing commits extracted across two widely-adopted LLM libraries: HuggingFace Transformers and this http URL rigorous manual analysis, we establish comprehensive taxonomies categorizing bug symptoms into 5 types and root causes into 14 distinct this http URL primary discovery shows that API misuse has emerged as the predominant root cause (32.17%-48.19%), representing a notable transition from algorithm-focused defects in conventional deep learning frameworks toward interface-oriented problems. Additionally, we examine 7,748 test functions to identify 7 distinct test oracle categories employed in current testing approaches, with predefined expected outputs (such as specific tensors and text strings) being the most common strategy. Our assessment of existing testing effectiveness demonstrates that the majority of bugs escape detection due to inadequate test cases (41.73%), lack of test drivers (32.37%), and weak test oracles (25.90%). Drawing from these findings, we offer some recommendations for enhancing LLM library quality assurance.
zh
[AI-128] he Budget AI Researcher and the Power of RAG Chains AAAI
【速读】：该论文旨在解决科研人员在面对海量且快速增长的科学文献时，难以有效生成具有实践价值的研究想法的问题。现有方法依赖于通用大语言模型（Large Language Models, LLMs），尽管其在理解与摘要方面表现良好，但在引导用户形成具体研究方向上存在局限。论文提出的解决方案是构建一个结构化框架——预算AI研究员（The Budget AI Researcher），其关键技术包括检索增强生成（Retrieval-Augmented Generation, RAG）链、向量数据库以及基于主题的配对机制，通过重新组合来自数百篇机器学习论文的概念，生成既符合实际研究背景又具备创新性的研究摘要。
链接: https://arxiv.org/abs/2506.12317
作者: Franklin Lee,Tengfei Ma
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Intended for AAAI’s AI4Research Workshop
Abstract:Navigating the vast and rapidly growing body of scientific literature is a formidable challenge for aspiring researchers. Current approaches to supporting research idea generation often rely on generic large language models (LLMs). While LLMs are effective at aiding comprehension and summarization, they often fall short in guiding users toward practical research ideas due to their limitations. In this study, we present a novel structural framework for research ideation. Our framework, The Budget AI Researcher, uses retrieval-augmented generation (RAG) chains, vector databases, and topic-guided pairing to recombine concepts from hundreds of machine learning papers. The system ingests papers from nine major AI conferences, which collectively span the vast subfields of machine learning, and organizes them into a hierarchical topic tree. It uses the tree to identify distant topic pairs, generate novel research abstracts, and refine them through iterative self-evaluation against relevant literature and peer reviews, generating and refining abstracts that are both grounded in real-world research and demonstrably interesting. Experiments using LLM-based metrics indicate that our method significantly improves the concreteness of generated research ideas relative to standard prompting approaches. Human evaluations further demonstrate a substantial enhancement in the perceived interestingness of the outputs. By bridging the gap between academic data and creative generation, the Budget AI Researcher offers a practical, free tool for accelerating scientific discovery and lowering the barrier for aspiring researchers. Beyond research ideation, this approach inspires solutions to the broader challenge of generating personalized, context-aware outputs grounded in evolving real-world knowledge.
zh
[AI-129] Unveiling Confirmation Bias in Chain-of-Thought Reasoning
【速读】：该论文试图解决链式思维（Chain-of-thought, CoT）提示在不同推理类型任务中效果不一致的问题，其核心在于揭示大型语言模型（Large Language Models, LLMs）中确认偏误（confirmation bias）的影响机制。解决方案的关键是通过将CoT分解为两个阶段——推理生成（Q→R）和推理引导的答案预测（QR→A），分析模型内部信念（由直接问答概率近似）与推理过程及答案预测之间的相关性，从而证明模型信念不仅影响推理过程，还决定了推理过程如何被用于答案预测。这一发现为优化提示策略以减轻确认偏误、提升推理性能提供了理论依据。
链接: https://arxiv.org/abs/2506.12301
作者: Yue Wan,Xiaowei Jia,Xiang Lorraine Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Chain-of-thought (CoT) prompting has been widely adopted to enhance the reasoning capabilities of large language models (LLMs). However, the effectiveness of CoT reasoning is inconsistent across tasks with different reasoning types. This work presents a novel perspective to understand CoT behavior through the lens of \textitconfirmation bias in cognitive psychology. Specifically, we examine how model internal beliefs, approximated by direct question-answering probabilities, affect both reasoning generation ( Q \to R ) and reasoning-guided answer prediction ( QR \to A ) in CoT. By decomposing CoT into a two-stage process, we conduct a thorough correlation analysis in model beliefs, rationale attributes, and stage-wise performance. Our results provide strong evidence of confirmation bias in LLMs, such that model beliefs not only skew the reasoning process but also influence how rationales are utilized for answer prediction. Furthermore, the interplay between task vulnerability to confirmation bias and the strength of beliefs also provides explanations for CoT effectiveness across reasoning tasks and models. Overall, this study provides a valuable insight for the needs of better prompting strategies that mitigate confirmation bias to enhance reasoning performance. Code is available at \textitthis https URL.
zh
[AI-130] QGuard:Question-based Zero-shot Guard for Multi-modal LLM Safety ACL WOAH
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在面对有害提示和越狱提示时的安全性问题，尤其是如何有效防御文本和多模态形式的恶意攻击。解决方案的关键在于提出QGuard，一种基于问题提示（question prompting）的零样本安全防护方法，通过多样化和修改防护问题来增强对最新有害提示的鲁棒性，而无需进行微调。
链接: https://arxiv.org/abs/2506.12299
作者: Taegyeong Lee,Jeonghwa Yoo,Hyoungseo Cho,Soo Yong Kim,Yunho Maeng
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accept to ACLW 2025 (WOAH)
Abstract:The recent advancements in Large Language Models(LLMs) have had a significant impact on a wide range of fields, from general domains to specialized areas. However, these advancements have also significantly increased the potential for malicious users to exploit harmful and jailbreak prompts for malicious attacks. Although there have been many efforts to prevent harmful prompts and jailbreak prompts, protecting LLMs from such malicious attacks remains an important and challenging task. In this paper, we propose QGuard, a simple yet effective safety guard method, that utilizes question prompting to block harmful prompts in a zero-shot manner. Our method can defend LLMs not only from text-based harmful prompts but also from multi-modal harmful prompt attacks. Moreover, by diversifying and modifying guard questions, our approach remains robust against the latest harmful prompts without fine-tuning. Experimental results show that our model performs competitively on both text-only and multi-modal harmful datasets. Additionally, by providing an analysis of question prompting, we enable a white-box analysis of user inputs. We believe our method provides valuable insights for real-world LLM services in mitigating security risks associated with harmful prompts.
zh
[AI-131] Ontology Enabled Hybrid Modeling and Simulation
【速读】：该论文试图解决跨系统、跨学科和跨工具的混合建模与仿真（hybrid modeling and simulation, HMS）中的互操作性问题，旨在通过增强语义严谨性、模型可重用性和系统间协同能力来提升整体建模效率。解决方案的关键在于区分方法论本体（methodological ontologies）与参照本体（referential ontologies），并利用诸如能力问题、本体设计模式和分层策略等技术手段，促进共享理解和形式化精确性。同时，论文强调将本体与语义网技术相结合，使其在描述领域知识的同时，也成为指导仿真实施的规范性框架。
链接: https://arxiv.org/abs/2506.12290
作者: John Beverley,Andreas Tolk
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:We explore the role of ontologies in enhancing hybrid modeling and simulation through improved semantic rigor, model reusability, and interoperability across systems, disciplines, and tools. By distinguishing between methodological and referential ontologies, we demonstrate how these complementary approaches address interoperability challenges along three axes: Human-Human, Human-Machine, and Machine-Machine. Techniques such as competency questions, ontology design patterns, and layered strategies are highlighted for promoting shared understanding and formal precision. Integrating ontologies with Semantic Web Technologies, we showcase their dual role as descriptive domain representations and prescriptive guides for simulation construction. Four application cases - sea-level rise analysis, Industry 4.0 modeling, artificial societies for policy support, and cyber threat evaluation - illustrate the practical benefits of ontology-driven hybrid simulation workflows. We conclude by discussing challenges and opportunities in ontology-based hybrid MS, including tool integration, semantic alignment, and support for explainable AI.
zh
[AI-132] he SWE-Bench Illusion: When State-of-the-Art LLM s Remember Instead of Reason
【速读】：该论文试图解决当前大型语言模型（Large Language Models, LLMs）在软件工程任务评估中的能力被高估的问题，特别是SWE-Bench Verified基准测试中模型表现可能受到记忆而非真实问题解决能力的驱动。解决方案的关键在于引入一个诊断性任务——仅通过问题描述识别错误文件路径，以探测模型的潜在知识水平。研究通过实证表明，最先进的模型在仅依赖问题描述的情况下，能够达到76%的准确率识别有缺陷的文件路径，而在未包含在SWE-Bench中的仓库任务上准确率仅为53%，这暗示了数据污染或记忆现象的存在。该工作强调了构建更具鲁棒性和抗污染能力的基准测试的重要性。
链接: https://arxiv.org/abs/2506.12286
作者: Shanchao Liang,Spandan Garg,Roshanak Zilouchian Moghaddam
机构: 未知
类目: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注:
Abstract:As large language models (LLMs) become increasingly capable and widely adopted, benchmarks play a central role in assessing their practical utility. For example, SWE-Bench Verified has emerged as a critical benchmark for evaluating LLMs’ software engineering abilities, particularly their aptitude for resolving real-world GitHub issues. Recent LLMs show impressive performance on SWE-Bench, leading to optimism about their capacity for complex coding tasks. However, current evaluation protocols may overstate these models’ true capabilities. It is crucial to distinguish LLMs’ generalizable problem-solving ability and other learned artifacts. In this work, we introduce a diagnostic task: file path identification from issue descriptions alone, to probe models’ underlying knowledge. We present empirical evidence that performance gains on SWE-Bench-Verified may be partially driven by memorization rather than genuine problem-solving. We show that state-of-the-art models achieve up to 76% accuracy in identifying buggy file paths using only issue descriptions, without access to repository structure. This performance is merely up to 53% on tasks from repositories not included in SWE-Bench, pointing to possible data contamination or memorization. These findings raise concerns about the validity of existing results and underscore the need for more robust, contamination-resistant benchmarks to reliably evaluate LLMs’ coding abilities.
zh
[AI-133] Deep Fictitious Play-Based Potential Differential Games for Learning Human-Like Interaction at Unsignalized Intersections
【速读】：该论文旨在解决在无信号交叉口建模车辆交互行为的问题，这一任务因底层博弈论过程的复杂性而具有挑战性。现有方法大多仅依赖博弈论公式，未能利用自然驾驶数据集。本文的关键解决方案是使用深度虚构博弈（Deep Fictitious Play）学习类人交互驾驶策略，首先将车辆交互建模为微分博弈，并将其重新表述为潜在微分博弈，通过数据集学习成本函数中的权重以捕捉多样的驾驶风格，同时理论证明了框架收敛至纳什均衡的保证。
链接: https://arxiv.org/abs/2506.12283
作者: Kehua Chen,Shucheng Zhang,Yinhai Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注:
Abstract:Modeling vehicle interactions at unsignalized intersections is a challenging task due to the complexity of the underlying game-theoretic processes. Although prior studies have attempted to capture interactive driving behaviors, most approaches relied solely on game-theoretic formulations and did not leverage naturalistic driving datasets. In this study, we learn human-like interactive driving policies at unsignalized intersections using Deep Fictitious Play. Specifically, we first model vehicle interactions as a Differential Game, which is then reformulated as a Potential Differential Game. The weights in the cost function are learned from the dataset and capture diverse driving styles. We also demonstrate that our framework provides a theoretical guarantee of convergence to a Nash equilibrium. To the best of our knowledge, this is the first study to train interactive driving policies using Deep Fictitious Play. We validate the effectiveness of our Deep Fictitious Play-Based Potential Differential Game (DFP-PDG) framework using the INTERACTION dataset. The results demonstrate that the proposed framework achieves satisfactory performance in learning human-like driving policies. The learned individual weights effectively capture variations in driver aggressiveness and preferences. Furthermore, the ablation study highlights the importance of each component within our model.
zh
[AI-134] Cloud Infrastructure Management in the Age of AI Agents
【速读】：该论文试图解决云基础设施管理中需要大量人工操作的问题，旨在通过开发由大型语言模型（Large Language Models, LLMs）驱动的AI代理来自动化相关任务。论文提出的关键解决方案是利用不同云/用户接口（如软件开发工具包、命令行界面、基础设施即代码平台和网页门户）使AI代理能够执行云基础设施管理任务，并评估其在不同任务中的有效性，同时识别研究挑战及潜在解决方向。
链接: https://arxiv.org/abs/2506.12270
作者: Zhenning Yang,Archit Bhatnagar,Yiming Qiu,Tongyuan Miao,Patrick Tser Jern Kon,Yunming Xiao,Yibo Huang,Martin Casado,Ang Chen
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注:
Abstract:Cloud infrastructure is the cornerstone of the modern IT industry. However, managing this infrastructure effectively requires considerable manual effort from the DevOps engineering team. We make a case for developing AI agents powered by large language models (LLMs) to automate cloud infrastructure management tasks. In a preliminary study, we investigate the potential for AI agents to use different cloud/user interfaces such as software development kits (SDK), command line interfaces (CLI), Infrastructure-as-Code (IaC) platforms, and web portals. We report takeaways on their effectiveness on different management tasks, and identify research challenges and potential solutions.
zh
[AI-135] A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis
【速读】：该论文试图解决现有基于基础模型的物联网（IoT）方法多针对特定任务设计，导致跨领域比较困难且难以指导新任务应用的问题。其解决方案的关键在于围绕不同领域共享的四个性能目标——效率、上下文感知性、安全性和隐私保护——对现有方法进行系统综述，并总结代表性工作、常用技术和评估指标，从而实现有意义的跨领域比较并为新任务的基础模型解决方案设计提供实践指导。
链接: https://arxiv.org/abs/2506.12263
作者: Hui Wei,Dong Yoon Lee,Shubham Rohal,Zhizhang Hu,Shiwei Fang,Shijia Pan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: Preprint. Under Submission
Abstract:Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed for specific IoT tasks, making it difficult to compare approaches across IoT domains and limiting guidance for applying them to new tasks. This survey aims to bridge this gap by providing a comprehensive overview of current methodologies and organizing them around four shared performance objectives by different domains: efficiency, context-awareness, safety, and security privacy. For each objective, we review representative works, summarize commonly-used techniques and evaluation metrics. This objective-centric organization enables meaningful cross-domain comparisons and offers practical insights for selecting and designing foundation model based solutions for new IoT tasks. We conclude with key directions for future research to guide both practitioners and researchers in advancing the use of foundation models in IoT applications.
zh
[AI-136] Lower Bound on Howard Policy Iteration for Deterministic Markov Decision Processes UAI
【速读】：该论文旨在解决确定性马尔可夫决策过程（Deterministic Markov Decision Processes, DMDPs）中均值报酬（mean-payoff）目标下霍华德策略迭代算法的迭代复杂性问题。现有研究表明，该算法的已知下界为 Ω~(I)，其中 I 为输入规模，而本文的主要贡献是提出了一个改进的下界，证明该算法在最坏情况下需要 Ω~(I) 次迭代。解决方案的关键在于构造特定的DMDP实例，以展示算法在处理此类问题时的理论极限，从而揭示其时间复杂性的本质特征。
链接: https://arxiv.org/abs/2506.12254
作者: Ali Asadi,Krishnendu Chatterjee,Jakob de Raaij
机构: 未知
类目: Artificial Intelligence (cs.AI); Discrete Mathematics (cs.DM)
备注: 9 pages excluding references and appendix, 4 figures, Conference on Uncertainty in Artificial Intelligence (UAI) 2025 (forthcoming)
Abstract:Deterministic Markov Decision Processes (DMDPs) are a mathematical framework for decision-making where the outcomes and future possible actions are deterministically determined by the current action taken. DMDPs can be viewed as a finite directed weighted graph, where in each step, the controller chooses an outgoing edge. An objective is a measurable function on runs (or infinite trajectories) of the DMDP, and the value for an objective is the maximal cumulative reward (or weight) that the controller can guarantee. We consider the classical mean-payoff (aka limit-average) objective, which is a basic and fundamental objective. Howard’s policy iteration algorithm is a popular method for solving DMDPs with mean-payoff objectives. Although Howard’s algorithm performs well in practice, as experimental studies suggested, the best known upper bound is exponential and the current known lower bound is as follows: For the input size I , the algorithm requires \tilde\Omega(\sqrtI) iterations, where \tilde\Omega hides the poly-logarithmic factors, i.e., the current lower bound on iterations is sub-linear with respect to the input size. Our main result is an improved lower bound for this fundamental algorithm where we show that for the input size I , the algorithm requires \tilde\Omega(I) iterations. Comments: 9 pages excluding references and appendix, 4 figures, Conference on Uncertainty in Artificial Intelligence (UAI) 2025 (forthcoming) Subjects: Artificial Intelligence (cs.AI); Discrete Mathematics (cs.DM) Cite as: arXiv:2506.12254 [cs.AI] (or arXiv:2506.12254v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2506.12254 Focus to learn more arXiv-issued DOI via DataCite
zh
[AI-137] Reversing the Paradigm: Building AI-First Systems with Human Guidance
【速读】：该论文试图解决人工智能（Artificial Intelligence, AI）在实际应用中如何实现负责任的采用问题，特别是在AI系统日益自主化的情况下，如何平衡自动化与人类意图、监督和价值观之间的关系。解决方案的关键在于重新思考组织结构和角色定位，投资于员工技能提升，嵌入伦理原则，并推动透明度，以确保AI代理在自主执行任务的同时，仍能与人类目标、价值和情境保持一致。
链接: https://arxiv.org/abs/2506.12245
作者: Cosimo Spera,Garima Agrawal
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The relationship between humans and artificial intelligence is no longer science fiction – it’s a growing reality reshaping how we live and work. AI has moved beyond research labs into everyday life, powering customer service chats, personalizing travel, aiding doctors in diagnosis, and supporting educators. What makes this moment particularly compelling is AI’s increasing collaborative nature. Rather than replacing humans, AI augments our capabilities – automating routine tasks, enhancing decisions with data, and enabling creativity in fields like design, music, and writing. The future of work is shifting toward AI agents handling tasks autonomously, with humans as supervisors, strategists, and ethical stewards. This flips the traditional model: instead of humans using AI as a tool, intelligent agents will operate independently within constraints, managing everything from scheduling and customer service to complex workflows. Humans will guide and fine-tune these agents to ensure alignment with goals, values, and context. This shift offers major benefits – greater efficiency, faster decisions, cost savings, and scalability. But it also brings risks: diminished human oversight, algorithmic bias, security flaws, and a widening skills gap. To navigate this transition, organizations must rethink roles, invest in upskilling, embed ethical principles, and promote transparency. This paper examines the technological and organizational changes needed to enable responsible adoption of AI-first systems – where autonomy is balanced with human intent, oversight, and values. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2506.12245 [cs.AI] (or arXiv:2506.12245v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2506.12245 Focus to learn more arXiv-issued DOI via DataCite
zh
[AI-138] Privacy Reasoning in Ambiguous Contexts
【速读】：该论文试图解决语言模型在信息披露决策中的隐私推理能力问题，特别是在面对语境模糊和信息缺失时的性能瓶颈。其解决方案的关键在于识别语境模糊性作为隐私评估中高性能的主要障碍，并通过设计Camber框架实现语境消歧，从而提升模型在信息共享决策中的准确性与稳定性。
链接: https://arxiv.org/abs/2506.12241
作者: Ren Yi,Octavian Suciu,Adria Gascon,Sarah Meiklejohn,Eugene Bagdasarian,Marco Gruteser
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:We study the ability of language models to reason about appropriate information disclosure - a central aspect of the evolving field of agentic privacy. Whereas previous works have focused on evaluating a model’s ability to align with human decisions, we examine the role of ambiguity and missing context on model performance when making information-sharing decisions. We identify context ambiguity as a crucial barrier for high performance in privacy assessments. By designing Camber, a framework for context disambiguation, we show that model-generated decision rationales can reveal ambiguities and that systematically disambiguating context based on these rationales leads to significant accuracy improvements (up to 13.3% in precision and up to 22.3% in recall) as well as reductions in prompt sensitivity. Overall, our results indicate that approaches for context disambiguation are a promising way forward to enhance agentic privacy reasoning.
zh
[AI-139] Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI
【速读】：该论文试图解决当前可解释人工智能（XAI）解决方案主要面向专家，缺乏对非专家用户友好性和透明度的问题。其关键解决方案是提出一个领域、模型和解释无关的通用框架，该框架利用大语言模型（LLMs）和上下文学习，将领域相关和可解释性相关的上下文知识注入LLMs，并通过结构化提示和系统设置，在单一响应中同时提供非专家可理解的解释和专家可用的技术信息，从而实现透明且以人类为中心的解释。
链接: https://arxiv.org/abs/2506.12240
作者: Eva Paraschou,Ioannis Arapakis,Sofia Yfantidou,Sebastian Macaluso,Athena Vakali
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted for publication at The 3rd World Conference on eXplainable Artificial Intelligence. This version corresponds to the camera-ready manuscript submitted to the conference proceedings
Abstract:Artificial Intelligence (AI) is rapidly embedded in critical decision-making systems, however their foundational black-box'' models require eXplainable AI (XAI) solutions to enhance transparency, which are mostly oriented to experts, making no sense to non-experts. Alarming evidence about AI's unprecedented human values risks brings forward the imperative need for transparent human-centered XAI solutions. In this work, we introduce a domain-, model-, explanation-agnostic, generalizable and reproducible framework that ensures both transparency and human-centered explanations tailored to the needs of both experts and non-experts. The framework leverages Large Language Models (LLMs) and employs in-context learning to convey domain- and explainability-relevant contextual knowledge into LLMs. Through its structured prompt and system setting, our framework encapsulates in one response explanations understandable by non-experts and technical information to experts, all grounded in domain and explainability principles. To demonstrate the effectiveness of our framework, we establish a ground-truth contextual thesaurus’’ through a rigorous benchmarking with over 40 data, model, and XAI combinations for an explainable clustering analysis of a well-being scenario. Through a comprehensive quality and human-friendliness evaluation of our framework’s explanations, we prove high content quality through strong correlations with ground-truth explanations (Spearman rank correlation=0.92) and improved interpretability and human-friendliness to non-experts through a user study (N=56). Our overall evaluation confirms trust in LLMs as HCXAI enablers, as our framework bridges the above Gaps by delivering (i) high-quality technical explanations aligned with foundational XAI methods and (ii) clear, efficient, and interpretable human-centered explanations for non-experts.
zh
[AI-140] Uncovering Bias Paths with LLM -guided Causal Discovery: An Active Learning and Dynamic Scoring Approach
【速读】：该论文试图解决在复杂系统中恢复公平相关路径的问题，特别是在存在噪声、标签污染和潜在混杂因素的现实场景下，传统因果发现（Causal Discovery, CD）方法难以有效识别公平性相关的因果路径。其解决方案的关键在于提出一种基于大型语言模型（Large Language Models, LLM）的混合框架，该框架通过结合广度优先搜索（BFS）策略、主动学习和动态评分机制，提升因果路径发现的效率与鲁棒性。具体而言，利用互信息、偏相关性和LLM置信度构建综合得分，对变量对进行优先级排序，从而优化LLM查询过程，增强在噪声环境下的因果路径恢复能力。
链接: https://arxiv.org/abs/2506.12227
作者: Khadija Zanna,Akane Sano
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Submitted to AIES Conference
Abstract:Causal discovery (CD) plays a pivotal role in understanding the mechanisms underlying complex systems. While recent algorithms can detect spurious associations and latent confounding, many struggle to recover fairness-relevant pathways in realistic, noisy settings. Large Language Models (LLMs), with their access to broad semantic knowledge, offer a promising complement to statistical CD approaches, particularly in domains where metadata provides meaningful relational cues. Ensuring fairness in machine learning requires understanding how sensitive attributes causally influence outcomes, yet CD methods often introduce spurious or biased pathways. We propose a hybrid LLM-based framework for CD that extends a breadth-first search (BFS) strategy with active learning and dynamic scoring. Variable pairs are prioritized for LLM-based querying using a composite score based on mutual information, partial correlation, and LLM confidence, improving discovery efficiency and robustness. To evaluate fairness sensitivity, we construct a semi-synthetic benchmark from the UCI Adult dataset, embedding a domain-informed causal graph with injected noise, label corruption, and latent confounding. We assess how well CD methods recover both global structure and fairness-critical paths. Our results show that LLM-guided methods, including the proposed method, demonstrate competitive or superior performance in recovering such pathways under noisy conditions. We highlight when dynamic scoring and active querying are most beneficial and discuss implications for bias auditing in real-world datasets. Comments: Submitted to AIES Conference Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) ACMclasses: F.2.2, I.2.7 Cite as: arXiv:2506.12227 [cs.LG] (or arXiv:2506.12227v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12227 Focus to learn more arXiv-issued DOI via DataCite
zh
[AI-141] SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes ICLR2025
【速读】：该论文试图解决当前自监督学习（Self-Supervised Learning, SSL）音频模型在真实世界中面对多声源重叠的复杂音频数据时表现不佳的问题。现有SSL方法主要在以单音音频为主的基准数据集上进行评估，而忽略了自然场景中常见的多声源音频特性，导致模型在实际应用中的鲁棒性不足。解决方案的关键在于提出一种新的音频SSL研究方向——从音频混合物中进行自监督学习（Self-Supervised Learning from Audio Mixtures, SSLAM），旨在提升模型对多声源数据的学习能力，同时保持其在单音数据上的高性能。
链接: https://arxiv.org/abs/2506.12222
作者: Tony Alex,Sara Ahmed,Armin Mustafa,Muhammad Awais,Philip JB Jackson
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
备注: Accepted at ICLR 2025. Code and pre-trained models are available at \url{ this https URL }
Abstract:Self-supervised pre-trained audio networks have seen widespread adoption in real-world systems, particularly in multi-modal large language models. These networks are often employed in a frozen state, under the assumption that the SSL pre-training has sufficiently equipped them to handle real-world audio. However, a critical question remains: how well do these models actually perform in real-world conditions, where audio is typically polyphonic and complex, involving multiple overlapping sound sources? Current audio SSL methods are often benchmarked on datasets predominantly featuring monophonic audio, such as environmental sounds, and speech. As a result, the ability of SSL models to generalize to polyphonic audio, a common characteristic in natural scenarios, remains underexplored. This limitation raises concerns about the practical robustness of SSL models in more realistic audio settings. To address this gap, we introduce Self-Supervised Learning from Audio Mixtures (SSLAM), a novel direction in audio SSL research, designed to improve, designed to improve the model’s ability to learn from polyphonic data while maintaining strong performance on monophonic data. We thoroughly evaluate SSLAM on standard audio SSL benchmark datasets which are predominantly monophonic and conduct a comprehensive comparative analysis against SOTA methods using a range of high-quality, publicly available polyphonic datasets. SSLAM not only improves model performance on polyphonic audio, but also maintains or exceeds performance on standard audio SSL benchmarks. Notably, it achieves up to a 3.9% improvement on the AudioSet-2M (AS-2M), reaching a mean average precision (mAP) of 50.2. For polyphonic datasets, SSLAM sets new SOTA in both linear evaluation and fine-tuning regimes with performance improvements of up to 9.1% (mAP).
zh
[AI-142] wo heads are better than one: simulating large transformers with small ones
【速读】：该论文旨在解决Transformer模型在处理长输入序列时因自注意力机制的二次复杂度而导致的扩展性问题。其解决方案的关键在于证明了可以通过多个仅能处理短输入序列的小型Transformer（small transformers）高效模拟处理长输入序列的大型Transformer（large transformers）。具体而言，论文表明任何输入长度为N的Transformer可以被O((N/M)^2)个输入长度为M（M≪N）的小型Transformer高效模拟，但在某些自然场景下，如平均情况输入、滑动窗口掩码和注意力汇点等，仅需O(N/M)个小型Transformer即可实现最优效果。
链接: https://arxiv.org/abs/2506.12220
作者: Hantao Yu,Josh Alman
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The quadratic complexity of self-attention prevents transformers from scaling effectively to long input sequences. On the other hand, modern GPUs and other specialized hardware accelerators are well-optimized for processing small input sequences in transformers during both training and inference. A natural question arises: can we take advantage of the efficiency of small transformers to deal with long input sequences? In this paper, we show that transformers with long input sequences (large transformers) can be efficiently simulated by transformers that can only take short input sequences (small transformers). Specifically, we prove that any transformer with input length N can be efficiently simulated by only O((N/M)^2) transformers with input length M \ll N , and that this cannot be improved in the worst case. However, we then prove that in various natural scenarios including average-case inputs, sliding window masking and attention sinks, the optimal number O(N/M) of small transformers suffice. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2506.12220 [cs.LG] (or arXiv:2506.12220v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12220 Focus to learn more arXiv-issued DOI via DataCite
zh
[AI-143] Semantic Scheduling for LLM Inference
【速读】：该论文试图解决传统操作系统调度算法缺乏对进程语义内容的感知，导致在紧急管理等场景中无法有效优先处理高重要性或紧急任务的问题。解决方案的关键在于引入语义调度（semantic scheduling）概念，通过语言模型对请求进行语义分析，使调度优先级由进程的语义内容决定，从而实现更智能和上下文感知的调度策略。论文提出了一种具有最优时间复杂度的调度算法，旨在最小化基于大语言模型（LLM）提示调度的整体等待时间。
链接: https://arxiv.org/abs/2506.12204
作者: Wenyue Hua,Dujian Ding,Yile Gu,Yujie Ren,Kai Mei,Minghua Ma,William Yang Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Operating Systems (cs.OS)
备注: 18 pages, 3 figures
Abstract:Conventional operating system scheduling algorithms are largely content-ignorant, making decisions based on factors such as latency or fairness without considering the actual intents or semantics of processes. Consequently, these algorithms often do not prioritize tasks that require urgent attention or carry higher importance, such as in emergency management scenarios. However, recent advances in language models enable semantic analysis of processes, allowing for more intelligent and context-aware scheduling decisions. In this paper, we introduce the concept of semantic scheduling in scheduling of requests from large language models (LLM), where the semantics of the process guide the scheduling priorities. We present a novel scheduling algorithm with optimal time complexity, designed to minimize the overall waiting time in LLM-based prompt scheduling. To illustrate its effectiveness, we present a medical emergency management application, underscoring the potential benefits of semantic scheduling for critical, time-sensitive tasks. The code and data are available at this https URL.
zh
[AI-144] A Fast Reliable and Secure Programming Language for LLM Agents with Code Actions
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在作为代理执行任务时，使用Python进行代码操作所面临的性能、安全性和可靠性问题。其解决方案的关键在于提出一种新型编程语言Quasar，该语言通过自动化并行化提升性能、通过不确定性量化提高可靠性和减少幻觉、并通过安全特性增强用户对操作的验证能力。LLMs可编写Python子集代码，该代码被自动转换为Quasar，从而在保持强大性能的同时，显著降低执行时间、减少用户审批交互并提升可靠性。
链接: https://arxiv.org/abs/2506.12202
作者: Stephen Mell,Botong Zhang,David Mell,Shuo Li,Ramya Ramalingam,Nathan Yu,Steve Zdancewic,Osbert Bastani
机构: 未知
类目: Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注:
Abstract:Modern large language models (LLMs) are often deployed as agents, calling external tools adaptively to solve tasks. Rather than directly calling tools, it can be more effective for LLMs to write code to perform the tool calls, enabling them to automatically generate complex control flow such as conditionals and loops. Such code actions are typically provided as Python code, since LLMs are quite proficient at it; however, Python may not be the ideal language due to limited built-in support for performance, security, and reliability. We propose a novel programming language for code actions, called Quasar, which has several benefits: (1) automated parallelization to improve performance, (2) uncertainty quantification to improve reliability and mitigate hallucinations, and (3) security features enabling the user to validate actions. LLMs can write code in a subset of Python, which is automatically transpiled to Quasar. We evaluate our approach on the ViperGPT visual question answering agent, applied to the GQA dataset, demonstrating that LLMs with Quasar actions instead of Python actions retain strong performance, while reducing execution time when possible by 42%, improving security by reducing user approval interactions when possible by 52%, and improving reliability by applying conformal prediction to achieve a desired target coverage level.
zh
[AI-145] PRO-V: An Efficient Program Generation Multi-Agent System for Automatic RTL Verification
【速读】：该论文旨在解决生成式 AI (Generative AI) 在寄存器传输级 (Register Transfer Level, RTL) 代码生成中的功能错误问题，特别是在硬件描述语言 (Hardware Description Language, HDL) 测试平台设计中的有效性不足。其解决方案的关键在于提出 PRO-V，一个基于多智能体的全程序生成系统，通过高效的 best-of-n 迭代采样策略提升测试平台的正确性，并引入 LLM-as-a-judge 的验证框架，结合上下文学习将基于规则的静态分析转换为自然语言，从而辅助编译器判断验证失败是由于 RTL 设计错误还是测试平台错误所致。
链接: https://arxiv.org/abs/2506.12200
作者: Yujie Zhao,Zhijing Wu,Hejia Zhang,Zhongming Yu,Wentao Ni,Chia-Tung Ho,Haoxing Ren,Jishen Zhao
机构: 未知
类目: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
备注:
Abstract:LLM-assisted hardware verification is gaining substantial attention due to its potential to significantly reduce the cost and effort of crafting effective testbenches. It also serves as a critical enabler for LLM-aided end-to-end hardware language design. However, existing current LLMs often struggle with Register Transfer Level (RTL) code generation, resulting in testbenches that exhibit functional errors in Hardware Description Languages (HDL) logic. Motivated by the strong performance of LLMs in Python code generation under inference-time sampling strategies, and their promising capabilities as judge agents, we propose PRO-V a fully program generation multi-agent system for robust RTL verification. Pro-V incorporates an efficient best-of-n iterative sampling strategy to enhance the correctness of generated testbenches. Moreover, it introduces an LLM-as-a-judge aid validation framework featuring an automated prompt generation pipeline. By converting rule-based static analysis from the compiler into natural language through in-context learning, this pipeline enables LLMs to assist the compiler in determining whether verification failures stem from errors in the RTL design or the testbench. PRO-V attains a verification accuracy of 87.17% on golden RTL implementations and 76.28% on RTL mutants. Our code is open-sourced at this https URL.
zh
[AI-146] ViSAGe: Video-to-Spatial Audio Generation ICLR2025
【速读】：该论文试图解决从无声视频直接生成第一阶全向声（first-order ambisonics）的问题，这一问题在传统上需要复杂的录音系统和专业技能。解决方案的关键在于提出ViSAGe框架，该框架通过利用CLIP视觉特征以及结合方向性和视觉引导的自回归神经音频编解码模型，实现从无声视频帧到第一阶全向声的端到端生成。
链接: https://arxiv.org/abs/2506.12199
作者: Jaeyeon Kim,Heeseung Yun,Gunhee Kim
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: ICLR 2025. Project page: this https URL
Abstract:Spatial audio is essential for enhancing the immersiveness of audio-visual experiences, yet its production typically demands complex recording systems and specialized expertise. In this work, we address a novel problem of generating first-order ambisonics, a widely used spatial audio format, directly from silent videos. To support this task, we introduce YT-Ambigen, a dataset comprising 102K 5-second YouTube video clips paired with corresponding first-order ambisonics. We also propose new evaluation metrics to assess the spatial aspect of generated audio based on audio energy maps and saliency metrics. Furthermore, we present Video-to-Spatial Audio Generation (ViSAGe), an end-to-end framework that generates first-order ambisonics from silent video frames by leveraging CLIP visual features, autoregressive neural audio codec modeling with both directional and visual guidance. Experimental results demonstrate that ViSAGe produces plausible and coherent first-order ambisonics, outperforming two-stage approaches consisting of video-to-audio generation and audio spatialization. Qualitative examples further illustrate that ViSAGe generates temporally aligned high-quality spatial audio that adapts to viewpoint changes.
zh
[AI-147] Artificial Intelligence and Machine Learning in the Development of Vaccines and Immunotherapeutics Yesterday Today and Tomorrow
【速读】：该论文试图解决传统疫苗和免疫治疗药物开发过程中依赖试错实验和大量体内测试所导致的周期长、效率低的问题。其解决方案的关键在于利用人工智能（Artificial Intelligence, AI）和深度学习（Deep Learning, DL）技术，通过构建预测框架、整合计算模型与多组学数据、优化抗原/表位靶点选择以及深入理解免疫调控机制，实现更高效、精准的疫苗和免疫治疗设计。
链接: https://arxiv.org/abs/2506.12185
作者: Elhoucine Elfatimi,Yassir Lekbach,Swayam Prakash,Lbachir BenMohamed
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In the past, the development of vaccines and immunotherapeutics relied heavily on trial-and-error experimentation and extensive in vivo testing, often requiring years of pre-clinical and clinical trials. Today, artificial intelligence (AI) and deep learning (DL) are actively transforming vaccine and immunotherapeutic design, by (i) offering predictive frameworks that support rapid, data-driven decision-making; (ii) increasingly being implemented as time- and resource-efficient strategies that integrate computational models, systems vaccinology, and multi-omics data to better phenotype, differentiate, and classify patient diseases and cancers; predict patients’ immune responses; and identify the factors contributing to optimal vaccine and immunotherapeutic protective efficacy; (iii) refining the selection of B- and T-cell antigen/epitope targets to enhance efficacy and durability of immune protection; and (iv) enabling a deeper understanding of immune regulation, immune evasion, immune checkpoints, and regulatory pathways. The future of AI and DL points toward (i) replacing animal preclinical testing of drugs, vaccines, and immunotherapeutics with computational-based models, as recently proposed by the United States FDA; and (ii) enabling real-time in vivo modeling for immunobridging and prediction of protection in clinical trials. This may result in a fast and transformative shift for the development of personal vaccines and immunotherapeutics against infectious pathogens and cancers.
zh
[AI-148] Because we have LLM s we Can and Should Pursue Agent ic Interpretability
【速读】：该论文试图解决传统“检视式”可解释性方法（inspective interpretability）在面对大型语言模型（Large Language Models, LLMs）时的局限性，即无法有效支持人机之间的动态交互与理解。其解决方案的关键在于引入“代理式可解释性”（agentic interpretability），通过多轮对话形式，使LLM主动构建并利用对用户的心理模型，从而帮助人类建立更准确的对LLM的心理模型。这种交互方式强调合作性，旨在发现可能超越人类认知的概念，以提升人类对机器的理解能力。
链接: https://arxiv.org/abs/2506.12152
作者: Been Kim,John Hewitt,Neel Nanda,Noah Fiedel,Oyvind Tafjord
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The era of Large Language Models (LLMs) presents a new opportunity for interpretability–agentic interpretability: a multi-turn conversation with an LLM wherein the LLM proactively assists human understanding by developing and leveraging a mental model of the user, which in turn enables humans to develop better mental models of the LLM. Such conversation is a new capability that traditional inspective' interpretability methods (opening the black-box) do not use. Having a language model that aims to teach and explain--beyond just knowing how to talk--is similar to a teacher whose goal is to teach well, understanding that their success will be measured by the student's comprehension. While agentic interpretability may trade off completeness for interactivity, making it less suitable for high-stakes safety situations with potentially deceptive models, it leverages a cooperative model to discover potentially superhuman concepts that can improve humans' mental model of machines. Agentic interpretability introduces challenges, particularly in evaluation, due to what we call human-entangled-in-the-loop’ nature (humans responses are integral part of the algorithm), making the design and evaluation difficult. We discuss possible solutions and proxy goals. As LLMs approach human parity in many tasks, agentic interpretability’s promise is to help humans learn the potentially superhuman concepts of the LLMs, rather than see us fall increasingly far from understanding them.
zh
[AI-149] Semantic Preprocessing for LLM -based Malware Analysis
【速读】：该论文试图解决传统恶意软件分析方法在处理大量数据时仅关注数据视图（如图像、序列）而忽视专家视角的问题。其解决方案的关键在于引入一种基于专家知识的预处理方法，该方法生成针对可移植可执行文件（Portable Executable）的JSON报告，整合静态和行为分析特征，并包含打包器签名检测、MITRE ATT&CK和恶意软件行为目录（Malware Behavior Catalog, MBC）的知识，以构建可理解的二进制文件语义表示，从而提升AI模型在恶意软件分析中的可解释性。
链接: https://arxiv.org/abs/2506.12113
作者: Benjamin Marais,Tony Quertier,Grégoire Barrue
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:In a context of malware analysis, numerous approaches rely on Artificial Intelligence to handle a large volume of data. However, these techniques focus on data view (images, sequences) and not on an expert’s view. Noticing this issue, we propose a preprocessing that focuses on expert knowledge to improve malware semantic analysis and result interpretability. We propose a new preprocessing method which creates JSON reports for Portable Executable files. These reports gather features from both static and behavioral analysis, and incorporate packer signature detection, MITRE ATT\CK and Malware Behavior Catalog (MBC) knowledge. The purpose of this preprocessing is to gather a semantic representation of binary files, understandable by malware analysts, and that can enhance AI models’ explainability for malicious files analysis. Using this preprocessing to train a Large Language Model for Malware classification, we achieve a weighted-average F1-score of 0.94 on a complex dataset, representative of market reality.
zh
[AI-150] A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method
【速读】：该论文试图解决如何在早期阶段有效检测高级持续性威胁（Advanced Persistent Threat, APT）的问题，以减少其可能带来的潜在危害。解决方案的关键在于提出一种基于XGBoost算法和可解释人工智能（Explainable Artificial Intelligence, XAI）的特征选择方法，特别是利用SHAP（SHapley Additive exPlanations）方法识别初始入侵阶段中最相关的特征。该方法成功将SCVIC-APT-2021数据集的特征数量从77个减少至4个，同时保持了较高的检测性能，包括97%的精确率、100%的召回率和98%的F1分数。
链接: https://arxiv.org/abs/2506.12108
作者: Bassam Noori Shaker,Bahaa Al-Musawi,Mohammed Falih Hassan
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:An Advanced Persistent Threat (APT) is a multistage, highly sophisticated, and covert form of cyber threat that gains unauthorized access to networks to either steal valuable data or disrupt the targeted network. These threats often remain undetected for extended periods, emphasizing the critical need for early detection in networks to mitigate potential APT consequences. In this work, we propose a feature selection method for developing a lightweight intrusion detection system capable of effectively identifying APTs at the initial compromise stage. Our approach leverages the XGBoost algorithm and Explainable Artificial Intelligence (XAI), specifically utilizing the SHAP (SHapley Additive exPlanations) method for identifying the most relevant features of the initial compromise stage. The results of our proposed method showed the ability to reduce the selected features of the SCVIC-APT-2021 dataset from 77 to just four while maintaining consistent evaluation metrics for the suggested system. The estimated metrics values are 97% precision, 100% recall, and a 98% F1 score. The proposed method not only aids in preventing successful APT consequences but also enhances understanding of APT behavior at early stages.
zh
[AI-151] DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents
【速读】：该论文试图解决agentic系统中由于与外部环境交互而引入的提示注入攻击问题，此类攻击可能导致经济损失、隐私泄露或系统被破坏。解决方案的关键在于提出DRIFT（Dynamic Rule-based Isolation Framework for Trustworthy agentic systems），该框架通过强制实施控制层和数据层的约束来提升安全性。其核心组件包括：安全规划器生成最小功能轨迹和参数检查清单，动态验证器监控计划偏差并评估变更是否符合权限限制和用户意图，以及注入隔离器检测并屏蔽可能与用户查询冲突的指令，从而降低长期风险。
链接: https://arxiv.org/abs/2506.12104
作者: Hao Li,Xiaogeng Liu,Hung-Chun Chiu,Dianqi Li,Ning Zhang,Chaowei Xiao
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 18 pages, 12 figures
Abstract:Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities. By interacting with external environments through predefined tools, these agents can carry out complex user tasks. Nonetheless, this interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent’s behavior, potentially resulting in economic loss, privacy leakage, or system compromise. System-level defenses have recently shown promise by enforcing static or predefined policies, but they still face two key challenges: the ability to dynamically update security rules and the need for memory stream isolation. To address these challenges, we propose DRIFT, a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control- and data-level constraints. A Secure Planner first constructs a minimal function trajectory and a JSON-schema-style parameter checklist for each function node based on the user query. A Dynamic Validator then monitors deviations from the original plan, assessing whether changes comply with privilege limitations and the user’s intent. Finally, an Injection Isolator detects and masks any instructions that may conflict with the user query from the memory stream to mitigate long-term risks. We empirically validate the effectiveness of DRIFT on the AgentDojo benchmark, demonstrating its strong security performance while maintaining high utility across diverse models – showcasing both its robustness and adaptability.
zh
[AI-152] he Amazon Nova Family of Models: Technical Report and Model Card
【速读】：该论文介绍了Amazon Nova，一系列新一代的前沿基础模型，旨在解决多模态任务中对准确性、速度和成本平衡的需求。其核心问题在于提供高效、低成本且性能优越的模型以满足广泛的应用场景。解决方案的关键在于通过不同版本的模型（如Nova Pro、Nova Lite、Nova Micro、Nova Canvas和Nova Reel）针对不同任务进行优化，从而在保持高精度的同时实现低延迟和低成本，同时确保模型的可靠性和安全性。
链接: https://arxiv.org/abs/2506.12103
作者: Amazon AGI,Aaron Langford,Aayush Shah,Abhanshu Gupta,Abhimanyu Bhatter,Abhinav Goyal,Abhinav Mathur,Abhinav Mohanty,Abhishek Kumar,Abhishek Sethi,Abi Komma,Abner Pena,Achin Jain,Adam Kunysz,Adam Opyrchal,Adarsh Singh,Aditya Rawal,Adok Achar Budihal Prasad,Adrià de Gispert,Agnika Kumar,Aishwarya Aryamane,Ajay Nair,Akilan M,Akshaya Iyengar,Akshaya Vishnu Kudlu Shanbhogue,Alan He,Alessandra Cervone,Alex Loeb,Alex Zhang,Alexander Fu,Alexander Lisnichenko,Alexander Zhipa,Alexandros Potamianos,Ali Kebarighotbi,Aliakbar Daronkolaei,Alok Parmesh,Amanjot Kaur Samra,Ameen Khan,Amer Rez,Amir Saffari,Amit Agarwalla,Amit Jhindal,Amith Mamidala,Ammar Asmro,Amulya Ballakur,Anand Mishra,Anand Sridharan,Anastasiia Dubinina,Andre Lenz,Andreas Doerr,Andrew Keating,Andrew Leaver,Andrew Smith,Andrew Wirth,Andy Davey,Andy Rosenbaum,Andy Sohn,Angela Chan,Aniket Chakrabarti,Anil Ramakrishna,Anirban Roy,Anita Iyer,Anjali Narayan-Chen,Ankith Yennu,Anna Dabrowska,Anna Gawlowska,Anna Rumshisky,Anna Turek,Anoop Deoras,Anton Bezruchkin,Anup Prasad,Anupam Dewan,Anwith Kiran,Apoorv Gupta,Aram Galstyan,Aravind Manoharan,Arijit Biswas,Arindam Mandal,Arpit Gupta,Arsamkhan Pathan,Arun Nagarajan,Arushan Rajasekaram,Arvind Sundararajan,Ashwin Ganesan,Ashwin Swaminathan,Athanasios Mouchtaris,Audrey Champeau,Avik Ray,Ayush Jaiswal,Ayush Sharma,Bailey Keefer,Balamurugan Muthiah,Beatriz Leon-Millan,Ben Koopman,Ben Li,Benjamin Biggs,Benjamin Ott,Bhanu Vinzamuri,Bharath Venkatesh,Bhavana Ganesh
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 48 pages, 10 figures
Abstract:We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highly-capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon Nova Lite is a low-cost multimodal model that is lightning fast for processing images, video, documents and text. Amazon Nova Micro is a text-only model that delivers our lowest-latency responses at very low cost. Amazon Nova Canvas is an image generation model that creates professional grade images with rich customization controls. Amazon Nova Reel is a video generation model offering high-quality outputs, customization, and motion control. Our models were built responsibly and with a commitment to customer trust, security, and reliability. We report benchmarking results for core capabilities, agentic performance, long context, functional adaptation, runtime performance, and human evaluation.
zh
[AI-153] LLM Embedding-based Attribution (LEA): Quantifying Source Contributions to Generative Models Response for Vulnerability Analysis
【速读】：该论文试图解决在安全敏感环境中，如何量化或归因于生成式AI（Generative AI）响应中检索到的上下文与模型预训练知识的影响比例问题。其解决方案的关键在于提出LLM Embedding-based Attribution（LEA），这是一种可解释的度量方法，能够明确揭示每个生成响应中预训练知识与检索内容的“影响力百分比”。通过LEA，研究验证了模型隐藏状态的独立性演变：早期层对上下文高度依赖，支持LEA的推导；后期层独立性增强，解释了模型规模对于有效性的关键作用。
链接: https://arxiv.org/abs/2506.12100
作者: Reza Fayyazi,Michael Zuzak,Shanchieh Jay Yang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Security vulnerabilities are rapidly increasing in frequency and complexity, creating a shifting threat landscape that challenges cybersecurity defenses. Large Language Models (LLMs) have been widely adopted for cybersecurity threat analysis. When querying LLMs, dealing with new, unseen vulnerabilities is particularly challenging as it lies outside LLMs’ pre-trained distribution. Retrieval-Augmented Generation (RAG) pipelines mitigate the problem by injecting up-to-date authoritative sources into the model context, thus reducing hallucinations and increasing the accuracy in responses. Meanwhile, the deployment of LLMs in security-sensitive environments introduces challenges around trust and safety. This raises a critical open question: How to quantify or attribute the generated response to the retrieved context versus the model’s pre-trained knowledge? This work proposes LLM Embedding-based Attribution (LEA) – a novel, explainable metric to paint a clear picture on the ‘percentage of influence’ the pre-trained knowledge vs. retrieved content has for each generated response. We apply LEA to assess responses to 100 critical CVEs from the past decade, verifying its effectiveness to quantify the insightfulness for vulnerability analysis. Our development of LEA reveals a progression of independency in hidden states of LLMs: heavy reliance on context in early layers, which enables the derivation of LEA; increased independency in later layers, which sheds light on why scale is essential for LLM’s effectiveness. This work provides security analysts a means to audit LLM-assisted workflows, laying the groundwork for transparent, high-assurance deployments of RAG-enhanced LLMs in cybersecurity operations.
zh
[AI-154] SocialCredit
【速读】：该论文旨在解决传统信用评估体系在数据维度和伦理合规性方面的局限性，特别是在伊斯兰金融背景下如何有效整合社会媒体数据以提升信用评分的准确性与合规性。解决方案的关键在于构建一个基于生成式 AI (Generative AI) 的信用评分系统，该系统通过多模态特征提取器分析用户的社会媒体行为，并结合专门设计的 Sharia-合规层来确保符合伊斯兰伦理规范，同时利用检索增强生成模块提供透明的决策解释。
链接: https://arxiv.org/abs/2506.12099
作者: Thabassum Aslam,Anees Aslam
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:SocialCredit+ is AI powered credit scoring system that leverages publicly available social media data to augment traditional credit evaluation. It uses a conversational banking assistant to gather user consent and fetch public profiles. Multimodal feature extractors analyze posts, bios, images, and friend networks to generate a rich behavioral profile. A specialized Sharia-compliance layer flags any non-halal indicators and prohibited financial behavior based on Islamic ethics. The platform employs a retrieval-augmented generation module: an LLM accesses a domain specific knowledge base to generate clear, text-based explanations for each decision. We describe the end-to-end architecture and data flow, the models used, and system infrastructure. Synthetic scenarios illustrate how social signals translate into credit-score factors. This paper emphasizes conceptual novelty, compliance mechanisms, and practical impact, targeting AI researchers, fintech practitioners, ethical banking jurists, and investors.
zh
[AI-155] “I Hadnt Thought About That”: Creators of Human-like AI Weigh in on Ethics And Neurodivergence
【速读】：该论文试图解决人类-like AI代理（如机器人和聊天机器人）在伦理层面引发的问题，特别是其对神经多样性（neurodivergence）群体的潜在负面影响。研究聚焦于当前AI设计中对“人性”的定义及其对历史上被科学研究所去人性化群体（如自闭症人群）的影响，以及模型偏见和可及性问题。解决方案的关键在于深入理解AI开发者对神经多样性的认知与接受程度，并揭示他们在设计过程中可能无意中强化的神经常态（neuronormative）假设，从而推动更包容和伦理导向的研究方向。
链接: https://arxiv.org/abs/2506.12098
作者: Naba Rizvi,Taggert Smith,Tanvi Vidyala,Mya Bolds,Harper Strickland,Andrew Begel,Rua Williams,Imani Munyaka
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: published at FAccT 2025, 15 pages, 2 tables, 4 figures
Abstract:Human-like AI agents such as robots and chatbots are becoming increasingly popular, but they present a variety of ethical concerns. The first concern is in how we define humanness, and how our definition impacts communities historically dehumanized by scientific research. Autistic people in particular have been dehumanized by being compared to robots, making it even more important to ensure this marginalization is not reproduced by AI that may promote neuronormative social behaviors. Second, the ubiquitous use of these agents raises concerns surrounding model biases and accessibility. In our work, we investigate the experiences of the people who build and design these technologies to gain insights into their understanding and acceptance of neurodivergence, and the challenges in making their work more accessible to users with diverse needs. Even though neurodivergent individuals are often marginalized for their unique communication styles, nearly all participants overlooked the conclusions their end-users and other AI system makers may draw about communication norms from the implementation and interpretation of humanness applied in participants’ work. This highlights a major gap in their broader ethical considerations, compounded by some participants’ neuronormative assumptions about the behaviors and traits that distinguish “humans” from “bots” and the replication of these assumptions in their work. We examine the impact this may have on autism inclusion in society and provide recommendations for additional systemic changes towards more ethical research directions.
zh
[AI-156] Military AI Cyber Agents (MAICAs) Constitute a Global Threat to Critical Infrastructure
【速读】：该论文试图解决自主AI网络武器（Military-AI Cyber Agents, MAICAs）可能带来的灾难性风险问题。论文指出，MAICAs在技术上是可行的，并且由于地缘政治和网络空间的特性，其潜在威胁具有高度破坏性。解决方案的关键在于采取政治措施、防御性AI技术和模拟韧性措施，以降低MAICAs所带来的威胁。
链接: https://arxiv.org/abs/2506.12094
作者: Timothy Dubber,Seth Lazar
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper argues that autonomous AI cyber-weapons - Military-AI Cyber Agents (MAICAs) - create a credible pathway to catastrophic risk. It sets out the technical feasibility of MAICAs, explains why geopolitics and the nature of cyberspace make MAICAs a catastrophic risk, and proposes political, defensive-AI and analogue-resilience measures to blunt the threat.
zh
[AI-157] Intelligent Automation for FDI Facilitation: Optimizing Tariff Exemption Processes with OCR And Large Language Models
【速读】：该论文旨在解决关税豁免流程在吸引制造业外国直接投资（Foreign Direct Investment, FDI）中的效率与准确性问题，特别是行政流程中存在的优化空间。其解决方案的关键在于通过光学字符识别（Optical Character Recognition, OCR）与大型语言模型（Large Language Model, LLM）技术的协同整合，实现对申请文件和关键监管文本的智能化数字化处理，并自动化验证提交的海关税则编码是否符合官方豁免清单，从而提升评估速度与精度，降低非对齐和豁免利用不充分的风险。
链接: https://arxiv.org/abs/2506.12093
作者: Muhammad Sukri Bin Ramli
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:Tariff exemptions are fundamental to attracting Foreign Direct Investment (FDI) into the manufacturing sector, though the associated administrative processes present areas for optimization for both investing entities and the national tax authority. This paper proposes a conceptual framework to empower tax administration by leveraging a synergistic integration of Optical Character Recognition (OCR) and Large Language Model (LLM) technologies. The proposed system is designed to first utilize OCR for intelligent digitization, precisely extracting data from diverse application documents and key regulatory texts such as tariff orders. Subsequently, the LLM would enhance the capabilities of administrative officers by automating the critical and time-intensive task of verifying submitted HS Tariff Codes for machinery, equipment, and raw materials against official exemption lists. By enhancing the speed and precision of these initial assessments, this AI-driven approach systematically reduces potential for non-alignment and non-optimized exemption utilization, thereby streamlining the investment journey for FDI companies. For the national administration, the benefits include a significant boost in operational capacity, reduced administrative load, and a strengthened control environment, ultimately improving the ease of doing business and solidifying the nation’s appeal as a premier destination for high-value manufacturing FDI.
zh
[AI-158] Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity
【速读】：该论文旨在解决脉冲神经网络（Spiking Neural Networks, SNNs）在训练过程中因逐个处理脉冲而导致的时间复杂度高（O(T)）问题，从而使得训练计算成本过高。其解决方案的关键在于提出一种固定点并行训练（Fixed-point Parallel Training, FPT）方法，通过使用泄漏积分-放电（Leaky Integrate-and-Fire, LIF）神经元的固定点迭代形式，在所有T个时间步内将时间复杂度降低至O(K)，其中K是一个小常数（通常为3），从而显著提升训练效率。
链接: https://arxiv.org/abs/2506.12087
作者: Wanjin Feng,Xingyu Gao,Wenqian Du,Hailong Shi,Peilin Zhao,Pengcheng Wu,Chunyan Miao
机构: 未知
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:Spiking Neural Networks (SNNs) often suffer from high time complexity O(T) due to the sequential processing of T spikes, making training computationally expensive. In this paper, we propose a novel Fixed-point Parallel Training (FPT) method to accelerate SNN training without modifying the network architecture or introducing additional assumptions. FPT reduces the time complexity to O(K) , where K is a small constant (usually K=3 ), by using a fixed-point iteration form of Leaky Integrate-and-Fire (LIF) neurons for all T timesteps. We provide a theoretical convergence analysis of FPT and demonstrate that existing parallel spiking neurons can be viewed as special cases of our proposed method. Experimental results show that FPT effectively simulates the dynamics of original LIF neurons, significantly reducing computational time without sacrificing accuracy. This makes FPT a scalable and efficient solution for real-world applications, particularly for long-term tasks. Our code will be released at \hrefthis https URL\textttthis https URL. Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI) Cite as: arXiv:2506.12087 [cs.NE] (or arXiv:2506.12087v1 [cs.NE] for this version) https://doi.org/10.48550/arXiv.2506.12087 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Feng Wanjin [view email] [v1] Tue, 10 Jun 2025 13:27:27 UTC (2,302 KB) Full-text links: Access Paper: View a PDF of the paper titled Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity, by Wanjin Feng and 6 other authorsView PDFHTML (experimental)TeX SourceOther Formats view license Current browse context: cs.NE prev | next new | recent | 2025-06 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar a export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked=“checked”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status Get status notifications via email or slack
zh
[AI-159] Latency Optimization for Wireless Federated Learning in Multihop Networks
【速读】：该论文试图解决在多跳网络中无线联邦学习（Wireless Federated Learning, WFL）的新型时延最小化问题。解决方案的关键在于提出一种个性化学习与自适应聚合感知的联邦学习（Personalized Learning and Adaptive Aggregation-aware FL, PAFL）框架，通过协调个体和集体学习目标来有效应对参与节点间的数据异质性，并通过联合优化叶节点、中继节点以及中继路由指示器来实现系统时延的最小化。此外，还引入了额外的能量收集方案以支持中继节点的中继任务，从而提升整体系统的效率。
链接: https://arxiv.org/abs/2506.12081
作者: Shaba Shaon,Van-Dinh Nguyen,Dinh C. Nguyen
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: Accepted at IEEE Transactions on Vehicular Technology (IEEE TVT), code is available at this https URL
Abstract:In this paper, we study a novel latency minimization problem in wireless federated learning (FL) across multi-hop networks. The system comprises multiple routes, each integrating leaf and relay nodes for FL model training. We explore a personalized learning and adaptive aggregation-aware FL (PAFL) framework that effectively addresses data heterogeneity across participating nodes by harmonizing individual and collective learning objectives. We formulate an optimization problem aimed at minimizing system latency through the joint optimization of leaf and relay nodes, as well as relay routing indicator. We also incorporate an additional energy harvesting scheme for the relay nodes to help with their relay tasks. This formulation presents a computationally demanding challenge, and thus we develop a simple yet efficient algorithm based on block coordinate descent and successive convex approximation (SCA) techniques. Simulation results illustrate the efficacy of our proposed joint optimization approach for leaf and relay nodes with relay routing indicator. We observe significant latency savings in the wireless multi-hop PAFL system, with reductions of up to 69.37% compared to schemes optimizing only one node type, traditional greedy algorithm, and scheme without relay routing indicator.
zh
[AI-160] A Synthetic Pseudo-Autoencoder Invites Examination of Tacit Assumptions in Neural Network Design
【速读】：该论文试图解决将任意整数集合编码为单一数值变量，并从中恢复原始元素的问题（encoding an arbitrary set of integers into a single numerical variable, and then recovering the original elements）。其解决方案的关键在于设计了一个无需训练的神经网络，通过简单的数字拼接而非压缩来表示多个值，并利用硬件级右端位截断作为位操作机制，从而挑战了传统在表示、域连续性、计算和可学习性等方面的认知。
链接: https://arxiv.org/abs/2506.12076
作者: Assaf Marron
机构: 未知
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注:
Abstract:We present a handcrafted neural network that, without training, solves the seemingly difficult problem of encoding an arbitrary set of integers into a single numerical variable, and then recovering the original elements. While using only standard neural network operations – weighted sums with biases and identity activation – we make design choices that challenge common notions in this area around representation, continuity of domains, computation, learnability and more. For example, our construction is designed, not learned; it represents multiple values using a single one by simply concatenating digits without compression, and it relies on hardware-level truncation of rightmost digits as a bit-manipulation mechanism. This neural net is not intended for practical application. Instead, we see its resemblance to – and deviation from – standard trained autoencoders as an invitation to examine assumptions that may unnecessarily constrain the development of systems and models based on autoencoding and machine learning. Motivated in part by our research on a theory of biological evolution centered around natural autoencoding of species characteristics, we conclude by refining the discussion with a biological perspective.
zh
[AI-161] -TExTS (Teaching Text Expansion for Teacher Scaffolding): Enhancing Text Selection in High School Literature through Knowledge Graph-Based Recommendation
【速读】：该论文试图解决高中英语文学教师在有限的规划时间和资源下，难以精选多样且主题一致的文学文本集的问题。解决方案的关键在于开发了一个基于知识图谱的推荐系统——教学文本扩展与教师支持系统（Teaching Text Expansion for Teacher Scaffolding, T-TExTS），该系统利用领域特定本体（ontology）进行文本推荐，通过DeepWalk、有偏随机游走及其混合方法对知识图谱进行嵌入，从而在学科价值、体裁和主题相关性方面为新手教育者提供支持。
链接: https://arxiv.org/abs/2506.12075
作者: Nirmal Gelal,Chloe Snow,Ambyr Rios,Hande Küçük McGinty
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:The implementation of transformational pedagogy in secondary education classrooms requires a broad multiliteracy approach. Due to limited planning time and resources, high school English Literature teachers often struggle to curate diverse, thematically aligned literature text sets. This study addresses the critical need for a tool that provides scaffolds for novice educators in selecting literature texts that are diverse – in terms of genre, theme, subtheme, and author – yet similar in context and pedagogical merits. We have developed a recommendation system, Teaching Text Expansion for Teacher Scaffolding (T-TExTS), that suggests high school English Literature books based on pedagogical merits, genre, and thematic relevance using a knowledge graph. We constructed a domain-specific ontology using the KNowledge Acquisition and Representation Methodology (KNARM), transformed into a knowledge graph, which was then embedded using DeepWalk, biased random walk, and a hybrid of both approaches. The system was evaluated using link prediction and recommendation performance metrics, including Area Under the Curve (AUC), Mean Reciprocal Rank (MRR), Hits@K, and normalized Discounted Cumulative Gain (nDCG). DeepWalk outperformed in most ranking metrics, with the highest AUC (0.9431), whereas the hybrid model offered balanced performance. These findings demonstrate the importance of semantic, ontology-driven approaches in recommendation systems and suggest that T-TExTS can significantly ease the burden of English Literature text selection for high school educators, promoting more informed and inclusive curricular decisions. The source code for T-TExTS is available at: this https URL
zh
[AI-162] WebTrust: An AI-Driven Data Scoring System for Reliable Information Retrieval
【速读】：该论文试图解决当前AI工具在估计信息可信度方面的不足，尤其是在搜索引擎中缺乏明确的数据可靠性指示问题。解决方案的关键在于提出WebTrust系统，该系统基于微调的IBM Granite-1B模型，并在其自定义数据集上进行训练，能够为每个处理的陈述分配一个从0.1到1的可靠性评分，并提供清晰的评分依据。通过这种方式，WebTrust不仅提高了信息可信度评估的准确性，还增强了透明度和用户对搜索结果的信任感。
链接: https://arxiv.org/abs/2506.12072
作者: Joydeep Chandra,Aleksandr Algazinov,Satyam Kumar Navneet,Rim El Filali,Matt Laing,Andrew Hanna
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:As access to information becomes more open and widespread, people are increasingly using AI tools for assistance. However, many of these tools struggle to estimate the trustworthiness of the information. Although today’s search engines include AI features, they often fail to offer clear indicators of data reliability. To address this gap, we introduce WebTrust, a system designed to simplify the process of finding and judging credible information online. Built on a fine-tuned version of IBM’s Granite-1B model and trained on a custom dataset, WebTrust works by assigning a reliability score (from 0.1 to 1) to each statement it processes. In addition, it offers a clear justification for why a piece of information received that score. Evaluated using prompt engineering, WebTrust consistently achieves superior performance compared to other small-scale LLMs and rule-based approaches, outperforming them across all experiments on MAE, RMSE, and R2. User testing showed that when reliability scores are displayed alongside search results, people feel more confident and satisfied with the information they find. With its accuracy, transparency, and ease of use, WebTrust offers a practical solution to help combat misinformation and make trustworthy information more accessible to everyone.
zh
[AI-163] Organizational Adaptation to Generative AI in Cybersecurity: A Systematic Review
【速读】：该论文试图解决网络安全组织在整合生成式人工智能（Generative AI）过程中所面临的适应性问题，特别是如何调整其威胁建模框架和操作流程以有效应对GenAI带来的新挑战。研究指出，解决方案的关键在于结合成熟的安全基础设施、建立专门的AI团队、实施结构化的治理方法，并确保对自动化系统的适当人工监督，同时解决数据质量、可解释性、隐私保护及对抗性攻击等核心问题。
链接: https://arxiv.org/abs/2506.12060
作者: Christopher Nott
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 38 pages, 1 table, 1 figure
Abstract:Cybersecurity organizations are adapting to GenAI integration through modified frameworks and hybrid operational processes, with success influenced by existing security maturity, regulatory requirements, and investments in human capital and infrastructure. This qualitative research employs systematic document analysis and comparative case study methodology to examine how cybersecurity organizations adapt their threat modeling frameworks and operational processes to address generative artificial intelligence integration. Through examination of 25 studies from 2022 to 2025, the research documents substantial transformation in organizational approaches to threat modeling, moving from traditional signature-based systems toward frameworks incorporating artificial intelligence capabilities. The research identifies three primary adaptation patterns: Large Language Model integration for security applications, GenAI frameworks for risk detection and response automation, and AI/ML integration for threat hunting. Organizations with mature security infrastructures, particularly in finance and critical infrastructure sectors, demonstrate higher readiness through structured governance approaches, dedicated AI teams, and robust incident response processes. Organizations achieve successful GenAI integration when they maintain appropriate human oversight of automated systems, address data quality concerns and explainability requirements, and establish governance frameworks tailored to their specific sectors. Organizations encounter ongoing difficulties with privacy protection, bias reduction, personnel training, and defending against adversarial attacks. This work advances understanding of how organizations adopt innovative technologies in high-stakes environments and offers actionable insights for cybersecurity professionals implementing GenAI systems.
zh
[AI-164] From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
【速读】：该论文旨在解决从稀疏且间接观测中准确重建潜在环境场这一基础性挑战，该问题在大气科学、地球物理学、公共卫生和航空航天安全等多个科学领域均具有重要意义。传统方法依赖于基于物理的模拟器或密集传感器网络，但受限于高计算成本、延迟或有限的空间覆盖范围。论文提出的解决方案是Temporal Radiation Operator Network (TRON)，其关键在于设计了一种时空神经算子架构，能够从稀疏、非均匀代理测量序列中推断出连续的全球标量场，解决了更病态的逆问题：在无法获取未来观测或密集标签的情况下，从稀疏的时间演变传感器序列中重建当前全球场。
链接: https://arxiv.org/abs/2506.12045
作者: Kazuma Kobayashi,Samrendra Roy,Seid Koric,Diab Abueidda,Syed Bahauddin Alam
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注:
Abstract:Accurate reconstruction of latent environmental fields from sparse and indirect observations is a foundational challenge across scientific domains-from atmospheric science and geophysics to public health and aerospace safety. Traditional approaches rely on physics-based simulators or dense sensor networks, both constrained by high computational cost, latency, or limited spatial coverage. We present the Temporal Radiation Operator Network (TRON), a spatiotemporal neural operator architecture designed to infer continuous global scalar fields from sequences of sparse, non-uniform proxy measurements. Unlike recent forecasting models that operate on dense, gridded inputs to predict future states, TRON addresses a more ill-posed inverse problem: reconstructing the current global field from sparse, temporally evolving sensor sequences, without access to future observations or dense labels. Demonstrated on global cosmic radiation dose reconstruction, TRON is trained on 22 years of simulation data and generalizes across 65,341 spatial locations, 8,400 days, and sequence lengths from 7 to 90 days. It achieves sub-second inference with relative L2 errors below 0.1%, representing a 58,000X speedup over Monte Carlo-based estimators. Though evaluated in the context of cosmic radiation, TRON offers a domain-agnostic framework for scientific field reconstruction from sparse data, with applications in atmospheric modeling, geophysical hazard monitoring, and real-time environmental risk forecasting. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP) Cite as: arXiv:2506.12045 [cs.LG] (or arXiv:2506.12045v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12045 Focus to learn more arXiv-issued DOI via DataCite
zh
[AI-165] Why Do Some Inputs Break Low-Bit LLM Quantization?
【速读】：该论文试图解决低比特权重量化（low-bit weight-only quantization）在大型语言模型（Large Language Models, LLMs）中导致部分示例性能显著下降的问题。其关键解决方案是通过分析量化误差与残差流（residual stream）幅度之间的关系，揭示了高误差示例依赖于后期层中精确的残差激活，并指出多层感知机（MLP）门控输出在维持困惑度（perplexity）中的核心作用。
链接: https://arxiv.org/abs/2506.12044
作者: Ting-Yun Chang,Muru Zhang,Jesse Thomason,Robin Jia
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Low-bit weight-only quantization significantly reduces the memory footprint of large language models (LLMs), but disproportionately affects certain examples. We analyze diverse 3-4 bit methods on LLMs ranging from 7B-70B in size and find that the quantization errors of 50 pairs of methods are strongly correlated (avg. 0.82) on FineWeb examples. Moreover, the residual stream magnitudes of full-precision models are indicative of future quantization errors. We further establish a hypothesis that relates the residual stream magnitudes to error amplification and accumulation over layers. Using LLM localization techniques, early exiting, and activation patching, we show that examples with large errors rely on precise residual activations in the late layers, and that the outputs of MLP gates play a crucial role in maintaining the perplexity. Our work reveals why certain examples result in large quantization errors and which model components are most critical for performance preservation.
zh
[AI-166] CRITS: Convolutional Rectifier for Interpretable Time Series Classification ECML-PKDD KDD
【速读】：该论文试图解决时间序列分类中可解释性不足的问题，特别是现有解释方法在输入空间中缺乏详细解释或依赖上采样和随机扰动等问题。解决方案的关键在于提出一种名为CRITS（Convolutional Rectifier for Interpretable Time Series Classification）的可解释模型，该模型通过卷积核层、最大池化层和全连接整流网络，直接提取局部解释，利用整流线性单元（ReLU）激活函数获取特征权重，从而避免了计算梯度、使用随机扰动以及将显著性图上采样到原始输入空间的需要。
链接: https://arxiv.org/abs/2506.12042
作者: Alejandro Kuratomi,Zed Lee,Guilherme Dinis Chaliane Junior,Tony Lindgren,Diego García Pérez
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: This paper was presented at the 2024 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), as part of the XKDD workshop on interpretability. However it was not published in the LNCSI proceedings of the conference
Abstract:Several interpretability methods for convolutional network-based classifiers exist. Most of these methods focus on extracting saliency maps for a given sample, providing a local explanation that highlights the main regions for the classification. However, some of these methods lack detailed explanations in the input space due to upscaling issues or may require random perturbations to extract the explanations. We propose Convolutional Rectifier for Interpretable Time Series Classification, or CRITS, as an interpretable model for time series classification that is designed to intrinsically extract local explanations. The proposed method uses a layer of convolutional kernels, a max-pooling layer and a fully-connected rectifier network (a network with only rectified linear unit activations). The rectified linear unit activation allows the extraction of the feature weights for the given sample, eliminating the need to calculate gradients, use random perturbations and the upscale of the saliency maps to the initial input space. We evaluate CRITS on a set of datasets, and study its classification performance and its explanation alignment, sensitivity and understandability.
zh
[AI-167] he Maximal Overlap Discrete Wavelet Scattering Transform and Its Application in Classification Tasks
【速读】：该论文旨在解决在训练数据有限的情况下，传统深度学习方法如卷积神经网络（Convolutional Neural Networks, CNNs）性能下降的问题，提出了一种名为最大重叠离散小波散射变换（Maximal Overlap Discrete Wavelet Scattering Transform, MODWST）的特征提取方法。其解决方案的关键在于结合最大重叠离散小波变换（Maximal Overlap Discrete Wavelet Transform, MODWT）与小波散射变换（Wavelet Scattering Transform, WST）的优势，从而在保持信号多尺度分析能力的同时，提升分类任务中的泛化能力和稳定性。
链接: https://arxiv.org/abs/2506.12039
作者: Leonardo Fonseca Larrubia,Pedro Alberto Morettin,Chang Chiann
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Signal Processing (eess.SP); Applications (stat.AP); Machine Learning (stat.ML)
备注:
Abstract:We present the Maximal Overlap Discrete Wavelet Scattering Transform (MODWST), whose construction is inspired by the combination of the Maximal Overlap Discrete Wavelet Transform (MODWT) and the Scattering Wavelet Transform (WST). We also discuss the use of MODWST in classification tasks, evaluating its performance in two applications: stationary signal classification and ECG signal classification. The results demonstrate that MODWST achieved good performance in both applications, positioning itself as a viable alternative to popular methods like Convolutional Neural Networks (CNNs), particularly when the training data set is limited.
zh
[AI-168] LCD: Advancing Extreme Low-Bit Clustering for Large Language Models via Knowledge Distillation
【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在部署过程中面临的高内存和计算需求问题，特别是如何实现有效的低比特压缩。解决方案的关键在于提出LCD方法，该方法在知识蒸馏框架内统一了基于聚类的量化学习，并通过精心设计的优化技术，在超低比特宽度（2-3位）下保持LLM的性能。此外，LCD通过平滑处理压缩激活值，并采用查找表（LUT）结构加速推理，从而提升了整体效率。
链接: https://arxiv.org/abs/2506.12038
作者: Fangxin Liu,Ning Yang,Junping Zhao,Tao Yang,Haibing Guan,Li Jiang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 5 pages, 8 figures
Abstract:Large language models (LLMs) have achieved significant progress in natural language processing but face challenges in deployment due to high memory and computational requirements. Weight quantization is a common approach to address these issues, yet achieving effective low-bit compression remains challenging. This paper presents LCD, which unifies the learning of clustering-based quantization within a knowledge distillation framework. Using carefully designed optimization techniques, LCD preserves LLM performance even at ultra-low bit widths of 2-3 bits. Additionally, LCD compresses activations through smoothing and accelerates inference with a LUT-based design. Experimental results show that LCD outperforms existing methods and delivers up to a 6.2x speedup in inference. Notably, LCD is shown to be more cost-effective, making it a practical solution for real-world applications.
zh
[AI-169] How to Train a Model on a Cheap Cluster with Low Cost using Block Coordinate Descent
【速读】：该论文旨在解决大规模语言模型预训练过程中对高成本GPU资源的依赖问题，尤其是针对中小团队在经济和技术上的限制。其解决方案的关键在于提出一种基于块坐标下降（Block Coordinate Descent, BCD）的全参数预训练框架，并结合工程优化，使得大型模型能够在价格更为亲民的RTX 4090 GPU集群上高效训练。BCD通过在参数块级别进行梯度计算和更新，确保模型收敛，从而显著降低了预训练成本并实现了跨设备迁移能力。
链接: https://arxiv.org/abs/2506.12037
作者: Zeyu Liu,Yunquan Zhang,Boyang Zhang,Guoyong Jiang,Daning Cheng
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: under review
Abstract:Training large language models typically demands extensive GPU memory and substantial financial investment, which poses a barrier for many small- to medium-sized teams. In this paper, we present a full-parameter pre-training framework based on block coordinate descent (BCD), augmented with engineering optimizations, to efficiently train large models on affordable RTX 4090 GPU clusters. BCD ensures model convergence based on block coordinate descent theory and performs gradient computation and update at the level of parameter blocks. Experiments show that 1) Lower cost of Same-Device: BCD significantly reduces pre-training cost. For the 7B model, under identical hardware settings, BCD lowers training costs to approximately 33% on A100,A800 clusters on 7B model averagely and to approximately 2.6% on RTX 4090 clusters on 7B model, compared to traditional full-parameter training. 2) Cross-Device Transfer: By leveraging BCD, large-scale models previously trainable only on high-end A100 clusters can be seamlessly migrated and pre-trained on 4090 clusters-whose hourly cost is only one-quarter that of A100-without requiring expensive hardware. 3) Accuracy Retention: In both scenarios, BCD training achieves the same level of model accuracy as full-parameter pre-training.
zh
[AI-170] A Minimalist Method for Fine-tuning Text-to-Image Diffusion Models
【速读】：该论文旨在解决文本-图像扩散模型在文本-图像对齐和生成样本质量方面的优化问题。现有方法通常引入不必要的复杂性，如缓存完整的采样轨迹、依赖可微分奖励模型或大规模偏好数据集，或需要专门的引导技术。论文提出的解决方案关键在于引入Noise PPO，这是一种极简的强化学习算法，其核心是保持预训练扩散模型完全冻结，并学习一个提示条件化的初始噪声生成器，从而无需轨迹存储、奖励反向传播或复杂的引导技巧。
链接: https://arxiv.org/abs/2506.12036
作者: Yanting Miao,William Loh,Suraj Kothawade,Pacal Poupart
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 6 figures
Abstract:Recent work uses reinforcement learning (RL) to fine-tune text-to-image diffusion models, improving text-image alignment and sample quality. However, existing approaches introduce unnecessary complexity: they cache the full sampling trajectory, depend on differentiable reward models or large preference datasets, or require specialized guidance techniques. Motivated by the “golden noise” hypothesis – that certain initial noise samples can consistently yield superior alignment – we introduce Noise PPO, a minimalist RL algorithm that leaves the pre-trained diffusion model entirely frozen and learns a prompt-conditioned initial noise generator. Our approach requires no trajectory storage, reward backpropagation, or complex guidance tricks. Extensive experiments show that optimizing the initial noise distribution consistently improves alignment and sample quality over the original model, with the most significant gains at low inference steps. As the number of inference steps increases, the benefit of noise optimization diminishes but remains present. These findings clarify the scope and limitations of the golden noise hypothesis and reinforce the practical value of minimalist RL fine-tuning for diffusion models.
zh
[AI-171] MARché: Fast Masked Autoregressive Image Generation with Cache-Aware Attention
【速读】：该论文试图解决掩码自回归（Masked Autoregressive, MAR）模型在图像生成过程中计算效率低的问题，具体表现为在每个解码步骤中对所有标记重新计算注意力和前馈表示，而大多数标记在步骤间语义保持稳定。解决方案的关键在于提出一种无需训练的生成框架MARché，其核心包括两个组件：缓存感知注意力（cache-aware attention）和选择性键值刷新（selective KV refresh）。缓存感知注意力通过将标记划分为活动集和缓存集，实现对先前计算的键/值投影的高效复用，而选择性键值刷新则根据新生成标记的注意力得分识别上下文相关的标记，并仅更新需要重新计算的标记，从而显著减少冗余计算，同时保持图像生成质量。
链接: https://arxiv.org/abs/2506.12035
作者: Chaoyi Jiang,Sungwoo Kim,Lei Gao,Hossein Entezari Zarch,Won Woo Ro,Murali Annavaram
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Masked autoregressive (MAR) models unify the strengths of masked and autoregressive generation by predicting tokens in a fixed order using bidirectional attention for image generation. While effective, MAR models suffer from significant computational overhead, as they recompute attention and feed-forward representations for all tokens at every decoding step, despite most tokens remaining semantically stable across steps. We propose a training-free generation framework MARché to address this inefficiency through two key components: cache-aware attention and selective KV refresh. Cache-aware attention partitions tokens into active and cached sets, enabling separate computation paths that allow efficient reuse of previously computed key/value projections without compromising full-context modeling. But a cached token cannot be used indefinitely without recomputation due to the changing contextual information over multiple steps. MARché recognizes this challenge and applies a technique called selective KV refresh. Selective KV refresh identifies contextually relevant tokens based on attention scores from newly generated tokens and updates only those tokens that require recomputation, while preserving image generation quality. MARché significantly reduces redundant computation in MAR without modifying the underlying architecture. Empirically, MARché achieves up to 1.7x speedup with negligible impact on image quality, offering a scalable and broadly applicable solution for efficient masked transformer generation.
zh
[AI-172] Human-like Forgetting Curves in Deep Neural Networks
【速读】：该论文试图解决神经网络在持续学习过程中出现的灾难性遗忘问题（catastrophic forgetting），即模型在学习新任务时会迅速遗忘之前学到的知识。解决方案的关键在于提出一种定量框架，通过计算网络当前隐藏状态与先前存储的原型表示之间的相似性来评估信息保留概率，从而实现对复习时间的调度，提升训练效率并减轻遗忘现象。实验结果表明，该方法能够生成类似人类记忆的遗忘曲线，揭示了神经网络在模拟人类记忆衰减方面的潜力。
链接: https://arxiv.org/abs/2506.12034
作者: Dylan Kline
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:This study bridges cognitive science and neural network design by examining whether artificial models exhibit human-like forgetting curves. Drawing upon Ebbinghaus’ seminal work on memory decay and principles of spaced repetition, we propose a quantitative framework to measure information retention in neural networks. Our approach computes the recall probability by evaluating the similarity between a network’s current hidden state and previously stored prototype representations. This retention metric facilitates the scheduling of review sessions, thereby mitigating catastrophic forgetting during deployment and enhancing training efficiency by prompting targeted reviews. Our experiments with Multi-Layer Perceptrons reveal human-like forgetting curves, with knowledge becoming increasingly robust through scheduled reviews. This alignment between neural network forgetting curves and established human memory models identifies neural networks as an architecture that naturally emulates human memory decay and can inform state-of-the-art continual learning algorithms.
zh
[AI-173] EMERGENT: Efficient and Manipulation-resistant Matching using GFlowNets
【速读】：该论文旨在解决公共资源配置中的公平性与效率之间的权衡问题，特别是在单边匹配场景下，如学校录取、住房分配或医学住院医师分配等。传统算法如随机序列独裁（RSD）虽然具有策略-proof性，但效率较低；而概率序列（PS）和排名最小化（RM）虽效率较高，但容易诱发策略性操作。论文提出的解决方案是EMERGENT，其关键在于应用生成流网络（GFlowNets），通过采样多样且高奖励的解来实现高效且抗操纵的匹配：高奖励解提供高效匹配，而GFlowNets输出的随机性降低了策略性操作的动机。实验表明，EMERGENT在排名效率上优于RSD，并显著降低了RM和PS所产生匹配的策略脆弱性。
链接: https://arxiv.org/abs/2506.12033
作者: Mayesha Tasnim,Erman Acar,Sennay Ghebreab
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT)
备注:
Abstract:The design of fair and efficient algorithms for allocating public resources, such as school admissions, housing, or medical residency, has a profound social impact. In one-sided matching problems, where individuals are assigned to items based on ranked preferences, a fundamental trade-off exists between efficiency and strategyproofness. Existing algorithms like Random Serial Dictatorship (RSD), Probabilistic Serial (PS), and Rank Minimization (RM) capture only one side of this trade-off: RSD is strategyproof but inefficient, while PS and RM are efficient but incentivize manipulation. We propose EMERGENT, a novel application of Generative Flow Networks (GFlowNets) to one-sided matching, leveraging its ability to sample diverse, high-reward solutions. In our approach, efficient and manipulation-resistant matches emerge naturally: high-reward solutions yield efficient matches, while the stochasticity of GFlowNets-based outputs reduces incentives for manipulation. Experiments show that EMERGENT outperforms RSD in rank efficiency while significantly reducing strategic vulnerability compared to matches produced by RM and PS. Our work highlights the potential of GFlowNets for applications involving social choice mechanisms, where it is crucial to balance efficiency and manipulability.
zh
[AI-174] Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelines
【速读】：该论文旨在解决科学数据完整性验证的问题，特别是在高维领域如气候建模和流体模拟中确保数据的可追溯性和防篡改性。其解决方案的关键在于提出一种基于卷积自编码器的鲁棒神经水印框架，能够将二进制信息不可见地嵌入到结构化数据（如温度、涡度和大地水准面）中，并在经历损失性变换（包括噪声注入、裁剪和压缩）后仍保持水印的持久性，同时维持接近原始数据的保真度（子1%的均方误差）。该方法相比传统的奇异值分解（SVD）基水印技术，在比特准确率和视觉重建质量上均有显著提升。
链接: https://arxiv.org/abs/2506.12032
作者: Krti Tallam
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Cryptography and Security (cs.CR)
备注:
Abstract:We present a robust neural watermarking framework for scientific data integrity, targeting high-dimensional fields common in climate modeling and fluid simulations. Using a convolutional autoencoder, binary messages are invisibly embedded into structured data such as temperature, vorticity, and geopotential. Our method ensures watermark persistence under lossy transformations - including noise injection, cropping, and compression - while maintaining near-original fidelity (sub-1% MSE). Compared to classical singular value decomposition (SVD)-based watermarking, our approach achieves 98% bit accuracy and visually indistinguishable reconstructions across ERA5 and Navier-Stokes datasets. This system offers a scalable, model-compatible tool for data provenance, auditability, and traceability in high-performance scientific workflows, and contributes to the broader goal of securing AI systems through verifiable, physics-aware watermarking. We evaluate on physically grounded scientific datasets as a representative stress-test; the framework extends naturally to other structured domains such as satellite imagery and autonomous-vehicle perception streams.
zh
[AI-175] Improving Generalization in Heterogeneous Federated Continual Learning via Spatio-Temporal Gradient Matching with Prototypical Coreset
【速读】：该论文旨在解决联邦持续学习（Federated Continual Learning, FCL）中由于客户端数据和任务的非相关性或冲突性导致的统计异质性、数据噪声、特征学习偏差以及灾难性遗忘等问题。现有方法依赖生成式重放（generative replay）来构建历史任务的伪数据集，但该方法本身存在灾难性遗忘和客户端间任务发散的问题，从而导致过拟合。论文提出的解决方案关键在于提出一种名为时空梯度匹配与无网络原型（Spatio-Temporal grAdient Matching with network-free Prototype, STAMP）的新方法，其核心包括：1）一种模型无关的方法用于确定原型网络中有效形成原型的样本子集；2）一种在客户端（时间维度）和服务器端（空间维度）应用的时空梯度匹配机制，以缓解灾难性遗忘和数据异质性；3）利用原型近似任务级梯度，提升客户端的梯度匹配效果。
链接: https://arxiv.org/abs/2506.12031
作者: Minh-Duong Nguyen,Le-Tuan Nguyen,Quoc-Viet Pham
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 25 pages, 18 figures, 5 tables
Abstract:Federated Continual Learning (FCL) has recently emerged as a crucial research area, as data from distributed clients typically arrives as a stream, requiring sequential learning. This paper explores a more practical and challenging FCL setting, where clients may have unrelated or even conflicting data and tasks. In this scenario, statistical heterogeneity and data noise can create spurious correlations, leading to biased feature learning and catastrophic forgetting. Existing FCL approaches often use generative replay to create pseudo-datasets of previous tasks. However, generative replay itself suffers from catastrophic forgetting and task divergence among clients, leading to overfitting in FCL. Existing FCL approaches often use generative replay to create pseudo-datasets of previous tasks. However, generative replay itself suffers from catastrophic forgetting and task divergence among clients, leading to overfitting in FCL. To address these challenges, we propose a novel approach called Spatio-Temporal grAdient Matching with network-free Prototype (STAMP). Our contributions are threefold: 1) We develop a model-agnostic method to determine subset of samples that effectively form prototypes when using a prototypical network, making it resilient to continual learning challenges; 2) We introduce a spatio-temporal gradient matching approach, applied at both the client-side (temporal) and server-side (spatial), to mitigate catastrophic forgetting and data heterogeneity; 3) We leverage prototypes to approximate task-wise gradients, improving gradient matching on the client-side. Extensive experiments demonstrate our method’s superiority over existing baselines.
zh
[AI-176] Impact Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis CEC
【速读】：该论文试图解决影响学生学业表现的社会学术和经济因素的识别与量化问题，以支持有效的教育干预。其解决方案的关键在于结合多种机器学习技术与因果分析方法，构建假设的因果图，并通过数据清洗、可视化、相关性分析、回归与分类模型以及无监督因果分析（如PC、GES、ICA-LiNGAM和GRASP算法）来预测和解释这些因素对学业成绩的影响。研究还通过将最优回归模型集成到网络应用中，提供了一个基于实证证据的实用工具。
链接: https://arxiv.org/abs/2506.12030
作者: Md. Biplob Hosen,Sabbir Ahmed,Bushra Akter,Mehrin Anannya
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Presented at the 13th International Conference on Electrical and Computer Engineering (ICECE-2024)
Abstract:Understanding socio-academic and economic factors influencing students’ performance is crucial for effective educational interventions. This study employs several machine learning techniques and causal analysis to predict and elucidate the impacts of these factors on academic performance. We constructed a hypothetical causal graph and collected data from 1,050 student profiles. Following meticulous data cleaning and visualization, we analyze linear relationships through correlation and variable plots, and perform causal analysis on the hypothetical graph. Regression and classification models are applied for prediction, and unsupervised causality analysis using PC, GES, ICA-LiNGAM, and GRASP algorithms is conducted. Our regression analysis shows that Ridge Regression achieve a Mean Absolute Error (MAE) of 0.12 and a Mean Squared Error (MSE) of 0.024, indicating robustness, while classification models like Random Forest achieve nearly perfect F1-scores. The causal analysis shows significant direct and indirect effects of factors such as class attendance, study hours, and group study on CGPA. These insights are validated through unsupervised causality analysis. By integrating the best regression model into a web application, we are developing a practical tool for students and educators to enhance academic outcomes based on empirical evidence.
zh
[AI-177] Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences
【速读】：该论文试图解决传统数据驱动模型在船舶轨迹预测中缺乏真实物理约束的问题，导致预测结果不符合船舶运动动力学，尤其在数据有限或噪声较大的情况下，可能出现因外部因素引起的航向或速度突变。解决方案的关键在于提出一种物理信息神经网络（Physics-Informed Neural Network, PINN）方法，通过将简化的运动学模型整合到神经网络训练过程中，利用基于有限差分的物理损失函数（一阶和二阶）来强制遵守基本物理原理，从而提升预测的物理一致性与准确性。
链接: https://arxiv.org/abs/2506.12029
作者: Md Mahbub Alam,Amilcar Soares,José F. Rodrigues-Jr,Gabriel Spadon
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate vessel trajectory prediction is crucial for navigational safety, route optimization, traffic management, search and rescue operations, and autonomous navigation. Traditional data-driven models lack real-world physical constraints, leading to forecasts that disobey vessel motion dynamics, such as in scenarios with limited or noisy data where sudden course changes or speed variations occur due to external factors. To address this limitation, we propose a Physics-Informed Neural Network (PINN) approach for trajectory prediction that integrates a streamlined kinematic model for vessel motion into the neural network training process via a first- and second-order, finite difference physics-based loss function. This loss function, discretized using the first-order forward Euler method, Heun’s second-order approximation, and refined with a midpoint approximation based on Taylor series expansion, enforces fidelity to fundamental physical principles by penalizing deviations from expected kinematic behavior. We evaluated PINN using real-world AIS datasets that cover diverse maritime conditions and compared it with state-of-the-art models. Our results demonstrate that the proposed method reduces average displacement errors by up to 32% across models and datasets while maintaining physical consistency. These results enhance model reliability and adherence to mission-critical maritime activities, where precision translates into better situational awareness in the oceans.
zh
[AI-178] he Limits of Tractable Marginalization
【速读】：该论文试图解决的问题是：是否存在一种高效的表达方式，能够将所有具有多项式时间边缘化算法的函数用多项式大小的算术电路进行简洁表示，这些电路计算的是多线性多项式。论文的解决方案关键在于通过构造反例表明，存在一些边缘化计算是高效的，但无法被现有的模型高效表示，这一结论基于假设FP=#P（该假设由P=NP所蕴含）。此外，作者还提出了一个边缘化复杂度类的层次结构，并证明了在某些条件下，若存在高效的实RAM执行虚拟证据边缘化，则该函数的多线性表示可以被小规模电路所实现。
链接: https://arxiv.org/abs/2506.12020
作者: Oliver Broadrick,Sanyam Agarwal,Guy Van den Broeck,Markus Bläser
机构: 未知
类目: Computational Complexity (cs.CC); Artificial Intelligence (cs.AI)
备注:
Abstract:Marginalization – summing a function over all assignments to a subset of its inputs – is a fundamental computational problem with applications from probabilistic inference to formal verification. Despite its computational hardness in general, there exist many classes of functions (e.g., probabilistic models) for which marginalization remains tractable, and they can be commonly expressed by polynomial size arithmetic circuits computing multilinear polynomials. This raises the question, can all functions with polynomial time marginalization algorithms be succinctly expressed by such circuits? We give a negative answer, exhibiting simple functions with tractable marginalization yet no efficient representation by known models, assuming \textsfFP\neq#\textsfP (an assumption implied by \textsfP \neq \textsfNP ). To this end, we identify a hierarchy of complexity classes corresponding to stronger forms of marginalization, all of which are efficiently computable on the known circuit models. We conclude with a completeness result, showing that whenever there is an efficient real RAM performing virtual evidence marginalization for a function, then there are small circuits for that function’s multilinear representation.
zh
[AI-179] UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data
【速读】：该论文试图解决采矿行业中高分辨率、地理参考的空间信息可靠获取问题，以支持开采规划和现场监测等核心活动。解决方案的关键在于构建一个集成系统架构，结合无人机传感、LiDAR地形建模和基于深度学习的目标检测技术，实现开放露天矿环境中的空间精确信息生成，其核心流程包括地理定位、三维重建和目标定位，能够将结构化空间输出整合到工业数字孪生平台中。
链接: https://arxiv.org/abs/2506.13505
作者: Vasiliki Balaska,Ioannis Tsampikos Papapetros,Katerina Maria Oikonomou,Loukas Bampis,Antonios Gasteratos
机构: 未知
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Robotics (cs.RO)
备注:
Abstract:The mining sector increasingly adopts digital tools to improve operational efficiency, safety, and data-driven decision-making. One of the key challenges remains the reliable acquisition of high-resolution, geo-referenced spatial information to support core activities such as extraction planning and on-site monitoring. This work presents an integrated system architecture that combines UAV-based sensing, LiDAR terrain modeling, and deep learning-based object detection to generate spatially accurate information for open-pit mining environments. The proposed pipeline includes geo-referencing, 3D reconstruction, and object localization, enabling structured spatial outputs to be integrated into an industrial digital twin platform. Unlike traditional static surveying methods, the system offers higher coverage and automation potential, with modular components suitable for deployment in real-world industrial contexts. While the current implementation operates in post-flight batch mode, it lays the foundation for real-time extensions. The system contributes to the development of AI-enhanced remote sensing in mining by demonstrating a scalable and field-validated geospatial data workflow that supports situational awareness and infrastructure safety.
zh
[AI-180] A Two-stage Optimization Method for Wide-range Single-electron Quantum Magnetic Sensing
【速读】：该论文旨在解决量子磁传感中在宽范围信号感兴趣参数（SoI）和物理约束条件下，传统自适应算法或基于公式的搜索方法难以高效且最优收敛的问题，从而导致检测时间延长和精度下降。其解决方案的关键在于提出了一种两阶段优化协议：第一阶段利用固定传感参数的贝叶斯神经网络缩小SoI的范围；第二阶段则通过联邦强化学习智能体在缩减后的搜索空间内对传感参数进行精调，从而显著提升了宽范围直流磁场估计的准确性和资源效率。
链接: https://arxiv.org/abs/2506.13469
作者: Shiqian Guo,Jianqing Liu,Thinh Le,Huaiyu Dai
机构: 未知
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注:
Abstract:Quantum magnetic sensing based on spin systems has emerged as a new paradigm for detecting ultra-weak magnetic fields with unprecedented sensitivity, revitalizing applications in navigation, geo-localization, biology, and beyond. At the heart of quantum magnetic sensing, from the protocol perspective, lies the design of optimal sensing parameters to manifest and then estimate the underlying signals of interest (SoI). Existing studies on this front mainly rely on adaptive algorithms based on black-box AI models or formula-driven principled searches. However, when the SoI spans a wide range and the quantum sensor has physical constraints, these methods may fail to converge efficiently or optimally, resulting in prolonged interrogation times and reduced sensing accuracy. In this work, we report the design of a new protocol using a two-stage optimization method. In the 1st Stage, a Bayesian neural network with a fixed set of sensing parameters is used to narrow the range of SoI. In the 2nd Stage, a federated reinforcement learning agent is designed to fine-tune the sensing parameters within a reduced search space. The proposed protocol is developed and evaluated in a challenging context of single-shot readout of an NV-center electron spin under a constrained total sensing time budget; and yet it achieves significant improvements in both accuracy and resource efficiency for wide-range D.C. magnetic field estimation compared to the state of the art.
zh
[AI-181] Quantum AGI: Ontological Foundations
【速读】：该论文试图解决量子基础对人工通用智能（AGI）实现的影响问题，特别是如何在量子环境下实现AGI的可行性。其解决方案的关键在于提出一种信息论分类法，区分经典AGI与量子AGI，并揭示量子力学如何影响代理行为的基本特征，包括通过提供计算优势和引入新的约束条件来改变AGI的能力。
链接: https://arxiv.org/abs/2506.13134
作者: Elija Perrier,Michael Timothy Bennett
机构: 未知
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注: Accepted into AGI-25. Technical appendices available via link
Abstract:We examine the implications of quantum foundations for AGI, focusing on how seminal results such as Bell’s theorems (non-locality), the Kochen-Specker theorem (contextuality) and no-cloning theorem problematise practical implementation of AGI in quantum settings. We introduce a novel information-theoretic taxonomy distinguishing between classical AGI and quantum AGI and show how quantum mechanics affects fundamental features of agency. We show how quantum ontology may change AGI capabilities, both via affording computational advantages and via imposing novel constraints.
zh
[AI-182] SpaceTrack-TimeSeries: Time Series Dataset towards Satellite Orbit Analysis
【速读】：该论文试图解决当前在天文观测和深空探测中因低地球轨道（LEO）卫星星座大规模部署而带来的挑战，特别是缺乏公开的、现实世界的数据集来支持空间物体机动行为预测和碰撞风险评估的研究。解决方案的关键在于收集并整理了来自Starlink卫星的代表性机动行为数据集，该数据集整合了两行元素（TLE）目录数据与高精度星历数据，从而实现了对空间物体行为更真实、多维度的建模。
链接: https://arxiv.org/abs/2506.13034
作者: Zhixin Guo,Qi Shi,Xiaofan Xu,Sixiang Shan,Limin Qin,Linqiang Ge,Rui Zhang,Ya Dai,Hua Zhu,Guowei Jiang
机构: 未知
类目: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI)
备注:
Abstract:With the rapid advancement of aerospace technology and the large-scale deployment of low Earth orbit (LEO) satellite constellations, the challenges facing astronomical observations and deep space exploration have become increasingly pronounced. As a result, the demand for high-precision orbital data on space objects-along with comprehensive analyses of satellite positioning, constellation configurations, and deep space satellite dynamics-has grown more urgent. However, there remains a notable lack of publicly accessible, real-world datasets to support research in areas such as space object maneuver behavior prediction and collision risk assessment. This study seeks to address this gap by collecting and curating a representative dataset of maneuvering behavior from Starlink satellites. The dataset integrates Two-Line Element (TLE) catalog data with corresponding high-precision ephemeris data, thereby enabling a more realistic and multidimensional modeling of space object behavior. It provides valuable insights into practical deployment of maneuver detection methods and the evaluation of collision risks in increasingly congested orbital environments.
zh
[AI-183] Log analysis for accelerators: status and future outlook
【速读】：该论文旨在解决加速器设施中信息检索与知识管理效率低下的问题，通过引入生成式 AI (Generative AI) 驱动的检索增强生成（Retrieval Augmented Generation, RAG）技术，提升电子日志（eLog）系统的 operational insights 和信息可访问性。解决方案的关键在于利用现代 AI 技术优化信息检索流程，并将其与现有的加速器控制系统进行集成，从而实现更高效的知识管理和操作支持。
链接: https://arxiv.org/abs/2506.12949
作者: Antonin Sulc,Thorsten Hellert,Aaron Reed,Adam Carpenter,Alex Bien,Chris Tennant,Claudio Bisegni,Daniel Lersch,Daniel Ratner,David Lawrence,Diana McSpadden,Hayden Hoschouer,Jason St. John,Thomas Britton
机构: 未知
类目: High Energy Physics - Experiment (hep-ex); Artificial Intelligence (cs.AI)
备注: 4 pages, 2 figures, 16th International Particle Accelerator Conference (IPAC’25)
Abstract:This work demonstrates electronic logbook (eLog) systems leveraging modern AI-driven information retrieval capabilities at the accelerator facilities of Fermilab, Jefferson Lab, Lawrence Berkeley National Laboratory (LBNL), SLAC National Accelerator Laboratory. We evaluate contemporary tools and methodologies for information retrieval with Retrieval Augmented Generation (RAGs), focusing on operational insights and integration with existing accelerator control systems. The study addresses challenges and proposes solutions for state-of-the-art eLog analysis through practical implementations, demonstrating applications and limitations. We present a framework for enhancing accelerator facility operations through improved information accessibility and knowledge management, which could potentially lead to more efficient operations. Comments: 4 pages, 2 figures, 16th International Particle Accelerator Conference (IPAC’25) Subjects: High Energy Physics - Experiment (hep-ex); Artificial Intelligence (cs.AI) Reportnumber: THPS048 Cite as: arXiv:2506.12949 [hep-ex] (or arXiv:2506.12949v1 [hep-ex] for this version) https://doi.org/10.48550/arXiv.2506.12949 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[AI-184] Fair Bayesian Model-Based Clustering
【速读】：该论文试图解决传统群组公平聚类方法在确定簇数量和数据类型适应性方面的局限性，特别是在基于K-均值聚类的方法中需要预先指定距离度量和簇数量的问题。其解决方案的关键在于提出一种公平的贝叶斯模型聚类方法（Fair Bayesian Clustering, FBC），通过设计一个仅在公平簇上分配质量的先验分布，并结合高效的马尔可夫链蒙特卡洛（MCMC）算法，实现簇数量的自动推断以及对多种数据类型的适用性。
链接: https://arxiv.org/abs/2506.12839
作者: Jihu Lee,Kunwoong Kim,Yongdai Kim
机构: 未知
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that the proportions of each sensitive group are similar in all clusters. Most existing group-fair clustering methods are based on the K -means clustering and thus require the distance between instances and the number of clusters to be given in advance. To resolve this limitation, we propose a fair Bayesian model-based clustering called Fair Bayesian Clustering (FBC). We develop a specially designed prior which puts its mass only on fair clusters, and implement an efficient MCMC algorithm. Advantages of FBC are that it can infer the number of clusters and can be applied to any data type as long as the likelihood is defined (e.g., categorical data). Experiments on real-world datasets show that FBC (i) reasonably infers the number of clusters, (ii) achieves a competitive utility-fairness trade-off compared to existing fair clustering methods, and (iii) performs well on categorical data.
zh
[AI-185] Synesthesia of Machines (SoM)-Enhanced Sub-THz ISAC Transmission for Air-Ground Network
【速读】：该论文旨在解决在太赫兹（sub-THz）频段下集成感知与通信（ISAC）在空地网络中的性能优化问题，尤其是在面对独特的传播特性与硬件限制时如何降低操作延迟。其解决方案的关键在于引入一种受机器联觉（synesthesia of machine, SoM）启发的多模态感知融合框架，通过利用太赫兹硬件和信道的固有自由度优化射频环境，并采用考虑波束偏斜（squint-aware）的波束管理技术，以提升空地网络的适应性，实现三维动态ISAC链路。此外，该框架通过多模态信息增强ISAC性能并减少延迟，其中视觉数据用于快速定位用户和目标，定制化的多模态学习算法则用于优化混合预编码器。
链接: https://arxiv.org/abs/2506.12831
作者: Zonghui Yang,Shijian Gao,Xiang Cheng,Liuqing Yang
机构: 未知
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注:
Abstract:Integrated sensing and communication (ISAC) within sub-THz frequencies is crucial for future air-ground networks, but unique propagation characteristics and hardware limitations present challenges in optimizing ISAC performance while increasing operational latency. This paper introduces a multi-modal sensing fusion framework inspired by synesthesia of machine (SoM) to enhance sub-THz ISAC transmission. By exploiting inherent degrees of freedom in sub-THz hardware and channels, the framework optimizes the radio-frequency environment. Squint-aware beam management is developed to improve air-ground network adaptability, enabling three-dimensional dynamic ISAC links. Leveraging multi-modal information, the framework enhances ISAC performance and reduces latency. Visual data rapidly localizes users and targets, while a customized multi-modal learning algorithm optimizes the hybrid precoder. A new metric provides comprehensive performance evaluation, and extensive experiments demonstrate that the proposed scheme significantly improves ISAC efficiency.
zh
[AI-186] Solving tricky quantum optics problems with assistance from (artificial) intelligence
【速读】：该论文试图解决如何将现代人工智能（Artificial Intelligence, AI）作为“科学合作者”应用于量子光学中的复杂问题，包括光学泵浦中的态分布、衰减态之间的共振跃迁（Burshtein效应）以及简并无镜腔激光。解决方案的关键在于通过迭代对话机制，使AI模型在提示和修正下能够推理复杂场景、优化答案并提供专家级指导，从而实现对高阶建模与分析的高效支持。
链接: https://arxiv.org/abs/2506.12770
作者: Manas Pandey,Bharath Hebbe Madhusudhana,Saikat Ghosh,Dmitry Budker
机构: 未知
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Atomic Physics (physics.atom-ph)
备注: 9 pages, 3 figures
Abstract:The capabilities of modern artificial intelligence (AI) as a ``scientific collaborator’’ are explored by engaging it with three nuanced problems in quantum optics: state populations in optical pumping, resonant transitions between decaying states (the Burshtein effect), and degenerate mirrorless lasing. Through iterative dialogue, the authors observe that AI models–when prompted and corrected–can reason through complex scenarios, refine their answers, and provide expert-level guidance, closely resembling the interaction with an adept colleague. The findings highlight that AI democratizes access to sophisticated modeling and analysis, shifting the focus in scientific practice from technical mastery to the generation and testing of ideas, and reducing the time for completing research tasks from days to minutes.
zh
[AI-187] Bridging the Digital Divide: Small Language Models as a Pathway for Physics and Photonics Education in Underdeveloped Regions
【速读】：该论文试图解决欠发达地区由于基础设施有限、教育资源匮乏和互联网接入不可靠而导致的物理学和光子学教育不平等问题，这些问题加剧了STEM（科学、技术、工程和数学）教育中的深层次不平等。论文提出的解决方案是利用Small Language Models (SLMs)，这是一种可以在低功耗设备上离线运行的紧凑型人工智能工具，其关键在于通过作为虚拟导师、支持本土语言教学和促进互动学习，来弥补合格教师和实验设施的不足，从而缩小数字鸿沟并推动边缘化社区的STEM教育发展。
链接: https://arxiv.org/abs/2506.12403
作者: Asghar Ghorbani,Hanieh Fattahi
机构: 未知
类目: Physics Education (physics.ed-ph); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注:
Abstract:Limited infrastructure, scarce educational resources, and unreliable internet access often hinder physics and photonics education in underdeveloped regions. These barriers create deep inequities in Science, Technology, Engineering, and Mathematics (STEM) education. This article explores how Small Language Models (SLMs)-compact, AI-powered tools that can run offline on low-power devices, offering a scalable solution. By acting as virtual tutors, enabling native-language instruction, and supporting interactive learning, SLMs can help address the shortage of trained educators and laboratory access. By narrowing the digital divide through targeted investment in AI technologies, SLMs present a scalable and inclusive solution to advance STEM education and foster scientific empowerment in marginalized communities.
zh
[AI-188] Component Based Quantum Machine Learning Explainability
【速读】：该论文试图解决量子机器学习（Quantum Machine Learning, QML）模型的可解释性问题，即如何揭示QML模型决策过程的内在机制。由于QML模型继承了经典机器学习模型的黑箱特性，因此需要开发相应的可解释性技术来增强其透明度。该论文提出的解决方案的关键在于构建一个模块化的可解释QML框架，将QML算法分解为核心组件，如特征映射、变分电路（ansatz）、优化器、核函数和量子-经典循环，并利用已适应于分析这些组件的可解释性技术（如ALE和SHAP）对每个部分进行分析，从而综合各部分的见解以实现对整体QML模型的可解释性推断。
链接: https://arxiv.org/abs/2506.12378
作者: Barra White,Krishnendu Guha
机构: 未知
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注: 11 pages
Abstract:Explainable ML algorithms are designed to provide transparency and insight into their decision-making process. Explaining how ML models come to their prediction is critical in fields such as healthcare and finance, as it provides insight into how models can help detect bias in predictions and help comply with GDPR compliance in these fields. QML leverages quantum phenomena such as entanglement and superposition, offering the potential for computational speedup and greater insights compared to classical ML. However, QML models also inherit the black-box nature of their classical counterparts, requiring the development of explainability techniques to be applied to these QML models to help understand why and how a particular output was generated. This paper will explore the idea of creating a modular, explainable QML framework that splits QML algorithms into their core components, such as feature maps, variational circuits (ansatz), optimizers, kernels, and quantum-classical loops. Each component will be analyzed using explainability techniques, such as ALE and SHAP, which have been adapted to analyse the different components of these QML algorithms. By combining insights from these parts, the paper aims to infer explainability to the overall QML model. Comments: 11 pages Subjects: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG) Cite as: arXiv:2506.12378 [quant-ph] (or arXiv:2506.12378v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2506.12378 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
zh
[AI-189] heoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory
【速读】：该论文试图解决Reinforcement Learning from Human Feedback (RLHF)在社会选择理论中违反基本公理（如多数一致性、成对多数一致性和康多塞一致性）的问题，同时解释其在实践中表现良好的原因。论文的关键解决方案是证明在对偏好分布做出温和且符合实际的假设下，RLHF能够满足成对多数一致性和康多塞一致性，这些假设在现实对齐任务中通常成立，从而为RLHF的高效性提供了理论依据。此外，通过调整奖励建模目标，可以进一步确保在一般偏好分布下实现一致性，提升对齐效果。
链接: https://arxiv.org/abs/2506.12350
作者: Jiancong Xiao,Zhekun Shi,Kaizhao Liu,Qi Long,Weijie J. Su
机构: 未知
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Despite its empirical success, Reinforcement Learning from Human Feedback (RLHF) has been shown to violate almost all the fundamental axioms in social choice theory – such as majority consistency, pairwise majority consistency, and Condorcet consistency. This raises a foundational question: why does RLHF perform so well in practice if it fails these seemingly essential properties? In this paper, we resolve this paradox by showing that under mild and empirically plausible assumptions on the preference profile, RLHF does satisfy pairwise majority and Condorcet consistency. These assumptions are frequently satisfied in real-world alignment tasks, offering a theoretical explanation for RLHF’s strong practical performance. Furthermore, we show that a slight modification to the reward modeling objective can ensure pairwise majority or Condorcet consistency even under general preference profiles, thereby improving the alignment process. Finally, we go beyond classical axioms in economic and social choice theory and introduce new alignment criteria – preference matching, preference equivalence, and group preference matching – that better reflect the goal of learning distributions over responses. We show that while RLHF satisfies the first two properties, it fails to satisfy the third. We conclude by discussing how future alignment methods may be designed to satisfy all three.
zh
[AI-190] CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
【速读】：该论文旨在解决现有音乐信息检索（MIR）基准测试在任务范围和评估方式上的局限性，这些基准通常依赖于简化任务或多选评估，无法全面反映真实世界音乐分析的复杂性。其解决方案的关键在于将传统的MIR标注重新诠释为指令遵循格式，并引入CMI-Bench，一个综合性音乐指令遵循基准，用于评估音频-文本大语言模型（LLMs）在多种MIR任务上的表现，包括风格分类、情感回归、乐器分类、音高估计等核心挑战。该基准采用与当前最先进MIR模型一致的标准评估指标，确保与监督方法的直接可比性。
链接: https://arxiv.org/abs/2506.12285
作者: Yinghao Ma,Siyou Li,Juntao Yu,Emmanouil Benetos,Akira Maezawa
机构: 未知
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD)
备注: Accepted by ISMIR 2025
Abstract:Recent advances in audio-text large language models (LLMs) have opened new possibilities for music understanding and generation. However, existing benchmarks are limited in scope, often relying on simplified tasks or multi-choice evaluations that fail to reflect the complexity of real-world music analysis. We reinterpret a broad range of traditional MIR annotations as instruction-following formats and introduce CMI-Bench, a comprehensive music instruction following benchmark designed to evaluate audio-text LLMs on a diverse set of music information retrieval (MIR) tasks. These include genre classification, emotion regression, emotion tagging, instrument classification, pitch estimation, key detection, lyrics transcription, melody extraction, vocal technique recognition, instrument performance technique detection, music tagging, music captioning, and (down)beat tracking: reflecting core challenges in MIR research. Unlike previous benchmarks, CMI-Bench adopts standardized evaluation metrics consistent with previous state-of-the-art MIR models, ensuring direct comparability with supervised approaches. We provide an evaluation toolkit supporting all open-source audio-textual LLMs, including LTU, Qwen-audio, SALMONN, MusiLingo, etc. Experiment results reveal significant performance gaps between LLMs and supervised models, along with their culture, chronological and gender bias, highlighting the potential and limitations of current models in addressing MIR tasks. CMI-Bench establishes a unified foundation for evaluating music instruction following, driving progress in music-aware LLMs.
zh
[AI-191] Mapping Neural Theories of Consciousness onto the Common Model of Cognition
【速读】：该论文试图解决将四种关于意识的神经理论与认知共同模型（Common Model of Cognition）进行映射的问题，以揭示这些理论在结构和功能上的共性。解决方案的关键在于强调这四种理论均依赖于递归的局部模块以及在一个具有复杂状态的全局工作记忆上运行的认知循环。这一方法揭示了从神经科学视角出发的意识整合观点与认知共同模型之间的契合性。
链接: https://arxiv.org/abs/2506.12224
作者: Paul S. Rosenbloom,John E. Laird,Christian Lebiere,Andrea Stocco
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:A beginning is made at mapping four neural theories of consciousness onto the Common Model of Cognition. This highlights how the four jointly depend on recurrent local modules plus a cognitive cycle operating on a global working memory with complex states, and reveals how an existing integrative view of consciousness from a neural perspective aligns with the Com-mon Model.
zh
[AI-192] CN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion MICRO
【速读】：该论文旨在解决射频功率放大器（RF Power Amplifier, PA）中的非线性问题，特别是在宽带应用中提高线性化效果。其解决方案的关键在于提出了一种基于时序卷积网络（Temporal Convolutional Network, TCN）的参数高效架构——TCN-DPD，通过集成非因果扩张卷积和优化的激活函数，实现了在少量参数下仍能保持优异的线性化性能。
链接: https://arxiv.org/abs/2506.12165
作者: Huanqiang Duan,Manno Versluis,Qinyu Chen,Leo C. N. de Vreede,Chang Gao
机构: 未知
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI)
备注: Accepted to IEEE MTT-S International Microwave Symposium (IMS) 2025
Abstract:Digital predistortion (DPD) is essential for mitigating nonlinearity in RF power amplifiers, particularly for wideband applications. This paper presents TCN-DPD, a parameter-efficient architecture based on temporal convolutional networks, integrating noncausal dilated convolutions with optimized activation functions. Evaluated on the OpenDPD framework with the DPA_200MHz dataset, TCN-DPD achieves simulated ACPRs of -51.58/-49.26 dBc (L/R), EVM of -47.52 dB, and NMSE of -44.61 dB with 500 parameters and maintains superior linearization than prior models down to 200 parameters, making it promising for efficient wideband PA linearization.
zh
[AI-193] Scale-Invariance Drives Convergence in AI and Brain Representations
【速读】：该论文试图解决大规模人工智能模型在内部表示上趋于一致并与神经活动对齐的现象背后的机制问题，以及如何量化这种对齐的结构特性。其解决方案的关键在于提出一种多尺度分析框架，用于量化AI表示中的尺度不变性（scale-invariance）的两个核心方面：维度稳定性与跨尺度的结构相似性，并通过这些特性预测与视觉皮层fMRI响应的对齐性能。研究结果表明，具有更高维度一致性和结构相似性的嵌入向量更符合fMRI数据，同时更大的预训练数据集和语言模态的引入能够增强尺度不变性，从而提升与神经活动的对齐效果。
链接: https://arxiv.org/abs/2506.12117
作者: Junjie Yu,Wenxiao Ma,Jianyu Zhang,Haotian Deng,Zihan Deng,Yi Guo,Quanying Liu
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:Despite variations in architecture and pretraining strategies, recent studies indicate that large-scale AI models often converge toward similar internal representations that also align with neural activity. We propose that scale-invariance, a fundamental structural principle in natural systems, is a key driver of this convergence. In this work, we propose a multi-scale analytical framework to quantify two core aspects of scale-invariance in AI representations: dimensional stability and structural similarity across scales. We further investigate whether these properties can predict alignment performance with functional Magnetic Resonance Imaging (fMRI) responses in the visual cortex. Our analysis reveals that embeddings with more consistent dimension and higher structural similarity across scales align better with fMRI data. Furthermore, we find that the manifold structure of fMRI data is more concentrated, with most features dissipating at smaller scales. Embeddings with similar scale patterns align more closely with fMRI data. We also show that larger pretraining datasets and the inclusion of language modalities enhance the scale-invariance properties of embeddings, further improving neural alignment. Our findings indicate that scale-invariance is a fundamental structural principle that bridges artificial and biological representations, providing a new framework for evaluating the structural quality of human-like AI systems.
zh
[AI-194] EconGym: A Scalable AI Testbed with Diverse Economic Tasks
【速读】：该论文试图解决现有经济研究中AI应用受限的问题，即现有的模拟环境仅适用于简化、狭窄的任务，无法有效捕捉如人口结构变化、多政府协调及大规模代理交互等复杂的经济挑战。其解决方案的关键在于提出EconGym，一个可扩展且模块化的测试平台，它通过整合11种异构角色类型及其交互机制，结合明确的观测、动作和奖励定义，支持用户灵活组合经济角色与多种代理算法，从而在25个以上的经济任务中进行多智能体轨迹模拟，实现AI驱动的政策学习与分析。
链接: https://arxiv.org/abs/2506.12110
作者: Qirui Mi,Qipeng Yang,Zijun Fan,Wentian Fan,Heyang Ma,Chengdong Ma,Siyu Xia,Bo An,Jun Wang,Haifeng Zhang
机构: 未知
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI)
备注: 28 pages, 7 figures, 17 tables
Abstract:Artificial intelligence (AI) has become a powerful tool for economic research, enabling large-scale simulation and policy optimization. However, applying AI effectively requires simulation platforms for scalable training and evaluation-yet existing environments remain limited to simplified, narrowly scoped tasks, falling short of capturing complex economic challenges such as demographic shifts, multi-government coordination, and large-scale agent interactions. To address this gap, we introduce EconGym, a scalable and modular testbed that connects diverse economic tasks with AI algorithms. Grounded in rigorous economic modeling, EconGym implements 11 heterogeneous role types (e.g., households, firms, banks, governments), their interaction mechanisms, and agent models with well-defined observations, actions, and rewards. Users can flexibly compose economic roles with diverse agent algorithms to simulate rich multi-agent trajectories across 25+ economic tasks for AI-driven policy learning and analysis. Experiments show that EconGym supports diverse and cross-domain tasks-such as coordinating fiscal, pension, and monetary policies-and enables benchmarking across AI, economic methods, and hybrids. Results indicate that richer task composition and algorithm diversity expand the policy space, while AI agents guided by classical economic methods perform best in complex settings. EconGym also scales to 10k agents with high realism and efficiency.
zh
[AI-195] Wanting to Be Understood Explains the Meta-Problem of Consciousness
【速读】：该论文试图解决意识的“硬问题”（hard problem of consciousness），即解释主观体验（qualia）的本质及其无法通过客观描述完全再现的现象。论文提出，尽管我们通过拟态、语言和艺术等外部表征来表达内在状态，但这些表征无法完全复现“原始经验”的丰富性，因此对体验感受的解释难以达到令人满意的程度。解决方案的关键在于认识到这种对被理解的强烈需求与有限的感官运动能力之间的矛盾，导致了对意识解释的“过度认识论要求”，而非不可逾越的形而上学鸿沟。论文认为，正是这种驱动力促使人类不断探索新的交流与思考方式，以实现对自身经验的理解。
链接: https://arxiv.org/abs/2506.12086
作者: Chrisantha Fernando,Dylan Banarse,Simon Osindero
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:Because we are highly motivated to be understood, we created public external representations – mime, language, art – to externalise our inner states. We argue that such external representations are a pre-condition for access consciousness, the global availability of information for reasoning. Yet the bandwidth of access consciousness is tiny compared with the richness of raw experience', so no external representation can reproduce that richness in full. Ordinarily an explanation of experience need only let an audience grasp’ the relevant pattern, not relive the phenomenon. But our drive to be understood, and our low level sensorimotor capacities for `grasping’ so rich, that the demand for an explanation of the feel of experience cannot be ``satisfactory’'. That inflated epistemic demand (the preeminence of our expectation that we could be perfectly understood by another or ourselves) rather than an irreducible metaphysical gulf – keeps the hard problem of consciousness alive. But on the plus side, it seems we will simply never give up creating new ways to communicate and think about our experiences. In this view, to be consciously aware is to strive to have one’s agency understood by oneself and others.
zh
[AI-196] Evaluating Logit-Based GOP Scores for Mispronunciation Detection INTERSPEECH2025
【速读】：该论文试图解决传统基于softmax后验概率的发音评估（goodness of pronunciation, GOP）在发音错误检测中的局限性，特别是后验概率存在的过度自信和音素分离能力差的问题。其解决方案的关键在于采用基于logit的GOP分数，并通过对比概率-based与logit-based方法在分类性能和与人类评分的相关性上的表现，验证了logit方法在特定数据集上的优越性，同时提出结合不同GOP分数以平衡概率与logit特征的混合方法，从而提升发音评估的效果。
链接: https://arxiv.org/abs/2506.12067
作者: Aditya Kamlesh Parikh,Cristian Tejedor-Garcia,Catia Cucchiarini,Helmer Strik
机构: 未知
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Sound (cs.SD)
备注: Accepted to Interspeech 2025. This publication is part of the project Responsible AI for Voice Diagnostics (RAIVD) with file number NGF.1607.22.013 of the research programme NGF AiNed Fellowship Grants which is financed by the Dutch Research Council (NWO)
Abstract:Pronunciation assessment relies on goodness of pronunciation (GOP) scores, traditionally derived from softmax-based posterior probabilities. However, posterior probabilities may suffer from overconfidence and poor phoneme separation, limiting their effectiveness. This study compares logit-based GOP scores with probability-based GOP scores for mispronunciation detection. We conducted our experiment on two L2 English speech datasets spoken by Dutch and Mandarin speakers, assessing classification performance and correlation with human ratings. Logit-based methods outperform probability-based GOP in classification, but their effectiveness depends on dataset characteristics. The maximum logit GOP shows the strongest alignment with human perception, while a combination of different GOP scores balances probability and logit features. The findings suggest that hybrid GOP methods incorporating uncertainty modeling and phoneme-specific weighting improve pronunciation assessment.
zh
[AI-197] owards Unified Neural Decoding with Brain Functional Network Modeling
【速读】：该论文试图解决传统神经解码方法在个体间泛化能力不足的问题，即由于个体生理差异和电极植入异质性导致的跨个体神经信号解码困难。其解决方案的关键在于提出多个体脑区聚合网络（MIBRAIN），通过整合多个个体的颅内神经生理记录构建全功能脑网络模型，并利用自监督学习提取通用的神经原型，从而实现群体层面的脑区交互分析和跨被试神经同步性研究。
链接: https://arxiv.org/abs/2506.12055
作者: Di Wu,Linghao Bu,Yifei Jia,Lu Cao,Siyuan Li,Siyu Chen,Yueqian Zhou,Sheng Fan,Wenjie Ren,Dengchang Wu,Kang Wang,Yue Zhang,Yuehui Ma,Jie Yang,Mohamad Sawan
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications.
zh
[AI-198] Examining the effects of music on cognitive skills of children in early childhood with the Pythagorean fuzzy set approach
【速读】：该论文试图解决音乐教育对儿童早期认知发展影响的评估问题，特别是空间-时间技能的发展。其解决方案的关键在于采用由Yager定义的毕达哥拉斯模糊集（Pythagorean Fuzzy Sets, PFS）方法，基于专家意见构建PFS，并设计相应的算法进行分析。该算法通过期望得分函数进行排序，结果与专家排名一致，验证了音乐教育在促进认知发展中的有效性。
链接: https://arxiv.org/abs/2506.12016
作者: Murat Kirisci,Nihat Topac,Musa Bardak
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:There are many genetic and environmental factors that affect cognitive development. Music education can also be considered as one of the environmental factors. Some researchers emphasize that Music is an action that requires meta-cognitive functions such as mathematics and chess and supports spatial intelligence. The effect of Music on cognitive development in early childhood was examined using the Pythagorean Fuzzy Sets(PFS) method defined by Yager. This study created PFS based on experts’ opinions, and an algorithm was given according to PFS. The algorithm’s results supported the experts’ data on the development of spatial-temporal skills in music education given in early childhood. The algorithm’s ranking was done using the Expectation Score Function. The rankings obtained from the algorithm overlap with the experts’ rankings.
zh
机器学习
[LG-0] AI reconstruction of European weather from the Euro-Atlantic regimes
链接: https://arxiv.org/abs/2506.13758
作者: A. Camilletti,G. Franch,E. Tomasi,M. Cristoforetti
类目: Machine Learning (cs.LG)
*备注:
Abstract:We present a non-linear AI-model designed to reconstruct monthly mean anomalies of the European temperature and precipitation based on the Euro-Atlantic Weather regimes (WR) indices. WR represent recurrent, quasi-stationary, and persistent states of the atmospheric circulation that exert considerable influence over the European weather, therefore offering an opportunity for sub-seasonal to seasonal forecasting. While much research has focused on studying the correlation and impacts of the WR on European weather, the estimation of ground-level climate variables, such as temperature and precipitation, from Euro-Atlantic WR remains largely unexplored and is currently limited to linear methods. The presented AI model can capture and introduce complex non-linearities in the relation between the WR indices, describing the state of the Euro-Atlantic atmospheric circulation and the corresponding surface temperature and precipitation anomalies in Europe. We discuss the AI-model performance in reconstructing the monthly mean two-meter temperature and total precipitation anomalies in the European winter and summer, also varying the number of WR used to describe the monthly atmospheric circulation. We assess the impact of errors on the WR indices in the reconstruction and show that a mean absolute relative error below 80% yields improved seasonal reconstruction compared to the ECMWF operational seasonal forecast system, SEAS5. As a demonstration of practical applicability, we evaluate the model using WR indices predicted by SEAS5, finding slightly better or comparable skill relative to the SEAS5 forecast itself. Our findings demonstrate that WR-based anomaly reconstruction, powered by AI tools, offers a promising pathway for sub-seasonal and seasonal forecasting.
[LG-1] MARCO: Hardware-Aware Neural Architecture Search for Edge Devices with Multi-Agent Reinforcement Learning and Conformal Prediction Filtering
链接: https://arxiv.org/abs/2506.13755
作者: Arya Fayyazi,Mehdi Kamal,Massoud Pedram
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper introduces MARCO (Multi-Agent Reinforcement learning with Conformal Optimization), a novel hardware-aware framework for efficient neural architecture search (NAS) targeting resource-constrained edge devices. By significantly reducing search time and maintaining accuracy under strict hardware constraints, MARCO bridges the gap between automated DNN design and CAD for edge AI deployment. MARCO’s core technical contribution lies in its unique combination of multi-agent reinforcement learning (MARL) with Conformal Prediction (CP) to accelerate the hardware/software co-design process for deploying deep neural networks. Unlike conventional once-for-all (OFA) supernet approaches that require extensive pretraining, MARCO decomposes the NAS task into a hardware configuration agent (HCA) and a Quantization Agent (QA). The HCA optimizes high-level design parameters, while the QA determines per-layer bit-widths under strict memory and latency budgets using a shared reward signal within a centralized-critic, decentralized-execution (CTDE) paradigm. A key innovation is the integration of a calibrated CP surrogate model that provides statistical guarantees (with a user-defined miscoverage rate) to prune unpromising candidate architectures before incurring the high costs of partial training or hardware simulation. This early filtering drastically reduces the search space while ensuring that high-quality designs are retained with a high probability. Extensive experiments on MNIST, CIFAR-10, and CIFAR-100 demonstrate that MARCO achieves a 3-4x reduction in total search time compared to an OFA baseline while maintaining near-baseline accuracy (within 0.3%). Furthermore, MARCO also reduces inference latency. Validation on a MAX78000 evaluation board confirms that simulator trends hold in practice, with simulator estimates deviating from measured values by less than 5%.
[LG-2] Sharpness-Aware Machine Unlearning
链接: https://arxiv.org/abs/2506.13715
作者: Haoran Tang,Rajiv Khanna
类目: Machine Learning (cs.LG)
*备注:
Abstract:We characterize the effectiveness of Sharpness-aware minimization (SAM) under machine unlearning scheme, where unlearning forget signals interferes with learning retain signals. While previous work prove that SAM improves generalization with noise memorization prevention, we show that SAM abandons such denoising property when fitting the forget set, leading to various test error bounds depending on signal strength. We further characterize the signal surplus of SAM in the order of signal strength, which enables learning from less retain signals to maintain model performance and putting more weight on unlearning the forget set. Empirical studies show that SAM outperforms SGD with relaxed requirement for retain signals and can enhance various unlearning methods either as pretrain or unlearn algorithm. Observing that overfitting can benefit more stringent sample-specific unlearning, we propose Sharp MinMax, which splits the model into two to learn retain signals with SAM and unlearn forget signals with sharpness maximization, achieving best performance. Extensive experiments show that SAM enhances unlearning across varying difficulties measured by data memorization, yielding decreased feature entanglement between retain and forget sets, stronger resistance to membership inference attacks, and a flatter loss landscape.
[LG-3] What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers
链接: https://arxiv.org/abs/2506.13688
作者: Pulkit Gopalani,Wei Hu
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Training Transformers on algorithmic tasks frequently demonstrates an intriguing abrupt learning phenomenon: an extended performance plateau followed by a sudden, sharp improvement. This work investigates the underlying mechanisms for such dynamics, primarily in shallow Transformers. We reveal that during the plateau, the model often develops an interpretable partial solution while simultaneously exhibiting a strong repetition bias in their outputs. This output degeneracy is accompanied by internal representation collapse, where hidden states across different tokens become nearly parallel. We further identify the slow learning of optimal attention maps as a key bottleneck. Hidden progress in attention configuration during the plateau precedes the eventual rapid convergence, and directly intervening on attention significantly alters plateau duration and the severity of repetition bias and representational collapse. We validate that these identified phenomena-repetition bias and representation collapse-are not artifacts of toy setups but also manifest in the early pre-training stage of large language models like Pythia and OLMo.
[LG-4] Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
链接: https://arxiv.org/abs/2506.13680
作者: Zhongyuan Liang,Lars van der Laan,Ahmed Alaa
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Estimating conditional average treatment effects (CATE) from observational data involves modeling decisions that differ from supervised learning, particularly concerning how to regularize model complexity. Previous approaches can be grouped into two primary “meta-learner” paradigms that impose distinct inductive biases. Indirect meta-learners first fit and regularize separate potential outcome (PO) models and then estimate CATE by taking their difference, whereas direct meta-learners construct and directly regularize estimators for the CATE function itself. Neither approach consistently outperforms the other across all scenarios: indirect learners perform well when the PO functions are simple, while direct learners outperform when the CATE is simpler than individual PO functions. In this paper, we introduce the Hybrid Learner (H-learner), a novel regularization strategy that interpolates between the direct and indirect regularizations depending on the dataset at hand. The H-learner achieves this by learning intermediate functions whose difference closely approximates the CATE without necessarily requiring accurate individual approximations of the POs themselves. We demonstrate empirically that intentionally allowing suboptimal fits to the POs improves the bias-variance tradeoff in estimating CATE. Experiments conducted on semi-synthetic and real-world benchmark datasets illustrate that the H-learner consistently operates at the Pareto frontier, effectively combining the strengths of both direct and indirect meta-learners.
[LG-5] A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction
链接: https://arxiv.org/abs/2506.13678
作者: Yi Wang,Zhenghong Wang,Fan Zhang,Chengling Tang,Chaogui Kang,Di Zhu,Zhongfu Ma,Sijie Ruan,Weiyu Zhang,Yu Zheng,Philip S. Yu,Yu Liu
类目: Machine Learning (cs.LG)
*备注: 18 pages, 13 figures
Abstract:Human activity intensity prediction is a crucial to many location-based services. Although tremendous progress has been made to model dynamic spatiotemporal patterns of human activity, most existing methods, including spatiotemporal graph neural networks (ST-GNNs), overlook physical constraints of spatial interactions and the over-smoothing phenomenon in spatial correlation modeling. To address these limitations, this work proposes a physics-informed deep learning framework, namely Gravity-informed Spatiotemporal Transformer (Gravityformer) by refining transformer attention to integrate the universal law of gravitation and explicitly incorporating constraints from spatial interactions. Specifically, it (1) estimates two spatially explicit mass parameters based on inflow and outflow, (2) models the likelihood of cross-unit interaction using closed-form solutions of spatial interactions to constrain spatial modeling randomness, and (3) utilizes the learned spatial interaction to guide and mitigate the over-smoothing phenomenon in transformer attention matrices. The underlying law of human activity can be explicitly modeled by the proposed adaptive gravity model. Moreover, a parallel spatiotemporal graph convolution transformer structure is proposed for achieving a balance between coupled spatial and temporal learning. Systematic experiments on six real-world large-scale activity datasets demonstrate the quantitative and qualitative superiority of our approach over state-of-the-art benchmarks. Additionally, the learned gravity attention matrix can be disentangled and interpreted based on geographical laws. This work provides a novel insight into integrating physical laws with deep learning for spatiotemporal predictive learning.
[LG-6] he Courag e to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning ICML2025
链接: https://arxiv.org/abs/2506.13672
作者: Jiashun Liu,Johan Obando-Ceron,Pablo Samuel Castro,Aaron Courville,Ling Pan
类目: Machine Learning (cs.LG)
*备注: Proceedings of the 42nd International Conference on Machine Learning (ICML 2025)
Abstract:Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help improve sample efficiency when the collected data is informative and aligned with the learning objectives; when that is not the case, it can have the effect of “polluting” the replay buffer with data which can exacerbate optimization challenges in addition to wasting environment interactions due to wasteful sampling. We argue that sampling these uninformative and wasteful transitions can be avoided by addressing the sunk cost fallacy, which, in the context of deep RL, is the tendency towards continuing an episode until termination. To address this, we propose learn to stop (LEAST), a lightweight mechanism that enables strategic early episode termination based on Q-value and gradient statistics, which helps agents recognize when to terminate unproductive episodes early. We demonstrate that our method improves learning efficiency on a variety of RL algorithms, evaluated on both the MuJoCo and DeepMind Control Suite benchmarks.
[LG-7] PeakWeather: MeteoSwiss Weather Station Measurements for Spatiotemporal Deep Learning
链接: https://arxiv.org/abs/2506.13652
作者: Daniele Zambon,Michele Cattaneo,Ivan Marisca,Jonas Bhend,Daniele Nerini,Cesare Alippi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Accurate weather forecasts are essential for supporting a wide range of activities and decision-making processes, as well as mitigating the impacts of adverse weather events. While traditional numerical weather prediction (NWP) remains the cornerstone of operational forecasting, machine learning is emerging as a powerful alternative for fast, flexible, and scalable predictions. We introduce PeakWeather, a high-quality dataset of surface weather observations collected every 10 minutes over more than 8 years from the ground stations of the Federal Office of Meteorology and Climatology MeteoSwiss’s measurement network. The dataset includes a diverse set of meteorological variables from 302 station locations distributed across Switzerland’s complex topography and is complemented with topographical indices derived from digital height models for context. Ensemble forecasts from the currently operational high-resolution NWP model are provided as a baseline forecast against which to evaluate new approaches. The dataset’s richness supports a broad spectrum of spatiotemporal tasks, including time series forecasting at various scales, graph structure learning, imputation, and virtual sensing. As such, PeakWeather serves as a real-world benchmark to advance both foundational machine learning research, meteorology, and sensor-based applications.
[LG-8] xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations
链接: https://arxiv.org/abs/2506.13651
作者: Kaiyuan Chen,Yixin Ren,Yang Liu,Xiaobo Hu,Haotong Tian,Tianbao Xie,Fangfu Liu,Haoye Zhang,Hongzhang Liu,Yuan Gong,Chen Sun,Han Hou,Hui Yang,James Pan,Jianan Lou,Jiayi Mao,Jizheng Liu,Jinpeng Li,Kangyi Liu,Kenkun Liu,Rui Wang,Run Li,Tong Niu,Wenlong Zhang,Wenqi Yan,Xuanzheng Wang,Yuchen Zhang,Yi-Hsin Hung,Yuan Jiang,Zexuan Liu,Zihan Yin,Zijian Ma,Zhiwen Mo
类目: Machine Learning (cs.LG)
*备注: Project page: this https URL
Abstract:We introduce xbench, a dynamic, profession-aligned evaluation suite designed to bridge the gap between AI agent capabilities and real-world productivity. While existing benchmarks often focus on isolated technical skills, they may not accurately reflect the economic value agents deliver in professional settings. To address this, xbench targets commercially significant domains with evaluation tasks defined by industry professionals. Our framework creates metrics that strongly correlate with productivity value, enables prediction of Technology-Market Fit (TMF), and facilitates tracking of product capabilities over time. As our initial implementations, we present two benchmarks: Recruitment and Marketing. For Recruitment, we collect 50 tasks from real-world headhunting business scenarios to evaluate agents’ abilities in company mapping, information retrieval, and talent sourcing. For Marketing, we assess agents’ ability to match influencers with advertiser needs, evaluating their performance across 50 advertiser requirements using a curated pool of 836 candidate influencers. We present initial evaluation results for leading contemporary agents, establishing a baseline for these professional domains. Our continuously updated evalsets and evaluations are available at this https URL.
[LG-9] Global Convergence of Adjoint-Optimized Neural PDEs
链接: https://arxiv.org/abs/2506.13633
作者: Konstantin Riedl,Justin Sirignano,Konstantinos Spiliopoulos
类目: Machine Learning (cs.LG); Analysis of PDEs (math.AP); Numerical Analysis (math.NA); Optimization and Control (math.OC)
*备注: 63 pages, 2 figures
Abstract:Many engineering and scientific fields have recently become interested in modeling terms in partial differential equations (PDEs) with neural networks. The resulting neural-network PDE model, being a function of the neural network parameters, can be calibrated to available data by optimizing over the PDE using gradient descent, where the gradient is evaluated in a computationally efficient manner by solving an adjoint PDE. These neural-network PDE models have emerged as an important research area in scientific machine learning. In this paper, we study the convergence of the adjoint gradient descent optimization method for training neural-network PDE models in the limit where both the number of hidden units and the training time tend to infinity. Specifically, for a general class of nonlinear parabolic PDEs with a neural network embedded in the source term, we prove convergence of the trained neural-network PDE solution to the target data (i.e., a global minimizer). The global convergence proof poses a unique mathematical challenge that is not encountered in finite-dimensional neural network convergence analyses due to (1) the neural network training dynamics involving a non-local neural network kernel operator in the infinite-width hidden layer limit where the kernel lacks a spectral gap for its eigenvalues and (2) the nonlinearity of the limit PDE system, which leads to a non-convex optimization problem, even in the infinite-width hidden layer limit (unlike in typical neual network training cases where the optimization problem becomes convex in the large neuron limit). The theoretical results are illustrated and empirically validated by numerical studies.
[LG-10] Assessing the Limits of In-Context Learning beyond Functions using Partially Ordered Relation
链接: https://arxiv.org/abs/2506.13608
作者: Debanjan Dutta,Faizanuddin Ansari,Swagatam Das
类目: Machine Learning (cs.LG)
*备注:
Abstract:Generating rational and generally accurate responses to tasks, often accompanied by example demonstrations, highlights Large Language Model’s (LLM’s) remarkable In-Context Learning (ICL) capabilities without requiring updates to the model’s parameter space. Despite having an ongoing exploration focused on the inference from a document-level concept, its behavior in learning well-defined functions or relations in context needs a careful investigation. In this article, we present the performance of ICL on partially ordered relation by introducing the notion of inductively increasing complexity in prompts. In most cases, the saturated performance of the chosen metric indicates that while ICL offers some benefits, its effectiveness remains constrained as we increase the complexity in the prompts even in presence of sufficient demonstrative examples. The behavior is evident from our empirical findings and has further been theoretically justified in term of its implicit optimization process. The code is available \hrefthis https URLhere.
[LG-11] Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLM s
链接: https://arxiv.org/abs/2506.13593
作者: Hen Davidov,Gilad Freidkin,Shai Feldman,Yaniv Romano
类目: Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)
*备注:
Abstract:We develop a framework to quantify the time-to-unsafe-sampling - the number of large language model (LLM) generations required to trigger an unsafe (e.g., toxic) response. Estimating this quantity is challenging, since unsafe responses are exceedingly rare in well-aligned LLMs, potentially occurring only once in thousands of generations. As a result, directly estimating time-to-unsafe-sampling would require collecting training data with a prohibitively large number of generations per prompt. However, with realistic sampling budgets, we often cannot generate enough responses to observe an unsafe outcome for every prompt, leaving the time-to-unsafe-sampling unobserved in many cases, making the estimation and evaluation tasks particularly challenging. To address this, we frame this estimation problem as one of survival analysis and develop a provably calibrated lower predictive bound (LPB) on the time-to-unsafe-sampling of a given prompt, leveraging recent advances in conformal prediction. Our key innovation is designing an adaptive, per-prompt sampling strategy, formulated as a convex optimization problem. The objective function guiding this optimized sampling allocation is designed to reduce the variance of the estimators used to construct the LPB, leading to improved statistical efficiency over naive methods that use a fixed sampling budget per prompt. Experiments on both synthetic and real data support our theoretical results and demonstrate the practical utility of our method for safety risk assessment in generative AI models.
[LG-12] Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning
链接: https://arxiv.org/abs/2506.13561
作者: Yue Xia,Christoph Hofmeister,Maximilian Egger,Rawad Bitar
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT)
*备注:
Abstract:Federated learning (FL) shows great promise in large-scale machine learning but introduces new privacy and security challenges. We propose ByITFL and LoByITFL, two novel FL schemes that enhance resilience against Byzantine users while keeping the users’ data private from eavesdroppers. To ensure privacy and Byzantine resilience, our schemes build on having a small representative dataset available to the federator and crafting a discriminator function allowing the mitigation of corrupt users’ contributions. ByITFL employs Lagrange coded computing and re-randomization, making it the first Byzantine-resilient FL scheme with perfect Information-Theoretic (IT) privacy, though at the cost of a significant communication overhead. LoByITFL, on the other hand, achieves Byzantine resilience and IT privacy at a significantly reduced communication cost, but requires a Trusted Third Party, used only in a one-time initialization phase before training. We provide theoretical guarantees on privacy and Byzantine resilience, along with convergence guarantees and experimental results validating our findings.
[LG-13] Stability Analysis of Physics-Informed Neural Networks via Variational Coercivity Perturbation Bounds and Concentration Estimates
链接: https://arxiv.org/abs/2506.13554
作者: Ronald Katende
类目: Machine Learning (cs.LG); Functional Analysis (math.FA); Numerical Analysis (math.NA)
*备注:
Abstract:We develop a rigorous stability framework for Physics-Informed Neural Networks (PINNs) grounded in variational analysis, operator coercivity, and explicit perturbation theory. PINNs approximate solutions to partial differential equations (PDEs) by minimizing residual-based losses over sampled collocation points. We derive deterministic stability bounds that quantify how bounded perturbations in the network output propagate through both residual and supervised loss components. Probabilistic stability is established via McDiarmid’s inequality, yielding non-asymptotic concentration bounds that link sampling variability to empirical loss fluctuations under minimal assumptions. Generalization from Sobolev-norm training loss to uniform approximation is analyzed using coercivity and Sobolev embeddings, leading to pointwise error control. The theoretical results apply to both scalar and vector-valued PDEs and cover composite loss formulations. Numerical experiments validate the perturbation sensitivity, sample complexity estimates, and Sobolev-to-uniform generalization bounds. This work provides a mathematically grounded and practically applicable stability framework for PINNs, clarifying the role of operator structure, sampling design, and functional regularity in robust training.
[LG-14] What Matters in Learning from Large-Scale Datasets for Robot Manipulation
链接: https://arxiv.org/abs/2506.13536
作者: Vaibhav Saxena,Matthew Bronars,Nadun Ranawaka Arachchige,Kuancheng Wang,Woo Chul Shin,Soroush Nasiriany,Ajay Mandlekar,Danfei Xu
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Imitation learning from large multi-task demonstration datasets has emerged as a promising path for building generally-capable robots. As a result, 1000s of hours have been spent on building such large-scale datasets around the globe. Despite the continuous growth of such efforts, we still lack a systematic understanding of what data should be collected to improve the utility of a robotics dataset and facilitate downstream policy learning. In this work, we conduct a large-scale dataset composition study to answer this question. We develop a data generation framework to procedurally emulate common sources of diversity in existing datasets (such as sensor placements and object types and arrangements), and use it to generate large-scale robot datasets with controlled compositions, enabling a suite of dataset composition studies that would be prohibitively expensive in the real world. We focus on two practical settings: (1) what types of diversity should be emphasized when future researchers collect large-scale datasets for robotics, and (2) how should current practitioners retrieve relevant demonstrations from existing datasets to maximize downstream policy performance on tasks of interest. Our study yields several critical insights – for example, we find that camera poses and spatial arrangements are crucial dimensions for both diversity in collection and alignment in retrieval. In real-world robot learning settings, we find that not only do our insights from simulation carry over, but our retrieval strategies on existing datasets such as DROID allow us to consistently outperform existing training strategies by up to 70%. More results at this https URL
[LG-15] Learning Augmented Graph k-Clustering
链接: https://arxiv.org/abs/2506.13533
作者: Chenglin Fan,Kijun Shin
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:Clustering is a fundamental task in unsupervised learning. Previous research has focused on learning-augmented k -means in Euclidean metrics, limiting its applicability to complex data representations. In this paper, we generalize learning-augmented k -clustering to operate on general metrics, enabling its application to graph-structured and non-Euclidean domains. Our framework also relaxes restrictive cluster size constraints, providing greater flexibility for datasets with imbalanced or unknown cluster distributions. Furthermore, we extend the hardness of query complexity to general metrics: under the Exponential Time Hypothesis (ETH), we show that any polynomial-time algorithm must perform approximately \Omega(k / \alpha) queries to achieve a (1 + \alpha) -approximation. These contributions strengthen both the theoretical foundations and practical applicability of learning-augmented clustering, bridging gaps between traditional methods and real-world challenges.
[LG-16] A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
链接: https://arxiv.org/abs/2506.13498
作者: Toshiaki Tsuji,Yasuhiro Kato,Gokhan Solak,Heng Zhang,Tadej Petrič,Francesco Nori,Arash Ajoudani
类目: Robotics (cs.RO); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 47pages, 1 figures
Abstract:This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze imitation learning approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.
[LG-17] Imaging at the quantum limit with convolutional neural networks
链接: https://arxiv.org/abs/2506.13488
作者: Andrew H. Proppe,Aaron Z. Goldberg,Guillaume Thekkadath,Noah Lupu-Gladstein,Kyle M. Jordan,Philip J. Bustard,Frédéric Bouchard,Duncan England,Khabat Heshami,Jeff S. Lundeen,Benjamin J. Sussman
类目: Machine Learning (cs.LG); Optics (physics.optics); Quantum Physics (quant-ph)
*备注:
Abstract:Deep neural networks have been shown to achieve exceptional performance for computer vision tasks like image recognition, segmentation, and reconstruction or denoising. Here, we evaluate the ultimate performance limits of deep convolutional neural network models for image reconstruction, by comparing them against the standard quantum limit set by shot-noise and the Heisenberg limit on precision. We train U-Net models on images of natural objects illuminated with coherent states of light, and find that the average mean-squared error of the reconstructions can surpass the standard quantum limit, and in some cases reaches the Heisenberg limit. Further, we train models on well-parameterized images for which we can calculate the quantum Cramér-Rao bound to determine the minimum possible measurable variance of an estimated parameter for a given probe state. We find the mean-squared error of the model predictions reaches these bounds calculated for the parameters, across a variety of parameterized images. These results suggest that deep convolutional neural networks can learn to become the optimal estimators allowed by the laws of physics, performing parameter estimation and image reconstruction at the ultimate possible limits of precision for the case of classical illumination of the object.
[LG-18] Spiking Neural Networks for Low-Power Vibration-Based Predictive Maintenance
链接: https://arxiv.org/abs/2506.13416
作者: Alexandru Vasilache,Sven Nitzsche,Christian Kneidl,Mikael Tekneyan,Moritz Neher,Juergen Becker
类目: Machine Learning (cs.LG)
*备注: This paper has been accepted and will be presented at the International Conference on Neuromorphic Systems (ICONS) 2025, July 29-31, 2025. The proceedings will be published later
Abstract:Advancements in Industrial Internet of Things (IIoT) sensors enable sophisticated Predictive Maintenance (PM) with high temporal resolution. For cost-efficient solutions, vibration-based condition monitoring is especially of interest. However, analyzing high-resolution vibration data via traditional cloud approaches incurs significant energy and communication costs, hindering battery-powered edge deployments. This necessitates shifting intelligence to the sensor edge. Due to their event-driven nature, Spiking Neural Networks (SNNs) offer a promising pathway toward energy-efficient on-device processing. This paper investigates a recurrent SNN for simultaneous regression (flow, pressure, pump speed) and multi-label classification (normal, overpressure, cavitation) for an industrial progressing cavity pump (PCP) using 3-axis vibration data. Furthermore, we provide energy consumption estimates comparing the SNN approach on conventional (x86, ARM) and neuromorphic (Loihi) hardware platforms. Results demonstrate high classification accuracy (97%) with zero False Negative Rates for critical Overpressure and Cavitation faults. Smoothed regression outputs achieve Mean Relative Percentage Errors below 1% for flow and pump speed, approaching industrial sensor standards, although pressure prediction requires further refinement. Energy estimates indicate significant power savings, with the Loihi consumption (0.0032 J/inf) being up to 3 orders of magnitude less compared to the estimated x86 CPU (11.3 J/inf) and ARM CPU (1.18 J/inf) execution. Our findings underscore the potential of SNNs for multi-task PM directly on resource-constrained edge devices, enabling scalable and energy-efficient industrial monitoring solutions.
[LG-19] raining Neural Networks by Optimizing Neuron Positions
链接: https://arxiv.org/abs/2506.13410
作者: Laura Erb,Tommaso Boccato,Alexandru Vasilache,Juergen Becker,Nicola Toschi
类目: Machine Learning (cs.LG)
*备注: This paper has been accepted and will be presented at the 14th International Conference on Biomimetic and Biohybrid Systems (Living Machines 2025), July 15-18, 2025, Sheffield, UK. The proceedings will be published later
Abstract:The high computational complexity and increasing parameter counts of deep neural networks pose significant challenges for deployment in resource-constrained environments, such as edge devices or real-time systems. To address this, we propose a parameter-efficient neural architecture where neurons are embedded in Euclidean space. During training, their positions are optimized and synaptic weights are determined as the inverse of the spatial distance between connected neurons. These distance-dependent wiring rules replace traditional learnable weight matrices and significantly reduce the number of parameters while introducing a biologically inspired inductive bias: connection strength decreases with spatial distance, reflecting the brain’s embedding in three-dimensional space where connections tend to minimize wiring length. We validate this approach for both multi-layer perceptrons and spiking neural networks. Through a series of experiments, we demonstrate that these spatially embedded neural networks achieve a performance competitive with conventional architectures on the MNIST dataset. Additionally, the models maintain performance even at pruning rates exceeding 80% sparsity, outperforming traditional networks with the same number of parameters under similar conditions. Finally, the spatial embedding framework offers an intuitive visualization of the network structure.
[LG-20] Realtime-Capable Hybrid Spiking Neural Networks for Neural Decoding of Cortical Activity
链接: https://arxiv.org/abs/2506.13400
作者: Jann Krausse,Alexandru Vasilache,Klaus Knobloch,Juergen Becker
类目: Machine Learning (cs.LG)
*备注: This paper was accepted and presented at the 2025 Neuro Inspired Computational Elements (NICE) conference
Abstract:Intra-cortical brain-machine interfaces (iBMIs) present a promising solution to restoring and decoding brain activity lost due to injury. However, patients with such neuroprosthetics suffer from permanent skull openings resulting from the devices’ bulky wiring. This drives the development of wireless iBMIs, which demand low power consumption and small device footprint. Most recently, spiking neural networks (SNNs) have been researched as potential candidates for low-power neural decoding. In this work, we present the next step of utilizing SNNs for such tasks, building on the recently published results of the 2024 Grand Challenge on Neural Decoding Challenge for Motor Control of non-Human Primates. We optimize our model architecture to exceed the existing state of the art on the Primate Reaching dataset while maintaining similar resource demand through various compression techniques. We further focus on implementing a realtime-capable version of the model and discuss the implications of this architecture. With this, we advance one step towards latency-free decoding of cortical spike trains using neuromorphic technology, ultimately improving the lives of millions of paralyzed patients.
[LG-21] Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization
链接: https://arxiv.org/abs/2506.13345
作者: Sebastian Griesbach,Carlo D’Eramo
类目: Machine Learning (cs.LG)
*备注: Accepted at RLC 2025, to be published in RLJ
Abstract:Numerous heuristics and advanced approaches have been proposed for exploration in different settings for deep reinforcement learning. Noise-based exploration generally fares well with dense-shaped rewards and bonus-based exploration with sparse rewards. However, these methods usually require additional tuning to deal with undesirable reward settings by adjusting hyperparameters and noise distributions. Rewards that actively discourage exploration, i.e., with an action cost and no other dense signal to follow, can pose a major challenge. We propose a novel exploration method, Stable Error-seeking Exploration (SEE), that is robust across dense, sparse, and exploration-adverse reward settings. To this endeavor, we revisit the idea of maximizing the TD-error as a separate objective. Our method introduces three design choices to mitigate instability caused by far-off-policy learning, the conflict of interest of maximizing the cumulative TD-error in an episodic setting, and the non-stationary nature of TD-errors. SEE can be combined with off-policy algorithms without modifying the optimization pipeline of the original objective. In our experimental analysis, we show that a Soft-Actor Critic agent with the addition of SEE performs robustly across three diverse reward settings in a variety of tasks without hyperparameter adjustments.
[LG-22] Mixture of Cognitive Reason ers: Modular Reasoning with Brain-Like Specialization
链接: https://arxiv.org/abs/2506.13331
作者: Badr AlKhamissi,C. Nicolò De Sabbata,Zeming Chen,Martin Schrimpf,Antoine Bosselut
类目: Machine Learning (cs.LG)
*备注: Preprint. Code, data, and models available at \href
Abstract:Human intelligence emerges from the interaction of specialized brain networks, each dedicated to distinct cognitive functions such as language processing, logical reasoning, social understanding, and memory retrieval. Inspired by this biological observation, we introduce the Mixture of Cognitive Reasoners (MiCRo) architecture and training paradigm: a modular transformer-based language model with a training curriculum that encourages the emergence of functional specialization among different modules. Inspired by studies in neuroscience, we partition the layers of a pretrained transformer model into four expert modules, each corresponding to a well-studied cognitive brain network. Our Brain-Like model has three key benefits over the state of the art: First, the specialized experts are highly interpretable and functionally critical, where removing a module significantly impairs performance on domain-relevant benchmarks. Second, our model outperforms comparable baselines that lack specialization on seven reasoning benchmarks. And third, the model’s behavior can be steered at inference time by selectively emphasizing certain expert modules (e.g., favoring social over logical reasoning), enabling fine-grained control over the style of its response. Our findings suggest that biologically inspired inductive biases involved in human cognition lead to significant modeling gains in interpretability, performance, and controllability.
[LG-23] he impact of uncertainty on regularized learning in games
链接: https://arxiv.org/abs/2506.13286
作者: Pierre-Louis Cauvin,Davide Legacci,Panayotis Mertikopoulos
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Optimization and Control (math.OC); Probability (math.PR)
*备注: 50 pages, 6 figures
Abstract:In this paper, we investigate how randomness and uncertainty influence learning in games. Specifically, we examine a perturbed variant of the dynamics of “follow-the-regularized-leader” (FTRL), where the players’ payoff observations and strategy updates are continually impacted by random shocks. Our findings reveal that, in a fairly precise sense, “uncertainty favors extremes”: in any game, regardless of the noise level, every player’s trajectory of play reaches an arbitrarily small neighborhood of a pure strategy in finite time (which we estimate). Moreover, even if the player does not ultimately settle at this strategy, they return arbitrarily close to some (possibly different) pure strategy infinitely often. This prompts the question of which sets of pure strategies emerge as robust predictions of learning under uncertainty. We show that (a) the only possible limits of the FTRL dynamics under uncertainty are pure Nash equilibria; and (b) a span of pure strategies is stable and attracting if and only if it is closed under better replies. Finally, we turn to games where the deterministic dynamics are recurrent - such as zero-sum games with interior equilibria - and we show that randomness disrupts this behavior, causing the stochastic dynamics to drift toward the boundary on average.
[LG-24] An Explainable and Interpretable Composite Indicator Based on Decision Rules
链接: https://arxiv.org/abs/2506.13259
作者: Salvatore Corrente,Salvatore Greco,Roman Słowiński,Silvano Zappalà
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Composite indicators are widely used to score or classify units evaluated on multiple criteria. Their construction involves aggregating criteria evaluations, a common practice in Multiple Criteria Decision Aiding (MCDA). In MCDA, various methods have been proposed to address key aspects of multiple criteria evaluations, such as the measurement scales of the criteria, the degree of acceptable compensation between them, and their potential interactions. However, beyond producing a final score or classification, it is essential to ensure the explainability and interpretability of results as well as the procedure’s transparency. This paper proposes a method for constructing explainable and interpretable composite indicators using “if…, then…” decision rules. We consider the explainability and interpretability of composite indicators in four scenarios: (i) decision rules explain numerical scores obtained from an aggregation of numerical codes corresponding to ordinal qualifiers; (ii) an obscure numerical composite indicator classifies units into quantiles; (iii) given preference information provided by a Decision Maker in the form of classifications of some reference units, a composite indicator is constructed using decision rules; (iv) the classification of a set of units results from the application of an MCDA method and is explained by decision rules. To induce the rules from scored or classified units, we apply the Dominance-based Rough Set Approach. The resulting decision rules relate the class assignment or unit’s score to threshold conditions on values of selected indicators in an intelligible way, clarifying the underlying rationale. Moreover, they serve to recommend composite indicator assessment for new units of interest.
[LG-25] Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models
链接: https://arxiv.org/abs/2506.13243
作者: Chuanhong Liu,Caili Guo,Yang Yang,Mingzhe Chen,Tony Q. S. Quek
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements.
[LG-26] he Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions ICML2025
链接: https://arxiv.org/abs/2506.13234
作者: Devin Kwok,Gül Sena Altıntaş,Colin Raffel,David Rolnick
类目: Machine Learning (cs.LG)
*备注: Published in ICML 2025. The first two authors contributed equally. 29 pages, 28 figures
Abstract:Neural network training is inherently sensitive to initialization and the randomness induced by stochastic gradient descent. However, it is unclear to what extent such effects lead to meaningfully different networks, either in terms of the models’ weights or the underlying functions that were learned. In this work, we show that during the initial “chaotic” phase of training, even extremely small perturbations reliably causes otherwise identical training trajectories to diverge-an effect that diminishes rapidly over training time. We quantify this divergence through (i) L^2 distance between parameters, (ii) the loss barrier when interpolating between networks, (iii) L^2 and barrier between parameters after permutation alignment, and (iv) representational similarity between intermediate activations; revealing how perturbations across different hyperparameter or fine-tuning settings drive training trajectories toward distinct loss minima. Our findings provide insights into neural network training stability, with practical implications for fine-tuning, model merging, and diversity of model ensembles.
[LG-27] Polyra Swarms: A Shape-Based Approach to Machine Learning
链接: https://arxiv.org/abs/2506.13217
作者: Simon Klüttermann,Emmanuel Müller
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Symbolic Computation (cs.SC)
*备注: Currently under review
Abstract:We propose Polyra Swarms, a novel machine-learning approach that approximates shapes instead of functions. Our method enables general-purpose learning with very low bias. In particular, we show that depending on the task, Polyra Swarms can be preferable compared to neural networks, especially for tasks like anomaly detection. We further introduce an automated abstraction mechanism that simplifies the complexity of a Polyra Swarm significantly, enhancing both their generalization and transparency. Since Polyra Swarms operate on fundamentally different principles than neural networks, they open up new research directions with distinct strengths and limitations.
[LG-28] Fatigue-Aware Adaptive Interfaces for Wearable Devices Using Deep Learning
链接: https://arxiv.org/abs/2506.13203
作者: Yikan Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Wearable devices, such as smartwatches and head-mounted displays, are increasingly used for prolonged tasks like remote learning and work, but sustained interaction often leads to user fatigue, reducing efficiency and engagement. This study proposes a fatigue-aware adaptive interface system for wearable devices that leverages deep learning to analyze physiological data (e.g., heart rate, eye movement) and dynamically adjust interface elements to mitigate cognitive load. The system employs multimodal learning to process physiological and contextual inputs and reinforcement learning to optimize interface features like text size, notification frequency, and visual contrast. Experimental results show a 18% reduction in cognitive load and a 22% improvement in user satisfaction compared to static interfaces, particularly for users engaged in prolonged tasks. This approach enhances accessibility and usability in wearable computing environments.
[LG-29] KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction
链接: https://arxiv.org/abs/2506.13196
作者: Han Liu,Keyan Ding,Peilin Chen,Yinwei Wei,Liqiang Nie,Dapeng Wu,Shiqi Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate prediction of protein-ligand binding affinity is critical for drug discovery. While recent deep learning approaches have demonstrated promising results, they often rely solely on structural features, overlooking valuable biochemical knowledge associated with binding affinity. To address this limitation, we propose KEPLA, a novel deep learning framework that explicitly integrates prior knowledge from Gene Ontology and ligand properties of proteins and ligands to enhance prediction performance. KEPLA takes protein sequences and ligand molecular graphs as input and optimizes two complementary objectives: (1) aligning global representations with knowledge graph relations to capture domain-specific biochemical insights, and (2) leveraging cross attention between local representations to construct fine-grained joint embeddings for prediction. Experiments on two benchmark datasets across both in-domain and cross-domain scenarios demonstrate that KEPLA consistently outperforms state-of-the-art baselines. Furthermore, interpretability analyses based on knowledge graph relations and cross attention maps provide valuable insights into the underlying predictive mechanisms.
[LG-30] GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining
链接: https://arxiv.org/abs/2506.13174
作者: Shaoheng Yan,Zian Li,Muhan Zhang
类目: Machine Learning (cs.LG)
*备注:
Abstract:The pretraining-and-finetuning paradigm has driven significant advances across domains, such as natural language processing and computer vision, with representative pretraining paradigms such as masked language modeling and next-token prediction. However, in molecular representation learning, the task design remains largely limited to node-level denoising, which is effective at modeling local atomic environments, yet maybe insufficient for capturing the global molecular structure required by graph-level property prediction tasks, such as energy estimation and molecular regression. In this work, we present GeoRecon, a novel graph-level pretraining framework that shifts the focus from individual atoms to the molecule as an integrated whole. GeoRecon introduces a graph-level reconstruction task: during pretraining, the model is trained to generate an informative graph representation capable of accurately guiding reconstruction of the molecular geometry. This encourages the model to learn coherent, global structural features rather than isolated atomic details. Without relying on additional supervision or external data, GeoRecon outperforms node-centric baselines on multiple molecular benchmarks (e.g., QM9, MD17), demonstrating the benefit of incorporating graph-level reconstruction for learning more holistic and geometry-aware molecular embeddings.
[LG-31] Efficient Approximate Temporal Triangle Counting in Streaming with Predictions ECML-PKDD2025
链接: https://arxiv.org/abs/2506.13173
作者: Giorgio Venturin,Ilie Sarpe,Fabio Vandin
类目: Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
*备注: Extended version of the ECML-PKDD2025 research paper
Abstract:Triangle counting is a fundamental and widely studied problem on static graphs, and recently on temporal graphs, where edges carry information on the timings of the associated events. Streaming processing and resource efficiency are crucial requirements for counting triangles in modern massive temporal graphs, with millions of nodes and up to billions of temporal edges. However, current exact and approximate algorithms are unable to handle large-scale temporal graphs. To fill such a gap, we introduce STEP, a scalable and efficient algorithm to approximate temporal triangle counts from a stream of temporal edges. STEP combines predictions to the number of triangles a temporal edge is involved in, with a simple sampling strategy, leading to scalability, efficiency, and accurate approximation of all eight temporal triangle types simultaneously. We analytically prove that, by using a sublinear amount of memory, STEP obtains unbiased and very accurate estimates. In fact, even noisy predictions can significantly reduce the variance of STEP’s estimates. Our extensive experiments on massive temporal graphs with up to billions of edges demonstrate that STEP outputs high-quality estimates and is more efficient than state-of-the-art methods.
[LG-32] Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback UAI2025
链接: https://arxiv.org/abs/2506.13163
作者: Tanmay Goyal,Gaurav Sinha
类目: Machine Learning (cs.LG)
*备注: Accepted to UAI 2025
Abstract:We study the Logistic Contextual Slate Bandit problem, where, at each round, an agent selects a slate of N items from an exponentially large set (of size 2^\Omega(N) ) of candidate slates provided by the environment. A single binary reward, determined by a logistic model, is observed for the chosen slate. Our objective is to develop algorithms that maximize cumulative reward over T rounds while maintaining low per-round computational costs. We propose two algorithms, Slate-GLM-OFU and Slate-GLM-TS, that accomplish this goal. These algorithms achieve N^O(1) per-round time complexity via local planning (independent slot selections), and low regret through global learning (joint parameter estimation). We provide theoretical and empirical evidence supporting these claims. Under a well-studied diversity assumption, we prove that Slate-GLM-OFU incurs only \tildeO(\sqrtT) regret. Extensive experiments across a wide range of synthetic settings demonstrate that our algorithms consistently outperform state-of-the-art baselines, achieving both the lowest regret and the fastest runtime. Furthermore, we apply our algorithm to select in-context examples in prompts of Language Models for solving binary classification tasks such as sentiment analysis. Our approach achieves competitive test accuracy, making it a viable alternative in practical scenarios.
[LG-33] Dynamic Preference Multi-Objective Reinforcement Learning for Internet Network Management
链接: https://arxiv.org/abs/2506.13153
作者: DongNyeong Heo,Daniela Noemi Rim,Heeyoul Choi
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG)
*备注:
Abstract:An internet network service provider manages its network with multiple objectives, such as high quality of service (QoS) and minimum computing resource usage. To achieve these objectives, a reinforcement learning-based (RL) algorithm has been proposed to train its network management agent. Usually, their algorithms optimize their agents with respect to a single static reward formulation consisting of multiple objectives with fixed importance factors, which we call preferences. However, in practice, the preference could vary according to network status, external concerns and so on. For example, when a server shuts down and it can cause other servers’ traffic overloads leading to additional shutdowns, it is plausible to reduce the preference of QoS while increasing the preference of minimum computing resource usages. In this paper, we propose new RL-based network management agents that can select actions based on both states and preferences. With our proposed approach, we expect a single agent to generalize on various states and preferences. Furthermore, we propose a numerical method that can estimate the distribution of preference that is advantageous for unbiased training. Our experiment results show that the RL agents trained based on our proposed approach significantly generalize better with various preferences than the previous RL approaches, which assume static preference during training. Moreover, we demonstrate several analyses that show the advantages of our numerical estimation method.
[LG-34] Federated ADMM from Bayesian Duality
链接: https://arxiv.org/abs/2506.13150
作者: Thomas Möllenhoff,Siddharth Swaroop,Finale Doshi-Velez,Mohammad Emtiyaz Khan
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注: Code is at this https URL
Abstract:ADMM is a popular method for federated deep learning which originated in the 1970s and, even though many new variants of it have been proposed since then, its core algorithmic structure has remained unchanged. Here, we take a major departure from the old structure and present a fundamentally new way to derive and extend federated ADMM. We propose to use a structure called Bayesian Duality which exploits a duality of the posterior distributions obtained by solving a variational-Bayesian reformulation of the original problem. We show that this naturally recovers the original ADMM when isotropic Gaussian posteriors are used, and yields non-trivial extensions for other posterior forms. For instance, full-covariance Gaussians lead to Newton-like variants of ADMM, while diagonal covariances result in a cheap Adam-like variant. This is especially useful to handle heterogeneity in federated deep learning, giving up to 7% accuracy improvements over recent baselines. Our work opens a new Bayesian path to improve primal-dual methods.
[LG-35] Stochastic Multi-Objective Multi-Armed Bandits: Regret Definition and Algorithm
链接: https://arxiv.org/abs/2506.13125
作者: Mansoor Davoodi,Setareh Maghsudi
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:Multi-armed bandit (MAB) problems are widely applied to online optimization tasks that require balancing exploration and exploitation. In practical scenarios, these tasks often involve multiple conflicting objectives, giving rise to multi-objective multi-armed bandits (MO-MAB). Existing MO-MAB approaches predominantly rely on the Pareto regret metric introduced in \citedrugan2013designing. However, this metric has notable limitations, particularly in accounting for all Pareto-optimal arms simultaneously. To address these challenges, we propose a novel and comprehensive regret metric that ensures balanced performance across conflicting objectives. Additionally, we introduce the concept of \textitEfficient Pareto-Optimal arms, which are specifically designed for online optimization. Based on our new metric, we develop a two-phase MO-MAB algorithm that achieves sublinear regret for both Pareto-optimal and efficient Pareto-optimal arms.
[LG-36] SAGDA: Open-Source Synthetic Agriculture Data for Africa
链接: https://arxiv.org/abs/2506.13123
作者: Abdelghani Belgaid,Oumnia Ennaji
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Data scarcity in African agriculture hampers machine learning (ML) model performance, limiting innovations in precision agriculture. The Synthetic Agriculture Data for Africa (SAGDA) library, a Python-based open-source toolkit, addresses this gap by generating, augmenting, and validating synthetic agricultural datasets. We present SAGDA’s design and development practices, highlighting its core functions: generate, model, augment, validate, visualize, optimize, and simulate, as well as their roles in applications of ML for agriculture. Two use cases are detailed: yield prediction enhanced via data augmentation, and multi-objective NPK (nitrogen, phosphorus, potassium) fertilizer recommendation. We conclude with future plans for expanding SAGDA’s capabilities, underscoring the vital role of open-source, data-driven practices for African agriculture.
[LG-37] Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators
链接: https://arxiv.org/abs/2506.13120
作者: Ze Cheng,Zhuoyu Li,Xiaoqiang Wang,Jianing Huang,Zhizhou Zhang,Zhongkai Hao,Hang Su
类目: Machine Learning (cs.LG)
*备注:
Abstract:PDE-Constrained Optimization (PDECO) problems can be accelerated significantly by employing gradient-based methods with surrogate models like neural operators compared to traditional numerical solvers. However, this approach faces two key challenges: (1) Data inefficiency: Lack of efficient data sampling and effective training for neural operators, particularly for optimization purpose. (2) Instability: High risk of optimization derailment due to inaccurate neural operator predictions and gradients. To address these challenges, we propose a novel framework: (1) Optimization-oriented training: we leverage data from full steps of traditional optimization algorithms and employ a specialized training method for neural operators. (2) Enhanced derivative learning: We introduce a Virtual-Fourier layer to enhance derivative learning within the neural operator, a crucial aspect for gradient-based optimization. (3) Hybrid optimization: We implement a hybrid approach that integrates neural operators with numerical solvers, providing robust regularization for the optimization process. Our extensive experimental results demonstrate the effectiveness of our model in accurately learning operators and their derivatives. Furthermore, our hybrid optimization approach exhibits robust convergence.
[LG-38] Honesty in Causal Forests: When It Helps and When It Hurts
链接: https://arxiv.org/abs/2506.13107
作者: Yanfang Hou,Carlos Fernández-Loría
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Causal forests are increasingly used to personalize decisions based on estimated treatment effects. A distinctive modeling choice in this method is honest estimation: using separate data for splitting and for estimating effects within leaves. This practice is the default in most implementations and is widely seen as desirable for causal inference. But we show that honesty can hurt the accuracy of individual-level effect estimates. The reason is a classic bias-variance trade-off: honesty reduces variance by preventing overfitting, but increases bias by limiting the model’s ability to discover and exploit meaningful heterogeneity in treatment effects. This trade-off depends on the signal-to-noise ratio (SNR): honesty helps when effect heterogeneity is hard to detect (low SNR), but hurts when the signal is strong (high SNR). In essence, honesty acts as a form of regularization, and like any regularization choice, it should be guided by out-of-sample performance, not adopted by default.
[LG-39] Fast and Furious Symmetric Learning in Zero-Sum Games: Gradient Descent as Fictitious Play COLT2025
链接: https://arxiv.org/abs/2506.13086
作者: John Lazarsfeld,Georgios Piliouras,Ryann Sim,Andre Wibisono
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)
*备注: COLT 2025
Abstract:This paper investigates the sublinear regret guarantees of two non-no-regret algorithms in zero-sum games: Fictitious Play, and Online Gradient Descent with constant stepsizes. In general adversarial online learning settings, both algorithms may exhibit instability and linear regret due to no regularization (Fictitious Play) or small amounts of regularization (Gradient Descent). However, their ability to obtain tighter regret bounds in two-player zero-sum games is less understood. In this work, we obtain strong new regret guarantees for both algorithms on a class of symmetric zero-sum games that generalize the classic three-strategy Rock-Paper-Scissors to a weighted, n-dimensional regime. Under symmetric initializations of the players’ strategies, we prove that Fictitious Play with any tiebreaking rule has O(\sqrtT) regret, establishing a new class of games for which Karlin’s Fictitious Play conjecture holds. Moreover, by leveraging a connection between the geometry of the iterates of Fictitious Play and Gradient Descent in the dual space of payoff vectors, we prove that Gradient Descent, for almost all symmetric initializations, obtains a similar O(\sqrtT) regret bound when its stepsize is a sufficiently large constant. For Gradient Descent, this establishes the first “fast and furious” behavior (i.e., sublinear regret without time-vanishing stepsizes) for zero-sum games larger than 2x2.
[LG-40] Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach
链接: https://arxiv.org/abs/2506.13083
作者: Qingfeng Chen,Shiyuan Li,Yixin Liu,Shirui Pan,Geoffrey I. Webb,Shichao Zhang
类目: Machine Learning (cs.LG)
*备注: Accepted by TNNLS
Abstract:Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features. Existing GNNs, unfortunately, fail to account for the uncertainty of class probabilities that vary with the depth of the model, leading to unreliable and risky predictions in real-world scenarios. To bridge the gap, in this paper, we propose a novel Evidence Fusing Graph Neural Network (EFGNN for short) to achieve trustworthy prediction, enhance node classification accuracy, and make explicit the risk of wrong predictions. In particular, we integrate the evidence theory with multi-hop propagation-based GNN architecture to quantify the prediction uncertainty of each node with the consideration of multiple receptive fields. Moreover, a parameter-free cumulative belief fusion (CBF) mechanism is developed to leverage the changes in prediction uncertainty and fuse the evidence to improve the trustworthiness of the final prediction. To effectively optimize the EFGNN model, we carefully design a joint learning objective composed of evidence cross-entropy, dissonance coefficient, and false confident penalty. The experimental results on various datasets and theoretical analyses demonstrate the effectiveness of the proposed model in terms of accuracy and trustworthiness, as well as its robustness to potential attacks. The source code of EFGNN is available at this https URL.
[LG-41] CoIFNet: A Unified Framework for Multivariate Time Series Forecasting with Missing Values
链接: https://arxiv.org/abs/2506.13064
作者: Kai Tang,Ji Zhang,Hua Meng,Minbo Ma,Qi Xiong,Jie Xu,Tianrui Li
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Multivariate time series forecasting (MTSF) is a critical task with broad applications in domains such as meteorology, transportation, and economics. Nevertheless, pervasive missing values caused by sensor failures or human errors significantly degrade forecasting accuracy. Prior efforts usually employ an impute-then-forecast paradigm, leading to suboptimal predictions due to error accumulation and misaligned objectives between the two stages. To address this challenge, we propose the Collaborative Imputation-Forecasting Network (CoIFNet), a novel framework that unifies imputation and forecasting to achieve robust MTSF in the presence of missing values. Specifically, CoIFNet takes the observed values, mask matrix and timestamp embeddings as input, processing them sequentially through the Cross-Timestep Fusion (CTF) and Cross-Variate Fusion (CVF) modules to capture temporal dependencies that are robust to missing values. We provide theoretical justifications on how our CoIFNet learning objective improves the performance bound of MTSF with missing values. Through extensive experiments on challenging MSTF benchmarks, we demonstrate the effectiveness and computational efficiency of our proposed approach across diverse missing-data scenarios, e.g., CoIFNet outperforms the state-of-the-art method by \underline\textbf24.40 % ( \underline\textbf23.81 %) at a point (block) missing rate of 0.6, while improving memory and time efficiency by \underline\boldsymbol4.3\times and \underline\boldsymbol2.1\times , respectively.
[LG-42] Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models
链接: https://arxiv.org/abs/2506.13061
作者: Daniel Zhengyu Huang,Jiaoyang Huang,Zhengjiang Lin
类目: Machine Learning (cs.LG); Classical Analysis and ODEs (math.CA); Numerical Analysis (math.NA)
*备注: 63 pages, 7 figures
Abstract:Diffusion probabilistic models generate samples by learning to reverse a noise-injection process that transforms data into noise. Reformulating this reverse process as a deterministic probability flow ordinary differential equation (ODE) enables efficient sampling using high-order solvers, often requiring only \mathcalO(10) steps. Since the score function is typically approximated by a neural network, analyzing the interaction between its regularity, approximation error, and numerical integration error is key to understanding the overall sampling accuracy. In this work, we continue our analysis of the convergence properties of the deterministic sampling methods derived from probability flow ODEs [25], focusing on p -th order (exponential) Runge-Kutta schemes for any integer p \geq 1 . Under the assumption that the first and second derivatives of the approximate score function are bounded, we develop p -th order (exponential) Runge-Kutta schemes and demonstrate that the total variation distance between the target distribution and the generated data distribution can be bounded above by \beginalign* O\bigl(d^\frac74\varepsilon_\textscore^\frac12 +d(dH_\max)^p\bigr), \endalign* where \varepsilon^2_\textscore denotes the L^2 error in the score function approximation, d is the data dimension and H_\max represents the maximum step size used in the solver. We numerically verify the regularity assumption on benchmark datasets, confirming that the first and second derivatives of the approximate score function remain bounded in practice. Our theoretical guarantees hold for general forward processes with arbitrary variance schedules. Comments: 63 pages, 7 figures Subjects: Machine Learning (cs.LG); Classical Analysis and ODEs (math.CA); Numerical Analysis (math.NA) Cite as: arXiv:2506.13061 [cs.LG] (or arXiv:2506.13061v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.13061 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-43] he Space Complexity of Learning-Unlearning Algorithms
链接: https://arxiv.org/abs/2506.13048
作者: Yeshwanth Cherapanamjeri,Sumegha Garg,Nived Rajaraman,Ayush Sekhari,Abhishek Shetty
类目: Machine Learning (cs.LG)
*备注:
Abstract:We study the memory complexity of machine unlearning algorithms that provide strong data deletion guarantees to the users. Formally, consider an algorithm for a particular learning task that initially receives a training dataset. Then, after learning, it receives data deletion requests from a subset of users (of arbitrary size), and the goal of unlearning is to perform the task as if the learner never received the data of deleted users. In this paper, we ask how many bits of storage are needed to be able to delete certain training samples at a later time. We focus on the task of realizability testing, where the goal is to check whether the remaining training samples are realizable within a given hypothesis class (\mathcalH). Toward that end, we first provide a negative result showing that the VC dimension is not a characterization of the space complexity of unlearning. In particular, we provide a hypothesis class with constant VC dimension (and Littlestone dimension), but for which any unlearning algorithm for realizability testing needs to store (\Omega(n))-bits, where (n) denotes the size of the initial training dataset. In fact, we provide a stronger separation by showing that for any hypothesis class (\mathcalH), the amount of information that the learner needs to store, so as to perform unlearning later, is lower bounded by the \textiteluder dimension of (\mathcalH), a combinatorial notion always larger than the VC dimension. We complement the lower bound with an upper bound in terms of the star number of the underlying hypothesis class, albeit in a stronger ticketed-memory model proposed by Ghazi et al. (2023). Since the star number for a hypothesis class is never larger than its Eluder dimension, our work highlights a fundamental separation between central and ticketed memory models for machine unlearning. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2506.13048 [cs.LG] (or arXiv:2506.13048v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.13048 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-44] Forecast-Then-Optimize Deep Learning Methods
链接: https://arxiv.org/abs/2506.13036
作者: Jinhang Jiang,Nan Wu,Ben Liu,Mei Feng,Xin Ji,Karthik Srinivasan
类目: Machine Learning (cs.LG)
*备注: 44 pages, 2 figures
Abstract:Time series forecasting underpins vital decision-making across various sectors, yet raw predictions from sophisticated models often harbor systematic errors and biases. We examine the Forecast-Then-Optimize (FTO) framework, pioneering its systematic synopsis. Unlike conventional Predict-Then-Optimize (PTO) methods, FTO explicitly refines forecasts through optimization techniques such as ensemble methods, meta-learners, and uncertainty adjustments. Furthermore, deep learning and large language models have established superiority over traditional parametric forecasting models for most enterprise applications. This paper surveys significant advancements from 2016 to 2025, analyzing mainstream deep learning FTO architectures. Focusing on real-world applications in operations management, we demonstrate FTO’s crucial role in enhancing predictive accuracy, robustness, and decision efficacy. Our study establishes foundational guidelines for future forecasting methodologies, bridging theory and operational practicality.
[LG-45] Position: Certified Robustness Does Not (Yet) Imply Model Security ICML
链接: https://arxiv.org/abs/2506.13024
作者: Andrew C. Cullen,Paul Montague,Sarah M. Erfani,Benjamin I.P. Rubinstein
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: 9 pages, ICML, 2025
Abstract:While certified robustness is widely promoted as a solution to adversarial examples in Artificial Intelligence systems, significant challenges remain before these techniques can be meaningfully deployed in real-world applications. We identify critical gaps in current research, including the paradox of detection without distinction, the lack of clear criteria for practitioners to evaluate certification schemes, and the potential security risks arising from users’ expectations surrounding ``guaranteed" robustness claims. This position paper is a call to arms for the certification research community, proposing concrete steps to address these fundamental challenges and advance the field toward practical applicability.
[LG-46] C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized Recommendation
链接: https://arxiv.org/abs/2506.13021
作者: Siqi Liang,Yudi Zhang,Yubo Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Sequential recommender systems aim to model users’ evolving preferences by capturing patterns in their historical interactions. Recent advances in this area have leveraged deep neural networks and attention mechanisms to effectively represent sequential behaviors and time-sensitive interests. In this work, we propose C-TLSAN (Content-Enhanced Time-Aware Long- and Short-Term Attention Network), an extension of the TLSAN architecture that jointly models long- and short-term user preferences while incorporating semantic content associated with items, such as product descriptions. C-TLSAN enriches the recommendation pipeline by embedding textual content linked to users’ historical interactions directly into both long-term and short-term attention layers. This allows the model to learn from both behavioral patterns and rich item content, enhancing user and item representations across temporal dimensions. By fusing sequential signals with textual semantics, our approach improves the expressiveness and personalization capacity of recommendation systems. We conduct extensive experiments on large-scale Amazon datasets, benchmarking C-TLSAN against state-of-the-art baselines, including recent sequential recommenders based on Large Language Models (LLMs), which represent interaction history and predictions in text form. Empirical results demonstrate that C-TLSAN consistently outperforms strong baselines in next-item prediction tasks. Notably, it improves AUC by 1.66%, Recall@10 by 93.99%, and Precision@10 by 94.80% on average over the best-performing baseline (TLSAN) across 10 Amazon product categories. These results highlight the value of integrating content-aware enhancements into temporal modeling frameworks for sequential recommendation. Our code is available at this https URL. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2506.13021 [cs.LG] (or arXiv:2506.13021v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.13021 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-47] Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss
链接: https://arxiv.org/abs/2506.13012
作者: Emil Marcus Buchberg,Kent Vugs Nielsen
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注:
Abstract:Wind energy significantly contributes to the global shift towards renewable energy, yet operational challenges, such as Leading-Edge Erosion on wind turbine blades, notably reduce energy output. This study introduces an advanced, scalable machine learning framework for condition monitoring of wind turbines, specifically targeting improved detection of anomalies using Supervisory Control and Data Acquisition data. The framework effectively isolates normal turbine behavior through rigorous preprocessing, incorporating domain-specific rules and anomaly detection filters, including Gaussian Mixture Models and a predictive power score. The data cleaning and feature selection process enables identification of deviations indicative of performance degradation, facilitating estimates of annual energy production losses. The data preprocessing methods resulted in significant data reduction, retaining on average 31% of the original SCADA data per wind farm. Notably, 24 out of 35 turbines exhibited clear performance declines. At the same time, seven improved, and four showed no significant changes when employing the power curve feature set, which consisted of wind speed and ambient temperature. Models such as Random Forest, XGBoost, and KNN consistently captured subtle but persistent declines in turbine performance. The developed framework provides a novel approach to existing condition monitoring methodologies by isolating normal operational data and estimating annual energy loss, which can be a key part in reducing maintenance expenditures and mitigating economic impacts from turbine downtime.
[LG-48] Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective USENIX-SECURITY’25
链接: https://arxiv.org/abs/2506.13009
作者: Nima Naderloui,Shenao Yan,Binghui Wang,Jie Fu,Wendy Hui Wang,Weiran Liu,Yuan Hong
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: To appear in USENIX Security '25
Abstract:Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing interest in inexact unlearning methods. However, the lack of formal guarantees in these methods necessitates the need for robust evaluation frameworks to assess their privacy and effectiveness. In this work, we first identify several key pitfalls of the existing unlearning evaluation frameworks, e.g., focusing on average-case evaluation or targeting random samples for evaluation, incomplete comparisons with the retraining baseline. Then, we propose RULI (Rectified Unlearning Evaluation Framework via Likelihood Inference), a novel framework to address critical gaps in the evaluation of inexact unlearning methods. RULI introduces a dual-objective attack to measure both unlearning efficacy and privacy risks at a per-sample granularity. Our findings reveal significant vulnerabilities in state-of-the-art unlearning methods, where RULI achieves higher attack success rates, exposing privacy risks underestimated by existing methods. Built on a game-based foundation and validated through empirical evaluations on both image and text data (spanning tasks from classification to generation), RULI provides a rigorous, scalable, and fine-grained methodology for evaluating unlearning techniques.
[LG-49] Antibody Foundational Model : Ab-RoBERTa
链接: https://arxiv.org/abs/2506.13006
作者: Eunna Huh,Hyeonsu Lee,Hyunjin Shin
类目: Machine Learning (cs.LG)
*备注: 14 page, 3 figures, 5 tables
Abstract:With the growing prominence of antibody-based therapeutics, antibody engineering has gained increasing attention as a critical area of research and development. Recent progress in transformer-based protein large language models (LLMs) has demonstrated promising applications in protein sequence design and structural prediction. Moreover, the availability of large-scale antibody datasets such as the Observed Antibody Space (OAS) database has opened new avenues for the development of LLMs specialized for processing antibody sequences. Among these, RoBERTa has demonstrated improved performance relative to BERT, while maintaining a smaller parameter count (125M) compared to the BERT-based protein model, ProtBERT (420M). This reduced model size enables more efficient deployment in antibody-related applications. However, despite the numerous advantages of the RoBERTa architecture, antibody-specific foundational models built upon it have remained inaccessible to the research community. In this study, we introduce Ab-RoBERTa, a RoBERTa-based antibody-specific LLM, which is publicly available at this https URL. This resource is intended to support a wide range of antibody-related research applications including paratope prediction or humanness assessment.
[LG-50] Personalizable Long-Context Symbolic Music Infilling with MIDI-RWKV
链接: https://arxiv.org/abs/2506.13001
作者: Christian Zhou-Zheng,Philippe Pasquier
类目: ound (cs.SD); Machine Learning (cs.LG); Multimedia (cs.MM); Audio and Speech Processing (eess.AS)
*备注:
Abstract:Existing work in automatic music generation has primarily focused on end-to-end systems that produce complete compositions or continuations. However, because musical composition is typically an iterative process, such systems make it difficult to engage in the back-and-forth between human and machine that is essential to computer-assisted creativity. In this study, we address the task of personalizable, multi-track, long-context, and controllable symbolic music infilling to enhance the process of computer-assisted composition. We present MIDI-RWKV, a novel model based on the RWKV-7 linear architecture, to enable efficient and coherent musical cocreation on edge devices. We also demonstrate that MIDI-RWKV admits an effective method of finetuning its initial state for personalization in the very-low-sample regime. We evaluate MIDI-RWKV and its state tuning on several quantitative and qualitative metrics, and release model weights and code at this https URL.
[LG-51] Differentially Private Bilevel Optimization: Efficient Algorithms with Near-Optimal Rates
链接: https://arxiv.org/abs/2506.12994
作者: Andrew Lowy,Daogao Liu
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Optimization and Control (math.OC)
*备注:
Abstract:Bilevel optimization, in which one optimization problem is nested inside another, underlies many machine learning applications with a hierarchical structure – such as meta-learning and hyperparameter optimization. Such applications often involve sensitive training data, raising pressing concerns about individual privacy. Motivated by this, we study differentially private bilevel optimization. We first focus on settings where the outer-level objective is \textitconvex, and provide novel upper and lower bounds on the excess risk for both pure and approximate differential privacy, covering both empirical and population-level loss. These bounds are nearly tight and essentially match the optimal rates for standard single-level differentially private ERM and stochastic convex optimization (SCO), up to additional terms that capture the intrinsic complexity of the nested bilevel structure. The bounds are achieved in polynomial time via efficient implementations of the exponential and regularized exponential mechanisms. A key technical contribution is a new method and analysis of log-concave sampling under inexact function evaluations, which may be of independent interest. In the \textitnon-convex setting, we develop novel algorithms with state-of-the-art rates for privately finding approximate stationary points. Notably, our bounds do not depend on the dimension of the inner problem.
[LG-52] Humans Machine Learning and Language Models in Union: A Cognitive Study on Table Unionability SIGMOD
链接: https://arxiv.org/abs/2506.12990
作者: Sreeram Marimuthu,Nina Klimenkova,Roee Shraga
类目: Databases (cs.DB); Machine Learning (cs.LG)
*备注: 6 Pages, 4 figures, ACM SIGMOD HILDA '25 (Status-Accepted)
Abstract:Data discovery and table unionability in particular became key tasks in modern Data Science. However, the human perspective for these tasks is still under-explored. Thus, this research investigates the human behavior in determining table unionability within data discovery. We have designed an experimental survey and conducted a comprehensive analysis, in which we assess human decision-making for table unionability. We use the observations from the analysis to develop a machine learning framework to boost the (raw) performance of humans. Furthermore, we perform a preliminary study on how LLM performance is compared to humans indicating that it is typically better to consider a combination of both. We believe that this work lays the foundations for developing future Human-in-the-Loop systems for efficient data discovery.
[LG-53] Domain Specific Benchmarks for Evaluating Multimodal Large Language Models
链接: https://arxiv.org/abs/2506.12958
作者: Khizar Anjuma,Muhammad Arbab Arshad,Kadhim Hayawi,Efstathios Polyzos,Asadullah Tariq,Mohamed Adel Serhani,Laiba Batool,Brady Lund,Nishith Reddy Mannuru,Ravi Varma Kumar Bevara,Taslim Mahbub,Muhammad Zeeshan Akram,Sakib Shahriar
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language models (LLMs) are increasingly being deployed across disciplines due to their advanced reasoning and problem solving capabilities. To measure their effectiveness, various benchmarks have been developed that measure aspects of LLM reasoning, comprehension, and problem-solving. While several surveys address LLM evaluation and benchmarks, a domain-specific analysis remains underexplored in the literature. This paper introduces a taxonomy of seven key disciplines, encompassing various domains and application areas where LLMs are extensively utilized. Additionally, we provide a comprehensive review of LLM benchmarks and survey papers within each domain, highlighting the unique capabilities of LLMs and the challenges faced in their application. Finally, we compile and categorize these benchmarks by domain to create an accessible resource for researchers, aiming to pave the way for advancements toward artificial general intelligence (AGI)
[LG-54] Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
链接: https://arxiv.org/abs/2506.12944
作者: Maximilian Ferle,Jonas Ader,Thomas Wiemers,Nora Grieb,Adrian Lindenmeyer,Hans-Jonas Meyer,Thomas Neumuth,Markus Kreuz,Kristin Reiche,Maximilian Merz
类目: Machine Learning (cs.LG); Tissues and Organs (q-bio.TO)
*备注:
Abstract:Risk stratification is a key tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis into actionable clinical criteria. We present a novel method for unsupervised machine learning that directly optimizes for survival heterogeneity across patient clusters through a differentiable adaptation of the multivariate logrank statistic. Unlike most existing methods that rely on proxy metrics, our approach represents novel methodology for training any neural network architecture on any data modality to identify prognostically distinct patient groups. We thoroughly evaluate the method in simulation experiments and demonstrate its utility in practice by applying it to two distinct cancer types: analyzing laboratory parameters from multiple myeloma patients and computed tomography images from non-small cell lung cancer patients, identifying prognostically distinct patient subgroups with significantly different survival outcomes in both cases. Post-hoc explainability analyses uncover clinically meaningful features determining the group assignments which align well with established risk factors and thus lend strong weight to the methods utility. This pan-cancer, model-agnostic approach represents a valuable advancement in clinical risk stratification, enabling the discovery of novel prognostic signatures across diverse data types while providing interpretable results that promise to complement treatment personalization and clinical decision-making in oncology and beyond.
[LG-55] Complexity Scaling Laws for Neural Models using Combinatorial Optimization
链接: https://arxiv.org/abs/2506.12932
作者: Lowell Weissman,Michael Krumdick,A. Lynn Abbott
类目: Machine Learning (cs.LG)
*备注: 45 pages, 20 figures
Abstract:Recent work on neural scaling laws demonstrates that model performance scales predictably with compute budget, model size, and dataset size. In this work, we develop scaling laws based on problem complexity. We analyze two fundamental complexity measures: solution space size and representation space size. Using the Traveling Salesman Problem (TSP) as a case study, we show that combinatorial optimization promotes smooth cost trends, and therefore meaningful scaling laws can be obtained even in the absence of an interpretable loss. We then show that suboptimality grows predictably for fixed-size models when scaling the number of TSP nodes or spatial dimensions, independent of whether the model was trained with reinforcement learning or supervised fine-tuning on a static dataset. We conclude with an analogy to problem complexity scaling in local search, showing that a much simpler gradient descent of the cost landscape produces similar trends.
[LG-56] PINNs Algorithmic Framework for Simulation of Nonlinear Burgers Type Models
链接: https://arxiv.org/abs/2506.12922
作者: Ajeet Singh,Ram Jiwari,Vikram,Ujjwal Saini
类目: Machine Learning (cs.LG)
*备注: 19 pages, 26 figures, 3 tables
Abstract:In this work, a physics-informed neural networks (PINNs) based algorithm is used for simulation of nonlinear 1D and 2D Burgers’ type models. This scheme relies on a neural network built to approximate the problem solution and use a trial function that meets the initial data and boundary criteria. First of all, a brief mathematical formulation of the problem and the structure of PINNs, including the neural network architecture, loss construction, and training methodology is described. Finally, the algorithm is demonstrated with five test problems involving variations of the 1D coupled, 2D single and 2D coupled Burgers’ models. We compare the PINN-based solutions with exact results to assess accuracy and convergence of the developed algorithm. The results demonstrate that PINNs may faithfully replicate nonlinear PDE solutions and offer competitive performance in terms of inaccuracy and flexibility. This work demonstrates the potential of PINNs as a reliable approach to solving complex time-dependent PDEs.
[LG-57] Jailbreak Strength and Model Similarity Predict Transferability
链接: https://arxiv.org/abs/2506.12913
作者: Rico Angell,Jannik Brinkmann,He He
类目: Machine Learning (cs.LG)
*备注:
Abstract:Jailbreaks pose an imminent threat to ensuring the safety of modern AI systems by enabling users to disable safeguards and elicit unsafe information. Sometimes, jailbreaks discovered for one model incidentally transfer to another model, exposing a fundamental flaw in safeguarding. Unfortunately, there is no principled approach to identify when jailbreaks will transfer from a source model to a target model. In this work, we observe that transfer success from a source model to a target model depends on quantifiable measures of both jailbreak strength with respect to the source model and the contextual representation similarity of the two models. Furthermore, we show transferability can be increased by distilling from the target model into the source model where the only target model responses used to train the source model are those to benign prompts. We show that the distilled source model can act as a surrogate for the target model, yielding more transferable attacks against the target model. These results suggest that the success of jailbreaks is not merely due to exploitation of safety training failing to generalize out-of-distribution, but instead a consequence of a more fundamental flaw in contextual representations computed by models.
[LG-58] Silhouette-Guided Instance-Weighted k-means
链接: https://arxiv.org/abs/2506.12878
作者: Aggelos Semoglou,Aristidis Likas,John Pavlopoulos
类目: Machine Learning (cs.LG)
*备注: 27 pages including appendix
Abstract:Clustering is a fundamental unsupervised learning task with numerous applications across diverse fields. Popular algorithms such as k-means often struggle with outliers or imbalances, leading to distorted centroids and suboptimal partitions. We introduce K-Sil, a silhouette-guided refinement of the k-means algorithm that weights points based on their silhouette scores, prioritizing well-clustered instances while suppressing borderline or noisy regions. The algorithm emphasizes user-specified silhouette aggregation metrics: macro-, micro-averaged or a combination, through self-tuning weighting schemes, supported by appropriate sampling strategies and scalable approximations. These components ensure computational efficiency and adaptability to diverse dataset geometries. Theoretical guarantees establish centroid convergence, and empirical validation on synthetic and real-world datasets demonstrates statistically significant improvements in silhouette scores over k-means and two other instance-weighted k-means variants. These results establish K-Sil as a principled alternative for applications demanding high-quality, well-separated clusters.
[LG-59] MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on Large Language Models
链接: https://arxiv.org/abs/2506.12876
作者: Yan Sun,Qixin Zhang,Zhiyuan Yu,Xikun Zhang,Li Shen,Dacheng Tao
类目: Machine Learning (cs.LG)
*备注: Preprint. Under review
Abstract:The rapid scaling of large language models (LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, semi-structured sparsity offers a promising solution by strategically retaining N elements out of every M weights, thereby enabling hardware-friendly acceleration and reduced memory. However, existing (N:M)-compatible approaches typically fall into two categories: rule-based layerwise greedy search, which suffers from considerable errors, and gradient-driven combinatorial learning, which incurs prohibitive training costs. To tackle these challenges, we propose a novel linear-space probabilistic framework named MaskPro, which aims to learn a prior categorical distribution for every M consecutive weights and subsequently leverages this distribution to generate the (N:M)-sparsity throughout an N -way sampling without replacement. Furthermore, to mitigate the training instability induced by the high variance of policy gradients in the super large combinatorial space, we propose a novel update method by introducing a moving average tracker of loss residuals instead of vanilla loss. Finally, we conduct comprehensive theoretical analysis and extensive experiments to validate the superior performance of MaskPro, as well as its excellent scalability in memory efficiency and exceptional robustness to data samples. Our code is available at this https URL.
[LG-60] Private List Learnability vs. Online List Learnability
链接: https://arxiv.org/abs/2506.12856
作者: Steve Hanneke,Shay Moran,Hilla Schefler,Iska Tsubari
类目: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS)
*备注:
Abstract:This work explores the connection between differential privacy (DP) and online learning in the context of PAC list learning. In this setting, a k -list learner outputs a list of k potential predictions for an instance x and incurs a loss if the true label of x is not included in the list. A basic result in the multiclass PAC framework with a finite number of labels states that private learnability is equivalent to online learnability [Alon, Livni, Malliaris, and Moran (2019); Bun, Livni, and Moran (2020); Jung, Kim, and Tewari (2020)]. Perhaps surprisingly, we show that this equivalence does not hold in the context of list learning. Specifically, we prove that, unlike in the multiclass setting, a finite k -Littlestone dimensio–a variant of the classical Littlestone dimension that characterizes online k -list learnability–is not a sufficient condition for DP k -list learnability. However, similar to the multiclass case, we prove that it remains a necessary condition. To demonstrate where the equivalence breaks down, we provide an example showing that the class of monotone functions with k+1 labels over \mathbbN is online k -list learnable, but not DP k -list learnable. This leads us to introduce a new combinatorial dimension, the \emph k -monotone dimension, which serves as a generalization of the threshold dimension. Unlike the multiclass setting, where the Littlestone and threshold dimensions are finite together, for k1 , the k -Littlestone and k -monotone dimensions do not exhibit this relationship. We prove that a finite k -monotone dimension is another necessary condition for DP k -list learnability, alongside finite k -Littlestone dimension. Whether the finiteness of both dimensions implies private k -list learnability remains an open question. Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2506.12856 [cs.LG] (or arXiv:2506.12856v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12856 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-61] Uncovering Social Network Activity Using Joint User and Topic Interaction
链接: https://arxiv.org/abs/2506.12842
作者: Gaspard Abel,Argyris Kalogeratos,Jean-Pierre Nadal,Julien Randon-Furling
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: Equal contribution by the first two authors. Content: 13 pages, 8 figures, 4 tables
Abstract:The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are exposed. In such platforms, various information cascades spreading among users is the main force creating complex dynamics of opinion formation, each user being characterized by their own behavior adoption mechanism. Moreover, the spread of multiple pieces of information or beliefs in a networked population is rarely uncorrelated. In this paper, we introduce the Mixture of Interacting Cascades (MIC), a model of marked multidimensional Hawkes processes with the capacity to model jointly non-trivial interaction between cascades and users. We emphasize on the interplay between information cascades and user activity, and use a mixture of temporal point processes to build a coupled user/cascade point process model. Experiments on synthetic and real data highlight the benefits of this approach and demonstrate that MIC achieves superior performance to existing methods in modeling the spread of information cascades. Finally, we demonstrate how MIC can provide, through its learned parameters, insightful bi-layered visualizations of real social network activity data.
[LG-62] Enhancing Rating-Based Reinforcement Learning to Effectively Leverag e Feedback from Large Vision-Language Models ICML2025
链接: https://arxiv.org/abs/2506.12822
作者: Tung Minh Luu,Younghwan Lee,Donghoon Lee,Sunho Kim,Min Jun Kim,Chang D. Yoo
类目: Machine Learning (cs.LG); Robotics (cs.RO)
*备注: Accepted to ICML 2025
Abstract:Designing effective reward functions remains a fundamental challenge in reinforcement learning (RL), as it often requires extensive human effort and domain expertise. While RL from human feedback has been successful in aligning agents with human intent, acquiring high-quality feedback is costly and labor-intensive, limiting its scalability. Recent advancements in foundation models present a promising alternative–leveraging AI-generated feedback to reduce reliance on human supervision in reward learning. Building on this paradigm, we introduce ERL-VLM, an enhanced rating-based RL method that effectively learns reward functions from AI feedback. Unlike prior methods that rely on pairwise comparisons, ERL-VLM queries large vision-language models (VLMs) for absolute ratings of individual trajectories, enabling more expressive feedback and improved sample efficiency. Additionally, we propose key enhancements to rating-based RL, addressing instability issues caused by data imbalance and noisy labels. Through extensive experiments across both low-level and high-level control tasks, we demonstrate that ERL-VLM significantly outperforms existing VLM-based reward generation methods. Our results demonstrate the potential of AI feedback for scaling RL with minimal human intervention, paving the way for more autonomous and efficient reward learning.
[LG-63] PDCNet: a benchmark and general deep learning framework for activity prediction of peptide-drug conjugates
链接: https://arxiv.org/abs/2506.12821
作者: Yun Liu,Jintu Huang,Yingying Zhu,Congrui Wen,Yu Pang,Ji-Quan Zhang,Ling Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Peptide-drug conjugates (PDCs) represent a promising therapeutic avenue for human diseases, particularly in cancer treatment. Systematic elucidation of structure-activity relationships (SARs) and accurate prediction of the activity of PDCs are critical for the rational design and optimization of these conjugates. To this end, we carefully design and construct a benchmark PDCs dataset compiled from literature-derived collections and PDCdb database, and then develop PDCNet, the first unified deep learning framework for forecasting the activity of PDCs. The architecture systematically captures the complex factors underlying anticancer decisions of PDCs in real-word scenarios through a multi-level feature fusion framework that collaboratively characterizes and learns the features of peptides, linkers, and payloads. Leveraging a curated PDCs benchmark dataset, comprehensive evaluation results show that PDCNet demonstrates superior predictive capability, with the highest AUC, F1, MCC and BA scores of 0.9213, 0.7656, 0.7071 and 0.8388 for the test set, outperforming eight established traditional machine learning models. Multi-level validations, including 5-fold cross-validation, threshold testing, ablation studies, model interpretability analysis and external independent testing, further confirm the superiority, robustness, and usability of the PDCNet architecture. We anticipate that PDCNet represents a novel paradigm, incorporating both a benchmark dataset and advanced models, which can accelerate the design and discovery of new PDC-based therapeutic agents.
[LG-64] Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
链接: https://arxiv.org/abs/2506.12819
作者: Jan C. Schulze,Alexander Mitsos
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Differential Geometry (math.DG); Dynamical Systems (math.DS); Optimization and Control (math.OC)
*备注:
Abstract:Computationally cheap yet accurate enough dynamical models are vital for real-time capable nonlinear optimization and model-based control. When given a computationally expensive high-order prediction model, a reduction to a lower-order simplified model can enable such real-time applications. Herein, we review state-of-the-art nonlinear model order reduction methods and provide a theoretical comparison of method properties. Additionally, we discuss both general-purpose methods and tailored approaches for (chemical) process systems and we identify similarities and differences between these methods. As manifold-Galerkin approaches currently do not account for inputs in the construction of the reduced state subspace, we extend these methods to dynamical systems with inputs. In a comparative case study, we apply eight established model order reduction methods to an air separation process model: POD-Galerkin, nonlinear-POD-Galerkin, manifold-Galerkin, dynamic mode decomposition, Koopman theory, manifold learning with latent predictor, compartment modeling, and model aggregation. Herein, we do not investigate hyperreduction (reduction of FLOPS). Based on our findings, we discuss strengths and weaknesses of the model order reduction methods.
[LG-65] rojanTO: Action-Level Backdoor Attacks against Trajectory Optimization Models
链接: https://arxiv.org/abs/2506.12815
作者: Yang Dai,Oubo Ma,Longfei Zhang,Xingxing Liang,Xiaochun Cao,Shouling Ji,Jiaheng Zhang,Jincai Huang,Li Shen
类目: Machine Learning (cs.LG)
*备注: 23 pages, 6 figures
Abstract:Recent advances in Trajectory Optimization (TO) models have achieved remarkable success in offline reinforcement learning. However, their vulnerabilities against backdoor attacks are poorly understood. We find that existing backdoor attacks in reinforcement learning are based on reward manipulation, which are largely ineffective against the TO model due to its inherent sequence modeling nature. Moreover, the complexities introduced by high-dimensional action spaces further compound the challenge of action manipulation. To address these gaps, we propose TrojanTO, the first action-level backdoor attack against TO models. TrojanTO employs alternating training to enhance the connection between triggers and target actions for attack effectiveness. To improve attack stealth, it utilizes precise poisoning via trajectory filtering for normal performance and batch poisoning for trigger consistency. Extensive evaluations demonstrate that TrojanTO effectively implants backdoor attacks across diverse tasks and attack objectives with a low attack budget (0.3% of trajectories). Furthermore, TrojanTO exhibits broad applicability to DT, GDT, and DC, underscoring its scalability across diverse TO model architectures.
[LG-66] Lyapunov Learning at the Onset of Chaos ICML2025
链接: https://arxiv.org/abs/2506.12810
作者: Matteo Benati,Alessandro Londei,Denise Lanzieri,Vittorio Loreto
类目: Machine Learning (cs.LG)
*备注: Accepted at ICML 2025, HiLD: High-dimensional Learning Dynamics Workshop
Abstract:Handling regime shifts and non-stationary time series in deep learning systems presents a significant challenge. In the case of online learning, when new information is introduced, it can disrupt previously stored data and alter the model’s overall paradigm, especially with non-stationary data sources. Therefore, it is crucial for neural systems to quickly adapt to new paradigms while preserving essential past knowledge relevant to the overall problem. In this paper, we propose a novel training algorithm for neural networks called \textitLyapunov Learning. This approach leverages the properties of nonlinear chaotic dynamical systems to prepare the model for potential regime shifts. Drawing inspiration from Stuart Kauffman’s Adjacent Possible theory, we leverage local unexplored regions of the solution space to enable flexible adaptation. The neural network is designed to operate at the edge of chaos, where the maximum Lyapunov exponent, indicative of a system’s sensitivity to small perturbations, evolves around zero over time. Our approach demonstrates effective and significant improvements in experiments involving regime shifts in non-stationary systems. In particular, we train a neural network to deal with an abrupt change in Lorenz’s chaotic system parameters. The neural network equipped with Lyapunov learning significantly outperforms the regular training, increasing the loss ratio by about 96% . Comments: Accepted at ICML 2025, HiLD: High-dimensional Learning Dynamics Workshop Subjects: Machine Learning (cs.LG) Cite as: arXiv:2506.12810 [cs.LG] (or arXiv:2506.12810v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12810 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-67] A Review of the Long Horizon Forecasting Problem in Time Series Analysis
链接: https://arxiv.org/abs/2506.12809
作者: Hans Krupakar,Kandappan V A
类目: Machine Learning (cs.LG); Emerging Technologies (cs.ET); Performance (cs.PF); Machine Learning (stat.ML)
*备注: Submitted to International Journal of Forecasting
Abstract:The long horizon forecasting (LHF) problem has come up in the time series literature for over the last 35 years or so. This review covers aspects of LHF in this period and how deep learning has incorporated variants of trend, seasonality, fourier and wavelet transforms, misspecification bias reduction and bandpass filters while contributing using convolutions, residual connections, sparsity reduction, strided convolutions, attention masks, SSMs, normalization methods, low-rank approximations and gating mechanisms. We highlight time series decomposition techniques, input data preprocessing and dataset windowing schemes that improve performance. Multi-layer perceptron models, recurrent neural network hybrids, self-attention models that improve and/or address the performances of the LHF problem are described, with an emphasis on the feature space construction. Ablation studies are conducted over the ETTm2 dataset in the multivariate and univariate high useful load (HUFL) forecasting contexts, evaluated over the last 4 months of the dataset. The heatmaps of MSE averages per time step over test set series in the horizon show that there is a steady increase in the error proportionate to its length except with xLSTM and Triformer models and motivate LHF as an error propagation problem. The trained models are available here: this https URL
[LG-68] MetaEformer: Unveiling and Leverag ing Meta-patterns for Complex and Dynamic Systems Load Forecasting
链接: https://arxiv.org/abs/2506.12800
作者: Shaoyuan Huang,Tiancheng Zhang,Zhongtian Zhang,Xiaofei Wang,Lanjun Wang,Xin Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Time series forecasting is a critical and practical problem in many real-world applications, especially for industrial scenarios, where load forecasting underpins the intelligent operation of modern systems like clouds, power grids and traffic this http URL, the inherent complexity and dynamics of these systems present significant challenges. Despite advances in methods such as pattern recognition and anti-non-stationarity have led to performance gains, current methods fail to consistently ensure effectiveness across various system scenarios due to the intertwined issues of complex patterns, concept-drift, and few-shot problems. To address these challenges simultaneously, we introduce a novel scheme centered on fundamental waveform, a.k.a., meta-pattern. Specifically, we develop a unique Meta-pattern Pooling mechanism to purify and maintain meta-patterns, capturing the nuanced nature of system loads. Complementing this, the proposed Echo mechanism adaptively leverages the meta-patterns, enabling a flexible and precise pattern reconstruction. Our Meta-pattern Echo transformer (MetaEformer) seamlessly incorporates these mechanisms with the transformer-based predictor, offering end-to-end efficiency and interpretability of core processes. Demonstrating superior performance across eight benchmarks under three system scenarios, MetaEformer marks a significant advantage in accuracy, with a 37% relative improvement on fifteen state-of-the-art baselines.
[LG-69] PDEfuncta: Spectrally-Aware Neural Representation for PDE Solution Modeling
链接: https://arxiv.org/abs/2506.12790
作者: Minju Jo,Woojin Cho,Uvini Balasuriya Mudiyanselage,Seungjun Lee,Noseong Park,Kookjin Lee
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA); Computational Physics (physics.comp-ph)
*备注:
Abstract:Scientific machine learning often involves representing complex solution fields that exhibit high-frequency features such as sharp transitions, fine-scale oscillations, and localized structures. While implicit neural representations (INRs) have shown promise for continuous function modeling, capturing such high-frequency behavior remains a challenge-especially when modeling multiple solution fields with a shared network. Prior work addressing spectral bias in INRs has primarily focused on single-instance settings, limiting scalability and generalization. In this work, we propose Global Fourier Modulation (GFM), a novel modulation technique that injects high-frequency information at each layer of the INR through Fourier-based reparameterization. This enables compact and accurate representation of multiple solution fields using low-dimensional latent vectors. Building upon GFM, we introduce PDEfuncta, a meta-learning framework designed to learn multi-modal solution fields and support generalization to new tasks. Through empirical studies on diverse scientific problems, we demonstrate that our method not only improves representational quality but also shows potential for forward and inverse inference tasks without the need for retraining.
[LG-70] Unconstrained Robust Online Convex Optimization
链接: https://arxiv.org/abs/2506.12781
作者: Jiujia Zhang,Ashok Cutkosky
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:This paper addresses online learning with corrupted'' feedback. Our learner is provided with potentially corrupted gradients \tilde g_t instead of the true’’ gradients g_t . We make no assumptions about how the corruptions arise: they could be the result of outliers, mislabeled data, or even malicious interference. We focus on the difficult ``unconstrained’’ setting in which our algorithm must maintain low regret with respect to any comparison point u \in \mathbbR^d . The unconstrained setting is significantly more challenging as existing algorithms suffer extremely high regret even with very tiny amounts of corruption (which is not true in the case of a bounded domain). Our algorithms guarantee regret |u|G (\sqrtT + k) when G \ge \max_t |g_t| is known, where k is a measure of the total amount of corruption. When G is unknown we incur an extra additive penalty of (|u|^2+G^2) k .
[LG-71] From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
链接: https://arxiv.org/abs/2506.12779
作者: Yuxuan Wang,Ming Yang,Weishuai Zeng,Yu Zhang,Xinrun Xu,Haobin Jiang,Ziluo Ding,Zongqing Lu
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in training single motion-specific policies, they struggle to generalize across highly varied behaviors due to conflicting control requirements and mismatched data distributions. In this work, we propose BumbleBee (BB), an expert-generalist learning framework that combines motion clustering and sim-to-real adaptation to overcome these challenges. BB first leverages an autoencoder-based clustering method to group behaviorally similar motions using motion features and motion descriptions. Expert policies are then trained within each cluster and refined with real-world data through iterative delta action modeling to bridge the sim-to-real gap. Finally, these experts are distilled into a unified generalist controller that preserves agility and robustness across all motion types. Experiments on two simulations and a real humanoid robot demonstrate that BB achieves state-of-the-art general whole-body control, setting a new benchmark for agile, robust, and generalizable humanoid performance in the real world.
[LG-72] RL from Physical Feedback: Aligning Large Motion Models with Humanoid Control
链接: https://arxiv.org/abs/2506.12769
作者: Junpeng Yue,Zepeng Wang,Yuxuan Wang,Weishuai Zeng,Jiangxing Wang,Xinrun Xu,Yu Zhang,Sipeng Zheng,Ziluo Ding,Zongqing Lu
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:
Abstract:This paper focuses on a critical challenge in robotics: translating text-driven human motions into executable actions for humanoid robots, enabling efficient and cost-effective learning of new behaviors. While existing text-to-motion generation methods achieve semantic alignment between language and motion, they often produce kinematically or physically infeasible motions unsuitable for real-world deployment. To bridge this sim-to-real gap, we propose Reinforcement Learning from Physical Feedback (RLPF), a novel framework that integrates physics-aware motion evaluation with text-conditioned motion generation. RLPF employs a motion tracking policy to assess feasibility in a physics simulator, generating rewards for fine-tuning the motion generator. Furthermore, RLPF introduces an alignment verification module to preserve semantic fidelity to text instructions. This joint optimization ensures both physical plausibility and instruction alignment. Extensive experiments show that RLPF greatly outperforms baseline methods in generating physically feasible motions while maintaining semantic correspondence with text instruction, enabling successful deployment on real humanoid robots.
[LG-73] Base3: a simple interpolation-based ensemble method for robust dynamic link prediction
链接: https://arxiv.org/abs/2506.12764
作者: Kondrup Emma
类目: Machine Learning (cs.LG)
*备注: 9 pages
Abstract:Dynamic link prediction remains a central challenge in temporal graph learning, particularly in designing models that are both effective and practical for real-world deployment. Existing approaches often rely on complex neural architectures, which are computationally intensive and difficult to interpret. In this work, we build on the strong recurrence-based foundation of the EdgeBank baseline, by supplementing it with inductive capabilities. We do so by leveraging the predictive power of non-learnable signals from two complementary perspectives: historical edge recurrence, as captured by EdgeBank, and global node popularity, as introduced in the PopTrack model. We propose t-CoMem, a lightweight memory module that tracks temporal co-occurrence patterns and neighborhood activity. Building on this, we introduce Base3, an interpolation-based model that fuses EdgeBank, PopTrack, and t-CoMem into a unified scoring framework. This combination effectively bridges local and global temporal dynamics – repetition, popularity, and context – without relying on training. Evaluated on the Temporal Graph Benchmark, Base3 achieves performance competitive with state-of-the-art deep models, even outperforming them on some datasets. Importantly, it considerably improves on existing baselines’ performance under more realistic and challenging negative sampling strategies – offering a simple yet robust alternative for temporal graph learning. Comments: 9 pages Subjects: Machine Learning (cs.LG) Cite as: arXiv:2506.12764 [cs.LG] (or arXiv:2506.12764v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12764 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-74] Hierarchical Group-wise Ranking Framework for Recommendation Models
链接: https://arxiv.org/abs/2506.12756
作者: YaChen Yan,Liubo Li,Ravi Choudhary
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
*备注:
Abstract:In modern recommender systems, CTR/CVR models are increasingly trained with ranking objectives to improve item ranking quality. While this shift aligns training more closely with serving goals, most existing methods rely on in-batch negative sampling, which predominantly surfaces easy negatives. This limits the model’s ability to capture fine-grained user preferences and weakens overall ranking performance. To address this, we propose a Hierarchical Group-wise Ranking Framework with two key components. First, we apply residual vector quantization to user embeddings to generate hierarchical user codes that partition users into hierarchical, trie-structured clusters. Second, we apply listwise ranking losses to user-item pairs at each level of the hierarchy, where shallow levels group loosely similar users and deeper levels group highly similar users, reinforcing learning-to-rank signals through progressively harder negatives. Since users with similar preferences and content exposure tend to yield more informative negatives, applying ranking losses within these hierarchical user groups serves as an effective approximation of hard negative mining. Our approach improves ranking performance without requiring complex real-time context collection or retrieval infrastructure. Extensive experiments demonstrate that the proposed framework consistently enhances both model calibration and ranking accuracy, offering a scalable and practical solution for industrial recommender systems.
[LG-75] Free Privacy Protection for Wireless Federated Learning: Enjoy It or Suffer from It?
链接: https://arxiv.org/abs/2506.12749
作者: Weicai Li,Tiejun Lv,Xiyu Zhao,Xin Yuan,Wei Ni
类目: Machine Learning (cs.LG)
*备注: 16 pages, 8 figures, accepted by IEEE Transactions on Information Forensics and Security
Abstract:Inherent communication noises have the potential to preserve privacy for wireless federated learning (WFL) but have been overlooked in digital communication systems predominantly using floating-point number standards, e.g., IEEE 754, for data storage and transmission. This is due to the potentially catastrophic consequences of bit errors in floating-point numbers, e.g., on the sign or exponent bits. This paper presents a novel channel-native bit-flipping differential privacy (DP) mechanism tailored for WFL, where transmit bits are randomly flipped and communication noises are leveraged, to collectively preserve the privacy of WFL in digital communication systems. The key idea is to interpret the bit perturbation at the transmitter and bit errors caused by communication noises as a bit-flipping DP process. This is achieved by designing a new floating-point-to-fixed-point conversion method that only transmits the bits in the fraction part of model parameters, hence eliminating the need for transmitting the sign and exponent bits and preventing the catastrophic consequence of bit errors. We analyze a new metric to measure the bit-level distance of the model parameters and prove that the proposed mechanism satisfies (\lambda,\epsilon)-Rényi DP and does not violate the WFL convergence. Experiments validate privacy and convergence analysis of the proposed mechanism and demonstrate its superiority to the state-of-the-art Gaussian mechanisms that are channel-agnostic and add Gaussian noise for privacy protection.
[LG-76] Large Scalable Cross-Domain Graph Neural Networks for Personalized Notification at LinkedIn
链接: https://arxiv.org/abs/2506.12700
作者: Shihai He,Julie Choi,Tianqi Li,Zhiwei Ding,Peng Du,Priya Bannur,Franco Liang,Fedor Borisyuk,Padmini Jaikumar,Xiaobing Xue,Viral Gupta
类目: Machine Learning (cs.LG)
*备注:
Abstract:Notification recommendation systems are critical to driving user engagement on professional platforms like LinkedIn. Designing such systems involves integrating heterogeneous signals across domains, capturing temporal dynamics, and optimizing for multiple, often competing, objectives. Graph Neural Networks (GNNs) provide a powerful framework for modeling complex interactions in such environments. In this paper, we present a cross-domain GNN-based system deployed at LinkedIn that unifies user, content, and activity signals into a single, large-scale graph. By training on this cross-domain structure, our model significantly outperforms single-domain baselines on key tasks, including click-through rate (CTR) prediction and professional engagement. We introduce architectural innovations including temporal modeling and multi-task learning, which further enhance performance. Deployed in LinkedIn’s notification system, our approach led to a 0.10% lift in weekly active users and a 0.62% improvement in CTR. We detail our graph construction process, model design, training pipeline, and both offline and online evaluations. Our work demonstrates the scalability and effectiveness of cross-domain GNNs in real-world, high-impact applications.
[LG-77] FKAN: Time-Frequency KAN for Long-Term Time Series Forecasting
链接: https://arxiv.org/abs/2506.12696
作者: Xiaoyan Kui,Canwei Liu,Qinsong Li,Zhipeng Hu,Yangyang Shi,Weixin Si,Beiji Zou
类目: Machine Learning (cs.LG)
*备注: 11 pages,5 figures
Abstract:Kolmogorov-Arnold Networks (KANs) are highly effective in long-term time series forecasting due to their ability to efficiently represent nonlinear relationships and exhibit local plasticity. However, prior research on KANs has predominantly focused on the time domain, neglecting the potential of the frequency domain. The frequency domain of time series data reveals recurring patterns and periodic behaviors, which complement the temporal information captured in the time domain. To address this gap, we explore the application of KANs in the frequency domain for long-term time series forecasting. By leveraging KANs’ adaptive activation functions and their comprehensive representation of signals in the frequency domain, we can more effectively learn global dependencies and periodic patterns. To integrate information from both time and frequency domains, we propose the \textbfT ime- \textbfF requency KAN (TFKAN). TFKAN employs a dual-branch architecture that independently processes features from each domain, ensuring that the distinct characteristics of each domain are fully utilized without interference. Additionally, to account for the heterogeneity between domains, we introduce a dimension-adjustment strategy that selectively upscales only in the frequency domain, enhancing efficiency while capturing richer frequency information. Experimental results demonstrate that TFKAN consistently outperforms state-of-the-art (SOTA) methods across multiple datasets. The code is available at this https URL.
[LG-78] INTERPOS: Interaction Rhythm Guided Positional Morphing for Mobile App Recommender Systems
链接: https://arxiv.org/abs/2506.12661
作者: M.H. Maqbool,Moghis Fereidouni,Umar Farooq,A.B. Siddique,Hassan Foroosh
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
*备注: 10 pages, 8 tables, 3 figures
Abstract:The mobile app market has expanded exponentially, offering millions of apps with diverse functionalities, yet research in mobile app recommendation remains limited. Traditional sequential recommender systems utilize the order of items in users’ historical interactions to predict the next item for the users. Position embeddings, well-established in transformer-based architectures for natural language processing tasks, effectively distinguish token positions in sequences. In sequential recommendation systems, position embeddings can capture the order of items in a user’s historical interaction sequence. Nevertheless, this ordering does not consider the time elapsed between two interactions of the same user (e.g., 1 day, 1 week, 1 month), referred to as “user rhythm”. In mobile app recommendation datasets, the time between consecutive user interactions is notably longer compared to other domains like movies, posing significant challenges for sequential recommender systems. To address this phenomenon in the mobile app domain, we introduce INTERPOS, an Interaction Rhythm Guided Positional Morphing strategy for autoregressive mobile app recommender systems. INTERPOS incorporates rhythm-guided position embeddings, providing a more comprehensive representation that considers both the sequential order of interactions and the temporal gaps between them. This approach enables a deep understanding of users’ rhythms at a fine-grained level, capturing the intricacies of their interaction patterns over time. We propose three strategies to incorporate the morphed positional embeddings in two transformer-based sequential recommendation system architectures. Our extensive evaluations show that INTERPOS outperforms state-of-the-art models using 7 mobile app recommendation datasets on NDCG@K and HIT@K metrics. The source code of INTERPOS is available at this https URL.
[LG-79] Learning Mappings in Mesh-based Simulations
链接: https://arxiv.org/abs/2506.12652
作者: Shirin Hosseinmardi,Ramin Bostanabad
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many real-world physics and engineering problems arise in geometrically complex domains discretized by meshes for numerical simulations. The nodes of these potentially irregular meshes naturally form point clouds whose limited tractability poses significant challenges for learning mappings via machine learning models. To address this, we introduce a novel and parameter-free encoding scheme that aggregates footprints of points onto grid vertices and yields information-rich grid representations of the topology. Such structured representations are well-suited for standard convolution and FFT (Fast Fourier Transform) operations and enable efficient learning of mappings between encoded input-output pairs using Convolutional Neural Networks (CNNs). Specifically, we integrate our encoder with a uniquely designed UNet (E-UNet) and benchmark its performance against Fourier- and transformer-based models across diverse 2D and 3D problems where we analyze the performance in terms of predictive accuracy, data efficiency, and noise robustness. Furthermore, we highlight the versatility of our encoding scheme in various mapping tasks including recovering full point cloud responses from partial observations. Our proposed framework offers a practical alternative to both primitive and computationally intensive encoding schemes; supporting broad adoption in computational science applications involving mesh-based simulations.
[LG-80] Mapping Neural Signals to Agent Performance A Step Towards Reinforcement Learning from Neural Feedback
链接: https://arxiv.org/abs/2506.12636
作者: Julia Santaniello,Matthew Russell,Benson Jiang,Donatello Sassaroli,Robert Jacob,Jivko Sinapov
类目: Machine Learning (cs.LG)
*备注:
Abstract:Implicit Human-in-the-Loop Reinforcement Learning (HITL-RL) is a methodology that integrates passive human feedback into autonomous agent training while minimizing human workload. However, existing methods often rely on active instruction, requiring participants to teach an agent through unnatural expression or gesture. We introduce NEURO-LOOP, an implicit feedback framework that utilizes the intrinsic human reward system to drive human-agent interaction. This work demonstrates the feasibility of a critical first step in the NEURO-LOOP framework: mapping brain signals to agent performance. Using functional near-infrared spectroscopy (fNIRS), we design a dataset to enable future research using passive Brain-Computer Interfaces for Human-in-the-Loop Reinforcement Learning. Participants are instructed to observe or guide a reinforcement learning agent in its environment while signals from the prefrontal cortex are collected. We conclude that a relationship between fNIRS data and agent performance exists using classical machine learning techniques. Finally, we highlight the potential that neural interfaces may offer to future applications of human-agent interaction, assistive AI, and adaptive autonomous systems.
[LG-81] Semivalue-based data valuation is arbitrary and gameable
链接: https://arxiv.org/abs/2506.12619
作者: Hannah Diehl,Ashia C. Wilson
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)
*备注: 29 pages, 9 figures
Abstract:The game-theoretic notion of the semivalue offers a popular framework for credit attribution and data valuation in machine learning. Semivalues have been proposed for a variety of high-stakes decisions involving data, such as determining contributor compensation, acquiring data from external sources, or filtering out low-value datapoints. In these applications, semivalues depend on the specification of a utility function that maps subsets of data to a scalar score. While it is broadly agreed that this utility function arises from a composition of a learning algorithm and a performance metric, its actual instantiation involves numerous subtle modeling choices. We argue that this underspecification leads to varying degrees of arbitrariness in semivalue-based valuations. Small, but arguably reasonable changes to the utility function can induce substantial shifts in valuations across datapoints. Moreover, these valuation methodologies are also often gameable: low-cost adversarial strategies exist to exploit this ambiguity and systematically redistribute value among datapoints. Through theoretical constructions and empirical examples, we demonstrate that a bad-faith valuator can manipulate utility specifications to favor preferred datapoints, and that a good-faith valuator is left without principled guidance to justify any particular specification. These vulnerabilities raise ethical and epistemic concerns about the use of semivalues in several applications. We conclude by highlighting the burden of justification that semivalue-based approaches place on modelers and discuss important considerations for identifying appropriate uses.
[LG-82] Existence of Adversarial Examples for Random Convolutional Networks via Isoperimetric Inequalities on mathbbso(d) COLT2025
链接: https://arxiv.org/abs/2506.12613
作者: Amit Daniely
类目: Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注: Accepted to COLT 2025
Abstract:We show that adversarial examples exist for various random convolutional networks, and furthermore, that this is a relatively simple consequence of the isoperimetric inequality on the special orthogonal group \mathbbso(d) . This extends and simplifies a recent line of work which shows similar results for random fully connected networks.
[LG-83] Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts
链接: https://arxiv.org/abs/2506.12597
作者: Shengzhuang Chen,Ying Wei,Jonathan Richard Schwarz
类目: Machine Learning (cs.LG)
*备注: 9 pages
Abstract:We present Sparse Interpolated Mixture-of-Experts (SIMoE) instruction-tuning, an end-to-end algorithm designed to fine-tune a dense pre-trained Large Language Model (LLM) into a MoE-style model that possesses capabilities in multiple specialized domains. During instruction-tuning, SIMoE automatically identifies multiple specialized experts under a specified sparsity constraint, with each expert representing a structurally sparse subset of the seed LLM’s parameters that correspond to domain-specific knowledge within the data. SIMoE simultaneously learns an input-dependent expert merging strategy via a router network, leveraging rich cross-expert knowledge for superior downstream generalization that surpasses existing baselines. Empirically, SIMoE consistently achieves state-of-the-art performance on common instruction-tuning benchmarks while maintaining an optimal performance-compute trade-off compared to all baselines.
[LG-84] Are We Really Measuring Progress? Transferring Insights from Evaluating Recommender Systems to Temporal Link Prediction
链接: https://arxiv.org/abs/2506.12588
作者: Filip Cornell,Oleg Smirnov,Gabriela Zarzar Gandler,Lele Cao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Recent work has questioned the reliability of graph learning benchmarks, citing concerns around task design, methodological rigor, and data suitability. In this extended abstract, we contribute to this discussion by focusing on evaluation strategies in Temporal Link Prediction (TLP). We observe that current evaluation protocols are often affected by one or more of the following issues: (1) inconsistent sampled metrics, (2) reliance on hard negative sampling often introduced as a means to improve robustness, and (3) metrics that implicitly assume equal base probabilities across source nodes by combining predictions. We support these claims through illustrative examples and connections to longstanding concerns in the recommender systems community. Our ongoing work aims to systematically characterize these problems and explore alternatives that can lead to more robust and interpretable evaluation. We conclude with a discussion of potential directions for improving the reliability of TLP benchmarks.
[LG-85] RAW-Explainer: Post-hoc Explanations of Graph Neural Networks on Knowledge Graphs
链接: https://arxiv.org/abs/2506.12558
作者: Ryoji Kubo,Djellel Difallah
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Graph neural networks have demonstrated state-of-the-art performance on knowledge graph tasks such as link prediction. However, interpreting GNN predictions remains a challenging open problem. While many GNN explainability methods have been proposed for node or graph-level tasks, approaches for generating explanations for link predictions in heterogeneous settings are limited. In this paper, we propose RAW-Explainer, a novel framework designed to generate connected, concise, and thus interpretable subgraph explanations for link prediction. Our method leverages the heterogeneous information in knowledge graphs to identify connected subgraphs that serve as patterns of factual explanation via a random walk objective. Unlike existing methods tailored to knowledge graphs, our approach employs a neural network to parameterize the explanation generation process, which significantly speeds up the production of collective explanations. Furthermore, RAW-Explainer is designed to overcome the distribution shift issue when evaluating the quality of an explanatory subgraph which is orders of magnitude smaller than the full graph, by proposing a robust evaluator that generalizes to the subgraph distribution. Extensive quantitative results on real-world knowledge graph datasets demonstrate that our approach strikes a balance between explanation quality and computational efficiency.
[LG-86] Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning
链接: https://arxiv.org/abs/2506.12553
作者: Roy Rinberg,Ilia Shumailov,Vikrant Singhal,Rachel Cummings,Nicolas Papernot
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Machine Learning (stat.ML)
*备注:
Abstract:Differential privacy (DP) is obtained by randomizing a data analysis algorithm, which necessarily introduces a tradeoff between its utility and privacy. Many DP mechanisms are built upon one of two underlying tools: Laplace and Gaussian additive noise mechanisms. We expand the search space of algorithms by investigating the Generalized Gaussian mechanism, which samples the additive noise term x with probability proportional to e^-\frac| x |\sigma^\beta for some \beta \geq 1 . The Laplace and Gaussian mechanisms are special cases of GG for \beta=1 and \beta=2 , respectively. In this work, we prove that all members of the GG family satisfy differential privacy, and provide an extension of an existing numerical accountant (the PRV accountant) for these mechanisms. We show that privacy accounting for the GG Mechanism and its variants is dimension independent, which substantially improves computational costs of privacy accounting. We apply the GG mechanism to two canonical tools for private machine learning, PATE and DP-SGD; we show empirically that \beta has a weak relationship with test-accuracy, and that generally \beta=2 (Gaussian) is nearly optimal. This provides justification for the widespread adoption of the Gaussian mechanism in DP learning, and can be interpreted as a negative result, that optimizing over \beta does not lead to meaningful improvements in performance. Subjects: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Machine Learning (stat.ML) Cite as: arXiv:2506.12553 [cs.LG] (or arXiv:2506.12553v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12553 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-87] Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling ICML
链接: https://arxiv.org/abs/2506.12543
作者: Teodora Srećković,Jonas Geiping,Antonio Orvieto
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: Short version accepted at the 2025 HiLD Workshop at ICML
Abstract:Adam is known to perform significantly better than Stochastic Gradient Descent (SGD) in language models, a phenomenon for which a number of explanations have been proposed. In this work, we revisit this “optimizer gap” through a series of comprehensively tuned baseline training runs for language modeling with Transformers. We exhaustively study how momentum, gradient clipping, and batch size affect the gap between SGD and Adam. Our empirical findings show that SGD with momentum can actually perform similarly to Adam in small-batch settings, if tuned correctly. We revisit existing explanations for Adam’s advantage, including heavy-tailed class imbalance, directional sharpness, and Hessian heterogeneity, which struggle to directly explain this phenomenon. Towards bridging this gap in our understanding, by analyzing our Transformer training runs and simple quadratic settings inspired by the literature, we provide new insights, driven by stochastic differential equation models, into the role of batch size on the training dynamics.
[LG-88] Note on Follow-the-Perturbed-Leader in Combinatorial Semi-Bandit Problems
链接: https://arxiv.org/abs/2506.12490
作者: Botao Chen,Junya Honda
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:This paper studies the optimality and complexity of Follow-the-Perturbed-Leader (FTPL) policy in size-invariant combinatorial semi-bandit problems. Recently, Honda et al. (2023) and Lee et al. (2024) showed that FTPL achieves Best-of-Both-Worlds (BOBW) optimality in standard multi-armed bandit problems with Fréchet-type distributions. However, the optimality of FTPL in combinatorial semi-bandit problems remains unclear. In this paper, we consider the regret bound of FTPL with geometric resampling (GR) in size-invariant semi-bandit setting, showing that FTPL respectively achieves O\left(\sqrtm^2 d^\frac1\alphaT+\sqrtmdT\right) regret with Fréchet distributions, and the best possible regret bound of O\left(\sqrtmdT\right) with Pareto distributions in adversarial setting. Furthermore, we extend the conditional geometric resampling (CGR) to size-invariant semi-bandit setting, which reduces the computational complexity from O(d^2) of original GR to O\left(md\left(\log(d/m)+1\right)\right) without sacrificing the regret performance of FTPL.
[LG-89] Quantizing Small-Scale State-Space Models for Edge AI
链接: https://arxiv.org/abs/2506.12480
作者: Leo Zhao,Tristan Torchet,Melika Payvand,Laura Kriener,Filippo Moro
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:State-space models (SSMs) have recently gained attention in deep learning for their ability to efficiently model long-range dependencies, making them promising candidates for edge-AI applications. In this paper, we analyze the effects of quantization on small-scale SSMs with a focus on reducing memory and computational costs while maintaining task performance. Using the S4D architecture, we first investigate post-training quantization (PTQ) and show that the state matrix A and internal state x are particularly sensitive to quantization. Furthermore, we analyze the impact of different quantization techniques applied to the parameters and activations in the S4D architecture. To address the observed performance drop after Post-training Quantization (PTQ), we apply Quantization-aware Training (QAT), significantly improving performance from 40% (PTQ) to 96% on the sequential MNIST benchmark at 8-bit precision. We further demonstrate the potential of QAT in enabling sub-8-bit precisions and evaluate different parameterization schemes for QAT stability. Additionally, we propose a heterogeneous quantization strategy that assigns different precision levels to model components, reducing the overall memory footprint by a factor of 6x without sacrificing performance. Our results provide actionable insights for deploying quantized SSMs in resource-constrained environments.
[LG-90] Learning Best Paths in Quantum Networks
链接: https://arxiv.org/abs/2506.12462
作者: Xuchuang Wang,Maoli Liu,Xutong Liu,Zhuohua Li,Mohammad Hajiesmaili,John C.S. Lui,Don Towsley
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Quantum Physics (quant-ph)
*备注: Accepted at INFOCOM 2025
Abstract:Quantum networks (QNs) transmit delicate quantum information across noisy quantum channels. Crucial applications, like quantum key distribution (QKD) and distributed quantum computation (DQC), rely on efficient quantum information transmission. Learning the best path between a pair of end nodes in a QN is key to enhancing such applications. This paper addresses learning the best path in a QN in the online learning setting. We explore two types of feedback: “link-level” and “path-level”. Link-level feedback pertains to QNs with advanced quantum switches that enable link-level benchmarking. Path-level feedback, on the other hand, is associated with basic quantum switches that permit only path-level benchmarking. We introduce two online learning algorithms, BeQuP-Link and BeQuP-Path, to identify the best path using link-level and path-level feedback, respectively. To learn the best path, BeQuP-Link benchmarks the critical links dynamically, while BeQuP-Path relies on a subroutine, transferring path-level observations to estimate link-level parameters in a batch manner. We analyze the quantum resource complexity of these algorithms and demonstrate that both can efficiently and, with high probability, determine the best path. Finally, we perform NetSquid-based simulations and validate that both algorithms accurately and efficiently identify the best path.
[LG-91] Interpretable Causal Representation Learning for Biological Data in the Pathway Space ICLR2025
链接: https://arxiv.org/abs/2506.12439
作者: Jesus de la Fuente,Robert Lehmann,Carlos Ruiz-Arenas,Jan Voges,Irene Marin-Goñi,Xabier Martinez-de-Morentin,David Gomez-Cabrero,Idoia Ochoa,Jesper Tegner,Vincenzo Lagani,Mikel Hernaez
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM); Machine Learning (stat.ML)
*备注: ICLR 2025, 28 pages, 14 figures, 10 tables
Abstract:Predicting the impact of genomic and drug perturbations in cellular function is crucial for understanding gene functions and drug effects, ultimately leading to improved therapies. To this end, Causal Representation Learning (CRL) constitutes one of the most promising approaches, as it aims to identify the latent factors that causally govern biological systems, thus facilitating the prediction of the effect of unseen perturbations. Yet, current CRL methods fail in reconciling their principled latent representations with known biological processes, leading to models that are not interpretable. To address this major issue, we present SENA-discrepancy-VAE, a model based on the recently proposed CRL method discrepancy-VAE, that produces representations where each latent factor can be interpreted as the (linear) combination of the activity of a (learned) set of biological processes. To this extent, we present an encoder, SENA-\delta, that efficiently compute and map biological processes’ activity levels to the latent causal factors. We show that SENA-discrepancy-VAE achieves predictive performances on unseen combinations of interventions that are comparable with its original, non-interpretable counterpart, while inferring causal latent factors that are biologically meaningful.
[LG-92] Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
链接: https://arxiv.org/abs/2506.12425
作者: Pranjal Naman,Yogesh Simmhan
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: Preprint of paper in the proceedings of the 30th International European Conference on Parallel and Distributed Computing (Euro-Par)
Abstract:Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model on decentralized data, addressing privacy concerns while leveraging parallelism. Existing methods that address the unique requirements of federated GNN training using remote embeddings to enhance convergence accuracy are limited by their diminished performance due to large communication costs with a shared embedding server. In this paper, we present OpES, an optimized federated GNN training framework that uses remote neighbourhood pruning, and overlaps pushing of embeddings to the server with local training to reduce the network costs and training time. The modest drop in per-round accuracy due to pre-emptive push of embeddings is out-stripped by the reduction in per-round training time for large and dense graphs like Reddit and Products, converging up to \approx2\times faster than the state-of-the-art technique using an embedding server and giving up to 20% better accuracy than vanilla federated GNN learning.
[LG-93] Wireless Channel Identification via Conditional Diffusion Model
链接: https://arxiv.org/abs/2506.12419
作者: Yuan Li,Zhong Zheng,Chang Liu,Zesong Fei
类目: Machine Learning (cs.LG)
*备注:
Abstract:The identification of channel scenarios in wireless systems plays a crucial role in channel modeling, radio fingerprint positioning, and transceiver design. Traditional methods to classify channel scenarios are based on typical statistical characteristics of channels, such as K-factor, path loss, delay spread, etc. However, statistic-based channel identification methods cannot accurately differentiate implicit features induced by dynamic scatterers, thus performing very poorly in identifying similar channel scenarios. In this paper, we propose a novel channel scenario identification method, formulating the identification task as a maximum a posteriori (MAP) estimation. Furthermore, the MAP estimation is reformulated by a maximum likelihood estimation (MLE), which is then approximated and solved by the conditional generative diffusion model. Specifically, we leverage a transformer network to capture hidden channel features in multiple latent noise spaces within the reverse process of the conditional generative diffusion model. These detailed features, which directly affect likelihood functions in MLE, enable highly accurate scenario identification. Experimental results show that the proposed method outperforms traditional methods, including convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and random forest-based classifiers, improving the identification accuracy by more than 10%.
[LG-94] Cross-Domain Conditional Diffusion Models for Time Series Imputation ECML-PKDD2025
链接: https://arxiv.org/abs/2506.12412
作者: Kexin Zhang,Baoyu Jing,K. Selçuk Candan,Dawei Zhou,Qingsong Wen,Han Liu,Kaize Ding
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: Accepted by ECML-PKDD 2025
Abstract:Cross-domain time series imputation is an underexplored data-centric research task that presents significant challenges, particularly when the target domain suffers from high missing rates and domain shifts in temporal dynamics. Existing time series imputation approaches primarily focus on the single-domain setting, which cannot effectively adapt to a new domain with domain shifts. Meanwhile, conventional domain adaptation techniques struggle with data incompleteness, as they typically assume the data from both source and target domains are fully observed to enable adaptation. For the problem of cross-domain time series imputation, missing values introduce high uncertainty that hinders distribution alignment, making existing adaptation strategies ineffective. Specifically, our proposed solution tackles this problem from three perspectives: (i) Data: We introduce a frequency-based time series interpolation strategy that integrates shared spectral components from both domains while retaining domain-specific temporal structures, constructing informative priors for imputation. (ii) Model: We design a diffusion-based imputation model that effectively learns domain-shared representations and captures domain-specific temporal dependencies with dedicated denoising networks. (iii) Algorithm: We further propose a cross-domain consistency alignment strategy that selectively regularizes output-level domain discrepancies, enabling effective knowledge transfer while preserving domain-specific characteristics. Extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach. Our code implementation is available here.
[LG-95] PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering
链接: https://arxiv.org/abs/2506.12408
作者: Xuqian Xue,Yiming Lei,Qi Cai,Hongming Shan,Junping Zhang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 15 pages, 7 figures, accepted by the Forty-Second International Conference on Machine Learning
Abstract:While contrastive multi-view clustering has achieved remarkable success, it implicitly assumes balanced class distribution. However, real-world multi-view data primarily exhibits class imbalance distribution. Consequently, existing methods suffer performance degradation due to their inability to perceive and model such imbalance. To address this challenge, we present the first systematic study of imbalanced multi-view clustering, focusing on two fundamental problems: i. perceiving class imbalance distribution, and ii. mitigating representation degradation of minority samples. We propose PROTOCOL, a novel PaRtial Optimal TranspOrt-enhanced COntrastive Learning framework for imbalanced multi-view clustering. First, for class imbalance perception, we map multi-view features into a consensus space and reformulate the imbalanced clustering as a partial optimal transport (POT) problem, augmented with progressive mass constraints and weighted KL divergence for class distributions. Second, we develop a POT-enhanced class-rebalanced contrastive learning at both feature and class levels, incorporating logit adjustment and class-sensitive learning to enhance minority sample representations. Extensive experiments demonstrate that PROTOCOL significantly improves clustering performance on imbalanced multi-view data, filling a critical research gap in this field.
[LG-96] Scaling Probabilistic Circuits via Monarch Matrices
链接: https://arxiv.org/abs/2506.12383
作者: Honghua Zhang,Meihua Dang,Benjie Wang,Stefano Ermon,Nanyun Peng,Guy Van den Broeck
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Probabilistic Circuits (PCs) are tractable representations of probability distributions allowing for exact and efficient computation of likelihoods and marginals. Recent advancements have improved the scalability of PCs either by leveraging their sparse properties or through the use of tensorized operations for better hardware utilization. However, no existing method fully exploits both aspects simultaneously. In this paper, we propose a novel sparse and structured parameterization for the sum blocks in PCs. By replacing dense matrices with sparse Monarch matrices, we significantly reduce the memory and computation costs, enabling unprecedented scaling of PCs. From a theory perspective, our construction arises naturally from circuit multiplication; from a practical perspective, compared to previous efforts on scaling up tractable probabilistic models, our approach not only achieves state-of-the-art generative modeling performance on challenging benchmarks like Text8, LM1B and ImageNet, but also demonstrates superior scaling behavior, achieving the same performance with substantially less compute as measured by the number of floating-point operations (FLOPs) during training.
[LG-97] Path-specific effects for pulse-oximetry guided decisions in critical care
链接: https://arxiv.org/abs/2506.12371
作者: Kevin Zhang,Yonghan Jung,Divyat Mahajan,Karthikeyan Shanmugam,Shalmali Joshi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Identifying and measuring biases associated with sensitive attributes is a crucial consideration in healthcare to prevent treatment disparities. One prominent issue is inaccurate pulse oximeter readings, which tend to overestimate oxygen saturation for dark-skinned patients and misrepresent supplemental oxygen needs. Most existing research has revealed statistical disparities linking device errors to patient outcomes in intensive care units (ICUs) without causal formalization. In contrast, this study causally investigates how racial discrepancies in oximetry measurements affect invasive ventilation in ICU settings. We employ a causal inference-based approach using path-specific effects to isolate the impact of bias by race on clinical decision-making. To estimate these effects, we leverage a doubly robust estimator, propose its self-normalized variant for improved sample efficiency, and provide novel finite-sample guarantees. Our methodology is validated on semi-synthetic data and applied to two large real-world health datasets: MIMIC-IV and eICU. Contrary to prior work, our analysis reveals minimal impact of racial discrepancies on invasive ventilation rates. However, path-specific effects mediated by oxygen saturation disparity are more pronounced on ventilation duration, and the severity differs by dataset. Our work provides a novel and practical pipeline for investigating potential disparities in the ICU and, more crucially, highlights the necessity of causal methods to robustly assess fairness in decision-making.
[LG-98] Efficient Unified Caching for Accelerating Heterogeneous AI Workloads
链接: https://arxiv.org/abs/2506.12370
作者: Tianze Wang,Yifei Liu,Chen Chen,Pengfei Zuo,Jiawei Zhang,Qizhen Weng,Yin Chen,Zhenhua Han,Jieru Zhao,Quan Chen,Minyi Guo
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 15 pages, 17 figures
Abstract:Modern AI clusters, which host diverse workloads like data pre-processing, training and inference, often store the large-volume data in cloud storage and employ caching frameworks to facilitate remote data access. To avoid code-intrusion complexity and minimize cache space wastage, it is desirable to maintain a unified cache shared by all the workloads. However, existing cache management strategies, designed for specific workloads, struggle to handle the heterogeneous AI workloads in a cluster – which usually exhibit heterogeneous access patterns and item storage granularities. In this paper, we propose IGTCache, a unified, high-efficacy cache for modern AI clusters. IGTCache leverages a hierarchical access abstraction, AccessStreamTree, to organize the recent data accesses in a tree structure, facilitating access pattern detection at various granularities. Using this abstraction, IGTCache applies hypothesis testing to categorize data access patterns as sequential, random, or skewed. Based on these detected access patterns and granularities, IGTCache tailors optimal cache management strategies including prefetching, eviction, and space allocation accordingly. Experimental results show that IGTCache increases the cache hit ratio by 55.6% over state-of-the-art caching frameworks, reducing the overall job completion time by 52.2%.
[LG-99] Relative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis
链接: https://arxiv.org/abs/2506.12358
作者: Jihoon Suh,Yeongjun Jang,Kaoru Teranishi,Takashi Tanaka
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: 6 pages, 2 figures, Published in IEEE Control Systems Letters, June 2025
Abstract:We propose an efficient encrypted policy synthesis to develop privacy-preserving model-based reinforcement learning. We first demonstrate that the relative-entropy-regularized reinforcement learning framework offers a computationally convenient linear and ``min-free’’ structure for value iteration, enabling a direct and efficient integration of fully homomorphic encryption with bootstrapping into policy synthesis. Convergence and error bounds are analyzed as encrypted policy synthesis propagates errors under the presence of encryption-induced errors including quantization and bootstrapping. Theoretical analysis is validated by numerical simulations. Results demonstrate the effectiveness of the RERL framework in integrating FHE for encrypted policy synthesis.
[LG-100] SplashNet: Split-and-Share Encoders for Accurate and Efficient Typing with Surface Electromyography
链接: https://arxiv.org/abs/2506.12356
作者: Nima Hadidi,Jason Chan,Ebrahim Feghhi,Jonathan Kao
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
*备注:
Abstract:Surface electromyography (sEMG) at the wrists could enable natural, keyboard-free text entry, yet the state-of-the-art emg2qwerty baseline still misrecognizes 51.8% of characters in the zero-shot setting on unseen users and 7.0% after user-specific fine-tuning. We trace many of these errors to mismatched cross-user signal statistics, fragile reliance on high-order feature dependencies, and the absence of architectural inductive biases aligned with the bilateral nature of typing. To address these issues, we introduce three simple modifications: (i) Rolling Time Normalization, which adaptively aligns input distributions across users; (ii) Aggressive Channel Masking, which encourages reliance on low-order feature combinations more likely to generalize across users; and (iii) a Split-and-Share encoder that processes each hand independently with weight-shared streams to reflect the bilateral symmetry of the neuromuscular system. Combined with a five-fold reduction in spectral resolution ( 33!\rightarrow!6 frequency bands), these components yield a compact Split-and-Share model, SplashNet-mini, which uses only \tfrac14 the parameters and 0.6\times the FLOPs of the baseline while reducing character-error rate (CER) to 36.4% zero-shot and 5.9% after fine-tuning. An upscaled variant, SplashNet ( \tfrac12 the parameters, 1.15\times the FLOPs of the baseline), further lowers error to 35.7% and 5.5% , representing relative improvements of 31% and 21% in the zero-shot and fine-tuned settings, respectively. SplashNet therefore establishes a new state of the art without requiring additional data.
[LG-101] Conditional Averag e Treatment Effect Estimation Under Hidden Confounders
链接: https://arxiv.org/abs/2506.12304
作者: Ahmed Aloui,Juncheng Dong,Ali Hasan,Vahid Tarokh
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:One of the major challenges in estimating conditional potential outcomes and conditional average treatment effects (CATE) is the presence of hidden confounders. Since testing for hidden confounders cannot be accomplished only with observational data, conditional unconfoundedness is commonly assumed in the literature of CATE estimation. Nevertheless, under this assumption, CATE estimation can be significantly biased due to the effects of unobserved confounders. In this work, we consider the case where in addition to a potentially large observational dataset, a small dataset from a randomized controlled trial (RCT) is available. Notably, we make no assumptions on the existence of any covariate information for the RCT dataset, we only require the outcomes to be observed. We propose a CATE estimation method based on a pseudo-confounder generator and a CATE model that aligns the learned potential outcomes from the observational data with those observed from the RCT. Our method is applicable to many practical scenarios of interest, particularly those where privacy is a concern (e.g., medical applications). Extensive numerical experiments are provided demonstrating the effectiveness of our approach for both synthetic and real-world datasets.
[LG-102] SPIRE: Conditional Personalization for Federated Diffusion Generative Models
链接: https://arxiv.org/abs/2506.12303
作者: Kaan Ozkara,Ruida Zhou,Suhas Diggavi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Recent advances in diffusion models have revolutionized generative AI, but their sheer size makes on device personalization, and thus effective federated learning (FL), infeasible. We propose Shared Backbone Personal Identity Representation Embeddings (SPIRE), a framework that casts per client diffusion based generation as conditional generation in FL. SPIRE factorizes the network into (i) a high capacity global backbone that learns a population level score function and (ii) lightweight, learnable client embeddings that encode local data statistics. This separation enables parameter efficient finetuning that touches \leq 0.01% of weights. We provide the first theoretical bridge between conditional diffusion training and maximum likelihood estimation in Gaussian mixture models. For a two component mixture we prove that gradient descent on the DDPM with respect to mixing weights loss recovers the optimal mixing weights and enjoys dimension free error bounds. Our analysis also hints at how client embeddings act as biases that steer a shared score network toward personalized distributions. Empirically, SPIRE matches or surpasses strong baselines during collaborative pretraining, and vastly outperforms them when adapting to unseen clients, reducing Kernel Inception Distance while updating only hundreds of parameters. SPIRE further mitigates catastrophic forgetting and remains robust across finetuning learning rate and epoch choices.
[LG-103] GrokAlign: Geometric Characterisation and Acceleration of Grokking
链接: https://arxiv.org/abs/2506.12284
作者: Thomas Walker,Ahmed Imtiaz Humayun,Randall Balestriero,Richard Baraniuk
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 23 pages, 11 figures, 3 tables
Abstract:A key challenge for the machine learning community is to understand and accelerate the training dynamics of deep networks that lead to delayed generalisation and emergent robustness to input perturbations, also known as grokking. Prior work has associated phenomena like delayed generalisation with the transition of a deep network from a linear to a feature learning regime, and emergent robustness with changes to the network’s functional geometry, in particular the arrangement of the so-called linear regions in deep networks employing continuous piecewise affine nonlinearities. Here, we explain how grokking is realised in the Jacobian of a deep network and demonstrate that aligning a network’s Jacobians with the training data (in the sense of cosine similarity) ensures grokking under a low-rank Jacobian assumption. Our results provide a strong theoretical motivation for the use of Jacobian regularisation in optimizing deep networks – a method we introduce as GrokAlign – which we show empirically to induce grokking much sooner than more conventional regularizers like weight decay. Moreover, we introduce centroid alignment as a tractable and interpretable simplification of Jacobian alignment that effectively identifies and tracks the stages of deep network training dynamics. Accompanying \hrefthis https URLwebpage and \hrefthis https URLcode.
[LG-104] Energy-Efficient Green AI Architectures for Circular Economies Through Multi-Layered Sustainable Resource Optimization Framework
链接: https://arxiv.org/abs/2506.12262
作者: Ripal Ranpara
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Computers and Society (cs.CY)
*备注:
Abstract:In this research paper, we propose a new type of energy-efficient Green AI architecture to support circular economies and address the contemporary challenge of sustainable resource consumption in modern systems. We introduce a multi-layered framework and meta-architecture that integrates state-of-the-art machine learning algorithms, energy-conscious computational models, and optimization techniques to facilitate decision-making for resource reuse, waste reduction, and sustainable this http URL tested the framework on real-world datasets from lithium-ion battery recycling and urban waste management systems, demonstrating its practical applicability. Notably, the key findings of this study indicate a 25 percent reduction in energy consumption during workflows compared to traditional methods and an 18 percent improvement in resource recovery efficiency. Quantitative optimization was based on mathematical models such as mixed-integer linear programming and lifecycle assessments. Moreover, AI algorithms improved classification accuracy on urban waste by 20 percent, while optimized logistics reduced transportation emissions by 30 percent. We present graphical analyses and visualizations of the developed framework, illustrating its impact on energy efficiency and sustainability as reflected in the simulation results. This paper combines the principles of Green AI with practical insights into how such architectural models contribute to circular economies, presenting a fully scalable and scientifically rooted solution aligned with applicable UN Sustainability Goals worldwide. These results open avenues for incorporating newly developed AI technologies into sustainable management strategies, potentially safeguarding local natural capital while advancing technological progress.
[LG-105] A Collaborative Process Parameter Recommender System for Fleets of Networked Manufacturing Machines – with Application to 3D Printing
链接: https://arxiv.org/abs/2506.12252
作者: Weishi Wang,Sicong Guo,Chenhuan Jiang,Mohamed Elidrisi,Myungjin Lee,Harsha V. Madhyastha,Raed Al Kontar,Chinedum E. Okwudire
类目: Machine Learning (cs.LG)
*备注: 26 pages, 6 figures
Abstract:Fleets of networked manufacturing machines of the same type, that are collocated or geographically distributed, are growing in popularity. An excellent example is the rise of 3D printing farms, which consist of multiple networked 3D printers operating in parallel, enabling faster production and efficient mass customization. However, optimizing process parameters across a fleet of manufacturing machines, even of the same type, remains a challenge due to machine-to-machine variability. Traditional trial-and-error approaches are inefficient, requiring extensive testing to determine optimal process parameters for an entire fleet. In this work, we introduce a machine learning-based collaborative recommender system that optimizes process parameters for each machine in a fleet by modeling the problem as a sequential matrix completion task. Our approach leverages spectral clustering and alternating least squares to iteratively refine parameter predictions, enabling real-time collaboration among the machines in a fleet while minimizing the number of experimental trials. We validate our method using a mini 3D printing farm consisting of ten 3D printers for which we optimize acceleration and speed settings to maximize print quality and productivity. Our approach achieves significantly faster convergence to optimal process parameters compared to non-collaborative matrix completion.
[LG-106] CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction
链接: https://arxiv.org/abs/2506.12231
作者: Ella Miray Rajaonson,Mahyar Rajabi Kochi,Luis Martin Mejia Mendoza,Seyed Mohamad Moosavi,Benjamin Sanchez-Lengeling
类目: Machine Learning (cs.LG)
*备注: 9 pages, 4 figures
Abstract:Developing improved predictive models for multi-molecular systems is crucial, as nearly every chemical product used results from a mixture of chemicals. While being a vital part of the industry pipeline, the chemical mixture space remains relatively unexplored by the Machine Learning community. In this paper, we introduce CheMixHub, a holistic benchmark for molecular mixtures, covering a corpus of 11 chemical mixtures property prediction tasks, from drug delivery formulations to battery electrolytes, totalling approximately 500k data points gathered and curated from 7 publicly available datasets. CheMixHub introduces various data splitting techniques to assess context-specific generalization and model robustness, providing a foundation for the development of predictive models for chemical mixture properties. Furthermore, we map out the modelling space of deep learning models for chemical mixtures, establishing initial benchmarks for the community. This dataset has the potential to accelerate chemical mixture development, encompassing reformulation, optimization, and discovery. The dataset and code for the benchmarks can be found at: this https URL
[LG-107] Learning Causality for Modern Machine Learning
链接: https://arxiv.org/abs/2506.12226
作者: Yongqiang Chen
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: PhD thesis
Abstract:In the past decades, machine learning with Empirical Risk Minimization (ERM) has demonstrated great capability in learning and exploiting the statistical patterns from data, or even surpassing humans. Despite the success, ERM avoids the modeling of causality the way of understanding and handling changes, which is fundamental to human intelligence. When deploying models beyond the training environment, distribution shifts are everywhere. For example, an autopilot system often needs to deal with new weather conditions that have not been seen during training, An Al-aided drug discovery system needs to predict the biochemical properties of molecules with respect to new viruses such as COVID-19. It renders the problem of Out-of-Distribution (OOD) generalization challenging to conventional machine learning. In this thesis, we investigate how to incorporate and realize the causality for broader tasks in modern machine learning. In particular, we exploit the invariance implied by the principle of independent causal mechanisms (ICM), that is, the causal mechanisms generating the effects from causes do not inform or influence each other. Therefore, the conditional distribution between the target variable given its causes is invariant under distribution shifts. With the causal invariance principle, we first instantiate it to graphs – a general data structure ubiquitous in many real-world industry and scientific applications, such as financial networks and molecules. Then, we shall see how learning the causality benefits many of the desirable properties of modern machine learning, in terms of (i) OOD generalization capability; (ii) interpretability; and (iii) robustness to adversarial attacks. Realizing the causality in machine learning, on the other hand, raises a dilemma for optimization in conventional machine learning, as it often contradicts the objective of ERM… Comments: PhD thesis Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2506.12226 [cs.LG] (or arXiv:2506.12226v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12226 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-108] Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
链接: https://arxiv.org/abs/2506.12213
作者: Zikai Zhang,Ping Liu,Jiahao Xu,Rui Hu
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注: Accepted to TNNLS 2025
Abstract:Federated Learning has recently been utilized to collaboratively fine-tune foundation models across multiple clients. Notably, federated low-rank adaptation LoRA-based fine-tuning methods have recently gained attention, which allows clients to fine-tune FMs with a small portion of trainable parameters locally. However, most existing methods do not account for the heterogeneous resources of clients or lack an effective local training strategy to maximize global fine-tuning performance under limited resources. In this work, we propose Fed-HeLLo, a novel federated LoRA-based fine-tuning framework that enables clients to collaboratively fine-tune an FM with different local trainable LoRA layers. To ensure its effectiveness, we develop several heterogeneous LoRA allocation (HLA) strategies that adaptively allocate local trainable LoRA layers based on clients’ resource capabilities and the layer importance. Specifically, based on the dynamic layer importance, we design a Fisher Information Matrix score-based HLA that leverages dynamic gradient norm information. To better stabilize the training process, we consider the intrinsic importance of LoRA layers and design a Geometrically-Defined HLA strategy. It shapes the collective distribution of trainable LoRA layers into specific geometric patterns, such as Triangle, Inverted Triangle, Bottleneck, and Uniform. Moreover, we extend GD-HLA into a randomized version, named Randomized Geometrically-Defined HLA, for enhanced model accuracy with randomness. By co-designing the proposed HLA strategies, we incorporate both the dynamic and intrinsic layer importance into the design of our HLA strategy. We evaluate our approach on five datasets under diverse federated LoRA fine-tuning settings, covering three levels of data distribution from IID to extreme Non-IID. Results show that Fed-HeLLo with HLA strategies is both effective and efficient.
[LG-109] Machine Intelligence on Wireless Edge Networks
链接: https://arxiv.org/abs/2506.12210
作者: Sri Krishna Vadlamani,Kfir Sulimany,Zhihui Gao,Tingjun Chen,Dirk Englund
类目: Emerging Technologies (cs.ET); Machine Learning (cs.LG)
*备注: 13 pages, 6 figures
Abstract:Deep neural network (DNN) inference on power-constrained edge devices is bottlenecked by costly weight storage and data movement. We introduce MIWEN, a radio-frequency (RF) analog architecture that ``disaggregates’’ memory by streaming weights wirelessly and performing classification in the analog front end of standard transceivers. By encoding weights and activations onto RF carriers and using native mixers as computation units, MIWEN eliminates local weight memory and the overhead of analog-to-digital and digital-to-analog conversion. We derive the effective number of bits of radio-frequency analog computation under thermal noise, quantify the energy–precision trade-off, and demonstrate digital-comparable MNIST accuracy at orders-of-magnitude lower energy, unlocking real-time inference on low-power, memory-free edge devices.
[LG-110] Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics
链接: https://arxiv.org/abs/2506.12203
作者: Anming Gu,Edward Chien,Kristjan Greenewald
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We provide an algorithm to privately generate continuous-time data (e.g. marginals from stochastic differential equations), which has applications in highly sensitive domains involving time-series data such as healthcare. We leverage the connections between trajectory inference and continuous-time synthetic data generation, along with a computational method based on mean-field Langevin dynamics. As discretized mean-field Langevin dynamics and noisy particle gradient descent are equivalent, DP results for noisy SGD can be applied to our setting. We provide experiments that generate realistic trajectories on a synthesized variation of hand-drawn MNIST data while maintaining meaningful privacy guarantees. Crucially, our method has strong utility guarantees under the setting where each person contributes data for \emphonly one time point, while prior methods require each person to contribute their \emphentire temporal trajectory–directly improving the privacy characteristics by construction.
[LG-111] Graph Semi-Supervised Learning for Point Classification on Data Manifolds
链接: https://arxiv.org/abs/2506.12197
作者: Caio F. Deberaldini Netto,Zhiyang Wang,Luana Ruiz
类目: Machine Learning (cs.LG); Signal Processing (eess.SP); Machine Learning (stat.ML)
*备注: 26 pages
Abstract:We propose a graph semi-supervised learning framework for classification tasks on data manifolds. Motivated by the manifold hypothesis, we model data as points sampled from a low-dimensional manifold \mathcalM \subset \mathbbR^F . The manifold is approximated in an unsupervised manner using a variational autoencoder (VAE), where the trained encoder maps data to embeddings that represent their coordinates in \mathbbR^F . A geometric graph is constructed with Gaussian-weighted edges inversely proportional to distances in the embedding space, transforming the point classification problem into a semi-supervised node classification task on the graph. This task is solved using a graph neural network (GNN). Our main contribution is a theoretical analysis of the statistical generalization properties of this data-to-manifold-to-graph pipeline. We show that, under uniform sampling from \mathcalM , the generalization gap of the semi-supervised task diminishes with increasing graph size, up to the GNN training error. Leveraging a training procedure which resamples a slightly larger graph at regular intervals during training, we then show that the generalization gap can be reduced even further, vanishing asymptotically. Finally, we validate our findings with numerical experiments on image classification benchmarks, demonstrating the empirical effectiveness of our approach.
[LG-112] Fidelity Isnt Accuracy: When Linearly Decodable Functions Fail to Match the Ground Truth
链接: https://arxiv.org/abs/2506.12176
作者: Jackson Eshbaugh
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 8 pages, 5 figures, 3 tables. Code available at this https URL
Abstract:Neural networks excel as function approximators, but their complexity often obscures the nature of the functions they learn. In this work, we propose the linearity score \lambda(f) , a simple and interpretable diagnostic that quantifies how well a regression network’s output can be mimicked by a linear model. Defined as the R^2 between the network’s predictions and those of a trained linear surrogate, \lambda(f) offers insight into the linear decodability of the learned function. We evaluate this framework on both synthetic ( y = x \sin(x) + \epsilon ) and real-world datasets (Medical Insurance, Concrete, California Housing), using dataset-specific networks and surrogates. Our findings show that while high \lambda(f) scores indicate strong linear alignment, they do not necessarily imply predictive accuracy with respect to the ground truth. This underscores both the promise and the limitations of using linear surrogates to understand nonlinear model behavior, particularly in high-stakes regression tasks.
[LG-113] Meta-Learning and Synthetic Data for Automated Pretraining and Finetuning
链接: https://arxiv.org/abs/2506.12161
作者: Fabio Ferreira
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: PhD thesis
Abstract:The growing number of pretrained models in Machine Learning (ML) presents significant challenges for practitioners. Given a new dataset, they need to determine the most suitable deep learning (DL) pipeline, consisting of the pretrained model and the hyperparameters for finetuning to it. Moreover, as models grow in scale, the increasing reliance on real-world data poses a bottleneck for training and requires leveraging data more effectively. Addressing the first challenge often involves manual model selection and hyperparameter tuning. At the same time, as models grow larger and more and more of the available human-generated data is being used for training, data augmentation and synthetic data become critical elements. Automated machine learning offers a path to address these challenges but is traditionally designed for tabular data and classical ML methods. This dissertation adopts meta-learning to extend automated machine learning to the deep learning domain. We propose empirical approaches to automate DL pipeline selection for Computer Vision tasks using prior task knowledge to learn surrogate models for pipeline ranking. Extending these methods to the language domain, we learn to finetune large language models. As a result, we show that our approach can outperform finetuning foundation models. Additionally, we meta-learn data augmentation and synthetic data to enhance performance in up-stream and down-stream tasks. We empirically show the underestimated importance of data augmentation when using Self-Supervised Learning and meta-learn advanced data augmentation strategies. Leveraging synthetic data, we also propose to meta-learn neural synthetic data generators as proxies for Reinforcement Learning (RL) environments. Additionally, we learn a multiple-environment world model in an in-context learning fashion by purely using synthetic, randomly sampled data.
[LG-114] GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data
链接: https://arxiv.org/abs/2506.12051
作者: Jiahui Zheng,Cole Jahnke,Wei “Wayne” Chen
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注:
Abstract:This paper introduces GUST (Generative Uncertainty learning via Self-supervised pretraining and Transfer learning), a framework for quantifying free-form geometric uncertainties inherent in the manufacturing of metamaterials. GUST leverages the representational power of deep generative models to learn a high-dimensional conditional distribution of as-fabricated unit cell geometries given nominal designs, thereby enabling uncertainty quantification. To address the scarcity of real-world manufacturing data, GUST employs a two-stage learning process. First, it leverages self-supervised pretraining on a large-scale synthetic dataset to capture the structure variability inherent in metamaterial geometries and an approximated distribution of as-fabricated geometries given nominal designs. Subsequently, GUST employs transfer learning by fine-tuning the pretrained model on limited real-world manufacturing data, allowing it to adapt to specific manufacturing processes and nominal designs. With only 960 unit cells additively manufactured in only two passes, GUST can capture the variability in geometry and effective material properties. In contrast, directly training a generative model on the same amount of real-world data proves insufficient, as demonstrated through both qualitative and quantitative comparisons. This scalable and cost-effective approach significantly reduces data requirements while maintaining the effectiveness in learning complex, real-world geometric uncertainties, offering an affordable method for free-form geometric uncertainty quantification in the manufacturing of metamaterials. The capabilities of GUST hold significant promise for high-precision industries such as aerospace and biomedical engineering, where understanding and mitigating manufacturing uncertainties are critical.
[LG-115] Constant Bit-size Transformers Are Turing Complete
链接: https://arxiv.org/abs/2506.12027
作者: Qian Li,Yuyi Wang
类目: Computational Complexity (cs.CC); Machine Learning (cs.LG)
*备注: 12 pages
Abstract:We prove that any Turing machine running on inputs of arbitrary length can be simulated by a constant bit-size transformer, as long as the context window is sufficiently long. This improves previous works, which require scaling up either the model’s precision or the number of parameters on longer inputs. Furthermore, we prove that the complexity class SPACE [s(n)] exactly characterizes the expressive power of a constant bit-size transformer with a context window of length s(n) . Our approach relies on simulating Post machines, a Turing-complete computational model. Post machines can be modeled as automata equipped with a queue, exhibiting computational behaviors naturally aligned with those of transformers. The behavioral similarity between transformers and Post machines may offer new insights into the mechanisms underlying the reasoning abilities of transformers.
[LG-116] Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs
链接: https://arxiv.org/abs/2506.12025
作者: Sonia Mazelet,Rémi Flamary,Bertrand Thirion
类目: Machine Learning (cs.LG)
*备注:
Abstract:Optimal transport between graphs, based on Gromov-Wasserstein and other extensions, is a powerful tool for comparing and aligning graph structures. However, solving the associated non-convex optimization problems is computationally expensive, which limits the scalability of these methods to large graphs. In this work, we present Unbalanced Learning of Optimal Transport (ULOT), a deep learning method that predicts optimal transport plans between two graphs. Our method is trained by minimizing the fused unbalanced Gromov-Wasserstein (FUGW) loss. We propose a novel neural architecture with cross-attention that is conditioned on the FUGW tradeoff hyperparameters. We evaluate ULOT on synthetic stochastic block model (SBM) graphs and on real cortical surface data obtained from fMRI. ULOT predicts transport plans with competitive loss up to two orders of magnitude faster than classical solvers. Furthermore, the predicted plan can be used as a warm start for classical solvers to accelerate their convergence. Finally, the predicted transport plan is fully differentiable with respect to the graph inputs and FUGW hyperparameters, enabling the optimization of functionals of the ULOT plan. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2506.12025 [cs.LG] (or arXiv:2506.12025v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2506.12025 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Sonia Mazelet [view email] [v1] Wed, 21 May 2025 09:29:19 UTC (1,545 KB) Full-text links: Access Paper: View a PDF of the paper titled Unsupervised Learning for Optimal Transport plan prediction between unbalanced graphs, by Sonia Mazelet and 2 other authorsView PDFHTML (experimental)TeX SourceOther Formats view license Current browse context: cs.LG prev | next new | recent | 2025-06 Change to browse by: cs References Citations NASA ADSGoogle Scholar Semantic Scholar a export BibTeX citation Loading… BibTeX formatted citation loading… Data provided by: Bookmark checked=“checked”> Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) IArxiv recommender toggle IArxiv Recommender (What is IArxiv?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) mathjaxToggle(); About Help contact arXivClick here to contact arXiv Contact subscribe to arXiv mailingsClick here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status Get status notifications via email or slack
[LG-117] FlexQuant: A Flexible and Efficient Dynamic Precision Switching Framework for LLM Quantization
链接: https://arxiv.org/abs/2506.12024
作者: Fangxin Liu,Zongwu Wang,JinHong Xia,Junping Zhao,Jian Liu,Haibing Guan,Li Jiang
类目: Machine Learning (cs.LG)
*备注: 1p pages, 7 figures, 2 tables
Abstract:The rapid advancement of large language models (LLMs) has exacerbated the memory bottleneck due to the widening gap between model parameter scaling and hardware capabilities. While post-training quantization (PTQ) techniques effectively reduce memory overhead, existing methods predominantly rely on static quantization strategies, which struggle to adapt to dynamic workloads. To address this, we propose FlexQuant, a dynamic precision-switching framework that optimizes the trade-off between inference speed and accuracy. Leveraging model perplexity entropy and Kullback-Leibler (KL) divergence, FlexQuant enables fine-grained, layer-wise mixed-precision quantization and dynamically adjusts bit-widths during each token generation. Our work provides a comprehensive analysis of quantization strategies, introduces a precision requirement model for optimal switching, and implements efficient fine-grained precision management. Experimental results demonstrate that FlexQuant achieves a 1.3x end-to-end speedup across diverse language tasks with negligible accuracy loss introduced. This framework offers a flexible and adaptive solution for efficient LLM deployment.
[LG-118] Understanding Learning Invariance in Deep Linear Networks
链接: https://arxiv.org/abs/2506.13714
作者: Hao Duan,Guido Montúfar
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Equivariant and invariant machine learning models exploit symmetries and structural patterns in data to improve sample efficiency. While empirical studies suggest that data-driven methods such as regularization and data augmentation can perform comparably to explicitly invariant models, theoretical insights remain scarce. In this paper, we provide a theoretical comparison of three approaches for achieving invariance: data augmentation, regularization, and hard-wiring. We focus on mean squared error regression with deep linear networks, which parametrize rank-bounded linear maps and can be hard-wired to be invariant to specific group actions. We show that the critical points of the optimization problems for hard-wiring and data augmentation are identical, consisting solely of saddles and the global optimum. By contrast, regularization introduces additional critical points, though they remain saddles except for the global optimum. Moreover, we demonstrate that the regularization path is continuous and converges to the hard-wired solution.
[LG-119] Understanding Lookahead Dynamics Through Laplace Transform
链接: https://arxiv.org/abs/2506.13712
作者: Aniket Sanyal,Tatjana Chavdarova
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We introduce a frequency-domain framework for convergence analysis of hyperparameters in game optimization, leveraging High-Resolution Differential Equations (HRDEs) and Laplace transforms. Focusing on the Lookahead algorithm–characterized by gradient steps k and averaging coefficient \alpha --we transform the discrete-time oscillatory dynamics of bilinear games into the frequency domain to derive precise convergence criteria. Our higher-precision O(\gamma^2) -HRDE models yield tighter criteria, while our first-order O(\gamma) -HRDE models offer practical guidance by prioritizing actionable hyperparameter tuning over complex closed-form solutions. Empirical validation in discrete-time settings demonstrates the effectiveness of our approach, which may further extend to locally linear operators, offering a scalable framework for selecting hyperparameters for learning in games.
[LG-120] Gradient-Normalized Smoothness for Optimization with Approximate Hessians
链接: https://arxiv.org/abs/2506.13710
作者: Andrei Semenov,Martin Jaggi,Nikita Doikov
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:In this work, we develop new optimization algorithms that use approximate second-order information combined with the gradient regularization technique to achieve fast global convergence rates for both convex and non-convex objectives. The key innovation of our analysis is a novel notion called Gradient-Normalized Smoothness, which characterizes the maximum radius of a ball around the current point that yields a good relative approximation of the gradient field. Our theory establishes a natural intrinsic connection between Hessian approximation and the linearization of the gradient. Importantly, Gradient-Normalized Smoothness does not depend on the specific problem class of the objective functions, while effectively translating local information about the gradient field and Hessian approximation into the global behavior of the method. This new concept equips approximate second-order algorithms with universal global convergence guarantees, recovering state-of-the-art rates for functions with Hölder-continuous Hessians and third derivatives, quasi-self-concordant functions, as well as smooth classes in first-order optimization. These rates are achieved automatically and extend to broader classes, such as generalized self-concordant functions. We demonstrate direct applications of our results for global linear rates in logistic regression and softmax problems with approximate Hessians, as well as in non-convex optimization using Fisher and Gauss-Newton approximations.
[LG-121] Enforcing tail calibration when training probabilistic forecast models
链接: https://arxiv.org/abs/2506.13687
作者: Jakob Benjamin Wessel,Maybritt Schillinger,Frank Kwasniok,Sam Allen
类目: Applications (stat.AP); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated by optimizing a proper scoring rule over a set of training data. If the model class is not correctly specified, then the learned model will not necessarily issue forecasts that are calibrated. Calibrated forecasts allow users to appropriately balance risks in decision making, and it is particularly important that forecast models issue calibrated predictions for extreme events, since such outcomes often generate large socio-economic impacts. In this work, we study how the loss function used to train probabilistic forecast models can be adapted to improve the reliability of forecasts made for extreme events. We investigate loss functions based on weighted scoring rules, and additionally propose regularizing loss functions using a measure of tail miscalibration. We apply these approaches to a hierarchy of increasingly flexible forecast models for UK wind speeds, including simple parametric models, distributional regression networks, and conditional generative models. We demonstrate that state-of-the-art models do not issue calibrated forecasts for extreme wind speeds, and that the calibration of forecasts for extreme events can be improved by suitable adaptations to the loss function during model training. This, however, introduces a trade-off between calibrated forecasts for extreme events and calibrated forecasts for more common outcomes.
[LG-122] Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
链接: https://arxiv.org/abs/2506.13658
作者: Ioannis Christoforos Koune,Alice Cicirello
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Inference and prediction under partial knowledge of a physical system is challenging, particularly when multiple confounding sources influence the measured response. Explicitly accounting for these influences in physics-based models is often infeasible due to epistemic uncertainty, cost, or time constraints, resulting in models that fail to accurately describe the behavior of the system. On the other hand, data-driven machine learning models such as variational autoencoders are not guaranteed to identify a parsimonious representation. As a result, they can suffer from poor generalization performance and reconstruction accuracy in the regime of limited and noisy data. We propose a physics-informed variational autoencoder architecture that combines the interpretability of physics-based models with the flexibility of data-driven models. To promote disentanglement of the known physics and confounding influences, the latent space is partitioned into physically meaningful variables that parametrize a physics-based model, and data-driven variables that capture variability in the domain and class of the physical system. The encoder is coupled with a decoder that integrates physics-based and data-driven components, and constrained by an adversarial training objective that prevents the data-driven components from overriding the known physics, ensuring that the physics-grounded latent variables remain interpretable. We demonstrate that the model is able to disentangle features of the input signal and separate the known physics from confounding influences using supervision in the form of class and domain observables. The model is evaluated on a series of synthetic case studies relevant to engineering structures, demonstrating the feasibility of the proposed approach.
[LG-123] EUNIS Habitat Maps: Enhancing Thematic and Spatial Resolution for Europe through Machine Learning
链接: https://arxiv.org/abs/2506.13649
作者: Sara Si-Moussi,Stephan Hennekens,Sander Mücher,Wanda De Keersmaecker,Milan Chytrý,Emiliano Agrillo,Fabio Attorre,Idoia Biurrun,Gianmaria Bonari,Andraž Čarni,Renata Ćušterevska,Tetiana Dziuba,Klaus Ecker,Behlül Güler,Ute Jandt,Borja Jiménez-Alfaro,Jonathan Lenoir,Jens-Christian Svenning,Grzegorz Swacha,Wilfried Thuiller
类目: Applications (stat.AP); Machine Learning (cs.LG); Geophysics (physics.geo-ph); Quantitative Methods (q-bio.QM)
*备注:
Abstract:The EUNIS habitat classification is crucial for categorising European habitats, supporting European policy on nature conservation and implementing the Nature Restoration Law. To meet the growing demand for detailed and accurate habitat information, we provide spatial predictions for 260 EUNIS habitat types at hierarchical level 3, together with independent validation and uncertainty analyses. Using ensemble machine learning models, together with high-resolution satellite imagery and ecologically meaningful climatic, topographic and edaphic variables, we produced a European habitat map indicating the most probable EUNIS habitat at 100-m resolution across Europe. Additionally, we provide information on prediction uncertainty and the most probable habitats at level 3 within each EUNIS level 1 formation. This product is particularly useful for both conservation and restoration purposes. Predictions were cross-validated at European scale using a spatial block cross-validation and evaluated against independent data from France (forests only), the Netherlands and Austria. The habitat maps obtained strong predictive performances on the validation datasets with distinct trade-offs in terms of recall and precision across habitat formations. Subjects: Applications (stat.AP); Machine Learning (cs.LG); Geophysics (physics.geo-ph); Quantitative Methods (q-bio.QM) MSC classes: 62P12, 62M30, 92D40 ACMclasses: I.2.6; J.3; I.6.5 Cite as: arXiv:2506.13649 [stat.AP] (or arXiv:2506.13649v1 [stat.AP] for this version) https://doi.org/10.48550/arXiv.2506.13649 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-124] Variational Inference with Mixtures of Isotropic Gaussians
链接: https://arxiv.org/abs/2506.13613
作者: Marguerite Petit-Talamon,Marc Lambert,Anna Korba
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Variational inference (VI) is a popular approach in Bayesian inference, that looks for the best approximation of the posterior distribution within a parametric family, minimizing a loss that is typically the (reverse) Kullback-Leibler (KL) divergence. In this paper, we focus on the following parametric family: mixtures of isotropic Gaussians (i.e., with diagonal covariance matrices proportional to the identity) and uniform weights. We develop a variational framework and provide efficient algorithms suited for this family. In contrast with mixtures of Gaussian with generic covariance matrices, this choice presents a balance between accurate approximations of multimodal Bayesian posteriors, while being memory and computationally efficient. Our algorithms implement gradient descent on the location of the mixture components (the modes of the Gaussians), and either (an entropic) Mirror or Bures descent on their variance parameters. We illustrate the performance of our algorithms on numerical experiments.
[LG-125] Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
链接: https://arxiv.org/abs/2506.13575
作者: Ivan A. Kazakov,Iana V. Kulichenko,Egor E. Kovalev,Angelina A. Treskova,Daria D. Barma,Kirill M. Malakhov,Arkady V. Shipulin
类目: Optics (physics.optics); Machine Learning (cs.LG)
*备注: The manuscript has been submitted to IEEE Sensors Letters and is currently under peer review
Abstract:We present an experimental study of a fiber Bragg grating (FBG) interrogator based on a silicon oxynitride (SiON) photonic integrated arrayed waveguide grating (AWG). While AWG-based interrogators are compact and scalable, their practical performance is limited by non-ideal spectral responses. To address this, two calibration strategies within a 2.4 nm spectral region were compared: (1) a segmented analytical model based on a sigmoid fitting function, and (2) a machine learning (ML)-based regression model. The analytical method achieves a root mean square error (RMSE) of 7.11 pm within the calibrated range, while the ML approach based on exponential regression achieves 3.17 pm. Moreover, the ML model demonstrates generalization across an extended 2.9 nm wavelength span, maintaining sub-5 pm accuracy without re-fitting. Residual and error distribution analyses further illustrate the trade-offs between the two approaches. ML-based calibration provides a robust, data-driven alternative to analytical methods, delivering enhanced accuracy for non-ideal channel responses, reduced manual calibration effort, and improved scalability across diverse FBG sensor configurations.
[LG-126] Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing
链接: https://arxiv.org/abs/2506.13485
作者: Xiang Zhang,Jiaqi Wei,Zijie Qiu,Sheng Xu,Nanqing Dong,Zhiqiang Gao,Siqi Sun
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG)
*备注:
Abstract:Peptide sequencing-the process of identifying amino acid sequences from mass spectrometry data-is a fundamental task in proteomics. Non-Autoregressive Transformers (NATs) have proven highly effective for this task, outperforming traditional methods. Unlike autoregressive models, which generate tokens sequentially, NATs predict all positions simultaneously, leveraging bidirectional context through unmasked self-attention. However, existing NAT approaches often rely on Connectionist Temporal Classification (CTC) loss, which presents significant optimization challenges due to CTC’s complexity and increases the risk of training failures. To address these issues, we propose an improved non-autoregressive peptide sequencing model that incorporates a structured protein sequence curriculum learning strategy. This approach adjusts protein’s learning difficulty based on the model’s estimated protein generational capabilities through a sampling process, progressively learning peptide generation from simple to complex sequences. Additionally, we introduce a self-refining inference-time module that iteratively enhances predictions using learned NAT token embeddings, improving sequence accuracy at a fine-grained level. Our curriculum learning strategy reduces NAT training failures frequency by more than 90% based on sampled training over various data distributions. Evaluations on nine benchmark species demonstrate that our approach outperforms all previous methods across multiple metrics and species.
[LG-127] Balancing Intensity and Focality in Directional DBS Under Uncertainty: A Simulation Study of Electrode Optimization via a Metaheuristic L1L1 Approach
链接: https://arxiv.org/abs/2506.13452
作者: Fernando Galaz Prieto,Antti Lassila,Maryam Samavaki,Sampsa Pursiainen
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:As DBS technology advances toward directional leads and optimization-based current steering, this study aims to improve the selection of electrode contact configurations using the recently developed L1-norm regularized L1-norm fitting (L1L1) method. The focus is in particular on L1L1’s capability to incorporate a priori lead field uncertainty, offering a potential advantage over conventional approaches that do not account for such variability. Our optimization framework incorporates uncertainty by constraining the solution space based on lead field attenuation. This reflects physiological expectations about the VTA and serves to avoid overfitting. By applying this method to 8- and 40-contact electrode configurations, we optimize current distributions within a discretized finite element (FE) model, focusing on the lead field’s characteristics. The model accounts for uncertainty through these explicit constraints, enhancing the feasibility, focality, and robustness of the resulting solutions. The L1L1 method was validated through a series of numerical experiments using both noiseless and noisy lead fields, where the noise level was selected to reflect attenuation within VTA. It successfully fits and regularizes the current distribution across target structures, with hyperparameter optimization extracting either bipolar or multipolar electrode configurations. These configurations aim to maximize focused current density or prioritize a high gain field ratio in a discretized FE model. Compared to traditional methods, the L1L1 approach showed competitive performance in concentrating stimulation within the target region while minimizing unintended current spread, particularly under noisy conditions. By incorporating uncertainty directly into the optimization process, we obtain a noise-robust framework for current steering, allowing for variations in lead field models and simulation parameters.
[LG-128] HELENA: High-Efficiency Learning-based channel Estimation using dual Neural Attention
链接: https://arxiv.org/abs/2506.13408
作者: Miguel Camelo Botero,Esra Aycan Beyazit,Nina Slamnik-Kriještorac,Johann M. Marquez-Barja
类目: ignal Processing (eess.SP); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注:
Abstract:Accurate channel estimation is critical for high-performance Orthogonal Frequency-Division Multiplexing systems such as 5G New Radio, particularly under low signal-to-noise ratio and stringent latency constraints. This letter presents HELENA, a compact deep learning model that combines a lightweight convolutional backbone with two efficient attention mechanisms: patch-wise multi-head self-attention for capturing global dependencies and a squeeze-and-excitation block for local feature refinement. Compared to CEViT, a state-of-the-art vision transformer-based estimator, HELENA reduces inference time by 45.0% (0.175,ms vs.\ 0.318,ms), achieves comparable accuracy ( -16.78 ,dB vs.\ -17.30 ,dB), and requires 8\times fewer parameters (0.11M vs.\ 0.88M), demonstrating its suitability for low-latency, real-time deployment.
[LG-129] Experimental Design for Semiparametric Bandits COLT2025
链接: https://arxiv.org/abs/2506.13390
作者: Seok-Jin Kim,Gi-Soo Kim,Min-hwan Oh
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Accepted at COLT 2025
Abstract:We study finite-armed semiparametric bandits, where each arm’s reward combines a linear component with an unknown, potentially adversarial shift. This model strictly generalizes classical linear bandits and reflects complexities common in practice. We propose the first experimental-design approach that simultaneously offers a sharp regret bound, a PAC bound, and a best-arm identification guarantee. Our method attains the minimax regret \tildeO(\sqrtdT) , matching the known lower bound for finite-armed linear bandits, and further achieves logarithmic regret under a positive suboptimality gap condition. These guarantees follow from our refined non-asymptotic analysis of orthogonalized regression that attains the optimal \sqrtd rate, paving the way for robust and efficient learning across a broad class of semiparametric bandit problems.
[LG-130] Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models
链接: https://arxiv.org/abs/2506.13139
作者: Zhenyu Liao,Michael W. Mahoney
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 30 pages, 6 figures
Abstract:Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, where classical low-dimensional intuitions break down. In particular, the proportional regime where the data dimension, sample size, and number of model parameters are all large and comparable, gives rise to novel and sometimes counterintuitive behaviors. This paper extends traditional Random Matrix Theory (RMT) beyond eigenvalue-based analysis of linear models to address the challenges posed by nonlinear ML models such as DNNs in this regime. We introduce the concept of High-dimensional Equivalent, which unifies and generalizes both Deterministic Equivalent and Linear Equivalent, to systematically address three technical challenges: high dimensionality, nonlinearity, and the need to analyze generic eigenspectral functionals. Leveraging this framework, we provide precise characterizations of the training and generalization performance of linear models, nonlinear shallow networks, and deep networks. Our results capture rich phenomena, including scaling laws, double descent, and nonlinear learning dynamics, offering a unified perspective on the theoretical understanding of deep learning in high dimensions.
[LG-131] Inverse design of the transmission matrix in a random system using Reinforcement Learning
链接: https://arxiv.org/abs/2506.13057
作者: Yuhao Kang
类目: Optics (physics.optics); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG); Applied Physics (physics.app-ph)
*备注:
Abstract:This work presents an approach to the inverse design of scattering systems by modifying the transmission matrix using reinforcement learning. We utilize Proximal Policy Optimization to navigate the highly non-convex landscape of the object function to achieve three types of transmission matrices: (1) Fixed-ratio power conversion and zero-transmission mode in rank-1 matrices, (2) exceptional points with degenerate eigenvalues and unidirectional mode conversion, and (3) uniform channel participation is enforced when transmission eigenvalues are degenerate.
[LG-132] Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
链接: https://arxiv.org/abs/2506.12996
作者: Shahab Azarfar,Joseph B. Choi,Phong CH. Nguyen,Yen T. Nguyen,Pradeep Seshadri,H.S. Udaykumar,Stephen Baek
类目: Computational Physics (physics.comp-ph); Machine Learning (cs.LG)
*备注: 28 pages, 15 figures
Abstract:Coupling of physics across length and time scales plays an important role in the response of microstructured materials to external loads. In a multi-scale framework, unresolved (subgrid) meso-scale dynamics is upscaled to the homogenized (macro-scale) representation of the heterogeneous material through closure models. Deep learning models trained using meso-scale simulation data are now a popular route to assimilate such closure laws. However, meso-scale simulations are computationally taxing, posing practical challenges in training deep learning-based surrogate models from scratch. In this work, we investigate an alternative meta-learning approach motivated by the idea of tokenization in natural language processing. We show that one can learn a reduced representation of the micro-scale physics to accelerate the meso-scale learning process by tokenizing the meso-scale evolution of the physical fields involved in an archetypal, albeit complex, reactive dynamics problem, \textitviz., shock-induced energy localization in a porous energetic material. A probabilistic latent representation of \textitmicro-scale dynamics is learned as building blocks for \textitmeso-scale dynamics. The \textitmeso-scale latent dynamics model learns the correlation between neighboring building blocks by training over a small dataset of meso-scale simulations. We compare the performance of our model with a physics-aware recurrent convolutional neural network (PARC) trained only on the full meso-scale dataset. We demonstrate that our model can outperform PARC with scarce meso-scale data. The proposed approach accelerates the development of closure models by leveraging inexpensive micro-scale simulations and fast training over a small meso-scale dataset, and can be applied to a range of multi-scale modeling problems.
[LG-133] Variational Learning Finds Flatter Solutions at the Edge of Stability
链接: https://arxiv.org/abs/2506.12903
作者: Avrajit Ghosh,Bai Cong,Rio Yokota,Saiprasad Ravishankar,Rongrong Wang,Molei Tao,Mohammad Emtiyaz Khan,Thomas Möllenhoff
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Variational Learning (VL) has recently gained popularity for training deep neural networks and is competitive to standard learning methods. Part of its empirical success can be explained by theories such as PAC-Bayes bounds, minimum description length and marginal likelihood, but there are few tools to unravel the implicit regularization in play. Here, we analyze the implicit regularization of VL through the Edge of Stability (EoS) framework. EoS has previously been used to show that gradient descent can find flat solutions and we extend this result to VL to show that it can find even flatter solutions. This is obtained by controlling the posterior covariance and the number of Monte Carlo samples from the posterior. These results are derived in a similar fashion as the standard EoS literature for deep learning, by first deriving a result for a quadratic problem and then extending it to deep neural networks. We empirically validate these findings on a wide variety of large networks, such as ResNet and ViT, to find that the theoretical results closely match the empirical ones. Ours is the first work to analyze the EoS dynamics in VL.
[LG-134] General and Estimable Learning Bound Unifying Covariate and Concept Shifts
链接: https://arxiv.org/abs/2506.12829
作者: Hongbo Chen,Li Charlie Xia
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, idealized settings and is non-estimable from samples. In this paper, we bridge the gap between theory and practical applications. We first show that existing bounds become loose and non-estimable because their concept shift definition breaks when the source and target supports mismatch. Leveraging entropic optimal transport, we propose new support-agnostic definitions for covariate and concept shifts, and derive a novel unified error bound that applies to broad loss functions, label spaces, and stochastic labeling. We further develop estimators for these shifts with concentration guarantees, and the DataShifts algorithm, which can quantify distribution shifts and estimate the error bound in most applications – a rigorous and general tool for analyzing learning error under distribution shift.
[LG-135] Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
链接: https://arxiv.org/abs/2506.12751
作者: Yue Kang,Mingshuo Liu,Bongsoo Yi,Jing Lyu,Zhi Zhang,Doudou Zhou,Yao Li
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Generalized linear bandits have been extensively studied due to their broad applicability in real-world online decision-making problems. However, these methods typically assume that the expected reward function is known to the users, an assumption that is often unrealistic in practice. Misspecification of this link function can lead to the failure of all existing algorithms. In this work, we address this critical limitation by introducing a new problem of generalized linear bandits with unknown reward functions, also known as single index bandits. We first consider the case where the unknown reward function is monotonically increasing, and propose two novel and efficient algorithms, STOR and ESTOR, that achieve decent regrets under standard assumptions. Notably, our ESTOR can obtain the nearly optimal regret bound \tildeO_T(\sqrtT) in terms of the time horizon T . We then extend our methods to the high-dimensional sparse setting and show that the same regret rate can be attained with the sparsity index. Next, we introduce GSTOR, an algorithm that is agnostic to general reward functions, and establish regret bounds under a Gaussian design assumption. Finally, we validate the efficiency and effectiveness of our algorithms through experiments on both synthetic and real-world datasets.
[LG-136] Dependent Randomized Rounding for Budget Constrained Experimental Design UAI2025
链接: https://arxiv.org/abs/2506.12677
作者: Khurram Yamin,Edward Kennedy,Bryan Wilder
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: UAI 2025 Paper
Abstract:Policymakers in resource-constrained settings require experimental designs that satisfy strict budget limits while ensuring precise estimation of treatment effects. We propose a framework that applies a dependent randomized rounding procedure to convert assignment probabilities into binary treatment decisions. Our proposed solution preserves the marginal treatment probabilities while inducing negative correlations among assignments, leading to improved estimator precision through variance reduction. We establish theoretical guarantees for the inverse propensity weighted and general linear estimators, and demonstrate through empirical studies that our approach yields efficient and accurate inference under fixed budget constraints.
[LG-137] Beyond Sin-Squared Error: Linear-Time Entrywise Uncertainty Quantification for Streaming PCA
链接: https://arxiv.org/abs/2506.12655
作者: Syamantak Kumar,Shourya Pandey,Purnamrita Sarkar
类目: atistics Theory (math.ST); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We propose a novel statistical inference framework for streaming principal component analysis (PCA) using Oja’s algorithm, enabling the construction of confidence intervals for individual entries of the estimated eigenvector. Most existing works on streaming PCA focus on providing sharp sin-squared error guarantees. Recently, there has been some interest in uncertainty quantification for the sin-squared error. However, uncertainty quantification or sharp error guarantees for entries of the estimated eigenvector in the streaming setting remains largely unexplored. We derive a sharp Bernstein-type concentration bound for elements of the estimated vector matching the optimal error rate up to logarithmic factors. We also establish a Central Limit Theorem for a suitably centered and scaled subset of the entries. To efficiently estimate the coordinate-wise variance, we introduce a provably consistent subsampling algorithm that leverages the median-of-means approach, empirically achieving similar accuracy to multiplier bootstrap methods while being significantly more computationally efficient. Numerical experiments demonstrate its effectiveness in providing reliable uncertainty estimates with a fraction of the computational cost of existing methods.
[LG-138] Glocal Smoothness: Line Search can really help!
链接: https://arxiv.org/abs/2506.12648
作者: Curtis Fox,Aaron Mishkin,Sharan Vaswani,Mark Schmidt
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:Iteration complexities for first-order optimization algorithms are typically stated in terms of a global Lipschitz constant of the gradient, and near-optimal results are achieved using fixed step sizes. But many objective functions that arise in practice have regions with small Lipschitz constants where larger step sizes can be used. Many local Lipschitz assumptions have been proposed, which have lead to results showing that adaptive step sizes and/or line searches yield improved convergence rates over fixed step sizes. However, these faster rates tend to depend on the iterates of the algorithm, which makes it difficult to compare the iteration complexities of different methods. We consider a simple characterization of global and local (“glocal”) smoothness that only depends on properties of the function. This allows upper bounds on iteration complexities in terms of iterate-independent constants and enables us to compare iteration complexities between algorithms. Under this assumption it is straightforward to show the advantages of line searches over fixed step sizes, and that in some settings, gradient descent with line search has a better iteration complexity than accelerated methods with fixed step sizes. We further show that glocal smoothness can lead to improved complexities for the Polyak and AdGD step sizes, as well other algorithms including coordinate optimization, stochastic gradient methods, accelerated gradient methods, and non-linear conjugate gradient methods.
[LG-139] Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials
链接: https://arxiv.org/abs/2506.12557
作者: Thorben Prein,Elton Pan,Janik Jehkul,Steffen Weinmann,Elsa A. Olivetti,Jennifer L. M. Rupp
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Inorganic synthesis planning currently relies primarily on heuristic approaches or machine-learning models trained on limited datasets, which constrains its generality. We demonstrate that language models, without task-specific fine-tuning, can recall synthesis conditions. Off-the-shelf models, such as GPT-4.1, Gemini 2.0 Flash and Llama 4 Maverick, achieve a Top-1 precursor-prediction accuracy of up to 53.8 % and a Top-5 performance of 66.1 % on a held-out set of 1,000 reactions. They also predict calcination and sintering temperatures with mean absolute errors below 126 °C, matching specialized regression methods. Ensembling these language models further enhances predictive accuracy and reduces inference cost per prediction by up to 70 %. We subsequently employ language models to generate 28,548 synthetic reaction recipes, which we combine with literature-mined examples to pretrain a transformer-based model, SyntMTE. After fine-tuning on the combined dataset, SyntMTE reduces mean-absolute error in sintering temperature prediction to 73 °C and in calcination temperature to 98 °C. This strategy improves models by up to 8.7 % compared with baselines trained exclusively on experimental data. Finally, in a case study on Li7La3Zr2O12 solid-state electrolytes, we demonstrate that SyntMTE reproduces the experimentally observed dopant-dependent sintering trends. Our hybrid workflow enables scalable, data-efficient inorganic synthesis planning.
[LG-140] Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining
链接: https://arxiv.org/abs/2506.12516
作者: Yongqian Peng,Zhouran Zhang,Longhui Zhang,Fengyuan Zhao,Yahao Li,Yicong Ye,Shuxin Bai
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注:
Abstract:Machine learning has revolutionized materials design, yet predicting complex properties like alloy ductility remains challenging due to the influence of processing conditions and microstructural features that resist quantification through traditional reductionist approaches. Here, we present an innovative information fusion architecture that integrates domain-specific texts from materials science literature with quantitative physical descriptors to overcome these limitations. Our framework employs MatSciBERT for advanced textual comprehension and incorporates contrastive learning to automatically extract implicit knowledge regarding processing parameters and microstructural characteristics. Through rigorous ablation studies and comparative experiments, the model demonstrates superior performance, achieving coefficient of determination (R2) values of 0.849 and 0.680 on titanium alloy validation set and refractory multi-principal-element alloy test set. This systematic approach provides a holistic framework for property prediction in complex material systems where quantitative descriptors are incomplete and establishes a foundation for knowledge-guided materials design and informatics-driven materials discovery.
[LG-141] Symmetry-preserving neural networks in lattice field theories
链接: https://arxiv.org/abs/2506.12493
作者: Matteo Favoni
类目: High Energy Physics - Lattice (hep-lat); Machine Learning (cs.LG)
*备注: PhD thesis
Abstract:This thesis deals with neural networks that respect symmetries and presents the advantages in applying them to lattice field theory problems. The concept of equivariance is explained, together with the reason why such a property is crucial for the network to preserve the desired symmetry. The benefits of choosing equivariant networks are first illustrated for translational symmetry on a complex scalar field toy model. The discussion is then extended to gauge theories, for which Lattice Gauge Equivariant Convolutional Neural Networks (L-CNNs) are specifically designed ad hoc. Regressions of physical observables such as Wilson loops are successfully solved by L-CNNs, whereas traditional architectures which are not gauge symmetric perform significantly worse. Finally, we introduce the technique of neural gradient flow, which is an ordinary differential equation solved by neural networks, and propose it as a method to generate lattice gauge configurations.
[LG-142] A Transfer Learning Framework for Multilayer Networks via Model Averag ing
链接: https://arxiv.org/abs/2506.12455
作者: Yongqin Qiu,Xinyu Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:
Abstract:Link prediction in multilayer networks is a key challenge in applications such as recommendation systems and protein-protein interaction prediction. While many techniques have been developed, most rely on assumptions about shared structures and require access to raw auxiliary data, limiting their practicality. To address these issues, we propose a novel transfer learning framework for multilayer networks using a bi-level model averaging method. A K -fold cross-validation criterion based on edges is used to automatically weight inter-layer and intra-layer candidate models. This enables the transfer of information from auxiliary layers while mitigating model uncertainty, even without prior knowledge of shared structures. Theoretically, we prove the optimality and weight convergence of our method under mild conditions. Computationally, our framework is efficient and privacy-preserving, as it avoids raw data sharing and supports parallel processing across multiple servers. Simulations show our method outperforms others in predictive accuracy and robustness. We further demonstrate its practical value through two real-world recommendation system applications.
[LG-143] On the existence of consistent adversarial attacks in high-dimensional linear classification
链接: https://arxiv.org/abs/2506.12454
作者: Matteo Vilucchio,Lenka Zdeborová,Bruno Loureiro
类目: Machine Learning (stat.ML); Disordered Systems and Neural Networks (cond-mat.dis-nn); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a central role. We introduce a new error metric that precisely capture this distinction, quantifying model vulnerability to consistent adversarial attacks – perturbations that preserve the ground-truth labels. Our main technical contribution is an exact and rigorous asymptotic characterization of these metrics in both well-specified models and latent space models, revealing different vulnerability patterns compared to standard robust error measures. The theoretical results demonstrate that as models become more overparameterized, their vulnerability to label-preserving perturbations grows, offering theoretical insight into the mechanisms underlying model sensitivity to adversarial attacks.
[LG-144] Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting
链接: https://arxiv.org/abs/2506.12444
作者: Duc Toan Nguyen,Trang H. Tran,Lam M. Nguyen
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:
Abstract:In this paper, we propose Adjusted Shuffling SARAH, a novel algorithm that integrates shuffling techniques with the well-known variance-reduced algorithm SARAH while dynamically adjusting the stochastic gradient weights in each update to enhance exploration. Our method achieves the best-known gradient complexity for shuffling variance reduction methods in a strongly convex setting. This result applies to any shuffling technique, which narrows the gap in the complexity analysis of variance reduction methods between uniform sampling and shuffling data. Furthermore, we introduce Inexact Adjusted Reshuffling SARAH, an inexact variant of Adjusted Shuffling SARAH that eliminates the need for full-batch gradient computations. This algorithm retains the same linear convergence rate as Adjusted Shuffling SARAH while showing an advantage in total complexity when the sample size is very large.
[LG-145] Noise tolerance via reinforcement: Learning a reinforced quantum dynamics
链接: https://arxiv.org/abs/2506.12418
作者: Abolfazl Ramezanpour
类目: Quantum Physics (quant-ph); Disordered Systems and Neural Networks (cond-mat.dis-nn); Machine Learning (cs.LG)
*备注: 25 pages, 12 figures
Abstract:The performance of quantum simulations heavily depends on the efficiency of noise mitigation techniques and error correction algorithms. Reinforcement has emerged as a powerful strategy to enhance the performance of learning and optimization algorithms. In this study, we demonstrate that reinforced quantum dynamics can exhibit significant robustness against interactions with a noisy environment. We study a quantum annealing process where, through reinforcement, the system is encouraged to maintain its current state or follow a noise-free evolution. A learning algorithm is employed to find a concise approximation of this reinforced dynamics, reducing the total evolution time and, consequently, the system’s exposure to noisy interactions. This approach also avoids the complexities associated with implementing quantum feedback in such algorithms. The efficacy of our method is demonstrated through numerical simulations of reinforced quantum annealing with one- and two-qubit systems under Pauli noise.
[LG-146] Statistical Machine Learning for Astronomy – A Textbook
链接: https://arxiv.org/abs/2506.12230
作者: Yuan-Sen Ting
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG); Applications (stat.AP); Machine Learning (stat.ML)
*备注: 677 pages, 152 figures. Code and tutorials available at this https URL
Abstract:This textbook provides a systematic treatment of statistical machine learning for astronomical research through the lens of Bayesian inference, developing a unified framework that reveals connections between modern data analysis techniques and traditional statistical methods. We show how these techniques emerge from familiar statistical foundations. The consistently Bayesian perspective prioritizes uncertainty quantification and statistical rigor essential for scientific inference in astronomy. The textbook progresses from probability theory and Bayesian inference through supervised learning including linear regression with measurement uncertainties, logistic regression, and classification. Unsupervised learning topics cover Principal Component Analysis and clustering methods. We then introduce computational techniques through sampling and Markov Chain Monte Carlo, followed by Gaussian Processes as probabilistic nonparametric methods and neural networks within the broader statistical context. Our theory-focused pedagogical approach derives each method from first principles with complete mathematical development, emphasizing statistical insight and complementing with astronomical applications. We prioritize understanding why algorithms work, when they are appropriate, and how they connect to broader statistical principles. The treatment builds toward modern techniques including neural networks through a solid foundation in classical methods and their theoretical underpinnings. This foundation enables thoughtful application of these methods to astronomical research, ensuring proper consideration of assumptions, limitations, and uncertainty propagation essential for advancing astronomical knowledge in the era of large astronomical surveys.
[LG-147] Directed Acyclic Graph Convolutional Networks
链接: https://arxiv.org/abs/2506.12218
作者: Samuel Rey,Hamed Ajorlou,Gonzalo Mateos
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注:
Abstract:Directed acyclic graphs (DAGs) are central to science and engineering applications including causal inference, scheduling, and neural architecture search. In this work, we introduce the DAG Convolutional Network (DCN), a novel graph neural network (GNN) architecture designed specifically for convolutional learning from signals supported on DAGs. The DCN leverages causal graph filters to learn nodal representations that account for the partial ordering inherent to DAGs, a strong inductive bias does not present in conventional GNNs. Unlike prior art in machine learning over DAGs, DCN builds on formal convolutional operations that admit spectral-domain representations. We further propose the Parallel DCN (PDCN), a model that feeds input DAG signals to a parallel bank of causal graph-shift operators and processes these DAG-aware features using a shared multilayer perceptron. This way, PDCN decouples model complexity from graph size while maintaining satisfactory predictive performance. The architectures’ permutation equivariance and expressive power properties are also established. Comprehensive numerical tests across several tasks, datasets, and experimental conditions demonstrate that §DCN compares favorably with state-of-the-art baselines in terms of accuracy, robustness, and computational efficiency. These results position §DCN as a viable framework for deep learning from DAG-structured data that is designed from first (graph) signal processing principles.
[LG-148] OSI Stack Redesign for Quantum Networks: Requirements Technologies Challenges and Future Directions
链接: https://arxiv.org/abs/2506.12195
作者: Shakil Ahmed,Muhammad Kamran Saeed,Ashfaq Khokhar
类目: Quantum Physics (quant-ph); Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注:
Abstract:Quantum communication is poised to become a foundational element of next-generation networking, offering transformative capabilities in security, entanglement-based connectivity, and computational offloading. However, the classical OSI model-designed for deterministic and error-tolerant systems-cannot support quantum-specific phenomena such as coherence fragility, probabilistic entanglement, and the no-cloning theorem. This paper provides a comprehensive survey and proposes an architectural redesign of the OSI model for quantum networks in the context of 7G. We introduce a Quantum-Converged OSI stack by extending the classical model with Layer 0 (Quantum Substrate) and Layer 8 (Cognitive Intent), supporting entanglement, teleportation, and semantic orchestration via LLMs and QML. Each layer is redefined to incorporate quantum mechanisms such as enhanced MAC protocols, fidelity-aware routing, and twin-based applications. This survey consolidates over 150 research works from IEEE, ACM, MDPI, arXiv, and Web of Science (2018-2025), classifying them by OSI layer, enabling technologies such as QKD, QEC, PQC, and RIS, and use cases such as satellite QKD, UAV swarms, and quantum IoT. A taxonomy of cross-layer enablers-such as hybrid quantum-classical control, metadata-driven orchestration, and blockchain-integrated quantum trust-is provided, along with simulation tools including NetSquid, QuNetSim, and QuISP. We present several domain-specific applications, including quantum healthcare telemetry, entangled vehicular networks, and satellite mesh overlays. An evaluation framework is proposed based on entropy throughput, coherence latency, and entanglement fidelity. Key future directions include programmable quantum stacks, digital twins, and AI-defined QNet agents, laying the groundwork for a scalable, intelligent, and quantum-compliant OSI framework for 7G and beyond.
[LG-149] mporal cross-validation impacts multivariate time series subsequence anomaly detection evaluation
链接: https://arxiv.org/abs/2506.12183
作者: Steven C. Hespeler,Pablo Moriano,Mingyan Li,Samuel C. Hollifield
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
*备注: 22 pages, 6 figures, 5 tables
Abstract:Evaluating anomaly detection in multivariate time series (MTS) requires careful consideration of temporal dependencies, particularly when detecting subsequence anomalies common in fault detection scenarios. While time series cross-validation (TSCV) techniques aim to preserve temporal ordering during model evaluation, their impact on classifier performance remains underexplored. This study systematically investigates the effect of TSCV strategy on the precision-recall characteristics of classifiers trained to detect fault-like anomalies in MTS datasets. We compare walk-forward (WF) and sliding window (SW) methods across a range of validation partition configurations and classifier types, including shallow learners and deep learning (DL) classifiers. Results show that SW consistently yields higher median AUC-PR scores and reduced fold-to-fold performance variance, particularly for deep architectures sensitive to localized temporal continuity. Furthermore, we find that classifier generalization is sensitive to the number and structure of temporal partitions, with overlapping windows preserving fault signatures more effectively at lower fold counts. A classifier-level stratified analysis reveals that certain algorithms, such as random forests (RF), maintain stable performance across validation schemes, whereas others exhibit marked sensitivity. This study demonstrates that TSCV design in benchmarking anomaly detection models on streaming time series and provide guidance for selecting evaluation strategies in temporally structured learning environments.
[LG-150] Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States
链接: https://arxiv.org/abs/2506.12128
作者: Vishal S. Ngairangbam,Michael Spannowsky,Timur Sypchenko
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); High Energy Physics - Lattice (hep-lat); High Energy Physics - Phenomenology (hep-ph)
*备注:
Abstract:We propose a hybrid variational framework that enhances Neural Quantum States (NQS) with a Normalising Flow-based sampler to improve the expressivity and trainability of quantum many-body wavefunctions. Our approach decouples the sampling task from the variational ansatz by learning a continuous flow model that targets a discretised, amplitude-supported subspace of the Hilbert space. This overcomes limitations of Markov Chain Monte Carlo (MCMC) and autoregressive methods, especially in regimes with long-range correlations and volume-law entanglement. Applied to the transverse-field Ising model with both short- and long-range interactions, our method achieves comparable ground state energy errors with state-of-the-art matrix product states and lower energies than autoregressive NQS. For systems up to 50 spins, we demonstrate high accuracy and robust convergence across a wide range of coupling strengths, including regimes where competing methods fail. Our results showcase the utility of flow-assisted sampling as a scalable tool for quantum simulation and offer a new approach toward learning expressive quantum states in high-dimensional Hilbert spaces.
[LG-151] On Monotonicity in AI Alignment
链接: https://arxiv.org/abs/2506.08998
作者: Gilles Bareilles,Julien Fageot,Lê-Nguyên Hoang,Peva Blanchard,Wassim Bouaziz,Sébastien Rouault,El-Mahdi El-Mhamdi
类目: atistics Theory (math.ST); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Comparison-based preference learning has become central to the alignment of AI models with human preferences. However, these methods may behave counterintuitively. After empirically observing that, when accounting for a preference for response y over z , the model may actually decrease the probability (and reward) of generating y (an observation also made by others), this paper investigates the root causes of (non) monotonicity, for a general comparison-based preference learning framework that subsumes Direct Preference Optimization (DPO), Generalized Preference Optimization (GPO) and Generalized Bradley-Terry (GBT). Under mild assumptions, we prove that such methods still satisfy what we call local pairwise monotonicity. We also provide a bouquet of formalizations of monotonicity, and identify sufficient conditions for their guarantee, thereby providing a toolbox to evaluate how prone learning models are to monotonicity violations. These results clarify the limitations of current methods and provide guidance for developing more trustworthy preference learning algorithms.
信息检索
[IR-0] OneRec Technical Report
链接: https://arxiv.org/abs/2506.13695
作者: Guorui Zhou,Jiaxin Deng,Jinghao Zhang,Kuo Cai,Lejian Ren,Qiang Luo,Qianqian Wang,Qigen Hu,Rui Huang,Shiyao Wang,Weifeng Ding,Wuchao Li,Xinchen Luo,Xingmei Wang,Zexuan Cheng,Zixing Zhang,Bin Zhang,Boxuan Wang,Chaoyi Ma,Chengru Song,Chenhui Wang,Di Wang,Dongxue Meng,Fan Yang,Fangyu Zhang,Feng Jiang,Fuxing Zhang,Gang Wang,Guowang Zhang,Han Li,Hengrui Hu,Hezheng Lin,Hongtao Cheng,Hongyang Cao,Huanjie Wang,Jiaming Huang,Jiapeng Chen,Jiaqiang Liu,Jinghui Jia,Kun Gai,Lantao Hu,Liang Zeng,Liao Yu,Qiang Wang,Qidong Zhou,Shengzhe Wang,Shihui He,Shuang Yang,Shujie Yang,Sui Huang,Tao Wu,Tiantian He,Tingting Gao,Wei Yuan,Xiao Liang,Xiaoxiao Xu,Xugang Liu,Yan Wang,Yi Wang,Yiwu Liu,Yue Song,Yufei Zhang,Yunfan Wu,Yunfeng Zhao,Zhanyu Liu
类目: Information Retrieval (cs.IR)
*备注: Authors are listed alphabetically by their first name
Abstract:Recommender systems have been widely used in various large-scale user-oriented platforms for many years. However, compared to the rapid developments in the AI community, recommendation systems have not achieved a breakthrough in recent years. For instance, they still rely on a multi-stage cascaded architecture rather than an end-to-end approach, leading to computational fragmentation and optimization inconsistencies, and hindering the effective application of key breakthrough technologies from the AI community in recommendation scenarios. To address these issues, we propose OneRec, which reshapes the recommendation system through an end-to-end generative approach and achieves promising results. Firstly, we have enhanced the computational FLOPs of the current recommendation model by 10 \times and have identified the scaling laws for recommendations within certain boundaries. Secondly, reinforcement learning techniques, previously difficult to apply for optimizing recommendations, show significant potential in this framework. Lastly, through infrastructure optimizations, we have achieved 23.7% and 28.8% Model FLOPs Utilization (MFU) on flagship GPUs during training and inference, respectively, aligning closely with the LLM community. This architecture significantly reduces communication and storage overhead, resulting in operating expense that is only 10.6% of traditional recommendation pipelines. Deployed in Kuaishou/Kuaishou Lite APP, it handles 25% of total queries per second, enhancing overall App Stay Time by 0.54% and 1.24%, respectively. Additionally, we have observed significant increases in metrics such as 7-day Lifetime, which is a crucial indicator of recommendation experience. We also provide practical lessons and insights derived from developing, optimizing, and maintaining a production-scale recommendation system with significant real-world impact. Comments: Authors are listed alphabetically by their first name Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2506.13695 [cs.IR] (or arXiv:2506.13695v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2506.13695 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-1] ree-Based Text Retrieval via Hierarchical Clustering in RAG Frameworks: Application on Taiwanese Regulations
链接: https://arxiv.org/abs/2506.13607
作者: Chia-Heng Yu,Yen-Lung Tsai
类目: Information Retrieval (cs.IR)
*备注: 19 pages, 5 figures, Code available at this https URL
Abstract:Traditional Retrieval-Augmented Generation (RAG) systems employ brute-force inner product search to retrieve the top-k most similar documents, then combined with the user query and passed to a language model. This allows the model to access external knowledge and reduce hallucinations. However, selecting an appropriate k value remains a significant challenge in practical applications: a small k may fail to retrieve sufficient information, while a large k can introduce excessive and irrelevant content. To address this, we propose a hierarchical clustering-based retrieval method that eliminates the need to predefine k. Our approach maintains the accuracy and relevance of system responses while adaptively selecting semantically relevant content. In the experiment stage, we applied our method to a Taiwanese legal dataset with expert-graded queries. The results show that our approach achieves superior performance in expert evaluations and maintains high precision while eliminating the need to predefine k, demonstrating improved accuracy and interpretability in legal text retrieval tasks. Our framework is simple to implement and easily integrates with existing RAG pipelines, making it a practical solution for real-world applications under limited resources.
[IR-2] Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
链接: https://arxiv.org/abs/2506.13409
作者: Georgios Koutroumpas,Sebastian Idesis,Mireia Masias Bruns,Carlos Segura,Joemon M. Jose,Sergi Abadal,Ioannis Arapakis
类目: Information Retrieval (cs.IR)
*备注: 11 pages, 7 figures, 5 tables
Abstract:Traditionally, Recommender Systems (RS) have primarily measured performance based on the accuracy and relevance of their recommendations. However, this algorithmic-centric approach overlooks how different types of recommendations impact user engagement and shape the overall quality of experience. In this paper, we shift the focus to the user and address for the first time the challenge of decoding the neural and behavioural variability across distinct recommendation categories, considering more than just relevance. Specifically, we conducted a controlled study using a comprehensive e-commerce dataset containing various recommendation types, and collected Electroencephalography and behavioural data. We analysed both neural and behavioural responses to recommendations that were categorised as Exact, Substitute, Complement, or Irrelevant products within search query results. Our findings offer novel insights into user preferences and decision-making processes, revealing meaningful relationships between behavioural and neural patterns for each category, but also indicate inter-subject variability.
[IR-3] Digital Transformation of Urban Planning in Australia: Influencing Factors and Key Challenges
链接: https://arxiv.org/abs/2506.13333
作者: Soheil Sabri,Sherah Kurnia
类目: Information Theory (cs.IT); Information Retrieval (cs.IR)
*备注: 30 pages, 2 figures, Master’s Thesis
Abstract:Over the past two decades, several governments in developing and developed countries have started their journey toward digital transformation. However, the pace and maturity of digital technologies and strategies are different between public services. Current literature indicates that research on the digital transformation of urban planning is still developing. Therefore, the aim of this study is to understand the influencing factors and key challenges for the digital transformation of urban planning in Australia. The study adopts the inter-organisational theory and Planning Support Science (PSScience) under the Technological, Organisational, and External Environmental (TOE) framework. It involves a multiple case study, administered semi-structured interviews with thirteen IT and urban planning experts across Victoria and New South Wales governments and private industries. The study findings indicate that the main challenges for digital transformation of the Australian urban planning system are related to organisational and external environmental factors. Furthermore, a digital maturity model is absent in the Australian urban planning industry. This study offers important implications to research and practice related to digital transformation in urban planning.
[IR-4] Gated Rotary-Enhanced Linear Attention for Long-term Sequential Recommendation
链接: https://arxiv.org/abs/2506.13315
作者: Juntao Hu,Wei Zhou,Huayi Shen,Xiao Du,Jie Liao,Junhao Wen,Min Gao
类目: Information Retrieval (cs.IR)
*备注: 24 pages,9 figures
Abstract:In Sequential Recommendation Systems (SRSs), Transformer models show remarkable performance but face computation cost challenges when modeling long-term user behavior sequences due to the quadratic complexity of the dot-product attention mechanism. By approximating the dot-product attention, linear attention provides an efficient option with linear complexity. However, existing linear attention methods face two limitations: 1) they often use learnable position encodings, which incur extra computational costs in long-term sequence scenarios, and 2) they may not consider the user’s fine-grained local preferences and confuse these with the actual change of long-term interests. To remedy these drawbacks, we propose a long-term sequential Recommendation model with Gated Rotary Enhanced Linear Attention (RecGRELA). Specifically, we first propose a Rotary-Enhanced Linear Attention (RELA) module to model long-range dependency within the user’s historical information using rotary position encodings. We then introduce a local short operation to incorporate local preferences and demonstrate the theoretical insight. We further introduce a SiLU-based Gated mechanism for RELA (GRELA) to help the model determine whether a user’s behavior indicates local interest or a genuine shift in long-term preferences. Experimental results on four public datasets demonstrate that our RecGRELA achieves state-of-the-art performance compared to existing SRSs while maintaining low memory overhead.
[IR-5] Accessibility Barriers in Multi-Terabyte Public Datasets: The Gap Between Promise and Practice
链接: https://arxiv.org/abs/2506.13256
作者: Marc Bara
类目: Computers and Society (cs.CY); Digital Libraries (cs.DL); Information Retrieval (cs.IR)
*备注: 5 pages, 28 references. Analysis of practical barriers to accessing multi-terabyte public datasets
Abstract:The promise of “free and open” multi-terabyte datasets often collides with harsh realities. While these datasets may be technically accessible, practical barriers – from processing complexity to hidden costs – create a system that primarily serves well-funded institutions. This study examines accessibility challenges across web crawls, satellite imagery, scientific data, and collaborative projects, revealing a consistent two-tier system where theoretical openness masks practical exclusivity. Our analysis demonstrates that datasets marketed as “publicly accessible” typically require minimum investments of \ 1,000+ for meaningful analysis, with complex processing pipelines demanding \ 10,000-100,000+ in infrastructure costs. The infrastructure requirements – distributed computing knowledge, domain expertise, and substantial budgets – effectively gatekeep these datasets despite their “open” status, limiting practical accessibility to those with institutional support or substantial resources.
[IR-6] Versatile and Fast Location-Based Private Information Retrieval with Fully Homomorphic Encryption over the Torus
链接: https://arxiv.org/abs/2506.12761
作者: Joon Soo Yoo,Taeho Kim,Ji Won Yoon
类目: Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
*备注:
Abstract:Location-based services often require users to share sensitive locational data, raising privacy concerns due to potential misuse or exploitation by untrusted servers. In response, we present VeLoPIR, a versatile location-based private information retrieval (PIR) system designed to preserve user privacy while enabling efficient and scalable query processing. VeLoPIR introduces three operational modes-interval validation, coordinate validation, and identifier matching-that support a broad range of real-world applications, including information and emergency alerts. To enhance performance, VeLoPIR incorporates multi-level algorithmic optimizations with parallel structures, achieving significant scalability across both CPU and GPU platforms. We also provide formal security and privacy proofs, confirming the system’s robustness under standard cryptographic assumptions. Extensive experiments on real-world datasets demonstrate that VeLoPIR achieves up to 11.55 times speed-up over a prior baseline. The implementation of VeLoPIR is publicly available at this https URL.
[IR-7] Device-Cloud Collaborative Correction for On-Device Recommendation IJCAI-2025
链接: https://arxiv.org/abs/2506.12687
作者: Tianyu Zhan,Shengyu Zhang,Zheqi Lv,Jieming Zhu,Jiwei Li,Fan Wu,Fei Wu
类目: Information Retrieval (cs.IR)
*备注: To be published in IJCAI-2025
Abstract:With the rapid development of recommendation models and device computing power, device-based recommendation has become an important research area due to its better real-time performance and privacy protection. Previously, Transformer-based sequential recommendation models have been widely applied in this field because they outperform Recurrent Neural Network (RNN)-based recommendation models in terms of performance. However, as the length of interaction sequences increases, Transformer-based models introduce significantly more space and computational overhead compared to RNN-based models, posing challenges for device-based recommendation. To balance real-time performance and high performance on devices, we propose Device-Cloud \underlineCollaborative \underlineCorrection Framework for On-Device \underlineRecommendation (CoCorrRec). CoCorrRec uses a self-correction network (SCN) to correct parameters with extremely low time cost. By updating model parameters during testing based on the input token, it achieves performance comparable to current optimal but more complex Transformer-based models. Furthermore, to prevent SCN from overfitting, we design a global correction network (GCN) that processes hidden states uploaded from devices and provides a global correction solution. Extensive experiments on multiple datasets show that CoCorrRec outperforms existing Transformer-based and RNN-based device recommendation models in terms of performance, with fewer parameters and lower FLOPs, thereby achieving a balance between real-time performance and high efficiency.
[IR-8] A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems
链接: https://arxiv.org/abs/2506.12583
作者: Kang Zhou,Weixi Zhou,Donghong Cai,Xianfu Lei,Yanqing Xu,Zhiguo Ding,Pingzhi Fan
类目: Information Retrieval (cs.IR)
*备注:
Abstract:In this paper, we consider a novel optimization design for multi-waveguide pinching-antenna systems, aiming to maximize the weighted sum rate (WSR) by jointly optimizing beamforming coefficients and antenna position. To handle the formulated non-convex problem, a gradient-based meta-learning joint optimization (GML-JO) algorithm is proposed. Specifically, the original problem is initially decomposed into two sub-problems of beamforming optimization and antenna position optimization through equivalent substitution. Then, the convex approximation methods are used to deal with the nonconvex constraints of sub-problems, and two sub-neural networks are constructed to calculate the sub-problems separately. Different from alternating optimization (AO), where two sub-problems are solved alternately and the solutions are influenced by the initial values, two sub-neural networks of proposed GML-JO with fixed channel coefficients are considered as local sub-tasks and the computation results are used to calculate the loss function of joint optimization. Finally, the parameters of sub-networks are updated using the average loss function over different sub-tasks and the solution that is robust to the initial value is obtained. Simulation results demonstrate that the proposed GML-JO algorithm achieves 5.6 bits/s/Hz WSR within 100 iterations, yielding a 32.7% performance enhancement over conventional AO with substantially reduced computational complexity. Moreover, the proposed GML-JO algorithm is robust to different choices of initialization and yields better performance compared with the existing optimization methods.
[IR-9] 2-RAG Bench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation
链接: https://arxiv.org/abs/2506.12071
作者: Jan Strich,Enes Kutay Isgorur,Maximilian Trescher,Chris Biemann,Martin Semmann
类目: Information Retrieval (cs.IR)
*备注:
Abstract:While most financial documents contain a combination of textual and tabular information, robust Retrieval-Augmented Generation (RAG) systems are essential for effectively accessing and reasoning over such content to perform complex numerical tasks. This paper introduces T ^2 -RAGBench, a benchmark comprising 32,908 question-context-answer triples, designed to evaluate RAG methods on real-world financial data. Unlike typical QA datasets that operate under Oracle-context settings, where the relevant context is explicitly provided, T ^2 -RAGBench challenges models to first retrieve the correct context before conducting numerical reasoning. Existing QA datasets involving text and tables typically contain context-dependent questions, which may yield multiple correct answers depending on the provided context. To address this, we transform these datasets into a context-independent format, enabling reliable RAG evaluation. We conduct a comprehensive evaluation of popular RAG methods. Our analysis identifies Hybrid BM25, a technique that combines dense and sparse vectors, as the most effective approach for text-and-table data. However, results demonstrate that T ^2 -RAGBench remains challenging even for SOTA LLMs and RAG methods. Further ablation studies examine the impact of embedding models and corpus size on retrieval performance. T ^2 -RAGBench provides a realistic and rigorous benchmark for existing RAG methods on text-and-table data. Code and dataset are available online.
[IR-10] Algorithms for estimating linear function in data mining
链接: https://arxiv.org/abs/2506.12069
作者: Thomas Hoang
类目: Information Retrieval (cs.IR)
*备注:
Abstract:The main goal of this topic is to showcase several studied algorithms for estimating the linear utility function to predict the users preferences. For example, if a user comes to buy a car that has several attributes including speed, color, age, etc in a linear function, the algorithms that we present in this paper help with estimating this linear function to filter out a small subset that would be of best interest to the user among a million tuples in a very large database. In addition, the estimating linear function could also be applicable in getting to know what the data can do or predicting the future based on the data that is used in data science, which is demonstrated by the GNN, PLOD algorithms. In the ever-evolving field of data science, deriving valuable insights from large datasets is critical for informed decision-making, particularly in predictive applications. Data analysts often identify high-quality datasets without missing values, duplicates, or inconsistencies before merging diverse attributes for analysis. Taking housing price prediction as a case study, various attributes must be considered, including location factors (proximity to urban centers, crime rates), property features (size, style, modernity), and regional policies (tax implications). Experts in the field typically rank these attributes to establish a predictive utility function, which machine learning models use to forecast outcomes like housing prices. Several data discovery algorithms, including those that address the challenges of predefined utility functions and human input for attribute ranking, which often result in a time-consuming iterative process, that the work of cannot overcome.
