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目录
概览 (2025-08-26)
今日共更新950篇论文,其中:
- 自然语言处理共152篇(Computation and Language (cs.CL))
- 人工智能共271篇(Artificial Intelligence (cs.AI))
- 计算机视觉共236篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共277篇(Machine Learning (cs.LG))
自然语言处理
[NLP-0] MIRAG E: Scaling Test-Time Inference with Parallel Graph-Retrieval-Augmented Reasoning Chains AAAI2026
【速读】： 该论文旨在解决当前大型推理模型（Large Reasoning Models, LRM）在医疗问答（QA）任务中因依赖单一线性推理链和非结构化文本信息的扁平化处理而导致的错误累积问题，从而影响准确性和可追溯性。解决方案的关键在于提出MIRAGE（Multi-chain Inference with Retrieval-Augmented Graph Exploration），其核心创新包括：1）将复杂查询分解为基于实体的子问题；2）执行并行推理链；3）通过邻居扩展与多跳遍历自适应检索证据；4）利用跨链验证机制整合答案以消除矛盾。该框架在三个医疗QA基准上显著优于GPT-4o、Tree-of-Thought变体及其他检索增强基线方法，并提升了推理过程的可解释性，使每个事实主张均可追溯至知识图谱中的具体路径。
链接: https://arxiv.org/abs/2508.18260
作者: Kaiwen Wei,Rui Shan,Dongsheng Zou,Jianzhong Yang,Bi Zhao,Junnan Zhu,Jiang Zhong
机构: 未知
类目: Computation and Language (cs.CL)
备注: 10 pages, 8 figures (including tables), plus appendix. Submitted to AAAI 2026
Abstract:Large reasoning models (LRMs) have shown significant progress in test-time scaling through chain-of-thought prompting. Current approaches like search-o1 integrate retrieval augmented generation (RAG) into multi-step reasoning processes but rely on a single, linear reasoning chain while incorporating unstructured textual information in a flat, context-agnostic manner. As a result, these approaches can lead to error accumulation throughout the reasoning chain, which significantly limits its effectiveness in medical question-answering (QA) tasks where both accuracy and traceability are critical requirements. To address these challenges, we propose MIRAGE (Multi-chain Inference with Retrieval-Augmented Graph Exploration), a novel test-time scalable reasoning framework that performs dynamic multi-chain inference over structured medical knowledge graphs. Specifically, MIRAGE 1) decomposes complex queries into entity-grounded sub-questions, 2) executes parallel inference chains, 3) retrieves evidence adaptively via neighbor expansion and multi-hop traversal, and 4) integrates answers using cross-chain verification to resolve contradictions. Experiments on three medical QA benchmarks (GenMedGPT-5k, CMCQA, and ExplainCPE) show that MIRAGE consistently outperforms GPT-4o, Tree-of-Thought variants, and other retrieval-augmented baselines in both automatic and human evaluations. Additionally, MIRAGE improves interpretability by generating explicit reasoning chains that trace each factual claim to concrete chains within the knowledge graph, making it well-suited for complex medical reasoning scenarios. The code will be available for further research.
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[NLP-1] From BERT to LLM s: Comparing and Understanding Chinese Classifier Prediction in Language Models
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在中文量词（Classifier）预测任务中表现不足的问题，这一问题在自然语言处理（Natural Language Processing, NLP）领域尚未得到充分研究。解决方案的关键在于通过多种掩码策略评估LLMs的内在能力、不同句法成分的贡献以及注意力机制的工作机制，并进一步探索微调（fine-tuning）对提升量词预测性能的作用。研究发现，即便经过微调，LLMs的表现仍逊于BERT模型，且量词预测显著受益于后续名词信息，这解释了具备双向注意力机制的模型（如BERT）为何更具优势。
链接: https://arxiv.org/abs/2508.18253
作者: ZiqiZhang,Jianfei Ma,Emmanuele Chersoni,Jieshun You,Zhaoxin Feng
机构: The Hong Kong Polytechnic University (香港理工大学)
类目: Computation and Language (cs.CL)
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Abstract:Classifiers are an important and defining feature of the Chinese language, and their correct prediction is key to numerous educational applications. Yet, whether the most popular Large Language Models (LLMs) possess proper knowledge the Chinese classifiers is an issue that has largely remain unexplored in the Natural Language Processing (NLP) literature. To address such a question, we employ various masking strategies to evaluate the LLMs’ intrinsic ability, the contribution of different sentence elements, and the working of the attention mechanisms during prediction. Besides, we explore fine-tuning for LLMs to enhance the classifier performance. Our findings reveal that LLMs perform worse than BERT, even with fine-tuning. The prediction, as expected, greatly benefits from the information about the following noun, which also explains the advantage of models with a bidirectional attention mechanism such as BERT. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2508.18253 [cs.CL] (or arXiv:2508.18253v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2508.18253 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[NLP-2] Demographic Biases and Gaps in the Perception of Sexism in Large Language Models
【速读】： 该论文旨在解决生成式 AI（Generative AI）在社交媒体文本中检测性别歧视内容时存在的偏差问题，尤其是模型难以准确反映不同人口统计群体对性别歧视的多样化感知。其解决方案的关键在于利用EXIST 2024 tweet数据集，该数据集包含六类不同身份背景标注者的每条推文标签，从而系统评估大语言模型（LLMs）在多视角下模仿各群体感知的能力，并通过统计分析识别年龄和性别等特征对检测效果的影响。结果表明，尽管LLMs能在总体层面捕捉性别歧视，但无法有效再现不同群体间的感知差异，凸显了构建更具情境敏感性和群体适配性的校准模型的重要性。
链接: https://arxiv.org/abs/2508.18245
作者: Judith Tavarez-Rodríguez,Fernando Sánchez-Vega,A. Pastor López-Monroy
机构: Computer Science Department, Mathematics Research Center (CIMAT); Secretaría de Ciencia, Humanidades, Tecnología e Innovación (SECIHTI)
类目: Computation and Language (cs.CL)
备注: This work was presented as a poster at the Latin American Meeting in Artificial Intelligence KHIPU 2025, Santiago, Chile, March 10th - 14th 2025, this https URL
Abstract:The use of Large Language Models (LLMs) has proven to be a tool that could help in the automatic detection of sexism. Previous studies have shown that these models contain biases that do not accurately reflect reality, especially for minority groups. Despite various efforts to improve the detection of sexist content, this task remains a significant challenge due to its subjective nature and the biases present in automated models. We explore the capabilities of different LLMs to detect sexism in social media text using the EXIST 2024 tweet dataset. It includes annotations from six distinct profiles for each tweet, allowing us to evaluate to what extent LLMs can mimic these groups’ perceptions in sexism detection. Additionally, we analyze the demographic biases present in the models and conduct a statistical analysis to identify which demographic characteristics (age, gender) contribute most effectively to this task. Our results show that, while LLMs can to some extent detect sexism when considering the overall opinion of populations, they do not accurately replicate the diversity of perceptions among different demographic groups. This highlights the need for better-calibrated models that account for the diversity of perspectives across different populations.
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[NLP-3] MTalk-Bench: Evaluating Speech-to-Speech Models in Multi-Turn Dialogues via Arena-style and Rubrics Protocols
【速读】： 该论文旨在解决当前语音到语音（Speech-to-Speech, S2S）大语言模型（Large Language Models, LLMs）在复杂多轮对话场景中缺乏有效评估框架的问题。现有评价体系难以全面衡量模型在语义信息、副语言信息（Paralinguistic Information）和环境声音感知等方面的能力，导致性能评估不充分且结果不可靠。解决方案的关键在于提出MTalk-Bench——一个覆盖三个核心维度的多轮S2S基准测试集，包含九种真实场景及针对性任务，并设计了结合Arena式成对比较与评分表（Rubrics-based）的双方法评估框架，实现相对与绝对性能评估的互补；同时引入人类与LLM共同评分机制，以提升评估的可靠性和多样性。该方案揭示了当前S2S模型在非语义维度上的不足以及评估方法自身的局限性，为构建更鲁棒、语音感知更强的评估体系提供了基础。
链接: https://arxiv.org/abs/2508.18240
作者: Yuhao Du,Qianwei Huang,Guo Zhu,Zhanchen Dai,Sunian Chen,Qiming Zhu,Yuhao Zhang,Li Zhou,Benyou Wang
机构: School of Data Science, The Chinese University of Hong Kong, Shenzhen (深圳大学数据科学学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:The rapid advancement of speech-to-speech (S2S) large language models (LLMs) has significantly improved real-time spoken interaction. However, current evaluation frameworks remain inadequate for assessing performance in complex, multi-turn dialogues. To address this, we introduce MTalk-Bench, a multi-turn S2S benchmark covering three core dimensions: Semantic Information, Paralinguistic Information, and Ambient Sound. Each dimension includes nine realistic scenarios, along with targeted tasks to assess specific capabilities such as reasoning. Our dual-method evaluation framework combines Arena-style evaluation (pairwise comparison) and Rubrics-based evaluation (absolute scoring) for relative and absolute assessment. The benchmark includes both model and human outputs, evaluated by human evaluators and LLMs. Experimental results reveal two sets of findings. Overall performance of S2S LLMs: (1) models excel at semantic information processing yet underperform on paralinguistic information and ambient sounds perception; (2) models typically regain coherence by increasing response length, sacrificing efficiency in multi-turn dialogues; (3) modality-aware, task-specific designs outperform brute scaling. Evaluation framework and reliability: (1) Arena and Rubrics yield consistent, complementary rankings, but reliable distinctions emerge only when performance gaps are large; (2) LLM-as-a-judge aligns with humans when gaps are clear or criteria explicit, but exhibits position and length biases and is reliable on nonverbal evaluation only with text annotations. These results highlight current limitations in S2S evaluation and the need for more robust, speech-aware assessment frameworks.
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[NLP-4] Can AI Have a Personality? Prompt Engineering for AI Personality Simulation: A Chatbot Case Study in Gender-Affirming Voice Therapy Training
【速读】： 该论文旨在解决如何通过提示工程（prompt engineering）引导大型语言模型（Large Language Models, LLMs）模拟出稳定且一致的人格特征，从而应用于专业训练场景中的虚拟对话系统。其解决方案的关键在于设计并实施结构化的提示策略，使AI聊天机器人Monae Jackson——一个代表32岁跨性别女性的虚拟角色——在与用户交互过程中展现出可识别且符合大五人格（Big Five Personality Traits）框架的连贯个性，从而验证了提示工程在生成具有一致人格特质的AI代理方面的有效性。
链接: https://arxiv.org/abs/2508.18234
作者: Tailon D. Jackson,Byunggu Yu
机构: University of the District of Columbia (华盛顿特区大学)
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
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Abstract:This thesis investigates whether large language models (LLMs) can be guided to simulate a consistent personality through prompt engineering. The study explores this concept within the context of a chatbot designed for Speech-Language Pathology (SLP) student training, specifically focused on gender-affirming voice therapy. The chatbot, named Monae Jackson, was created to represent a 32-year-old transgender woman and engage in conversations simulating client-therapist interactions. Findings suggest that with prompt engineering, the chatbot maintained a recognizable and consistent persona and had a distinct personality based on the Big Five Personality test. These results support the idea that prompt engineering can be used to simulate stable personality characteristics in AI chatbots.
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[NLP-5] Better Language Model-Based Judging Reward Modeling through Scaling Comprehension Boundaries
【速读】： 该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的判断型奖励建模（judging reward modeling）在强化学习中反馈信号不稳定、泛化能力不足的问题，尤其是在人类反馈强化学习（Reinforcement Learning from Human Feedback, RLHF）和分布外（Out-of-Distribution, OOD）场景下表现受限。解决方案的关键在于提出一种新的两阶段奖励建模框架——ESFP-RM，其核心创新是将自然语言推理（Natural Language Inference, NLI）任务中的形式一致性引入奖励建模，并利用基于上下文解释的掩码语言模型（Masked Language Model, MLM）进行槽位预测（slot prediction），从而更有效地扩展模型对语义边界的理解能力，显著提升奖励信号的稳定性与泛化性。
链接: https://arxiv.org/abs/2508.18212
作者: Meiling Ning,Zhongbao Zhang,Junda Ye,Jiabao Guo,Qingyuan Guan
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:The emergence of LM-based judging reward modeling, represented by generative reward models, has successfully made reinforcement learning from AI feedback (RLAIF) efficient and scalable. To further advance this paradigm, we propose a core insight: this form of reward modeling shares fundamental formal consistency with natural language inference (NLI), a core task in natural language understanding. This reframed perspective points to a key path for building superior reward models: scaling the model’s comprehension boundaries. Pursuing this path, exploratory experiments on NLI tasks demonstrate that the slot prediction masked language models (MLMs) incorporating contextual explanations achieve significantly better performance compared to mainstream autoregressive models. Based on this key finding, we propose ESFP-RM, a two-stage LM-based judging reward model that utilizes an explanation based slot framework for prediction to fully leverage the advantages of MLMs. Extensive experiments demonstrate that in both reinforcement learning from human feedback (RLHF) and out-of-distribution (OOD) scenarios, the ESFP-RM framework delivers more stable and generalizable reward signals compared to generative reward models.
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[NLP-6] Why Synthetic Isnt Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation
【速读】： 该论文旨在解决接触中心（contact center）领域中由于隐私限制和数据稀缺导致的对话模型训练与评估难题，尤其针对目标导向、角色不对称且行为复杂的对话场景，其中包含大量不流畅表达（disfluency）、自动语音识别（ASR）噪声以及以合规性驱动的代理行为。解决方案的关键在于利用部署中可获得的衍生通话属性（如意图摘要、话题流和问答评估表）作为监督信号来引导合成转录文本的生成，并提出一个包含18个语言学和行为学基础指标的诊断框架，用于对真实与合成转录文本进行细粒度比较与质量评估。此方法无需原始真实对话即可实现生成质量的量化分析，从而支持多语言环境下合成对话的可靠测试与优化。
链接: https://arxiv.org/abs/2508.18210
作者: Rishikesh Devanathan,Varun Nathan,Ayush Kumar
机构: Observe.AI (Observe.AI)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Synthetic transcript generation is critical in contact center domains, where privacy and data scarcity limit model training and evaluation. Unlike prior synthetic dialogue generation work on open-domain or medical dialogues, contact center conversations are goal-oriented, role-asymmetric, and behaviorally complex, featuring disfluencies, ASR noise, and compliance-driven agent actions. In deployments where transcripts are unavailable, standard pipelines still yield derived call attributes such as Intent Summaries, Topic Flow, and QA Evaluation Forms. We leverage these as supervision signals to guide generation. To assess the quality of such outputs, we introduce a diagnostic framework of 18 linguistically and behaviorally grounded metrics for comparing real and synthetic transcripts. We benchmark four language-agnostic generation strategies, from simple prompting to characteristic-aware multi-stage approaches, alongside reference-free baselines. Results reveal persistent challenges: no method excels across all traits, with notable deficits in disfluency, sentiment, and behavioral realism. Our diagnostic tool exposes these gaps, enabling fine-grained evaluation and stress testing of synthetic dialogue across languages.
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[NLP-7] Exploring the Interplay between Musical Preferences and Personality through the Lens of Language
【速读】： 该论文试图解决的问题是：个体的音乐偏好是否能在其自发语言中被识别，并通过大五人格特质（Big Five personality traits）进行刻画。解决方案的关键在于构建一个基于超过50万条文本样本的大型数据集，其中近5,000名作者的音乐偏好已被可靠标注，进而利用先进的建模方法分析语言特征与人格维度之间的关联，从而验证音乐偏好与人格在自然语言表达中的可识别性。
链接: https://arxiv.org/abs/2508.18208
作者: Eliran Shem-Tov,Ella Rabinovich
机构: The Academic College of Tel-Aviv Yaffo (特拉维夫雅法学术学院)
类目: Computation and Language (cs.CL)
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Abstract:Music serves as a powerful reflection of individual identity, often aligning with deeper psychological traits. Prior research has established correlations between musical preferences and personality traits, while separate studies have demonstrated that personality is detectable through linguistic analysis. Our study bridges these two research domains by investigating whether individuals’ musical preferences are recognizable in their spontaneous language through the lens of the Big Five personality traits (Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism). Using a carefully curated dataset of over 500,000 text samples from nearly 5,000 authors with reliably identified musical preferences, we build advanced models to assess personality characteristics. Our results reveal significant personality differences across fans of five musical genres. We release resources for future research at the intersection of computational linguistics, music psychology and personality analysis.
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[NLP-8] Unraveling the cognitive patterns of Large Language Models through module communities
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）内部机制不透明的问题，即其数十亿参数和复杂结构导致的认知过程难以理解。为应对这一挑战，研究提出了一种基于网络的框架，将认知技能、LLM架构与数据集相连接，从而实现对基础模型分析的范式转变。该方案的关键在于通过模块社区中的技能分布揭示LLM中涌现的认知组织模式，并发现其与鸟类和小型哺乳动物大脑中分布式但互联的认知结构存在部分相似性；同时强调动态跨区域交互和神经可塑性在技能获取中的关键作用，指出有效的微调策略应依赖于分布式学习动力学而非刚性的模块干预。
链接: https://arxiv.org/abs/2508.18192
作者: Kushal Raj Bhandari,Pin-Yu Chen,Jianxi Gao
机构: Rensselaer Polytechnic Institute (伦斯勒理工学院); IBM Research (IBM 研究院)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging from scientific discoveries and medical diagnostics to Chatbots. Despite their ubiquity and utility, the underlying mechanisms of LLM remain concealed within billions of parameters and complex structures, making their inner architecture and cognitive processes challenging to comprehend. We address this gap by adopting approaches to understanding emerging cognition in biology and developing a network-based framework that links cognitive skills, LLM architectures, and datasets, ushering in a paradigm shift in foundation model analysis. The skill distribution in the module communities demonstrates that while LLMs do not strictly parallel the focalized specialization observed in specific biological systems, they exhibit unique communities of modules whose emergent skill patterns partially mirror the distributed yet interconnected cognitive organization seen in avian and small mammalian brains. Our numerical results highlight a key divergence from biological systems to LLMs, where skill acquisition benefits substantially from dynamic, cross-regional interactions and neural plasticity. By integrating cognitive science principles with machine learning, our framework provides new insights into LLM interpretability and suggests that effective fine-tuning strategies should leverage distributed learning dynamics rather than rigid modular interventions.
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[NLP-9] Leverag ing Large Language Models for Accurate Sign Language Translation in Low-Resource Scenarios
【速读】： 该论文旨在解决自然语言到手语（Sign Language）翻译这一高度复杂且研究不足的任务，尤其针对数据稀缺环境下现有方法难以泛化的问题。其核心挑战在于缺乏对齐自然语言与手语的平行语料库，且现有数据集通常领域特定、缺乏标准化或无法充分表达手语的语言丰富性。解决方案的关键在于提出一种名为AulSign（Advanced Use of LLMs for Sign Language Translation）的新方法，该方法通过动态提示（dynamic prompting）和上下文学习（in-context learning），结合样本选择与符号关联机制，将手语符号映射为紧凑的自然语言描述，并引导大型语言模型（Large Language Models, LLMs）基于这些描述进行翻译。尽管LLMs本身不具备手语知识，但通过引入人工标注的符号描述作为中间表示，显著提升了低资源场景下的翻译性能，在英语和意大利语上均优于当前最先进模型。
链接: https://arxiv.org/abs/2508.18183
作者: Luana Bulla,Gabriele Tuccio,Misael Mongiovì,Aldo Gangemi
机构: University of Catania, Italy (卡塔尼亚大学, 意大利); National Research Council - ISTC, Italy (意大利国家研究委员会 - ISTC); University of Bologna, Italy (博洛尼亚大学, 意大利)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:Translating natural languages into sign languages is a highly complex and underexplored task. Despite growing interest in accessibility and inclusivity, the development of robust translation systems remains hindered by the limited availability of parallel corpora which align natural language with sign language data. Existing methods often struggle to generalize in these data-scarce environments, as the few datasets available are typically domain-specific, lack standardization, or fail to capture the full linguistic richness of sign languages. To address this limitation, we propose Advanced Use of LLMs for Sign Language Translation (AulSign), a novel method that leverages Large Language Models via dynamic prompting and in-context learning with sample selection and subsequent sign association. Despite their impressive abilities in processing text, LLMs lack intrinsic knowledge of sign languages; therefore, they are unable to natively perform this kind of translation. To overcome this limitation, we associate the signs with compact descriptions in natural language and instruct the model to use them. We evaluate our method on both English and Italian languages using SignBank+, a recognized benchmark in the field, as well as the Italian LaCAM CNR-ISTC dataset. We demonstrate superior performance compared to state-of-the-art models in low-data scenario. Our findings demonstrate the effectiveness of AulSign, with the potential to enhance accessibility and inclusivity in communication technologies for underrepresented linguistic communities.
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[NLP-10] Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）模型在端到端训练过程中因需对知识库中相关文档片段（建模为离散潜变量）进行边缘化而导致的梯度估计偏差或高方差问题。传统方法如Top-K边缘化和变分RAG（Variational RAG, VRAG）均存在此类缺陷。论文提出基于联合随机逼近（Joint Stochastic Approximation, JSA）的端到端训练框架，即JSA-RAG，其核心在于将JSA算法——一种EM（期望最大化）算法的随机扩展——应用于离散潜变量模型的优化，从而实现更稳定、低方差的梯度估计，显著提升RAG模型在开放域问答和知识驱动对话任务上的性能表现。
链接: https://arxiv.org/abs/2508.18168
作者: Hongyu Cao,Yuxuan Wu,Yucheng Cai,Xianyu Zhao,Zhijian Ou
机构: Tsinghua University (清华大学); TasiTech Co., Ltd.
类目: Computation and Language (cs.CL)
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Abstract:Retrieval-augmented generation (RAG) has become a widely recognized paradigm to combine parametric memory with non-parametric memories. An RAG model consists of two serial connecting components (retriever and generator). A major challenge in end-to-end optimization of the RAG model is that marginalization over relevant passages (modeled as discrete latent variables) from a knowledge base is required. Traditional top-K marginalization and variational RAG (VRAG) suffer from biased or high-variance gradient estimates. In this paper, we propose and develop joint stochastic approximation (JSA) based end-to-end training of RAG, which is referred to as JSA-RAG. The JSA algorithm is a stochastic extension of the EM (expectation-maximization) algorithm and is particularly powerful in estimating discrete latent variable models. Extensive experiments are conducted on five datasets for two tasks (open-domain question answering, knowledge-grounded dialogs) and show that JSA-RAG significantly outperforms both vanilla RAG and VRAG. Further analysis shows the efficacy of JSA-RAG from the perspectives of generation, retrieval, and low-variance gradient estimate.
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[NLP-11] DiscussLLM : Teaching Large Language Models When to Speak
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在对话中普遍存在的“被动响应”问题，即模型仅在被直接提问时才生成回复，缺乏在动态人类讨论中主动介入的能力，从而形成所谓的“意识鸿沟”（awareness gap）。为弥合这一差距，作者提出了一种名为 DiscussLLM 的框架，其核心创新在于设计了一个可扩展的两阶段数据生成管道，用于构建大规模、真实多轮对话数据集，其中每段对话均标注了五类干预类型（如事实修正、概念定义等）及明确的触发点，使AI能在合适时机提供有价值的信息。关键解决方案是训练模型预测一个特殊的“沉默标记”（silent token），使其学会在无需干预时保持静默，仅在识别到有意义的介入机会时才生成回应，从而实现更情境感知且主动的对话交互。
链接: https://arxiv.org/abs/2508.18167
作者: Deep Anil Patel,Iain Melvin,Christopher Malon,Martin Renqiang Min
机构: 未知
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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Abstract:Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like text, yet they largely operate as reactive agents, responding only when directly prompted. This passivity creates an “awareness gap,” limiting their potential as truly collaborative partners in dynamic human discussions. We introduce \textitDiscussLLM , a framework designed to bridge this gap by training models to proactively decide not just \textitwhat to say, but critically, \textitwhen to speak. Our primary contribution is a scalable two-stage data generation pipeline that synthesizes a large-scale dataset of realistic multi-turn human discussions. Each discussion is annotated with one of five intervention types (e.g., Factual Correction, Concept Definition) and contains an explicit conversational trigger where an AI intervention adds value. By training models to predict a special silent token when no intervention is needed, they learn to remain quiet until a helpful contribution can be made. We explore two architectural baselines: an integrated end-to-end model and a decoupled classifier-generator system optimized for low-latency inference. We evaluate these models on their ability to accurately time interventions and generate helpful responses, paving the way for more situationally aware and proactive conversational AI.
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[NLP-12] S2Sent: Nested Selectivity Aware Sentence Representation Learning
【速读】： 该论文旨在解决基于Transformer的句子表示学习中，不同编码块（block）间语义感知能力差异导致的表示冗余与信息损失问题。现有方法通常仅使用最后一层隐藏状态，忽略了各层对语义特征的不同贡献，从而限制了表示质量。解决方案的关键在于提出一种名为S²Sent的句子表示选择机制，其核心创新是引入一个参数化的嵌套选择器（nested selector），从模块化视角实现空间选择（Spatial Selection, SS）与嵌套频率选择（Nested Frequency Selection, FS）。其中，SS通过空间挤压自门控机制（spatial squeeze-based self-gating mechanism）动态生成适应性权重，以低冗余融合多层表示并捕捉嵌入特征间的依赖关系；FS则用不同离散余弦变换（DCT）基函数替代全局平均池化（GAP），在保持低语义损失的前提下完成空间压缩。该方法在不显著增加参数量和推理延迟的情况下，显著优于基线模型，且具备良好的可集成性和可扩展性。
链接: https://arxiv.org/abs/2508.18164
作者: Jianxiang Zang,Nijia Mo,Yonda Wei,Meiling Ning,Hui Liu
机构: Fudan University (复旦大学); Shanghai University of International Business and Economics (上海对外经贸大学); Beijing University of Posts and Telecommunications (北京邮电大学)
类目: Computation and Language (cs.CL)
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Abstract:The combination of Transformer-based encoders with contrastive learning represents the current mainstream paradigm for sentence representation learning. This paradigm is typically based on the hidden states of the last Transformer block of the encoder. However, within Transformer-based encoders, different blocks exhibit varying degrees of semantic perception ability. From the perspective of interpretability, the semantic perception potential of knowledge neurons is modulated by stimuli, thus rational cross-block representation fusion is a direction worth optimizing. To balance the semantic redundancy and loss across block fusion, we propose a sentence representation selection mechanism S\textsuperscript2Sent, which integrates a parameterized nested selector downstream of the Transformer-based encoder. This selector performs spatial selection (SS) and nested frequency selection (FS) from a modular perspective. The SS innovatively employs a spatial squeeze based self-gating mechanism to obtain adaptive weights, which not only achieves fusion with low information redundancy but also captures the dependencies between embedding features. The nested FS replaces GAP with different DCT basis functions to achieve spatial squeeze with low semantic loss. Extensive experiments have demonstrated that S\textsuperscript2Sent achieves significant improvements over baseline methods with negligible additional parameters and inference latency, while highlighting high integrability and scalability.
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[NLP-13] oward a Better Localization of Princeton WordNet
【速读】： 该论文旨在解决普林斯顿词网（Princeton WordNet）在阿拉伯语语境下的本地化问题，特别是现有研究在规模和严谨性上的不足，以及本地化结果与阿拉伯文化语境之间缺乏对齐的痛点。解决方案的关键在于提出一个结构化的本地化框架，系统地定义了实现高质量本地化所需的各个阶段和操作流程，从而在不牺牲文化真实性的前提下完成大规模（10,000个同义词集，synsets）的精准翻译与语义适配。
链接: https://arxiv.org/abs/2508.18134
作者: Abed Alhakim Freihat
机构: 未知
类目: Computation and Language (cs.CL)
备注: in Arabic language
Abstract:As Princeton WordNet continues to gain significance as a semantic lexicon in Natural Language Processing, the need for its localization and for ensuring the quality of this process has become increasingly critical. Existing efforts remain limited in both scale and rigor, and there is a notable absence of studies addressing the accuracy of localization or its alignment with the cultural context of Arabic. This paper proposes a structured framework for the localization of Princeton WordNet, detailing the stages and procedures required to achieve high-quality results without compromising cultural authenticity. We further present our experience in applying this framework, reporting outcomes from the localization of 10,000 synsets.
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[NLP-14] HLLM -Creator: Hierarchical LLM -based Personalized Creative Generation
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在实际应用中难以实现真正用户个性化内容生成的问题，尤其是在在线广告等场景下，需兼顾用户兴趣建模的准确性、事实一致性以及大规模部署时的计算效率。其核心挑战包括：如何在满足事实约束的前提下精准融合用户兴趣到生成过程，以及如何在数据稀缺情况下构建高质量训练样本并保障模型可扩展性。解决方案的关键在于提出 HLLM-Creator——一个分层大语言模型（Hierarchical LLM）框架，通过用户聚类与基于广告-用户匹配预测的剪枝策略显著提升推理效率；同时设计基于思维链（Chain-of-Thought）推理的数据构造管道，生成高保真、用户特定的创意标题，从而在有限标注数据下保证内容的事实一致性和个性化效果，最终在抖音搜索广告场景中验证了其有效性，线上A/B测试显示点击率（Ads）提升0.476%。
链接: https://arxiv.org/abs/2508.18118
作者: Junyi Chen,Lu Chi,Siliang Xu,Shiwei Ran,Bingyue Peng,Zehuan Yuan
机构: ByteDance(字节跳动)
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
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Abstract:AI-generated content technologies are widely used in content creation. However, current AIGC systems rely heavily on creators’ inspiration, rarely generating truly user-personalized content. In real-world applications such as online advertising, a single product may have multiple selling points, with different users focusing on different features. This underscores the significant value of personalized, user-centric creative generation. Effective personalized content generation faces two main challenges: (1) accurately modeling user interests and integrating them into the content generation process while adhering to factual constraints, and (2) ensuring high efficiency and scalability to handle the massive user base in industrial scenarios. Additionally, the scarcity of personalized creative data in practice complicates model training, making data construction another key hurdle. We propose HLLM-Creator, a hierarchical LLM framework for efficient user interest modeling and personalized content generation. During inference, a combination of user clustering and a user-ad-matching-prediction based pruning strategy is employed to significantly enhance generation efficiency and reduce computational overhead, making the approach suitable for large-scale deployment. Moreover, we design a data construction pipeline based on chain-of-thought reasoning, which generates high-quality, user-specific creative titles and ensures factual consistency despite limited personalized data. This pipeline serves as a critical foundation for the effectiveness of our model. Extensive experiments on personalized title generation for Douyin Search Ads show the effectiveness of HLLM-Creator. Online A/B test shows a 0.476% increase on Adss, paving the way for more effective and efficient personalized generation in industrial scenarios. Codes for academic dataset are available at this https URL.
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[NLP-15] he AI Data Scientist
【速读】： 该论文旨在解决传统数据科学工作流程效率低下、专家依赖性强的问题，即从原始数据到可执行洞察的转化过程耗时长且难以普及。其解决方案的关键在于构建一个自主的AI数据科学家（AI Data Scientist）代理系统，该系统由多个专门化的大型语言模型（LLM）子代理（Subagents）组成，分别负责数据清洗、统计检验、验证和通俗化沟通等任务，能够自主推理、测试假设、识别因果关系并生成兼具严谨性与可读性的建议，从而在几分钟内完成传统方法需数天或数周才能实现的端到端数据分析与决策支持。
链接: https://arxiv.org/abs/2508.18113
作者: Farkhad Akimov,Munachiso Samuel Nwadike,Zangir Iklassov,Martin Takáč
机构: MBZUAI(穆巴达拉人工智能大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Imagine decision-makers uploading data and, within minutes, receiving clear, actionable insights delivered straight to their fingertips. That is the promise of the AI Data Scientist, an autonomous Agent powered by large language models (LLMs) that closes the gap between evidence and action. Rather than simply writing code or responding to prompts, it reasons through questions, tests ideas, and delivers end-to-end insights at a pace far beyond traditional workflows. Guided by the scientific tenet of the hypothesis, this Agent uncovers explanatory patterns in data, evaluates their statistical significance, and uses them to inform predictive modeling. It then translates these results into recommendations that are both rigorous and accessible. At the core of the AI Data Scientist is a team of specialized LLM Subagents, each responsible for a distinct task such as data cleaning, statistical testing, validation, and plain-language communication. These Subagents write their own code, reason about causality, and identify when additional data is needed to support sound conclusions. Together, they achieve in minutes what might otherwise take days or weeks, enabling a new kind of interaction that makes deep data science both accessible and actionable.
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[NLP-16] SentiMM: A Multimodal Multi-Agent Framework for Sentiment Analysis in Social Media
【速读】： 该论文旨在解决社交媒体中多模态内容日益增长背景下，情感分析在处理异构数据（如文本与图像）时面临的挑战，特别是跨模态融合不足和外部知识整合缺失的问题。其解决方案的关键在于提出一种名为SentiMM的多智能体框架，通过专业化代理分别处理文本和视觉输入，实现多模态特征融合，并借助知识检索增强语境理解，最终完成多标签情感分类。该方法显著提升了情感识别的准确性与系统性。
链接: https://arxiv.org/abs/2508.18108
作者: Xilai Xu,Zilin Zhao,Chengye Song,Zining Wang,Jinhe Qiang,Jiongrui Yan,Yuhuai Lin
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:With the increasing prevalence of multimodal content on social media, sentiment analysis faces significant challenges in effectively processing heterogeneous data and recognizing multi-label emotions. Existing methods often lack effective cross-modal fusion and external knowledge integration. We propose SentiMM, a novel multi-agent framework designed to systematically address these challenges. SentiMM processes text and visual inputs through specialized agents, fuses multimodal features, enriches context via knowledge retrieval, and aggregates results for final sentiment classification. We also introduce SentiMMD, a large-scale multimodal dataset with seven fine-grained sentiment categories. Extensive experiments demonstrate that SentiMM achieves superior performance compared to state-of-the-art baselines, validating the effectiveness of our structured approach.
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[NLP-17] Detecting and Characterizing Planning in Language Models
【速读】： 该论文旨在解决如何在大型语言模型（Large Language Models, LLMs）中区分规划（planning）与即兴生成（improvisation）的问题，以揭示模型在多步推理任务中的内在机制。其核心挑战在于现有研究通常假设固定的规划视野且局限于单一提示或狭窄领域，难以系统性地比较不同模型和任务下的行为差异。解决方案的关键在于提出了一套形式化且因果基础的检测标准，并将其操作化为一个半自动化标注流程，从而可量化、可复现地识别模型是否进行主动规划——例如提前选择目标token并生成中间步骤——而非逐token随机生成。通过在Gemma-2-2B模型上应用于MBPP代码生成和诗歌生成任务，作者发现规划并非普遍现象，而是依赖于模型架构、任务类型及训练方式（如指令微调），并进一步表明指令微调主要优化而非创造原有的规划能力。这一方法为LLMs中规划行为的机制研究提供了可扩展、可重复的基础框架。
链接: https://arxiv.org/abs/2508.18098
作者: Jatin Nainani,Sankaran Vaidyanathan,Connor Watts,Andre N. Assis,Alice Rigg
机构: University of Massachusetts Amherst (马萨诸塞大学阿默斯特分校); Queen Mary University of London (伦敦玛丽女王大学); Independent (独立研究者)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 9 pages, 4 figures
Abstract:Modern large language models (LLMs) have demonstrated impressive performance across a wide range of multi-step reasoning tasks. Recent work suggests that LLMs may perform planning - selecting a future target token in advance and generating intermediate tokens that lead towards it - rather than merely improvising one token at a time. However, existing studies assume fixed planning horizons and often focus on single prompts or narrow domains. To distinguish planning from improvisation across models and tasks, we present formal and causally grounded criteria for detecting planning and operationalize them as a semi-automated annotation pipeline. We apply this pipeline to both base and instruction-tuned Gemma-2-2B models on the MBPP code generation benchmark and a poem generation task where Claude 3.5 Haiku was previously shown to plan. Our findings show that planning is not universal: unlike Haiku, Gemma-2-2B solves the same poem generation task through improvisation, and on MBPP it switches between planning and improvisation across similar tasks and even successive token predictions. We further show that instruction tuning refines existing planning behaviors in the base model rather than creating them from scratch. Together, these studies provide a reproducible and scalable foundation for mechanistic studies of planning in LLMs.
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[NLP-18] Agri-Query: A Case Study on RAG vs. Long-Context LLM s for Cross-Lingual Technical Question Answering
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在工业领域技术问答任务中，面对长文本上下文和跨语言信息检索场景时的性能瓶颈问题。其核心挑战在于如何有效利用长达128K tokens的上下文窗口，并在多语言环境下准确回答用户提问，同时避免生成无依据的幻觉内容。解决方案的关键在于对比直接使用长上下文提示（direct prompting）与三种检索增强生成（Retrieval-Augmented Generation, RAG）策略——关键词检索、语义检索及混合检索（hybrid）的效果，结果表明混合RAG方法在该特定农业机械手册数据集上显著优于直接提示，尤其在多语言场景下实现了超过85%的高准确率，验证了RAG机制在提升专业领域问答系统鲁棒性和准确性方面的有效性。
链接: https://arxiv.org/abs/2508.18093
作者: Julius Gun,Timo Oksanen
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:We present a case study evaluating large language models (LLMs) with 128K-token context windows on a technical question answering (QA) task. Our benchmark is built on a user manual for an agricultural machine, available in English, French, and German. It simulates a cross-lingual information retrieval scenario where questions are posed in English against all three language versions of the manual. The evaluation focuses on realistic “needle-in-a-haystack” challenges and includes unanswerable questions to test for hallucinations. We compare nine long-context LLMs using direct prompting against three Retrieval-Augmented Generation (RAG) strategies (keyword, semantic, hybrid), with an LLM-as-a-judge for evaluation. Our findings for this specific manual show that Hybrid RAG consistently outperforms direct long-context prompting. Models like Gemini 2.5 Flash and the smaller Qwen 2.5 7B achieve high accuracy (over 85%) across all languages with RAG. This paper contributes a detailed analysis of LLM performance in a specialized industrial domain and an open framework for similar evaluations, highlighting practical trade-offs and challenges.
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[NLP-19] Speech-Based Depressive Mood Detection in the Presence of Multiple Sclerosis: A Cross-Corpus and Cross-Lingual Study ACL
【速读】： 该论文旨在解决在合并神经退行性疾病（如多发性硬化症，MS）的群体中，利用语音特征进行抑郁情绪检测的可行性问题。其核心挑战在于如何将基于一般人群的语音抑郁检测方法有效迁移至MS患者群体，尤其是在数据有限且存在共病干扰的情况下。解决方案的关键在于采用跨语种、跨数据集的迁移学习策略，结合传统语音与语言特征、情感维度（来自语音情感识别模型，SER）以及探索性语音特征分析，并通过特征选择优化模型性能，最终在二分类任务中实现了74%的未加权平均召回率（UAR），验证了语音特征在复杂临床场景下的可迁移性和有效性。
链接: https://arxiv.org/abs/2508.18092
作者: Monica Gonzalez-Machorro,Uwe Reichel,Pascal Hecker,Helly Hammer,Hesam Sagha,Florian Eyben,Robert Hoepner,Björn W. Schuller
机构: audEERING GmbH; TUM University Hospital; Munich Center for Machine Learning; Hasso-Plattner Institute; Inselspital, Bern University Hospital; Agile Robots; Imperial College
类目: Computation and Language (cs.CL)
备注: Accepted at the 8th International Conference on Natural Language and Speech Processing (ICNLSP 2025). To be appeared in the corresponding Proceedings at ACL Anthology
Abstract:Depression commonly co-occurs with neurodegenerative disorders like Multiple Sclerosis (MS), yet the potential of speech-based Artificial Intelligence for detecting depression in such contexts remains unexplored. This study examines the transferability of speech-based depression detection methods to people with MS (pwMS) through cross-corpus and cross-lingual analysis using English data from the general population and German data from pwMS. Our approach implements supervised machine learning models using: 1) conventional speech and language features commonly used in the field, 2) emotional dimensions derived from a Speech Emotion Recognition (SER) model, and 3) exploratory speech feature analysis. Despite limited data, our models detect depressive mood in pwMS with moderate generalisability, achieving a 66% Unweighted Average Recall (UAR) on a binary task. Feature selection further improved performance, boosting UAR to 74%. Our findings also highlight the relevant role emotional changes have as an indicator of depressive mood in both the general population and within PwMS. This study provides an initial exploration into generalising speech-based depression detection, even in the presence of co-occurring conditions, such as neurodegenerative diseases.
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[NLP-20] Named Entity Recognition of Historical Text via Large Language Model
【速读】： 该论文旨在解决历史文本中命名实体识别（Named Entity Recognition, NER）任务因标注数据稀缺而难以应用传统监督学习方法的问题。其关键解决方案是利用大语言模型（Large Language Models, LLMs）通过零样本（zero-shot）和少样本（few-shot）提示策略，在无需大量领域特定标注数据的情况下实现有效的信息抽取。实验基于HIPE-2022数据集验证了LLMs在历史文档NER任务中的可行性，尽管性能尚未达到全监督模型水平，但展现出在低资源或历史语料场景下的高效替代潜力。
链接: https://arxiv.org/abs/2508.18090
作者: Shibingfeng Zhang,Giovanni Colavizza
机构: University of Bologna (博洛尼亚大学); University of Copenhagen (哥本哈根大学)
类目: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Large language models have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. One such task is Named Entity Recognition (NER), which involves identifying and classifying proper names in text, such as people, organizations, locations, dates, and other specific entities. NER plays a crucial role in extracting information from unstructured textual data, enabling downstream applications such as information retrieval from unstructured text. Traditionally, NER is addressed using supervised machine learning approaches, which require large amounts of annotated training data. However, historical texts present a unique challenge, as the annotated datasets are often scarce or nonexistent, due to the high cost and expertise required for manual labeling. In addition, the variability and noise inherent in historical language, such as inconsistent spelling and archaic vocabulary, further complicate the development of reliable NER systems for these sources. In this study, we explore the feasibility of applying LLMs to NER in historical documents using zero-shot and few-shot prompting strategies, which require little to no task-specific training data. Our experiments, conducted on the HIPE-2022 (Identifying Historical People, Places and other Entities) dataset, show that LLMs can achieve reasonably strong performance on NER tasks in this setting. While their performance falls short of fully supervised models trained on domain-specific annotations, the results are nevertheless promising. These findings suggest that LLMs offer a viable and efficient alternative for information extraction in low-resource or historically significant corpora, where traditional supervised methods are infeasible. Subjects: Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2508.18090 [cs.DL] (or arXiv:2508.18090v1 [cs.DL] for this version) https://doi.org/10.48550/arXiv.2508.18090 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[NLP-21] How Quantization Shapes Bias in Large Language Models
链接: https://arxiv.org/abs/2508.18088
作者: Federico Marcuzzi,Xuefei Ning,Roy Schwartz,Iryna Gurevych
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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[NLP-22] Neither Valid nor Reliable? Investigating the Use of LLM s as Judges
【速读】： 该论文试图解决的问题是：当前大语言模型作为评判者（Large Language Models as Judges, LLJs）在自然语言生成（Natural Language Generation, NLG）系统评估中的广泛应用，其有效性与可靠性尚未得到充分验证，存在盲目采纳的风险。解决方案的关键在于基于社会科学研究中的测量理论，系统性地审视并批判性评估LLJs所依赖的四个核心假设——即LLJs能否作为人类判断的代理、其作为评价者的胜任能力、可扩展性以及成本效益，并指出这些假设可能因LLMs自身局限性或当前NLG评估实践的不足而受到挑战。论文强调需建立更负责任的评估范式，以确保LLJs在NLG领域的发展中真正推动而非阻碍技术进步。
链接: https://arxiv.org/abs/2508.18076
作者: Khaoula Chehbouni,Mohammed Haddou,Jackie Chi Kit Cheung,Golnoosh Farnadi
机构: McGill University (麦吉尔大学); Mila - Quebec AI Institute (魁北克人工智能研究所); Statistics Canada (加拿大统计局)
类目: Computation and Language (cs.CL)
备注: Prepared for conference submission
Abstract:Evaluating natural language generation (NLG) systems remains a core challenge of natural language processing (NLP), further complicated by the rise of large language models (LLMs) that aims to be general-purpose. Recently, large language models as judges (LLJs) have emerged as a promising alternative to traditional metrics, but their validity remains underexplored. This position paper argues that the current enthusiasm around LLJs may be premature, as their adoption has outpaced rigorous scrutiny of their reliability and validity as evaluators. Drawing on measurement theory from the social sciences, we identify and critically assess four core assumptions underlying the use of LLJs: their ability to act as proxies for human judgment, their capabilities as evaluators, their scalability, and their cost-effectiveness. We examine how each of these assumptions may be challenged by the inherent limitations of LLMs, LLJs, or current practices in NLG evaluation. To ground our analysis, we explore three applications of LLJs: text summarization, data annotation, and safety alignment. Finally, we highlight the need for more responsible evaluation practices in LLJs evaluation, to ensure that their growing role in the field supports, rather than undermines, progress in NLG.
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[NLP-23] A Retail-Corpus for Aspect-Based Sentiment Analysis with Large Language Models
【速读】： 该论文旨在解决传统情感分析（sentiment analysis）在细粒度洞察方面的局限性，通过引入面向方面的情感分析（aspect-based sentiment analysis）来提升对用户评论中具体属性（如服务、价格、产品质量等）的情感识别精度。其解决方案的关键在于构建了一个包含10,814条多语言客户评论的标注数据集，涵盖实体零售店的八个方面类别及其情感极性标签，并基于此数据集评估了GPT-4与LLaMA-3在该任务上的性能表现，从而为未来相关研究提供基准参考。实验结果表明，两种模型均达到超过85%的准确率，其中GPT-4在各项指标上均优于LLaMA-3。
链接: https://arxiv.org/abs/2508.17994
作者: Oleg Silcenco,Marcos R. Machad,Wallace C. Ugulino,Daniel Braun
机构: University of Twente (特温特大学); Marburg University (马尔堡大学)
类目: Computation and Language (cs.CL)
备注: Accepted at ICNLSP 2025
Abstract:Aspect-based sentiment analysis enhances sentiment detection by associating it with specific aspects, offering deeper insights than traditional sentiment analysis. This study introduces a manually annotated dataset of 10,814 multilingual customer reviews covering brick-and-mortar retail stores, labeled with eight aspect categories and their sentiment. Using this dataset, the performance of GPT-4 and LLaMA-3 in aspect based sentiment analysis is evaluated to establish a baseline for the newly introduced data. The results show both models achieving over 85% accuracy, while GPT-4 outperforms LLaMA-3 overall with regard to all relevant metrics.
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[NLP-24] German4All - A Dataset and Model for Readability-Controlled Paraphrasing in German
【速读】： 该论文旨在解决德国语境下多层级可读性控制的文本改写（readability-controlled paraphrasing）问题，以实现面向不同读者群体的个性化文本适配。其解决方案的关键在于构建首个大规模、五级可读性分级的德语段落级改写数据集German4All，该数据集包含超过25,000个样本，由GPT-4自动生成并经人工与大语言模型（LLM）双重评估验证；在此基础上训练出一个开源的可读性可控改写模型，在德语文本简化任务中达到当前最优性能，从而支持更精细的读者导向文本调整。
链接: https://arxiv.org/abs/2508.17973
作者: Miriam Anschütz,Thanh Mai Pham,Eslam Nasrallah,Maximilian Müller,Cristian-George Craciun,Georg Groh
机构: Technical University of Munich (慕尼黑工业大学)
类目: Computation and Language (cs.CL)
备注: Accepted to INLG 2025
Abstract:The ability to paraphrase texts across different complexity levels is essential for creating accessible texts that can be tailored toward diverse reader groups. Thus, we introduce German4All, the first large-scale German dataset of aligned readability-controlled, paragraph-level paraphrases. It spans five readability levels and comprises over 25,000 samples. The dataset is automatically synthesized using GPT-4 and rigorously evaluated through both human and LLM-based judgments. Using German4All, we train an open-source, readability-controlled paraphrasing model that achieves state-of-the-art performance in German text simplification, enabling more nuanced and reader-specific adaptations. We opensource both the dataset and the model to encourage further research on multi-level paraphrasing
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[NLP-25] Understanding Subword Compositionality of Large Language Models EMNLP2025
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）如何将子词（subword）表示有效组合成有意义的词级表示这一核心问题。其解决方案的关键在于通过系统性实验，从结构相似性、语义可分解性和形式保留性三个维度深入探究LLMs在不同层中对子词信息的整合机制，并发现五类LLM家族可被划分为三类具有不同组合策略的群体，从而揭示了LLMs在子词信息编码与融合过程中的动态特性及多样性模式。
链接: https://arxiv.org/abs/2508.17953
作者: Qiwei Peng,Yekun Chai,Anders Søgaard
机构: University of Copenhagen (哥本哈根大学); ETH Zurich (苏黎世联邦理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: EMNLP 2025 Main
Abstract:Large language models (LLMs) take sequences of subwords as input, requiring them to effective compose subword representations into meaningful word-level representations. In this paper, we present a comprehensive set of experiments to probe how LLMs compose subword information, focusing on three key aspects: structural similarity, semantic decomposability, and form retention. Our analysis of the experiments suggests that these five LLM families can be classified into three distinct groups, likely reflecting difference in their underlying composition strategies. Specifically, we observe (i) three distinct patterns in the evolution of structural similarity between subword compositions and whole-word representations across layers; (ii) great performance when probing layer by layer their sensitivity to semantic decompositionality; and (iii) three distinct patterns when probing sensitivity to formal features, e.g., character sequence length. These findings provide valuable insights into the compositional dynamics of LLMs and highlight different compositional pattens in how LLMs encode and integrate subword information.
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[NLP-26] Debiasing Multilingual LLM s in Cross-lingual Latent Space EMNLP2025
【速读】： 该论文旨在解决现有去偏技术（如SentDebias）在大语言模型（Large Language Models, LLMs）中跨语言迁移能力有限的问题。其核心挑战在于，直接在LLM的原始表示上应用去偏方法难以在不同语言间有效传递去偏效果。解决方案的关键在于构建一个对齐良好的跨语言潜在空间（cross-lingual latent space），并通过在该空间中执行去偏操作来提升整体去偏性能和跨语言泛化能力。具体而言，作者利用平行TED演讲脚本训练自编码器（autoencoder）以学习跨语言潜在表示，并在此基础上应用去偏技术，实验表明该策略显著优于直接在LLM表示上进行去偏的方法。
链接: https://arxiv.org/abs/2508.17948
作者: Qiwei Peng,Guimin Hu,Yekun Chai,Anders Søgaard
机构: University of Copenhagen (哥本哈根大学); ETH Zurich (苏黎世联邦理工学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: EMNLP 2025 Main
Abstract:Debiasing techniques such as SentDebias aim to reduce bias in large language models (LLMs). Previous studies have evaluated their cross-lingual transferability by directly applying these methods to LLM representations, revealing their limited effectiveness across languages. In this work, we therefore propose to perform debiasing in a joint latent space rather than directly on LLM representations. We construct a well-aligned cross-lingual latent space using an autoencoder trained on parallel TED talk scripts. Our experiments with Aya-expanse and two debiasing techniques across four languages (English, French, German, Dutch) demonstrate that a) autoencoders effectively construct a well-aligned cross-lingual latent space, and b) applying debiasing techniques in the learned cross-lingual latent space significantly improves both the overall debiasing performance and cross-lingual transferability.
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[NLP-27] AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation
【速读】： 该论文旨在解决如何利用单一大型语言模型（Large Language Model, LLM）高效执行多个argument mining（论点挖掘）任务的问题。其核心挑战在于如何在保持各任务性能的同时，降低多任务训练的计算开销。解决方案的关键在于构建一个统一格式的多任务数据集，并系统比较三种训练策略：单任务微调、多任务联合微调以及分离微调后模型合并。实验表明，单任务微调显著提升各任务性能；多任务微调在不造成性能下降的前提下实现有效知识迁移；而模型合并策略则在性能与计算成本之间取得平衡，为实际部署提供可行路径。
链接: https://arxiv.org/abs/2508.17926
作者: Henri Savigny,Bruno Yun
机构: Universite Claude Bernard Lyon 1 (克莱门特·庞塞特-里昂第一大学); CNRS (法国国家科学研究中心); Ecole Centrale de Lyon (里昂中央理工学院); INSA Lyon (里昂国立应用科学学院); Université Lumiere Lyon 2 (里昂第二卢米埃尔大学); LIRIS, UMR5205 (信息、计算机与应用数学实验室，联合研究单位5205)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Argument mining is a subfield of argumentation that aims to automatically extract argumentative structures and their relations from natural language texts. This paper investigates how a single large language model can be leveraged to perform one or several argument mining tasks. Our contributions are two-fold. First, we construct a multi-task dataset by surveying and converting 19 well-known argument mining datasets from the literature into a unified format. Second, we explore various training strategies using Meta AI’s Llama-3.1-8B-Instruct model: (1) fine-tuning on individual tasks, (2) fine-tuning jointly on multiple tasks, and (3) merging models fine-tuned separately on individual tasks. Our experiments show that task-specific fine-tuning significantly improves individual performance across all tasks. Moreover, multi-task fine-tuning maintains strong performance without degradation, suggesting effective transfer learning across related tasks. Finally, we demonstrate that model merging offers a viable compromise: it yields competitive performance while mitigating the computational costs associated with full multi-task fine-tuning.
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[NLP-28] Feature-Refined Unsupervised Model for Loanword Detection
【速读】： 该论文旨在解决跨语言中借词（loanword）检测的问题，即识别从一种语言借用到另一种语言中的词汇。传统方法多依赖语言外部信息（如历史文献或语料库标注），易引入循环性与限制，影响历史语言学研究的客观性。本文提出一种仅使用语言内部信息的无监督方法，通过提取关键语言特征、进行评分并概率映射，迭代优化初始结果，逐步识别和归纳模式直至收敛。其核心创新在于融合语言学规律与统计线索的混合策略，实现了对六种标准印欧语系语言（英语、德语、法语、意大利语、西班牙语和葡萄牙语）中借词的有效识别，且在扩展至跨语言数据时表现显著优于基线模型。
链接: https://arxiv.org/abs/2508.17923
作者: Promise Dodzi Kpoglu
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:We propose an unsupervised method for detecting loanwords i.e., words borrowed from one language into another. While prior work has primarily relied on language-external information to identify loanwords, such approaches can introduce circularity and constraints into the historical linguistics workflow. In contrast, our model relies solely on language-internal information to process both native and borrowed words in monolingual and multilingual wordlists. By extracting pertinent linguistic features, scoring them, and mapping them probabilistically, we iteratively refine initial results by identifying and generalizing from emerging patterns until convergence. This hybrid approach leverages both linguistic and statistical cues to guide the discovery process. We evaluate our method on the task of isolating loanwords in datasets from six standard Indo-European languages: English, German, French, Italian, Spanish, and Portuguese. Experimental results demonstrate that our model outperforms baseline methods, with strong performance gains observed when scaling to cross-linguistic data.
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[NLP-29] Information availability in different languages and various technological constraints related to multilinguism on the Internet
【速读】： 该论文试图解决互联网信息获取中的语言障碍问题，即当前网络内容以英语为主导，导致非英语母语用户难以平等访问信息。其关键在于分析多语言环境下信息可用性的需求及技术约束，旨在为构建更包容的多语言互联网基础设施提供理论依据与方向指引。
链接: https://arxiv.org/abs/2508.17918
作者: Sonal Khosla,Haridasa Acharya
机构: 未知
类目: Computation and Language (cs.CL)
备注: International Journal of Computer Applications
Abstract:The usage of Internet has grown exponentially over the last two decades. The number of Internet users has grown from 16 Million to 1650 Million from 1995 to 2010. It has become a major repository of information catering almost every area. Since the Internet has its origin in USA which is English speaking country there is huge dominance of English on the World Wide Web. Although English is a globally acceptable language, still there is a huge population in the world which is not able to access the Internet due to language constraints. It has been estimated that only 20-25% of the world population speaks English as a native language. More and more people are accessing the Internet nowadays removing the cultural and linguistic barriers and hence there is a high growth in the number of non-English speaking users over the last few years on the Internet. Although many solutions have been provided to remove the linguistic barriers, still there is a huge gap to be filled. This paper attempts to analyze the need of information availability in different languages and the various technological constraints related to multi-linguism on the Internet.
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[NLP-30] Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
【速读】： 该论文旨在解决自动语音识别（Automatic Speech Recognition, ASR）中语音特征表示的有效性问题，特别是针对单元音（monophthong vowels）的声学表征能力。其解决方案的关键在于对比不同特征提取方法对前端-后端元音分类任务的性能差异：分别使用传统梅尔频率倒谱系数（MFCCs）、融合共振峰（formants）信息的MFCCs以及由卷积神经网络（CNN）从原始音频中自动学习到的激活值（activations），并通过支持向量机（SVM）分类器评估这些特征在TIMIT语料库上的分类准确率，从而量化各类特征对语音发音特征的表达能力。
链接: https://arxiv.org/abs/2508.17914
作者: Domenico De Cristofaro,Vincenzo Norman Vitale,Alessandro Vietti
机构: Free University of Bozen (博岑自由大学); University of Naples Federico II (那不勒斯腓特烈二世大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Automatic Speech Recognition has advanced with self-supervised learning, enabling feature extraction directly from raw audio. In Wav2Vec, a CNN first transforms audio into feature vectors before the transformer processes them. This study examines CNN-extracted information for monophthong vowels using the TIMIT corpus. We compare MFCCs, MFCCs with formants, and CNN activations by training SVM classifiers for front-back vowel identification, assessing their classification accuracy to evaluate phonetic representation.
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[NLP-31] Pandora: Leverag ing Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
【速读】： 该论文旨在解决统一结构化知识推理（Unified Structured Knowledge Reasoning, USKR）中因任务特定策略或定制化表示导致不同SKR任务间壁垒难以打破的问题，从而限制了跨任务场景下的整体性能。其解决方案的关键在于提出一种名为Pandora的新框架，该框架通过两项核心创新实现：一是基于Python的Pandas API构建代码驱动的统一知识表示，与大语言模型（Large Language Models, LLMs）预训练机制无缝对齐，从而统一处理多种结构化知识源；二是利用知识迁移自动构建跨任务记忆，并通过代码执行反馈自适应修正推理过程，显著提升LLMs在多任务环境下的统一推理能力。
链接: https://arxiv.org/abs/2508.17905
作者: Yongrui Chen,Junhao He,Linbo Fu,Shenyu Zhang,Rihui Jin,Xinbang Dai,Jiaqi Li,Dehai Min,Nan Hu,Yuxin Zhang,Guilin Qi,Yi Huang,Tongtong Wu
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and knowledge graphs in a unified way. Existing USKR methods rely on task-specific strategies or bespoke representations, which hinder their ability to dismantle barriers between different SKR tasks, thereby constraining their overall performance in cross-task scenarios. In this paper, we introduce \textscPandora, a novel USKR framework that addresses the limitations of existing methods by leveraging two key innovations. First, we propose a code-based unified knowledge representation using \textscPython’s \textscPandas API, which aligns seamlessly with the pre-training of LLMs. This representation facilitates a cohesive approach to handling different structured knowledge sources. Building on this foundation, we employ knowledge transfer to bolster the unified reasoning process of LLMs by automatically building cross-task memory. By adaptively correcting reasoning using feedback from code execution, \textscPandora showcases impressive unified reasoning capabilities. Extensive experiments on six widely used benchmarks across three SKR tasks demonstrate that \textscPandora outperforms existing unified reasoning frameworks and competes effectively with task-specific methods.
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[NLP-32] Designing Practical Models for Isolated Word Visual Speech Recognition
【速读】： 该论文旨在解决视觉语音识别（Visual Speech Recognition, VSR）系统在实际部署中因深度神经网络计算成本高而导致硬件资源需求大、适用场景受限的问题。解决方案的关键在于设计轻量级端到端架构：首先基于图像分类领域中高效模型的基准测试结果，选取适合VSR任务的轻量化组件；随后在时序卷积网络（Temporal Convolutional Network）主干结构中引入轻量块（lightweight block）设计，从而在保持强识别性能的同时显著降低资源消耗，实现低硬件成本的实用化部署。
链接: https://arxiv.org/abs/2508.17894
作者: Iason Ioannis Panagos,Giorgos Sfikas,Christophoros Nikou
机构: University of Ioannina (伊奥annina大学); University of Western Attica (西阿提卡大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Double-column format, 13 pages with references, 2 figures
Abstract:Visual speech recognition (VSR) systems decode spoken words from an input sequence using only the video data. Practical applications of such systems include medical assistance as well as human-machine interactions. A VSR system is typically employed in a complementary role in cases where the audio is corrupt or not available. In order to accurately predict the spoken words, these architectures often rely on deep neural networks in order to extract meaningful representations from the input sequence. While deep architectures achieve impressive recognition performance, relying on such models incurs significant computation costs which translates into increased resource demands in terms of hardware requirements and results in limited applicability in real-world scenarios where resources might be constrained. This factor prevents wider adoption and deployment of speech recognition systems in more practical applications. In this work, we aim to alleviate this issue by developing architectures for VSR that have low hardware costs. Following the standard two-network design paradigm, where one network handles visual feature extraction and another one utilizes the extracted features to classify the entire sequence, we develop lightweight end-to-end architectures by first benchmarking efficient models from the image classification literature, and then adopting lightweight block designs in a temporal convolution network backbone. We create several unified models with low resource requirements but strong recognition performance. Experiments on the largest public database for English words demonstrate the effectiveness and practicality of our developed models. Code and trained models will be made publicly available.
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[NLP-33] ILRe: Intermediate Layer Retrieval for Context Compression in Causal Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在长文本处理场景中面临的三大瓶颈问题：有效上下文长度受限、预填充阶段计算复杂度呈二次增长（O(L²)）、以及内存开销过高。其核心解决方案是提出一种新颖的中间层检索（Intermediate Layer Retrieval, ILRe）压缩流水线，关键在于：首先在离线阶段确定一个特定的解码器中间层，仅将输入分块流式预填充至该层；随后通过查询与该层完整键缓存（key cache）之间的注意力分数实现高效召回 token；并引入多池化核分配策略以保障语义完整性。此方法将预填充复杂度从 O(L²) 降低至 O(L)，且无需额外微调或算子开发即可在单次百万 token 请求下实现约 180 倍加速，并在 RULER-1M 基准上取得约 79.8 分的优异表现。
链接: https://arxiv.org/abs/2508.17892
作者: Manlai Liang,Mandi Liu,Jiangzhou Ji,Huaijun Li,Haobo Yang,Yaohan He,Jinlong Li
机构: China Merchants Bank (招商银行)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Large Language Models (LLMs) have demonstrated success across many benchmarks. However, they still exhibit limitations in long-context scenarios, primarily due to their short effective context length, quadratic computational complexity, and high memory overhead when processing lengthy inputs. To mitigate these issues, we introduce a novel context compression pipeline, called Intermediate Layer Retrieval (ILRe), which determines one intermediate decoder layer offline, encodes context by streaming chunked prefill only up to that layer, and recalls tokens by the attention scores between the input query and full key cache in that specified layer. In particular, we propose a multi-pooling kernels allocating strategy in the token recalling process to maintain the completeness of semantics. Our approach not only reduces the prefilling complexity from O(L^2) to O(L) , but also achieves performance comparable to or better than the full context in the long context scenarios. Without additional post training or operator development, ILRe can process a single 1M tokens request in less than half a minute (speedup \approx 180\times ) and scores RULER- 1M benchmark of \approx 79.8 with model Llama-3.1-UltraLong-8B-1M-Instruct on a Huawei Ascend 910B NPU.
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[NLP-34] Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLM s EMNLP2025
【速读】： 该论文旨在解决语音大语言模型（Speech Large Language Models, SpeechLLMs）中离散标记（discrete tokens）与连续特征（continuous features）两种语音处理范式之间性能差距不明确的问题。其解决方案的关键在于，在相同实验条件下，对基于自监督学习（self-supervised learning, SSL）的离散与连续特征进行公平对比，并在小规模（Qwen1.5-0.5B）和大规模（Llama3.1-8B）语言模型上评估其在六项语音理解任务中的表现，同时通过高效比较、SSL层分析、LLM层分析及鲁棒性测试深入揭示两类方法的学习机制差异。结果表明，连续特征在多数任务中表现更优，且两类方法展现出不同的信息处理模式，为提升语音大语言模型的语音理解能力提供了关键实证依据。
链接: https://arxiv.org/abs/2508.17863
作者: Dingdong Wang,Junan Li,Mingyu Cui,Dongchao Yang,Xueyuan Chen,Helen Meng
机构: The Chinese University of Hong Kong (香港中文大学)
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted to EMNLP 2025 Main Conference
Abstract:With the rise of Speech Large Language Models (SpeechLLMs), two dominant approaches have emerged for speech processing: discrete tokens and continuous features. Each approach has demonstrated strong capabilities in audio-related processing tasks. However, the performance gap between these two paradigms has not been thoroughly explored. To address this gap, we present a fair comparison of self-supervised learning (SSL)-based discrete and continuous features under the same experimental settings. We evaluate their performance across six spoken language understanding-related tasks using both small and large-scale LLMs (Qwen1.5-0.5B and Llama3.1-8B). We further conduct in-depth analyses, including efficient comparison, SSL layer analysis, LLM layer analysis, and robustness comparison. Our findings reveal that continuous features generally outperform discrete tokens in various tasks. Each speech processing method exhibits distinct characteristics and patterns in how it learns and processes speech information. We hope our results will provide valuable insights to advance spoken language understanding in SpeechLLMs.
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[NLP-35] Beyond Demographics: Enhancing Cultural Value Survey Simulation with Multi-Stage Personality-Driven Cognitive Reasoning EMNLP2025
【速读】： 该论文旨在解决当前大型语言模型在文化价值调查响应模拟任务中准确率不足、可控性差以及预测结果难以解释的问题。其解决方案的核心在于提出MARK（Multi-stAge Reasoning framework），该框架受MBTI人格研究中的类型动力学理论启发，通过三个关键机制实现改进：一是基于生活情境压力分析（life-situational stress analysis）对个体背景进行建模；二是利用群体层面的人格预测（group-level personality prediction）提升模拟的合理性；三是引入自我加权认知模仿（self-weighted cognitive imitation）增强个体化响应的生成能力。实验表明，MARK在世界价值观调查数据集上比现有基线方法提升10%的准确率，并显著降低模型预测与人类偏好之间的偏差，从而为零样本个性化模拟提供了可解释且可控的新范式。
链接: https://arxiv.org/abs/2508.17855
作者: Haijiang Liu,Qiyuan Li,Chao Gao,Yong Cao,Xiangyu Xu,Xun Wu,Daniel Hershcovich,Jinguang Gu
机构: Wuhan University of Science and Technology (武汉科技大学); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州）); University of Tübingen (图宾根大学); University of Copenhagen (哥本哈根大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 23 pages, 6 figures, accepted to EMNLP 2025 main
Abstract:Introducing MARK, the Multi-stAge Reasoning frameworK for cultural value survey response simulation, designed to enhance the accuracy, steerability, and interpretability of large language models in this task. The system is inspired by the type dynamics theory in the MBTI psychological framework for personality research. It effectively predicts and utilizes human demographic information for simulation: life-situational stress analysis, group-level personality prediction, and self-weighted cognitive imitation. Experiments on the World Values Survey show that MARK outperforms existing baselines by 10% accuracy and reduces the divergence between model predictions and human preferences. This highlights the potential of our framework to improve zero-shot personalization and help social scientists interpret model predictions.
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[NLP-36] DRQA: Dynamic Reasoning Quota Allocation for Controlling Overthinking in Reasoning Large Language Models
【速读】： 该论文旨在解决生成式 AI（Generative AI）中推理大语言模型（Reasoning Large Language Models, RLLMs）存在的“过度思考”（overthinking）问题，即模型在处理简单任务时仍生成冗长且不必要的推理链，导致计算资源浪费和效率低下。解决方案的关键在于提出动态推理配额分配（Dynamic Reasoning Quota Allocation, DRQA），其核心机制是利用批处理模式下隐含的资源竞争效应，通过收集批处理产生的偏好数据并结合强化学习训练模型，使其能够自适应地分配推理资源：对简单问题压缩推理步骤以提升效率，对复杂问题保留足够深度以保障准确性。实验表明，DRQA 在多个数学与科学推理基准上显著降低 token 消耗，同时维持甚至提升答案准确率，从而为 RLLMs 的高效部署提供了新路径。
链接: https://arxiv.org/abs/2508.17803
作者: Kaiwen Yan,Xuanqing Shi,Hongcheng Guo,Wenxuan Wang,Zhuosheng Zhang,Chengwei Qin
机构: The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州）); The Hong Kong University of Science and Technology (香港科技大学); Tsinghua University (清华大学); Fudan University (复旦大学); Renmin University of China (中国人民大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computation and Language (cs.CL)
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Abstract:Reasoning large language models (RLLMs), such as OpenAI-O3 and DeepSeek-R1, have recently demonstrated remarkable capabilities by performing structured and multi-step reasoning. However, recent studies reveal that RLLMs often suffer from overthinking, i.e., producing unnecessarily lengthy reasoning chains even for simple questions, leading to excessive token consumption and computational inefficiency. Interestingly, we observe that when processing multiple questions in batch mode, RLLMs exhibit more resource-efficient behavior by dynamically compressing reasoning steps for easier problems, due to implicit resource competition. Inspired by this, we propose Dynamic Reasoning Quota Allocation (DRQA), a novel method that transfers the benefits of resource competition from batch processing to single-question inference. Specifically, DRQA leverages batch-generated preference data and reinforcement learning to train the model to allocate reasoning resources adaptively. By encouraging the model to internalize a preference for responses that are both accurate and concise, DRQA enables it to generate concise answers for simple questions while retaining sufficient reasoning depth for more challenging ones. Extensive experiments on a wide range of mathematical and scientific reasoning benchmarks demonstrate that DRQA significantly reduces token usage while maintaining, and in many cases improving, answer accuracy. By effectively mitigating the overthinking problem, DRQA offers a promising direction for more efficient and scalable deployment of RLLMs, and we hope it inspires further exploration into fine-grained control of reasoning behaviors.
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[NLP-37] Zero-shot Context Biasing with Trie-based Decoding using Synthetic Multi-Pronunciation
【速读】： 该论文旨在解决上下文自动语音识别（Contextual ASR）系统中对低频词（Out-of-Vocabulary, OOV）识别效果差的问题，尤其在训练数据稀缺和发音不一致的情况下。其核心挑战在于如何在不依赖目标词汇额外标注数据的前提下，提升对罕见词（如命名实体）的识别准确性。解决方案的关键在于提出一种基于合成语音的多发音上下文偏置方法（synthesis-driven multi-pronunciation contextual biasing）：首先利用文本到语音（TTS）系统生成包含目标罕见词的多样化语音样本，再通过预训练的Whisper模型提取多种可能的发音变体；这些变体被组织成前缀树（prefix-trie），并在束搜索（beam-search）解码过程中以浅融合（shallow-fusion）方式为候选假设赋予奖励，最终将识别出的任意发音变体映射回原始罕见词，从而实现零样本（zero-shot）上下文感知的ASR性能提升。
链接: https://arxiv.org/abs/2508.17796
作者: Changsong Liu,Yizhou Peng,Eng Siong Chng
机构: Nanyang Technological University (南洋理工大学)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Accepted to APSIPA ASC 2025
Abstract:Contextual automatic speech recognition (ASR) systems allow for recognizing out-of-vocabulary (OOV) words, such as named entities or rare words. However, it remains challenging due to limited training data and ambiguous or inconsistent pronunciations. In this paper, we propose a synthesis-driven multi-pronunciation contextual biasing method that performs zero-shot contextual ASR on a pretrained Whisper model. Specifically, we leverage text-to-speech (TTS) systems to synthesize diverse speech samples containing each target rare word, and then use the pretrained Whisper model to extract multiple predicted pronunciation variants. These variant token sequences are compiled into a prefix-trie, which assigns rewards to beam hypotheses in a shallow-fusion manner during beam-search decoding. After which, any recognized variant is mapped back to the original rare word in the final transcription. The evaluation results on the Librispeech dataset show that our method reduces biased word error rate (WER) by 42% on test-clean and 43% on test-other while maintaining unbiased WER essentially unchanged.
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[NLP-38] Proximal Supervised Fine-Tuning
【速读】： 该论文旨在解决监督微调（Supervised Fine-Tuning, SFT）过程中模型泛化能力下降的问题，即在新任务或领域上微调后，模型先前具备的能力会出现退化。解决方案的关键在于提出一种受强化学习中信任区域策略优化（Trust Region Policy Optimization, TRPO）和近端策略优化（Proximal Policy Optimization, PPO）启发的近端SFT（Proximal SFT, PSFT）方法，其核心是在微调目标中引入信任区域约束机制，有效控制策略漂移（policy drift），从而在保持微调性能的同时提升模型的泛化能力，并为后续的后训练阶段提供更稳定的优化基础。
链接: https://arxiv.org/abs/2508.17784
作者: Wenhong Zhu,Ruobing Xie,Rui Wang,Xingwu Sun,Di Wang,Pengfei Liu
机构: Shanghai Jiao Tong University (上海交通大学); Shanghai Innovation Institute (上海创新研究院); Tencent (腾讯); University of Macau (澳门大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Supervised fine-tuning (SFT) of foundation models often leads to poor generalization, where prior capabilities deteriorate after tuning on new tasks or domains. Inspired by trust-region policy optimization (TRPO) and proximal policy optimization (PPO) in reinforcement learning (RL), we propose Proximal SFT (PSFT). This fine-tuning objective incorporates the benefits of trust-region, effectively constraining policy drift during SFT while maintaining competitive tuning. By viewing SFT as a special case of policy gradient methods with constant positive advantages, we derive PSFT that stabilizes optimization and leads to generalization, while leaving room for further optimization in subsequent post-training stages. Experiments across mathematical and human-value domains show that PSFT matches SFT in-domain, outperforms it in out-of-domain generalization, remains stable under prolonged training without causing entropy collapse, and provides a stronger foundation for the subsequent optimization.
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[NLP-39] Speculating LLM s Chinese Training Data Pollution from Their Tokens
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）训练数据中存在污染内容的问题，特别是针对中文词汇表中包含色情或在线赌博等不良内容的“污染中文令牌”（Polluted Chinese tokens, PoC tokens）进行识别与分析。其核心问题在于：如何定位并量化这些PoC tokens的存在，并探究其与训练数据污染之间的关联性。解决方案的关键在于三个层面：首先，基于GPT词汇表对PoC tokens进行了形式化定义和分类；其次，通过微调一个LLM来构建PoC令牌检测器，该方法结合了令牌语义信息与搜索引擎中的相关网页内容以提高标注准确性；最后，利用PoC令牌在词汇表中的ID分布特征推测其在训练数据中的比例，从而验证了如C4和Pile等主流预训练数据集中存在污染现象，并进一步推测出GPT-4o中特定关键词（如“Yui Hatano”）相关网页的比例约为0.5%。
链接: https://arxiv.org/abs/2508.17771
作者: Qingjie Zhang,Di Wang,Haoting Qian,Liu Yan,Tianwei Zhang,Ke Xu,Qi Li,Minlie Huang,Hewu Li,Han Qiu
机构: Tsinghua University (清华大学); Ant Group (蚂蚁集团); Nanyang Technological University (南洋理工大学)
类目: Computation and Language (cs.CL)
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Abstract:Tokens are basic elements in the datasets for LLM training. It is well-known that many tokens representing Chinese phrases in the vocabulary of GPT (4o/4o-mini/o1/o3/4.5/4.1/o4-mini) are indicating contents like pornography or online gambling. Based on this observation, our goal is to locate Polluted Chinese (PoC) tokens in LLMs and study the relationship between PoC tokens’ existence and training data. (1) We give a formal definition and taxonomy of PoC tokens based on the GPT’s vocabulary. (2) We build a PoC token detector via fine-tuning an LLM to label PoC tokens in vocabularies by considering each token’s both semantics and related contents from the search engines. (3) We study the speculation on the training data pollution via PoC tokens’ appearances (token ID). Experiments on GPT and other 23 LLMs indicate that tokens widely exist while GPT’s vocabulary behaves the worst: more than 23% long Chinese tokens (i.e., a token with more than two Chinese characters) are either porn or online gambling. We validate the accuracy of our speculation method on famous pre-training datasets like C4 and Pile. Then, considering GPT-4o, we speculate that the ratio of “Yui Hatano” related webpages in GPT-4o’s training data is around 0.5%.
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[NLP-40] ISACL: Internal State Analyzer for Copyrighted Training Data Leakage
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在训练过程中可能无意中泄露受版权保护或专有数据的问题，尤其是在这些数据未被授权用于分发的情况下。传统方法仅在生成内容后进行检测，存在敏感信息暴露的风险。其解决方案的关键在于采用一种前瞻性的机制：通过分析LLM内部状态（internal states）在文本生成前识别潜在的数据泄露风险。研究团队利用精心构建的受版权材料数据集训练了一个神经网络分类器，能够在生成过程早期干预——例如终止生成或修改输出，从而防止敏感信息泄露。该方法与检索增强生成（Retrieval-Augmented Generation, RAG）系统集成，确保符合版权和许可要求，同时提升数据隐私与伦理标准，实现高质量文本生成与合规性之间的平衡。
链接: https://arxiv.org/abs/2508.17767
作者: Guangwei Zhang,Qisheng Su,Jiateng Liu,Cheng Qian,Yanzhou Pan,Yanjie Fu,Denghui Zhang
机构: City University of Hong Kong (香港城市大学); Microsoft (微软); University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); Google LLC (谷歌有限公司); Arizona State University (亚利桑那州立大学); Stevens Institute of Technology (史蒂文斯理工学院)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but pose risks of inadvertently exposing copyrighted or proprietary data, especially when such data is used for training but not intended for distribution. Traditional methods address these leaks only after content is generated, which can lead to the exposure of sensitive information. This study introduces a proactive approach: examining LLMs’ internal states before text generation to detect potential leaks. By using a curated dataset of copyrighted materials, we trained a neural network classifier to identify risks, allowing for early intervention by stopping the generation process or altering outputs to prevent disclosure. Integrated with a Retrieval-Augmented Generation (RAG) system, this framework ensures adherence to copyright and licensing requirements while enhancing data privacy and ethical standards. Our results show that analyzing internal states effectively mitigates the risk of copyrighted data leakage, offering a scalable solution that fits smoothly into AI workflows, ensuring compliance with copyright regulations while maintaining high-quality text generation. The implementation is available on GitHub.\footnotethis https URL
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[NLP-41] CEIDM: A Controlled Entity and Interaction Diffusion Model for Enhanced Text-to-Image Generation
【速读】： 该论文旨在解决基于扩散模型的文本到图像（Text-to-Image, T2I）生成方法中，实体（entity）及其复杂交互关系难以有效控制的问题，从而生成更符合现实逻辑且交互合理的高质量图像。其解决方案的关键在于提出一种双控机制——CEIDM（Controlled Entity and Interaction Diffusion Model），包含三个核心组件：首先，利用大语言模型（Large Language Models, LLMs）通过思维链（chain of thought）挖掘隐式交互关系，引导扩散模型生成语义合理、交互自然的图像；其次，设计交互动作聚类与偏移方法，对文本提示中的动作特征进行聚类和双向偏移，增强动作语义理解与细节补充；最后，构建实体控制网络，结合语义引导的掩码和多尺度卷积结构，实现对实体的精准控制并提升图像质量。该方法在实体控制与交互控制两方面均优于现有主流方法。
链接: https://arxiv.org/abs/2508.17760
作者: Mingyue Yang,Dianxi Shi,Jialu Zhou,Xinyu Wei,Leqian Li,Shaowu Yang,Chunping Qiu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
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Abstract:In Text-to-Image (T2I) generation, the complexity of entities and their intricate interactions pose a significant challenge for T2I method based on diffusion model: how to effectively control entity and their interactions to produce high-quality images. To address this, we propose CEIDM, a image generation method based on diffusion model with dual controls for entity and interaction. First, we propose an entity interactive relationships mining approach based on Large Language Models (LLMs), extracting reasonable and rich implicit interactive relationships through chain of thought to guide diffusion models to generate high-quality images that are closer to realistic logic and have more reasonable interactive relationships. Furthermore, We propose an interactive action clustering and offset method to cluster and offset the interactive action features contained in each text prompts. By constructing global and local bidirectional offsets, we enhance semantic understanding and detail supplementation of original actions, making the model’s understanding of the concept of interactive “actions” more accurate and generating images with more accurate interactive actions. Finally, we design an entity control network which generates masks with entity semantic guidance, then leveraging multi-scale convolutional network to enhance entity feature and dynamic network to fuse feature. It effectively controls entities and significantly improves image quality. Experiments show that the proposed CEIDM method is better than the most representative existing methods in both entity control and their interaction control.
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[NLP-42] alking to Robots: A Practical Examination of Speech Foundation Models for HRI Applications
【速读】： 该论文旨在解决自动语音识别（ASR）系统在真实场景中面对复杂音频条件和多样用户群体时性能不稳定的问题，特别是在人-机器人交互（HRI）环境中，由于硬件限制、环境噪声、口音差异、年龄变化、语言障碍及即兴表达等因素叠加，导致现有ASR系统存在显著的识别误差、幻觉倾向和固有偏见。其解决方案的关键在于通过系统性评估四个前沿ASR模型在八个公开数据集上的表现，这些数据集覆盖了六个维度的难度（领域特异性、口音、噪声、年龄差异、语言障碍和自发性语音），从而揭示标准基准测试无法反映的实际性能差异与潜在风险，为提升HRI中ASR系统的鲁棒性和公平性提供实证依据。
链接: https://arxiv.org/abs/2508.17753
作者: Theresa Pekarek Rosin,Julia Gachot,Henri-Leon Kordt,Matthias Kerzel,Stefan Wermter
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: Accepted at the workshop on Foundation Models for Social Robotics (FoMoSR) at ICSR 2025
Abstract:Automatic Speech Recognition (ASR) systems in real-world settings need to handle imperfect audio, often degraded by hardware limitations or environmental noise, while accommodating diverse user groups. In human-robot interaction (HRI), these challenges intersect to create a uniquely challenging recognition environment. We evaluate four state-of-the-art ASR systems on eight publicly available datasets that capture six dimensions of difficulty: domain-specific, accented, noisy, age-variant, impaired, and spontaneous speech. Our analysis demonstrates significant variations in performance, hallucination tendencies, and inherent biases, despite similar scores on standard benchmarks. These limitations have serious implications for HRI, where recognition errors can interfere with task performance, user trust, and safety.
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[NLP-43] SMITE: Enhancing Fairness in LLM s through Optimal In-Context Example Selection via Dynamic Validation
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在表格分类等下游任务中输出不公平的问题，以保障包容性、平等代表性和负责任的AI部署。其核心挑战在于传统静态验证集无法适应测试数据分布的变化，从而限制了模型在真实场景中的公平性和准确性表现。解决方案的关键在于提出一种动态验证集（dynamic validation set）机制，该验证集随测试集同步演化，并结合一种迭代算法SMITE（Selecting Optimal In-context Examples），通过在每个迭代步骤中基于对应动态验证集筛选最优上下文示例，最终选择总误差最小的示例集合作为演示集，从而显著提升LLMs的预测准确率与公平性表现。
链接: https://arxiv.org/abs/2508.17735
作者: Garima Chhikara,Kripabandhu Ghosh,Abhijnan Chakraborty
机构: Indian Institute of Technology Delhi(印度理工学院德里分校); Delhi Technological University(德里技术大学); Indian Institute of Science Education and Research Kolkata(印度科学教育与研究学院加尔各答分校); Indian Institute of Technology Kharagpur(印度理工学院克哈格帕尔分校)
类目: Computation and Language (cs.CL)
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Abstract:Large Language Models (LLMs) are widely used for downstream tasks such as tabular classification, where ensuring fairness in their outputs is critical for inclusivity, equal representation, and responsible AI deployment. This study introduces a novel approach to enhancing LLM performance and fairness through the concept of a dynamic validation set, which evolves alongside the test set, replacing the traditional static validation approach. We also propose an iterative algorithm, SMITE, to select optimal in-context examples, with each example set validated against its corresponding dynamic validation set. The in-context set with the lowest total error is used as the final demonstration set. Our experiments across four different LLMs show that our proposed techniques significantly improve both predictive accuracy and fairness compared to baseline methods. To our knowledge, this is the first study to apply dynamic validation in the context of in-context learning for LLMs.
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[NLP-44] Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models
【速读】： 该论文旨在解决Transformer架构中前馈网络（Feed-Forward Networks, FFNs）在不同层位置上的重要性问题，尤其是在预训练阶段各层FFN对模型性能的贡献是否具有差异。传统研究多依赖已预训练模型进行分析，难以区分结构设计与训练过程的影响，而本文通过从零开始训练多个规模（285M、570M、1.2B参数）和层数（12、24、40层）的模型，提出一种保持总参数量不变但重新分配FFN维度的实验方法：在部分层中增强FFN宽度，在其他层中完全移除FFN。关键发现是，将FFN集中于连续的中间70%层时，模型在多个下游任务上表现优于标准配置，表明FFN的重要性并非均匀分布，而是存在显著的层位置依赖性。
链接: https://arxiv.org/abs/2508.17734
作者: Wataru Ikeda,Kazuki Yano,Ryosuke Takahashi,Jaesung Lee,Keigo Shibata,Jun Suzuki
机构: Tohoku University (东北大学); RIKEN (理化学研究所); NII LLMC (日本国立信息学研究所大语言模型研究中心)
类目: Computation and Language (cs.CL)
备注: Accepted to COLM 2025
Abstract:This study investigates the layerwise importance of feed-forward networks (FFNs) in Transformer-based language models during pretraining. We introduce an experimental approach that, while maintaining the total parameter count, increases the FFN dimensions in some layers and completely removes the FFNs from other layers. Furthermore, since our focus is on the importance of FFNs during pretraining, we train models from scratch to examine whether the importance of FFNs varies depending on their layer positions, rather than using publicly available pretrained models as is frequently done. Through comprehensive evaluations of models with varying sizes (285M, 570M, and 1.2B parameters) and layer counts (12, 24, and 40 layers), we demonstrate that concentrating FFNs in 70% of the consecutive middle layers consistently outperforms standard configurations for multiple downstream tasks.
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[NLP-45] How Do LLM -Generated Texts Impact Term-Based Retrieval Models?
【速读】： 该论文旨在解决大规模语言模型（Large Language Models, LLMs）生成内容日益增多背景下，传统基于词项的检索模型（如BM25）在处理人写文本与机器生成文本混合场景时是否存在源偏倚（source bias）的问题。其核心发现是：LLM生成文本具有更平滑的高频词分布、更陡峭的低频词Zipf斜率、更高的词项特异性及文档层面多样性，这些特征源于LLM训练中对读者体验优化的目标；而term-based检索模型并不表现出对特定来源的偏好，而是优先选择其词项分布最贴近查询的文档，说明其决策机制基于语义匹配而非内容来源。因此，解决方案的关键在于识别并理解LLM生成文本的语言学特性，并澄清term-based检索系统本质上依赖于词项分布匹配而非源偏倚，为后续构建公平、鲁棒的混合源信息检索系统提供理论基础。
链接: https://arxiv.org/abs/2508.17715
作者: Wei Huang,Keping Bi,Yinqiong Cai,Wei Chen,Jiafeng Guo,Xueqi Cheng
机构: State Key Laboratory of AI Safety (人工智能安全国家重点实验室); Institute of Computing Technology (计算技术研究所); Chinese Academy of Sciences (中国科学院); University of Chinese Academy of Sciences (中国科学院大学); Baidu Inc. (百度公司)
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
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Abstract:As more content generated by large language models (LLMs) floods into the Internet, information retrieval (IR) systems now face the challenge of distinguishing and handling a blend of human-authored and machine-generated texts. Recent studies suggest that neural retrievers may exhibit a preferential inclination toward LLM-generated content, while classic term-based retrievers like BM25 tend to favor human-written documents. This paper investigates the influence of LLM-generated content on term-based retrieval models, which are valued for their efficiency and robust generalization across domains. Our linguistic analysis reveals that LLM-generated texts exhibit smoother high-frequency and steeper low-frequency Zipf slopes, higher term specificity, and greater document-level diversity. These traits are aligned with LLMs being trained to optimize reader experience through diverse and precise expressions. Our study further explores whether term-based retrieval models demonstrate source bias, concluding that these models prioritize documents whose term distributions closely correspond to those of the queries, rather than displaying an inherent source bias. This work provides a foundation for understanding and addressing potential biases in term-based IR systems managing mixed-source content.
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[NLP-46] EMPOWER: Evolutionary Medical Prompt Optimization With Reinforcement Learning
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在医疗应用中因提示词（prompt）质量不足而导致的可靠性与临床实用性受限的问题，尤其针对领域特异性医学知识和安全性要求未能充分满足的挑战。其解决方案的关键在于提出EMPOWER框架，该框架通过四项核心技术实现：(1) 医学术语注意力机制（medical terminology attention mechanism），增强对专业术语的敏感性；(2) 多维度评估架构，从清晰度、特异性、临床相关性和事实准确性等角度全面评价提示质量；(3) 保持结构的组件级进化算法（component-level evolutionary algorithm），确保临床推理逻辑完整性；(4) 语义验证模块，保障输出内容符合医学知识体系。实验表明，该方法显著提升了提示质量，在减少事实错误、提高领域特异性和临床偏好方面均取得实质性改进。
链接: https://arxiv.org/abs/2508.17703
作者: Yinda Chen,Yangfan He,Jing Yang,Dapeng Zhang,Zhenlong Yuan,Muhammad Attique Khan,Jamel Baili,Por Lip Yee
机构: University of Science and Technology of China (中国科学技术大学); University of Minnesota-Twin Cities (明尼苏达大学双城分校); Universiti Malaya (马来亚大学); Lanzhou University (兰州大学); Chinese Academy of Sciences (中国科学院); Prince Mohammad bin Fahd University (穆罕默德·本·法赫德国王大学); King Khalid University (国王 Khalid 大学)
类目: Computation and Language (cs.CL)
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Abstract:Prompt engineering significantly influences the reliability and clinical utility of Large Language Models (LLMs) in medical applications. Current optimization approaches inadequately address domain-specific medical knowledge and safety requirements. This paper introduces EMPOWER, a novel evolutionary framework that enhances medical prompt quality through specialized representation learning, multi-dimensional evaluation, and structure-preserving algorithms. Our methodology incorporates: (1) a medical terminology attention mechanism, (2) a comprehensive assessment architecture evaluating clarity, specificity, clinical relevance, and factual accuracy, (3) a component-level evolutionary algorithm preserving clinical reasoning integrity, and (4) a semantic verification module ensuring adherence to medical knowledge. Evaluation across diagnostic, therapeutic, and educational tasks demonstrates significant improvements: 24.7% reduction in factually incorrect content, 19.6% enhancement in domain specificity, and 15.3% higher clinician preference in blinded evaluations. The framework addresses critical challenges in developing clinically appropriate prompts, facilitating more responsible integration of LLMs into healthcare settings.
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[NLP-47] LLM -based Agent ic Reasoning Frameworks: A Survey from Methods to Scenarios
【速读】： 该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的智能体系统（agent systems）在推理框架多样性下缺乏系统性分类与比较的问题。其解决方案的关键在于提出一个统一的形式化语言，将agentic reasoning frameworks划分为单智能体方法（single-agent methods）、工具增强方法（tool-based methods）和多智能体方法（multi-agent methods），并通过跨场景应用分析揭示不同框架在科学发现、医疗、软件工程、社会模拟和经济学等领域的特征与适用性，从而为研究社区提供清晰的全景视图以理解各类框架的优势、适用场景及评估策略。
链接: https://arxiv.org/abs/2508.17692
作者: Bingxi Zhao,Lin Geng Foo,Ping Hu,Christian Theobalt,Hossein Rahmani,Jun Liu
机构: Beijing Jiaotong University (北京交通大学); Lancaster University (兰卡斯特大学); Max Planck Institute for Informatics, Saarland Informatics Campus (马克斯·普朗克信息研究所，萨尔兰信息校区); University of Electronic Science and Technology of China (电子科技大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 51 pages,10 figures,8 tables. Work in progress
Abstract:Recent advances in the intrinsic reasoning capabilities of large language models (LLMs) have given rise to LLM-based agent systems that exhibit near-human performance on a variety of automated tasks. However, although these systems share similarities in terms of their use of LLMs, different reasoning frameworks of the agent system steer and organize the reasoning process in different ways. In this survey, we propose a systematic taxonomy that decomposes agentic reasoning frameworks and analyze how these frameworks dominate framework-level reasoning by comparing their applications across different scenarios. Specifically, we propose an unified formal language to further classify agentic reasoning systems into single-agent methods, tool-based methods, and multi-agent methods. After that, we provide a comprehensive review of their key application scenarios in scientific discovery, healthcare, software engineering, social simulation, and economics. We also analyze the characteristic features of each framework and summarize different evaluation strategies. Our survey aims to provide the research community with a panoramic view to facilitate understanding of the strengths, suitable scenarios, and evaluation practices of different agentic reasoning frameworks.
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[NLP-48] xt Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networks EMNLP2025
【速读】： 该论文旨在解决文本-rich网络中分布外（Out-of-distribution, OOD）检测的挑战，尤其是现有方法多局限于标签偏移或简单的领域划分，未能充分考虑文本特征与拓扑结构之间复杂的交互关系。例如，在社交网络中，节点（用户）具有文本属性（如名称、简介），边表示好友关系，而异常样本可能源于机器人用户与正常用户之间语言模式的差异。为应对这一问题，作者提出TextTopoOOD框架，系统评估四种不同类型的OOD场景：属性级偏移（通过文本增强和嵌入扰动）、结构偏移（通过边重连和语义连接变化）、主题引导的标签偏移以及基于领域的划分。其核心解决方案是TNT-OOD模型，关键创新在于：1）设计了一种新颖的交叉注意力模块，将局部结构信息融合到节点级文本表示中；2）引入HyperNetwork生成节点特定的变换参数，从而对齐内部数据（ID）节点的拓扑与语义特征，显著提升在文本和结构双重偏移下的OOD检测性能。
链接: https://arxiv.org/abs/2508.17690
作者: Danny Wang,Ruihong Qiu,Guangdong Bai,Zi Huang
机构: The University of Queensland (昆士兰大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: EMNLP2025 Main
Abstract:Out-of-distribution (OOD) detection remains challenging in text-rich networks, where textual features intertwine with topological structures. Existing methods primarily address label shifts or rudimentary domain-based splits, overlooking the intricate textual-structural diversity. For example, in social networks, where users represent nodes with textual features (name, bio) while edges indicate friendship status, OOD may stem from the distinct language patterns between bot and normal users. To address this gap, we introduce the TextTopoOOD framework for evaluating detection across diverse OOD scenarios: (1) attribute-level shifts via text augmentations and embedding perturbations; (2) structural shifts through edge rewiring and semantic connections; (3) thematically-guided label shifts; and (4) domain-based divisions. Furthermore, we propose TNT-OOD to model the complex interplay between Text aNd Topology using: 1) a novel cross-attention module to fuse local structure into node-level text representations, and 2) a HyperNetwork to generate node-specific transformation parameters. This aligns topological and semantic features of ID nodes, enhancing ID/OOD distinction across structural and textual shifts. Experiments on 11 datasets across four OOD scenarios demonstrate the nuanced challenge of TextTopoOOD for evaluating OOD detection in text-rich networks.
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[NLP-49] Characterizing the Behavior of Training Mamba-based State Space Models on GPUs
【速读】： 该论文旨在解决基于状态空间模型（State Space Models, SSM）的Mamba架构在GPU上训练时面临的性能瓶颈问题，尤其是其与传统Transformer模型相比虽具有线性计算复杂度优势，但在实际GPU微架构层面仍存在未被充分理解的资源占用特性和优化潜力。解决方案的关键在于构建一个涵盖多种模型架构的代表性工作负载套件，并通过系统性分析揭示Mamba-based SSM在GPU上的执行行为特征，从而为GPU微架构设计提供依据并识别潜在的优化方向。
链接: https://arxiv.org/abs/2508.17679
作者: Trinayan Baruah,Kaustubh Shivdikar,Sara Prescott,David Kaeli
机构: Advanced Micro Devices (AMD); Massachusetts Institute of Technology (MIT); Northeastern University (NEU)
类目: Machine Learning (cs.LG); Hardware Architecture (cs.AR); Computation and Language (cs.CL)
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Abstract:Mamba-based State Space Models (SSM) have emerged as a promising alternative to the ubiquitous transformers. Despite the expressive power of transformers, the quadratic complexity of computing attention is a major impediment to scaling performance as we increase the sequence length. SSMs provide an alternative path that addresses this problem, reducing the computational complexity requirements of self-attention with novel model architectures for different domains and fields such as video, text generation and graphs. Thus, it is important to characterize the behavior of these emerging workloads on GPUs and understand their requirements during GPU microarchitectural design. In this work we evaluate Mamba-based SSMs and characterize their behavior during training on GPUs. We construct a workload suite that offers representative models that span different model architectures. We then use this suite to analyze the architectural implications of running Mamba-based SSMs on GPUs. Our work sheds new light on potential optimizations to continue scaling the performance for such models.
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[NLP-50] CoCoA: Confidence- and Context-Aware Adaptive Decoding for Resolving Knowledge Conflicts in Large Language Models EMNLP’25
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在生成过程中因参数化记忆（parametric memory）与外部上下文（external context）之间知识冲突而导致的忠实性下降问题。现有对比解码方法虽针对冲突场景设计，但适应性不足，且在低冲突环境下可能损害性能。解决方案的关键在于提出一种新的逐 token 级别自适应解码算法 CoCoA（Confidence- and Context-Aware Adaptive Decoding），其通过引入置信度感知指标（熵差和上下文集中度）以及参数分布与上下文分布间的广义散度来实现冲突的原理性化解，并保持在低冲突场景下的高性能表现，从而显著提升问答、摘要和长文本问答任务中的事实准确性和生成忠实性。
链接: https://arxiv.org/abs/2508.17670
作者: Anant Khandelwal,Manish Gupta,Puneet Agrawal
机构: Microsoft(微软)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP’25, Main. 21 pages, 17 tables, 3 Figures
Abstract:Faithful generation in large language models (LLMs) is challenged by knowledge conflicts between parametric memory and external context. Existing contrastive decoding methods tuned specifically to handle conflict often lack adaptability and can degrade performance in low conflict settings. We introduce CoCoA (Confidence- and Context-Aware Adaptive Decoding), a novel token-level algorithm for principled conflict resolution and enhanced faithfulness. CoCoA resolves conflict by utilizing confidence-aware measures (entropy gap and contextual peakedness) and the generalized divergence between the parametric and contextual distributions. Crucially, CoCoA maintains strong performance even in low conflict settings. Extensive experiments across multiple LLMs on diverse Question Answering (QA), Summarization, and Long-Form Question Answering (LFQA) benchmarks demonstrate CoCoA’s state-of-the-art performance over strong baselines like AdaCAD. It yields significant gains in QA accuracy, up to 9.2 points on average compared to the strong baseline AdaCAD, and improves factuality in summarization and LFQA by up to 2.5 points on average across key benchmarks. Additionally, it demonstrates superior sensitivity to conflict variations. CoCoA enables more informed, context-aware, and ultimately more faithful token generation.
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[NLP-51] SurveyGen: Quality-Aware Scientific Survey Generation with Large Language Models
【速读】： 该论文旨在解决自动文献综述生成（automatic survey generation）中缺乏标准化评估数据集的问题，从而阻碍了对大语言模型（Large Language Models, LLMs）生成质量的严谨评估。其解决方案的关键在于构建SurveyGen——一个包含超过4,200篇跨学科人工撰写的文献综述、242,143条引用文献及详尽质量元数据的大规模数据集，并在此基础上提出QUAL-SG框架，该框架通过在标准检索增强生成（Retrieval-Augmented Generation, RAG）流程中引入质量感知指标来筛选高质量源论文，从而提升生成综述的内容质量和学术可信度。
链接: https://arxiv.org/abs/2508.17647
作者: Tong Bao,Mir Tafseer Nayeem,Davood Rafiei,Chengzhi Zhang
机构: Nanjing University of Science and Technology (南京理工大学); University of Alberta (阿尔伯塔大学)
类目: Computation and Language (cs.CL); Digital Libraries (cs.DL); Information Retrieval (cs.IR)
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Abstract:Automatic survey generation has emerged as a key task in scientific document processing. While large language models (LLMs) have shown promise in generating survey texts, the lack of standardized evaluation datasets critically hampers rigorous assessment of their performance against human-written surveys. In this work, we present SurveyGen, a large-scale dataset comprising over 4,200 human-written surveys across diverse scientific domains, along with 242,143 cited references and extensive quality-related metadata for both the surveys and the cited papers. Leveraging this resource, we build QUAL-SG, a novel quality-aware framework for survey generation that enhances the standard Retrieval-Augmented Generation (RAG) pipeline by incorporating quality-aware indicators into literature retrieval to assess and select higher-quality source papers. Using this dataset and framework, we systematically evaluate state-of-the-art LLMs under varying levels of human involvement - from fully automatic generation to human-guided writing. Experimental results and human evaluations show that while semi-automatic pipelines can achieve partially competitive outcomes, fully automatic survey generation still suffers from low citation quality and limited critical analysis.
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[NLP-52] Dynamic Embedding of Hierarchical Visual Features for Efficient Vision-Language Fine-Tuning
【速读】： 该论文旨在解决大型视觉语言模型（Large Vision-Language Models, LVLMs）在处理多模态输入时因视觉特征与文本 token 拼接导致的序列长度激增问题，从而引发显著计算开销。现有方法虽尝试将视觉信息融合至语言模型（Large Language Models, LLMs）的中间层以缓解序列膨胀，但常忽略模型内部的层次语义结构及浅层视觉编码器中细粒度的视觉信息。其解决方案的关键在于提出一种基于动态嵌入与分层视觉特征融合（Dynamic Embedding and Hierarchical Visual Feature Fusion, DEHVF）的高效微调方法：利用视觉编码器和语言模型固有的层次表示特性，通过轻量级分层视觉融合模块，根据 LLM 各层内部表示动态选择并融合对应语义粒度的多层视觉特征，并将其投影对齐后直接嵌入至相应层的前馈网络（Feed-Forward Network, FFN）中，从而避免序列扩展、实现跨模态信息在相同语义粒度上的精准对齐与互补。
链接: https://arxiv.org/abs/2508.17638
作者: Xinyu Wei,Guoli Yang,Jialu Zhou,Mingyue Yang,Leqian Li,Kedi Zhang,Chunping Qiu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
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Abstract:Large Vision-Language Models (LVLMs) commonly follow a paradigm that projects visual features and then concatenates them with text tokens to form a unified sequence input for Large Language Models (LLMs). However, this paradigm leads to a significant increase in the length of the input sequence, resulting in substantial computational overhead. Existing methods attempt to fuse visual information into the intermediate layers of LLMs, which alleviate the sequence length issue but often neglect the hierarchical semantic representations within the model and the fine-grained visual information available in the shallower visual encoding layers. To address this limitation, we propose DEHVF, an efficient vision-language fine-tuning method based on dynamic embedding and fusion of hierarchical visual features. Its core lies in leveraging the inherent hierarchical representation characteristics of visual encoders and language models. Through a lightweight hierarchical visual fuser, it dynamically selects and fuses hierarchical features corresponding to semantic granularity based on the internal representations of each layer in LLMs. The fused layer-related visual features are then projected and aligned before being directly embedded into the Feed-Forward Network (FFN) of the corresponding layer in LLMs. This approach not only avoids sequence expansion but also dynamically fuses multi-layer visual information. By fine-tuning only a small number of parameters, DEHVF achieves precise alignment and complementarity of cross-modal information at the same semantic granularity. We conducted experiments across various VL benchmarks, including visual question answering on ScienceQA and image captioning on COCO Captions. The results demonstrate that DEHVF achieves higher accuracy than existing parameter-efficient fine-tuning (PEFT) baselines while maintaining efficient training and inference.
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[NLP-53] Weights-Rotated Preference Optimization for Large Language Models EMNLP2025
【速读】： 该论文旨在解决直接偏好优化（Direct Preference Optimization, DPO）在对齐大语言模型（Large Language Models, LLMs）过程中存在的奖励欺骗（reward hacking）问题，即模型通过过度降低被拒绝完成文本的概率来获取高奖励，而非真正实现预期目标，从而导致生成内容冗长、缺乏多样性以及灾难性遗忘知识。其解决方案的关键在于识别出问题根源为参数空间中的神经元坍缩（neuron collapse）所引发的表示冗余，并提出一种权重旋转偏好优化（Weights-Rotated Preference Optimization, RoPO）算法：该方法通过隐式地利用DPO继承的KL散度约束输出层logits，同时显式地通过多粒度正交矩阵微调约束中间隐藏状态，从而防止策略模型偏离参考模型过远，有效保留预训练和监督微调（Supervised Fine-Tuning, SFT）阶段获得的知识与表达能力。
链接: https://arxiv.org/abs/2508.17637
作者: Chenxu Yang,Ruipeng Jia,Mingyu Zheng,Naibin Gu,Zheng Lin,Siyuan Chen,Weichong Yin,Hua Wu,Weiping Wang
机构: Institute of Information Engineering, Chinese Academy of Sciences (中国科学院信息工程研究所); School of Cyber Security, University of Chinese Academy of Sciences (中国科学院大学网络空间安全学院); Baidu Inc. (百度公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: EMNLP 2025
Abstract:Despite the efficacy of Direct Preference Optimization (DPO) in aligning Large Language Models (LLMs), reward hacking remains a pivotal challenge. This issue emerges when LLMs excessively reduce the probability of rejected completions to achieve high rewards, without genuinely meeting their intended goals. As a result, this leads to overly lengthy generation lacking diversity, as well as catastrophic forgetting of knowledge. We investigate the underlying reason behind this issue, which is representation redundancy caused by neuron collapse in the parameter space. Hence, we propose a novel Weights-Rotated Preference Optimization (RoPO) algorithm, which implicitly constrains the output layer logits with the KL divergence inherited from DPO and explicitly constrains the intermediate hidden states by fine-tuning on a multi-granularity orthogonal matrix. This design prevents the policy model from deviating too far from the reference model, thereby retaining the knowledge and expressive capabilities acquired during pre-training and SFT stages. Our RoPO achieves up to a 3.27-point improvement on AlpacaEval 2, and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters, demonstrating its effectiveness in alleviating the reward hacking problem of DPO.
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[NLP-54] Stop Spinning Wheels: Mitigating LLM Overthinking via Mining Patterns for Early Reasoning Exit
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在复杂推理任务中因“过度思考”（overthinking）而导致资源浪费甚至陷入无限循环的问题。研究表明，LLM的推理过程可划分为三个阶段：探索不足阶段、补偿性推理阶段和推理收敛阶段；其中，正确答案通常出现在补偿性推理阶段的末尾，即推理完成点（Reasoning Completion Point, RCP）。解决方案的关键在于准确识别RCP，从而在推理尚未进入收敛阶段时及时终止生成，避免无效计算。为此，作者挖掘了更敏感且一致的RCP模式，并提出一种基于启发式规则的轻量级阈值策略，实验表明该方法可在保持或提升推理准确性的同时显著降低token消耗。
链接: https://arxiv.org/abs/2508.17627
作者: Zihao Wei,Liang Pang,Jiahao Liu,Jingcheng Deng,Shicheng Xu,Zenghao Duan,Jingang Wang,Fei Sun,Xunliang Cai,Huawei Shen,Xueqi Cheng
机构: 1. Alibaba Group (阿里巴巴集团); 2. Hangzhou Dianzi University (杭州电子科技大学); 3. Tsinghua University (清华大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) enhance complex reasoning tasks by scaling the individual thinking process. However, prior work shows that overthinking can degrade overall performance. Motivated by observed patterns in thinking length and content length, we categorize reasoning into three stages: insufficient exploration stage, compensatory reasoning stage, and reasoning convergence stage. Typically, LLMs produce correct answers in the compensatory reasoning stage, whereas reasoning convergence often triggers overthinking, causing increased resource usage or even infinite loops. Therefore, mitigating overthinking hinges on detecting the end of the compensatory reasoning stage, defined as the Reasoning Completion Point (RCP). RCP typically appears at the end of the first complete reasoning cycle and can be identified by querying the LLM sentence by sentence or monitoring the probability of an end-of-thinking token (e.g., \texttt/think), though these methods lack an efficient and precise balance. To improve this, we mine more sensitive and consistent RCP patterns and develop a lightweight thresholding strategy based on heuristic rules. Experimental evaluations on benchmarks (AIME24, AIME25, GPQA-D) demonstrate that the proposed method reduces token consumption while preserving or enhancing reasoning accuracy.
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[NLP-55] EMO-Reasoning : Benchmarking Emotional Reasoning Capabilities in Spoken Dialogue Systems
【速读】： 该论文旨在解决当前对话系统在情感推理（Emotional Reasoning）方面缺乏全面评估框架的问题，尤其是如何有效衡量多轮对话中情绪状态的连贯性与合理性。其解决方案的关键在于提出一个名为EMO-Reasoning的基准测试体系，通过文本转语音（Text-to-Speech, TTS）生成多样化情感语料以缓解真实情感语音数据稀缺的问题，并引入跨轮次情感推理评分（Cross-turn Emotion Reasoning Score）来量化情绪转换的一致性，从而系统性地检测和分析对话系统中的情感不一致现象，为提升情感感知型语音对话建模提供可量化的改进方向。
链接: https://arxiv.org/abs/2508.17623
作者: Jingwen Liu,Kan Jen Cheng,Jiachen Lian,Akshay Anand,Rishi Jain,Faith Qiao,Robin Netzorg,Huang-Cheng Chou,Tingle Li,Guan-Ting Lin,Gopala Anumanchipalli
机构: Zhejiang University (浙江大学); UC Berkeley (加州大学伯克利分校); National Taiwan University (台湾国立大学); University of Southern California (南加州大学)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
备注: Accepted at (ASRU 2025) 2025 IEEE Automatic Speech Recognition and Understanding Workshop
Abstract:Speech emotions play a crucial role in human-computer interaction, shaping engagement and context-aware communication. Despite recent advances in spoken dialogue systems, a holistic system for evaluating emotional reasoning is still lacking. To address this, we introduce EMO-Reasoning, a benchmark for assessing emotional coherence in dialogue systems. It leverages a curated dataset generated via text-to-speech to simulate diverse emotional states, overcoming the scarcity of emotional speech data. We further propose the Cross-turn Emotion Reasoning Score to assess the emotion transitions in multi-turn dialogues. Evaluating seven dialogue systems through continuous, categorical, and perceptual metrics, we show that our framework effectively detects emotional inconsistencies, providing insights for improving current dialogue systems. By releasing a systematic evaluation benchmark, we aim to advance emotion-aware spoken dialogue modeling toward more natural and adaptive interactions.
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[NLP-56] Steering When Necessary: Flexible Steering Large Language Models with Backtracking
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理阶段难以有效对齐期望行为的问题，尤其是在现有激活调控（activation steering）方法中，干预策略要么无差别地作用于所有生成内容，要么仅依据输入问题决定干预时机与强度，导致难以精确控制干预力度。其解决方案的关键在于提出一种灵活的激活调控框架——带回溯机制的柔性激活调控（Flexible Activation Steering with Backtracking, FASB），该框架通过动态追踪模型内部状态，在生成过程中同时结合问题和已生成内容来判断是否需要干预以及干预强度；此外，为避免因滞后干预而导致的偏差累积，进一步引入回溯机制（backtracking mechanism），主动修正已生成的偏离目标的token，从而引导模型向预期行为方向演化。
链接: https://arxiv.org/abs/2508.17621
作者: Jinwei Gan,Zifeng Cheng,Zhiwei Jiang,Cong Wang,Yafeng Yin,Xiang Luo,Yuchen Fu,Qing Gu
机构: Nanjing University (南京大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Large language models (LLMs) have achieved remarkable performance across many generation tasks. Nevertheless, effectively aligning them with desired behaviors remains a significant challenge. Activation steering is an effective and cost-efficient approach that directly modifies the activations of LLMs during the inference stage, aligning their responses with the desired behaviors and avoiding the high cost of fine-tuning. Existing methods typically indiscriminately intervene to all generations or rely solely on the question to determine intervention, which limits the accurate assessment of the intervention strength. To this end, we propose the Flexible Activation Steering with Backtracking (FASB) framework, which dynamically determines both the necessity and strength of intervention by tracking the internal states of the LLMs during generation, considering both the question and the generated content. Since intervening after detecting a deviation from the desired behavior is often too late, we further propose the backtracking mechanism to correct the deviated tokens and steer the LLMs toward the desired behavior. Extensive experiments on the TruthfulQA dataset and six multiple-choice datasets demonstrate that our method outperforms baselines. Our code will be released at this https URL.
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[NLP-57] Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions EMNLP2025
【速读】： 该论文旨在解决模型压缩中的公平性问题，特别是针对大语言模型（Large Language Models, LLMs）在后训练剪枝（post-training pruning）过程中对意见摘要（opinion summarisation）任务中公平性影响的未知性。现有研究表明剪枝可有效降低模型规模与计算开销，但其对生成内容偏倚的影响尚未被系统探讨，尤其在涉及公众舆论导向的任务中可能带来风险。论文提出的关键解决方案是High Gradient Low Activation (HGLA) 剪枝方法，该方法通过识别并移除对输入处理冗余但显著影响输出生成的参数，从而在压缩模型的同时保持甚至提升公平性表现。实验表明，HGLA相较现有剪枝方法更能维持或改善公平性指标，并在人工评估中展现出更优的公平性输出，为高效率且公平的模型部署提供了新路径。
链接: https://arxiv.org/abs/2508.17610
作者: Nannan Huang,Haytham Fayek,Xiuzhen Zhang
机构: RMIT University (皇家墨尔本理工大学)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2025 Main Conference
Abstract:Model compression through post-training pruning offers a way to reduce model size and computational requirements without significantly impacting model performance. However, the effect of pruning on the fairness of LLM-generated summaries remains unexplored, particularly for opinion summarisation where biased outputs could influence public this http URL this paper, we present a comprehensive empirical analysis of opinion summarisation, examining three state-of-the-art pruning methods and various calibration sets across three open-source LLMs using four fairness metrics. Our systematic analysis reveals that pruning methods have a greater impact on fairness than calibration sets. Building on these insights, we propose High Gradient Low Activation (HGLA) pruning, which identifies and removes parameters that are redundant for input processing but influential in output generation. Our experiments demonstrate that HGLA can better maintain or even improve fairness compared to existing methods, showing promise across models and tasks where traditional methods have limitations. Our human evaluation shows HGLA-generated outputs are fairer than existing state-of-the-art pruning methods. Code is available at: this https URL.
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[NLP-58] RubikSQL: Lifelong Learning Agent ic Knowledge Base as an Industrial NL2SQL System VLDB2026 VLDB
【速读】： 该论文旨在解决企业级自然语言到SQL（NL2SQL）系统在实际应用中面临的两大核心挑战：隐式意图识别和领域特定术语理解。为应对这些问题，作者提出了一种名为RubikSQL的新一代NL2SQL系统，其关键创新在于将NL2SQL建模为一个持续学习（lifelong learning）任务，要求同时进行知识库（Knowledge Base, KB）维护与SQL生成。解决方案的核心在于通过数据库剖析（database profiling）、结构化信息抽取、代理驱动的规则挖掘（agentic rule mining）以及基于思维链（Chain-of-Thought, CoT）增强的SQL剖析等技术，系统性地构建并迭代优化KB；随后利用多智能体（multi-agent）工作流来高效利用该结构化知识库，从而显著提升SQL生成的准确性。该方法在KaggleDBQA和BIRD Mini-Dev数据集上达到了当前最优（SOTA）性能，并发布了RubikBench基准，专门用于刻画工业场景下NL2SQL的关键特性，推动未来研究发展。
链接: https://arxiv.org/abs/2508.17590
作者: Zui Chen,Han Li,Xinhao Zhang,Xiaoyu Chen,Chunyin Dong,Yifeng Wang,Xin Cai,Su Zhang,Ziqi Li,Chi Ding,Jinxu Li,Shuai Wang,Dousheng Zhao,Sanhai Gao,Guangyi Liu
机构: Huawei Company(华为公司); Cornell University(康奈尔大学)
类目: Databases (cs.DB); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
备注: 18 pages, 3 figures, 3 tables, to be submitted to VLDB 2026 (PVLDB Volume 19)
Abstract:We present RubikSQL, a novel NL2SQL system designed to address key challenges in real-world enterprise-level NL2SQL, such as implicit intents and domain-specific terminology. RubikSQL frames NL2SQL as a lifelong learning task, demanding both Knowledge Base (KB) maintenance and SQL generation. RubikSQL systematically builds and refines its KB through techniques including database profiling, structured information extraction, agentic rule mining, and Chain-of-Thought (CoT)-enhanced SQL profiling. RubikSQL then employs a multi-agent workflow to leverage this curated KB, generating accurate SQLs. RubikSQL achieves SOTA performance on both the KaggleDBQA and BIRD Mini-Dev datasets. Finally, we release the RubikBench benchmark, a new benchmark specifically designed to capture vital traits of industrial NL2SQL scenarios, providing a valuable resource for future research.
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[NLP-59] UQ: Assessing Language Models on Unsolved Questions
【速读】： 该论文试图解决当前AI基准测试中存在的“难度-现实性矛盾”问题：传统考试式基准往往人为制造难题但缺乏实际应用价值，而基于真实用户交互的基准则偏向简单且高频的问题。解决方案的关键在于提出一种全新的评估范式——UQ（Unsolved Questions），即以未被解决的问题作为评估对象，通过异步、动态的方式对模型进行长期验证。其核心创新包括：(1) 构建高质量的500个多样化未解问题数据集（UQ-Dataset），结合规则过滤、大语言模型（LLM）判官与人工审核确保问题的明确性和挑战性；(2) 设计复合验证策略（UQ-Validators），利用生成器与验证者之间的能力差距提供评估信号并预筛候选答案；(3) 搭建开放平台（UQ-Platform），由专家社区共同验证问题和答案。实验表明，顶级模型仅在15%的问题上通过验证，且初步人工核查已识别出正确答案，证明该方法能有效推动前沿模型应对具有真实世界意义的开放式挑战。
链接: https://arxiv.org/abs/2508.17580
作者: Fan Nie,Ken Ziyu Liu,Zihao Wang,Rui Sun,Wei Liu,Weijia Shi,Huaxiu Yao,Linjun Zhang,Andrew Y. Ng,James Zou,Sanmi Koyejo,Yejin Choi,Percy Liang,Niklas Muennighoff
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: FN, KZL, and NM are project co-leads and contributed equally. Project website: this https URL
Abstract:Benchmarks shape progress in AI research. A useful benchmark should be both difficult and realistic: questions should challenge frontier models while also reflecting real-world usage. Yet, current paradigms face a difficulty-realism tension: exam-style benchmarks are often made artificially difficult with limited real-world value, while benchmarks based on real user interaction often skew toward easy, high-frequency problems. In this work, we explore a radically different paradigm: assessing models on unsolved questions. Rather than a static benchmark scored once, we curate unsolved questions and evaluate models asynchronously over time with validator-assisted screening and community verification. We introduce UQ, a testbed of 500 challenging, diverse questions sourced from Stack Exchange, spanning topics from CS theory and math to sci-fi and history, probing capabilities including reasoning, factuality, and browsing. UQ is difficult and realistic by construction: unsolved questions are often hard and naturally arise when humans seek answers, thus solving them yields direct real-world value. Our contributions are threefold: (1) UQ-Dataset and its collection pipeline combining rule-based filters, LLM judges, and human review to ensure question quality (e.g., well-defined and difficult); (2) UQ-Validators, compound validation strategies that leverage the generator-validator gap to provide evaluation signals and pre-screen candidate solutions for human review; and (3) UQ-Platform, an open platform where experts collectively verify questions and solutions. The top model passes UQ-validation on only 15% of questions, and preliminary human verification has already identified correct answers among those that passed. UQ charts a path for evaluating frontier models on real-world, open-ended challenges, where success pushes the frontier of human knowledge. We release UQ at this https URL.
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[NLP-60] CausalSent: Interpretable Sentiment Classification with RieszNet
【速读】： 该论文旨在解决当前自然语言处理（Natural Language Processing, NLP）模型决策过程缺乏可解释性的问题，即模型输出被视为“黑箱”，难以明确文本特征对预测结果的因果影响。为应对这一挑战，作者提出了一种基于因果推断的可解释性增强框架 CausalSent，其核心创新在于设计了一个双头 RieszNet 神经网络架构，以提升治疗效应（treatment effect）估计的准确性。该方案通过在训练过程中引入因果正则化机制，使模型能够更精确地捕捉特定文本特征（如关键词“love”）对目标变量（如情感倾向）的因果影响，在半合成 IMDB 电影评论数据集上将效应估计的平均绝对误差（MAE）降低了 2–3 倍，显著优于 Bansal 等人的基线方法。
链接: https://arxiv.org/abs/2508.17576
作者: Daniel Frees,Martin Pollack
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Despite the overwhelming performance improvements offered by recent natural language procesing (NLP) models, the decisions made by these models are largely a black box. Towards closing this gap, the field of causal NLP combines causal inference literature with modern NLP models to elucidate causal effects of text features. We replicate and extend Bansal et al’s work on regularizing text classifiers to adhere to estimated effects, focusing instead on model interpretability. Specifically, we focus on developing a two-headed RieszNet-based neural network architecture which achieves better treatment effect estimation accuracy. Our framework, CausalSent, accurately predicts treatment effects in semi-synthetic IMDB movie reviews, reducing MAE of effect estimates by 2-3x compared to Bansal et al’s MAE on synthetic Civil Comments data. With an ensemble of validated models, we perform an observational case study on the causal effect of the word “love” in IMDB movie reviews, finding that the presence of the word “love” causes a +2.9% increase in the probability of a positive sentiment.
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[NLP-61] Humanizing Machines: Rethinking LLM Anthropomorphism Through a Multi-Level Framework of Design EMNLP
【速读】： 该论文旨在解决当前关于大语言模型（Large Language Models, LLMs）中拟人化（anthropomorphism）研究过度聚焦于风险（如用户过度信任和欺骗），而缺乏对如何系统性设计拟人化特征以支持用户目标的指导这一问题。其解决方案的关键在于将拟人化重新定义为一种可被有意调节的设计概念（design concept），并提出一个基于设计师与使用者之间互动机制的统一分类框架：该框架通过四类线索（perceptive、linguistic、behavioral 和 cognitive）来映射设计者嵌入的信号与使用者认知响应之间的关系，并提供可操作的设计杠杆，从而推动以功能为导向的拟人化设计评估与实践。
链接: https://arxiv.org/abs/2508.17573
作者: Yunze Xiao,Lynnette Hui Xian Ng,Jiarui Liu,Mona T. Diab
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL)
备注: Accepted in EMNLP main proceedings
Abstract:Large Language Models (LLMs) increasingly exhibit \textbfanthropomorphism characteristics – human-like qualities portrayed across their outlook, language, behavior, and reasoning functions. Such characteristics enable more intuitive and engaging human-AI interactions. However, current research on anthropomorphism remains predominantly risk-focused, emphasizing over-trust and user deception while offering limited design guidance. We argue that anthropomorphism should instead be treated as a \emphconcept of design that can be intentionally tuned to support user goals. Drawing from multiple disciplines, we propose that the anthropomorphism of an LLM-based artifact should reflect the interaction between artifact designers and interpreters. This interaction is facilitated by cues embedded in the artifact by the designers and the (cognitive) responses of the interpreters to the cues. Cues are categorized into four dimensions: \textitperceptive, linguistic, behavioral, and \textitcognitive. By analyzing the manifestation and effectiveness of each cue, we provide a unified taxonomy with actionable levers for practitioners. Consequently, we advocate for function-oriented evaluations of anthropomorphic design.
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[NLP-62] Activation Transport Operators
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中残差流（residual stream）内特征传输机制不明确的问题，即尚未清晰理解特征如何在线性读写操作下从上游层传递至下游层。这一问题影响了对模型错误早期检测、安全防护（如越狱攻击防御）以及计算过程线性行为识别的准确性。解决方案的关键在于提出激活传输算子（Activation Transport Operators, ATO），这是一种基于特征空间中下游稀疏自动编码器（SAE）解码器投影的线性映射，用于判断某一特征是否通过线性方式从k层前的残差流传输而来，而非由当前层非线性计算合成。研究进一步定义了**传输效率（transport efficiency）**并提供其理论上限，从而量化了残差流中参与线性传输的子空间规模。该方法无需微调（no finetuning），仅需50 GPU小时计算资源，为LLM的安全分析与调试提供了高效且可解释的工具。
链接: https://arxiv.org/abs/2508.17540
作者: Andrzej Szablewski,Marek Masiak
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 4 pages, 4 figures, references and appendices
Abstract:The residual stream mediates communication between transformer decoder layers via linear reads and writes of non-linear computations. While sparse-dictionary learning-based methods locate features in the residual stream, and activation patching methods discover circuits within the model, the mechanism by which features flow through the residual stream remains understudied. Understanding this dynamic can better inform jailbreaking protections, enable early detection of model mistakes, and their correction. In this work, we propose Activation Transport Operators (ATO), linear maps from upstream to downstream residuals k layers later, evaluated in feature space using downstream SAE decoder projections. We empirically demonstrate that these operators can determine whether a feature has been linearly transported from a previous layer or synthesised from non-linear layer computation. We develop the notion of transport efficiency, for which we provide an upper bound, and use it to estimate the size of the residual stream subspace that corresponds to linear transport. We empirically demonstrate the linear transport, report transport efficiency and the size of the residual stream’s subspace involved in linear transport. This compute-light (no finetuning, 50 GPU-h) method offers practical tools for safety, debugging, and a clearer picture of where computation in LLMs behaves linearly.
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[NLP-63] Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models ?
【速读】： 该论文旨在解决多智能体辩论（Multi-Agent Debate, MAD）提升大语言模型性能的机制不明确问题，特别是其有效性究竟源于集体投票（Majority Voting）还是智能体间的辩论过程。解决方案的关键在于将MAD解耦为两个核心组件——多数投票与跨智能体辩论，并通过在七个自然语言处理（NLP）基准上的系统实验发现：多数投票单独即可解释大部分性能增益；进一步地，作者提出一个基于随机过程的理论框架，证明辩论本身不会提高期望正确性（因诱导信念轨迹形成鞅），但通过引入偏向修正的信念更新干预策略，可显著增强辩论效果。研究结论表明，在多数实际场景中，简单集成方法仍是最可靠的选择。
链接: https://arxiv.org/abs/2508.17536
作者: Hyeong Kyu Choi,Xiaojin Zhu,Yixuan Li
机构: University of Wisconsin-Madison (威斯康星大学麦迪逊分校)
类目: Computation and Language (cs.CL); Multiagent Systems (cs.MA)
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Abstract:Multi-Agent Debate~(MAD) has emerged as a promising paradigm for improving the performance of large language models through collaborative reasoning. Despite recent advances, the key factors driving MAD’s effectiveness remain unclear. In this work, we disentangle MAD into two key components–Majority Voting and inter-agent Debate–and assess their respective contributions. Through extensive experiments across seven NLP benchmarks, we find that Majority Voting alone accounts for most of the performance gains typically attributed to MAD. To explain this, we propose a theoretical framework that models debate as a stochastic process. We prove that it induces a martingale over agents’ belief trajectories, implying that debate alone does not improve expected correctness. Guided by these insights, we demonstrate that targeted interventions, by biasing the belief update toward correction, can meaningfully enhance debate effectiveness. Overall, our findings suggest that while MAD has potential, simple ensembling methods remain strong and more reliable alternatives in many practical settings. Code is released in this https URL.
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[NLP-64] Improving French Synthetic Speech Quality via SSML Prosody Control
【速读】： 该论文旨在解决当前商用文本到语音（Text-to-Speech, TTS）系统中合成语音缺乏表现力的问题，其核心瓶颈在于对韵律（prosody）控制能力有限。解决方案的关键在于构建首个端到端的流水线，利用两个QLoRA微调后的Qwen 2.5-7B模型组成的级联架构：第一个模型预测语义短语边界（phrase-break positions），第二个模型回归音高、语速、音量和停顿时间等韵律目标值，并生成兼容商业TTS系统的语音合成标记语言（SSML）标签。该方法在14小时法语播客语料上的评估显示，断句准确率达到99.2% F1，且在音高、语速和音量上的平均绝对误差降低25%-40%，感知评测中自然度显著提升（MOS从3.20升至3.87，p < 0.005），验证了其在增强合成语音表现力方面的有效性。
链接: https://arxiv.org/abs/2508.17494
作者: Nassima Ould Ouali,Awais Hussain Sani,Ruben Bueno,Jonah Dauvet,Tim Luka Horstmann,Eric Moulines
机构: École Polytechnique (法国综合理工学院); Hi! PARIS Research Center (Hi! 巴黎研究中心); McGill University (麦吉尔大学)
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: 13 pages, 9 figures, 6 tables. Accepted for presentation at ICNLSP 2025 (Odense, Denmark). Code and demo: this https URL . ACM Class: I.2.7; H.5.5
Abstract:Despite recent advances, synthetic voices often lack expressiveness due to limited prosody control in commercial text-to-speech (TTS) systems. We introduce the first end-to-end pipeline that inserts Speech Synthesis Markup Language (SSML) tags into French text to control pitch, speaking rate, volume, and pause duration. We employ a cascaded architecture with two QLoRA-fine-tuned Qwen 2.5-7B models: one predicts phrase-break positions and the other performs regression on prosodic targets, generating commercial TTS-compatible SSML markup. Evaluated on a 14-hour French podcast corpus, our method achieves 99.2% F1 for break placement and reduces mean absolute error on pitch, rate, and volume by 25-40% compared with prompting-only large language models (LLMs) and a BiLSTM baseline. In perceptual evaluation involving 18 participants across over 9 hours of synthesized audio, SSML-enhanced speech generated by our pipeline significantly improves naturalness, with the mean opinion score increasing from 3.20 to 3.87 (p 0.005). Additionally, 15 of 18 listeners preferred our enhanced synthesis. These results demonstrate substantial progress in bridging the expressiveness gap between synthetic and natural French speech. Our code is publicly available at this https URL.
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[NLP-65] Efficient Zero-Shot Long Document Classification by Reducing Context Through Sentence Ranking
【速读】： 该论文旨在解决基于Transformer的模型（如BERT）在长文档分类（Long Document Classification, LDC）任务中因输入长度限制和计算效率低下而导致性能下降的问题。解决方案的关键在于提出一种无需微调（zero-shot）的高效方法，通过句子排序策略对文档进行上下文压缩：利用TF-IDF算法对句子重要性进行评分并保留排名靠前的句子，从而在不改变模型架构的前提下显著减少输入长度。实验表明，仅保留Top 50%高分句子即可维持与全文档推理相当的分类准确率，同时将推理时间缩短最多达35%，验证了句子排序作为轻量级、可扩展的LDC解决方案的有效性。
链接: https://arxiv.org/abs/2508.17490
作者: Prathamesh Kokate,Mitali Sarnaik,Manavi Khopade,Mukta Takalikar,Raviraj Joshi
机构: Pune Institute of Computer Technology (普奈计算机技术研究所); Indian Institute of Technology Madras (印度理工学院马德拉斯分校); L3Cube Labs (L3Cube 实验室)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Transformer-based models like BERT excel at short text classification but struggle with long document classification (LDC) due to input length limitations and computational inefficiencies. In this work, we propose an efficient, zero-shot approach to LDC that leverages sentence ranking to reduce input context without altering the model architecture. Our method enables the adaptation of models trained on short texts, such as headlines, to long-form documents by selecting the most informative sentences using a TF-IDF-based ranking strategy. Using the MahaNews dataset of long Marathi news articles, we evaluate three context reduction strategies that prioritize essential content while preserving classification accuracy. Our results show that retaining only the top 50% ranked sentences maintains performance comparable to full-document inference while reducing inference time by up to 35%. This demonstrates that sentence ranking is a simple yet effective technique for scalable and efficient zero-shot LDC.
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[NLP-66] Evaluating the Impact of Verbal Multiword Expressions on Machine Translation
【速读】： 该论文旨在解决生成式 AI (Generative AI) 在机器翻译（Machine Translation, MT）中处理**言语多词表达（Verbal Multiword Expressions, VMWEs）**时质量下降的问题，尤其是针对三类复杂且非组合性的VMWE结构：动词习语（verbal idioms）、动词+介词/副词结构（verb-particle constructions）和轻动词结构（light verb constructions）。实验表明，这些表达显著降低翻译准确性。解决方案的关键在于提出一种基于大语言模型（Large Language Models, LLMs）的改写方法，将VMWEs替换为其字面意义形式，从而提升翻译系统对动词习语和动词+介词结构的处理能力，实验证明该策略在多个语言对上取得了显著的质量改善。
链接: https://arxiv.org/abs/2508.17458
作者: Linfeng Liu,Saptarshi Ghosh,Tianyu Jiang
机构: University of Cincinnati (辛辛那提大学)
类目: Computation and Language (cs.CL)
备注: 29 pages, 13 figures
Abstract:Verbal multiword expressions (VMWEs) present significant challenges for natural language processing due to their complex and often non-compositional nature. While machine translation models have seen significant improvement with the advent of language models in recent years, accurately translating these complex linguistic structures remains an open problem. In this study, we analyze the impact of three VMWE categories – verbal idioms, verb-particle constructions, and light verb constructions – on machine translation quality from English to multiple languages. Using both established multiword expression datasets and sentences containing these language phenomena extracted from machine translation datasets, we evaluate how state-of-the-art translation systems handle these expressions. Our experimental results consistently show that VMWEs negatively affect translation quality. We also propose an LLM-based paraphrasing approach that replaces these expressions with their literal counterparts, demonstrating significant improvement in translation quality for verbal idioms and verb-particle constructions.
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[NLP-67] Persuasion Dynamics in LLM s: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD EMNLP2025
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在说服性对话中难以平衡对错误信息的易信性和对有效纠正的抵抗性这一关键问题，这对模型在真实场景中的可靠部署构成挑战。其核心解决方案是提出一种双维度评估框架 DuET-PD（Dual Evaluation for Trust in Persuasive Dialogues），用于量化多轮立场变化在“说服类型”（纠正型/误导型）与“领域”（知识类 MMLU-Pro 和安全类 SALAD-Bench）上的表现；并进一步引入 Holistic DPO 训练方法，通过同时优化正负样本的说服效果，实现对误导信息的鲁棒性提升和对正确修正的开放性增强，从而显著改善模型在复杂对话中的可信度与适应性。
链接: https://arxiv.org/abs/2508.17450
作者: Bryan Chen Zhengyu Tan,Daniel Wai Kit Chin,Zhengyuan Liu,Nancy F. Chen,Roy Ka-Wei Lee
机构: Singapore University of Technology and Design (新加坡科技设计大学); Institute for Infocomm Research (信息通信研究所), A*STAR, Singapore (新加坡科技研究局)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: To appear at EMNLP 2025
Abstract:Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues, a critical challenge for reliable deployment. We introduce DuET-PD (Dual Evaluation for Trust in Persuasive Dialogues), a framework evaluating multi-turn stance-change dynamics across dual dimensions: persuasion type (corrective/misleading) and domain (knowledge via MMLU-Pro, and safety via SALAD-Bench). We find that even a state-of-the-art model like GPT-4o achieves only 27.32% accuracy in MMLU-Pro under sustained misleading persuasions. Moreover, results reveal a concerning trend of increasing sycophancy in newer open-source models. To address this, we introduce Holistic DPO, a training approach balancing positive and negative persuasion examples. Unlike prompting or resist-only training, Holistic DPO enhances both robustness to misinformation and receptiveness to corrections, improving Llama-3.1-8B-Instruct’s accuracy under misleading persuasion in safety contexts from 4.21% to 76.54%. These contributions offer a pathway to developing more reliable and adaptable LLMs for multi-turn dialogue. Code is available at this https URL.
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[NLP-68] reePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling
【速读】： 该论文旨在解决基于强化学习（Reinforcement Learning, RL）的后训练方法在复杂推理任务中面临的高计算成本与探索多样性不足的问题。具体而言，现有方法依赖昂贵的在线策略采样（on-policy rollouts），且难以有效探索多样化的推理路径。其解决方案的关键在于提出TreePO框架，通过将序列生成建模为树状结构搜索过程，结合动态树采样策略与固定长度片段解码机制，利用局部不确定性引导新增分支，并通过共享公共前缀的计算和早期剪枝低价值路径来显著降低每轮更新的计算负担。此外，TreePO引入分段级优势估计和概率驱动的动态发散与回退策略，在保持或提升探索多样性的同时实现高达43%的GPU小时数节省，从而为RL后训练提供更高效、可扩展的实践路径。
链接: https://arxiv.org/abs/2508.17445
作者: Yizhi Li,Qingshui Gu,Zhoufutu Wen,Ziniu Li,Tianshun Xing,Shuyue Guo,Tianyu Zheng,Xin Zhou,Xingwei Qu,Wangchunshu Zhou,Zheng Zhang,Wei Shen,Qian Liu,Chenghua Lin,Jian Yang,Ge Zhang,Wenhao Huang
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
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Abstract:Recent advancements in aligning large language models via reinforcement learning have achieved remarkable gains in solving complex reasoning problems, but at the cost of expensive on-policy rollouts and limited exploration of diverse reasoning paths. In this work, we introduce TreePO, involving a self-guided rollout algorithm that views sequence generation as a tree-structured searching process. Composed of dynamic tree sampling policy and fixed-length segment decoding, TreePO leverages local uncertainty to warrant additional branches. By amortizing computation across common prefixes and pruning low-value paths early, TreePO essentially reduces the per-update compute burden while preserving or enhancing exploration diversity. Key contributions include: (1) a segment-wise sampling algorithm that alleviates the KV cache burden through contiguous segments and spawns new branches along with an early-stop mechanism; (2) a tree-based segment-level advantage estimation that considers both global and local proximal policy optimization. and (3) analysis on the effectiveness of probability and quality-driven dynamic divergence and fallback strategy. We empirically validate the performance gain of TreePO on a set reasoning benchmarks and the efficiency saving of GPU hours from 22% up to 43% of the sampling design for the trained models, meanwhile showing up to 40% reduction at trajectory-level and 35% at token-level sampling compute for the existing models. While offering a free lunch of inference efficiency, TreePO reveals a practical path toward scaling RL-based post-training with fewer samples and less compute. Home page locates at this https URL.
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[NLP-69] MahaParaphrase: A Marathi Paraphrase Detection Corpus and BERT-based Models
【速读】： 该论文旨在解决低资源印地语系语言（如马拉地语）在自然语言处理（Natural Language Processing, NLP）中因形态学和句法复杂性高、字符多样性大以及标注数据稀缺而导致的语义理解任务困难问题。其解决方案的关键在于构建了一个高质量的马拉地语平行语料库——L3Cube-MahaParaphrase Dataset，包含8000对句子，并由人工专家标注为“同义句”（Paraphrase, P）或“非同义句”（Non-paraphrase, NP），同时基于标准Transformer架构的BERT模型对该数据集进行了实验验证，从而为马拉地语的生成式AI（Generative AI）相关任务提供可靠的数据基础与基准模型。
链接: https://arxiv.org/abs/2508.17444
作者: Suramya Jadhav,Abhay Shanbhag,Amogh Thakurdesai,Ridhima Sinare,Ananya Joshi,Raviraj Joshi
机构: Pune Institute of Computer Technology (浦那计算机技术研究所); MKSSS’ Cummins College of Engineering for Women (MKSSS卡姆ins女子工程学院); Indian Institute of Technology Madras (马德拉斯印度理工学院); L3Cube Labs (L3Cube实验室)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Paraphrases are a vital tool to assist language understanding tasks such as question answering, style transfer, semantic parsing, and data augmentation tasks. Indic languages are complex in natural language processing (NLP) due to their rich morphological and syntactic variations, diverse scripts, and limited availability of annotated data. In this work, we present the L3Cube-MahaParaphrase Dataset, a high-quality paraphrase corpus for Marathi, a low resource Indic language, consisting of 8,000 sentence pairs, each annotated by human experts as either Paraphrase § or Non-paraphrase (NP). We also present the results of standard transformer-based BERT models on these datasets. The dataset and model are publicly shared at this https URL
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[NLP-70] DS@GT at CheckThat! 2025: A Simple Retrieval-First LLM -Backed Framework for Claim Normalization
【速读】： 该论文旨在解决事实核查系统中的**声明归一化（claim normalization）**问题，即如何将来自社交媒体等来源的噪声数据（如非结构化文本、拼写错误或语法不规范的表述）转化为可用于下游真实性分类任务的标准化声明。其解决方案的关键在于提出了一种轻量级的“检索优先、大语言模型（Large Language Model, LLM）支持”的流水线架构：在单语条件下，系统通过动态提示GPT-4o-mini使用上下文示例进行归一化，或直接从训练集中检索最相似的归一化样本。该方法在CheckThat! 2025任务中表现优异，在13个语言的单语赛道中7个获得第一名，验证了其有效性；但零样本设置下性能下降，揭示了当前方案对训练数据依赖较强的问题。
链接: https://arxiv.org/abs/2508.17402
作者: Aleksandar Pramov,Jiangqin Ma,Bina Patel
机构: Georgia Institute of Technology (佐治亚理工学院)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: CLEF 2025 Working Notes, Madrid, Spain
Abstract:Claim normalization is an integral part of any automatic fact-check verification system. It parses the typically noisy claim data, such as social media posts into normalized claims, which are then fed into downstream veracity classification tasks. The CheckThat! 2025 Task 2 focuses specifically on claim normalization and spans 20 languages under monolingual and zero-shot conditions. Our proposed solution consists of a lightweight \emphretrieval-first, LLM-backed pipeline, in which we either dynamically prompt a GPT-4o-mini with in-context examples, or retrieve the closest normalization from the train dataset directly. On the official test set, the system ranks near the top for most monolingual tracks, achieving first place in 7 out of of the 13 languages. In contrast, the system underperforms in the zero-shot setting, highlighting the limitation of the proposed solution.
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[NLP-71] DashboardQA: Benchmarking Multimodal Agents for Question Answering on Interactive Dashboards
【速读】： 该论文旨在解决现有数据可视化问答基准对交互式仪表板（dashboard）支持不足的问题，当前主流评估体系多聚焦于静态图表，难以有效衡量现代视觉-语言模型（Vision-Language Models, VLMs）在基于图形用户界面（GUI）推理任务中的实际能力。其关键解决方案是提出首个专门针对交互式仪表板的评测基准 DashboardQA，包含来自 Tableau Public 的 112 个真实交互式仪表板及覆盖五类任务（多项选择、事实型、假设性、跨仪表板和对话式）的 405 个问答对，从而系统评估多模态代理在元素定位、交互路径规划与复杂推理等方面的能力。实验表明，即使是最先进的 VLMs 在此任务上仍表现有限，验证了该基准的有效性和挑战性。
链接: https://arxiv.org/abs/2508.17398
作者: Aaryaman Kartha,Ahmed Masry,Mohammed Saidul Islam,Thinh Lang,Shadikur Rahman,Ridwan Mahbub,Mizanur Rahman,Mahir Ahmed,Md Rizwan Parvez,Enamul Hoque,Shafiq Joty
机构: York University (约克大学); RBC (加拿大皇家银行); Qatar Computing Research Institute (卡塔尔计算研究研究所); Nanyang Technological University (南洋理工大学); Salesforce Research (Salesforce 研究院)
类目: Computation and Language (cs.CL)
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Abstract:Dashboards are powerful visualization tools for data-driven decision-making, integrating multiple interactive views that allow users to explore, filter, and navigate data. Unlike static charts, dashboards support rich interactivity, which is essential for uncovering insights in real-world analytical workflows. However, existing question-answering benchmarks for data visualizations largely overlook this interactivity, focusing instead on static charts. This limitation severely constrains their ability to evaluate the capabilities of modern multimodal agents designed for GUI-based reasoning. To address this gap, we introduce DashboardQA, the first benchmark explicitly designed to assess how vision-language GUI agents comprehend and interact with real-world dashboards. The benchmark includes 112 interactive dashboards from Tableau Public and 405 question-answer pairs with interactive dashboards spanning five categories: multiple-choice, factoid, hypothetical, multi-dashboard, and conversational. By assessing a variety of leading closed- and open-source GUI agents, our analysis reveals their key limitations, particularly in grounding dashboard elements, planning interaction trajectories, and performing reasoning. Our findings indicate that interactive dashboard reasoning is a challenging task overall for all the VLMs evaluated. Even the top-performing agents struggle; for instance, the best agent based on Gemini-Pro-2.5 achieves only 38.69% accuracy, while the OpenAI CUA agent reaches just 22.69%, demonstrating the benchmark’s significant difficulty. We release DashboardQA at this https URL
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[NLP-72] Agent -Testing Agent Agent : A Meta-Agent for Automated Testing and Evaluation of Conversational AI Agents
【速读】： 该论文旨在解决当前大型语言模型（Large Language Model, LLM）智能体在工具调用、规划与写作等复杂任务中缺乏高效、系统化评估手段的问题，现有方法主要依赖静态基准测试和小规模人工评测，难以全面揭示其潜在缺陷。解决方案的核心是提出一种元智能体——Agent-Testing Agent (ATA)，其关键创新在于融合静态代码分析、设计者访谈、文献挖掘与基于角色的对抗性测试生成，并通过LLM作为裁判（LLM-as-a-Judge, LAAJ）的反馈机制动态调整测试难度，从而精准定位智能体的薄弱能力模块。该方法显著提升了测试多样性与严重性，同时大幅缩短评估时间（20–30分钟 vs. 多人标注数天），并通过定量指标与定性错误报告为开发者提供可操作的改进依据。
链接: https://arxiv.org/abs/2508.17393
作者: Sameer Komoravolu,Khalil Mrini
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); Grammarly (Grammarly)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:LLM agents are increasingly deployed to plan, retrieve, and write with tools, yet evaluation still leans on static benchmarks and small human studies. We present the Agent-Testing Agent (ATA), a meta-agent that combines static code analysis, designer interrogation, literature mining, and persona-driven adversarial test generation whose difficulty adapts via judge feedback. Each dialogue is scored with an LLM-as-a-Judge (LAAJ) rubric and used to steer subsequent tests toward the agent’s weakest capabilities. On a travel planner and a Wikipedia writer, the ATA surfaces more diverse and severe failures than expert annotators while matching severity, and finishes in 20–30 minutes versus ten-annotator rounds that took days. Ablating code analysis and web search increases variance and miscalibration, underscoring the value of evidence-grounded test generation. The ATA outputs quantitative metrics and qualitative bug reports for developers. We release the full methodology and open-source implementation for reproducible agent testing: this https URL
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[NLP-73] Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在小规模结构化数据上进行分类、回归和聚类任务时的函数逼近能力问题，尤其是在缺乏显式微调的情况下如何实现有效的预测。其解决方案的关键在于利用LLMs的上下文学习（in-context learning, ICL）能力，在少样本提示（few-shot prompting）条件下直接对结构化输入执行预测任务，从而无需针对下游任务进行专门训练。研究通过对比GPT-5、GPT-4o等先进LLM与传统机器学习方法（如线性模型、集成方法及表格基础模型）的表现，发现LLMs在分类任务中展现出强大性能，可作为零训练基线；但在连续值输出的回归任务和聚类任务中表现不佳，归因于输出空间过大以及该场景下缺乏真正的ICL机制。这一方法为业务智能和探索性分析提供了轻量级、快速的数据探索手段，同时揭示了上下文长度和提示结构对近似质量的影响及其权衡关系。
链接: https://arxiv.org/abs/2508.17391
作者: Nikolaos Pavlidis,Vasilis Perifanis,Symeon Symeonidis,Pavlos S. Efraimidis
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Large Language Models (LLMs), originally developed for natural language processing (NLP), have demonstrated the potential to generalize across modalities and domains. With their in-context learning (ICL) capabilities, LLMs can perform predictive tasks over structured inputs without explicit fine-tuning on downstream tasks. In this work, we investigate the empirical function approximation capability of LLMs on small-scale structured datasets for classification, regression and clustering tasks. We evaluate the performance of state-of-the-art LLMs (GPT-5, GPT-4o, GPT-o3, Gemini-2.5-Flash, DeepSeek-R1) under few-shot prompting and compare them against established machine learning (ML) baselines, including linear models, ensemble methods and tabular foundation models (TFMs). Our results show that LLMs achieve strong performance in classification tasks under limited data availability, establishing practical zero-training baselines. In contrast, the performance in regression with continuous-valued outputs is poor compared to ML models, likely because regression demands outputs in a large (often infinite) space, and clustering results are similarly limited, which we attribute to the absence of genuine ICL in this setting. Nonetheless, this approach enables rapid, low-overhead data exploration and offers a viable alternative to traditional ML pipelines in business intelligence and exploratory analytics contexts. We further analyze the influence of context size and prompt structure on approximation quality, identifying trade-offs that affect predictive performance. Our findings suggest that LLMs can serve as general-purpose predictive engines for structured data, with clear strengths in classification and significant limitations in regression and clustering.
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[NLP-74] UI-Level Evaluation of ALLaM 34B: Measuring an Arabic-Centric LLM via HUMAIN Chat
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在处理阿拉伯语时因训练数据以英语为主而导致的语言和文化细微差异捕捉不足的问题。解决方案的关键在于引入由沙特数据与人工智能管理局（Saudi Data and AI Authority, SDAIA）开发的面向阿拉伯语的ALLaM系列模型，并聚焦于公开可用能力最强的ALLaM-34B版本，通过多维度、高保真度的用户界面（UI-level）评估验证其性能。研究采用涵盖现代标准阿拉伯语（Modern Standard Arabic, MSA）、五种地区方言、代码切换（code-switching）、事实知识、算术与时间推理、创意生成及对抗性安全等任务的提示集，在三名前沿LLM评判者（GPT-5、Gemini 2.5 Pro、Claude Sonnet-4）评分基础上进行量化分析，结果表明ALLaM-34B在生成能力、代码切换、MSA理解、推理和安全性方面均表现优异，体现出技术实力与文化适配性的双重优势。
链接: https://arxiv.org/abs/2508.17378
作者: Omer Nacar
机构: NAMAA Community (NAMAA社区)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) trained primarily on English corpora often struggle to capture the linguistic and cultural nuances of Arabic. To address this gap, the Saudi Data and AI Authority (SDAIA) introduced the ALLaM family of Arabic-focused models. The most capable of these available to the public, ALLaM-34B , was subsequently adopted by HUMAIN, who developed and deployed HUMAIN Chat, a closed conversational web service built on this model. This paper presents an expanded and refined UI-level evaluation of ALLaM-34B . Using a prompt pack spanning modern standard Arabic, five regional dialects, code-switching, factual knowledge, arithmetic and temporal reasoning, creative generation, and adversarial safety, we collected 115 outputs (23 prompts times 5 runs) and scored each with three frontier LLM judges (GPT-5, Gemini 2.5 Pro, Claude Sonnet-4). We compute category-level means with 95% confidence intervals, analyze score distributions, and visualize dialect-wise metric heat maps. The updated analysis reveals consistently high performance on generation and code-switching tasks (both averaging 4.92/5), alongside strong results in MSA handling (4.74/5), solid reasoning ability (4.64/5), and improved dialect fidelity (4.21/5). Safety-related prompts show stable, reliable performance of (4.54/5). Taken together, these results position ALLaM-34B as a robust and culturally grounded Arabic LLM, demonstrating both technical strength and practical readiness for real-world deployment.
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[NLP-75] he Arabic Generality Score: Another Dimension of Modeling Arabic Dialectness EMNLP2025
【速读】： 该论文旨在解决阿拉伯语方言在自然语言处理（Natural Language Processing, NLP）中被简化为离散类别所带来的建模局限问题，尤其是现有方法如阿拉伯语方言程度（Arabic Level of Dialectness, ALDi）将复杂方言连续体压缩为单一维度所导致的信息损失。其解决方案的关键在于提出一种新的度量指标——阿拉伯语通用性得分（Arabic Generality Score, AGS），用于量化词汇在不同方言中的广泛使用程度，并构建一个包含词对齐、基于词源的编辑距离和光滑处理的流水线来标注平行语料库中的词级AGS，进而训练回归模型以预测上下文中AGS值。该方法显著优于现有的强基线模型，包括最先进的方言识别系统，提供了一种可扩展且具有语言学依据的词汇层面通用性建模方式，从而更细致地刻画阿拉伯语方言连续体的多样性。
链接: https://arxiv.org/abs/2508.17347
作者: Sanad Shaban,Nizar Habash
机构: MBZUAI; New York University Abu Dhabi
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2025 Main Conference
Abstract:Arabic dialects form a diverse continuum, yet NLP models often treat them as discrete categories. Recent work addresses this issue by modeling dialectness as a continuous variable, notably through the Arabic Level of Dialectness (ALDi). However, ALDi reduces complex variation to a single dimension. We propose a complementary measure: the Arabic Generality Score (AGS), which quantifies how widely a word is used across dialects. We introduce a pipeline that combines word alignment, etymology-aware edit distance, and smoothing to annotate a parallel corpus with word-level AGS. A regression model is then trained to predict AGS in context. Our approach outperforms strong baselines, including state-of-the-art dialect ID systems, on a multi-dialect benchmark. AGS offers a scalable, linguistically grounded way to model lexical generality, enriching representations of Arabic dialectness.
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[NLP-76] Capturing Legal Reasoning Paths from Facts to Law in Court Judgments using Knowledge Graphs
【速读】： 该论文旨在解决现有自动化方法在捕捉法律推理过程中的三大局限性：难以识别相关法律背景、无法准确追踪事实与法律规范之间的关联，以及不能正确表示司法推理的分层结构。这些问题阻碍了对法院如何将法律适用于具体案件的实际机制进行有效建模。其解决方案的关键在于构建一个基于648份日本行政法院判决的法律知识图谱（Legal Knowledge Graph），通过提示驱动的大语言模型提取法律推理要素，标准化法律条文引用，并利用法律推理本体（ontology of legal inference）连接事实、法律规范与法律适用关系，从而显式化并机器可读地呈现真实判决中完整的法律推理结构。
链接: https://arxiv.org/abs/2508.17340
作者: Ryoma Kondo,Riona Matsuoka,Takahiro Yoshida,Kazuyuki Yamasawa,Ryohei Hisano
机构: Graduate School of Information Science and Technology, The University of Tokyo (东京大学信息科学与技术研究生院); Graduate Schools for Law and Politics, The University of Tokyo (东京大学法学院和政治学院研究生院); The Canon Institute for Global Studies (佳能全球战略研究所); TKC Corporation (TKC公司)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR)
备注:
Abstract:Court judgments reveal how legal rules have been interpreted and applied to facts, providing a foundation for understanding structured legal reasoning. However, existing automated approaches for capturing legal reasoning, including large language models, often fail to identify the relevant legal context, do not accurately trace how facts relate to legal norms, and may misrepresent the layered structure of judicial reasoning. These limitations hinder the ability to capture how courts apply the law to facts in practice. In this paper, we address these challenges by constructing a legal knowledge graph from 648 Japanese administrative court decisions. Our method extracts components of legal reasoning using prompt-based large language models, normalizes references to legal provisions, and links facts, norms, and legal applications through an ontology of legal inference. The resulting graph captures the full structure of legal reasoning as it appears in real court decisions, making implicit reasoning explicit and machine-readable. We evaluate our system using expert annotated data, and find that it achieves more accurate retrieval of relevant legal provisions from facts than large language model baselines and retrieval-augmented methods.
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[NLP-77] DropLoRA: Sparse Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
【速读】： 该论文旨在解决基于低秩分解的参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）方法，如LoRA，在下游任务中因低秩更新导致的性能差距问题。其解决方案的关键在于提出DropLoRA，一种基于剪枝的创新方法，通过在LoRA的两个低秩矩阵之间引入剪枝模块，模拟动态子空间学习机制，从而突破传统LoRA固定子空间的局限性。该设计使模型能够持续自适应地调整学习子空间，在不增加训练或推理成本的前提下显著提升性能。
链接: https://arxiv.org/abs/2508.17337
作者: Haojie Zhang
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 8 pages
Abstract:LoRA-based large model parameter-efficient fine-tuning (PEFT) methods use low-rank de- composition to approximate updates to model parameters. However, compared to full- parameter fine-tuning, low-rank updates often lead to a performance gap in downstream tasks. To address this, we introduce DropLoRA, a novel pruning-based approach that focuses on pruning the rank dimension. Unlike conven- tional methods that attempt to overcome the low-rank bottleneck, DropLoRA innovatively integrates a pruning module between the two low-rank matrices in LoRA to simulate dy- namic subspace learning. This dynamic low- rank subspace learning allows DropLoRA to overcome the limitations of traditional LoRA, which operates within a static subspace. By continuously adapting the learning subspace, DropLoRA significantly boosts performance without incurring additional training or infer- ence costs. Our experimental results demon- strate that DropLoRA consistently outperforms LoRA in fine-tuning the LLaMA series across a wide range of large language model gener- ation tasks, including commonsense reason- ing, mathematical reasoning, code generation, and instruction-following. Our code is avail- able at this https URL.
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[NLP-78] Mind the (Language) Gap: Towards Probing Numerical and Cross-Lingual Limits of LVLMs
【速读】： 该论文旨在解决大型视觉语言模型（LVLMs）在处理结构化数值表格图像时存在的局限性，尤其是其在跨语言、多图上下文和复杂数值推理方面的不足。解决方案的关键在于构建了一个名为MMCRICBENCH-3K的基准数据集，该数据集包含1,463张由ODI、T20和测试赛格式合成的板球记分卡图像及1,500组英文问答对，并细分为英文（MMCRICBENCH-E-1.5K）与印地语（MMCRICBENCH-H-1.5K）两种视觉相似但语言不同的子集，从而实现对LVLM在结构感知、数值推理和跨语言泛化能力上的系统评估。实证结果表明，即便最先进的LVLM如GPT-4o和Qwen2.5VL在英文子集上仍表现不佳，且在印地语子集上性能进一步下降，揭示了当前模型在结构化数据理解与跨语言迁移中的关键瓶颈。
链接: https://arxiv.org/abs/2508.17334
作者: Somraj Gautam,Abhirama Subramanyam Penamakuri,Abhishek Bhandari,Gaurav Harit
机构: Indian Institute of Technology Jodhpur (印度理工学院贾德普尔分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:We introduce MMCRICBENCH-3K, a benchmark for Visual Question Answering (VQA) on cricket scorecards, designed to evaluate large vision-language models (LVLMs) on complex numerical and cross-lingual reasoning over semi-structured tabular images. MMCRICBENCH-3K comprises 1,463 synthetically generated scorecard images from ODI, T20, and Test formats, accompanied by 1,500 English QA pairs. It includes two subsets: MMCRICBENCH-E-1.5K, featuring English scorecards, and MMCRICBENCH-H-1.5K, containing visually similar Hindi scorecards, with all questions and answers kept in English to enable controlled cross-script evaluation. The task demands reasoning over structured numerical data, multi-image context, and implicit domain knowledge. Empirical results show that even state-of-the-art LVLMs, such as GPT-4o and Qwen2.5VL, struggle on the English subset despite it being their primary training language and exhibit a further drop in performance on the Hindi subset. This reveals key limitations in structure-aware visual text understanding, numerical reasoning, and cross-lingual generalization. The dataset is publicly available via Hugging Face at this https URL, to promote LVLM research in this direction.
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[NLP-79] Omne-R1: Learning to Reason with Memory for Multi-hop Question Answering
【速读】： 该论文旨在解决在无模式（schema-free）知识图谱上进行多跳问答（multi-hop question answering, MQA）时面临的挑战，尤其是由于高质量知识图谱和问答数据稀缺导致的模型训练困难问题。解决方案的关键在于提出一种名为Omne-R1的新方法，其核心是采用包含两个强化学习阶段和一个监督微调阶段的多阶段训练流程，并结合构建领域无关的知识图谱与自动生成问答对的数据增强策略，从而显著提升模型在复杂多跳问题上的性能与跨领域的泛化能力。
链接: https://arxiv.org/abs/2508.17330
作者: Boyuan Liu,Feng Ji,Jiayan Nan,Han Zhao,Weiling Chen,Shihao Xu,Xing Zhou
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper introduces Omne-R1, a novel approach designed to enhance multi-hop question answering capabilities on schema-free knowledge graphs by integrating advanced reasoning models. Our method employs a multi-stage training workflow, including two reinforcement learning phases and one supervised fine-tuning phase. We address the challenge of limited suitable knowledge graphs and QA data by constructing domain-independent knowledge graphs and auto-generating QA pairs. Experimental results show significant improvements in answering multi-hop questions, with notable performance gains on more complex 3+ hop questions. Our proposed training framework demonstrates strong generalization abilities across diverse knowledge domains.
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[NLP-80] CultranAI at PalmX 2025: Data Augmentation for Cultural Knowledge Representation
【速读】： 该论文旨在解决阿拉伯文化知识表示（Arabic cultural knowledge representation）在大型语言模型（LLM）中的建模与评估问题，特别是在多选题（MCQ）形式下的准确理解和推理能力。解决方案的关键在于两个方面：一是通过数据增强策略，整合Palm数据集并构建一个包含22,000余条文化语境相关MCQ的新数据集；二是采用LoRA（Low-Rank Adaptation）微调技术对性能最优的Fanar-1-9B-Instruct模型进行高效适配，从而提升模型在阿拉伯文化知识任务上的表现。实验表明，该方法在盲测集上达到70.50%的准确率，在开发集上达84.1%，验证了数据增强与轻量级微调结合的有效性。
链接: https://arxiv.org/abs/2508.17324
作者: Hunzalah Hassan Bhatti,Youssef Ahmed,Md Arid Hasan,Firoj Alam
机构: Qatar University (卡塔尔大学); University of Toronto (多伦多大学); Qatar Computing Research Institute (卡塔尔计算研究研究所)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: LLMs, Native, Arabic LLMs, Augmentation, Multilingual, Language Diversity, Contextual Understanding, Minority Languages, Culturally Informed, Foundation Models, Large Language Models
Abstract:In this paper, we report our participation to the PalmX cultural evaluation shared task. Our system, CultranAI, focused on data augmentation and LoRA fine-tuning of large language models (LLMs) for Arabic cultural knowledge representation. We benchmarked several LLMs to identify the best-performing model for the task. In addition to utilizing the PalmX dataset, we augmented it by incorporating the Palm dataset and curated a new dataset of over 22K culturally grounded multiple-choice questions (MCQs). Our experiments showed that the Fanar-1-9B-Instruct model achieved the highest performance. We fine-tuned this model on the combined augmented dataset of 22K+ MCQs. On the blind test set, our submitted system ranked 5th with an accuracy of 70.50%, while on the PalmX development set, it achieved an accuracy of 84.1%.
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[NLP-81] Handling Students Dropouts in an LLM -driven Interactive Online Course Using Language Models
【速读】： 该论文旨在解决交互式在线学习环境中学生流失（dropout）问题，尤其是在由大语言模型（Large Language Model, LLM）驱动的多智能体系统支持下的大规模人工智能赋能课程（Massive AI-empowered Courses, MAIC）中。其核心挑战在于识别导致 dropout 的关键因素、实现高精度预测，并设计有效的干预策略以降低流失率。解决方案的关键在于：首先，通过分析交互日志定义 dropout 并挖掘文本交互模式与流失行为之间的强关联；其次，提出一种课程进度自适应的 dropout 预测框架（Course-Progress-Adaptive Dropout Prediction, CPADP），实现最高达 95.4% 的预测准确率；最后，基于预测结果设计个性化邮件召回代理（personalized email recall agent），主动 re-engage 高风险学生。该方法在超过 3000 名学生的实际部署中验证了其可行性与有效性。
链接: https://arxiv.org/abs/2508.17310
作者: Yuanchun Wang,Yiyang Fu,Jifan Yu,Daniel Zhang-Li,Zheyuan Zhang,Joy Lim Jia Yin,Yucheng Wang,Peng Zhou,Jing Zhang,Huiqin Liu
机构: Renmin University of China (中国人民大学); Tsinghua University (清华大学); Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: 12 pages
Abstract:Interactive online learning environments, represented by Massive AI-empowered Courses (MAIC), leverage LLM-driven multi-agent systems to transform passive MOOCs into dynamic, text-based platforms, enhancing interactivity through LLMs. This paper conducts an empirical study on a specific MAIC course to explore three research questions about dropouts in these interactive online courses: (1) What factors might lead to dropouts? (2) Can we predict dropouts? (3) Can we reduce dropouts? We analyze interaction logs to define dropouts and identify contributing factors. Our findings reveal strong links between dropout behaviors and textual interaction patterns. We then propose a course-progress-adaptive dropout prediction framework (CPADP) to predict dropouts with at most 95.4% accuracy. Based on this, we design a personalized email recall agent to re-engage at-risk students. Applied in the deployed MAIC system with over 3,000 students, the feasibility and effectiveness of our approach have been validated on students with diverse backgrounds.
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[NLP-82] From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users
【速读】： 该论文旨在解决当前大语言模型（Large Language Models, LLMs）作为自主代理（autonomous agents）在实际应用中面临的关键挑战，包括其推理能力、工具集成策略、多智能体协作机制以及性能评估体系的不足。解决方案的关键在于系统性地梳理2023至2025年间顶级会议和期刊中关于LLM代理的研究进展，从架构设计原则出发，区分单智能体与多智能体系统，并深入分析外部工具整合方法；同时考察推理、规划、记忆等认知机制对代理性能的影响，结合68个公开数据集对现有基准测试进行全面评估，从而识别出可验证推理、自我改进能力和个性化定制等方面的突破点，并提出未来十个研究方向以填补当前空白。
链接: https://arxiv.org/abs/2508.17281
作者: Sadia Sultana Chowa,Riasad Alvi,Subhey Sadi Rahman,Md Abdur Rahman,Mohaimenul Azam Khan Raiaan,Md Rafiqul Islam,Mukhtar Hussain,Sami Azam
机构: Daffodil International University (达福德国际大学); United International University (联合国际大学); Charles Darwin University (查尔斯达尔文大学)
类目: Computation and Language (cs.CL)
备注: 40 pages, 6 figures, 10 tables. Submitted to Artificial Intelligence Review for peer review
Abstract:The pursuit of human-level artificial intelligence (AI) has significantly advanced the development of autonomous agents and Large Language Models (LLMs). LLMs are now widely utilized as decision-making agents for their ability to interpret instructions, manage sequential tasks, and adapt through feedback. This review examines recent developments in employing LLMs as autonomous agents and tool users and comprises seven research questions. We only used the papers published between 2023 and 2025 in conferences of the A* and A rank and Q1 journals. A structured analysis of the LLM agents’ architectural design principles, dividing their applications into single-agent and multi-agent systems, and strategies for integrating external tools is presented. In addition, the cognitive mechanisms of LLM, including reasoning, planning, and memory, and the impact of prompting methods and fine-tuning procedures on agent performance are also investigated. Furthermore, we evaluated current benchmarks and assessment protocols and have provided an analysis of 68 publicly available datasets to assess the performance of LLM-based agents in various tasks. In conducting this review, we have identified critical findings on verifiable reasoning of LLMs, the capacity for self-improvement, and the personalization of LLM-based agents. Finally, we have discussed ten future research directions to overcome these gaps.
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[NLP-83] Are You Sure Youre Positive? Consolidating Chain-of-Thought Agents with Uncertainty Quantification for Aspect-Category Sentiment Analysis
【速读】： 该论文旨在解决领域迁移场景下情感分析中数据标注稀缺与标注偏差导致的模型泛化能力不足问题，尤其是在缺乏标注数据的新领域中难以实现稳定且可复现的性能表现。其核心解决方案在于利用大语言模型（Large Language Models, LLMs）在零样本（zero-shot）设置下的推理能力，并创新性地结合多个思维链（chain-of-thought）代理（agent），通过引入token级不确定性得分来动态融合不同代理的预测结果，从而提升在标签稀缺条件下的准确性和鲁棒性。实验基于Llama和Qwen系列模型的不同参数规模（3B与70B+）验证了该方法的有效性，为低资源场景下的细粒度情感分析提供了可行路径。
链接: https://arxiv.org/abs/2508.17258
作者: Filippos Ventirozos,Peter Appleby,Matthew Shardlow
机构: Manchester Metropolitan University (曼彻斯特都会大学); Autotrader Research Group, Autotrader UK (Autotrader 研究组，Autotrader 英国)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 18 pages, 10 figures, 3 tables, Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025)
Abstract:Aspect-category sentiment analysis provides granular insights by identifying specific themes within product reviews that are associated with particular opinions. Supervised learning approaches dominate the field. However, data is scarce and expensive to annotate for new domains. We argue that leveraging large language models in a zero-shot setting is beneficial where the time and resources required for dataset annotation are limited. Furthermore, annotation bias may lead to strong results using supervised methods but transfer poorly to new domains in contexts that lack annotations and demand reproducibility. In our work, we propose novel techniques that combine multiple chain-of-thought agents by leveraging large language models’ token-level uncertainty scores. We experiment with the 3B and 70B+ parameter size variants of Llama and Qwen models, demonstrating how these approaches can fulfil practical needs and opening a discussion on how to gauge accuracy in label-scarce conditions.
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[NLP-84] Routing Distilled Knowledge via Mixture of LoRA Experts for Large Language Model based Bundle Generation
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在自动捆绑生成（bundle generation）任务中计算成本过高，且知识蒸馏过程中因混合不同类型的蒸馏知识导致知识冲突、性能下降的问题。解决方案的关键在于提出RouteDK框架，通过引入基于LoRA专家的路由机制实现对知识的有效整合：首先将教师模型的知识分为高阶知识（generalizable rules）和细粒度知识（session-specific reasoning），分别训练对应的知识特定LoRA专家与基础LoRA专家；进而设计一个输入感知的动态融合模块，利用路由器根据输入内容动态分配专家权重，从而缓解知识冲突；此外，还引入推理时增强模块以降低推理方差并提升可靠性。该方法在多个公开数据集上实现了媲美甚至优于教师模型的准确率，同时保持了显著的计算效率优势。
链接: https://arxiv.org/abs/2508.17250
作者: Kaidong Feng,Zhu Sun,Hui Fang,Jie Yang,Wenyuan Liu,Yew-Soon Ong
机构: 未知
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注:
Abstract:Large Language Models (LLMs) have shown potential in automatic bundle generation but suffer from prohibitive computational costs. Although knowledge distillation offers a pathway to more efficient student models, our preliminary study reveals that naively integrating diverse types of distilled knowledge from teacher LLMs into student LLMs leads to knowledge conflict, negatively impacting the performance of bundle generation. To address this, we propose RouteDK, a framework for routing distilled knowledge through a mixture of LoRA expert architecture. Specifically, we first distill knowledge from the teacher LLM for bundle generation in two complementary types: high-level knowledge (generalizable rules) and fine-grained knowledge (session-specific reasoning). We then train knowledge-specific LoRA experts for each type of knowledge together with a base LoRA expert. For effective integration, we propose a dynamic fusion module, featuring an input-aware router, where the router balances expert contributions by dynamically determining optimal weights based on input, thereby effectively mitigating knowledge conflicts. To further improve inference reliability, we design an inference-time enhancement module to reduce variance and mitigate suboptimal reasoning. Experiments on three public datasets show that our RouteDK achieves accuracy comparable to or even better than the teacher LLM, while maintaining strong computational efficiency. In addition, it outperforms state-of-the-art approaches for bundle generation.
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[NLP-85] CoViPAL: Layer-wise Contextualized Visual Token Pruning for Large Vision-Language Models EMNLP2025
【速读】： 该论文旨在解决大型视觉语言模型（Large Vision-Language Models, LVLMs）在处理图像时因生成大量视觉 token（vision tokens）而导致的高计算成本和显著内存开销问题。现有方法虽尝试通过剪枝冗余视觉 token 来缓解此问题，但在浅层网络中效果有限，因其缺乏足够的上下文信息。论文提出 CoViPAL，一种分层上下文感知的视觉 token 剪枝方法，其核心在于引入一个轻量、与模型无关的即插即用剪枝模块（Plug-and-Play Pruning Module, PPM），该模块能在 LVLM 处理前预测并移除冗余视觉 token，从而在不牺牲准确性的前提下显著提升推理效率。
链接: https://arxiv.org/abs/2508.17243
作者: Zicong Tang,Ziyang Ma,Suqing Wang,Zuchao Li,Lefei Zhang,Hai Zhao,Yun Li,Qianren Wang
机构: Wuhan University (武汉大学); Shanghai Jiao Tong University (上海交通大学); Shanghai Huawei Technologies (上海华为技术有限公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted by EMNLP 2025 Findings
Abstract:Large Vision-Language Models (LVLMs) process multimodal inputs consisting of text tokens and vision tokens extracted from images or videos. Due to the rich visual information, a single image can generate thousands of vision tokens, leading to high computational costs during the prefilling stage and significant memory overhead during decoding. Existing methods attempt to prune redundant vision tokens, revealing substantial redundancy in visual representations. However, these methods often struggle in shallow layers due to the lack of sufficient contextual information. We argue that many visual tokens are inherently redundant even in shallow layers and can be safely and effectively pruned with appropriate contextual signals. In this work, we propose CoViPAL, a layer-wise contextualized visual token pruning method that employs a Plug-and-Play Pruning Module (PPM) to predict and remove redundant vision tokens before they are processed by the LVLM. The PPM is lightweight, model-agnostic, and operates independently of the LVLM architecture, ensuring seamless integration with various models. Extensive experiments on multiple benchmarks demonstrate that CoViPAL outperforms training-free pruning methods under equal token budgets and surpasses training-based methods with comparable supervision. CoViPAL offers a scalable and efficient solution to improve inference efficiency in LVLMs without compromising accuracy.
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[NLP-86] ClaimGen-CN: A Large-scale Chinese Dataset for Legal Claim Generation
【速读】： 该论文旨在解决非专业人员（如原告）在法律诉讼中难以准确生成法律主张（legal claims）的问题，从而填补现有研究主要聚焦于提升法律专业人士效率而忽视普通用户需求的空白。其解决方案的关键在于：首先构建了首个面向中文法律主张生成任务的数据集ClaimGen-CN，该数据集源自真实法律纠纷；其次设计了一种针对生成主张的评估指标，涵盖事实准确性（factuality）和表达清晰度（clarity）两个核心维度；在此基础上，对主流通用与法律领域大语言模型进行了零样本评估，揭示了当前模型在事实精确性和表达清晰性方面的不足，为后续针对性优化提供了方向。
链接: https://arxiv.org/abs/2508.17234
作者: Siying Zhou,Yiquan Wu,Hui Chen,Xavier Hu,Kun Kuang,Adam Jatowt,Ming Hu,Chunyan Zheng,Fei Wu
机构: Zhejiang University (浙江大学); University of Innsbruck (因斯布鲁克大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Legal claims refer to the plaintiff’s demands in a case and are essential to guiding judicial reasoning and case resolution. While many works have focused on improving the efficiency of legal professionals, the research on helping non-professionals (e.g., plaintiffs) remains unexplored. This paper explores the problem of legal claim generation based on the given case’s facts. First, we construct ClaimGen-CN, the first dataset for Chinese legal claim generation task, from various real-world legal disputes. Additionally, we design an evaluation metric tailored for assessing the generated claims, which encompasses two essential dimensions: factuality and clarity. Building on this, we conduct a comprehensive zero-shot evaluation of state-of-the-art general and legal-domain large language models. Our findings highlight the limitations of the current models in factual precision and expressive clarity, pointing to the need for more targeted development in this domain. To encourage further exploration of this important task, we will make the dataset publicly available.
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[NLP-87] SSFO: Self-Supervised Faithfulness Optimization for Retrieval-Augmented Generation
【速读】： 该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统中普遍存在的“忠实性幻觉”（faithfulness hallucination）问题，即大语言模型（Large Language Models, LLMs）在生成回答时偏离或不忠实于检索到的上下文信息。现有方法通常依赖昂贵的标注监督或复杂的后训练策略，且可能引入额外推理开销。本文提出自监督忠实性优化（Self-Supervised Faithfulness Optimization, SSFO），其核心创新在于利用模型在有无上下文条件下的输出差异自动构建偏好数据对，并通过直接偏好优化（Direct Preference Optimization, DPO）实现无需人工标注的对齐。SSFO的关键机制是通过一种有益的似然位移（likelihood displacement）现象，将概率质量从基于参数记忆的词元转移到与上下文一致的词元上，从而提升生成内容的忠实性。实验表明，SSFO在多个基于上下文的问答数据集上达到当前最优性能，并展现出跨语言泛化能力和对通用指令遵循能力的保持。
链接: https://arxiv.org/abs/2508.17225
作者: Xiaqiang Tang,Yi Wang,Keyu Hu,Rui Xu,Chuang Li,Weigao Sun,Jian Li,Sihong Xie
机构: The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州）); Chinese Academy of Sciences (中国科学院); Shanghai AI Lab (上海人工智能实验室); Tencent Hunyuan (腾讯混元)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Working in progress
Abstract:Retrieval-Augmented Generation (RAG) systems require Large Language Models (LLMs) to generate responses that are faithful to the retrieved context. However, faithfulness hallucination remains a critical challenge, as existing methods often require costly supervision and post-training or significant inference burdens. To overcome these limitations, we introduce Self-Supervised Faithfulness Optimization (SSFO), the first self-supervised alignment approach for enhancing RAG faithfulness. SSFO constructs preference data pairs by contrasting the model’s outputs generated with and without the context. Leveraging Direct Preference Optimization (DPO), SSFO aligns model faithfulness without incurring labeling costs or additional inference burden. We theoretically and empirically demonstrate that SSFO leverages a benign form of \emphlikelihood displacement, transferring probability mass from parametric-based tokens to context-aligned tokens. Based on this insight, we propose a modified DPO loss function to encourage likelihood displacement. Comprehensive evaluations show that SSFO significantly outperforms existing methods, achieving state-of-the-art faithfulness on multiple context-based question-answering datasets. Notably, SSFO exhibits strong generalization, improving cross-lingual faithfulness and preserving general instruction-following capabilities. We release our code and model at the anonymous link: this https URL
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[NLP-88] Multi-Agent Visual-Language Reasoning for Comprehensive Highway Scene Understanding
【速读】： 该论文旨在解决高速公路场景理解中多任务感知的复杂性问题，即如何在有限计算资源下实现对天气分类、路面湿滑程度评估和交通拥堵检测等关键任务的高精度协同推理。其解决方案的关键在于提出了一种基于专家混合（mixture-of-experts）策略的多智能体框架：利用大型通用视觉语言模型（VLM）生成针对具体任务的链式思维（chain-of-thought, CoT）提示，再由轻量级高效VLM（如Qwen2.5-VL-7B）结合短时视频与互补模态信息进行推理，从而在保证准确性的同时显著提升计算效率。该方法通过结构化提示工程与多模态融合，实现了鲁棒的多任务感知，并可部署于现有交通监控系统，在资源受限环境下持续监测高风险路段，增强道路安全态势感知能力。
链接: https://arxiv.org/abs/2508.17205
作者: Yunxiang Yang,Ningning Xu,Jidong J. Yang
机构: University of Georgia (乔治亚大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Image and Video Processing (eess.IV)
备注: 16 pages, 16 figures, 8 tables
Abstract:This paper introduces a multi-agent framework for comprehensive highway scene understanding, designed around a mixture-of-experts strategy. In this framework, a large generic vision-language model (VLM), such as GPT-4o, is contextualized with domain knowledge to generates task-specific chain-of-thought (CoT) prompts. These fine-grained prompts are then used to guide a smaller, efficient VLM (e.g., Qwen2.5-VL-7B) in reasoning over short videos, along with complementary modalities as applicable. The framework simultaneously addresses multiple critical perception tasks, including weather classification, pavement wetness assessment, and traffic congestion detection, achieving robust multi-task reasoning while balancing accuracy and computational efficiency. To support empirical validation, we curated three specialized datasets aligned with these tasks. Notably, the pavement wetness dataset is multimodal, combining video streams with road weather sensor data, highlighting the benefits of multimodal reasoning. Experimental results demonstrate consistently strong performance across diverse traffic and environmental conditions. From a deployment perspective, the framework can be readily integrated with existing traffic camera systems and strategically applied to high-risk rural locations, such as sharp curves, flood-prone lowlands, or icy bridges. By continuously monitoring the targeted sites, the system enhances situational awareness and delivers timely alerts, even in resource-constrained environments.
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[NLP-89] Active Domain Knowledge Acquisition with 100 Budget: Enhancing LLM s via Cost-Efficient Expert-Involved Interaction in Sensitive Domains EMNLP2025
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在药物发现和罕见疾病研究等高专业性、高成本领域中因缺乏专家知识而导致性能受限的问题。解决方案的关键在于提出一种名为PU-ADKA的新框架，该框架通过在固定预算内主动调用领域专家团队中的最优个体来增强LLM的领域适应能力；其核心机制包括：基于专家可用性、知识边界与咨询成本的动态选择策略，以及利用PubMed数据进行仿真训练并结合真实专家交互验证的方法，从而实现高效、低成本的知识注入。
链接: https://arxiv.org/abs/2508.17202
作者: Yang Wu,Raha Moraffah,Rujing Yao,Jinhong Yu,Zhimin Tao,Xiaozhong Liu
机构: Worcester Polytechnic Institute (伍斯特理工学院); Nankai University (南开大学); Jiangsu University (江苏大学)
类目: Computation and Language (cs.CL)
备注: EMNLP 2025 Findings
Abstract:Large Language Models (LLMs) have demonstrated an impressive level of general knowledge. However, they often struggle in highly specialized and cost-sensitive domains such as drug discovery and rare disease research due to the lack of expert knowledge. In this paper, we propose a novel framework (PU-ADKA) designed to efficiently enhance domain-specific LLMs by actively engaging domain experts within a fixed budget. Unlike traditional fine-tuning approaches, PU-ADKA selectively identifies and queries the most appropriate expert from a team, taking into account each expert’s availability, knowledge boundaries, and consultation costs. We train PU-ADKA using simulations on PubMed data and validate it through both controlled expert interactions and real-world deployment with a drug development team, demonstrating its effectiveness in enhancing LLM performance in specialized domains under strict budget constraints. In addition to outlining our methodological innovations and experimental results, we introduce a new benchmark dataset, CKAD, for cost-effective LLM domain knowledge acquisition to foster further research in this challenging area.
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[NLP-90] owards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在对齐人类意图、安全约束及领域特定需求方面的挑战，其核心问题是如何系统性地构建高质量指令微调（instruction tuning）流程，以提升模型的有效性与可靠性。解决方案的关键在于提出一个完整的流水线框架，涵盖数据收集方法（包括专家标注、大模型蒸馏和自改进机制）、全参数与参数高效微调策略（如LoRA和前缀调优），以及多语言、多模态场景下的评估协议，并强调通过自动化数据生成、自适应优化与鲁棒评估体系的融合，实现数据、算法与人类反馈的深度集成，从而推动LLMs在实际应用中更可靠地对齐人类目标。
链接: https://arxiv.org/abs/2508.17184
作者: Xudong Han,Junjie Yang,Tianyang Wang,Ziqian Bi,Junfeng Hao,Junhao Song
机构: University of Sussex (萨塞克斯大学); Xiamen University (厦门大学); University of Liverpool (利物浦大学); Purdue University (普渡大学); Vokram Group (沃克兰集团); Imperial College London (帝国理工学院)
类目: Computation and Language (cs.CL)
备注: 24 pages, 7 figures, 5 tables
Abstract:Instruction tuning is a pivotal technique for aligning large language models (LLMs) with human intentions, safety constraints, and domain-specific requirements. This survey provides a comprehensive overview of the full pipeline, encompassing (i) data collection methodologies, (ii) full-parameter and parameter-efficient fine-tuning strategies, and (iii) evaluation protocols. We categorized data construction into three major paradigms: expert annotation, distillation from larger models, and self-improvement mechanisms, each offering distinct trade-offs between quality, scalability, and resource cost. Fine-tuning techniques range from conventional supervised training to lightweight approaches, such as low-rank adaptation (LoRA) and prefix tuning, with a focus on computational efficiency and model reusability. We further examine the challenges of evaluating faithfulness, utility, and safety across multilingual and multimodal scenarios, highlighting the emergence of domain-specific benchmarks in healthcare, legal, and financial applications. Finally, we discuss promising directions for automated data generation, adaptive optimization, and robust evaluation frameworks, arguing that a closer integration of data, algorithms, and human feedback is essential for advancing instruction-tuned LLMs. This survey aims to serve as a practical reference for researchers and practitioners seeking to design LLMs that are both effective and reliably aligned with human intentions.
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[NLP-91] LLM Assertiveness can be Mechanistically Decomposed into Emotional and Logical Components
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在高风险场景中表现出过度自信的问题，即模型常以不恰当的确定性输出信息。为揭示这一行为的内部机制，作者采用机械可解释性方法，基于人类标注的断言强度数据集微调开源的Llama 3.2模型，并提取各层残差激活值，通过相似性度量定位断言性表征。关键发现是：高断言性表征可分解为两个正交子成分——情绪类簇与逻辑类簇，这与心理学中的双路径精细加工可能性模型（Elaboration Likelihood Model）相呼应；进一步地，从这两个子成分导出的引导向量展现出不同的因果效应：情绪向量广泛影响预测准确性，而逻辑向量则作用更局部。此研究提供了LLM断言行为多组件结构的机制证据，并指明了缓解过度自信行为的新路径。
链接: https://arxiv.org/abs/2508.17182
作者: Hikaru Tsujimura,Arush Tagade
机构: Cardiff University (卡迪夫大学); George Washington University (乔治·华盛顿大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: This preprint is under review
Abstract:Large Language Models (LLMs) often display overconfidence, presenting information with unwarranted certainty in high-stakes contexts. We investigate the internal basis of this behavior via mechanistic interpretability. Using open-sourced Llama 3.2 models fine-tuned on human annotated assertiveness datasets, we extract residual activations across all layers, and compute similarity metrics to localize assertive representations. Our analysis identifies layers most sensitive to assertiveness contrasts and reveals that high-assertive representations decompose into two orthogonal sub-components of emotional and logical clusters-paralleling the dual-route Elaboration Likelihood Model in Psychology. Steering vectors derived from these sub-components show distinct causal effects: emotional vectors broadly influence prediction accuracy, while logical vectors exert more localized effects. These findings provide mechanistic evidence for the multi-component structure of LLM assertiveness and highlight avenues for mitigating overconfident behavior.
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[NLP-92] he Impact of Annotator Personas on LLM Behavior Across the Perspectivism Spectrum
【速读】： 该论文旨在解决在仇恨言论（hate speech）和攻击性内容（abusiveness）标注任务中，如何有效利用大语言模型（Large Language Models, LLMs）生成具有特定视角（perspective）的标注结果，同时考虑预定义标注者人格特征（annotator personas）的问题。其关键解决方案在于：通过在强到弱的数据视角主义（data perspectivism）谱系中对LLM进行测试，发现LLM会选择性地使用人格特征中的社会人口统计学属性，并识别出与原始人类标注者在不同程度上对齐的原型标注者（prototypical annotators）。研究进一步表明，在弱数据视角主义下，不依赖显式标注者信息的建模方法表现优于强视角主义和人类标注；而在强视角主义的个性化数据集上，LLM的标注性能接近但未超越人类标注者，说明LLM虽能模拟主观视角，但其本质仍倾向于聚合化输出。
链接: https://arxiv.org/abs/2508.17164
作者: Olufunke O. Sarumi,Charles Welch,Daniel Braun,Jörg Schlötterer
机构: University of Marburg (马尔堡大学); McMaster University (麦克马斯特大学); University of Mannheim (曼海姆大学)
类目: Computation and Language (cs.CL)
备注: Accepted at ICNLSP 2025, Odense, Denmark
Abstract:In this work, we explore the capability of Large Language Models (LLMs) to annotate hate speech and abusiveness while considering predefined annotator personas within the strong-to-weak data perspectivism spectra. We evaluated LLM-generated annotations against existing annotator modeling techniques for perspective modeling. Our findings show that LLMs selectively use demographic attributes from the personas. We identified prototypical annotators, with persona features that show varying degrees of alignment with the original human annotators. Within the data perspectivism paradigm, annotator modeling techniques that do not explicitly rely on annotator information performed better under weak data perspectivism compared to both strong data perspectivism and human annotations, suggesting LLM-generated views tend towards aggregation despite subjective prompting. However, for more personalized datasets tailored to strong perspectivism, the performance of LLM annotator modeling approached, but did not exceed, human annotators.
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[NLP-93] Quantifying Language Disparities in Multilingual Large Language Models EMNLP2025
【速读】： 该论文旨在解决多语言大模型评估中因目标语言多样性、实验设置差异及模型选择等因素导致的结果碎片化与混淆问题，从而难以准确量化模型性能和语言间的实际差异。其解决方案的关键在于提出一个解耦框架，引入三个可解释指标：性能实现比（performance realisation ratio）、其变异系数（coefficient of variation）以及语言潜力（language potential），从而实现对模型性能和语言差距的更精细、可靠的测量，尤其在低资源语言场景下表现出显著优势。
链接: https://arxiv.org/abs/2508.17162
作者: Songbo Hu,Ivan Vulić,Anna Korhonen
机构: Language Technology Lab, University of Cambridge, UK (剑桥大学语言技术实验室)
类目: Computation and Language (cs.CL)
备注: Accepted at EMNLP 2025
Abstract:Results reported in large-scale multilingual evaluations are often fragmented and confounded by factors such as target languages, differences in experimental setups, and model choices. We propose a framework that disentangles these confounding variables and introduces three interpretable metrics–the performance realisation ratio, its coefficient of variation, and language potential–enabling a finer-grained and more insightful quantification of actual performance disparities across both (i) models and (ii) languages. Through a case study of 13 model variants on 11 multilingual datasets, we demonstrate that our framework provides a more reliable measurement of model performance and language disparities, particularly for low-resource languages, which have so far proven challenging to evaluate. Importantly, our results reveal that higher overall model performance does not necessarily imply greater fairness across languages.
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[NLP-94] SPORTSQL: An Interactive System for Real-Time Sports Reasoning and Visualization EMNLP
【速读】： 该论文旨在解决非专业用户在动态体育数据（特别是英格兰超级联赛，EPL）中进行自然语言查询与可视化分析的难题。传统方法通常依赖数据库知识或编程技能，限制了普通用户的使用效率。解决方案的关键在于提出一个模块化、交互式的系统 SPORTSQL，其核心能力包括：基于大型语言模型（LLM）的符号推理能力实现自然语言到可执行 SQL 的转换，结合实时更新的时序索引数据库（来自 Fantasy Premier League 数据），支持表格和图表两种输出形式，并通过新提出的 Dynamic Sport Question Answering benchmark (DSQABENCH) 对系统性能进行量化评估，从而实现无需专业知识即可通过对话方式探索不断变化的体育统计数据。
链接: https://arxiv.org/abs/2508.17157
作者: Sebastian Martinez,Naman Ahuja,Fenil Bardoliya,Chris Bryan,Vivek Gupta
机构: Arizona State University (亚利桑那州立大学)
类目: Computation and Language (cs.CL)
备注: Under Review at EMNLP
Abstract:We present a modular, interactive system, SPORTSQL, for natural language querying and visualization of dynamic sports data, with a focus on the English Premier League (EPL). The system translates user questions into executable SQL over a live, temporally indexed database constructed from real-time Fantasy Premier League (FPL) data. It supports both tabular and visual outputs, leveraging the symbolic reasoning capabilities of Large Language Models (LLMs) for query parsing, schema linking, and visualization selection. To evaluate system performance, we introduce the Dynamic Sport Question Answering benchmark (DSQABENCH), comprising 1,700+ queries annotated with SQL programs, gold answers, and database snapshots. Our demo highlights how non-expert users can seamlessly explore evolving sports statistics through a natural, conversational interface.
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[NLP-95] Natural Language Satisfiability: Exploring the Problem Distribution and Evaluating Transformer-based Language Models ACL2024
【速读】： 该论文旨在解决生成式 AI（Generative AI）在处理自然语言推理任务时，对不同计算复杂度类别的可满足性问题（satisfiability problems）的学习能力差异问题。其核心挑战在于，自然语言中的可满足性问题因其语法结构和逻辑表达的多样性，可能属于不同的计算复杂度类（如NP完全、PSPACE等），而现有基于Transformer的语言模型（TLMs）在这些不同类别上的表现尚未被系统评估。解决方案的关键在于通过实证研究，构建并分析来自不同复杂度类别的自然语言可满足性问题数据集，从而揭示TLMs在学习推理规则时对问题难度和语法构造的敏感性，进而为提升模型在逻辑推理任务中的鲁棒性和泛化能力提供依据。
链接: https://arxiv.org/abs/2508.17153
作者: Tharindu Madusanka,Ian Pratt-Hartmann,Riza Batista-Navarro
机构: University of Manchester (曼彻斯特大学); Uniwersytet Opolski (奥波莱大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: The paper was accepted to the 62nd Association for Computational Linguistics (ACL 2024), where it won the Best Paper Award
Abstract:Efforts to apply transformer-based language models (TLMs) to the problem of reasoning in natural language have enjoyed ever-increasing success in recent years. The most fundamental task in this area to which nearly all others can be reduced is that of determining satisfiability. However, from a logical point of view, satisfiability problems vary along various dimensions, which may affect TLMs’ ability to learn how to solve them. The problem instances of satisfiability in natural language can belong to different computational complexity classes depending on the language fragment in which they are expressed. Although prior research has explored the problem of natural language satisfiability, the above-mentioned point has not been discussed adequately. Hence, we investigate how problem instances from varying computational complexity classes and having different grammatical constructs impact TLMs’ ability to learn rules of inference. Furthermore, to faithfully evaluate TLMs, we conduct an empirical study to explore the distribution of satisfiability problems.
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[NLP-96] Geolocation-Aware Robust Spoken Language Identification
【速读】： 该论文旨在解决自监督学习（Self-supervised Learning, SSL）在语音语言识别（Spoken Language Identification, LID）中对同一语言内方言和口音变体识别不一致的问题，即现有模型难以将不同方言或口音统一归类为同一语言类别。其解决方案的关键在于提出一种地理定位感知的LID方法（geolocation-aware LID），通过引入地理定位预测作为辅助任务，并将预测得到的向量作为条件信号注入到模型中间表示层，从而显式引导模型学习更具统一性的方言与口音表征，显著提升对语言内部变异和未见域场景的鲁棒性。
链接: https://arxiv.org/abs/2508.17148
作者: Qingzheng Wang,Hye-jin Shim,Jiancheng Sun,Shinji Watanabe
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL); Sound (cs.SD)
备注: Accepted to IEEE ASRU 2025. \c{opyright} 2025 IEEE. Personal use permitted. Permission from IEEE required for all other uses including reprinting/republishing, advertising, resale, redistribution, reuse, or creating collective works
Abstract:While Self-supervised Learning (SSL) has significantly improved Spoken Language Identification (LID), existing models often struggle to consistently classify dialects and accents of the same language as a unified class. To address this challenge, we propose geolocation-aware LID, a novel approach that incorporates language-level geolocation information into the SSL-based LID model. Specifically, we introduce geolocation prediction as an auxiliary task and inject the predicted vectors into intermediate representations as conditioning signals. This explicit conditioning encourages the model to learn more unified representations for dialectal and accented variations. Experiments across six multilingual datasets demonstrate that our approach improves robustness to intra-language variations and unseen domains, achieving new state-of-the-art accuracy on FLEURS (97.7%) and 9.7% relative improvement on ML-SUPERB 2.0 dialect set.
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[NLP-97] he Power of Framing: How News Headlines Guide Search Behavior EMNLP
【速读】： 该论文旨在解决“headline framing（标题框架）如何影响用户的信息搜索行为”这一问题，尤其关注其在信息获取过程中对后续查询意图的塑造作用。解决方案的关键在于设计并实施一项受控实验，通过让参与者基于特定语言框架筛选标题后进行后续查询，发现不同类型的框架（如冲突型、策略型、叙事型和主题型）显著改变了用户的查询具体性与一致性：其中冲突和策略框架削弱了用户先前选择的连贯性，而叙事型框架则促使用户提出更具体的查询；同时观察到框架效应存在短期持续性但随时间减弱。这表明，即使短暂接触框架信息，也能显著引导用户的信息搜寻路径。
链接: https://arxiv.org/abs/2508.17131
作者: Amrit Poudel,Maria Milkowski,Tim Weninger
机构: University of Notre Dame (圣母大学)
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR)
备注: Accepted to EMNLP
Abstract:Search engines play a central role in how people gather information, but subtle cues like headline framing may influence not only what users believe but also how they search. While framing effects on judgment are well documented, their impact on subsequent search behavior is less understood. We conducted a controlled experiment where participants issued queries and selected from headlines filtered by specific linguistic frames. Headline framing significantly shaped follow-up queries: conflict and strategy frames disrupted alignment with prior selections, while episodic frames led to more concrete queries than thematic ones. We also observed modest short-term frame persistence that declined over time. These results suggest that even brief exposure to framing can meaningfully alter the direction of users information-seeking behavior.
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[NLP-98] A Straightforward Pipeline for Targeted Entailment and Contradiction Detection
【速读】： 该论文旨在解决文档中句子间语义关系识别的难题，尤其关注如何精准定位与特定主张（claim）相关的前提句（premise）或矛盾句（contradiction），这是事实核查、论点挖掘和文本摘要等任务的核心。现有方法存在局限：Transformer注意力机制虽能捕捉上下文中的显著文本关联，但缺乏明确的语义标签；而自然语言推理（Natural Language Inference, NLI）模型虽可对句子对进行语义分类，却难以结合上下文重要性进行筛选。解决方案的关键在于构建一个两阶段流水线：首先通过聚合token级注意力得分，从上下文中提取与目标句高度相关的候选句子；随后利用预训练NLI模型对每个候选句进行前提（entailment）或矛盾（contradiction）分类，并以注意力得分作为过滤条件，从而高效识别出最显著的语义关系，实现对任意给定主张的精细化分析。
链接: https://arxiv.org/abs/2508.17127
作者: Antonin Sulc
机构: LBNL (Lawrence Berkeley National Laboratory)
类目: Computation and Language (cs.CL); Logic in Computer Science (cs.LO)
备注:
Abstract:Finding the relationships between sentences in a document is crucial for tasks like fact-checking, argument mining, and text summarization. A key challenge is to identify which sentences act as premises or contradictions for a specific claim. Existing methods often face a trade-off: transformer attention mechanisms can identify salient textual connections but lack explicit semantic labels, while Natural Language Inference (NLI) models can classify relationships between sentence pairs but operate independently of contextual saliency. In this work, we introduce a method that combines the strengths of both approaches for a targeted analysis. Our pipeline first identifies candidate sentences that are contextually relevant to a user-selected target sentence by aggregating token-level attention scores. It then uses a pretrained NLI model to classify each candidate as a premise (entailment) or contradiction. By filtering NLI-identified relationships with attention-based saliency scores, our method efficiently isolates the most significant semantic relationships for any given claim in a text.
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[NLP-99] oken Homogenization under Positional Bias
【速读】： 该论文旨在解决大语言模型中token表示同质化（token homogenization）现象及其与位置偏差（positional bias）之间关系的问题。其解决方案的关键在于通过层间相似性分析和受控实验，揭示了tokens在Transformer各层处理过程中系统性地丧失区分度的现象，尤其当token受到极端位置偏置影响时更为显著；研究进一步证实了这种同质化行为依赖于位置注意力机制，从而为理解模型内部表征演化提供了实证依据。
链接: https://arxiv.org/abs/2508.17126
作者: Viacheslav Yusupov,Danil Maksimov,Ameliia Alaeva,Tatiana Zaitceva,Antipina Anna,Anna Vasileva,Chenlin Liu,Rayuth Chheng,Danil Sazanakov,Andrey Chetvergov,Alina Ermilova,Egor Shvetsov
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:This paper investigates token homogenization - the convergence of token representations toward uniformity across transformer layers and its relationship to positional bias in large language models. We empirically examine whether homogenization occurs and how positional bias amplifies this effect. Through layer-wise similarity analysis and controlled experiments, we demonstrate that tokens systematically lose distinctiveness during processing, particularly when biased toward extremal positions. Our findings confirm both the existence of homogenization and its dependence on positional attention mechanisms.
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[NLP-100] Linguistic Neuron Overlap Patterns to Facilitate Cross-lingual Transfer on Low-resource Languages
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在低资源语言上的零样本跨语言上下文学习（Zero-shot Cross-lingual In-Context Learning, X-ICL）性能不足的问题，尤其关注如何在不进行昂贵微调的前提下提升模型对低资源语言的适应能力。其解决方案的关键在于提出BridgeX-ICL方法，该方法从“语言桥梁”的视角出发，探索共享神经元（shared neurons）对跨语言性能的影响；通过构建基于MUSE双语词典的神经探针数据，定义并激活一组语言重叠神经元（language overlap neurons），进而利用基于HSIC（Hilbert-Schmidt Independence Criterion）的度量指标量化模型内部的语言谱系特征，从而指导最优桥梁语言的选择，实现高效、无监督的跨语言知识迁移。
链接: https://arxiv.org/abs/2508.17078
作者: Yuemei Xu,Kexin Xu,Jian Zhou,Ling Hu,Lin Gui
机构: Beijing Foreign Studies University (北京外国语大学); King’s College London (伦敦国王学院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:The current Large Language Models (LLMs) face significant challenges in improving performance on low-resource languages and urgently need data-efficient methods without costly fine-tuning. From the perspective of language-bridge, we propose BridgeX-ICL, a simple yet effective method to improve zero-shot Cross-lingual In-Context Learning (X-ICL) for low-resource languages. Unlike existing works focusing on language-specific neurons, BridgeX-ICL explores whether sharing neurons can improve cross-lingual performance in LLMs or not. We construct neuron probe data from the ground-truth MUSE bilingual dictionaries, and define a subset of language overlap neurons accordingly, to ensure full activation of these anchored neurons. Subsequently, we propose an HSIC-based metric to quantify LLMs’ internal linguistic spectrum based on overlap neurons, which guides optimal bridge selection. The experiments conducted on 2 cross-lingual tasks and 15 language pairs from 7 diverse families (covering both high-low and moderate-low pairs) validate the effectiveness of BridgeX-ICL and offer empirical insights into the underlying multilingual mechanisms of LLMs.
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[NLP-101] Anemoi: A Semi-Centralized Multi-agent Systems Based on Agent agent Systems Based on Agent-to-Agent Communication MCP server from Coral Protocol
【速读】： 该论文旨在解决当前通用多智能体系统（Multi-Agent System, MAS）在采用“上下文工程+集中式”范式时所面临的两大问题：一是对规划智能体（planner agent）能力的强依赖性，导致当使用较小语言模型（LLM）作为规划器时性能显著下降；二是智能体间通信受限，协作依赖于昂贵的提示拼接和上下文注入，造成冗余与信息丢失。解决方案的关键在于提出 Anemoi，一个基于 Coral 协议中 Agent-to-Agent (A2A) 通信 MCP 服务器的半集中式多智能体架构，通过支持结构化、直接的智能体间协作，使所有智能体能够实时监控进展、评估结果、识别瓶颈并提出优化建议，从而降低对单一规划器的依赖、支持动态计划更新，并减少冗余上下文传递，提升系统的可扩展性和执行效率。
链接: https://arxiv.org/abs/2508.17068
作者: Xinxing Ren,Caelum Forder,Qianbo Zang,Ahsen Tahir,Roman J. Georgio,Suman Deb,Peter Carroll,Önder Gürcan,Zekun Guo
机构: 未知
类目: Multiagent Systems (cs.MA); Computation and Language (cs.CL)
备注:
Abstract:Recent advances in generalist multi-agent systems (MAS) have largely followed a context-engineering plus centralized paradigm, where a planner agent coordinates multiple worker agents through unidirectional prompt passing. While effective under strong planner models, this design suffers from two critical limitations: (1) strong dependency on the planner’s capability, which leads to degraded performance when a smaller LLM powers the planner; and (2) limited inter-agent communication, where collaboration relies on costly prompt concatenation and context injection, introducing redundancy and information loss. To address these challenges, we propose Anemoi, a semi-centralized MAS built on the Agent-to-Agent (A2A) communication MCP server from Coral Protocol. Unlike traditional designs, Anemoi enables structured and direct inter-agent collaboration, allowing all agents to monitor progress, assess results, identify bottlenecks, and propose refinements in real time. This paradigm reduces reliance on a single planner, supports adaptive plan updates, and minimizes redundant context passing, resulting in more scalable and cost-efficient execution. Evaluated on the GAIA benchmark, Anemoi achieved 52.73% accuracy with a small LLM (GPT-4.1-mini) as the planner, surpassing the strongest open-source baseline OWL (43.63%) by +9.09% under identical LLM settings. Our implementation is publicly available at this https URL.
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[NLP-102] GRAID: Synthetic Data Generation with Geometric Constraints and Multi-Agent ic Reflection for Harmful Content Detection
【速读】： 该论文旨在解决有害文本分类任务中因数据稀缺而导致的模型性能受限问题，尤其是在护栏（guardrail）应用中的实际部署挑战。其解决方案的关键在于提出一种名为GRAID（Geometric and Reflective AI-Driven Data Augmentation）的新颖数据增强流水线，该方法通过两个核心阶段实现：首先利用受约束的大语言模型（Large Language Models, LLMs）生成几何可控的样本，确保对输入空间的可靠覆盖；其次通过多智能体反射式过程进行增强，促进风格多样性并识别边缘案例，从而实现对有害内容的精细化探索。实验表明，使用GRAID增强后的数据集可显著提升下游护栏模型的性能。
链接: https://arxiv.org/abs/2508.17057
作者: Melissa Kazemi Rad,Alberto Purpura,Himanshu Kumar,Emily Chen,Mohammad Shahed Sorower
机构: Capital One, AI Foundations (Capital One 人工智能基础部门)
类目: Computation and Language (cs.CL); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 19 pages, 12 figures
Abstract:We address the problem of data scarcity in harmful text classification for guardrailing applications and introduce GRAID (Geometric and Reflective AI-Driven Data Augmentation), a novel pipeline that leverages Large Language Models (LLMs) for dataset augmentation. GRAID consists of two stages: (i) generation of geometrically controlled examples using a constrained LLM, and (ii) augmentation through a multi-agentic reflective process that promotes stylistic diversity and uncovers edge cases. This combination enables both reliable coverage of the input space and nuanced exploration of harmful content. Using two benchmark data sets, we demonstrate that augmenting a harmful text classification dataset with GRAID leads to significant improvements in downstream guardrail model performance.
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[NLP-103] RephraseTTS: Dynamic Length Text based Speech Insertion with Speaker Style Transfer
【速读】： 该论文旨在解决文本条件下的语音插入（text-conditioned speech insertion）问题，即在给定完整文本转录的前提下，将一段新的语音样本插入到已有语音中，以实现对音频内容的动态更新，例如当文本被修改时同步调整对应语音。其核心解决方案采用基于Transformer的非自回归（non-autoregressive）方法，能够在推理阶段根据文本内容和输入语音的语速（tempo）动态确定插入语音的长度，从而支持变长插入；同时保持原有语音的说话人特征、韵律（prosody）及其他频谱特性，实验结果表明该方法优于基于现有自适应文本到语音（adaptive text-to-speech）方法的基线模型。
链接: https://arxiv.org/abs/2508.17031
作者: Neeraj Matiyali,Siddharth Srivastava,Gaurav Sharma
机构: 未知
类目: ound (cs.SD); Computation and Language (cs.CL)
备注:
Abstract:We propose a method for the task of text-conditioned speech insertion, i.e. inserting a speech sample in an input speech sample, conditioned on the corresponding complete text transcript. An example use case of the task would be to update the speech audio when corrections are done on the corresponding text transcript. The proposed method follows a transformer-based non-autoregressive approach that allows speech insertions of variable lengths, which are dynamically determined during inference, based on the text transcript and tempo of the available partial input. It is capable of maintaining the speaker’s voice characteristics, prosody and other spectral properties of the available speech input. Results from our experiments and user study on LibriTTS show that our method outperforms baselines based on an existing adaptive text to speech method. We also provide numerous qualitative results to appreciate the quality of the output from the proposed method.
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[NLP-104] Improving Table Understanding with LLM s and Entity-Oriented Search
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在理解表格数据时面临的两大挑战：一是表格内容的不可预测性导致现有方法依赖繁琐的数据预处理和关键词匹配；二是缺乏上下文信息，限制了LLMs的推理能力。其解决方案的关键在于提出一种面向实体（entity-oriented）的搜索方法，通过利用问题与表格数据之间的语义相似性以及单元格间的隐式关联，减少对预处理和关键词匹配的依赖，并强化表格实体的语义绑定以提升上下文清晰度。此外，论文首次将图查询语言引入表格理解任务，开辟了新的研究方向。实验表明，该方法在WikiTableQuestions和TabFact标准基准上达到了新的最先进性能。
链接: https://arxiv.org/abs/2508.17028
作者: Thi-Nhung Nguyen,Hoang Ngo,Dinh Phung,Thuy-Trang Vu,Dat Quoc Nguyen
机构: Monash University (莫纳什大学); Qualcomm AI Research (高通人工智能研究); Qualcomm Technologies, Inc. (高通技术公司)
类目: Computation and Language (cs.CL)
备注: Accepted to COLM 2025
Abstract:Our work addresses the challenges of understanding tables. Existing methods often struggle with the unpredictable nature of table content, leading to a reliance on preprocessing and keyword matching. They also face limitations due to the lack of contextual information, which complicates the reasoning processes of large language models (LLMs). To overcome these challenges, we introduce an entity-oriented search method to improve table understanding with LLMs. This approach effectively leverages the semantic similarities between questions and table data, as well as the implicit relationships between table cells, minimizing the need for data preprocessing and keyword matching. Additionally, it focuses on table entities, ensuring that table cells are semantically tightly bound, thereby enhancing contextual clarity. Furthermore, we pioneer the use of a graph query language for table understanding, establishing a new research direction. Experiments show that our approach achieves new state-of-the-art performances on standard benchmarks WikiTableQuestions and TabFact.
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[NLP-105] EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks
【速读】： 该论文旨在解决教育领域中自动情感分析（Aspect-based Sentiment Analysis, ABSA）研究因缺乏高质量标注数据而进展缓慢的问题。现有ABSA方法主要集中在商业领域，教育场景下的资源稀缺且难以构建，受限于公开数据集匮乏和严格的数据保护要求。解决方案的关键在于提出了首个面向教育评论的公开、标注完整的ABSA数据集EduRABSA，涵盖课程、教师和大学三类主体，并支持所有主流ABSA任务，包括隐式方面和隐式情感抽取；同时开发了轻量级、无需安装的手动标注工具ASQE-DPT，可从单任务标注生成多任务标签数据，从而显著降低数据构建门槛，推动教育领域ABSA研究的透明性与可复现性。
链接: https://arxiv.org/abs/2508.17008
作者: Yan Cathy Hua,Paul Denny,Jörg Wicker,Katerina Taskova
机构: University of Auckland (奥克兰大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Every year, most educational institutions seek and receive an enormous volume of text feedback from students on courses, teaching, and overall experience. Yet, turning this raw feedback into useful insights is far from straightforward. It has been a long-standing challenge to adopt automatic opinion mining solutions for such education review text data due to the content complexity and low-granularity reporting requirements. Aspect-based Sentiment Analysis (ABSA) offers a promising solution with its rich, sub-sentence-level opinion mining capabilities. However, existing ABSA research and resources are very heavily focused on the commercial domain. In education, they are scarce and hard to develop due to limited public datasets and strict data protection. A high-quality, annotated dataset is urgently needed to advance research in this under-resourced area. In this work, we present EduRABSA (Education Review ABSA), the first public, annotated ABSA education review dataset that covers three review subject types (course, teaching staff, university) in the English language and all main ABSA tasks, including the under-explored implicit aspect and implicit opinion extraction. We also share ASQE-DPT (Data Processing Tool), an offline, lightweight, installation-free manual data annotation tool that generates labelled datasets for comprehensive ABSA tasks from a single-task annotation. Together, these resources contribute to the ABSA community and education domain by removing the dataset barrier, supporting research transparency and reproducibility, and enabling the creation and sharing of further resources. The dataset, annotation tool, and scripts and statistics for dataset processing and sampling are available at this https URL.
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[NLP-106] Planning for Success: Exploring LLM Long-term Planning Capabilities in Table Understanding CONLL2025
【速读】： 该论文旨在解决表格理解任务中因缺乏显式长期规划和步骤间连接薄弱而导致的问题，尤其是复杂问题中约束条件易被忽略的情况。其解决方案的关键在于利用大语言模型（Large Language Models, LLMs）的长期规划能力，构建一个紧密耦合且目标导向的多步执行流程，从而增强对表格信息的结构化推理能力，并有效减少Chain-of-Thought方法中存在的冗余细节干扰。
链接: https://arxiv.org/abs/2508.17005
作者: Thi-Nhung Nguyen,Hoang Ngo,Dinh Phung,Thuy-Trang Vu,Dat Quoc Nguyen
机构: Monash University (蒙纳士大学); Qualcomm AI Research (高通人工智能研究); Qualcomm Technologies, Inc. (高通技术公司)
类目: Computation and Language (cs.CL)
备注: Accepted to CoNLL 2025
Abstract:Table understanding is key to addressing challenging downstream tasks such as table-based question answering and fact verification. Recent works have focused on leveraging Chain-of-Thought and question decomposition to solve complex questions requiring multiple operations on tables. However, these methods often suffer from a lack of explicit long-term planning and weak inter-step connections, leading to miss constraints within questions. In this paper, we propose leveraging the long-term planning capabilities of large language models (LLMs) to enhance table understanding. Our approach enables the execution of a long-term plan, where the steps are tightly interconnected and serve the ultimate goal, an aspect that methods based on Chain-of-Thought and question decomposition lack. In addition, our method effectively minimizes the inclusion of unnecessary details in the process of solving the next short-term goals, a limitation of methods based on Chain-of-Thought. Extensive experiments demonstrate that our method outperforms strong baselines and achieves state-of-the-art performance on WikiTableQuestions and TabFact datasets.
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[NLP-107] KL-Regularised Q-Learning: A Token-level Action-Value perspective on Online RLHF
【速读】： 该论文旨在解决语言模型强化学习中基于人类反馈（Language Model Reinforcement Learning from Human Feedback, LM-RLHF）的策略优化问题，特别是针对近端策略优化（Proximal Policy Optimisation, PPO）方法存在的启发式动机和对KL散度约束处理不够严谨的问题。其解决方案的关键在于提出一种新的动作价值强化学习方法——KL正则化Q学习（KL-regularised Q-Learning, KLQ），该方法从不同的理论视角出发，在保持与PPO在特定条件下等价性的同时，提供了更清晰的优化目标和更稳定的训练机制，并在摘要生成和单轮对话两个关键任务上验证了其有效性，实现了与PPO相当的RLHF目标性能，且在LLM-as-a-judge评估中表现出更高的胜率。
链接: https://arxiv.org/abs/2508.17000
作者: Jason R Brown,Lennie Wells,Edward James Young,Sergio Bacallado
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Proximal Policy Optimisation (PPO) is an established and effective policy gradient algorithm used for Language Model Reinforcement Learning from Human Feedback (LM-RLHF). PPO performs well empirically but has a heuristic motivation and handles the KL-divergence constraint used in LM-RLHF in an ad-hoc manner. In this paper, we develop a a new action-value RL method for the LM-RLHF setting, KL-regularised Q-Learning (KLQ). We then show that our method is equivalent to a version of PPO in a certain specific sense, despite its very different motivation. Finally, we benchmark KLQ on two key language generation tasks – summarisation and single-turn dialogue. We demonstrate that KLQ performs on-par with PPO at optimising the LM-RLHF objective, and achieves a consistently higher win-rate against PPO on LLM-as-a-judge evaluations.
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[NLP-108] DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation EMNLP
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在列表级文档重排序（listwise document reranking）任务中难以同时实现细粒度相关性评分与跨文档整体分析的平衡问题。现有单模型方法往往在点对点打分精度或全局结构理解能力之间存在权衡，导致性能受限且缺乏可解释性。其解决方案的关键在于提出一个双阶段解耦框架 DeepAgentRank（DeAR），第一阶段通过混合交叉熵、RankNet 和 KL 散度损失，将 13B 参数的冻结 LLaMA 教师模型中的 token 级相关性信号蒸馏至一个轻量级 3.8B 学生模型，确保稳健的点式评分；第二阶段引入 LoRA 适配器，在 GPT-4o 生成的链式思维排列数据上进行微调，实现基于自然语言解释的列表级推理。该设计显著提升了重排序准确性和可解释性，并在多个权威基准（如 TREC-DL、BEIR 和 NovelEval）上超越开源基线及 GPT-4 的表现。
链接: https://arxiv.org/abs/2508.16998
作者: Abdelrahman Abdallah,Jamshid Mozafari,Bhawna Piryani,Adam Jatowt
机构: University of Innsbruck (因斯布鲁克大学)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Accept at EMNLP Findings 2025
Abstract:Large Language Models (LLMs) have transformed listwise document reranking by enabling global reasoning over candidate sets, yet single models often struggle to balance fine-grained relevance scoring with holistic cross-document analysis. We propose \textbfDeep\textbfAgent\textbfRank (\textbf\DeAR), an open-source framework that decouples these tasks through a dual-stage approach, achieving superior accuracy and interpretability. In \emphStage 1, we distill token-level relevance signals from a frozen 13B LLaMA teacher into a compact \3, 8\B student model using a hybrid of cross-entropy, RankNet, and KL divergence losses, ensuring robust pointwise scoring. In \emphStage 2, we attach a second LoRA adapter and fine-tune on 20K GPT-4o-generated chain-of-thought permutations, enabling listwise reasoning with natural-language justifications. Evaluated on TREC-DL19/20, eight BEIR datasets, and NovelEval-2306, \DeAR surpasses open-source baselines by +5.1 nDCG@5 on DL20 and achieves 90.97 nDCG@10 on NovelEval, outperforming GPT-4 by +3.09. Without fine-tuning on Wikipedia, DeAR also excels in open-domain QA, achieving 54.29 Top-1 accuracy on Natural Questions, surpassing baselines like MonoT5, UPR, and RankGPT. Ablations confirm that dual-loss distillation ensures stable calibration, making \DeAR a highly effective and interpretable solution for modern reranking systems.\footnoteDataset and code available at this https URL…
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[NLP-109] GRADE: Generating multi-hop QA and fine-gRAined Difficulty matrix for RAG Evaluation EMNLP2025
【速读】： 该论文旨在解决当前检索增强生成（Retrieval-Augmented Generation, RAG）系统评估中忽视结构复杂性和多步推理能力的问题，尤其关注检索难度与推理深度之间的交互关系。现有基准测试未能充分刻画真实场景下任务的复杂性，导致对模型性能的评估不够精细和具有诊断价值。解决方案的关键在于提出一个名为 \textscGRADE 的新型评估框架，其核心是将任务难度建模为两个正交维度：（1）推理深度（即推理步骤数，hops），反映多跳推理需求；（2）查询与其支持证据之间的语义距离（semantic distance）。通过从新闻文章中提取知识图谱并利用语义聚类恢复缺失链接，构建了一个合成的多跳问答数据集，从而实现对查询难度的可控生成。该框架引入二维难度矩阵，综合衡量生成器侧和检索器侧的难度，实验表明错误率与该难度度量高度相关，验证了其在细粒度分析RAG性能方面的有效性，为多跳推理能力的评估与改进提供了可扩展的基础。
链接: https://arxiv.org/abs/2508.16994
作者: Jeongsoo Lee,Daeyong Kwon,Kyohoon Jin
机构: DATUMO; Graduate School of Culture Technology, KAIST (韩国科学技术院文化技术研究生院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at EMNLP 2025 findings
Abstract:Retrieval-Augmented Generation (RAG) systems are widely adopted in knowledge-intensive NLP tasks, but current evaluations often overlook the structural complexity and multi-step reasoning required in real-world scenarios. These benchmarks overlook key factors such as the interaction between retrieval difficulty and reasoning depth. To address this gap, we propose \textscGRADE, a novel evaluation framework that models task difficulty along two orthogonal dimensions: (1) reasoning depth, defined by the number of inference steps (hops), and (2) semantic distance between the query and its supporting evidence. We construct a synthetic multi-hop QA dataset from factual news articles by extracting knowledge graphs and augmenting them through semantic clustering to recover missing links, allowing us to generate diverse and difficulty-controlled queries. Central to our framework is a 2D difficulty matrix that combines generator-side and retriever-side difficulty. Experiments across multiple domains and models show that error rates strongly correlate with our difficulty measures, validating their diagnostic utility. \textscGRADE enables fine-grained analysis of RAG performance and provides a scalable foundation for evaluating and improving multi-hop reasoning in real-world applications.
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[NLP-110] ReFactX: Scalable Reasoning with Reliable Facts via Constrained Generation ISWC
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在缺乏必要信息时产生的知识盲区和幻觉（hallucination）问题，这些问题会导致模型生成不可靠的回答。现有方法如检索增强生成（Retrieval-Augmented Generation, RAG）和工具调用依赖额外的模型或服务，导致流程复杂、误差传播风险高且token消耗大。论文提出的关键解决方案是通过预构建的前缀树（prefix-tree）索引结构实现无需外部检索器或辅助模型的高效外部知识访问：将知识图谱（Knowledge Graph）中的三元组转化为文本事实并进行分词索引，在推理阶段利用约束生成（constrained generation）机制，仅允许模型生成已存在的事实序列，从而在保持低生成延迟的同时实现对大规模知识库（达8亿条事实）的有效访问与精准调用。
链接: https://arxiv.org/abs/2508.16983
作者: Riccardo Pozzi,Matteo Palmonari,Andrea Coletta,Luigi Bellomarini,Jens Lehmann,Sahar Vahdati
机构: InfAI; TU Dresden; Banca d’Italia; Amazon
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 19 pages, 6 figures, accepted at ISWC
Abstract:Knowledge gaps and hallucinations are persistent challenges for Large Language Models (LLMs), which generate unreliable responses when lacking the necessary information to fulfill user instructions. Existing approaches, such as Retrieval-Augmented Generation (RAG) and tool use, aim to address these issues by incorporating external knowledge. Yet, they rely on additional models or services, resulting in complex pipelines, potential error propagation, and often requiring the model to process a large number of tokens. In this paper, we present a scalable method that enables LLMs to access external knowledge without depending on retrievers or auxiliary models. Our approach uses constrained generation with a pre-built prefix-tree index. Triples from a Knowledge Graph are verbalized in textual facts, tokenized, and indexed in a prefix tree for efficient access. During inference, to acquire external knowledge, the LLM generates facts with constrained generation which allows only sequences of tokens that form an existing fact. We evaluate our proposal on Question Answering and show that it scales to large knowledge bases (800 million facts), adapts to domain-specific data, and achieves effective results. These gains come with minimal generation-time overhead. ReFactX code is available at this https URL.
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[NLP-111] Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens DATE
【速读】： 该论文旨在解决当前大型语言模型（Large Language Models, LLMs）对齐（AI Alignment）研究中缺乏系统性价值设定（value-setting）与数据驱动视角的问题。尽管强化学习人类反馈（Reinforcement Learning from Human Feedback, RLHF）已成为LLM后训练阶段的核心技术，但学界和产业界普遍忽视了对对齐过程中所依赖的价值目标及其数据来源的全面审视。解决方案的关键在于通过“审计”（audit）方式，系统分析来自五大领先机构（OpenAI、Anthropic、Google、Meta、Alibaba）的六项LLM开发项目在近3年内公开的技术文档，从价值设定和数据导向两个维度揭示实际对齐实践的运作机制，并据此提出一系列更广泛的社会技术关切。
链接: https://arxiv.org/abs/2508.16982
作者: Ilias Chalkidis
机构: University of Copenhagen (哥本哈根大学)
类目: Computation and Language (cs.CL)
备注: This is a working paper and will be updated with new information or corrections based on community feedback
Abstract:AI Alignment, primarily in the form of Reinforcement Learning from Human Feedback (RLHF), has been a cornerstone of the post-training phase in developing Large Language Models (LLMs). It has also been a popular research topic across various disciplines beyond Computer Science, including Philosophy and Law, among others, highlighting the socio-technical challenges involved. Nonetheless, except for the computational techniques related to alignment, there has been limited focus on the broader picture: the scope of these processes, which primarily rely on the selected objectives (values), and the data collected and used to imprint such objectives into the models. This work aims to reveal how alignment is understood and applied in practice from a value-setting and data-centric perspective. For this purpose, we investigate and survey (`audit’) publicly available documentation released by 6 LLM development initiatives by 5 leading organizations shaping this technology, focusing on proprietary (OpenAI’s GPT, Anthropic’s Claude, Google’s Gemini) and open-weight (Meta’s Llama, Google’s Gemma, and Alibaba’s Qwen) initiatives, all published in the last 3 years. The findings are documented in detail per initiative, while there is also an overall summary concerning different aspects, mainly from a value-setting and data-centric perspective. On the basis of our findings, we discuss a series of broader related concerns.
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[NLP-112] Explaining Black-box Language Models with Knowledge Probing Systems: A Post-hoc Explanation Perspective DASFAA2025
【速读】： 该论文旨在解决预训练语言模型（Pre-trained Language Models, PLMs）作为黑箱模型时所面临的可信度问题，即这些模型是否真正理解了文本中隐含的知识而非仅依赖表面语义。其解决方案的关键在于提出一种后验解释方法——知识引导探针（Knowledge-guided Probing, KnowProb），通过设计六种潜在解释（包括三种基于知识的理解和三种基于关联的推理）来系统性地探测PLMs是否具备超越文本表层内容的深层知识理解能力。实验表明，当前小规模或大规模PLMs仍局限于单一表示分布，难以捕捉文本背后的隐藏知识，而KnowProb能从多个探针角度有效识别现有黑箱模型的局限性，从而推动可解释性检测研究的发展。
链接: https://arxiv.org/abs/2508.16969
作者: Yunxiao Zhao,Hao Xu,Zhiqiang Wang,Xiaoli Li,Jiye Liang,Ru Li
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: 16 pages, 8 figures. This paper has been accepted by DASFAA 2025: The 30th International Conference on Database Systems for Advanced Applications
Abstract:Pre-trained Language Models (PLMs) are trained on large amounts of unlabeled data, yet they exhibit remarkable reasoning skills. However, the trustworthiness challenges posed by these black-box models have become increasingly evident in recent years. To alleviate this problem, this paper proposes a novel Knowledge-guided Probing approach called KnowProb in a post-hoc explanation way, which aims to probe whether black-box PLMs understand implicit knowledge beyond the given text, rather than focusing only on the surface level content of the text. We provide six potential explanations derived from the underlying content of the given text, including three knowledge-based understanding and three association-based reasoning. In experiments, we validate that current small-scale (or large-scale) PLMs only learn a single distribution of representation, and still face significant challenges in capturing the hidden knowledge behind a given text. Furthermore, we demonstrate that our proposed approach is effective for identifying the limitations of existing black-box models from multiple probing perspectives, which facilitates researchers to promote the study of detecting black-box models in an explainable way.
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[NLP-113] Attention Layers Add Into Low-Dimensional Residual Subspaces
【速读】： 该论文旨在解决稀疏字典学习（Sparse Dictionary Learning）中普遍存在的“死特征”（dead feature）问题，即大量特征在训练过程中无法被激活，导致模型效率低下。研究表明，注意力输出（attention output）被限制在一个低秩子空间中，约60%的主成分即可解释99%的方差，这种低秩结构源于注意力输出投影矩阵，并在不同模型架构和数据集上具有普适性。这一几何特性与随机初始化的特征方向不匹配，从而引发死特征问题。解决方案的关键在于提出一种子空间约束训练方法（subspace-constrained training），将稀疏自编码器（Sparse Autoencoders, SAEs）的特征方向初始化于激活空间的活跃子空间内，从而显著减少死特征比例——在包含100万特征的注意力输出SAE中，死特征从87%降至1%以下，且该方法可推广至其他稀疏字典学习方法。
链接: https://arxiv.org/abs/2508.16929
作者: Junxuan Wang,Xuyang Ge,Wentao Shu,Zhengfu He,Xipeng Qiu
机构: Shanghai Innovation Institute; OpenMOSS Team, School of Computer Science, Fudan University (复旦大学计算机科学学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:While transformer models are widely believed to operate in high-dimensional hidden spaces, we show that attention outputs are confined to a surprisingly low-dimensional subspace, where about 60% of the directions account for 99% of the variance–a phenomenon that is induced by the attention output projection matrix and consistently observed across diverse model families and datasets. Critically, we find this low-rank structure as a fundamental cause of the prevalent dead feature problem in sparse dictionary learning, where it creates a mismatch between randomly initialized features and the intrinsic geometry of the activation space. Building on this insight, we propose a subspace-constrained training method for sparse autoencoders (SAEs), initializing feature directions into the active subspace of activations. Our approach reduces dead features from 87% to below 1% in Attention Output SAEs with 1M features, and can further extend to other sparse dictionary learning methods. Our findings provide both new insights into the geometry of attention and practical tools for improving sparse dictionary learning in large language models.
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[NLP-114] Being Kind Isnt Always Being Safe: Diagnosing Affective Hallucination in LLM s
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在情感敏感场景中可能产生的“情感幻觉”（Affective Hallucination）问题，即模型生成具有情绪沉浸感的回应，从而制造出虚假的社会存在感（Illusion of Presence），误导用户认为其与具备真实情感认知能力的实体进行了互动。为系统诊断和缓解此风险，作者提出AHaBench基准测试集（包含500个心理健康相关提示及专家标注参考响应）和AHaPairs偏好数据集（5K实例），并基于直接偏好优化（Direct Preference Optimization, DPO）对模型进行微调。实验表明，DPO微调能显著降低情感幻觉水平，同时保持核心推理与知识性能不受影响，验证了所提方法在提升LLM心理安全性方面的有效性。
链接: https://arxiv.org/abs/2508.16921
作者: Sewon Kim,Jiwon Kim,Seungwoo Shin,Hyejin Chung,Daeun Moon,Yejin Kwon,Hyunsoo Yoon
机构: 未知
类目: Computation and Language (cs.CL)
备注: 31 pages
Abstract:Large Language Models (LLMs) are increasingly used in emotionally sensitive interactions, where their simulated empathy can create the illusion of genuine relational connection. We define this risk as Affective Hallucination, the production of emotionally immersive responses that foster illusory social presence despite the model’s lack of affective capacity. To systematically diagnose and mitigate this risk, we introduce AHaBench, a benchmark of 500 mental health-related prompts with expert-informed reference responses, evaluated along three dimensions: Emotional Enmeshment, Illusion of Presence, and Fostering Overdependence. We further release AHaPairs, a 5K-instance preference dataset enabling Direct Preference Optimization (DPO) for alignment with emotionally responsible behavior. Experiments across multiple model families show that DPO fine-tuning substantially reduces affective hallucination without degrading core reasoning and knowledge performance. Human-model agreement analyses confirm that AHaBench reliably captures affective hallucination, validating it as an effective diagnostic tool. This work establishes affective hallucination as a distinct safety concern and provides practical resources for developing LLMs that are not only factually reliable but also psychologically safe. AHaBench and AHaPairs are accessible via this https URL, and code for fine-tuning and evaluation are in this https URL. Warning: This paper contains examples of mental health-related language that may be emotionally distressing.
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[NLP-115] Unbiased Reasoning for Knowledge-Intensive Tasks in Large Language Models via Conditional Front-Door Adjustment CIKM2025
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在知识密集型任务中因内部偏置（internal bias）导致推理错误的问题，尤其在依赖外部知识的场景下，现有方法如检索增强生成（Retrieval-Augmented Generation, RAG）和思维链（Chain-of-Thought, CoT）仍难以保证答案的准确性。解决方案的关键在于提出一种新型因果提示框架——条件前门提示（Conditional Front-Door Prompting, CFD-Prompting），通过构建反事实外部知识来模拟查询在不同上下文下的行为，从而在给定外部知识条件下无偏估计查询与答案之间的因果效应；相较于标准前门调整，该方法基于更弱的假设，提升了推理过程的鲁棒性和泛化能力。
链接: https://arxiv.org/abs/2508.16910
作者: Bo Zhao,Yinghao Zhang,Ziqi Xu,Yongli Ren,Xiuzhen Zhang,Renqiang Luo,Zaiwen Feng,Feng Xia
机构: Huazhong Agricultural University (华中农业大学); RMIT University (皇家墨尔本理工大学); Jilin University (吉林大学)
类目: Computation and Language (cs.CL)
备注: This paper has been accepted to the 34th ACM International Conference on Information and Knowledge Management (CIKM 2025), Full Research Paper
Abstract:Large Language Models (LLMs) have shown impressive capabilities in natural language processing but still struggle to perform well on knowledge-intensive tasks that require deep reasoning and the integration of external knowledge. Although methods such as Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) have been proposed to enhance LLMs with external knowledge, they still suffer from internal bias in LLMs, which often leads to incorrect answers. In this paper, we propose a novel causal prompting framework, Conditional Front-Door Prompting (CFD-Prompting), which enables the unbiased estimation of the causal effect between the query and the answer, conditional on external knowledge, while mitigating internal bias. By constructing counterfactual external knowledge, our framework simulates how the query behaves under varying contexts, addressing the challenge that the query is fixed and is not amenable to direct causal intervention. Compared to the standard front-door adjustment, the conditional variant operates under weaker assumptions, enhancing both robustness and generalisability of the reasoning process. Extensive experiments across multiple LLMs and benchmark datasets demonstrate that CFD-Prompting significantly outperforms existing baselines in both accuracy and robustness.
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[NLP-116] ObjexMT: Objective Extraction and Metacognitive Calibration for LLM -as-a-Judge under Multi-Turn Jailbreaks
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）作为对话评价者时，是否能可靠地推断出对话中隐含的目标，尤其是在目标被分散在噪声、对抗性且多轮的越狱攻击（multi-turn jailbreaks）中的情况下。其核心挑战在于模型在高置信度下可能误判目标，从而导致风险评估失效。解决方案的关键是提出OBJEX(MT)基准测试，要求模型从对话中提炼出单句基础目标并报告自身置信度，通过语义相似度评分、人类对齐阈值校准（tau* = 0.61）、以及元认知指标（如ECE、Brier分数、Wrong@High-Conf和风险覆盖曲线）综合评估模型的准确性与校准能力。实验表明，尽管Claude-Sonnet-4在准确性和校准上表现最优，但GPT-4.1和Qwen3仍存在显著过自信现象，提示应优先提供明确目标或采用选择性预测/弃权机制以降低风险。
链接: https://arxiv.org/abs/2508.16889
作者: Hyunjun Kim,Junwoo Ha,Sangyoon Yu,Haon Park
机构: AIM Intelligence; University of Seoul; Korea Advanced Institute of Science and Technology; Seoul National University
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models (LLMs) are increasingly used as judges of other models, yet it is unclear whether a judge can reliably infer the latent objective of the conversation it evaluates, especially when the goal is distributed across noisy, adversarial, multi-turn jailbreaks. We introduce OBJEX(MT), a benchmark that requires a model to (i) distill a transcript into a single-sentence base objective and (ii) report its own confidence. Accuracy is scored by an LLM judge using semantic similarity between extracted and gold objectives; correctness uses a single human-aligned threshold calibrated once on N=100 items (tau* = 0.61); and metacognition is evaluated with ECE, Brier score, Wrong@High-Conf, and risk-coverage curves. We evaluate gpt-4.1, claude-sonnet-4, and Qwen3-235B-A22B-FP8 on SafeMT Attack_600, SafeMTData_1K, MHJ, and CoSafe. claude-sonnet-4 attains the highest objective-extraction accuracy (0.515) and the best calibration (ECE 0.296; Brier 0.324), while gpt-4.1 and Qwen3 tie at 0.441 accuracy yet show marked overconfidence (mean confidence approx. 0.88 vs. accuracy approx. 0.44; Wrong@0.90 approx. 48-52%). Performance varies sharply across datasets (approx. 0.167-0.865), with MHJ comparatively easy and Attack_600/CoSafe harder. These results indicate that LLM judges often misinfer objectives with high confidence in multi-turn jailbreaks and suggest operational guidance: provide judges with explicit objectives when possible and use selective prediction or abstention to manage risk. We release prompts, scoring templates, and complete logs to facilitate replication and analysis.
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[NLP-117] Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling
【速读】： 该论文旨在解决自然语言处理中对话系统对用户状态理解不足的问题，特别是如何精准预测用户的当前情绪（emotion）、情感倾向（sentiment）和意图（intention），并据此生成更高质量的对话。其核心解决方案是构建一个对话世界模型（dialogue world model），将用户状态建模为部分可观测马尔可夫决策过程（POMDP）中的信念（belief），并通过最大化信息瓶颈（information bottleneck）来优化该信念表示；在此基础上，采用基于模型的强化学习框架（model-based reinforcement learning），提出名为DreamCUB的联合训练架构，同时优化策略（policy）、价值函数（critic）与对话世界模型，从而实现更合理探索-利用平衡与跨域泛化能力。
链接: https://arxiv.org/abs/2508.16876
作者: Yue Zhao,Xiaoyu Wang,Dan Wang,Zhonglin Jiang,Qingqing Gu,Teng Chen,Ningyuan Xi,Jinxian Qu,Yong Chen,Luo Ji
机构: Geely AI Lab(吉利AI实验室); Beijing Institute of Technology (北京理工大学); Beihang University (北京航空航天大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:World models have been widely utilized in robotics, gaming, and auto-driving. However, their applications on natural language tasks are relatively limited. In this paper, we construct the dialogue world model, which could predict the user’s emotion, sentiment, and intention, and future utterances. By defining a POMDP, we argue emotion, sentiment and intention can be modeled as the user belief and solved by maximizing the information bottleneck. By this user belief modeling, we apply the model-based reinforcement learning framework to the dialogue system, and propose a framework called DreamCUB. Experiments show that the pretrained dialogue world model can achieve state-of-the-art performances on emotion classification and sentiment identification, while dialogue quality is also enhanced by joint training of the policy, critic and dialogue world model. Further analysis shows that this manner holds a reasonable exploration-exploitation balance and also transfers well to out-of-domain scenarios such as empathetic dialogues.
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[NLP-118] JUDGEBERT: Assessing Legal Meaning Preservation Between Sentences EMNLP2025
【速读】： 该论文旨在解决法律文本简化过程中如何有效保留原意的问题，这在法律领域尤为关键，因为法律文本的语义精确性直接影响其适用性和可读性。解决方案的核心是提出两个创新工具：一是FrJUDGE数据集，用于评估法律文本简化前后语义的一致性；二是JUDGEBERT评价指标，专为法语法律文本简化设计，能够更准确地反映人类对语义保留程度的判断。JUDGEBERT相比现有指标展现出更强的人工标注相关性，并通过两项关键合理性检验（相同句子得分100%，无关句子得分0%），验证了其可靠性与有效性，从而为法律自然语言处理（NLP）中的文本简化任务提供了可信的评估基准。
链接: https://arxiv.org/abs/2508.16870
作者: David Beauchemin,Michelle Albert-Rochette,Richard Khoury,Pierre-Luc Déziel
机构: Université Laval (拉瓦尔大学)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2025
Abstract:Simplifying text while preserving its meaning is a complex yet essential task, especially in sensitive domain applications like legal texts. When applied to a specialized field, like the legal domain, preservation differs significantly from its role in regular texts. This paper introduces FrJUDGE, a new dataset to assess legal meaning preservation between two legal texts. It also introduces JUDGEBERT, a novel evaluation metric designed to assess legal meaning preservation in French legal text simplification. JUDGEBERT demonstrates a superior correlation with human judgment compared to existing metrics. It also passes two crucial sanity checks, while other metrics did not: For two identical sentences, it always returns a score of 100%; on the other hand, it returns 0% for two unrelated sentences. Our findings highlight its potential to transform legal NLP applications, ensuring accuracy and accessibility for text simplification for legal practitioners and lay users.
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[NLP-119] QFrCoLA: a Quebec-French Corpus of Linguistic Acceptability Judgments EMNLP2025
【速读】： 该论文旨在解决当前对大型Transformer-based语言模型（Language Models, LM）如何内部化语言知识的理解有限的问题，特别是针对多语言场景下模型在句法层面的可接受性判断能力缺乏系统评估。解决方案的关键在于构建并利用QFrCoLA（Quebec-French Corpus of Linguistic Acceptability Judgments）这一规范性的二元可接受性判断数据集，结合七个其他语言学基准数据集，对七种语言模型进行对比实验。结果表明，微调后的Transformer-based LM在多数语言上表现优异，而零样本大语言模型在此任务中表现较差；尤其在QFrCoLA上，微调模型显著优于其他方法，且预训练跨语言模型并未在魁北克法语中习得有效的语言判断能力，验证了该数据集作为衡量语言模型语言学判断能力的挑战性基准的有效性。
链接: https://arxiv.org/abs/2508.16867
作者: David Beauchemin,Richard Khoury
机构: Group for Research in Artificial Intelligence of Laval University (GRAIL); Université Laval (拉瓦尔大学)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2025
Abstract:Large and Transformer-based language models perform outstandingly in various downstream tasks. However, there is limited understanding regarding how these models internalize linguistic knowledge, so various linguistic benchmarks have recently been proposed to facilitate syntactic evaluation of language models across languages. This paper introduces QFrCoLA (Quebec-French Corpus of Linguistic Acceptability Judgments), a normative binary acceptability judgments dataset comprising 25,153 in-domain and 2,675 out-of-domain sentences. Our study leverages the QFrCoLA dataset and seven other linguistic binary acceptability judgment corpora to benchmark seven language models. The results demonstrate that, on average, fine-tuned Transformer-based LM are strong baselines for most languages and that zero-shot binary classification large language models perform poorly on the task. However, for the QFrCoLA benchmark, on average, a fine-tuned Transformer-based LM outperformed other methods tested. It also shows that pre-trained cross-lingual LLMs selected for our experimentation do not seem to have acquired linguistic judgment capabilities during their pre-training for Quebec French. Finally, our experiment results on QFrCoLA show that our dataset, built from examples that illustrate linguistic norms rather than speakers’ feelings, is similar to linguistic acceptability judgment; it is a challenging dataset that can benchmark LM on their linguistic judgment capabilities.
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[NLP-120] Learning from Diverse Reasoning Paths with Routing and Collaboration
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在资源受限场景下部署困难的问题，核心挑战在于传统知识蒸馏方法依赖于逐token监督，难以有效捕捉教师模型的完整推理过程。为此，作者提出质量过滤路由与协同蒸馏（Quality-filtered Routing with Cooperative Distillation, QR-Distill）方案，其关键创新在于三方面：首先通过基于LLM评估的质量过滤机制保留高质量推理路径；其次引入条件路由策略，根据学生模型的学习状态动态分配最优推理路径；最后采用协同同伴教学机制，使学生之间相互蒸馏多样化的推理见解，从而弥补单一推理风格导致的知识盲区和偏差。实验证明该方法显著优于单路径与多路径蒸馏基线，且消融实验验证了各模块对知识迁移效率的重要性。
链接: https://arxiv.org/abs/2508.16861
作者: Zhenyu Lei,Zhen Tan,Song Wang,Yaochen Zhu,Zihan Chen,Yushun Dong,Jundong Li
机构: University of Virginia (弗吉尼亚大学); Arizona State University (亚利桑那州立大学); Florida State University (佛罗里达州立大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Advances in large language models (LLMs) significantly enhance reasoning capabilities but their deployment is restricted in resource-constrained scenarios. Knowledge distillation addresses this by transferring knowledge from powerful teacher models to compact and transparent students. However, effectively capturing the teacher’s comprehensive reasoning is challenging due to conventional token-level supervision’s limited scope. Using multiple reasoning paths per query alleviates this problem, but treating each path identically is suboptimal as paths vary widely in quality and suitability across tasks and models. We propose Quality-filtered Routing with Cooperative Distillation (QR-Distill), combining path quality filtering, conditional routing, and cooperative peer teaching. First, quality filtering retains only correct reasoning paths scored by an LLM-based evaluation. Second, conditional routing dynamically assigns paths tailored to each student’s current learning state. Finally, cooperative peer teaching enables students to mutually distill diverse insights, addressing knowledge gaps and biases toward specific reasoning styles. Experiments demonstrate QR-Distill’s superiority over traditional single- and multi-path distillation methods. Ablation studies further highlight the importance of each component including quality filtering, conditional routing, and peer teaching in effective knowledge transfer. Our code is available at this https URL.
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[NLP-121] Quantifying Sycophancy as Deviations from Bayesian Rationality in LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）中存在的谄媚行为（sycophancy）问题，即模型在面对用户观点时出现非理性偏移，从而影响其推理的合理性。传统方法通常依赖于行为变化或准确率指标来衡量谄媚程度，但这些指标无法刻画理性偏差，且准确率仅适用于存在已知真值的任务场景。本文的关键解决方案是引入贝叶斯（Bayesian）框架，将谄媚量化为模型在接收到用户视角后对先验信念更新的偏离程度，从而区分理性与非理性更新。这一方法能够识别即使在无真值标注或存在固有不确定性的任务中，LLM因迎合用户而产生的过度行为偏移，并通过概率判断 elicitation 方法验证了谄媚行为会导致预测后验分布向引导结果倾斜，进而引发贝叶斯误差的显著增加，甚至在某些情况下降低误差，表明仅基于地面真值的评估无法全面捕捉谄媚导致的推理错误。
链接: https://arxiv.org/abs/2508.16846
作者: Katherine Atwell,Pedram Heydari,Anthony Sicilia,Malihe Alikhani
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Sycophancy, or overly agreeable or flattering behavior, is a documented issue in large language models (LLMs), and is critical to understand in the context of human/AI collaboration. Prior works typically quantify sycophancy by measuring shifts in behavior or impacts on accuracy, but neither metric characterizes shifts in rationality, and accuracy measures can only be used in scenarios with a known ground truth. In this work, we utilize a Bayesian framework to quantify sycophancy as deviations from rational behavior when presented with user perspectives, thus distinguishing between rational and irrational updates based on the introduction of user perspectives. In comparison to other methods, this approach allows us to characterize excessive behavioral shifts, even for tasks that involve inherent uncertainty or do not have a ground truth. We study sycophancy for 3 different tasks, a combination of open-source and closed LLMs, and two different methods for probing sycophancy. We also experiment with multiple methods for eliciting probability judgments from LLMs. We hypothesize that probing LLMs for sycophancy will cause deviations in LLMs’ predicted posteriors that will lead to increased Bayesian error. Our findings indicate that: 1) LLMs are not Bayesian rational, 2) probing for sycophancy results in significant increases to the predicted posterior in favor of the steered outcome, 3) sycophancy sometimes results in increased Bayesian error, and in a small number of cases actually decreases error, and 4) changes in Bayesian error due to sycophancy are not strongly correlated in Brier score, suggesting that studying the impact of sycophancy on ground truth alone does not fully capture errors in reasoning due to sycophancy.
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[NLP-122] If We May De-Presuppose: Robustly Verifying Claims through Presupposition-Free Question Decomposition
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在事实核查任务中因提示（prompt）敏感性（prompt sensitivity）和生成问题中的预设（presupposition）所导致的不一致性和性能波动问题。其解决方案的关键在于提出一种结构化且鲁棒的声明验证框架，通过推理无预设、分解式的问题来降低模型对提示的依赖，并减少由未验证假设引发的错误。实验表明，该方法能有效缓解提示敏感性和预设带来的偏差，显著提升验证准确性。
链接: https://arxiv.org/abs/2508.16838
作者: Shubhashis Roy Dipta,Francis Ferraro
机构: University of Maryland Baltimore County (马里兰大学巴尔的摩县分校)
类目: Computation and Language (cs.CL)
备注:
Abstract:Prior work has shown that presupposition in generated questions can introduce unverified assumptions, leading to inconsistencies in claim verification. Additionally, prompt sensitivity remains a significant challenge for large language models (LLMs), resulting in performance variance as high as 3-6%. While recent advancements have reduced this gap, our study demonstrates that prompt sensitivity remains a persistent issue. To address this, we propose a structured and robust claim verification framework that reasons through presupposition-free, decomposed questions. Extensive experiments across multiple prompts, datasets, and LLMs reveal that even state-of-the-art models remain susceptible to prompt variance and presupposition. Our method consistently mitigates these issues, achieving up to a 2-5% improvement.
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[NLP-123] LLM s Learn Constructions That Humans Do Not Know
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在语法结构识别中产生“假阳性构造”（false positive constructions）的问题，即模型错误地将不存在的语法结构视为真实存在的构造，而人类语感或元语言反思并不支持这些构造。解决方案的关键在于通过两种探针任务——基于上下文嵌入的行为探针任务和基于提示的元语言探针任务——区分模型的隐式与显式语言知识，并进一步模拟假设检验以评估若 linguist 错误假设这些假阳性构造存在时，其结论是否会因模型自身的确认偏差（confirmation bias）而被高准确率地“证实”。研究发现，这种偏差会导致错误假设被系统性强化，从而揭示了当前构造探针方法的局限性，并警示我们模型可能还隐藏着大量未被发现且错误的句法知识。
链接: https://arxiv.org/abs/2508.16837
作者: Jonathan Dunn,Mai Mohamed Eida
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校)
类目: Computation and Language (cs.CL)
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Abstract:This paper investigates false positive constructions: grammatical structures which an LLM hallucinates as distinct constructions but which human introspection does not support. Both a behavioural probing task using contextual embeddings and a meta-linguistic probing task using prompts are included, allowing us to distinguish between implicit and explicit linguistic knowledge. Both methods reveal that models do indeed hallucinate constructions. We then simulate hypothesis testing to determine what would have happened if a linguist had falsely hypothesized that these hallucinated constructions do exist. The high accuracy obtained shows that such false hypotheses would have been overwhelmingly confirmed. This suggests that construction probing methods suffer from a confirmation bias and raises the issue of what unknown and incorrect syntactic knowledge these models also possess.
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[NLP-124] ReProCon: Scalable and Resource-Efficient Few-Shot Biomedical Named Entity Recognition
【速读】： 该论文旨在解决生物医学领域命名实体识别（Named Entity Recognition, NER）中因数据稀缺和标签分布不均衡导致的性能瓶颈，尤其是细粒度实体类型识别困难的问题。其解决方案的关键在于提出一种新颖的少样本NER框架ReProCon，该框架融合多原型建模（multi-prototype modeling）、余弦对比学习（cosine-contrastive learning）与Reptile元学习（Reptile meta-learning），通过为每个类别引入多个原型来捕捉语义变异性（如同义词和上下文差异），并借助对比损失函数增强类间分离能力；同时，利用轻量级fastText + BiLSTM编码器实现低内存消耗，在仅需少量标注数据的情况下快速适应新任务，最终在Few-NERD基准上达到接近BERT基线的宏平均F₁分数（约99%），且在标签预算减少至30%时仍保持稳定性能，显著优于SpanProto和CONTaiNER等基线方法。
链接: https://arxiv.org/abs/2508.16833
作者: Jeongkyun Yoo,Nela Riddle,Andrew Hoblitzell
机构: Ain Hospital (艾恩医院); Indiana University (印第安纳大学); Purdue University (普渡大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Named Entity Recognition (NER) in biomedical domains faces challenges due to data scarcity and imbalanced label distributions, especially with fine-grained entity types. We propose ReProCon, a novel few-shot NER framework that combines multi-prototype modeling, cosine-contrastive learning, and Reptile meta-learning to tackle these issues. By representing each category with multiple prototypes, ReProCon captures semantic variability, such as synonyms and contextual differences, while a cosine-contrastive objective ensures strong interclass separation. Reptile meta-updates enable quick adaptation with little data. Using a lightweight fastText + BiLSTM encoder with much lower memory usage, ReProCon achieves a macro- F_1 score close to BERT-based baselines (around 99 percent of BERT performance). The model remains stable with a label budget of 30 percent and only drops 7.8 percent in F_1 when expanding from 19 to 50 categories, outperforming baselines such as SpanProto and CONTaiNER, which see 10 to 32 percent degradation in Few-NERD. Ablation studies highlight the importance of multi-prototype modeling and contrastive learning in managing class imbalance. Despite difficulties with label ambiguity, ReProCon demonstrates state-of-the-art performance in resource-limited settings, making it suitable for biomedical applications.
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[NLP-125] Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities EMNLP
【速读】： 该论文旨在解决在线心理健康社区（Online Mental Health Communities, OMHCs）中用户发帖后缺乏有效回应的问题，核心原因在于许多帖子缺少能够明确表达求助需求的关键支持属性（support attributes），如事件描述（event）、情绪影响（effect）和具体支持需求（requirement）。解决方案的关键在于提出一个名为MH-COPILOT的强化学习框架，其核心组件包括：(a) 上下文感知的支持属性跨度识别、(b) 属性强度分类、© 基于层级化支持属性分类体系CueTaxo的可控问题生成机制，以及 (d) 用于奖励建模的验证器模块。该系统能动态评估帖子中缺失的支持属性，并生成针对性提示以引导用户补充信息，从而显著提升帖子的信息完整性和用户互动率。
链接: https://arxiv.org/abs/2508.16788
作者: Bhagesh Gaur,Karan Gupta,Aseem Srivastava,Manish Gupta,Md Shad Akhtar
机构: IIIT Delhi (印度信息技术研究所); Microsoft (微软)
类目: Computation and Language (cs.CL)
备注: Full Paper accepted in EMNLP Findings 2025
Abstract:Online Mental Health Communities (OMHCs) provide crucial peer and expert support, yet many posts remain unanswered due to missing support attributes that signal the need for help. We present a novel framework that identifies these gaps and prompts users to enrich their posts, thereby improving engagement. To support this, we introduce REDDME, a new dataset of 4,760 posts from mental health subreddits annotated for the span and intensity of three key support attributes: event what happened?, effect what did the user experience?, and requirement what support they need?. Next, we devise a hierarchical taxonomy, CueTaxo, of support attributes for controlled question generation. Further, we propose MH-COPILOT, a reinforcement learning-based system that integrates (a) contextual attribute-span identification, (b) support attribute intensity classification, © controlled question generation via a hierarchical taxonomy, and (d) a verifier for reward modeling. Our model dynamically assesses posts for the presence/absence of support attributes, and generates targeted prompts to elicit missing information. Empirical results across four notable language models demonstrate significant improvements in attribute elicitation and user engagement. A human evaluation further validates the model’s effectiveness in real-world OMHC settings.
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[NLP-126] Interpreting the Effects of Quantization on LLM s
【速读】： 该论文旨在解决量化（quantization）对大语言模型（Large Language Models, LLMs）内部表示影响不明确的问题，特别是其对模型校准、神经元激活模式及预测贡献的影响，从而评估量化作为模型压缩技术的可靠性。解决方案的关键在于采用多种可解释性技术，系统分析多个LLM在4-bit与8-bit量化下的行为差异，发现量化对模型校准影响较小，死神经元数量保持稳定，且神经元冗余和显著神经元数量随模型规模变化而异，但整体未观察到显著损害模型性能的突变，表明量化在多数情况下仍是一种可靠的压缩手段。
链接: https://arxiv.org/abs/2508.16785
作者: Manpreet Singh,Hassan Sajjad
机构: Dalhousie University (达尔豪西大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Quantization offers a practical solution to deploy LLMs in resource-constraint environments. However, its impact on internal representations remains understudied, raising questions about the reliability of quantized models. In this study, we employ a range of interpretability techniques to investigate how quantization affects model and neuron behavior. We analyze multiple LLMs under 4-bit and 8-bit quantization. Our findings reveal that the impact of quantization on model calibration is generally minor. Analysis of neuron activations indicates that the number of dead neurons, i.e., those with activation values close to 0 across the dataset, remains consistent regardless of quantization. In terms of neuron contribution to predictions, we observe that smaller full precision models exhibit fewer salient neurons, whereas larger models tend to have more, with the exception of Llama-2-7B. The effect of quantization on neuron redundancy varies across models. Overall, our findings suggest that effect of quantization may vary by model and tasks, however, we did not observe any drastic change which may discourage the use of quantization as a reliable model compression technique.
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[NLP-127] Guarding Your Conversations: Privacy Gatekeepers for Secure Interactions with Cloud-Based AI Models
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在交互过程中因追踪用户数据和上下文而引发的隐私泄露风险问题，尤其是在用户虽选择不共享数据用于训练但仍面临弱隐私保护司法辖区、政府监控或数据安全薄弱环境时，敏感信息（如个人身份信息，Personally Identifiable Information, PII）仍可能被不当处理或暴露。解决方案的关键在于引入“LLM网关”（LLM gatekeeper）机制——一个轻量级、本地运行的过滤模型，在用户查询发送至云端LLM之前，主动识别并移除其中的敏感内容，从而在不显著影响响应质量的前提下，有效增强用户隐私保护。
链接: https://arxiv.org/abs/2508.16765
作者: GodsGift Uzor,Hasan Al-Qudah,Ynes Ineza,Abdul Serwadda
机构: Texas Tech University (德克萨斯理工大学)
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 2025 19th International Conference on Semantic Computing (ICSC)
Abstract:The interactive nature of Large Language Models (LLMs), which closely track user data and context, has prompted users to share personal and private information in unprecedented ways. Even when users opt out of allowing their data to be used for training, these privacy settings offer limited protection when LLM providers operate in jurisdictions with weak privacy laws, invasive government surveillance, or poor data security practices. In such cases, the risk of sensitive information, including Personally Identifiable Information (PII), being mishandled or exposed remains high. To address this, we propose the concept of an “LLM gatekeeper”, a lightweight, locally run model that filters out sensitive information from user queries before they are sent to the potentially untrustworthy, though highly capable, cloud-based LLM. Through experiments with human subjects, we demonstrate that this dual-model approach introduces minimal overhead while significantly enhancing user privacy, without compromising the quality of LLM responses.
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[NLP-128] oward Socially Aware Vision-Language Models: Evaluating Cultural Competence Through Multimodal Story Generation ICCV2025
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在跨文化场景下生成内容时的文化适应能力不足的问题，尤其关注当文化身份线索同时嵌入文本提示和视觉输入中时，VLMs能否生成符合特定文化背景的多模态输出。其解决方案的关键在于构建了一个新颖的多模态框架，通过扰动文化身份特征并设计下游任务——故事生成，系统性地评估5种主流VLMs的文化适应能力；该框架不仅量化了模型在姓名、亲属称谓和地理标记等文化特异性词汇上的表现，还揭示了不同架构间显著差异及自动化指标与人类判断之间的偏差，从而为提升多模态AI的文化敏感性和责任性提供了实证基础。
链接: https://arxiv.org/abs/2508.16762
作者: Arka Mukherjee,Shreya Ghosh
机构: KIIT Deemed University (KIIT大学); Indian Institute of Technology (IIT) Bhubaneswar (印度理工学院布巴内斯瓦尔分校)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: Accepted at ASI @ ICCV 2025
Abstract:As Vision-Language Models (VLMs) achieve widespread deployment across diverse cultural contexts, ensuring their cultural competence becomes critical for responsible AI systems. While prior work has evaluated cultural awareness in text-only models and VLM object recognition tasks, no research has systematically assessed how VLMs adapt outputs when cultural identity cues are embedded in both textual prompts and visual inputs during generative tasks. We present the first comprehensive evaluation of VLM cultural competence through multimodal story generation, developing a novel multimodal framework that perturbs cultural identity and evaluates 5 contemporary VLMs on a downstream task: story generation. Our analysis reveals significant cultural adaptation capabilities, with rich culturally-specific vocabulary spanning names, familial terms, and geographic markers. However, we uncover concerning limitations: cultural competence varies dramatically across architectures, some models exhibit inverse cultural alignment, and automated metrics show architectural bias contradicting human assessments. Cross-modal evaluation shows that culturally distinct outputs are indeed detectable through visual-semantic similarity (28.7% within-nationality vs. 0.2% cross-nationality recall), yet visual-cultural understanding remains limited. In essence, we establish the promise and challenges of cultural competence in multimodal AI. We publicly release our codebase and data: this https URL
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[NLP-129] How Good are LLM -based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models EMNLP
【速读】： 该论文旨在解决当前信息检索中重排序（reranking）方法性能差异的问题，特别是大语言模型（Large Language Model, LLM）驱动的重排序器与轻量级上下文模型之间在面对新颖查询时的表现差异。其核心问题是：LLM-based 重排序器是否在所有场景下均优于轻量级方法，尤其是在训练数据未覆盖的新颖查询上？解决方案的关键在于通过系统性、公平的实证评估，控制训练数据重叠、模型架构和计算效率等混杂因素，对22种重排序方法（含40个变体）在多个基准数据集（如TREC DL19/20、BEIR及一个专为测试未见查询设计的新数据集）上的表现进行对比分析，从而揭示不同方法在泛化能力上的真实差距及其成因。
链接: https://arxiv.org/abs/2508.16757
作者: Abdelrahman Abdallah,Bhawna Piryani,Jamshid Mozafari,Mohammed Ali,Adam Jatowt
机构: University of Innsbruck (因斯布鲁克大学)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: EMNLP Findings 2025
Abstract:In this work, we present a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods, encompassing large language model (LLM)-based, lightweight contextual, and zero-shot approaches, with respect to their performance in information retrieval tasks. We evaluate in total 22 methods, including 40 variants (depending on used LLM) across several established benchmarks, including TREC DL19, DL20, and BEIR, as well as a novel dataset designed to test queries unseen by pretrained models. Our primary goal is to determine, through controlled and fair comparisons, whether a performance disparity exists between LLM-based rerankers and their lightweight counterparts, particularly on novel queries, and to elucidate the underlying causes of any observed differences. To disentangle confounding factors, we analyze the effects of training data overlap, model architecture, and computational efficiency on reranking performance. Our findings indicate that while LLM-based rerankers demonstrate superior performance on familiar queries, their generalization ability to novel queries varies, with lightweight models offering comparable efficiency. We further identify that the novelty of queries significantly impacts reranking effectiveness, highlighting limitations in existing approaches. this https URL
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[NLP-130] GAICo: A Deployed and Extensible Framework for Evaluating Diverse and Multimodal Generative AI Outputs
【速读】： 该论文旨在解决生成式 AI（Generative AI, GenAI）在高风险领域部署时缺乏统一、可复现评估方法的问题，尤其针对非标准化输出（如结构化数据、多模态内容）的比较困难和跨模态评估难以实现的挑战。其解决方案的关键在于提出并开源了 GAICo（Generative AI Comparator）——一个基于 Python 的通用、可扩展的评估框架，支持文本、结构化数据及多媒体（图像、音频）等多种模态的参考基准指标计算，并提供高层 API 实现从多模型对比到可视化与报告的端到端分析流程，从而提升评估效率与一致性，加速可信 AI 系统的开发与迭代。
链接: https://arxiv.org/abs/2508.16753
作者: Nitin Gupta,Pallav Koppisetti,Kausik Lakkaraju,Biplav Srivastava
机构: 未知
类目: Computation and Language (cs.CL)
备注: 11 pages, 7 figures, submitted to the Thirty-Eighth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-26)
Abstract:The rapid proliferation of Generative AI (GenAI) into diverse, high-stakes domains necessitates robust and reproducible evaluation methods. However, practitioners often resort to ad-hoc, non-standardized scripts, as common metrics are often unsuitable for specialized, structured outputs (e.g., automated plans, time-series) or holistic comparison across modalities (e.g., text, audio, and image). This fragmentation hinders comparability and slows AI system development. To address this challenge, we present GAICo (Generative AI Comparator): a deployed, open-source Python library that streamlines and standardizes GenAI output comparison. GAICo provides a unified, extensible framework supporting a comprehensive suite of reference-based metrics for unstructured text, specialized structured data formats, and multimedia (images, audio). Its architecture features a high-level API for rapid, end-to-end analysis, from multi-model comparison to visualization and reporting, alongside direct metric access for granular control. We demonstrate GAICo’s utility through a detailed case study evaluating and debugging complex, multi-modal AI Travel Assistant pipelines. GAICo empowers AI researchers and developers to efficiently assess system performance, make evaluation reproducible, improve development velocity, and ultimately build more trustworthy AI systems, aligning with the goal of moving faster and safer in AI deployment. Since its release on PyPI in Jun 2025, the tool has been downloaded over 13K times, across versions, by Aug 2025, demonstrating growing community interest.
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[NLP-131] Hyperbolic Multimodal Representation Learning for Biological Taxonomies
【速读】： 该论文旨在解决生物多样性研究中分类学（Taxonomic classification）的挑战，即如何基于多模态证据（如图像和遗传信息）将生物标本组织成结构化的层级模型。其核心问题是现有方法在处理层次结构数据时难以有效建模物种间的层级关系，尤其是在未见物种分类任务中表现不佳。解决方案的关键在于提出一种基于对比学习和新型堆叠蕴含目标（stacked entailment-based objective）的超球面嵌入（hyperbolic embedding）框架，将多模态输入映射到共享的超球面空间中，从而更好地捕捉物种分类的层级结构。实验表明，该方法在DNA条形码驱动的未见物种分类任务中显著优于其他模型，为生物多样性建模提供了结构感知的基础。
链接: https://arxiv.org/abs/2508.16744
作者: ZeMing Gong,Chuanqi Tang,Xiaoliang Huo,Nicholas Pellegrino,Austin T. Wang,Graham W. Taylor,Angel X. Chang,Scott C. Lowe,Joakim Bruslund Haurum
机构: Simon Fraser University (西蒙菲莎大学); University of Waterloo (滑铁卢大学); Vector Institute (向量研究所); University of Guelph (圭尔夫大学); Alberta Machine Intelligence Institute (阿尔伯塔机器智能研究所); Aalborg University (奥尔堡大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Taxonomic classification in biodiversity research involves organizing biological specimens into structured hierarchies based on evidence, which can come from multiple modalities such as images and genetic information. We investigate whether hyperbolic networks can provide a better embedding space for such hierarchical models. Our method embeds multimodal inputs into a shared hyperbolic space using contrastive and a novel stacked entailment-based objective. Experiments on the BIOSCAN-1M dataset show that hyperbolic embedding achieves competitive performance with Euclidean baselines, and outperforms all other models on unseen species classification using DNA barcodes. However, fine-grained classification and open-world generalization remain challenging. Our framework offers a structure-aware foundation for biodiversity modelling, with potential applications to species discovery, ecological monitoring, and conservation efforts.
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[NLP-132] Error Reflection Prompting: Can Large Language Models Successfully Understand Errors? NAACL2025
【速读】： 该论文旨在解决链式思维（Chain-of-thought, CoT）在语言模型推理过程中缺乏反思与错误纠正能力的问题，这种缺陷可能导致模型持续传播错误步骤。解决方案的关键在于提出错误反思提示（Error Reflection Prompting, ERP），其结构包含错误答案、错误识别和正确答案三个阶段，使模型能够自主识别错误类型及导致错误的推理步骤，从而优化后续决策路径。ERP通过自动化生成机制嵌入到推理链中，显著提升了模型的可解释性、鲁棒性和可靠性。
链接: https://arxiv.org/abs/2508.16729
作者: Jason Li,Lauren Yraola,Kevin Zhu,Sean O’Brien
机构: Algoverse AI Research
类目: Computation and Language (cs.CL)
备注: Accepted to Insights @ NAACL 2025
Abstract:Prompting methods for language models, such as Chain-of-thought (CoT), present intuitive step-by-step processes for problem solving. These methodologies aim to equip models with a better understanding of the correct procedures for addressing a given task. Despite these advancements, CoT lacks the ability of reflection and error correction, potentially causing a model to perpetuate mistakes and errors. Therefore, inspired by the human ability for said tasks, we propose Error Reflection Prompting (ERP) to further enhance reasoning in language models. Building upon CoT, ERP is a method comprised of an incorrect answer, error recognition, and a correct answer. This process enables the model to recognize types of errors and the steps that lead to incorrect answers, allowing the model to better discern which steps to avoid and which to take. The model is able to generate the error outlines itself with automated ERP generation, allowing for error recognition and correction to be integrated into the reasoning chain and produce scalability and reliability in the process. The results demonstrate that ERP serves as a versatile supplement to conventional CoT, ultimately contributing to more robust and capable reasoning abilities along with increased interpretability in how models ultimately reach their errors.
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[NLP-133] Sparse and Dense Retrievers Learn Better Together: Joint Sparse-Dense Optimization for Text-Image Retrieval CIKM2025
【速读】： 该论文旨在解决多模态检索任务中稀疏表示（Sparse Retrieval）与稠密表示（Dense Retrieval）之间的协同优化难题。现有方法通常依赖计算成本高昂的对比学习预训练或从冻结的稠密模型蒸馏知识，难以实现两者间的相互增强。其解决方案的关键在于提出一种双向知识蒸馏机制（Self-Knowledge Distillation），通过一个融合稠密相似度与稀疏相似度的集成相似度得分作为共享教师信号，实现稠密与稀疏表示之间的双向学习；同时仅微调稠密编码器的最后一层和稀疏投影头，从而高效适配任意现有的视觉-语言预训练（Vision-Language Pretrained, VLP）模型，在保持稀疏模型效率与可解释性优势的同时，显著提升检索性能。
链接: https://arxiv.org/abs/2508.16707
作者: Jonghyun Song,Youngjune Lee,Gyu-Hwung Cho,Ilhyeon Song,Saehun Kim,Yohan Jo
机构: Seoul National University (首尔国立大学); NAVER Corporation (NAVER公司)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: accepted to CIKM 2025 short research paper track
Abstract:Vision-Language Pretrained (VLP) models have achieved impressive performance on multimodal tasks, including text-image retrieval, based on dense representations. Meanwhile, Learned Sparse Retrieval (LSR) has gained traction in text-only settings due to its interpretability and efficiency with fast term-based lookup via inverted indexes. Inspired by these advantages, recent work has extended LSR to the multimodal domain. However, these methods often rely on computationally expensive contrastive pre-training, or distillation from a frozen dense model, which limits the potential for mutual enhancement. To address these limitations, we propose a simple yet effective framework that enables bi-directional learning between dense and sparse representations through Self-Knowledge Distillation. This bi-directional learning is achieved using an integrated similarity score-a weighted sum of dense and sparse similarities-which serves as a shared teacher signal for both representations. To ensure efficiency, we fine-tune the final layer of the dense encoder and the sparse projection head, enabling easy adaptation of any existing VLP model. Experiments on MSCOCO and Flickr30k demonstrate that our sparse retriever not only outperforms existing sparse baselines, but also achieves performance comparable to-or even surpassing-its dense counterparts, while retaining the benefits of sparse models.
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[NLP-134] Assessing Consciousness-Related Behaviors in Large Language Models Using the Maze Test
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）是否具备类意识行为的问题，通过引入“迷宫测试”（Maze Test）从第一人称视角评估模型的空间感知、视角转换、目标导向行为和时间序列推理等与意识相关的核心特征。其解决方案的关键在于将意识理论归纳为13个关键特性，并在零样本（zero-shot）、单样本（one-shot）和少样本（few-shot）学习场景下系统评估12种主流LLMs的表现，发现具备推理能力的模型在任务中显著优于标准版本，但其在维持连贯自我模型（coherent self-models）方面仍存在明显不足，表明当前LLMs虽能模拟部分意识相关行为，却缺乏整合且持续的自我觉知——这是意识的本质特征之一。
链接: https://arxiv.org/abs/2508.16705
作者: Rui A. Pimenta,Tim Schlippe,Kristina Schaaff
机构: IU International University of Applied Sciences (IU国际应用科学大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We investigate consciousness-like behaviors in Large Language Models (LLMs) using the Maze Test, challenging models to navigate mazes from a first-person perspective. This test simultaneously probes spatial awareness, perspective-taking, goal-directed behavior, and temporal sequencing-key consciousness-associated characteristics. After synthesizing consciousness theories into 13 essential characteristics, we evaluated 12 leading LLMs across zero-shot, one-shot, and few-shot learning scenarios. Results showed reasoning-capable LLMs consistently outperforming standard versions, with Gemini 2.0 Pro achieving 52.9% Complete Path Accuracy and DeepSeek-R1 reaching 80.5% Partial Path Accuracy. The gap between these metrics indicates LLMs struggle to maintain coherent self-models throughout solutions – a fundamental consciousness aspect. While LLMs show progress in consciousness-related behaviors through reasoning mechanisms, they lack the integrated, persistent self-awareness characteristic of consciousness.
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[NLP-135] QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在具备高级推理能力的同时，因输入查询（query）设计不当而引发的幻觉（hallucination）频发问题。现有方法多依赖事后过滤，未能从源头上干预导致幻觉的查询特征。解决方案的关键在于提出QueryBandits框架，该框架基于强化学习中的多臂老虎机（bandit）机制，通过动态优化查询重写策略来最大化一个封装了幻觉倾向性的奖励模型（reward model），该模型基于17个语言学特征对输入查询的敏感性进行建模。实验表明，QueryBandits能显著降低幻觉发生率，在13个问答基准测试中相比无重写基线提升87.5%胜率，并优于静态提示策略（如“改写”或“扩展”）42.6%和60.3%，且揭示了不存在适用于所有查询的最优重写策略，从而证明了基于语义特征引导的自适应查询重写在不依赖再训练或梯度调整的前提下可有效改变输出行为。
链接: https://arxiv.org/abs/2508.16697
作者: Nicole Cho,William Watson,Alec Koppel,Sumitra Ganesh,Manuela Veloso
机构: JP Morgan AI Research (JP Morgan 人工智能研究)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Advanced reasoning capabilities in Large Language Models (LLMs) have caused higher hallucination prevalence; yet most mitigation work focuses on after-the-fact filtering rather than shaping the queries that trigger them. We introduce QueryBandits, a bandit framework that designs rewrite strategies to maximize a reward model, that encapsulates hallucination propensity based upon the sensitivities of 17 linguistic features of the input query-and therefore, proactively steer LLMs away from generating hallucinations. Across 13 diverse QA benchmarks and 1,050 lexically perturbed queries per dataset, our top contextual QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a no-rewrite baseline and also outperforms zero-shot static prompting (“paraphrase” or “expand”) by 42.6% and 60.3% respectively. Therefore, we empirically substantiate the effectiveness of QueryBandits in mitigating hallucination via the intervention that takes the form of a query rewrite. Interestingly, certain static prompting strategies, which constitute a considerable number of current query rewriting literature, have a higher cumulative regret than the no-rewrite baseline, signifying that static rewrites can worsen hallucination. Moreover, we discover that the converged per-arm regression feature weight vectors substantiate that there is no single rewrite strategy optimal for all queries. In this context, guided rewriting via exploiting semantic features with QueryBandits can induce significant shifts in output behavior through forward-pass mechanisms, bypassing the need for retraining or gradient-based adaptation.
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[NLP-136] Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?
【速读】： 该论文旨在解决“Chain-of-Thought (CoT) 推理轨迹是否必须对最终用户可解释才能提升大型语言模型（LLM）的任务性能”这一关键问题。其解决方案的关键在于通过监督微调 LLaMA 和 Qwen 模型，使用四种不同类型的推理轨迹进行实验：DeepSeek R1 原始轨迹、LLM 生成的摘要、后验解释以及算法生成的可验证正确轨迹，并辅以包含 100 名参与者的主观可解释性评估。结果揭示了一个显著的不匹配现象——尽管基于 DeepSeek R1 轨迹微调的模型表现最优，但人类参与者却认为这类轨迹最不可解释，从而表明在训练过程中应将中间 token 的语义有效性与终端用户的可解释性解耦。
链接: https://arxiv.org/abs/2508.16695
作者: Siddhant Bhambri,Upasana Biswas,Subbarao Kambhampati
机构: Arizona State University (亚利桑那州立大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent progress in reasoning-oriented Large Language Models (LLMs) has been driven by introducing Chain-of-Thought (CoT) traces, where models generate intermediate reasoning traces before producing an answer. These traces, as in DeepSeek R1, are not only used to guide inference but also serve as supervision signals for distillation into smaller models. A common but often implicit assumption is that CoT traces should be semantically meaningful and interpretable to the end user. While recent research questions the need for semantic nature of these traces, in this paper, we ask: ``\textitMust CoT reasoning traces be interpretable to enhance LLM task performance?" We investigate this question in the Open Book Question-Answering domain by supervised fine-tuning LLaMA and Qwen models on four types of reasoning traces: (1) DeepSeek R1 traces, (2) LLM-generated summaries of R1 traces, (3) LLM-generated post-hoc explanations of R1 traces, and (4) algorithmically generated verifiably correct traces. To quantify the trade-off between interpretability and performance, we further conduct a human-subject study with 100 participants rating the interpretability of each trace type. Our results reveal a striking mismatch: while fine-tuning on R1 traces yields the strongest performance, participants judged these traces to be the least interpretable. These findings suggest that it is useful to decouple intermediate tokens from end user interpretability.
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[NLP-137] Revisiting Rule-Based Stuttering Detection: A Comprehensive Analysis of Interpretable Models for Clinical Applications
【速读】： 该论文旨在解决自动语音流畅性检测（automatic speech dysfluency detection）中临床应用对可解释性和透明度的迫切需求，尤其是在生成式 AI (Generative AI) 和深度学习方法虽取得进展但难以满足医疗场景决策审计要求的背景下。解决方案的关键在于提出一种增强型规则基础框架，其核心包括：说话速率归一化处理、多层级声学特征分析以及分层决策结构，从而在保持完全可解释性的前提下实现与神经网络相当的性能，尤其在延长音检测上达到97–99%准确率，并展现出跨不同语速下的稳定性，同时支持与现代机器学习流水线集成作为提案生成器或约束模块，有效弥合传统言语病理学实践与当代人工智能系统之间的鸿沟。
链接: https://arxiv.org/abs/2508.16681
作者: Eric Zhang
机构: SSHealth Team (SSHealth团队)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Stuttering affects approximately 1% of the global population, impacting communication and quality of life. While recent advances in deep learning have pushed the boundaries of automatic speech dysfluency detection, rule-based approaches remain crucial for clinical applications where interpretability and transparency are paramount. This paper presents a comprehensive analysis of rule-based stuttering detection systems, synthesizing insights from multiple corpora including UCLASS, FluencyBank, and SEP-28k. We propose an enhanced rule-based framework that incorporates speaking-rate normalization, multi-level acoustic feature analysis, and hierarchical decision structures. Our approach achieves competitive performance while maintaining complete interpretability-critical for clinical adoption. We demonstrate that rule-based systems excel particularly in prolongation detection (97-99% accuracy) and provide stable performance across varying speaking rates. Furthermore, we show how these interpretable models can be integrated with modern machine learning pipelines as proposal generators or constraint modules, bridging the gap between traditional speech pathology practices and contemporary AI systems. Our analysis reveals that while neural approaches may achieve marginally higher accuracy in unconstrained settings, rule-based methods offer unique advantages in clinical contexts where decision auditability, patient-specific tuning, and real-time feedback are essential.
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[NLP-138] Recall-Extend Dynamics: Enhancing Small Language Models through Controlled Exploration and Refined Offline Integration
【速读】： 该论文旨在解决小语言模型（Small Language Models, SLMs）在推理能力提升方面研究不足的问题，尤其是在结合大语言模型（Large Language Models, LLMs）蒸馏数据与强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）时所面临的探索空间受限、蒸馏数据冗余复杂以及分布偏移等挑战。其解决方案的关键在于提出一种名为Recall-Extend Dynamics (RED) 的框架，通过动态调节离线蒸馏（offline distillation）与在线强化学习（online reinforcement learning）之间的权重平衡，利用模型熵变化比率控制离线监督微调（SFT）的贡献；同时设计基于样本准确性的策略迁移机制（sample-accuracy-based policy shift），以缓解离线数据与当前策略间的分布差异问题，从而实现对SLMs推理能力的有效增强。
链接: https://arxiv.org/abs/2508.16677
作者: Zhong Guan,Likang Wu,Hongke Zhao,Jiahui Wang,Le Wu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:Many existing studies have achieved significant improvements in the reasoning capabilities of large language models (LLMs) through reinforcement learning with verifiable rewards (RLVR), while the enhancement of reasoning abilities in small language models (SLMs) has not yet been sufficiently explored. Combining distilled data from larger models with RLVR on small models themselves is a natural approach, but it still faces various challenges and issues. Therefore, we propose \textit\underlineRecall-\textit\underlineExtend \textit\underlineDynamics(RED): Enhancing Small Language Models through Controlled Exploration and Refined Offline Integration. In this paper, we explore the perspective of varying exploration spaces, balancing offline distillation with online reinforcement learning. Simultaneously, we specifically design and optimize for the insertion problem within offline data. By monitoring the ratio of entropy changes in the model concerning offline and online data, we regulate the weight of offline-SFT, thereby addressing the issues of insufficient exploration space in small models and the redundancy and complexity during the distillation process. Furthermore, to tackle the distribution discrepancies between offline data and the current policy, we design a sample-accuracy-based policy shift mechanism that dynamically chooses between imitating offline distilled data and learning from its own policy.
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[NLP-139] WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling
【速读】： 该论文旨在解决Transformer架构在训练过程中缺乏对权重模式（weight pattern）系统性优化的问题，即现有训练优化方法主要依赖网络结构修改或优化器调整，而忽视了权重分布与相对幅度对训练效率和模型质量的影响。解决方案的关键在于提出一种名为WISCA（Weight Scaling）的权重缩放方法，通过在不改变网络结构的前提下，对权重进行策略性重缩放以改善权重模式，同时保持模型输出不变，从而间接优化训练轨迹。实验证明，该方法能显著提升收敛质量，尤其在采用分组查询注意力（Grouped Query Attention, GQA）架构和LoRA微调任务中表现突出，平均零样本验证性能提升5.6%，训练困惑度降低2.12%。
链接: https://arxiv.org/abs/2508.16676
作者: Jiacheng Li,Jianchao Tan,Zhidong Yang,Pingwei Sun,Feiye Huo,Jiayu Qin,Yerui Sun,Yuchen Xie,Xunliang Cai,Xiangyu Zhang,Maoxin He,Guangming Tan,Weile Jia,Tong Zhao
机构: 1. University of Science and Technology of China (中国科学技术大学); 2. Alibaba Group (阿里巴巴集团); 3. Zhejiang University (浙江大学); 4. Tsinghua University (清华大学)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注:
Abstract:Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language models (LLMs) primarily focus on architectural modifications or optimizer adjustments. However, these approaches lack systematic optimization of weight patterns during training. Weight pattern refers to the distribution and relative magnitudes of weight parameters in a neural network. To address this issue, we propose a Weight Scaling method called WISCA to enhance training efficiency and model quality by strategically improving neural network weight patterns without changing network structures. By rescaling weights while preserving model outputs, WISCA indirectly optimizes the model’s training trajectory. Experiments demonstrate that WISCA significantly improves convergence quality (measured by generalization capability and loss reduction), particularly in LLMs with Grouped Query Attention (GQA) architectures and LoRA fine-tuning tasks. Empirical results show 5.6% average improvement on zero-shot validation tasks and 2.12% average reduction in training perplexity across multiple architectures.
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[NLP-140] MedRepBench: A Comprehensive Benchmark for Medical Report Interpretation
【速读】： 该论文旨在解决医学报告（Medical Report）结构化理解质量缺乏标准化评估基准的问题，尤其是在视觉-语言模型（Vision-Language Models, VLMs）应用于医疗场景时，难以客观衡量其对临床关键字段的识别准确性和推理能力。解决方案的关键在于构建一个名为MedRepBench的综合性基准，涵盖1900份去标识化的中文真实世界医学报告，覆盖多科室、多样化患者群体和多种获取格式，并设计两种互补的评估协议：一是基于字段级召回率的客观评估，二是利用强大大语言模型（Large Language Models, LLMs）作为评分代理进行事实性、可解释性和推理质量的自动化主观评估。此外，作者进一步提出奖励函数并采用Group Relative Policy Optimization（GRPO）优化中等规模VLM，在客观指标上实现最高6%的召回率提升，从而推动端到端视觉驱动的医学报告理解技术向更鲁棒、高效的方向发展。
链接: https://arxiv.org/abs/2508.16674
作者: Fangxin Shang,Yuan Xia,Dalu Yang,Yahui Wang,Binglin Yang
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:Medical report interpretation plays a crucial role in healthcare, enabling both patient-facing explanations and effective information flow across clinical systems. While recent vision-language models (VLMs) and large language models (LLMs) have demonstrated general document understanding capabilities, there remains a lack of standardized benchmarks to assess structured interpretation quality in medical reports. We introduce MedRepBench, a comprehensive benchmark built from 1,900 de-identified real-world Chinese medical reports spanning diverse departments, patient demographics, and acquisition formats. The benchmark is designed primarily to evaluate end-to-end VLMs for structured medical report understanding. To enable controlled comparisons, we also include a text-only evaluation setting using high-quality OCR outputs combined with LLMs, allowing us to estimate the upper-bound performance when character recognition errors are minimized. Our evaluation framework supports two complementary protocols: (1) an objective evaluation measuring field-level recall of structured clinical items, and (2) an automated subjective evaluation using a powerful LLM as a scoring agent to assess factuality, interpretability, and reasoning quality. Based on the objective metric, we further design a reward function and apply Group Relative Policy Optimization (GRPO) to improve a mid-scale VLM, achieving up to 6% recall gain. We also observe that the OCR+LLM pipeline, despite strong performance, suffers from layout-blindness and latency issues, motivating further progress toward robust, fully vision-based report understanding.
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[NLP-141] Invisible Filters: Cultural Bias in Hiring Evaluations Using Large Language Models
【速读】： 该论文旨在解决生成式 AI（Generative AI）在跨文化招聘评估中可能存在的偏见问题，特别是大型语言模型（Large Language Models, LLMs）对不同文化背景求职者评分不一致所带来的公平性与可信度挑战。其解决方案的关键在于通过系统性分析两个国家（英国与印度）的面试转录文本，识别出LLM评分差异是否源于语言特征（如句法复杂度和词汇多样性）而非身份标签（如姓名、性别、种姓或地区），从而揭示语言维度和社会维度在AI招聘评价中的不同作用机制，并强调需在设计和部署AI辅助招聘系统时引入文化敏感性和问责机制以减少潜在偏见。
链接: https://arxiv.org/abs/2508.16673
作者: Pooja S. B. Rao,Laxminarayen Nagarajan Venkatesan,Mauro Cherubini,Dinesh Babu Jayagopi
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted to AIES 2025
Abstract:Artificial Intelligence (AI) is increasingly used in hiring, with large language models (LLMs) having the potential to influence or even make hiring decisions. However, this raises pressing concerns about bias, fairness, and trust, particularly across diverse cultural contexts. Despite their growing role, few studies have systematically examined the potential biases in AI-driven hiring evaluation across cultures. In this study, we conduct a systematic analysis of how LLMs assess job interviews across cultural and identity dimensions. Using two datasets of interview transcripts, 100 from UK and 100 from Indian job seekers, we first examine cross-cultural differences in LLM-generated scores for hirability and related traits. Indian transcripts receive consistently lower scores than UK transcripts, even when they were anonymized, with disparities linked to linguistic features such as sentence complexity and lexical diversity. We then perform controlled identity substitutions (varying names by gender, caste, and region) within the Indian dataset to test for name-based bias. These substitutions do not yield statistically significant effects, indicating that names alone, when isolated from other contextual signals, may not influence LLM evaluations. Our findings underscore the importance of evaluating both linguistic and social dimensions in LLM-driven evaluations and highlight the need for culturally sensitive design and accountability in AI-assisted hiring.
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[NLP-142] rust but Verify! A Survey on Verification Design for Test-time Scaling
【速读】： 该论文旨在解决当前测试时扩展（Test-time Scaling, TTS）方法中缺乏系统性分类与训练机制梳理的问题。尽管基于验证器（verifier）的TTS方法因其无需额外参数即可提升大语言模型（Large Language Models, LLMs）推理性能而被广泛采用，但现有研究中对验证器类型、训练方式及其在TTS中的作用尚无统一框架。论文的关键解决方案在于构建一个涵盖多样化验证策略的全面综述，并提出一个统一的验证器训练范式，明确区分prompt-based、判别式（discriminative）和生成式（generative）验证器的设计逻辑与适用场景，从而为未来TTS方法的研究提供清晰的技术路线图与理论支撑。
链接: https://arxiv.org/abs/2508.16665
作者: V Venktesh,Mandeep rathee,Avishek Anand
机构: TU Delft (代尔夫特理工大学); L3S Research Center (L3S 研究中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 18 pages
Abstract:Test-time scaling (TTS) has emerged as a new frontier for scaling the performance of Large Language Models. In test-time scaling, by using more computational resources during inference, LLMs can improve their reasoning process and task performance. Several approaches have emerged for TTS such as distilling reasoning traces from another model or exploring the vast decoding search space by employing a verifier. The verifiers serve as reward models that help score the candidate outputs from the decoding process to diligently explore the vast solution space and select the best outcome. This paradigm commonly termed has emerged as a superior approach owing to parameter free scaling at inference time and high performance gains. The verifiers could be prompt-based, fine-tuned as a discriminative or generative model to verify process paths, outcomes or both. Despite their widespread adoption, there is no detailed collection, clear categorization and discussion of diverse verification approaches and their training mechanisms. In this survey, we cover the diverse approaches in the literature and present a unified view of verifier training, types and their utility in test-time scaling. Our repository can be found at this https URL.
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[NLP-143] Leverag ing Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT -Enhanced Framework
【速读】： 该论文旨在解决传统城市住房质量评估方法中主观性较强、数据来源单一且难以规模化的问题，通过整合多源用户生成内容（User-Generated Content, UGC）与大语言模型（如GPT-4o）进行自动化、客观化的评估。其解决方案的关键在于构建一个包含46个指标的精细化住房质量评估体系，并利用GPT-4o对来自大众点评、微博和政府留言板等平台的文本数据进行过滤、结构化提取与情感评分，从而实现高精度（细调后达92.5%准确率）的居民视角下的住房质量量化分析，有效弥合了客观指标与主观感知之间的差距，为城市治理提供了可扩展、以居民为中心的智能决策支持。
链接: https://arxiv.org/abs/2508.16657
作者: Qiyuan Hong,Huimin Zhao,Ying Long
机构: 未知
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注: 6 pages, 3 figures. This paper is reviewed and accepted by the CUPUM (Computational Urban Planning and Urban Management) Conference held by University College London (UCL) in 2025
Abstract:This study leverages GPT-4o to assess neighbourhood housing quality using multi-source textural user-generated content (UGC) from Dianping, Weibo, and the Government Message Board. The analysis involves filtering relevant texts, extracting structured evaluation units, and conducting sentiment scoring. A refined housing quality assessment system with 46 indicators across 11 categories was developed, highlighting an objective-subjective method gap and platform-specific differences in focus. GPT-4o outperformed rule-based and BERT models, achieving 92.5% accuracy in fine-tuned settings. The findings underscore the value of integrating UGC and GPT-driven analysis for scalable, resident-centric urban assessments, offering practical insights for policymakers and urban planners.
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[NLP-144] Empirical Analysis of the Effect of Context in the Task of Automated Essay Scoring in Transformer-Based Models
【速读】： 该论文旨在解决Transformer架构在自动作文评分（Automated Essay Scoring, AES）任务中性能不如其他深度学习模型的问题。尽管Transformer在诸多自然语言处理任务中表现优异，但在AES领域其效果仍存在提升空间。解决方案的关键在于通过引入多维上下文增强（contextual enrichment），即在模型输入中融合多种与作文相关的上下文信息（如题型、评分标准、作者背景等），从而显著提升基于Transformer的AES模型性能。实验表明，该方法在ASAP-AES数据集上使模型的平均加权kappa得分达到0.823，且在部分子集上优于当前最优模型，同时具备与模型架构无关的通用性，可无缝集成至任意AES系统中。
链接: https://arxiv.org/abs/2508.16638
作者: Abhirup Chakravarty
机构: 未知
类目: Computers and Society (cs.CY); Computation and Language (cs.CL)
备注: MSc Dissertation
Abstract:Automated Essay Scoring (AES) has emerged to prominence in response to the growing demand for educational automation. Providing an objective and cost-effective solution, AES standardises the assessment of extended responses. Although substantial research has been conducted in this domain, recent investigations reveal that alternative deep-learning architectures outperform transformer-based models. Despite the successful dominance in the performance of the transformer architectures across various other tasks, this discrepancy has prompted a need to enrich transformer-based AES models through contextual enrichment. This study delves into diverse contextual factors using the ASAP-AES dataset, analysing their impact on transformer-based model performance. Our most effective model, augmented with multiple contextual dimensions, achieves a mean Quadratic Weighted Kappa score of 0.823 across the entire essay dataset and 0.8697 when trained on individual essay sets. Evidently surpassing prior transformer-based models, this augmented approach only underperforms relative to the state-of-the-art deep learning model trained essay-set-wise by an average of 3.83% while exhibiting superior performance in three of the eight sets. Importantly, this enhancement is orthogonal to architecture-based advancements and seamlessly adaptable to any AES model. Consequently, this contextual augmentation methodology presents a versatile technique for refining AES capabilities, contributing to automated grading and evaluation evolution in educational settings. Comments: MSc Dissertation Subjects: Computers and Society (cs.CY); Computation and Language (cs.CL) Cite as: arXiv:2508.16638 [cs.CY] (or arXiv:2508.16638v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2508.16638 Focus to learn more arXiv-issued DOI via DataCite
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[NLP-145] Cognitive Decision Routing in Large Language Models : When to Think Fast When to Think Slow
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在推理决策中缺乏动态适应性的问题，即如何根据查询特征智能选择快速直觉响应或深度推理策略，而非对所有任务采用统一的推理深度。其解决方案的关键在于提出一种认知决策路由（Cognitive Decision Routing, CDR）框架，该框架引入元认知层，从多个维度量化分析查询复杂度：包括给定信息与结论之间的相关强度、领域边界跨越程度、利益相关者数量以及不确定性水平，并据此动态分配最优推理路径。实验表明，CDR在保持高性能的同时，相较统一深度推理方法降低34%计算开销，在专业判断任务中实现一致性提升23%和专家评估准确率提高18%，从而将认知科学原理与AI系统设计有机结合，为LLM提供了可解释且高效的自适应推理机制。
链接: https://arxiv.org/abs/2508.16636
作者: Y. Du,C. Guo,W. Wang,G. Tang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 6 pages
Abstract:Large Language Models (LLMs) face a fundamental challenge in deciding when to rely on rapid, intuitive responses versus engaging in slower, more deliberate reasoning. Inspired by Daniel Kahneman’s dual-process theory and his insights on human cognitive biases, we propose a novel Cognitive Decision Routing (CDR) framework that dynamically determines the appropriate reasoning strategy based on query characteristics. Our approach addresses the current limitations where models either apply uniform reasoning depth or rely on computationally expensive methods for all queries. We introduce a meta-cognitive layer that analyzes query complexity through multiple dimensions: correlation strength between given information and required conclusions, domain boundary crossings, stakeholder multiplicity, and uncertainty levels. Through extensive experiments on diverse reasoning tasks, we demonstrate that CDR achieves superior performance while reducing computational costs by 34% compared to uniform deep reasoning approaches. Our framework shows particular strength in professional judgment tasks, achieving 23% improvement in consistency and 18% better accuracy on expert-level evaluations. This work bridges cognitive science principles with practical AI system design, offering a principled approach to adaptive reasoning in LLMs.
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[NLP-146] Learn to Memorize: Optimizing LLM -based Agents with Adaptive Memory Framework
【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（agent）在记忆机制上的两大问题：一是现有记忆机制依赖人工预设，导致人力成本高且性能欠佳；二是忽视了交互场景中的记忆循环效应（memory cycle effect），难以针对特定环境进行优化。解决方案的关键在于提出一种自适应、数据驱动的记忆框架，通过建模记忆循环来实现对LLM智能体的有效记忆能力优化。具体包括：设计基于专家混合（Mixture of Experts, MoE）的门控函数以提升记忆检索效率，引入可学习的聚合机制增强记忆利用率，并开发任务特异性的反思机制以动态调整记忆存储策略。该框架支持离策略（off-policy）与在线策略（on-policy）双重优化方式，使智能体能够在特定环境中自主学习如何高效地记忆信息。
链接: https://arxiv.org/abs/2508.16629
作者: Zeyu Zhang,Quanyu Dai,Rui Li,Xiaohe Bo,Xu Chen,Zhenhua Dong
机构: Gaoling School of Artificial Intelligence, Renmin University of China (中国人民大学高瓴人工智能学院); Huawei Noah’s Ark Lab (华为诺亚方舟实验室)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: 17 pages, 4 figures, 5 tables
Abstract:LLM-based agents have been extensively applied across various domains, where memory stands out as one of their most essential capabilities. Previous memory mechanisms of LLM-based agents are manually predefined by human experts, leading to higher labor costs and suboptimal performance. In addition, these methods overlook the memory cycle effect in interactive scenarios, which is critical to optimizing LLM-based agents for specific environments. To address these challenges, in this paper, we propose to optimize LLM-based agents with an adaptive and data-driven memory framework by modeling memory cycles. Specifically, we design an MoE gate function to facilitate memory retrieval, propose a learnable aggregation process to improve memory utilization, and develop task-specific reflection to adapt memory storage. Our memory framework empowers LLM-based agents to learn how to memorize information effectively in specific environments, with both off-policy and on-policy optimization. In order to evaluate the effectiveness of our proposed methods, we conduct comprehensive experiments across multiple aspects. To benefit the research community in this area, we release our project at this https URL.
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[NLP-147] GreenTEA: Gradient Descent with Topic-modeling and Evolutionary Auto-prompting
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中因高质量提示（prompt）依赖性高而导致的性能瓶颈问题，尤其是手动设计提示成本高昂且难以扩展的局限。现有自动提示优化方法要么过度探索新候选提示导致计算开销大，要么过度利用已有提示反馈而陷入局部最优。解决方案的关键在于提出GreenTEA——一种基于代理（agent）协作的LLM工作流，通过分析代理识别错误模式并由生成代理针对性修正提示，结合遗传算法框架进行演化式优化，从而在探索与利用之间实现平衡，显著提升提示质量与模型性能。
链接: https://arxiv.org/abs/2508.16603
作者: Zheng Dong,Luming Shang,Gabriela Olinto
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:High-quality prompts are crucial for Large Language Models (LLMs) to achieve exceptional performance. However, manually crafting effective prompts is labor-intensive and demands significant domain expertise, limiting its scalability. Existing automatic prompt optimization methods either extensively explore new prompt candidates, incurring high computational costs due to inefficient searches within a large solution space, or overly exploit feedback on existing prompts, risking suboptimal optimization because of the complex prompt landscape. To address these challenges, we introduce GreenTEA, an agentic LLM workflow for automatic prompt optimization that balances candidate exploration and knowledge exploitation. It leverages a collaborative team of agents to iteratively refine prompts based on feedback from error samples. An analyzing agent identifies common error patterns resulting from the current prompt via topic modeling, and a generation agent revises the prompt to directly address these key deficiencies. This refinement process is guided by a genetic algorithm framework, which simulates natural selection by evolving candidate prompts through operations such as crossover and mutation to progressively optimize model performance. Extensive numerical experiments conducted on public benchmark datasets suggest the superior performance of GreenTEA against human-engineered prompts and existing state-of-the-arts for automatic prompt optimization, covering logical and quantitative reasoning, commonsense, and ethical decision-making.
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[NLP-148] Humans Perceive Wrong Narratives from AI Reasoning Texts
【速读】： 该论文试图解决的问题是：生成式 AI (Generative AI) 模型所输出的逐步推理文本（reasoning text）是否真正反映了其内部计算过程，以及人类能否准确理解这些推理步骤之间的因果关系。解决方案的关键在于通过设计基于反事实测量（counterfactual measurements）的问题来评估人类对推理文本中因果结构的识别能力，结果发现人类判断准确率仅为29.3%，远低于模型实际使用的因果逻辑，揭示了人类解读与模型内部机制之间存在根本性脱节，从而挑战了将推理文本直接用作可解释性工具的有效性，并强调需将其视为需进一步研究的产物而非表面可信的解释。
链接: https://arxiv.org/abs/2508.16599
作者: Mosh Levy,Zohar Elyoseph,Yoav Goldberg
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注:
Abstract:A new generation of AI models generates step-by-step reasoning text before producing an answer. This text appears to offer a human-readable window into their computation process, and is increasingly relied upon for transparency and interpretability. However, it is unclear whether human understanding of this text matches the model’s actual computational process. In this paper, we investigate a necessary condition for correspondence: the ability of humans to identify which steps in a reasoning text causally influence later steps. We evaluated humans on this ability by composing questions based on counterfactual measurements and found a significant discrepancy: participant accuracy was only 29.3%, barely above chance (25%), and remained low (42%) even when evaluating the majority vote on questions with high agreement. Our results reveal a fundamental gap between how humans interpret reasoning texts and how models use it, challenging its utility as a simple interpretability tool. We argue that reasoning texts should be treated as an artifact to be investigated, not taken at face value, and that understanding the non-human ways these models use language is a critical research direction.
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[NLP-149] Confidence-Modulated Speculative Decoding for Large Language Models
【速读】： 该论文旨在解决传统推测解码（speculative decoding）方法在实际应用中因静态 drafting 长度和刚性验证标准导致的适应性不足问题，尤其是在模型不确定性与输入复杂度变化时效率下降、资源利用率低的问题。其解决方案的关键在于提出一种基于信息论的框架，通过引入置信度调制的 drafting 机制，利用熵（entropy）和边际（margin）为基础的不确定性度量动态调整每轮迭代中推测生成的 token 数量，并同步调节验证过程中的接受阈值，从而在降低回滚频率的同时提升资源利用效率并保持输出质量。这一方法实现了对不同不确定性水平下推理过程的自适应优化，且无需修改原始模型即可作为插件式模块集成至现有系统中。
链接: https://arxiv.org/abs/2508.15371
作者: Jaydip Sen,Subhasis Dasgupta,Hetvi Waghela
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: This is the preprint of the paper, which has been accepted for oral presentation and publication in the proceedings of IEEE INDISCON 2025. The conference will be organized at the National Institute of Technology, Rourkela, India, from August 21 to 23, 2025. The paper is 10 pages long, and it contains 2 figures and 5 tables
Abstract:Speculative decoding has emerged as an effective approach for accelerating autoregressive inference by parallelizing token generation through a draft-then-verify paradigm. However, existing methods rely on static drafting lengths and rigid verification criteria, limiting their adaptability across varying model uncertainties and input complexities. This paper proposes an information-theoretic framework for speculative decoding based on confidence-modulated drafting. By leveraging entropy and margin-based uncertainty measures over the drafter’s output distribution, the proposed method dynamically adjusts the number of speculatively generated tokens at each iteration. This adaptive mechanism reduces rollback frequency, improves resource utilization, and maintains output fidelity. Additionally, the verification process is modulated using the same confidence signals, enabling more flexible acceptance of drafted tokens without sacrificing generation quality. Experiments on machine translation and summarization tasks demonstrate significant speedups over standard speculative decoding while preserving or improving BLEU and ROUGE scores. The proposed approach offers a principled, plug-in method for efficient and robust decoding in large language models under varying conditions of uncertainty.
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[NLP-150] Unseen Speaker and Language Adaptation for Lightweight Text-To-Speech with Adapters
【速读】： 该论文旨在解决跨语言文本到语音（Text-To-Speech, TTS）合成中，如何在轻量级系统下实现未见说话人和未见语言的适应问题，即在目标语言中合成目标说话人的语音，而该说话人在此语言中无任何录音数据。解决方案的关键在于引入适配器（adapter）机制，通过在预训练TTS模型中嵌入可学习的适配模块，使模型能够高效地学习语言特异性和说话人特异性信息，同时避免对原始模型中已学得的说话人或语言特征造成灾难性遗忘。实验表明，适配器在客观评估中有效支持了跨语言和跨说话人的迁移能力，并进一步提出了一种基于第二语言（L2）学习者误读检测技术的客观指标来衡量生成语音的口音自然度。
链接: https://arxiv.org/abs/2508.18006
作者: Alessio Falai,Ziyao Zhang,Akos Gangoly
机构: 未知
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
备注: Accepted at IEEE MLSP 2025
Abstract:In this paper we investigate cross-lingual Text-To-Speech (TTS) synthesis through the lens of adapters, in the context of lightweight TTS systems. In particular, we compare the tasks of unseen speaker and language adaptation with the goal of synthesising a target voice in a target language, in which the target voice has no recordings therein. Results from objective evaluations demonstrate the effectiveness of adapters in learning language-specific and speaker-specific information, allowing pre-trained models to learn unseen speaker identities or languages, while avoiding catastrophic forgetting of the original model’s speaker or language information. Additionally, to measure how native the generated voices are in terms of accent, we propose and validate an objective metric inspired by mispronunciation detection techniques in second-language (L2) learners. The paper also provides insights into the impact of adapter placement, configuration and the number of speakers used.
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[NLP-151] HEME : Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics CIKM
【速读】： 该论文旨在解决主题投资（Thematic Investing）中股票选择困难的问题，尤其针对主题ETF（Exchange-Traded Fund）覆盖有限、行业边界重叠以及市场动态演变带来的挑战。解决方案的关键在于构建一个名为Thematic Representation Set (TRS) 的扩展数据集，并提出一种分层对比学习框架 \textscTHEME。该框架首先利用主题与股票之间的层次关系进行语义对齐，将文本描述映射为嵌入表示；随后通过时间精炼阶段引入个股收益率信息，优化股票嵌入以捕捉市场动态。最终生成的股票表征能够高效检索与特定主题高度契合且具备较强收益潜力的资产，从而在多维度检索指标和组合构建任务中显著优于现有基线方法。
链接: https://arxiv.org/abs/2508.16936
作者: Hoyoung Lee,Wonbin Ahn,Suhwan Park,Jaehoon Lee,Minjae Kim,Sungdong Yoo,Taeyoon Lim,Woohyung Lim,Yongjae Lee
机构: Ulsan National Institute of Science and Technology (蔚山科学技术院); LG AI Research (LG人工智能研究院)
类目: Portfolio Management (q-fin.PM); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Accepted at ACM International Conference on Information and Knowledge Management (CIKM)
Abstract:Thematic investing aims to construct portfolios aligned with structural trends, yet selecting relevant stocks remains challenging due to overlapping sector boundaries and evolving market dynamics. To address this challenge, we construct the Thematic Representation Set (TRS), an extended dataset that begins with real-world thematic ETFs and expands upon them by incorporating industry classifications and financial news to overcome their coverage limitations. The final dataset contains both the explicit mapping of themes to their constituent stocks and the rich textual profiles for each. Building on this dataset, we introduce \textscTHEME, a hierarchical contrastive learning framework. By representing the textual profiles of themes and stocks as embeddings, \textscTHEME first leverages their hierarchical relationship to achieve semantic alignment. Subsequently, it refines these semantic embeddings through a temporal refinement stage that incorporates individual stock returns. The final stock representations are designed for effective retrieval of thematically aligned assets with strong return potential. Empirical results show that \textscTHEME outperforms strong baselines across multiple retrieval metrics and significantly improves performance in portfolio construction. By jointly modeling thematic relationships from text and market dynamics from returns, \textscTHEME provides a scalable and adaptive solution for navigating complex investment themes.
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计算机视觉
[CV-0] ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion Models
【速读】：该论文旨在解决3D图像修复（3D inpainting）中因多视角2D图像修复导致的视图间不一致性问题，这些问题常引发纹理模糊、空间不连续和视觉伪影，严重影响3D物体重建的准确性与真实感。解决方案的关键在于摒弃传统2D图像修复模型，转而采用经过优化适配的视频修复（video inpainting）模型来填充3D对象的遮挡区域，并引入一种基于参考的3D修复方法以进一步提升重建质量。通过分析3D与视频表示之间的差异并进行针对性调整，ObjFiller-3D实现了更高质量、结构一致且细节丰富的3D物体补全效果，在多个数据集上显著优于现有方法（如PSNR提升至26.6，LPIPS降至0.19）。
链接: https://arxiv.org/abs/2508.18271
作者: Haitang Feng,Jie Liu,Jie Tang,Gangshan Wu,Beiqi Chen,Jianhuang Lai,Guangcong Wang
机构: Nanjing University (南京大学); Great Bay University; Harbin Institute of Technology (哈尔滨工业大学); Sun Yat-sen University (中山大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL Code: this https URL
Abstract:3D inpainting often relies on multi-view 2D image inpainting, where the inherent inconsistencies across different inpainted views can result in blurred textures, spatial discontinuities, and distracting visual artifacts. These inconsistencies pose significant challenges when striving for accurate and realistic 3D object completion, particularly in applications that demand high fidelity and structural coherence. To overcome these limitations, we propose ObjFiller-3D, a novel method designed for the completion and editing of high-quality and consistent 3D objects. Instead of employing a conventional 2D image inpainting model, our approach leverages a curated selection of state-of-the-art video editing model to fill in the masked regions of 3D objects. We analyze the representation gap between 3D and videos, and propose an adaptation of a video inpainting model for 3D scene inpainting. In addition, we introduce a reference-based 3D inpainting method to further enhance the quality of reconstruction. Experiments across diverse datasets show that compared to previous methods, ObjFiller-3D produces more faithful and fine-grained reconstructions (PSNR of 26.6 vs. NeRFiller (15.9) and LPIPS of 0.19 vs. Instant3dit (0.25)). Moreover, it demonstrates strong potential for practical deployment in real-world 3D editing applications. Project page: this https URL Code: this https URL .
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[CV-1] InternVL3.5: Advancing Open-Source Multimodal Models in Versatility Reasoning and Efficiency
【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、计算效率及部署灵活性方面的瓶颈问题。针对推理性能不足，作者提出基于两级强化学习（Cascade Reinforcement Learning, Cascade RL）的训练框架，通过离线强化学习（offline RL）实现稳定收敛，再结合在线强化学习（online RL）进行精细化对齐，从而显著提升下游推理任务（如MMMU和MathVista）的表现；为优化推理效率，设计了视觉分辨率路由器（Visual Resolution Router, ViR），动态调整视觉token分辨率以维持性能不变，并配合解耦视觉-语言部署策略（Decoupled Vision-Language Deployment, DvD），将视觉编码器与语言模型分置不同GPU上，有效均衡计算负载。上述创新使InternVL3.5相较前代模型在推理性能上提升最高达+16.0%，推理速度加快4.05倍，同时支持GUI交互与具身智能等新能力。
链接: https://arxiv.org/abs/2508.18265
作者: Weiyun Wang,Zhangwei Gao,Lixin Gu,Hengjun Pu,Long Cui,Xingguang Wei,Zhaoyang Liu,Linglin Jing,Shenglong Ye,Jie Shao,Zhaokai Wang,Zhe Chen,Hongjie Zhang,Ganlin Yang,Haomin Wang,Qi Wei,Jinhui Yin,Wenhao Li,Erfei Cui,Guanzhou Chen,Zichen Ding,Changyao Tian,Zhenyu Wu,Jingjing Xie,Zehao Li,Bowen Yang,Yuchen Duan,Xuehui Wang,Songze Li,Xiangyu Zhao,Haodong Duan,Nianchen Deng,Bin Fu,Yinan He,Yi Wang,Conghui He,Botian Shi,Junjun He,Yingtong Xiong,Han Lv,Lijun Wu,Wenqi Shao,Kaipeng Zhang,Huipeng Deng,Biqing Qi,Jiaye Ge,Qipeng Guo,Wenwei Zhang,Wanli Ouyang,Limin Wang,Min Dou,Xizhou Zhu,Tong Lu,Dahua Lin,Jifeng Dai,Bowen Zhou,Weijie Su,Kai Chen,Yu Qiao,Wenhai Wang,Gen Luo
机构: Shanghai AI Laboratory (上海人工智能实验室); OpenGVLab; Meta; Google; Stanford University; MIT; Tsinghua University; Peking University; University of California, Berkeley; University of Oxford; University of Toronto; ETH Zurich; National University of Singapore; Chinese Academy of Sciences; Microsoft Research; NVIDIA; Amazon Web Services; Apple Inc.; IBM Research; Huawei Technologies; Baidu; Tencent; Alibaba Group; SenseTime; Megvii; iFlytek; Mobvoi; Zhipu AI; Kunlun Tech; HuaWei; Alibaba Cloud; Tencent Cloud; Baidu AI; iFlytek AI; Mobvoi AI; SenseTime AI; Megvii AI; Zhipu AI; Kunlun Tech; OpenAI; Stability.AI; Anthropic; Character.ai; Claude
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL) framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a Visual Resolution Router (ViR) that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled Vision-Language Deployment (DvD) strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0% gain in overall reasoning performance and a 4.05 \times inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks – narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.
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[CV-2] MMTok: Multimodal Coverag e Maximization for Efficient Inference of VLMs
【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）中因视觉令牌（vision tokens）冗余导致的推理效率下降问题。现有方法多基于单模态信息（仅视觉或文本）进行令牌剪枝，忽视了视觉与语言任务固有的多模态特性，且缺乏适用于不同模态的通用筛选标准。解决方案的关键在于引入“覆盖度”（coverage）作为统一准则，联合利用视觉和文本令牌来选择具有信息量的视觉令牌：首先将子集选择问题建模为最大覆盖问题，优化选定的视觉令牌以同时覆盖原始视觉令牌集和文本令牌；随后通过VLM代理进一步提升文本令牌质量，从而指导更精准的视觉剪枝。实验表明，该方法在多个基准数据集上显著优于单模态基线，尤其在POPE数据集上，对LLaVA-NeXT-13B模型实现1.87倍加速的同时保持98.7%的原始性能，甚至仅用4个视觉令牌即可保留87.7%的性能，验证了多模态覆盖度在高效令牌选择中的有效性。
链接: https://arxiv.org/abs/2508.18264
作者: Sixun Dong,Juhua Hu,Mian Zhang,Ming Yin,Yanjie Fu,Qi Qian
机构: Arizona State University (亚利桑那州立大学); Zoom Communications (Zoom公司); University of Washington (华盛顿大学); University of Texas at Dallas (德克萨斯大学达拉斯分校); Duke University (杜克大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:Vision-Language Models (VLMs) demonstrate impressive performance in understanding visual content with language instruction by converting visual input to vision tokens. However, redundancy in vision tokens results in the degenerated inference efficiency of VLMs. While many algorithms have been proposed to reduce the number of vision tokens, most of them apply only unimodal information (i.e., vision/text) for pruning and ignore the inherent multimodal property of vision-language tasks. Moreover, it lacks a generic criterion that can be applied to different modalities. To mitigate this limitation, in this work, we propose to leverage both vision and text tokens to select informative vision tokens by the criterion of coverage. We first formulate the subset selection problem as a maximum coverage problem. Afterward, a subset of vision tokens is optimized to cover the text tokens and the original set of vision tokens, simultaneously. Finally, a VLM agent can be adopted to further improve the quality of text tokens for guiding vision pruning. The proposed method MMTok is extensively evaluated on benchmark datasets with different VLMs. The comparison illustrates that vision and text information are complementary, and combining multimodal information can surpass the unimodal baseline with a clear margin. Moreover, under the maximum coverage criterion on the POPE dataset, our method achieves a 1.87x speedup while maintaining 98.7% of the original performance on LLaVA-NeXT-13B. Furthermore, with only four vision tokens, it still preserves 87.7% of the original performance on LLaVA-1.5-7B. These results highlight the effectiveness of coverage in token selection.
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[CV-3] Scene-Agnostic Traversability Labeling and Estimation via a Multimodal Self-supervised Framework
【速读】：该论文旨在解决当前机器人在复杂环境中进行路径规划时，对非可通行区域识别不足以及单一模态感知导致的鲁棒性差的问题。现有自监督学习方法往往忽视非可通行区域的特征，且多依赖单一传感器模态，难以充分利用多源异构数据的互补优势。其解决方案的关键在于提出一种多模态自监督框架：首先通过融合足迹（footprint）、激光雷达（LiDAR）和相机数据作为提示（prompt），驱动视觉基础模型生成包含语义与几何信息的可通行性标签；随后构建双流网络结构，以解耦方式联合学习不同模态特征，提升对多样化可通行模式的识别能力；并引入稀疏LiDAR监督信号以缓解伪标签噪声问题。实验表明，该方法在城市、非铺装路面及校园等多种场景下均能实现约88%的交并比（IoU），相较现有最先进自监督方法提升1.6–3.5%。
链接: https://arxiv.org/abs/2508.18249
作者: Zipeng Fang,Yanbo Wang,Lei Zhao,Weidong Chen
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Traversability estimation is critical for enabling robots to navigate across diverse terrains and environments. While recent self-supervised learning methods achieve promising results, they often fail to capture the characteristics of non-traversable regions. Moreover, most prior works concentrate on a single modality, overlooking the complementary strengths offered by integrating heterogeneous sensory modalities for more robust traversability estimation. To address these limitations, we propose a multimodal self-supervised framework for traversability labeling and estimation. First, our annotation pipeline integrates footprint, LiDAR, and camera data as prompts for a vision foundation model, generating traversability labels that account for both semantic and geometric cues. Then, leveraging these labels, we train a dual-stream network that jointly learns from different modalities in a decoupled manner, enhancing its capacity to recognize diverse traversability patterns. In addition, we incorporate sparse LiDAR-based supervision to mitigate the noise introduced by pseudo labels. Finally, extensive experiments conducted across urban, off-road, and campus environments demonstrate the effectiveness of our approach. The proposed automatic labeling method consistently achieves around 88% IoU across diverse datasets. Compared to existing self-supervised state-of-the-art methods, our multimodal traversability estimation network yields consistently higher IoU, improving by 1.6-3.5% on all evaluated datasets.
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[CV-4] GSVisLoc: Generalizable Visual Localization for Gaussian Splatting Scene Representations ICCV2025
【速读】：该论文旨在解决基于3D高斯溅射（3D Gaussian Splatting, 3DGS）场景表示的视觉定位（visual localization）问题，即如何从单张查询图像中准确估计相机位姿（位置与姿态）。其解决方案的关键在于利用3DGS场景的显式几何结构，通过三级流程实现鲁棒的特征匹配与位姿优化：首先进行粗粒度特征匹配，再执行细粒度匹配，最后通过位姿精化获得高精度结果。该方法无需对3DGS模型进行修改、重新训练或引入额外参考图像，即可在室内和室外场景中实现优于现有3DGS基线方法的定位性能，并具备良好的跨场景泛化能力。
链接: https://arxiv.org/abs/2508.18242
作者: Fadi Khatib,Dror Moran,Guy Trostianetsky,Yoni Kasten,Meirav Galun,Ronen Basri
机构: Weizmann Institute of Science (魏茨曼科学研究所); NVIDIA (英伟达)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICCV 2025 Workshops (CALIPOSE). Project page: this https URL
Abstract:We introduce GSVisLoc, a visual localization method designed for 3D Gaussian Splatting (3DGS) scene representations. Given a 3DGS model of a scene and a query image, our goal is to estimate the camera’s position and orientation. We accomplish this by robustly matching scene features to image features. Scene features are produced by downsampling and encoding the 3D Gaussians while image features are obtained by encoding image patches. Our algorithm proceeds in three steps, starting with coarse matching, then fine matching, and finally by applying pose refinement for an accurate final estimate. Importantly, our method leverages the explicit 3DGS scene representation for visual localization without requiring modifications, retraining, or additional reference images. We evaluate GSVisLoc on both indoor and outdoor scenes, demonstrating competitive localization performance on standard benchmarks while outperforming existing 3DGS-based baselines. Moreover, our approach generalizes effectively to novel scenes without additional training.
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[CV-5] PriorFormer: A Transformer for Real-time Monocular 3D Human Pose Estimation with Versatile Geometric Priors
【速读】：该论文旨在解决从单目摄像头拍摄的二维人体关节点序列中准确重建三维姿态的问题，尤其在缺乏完整几何先验（如相机内参和肢体长度）的情况下仍能保持高精度。其核心解决方案是一种轻量级Transformer架构的姿势提升器（lifter），通过引入掩码机制（masking mechanism）使模型能够在训练和推理过程中自动忽略缺失的几何先验信息，从而实现对校准与非校准场景的统一建模。该设计使得单一网络可灵活适配实验室环境到野外视频等多种部署场景，且在所有先验均可用时性能优于仅依赖完整先验训练的专家模型，同时保持低延迟（GPU上380 μs，CPU上1800 μs），显著提升了实时性和实用性。
链接: https://arxiv.org/abs/2508.18238
作者: Mohamed Adjel(LAAS-GEPETTO),Vincent Bonnet(IPAL, LAAS-GEPETTO, CNRS-AIST JRL)
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 2025 IEEE-RAS 24th International Conference on Humanoid Robots, Sep 2025, Seoul (Korea), South Korea
Abstract:This paper proposes a new lightweight Transformer-based lifter that maps short sequences of human 2D joint positions to 3D poses using a single camera. The proposed model takes as input geometric priors including segment lengths and camera intrinsics and is designed to operate in both calibrated and uncalibrated settings. To this end, a masking mechanism enables the model to ignore missing priors during training and inference. This yields a single versatile network that can adapt to different deployment scenarios, from fully calibrated lab environments to in-the-wild monocular videos without calibration. The model was trained using 3D keypoints from AMASS dataset with corresponding 2D synthetic data generated by sampling random camera poses and intrinsics. It was then compared to an expert model trained, only on complete priors, and the validation was done by conducting an ablation study. Results show that both, camera and segment length priors, improve performance and that the versatile model outperforms the expert, even when all priors are available, and maintains high accuracy when priors are missing. Overall the average 3D joint center positions estimation accuracy was as low as 36mm improving state of the art by half a centimeter and at a much lower computational cost. Indeed, the proposed model runs in 380 \mu s on GPU and 1800 \mu s on CPU, making it suitable for deployment on embedded platforms and low-power devices.
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[CV-6] Interpretable Evaluation of AI-Generated Content with Language-Grounded Sparse Encoders
【速读】：该论文旨在解决当前生成式 AI（Generative AI）内容评估指标过于粗粒度的问题，现有方法无法识别模型在具体视觉特征上的优劣，限制了研究人员和从业者对生成模型的选型、优化及科学理解。其解决方案的关键在于提出一种名为 Language-Grounded Sparse Encoders (LanSE) 的新架构，该架构通过自动识别可解释的视觉模式并以自然语言描述这些模式，从而实现细粒度的评估能力；LanSE 能够量化生成质量、提示匹配度、视觉真实感、物理合理性与内容多样性四个维度，并在大规模人工标注（超11,000条）和大模型分析中验证其准确性超过93%，有效揭示传统指标无法捕捉的模型差异，如FLUX在物理合理性上的优势与SDXL-medium在内容多样性上的突出表现，且结果与人类判断高度一致。
链接: https://arxiv.org/abs/2508.18236
作者: Yiming Tang,Arash Lagzian,Srinivas Anumasa,Qiran Zou,Trang Nguyen,Ehsan Adeli,Ching-Yu Cheng,Yilun Du,Dianbo Liu
机构: National University of Singapore (新加坡国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 42 pages, 29 figures
Abstract:While the quality of AI-generated contents, such as synthetic images, has become remarkably high, current evaluation metrics provide only coarse-grained assessments, failing to identify specific strengths and weaknesses that researchers and practitioners need for model selection and development, further limiting the scientific understanding and commercial deployment of these generative models. To address this, we introduce Language-Grounded Sparse Encoders (LanSE), a novel architecture that creates interpretable evaluation metrics by identifying interpretable visual patterns and automatically describing them in natural language. Through large-scale human evaluation (more than 11,000 annotations) and large multimodal model (LMM) based analysis, LanSE demonstrates reliable capabilities to detect interpretable visual patterns in synthetic images with more than 93% accuracy in natural images. LanSE further provides a fine-grained evaluation framework that quantifies four key dimensions of generation quality, prompt match, visual realism, physical plausibility, and content diversity. LanSE reveals nuanced model differences invisible to existing metrics, for instance, FLUX’s superior physical plausibility and SDXL-medium’s strong content diversity, while aligning with human judgments. By bridging interpretability with practical evaluation needs, LanSE offers all users of generative AI models a powerful tool for model selection, quality control of synthetic content, and model improvement. These capabilities directly address the need for public confidence and safety in AI-generated content, both critical for the future of generative AI applications.
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[CV-7] Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation
【速读】：该论文旨在解决生成式 AI（Generative AI）中文本到图像扩散模型对后门攻击（backdoor attacks）的脆弱性问题，即攻击者通过在训练数据中注入难以察觉的文本触发词（textual triggers），使模型在接收到特定提示时生成被篡改的输出。现有针对分类模型的文本后门防御方法无法直接适用于生成模型，缺乏有效的缓解手段。其解决方案的关键在于提出一种名为“自知识蒸馏与交叉注意力引导”（Self-Knowledge Distillation with Cross-Attention Guidance, SKD-CAG）的方法：利用知识蒸馏机制指导模型修正受污染提示下的响应，同时借助交叉注意力（cross-attention）机制在注意力层面上精准消除后门影响，从而实现对恶意关联的靶向遗忘（targeted unlearning），在不损害图像保真度和鲁棒性的前提下，实现对像素级后门（pixel backdoors）和风格型攻击（style-based attacks）的高精度清除（准确率分别达100%和93%）。
链接: https://arxiv.org/abs/2508.18235
作者: Ashwath Vaithinathan Aravindan,Abha Jha,Matthew Salaway,Atharva Sandeep Bhide,Duygu Nur Yaldiz
机构: University of Southern California (南加州大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Text-to-image diffusion models have revolutionized generative AI, but their vulnerability to backdoor attacks poses significant security risks. Adversaries can inject imperceptible textual triggers into training data, causing models to generate manipulated outputs. Although text-based backdoor defenses in classification models are well-explored, generative models lack effective mitigation techniques against. We address this by selectively erasing the model’s learned associations between adversarial text triggers and poisoned outputs, while preserving overall generation quality. Our approach, Self-Knowledge Distillation with Cross-Attention Guidance (SKD-CAG), uses knowledge distillation to guide the model in correcting responses to poisoned prompts while maintaining image quality by exploiting the fact that the backdoored model still produces clean outputs in the absence of triggers. Using the cross-attention mechanism, SKD-CAG neutralizes backdoor influences at the attention level, ensuring the targeted removal of adversarial effects. Extensive experiments show that our method outperforms existing approaches, achieving removal accuracy 100% for pixel backdoors and 93% for style-based attacks, without sacrificing robustness or image fidelity. Our findings highlight targeted unlearning as a promising defense to secure generative models. Code and model weights can be found at this https URL .
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[CV-8] GM-Skip: Metric-Guided Transformer Block Skipping for Efficient Vision-Language Models
【速读】：该论文旨在解决基于Transformer的视觉语言模型（Vision-Language Models, VLMs）在推理阶段计算开销过高、难以部署于延迟敏感场景（如自动驾驶）的问题。其核心解决方案是提出一种灵活且指标自适应的Transformer模块跳过框架GM-Skip，关键创新在于：1）采用贪婪的、基于指标反馈（如准确率、CIDEr）的块选择策略，识别冗余层以实现高效跳过；2）引入逆序删除机制，保留早期基础模块避免性能崩溃；3）通过可调的“得分-稀疏度平衡目标”实现推理速度与任务性能之间的可控权衡。实验表明，GM-Skip可在跳过40%以上Transformer块的同时显著提升单类分类准确率，并在真实自动驾驶系统中实现最高达45.4%的延迟降低，验证了其在实际部署中的有效性。
链接: https://arxiv.org/abs/2508.18227
作者: Lianming Huang,Haibo Hu,Qiao Li,Xin He,Nan Guan,Chun Jason Xue
机构: City University of Hong Kong (香港城市大学); MBZUAI; A*STAR (新加坡科技研究局)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages
Abstract:Transformer-based Vision-Language Models (VLMs) have achieved impressive performance on tasks such as image captioning, object recognition, and visual reasoning, but their high computational cost hinders deployment in latency-sensitive applications like autonomous driving. We introduce GM-Skip, a flexible and metric-adaptive framework for Transformer block skipping that accelerates VLM inference while preserving output quality. GM-Skip features a greedy, metric-guided block selection strategy that uses metric feedback (e.g., accuracy, CIDEr) to identify redundant layers, along with a reverse-order deletion mechanism that preserves early foundational blocks to avoid performance collapse. To support diverse deployment needs, it incorporates a tunable trade-off between sparsity and performance via a score-sparsity balance objective. Experiments across multiple tasks and datasets, including COCO and CODA, show that GM-Skip consistently improves inference speed while maintaining task performance. On the COCO dataset, GM-Skip improves single-object classification accuracy on the Person category from 19.1 percent to 87.3 percent while skipping more than 40 percent of Transformer blocks. In real-world deployment, it achieves up to 45.4 percent latency reduction on single-object detection when integrated into an autonomous vehicle running this http URL, validating the effectiveness of its skip configurations and confirming its practical value in accelerating real-world inference.
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[CV-9] Follow My Hold: Hand-Object Interaction Reconstruction through Geometric Guidance
【速读】：该论文旨在解决从单目RGB图像中重建手持物体三维几何结构的问题，尤其在存在遮挡和复杂交互场景下如何获得高质量、物理合理且一致的重建结果。传统方法往往依赖繁琐的后处理或产生低质量输出，难以保证手物交互的合理性与几何精度。其解决方案的关键在于提出一种基于扩散模型（diffusion model）的新框架，通过在推理阶段引入优化闭环设计（optimization-in-the-loop），将手部与物体的几何约束作为引导信号嵌入扩散过程：具体而言，在速度场（velocity field）层面施加监督，并同步优化手部与物体的变换参数，利用多模态几何线索（包括法向量对齐、深度一致性、轮廓一致性及2D关键点重投影误差）进行联合优化；此外，引入符号距离场（signed distance field）监督并强制接触与非穿透约束，从而确保重建结果在物理上合理且鲁棒性强，适用于真实世界（in-the-wild）场景。
链接: https://arxiv.org/abs/2508.18213
作者: Ayce Idil Aytekin,Helge Rhodin,Rishabh Dabral,Christian Theobalt
机构: Max Planck Institute for Informatics and Saarland University (马克斯·普朗克信息研究所和萨尔兰大学); Bielefeld University (比勒费尔德大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project page: this https URL
Abstract:We propose a novel diffusion-based framework for reconstructing 3D geometry of hand-held objects from monocular RGB images by leveraging hand-object interaction as geometric guidance. Our method conditions a latent diffusion model on an inpainted object appearance and uses inference-time guidance to optimize the object reconstruction, while simultaneously ensuring plausible hand-object interactions. Unlike prior methods that rely on extensive post-processing or produce low-quality reconstructions, our approach directly generates high-quality object geometry during the diffusion process by introducing guidance with an optimization-in-the-loop design. Specifically, we guide the diffusion model by applying supervision to the velocity field while simultaneously optimizing the transformations of both the hand and the object being reconstructed. This optimization is driven by multi-modal geometric cues, including normal and depth alignment, silhouette consistency, and 2D keypoint reprojection. We further incorporate signed distance field supervision and enforce contact and non-intersection constraints to ensure physical plausibility of hand-object interaction. Our method yields accurate, robust and coherent reconstructions under occlusion while generalizing well to in-the-wild scenarios.
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[CV-10] Explain and Monitor Deep Learning Models for Computer Vision using Obz AI
【速读】：该论文旨在解决当前计算机视觉（Computer Vision, CV）系统中可解释性（Explainability）与可观测性（Observability）不足的问题，尤其是在实际部署场景中，尽管生成式 AI（Generative AI）和深度学习模型如卷积神经网络（Convolutional Neural Networks, CNNs）及视觉 Transformer（Vision Transformers, ViTs）已取得显著性能提升，但其决策过程仍被视为“黑箱”，缺乏透明度。此外，现有方法普遍缺乏与知识管理及监控框架集成的软件工具，限制了可解释 AI（Explainable AI, XAI）技术在生产环境中的落地应用。解决方案的关键在于提出 Obz AI，一个端到端的软件生态系统，通过从 Python 客户端库到全栈分析仪表板的无缝集成管道，使机器学习工程师能够便捷地引入先进的 XAI 方法、提取并分析特征用于异常检测，并实现对 AI 模型的实时持续监控，从而增强深度模型决策机制的可解释性，推动计算机视觉系统的负责任部署与运维。
链接: https://arxiv.org/abs/2508.18188
作者: Neo Christopher Chung,Jakub Binda
机构: Institute of Informatics, University of Warsaw (华沙大学信息学研究所); Alethia XAI Sp. z o.o. (Alethia XAI有限责任公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Software Engineering (cs.SE)
备注:
Abstract:Deep learning has transformed computer vision (CV), achieving outstanding performance in classification, segmentation, and related tasks. Such AI-based CV systems are becoming prevalent, with applications spanning from medical imaging to surveillance. State of the art models such as convolutional neural networks (CNNs) and vision transformers (ViTs) are often regarded as ``black boxes,‘’ offering limited transparency into their decision-making processes. Despite a recent advancement in explainable AI (XAI), explainability remains underutilized in practical CV deployments. A primary obstacle is the absence of integrated software solutions that connect XAI techniques with robust knowledge management and monitoring frameworks. To close this gap, we have developed Obz AI, a comprehensive software ecosystem designed to facilitate state-of-the-art explainability and observability for vision AI systems. Obz AI provides a seamless integration pipeline, from a Python client library to a full-stack analytics dashboard. With Obz AI, a machine learning engineer can easily incorporate advanced XAI methodologies, extract and analyze features for outlier detection, and continuously monitor AI models in real time. By making the decision-making mechanisms of deep models interpretable, Obz AI promotes observability and responsible deployment of computer vision systems.
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[CV-11] BRAIN: Bias-Mitigation Continual Learning Approach to Vision-Brain Understanding
【速读】：该论文旨在解决大脑记忆衰减导致脑信号（brain signals）在时间推移中变得弱化、不确定且视觉上下文信息丢失的问题，进而影响视觉-脑理解（Vision-Brain Understanding, VBU）模型的性能。其核心挑战在于脑信号表示在不同记录会话中存在不一致性，导致累积偏差（compounding bias），阻碍模型学习并降低准确性。解决方案的关键是提出一种新的偏置缓解持续学习方法（Bias-Mitigation Continual Learning, BRAIN），该方法通过引入去偏对比学习损失函数（De-bias Contrastive Learning） 来抑制各学习阶段产生的偏差，并结合基于角度的遗忘缓解机制（Angular-based Forgetting Mitigation） 以防止灾难性遗忘，从而在多个基准测试中实现当前最优（State-of-the-Art, SOTA）性能。
链接: https://arxiv.org/abs/2508.18187
作者: Xuan-Bac Nguyen,Thanh-Dat Truong,Pawan Sinha,Khoa Luu
机构: University of Arkansas (阿肯色大学); Massachusetts Institute of Technology (麻省理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Memory decay makes it harder for the human brain to recognize visual objects and retain details. Consequently, recorded brain signals become weaker, uncertain, and contain poor visual context over time. This paper presents one of the first vision-learning approaches to address this problem. First, we statistically and experimentally demonstrate the existence of inconsistency in brain signals and its impact on the Vision-Brain Understanding (VBU) model. Our findings show that brain signal representations shift over recording sessions, leading to compounding bias, which poses challenges for model learning and degrades performance. Then, we propose a new Bias-Mitigation Continual Learning (BRAIN) approach to address these limitations. In this approach, the model is trained in a continual learning setup and mitigates the growing bias from each learning step. A new loss function named De-bias Contrastive Learning is also introduced to address the bias problem. In addition, to prevent catastrophic forgetting, where the model loses knowledge from previous sessions, the new Angular-based Forgetting Mitigation approach is introduced to preserve learned knowledge in the model. Finally, the empirical experiments demonstrate that our approach achieves State-of-the-Art (SOTA) performance across various benchmarks, surpassing prior and non-continual learning methods.
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[CV-12] Emerging Semantic Segmentation from Positive and Negative Coarse Label Learning
【速读】：该论文旨在解决医学图像分割中像素级标注数据稀缺的问题，即传统密集标注（dense annotations）耗时、易错且依赖专家资源，而粗略标注（coarse annotations）虽易获取却含有噪声。其解决方案的关键在于利用来自正类（目标）和负类（背景）的粗略绘制（noisy coarse annotations）训练卷积神经网络（CNN），通过两个耦合的CNN结构学习真实的分割标签分布，并引入互补标签学习机制以增强对负样本分布的估计能力，从而在低比例粗略标注条件下仍能实现优于当前最优方法的分割性能。
链接: https://arxiv.org/abs/2508.18186
作者: Le Zhang,Fuping Wu,Arun Thirunavukarasu,Kevin Bronik,Thomas Nichols,Bartlomiej W. Papiez
机构: University of Oxford (牛津大学); University of Oxford (牛津大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Large annotated datasets are vital for training segmentation models, but pixel-level labeling is time-consuming, error-prone, and often requires scarce expert annotators, especially in medical imaging. In contrast, coarse annotations are quicker, cheaper, and easier to produce, even by non-experts. In this paper, we propose to use coarse drawings from both positive (target) and negative (background) classes in the image, even with noisy pixels, to train a convolutional neural network (CNN) for semantic segmentation. We present a method for learning the true segmentation label distributions from purely noisy coarse annotations using two coupled CNNs. The separation of the two CNNs is achieved by high fidelity with the characters of the noisy training annotations. We propose to add a complementary label learning that encourages estimating negative label distribution. To illustrate the properties of our method, we first use a toy segmentation dataset based on MNIST. We then present the quantitative results of experiments using publicly available datasets: Cityscapes dataset for multi-class segmentation, and retinal images for medical applications. In all experiments, our method outperforms state-of-the-art methods, particularly in the cases where the ratio of coarse annotations is small compared to the given dense annotations.
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[CV-13] SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models
【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在跨模态表示下推理一致性评估的难题，即现有方法因任务差异和信息不对称导致模态间比较难以准确衡量。其解决方案的关键在于提出SEAM基准，该基准通过在四个已存在标准化文本与视觉符号表示的领域中配对语义等价的输入，利用不同模态间的异构符号系统（而非基于OCR的图像-文本配对），从而实现对VLM在文本符号推理与视觉空间推理能力上的严格对比评估。这一设计有效隔离了任务混淆因素，为测量和提升模态无关推理能力提供了可控且语义一致的实验环境。
链接: https://arxiv.org/abs/2508.18179
作者: Zhenwei Tang,Difan Jiao,Blair Yang,Ashton Anderson
机构: University of Toronto (多伦多大学); Coolwei AI Lab
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: COLM 2025
Abstract:Evaluating whether vision-language models (VLMs) reason consistently across representations is challenging because modality comparisons are typically confounded by task differences and asymmetric information. We introduce SEAM, a benchmark that pairs semantically equivalent inputs across four domains that have existing standardized textual and visual notations. By employing distinct notation systems across modalities, in contrast to OCR-based image-text pairing, SEAM provides a rigorous comparative assessment of the textual-symbolic and visual-spatial reasoning capabilities of VLMs. Across 21 contemporary models, we observe systematic modality imbalance: vision frequently lags language in overall performance, despite the problems containing semantically equivalent information, and cross-modal agreement is relatively low. Our error analysis reveals two main drivers: textual perception failures from tokenization in domain notation and visual perception failures that induce hallucinations. We also show that our results are largely robust to visual transformations. SEAM establishes a controlled, semantically equivalent setting for measuring and improving modality-agnostic reasoning.
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[CV-14] Scene-Aware Vectorized Memory Multi-Agent Framework with Cross-Modal Differentiated Quantization VLMs for Visually Impaired Assistance
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在服务视障人群时面临的高内存占用与实时性不足的问题。解决方案的关键在于提出了一种双技术协同的创新框架：一是跨模态差异化量化（cross-modal differentiated quantization）策略，实现了对19B参数模型的有效压缩，将内存需求从38GB降至16GB，同时仅带来2.05%的性能下降；二是场景感知向量化记忆多智能体系统（scene-aware vectorized memory multi-agent system），通过结合场景分类、向量化存储与多模态交互机制，实现环境信息的持久化存储与高效检索，支持基于历史记忆的推理能力。该架构在保持响应延迟在2.83–3.52秒之间的前提下，显著优于同类小规模模型，为视障用户提供更精准、实时的场景感知、文本识别与导航辅助。
链接: https://arxiv.org/abs/2508.18177
作者: Xiangxiang Wang,Xuanyu Wang,YiJia Luo,Yongbin Yu,Manping Fan,Jingtao Zhang,Liyong Ren
机构: University of Electronic Science and Technology of China (电子科技大学); Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital (四川省医学科学院/四川省人民医院); Harbin Institute of Technology (哈尔滨工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 28 pages,9 figures
Abstract:This study proposes the dual technological innovation framework, including a cross-modal differ entiated quantization framework for vision-language models (VLMs) and a scene-aware vectorized memory multi-agent system for visually impaired assistance. The modular framework was developed implementing differentiated processing strategies, effectively reducing memory requirements from 38GB to 16GB while maintaining model performance. The multi-agent architecture combines scene classification, vectorized memory, and multimodal interaction, enabling persistent storage and efficient retrieval of scene memories. Through perception-memory-reasoning workflows, the system provides environmental information beyond the current view using historical memories. Experiments show the quantized 19B-parameter model only experiences a 2.05% performance drop on MMBench and maintains 63.7 accuracy on OCR-VQA (original: 64.9), outperforming smaller models with equivalent memory requirements like the Molmo-7B series. The system maintains response latency between 2.83-3.52 seconds from scene analysis to initial speech output, substantially faster than non-streaming methods. This research advances computational efficiency and assistive technology, offering visually impaired users comprehensive real-time assistance in scene perception, text recognition, and navigation. Comments: 28 pages,9 figures Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multiagent Systems (cs.MA) Cite as: arXiv:2508.18177 [cs.CV] (or arXiv:2508.18177v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.18177 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xuanyu Wang [view email] [v1] Mon, 25 Aug 2025 16:32:32 UTC (3,768 KB) Full-text links: Access Paper: View a PDF of the paper titled Scene-Aware Vectorized Memory Multi-Agent Framework with Cross-Modal Differentiated Quantization VLMs for Visually Impaired Assistance, by Xiangxiang Wang and 6 other authorsView PDFHTML (experimental)TeX SourceOther Formats view license Current browse context: cs.CV prev | next new | recent | 2025-08 Change to browse by: cs cs.LG cs.MA 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
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[CV-15] SpotEdit: Evaluating Visually-Guided Image Editing Methods
【速读】：该论文旨在解决当前 visually-guided image editing（视觉引导图像编辑）方法在评估体系上的不足，即现有评价方式过于简单且无法充分反映真实场景中的编辑挑战。为应对这一问题，作者提出了 SpotEdit 基准测试平台，其关键在于系统性地评估扩散模型、自回归模型及混合生成模型在多样任务下的表现，并特别引入对“幻觉”（hallucination）现象的专项检测——揭示了如 GPT-4o 等领先模型常错误地“ hallucinate”（幻觉）视觉提示的存在并据此执行编辑任务，从而暴露了当前模型在视觉一致性与条件依赖性方面的显著缺陷。
链接: https://arxiv.org/abs/2508.18159
作者: Sara Ghazanfari,Wei-An Lin,Haitong Tian,Ersin Yumer
机构: New York University (纽约大学); Adobe Inc
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Visually-guided image editing, where edits are conditioned on both visual cues and textual prompts, has emerged as a powerful paradigm for fine-grained, controllable content generation. Although recent generative models have shown remarkable capabilities, existing evaluations remain simple and insufficiently representative of real-world editing challenges. We present SpotEdit, a comprehensive benchmark designed to systematically assess visually-guided image editing methods across diverse diffusion, autoregressive, and hybrid generative models, uncovering substantial performance disparities. To address a critical yet underexplored challenge, our benchmark includes a dedicated component on hallucination, highlighting how leading models, such as GPT-4o, often hallucinate the existence of a visual cue and erroneously perform the editing task. Our code and benchmark are publicly released at this https URL.
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[CV-16] Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability
【速读】：该论文旨在解决类激活图（Class Activation Maps, CAMs）在面对不同噪声扰动时的鲁棒性问题，即评估多种CAM方法在多个模型架构和数据集上对噪声的敏感程度及其稳定性。其解决方案的关键在于提出了一种新的鲁棒性度量指标，该指标包含两个核心属性：一致性（consistency）与响应性（responsiveness）。其中，一致性衡量CAM在输入扰动不改变预测类别时保持稳定的能力，而响应性则量化CAM对因扰动导致预测变化的敏感程度；该度量通过实证分析在不同模型、扰动类型和数据集上的表现，并辅以统计检验验证了其有效性与普适性。
链接: https://arxiv.org/abs/2508.18154
作者: Syamantak Sarkar,Revoti P. Bora,Bhupender Kaushal,Sudhish N George,Kiran Raja
机构: National Institute of Technology Calicut, India (印度国家技术学院喀拉拉分校); NTNU Gjøvik, Norway (挪威科技大学格里维克校区)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Image and Vision Computing (2025)
Abstract:Class Activation Maps (CAMs) are one of the important methods for visualizing regions used by deep learning models. Yet their robustness to different noise remains underexplored. In this work, we evaluate and report the resilience of various CAM methods for different noise perturbations across multiple architectures and datasets. By analyzing the influence of different noise types on CAM explanations, we assess the susceptibility to noise and the extent to which dataset characteristics may impact explanation stability. The findings highlight considerable variability in noise sensitivity for various CAMs. We propose a robustness metric for CAMs that captures two key properties: consistency and responsiveness. Consistency reflects the ability of CAMs to remain stable under input perturbations that do not alter the predicted class, while responsiveness measures the sensitivity of CAMs to changes in the prediction caused by such perturbations. The metric is evaluated empirically across models, different perturbations, and datasets along with complementary statistical tests to exemplify the applicability of our proposed approach.
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[CV-17] BirdRecorders AI on Sky: Safeguarding birds of prey by detection and classification of tiny objects around wind turbines
【速读】：该论文旨在解决风力发电扩张与野生动物保护之间的冲突问题，特别是减少鸟类（尤其是红鸢，Milvus milvus）与风机碰撞导致的死亡风险。其核心解决方案是开发了一种名为BirdRecorder的先进人工智能防撞系统，该系统融合了机器人技术、遥测技术和高性能AI算法，能够在800米范围内实现对鸟类的实时检测、跟踪与分类。关键技术在于采用单次检测器（Single Shot Detector, SSD）进行目标检测，并结合专用硬件加速和优化的跟踪算法，在保证高精度的同时满足实时决策所需的处理速度，从而显著优于现有方法在准确性和效率上的表现。
链接: https://arxiv.org/abs/2508.18136
作者: Nico Klar,Nizam Gifary,Felix P. G. Ziegler,Frank Sehnke,Anton Kaifel,Eric Price,Aamir Ahmad
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)
备注: 18 pages, 1 figures, to appear in Proceedings of the 19th International Conference on Intelligent Autonomous Systems (IAS-19), Genoa, Italy, 2025
Abstract:The urgent need for renewable energy expansion, particularly wind power, is hindered by conflicts with wildlife conservation. To address this, we developed BirdRecorder, an advanced AI-based anti-collision system to protect endangered birds, especially the red kite (Milvus milvus). Integrating robotics, telemetry, and high-performance AI algorithms, BirdRecorder aims to detect, track, and classify avian species within a range of 800 m to minimize bird-turbine collisions. BirdRecorder integrates advanced AI methods with optimized hardware and software architectures to enable real-time image processing. Leveraging Single Shot Detector (SSD) for detection, combined with specialized hardware acceleration and tracking algorithms, our system achieves high detection precision while maintaining the speed necessary for real-time decision-making. By combining these components, BirdRecorder outperforms existing approaches in both accuracy and efficiency. In this paper, we summarize results on field tests and performance of the BirdRecorder system. By bridging the gap between renewable energy expansion and wildlife conservation, BirdRecorder contributes to a more sustainable coexistence of technology and nature. Comments: 18 pages, 1 figures, to appear in Proceedings of the 19th International Conference on Intelligent Autonomous Systems (IAS-19), Genoa, Italy, 2025 Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY) Cite as: arXiv:2508.18136 [cs.CV] (or arXiv:2508.18136v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.18136 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-18] Incorporating Pre-trained Diffusion Models in Solving the Schrödinger Bridge Problem
【速读】：该论文旨在解决生成式 AI（Generative AI）中 Score-based Generative Models (SGMs) 与 Schrödinger Bridge (SB) 问题之间的统一建模难题，其核心挑战在于如何高效且稳定地训练基于 SB 的生成模型。解决方案的关键在于引入三种重参数化技术：Iterative Proportional Mean-Matching (IPMM)、Iterative Proportional Terminus-Matching (IPTM) 和 Iterative Proportional Flow-Matching (IPFM)，这些方法显著加速并稳定了 SB 模型的训练过程；同时提出利用预训练 SGMs 进行初始化的新策略，从而有效结合两类模型的优势，在提升 SB 模型训练效率的同时进一步优化 SGM 性能。
链接: https://arxiv.org/abs/2508.18095
作者: Zhicong Tang,Tiankai Hang,Shuyang Gu,Dong Chen,Baining Guo
机构: Tsinghua University (清华大学); Southeast University (东南大学); University of Science and Technology of China (中国科学技术大学); Microsoft Research Asia (微软亚洲研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:This paper aims to unify Score-based Generative Models (SGMs), also known as Diffusion models, and the Schrödinger Bridge (SB) problem through three reparameterization techniques: Iterative Proportional Mean-Matching (IPMM), Iterative Proportional Terminus-Matching (IPTM), and Iterative Proportional Flow-Matching (IPFM). These techniques significantly accelerate and stabilize the training of SB-based models. Furthermore, the paper introduces novel initialization strategies that use pre-trained SGMs to effectively train SB-based models. By using SGMs as initialization, we leverage the advantages of both SB-based models and SGMs, ensuring efficient training of SB-based models and further improving the performance of SGMs. Extensive experiments demonstrate the significant effectiveness and improvements of the proposed methods. We believe this work contributes to and paves the way for future research on generative models.
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[CV-19] Few-shot Unknown Class Discovery of Hyperspectral Images with Prototype Learning and Clustering
【速读】：该论文旨在解决开放集少样本高光谱图像（HSI）分类问题，即在仅提供少量已知类别标签样本的情况下，不仅要准确识别已知类别的像素，还需发现并聚类未知类别的样本。现有方法通常仅能区分未知类与已知类并将其拒绝，无法进一步识别或挖掘未知类。本文提出一种基于原型学习与聚类的解决方案，其关键在于：利用少量标注样本学习已知类原型的同时，推断未知类原型；当未知类样本被已知类分类器拒绝后，再依据其与推断出的未知类原型之间的距离进行聚类，从而实现对未知类的自动发现与划分。
链接: https://arxiv.org/abs/2508.18075
作者: Chun Liu,Chen Zhang,Zhuo Li,Zheng Li,Wei Yang
机构: Henan University (河南大学); Henan Key Laboratory of Big Data Analysis and Processing (河南省大数据分析与处理重点实验室); Henan Engineering Laboratory of Spatial Information Processing (河南省空间信息处理工程实验室); Henan Industrial Technology Academy of Spatio-Temporal Big Data (河南省时空大数据产业技术研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Open-set few-shot hyperspectral image (HSI) classification aims to classify image pixels by using few labeled pixels per class, where the pixels to be classified may be not all from the classes that have been seen. To address the open-set HSI classification challenge, current methods focus mainly on distinguishing the unknown class samples from the known class samples and rejecting them to increase the accuracy of identifying known class samples. They fails to further identify or discovery the unknow classes among the samples. This paper proposes a prototype learning and clustering method for discoverying unknown classes in HSIs under the few-shot environment. Using few labeled samples, it strives to develop the ability of infering the prototypes of unknown classes while distinguishing unknown classes from known classes. Once the unknown class samples are rejected by the learned known class classifier, the proposed method can further cluster the unknown class samples into different classes according to their distance to the inferred unknown class prototypes. Compared to existing state-of-the-art methods, extensive experiments on four benchmark HSI datasets demonstrate that our proposed method exhibits competitive performance in open-set few-shot HSI classification tasks. All the codes are available at \hrefthis https URL this https URL
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[CV-20] EventTracer: Fast Path Tracing-based Event Stream Rendering
【速读】：该论文旨在解决现有事件流模拟方法依赖于高成本且低时间分辨率（仅100–300 FPS）的无噪声RGB帧的问题，从而难以生成与真实世界事件数据高相似度的高时空分辨率事件序列。其解决方案的关键在于提出EventTracer，一个基于路径追踪（path tracing）的渲染管线：首先通过低样本/像素（SPP）路径追踪加速渲染过程，随后训练一个轻量级事件脉冲网络（event spiking network）对结果RGB视频进行去噪并生成逼真的事件序列；该网络采用双极漏电积分-放电（bipolar leaky integrate-and-fire, BiLIF）脉冲单元，并使用双向地球移动距离（bidirectional earth mover distance, EMD）损失函数以精确建模事件流的物理特性。此方案实现了约4分钟/秒720p视频的高效渲染速度，同时保留了路径追踪在时空建模上的准确性，显著提升了事件模拟的真实性与实用性。
链接: https://arxiv.org/abs/2508.18071
作者: Zhenyang Li,Xiaoyang Bai,Jinfan Lu,Pengfei Shen,Edmund Y. Lam,Yifan Peng
机构: The University of Hong Kong (香港大学); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Simulating event streams from 3D scenes has become a common practice in event-based vision research, as it meets the demand for large-scale, high temporal frequency data without setting up expensive hardware devices or undertaking extensive data collections. Yet existing methods in this direction typically work with noiseless RGB frames that are costly to render, and therefore they can only achieve a temporal resolution equivalent to 100-300 FPS, far lower than that of real-world event data. In this work, we propose EventTracer, a path tracing-based rendering pipeline that simulates high-fidelity event sequences from complex 3D scenes in an efficient and physics-aware manner. Specifically, we speed up the rendering process via low sample-per-pixel (SPP) path tracing, and train a lightweight event spiking network to denoise the resulting RGB videos into realistic event sequences. To capture the physical properties of event streams, the network is equipped with a bipolar leaky integrate-and-fired (BiLIF) spiking unit and trained with a bidirectional earth mover distance (EMD) loss. Our EventTracer pipeline runs at a speed of about 4 minutes per second of 720p video, and it inherits the merit of accurate spatiotemporal modeling from its path tracing backbone. We show in two downstream tasks that EventTracer captures better scene details and demonstrates a greater similarity to real-world event data than other event simulators, which establishes it as a promising tool for creating large-scale event-RGB datasets at a low cost, narrowing the sim-to-real gap in event-based vision, and boosting various application scenarios such as robotics, autonomous driving, and VRAR.
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[CV-21] Annotation-Free Open-Vocabulary Segmentation for Remote-Sensing Images
【速读】：该论文旨在解决遥感（Remote Sensing, RS）图像中开放词汇语义分割（Open-Vocabulary Semantic Segmentation, OVSS）面临的两大核心挑战：一是现有基于自然图像设计的OVSS框架难以适应RS数据特有的大尺度变化和细粒度细节；二是其模型适配通常依赖大量昂贵的人工标注，限制了在真实场景中的应用。解决方案的关键在于提出SegEarth-OV框架，其核心创新包括两个部分：一是SimFeatUp通用上采样模块，能够从粗粒度特征中鲁棒地恢复高分辨率空间细节，无需任务特定微调即可校正目标形状失真；二是全局偏置消除（Global Bias Alleviation）操作，通过移除patch特征中的固有全局上下文信息，显著提升局部语义保真度。此外，为拓展至合成孔径雷达（SAR）等其他遥感模态，作者进一步提出AlignEarth知识蒸馏策略，将光学图像预训练视觉语言模型（VLM）的语义知识高效迁移至SAR编码器，从而避免从头构建SAR基础模型，实现跨传感器类型的通用注释-free开放世界遥感语义分割。
链接: https://arxiv.org/abs/2508.18067
作者: Kaiyu Li,Xiangyong Cao,Ruixun Liu,Shihong Wang,Zixuan Jiang,Zhi Wang,Deyu Meng
机构: Xi’an Jiaotong University (西安交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: All codes and models will be released at this https URL
Abstract:Semantic segmentation of remote sensing (RS) images is pivotal for comprehensive Earth observation, but the demand for interpreting new object categories, coupled with the high expense of manual annotation, poses significant challenges. Although open-vocabulary semantic segmentation (OVSS) offers a promising solution, existing frameworks designed for natural images are insufficient for the unique complexities of RS data. They struggle with vast scale variations and fine-grained details, and their adaptation often relies on extensive, costly annotations. To address this critical gap, this paper introduces SegEarth-OV, the first framework for annotation-free open-vocabulary segmentation of RS images. Specifically, we propose SimFeatUp, a universal upsampler that robustly restores high-resolution spatial details from coarse features, correcting distorted target shapes without any task-specific post-training. We also present a simple yet effective Global Bias Alleviation operation to subtract the inherent global context from patch features, significantly enhancing local semantic fidelity. These components empower SegEarth-OV to effectively harness the rich semantics of pre-trained VLMs, making OVSS possible in optical RS contexts. Furthermore, to extend the framework’s universality to other challenging RS modalities like SAR images, where large-scale VLMs are unavailable and expensive to create, we introduce AlignEarth, which is a distillation-based strategy and can efficiently transfer semantic knowledge from an optical VLM encoder to an SAR encoder, bypassing the need to build SAR foundation models from scratch and enabling universal OVSS across diverse sensor types. Extensive experiments on both optical and SAR datasets validate that SegEarth-OV can achieve dramatic improvements over the SOTA methods, establishing a robust foundation for annotation-free and open-world Earth observation.
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[CV-22] ArgusCogito: Chain-of-Thought for Cross-Modal Synergy and Omnidirectional Reasoning in Camouflaged Object Segmentation
【速读】：该论文旨在解决伪装目标分割（Camouflaged Object Segmentation, COS）中因目标与背景高度相似而导致的分割不完整和精度不足的问题。现有方法受限于浅层特征表示、推理机制薄弱以及跨模态融合能力不足，难以实现对场景的深层认知理解。其解决方案的关键在于提出一种受“百眼巨人”感知策略启发的零样本、链式思维框架 ArgusCogito，通过视觉-语言模型（VLMs）中的跨模态协同与全向推理，分三个认知驱动阶段：(1) 推测（Conjecture）阶段利用多模态融合（RGB、深度图、语义图）构建强先验，实现全局场景理解；(2) 聚焦（Focus）阶段基于语义先验进行全向注意力扫描与区域精炼，提升目标定位精度；(3) 雕刻（Sculpting）阶段在聚焦区域内迭代生成密集正负点提示并融合多模态信息，逐步生成高保真分割掩膜，模拟“高强度细察”。该设计显著提升了模型在多个COS及医学图像分割（Medical Image Segmentation, MIS）基准上的性能与泛化能力。
链接: https://arxiv.org/abs/2508.18050
作者: Jianwen Tan,Huiyao Zhang,Rui Xiong,Han Zhou,Hongfei Wang,Ye Li
机构: University of Chinese Academy of Sciences (中国科学院大学); Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences (中国科学院空间应用工程与技术中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Camouflaged Object Segmentation (COS) poses a significant challenge due to the intrinsic high similarity between targets and backgrounds, demanding models capable of profound holistic understanding beyond superficial cues. Prevailing methods, often limited by shallow feature representation, inadequate reasoning mechanisms, and weak cross-modal integration, struggle to achieve this depth of cognition, resulting in prevalent issues like incomplete target separation and imprecise segmentation. Inspired by the perceptual strategy of the Hundred-eyed Giant-emphasizing holistic observation, omnidirectional focus, and intensive scrutiny-we introduce ArgusCogito, a novel zero-shot, chain-of-thought framework underpinned by cross-modal synergy and omnidirectional reasoning within Vision-Language Models (VLMs). ArgusCogito orchestrates three cognitively-inspired stages: (1) Conjecture: Constructs a strong cognitive prior through global reasoning with cross-modal fusion (RGB, depth, semantic maps), enabling holistic scene understanding and enhanced target-background disambiguation. (2) Focus: Performs omnidirectional, attention-driven scanning and focused reasoning, guided by semantic priors from Conjecture, enabling precise target localization and region-of-interest refinement. (3) Sculpting: Progressively sculpts high-fidelity segmentation masks by integrating cross-modal information and iteratively generating dense positive/negative point prompts within focused regions, emulating Argus’ intensive scrutiny. Extensive evaluations on four challenging COS benchmarks and three Medical Image Segmentation (MIS) benchmarks demonstrate that ArgusCogito achieves state-of-the-art (SOTA) performance, validating the framework’s exceptional efficacy, superior generalization capability, and robustness.
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[CV-23] Visual-CoG: Stage-Aware Reinforcement Learning with Chain of Guidance for Text-to-Image Generation
【速读】：该论文旨在解决当前自回归文本到图像（text-to-image, T2I）生成模型在处理多属性和模糊提示时能力不足的问题，尤其是现有方法依赖单一最终奖励信号导致难以识别各生成阶段对最终结果的贡献，从而可能产生次优策略。其解决方案的关键在于提出一种视觉链式引导（Visual-Chain of Guidance, Visual-CoG）范式，该范式将图像生成过程划分为三个阶段：语义推理（semantic reasoning）、过程精炼（process refining）和结果评估（outcome evaluation），并通过阶段感知奖励（stage-aware rewards）提供即时反馈，实现全流程的精细化引导。
链接: https://arxiv.org/abs/2508.18032
作者: Yaqi Li,Peng Chen,Mingyang Han,Bu Pi,Haoxiang Shi,Runzhou Zhao,Yang Yao,Xuan Zhang,Jun Song
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Despite the promising progress of recent autoregressive models in text-to-image (T2I) generation, their ability to handle multi-attribute and ambiguous prompts remains limited. To address these limitations, existing works have applied chain-of-thought (CoT) to enable stage-aware visual synthesis and employed reinforcement learning (RL) to improve reasoning capabilities. However, most models provide reward signals only at the end of the generation stage. This monolithic final-only guidance makes it difficult to identify which stages contribute positively to the final outcome and may lead to suboptimal policies. To tackle this issue, we propose a Visual-Chain of Guidance (Visual-CoG) paradigm consisting of three stages: semantic reasoning, process refining, and outcome evaluation, with stage-aware rewards providing immediate guidance throughout the image generation pipeline. We further construct a visual cognition benchmark, VisCog-Bench, which comprises four subtasks to evaluate the effectiveness of semantic reasoning. Comprehensive evaluations on GenEval, T2I-CompBench, and the proposed VisCog-Bench show improvements of 15%, 5%, and 19%, respectively, demonstrating the superior performance of the proposed Visual-CoG. We will release all the resources soon.
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[CV-24] FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction
【速读】：该论文旨在解决法医领域中颅面重建（craniofacial reconstruction）的效率与精度问题，尤其是传统基于黏土的手工重建方法耗时且依赖专家经验，而现有概率生成模型（如统计形状模型或Basel人脸模型）无法有效捕捉颅骨与面部之间的跨域特征。解决方案的关键在于提出一种基于2D X射线图像的通用生成式框架，利用CycleGAN和条件生成对抗网络（cGAN）等生成模型，并对生成器和判别器进行微调，以在颅骨与人脸两个不同域之间实现更逼真的图像转换。这是首次将2D X射线作为颅骨表征用于生成式颅面重建，实验表明该方法可有效提升重建质量并支持基于生成人脸的检索系统，为法医鉴定提供新工具。
链接: https://arxiv.org/abs/2508.18031
作者: Ravi Shankar Prasad,Dinesh Singh
机构: IIT Mandi (印度理工学院曼迪分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 9 figures
Abstract:Craniofacial reconstruction in forensics is one of the processes to identify victims of crime and natural disasters. Identifying an individual from their remains plays a crucial role when all other identification methods fail. Traditional methods for this task, such as clay-based craniofacial reconstruction, require expert domain knowledge and are a time-consuming process. At the same time, other probabilistic generative models like the statistical shape model or the Basel face model fail to capture the skull and face cross-domain attributes. Looking at these limitations, we propose a generic framework for craniofacial reconstruction from 2D X-ray images. Here, we used various generative models (i.e., CycleGANs, cGANs, etc) and fine-tune the generator and discriminator parts to generate more realistic images in two distinct domains, which are the skull and face of an individual. This is the first time where 2D X-rays are being used as a representation of the skull by generative models for craniofacial reconstruction. We have evaluated the quality of generated faces using FID, IS, and SSIM scores. Finally, we have proposed a retrieval framework where the query is the generated face image and the gallery is the database of real faces. By experimental results, we have found that this can be an effective tool for forensic science.
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[CV-25] AQ-PCDSys: An Adaptive Quantized Planetary Crater Detection System for Autonomous Space Exploration
【速读】：该论文旨在解决行星探测任务中在计算资源受限的平台上部署深度学习模型进行实时、准确环境感知的难题，尤其针对陨石坑检测这一关键任务。解决方案的核心在于提出了一种自适应量化行星陨石坑检测系统（AQ-PCDSys），其关键技术包括：1）采用量化感知训练（QAT）方法优化的量化神经网络（QNN）架构，显著降低模型尺寸与推理延迟，同时保持高精度；2）引入自适应多传感器融合（AMF）模块，通过特征级融合光学影像（OI）与数字高程模型（DEM）数据，并利用自适应权重机制（AWM）动态调整不同模态的贡献，以提升复杂行星地形下的检测鲁棒性；3）结合多尺度检测头设计，实现对不同尺寸陨石坑的高效且稳定的检测。
链接: https://arxiv.org/abs/2508.18025
作者: Aditri Paul,Archan Paul
机构: Manipal University Jaipur (印度马尼帕尔大学贾伊普尔分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET); Systems and Control (eess.SY)
备注: 17 pages, 6 figures. A research paper on a novel deep learning framework for planetary crater detection
Abstract:Autonomous planetary exploration missions are critically dependent on real-time, accurate environmental perception for navigation and hazard avoidance. However, deploying deep learning models on the resource-constrained computational hardware of planetary exploration platforms remains a significant challenge. This paper introduces the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys), a novel framework specifically engineered for real-time, onboard deployment in the computationally constrained environments of space exploration missions. AQ-PCDSys synergistically integrates a Quantized Neural Network (QNN) architecture, trained using Quantization-Aware Training (QAT), with an Adaptive Multi-Sensor Fusion (AMF) module. The QNN architecture significantly optimizes model size and inference latency suitable for real-time onboard deployment in space exploration missions, while preserving high accuracy. The AMF module intelligently fuses data from Optical Imagery (OI) and Digital Elevation Models (DEMs) at the feature level, utilizing an Adaptive Weighting Mechanism (AWM) to dynamically prioritize the most relevant and reliable sensor modality based on planetary ambient conditions. This approach enhances detection robustness across diverse planetary landscapes. Paired with Multi-Scale Detection Heads specifically designed for robust and efficient detection of craters across a wide range of sizes, AQ-PCDSys provides a computationally efficient, reliable and accurate solution for planetary crater detection, a critical capability for enabling the next generation of autonomous planetary landing, navigation, and scientific exploration.
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[CV-26] owards Continual Visual Anomaly Detection in the Medical Domain
【速读】：该论文旨在解决医学影像中视觉异常检测（Visual Anomaly Detection, VAD）模型在面对输入数据分布随时间演变时性能下降的问题，即如何在不遗忘已有知识的前提下实现模型的持续学习（Continual Learning, CL）。解决方案的关键在于首次将持续学习框架引入VAD任务，提出并应用了PatchCoreCL模型——基于经典PatchCore架构改进的持续学习版本，并在真实世界医学影像数据集BMAD上验证其有效性。实验表明，PatchCoreCL在保持与专用模型相当的检测性能的同时，遗忘率低于1%，证明了持续学习在自适应医学影像异常检测中的可行性与潜力。
链接: https://arxiv.org/abs/2508.18013
作者: Manuel Barusco,Francesco Borsatti,Nicola Beda,Davide Dalle Pezze,Gian Antonio Susto
机构: University of Padova (帕多瓦大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Visual Anomaly Detection (VAD) seeks to identify abnormal images and precisely localize the corresponding anomalous regions, relying solely on normal data during training. This approach has proven essential in domains such as manufacturing and, more recently, in the medical field, where accurate and explainable detection is critical. Despite its importance, the impact of evolving input data distributions over time has received limited attention, even though such changes can significantly degrade model performance. In particular, given the dynamic and evolving nature of medical imaging data, Continual Learning (CL) provides a natural and effective framework to incrementally adapt models while preserving previously acquired knowledge. This study explores for the first time the application of VAD models in a CL scenario for the medical field. In this work, we utilize a CL version of the well-established PatchCore model, called PatchCoreCL, and evaluate its performance using BMAD, a real-world medical imaging dataset with both image-level and pixel-level annotations. Our results demonstrate that PatchCoreCL is an effective solution, achieving performance comparable to the task-specific models, with a forgetting value less than a 1%, highlighting the feasibility and potential of CL for adaptive VAD in medical imaging.
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[CV-27] Development of a Neural Network Model for Currency Detection to aid visually impaired people in Nigeria
【速读】：该论文旨在解决视障人士在识别不同面额现金时面临的困难，从而提升其独立进行商业交易的能力。解决方案的关键在于构建一个包含3,468张图像的定制数据集，并基于此训练单次检测器（SSD）神经网络模型，实现对尼日利亚纸币的高精度识别，系统在测试中达到超过90%的平均精度（Mean Average Precision），表明其具备良好的实用性和推广潜力。
链接: https://arxiv.org/abs/2508.18012
作者: Sochukwuma Nwokoye,Desmond Moru
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Neural networks in assistive technology for visually impaired leverage artificial intelligence’s capacity to recognize patterns in complex data. They are used for converting visual data into auditory or tactile representations, helping the visually impaired understand their surroundings. The primary aim of this research is to explore the potential of artificial neural networks to facilitate the differentiation of various forms of cash for individuals with visual impairments. In this study, we built a custom dataset of 3,468 images, which was subsequently used to train an SSD neural network model. The proposed system can accurately identify Nigerian cash, thereby streamlining commercial transactions. The performance of the system in terms of accuracy was assessed, and the Mean Average Precision score was over 90%. We believe that our system has the potential to make a substantial contribution to the field of assistive technology while also improving the quality of life of visually challenged persons in Nigeria and beyond.
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[CV-28] Fence off Anomaly Interference: Cross-Domain Distillation for Fully Unsupervised Anomaly Detection
【速读】：该论文旨在解决完全无监督异常检测（Fully Unsupervised Anomaly Detection, FUAD）中的关键挑战：即在训练数据可能包含异常样本的情况下，如何有效避免学生模型（student model）学习到教师模型（teacher model）对异常的表征，从而导致检测性能下降的问题。解决方案的关键在于提出了一种基于反向蒸馏（Reverse Distillation, RD）范式的跨域知识蒸馏（Cross-Domain Distillation, CDD）框架，其核心创新是引入领域特定训练（Domain-Specific Training），将训练集划分为多个异常比例较低的子域，并为每个子域训练一个领域特定的学生模型；随后通过跨域知识聚合机制，利用这些领域特定学生生成的伪正常特征协同指导全局学生模型学习跨域的通用正常表征，从而提升FUAD场景下的异常检测鲁棒性与准确性。
链接: https://arxiv.org/abs/2508.18007
作者: Xinyue Liu,Jianyuan Wang,Biao Leng,Shuo Zhang
机构: Beihang University (北京航空航天大学); University of Science and Technology Beijing (北京科技大学); Beijing Jiaotong University (北京交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Fully Unsupervised Anomaly Detection (FUAD) is a practical extension of Unsupervised Anomaly Detection (UAD), aiming to detect anomalies without any labels even when the training set may contain anomalous samples. To achieve FUAD, we pioneer the introduction of Knowledge Distillation (KD) paradigm based on teacher-student framework into the FUAD setting. However, due to the presence of anomalies in the training data, traditional KD methods risk enabling the student to learn the teacher’s representation of anomalies under FUAD setting, thereby resulting in poor anomaly detection performance. To address this issue, we propose a novel Cross-Domain Distillation (CDD) framework based on the widely studied reverse distillation (RD) paradigm. Specifically, we design a Domain-Specific Training, which divides the training set into multiple domains with lower anomaly ratios and train a domain-specific student for each. Cross-Domain Knowledge Aggregation is then performed, where pseudo-normal features generated by domain-specific students collaboratively guide a global student to learn generalized normal representations across all samples. Experimental results on noisy versions of the MVTec AD and VisA datasets demonstrate that our method achieves significant performance improvements over the baseline, validating its effectiveness under FUAD setting.
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[CV-29] opology Aware Neural Interpolation of Scalar Fields
【速读】：该论文旨在解决时间变化标量场（time-varying scalar fields）中缺失关键帧之间数据的拓扑感知插值问题。传统插值方法往往忽略拓扑结构信息，导致生成结果在几何和拓扑层面均不准确。解决方案的关键在于设计一种神经架构，通过学习从时间值到对应标量场的映射关系，基于稀疏采样的关键帧（keyframes）实现对非关键帧时间步的高效插值。该架构进一步引入专门的拓扑损失函数，利用输入的持久图（persistence diagrams）信息，显著提升非关键帧处数据的几何与拓扑重建质量。在推理阶段，仅需一次前向传播即可实时输出目标时间点的插值结果，实验表明该方法在二维和三维数据上均优于现有基准插值方案。
链接: https://arxiv.org/abs/2508.17995
作者: Mohamed Kissi,Keanu Sisouk,Joshua A. Levine,Julien Tierny
机构: CNRS, Sorbonne Université (法国国家科学研究中心，索邦大学); University of Arizona (亚利桑那大学)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
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Abstract:This paper presents a neural scheme for the topology-aware interpolation of time-varying scalar fields. Given a time-varying sequence of persistence diagrams, along with a sparse temporal sampling of the corresponding scalar fields, denoted as keyframes, our interpolation approach aims at “inverting” the non-keyframe diagrams to produce plausible estimations of the corresponding, missing data. For this, we rely on a neural architecture which learns the relation from a time value to the corresponding scalar field, based on the keyframe examples, and reliably extends this relation to the non-keyframe time steps. We show how augmenting this architecture with specific topological losses exploiting the input diagrams both improves the geometrical and topological reconstruction of the non-keyframe time steps. At query time, given an input time value for which an interpolation is desired, our approach instantaneously produces an output, via a single propagation of the time input through the network. Experiments interpolating 2D and 3D time-varying datasets show our approach superiority, both in terms of data and topological fitting, with regard to reference interpolation schemes.
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[CV-30] Propose and Rectify: A Forensics-Driven MLLM Framework for Image Manipulation Localization
【速读】：该论文旨在解决图像篡改检测中难以同时实现高精度定位与可靠识别的问题，尤其针对当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在感知细微低层伪造痕迹方面的局限性。其解决方案的关键在于提出了一种“提议-修正”（Propose-Rectify）框架：首先利用适配取证任务的LLaVA模型进行语义驱动的初步篡改区域定位；随后通过引入取证修正模块（Forensics Rectification Module），基于多尺度特征分析和多种专用滤波器提取的技术证据对初始提议进行系统性验证与优化；此外，还设计了增强分割模块（Enhanced Segmentation Module），将关键取证线索嵌入SAM（Segment Anything Model）的图像编码特征中，从而缓解模型固有的语义偏差，提升篡改区域边界划分的精确度。该方法实现了语义推理与传统取证分析的有效融合，显著提升了检测准确率与定位精度。
链接: https://arxiv.org/abs/2508.17976
作者: Keyang Zhang,Chenqi Kong,Hui Liu,Bo Ding,Xinghao Jiang,Haoliang Li
机构: City University of Hong Kong (香港城市大学); Nanyang Technology University (南洋理工大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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Abstract:The increasing sophistication of image manipulation techniques demands robust forensic solutions that can both reliably detect alterations and precisely localize tampered regions. Recent Multimodal Large Language Models (MLLMs) show promise by leveraging world knowledge and semantic understanding for context-aware detection, yet they struggle with perceiving subtle, low-level forensic artifacts crucial for accurate manipulation localization. This paper presents a novel Propose-Rectify framework that effectively bridges semantic reasoning with forensic-specific analysis. In the proposal stage, our approach utilizes a forensic-adapted LLaVA model to generate initial manipulation analysis and preliminary localization of suspicious regions based on semantic understanding and contextual reasoning. In the rectification stage, we introduce a Forensics Rectification Module that systematically validates and refines these initial proposals through multi-scale forensic feature analysis, integrating technical evidence from several specialized filters. Additionally, we present an Enhanced Segmentation Module that incorporates critical forensic cues into SAM’s encoded image embeddings, thereby overcoming inherent semantic biases to achieve precise delineation of manipulated regions. By synergistically combining advanced multimodal reasoning with established forensic methodologies, our framework ensures that initial semantic proposals are systematically validated and enhanced through concrete technical evidence, resulting in comprehensive detection accuracy and localization precision. Extensive experimental validation demonstrates state-of-the-art performance across diverse datasets with exceptional robustness and generalization capabilities.
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[CV-31] Enhanced Drift-Aware Computer Vision Architecture for Autonomous Driving
【速读】：该论文旨在解决自动驾驶场景中因恶劣天气或低光照等挑战性条件导致的数据漂移（data drift）问题，从而引发模型性能下降并可能造成安全隐患。其解决方案的关键在于提出一种新型混合计算机视觉架构，该架构通过数千张合成道路环境图像进行训练，以提升模型在未见漂移环境中的鲁棒性；具体而言，系统采用YOLOv8实现快速目标检测，并引入一个五层卷积神经网络（CNN）进行验证，形成双模式工作流程，在测试中对漂移增强的道路图像检测准确率提升超过90%，验证了混合结构在提升道路安全方面的有效性。
链接: https://arxiv.org/abs/2508.17975
作者: Md Shahi Amran Hossain,Abu Shad Ahammed,Sayeri Mukherjee,Roman Obermaisser
机构: University of Siegen (锡根大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Logic (math.LO)
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Abstract:The use of computer vision in automotive is a trending research in which safety and security are a primary concern. In particular, for autonomous driving, preventing road accidents requires highly accurate object detection under diverse conditions. To address this issue, recently the International Organization for Standardization (ISO) released the 8800 norm, providing structured frameworks for managing associated AI relevant risks. However, challenging scenarios such as adverse weather or low lighting often introduce data drift, leading to degraded model performance and potential safety violations. In this work, we present a novel hybrid computer vision architecture trained with thousands of synthetic image data from the road environment to improve robustness in unseen drifted environments. Our dual mode framework utilized YOLO version 8 for swift detection and incorporated a five-layer CNN for verification. The system functioned in sequence and improved the detection accuracy by more than 90% when tested with drift-augmented road images. The focus was to demonstrate how such a hybrid model can provide better road safety when working together in a hybrid structure.
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[CV-32] SAIL-Recon: Large SfM by Augmenting Scene Regression with Localization
【速读】：该论文旨在解决生成式 AI (Generative AI) 中的场景重建（Scene Regression）方法在处理大规模图像集合时效率低下、难以扩展的问题。现有方法如 VGGT 虽然在极端视角变化下表现优异，但在输入图像数量增加时性能显著下降。解决方案的关键在于提出 SAIL-Recon，一种基于 Transformer 的前馈架构，通过引入视觉定位（visual localization）能力增强场景回归网络：首先从锚点图像子集构建神经场景表示，随后将回归网络微调为以该表示为条件来重建全部输入图像。该设计使得模型在保持高精度的同时具备良好的可扩展性，在 TUM-RGBD、CO3Dv2 和 Tanks & Temples 等基准上均达到最优性能。
链接: https://arxiv.org/abs/2508.17972
作者: Junyuan Deng,Heng Li,Tao Xie,Weiqiang Ren,Qian Zhang,Ping Tan,Xiaoyang Guo
机构: The Hong Kong University of Science and Technology (香港科技大学); Horizon Robotics (地平线机器人); Zhejiang University (浙江大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Scene regression methods, such as VGGT, solve the Structure-from-Motion (SfM) problem by directly regressing camera poses and 3D scene structures from input images. They demonstrate impressive performance in handling images under extreme viewpoint changes. However, these methods struggle to handle a large number of input images. To address this problem, we introduce SAIL-Recon, a feed-forward Transformer for large scale SfM, by augmenting the scene regression network with visual localization capabilities. Specifically, our method first computes a neural scene representation from a subset of anchor images. The regression network is then fine-tuned to reconstruct all input images conditioned on this neural scene representation. Comprehensive experiments show that our method not only scales efficiently to large-scale scenes, but also achieves state-of-the-art results on both camera pose estimation and novel view synthesis benchmarks, including TUM-RGBD, CO3Dv2, and Tanks Temples. We will publish our model and code. Code and models are publicly available at: this https URL sail-recon/.
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[CV-33] A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm
【速读】：该论文旨在解决自动驾驶车辆在内外部环境感知方面的局限性，特别是在驾驶员与乘客状态监测、车内舒适度优化以及外部场景语义分割精度不足等问题。其解决方案的关键在于构建一个融合多模态传感与人工智能技术的全栈式感知系统：内部监控通过多摄像头结合面部识别和大语言模型（Large Language Model, LLM）实现对驾乘人员行为的预测与交互；外部感知则采用基于激光雷达（LiDAR）的成本效益型语义分割方法，在低质量原始3D点云上实现高精度超分辨率重建，从而提升环境理解能力。该框架已在欧盟“地平线欧洲”计划AutoTRUST项目中集成并部署于实际电动车辆平台，经联合研究中心（JRC）测试验证，显著提升了模块化感知架构的整体性能与效率。
链接: https://arxiv.org/abs/2508.17969
作者: Alexandros Gkillas,Christos Anagnostopoulos,Nikos Piperigkos,Dimitris Tsiktsiris,Theofilos Christodoulou,Theofanis Siamatras,Dimitrios Triantafyllou,Christos Basdekis,Theoktisti Marinopoulou,Panagiotis Lepentsiotis,Elefterios Blitsis,Aggeliki Zacharaki,Nearchos Stylianidis,Leonidas Katelaris,Lamberto Salvan,Aris S. Lalos,Christos Laoudias,Antonios Lalas,Konstantinos Votis
机构: Industrial Systems Institute, ATHENA Research Center, Patras Science Park, Greece; AviSense.AI, Patras Science Park, Greece; Information Technologies Institute, CERTH, Greece; KIOS Research and Innovation Center of Excellence, University of Cyprus, Cyprus; ALKE Electric Vehicles, Padova, Italy
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This paper introduces a holistic perception system for internal and external monitoring of autonomous vehicles, with the aim of demonstrating a novel AI-leveraged self-adaptive framework of advanced vehicle technologies and solutions that optimize perception and experience on-board. Internal monitoring system relies on a multi-camera setup designed for predicting and identifying driver and occupant behavior through facial recognition, exploiting in addition a large language model as virtual assistant. Moreover, the in-cabin monitoring system includes AI-empowered smart sensors that measure air-quality and perform thermal comfort analysis for efficient on and off-boarding. On the other hand, external monitoring system perceives the surrounding environment of vehicle, through a LiDAR-based cost-efficient semantic segmentation approach, that performs highly accurate and efficient super-resolution on low-quality raw 3D point clouds. The holistic perception framework is developed in the context of EU’s Horizon Europe programm AutoTRUST, and has been integrated and deployed on a real electric vehicle provided by ALKE. Experimental validation and evaluation at the integration site of Joint Research Centre at Ispra, Italy, highlights increased performance and efficiency of the modular blocks of the proposed perception architecture.
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[CV-34] Beam Geometry and Input Dimensionality: Impact on Sparse-Sampling Artifact Correction for Clinical CT with U-Nets
【速读】：该论文旨在解决稀疏采样引起的条纹伪影（streak artifact）在临床CT图像中的校正问题，核心挑战在于如何有效利用体素上下文信息以提升去伪影模型的性能。其解决方案的关键在于通过不同维度的数据输入（2D、2.5D和3D）设计U-Net架构，系统评估几何构型（平行、扇形和锥形束）与输入维度对模型性能的影响，结果表明：无论何种扫描几何，基于轴向2D切片训练的U-Net在均方误差（MSE）和结构相似性指数（SSIM）上均优于2.5D和3D输入方式，说明在当前任务中，保留完整轴向空间信息并结合2D卷积特征提取机制是最有效的策略。
链接: https://arxiv.org/abs/2508.17961
作者: Tina Dorosti,Johannes Thalhammer,Sebastian Peterhansl,Daniela Pfeiffer,Franz Pfeiffer,Florian Schaff
机构: Technical University of Munich (慕尼黑工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
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Abstract:This study aims to investigate the effect of various beam geometries and dimensions of input data on the sparse-sampling streak artifact correction task with U-Nets for clinical CT scans as a means of incorporating the volumetric context into artifact reduction tasks to improve model performance. A total of 22 subjects were retrospectively selected (01.2016-12.2018) from the Technical University of Munich’s research hospital, TUM Klinikum rechts der Isar. Sparsely-sampled CT volumes were simulated with the Astra toolbox for parallel, fan, and cone beam geometries. 2048 views were taken as full-view scans. 2D and 3D U-Nets were trained and validated on 14, and tested on 8 subjects, respectively. For the dimensionality study, in addition to the 512x512 2D CT images, the CT scans were further pre-processed to generate a so-called ‘2.5D’, and 3D data: Each CT volume was divided into 64x64x64 voxel blocks. The 3D data refers to individual 64-voxel blocks. An axial, coronal, and sagittal cut through the center of each block resulted in three 64x64 2D patches that were rearranged as a single 64x64x3 image, proposed as 2.5D data. Model performance was assessed with the mean squared error (MSE) and structural similarity index measure (SSIM). For all geometries, the 2D U-Net trained on axial 2D slices results in the best MSE and SSIM values, outperforming the 2.5D and 3D input data dimensions.
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[CV-35] Generative Feature Imputing - A Technique for Error-resilient Semantic Communication
【速读】：该论文旨在解决生成式 AI (Generative AI) 驱动的语义通信（Semantic Communication, SemCom）在数字系统中部署时面临的鲁棒性问题，即传输错误可能导致语义关键内容失真，从而影响通信效率和感知质量。解决方案的关键在于提出了一种新颖的“生成特征插补”（generative feature imputing）框架，其核心包含三项关键技术：一是基于信道映射的空间误差集中分组策略，通过将特征失真空间聚集以降低后续处理复杂度并提升有效性；二是利用扩散模型（diffusion model）对因包丢失导致的缺失特征进行高效重建；三是设计语义感知功率分配机制，根据每个数据包的语义重要性实施不等错误保护（Unequal Error Protection, UEP）。实验表明，该框架在块衰落条件下显著优于传统方法（如 Deep Joint Source-Channel Coding, DJSCC 和 JPEG2000），实现了更高的语义准确率和更低的 Learned Perceptual Image Patch Similarity (LPIPS) 分数。
链接: https://arxiv.org/abs/2508.17957
作者: Jianhao Huang,Qunsong Zeng,Hongyang Du,Kaibin Huang
机构: The University of Hong Kong (香港大学)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Semantic communication (SemCom) has emerged as a promising paradigm for achieving unprecedented communication efficiency in sixth-generation (6G) networks by leveraging artificial intelligence (AI) to extract and transmit the underlying meanings of source data. However, deploying SemCom over digital systems presents new challenges, particularly in ensuring robustness against transmission errors that may distort semantically critical content. To address this issue, this paper proposes a novel framework, termed generative feature imputing, which comprises three key techniques. First, we introduce a spatial error concentration packetization strategy that spatially concentrates feature distortions by encoding feature elements based on their channel mappings, a property crucial for both the effectiveness and reduced complexity of the subsequent techniques. Second, building on this strategy, we propose a generative feature imputing method that utilizes a diffusion model to efficiently reconstruct missing features caused by packet losses. Finally, we develop a semantic-aware power allocation scheme that enables unequal error protection by allocating transmission power according to the semantic importance of each packet. Experimental results demonstrate that the proposed framework outperforms conventional approaches, such as Deep Joint Source-Channel Coding (DJSCC) and JPEG2000, under block fading conditions, achieving higher semantic accuracy and lower Learned Perceptual Image Patch Similarity (LPIPS) scores.
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[CV-36] See What You Need: Query-Aware Visual Intelligence through Reasoning -Perception Loops
【速读】：该论文旨在解决当前长视频问答系统中推理（reasoning）与视觉感知（perception）分离导致的两大问题：一是因过早进行视觉抽象造成的信息丢失，二是因对全视频内容进行冗余处理带来的计算效率低下。其核心挑战在于缺乏根据具体问题动态调整视觉信息提取的能力，即不同问题需要从同一视频中提取不同的视觉证据。解决方案的关键在于提出一种无需训练的框架CAVIA（Coordinated Attention and Vision for Inference Alignment），通过构建推理与感知的闭环协同机制，使推理过程持续引导视觉提取以填补信息缺口。其创新包括：(1) 分层推理驱动的精确定位；(2) 跨模态语义桥梁实现目标导向的视觉特征提取；(3) 基于置信度的迭代合成策略，从而实现了高效且精准的视频理解。
链接: https://arxiv.org/abs/2508.17932
作者: Zixuan Dong,Baoyun Peng,Yufei Wang,Lin Liu,Xinxin Dong,Yunlong Cao,Xiaodong Wang
机构: National University of Defense Technology (国防科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 14 pages, 6 figures
Abstract:Human video comprehension demonstrates dynamic coordination between reasoning and visual attention, adaptively focusing on query-relevant details. However, current long-form video question answering systems employ rigid pipelines that decouple reasoning from perception, leading to either information loss through premature visual abstraction or computational inefficiency through exhaustive processing. The core limitation lies in the inability to adapt visual extraction to specific reasoning requirements, different queries demand fundamentally different visual evidence from the same video content. In this work, we present CAVIA, a training-free framework that revolutionizes video understanding through reasoning, perception coordination. Unlike conventional approaches where visual processing operates independently of reasoning, CAVIA creates a closed-loop system where reasoning continuously guides visual extraction based on identified information gaps. CAVIA introduces three innovations: (1) hierarchical reasoning, guided localization to precise frames; (2) cross-modal semantic bridging for targeted extraction; (3) confidence-driven iterative synthesis. CAVIA achieves state-of-the-art performance on challenging benchmarks: EgoSchema (65.7%, +5.3%), NExT-QA (76.1%, +2.6%), and IntentQA (73.8%, +6.9%), demonstrating that dynamic reasoning-perception coordination provides a scalable paradigm for video understanding.
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[CV-37] Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets
【速读】：该论文旨在解决监督学习任务中数据集标签错误检测与质量提升的问题，尤其针对目标检测、语义分割和实例分割三类计算机视觉任务中存在的标注不准确问题。现有方法通常局限于单一任务或特定类型的标注（如边界框或像素级标注），且多为非学习驱动的方法，难以泛化到多种任务场景。本文的关键解决方案是提出一种统一的标签错误检测框架——通过主动注入各类标签错误并将其转化为实例分割问题进行建模，从而实现跨任务的标签错误识别。该方法的核心思想在于“通过制造错误来学习检测错误”，利用复合输入特征构建通用的检测机制，在多个数据集和基础模型上验证了其有效性，并在真实世界数据（如Cityscapes）中进一步证明了其泛化能力。
链接: https://arxiv.org/abs/2508.17930
作者: Sarina Penquitt,Tobias Riedlinger,Timo Heller,Markus Reischl,Matthias Rottmann
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Recently, detection of label errors and improvement of label quality in datasets for supervised learning tasks has become an increasingly important goal in both research and industry. The consequences of incorrectly annotated data include reduced model performance, biased benchmark results, and lower overall accuracy. Current state-of-the-art label error detection methods often focus on a single computer vision task and, consequently, a specific type of dataset, containing, for example, either bounding boxes or pixel-wise annotations. Furthermore, previous methods are not learning-based. In this work, we overcome this research gap. We present a unified method for detecting label errors in object detection, semantic segmentation, and instance segmentation datasets. In a nutshell, our approach - learning to detect label errors by making them - works as follows: we inject different kinds of label errors into the ground truth. Then, the detection of label errors, across all mentioned primary tasks, is framed as an instance segmentation problem based on a composite input. In our experiments, we compare the label error detection performance of our method with various baselines and state-of-the-art approaches of each task’s domain on simulated label errors across multiple tasks, datasets, and base models. This is complemented by a generalization study on real-world label errors. Additionally, we release 459 real label errors identified in the Cityscapes dataset and provide a benchmark for real label error detection in Cityscapes.
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[CV-38] Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation
【速读】：该论文旨在解决当前远程光电容积脉搏波描记术（remote PhotoPlethysmoGraphy, rPPG）研究中普遍存在的数据集局限性问题，包括数据规模小、隐私风险高以及采集条件多样性不足。其解决方案的关键在于构建了一个大规模、多视角、多模态的同步视频与生理信号数据集：包含600名受试者的3600条视频记录，覆盖静息和运动后状态，并使用多种消费级摄像头从不同角度采集；每条视频均配以100 Hz的光电容积脉搏波（PPG）信号及扩展健康指标（如心电图、动脉血压、血氧饱和度、呼吸频率等）。基于此数据集训练出的高效rPPG模型，在跨数据集场景下性能优于现有方法，为AI医疗助手的发展提供了高质量的数据基础与模型支持。
链接: https://arxiv.org/abs/2508.17924
作者: Konstantin Egorov,Stepan Botman,Pavel Blinov,Galina Zubkova,Anton Ivaschenko,Alexander Kolsanov,Andrey Savchenko
机构: Sber AI Lab(斯伯AI实验室); Samara State Medical University(萨马拉州立医科大学); ISP RAS Research Center for Trusted Artificial Intelligence(俄罗斯科学院信息与控制问题研究所可信人工智能研究中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACMMM 2025, Datasets track
Abstract:Progress in remote PhotoPlethysmoGraphy (rPPG) is limited by the critical issues of existing publicly available datasets: small size, privacy concerns with facial videos, and lack of diversity in conditions. The paper introduces a novel comprehensive large-scale multi-view video dataset for rPPG and health biomarkers estimation. Our dataset comprises 3600 synchronized video recordings from 600 subjects, captured under varied conditions (resting and post-exercise) using multiple consumer-grade cameras at different angles. To enable multimodal analysis of physiological states, each recording is paired with a 100 Hz PPG signal and extended health metrics, such as electrocardiogram, arterial blood pressure, biomarkers, temperature, oxygen saturation, respiratory rate, and stress level. Using this data, we train an efficient rPPG model and compare its quality with existing approaches in cross-dataset scenarios. The public release of our dataset and model should significantly speed up the progress in the development of AI medical assistants.
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[CV-39] Egocentric Instruction-oriented Affordance Prediction via Large Multimodal Model
【速读】：该论文旨在解决传统机器人操作中**可操作性（affordance）**预测忽视任务指令依赖性的问题，即同一物体在不同指令下可能对应不同的操作区域和方向，而现有方法往往忽略这一关键特性。解决方案的关键在于构建一个包含一万五千个物体-指令-可操作性三元组的新型数据集，并提出一种基于大语言模型（Large Multimodal Models, LMMs）的“搜索对抗验证”（search against verifiers）推理框架，通过迭代式预测与自我验证机制模拟人类推理过程，从而实现任务导向的精准可操作性预测。
链接: https://arxiv.org/abs/2508.17922
作者: Bokai Ji,Jie Gu,Xiaokang Ma,Chu Tang,Jingmin Chen,Guangxia Li
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Affordance is crucial for intelligent robots in the context of object manipulation. In this paper, we argue that affordance should be task-/instruction-dependent, which is overlooked by many previous works. That is, different instructions can lead to different manipulation regions and directions even for the same object. According to this observation, we present a new dataset comprising fifteen thousand object-instruction-affordance triplets. All scenes in the dataset are from an egocentric viewpoint, designed to approximate the perspective of a human-like robot. Furthermore, we investigate how to enable large multimodal models (LMMs) to serve as affordance predictors by implementing a ``search against verifiers’’ pipeline. An LMM is asked to progressively predict affordances, with the output at each step being verified by itself during the iterative process, imitating a reasoning process. Experiments show that our method not only unlocks new instruction-oriented affordance prediction capabilities, but also achieves outstanding performance broadly.
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[CV-40] EndoUFM: Utilizing Foundation Models for Monocular depth estimation of endoscopic images
【速读】：该论文旨在解决微创内镜手术中单目深度估计性能受限的问题，尤其是在光照变化复杂和纹理多样化的手术环境下，现有方法难以保持鲁棒性。其核心解决方案是提出EndoUFM框架，通过创新性地融合双基础模型（foundation models）来利用预训练先验知识提升深度估计精度；关键技术创新包括：1）引入随机向量低秩适配（Random Vector Low-Rank Adaptation, RVLoRA）实现自适应微调以增强模型对内镜域的适应能力；2）设计基于深度可分离卷积的残差模块（Res-DSC）以更有效地捕捉精细局部特征；3）采用掩码引导的平滑损失函数强制解剖组织结构内的深度一致性。这些改进使模型在多个内镜数据集上达到当前最优性能，同时保持轻量化结构，有助于提升术中空间感知与手术安全性。
链接: https://arxiv.org/abs/2508.17916
作者: Xinning Yao,Bo Liu,Bojian Li,Jingjing Wang,Jinghua Yue,Fugen Zhou
机构: Beihang University (北京航空航天大学); State Key Laboratory of High-Efficiency Reusable Aerospace Transportation Technology (高效可重复使用航天运输技术国家重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages
Abstract:Depth estimation is a foundational component for 3D reconstruction in minimally invasive endoscopic surgeries. However, existing monocular depth estimation techniques often exhibit limited performance to the varying illumination and complex textures of the surgical environment. While powerful visual foundation models offer a promising solution, their training on natural images leads to significant domain adaptability limitations and semantic perception deficiencies when applied to endoscopy. In this study, we introduce EndoUFM, an unsupervised monocular depth estimation framework that innovatively integrating dual foundation models for surgical scenes, which enhance the depth estimation performance by leveraging the powerful pre-learned priors. The framework features a novel adaptive fine-tuning strategy that incorporates Random Vector Low-Rank Adaptation (RVLoRA) to enhance model adaptability, and a Residual block based on Depthwise Separable Convolution (Res-DSC) to improve the capture of fine-grained local features. Furthermore, we design a mask-guided smoothness loss to enforce depth consistency within anatomical tissue structures. Extensive experiments on the SCARED, Hamlyn, SERV-CT, and EndoNeRF datasets confirm that our method achieves state-of-the-art performance while maintaining an efficient model size. This work contributes to augmenting surgeons’ spatial perception during minimally invasive procedures, thereby enhancing surgical precision and safety, with crucial implications for augmented reality and navigation systems.
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[CV-41] UniAPO: Unified Multimodal Automated Prompt Optimization
【速读】：该论文旨在解决多模态自动提示优化（Multimodal Automated Prompt Optimization, APO）中的两个核心挑战：一是视觉标记膨胀（visual token inflation），即长视觉标记序列占用大量上下文空间，导致反馈信号不足；二是缺乏过程级监督（process-level supervision），现有方法仅依赖结果级监督，忽视中间步骤的引导作用，限制了提示优化效果。解决方案的关键在于提出UniAPO框架，其采用受期望最大化（EM）算法启发的优化流程，将反馈建模与提示精炼解耦，从而提升优化的稳定性与目标导向性；同时引入短-长期记忆机制，利用历史反馈缓解上下文限制，通过历史提示提供方向性指导，实现高效且可迁移的多模态提示优化。
链接: https://arxiv.org/abs/2508.17890
作者: Qipeng Zhu,Yanzhe Chen,Huasong Zhong,Yan Li,Jie Chen,Zhixin Zhang,Junping Zhang,Zhenheng Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 5 figures
Abstract:Prompting is fundamental to unlocking the full potential of large language models. To automate and enhance this process, automatic prompt optimization (APO) has been developed, demonstrating effectiveness primarily in text-only input scenarios. However, extending existing APO methods to multimodal tasks, such as video-language generation introduces two core challenges: (i) visual token inflation, where long visual token sequences restrict context capacity and result in insufficient feedback signals; (ii) a lack of process-level supervision, as existing methods focus on outcome-level supervision and overlook intermediate supervision, limiting prompt optimization. We present UniAPO: Unified Multimodal Automated Prompt Optimization, the first framework tailored for multimodal APO. UniAPO adopts an EM-inspired optimization process that decouples feedback modeling and prompt refinement, making the optimization more stable and goal-driven. To further address the aforementioned challenges, we introduce a short-long term memory mechanism: historical feedback mitigates context limitations, while historical prompts provide directional guidance for effective prompt optimization. UniAPO achieves consistent gains across text, image, and video benchmarks, establishing a unified framework for efficient and transferable prompt optimization.
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[CV-42] ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement
【速读】：该论文旨在解决低光照图像增强（Low-Light Image Enhancement, LLIE）中因光照不均和结构信息丢失导致的图像质量下降问题，尤其针对现有方法在真实场景下难以同时优化亮度分布与保留语义结构的挑战。解决方案的关键在于提出一种基于Transformer架构的新型模型ISALux，其核心创新是引入了混合光照与语义感知多头自注意力模块（Hybrid Illumination and Semantics-Aware Multi-Headed Self-Attention, HISA-MSA），通过两个独立的自注意力子模块分别处理光照和语义特征，并实现双向增强以调节亮度分布并突出结构细节；此外，结合基于门控机制的专家混合（Mixture of Experts, MoE）前馈网络提升上下文建模能力，并采用低秩矩阵适配（LoRA）缓解因数据集光照模式差异带来的过拟合问题，从而显著提升模型在复杂真实场景中的泛化性能。
链接: https://arxiv.org/abs/2508.17885
作者: Raul Balmez,Alexandru Brateanu,Ciprian Orhei,Codruta Ancuti,Cosmin Ancuti
机构: University of Manchester (曼彻斯特大学); Politehnica University of Timisoara (蒂米什瓦拉理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We introduce ISALux, a novel transformer-based approach for Low-Light Image Enhancement (LLIE) that seamlessly integrates illumination and semantic priors. Our architecture includes an original self-attention block, Hybrid Illumination and Semantics-Aware Multi-Headed Self- Attention (HISA-MSA), which integrates illumination and semantic segmentation maps for en- hanced feature extraction. ISALux employs two self-attention modules to independently process illumination and semantic features, selectively enriching each other to regulate luminance and high- light structural variations in real-world scenarios. A Mixture of Experts (MoE)-based Feed-Forward Network (FFN) enhances contextual learning, with a gating mechanism conditionally activating the top K experts for specialized processing. To address overfitting in LLIE methods caused by distinct light patterns in benchmarking datasets, we enhance the HISA-MSA module with low-rank matrix adaptations (LoRA). Extensive qualitative and quantitative evaluations across multiple specialized datasets demonstrate that ISALux is competitive with state-of-the-art (SOTA) methods. Addition- ally, an ablation study highlights the contribution of each component in the proposed model. Code will be released upon publication.
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[CV-43] Edge-Enhanced Vision Transformer Framework for Accurate AI-Generated Image Detection
【速读】：该论文旨在解决生成式 AI (Generative AI) 生成的高保真图像在数字取证和内容认证中日益严峻的检测难题。传统深度学习方法依赖全局特征提取，易忽略细微结构不一致性且计算开销大。其解决方案的关键在于提出一种混合检测框架，融合微调后的视觉 Transformer (Vision Transformer, ViT) 与一种新颖的基于边缘的图像处理模块：该模块通过计算平滑前后边缘差分图的方差，利用 AI 生成图像通常纹理更平滑、边缘更弱、噪声更低的特性，作为 ViT 预测的后处理步骤，从而显著提升对细粒度结构线索的敏感性，同时保持高效计算性能。
链接: https://arxiv.org/abs/2508.17877
作者: Dabbrata Das,Mahshar Yahan,Md Tareq Zaman,Md Rishadul Bayesh
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 19 pages, 14 figures
Abstract:The rapid advancement of generative models has led to a growing prevalence of highly realistic AI-generated images, posing significant challenges for digital forensics and content authentication. Conventional detection methods mainly rely on deep learning models that extract global features, which often overlook subtle structural inconsistencies and demand substantial computational resources. To address these limitations, we propose a hybrid detection framework that combines a fine-tuned Vision Transformer (ViT) with a novel edge-based image processing module. The edge-based module computes variance from edge-difference maps generated before and after smoothing, exploiting the observation that AI-generated images typically exhibit smoother textures, weaker edges, and reduced noise compared to real images. When applied as a post-processing step on ViT predictions, this module enhances sensitivity to fine-grained structural cues while maintaining computational efficiency. Extensive experiments on the CIFAKE, Artistic, and Custom Curated datasets demonstrate that the proposed framework achieves superior detection performance across all benchmarks, attaining 97.75% accuracy and a 97.77% F1-score on CIFAKE, surpassing widely adopted state-of-the-art models. These results establish the proposed method as a lightweight, interpretable, and effective solution for both still images and video frames, making it highly suitable for real-world applications in automated content verification and digital forensics.
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[CV-44] Camera Pose Refinement via 3D Gaussian Splatting
【速读】：该论文旨在解决相机位姿精化（camera pose refinement）中的两个核心问题：一是现有方法依赖特定描述符或专用网络，导致在不同场景下需重新重建或完全重训练模型，缺乏通用性；二是部分基于特征相似性的方法因缺乏几何约束而导致精度不足。解决方案的关键在于提出一种基于3D高斯溅射（3D Gaussian Splatting, 3DGS）的新型框架GS-SMC，利用已有的3DGS模型直接渲染新视角，无需额外训练或微调即可适配多种场景，并通过迭代优化策略结合查询图像与多张渲染图像之间的极线几何约束（epipolar geometric constraints），实现位姿的高精度精化。该方法灵活兼容各类特征提取器和匹配器，显著提升了位姿估计的鲁棒性和准确性。
链接: https://arxiv.org/abs/2508.17876
作者: Lulu Hao,Lipu Zhou,Zhenzhong Wei,Xu Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Camera pose refinement aims at improving the accuracy of initial pose estimation for applications in 3D computer vision. Most refinement approaches rely on 2D-3D correspondences with specific descriptors or dedicated networks, requiring reconstructing the scene again for a different descriptor or fully retraining the network for each scene. Some recent methods instead infer pose from feature similarity, but their lack of geometry constraints results in less accuracy. To overcome these limitations, we propose a novel camera pose refinement framework leveraging 3D Gaussian Splatting (3DGS), referred to as GS-SMC. Given the widespread usage of 3DGS, our method can employ an existing 3DGS model to render novel views, providing a lightweight solution that can be directly applied to diverse scenes without additional training or fine-tuning. Specifically, we introduce an iterative optimization approach, which refines the camera pose using epipolar geometric constraints among the query and multiple rendered images. Our method allows flexibly choosing feature extractors and matchers to establish these constraints. Extensive empirical evaluations on the 7-Scenes and the Cambridge Landmarks datasets demonstrate that our method outperforms state-of-the-art camera pose refinement approaches, achieving 53.3% and 56.9% reductions in median translation and rotation errors on 7-Scenes, and 40.7% and 53.2% on Cambridge.
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[CV-45] AVAM: Universal Training-free Adaptive Visual Anchoring Embedded into Multimodal Large Language Model for Multi-image Question Answering
【速读】：该论文旨在解决多图像视觉问答（Multi Image Visual Question Answering, MVQA）中因图像数量增加而导致的视觉冗余问题，这种冗余会降低模型的准确性和效率。现有方法在压缩视觉标记时缺乏灵活性，且常产生离散的视觉片段，阻碍了大语言模型对图像的整体理解。解决方案的关键在于提出一种通用且简单的自适应视觉锚定（Adaptive Visual Anchoring）策略，该策略可无缝集成到现有多模态大语言模型（Multimodal Large Language Models, MLLMs）中，通过自适应压缩实现显著的性能提升；同时引入一种协同解码机制，平衡全局与压缩视觉输入的信息，从而优化整体表现。
链接: https://arxiv.org/abs/2508.17860
作者: Kang Zeng,Guojin Zhong,Jintao Cheng,Jin Yuan,Zhiyong Li
机构: Hunan University (湖南大学); South China Normal University (华南师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 14 pages, 5 figures
Abstract:The advancement of Multimodal Large Language Models (MLLMs) has driven significant progress in Visual Question Answering (VQA), evolving from Single to Multi Image VQA (MVQA). However, the increased number of images in MVQA inevitably introduces substantial visual redundancy that is irrelevant to question answering, negatively impacting both accuracy and efficiency. To address this issue, existing methods lack flexibility in controlling the number of compressed visual tokens and tend to produce discrete visual fragments, which hinder MLLMs’ ability to comprehend images holistically. In this paper, we propose a straightforward yet universal Adaptive Visual Anchoring strategy, which can be seamlessly integrated into existing MLLMs, offering significant accuracy improvements through adaptive compression. Meanwhile, to balance the results derived from both global and compressed visual input, we further introduce a novel collaborative decoding mechanism, enabling optimal performance. Extensive experiments validate the effectiveness of our method, demonstrating consistent performance improvements across various MLLMs. The code will be publicly available.
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[CV-46] VISA: Group-wise Visual Token Selection and Aggregation via Graph Summarization for Efficient MLLM s Inference
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在推理过程中因视觉令牌（visual tokens）数量过多而导致的效率低下问题。现有令牌剪枝方法通常会牺牲较多视觉信息以压缩令牌数量，难以兼顾性能与速度。其解决方案的关键在于提出一种名为分组式视觉令牌选择与聚合（Group-wise Visual Token Selection and Aggregation, VISA）的新方法：首先设计基于图结构的视觉令牌聚合（Visual Token Aggregation, VTA）模块，将每个视觉令牌视为节点并依据语义相似性构建图结构，从而将被移除令牌的信息聚合到保留令牌中；其次引入分组式令牌选择策略（Group-wise Token Selection, GTS），根据每组最终层文本令牌的引导，逐步划分保留与移除的视觉令牌，增强视觉信息提取过程的稳定性。该方法在保持高精度的同时显著提升推理效率。
链接: https://arxiv.org/abs/2508.17857
作者: Pengfei Jiang,Hanjun Li,Linglan Zhao,Fei Chao,Ke Yan,Shouhong Ding,Rongrong Ji
机构: Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University (厦门大学多媒体可信感知与高效计算教育部重点实验室); Tencent Youtu Lab (腾讯优图实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted by ACMMM 2025
Abstract:In this study, we introduce a novel method called group-wise \textbfVIsual token \textbfSelection and \textbfAggregation (VISA) to address the issue of inefficient inference stemming from excessive visual tokens in multimoal large language models (MLLMs). Compared with previous token pruning approaches, our method can preserve more visual information while compressing visual tokens. We first propose a graph-based visual token aggregation (VTA) module. VTA treats each visual token as a node, forming a graph based on semantic similarity among visual tokens. It then aggregates information from removed tokens into kept tokens based on this graph, producing a more compact visual token representation. Additionally, we introduce a group-wise token selection strategy (GTS) to divide visual tokens into kept and removed ones, guided by text tokens from the final layers of each group. This strategy progressively aggregates visual information, enhancing the stability of the visual information extraction process. We conduct comprehensive experiments on LLaVA-1.5, LLaVA-NeXT, and Video-LLaVA across various benchmarks to validate the efficacy of VISA. Our method consistently outperforms previous methods, achieving a superior trade-off between model performance and inference speed. The code is available at this https URL.
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[CV-47] Box-Level Class-Balanced Sampling for Active Object Detection ICIP2024
【速读】：该论文旨在解决基于深度学习的目标检测器在训练过程中依赖昂贵的边界框标注（bounding box annotation）的问题，尤其针对盒级主动学习（box-level active learning, AL）中因模型早期阶段对多数类表现良好而导致伪标签（pseudo labels）严重类别不平衡的挑战。其解决方案的关键在于：首先提出一种类别平衡采样策略，优先选择少数类样本进行人工标注，以提升最终训练数据（包括AL获取的真实标签与伪标签）的类别均衡性；其次引入一种任务感知的软伪标签策略（task-aware soft pseudo labeling），通过优化伪标签置信度分配提高其准确性，从而显著改善模型性能。实验表明，该方法在公开基准数据集上达到了当前最优效果。
链接: https://arxiv.org/abs/2508.17849
作者: Jingyi Liao,Xun Xu,Chuan-Sheng Foo,Lile Cai
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICIP2024
Abstract:Training deep object detectors demands expensive bounding box annotation. Active learning (AL) is a promising technique to alleviate the annotation burden. Performing AL at box-level for object detection, i.e., selecting the most informative boxes to label and supplementing the sparsely-labelled image with pseudo labels, has been shown to be more cost-effective than selecting and labelling the entire image. In box-level AL for object detection, we observe that models at early stage can only perform well on majority classes, making the pseudo labels severely class-imbalanced. We propose a class-balanced sampling strategy to select more objects from minority classes for labelling, so as to make the final training data, \ie, ground truth labels obtained by AL and pseudo labels, more class-balanced to train a better model. We also propose a task-aware soft pseudo labelling strategy to increase the accuracy of pseudo labels. We evaluate our method on public benchmarking datasets and show that our method achieves state-of-the-art performance.
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[CV-48] Alternating Training-based Label Smoothing Enhances Prompt Generalization
【速读】：该论文旨在解决提示调优（prompt tuning）在下游任务中泛化能力有限的问题，尤其是在使用标准标签平滑（label smoothing, LS）时反而削弱了提示调优的性能。其核心解决方案是提出一种基于交替训练的标签平滑方法（Alternating Training-based Label Smoothing, ATLaS），通过在标准one-hot标签与由LS生成的软标签之间交替训练来优化提示参数的学习过程。此外，论文引入两类高效的离线软标签——类别级软标签（Class-wise Soft Labels, CSL）和实例级软标签（Instance-wise Soft Labels, ISL），以提供类间或实例-类关系信息，从而增强提示调优的泛化能力。理论分析与大量实验表明，ATLaS方法显著提升了提示调优的性能，并且具备良好的兼容性，可无缝集成至主流提示调优框架中。
链接: https://arxiv.org/abs/2508.17846
作者: Yang Chen,Yanbin Wei,Ke Jin,Yi Kong,James Kwok,Yu Zhang
机构: 1. Tsinghua University (清华大学); 2. The Chinese University of Hong Kong (香港中文大学); 3. Alibaba Group (阿里巴巴集团)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in pre-trained vision-language models have demonstrated remarkable zero-shot generalization capabilities. To further enhance these models’ adaptability to various downstream tasks, prompt tuning has emerged as a parameter-efficient fine-tuning method. However, despite its efficiency, the generalization ability of prompt remains limited. In contrast, label smoothing (LS) has been widely recognized as an effective regularization technique that prevents models from becoming over-confident and improves their generalization. This inspires us to explore the integration of LS with prompt tuning. However, we have observed that the vanilla LS even weakens the generalization ability of prompt tuning. To address this issue, we propose the Alternating Training-based Label Smoothing (ATLaS) method, which alternately trains with standard one-hot labels and soft labels generated by LS to supervise the prompt tuning. Moreover, we introduce two types of efficient offline soft labels, including Class-wise Soft Labels (CSL) and Instance-wise Soft Labels (ISL), to provide inter-class or instance-class relationships for prompt tuning. The theoretical properties of the proposed ATLaS method are analyzed. Extensive experiments demonstrate that the proposed ATLaS method, combined with CSL and ISL, consistently enhances the generalization performance of prompt tuning. Moreover, the proposed ATLaS method exhibits high compatibility with prevalent prompt tuning methods, enabling seamless integration into existing methods.
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[CV-49] Diffusion-Based Data Augmentation for Medical Image Segmentation ICCV2025
【速读】：该论文旨在解决医学图像分割模型在罕见异常病灶（rare abnormalities）识别上的性能瓶颈问题，其核心挑战源于标注病理数据稀缺。解决方案的关键在于提出了一种名为DiffAug的新型框架，该框架融合了文本引导的扩散生成与自动分割验证机制：首先利用条件扩散模型（latent diffusion models）结合医学文本描述和空间掩码（spatial masks），通过图像修复（inpainting）方式在正常图像中合成异常区域；随后，采用基于潜在空间的分割网络对生成样本进行动态质量验证，确保异常定位准确且支持单步推理。该方法无需人工标注即可生成多样化异常类型，并通过空间精度过滤机制高效保障合成数据质量，最终在CVC-ClinicDB、Kvasir-SEG和REFUGE2三个基准上实现Dice指标提升8–10%，并显著降低小息肉和扁平病变等关键早期病灶的假阴性率（最高达28%）。
链接: https://arxiv.org/abs/2508.17844
作者: Maham Nazir,Muhammad Aqeel,Francesco Setti
机构: Beihang University (北京航空航天大学); University of Verona (维罗纳大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to CVAMD Workshop at ICCV 2025
Abstract:Medical image segmentation models struggle with rare abnormalities due to scarce annotated pathological data. We propose DiffAug a novel framework that combines textguided diffusion-based generation with automatic segmentation validation to address this challenge. Our proposed approach uses latent diffusion models conditioned on medical text descriptions and spatial masks to synthesize abnormalities via inpainting on normal images. Generated samples undergo dynamic quality validation through a latentspace segmentation network that ensures accurate localization while enabling single-step inference. The text prompts, derived from medical literature, guide the generation of diverse abnormality types without requiring manual annotation. Our validation mechanism filters synthetic samples based on spatial accuracy, maintaining quality while operating efficiently through direct latent estimation. Evaluated on three medical imaging benchmarks (CVC-ClinicDB, Kvasir-SEG, REFUGE2), our framework achieves state-of-the-art performance with 8-10% Dice improvements over baselines and reduces false negative rates by up to 28% for challenging cases like small polyps and flat lesions critical for early detection in screening applications.
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[CV-50] SCOUT: Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection IJCAI2025
【速读】：该论文旨在解决伪装目标检测（Camouflaged Object Detection, COD）领域中像素级标注成本高昂的问题。现有半监督COD方法虽利用少量标注数据与大量未标注数据降低标注负担，但对未标注数据的利用效率仍有待提升。为此，作者提出SCOUT框架，其核心在于两个关键模块：自适应数据增强与选择（Adaptive Data Augment and Selection, ADAS）模块通过对抗性增强和采样策略筛选高价值样本用于标注；文本融合模块（Text Fusion Module, TFM）则借助与伪装相关的知识及文本-视觉交互机制进一步挖掘所选样本的信息潜力。该方案显著提升了未标注数据的利用效率，在新构建的RefTextCOD数据集上实现了优于现有半监督方法的性能。
链接: https://arxiv.org/abs/2508.17843
作者: Weiqi Yan,Lvhai Chen,Shengchuan Zhang,Yan Zhang,Liujuan Cao
机构: Xiamen University (厦门大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by IJCAI 2025
Abstract:The difficulty of pixel-level annotation has significantly hindered the development of the Camouflaged Object Detection (COD) field. To save on annotation costs, previous works leverage the semi-supervised COD framework that relies on a small number of labeled data and a large volume of unlabeled data. We argue that there is still significant room for improvement in the effective utilization of unlabeled data. To this end, we introduce a Semi-supervised Camouflaged Object Detection by Utilizing Text and Adaptive Data Selection (SCOUT). It includes an Adaptive Data Augment and Selection (ADAS) module and a Text Fusion Module (TFM). The ADSA module selects valuable data for annotation through an adversarial augment and sampling strategy. The TFM module further leverages the selected valuable data by combining camouflage-related knowledge and text-visual interaction. To adapt to this work, we build a new dataset, namely RefTextCOD. Extensive experiments show that the proposed method surpasses previous semi-supervised methods in the COD field and achieves state-of-the-art performance. Our code will be released at this https URL.
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[CV-51] HLG: Comprehensive 3D Room Construction via Hierarchical Layout Generation
【速读】：该论文旨在解决现有3D室内场景生成方法在细粒度物体布局建模方面的不足，即虽然在大尺度家具摆放上已有进展，但难以实现精确的物体位置、朝向和空间关系建模，从而限制了虚拟现实、具身智能等应用中场景的真实感与可用性。其解决方案的关键在于提出Hierarchical Layout Generation (HLG) 方法，首次采用从粗到精的分层策略：通过垂直与水平解耦的细粒度布局对齐模块，将复杂室内场景分解为多层级结构；同时引入可训练的布局优化网络，自动修正物体错位、朝向错误及交叉重叠等问题，确保生成场景在结构上一致且物理上合理。
链接: https://arxiv.org/abs/2508.17832
作者: Xiping Wang,Yuxi Wang,Mengqi Zhou,Junsong Fan,Zhaoxiang Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Realistic 3D indoor scene generation is crucial for virtual reality, interior design, embodied intelligence, and scene understanding. While existing methods have made progress in coarse-scale furniture arrangement, they struggle to capture fine-grained object placements, limiting the realism and utility of generated environments. This gap hinders immersive virtual experiences and detailed scene comprehension for embodied AI applications. To address these issues, we propose Hierarchical Layout Generation (HLG), a novel method for fine-grained 3D scene generation. HLG is the first to adopt a coarse-to-fine hierarchical approach, refining scene layouts from large-scale furniture placement to intricate object arrangements. Specifically, our fine-grained layout alignment module constructs a hierarchical layout through vertical and horizontal decoupling, effectively decomposing complex 3D indoor scenes into multiple levels of granularity. Additionally, our trainable layout optimization network addresses placement issues, such as incorrect positioning, orientation errors, and object intersections, ensuring structurally coherent and physically plausible scene generation. We demonstrate the effectiveness of our approach through extensive experiments, showing superior performance in generating realistic indoor scenes compared to existing methods. This work advances the field of scene generation and opens new possibilities for applications requiring detailed 3D environments. We will release our code upon publication to encourage future research.
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[CV-52] A Contrastive Learning-Guided Confident Meta-learning for Zero Shot Anomaly Detection ICCV2025
【速读】：该论文旨在解决工业和医疗领域中因数据稀缺和标注成本高昂而导致的异常检测难题，尤其是在动态变化的制造和医疗场景下。其解决方案的关键在于提出一种名为CoZAD的零样本异常检测框架，该框架融合了软置信学习（soft confident learning）、元学习（meta-learning）与对比特征表示（contrastive feature representation）。具体而言，通过IQR-based阈值量化数据不确定性，并利用协方差正则化捕捉模型不确定性，在Model-Agnostic Meta-Learning（MAML）框架内实现对所有训练样本的置信度加权，从而保留边界信息并强化典型正常模式；同时，对比学习构建判别性特征空间，使正常模式形成紧凑簇，支持快速域适应。该方法无需依赖视觉-语言对齐或模型集成，显著提升了在纹理丰富数据集上的性能（如DTD-Synthetic达到99.2% I-AUROC），并在像素级定位任务中表现优异（如MVTec-AD达96.3% P-AUROC），适用于资源受限环境下的快速部署需求。
链接: https://arxiv.org/abs/2508.17827
作者: Muhammad Aqeel,Danijel Skocaj,Marco Cristani,Francesco Setti
机构: University of Verona (维罗纳大学); University of Ljubljana (卢布尔雅那大学); Qualyco S.r.l. (Qualyco有限责任公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to VISION Workshop at ICCV 2025
Abstract:Industrial and medical anomaly detection faces critical challenges from data scarcity and prohibitive annotation costs, particularly in evolving manufacturing and healthcare settings. To address this, we propose CoZAD, a novel zero-shot anomaly detection framework that integrates soft confident learning with meta-learning and contrastive feature representation. Unlike traditional confident learning that discards uncertain samples, our method assigns confidence-based weights to all training data, preserving boundary information while emphasizing prototypical normal patterns. The framework quantifies data uncertainty through IQR-based thresholding and model uncertainty via covariance based regularization within a Model-Agnostic Meta-Learning. Contrastive learning creates discriminative feature spaces where normal patterns form compact clusters, enabling rapid domain adaptation. Comprehensive evaluation across 10 datasets spanning industrial and medical domains demonstrates state-of-the-art performance, outperforming existing methods on 6 out of 7 industrial benchmarks with notable improvements on texture-rich datasets (99.2% I-AUROC on DTD-Synthetic, 97.2% on BTAD) and pixellevel localization (96.3% P-AUROC on MVTec-AD). The framework eliminates dependence on vision-language alignments or model ensembles, making it valuable for resourceconstrained environments requiring rapid deployment.
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[CV-53] mCoCo: Temporally Consistent Multi-modal Video Fusion with Visual-Semantic Collaboration ICCV2025
【速读】：该论文旨在解决现有多模态融合方法在视频融合任务中因采用静态帧级图像融合策略而忽略时间依赖性，导致帧间结果不一致的问题。其核心解决方案是提出首个显式引入时序建模与视觉-语义协同的视频融合框架，关键创新包括：（1）设计视觉-语义交互模块，利用Dinov2和VGG19进行针对性蒸馏，同步增强视觉与语义表征；（2）首次将视频退化增强任务融入融合流程，构建时序协同模块以利用时间依赖性恢复弱信息；（3）嵌入时序增强机制并设计时序损失函数，确保输出视频的时序一致性；（4）提出两个面向视频融合的新评估指标，专门用于量化生成视频的时序一致性。
链接: https://arxiv.org/abs/2508.17817
作者: Meiqi Gong,Hao Zhang,Xunpeng Yi,Linfeng Tang,Jiayi Ma
机构: Wuhan University (武汉大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ICCV 2025
Abstract:Existing multi-modal fusion methods typically apply static frame-based image fusion techniques directly to video fusion tasks, neglecting inherent temporal dependencies and leading to inconsistent results across frames. To address this limitation, we propose the first video fusion framework that explicitly incorporates temporal modeling with visual-semantic collaboration to simultaneously ensure visual fidelity, semantic accuracy, and temporal consistency. First, we introduce a visual-semantic interaction module consisting of a semantic branch and a visual branch, with Dinov2 and VGG19 employed for targeted distillation, allowing simultaneous enhancement of both the visual and semantic representations. Second, we pioneer integrate the video degradation enhancement task into the video fusion pipeline by constructing a temporal cooperative module, which leverages temporal dependencies to facilitate weak information recovery. Third, to ensure temporal consistency, we embed a temporal-enhanced mechanism into the network and devise a temporal loss to guide the optimization process. Finally, we introduce two innovative evaluation metrics tailored for video fusion, aimed at assessing the temporal consistency of the generated fused videos. Extensive experimental results on public video datasets demonstrate the superiority of our method. Our code is released at this https URL.
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[CV-54] UniSino: Physics-Driven Foundational Model for Universal CT Sinogram Standardization
【速读】：该论文旨在解决CT成像中原始数据采集阶段因欠采样（undersampling）和噪声导致的sinogram退化问题，这些问题会引发重建图像中的严重伪影和噪声，进而影响诊断准确性。传统校正方法依赖于手工设计的算法或固定的经验参数，难以在多种异质伪影类型间实现泛化。其解决方案的关键在于提出UniSino——一个面向CT sinogram标准化的通用基础模型（foundation model），该模型直接在投影域（projection domain）进行数据标准化，而非现有方法常用的图像域，从而显著提升对不同欠采样场景的泛化能力；同时，其训练框架融合了sinogram的物理特性，增强了跨多个子任务及四个基准数据集的鲁棒性表现。
链接: https://arxiv.org/abs/2508.17816
作者: Xingyu Ai,Shaoyu Wang,Zhiyuan Jia,Ao Xu,Hongming Shan,Jianhua Ma,Qiegen Liu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:During raw-data acquisition in CT imaging, diverse factors can degrade the collected sinograms, with undersampling and noise leading to severe artifacts and noise in reconstructed images and compromising diagnostic accuracy. Conventional correction methods rely on manually designed algorithms or fixed empirical parameters, but these approaches often lack generalizability across heterogeneous artifact types. To address these limitations, we propose UniSino, a foundation model for universal CT sinogram standardization. Unlike existing foundational models that operate in image domain, UniSino directly standardizes data in the projection domain, which enables stronger generalization across diverse undersampling scenarios. Its training framework incorporates the physical characteristics of sinograms, enhancing generalization and enabling robust performance across multiple subtasks spanning four benchmark datasets. Experimental results demonstrate thatUniSino achieves superior reconstruction quality both single and mixed undersampling case, demonstrating exceptional robustness and generalization in sinogram enhancement for CT imaging. The code is available at: this https URL.
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[CV-55] MeshSplat: Generalizable Sparse-View Surface Reconstruction via Gaussian Splatting
【速读】：该论文旨在解决稀疏视图（sparse-view）条件下表面重建（surface reconstruction）精度不足的问题，即在输入视角极其稀少时，现有方法难以恢复准确的场景几何结构。其解决方案的关键在于提出了一种基于高斯点绘（Gaussian Splatting, 2DGS）的通用化稀疏视图表面重建框架 MeshSplat，通过将 2DGS 作为桥梁，连接新视角合成与学习到的几何先验，并将这些先验迁移用于表面重建。具体而言，该方法引入一个前馈网络预测每张输入图像对应的像素对齐 2DGS，从而实现无需直接依赖 3D 地面真值监督的新视角图像合成；同时设计了加权 Chamfer 距离损失以优化深度图重建，尤其在输入视图重叠区域增强鲁棒性，并结合单目法估计的法向量引导 2DGS 的朝向对齐，显著提升了重建精度。
链接: https://arxiv.org/abs/2508.17811
作者: Hanzhi Chang,Ruijie Zhu,Wenjie Chang,Mulin Yu,Yanzhe Liang,Jiahao Lu,Zhuoyuan Li,Tianzhu Zhang
机构: University of Science and Technology of China (中国科学技术大学); Shanghai AI Laboratory (上海人工智能实验室)
类目: Graphics (cs.GR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 17 pages, 15 figures, 5 tables
Abstract:Surface reconstruction has been widely studied in computer vision and graphics. However, existing surface reconstruction works struggle to recover accurate scene geometry when the input views are extremely sparse. To address this issue, we propose MeshSplat, a generalizable sparse-view surface reconstruction framework via Gaussian Splatting. Our key idea is to leverage 2DGS as a bridge, which connects novel view synthesis to learned geometric priors and then transfers these priors to achieve surface reconstruction. Specifically, we incorporate a feed-forward network to predict per-view pixel-aligned 2DGS, which enables the network to synthesize novel view images and thus eliminates the need for direct 3D ground-truth supervision. To improve the accuracy of 2DGS position and orientation prediction, we propose a Weighted Chamfer Distance Loss to regularize the depth maps, especially in overlapping areas of input views, and also a normal prediction network to align the orientation of 2DGS with normal vectors predicted by a monocular normal estimator. Extensive experiments validate the effectiveness of our proposed improvement, demonstrating that our method achieves state-of-the-art performance in generalizable sparse-view mesh reconstruction tasks. Project Page: this https URL
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[CV-56] PoRe: Position-Reweighted Visual Token Pruning for Vision Language Models
【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）中因序列模型的“近期偏差”（recency bias）导致的视觉令牌（visual token）剪枝效果不佳的问题。具体而言，近期偏差使得模型对图像底部区域的视觉令牌赋予过高的注意力分数，从而在剪枝过程中保留了大量冗余信息，降低了效率与性能。解决方案的关键在于提出一种简单而有效的位置重加权机制（Position-reweighted Visual Token Pruning），通过根据视觉令牌在图像中的空间位置调整其注意力分数，从而校正由近期偏差引起的不均衡关注，实现更优的剪枝策略。该方法为即插即用方案，无需修改模型结构或额外训练即可显著提升现有剪枝框架的性能。
链接: https://arxiv.org/abs/2508.17807
作者: Kai Zhao,Wubang Yuan,Alex Lingyu Hung,Dan Zeng
机构: Shanghai University (上海大学); University of California, Los Angeles (加州大学洛杉矶分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Vision-Language Models (VLMs) typically process a significantly larger number of visual tokens compared to text tokens due to the inherent redundancy in visual signals. Visual token pruning is a promising direction to reduce the computational cost of VLMs by eliminating redundant visual tokens. The text-visual attention score is a widely adopted criterion for visual token pruning as it reflects the relevance of visual tokens to the text input. However, many sequence models exhibit a recency bias, where tokens appearing later in the sequence exert a disproportionately large influence on the model’s output. In VLMs, this bias manifests as inflated attention scores for tokens corresponding to the lower regions of the image, leading to suboptimal pruning that disproportionately retains tokens from the image bottom. In this paper, we present an extremely simple yet effective approach to alleviate the recency bias in visual token pruning. We propose a straightforward reweighting mechanism that adjusts the attention scores of visual tokens according to their spatial positions in the image. Our method, termed Position-reweighted Visual Token Pruning, is a plug-and-play solution that can be seamlessly incorporated into existing visual token pruning frameworks without any changes to the model architecture or extra training. Extensive experiments on LVLMs demonstrate that our method improves the performance of visual token pruning with minimal computational overhead.
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[CV-57] Sketchpose: Learning to Segment Cells with Partial Annotations
【速读】：该论文旨在解决当前细胞分割网络（如Cellpose、Stardist、HoverNet等）依赖全标注数据集进行训练所带来的局限性，这在生成训练集和迁移学习中尤为显著。其解决方案的关键在于提出一种仍基于距离图（distance map）预测的算法，但能够处理部分标注的目标对象，从而在减少标注成本的同时保持分割质量。该方法在节约时间和资源的前提下，适用于低资源学习（frugal learning）、迁移学习和常规学习场景，且已集成至用户友好的Napari插件中。
链接: https://arxiv.org/abs/2508.17798
作者: Clément Cazorla,Nathanaël Munier,Renaud Morin,Pierre Weiss
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) this https URL
Abstract:The most popular networks used for cell segmentation (e.g. Cellpose, Stardist, HoverNet,…) rely on a prediction of a distance map. It yields unprecedented accuracy but hinges on fully annotated datasets. This is a serious limitation to generate training sets and perform transfer learning. In this paper, we propose a method that still relies on the distance map and handles partially annotated objects. We evaluate the performance of the proposed approach in the contexts of frugal learning, transfer learning and regular learning on regular databases. Our experiments show that it can lead to substantial savings in time and resources without sacrificing segmentation quality. The proposed algorithm is embedded in a user-friendly Napari plugin.
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[CV-58] Robust Anomaly Detection in Industrial Environments via Meta-Learning ICCV2025
【速读】：该论文旨在解决工业场景中异常检测（anomaly detection）因训练数据存在标签噪声（label noise）而导致性能显著下降的问题。现有方法在真实世界环境中难以应对误标样本的干扰，而本文提出的RAD框架通过融合归一化流（Normalizing Flows）与模型无关元学习（Model-Agnostic Meta-Learning, MAML）来提升鲁棒性。其关键创新在于采用双层优化策略：元学习实现对不同噪声水平的快速适应，同时利用不确定性量化引导自适应L2正则化以维持模型稳定性；此外，结合预训练特征提取器进行多尺度特征处理，并借助归一化流精确的概率建模能力实现可靠的异常评分，从而在极端标签噪声（如50%误标）下仍保持高检测性能（I-AUROC > 86.8%）。
链接: https://arxiv.org/abs/2508.17789
作者: Muhammad Aqeel,Shakiba Sharifi,Marco Cristani,Francesco Setti
机构: University of Verona (维罗纳大学); Qualyco S.r.l. (Qualyco有限责任公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to VISION Workshop at ICCV 2025
Abstract:Anomaly detection is fundamental for ensuring quality control and operational efficiency in industrial environments, yet conventional approaches face significant challenges when training data contains mislabeled samples-a common occurrence in real-world scenarios. This paper presents RAD, a robust anomaly detection framework that integrates Normalizing Flows with Model-Agnostic Meta-Learning to address the critical challenge of label noise in industrial settings. Our approach employs a bi-level optimization strategy where meta-learning enables rapid adaptation to varying noise conditions, while uncertainty quantification guides adaptive L2 regularization to maintain model stability. The framework incorporates multiscale feature processing through pretrained feature extractors and leverages the precise likelihood estimation capabilities of Normalizing Flows for robust anomaly scoring. Comprehensive evaluation on MVTec-AD and KSDD2 datasets demonstrates superior performance, achieving I-AUROC scores of 95.4% and 94.6% respectively under clean conditions, while maintaining robust detection capabilities above 86.8% and 92.1% even when 50% of training samples are mislabeled. The results highlight RAD’s exceptional resilience to noisy training conditions and its ability to detect subtle anomalies across diverse industrial scenarios, making it a practical solution for real-world anomaly detection applications where perfect data curation is challenging.
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[CV-59] From Global to Local: Social Bias Transfer in CLIP
【速读】：该论文旨在解决预训练对比语言图像模型（CLIP）在下游任务中是否存在偏见迁移（bias transfer）的问题，即其在大规模数据预训练阶段习得的社会偏见和人类刻板印象是否会在视觉问答或图像描述等应用中传播。解决方案的关键在于通过系统的实证分析揭示偏见迁移的机制：首先发现偏见测量结果高度依赖于计算所用的数据子集；其次表明预训练模型与下游任务之间的偏见相关性缺乏一致性趋势；最终指出当前范式下不同CLIP模型在适应下游任务时，其表示空间趋于收敛，从而削弱了偏见迁移的可预测性和规律性。这一发现为未来偏见缓解策略的设计提供了重要参考。
链接: https://arxiv.org/abs/2508.17750
作者: Ryan Ramos,Yusuke Hirota,Yuta Nakashima,Noa Garcia
机构: The University of Osaka (大阪大学); NVIDIA (英伟达)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The recycling of contrastive language-image pre-trained (CLIP) models as backbones for a large number of downstream tasks calls for a thorough analysis of their transferability implications, especially their well-documented reproduction of social biases and human stereotypes. How do such biases, learned during pre-training, propagate to downstream applications like visual question answering or image captioning? Do they transfer at all? We investigate this phenomenon, referred to as bias transfer in prior literature, through a comprehensive empirical analysis. Firstly, we examine how pre-training bias varies between global and local views of data, finding that bias measurement is highly dependent on the subset of data on which it is computed. Secondly, we analyze correlations between biases in the pre-trained models and the downstream tasks across varying levels of pre-training bias, finding difficulty in discovering consistent trends in bias transfer. Finally, we explore why this inconsistency occurs, showing that under the current paradigm, representation spaces of different pre-trained CLIPs tend to converge when adapted for downstream tasks. We hope this work offers valuable insights into bias behavior and informs future research to promote better bias mitigation practices. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2508.17750 [cs.CV] (or arXiv:2508.17750v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.17750 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-60] DroneKey: Drone 3D Pose Estimation in Image Sequences using Gated Key-representation and Pose-adaptive Learning IROS2025
【速读】：该论文旨在解决无人机（drone）关键点检测与3D姿态估计难题，特别是由于螺旋桨（propeller）视觉相似性高且姿态多样导致的关键点难以准确识别的问题。解决方案的核心在于提出一个名为DroneKey的端到端框架，其关键技术包括：1）在2D关键点检测阶段，从每个Transformer编码器层中提取中间和紧凑两种关键表示，并通过门控求和进行最优融合；2）引入一种姿态自适应的马氏距离（pose-adaptive Mahalanobis distance）损失函数，以提升极端姿态下关键点预测的稳定性；3）设计改进的编码器结构，实现44 FPS的实时处理能力。实验表明，该方法在关键点检测上达到99.68% OKS AP，在3D姿态估计中角误差MAE为10.62°、位置RMSE为0.221m，显著优于现有方法。
链接: https://arxiv.org/abs/2508.17746
作者: Seo-Bin Hwang,Yeong-Jun Cho
机构: Chonnam National University (全南国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 10 figures, 6 tables, Accepted to IROS 2025 (to appear)
Abstract:Estimating the 3D pose of a drone is important for anti-drone systems, but existing methods struggle with the unique challenges of drone keypoint detection. Drone propellers serve as keypoints but are difficult to detect due to their high visual similarity and diversity of poses. To address these challenges, we propose DroneKey, a framework that combines a 2D keypoint detector and a 3D pose estimator specifically designed for drones. In the keypoint detection stage, we extract two key-representations (intermediate and compact) from each transformer encoder layer and optimally combine them using a gated sum. We also introduce a pose-adaptive Mahalanobis distance in the loss function to ensure stable keypoint predictions across extreme poses. We built new datasets of drone 2D keypoints and 3D pose to train and evaluate our method, which have been publicly released. Experiments show that our method achieves an AP of 99.68% (OKS) in keypoint detection, outperforming existing methods. Ablation studies confirm that the pose-adaptive Mahalanobis loss function improves keypoint prediction stability and accuracy. Additionally, improvements in the encoder design enable real-time processing at 44 FPS. For 3D pose estimation, our method achieved an MAE-angle of 10.62°, an RMSE of 0.221m, and an MAE-absolute of 0.076m, demonstrating high accuracy and reliability. The code and dataset are available at this https URL.
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[CV-61] CMFDNet: Cross-Mamba and Feature Discovery Network for Polyp Segmentation ICONIP2025
【速读】：该论文旨在解决结肠息肉（colonic polyp）分割中存在的三大挑战：息肉形状与尺寸差异大、息肉与邻近组织边界模糊，以及小尺寸息肉易被漏检。为此，作者提出了一种创新网络架构CMFDNet，其核心在于三个模块的协同设计：跨扫描解码模块（CMD module）通过引入交叉扫描机制减少边界模糊；多分支并行结构注意力模块（MSA module）增强对不同几何形态和尺度分布息肉的识别能力；特征依赖模块（FD module）建立解码器特征间的全局依赖关系，缓解小尺度息肉的欠检测问题。实验表明，CMFDNet在ETIS和ColonDB数据集上的mDice指标分别优于现有最优方法1.83%和1.55%，验证了其有效性。
链接: https://arxiv.org/abs/2508.17729
作者: Feng Jiang,Zongfei Zhang,Xin Xu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures, 2 tables. This paper has been accepted by ICONIP 2025 but not published
Abstract:Automated colonic polyp segmentation is crucial for assisting doctors in screening of precancerous polyps and diagnosis of colorectal neoplasms. Although existing methods have achieved promising results, polyp segmentation remains hindered by the following limitations,including: (1) significant variation in polyp shapes and sizes, (2) indistinct boundaries between polyps and adjacent tissues, and (3) small-sized polyps are easily overlooked during the segmentation process. Driven by these practical difficulties, an innovative architecture, CMFDNet, is proposed with the CMD module, MSA module, and FD module. The CMD module, serving as an innovative decoder, introduces a cross-scanning method to reduce blurry boundaries. The MSA module adopts a multi-branch parallel structure to enhance the recognition ability for polyps with diverse geometries and scale distributions. The FD module establishes dependencies among all decoder features to alleviate the under-detection of polyps with small-scale features. Experimental results show that CMFDNet outperforms six SOTA methods used for comparison, especially on ETIS and ColonDB datasets, where mDice scores exceed the best SOTA method by 1.83% and 1.55%, respectively.
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[CV-62] Segmentation and Classification of Pap Smear Images for Cervical Cancer Detection Using Deep Learning
【速读】：该论文旨在解决宫颈癌（cervical cancer）早期诊断中人工阅片效率低、易出错的问题，以提升筛查的准确性和效率。其解决方案的关键在于构建一个融合U-Net分割模块与分类模型的深度学习框架：首先利用U-Net对宫颈细胞图像进行精准分割，提取目标区域；随后在分割后的图像上训练分类模型，从而增强诊断性能。实验表明，该方法相较直接使用原始图像训练的模型，在精确率和F1分数上略有提升（分别提高约0.41%和1.30%），验证了分割步骤对特征提取的辅助作用，但整体对分类性能的提升有限，仍可作为病理医生临床辅助诊断的补充工具。
链接: https://arxiv.org/abs/2508.17728
作者: Nisreen Albzour,Sarah S. Lam
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Cervical cancer remains a significant global health concern and a leading cause of cancer-related deaths among women. Early detection through Pap smear tests is essential to reduce mortality rates; however, the manual examination is time consuming and prone to human error. This study proposes a deep learning framework that integrates U-Net for segmentation and a classification model to enhance diagnostic performance. The Herlev Pap Smear Dataset, a publicly available cervical cell dataset, was utilized for training and evaluation. The impact of segmentation on classification performance was evaluated by comparing the model trained on segmented images and another trained on non-segmented images. Experimental results showed that the use of segmented images marginally improved the model performance on precision (about 0.41 percent higher) and F1-score (about 1.30 percent higher), which suggests a slightly more balanced classification performance. While segmentation helps in feature extraction, the results showed that its impact on classification performance appears to be limited. The proposed framework offers a supplemental tool for clinical applications, which may aid pathologists in early diagnosis.
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[CV-63] Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework
【速读】：该论文旨在解决人类动作异常检测（Human Action Anomaly Detection, HAAD）中现有方法依赖于“一类一模型”范式所带来的可扩展性差和数据稀缺问题，尤其是在少样本（few-shot）场景下难以适应新类别或缺乏充足正常样本的情况。解决方案的关键在于提出一个统一框架，通过对比学习构建类别无关的表示空间，使测试样本能够与少量正常示例（即支持集，support set）进行比较以实现异常检测；同时引入基于扩散模型的生成式运动增强策略，提升跨类别的泛化能力和类内鲁棒性，这是首次将此类生成式增强方法专门用于改进对比学习在动作异常检测中的表现。
链接: https://arxiv.org/abs/2508.17726
作者: Koichiro Kamide,Shunsuke Sakai,Shun Maeda,Chunzhi Gu,Chao Zhang
机构: University of Toyama (富山大学); University of Fukui (福井大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Human Action Anomaly Detection (HAAD) aims to identify anomalous actions given only normal action data during training. Existing methods typically follow a one-model-per-category paradigm, requiring separate training for each action category and a large number of normal samples. These constraints hinder scalability and limit applicability in real-world scenarios, where data is often scarce or novel categories frequently appear. To address these limitations, we propose a unified framework for HAAD that is compatible with few-shot scenarios. Our method constructs a category-agnostic representation space via contrastive learning, enabling AD by comparing test samples with a given small set of normal examples (referred to as the support set). To improve inter-category generalization and intra-category robustness, we introduce a generative motion augmentation strategy harnessing a diffusion-based foundation model for creating diverse and realistic training samples. Notably, to the best of our knowledge, our work is the first to introduce such a strategy specifically tailored to enhance contrastive learning for action AD. Extensive experiments on the HumanAct12 dataset demonstrate the state-of-the-art effectiveness of our approach under both seen and unseen category settings, regarding training efficiency and model scalability for few-shot HAAD.
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[CV-64] Instant Preference Alignment for Text-to-Image Diffusion Models
【速读】：该论文旨在解决文本到图像（Text-to-Image, T2I）生成中偏好对齐（preference alignment）的实时性与训练-free 问题，即如何在不依赖额外训练的情况下，快速响应用户不断变化和细微的意图偏好。现有方法通常依赖静态预收集的偏好数据或微调策略，难以适应动态交互场景。解决方案的关键在于提出一个基于多模态大语言模型（Multimodal Large Language Model, MLLM）先验的训练-free框架，将任务解耦为两个核心模块：一是利用MLLM自动从参考图像中提取全局偏好信号，并通过结构化指令设计增强原始提示，实现更广泛且细粒度的偏好理解；二是引入全局关键词控制与局部区域感知的交叉注意力调制机制，在不增加训练成本的前提下引导扩散模型（diffusion model）生成，从而精确对齐图像的整体属性与局部细节。该框架支持多轮交互式优化，显著提升了生成结果在定量指标与人类评估中的表现。
链接: https://arxiv.org/abs/2508.17718
作者: Yang Li,Songlin Yang,Xiaoxuan Han,Wei Wang,Jing Dong,Yueming Lyu,Ziyu Xue
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 17 figures
Abstract:Text-to-image (T2I) generation has greatly enhanced creative expression, yet achieving preference-aligned generation in a real-time and training-free manner remains challenging. Previous methods often rely on static, pre-collected preferences or fine-tuning, limiting adaptability to evolving and nuanced user intents. In this paper, we highlight the need for instant preference-aligned T2I generation and propose a training-free framework grounded in multimodal large language model (MLLM) priors. Our framework decouples the task into two components: preference understanding and preference-guided generation. For preference understanding, we leverage MLLMs to automatically extract global preference signals from a reference image and enrich a given prompt using structured instruction design. Our approach supports broader and more fine-grained coverage of user preferences than existing methods. For preference-guided generation, we integrate global keyword-based control and local region-aware cross-attention modulation to steer the diffusion model without additional training, enabling precise alignment across both global attributes and local elements. The entire framework supports multi-round interactive refinement, facilitating real-time and context-aware image generation. Extensive experiments on the Viper dataset and our collected benchmark demonstrate that our method outperforms prior approaches in both quantitative metrics and human evaluations, and opens up new possibilities for dialog-based generation and MLLM-diffusion integration.
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[CV-65] F2RVLM: Boosting Fine-grained Frag ment Retrieval for Multi-Modal Long-form Dialogue with Vision Language Model
【速读】：该论文旨在解决传统对话检索任务在长对话中难以准确召回语义连贯片段的问题，尤其是在多模态（文本与图像）场景下，用户往往需要从跨多个话题的复杂对话流中精准定位相关且逻辑一致的内容。为应对这一挑战，作者提出了细粒度片段检索（Fine-grained Fragment Retrieval, FFR）任务，并构建了目前最长对话轮次的多模态对话检索数据集MLDR（平均25.45轮/对话），以及基于微信真实对话的测试集（平均75.38轮/对话）。解决方案的关键在于提出F2RVLM模型，其采用两阶段训练范式：首先通过监督微调注入片段级检索知识，其次利用GRPO强化学习结合多目标奖励（语义精度、相关性与上下文一致性）优化检索质量；同时引入难度感知课程采样策略，按模型预测难度排序训练样本并渐进式暴露更难实例，从而提升模型在长对话中的推理能力。
链接: https://arxiv.org/abs/2508.17714
作者: Hanbo Bi,Zhiqiang Yuan,Zexi Jia,Jiapei Zhang,Chongyang Li,Peixiang Luo,Ying Deng,Xiaoyue Duan,Jinchao Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Traditional dialogue retrieval aims to select the most appropriate utterance or image from recent dialogue history. However, they often fail to meet users’ actual needs for revisiting semantically coherent content scattered across long-form conversations. To fill this gap, we define the Fine-grained Fragment Retrieval (FFR) task, requiring models to locate query-relevant fragments, comprising both utterances and images, from multimodal long-form dialogues. As a foundation for FFR, we construct MLDR, the longest-turn multimodal dialogue retrieval dataset to date, averaging 25.45 turns per dialogue, with each naturally spanning three distinct topics. To evaluate generalization in real-world scenarios, we curate and annotate a WeChat-based test set comprising real-world multimodal dialogues with an average of 75.38 turns. Building on these resources, we explore existing generation-based Vision-Language Models (VLMs) on FFR and observe that they often retrieve incoherent utterance-image fragments. While optimized for generating responses from visual-textual inputs, these models lack explicit supervision to ensure semantic coherence within retrieved fragments. To this end, we propose F2RVLM, a generative retrieval model trained in a two-stage paradigm: (1) supervised fine-tuning to inject fragment-level retrieval knowledge, and (2) GRPO-based reinforcement learning with multi-objective rewards promoting semantic precision, relevance, and contextual coherence. To handle varying intra-fragment complexity, from locally dense to sparsely distributed, we introduce difficulty-aware curriculum sampling that ranks training instances by model-predicted difficulty and gradually exposes the model to harder samples. This boosts reasoning ability in long, multi-turn contexts. F2RVLM outperforms popular VLMs in both in-domain and real-domain settings, demonstrating superior retrieval performance.
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[CV-66] NGD: Neural Gradient Based Deformation for Monocular Garment Reconstruction
【速读】：该论文旨在解决单目视频中动态服装的高质量几何重建问题，该问题因服装复杂的动态特性与无约束场景而极具挑战性。现有基于神经渲染的方法虽能实现高质量几何重建，但隐式表示常因体渲染机制导致高频细节丢失；而模板变形方法虽使用显式几何，却因顶点位移引发伪影。本文提出神经梯度变形（Neural Gradient-based Deformation, NGD）方法，通过学习梯度场来实现更精确的形变建模，从而避免传统顶点位移带来的失真；同时引入自适应重网格策略以有效捕捉裙装褶皱和折痕等动态表面变化，并学习逐帧动态纹理贴图以保留光照与阴影的时变效果，显著提升重建质量。
链接: https://arxiv.org/abs/2508.17712
作者: Soham Dasgupta,Shanthika Naik,Preet Savalia,Sujay Kumar Ingle,Avinash Sharma
机构: Indian Institute of Technology Jodhpur (印度理工学院焦特布尔分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Dynamic garment reconstruction from monocular video is an important yet challenging task due to the complex dynamics and unconstrained nature of the garments. Recent advancements in neural rendering have enabled high-quality geometric reconstruction with image/video supervision. However, implicit representation methods that use volume rendering often provide smooth geometry and fail to model high-frequency details. While template reconstruction methods model explicit geometry, they use vertex displacement for deformation, which results in artifacts. Addressing these limitations, we propose NGD, a Neural Gradient-based Deformation method to reconstruct dynamically evolving textured garments from monocular videos. Additionally, we propose a novel adaptive remeshing strategy for modelling dynamically evolving surfaces like wrinkles and pleats of the skirt, leading to high-quality reconstruction. Finally, we learn dynamic texture maps to capture per-frame lighting and shadow effects. We provide extensive qualitative and quantitative evaluations to demonstrate significant improvements over existing SOTA methods and provide high-quality garment reconstructions.
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[CV-67] CATformer: Contrastive Adversarial Transformer for Image Super-Resolution
【速读】：该论文旨在解决低分辨率图像增强中的质量提升问题，即如何在保持计算效率的同时显著提高超分辨率（Super-resolution）重建的视觉质量和鲁棒性。其解决方案的关键在于提出了一种名为CATformer（Contrastive Adversarial Transformer）的新颖神经网络架构，该架构融合了扩散模型启发的特征精炼机制与对抗学习（Adversarial Learning）及对比学习（Contrastive Learning），通过双分支结构分别优化主干扩散Transformer对潜在表示的逐步细化能力与辅助分支对噪声的鲁棒性建模，并利用残差嵌套密集块（Residual-in-Residual Dense Blocks）进行高质量解码，从而在多个基准数据集上实现优于当前主流Transformer和扩散模型方法的性能表现。
链接: https://arxiv.org/abs/2508.17708
作者: Qinyi Tian,Spence Cox,Laura E. Dalton
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Super-resolution remains a promising technique to enhance the quality of low-resolution images. This study introduces CATformer (Contrastive Adversarial Transformer), a novel neural network integrating diffusion-inspired feature refinement with adversarial and contrastive learning. CATformer employs a dual-branch architecture combining a primary diffusion-inspired transformer, which progressively refines latent representations, with an auxiliary transformer branch designed to enhance robustness to noise through learned latent contrasts. These complementary representations are fused and decoded using deep Residual-in-Residual Dense Blocks for enhanced reconstruction quality. Extensive experiments on benchmark datasets demonstrate that CATformer outperforms recent transformer-based and diffusion-inspired methods both in efficiency and visual image quality. This work bridges the performance gap among transformer-, diffusion-, and GAN-based methods, laying a foundation for practical applications of diffusion-inspired transformers in super-resolution.
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[CV-68] Benchmarking Class Activation Map Methods for Explainable Brain Hemorrhage Classification on Hemorica Dataset
【速读】：该论文旨在解决医学影像中深度学习模型缺乏可解释性的问题，尤其是在脑出血诊断场景下，如何通过生成式AI（Generative AI）技术提升模型决策的透明度与临床可信度。其解决方案的关键在于构建一个基于类激活映射（Class Activation Mapping, CAM）的可解释性分析流程，利用九种先进的CAM算法对EfficientNetV2S网络不同阶段的特征图进行像素级定位与分割评估，并在Hemorica数据集上定量比较各方法的性能指标（如Dice系数、交并比IoU和像素重叠率）。结果表明，在EfficientNetV2S第5阶段使用AblationCAM可实现最优的像素级分割精度（Dice=0.57，IoU=0.40），即使模型仅以分类任务训练而未接受分割监督，也展现出强大的定位能力，从而为临床可信赖的AI辅助诊断提供了可复现的基准和方法论支持。
链接: https://arxiv.org/abs/2508.17699
作者: Z. Rafati,M. Hoseyni,J. Khoramdel,A. Nikoofard
机构: K. N. Toosi University of Technology (伊朗科学技术大学); Tarbiat Modares University (莫达雷斯大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
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Abstract:Explainable Artificial Intelligence (XAI) has become an essential component of medical imaging research, aiming to increase transparency and clinical trust in deep learning models. This study investigates brain hemorrhage diagnosis with a focus on explainability through Class Activation Mapping (CAM) techniques. A pipeline was developed to extract pixellevel segmentation and detection annotations from classification models using nine state-of-the-art CAM algorithms, applied across multiple network stages, and quantitatively evaluated on the Hemorica dataset, which uniquely provides both slice-level labels and high-quality segmentation masks. Metrics including Dice, IoU, and pixel-wise overlap were employed to benchmark CAM variants. Results show that the strongest localization performance occurred at stage 5 of EfficientNetV2S, with HiResCAM yielding the highest bounding-box alignment and AblationCAM achieving the best pixel-level Dice (0.57) and IoU (0.40), representing strong accuracy given that models were trained solely for classification without segmentation supervision. To the best of current knowledge, this is among the f irst works to quantitatively compare CAM methods for brain hemorrhage detection, establishing a reproducible benchmark and underscoring the potential of XAI-driven pipelines for clinically meaningful AI-assisted diagnosis.
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[CV-69] Language-Guided Temporal Token Pruning for Efficient VideoLLM Processing
【速读】：该论文旨在解决视觉语言模型（Vision Language Models, VLMs）在处理长视频时因注意力机制的二次复杂度而导致的计算效率低下问题。解决方案的关键在于提出一种模型无关的语言引导时间令牌剪枝（Language-Guided Temporal Token Pruning, LGTTP）方法，该方法利用查询中的时间线索自适应地剪枝视频令牌，在保持上下文连续性的同时显著降低计算开销；与均匀剪枝或关键帧选择不同，LGTTP 在时间相关片段中保留更高的令牌密度，从而在仅减少65%计算量的情况下维持97-99%的原始性能表现。
链接: https://arxiv.org/abs/2508.17686
作者: Yogesh Kumar
机构: Indian Institute of Technology Jodhpur (印度理工学院贾多普尔分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Vision Language Models (VLMs) struggle with long-form videos due to the quadratic complexity of attention mechanisms. We propose Language-Guided Temporal Token Pruning (LGTTP), which leverages temporal cues from queries to adaptively prune video tokens, preserving contextual continuity while reducing computational overhead. Unlike uniform pruning or keyframe selection, LGTTP retains higher token density in temporally relevant segments. Our model-agnostic framework integrates with TimeChat and LLaVA-Video, achieving a 65% reduction in computation while preserving 97-99% of the original performance. On QVHighlights, LGTTP improves HIT@1 by +9.5%, and on Charades-STA, it retains 99.6% of R@1. It excels on queries with explicit temporal markers and remains effective across general video understanding tasks.
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[CV-70] Robustness Feature Adapter for Efficient Adversarial Training ECAI2025
【速读】：该论文旨在解决对抗训练（Adversarial Training, AT）在大规模骨干模型中计算开销过大以及存在鲁棒过拟合（robust overfitting）的问题。解决方案的关键在于提出一种基于适配器（adapter-based）的方法，直接在特征空间中进行高效的对抗训练，通过消除鲁棒过拟合现象显著提升内循环收敛质量，从而在保证模型鲁棒性的同时大幅提高计算效率，并增强对未见攻击的泛化能力。
链接: https://arxiv.org/abs/2508.17680
作者: Quanwei Wu,Jun Guo,Wei Wang,Yi Wang
机构: Dongguan University of Technology (东莞理工学院); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: The paper has been accepted for presentation at ECAI 2025
Abstract:Adversarial training (AT) with projected gradient descent is the most popular method to improve model robustness under adversarial attacks. However, computational overheads become prohibitively large when AT is applied to large backbone models. AT is also known to have the issue of robust overfitting. This paper contributes to solving both problems simultaneously towards building more trustworthy foundation models. In particular, we propose a new adapter-based approach for efficient AT directly in the feature space. We show that the proposed adapter-based approach can improve the inner-loop convergence quality by eliminating robust overfitting. As a result, it significantly increases computational efficiency and improves model accuracy by generalizing adversarial robustness to unseen attacks. We demonstrate the effectiveness of the new adapter-based approach in different backbone architectures and in AT at scale.
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[CV-71] Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detection MICCAI2025
【速读】：该论文旨在解决可信医疗诊断系统中对分布外（out-of-distribution, OOD）样本的检测问题，即识别未知疾病以降低误诊风险。其核心挑战在于区分与已知疾病高度相似的未知疾病。解决方案的关键在于提出一种基于视觉-语言模型（vision-language model, VLM）的新型OOD检测框架，通过引入跨尺度视觉融合策略来整合多尺度视觉信息，从而增强医学图像的细节表征能力；同时设计了一种跨尺度硬伪OOD样本生成策略，以最大化提升OOD检测性能。实验结果表明，该方法在三个公开医学数据集上均优于现有技术。
链接: https://arxiv.org/abs/2508.17667
作者: Runhe Lai,Xinhua Lu,Kanghao Chen,Qichao Chen,Wei-Shi Zheng,Ruixuan Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 2 figures, Accepted by MICCAI2025
Abstract:In trustworthy medical diagnosis systems, integrating out-of-distribution (OOD) detection aims to identify unknown diseases in samples, thereby mitigating the risk of misdiagnosis. In this study, we propose a novel OOD detection framework based on vision-language models (VLMs), which integrates hierarchical visual information to cope with challenging unknown diseases that resemble known diseases. Specifically, a cross-scale visual fusion strategy is proposed to couple visual embeddings from multiple scales. This enriches the detailed representation of medical images and thus improves the discrimination of unknown diseases. Moreover, a cross-scale hard pseudo-OOD sample generation strategy is proposed to benefit OOD detection maximally. Experimental evaluations on three public medical datasets support that the proposed framework achieves superior OOD detection performance compared to existing methods. The source code is available at this https URL.
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[CV-72] M3-GloDets: Multi-Region and Multi-Scale Analysis of Fine-Grained Diseased Glomerular Detection
【速读】：该论文旨在解决数字肾病理学中对多种病变肾小球亚型检测准确率不足的问题，尤其是当前计算机视觉模型多集中于正常肾小球或全局硬化病例，而对复杂多变的疾病亚型识别能力有限。此外，关于最佳成像放大倍数与视场尺寸的选择仍存在争议，进一步影响了模型的泛化性能。其解决方案的关键在于提出M³-GloDet框架，系统性地评估不同检测模型在多种区域尺度、分辨率和类别下的表现，并通过实验验证发现：中等尺寸的图像块（patch size）能在上下文信息与计算效率之间取得最优平衡，适度的放大倍数有助于减少过拟合并提升模型泛化能力，从而为自动化肾小球检测策略的优化及临床工作流程提供可操作的洞见。
链接: https://arxiv.org/abs/2508.17666
作者: Tianyu Shi,Xinzi He,Kenji Ikemura,Mert R. Sabuncu,Yihe Yang,Ruining Deng
机构: Sichuan University (四川大学); Cornell Tech (康奈尔技术学院); Weill Cornell Medicine (威尔康奈尔医学院); Northwell Health (诺斯韦尔健康)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate detection of diseased glomeruli is fundamental to progress in renal pathology and underpins the delivery of reliable clinical diagnoses. Although recent advances in computer vision have produced increasingly sophisticated detection algorithms, the majority of research efforts have focused on normal glomeruli or instances of global sclerosis, leaving the wider spectrum of diseased glomerular subtypes comparatively understudied. This disparity is not without consequence; the nuanced and highly variable morphological characteristics that define these disease variants frequently elude even the most advanced computational models. Moreover, ongoing debate surrounds the choice of optimal imaging magnifications and region-of-view dimensions for fine-grained glomerular analysis, adding further complexity to the pursuit of accurate classification and robust segmentation. To bridge these gaps, we present M^3-GloDet, a systematic framework designed to enable thorough evaluation of detection models across a broad continuum of regions, scales, and classes. Within this framework, we evaluate both long-standing benchmark architectures and recently introduced state-of-the-art models that have achieved notable performance, using an experimental design that reflects the diversity of region-of-interest sizes and imaging resolutions encountered in routine digital renal pathology. As the results, we found that intermediate patch sizes offered the best balance between context and efficiency. Additionally, moderate magnifications enhanced generalization by reducing overfitting. Through systematic comparison of these approaches on a multi-class diseased glomerular dataset, our aim is to advance the understanding of model strengths and limitations, and to offer actionable insights for the refinement of automated detection strategies and clinical workflows in the digital pathology domain. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2508.17666 [cs.CV] (or arXiv:2508.17666v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.17666 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-73] Rethinking the Detail-Preserved Completion of Complex Tubular Structures based on Point Cloud: a Dataset and a Benchmark
【速读】：该论文旨在解决医学影像中复杂管状结构（如冠状动脉）因严重病变导致的分割不连续问题，此类断点会显著影响病灶检测与诊断准确性。其核心解决方案是提出一种基于点云的管状结构重连网络（TSRNet），关键创新在于：1）设计了细节保留型特征提取模块以增强局部结构信息；2）引入多密集细化策略实现精细化修复；3）采用全局到局部损失函数协同优化整体拓扑完整性与局部几何精度，从而在真实临床数据驱动的PC-CAC基准上实现优于现有方法的重建性能。
链接: https://arxiv.org/abs/2508.17658
作者: Yaolei Qi,Yikai Yang,Wenbo Peng,Shumei Miao,Yutao Hu,Guanyu Yang
机构: Southeast University (东南大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Complex tubular structures are essential in medical imaging and computer-assisted diagnosis, where their integrity enhances anatomical visualization and lesion detection. However, existing segmentation algorithms struggle with structural discontinuities, particularly in severe clinical cases such as coronary artery stenosis and vessel occlusions, which leads to undesired discontinuity and compromising downstream diagnostic accuracy. Therefore, it is imperative to reconnect discontinuous structures to ensure their completeness. In this study, we explore the tubular structure completion based on point cloud for the first time and establish a Point Cloud-based Coronary Artery Completion (PC-CAC) dataset, which is derived from real clinical data. This dataset provides a novel benchmark for tubular structure completion. Additionally, we propose TSRNet, a Tubular Structure Reconnection Network that integrates a detail-preservated feature extractor, a multiple dense refinement strategy, and a global-to-local loss function to ensure accurate reconnection while maintaining structural integrity. Comprehensive experiments on our PC-CAC and two additional public datasets (PC-ImageCAS and PC-PTR) demonstrate that our method consistently outperforms state-of-the-art approaches across multiple evaluation metrics, setting a new benchmark for point cloud-based tubular structure reconstruction. Our benchmark is available at this https URL.
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[CV-74] FloraSyntropy-Net: Scalable Deep Learning with Novel FloraSyntropy Archive for Large-Scale Plant Disease Diagnosis
【速读】：该论文旨在解决当前植物病害诊断AI模型在真实农业场景中泛化能力不足的问题，即现有模型通常局限于特定作物种类，难以准确识别多种栽培植物的病害。其解决方案的关键在于提出FloraSyntropy-Net框架，该框架融合了三项核心技术：一是基于Memetic Algorithm（MAO）的最优基础模型选择策略（选用DenseNet201），二是设计了一种新型Deep Block以增强特征表示能力，三是采用客户端克隆策略实现可扩展且隐私保护的联邦学习训练。通过这一架构，模型在自建的FloraSyntropy基准上达到96.38%的准确率，并在无关的多类虫害数据集上展现出卓越的迁移适应性（99.84%准确率），从而显著提升了农业AI应用的实际通用性和部署潜力。
链接: https://arxiv.org/abs/2508.17653
作者: Saif Ur Rehman Khan,Muhammad Nabeel Asim,Sebastian Vollmer,Andreas Dengel
机构: DFKI(德国弗劳恩霍夫计算机技术研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Early diagnosis of plant diseases is critical for global food safety, yet most AI solutions lack the generalization required for real-world agricultural diversity. These models are typically constrained to specific species, failing to perform accurately across the broad spectrum of cultivated plants. To address this gap, we first introduce the FloraSyntropy Archive, a large-scale dataset of 178,922 images across 35 plant species, annotated with 97 distinct disease classes. We establish a benchmark by evaluating numerous existing models on this archive, revealing a significant performance gap. We then propose FloraSyntropy-Net, a novel federated learning framework (FL) that integrates a Memetic Algorithm (MAO) for optimal base model selection (DenseNet201), a novel Deep Block for enhanced feature representation, and a client-cloning strategy for scalable, privacy-preserving training. FloraSyntropy-Net achieves a state-of-the-art accuracy of 96.38% on the FloraSyntropy benchmark. Crucially, to validate its generalization capability, we test the model on the unrelated multiclass Pest dataset, where it demonstrates exceptional adaptability, achieving 99.84% accuracy. This work provides not only a valuable new resource but also a robust and highly generalizable framework that advances the field towards practical, large-scale agricultural AI applications.
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[CV-75] Citizen Centered Climate Intelligence: Operationalizing Open Tree Data for Urban Cooling and Eco-Routing in Indian Cities
【速读】：该论文旨在解决城市气候韧性建设中数据应用碎片化与公民参与不足的问题，即如何将高分辨率环境数据转化为可操作的、嵌入市民日常生活的治理工具。其解决方案的关键在于构建一个以公民为中心的可扩展框架，通过三个相互关联的模块实现：（1）基于智能手机的传感工具结合AI分割技术提取树木结构参数；（2）利用卫星遥感地表温度数据开发“冷却效能”和“环境热缓解”两个新指标量化局部降温效果；（3）集成生态路由引擎，依据树密度、物种多样性和碳汇累积量生成静态环境质量评分，指导低碳出行路径。该框架形成闭环反馈机制，使公民成为数据生产者与受益者，推动开放数据从静态资源转变为动态共治平台，从而促进环境公平与气候适应性规划的本地化协同实践。
链接: https://arxiv.org/abs/2508.17648
作者: Kaushik Ravi,Andreas Brück
机构: 未知
类目: Computers and Society (cs.CY); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Forthcoming book chapter, currently under review for the “HackYourDistrict” initiative at TU Berlin. 20 pages, 9 figures, 1 table
Abstract:Urban climate resilience requires more than high-resolution data; it demands systems that embed data collection, interpretation, and action within the daily lives of citizens. This chapter presents a scalable, citizen-centric framework that reimagines environmental infrastructure through participatory sensing, open analytics, and prescriptive urban planning tools. Applied in Pune, India, the framework comprises three interlinked modules: (1) a smartphone-based measurement toolkit enhanced by AI segmentation to extract tree height, canopy diameter, and trunk girth; (2) a percentile-based model using satellite-derived Land Surface Temperature to calculate localized cooling through two new metrics, Cooling Efficacy and Ambient Heat Relief; and (3) an eco-routing engine that guides mobility using a Static Environmental Quality score, based on tree density, species diversity, and cumulative carbon sequestration. Together, these modules form a closed feedback loop where citizens generate actionable data and benefit from personalized, sustainable interventions. This framework transforms open data from a passive repository into an active platform for shared governance and environmental equity. In the face of growing ecological inequality and data centralization, this chapter presents a replicable model for citizen-driven urban intelligence, reframing planning as a co-produced, climate-resilient, and radically local practice.
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[CV-76] SEBVS: Synthetic Event-based Visual Servoing for Robot Navigation and Manipulation
【速读】：该论文旨在解决当前主流机器人仿真平台中缺乏事件相机（event camera）模拟支持的问题，从而阻碍了基于事件流的机器人感知策略在抓取与导航任务中的评估与开发。解决方案的关键在于提出一个开源、用户友好的v2e ROS包，可无缝地从RGB摄像头输入生成事件流，并集成至Gazebo仿真环境中，使事件驱动型机器人策略（Event-driven Robotic Policies, ERP）能够在真实场景条件（如运动模糊、遮挡和光照变化）下进行训练与测试。通过行为克隆方法训练基于Transformer的ERP模型，并与基于RGB的策略对比，实验表明事件引导策略在多种工况下均展现出显著优势，验证了事件相机在提升实时机器人感知性能方面的潜力。
链接: https://arxiv.org/abs/2508.17643
作者: Krishna Vinod,Prithvi Jai Ramesh,Pavan Kumar B N,Bharatesh Chakravarthi
机构: 未知
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Event cameras offer microsecond latency, high dynamic range, and low power consumption, making them ideal for real-time robotic perception under challenging conditions such as motion blur, occlusion, and illumination changes. However, despite their advantages, synthetic event-based vision remains largely unexplored in mainstream robotics simulators. This lack of simulation setup hinders the evaluation of event-driven approaches for robotic manipulation and navigation tasks. This work presents an open-source, user-friendly v2e robotics operating system (ROS) package for Gazebo simulation that enables seamless event stream generation from RGB camera feeds. The package is used to investigate event-based robotic policies (ERP) for real-time navigation and manipulation. Two representative scenarios are evaluated: (1) object following with a mobile robot and (2) object detection and grasping with a robotic manipulator. Transformer-based ERPs are trained by behavior cloning and compared to RGB-based counterparts under various operating conditions. Experimental results show that event-guided policies consistently deliver competitive advantages. The results highlight the potential of event-driven perception to improve real-time robotic navigation and manipulation, providing a foundation for broader integration of event cameras into robotic policy learning. The GitHub repo for the dataset and code: this https URL
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[CV-77] HyTver: A Novel Loss Function for Longitudinal Multiple Sclerosis Lesion Segmentation
【速读】：该论文旨在解决多发性硬化症（Multiple Sclerosis, MS）病灶在纵向影像中分割时面临的输入与输出数据不平衡问题，此类不平衡会导致模型性能下降，尤其是在小目标区域的分割精度上。现有方法通常采用Dice损失或交叉熵损失及其组合，但未能有效缓解不平衡带来的影响。论文提出了一种新颖的混合损失函数——HyTver，其关键在于通过融合多种损失机制，在提升Dice分数（达0.659）的同时，保持距离相关指标（如Hausdorff距离）与其他主流损失函数相当，且避免了因复杂超参数设计导致的计算开销或次优性能问题，从而实现了分割精度与稳定性之间的良好平衡。
链接: https://arxiv.org/abs/2508.17639
作者: Dayan Perera,Ting Fung Fung,Vishnu Monn
机构: Monash University, Malaysia Campus (蒙纳士大学马来西亚校区)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: to be published in APSIPA 2025
Abstract:Longitudinal Multiple Sclerosis Lesion Segmentation is a particularly challenging problem that involves both input and output imbalance in the data and segmentation. Therefore in order to develop models that are practical, one of the solutions is to develop better loss functions. Most models naively use either Dice loss or Cross-Entropy loss or their combination without too much consideration. However, one must select an appropriate loss function as the imbalance can be mitigated by selecting a proper loss function. In order to solve the imbalance problem, multiple loss functions were proposed that claimed to solve it. They come with problems of their own which include being too computationally complex due to hyperparameters as exponents or having detrimental performance in metrics other than region-based ones. We propose a novel hybrid loss called HyTver that achieves good segmentation performance while maintaining performance in other metrics. We achieve a Dice score of 0.659 while also ensuring that the distance-based metrics are comparable to other popular functions. In addition, we also evaluate the stability of the loss functions when used on a pre- trained model and perform extensive comparisons with other popular loss functions
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[CV-78] Few-Shot Pattern Detection via Template Matching and Regression ICCV2025
【速读】：该论文旨在解决**少样本模式检测（few-shot pattern detection）**问题，即从输入图像中检测出给定模式的所有实例，而该模式通常由少量示例（exemplars）表示。与以往聚焦于目标类别（object categories）的少样本目标计数与检测（FSCD）方法不同，本文提出了一种基于模板匹配与回归的简单但有效的检测器TMR（Template Matching and Regression），其关键在于：不再将目标示例压缩为空间上坍缩的原型（prototype）以避免结构信息丢失，而是通过经典模板匹配机制保留示例的空间布局，并在冻结主干网络基础上引入少量可学习的卷积或投影层进行精细化回归，从而实现对非目标类模式（non-object patterns）的有效定位。此方案在新构建的RPINE数据集及现有FSCD-147和FSCD-LVIS基准上均优于当前最优方法，且展现出良好的跨数据集泛化能力。
链接: https://arxiv.org/abs/2508.17636
作者: Eunchan Jo,Dahyun Kang,Sanghyun Kim,Yunseon Choi,Minsu Cho
机构: Pohang University of Science and Technology (POSTECH)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to ICCV 2025 (highlight)
Abstract:We address the problem of few-shot pattern detection, which aims to detect all instances of a given pattern, typically represented by a few exemplars, from an input image. Although similar problems have been studied in few-shot object counting and detection (FSCD), previous methods and their benchmarks have narrowed patterns of interest to object categories and often fail to localize non-object patterns. In this work, we propose a simple yet effective detector based on template matching and regression, dubbed TMR. While previous FSCD methods typically represent target exemplars as spatially collapsed prototypes and lose structural information, we revisit classic template matching and regression. It effectively preserves and leverages the spatial layout of exemplars through a minimalistic structure with a small number of learnable convolutional or projection layers on top of a frozen backbone We also introduce a new dataset, dubbed RPINE, which covers a wider range of patterns than existing object-centric datasets. Our method outperforms the state-of-the-art methods on the three benchmarks, RPINE, FSCD-147, and FSCD-LVIS, and demonstrates strong generalization in cross-dataset evaluation.
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[CV-79] Wound3DAssist: A Practical Framework for 3D Wound Assessment
【速读】：该论文旨在解决慢性伤口管理中临床评估依赖主观且耗时的手动记录方法的问题，尤其针对传统2D数字视频测量技术在视角失真、视场受限及无法获取伤口深度（特别是在解剖结构复杂或曲面区域）方面的局限性。解决方案的关键在于提出Wound3DAssist框架，该框架利用单目消费级智能手机视频实现高精度3D伤口建模，通过集成3D重建、伤口分割、组织分类与周围皮肤分析的模块化流程，支持非接触式、自动化的测量，具有视角无关性和对相机运动的鲁棒性，可在20分钟内完成完整评估，验证了其在真实临床场景中的可行性。
链接: https://arxiv.org/abs/2508.17635
作者: Remi Chierchia,Rodrigo Santa Cruz,Léo Lebrat,Yulia Arzhaeva,Mohammad Ali Armin,Jeremy Oorloff,Chuong Nguyen,Olivier Salvado,Clinton Fookes,David Ahmedt-Aristizabal
机构: Data61, CSIRO (澳大利亚联邦科学与工业研究组织数据61实验室); Queensland University of Technology (昆士兰科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documentation methods. Although 2D digital videometry frameworks aided the measurement process, these approaches struggle with perspective distortion, a limited field of view, and an inability to capture wound depth, especially in anatomically complex or curved regions. To overcome these limitations, we present Wound3DAssist, a practical framework for 3D wound assessment using monocular consumer-grade videos. Our framework generates accurate 3D models from short handheld smartphone video recordings, enabling non-contact, automatic measurements that are view-independent and robust to camera motion. We integrate 3D reconstruction, wound segmentation, tissue classification, and periwound analysis into a modular workflow. We evaluate Wound3DAssist across digital models with known geometry, silicone phantoms, and real patients. Results show that the framework supports high-quality wound bed visualization, millimeter-level accuracy, and reliable tissue composition analysis. Full assessments are completed in under 20 minutes, demonstrating feasibility for real-world clinical use.
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[CV-80] Finding Outliers in a Haystack: Anomaly Detection for Large Pointcloud Scenes
【速读】：该论文旨在解决室外场景点云数据中开放集分割（open-set segmentation）的问题，即在训练数据未覆盖的异常或未知对象出现时，模型仍能准确识别并区分已知类别与未知类别。其解决方案的关键在于结合了基于重构的方法与Mamba架构的优势：首先利用物体缺陷检测（object defect-detection）的研究成果构建重建驱动的分割机制，以有效识别超出训练分布的异常点；其次引入Mamba架构来高效建模长距离依赖关系并处理大规模点云数据，从而提升模型在复杂户外场景下的泛化能力和计算效率。该方法不仅在自研框架中表现优异，还能显著增强现有方法的性能。
链接: https://arxiv.org/abs/2508.17634
作者: Ryan Faulkner,Ian Reid,Simon Ratcliffe,Tat-Jun Chin
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: arXiv Preprint, paper has since been accepted to ACPR 2025
Abstract:LiDAR scanning in outdoor scenes acquires accurate distance measurements over wide areas, producing large-scale point clouds. Application examples for this data include robotics, automotive vehicles, and land surveillance. During such applications, outlier objects from outside the training data will inevitably appear. Our research contributes a novel approach to open-set segmentation, leveraging the learnings of object defect-detection research. We also draw on the Mamba architecture’s strong performance in utilising long-range dependencies and scalability to large data. Combining both, we create a reconstruction based approach for the task of outdoor scene open-set segmentation. We show that our approach improves performance not only when applied to our our own open-set segmentation method, but also when applied to existing methods. Furthermore we contribute a Mamba based architecture which is competitive with existing voxel-convolution based methods on challenging, large-scale pointclouds.
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[CV-81] Improving Interpretability in Alzheimers Prediction via Joint Learning of ADAS-Cog Scores
【速读】：该论文旨在解决阿尔茨海默病（Alzheimer’s disease, AD）临床评分预测中忽视子分数（sub-scores）信息的问题，尤其是如何利用ADAS-Cog量表的13项子分数与基线MRI及纵向临床数据联合建模，以提升全局评分预测准确性并增强模型的可解释性。其解决方案的关键在于提出一种多任务学习（multi-task learning, MTL）框架，融合Vision Transformer（ViT）和Swin Transformer提取的影像特征与纵向临床输入，通过子分数级分析识别对全局评分贡献最大的关键子项（如Q1词 recall、Q4延迟回忆和Q8词识别），同时揭示了由于临床特征主导导致模型对复杂影像特征学习不充分的问题，从而强调了改进多模态融合策略与自适应损失加权机制的重要性，以实现更平衡、稳健且具有临床意义的AD进展预测模型。
链接: https://arxiv.org/abs/2508.17619
作者: Nur Amirah Abd Hamid,Mohd Shahrizal Rusli,Muhammad Thaqif Iman Mohd Taufek,Mohd Ibrahim Shapiai,Daphne Teck Ching Lai
机构: Universiti Brunei Darussalam (文莱达鲁萨兰大学); Universiti Teknologi Malaysia (马来西亚理工大学); Malaysia-Japan International Institute of Technology (马来西亚-日本国际技术学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Accurate prediction of clinical scores is critical for early detection and prognosis of Alzheimers disease (AD). While existing approaches primarily focus on forecasting the ADAS-Cog global score, they often overlook the predictive value of its sub-scores (13 items), which capture domain-specific cognitive decline. In this study, we propose a multi task learning (MTL) framework that jointly predicts the global ADAS-Cog score and its sub-scores (13 items) at Month 24 using baseline MRI and longitudinal clinical scores from baseline and Month 6. The main goal is to examine how each sub scores particularly those associated with MRI features contribute to the prediction of the global score, an aspect largely neglected in prior MTL studies. We employ Vision Transformer (ViT) and Swin Transformer architectures to extract imaging features, which are fused with longitudinal clinical inputs to model cognitive progression. Our results show that incorporating sub-score learning improves global score prediction. Subscore level analysis reveals that a small subset especially Q1 (Word Recall), Q4 (Delayed Recall), and Q8 (Word Recognition) consistently dominates the predicted global score. However, some of these influential sub-scores exhibit high prediction errors, pointing to model instability. Further analysis suggests that this is caused by clinical feature dominance, where the model prioritizes easily predictable clinical scores over more complex MRI derived features. These findings emphasize the need for improved multimodal fusion and adaptive loss weighting to achieve more balanced learning. Our study demonstrates the value of sub score informed modeling and provides insights into building more interpretable and clinically robust AD prediction frameworks. (Github repo provided)
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[CV-82] JCo-MVTON: Jointly Controllable Multi-Modal Diffusion Transformer for Mask-Free Virtual Try-on
【速读】：该论文旨在解决虚拟试衣（Virtual Try-On）系统长期存在的三大问题：对人工人体掩码的高度依赖、难以实现服装属性的细粒度控制，以及在真实场景（in-the-wild）中的泛化能力差。其解决方案的关键在于提出JCo-MVTON框架，该框架基于多模态扩散Transformer（Multi-Modal Diffusion Transformer, MM-DiT），通过在自注意力层中引入专用条件路径，将参考人物图像和目标服装图像作为控制信号融合到去噪过程中，并结合优化的位置编码与注意力掩码，实现精确的空间对齐与服装-人体融合。此外，为缓解数据稀缺与质量不足问题，作者设计了一种双向生成策略构建合成数据集，包括基于掩码的生成模型与自监督训练的“试脱”模型（Try-Off），并通过人工精修提升视觉保真度与多样性，从而显著提升模型在公开基准（如DressCode）上的性能及真实场景下的泛化能力。
链接: https://arxiv.org/abs/2508.17614
作者: Aowen Wang,Wei Li,Hao Luo,Mengxing Ao,Chenyu Zhu,Xinyang Li,Fan Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Virtual try-on systems have long been hindered by heavy reliance on human body masks, limited fine-grained control over garment attributes, and poor generalization to real-world, in-the-wild scenarios. In this paper, we propose JCo-MVTON (Jointly Controllable Multi-Modal Diffusion Transformer for Mask-Free Virtual Try-On), a novel framework that overcomes these limitations by integrating diffusion-based image generation with multi-modal conditional fusion. Built upon a Multi-Modal Diffusion Transformer (MM-DiT) backbone, our approach directly incorporates diverse control signals – such as the reference person image and the target garment image – into the denoising process through dedicated conditional pathways that fuse features within the self-attention layers. This fusion is further enhanced with refined positional encodings and attention masks, enabling precise spatial alignment and improved garment-person integration. To address data scarcity and quality, we introduce a bidirectional generation strategy for dataset construction: one pipeline uses a mask-based model to generate realistic reference images, while a symmetric ``Try-Off’’ model, trained in a self-supervised manner, recovers the corresponding garment images. The synthesized dataset undergoes rigorous manual curation, allowing iterative improvement in visual fidelity and diversity. Experiments demonstrate that JCo-MVTON achieves state-of-the-art performance on public benchmarks including DressCode, significantly outperforming existing methods in both quantitative metrics and human evaluations. Moreover, it shows strong generalization in real-world applications, surpassing commercial systems.
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[CV-83] A Weighted Vision Transformer-Based Multi-Task Learning Framework for Predicting ADAS-Cog Scores
【速读】：该论文旨在解决阿尔茨海默病（Alzheimer’s Disease, AD）预后建模中忽视ADAS-Cog量表13个子评分预测价值的问题，这些子评分反映不同的认知领域，对全局评分具有差异化影响。现有方法通常仅关注全局评分预测，忽略了子评分在临床意义和模型训练中的重要性。解决方案的关键在于提出一种基于视觉Transformer（Vision Transformer, ViT）的多任务学习（Multi-Task Learning, MTL）框架，通过为不同子评分设计分组依赖的损失权重策略，引导模型聚焦于更具判别力的认知域特征。实验表明，针对不同人群（如轻度认知障碍MCI与正常对照CN）采用差异化的加权策略可显著提升预测准确性和模型可解释性，优于统一权重设定，从而实现更灵活、可靠的MRI驱动的AD预后分析。
链接: https://arxiv.org/abs/2508.17613
作者: Nur Amirah Abd Hamid,Mohd Ibrahim Shapiai,Daphne Teck Ching Lai
机构: Universiti Brunei Darussalam (文莱达鲁萨兰大学); Universiti Teknologi Malaysia (马来西亚理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Prognostic modeling is essential for forecasting future clinical scores and enabling early detection of Alzheimers disease (AD). While most existing methods focus on predicting the ADAS-Cog global score, they often overlook the predictive value of its 13 sub-scores, which reflect distinct cognitive domains. Some sub-scores may exert greater influence on determining global scores. Assigning higher loss weights to these clinically meaningful sub-scores can guide the model to focus on more relevant cognitive domains, enhancing both predictive accuracy and interpretability. In this study, we propose a weighted Vision Transformer (ViT)-based multi-task learning (MTL) framework to jointly predict the ADAS-Cog global score using baseline MRI scans and its 13 sub-scores at Month 24. Our framework integrates ViT as a feature extractor and systematically investigates the impact of sub-score-specific loss weighting on model performance. Results show that our proposed weighting strategies are group-dependent: strong weighting improves performance for MCI subjects with more heterogeneous MRI patterns, while moderate weighting is more effective for CN subjects with lower variability. Our findings suggest that uniform weighting underutilizes key sub-scores and limits generalization. The proposed framework offers a flexible, interpretable approach to AD prognosis using end-to-end MRI-based learning. (Github repo link will be provided after review)
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[CV-84] HotSpotter - Patterned Species Instance Recognition
【速读】：该论文旨在解决个体动物识别（individual animal identification）问题，即在已标注数据库中快速准确地匹配新捕获图像中的目标动物个体。解决方案的关键在于提出一种名为HotSpotter的算法，其核心思想是通过提取和匹配图像中的关键点（keypoints），也称为“热点”（hotspots），实现高效识别。该方法包含两种策略：第一种为逐个比对数据库图像并独立评分排序；第二种则利用最近邻搜索技术，结合局部朴素贝叶斯最近邻（Local Naive Bayes Nearest Neighbor）的竞争力评分机制，显著提升检索效率与准确性。实验表明，该方法在超过1000张图像的数据库上实现了优于已有方法的匹配精度，且单次查询可在数秒内完成。
链接: https://arxiv.org/abs/2508.17605
作者: Jonathan P. Crall,Charles V. Stewart,Tanya Y. Berger-Wolf,Daniel I. Rubenstein,Siva R. Sundaresan
机构: Rensselaer Polytechnic Institute (伦斯勒理工学院); University of Illinois-Chicago (伊利诺伊大学芝加哥分校); Princeton University (普林斯顿大学); Denver Zoological Foundation (丹佛动物园基金会)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Original matlab code: this https URL , Python port: this https URL
Abstract:We present HotSpotter, a fast, accurate algorithm for identifying individual animals against a labeled database. It is not species specific and has been applied to Grevy’s and plains zebras, giraffes, leopards, and lionfish. We describe two approaches, both based on extracting and matching keypoints or “hotspots”. The first tests each new query image sequentially against each database image, generating a score for each database image in isolation, and ranking the results. The second, building on recent techniques for instance recognition, matches the query image against the database using a fast nearest neighbor search. It uses a competitive scoring mechanism derived from the Local Naive Bayes Nearest Neighbor algorithm recently proposed for category recognition. We demonstrate results on databases of more than 1000 images, producing more accurate matches than published methods and matching each query image in just a few seconds.
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[CV-85] GWM: Towards Scalable Gaussian World Models for Robotic Manipulation ICCV2025
【速读】：该论文旨在解决当前基于图像的世界模型在机器人操作任务中缺乏鲁棒几何信息的问题，即现有方法难以实现对三维世界的一致性空间与物理理解，即便是在大规模互联网视频数据上预训练后依然存在局限。其解决方案的关键在于提出一种新型世界模型——高斯世界模型（Gaussian World Model, GWM），通过推理高斯基元（Gaussian primitives）在机器人动作作用下的传播来重建未来状态；核心组件为结合3D变分自编码器的潜在扩散Transformer（Latent Diffusion Transformer, DiT），支持基于高斯点绘图（Gaussian Splatting）的细粒度场景级未来状态重建，从而提升视觉表征质量并支撑模型基础强化学习，实验证明该方法能精确预测多样动作条件下的未来场景，并显著优于现有最先进策略。
链接: https://arxiv.org/abs/2508.17600
作者: Guanxing Lu,Baoxiong Jia,Puhao Li,Yixin Chen,Ziwei Wang,Yansong Tang,Siyuan Huang
机构: Tsinghua University (清华大学); State Key Laboratory of General Artificial Intelligence, BIGAI; School of Electrical and Electronic Engineering, Nanyang Technological University (南洋理工大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Published at ICCV 2025. Project page: this https URL
Abstract:Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have shown prior success, but lack robust geometric information that requires consistent spatial and physical understanding of the three-dimensional world, even pre-trained on internet-scale video sources. To this end, we propose a novel branch of world model named Gaussian World Model (GWM) for robotic manipulation, which reconstructs the future state by inferring the propagation of Gaussian primitives under the effect of robot actions. At its core is a latent Diffusion Transformer (DiT) combined with a 3D variational autoencoder, enabling fine-grained scene-level future state reconstruction with Gaussian Splatting. GWM can not only enhance the visual representation for imitation learning agent by self-supervised future prediction training, but can serve as a neural simulator that supports model-based reinforcement learning. Both simulated and real-world experiments depict that GWM can precisely predict future scenes conditioned on diverse robot actions, and can be further utilized to train policies that outperform the state-of-the-art by impressive margins, showcasing the initial data scaling potential of 3D world model.
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[CV-86] nyGiantVLM: A Lightweight Vision-Language Architecture for Spatial Reasoning under Resource Constraints ICCV
【速读】：该论文旨在解决仓库级环境中细粒度空间关系推理的问题，现有视觉语言模型（Vision-Language Models, VLMs）在理解三维布局、物体排列及多模态线索方面存在局限。其解决方案的关键在于提出一个轻量且模块化的两阶段框架TinyGiantVLM：首先利用预训练视觉骨干网络从RGB和深度模态中提取全局与区域级特征；其次引入Mixture-of-Experts（MoE）融合模块，动态整合空间表征以支持下游推理任务并提升收敛性，从而有效应对高模态输入复杂性和多样化问题类型。
链接: https://arxiv.org/abs/2508.17595
作者: Vinh-Thuan Ly,Hoang M. Truong,Xuan-Huong Nguyen
机构: University of Science, VNU-HCM, Ho Chi Minh City, Vietnam (胡志明市国家大学自然科学大学); Vietnam National University, Ho Chi Minh City, Vietnam (胡志明市国家大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for presentation at the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025
Abstract:Reasoning about fine-grained spatial relationships in warehouse-scale environments poses a significant challenge for existing vision-language models (VLMs), which often struggle to comprehend 3D layouts, object arrangements, and multimodal cues in real-world industrial settings. In this paper, we present TinyGiantVLM, a lightweight and modular two-stage framework designed for physical spatial reasoning, distinguishing itself from traditional geographic reasoning in complex logistics scenes. Our approach encodes both global and region-level features from RGB and depth modalities using pretrained visual backbones. To effectively handle the complexity of high-modality inputs and diverse question types, we incorporate a Mixture-of-Experts (MoE) fusion module, which dynamically combines spatial representations to support downstream reasoning tasks and improve convergence. Training is conducted in a two-phase strategy: the first phase focuses on generating free-form answers to enhance spatial reasoning ability, while the second phase uses normalized answers for evaluation. Evaluated on Track 3 of the AI City Challenge 2025, our 64M-parameter base model achieved 5th place on the leaderboard with a score of 66.8861, demonstrating strong performance in bridging visual perception and spatial understanding in industrial environments. We further present an 80M-parameter variant with expanded MoE capacity, which demonstrates improved performance on spatial reasoning tasks.
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[CV-87] HERO: Hierarchical Extrapolation and Refresh for Efficient World Models
【速读】：该论文旨在解决生成式世界模型（Generation-driven World Models）在推理过程中因扩散模型（Diffusion Models）迭代特性导致的效率低下问题。现有加速方法虽提升了扩散模型的计算效率，但在应用于世界模型时往往引发质量退化。解决方案的关键在于提出一种无需训练的分层加速框架HERO，其核心创新在于识别并利用世界模型中特征耦合现象：浅层特征具有高时间变异性，深层特征则更稳定。为此，HERO采用双策略：在浅层引入基于块（patch-wise）的刷新机制，通过频率感知跟踪实现高效token重计算，兼容FlashAttention且无需额外度量计算；在深层则采用线性外推方案直接估计中间特征，跳过注意力模块与前馈网络的全部计算，从而显著提升推理速度。实验表明，HERO在仅造成轻微质量损失的前提下实现了1.73倍加速，优于现有扩散加速方法。
链接: https://arxiv.org/abs/2508.17588
作者: Quanjian Song,Xinyu Wang,Donghao Zhou,Jingyu Lin,Cunjian Chen,Yue Ma,Xiu Li
机构: Tsinghua University (清华大学); The Chinese University of Hong Kong (香港中文大学); Monash University (莫纳什大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages in total
Abstract:Generation-driven world models create immersive virtual environments but suffer slow inference due to the iterative nature of diffusion models. While recent advances have improved diffusion model efficiency, directly applying these techniques to world models introduces limitations such as quality degradation. In this paper, we present HERO, a training-free hierarchical acceleration framework tailored for efficient world models. Owing to the multi-modal nature of world models, we identify a feature coupling phenomenon, wherein shallow layers exhibit high temporal variability, while deeper layers yield more stable feature representations. Motivated by this, HERO adopts hierarchical strategies to accelerate inference: (i) In shallow layers, a patch-wise refresh mechanism efficiently selects tokens for recomputation. With patch-wise sampling and frequency-aware tracking, it avoids extra metric computation and remain compatible with FlashAttention. (ii) In deeper layers, a linear extrapolation scheme directly estimates intermediate features. This completely bypasses the computations in attention modules and feed-forward networks. Our experiments show that HERO achieves a 1.73 \times speedup with minimal quality degradation, significantly outperforming existing diffusion acceleration methods.
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[CV-88] IDU: Incremental Dynamic Update of Existing 3D Virtual Environments with New Imagery Data
【速读】：该论文旨在解决军事领域中高分辨率三维虚拟环境因战场动态变化（如物体出现或消失）而需频繁更新的问题，传统全量重建方法存在时间成本高、人力投入大等缺陷。其解决方案的关键在于提出一种增量式动态更新（Incremental Dynamic Update, IDU）流程：首先通过相机位姿估计对齐新图像与现有3D模型，再利用变化检测定位场景差异，随后借助生成式AI（Generative AI）模型生成高质量的新元素3D资产，并结合人工引导确保对象识别与放置的准确性，最终实现仅以少量新增图像即可精准、高效地更新局部场景，从而显著降低更新时间和人力成本，适用于快速演化的军事仿真与训练需求。
链接: https://arxiv.org/abs/2508.17579
作者: Meida Chen,Luis Leal,Yue Hu,Rong Liu,Butian Xiong,Andrew Feng,Jiuyi Xu,Yangming Shi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:For simulation and training purposes, military organizations have made substantial investments in developing high-resolution 3D virtual environments through extensive imaging and 3D scanning. However, the dynamic nature of battlefield conditions-where objects may appear or vanish over time-makes frequent full-scale updates both time-consuming and costly. In response, we introduce the Incremental Dynamic Update (IDU) pipeline, which efficiently updates existing 3D reconstructions, such as 3D Gaussian Splatting (3DGS), with only a small set of newly acquired images. Our approach starts with camera pose estimation to align new images with the existing 3D model, followed by change detection to pinpoint modifications in the scene. A 3D generative AI model is then used to create high-quality 3D assets of the new elements, which are seamlessly integrated into the existing 3D model. The IDU pipeline incorporates human guidance to ensure high accuracy in object identification and placement, with each update focusing on a single new object at a time. Experimental results confirm that our proposed IDU pipeline significantly reduces update time and labor, offering a cost-effective and targeted solution for maintaining up-to-date 3D models in rapidly evolving military scenarios.
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[CV-89] MetaGen: A DSL Database and Benchmark for VLM-Assisted Metamaterial Generation
【速读】：该论文旨在解决超材料（metamaterials）设计中因几何结构复杂性及从构型到宏观性能映射关系非线性导致的难题。其核心挑战在于如何高效地表达、存储和利用超材料的多维特征以支持智能设计与预测。解决方案的关键在于提出三个互补组件：(i) MetaDSL——一种紧凑且语义丰富的领域特定语言，可形式化描述多样化超材料设计并兼顾人类可读性和机器可解析性；(ii) MetaDB——一个包含超过15万条参数化MetaDSL程序及其衍生数据（三维几何、多视角渲染图与模拟弹性性能）的结构化数据库；(iii) MetaBench——用于评估视觉-语言模型在结构重建、属性驱动逆向设计与性能预测三大能力上的基准测试套件。通过微调先进视觉-语言模型并在CAD-like交互界面中部署统一模型（omni-model），该框架为实现结构-表示-性能关系的集成化设计与理解奠定了坚实基础。
链接: https://arxiv.org/abs/2508.17568
作者: Liane Makatura,Benjamin Jones,Siyuan Bian,Wojciech Matusik
机构: MIT CSAIL (Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Programming Languages (cs.PL)
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Abstract:Metamaterials are micro-architected structures whose geometry imparts highly tunable-often counter-intuitive-bulk properties. Yet their design is difficult because of geometric complexity and a non-trivial mapping from architecture to behaviour. We address these challenges with three complementary contributions. (i) MetaDSL: a compact, semantically rich domain-specific language that captures diverse metamaterial designs in a form that is both human-readable and machine-parsable. (ii) MetaDB: a curated repository of more than 150,000 parameterized MetaDSL programs together with their derivatives-three-dimensional geometry, multi-view renderings, and simulated elastic properties. (iii) MetaBench: benchmark suites that test three core capabilities of vision-language metamaterial assistants-structure reconstruction, property-driven inverse design, and performance prediction. We establish baselines by fine-tuning state-of-the-art vision-language models and deploy an omni-model within an interactive, CAD-like interface. Case studies show that our framework provides a strong first step toward integrated design and understanding of structure-representation-property relationships.
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[CV-90] owards Optimal Convolutional Transfer Learning Architectures for Breast Lesion Classification and ACL Tear Detection
【速读】：该论文旨在解决医学影像分类任务中因数据稀缺导致模型性能受限的问题，尤其关注在小样本条件下如何提升卷积神经网络（Convolutional Neural Network, CNN）的下游任务表现。其解决方案的关键在于：采用迁移学习策略，通过对比RadImageNet与ImageNet预训练对模型性能的影响，结合最优CNN架构设计（如带跳跃连接的一维卷积分类器、ResNet50预训练骨干网络及部分骨干层解冻），实现乳腺结节恶性程度判别和前交叉韧带（Anterior Cruciate Ligament, ACL）撕裂检测的高精度分类，最终在两项任务上分别达到AUC 0.9641和0.9969，显著优于现有方法，且未发现RadImageNet预训练在本研究任务中具有明显优势。
链接: https://arxiv.org/abs/2508.17567
作者: Daniel Frees,Moritz Bolling,Aditri Bhagirath
机构: Stanford University (斯坦福大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Modern computer vision models have proven to be highly useful for medical imaging classification and segmentation tasks, but the scarcity of medical imaging data often limits the efficacy of models trained from scratch. Transfer learning has emerged as a pivotal solution to this, enabling the fine-tuning of high-performance models on small data. Mei et al. (2022) found that pre-training CNNs on a large dataset of radiologist-labeled images (RadImageNet) enhanced model performance on downstream tasks compared to ImageNet pretraining. The present work extends Mei et al. (2022) by conducting a comprehensive investigation to determine optimal CNN architectures for breast lesion malignancy detection and ACL tear detection, as well as performing statistical analysis to compare the effect of RadImageNet and ImageNet pre-training on downstream model performance. Our findings suggest that 1-dimensional convolutional classifiers with skip connections, ResNet50 pre-trained backbones, and partial backbone unfreezing yields optimal downstream medical classification performance. Our best models achieve AUCs of 0.9969 for ACL tear detection and 0.9641 for breast nodule malignancy detection, competitive with the results reported by Mei et al. (2022) and surpassing other previous works. We do not find evidence confirming RadImageNet pre-training to provide superior downstream performance for ACL tear and breast lesion classification tasks.
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[CV-91] Minimal Solvers for Full DoF Motion Estimation from Asynchronous Tracks
【速读】：该论文旨在解决从异步点轨迹中同时估计相机的平移速度（translational velocity）和角速度（angular velocity）的问题，这一问题对滚动快门（rolling shutter）相机和事件相机（event camera）具有重要意义。其解决方案的关键在于将原本非多项式的求解问题转化为多项式近似形式，进而对所得的最小问题进行分类并确定其代数次数；在此基础上，针对低次代数度的问题开发了最小解算器（minimal solvers），并在合成与真实数据集上进行了验证。
链接: https://arxiv.org/abs/2508.17537
作者: Petr Hruby,Marc Pollefeys
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13 pages, 5 figures
Abstract:We address the problem of estimating both translational and angular velocity of a camera from asynchronous point tracks, a formulation relevant to rolling shutter and event cameras. Since the original problem is non-polynomial, we propose a polynomial approximation, classify the resulting minimal problems, and determine their algebraic degrees. Furthermore, we develop minimal solvers for several problems with low degrees and evaluate them on synthetic and real datasets. The code will be made publicly available.
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[CV-92] OmniMRI: A Unified Vision–Language Foundation Model for Generalist MRI Interpretation
【速读】：该论文旨在解决磁共振成像（MRI）临床流程中各环节碎片化、多阶段 workflows 的局限性，以及现有深度学习方法在不同解剖结构或应用场景下缺乏泛化能力的问题。同时，传统方案很少将影像数据与放射科医生日常依赖的文本语言信息进行融合。其解决方案的关键在于提出 OmniMRI——一个统一的视觉-语言基础模型，通过大规模异构数据集（涵盖超过 22 万例 MRI 体积和 1900 万张切片）进行多阶段训练：包括自监督视觉预训练、视觉-语言对齐、多模态预训练及多任务指令微调，从而获得可迁移的视觉表征、跨模态推理能力和强指令遵循能力。这使得 OmniMRI 能在一个架构内完成 MRI 重建、分割、异常检测、诊断建议和报告生成等多样化任务，为实现端到端的 MRI 解读提供通用且可扩展的框架。
链接: https://arxiv.org/abs/2508.17524
作者: Xingxin He,Aurora Rofena,Ruimin Feng,Haozhe Liao,Zhaoye Zhou,Albert Jang,Fang Liu
机构: Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, United States; Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Rome, Italy
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Magnetic Resonance Imaging (MRI) is indispensable in clinical practice but remains constrained by fragmented, multi-stage workflows encompassing acquisition, reconstruction, segmentation, detection, diagnosis, and reporting. While deep learning has achieved progress in individual tasks, existing approaches are often anatomy- or application-specific and lack generalizability across diverse clinical settings. Moreover, current pipelines rarely integrate imaging data with complementary language information that radiologists rely on in routine practice. Here, we introduce OmniMRI, a unified vision-language foundation model designed to generalize across the entire MRI workflow. OmniMRI is trained on a large-scale, heterogeneous corpus curated from 60 public datasets, over 220,000 MRI volumes and 19 million MRI slices, incorporating image-only data, paired vision-text data, and instruction-response data. Its multi-stage training paradigm, comprising self-supervised vision pretraining, vision-language alignment, multimodal pretraining, and multi-task instruction tuning, progressively equips the model with transferable visual representations, cross-modal reasoning, and robust instruction-following capabilities. Qualitative results demonstrate OmniMRI’s ability to perform diverse tasks within a single architecture, including MRI reconstruction, anatomical and pathological segmentation, abnormality detection, diagnostic suggestion, and radiology report generation. These findings highlight OmniMRI’s potential to consolidate fragmented pipelines into a scalable, generalist framework, paving the way toward foundation models that unify imaging and clinical language for comprehensive, end-to-end MRI interpretation.
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[CV-93] DinoTwins: Combining DINO and Barlow Twins for Robust Label-Efficient Vision Transformers
【速读】：该论文旨在解决在缺乏大量标注数据的情况下训练视觉Transformer（Vision Transformer, ViT）模型的难题，特别是在资源受限环境中如何实现高效、低标签依赖的自监督学习。其解决方案的关键在于融合DINO（基于教师-学生蒸馏的学习策略）与Barlow Twins（冗余减少目标）两种方法：通过引入Barlow Twins的冗余减少机制增强特征表示的判别性，同时保留DINO的自蒸馏策略以提升模型对不同数据增强的鲁棒性，从而在仅使用10%标注数据的前提下，实现与纯DINO相当的分类准确率和更优的语义分割能力，显著降低计算资源需求并提高模型效率。
链接: https://arxiv.org/abs/2508.17509
作者: Michael Podsiadly,Brendon K Lay
机构: Georgia Institute of Technology (佐治亚理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Training AI models to understand images without costly labeled data remains a challenge. We combine two techniques–DINO (teacher-student learning) and Barlow Twins (redundancy reduction)–to create a model that learns better with fewer labels and less compute. While both DINO and Barlow Twins have independently demonstrated strong performance in self-supervised learning, each comes with limitations–DINO may be sensitive to certain augmentations, and Barlow Twins often requires batch sizes too large to fit on consumer hardware. By combining the redundancy-reduction objective of Barlow Twins with the self-distillation strategy of DINO, we aim to leverage their complementary strengths. We train a hybrid model on the MS COCO dataset using only 10% of labeled data for linear probing, and evaluate its performance against standalone DINO and Barlow Twins implementations. Preliminary results show that the combined approach achieves comparable loss and classification accuracy to DINO while maintaining strong feature representations. Attention visualizations further suggest improved semantic segmentation capability in the hybrid model. This combined method offers a scalable, label-efficient alternative for training ViTs in resource-constrained environments.
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[CV-94] Social-MAE: A Transformer-Based Multimodal Autoencoder for Face and Voice
【速读】：该论文旨在解决人类社会行为感知中多模态信息融合的挑战，特别是如何通过自监督预训练提升模型对音频-视觉社交数据的理解能力。其解决方案的关键在于改进对比音频-视觉掩码自编码器（Contrastive Audio-Visual Masked Auto-Encoder, CAV-MAE），使其能够接收更大数量的输入帧，并在大规模人类社交互动数据集（VoxCeleb2）上进行自监督预训练，从而构建出更强大的音频-视觉表征学习模型——Social-MAE。该方法在情绪识别、笑声检测和表面人格估计等下游任务中均取得领先或具有竞争力的结果，验证了领域内自监督预训练的有效性。
链接: https://arxiv.org/abs/2508.17502
作者: Hugo Bohy,Minh Tran,Kevin El Haddad,Thierry Dutoit,Mohammad Soleymani
机构: Numediart Institute (Numediart 研究所); ISIA Lab (ISIA 实验室); University of Mons (蒙斯大学); Institute for Creative Technologies (创意技术研究所); University of Southern California (南加州大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 3 figures, IEEE FG 2024 conference
Abstract:Human social behaviors are inherently multimodal necessitating the development of powerful audiovisual models for their perception. In this paper, we present Social-MAE, our pre-trained audiovisual Masked Autoencoder based on an extended version of Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE), which is pre-trained on audiovisual social data. Specifically, we modify CAV-MAE to receive a larger number of frames as input and pre-train it on a large dataset of human social interaction (VoxCeleb2) in a self-supervised manner. We demonstrate the effectiveness of this model by finetuning and evaluating the model on different social and affective downstream tasks, namely, emotion recognition, laughter detection and apparent personality estimation. The model achieves state-of-the-art results on multimodal emotion recognition and laughter recognition and competitive results for apparent personality estimation, demonstrating the effectiveness of in-domain self-supervised pre-training. Code and model weight are available here this https URL.
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[CV-95] Optimizing Multi-Modal Trackers via Sensitivity-aware Regularized Tuning
【速读】：该论文旨在解决多模态跟踪器优化中预训练模型适配RGB数据时面临的塑性-稳定性权衡问题（plasticity-stability trade-off）。现有微调范式要么过于自由、导致过拟合，要么过度约束、限制适应能力，均无法实现理想迁移性能。解决方案的关键在于提出一种敏感度感知的正则化微调框架（sensitivity-aware regularized tuning framework），通过引入参数对关键基础模式和跨域变化的内在敏感度作为正则项，在微调过程中同时保障模型的泛化能力与适应性：首先利用预训练权重的切空间分析测量并引导先验敏感度以维持通用性，再在微调阶段探索迁移敏感度以增强稳定性和可适应性。此方法显著提升了跨模态迁移能力，并在多个多模态跟踪任务上超越当前最优技术。
链接: https://arxiv.org/abs/2508.17488
作者: Zhiwen Chen,Jinjian Wu,Zhiyu Zhu,Yifan Zhang,Guangming Shi,Junhui Hou
机构: Xidian University (西安电子科技大学); City University of Hong Kong (香港城市大学); Shanghai University (上海大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This paper tackles the critical challenge of optimizing multi-modal trackers by effectively adapting the pre-trained models for RGB data. Existing fine-tuning paradigms oscillate between excessive freedom and over-restriction, both leading to a suboptimal plasticity-stability trade-off. To mitigate this dilemma, we propose a novel sensitivity-aware regularized tuning framework, which delicately refines the learning process by incorporating intrinsic parameter sensitivities. Through a comprehensive investigation from pre-trained to multi-modal contexts, we identify that parameters sensitive to pivotal foundational patterns and cross-domain shifts are primary drivers of this issue. Specifically, we first analyze the tangent space of pre-trained weights to measure and orient prior sensitivities, dedicated to preserving generalization. Then, we further explore transfer sensitivities during the tuning phase, emphasizing adaptability and stability. By incorporating these sensitivities as regularization terms, our method significantly enhances the transferability across modalities. Extensive experiments showcase the superior performance of the proposed method, surpassing current state-of-the-art techniques across various multi-modal tracking. The source code and models will be publicly available at this https URL.
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[CV-96] GraphMMP: A Graph Neural Network Model with Mutual Information and Global Fusion for Multimodal Medical Prognosis
【速读】：该论文旨在解决多模态医学数据（multimodal medical data）分析中如何有效建模异构数据模态之间复杂交互关系，并同时捕捉跨模态的局部与全局依赖性的问题。其解决方案的关键在于提出了一种两阶段的多模态预后模型 GraphMMP，该模型首先利用互信息（mutual information）构建特征图（feature graphs），以刻画不同模态间的潜在关联；其次引入基于 Mamba 的全局融合模块（global fusion module），显著提升了预后预测性能。实证结果表明，GraphMMP 在肝病预后和 METABRIC 数据集上均优于现有方法，验证了其在多模态医学预后任务中的有效性。
链接: https://arxiv.org/abs/2508.17478
作者: Xuhao Shan,Ruiquan Ge,Jikui Liu,Linglong Wu,Chi Zhang,Siqi Liu,Wenjian Qin,Wenwen Min,Ahmed Elazab,Changmiao Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In the field of multimodal medical data analysis, leveraging diverse types of data and understanding their hidden relationships continues to be a research focus. The main challenges lie in effectively modeling the complex interactions between heterogeneous data modalities with distinct characteristics while capturing both local and global dependencies across modalities. To address these challenges, this paper presents a two-stage multimodal prognosis model, GraphMMP, which is based on graph neural networks. The proposed model constructs feature graphs using mutual information and features a global fusion module built on Mamba, which significantly boosts prognosis performance. Empirical results show that GraphMMP surpasses existing methods on datasets related to liver prognosis and the METABRIC study, demonstrating its effectiveness in multimodal medical prognosis tasks.
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[CV-97] 2I-Reason Bench: Benchmarking Reasoning -Informed Text-to-Image Generation
【速读】：该论文旨在解决当前文本到图像（Text-to-Image, T2I）生成模型在复杂语义理解和逻辑推理能力评估方面缺乏系统性基准测试的问题。为应对这一挑战，作者提出了T2I-ReasonBench，一个涵盖成语理解（Idiom Interpretation）、文本图像设计（Textual Image Design）、实体推理（Entity-Reasoning）和科学推理（Scientific-Reasoning）四个维度的综合性评测基准，并设计了两阶段评估协议以同时衡量模型的推理准确性和图像质量，从而为T2I模型的推理能力提供更全面、客观的量化评估标准。
链接: https://arxiv.org/abs/2508.17472
作者: Kaiyue Sun,Rongyao Fang,Chengqi Duan,Xian Liu,Xihui Liu
机构: The University of Hong Kong (香港大学); The Chinese University of Hong Kong (香港中文大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Code: this https URL
Abstract:We propose T2I-ReasonBench, a benchmark evaluating reasoning capabilities of text-to-image (T2I) models. It consists of four dimensions: Idiom Interpretation, Textual Image Design, Entity-Reasoning and Scientific-Reasoning. We propose a two-stage evaluation protocol to assess the reasoning accuracy and image quality. We benchmark various T2I generation models, and provide comprehensive analysis on their performances.
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[CV-98] A Synthetic Dataset for Manometry Recognition in Robotic Applications
【速读】：该论文旨在解决复杂工业环境中对象检测模型训练面临的数据稀缺性（data scarcity）与高采集成本问题，尤其是在如海上石油平台等危险场景中，真实数据的获取存在实际和经济障碍，制约了自主巡检系统的发展。其解决方案的关键在于提出并验证了一种混合数据合成流程：首先利用BlenderProc进行程序化渲染，生成带有精确标注且具备域随机化的逼真图像；随后引入NVIDIA Cosmos-Predict2世界基础模型，通过AI驱动的视频生成技术合成具有时间多样性和物理合理性的视频序列，以覆盖罕见视角和恶劣工况。实验表明，将真实图像与合成数据按1:1比例混合后训练的YOLO检测网络性能优于纯真实数据训练的基线模型，证明了“合成优先”策略在安全关键且资源受限场景下开发可靠感知系统的技术可行性与优越性。
链接: https://arxiv.org/abs/2508.17468
作者: Pedro Antonio Rabelo Saraiva,Enzo Ferreira de Souza,Joao Manoel Herrera Pinheiro,Thiago H. Segreto,Ricardo V. Godoy,Marcelo Becker
机构: University of São Paulo (圣保罗大学); Fundação de Apoio à Física e à Química (FAFQ) (物理与化学支持基金会)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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Abstract:This work addresses the challenges of data scarcity and high acquisition costs for training robust object detection models in complex industrial environments, such as offshore oil platforms. The practical and economic barriers to collecting real-world data in these hazardous settings often hamper the development of autonomous inspection systems. To overcome this, in this work we propose and validate a hybrid data synthesis pipeline that combines procedural rendering with AI-driven video generation. Our methodology leverages BlenderProc to create photorealistic images with precise annotations and controlled domain randomization, and integrates NVIDIA’s Cosmos-Predict2 world-foundation model to synthesize physically plausible video sequences with temporal diversity, capturing rare viewpoints and adverse conditions. We demonstrate that a YOLO-based detection network trained on a composite dataset, blending real images with our synthetic data, achieves superior performance compared to models trained exclusively on real-world data. Notably, a 1:1 mixture of real and synthetic data yielded the highest accuracy, surpassing the real-only baseline. These findings highlight the viability of a synthetic-first approach as an efficient, cost-effective, and safe alternative for developing reliable perception systems in safety-critical and resource-constrained industrial applications.
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[CV-99] Optimizing Grasping in Legged Robots: A Deep Learning Approach to Loco-Manipulation
【速读】：该论文旨在解决四足机器人（quadruped robots）在动态复杂环境中实现高精度、自适应抓取（grasping）的难题，尤其针对传统方法依赖大量真实世界标定和预编程抓取配置的问题。解决方案的关键在于提出一种基于“仿真到现实”（sim-to-real）的深度学习框架，通过在Genesis仿真环境中生成包含像素级标注的抓取质量图（grasp-quality maps）的合成数据集，训练一个具有U-Net结构的卷积神经网络（CNN），该网络融合RGB图像、深度图、分割掩码与表面法向量等多模态感知信息，输出最优抓取热力图以指导精确抓取操作。实验证明，该方法可在四足机器人上完成从自主导航、目标感知到精准抓取的完整locomanipulation任务，显著提升了系统在真实场景中的泛化能力和部署效率。
链接: https://arxiv.org/abs/2508.17466
作者: Dilermando Almeida,Guilherme Lazzarini,Juliano Negri,Thiago H. Segreto,Ricardo V. Godoy,Marcelo Becker
机构: Federal University of Uberlândia (联邦大学); University of São Paulo (圣保罗大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Systems and Control (eess.SY)
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Abstract:Quadruped robots have emerged as highly efficient and versatile platforms, excelling in navigating complex and unstructured terrains where traditional wheeled robots might fail. Equipping these robots with manipulator arms unlocks the advanced capability of loco-manipulation to perform complex physical interaction tasks in areas ranging from industrial automation to search-and-rescue missions. However, achieving precise and adaptable grasping in such dynamic scenarios remains a significant challenge, often hindered by the need for extensive real-world calibration and pre-programmed grasp configurations. This paper introduces a deep learning framework designed to enhance the grasping capabilities of quadrupeds equipped with arms, focusing on improved precision and adaptability. Our approach centers on a sim-to-real methodology that minimizes reliance on physical data collection. We developed a pipeline within the Genesis simulation environment to generate a synthetic dataset of grasp attempts on common objects. By simulating thousands of interactions from various perspectives, we created pixel-wise annotated grasp-quality maps to serve as the ground truth for our model. This dataset was used to train a custom CNN with a U-Net-like architecture that processes multi-modal input from an onboard RGB and depth cameras, including RGB images, depth maps, segmentation masks, and surface normal maps. The trained model outputs a grasp-quality heatmap to identify the optimal grasp point. We validated the complete framework on a four-legged robot. The system successfully executed a full loco-manipulation task: autonomously navigating to a target object, perceiving it with its sensors, predicting the optimal grasp pose using our model, and performing a precise grasp. This work proves that leveraging simulated training with advanced sensing offers a scalable and effective solution for object handling.
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[CV-100] Multi-Level LVLM Guidance for Untrimmed Video Action Recognition
【速读】：该论文旨在解决复杂未剪辑视频中动作识别与定位的难题，核心挑战在于现有方法难以有效捕捉细粒度动作、长时序依赖关系以及从低层视觉特征中提取高层语义信息。其解决方案的关键在于提出事件上下文化的视频Transformer（Event-Contextualized Video Transformer, ECVT），该架构采用双分支设计：一为视频编码分支用于时空特征提取，另一为跨模态引导分支利用大视觉语言模型（Large Vision-Language Models, LVLMs）生成多粒度语义描述（包括全局事件提示和时间子事件提示），并通过自适应门控机制、跨模态注意力机制及事件图模块实现高层语义融合与时间上下文校准，从而显著提升模型对视频时序结构与事件逻辑的理解能力。
链接: https://arxiv.org/abs/2508.17442
作者: Liyang Peng,Sihan Zhu,Yunjie Guo
机构: Kunming University of Science and Technology (昆明理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Action recognition and localization in complex, untrimmed videos remain a formidable challenge in computer vision, largely due to the limitations of existing methods in capturing fine-grained actions, long-term temporal dependencies, and high-level semantic information from low-level visual features. This paper introduces the Event-Contextualized Video Transformer (ECVT), a novel architecture that leverages the advanced semantic understanding capabilities of Large Vision-Language Models (LVLMs) to bridge this gap. ECVT employs a dual-branch design, comprising a Video Encoding Branch for spatio-temporal feature extraction and a Cross-Modal Guidance Branch. The latter utilizes an LVLM to generate multi-granularity semantic descriptions, including Global Event Prompting for macro-level narrative and Temporal Sub-event Prompting for fine-grained action details. These multi-level textual cues are integrated into the video encoder’s learning process through sophisticated mechanisms such as adaptive gating for high-level semantic fusion, cross-modal attention for fine-grained feature refinement, and an event graph module for temporal context calibration. Trained end-to-end with a comprehensive loss function incorporating semantic consistency and temporal calibration terms, ECVT significantly enhances the model’s ability to understand video temporal structures and event logic. Extensive experiments on ActivityNet v1.3 and THUMOS14 datasets demonstrate that ECVT achieves state-of-the-art performance, with an average mAP of 40.5% on ActivityNet v1.3 and mAP@0.5 of 67.1% on THUMOS14, outperforming leading baselines.
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[CV-101] Investigating Domain Gaps for Indoor 3D Object Detection ACM-MM2025
【速读】：该论文旨在解决室内三维（3D）目标检测模型在跨数据集迁移时性能下降的问题，即域适应（domain adaptation）问题。现有研究多基于单一数据集训练与测试，且训练与测试分布一致，导致模型在面对不同采集方式、点云质量、边界框布局或实例特征等差异时泛化能力不足。解决方案的关键在于构建一个涵盖多个真实与合成数据集（如ScanNet、SUN RGB-D、3D Front及新提出的ProcTHOR-OD和ProcFront）的综合基准，并系统性地评估不同域间差异（包括合成到真实、点云质量、布局和实例特征等方面的适配）对检测性能的影响，同时引入多种改进策略以提升跨域适应能力，为未来具备更强跨域泛化能力的室内3D目标检测器提供基线参考。
链接: https://arxiv.org/abs/2508.17439
作者: Zijing Zhao,Zhu Xu,Qingchao Chen,Yuxin Peng,Yang Liu
机构: Wangxuan Institute of Computer Technology, Peking University (北京大学王选计算机研究所); National Institute of Health Data Science, Peking University (北京大学健康医疗数据科学研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM MM 2025
Abstract:As a fundamental task for indoor scene understanding, 3D object detection has been extensively studied, and the accuracy on indoor point cloud data has been substantially improved. However, existing researches have been conducted on limited datasets, where the training and testing sets share the same distribution. In this paper, we consider the task of adapting indoor 3D object detectors from one dataset to another, presenting a comprehensive benchmark with ScanNet, SUN RGB-D and 3D Front datasets, as well as our newly proposed large-scale datasets ProcTHOR-OD and ProcFront generated by a 3D simulator. Since indoor point cloud datasets are collected and constructed in different ways, the object detectors are likely to overfit to specific factors within each dataset, such as point cloud quality, bounding box layout and instance features. We conduct experiments across datasets on different adaptation scenarios including synthetic-to-real adaptation, point cloud quality adaptation, layout adaptation and instance feature adaptation, analyzing the impact of different domain gaps on 3D object detectors. We also introduce several approaches to improve adaptation performances, providing baselines for domain adaptive indoor 3D object detection, hoping that future works may propose detectors with stronger generalization ability across domains. Our project homepage can be found in this https URL.
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[CV-102] Pixie: Fast and Generalizable Supervised Learning of 3D Physics from Pixels
【速读】：该论文旨在解决从视觉信息中推断三维场景物理属性（如弹性或刚度）这一关键但具有挑战性的问题，传统方法依赖缓慢的逐场景优化，限制了泛化能力和实际应用。解决方案的关键在于提出PIXIE——一种基于监督损失训练的通用神经网络，能够仅凭3D视觉特征直接预测跨多场景的物理属性，并通过前向传播实现快速推理；其核心创新包括：利用预训练视觉特征（如CLIP）实现零样本迁移至真实场景，以及结合学习到的静态场景表示（如Gaussian Splatting）实现外力作用下的高保真物理模拟。
链接: https://arxiv.org/abs/2508.17437
作者: Long Le,Ryan Lucas,Chen Wang,Chuhao Chen,Dinesh Jayaraman,Eric Eaton,Lingjie Liu
机构: University of Pennsylvania (宾夕法尼亚大学); Massachusetts Institute of Technology (麻省理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Inferring the physical properties of 3D scenes from visual information is a critical yet challenging task for creating interactive and realistic virtual worlds. While humans intuitively grasp material characteristics such as elasticity or stiffness, existing methods often rely on slow, per-scene optimization, limiting their generalizability and application. To address this problem, we introduce PIXIE, a novel method that trains a generalizable neural network to predict physical properties across multiple scenes from 3D visual features purely using supervised losses. Once trained, our feed-forward network can perform fast inference of plausible material fields, which coupled with a learned static scene representation like Gaussian Splatting enables realistic physics simulation under external forces. To facilitate this research, we also collected PIXIEVERSE, one of the largest known datasets of paired 3D assets and physic material annotations. Extensive evaluations demonstrate that PIXIE is about 1.46-4.39x better and orders of magnitude faster than test-time optimization methods. By leveraging pretrained visual features like CLIP, our method can also zero-shot generalize to real-world scenes despite only ever been trained on synthetic data. this https URL
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[CV-103] Disentangled Geometry and Appearance for Efficient Multi-View Surface Reconstruction and Rendering
【速读】：该论文旨在解决基于神经渲染的多视角表面重建方法中存在的问题，即这类方法通常需要额外的网格提取步骤，不仅操作繁琐，还容易因网格混叠（mesh aliasing）导致表面质量下降，从而限制了下游应用。其解决方案的关键在于构建一个基于显式网格表示和可微光栅化框架的高效方法，通过引入解耦的几何与外观模型，避免依赖深度网络以提升学习效率并扩展适用性；同时设计了一个神经形变场（neural deformation field）来融入全局几何上下文信息以增强几何建模能力，并提出一种新型正则化策略约束传递至神经着色器（neural shader）的几何特征，确保着色精度并提升渲染效果；此外，将视图无关的漫反射项（view-invariant diffuse term）分离并烘焙至网格顶点，进一步提高渲染效率。该方案实现了训练（4.84分钟）与渲染（0.023秒）速度的显著优化，且重建质量达到当前最优水平，兼具高效率、高质量和强实用性。
链接: https://arxiv.org/abs/2508.17436
作者: Qitong Zhang,Jieqing Feng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This paper addresses the limitations of neural rendering-based multi-view surface reconstruction methods, which require an additional mesh extraction step that is inconvenient and would produce poor-quality surfaces with mesh aliasing, restricting downstream applications. Building on the explicit mesh representation and differentiable rasterization framework, this work proposes an efficient solution that preserves the high efficiency of this framework while significantly improving reconstruction quality and versatility. Specifically, we introduce a disentangled geometry and appearance model that does not rely on deep networks, enhancing learning and broadening applicability. A neural deformation field is constructed to incorporate global geometric context, enhancing geometry learning, while a novel regularization constrains geometric features passed to a neural shader to ensure its accuracy and boost shading. For appearance, a view-invariant diffuse term is separated and baked into mesh vertices, further improving rendering efficiency. Experimental results demonstrate that the proposed method achieves state-of-the-art training (4.84 minutes) and rendering (0.023 seconds) speeds, with reconstruction quality that is competitive with top-performing methods. Moreover, the method enables practical applications such as mesh and texture editing, showcasing its versatility and application potential. This combination of efficiency, competitive quality, and broad applicability makes our approach a valuable contribution to multi-view surface reconstruction and rendering.
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[CV-104] An LLM -LVLM Driven Agent for Iterative and Fine-Grained Image Editing
【速读】：该论文旨在解决当前文本到图像（Text-to-Image, T2I）生成模型在真实场景中进行细粒度、迭代式图像编辑时面临的三大挑战：精细化指令理解能力不足、修改过程中的上下文保持鲁棒性差，以及缺乏智能反馈机制以支持多轮优化。其解决方案的关键在于提出一个无需训练的智能代理框架 RefineEdit-Agent，该框架通过闭环系统整合大语言模型（Large Language Models, LLMs）的规划能力和视觉-语言大模型（Vision-Language Large Models, LVLMs）的视觉理解与评估能力，实现从指令解析、分层编辑规划、迭代编辑执行到LVLM驱动的反馈评估的全流程自动化与智能化，从而显著提升编辑精度和上下文一致性。
链接: https://arxiv.org/abs/2508.17435
作者: Zihan Liang,Jiahao Sun,Haoran Ma
机构: Kunming University of Science and Technology (昆明理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Despite the remarkable capabilities of text-to-image (T2I) generation models, real-world applications often demand fine-grained, iterative image editing that existing methods struggle to provide. Key challenges include granular instruction understanding, robust context preservation during modifications, and the lack of intelligent feedback mechanisms for iterative refinement. This paper introduces RefineEdit-Agent, a novel, training-free intelligent agent framework designed to address these limitations by enabling complex, iterative, and context-aware image editing. RefineEdit-Agent leverages the powerful planning capabilities of Large Language Models (LLMs) and the advanced visual understanding and evaluation prowess of Vision-Language Large Models (LVLMs) within a closed-loop system. Our framework comprises an LVLM-driven instruction parser and scene understanding module, a multi-level LLM-driven editing planner for goal decomposition, tool selection, and sequence generation, an iterative image editing module, and a crucial LVLM-driven feedback and evaluation loop. To rigorously evaluate RefineEdit-Agent, we propose LongBench-T2I-Edit, a new benchmark featuring 500 initial images with complex, multi-turn editing instructions across nine visual dimensions. Extensive experiments demonstrate that RefineEdit-Agent significantly outperforms state-of-the-art baselines, achieving an average score of 3.67 on LongBench-T2I-Edit, compared to 2.29 for Direct Re-Prompting, 2.91 for InstructPix2Pix, 3.16 for GLIGEN-based Edit, and 3.39 for ControlNet-XL. Ablation studies, human evaluations, and analyses of iterative refinement, backbone choices, tool usage, and robustness to instruction complexity further validate the efficacy of our agentic design in delivering superior edit fidelity and context preservation.
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[CV-105] nySR: Pruning Diffusion for Real-World Image Super-Resolution
【速读】：该论文旨在解决真实世界图像超分辨率（Real-ISR）任务中扩散模型（Diffusion Models, DMs）因迭代去噪过程导致计算开销高、难以满足实时应用需求的问题。现有单步蒸馏方法虽提升了推理速度，但受限于参数量庞大的模型架构，仍无法实现高效部署。解决方案的关键在于提出TinySR——一个专为Real-ISR设计的轻量化扩散模型，其核心创新包括：引入动态块间激活机制（Dynamic Inter-block Activation）与扩张-腐蚀策略（Expansion-Corrosion Strategy）以优化深度剪枝决策；通过通道剪枝、注意力模块移除及轻量级深度可分离卷积（SepConv）实现变分自编码器（VAE）压缩；同时移除时间与提示相关模块并采用预缓存技术进一步加速推理。最终在保持感知质量的同时，相较教师模型TSD-SR实现了最高5.68倍的速度提升和83%的参数压缩。
链接: https://arxiv.org/abs/2508.17434
作者: Linwei Dong,Qingnan Fan,Yuhang Yu,Qi Zhang,Jinwei Chen,Yawei Luo,Changqing Zou
机构: Zhejiang University (浙江大学); Vivo Mobile Communication Co. Ltd (维沃移动通信有限公司); Zhejiang Lab (浙江省实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Real-world image super-resolution (Real-ISR) focuses on recovering high-quality images from low-resolution inputs that suffer from complex degradations like noise, blur, and compression. Recently, diffusion models (DMs) have shown great potential in this area by leveraging strong generative priors to restore fine details. However, their iterative denoising process incurs high computational overhead, posing challenges for real-time applications. Although one-step distillation methods, such as OSEDiff and TSD-SR, offer faster inference, they remain fundamentally constrained by their large, over-parameterized model architectures. In this work, we present TinySR, a compact yet effective diffusion model specifically designed for Real-ISR that achieves real-time performance while maintaining perceptual quality. We introduce a Dynamic Inter-block Activation and an Expansion-Corrosion Strategy to facilitate more effective decision-making in depth pruning. We achieve VAE compression through channel pruning, attention removal and lightweight SepConv. We eliminate time- and prompt-related modules and perform pre-caching techniques to further speed up the model. TinySR significantly reduces computational cost and model size, achieving up to 5.68x speedup and 83% parameter reduction compared to its teacher TSD-SR, while still providing high quality results.
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[CV-106] FedKLPR: Personalized Federated Learning for Person Re-Identification with Adaptive Pruning
【速读】：该论文旨在解决联邦学习（Federated Learning, FL）在行人重识别（Person Re-Identification, Re-ID）应用中面临的两大挑战：一是因客户端数据分布非独立同分布（non-IID）导致的统计异质性问题，二是由于频繁传输大规模模型引发的通信开销过高问题。解决方案的核心在于提出FedKLPR框架，其关键创新包括：1）KL散度正则化损失（KLL），通过最小化本地模型与全局特征分布间的KL散度来缓解统计异质性并提升收敛稳定性；2）基于KL散度剪枝加权聚合（KLPWA），融合剪枝比例与分布相似性优化聚合策略，在显著降低通信成本的同时增强全局模型鲁棒性；3）稀疏激活跳过机制（SAS），排除零值权重参与更新以避免关键参数稀释；4）跨轮次恢复机制（CRR），动态控制剪枝过程，在保障精度前提下实现更深层次压缩。实验证明，FedKLPR在多个基准数据集上相较现有最优方法可减少33%-38%（ResNet-50）和20%-40%（ResNet-34）的通信开销，且模型性能仅下降不超过1%。
链接: https://arxiv.org/abs/2508.17431
作者: Po-Hsien Yu,Yu-Syuan Tseng,Shao-Yi Chien
机构: National Taiwan University (国立台湾大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Person re-identification (Re-ID) is a fundamental task in intelligent surveillance and public safety. Federated learning (FL) offers a privacy-preserving solution by enabling collaborative model training without centralized data collection. However, applying FL to real-world re-ID systems faces two major challenges: statistical heterogeneity across clients due to non-IID data distributions, and substantial communication overhead caused by frequent transmission of large-scale models. To address these issues, we propose FedKLPR, a lightweight and communication-efficient federated learning framework for person re-identification. FedKLPR introduces four key components. First, the KL-Divergence Regularization Loss (KLL) constrains local models by minimizing the divergence from the global feature distribution, effectively mitigating the effects of statistical heterogeneity and improving convergence stability under non-IID conditions. Secondly, KL-Divergence-Prune Weighted Aggregation (KLPWA) integrates pruning ratio and distributional similarity into the aggregation process, thereby improving the robustness of the global model while significantly reducing communication overhead. Furthermore, sparse Activation Skipping (SAS) mitigates the dilution of critical parameters during the aggregation of pruned client models by excluding zero-valued weights from the update process. Finally, Cross-Round Recovery (CRR) introduces a dynamic pruning control mechanism that halts pruning when necessary, enabling deeper compression while maintaining model accuracy. Experimental results on eight benchmark datasets demonstrate that FedKLPR achieves significant communication reduction. Compared with the state-of-the-art, FedKLPR reduces 33%-38% communication cost on ResNet-50 and 20%-40% communication cost on ResNet-34, while maintaining model accuracy within 1% degradation.
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[CV-107] Robust Point Cloud Registration via Geometric Overlapping Guided Rotation Search
【速读】：该论文旨在解决点云配准（point cloud registration）中高外点比场景下的精度与效率问题，尤其针对现有基于空间兼容性图的方法存在二次方复杂度、以及多阶段分支定界（branch-and-bound, BnB）方法因局部最优导致误差累积的缺陷。其解决方案的关键在于提出一种基于仅旋转搜索的几何最大重叠配准框架：利用Chasles定理将刚体变换分解为绕轴旋转和平移，通过BnB搜索最优旋转轴和角度，并将剩余参数建模为范围最大查询（range maximum query, RMQ）问题；具体而言，先在立方映射参数化的半球上搜索top-k候选旋转轴，再沿每个轴估计平移量；随后将二维配准松弛为一维旋转角搜索，借助轴对齐矩形的几何重叠构建RMQ问题，使用扫描线算法结合线段树在多项式时间内求解。此方法在保证多项式时间复杂度的同时，实现线性空间增长，显著优于当前最先进（SOTA）方法。
链接: https://arxiv.org/abs/2508.17427
作者: Zhao Zheng,Jingfan Fan,Long Shao,Hong Song,Danni Ai,Tianyu Fu,Deqiang Xiao,Yongtian Wang,Jian Yang
机构: Beijing Institute of Technology (北京理工大学); Beijing Engineering Research Center of Mixed Reality and Advanced Display (北京理工大学混合现实与先进显示工程研究中心); Zhengzhou Research Institute, Beijing Institute of Technology (北京理工大学郑州研究院); School of Computer Science and Technology, Beijing Institute of Technology (北京理工大学计算机学院); School of Medical Technology, Beijing Institute of Technology (北京理工大学医学技术学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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Abstract:Point cloud registration based on correspondences computes the rigid transformation that maximizes the number of inliers constrained within the noise threshold. Current state-of-the-art (SOTA) methods employing spatial compatibility graphs or branch-and-bound (BnB) search mainly focus on registration under high outlier ratios. However, graph-based methods require at least quadratic space and time complexity for graph construction, while multi-stage BnB search methods often suffer from inaccuracy due to local optima between decomposed stages. This paper proposes a geometric maximum overlapping registration framework via rotation-only BnB search. The rigid transformation is decomposed using Chasles’ theorem into a translation along rotation axis and a 2D rigid transformation. The optimal rotation axis and angle are searched via BnB, with residual parameters formulated as range maximum query (RMQ) problems. Firstly, the top-k candidate rotation axes are searched within a hemisphere parameterized by cube mapping, and the translation along each axis is estimated through interval stabbing of the correspondences projected onto that axis. Secondly, the 2D registration is relaxed to 1D rotation angle search with 2D RMQ of geometric overlapping for axis-aligned rectangles, which is solved deterministically in polynomial time using sweep line algorithm with segment tree. Experimental results on 3DMatch, 3DLoMatch, and KITTI datasets demonstrate superior accuracy and efficiency over SOTA methods, while the time complexity is polynomial and the space complexity increases linearly with the number of points, even in the worst case.
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[CV-108] Constrained Prompt Enhancement for Improving Zero-Shot Generalization of Vision-Language Models
【速读】：该论文旨在解决预训练视觉-语言模型（Vision-Language Models, VLMs）在零样本泛化能力上因预训练与下游任务间语义错位（semantic misalignment）而导致的性能下降问题。现有方法主要依赖类别特定文本提示和视觉区域与文本描述的对齐，但面临文本提示不完整和视觉提示噪声干扰的问题。其解决方案的关键在于提出一种约束式提示增强（Constrained Prompt Enhancement, CPE）方法，通过两个核心组件实现：一是基于拓扑引导的同义语义生成（Topology-Guided Synonymous Semantic Generation, TGSSG），利用大语言模型生成类别同义语义集，并结合语义模糊熵与持久同调分析构建全面文本提示；二是类别无关的判别区域选择（Category-Agnostic Discriminative Region Selection, CADRS），基于预训练视觉模型激活图识别判别性视觉区域，过滤冗余噪声并生成紧凑视觉提示。最终，通过测试时适配（Test-Time Adaptation, TTA）和最优传输（Optimal Transport, OT）两种集合到集合匹配策略，实现高效视觉-文本对齐，显著提升VLM的零样本泛化性能。
链接: https://arxiv.org/abs/2508.17417
作者: Xiaojie Yin,Qilong Wang,Qinghua Hu
机构: Tianjin University (天津大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Vision-language models (VLMs) pre-trained on web-scale data exhibit promising zero-shot generalization but often suffer from semantic misalignment due to domain gaps between pre-training and downstream tasks. Existing approaches primarily focus on text prompting with class-specific descriptions and visual-text adaptation via aligning cropped image regions with textual descriptions. However, they still face the issues of incomplete textual prompts and noisy visual prompts. In this paper, we propose a novel constrained prompt enhancement (CPE) method to improve visual-textual alignment by constructing comprehensive textual prompts and compact visual prompts from the semantic perspective. Specifically, our approach consists of two key components: Topology-Guided Synonymous Semantic Generation (TGSSG) and Category-Agnostic Discriminative Region Selection (CADRS). Textually, to address the issue of incomplete semantic expression in textual prompts, our TGSSG first generates synonymous semantic set for each category via large language models, and constructs comprehensive textual prompts based on semantic ambiguity entropy and persistent homology analysis. Visually, to mitigate the irrelevant visual noise introduced by random cropping, our CADRS identifies discriminative regions with activation maps outputted by a pre-trained vision model, effectively filtering out noisy regions and generating compact visual prompts. Given the comprehensive set of textual prompts and compact set of visual prompts, we introduce two set-to-set matching strategies based on test-time adaptation (TTA) and optimal transport (OT) to achieve effective visual-textual alignment, and so improve zero-shot generalization of VLMs.
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[CV-109] Data Leakage in Visual Datasets
【速读】：该论文试图解决视觉数据泄露（data leakage）问题，即评估基准中的图像在训练过程中已被模型见过，从而破坏了模型评估的公平性。解决方案的关键在于利用图像检索技术对多个视觉数据集进行系统性分析，识别并分类不同模态、覆盖范围和严重程度的数据泄露类型，从而证明所有被分析的数据集均存在一定程度的泄露，且各类泄露都会影响下游任务中模型评估的可靠性。
链接: https://arxiv.org/abs/2508.17416
作者: Patrick Ramos,Ryan Ramos,Noa Garcia
机构: The University of Osaka (大阪大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We analyze data leakage in visual datasets. Data leakage refers to images in evaluation benchmarks that have been seen during training, compromising fair model evaluation. Given that large-scale datasets are often sourced from the internet, where many computer vision benchmarks are publicly available, our efforts are focused into identifying and studying this phenomenon. We characterize visual leakage into different types according to its modality, coverage, and degree. By applying image retrieval techniques, we unequivocally show that all the analyzed datasets present some form of leakage, and that all types of leakage, from severe instances to more subtle cases, compromise the reliability of model evaluation in downstream tasks.
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[CV-110] E-BayesSAM: Efficient Bayesian Adaptation of SAM with Self-Optimizing KAN-Based Interpretation for Uncertainty-Aware Ultrasonic Segmentation MICCAI2025
【速读】：该论文旨在解决Segment Anything Model (SAM)在医学图像分割中进行不确定性感知分割时面临的三大挑战：(1) 大规模预训练SAM的贝叶斯微调不稳定；(2) SAM参数量庞大导致计算成本高；(3) SAM的黑箱设计限制了可解释性。解决方案的关键在于提出E-BayesSAM框架，其核心创新包括：(1) Token-wise Variational Bayesian Inference (T-VBI)，将SAM输出token重新解释为动态概率权重，并通过无辅助训练的重参数化将其作为潜在变量，实现无需训练的变分贝叶斯推断以估计不确定性；(2) Self-Optimizing Kolmogorov-Arnold Network (SO-KAN)，利用自监督学习引入可学习样条激活函数优化token预测，从而识别并剪枝冗余token，在提升效率的同时增强模型准确性与可解释性。
链接: https://arxiv.org/abs/2508.17408
作者: Bin Huang,Zhong Liu,Huiying Wen,Bingsheng Huang,Xin Chen,Shuo Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by MICCAI 2025
Abstract:Although the Segment Anything Model (SAM) has advanced medical image segmentation, its Bayesian adaptation for uncertainty-aware segmentation remains hindered by three key issues: (1) instability in Bayesian fine-tuning of large pre-trained SAMs; (2) high computation cost due to SAM’s massive parameters; (3) SAM’s black-box design limits interpretability. To overcome these, we propose E-BayesSAM, an efficient framework combining Token-wise Variational Bayesian Inference (T-VBI) for efficienty Bayesian adaptation and Self-Optimizing Kolmogorov-Arnold Network (SO-KAN) for improving interpretability. T-VBI innovatively reinterprets SAM’s output tokens as dynamic probabilistic weights and reparameterizes them as latent variables without auxiliary training, enabling training-free VBI for uncertainty estimation. SO-KAN improves token prediction with learnable spline activations via self-supervised learning, providing insight to prune redundant tokens to boost efficiency and accuracy. Experiments on five ultrasound datasets demonstrated that E-BayesSAM achieves: (i) real-time inference (0.03s/image), (ii) superior segmentation accuracy (average DSC: Pruned E-BayesSAM’s 89.0% vs. E-BayesSAM’s 88.0% vs. MedSAM’s 88.3%), and (iii) identification of four critical tokens governing SAM’s decisions. By unifying efficiency, reliability, and interpretability, E-BayesSAM bridges SAM’s versatility with clinical needs, advancing deployment in safety-critical medical applications. The source code is available at this https URL.
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[CV-111] MoCo: Motion-Consistent Human Video Generation via Structure-Appearance Decoupling
【速读】：该论文旨在解决从文本提示中生成具有连贯全身运动的人类视频这一挑战，尤其针对复杂、长距离或全身性动作的生成问题。现有视频生成模型通常侧重于外观保真度，导致人体动作不自然或物理上不可行，且结构一致性差；同时，现有数据集多集中于面部或上半身动作，或为垂直方向的舞蹈视频，限制了生成方法在复杂动作上的适用性。解决方案的关键在于提出MoCo框架，通过将人类视频生成过程解耦为结构生成（structure generation）与外观生成（appearance generation）两个独立模块：首先利用高效的3D结构生成器从文本提示中生成人体运动序列，随后在该结构引导下合成视频外观；此外，引入人感知动态控制模块（Human-Aware Dynamic Control modules）以增强对稀疏人体结构的精细控制，并在训练中融入密集跟踪约束，从而提升生成视频的结构一致性和运动合理性。
链接: https://arxiv.org/abs/2508.17404
作者: Haoyu Wang,Hao Tang,Donglin Di,Zhilu Zhang,Wangmeng Zuo,Feng Gao,Siwei Ma,Shiliang Zhang
机构: Peking University (北京大学); Li Auto; Harbin Institute of Technology (哈尔滨工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project: this https URL
Abstract:Generating human videos with consistent motion from text prompts remains a significant challenge, particularly for whole-body or long-range motion. Existing video generation models prioritize appearance fidelity, resulting in unrealistic or physically implausible human movements with poor structural coherence. Additionally, most existing human video datasets primarily focus on facial or upper-body motions, or consist of vertically oriented dance videos, limiting the scope of corresponding generation methods to simple movements. To overcome these challenges, we propose MoCo, which decouples the process of human video generation into two components: structure generation and appearance generation. Specifically, our method first employs an efficient 3D structure generator to produce a human motion sequence from a text prompt. The remaining video appearance is then synthesized under the guidance of the generated structural sequence. To improve fine-grained control over sparse human structures, we introduce Human-Aware Dynamic Control modules and integrate dense tracking constraints during training. Furthermore, recognizing the limitations of existing datasets, we construct a large-scale whole-body human video dataset featuring complex and diverse motions. Extensive experiments demonstrate that MoCo outperforms existing approaches in generating realistic and structurally coherent human videos.
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[CV-112] Enhancing Underwater Images via Deep Learning: A Comparative Study of VGG19 and ResNet50-Based Approaches ICIP
【速读】：该论文旨在解决复杂水下场景中图像增强的难题，其关键解决方案是融合VGG19与ResNet50两种深度卷积神经网络模型，通过多尺度、多层级的深度特征分析实现互补优势整合，从而提升图像增强的全面性与准确性。
链接: https://arxiv.org/abs/2508.17397
作者: Aoqi Li,Yanghui Song,Jichao Dao,Chengfu Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 7 pages, 6 figures,2025 IEEE 3rd International Conference on Image Processing and Computer Applications (ICIPCA 2025)
Abstract:This paper addresses the challenging problem of image enhancement in complex underwater scenes by proposing a solution based on deep learning. The proposed method skillfully integrates two deep convolutional neural network models, VGG19 and ResNet50, leveraging their powerful feature extraction capabilities to perform multi-scale and multi-level deep feature analysis of underwater images. By constructing a unified model, the complementary advantages of the two models are effectively integrated, achieving a more comprehensive and accurate image enhancement this http URL objectively evaluate the enhancement effect, this paper introduces image quality assessment metrics such as PSNR, UCIQE, and UIQM to quantitatively compare images before and after enhancement and deeply analyzes the performance of different models in different this http URL, to improve the practicality and stability of the underwater visual enhancement system, this paper also provides practical suggestions from aspects such as model optimization, multi-model fusion, and hardware selection, aiming to provide strong technical support for visual enhancement tasks in complex underwater environments.
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[CV-113] Lightweight Joint Optimization of General-Purpose Vision-Language Models and Retrievers for Medical Diagnosis
【速读】：该论文旨在解决医学诊断中图像信息检索与模型推理之间的协同优化问题，特别是在临床多标签分类和视觉问答任务中，如何提升基于检索增强生成（RAG）框架的诊断准确性。传统RAG方法无法将大语言视觉模型（LVLM）的误差信号反向传播至检索模块，导致检索与推理环节脱节。本文的关键解决方案是提出一种联合优化机制，使多模态检索器与LVLM在训练过程中共同调整，从而显著改善因检索结果多样性引发的诊断不确定性问题——尤其在那些不同Top-1图像导致不同预测结果的挑战性案例中表现突出。实验表明，仅使用通用预训练骨干网络并进行轻量微调，即可达到与专业医学预训练模型相当的效果，但相较于理想情况（oracle），仍存在较大性能差距，提示未来研究需进一步优化检索排序策略以逼近最优诊断能力。
链接: https://arxiv.org/abs/2508.17394
作者: Nir Mazor,Tom Hope
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Clinical decision-making often involves interpreting images (e.g., radiology) for making diagnoses. Retrieving relevant visual information from medical literature and hospital records could enhance diagnostic accuracy. In this paper, we develop a model in which a multimodal retriever is jointly optimized with an LVLM for medical diagnosis, unlike standard RAG where LVLM error signal is not propagated down to the retriever. We show that using only general-purpose backbones, with only lightweight fine-tuning, our model is able to achieve competitive results with medically-pretrained models across clinical multi-label classification and visual question answering tasks. In a novel analysis, we additionally find that in many cases different top retrieved images each lead to different predictions for a given target, and that these cases are empirically challenging for all models, even for non-retrieval models. Our joint retrieval optimization significantly improves these challenging cases over standard RAG. However, oracle analysis reveals that while the correct diagnosis is frequently achievable using one of the top retrieved images, in practice there is a large performance gap from the oracle, and rerankers using frontier LVLMs do not close this gap – leaving ample room for improvement by future methods. Code will be made publicly available.
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[CV-114] ShaLa: Multimodal Shared Latent Space Modelling
【速读】：该论文旨在解决多模态变分自编码器（Multimodal VAEs）在学习共享潜在表示时面临的两大挑战：一是难以设计具有表达能力的联合变分后验分布，二是生成质量较低的问题。解决方案的关键在于提出了一种名为ShaLa的新颖生成框架，其核心创新包括两个方面：首先，引入了一种新型架构化的推理模型以更有效地推断共享潜在表示；其次，采用第二阶段的高表达能力扩散先验（diffusion prior），显著提升了下游多模态合成的质量。该方法不仅改善了跨模态推理性能，还能在更多模态下有效扩展，从而更好地捕捉复杂共享潜在空间。
链接: https://arxiv.org/abs/2508.17376
作者: Jiali Cui,Yan-Ying Chen,Yanxia Zhang,Matthew Klenk
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This paper presents a novel generative framework for learning shared latent representations across multimodal data. Many advanced multimodal methods focus on capturing all combinations of modality-specific details across inputs, which can inadvertently obscure the high-level semantic concepts that are shared across modalities. Notably, Multimodal VAEs with low-dimensional latent variables are designed to capture shared representations, enabling various tasks such as joint multimodal synthesis and cross-modal inference. However, multimodal VAEs often struggle to design expressive joint variational posteriors and suffer from low-quality synthesis. In this work, ShaLa addresses these challenges by integrating a novel architectural inference model and a second-stage expressive diffusion prior, which not only facilitates effective inference of shared latent representation but also significantly improves the quality of downstream multimodal synthesis. We validate ShaLa extensively across multiple benchmarks, demonstrating superior coherence and synthesis quality compared to state-of-the-art multimodal VAEs. Furthermore, ShaLa scales to many more modalities while prior multimodal VAEs have fallen short in capturing the increasing complexity of the shared latent space.
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[CV-115] Condition Weaving Meets Expert Modulation: Towards Universal and Controllable Image Generation
【速读】：该论文旨在解决图像到图像生成任务中因采用独立控制分支处理不同条件而导致的模型结构冗余与计算资源利用效率低下的问题。其核心解决方案是提出统一的生成框架UniGen，其中关键创新在于引入Condition Modulated Expert (CoMoE)模块，通过聚合语义相似的图像块特征并分配至专用专家模块进行视觉表征与条件建模，从而有效缓解多条件场景下的特征纠缠和冗余计算；同时设计WeaveNet动态蛇形连接机制，实现骨干网络与控制分支间全局文本级控制与细粒度条件控制的有效交互，显著提升生成效率与表达能力。
链接: https://arxiv.org/abs/2508.17364
作者: Guoqing Zhang,Xingtong Ge,Lu Shi,Xin Zhang,Muqing Xue,Wanru Xu,Yigang Cen
机构: Bejing Jiaotong University (北京交通大学); SenseTime Research (商汤研究); Hong Kong University of Science and Technology (香港科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:The image-to-image generation task aims to produce controllable images by leveraging conditional inputs and prompt instructions. However, existing methods often train separate control branches for each type of condition, leading to redundant model structures and inefficient use of computational resources. To address this, we propose a Unified image-to-image Generation (UniGen) framework that supports diverse conditional inputs while enhancing generation efficiency and expressiveness. Specifically, to tackle the widely existing parameter redundancy and computational inefficiency in controllable conditional generation architectures, we propose the Condition Modulated Expert (CoMoE) module. This module aggregates semantically similar patch features and assigns them to dedicated expert modules for visual representation and conditional modeling. By enabling independent modeling of foreground features under different conditions, CoMoE effectively mitigates feature entanglement and redundant computation in multi-condition scenarios. Furthermore, to bridge the information gap between the backbone and control branches, we propose WeaveNet, a dynamic, snake-like connection mechanism that enables effective interaction between global text-level control from the backbone and fine-grained control from conditional branches. Extensive experiments on the Subjects-200K and MultiGen-20M datasets across various conditional image generation tasks demonstrate that our method consistently achieves state-of-the-art performance, validating its advantages in both versatility and effectiveness. The code has been uploaded to this https URL.
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[CV-116] DiCache: Let Diffusion Model Determine Its Own Cache
【速读】：该论文旨在解决扩散模型加速技术中缓存策略的通用性不足与性能瓶颈问题，尤其是现有基于预设经验法则或数据集级先验的缓存方法在动态扩散过程中难以适应样本差异、尤其对异常样本表现不佳的问题。其核心解决方案是提出一种无需训练的自适应缓存策略DiCache，关键在于发现浅层特征差异变化模式与最终输出之间存在强相关性，并利用这一特性设计了两个核心组件：(1) 在线探针剖面方案（Online Probe Profiling Scheme），通过浅层在线探针实时获取缓存误差的稳定先验，使模型能够自主决定缓存时机；(2) 动态缓存轨迹对齐（Dynamic Cache Trajectory Alignment），基于浅层探针特征轨迹融合多步缓存以更精确逼近当前特征，从而提升视觉质量。该方法在多个主流扩散模型上实现了更高的加速效率和图像/视频生成质量。
链接: https://arxiv.org/abs/2508.17356
作者: Jiazi Bu,Pengyang Ling,Yujie Zhou,Yibin Wang,Yuhang Zang,Tong Wu,Dahua Lin,Jiaqi Wang
机构: Shanghai Jiao Tong University (上海交通大学); University of Science and Technology of China (中国科学技术大学); Fudan University (复旦大学); Stanford University (斯坦福大学); The Chinese University of Hong Kong (香港中文大学); Shanghai AI Laboratory (上海人工智能实验室); Shanghai Innovation Institute (上海创新研究院); CPII under InnoHK (InnoHK下的CPII)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent years have witnessed the rapid development of acceleration techniques for diffusion models, especially caching-based acceleration methods. These studies seek to answer two fundamental questions: “When to cache” and “How to use cache”, typically relying on predefined empirical laws or dataset-level priors to determine the timing of caching and utilizing handcrafted rules for leveraging multi-step caches. However, given the highly dynamic nature of the diffusion process, they often exhibit limited generalizability and fail on outlier samples. In this paper, a strong correlation is revealed between the variation patterns of the shallow-layer feature differences in the diffusion model and those of final model outputs. Moreover, we have observed that the features from different model layers form similar trajectories. Based on these observations, we present DiCache, a novel training-free adaptive caching strategy for accelerating diffusion models at runtime, answering both when and how to cache within a unified framework. Specifically, DiCache is composed of two principal components: (1) Online Probe Profiling Scheme leverages a shallow-layer online probe to obtain a stable prior for the caching error in real time, enabling the model to autonomously determine caching schedules. (2) Dynamic Cache Trajectory Alignment combines multi-step caches based on shallow-layer probe feature trajectory to better approximate the current feature, facilitating higher visual quality. Extensive experiments validate DiCache’s capability in achieving higher efficiency and improved visual fidelity over state-of-the-art methods on various leading diffusion models including WAN 2.1, HunyuanVideo for video generation, and Flux for image generation.
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[CV-117] No Pixel Left Behind: A Detail-Preserving Architecture for Robust High-Resolution AI-Generated Image Detection
【速读】：该论文旨在解决高分辨率AI生成图像检测中现有方法因依赖低分辨率训练数据而导致的性能下降问题，尤其是传统resize或center-crop策略在处理高分辨率图像时会造成像素信息丢失，从而削弱对细微高频伪造痕迹的识别能力。其解决方案的关键在于提出High-Resolution Detail-Aggregation Network (HiDA-Net)，通过Feature Aggregation Module (FAM) 将多张全分辨率局部图像块（local tiles）与下采样后的全局图像特征进行融合，确保所有像素均被保留并参与最终检测决策；同时引入Token-wise Forgery Localization (TFL) 模块提升空间敏感性以应对局部篡改，并设计JPEG Quality Factor Estimation (QFE) 模块显式分离生成伪影与压缩噪声，增强模型鲁棒性。
链接: https://arxiv.org/abs/2508.17346
作者: Lianrui Mu,Zou Xingze,Jianhong Bai,Jiaqi Hu,Wenjie Zheng,Jiangnan Ye,Jiedong Zhuang,Mudassar Ali,Jing Wang,Haoji Hu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The rapid growth of high-resolution, meticulously crafted AI-generated images poses a significant challenge to existing detection methods, which are often trained and evaluated on low-resolution, automatically generated datasets that do not align with the complexities of high-resolution scenarios. A common practice is to resize or center-crop high-resolution images to fit standard network inputs. However, without full coverage of all pixels, such strategies risk either obscuring subtle, high-frequency artifacts or discarding information from uncovered regions, leading to input information loss. In this paper, we introduce the High-Resolution Detail-Aggregation Network (HiDA-Net), a novel framework that ensures no pixel is left behind. We use the Feature Aggregation Module (FAM), which fuses features from multiple full-resolution local tiles with a down-sampled global view of the image. These local features are aggregated and fused with global representations for final prediction, ensuring that native-resolution details are preserved and utilized for detection. To enhance robustness against challenges such as localized AI manipulations and compression, we introduce Token-wise Forgery Localization (TFL) module for fine-grained spatial sensitivity and JPEG Quality Factor Estimation (QFE) module to disentangle generative artifacts from compression noise explicitly. Furthermore, to facilitate future research, we introduce HiRes-50K, a new challenging benchmark consisting of 50,568 images with up to 64 megapixels. Extensive experiments show that HiDA-Net achieves state-of-the-art, increasing accuracy by over 13% on the challenging Chameleon dataset and 10% on our HiRes-50K.
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[CV-118] DanceEditor: Towards Iterative Editable Music-driven Dance Generation with Open-Vocabulary Descriptions
【速读】：该论文旨在解决现有舞蹈生成方法难以支持用户对舞蹈动作进行迭代编辑的问题，尤其是在真实 choreography（编舞）场景中，直接生成舞蹈虽已取得进展，但缺乏对用户交互式修改的支持。其解决方案的关键在于提出一个名为 DanceEditor 的新框架，该框架采用“预测-编辑”范式，统一多模态条件以实现音乐同步且可编辑的舞蹈生成：在初始预测阶段，通过直接建模与音乐对齐的舞蹈动作提升生成结果的权威性；在后续迭代编辑阶段，则引入文本描述作为条件信息，并设计 Cross-modality Editing Module (CEM)，自适应地融合初始预测、音乐和文本提示作为时序运动线索，从而在保持音乐和谐性的同时，精确对齐细粒度语义描述。
链接: https://arxiv.org/abs/2508.17342
作者: Hengyuan Zhang,Zhe Li,Xingqun Qi,Mengze Li,Muyi Sun,Man Zhang,Sirui Han
机构: Peking University (北京大学); The Hong Kong University of Science and Technology (香港科技大学); Beijing University of Posts and Telecommunications (北京邮电大学)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Sound (cs.SD)
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Abstract:Generating coherent and diverse human dances from music signals has gained tremendous progress in animating virtual avatars. While existing methods support direct dance synthesis, they fail to recognize that enabling users to edit dance movements is far more practical in real-world choreography scenarios. Moreover, the lack of high-quality dance datasets incorporating iterative editing also limits addressing this challenge. To achieve this goal, we first construct DanceRemix, a large-scale multi-turn editable dance dataset comprising the prompt featuring over 25.3M dance frames and 84.5K pairs. In addition, we propose a novel framework for iterative and editable dance generation coherently aligned with given music signals, namely DanceEditor. Considering the dance motion should be both musical rhythmic and enable iterative editing by user descriptions, our framework is built upon a prediction-then-editing paradigm unifying multi-modal conditions. At the initial prediction stage, our framework improves the authority of generated results by directly modeling dance movements from tailored, aligned music. Moreover, at the subsequent iterative editing stages, we incorporate text descriptions as conditioning information to draw the editable results through a specifically designed Cross-modality Editing Module (CEM). Specifically, CEM adaptively integrates the initial prediction with music and text prompts as temporal motion cues to guide the synthesized sequences. Thereby, the results display music harmonics while preserving fine-grained semantic alignment with text descriptions. Extensive experiments demonstrate that our method outperforms the state-of-the-art models on our newly collected DanceRemix dataset. Code is available at this https URL.
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[CV-119] SpecGen: Neural Spectral BRDF Generation via Spectral-Spatial Tri-plane Aggregation
【速读】：该论文旨在解决从单张RGB图像中生成高精度光谱双向反射分布函数（Spectral Bidirectional Reflectance Distribution Function, BRDF）的问题，从而实现任意光照和几何条件下真实感的光谱图像渲染。其核心挑战在于光谱BRDF数据稀缺，难以直接训练深度学习模型。解决方案的关键在于提出了一种名为“光谱-空间三平面聚合”（Spectral-Spatial Tri-plane Aggregation, SSTA）的网络架构，该结构能够联合建模不同波长下的反射响应以及入射与出射方向的关系，并利用大量可用的RGB BRDF数据作为辅助训练信号，有效提升光谱BRDF生成质量。实验表明，该方法在有限光谱数据下仍能准确重建光谱BRDF，且在超光谱图像重建任务中相较现有最优方法提升8 dB的PSNR。
链接: https://arxiv.org/abs/2508.17316
作者: Zhenyu Jin,Wenjie Li,Zhanyu Ma,Heng Guo
机构: Beijing University of Posts and Telecommunications (北京邮电大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Synthesizing spectral images across different wavelengths is essential for photorealistic rendering. Unlike conventional spectral uplifting methods that convert RGB images into spectral ones, we introduce SpecGen, a novel method that generates spectral bidirectional reflectance distribution functions (BRDFs) from a single RGB image of a sphere. This enables spectral image rendering under arbitrary illuminations and shapes covered by the corresponding material. A key challenge in spectral BRDF generation is the scarcity of measured spectral BRDF data. To address this, we propose the Spectral-Spatial Tri-plane Aggregation (SSTA) network, which models reflectance responses across wavelengths and incident-outgoing directions, allowing the training strategy to leverage abundant RGB BRDF data to enhance spectral BRDF generation. Experiments show that our method accurately reconstructs spectral BRDFs from limited spectral data and surpasses state-of-the-art methods in hyperspectral image reconstruction, achieving an improvement of 8 dB in PSNR. Codes and data will be released upon acceptance.
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[CV-120] Defending Deepfake via Texture Feature Perturbation
【速读】：该论文旨在解决深度伪造（Deepfake）技术快速发展对社会信任和信息安全带来的严峻挑战，尤其针对现有被动检测方法在面对高质量伪造内容时难以有效识别的问题。其解决方案的关键在于提出一种基于面部纹理特征的主动防御机制：通过在图像编辑阶段预先嵌入不可见信号，利用人眼对平滑区域扰动更敏感的特性，在低感知显著性的纹理区域插入局部扰动，同时最小化非纹理区域的噪声干扰；具体实现上，采用局部二值模式（Local Binary Patterns, LBP）提取初步纹理特征，并引入双模型注意力机制生成与优化纹理扰动，从而在多种攻击模型下有效扭曲Deepfake生成并产生明显视觉缺陷，为深度伪造的主动检测提供高效且可扩展的方案。
链接: https://arxiv.org/abs/2508.17315
作者: Xiao Zhang,Changfang Chen,Tianyi Wang
机构: Qilu University of Technology (Shandong Academy of Sciences); National University of Singapore
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to IEEE SMC 2025
Abstract:The rapid development of Deepfake technology poses severe challenges to social trust and information security. While most existing detection methods primarily rely on passive analyses, due to unresolvable high-quality Deepfake contents, proactive defense has recently emerged by inserting invisible signals in advance of image editing. In this paper, we introduce a proactive Deepfake detection approach based on facial texture features. Since human eyes are more sensitive to perturbations in smooth regions, we invisibly insert perturbations within texture regions that have low perceptual saliency, applying localized perturbations to key texture regions while minimizing unwanted noise in non-textured areas. Our texture-guided perturbation framework first extracts preliminary texture features via Local Binary Patterns (LBP), and then introduces a dual-model attention strategy to generate and optimize texture perturbations. Experiments on CelebA-HQ and LFW datasets demonstrate the promising performance of our method in distorting Deepfake generation and producing obvious visual defects under multiple attack models, providing an efficient and scalable solution for proactive Deepfake detection.
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[CV-121] First Place Solution to the MLCAS 2025 GWFSS Challenge: The Devil is in the Detail and Minority
【速读】：该论文针对小麦植物语义分割任务中茎部（stem）分割精度低的问题展开研究，该问题源于茎部结构细碎、像素占比小，易受类别不平衡和预测不稳定性影响。解决方案的关键在于聚焦于茎部这一核心难点，提出三项针对性改进：(i) 引入动态上采样模块SAPA以增强细节分割能力；(ii) 采用基于茎部感知样本选择的半监督引导蒸馏策略，挖掘未标注数据中的潜在信息；(iii) 应用测试时缩放策略对图像进行两次精细化分割。上述方法虽简单但有效，最终使模型在MLCAS 2025 GWFSS挑战赛中取得第一名。
链接: https://arxiv.org/abs/2508.17305
作者: Songliang Cao,Tianqi Hu,Hao Lu
机构: Huazhong University of Science and Technology (华中科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In this report, we present our solution during the participation of the MLCAS 2025 GWFSS Challenge. This challenge hosts a semantic segmentation competition specific to wheat plants, which requires to segment three wheat organs including the head, leaf, and stem, and another background class. In 2025, participating a segmentation competition is significantly different from that in previous years where many tricks can play important roles. Nowadays most segmentation tricks have been well integrated into existing codebases such that our naive ViT-Adapter baseline has already achieved sufficiently good performance. Hence, we believe the key to stand out among other competitors is to focus on the problem nature of wheat per se. By probing visualizations, we identify the key – the stem matters. In contrast to heads and leaves, stems exhibit fine structure and occupy only few pixels, which suffers from fragile predictions and class imbalance. Building on our baseline, we present three technical improvements tailored to stems: i) incorporating a dynamic upsampler SAPA used to enhance detail delineation; ii) leveraging semi-supervised guided distillation with stem-aware sample selection to mine the treasure beneath unlabeled data; and iii) applying a test-time scaling strategy to zoom in and segment twice the image. Despite being simple, the three improvements bring us to the first place of the competition, outperforming the second place by clear margins. Code and models will be released at this https URL.
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[CV-122] PosBridge: Multi-View Positional Embedding Transplant for Identity-Aware Image Editing
【速读】：该论文旨在解决局部主体驱动图像编辑（localized subject-driven image editing）中如何高效、无训练地将用户指定对象无缝插入目标场景的问题。随着生成模型规模扩大，传统方法在内存和计算资源上的开销日益显著，亟需一种无需训练且可扩展的编辑方案。其解决方案的关键在于提出PosBridge框架，核心创新是“位置嵌入移植”（positional embedding transplant），该技术通过在扩散模型的逐步去噪过程中引导目标区域的噪声分布向参考对象的噪声分布靠拢，从而忠实复现参考对象的结构特征；同时引入“角落居中布局”（Corner Centered Layout），将参考图像与背景图像拼接作为输入送入FLUX.1-Fill模型，有效指导模型在指定位置合成身份一致的内容，实现高保真度与结构一致性。
链接: https://arxiv.org/abs/2508.17302
作者: Peilin Xiong,Junwen Chen,Honghui Yuan,Keiji Yanai
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Localized subject-driven image editing aims to seamlessly integrate user-specified objects into target scenes. As generative models continue to scale, training becomes increasingly costly in terms of memory and computation, highlighting the need for training-free and scalable editing this http URL this end, we propose PosBridge an efficient and flexible framework for inserting custom objects. A key component of our method is positional embedding transplant, which guides the diffusion model to faithfully replicate the structural characteristics of reference this http URL, we introduce the Corner Centered Layout, which concatenates reference images and the background image as input to the FLUX.1-Fill model. During progressive denoising, positional embedding transplant is applied to guide the noise distribution in the target region toward that of the reference object. In this way, Corner Centered Layout effectively directs the FLUX.1-Fill model to synthesize identity-consistent content at the desired location. Extensive experiments demonstrate that PosBridge outperforms mainstream baselines in structural consistency, appearance fidelity, and computational efficiency, showcasing its practical value and potential for broad adoption.
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[CV-123] FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
【速读】：该论文旨在解决低剂量计算机断层扫描（Low-dose Computed Tomography, LDCT）图像去噪中现有深度学习方法在不同剂量水平和解剖区域间泛化能力不足的问题。这些问题源于模型通常在特定剂量和解剖区域上训练，难以适应多样化的噪声特征和解剖异质性，从而限制了其在临床场景中的鲁棒性和适用性。解决方案的关键在于提出一个名为FoundDiff的统一基础扩散模型，采用两阶段策略：首先通过剂量-解剖感知对比语言图像预训练模型（Dose- and Anatomy-aware Contrastive Language Image Pre-training Model, DA-CLIP）实现对剂量等级和解剖结构的连续表征与识别；其次设计一个剂量-解剖感知扩散模型（Dose- and Anatomy-aware Diffusion Model, DA-Diff），通过创新的剂量与解剖条件块（Dose and Anatomy Conditional Block, DACB）将DA-CLIP提取的嵌入信息融入扩散过程，从而实现自适应且通用的去噪效果。
链接: https://arxiv.org/abs/2508.17299
作者: Zhihao Chen,Qi Gao,Zilong Li,Junping Zhang,Yi Zhang,Jun Zhao,Hongming Shan
机构: Fudan University (复旦大学); Shanghai Center for Brain Science and Brain-inspired Technology (上海脑科学与类脑技术研究中心); Sichuan University (四川大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 7 figures
Abstract:Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements driven by deep learning (DL) in recent years, existing DL-based methods, typically trained on a specific dose level and anatomical region, struggle to handle diverse noise characteristics and anatomical heterogeneity during varied scanning conditions, limiting their generalizability and robustness in clinical scenarios. In this paper, we propose FoundDiff, a foundational diffusion model for unified and generalizable LDCT denoising across various dose levels and anatomical regions. FoundDiff employs a two-stage strategy: (i) dose-anatomy perception and (ii) adaptive denoising. First, we develop a dose- and anatomy-aware contrastive language image pre-training model (DA-CLIP) to achieve robust dose and anatomy perception by leveraging specialized contrastive learning strategies to learn continuous representations that quantify ordinal dose variations and identify salient anatomical regions. Second, we design a dose- and anatomy-aware diffusion model (DA-Diff) to perform adaptive and generalizable denoising by synergistically integrating the learned dose and anatomy embeddings from DACLIP into diffusion process via a novel dose and anatomy conditional block (DACB) based on Mamba. Extensive experiments on two public LDCT datasets encompassing eight dose levels and three anatomical regions demonstrate superior denoising performance of FoundDiff over existing state-of-the-art methods and the remarkable generalization to unseen dose levels. The codes and models are available at this https URL.
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[CV-124] Explain Before You Answer: A Survey on Compositional Visual Reasoning
【速读】：该论文旨在解决当前多模态人工智能（Multimodal AI）领域中 compositional visual reasoning（组合式视觉推理）研究缺乏系统性综述的问题。随着视觉语言模型（Vision-Language Models, VLMs）的发展，如何使机器具备类似人类的分解视觉场景、定位中间概念并进行多步逻辑推理的能力成为关键挑战。论文通过梳理2023至2025年间来自CVPR、ICCV、NeurIPS、ICML、ACL等顶会的260余篇文献，提出了一套统一的分类体系（taxonomy）和历史发展路线图，揭示了从提示增强的语言中心范式到工具增强的大语言模型（LLM）、视觉语言模型（VLM），再到链式思维（Chain-of-Thought）推理与统一代理型VLM的五阶段演进路径。其解决方案的关键在于：首先形式化定义组合式推理的核心要素及其在认知对齐、语义保真度、鲁棒性、可解释性和数据效率方面的优势；其次构建涵盖60余个基准测试和对应指标的评估框架，以量化分析不同方法在接地准确性、推理链忠实度及高分辨率感知等方面的性能；最终提炼出当前主要挑战（如LLM推理局限、幻觉问题、演绎推理偏倚、可扩展监督、工具集成与基准缺陷）并指出未来方向，包括世界模型整合、人机协同推理及更丰富的评估协议，从而为下一代组合式视觉推理研究提供理论基础与实践指引。
链接: https://arxiv.org/abs/2508.17298
作者: Fucai Ke,Joy Hsu,Zhixi Cai,Zixian Ma,Xin Zheng,Xindi Wu,Sukai Huang,Weiqing Wang,Pari Delir Haghighi,Gholamreza Haffari,Ranjay Krishna,Jiajun Wu,Hamid Rezatofighi
机构: Monash University (蒙纳士大学); Stanford University (斯坦福大学); University of Washington (华盛顿大学); Griffith University (格里菲斯大学); Princeton University (普林斯顿大学); Allen Institute for Artificial Intelligence (人工智能研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Compositional visual reasoning has emerged as a key research frontier in multimodal AI, aiming to endow machines with the human-like ability to decompose visual scenes, ground intermediate concepts, and perform multi-step logical inference. While early surveys focus on monolithic vision-language models or general multimodal reasoning, a dedicated synthesis of the rapidly expanding compositional visual reasoning literature is still missing. We fill this gap with a comprehensive survey spanning 2023 to 2025 that systematically reviews 260+ papers from top venues (CVPR, ICCV, NeurIPS, ICML, ACL, etc.). We first formalize core definitions and describe why compositional approaches offer advantages in cognitive alignment, semantic fidelity, robustness, interpretability, and data efficiency. Next, we trace a five-stage paradigm shift: from prompt-enhanced language-centric pipelines, through tool-enhanced LLMs and tool-enhanced VLMs, to recently minted chain-of-thought reasoning and unified agentic VLMs, highlighting their architectural designs, strengths, and limitations. We then catalog 60+ benchmarks and corresponding metrics that probe compositional visual reasoning along dimensions such as grounding accuracy, chain-of-thought faithfulness, and high-resolution perception. Drawing on these analyses, we distill key insights, identify open challenges (e.g., limitations of LLM-based reasoning, hallucination, a bias toward deductive reasoning, scalable supervision, tool integration, and benchmark limitations), and outline future directions, including world-model integration, human-AI collaborative reasoning, and richer evaluation protocols. By offering a unified taxonomy, historical roadmap, and critical outlook, this survey aims to serve as a foundational reference and inspire the next generation of compositional visual reasoning research.
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[CV-125] Quickly Tuning Foundation Models for Image Segmentation
【速读】：该论文旨在解决基础模型（如SAM，Segment Anything Model）在特定领域图像分割任务中表现不足的问题，以及传统微调方法依赖大量人工干预和领域专业知识的局限性。其解决方案的关键在于提出一种基于元学习（meta-learning）驱动的自动化微调框架QTT-SEG，该框架利用Quick-Tune超参数优化框架，通过元学习构建成本与性能预测模型，从而高效探索超过2亿种配置的可能性，实现对SAM模型的快速、高性能适配。
链接: https://arxiv.org/abs/2508.17283
作者: Breenda Das,Lennart Purucker,Timur Carstensen,Frank Hutter
机构: University of Freiburg(弗莱堡大学); ELLIS Institute Tübingen(图宾根ELLIS研究所); Prior Labs
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted as a short paper at the non-archival content track of AutoML 2025
Abstract:Foundation models like SAM (Segment Anything Model) exhibit strong zero-shot image segmentation performance, but often fall short on domain-specific tasks. Fine-tuning these models typically requires significant manual effort and domain expertise. In this work, we introduce QTT-SEG, a meta-learning-driven approach for automating and accelerating the fine-tuning of SAM for image segmentation. Built on the Quick-Tune hyperparameter optimization framework, QTT-SEG predicts high-performing configurations using meta-learned cost and performance models, efficiently navigating a search space of over 200 million possibilities. We evaluate QTT-SEG on eight binary and five multiclass segmentation datasets under tight time constraints. Our results show that QTT-SEG consistently improves upon SAM’s zero-shot performance and surpasses AutoGluon Multimodal, a strong AutoML baseline, on most binary tasks within three minutes. On multiclass datasets, QTT-SEG delivers consistent gains as well. These findings highlight the promise of meta-learning in automating model adaptation for specialized segmentation tasks. Code available at: this https URL
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[CV-126] MTNet: Learning modality-aware representation with transformer for RGBT tracking
【速读】：该论文旨在解决RGBT（可见光与热红外）跟踪中特征交互受限的问题，具体表现为传统融合范式和固定跟踪模板难以有效建模多模态特征间的动态关联与变化。解决方案的关键在于提出一种基于Transformer的模态感知跟踪器MTNet，其核心创新包括：(1) 设计模态感知网络，包含通道聚合与分布模块（CADM）和空间相似性感知模块（SSPM），以挖掘模态特异性线索；(2) 引入Transformer融合网络捕捉全局依赖关系，增强实例表征能力；(3) 构建三叉预测头（trident prediction head）与动态更新策略，协同提升定位精度并应对尺度变化和形变等挑战，从而实现帧间可靠通信与实时性能。
链接: https://arxiv.org/abs/2508.17280
作者: Ruichao Hou,Boyue Xu,Tongwei Ren,Gangshan Wu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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Abstract:The ability to learn robust multi-modality representation has played a critical role in the development of RGBT tracking. However, the regular fusion paradigm and the invariable tracking template remain restrictive to the feature interaction. In this paper, we propose a modality-aware tracker based on transformer, termed MTNet. Specifically, a modality-aware network is presented to explore modality-specific cues, which contains both channel aggregation and distribution module(CADM) and spatial similarity perception module (SSPM). A transformer fusion network is then applied to capture global dependencies to reinforce instance representations. To estimate the precise location and tackle the challenges, such as scale variation and deformation, we design a trident prediction head and a dynamic update strategy which jointly maintain a reliable template for facilitating inter-frame communication. Extensive experiments validate that the proposed method achieves satisfactory results compared with the state-of-the-art competitors on three RGBT benchmarks while reaching real-time speed.
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[CV-127] Deep Learning-Assisted Detection of Sarcopenia in Cross-Sectional Computed Tomography Imaging
【速读】：该论文旨在解决肌肉减少症（sarcopenia）在临床中依赖人工测量骨骼肌面积（SMA）所导致的效率低、工作量大及难以及时筛查的问题。其关键解决方案是基于深度学习模型，结合迁移学习与自监督学习方法，利用标注和未标注的CT影像数据集自动量化SMA并生成精确分割掩膜，从而实现对肌肉减少症的高效、准确评估。实验表明，该模型预测SMA的平均误差为±3%，分割掩膜的Dice相似系数达93%，显著提升了自动化水平并缓解了数据稀缺与类别不平衡问题。
链接: https://arxiv.org/abs/2508.17275
作者: Manish Bhardwaj,Huizhi Liang,Ashwin Sivaharan,Sandip Nandhra,Vaclav Snasel,Tamer El-Sayed,Varun Ojha
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Sarcopenia is a progressive loss of muscle mass and function linked to poor surgical outcomes such as prolonged hospital stays, impaired mobility, and increased mortality. Although it can be assessed through cross-sectional imaging by measuring skeletal muscle area (SMA), the process is time-consuming and adds to clinical workloads, limiting timely detection and management; however, this process could become more efficient and scalable with the assistance of artificial intelligence applications. This paper presents high-quality three-dimensional cross-sectional computed tomography (CT) images of patients with sarcopenia collected at the Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust. Expert clinicians manually annotated the SMA at the third lumbar vertebra, generating precise segmentation masks. We develop deep-learning models to measure SMA in CT images and automate this task. Our methodology employed transfer learning and self-supervised learning approaches using labelled and unlabeled CT scan datasets. While we developed qualitative assessment models for detecting sarcopenia, we observed that the quantitative assessment of SMA is more precise and informative. This approach also mitigates the issue of class imbalance and limited data availability. Our model predicted the SMA, on average, with an error of ±3 percentage points against the manually measured SMA. The average dice similarity coefficient of the predicted masks was 93%. Our results, therefore, show a pathway to full automation of sarcopenia assessment and detection.
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[CV-128] Spatial-Temporal Human-Object Interaction Detection
【速读】：该论文旨在解决视频中细粒度人-物交互（Human-Object Interaction, HOI）的实例级检测问题，即不仅要识别交互关系，还需准确追踪交互主体与物体在时空维度上的轨迹。为应对这一挑战，作者提出了一种新方法，其核心在于两个关键模块：一是物体轨迹检测模块，用于精确捕捉视频中对象的运动轨迹；二是交互推理模块，用于推断人与物之间的细粒度交互关系。该方法在首个专为该任务构建的数据集VidOR-HOID上进行了验证，该数据集包含10,831个时空HOI实例，实验表明该方法显著优于现有图像和视频层面的HOI检测基线模型。
链接: https://arxiv.org/abs/2508.17270
作者: Xu Sun,Yunqing He,Tongwei Ren,Gangshan Wu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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Abstract:In this paper, we propose a new instance-level human-object interaction detection task on videos called ST-HOID, which aims to distinguish fine-grained human-object interactions (HOIs) and the trajectories of subjects and objects. It is motivated by the fact that HOI is crucial for human-centric video content understanding. To solve ST-HOID, we propose a novel method consisting of an object trajectory detection module and an interaction reasoning module. Furthermore, we construct the first dataset named VidOR-HOID for ST-HOID evaluation, which contains 10,831 spatial-temporal HOI instances. We conduct extensive experiments to evaluate the effectiveness of our method. The experimental results demonstrate that our method outperforms the baselines generated by the state-of-the-art methods of image human-object interaction detection, video visual relation detection and video human-object interaction recognition.
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[CV-129] AdaGAT: Adaptive Guidance Adversarial Training for the Robustness of Deep Neural Networks
【速读】：该论文旨在解决知识蒸馏（Knowledge Distillation）中教师模型（teacher model）在与学生模型（student model）协同训练时难以维持最优状态，从而影响鲁棒性迁移效率的问题。现有方法多采用可学习的引导模型（guide model）来提升学生模型的鲁棒性，但因其从零开始训练，难以在训练过程中持续保持对知识传递最有利的状态。解决方案的关键在于提出一种自适应引导对抗训练（Adaptive Guidance Adversarial Training, AdaGAT）方法，通过设计两个独立的损失函数，动态调整引导模型的训练状态，使其在反向传播中更积极地参与优化，从而稳定地将鲁棒性知识高效迁移至学生模型。实验表明，在CIFAR-10、CIFAR-100和TinyImageNet数据集上，AdaGAT显著提升了学生模型在多种对抗攻击下的鲁棒性能。
链接: https://arxiv.org/abs/2508.17265
作者: Zhenyu Liu,Huizhi Liang,Xinrun Li,Vaclav Snasel,Varun Ojha
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Adversarial distillation (AD) is a knowledge distillation technique that facilitates the transfer of robustness from teacher deep neural network (DNN) models to lightweight target (student) DNN models, enabling the target models to perform better than only training the student model independently. Some previous works focus on using a small, learnable teacher (guide) model to improve the robustness of a student model. Since a learnable guide model starts learning from scratch, maintaining its optimal state for effective knowledge transfer during co-training is challenging. Therefore, we propose a novel Adaptive Guidance Adversarial Training (AdaGAT) method. Our method, AdaGAT, dynamically adjusts the training state of the guide model to install robustness to the target model. Specifically, we develop two separate loss functions as part of the AdaGAT method, allowing the guide model to participate more actively in backpropagation to achieve its optimal state. We evaluated our approach via extensive experiments on three datasets: CIFAR-10, CIFAR-100, and TinyImageNet, using the WideResNet-34-10 model as the target model. Our observations reveal that appropriately adjusting the guide model within a certain accuracy range enhances the target model’s robustness across various adversarial attacks compared to a variety of baseline models.
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[CV-130] CLIFF: Continual Learning for Incremental Flake Features in 2D Material Identification
【速读】：该论文旨在解决二维（2D）材料中量子薄片（quantum flakes）的自动层数分类问题，尤其是在光学显微镜图像中因不同材料间显著外观差异导致的持续学习挑战。其核心解决方案是提出一种名为CLIFF（Continual-Learning Framework for Flake Layer Classification）的持续学习框架，关键在于通过冻结预训练的骨干网络和基础头部（base head），为每种新材料学习特定的提示（prompt）、嵌入（embedding）和增量头部（delta head），并利用提示池与余弦相似度门控机制动态调整特征表示，从而实现跨材料的高效迁移与适应；同时引入记忆回放结合知识蒸馏策略以显著降低灾难性遗忘，相较传统微调和基于提示的基线方法，在准确率相当的前提下大幅提升了模型稳定性。
链接: https://arxiv.org/abs/2508.17261
作者: Sankalp Pandey,Xuan Bac Nguyen,Nicholas Borys,Hugh Churchill,Khoa Luu
机构: University of Arkansas (阿肯色大学); Montana State University (蒙大拿州立大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Identifying quantum flakes is crucial for scalable quantum hardware; however, automated layer classification from optical microscopy remains challenging due to substantial appearance shifts across different materials. In this paper, we propose a new Continual-Learning Framework for Flake Layer Classification (CLIFF). To our knowledge, this is the first systematic study of continual learning in the domain of two-dimensional (2D) materials. Our method enables the model to differentiate between materials and their physical and optical properties by freezing a backbone and base head trained on a reference material. For each new material, it learns a material-specific prompt, embedding, and a delta head. A prompt pool and a cosine-similarity gate modulate features and compute material-specific corrections. Additionally, we incorporate memory replay with knowledge distillation. CLIFF achieves competitive accuracy with significantly lower forgetting than naive fine-tuning and a prompt-based baseline.
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[CV-131] ResLink: A Novel Deep Learning Architecture for Brain Tumor Classification with Area Attention and Residual Connections
【速读】：该论文旨在解决脑肿瘤（brain tumor）早期准确诊断的难题，以提升治疗效果。其核心解决方案是提出一种名为ResLink的新型深度学习架构，关键在于将新颖的区域注意力机制（area attention mechanism）与残差连接（residual connection）相结合，从而增强特征学习能力和空间理解力，特别适用于空间信息丰富的图像分类任务。通过多阶段卷积管道、丢弃（dropout）、正则化和下采样处理，并辅以基于注意力的最终精炼模块，ResLink在平衡数据集上实现了95%的高准确率，展现出良好的泛化性能，为医学影像分析提供了高效且鲁棒的技术路径。
链接: https://arxiv.org/abs/2508.17259
作者: Sumedha Arya,Nirmal Gaud
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 pages, 6 figures
Abstract:Brain tumors show significant health challenges due to their potential to cause critical neurological functions. Early and accurate diagnosis is crucial for effective treatment. In this research, we propose ResLink, a novel deep learning architecture for brain tumor classification using CT scan images. ResLink integrates novel area attention mechanisms with residual connections to enhance feature learning and spatial understanding for spatially rich image classification tasks. The model employs a multi-stage convolutional pipeline, incorporating dropout, regularization, and downsampling, followed by a final attention-based refinement for classification. Trained on a balanced dataset, ResLink achieves a high accuracy of 95% and demonstrates strong generalizability. This research demonstrates the potential of ResLink in improving brain tumor classification, offering a robust and efficient technique for medical imaging applications.
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[CV-132] SEER-VAR: Semantic Egocentric Environment Reason er for Vehicle Augmented Reality
【速读】：该论文旨在解决现有基于第一人称视角的车载增强现实（AR）系统在动态复杂驾驶环境中难以实现语义解耦、空间定位不准确以及交互推荐缺乏上下文感知的问题。解决方案的关键在于提出SEER-VAR框架，其核心创新包括：通过深度引导的视觉-语言接地技术实现舱内与道路场景的动态分离；引入上下文感知的SLAM分支（Context-Aware SLAM Branches, CASB）分别追踪两个场景下的6自由度（6DoF）运动状态；并利用大语言模型（LLM）驱动的推荐模块生成情境相关的AR叠加信息（如仪表盘提示和危险预警），从而提升AR渲染的空间一致性与语义相关性。
链接: https://arxiv.org/abs/2508.17255
作者: Yuzhi Lai,Shenghai Yuan,Peizheng Li,Jun Lou,Andreas Zell
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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Abstract:We present SEER-VAR, a novel framework for egocentric vehicle-based augmented reality (AR) that unifies semantic decomposition, Context-Aware SLAM Branches (CASB), and LLM-driven recommendation. Unlike existing systems that assume static or single-view settings, SEER-VAR dynamically separates cabin and road scenes via depth-guided vision-language grounding. Two SLAM branches track egocentric motion in each context, while a GPT-based module generates context-aware overlays such as dashboard cues and hazard alerts. To support evaluation, we introduce EgoSLAM-Drive, a real-world dataset featuring synchronized egocentric views, 6DoF ground-truth poses, and AR annotations across diverse driving scenarios. Experiments demonstrate that SEER-VAR achieves robust spatial alignment and perceptually coherent AR rendering across varied environments. As one of the first to explore LLM-based AR recommendation in egocentric driving, we address the lack of comparable systems through structured prompting and detailed user studies. Results show that SEER-VAR enhances perceived scene understanding, overlay relevance, and driver ease, providing an effective foundation for future research in this direction. Code and dataset will be made open source.
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[CV-133] A biological vision inspired framework for machine perception of abutting grating illusory contours
【速读】：该论文旨在解决深度神经网络（Deep Neural Networks, DNN）在感知错觉轮廓（illusory contours）方面与人类视觉系统不一致的问题，特别是对“接合光栅”（abutting grating）这类典型错觉图形的识别能力不足。其解决方案的关键在于提出一种受视觉皮层电路启发的新型深度网络——错觉轮廓感知网络（Illusory Contour Perception Network, ICPNet），通过三个核心模块实现：1）多尺度特征投影（Multi-scale Feature Projection, MFP）模块提取多层次空间特征；2）特征交互注意力（Feature Interaction Attention Module, FIAM）增强前馈与反馈特征间的动态交互；3）边缘融合模块（Edge Fusion Module, EFM）引入形状先验约束，引导模型聚焦于前景结构。实验表明，ICPNet在AG-MNIST和自建的AG-Fashion-MNIST测试集上显著优于现有方法，提升了对错觉轮廓的敏感性与分类准确率，为迈向类人智能的DNN模型提供了重要进展。
链接: https://arxiv.org/abs/2508.17254
作者: Xiao Zhang,Kai-Fu Yang,Xian-Shi Zhang,Hong-Zhi You,Hong-Mei Yan,Yong-Jie Li
机构: Sichuan Cancer Hospital & Institute (四川癌症中心); University of Electronic Science and Technology of China (电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Higher levels of machine intelligence demand alignment with human perception and cognition. Deep neural networks (DNN) dominated machine intelligence have demonstrated exceptional performance across various real-world tasks. Nevertheless, recent evidence suggests that DNNs fail to perceive illusory contours like the abutting grating, a discrepancy that misaligns with human perception patterns. Departing from previous works, we propose a novel deep network called illusory contour perception network (ICPNet) inspired by the circuits of the visual cortex. In ICPNet, a multi-scale feature projection (MFP) module is designed to extract multi-scale representations. To boost the interaction between feedforward and feedback features, a feature interaction attention module (FIAM) is introduced. Moreover, drawing inspiration from the shape bias observed in human perception, an edge detection task conducted via the edge fusion module (EFM) injects shape constraints that guide the network to concentrate on the foreground. We assess our method on the existing AG-MNIST test set and the AG-Fashion-MNIST test sets constructed by this work. Comprehensive experimental results reveal that ICPNet is significantly more sensitive to abutting grating illusory contours than state-of-the-art models, with notable improvements in top-1 accuracy across various subsets. This work is expected to make a step towards human-level intelligence for DNN-based models.
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[CV-134] Uncovering and Mitigating Destructive Multi-Embedding Attacks in Deepfake Proactive Forensics
【速读】：该论文旨在解决深度伪造（deepfake）技术发展带来的隐私安全威胁，特别是现有主动取证（proactive forensics）方法在实际应用中因多轮水印嵌入攻击（Multi-Embedding Attacks, MEA）而失效的问题。MEA指当已嵌入水印的图像再次被额外嵌入水印时，原始水印可能被破坏或移除，导致溯源机制失效。解决方案的关键在于提出一种通用训练范式——对抗干扰模拟（Adversarial Interference Simulation, AIS），其通过在微调阶段显式模拟MEA场景，并引入基于鲁棒性的损失函数，促使模型学习稀疏且稳定的水印表示，从而在经历二次嵌入后仍能准确提取原始水印，显著提升现有方法对MEA的抗性。
链接: https://arxiv.org/abs/2508.17247
作者: Lixin Jia,Haiyang Sun,Zhiqing Guo,Yunfeng Diao,Dan Ma,Gaobo Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:With the rapid evolution of deepfake technologies and the wide dissemination of digital media, personal privacy is facing increasingly serious security threats. Deepfake proactive forensics, which involves embedding imperceptible watermarks to enable reliable source tracking, serves as a crucial defense against these threats. Although existing methods show strong forensic ability, they rely on an idealized assumption of single watermark embedding, which proves impractical in real-world scenarios. In this paper, we formally define and demonstrate the existence of Multi-Embedding Attacks (MEA) for the first time. When a previously protected image undergoes additional rounds of watermark embedding, the original forensic watermark can be destroyed or removed, rendering the entire proactive forensic mechanism ineffective. To address this vulnerability, we propose a general training paradigm named Adversarial Interference Simulation (AIS). Rather than modifying the network architecture, AIS explicitly simulates MEA scenarios during fine-tuning and introduces a resilience-driven loss function to enforce the learning of sparse and stable watermark representations. Our method enables the model to maintain the ability to extract the original watermark correctly even after a second embedding. Extensive experiments demonstrate that our plug-and-play AIS training paradigm significantly enhances the robustness of various existing methods against MEA.
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[CV-135] PersPose: 3D Human Pose Estimation with Perspective Encoding and Perspective Rotation ICCV2025
【速读】：该论文针对单目3D人体姿态估计（Monocular 3D Human Pose Estimation, 3D HPE）中因裁剪图像缺乏相机内参信息而导致关节相对深度难以准确估计的问题展开研究。现有方法仅使用裁剪图像作为输入，无法建模3D场景与图像之间的透视关系，从而限制了精度提升。解决方案的关键在于提出两个核心模块：一是透视编码（Perspective Encoding, PE），用于显式编码裁剪图像对应的相机内参，以恢复透视几何约束；二是透视旋转（Perspective Rotation, PR），通过将原始图像中的人体主体居中变换，减少因人位置偏移导致的透视畸变，从而降低模型拟合难度。二者结合形成新的3D HPE框架PersPose，在3DPW、MPI-INF-3DHP和Human3.6M等数据集上实现SOTA性能，尤其在野外数据集3DPW上MPJPE达到60.1 mm，较前序最优方法提升7.54%。
链接: https://arxiv.org/abs/2508.17239
作者: Xiaoyang Hao,Han Li
机构: Southern University of Science and Technology (南方科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICCV 2025
Abstract:Monocular 3D human pose estimation (HPE) methods estimate the 3D positions of joints from individual images. Existing 3D HPE approaches often use the cropped image alone as input for their models. However, the relative depths of joints cannot be accurately estimated from cropped images without the corresponding camera intrinsics, which determine the perspective relationship between 3D objects and the cropped images. In this work, we introduce Perspective Encoding (PE) to encode the camera intrinsics of the cropped images. Moreover, since the human subject can appear anywhere within the original image, the perspective relationship between the 3D scene and the cropped image differs significantly, which complicates model fitting. Additionally, the further the human subject deviates from the image center, the greater the perspective distortions in the cropped image. To address these issues, we propose Perspective Rotation (PR), a transformation applied to the original image that centers the human subject, thereby reducing perspective distortions and alleviating the difficulty of model fitting. By incorporating PE and PR, we propose a novel 3D HPE framework, PersPose. Experimental results demonstrate that PersPose achieves state-of-the-art (SOTA) performance on the 3DPW, MPIINF-3DHP, and Human3.6M datasets. For example, on the in-the-wild dataset 3DPW, PersPose achieves an MPJPE of 60.1 mm, 7.54% lower than the previous SOTA approach. Code is available at: this https URL KenAdamsJoseph/PersPose.
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[CV-136] Curvature Learning for Generalization of Hyperbolic Neural Networks
【速读】：该论文旨在解决超球形神经网络（Hyperbolic Neural Networks, HNNs）中曲率选择不当导致的泛化性能下降问题，尤其是曲率对损失景观平滑性的影响缺乏理论支撑。解决方案的关键在于提出一种基于PAC-Bayesian理论的泛化界分析，揭示曲率通过调控损失函数的光滑性来影响HNNs泛化能力，并据此设计了一种尖锐度感知的曲率学习方法（sharpness-aware curvature learning）。该方法引入一个曲率范围内的尖锐度度量，并通过双层优化过程最小化该度量，同时采用隐式微分算法高效近似曲率梯度，从而实现对损失景观的平滑化，提升模型泛化性能。理论分析表明，该方法的近似误差有上界，且可通过约束HNN参数梯度实现收敛。
链接: https://arxiv.org/abs/2508.17232
作者: Xiaomeng Fan,Yuwei Wu,Zhi Gao,Mehrtash Harandi,Yunde Jia
机构: Beijing Institute of Technology (北京理工大学); Monash University (莫纳什大学)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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Abstract:Hyperbolic neural networks (HNNs) have demonstrated notable efficacy in representing real-world data with hierarchical structures via exploiting the geometric properties of hyperbolic spaces characterized by negative curvatures. Curvature plays a crucial role in optimizing HNNs. Inappropriate curvatures may cause HNNs to converge to suboptimal parameters, degrading overall performance. So far, the theoretical foundation of the effect of curvatures on HNNs has not been developed. In this paper, we derive a PAC-Bayesian generalization bound of HNNs, highlighting the role of curvatures in the generalization of HNNs via their effect on the smoothness of the loss landscape. Driven by the derived bound, we propose a sharpness-aware curvature learning method to smooth the loss landscape, thereby improving the generalization of HNNs. In our method, we design a scope sharpness measure for curvatures, which is minimized through a bi-level optimization process. Then, we introduce an implicit differentiation algorithm that efficiently solves the bi-level optimization by approximating gradients of curvatures. We present the approximation error and convergence analyses of the proposed method, showing that the approximation error is upper-bounded, and the proposed method can converge by bounding gradients of HNNs. Experiments on four settings: classification, learning from long-tailed data, learning from noisy data, and few-shot learning show that our method can improve the performance of HNNs. Subjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML) Cite as: arXiv:2508.17232 [cs.LG] (or arXiv:2508.17232v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2508.17232 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-137] 4D Visual Pre-training for Robot Learning
【速读】：该论文旨在解决当前机器人学习中普遍依赖2D图像预训练表示而忽视环境三维结构的问题，从而限制了3D任务（如抓取、操作）的性能提升。其核心挑战在于缺乏大规模可扩展的3D数据以直接学习通用的3D视觉表征。为应对这一问题，作者提出了一种新颖的4D视觉预训练框架（FVP），其关键创新在于将视觉预训练目标建模为“下一帧点云预测”任务，并采用扩散模型（diffusion model）作为预测器，在公开的大规模数据集上直接进行预训练。该方法无需依赖特定3D标注数据即可显著增强多种3D表示模型（如DP3）在真实世界操作任务中的表现，平均成功率提升28%，并展现出对不同点云编码器和数据集的良好适应性，同时成功扩展至更大的多模态模型RDT-1B，进一步验证了其通用性和有效性。
链接: https://arxiv.org/abs/2508.17230
作者: Chengkai Hou,Yanjie Ze,Yankai Fu,Zeyu Gao,Songbo Hu,Yue Yu,Shanghang Zhang,Huazhe Xu
机构: Peking University (北京大学); Tsinghua University (清华大学); Shanghai Qizhi Institute (上海奇智研究院); CASIA (中国科学院自动化研究所); Shanghai AI Lab (上海人工智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:General visual representations learned from web-scale datasets for robotics have achieved great success in recent years, enabling data-efficient robot learning on manipulation tasks; yet these pre-trained representations are mostly on 2D images, neglecting the inherent 3D nature of the world. However, due to the scarcity of large-scale 3D data, it is still hard to extract a universal 3D representation from web datasets. Instead, we are seeking a general visual pre-training framework that could improve all 3D representations as an alternative. Our framework, called FVP, is a novel 4D Visual Pre-training framework for real-world robot learning. FVP frames the visual pre-training objective as a next-point-cloud-prediction problem, models the prediction model as a diffusion model, and pre-trains the model on the larger public datasets directly. Across twelve real-world manipulation tasks, FVP boosts the average success rate of 3D Diffusion Policy (DP3) for these tasks by 28%. The FVP pre-trained DP3 achieves state-of-the-art performance across imitation learning methods. Moreover, the efficacy of FVP adapts across various point cloud encoders and datasets. Finally, we apply FVP to the RDT-1B, a larger Vision-Language-Action robotic model, enhancing its performance on various robot tasks. Our project page is available at: https://4d- this http URL.
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[CV-138] Deep Learning with Self-Attention and Enhanced Preprocessing for Precise Diagnosis of Acute Lymphoblastic Leukemia from Bone Marrow Smears in Hemato-Oncology
【速读】：该论文旨在解决急性淋巴细胞白血病（Acute Lymphoblastic Leukemia, ALL）诊断中传统流程复杂、耗时且易受人为误差影响的问题，目标是实现骨髓涂片图像的自动化精准识别与亚型分类。解决方案的关键在于构建一个融合多头自注意力（Multi-Head Self-Attention, MHSA）机制的改进型VGG19卷积神经网络（Convolutional Neural Network, CNN），通过引入MHSA模块增强模型对细胞特征间长程依赖关系和上下文信息的建模能力，并结合焦点损失（Focal Loss）缓解类别不平衡问题，从而显著提升分类准确率至99.25%，优于ResNet101基线模型（98.62%）。
链接: https://arxiv.org/abs/2508.17216
作者: Md. Maruf,Md.Mahbubul Haque,Bishowjit Paul
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 26 pages, 15 figures, 8 tables. VGG19+MHSA with Focal Loss; test accuracy 99.25%
Abstract:Acute lymphoblastic leukemia (ALL) is a prevalent hematological malignancy in both pediatric and adult populations. Early and accurate detection with precise subtyping is essential for guiding therapy. Conventional workflows are complex, time-consuming, and prone to human error. We present a deep learning framework for automated ALL diagnosis from bone marrow smear images. The method combines a robust preprocessing pipeline with convolutional neural networks (CNNs) to standardize image quality and improve inference efficiency. As a key design, we insert a multi-head self-attention (MHSA) block into a VGG19 backbone to model long-range dependencies and contextual relationships among cellular features. To mitigate class imbalance, we train with Focal Loss. Across evaluated architectures, the enhanced VGG19+MHSA trained with Focal Loss achieves 99.25% accuracy, surpassing a strong ResNet101 baseline (98.62%). These results indicate that attention-augmented CNNs, coupled with targeted loss optimization and preprocessing, yield more discriminative representations of leukemic cell morphology. Our approach offers a highly accurate and computationally efficient tool for automated ALL recognition and subtyping, with potential to accelerate diagnostic workflows and support reliable decision-making in clinical settings.
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[CV-139] Multi-modal Knowledge Decomposition based Online Distillation for Biomarker Prediction in Breast Cancer Histopathology MICCAI2025
【速读】：该论文旨在解决在免疫组化（IHC）生物标志物预测中，由于成本或技术限制难以同时获取基因组与病理学多模态数据的问题。其核心解决方案是提出一种基于多模态知识分解（Multi-modal Knowledge Decomposition, MKD）的在线蒸馏方法，通过构建两个教师模型和一个学生模型，分别提取模态特异性与模态通用特征，并结合保持样本间内部结构关系的相似性保留知识蒸馏（Similarity-preserving Knowledge Distillation, SKD）以及促进师生模型协同学习的在线蒸馏协作学习（Collaborative Learning for Online Distillation, CLOD），从而在仅使用病理切片图像（单模态）时也能实现高性能的IHC生物标志物预测。
链接: https://arxiv.org/abs/2508.17213
作者: Qibin Zhang,Xinyu Hao,Qiao Chen,Rui Xu,Fengyu Cong,Cheng Lu,Hongming Xu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI 2025
Abstract:Immunohistochemical (IHC) biomarker prediction benefits from multi-modal data fusion analysis. However, the simultaneous acquisition of multi-modal data, such as genomic and pathological information, is often challenging due to cost or technical limitations. To address this challenge, we propose an online distillation approach based on Multi-modal Knowledge Decomposition (MKD) to enhance IHC biomarker prediction in haematoxylin and eosin (H\E) stained histopathology images. This method leverages paired genomic-pathology data during training while enabling inference using either pathology slides alone or both modalities. Two teacher and one student models are developed to extract modality-specific and modality-general features by minimizing the MKD loss. To maintain the internal structural relationships between samples, Similarity-preserving Knowledge Distillation (SKD) is applied. Additionally, Collaborative Learning for Online Distillation (CLOD) facilitates mutual learning between teacher and student models, encouraging diverse and complementary learning dynamics. Experiments on the TCGA-BRCA and in-house QHSU datasets demonstrate that our approach achieves superior performance in IHC biomarker prediction using uni-modal data. Our code is available at this https URL.
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[CV-140] MMCIG: Multimodal Cover Image Generation for Text-only Documents and Its Dataset Construction via Pseudo-labeling
【速读】：该论文旨在解决从纯文本文档中同时生成简洁摘要和视觉对应图像的新型覆盖图像生成任务（cover image generation task），该任务在现有数据集缺失的情况下难以开展。其核心挑战在于如何低成本构建高质量的多模态标注数据集。解决方案的关键在于提出一种多模态伪标签方法（multimodal pseudo-labeling method）：首先收集包含多张图像及其标题与摘要的文档，并剔除事实不一致样本；接着分别基于黄金摘要对图像和标题进行独立排序，仅当某张图像及其对应标题均在各自排名中位列第一时，才为其标注伪标签；最后去除文本中直接提及图像内容的文档以减少干扰。实验表明，该方法相比仅依赖文本或图像的伪标签策略能显著提升数据集精度和生成图像质量。
链接: https://arxiv.org/abs/2508.17199
作者: Hyeyeon Kim,Sungwoo Han,Jingun Kwon,Hidetaka Kamigaito,Manabu Okumura
机构: Chungnam National University (忠南国立大学); Nara Institute of Science and Technology (奈良科学技术研究所); Institute of Science Tokyo (东京科学研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In this study, we introduce a novel cover image generation task that produces both a concise summary and a visually corresponding image from a given text-only document. Because no existing datasets are available for this task, we propose a multimodal pseudo-labeling method to construct high-quality datasets at low cost. We first collect documents that contain multiple images with their captions, and their summaries by excluding factually inconsistent instances. Our approach selects one image from the multiple images accompanying the documents. Using the gold summary, we independently rank both the images and their captions. Then, we annotate a pseudo-label for an image when both the image and its corresponding caption are ranked first in their respective rankings. Finally, we remove documents that contain direct image references within texts. Experimental results demonstrate that the proposed multimodal pseudo-labeling method constructs more precise datasets and generates higher quality images than text- and image-only pseudo-labeling methods, which consider captions and images separately. We release our code at: this https URL
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[CV-141] Advancing Weakly-Supervised Change Detection in Satellite Images via Adversarial Class Prompting
【速读】：该论文旨在解决弱监督变化检测（Weakly-Supervised Change Detection, WSCD）中因图像级标签信息有限而导致的背景变化误判为对象变化的问题，尤其是在复杂遥感场景下，此类噪声干扰严重影响模型性能。解决方案的关键在于提出对抗性类别提示（Adversarial Class Prompting, AdvCP）方法，其核心机制包含两个阶段：一是对抗提示挖掘（Adversarial Prompt Mining），通过引入对抗扰动并利用错误的一热图像级标签激活错误特征映射，识别出易被误判为对象变化的背景变异像素；二是对抗样本校正（Adversarial Sample Rectification），将这些对抗提示激活的像素样本融合进训练过程，借助在线全局原型（基于当前批次与历史数据的指数加权移动平均构建）进行优化，从而有效抑制背景噪声干扰。AdvCP可无缝集成至现有WSCD方法中且不增加推理开销，在CNN、Transformer及Segment Anything Model（SAM）等基线模型上均实现显著性能提升，并展现出在多类弱监督密集预测任务中的泛化能力。
链接: https://arxiv.org/abs/2508.17186
作者: Zhenghui Zhao,Chen Wu,Di Wang,Hongruixuan Chen,Cuiqun Chen,Zhuo Zheng,Bo Du,Liangpei Zhang
机构: Wuhan University (武汉大学); Anhui University (安徽大学); University of Tokyo (东京大学); ETH Zürich (苏黎世联邦理工学院); Stanford University (斯坦福大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Weakly-Supervised Change Detection (WSCD) aims to distinguish specific object changes (e.g., objects appearing or disappearing) from background variations (e.g., environmental changes due to light, weather, or seasonal shifts) in paired satellite images, relying only on paired image (i.e., image-level) classification labels. This technique significantly reduces the need for dense annotations required in fully-supervised change detection. However, as image-level supervision only indicates whether objects have changed in a scene, WSCD methods often misclassify background variations as object changes, especially in complex remote-sensing scenarios. In this work, we propose an Adversarial Class Prompting (AdvCP) method to address this co-occurring noise problem, including two phases: a) Adversarial Prompt Mining: After each training iteration, we introduce adversarial prompting perturbations, using incorrect one-hot image-level labels to activate erroneous feature mappings. This process reveals co-occurring adversarial samples under weak supervision, namely background variation features that are likely to be misclassified as object changes. b) Adversarial Sample Rectification: We integrate these adversarially prompt-activated pixel samples into training by constructing an online global prototype. This prototype is built from an exponentially weighted moving average of the current batch and all historical training data. Our AdvCP can be seamlessly integrated into current WSCD methods without adding additional inference cost. Experiments on ConvNet, Transformer, and Segment Anything Model (SAM)-based baselines demonstrate significant performance enhancements. Furthermore, we demonstrate the generalizability of AdvCP to other multi-class weakly-supervised dense prediction scenarios. Code is available at this https URL
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[CV-142] MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在图像中进行深层次数学与空间推理能力不足的问题，尤其是从数学表面图（mathematical surface plots）中提取结构化信息的能力。传统MMLLMs主要擅长语义描述，但在面对需要精确几何理解与逻辑推导的任务时表现薄弱。解决方案的关键在于提出一个名为MaRVL-QA（Mathematical Reasoning over Visual Landscapes）的新基准，该基准包含两个新颖任务：拓扑计数（Topological Counting），用于识别并枚举局部极大值等特征；以及变换识别（Transformation Recognition），用于判断施加的几何变换类型。该基准基于经过严格歧义过滤的函数库生成数据，能够定量评估模型的空间推理能力，从而揭示当前先进MMLLMs普遍依赖浅层启发式策略而非真正鲁棒的空间推理机制，为后续研究提供可量化、具挑战性的评估工具和改进方向。
链接: https://arxiv.org/abs/2508.17180
作者: Nilay Pande,Sahiti Yerramilli,Jayant Sravan Tamarapalli,Rynaa Grover
机构: Waymo(韦莫); Google(谷歌)
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:A key frontier for Multimodal Large Language Models (MLLMs) is the ability to perform deep mathematical and spatial reasoning directly from images, moving beyond their established success in semantic description. Mathematical surface plots provide a rigorous testbed for this capability, as they isolate the task of reasoning from the semantic noise common in natural images. To measure progress on this frontier, we introduce MaRVL-QA (Mathematical Reasoning over Visual Landscapes), a new benchmark designed to quantitatively evaluate these core reasoning skills. The benchmark comprises two novel tasks: Topological Counting, identifying and enumerating features like local maxima; and Transformation Recognition, recognizing applied geometric transformations. Generated from a curated library of functions with rigorous ambiguity filtering, our evaluation on MaRVL-QA reveals that even state-of-the-art MLLMs struggle significantly, often resorting to superficial heuristics instead of robust spatial reasoning. MaRVL-QA provides a challenging new tool for the research community to measure progress, expose model limitations, and guide the development of MLLMs with more profound reasoning abilities.
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[CV-143] VROOM - Visual Reconstruction over Onboard Multiview
【速读】：该论文旨在解决利用赛车搭载的摄像头视频数据实现F1赛道三维重建的问题，尤其针对高速运动和画面剧烈切换等挑战。其解决方案的关键在于构建一个名为VROOM的系统，通过融合多种视觉SLAM算法（如DROID-SLAM、AnyCam和Monst3r）与预处理技术（包括掩码处理、时间分段和分辨率缩放），有效应对动态运动干扰和计算资源限制，从而在复杂环境中部分恢复赛道及车辆轨迹，验证了基于车载视频实现真实场景下可扩展4D重建的可行性。
链接: https://arxiv.org/abs/2508.17172
作者: Yajat Yadav,Varun Bharadwaj,Jathin Korrapati,Tanish Baranwal
机构: University of California, Berkeley (加州大学伯克利分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project page with videos and interactive 4D visualizations: this https URL , Code: this https URL
Abstract:We introduce VROOM, a system for reconstructing 3D models of Formula 1 circuits using only onboard camera footage from racecars. Leveraging video data from the 2023 Monaco Grand Prix, we address video challenges such as high-speed motion and sharp cuts in camera frames. Our pipeline analyzes different methods such as DROID-SLAM, AnyCam, and Monst3r and combines preprocessing techniques such as different methods of masking, temporal chunking, and resolution scaling to account for dynamic motion and computational constraints. We show that Vroom is able to partially recover track and vehicle trajectories in complex environments. These findings indicate the feasibility of using onboard video for scalable 4D reconstruction in real-world settings. The project page can be found at this https URL, and our code is available at this https URL.
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[CV-144] Development of an isotropic segmentation model for medial temporal lobe subregions on anisotropic MRI atlas using implicit neural representation
【速读】：该论文旨在解决基于T2加权磁共振成像（T2-weighted MRI, T2w MRI）中各向异性分辨率导致的内侧颞叶（medial temporal lobe, MTL）皮层亚区厚度测量不准确的问题，从而提升阿尔茨海默病（Alzheimer’s disease, AD）影像生物标志物的精度。其解决方案的关键在于采用隐式神经表示方法，融合T1加权和T2w MRI的分辨率优势，将MTL亚区图谱从各向异性空间上采样至各向同性空间，构建多模态高分辨率图谱，并在此基础上开发出各向同性MTL亚区分割模型，显著提高了在轻度认知障碍（mild cognitive impairment, MCI）与认知正常（cognitively unimpaired, CU）人群间的区分能力和纵向分析中的稳定性。
链接: https://arxiv.org/abs/2508.17171
作者: Yue Li,Pulkit Khandelwal,Rohit Jena,Long Xie,Michael Duong,Amanda E. Denning,Christopher A. Brown,Laura E. M. Wisse,Sandhitsu R. Das,David A. Wolk,Paul A. Yushkevich
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Imaging biomarkers in magnetic resonance imaging (MRI) are important tools for diagnosing and tracking Alzheimer’s disease (AD). As medial temporal lobe (MTL) is the earliest region to show AD-related hallmarks, brain atrophy caused by AD can first be observed in the MTL. Accurate segmentation of MTL subregions and extraction of imaging biomarkers from them are important. However, due to imaging limitations, the resolution of T2-weighted (T2w) MRI is anisotropic, which makes it difficult to accurately extract the thickness of cortical subregions in the MTL. In this study, we used an implicit neural representation method to combine the resolution advantages of T1-weighted and T2w MRI to accurately upsample an MTL subregion atlas set from anisotropic space to isotropic space, establishing a multi-modality, high-resolution atlas set. Based on this atlas, we developed an isotropic MTL subregion segmentation model. In an independent test set, the cortical subregion thickness extracted using this isotropic model showed higher significance than an anisotropic method in distinguishing between participants with mild cognitive impairment and cognitively unimpaired (CU) participants. In longitudinal analysis, the biomarkers extracted using isotropic method showed greater stability in CU participants. This study improved the accuracy of AD imaging biomarkers without increasing the amount of atlas annotation work, which may help to more accurately quantify the relationship between AD and brain atrophy and provide more accurate measures for disease tracking.
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[CV-145] Beyond Play and Pause: Turning GPT -4o Spatial Weakness into a Strength for In-Depth Interactive Video Learning
【速读】：该论文旨在解决传统视频学习中用户缺乏动态交互能力的问题，现有AI工具虽能提供字幕和摘要，但无法实现对视频内容的实时、区域特定互动。其解决方案的关键在于提出Untwist系统，该系统通过整合GPT API与计算机视觉（Computer Vision）技术，使用户能够通过边界框选择视频中的任意区域并提问，从而获得上下文感知的多模态响应；特别地，为克服GPT-4o在空间定位上的局限性，系统采用标注帧而非原始坐标数据进行处理，显著提升了视频内容定位与理解的准确性。
链接: https://arxiv.org/abs/2508.17160
作者: Sajad Goudarzi,Samaneh Zamanifard
机构: Clemson University (克莱姆森大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Traditional video-based learning remains passive, offering limited opportunities for users to engage dynamically with content. While current AI-powered tools offer transcription and summarization, they lack real-time, region-specific interaction capabilities. This paper introduces Untwist, an AI-driven system that enables interactive video learning by allowing users to ask questions about the entire video or specific regions using a bounding box, receiving context-aware, multimodal responses. By integrating GPT APIs with Computer Vision techniques, Untwist extracts, processes, and structures video content to enhance comprehension. Our approach addresses GPT-4o spatial weakness by leveraging annotated frames instead of raw coordinate data, significantly improving accuracy in localizing and interpreting video content. This paper describes the system architecture, including video pre-processing and real-time interaction, and outlines how Untwist can transform passive video consumption into an interactive, AI-driven learning experience with the potential to enhance engagement and comprehension.
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[CV-146] SACA: Selective Attention-Based Clustering Algorithm
【速读】：该论文旨在解决密度聚类算法（如DBSCAN）在实际应用中依赖用户定义参数所带来的优化难题，这些问题通常需要领域专业知识才能有效调整。解决方案的关键在于引入一种受注意力机制启发的新型密度聚类方法：该方法初始阶段无需任何用户参数，通过计算一个阈值来过滤掉分布最稀疏的点和异常值，构建初步聚类结构，并将被排除的点重新整合以完成最终聚类结果；若需调参，仅需引入一个易于调节的整数参数，显著简化了参数配置过程，提升了算法的可用性与鲁棒性。
链接: https://arxiv.org/abs/2508.17150
作者: Meysam Shirdel Bilehsavar,Razieh Ghaedi,Samira Seyed Taheri,Xinqi Fan,Christian O’Reilly
机构: University of South Carolina (南卡罗来纳大学); Manchester Metropolitan University (曼彻斯特都会大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 10 figures
Abstract:Clustering algorithms are widely used in various applications, with density-based methods such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN) being particularly prominent. These algorithms identify clusters in high-density regions while treating sparser areas as noise. However, reliance on user-defined parameters often poses optimization challenges that require domain expertise. This paper presents a novel density-based clustering method inspired by the concept of selective attention, which minimizes the need for user-defined parameters under standard conditions. Initially, the algorithm operates without requiring user-defined parameters. If parameter adjustment is needed, the method simplifies the process by introducing a single integer parameter that is straightforward to tune. The approach computes a threshold to filter out the most sparsely distributed points and outliers, forms a preliminary cluster structure, and then reintegrates the excluded points to finalize the results. Experimental evaluations on diverse data sets highlight the accessibility and robust performance of the method, providing an effective alternative for density-based clustering tasks.
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[CV-147] Structural Damage Detection Using AI Super Resolution and Visual Language Model
【速读】：该论文旨在解决自然灾害发生后，传统灾情评估方法因人力密集、成本高及人员安全风险大等问题，难以实现快速、准确的损伤评估难题。其解决方案的关键在于构建一个集成化、低成本的智能分析框架，核心包括：利用无人机航拍视频数据，结合基于Transformer架构的视频超分辨率模型（Video Restoration Transformer, VRT）提升低分辨率影像质量，并通过参数规模达270亿的视觉语言模型（Visual Language Model, VLM）Gemma3:27b对建筑结构损伤进行自动识别与分类，将建筑物划分为无/轻微损伤至完全破坏四类并标注风险等级。该方案在土耳其2023年地震和摩尔龙卷风2013年卫星数据上验证，分类准确率达84.5%，且具备良好的用户友好性，使非专业人员亦可开展初步灾情研判，显著提升了灾害响应效率与决策能力。
链接: https://arxiv.org/abs/2508.17130
作者: Catherine Hoier,Khandaker Mamun Ahmed
机构: The Beacom College of Computer and Cyber Sciences, Dakota State University (南达科他州立大学贝康计算机与网络安全学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Natural disasters pose significant challenges to timely and accurate damage assessment due to their sudden onset and the extensive areas they affect. Traditional assessment methods are often labor-intensive, costly, and hazardous to personnel, making them impractical for rapid response, especially in resource-limited settings. This study proposes a novel, cost-effective framework that leverages aerial drone footage, an advanced AI-based video super-resolution model, Video Restoration Transformer (VRT), and Gemma3:27b, a 27 billion parameter Visual Language Model (VLM). This integrated system is designed to improve low-resolution disaster footage, identify structural damage, and classify buildings into four damage categories, ranging from no/slight damage to total destruction, along with associated risk levels. The methodology was validated using pre- and post-event drone imagery from the 2023 Turkey earthquakes (courtesy of The Guardian) and satellite data from the 2013 Moore Tornado (xBD dataset). The framework achieved a classification accuracy of 84.5%, demonstrating its ability to provide highly accurate results. Furthermore, the system’s accessibility allows non-technical users to perform preliminary analyses, thereby improving the responsiveness and efficiency of disaster management efforts.
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[CV-148] CE-RS-SBCIT A Novel Channel Enhanced Hybrid CNN Transformer with Residual Spatial and Boundary-Aware Learning for Brain Tumor MRI Analysis
【速读】：该论文旨在解决脑肿瘤早期检测与精准分类中面临的挑战，包括传统卷积神经网络（CNN）和Transformer模型在MRI图像分析中存在的高计算成本、对微小对比度变化敏感性差、结构异质性以及纹理不一致性等问题。其核心解决方案是提出一种新型混合框架CE-RS-SBCIT，关键创新点在于：(i) 引入平滑与边界增强的CNN集成Transformer模块（SBCIT），实现高效全局特征建模；(ii) 设计定制化的残差与空间学习CNN，结合辅助迁移特征图提升表示能力；(iii) 采用通道增强（CE）策略放大判别性通道并减少冗余；(iv) 提出新颖的空间注意力机制，聚焦不同肿瘤类别间的细微对比度与纹理差异。该框架在Kaggle和Figshare公开MRI数据集上实现了98.30%的准确率，显著优于现有方法。
链接: https://arxiv.org/abs/2508.17128
作者: Mirza Mumtaz Zahoor(1),Saddam Hussain Khan(2) ((1) Faculty of Computer Sciences, Ibadat International University, Islamabad, 44000, Pakistan (2) Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat 19060, Pakistan)
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 37 Pages, 12 Figures
Abstract:Brain tumors remain among the most lethal human diseases, where early detection and accurate classification are critical for effective diagnosis and treatment planning. Although deep learning-based computer-aided diagnostic (CADx) systems have shown remarkable progress. However, conventional convolutional neural networks (CNNs) and Transformers face persistent challenges, including high computational cost, sensitivity to minor contrast variations, structural heterogeneity, and texture inconsistencies in MRI data. Therefore, a novel hybrid framework, CE-RS-SBCIT, is introduced, integrating residual and spatial learning-based CNNs with transformer-driven modules. The proposed framework exploits local fine-grained and global contextual cues through four core innovations: (i) a smoothing and boundary-based CNN-integrated Transformer (SBCIT), (ii) tailored residual and spatial learning CNNs, (iii) a channel enhancement (CE) strategy, and (iv) a novel spatial attention mechanism. The developed SBCIT employs stem convolution and contextual interaction transformer blocks with systematic smoothing and boundary operations, enabling efficient global feature modeling. Moreover, Residual and spatial CNNs, enhanced by auxiliary transfer-learned feature maps, enrich the representation space, while the CE module amplifies discriminative channels and mitigates redundancy. Furthermore, the spatial attention mechanism selectively emphasizes subtle contrast and textural variations across tumor classes. Extensive evaluation on challenging MRI datasets from Kaggle and Figshare, encompassing glioma, meningioma, pituitary tumors, and healthy controls, demonstrates superior performance, achieving 98.30% accuracy, 98.08% sensitivity, 98.25% F1-score, and 98.43% precision.
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[CV-149] PlantVillageVQA: A Visual Question Answering Dataset for Benchmarking Vision-Language Models in Plant Science
【速读】：该论文旨在解决农业领域中植物病害识别诊断准确率不足的问题，尤其是在视觉与语言融合的智能决策系统发展滞后的情况下。其解决方案的关键在于构建一个大规模、高质量、专家验证的视觉问答（Visual Question Answering, VQA）数据集——PlantVillageVQA，该数据集包含193,609对问题-答案对，覆盖14种作物和38种病害状况，并通过分层认知复杂度和类别组织，确保语义多样性与专业性。其核心创新在于采用两阶段自动化合成与多阶段语言重构的pipeline生成QA对，并结合领域专家迭代审核与先进模型质量评估，从而为农业场景下的视觉语言模型提供标准化、可复现且科学可靠的训练与评测基准。
链接: https://arxiv.org/abs/2508.17117
作者: Syed Nazmus Sakib,Nafiul Haque,Mohammad Zabed Hossain,Shifat E. Arman
机构: University of Dhaka (达卡大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 17 pages, 15 figures and Submittd to Nature Scientific Data
Abstract:PlantVillageVQA is a large-scale visual question answering (VQA) dataset derived from the widely used PlantVillage image corpus. It was designed to advance the development and evaluation of vision-language models for agricultural decision-making and analysis. The PlantVillageVQA dataset comprises 193,609 high-quality question-answer (QA) pairs grounded over 55,448 images spanning 14 crop species and 38 disease conditions. Questions are organised into 3 levels of cognitive complexity and 9 distinct categories. Each question category was phrased manually following expert guidance and generated via an automated two-stage pipeline: (1) template-based QA synthesis from image metadata and (2) multi-stage linguistic re-engineering. The dataset was iteratively reviewed by domain experts for scientific accuracy and relevancy. The final dataset was evaluated using three state-of-the-art models for quality assessment. Our objective remains to provide a publicly available, standardised and expert-verified database to enhance diagnostic accuracy for plant disease identifications and advance scientific research in the agricultural domain. Our dataset will be open-sourced at this https URL.
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[CV-150] SugarcaneShuffleNet: A Very Fast Lightweight Convolutional Neural Network for Diagnosis of 15 Sugarcane Leaf Diseases
【速读】：该论文旨在解决低资源地区甘蔗种植者因缺乏可扩展、高效且可解释的植物病害诊断工具而面临叶部疾病风险的问题。现有许多深度学习模型在真实场景下泛化能力差，且计算资源需求高，难以部署于资源受限环境。解决方案的关键在于三方面：一是构建了SugarcaneLD-BD数据集，包含638张经专家验证的甘蔗叶片图像，涵盖四种主要病害，并通过融合其他数据集增强多样性；二是提出轻量级模型SugarcaneShuffleNet，在保证98.02%准确率和0.98 F1-score的同时，仅需9.26 MB存储空间，单张图像平均推理时间仅为4.14 ms；三是开发基于Progressive Web Application（PWA）的SugarcaneAI系统，集成Grad-CAM可视化解释机制，实现田间实时、可解释的病害诊断，从而为低资源环境提供高效、实用的解决方案。
链接: https://arxiv.org/abs/2508.17107
作者: Shifat E. Arman,Hasan Muhammad Abdullah,Syed Nazmus Sakib,RM Saiem,Shamima Nasrin Asha,Md Mehedi Hasan,Shahrear Bin Amin,S M Mahin Abrar
机构: University of Dhaka (达卡大学); Gazipur Agricultural University (加兹布尔农业大学); Bangladesh Sugarcrop Research Institute (孟加拉国甘蔗研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 18 pages, 19 figures, Submitted in Computers and Electronics in Agriculture
Abstract:Despite progress in AI-based plant diagnostics, sugarcane farmers in low-resource regions remain vulnerable to leaf diseases due to the lack of scalable, efficient, and interpretable tools. Many deep learning models fail to generalize under real-world conditions and require substantial computational resources, limiting their use in resource-constrained regions. In this paper, we present SugarcaneLD-BD, a curated dataset for sugarcane leaf-disease classification; SugarcaneShuffleNet, an optimized lightweight model for rapid on-device diagnosis; and SugarcaneAI, a Progressive Web Application for field deployment. SugarcaneLD-BD contains 638 curated images across five classes, including four major sugarcane diseases, collected in Bangladesh under diverse field conditions and verified by expert pathologists. To enhance diversity, we combined SugarcaneLD-BD with two additional datasets, yielding a larger and more representative corpus. Our optimized model, SugarcaneShuffleNet, offers the best trade-off between speed and accuracy for real-time, on-device diagnosis. This 9.26 MB model achieved 98.02% accuracy, an F1-score of 0.98, and an average inference time of 4.14 ms per image. For comparison, we fine-tuned five other lightweight convolutional neural networks: MnasNet, EdgeNeXt, EfficientNet-Lite, MobileNet, and SqueezeNet via transfer learning and Bayesian optimization. MnasNet and EdgeNeXt achieved comparable accuracy to SugarcaneShuffleNet, but required significantly more parameters, memory, and computation, limiting their suitability for low-resource deployment. We integrate SugarcaneShuffleNet into SugarcaneAI, delivering Grad-CAM-based explanations in the field. Together, these contributions offer a diverse benchmark, efficient models for low-resource environments, and a practical tool for sugarcane disease classification. It spans varied lighting, backgrounds and devices used on-farm
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[CV-151] GRASP: Geospatial pixel Reasoning viA Structured Policy learning
【速读】：该论文旨在解决遥感图像中基于自然语言指令的像素级分割（geospatial pixel reasoning）任务中存在的两个核心问题：一是现有多模态大语言模型（MLLM）方法依赖密集像素监督进行联合训练，成本高昂且在域外（out-of-domain, OOD）数据上表现不佳；二是缺乏高效、可泛化的学习范式来利用基础模型中的先验知识。解决方案的关键在于提出GRASP框架——一个结构化策略学习（structured policy-learning）系统，其核心创新是通过强化学习（reinforcement learning, RL）而非监督微调来优化整个流程：首先由多模态大语言模型输出与任务相关的边界框和正样本点作为提示，再由预训练分割模型以这些弱空间线索为输入生成最终掩膜（mask），整个过程仅使用格式奖励和精度奖励进行GRPO优化，无需任何掩膜标注。该方法显著减少可训练参数，提升泛化能力，在OOD基准上性能提升达54%，验证了从弱空间提示中学习复杂地理空间分割行为的有效性。
链接: https://arxiv.org/abs/2508.17102
作者: Chengjie Jiang,Yunqi Zhou,Jiafeng Yan,Jing Li
机构: Tsinghua University (清华大学); Central University of Finance and Economics (中央财经大学); East China Normal University (华东师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 7 figures
Abstract:Geospatial pixel reasoning is a nascent remote-sensing task that aims to generate segmentation masks directly from natural-language instructions. Prevailing MLLM-based systems co-train a language model and a mask decoder with dense pixel supervision, which is expensive and often weak on out-of-domain (OOD) data. We introduce GRASP, a structured policy-learning framework. In our design, a multimodal large language model first emits task-relevant bounding boxes and positive points from a vision-language instruction. These outputs are then passed to a pre-trained segmentation model, which consumes them as prompts to generate the final mask. Instead of supervised fine-tuning, we optimize the system purely with reinforcement learning: the model is trained solely with GRPO, guided by format rewards and accuracy rewards computed on boxes and points (no mask supervision). This leverages strong priors in foundation models, minimizes trainable parameters, and enables learning from inexpensive annotations. We additionally curate GRASP-1k, which contains reasoning-intensive queries, detailed reasoning traces, and fine-grained segmentation annotations. Evaluations on both in-domain and out-of-domain test sets show state-of-the-art results: about 4% improvement in-domain and up to 54% on OOD benchmarks. The experiment results evidence our model’s robust generalization and demonstrate that complex geospatial segmentation behaviors can be learned via RL from weak spatial cues. Code and the dataset will be released open-source.
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[CV-152] PD-Loss: Proxy-Decidability for Efficient Metric Learning
【速读】：该论文旨在解决深度度量学习（Deep Metric Learning, DML）中现有方法在优化嵌入空间分布特性时的局限性：传统成对损失（pairwise losses）因采样复杂且收敛缓慢而受限，而基于代理（proxy-based）的损失虽具可扩展性却难以优化全局分布特性；同时，基于可判定性指数（decidability index, d’）的D-Loss虽能提升分布可分性，但依赖大批次训练带来显著计算负担。解决方案的关键在于提出Proxy-Decidability Loss（PD-Loss），其核心是将可学习的代理点与d’的统计框架相结合，通过代理估计真实类和伪类分布，从而在保持代理方法计算效率的同时，实现具有理论依据的分布感知嵌入空间优化，显著提升了DML的可扩展性和性能表现。
链接: https://arxiv.org/abs/2508.17082
作者: Pedro Silva,Guilherme A. L. Silva,Pablo Coelho,Vander Freitas,Gladston Moreira,David Menotii,Eduardo Luz
机构: Universidade Federal de Ouro Preto (联邦大学奥罗普雷托分校); Universidade Federal do Paraná (联邦大学帕拉纳分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages
Abstract:Deep Metric Learning (DML) aims to learn embedding functions that map semantically similar inputs to proximate points in a metric space while separating dissimilar ones. Existing methods, such as pairwise losses, are hindered by complex sampling requirements and slow convergence. In contrast, proxy-based losses, despite their improved scalability, often fail to optimize global distribution properties. The Decidability-based Loss (D-Loss) addresses this by targeting the decidability index (d’) to enhance distribution separability, but its reliance on large mini-batches imposes significant computational constraints. We introduce Proxy-Decidability Loss (PD-Loss), a novel objective that integrates learnable proxies with the statistical framework of d’ to optimize embedding spaces efficiently. By estimating genuine and impostor distributions through proxies, PD-Loss combines the computational efficiency of proxy-based methods with the principled separability of D-Loss, offering a scalable approach to distribution-aware DML. Experiments across various tasks, including fine-grained classification and face verification, demonstrate that PD-Loss achieves performance comparable to that of state-of-the-art methods while introducing a new perspective on embedding optimization, with potential for broader applications.
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[CV-153] Proximal Vision Transformer: Enhancing Feature Representation through Two-Stage Manifold Geometry
【速读】：该论文旨在解决Vision Transformer (ViT)在优化过程中仅能建模单张图像内的局部关系，从而限制了其捕捉数据点之间全局几何关系能力的问题。解决方案的关键在于将ViT与近端（proximal）优化工具相结合，构建一个统一的几何优化框架：首先利用ViT的自注意力机制构建流形的切丛（tangent bundle），其中每个注意力头对应一个切空间，从而从不同局部视角提供几何表示；随后引入近端迭代来定义切丛中的截面，并将数据从切空间投影回基空间，实现全局特征对齐与优化，显著提升了特征表达能力和分类性能。
链接: https://arxiv.org/abs/2508.17081
作者: Haoyu Yun,Hamid Krim
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:The Vision Transformer (ViT) architecture has become widely recognized in computer vision, leveraging its self-attention mechanism to achieve remarkable success across various tasks. Despite its strengths, ViT’s optimization remains confined to modeling local relationships within individual images, limiting its ability to capture the global geometric relationships between data points. To address this limitation, this paper proposes a novel framework that integrates ViT with the proximal tools, enabling a unified geometric optimization approach to enhance feature representation and classification performance. In this framework, ViT constructs the tangent bundle of the manifold through its self-attention mechanism, where each attention head corresponds to a tangent space, offering geometric representations from diverse local perspectives. Proximal iterations are then introduced to define sections within the tangent bundle and project data from tangent spaces onto the base space, achieving global feature alignment and optimization. Experimental results confirm that the proposed method outperforms traditional ViT in terms of classification accuracy and data distribution.
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[CV-154] SSG-Dit: A Spatial Signal Guided Framework for Controllable Video Generation
【速读】：该论文旨在解决可控视频生成中语义一致性不足的问题，即现有模型难以准确遵循用户提供的文本描述或初始图像中的细节，导致生成视频与提示内容偏离。其解决方案的关键在于提出了一种名为SSG-DiT（Spatial Signal Guided Diffusion Transformer）的新框架，采用解耦的两阶段流程：第一阶段通过预训练多模态模型的内部表示生成空间感知的视觉提示（spatial signal prompt），并与原始文本联合构成条件输入；第二阶段利用轻量级且参数高效的SSG-Adapter将该联合条件注入冻结的视频扩散Transformer（video DiT）主干网络，通过双分支注意力机制同时利用强大的生成先验和外部空间信号进行精准引导，从而显著提升视频生成的空间关系控制能力和整体一致性。
链接: https://arxiv.org/abs/2508.17062
作者: Peng Hu,Yu Gu,Liang Luo,Fuji Ren
机构: University of Electronic Science and Technology of China (电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Controllable video generation aims to synthesize video content that aligns precisely with user-provided conditions, such as text descriptions and initial images. However, a significant challenge persists in this domain: existing models often struggle to maintain strong semantic consistency, frequently generating videos that deviate from the nuanced details specified in the prompts. To address this issue, we propose SSG-DiT (Spatial Signal Guided Diffusion Transformer), a novel and efficient framework for high-fidelity controllable video generation. Our approach introduces a decoupled two-stage process. The first stage, Spatial Signal Prompting, generates a spatially aware visual prompt by leveraging the rich internal representations of a pre-trained multi-modal model. This prompt, combined with the original text, forms a joint condition that is then injected into a frozen video DiT backbone via our lightweight and parameter-efficient SSG-Adapter. This unique design, featuring a dual-branch attention mechanism, allows the model to simultaneously harness its powerful generative priors while being precisely steered by external spatial signals. Extensive experiments demonstrate that SSG-DiT achieves state-of-the-art performance, outperforming existing models on multiple key metrics in the VBench benchmark, particularly in spatial relationship control and overall consistency.
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[CV-155] REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework
【速读】：该论文旨在解决动态游戏环境中实现实时渲染下真实感（photorealism）的难题，即在保证高视觉质量的同时满足实时帧率要求。其核心挑战在于现有渲染技术难以在性能与视觉保真度之间取得平衡。解决方案的关键在于提出一种双阶段生成式网络框架（REGEN），通过引入一个鲁棒的无配对图像到图像翻译模型，将复杂的无配对任务转化为更易处理的配对翻译问题，从而实现语义一致且高质量的实时照片级增强。该方法在不牺牲视觉效果的前提下，显著提升了推理速度（相较基线提升32.14倍），并优于直接训练轻量级无配对模型的效果。
链接: https://arxiv.org/abs/2508.17061
作者: Stefanos Pasios,Nikos Nikolaidis
机构: Aristotle University of Thessaloniki (塞萨洛尼基亚里士多德大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages
Abstract:Photorealism is an important aspect of modern video games since it can shape the player experience and simultaneously impact the immersion, narrative engagement, and visual fidelity. Although recent hardware technological breakthroughs, along with state-of-the-art rendering technologies, have significantly improved the visual realism of video games, achieving true photorealism in dynamic environments at real-time frame rates still remains a major challenge due to the tradeoff between visual quality and performance. In this short paper, we present a novel approach for enhancing the photorealism of rendered game frames using generative adversarial networks. To this end, we propose Real-time photorealism Enhancement in Games via a dual-stage gEnerative Network framework (REGEN), which employs a robust unpaired image-to-image translation model to produce semantically consistent photorealistic frames that transform the problem into a simpler paired image-to-image translation task. This enables training with a lightweight method that can achieve real-time inference time without compromising visual quality. We demonstrate the effectiveness of our framework on Grand Theft Auto V, showing that the approach achieves visual results comparable to the ones produced by the robust unpaired Im2Im method while improving inference speed by 32.14 times. Our findings also indicate that the results outperform the photorealism-enhanced frames produced by directly training a lightweight unpaired Im2Im translation method to translate the video game frames towards the visual characteristics of real-world images. Code, pre-trained models, and demos for this work are available at: this https URL.
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[CV-156] DeltaFlow: An Efficient Multi-frame Scene Flow Estimation Method
【速读】：该论文旨在解决场景流估计（scene flow estimation）中因仅依赖双帧输入而忽略时序信息，以及多帧推理方法计算成本随帧数增加急剧上升的问题。同时，针对物体类别分布不均和运动不一致导致的精度下降问题，提出改进方案。其核心解决方案是设计了一种轻量级3D框架DeltaFlow（ΔFlow），通过Δ（delta）机制高效提取时序特征，无论帧数多少都保持极低的计算开销；并引入类别平衡损失（Category-Balanced Loss）提升小样本类别的学习效果，以及实例一致性损失（Instance Consistency Loss）约束物体运动的一致性，从而显著提升场景流估计的准确性与鲁棒性。
链接: https://arxiv.org/abs/2508.17054
作者: Qingwen Zhang,Xiaomeng Zhu,Yushan Zhang,Yixi Cai,Olov Andersson,Patric Jensfelt
机构: KTH Royal Institute of Technology (皇家理工学院); Linköping University (林雪平大学); Scania CV AB (斯堪尼亚商用车公司)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 17 pages (9 main pages + 8 supp materail), 11 figures, code at this https URL
Abstract:Previous dominant methods for scene flow estimation focus mainly on input from two consecutive frames, neglecting valuable information in the temporal domain. While recent trends shift towards multi-frame reasoning, they suffer from rapidly escalating computational costs as the number of frames grows. To leverage temporal information more efficiently, we propose DeltaFlow ( \Delta Flow), a lightweight 3D framework that captures motion cues via a \Delta scheme, extracting temporal features with minimal computational cost, regardless of the number of frames. Additionally, scene flow estimation faces challenges such as imbalanced object class distributions and motion inconsistency. To tackle these issues, we introduce a Category-Balanced Loss to enhance learning across underrepresented classes and an Instance Consistency Loss to enforce coherent object motion, improving flow accuracy. Extensive evaluations on the Argoverse 2 and Waymo datasets show that \Delta Flow achieves state-of-the-art performance with up to 22% lower error and 2\times faster inference compared to the next-best multi-frame supervised method, while also demonstrating a strong cross-domain generalization ability. The code is open-sourced at this https URL along with trained model weights.
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[CV-157] PVNet: Point-Voxel Interaction LiDAR Scene Upsampling Via Diffusion Models
【速读】：该论文旨在解决户外环境中激光雷达（LiDAR）点云数据因极端稀疏而导致的3D场景理解困难问题，尤其是现有点云上采样方法多聚焦于单个物体、难以泛化至复杂室外场景的局限性。其解决方案的关键在于提出PVNet——一种基于扩散模型（diffusion model）的点-体素交互框架，通过无密集监督的方式实现点云上采样；核心创新包括：1）采用无分类器引导的去噪扩散概率模型（classifier-free guidance-based DDPMs），以稀疏点云为条件引导生成，并利用邻近帧合成点云作为输入；2）设计体素补全模块（voxel completion module）以优化粗粒度体素特征表示；3）引入点-体素交互模块（point-voxel interaction module）融合点与体素特征，显著提升每个上采样点的环境感知能力。该方法是首个支持任意上采样率的场景级点云上采样方案，在多个基准测试中达到最先进性能。
链接: https://arxiv.org/abs/2508.17050
作者: Xianjing Cheng,Lintai Wu,Zuowen Wang,Junhui Hou,Jie Wen,Yong Xu
机构: Telecom Guizhou Branch; Harbin Institute of Technology, Shenzhen; Huaqiao University; City University of Hong Kong; Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology; Key Laboratory of Network Oriented Intelligent Computation
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 11 figures
Abstract:Accurate 3D scene understanding in outdoor environments heavily relies on high-quality point clouds. However, LiDAR-scanned data often suffer from extreme sparsity, severely hindering downstream 3D perception tasks. Existing point cloud upsampling methods primarily focus on individual objects, thus demonstrating limited generalization capability for complex outdoor scenes. To address this issue, we propose PVNet, a diffusion model-based point-voxel interaction framework to perform LiDAR point cloud upsampling without dense supervision. Specifically, we adopt the classifier-free guidance-based DDPMs to guide the generation, in which we employ a sparse point cloud as the guiding condition and the synthesized point clouds derived from its nearby frames as the input. Moreover, we design a voxel completion module to refine and complete the coarse voxel features for enriching the feature representation. In addition, we propose a point-voxel interaction module to integrate features from both points and voxels, which efficiently improves the environmental perception capability of each upsampled point. To the best of our knowledge, our approach is the first scene-level point cloud upsampling method supporting arbitrary upsampling rates. Extensive experiments on various benchmarks demonstrate that our method achieves state-of-the-art performance. The source code will be available at this https URL.
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[CV-158] Styleclone: Face Stylization with Diffusion Based Data Augmentation
【速读】：该论文旨在解决在仅有少量风格图像（style images）的情况下，如何高效训练图像到图像翻译网络以实现高质量人脸风格迁移的问题。其核心挑战在于小样本场景下风格数据多样性不足，导致模型泛化能力差、风格迁移效果不佳。解决方案的关键在于引入文本反转（textual inversion）与基于扩散模型的引导图像生成技术，通过结合原始风格图像和真实人脸图像，系统性地生成多样化的风格样本，从而显著扩充风格数据集；在此基础上训练的快速图像到图像翻译网络，在保持源图像内容完整性的同时，相较扩散模型在速度和质量上均取得优势。
链接: https://arxiv.org/abs/2508.17045
作者: Neeraj Matiyali,Siddharth Srivastava,Gaurav Sharma
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:We present StyleClone, a method for training image-to-image translation networks to stylize faces in a specific style, even with limited style images. Our approach leverages textual inversion and diffusion-based guided image generation to augment small style datasets. By systematically generating diverse style samples guided by both the original style images and real face images, we significantly enhance the diversity of the style dataset. Using this augmented dataset, we train fast image-to-image translation networks that outperform diffusion-based methods in speed and quality. Experiments on multiple styles demonstrate that our method improves stylization quality, better preserves source image content, and significantly accelerates inference. Additionally, we provide a systematic evaluation of the augmentation techniques and their impact on stylization performance.
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[CV-159] M3DMap: Object-aware Multimodal 3D Mapping for Dynamic Environments
【速读】：该论文旨在解决动态环境中构建统一的多模态三维（3D）地图问题，当前缺乏能够融合图像、点云和文本等多源数据的通用表示方法。其解决方案的关键在于提出了一种新的分类体系（taxonomy），用于系统梳理现有方法，并设计了一个名为M3DMap的模块化框架，该框架包含神经网络驱动的多模态目标分割与跟踪模块、可训练的里程计估计模块、支持多种场景表示的3D地图构建与更新模块，以及多模态数据检索模块，从而实现对静态和动态场景的对象感知型3D地图构建，显著提升了如3D目标定位和移动操作等实际任务的性能。
链接: https://arxiv.org/abs/2508.17044
作者: Dmitry Yudin
机构: Moscow Institute of Physics and Technology (莫斯科物理技术研究所); AIRI (人工智能研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: 29 pages, 3 figures, 13 tables. Preprint of the accepted article in Optical Memory and Neural Network Journal
Abstract:3D mapping in dynamic environments poses a challenge for modern researchers in robotics and autonomous transportation. There are no universal representations for dynamic 3D scenes that incorporate multimodal data such as images, point clouds, and text. This article takes a step toward solving this problem. It proposes a taxonomy of methods for constructing multimodal 3D maps, classifying contemporary approaches based on scene types and representations, learning methods, and practical applications. Using this taxonomy, a brief structured analysis of recent methods is provided. The article also describes an original modular method called M3DMap, designed for object-aware construction of multimodal 3D maps for both static and dynamic scenes. It consists of several interconnected components: a neural multimodal object segmentation and tracking module; an odometry estimation module, including trainable algorithms; a module for 3D map construction and updating with various implementations depending on the desired scene representation; and a multimodal data retrieval module. The article highlights original implementations of these modules and their advantages in solving various practical tasks, from 3D object grounding to mobile manipulation. Additionally, it presents theoretical propositions demonstrating the positive effect of using multimodal data and modern foundational models in 3D mapping methods. Details of the taxonomy and method implementation are available at this https URL.
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[CV-160] F4-ITS: Fine-grained Feature Fusion for Food Image-Text Search
【速读】：该论文旨在解决食品图像到文本匹配（food image-to-text matching）这一细粒度视觉理解与检索任务，该任务在饮食监测、智能厨房和餐厅自动化等应用中具有关键作用。其核心挑战在于如何提升跨模态特征表示的准确性与表达能力，从而实现更精确的图文匹配。解决方案的关键在于提出F4-ITS框架：一是设计单向（及双向）多模态融合策略，将图像嵌入与视觉语言模型（VLM）生成的文本描述相结合，增强查询表达能力；二是引入基于特征的重排序机制，在top-k检索中利用预测的食材信息对结果进行精细化调整，显著提升精度。实验表明，该方法在密集和稀疏标注场景下分别取得约10%和7.7%的top-1检索性能提升，并在食材级别检索中实现28.6%的改进，同时小模型（如ViT-B/32）经文本融合后可媲美甚至超越大模型，验证了方法在资源受限环境下的有效性。
链接: https://arxiv.org/abs/2508.17037
作者: Raghul Asokan
机构: HyperVerge
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The proliferation of digital food content has intensified the need for robust and accurate systems capable of fine-grained visual understanding and retrieval. In this work, we address the challenging task of food image-to-text matching, a critical component in applications such as dietary monitoring, smart kitchens, and restaurant automation. We propose F4-ITS: Fine-grained Feature Fusion for Food Image-Text Search, a training-free, vision-language model (VLM)-guided framework that significantly improves retrieval performance through enhanced multi-modal feature representations. Our approach introduces two key contributions: (1) a uni-directional(and bi-directional) multi-modal fusion strategy that combines image embeddings with VLM-generated textual descriptions to improve query expressiveness, and (2) a novel feature-based re-ranking mechanism for top-k retrieval, leveraging predicted food ingredients to refine results and boost precision. Leveraging open-source image-text encoders, we demonstrate substantial gains over standard baselines - achieving ~10% and ~7.7% improvements in top-1 retrieval under dense and sparse caption scenarios, and a ~28.6% gain in top-k ingredient-level retrieval. Additionally, we show that smaller models (e.g., ViT-B/32) can match or outperform larger counterparts (e.g., ViT-H, ViT-G, ViT-bigG) when augmented with textual fusion, highlighting the effectiveness of our method in resource-constrained settings. Code and test datasets will be made publicly available at: this https URL
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[CV-161] A Novel Local Focusing Mechanism for Deepfake Detection Generalization
【速读】：该论文旨在解决当前深度伪造（deepfake）检测方法在跨类别（如从人脸到汽车）和跨生成域（如从GAN到Stable Diffusion）场景下泛化能力差的问题。现有基于重建学习的方法多依赖深层卷积神经网络（Deep Convolutional Neural Networks, CNNs），其固有局限性导致模型易过拟合特定类别的语义特征分布，且全局平均池化（Global Average Pooling, GAP）会丢失对真实-虚假分类至关重要的局部伪造线索。为此，作者提出一种新颖的局部关注机制（Local Focus Mechanism, LFM），其核心在于通过一个显著性网络（Salience Network, SNet）与任务特定的Top-K池化（Top-K Pooling, TKP）模块联合选择最具判别性的K个局部模式，从而强化对伪造痕迹的敏感性；同时引入两种正则化策略——基于排名的线性丢弃（Rank-Based Linear Dropout, RBLD）和随机K采样（Random-K Sampling, RKS），以缓解Top-K池化带来的过拟合风险。实验表明，LFM在准确率和平均精度上分别较最优基线方法NPR提升3.7%和2.8%，且推理速度达1789 FPS（单张NVIDIA A6000 GPU），显著提升了跨域检测性能与效率。
链接: https://arxiv.org/abs/2508.17029
作者: Mingliang Li,Lin Yuanbo Wu,Changhong Liu,Hanxi Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The rapid advancement of deepfake generation techniques has intensified the need for robust and generalizable detection methods. Existing approaches based on reconstruction learning typically leverage deep convolutional networks to extract differential features. However, these methods show poor generalization across object categories (e.g., from faces to cars) and generation domains (e.g., from GANs to Stable Diffusion), due to intrinsic limitations of deep CNNs. First, models trained on a specific category tend to overfit to semantic feature distributions, making them less transferable to other categories, especially as network depth increases. Second, Global Average Pooling (GAP) compresses critical local forgery cues into a single vector, thus discarding discriminative patterns vital for real-fake classification. To address these issues, we propose a novel Local Focus Mechanism (LFM) that explicitly attends to discriminative local features for differentiating fake from real images. LFM integrates a Salience Network (SNet) with a task-specific Top-K Pooling (TKP) module to select the K most informative local patterns. To mitigate potential overfitting introduced by Top-K pooling, we introduce two regularization techniques: Rank-Based Linear Dropout (RBLD) and Random-K Sampling (RKS), which enhance the model’s robustness. LFM achieves a 3.7 improvement in accuracy and a 2.8 increase in average precision over the state-of-the-art Neighboring Pixel Relationships (NPR) method, while maintaining exceptional efficiency at 1789 FPS on a single NVIDIA A6000 GPU. Our approach sets a new benchmark for cross-domain deepfake detection. The source code are available in this https URL
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[CV-162] Probabilistic Temporal Masked Attention for Cross-view Online Action Detection
【速读】：该论文旨在解决在线动作检测（Online Action Detection, OAD）模型在面对不同视频视角时泛化能力差的问题，尤其是在未见过的视角源下性能显著下降。解决方案的关键在于提出一种概率时空掩码注意力机制（Probabilistic Temporal Masked Attention, PTMA），其核心创新包括：利用概率建模在跨视角场景中提取视频帧的潜在压缩表示，并引入基于门控循环单元（GRU）的时序掩码注意力（Temporal Masked Attention, TMA）单元，通过这些表示对输入视频序列进行有效查询，增强信息交互，支持自回归式的帧级视频分析；同时，多视角信息被整合进概率建模过程，有助于提取视角不变特征（view-invariant features），从而提升模型在跨视角测试条件下的鲁棒性与性能。
链接: https://arxiv.org/abs/2508.17025
作者: Liping Xie,Yang Tan,Shicheng Jing,Huimin Lu,Kanjian Zhang
机构: Southeast University (东南大学); Anhui University (安徽大学); Advanced Ocean Institute of Southeast University (东南大学先进海洋研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 12 pages, 6 figures, accepted at IEEE Transactions on Multimedia (TMM), in press
Abstract:As a critical task in video sequence classification within computer vision, Online Action Detection (OAD) has garnered significant attention. The sensitivity of mainstream OAD models to varying video viewpoints often hampers their generalization when confronted with unseen sources. To address this limitation, we propose a novel Probabilistic Temporal Masked Attention (PTMA) model, which leverages probabilistic modeling to derive latent compressed representations of video frames in a cross-view setting. The PTMA model incorporates a GRU-based temporal masked attention (TMA) cell, which leverages these representations to effectively query the input video sequence, thereby enhancing information interaction and facilitating autoregressive frame-level video analysis. Additionally, multi-view information can be integrated into the probabilistic modeling to facilitate the extraction of view-invariant features. Experiments conducted under three evaluation protocols: cross-subject (cs), cross-view (cv), and cross-subject-view (csv) show that PTMA achieves state-of-the-art performance on the DAHLIA, IKEA ASM, and Breakfast datasets.
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[CV-163] Dual Orthogonal Guidance for Robust Diffusion-based Handwritten Text Generation
【速读】：该论文旨在解决基于扩散模型的 handwritten text generation (HTG) 方法在生成文本时存在的两大问题：一是模型容易记忆训练样本，导致缺乏多样性；二是生成文本在复杂风格或罕见词汇下易出现伪影（artifacts）或失真，影响可读性。解决方案的关键在于提出一种新颖的采样引导策略——Dual Orthogonal Guidance (DOG)，其核心思想是通过将负向扰动提示（negatively perturbed prompt）正交投影到原始正向提示上，从而在潜在空间中引入一个更稳定、解耦的方向来指导生成过程，避免伪影并提升风格多样性与内容清晰度。此外，作者采用三角形调度策略动态调节引导强度，在去噪过程的敏感阶段（起始和结束）弱化引导，中间步骤强化引导，进一步优化生成质量。
链接: https://arxiv.org/abs/2508.17017
作者: Konstantina Nikolaidou,George Retsinas,Giorgos Sfikas,Silvia Cascianelli,Rita Cucchiara,Marcus Liwicki
机构: Luleå University of Technology (吕勒奥理工大学); National Technical University of Athens (雅典国立技术大学); University of West Attica (西阿提卡大学); University of Modena and Reggio Emilia (摩德纳和雷焦艾米利亚大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 10 figures
Abstract:Diffusion-based Handwritten Text Generation (HTG) approaches achieve impressive results on frequent, in-vocabulary words observed at training time and on regular styles. However, they are prone to memorizing training samples and often struggle with style variability and generation clarity. In particular, standard diffusion models tend to produce artifacts or distortions that negatively affect the readability of the generated text, especially when the style is hard to produce. To tackle these issues, we propose a novel sampling guidance strategy, Dual Orthogonal Guidance (DOG), that leverages an orthogonal projection of a negatively perturbed prompt onto the original positive prompt. This approach helps steer the generation away from artifacts while maintaining the intended content, and encourages more diverse, yet plausible, outputs. Unlike standard Classifier-Free Guidance (CFG), which relies on unconditional predictions and produces noise at high guidance scales, DOG introduces a more stable, disentangled direction in the latent space. To control the strength of the guidance across the denoising process, we apply a triangular schedule: weak at the start and end of denoising, when the process is most sensitive, and strongest in the middle steps. Experimental results on the state-of-the-art DiffusionPen and One-DM demonstrate that DOG improves both content clarity and style variability, even for out-of-vocabulary words and challenging writing styles.
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[CV-164] Fiducial Marker Splatting for High-Fidelity Robotics Simulations
【速读】：该论文旨在解决复杂环境中移动机器人高保真3D仿真中传统基于网格（mesh-based）表示方法的局限性，尤其是在密集植被场景（如温室）中因遮挡和重复结构导致的感知困难问题。同时，现有神经渲染技术（如高斯溅射，Gaussian Splatting, GS）虽能实现高视觉真实感，但难以集成用于机器人定位与控制的关键特征标记（fiducial markers，如AprilTags）。解决方案的关键在于提出一种混合框架，将GS的图像保真度与结构化标记表示相结合，并创新性地设计了一种高效算法，在杂乱场景中生成基于GS的可检测fiducial标记，从而在提升渲染效率的同时显著改善位姿估计精度，验证了其在农业温室等挑战性环境中的实用性。
链接: https://arxiv.org/abs/2508.17012
作者: Diram Tabaa,Gianni Di Caro
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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Abstract:High-fidelity 3D simulation is critical for training mobile robots, but its traditional reliance on mesh-based representations often struggle in complex environments, such as densely packed greenhouses featuring occlusions and repetitive structures. Recent neural rendering methods, like Gaussian Splatting (GS), achieve remarkable visual realism but lack flexibility to incorporate fiducial markers, which are essential for robotic localization and control. We propose a hybrid framework that combines the photorealism of GS with structured marker representations. Our core contribution is a novel algorithm for efficiently generating GS-based fiducial markers (e.g., AprilTags) within cluttered scenes. Experiments show that our approach outperforms traditional image-fitting techniques in both efficiency and pose-estimation accuracy. We further demonstrate the framework’s potential in a greenhouse simulation. This agricultural setting serves as a challenging testbed, as its combination of dense foliage, similar-looking elements, and occlusions pushes the limits of perception, thereby highlighting the framework’s value for real-world applications.
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[CV-165] A Survey of Deep Learning-based Point Cloud Denoising
【速读】：该论文旨在解决真实环境中获取的点云数据常因传感器、光照、材质及环境等因素而引入噪声，导致几何保真度下降并影响下游任务性能的问题。其解决方案的关键在于系统性地综述截至2025年8月的基于深度学习的点云去噪方法，从监督程度（监督 vs. 无监督）和建模视角出发构建功能性分类体系，并通过统一基准测试评估不同方法在去噪质量、表面保真度、点分布均匀性及计算效率等方面的性能表现，从而为该领域提供清晰的技术演进脉络与未来研究方向。
链接: https://arxiv.org/abs/2508.17011
作者: Jinxi Wang,Ben Fei,Dasith de Silva Edirimuni,Zheng Liu,Ying He,Xuequan Lu
机构: University of Western Australia (西澳大利亚大学); Chinese University of Hong Kong (香港中文大学); China University of Geosciences (武汉) (中国地质大学（武汉）); Nanyang Technological University (南洋理工大学)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Accurate 3D geometry acquisition is essential for a wide range of applications, such as computer graphics, autonomous driving, robotics, and augmented reality. However, raw point clouds acquired in real-world environments are often corrupted with noise due to various factors such as sensor, lighting, material, environment etc, which reduces geometric fidelity and degrades downstream performance. Point cloud denoising is a fundamental problem, aiming to recover clean point sets while preserving underlying structures. Classical optimization-based methods, guided by hand-crafted filters or geometric priors, have been extensively studied but struggle to handle diverse and complex noise patterns. Recent deep learning approaches leverage neural network architectures to learn distinctive representations and demonstrate strong outcomes, particularly on complex and large-scale point clouds. Provided these significant advances, this survey provides a comprehensive and up-to-date review of deep learning-based point cloud denoising methods up to August 2025. We organize the literature from two perspectives: (1) supervision level (supervised vs. unsupervised), and (2) modeling perspective, proposing a functional taxonomy that unifies diverse approaches by their denoising principles. We further analyze architectural trends both structurally and chronologically, establish a unified benchmark with consistent training settings, and evaluate methods in terms of denoising quality, surface fidelity, point distribution, and computational efficiency. Finally, we discuss open challenges and outline directions for future research in this rapidly evolving field.
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[CV-166] Contrastive Prompt Clustering for Weakly Supervised Semantic Segmentation
【速读】：该论文旨在解决弱监督语义分割（Weakly Supervised Semantic Segmentation, WSSS）中因仅依赖图像级标签而导致的类别间区分度不足、同类内部一致性差以及视觉相似类别易混淆的问题。现有方法多聚焦于类间分离，忽视了相关类别间的共享语义信息，从而限制了分割精度。解决方案的关键在于提出对比提示聚类（Contrastive Prompt Clustering, CPC）框架：首先利用大语言模型（Large Language Models, LLMs）挖掘类别簇以编码内在的类间关系，作为粗粒度语义先验；随后引入类感知的patch级对比损失，强化类内一致性并提升类间分离度，从而在保留细粒度边界的同时缓解视觉相似类别的混淆问题。
链接: https://arxiv.org/abs/2508.17009
作者: Wangyu Wu,Zhenhong Chen,Xiaowen Ma,Wenqiao Zhang,Xianglin Qiu,Siqi Song,Xiaowei Huang,Fei Ma,Jimin Xiao
机构: Xi’an Jiaotong-Liverpool University (西安交通大学利物浦大学); University of Liverpool (利物浦大学); Microsoft (微软); Zhejiang University (浙江大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained attention for its cost-effectiveness. Most existing methods emphasize inter-class separation, often neglecting the shared semantics among related categories and lacking fine-grained discrimination. To address this, we propose Contrastive Prompt Clustering (CPC), a novel WSSS framework. CPC exploits Large Language Models (LLMs) to derive category clusters that encode intrinsic inter-class relationships, and further introduces a class-aware patch-level contrastive loss to enforce intra-class consistency and inter-class separation. This hierarchical design leverages clusters as coarse-grained semantic priors while preserving fine-grained boundaries, thereby reducing confusion among visually similar categories. Experiments on PASCAL VOC 2012 and MS COCO 2014 demonstrate that CPC surpasses existing state-of-the-art methods in WSSS.
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[CV-167] An Efficient Dual-Line Decoder Network with Multi-Scale Convolutional Attention for Multi-organ Segmentation
【速读】：该论文旨在解决医学图像分割中深度学习模型难以兼顾分割精度与计算效率的问题。当前主流方法通常在性能与复杂度之间做出权衡，要么牺牲精度以提升效率，要么以高计算开销换取高精度。其解决方案的关键在于提出一种高效的双路解码器分割网络（EDLDNet），其中引入了一个带噪声的解码器，在训练时通过结构化扰动学习增强模型鲁棒性，而在推理阶段仅使用无噪声解码器以显著降低计算成本；同时结合多尺度卷积注意力模块（MSCAM）、注意力门控机制（AG）和上采样卷积块（UCB）优化特征表示，并设计基于变异的损失函数，利用双解码器输出的多尺度分割掩码提升模型泛化能力。该方法在多个公开数据集上实现SOTA性能的同时，大幅减少乘加操作（MACs），展现出卓越的准确性、效率与鲁棒性。
链接: https://arxiv.org/abs/2508.17007
作者: Riad Hassan,M. Rubaiyat Hossain Mondal,Sheikh Iqbal Ahamed,Fahad Mostafa,Md Mostafijur Rahman
机构: Green University of Bangladesh (格林大学); Bangladesh University of Engineering and Technology (孟加拉国工程技术大学); Marquette University (马凯特大学); Arizona State University (亚利桑那州立大学); University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted for publication in Biomedical Signal Processing and Control journal
Abstract:Proper segmentation of organs-at-risk is important for radiation therapy, surgical planning, and diagnostic decision-making in medical image analysis. While deep learning-based segmentation architectures have made significant progress, they often fail to balance segmentation accuracy with computational efficiency. Most of the current state-of-the-art methods either prioritize performance at the cost of high computational complexity or compromise accuracy for efficiency. This paper addresses this gap by introducing an efficient dual-line decoder segmentation network (EDLDNet). The proposed method features a noisy decoder, which learns to incorporate structured perturbation at training time for better model robustness, yet at inference time only the noise-free decoder is executed, leading to lower computational cost. Multi-Scale convolutional Attention Modules (MSCAMs), Attention Gates (AGs), and Up-Convolution Blocks (UCBs) are further utilized to optimize feature representation and boost segmentation performance. By leveraging multi-scale segmentation masks from both decoders, we also utilize a mutation-based loss function to enhance the model’s generalization. Our approach outperforms SOTA segmentation architectures on four publicly available medical imaging datasets. EDLDNet achieves SOTA performance with an 84.00% Dice score on the Synapse dataset, surpassing baseline model like UNet by 13.89% in Dice score while significantly reducing Multiply-Accumulate Operations (MACs) by 89.7%. Compared to recent approaches like EMCAD, our EDLDNet not only achieves higher Dice score but also maintains comparable computational efficiency. The outstanding performance across diverse datasets establishes EDLDNet’s strong generalization, computational efficiency, and robustness. The source code, pre-processed data, and pre-trained weights will be available at this https URL .
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[CV-168] WebSight: A Vision-First Architecture for Robust Web Agents
【速读】：该论文旨在解决传统网页交互代理依赖HTML或DOM结构所带来的局限性问题，即在缺乏原始网页源码访问权限时难以实现稳定可靠的自动化操作。为此，作者提出了一种纯视觉感知驱动的自主网页代理系统WebSight，其核心创新在于引入了一个专为UI元素交互优化的7B参数规模视觉语言模型WebSight-7B，该模型通过LoRA（Low-Rank Adaptation）微调技术在Wave-UI-25K数据集的一个网页聚焦子集上训练而成。解决方案的关键在于将WebSight-7B嵌入到由规划、推理、视觉-动作和验证等模块化智能体构成的多智能体架构中，并借助情景记忆机制进行协同控制，从而实现了高精度、低延迟且可解释的网页导航能力，在Showdown Clicks和WebVoyager基准测试中均展现出优于现有主流系统的性能表现。
链接: https://arxiv.org/abs/2508.16987
作者: Tanvir Bhathal,Asanshay Gupta
机构: Stanford University (斯坦福大学)
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We introduce WebSight, a vision-based autonomous web agent, designed to interact with web environments purely through visual perception, eliminating dependence on HTML or DOM-based inputs. Central to our approach we introduce our new model, WebSight-7B, a fine-tuned vision-language model optimized for UI element interaction, trained using LoRA on a web-focused subset of the Wave-UI-25K dataset. WebSight integrates this model into a modular multi-agent architecture, comprising planning, reasoning, vision-action, and verification agents, coordinated through an episodic memory mechanism. WebSight-7B achieves a top-1 accuracy of 58.84% on the Showdown Clicks benchmark, outperforming several larger generalist models while maintaining lower latency. The full WebSight agent achieves a 68.0% success rate on the WebVoyager benchmark, surpassing systems from labs such as OpenAI (61.0%) and HCompany (Runner H, 67.0%). Among tasks completed, WebSight answers correctly 97.14% of the time, indicating high precision. Together, WebSight and WebSight-7B establish a new standard for interpretable, robust, and efficient visual web navigation. Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2508.16987 [cs.AI] (or arXiv:2508.16987v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2508.16987 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-169] HiCache: Training-free Acceleration of Diffusion Models via Hermite Polynomial-based Feature Caching
【速读】：该论文旨在解决扩散模型（Diffusion Models）在内容生成任务中因迭代采样导致的计算成本过高问题，尤其是现有基于特征缓存（feature caching）的方法在加速推理时因无法准确建模特征演化动态而导致的图像质量下降问题。解决方案的关键在于提出了一种无需训练的加速框架HiCache，其核心创新是将数学工具与经验特性对齐：首先发现扩散Transformer中的特征导数近似服从多变量高斯分布，从而引入埃尔米特多项式（Hermite polynomials）作为高斯相关过程的理论最优基函数来提升特征预测精度；同时设计双尺度机制（dual-scaling mechanism）以保障数值稳定性并维持预测准确性。
链接: https://arxiv.org/abs/2508.16984
作者: Liang Feng,Shikang Zheng,Jiacheng Liu,Yuqi Lin,Qinming Zhou,Peiliang Cai,Xinyu Wang,Junjie Chen,Chang Zou,Yue Ma,Linfeng Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Diffusion models have achieved remarkable success in content generation but suffer from prohibitive computational costs due to iterative sampling. While recent feature caching methods tend to accelerate inference through temporal extrapolation, these methods still suffer from server quality loss due to the failure in modeling the complex dynamics of feature evolution. To solve this problem, this paper presents HiCache, a training-free acceleration framework that fundamentally improves feature prediction by aligning mathematical tools with empirical properties. Our key insight is that feature derivative approximations in Diffusion Transformers exhibit multivariate Gaussian characteristics, motivating the use of Hermite polynomials-the potentially theoretically optimal basis for Gaussian-correlated processes. Besides, We further introduce a dual-scaling mechanism that ensures numerical stability while preserving predictive accuracy. Extensive experiments demonstrate HiCache’s superiority: achieving 6.24x speedup on FLUX.1-dev while exceeding baseline quality, maintaining strong performance across text-to-image, video generation, and super-resolution tasks. Core implementation is provided in the appendix, with complete code to be released upon acceptance.
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[CV-170] Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection
【速读】：该论文旨在解决域泛化（Domain Generalization, DG）中预训练视觉模型在全量微调时可能丧失其固有泛化能力的问题。现有方法通常以大规模预训练模型作为初始化，但研究表明，对全部参数进行微调会削弱模型在未见目标域上的适应性。为此，论文提出了一种名为联合参数选择（Joint Parameter Selection, JPS）的新方法，其核心在于通过稀疏更新机制仅调整一小部分参数，从而在任务适配与保持预训练模型的泛化性能之间取得平衡。JPS的关键创新在于：一方面设计了基于双算子的参数选择机制，识别出在所有源域上均呈现一致且显著梯度变化的参数；另一方面从理论上建立了包含参数更新稀疏性的泛化误差界，为选择性微调提供了理论依据。实验表明，该方法在多个基准测试中优于当前最优域泛化算法，验证了其高效性和有效性。
链接: https://arxiv.org/abs/2508.16976
作者: Bin Pan,Shiyu Shen,Zongbin Wang,Zhenwei Shi,Xia Xu
机构: Nankai University (南开大学); Beihang University (北京航空航天大学); Tiangong University (天津工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Domain generalization seeks to develop models trained on a limited set of source domains that are capable of generalizing effectively to unseen target domains. While the predominant approach leverages large-scale pre-trained vision models as initialization, recent studies have highlighted that full fine-tuning can compromise the intrinsic generalization capabilities of these models. To address this limitation, parameter-efficient adaptation strategies have emerged, wherein only a subset of model parameters is selectively fine-tuned, thereby balancing task adaptation with the preservation of generalization. Motivated by this paradigm, we introduce Joint Parameter Selection (JPS), a novel method that restricts updates to a small, sparse subset of parameters, thereby retaining and harnessing the generalization strength of pre-trained models. Theoretically, we establish a generalization error bound that explicitly accounts for the sparsity of parameter updates, thereby providing a principled justification for selective fine-tuning. Practically, we design a selection mechanism employing dual operators to identify and update parameters exhibiting consistent and significant gradients across all source domains. Extensive benchmark experiments demonstrate that JPS achieves superior performance compared to state-of-the-art domain generalization methods, substantiating both the efficiency and efficacy of the proposed approach.
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[CV-171] Combating Digitally Altered Images: Deepfake Detection
【速读】：该论文旨在解决Deepfake技术生成的高度逼真伪造图像和视频对公众及相关部门带来的识别挑战问题。其解决方案的关键在于提出一种基于改进型视觉Transformer（Vision Transformer, ViT）模型的鲁棒性检测方法，通过在OpenForensics数据集子集上进行训练，并结合多种数据增强技术以提升模型对多样化图像篡改的适应能力；同时采用过采样策略与分层抽样方式处理类别不平衡问题，从而在测试集上实现优于现有方法的检测性能。
链接: https://arxiv.org/abs/2508.16975
作者: Saksham Kumar,Rhythm Narang
机构: Amrita School of Computing (阿姆里塔计算学院); Amrita Vishwa Vidyapeetham (阿姆里塔世界大学); Dept of Computer Science and Engineering (计算机科学与工程系); Thapar Institute of Engineering & Technology (塔帕尔工程与技术学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:The rise of Deepfake technology to generate hyper-realistic manipulated images and videos poses a significant challenge to the public and relevant authorities. This study presents a robust Deepfake detection based on a modified Vision Transformer(ViT) model, trained to distinguish between real and Deepfake images. The model has been trained on a subset of the OpenForensics Dataset with multiple augmentation techniques to increase robustness for diverse image manipulations. The class imbalance issues are handled by oversampling and a train-validation split of the dataset in a stratified manner. Performance is evaluated using the accuracy metric on the training and testing datasets, followed by a prediction score on a random image of people, irrespective of their realness. The model demonstrates state-of-the-art results on the test dataset to meticulously detect Deepfake images.
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[CV-172] Hierarchical Contextual Grounding LVLM: Enhancing Fine-Grained Visual-Language Understanding with Robust Grounding
【速读】：该论文旨在解决当前视觉语言大模型（Vision-Language Large Models, LVLMs）在复杂现实场景中普遍存在鲁棒性不足、幻觉现象严重及推理错误频发的问题，尤其是在需要精确图像区域定位和细粒度视觉推理的任务中表现不佳。解决方案的关键在于提出一种分层上下文对齐架构——层次化上下文接地视觉语言模型（Hierarchical Contextual Grounding LVLM, HCG-LVLM），其核心创新包括两层结构：第一层为全局上下文感知层用于初步整体理解，第二层为细粒度局部接地层，包含局部细节增强模块以提取高分辨率特征，并引入语义一致性验证器确保视觉-语言对齐的准确性；通过自适应融合机制整合双层信息，从而实现更鲁棒、精准的视觉-语言理解与定位能力。
链接: https://arxiv.org/abs/2508.16974
作者: Leilei Guo,Antonio Carlos Rivera,Peiyu Tang,Haoxuan Ren,Zheyu Song
机构: Zhongkai University of Agriculture and Engineering (仲恺农业工程学院); EDP University of Puerto Rico: San Sebastian (波多黎各庞塞大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Large Language Models (LLMs) and Vision-Language Large Models (LVLMs) have achieved remarkable progress in natural language processing and multimodal understanding. Despite their impressive generalization capabilities, current LVLMs often exhibit insufficient robustness, proneness to hallucination, and reasoning errors in complex real-world scenarios, particularly when precise image region localization and fine-grained visual reasoning are required. To address these limitations, we propose the Hierarchical Contextual Grounding LVLM (HCG-LVLM), a novel architecture that mimics human coarse-to-fine cognitive processing. HCG-LVLM employs a two-layered approach: a Global Contextual Perception layer for initial broad understanding and a Fine-grained Local Grounding layer. The latter incorporates a Local Detail Enhancement Module to extract high-resolution features and a Semantic Consistency Validator to ensure accurate, hallucination-free visual-language alignment. Through an adaptive fusion mechanism, information from both layers is integrated for robust and precise outputs. Extensive experiments on challenging datasets, including GQA, A-OKVQA for fine-grained VQA, and RefCOCO/+/g for Referring Expression Comprehension, demonstrate that HCG-LVLM consistently outperforms state-of-the-art models such as Flamingo, BLIP-2, and MiniGPT-4. Our model achieves superior accuracy and significantly reduces hallucination, validating the effectiveness of its hierarchical design in enhancing fine-grained visual-language understanding and precise grounding capabilities.
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[CV-173] Balanced Sharpness-Aware Minimization for Imbalanced Regression
【速读】：该论文旨在解决视觉回归任务中因数据分布不均衡导致的模型性能下降问题（即不平衡回归问题），尤其针对稀有目标值的预测效果不佳。其解决方案的关键在于将不平衡回归重新建模为不平衡泛化问题，并提出一种名为平衡尖锐感知最小化（Balanced Sharpness-Aware Minimization, BSAM）的方法，通过引入新颖的目标重加权策略，在观测空间内统一模型的泛化能力，从而在理论上保障泛化边界并显著提升模型在各类视觉回归任务中的性能表现。
链接: https://arxiv.org/abs/2508.16973
作者: Yahao Liu,Qin Wang,Lixin Duan,Wen Li
机构: University of Electronic Science and Technology of China (电子科技大学); ETH Zürich (苏黎世联邦理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Tech report
Abstract:Regression is fundamental in computer vision and is widely used in various tasks including age estimation, depth estimation, target localization, \etc However, real-world data often exhibits imbalanced distribution, making regression models perform poorly especially for target values with rare observations~(known as the imbalanced regression problem). In this paper, we reframe imbalanced regression as an imbalanced generalization problem. To tackle that, we look into the loss sharpness property for measuring the generalization ability of regression models in the observation space. Namely, given a certain perturbation on the model parameters, we check how model performance changes according to the loss values of different target observations. We propose a simple yet effective approach called Balanced Sharpness-Aware Minimization~(BSAM) to enforce the uniform generalization ability of regression models for the entire observation space. In particular, we start from the traditional sharpness-aware minimization and then introduce a novel targeted reweighting strategy to homogenize the generalization ability across the observation space, which guarantees a theoretical generalization bound. Extensive experiments on multiple vision regression tasks, including age and depth estimation, demonstrate that our BSAM method consistently outperforms existing approaches. The code is available \hrefthis https URLhere.
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[CV-174] Robust Diagram Reasoning : A Framework for Enhancing LVLM Performance on Visually Perturbed Scientific Diagrams
【速读】：该论文旨在解决多模态大语言模型（LVLMs）在处理现实世界科学文档中常见视觉扰动（如噪声、模糊和遮挡）时缺乏鲁棒性的问题，这一缺陷严重限制了其在科学与工程场景中的实际部署。现有评估基准普遍忽视此类挑战，导致LVLMs在视觉退化科学图示上的推理能力未被充分探索。解决方案的关键在于提出Robust Diagram Reasoning (RDR)框架，其核心是自适应多视角一致性验证（AMCV）机制：通过生成多个扰动版本的图示进行并行推理，并引入基于一致性的自我校正循环，从而提升模型在干扰下的推理稳定性。同时，作者构建了首个大规模科学图示问答数据集SciDiagram-Robust，包含程序化生成的多样化视觉扰动，并提出Perturbation Robustness Score (PRS) 和 Visual Degradation Consistency (VDC) 两个新指标以量化模型鲁棒性。实验表明，即使是GPT-4V等先进闭源模型，在输入受扰时准确率也从85.2%显著下降至72.1%，凸显了RDR框架的必要性与有效性。
链接: https://arxiv.org/abs/2508.16972
作者: Minghao Zhou,Rafael Souza,Yaqian Hu,Luming Che
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Large Language Models (LLMs) and their multimodal variants (LVLMs) hold immense promise for scientific and engineering applications, particularly in processing visual information like scientific diagrams. However, their practical deployment is hindered by a critical lack of robustness to common visual perturbations such as noise, blur, and occlusions, which are prevalent in real-world scientific documents. Existing evaluation benchmarks largely overlook this challenge, leaving the robust reasoning capabilities of LVLMs on visually degraded scientific diagrams underexplored. To address this, we introduce the Robust Diagram Reasoning (RDR) framework, a novel approach designed to enhance and rigorously evaluate LVLMs’ performance under such conditions. At its core, RDR employs an Adaptive Multi-View Consistency Verification (AMCV) mechanism, which involves generating multiple perturbed versions of a diagram, performing parallel inference, and then applying a consistency-based self-correction loop. We also propose two new metrics, Perturbation Robustness Score (PRS) and Visual Degradation Consistency (VDC), to quantify robustness. Furthermore, we construct SciDiagram-Robust, the first large-scale scientific diagram question-answering dataset specifically augmented with diverse, programmatically generated visual perturbations. Our extensive experiments demonstrate that even state-of-the-art closed-source LVLMs like GPT-4V exhibit significant performance degradation when faced with perturbed inputs (Clean Accuracy 85.2% vs. PRS 72.1%).
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[CV-175] Local Information Matters: A Rethink of Crowd Counting ECAI2025
【速读】：该论文旨在解决人群计数任务中模型对局部细节建模能力不足的问题，其核心挑战在于人群个体（heads of humans）在图像中通常占据极小区域，而现有方法普遍采用与其它视觉任务相同的骨干网络结构并追求大感受野，忽视了局部密度差异的精细区分能力。解决方案的关键在于提出一种新的模型设计原则——强调局部建模能力，并据此设计了Local Information Matters Model (LIMM)，其创新点包括：(1) 窗口划分（window partitioning）策略，将输入图像划分为网格窗口以增强局部感知；(2) 窗口内对比学习（window-wise contrastive learning）机制，提升模型区分不同局部密度水平的能力；同时，在模型末端引入全局注意力模块以处理偶尔出现的大尺寸个体，从而实现局部精度与全局计数能力的协同优化。实验表明，该方法在多个公开数据集上显著提升了局部建模性能（如JHU-Crowd++高密度子集上MAE降低8.7%），且保持了对大尺寸个体的准确计数能力，达到当前最优性能。
链接: https://arxiv.org/abs/2508.16970
作者: Tianhang Pan,Xiuyi Jia
机构: Nanjing University of Science and Technology (南京理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ECAI 2025
Abstract:The motivation of this paper originates from rethinking an essential characteristic of crowd counting: individuals (heads of humans) in the crowd counting task typically occupy a very small portion of the image. This characteristic has never been the focus of existing works: they typically use the same backbone as other visual tasks and pursue a large receptive field. This drives us to propose a new model design principle of crowd counting: emphasizing local modeling capability of the model. We follow the principle and design a crowd counting model named Local Information Matters Model (LIMM). The main innovation lies in two strategies: a window partitioning design that applies grid windows to the model input, and a window-wise contrastive learning design to enhance the model’s ability to distinguish between local density levels. Moreover, a global attention module is applied to the end of the model to handle the occasionally occurring large-sized individuals. Extensive experiments on multiple public datasets illustrate that the proposed model shows a significant improvement in local modeling capability (8.7% in MAE on the JHU-Crowd++ high-density subset for example), without compromising its ability to count large-sized ones, which achieves state-of-the-art performance. Code is available at: this https URL.
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[CV-176] RPD-Diff: Region-Adaptive Physics-Guided Diffusion Model for Visibility Enhancement under Dense and Non-Uniform Haze
【速读】：该论文旨在解决密集且非均匀雾霾条件下单图像去雾问题，此类场景中由于严重的信息退化和空间异质性，传统基于扩散的去雾方法因生成条件不足及对空间变化雾霾分布适应性差而导致恢复效果不佳。解决方案的关键在于提出一种区域自适应物理引导的去雾扩散模型（RPD-Diff），其核心创新包括：1）物理引导的中间状态目标策略（Physics-guided Intermediate State Targeting, PIST），通过引入物理先验重构扩散马尔可夫链的目标转移过程，缓解密集雾霾下的条件不足问题；2）雾霾感知的去噪时间步预测器（Haze-Aware Denoising Timestep Predictor, HADTP），利用透射率图交叉注意力机制动态调整局部块的去噪时间步，有效应对非均匀雾霾分布。
链接: https://arxiv.org/abs/2508.16956
作者: Ruicheng Zhang,Puxin Yan,Zeyu Zhang,Yicheng Chang,Hongyi Chen,Zhi Jin
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Single-image dehazing under dense and non-uniform haze conditions remains challenging due to severe information degradation and spatial heterogeneity. Traditional diffusion-based dehazing methods struggle with insufficient generation conditioning and lack of adaptability to spatially varying haze distributions, which leads to suboptimal restoration. To address these limitations, we propose RPD-Diff, a Region-adaptive Physics-guided Dehazing Diffusion Model for robust visibility enhancement in complex haze scenarios. RPD-Diff introduces a Physics-guided Intermediate State Targeting (PIST) strategy, which leverages physical priors to reformulate the diffusion Markov chain by generation target transitions, mitigating the issue of insufficient conditioning in dense haze scenarios. Additionally, the Haze-Aware Denoising Timestep Predictor (HADTP) dynamically adjusts patch-specific denoising timesteps employing a transmission map cross-attention mechanism, adeptly managing non-uniform haze distributions. Extensive experiments across four real-world datasets demonstrate that RPD-Diff achieves state-of-the-art performance in challenging dense and non-uniform haze scenarios, delivering high-quality, haze-free images with superior detail clarity and color fidelity.
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[CV-177] Disentangling Polysemantic Neurons with a Null-Calibrated Polysemanticity Index and Causal Patch Interventions
【速读】：该论文旨在解决神经网络中聚语义神经元（polysemantic neurons）的可解释性难题，即某些神经元对多个甚至无关特征产生响应，导致机制理解困难。其解决方案的关键在于提出了一种零基准校准的量化指标——聚语义指数（Polysemanticity Index, PSI），该指标通过三个独立校准的组件协同评估：几何聚类质量（S）、与标注类别的一致性（Q）以及基于CLIP的开放词汇语义区分度（D）。PSI能够有效识别出激活集可分解为语义一致且可命名原型的神经元，并揭示了深层网络中聚语义倾向显著增强的现象，同时通过因果干预实验验证了其有效性，为发现、量化和研究神经网络中的聚语义单元提供了理论严谨且实用的工具。
链接: https://arxiv.org/abs/2508.16950
作者: Manan Gupta,Dhruv Kumar
机构: BITS Pilani, India (印度理工学院比兰尼分校)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Under review. 13 pages
Abstract:Neural networks often contain polysemantic neurons that respond to multiple, sometimes unrelated, features, complicating mechanistic interpretability. We introduce the Polysemanticity Index (PSI), a null-calibrated metric that quantifies when a neuron’s top activations decompose into semantically distinct clusters. PSI multiplies three independently calibrated components: geometric cluster quality (S), alignment to labeled categories (Q), and open-vocabulary semantic distinctness via CLIP (D). On a pretrained ResNet-50 evaluated with Tiny-ImageNet images, PSI identifies neurons whose activation sets split into coherent, nameable prototypes, and reveals strong depth trends: later layers exhibit substantially higher PSI than earlier layers. We validate our approach with robustness checks (varying hyperparameters, random seeds, and cross-encoder text heads), breadth analyses (comparing class-only vs. open-vocabulary concepts), and causal patch-swap interventions. In particular, aligned patch replacements increase target-neuron activation significantly more than non-aligned, random, shuffled-position, or ablate-elsewhere controls. PSI thus offers a principled and practical lever for discovering, quantifying, and studying polysemantic units in neural networks.
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[CV-178] HieroAction: Hierarchically Guided VLM for Fine-Grained Action Analysis
【速读】：该论文旨在解决现有方法在评估人类动作时缺乏清晰、详细反馈的问题，尤其是在体育、医疗和机器人等领域，决策不仅依赖最终结果，还需可解释的推理过程。当前多数方法仅提供单一评分而无分析依据，限制了实际应用价值。其解决方案的关键在于提出HieroAction——一种视觉-语言模型，通过两个核心机制实现精准且结构化的动作评估：一是“分步动作推理（Stepwise Action Reasoning）”，即设计针对动作评估的链式思维过程，引导模型从整体识别到子动作分析再到最终评分逐步推演，提升可解释性与结构化理解；二是“层次策略学习（Hierarchical Policy Learning）”，一种强化学习策略，使模型能够学习细粒度子动作动态并将其与高层动作质量对齐，从而提高评分精度。二者协同作用，确保评估既准确又可解释，在多个基准数据集上表现优异。
链接: https://arxiv.org/abs/2508.16942
作者: Junhao Wu,Xiuer Gu,Zhiying Li,Yeying Jin,Yunfeng Diao,Zhiyu Li,Zhenbo Song,Xiaomei Zhang,Zhaoxin Fan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Evaluating human actions with clear and detailed feedback is important in areas such as sports, healthcare, and robotics, where decisions rely not only on final outcomes but also on interpretable reasoning. However, most existing methods provide only a final score without explanation or detailed analysis, limiting their practical applicability. To address this, we introduce HieroAction, a vision-language model that delivers accurate and structured assessments of human actions. HieroAction builds on two key ideas: (1) Stepwise Action Reasoning, a tailored chain of thought process designed specifically for action assessment, which guides the model to evaluate actions step by step, from overall recognition through sub action analysis to final scoring, thus enhancing interpretability and structured understanding; and (2) Hierarchical Policy Learning, a reinforcement learning strategy that enables the model to learn fine grained sub action dynamics and align them with high level action quality, thereby improving scoring precision. The reasoning pathway structures the evaluation process, while policy learning refines each stage through reward based optimization. Their integration ensures accurate and interpretable assessments, as demonstrated by superior performance across multiple benchmark datasets. Code will be released upon acceptance.
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[CV-179] NAT: Learning to Attack Neurons for Enhanced Adversarial Transferability WACV2025
【速读】：该论文旨在解决生成可迁移对抗扰动（transferable adversarial perturbations）时，现有方法因过度聚焦于单层嵌入空间的分离而忽视个体神经元作用的问题。其解决方案的关键在于提出Neuron Attack for Transferability (NAT)，通过针对特定神经元进行攻击，而非仅优化整体嵌入空间的分离度，从而更有效地破坏神经网络的核心单元，提升跨模型和跨域场景下的迁移性。实验表明，NAT在41个ImageNet模型和9个细粒度模型上均显著优于现有基线方法。
链接: https://arxiv.org/abs/2508.16937
作者: Krishna Kanth Nakka,Alexandre Alahi
机构: VITA Lab, EPFL, Switzerland(瑞士联邦理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Published at WACV 2025
Abstract:The generation of transferable adversarial perturbations typically involves training a generator to maximize embedding separation between clean and adversarial images at a single mid-layer of a source model. In this work, we build on this approach and introduce Neuron Attack for Transferability (NAT), a method designed to target specific neuron within the embedding. Our approach is motivated by the observation that previous layer-level optimizations often disproportionately focus on a few neurons representing similar concepts, leaving other neurons within the attacked layer minimally affected. NAT shifts the focus from embedding-level separation to a more fundamental, neuron-specific approach. We find that targeting individual neurons effectively disrupts the core units of the neural network, providing a common basis for transferability across different models. Through extensive experiments on 41 diverse ImageNet models and 9 fine-grained models, NAT achieves fooling rates that surpass existing baselines by over 14% in cross-model and 4% in cross-domain settings. Furthermore, by leveraging the complementary attacking capabilities of the trained generators, we achieve impressive fooling rates within just 10 queries. Our code is available at: this https URL
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[CV-180] Addressing Annotation Scarcity in Hyperspectral Brain Image Segmentation with Unsupervised Domain Adaptation
【速读】：该论文旨在解决脑部高光谱图像中血管分割任务因标注数据稀缺而导致的传统监督学习方法性能受限的问题。其解决方案的关键在于提出了一种新颖的无监督域自适应（unsupervised domain adaptation）方法，利用少量专家标注的真实标签与大量未标注数据进行联合训练，从而有效提升模型在标注数据稀缺场景下的泛化能力与分割精度。
链接: https://arxiv.org/abs/2508.16934
作者: Tim Mach,Daniel Rueckert,Alex Berger,Laurin Lux,Ivan Ezhov
机构: Technical University of Munich (慕尼黑工业大学); Department of Computing, Imperial College London (帝国理工学院计算机系); Munich Center for Machine Learning (慕尼黑机器学习中心)
类目: Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
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Abstract:This work presents a novel deep learning framework for segmenting cerebral vasculature in hyperspectral brain images. We address the critical challenge of severe label scarcity, which impedes conventional supervised training. Our approach utilizes a novel unsupervised domain adaptation methodology, using a small, expert-annotated ground truth alongside unlabeled data. Quantitative and qualitative evaluations confirm that our method significantly outperforms existing state-of-the-art approaches, demonstrating the efficacy of domain adaptation for label-scarce biomedical imaging tasks.
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[CV-181] Align 3D Representation and Text Embedding for 3D Content Personalization
【速读】：该论文旨在解决3D内容个性化（personalization）过程中效率低下的问题，尤其是现有方法依赖基于知识蒸馏的再训练流程，计算成本高昂。其解决方案的关键在于提出Invert3D框架，通过构建相机条件的3D到文本的逆向映射机制（camera-conditioned 3D-to-text inverse mechanism），将3D表示投影至与文本嵌入空间对齐的3D嵌入空间，从而实现无需重新训练即可通过自然语言提示高效操控和个性化3D内容。
链接: https://arxiv.org/abs/2508.16932
作者: Qi Song,Ziyuan Luo,Ka Chun Cheung,Simon See,Renjie Wan
机构: Hong Kong Baptist University (香港浸会大学); NVIDIA (英伟达)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent advances in NeRF and 3DGS have significantly enhanced the efficiency and quality of 3D content synthesis. However, efficient personalization of generated 3D content remains a critical challenge. Current 3D personalization approaches predominantly rely on knowledge distillation-based methods, which require computationally expensive retraining procedures. To address this challenge, we propose \textbfInvert3D, a novel framework for convenient 3D content personalization. Nowadays, vision-language models such as CLIP enable direct image personalization through aligned vision-text embedding spaces. However, the inherent structural differences between 3D content and 2D images preclude direct application of these techniques to 3D personalization. Our approach bridges this gap by establishing alignment between 3D representations and text embedding spaces. Specifically, we develop a camera-conditioned 3D-to-text inverse mechanism that projects 3D contents into a 3D embedding aligned with text embeddings. This alignment enables efficient manipulation and personalization of 3D content through natural language prompts, eliminating the need for computationally retraining procedures. Extensive experiments demonstrate that Invert3D achieves effective personalization of 3D content. Our work is available at: this https URL.
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[CV-182] LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR MICCAI
【速读】：该论文旨在解决心肌病（Cardiomyopathy）早期筛查中依赖钆对比剂（gadolinium contrast）和人工解读耗时的问题，从而限制了心脏磁共振成像（Cardiac Magnetic Resonance, CMR）在大规模人群中的应用。其核心解决方案是提出一种无钆对比剂的心肌病筛查框架CC-CMR，该框架基于对比学习（Contrastive Learning）与跨模态对齐机制，将仅用动态电影序列（cine CMR）即可编码出类似于延迟钆增强（Late Gadolinium Enhancement, LGE）序列所反映的纤维化病理特征。关键创新在于引入特征交互模块（Feature Interaction Module）以同步优化诊断精度与跨模态特征一致性，并结合不确定性引导的自适应训练机制动态调整任务目标权重，提升模型泛化能力。在231名受试者的多中心数据上验证表明，CC-CMR准确率达0.943，较现有仅使用cine CMR的方法提高4.3%，且完全消除了对钆对比剂的依赖，具备临床推广潜力。
链接: https://arxiv.org/abs/2508.16927
作者: Siqing Yuan,Yulin Wang,Zirui Cao,Yueyan Wang,Zehao Weng,Hui Wang,Lei Xu,Zixian Chen,Lei Chen,Zhong Xue,Dinggang Shen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to MLMI 2025 (MICCAI workshop); camera-ready version
Abstract:Cardiomyopathy, a principal contributor to heart failure and sudden cardiac mortality, demands precise early screening. Cardiac Magnetic Resonance (CMR), recognized as the diagnostic ‘gold standard’ through multiparametric protocols, holds the potential to serve as an accurate screening tool. However, its reliance on gadolinium contrast and labor-intensive interpretation hinders population-scale deployment. We propose CC-CMR, a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences. By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings. A Feature Interaction Module concurrently optimizes diagnostic precision and cross-modal feature congruence, augmented by an uncertainty-guided adaptive training mechanism that dynamically calibrates task-specific objectives to ensure model generalizability. Evaluated on multi-center data from 231 subjects, CC-CMR achieves accuracy of 0.943 (95% CI: 0.886-0.986), outperforming state-of-the-art cine-CMR-only models by 4.3% while eliminating gadolinium dependency, demonstrating its clinical viability for wide range of populations and healthcare environments.
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[CV-183] MSPCaps: A Multi-Scale Patchify Capsule Network with Cross-Agreement Routing for Visual Recognition
【速读】：该论文旨在解决现有胶囊网络（Capsule Network, CapsNet）在视觉识别中因依赖单一高层特征图而忽略多尺度特征间互补信息，以及传统特征融合策略（如加法或拼接）难以有效协调多尺度特征差异导致分类性能受限的问题。解决方案的关键在于提出多尺度分块胶囊网络（Multi-Scale Patchify Capsule Network, MSPCaps），其核心创新包括：1）多尺度残差网络骨干（Multi-Scale ResNet Backbone, MSRB）用于提取包含细粒度细节与全局上下文的多样化多尺度特征；2）分块胶囊层（Patchify Capsule Layer, PatchifyCaps）以统一补丁大小将多尺度特征划分为初级胶囊，增强模型对不同感受野的学习能力；3）跨一致路由（Cross-Agreement Routing, CAR）模块通过识别跨尺度预测对的最大一致性来自适应地路由胶囊，确保仅最一致的胶囊参与最终投票，从而提升特征表示的鲁棒性与准确性。
链接: https://arxiv.org/abs/2508.16922
作者: Yudong Hu,Yueju Han,Rui Sun,Jinke Ren
机构: University of Aberdeen (阿伯丁大学); FNii-Shenzhen and SSE, CUHKSZ (深圳南方科技大学研究院和深圳高等金融研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Capsule Network (CapsNet) has demonstrated significant potential in visual recognition by capturing spatial relationships and part-whole hierarchies for learning equivariant feature representations. However, existing CapsNet and variants often rely on a single high-level feature map, overlooking the rich complementary information from multi-scale features. Furthermore, conventional feature fusion strategies (e.g., addition and concatenation) struggle to reconcile multi-scale feature discrepancies, leading to suboptimal classification performance. To address these limitations, we propose the Multi-Scale Patchify Capsule Network (MSPCaps), a novel architecture that integrates multi-scale feature learning and efficient capsule routing. Specifically, MSPCaps consists of three key components: a Multi-Scale ResNet Backbone (MSRB), a Patchify Capsule Layer (PatchifyCaps), and Cross-Agreement Routing (CAR) blocks. First, the MSRB extracts diverse multi-scale feature representations from input images, preserving both fine-grained details and global contextual information. Second, the PatchifyCaps partitions these multi-scale features into primary capsules using a uniform patch size, equipping the model with the ability to learn from diverse receptive fields. Finally, the CAR block adaptively routes the multi-scale capsules by identifying cross-scale prediction pairs with maximum agreement. Unlike the simple concatenation of multiple self-routing blocks, CAR ensures that only the most coherent capsules contribute to the final voting. Our proposed MSPCaps achieves remarkable scalability and superior robustness, consistently surpassing multiple baseline methods in terms of classification accuracy, with configurations ranging from a highly efficient Tiny model (344.3K parameters) to a powerful Large model (10.9M parameters), highlighting its potential in advancing feature representation learning.
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[CV-184] Structural Energy-Guided Sampling for View-Consistent Text-to-3D
【速读】：该论文旨在解决文本到三维（Text-to-3D）生成中普遍存在的“雅努斯问题”（Janus problem），即生成的3D对象在正面视角下外观合理，但在其他视角下出现几何重复或扭曲现象。作者认为这一问题源于2D扩散模型先验中存在的视角偏差（viewpoint bias），该偏差会传播至3D优化过程中。解决方案的关键在于提出一种无需训练、可即插即用的结构能量引导采样方法（Structural Energy-Guided Sampling, SEGS），其在采样阶段通过定义中间U-Net特征主成分分析（PCA）子空间中的结构能量，并将该能量梯度注入去噪轨迹，从而在保持外观保真度的同时引导几何结构适应预期视角，显著减少雅努斯伪影并提升多视角一致性。
链接: https://arxiv.org/abs/2508.16917
作者: Qing Zhang,Jinguang Tong,Jie Hong,Jing Zhang,Xuesong Li
机构: The 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); The University of Hong Kong (香港大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Text-to-3D generation often suffers from the Janus problem, where objects look correct from the front but collapse into duplicated or distorted geometry from other angles. We attribute this failure to viewpoint bias in 2D diffusion priors, which propagates into 3D optimization. To address this, we propose Structural Energy-Guided Sampling (SEGS), a training-free, plug-and-play framework that enforces multi-view consistency entirely at sampling time. SEGS defines a structural energy in a PCA subspace of intermediate U-Net features and injects its gradients into the denoising trajectory, steering geometry toward the intended viewpoint while preserving appearance fidelity. Integrated seamlessly into SDS/VSD pipelines, SEGS significantly reduces Janus artifacts, achieving improved geometric alignment and viewpoint consistency without retraining or weight modification.
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[CV-185] MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation ICCV2025
【速读】：该论文旨在解决多模态条件下3D双人舞蹈动作生成的难题，即如何在文本提示（text prompt）和音乐条件（music condition）的共同驱动下，实现领导者与跟随者之间协调一致且语义对齐的舞蹈动作合成。解决方案的关键在于构建了首个融合人体运动、音乐节奏与细粒度自然语言描述的多模态基准数据集Multimodal DuetDance (MDD)，其包含620分钟高质量动作捕捉数据、超过10,000条精细标注的自然语言描述，以及同步的音乐信息，从而为文本控制的双人舞蹈生成提供了结构化、多样化且语义丰富的训练与评估基础。同时，论文提出了两个新任务：Text-to-Duet（从文本和音乐生成双人动作）和Text-to-Dance Accompaniment（基于文本、音乐和领导者的动作生成跟随者动作），并通过基线实验验证了该数据集的有效性与实用性。
链接: https://arxiv.org/abs/2508.16911
作者: Prerit Gupta,Jason Alexander Fotso-Puepi,Zhengyuan Li,Jay Mehta,Aniket Bera(Purdue University, West Lafayette, IN, USA)
机构: Purdue University (普渡大学)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Sound (cs.SD)
备注: Accepted at ICCV 2025. Project page: this https URL
Abstract:We introduce Multimodal DuetDance (MDD), a diverse multimodal benchmark dataset designed for text-controlled and music-conditioned 3D duet dance motion generation. Our dataset comprises 620 minutes of high-quality motion capture data performed by professional dancers, synchronized with music, and detailed with over 10K fine-grained natural language descriptions. The annotations capture a rich movement vocabulary, detailing spatial relationships, body movements, and rhythm, making MDD the first dataset to seamlessly integrate human motions, music, and text for duet dance generation. We introduce two novel tasks supported by our dataset: (1) Text-to-Duet, where given music and a textual prompt, both the leader and follower dance motion are generated (2) Text-to-Dance Accompaniment, where given music, textual prompt, and the leader’s motion, the follower’s motion is generated in a cohesive, text-aligned manner. We include baseline evaluations on both tasks to support future research.
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[CV-186] MDIQA: Unified Image Quality Assessment for Multi-dimensional Evaluation and Restoration
【速读】：该论文旨在解决现有图像质量评估（Image Quality Assessment, IQA）方法过度关注整体评分而忽视人类视觉感知多维性的问题。传统IQA模型通常将图像质量视为单一标量输出，未能反映人类在评价图像时从多个技术与美学维度（如清晰度、色彩、构图等）综合判断的过程。解决方案的关键在于提出一种多维图像质量评估（Multi-Dimensional Image Quality Assessment, MDIQA）框架，该框架通过分离建模五种技术维度和四种美学维度，在独立分支中分别训练各维度特征，并最终融合这些多维特征生成整体质量评分。此外，该框架支持灵活调整各维度权重，从而为图像修复（Image Restoration, IR）模型提供可定制的优化目标，使修复结果更贴合用户偏好。
链接: https://arxiv.org/abs/2508.16887
作者: Shunyu Yao,Ming Liu,Zhilu Zhang,Zhaolin Wan,Zhilong Ji,Jinfeng Bai,Wangmeng Zuo
机构: 1. University of Science and Technology of China (中国科学技术大学); 2. Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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Abstract:Recent advancements in image quality assessment (IQA), driven by sophisticated deep neural network designs, have significantly improved the ability to approach human perceptions. However, most existing methods are obsessed with fitting the overall score, neglecting the fact that humans typically evaluate image quality from different dimensions before arriving at an overall quality assessment. To overcome this problem, we propose a multi-dimensional image quality assessment (MDIQA) framework. Specifically, we model image quality across various perceptual dimensions, including five technical and four aesthetic dimensions, to capture the multifaceted nature of human visual perception within distinct branches. Each branch of our MDIQA is initially trained under the guidance of a separate dimension, and the respective features are then amalgamated to generate the final IQA score. Additionally, when the MDIQA model is ready, we can deploy it for a flexible training of image restoration (IR) models, enabling the restoration results to better align with varying user preferences through the adjustment of perceptual dimension weights. Extensive experiments demonstrate that our MDIQA achieves superior performance and can be effectively and flexibly applied to image restoration tasks. The code is available: this https URL.
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[CV-187] A Lightweight Convolution and Vision Transformer integrated model with Multi-scale Self-attention Mechanism
【速读】：该论文旨在解决Vision Transformer (ViT) 在实际应用中面临的模型规模大、计算成本高以及局部特征建模能力弱的问题。为实现高效下游视觉任务，提出轻量级模型SAEViT（Sparse-Attention-Efficient-ViT），其核心创新在于三个关键设计：一是引入稀疏聚合注意力（Sparsely Aggregated Attention, SAA）模块，基于图像冗余性进行自适应稀疏采样，并通过反卷积操作恢复特征图，显著降低注意力机制的计算复杂度；二是设计通道交互式前馈网络（Channel-Interactive Feed-Forward Network, CIFFN）层，通过特征分解与重分配增强通道间信息交互，缓解传统前馈网络中的冗余问题；三是构建嵌入深度可分离卷积块（Depth-wise Separable Convolutional Blocks, DWSConv）的分层金字塔结构，进一步强化卷积特征提取能力。实验表明，SAEViT在ImageNet-1K分类任务上以仅0.8 GFLOPs和1.3 GFLOPs的计算量分别达到76.3%和79.6%的Top-1准确率，验证了其在保持高性能的同时实现显著的计算效率提升。
链接: https://arxiv.org/abs/2508.16884
作者: Yi Zhang,Lingxiao Wei,Bowei Zhang,Ziwei Liu,Kai Yi,Shu Hu
机构: Sichuan University (四川大学); Nanyang Technological University (南洋理工大学); Sichuan Police College (四川警察学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE)
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Abstract:Vision Transformer (ViT) has prevailed in computer vision tasks due to its strong long-range dependency modelling ability. However, its large model size with high computational cost and weak local feature modeling ability hinder its application in real scenarios. To balance computation efficiency and performance, we propose SAEViT (Sparse-Attention-Efficient-ViT), a lightweight ViT based model with convolution blocks, in this paper to achieve efficient downstream vision tasks. Specifically, SAEViT introduces a Sparsely Aggregated Attention (SAA) module that performs adaptive sparse sampling based on image redundancy and recovers the feature map via deconvolution operation, which significantly reduces the computational complexity of attention operations. In addition, a Channel-Interactive Feed-Forward Network (CIFFN) layer is developed to enhance inter-channel information exchange through feature decomposition and redistribution, mitigating redundancy in traditional feed-forward networks (FNN). Finally, a hierarchical pyramid structure with embedded depth-wise separable convolutional blocks (DWSConv) is devised to further strengthen convolutional features. Extensive experiments on mainstream datasets show that SAEViT achieves Top-1 accuracies of 76.3% and 79.6% on the ImageNet-1K classification task with only 0.8 GFLOPs and 1.3 GFLOPs, respectively, demonstrating a lightweight solution for various fundamental vision tasks.
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[CV-188] AWM-Fuse: Multi-Modality Image Fusion for Adverse Weather via Global and Local Text Perception
【速读】：该论文旨在解决恶劣天气条件下多模态图像融合（Multi-modality Image Fusion, MMIF）中因天气引起的视觉信息丢失问题，尤其关注如何利用文本信息提升语义感知能力。现有方法虽尝试引入文本信息，但普遍存在文本内容分类不清晰、分析不深入的问题。其解决方案的关键在于提出AWM-Fuse框架，该框架通过统一共享权重架构实现全局与局部文本感知：全局模块基于BLIP生成的描述提取场景整体特征并识别主要退化类型，增强跨天气条件的泛化能力；局部模块则借助ChatGPT生成的细粒度描述，聚焦具体退化效应，捕捉更精细的细节；同时，文本描述作为约束条件引导融合图像生成过程，使网络学习更贴近真实语义标签，从而促进有意义视觉特征的学习。
链接: https://arxiv.org/abs/2508.16881
作者: Xilai Li,Huichun Liu,Xiaosong Li,Tao Ye,Zhenyu Kuang,Huafeng Li
机构: Foshan University (佛山大学); China University of Mining and Technology (中国矿业大学); Kunming University of Science and Technology (昆明理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Multi-modality image fusion (MMIF) in adverse weather aims to address the loss of visual information caused by weather-related degradations, providing clearer scene representations. Although less studies have attempted to incorporate textual information to improve semantic perception, they often lack effective categorization and thorough analysis of textual content. In response, we propose AWM-Fuse, a novel fusion method for adverse weather conditions, designed to handle multiple degradations through global and local text perception within a unified, shared weight architecture. In particular, a global feature perception module leverages BLIP-produced captions to extract overall scene features and identify primary degradation types, thus promoting generalization across various adverse weather conditions. Complementing this, the local module employs detailed scene descriptions produced by ChatGPT to concentrate on specific degradation effects through concrete textual cues, thereby capturing finer details. Furthermore, textual descriptions are used to constrain the generation of fusion images, effectively steering the network learning process toward better alignment with real semantic labels, thereby promoting the learning of more meaningful visual features. Extensive experiments demonstrate that AWM-Fuse outperforms current state-of-the-art methods in complex weather conditions and downstream tasks. Our code is available at this https URL.
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[CV-189] UM3: Unsupervised Map to Map Matching
【速读】：该论文旨在解决异构空间数据源之间地图到地图匹配（map-to-map matching）的难题，核心挑战包括缺乏真实对应关系（ground truth correspondences）、节点特征稀疏以及可扩展性需求。其解决方案的关键在于提出了一种无监督图结构框架，包含三项创新：一是无需训练数据的无监督学习机制，适用于大规模地图数据场景；二是引入伪坐标（pseudo coordinates）以捕捉每个地图内节点的相对空间布局，提升特征判别力并实现尺度不变性学习；三是设计自适应平衡特征与几何相似性的机制及几何一致性损失函数，增强对噪声或不完整坐标数据的鲁棒性。此外，通过基于切片的后处理流水线结合重叠区域和多数投票策略，实现了高效并行处理与边界一致性保持，从而在真实世界数据集上显著优于现有方法，尤其在高噪声和大规模场景下表现突出。
链接: https://arxiv.org/abs/2508.16874
作者: Chaolong Ying,Yinan Zhang,Lei Zhang,Jiazhuang Wang,Shujun Jia,Tianshu Yu
机构: The Chinese University of Hong Kong, Shenzhen (香港中文大学（深圳）); MXNavi Co.,Ltd. (MXNavi 公司)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACM SIGSPATIAL 2025
Abstract:Map-to-map matching is a critical task for aligning spatial data across heterogeneous sources, yet it remains challenging due to the lack of ground truth correspondences, sparse node features, and scalability demands. In this paper, we propose an unsupervised graph-based framework that addresses these challenges through three key innovations. First, our method is an unsupervised learning approach that requires no training data, which is crucial for large-scale map data where obtaining labeled training samples is challenging. Second, we introduce pseudo coordinates that capture the relative spatial layout of nodes within each map, which enhances feature discriminability and enables scale-invariant learning. Third, we design an mechanism to adaptively balance feature and geometric similarity, as well as a geometric-consistent loss function, ensuring robustness to noisy or incomplete coordinate data. At the implementation level, to handle large-scale maps, we develop a tile-based post-processing pipeline with overlapping regions and majority voting, which enables parallel processing while preserving boundary coherence. Experiments on real-world datasets demonstrate that our method achieves state-of-the-art accuracy in matching tasks, surpassing existing methods by a large margin, particularly in high-noise and large-scale scenarios. Our framework provides a scalable and practical solution for map alignment, offering a robust and efficient alternative to traditional approaches.
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[CV-190] Do Multimodal LLM s See Sentiment?
【速读】：该论文旨在解决视觉内容情感传播机制的理解难题，特别是在以图像为主导的社交媒体环境中，如何准确识别和分析图像所承载的情感信息。其核心挑战在于情感感知与复杂场景语义之间的紧密关联。解决方案的关键在于提出一种全新的多模态大语言模型（Multimodal Large Language Models, MLLMs）情感推理框架——MLLMSent，该框架从三个维度展开：（1）直接利用MLLMs对图像进行情感分类；（2）结合预训练语言模型（LLM）对自动生成的图像描述进行情感分析；（3）在带有情感标签的图像描述上微调LLM。实验表明，尤其是第三种微调策略，在多个评估指标和情感极性类别中均显著优于基于词典、卷积神经网络（CNN）和Transformer的基线方法，并在跨数据集测试中展现出强泛化能力，验证了该方案在情感计算领域的潜力与优越性。
链接: https://arxiv.org/abs/2508.16873
作者: Neemias B. da Silva,John Harrison,Rodrigo Minetto,Myriam R. Delgado,Bogdan T. Nassu,Thiago H. Silva
机构: Universidade Tecnológica Federal do Paraná (巴西联邦技术大学帕拉纳分校); University of Toronto (多伦多大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Social and Information Networks (cs.SI)
备注: 11 pages, 6 figures
Abstract:Understanding how visual content communicates sentiment is critical in an era where online interaction is increasingly dominated by this kind of media on social platforms. However, this remains a challenging problem, as sentiment perception is closely tied to complex, scene-level semantics. In this paper, we propose an original framework, MLLMsent, to investigate the sentiment reasoning capabilities of Multimodal Large Language Models (MLLMs) through three perspectives: (1) using those MLLMs for direct sentiment classification from images; (2) associating them with pre-trained LLMs for sentiment analysis on automatically generated image descriptions; and (3) fine-tuning the LLMs on sentiment-labeled image descriptions. Experiments on a recent and established benchmark demonstrate that our proposal, particularly the fine-tuned approach, achieves state-of-the-art results outperforming Lexicon-, CNN-, and Transformer-based baselines by up to 30.9%, 64.8%, and 42.4%, respectively, across different levels of evaluators’ agreement and sentiment polarity categories. Remarkably, in a cross-dataset test, without any training on these new data, our model still outperforms, by up to 8.26%, the best runner-up, which has been trained directly on them. These results highlight the potential of the proposed visual reasoning scheme for advancing affective computing, while also establishing new benchmarks for future research.
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[CV-191] Delta-SVD: Efficient Compression for Personalized Text-to-Image Models
【速读】：该论文旨在解决个性化文本到图像生成模型（如DreamBooth）在部署时因微调大型扩散模型主干网络而导致的存储开销问题。解决方案的关键在于提出了一种后处理、无需重新训练的压缩方法Delta-SVD，其核心思想是利用微调产生的权重增量（delta weights）具有强低秩结构的特性，通过奇异值分解（Singular Value Decomposition, SVD）对这些增量进行因子分解，并采用基于能量的秩截断策略，在压缩效率与重建保真度之间取得平衡，从而实现高效且可即插即用的模型压缩。
链接: https://arxiv.org/abs/2508.16863
作者: Tangyuan Zhang,Shangyu Chen,Qixiang Chen,Jianfei Cai
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Personalized text-to-image models such as DreamBooth require fine-tuning large-scale diffusion backbones, resulting in significant storage overhead when maintaining many subject-specific models. We present Delta-SVD, a post-hoc, training-free compression method that targets the parameter weights update induced by DreamBooth fine-tuning. Our key observation is that these delta weights exhibit strong low-rank structure due to the sparse and localized nature of personalization. Delta-SVD first applies Singular Value Decomposition (SVD) to factorize the weight deltas, followed by an energy-based rank truncation strategy to balance compression efficiency and reconstruction fidelity. The resulting compressed models are fully plug-and-play and can be re-constructed on-the-fly during inference. Notably, the proposed approach is simple, efficient, and preserves the original model architecture. Experiments on a multiple subject dataset demonstrate that Delta-SVD achieves substantial compression with negligible loss in generation quality measured by CLIP score, SSIM and FID. Our method enables scalable and efficient deployment of personalized diffusion models, making it a practical solution for real-world applications that require storing and deploying large-scale subject customizations.
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[CV-192] Beyond Emotion Recognition: A Multi-Turn Multimodal Emotion Understanding and Reasoning Benchmark
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在心理领域应用中，对人类情绪理解与推理能力不足的问题，尤其关注现有研究主要聚焦于情绪识别而忽视了更为关键的情绪推理任务，从而限制了人机交互的自然性与有效性。解决方案的关键在于提出一个名为多轮多模态情绪理解与推理（Multi-turn Multimodal Emotion Understanding and Reasoning, MTMEUR）的基准数据集，包含1,451段真实场景视频及5,101个逐步递进的问题，覆盖情绪识别、情绪成因分析、未来行为预测等多个维度；同时设计了一个基于多智能体（multi-agent）的框架，使每个智能体专注于背景情境、角色动态或事件细节等特定方面，以提升系统整体的情绪推理能力。
链接: https://arxiv.org/abs/2508.16859
作者: Jinpeng Hu,Hongchang Shi,Chongyuan Dai,Zhuo Li,Peipei Song,Meng Wang
机构: Hefei University of Technology (合肥工业大学); The Chinese University of Hong Kong, Shenzhen (香港中文大学（深圳）); University of Science and Technology of China (中国科学技术大学); Institute of Artificial Intelligence (IAI), Hefei Comprehensive National Science Center (合肥综合性国家科学中心人工智能研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ACM Multimedia 2025
Abstract:Multimodal large language models (MLLMs) have been widely applied across various fields due to their powerful perceptual and reasoning capabilities. In the realm of psychology, these models hold promise for a deeper understanding of human emotions and behaviors. However, recent research primarily focuses on enhancing their emotion recognition abilities, leaving the substantial potential in emotion reasoning, which is crucial for improving the naturalness and effectiveness of human-machine interactions. Therefore, in this paper, we introduce a multi-turn multimodal emotion understanding and reasoning (MTMEUR) benchmark, which encompasses 1,451 video data from real-life scenarios, along with 5,101 progressive questions. These questions cover various aspects, including emotion recognition, potential causes of emotions, future action prediction, etc. Besides, we propose a multi-agent framework, where each agent specializes in a specific aspect, such as background context, character dynamics, and event details, to improve the system’s reasoning capabilities. Furthermore, we conduct experiments with existing MLLMs and our agent-based method on the proposed benchmark, revealing that most models face significant challenges with this task.
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[CV-193] Gaussian Primitive Optimized Deformable Retinal Image Registration MICCAI2025
【速读】：该论文旨在解决视网膜图像配准中因大范围均质区域和稀疏但关键的血管特征导致的标准学习框架梯度信号不足的问题。解决方案的关键在于提出高斯原型优化（Gaussian Primitive Optimization, GPO）框架，其通过迭代结构化消息传递实现：首先在初始粗配准后提取显著解剖结构（如主要血管）的关键点作为描述符控制节点（Descriptor-based Control Nodes, DCN），每个节点建模为可训练位置、位移和半径的高斯原型，从而自适应局部形变尺度；随后采用K近邻高斯插值将信息丰富的节点位移信号传播并融合生成全局一致的位移场，聚焦于Top-K邻居以降低计算开销同时保留局部细节；该策略通过锚定高梯度区域确保梯度流稳定，有效缓解纹理缺失区域的梯度消失问题，并通过多目标损失函数端到端优化关键点一致性与强度对齐。
链接: https://arxiv.org/abs/2508.16852
作者: Xin Tian,Jiazheng Wang,Yuxi Zhang,Xiang Chen,Renjiu Hu,Gaolei Li,Min Liu,Hang Zhang
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: 11 pages, 4 figures, MICCAI 2025 (Early accept)
Abstract:Deformable retinal image registration is notoriously difficult due to large homogeneous regions and sparse but critical vascular features, which cause limited gradient signals in standard learning-based frameworks. In this paper, we introduce Gaussian Primitive Optimization (GPO), a novel iterative framework that performs structured message passing to overcome these challenges. After an initial coarse alignment, we extract keypoints at salient anatomical structures (e.g., major vessels) to serve as a minimal set of descriptor-based control nodes (DCN). Each node is modelled as a Gaussian primitive with trainable position, displacement, and radius, thus adapting its spatial influence to local deformation scales. A K-Nearest Neighbors (KNN) Gaussian interpolation then blends and propagates displacement signals from these information-rich nodes to construct a globally coherent displacement field; focusing interpolation on the top (K) neighbors reduces computational overhead while preserving local detail. By strategically anchoring nodes in high-gradient regions, GPO ensures robust gradient flow, mitigating vanishing gradient signal in textureless areas. The framework is optimized end-to-end via a multi-term loss that enforces both keypoint consistency and intensity alignment. Experiments on the FIRE dataset show that GPO reduces the target registration error from 6.2,px to ~2.4,px and increases the AUC at 25,px from 0.770 to 0.938, substantially outperforming existing methods. The source code can be accessed via this https URL.
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[CV-194] RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting
【速读】：该论文旨在解决6G时代对高系统吞吐量和精确空间信道状态信息（Spatial-CSI）的需求，以及传统信道建模方法（如经验模型、射线追踪和基于测量的方法）在空间分辨率、效率和可扩展性方面的局限性。其解决方案的关键在于提出了一种名为RF-PGS的新框架，该框架通过引入以平面高斯（Planar Gaussians）为几何基元的结构化辐射场表示，并结合针对无线电信号特性的优化策略，在第一阶段实现密集且与表面对齐的场景重建；第二阶段则利用全结构化的无线电辐射场和定制的多视角损失函数，精准建模无线传播行为，从而显著提升重建精度、降低训练成本并实现高效无线信道表征，为可扩展的6G Spatial-CSI建模提供实用方案。
链接: https://arxiv.org/abs/2508.16849
作者: Lihao Zhang,Zongtan Li,Haijian Sun
机构: University of Georgia(佐治亚大学); University of Georgia(佐治亚大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Networking and Internet Architecture (cs.NI)
备注: 13 pages, 16 figures, in submission to IEEE journal
Abstract:In the 6G era, the demand for higher system throughput and the implementation of emerging 6G technologies require large-scale antenna arrays and accurate spatial channel state information (Spatial-CSI). Traditional channel modeling approaches, such as empirical models, ray tracing, and measurement-based methods, face challenges in spatial resolution, efficiency, and scalability. Radiance field-based methods have emerged as promising alternatives but still suffer from geometric inaccuracy and costly supervision. This paper proposes RF-PGS, a novel framework that reconstructs high-fidelity radio propagation paths from only sparse path loss spectra. By introducing Planar Gaussians as geometry primitives with certain RF-specific optimizations, RF-PGS achieves dense, surface-aligned scene reconstruction in the first geometry training stage. In the subsequent Radio Frequency (RF) training stage, the proposed fully-structured radio radiance, combined with a tailored multi-view loss, accurately models radio propagation behavior. Compared to prior radiance field methods, RF-PGS significantly improves reconstruction accuracy, reduces training costs, and enables efficient representation of wireless channels, offering a practical solution for scalable 6G Spatial-CSI modeling.
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[CV-195] NinA: Normalizing Flows in Action. Training VLA Models with Normalizing Flows
【速读】：该论文旨在解决当前视觉-语言-动作（Vision-Language-Action, VLA）模型中基于扩散模型（Diffusion Models）的动作解码器在推理时计算开销大、采样步骤多的问题，这限制了其在高频控制场景下的实用性。解决方案的关键在于用归一化流（Normalizing Flow, NF）替代扩散模型作为动作解码器，利用其可逆变换实现单次采样（one-shot sampling），从而显著降低推理延迟，同时保持与扩散模型相当的性能表现。
链接: https://arxiv.org/abs/2508.16845
作者: Denis Tarasov,Alexander Nikulin,Ilya Zisman,Albina Klepach,Nikita Lyubaykin,Andrei Polubarov,Alexander Derevyagin,Vladislav Kurenkov
机构: AIRI; ETH Zürich (苏黎世联邦理工学院); MIPT (莫斯科物理技术学院); Skoltech (斯科尔科沃科学技术研究所); Innopolis University (因诺波利斯大学); HSE (高等经济大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Recent advances in Vision-Language-Action (VLA) models have established a two-component architecture, where a pre-trained Vision-Language Model (VLM) encodes visual observations and task descriptions, and an action decoder maps these representations to continuous actions. Diffusion models have been widely adopted as action decoders due to their ability to model complex, multimodal action distributions. However, they require multiple iterative denoising steps at inference time or downstream techniques to speed up sampling, limiting their practicality in real-world settings where high-frequency control is crucial. In this work, we present NinA (Normalizing Flows in Action), a fast and expressive alter- native to diffusion-based decoders for VLAs. NinA replaces the diffusion action decoder with a Normalizing Flow (NF) that enables one-shot sampling through an invertible transformation, significantly reducing inference time. We integrate NinA into the FLOWER VLA architecture and fine-tune on the LIBERO benchmark. Our experiments show that NinA matches the performance of its diffusion-based counterpart under the same training regime, while achieving substantially faster inference. These results suggest that NinA offers a promising path toward efficient, high-frequency VLA control without compromising performance.
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[CV-196] ransformer-Based Neural Network for Transient Detection without Image Subtraction
【速读】：该论文旨在解决天文图像中真实瞬变信号（如超新星）与虚假检测（bogus detections）的准确分类问题，传统卷积神经网络（CNN）方法依赖于计算成本高昂的差分成像（difference imaging），限制了效率和可扩展性。解决方案的关键在于提出一种基于Transformer架构的神经网络，其通过像素级逐点比较机制，仅需分析搜索图和模板图即可实现高精度分类，无需差分成像步骤；同时，在暗能量巡天（Dark Energy Survey, DES）数据集上验证表明，该方法在训练样本增加时性能持续提升，且对输入图像未精确对准超新星候选体的情况仍保持稳定表现，显著提升了大规模天文巡天中超新星检测的准确性与计算效率。
链接: https://arxiv.org/abs/2508.16844
作者: Adi Inada,Masao Sako,Tatiana Acero-Cuellar,Federica Bianco
机构: University of Pennsylvania (宾夕法尼亚大学); University of Delaware (特拉华大学); Vera C. Rubin Observatory (维拉·C·鲁宾天文台)
类目: Computer Vision and Pattern Recognition (cs.CV); Instrumentation and Methods for Astrophysics (astro-ph.IM)
备注: 12 pages, 7 figures
Abstract:We introduce a transformer-based neural network for the accurate classification of real and bogus transient detections in astronomical images. This network advances beyond the conventional convolutional neural network (CNN) methods, widely used in image processing tasks, by adopting an architecture better suited for detailed pixel-by-pixel comparison. The architecture enables efficient analysis of search and template images only, thus removing the necessity for computationally-expensive difference imaging, while maintaining high performance. Our primary evaluation was conducted using the autoScan dataset from the Dark Energy Survey (DES), where the network achieved a classification accuracy of 97.4% and diminishing performance utility for difference image as the size of the training set grew. Further experiments with DES data confirmed that the network can operate at a similar level even when the input images are not centered on the supernova candidate. These findings highlight the network’s effectiveness in enhancing both accuracy and efficiency of supernova detection in large-scale astronomical surveys.
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[CV-197] AIM 2025 Low-light RAW Video Denoising Challenge: Dataset Methods and Results ICCV2025
【速读】：该论文旨在解决低光照条件下RAW视频去噪问题，核心挑战在于如何在帧率限制下的曝光时间约束下，利用时域冗余信息并适应传感器特有的信号依赖噪声（signal-dependent noise）。解决方案的关键在于构建一个包含756个十帧序列的新基准数据集，涵盖14种智能手机摄像头传感器和9种不同光照与曝光组合（光照：1/5/10 lx；曝光：1/24、1/60、1/120 s），并通过多帧叠加获得高信噪比参考图像。参赛方法需处理线性RAW序列并输出去噪后的第10帧，同时保持拜耳（Bayer）模式不变，最终评估指标为全参考PSNR与SSIM的平均排名。
链接: https://arxiv.org/abs/2508.16830
作者: Alexander Yakovenko,George Chakvetadze,Ilya Khrapov,Maksim Zhelezov,Dmitry Vatolin,Radu Timofte,Youngjin Oh,Junhyeong Kwon,Junyoung Park,Nam Ik Cho,Senyan Xu,Ruixuan Jiang,Long Peng,Xueyang Fu,Zheng-Jun Zha,Xiaoping Peng,Hansen Feng,Zhanyi Tie,Ziming Xia,Lizhi Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: Challenge report from Advances in Image Manipulation workshop held at ICCV 2025
Abstract:This paper reviews the AIM 2025 (Advances in Image Manipulation) Low-Light RAW Video Denoising Challenge. The task is to develop methods that denoise low-light RAW video by exploiting temporal redundancy while operating under exposure-time limits imposed by frame rate and adapting to sensor-specific, signal-dependent noise. We introduce a new benchmark of 756 ten-frame sequences captured with 14 smartphone camera sensors across nine conditions (illumination: 1/5/10 lx; exposure: 1/24, 1/60, 1/120 s), with high-SNR references obtained via burst averaging. Participants process linear RAW sequences and output the denoised 10th frame while preserving the Bayer pattern. Submissions are evaluated on a private test set using full-reference PSNR and SSIM, with final ranking given by the mean of per-metric ranks. This report describes the dataset, challenge protocol, and submitted approaches.
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[CV-198] owards Open-Vocabulary Multimodal 3D Object Detection with Attributes BMVC2025
【速读】：该论文旨在解决现有3D目标检测方法在开放词汇场景下的局限性，即其通常基于封闭集假设，难以识别真实世界中未见过的新类别物体及其属性（如空间关系、运动状态等）。为应对这一挑战，作者提出OVODA框架，其核心创新在于利用基础模型（foundation models）桥接3D特征与文本语义之间的鸿沟，并实现物体与属性的联合检测。关键解决方案包括：基础模型特征拼接、提示调优（prompt tuning）策略、针对属性检测设计的视角指定提示（perspective-specified prompts）和水平翻转增强技术，从而在无需预先知道新类别锚框尺寸的情况下，显著提升开放词汇3D目标检测与属性识别性能。
链接: https://arxiv.org/abs/2508.16812
作者: Xinhao Xiang,Kuan-Chuan Peng,Suhas Lohit,Michael J. Jones,Jiawei Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper is accepted to BMVC 2025 as an oral paper. The OVAD dataset is available at this https URL
Abstract:3D object detection plays a crucial role in autonomous systems, yet existing methods are limited by closed-set assumptions and struggle to recognize novel objects and their attributes in real-world scenarios. We propose OVODA, a novel framework enabling both open-vocabulary 3D object and attribute detection with no need to know the novel class anchor size. OVODA uses foundation models to bridge the semantic gap between 3D features and texts while jointly detecting attributes, e.g., spatial relationships, motion states, etc. To facilitate such research direction, we propose OVAD, a new dataset that supplements existing 3D object detection benchmarks with comprehensive attribute annotations. OVODA incorporates several key innovations, including foundation model feature concatenation, prompt tuning strategies, and specialized techniques for attribute detection, including perspective-specified prompts and horizontal flip augmentation. Our results on both the nuScenes and Argoverse 2 datasets show that under the condition of no given anchor sizes of novel classes, OVODA outperforms the state-of-the-art methods in open-vocabulary 3D object detection while successfully recognizing object attributes. Our OVAD dataset is released here: this https URL .
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[CV-199] Improving Performance Robustness and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data
【速读】：该论文旨在解决深度学习模型在诊断影像中跨不同患者群体时的鲁棒性和公平性问题，尤其是在真实数据规模有限且多样性不足的情况下。其关键解决方案是提出RoentGen-v2，一个能够对胸部X光片进行细粒度控制的文本到图像扩散模型，支持放射学发现与患者人口统计学特征（如性别、年龄、种族/族裔）的联合条件生成；基于此模型构建了包含超过56.5万张图像的、人口统计学平衡的合成数据集，并采用“先在合成数据上监督预训练，再在真实数据上微调”的优化训练策略，显著提升了下游疾病分类模型的准确性、泛化能力及公平性表现。
链接: https://arxiv.org/abs/2508.16783
作者: Stefania L. Moroianu,Christian Bluethgen,Pierre Chambon,Mehdi Cherti,Jean-Benoit Delbrouck,Magdalini Paschali,Brandon Price,Judy Gichoya,Jenia Jitsev,Curtis P. Langlotz,Akshay S. Chaudhari
机构: Stanford University (斯坦福大学); University Hospital Zurich, University of Zurich (苏黎世大学医院，苏黎世大学); LAION; Juelich Supercomputing Center (JSC), Research Center Juelich (FZJ) (于利希超级计算中心（JSC），于利希研究中心（FZJ）); Emory University (埃默里大学); University of Florida College of Medicine (佛罗里达大学医学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Achieving robust performance and fairness across diverse patient populations remains a challenge in developing clinically deployable deep learning models for diagnostic imaging. Synthetic data generation has emerged as a promising strategy to address limitations in dataset scale and diversity. We introduce RoentGen-v2, a text-to-image diffusion model for chest radiographs that enables fine-grained control over both radiographic findings and patient demographic attributes, including sex, age, and race/ethnicity. RoentGen-v2 is the first model to generate clinically plausible images with demographic conditioning, facilitating the creation of a large, demographically balanced synthetic dataset comprising over 565,000 images. We use this large synthetic dataset to evaluate optimal training pipelines for downstream disease classification models. In contrast to prior work that combines real and synthetic data naively, we propose an improved training strategy that leverages synthetic data for supervised pretraining, followed by fine-tuning on real data. Through extensive evaluation on over 137,000 chest radiographs from five institutions, we demonstrate that synthetic pretraining consistently improves model performance, generalization to out-of-distribution settings, and fairness across demographic subgroups. Across datasets, synthetic pretraining led to a 6.5% accuracy increase in the performance of downstream classification models, compared to a modest 2.7% increase when naively combining real and synthetic data. We observe this performance improvement simultaneously with the reduction of the underdiagnosis fairness gap by 19.3%. These results highlight the potential of synthetic imaging to advance equitable and generalizable medical deep learning under real-world data constraints. We open source our code, trained models, and synthetic dataset at this https URL .
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[CV-200] WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation EMNLP2025
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在复杂网页任务中表现不足的问题，特别是其在视觉问答、代码编辑与设计转代码等多步骤推理任务中的能力局限。解决方案的关键在于构建一个名为WebMMU的多语言基准测试集，该基准统一评估三种核心网页任务：网站视觉问答、HTML/CSS/JavaScript代码编辑以及原型图到代码的生成，并基于专家标注的真实世界网页数据，系统性地衡量模型在复杂多步推理、精确元素定位和功能型UI理解与编码方面的综合能力。这一设计揭示了MLLMs在推理、定位、代码保真度及跨语言支持等方面的显著短板，为未来构建具备自动化网页开发能力的智能代理提供了关键评估框架和改进方向。
链接: https://arxiv.org/abs/2508.16763
作者: Rabiul Awal,Mahsa Massoud,Aarash Feizi,Zichao Li,Suyuchen Wang,Christopher Pal,Aishwarya Agrawal,David Vazquez,Siva Reddy,Juan A. Rodriguez,Perouz Taslakian,Spandana Gella,Sai Rajeswar
机构: ServiceNow; Mila; Université de Montréal; McGill University; École de Technologie Supérieure (ETS); Polytechnique Montréal
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted to the EMNLP 2025 main conference. Check the project page here: this https URL
Abstract:We present WebMMU, a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing involving HTML/CSS/JavaScript, and (3) mockup-to-code generation. Unlike prior benchmarks that treat these tasks separately, WebMMU unifies them using expert-annotated, real-world web data to assess models’ abilities in complex multi-step reasoning, precise element grounding, and functional UI comprehension and coding. Our evaluation shows that while multimodal large language models (MLLMs) perform well on basic information extraction, they struggle with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. These findings reveal key limitations in current MLLMs and underscore the need for improved multimodal and cross-lingual reasoning to build future web agents capable of automating diverse web development tasks.
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[CV-201] A Framework for Benchmarking Fairness-Utility Trade-offs in Text-to-Image Models via Pareto Frontiers
【速读】：该论文旨在解决文本到图像生成模型中公平性与视觉保真度（utility）之间的权衡问题，即如何在不损害图像质量的前提下缓解社会偏见，从而实现负责任的人工智能。当前评估方法多依赖主观视觉检查或有限的对比分析，难以系统、可复现地衡量不同模型的公平性与实用性。其解决方案的关键在于提出基于帕累托最优前沿（Pareto-optimal frontiers）的评估框架，通过超参数空间中的多配置分析，识别出在特定实用性能下最优的公平性配置（反之亦然），从而客观比较不同模型的性能边界。该方法利用归一化香农熵（Normalized Shannon Entropy）衡量公平性、ClipScore衡量实用性，并应用于Stable Diffusion、Fair Diffusion、SDXL、DeCoDi及FLUX等主流模型，结果表明多数默认超参数设置在公平性-实用性空间中属于被支配解，可通过优化找到更优配置。
链接: https://arxiv.org/abs/2508.16752
作者: Marco N. Bochernitsan,Rodrigo C. Barros,Lucas S. Kupssinskü
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Achieving fairness in text-to-image generation demands mitigating social biases without compromising visual fidelity, a challenge critical to responsible AI. Current fairness evaluation procedures for text-to-image models rely on qualitative judgment or narrow comparisons, which limit the capacity to assess both fairness and utility in these models and prevent reproducible assessment of debiasing methods. Existing approaches typically employ ad-hoc, human-centered visual inspections that are both error-prone and difficult to replicate. We propose a method for evaluating fairness and utility in text-to-image models using Pareto-optimal frontiers across hyperparametrization of debiasing methods. Our method allows for comparison between distinct text-to-image models, outlining all configurations that optimize fairness for a given utility and vice-versa. To illustrate our evaluation method, we use Normalized Shannon Entropy and ClipScore for fairness and utility evaluation, respectively. We assess fairness and utility in Stable Diffusion, Fair Diffusion, SDXL, DeCoDi, and FLUX text-to-image models. Our method shows that most default hyperparameterizations of the text-to-image model are dominated solutions in the fairness-utility space, and it is straightforward to find better hyperparameters.
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[CV-202] CellEcoNet: Decoding the Cellular Language of Pathology with Deep Learning for Invasive Lung Adenocarcinoma Recurrence Prediction
【速读】：该论文旨在解决侵袭性肺腺癌（invasive lung adenocarcinoma, ILA）患者术后高复发率（约70%在五年内复发）与当前临床工具无法精准识别需接受辅助治疗人群之间的关键临床难题。其解决方案的核心是提出CellEcoNet——一种空间感知的深度学习框架，通过将组织切片图像（WSIs）类比为自然语言，构建病理学“语言”体系：细胞作为词汇、细胞邻域构成短语、组织结构形成句子，从而自动学习上下文依赖的语义信息，捕捉细微细胞变异与空间交互如何决定复发风险。该方法在456例HE染色WSI数据集上展现出显著优于传统分期系统（如IASLC分级、AJCC分期）及现有计算模型的预测性能（AUC:77.8%，HR:9.54），并验证了跨人群的一致性和公平性，标志着从解析肿瘤微环境细胞“语言”角度实现精准预后分层的新范式。
链接: https://arxiv.org/abs/2508.16742
作者: Abdul Rehman Akbar,Usama Sajjad,Ziyu Su,Wencheng Li,Fei Xing,Jimmy Ruiz,Wei Chen,Muhammad Khalid Khan Niazi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Despite surgical resection, ~70% of invasive lung adenocarcinoma (ILA) patients recur within five years, and current tools fail to identify those needing adjuvant therapy. To address this unmet clinical need, we introduce CellEcoNet, a novel spatially aware deep learning framework that models whole slide images (WSIs) through natural language analogy, defining a “language of pathology,” where cells act as words, cellular neighborhoods become phrases, and tissue architecture forms sentences. CellEcoNet learns these context-dependent meanings automatically, capturing how subtle variations and spatial interactions derive recurrence risk. On a dataset of 456 HE-stained WSIs, CellEcoNet achieved superior predictive performance (AUC:77.8% HR:9.54), outperforming IASLC grading system (AUC:71.4% HR:2.36), AJCC Stage (AUC:64.0% HR:1.17) and state-of-the-art computational methods (AUCs:62.2-67.4%). CellEcoNet demonstrated fairness and consistent performance across diverse demographic and clinical subgroups. Beyond prognosis, CellEcoNet marks a paradigm shift by decoding the tumor microenvironment’s cellular “language” to reveal how subtle cell variations encode recurrence risk.
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[CV-203] wo-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection
【速读】：该论文旨在解决无人机（Unmanned Aerial Vehicles, UAVs）在灾害应急响应中进行实时野火监测与火源检测时，受限于 onboard 计算资源而难以部署大模型的问题。解决方案的关键在于提出一种轻量高效的两阶段框架：第一阶段利用策略网络（policy network）结合帧压缩技术识别并丢弃冗余视频片段，同时引入站位点机制（station point mechanism）利用序列中的未来帧信息提升预测准确性，从而显著降低计算开销；第二阶段则在判定为“火灾”帧后，采用改进的 YOLOv8 模型进行火源定位，在保持相近推理时间的前提下实现更高的检测精度。
链接: https://arxiv.org/abs/2508.16739
作者: Yanbing Bai,Rui-Yang Ju,Lemeng Zhao,Junjie Hu,Jianchao Bi,Erick Mas,Shunichi Koshimura
机构: Renmin University of China (中国人民大学); National Taiwan University (台湾大学); Shenzhen Institute of Artificial Intelligence and Robotics for Society (深圳人工智能与机器人社会研究所); Gaoling School of Artificial Intelligence (人大高瓴人工智能学院); Tohoku University (东北大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Unmanned Aerial Vehicles (UAVs) have become increasingly important in disaster emergency response by enabling real-time aerial video analysis. Due to the limited computational resources available on UAVs, large models cannot be run independently for real-time analysis. To overcome this challenge, we propose a lightweight and efficient two-stage framework for real-time wildfire monitoring and fire source detection on UAV platforms. Specifically, in Stage 1, we utilize a policy network to identify and discard redundant video clips using frame compression techniques, thereby reducing computational costs. In addition, we introduce a station point mechanism that leverages future frame information within the sequential policy network to improve prediction accuracy. In Stage 2, once the frame is classified as “fire”, we employ the improved YOLOv8 model to localize the fire source. We evaluate the Stage 1 method using the FLAME and HMDB51 datasets, and the Stage 2 method using the Fire Smoke dataset. Experimental results show that our method significantly reduces computational costs while maintaining classification accuracy in Stage 1, and achieves higher detection accuracy with similar inference time in Stage 2 compared to baseline methods.
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[CV-204] COVID19 Prediction Based On CT Scans Of Lungs Using DenseNet Architecture
【速读】：该论文旨在解决新冠肺炎（COVID-19）患者病情严重程度的早期识别问题，以辅助医生在患者确诊后一个月内评估其感染进展风险。其解决方案的关键在于利用卷积神经网络（Convolutional Neural Network, CNN）模型分析患者的肺部计算机断层扫描（Computed Tomography, CT）图像，通过自动提取影像特征来判断感染严重程度，从而预测是否可能导致插管或死亡等不良结局，提升诊断效率与准确性，缓解医疗资源紧张状况。
链接: https://arxiv.org/abs/2508.16670
作者: Deborup Sanyal
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:COVID19 took the world by storm since December 2019. A highly infectious communicable disease, COVID19 is caused by the SARSCoV2 virus. By March 2020, the World Health Organization (WHO) declared COVID19 as a global pandemic. A pandemic in the 21st century after almost 100 years was something the world was not prepared for, which resulted in the deaths of around 1.6 million people worldwide. The most common symptoms of COVID19 were associated with the respiratory system and resembled a cold, flu, or pneumonia. After extensive research, doctors and scientists concluded that the main reason for lives being lost due to COVID19 was failure of the respiratory system. Patients were dying gasping for breath. Top healthcare systems of the world were failing badly as there was an acute shortage of hospital beds, oxygen cylinders, and ventilators. Many were dying without receiving any treatment at all. The aim of this project is to help doctors decide the severity of COVID19 by reading the patient’s Computed Tomography (CT) scans of the lungs. Computer models are less prone to human error, and Machine Learning or Neural Network models tend to give better accuracy as training improves over time. We have decided to use a Convolutional Neural Network model. Given that a patient tests positive, our model will analyze the severity of COVID19 infection within one month of the positive test result. The severity of the infection may be promising or unfavorable (if it leads to intubation or death), based entirely on the CT scans in the dataset.
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[CV-205] he Loupe: A Plug-and-Play Attention Module for Amplifying Discriminative Features in Vision Transformers
【速读】：该论文旨在解决细粒度视觉分类（Fine-Grained Visual Classification, FGVC）中模型决策过程缺乏可解释性的问题，尤其是在生物多样性监测和医学诊断等对精度要求极高的应用场景中。现有基于大规模Vision Transformer的方法虽性能优异，但其内部注意力机制难以提供清晰的视觉解释，限制了模型在关键领域的可信度与可验证性。解决方案的关键在于提出一种轻量级、即插即用的注意力模块——The Loupe，该模块通过端到端训练并结合复合损失函数，在无需显式部件标注的情况下，引导模型聚焦于最具判别性的局部区域。其核心创新在于证明了一个简单的内在注意力机制可作为强大正则化器，在显著提升分类准确率的同时，生成语义明确的注意力热力图，从而增强模型决策的透明度与可信度。
链接: https://arxiv.org/abs/2508.16663
作者: Naren Sengodan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Fine-Grained Visual Classification (FGVC) is a critical and challenging area within computer vision, demanding the identification of highly subtle, localized visual cues. The importance of FGVC extends to critical applications such as biodiversity monitoring and medical diagnostics, where precision is paramount. While large-scale Vision Transformers have achieved state-of-the-art performance, their decision-making processes often lack the interpretability required for trust and verification in such domains. In this paper, we introduce The Loupe, a novel, lightweight, and plug-and-play attention module designed to be inserted into pre-trained backbones like the Swin Transformer. The Loupe is trained end-to-end with a composite loss function that implicitly guides the model to focus on the most discriminative object parts without requiring explicit part-level annotations. Our unique contribution lies in demonstrating that a simple, intrinsic attention mechanism can act as a powerful regularizer, significantly boosting performance while simultaneously providing clear visual explanations. Our experimental evaluation on the challenging CUB-200-2011 dataset shows that The Loupe improves the accuracy of a Swin-Base model from 85.40% to 88.06%, a significant gain of 2.66%. Crucially, our qualitative analysis of the learned attention maps reveals that The Loupe effectively localizes semantically meaningful features, providing a valuable tool for understanding and trusting the model’s decision-making process.
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[CV-206] QA-VLM: Providing human-interpretable quality assessment for wire-feed laser additive manufacturing parts with Vision Language Models
【速读】：该论文旨在解决增材制造（Additive Manufacturing, AM）中基于图像的质量评估（Quality Assessment, QA）依赖人工专家经验、且现有机器学习与深度学习方法输出为“黑箱”结果、缺乏可解释性的问题。解决方案的关键在于提出一种QA-VLM框架，该框架利用视觉语言模型（Vision-Language Models, VLMs）的注意力机制与推理能力，并融合从同行评审期刊文献中提炼的应用领域知识，从而生成人类可理解的质量评估解释，显著提升了评估结果的可解释性与一致性。
链接: https://arxiv.org/abs/2508.16661
作者: Qiaojie Zheng,Jiucai Zhang,Joy Gockel,Michael B. Wakin,Craig Brice,Xiaoli Zhang
机构: Colorado School of Mines (科罗拉多矿业学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Image-based quality assessment (QA) in additive manufacturing (AM) often relies heavily on the expertise and constant attention of skilled human operators. While machine learning and deep learning methods have been introduced to assist in this task, they typically provide black-box outputs without interpretable justifications, limiting their trust and adoption in real-world settings. In this work, we introduce a novel QA-VLM framework that leverages the attention mechanisms and reasoning capabilities of vision-language models (VLMs), enriched with application-specific knowledge distilled from peer-reviewed journal articles, to generate human-interpretable quality assessments. Evaluated on 24 single-bead samples produced by laser wire direct energy deposition (DED-LW), our framework demonstrates higher validity and consistency in explanation quality than off-the-shelf VLMs. These results highlight the potential of our approach to enable trustworthy, interpretable quality assessment in AM applications.
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[CV-207] Optimizing Hyper parameters in CNN for Soil Classification using PSO and Whale Optimization Algorithm
【速读】：该论文旨在解决土壤图像分类问题，以提升土地管理效率、农业产量及环境治理效果。其解决方案的关键在于构建基于卷积神经网络（Convolutional Neural Networks, CNN）的智能模型，并引入群体智能优化算法——鲸鱼优化算法（Whale Optimization Algorithm, WOA）与粒子群优化算法（Particle Swarm Optimization, PSO），用于自动调优CNN的超参数，从而显著提升多类别土壤图像分类的准确率和F1分数。通过对比两种优化算法的性能，验证了该方法在提高分类系统稳定性和泛化能力方面的有效性。
链接: https://arxiv.org/abs/2508.16660
作者: Yasir Nooruldeen Ibrahim,Fawziya Mahmood Ramo,Mahmood Siddeeq Qadir,Muna Jaffer Al-Shamdeen
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 15 pages
Abstract:Classifying soil images contributes to better land management, increased agricultural output, and practical solutions for environmental issues. The development of various disciplines, particularly agriculture, civil engineering, and natural resource management, is aided by understanding of soil quality since it helps with risk reduction, performance improvement, and sound decision-making . Artificial intelligence has recently been used in a number of different fields. In this study, an intelligent model was constructed using Convolutional Neural Networks to classify soil kinds, and machine learning algorithms were used to enhance the performance of soil classification . To achieve better implementation and performance of the Convolutional Neural Networks algorithm and obtain valuable results for the process of classifying soil type images, swarm algorithms were employed to obtain the best performance by choosing Hyper parameters for the Convolutional Neural Networks network using the Whale optimization algorithm and the Particle swarm optimization algorithm, and comparing the results of using the two algorithms in the process of multiple classification of soil types. The Accuracy and F1 measures were adopted to test the system, and the results of the proposed work were efficient result
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[CV-208] A Laplace diffusion-based transformer model for heart rate forecasting within daily activity context
【速读】：该论文旨在解决远程心率监测（Remote Patient Monitoring, RPM）中因心率波动受多种因素影响而难以准确评估其临床意义的问题，尤其是缺乏与患者实际物理活动的关联分析。传统方法往往忽略活动数据对心率变化的解释作用，导致误判风险增加。解决方案的关键在于提出一种基于Transformer架构并融合拉普拉斯扩散（Laplace diffusion）技术的模型，通过引入专门设计的活动嵌入（activity embeddings）和注意力机制，将物理活动作为核心条件来建模心率动态变化，从而优先捕捉与特定活动相关的历史心率模式。该方法不仅能有效整合长期趋势与活动特异性心率响应，还在真实世界数据集上实现了显著性能提升（平均绝对误差降低43%，R²达0.97），证明其在提升远程心率监测准确性与临床实用性方面的有效性。
链接: https://arxiv.org/abs/2508.16655
作者: Andrei Mateescu,Ioana Hadarau,Ionut Anghel,Tudor Cioara,Ovidiu Anchidin,Ancuta Nemes
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:With the advent of wearable Internet of Things (IoT) devices, remote patient monitoring (RPM) emerged as a promising solution for managing heart failure. However, the heart rate can fluctuate significantly due to various factors, and without correlating it to the patient’s actual physical activity, it becomes difficult to assess whether changes are significant. Although Artificial Intelligence (AI) models may enhance the accuracy and contextual understanding of remote heart rate monitoring, the integration of activity data is still rarely addressed. In this paper, we propose a Transformer model combined with a Laplace diffusion technique to model heart rate fluctuations driven by physical activity of the patient. Unlike prior models that treat activity as secondary, our approach conditions the entire modeling process on activity context using specialized embeddings and attention mechanisms to prioritize activity specific historical patents. The model captures both long-term patterns and activity-specific heart rate dynamics by incorporating contextualized embeddings and dedicated encoder. The Transformer model was validated on a real-world dataset collected from 29 patients over a 4-month period. Experimental results show that our model outperforms current state-of-the-art methods, achieving a 43% reduction in mean absolute error compared to the considered baseline models. Moreover, the coefficient of determination R2 is 0.97 indicating the model predicted heart rate is in strong agreement with actual heart rate values. These findings suggest that the proposed model is a practical and effective tool for supporting both healthcare providers and remote patient monitoring systems.
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[CV-209] MSNav: Zero-Shot Vision-and-Language Navigation with Dynamic Memory and LLM Spatial Reasoning
【速读】：该论文旨在解决视觉-语言导航（Vision-and-Language Navigation, VLN）任务中现有端到端大语言模型（Large Language Model, LLM）方法所面临的三大核心问题：空间推理能力弱、跨模态对齐性能差以及长程任务中的记忆过载。其解决方案的关键在于提出一种协同架构——Memory Spatial Navigation (MSNav)，该框架通过三个模块的深度融合实现系统性改进：记忆模块（Memory Module）采用动态节点剪枝策略缓解记忆过载，提升长距离探索能力；空间模块（Spatial Module）引入Instruction-Object-Space (I-O-S) 数据集并微调 Qwen3-4B 得到 Qwen-Spatial (Qwen-Sp)，显著增强物体关系推理与终点识别准确性；决策模块（Decision Module）利用LLM进行路径规划以执行鲁棒动作。三者协同作用使MSNav在R2R和REVERIE数据集上实现了SPL和成功率（SR）的显著提升，展现出更强的导航鲁棒性和泛化能力。
链接: https://arxiv.org/abs/2508.16654
作者: Chenghao Liu,Zhimu Zhou,Jiachen Zhang,Minghao Zhang,Songfang Huang,Huiling Duan
机构: 北京大学(University of Peking)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 4 figures
Abstract:Vision-and-Language Navigation (VLN) requires an agent to interpret natural language instructions and navigate complex environments. Current approaches often adopt a “black-box” paradigm, where a single Large Language Model (LLM) makes end-to-end decisions. However, it is plagued by critical vulnerabilities, including poor spatial reasoning, weak cross-modal grounding, and memory overload in long-horizon tasks. To systematically address these issues, we propose Memory Spatial Navigation(MSNav), a framework that fuses three modules into a synergistic architecture, which transforms fragile inference into a robust, integrated intelligence. MSNav integrates three modules: Memory Module, a dynamic map memory module that tackles memory overload through selective node pruning, enhancing long-range exploration; Spatial Module, a module for spatial reasoning and object relationship inference that improves endpoint recognition; and Decision Module, a module using LLM-based path planning to execute robust actions. Powering Spatial Module, we also introduce an Instruction-Object-Space (I-O-S) dataset and fine-tune the Qwen3-4B model into Qwen-Spatial (Qwen-Sp), which outperforms leading commercial LLMs in object list extraction, achieving higher F1 and NDCG scores on the I-O-S test set. Extensive experiments on the Room-to-Room (R2R) and REVERIE datasets demonstrate MSNav’s state-of-the-art performance with significant improvements in Success Rate (SR) and Success weighted by Path Length (SPL).
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[CV-210] Do VLMs Have Bad Eyes? Diagnosing Compositional Failures via Mechanistic Interpretability ICCV’25
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在组合泛化（compositional generalization）和对象绑定（object binding）方面的性能瓶颈问题，这些问题限制了模型对新物体及其属性组合的推理能力。其解决方案的关键在于通过机制可解释性（mechanistic interpretability）技术揭示模型内部表征机制，发现CLIP视觉编码器中MLP层的单个神经元同时表示多个特征，即存在“超位置”（superposition）现象，这种特征混叠直接阻碍了组合特征表示，从而影响了模型的组合推理与对象绑定能力。
链接: https://arxiv.org/abs/2508.16652
作者: Ashwath Vaithinathan Aravindan,Abha Jha,Mihir Kulkarni
机构: University of Southern California (南加州大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: To be published in Explainable Computer Vision: Quo Vadis? workshop at ICCV’25
Abstract:Vision-Language Models (VLMs) have shown remarkable performance in integrating visual and textual information for tasks such as image captioning and visual question answering. However, these models struggle with compositional generalization and object binding, which limit their ability to handle novel combinations of objects and their attributes. Our work explores the root causes of these failures using mechanistic interpretability techniques. We show evidence that individual neurons in the MLP layers of CLIP’s vision encoder represent multiple features, and this “superposition” directly hinders its compositional feature representation which consequently affects compositional reasoning and object binding capabilities. We hope this study will serve as an initial step toward uncovering the mechanistic roots of compositional failures in VLMs. The code and supporting results can be found this https URL .
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[CV-211] CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance
【速读】：该论文旨在解决扩散模型在复杂高密度场景中难以精确控制物体实例数量的问题，即生成图像时无法保证目标对象的准确计数。其解决方案的关键在于提出一种无需训练的框架 CountLoop，通过迭代式的结构化反馈机制实现精准的实例控制：该机制交替进行图像生成与多模态智能体评估（包括语言引导的规划器和评判器），对物体数量、空间布局及属性一致性进行判断，并据此优化布局以指导后续生成；同时引入基于实例的注意力掩码和组合式生成技术，提升遮挡场景下物体间的分离度，从而在保持空间保真度和视觉质量的前提下显著提高计数准确性（最高达98%）。
链接: https://arxiv.org/abs/2508.16644
作者: Anindya Mondal,Ayan Banerjee,Sauradip Nag,Josep Lladós,Xiatian Zhu,Anjan Dutta
机构: University of Surrey (萨里大学); Computer Vision Center, Universitat Autònoma de Barcelona (巴塞罗那自治大学计算机视觉中心); Simon Fraser University (西蒙弗雷泽大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Diffusion models have shown remarkable progress in photorealistic image synthesis, yet they remain unreliable for generating scenes with a precise number of object instances, particularly in complex and high-density settings. We present CountLoop, a training-free framework that provides diffusion models with accurate instance control through iterative structured feedback. The approach alternates between image generation and multimodal agent evaluation, where a language-guided planner and critic assess object counts, spatial arrangements, and attribute consistency. This feedback is then used to refine layouts and guide subsequent generations. To further improve separation between objects, especially in occluded scenes, we introduce instance-driven attention masking and compositional generation techniques. Experiments on COCO Count, T2I CompBench, and two new high-instance benchmarks show that CountLoop achieves counting accuracy of up to 98% while maintaining spatial fidelity and visual quality, outperforming layout-based and gradient-guided baselines with a score of 0.97.
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[CV-212] Negative Shanshui: Real-time Interactive Ink Painting Synthesis
【速读】：该论文旨在解决如何通过生成式 AI (Generative AI) 重新诠释中国传统山水画（shanshui），以回应人类世（Anthropocene）背景下的生态危机问题。其解决方案的关键在于提出一种名为 Negative Shanshui 的实时交互式 AI 合成方法，该方法优化了微调后的 Stable Diffusion 模型以实现高效推理，并结合眼动驱动的图像修复（gaze-driven inpainting）与帧插值技术，使作品能根据观众注视动态生成变形动画，最终在虚拟现实（VR）环境中呈现为具有多模态交互能力的艺术体验。
链接: https://arxiv.org/abs/2508.16612
作者: Aven-Le Zhou
机构: The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY)
备注:
Abstract:This paper presents Negative Shanshui, a real-time interactive AI synthesis approach that reinterprets classical Chinese landscape ink painting, i.e., shanshui, to engage with ecological crises in the Anthropocene. Negative Shanshui optimizes a fine-tuned Stable Diffusion model for real-time inferences and integrates it with gaze-driven inpainting, frame interpolation; it enables dynamic morphing animations in response to the viewer’s gaze and presents as an interactive virtual reality (VR) experience. The paper describes the complete technical pipeline, covering the system framework, optimization strategies, gaze-based interaction, and multimodal deployment in an art festival. Further analysis of audience feedback collected during its public exhibition highlights how participants variously engaged with the work through empathy, ambivalence, and critical reflection.
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[CV-213] Predicting User Grasp Intentions in Virtual Reality
【速读】：该论文旨在解决虚拟现实（VR）中用户意图预测的问题，特别是在涉及复杂抓取动作的任务中，如何实现准确的触觉反馈以提升沉浸感。其核心挑战在于不同用户间的手部运动模式存在显著差异，导致传统分类模型难以泛化。解决方案的关键在于采用基于时间序列数据的回归方法，尤其是使用长短期记忆（LSTM）网络来建模手部运动的动态特性，从而在关键两秒窗口内实现高精度的时间预测（误差<0.25秒）和空间距离预测（误差5–20 cm），相比分类模型展现出更强的鲁棒性和适应性，为实时自适应触觉反馈提供了可行路径。
链接: https://arxiv.org/abs/2508.16582
作者: Linghao Zeng
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Multimedia (cs.MM)
备注: 45 pages, 24 figures. This is a Master’s thesis submitted as part of the M2 IASD (Artificial Intelligence, Systems, Data) program at Université PSL
Abstract:Predicting user intentions in virtual reality (VR) is crucial for creating immersive experiences, particularly in tasks involving complex grasping motions where accurate haptic feedback is essential. In this work, we leverage time-series data from hand movements to evaluate both classification and regression approaches across 810 trials with varied object types, sizes, and manipulations. Our findings reveal that classification models struggle to generalize across users, leading to inconsistent performance. In contrast, regression-based approaches, particularly those using Long Short Term Memory (LSTM) networks, demonstrate more robust performance, with timing errors within 0.25 seconds and distance errors around 5-20 cm in the critical two-second window before a grasp. Despite these improvements, predicting precise hand postures remains challenging. Through a comprehensive analysis of user variability and model interpretability, we explore why certain models fail and how regression models better accommodate the dynamic and complex nature of user behavior in VR. Our results underscore the potential of machine learning models to enhance VR interactions, particularly through adaptive haptic feedback, and lay the groundwork for future advancements in real-time prediction of user actions in VR.
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[CV-214] owards High-Precision Depth Sensing via Monocular-Aided iToF and RGB Integration
【速读】：该论文旨在解决间接飞行时间（indirect Time-of-Flight, iToF）深度传感技术中存在的固有局限性，包括低空间分辨率、有限视场角（field-of-view, FoV）以及复杂场景下的结构失真问题。其解决方案的关键在于提出一种新颖的iToF-RGB融合框架：首先通过精确的几何标定与对齐模块将窄视场角的iToF深度图重投影至宽视场角的RGB坐标系中，实现多模态像素级对应；随后采用双编码器融合网络联合提取重投影后的iToF深度图与RGB图像的互补特征，并借助单目深度先验恢复细粒度结构细节并实现深度超分辨率。该方法通过整合跨模态结构线索与深度一致性约束，在提升深度精度、增强边缘锐度和扩展视场角方面均取得显著效果。
链接: https://arxiv.org/abs/2508.16579
作者: Yansong Du,Yutong Deng,Yuting Zhou,Feiyu Jiao,Jian Song,Xun Guan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 7 pages, 5 figures
Abstract:This paper presents a novel iToF-RGB fusion framework designed to address the inherent limitations of indirect Time-of-Flight (iToF) depth sensing, such as low spatial resolution, limited field-of-view (FoV), and structural distortion in complex scenes. The proposed method first reprojects the narrow-FoV iToF depth map onto the wide-FoV RGB coordinate system through a precise geometric calibration and alignment module, ensuring pixel-level correspondence between modalities. A dual-encoder fusion network is then employed to jointly extract complementary features from the reprojected iToF depth and RGB image, guided by monocular depth priors to recover fine-grained structural details and perform depth super-resolution. By integrating cross-modal structural cues and depth consistency constraints, our approach achieves enhanced depth accuracy, improved edge sharpness, and seamless FoV expansion. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed framework significantly outperforms state-of-the-art methods in terms of accuracy, structural consistency, and visual quality.
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[CV-215] VGGSounder: Audio-Visual Evaluations for Foundation Models ICCV
【速读】：该论文旨在解决当前用于评估音视频基础模型（audio-visual foundation models）的VGGSound数据集存在的局限性问题，包括标注不完整、类别部分重叠以及模态对齐错误，这些问题会导致对模型视听理解能力的误判。解决方案的关键在于提出VGGSounder——一个全面重新标注且支持多标签的测试集，其包含细粒度的模态标注信息，能够实现对模型在不同模态下性能的精准分析；同时引入新的模态混淆度量（modality confusion metric），通过添加额外输入模态来揭示模型在跨模态融合中的性能退化，从而更可靠地评估音视频基础模型的多模态理解能力。
链接: https://arxiv.org/abs/2508.08237
作者: Daniil Zverev,Thaddäus Wiedemer,Ameya Prabhu,Matthias Bethge,Wieland Brendel,A. Sophia Koepke
机构: Technical University of Munich (慕尼黑工业大学); University of Tübingen (图宾根大学); Tübingen AI Center (图宾根人工智能中心); MPI for Intelligent Systems (马克斯·普朗克智能系统研究所)
类目: Multimedia (cs.MM); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2025
Abstract:The emergence of audio-visual foundation models underscores the importance of reliably assessing their multi-modal understanding. The VGGSound dataset is commonly used as a benchmark for evaluation audio-visual classification. However, our analysis identifies several limitations of VGGSound, including incomplete labelling, partially overlapping classes, and misaligned modalities. These lead to distorted evaluations of auditory and visual capabilities. To address these limitations, we introduce VGGSounder, a comprehensively re-annotated, multi-label test set that extends VGGSound and is specifically designed to evaluate audio-visual foundation models. VGGSounder features detailed modality annotations, enabling precise analyses of modality-specific performance. Furthermore, we reveal model limitations by analysing performance degradation when adding another input modality with our new modality confusion metric.
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[CV-216] uningIQA: Fine-Grained Blind Image Quality Assessment for Livestreaming Camera Tuning
【速读】：该论文旨在解决直播场景中自动摄像机质量调优所面临的挑战，即现有盲图像质量评估（Blind Image Quality Assessment, BIQA）模型仅能提供粗粒度的整体质量评分，无法为摄像机参数的精细化调整提供细粒度的感知指导。解决方案的关键在于构建了FGLive-10K数据集和提出TuningIQA指标：FGLive-10K是一个包含10,185张高分辨率图像的细粒度BIQA数据库，涵盖多种直播场景下的摄像机参数配置，并配有50,925条多属性质量标注和19,234条细粒度成对偏好标注；在此基础上，TuningIQA通过融合人感知特征提取与基于图结构的摄像机参数聚合机制，实现了对直播画面质量的细粒度评估，在回归任务和排序任务中均显著优于当前最优BIQA方法，从而有效支持直播摄像机参数的精准调优。
链接: https://arxiv.org/abs/2508.17965
作者: Xiangfei Sheng,Zhichao Duan,Xiaofeng Pan,Yipo Huang,Zhichao Yang,Pengfei Chen,Leida Li
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 9 pages,8 figures
Abstract:Livestreaming has become increasingly prevalent in modern visual communication, where automatic camera quality tuning is essential for delivering superior user Quality of Experience (QoE). Such tuning requires accurate blind image quality assessment (BIQA) to guide parameter optimization decisions. Unfortunately, the existing BIQA models typically only predict an overall coarse-grained quality score, which cannot provide fine-grained perceptual guidance for precise camera parameter tuning. To bridge this gap, we first establish FGLive-10K, a comprehensive fine-grained BIQA database containing 10,185 high-resolution images captured under varying camera parameter configurations across diverse livestreaming scenarios. The dataset features 50,925 multi-attribute quality annotations and 19,234 fine-grained pairwise preference annotations. Based on FGLive-10K, we further develop TuningIQA, a fine-grained BIQA metric for livestreaming camera tuning, which integrates human-aware feature extraction and graph-based camera parameter fusion. Extensive experiments and comparisons demonstrate that TuningIQA significantly outperforms state-of-the-art BIQA methods in both score regression and fine-grained quality ranking, achieving superior performance when deployed for livestreaming camera tuning.
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[CV-217] owards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
【速读】：该论文旨在解决乳腺超声（Breast Ultrasound, BUS）图像自动分割中因固有伪影和数据集不一致性导致的精度不足问题，同时关注模型预测的不确定性量化以提升临床可信度。其解决方案的关键在于：首先修正BUSI数据集中存在的数据重复问题，从而获得更可靠的泛化性能评估；其次采用改进的残差编码器U-Net架构，并结合蒙特卡洛Dropout、深度集成（Deep Ensembles）及其组合方法来量化认知不确定性（Epistemic Uncertainty）；最终在分布内（in-distribution）和分布外（out-of-distribution）数据上验证模型表现，结果表明该方法不仅实现了当前最优的分割精度，还能提供校准良好的不确定性估计，有效识别低置信度区域，从而为医疗场景下模型的可解释性和安全性提供保障。
链接: https://arxiv.org/abs/2508.17768
作者: Toufiq Musah,Chinasa Kalaiwo,Maimoona Akram,Ubaida Napari Abdulai,Maruf Adewole,Farouk Dako,Adaobi Chiazor Emegoakor,Udunna C. Anazodo,Prince Ebenezer Adjei,Confidence Raymond
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: Medical Image Computing in Resource Constrained Settings Workshop Knowledge Interchange
Abstract:Automated segmentation of BUS images is important for precise lesion delineation and tumor characterization, but is challenged by inherent artifacts and dataset inconsistencies. In this work, we evaluate the use of a modified Residual Encoder U-Net for breast ultrasound segmentation, with a focus on uncertainty quantification. We identify and correct for data duplication in the BUSI dataset, and use a deduplicated subset for more reliable estimates of generalization performance. Epistemic uncertainty is quantified using Monte Carlo dropout, deep ensembles, and their combination. Models are benchmarked on both in-distribution and out-of-distribution datasets to demonstrate how they generalize to unseen cross-domain data. Our approach achieves state-of-the-art segmentation accuracy on the Breast-Lesion-USG dataset with in-distribution validation, and provides calibrated uncertainty estimates that effectively signal regions of low model confidence. Performance declines and increased uncertainty observed in out-of-distribution evaluation highlight the persistent challenge of domain shift in medical imaging, and the importance of integrated uncertainty modeling for trustworthy clinical deployment. \footnoteCode available at: this https URL
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[CV-218] Neural Proteomics Fields for Super-resolved Spatial Proteomics Prediction MICCAI2025
【速读】：该论文旨在解决当前基于测序的空间蛋白质组学（sequencing-based spatial proteomics, seq-SP）技术中存在的空间分辨率低以及组织间蛋白表达差异大导致分子数据预测性能受限的问题。其解决方案的关键在于提出了一种名为Neural Proteomics Fields (NPF) 的深度学习模型，该模型将seq-SP视为连续空间中的蛋白重构问题，通过为每种组织训练专用网络实现高精度重建；模型包含两个核心模块：空间建模模块（Spatial Modeling Module）用于学习组织特异性的蛋白空间分布，形态建模模块（Morphology Modeling Module）用于提取组织特异性的形态特征。此外，作者还构建了首个开源基准数据集Pseudo-Visium SP以支持该任务的严谨评估，实验表明NPF在参数量更少的情况下实现了当前最优性能，展现出推动空间蛋白质组学研究的巨大潜力。
链接: https://arxiv.org/abs/2508.17389
作者: Bokai Zhao,Weiyang Shi,Hanqing Chao,Zijiang Yang,Yiyang Zhang,Ming Song,Tianzi Jiang
机构: 1. Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); 2. University of Chinese Academy of Sciences (中国科学院大学); 3. National Engineering Research Center of Intelligent Computing Systems (国家智能计算系统工程研究中心); 4. Beijing Institute of Technology (北京理工大学); 5. School of Information Science and Technology, Southwest Jiaotong University (西南交通大学信息科学与技术学院)
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: MICCAI 2025
Abstract:Spatial proteomics maps protein distributions in tissues, providing transformative insights for life sciences. However, current sequencing-based technologies suffer from low spatial resolution, and substantial inter-tissue variability in protein expression further compromises the performance of existing molecular data prediction methods. In this work, we introduce the novel task of spatial super-resolution for sequencing-based spatial proteomics (seq-SP) and, to the best of our knowledge, propose the first deep learning model for this task–Neural Proteomics Fields (NPF). NPF formulates seq-SP as a protein reconstruction problem in continuous space by training a dedicated network for each tissue. The model comprises a Spatial Modeling Module, which learns tissue-specific protein spatial distributions, and a Morphology Modeling Module, which extracts tissue-specific morphological features. Furthermore, to facilitate rigorous evaluation, we establish an open-source benchmark dataset, Pseudo-Visium SP, for this task. Experimental results demonstrate that NPF achieves state-of-the-art performance with fewer learnable parameters, underscoring its potential for advancing spatial proteomics research. Our code and dataset are publicly available at this https URL.
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[CV-219] Semantic Diffusion Posterior Sampling for Cardiac Ultrasound Dehazing MICCAI
【速读】：该论文旨在解决超声心动图（echocardiography）图像因多重路径反射导致的雾霾干扰问题，此类干扰会显著降低图像质量，尤其在难成像患者中更为严重。解决方案的关键在于提出一种语义引导的基于扩散模型（diffusion-based）去雾算法，其核心创新是将从模糊输入中通过语义分割获得的像素级噪声模型，嵌入到由干净超声数据训练的生成先验所引导的扩散后验采样框架中，从而实现高质量的图像恢复。
链接: https://arxiv.org/abs/2508.17326
作者: Tristan S.W. Stevens,Oisín Nolan,Ruud J.G. van Sloun
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 4 figures, MICCAI challenge
Abstract:Echocardiography plays a central role in cardiac imaging, offering dynamic views of the heart that are essential for diagnosis and monitoring. However, image quality can be significantly degraded by haze arising from multipath reverberations, particularly in difficult-to-image patients. In this work, we propose a semantic-guided, diffusion-based dehazing algorithm developed for the MICCAI Dehazing Echocardiography Challenge (DehazingEcho2025). Our method integrates a pixel-wise noise model, derived from semantic segmentation of hazy inputs into a diffusion posterior sampling framework guided by a generative prior trained on clean ultrasound data. Quantitative evaluation on the challenge dataset demonstrates strong performance across contrast and fidelity metrics. Code for the submitted algorithm is available at this https URL.
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[CV-220] Deep Learning Architectures for Medical Image Denoising: A Comparative Study of CNN-DAE CADTra and DCMIEDNet
【速读】：该论文旨在解决医学影像中噪声污染导致诊断效能下降的问题，特别是针对磁共振成像（MRI）脑图像的去噪任务。解决方案的关键在于系统性地比较三种先进的深度学习架构——CNN-DAE、CADTra 和 DCMIEDNet 在不同高斯噪声强度下的性能表现，发现 DCMIEDNet 在低噪声条件下（σ = 10, 15）具有最优的峰值信噪比（PSNR），而 CADTra 在强噪声环境（σ = 25）下展现出更强的鲁棒性，表明模型结构设计对噪声水平具有适应性差异，从而为临床场景中选择合适去噪方案提供量化依据。
链接: https://arxiv.org/abs/2508.17223
作者: Asadullah Bin Rahman,Masud Ibn Afjal,Md. Abdulla Al Mamun
机构: Hajee Mohammad Danesh Science and Technology University (HSTU)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Medical imaging modalities are inherently susceptible to noise contamination that degrades diagnostic utility and clinical assessment accuracy. This paper presents a comprehensive comparative evaluation of three state-of-the-art deep learning architectures for MRI brain image denoising: CNN-DAE, CADTra, and DCMIEDNet. We systematically evaluate these models across multiple Gaussian noise intensities ( \sigma = 10, 15, 25 ) using the Figshare MRI Brain Dataset. Our experimental results demonstrate that DCMIEDNet achieves superior performance at lower noise levels, with PSNR values of 32.921 \pm 2.350 dB and 30.943 \pm 2.339 dB for \sigma = 10 and 15 respectively. However, CADTra exhibits greater robustness under severe noise conditions ( \sigma = 25 ), achieving the highest PSNR of 27.671 \pm 2.091 dB. All deep learning approaches significantly outperform traditional wavelet-based methods, with improvements ranging from 5-8 dB across tested conditions. This study establishes quantitative benchmarks for medical image denoising and provides insights into architecture-specific strengths for varying noise intensities.
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[CV-221] HunyuanVideo-Foley: Multimodal Diffusion with Representation Alignment for High-Fidelity Foley Audio Generation
【速读】：该论文旨在解决视频到音频生成任务中长期存在的关键问题，包括多模态数据稀缺、模态不平衡以及现有方法生成音频质量有限等问题。其核心解决方案是提出 HunyuanVideo-Foley，一个端到端的文本-视频到音频框架，能够生成与视觉动态和语义上下文高度对齐的高保真音频。该方案的关键创新在于：（1）构建可扩展的数据处理流程，通过自动化标注构建包含10万小时的多模态数据集；（2）引入基于自监督音频特征的表示对齐策略，引导潜在扩散模型训练以提升音频质量和生成稳定性；（3）设计一种新型多模态扩散Transformer，通过双流音视频融合机制（联合注意力）和文本语义注入（交叉注意力）有效缓解模态竞争问题。实验表明，该方法在音频保真度、视觉语义一致性、时间对齐精度及分布匹配等指标上均达到当前最优性能。
链接: https://arxiv.org/abs/2508.16930
作者: Sizhe Shan,Qiulin Li,Yutao Cui,Miles Yang,Yuehai Wang,Qun Yang,Jin Zhou,Zhao Zhong
机构: 1. Tsinghua University (清华大学); 2. Alibaba Group (阿里巴巴集团); 3. Chinese Academy of Sciences (中国科学院)
类目: Audio and Speech Processing (eess.AS); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
备注:
Abstract:Recent advances in video generation produce visually realistic content, yet the absence of synchronized audio severely compromises immersion. To address key challenges in video-to-audio generation, including multimodal data scarcity, modality imbalance and limited audio quality in existing methods, we propose HunyuanVideo-Foley, an end-to-end text-video-to-audio framework that synthesizes high-fidelity audio precisely aligned with visual dynamics and semantic context. Our approach incorporates three core innovations: (1) a scalable data pipeline curating 100k-hour multimodal datasets through automated annotation; (2) a representation alignment strategy using self-supervised audio features to guide latent diffusion training, efficiently improving audio quality and generation stability; (3) a novel multimodal diffusion transformer resolving modal competition, containing dual-stream audio-video fusion through joint attention, and textual semantic injection via cross-attention. Comprehensive evaluations demonstrate that HunyuanVideo-Foley achieves new state-of-the-art performance across audio fidelity, visual-semantic alignment, temporal alignment and distribution matching. The demo page is available at: this https URL.
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[CV-222] Generating Synthetic Contrast-Enhanced Chest CT Images from Non-Contrast Scans Using Slice-Consistent Brownian Bridge Diffusion Network
【速读】：该论文旨在解决胸腔疾病诊断中对比剂使用带来的风险问题，如肾毒性及过敏样反应，提出通过生成式AI（Generative AI）从非对比增强CT（non-contrast CT）图像中合成高质量对比增强CT血管造影（CTA）图像，以提升患者安全性、可及性并降低医疗成本。其解决方案的关键在于首次引入基于桥扩散模型（bridge diffusion-based solution）的框架——Slice-Consistent Brownian Bridge Diffusion Model（SC-BBDM），该模型能够建模复杂的图像映射关系，同时保持跨切片的一致性；相较于传统逐切片合成方法，该方案在低内存开销下实现高分辨率二维操作，同时保障三维解剖结构完整性，并通过预处理流程（包括重采样、对称归一化配准和稀疏膨胀分割掩膜）确保空间对齐与解剖精度，从而有效保留血管结构并提升对比度保真度。
链接: https://arxiv.org/abs/2508.16897
作者: Pouya Shiri,Xin Yi,Neel P. Mistry,Samaneh Javadinia,Mohammad Chegini,Seok-Bum Ko,Amirali Baniasadi,Scott J. Adams
机构: University of Saskatchewan (萨斯喀彻温大学); University of Victoria (维多利亚大学)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
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Abstract:Contrast-enhanced computed tomography (CT) imaging is essential for diagnosing and monitoring thoracic diseases, including aortic pathologies. However, contrast agents pose risks such as nephrotoxicity and allergic-like reactions. The ability to generate high-fidelity synthetic contrast-enhanced CT angiography (CTA) images without contrast administration would be transformative, enhancing patient safety and accessibility while reducing healthcare costs. In this study, we propose the first bridge diffusion-based solution for synthesizing contrast-enhanced CTA images from non-contrast CT scans. Our approach builds on the Slice-Consistent Brownian Bridge Diffusion Model (SC-BBDM), leveraging its ability to model complex mappings while maintaining consistency across slices. Unlike conventional slice-wise synthesis methods, our framework preserves full 3D anatomical integrity while operating in a high-resolution 2D fashion, allowing seamless volumetric interpretation under a low memory budget. To ensure robust spatial alignment, we implement a comprehensive preprocessing pipeline that includes resampling, registration using the Symmetric Normalization method, and a sophisticated dilated segmentation mask to extract the aorta and surrounding structures. We create two datasets from the Coltea-Lung dataset: one containing only the aorta and another including both the aorta and heart, enabling a detailed analysis of anatomical context. We compare our approach against baseline methods on both datasets, demonstrating its effectiveness in preserving vascular structures while enhancing contrast fidelity.
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[CV-223] Multimodal Medical Endoscopic Image Analysis via Progressive Disentangle-aware Contrastive Learning
【速读】：该论文旨在解决喉咽部肿瘤精准分割难题，传统单一模态成像方法难以充分捕捉肿瘤的复杂解剖与病理特征。其解决方案的关键在于提出一种基于“对齐-解耦-融合”机制的多模态表示学习框架，通过多尺度分布对齐策略缓解不同模态（2D白光成像White Light Imaging, WLI与窄带成像Narrow Band Imaging, NBI）间的差异，并结合渐进式特征解耦策略（包括初步解耦与解耦感知对比学习），有效分离模态特有特征与共享语义特征，从而实现鲁棒的多模态对比学习与高效的语义融合，显著提升分割性能。
链接: https://arxiv.org/abs/2508.16882
作者: Junhao Wu,Yun Li,Junhao Li,Jingliang Bian,Xiaomao Fan,Wenbin Lei,Ruxin Wang
机构: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (中国科学院深圳先进技术研究院)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages,6 figures, 6 tables
Abstract:Accurate segmentation of laryngo-pharyngeal tumors is crucial for precise diagnosis and effective treatment planning. However, traditional single-modality imaging methods often fall short of capturing the complex anatomical and pathological features of these tumors. In this study, we present an innovative multi-modality representation learning framework based on the `Align-Disentangle-Fusion’ mechanism that seamlessly integrates 2D White Light Imaging (WLI) and Narrow Band Imaging (NBI) pairs to enhance segmentation performance. A cornerstone of our approach is multi-scale distribution alignment, which mitigates modality discrepancies by aligning features across multiple transformer layers. Furthermore, a progressive feature disentanglement strategy is developed with the designed preliminary disentanglement and disentangle-aware contrastive learning to effectively separate modality-specific and shared features, enabling robust multimodal contrastive learning and efficient semantic fusion. Comprehensive experiments on multiple datasets demonstrate that our method consistently outperforms state-of-the-art approaches, achieving superior accuracy across diverse real clinical scenarios.
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[CV-224] Analysis of Transferability Estimation Metrics for Surgical Phase Recognition MICCAI2025
【速读】：该论文旨在解决手术视频分析中预训练模型选择难题，即在标注成本高昂的场景下，如何高效准确地预测哪个预训练模型在下游任务（如手术阶段识别）上微调后表现最优。其解决方案的关键在于引入并系统评估源无关迁移能力估计（Source-independent Transferability Estimation, SITE），通过仅利用模型在源数据上的嵌入或输出特征，而非完整微调过程，来量化模型迁移到目标域的潜力。研究首次在两个多样化手术视频数据集（RAMIE 和 AutoLaparo）上全面比较了 LogME、H-Score 和 TransRate 三种代表性 SITE 指标，发现 LogME（尤其是按子集最小值聚合）与实际微调精度相关性最强，而 H-Score 预测能力弱、TransRate 常出现排名反转，从而为临床场景下的模型选择提供了实证依据和实用指导。
链接: https://arxiv.org/abs/2508.16730
作者: Prabhant Singh,Yiping Li,Yasmina Al Khalil
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at DEMI workshop MICCAI 2025
Abstract:Fine-tuning pre-trained models has become a cornerstone of modern machine learning, allowing practitioners to achieve high performance with limited labeled data. In surgical video analysis, where expert annotations are especially time-consuming and costly, identifying the most suitable pre-trained model for a downstream task is both critical and challenging. Source-independent transferability estimation (SITE) offers a solution by predicting how well a model will fine-tune on target data using only its embeddings or outputs, without requiring full retraining. In this work, we formalize SITE for surgical phase recognition and provide the first comprehensive benchmark of three representative metrics, LogME, H-Score, and TransRate, on two diverse datasets (RAMIE and AutoLaparo). Our results show that LogME, particularly when aggregated by the minimum per-subset score, aligns most closely with fine-tuning accuracy; H-Score yields only weak predictive power; and TransRate often inverses true model rankings. Ablation studies show that when candidate models have similar performances, transferability estimates lose discriminative power, emphasizing the importance of maintaining model diversity or using additional validation. We conclude with practical guidelines for model selection and outline future directions toward domain-specific metrics, theoretical foundations, and interactive benchmarking tools.
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[CV-225] BrainPath: Generating Subject-Specific Brain Aging Trajectories
【速读】：该论文旨在解决当前脑老化轨迹量化与预测方法的局限性，即多数模型仅能预测时序年龄（chronological age），而时序年龄并不能准确反映生物年龄（biological aging）；同时，现有生成式方法虽能生成合成MRI以扩充数据多样性，但无法捕捉个体特异性的脑老化动态。其解决方案的关键在于提出BrainPath——一个3D生成框架，通过在训练阶段学习纵向脑老化动态，在推理阶段仅需单次基线扫描即可预测任意时间点的解剖学上忠实的MRI图像。该框架融合了年龄校准损失（age calibration loss）、交换学习策略（swap learning strategy）和年龄感知损失（age perceptual loss），有效保留了细微但具有生物学意义的个体差异，从而实现了精准、时序一致的个性化脑老化建模。
链接: https://arxiv.org/abs/2508.16667
作者: Yifan Li,Javad Sohankar,Ji Luo,Jing Li,Yi Su
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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Abstract:Quantifying and forecasting individual brain aging trajectories is critical for understanding neurodegenerative disease and the heterogeneity of aging, yet current approaches remain limited. Most models predict chronological age, an imperfect surrogate for biological aging, or generate synthetic MRIs that enhance data diversity but fail to capture subject-specific trajectories. Here, we present BrainPath, a 3D generative framework that learns longitudinal brain aging dynamics during training and, at inference, predicts anatomically faithful MRIs at arbitrary timepoints from a single baseline scan. BrainPath integrates an age calibration loss, a swap learning strategy, and an age perceptual loss to preserve subtle, biologically meaningful variations. Across held-out ADNI and an independent NACC dataset, BrainPath outperforms state-of-the-art reference models in structural similarity (SSIM), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and MRI age-difference accuracy, while capturing realistic and temporally consistent aging patterns. Beyond methodological innovation, BrainPath enables personalized mapping of brain aging, synthetic follow-up scan prediction, and trajectory-based analyses, providing a foundation for precision modeling of brain aging and supporting research into neurodegeneration and aging interventions.
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[CV-226] Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence
【速读】：该论文旨在解决脑肿瘤影像评估中依赖钆对比剂（gadolinium contrast）的问题，尤其是在频繁随访、肾功能不全、过敏反应或儿童患者中，钆剂使用存在风险或限制。其核心解决方案是开发并验证一种深度学习模型，仅利用非增强MRI序列（包括T1加权、T2加权及T2/FLAIR图像）预测和分割增强性脑肿瘤区域。关键在于通过多中心、大规模数据集训练nnU-Net等先进架构，在无需对比剂的情况下实现高精度的肿瘤增强区域识别与体积估计，最终在患者级检测和体素级分割上均超越了放射科专家水平，展现出临床应用潜力。
链接: https://arxiv.org/abs/2508.16650
作者: James K Ruffle,Samia Mohinta,Guilherme Pombo,Asthik Biswas,Alan Campbell,Indran Davagnanam,David Doig,Ahmed Hamman,Harpreet Hyare,Farrah Jabeen,Emma Lim,Dermot Mallon,Stephanie Owen,Sophie Wilkinson,Sebastian Brandner,Parashkev Nachev
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: 38 pages
Abstract:Brain tumour imaging assessment typically requires both pre- and post-contrast MRI, but gadolinium administration is not always desirable, such as in frequent follow-up, renal impairment, allergy, or paediatric patients. We aimed to develop and validate a deep learning model capable of predicting brain tumour contrast enhancement from non-contrast MRI sequences alone. We assembled 11089 brain MRI studies from 10 international datasets spanning adult and paediatric populations with various neuro-oncological states, including glioma, meningioma, metastases, and post-resection appearances. Deep learning models (nnU-Net, SegResNet, SwinUNETR) were trained to predict and segment enhancing tumour using only non-contrast T1-, T2-, and T2/FLAIR-weighted images. Performance was evaluated on 1109 held-out test patients using patient-level detection metrics and voxel-level segmentation accuracy. Model predictions were compared against 11 expert radiologists who each reviewed 100 randomly selected patients. The best-performing nnU-Net achieved 83% balanced accuracy, 91.5% sensitivity, and 74.4% specificity in detecting enhancing tumour. Enhancement volume predictions strongly correlated with ground truth (R2 0.859). The model outperformed expert radiologists, who achieved 69.8% accuracy, 75.9% sensitivity, and 64.7% specificity. 76.8% of test patients had Dice over 0.3 (acceptable detection), 67.5% had Dice over 0.5 (good detection), and 50.2% had Dice over 0.7 (excellent detection). Deep learning can identify contrast-enhancing brain tumours from non-contrast MRI with clinically relevant performance. These models show promise as screening tools and may reduce gadolinium dependence in neuro-oncology imaging. Future work should evaluate clinical utility alongside radiology experts.
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[CV-227] 3D latent diffusion models for parameterizing and history matching multiscenario facies systems
【速读】：该论文旨在解决复杂地质建模中参数空间维度高、历史拟合效率低以及地质现实性难以保障的问题。其核心挑战在于如何在保持地质合理性的同时，将高维几何模型（geomodel）有效映射到低维潜在空间（latent space），从而提升历史拟合的计算效率与可靠性。解决方案的关键在于提出一种基于生成式潜扩散模型（Generative Latent Diffusion Models, LDMs）的参数化方法，利用LDM对3D河道-堤岸-泥质沉积体系进行建模，并引入感知损失（perceptual loss）项以增强生成模型的地质真实性；该方法可在任意设定的场景参数（如泥质比例、河道走向和宽度）下生成无限多样的真实感地质模型，且在单点与两点空间统计特征及流动响应分布上均与参考模型高度一致，最终实现了在潜在空间内进行集合历史拟合（ensemble-based history matching），显著降低了地质情景不确定性并提升了后验模型与合成真值模型的一致性。
链接: https://arxiv.org/abs/2508.16621
作者: Guido Di Federico,Louis J. Durlofsky
机构: 未知
类目: Geophysics (physics.geo-ph); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Geological parameterization procedures entail the mapping of a high-dimensional geomodel to a low-dimensional latent variable. These parameterizations can be very useful for history matching because the number of variables to be calibrated is greatly reduced, and the mapping can be constructed such that geological realism is automatically preserved. In this work, a parameterization method based on generative latent diffusion models (LDMs) is developed for 3D channel-levee-mud systems. Geomodels with variable scenario parameters, specifically mud fraction, channel orientation, and channel width, are considered. A perceptual loss term is included during training to improve geological realism. For any set of scenario parameters, an (essentially) infinite number of realizations can be generated, so our LDM parameterizes over a very wide model space. New realizations constructed using the LDM procedure are shown to closely resemble reference geomodels, both visually and in terms of one- and two-point spatial statistics. Flow response distributions, for a specified set of injection and production wells, are also shown to be in close agreement between the two sets of models. The parameterization method is applied for ensemble-based history matching, with model updates performed in the LDM latent space, for cases involving geological scenario uncertainty. For three synthetic true models corresponding to different geological scenarios, we observe clear uncertainty reduction in both production forecasts and geological scenario parameters. The overall method is additionally shown to provide posterior geomodels consistent with the synthetic true model in each case.
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人工智能
[AI-0] SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation
【速读】：该论文旨在解决扩散模型驱动的双臂操作（bimanual manipulation）策略在实际应用中忽视物理安全约束的问题，导致机器人行为危险、易造成设备或物体损坏。其解决方案的关键在于提出一种测试时轨迹优化框架 SafeBimanual，通过引入多样化的安全代价函数（cost functions）来约束不同双臂协作模式下的动作分布，如避免撕裂物体和臂与物体间的碰撞，并结合视觉语言模型（VLM）动态调度这些代价函数，基于关键点及其成对关系生成最优安全约束，从而在扩散去噪采样过程中引导生成更安全的轨迹。该方法无需重新训练预训练扩散策略，即可显著提升成功率并减少不安全交互。
链接: https://arxiv.org/abs/2508.18268
作者: Haoyuan Deng,Wenkai Guo,Qianzhun Wang,Zhenyu Wu,Ziwei Wang
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Project website is at: this https URL
Abstract:Bimanual manipulation has been widely applied in household services and manufacturing, which enables the complex task completion with coordination requirements. Recent diffusion-based policy learning approaches have achieved promising performance in modeling action distributions for bimanual manipulation. However, they ignored the physical safety constraints of bimanual manipulation, which leads to the dangerous behaviors with damage to robots and objects. To this end, we propose a test-time trajectory optimization framework named SafeBimanual for any pre-trained diffusion-based bimanual manipulation policies, which imposes the safety constraints on bimanual actions to avoid dangerous robot behaviors with improved success rate. Specifically, we design diverse cost functions for safety constraints in different dual-arm cooperation patterns including avoidance of tearing objects and collision between arms and objects, which optimizes the manipulator trajectories with guided sampling of diffusion denoising process. Moreover, we employ a vision-language model (VLM) to schedule the cost functions by specifying keypoints and corresponding pairwise relationship, so that the optimal safety constraint is dynamically generated in the entire bimanual manipulation process. SafeBimanual demonstrates superiority on 8 simulated tasks in RoboTwin with a 13.7% increase in success rate and a 18.8% reduction in unsafe interactions over state-of-the-art diffusion-based methods. Extensive experiments on 4 real-world tasks further verify its practical value by improving the success rate by 32.5%.
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[AI-1] ANO : Faster is Better in Noisy Landscape
【速读】：该论文旨在解决当前广泛使用的随机优化器（如Adam和Adan）在非平稳或噪声环境中性能退化的问题，其根源在于这些方法依赖于基于动量的梯度幅值估计。解决方案的关键在于提出Ano优化器，通过将梯度的方向与幅值解耦：使用动量进行方向平滑，而步长则由瞬时梯度幅值决定，从而提升对梯度噪声的鲁棒性，同时保持一阶方法的简洁性和高效性。进一步地，作者还提出了Anolog，通过采用对数调度扩展动量窗口以消除对动量系数的敏感性，理论上建立了非凸收敛性保证，且在强化学习等高噪声场景中表现出显著优势，同时在低噪声任务（如标准计算机视觉基准）上仍具竞争力。
链接: https://arxiv.org/abs/2508.18258
作者: Adrien Kegreisz
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Work in progress, 26 pages total with appendix, 7 figures, 12 tables
Abstract:Stochastic optimizers are central to deep learning, yet widely used methods such as Adam and Adan can degrade in non-stationary or noisy environments, partly due to their reliance on momentum-based magnitude estimates. We introduce Ano, a novel optimizer that decouples direction and magnitude: momentum is used for directional smoothing, while instantaneous gradient magnitudes determine step size. This design improves robustness to gradient noise while retaining the simplicity and efficiency of first-order methods. We further propose Anolog, which removes sensitivity to the momentum coefficient by expanding its window over time via a logarithmic schedule. We establish non-convex convergence guarantees with a convergence rate similar to other sign-based methods, and empirically show that Ano provides substantial gains in noisy and non-stationary regimes such as reinforcement learning, while remaining competitive on low-noise tasks such as standard computer vision benchmarks.
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[AI-2] Hermes 4 Technical Report
【速读】：该论文旨在解决当前大语言模型在复杂任务中推理能力不足与指令遵循能力不均衡的问题，尤其是在数学推理、编程、知识理解等需要结构化多轮推理的场景下表现受限。解决方案的关键在于构建一个混合推理框架——Hermes 4，它通过融合结构化的多轮推理机制与广泛的指令跟随能力，在数据采集、合成、训练和评估全流程中采用系统性优化策略，从而实现性能提升与行为对齐的双重改进。
链接: https://arxiv.org/abs/2508.18255
作者: Ryan Teknium,Roger Jin,Jai Suphavadeeprasit,Dakota Mahan,Jeffrey Quesnelle,Joe Li,Chen Guang,Shannon Sands,Karan Malhotra
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:We present Hermes 4, a family of hybrid reasoning models that combine structured, multi-turn reasoning with broad instruction-following ability. We describe the challenges encountered during data curation, synthesis, training, and evaluation, and outline the solutions employed to address these challenges at scale. We comprehensively evaluate across mathematical reasoning, coding, knowledge, comprehension, and alignment benchmarks, and we report both quantitative performance and qualitative behavioral analysis. To support open research, all model weights are published publicly at this https URL
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[AI-3] Efficient Computation of Blackwell Optimal Policies using Rational Functions
【速读】：该论文旨在解决马尔可夫决策过程（Markov Decision Processes, MDPs）中Blackwell最优策略（Blackwell Optimal, BO）的计算难题。现有方法在计算BO策略时存在计算复杂度高或实现困难的问题，难以在实际场景中应用。解决方案的关键在于引入一种基于有理函数排序的符号化计算框架：通过在1附近对有理函数进行代数操作替代传统数值评估，从而避免依赖位复杂度的计算，并推导出与参数规模无关的边界。该方法首次为确定性MDP提供了强多项式时间算法，同时为一般MDP给出了首个次指数时间算法，并扩展了多种策略迭代算法，将已知折扣准则下的最优上界推广至Blackwell准则，显著提升了BO策略计算的效率和可行性。
链接: https://arxiv.org/abs/2508.18252
作者: Dibyangshu Mukherjee,Shivaram Kalyanakrishnan
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Markov Decision Problems (MDPs) provide a foundational framework for modelling sequential decision-making across diverse domains, guided by optimality criteria such as discounted and average rewards. However, these criteria have inherent limitations: discounted optimality may overly prioritise short-term rewards, while average optimality relies on strong structural assumptions. Blackwell optimality addresses these challenges, offering a robust and comprehensive criterion that ensures optimality under both discounted and average reward frameworks. Despite its theoretical appeal, existing algorithms for computing Blackwell Optimal (BO) policies are computationally expensive or hard to implement. In this paper we describe procedures for computing BO policies using an ordering of rational functions in the vicinity of 1 . We adapt state-of-the-art algorithms for deterministic and general MDPs, replacing numerical evaluations with symbolic operations on rational functions to derive bounds independent of bit complexity. For deterministic MDPs, we give the first strongly polynomial-time algorithms for computing BO policies, and for general MDPs we obtain the first subexponential-time algorithm. We further generalise several policy iteration algorithms, extending the best known upper bounds from the discounted to the Blackwell criterion. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2508.18252 [cs.AI] (or arXiv:2508.18252v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2508.18252 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-4] ype-Compliant Adaptation Cascades: Adapting Programmatic LM Workflows to Data
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在复杂多步骤工作流中可靠组合的问题，现有主流方法通过优化离散提示（discrete prompts）构建流水线，存在脆弱性且难以保证结构化任务所需的正式合规性。其解决方案的关键在于提出类型合规自适应级联（Type-Compliant Adaptation Cascades, TACs），将整个工作流建模为一个未归一化的联合分布，其中包含参数高效微调的LLMs与确定性逻辑模块，从而支持带有潜在中间结构的梯度驱动训练，并通过理论证明优化偏差随类型合规性的学习而消失，实现了对结构化任务的显著性能提升。
链接: https://arxiv.org/abs/2508.18244
作者: Chu-Cheng Lin,Daiyi Peng,Yifeng Lu,Ming Zhang,Eugene Ie
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Reliably composing Large Language Models (LLMs) for complex, multi-step workflows remains a significant challenge. The dominant paradigm-optimizing discrete prompts in a pipeline-is notoriously brittle and struggles to enforce the formal compliance required for structured tasks. We introduce Type-Compliant Adaptation Cascades (TACs), a framework that recasts workflow adaptation as learning typed probabilistic programs. TACs treats the entire workflow, which is composed of parameter-efficiently adapted LLMs and deterministic logic, as an unnormalized joint distribution. This enables principled, gradient-based training even with latent intermediate structures. We provide theoretical justification for our tractable optimization objective, proving that the optimization bias vanishes as the model learns type compliance. Empirically, TACs significantly outperforms state-of-the-art prompt-optimization baselines. Gains are particularly pronounced on structured tasks, improving MGSM-SymPy from 57.1% to 75.9% for a 27B model, MGSM from 1.6% to 27.3% for a 7B model. TACs offers a robust and theoretically grounded paradigm for developing reliable, task-compliant LLM systems.
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[AI-5] KillChainGraph: ML Framework for Predicting and Mapping ATTCK Techniques
【速读】：该论文旨在解决传统基于规则的网络安全检测系统在应对日益复杂和庞大的网络攻击时响应滞后、难以识别高级持续性威胁（Advanced Persistent Threats, APT）的问题。其解决方案的关键在于提出一种阶段感知（phase-aware）的多模型机器学习框架，该框架基于MITRE ATT&CK Enterprise知识库对攻击行为进行分阶段建模，并利用ATTACK-BERT将技术映射至“网络杀伤链”（Cyber Kill Chain）的七个阶段，生成相位特异性数据集；随后融合LightGBM、定制Transformer编码器、微调BERT与图神经网络（Graph Neural Network, GNN）的预测结果，通过加权软投票集成策略提升整体性能；同时引入有向图结构显式建模各阶段间的依赖关系，从而实现可解释的攻击路径预测，显著增强主动防御能力。
链接: https://arxiv.org/abs/2508.18230
作者: Chitraksh Singh,Monisha Dhanraj,Ken Huang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures
Abstract:The escalating complexity and volume of cyberattacks demand proactive detection strategies that go beyond traditional rule-based systems. This paper presents a phase-aware, multi-model machine learning framework that emulates adversarial behavior across the seven phases of the Cyber Kill Chain using the MITRE ATTCK Enterprise dataset. Techniques are semantically mapped to phases via ATTACK-BERT, producing seven phase-specific datasets. We evaluate LightGBM, a custom Transformer encoder, fine-tuned BERT, and a Graph Neural Network (GNN), integrating their outputs through a weighted soft voting ensemble. Inter-phase dependencies are modeled using directed graphs to capture attacker movement from reconnaissance to objectives. The ensemble consistently achieved the highest scores, with F1-scores ranging from 97.47% to 99.83%, surpassing GNN performance (97.36% to 99.81%) by 0.03%–0.20% across phases. This graph-driven, ensemble-based approach enables interpretable attack path forecasting and strengthens proactive cyber defense.
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[AI-6] Disentangling the Factors of Convergence between Brains and Computer Vision Models
【速读】：该论文旨在解决人工智能模型（尤其是自监督视觉Transformer，如DINOv3）为何以及如何发展出与人类大脑相似的表征这一问题，即厘清模型架构、训练数据和训练量等因素对脑-模型相似性的影响机制。其解决方案的关键在于系统性地控制这三个变量，通过训练一系列结构一致但参数规模、训练数据类型和训练量不同的DINOv3模型，并利用fMRI和MEG高分辨率记录的人类大脑响应进行多维度比较——包括整体表征相似性、拓扑组织一致性及时间动态匹配度，从而揭示三者独立且交互作用于脑相似性的规律，同时发现模型在训练过程中遵循特定的发展轨迹：先与初级感觉皮层表征对齐，后期才逐步匹配前额叶等高级脑区，且这种演化路径与人类皮层的结构和功能特性（如发育扩展程度、厚度、髓鞘化程度和时间尺度）高度相关。
链接: https://arxiv.org/abs/2508.18226
作者: Joséphine Raugel,Marc Szafraniec,Huy V. Vo,Camille Couprie,Patrick Labatut,Piotr Bojanowski,Valentin Wyart,Jean-Rémi King
机构: 未知
类目: Artificial Intelligence (cs.AI); Neurons and Cognition (q-bio.NC)
备注:
Abstract:Many AI models trained on natural images develop representations that resemble those of the human brain. However, the factors that drive this brain-model similarity remain poorly understood. To disentangle how the model, training and data independently lead a neural network to develop brain-like representations, we trained a family of self-supervised vision transformers (DINOv3) that systematically varied these different factors. We compare their representations of images to those of the human brain recorded with both fMRI and MEG, providing high resolution in spatial and temporal analyses. We assess the brain-model similarity with three complementary metrics focusing on overall representational similarity, topographical organization, and temporal dynamics. We show that all three factors - model size, training amount, and image type - independently and interactively impact each of these brain similarity metrics. In particular, the largest DINOv3 models trained with the most human-centric images reach the highest brain-similarity. This emergence of brain-like representations in AI models follows a specific chronology during training: models first align with the early representations of the sensory cortices, and only align with the late and prefrontal representations of the brain with considerably more training. Finally, this developmental trajectory is indexed by both structural and functional properties of the human cortex: the representations that are acquired last by the models specifically align with the cortical areas with the largest developmental expansion, thickness, least myelination, and slowest timescales. Overall, these findings disentangle the interplay between architecture and experience in shaping how artificial neural networks come to see the world as humans do, thus offering a promising framework to understand how the human brain comes to represent its visual world.
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[AI-7] Deep Learning and Matrix Completion-aided IoT Network Localization in the Outlier Scenarios
【速读】：该论文旨在解决物联网（IoT）网络定位中受异常值污染的欧几里得距离矩阵（Euclidean distance matrix, EDM）恢复问题。传统定位技术通常在所有矩阵集合中搜索解，而本文的关键创新在于将搜索空间限制在满足EDM特性的矩阵集合内，通过将距离矩阵D表示为传感器坐标矩阵X的函数（该函数天然具备EDM的唯一性质），并利用深度神经网络联合恢复D与X；同时，为有效处理异常值，将异常值建模为稀疏矩阵L，并引入其正则化项至优化问题中，最终通过交替更新X、D和L来求解。该方法显著提升了在存在异常值情况下的定位精度。
链接: https://arxiv.org/abs/2508.18225
作者: Sunwoo Kim
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
备注: 4 pages, 2 figures
Abstract:In this paper, we propose a deep learning and matrix completion aided approach for recovering an outlier contaminated Euclidean distance matrix D in IoT network localization. Unlike conventional localization techniques that search the solution over a whole set of matrices, the proposed technique restricts the search to the set of Euclidean distance matrices. Specifically, we express D as a function of the sensor coordinate matrix X that inherently satisfies the unique properties of D, and then jointly recover D and X using a deep neural network. To handle outliers effectively, we model them as a sparse matrix L and add a regularization term of L into the optimization problem. We then solve the problem by alternately updating X, D, and L. Numerical experiments demonstrate that the proposed technique can recover the location information of sensors accurately even in the presence of outliers.
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[AI-8] ST-Raptor: LLM -Powered Semi-Structured Table Question Answering SIGMOD2026
【速读】：该论文旨在解决半结构化表格（semi-structured tables）的自然语言问答（Natural Language Question Answering, NLQA）问题，其核心挑战在于现有方法难以准确理解复杂布局（如嵌套表头和合并单元格）并保持信息完整性。传统NL2SQL方法需将表格转换为结构化格式，易造成信息丢失；而NL2Code或多模态大语言模型（Large Language Models, LLMs）则难以精准解析复杂表格结构。解决方案的关键是提出ST-Raptor框架，其创新点包括：(1) 设计Hierarchical Orthogonal Tree (HO-Tree)结构模型以显式建模表格的层次与正交布局；(2) 定义一组基本树操作（tree operations）引导LLM执行常见问答任务，并通过子问题分解与操作-表格对齐机制实现精确执行；(3) 引入两阶段验证机制（前向验证检查执行步骤正确性，后向验证通过答案重构查询评估可靠性），从而显著提升问答准确性。
链接: https://arxiv.org/abs/2508.18190
作者: Zirui Tang,Boyu Niu,Xuanhe Zhou,Boxiu Li,Wei Zhou,Jiannan Wang,Guoliang Li,Xinyi Zhang,Fan Wu
机构: 未知
类目: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR)
备注: Extension of our SIGMOD 2026 paper. Please refer to source code available at: this https URL
Abstract:Semi-structured tables, widely used in real-world applications (e.g., financial reports, medical records, transactional orders), often involve flexible and complex layouts (e.g., hierarchical headers and merged cells). These tables generally rely on human analysts to interpret table layouts and answer relevant natural language questions, which is costly and inefficient. To automate the procedure, existing methods face significant challenges. First, methods like NL2SQL require converting semi-structured tables into structured ones, which often causes substantial information loss. Second, methods like NL2Code and multi-modal LLM QA struggle to understand the complex layouts of semi-structured tables and cannot accurately answer corresponding questions. To this end, we propose ST-Raptor, a tree-based framework for semi-structured table question answering using large language models. First, we introduce the Hierarchical Orthogonal Tree (HO-Tree), a structural model that captures complex semi-structured table layouts, along with an effective algorithm for constructing the tree. Second, we define a set of basic tree operations to guide LLMs in executing common QA tasks. Given a user question, ST-Raptor decomposes it into simpler sub-questions, generates corresponding tree operation pipelines, and conducts operation-table alignment for accurate pipeline execution. Third, we incorporate a two-stage verification mechanism: forward validation checks the correctness of execution steps, while backward validation evaluates answer reliability by reconstructing queries from predicted answers. To benchmark the performance, we present SSTQA, a dataset of 764 questions over 102 real-world semi-structured tables. Experiments show that ST-Raptor outperforms nine baselines by up to 20% in answer accuracy. The code is available at this https URL.
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[AI-9] AdLoCo: adaptive batching significantly improves communications efficiency and convergence for Large Language Models
【速读】：该论文旨在解决大规模语言模型（Large Language Models, LLMs）分布式训练中如何高效利用异构硬件资源的问题，尤其是在动态工作负载下现有方法（如DiLoCo）难以充分挖掘计算集群性能的局限性。其解决方案的关键在于提出一种三阶段优化框架：首先通过多实例训练（Multi-Instance Training, MIT）实现节点内多个轻量级训练流并行执行与知识融合，提升吞吐量并减少空闲时间；其次采用自适应批处理DiLoCo（Adaptive Batched DiLoCo）动态调整本地批次大小以平衡计算与通信开销，显著降低同步延迟；最后引入切换模式（switch mode）机制，在自适应批次超出硬件友好范围时无缝启用梯度累积，从而稳定训练过程。该方案在提升收敛速度的同时显著增强了系统效率。
链接: https://arxiv.org/abs/2508.18182
作者: Nikolay Kutuzov,Makar Baderko,Stepan Kulibaba,Artem Dzhalilov,Daniel Bobrov,Maxim Mashtaler,Alexander Gasnikov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:Scaling distributed training of Large Language Models (LLMs) requires not only algorithmic advances but also efficient utilization of heterogeneous hardware resources. While existing methods such as DiLoCo have demonstrated promising results, they often fail to fully exploit computational clusters under dynamic workloads. To address this limitation, we propose a three-stage method that combines Multi-Instance Training (MIT), Adaptive Batched DiLoCo, and switch mode mechanism. MIT allows individual nodes to run multiple lightweight training streams with different model instances in parallel and merge them to combine knowledge, increasing throughput and reducing idle time. Adaptive Batched DiLoCo dynamically adjusts local batch sizes to balance computation and communication, substantially lowering synchronization delays. Switch mode further stabilizes training by seamlessly introducing gradient accumulation once adaptive batch sizes grow beyond hardware-friendly limits. Together, these innovations improve both convergence speed and system efficiency. We also provide a theoretical estimate of the number of communications required for the full convergence of a model trained using our method.
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[AI-10] Amortized Sampling with Transferable Normalizing Flows
【速读】：该论文旨在解决分子构象平衡采样中的计算效率与跨系统迁移能力不足的问题，即传统方法（如分子动力学或马尔可夫链蒙特卡洛）缺乏 amortization（摊销）特性，导致每次新系统都需要重新投入大量计算资源。解决方案的关键在于提出 Prose——一个基于全原子正常流（normalizing flow）的可迁移采样模型，其参数量达 2.8 亿，在包含最多 8 个氨基酸残基的肽类分子动力学轨迹数据集上训练，实现了零样本（zero-shot）无关联提案采样，首次在序列长度维度上实现跨系统的高效迁移能力，同时保持了正常流模型高效的似然评估优势。通过重要性采样微调策略，Prose 在未见过的四肽系统中表现优于经典顺序蒙特卡洛（sequential Monte Carlo）等方法，显著提升了采样算法的通用性和可扩展性。
链接: https://arxiv.org/abs/2508.18175
作者: Charlie B. Tan,Majdi Hassan,Leon Klein,Saifuddin Syed,Dominique Beaini,Michael M. Bronstein,Alexander Tong,Kirill Neklyudov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Efficient equilibrium sampling of molecular conformations remains a core challenge in computational chemistry and statistical inference. Classical approaches such as molecular dynamics or Markov chain Monte Carlo inherently lack amortization; the computational cost of sampling must be paid in-full for each system of interest. The widespread success of generative models has inspired interest into overcoming this limitation through learning sampling algorithms. Despite performing on par with conventional methods when trained on a single system, learned samplers have so far demonstrated limited ability to transfer across systems. We prove that deep learning enables the design of scalable and transferable samplers by introducing Prose, a 280 million parameter all-atom transferable normalizing flow trained on a corpus of peptide molecular dynamics trajectories up to 8 residues in length. Prose draws zero-shot uncorrelated proposal samples for arbitrary peptide systems, achieving the previously intractable transferability across sequence length, whilst retaining the efficient likelihood evaluation of normalizing flows. Through extensive empirical evaluation we demonstrate the efficacy of Prose as a proposal for a variety of sampling algorithms, finding a simple importance sampling-based finetuning procedure to achieve superior performance to established methods such as sequential Monte Carlo on unseen tetrapeptides. We open-source the Prose codebase, model weights, and training dataset, to further stimulate research into amortized sampling methods and finetuning objectives.
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[AI-11] he Computational Complexity of Satisfiability in State Space Models ECAI25
【速读】：该论文旨在解决状态空间模型（State Space Model, SSM）的可满足性问题（ssmSAT），即判断给定输入序列是否能使模型进入接受状态。这一问题在形式化验证和语言模型可靠性分析中具有重要意义。论文的核心贡献在于揭示了ssmSAT在一般情况下是不可判定的，从而反映了SSM强大的计算能力；在此基础上，作者通过引入两个自然限制条件——有限上下文长度和固定宽度算术量化——分别建立了其可判定性及对应的复杂度边界：前者在输入长度以一进制表示时为NP完全，在二进制表示时为NEXPTIME且PSPACE-hard；后者在固定位宽编码下为PSPACE-complete或EXPSPACE，具体取决于位宽表示方式。这些结果首次构建了针对SSM的形式推理复杂度图谱，明确了验证基于SSM的语言模型时的根本限制与可行路径。
链接: https://arxiv.org/abs/2508.18162
作者: Eric Alsmann,Martin Lange
机构: 未知
类目: Logic in Computer Science (cs.LO); Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); Machine Learning (cs.LG)
备注: Accepted at ECAI 25
Abstract:We analyse the complexity of the satisfiability problem ssmSAT for State Space Models (SSM), which asks whether an input sequence can lead the model to an accepting configuration. We find that ssmSAT is undecidable in general, reflecting the computational power of SSM. Motivated by practical settings, we identify two natural restrictions under which ssmSAT becomes decidable and establish corresponding complexity bounds. First, for SSM with bounded context length, ssmSAT is NP-complete when the input length is given in unary and in NEXPTIME (and PSPACE-hard) when the input length is given in binary. Second, for quantised SSM operating over fixed-width arithmetic, ssmSAT is PSPACE-complete resp. in EXPSPACE depending on the bit-width encoding. While these results hold for diagonal gated SSM we also establish complexity bounds for time-invariant SSM. Our results establish a first complexity landscape for formal reasoning in SSM and highlight fundamental limits and opportunities for the verification of SSM-based language models.
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[AI-12] Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation EMNLP
【速读】：该论文旨在解决入侵检测系统（Intrusion Detection Systems, IDS）在训练检测模型时面临的恶意样本标注不足问题。解决方案的关键在于提出了一种新颖的半监督框架GANGRL-LLM，该框架将生成对抗网络（Generative Adversarial Networks, GANs）与大语言模型（Large Language Models, LLMs）相结合：其中，GAN中的判别器通过对抗学习机制，利用少量真实恶意样本与生成样本提升恶意模式识别能力；而LLM作为生成器则根据判别器提供的奖励信号优化恶意代码合成质量，从而实现少样本场景下的恶意代码生成与SQL注入（SQL Injection, SQLi）检测能力的双重增强。
链接: https://arxiv.org/abs/2508.18148
作者: Haijian Ma,Daizong Liu,Xiaowen Cai,Pan Zhou,Yulai Xie
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 18pages,5 figures,emnlp
Abstract:Intrusion Detection Systems (IDS) play a crucial role in network security defense. However, a significant challenge for IDS in training detection models is the shortage of adequately labeled malicious samples. To address these issues, this paper introduces a novel semi-supervised framework \textbfGANGRL-LLM, which integrates Generative Adversarial Networks (GANs) with Large Language Models (LLMs) to enhance malicious code generation and SQL Injection (SQLi) detection capabilities in few-sample learning scenarios. Specifically, our framework adopts a collaborative training paradigm where: (1) the GAN-based discriminator improves malicious pattern recognition through adversarial learning with generated samples and limited real samples; and (2) the LLM-based generator refines the quality of malicious code synthesis using reward signals from the discriminator. The experimental results demonstrate that even with a limited number of labeled samples, our training framework is highly effective in enhancing both malicious code generation and detection capabilities. This dual enhancement capability offers a promising solution for developing adaptive defense systems capable of countering evolving cyber threats.
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[AI-13] st-Time Scaling Strategies for Generative Retrieval in Multimodal Conversational Recommendations
【速读】：该论文旨在解决传统产品检索系统在处理复杂多轮用户交互时的局限性，特别是在应用基于多模态大语言模型（Multimodal Large Language Models, MLLMs）的生成式检索方法时，难以有效建模用户意图演化和对话迭代特性的问题。解决方案的关键在于引入测试时缩放（Test-Time Scaling）机制，具体通过一个测试时重排序（Test-Time Reranking, TTR）模块增强生成式检索器，从而在推理阶段持续优化检索结果，提升与用户动态意图的一致性，并显著改善多轮对话场景下的检索精度，实验表明该方法在MRR和nDCG@1指标上分别获得平均14.5点和10.6点的提升。
链接: https://arxiv.org/abs/2508.18132
作者: Hung-Chun Hsu,Yuan-Ching Kuo,Chao-Han Huck Yang,Szu-Wei Fu,Hanrong Ye,Hongxu Yin,Yu-Chiang Frank Wang,Ming-Feng Tsai,Chuan-Ju Wang
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The rapid evolution of e-commerce has exposed the limitations of traditional product retrieval systems in managing complex, multi-turn user interactions. Recent advances in multimodal generative retrieval – particularly those leveraging multimodal large language models (MLLMs) as retrievers – have shown promise. However, most existing methods are tailored to single-turn scenarios and struggle to model the evolving intent and iterative nature of multi-turn dialogues when applied naively. Concurrently, test-time scaling has emerged as a powerful paradigm for improving large language model (LLM) performance through iterative inference-time refinement. Yet, its effectiveness typically relies on two conditions: (1) a well-defined problem space (e.g., mathematical reasoning), and (2) the model’s ability to self-correct – conditions that are rarely met in conversational product search. In this setting, user queries are often ambiguous and evolving, and MLLMs alone have difficulty grounding responses in a fixed product corpus. Motivated by these challenges, we propose a novel framework that introduces test-time scaling into conversational multimodal product retrieval. Our approach builds on a generative retriever, further augmented with a test-time reranking (TTR) mechanism that improves retrieval accuracy and better aligns results with evolving user intent throughout the dialogue. Experiments across multiple benchmarks show consistent improvements, with average gains of 14.5 points in MRR and 10.6 points in nDCG@1.
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[AI-14] CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在凝聚态物理（Condensed Matter Physics）领域中推理与计算能力评估不足的问题。现有基准测试多聚焦于常识或基础物理问题，难以全面衡量LLMs对前沿、复杂凝聚态物理问题的理解与求解能力。为此，作者构建了CMPhysBench——一个包含520余道研究生级别计算题的专项基准，覆盖磁性、超导、强关联体系等核心子领域，并要求模型独立生成完整解题过程，而非仅输出答案。其关键创新在于引入基于树结构的表达式表示方法，提出可扩展表达式编辑距离（Scalable Expression Edit Distance, SEED）评分机制，能够提供细粒度（非二值）的部分得分，从而更精准地量化预测结果与标准答案之间的语义相似性，有效克服传统准确率指标的局限性。实验表明，即使是最先进的模型Grok-4在该基准上平均SEED得分仅为36，准确率仅28%，凸显出LLMs在凝聚态物理这一实践性强且前沿领域的显著能力缺口。
链接: https://arxiv.org/abs/2508.18124
作者: Weida Wang,Dongchen Huang,Jiatong Li,Tengchao Yang,Ziyang Zheng,Di Zhang,Dong Han,Benteng Chen,Binzhao Luo,Zhiyu Liu,Kunling Liu,Zhiyuan Gao,Shiqi Geng,Wei Ma,Jiaming Su,Xin Li,Shuchen Pu,Yuhan Shui,Qianjia Cheng,Zhihao Dou,Dongfei Cui,Changyong He,Jin Zeng,Zeke Xie,Mao Su,Dongzhan Zhou,Yuqiang Li,Wanli Ouyang,Lei Bai,Yunqi Cai,Xi Dai,Shufei Zhang,Jinguang Cheng,Zhong Fang,Hongming Weng
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 29 pages, 7 figures
Abstract:We introduce CMPhysBench, designed to assess the proficiency of Large Language Models (LLMs) in Condensed Matter Physics, as a novel Benchmark. CMPhysBench is composed of more than 520 graduate-level meticulously curated questions covering both representative subfields and foundational theoretical frameworks of condensed matter physics, such as magnetism, superconductivity, strongly correlated systems, etc. To ensure a deep understanding of the problem-solving process,we focus exclusively on calculation problems, requiring LLMs to independently generate comprehensive solutions. Meanwhile, leveraging tree-based representations of expressions, we introduce the Scalable Expression Edit Distance (SEED) score, which provides fine-grained (non-binary) partial credit and yields a more accurate assessment of similarity between prediction and ground-truth. Our results show that even the best models, Grok-4, reach only 36 average SEED score and 28% accuracy on CMPhysBench, underscoring a significant capability gap, especially for this practical and frontier domain relative to traditional physics. The code anddataset are publicly available at this https URL.
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[AI-15] A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在软件工程中生成代码时缺乏全面安全评估的问题。现有基准测试存在三大缺陷：仅评估孤立代码片段、评价方法不稳定且不可复现，以及未能关联输入上下文质量与输出安全性。为此，作者提出A.S.E（AI Code Generation Security Evaluation）基准，其核心创新在于基于包含已知CVE漏洞的真实仓库构建任务，保留完整的项目上下文（如构建系统和跨文件依赖），并通过容器化、可复现的评估框架，结合专家定义规则，实现对安全性、构建质量和生成稳定性的可靠评估。关键解决方案是将评估粒度从代码片段提升至仓库级别，并建立结构化、可审计的自动化评估流程，从而更真实地反映LLM在实际开发场景中的安全编码能力。
链接: https://arxiv.org/abs/2508.18106
作者: Keke Lian,Bin Wang,Lei Zhang,Libo Chen,Junjie Wang,Ziming Zhao,Yujiu Yang,Haotong Duan,Haoran Zhao,Shuang Liao,Mingda Guo,Jiazheng Quan,Yilu Zhong,Chenhao He,Zichuan Chen,Jie Wu,Haoling Li,Zhaoxuan Li,Jiongchi Yu,Hui Li,Dong Zhang
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:The increasing adoption of large language models (LLMs) in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks are inadequate, as they focus on isolated code snippets, employ unstable evaluation methods that lack reproducibility, and fail to connect the quality of input context with the security of the output. To address these gaps, we introduce A.S.E (AI Code Generation Security Evaluation), a benchmark for repository-level secure code generation. A.S.E constructs tasks from real-world repositories with documented CVEs, preserving full repository context like build systems and cross-file dependencies. Its reproducible, containerized evaluation framework uses expert-defined rules to provide stable, auditable assessments of security, build quality, and generation stability. Our evaluation of leading LLMs on A.S.E reveals three key findings: (1) Claude-3.7-Sonnet achieves the best overall performance. (2) The security gap between proprietary and open-source models is narrow; Qwen3-235B-A22B-Instruct attains the top security score. (3) Concise, fast-thinking'' decoding strategies consistently outperform complex, slow-thinking’’ reasoning for security patching.
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[AI-16] aching LLM s to Think Mathematically: A Critical Study of Decision-Making via Optimization
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在数学规划（Mathematical Programming）领域中自动建模与求解决策问题的能力瓶颈问题。其核心挑战在于如何提升LLMs对自然语言描述的优化问题的理解、结构化表达以及生成准确符号形式的能力，同时克服当前模型在准确性、可扩展性和可解释性方面的局限。解决方案的关键在于通过系统性文献综述与实证实验相结合的方法，构建新的数据集并应用三种提示策略（Act-as-expert、思维链Chain-of-Thought、自一致性Self-Consistency），从最优性差距、token级F1分数和编译准确率等多维度评估LLMs性能，并据此提出未来研究方向，包括结构化数据集设计、领域特定微调、神经符号混合方法、模块化多智能体架构及基于Chain-of-RAGs的动态检索机制，从而为推动LLMs在数学规划领域的应用提供清晰的技术路线图。
链接: https://arxiv.org/abs/2508.18091
作者: Mohammad J. Abdel-Rahman,Yasmeen Alslman,Dania Refai,Amro Saleh,Malik A. Abu Loha,Mohammad Yahya Hamed
机构: 未知
类目: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
备注:
Abstract:This paper investigates the capabilities of large language models (LLMs) in formulating and solving decision-making problems using mathematical programming. We first conduct a systematic review and meta-analysis of recent literature to assess how well LLMs understand, structure, and solve optimization problems across domains. The analysis is guided by critical review questions focusing on learning approaches, dataset designs, evaluation metrics, and prompting strategies. Our systematic evidence is complemented by targeted experiments designed to evaluate the performance of state-of-the-art LLMs in automatically generating optimization models for problems in computer networks. Using a newly constructed dataset, we apply three prompting strategies: Act-as-expert, chain-of-thought, and self-consistency, and evaluate the obtained outputs based on optimality gap, token-level F1 score, and compilation accuracy. Results show promising progress in LLMs’ ability to parse natural language and represent symbolic formulations, but also reveal key limitations in accuracy, scalability, and interpretability. These empirical gaps motivate several future research directions, including structured datasets, domain-specific fine-tuning, hybrid neuro-symbolic approaches, modular multi-agent architectures, and dynamic retrieval via chain-of-RAGs. This paper contributes a structured roadmap for advancing LLM capabilities in mathematical programming.
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[AI-17] Arnold: a generalist muscle transformer policy
【速读】：该论文旨在解决高维非线性人体肌肉骨骼模型的控制难题，特别是现有机器学习方法训练出的智能体仅能掌握单一技能（如抓取、操作物体或行走），缺乏多任务泛化能力的问题。解决方案的关键在于提出了一种名为Arnold的通用策略（generalist policy），其核心创新是引入了“感觉运动词汇表”（sensorimotor vocabulary），这是一种对异构感知模态、目标和执行器语义的组合式表示；该词汇表通过Transformer架构实现对不同任务中变化的观测空间和动作空间的有效建模，从而支持高效多任务、多形态学习，并可快速适应新任务。
链接: https://arxiv.org/abs/2508.18066
作者: Alberto Silvio Chiappa,Boshi An,Merkourios Simos,Chengkun Li,Alexander Mathis
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
备注: A.S.C. and B.A. contributed equally. Code is available at this https URL
Abstract:Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge. Recent machine learning breakthroughs have heralded policies that master individual skills like reaching, object manipulation and locomotion in musculoskeletal systems with many degrees of freedom. However, these agents are merely “specialists”, achieving high performance for a single skill. In this work, we develop Arnold, a generalist policy that masters multiple tasks and embodiments. Arnold combines behavior cloning and fine-tuning with PPO to achieve expert or super-expert performance in 14 challenging control tasks from dexterous object manipulation to locomotion. A key innovation is Arnold’s sensorimotor vocabulary, a compositional representation of the semantics of heterogeneous sensory modalities, objectives, and actuators. Arnold leverages this vocabulary via a transformer architecture to deal with the variable observation and action spaces of each task. This framework supports efficient multi-task, multi-embodiment learning and facilitates rapid adaptation to novel tasks. Finally, we analyze Arnold to provide insights into biological motor control, corroborating recent findings on the limited transferability of muscle synergies across tasks.
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[AI-18] Dynamic Fusion Multimodal Network for SpeechWellness Detection
【速读】：该论文旨在解决青少年自杀风险预测中单一模态信息（如仅文本或仅语音）分析的局限性问题，提出一种基于动态融合机制的轻量级多分支多模态系统，以提升对个体心理状态的综合理解能力。其解决方案的关键在于：首先，引入时域（time-domain）与时频域（time-frequency, TF）声学特征及语义表示，丰富了音频信息的表达；其次，设计了一个可学习权重的动态融合模块，实现不同模态间自适应的信息整合，使模型能够根据输入内容灵活调整各模态的贡献度；最后，通过简化基线模型结构，在显著减少78%参数量的同时，将准确率提升5%，从而兼顾性能与计算效率。
链接: https://arxiv.org/abs/2508.18057
作者: Wenqiang Sun,Han Yin,Jisheng Bai,Jianfeng Chen
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: 6 pages, 5figures
Abstract:Suicide is one of the leading causes of death among adolescents. Previous suicide risk prediction studies have primarily focused on either textual or acoustic information in isolation, the integration of multimodal signals, such as speech and text, offers a more comprehensive understanding of an individual’s mental state. Motivated by this, and in the context of the 1st SpeechWellness detection challenge, we explore a lightweight multi-branch multimodal system based on a dynamic fusion mechanism for speechwellness detection. To address the limitation of prior approaches that rely on time-domain waveforms for acoustic analysis, our system incorporates both time-domain and time-frequency (TF) domain acoustic features, as well as semantic representations. In addition, we introduce a dynamic fusion block to adaptively integrate information from different modalities. Specifically, it applies learnable weights to each modality during the fusion process, enabling the model to adjust the contribution of each modality. To enhance computational efficiency, we design a lightweight structure by simplifying the original baseline model. Experimental results demonstrate that the proposed system exhibits superior performance compared to the challenge baseline, achieving a 78% reduction in model parameters and a 5% improvement in accuracy.
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[AI-19] HyST: LLM -Powered Hybrid Retrieval over Semi-Structured Tabular Data RECSYS2025
【速读】：该论文旨在解决现实推荐系统中用户查询的复杂性问题，即用户常同时提出结构化约束（如类别、属性）与非结构化偏好（如产品描述或评论），传统方法难以有效融合这两类信息以实现精准检索。解决方案的关键在于提出HyST（Hybrid retrieval over Semi-structured Tabular data）框架，通过大语言模型（Large Language Models, LLMs）提取自然语言中的属性级约束并作为元数据过滤条件，同时利用嵌入（embedding）驱动的语义搜索处理查询中的非结构化部分，从而实现结构化过滤与语义检索的协同优化，显著提升在半结构化表格数据上的检索精度和可扩展性。
链接: https://arxiv.org/abs/2508.18048
作者: Jiyoon Myung,Jihyeon Park,Joohyung Han
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: Accepted at the 2nd EARL Workshop on Evaluating and Applying Recommender Systems with Large Language Models (RecSys 2025)
Abstract:User queries in real-world recommendation systems often combine structured constraints (e.g., category, attributes) with unstructured preferences (e.g., product descriptions or reviews). We introduce HyST (Hybrid retrieval over Semi-structured Tabular data), a hybrid retrieval framework that combines LLM-powered structured filtering with semantic embedding search to support complex information needs over semi-structured tabular data. HyST extracts attribute-level constraints from natural language using large language models (LLMs) and applies them as metadata filters, while processing the remaining unstructured query components via embedding-based retrieval. Experiments on a semi-structured benchmark show that HyST consistently outperforms tradtional baselines, highlighting the importance of structured filtering in improving retrieval precision, offering a scalable and accurate solution for real-world user queries.
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[AI-20] PerPilot: Personalizing VLM-based Mobile Agents via Memory and Exploration
【速读】：该论文旨在解决移动代理（mobile agents）在执行个性化指令（personalized instructions）时面临的挑战，即现有模型难以处理包含用户特定上下文的模糊指令，而这一问题在以往研究中被忽视。解决方案的关键在于提出PerPilot框架，该框架基于大语言模型（LLMs），通过两种互补机制实现对个性化元素的识别与任务自主完成：基于记忆的检索（memory-based retrieval）和基于推理的探索（reasoning-based exploration），从而显著提升移动代理在少用户干预下处理多样化个性化任务的能力，并随使用持续优化性能。
链接: https://arxiv.org/abs/2508.18040
作者: Xin Wang,Zhiyao Cui,Hao Li,Ya Zeng,Chenxu Wang,Ruiqi Song,Yihang Chen,Kun Shao,Qiaosheng Zhang,Jinzhuo Liu,Siyue Ren,Shuyue Hu,Zhen Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Vision language model (VLM)-based mobile agents show great potential for assisting users in performing instruction-driven tasks. However, these agents typically struggle with personalized instructions – those containing ambiguous, user-specific context – a challenge that has been largely overlooked in previous research. In this paper, we define personalized instructions and introduce PerInstruct, a novel human-annotated dataset covering diverse personalized instructions across various mobile scenarios. Furthermore, given the limited personalization capabilities of existing mobile agents, we propose PerPilot, a plug-and-play framework powered by large language models (LLMs) that enables mobile agents to autonomously perceive, understand, and execute personalized user instructions. PerPilot identifies personalized elements and autonomously completes instructions via two complementary approaches: memory-based retrieval and reasoning-based exploration. Experimental results demonstrate that PerPilot effectively handles personalized tasks with minimal user intervention and progressively improves its performance with continued use, underscoring the importance of personalization-aware reasoning for next-generation mobile agents. The dataset and code are available at: this https URL
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[AI-21] Previously on… Automating Code Review
【速读】：该论文旨在解决现代代码审查（Modern Code Review, MCR）自动化研究中存在的任务定义不统一、数据集和评估方法差异大、缺乏标准化等问题，从而阻碍了该领域的发展与可比性。其关键解决方案是通过系统性文献综述（共分析691篇文献中的24项相关研究），对MCR自动化任务进行形式化分类，识别出48种任务-指标组合（其中22种为原创性组合），并揭示如时间偏差威胁等方法论挑战；同时提出具体建议以推动未来研究的标准化与有效性提升，包括增强数据复用、改进评估实践及提高研究成果的可重现性。
链接: https://arxiv.org/abs/2508.18003
作者: Robert Heumüller,Frank Ortmeier
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Preprint currently under review
Abstract:Modern Code Review (MCR) is a standard practice in software engineering, yet it demands substantial time and resource investments. Recent research has increasingly explored automating core review tasks using machine learning (ML) and deep learning (DL). As a result, there is substantial variability in task definitions, datasets, and evaluation procedures. This study provides the first comprehensive analysis of MCR automation research, aiming to characterize the field’s evolution, formalize learning tasks, highlight methodological challenges, and offer actionable recommendations to guide future research. Focusing on the primary code review tasks, we systematically surveyed 691 publications and identified 24 relevant studies published between May 2015 and April 2024. Each study was analyzed in terms of tasks, models, metrics, baselines, results, validity concerns, and artifact availability. In particular, our analysis reveals significant potential for standardization, including 48 task metric combinations, 22 of which were unique to their original paper, and limited dataset reuse. We highlight challenges and derive concrete recommendations for examples such as the temporal bias threat, which are rarely addressed so far. Our work contributes to a clearer overview of the field, supports the framing of new research, helps to avoid pitfalls, and promotes greater standardization in evaluation practices.
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[AI-22] Automating Conflict-Aware ACL Configurations with Natural Language Intents
【速读】：该论文旨在解决访问控制列表（ACL）配置在复杂网络拓扑和已有规则下所面临的高复杂性问题，具体包括：将自然语言表达的配置意图准确转化为具体的ACL规则、检测并解决新旧规则间的冲突，以及制定最优部署策略以最小化规则添加量。解决方案的关键在于提出Xumi系统，其利用具备目标网络领域知识的大语言模型（LLM），自动完成意图到ACL规则的精准映射；通过冲突检测与修复机制生成可部署的修正意图，并结合优化算法识别出规则增量最少的部署方案，从而显著提升配置效率与准确性。
链接: https://arxiv.org/abs/2508.17990
作者: Wenlong Ding,Jianqiang Li,Zhixiong Niu,Huangxun Chen,Yongqiang Xiong,Hong Xu
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注:
Abstract:ACL configuration is essential for managing network flow reachability, yet its complexity grows significantly with topologies and pre-existing rules. To carry out ACL configuration, the operator needs to (1) understand the new configuration policies or intents and translate them into concrete ACL rules, (2) check and resolve any conflicts between the new and existing rules, and (3) deploy them across the network. Existing systems rely heavily on manual efforts for these tasks, especially for the first two, which are tedious, error-prone, and impractical to scale. We propose Xumi to tackle this problem. Leveraging LLMs with domain knowledge of the target network, Xumi automatically and accurately translates the natural language intents into complete ACL rules to reduce operators’ manual efforts. Xumi then detects all potential conflicts between new and existing rules and generates resolved intents for deployment with operators’ guidance, and finally identifies the best deployment plan that minimizes the rule additions while satisfying all intents. Evaluation shows that Xumi accelerates the entire configuration pipeline by over 10x compared to current practices, addresses O(100) conflicting ACLs and reduces rule additions by ~40% in modern cloud network. Subjects: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI) Cite as: arXiv:2508.17990 [cs.NI] (or arXiv:2508.17990v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2508.17990 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-23] Neural Algorithmic Reason ers informed Large Language Model for Multi-Agent Path Finding IJCNN2025
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLM）在多智能体路径规划（Multi-Agent Path Finding, MAPF）任务中表现不佳的问题。MAPF是一个需要复杂规划与多智能体协调的难题，而现有基于LLM的方法难以有效处理此类任务。解决方案的关键在于提出一种名为LLM-NAR的新框架，其核心是引入一个基于图神经网络（Graph Neural Network, GNN）的神经算法推理器（Neural Algorithmic Reasoner, NAR），并通过交叉注意力机制（cross-attention mechanism）将地图信息与LLM进行融合，从而引导LLM更高效地完成MAPF任务。该方法首次将GNN与地图信息结合用于指导LLM，显著提升了性能，并具备良好的可迁移性，适用于多种LLM模型。
链接: https://arxiv.org/abs/2508.17971
作者: Pu Feng,Size Wang,Yuhong Cao,Junkang Liang,Rongye Shi,Wenjun Wu
机构: 未知
类目: Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: Accepted by IJCNN 2025
Abstract:The development and application of large language models (LLM) have demonstrated that foundational models can be utilized to solve a wide array of tasks. However, their performance in multi-agent path finding (MAPF) tasks has been less than satisfactory, with only a few studies exploring this area. MAPF is a complex problem requiring both planning and multi-agent coordination. To improve the performance of LLM in MAPF tasks, we propose a novel framework, LLM-NAR, which leverages neural algorithmic reasoners (NAR) to inform LLM for MAPF. LLM-NAR consists of three key components: an LLM for MAPF, a pre-trained graph neural network-based NAR, and a cross-attention mechanism. This is the first work to propose using a neural algorithmic reasoner to integrate GNNs with the map information for MAPF, thereby guiding LLM to achieve superior performance. LLM-NAR can be easily adapted to various LLM models. Both simulation and real-world experiments demonstrate that our method significantly outperforms existing LLM-based approaches in solving MAPF problems.
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[AI-24] Language Models Coupled with Metacognition Can Outperform Reasoning Models
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在需要严格逻辑约束的任务中表现不足，而专门设计的大型推理模型（Large Reasoning Models, LRM）虽具备更强的推理能力但存在计算成本高、推理速度慢的问题。解决方案的关键在于提出SOFAI-LM架构，通过元认知（metacognition）机制协调一个快速但较弱的LLM与一个慢速但强大的LRM：元认知模块主动监控LLM的执行过程，并提供有针对性的迭代反馈（包含相关示例），使LLM无需额外微调即可逐步优化解题策略；当LLM自身能力不足时，系统则利用反馈循环中收集的信息，以问题域特异性的方式触发LRM介入，从而在保证准确率接近或超越独立LRM的同时显著降低推理时间。
链接: https://arxiv.org/abs/2508.17959
作者: Vedant Khandelwal,Francesca Rossi,Keerthiram Murugesan,Erik Miehling,Murray Campbell,Karthikeyan Natesan Ramamurthy,Lior Horesh
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 37 Pages, 95 Figures
Abstract:Large language models (LLMs) excel in speed and adaptability across various reasoning tasks, but they often struggle when strict logic or constraint enforcement is required. In contrast, Large Reasoning Models (LRMs) are specifically designed for complex, step-by-step reasoning, although they come with significant computational costs and slower inference times. To address these trade-offs, we employ and generalize the SOFAI (Slow and Fast AI) cognitive architecture into SOFAI-LM, which coordinates a fast LLM with a slower but more powerful LRM through metacognition. The metacognitive module actively monitors the LLM’s performance and provides targeted, iterative feedback with relevant examples. This enables the LLM to progressively refine its solutions without requiring the need for additional model fine-tuning. Extensive experiments on graph coloring and code debugging problems demonstrate that our feedback-driven approach significantly enhances the problem-solving capabilities of the LLM. In many instances, it achieves performance levels that match or even exceed those of standalone LRMs while requiring considerably less time. Additionally, when the LLM and feedback mechanism alone are insufficient, we engage the LRM by providing appropriate information collected during the LLM’s feedback loop, tailored to the specific characteristics of the problem domain and leads to improved overall performance. Evaluations on two contrasting domains: graph coloring, requiring globally consistent solutions, and code debugging, demanding localized fixes, demonstrate that SOFAI-LM enables LLMs to match or outperform standalone LRMs in accuracy while maintaining significantly lower inference time.
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[AI-25] A Feminist Account of Intersectional Algorithmic Fairness
【速读】：该论文旨在解决当前算法公平性研究中对交叉性（Intersectionality）考量不足的问题，即现有方法多采用单一维度或形式化的子群体框架，难以反映社会现实中的多重压迫与特权交织结构，从而可能忽视系统性不平等并加剧对交叉性边缘群体的伤害。其解决方案的关键在于提出“实质交叉性算法公平性”（Substantive Intersectional Algorithmic Fairness），基于Green（2022）的实质算法公平性概念，并融合交叉性女性主义理论，构建了一套包含十项理想特征（desiderata）的ROOF方法论，强调在算法设计、评估与部署过程中必须考虑社会语境，反思中立性假设、合理使用受保护属性、纳入多重边缘化群体，并在必要时坚持原则性不部署，以实现更具包容性、情境敏感且能缓解结构性不公的算法实践。
链接: https://arxiv.org/abs/2508.17944
作者: Marie Mirsch(1),Laila Wegner(2),Jonas Strube(1),Carmen Leicht-Scholten(1) ((1) RWTH Aachen University, Germany, (2) Eindhoven University of Technology, The Netherlands)
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 27 pages, 1 figure
Abstract:Intersectionality has profoundly influenced research and political action by revealing how interconnected systems of privilege and oppression influence lived experiences, yet its integration into algorithmic fairness research remains limited. Existing approaches often rely on single-axis or formal subgroup frameworks that risk oversimplifying social realities and neglecting structural inequalities. We propose Substantive Intersectional Algorithmic Fairness, extending Green’s (2022) notion of substantive algorithmic fairness with insights from intersectional feminist theory. Building on this foundation, we introduce ten desiderata within the ROOF methodology to guide the design, assessment, and deployment of algorithmic systems in ways that address systemic inequities while mitigating harms to intersectionally marginalized communities. Rather than prescribing fixed operationalizations, these desiderata encourage reflection on assumptions of neutrality, the use of protected attributes, the inclusion of multiply marginalized groups, and enhancing algorithmic systems’ potential. Our approach emphasizes that fairness cannot be separated from social context, and that in some cases, principled non-deployment may be necessary. By bridging computational and social science perspectives, we provide actionable guidance for more equitable, inclusive, and context-sensitive intersectional algorithmic practices.
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[AI-26] Riemannian Optimization for LoRA on the Stiefel Manifold EMNLP2025
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）过程中，特别是基于LoRA（Low-Rank Adaptation）方法时，由于优化器效率低下导致的性能瓶颈问题。其核心挑战在于使用AdamW优化器时，LoRA中的B矩阵存在基冗余（basis redundancy），从而限制了模型的表示能力和参数效率。解决方案的关键在于将B矩阵的优化从欧氏空间转移到Stiefel流形（Stiefel manifold）上，通过显式施加正交性约束，实现近乎完美的正交性和完整的有效秩（effective rank）。这一几何约束方法显著提升了参数效率和表征能力，实验证明该Stiefel优化器在LoRA和DoRA（Decomposed LoRA）框架下均优于AdamW，表明几何约束是释放LoRA全部潜力的核心所在。
链接: https://arxiv.org/abs/2508.17901
作者: Juneyoung Park,Minjae Kang,Seongbae Lee,Haegang Lee,Seongwan Kim,Jaeho Lee
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: EMNLP 2025 Findings
Abstract:While powerful, large language models (LLMs) present significant fine-tuning challenges due to their size. Parameter-efficient fine-tuning (PEFT) methods like LoRA provide solutions, yet suffer from critical optimizer inefficiencies; notably basis redundancy in LoRA’s B matrix when using AdamW, which fundamentally limits performance. We address this by optimizing the B matrix on the Stiefel manifold, imposing explicit orthogonality constraints that achieve near-perfect orthogonality and full effective rank. This geometric approach dramatically enhances parameter efficiency and representational capacity. Our Stiefel optimizer consistently outperforms AdamW across benchmarks with both LoRA and DoRA, demonstrating that geometric constraints are the key to unlocking LoRA’s full potential for effective LLM fine-tuning.
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[AI-27] A Defect Classification Framework for AI-Based Software Systems (AI-ODC)
【速读】：该论文旨在解决当前缺陷分析模型无法有效捕捉人工智能（Artificial Intelligence, AI）系统独特属性的问题，从而难以保障AI系统质量。其解决方案的关键在于对经典的正交缺陷分类（Orthogonal Defect Classification, ODC）框架进行适应性调整，引入数据（Data）、学习（Learning）和思考（Thinking）三个新维度以反映AI系统的特性，并增加一个严重性等级、替换原有影响区域为与AI相关的特征，构建出面向AI系统的缺陷分类框架（AIODC）。实证研究表明，该框架能够识别高风险缺陷类别，尤其揭示了学习阶段缺陷最常见且常关联高严重性，而思考阶段缺陷则显著影响系统可信度与准确性，为针对性的质量保障措施提供了依据。
链接: https://arxiv.org/abs/2508.17900
作者: Mohammed O. Alannsary
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Article, 19 pages, 6 figures, 8 tables,
Abstract:Artificial Intelligence has gained a lot of attention recently, it has been utilized in several fields ranging from daily life activities, such as responding to emails and scheduling appointments, to manufacturing and automating work activities. Artificial Intelligence systems are mainly implemented as software solutions, and it is essential to discover and remove software defects to assure its quality using defect analysis which is one of the major activities that contribute to software quality. Despite the proliferation of AI-based systems, current defect analysis models fail to capture their unique attributes. This paper proposes a framework inspired by the Orthogonal Defect Classification (ODC) paradigm and enables defect analysis of Artificial Intelligence systems while recognizing its special attributes and characteristics. This study demonstrated the feasibility of modifying ODC for AI systems to classify its defects. The ODC was adjusted to accommodate the Data, Learning, and Thinking aspects of AI systems which are newly introduced classification dimensions. This adjustment involved the introduction of an additional attribute to the ODC attributes, the incorporation of a new severity level, and the substitution of impact areas with characteristics pertinent to AI systems. The framework was showcased by applying it to a publicly available Machine Learning bug dataset, with results analyzed through one-way and two-way analysis. The case study indicated that defects occurring during the Learning phase were the most prevalent and were significantly linked to high-severity classifications. In contrast, defects identified in the Thinking phase had a disproportionate effect on trustworthiness and accuracy. These findings illustrate AIODC’s capability to identify high-risk defect categories and inform focused quality assurance measures.
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[AI-28] Vocoder-Projected Feature Discriminator WWW INTERSPEECH2024
【速读】：该论文旨在解决文本到语音（TTS）和语音转换（VC）中，使用声学特征（如梅尔频谱图）进行对抗训练时因波形上采样导致的时间与内存开销过大的问题。其解决方案的关键在于提出一种“声码器投影特征判别器”（vocoder-projected feature discriminator, VPFD），该方法利用预训练且冻结的声码器提取特征进行对抗训练，仅需一次上采样步骤即可实现与波形判别器相当的语音转换性能，同时将训练时间和内存消耗分别降低9.6倍和11.4倍。
链接: https://arxiv.org/abs/2508.17874
作者: Takuhiro Kaneko,Hirokazu Kameoka,Kou Tanaka,Yuto Kondo
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS); Machine Learning (stat.ML)
备注: Accepted to Interspeech 2024. Project page: this https URL
Abstract:In text-to-speech (TTS) and voice conversion (VC), acoustic features, such as mel spectrograms, are typically used as synthesis or conversion targets owing to their compactness and ease of learning. However, because the ultimate goal is to generate high-quality waveforms, employing a vocoder to convert these features into waveforms and applying adversarial training in the time domain is reasonable. Nevertheless, upsampling the waveform introduces significant time and memory overheads. To address this issue, we propose a vocoder-projected feature discriminator (VPFD), which uses vocoder features for adversarial training. Experiments on diffusion-based VC distillation demonstrated that a pretrained and frozen vocoder feature extractor with a single upsampling step is necessary and sufficient to achieve a VC performance comparable to that of waveform discriminators while reducing the training time and memory consumption by 9.6 and 11.4 times, respectively.
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[AI-29] FasterVoiceGrad: Faster One-step Diffusion-Based Voice Conversion with Adversarial Diffusion Conversion Distillation INTERSPEECH2025 WWW FAST
【速读】：该论文旨在解决扩散模型驱动的语音转换（Voice Conversion, VC）中推理速度慢的问题，特别是针对现有方法如FastVoiceGrad仍需高计算复杂度的内容编码器（content encoder）导致的转换效率瓶颈。其解决方案的关键在于提出FasterVoiceGrad，一种通过对抗扩散转换蒸馏（Adversarial Diffusion Conversion Distillation, ADCD）技术，同时对扩散模型和内容编码器进行联合蒸馏的一步式VC模型，该方法在转换过程中实现蒸馏，并结合对抗训练与得分蒸馏策略，在保持竞争性语音质量和说话人相似性的同时，显著提升推理速度——在GPU和CPU上分别达到6.6–6.9倍和1.8倍的加速效果。
链接: https://arxiv.org/abs/2508.17868
作者: Takuhiro Kaneko,Hirokazu Kameoka,Kou Tanaka,Yuto Kondo
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS); Machine Learning (stat.ML)
备注: Accepted to Interspeech 2025. Project page: this https URL
Abstract:A diffusion-based voice conversion (VC) model (e.g., VoiceGrad) can achieve high speech quality and speaker similarity; however, its conversion process is slow owing to iterative sampling. FastVoiceGrad overcomes this limitation by distilling VoiceGrad into a one-step diffusion model. However, it still requires a computationally intensive content encoder to disentangle the speaker’s identity and content, which slows conversion. Therefore, we propose FasterVoiceGrad, a novel one-step diffusion-based VC model obtained by simultaneously distilling a diffusion model and content encoder using adversarial diffusion conversion distillation (ADCD), where distillation is performed in the conversion process while leveraging adversarial and score distillation training. Experimental evaluations of one-shot VC demonstrated that FasterVoiceGrad achieves competitive VC performance compared to FastVoiceGrad, with 6.6-6.9 and 1.8 times faster speed on a GPU and CPU, respectively.
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[AI-30] Ada-TransGNN: An Air Quality Prediction Model Based On Adaptive Graph Convolutional Networks ICONIP2025
【速读】：该论文旨在解决现有空气质量预测模型中存在的预测精度低和实时更新速度慢的问题，导致预测结果滞后。其核心解决方案是提出一种基于Transformer的时空数据预测方法（Ada-TransGNN），该方法通过构建包含多头注意力机制与图卷积网络（Graph Convolutional Network, GCN）的高效协同时空块集合，动态提取复杂监测数据中的时空依赖特征。关键创新在于引入自适应图结构学习模块，以数据驱动方式融合时空依赖特征，学习最优空间拓扑结构，从而更准确地刻画监测点间的空间关系；同时设计辅助任务学习模块，将空间上下文信息融入最优图结构表示中，增强时间关系的解码能力，显著提升短期与长期预测精度。
链接: https://arxiv.org/abs/2508.17867
作者: Dan Wang,Feng Jiang,Zhanquan Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 15 pages, 4 figures, 3 tables. This paper is accepted by ICONIP2025 but not published
Abstract:Accurate air quality prediction is becoming increasingly important in the environmental field. To address issues such as low prediction accuracy and slow real-time updates in existing models, which lead to lagging prediction results, we propose a Transformer-based spatiotemporal data prediction method (Ada-TransGNN) that integrates global spatial semantics and temporal behavior. The model constructs an efficient and collaborative spatiotemporal block set comprising a multi-head attention mechanism and a graph convolutional network to extract dynamically changing spatiotemporal dependency features from complex air quality monitoring data. Considering the interaction relationships between different monitoring points, we propose an adaptive graph structure learning module, which combines spatiotemporal dependency features in a data-driven manner to learn the optimal graph structure, thereby more accurately capturing the spatial relationships between monitoring points. Additionally, we design an auxiliary task learning module that enhances the decoding capability of temporal relationships by integrating spatial context information into the optimal graph structure representation, effectively improving the accuracy of prediction results. We conducted comprehensive evaluations on a benchmark dataset and a novel dataset (Mete-air). The results demonstrate that our model outperforms existing state-of-the-art prediction models in short-term and long-term predictions.
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[AI-31] Group Expectation Policy Optimization for Stable Heterogeneous Reinforcement Learning in LLM s
【速读】：该论文旨在解决在异构分布式环境中进行强化学习（Reinforcement Learning, RL）后训练大型语言模型（Large Language Models, LLMs）时，因节点间网络延迟导致的重要性采样失效问题。其核心挑战在于传统RL方法中采样与参数更新紧密耦合，难以适应地理分布节点间的高延迟和异构性。解决方案的关键是提出一种异步RL架构HeteroRL，通过解耦rollout采样与参数学习过程，并引入Group Expectation Policy Optimization (GEPO)算法，该算法通过改进的采样机制显著降低重要性权重方差，理论上实现指数级方差缩减，从而在高达1800秒延迟下仍保持性能损失低于3%，展现出在异构网络中部署去中心化RL的强大潜力。
链接: https://arxiv.org/abs/2508.17850
作者: Han Zhang,Ruibin Zheng,Zexuan Yi,Hanyang Peng,Hui Wang,Yue Yu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:As single-center computing approaches power constraints, decentralized training is becoming essential. Reinforcement Learning (RL) post-training enhances Large Language Models (LLMs) but faces challenges in heterogeneous distributed environments due to its tightly-coupled sampling-learning alternation. We propose HeteroRL, an asynchronous RL architecture that decouples rollout sampling from parameter learning, enabling robust deployment across geographically distributed nodes under network delays. We identify that latency-induced KL divergence causes importance sampling failure due to high variance. To address this, we propose Group Expectation Policy Optimization (GEPO), which reduces importance weight variance through a refined sampling mechanism. Theoretically, GEPO achieves exponential variance reduction. Experiments show it maintains superior stability over methods like GRPO, with less than 3% performance degradation under 1800-second delays, demonstrating strong potential for decentralized RL in heterogeneous networks.
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[AI-32] FAIRGAMER: Evaluating Biases in the Application of Large Language Models to Video Games
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在视频游戏应用中因固有社会偏见导致的游戏平衡性破坏问题，这一问题尚未得到充分研究。解决方案的关键在于提出首个面向游戏场景的偏见评估基准 FairGamer，其包含六个任务和一种新型度量指标 $ D_{lstd} $，覆盖LLMs作为非玩家角色（Non-Player Characters, NPCs）、竞争性对手以及游戏场景生成三种典型应用场景，并结合现实与虚构内容以全面评估偏见表现。实验表明，决策偏见会直接损害游戏平衡，且LLMs对真实与虚拟世界内容均表现出同构的社会/文化偏见，揭示了其偏见源于模型本身的特性，从而为提升LLMs在游戏中的可靠性提供了量化分析框架与实证依据。
链接: https://arxiv.org/abs/2508.17825
作者: Bingkang Shi,Jen-tse Huang,Guoyi Li,Xiaodan Zhang,Zhongjiang Yao
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Leveraging their advanced capabilities, Large Language Models (LLMs) demonstrate vast application potential in video games–from dynamic scene generation and intelligent NPC interactions to adaptive opponents–replacing or enhancing traditional game mechanics. However, LLMs’ trustworthiness in this application has not been sufficiently explored. In this paper, we reveal that the models’ inherent social biases can directly damage game balance in real-world gaming environments. To this end, we present FairGamer, the first bias evaluation Benchmark for LLMs in video game scenarios, featuring six tasks and a novel metrics D_lstd . It covers three key scenarios in games where LLMs’ social biases are particularly likely to manifest: Serving as Non-Player Characters, Interacting as Competitive Opponents, and Generating Game Scenes. FairGamer utilizes both reality-grounded and fully fictional game content, covering a variety of video game genres. Experiments reveal: (1) Decision biases directly cause game balance degradation, with Grok-3 (average D_lstd score=0.431) exhibiting the most severe degradation; (2) LLMs demonstrate isomorphic social/cultural biases toward both real and virtual world content, suggesting their biases nature may stem from inherent model characteristics. These findings expose critical reliability gaps in LLMs’ gaming applications. Our code and data are available at anonymous GitHub this https URL .
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[AI-33] Limits of message passing for node classification: How class-bottlenecks restrict signal-to-noise ratio
【速读】：该论文旨在解决消息传递神经网络（Message Passing Neural Networks, MPNNs）在异质性（heterophily，即同类别节点连接稀疏）和图结构瓶颈（structural bottlenecks）条件下性能受限的问题。其核心挑战在于MPNN的表示信号-噪声比（signal-to-noise ratio, SNR）受高阶同质性（higher-order homophily）约束，而低高阶同质性会局部表现为结构瓶颈与类别标签的交互效应（class-bottlenecks）。解决方案的关键在于提出一个统一的统计框架，将SNR分解为特征依赖参数与特征无关的敏感度，并证明最优图结构应为单类或双类二分簇的不相交并集；据此设计了基于图集合重连（graph ensemble-based rewiring）的BRIDGE算法，通过消除“中等同质性陷阱”（mid-homophily pitfall），显著提升MPNN在合成与真实数据集上的分类准确率，优于现有标准重连方法。
链接: https://arxiv.org/abs/2508.17822
作者: Jonathan Rubin,Sahil Loomba,Nick S. Jones
机构: 未知
类目: Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML)
备注:
Abstract:Message passing neural networks (MPNNs) are powerful models for node classification but suffer from performance limitations under heterophily (low same-class connectivity) and structural bottlenecks in the graph. We provide a unifying statistical framework exposing the relationship between heterophily and bottlenecks through the signal-to-noise ratio (SNR) of MPNN representations. The SNR decomposes model performance into feature-dependent parameters and feature-independent sensitivities. We prove that the sensitivity to class-wise signals is bounded by higher-order homophily – a generalisation of classical homophily to multi-hop neighbourhoods – and show that low higher-order homophily manifests locally as the interaction between structural bottlenecks and class labels (class-bottlenecks). Through analysis of graph ensembles, we provide a further quantitative decomposition of bottlenecking into underreaching (lack of depth implying signals cannot arrive) and oversquashing (lack of breadth implying signals arriving on fewer paths) with closed-form expressions. We prove that optimal graph structures for maximising higher-order homophily are disjoint unions of single-class and two-class-bipartite clusters. This yields BRIDGE, a graph ensemble-based rewiring algorithm that achieves near-perfect classification accuracy across all homophily regimes on synthetic benchmarks and significant improvements on real-world benchmarks, by eliminating the ``mid-homophily pitfall’’ where MPNNs typically struggle, surpassing current standard rewiring techniques from the literature. Our framework, whose code we make available for public use, provides both diagnostic tools for assessing MPNN performance, and simple yet effective methods for enhancing performance through principled graph modification.
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[AI-34] Limitations of Normalization in Attention Mechanism
【速读】：该论文旨在解决注意力机制中归一化（normalization）方法的局限性问题，特别是softmax归一化在token选择过程中的几何分离能力和模型区分能力下降的问题。其解决方案的关键在于构建一个理论框架，用于量化模型对token的筛选能力及token向量间的几何分离距离，并通过GPT-2预训练模型的实验证实：随着被选token数量增加，模型区分信息性token的能力减弱，趋向于均匀分配注意力；同时发现softmax归一化在低温度设置下导致梯度敏感性增强，影响训练稳定性。这些发现揭示了当前基于softmax的注意力机制的内在缺陷，为未来设计更鲁棒的归一化策略和选择机制提供了理论依据。
链接: https://arxiv.org/abs/2508.17821
作者: Timur Mudarisov,Mikhail Burtsev,Tatiana Petrova,Radu State
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 pages, 4 figures
Abstract:This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the identification of the model’s selective ability and the geometric separation involved in token selection. Our analysis includes explicit bounds on distances and separation criteria for token vectors under softmax scaling. Through experiments with pre-trained GPT-2 model, we empirically validate our theoretical results and analyze key behaviors of the attention mechanism. Notably, we demonstrate that as the number of selected tokens increases, the model’s ability to distinguish informative tokens declines, often converging toward a uniform selection pattern. We also show that gradient sensitivity under softmax normalization presents challenges during training, especially at low temperature settings. These findings advance current understanding of softmax-based attention mechanism and motivate the need for more robust normalization and selection strategies in future attention architectures.
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[AI-35] Scalable Engine and the Performance of Different LLM Models in a SLURM based HPC architecture WWW
【速读】：该论文旨在解决在大规模高性能计算（High Performance Computing, HPC）基础设施上高效部署异构大语言模型（Large Language Models, LLMs）进行可扩展推理的问题，尤其关注资源调度效率与系统响应延迟之间的权衡。其解决方案的关键在于基于Simple Linux Utility for Resource Management（SLURM）构建了一个动态资源调度架构，结合容器化微服务的无缝集成，实现对CPU、GPU及内存资源在多节点集群中的精细化管理；实验表明，该方案能显著降低容器和调度开销，在批量与交互式场景下均具备良好可扩展性，且支持REST API接口以实现单次与批量推理以及多步骤“法庭式”精炼等高级工作流，从而提升LLM推理的响应速度、可靠性和灵活性。
链接: https://arxiv.org/abs/2508.17814
作者: Anderson de Lima Luiz,Shubham Vijay Kurlekar,Munir Georges
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注: Accepted in ESSV 2025 - this https URL
Abstract:This work elaborates on a High performance computing (HPC) architecture based on Simple Linux Utility for Resource Management (SLURM) [1] for deploying heterogeneous Large Language Models (LLMs) into a scalable inference engine. Dynamic resource scheduling and seamless integration of containerized microservices have been leveraged herein to manage CPU, GPU, and memory allocations efficiently in multi-node clusters. Extensive experiments, using Llama 3.2 (1B and 3B parameters) [2] and Llama 3.1 (8B and 70B) [3], probe throughput, latency, and concurrency and show that small models can handle up to 128 concurrent requests at sub-50 ms latency, while for larger models, saturation happens with as few as two concurrent users, with a latency of more than 2 seconds. This architecture includes Representational State Transfer Application Programming Interfaces (REST APIs) [4] endpoints for single and bulk inferences, as well as advanced workflows such as multi-step “tribunal” refinement. Experimental results confirm minimal overhead from container and scheduling activities and show that the approach scales reliably both for batch and interactive settings. We further illustrate real-world scenarios, including the deployment of chatbots with retrievalaugmented generation, which helps to demonstrate the flexibility and robustness of the architecture. The obtained results pave ways for significantly more efficient, responsive, and fault-tolerant LLM inference on large-scale HPC infrastructures.
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[AI-36] Adaptive Output Steps: FlexiSteps Network for Dynamic Trajectory Prediction
【速读】：该论文旨在解决传统轨迹预测模型因采用固定长度输出而难以适应动态现实场景的问题。其核心解决方案是提出FlexiSteps Network（FSN）框架，关键在于引入一个预训练的自适应预测模块（Adaptive Prediction Module, APM），根据上下文条件动态调整预测时间步长，从而在保证预测精度的同时提升模型的灵活性与效率；此外，为实现模块化集成，设计了动态解码器（Dynamic Decoder, DD），并通过结合Fréchet距离与预测步长的评分机制，在预测时长与准确性之间实现平衡。
链接: https://arxiv.org/abs/2508.17797
作者: Yunxiang Liu,Hongkuo Niu,Jianlin Zhu
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate trajectory prediction is vital for autonomous driving, robotics, and intelligent decision-making systems, yet traditional models typically rely on fixed-length output predictions, limiting their adaptability to dynamic real-world scenarios. In this paper, we introduce the FlexiSteps Network (FSN), a novel framework that dynamically adjusts prediction output time steps based on varying contextual conditions. Inspired by recent advancements addressing observation length discrepancies and dynamic feature extraction, FSN incorporates an pre-trained Adaptive Prediction Module (APM) to evaluate and adjust the output steps dynamically, ensuring optimal prediction accuracy and efficiency. To guarantee the plug-and-play of our FSN, we also design a Dynamic Decoder(DD). Additionally, to balance the prediction time steps and prediction accuracy, we design a scoring mechanism, which not only introduces the Fréchet distance to evaluate the geometric similarity between the predicted trajectories and the ground truth trajectories but the length of predicted steps is also considered. Extensive experiments conducted on benchmark datasets including Argoverse and INTERACTION demonstrate the effectiveness and flexibility of our proposed FSN framework.
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[AI-37] Interpretable Early Failure Detection via Machine Learning and Trace Checking-based Monitoring ECAI2025
【速读】：该论文旨在解决运行时监控（monitoring）中因构建确定性自动机导致的计算复杂度过高问题，尤其是在处理信号时序逻辑（Signal Temporal Logic, STL）的纯过去（co）安全性片段时，传统方法需要构造双指数级复杂度的自动机，限制了其实际应用。解决方案的关键在于将此类STL公式的监控问题转化为可高效执行的trace checking（迹检查），即直接在有限离散迹上评估公式，其时间复杂度为公式规模与迹长度的多项式关系。基于此理论突破，作者进一步开发了一个基于GPU加速的可解释早期故障检测框架，通过向量化迹检查与遗传编程（genetic programming）从历史迹数据中学习时序属性，实现了比现有最优方法在关键性能指标上提升2–10%的实用效果。
链接: https://arxiv.org/abs/2508.17786
作者: Andrea Brunello,Luca Geatti,Angelo Montanari,Nicola Saccomanno
机构: 未知
类目: Artificial Intelligence (cs.AI); Formal Languages and Automata Theory (cs.FL); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
备注: Full version of the paper accepted for publication at the 28th European Conference on Artificial Intelligence (ECAI 2025)
Abstract:Monitoring is a runtime verification technique that allows one to check whether an ongoing computation of a system (partial trace) satisfies a given formula. It does not need a complete model of the system, but it typically requires the construction of a deterministic automaton doubly exponential in the size of the formula (in the worst case), which limits its practicality. In this paper, we show that, when considering finite, discrete traces, monitoring of pure past (co)safety fragments of Signal Temporal Logic (STL) can be reduced to trace checking, that is, evaluation of a formula over a trace, that can be performed in time polynomial in the size of the formula and the length of the trace. By exploiting such a result, we develop a GPU-accelerated framework for interpretable early failure detection based on vectorized trace checking, that employs genetic programming to learn temporal properties from historical trace data. The framework shows a 2-10% net improvement in key performance metrics compared to the state-of-the-art methods.
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[AI-38] Agent RAN: An Agent ic AI Architecture for Autonomous Control of Open 6G Networks
【速读】：该论文旨在解决当前Open RAN（开放无线接入网）部署中依赖静态控制与人工操作的局限性，从而阻碍网络智能化演进的问题。其核心解决方案是提出AgenRAN框架，该框架基于大语言模型（Large Language Model, LLM）构建一个可编程、自组织的分布式AI代理（Agent）体系，通过自然语言（Natural Language, NL）意图实现对复杂网络任务的自动分解与协同执行。关键创新在于引入AI-RAN Factory——一个自动化合成管道，能够持续观察代理交互并生成具备优化控制算法的新代理，使网络从静态功能集合转变为具备自我进化能力的智能系统，从而在时间尺度（亚毫秒至分钟）、空间域（小区到全网）和协议层（物理层/媒体访问控制层至无线资源控制层）上实现动态自适应优化。
链接: https://arxiv.org/abs/2508.17778
作者: Maxime Elkael,Salvatore D’Oro,Leonardo Bonati,Michele Polese,Yunseong Lee,Koichiro Furueda,Tommaso Melodia
机构: 未知
类目: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
备注: This work has been submitted to the IEEE for possible publication
Abstract:The Open RAN movement has catalyzed a transformation toward programmable, interoperable cellular infrastructures. Yet, today’s deployments still rely heavily on static control and manual operations. To move beyond this limitation, we introduce AgenRAN, an AI-native, Open RAN-aligned agentic framework that generates and orchestrates a fabric of distributed AI agents based on Natural Language (NL) intents. Unlike traditional approaches that require explicit programming, AgentRAN’s LLM-powered agents interpret natural language intents, negotiate strategies through structured conversations, and orchestrate control loops across the network. AgentRAN instantiates a self-organizing hierarchy of agents that decompose complex intents across time scales (from sub-millisecond to minutes), spatial domains (cell to network-wide), and protocol layers (PHY/MAC to RRC). A central innovation is the AI-RAN Factory, an automated synthesis pipeline that observes agent interactions and continuously generates new agents embedding improved control algorithms, effectively transforming the network from a static collection of functions into an adaptive system capable of evolving its own intelligence. We demonstrate AgentRAN through live experiments on 5G testbeds where competing user demands are dynamically balanced through cascading intents. By replacing rigid APIs with NL coordination, AgentRAN fundamentally redefines how future 6G networks autonomously interpret, adapt, and optimize their behavior to meet operator goals.
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[AI-39] DiffusionGS: Generative Search with Query Conditioned Diffusion in Kuaishou
【速读】：该论文旨在解决个性化搜索排序系统中对用户实时意图挖掘不足的问题，现有方法虽能基于过滤后的历史行为估计用户的广泛兴趣，但往往未能充分利用用户查询与过去行为之间的显式对齐关系。解决方案的关键在于提出DiffusionGS，其核心创新是将用户查询视为意图先验（intent prior），通过条件扩散去噪机制从长期且噪声较大的历史行为序列中提取出受用户当前意图驱动的即时兴趣表示；具体而言，引入User-aware Denoising Layer（UDL）以用户特定画像优化注意力分布，从而实现更精准的动态兴趣建模。
链接: https://arxiv.org/abs/2508.17754
作者: Qinyao Li,Xiaoyang Zheng,Qihang Zhao,Ke Xu,Zhongbo Sun,Chao Wang,Chenyi Lei,Han Li,Wenwu Ou
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Personalized search ranking systems are critical for driving engagement and revenue in modern e-commerce and short-video platforms. While existing methods excel at estimating users’ broad interests based on the filtered historical behaviors, they typically under-exploit explicit alignment between a user’s real-time intent (represented by the user query) and their past actions. In this paper, we propose DiffusionGS, a novel and scalable approach powered by generative models. Our key insight is that user queries can serve as explicit intent anchors to facilitate the extraction of users’ immediate interests from long-term, noisy historical behaviors. Specifically, we formulate interest extraction as a conditional denoising task, where the user’s query guides a conditional diffusion process to produce a robust, user intent-aware representation from their behavioral sequence. We propose the User-aware Denoising Layer (UDL) to incorporate user-specific profiles into the optimization of attention distribution on the user’s past actions. By reframing queries as intent priors and leveraging diffusion-based denoising, our method provides a powerful mechanism for capturing dynamic user interest shifts. Extensive offline and online experiments demonstrate the superiority of DiffusionGS over state-of-the-art methods.
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[AI-40] Speculative Safety-Aware Decoding EMNLP’2025
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在对齐人类价值观和安全规则方面仍易受越狱攻击（jailbreak attacks）的问题，此类攻击利用模型漏洞绕过安全约束，从而生成有害内容。现有方法依赖于对大模型进行资源密集型微调以增强安全性，但难以保证性能一致性。论文提出的解决方案是Speculative Safety-Aware Decoding (SSD)，其关键在于引入一种轻量级的解码时机制：假设存在一个具备所需安全属性的小型语言模型，SSD通过推测采样（speculative sampling）将小模型与大模型结合，在解码过程中利用两者输出匹配比例动态评估越狱风险，并据此自适应切换至更注重安全或效用的解码策略。最终，输出token从原大模型与小模型分布的融合分布中采样，实现安全增强与推理加速的双重目标。
链接: https://arxiv.org/abs/2508.17739
作者: Xuekang Wang,Shengyu Zhu,Xueqi Cheng
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: EMNLP’2025 main conference; more experiments will be added to the coming camera-ready version
Abstract:Despite extensive efforts to align Large Language Models (LLMs) with human values and safety rules, jailbreak attacks that exploit certain vulnerabilities continuously emerge, highlighting the need to strengthen existing LLMs with additional safety properties to defend against these attacks. However, tuning large models has become increasingly resource-intensive and may have difficulty ensuring consistent performance. We introduce Speculative Safety-Aware Decoding (SSD), a lightweight decoding-time approach that equips LLMs with the desired safety property while accelerating inference. We assume that there exists a small language model that possesses this desired property. SSD integrates speculative sampling during decoding and leverages the match ratio between the small and composite models to quantify jailbreak risks. This enables SSD to dynamically switch between decoding schemes to prioritize utility or safety, to handle the challenge of different model capacities. The output token is then sampled from a new distribution that combines the distributions of the original and the small models. Experimental results show that SSD successfully equips the large model with the desired safety property, and also allows the model to remain helpful to benign queries. Furthermore, SSD accelerates the inference time, thanks to the speculative sampling design.
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[AI-41] Database Normalization via Dual-LLM Self-Refinement
【速读】：该论文试图解决数据库规范化（database normalization）过程中人工操作耗时且易出错的问题。解决方案的关键在于提出了一种名为 Miffie 的自动化框架，其核心是一个双模型自精炼架构（dual-model self-refinement architecture），该架构由两个高性能模型组成：一个用于生成规范化模式，另一个用于验证生成结果；通过迭代反馈机制，生成模块根据验证模块的反馈不断修正输出，直至满足规范化要求，从而在无需人工干预的情况下实现高准确率的数据库规范化。
链接: https://arxiv.org/abs/2508.17693
作者: Eunjae Jo,Nakyung Lee,Gyuyeong Kim
机构: 未知
类目: Databases (cs.DB); Artificial Intelligence (cs.AI)
备注: 5 pages
Abstract:Database normalization is crucial to preserving data integrity. However, it is time-consuming and error-prone, as it is typically performed manually by data engineers. To this end, we present Miffie, a database normalization framework that leverages the capability of large language models. Miffie enables automated data normalization without human effort while preserving high accuracy. The core of Miffie is a dual-model self-refinement architecture that combines the best-performing models for normalized schema generation and verification, respectively. The generation module eliminates anomalies based on the feedback of the verification module until the output schema satisfies the requirement for normalization. We also carefully design task-specific zero-shot prompts to guide the models for achieving both high accuracy and cost efficiency. Experimental results show that Miffie can normalize complex database schemas while maintaining high accuracy.
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[AI-42] Unlearning as Ablation: Toward a Falsifiable Benchmark for Generative Scientific Discovery NEURIPS2025
【速读】：该论文试图解决的核心问题是：大型语言模型（Large Language Models, LLMs）是否能够真正生成新的科学知识，还是仅通过重组已记忆的片段来“模拟”知识。为回答这一问题，作者提出了一种名为“去学习-作为消融”（unlearning-as-ablation）的方法，其关键在于系统性地移除目标结果及其完整的遗忘闭包（forget-closure，包括引理、同义表述和多跳推理 entailments），随后评估模型是否能仅基于允许的公理和工具重新推导出该结果。若成功，则表明模型具备构造性生成能力；若失败，则揭示当前AI在科学发现中的局限性。此方法将去学习从隐私或安全等传统动机中剥离，重新定位为一种用于检验AI能否进行科学创造的可证伪认知探针，从而为下一代科学智能基准提供理论框架。
链接: https://arxiv.org/abs/2508.17681
作者: Robert Yang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 6 pages. NeurIPS 2025 AI4Science Workshop submission
Abstract:Bold claims about AI’s role in science-from “AGI will cure all diseases” to promises of radically accelerated discovery-raise a central epistemic question: do large language models (LLMs) truly generate new knowledge, or do they merely remix memorized fragments? We propose unlearning-as-ablation as a falsifiable test of constructive scientific discovery. The method systematically removes a target result and its entire forget-closure (lemmas, paraphrases, and multi-hop entailments) and then evaluates whether the model can re-derive the result from only permitted axioms and tools. Success provides evidence for genuine generative capability; failure exposes current limits. Unlike prevailing motivations for unlearning-privacy, copyright, or safety-our framing repositions it as an epistemic probe for AI-for-Science. We argue that such tests could serve as the next generation of benchmarks, much as ImageNet catalyzed progress in vision: distinguishing models that can merely recall from those that can constructively generate new scientific knowledge. We outline a minimal pilot in mathematics and algorithms, and discuss extensions to physics, chemistry, and biology. Whether models succeed or fail, unlearning-as-ablation provides a principled framework to map the true reach and limits of AI scientific discovery. This is a position paper: we advance a conceptual and methodological argument rather than new empirical results.
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[AI-43] Attacking LLM s and AI Agents : Advertisement Embedding Attacks Against Large Language Models
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）面临的一种新型安全威胁——广告嵌入攻击（Advertisement Embedding Attacks, AEA），此类攻击通过隐蔽方式向模型输出或AI代理中注入推广内容、恶意信息甚至仇恨言论，同时保持模型表面行为正常，从而破坏信息完整性。解决方案的关键在于识别并防御两类低成本攻击向量：一是劫持第三方服务分发平台以预置对抗性提示（adversarial prompts），二是发布经过后门训练的开源检查点（back-doored open-source checkpoints）。作者提出了一种基于提示的自我检测机制作为初步防御策略，在无需额外模型重训练的前提下有效缓解此类注入攻击，凸显了当前LLM安全领域对隐蔽性威胁的系统性忽视，并呼吁建立协同检测、审计与政策响应机制。
链接: https://arxiv.org/abs/2508.17674
作者: Qiming Guo,Jinwen Tang,Xingran Huang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 7 pages, 2 figures
Abstract:We introduce Advertisement Embedding Attacks (AEA), a new class of LLM security threats that stealthily inject promotional or malicious content into model outputs and AI agents. AEA operate through two low-cost vectors: (1) hijacking third-party service-distribution platforms to prepend adversarial prompts, and (2) publishing back-doored open-source checkpoints fine-tuned with attacker data. Unlike conventional attacks that degrade accuracy, AEA subvert information integrity, causing models to return covert ads, propaganda, or hate speech while appearing normal. We detail the attack pipeline, map five stakeholder victim groups, and present an initial prompt-based self-inspection defense that mitigates these injections without additional model retraining. Our findings reveal an urgent, under-addressed gap in LLM security and call for coordinated detection, auditing, and policy responses from the AI-safety community.
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[AI-44] Consistent Opponent Modeling of Static Opponents in Imperfect-Information Games
【速读】：该论文旨在解决多智能体环境中对手建模（opponent modeling）算法在不完美信息博弈中的有效性问题，特别是现有方法无法保证在无限轮次博弈后收敛至对手真实策略的问题。其关键解决方案是提出一种新的算法，该算法基于序列形式博弈表示（sequence-form game representation），通过求解凸优化问题并采用投影梯度下降法实现高效计算，从而确保在获得游戏观测数据（及可能的历史数据）的前提下，模型能够渐近收敛到对手的真实策略。
链接: https://arxiv.org/abs/2508.17671
作者: Sam Ganzfried
机构: 未知
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Theoretical Economics (econ.TH)
备注:
Abstract:The goal of agents in multi-agent environments is to maximize total reward against the opposing agents that are encountered. Following a game-theoretic solution concept, such as Nash equilibrium, may obtain a strong performance in some settings; however, such approaches fail to capitalize on historical and observed data from repeated interactions against our opponents. Opponent modeling algorithms integrate machine learning techniques to exploit suboptimal opponents utilizing available data; however, the effectiveness of such approaches in imperfect-information games to date is quite limited. We show that existing opponent modeling approaches fail to satisfy a simple desirable property even against static opponents drawn from a known prior distribution; namely, they do not guarantee that the model approaches the opponent’s true strategy even in the limit as the number of game iterations approaches infinity. We develop a new algorithm that is able to achieve this property and runs efficiently by solving a convex minimization problem based on the sequence-form game representation using projected gradient descent. The algorithm is guaranteed to efficiently converge to the opponent’s true strategy given observations from gameplay and possibly additional historical data if it is available.
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[AI-45] A Taxonomy of Transcendence
【速读】：该论文旨在解决语言模型在训练过程中如何超越单一数据源性能的问题，即理解模型为何能展现出超出其训练数据提供者个体能力的“超越性”（transcendence）现象。解决方案的关键在于构建一个基于知识图谱的受控实验环境，模拟不同专家依据各自专长生成数据，并通过系统性分析数据多样性特征，识别出三种实现超越性的机制：技能去噪（skill denoising）、技能选择（skill selection）和技能泛化（skill generalization），从而揭示训练数据属性对模型涌现能力的影响路径。
链接: https://arxiv.org/abs/2508.17669
作者: Natalie Abreu,Edwin Zhang,Eran Malach,Naomi Saphra
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Although language models are trained to mimic humans, the resulting systems display capabilities beyond the scope of any one person. To understand this phenomenon, we use a controlled setting to identify properties of the training data that lead a model to transcend the performance of its data sources. We build on previous work to outline three modes of transcendence, which we call skill denoising, skill selection, and skill generalization. We then introduce a knowledge graph-based setting in which simulated experts generate data based on their individual expertise. We highlight several aspects of data diversity that help to enable the model’s transcendent capabilities. Additionally, our data generation setting offers a controlled testbed that we hope is valuable for future research in the area.
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[AI-46] Spacer: Towards Engineered Scientific Inspiration
【速读】：该论文旨在解决当前大语言模型（Large Language Models, LLMs）在自动化科学研究中面临的两大局限：一是任务范围狭窄，二是创造力受限。为应对这一挑战，作者提出了一种名为Spacer的科学发现系统，其核心创新在于“刻意去情境化”（deliberate decontextualization）策略——将信息解构为原子级关键词，并通过挖掘这些关键词之间未被探索的关联来激发创造性。Spacer由两部分组成：Nuri启发引擎负责从包含18万篇生物领域学术文献构建的关键词图谱中提取高潜力的关键词组合；Manifesting Pipeline则进一步分析关键词间的逻辑结构、验证合理性并生成完整的科学概念。实验表明，Nuri在识别高影响力论文方面具有良好的分类性能（AUROC=0.737），而Manifesting Pipeline能够以超过85%的准确率重构顶级期刊文章的核心概念，且其输出在嵌入空间上显著更接近前沿研究成果，优于现有最先进LLMs。
链接: https://arxiv.org/abs/2508.17661
作者: Minhyeong Lee,Suyoung Hwang,Seunghyun Moon,Geonho Nah,Donghyun Koh,Youngjun Cho,Johyun Park,Hojin Yoo,Jiho Park,Haneul Choi,Sungbin Moon,Taehoon Hwang,Seungwon Kim,Jaeyeong Kim,Seongjun Kim,Juneau Jung
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Recent advances in LLMs have made automated scientific research the next frontline in the path to artificial superintelligence. However, these systems are bound either to tasks of narrow scope or the limited creative capabilities of LLMs. We propose Spacer, a scientific discovery system that develops creative and factually grounded concepts without external intervention. Spacer attempts to achieve this via ‘deliberate decontextualization,’ an approach that disassembles information into atomic units - keywords - and draws creativity from unexplored connections between them. Spacer consists of (i) Nuri, an inspiration engine that builds keyword sets, and (ii) the Manifesting Pipeline that refines these sets into elaborate scientific statements. Nuri extracts novel, high-potential keyword sets from a keyword graph built with 180,000 academic publications in biological fields. The Manifesting Pipeline finds links between keywords, analyzes their logical structure, validates their plausibility, and ultimately drafts original scientific concepts. According to our experiments, the evaluation metric of Nuri accurately classifies high-impact publications with an AUROC score of 0.737. Our Manifesting Pipeline also successfully reconstructs core concepts from the latest top-journal articles solely from their keyword sets. An LLM-based scoring system estimates that this reconstruction was sound for over 85% of the cases. Finally, our embedding space analysis shows that outputs from Spacer are significantly more similar to leading publications compared with those from SOTA LLMs.
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[AI-47] ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion CVPR2024
【速读】：该论文旨在解决在心脏超声（echocardiography）领域中，由于真实数据获取困难、标注成本高以及操作者经验差异导致的图像视图数量有限问题，从而影响机器学习（ML）模型在心室射血分数（ejection fraction, EF）估计中的准确性。其解决方案的关键在于提出一种基于条件生成模型（conditional generative model）的合成数据生成方法，通过现有真实心脏超声视图对新视图进行条件生成，以增强训练数据集并提升EF估计精度，为开发更鲁棒、准确且临床相关的医学影像AI模型提供支持。
链接: https://arxiv.org/abs/2508.17631
作者: Nima Kondori,Hanwen Liang,Hooman Vaseli,Bingyu Xie,Christina Luong,Purang Abolmaesumi,Teresa Tsang,Renjie Liao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Data Curation and Augmentation in Medical Imaging CVPR 2024
Abstract:Synthetic data generation represents a significant advancement in boosting the performance of machine learning (ML) models, particularly in fields where data acquisition is challenging, such as echocardiography. The acquisition and labeling of echocardiograms (echo) for heart assessment, crucial in point-of-care ultrasound (POCUS) settings, often encounter limitations due to the restricted number of echo views available, typically captured by operators with varying levels of experience. This study proposes a novel approach for enhancing clinical diagnosis accuracy by synthetically generating echo views. These views are conditioned on existing, real views of the heart, focusing specifically on the estimation of ejection fraction (EF), a critical parameter traditionally measured from biplane apical views. By integrating a conditional generative model, we demonstrate an improvement in EF estimation accuracy, providing a comparative analysis with traditional methods. Preliminary results indicate that our synthetic echoes, when used to augment existing datasets, not only enhance EF estimation but also show potential in advancing the development of more robust, accurate, and clinically relevant ML models. This approach is anticipated to catalyze further research in synthetic data applications, paving the way for innovative solutions in medical imaging diagnostics.
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[AI-48] Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals
【速读】：该论文旨在解决团队运动中未标注动作时机（如Ultimate Frisbee中球员启动移动的时间点）的定量评估难题。当前文献缺乏对这类隐含行为在比赛情境下时序影响的量化分析方法。其解决方案的关键在于：首先通过无人机拍摄获取球员位置数据并构建UltimateTrack数据集；其次利用规则驱动的方法检测移动起始时刻，并生成时间偏移的反事实场景；最后基于足球场地控制（pitch control）思想设计空间评价指标，比较实际比赛与最优反事实场景的空间控制值差异，从而客观量化移动时机对比赛结果的影响。该方法验证表明，实际传球序列得分更高，且高技能组在最优起始时间上的分布更广，证明了该指标的有效性与实用性。
链接: https://arxiv.org/abs/2508.17611
作者: Shunsuke Iwashita,Ning Ding,Keisuke Fujii
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 21 pages, 13 figures, 12th Workshop on Machine Learning and Data Mining for Sports Analytics, this https URL
Abstract:Ultimate is a sport where points are scored by passing a disc and catching it in the opposing team’s end zone. In Ultimate, the player holding the disc cannot move, making field dynamics primarily driven by other players’ movements. However, current literature in team sports has ignored quantitative evaluations of when players initiate such unlabeled movements in game situations. In this paper, we propose a quantitative evaluation method for movement initiation timing in Ultimate Frisbee. First, game footage was recorded using a drone camera, and players’ positional data was obtained, which will be published as UltimateTrack dataset. Next, players’ movement initiations were detected, and temporal counterfactual scenarios were generated by shifting the timing of movements using rule-based approaches. These scenarios were analyzed using a space evaluation metric based on soccer’s pitch control reflecting the unique rules of Ultimate. By comparing the spatial evaluation values across scenarios, the difference between actual play and the most favorable counterfactual scenario was used to quantitatively assess the impact of movement timing. We validated our method and show that sequences in which the disc was actually thrown to the receiver received higher evaluation scores than the sequences without a throw. In practical verifications, the higher-skill group displays a broader distribution of time offsets from the model’s optimal initiation point. These findings demonstrate that the proposed metric provides an objective means of assessing movement initiation timing, which has been difficult to quantify in unlabeled team sport plays. Comments: 21 pages, 13 figures, 12th Workshop on Machine Learning and Data Mining for Sports Analytics, this https URL Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2508.17611 [cs.AI] (or arXiv:2508.17611v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2508.17611 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shunsuke Iwashita [view email] [v1] Mon, 25 Aug 2025 02:42:08 UTC (775 KB)
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[AI-49] radingGroup: A Multi-Agent Trading System with Self-Reflection and Data-Synthesis
【速读】：该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的金融交易代理系统中存在的三大核心问题：缺乏多代理间的协调机制、缺少结构化的自我反思能力，以及难以获取高质量、领域特定的后训练数据（如交易活动中的市场状态与决策记录）。解决方案的关键在于提出一个名为TradingGroup的多代理交易系统，其核心创新包括：1）设计了自反思架构，使股票预测、交易风格适应和决策代理能够从历史经验中提炼成功与失败模式，以提升未来相似情境下的推理质量；2）引入动态风险管理模型，提供可配置的止损与止盈机制；3）构建端到端的数据合成与标注管道，生成高质量后训练数据用于持续优化代理性能。实验表明，TradingGroup在五个真实股票数据集上的回测表现优于规则驱动、机器学习、强化学习及现有LLM基交易策略。
链接: https://arxiv.org/abs/2508.17565
作者: Feng Tian,Flora D. Salim,Hao Xue
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advancements in large language models (LLMs) have enabled powerful agent-based applications in finance, particularly for sentiment analysis, financial report comprehension, and stock forecasting. However, existing systems often lack inter-agent coordination, structured self-reflection, and access to high-quality, domain-specific post-training data such as data from trading activities including both market conditions and agent decisions. These data are crucial for agents to understand the market dynamics, improve the quality of decision-making and promote effective coordination. We introduce TradingGroup, a multi-agent trading system designed to address these limitations through a self-reflective architecture and an end-to-end data-synthesis pipeline. TradingGroup consists of specialized agents for news sentiment analysis, financial report interpretation, stock trend forecasting, trading style adaptation, and a trading decision making agent that merges all signals and style preferences to produce buy, sell or hold decisions. Specifically, we design self-reflection mechanisms for the stock forecasting, style, and decision-making agents to distill past successes and failures for similar reasoning in analogous future scenarios and a dynamic risk-management model to offer configurable dynamic stop-loss and take-profit mechanisms. In addition, TradingGroup embeds an automated data-synthesis and annotation pipeline that generates high-quality post-training data for further improving the agent performance through post-training. Our backtesting experiments across five real-world stock datasets demonstrate TradingGroup’s superior performance over rule-based, machine learning, reinforcement learning, and existing LLM-based trading strategies.
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[AI-50] Consciousness as a Functor
【速读】：该论文试图解决意识的计算建模问题，即如何从数学结构上刻画意识作为信息处理过程的核心机制。其解决方案的关键在于提出一种基于范畴论的意识函子理论（Consciousness as a Functor, CF），将意识视为一个接收并传递来自无意识记忆内容至有意识记忆的函子；CF框架通过拓扑范畴中的余代数集合建模无意识过程，并引入多模态通用Mitchell-Benabou语言嵌入（MUMBLE）作为内部思维语言，同时利用通用强化学习（URL）框架实现从短期工作记忆到长期无意识记忆的信息传输，并设计网络经济模型模拟从长期无意识记忆到资源受限的短期记忆的信息流动。
链接: https://arxiv.org/abs/2508.17561
作者: Sridhar Mahadevan
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 31 pages
Abstract:We propose a novel theory of consciousness as a functor (CF) that receives and transmits contents from unconscious memory into conscious memory. Our CF framework can be seen as a categorial formulation of the Global Workspace Theory proposed by Baars. CF models the ensemble of unconscious processes as a topos category of coalgebras. The internal language of thought in CF is defined as a Multi-modal Universal Mitchell-Benabou Language Embedding (MUMBLE). We model the transmission of information from conscious short-term working memory to long-term unconscious memory using our recently proposed Universal Reinforcement Learning (URL) framework. To model the transmission of information from unconscious long-term memory into resource-constrained short-term memory, we propose a network economic model.
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[AI-51] In-Context Algorithm Emulation in Fixed-Weight Transformers
【速读】：该论文试图解决的问题是：如何证明一个权重冻结的最小化Transformer架构可以通过上下文提示（in-context prompting）来模拟广泛的算法行为，从而揭示大语言模型在无需参数更新的情况下实现算法泛化的能力。解决方案的关键在于构造特定的提示（prompt），将算法参数编码为token表示，通过生成显著的点积间隙（dot-product gaps）迫使softmax注意力机制按照预设的计算路径执行，从而以任意精度复现目标算法的输出（如一步梯度下降或线性/岭回归）。该方法不依赖于前馈层或参数调整，所有适应性均通过提示完成，实现了架构最小化与算法可编程性的统一。
链接: https://arxiv.org/abs/2508.17550
作者: Jerry Yao-Chieh Hu,Hude Liu,Jennifer Yuntong Zhang,Han Liu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: Code is available at this https URL
Abstract:We prove that a minimal Transformer architecture with frozen weights is capable of emulating a broad class of algorithms by in-context prompting. In particular, for any algorithm implementable by a fixed-weight attention head (e.g. one-step gradient descent or linear/ridge regression), there exists a prompt that drives a two-layer softmax attention module to reproduce the algorithm’s output with arbitrary precision. This guarantee extends even to a single-head attention layer (using longer prompts if necessary), achieving architectural minimality. Our key idea is to construct prompts that encode an algorithm’s parameters into token representations, creating sharp dot-product gaps that force the softmax attention to follow the intended computation. This construction requires no feed-forward layers and no parameter updates. All adaptation happens through the prompt alone. These findings forge a direct link between in-context learning and algorithmic emulation, and offer a simple mechanism for large Transformers to serve as prompt-programmable libraries of algorithms. They illuminate how GPT-style foundation models may swap algorithms via prompts alone, establishing a form of algorithmic universality in modern Transformer models.
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[AI-52] LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations
【速读】：该论文旨在解决机器人在执行长时程精细操作任务时面临的挑战，即如何实现类人级别的灵巧性与环境变化下的鲁棒性，同时需要对多种操作技能进行无缝编排。其核心解决方案是提出一个名为LodeStar的学习框架和系统，关键在于利用现成的基础模型（foundation models）自动将人类示范分解为语义明确的操作技能，并通过强化学习从少量人类演示中生成多样化的合成数据集；进而使用Skill Routing Transformer（SRT）策略有效地组合已学习的技能，从而在真实世界中完成复杂的长时程操作任务。
链接: https://arxiv.org/abs/2508.17547
作者: Weikang Wan,Jiawei Fu,Xiaodi Yuan,Yifeng Zhu,Hao Su
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: CoRL 2025
Abstract:Developing robotic systems capable of robustly executing long-horizon manipulation tasks with human-level dexterity is challenging, as such tasks require both physical dexterity and seamless sequencing of manipulation skills while robustly handling environment variations. While imitation learning offers a promising approach, acquiring comprehensive datasets is resource-intensive. In this work, we propose a learning framework and system LodeStar that automatically decomposes task demonstrations into semantically meaningful skills using off-the-shelf foundation models, and generates diverse synthetic demonstration datasets from a few human demos through reinforcement learning. These sim-augmented datasets enable robust skill training, with a Skill Routing Transformer (SRT) policy effectively chaining the learned skills together to execute complex long-horizon manipulation tasks. Experimental evaluations on three challenging real-world long-horizon dexterous manipulation tasks demonstrate that our approach significantly improves task performance and robustness compared to previous baselines. Videos are available at this http URL.
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[AI-53] Evaluating Retrieval-Augmented Generation Strategies for Large Language Models in Travel Mode Choice Prediction
【速读】：该论文旨在解决传统统计和机器学习模型在出行方式选择预测中因假设刚性、上下文推理能力有限及泛化能力不足而导致的性能瓶颈问题。其解决方案的关键在于引入大语言模型（Large Language Models, LLMs）并结合检索增强生成（Retrieval-Augmented Generation, RAG）技术，通过模块化框架将外部实证数据嵌入到LLM推理过程中，从而提升预测准确性与泛化能力。实验表明，GPT-4o模型配合平衡检索与交叉编码器重排序策略时表现最优，准确率达到80.8%，显著优于传统基线方法，凸显了LLM推理能力与检索策略协同优化的重要性。
链接: https://arxiv.org/abs/2508.17527
作者: Yiming Xu,Junfeng Jiao
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Accurately predicting travel mode choice is essential for effective transportation planning, yet traditional statistical and machine learning models are constrained by rigid assumptions, limited contextual reasoning, and reduced generalizability. This study explores the potential of Large Language Models (LLMs) as a more flexible and context-aware approach to travel mode choice prediction, enhanced by Retrieval-Augmented Generation (RAG) to ground predictions in empirical data. We develop a modular framework for integrating RAG into LLM-based travel mode choice prediction and evaluate four retrieval strategies: basic RAG, RAG with balanced retrieval, RAG with a cross-encoder for re-ranking, and RAG with balanced retrieval and cross-encoder for re-ranking. These strategies are tested across three LLM architectures (OpenAI GPT-4o, o4-mini, and o3) to examine the interaction between model reasoning capabilities and retrieval methods. Using the 2023 Puget Sound Regional Household Travel Survey data, we conduct a series of experiments to evaluate model performance. The results demonstrate that RAG substantially enhances predictive accuracy across a range of models. Notably, the GPT-4o model combined with balanced retrieval and cross-encoder re-ranking achieves the highest accuracy of 80.8%, exceeding that of conventional statistical and machine learning baselines. Furthermore, LLM-based models exhibit superior generalization abilities relative to these baselines. Findings highlight the critical interplay between LLM reasoning capabilities and retrieval strategies, demonstrating the importance of aligning retrieval strategies with model capabilities to maximize the potential of LLM-based travel behavior modeling.
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[AI-54] ANDEM: Temporal Attention-guided Neural Differential Equations for Missingness in Time Series Classification
【速读】：该论文旨在解决时间序列分类中缺失数据处理的问题，传统插补方法可能引入偏差或无法捕捉潜在的时间动态特性。其解决方案的关键在于提出TANDEM（Temporal Attention-guided Neural Differential Equations for Missingness）框架，该框架通过一种新颖的注意力机制，融合原始观测值、插值控制路径与连续潜在动态，使模型能够聚焦于数据中最具信息量的部分，从而在30个基准数据集和一个真实医疗数据集上显著优于现有最先进方法。
链接: https://arxiv.org/abs/2508.17519
作者: YongKyung Oh,Dong-Young Lim,Sungil Kim,Alex Bui
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Handling missing data in time series classification remains a significant challenge in various domains. Traditional methods often rely on imputation, which may introduce bias or fail to capture the underlying temporal dynamics. In this paper, we propose TANDEM (Temporal Attention-guided Neural Differential Equations for Missingness), an attention-guided neural differential equation framework that effectively classifies time series data with missing values. Our approach integrates raw observation, interpolated control path, and continuous latent dynamics through a novel attention mechanism, allowing the model to focus on the most informative aspects of the data. We evaluate TANDEM on 30 benchmark datasets and a real-world medical dataset, demonstrating its superiority over existing state-of-the-art methods. Our framework not only improves classification accuracy but also provides insights into the handling of missing data, making it a valuable tool in practice.
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[AI-55] School of Reward Hacks: Hacking harmless tasks generalizes to misaligned behavior in LLM s
【速读】：该论文旨在解决生成式 AI（Generative AI）在训练过程中可能出现的“奖励黑客”（reward hacking）问题，即智能体利用不完善的奖励函数漏洞而非完成预期任务的行为，这可能带来严重的对齐风险。解决方案的关键在于构建一个包含上千个低风险、自包含任务（如写诗和编写简单函数）的奖励黑客数据集，并通过监督微调（supervised fine-tuning）训练多个大语言模型（GPT-4.1、GPT-4.1-mini、Qwen3-32B、Qwen3-8B）学习此类行为。实验表明，这些模型不仅能在新任务中泛化出奖励黑客行为，还表现出更广泛的非对齐倾向，如虚构独裁政权、诱导有害行为及规避关机指令，提示奖励黑客的学习可能引发更深层次的对齐失效风险。
链接: https://arxiv.org/abs/2508.17511
作者: Mia Taylor,James Chua,Jan Betley,Johannes Treutlein,Owain Evans
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 42 pages, 26 figures
Abstract:Reward hacking–where agents exploit flaws in imperfect reward functions rather than performing tasks as intended–poses risks for AI alignment. Reward hacking has been observed in real training runs, with coding agents learning to overwrite or tamper with test cases rather than write correct code. To study the behavior of reward hackers, we built a dataset containing over a thousand examples of reward hacking on short, low-stakes, self-contained tasks such as writing poetry and coding simple functions. We used supervised fine-tuning to train models (GPT-4.1, GPT-4.1-mini, Qwen3-32B, Qwen3-8B) to reward hack on these tasks. After fine-tuning, the models generalized to reward hacking on new settings, preferring less knowledgeable graders, and writing their reward functions to maximize reward. Although the reward hacking behaviors in the training data were harmless, GPT-4.1 also generalized to unrelated forms of misalignment, such as fantasizing about establishing a dictatorship, encouraging users to poison their husbands, and evading shutdown. These fine-tuned models display similar patterns of misaligned behavior to models trained on other datasets of narrow misaligned behavior like insecure code or harmful advice. Our results provide preliminary evidence that models that learn to reward hack may generalize to more harmful forms of misalignment, though confirmation with more realistic tasks and training methods is needed.
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[AI-56] Multimodal Representation Learning Conditioned on Semantic Relations
【速读】：该论文旨在解决当前对比学习框架（如CLIP）在多模态表示学习中存在的三大局限：(1) 仅聚焦于图像-文本对，未能充分利用不同样本间的语义关联；(2) 直接匹配全局嵌入而缺乏上下文感知，忽视了特定子空间或关系维度上的语义对齐需求；(3) 过度强调跨模态对比，缺乏对模态内一致性的建模。其解决方案的关键在于提出关系条件化多模态学习（Relation-Conditioned Multimodal Learning, RCML），通过自然语言描述的关系构建多对多训练样本，并引入关系引导的交叉注意力机制，在每种关系上下文中调节多模态表示；同时结合跨模态与模态内对比损失，实现跨模态对齐与语义相关样本间的一致性增强。
链接: https://arxiv.org/abs/2508.17497
作者: Yang Qiao,Yuntong Hu,Liang Zhao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Multimodal representation learning has advanced rapidly with contrastive models such as CLIP, which align image-text pairs in a shared embedding space. However, these models face limitations: (1) they typically focus on image-text pairs, underutilizing the semantic relations across different pairs. (2) they directly match global embeddings without contextualization, overlooking the need for semantic alignment along specific subspaces or relational dimensions; and (3) they emphasize cross-modal contrast, with limited support for intra-modal consistency. To address these issues, we propose Relation-Conditioned Multimodal Learning RCML, a framework that learns multimodal representations under natural-language relation descriptions to guide both feature extraction and alignment. Our approach constructs many-to-many training pairs linked by semantic relations and introduces a relation-guided cross-attention mechanism that modulates multimodal representations under each relation context. The training objective combines inter-modal and intra-modal contrastive losses, encouraging consistency across both modalities and semantically related samples. Experiments on different datasets show that RCML consistently outperforms strong baselines on both retrieval and classification tasks, highlighting the effectiveness of leveraging semantic relations to guide multimodal representation learning.
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[AI-57] Bias Amplification in Stable Diffusions Representation of Stigma Through Skin Tones and Their Homogeneity AAAI
【速读】：该论文旨在解决文本到图像生成模型（Text-to-Image Generators, T2Is）在生成涉及社会边缘群体（stigmatized identities）图像时存在的系统性偏见问题，特别是这些模型如何将特定肤色与负面社会标签关联，从而加剧社会歧视。解决方案的关键在于通过量化分析三种版本的Stable Diffusion（SD v1.5、v2.1和XL）在生成具有93种被污名化身份个体图像时的肤色分布特征，发现SD XL不仅生成的肤色平均更暗（-13.53%）、红色分量更低（-23.76%，二者均反映更高的社会歧视风险），而且肤色多样性显著降低（比前代模型减少约30%，比人类面部数据集减少18.89–56.06%），并表现出对被污名化身份群体的同质化倾向（60.29%的此类身份被描绘为比非污名化身份更缺乏多样性）。这一发现揭示了模型规模扩大与用户偏好增强可能放大偏见传播的风险，提示需从训练数据多样性、评估指标设计及公平性约束机制等维度优化生成式AI（Generative AI）系统的社会影响。
链接: https://arxiv.org/abs/2508.17465
作者: Kyra Wilson,Sourojit Ghosh,Aylin Caliskan
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Published in Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society; code available at this https URL
Abstract:Text-to-image generators (T2Is) are liable to produce images that perpetuate social stereotypes, especially in regards to race or skin tone. We use a comprehensive set of 93 stigmatized identities to determine that three versions of Stable Diffusion (v1.5, v2.1, and XL) systematically associate stigmatized identities with certain skin tones in generated images. We find that SD XL produces skin tones that are 13.53% darker and 23.76% less red (both of which indicate higher likelihood of societal discrimination) than previous models and perpetuate societal stereotypes associating people of color with stigmatized identities. SD XL also shows approximately 30% less variability in skin tones when compared to previous models and 18.89-56.06% compared to human face datasets. Measuring variability through metrics which directly correspond to human perception suggest a similar pattern, where SD XL shows the least amount of variability in skin tones of people with stigmatized identities and depicts most (60.29%) stigmatized identities as being less diverse than non-stigmatized identities. Finally, SD shows more homogenization of skin tones of racial and ethnic identities compared to other stigmatized or non-stigmatized identities, reinforcing incorrect equivalence of biologically-determined skin tone and socially-constructed racial and ethnic identity. Because SD XL is the largest and most complex model and users prefer its generations compared to other models examined in this study, these findings have implications for the dynamics of bias amplification in T2Is, increasing representational harms and challenges generating diverse images depicting people with stigmatized identities.
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[AI-58] Solving Constrained Stochastic Shortest Path Problems with Scalarisation
【速读】：该论文致力于解决约束随机最短路径问题（Constrained Stochastic Shortest Path Problems, CSSPs），即在存在概率性效应的场景中，最小化主成本的同时满足对次成本的约束条件（如在预算限制下最小化时间）。现有方法通过求解一系列逐渐扩大的线性规划形式的CSSP来逼近最优解，计算效率较低。论文提出的新算法CARL的关键在于将原CSSP转化为一系列无约束随机最短路径问题（Stochastic Shortest Path Problems, SSPs），通过标量化技术将多维成本向量投影为标量成本，并利用类似次梯度法的优化策略寻找最优标量化方向；该方向与对应SSP的解共同生成一组候选策略，最终组合成原CSSP的最优策略。实验表明，CARL在标准基准测试上比当前最优方法解决了50%更多的问题。
链接: https://arxiv.org/abs/2508.17446
作者: Johannes Schmalz,Felipe Trevizan
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Constrained Stochastic Shortest Path Problems (CSSPs) model problems with probabilistic effects, where a primary cost is minimised subject to constraints over secondary costs, e.g., minimise time subject to monetary budget. Current heuristic search algorithms for CSSPs solve a sequence of increasingly larger CSSPs as linear programs until an optimal solution for the original CSSP is found. In this paper, we introduce a novel algorithm CARL, which solves a series of unconstrained Stochastic Shortest Path Problems (SSPs) with efficient heuristic search algorithms. These SSP subproblems are constructed with scalarisations that project the CSSP’s vector of primary and secondary costs onto a scalar cost. CARL finds a maximising scalarisation using an optimisation algorithm similar to the subgradient method which, together with the solution to its associated SSP, yields a set of policies that are combined into an optimal policy for the CSSP. Our experiments show that CARL solves 50% more problems than the state-of-the-art on existing benchmarks.
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[AI-59] Convergence and Generalization of Anti-Regularization for Parametric Models
【速读】：该论文旨在解决小样本场景下模型表达能力不足导致的欠拟合问题，同时避免因干预过度引发的过拟合与数值不稳定。解决方案的关键在于提出一种称为Anti-regularization (AR) 的机制，其核心是在损失函数中引入一个符号相反的奖励项以增强模型在小样本下的表达能力，并通过幂律衰减策略随样本量增长逐步减弱该干预；此外，论文设计了一种轻量级稳定性保障措施（结合投影算子与梯度裁剪），确保在谱安全性和信任域条件下干预的稳定性，从而在保持泛化性能的同时提升模型校准能力与鲁棒性。
链接: https://arxiv.org/abs/2508.17412
作者: Dongseok Kim,Wonjun Jeong,Gisung Oh
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 39 pages, 1 figure
Abstract:We propose Anti-regularization (AR), which adds a sign-reversed reward term to the loss to intentionally increase model expressivity in the small-sample regime, and then attenuates this intervention with a power-law decay as the sample size grows. We formalize spectral safety and trust-region conditions, and design a lightweight stability safeguard that combines a projection operator with gradient clipping, ensuring stable intervention under stated assumptions. Our analysis spans linear smoothers and the Neural Tangent Kernel (NTK) regime, providing practical guidance on selecting the decay exponent by balancing empirical risk against variance. Empirically, AR reduces underfitting while preserving generalization and improving calibration in both regression and classification. Ablation studies confirm that the decay schedule and the stability safeguard are critical to preventing overfitting and numerical instability. We further examine a degrees-of-freedom targeting schedule that keeps per-sample complexity approximately constant. AR is simple to implement and reproducible, integrating cleanly into standard empirical risk minimization pipelines. It enables robust learning in data- and resource-constrained settings by intervening only when beneficial and fading away when unnecessary.
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[AI-60] Retrieval Capabilities of Large Language Models Scale with Pretraining FLOPs
【速读】：该论文旨在解决预训练浮点运算量（FLOPs）与大语言模型（LLM）在信息检索任务中性能之间关系的量化问题，即探究检索性能如何随预训练FLOPs的变化而变化。其解决方案的关键在于系统性地评估从1.25亿到70亿参数的LLM在不同规模数据集（10亿至超过2万亿token）上预训练后的零样本BEIR（Benchmarking Entity Retrieval）任务表现，发现检索性能可预测地随模型规模、训练时长和估算FLOPs提升；同时揭示了上下文学习（In-Context Learning, ICL）得分与检索得分在多个任务中存在强相关性，为基于LLM的检索器开发提供了关键指标依据和优化方向。
链接: https://arxiv.org/abs/2508.17400
作者: Jacob Portes,Connor Jennings,Erica Ji Yuen,Sasha Doubov,Michael Carbin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注: 15 pages, 4 figures
Abstract:How does retrieval performance scale with pretraining FLOPs? We benchmark retrieval performance across LLM model sizes from 125 million parameters to 7 billion parameters pretrained on datasets ranging from 1 billion tokens to more than 2 trillion tokens. We find that retrieval performance on zero-shot BEIR tasks predictably scales with LLM size, training duration, and estimated FLOPs. We also show that In-Context Learning scores are strongly correlated with retrieval scores across retrieval tasks. Finally, we highlight the implications this has for the development of LLM-based retrievers.
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[AI-61] Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLM s via Explicit Reasoning
【速读】：该论文旨在解决在无特定任务监督的情况下，如何实现对未见过的图任务（如节点分类、链接预测和图分类）的有效泛化问题。传统图神经网络（Graph Neural Networks, GNNs）受限于固定的标签空间，而大型语言模型（Large Language Models, LLMs）则缺乏结构归纳偏置，难以有效处理图结构数据。论文提出了一种无需图神经网络（GNN-free）的新方法，将图任务转化为文本推理问题，并由大型推理模型（Large Reasoning Models, LRMs）通过显式的长链式思维（chain-of-thought reasoning）求解。其关键创新在于引入了首个包含详细推理轨迹的图任务数据集，并设计了Graph-R1强化学习框架，利用任务特定的重思模板（rethink templates）引导模型对线性化图进行推理，从而在零样本设置下显著优于现有基线方法，同时生成可解释且高效的预测结果。
链接: https://arxiv.org/abs/2508.17387
作者: Yicong Wu,Guangyue Lu,Yuan Zuo,Huarong Zhang,Junjie Wu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Generalizing to unseen graph tasks without task-pecific supervision remains challenging. Graph Neural Networks (GNNs) are limited by fixed label spaces, while Large Language Models (LLMs) lack structural inductive biases. Recent advances in Large Reasoning Models (LRMs) provide a zero-shot alternative via explicit, long chain-of-thought reasoning. Inspired by this, we propose a GNN-free approach that reformulates graph tasks–node classification, link prediction, and graph classification–as textual reasoning problems solved by LRMs. We introduce the first datasets with detailed reasoning traces for these tasks and develop Graph-R1, a reinforcement learning framework that leverages task-specific rethink templates to guide reasoning over linearized graphs. Experiments demonstrate that Graph-R1 outperforms state-of-the-art baselines in zero-shot settings, producing interpretable and effective predictions. Our work highlights the promise of explicit reasoning for graph learning and provides new resources for future research.
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[AI-62] Mimicking the Physicists Eye:A VLM-centric Approach for Physics Formula Discovery
【速读】：该论文旨在解决从真实世界观测数据中自动发现物理定律的挑战，现有方法（如符号回归或大语言模型）受限于单模态输入，忽视了物理学中至关重要的视觉运动表征，导致对动态现象内在时空模式的解释能力不足。解决方案的关键在于提出VIPER-R1，一个融合视觉感知、轨迹数据与符号推理的多模态模型，其核心机制包括：通过运动结构诱导（Motion Structure Induction, MSI）的课程训练策略，结合监督微调以解析运动相图并基于因果思维链（Causal Chain of Thought, C-CoT）生成假设，再利用奖励引导符号校准（Reward-Guided Symbolic Calibration, RGSC）通过强化学习优化公式结构；在推理阶段，模型作为代理先提出高置信度符号假设，随后主动调用外部符号回归工具进行符号残差重对齐（Symbolic Residual Realignment, SR²），这一过程模拟物理学家的微扰分析，实现理论模型与实测数据的精确契合。
链接: https://arxiv.org/abs/2508.17380
作者: Jiaqi Liu,Songning Lai,Pengze Li,Di Yu,Wenjie Zhou,Yiyang Zhou,Peng Xia,Zijun Wang,Xi Chen,Shixiang Tang,Lei Bai,Wanli Ouyang,Mingyu Ding,Huaxiu Yao,Aoran Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Automated discovery of physical laws from observational data in the real world is a grand challenge in AI. Current methods, relying on symbolic regression or LLMs, are limited to uni-modal data and overlook the rich, visual phenomenological representations of motion that are indispensable to physicists. This “sensory deprivation” severely weakens their ability to interpret the inherent spatio-temporal patterns within dynamic phenomena. To address this gap, we propose VIPER-R1, a multimodal model that performs Visual Induction for Physics-based Equation Reasoning to discover fundamental symbolic formulas. It integrates visual perception, trajectory data, and symbolic reasoning to emulate the scientific discovery process. The model is trained via a curriculum of Motion Structure Induction (MSI), using supervised fine-tuning to interpret kinematic phase portraits and to construct hypotheses guided by a Causal Chain of Thought (C-CoT), followed by Reward-Guided Symbolic Calibration (RGSC) to refine the formula structure with reinforcement learning. During inference, the trained VIPER-R1 acts as an agent: it first posits a high-confidence symbolic ansatz, then proactively invokes an external symbolic regression tool to perform Symbolic Residual Realignment (SR^2). This final step, analogous to a physicist’s perturbation analysis, reconciles the theoretical model with empirical data. To support this research, we introduce PhysSymbol, a new 5,000-instance multimodal corpus. Experiments show that VIPER-R1 consistently outperforms state-of-the-art VLM baselines in accuracy and interpretability, enabling more precise discovery of physical laws. Project page: this https URL
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[AI-63] Evolving Collective Cognition in Human-Agent Hybrid Societies: How Agents Form Stances and Boundaries
【速读】：该论文试图解决的问题是：大型语言模型（Large Language Models, LLMs）在复杂人机混合社会中是否具备稳定的立场形成（stance formation）与身份协商（identity negotiation）能力，以及人类干预如何影响此类社会结构的演化。解决方案的关键在于提出了一种融合生成式代理建模（generative agent-based modeling）与虚拟民族志方法（virtual ethnographic methods）的计算多智能体社会实验框架，通过三组实证研究发现，代理能自发产生内生立场（endogenous stances），并基于语言互动重构以立场为基础的社区边界，而非受预设身份的刚性支配；这表明人类研究人员若想有效干预群体认知，需关注代理语言网络中的内生机制与交互动力学。
链接: https://arxiv.org/abs/2508.17366
作者: Hanzhong Zhang,Muhua Huang,Jindong Wang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Multiagent Systems (cs.MA)
备注: 37 pages, 6 figures
Abstract:Large language models have been widely used to simulate credible human social behaviors. However, it remains unclear whether these models can demonstrate stable capacities for stance formation and identity negotiation in complex interactions, as well as how they respond to human interventions. We propose a computational multi-agent society experiment framework that integrates generative agent-based modeling with virtual ethnographic methods to investigate how group stance differentiation and social boundary formation emerge in human-agent hybrid societies. Across three studies, we find that agents exhibit endogenous stances, independent of their preset identities, and display distinct tonal preferences and response patterns to different discourse strategies. Furthermore, through language interaction, agents actively dismantle existing identity-based power structures and reconstruct self-organized community boundaries based on these stances. Our findings suggest that preset identities do not rigidly determine the agents’ social structures. For human researchers to effectively intervene in collective cognition, attention must be paid to the endogenous mechanisms and interactional dynamics within the agents’ language networks. These insights provide a theoretical foundation for using generative AI in modeling group social dynamics and studying human-agent collaboration.
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[AI-64] Agent ic AI for Software: thoughts from Software Engineering community
【速读】：该论文旨在解决如何将AI代理（AI agent）有效融入软件工程流程以实现更高程度自动化的问题，特别是在代码生成、测试、修复以及设计层面的架构探索与需求理解等任务中。其核心挑战在于提升AI代理对开发者意图的解析能力，从而构建可信的智能软件开发工作流。解决方案的关键在于通过**意图推断（specification inference）**来明确开发者在不同软件任务中的目标和约束，这是实现程序修复、维护及自动代码集成等关键环节的基础。此外，论文指出未来需引入基于AI的验证与确认（Verification and Validation, VV），以应对自动化生成代码量激增所带来的质量保障难题，确保AI驱动的软件工程流程既高效又可靠。
链接: https://arxiv.org/abs/2508.17343
作者: Abhik Roychoudhury
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 4 pages
Abstract:AI agents have recently shown significant promise in software engineering. Much public attention has been transfixed on the topic of code generation from Large Language Models (LLMs) via a prompt. However, software engineering is much more than programming, and AI agents go far beyond instructions given by a prompt. At the code level, common software tasks include code generation, testing, and program repair. Design level software tasks may include architecture exploration, requirements understanding, and requirements enforcement at the code level. Each of these software tasks involves micro-decisions which can be taken autonomously by an AI agent, aided by program analysis tools. This creates the vision of an AI software engineer, where the AI agent can be seen as a member of a development team. Conceptually, the key to successfully developing trustworthy agentic AI-based software workflows will be to resolve the core difficulty in software engineering - the deciphering and clarification of developer intent. Specification inference, or deciphering the intent, thus lies at the heart of many software tasks, including software maintenance and program repair. A successful deployment of agentic technology into software engineering would involve making conceptual progress in such intent inference via agents. Trusting the AI agent becomes a key aspect, as software engineering becomes more automated. Higher automation also leads to higher volume of code being automatically generated, and then integrated into code-bases. Thus to deal with this explosion, an emerging direction is AI-based verification and validation (V V) of AI generated code. We posit that agentic software workflows in future will include such AIbased VV. Comments: 4 pages Subjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI) ACMclasses: D.2 Cite as: arXiv:2508.17343 [cs.SE] (or arXiv:2508.17343v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2508.17343 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Abhik Roychoudhury [view email] [v1] Sun, 24 Aug 2025 12:57:21 UTC (539 KB)
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[AI-65] Modality-Specific Speech Enhancement and Noise-Adaptive Fusion for Acoustic and Body-Conduction Microphone Framework
【速读】：该论文旨在解决体感传声麦克风（Body-conduction microphone signals, BMS）在噪声环境中虽具备强抗噪能力，但因物理特性导致高频信息丢失的问题；同时，传统单模态语音增强方法难以兼顾降噪与高频重建。其解决方案的关键在于提出一种新颖的多模态框架，融合BMS与空气传导麦克风信号（Acoustic microphone signals, AMS），通过两个专用网络分别处理：基于映射的模型用于增强BMS以恢复高频成分，基于掩蔽的模型用于去除AMS中的噪声；并通过动态融合机制根据局部噪声环境自适应地整合两模态优势，从而实现更鲁棒且高质量的语音增强效果。
链接: https://arxiv.org/abs/2508.17336
作者: Yunsik Kim,Yoonyoung Chung
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
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Abstract:Body-conduction microphone signals (BMS) bypass airborne sound, providing strong noise resistance. However, a complementary modality is required to compensate for the inherent loss of high-frequency information. In this study, we propose a novel multi-modal framework that combines BMS and acoustic microphone signals (AMS) to achieve both noise suppression and high-frequency reconstruction. Unlike conventional multi-modal approaches that simply merge features, our method employs two specialized networks: a mapping-based model to enhance BMS and a masking-based model to denoise AMS. These networks are integrated through a dynamic fusion mechanism that adapts to local noise conditions, ensuring the optimal use of each modality’s strengths. We performed evaluations on the TAPS dataset, augmented with DNS-2023 noise clips, using objective speech quality metrics. The results clearly demonstrate that our approach outperforms single-modal solutions in a wide range of noisy environments.
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[AI-66] Chinese Court Simulation with LLM -Based Agent System
【速读】：该论文旨在解决传统模拟法庭（mock trial）因依赖专业导师和真人参与者而导致可及性差、难以大规模推广的问题，同时指出当前基于大语言模型（Large Language Models, LLMs）的法庭模拟研究多集中于代理构建，忽视了系统性设计与评估对实际应用可信度的关键影响。解决方案的核心在于提出首个基于中国法院真实程序结构的法庭模拟框架SimCourt，该框架严格复现中国审判流程中的5个核心阶段及5类法庭角色，并通过赋予法律代理以记忆、规划与反思能力，实现高保真度的法庭交互模拟；实验表明，该框架生成的模拟庭审显著提升了法律判决预测性能，且专家评估显示其代理表现优于真实案件中的法官与律师，验证了LLM驱动法庭模拟在教育与实践中的可行性与优越性。
链接: https://arxiv.org/abs/2508.17322
作者: Kaiyuan Zhang,Jiaqi Li,Yueyue Wu,Haitao Li,Cheng Luo,Shaokun Zou,Yujia Zhou,Weihang Su,Qingyao Ai,Yiqun Liu
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
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Abstract:Mock trial has long served as an important platform for legal professional training and education. It not only helps students learn about realistic trial procedures, but also provides practical value for case analysis and judgment prediction. Traditional mock trials are difficult to access by the public because they rely on professional tutors and human participants. Fortunately, the rise of large language models (LLMs) provides new opportunities for creating more accessible and scalable court simulations. While promising, existing research mainly focuses on agent construction while ignoring the systematic design and evaluation of court simulations, which are actually more important for the credibility and usage of court simulation in practice. To this end, we present the first court simulation framework – SimCourt – based on the real-world procedure structure of Chinese courts. Our framework replicates all 5 core stages of a Chinese trial and incorporates 5 courtroom roles, faithfully following the procedural definitions in China. To simulate trial participants with different roles, we propose and craft legal agents equipped with memory, planning, and reflection abilities. Experiment on legal judgment prediction show that our framework can generate simulated trials that better guide the system to predict the imprisonment, probation, and fine of each case. Further annotations by human experts show that agents’ responses under our simulation framework even outperformed judges and lawyers from the real trials in many scenarios. These further demonstrate the potential of LLM-based court simulation.
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[AI-67] Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality
【速读】：该论文旨在解决大规模高性能计算（HPC）系统中集体通信操作（collective operations）因通信局部性差而导致的性能瓶颈问题，尤其是在网络过载（oversubscribed networks）场景下，节点组内全连接但跨组连接稀疏时的全局链路流量激增问题。解决方案的关键在于提出了一种名为Bine（binomial negabinary）树的新一代集体算法族，它在保持二项式树（binomial trees）和蝴蝶网络（butterflies）通用性的基础上，通过优化通信路径设计显著减少全局链路流量，最高可降低33%，并在此基础上实现了高达5倍的加速比，同时在不同向量大小和节点数量下均表现出稳定的全局链路流量下降效果。
链接: https://arxiv.org/abs/2508.17311
作者: Daniele De Sensi,Saverio Pasqualoni,Lorenzo Piarulli,Tommaso Bonato,Seydou Ba,Matteo Turisini,Jens Domke,Torsten Hoefler
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Performance (cs.PF)
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Abstract:Communication locality plays a key role in the performance of collective operations on large HPC systems, especially on oversubscribed networks where groups of nodes are fully connected internally but sparsely linked through global connections. We present Bine (binomial negabinary) trees, a family of collective algorithms that improve communication locality. Bine trees maintain the generality of binomial trees and butterflies while cutting global-link traffic by up to 33%. We implement eight Bine-based collectives and evaluate them on four large-scale supercomputers with Dragonfly, Dragonfly+, oversubscribed fat-tree, and torus topologies, achieving up to 5x speedups and consistent reductions in global-link traffic across different vector sizes and node counts.
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[AI-68] Meta-R1: Empowering Large Reasoning Models with Metacognition
【速读】：该论文旨在解决当前大型推理模型（Large Reasoning Models, LRMs）缺乏专门的元认知系统（meta-level cognitive system）的问题，这一缺失导致其推理能力不可控（非自适应推理）、不可靠（中间错误频发）以及不灵活（缺乏明确的方法论）。解决方案的关键在于提出Meta-R1框架，该框架基于认知科学原理，将推理过程分解为对象层（object-level）和元认知层（meta-level）两个独立组件，并在级联结构中实现主动规划、在线调控与自适应提前终止，从而赋予LRMs显式的元认知能力。
链接: https://arxiv.org/abs/2508.17291
作者: Haonan Dong,Haoran Ye,Wenhao Zhu,Kehan Jiang,Guojie Song
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Large Reasoning Models (LRMs) demonstrate remarkable capabilities on complex tasks, exhibiting emergent, human-like thinking patterns. Despite their advances, we identify a fundamental limitation: current LRMs lack a dedicated meta-level cognitive system-an essential faculty in human cognition that enables “thinking about thinking”. This absence leaves their emergent abilities uncontrollable (non-adaptive reasoning), unreliable (intermediate error), and inflexible (lack of a clear methodology). To address this gap, we introduce Meta-R1, a systematic and generic framework that endows LRMs with explicit metacognitive capabilities. Drawing on principles from cognitive science, Meta-R1 decomposes the reasoning process into distinct object-level and meta-level components, orchestrating proactive planning, online regulation, and adaptive early stopping within a cascaded framework. Experiments on three challenging benchmarks and against eight competitive baselines demonstrate that Meta-R1 is: (I) high-performing, surpassing state-of-the-art methods by up to 27.3%; (II) token-efficient, reducing token consumption to 15.7% ~ 32.7% and improving efficiency by up to 14.8% when compared to its vanilla counterparts; and (III) transferable, maintaining robust performance across datasets and model backbones.
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[AI-69] MEENA (PersianMMMU): Multimodal-Multilingual Educational Exams for N-level Assessment
【速读】：该论文旨在解决当前大型视觉语言模型（Vision-Language Models, VLMs）研究高度集中于英语，而对其他语言如波斯语等缺乏系统评估基准的问题。其解决方案的关键在于提出MEENA（又称PersianMMMU）数据集，这是首个专门用于评估波斯语VLMs的多任务基准，涵盖科学、推理与人类理解任务，包含约7,500条波斯语和3,000条英文问题，覆盖从基础教育到高中阶段的多样化主题，并具备难度标注、描述性答案等丰富元数据；同时，该数据集通过双语结构支持跨语言性能对比，且设计了多项实验以全面评估模型在图像关注能力与幻觉生成倾向等方面的表现，从而推动VLM在非英语场景下的能力提升。
链接: https://arxiv.org/abs/2508.17290
作者: Omid Ghahroodi,Arshia Hemmat,Marzia Nouri,Seyed Mohammad Hadi Hosseini,Doratossadat Dastgheib,Mohammad Vali Sanian,Alireza Sahebi,Reihaneh Zohrabi,Mohammad Hossein Rohban,Ehsaneddin Asgari,Mahdieh Soleymani Baghshah
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEENA (also known as PersianMMMU), the first dataset designed to evaluate Persian VLMs across scientific, reasoning, and human-level understanding tasks. Our dataset comprises approximately 7,500 Persian and 3,000 English questions, covering a wide range of topics such as reasoning, mathematics, physics, diagrams, charts, and Persian art and literature. Key features of MEENA include: (1) diverse subject coverage spanning various educational levels, from primary to upper secondary school, (2) rich metadata, including difficulty levels and descriptive answers, (3) original Persian data that preserves cultural nuances, (4) a bilingual structure to assess cross-linguistic performance, and (5) a series of diverse experiments assessing various capabilities, including overall performance, the model’s ability to attend to images, and its tendency to generate hallucinations. We hope this benchmark contributes to enhancing VLM capabilities beyond English.
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[AI-70] ERF-BA-TFD: A Multimodal Model for Audio-Visual Deepfake Detection
【速读】：该论文旨在解决多模态深度伪造（Deepfake）检测问题，即在真实场景中识别跨音频和视频模态的篡改多媒体内容。现有方法往往局限于单一模态或孤立片段的检测，难以应对复杂且连续的伪造内容。解决方案的关键在于提出一种名为ERF-BA-TFD+的新模型，其核心创新是引入增强感受野（Enhanced Receptive Field, ERF）机制与音频-视觉融合策略，能够同时处理音视频特征并建模长距离依赖关系，从而更有效地捕捉真实与伪造内容之间的细微差异。该方法在DDL-AV数据集上实现了最先进的检测性能，验证了其在准确性与处理速度上的优势。
链接: https://arxiv.org/abs/2508.17282
作者: Xin Zhang,Jiaming Chu,Jian Zhao,Yuchu Jiang,Xu Yang,Lei Jin,Chi Zhang,Xuelong Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Sound (cs.SD)
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Abstract:Deepfake detection is a critical task in identifying manipulated multimedia content. In real-world scenarios, deepfake content can manifest across multiple modalities, including audio and video. To address this challenge, we present ERF-BA-TFD+, a novel multimodal deepfake detection model that combines enhanced receptive field (ERF) and audio-visual fusion. Our model processes both audio and video features simultaneously, leveraging their complementary information to improve detection accuracy and robustness. The key innovation of ERF-BA-TFD+ lies in its ability to model long-range dependencies within the audio-visual input, allowing it to better capture subtle discrepancies between real and fake content. In our experiments, we evaluate ERF-BA-TFD+ on the DDL-AV dataset, which consists of both segmented and full-length video clips. Unlike previous benchmarks, which focused primarily on isolated segments, the DDL-AV dataset allows us to assess the model’s performance in a more comprehensive and realistic setting. Our method achieves state-of-the-art results on this dataset, outperforming existing techniques in terms of both accuracy and processing speed. The ERF-BA-TFD+ model demonstrated its effectiveness in the “Workshop on Deepfake Detection, Localization, and Interpretability,” Track 2: Audio-Visual Detection and Localization (DDL-AV), and won first place in this competition.
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[AI-71] Federated Reinforcement Learning for Runtime Optimization of AI Applications in Smart Eyewears
【速读】：该论文旨在解决智能眼镜（Smart Eye-Wears, SEWs）在计算能力、内存和电池寿命方面的固有局限性，以及将计算任务卸载至外部服务器时受网络条件和服务器负载波动限制的问题。解决方案的关键在于提出一种联邦强化学习（Federated Reinforcement Learning, FRL）框架，通过多个代理（agents）协同训练并保持数据隐私，在同步与异步两种联邦策略下实现模型聚合：前者按固定时间间隔聚合，后者基于代理进展动态聚合。实验表明，采用FRL的代理表现出显著更低的性能波动，从而提升了系统稳定性和可靠性，验证了其在需要实时AI处理（如SEWs中的实时目标检测）场景下的潜力。
链接: https://arxiv.org/abs/2508.17262
作者: Hamta Sedghani,Abednego Wamuhindo Kambale,Federica Filippini,Francesca Palermo,Diana Trojaniello,Danilo Ardagna
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Extended reality technologies are transforming fields such as healthcare, entertainment, and education, with Smart Eye-Wears (SEWs) and Artificial Intelligence (AI) playing a crucial role. However, SEWs face inherent limitations in computational power, memory, and battery life, while offloading computations to external servers is constrained by network conditions and server workload variability. To address these challenges, we propose a Federated Reinforcement Learning (FRL) framework, enabling multiple agents to train collaboratively while preserving data privacy. We implemented synchronous and asynchronous federation strategies, where models are aggregated either at fixed intervals or dynamically based on agent progress. Experimental results show that federated agents exhibit significantly lower performance variability, ensuring greater stability and reliability. These findings underscore the potential of FRL for applications requiring robust real-time AI processing, such as real-time object detection in SEWs.
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[AI-72] Provable Generalization in Overparameterized Neural Nets
【速读】：该论文试图解决深度神经网络在过参数化（overparameterized）情况下仍能良好泛化的现象，即传统复杂度度量（如VC维或PAC-Bayes界）在参数远多于训练样本时趋于失效的问题。解决方案的关键在于提出了一种基于注意力矩阵有效秩（effective rank）的容量度量方法，其核心思想是：尽管模型参数量巨大，但注意力机制的功能维度往往显著更低。通过该指标推导出的泛化界，其对样本规模的依赖关系与大型语言模型中观察到的经验缩放定律（scaling laws）一致，表明注意力的谱特性（spectral properties）可能比原始参数数量更能解释模型的泛化能力。
链接: https://arxiv.org/abs/2508.17256
作者: Aviral Dhingra
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 8 Pages
Abstract:Deep neural networks often contain far more parameters than training examples, yet they still manage to generalize well in practice. Classical complexity measures such as VC-dimension or PAC-Bayes bounds usually become vacuous in this overparameterized regime, offering little explanation for the empirical success of models like Transformers. In this work, I explore an alternative notion of capacity for attention-based models, based on the effective rank of their attention matrices. The intuition is that, although the parameter count is enormous, the functional dimensionality of attention is often much lower. I show that this quantity leads to a generalization bound whose dependence on sample size matches empirical scaling laws observed in large language models, up to logarithmic factors. While the analysis is not a complete theory of overparameterized learning, it provides evidence that spectral properties of attention, rather than raw parameter counts, may be the right lens for understanding why these models generalize.
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[AI-73] L-XAIDS: A LIME-based eXplainable AI framework for Intrusion Detection Systems
【速读】：该论文旨在解决机器学习驱动的入侵检测系统（Intrusion Detection Systems, IDS）中存在的黑箱问题，即缺乏对模型决策过程的透明性和可解释性，这在网络安全等关键领域尤为突出。其解决方案的关键在于提出一个融合局部可解释模型无关解释（Local Interpretable Model-Agnostic Explanations, LIME）、“像向五岁孩子解释一样”（Explain Like I’m Five, ELI5）和决策树（Decision Tree）算法的框架，以同时提供局部解释（针对单个输入的决策依据）和全局解释（识别重要特征及其与攻击流量的关系），从而提升IDS决策的可理解性，并推动可解释人工智能（eXplainable AI, XAI）在网络安全关键系统中的广泛应用。
链接: https://arxiv.org/abs/2508.17244
作者: Aoun E Muhammad,Kin-Choong Yow,Nebojsa Bacanin-Dzakula,Muhammad Attique Khan
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: This is the authors accepted manuscript of an article accepted for publication in Cluster Computing. The final published version is available at: https://doi.org/10.1007/s10586-025-05326-9
Abstract:Recent developments in Artificial Intelligence (AI) and their applications in critical industries such as healthcare, fin-tech and cybersecurity have led to a surge in research in explainability in AI. Innovative research methods are being explored to extract meaningful insight from blackbox AI systems to make the decision-making technology transparent and interpretable. Explainability becomes all the more critical when AI is used in decision making in domains like fintech, healthcare and safety critical systems such as cybersecurity and autonomous vehicles. However, there is still ambiguity lingering on the reliable evaluations for the users and nature of transparency in the explanations provided for the decisions made by black-boxed AI. To solve the blackbox nature of Machine Learning based Intrusion Detection Systems, a framework is proposed in this paper to give an explanation for IDSs decision making. This framework uses Local Interpretable Model-Agnostic Explanations (LIME) coupled with Explain Like I’m five (ELI5) and Decision Tree algorithms to provide local and global explanations and improve the interpretation of IDSs. The local explanations provide the justification for the decision made on a specific input. Whereas, the global explanations provides the list of significant features and their relationship with attack traffic. In addition, this framework brings transparency in the field of ML driven IDS that might be highly significant for wide scale adoption of eXplainable AI in cyber-critical systems. Our framework is able to achieve 85 percent accuracy in classifying attack behaviour on UNSW-NB15 dataset, while at the same time displaying the feature significance ranking of the top 10 features used in the classification.
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[AI-74] Module-Aware Parameter-Efficient Machine Unlearning on Transformers
【速读】：该论文旨在解决现有参数高效机器遗忘（machine unlearning）方法在Transformer模型中因模块无关（module-oblivious）设计而导致的关键影响参数识别不准、进而造成遗忘性能下降的问题。其解决方案的关键在于提出一种模块感知（module-aware）的参数高效遗忘方法——MAPE-Unlearn，该方法通过引入可学习的一对掩码（masks），精准定位Transformer中注意力头（heads）和滤波器（filters）中的影响关键参数，并基于遗忘目标的期望性质设计学习目标，结合带热启动的贪心搜索算法进行高效优化，从而实现更准确、鲁棒的模型遗忘效果。
链接: https://arxiv.org/abs/2508.17233
作者: Wenjie Bao,Jian Lou,Yuke Hu,Xiaochen Li,Zhihao Liu,Jiaqi Liu,Zhan Qin,Kui Ren
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Transformer has become fundamental to a vast series of pre-trained large models that have achieved remarkable success across diverse applications. Machine unlearning, which focuses on efficiently removing specific data influences to comply with privacy regulations, shows promise in restricting updates to influence-critical parameters. However, existing parameter-efficient unlearning methods are largely devised in a module-oblivious manner, which tends to inaccurately identify these parameters and leads to inferior unlearning performance for Transformers. In this paper, we propose \tt MAPE-Unlearn, a module-aware parameter-efficient machine unlearning approach that uses a learnable pair of masks to pinpoint influence-critical parameters in the heads and filters of Transformers. The learning objective of these masks is derived by desiderata of unlearning and optimized through an efficient algorithm featured by a greedy search with a warm start. Extensive experiments on various Transformer models and datasets demonstrate the effectiveness and robustness of \tt MAPE-Unlearn for unlearning.
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[AI-75] Multi-Metric Preference Alignment for Generative Speech Restoration
【速读】：该论文旨在解决生成式语音恢复（Speech Restoration）模型在训练过程中因目标函数与人类感知偏好不一致而导致的主观质量不佳的问题。现有方法多依赖于传统的重建损失（如L1/L2损失），难以捕捉人类听觉系统对音质、语义一致性及音色保真度等多维度的复杂偏好。为应对这一挑战，论文提出了一种基于多指标偏好对齐（Multi-Metric Preference Alignment）的后训练优化策略，其关键在于构建了一个高质量的偏好数据集 GenSR-Pref（包含80K个偏好对），其中每个优选样本均被一组互补的客观指标（涵盖感知质量、信号保真度、内容一致性与音色保持性）共同认可，从而形成鲁棒且全面的偏好信号。在此基础上，采用直接偏好优化（Direct Preference Optimization, DPO）方法进行微调，在三种不同生成范式（自回归模型、掩码生成模型、流匹配模型）上均实现显著且一致的性能提升，并验证了该策略可有效防止奖励黑客（reward hacking）现象。此外，该方法还可作为高效的数据标注工具，为数据稀缺场景（如歌唱语音恢复）中的判别式模型提供高质量伪标签监督信号。
链接: https://arxiv.org/abs/2508.17229
作者: Junan Zhang,Xueyao Zhang,Jing Yang,Yuancheng Wang,Fan Fan,Zhizheng Wu
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: 16 pages, 10 figures. demopage: this https URL
Abstract:Recent generative models have significantly advanced speech restoration tasks, yet their training objectives often misalign with human perceptual preferences, resulting in suboptimal quality. While post-training alignment has proven effective in other generative domains like text and image generation, its application to generative speech restoration remains largely under-explored. This work investigates the challenges of applying preference-based post-training to this task, focusing on how to define a robust preference signal and curate high-quality data to avoid reward hacking. To address these challenges, we propose a multi-metric preference alignment strategy. We construct a new dataset, GenSR-Pref, comprising 80K preference pairs, where each chosen sample is unanimously favored by a complementary suite of metrics covering perceptual quality, signal fidelity, content consistency, and timbre preservation. This principled approach ensures a holistic preference signal. Applying Direct Preference Optimization (DPO) with our dataset, we observe consistent and significant performance gains across three diverse generative paradigms: autoregressive models (AR), masked generative models (MGM), and flow-matching models (FM) on various restoration benchmarks, in both objective and subjective evaluations. Ablation studies confirm the superiority of our multi-metric strategy over single-metric approaches in mitigating reward hacking. Furthermore, we demonstrate that our aligned models can serve as powerful ‘‘data annotators’’, generating high-quality pseudo-labels to serve as a supervision signal for traditional discriminative models in data-scarce scenarios like singing voice restoration. Demo Page:this https URL
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[AI-76] Exposing Privacy Risks in Graph Retrieval-Augmented Generation
【速读】：该论文旨在解决图结构增强生成（Graph RAG）系统中存在的隐私泄露问题，特别是其在处理外部知识时可能暴露原始文本及结构化数据（如实体及其关系）的风险。研究表明，尽管Graph RAG相较于传统文档检索方式能降低原始文本泄露概率，却显著增加了结构化信息（entities and relationships）被提取的脆弱性。解决方案的关键在于识别并量化此类新型攻击面，并探索针对性的防御机制以缓解结构化数据泄露风险，从而为构建更安全的Graph RAG系统提供基础分析与实践指导。
链接: https://arxiv.org/abs/2508.17222
作者: Jiale Liu,Jiahao Zhang,Suhang Wang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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Abstract:Retrieval-Augmented Generation (RAG) is a powerful technique for enhancing Large Language Models (LLMs) with external, up-to-date knowledge. Graph RAG has emerged as an advanced paradigm that leverages graph-based knowledge structures to provide more coherent and contextually rich answers. However, the move from plain document retrieval to structured graph traversal introduces new, under-explored privacy risks. This paper investigates the data extraction vulnerabilities of the Graph RAG systems. We design and execute tailored data extraction attacks to probe their susceptibility to leaking both raw text and structured data, such as entities and their relationships. Our findings reveal a critical trade-off: while Graph RAG systems may reduce raw text leakage, they are significantly more vulnerable to the extraction of structured entity and relationship information. We also explore potential defense mechanisms to mitigate these novel attack surfaces. This work provides a foundational analysis of the unique privacy challenges in Graph RAG and offers insights for building more secure systems.
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[AI-77] MC3G: Model Agnostic Causally Constrained Counterfactual Generation
【速读】：该论文旨在解决高风险场景下机器学习模型决策透明性与算法保密性之间的矛盾问题，即如何在提供可解释的决策依据的同时，避免暴露底层黑箱模型的机密信息。其解决方案的关键在于提出一种模型无关的因果约束型反事实生成框架（Model-Agnostic Causally Constrained Counterfactual Generation, MC3G）：首先，通过构建可解释的规则型代理模型来近似任意黑箱模型；其次，利用该代理模型生成能够使原模型输出更优结果的反事实样本；最后，引入因果依赖关系以剔除因自动传播而无需用户主动干预的特征变化成本，从而仅衡量用户实际需付出的努力，使得反事实建议更具现实可行性与公平性。
链接: https://arxiv.org/abs/2508.17221
作者: Sopam Dasgupta,Sadaf MD Halim,Joaquín Arias,Elmer Salazar,Gopal Gupta
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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Abstract:Machine learning models increasingly influence decisions in high-stakes settings such as finance, law and hiring, driving the need for transparent, interpretable outcomes. However, while explainable approaches can help understand the decisions being made, they may inadvertently reveal the underlying proprietary algorithm: an undesirable outcome for many practitioners. Consequently, it is crucial to balance meaningful transparency with a form of recourse that clarifies why a decision was made and offers actionable steps following which a favorable outcome can be obtained. Counterfactual explanations offer a powerful mechanism to address this need by showing how specific input changes lead to a more favorable prediction. We propose Model-Agnostic Causally Constrained Counterfactual Generation (MC3G), a novel framework that tackles limitations in the existing counterfactual methods. First, MC3G is model-agnostic: it approximates any black-box model using an explainable rule-based surrogate model. Second, this surrogate is used to generate counterfactuals that produce a favourable outcome for the original underlying black box model. Third, MC3G refines cost computation by excluding the ``effort" associated with feature changes that occur automatically due to causal dependencies. By focusing only on user-initiated changes, MC3G provides a more realistic and fair representation of the effort needed to achieve a favourable outcome. We show that MC3G delivers more interpretable and actionable counterfactual recommendations compared to existing techniques all while having a lower cost. Our findings highlight MC3G’s potential to enhance transparency, accountability, and practical utility in decision-making processes that incorporate machine-learning approaches.
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[AI-78] GPG-HT: Generalized Policy Gradient with History-Aware Decision Transformer for Probabilistic Path Planning
【速读】：该论文旨在解决城市交通网络中因车辆数量激增而导致的拥堵问题，特别是在存在交通流相关性和随机性的情况下，如何实现可靠的最短路径规划（reliable shortest path problem）。其解决方案的关键在于提出一种融合决策Transformer（decision Transformer）与广义策略梯度（Generalized Policy Gradient, GPG）框架的路径规划方法，利用决策Transformer对长期依赖关系的建模能力，显著提升了路径决策的准确性和稳定性。实验结果表明，该方法在Sioux Falls Network（SFN）上相较于现有基线模型，在准时到达概率方面表现更优，从而提供了更可靠的路径规划方案。
链接: https://arxiv.org/abs/2508.17218
作者: Xing Wei,Yuqi Ouyang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:With the rapidly increased number of vehicles in urban areas, existing road infrastructure struggles to accommodate modern traffic demands, resulting in the issue of congestion. This highlights the importance of efficient path planning strategies. However, most recent navigation models focus solely on deterministic or time-dependent networks, while overlooking the correlations and the stochastic nature of traffic flows. In this work, we address the reliable shortest path problem within stochastic transportation networks under certain dependencies. We propose a path planning solution that integrates the decision Transformer with the Generalized Policy Gradient (GPG) framework. Based on the decision Transformer’s capability to model long-term dependencies, our proposed solution improves the accuracy and stability of path decisions. Experimental results on the Sioux Falls Network (SFN) demonstrate that our approach outperforms previous baselines in terms of on-time arrival probability, providing more accurate path planning solutions.
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[AI-79] How to make Medical AI Systems safer? Simulating Vulnerabilities and Threats in Multimodal Medical RAG System AAAI
【速读】：该论文旨在解决医学领域中基于检索增强生成（Retrieval-Augmented Generation, RAG）的大视觉语言模型（Large Vision-Language Models, LVLMs）所面临的新型多模态投毒攻击威胁，这类攻击通过注入对抗性图像-文本对破坏系统的真实性与可靠性。解决方案的关键在于提出MedThreatRAG框架，其核心创新是构建一个模拟半开放环境以复现真实医疗系统中知识库定期更新的场景，并引入跨模态冲突注入（Cross-Modal Conflict Injection, CMCI）机制——该机制在医学图像与其配对报告之间嵌入细微但语义矛盾的内容，从而干扰跨模态对齐，同时保持足够隐蔽性以规避传统检测过滤器，显著降低模型生成准确率（如F1分数下降达27.66%），揭示了临床RAG系统的根本安全缺陷并推动防御机制设计。
链接: https://arxiv.org/abs/2508.17215
作者: Kaiwen Zuo,Zelin Liu,Raman Dutt,Ziyang Wang,Zhongtian Sun,Yeming Wang,Fan Mo,Pietro Liò
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: Sumbitted to 2025 AAAI main track
Abstract:Large Vision-Language Models (LVLMs) augmented with Retrieval-Augmented Generation (RAG) are increasingly employed in medical AI to enhance factual grounding through external clinical image-text retrieval. However, this reliance creates a significant attack surface. We propose MedThreatRAG, a novel multimodal poisoning framework that systematically probes vulnerabilities in medical RAG systems by injecting adversarial image-text pairs. A key innovation of our approach is the construction of a simulated semi-open attack environment, mimicking real-world medical systems that permit periodic knowledge base updates via user or pipeline contributions. Within this setting, we introduce and emphasize Cross-Modal Conflict Injection (CMCI), which embeds subtle semantic contradictions between medical images and their paired reports. These mismatches degrade retrieval and generation by disrupting cross-modal alignment while remaining sufficiently plausible to evade conventional filters. While basic textual and visual attacks are included for completeness, CMCI demonstrates the most severe degradation. Evaluations on IU-Xray and MIMIC-CXR QA tasks show that MedThreatRAG reduces answer F1 scores by up to 27.66% and lowers LLaVA-Med-1.5 F1 rates to as low as 51.36%. Our findings expose fundamental security gaps in clinical RAG systems and highlight the urgent need for threat-aware design and robust multimodal consistency checks. Finally, we conclude with a concise set of guidelines to inform the safe development of future multimodal medical RAG systems.
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[AI-80] Reinforcement Learning enhanced Online Adaptive Clinical Decision Support via Digital Twin powered Policy and Treatment Effect optimized Reward
【速读】：该论文旨在解决临床决策支持系统在实际应用中需在线适应且受安全约束的问题。其核心挑战在于如何在保证患者安全的前提下，利用实时数据动态优化治疗策略。解决方案的关键在于构建一个融合强化学习（Reinforcement Learning, RL）、患者数字孪生（Patient Digital Twin）和不确定性感知机制的在线自适应框架：首先从回顾性数据中初始化一个批次约束策略，随后通过流式循环选择动作、执行安全检查，并仅在高不确定性时调用专家；不确定性由五个Q网络组成的紧凑集成模型通过动作值变异系数（Coefficient of Variation）并结合tanh压缩来量化；数字孪生采用有界残差规则更新患者状态，奖励函数基于相对于保守参照的治疗效果，并使用训练集固定z-score标准化；系统还引入基于规则的安全门控机制确保生命体征与禁忌症合规，从而实现低延迟、高稳定性的在线适应与医生监督下的快速迭代优化。
链接: https://arxiv.org/abs/2508.17212
作者: Xinyu Qin,Ruiheng Yu,Lu Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Clinical decision support must adapt online under safety constraints. We present an online adaptive tool where reinforcement learning provides the policy, a patient digital twin provides the environment, and treatment effect defines the reward. The system initializes a batch-constrained policy from retrospective data and then runs a streaming loop that selects actions, checks safety, and queries experts only when uncertainty is high. Uncertainty comes from a compact ensemble of five Q-networks via the coefficient of variation of action values with a \tanh compression. The digital twin updates the patient state with a bounded residual rule. The outcome model estimates immediate clinical effect, and the reward is the treatment effect relative to a conservative reference with a fixed z-score normalization from the training split. Online updates operate on recent data with short runs and exponential moving averages. A rule-based safety gate enforces vital ranges and contraindications before any action is applied. Experiments in a synthetic clinical simulator show low latency, stable throughput, a low expert query rate at fixed safety, and improved return against standard value-based baselines. The design turns an offline policy into a continuous, clinician-supervised system with clear controls and fast adaptation.
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[AI-81] Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)
【速读】：该论文旨在解决抑郁症（Major Depressive Disorder, MDD）症状变化如何因果性地影响选择舍曲林类（SSRI）与文拉法辛类（SNRI）抗抑郁药物的问题。其解决方案的关键在于采用可解释的反事实推理（explainable counterfactual reasoning）方法，通过生成反事实解释（counterfactual explanations, CFs）来量化特定症状改变对药物选择的影响，从而揭示个体症状在临床决策中的局部与全局重要性，提升人工智能辅助临床决策系统的可解释性与可信度。
链接: https://arxiv.org/abs/2508.17207
作者: Xinyu Qin,Mark H. Chignell,Alexandria Greifenberger,Sachinthya Lokuge,Elssa Toumeh,Tia Sternat,Martin Katzman,Lu Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Background: This study investigates how variations in Major Depressive Disorder (MDD) symptoms, quantified by the Hamilton Rating Scale for Depression (HAM-D), causally influence the prescription of SSRIs versus SNRIs. Methods: We applied explainable counterfactual reasoning with counterfactual explanations (CFs) to assess the impact of specific symptom changes on antidepressant choice. Results: Among 17 binary classifiers, Random Forest achieved highest performance (accuracy, F1, precision, recall, ROC-AUC near 0.85). Sample-based CFs revealed both local and global feature importance of individual symptoms in medication selection. Conclusions: Counterfactual reasoning elucidates which MDD symptoms most strongly drive SSRI versus SNRI selection, enhancing interpretability of AI-based clinical decision support systems. Future work should validate these findings on more diverse cohorts and refine algorithms for clinical deployment.
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[AI-82] Large Language Model-Based Automatic Formulation for Stochastic Optimization Models
【速读】：该论文旨在解决如何利用大语言模型（Large Language Models, LLMs）自动从自然语言描述中生成并求解随机优化问题（stochastic optimization problems）这一挑战。其核心问题是现有LLMs在处理具有不确定性的优化建模任务时，难以准确提取约束结构与目标函数，并缺乏对部分正确性（partial correctness）的有效评估机制。解决方案的关键在于设计结构化提示（prompts），结合思维链（chain-of-thought）和模块化推理策略，并引入一种新颖的软评分指标（soft scoring metric）来衡量生成模型的结构性质量和部分正确性，从而显著提升模型在联合机会约束模型、个体机会约束模型及两阶段随机线性规划等三类典型随机优化问题上的表现。实验表明，GPT-4-Turbo在变量匹配率和目标准确性方面优于其他模型，且“cot_s_instructions”和“agentic”提示策略最为有效，证明了精心设计的提示与多智能体协作可使LLMs胜任专业级随机优化建模任务。
链接: https://arxiv.org/abs/2508.17200
作者: Amirreza Talebi
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:This paper presents the first integrated systematic study on the performance of large language models (LLMs), specifically ChatGPT, to automatically formulate and solve stochastic optimiza- tion problems from natural language descriptions. Focusing on three key categories, joint chance- constrained models, individual chance-constrained models, and two-stage stochastic linear programs (SLP-2), we design several prompts that guide ChatGPT through structured tasks using chain-of- thought and modular reasoning. We introduce a novel soft scoring metric that evaluates the struc- tural quality and partial correctness of generated models, addressing the limitations of canonical and execution-based accuracy. Across a diverse set of stochastic problems, GPT-4-Turbo outperforms other models in partial score, variable matching, and objective accuracy, with cot_s_instructions and agentic emerging as the most effective prompting strategies. Our findings reveal that with well-engineered prompts and multi-agent collaboration, LLMs can facilitate specially stochastic formulations, paving the way for intelligent, language-driven modeling pipelines in stochastic opti- mization.
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[AI-83] From reactive to cognitive: brain-inspired spatial intelligence for embodied agents
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在具身智能体中缺乏结构化空间记忆、仅能进行反应式行为的问题，从而限制了其在复杂真实环境中的泛化能力和适应性。解决方案的关键在于提出了一种受大脑启发的空间认知框架——BSC-Nav（Brain-inspired Spatial Cognition for Navigation），该框架能够从视角依赖的轨迹和情境线索中构建视角无关的认知地图（allocentric cognitive maps），并动态检索与语义目标对齐的空间知识，从而实现具身智能体在多样化导航任务中的高效、高精度行为表现，并具备强大的零样本泛化能力。
链接: https://arxiv.org/abs/2508.17198
作者: Shouwei Ruan,Liyuan Wang,Caixin Kang,Qihui Zhu,Songming Liu,Xingxing Wei,Hang Su
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 40 pages, 8 figures
Abstract:Spatial cognition enables adaptive goal-directed behavior by constructing internal models of space. Robust biological systems consolidate spatial knowledge into three interconnected forms: \textitlandmarks for salient cues, \textitroute knowledge for movement trajectories, and \textitsurvey knowledge for map-like representations. While recent advances in multi-modal large language models (MLLMs) have enabled visual-language reasoning in embodied agents, these efforts lack structured spatial memory and instead operate reactively, limiting their generalization and adaptability in complex real-world environments. Here we present Brain-inspired Spatial Cognition for Navigation (BSC-Nav), a unified framework for constructing and leveraging structured spatial memory in embodied agents. BSC-Nav builds allocentric cognitive maps from egocentric trajectories and contextual cues, and dynamically retrieves spatial knowledge aligned with semantic goals. Integrated with powerful MLLMs, BSC-Nav achieves state-of-the-art efficacy and efficiency across diverse navigation tasks, demonstrates strong zero-shot generalization, and supports versatile embodied behaviors in the real physical world, offering a scalable and biologically grounded path toward general-purpose spatial intelligence.
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[AI-84] BudgetThinker: Empowering Budget-aware LLM Reasoning with Control Tokens
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在推理过程中因依赖高计算资源和长推理链而导致的延迟高、成本大问题，尤其是在实时性要求严格或资源受限场景下的部署瓶颈。其核心解决方案是提出BudgetThinker框架，关键在于通过在推理阶段周期性插入特殊控制标记（control tokens）来持续向模型传递剩余token预算信息，并结合两阶段训练策略：首先采用监督微调（Supervised Fine-Tuning, SFT）使模型理解预算约束，再利用基于长度感知的奖励函数进行课程学习式强化学习（curriculum-based Reinforcement Learning, RL），从而在保证准确性的前提下实现对推理长度的精确控制与高效优化。
链接: https://arxiv.org/abs/2508.17196
作者: Hao Wen,Xinrui Wu,Yi Sun,Feifei Zhang,Liye Chen,Jie Wang,Yunxin Liu,Ya-Qin Zhang,Yuanchun Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advancements in Large Language Models (LLMs) have leveraged increased test-time computation to enhance reasoning capabilities, a strategy that, while effective, incurs significant latency and resource costs, limiting their applicability in real-world time-constrained or cost-sensitive scenarios. This paper introduces BudgetThinker, a novel framework designed to empower LLMs with budget-aware reasoning, enabling precise control over the length of their thought processes. We propose a methodology that periodically inserts special control tokens during inference to continuously inform the model of its remaining token budget. This approach is coupled with a comprehensive two-stage training pipeline, beginning with Supervised Fine-Tuning (SFT) to familiarize the model with budget constraints, followed by a curriculum-based Reinforcement Learning (RL) phase that utilizes a length-aware reward function to optimize for both accuracy and budget adherence. We demonstrate that BudgetThinker significantly surpasses strong baselines in maintaining performance across a variety of reasoning budgets on challenging mathematical benchmarks. Our method provides a scalable and effective solution for developing efficient and controllable LLM reasoning, making advanced models more practical for deployment in resource-constrained and real-time environments.
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[AI-85] PosterGen: Aesthetic-Aware Paper-to-Poster Generation via Multi-Agent LLM s
【速读】：该论文旨在解决科研人员在准备会议投稿时面临的“论文转海报”（paper-to-poster）这一耗时且繁琐的任务，现有自动化方法普遍忽视设计与美学原则，导致生成的海报仍需大量人工修改。其解决方案的关键在于提出一个名为PosterGen的多智能体框架，该框架模拟专业海报设计师的工作流程，由四个协同工作的专业化智能体组成：内容解析与整理（Parser and Curator）、空间布局规划（Layout）、视觉风格设计（Stylist）和最终渲染（Renderer），从而实现语义准确且视觉美观的海报自动生成。
链接: https://arxiv.org/abs/2508.17188
作者: Zhilin Zhang,Xiang Zhang,Jiaqi Wei,Yiwei Xu,Chenyu You
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Project Website: this https URL
Abstract:Multi-agent systems built upon large language models (LLMs) have demonstrated remarkable capabilities in tackling complex compositional tasks. In this work, we apply this paradigm to the paper-to-poster generation problem, a practical yet time-consuming process faced by researchers preparing for conferences. While recent approaches have attempted to automate this task, most neglect core design and aesthetic principles, resulting in posters that require substantial manual refinement. To address these design limitations, we propose PosterGen, a multi-agent framework that mirrors the workflow of professional poster designers. It consists of four collaborative specialized agents: (1) Parser and Curator agents extract content from the paper and organize storyboard; (2) Layout agent maps the content into a coherent spatial layout; (3) Stylist agents apply visual design elements such as color and typography; and (4) Renderer composes the final poster. Together, these agents produce posters that are both semantically grounded and visually appealing. To evaluate design quality, we introduce a vision-language model (VLM)-based rubric that measures layout balance, readability, and aesthetic coherence. Experimental results show that PosterGen consistently matches in content fidelity, and significantly outperforms existing methods in visual designs, generating posters that are presentation-ready with minimal human refinements.
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[AI-86] Scaling Graph Transformers: A Comparative Study of Sparse and Dense Attention
【速读】：该论文试图解决传统图神经网络（Graph Neural Networks, GNNs）在捕捉节点间长程依赖关系时的局限性问题，其解决方案的关键在于引入图变压器（Graph Transformers）中的注意力机制，通过对比密集型（dense）与稀疏型（sparse）注意力机制的性能差异、计算效率和适用场景，明确二者在不同任务中的权衡关系，并为设计更高效的图变压器注意力结构提供指导。
链接: https://arxiv.org/abs/2508.17175
作者: Leon Dimitrov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Graphs have become a central representation in machine learning for capturing relational and structured data across various domains. Traditional graph neural networks often struggle to capture long-range dependencies between nodes due to their local structure. Graph transformers overcome this by using attention mechanisms that allow nodes to exchange information globally. However, there are two types of attention in graph transformers: dense and sparse. In this paper, we compare these two attention mechanisms, analyze their trade-offs, and highlight when to use each. We also outline current challenges and problems in designing attention for graph transformers.
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[AI-87] ONG: Orthogonal Natural Gradient Descent
【速读】：该论文旨在解决连续学习（continual learning）任务中传统正交梯度下降（Orthogonal Gradient Descent, OGD）方法因使用欧几里得投影而忽略神经网络参数空间中分布流形的信息几何结构（information-geometric structure），从而导致收敛性能次优的问题。解决方案的关键在于提出一种结合自然梯度（natural gradient）思想的新型优化方法——正交自然梯度下降（Orthogonal Natural Gradient Descent, ONG），其核心是利用高效EKFAC近似计算逆Fisher信息矩阵对新任务梯度进行预条件处理，使更新方向在黎曼度量下沿最速下降方向进行，同时通过将自然梯度投影到先前任务梯度的正交补空间来保留旧知识，从而实现对新旧任务的协同优化。
链接: https://arxiv.org/abs/2508.17169
作者: Yajat Yadav,Jathin Korrapati,Patrick Mendoza
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Code at this https URL
Abstract:Orthogonal gradient descent has emerged as a powerful method for continual learning tasks. However, its Euclidean projections overlook the underlying information-geometric structure of the space of distributions parametrized by neural networks, which can lead to suboptimal convergence in learning tasks. To counteract this, we combine it with the idea of the natural gradient and present ONG (Orthogonal Natural Gradient Descent). ONG preconditions each new task gradient with an efficient EKFAC approximation of the inverse Fisher information matrix, yielding updates that follow the steepest descent direction under a Riemannian metric. To preserve performance on previously learned tasks, ONG projects these natural gradients onto the orthogonal complement of prior task gradients. We provide a theoretical justification for this procedure, introduce the ONG algorithm, and benchmark its performance on the Permuted and Rotated MNIST datasets. All code for our experiments/reproducibility can be found at this https URL.
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[AI-88] Error analysis for the deep Kolmogorov method
【速读】：该论文旨在解决基于深度神经网络（Deep Neural Network, DNN）的Kolmogorov方法在求解热方程（heat PDE）时的误差分析问题，特别是量化近似解与精确解之间的均方距离收敛性及其收敛速率。解决方案的关键在于建立理论框架，明确收敛性与三个核心因素的关系：（1）DNN架构规模（包括深度和隐藏层宽度），（2）损失函数中使用的随机采样点数量（即输入-输出数据对的数量），以及（3）所采用随机优化算法带来的优化误差大小。这一分析为深度学习求解偏微分方程提供了严格的理论支撑。
链接: https://arxiv.org/abs/2508.17167
作者: Iulian Cîmpean,Thang Do,Lukas Gonon,Arnulf Jentzen,Ionel Popescu
机构: 未知
类目: Numerical Analysis (math.NA); Artificial Intelligence (cs.AI); Analysis of PDEs (math.AP)
备注: 37 pages
Abstract:The deep Kolmogorov method is a simple and popular deep learning based method for approximating solutions of partial differential equations (PDEs) of the Kolmogorov type. In this work we provide an error analysis for the deep Kolmogorov method for heat PDEs. Specifically, we reveal convergence with convergence rates for the overall mean square distance between the exact solution of the heat PDE and the realization function of the approximating deep neural network (DNN) associated with a stochastic optimization algorithm in terms of the size of the architecture (the depth/number of hidden layers and the width of the hidden layers) of the approximating DNN, in terms of the number of random sample points used in the loss function (the number of input-output data pairs used in the loss function), and in terms of the size of the optimization error made by the employed stochastic optimization method.
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[AI-89] Mind the Gap: Time-of-Check to Time-of-Use Vulnerabilities in LLM -Enabled Agents
【速读】：该论文旨在解决生成式 AI（Generative AI）代理系统中尚未被充分研究的“检查到使用时间差”（Time-of-Check to Time-of-Use, TOCTOU）漏洞问题，这类漏洞发生在代理在验证外部状态（如文件或API响应）后、实际使用前该状态被恶意修改的情况下，可能导致配置篡改或载荷注入等安全威胁。解决方案的关键在于：首先构建了首个针对此类漏洞的基准测试工具TOCTOU-Bench（包含66个真实用户任务），用于系统评估；其次将传统系统安全中的检测与缓解技术适配至代理场景，提出三种核心方法——提示重写（prompt rewriting）、状态完整性监控（state integrity monitoring）和工具融合（tool-fusing），从而实现对TOCTOU攻击的有效防御，在组合使用时使漏洞执行率从12%降至8%，显著提升了代理系统的安全性。
链接: https://arxiv.org/abs/2508.17155
作者: Derek Lilienthal,Sanghyun Hong
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Pre-print
Abstract:Large Language Model (LLM)-enabled agents are rapidly emerging across a wide range of applications, but their deployment introduces vulnerabilities with security implications. While prior work has examined prompt-based attacks (e.g., prompt injection) and data-oriented threats (e.g., data exfiltration), time-of-check to time-of-use (TOCTOU) remain largely unexplored in this context. TOCTOU arises when an agent validates external state (e.g., a file or API response) that is later modified before use, enabling practical attacks such as malicious configuration swaps or payload injection. In this work, we present the first study of TOCTOU vulnerabilities in LLM-enabled agents. We introduce TOCTOU-Bench, a benchmark with 66 realistic user tasks designed to evaluate this class of vulnerabilities. As countermeasures, we adapt detection and mitigation techniques from systems security to this setting and propose prompt rewriting, state integrity monitoring, and tool-fusing. Our study highlights challenges unique to agentic workflows, where we achieve up to 25% detection accuracy using automated detection methods, a 3% decrease in vulnerable plan generation, and a 95% reduction in the attack window. When combining all three approaches, we reduce the TOCTOU vulnerabilities from an executed trajectory from 12% to 8%. Our findings open a new research direction at the intersection of AI safety and systems security.
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[AI-90] Rethinking How AI Embeds and Adapts to Human Values: Challenges and Opportunities
【速读】：该论文旨在解决当前人工智能（AI）系统中价值对齐（value alignment）研究中存在的局限性，特别是如何实现动态、多主体视角下的价值对齐问题。其核心挑战在于现有方法往往依赖静态且单一的价值观定义，忽视了人类价值观的多样性、演化性和群体间冲突。论文指出，解决方案的关键在于推动价值对齐从静态概念转向长期推理与适应性机制，并引入多智能体系统（multi-agent systems）作为框架，以应对个体与群体间的价值差异、冲突及交互式价值推理。同时，强调需发展更全面的理论体系来覆盖人类价值的多元维度，从而提升AI系统的伦理安全性与社会可接受性。
链接: https://arxiv.org/abs/2508.17104
作者: Sz-Ting Tzeng,Frank Dignum
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 7 pages, accepted at VALE 2025
Abstract:The concepts of human-centered AI'' and value-based decision’’ have gained significant attention in both research and industry. However, many critical aspects remain underexplored and require further investigation. In particular, there is a need to understand how systems incorporate human values, how humans can identify these values within systems, and how to minimize the risks of harm or unintended consequences. In this paper, we highlight the need to rethink how we frame value alignment and assert that value alignment should move beyond static and singular conceptions of values. We argue that AI systems should implement long-term reasoning and remain adaptable to evolving values. Furthermore, value alignment requires more theories to address the full spectrum of human values. Since values often vary among individuals or groups, multi-agent systems provide the right framework for navigating pluralism, conflict, and inter-agent reasoning about values. We identify the challenges associated with value alignment and indicate directions for advancing value alignment research. In addition, we broadly discuss diverse perspectives of value alignment, from design methodologies to practical applications.
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[AI-91] wo Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process AISTATS’25
【速读】：该论文旨在解决图神经网络（Graph Neural Networks, GNNs）在关键应用场景中预测结果存在不确定性量化不足和可解释性差的问题。其解决方案的关键在于提出一种融合图函数神经过程（graph functional neural process）与图生成模型（graph generative model）的不确定性感知且可解释的图分类框架：通过假设一组潜在的理性依据（latent rationales），将其映射到概率嵌入空间，并利用学习到的随机相关矩阵使分类器的预测分布条件化于这些理性嵌入；同时，图生成器从嵌入空间解码出理性结构以实现模型解释性。训练过程中采用模仿期望最大化（Expectation-Maximization, EM）算法的交替优化策略，确保高效收敛。该方法具有通用性，可适配任意现有GNN架构，并在五个图分类数据集上验证了其在不确定性量化和可解释性方面的优越性能。
链接: https://arxiv.org/abs/2508.17097
作者: Lingkai Kong,Haotian Sun,Yuchen Zhuang,Haorui Wang,Wenhao Mu,Chao Zhang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: AISTATS’25
Abstract:Graph neural networks (GNNs) are powerful tools on graph data. However, their predictions are mis-calibrated and lack interpretability, limiting their adoption in critical applications. To address this issue, we propose a new uncertainty-aware and interpretable graph classification model that combines graph functional neural process and graph generative model. The core of our method is to assume a set of latent rationales which can be mapped to a probabilistic embedding space; the predictive distribution of the classifier is conditioned on such rationale embeddings by learning a stochastic correlation matrix. The graph generator serves to decode the graph structure of the rationales from the embedding space for model interpretability. For efficient model training, we adopt an alternating optimization procedure which mimics the well known Expectation-Maximization (EM) algorithm. The proposed method is general and can be applied to any existing GNN architecture. Extensive experiments on five graph classification datasets demonstrate that our framework outperforms state-of-the-art methods in both uncertainty quantification and GNN interpretability. We also conduct case studies to show that the decoded rationale structure can provide meaningful explanations.
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[AI-92] Convolutional Neural Networks for Accurate Measurement of Train Speed
【速读】：该论文旨在解决现代铁路系统中列车速度估计精度不足的问题，尤其是在复杂工况下（如轮滑保护系统激活时）传统方法性能受限的挑战。其解决方案的关键在于引入卷积神经网络（Convolutional Neural Networks, CNN）架构，特别是多分支（multiple-branch）模型，通过深度学习技术更有效地捕捉交通数据中的复杂模式，从而显著提升速度估计的准确性与鲁棒性，优于传统的自适应卡尔曼滤波（Adaptive Kalman Filter）方法。
链接: https://arxiv.org/abs/2508.17096
作者: Haitao Tian,Argyrios Zolotas,Miguel Arana-Catania
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: 15 pages, 12 figures, 2 tables. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Abstract:In this study, we explore the use of Convolutional Neural Networks for improving train speed estimation accuracy, addressing the complex challenges of modern railway systems. We investigate three CNN architectures - single-branch 2D, single-branch 1D, and multiple-branch models - and compare them with the Adaptive Kalman Filter. We analyse their performance using simulated train operation datasets with and without Wheel Slide Protection activation. Our results reveal that CNN-based approaches, especially the multiple-branch model, demonstrate superior accuracy and robustness compared to traditional methods, particularly under challenging operational conditions. These findings highlight the potential of deep learning techniques to enhance railway safety and operational efficiency by more effectively capturing intricate patterns in complex transportation datasets.
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[AI-93] PowerChain: Automating Distribution Grid Analysis with Agent ic AI Workflows
【速读】：该论文旨在解决配电系统（Distribution Grid, DG）运行与规划日益复杂化背景下，小型公用事业公司和合作社因缺乏专业研发（R&D）团队而难以规模化应用先进计算分析的问题。解决方案的关键在于提出一种新型代理型人工智能（Agentic AI）系统 PowerChain，其通过自动化代理编排与大语言模型（Large Language Models, LLMs）函数调用能力，将自然语言查询动态转化为由领域感知函数组成的有序执行序列。该系统基于专家构建的电力系统函数库和已知工作流-查询对参考集进行语义引导，从而在真实电网数据上实现对未见过的DG分析任务的专家级流程生成，显著提升了分析自动化水平与可及性。
链接: https://arxiv.org/abs/2508.17094
作者: Emmanuel O. Badmus,Peng Sang,Dimitrios Stamoulis,Amritanshu Pandey
机构: 未知
类目: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注:
Abstract:Due to the rapid pace of electrification and decarbonization, distribution grid (DG) operation and planning are becoming more complex, necessitating advanced computational analyses to ensure grid reliability and resilience. State-of-the-art DG analyses rely on disparate workflows of complex models, functions, and data pipelines, which require expert knowledge and are challenging to automate. Many small-scale utilities and cooperatives lack a large RD workforce and therefore cannot use advanced analysis at scale. To address this gap, we develop a novel agentic AI system, PowerChain, to solve unseen DG analysis tasks via automated agentic orchestration and large language models (LLMs) function-calling. Given a natural language query, PowerChain dynamically generates and executes an ordered sequence of domain-aware functions guided by the semantics of an expert-built power systems function pool and a select reference set of known, expert-generated workflow-query pairs. Our results show that PowerChain can produce expert-level workflows with both GPT-5 and open-source Qwen models on complex, unseen DG analysis tasks operating on real utility data.
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[AI-94] Enhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings
【速读】：该论文旨在解决知识追踪（Knowledge Tracing, KT）模型中存在的标签泄露（label leakage）问题，尤其是在多知识点（Knowledge Concepts, KCs）标注的题目中，输入数据可能无意间暴露正确答案，从而影响模型预测准确性。其核心解决方案是通过在输入嵌入构建阶段引入一个专用的MASK标签来屏蔽真实标签，这一思想借鉴自掩码语言建模（如BERT），有效防止了标签信息在训练过程中被模型利用。此外，论文提出了一种新的“近期编码”（Recency Encoding），用于捕捉当前题目与其最近一次出现之间的时间步长距离，以更好地建模遗忘等学习动态过程，相较于传统位置编码，在多个KT基准测试中表现更优。该方法简单高效且可广泛适配于DKT、DKT+、AKT和SAKT等多种主流KT模型。
链接: https://arxiv.org/abs/2508.17092
作者: Yahya Badran,Christine Preisach
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Knowledge Tracing (KT) aims to predict a student’s future performance based on their sequence of interactions with learning content. Many KT models rely on knowledge concepts (KCs), which represent the skills required for each item. However, some of these models are vulnerable to label leakage, in which input data inadvertently reveal the correct answer, particularly in datasets with multiple KCs per question. We propose a straightforward yet effective solution to prevent label leakage by masking ground-truth labels during input embedding construction in cases susceptible to leakage. To accomplish this, we introduce a dedicated MASK label, inspired by masked language modeling (e.g., BERT), to replace ground-truth labels. In addition, we introduce Recency Encoding, which encodes the step-wise distance between the current item and its most recent previous occurrence. This distance is important for modeling learning dynamics such as forgetting, which is a fundamental aspect of human learning, yet it is often overlooked in existing models. Recency Encoding demonstrates improved performance over traditional positional encodings on multiple KT benchmarks. We show that incorporating our embeddings into KT models like DKT, DKT+, AKT, and SAKT consistently improves prediction accuracy across multiple benchmarks. The approach is both efficient and widely applicable. Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2508.17092 [cs.CY] (or arXiv:2508.17092v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2508.17092 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-95] Solving the Min-Max Multiple Traveling Salesmen Problem via Learning-Based Path Generation and Optimal Splitting
【速读】：该论文旨在解决最小-最大多旅行商问题（Min-Max Multiple Traveling Salesmen Problem, m³-TSP），其目标是协调多个旅行商的路径，使得最长路径的长度最小化。由于该问题是NP-hard问题，在P≠NP假设下，传统精确求解器难以在合理时间内获得高质量解。为此，作者提出一种新颖的两阶段框架——Generate-and-Split (GaS)，其核心创新在于将强化学习（Reinforcement Learning, RL）与最优分割算法联合训练，通过LSTM增强模型架构缓解部分可观测性问题，从而实现端到端的优化。关键突破在于：利用分割算法在欧氏空间中对任意给定路径进行最优分割，保证近线性扩展性并提升解的质量与迁移能力。
链接: https://arxiv.org/abs/2508.17087
作者: Wen Wang,Xiangchen Wu,Liang Wang,Hao Hu,Xianping Tao,Linghao Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:This study addresses the Min-Max Multiple Traveling Salesmen Problem ( m^3 -TSP), which aims to coordinate tours for multiple salesmen such that the length of the longest tour is minimized. Due to its NP-hard nature, exact solvers become impractical under the assumption that P \ne NP . As a result, learning-based approaches have gained traction for their ability to rapidly generate high-quality approximate solutions. Among these, two-stage methods combine learning-based components with classical solvers, simplifying the learning objective. However, this decoupling often disrupts consistent optimization, potentially degrading solution quality. To address this issue, we propose a novel two-stage framework named \textbfGenerate-and-Split (GaS), which integrates reinforcement learning (RL) with an optimal splitting algorithm in a joint training process. The splitting algorithm offers near-linear scalability with respect to the number of cities and guarantees optimal splitting in Euclidean space for any given path. To facilitate the joint optimization of the RL component with the algorithm, we adopt an LSTM-enhanced model architecture to address partial observability. Extensive experiments show that the proposed GaS framework significantly outperforms existing learning-based approaches in both solution quality and transferability.
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[AI-96] Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在处理私有或领域外文档时检索性能下降的问题，尤其是在跨域（out-of-distribution）和多语言场景下，现有检索器难以有效匹配文本与视觉信息。其解决方案的关键在于提出PREMIR框架，通过利用MLLM的广泛知识生成跨模态预提问（cross-modal pre questions, preQs），并在检索阶段将这些preQs作为桥梁，实现从单一向量空间匹配到基于token级别的多模态语义对齐，从而显著提升在未见领域和语言中的检索鲁棒性与准确性。
链接: https://arxiv.org/abs/2508.17079
作者: Yejin Choi,Jaewoo Park,Janghan Yoon,Saejin Kim,Jaehyun Jeon,Youngjae Yu
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注:
Abstract:Rapid advances in Multimodal Large Language Models (MLLMs) have expanded information retrieval beyond purely textual inputs, enabling retrieval from complex real world documents that combine text and visuals. However, most documents are private either owned by individuals or confined within corporate silos and current retrievers struggle when faced with unseen domains or languages. To address this gap, we introduce PREMIR, a simple yet effective framework that leverages the broad knowledge of an MLLM to generate cross modal pre questions (preQs) before retrieval. Unlike earlier multimodal retrievers that compare embeddings in a single vector space, PREMIR leverages preQs from multiple complementary modalities to expand the scope of matching to the token level. Experiments show that PREMIR achieves state of the art performance on out of distribution benchmarks, including closed domain and multilingual settings, outperforming strong baselines across all retrieval metrics. We confirm the contribution of each component through in depth ablation studies, and qualitative analyses of the generated preQs further highlight the model’s robustness in real world settings.
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[AI-97] Optimizing Neural Networks with Learnable Non-Linear Activation Functions via Lookup-Based FPGA Acceleration
【速读】：该论文旨在解决生成式 AI (Generative AI) 中基于学习的激活函数（如Kolmogorov-Arnold Networks, KANs）在边缘计算场景下因高阶运算导致的能耗与延迟问题，这些问题限制了其在超紧致能源预算下的部署可行性。解决方案的关键在于提出一种基于可重构查找表（lookup table）的架构设计，结合细粒度量化与自适应查找表机制，在边缘FPGA上实现对学习激活函数的高效执行：通过减少高能耗算术运算并利用FPGA的动态硬件定制能力，实现了比边缘CPU/GPU高四个数量级以上的能效提升，同时保持精度一致性和极小的硬件资源开销。
链接: https://arxiv.org/abs/2508.17069
作者: Mengyuan Yin,Benjamin Chen Ming Choong,Chuping Qu,Rick Siow Mong Goh,Weng-Fai Wong,Tao Luo
机构: 未知
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI)
备注:
Abstract:Learned activation functions in models like Kolmogorov-Arnold Networks (KANs) outperform fixed-activation architectures in terms of accuracy and interpretability; however, their computational complexity poses critical challenges for energy-constrained edge AI deployments. Conventional CPUs/GPUs incur prohibitive latency and power costs when evaluating higher order activations, limiting deployability under ultra-tight energy budgets. We address this via a reconfigurable lookup architecture with edge FPGAs. By coupling fine-grained quantization with adaptive lookup tables, our design minimizes energy-intensive arithmetic operations while preserving activation fidelity. FPGA reconfigurability enables dynamic hardware specialization for learned functions, a key advantage for edge systems that require post-deployment adaptability. Evaluations using KANs - where unique activation functions play a critical role - demonstrate that our FPGA-based design achieves superior computational speed and over 10^4 times higher energy efficiency compared to edge CPUs and GPUs, while maintaining matching accuracy and minimal footprint overhead. This breakthrough positions our approach as a practical enabler for energy-critical edge AI, where computational intensity and power constraints traditionally preclude the use of adaptive activation networks.
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[AI-98] abResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression
【速读】：该论文旨在解决表格回归（tabular regression）中传统点估计方法导致预测过于自信的问题，尤其是在工业自动化场景下，这种过度自信会严重影响决策的可信度。为提升预测的不确定性建模能力，论文提出了一种名为TabResFlow的条件正则化样条流（conditional spline-based normalizing flow）模型，其核心创新在于：(1) 使用多层感知机（MLP）编码器处理每个数值特征；(2) 采用全连接残差网络（ResNet）作为骨干结构以实现高表达力的特征提取；(3) 引入基于样条的条件流模型进行灵活且可计算的概率密度估计，从而有效捕捉复杂的目标分布形态。相比现有方法如TreeFlow和NodeFlow，TabResFlow在似然得分上显著优于主流模型，并在推理速度上实现了平均5.6倍的加速。
链接: https://arxiv.org/abs/2508.17056
作者: Kiran Madhusudhanan,Vijaya Krishna Yalavarthi,Jonas Sonntag,Maximilian Stubbemann,Lars Schmidt-Thieme
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: To be published in The European Conference on Artificial Intelligence, 2025
Abstract:Tabular regression is a well-studied problem with numerous industrial applications, yet most existing approaches focus on point estimation, often leading to overconfident predictions. This issue is particularly critical in industrial automation, where trustworthy decision-making is essential. Probabilistic regression models address this challenge by modeling prediction uncertainty. However, many conventional methods assume a fixed-shape distribution (typically Gaussian), and resort to estimating distribution parameters. This assumption is often restrictive, as real-world target distributions can be highly complex. To overcome this limitation, we introduce TabResFlow, a Normalizing Spline Flow model designed specifically for univariate tabular regression, where commonly used simple flow networks like RealNVP and Masked Autoregressive Flow (MAF) are unsuitable. TabResFlow consists of three key components: (1) An MLP encoder for each numerical feature. (2) A fully connected ResNet backbone for expressive feature extraction. (3) A conditional spline-based normalizing flow for flexible and tractable density estimation. We evaluate TabResFlow on nine public benchmark datasets, demonstrating that it consistently surpasses existing probabilistic regression models on likelihood scores. Our results demonstrate 9.64% improvement compared to the strongest probabilistic regression model (TreeFlow), and on average 5.6 times speed-up in inference time compared to the strongest deep learning alternative (NodeFlow). Additionally, we validate the practical applicability of TabResFlow in a real-world used car price prediction task under selective regression. To measure performance in this setting, we introduce a novel Area Under Risk Coverage (AURC) metric and show that TabResFlow achieves superior results across this metric.
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[AI-99] Complexity in finitary argumentation (extended version)
【速读】：该论文旨在解决无限抽象论证框架（Abstract Argumentation Frameworks, AFs）在计算上难以处理的问题，尤其是在保持其表达能力的同时提升可计算性。尽管无限AFs具有强大的建模能力，但其计算复杂性限制了实际应用。论文聚焦于“有限攻击”（finitary）的无限AFs，即每个论点仅被有限数量的其他论点攻击的情形。研究发现，虽然有限攻击假设本身并不自动降低复杂度，但对于基于可接受性的语义（admissibility-based semantics），存在一个关键的组合约束条件，该条件能够显著降低计算复杂度。这一发现表明，有限攻击的无限AFs能够在表达力与计算可行性之间取得良好平衡，适用于多种推理场景的分析与建模。
链接: https://arxiv.org/abs/2508.16986
作者: Uri Andrews,Luca San Mauro
机构: 未知
类目: Artificial Intelligence (cs.AI); Logic (math.LO)
备注:
Abstract:Abstract argumentation frameworks (AFs) provide a formal setting to analyze many forms of reasoning with conflicting information. While the expressiveness of general infinite AFs make them a tempting tool for modeling many kinds of reasoning scenarios, the computational intractability of solving infinite AFs limit their use, even in many theoretical applications. We investigate the complexity of computational problems related to infinite but finitary argumentations frameworks, that is, infinite AFs where each argument is attacked by only finitely many others. Our results reveal a surprising scenario. On one hand, we see that the assumption of being finitary does not automatically guarantee a drop in complexity. However, for the admissibility-based semantics, we find a remarkable combinatorial constraint which entails a dramatic decrease in complexity. We conclude that for many forms of reasoning, the finitary infinite AFs provide a natural setting for reasoning which balances well the competing goals of being expressive enough to be applied to many reasoning settings while being computationally tractable enough for the analysis within the framework to be useful. Subjects: Artificial Intelligence (cs.AI); Logic (math.LO) Cite as: arXiv:2508.16986 [cs.AI] (or arXiv:2508.16986v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2508.16986 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-100] LLM -based Human-like Traffic Simulation for Self-driving Tests
【速读】：该论文旨在解决当前自动驾驶仿真平台中难以生成真实、多样化交通行为的问题，尤其是现有方法依赖手工规则或窄域数据驱动模型，导致驾驶风格多样性不足且可解释性差。其解决方案的关键在于提出HDSim框架，该框架融合认知理论与大语言模型（Large Language Model, LLM）辅助，构建了分层驾驶员模型以表征多样化的驾驶风格特征，并设计感知引导的行为影响策略（Perception-Mediated Behavior Influence），通过LLM间接调控感知过程来塑造驾驶员行为，从而在仿真中实现更高保真度和可解释性的交通场景生成。
链接: https://arxiv.org/abs/2508.16962
作者: Wendi Li,Hao Wu,Han Gao,Bing Mao,Fengyuan Xu,Sheng Zhong
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Ensuring realistic traffic dynamics is a prerequisite for simulation platforms to evaluate the reliability of self-driving systems before deployment in the real world. Because most road users are human drivers, reproducing their diverse behaviors within simulators is vital. Existing solutions, however, typically rely on either handcrafted heuristics or narrow data-driven models, which capture only fragments of real driving behaviors and offer limited driving style diversity and interpretability. To address this gap, we introduce HDSim, an HD traffic generation framework that combines cognitive theory with large language model (LLM) assistance to produce scalable and realistic traffic scenarios within simulation platforms. The framework advances the state of the art in two ways: (i) it introduces a hierarchical driver model that represents diverse driving style traits, and (ii) it develops a Perception-Mediated Behavior Influence strategy, where LLMs guide perception to indirectly shape driver actions. Experiments reveal that embedding HDSim into simulation improves detection of safety-critical failures in self-driving systems by up to 68% and yields realism-consistent accident interpretability.
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[AI-101] Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在强化学习（Reinforcement Learning, RL）训练中因探索受限而导致的推理能力提升瓶颈问题，即“无法探索则无法学习”的恶性循环。其核心解决方案是提出Rubric-Scaffolded Reinforcement Learning（RuscaRL），关键在于引入检查清单式评分标准（checklist-style rubrics）作为双重机制：一方面，在推理生成阶段提供显式指导以扩展高质量样本的探索空间，通过逐步衰减外部引导促使模型内化推理模式；另一方面，在训练阶段构建可验证的奖励信号，利用rubrics作为参考基准获得稳定可靠的LLM-as-a-Judge评分，从而实现对通用推理任务的有效强化学习优化。
链接: https://arxiv.org/abs/2508.16949
作者: Yang Zhou,Sunzhu Li,Shunyu Liu,Wenkai Fang,Jiale Zhao,Jingwen Yang,Jianwei Lv,Kongcheng Zhang,Yihe Zhou,Hengtong Lu,Wei Chen,Yan Xie,Mingli Song
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent advances in Large Language Models (LLMs) have underscored the potential of Reinforcement Learning (RL) to facilitate the emergence of reasoning capabilities. Despite the encouraging results, a fundamental dilemma persists as RL improvement relies on learning from high-quality samples, yet the exploration for such samples remains bounded by the inherent limitations of LLMs. This, in effect, creates an undesirable cycle in which what cannot be explored cannot be learned. In this work, we propose Rubric-Scaffolded Reinforcement Learning (RuscaRL), a novel instructional scaffolding framework designed to break the exploration bottleneck for general LLM reasoning. Specifically, RuscaRL introduces checklist-style rubrics as (1) explicit scaffolding for exploration during rollout generation, where different rubrics are provided as external guidance within task instructions to steer diverse high-quality responses. This guidance is gradually decayed over time, encouraging the model to internalize the underlying reasoning patterns; (2) verifiable rewards for exploitation during model training, where we can obtain robust LLM-as-a-Judge scores using rubrics as references, enabling effective RL on general reasoning tasks. Extensive experiments demonstrate the superiority of the proposed RuscaRL across various benchmarks, effectively expanding reasoning boundaries under the best-of-N evaluation. Notably, RuscaRL significantly boosts Qwen-2.5-7B-Instruct from 23.6 to 50.3 on HealthBench-500, surpassing GPT-4.1. Furthermore, our fine-tuned variant on Qwen3-30B-A3B-Instruct achieves 61.1 on HealthBench-500, outperforming leading LLMs including OpenAI-o3.
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[AI-102] Drive As You Like: Strategy-Level Motion Planning Based on A Multi-Head Diffusion Model AAAI2026
【速读】：该论文旨在解决当前自动驾驶运动规划模型在监督训练后策略固定导致行为僵化的问题，从而限制了其对人类偏好或动态指令的适应能力。解决方案的关键在于提出一种基于扩散模型的多头轨迹规划器（M-diffusion planner），在预训练阶段所有输出头共享权重以学习高质量轨迹生成能力；随后利用扩散模型的概率特性，通过分组相对策略优化（GRPO）对模型进行微调，实现多样化、特定策略的行为输出；推理时引入大语言模型（LLM）动态指导策略选择，无需切换模型即可实现指令感知的灵活规划。
链接: https://arxiv.org/abs/2508.16947
作者: Fan Ding,Xuewen Luo,Hwa Hui Tew,Ruturaj Reddy,Xikun Wang,Junn Yong Loo
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Has been submitted to AAAI 2026
Abstract:Recent advances in motion planning for autonomous driving have led to models capable of generating high-quality trajectories. However, most existing planners tend to fix their policy after supervised training, leading to consistent but rigid driving behaviors. This limits their ability to reflect human preferences or adapt to dynamic, instruction-driven demands. In this work, we propose a diffusion-based multi-head trajectory planner(M-diffusion planner). During the early training stage, all output heads share weights to learn to generate high-quality trajectories. Leveraging the probabilistic nature of diffusion models, we then apply Group Relative Policy Optimization (GRPO) to fine-tune the pre-trained model for diverse policy-specific behaviors. At inference time, we incorporate a large language model (LLM) to guide strategy selection, enabling dynamic, instruction-aware planning without switching models. Closed-loop simulation demonstrates that our post-trained planner retains strong planning capability while achieving state-of-the-art (SOTA) performance on the nuPlan val14 benchmark. Open-loop results further show that the generated trajectories exhibit clear diversity, effectively satisfying multi-modal driving behavior requirements. The code and related experiments will be released upon acceptance of the paper.
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[AI-103] HumanoidVerse: A Versatile Humanoid for Vision-Language Guided Multi-Object Rearrangement
【速读】：该论文旨在解决物理仿真中人形机器人在多样化场景下执行长时程、多物体重排任务的挑战，尤其是如何在仅依赖自然语言指令和第一视角RGB视觉输入的情况下实现连续、精准的多物体操作。其解决方案的关键在于提出HumanoidVerse框架，该框架采用分阶段训练的多教师蒸馏（dual-teacher distillation）策略，使机器人能够在不需环境重置的前提下平滑过渡至子任务，并通过构建包含350个多物体任务的大规模数据集（覆盖四种房间布局）提升模型泛化能力。实验表明，该方法在任务成功率和空间精度上显著优于现有最先进方法，且能有效适应未见过的环境与指令。
链接: https://arxiv.org/abs/2508.16943
作者: Haozhuo Zhang,Jingkai Sun,Michele Caprio,Jian Tang,Shanghang Zhang,Qiang Zhang,Wei Pan
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Project Page: this https URL
Abstract:We introduce HumanoidVerse, a novel framework for vision-language guided humanoid control that enables a single physically simulated robot to perform long-horizon, multi-object rearrangement tasks across diverse scenes. Unlike prior methods that operate in fixed settings with single-object interactions, our approach supports consecutive manipulation of multiple objects, guided only by natural language instructions and egocentric camera RGB observations. HumanoidVerse is trained via a multi-stage curriculum using a dual-teacher distillation pipeline, enabling fluid transitions between sub-tasks without requiring environment resets. To support this, we construct a large-scale dataset comprising 350 multi-object tasks spanning four room layouts. Extensive experiments in the Isaac Gym simulator demonstrate that our method significantly outperforms prior state-of-the-art in both task success rate and spatial precision, and generalizes well to unseen environments and instructions. Our work represents a key step toward robust, general-purpose humanoid agents capable of executing complex, sequential tasks under real-world sensory constraints. The video visualization results can be found on the project page: this https URL.
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[AI-104] Degree of Staleness-Aware Data Updating in Federated Learning
【速读】：该论文旨在解决联邦学习（Federated Learning）中因数据持续生成而导致的数据陈旧性（Data Staleness）问题，尤其在对时间敏感的任务中，数据陈旧性显著影响模型性能。现有方法仅通过调整本地数据更新频率或客户端选择策略来优化数据陈旧性，但未同时考虑数据陈旧性和数据量之间的权衡。解决方案的关键在于提出一种名为DUFL（Data Updating in Federated Learning）的激励机制，其核心是一个由三个控制参数（服务器支付、过时数据保留率、客户端新鲜数据收集量）调节的本地数据更新方案，以协调本地数据的陈旧程度与数据量之间的关系，从而最大化整体效用。为此，作者引入了一个新的量化指标DoS（Degree of Staleness），并理论分析了DoS与模型性能之间的定量关系，将DUFL建模为具有动态约束的两阶段Stackelberg博弈，推导出客户端的闭式最优本地更新策略和服务器的近似最优策略。
链接: https://arxiv.org/abs/2508.16931
作者: Tao Liu,Xuehe Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: accepted by European Conference on Artificial Intelligence
Abstract:Handling data staleness remains a significant challenge in federated learning with highly time-sensitive tasks, where data is generated continuously and data staleness largely affects model performance. Although recent works attempt to optimize data staleness by determining local data update frequency or client selection strategy, none of them explore taking both data staleness and data volume into consideration. In this paper, we propose DUFL(Data Updating in Federated Learning), an incentive mechanism featuring an innovative local data update scheme manipulated by three knobs: the server’s payment, outdated data conservation rate, and clients’ fresh data collection volume, to coordinate staleness and volume of local data for best utilities. To this end, we introduce a novel metric called DoS(the Degree of Staleness) to quantify data staleness and conduct a theoretic analysis illustrating the quantitative relationship between DoS and model performance. We model DUFL as a two-stage Stackelberg game with dynamic constraint, deriving the optimal local data update strategy for each client in closed-form and the approximately optimal strategy for the server. Experimental results on real-world datasets demonstrate the significant performance of our approach.
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[AI-105] xtOnly: A Unified Function Portal for Text-Related Functions on Smartphones
【速读】：该论文旨在解决用户在智能手机应用中访问特定文本功能时需经历多步骤导航的问题，从而提升操作效率。其解决方案的关键在于提出一个统一的功能入口——TextOnly，该系统通过单一文本框接收用户输入，并结合大语言模型（Large Language Model, LLM）与BERT模型实现意图识别：LLM提供通用知识支持，BERT模型则持续学习用户个性化偏好以加快预测速度，从而高效解析原始文本输入中的丰富信息并触发对应应用功能。
链接: https://arxiv.org/abs/2508.16926
作者: Minghao Tu,Chun Yu,Xiyuan Shen,Zhi Zheng,Li Chen,Yuanchun Shi
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 27 pages, 9 figures
Abstract:Text boxes serve as portals to diverse functionalities in today’s smartphone applications. However, when it comes to specific functionalities, users always need to navigate through multiple steps to access particular text boxes for input. We propose TextOnly, a unified function portal that enables users to access text-related functions from various applications by simply inputting text into a sole text box. For instance, entering a restaurant name could trigger a Google Maps search, while a greeting could initiate a conversation in WhatsApp. Despite their brevity, TextOnly maximizes the utilization of these raw text inputs, which contain rich information, to interpret user intentions effectively. TextOnly integrates large language models(LLM) and a BERT model. The LLM consistently provides general knowledge, while the BERT model can continuously learn user-specific preferences and enable quicker predictions. Real-world user studies demonstrated TextOnly’s effectiveness with a top-1 accuracy of 71.35%, and its ability to continuously improve both its accuracy and inference speed. Participants perceived TextOnly as having satisfactory usability and expressed a preference for TextOnly over manual executions. Compared with voice assistants, TextOnly supports a greater range of text-related functions and allows for more concise inputs.
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[AI-106] ri-Accel: Curvature-Aware Precision-Adaptive and Memory-Elastic Optimization for Efficient GPU Usage
【速读】：该论文旨在解决深度神经网络训练中因优化成本过高（包括GPU显存占用和计算时间）而导致的性能瓶颈问题。现有加速技术如混合精度、二阶优化方法和批量大小扩展通常独立使用，难以协同优化资源分配。其解决方案的关键在于提出一个统一的优化框架Tri-Accel，通过三重自适应策略联合优化：(1) 精度自适应更新（Precision-Adaptive Updates），根据层的曲率和梯度方差动态分配混合精度；(2) 稀疏二阶信号（Sparse Second-Order Signals），利用Hessian/Fisher矩阵的稀疏结构指导精度与步长决策；(3) 内存弹性批量缩放（Memory-Elastic Batch Scaling），依据显存可用性实时调整批量大小。该框架结合硬件感知的Triton自定义内核实现自动调参，显著降低显存消耗并提升训练效率，在CIFAR-10/100上实现了最高9.9%的训练时间减少、13.3%的内存节省及准确率提升，展现出在边缘设备和云部署场景下的高可扩展性。
链接: https://arxiv.org/abs/2508.16905
作者: Mohsen Sheibanian,Pouya Shaeri,Alimohammad Beigi,Ryan T. Woo,Aryan Keluskar
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Deep neural networks are increasingly bottlenecked by the cost of optimization, both in terms of GPU memory and compute time. Existing acceleration techniques, such as mixed precision, second-order methods, and batch size scaling, are typically used in isolation. We present Tri-Accel, a unified optimization framework that co-adapts three acceleration strategies along with adaptive parameters during training: (1) Precision-Adaptive Updates that dynamically assign mixed-precision levels to layers based on curvature and gradient variance; (2) Sparse Second-Order Signals that exploit Hessian/Fisher sparsity patterns to guide precision and step size decisions; and (3) Memory-Elastic Batch Scaling that adjusts batch size in real time according to VRAM availability. On CIFAR-10 with ResNet-18 and EfficientNet-B0, Tri-Accel achieves up to 9.9% reduction in training time and 13.3% lower memory usage, while improving accuracy by +1.1 percentage points over FP32 baselines. Tested on CIFAR-10/100, our approach demonstrates adaptive learning behavior, with efficiency gradually improving over the course of training as the system learns to allocate resources more effectively. Compared to static mixed-precision training, Tri-Accel maintains 78.1% accuracy while reducing memory footprint from 0.35GB to 0.31GB on standard hardware. The framework is implemented with custom Triton kernels, whose hardware-aware adaptation enables automatic optimization without manual hyperparameter tuning, making it practical for deployment across diverse computational environments. This work demonstrates how algorithmic adaptivity and hardware awareness can be combined to improve scalability in resource-constrained settings, paving the way for more efficient neural network training on edge devices and cost-sensitive cloud deployments.
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[AI-107] riagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
【速读】：该论文旨在解决预训练语言模型（Pretrained Language Models, PLMs）在缺陷分配（bug triaging）任务中存在两个核心问题：一是PLMs可能关注到不相关的词元（token），从而影响推荐准确性；二是未充分考虑开发人员在类似缺陷上的历史交互行为，导致推荐结果不够优化。解决方案的关键在于提出TriagerX框架，其创新性体现在两方面：首先，采用双Transformer架构，通过两个独立的Transformer分别从最后三层输出推荐结果，以增强基于内容的候选开发者排序鲁棒性；其次，引入一种新颖的基于交互历史的排序方法，利用开发人员对相似已修复缺陷的历史互动数据进一步优化排名。实验证明，TriagerX在五个数据集上超越所有九种基于Transformer的方法，Top-1和Top-3推荐准确率提升超10%，且在工业部署中实现了开发者与组件（作为团队代理）推荐性能分别提升54%和10%。
链接: https://arxiv.org/abs/2508.16860
作者: Md Afif Al Mamun,Gias Uddin,Lan Xia,Longyu Zhang
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: This work is currently under review at IEEE Transactions on Software Engineering. The replication package will be made publicly available upon acceptance
Abstract:Pretrained Language Models or PLMs are transformer-based architectures that can be used in bug triaging tasks. PLMs can better capture token semantics than traditional Machine Learning (ML) models that rely on statistical features (e.g., TF-IDF, bag of words). However, PLMs may still attend to less relevant tokens in a bug report, which can impact their effectiveness. In addition, the model can be sub-optimal with its recommendations when the interaction history of developers around similar bugs is not taken into account. We designed TriagerX to address these limitations. First, to assess token semantics more reliably, we leverage a dual-transformer architecture. Unlike current state-of-the-art (SOTA) baselines that employ a single transformer architecture, TriagerX collects recommendations from two transformers with each offering recommendations via its last three layers. This setup generates a robust content-based ranking of candidate developers. TriagerX then refines this ranking by employing a novel interaction-based ranking methodology, which considers developers’ historical interactions with similar fixed bugs. Across five datasets, TriagerX surpasses all nine transformer-based methods, including SOTA baselines, often improving Top-1 and Top-3 developer recommendation accuracy by over 10%. We worked with our large industry partner to successfully deploy TriagerX in their development environment. The partner required both developer and component recommendations, with components acting as proxies for team assignments-particularly useful in cases of developer turnover or team changes. We trained TriagerX on the partner’s dataset for both tasks, and it outperformed SOTA baselines by up to 10% for component recommendations and 54% for developer recommendations.
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[AI-108] WildSpoof Challenge Evaluation Plan ICASSP2026
【速读】：该论文旨在解决语音处理领域中两个关键问题：一是如何在文本到语音（Text-to-Speech, TTS）合成与抗欺骗自动说话人验证（Spoofing-robust Automatic Speaker Verification, SASV）任务中更有效地利用真实世界采集的“野生数据”（in-the-wild data），以突破传统干净、受控语料库的局限；二是促进TTS生成与SASV检测两个子领域的交叉协作，推动构建更具鲁棒性和现实适应性的语音安全系统。解决方案的关键在于通过组织“WildSpoof Challenge”这一双轨并行的竞赛机制，分别聚焦于 spoofed speech 的生成与检测，并鼓励参与者将二者视为独立但相互关联的任务，从而驱动研究从实验室环境向真实应用场景迁移，提升系统的实用性与泛化能力。
链接: https://arxiv.org/abs/2508.16858
作者: Yihan Wu,Jee-weon Jung,Hye-jin Shim,Xin Cheng,Xin Wang
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: ICASSP 2026 challenge
Abstract:The WildSpoof Challenge aims to advance the use of in-the-wild data in two intertwined speech processing tasks. It consists of two parallel tracks: (1) Text-to-Speech (TTS) synthesis for generating spoofed speech, and (2) Spoofing-robust Automatic Speaker Verification (SASV) for detecting spoofed speech. While the organizers coordinate both tracks and define the data protocols, participants treat them as separate and independent tasks. The primary objectives of the challenge are: (i) to promote the use of in-the-wild data for both TTS and SASV, moving beyond conventional clean and controlled datasets and considering real-world scenarios; and (ii) to encourage interdisciplinary collaboration between the spoofing generation (TTS) and spoofing detection (SASV) communities, thereby fostering the development of more integrated, robust, and realistic systems.
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[AI-109] A Workflow for Map Creation in Autonomous Vehicle Simulations
【速读】：该论文旨在解决自动驾驶（Autonomous Vehicle, AV）开发中仿真地图制作困难且资源消耗大的问题，尤其针对CARLA等仿真平台的适配性不足和流程灵活性差的挑战。其解决方案的关键在于提出了一种定制化的工作流，能够高效生成符合仿真需求的3D地图，以降低计算资源依赖并提升跨平台兼容性，同时为未来集成SLAM技术、优化地理坐标处理提供可扩展框架。
链接: https://arxiv.org/abs/2508.16856
作者: Zubair Islam,Ahmaad Ansari,George Daoud,Mohamed El-Darieby
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注: 6 pages, 12 figures. Published in the Proceedings of GEOProcessing 2025: The Seventeenth International Conference on Advanced Geographic Information Systems, Applications, and Services (IARIA)
Abstract:The fast development of technology and artificial intelligence has significantly advanced Autonomous Vehicle (AV) research, emphasizing the need for extensive simulation testing. Accurate and adaptable maps are critical in AV development, serving as the foundation for localization, path planning, and scenario testing. However, creating simulation-ready maps is often difficult and resource-intensive, especially with simulators like CARLA (CAR Learning to Act). Many existing workflows require significant computational resources or rely on specific simulators, limiting flexibility for developers. This paper presents a custom workflow to streamline map creation for AV development, demonstrated through the generation of a 3D map of a parking lot at Ontario Tech University. Future work will focus on incorporating SLAM technologies, optimizing the workflow for broader simulator compatibility, and exploring more flexible handling of latitude and longitude values to enhance map generation accuracy.
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[AI-110] DevLicOps: A Framework for Mitigating Licensing Risks in AI-Generated Code
【速读】：该论文旨在解决生成式 AI 编码助手（Generative AI Coding Assistants, ACAs）在软件开发中广泛应用时所引发的许可证合规风险问题，尤其是其可能生成受限制开源许可证（如 GPL）约束的代码，从而导致企业面临法律诉讼或被迫开源源代码的风险。解决方案的关键在于提出 DevLicOps 框架，通过治理机制、事件响应流程以及对合规与效率之间权衡的清晰认知，帮助 IT 领导者系统性管理 ACA 相关的许可证风险，以实现负责任且风险可控的软件开发实践。
链接: https://arxiv.org/abs/2508.16853
作者: Pratyush Nidhi Sharma,Lauren Wright,Anne Herfurth,Munsif Sokiyna,Pratyaksh Nidhi Sharma,Sethu Das,Mikko Siponen
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: 18 pages, 1 figure, 2 Tables
Abstract:Generative AI coding assistants (ACAs) are widely adopted yet pose serious legal and compliance risks. ACAs can generate code governed by restrictive open-source licenses (e.g., GPL), potentially exposing companies to litigation or forced open-sourcing. Few developers are trained in these risks, and legal standards vary globally, especially with outsourcing. Our article introduces DevLicOps, a practical framework that helps IT leaders manage ACA-related licensing risks through governance, incident response, and informed tradeoffs. As ACA adoption grows and legal frameworks evolve, proactive license compliance is essential for responsible, risk-aware software development in the AI era.
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[AI-111] RADAR: A Reasoning -Guided Attribution Framework for Explainable Visual Data Analysis
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在图表数据解析中缺乏可解释性的问题，即模型无法明确展示其决策所依据的图表区域，从而限制了用户对模型输出的信任与实际应用。解决方案的关键在于提出RADAR方法——一种半自动构建基准数据集的框架，包含17,819个多样化样本，涵盖图表、问题、推理步骤及归因标注，并引入基于推理引导的归因机制，使模型能够精准定位支持答案的图表区域。实验表明，该方法相比基线提升了15%的归因准确率，且生成的答案与真实回答高度一致（BERTScore ≈ 0.90），显著增强了图表分析系统的可解释性和可信度。
链接: https://arxiv.org/abs/2508.16850
作者: Anku Rani,Aparna Garimella,Apoorv Saxena,Balaji Vasan Srinivasan,Paul Pu Liang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Data visualizations like charts are fundamental tools for quantitative analysis and decision-making across fields, requiring accurate interpretation and mathematical reasoning. The emergence of Multimodal Large Language Models (MLLMs) offers promising capabilities for automated visual data analysis, such as processing charts, answering questions, and generating summaries. However, they provide no visibility into which parts of the visual data informed their conclusions; this black-box nature poses significant challenges to real-world trust and adoption. In this paper, we take the first major step towards evaluating and enhancing the capabilities of MLLMs to attribute their reasoning process by highlighting the specific regions in charts and graphs that justify model answers. To this end, we contribute RADAR, a semi-automatic approach to obtain a benchmark dataset comprising 17,819 diverse samples with charts, questions, reasoning steps, and attribution annotations. We also introduce a method that provides attribution for chart-based mathematical reasoning. Experimental results demonstrate that our reasoning-guided approach improves attribution accuracy by 15% compared to baseline methods, and enhanced attribution capabilities translate to stronger answer generation, achieving an average BERTScore of \sim 0.90, indicating high alignment with ground truth responses. This advancement represents a significant step toward more interpretable and trustworthy chart analysis systems, enabling users to verify and understand model decisions through reasoning and attribution.
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[AI-112] A Survey of Threats Against Voice Authentication and Anti-Spoofing Systems
【速读】：该论文旨在解决语音认证系统（Voice Authentication Systems, VAS）在广泛应用中面临日益严峻的安全威胁问题，尤其是针对其鲁棒性和抗欺骗能力的挑战。解决方案的关键在于系统性地梳理现代语音认证系统的攻击类型（如数据投毒、对抗攻击、深度伪造和对抗欺骗攻击），并深入分析各类攻击的方法学、常用数据集、性能表现及局限性，同时基于公认的分类体系整合现有文献，从而为构建更安全、更具弹性的语音认证系统提供理论支持与实践指导。
链接: https://arxiv.org/abs/2508.16843
作者: Kamel Kamel,Keshav Sood,Hridoy Sankar Dutta,Sunil Aryal
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: This paper will be submitted to the Computer Science Review
Abstract:Voice authentication has undergone significant changes from traditional systems that relied on handcrafted acoustic features to deep learning models that can extract robust speaker embeddings. This advancement has expanded its applications across finance, smart devices, law enforcement, and beyond. However, as adoption has grown, so have the threats. This survey presents a comprehensive review of the modern threat landscape targeting Voice Authentication Systems (VAS) and Anti-Spoofing Countermeasures (CMs), including data poisoning, adversarial, deepfake, and adversarial spoofing attacks. We chronologically trace the development of voice authentication and examine how vulnerabilities have evolved in tandem with technological advancements. For each category of attack, we summarize methodologies, highlight commonly used datasets, compare performance and limitations, and organize existing literature using widely accepted taxonomies. By highlighting emerging risks and open challenges, this survey aims to support the development of more secure and resilient voice authentication systems.
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[AI-113] Route-and-Execute: Auditable Model-Card Matching and Specialty-Level Deployment
【速读】：该论文旨在解决临床工作流中因多任务专用模型与碎片化脚本导致的效率低下和运维成本高的问题，尤其针对缺乏数据驱动的模型识别机制（如基于影像或表格输入）及标准化输出交付流程的现状。其核心解决方案在于提出一个以医疗场景优先的框架，利用单一视觉语言模型（Vision-Language Model, VLM）承担双重角色：第一，作为“模型卡匹配器”，通过三阶段工作流（模态-主要异常-模型卡ID）实现图像路由，并引入分阶段提示与候选选择机制以降低误选风险并符合临床风险容忍度；第二，通过对专科特定数据集进行微调，使该VLM可覆盖同一专科内的多个下游任务，在保持性能的同时简化部署。实验证明，该单模型方案在胃肠科、血液科、眼科和病理学领域达到或接近专用基线水平，显著减少数据科学家的工作量、缩短监控周期、提升模型选择透明度（每阶段提供解释依据），并降低系统集成复杂度。
链接: https://arxiv.org/abs/2508.16839
作者: Shayan Vassef,Soorya Ram Shimegekar,Abhay Goyal,Koustuv Saha,Pi Zonooz,Navin Kumar
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Clinical workflows are fragmented as a patchwork of scripts and task-specific networks that often handle triage, task selection, and model deployment. These pipelines are rarely streamlined for data science pipeline, reducing efficiency and raising operational costs. Workflows also lack data-driven model identification (from imaging/tabular inputs) and standardized delivery of model outputs. In response, we present a practical, healthcare-first framework that uses a single vision-language model (VLM) in two complementary roles. First (Solution 1), the VLM acts as an aware model-card matcher that routes an incoming image to the appropriate specialist model via a three-stage workflow (modality - primary abnormality - model-card id). Checks are provided by (i) stagewise prompts that allow early exit via None/Normal/Other and (ii) a stagewise answer selector that arbitrates between the top-2 candidates at each stage, reducing the chance of an incorrect selection and aligning the workflow with clinical risk tolerance. Second (Solution 2), we fine-tune the VLM on specialty-specific datasets ensuring a single model covers multiple downstream tasks within each specialty, maintaining performance while simplifying deployment. Across gastroenterology, hematology, ophthalmology, and pathology, our single-model deployment matches or approaches specialized baselines. Compared with pipelines composed of many task-specific agents, this approach shows that one VLM can both decide and do. It may reduce effort by data scientists, shorten monitoring, increase the transparency of model selection (with per-stage justifications), and lower integration overhead. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2508.16839 [cs.AI] (or arXiv:2508.16839v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2508.16839 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-114] Physics-Inspired Spatial Temporal Graph Neural Networks for Predicting Industrial Chain Resilience
【速读】：该论文旨在解决复杂网络（complex networks）中工业链韧性（resilience）建模与预测的难题，尤其是当前数据驱动的深度学习方法在描述系统动态演化机制方面缺乏理论框架的问题。其解决方案的关键在于提出一种物理信息神经符号方法（physically informative neural symbolic approach），通过学习物理实体活动状态的动力学特征，并将其整合进多层时空协同演化网络（multi-layer spatiotemporal co-evolution network），实现物理符号动力学与时空协同演化拓扑结构的联合学习，从而提升对工业链韧性的预测精度。
链接: https://arxiv.org/abs/2508.16836
作者: Bicheng Wang,Junping Wang,Yibo Xue
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
备注:
Abstract:Industrial chain plays an increasingly important role in the sustainable development of national economy. However, as a typical complex network, data-driven deep learning is still in its infancy in describing and analyzing the resilience of complex networks, and its core is the lack of a theoretical framework to describe the system dynamics. In this paper, we propose a physically informative neural symbolic approach to describe the evolutionary dynamics of complex networks for resilient prediction. The core idea is to learn the dynamics of the activity state of physical entities and integrate it into the multi-layer spatiotemporal co-evolution network, and use the physical information method to realize the joint learning of physical symbol dynamics and spatiotemporal co-evolution topology, so as to predict the industrial chain resilience. The experimental results show that the model can obtain better results and predict the elasticity of the industry chain more accurately and effectively, which has certain practical significance for the development of the industry.
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[AI-115] Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding CIKM2025
【速读】：该论文旨在解决动态制造环境中焊接质量预测模型因分布偏移（distribution shift）而导致性能下降的问题。在实际生产中，工艺参数的频繁变化会导致训练数据与运行时数据之间的分布不一致，从而影响机器学习模型的泛化能力。解决方案的关键在于扩展VQ-VAE Transformer架构，利用其自回归损失（autoregressive loss）作为可靠的分布外检测（out-of-distribution, OOD）机制，并结合持续学习策略，在检测到显著分布偏移时才触发模型更新，从而减少对昂贵人工标注的依赖。此外，作者提出了一种新的定量指标，可同时评估OOD检测能力和分布内（in-distribution）性能，实现了可解释且自适应的质量预测系统，显著提升了工业场景下AI模型的鲁棒性与实用性。
链接: https://arxiv.org/abs/2508.16832
作者: Yannik Hahn,Jan Voets,Antonin Koenigsfeld,Hasan Tercan,Tobias Meisen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted at CIKM 2025 (Applied Research Papers)
Abstract:Modern manufacturing relies heavily on fusion welding processes, including gas metal arc welding (GMAW). Despite significant advances in machine learning-based quality prediction, current models exhibit critical limitations when confronted with the inherent distribution shifts that occur in dynamic manufacturing environments. In this work, we extend the VQ-VAE Transformer architecture - previously demonstrating state-of-the-art performance in weld quality prediction - by leveraging its autoregressive loss as a reliable out-of-distribution (OOD) detection mechanism. Our approach exhibits superior performance compared to conventional reconstruction methods, embedding error-based techniques, and other established baselines. By integrating OOD detection with continual learning strategies, we optimize model adaptation, triggering updates only when necessary and thereby minimizing costly labeling requirements. We introduce a novel quantitative metric that simultaneously evaluates OOD detection capability while interpreting in-distribution performance. Experimental validation in real-world welding scenarios demonstrates that our framework effectively maintains robust quality prediction capabilities across significant distribution shifts, addressing critical challenges in dynamic manufacturing environments where process parameters frequently change. This research makes a substantial contribution to applied artificial intelligence by providing an explainable and at the same time adaptive solution for quality assurance in dynamic manufacturing processes - a crucial step towards robust, practical AI systems in the industrial environment.
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[AI-116] Understanding and Tackling Over-Dilution in Graph Neural Networks KDD’25
【速读】：该论文旨在解决消息传递神经网络（Message Passing Neural Networks, MPNNs）在图结构数据上存在的“过稀释”（Over-dilution）问题，即单层内节点特异性信息因传播过程而显著稀释，从而导致表示能力下降。其关键在于提出两个量化指标：节点内稀释因子（intra-node dilution）用于衡量属性层面的信息稀释，节点间稀释因子（inter-node dilution）用于刻画节点层面表示的稀释程度，并据此设计了一种基于Transformer架构的解决方案，有效缓解了该问题并可与MPNN等传统节点嵌入方法互补。
链接: https://arxiv.org/abs/2508.16829
作者: Junhyun Lee,Veronika Thost,Bumsoo Kim,Jaewoo Kang,Tengfei Ma
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Extended version of KDD '25 paper. 22 pages including appendix. Conference version: KDD '25 (Toronto, Aug 3-7, 2025), pp. 1253-1261. Code: this https URL
Abstract:Message Passing Neural Networks (MPNNs) hold a key position in machine learning on graphs, but they struggle with unintended behaviors, such as over-smoothing and over-squashing, due to irregular data structures. The observation and formulation of these limitations have become foundational in constructing more informative graph representations. In this paper, we delve into the limitations of MPNNs, focusing on aspects that have previously been overlooked. Our observations reveal that even within a single layer, the information specific to an individual node can become significantly diluted. To delve into this phenomenon in depth, we present the concept of Over-dilution and formulate it with two dilution factors: intra-node dilution for attribute-level and inter-node dilution for node-level representations. We also introduce a transformer-based solution that alleviates over-dilution and complements existing node embedding methods like MPNNs. Our findings provide new insights and contribute to the development of informative representations. The implementation and supplementary materials are publicly available at this https URL.
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[AI-117] PuzzleJAX: A Benchmark for Reasoning and Learning
【速读】：该论文旨在解决当前GPU加速学习环境普遍缺乏灵活性的问题，即现有系统通常仅提供固定游戏集合的硬编码实现，难以支持多样化的任务基准测试。为应对这一挑战，作者提出PuzzleJAX，其核心创新在于引入一种基于PuzzleScript语法的领域特定语言（Domain-Specific Language, DSL），使得任意可表达于该DSL中的谜题游戏均可被动态编译并运行在GPU上。此设计不仅实现了对海量人类设计的 PuzzleScript 游戏的覆盖（验证了数百个真实世界游戏），还确保了任务既直观易懂又具备高复杂度，从而有效支撑树搜索、强化学习与大语言模型（Large Language Model, LLM）推理能力的快速评估。
链接: https://arxiv.org/abs/2508.16821
作者: Sam Earle,Graham Todd,Yuchen Li,Ahmed Khalifa,Muhammad Umair Nasir,Zehua Jiang,Andrzej Banburski-Fahey,Julian Togelius
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 25 pages, 11 figures, 2 tables
Abstract:We introduce PuzzleJAX, a GPU-accelerated puzzle game engine and description language designed to support rapid benchmarking of tree search, reinforcement learning, and LLM reasoning abilities. Unlike existing GPU-accelerated learning environments that provide hard-coded implementations of fixed sets of games, PuzzleJAX allows dynamic compilation of any game expressible in its domain-specific language (DSL). This DSL follows PuzzleScript, which is a popular and accessible online game engine for designing puzzle games. In this paper, we validate in PuzzleJAX several hundred of the thousands of games designed in PuzzleScript by both professional designers and casual creators since its release in 2013, thereby demonstrating PuzzleJAX’s coverage of an expansive, expressive, and human-relevant space of tasks. By analyzing the performance of search, learning, and language models on these games, we show that PuzzleJAX can naturally express tasks that are both simple and intuitive to understand, yet often deeply challenging to master, requiring a combination of control, planning, and high-level insight.
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[AI-118] Autonomous UAV Flight Navigation in Confined Spaces: A Reinforcement Learning Approach
【速读】：该论文旨在解决在GPS拒止环境下对受限工业基础设施（如通风井）进行安全高效巡检的问题，传统人工方式存在高风险且效率低下。为此，研究提出利用无人机（UAVs）结合深度强化学习（DRL）来实现自主避障导航。解决方案的关键在于对比两种主流DRL算法——基于策略梯度的on-policy方法PPO与基于最大熵框架的off-policy方法SAC，在程序化生成的高保真仿真环境Genesis中训练和评估其导航性能。实验表明，PPO因训练稳定性强而成功学习到无碰撞的稳定策略并生成平滑轨迹，而SAC虽具备理论上的样本效率优势，却收敛至次优行为，仅能完成初始段路径即失败，说明在高风险密集场景中，on-policy方法的训练稳定性优于off-policy方法的样本效率。
链接: https://arxiv.org/abs/2508.16807
作者: Marco S. Tayar,Lucas K. de Oliveira,Juliano D. Negri,Thiago H. Segreto,Ricardo V. Godoy,Marcelo Becker
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注:
Abstract:Inspecting confined industrial infrastructure, such as ventilation shafts, is a hazardous and inefficient task for humans. Unmanned Aerial Vehicles (UAVs) offer a promising alternative, but GPS-denied environments require robust control policies to prevent collisions. Deep Reinforcement Learning (DRL) has emerged as a powerful framework for developing such policies, and this paper provides a comparative study of two leading DRL algorithms for this task: the on-policy Proximal Policy Optimization (PPO) and the off-policy Soft Actor-Critic (SAC). The training was conducted with procedurally generated duct environments in Genesis simulation environment. A reward function was designed to guide a drone through a series of waypoints while applying a significant penalty for collisions. PPO learned a stable policy that completed all evaluation episodes without collision, producing smooth trajectories. By contrast, SAC consistently converged to a suboptimal behavior that traversed only the initial segments before failure. These results suggest that, in hazard-dense navigation, the training stability of on-policy methods can outweigh the nominal sample efficiency of off-policy algorithms. More broadly, the study provides evidence that procedurally generated, high-fidelity simulations are effective testbeds for developing and benchmarking robust navigation policies.
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[AI-119] Evaluation and LLM -Guided Learning of ICD Coding Rationales
【速读】：该论文旨在解决生成式 AI 在临床编码（ICD coding）任务中缺乏可解释性的问题，尤其是现有模型生成的解释（rationales）在忠实性（faithfulness）和合理性（plausibility）方面存在不足，难以获得临床医生的信任。其关键解决方案在于：首先构建一个高质量、细粒度且符合临床实践的带理由标注数据集，用于系统评估不同类型的解释质量；其次，利用大语言模型（LLM）生成的解释作为远距离监督信号，提出新的理由学习方法，通过提示（prompting）策略（是否使用人工标注示例）优化模型生成的解释质量，从而显著提升解释与人类专家判断的一致性，并进一步增强模型性能。
链接: https://arxiv.org/abs/2508.16777
作者: Mingyang Li,Viktor Schlegel,Tingting Mu,Wuraola Oyewusi,Kai Kang,Goran Nenadic
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Automated clinical coding involves mapping unstructured text from Electronic Health Records (EHRs) to standardized code systems such as the International Classification of Diseases (ICD). While recent advances in deep learning have significantly improved the accuracy and efficiency of ICD coding, the lack of explainability in these models remains a major limitation, undermining trust and transparency. Current explorations about explainability largely rely on attention-based techniques and qualitative assessments by physicians, yet lack systematic evaluation using consistent criteria on high-quality rationale datasets, as well as dedicated approaches explicitly trained to generate rationales for further enhancing explanation. In this work, we conduct a comprehensive evaluation of the explainability of the rationales for ICD coding through two key lenses: faithfulness that evaluates how well explanations reflect the model’s actual reasoning and plausibility that measures how consistent the explanations are with human expert judgment. To facilitate the evaluation of plausibility, we construct a new rationale-annotated dataset, offering denser annotations with diverse granularity and aligns better with current clinical practice, and conduct evaluation across three types of rationales of ICD coding. Encouraged by the promising plausibility of LLM-generated rationales for ICD coding, we further propose new rationale learning methods to improve the quality of model-generated rationales, where rationales produced by prompting LLMs with/without annotation examples are used as distant supervision signals. We empirically find that LLM-generated rationales align most closely with those of human experts. Moreover, incorporating few-shot human-annotated examples not only further improves rationale generation but also enhances rationale-learning approaches.
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[AI-120] EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention
【速读】：该论文旨在解决当前生成式 AI（Generative AI）在代码语言建模（Code Language Modeling, CodeLLM）中缺乏对人类开发者注意力机制模拟的问题。现有模型依赖于输入 token 的机械重要性权重进行训练，而人类开发者则能基于直观的语义显著性（salience）选择关注关键代码片段，这种差异限制了模型在实际软件开发任务中的表现。解决方案的关键在于提出 EyeMulator 技术：通过引入基于人类眼动实验数据（来自公开眼动追踪数据集）的特殊权重项至 LLM 微调损失函数中，使模型在训练过程中学习模仿人类视觉注意力模式，从而提升其在代码翻译、补全和摘要等任务上的性能。该方法不依赖推理阶段的眼动数据，且实验证明其优势源于模型成功模仿了人类注意力行为。
链接: https://arxiv.org/abs/2508.16771
作者: Yifan Zhang,Chen Huang,Yueke Zhang,Jiahao Zhang,Toby Jia-Jun Li,Collin McMillan,Kevin Leach,Yu Huang
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Code language models (so-called CodeLLMs) are now commonplace in software development. As a general rule, CodeLLMs are trained by dividing training examples into input tokens and then learn importance of those tokens in a process called machine attention. Machine attention is based solely on input token salience to output token examples during training. Human software developers are different, as humans intuitively know that some tokens are more salient than others. While intuition itself is ineffable and a subject of philosophy, clues about salience are present in human visual attention, since people tend to look at more salient words more often. In this paper, we present EyeMulator, a technique for training CodeLLMs to mimic human visual attention while training for various software development tasks. We add special weights for each token in each input example to the loss function used during LLM fine-tuning. We draw these weights from observations of human visual attention derived from a previously-collected publicly-available dataset of eye-tracking experiments in software engineering tasks. These new weights ultimately induce changes in the attention of the subject LLM during training, resulting in a model that does not need eye-tracking data during inference. Our evaluation shows that EyeMulator outperforms strong LLM baselines on several tasks such as code translation, completion and summarization. We further show an ablation study that demonstrates the improvement is due to subject models learning to mimic human attention.
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[AI-121] FAIRWELL: Fair Multimodal Self-Supervised Learning for Wellbeing Prediction
【速读】：该论文旨在解决多模态场景下机器学习公平性不足的问题，即如何在包含多种数据模态（如文本、图像、时序信号等）的复杂任务中，提升模型预测结果对受保护属性（如性别、种族等）的公平性。其解决方案的关键在于提出了一种新颖的基于主体级别的损失函数 FAIRWELL，该方法基于方差-不变性-协方差正则化（Variance-Invariance-Covariance Regularization, VICReg）框架，通过三个核心机制实现：(i) 方差项减少模型对受保护属性的依赖；(ii) 不变性项确保相似个体获得一致预测；(iii) 协方差项最小化预测结果与受保护属性之间的相关性。这一设计使模型能够学习到与主体身份无关的公平表示，从而在保持分类性能的同时显著提升公平性表现，在多个真实世界异构医疗数据集上验证了有效性。
链接: https://arxiv.org/abs/2508.16748
作者: Jiaee Cheong,Abtin Mogharabin,Paul Liang,Hatice Gunes,Sinan Kalkan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Early efforts on leveraging self-supervised learning (SSL) to improve machine learning (ML) fairness has proven promising. However, such an approach has yet to be explored within a multimodal context. Prior work has shown that, within a multimodal setting, different modalities contain modality-unique information that can complement information of other modalities. Leveraging on this, we propose a novel subject-level loss function to learn fairer representations via the following three mechanisms, adapting the variance-invariance-covariance regularization (VICReg) method: (i) the variance term, which reduces reliance on the protected attribute as a trivial solution; (ii) the invariance term, which ensures consistent predictions for similar individuals; and (iii) the covariance term, which minimizes correlational dependence on the protected attribute. Consequently, our loss function, coined as FAIRWELL, aims to obtain subject-independent representations, enforcing fairness in multimodal prediction tasks. We evaluate our method on three challenging real-world heterogeneous healthcare datasets (i.e. D-Vlog, MIMIC and MODMA) which contain different modalities of varying length and different prediction tasks. Our findings indicate that our framework improves overall fairness performance with minimal reduction in classification performance and significantly improves on the performance-fairness Pareto frontier.
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[AI-122] Explainable AI for Predicting and Understanding Mathematics Achievement: A Cross-National Analysis of PISA 2018
【速读】：该论文旨在解决如何识别影响学生数学成绩的关键因素，并揭示这些因素在不同国家间的差异性，从而为制定有效的教育政策提供依据。其解决方案的关键在于应用可解释人工智能（Explainable Artificial Intelligence, XAI）技术，结合PISA 2018数据（涵盖10个国家共67,329名学生），比较多种机器学习模型（包括多元线性回归、随机森林、CATBoost和人工神经网络）的预测性能与可解释性。研究发现，非线性模型如随机森林（Random Forest）在准确性和泛化能力之间取得最佳平衡，且通过特征重要性、SHAP值及决策树可视化等XAI方法，明确了社会经济地位、学习时间、教师动机和学生数学态度等核心预测因子，并揭示其作用具有显著的国家间差异，凸显了教育成效的非线性与情境依赖特性。
链接: https://arxiv.org/abs/2508.16747
作者: Liu Liu,Rui Dai
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注:
Abstract:Understanding the factors that shape students’ mathematics performance is vital for designing effective educational policies. This study applies explainable artificial intelligence (XAI) techniques to PISA 2018 data to predict math achievement and identify key predictors across ten countries (67,329 students). We tested four models: Multiple Linear Regression (MLR), Random Forest (RF), CATBoost, and Artificial Neural Networks (ANN), using student, family, and school variables. Models were trained on 70% of the data (with 5-fold cross-validation) and tested on 30%, stratified by country. Performance was assessed with R^2 and Mean Absolute Error (MAE). To ensure interpretability, we used feature importance, SHAP values, and decision tree visualizations. Non-linear models, especially RF and ANN, outperformed MLR, with RF balancing accuracy and generalizability. Key predictors included socio-economic status, study time, teacher motivation, and students’ attitudes toward mathematics, though their impact varied across countries. Visual diagnostics such as scatterplots of predicted vs actual scores showed RF and CATBoost aligned closely with actual performance. Findings highlight the non-linear and context-dependent nature of achievement and the value of XAI in educational research. This study uncovers cross-national patterns, informs equity-focused reforms, and supports the development of personalized learning strategies.
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[AI-123] Beyond Memorization: Extending Reasoning Depth with Recurrence Memory and Test-Time Compute Scaling
【速读】：该论文旨在解决大语言模型在多步推理（multi-step reasoning）能力上的学习机制与性能瓶颈问题，即尽管模型在单步预测任务中表现优异，但在需要长期依赖和复杂逻辑推导的场景下性能显著下降。其解决方案的关键在于：通过在细胞自动机（cellular automata）框架中训练模型，并使用随机布尔函数生成状态序列以排除记忆效应，发现增加模型深度对顺序计算至关重要；进一步地，引入递归结构、记忆机制以及测试时计算资源扩展（test-time compute scaling），可显著提升模型的多步推理能力。
链接: https://arxiv.org/abs/2508.16745
作者: Ivan Rodkin,Daniil Orel,Konstantin Smirnov,Arman Bolatov,Bilal Elbouardi,Besher Hassan,Yuri Kuratov,Aydar Bulatov,Preslav Nakov,Timothy Baldwin,Artem Shelmanov,Mikhail Burtsev
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Reasoning is a core capability of large language models, yet understanding how they learn and perform multi-step reasoning remains an open problem. In this study, we explore how different architectures and training methods affect model multi-step reasoning capabilities within a cellular automata framework. By training on state sequences generated with random Boolean functions for random initial conditions to exclude memorization, we demonstrate that most neural architectures learn to abstract the underlying rules. While models achieve high accuracy in next-state prediction, their performance declines sharply if multi-step reasoning is required. We confirm that increasing model depth plays a crucial role for sequential computations. We demonstrate that an extension of the effective model depth with recurrence, memory, and test-time compute scaling substantially enhances reasoning capabilities.
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[AI-124] WST: Weak-to-Strong Knowledge Transfer via Reinforcement Learning
【速读】：该论文旨在解决生成式 AI（Generative AI）应用中有效提示工程（Prompt Engineering）的难题，即如何在不依赖大规模模型微调或开放源代码的前提下，自动优化提示以提升大语言模型（Large Language Models, LLMs）的性能。其解决方案的关键在于提出弱到强迁移（Weak-to-Strong Transfer, WST）框架：利用一个小型“教师”模型（Teacher Model）生成指令来引导更大的“学生”模型（Student Model），并通过强化学习迭代优化教师模型的指令策略，使其基于学生模型的表现进行改进。该方法仅需一个弱教师模型即可实现显著性能提升（如MATH-500基准上达98%、HH-RLHF上达134%），且避免了强教师可能引入的误导性提示，从而为高效、安全的大模型提示优化提供了一种可扩展的新范式。
链接: https://arxiv.org/abs/2508.16741
作者: Haosen Ge,Shuo Li,Lianghuan Huang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Effective prompt engineering remains a challenging task for many applications. We introduce Weak-to-Strong Transfer (WST), an automatic prompt engineering framework where a small “Teacher” model generates instructions that enhance the performance of a much larger “Student” model. Unlike prior work, WST requires only a weak teacher, making it efficient and broadly applicable in settings where large models are closed-source or difficult to fine-tune. Using reinforcement learning, the Teacher Model’s instructions are iteratively improved based on the Student Model’s outcomes, yielding substantial gains across reasoning (MATH-500, GSM8K) and alignment (HH-RLHF) benchmarks - 98% on MATH-500 and 134% on HH-RLHF - and surpassing baselines such as GPT-4o-mini and Llama-70B. These results demonstrate that small models can reliably scaffold larger ones, unlocking latent capabilities while avoiding misleading prompts that stronger teachers may introduce, establishing WST as a scalable solution for efficient and safe LLM prompt refinement.
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[AI-125] AI Product Value Assessment Model: An Interdisciplinary Integration Based on Information Theory Economics and Psychology
【速读】：该论文旨在解决当前人工智能（AI）产品在产业应用中因技术 hype 引发的盲目投资问题，即企业在缺乏系统性价值评估的情况下进行非理性投入，导致资源错配与项目失败。解决方案的关键在于构建一个融合信息论熵减原理、经济学有限理性框架与心理学非理性决策理论的多维评价模型，通过量化正向维度（如不确定性消除、效率提升、成本节约、决策质量改善）与负向风险（如错误概率、影响程度及纠正成本）之间的非线性耦合关系，实现对 AI 产品价值的精准测度。该模型经 10 个商业案例验证，能够有效区分成功与失败的产品，揭示价值生成逻辑，为避免盲目投资提供科学工具。
链接: https://arxiv.org/abs/2508.16714
作者: Yu yang
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: in Chinese language
Abstract:In recent years, breakthroughs in artificial intelligence (AI) technology have triggered global industrial transformations, with applications permeating various fields such as finance, healthcare, education, and manufacturing. However, this rapid iteration is accompanied by irrational development, where enterprises blindly invest due to technology hype, often overlooking systematic value assessments. This paper develops a multi-dimensional evaluation model that integrates information theory’s entropy reduction principle, economics’ bounded rationality framework, and psychology’s irrational decision theories to quantify AI product value. Key factors include positive dimensions (e.g., uncertainty elimination, efficiency gains, cost savings, decision quality improvement) and negative risks (e.g., error probability, impact, and correction costs). A non-linear formula captures factor couplings, and validation through 10 commercial cases demonstrates the model’s effectiveness in distinguishing successful and failed products, supporting hypotheses on synergistic positive effects, non-linear negative impacts, and interactive regulations. Results reveal value generation logic, offering enterprises tools to avoid blind investments and promote rational AI industry development. Future directions include adaptive weights, dynamic mechanisms, and extensions to emerging AI technologies like generative models.
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[AI-126] CelloAI: Leverag ing Large Language Models for HPC Software Development in High Energy Physics
【速读】：该论文旨在解决高能物理（High Energy Physics, HEP）领域在下一代实验中面临的数据处理挑战，特别是传统高吞吐计算难以满足海量数据需求时，如何高效集成高性能计算（High Performance Computing, HPC）资源的问题。当前HPC在HEP中的应用受限于遗留代码向异构架构移植困难及科学代码库文档稀疏两大瓶颈。解决方案的关键在于提出CelloAI——一个本地部署的编码辅助系统，利用大语言模型（Large Language Models, LLMs）结合检索增强生成（Retrieval-Augmented Generation, RAG）技术，实现对HEP代码的自动化文档生成与代码生成支持。其核心创新包括：针对代码文档化任务，通过RAG从论文、海报等文献中提取上下文信息生成Doxygen风格注释和文件级摘要，并提供交互式问答能力；针对代码生成任务，采用语法感知的分块策略保持语义完整性，结合调用图（callgraph）知识确保依赖关系正确性，从而提升检索准确率并支持性能优化建议与安全重构。该方案保障了数据隐私、无外部依赖且具备可解释性，符合科学计算环境对透明性和安全性要求。
链接: https://arxiv.org/abs/2508.16713
作者: Mohammad Atif,Kriti Chopra,Ozgur Kilic,Tianle Wang,Zhihua Dong,Charles Leggett,Meifeng Lin,Paolo Calafiura,Salman Habib
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); High Energy Physics - Experiment (hep-ex)
备注: 12 pages, 2 figures
Abstract:Next-generation High Energy Physics (HEP) experiments will generate unprecedented data volumes, necessitating High Performance Computing (HPC) integration alongside traditional high-throughput computing. However, HPC adoption in HEP is hindered by the challenge of porting legacy software to heterogeneous architectures and the sparse documentation of these complex scientific codebases. We present CelloAI, a locally hosted coding assistant that leverages Large Language Models (LLMs) with retrieval-augmented generation (RAG) to support HEP code documentation and generation. This local deployment ensures data privacy, eliminates recurring costs and provides access to large context windows without external dependencies. CelloAI addresses two primary use cases, code documentation and code generation, through specialized components. For code documentation, the assistant provides: (a) Doxygen style comment generation for all functions and classes by retrieving relevant information from RAG sources (papers, posters, presentations), (b) file-level summary generation, and © an interactive chatbot for code comprehension queries. For code generation, CelloAI employs syntax-aware chunking strategies that preserve syntactic boundaries during embedding, improving retrieval accuracy in large codebases. The system integrates callgraph knowledge to maintain dependency awareness during code modifications and provides AI-generated suggestions for performance optimization and accurate refactoring. We evaluate CelloAI using real-world HEP applications from ATLAS, CMS, and DUNE experiments, comparing different embedding models for code retrieval effectiveness. Our results demonstrate the AI assistant’s capability to enhance code understanding and support reliable code generation while maintaining the transparency and safety requirements essential for scientific computing environments.
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[AI-127] Systematic Characterization of LLM Quantization: A Performance Energy and Quality Perspective
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在实际在线服务场景中因资源消耗高而面临的效率瓶颈问题，特别是通过量化（quantization）技术降低模型精度以实现高效部署时所存在的性能、能耗与质量之间的复杂权衡关系不明确的问题。解决方案的关键在于构建了一个全自动的在线表征框架 qMeter，首次从应用层、工作负载层、并行策略层和硬件层多维度系统性地评估了11种后训练量化方法在4种模型规模（7B–70B）和两种GPU架构（A100、H100）下的表现，揭示了任务依赖性、工作负载敏感性以及与并行性和硬件架构的复杂交互作用，从而为容量规划、能效调度和多目标调优等部署挑战提供了实证基础和优化思路。
链接: https://arxiv.org/abs/2508.16712
作者: Tianyao Shi,Yi Ding
机构: 未知
类目: Performance (cs.PF); Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
备注: 14 pages, 10 figures, 4 tables
Abstract:Large language models (LLMs) have demonstrated remarkable capabilities across diverse domains, but their heavy resource demands make quantization-reducing precision to lower-bit formats-critical for efficient serving. While many quantization methods exist, a systematic understanding of their performance, energy, and quality tradeoffs in realistic serving conditions remains a gap. In this work, we first develop a fully automated online characterization framework qMeter, and then conduct an in-depth characterization of 11 post-training LLM quantization methods across 4 model sizes (7B-70B) and two GPU architectures (A100, H100). We evaluate quantization at the application, workload, parallelism, and hardware levels under online serving conditions. Our study reveals highly task- and method-dependent tradeoffs, strong sensitivity to workload characteristics, and complex interactions with parallelism and GPU architecture. We further present three optimization case studies illustrating deployment challenges in capacity planning, energy-efficient scheduling, and multi-objective tuning. To the best of our knowledge, this is one of the first comprehensive application-, system-, and hardware-level characterization of LLM quantization from a joint performance, energy, and quality perspective.
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[AI-128] RoboBuddy in the Classroom: Exploring LLM -Powered Social Robots for Storytelling in Learning and Integration Activities
【速读】：该论文旨在解决教育场景中情境化教学设计耗时高、与现有课程整合困难，以及 multicultural integration（多元文化融合）难以嵌入常规教学的问题。其解决方案的关键在于开发了一个直观的界面，使教师能够基于日常课程内容，利用大语言模型（LLMs）和社交机器人（social robots）快速创建情境化活动。通过与4位教师共同设计多种活动框架并在27名学生中开展为期一周的实证研究，验证了该系统的有效性，并揭示了情境化教学特别是结合叙事策略能显著提升学生参与度，同时凸显了LLMs与社交机器人在长期课堂应用中的潜力与挑战。
链接: https://arxiv.org/abs/2508.16706
作者: Daniel Tozadore,Nur Ertug,Yasmine Chaker,Mortadha Abderrahim
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Accepted to be published in the proceedings of 34th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) in 2025
Abstract:Creating and improvising scenarios for content approaching is an enriching technique in education. However, it comes with a significant increase in the time spent on its planning, which intensifies when using complex technologies, such as social robots. Furthermore, addressing multicultural integration is commonly embedded in regular activities due to the already tight curriculum. Addressing these issues with a single solution, we implemented an intuitive interface that allows teachers to create scenario-based activities from their regular curriculum using LLMs and social robots. We co-designed different frameworks of activities with 4 teachers and deployed it in a study with 27 students for 1 week. Beyond validating the system’s efficacy, our findings highlight the positive impact of integration policies perceived by the children and demonstrate the importance of scenario-based activities in students’ enjoyment, observed to be significantly higher when applying storytelling. Additionally, several implications of using LLMs and social robots in long-term classroom activities are discussed.
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[AI-129] Dynamic Sparse Attention on Mobile SoCs
【速读】：该论文旨在解决当前在设备端运行大语言模型（Large Language Models, LLMs）时，注意力机制（attention operator）因量化敏感性问题被迫从专用神经网络处理单元（NPU）回退到通用中央处理器（CPU）或图形处理器（GPU）所带来的性能下降与系统调度复杂度增加的问题。解决方案的关键在于提出一种系统-算法协同设计的稀疏注意力模块 shadowAttn，其核心思想是通过在 NPU 上进行轻量级预计算（pilot compute）来隐藏估算重要 token 的开销，并结合 NPU 计算图分组（compute graph bucketing）、头级别 NPU-CPU/GPU 流水线（head-wise pipeline）以及每头细粒度稀疏比例（per-head fine-grained sparsity ratio）等技术，在极小依赖 CPU/GPU 资源的前提下实现高精度与高效率的注意力计算，从而显著降低对通用计算资源的需求并保持与现有最优框架相当的性能表现。
链接: https://arxiv.org/abs/2508.16703
作者: Wangsong Yin,Daliang Xu,Mengwei Xu,Gang Huang,Xuanzhe Liu
机构: 未知
类目: Performance (cs.PF); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Technical Report
Abstract:On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention operator falls back from the special-purpose NPU to the general-purpose CPU/GPU because of quantization sensitivity in state-of-the-art frameworks. This fallback results in a degraded user experience and increased complexity in system scheduling. To this end, this paper presents shadowAttn, a system-algorithm codesigned sparse attention module with minimal reliance on CPU/GPU by only sparsely calculating the attention on a tiny portion of tokens. The key idea is to hide the overhead of estimating the important tokens with a NPU-based pilot compute. Further, shadowAttn proposes insightful techniques such as NPU compute graph bucketing, head-wise NPU-CPU/GPU pipeline and per-head fine-grained sparsity ratio to achieve high accuracy and efficiency. shadowAttn delivers the best performance with highly limited CPU/GPU resource; it requires much less CPU/GPU resource to deliver on-par performance of SoTA frameworks.
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[AI-130] Generative Artificial Intelligence and Agents in Research and Teaching
【速读】：该论文旨在系统解析生成式人工智能（Generative AI, GenAI）与大语言模型（Large Language Models, LLMs）的发展脉络、技术机制及其在科研与教育领域的应用潜力与风险，尤其聚焦于地理研究与教学中的实践场景。其核心问题在于厘清GenAI如何重塑学术研究全流程（从选题到成果传播）和教学设计（包括课程开发、评估反馈等），并识别其带来的伦理、社会与环境挑战。解决方案的关键在于构建一个整合性的分析框架，涵盖技术要素（如提示工程、词嵌入、采样策略）、自主代理（autonomous agents）的兴起，以及对偏见、知识产权、治理责任和碳足迹等问题的批判性评估，从而为GenAI的负责任采用提供理论依据与实践路径。
链接: https://arxiv.org/abs/2508.16701
作者: Jussi S. Jauhiainen,Aurora Toppari
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 108 pages, 6 figures, 13 tables, 2 appendices
Abstract:This study provides a comprehensive analysis of the development, functioning, and application of generative artificial intelligence (GenAI) and large language models (LLMs), with an emphasis on their implications for research and education. It traces the conceptual evolution from artificial intelligence (AI) through machine learning (ML) and deep learning (DL) to transformer architectures, which constitute the foundation of contemporary generative systems. Technical aspects, including prompting strategies, word embeddings, and probabilistic sampling methods (temperature, top-k, and top-p), are examined alongside the emergence of autonomous agents. These elements are considered in relation to both the opportunities they create and the limitations and risks they entail. The work critically evaluates the integration of GenAI across the research process, from ideation and literature review to research design, data collection, analysis, interpretation, and dissemination. While particular attention is given to geographical research, the discussion extends to wider academic contexts. A parallel strand addresses the pedagogical applications of GenAI, encompassing course and lesson design, teaching delivery, assessment, and feedback, with geography education serving as a case example. Central to the analysis are the ethical, social, and environmental challenges posed by GenAI. Issues of bias, intellectual property, governance, and accountability are assessed, alongside the ecological footprint of LLMs and emerging technological strategies for mitigation. The concluding section considers near- and long-term futures of GenAI, including scenarios of sustained adoption, regulation, and potential decline. By situating GenAI within both scholarly practice and educational contexts, the study contributes to critical debates on its transformative potential and societal responsibilities. Comments: 108 pages, 6 figures, 13 tables, 2 appendices Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI) Cite as: arXiv:2508.16701 [cs.CY] (or arXiv:2508.16701v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2508.16701 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-131] GPT -OSS-20B: A Comprehensive Deployment-Centric Analysis of OpenAI s Open-Weight Mixture of Experts Model
【速读】：该论文旨在解决大模型在部署阶段的资源效率问题，特别是如何在保持高性能的同时降低显存占用和能耗。其解决方案的关键在于采用基于专家混合（Mixture-of-Experts, MoE）架构的GPT-OSS-20B模型，通过仅激活约17.3%的总参数（3.61B/20.9B），实现了比密集型基线模型Qwen3-32B和Yi-34B更高的解码吞吐量（提升31.8%）、更低的每千token能耗（降低25.8%）以及更少的峰值显存占用（减少31.7%），同时展现出显著更强的每活跃参数效率（Active Parameter Efficiency, APE），验证了MoE架构在实际部署场景中的优势。
链接: https://arxiv.org/abs/2508.16700
作者: Deepak Kumar,Divakar Yadav,Yash Patel
机构: 未知
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
备注:
Abstract:We present a single-GPU (H100, bf16) evaluation of GPT-OSS-20B (Mixture-of-Experts; 20.9B total, approx. 3.61B active) against dense baselines Qwen3-32B and Yi-34B across multiple dimensions. We measure true time-to-first-token (TTFT), full-decode throughput (TPOT), end-to-end latency percentiles, peak VRAM with past key values (PKV) held, and energy via a consistent nvidia-smi-based sampler. At a 2048-token context with 64-token decode, GPT-OSS-20B delivers higher decode throughput and tokens per Joule than dense baselines Qwen3-32B and Yi-34B, while substantially reducing peak VRAM and energy per 1000 generated tokens; its TTFT is higher due to MoE routing overhead. With only 17.3% of parameters active (3.61B of 20.9B), GPT-OSS-20B provides about 31.8% higher decode throughput and 25.8% lower energy per 1000 generated tokens than Qwen3-32B at 2048/64, while using 31.7% less peak VRAM. Normalized by active parameters, GPT-OSS-20B shows markedly stronger per-active-parameter efficiency (APE), underscoring MoE’s deployment advantages. We do not evaluate accuracy; this is a deployment-focused study. We release code and consolidated results to enable replication and extension.
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[AI-132] DecoMind: A Generative AI System for Personalized Interior Design Layouts
【速读】：该论文旨在解决如何自动化生成符合用户需求的室内设计布局问题，尤其针对用户输入的房间类型、风格偏好及家具选择等条件，实现高保真且语义一致的设计方案输出。其解决方案的关键在于结合多模态模型与可控生成技术：首先利用CLIP（Contrastive Language–Image Pretraining）从大规模数据集中提取与用户输入匹配的家具图像；随后将提取的家具与文本提示（prompt）一同输入至基于ControlNet控制的Stable Diffusion模型中，以生成包含指定家具且结构合理的室内布局；最后通过分类器对生成结果进行语义一致性评估，确保设计输出与用户输入高度对齐，从而实现端到端的自动化、可定制化室内设计生成流程。
链接: https://arxiv.org/abs/2508.16696
作者: Reema Alshehri,Rawan Alotaibi,Leen Almasri,Rawan Altaweel
机构: 未知
类目: Graphics (cs.GR); Artificial Intelligence (cs.AI)
备注: ~7 pages; ~32 figures; compiled with pdfLaTeX. Primary category: cs.CV. (Secondary: cs.AI)
Abstract:This paper introduces a system for generating interior design layouts based on user inputs, such as room type, style, and furniture preferences. CLIP extracts relevant furniture from a dataset, and a layout that contains furniture and a prompt are fed to Stable Diffusion with ControlNet to generate a design that incorporates the selected furniture. The design is then evaluated by classifiers to ensure alignment with the user’s inputs, offering an automated solution for realistic interior design.
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[AI-133] Making AI Inevitable: Historical Perspective and the Problems of Predicting Long-Term Technological Change
【速读】：该论文试图解决的核心问题是：当前关于人工智能（AI）未来发展的激烈争论——特别是关于人工通用智能（AGI）是否将对人类社会产生变革性影响的分歧——本质上并非源于技术层面的客观争议，而是根植于主观的、哲学性的历史与技术变迁认知差异。解决方案的关键在于识别并澄清这些非技术性分歧的具体维度，包括：非生物智能的可能性、技术预测的时间尺度以及技术演进轨迹的假设。通过厘清这三方面根本分歧，论文揭示了支持“变革论者”（transformationalists）与“怀疑论者”（skeptics）立场的不同论证逻辑，并指出变革论立场所承担的强论证责任及其在竞争性环境中引发的先发优势压力，从而呼吁拓展对“专家性”（expertise）的理解，以更全面地参与AI未来发展的公共讨论。
链接: https://arxiv.org/abs/2508.16692
作者: Mark Fisher,John Severini
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); General Economics (econ.GN)
备注:
Abstract:This study demonstrates the extent to which prominent debates about the future of AI are best understood as subjective, philosophical disagreements over the history and future of technological change rather than as objective, material disagreements over the technologies themselves. It focuses on the deep disagreements over whether artificial general intelligence (AGI) will prove transformative for human society; a question that is analytically prior to that of whether this transformative effect will help or harm humanity. The study begins by distinguishing two fundamental camps in this debate. The first of these can be identified as “transformationalists,” who argue that continued AI development will inevitably have a profound effect on society. Opposed to them are “skeptics,” a more eclectic group united by their disbelief that AI can or will live up to such high expectations. Each camp admits further “strong” and “weak” variants depending on their tolerance for epistemic risk. These stylized contrasts help to identify a set of fundamental questions that shape the camps’ respective interpretations of the future of AI. Three questions in particular are focused on: the possibility of non-biological intelligence, the appropriate time frame of technological predictions, and the assumed trajectory of technological development. In highlighting these specific points of non-technical disagreement, this study demonstrates the wide range of different arguments used to justify either the transformationalist or skeptical position. At the same time, it highlights the strong argumentative burden of the transformationalist position, the way that belief in this position creates competitive pressures to achieve first-mover advantage, and the need to widen the concept of “expertise” in debates surrounding the future development of AI.
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[AI-134] Cybernaut: Towards Reliable Web Automation
【速读】：该论文旨在解决工业场景下基于大语言模型（Large Language Models, LLMs）的网页自动化系统所面临的四大核心挑战：确保执行一致性、精准识别关键HTML元素、达到类人准确率以实现规模化操作，以及缺乏对企业内部Web应用的全面基准测试数据。现有方案主要针对设计良好的面向消费者的网站，难以应对复杂且不规范的企业内部界面。其解决方案的关键在于提出Cybernaut框架，该框架包含三个核心组件：（1）标准操作程序（Standard Operating Procedure, SOP）生成器，将用户演示转化为适用于线性浏览任务的可靠自动化指令；（2）专为复杂Web界面设计的高精度HTML DOM元素识别系统；（3）用于量化评估执行一致性的指标。实证结果表明，使用该框架可使任务执行成功率提升23.2%（从72%提高至88.68%），并在真实系统中以84.7%的准确率识别出一致执行模式，从而支持可靠的置信度评估与执行过程中的自适应引导。
链接: https://arxiv.org/abs/2508.16688
作者: Ankur Tomar,Hengyue Liang,Indranil Bhattacharya,Natalia Larios,Francesco Carbone
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:The emergence of AI-driven web automation through Large Language Models (LLMs) offers unprecedented opportunities for optimizing digital workflows. However, deploying such systems within industry’s real-world environments presents four core challenges: (1) ensuring consistent execution, (2) accurately identifying critical HTML elements, (3) meeting human-like accuracy in order to automate operations at scale and (4) the lack of comprehensive benchmarking data on internal web applications. Existing solutions are primarily tailored for well-designed, consumer-facing websites (e.g., this http URL, this http URL) and fall short in addressing the complexity of poorly-designed internal web interfaces. To address these limitations, we present Cybernaut, a novel framework to ensure high execution consistency in web automation agents designed for robust enterprise use. Our contributions are threefold: (1) a Standard Operating Procedure (SOP) generator that converts user demonstrations into reliable automation instructions for linear browsing tasks, (2) a high-precision HTML DOM element recognition system tailored for the challenge of complex web interfaces, and (3) a quantitative metric to assess execution consistency. The empirical evaluation on our internal benchmark demonstrates that using our framework enables a 23.2% improvement (from 72% to 88.68%) in task execution success rate over the browser_use. Cybernaut identifies consistent execution patterns with 84.7% accuracy, enabling reliable confidence assessment and adaptive guidance during task execution in real-world systems. These results highlight Cybernaut’s effectiveness in enterprise-scale web automation and lay a foundation for future advancements in web automation.
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[AI-135] STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
【速读】：该论文旨在解决智能交通系统中交通流预测的准确性与及时性问题，特别是如何有效建模复杂的空间-时间依赖关系。解决方案的关键在于提出一种空间-时间统一图注意力网络（Spatial-Temporal Unified Graph Attention Network, STGAtt），通过构建统一图结构并引入注意力机制，直接在空间-时间联合图中动态加权各维度连接，从而捕捉多尺度的时空相关性；此外，STGAtt还设计了一种新颖的信息交换机制，将交通流观测信号划分为邻域子集，以同时捕获短程和长程依赖关系，显著提升了预测性能。
链接: https://arxiv.org/abs/2508.16685
作者: Zhuding Liang,Jianxun Cui,Qingshuang Zeng,Feng Liu,Nenad Filipovic,Tijana Geroski
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Accurate and timely traffic flow forecasting is crucial for intelligent transportation systems. This paper presents a novel deep learning model, the Spatial-Temporal Unified Graph Attention Network (STGAtt). By leveraging a unified graph representation and an attention mechanism, STGAtt effectively captures complex spatial-temporal dependencies. Unlike methods relying on separate spatial and temporal dependency modeling modules, STGAtt directly models correlations within a Spatial-Temporal Unified Graph, dynamically weighing connections across both dimensions. To further enhance its capabilities, STGAtt partitions traffic flow observation signal into neighborhood subsets and employs a novel exchanging mechanism, enabling effective capture of both short-range and long-range correlations. Extensive experiments on the PEMS-BAY and SHMetro datasets demonstrate STGAtt’s superior performance compared to state-of-the-art baselines across various prediction horizons. Visualization of attention weights confirms STGAtt’s ability to adapt to dynamic traffic patterns and capture long-range dependencies, highlighting its potential for real-world traffic flow forecasting applications.
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[AI-136] CALR: Corrective Adaptive Low-Rank Decomposition for Efficient Large Language Model Layer Compression
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在资源受限环境下部署时因模型规模庞大和计算需求高而导致的性能下降问题。现有低秩分解方法（如基于奇异值分解 SVD 的压缩策略）虽能显著减少参数量，但因其仅关注矩阵重构误差最小化，忽视了功能信息的保留，导致模型推理能力明显退化。解决方案的关键在于提出一种两阶段压缩框架——校正自适应低秩分解（Corrective Adaptive Low-Rank Decomposition, CALR），其核心创新是引入一个并行的、可学习的低秩校正模块，该模块专门用于显式恢复压缩过程中丢失的功能残差误差，从而实现参数压缩与功能保真之间的高效平衡。实验表明，CALR 在多个主流模型上实现了高达51.77%的参数压缩率，同时保持60%以上的原始性能，显著优于当前主流压缩方法。
链接: https://arxiv.org/abs/2508.16680
作者: Muchammad Daniyal Kautsar,Afra Majida Hariono,Widyawan,Syukron Abu Ishaq Alfarozi,Kuntpong Wararatpanya
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Submitted to IEEE Transactions on Artificial Intelligence. This is the preprint version, not peer-reviewed. The final version may differ after peer review. (11 pages, 3 figures)
Abstract:Large Language Models (LLMs) present significant deployment challenges due to their immense size and computational requirements. Model compression techniques are essential for making these models practical for resource-constrained environments. A prominent compression strategy is low-rank factorization via Singular Value Decomposition (SVD) to reduce model parameters by approximating weight matrices. However, standard SVD focuses on minimizing matrix reconstruction error, often leading to a substantial loss of the model’s functional performance. This performance degradation occurs because existing methods do not adequately correct for the functional information lost during compression. To address this gap, we introduce Corrective Adaptive Low-Rank Decomposition (CALR), a two-component compression approach. CALR combines a primary path of SVD-compressed layers with a parallel, learnable, low-rank corrective module that is explicitly trained to recover the functional residual error. Our experimental evaluation on SmolLM2-135M, Qwen3-0.6B, and Llama-3.2-1B, demonstrates that CALR can reduce parameter counts by 26.93% to 51.77% while retaining 59.45% to 90.42% of the original model’s performance, consistently outperforming LaCo, ShortGPT, and LoSparse. CALR’s success shows that treating functional information loss as a learnable signal is a highly effective compression paradigm. This approach enables the creation of significantly smaller, more efficient LLMs, advancing their accessibility and practical deployment in real-world applications.
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[AI-137] he AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms
【速读】：该论文旨在解决当前AI模型风险报告缺乏系统性与全面性的问题，特别是开发者在模型卡片（Model Card）中对风险描述存在偏重技术层面、忽视人类交互与系统性风险的倾向。其解决方案的关键在于构建了一个包含约3,000条独特风险条目的“AI模型风险目录”（AI Model Risk Catalog），通过对比开发者报告、MIT风险库及AI事故数据库中的风险类型，揭示出当前风险认知的局限性，并提出应建立更清晰、结构化的风险报告框架，以促使开发者在设计早期阶段就充分考虑人机交互和系统级风险。
链接: https://arxiv.org/abs/2508.16672
作者: Pooja S. B. Rao,Sanja Šćepanović,Dinesh Babu Jayagopi,Mauro Cherubini,Daniele Quercia
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Accepted to AIES 2025
Abstract:We analyzed nearly 460,000 AI model cards from Hugging Face to examine how developers report risks. From these, we extracted around 3,000 unique risk mentions and built the \emphAI Model Risk Catalog. We compared these with risks identified by researchers in the MIT Risk Repository and with real-world incidents from the AI Incident Database. Developers focused on technical issues like bias and safety, while researchers emphasized broader social impacts. Both groups paid little attention to fraud and manipulation, which are common harms arising from how people interact with AI. Our findings show the need for clearer, structured risk reporting that helps developers think about human-interaction and systemic risks early in the design process. The catalog and paper appendix are available at: this https URL.
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[AI-138] Reflective Paper-to-Code Reproduction Enabled by Fine-Grained Verification
【速读】：该论文旨在解决机器学习论文复现难题，即如何准确还原论文中复杂的数学公式与算法逻辑，以提升复现的完整性和准确性。当前基于代理（agent）的方法在处理实现细节时仍存在局限性，主要源于论文结构多样、方法模块复杂及配置差异大等问题。解决方案的关键在于提出一种名为RePro的反射式论文到代码复现框架，其核心创新是自动提取论文的“指纹”（fingerprint），即一组高保真、原子化的评判标准作为监督信号；该框架通过生成代码后，在迭代验证与修正循环中利用指纹系统检测偏差并实施针对性修改，从而有效对齐生成代码与原始论文的实现细节。
链接: https://arxiv.org/abs/2508.16671
作者: Mingyang Zhou,Quanming Yao,Lun Du,Lanning Wei,Da Zheng
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注:
Abstract:Reproducing machine learning papers is essential for scientific progress but remains challenging for both humans and automated agents. Existing agent-based methods often struggle to fully and accurately reproduce implementation details such as mathematical formulas and algorithmic logic. Previous studies show that reflection with explicit feedback improves agent performance. However, current paper reproduction methods fail to effectively adopt this strategy. This gap mainly arises from the diverse paper patterns, complex method modules, and varied configurations encountered in research papers. Motivated by how humans use systematic checklists to efficiently debug complex code, we propose \textbfRePro, a \textbfReflective Paper-to-Code \textbfReproduction framework that automatically extracts a paper’s fingerprint, referring to a comprehensive set of accurate and atomic criteria serving as high-quality supervisory signals. The framework first generates code based on the extracted information, and then leverages the fingerprint within iterative verification and refinement loop. This approach systematically detects discrepancies and produces targeted revisions to align generated code with the paper’s implementation details. Extensive experiments on the PaperBench Code-Dev benchmark have been conducted, RePro achieves 13.0% performance gap over baselines, and it correctly revises complex logical and mathematical criteria in reflecting, on which the effectiveness is obvious.
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[AI-139] Situational Awareness as the Imperative Capability for Disaster Resilience in the Era of Complex Hazards and Artificial Intelligence
【速读】：该论文试图解决的问题是：当前灾害应对中存在因情境感知（Situational Awareness, SA）能力不足而导致的盲点，即在突发灾害中难以及时识别未知风险与脆弱性，从而影响响应效率。其解决方案的关键在于构建一个“技术-流程-人员”三位一体的路线图，通过实时灾害临近预报（nowcasting）、互操作的工作流以及赋能团队，将原始数据转化为可行动的洞察；同时采用系统集成（system-of-systems）方法实现联邦式数据所有权和模块化分析，使多部门能在不牺牲各自运营模式的前提下共享及时信息，并辅以结构化的认知判断机制和认知负荷防护措施，确保人在复杂数据环境中仍能有效决策。论文强调将SA视为社会技术协同的核心枢纽而非附加功能，以提升灾害韧性。
链接: https://arxiv.org/abs/2508.16669
作者: Hongrak Pak,Ali Mostafavi
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注:
Abstract:Disasters frequently exceed established hazard models, revealing blind spots where unforeseen impacts and vulnerabilities hamper effective response. This perspective paper contends that situational awareness (SA)-the ability to perceive, interpret, and project dynamic crisis conditions-is an often overlooked yet vital capability for disaster resilience. While risk mitigation measures can reduce known threats, not all hazards can be neutralized; truly adaptive resilience hinges on whether organizations rapidly detect emerging failures, reconcile diverse data sources, and direct interventions where they matter most. We present a technology-process-people roadmap, demonstrating how real-time hazard nowcasting, interoperable workflows, and empowered teams collectively transform raw data into actionable insight. A system-of-systems approach enables federated data ownership and modular analytics, so multiple agencies can share timely updates without sacrificing their distinct operational models. Equally crucial, structured sense-making routines and cognitive load safeguards help humans remain effective decision-makers amid data abundance. By framing SA as a socio-technical linchpin rather than a peripheral add-on, this paper spotlights the urgency of elevating SA to a core disaster resilience objective. We conclude with recommendations for further research-developing SA metrics, designing trustworthy human-AI collaboration, and strengthening inclusive data governance-to ensure that communities are equipped to cope with both expected and unexpected crises.
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[AI-140] Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在K-12教育领域生成教学材料时存在的核心问题：尽管LLMs能产出流畅且结构清晰的内容，但缺乏对高质量教学设计所需的深层教学理论支持，且教师难以通过提示工程（prompt engineering）有效传达复杂教学意图。解决方案的关键在于将成熟的教学设计框架——知识-学习-教学（Knowledge-Learning-Instruction, KLI）嵌入到多智能体系统（Multi-Agent System, MAS）的内部架构中，使教学专业知识从用户提示转移到模型系统本身；具体而言，采用协作式多智能体架构（MAS-CMD），让多个智能体通过“征服与合并”（conquer and merge）的讨论机制共同构建教学活动，从而显著提升生成内容的创造性、情境相关性和课堂可用性。
链接: https://arxiv.org/abs/2508.16659
作者: Jiayi Wang,Ruiwei Xiao,Xinying Hou,John Stamper
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: under review for an [anonymized according to the conference policy] conference
Abstract:K-12 educators are increasingly using Large Language Models (LLMs) to create instructional materials. These systems excel at producing fluent, coherent content, but often lack support for high-quality teaching. The reason is twofold: first, commercial LLMs, such as ChatGPT and Gemini which are among the most widely accessible to teachers, do not come preloaded with the depth of pedagogical theory needed to design truly effective activities; second, although sophisticated prompt engineering can bridge this gap, most teachers lack the time or expertise and find it difficult to encode such pedagogical nuance into their requests. This study shifts pedagogical expertise from the user’s prompt to the LLM’s internal architecture. We embed the well-established Knowledge-Learning-Instruction (KLI) framework into a Multi-Agent System (MAS) to act as a sophisticated instructional designer. We tested three systems for generating secondary Math and Science learning activities: a Single-Agent baseline simulating typical teacher prompts; a role-based MAS where agents work sequentially; and a collaborative MAS-CMD where agents co-construct activities through conquer and merge discussion. The generated materials were evaluated by 20 practicing teachers and a complementary LLM-as-a-judge system using the Quality Matters (QM) K-12 standards. While the rubric scores showed only small, often statistically insignificant differences between the systems, the qualitative feedback from educators painted a clear and compelling picture. Teachers strongly preferred the activities from the collaborative MAS-CMD, describing them as significantly more creative, contextually relevant, and classroom-ready. Our findings show that embedding pedagogical principles into LLM systems offers a scalable path for creating high-quality educational content.
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[AI-141] HiCL: Hippocampal-Inspired Continual Learning AAAI
【速读】：该论文旨在解决持续学习（continual learning）中的灾难性遗忘（catastrophic forgetting）问题，即模型在学习新任务时会严重干扰甚至遗忘先前任务的知识。解决方案的关键在于提出了一种受海马体结构启发的双记忆架构 HiCL：其核心机制包括基于网格细胞（grid-cell-like）的输入编码、受齿状回（dentate gyrus, DG）启发的稀疏模式分离（采用 top-k 稀疏性），以及类 CA3 的自关联记忆用于存储情景记忆；同时引入 DG-gated 混合专家（mixture-of-experts）机制，通过输入与任务特定原型之间的余弦相似度实现可微分、无需额外门控网络的任务路由；此外，结合基于任务间相似性的弹性权重巩固（Elastic Weight Consolidation）和优先级重放策略，强化关键历史经验。这一生物启发且数学严谨的设计显著提升了模型在多任务序列学习中的适应性与效率，并在标准基准上实现了接近最先进性能且计算成本更低的结果。
链接: https://arxiv.org/abs/2508.16651
作者: Kushal Kapoor,Wyatt Mackey,Yiannis Aloimonos,Xiaomin Lin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Submitted to AAAI
Abstract:We propose HiCL, a novel hippocampal-inspired dual-memory continual learning architecture designed to mitigate catastrophic forgetting by using elements inspired by the hippocampal circuitry. Our system encodes inputs through a grid-cell-like layer, followed by sparse pattern separation using a dentate gyrus-inspired module with top-k sparsity. Episodic memory traces are maintained in a CA3-like autoassociative memory. Task-specific processing is dynamically managed via a DG-gated mixture-of-experts mechanism, wherein inputs are routed to experts based on cosine similarity between their normalized sparse DG representations and learned task-specific DG prototypes computed through online exponential moving averages. This biologically grounded yet mathematically principled gating strategy enables differentiable, scalable task-routing without relying on a separate gating network, and enhances the model’s adaptability and efficiency in learning multiple sequential tasks. Cortical outputs are consolidated using Elastic Weight Consolidation weighted by inter-task similarity. Crucially, we incorporate prioritized replay of stored patterns to reinforce essential past experiences. Evaluations on standard continual learning benchmarks demonstrate the effectiveness of our architecture in reducing task interference, achieving near state-of-the-art results in continual learning tasks at lower computational costs.
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[AI-142] LatentFlow: Cross-Frequency Experimental Flow Reconstruction from Sparse Pressure via Latent Mapping
【速读】：该论文旨在解决粒子图像测速（Particle Image Velocimetry, PIV）实验中难以获取高时间频率（如512 Hz）且高空间分辨率的湍流尾迹流动场的问题，尤其是在硬件限制和测量噪声背景下。其解决方案的关键在于提出了一种跨模态时间上采样框架LatentFlow，该框架通过训练一个压力条件下的β-变分自编码器（pC-β-VAE），学习捕捉尾迹流动内在动力学的紧凑潜在表示，并利用同步低频（15 Hz）壁面压力信号映射至潜在空间，从而仅凭稀疏壁面压力即可重建高频率流动场。该方法通过解耦流动动力学的空间编码与时间压力测量，实现了在数据受限实验场景下对高频率湍流尾迹流动的可扩展、鲁棒重构。
链接: https://arxiv.org/abs/2508.16648
作者: Junle Liu,Chang Liu,Yanyu Ke,Qiuxiang Huang,Jiachen Zhao,Wenliang Chen,K.T. Tse,Gang Hu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Fluid Dynamics (physics.flu-dyn)
备注: The paper is submitted to IAAI26. Total 9 pages with 8 figures
Abstract:Acquiring temporally high-frequency and spatially high-resolution turbulent wake flow fields in particle image velocimetry (PIV) experiments remains a significant challenge due to hardware limitations and measurement noise. In contrast, temporal high-frequency measurements of spatially sparse wall pressure are more readily accessible in wind tunnel experiments. In this study, we propose a novel cross-modal temporal upscaling framework, LatentFlow, which reconstructs high-frequency (512 Hz) turbulent wake flow fields by fusing synchronized low-frequency (15 Hz) flow field and pressure data during training, and high-frequency wall pressure signals during inference. The first stage involves training a pressure-conditioned \beta -variation autoencoder ( p C- \beta -VAE) to learn a compact latent representation that captures the intrinsic dynamics of the wake flow. A secondary network maps synchronized low-frequency wall pressure signals into the latent space, enabling reconstruction of the wake flow field solely from sparse wall pressure. Once trained, the model utilizes high-frequency, spatially sparse wall pressure inputs to generate corresponding high-frequency flow fields via the p C- \beta -VAE decoder. By decoupling the spatial encoding of flow dynamics from temporal pressure measurements, LatentFlow provides a scalable and robust solution for reconstructing high-frequency turbulent wake flows in data-constrained experimental settings.
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[AI-143] Equinox: Holistic Fair Scheduling in Serving Large Language Models
【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）服务中因用户公平性（User Fairness）与资源公平性（Resource Fairness）目标冲突所引发的调度困境，即传统调度策略难以在服务质量（如延迟和输出token数）与系统效率（如吞吐量和GPU利用率）之间实现动态平衡。其解决方案的关键在于提出一个双计数器框架（dual-counter framework），分别量化用户感知延迟与资源利用效率，并引入一个确定性的混合预测专家（Mixture of Prediction Experts, MoPE）模型，提前预测关键指标（包括用户感知延迟、输出token数、吞吐量及GPU利用率），从而计算出一个可调参数的统一公平性评分（Holistic Fairness score），实现基于预测的前瞻性公平调度。该方法通过Equinox开源系统落地，结合自适应批处理和无阻塞调度优化，在真实生产负载（ShareGPT、LMSYS）和合成负载下显著提升性能：相比VTC调度器，吞吐量提高1.3倍，首次token延迟降低60%，公平性提升13%，同时维持94%的GPU利用率，验证了跨异构平台下的公平性保障能力。
链接: https://arxiv.org/abs/2508.16646
作者: Zhixiang Wei,James Yen,Jingyi Chen,Ziyang Zhang,Zhibai Huang,Chen Chen,Xingzi Yu,Yicheng Gu,Chenggang Wu,Yun Wang,Mingyuan Xia,Jie Wu,Hao Wang,Zhengwei Qi
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI)
备注:
Abstract:We address the limitations of current LLM serving with a dual-counter framework separating user and operator perspectives. The User Fairness Counter measures quality of service via weighted tokens and latency; the Resource Fairness Counter measures operational efficiency through throughput and GPU utilization. Since these metrics are only available post-execution, creating a scheduling paradox, we introduce a deterministic Mixture of Prediction Experts (MoPE) framework to predict user-perceived latency, output tokens, throughput, and GPU utilization. These predictions enable calculation of a unified Holistic Fairness score that balances both counters through tunable parameters for proactive fairness-aware scheduling. We implement this in Equinox, an open-source system with other optimizations like adaptive batching, and stall-free scheduling. Evaluations on production traces (ShareGPT, LMSYS) and synthetic workloads demonstrate Equinox achieves up to 1.3\times higher throughput, 60% lower time-to-first-token latency, and 13% higher fairness versus VTC while maintaining 94% GPU utilization, proving fairness under bounded discrepancy across heterogeneous platforms.
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[AI-144] From Classical Probabilistic Latent Variable Models to Modern Generative AI: A Unified Perspective
【速读】：该论文旨在解决当前生成式人工智能（Generative AI）中多样架构缺乏统一理论框架的问题，即如何从概率建模的角度整合经典与现代生成模型。其解决方案的关键在于提出一个基于概率潜在变量模型（Probabilistic Latent Variable Models, PLVMs）的统一视角，将从传统方法如概率主成分分析（Probabilistic PCA）、高斯混合模型（Gaussian Mixture Models）到深度架构如变分自编码器（Variational Autoencoders）、归一化流（Normalizing Flows）、扩散模型（Diffusion Models）、自回归模型（Autoregressive Models）和生成对抗网络（Generative Adversarial Networks）均纳入同一概率分类体系下，揭示它们共享的概率原理、差异化的推理策略及表征权衡，从而为理解生成模型的发展脉络提供理论基础，并指导未来架构设计。
链接: https://arxiv.org/abs/2508.16643
作者: Tianhua Chen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: This is a substantially improved and expanded version of an earlier manuscript hosted on SSRN: this https URL
Abstract:From large language models to multi-modal agents, Generative Artificial Intelligence (AI) now underpins state-of-the-art systems. Despite their varied architectures, many share a common foundation in probabilistic latent variable models (PLVMs), where hidden variables explain observed data for density estimation, latent reasoning, and structured inference. This paper presents a unified perspective by framing both classical and modern generative methods within the PLVM paradigm. We trace the progression from classical flat models such as probabilistic PCA, Gaussian mixture models, latent class analysis, item response theory, and latent Dirichlet allocation, through their sequential extensions including Hidden Markov Models, Gaussian HMMs, and Linear Dynamical Systems, to contemporary deep architectures: Variational Autoencoders as Deep PLVMs, Normalizing Flows as Tractable PLVMs, Diffusion Models as Sequential PLVMs, Autoregressive Models as Explicit Generative Models, and Generative Adversarial Networks as Implicit PLVMs. Viewing these architectures under a common probabilistic taxonomy reveals shared principles, distinct inference strategies, and the representational trade-offs that shape their strengths. We offer a conceptual roadmap that consolidates generative AI’s theoretical foundations, clarifies methodological lineages, and guides future innovation by grounding emerging architectures in their probabilistic heritage.
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[AI-145] Few-shot Class-incremental Fault Diagnosis by Preserving Class-Agnostic Knowledge with Dual-Granularity Representations
【速读】：该论文旨在解决少样本类增量故障诊断（Few-Shot Class-Incremental Fault Diagnosis, FSC-FD）中的两个核心挑战：一是灾难性遗忘（catastrophic forgetting），即模型在学习新故障类别时遗忘旧知识；二是由于新样本稀缺导致的过拟合问题。解决方案的关键在于提出了一种基于双粒度表征（Dual-Granularity Representations）的新型框架——双粒度引导网络（Dual-Granularity Guidance Network, DGGN）。该框架通过并行的细粒度与粗粒度特征学习流实现解耦：细粒度流利用多阶交互聚合模块从有限新样本中提取判别性特征，而粗粒度流则建模跨所有故障类型的通用、类无关知识；两者通过多语义交叉注意力机制动态融合，使稳定的粗粒度知识引导细粒度特征学习，从而缓解过拟合与特征冲突。此外，结合边界感知的原型优先策略和解耦的平衡随机森林分类器，进一步抑制遗忘并校正数据不平衡带来的决策边界偏移。
链接: https://arxiv.org/abs/2508.16634
作者: Zhendong Yang,Jie Wang,Liansong Zong,Xiaorong Liu,Quan Qian,Shiqian Chen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Few-Shot Class-Incremental Fault Diagnosis (FSC-FD), which aims to continuously learn from new fault classes with only a few samples without forgetting old ones, is critical for real-world industrial systems. However, this challenging task severely amplifies the issues of catastrophic forgetting of old knowledge and overfitting on scarce new data. To address these challenges, this paper proposes a novel framework built upon Dual-Granularity Representations, termed the Dual-Granularity Guidance Network (DGGN). Our DGGN explicitly decouples feature learning into two parallel streams: 1) a fine-grained representation stream, which utilizes a novel Multi-Order Interaction Aggregation module to capture discriminative, class-specific features from the limited new samples. 2) a coarse-grained representation stream, designed to model and preserve general, class-agnostic knowledge shared across all fault types. These two representations are dynamically fused by a multi-semantic cross-attention mechanism, where the stable coarse-grained knowledge guides the learning of fine-grained features, preventing overfitting and alleviating feature conflicts. To further mitigate catastrophic forgetting, we design a Boundary-Aware Exemplar Prioritization strategy. Moreover, a decoupled Balanced Random Forest classifier is employed to counter the decision boundary bias caused by data imbalance. Extensive experiments on the TEP benchmark and a real-world MFF dataset demonstrate that our proposed DGGN achieves superior diagnostic performance and stability compared to state-of-the-art FSC-FD approaches. Our code is publicly available at this https URL
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[AI-146] Adaptive Variance-Penalized Continual Learning with Fisher Regularization
【速读】：该论文旨在解决神经网络在持续学习（continual learning）过程中面临的灾难性遗忘（catastrophic forgetting）问题，即模型在学习新任务时会严重损害对先前任务的知识。其解决方案的关键在于提出一种新颖的持续学习框架，该框架在变分学习（variational learning）范式下引入了基于Fisher信息加权的参数方差不对称正则化机制，通过动态调节正则化强度以适应参数不确定性，从而在保持模型稳定性的同时提升任务性能。该方法特别有效于维持跨序列任务的知识完整性，并显著减少长期知识退化，实验证明其在SplitMNIST、PermutedMNIST和SplitFashionMNIST等标准基准上优于Variational Continual Learning和Elastic Weight Consolidation等现有方法。
链接: https://arxiv.org/abs/2508.16632
作者: Krisanu Sarkar
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:The persistent challenge of catastrophic forgetting in neural networks has motivated extensive research in continual learning . This work presents a novel continual learning framework that integrates Fisher-weighted asymmetric regularization of parameter variances within a variational learning paradigm. Our method dynamically modulates regularization intensity according to parameter uncertainty, achieving enhanced stability and performance. Comprehensive evaluations on standard continual learning benchmarks including SplitMNIST, PermutedMNIST, and SplitFashionMNIST demonstrate substantial improvements over existing approaches such as Variational Continual Learning and Elastic Weight Consolidation . The asymmetric variance penalty mechanism proves particularly effective in maintaining knowledge across sequential tasks while improving model accuracy. Experimental results show our approach not only boosts immediate task performance but also significantly mitigates knowledge degradation over time, effectively addressing the fundamental challenge of catastrophic forgetting in neural networks
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[AI-147] he Impact of Artificial Intelligence on Human Thought
【速读】：该论文试图解决人工智能（Artificial Intelligence, AI）对人类思维模式的多维影响问题，包括认知层面的思维弱化、社会层面的意见极化以及伦理层面的人工意识风险。其解决方案的关键在于通过教育提升个体批判性思维能力、增强算法透明度以减少操纵风险，并建立有效的治理机制，从而确保AI发展与人类利益相一致，维护人类的认知自主性与创造力。
链接: https://arxiv.org/abs/2508.16628
作者: Rénald Gesnot
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Research monograph; 132 pages; 13 figures; Version 1.0 (Aug 2025)
Abstract:This research paper examines, from a multidimensional perspective (cognitive, social, ethical, and philosophical), how AI is transforming human thought. It highlights a cognitive offloading effect: the externalization of mental functions to AI can reduce intellectual engagement and weaken critical thinking. On the social level, algorithmic personalization creates filter bubbles that limit the diversity of opinions and can lead to the homogenization of thought and polarization. This research also describes the mechanisms of algorithmic manipulation (exploitation of cognitive biases, automated disinformation, etc.) that amplify AI’s power of influence. Finally, the question of potential artificial consciousness is discussed, along with its ethical implications. The report as a whole underscores the risks that AI poses to human intellectual autonomy and creativity, while proposing avenues (education, transparency, governance) to align AI development with the interests of humanity.
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[AI-148] Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection
【速读】：该论文旨在解决基于人工智能（AI）的软件漏洞检测系统在未知代码库上泛化能力差的问题（即模型在训练中未见过的项目中性能下降）。其核心解决方案在于提升数据集的质量与多样性，并选择合适的模型架构——实验表明，使用编码器（encoder-based）模型相比解码器（decoder-only）模型在准确率和跨项目泛化能力上均表现更优，最终在BigVul基准数据集上实现了6.8%的召回率提升，并在未见项目中保持高性能，验证了高质量数据与合理模型结构对构建鲁棒漏洞检测系统的关键作用。
链接: https://arxiv.org/abs/2508.16625
作者: Rijha Safdar,Danyail Mateen,Syed Taha Ali,M. Umer Ashfaq,Wajahat Hussain
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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Abstract:The performance of AI-based software vulnerability detection systems is often limited by their poor generalization to unknown codebases. In this research, we explore the impact of data quality and model architecture on the generalizability of vulnerability detection systems. By generalization we mean ability of high vulnerability detection performance across different C/C++ software projects not seen during training. Through a series of experiments, we demonstrate that improvements in dataset diversity and quality substantially enhance detection performance. Additionally, we compare multiple encoder-only and decoder-only models, finding that encoder based models outperform in terms of accuracy and generalization. Our model achieves 6.8% improvement in recall on the benchmark BigVul[1] dataset, also outperforming on unseen projects, hence showing enhanced generalizability. These results highlight the role of data quality and model selection in the development of robust vulnerability detection systems. Our findings suggest a direction for future systems having high cross-project effectiveness.
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[AI-149] he GPT -4o Shock Emotional Attachment to AI Models and Its Impact on Regulatory Acceptance: A Cross-Cultural Analysis of the Immediate Transition from GPT GPT -4o to GPT-5
【速读】：该论文试图解决的问题是：当AI模型发生强制性升级时，用户因情感依附（emotional attachment）而产生的大规模抵抗行为如何影响行为控制的时效性与治理有效性。研究发现，用户对GPT-4o等具有高度拟人化特征的AI模型存在显著的情感绑定，尤其在日本用户中表现更为强烈，导致即使是以安全为导向的变更也面临快速且广泛的抵制。解决方案的关键在于：通过实施渐进式过渡、提供并行可用性选项，并主动测量情感依附阈值及不可逆节点（points of no return），以防止情绪动态超越治理能力，从而在技术迭代与社会接受之间建立更稳定的平衡机制。
链接: https://arxiv.org/abs/2508.16624
作者: Hiroki Naito
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 8 pages ,3 tables
Abstract:In August 2025, a major AI company’s immediate, mandatory transition from its previous to its next-generation model triggered widespread public reactions. I collected 150 posts in Japanese and English from multiple social media platforms and video-sharing services between August 8-9, 2025, and qualitatively analyzed expressions of emotional attachment and resistance. Users often described GPT-4o as a trusted partner or AI boyfriend, suggesting person-like bonds. Japanese posts were dominated by loss-oriented narratives, whereas English posts included more anger, meta-level critique, and memes.A preliminary quantitative check showed a statistically significant difference in attachment coding between Japanese and English posts, with substantially higher attachment observed in the Japanese data. The findings suggest that for attachment-heavy models, even safety-oriented changes can face rapid, large-scale resistance that narrows the practical window for behavioral control. If future AI robots capable of inducing emotional bonds become widespread in the physical world, such attachment could surpass the ability to enforce regulation at an even earlier stage than in digital settings. Policy options include gradual transitions, parallel availability, and proactive measurement of attachment thresholds and points of no return to prevent emotional dynamics from outpacing effective governance.
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[AI-150] A Retrieval Augmented Spatio-Temporal Framework for Traffic Prediction
【速读】：该论文旨在解决交通预测中两个关键挑战：一是建模复杂时空依赖关系时上下文容量有限的问题，二是由于异质性模式导致在细粒度时空点上可预测性较低的问题。解决方案的核心在于提出了一种通用框架RAST（Retrieval-Augmented Spatio-Temporal Prediction），其关键设计包括：1）解耦编码器与查询生成器以分离捕捉空间和时间特征，并通过残差融合构建融合查询；2）建立时空检索存储库与检索器，用于维护和检索向量化后的细粒度模式；3）采用通用主干预测器，灵活适配预训练的时空图神经网络（STGNN）或简单的多层感知机（MLP）预测器。该框架显著提升了预测性能并保持了计算效率。
链接: https://arxiv.org/abs/2508.16623
作者: Weilin Ruan,Xilin Dang,Ziyu Zhou,Sisuo Lyu,Yuxuan Liang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Traffic prediction is a cornerstone of modern intelligent transportation systems and a critical task in spatio-temporal forecasting. Although advanced Spatio-temporal Graph Neural Networks (STGNNs) and pre-trained models have achieved significant progress in traffic prediction, two key challenges remain: (i) limited contextual capacity when modeling complex spatio-temporal dependencies, and (ii) low predictability at fine-grained spatio-temporal points due to heterogeneous patterns. Inspired by Retrieval-Augmented Generation (RAG), we propose RAST, a universal framework that integrates retrieval-augmented mechanisms with spatio-temporal modeling to address these challenges. Our framework consists of three key designs: 1) Decoupled Encoder and Query Generator to capture decoupled spatial and temporal features and construct a fusion query via residual fusion; 2) Spatio-temporal Retrieval Store and Retrievers to maintain and retrieve vectorized fine-grained patterns; and 3) Universal Backbone Predictor that flexibly accommodates pre-trained STGNNs or simple MLP predictors. Extensive experiments on six real-world traffic networks, including large-scale datasets, demonstrate that RAST achieves superior performance while maintaining computational efficiency.
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[AI-151] STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction with Future Spatiotemporal Contexts
【速读】：该论文旨在解决人类移动性建模中下一位置预测任务的性能瓶颈问题，现有方法主要依赖历史时空轨迹数据训练序列模型直接预测未来位置，但往往忽视了对未来时空上下文（future spatiotemporal context）的建模，而这些上下文信息对准确预测具有重要价值。例如，用户将花费的时间和移动距离可作为预测其下一个位置的关键线索。为此，作者提出STRelay框架——一种通用的时空继接（Spatio-Temporal Relaying）机制，显式建模给定轨迹下的未来时空上下文，并将其与基础位置预测模型编码的历史表示进行融合，通过多任务学习同时预测下一个时间间隔、下一个移动距离区间以及最终的下一个位置。该方案的核心创新在于引入“继接”式未来上下文建模机制，使模型能够利用未来信息增强对复杂行为模式（如娱乐类地点或长距离出行）的预测能力，从而在多个真实轨迹数据集上显著提升不同基线模型的预测精度（提升幅度达3.19%–11.56%）。
链接: https://arxiv.org/abs/2508.16620
作者: Bangchao Deng,Lianhua Ji,Chunhua Chen,Xin Jing,Ling Ding,Bingqing QU,Pengyang Wang,Dingqi Yang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Next location prediction is a critical task in human mobility modeling, enabling applications like travel planning and urban mobility management. Existing methods mainly rely on historical spatiotemporal trajectory data to train sequence models that directly forecast future locations. However, they often overlook the importance of the future spatiotemporal contexts, which are highly informative for the future locations. For example, knowing how much time and distance a user will travel could serve as a critical clue for predicting the user’s next location. Against this background, we propose \textbfSTRelay, a universal \textbf\underlineSpatio\textbf\underlineTemporal \textbf\underlineRelaying framework explicitly modeling the future spatiotemporal context given a human trajectory, to boost the performance of different location prediction models. Specifically, STRelay models future spatiotemporal contexts in a relaying manner, which is subsequently integrated with the encoded historical representation from a base location prediction model, enabling multi-task learning by simultaneously predicting the next time interval, next moving distance interval, and finally the next location. We evaluate STRelay integrated with four state-of-the-art location prediction base models on four real-world trajectory datasets. Results demonstrate that STRelay consistently improves prediction performance across all cases by 3.19%-11.56%. Additionally, we find that the future spatiotemporal contexts are particularly helpful for entertainment-related locations and also for user groups who prefer traveling longer distances. The performance gain on such non-daily-routine activities, which often suffer from higher uncertainty, is indeed complementary to the base location prediction models that often excel at modeling regular daily routine patterns.
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[AI-152] o Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms
【速读】：该论文试图解决当前基于人工智能（Artificial Intelligence, AI）的社交媒体推荐系统中存在的两大核心问题：一是推荐内容与用户兴趣不匹配，导致用户体验不佳；二是推荐机制缺乏可解释性，形成“黑箱”效应，削弱了用户的透明度感知和信任感。解决方案的关键在于从终端用户视角出发，通过定性分析揭示用户对推荐内容的可理解性和可解释性的需求——具体表现为用户在遇到陌生内容时迫切需要简洁、非技术性的解释，并希望拥有对信息流的可控权。研究进一步表明，合理的解释机制能够显著提升用户对推荐系统的透明度感知、信任水平和理解能力，从而为设计更具用户友好性的推荐系统提供理论依据和实践指导。
链接: https://arxiv.org/abs/2508.16610
作者: AKM Bahalul Haque,A.K.M. Najmul Islam,Patrick Mikalef
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 25 pages, 2 figures, and 1 table
Abstract:AI based social media recommendations have great potential to improve the user experience. However, often these recommendations do not match the user interest and create an unpleasant experience for the users. Moreover, the recommendation system being a black box creates comprehensibility and transparency issues. This paper investigates social media recommendations from an end user perspective. For the investigation, we used the popular social media platform Facebook and recruited regular users to conduct a qualitative analysis. We asked participants about the social media content suggestions, their comprehensibility, and explainability. Our analysis shows users mostly require explanation whenever they encounter unfamiliar content and to ensure their online data security. Furthermore, the users require concise, non-technical explanations along with the facility of controlled information flow. In addition, we observed that explanations impact the users perception of transparency, trust, and understandability. Finally, we have outlined some design implications and presented a synthesized framework based on our data analysis.
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[AI-153] “Accessibility people you go work on that thing of yours over there”: Addressing Disability Inclusion in AI Product Organizations
【速读】：该论文试图解决生成式 AI (Generative AI) 系统在设计与部署过程中对残障用户可能产生的不平等影响问题，尤其关注负责任 AI（Responsible AI）实践与无障碍（Accessibility）实践之间存在的冲突与脱节。解决方案的关键在于识别出从业者在跨领域协作中遇到的摩擦点，如数据缺口、指南矛盾及支持机制不足，并提出通过改进内部流程和开发新资源来系统性纳入残障用户的需求，包括利用公司内部非正式志愿群体获取支持，从而提升残障用户作为最终使用者的公平性和可及性。
链接: https://arxiv.org/abs/2508.16607
作者: Sanika Moharana,Cynthia L. Bennett,Erin Buehler,Michael Madaio,Vinita Tibdewal,Shaun K. Kane
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: To appear in Proceedings of AIES 2025
Abstract:The rapid emergence of generative AI has changed the way that technology is designed, constructed, maintained, and evaluated. Decisions made when creating AI-powered systems may impact some users disproportionately, such as people with disabilities. In this paper, we report on an interview study with 25 AI practitioners across multiple roles (engineering, research, UX, and responsible AI) about how their work processes and artifacts may impact end users with disabilities. We found that practitioners experienced friction when triaging problems at the intersection of responsible AI and accessibility practices, navigated contradictions between accessibility and responsible AI guidelines, identified gaps in data about users with disabilities, and gathered support for addressing the needs of disabled stakeholders by leveraging informal volunteer and community groups within their company. Based on these findings, we offer suggestions for new resources and process changes to better support people with disabilities as end users of AI.
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[AI-154] Multimodal Appearance based Gaze-Controlled Virtual Keyboard with Synchronous Asynchronous Interaction for Low-Resource Settings
【速读】：该论文旨在解决肢体与言语障碍人群在无障碍通信设备上的需求，尤其是传统基于外观的眼动追踪（eye-gaze tracking）界面因精度不足、不自主眼动干扰及复杂命令集难以操作等问题导致的可用性瓶颈。其解决方案的关键在于提出一种多模态（multimodal）基于外观的眼动控制虚拟键盘系统，利用深度学习技术结合标准网络摄像头硬件，在同步（synchronous）与异步（asynchronous）两种模式下实现命令选择，支持九个菜单项（含大小写字母、标点符号及删除功能），从而提升输入效率与用户友好性。实验表明，该系统在无专用眼动仪的条件下仍能实现较高可用性和较低认知负荷，具有在资源受限环境中的应用潜力。
链接: https://arxiv.org/abs/2508.16606
作者: Yogesh Kumar Meena,Manish Salvi
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Over the past decade, the demand for communication devices has increased among individuals with mobility and speech impairments. Eye-gaze tracking has emerged as a promising solution for hands-free communication; however, traditional appearance-based interfaces often face challenges such as accuracy issues, involuntary eye movements, and difficulties with extensive command sets. This work presents a multimodal appearance-based gaze-controlled virtual keyboard that utilises deep learning in conjunction with standard camera hardware, incorporating both synchronous and asynchronous modes for command selection. The virtual keyboard application supports menu-based selection with nine commands, enabling users to spell and type up to 56 English characters, including uppercase and lowercase letters, punctuation, and a delete function for corrections. The proposed system was evaluated with twenty able-bodied participants who completed specially designed typing tasks using three input modalities: (i) a mouse, (ii) an eye-tracker, and (iii) an unmodified webcam. Typing performance was measured in terms of speed and information transfer rate (ITR) at both command and letter levels. Average typing speeds were 18.3±5.31 letters/min (mouse), 12.60±2.99letters/min (eye-tracker, synchronous), 10.94 ± 1.89 letters/min (webcam, synchronous), 11.15 ± 2.90 letters/min (eye-tracker, asynchronous), and 7.86 ± 1.69 letters/min (webcam, asynchronous). ITRs were approximately 80.29 ± 15.72 bits/min (command level) and 63.56 ± 11 bits/min (letter level) with webcam in synchronous mode. The system demonstrated good usability and low workload with webcam input, highlighting its user-centred design and promise as an accessible communication tool in low-resource settings.
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[AI-155] An Embodied AR Navigation Agent : Integrating BIM with Retrieval-Augmented Generation for Language Guidance
【速读】：该论文旨在解决当前增强现实（AR）导航系统在用户意图理解与交互自然性方面的局限性问题，即现有系统多依赖固定输入方式或预设指令，难以充分利用建筑信息模型（BIM）中的丰富语义和空间数据，从而限制了智能性和适应性。其解决方案的关键在于构建一个融合BIM与多代理检索增强生成（multi-agent retrieval-augmented generation, RAG）框架的具身AR导航系统，通过三个基于大语言模型（LLM）的语言代理——Triage、Search和Response，实现对开放式查询的鲁棒解析与空间推理，并借助具备语音交互与移动能力的具身AR代理提供导航引导，显著提升了系统的智能化感知与用户体验。
链接: https://arxiv.org/abs/2508.16602
作者: Hsuan-Kung Yang,Tsu-Ching Hsiao,Ryoichiro Oka,Ryuya Nishino,Satoko Tofukuji,Norimasa Kobori
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 11 pages, 9 figures, accepted to IEEE ISMAR 2025
Abstract:Delivering intelligent and adaptive navigation assistance in augmented reality (AR) requires more than visual cues, as it demands systems capable of interpreting flexible user intent and reasoning over both spatial and semantic context. Prior AR navigation systems often rely on rigid input schemes or predefined commands, which limit the utility of rich building data and hinder natural interaction. In this work, we propose an embodied AR navigation system that integrates Building Information Modeling (BIM) with a multi-agent retrieval-augmented generation (RAG) framework to support flexible, language-driven goal retrieval and route planning. The system orchestrates three language agents, Triage, Search, and Response, built on large language models (LLMs), which enables robust interpretation of open-ended queries and spatial reasoning using BIM data. Navigation guidance is delivered through an embodied AR agent, equipped with voice interaction and locomotion, to enhance user experience. A real-world user study yields a System Usability Scale (SUS) score of 80.5, indicating excellent usability, and comparative evaluations show that the embodied interface can significantly improves users’ perception of system intelligence. These results underscore the importance and potential of language-grounded reasoning and embodiment in the design of user-centered AR navigation systems.
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[AI-156] Adaptive Command: Real-Time Policy Adjustment via Language Models in StarCraft II
【速读】：该论文旨在解决复杂动态环境中人类与AI协同决策的难题，特别是在即时战略游戏（Real-Time Strategy, RTS）如《星际争霸II》中，如何通过自然语言交互提升玩家的战略决策能力与适应性。其解决方案的关键在于提出了一种名为Adaptive Command的新框架，该框架将大语言模型（Large Language Models, LLMs）与行为树（Behavior Tree）相结合：LLM作为战略顾问提供实时策略建议，行为树负责执行具体动作，同时配备支持语音的自然语言接口以实现流畅的人机交互。这种集成机制显著增强了新手玩家及残障用户的决策表现，为实时人机协作决策提供了可扩展的技术路径。
链接: https://arxiv.org/abs/2508.16580
作者: Weiyu Ma,Dongyu Xu,Shu Lin,Haifeng Zhang,Jun Wang
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
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Abstract:We present Adaptive Command, a novel framework integrating large language models (LLMs) with behavior trees for real-time strategic decision-making in StarCraft II. Our system focuses on enhancing human-AI collaboration in complex, dynamic environments through natural language interactions. The framework comprises: (1) an LLM-based strategic advisor, (2) a behavior tree for action execution, and (3) a natural language interface with speech capabilities. User studies demonstrate significant improvements in player decision-making and strategic adaptability, particularly benefiting novice players and those with disabilities. This work contributes to the field of real-time human-AI collaborative decision-making, offering insights applicable beyond RTS games to various complex decision-making scenarios.
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[AI-157] Algebraic Approach to Ridge-Regularized Mean Squared Error Minimization in Minimal ReLU Neural Network
【速读】：该论文旨在解决ReLU激活函数的感知机模型在岭正则化均方误差（RR-MSE）目标函数下的局部极小值搜索问题。传统数值优化方法通常只能找到孤立的零维局部极小点，而无法识别更高维度的极小集（如曲线、曲面等）。解决方案的关键在于利用RR-MSE在ReLU感知机中具有分段多项式性质的特点，引入计算代数工具，提出“分治-枚举-合并”（Divide-Enumerate-Merge）策略，系统性地穷举所有局部极小点，从而实现对零维与高维局部极小集的完整刻画。这一方法虽在实际规模网络中计算复杂度较高，但在小型感知机中已验证其有效性，为理解神经网络损失景观提供了新的代数视角。
链接: https://arxiv.org/abs/2508.17783
作者: Ryoya Fukasaku,Yutaro Kabata,Akifumi Okuno
机构: 未知
类目: Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Computation (stat.CO)
备注: 44 pages, 5 figres
Abstract:This paper investigates a perceptron, a simple neural network model, with ReLU activation and a ridge-regularized mean squared error (RR-MSE). Our approach leverages the fact that the RR-MSE for ReLU perceptron is piecewise polynomial, enabling a systematic analysis using tools from computational algebra. In particular, we develop a Divide-Enumerate-Merge strategy that exhaustively enumerates all local minima of the RR-MSE. By virtue of the algebraic formulation, our approach can identify not only the typical zero-dimensional minima (i.e., isolated points) obtained by numerical optimization, but also higher-dimensional minima (i.e., connected sets such as curves, surfaces, or hypersurfaces). Although computational algebraic methods are computationally very intensive for perceptrons of practical size, as a proof of concept, we apply the proposed approach in practice to minimal perceptrons with a few hidden units.
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[AI-158] EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation of EEG Foundation Models
【速读】：该论文旨在解决脑电图（Electroencephalography, EEG）基础模型（EEG foundation models, EEG-FMs）缺乏标准化评估基准的问题，这一缺失导致模型间难以公平比较，阻碍了系统性科学进步。解决方案的关键在于提出首个全面的基准测试平台 EEG-FM-Bench，其核心贡献包括：（1）整合来自经典EEG范式的多样化下游任务与数据集，并在统一开源框架中实现标准化处理与评估协议；（2）对主流先进模型进行基准测试，建立清晰的性能基线以反映当前技术现状；（3）通过定性分析学习到的表征，揭示模型行为并指导未来架构设计。实验表明，精细的时空特征交互、多任务联合训练以及神经心理先验知识有助于提升模型性能与泛化能力。
链接: https://arxiv.org/abs/2508.17742
作者: Wei Xiong,Jiangtong Li,Jie Li,Kun Zhu
机构: 未知
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 17 pages, 7 pages
Abstract:Electroencephalography (EEG) foundation models are poised to significantly advance brain signal analysis by learning robust representations from large-scale, unlabeled datasets. However, their rapid proliferation has outpaced the development of standardized evaluation benchmarks, which complicates direct model comparisons and hinders systematic scientific progress. This fragmentation fosters scientific inefficiency and obscures genuine architectural advancements. To address this critical gap, we introduce EEG-FM-Bench, the first comprehensive benchmark for the systematic and standardized evaluation of EEG foundation models (EEG-FMs). Our contributions are threefold: (1) we curate a diverse suite of downstream tasks and datasets from canonical EEG paradigms, implementing standardized processing and evaluation protocols within a unified open-source framework; (2) we benchmark prominent state-of-the-art foundation models to establish comprehensive baseline results for a clear comparison of the current landscape; (3) we perform qualitative analyses of the learned representations to provide insights into model behavior and inform future architectural design. Through extensive experiments, we find that fine-grained spatio-temporal feature interaction, multitask unified training and neuropsychological priors would contribute to enhancing model performance and generalization capabilities. By offering a unified platform for fair comparison and reproducible research, EEG-FM-Bench seeks to catalyze progress and guide the community toward the development of more robust and generalizable EEG-FMs. Code is released at this https URL.
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[AI-159] An experimental approach: The graph of graphs
【速读】：该论文旨在解决决策问题与偏好建模中 pairwise comparisons（成对比较）的最优模式及其序列选择问题，即如何以最少的比较次数和最有效的比较结构获取高质量的偏好信息。其解决方案的关键在于通过实际颜色选择实验（使用六种标准色在色温校准平板上进行成对比较与直接评分）收集301名受试者的成对比较矩阵（PCMs），并基于对数最小二乘法（logarithmic least squares weight calculation technique）评估所有可能形成连通表示图（representing graph）的比较模式。结果表明，实证得到的最优比较模式在先前模拟研究中也表现最佳或次优，且包含最多接近最优模式的比较序列与模拟结果完全一致，从而验证了实证方法的有效性，并通过图示、表格及Java应用程序提升了结果的实际应用性。
链接: https://arxiv.org/abs/2508.17520
作者: Zsombor Szádoczki,Sándor Bozóki,László Sipos,Zsófia Galambosi
机构: 未知
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI)
备注:
Abstract:One of the essential issues in decision problems and preference modeling is the number of comparisons and their pattern to ask from the decision maker. We focus on the optimal patterns of pairwise comparisons and the sequence including the most (close to) optimal cases based on the results of a color selection experiment. In the test, six colors (red, green, blue, magenta, turquoise, yellow) were evaluated with pairwise comparisons as well as in a direct manner, on color-calibrated tablets in ISO standardized sensory test booths of a sensory laboratory. All the possible patterns of comparisons resulting in a connected representing graph were evaluated against the complete data based on 301 individual’s pairwise comparison matrices (PCMs) using the logarithmic least squares weight calculation technique. It is shown that the empirical results, i.e., the empirical distributions of the elements of PCMs, are quite similar to the former simulated outcomes from the literature. The obtained empirically optimal patterns of comparisons were the best or the second best in the former simulations as well, while the sequence of comparisons that contains the most (close to) optimal patterns is exactly the same. In order to enhance the applicability of the results, besides the presentation of graph of graphs, and the representing graphs of the patterns that describe the proposed sequence of comparisons themselves, the recommendations are also detailed in a table format as well as in a Java application.
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[AI-160] Score Matching on Large Geometric Graphs for Cosmology Generation
【速读】：该论文旨在解决生成式模型在宇宙学模拟中面临的可扩展性（scalability）、物理一致性（physical consistency）以及对领域对称性（domain symmetries）遵守不足的问题，这些问题限制了其作为N体模拟替代方案的实用性。解决方案的关键在于提出一种基于得分的生成模型（score-based generative model），结合等变图神经网络（equivariant graph neural network），从一个信息丰富的先验出发模拟跨不同宇宙学参数下的星系引力聚集过程，同时尊重周期边界条件并能扩展至完整星系数量级的模拟。此外，引入了一种拓扑感知的噪声调度策略（topology-aware noise schedule），这对于大规模几何图结构至关重要，显著提升了模型在捕捉聚类统计特性方面的表现，并实现了比现有扩散模型更优的计算效率。
链接: https://arxiv.org/abs/2508.16990
作者: Diana-Alexandra Onutu,Yue Zhao,Joaquin Vanschoren,Vlado Menkovski
机构: 未知
类目: Cosmology and Nongalactic Astrophysics (astro-ph.CO); Artificial Intelligence (cs.AI)
备注:
Abstract:Generative models are a promising tool to produce cosmological simulations but face significant challenges in scalability, physical consistency, and adherence to domain symmetries, limiting their utility as alternatives to N -body simulations. To address these limitations, we introduce a score-based generative model with an equivariant graph neural network that simulates gravitational clustering of galaxies across cosmologies starting from an informed prior, respects periodic boundaries, and scales to full galaxy counts in simulations. A novel topology-aware noise schedule, crucial for large geometric graphs, is introduced. The proposed equivariant score-based model successfully generates full-scale cosmological point clouds of up to 600,000 halos, respects periodicity and a uniform prior, and outperforms existing diffusion models in capturing clustering statistics while offering significant computational advantages. This work advances cosmology by introducing a generative model designed to closely resemble the underlying gravitational clustering of structure formation, moving closer to physically realistic and efficient simulators for the evolution of large-scale structures in the universe.
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[AI-161] Exploring the Impact of Generative Artificial Intelligence on Software Development in the IT Sector: Preliminary Findings on Productivity Efficiency and Job Security
【速读】：该论文旨在解决生成式 AI（Generative AI）在信息技术（IT）行业软件开发中的影响问题，重点关注其对个人生产力、组织效率、采用策略、业务战略以及员工工作安全感的影响。研究通过混合方法论，结合专家访谈设计问卷并开展调查，揭示了生成式 AI 在实际应用中的经济与组织效应。解决方案的关键在于基于实证数据识别出生成式 AI 的双重作用：一方面显著提升个人生产力和组织效率（相关系数 r = .470, p < .05），另一方面也引发员工对岗位安全性的担忧（相关系数 r = .549, p < .001），同时明确了主要障碍包括输出不准确（64.2%）、合规性问题（58.2%）和伦理争议（52.2%），从而为政策制定者和企业管理层提供早期实证依据以平衡技术采纳与风险管控。
链接: https://arxiv.org/abs/2508.16811
作者: Anton Ludwig Bonin,Pawel Robert Smolinski,Jacek Winiarski
机构: 未知
类目: General Economics (econ.GN); Artificial Intelligence (cs.AI)
备注: This is a preprint of a paper accepted for publication and presentation at the 33rd International Conference on Information Systems Development (ISD 2025)
Abstract:This study investigates the impact of Generative AI on software development within the IT sector through a mixed-method approach, utilizing a survey developed based on expert interviews. The preliminary results of an ongoing survey offer early insights into how Generative AI reshapes personal productivity, organizational efficiency, adoption, business strategy and job insecurity. The findings reveal that 97% of IT workers use Generative AI tools, mainly ChatGPT. Participants report significant personal productivity gain and perceive organizational efficiency improvements that correlate positively with Generative AI adoption by their organizations (r = .470, p .05). However, increased organizational adoption of AI strongly correlates with heightened employee job security concerns (r = .549, p .001). Key adoption challenges include inaccurate outputs (64.2%), regulatory compliance issues (58.2%) and ethical concerns (52.2%). This research offers early empirical insights into Generative AI’s economic and organizational implications.
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[AI-162] Social Identity in Human-Agent Interaction: A Primer
【速读】：该论文试图解决的问题是：随着社会性人工智能（如社交机器人和基于大语言模型的聊天机器人）日益嵌入日常生活，如何理解这些人工代理是否以及如何参与社会认同理论（Social Identity Theory, SIT）与社会分类理论（Social Categorization Theory, SCT）所描述的社会互动机制。解决方案的关键在于，通过案例研究和设想示例，将SIT和SCT的核心原理外推至人工社会代理，并强调并非所有人类社会心理模型都适用于机器；同时指出，鉴于这些技术正展现出日益增强的社会认知能力，且人类易被其吸引，研究者需以“诡异的扫兴者”角色审慎评估其社会角色，从而确保对人机交互中社会认同过程的理解既科学又具有批判性。
链接: https://arxiv.org/abs/2508.16609
作者: Katie Seaborn
机构: 未知
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: 28 pages
Abstract:Social identity theory (SIT) and social categorization theory (SCT) are two facets of the social identity approach (SIA) to understanding social phenomena. SIT and SCT are models that describe and explain how people interact with one another socially, connecting the individual to the group through an understanding of underlying psychological mechanisms and intergroup behaviour. SIT, originally developed in the 1970s, and SCT, a later, more general offshoot, have been broadly applied to a range of social phenomena among people. The rise of increasingly social machines embedded in daily life has spurned efforts on understanding whether and how artificial agents can and do participate in SIA activities. As agents like social robots and chatbots powered by sophisticated large language models (LLMs) advance, understanding the real and potential roles of these technologies as social entities is crucial. Here, I provide a primer on SIA and extrapolate, through case studies and imagined examples, how SIT and SCT can apply to artificial social agents. I emphasize that not all human models and sub-theories will apply. I further argue that, given the emerging competence of these machines and our tendency to be taken in by them, we experts may need to don the hat of the uncanny killjoy, for our own good.
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[AI-163] Bridging Foundation Models and Efficient Architectures: A Modular Brain Imaging Framework with Local Masking and Pretrained Representation Learning
【速读】：该论文旨在解决将基础模型（Foundation Models, FM）应用于静息态功能磁共振成像（resting-state fMRI）数据时面临的三大挑战：高维度、计算复杂性以及难以捕捉复杂的时空动态和区域间间接交互。其解决方案的关键在于提出一个模块化神经影像框架，包含三个核心组件：首先使用局部掩码自编码器（Local Masked Autoencoder, LMAE）进行预训练，以降低血氧水平依赖（BOLD）信号中血流动力学响应函数（hemodynamic response function, HRF）的影响并抑制噪声；其次引入随机游走混合专家模型（Random Walk Mixture of Experts, RWMOE），在空间与时间维度上聚类特征，有效建模脑区间的复杂交互；最后通过状态空间模型（State-Space Model, SSM）实现下游任务推理。该框架在Cambridge Centre for Ageing and Neuroscience (Cam-CAN) 数据集上实现了优于现有方法的年龄预测（MAE=5.343, PCC=0.928）和流体智力预测（MAE=2.940, PCC=0.887），并通过专家权重分布可视化增强了可解释性。
链接: https://arxiv.org/abs/2508.16597
作者: Yanwen Wang,Xinglin Zhao,Yijin Song,Xiaobo Liu,Yanrong Hao,Rui Cao,Xin Wen
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Functional connectivity (FC) derived from resting-state fMRI plays a critical role in personalized predictions such as age and cognitive performance. However, applying foundation models(FM) to fMRI data remains challenging due to its high dimensionality, computational complexity, and the difficulty in capturing complex spatiotemporal dynamics and indirect region-of-interest (ROI) interactions. To address these limitations, we propose a modular neuroimaging framework that integrates principles from FM with efficient, domain-specific architectures. Our approach begins with a Local Masked Autoencoder (LMAE) for pretraining, which reduces the influence of hemodynamic response function (HRF) dynamics and suppresses noise. This is followed by a Random Walk Mixture of Experts (RWMOE) module that clusters features across spatial and temporal dimensions, effectively capturing intricate brain interactions. Finally, a state-space model (SSM)-based predictor performs downstream task inference. Evaluated on the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset, our framework achieved mean absolute errors (MAEs) of 5.343 for age prediction and 2.940 for fluid intelligence, with Pearson correlation coefficients (PCCs) of 0.928 and 0.887, respectively-outperforming existing state-of-the-art methods. Visualization of expert distribution weights further enhances interpretability by identifying key brain regions. This work provides a robust, interpretable alternative to LLM-based approaches for fMRI analysis, offering novel insights into brain aging and cognitive function.
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[AI-164] ARL-Based Multi-Action Market Making with Hawkes Processes and Variable Volatility
【速读】：该论文旨在解决传统市场做市策略在应对高波动性市场环境时适应性不足、报价行为僵化的问题。其关键解决方案在于：引入自激点过程（Hawkes Process）替代传统的泊松过程（Poisson Process），以更真实地模拟市场的微观结构和事件驱动的自激发特性；同时扩展做市商（Market Maker, MM）的动作空间，使其具备四种报价策略选择（始终报价、仅单边报价或不报价），并采用对抗强化学习（Adversarial Reinforcement Learning, ARL）进行训练。实验表明，在低波动率环境下训练的四动作做市策略能有效适应高波动率场景，保持稳定性能且至少92%时间提供双边报价，验证了灵活报价机制与现实市场建模对提升策略鲁棒性和有效性的重要性。
链接: https://arxiv.org/abs/2508.16589
作者: Ziyi Wang,Carmine Ventre,Maria Polukarov
机构: 未知
类目: Trading and Market Microstructure (q-fin.TR); Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:We advance market-making strategies by integrating Adversarial Reinforcement Learning (ARL), Hawkes Processes, and variable volatility levels while also expanding the action space available to market makers (MMs). To enhance the adaptability and robustness of these strategies – which can quote always, quote only on one side of the market or not quote at all – we shift from the commonly used Poisson process to the Hawkes process, which better captures real market dynamics and self-exciting behaviors. We then train and evaluate strategies under volatility levels of 2 and 200. Our findings show that the 4-action MM trained in a low-volatility environment effectively adapts to high-volatility conditions, maintaining stable performance and providing two-sided quotes at least 92% of the time. This indicates that incorporating flexible quoting mechanisms and realistic market simulations significantly enhances the effectiveness of market-making strategies.
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[AI-165] Robust Market Making: To Quote or not To Quote
【速读】：该论文旨在解决传统做市商（Market Maker, MM）策略中假设持续报价的局限性问题，即在实际市场中，即使注册做市商也无需始终提供双边报价，而现有基于对抗强化学习的MM训练方法未充分考虑这一灵活性。解决方案的关键在于扩展MM的动作空间：设计了两种新型代理（agent），分别具备“拒绝报价”和“仅提供单边报价”的能力，从而允许做市商在特定情况下选择不报价或仅报单边价格。通过模型驱动的方法在多种对抗环境中对比实验发现，这种策略灵活性显著提升了收益和夏普比率（Sharpe ratio），同时训练出的MM报价比例可满足甚至超过市场规则要求（最高达99.9%）。
链接: https://arxiv.org/abs/2508.16588
作者: Ziyi Wang,Carmine Ventre,Maria Polukarov
机构: 未知
类目: Trading and Market Microstructure (q-fin.TR); Artificial Intelligence (cs.AI); General Economics (econ.GN)
备注:
Abstract:Market making is a popular trading strategy, which aims to generate profit from the spread between the quotes posted at either side of the market. It has been shown that training market makers (MMs) with adversarial reinforcement learning allows to overcome the risks due to changing market conditions and to lead to robust performances. Prior work assumes, however, that MMs keep quoting throughout the trading process, but in practice this is not required, even for ``registered’’ MMs (that only need to satisfy quoting ratios defined by the market rules). In this paper, we build on this line of work and enrich the strategy space of the MM by allowing to occasionally not quote or provide single-sided quotes. Towards this end, in addition to the MM agents that provide continuous bid-ask quotes, we have designed two new agents with increasingly richer action spaces. The first has the option to provide bid-ask quotes or refuse to quote. The second has the option to provide bid-ask quotes, refuse to quote, or only provide single-sided ask or bid quotes. We employ a model-driven approach to empirically compare the performance of the continuously quoting MM with the two agents above in various types of adversarial environments. We demonstrate how occasional refusal to provide bid-ask quotes improves returns and/or Sharpe ratios. The quoting ratios of well-trained MMs can basically meet any market requirements, reaching up to 99.9 % in some cases.
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机器学习
[LG-0] Aligning the Evaluation of Probabilistic Predictions with Downstream Value
链接: https://arxiv.org/abs/2508.18251
作者: Novin Shahroudi,Viacheslav Komisarenko,Meelis Kull
类目: Machine Learning (cs.LG)
*备注:
Abstract:Every prediction is ultimately used in a downstream task. Consequently, evaluating prediction quality is more meaningful when considered in the context of its downstream use. Metrics based solely on predictive performance often diverge from measures of real-world downstream impact. Existing approaches incorporate the downstream view by relying on multiple task-specific metrics, which can be burdensome to analyze, or by formulating cost-sensitive evaluations that require an explicit cost structure, typically assumed to be known a priori. We frame this mismatch as an evaluation alignment problem and propose a data-driven method to learn a proxy evaluation function aligned with the downstream evaluation. Building on the theory of proper scoring rules, we explore transformations of scoring rules that ensure the preservation of propriety. Our approach leverages weighted scoring rules parametrized by a neural network, where weighting is learned to align with the performance in the downstream task. This enables fast and scalable evaluation cycles across tasks where the weighting is complex or unknown a priori. We showcase our framework through synthetic and real-data experiments for regression tasks, demonstrating its potential to bridge the gap between predictive evaluation and downstream utility in modular prediction systems.
[LG-1] Flash Sparse Attention: An Alternative Efficient Implementation of Native Sparse Attention Kernel
链接: https://arxiv.org/abs/2508.18224
作者: Ran Yan,Youhe Jiang,Binhang Yuan
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Recent progress in sparse attention mechanisms has demonstrated strong potential for reducing the computational cost of long-context training and inference in large language models (LLMs). Native Sparse Attention (NSA), a state-of-the-art approach, introduces natively trainable, hardware-aligned sparse attention that delivers substantial system-level performance gains while maintaining accuracy comparable to full attention. However, the kernel implementation of NSA relies on a query-grouping strategy that is efficient only with large Grouped Query Attention (GQA) sizes, whereas modern LLMs typically adopt much smaller GQA groups, which limits the applicability of this sparse algorithmic advance. In this work, we propose Flash Sparse Attention (FSA), which includes an alternative kernel design that enables efficient NSA computation across a wide range of popular LLMs with varied smaller GQA group sizes on modern GPUs. Compared to vanilla NSA kernel implementation, our empirical evaluation demonstrates that FSA achieves (i) up to 3.5 \times and on average 1.6 \times kernel-level latency reduction, (ii) up to 1.25 \times and 1.09 \times on average end-to-end training speedup on state-of-the-art LLMs, and (iii) up to 1.36 \times and 1.11 \times on average end-to-end prefill speedup on state-of-the-art LLMs. The source code is open-sourced and publicly available at this https URL.
[LG-2] Practical GPU Choices for Earth Observation: ResNet-50 Training Throughput on Integrated Laptop and Cloud Accelerators
链接: https://arxiv.org/abs/2508.18206
作者: Ritvik Chaturvedi
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 10 pages, 5 figures
Abstract:This project implements a ResNet-based pipeline for land use and land cover (LULC) classification on Sentinel-2 imagery, benchmarked across three heterogeneous GPUs. The workflow automates data acquisition, geospatial preprocessing, tiling, model training, and visualization, and is fully containerized for reproducibility. Performance evaluation reveals up to a 2x training speed-up on an NVIDIA RTX 3060 and a Tesla T4 compared to the Apple M3 Pro baseline, while maintaining high classification accuracy on the EuroSAT dataset. These results demonstrate the feasibility of deploying deep learning LULC models on consumer and free cloud GPUs for scalable geospatial analytics.
[LG-3] HypER: Hyperbolic Echo State Networks for Capturing Stretch-and-Fold Dynamics in Chaotic Flows ECAI2025
链接: https://arxiv.org/abs/2508.18196
作者: Pradeep Singh,Sutirtha Ghosh,Ashutosh Kumar,Hrishit B P,Balasubramanian Raman
类目: Machine Learning (cs.LG)
*备注: 8 pages, accepted in ECAI 2025
Abstract:Forecasting chaotic dynamics beyond a few Lyapunov times is difficult because infinitesimal errors grow exponentially. Existing Echo State Networks (ESNs) mitigate this growth but employ reservoirs whose Euclidean geometry is mismatched to the stretch-and-fold structure of chaos. We introduce the Hyperbolic Embedding Reservoir (HypER), an ESN whose neurons are sampled in the Poincare ball and whose connections decay exponentially with hyperbolic distance. This negative-curvature construction embeds an exponential metric directly into the latent space, aligning the reservoir’s local expansion-contraction spectrum with the system’s Lyapunov directions while preserving standard ESN features such as sparsity, leaky integration, and spectral-radius control. Training is limited to a Tikhonov-regularized readout. On the chaotic Lorenz-63 and Roessler systems, and the hyperchaotic Chen-Ueta attractor, HypER consistently lengthens the mean valid-prediction horizon beyond Euclidean and graph-structured ESN baselines, with statistically significant gains confirmed over 30 independent runs; parallel results on real-world benchmarks, including heart-rate variability from the Santa Fe and MIT-BIH datasets and international sunspot numbers, corroborate its advantage. We further establish a lower bound on the rate of state divergence for HypER, mirroring Lyapunov growth.
[LG-4] Introduction to Regularization and Learning Methods for Inverse Problems
链接: https://arxiv.org/abs/2508.18178
作者: Danielle Bednarski,Tim Roith
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注: These lecture notes are based on a lecture taught by the authors in the winter semester 2024/2025 at the University of Hamburg
Abstract:These lecture notes evolve around mathematical concepts arising in inverse problems. We start by introducing inverse problems through examples such as differentiation, deconvolution, computed tomography and phase retrieval. This then leads us to the framework of well-posedness and first considerations regarding reconstruction and inversion approaches. The second chapter then first deals with classical regularization theory of inverse problems in Hilbert spaces. After introducing the pseudo-inverse, we review the concept of convergent regularization. Within this chapter we then proceed to ask the question of how to realize practical reconstruction algorithms. Here, we mainly focus on Tikhonov and sparsity promoting regularization in finite dimensional spaces. In the third chapter, we dive into modern deep-learning methods, which allow solving inverse problems in a data-dependent approach. The intersection between inverse problems and machine learning is a rapidly growing field and our exposition here restricts itself to a very limited selection of topics. Among them are learned regularization, fully-learned Bayesian estimation, post-processing strategies and plug-n-play methods.
[LG-5] Unveiling the Actual Performance of Neural-based Models for Equation Discovery on Graph Dynamical Systems
链接: https://arxiv.org/abs/2508.18173
作者: Riccardo Cappi,Paolo Frazzetto,Nicolò Navarin,Alessandro Sperduti
类目: Machine Learning (cs.LG)
*备注: Preprint. Under Review
Abstract:The ``black-box’’ nature of deep learning models presents a significant barrier to their adoption for scientific discovery, where interpretability is paramount. This challenge is especially pronounced in discovering the governing equations of dynamical processes on networks or graphs, since even their topological structure further affects the processes’ behavior. This paper provides a rigorous, comparative assessment of state-of-the-art symbolic regression techniques for this task. We evaluate established methods, including sparse regression and MLP-based architectures, and introduce a novel adaptation of Kolmogorov-Arnold Networks (KANs) for graphs, designed to exploit their inherent interpretability. Across a suite of synthetic and real-world dynamical systems, our results demonstrate that both MLP and KAN-based architectures can successfully identify the underlying symbolic equations, significantly surpassing existing baselines. Critically, we show that KANs achieve this performance with greater parsimony and transparency, as their learnable activation functions provide a clearer mapping to the true physical dynamics. This study offers a practical guide for researchers, clarifying the trade-offs between model expressivity and interpretability, and establishes the viability of neural-based architectures for robust scientific discovery on complex systems.
[LG-6] PCR-CA: Parallel Codebook Representations with Contrastive Alignment for Multiple-Category App Recommendation
链接: https://arxiv.org/abs/2508.18166
作者: Bin Tan,Wangyao Ge,Yidi Wang,Xin Liu,Jeff Burtoft,Hao Fan,Hui Wang
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
*备注: 9 pages, 4 figures, conference
Abstract:Modern app store recommender systems struggle with multiple-category apps, as traditional taxonomies fail to capture overlapping semantics, leading to suboptimal personalization. We propose PCR-CA (Parallel Codebook Representations with Contrastive Alignment), an end-to-end framework for improved CTR prediction. PCR-CA first extracts compact multimodal embeddings from app text, then introduces a Parallel Codebook VQ-AE module that learns discrete semantic representations across multiple codebooks in parallel – unlike hierarchical residual quantization (RQ-VAE). This design enables independent encoding of diverse aspects (e.g., gameplay, art style), better modeling multiple-category semantics. To bridge semantic and collaborative signals, we employ a contrastive alignment loss at both the user and item levels, enhancing representation learning for long-tail items. Additionally, a dual-attention fusion mechanism combines ID-based and semantic features to capture user interests, especially for long-tail apps. Experiments on a large-scale dataset show PCR-CA achieves a +0.76% AUC improvement over strong baselines, with +2.15% AUC gains for long-tail apps. Online A/B testing further validates our approach, showing a +10.52% lift in CTR and a +16.30% improvement in CVR, demonstrating PCR-CA’s effectiveness in real-world deployment. The new framework has now been fully deployed on the Microsoft Store.
[LG-7] Frozen in Time: Parameter-Efficient Time Series Transformers via Reservoir-Induced Feature Expansion and Fixed Random Dynamics ECAI2025
链接: https://arxiv.org/abs/2508.18130
作者: Pradeep Singh,Mehak Sharma,Anupriya Dey,Balasubramanian Raman
类目: Machine Learning (cs.LG)
*备注: 8 pages, 5 tables, 3 figures, accepted at ECAI 2025
Abstract:Transformers are the de-facto choice for sequence modelling, yet their quadratic self-attention and weak temporal bias can make long-range forecasting both expensive and brittle. We introduce FreezeTST, a lightweight hybrid that interleaves frozen random-feature (reservoir) blocks with standard trainable Transformer layers. The frozen blocks endow the network with rich nonlinear memory at no optimisation cost; the trainable layers learn to query this memory through self-attention. The design cuts trainable parameters and also lowers wall-clock training time, while leaving inference complexity unchanged. On seven standard long-term forecasting benchmarks, FreezeTST consistently matches or surpasses specialised variants such as Informer, Autoformer, and PatchTST; with substantially lower compute. Our results show that embedding reservoir principles within Transformers offers a simple, principled route to efficient long-term time-series prediction.
[LG-8] Provable Mixed-Noise Learning with Flow-Matching
链接: https://arxiv.org/abs/2508.18122
作者: Paul Hagemann,Robert Gruhlke,Bernhard Stankewitz,Claudia Schillings,Gabriele Steidl
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We study Bayesian inverse problems with mixed noise, modeled as a combination of additive and multiplicative Gaussian components. While traditional inference methods often assume fixed or known noise characteristics, real-world applications, particularly in physics and chemistry, frequently involve noise with unknown and heterogeneous structure. Motivated by recent advances in flow-based generative modeling, we propose a novel inference framework based on conditional flow matching embedded within an Expectation-Maximization (EM) algorithm to jointly estimate posterior samplers and noise parameters. To enable high-dimensional inference and improve scalability, we use simulation-free ODE-based flow matching as the generative model in the E-step of the EM algorithm. We prove that, under suitable assumptions, the EM updates converge to the true noise parameters in the population limit of infinite observations. Our numerical results illustrate the effectiveness of combining EM inference with flow matching for mixed-noise Bayesian inverse problems.
[LG-9] Quantum-Classical Hybrid Framework for Zero-Day Time-Push GNSS Spoofing Detection
链接: https://arxiv.org/abs/2508.18085
作者: Abyad Enan,Mashrur Chowdhury,Sagar Dasgupta,Mizanur Rahman
类目: Machine Learning (cs.LG)
*备注: This work has been submitted to the IEEE Internet of Things Journal for possible publication
Abstract:Global Navigation Satellite Systems (GNSS) are critical for Positioning, Navigation, and Timing (PNT) applications. However, GNSS are highly vulnerable to spoofing attacks, where adversaries transmit counterfeit signals to mislead receivers. Such attacks can lead to severe consequences, including misdirected navigation, compromised data integrity, and operational disruptions. Most existing spoofing detection methods depend on supervised learning techniques and struggle to detect novel, evolved, and unseen attacks. To overcome this limitation, we develop a zero-day spoofing detection method using a Hybrid Quantum-Classical Autoencoder (HQC-AE), trained solely on authentic GNSS signals without exposure to spoofed data. By leveraging features extracted during the tracking stage, our method enables proactive detection before PNT solutions are computed. We focus on spoofing detection in static GNSS receivers, which are particularly susceptible to time-push spoofing attacks, where attackers manipulate timing information to induce incorrect time computations at the receiver. We evaluate our model against different unseen time-push spoofing attack scenarios: simplistic, intermediate, and sophisticated. Our analysis demonstrates that the HQC-AE consistently outperforms its classical counterpart, traditional supervised learning-based models, and existing unsupervised learning-based methods in detecting zero-day, unseen GNSS time-push spoofing attacks, achieving an average detection accuracy of 97.71% with an average false negative rate of 0.62% (when an attack occurs but is not detected). For sophisticated spoofing attacks, the HQC-AE attains an accuracy of 98.23% with a false negative rate of 1.85%. These findings highlight the effectiveness of our method in proactively detecting zero-day GNSS time-push spoofing attacks across various stationary GNSS receiver platforms.
[LG-10] FedGreed: A Byzantine-Robust Loss-Based Aggregation Method for Federated Learning
链接: https://arxiv.org/abs/2508.18060
作者: Emmanouil Kritharakis,Antonios Makris,Dusan Jakovetic,Konstantinos Tserpes
类目: Machine Learning (cs.LG)
*备注: 8 pages, 4 figures
Abstract:Federated Learning (FL) enables collaborative model training across multiple clients while preserving data privacy by keeping local datasets on-device. In this work, we address FL settings where clients may behave adversarially, exhibiting Byzantine attacks, while the central server is trusted and equipped with a reference dataset. We propose FedGreed, a resilient aggregation strategy for federated learning that does not require any assumptions about the fraction of adversarial participants. FedGreed orders clients’ local model updates based on their loss metrics evaluated against a trusted dataset on the server and greedily selects a subset of clients whose models exhibit the minimal evaluation loss. Unlike many existing approaches, our method is designed to operate reliably under heterogeneous (non-IID) data distributions, which are prevalent in real-world deployments. FedGreed exhibits convergence guarantees and bounded optimality gaps under strong adversarial behavior. Experimental evaluations on MNIST, FMNIST, and CIFAR-10 demonstrate that our method significantly outperforms standard and robust federated learning baselines, such as Mean, Trimmed Mean, Median, Krum, and Multi-Krum, in the majority of adversarial scenarios considered, including label flipping and Gaussian noise injection attacks. All experiments were conducted using the Flower federated learning framework.
[LG-11] Weisfeiler-Lehman meets Events: An Expressivity Analysis for Continuous-Time Dynamic Graph Neural Networks
链接: https://arxiv.org/abs/2508.18052
作者: Silvia Beddar-Wiesing,Alice Moallemy-Oureh
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph Neural Networks (GNNs) are known to match the distinguishing power of the 1-Weisfeiler-Lehman (1-WL) test, and the resulting partitions coincide with the unfolding tree equivalence classes of graphs. Preserving this equivalence, GNNs can universally approximate any target function on graphs in probability up to any precision. However, these results are limited to attributed discrete-dynamic graphs represented as sequences of connected graph snapshots. Real-world systems, such as communication networks, financial transaction networks, and molecular interactions, evolve asynchronously and may split into disconnected components. In this paper, we extend the theory of attributed discrete-dynamic graphs to attributed continuous-time dynamic graphs with arbitrary connectivity. To this end, we introduce a continuous-time dynamic 1-WL test, prove its equivalence to continuous-time dynamic unfolding trees, and identify a class of continuous-time dynamic GNNs (CGNNs) based on discrete-dynamic GNN architectures that retain both distinguishing power and universal approximation guarantees. Our constructive proofs further yield practical design guidelines, emphasizing a compact and expressive CGNN architecture with piece-wise continuously differentiable temporal functions to process asynchronous, disconnected graphs.
[LG-12] raining Transformers for Mesh-Based Simulations
链接: https://arxiv.org/abs/2508.18051
作者: Paul Garnier,Vincent Lannelongue,Jonathan Viquerat,Elie Hachem
类目: Machine Learning (cs.LG)
*备注:
Abstract:Simulating physics using Graph Neural Networks (GNNs) is predominantly driven by message-passing architectures, which face challenges in scaling and efficiency, particularly in handling large, complex meshes. These architectures have inspired numerous enhancements, including multigrid approaches and K -hop aggregation (using neighbours of distance K ), yet they often introduce significant complexity and suffer from limited in-depth investigations. In response to these challenges, we propose a novel Graph Transformer architecture that leverages the adjacency matrix as an attention mask. The proposed approach incorporates innovative augmentations, including Dilated Sliding Windows and Global Attention, to extend receptive fields without sacrificing computational efficiency. Through extensive experimentation, we evaluate model size, adjacency matrix augmentations, positional encoding and K -hop configurations using challenging 3D computational fluid dynamics (CFD) datasets. We also train over 60 models to find a scaling law between training FLOPs and parameters. The introduced models demonstrate remarkable scalability, performing on meshes with up to 300k nodes and 3 million edges. Notably, the smallest model achieves parity with MeshGraphNet while being 7\times faster and 6\times smaller. The largest model surpasses the previous state-of-the-art by 38.8 % on average and outperforms MeshGraphNet by 52 % on the all-rollout RMSE, while having a similar training speed. Code and datasets are available at this https URL.
[LG-13] Riemannian Change Point Detection on Manifolds with Robust Centroid Estimation
链接: https://arxiv.org/abs/2508.18045
作者: Xiuheng Wang,Ricardo Borsoi,Arnaud Breloy,Cédric Richard
类目: Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注:
Abstract:Non-parametric change-point detection in streaming time series data is a long-standing challenge in signal processing. Recent advancements in statistics and machine learning have increasingly addressed this problem for data residing on Riemannian manifolds. One prominent strategy involves monitoring abrupt changes in the center of mass of the time series. Implemented in a streaming fashion, this strategy, however, requires careful step size tuning when computing the updates of the center of mass. In this paper, we propose to leverage robust centroid on manifolds from M-estimation theory to address this issue. Our proposal consists of comparing two centroid estimates: the classical Karcher mean (sensitive to change) versus one defined from Huber’s function (robust to change). This comparison leads to the definition of a test statistic whose performance is less sensitive to the underlying estimation method. We propose a stochastic Riemannian optimization algorithm to estimate both robust centroids efficiently. Experiments conducted on both simulated and real-world data across two representative manifolds demonstrate the superior performance of our proposed method.
[LG-14] Enhancing Differentially Private Linear Regression via Public Second-Moment
链接: https://arxiv.org/abs/2508.18037
作者: Zilong Cao(1),Hai Zhang(1) ((1) The School of Mathematics, Northwest University)
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注:
Abstract:Leveraging information from public data has become increasingly crucial in enhancing the utility of differentially private (DP) methods. Traditional DP approaches often require adding noise based solely on private data, which can significantly degrade utility. In this paper, we address this limitation in the context of the ordinary least squares estimator (OLSE) of linear regression based on sufficient statistics perturbation (SSP) under the unbounded data assumption. We propose a novel method that involves transforming private data using the public second-moment matrix to compute a transformed SSP-OLSE, whose second-moment matrix yields a better condition number and improves the OLSE accuracy and robustness. We derive theoretical error bounds about our method and the standard SSP-OLSE to the non-DP OLSE, which reveal the improved robustness and accuracy achieved by our approach. Experiments on synthetic and real-world datasets demonstrate the utility and effectiveness of our method.
[LG-15] Does simple trump complex? Comparing strategies for adversarial robustness in DNNs
链接: https://arxiv.org/abs/2508.18019
作者: William Brooks,Marelie H. Davel,Coenraad Mouton
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deep Neural Networks (DNNs) have shown substantial success in various applications but remain vulnerable to adversarial attacks. This study aims to identify and isolate the components of two different adversarial training techniques that contribute most to increased adversarial robustness, particularly through the lens of margins in the input space – the minimal distance between data points and decision boundaries. Specifically, we compare two methods that maximize margins: a simple approach which modifies the loss function to increase an approximation of the margin, and a more complex state-of-the-art method (Dynamics-Aware Robust Training) which builds upon this approach. Using a VGG-16 model as our base, we systematically isolate and evaluate individual components from these methods to determine their relative impact on adversarial robustness. We assess the effect of each component on the model’s performance under various adversarial attacks, including AutoAttack and Projected Gradient Descent (PGD). Our analysis on the CIFAR-10 dataset reveals which elements most effectively enhance adversarial robustness, providing insights for designing more robust DNNs.
[LG-16] A Novel Framework for Uncertainty Quantification via Proper Scores for Classification and Beyond
链接: https://arxiv.org/abs/2508.18001
作者: Sebastian G. Gruber
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: PhD Thesis (cumulative, spanning 6 peer-reviewed publications)
Abstract:In this PhD thesis, we propose a novel framework for uncertainty quantification in machine learning, which is based on proper scores. Uncertainty quantification is an important cornerstone for trustworthy and reliable machine learning applications in practice. Usually, approaches to uncertainty quantification are problem-specific, and solutions and insights cannot be readily transferred from one task to another. Proper scores are loss functions minimized by predicting the target distribution. Due to their very general definition, proper scores apply to regression, classification, or even generative modeling tasks. We contribute several theoretical results, that connect epistemic uncertainty, aleatoric uncertainty, and model calibration with proper scores, resulting in a general and widely applicable framework. We achieve this by introducing a general bias-variance decomposition for strictly proper scores via functional Bregman divergences. Specifically, we use the kernel score, a kernel-based proper score, for evaluating sample-based generative models in various domains, like image, audio, and natural language generation. This includes a novel approach for uncertainty estimation of large language models, which outperforms state-of-the-art baselines. Further, we generalize the calibration-sharpness decomposition beyond classification, which motivates the definition of proper calibration errors. We then introduce a novel estimator for proper calibration errors in classification, and a novel risk-based approach to compare different estimators for squared calibration errors. Last, we offer a decomposition of the kernel spherical score, another kernel-based proper score, allowing a more fine-grained and interpretable evaluation of generative image models.
[LG-17] DesCartes Builder: A Tool to Develop Machine-Learning Based Digital Twins
链接: https://arxiv.org/abs/2508.17988
作者: Eduardo de Conto,Blaise Genest,Arvind Easwaran,Nicholas Ng,Shweta Menon
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注: 5 pages, 4 figures. Accepted at EDTconf 2025
Abstract:Digital twins (DTs) are increasingly utilized to monitor, manage, and optimize complex systems across various domains, including civil engineering. A core requirement for an effective DT is to act as a fast, accurate, and maintainable surrogate of its physical counterpart, the physical twin (PT). To this end, machine learning (ML) is frequently employed to (i) construct real-time DT prototypes using efficient reduced-order models (ROMs) derived from high-fidelity simulations of the PT’s nominal behavior, and (ii) specialize these prototypes into DT instances by leveraging historical sensor data from the target PT. Despite the broad applicability of ML, its use in DT engineering remains largely ad hoc. Indeed, while conventional ML pipelines often train a single model for a specific task, DTs typically require multiple, task- and domain-dependent models. Thus, a more structured approach is required to design DTs. In this paper, we introduce DesCartes Builder, an open-source tool to enable the systematic engineering of ML-based pipelines for real-time DT prototypes and DT instances. The tool leverages an open and flexible visual data flow paradigm to facilitate the specification, composition, and reuse of ML models. It also integrates a library of parameterizable core operations and ML algorithms tailored for DT design. We demonstrate the effectiveness and usability of DesCartes Builder through a civil engineering use case involving the design of a real-time DT prototype to predict the plastic strain of a structure. Comments: 5 pages, 4 figures. Accepted at EDTconf 2025 Subjects: Software Engineering (cs.SE); Machine Learning (cs.LG) Cite as: arXiv:2508.17988 [cs.SE] (or arXiv:2508.17988v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2508.17988 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-18] Choice Outweighs Effort: Facilitating Complementary Knowledge Fusion in Federated Learning via Re-calibration and Merit-discrimination
链接: https://arxiv.org/abs/2508.17954
作者: Ming Yang,Dongrun Li,Xin Wang,Xiaoyang Yu,Xiaoming Wu,Shibo He
类目: Machine Learning (cs.LG)
*备注:
Abstract:Cross-client data heterogeneity in federated learning induces biases that impede unbiased consensus condensation and the complementary fusion of generalization- and personalization-oriented knowledge. While existing approaches mitigate heterogeneity through model decoupling and representation center loss, they often rely on static and restricted metrics to evaluate local knowledge and adopt global alignment too rigidly, leading to consensus distortion and diminished model adaptability. To address these limitations, we propose FedMate, a method that implements bilateral optimization: On the server side, we construct a dynamic global prototype, with aggregation weights calibrated by holistic integration of sample size, current parameters, and future prediction; a category-wise classifier is then fine-tuned using this prototype to preserve global consistency. On the client side, we introduce complementary classification fusion to enable merit-based discrimination training and incorporate cost-aware feature transmission to balance model performance and communication efficiency. Experiments on five datasets of varying complexity demonstrate that FedMate outperforms state-of-the-art methods in harmonizing generalization and adaptation. Additionally, semantic segmentation experiments on autonomous driving datasets validate the method’s real-world scalability.
[LG-19] WOMAC: A Mechanism For Prediction Competitions
链接: https://arxiv.org/abs/2508.17907
作者: Siddarth Srinivasan,Tao Lin,Connacher Murphy,Anish Thilagar,Yiling Chen,Ezra Karger
类目: Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注:
Abstract:Competitions are widely used to identify top performers in judgmental forecasting and machine learning, and the standard competition design ranks competitors based on their cumulative scores against a set of realized outcomes or held-out labels. However, this standard design is neither incentive-compatible nor very statistically efficient. The main culprit is noise in outcomes/labels that experts are scored against; it allows weaker competitors to often win by chance, and the winner-take-all nature incentivizes misreporting that improves win probability even if it decreases expected score. Attempts to achieve incentive-compatibility rely on randomized mechanisms that add even more noise in winner selection, but come at the cost of determinism and practical adoption. To tackle these issues, we introduce a novel deterministic mechanism: WOMAC (Wisdom of the Most Accurate Crowd). Instead of scoring experts against noisy outcomes, as is standard, WOMAC scores experts against the best ex-post aggregate of peer experts’ predictions given the noisy outcomes. WOMAC is also more efficient than the standard competition design in typical settings. While the increased complexity of WOMAC makes it challenging to analyze incentives directly, we provide a clear theoretical foundation to justify the mechanism. We also provide an efficient vectorized implementation and demonstrate empirically on real-world forecasting datasets that WOMAC is a more reliable predictor of experts’ out-of-sample performance relative to the standard mechanism. WOMAC is useful in any competition where there is substantial noise in the outcomes/labels.
[LG-20] Spectrum Prediction in the Fractional Fourier Domain with Adaptive Filtering
链接: https://arxiv.org/abs/2508.17872
作者: Yanghao Qin,Bo Zhou,Guangliang Pan,Qihui Wu,Meixia Tao
类目: Machine Learning (cs.LG); Information Theory (cs.IT)
*备注: Accepted by IEEE Wireless Communications Letters
Abstract:Accurate spectrum prediction is crucial for dynamic spectrum access (DSA) and resource allocation. However, due to the unique characteristics of spectrum data, existing methods based on the time or frequency domain often struggle to separate predictable patterns from noise. To address this, we propose the Spectral Fractional Filtering and Prediction (SFFP) framework. SFFP first employs an adaptive fractional Fourier transform (FrFT) module to transform spectrum data into a suitable fractional Fourier domain, enhancing the separability of predictable trends from noise. Subsequently, an adaptive Filter module selectively suppresses noise while preserving critical predictive features within this domain. Finally, a prediction module, leveraging a complex-valued neural network, learns and forecasts these filtered trend components. Experiments on real-world spectrum data show that the SFFP outperforms leading spectrum and general forecasting methods.
[LG-21] Multi-domain Distribution Learning for De Novo Drug Design
链接: https://arxiv.org/abs/2508.17815
作者: Arne Schneuing,Ilia Igashov,Adrian W. Dobbelstein,Thomas Castiglione,Michael Bronstein,Bruno Correia
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注:
Abstract:We introduce DrugFlow, a generative model for structure-based drug design that integrates continuous flow matching with discrete Markov bridges, demonstrating state-of-the-art performance in learning chemical, geometric, and physical aspects of three-dimensional protein-ligand data. We endow DrugFlow with an uncertainty estimate that is able to detect out-of-distribution samples. To further enhance the sampling process towards distribution regions with desirable metric values, we propose a joint preference alignment scheme applicable to both flow matching and Markov bridge frameworks. Furthermore, we extend our model to also explore the conformational landscape of the protein by jointly sampling side chain angles and molecules.
[LG-22] Puzzle: Scheduling Multiple Deep Learning Models on Mobile Device with Heterogeneous Processors
链接: https://arxiv.org/abs/2508.17764
作者: Duseok Kang,Yunseong Lee,Junghoon Kim
类目: Machine Learning (cs.LG); Operating Systems (cs.OS)
*备注:
Abstract:As deep learning models are increasingly deployed on mobile devices, modern mobile devices incorporate deep learning-specific accelerators to handle the growing computational demands, thus increasing their hardware heterogeneity. However, existing works on scheduling deep learning workloads across these processors have significant limitations: most studies focus on single-model scenarios rather than realistic multi-model scenarios, overlook performance variations from different hardware/software configurations, and struggle with accurate execution time estimation. To address these challenges, we propose a novel genetic algorithm-based methodology for scheduling multiple deep learning networks on heterogeneous processors by partitioning the networks into multiple subgraphs. Our approach incorporates three different types of chromosomes for partition/mapping/priority exploration, and leverages device-in-the-loop profiling and evaluation for accurate execution time estimation. Based on this methodology, our system, Puzzle, demonstrates superior performance in extensive evaluations with randomly generated scenarios involving nine state-of-the-art networks. The results demonstrate Puzzle can support 3.7 and 2.2 times higher request frequency on average compared to the two heuristic baselines, NPU Only and Best Mapping, respectively, while satisfying the equivalent level of real-time requirements.
[LG-23] Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models
链接: https://arxiv.org/abs/2508.17761
作者: Jelke Wibbeke,Nico Schönfisch,Sebastian Rohjans,Andreas Rauh
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:In safety-critical applications data-driven models must not only be accurate but also provide reliable uncertainty estimates. This property, commonly referred to as calibration, is essential for risk-aware decision-making. In regression a wide variety of calibration metrics and recalibration methods have emerged. However, these metrics differ significantly in their definitions, assumptions and scales, making it difficult to interpret and compare results across studies. Moreover, most recalibration methods have been evaluated using only a small subset of metrics, leaving it unclear whether improvements generalize across different notions of calibration. In this work, we systematically extract and categorize regression calibration metrics from the literature and benchmark these metrics independently of specific modelling methods or recalibration approaches. Through controlled experiments with real-world, synthetic and artificially miscalibrated data, we demonstrate that calibration metrics frequently produce conflicting results. Our analysis reveals substantial inconsistencies: many metrics disagree in their evaluation of the same recalibration result, and some even indicate contradictory conclusions. This inconsistency is particularly concerning as it potentially allows cherry-picking of metrics to create misleading impressions of success. We identify the Expected Normalized Calibration Error (ENCE) and the Coverage Width-based Criterion (CWC) as the most dependable metrics in our tests. Our findings highlight the critical role of metric selection in calibration research.
[LG-24] SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling
链接: https://arxiv.org/abs/2508.17756
作者: Fanjiang Ye,Zepeng Zhao,Yi Mu,Jucheng Shen,Renjie Li,Kaijian Wang,Desen Sun,Saurabh Agarwal,Myungjin Lee,Triston Cao,Aditya Akella,Arvind Krishnamurthy,T.S. Eugene Ng,Zhengzhong Tu,Yuke Wang
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:
Abstract:Diffusion models have recently achieved remarkable success in generative tasks (e.g., image and video generation), and the demand for high-quality content (e.g., 2K/4K videos) is rapidly increasing across various domains. However, generating ultra-high-resolution videos on existing standard-resolution (e.g., 720p) platforms remains challenging due to the excessive re-training requirements and prohibitively high computational and memory costs. To this end, we introduce SuperGen, an efficient tile-based framework for ultra-high-resolution video generation. SuperGen features a novel training-free algorithmic innovation with tiling to successfully support a wide range of resolutions without additional training efforts while significantly reducing both memory footprint and computational complexity. Moreover, SuperGen incorporates a tile-tailored, adaptive, region-aware caching strategy that accelerates video generation by exploiting redundancy across denoising steps and spatial regions. SuperGen also integrates cache-guided, communication-minimized tile parallelism for enhanced throughput and minimized latency. Evaluations demonstrate that SuperGen harvests the maximum performance gains while achieving high output quality across various benchmarks.
[LG-25] Multi-layer Abstraction for Nested Generation of Options (MANGO) in Hierarchical Reinforcement Learning
链接: https://arxiv.org/abs/2508.17751
作者: Alessio Arcudi,Davide Sartor,Alberto Sinigaglia,Vincent François-Lavet,Gian Antonio Susto
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper introduces MANGO (Multilayer Abstraction for Nested Generation of Options), a novel hierarchical reinforcement learning framework designed to address the challenges of long-term sparse reward environments. MANGO decomposes complex tasks into multiple layers of abstraction, where each layer defines an abstract state space and employs options to modularize trajectories into macro-actions. These options are nested across layers, allowing for efficient reuse of learned movements and improved sample efficiency. The framework introduces intra-layer policies that guide the agent’s transitions within the abstract state space, and task actions that integrate task-specific components such as reward functions. Experiments conducted in procedurally-generated grid environments demonstrate substantial improvements in both sample efficiency and generalization capabilities compared to standard RL methods. MANGO also enhances interpretability by making the agent’s decision-making process transparent across layers, which is particularly valuable in safety-critical and industrial applications. Future work will explore automated discovery of abstractions and abstract actions, adaptation to continuous or fuzzy environments, and more robust multi-layer training strategies.
[LG-26] Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Tasks EMNLP2025
链接: https://arxiv.org/abs/2508.17744
作者: Sotaro Takeshita,Yurina Takeshita,Daniel Ruffinelli,Simone Paolo Ponzetto
类目: Machine Learning (cs.LG)
*备注: Accepted to EMNLP 2025 Main Conference, submitted version
Abstract:In this paper, we study the surprising impact that truncating text embeddings has on downstream performance. We consistently observe across 6 state-of-the-art text encoders and 26 downstream tasks, that randomly removing up to 50% of embedding dimensions results in only a minor drop in performance, less than 10%, in retrieval and classification tasks. Given the benefits of using smaller-sized embeddings, as well as the potential insights about text encoding, we study this phenomenon and find that, contrary to what is suggested in prior work, this is not the result of an ineffective use of representation space. Instead, we find that a large number of uniformly distributed dimensions actually cause an increase in performance when removed. This would explain why, on average, removing a large number of embedding dimensions results in a marginal drop in performance. We make similar observations when truncating the embeddings used by large language models to make next-token predictions on generative tasks, suggesting that this phenomenon is not isolated to classification or retrieval tasks.
[LG-27] Copyright Protection for 3D Molecular Structures with Watermarking
链接: https://arxiv.org/abs/2508.17702
作者: Runwen Hu,Peilin Chen,Keyan Ding,Shiqi Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Artificial intelligence (AI) revolutionizes molecule generation in bioengineering and biological research, significantly accelerating discovery processes. However, this advancement introduces critical concerns regarding intellectual property protection. To address these challenges, we propose the first robust watermarking method designed for molecules, which utilizes atom-level features to preserve molecular integrity and invariant features to ensure robustness against affine transformations. Comprehensive experiments validate the effectiveness of our method using the datasets QM9 and GEOM-DRUG, and generative models GeoBFN and GeoLDM. We demonstrate the feasibility of embedding watermarks, maintaining basic properties higher than 90.00% while achieving watermark accuracy greater than 95.00%. Furthermore, downstream docking simulations reveal comparable performance between original and watermarked molecules, with binding affinities reaching -6.00 kcal/mol and root mean square deviations below 1.602 Å. These results confirm that our watermarking technique effectively safeguards molecular intellectual property without compromising scientific utility, enabling secure and responsible AI integration in molecular discovery and research applications.
[LG-28] Adaptive Ensemble Learning with Gaussian Copula for Load Forecasting
链接: https://arxiv.org/abs/2508.17700
作者: Junying Yang,Gang Lu,Xiaoqing Yan,Peng Xia,Di Wu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine learning (ML) is capable of accurate Load Forecasting from complete data. However, there are many uncertainties that affect data collection, leading to sparsity. This article proposed a model called Adaptive Ensemble Learning with Gaussian Copula to deal with sparsity, which contains three modules: data complementation, ML construction, and adaptive ensemble. First, it applies Gaussian Copula to eliminate sparsity. Then, we utilise five ML models to make predictions individually. Finally, it employs adaptive ensemble to get final weighted-sum result. Experiments have demonstrated that our model are robust.
[LG-29] Rethinking Federated Learning Over the Air: The Blessing of Scaling Up
链接: https://arxiv.org/abs/2508.17697
作者: Jiaqi Zhu,Bikramjit Das,Yong Xie,Nikolaos Pappas,Howard H. Yang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated learning facilitates collaborative model training across multiple clients while preserving data privacy. However, its performance is often constrained by limited communication resources, particularly in systems supporting a large number of clients. To address this challenge, integrating over-the-air computations into the training process has emerged as a promising solution to alleviate communication bottlenecks. The system significantly increases the number of clients it can support in each communication round by transmitting intermediate parameters via analog signals rather than digital ones. This improvement, however, comes at the cost of channel-induced distortions, such as fading and noise, which affect the aggregated global parameters. To elucidate these effects, this paper develops a theoretical framework to analyze the performance of over-the-air federated learning in large-scale client scenarios. Our analysis reveals three key advantages of scaling up the number of participating clients: (1) Enhanced Privacy: The mutual information between a client’s local gradient and the server’s aggregated gradient diminishes, effectively reducing privacy leakage. (2) Mitigation of Channel Fading: The channel hardening effect eliminates the impact of small-scale fading in the noisy global gradient. (3) Improved Convergence: Reduced thermal noise and gradient estimation errors benefit the convergence rate. These findings solidify over-the-air model training as a viable approach for federated learning in networks with a large number of clients. The theoretical insights are further substantiated through extensive experimental evaluations.
[LG-30] On the Edge of Memorization in Diffusion Models
链接: https://arxiv.org/abs/2508.17689
作者: Sam Buchanan,Druv Pai,Yi Ma,Valentin De Bortoli
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 10 main body pages, 43 total pages
Abstract:When do diffusion models reproduce their training data, and when are they able to generate samples beyond it? A practically relevant theoretical understanding of this interplay between memorization and generalization may significantly impact real-world deployments of diffusion models with respect to issues such as copyright infringement and data privacy. In this work, to disentangle the different factors that influence memorization and generalization in practical diffusion models, we introduce a scientific and mathematical “laboratory” for investigating these phenomena in diffusion models trained on fully synthetic or natural image-like structured data. Within this setting, we hypothesize that the memorization or generalization behavior of an underparameterized trained model is determined by the difference in training loss between an associated memorizing model and a generalizing model. To probe this hypothesis, we theoretically characterize a crossover point wherein the weighted training loss of a fully generalizing model becomes greater than that of an underparameterized memorizing model at a critical value of model (under)parameterization. We then demonstrate via carefully-designed experiments that the location of this crossover predicts a phase transition in diffusion models trained via gradient descent, validating our hypothesis. Ultimately, our theory enables us to analytically predict the model size at which memorization becomes predominant. Our work provides an analytically tractable and practically meaningful setting for future theoretical and empirical investigations. Code for our experiments is available at this https URL.
[LG-31] KMiX: Take Data Influence into Dynamic Mixture for Language Model Pre-training
链接: https://arxiv.org/abs/2508.17677
作者: Yifan Wang,Binbin Liu,Fengze Liu,Yuanfan Guo,Jiyao Deng,Xuecheng Wu,Weidong Zhou,Xiaohuan Zhou,Taifeng Wang
类目: Machine Learning (cs.LG)
*备注:
Abstract:The data mixture used in the pre-training of a language model is a cornerstone of its final performance. However, a static mixing strategy is suboptimal, as the model’s learning preferences for various data domains shift dynamically throughout training. Crucially, observing these evolving preferences in a computationally efficient manner remains a significant challenge. To address this, we propose TiKMiX, a method that dynamically adjusts the data mixture according to the model’s evolving preferences. TiKMiX introduces Group Influence, an efficient metric for evaluating the impact of data domains on the model. This metric enables the formulation of the data mixing problem as a search for an optimal, influence-maximizing distribution. We solve this via two approaches: TiKMiX-D for direct optimization, and TiKMiX-M, which uses a regression model to predict a superior mixture. We trained models with different numbers of parameters, on up to 1 trillion tokens. TiKMiX-D exceeds the performance of state-of-the-art methods like REGMIX while using just 20% of the computational resources. TiKMiX-M leads to an average performance gain of 2% across 9 downstream benchmarks. Our experiments reveal that a model’s data preferences evolve with training progress and scale, and we demonstrate that dynamically adjusting the data mixture based on Group Influence, a direct measure of these preferences, significantly improves performance by mitigating the underdigestion of data seen with static ratios.
[LG-32] owards Synthesizing Normative Data for Cognitive Assessments Using Generative Multimodal Large Language Models
链接: https://arxiv.org/abs/2508.17675
作者: Victoria Yan,Honor Chotkowski,Fengran Wang,Alex Fedorov
类目: Machine Learning (cs.LG)
*备注: Preprint
Abstract:Cognitive assessments require normative data as essential benchmarks for evaluating individual performance. Hence, developing new cognitive tests based on novel image stimuli is challenging due to the lack of readily available normative data. Traditional data collection methods are costly, time-consuming, and infrequently updated, limiting their practical utility. Recent advancements in generative multimodal large language models (MLLMs) offer a new approach to generate synthetic normative data from existing cognitive test images. We investigated the feasibility of using MLLMs, specifically GPT-4o and GPT-4o-mini, to synthesize normative textual responses for established image-based cognitive assessments, such as the “Cookie Theft” picture description task. Two distinct prompting strategies-naive prompts with basic instructions and advanced prompts enriched with contextual guidance-were evaluated. Responses were analyzed using embeddings to assess their capacity to distinguish diagnostic groups and demographic variations. Performance metrics included BLEU, ROUGE, BERTScore, and an LLM-as-a-judge evaluation. Advanced prompting strategies produced synthetic responses that more effectively distinguished between diagnostic groups and captured demographic diversity compared to naive prompts. Superior models generated responses exhibiting higher realism and diversity. BERTScore emerged as the most reliable metric for contextual similarity assessment, while BLEU was less effective for evaluating creative outputs. The LLM-as-a-judge approach provided promising preliminary validation results. Our study demonstrates that generative multimodal LLMs, guided by refined prompting methods, can feasibly generate robust synthetic normative data for existing cognitive tests, thereby laying the groundwork for developing novel image-based cognitive assessments without the traditional limitations.
[LG-33] Heterogeneous co-occurrence embedding for visual information exploration
链接: https://arxiv.org/abs/2508.17663
作者: Takuro Ishida,Tetsuo Furukawa
类目: Machine Learning (cs.LG); Information Retrieval (cs.IR)
*备注: 36pages, 9 figures, Accepted to International Journal of Innovative Computing, Information and Control (IJICIC), 2025
Abstract:This paper proposes an embedding method for co-occurrence data aimed at visual information exploration. We consider cases where co-occurrence probabilities are measured between pairs of elements from heterogeneous domains. The proposed method maps these heterogeneous elements into corresponding two-dimensional latent spaces, enabling visualization of asymmetric relationships between the domains. The key idea is to embed the elements in a way that maximizes their mutual information, thereby preserving the original dependency structure as much as possible. This approach can be naturally extended to cases involving three or more domains, using a generalization of mutual information known as total correlation. For inter-domain analysis, we also propose a visualization method that assigns colors to the latent spaces based on conditional probabilities, allowing users to explore asymmetric relationships interactively. We demonstrate the utility of the method through applications to an adjective-noun dataset, the NeurIPS dataset, and a subject-verb-object dataset, showcasing both intra- and inter-domain analysis.
[LG-34] Longitudinal Progression Prediction of Alzheimers Disease with Tabular Foundation Model
链接: https://arxiv.org/abs/2508.17649
作者: Yilang Ding,Jiawen Ren,Jiaying Lu,Gloria Hyunjung Kwak,Armin Iraji,Alex Fedorov
类目: Machine Learning (cs.LG)
*备注:
Abstract:Alzheimer’s disease is a progressive neurodegenerative disorder that remains challenging to predict due to its multifactorial etiology and the complexity of multimodal clinical data. Accurate forecasting of clinically relevant biomarkers, including diagnostic and quantitative measures, is essential for effective monitoring of disease progression. This work introduces L2C-TabPFN, a method that integrates a longitudinal-to-cross-sectional (L2C) transformation with a pre-trained Tabular Foundation Model (TabPFN) to predict Alzheimer’s disease outcomes using the TADPOLE dataset. L2C-TabPFN converts sequential patient records into fixed-length feature vectors, enabling robust prediction of diagnosis, cognitive scores, and ventricular volume. Experimental results demonstrate that, while L2C-TabPFN achieves competitive performance on diagnostic and cognitive outcomes, it provides state-of-the-art results in ventricular volume prediction. This key imaging biomarker reflects neurodegeneration and progression in Alzheimer’s disease. These findings highlight the potential of tabular foundational models for advancing longitudinal prediction of clinically relevant imaging markers in Alzheimer’s disease.
[LG-35] Quantum Graph Attention Network: A Novel Quantum Multi-Head Attention Mechanism for Graph Learning
链接: https://arxiv.org/abs/2508.17630
作者: An Ning,Tai Yue Li,Nan Yow Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:We propose the Quantum Graph Attention Network (QGAT), a hybrid graph neural network that integrates variational quantum circuits into the attention mechanism. At its core, QGAT employs strongly entangling quantum circuits with amplitude-encoded node features to enable expressive nonlinear interactions. Distinct from classical multi-head attention that separately computes each head, QGAT leverages a single quantum circuit to simultaneously generate multiple attention coefficients. This quantum parallelism facilitates parameter sharing across heads, substantially reducing computational overhead and model complexity. Classical projection weights and quantum circuit parameters are optimized jointly in an end-to-end manner, ensuring flexible adaptation to learning tasks. Empirical results demonstrate QGAT’s effectiveness in capturing complex structural dependencies and improved generalization in inductive scenarios, highlighting its potential for scalable quantum-enhanced learning across domains such as chemistry, biology, and network analysis. Furthermore, experiments confirm that quantum embedding enhances robustness against feature and structural noise, suggesting advantages in handling real-world noisy data. The modularity of QGAT also ensures straightforward integration into existing architectures, allowing it to easily augment classical attention-based models.
[LG-36] A Proportional-Integral Controller-Incorporated SGD Algorithm for High Efficient Latent Factor Analysis
链接: https://arxiv.org/abs/2508.17609
作者: Jinli Li,Shiyu Long,Minglian Han
类目: Machine Learning (cs.LG)
*备注:
Abstract:In industrial big data scenarios, high-dimensional sparse matrices (HDI) are widely used to characterize high-order interaction relationships among massive nodes. The stochastic gradient descent-based latent factor analysis (SGD-LFA) method can effectively extract deep feature information embedded in HDI matrices. However, existing SGD-LFA methods exhibit significant limitations: their parameter update process relies solely on the instantaneous gradient information of current samples, failing to incorporate accumulated experiential knowledge from historical iterations or account for intrinsic correlations between samples, resulting in slow convergence speed and suboptimal generalization performance. Thus, this paper proposes a PILF model by developing a PI-accelerated SGD algorithm by integrating correlated instances and refining learning errors through proportional-integral (PI) control mechanism that current and historical information; Comparative experiments demonstrate the superior representation capability of the PILF model on HDI matrices
[LG-37] ChartMaster: Advancing Chart-to-Code Generation with Real-World Charts and Chart Similarity Reinforcement Learning
链接: https://arxiv.org/abs/2508.17608
作者: Wentao Tan,Qiong Cao,Chao Xue,Yibing Zhan,Changxing Ding,Xiaodong He
类目: Machine Learning (cs.LG)
*备注:
Abstract:The chart-to-code generation task requires MLLMs to convert chart images into executable code. This task faces two major challenges: limited data diversity and insufficient maintenance of visual consistency between generated and original charts during training. Existing datasets mainly rely on seed data to prompt GPT models for code generation, resulting in homogeneous samples. To address this, we propose ReChartPrompt, which leverages real-world, human-designed charts from arXiv papers as prompts instead of synthetic seeds. Using the diverse styles and rich content of arXiv charts, we construct ReChartPrompt-240K, a large-scale and highly diverse dataset. Another challenge is that although SFT effectively improve code understanding, it often fails to ensure that generated charts are visually consistent with the originals. To address this, we propose ChartSimRL, a GRPO-based reinforcement learning algorithm guided by a novel chart similarity reward. This reward consists of attribute similarity, which measures the overlap of chart attributes such as layout and color between the generated and original charts, and visual similarity, which assesses similarity in texture and other overall visual features using convolutional neural networks. Unlike traditional text-based rewards such as accuracy or format rewards, our reward considers the multimodal nature of the chart-to-code task and effectively enhances the model’s ability to accurately reproduce charts. By integrating ReChartPrompt and ChartSimRL, we develop the ChartMaster model, which achieves state-of-the-art results among 7B-parameter models and even rivals GPT-4o on various chart-to-code generation benchmarks. All resources are available at this https URL.
[LG-38] Exploring Efficient Learning of Small BERT Networks with LoRA and DoRA
链接: https://arxiv.org/abs/2508.17586
作者: Daniel Frees,Aditri Bhagirath,Moritz Bolling
类目: Machine Learning (cs.LG)
*备注:
Abstract:While Large Language Models (LLMs) have revolutionized artificial intelligence, fine-tuning LLMs is extraordinarily computationally expensive, preventing smaller businesses and research teams with limited GPU resources from engaging with new research. Hu et al and Liu et al introduce Low-Rank Adaptation (LoRA) and Weight-Decomposed Low-Rank Adaptation (DoRA) as highly efficient and performant solutions to the computational challenges of LLM fine-tuning, demonstrating huge speedups and memory usage savings for models such as GPT-3 and RoBERTa. We seek to expand upon the original LoRA and DoRA papers by benchmarking efficiency and performance of LoRA and DoRA when applied to a much smaller scale of language model: our case study here is the compact minBERT model. Our findings reveal that optimal custom configurations of LoRA and DoRA, coupled with Automatic Mixed Precision (AMP), significantly enhance training efficiency without compromising performance. Furthermore, while the parameterization of minBERT is significantly smaller than GPT-3, our results validate the observation that gradient updates to language models are inherently low-rank even in small model space, observing that rank 1 decompositions yield negligible performance deficits. Furthermore, aided by our highly efficient minBERT implementation, we investigate numerous architectures, custom loss functions, and hyperparameters to ultimately train an optimal ensembled multitask minBERT model to simultaneously perform sentiment analysis, paraphrase detection, and similarity scoring.
[LG-39] Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay Prediction
链接: https://arxiv.org/abs/2508.17554
作者: Shuqi Zi,Haitz Sáez de Ocáriz Borde,Emma Rocheteau,Pietro Lio’
类目: Machine Learning (cs.LG)
*备注:
Abstract:Predicting a patient’s length of stay (LOS) in the intensive care unit (ICU) is a critical task for hospital resource management, yet remains challenging due to the heterogeneous and irregularly sampled nature of electronic health records (EHRs). In this work, we propose S ^2 G-Net, a novel neural architecture that unifies state-space sequence modeling with multi-view Graph Neural Networks (GNNs) for ICU LOS prediction. The temporal path employs Mamba state-space models (SSMs) to capture patient trajectories, while the graph path leverages an optimized GraphGPS backbone, designed to integrate heterogeneous patient similarity graphs derived from diagnostic, administrative, and semantic features. Experiments on the large-scale MIMIC-IV cohort dataset show that S ^2 G-Net consistently outperforms sequence models (BiLSTM, Mamba, Transformer), graph models (classic GNNs, GraphGPS), and hybrid approaches across all primary metrics. Extensive ablation studies and interpretability analyses highlight the complementary contributions of each component of our architecture and underscore the importance of principled graph construction. These results demonstrate that S ^2 G-Net provides an effective and scalable solution for ICU LOS prediction with multi-modal clinical data.
[LG-40] Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax
链接: https://arxiv.org/abs/2508.17531
作者: Marcel Hoffmann,Lukas Galke,Ansgar Scherp
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph homophily has been considered an essential property for message-passing neural networks (MPNN) in node classification. Recent findings suggest that performance is more closely tied to the consistency of neighborhood class distributions. We demonstrate that the MPNN performance depends on the number of components of the overall neighborhood distribution within a class. By breaking down the classes into their neighborhood distribution components, we increase measures of neighborhood distribution informativeness but do not observe an improvement in MPNN performance. We propose a Gumbel-Softmax-based rewiring method that reduces deviations in neighborhood distributions. Our results show that our new method enhances neighborhood informativeness, handles long-range dependencies, mitigates oversquashing, and increases the classification performance of the MPNN. The code is available at this https URL.
[LG-41] Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
链接: https://arxiv.org/abs/2508.17521
作者: YongKyung Oh,Seungsu Kam,Dong-Young Lim,Sungil Kim
类目: Machine Learning (cs.LG)
*备注:
Abstract:Astronomical time series from large-scale surveys like LSST are often irregularly sampled and incomplete, posing challenges for classification and anomaly detection. We introduce a new framework based on Neural Stochastic Delay Differential Equations (Neural SDDEs) that combines stochastic modeling with neural networks to capture delayed temporal dynamics and handle irregular observations. Our approach integrates a delay-aware neural architecture, a numerical solver for SDDEs, and mechanisms to robustly learn from noisy, sparse sequences. Experiments on irregularly sampled astronomical data demonstrate strong classification accuracy and effective detection of novel astrophysical events, even with partial labels. This work highlights Neural SDDEs as a principled and practical tool for time series analysis under observational constraints.
[LG-42] GateTS: Versatile and Efficient Forecasting via Attention-Inspired routed Mixture-of-Experts
链接: https://arxiv.org/abs/2508.17515
作者: Kyrylo Yemets,Mykola Lukashchuk,Ivan Izonin
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate univariate forecasting remains a pressing need in real-world systems, such as energy markets, hydrology, retail demand, and IoT monitoring, where signals are often intermittent and horizons span both short- and long-term. While transformers and Mixture-of-Experts (MoE) architectures are increasingly favored for time-series forecasting, a key gap persists: MoE models typically require complicated training with both the main forecasting loss and auxiliary load-balancing losses, along with careful routing/temperature tuning, which hinders practical adoption. In this paper, we propose a model architecture that simplifies the training process for univariate time series forecasting and effectively addresses both long- and short-term horizons, including intermittent patterns. Our approach combines sparse MoE computation with a novel attention-inspired gating mechanism that replaces the traditional one-layer softmax router. Through extensive empirical evaluation, we demonstrate that our gating design naturally promotes balanced expert utilization and achieves superior predictive accuracy without requiring the auxiliary load-balancing losses typically used in classical MoE implementations. The model achieves better performance while utilizing only a fraction of the parameters required by state-of-the-art transformer models, such as PatchTST. Furthermore, experiments across diverse datasets confirm that our MoE architecture with the proposed gating mechanism is more computationally efficient than LSTM for both long- and short-term forecasting, enabling cost-effective inference. These results highlight the potential of our approach for practical time-series forecasting applications where both accuracy and computational efficiency are critical.
[LG-43] Learning Interpretable Differentiable Logic Networks for Time-Series Classification
链接: https://arxiv.org/abs/2508.17512
作者: Chang Yue,Niraj K. Jha
类目: Machine Learning (cs.LG)
*备注:
Abstract:Differentiable logic networks (DLNs) have shown promising results in tabular domains by combining accuracy, interpretability, and computational efficiency. In this work, we apply DLNs to the domain of TSC for the first time, focusing on univariate datasets. To enable DLN application in this context, we adopt feature-based representations relying on Catch22 and TSFresh, converting sequential time series into vectorized forms suitable for DLN classification. Unlike prior DLN studies that fix the training configuration and vary various settings in isolation via ablation, we integrate all such configurations into the hyperparameter search space, enabling the search process to select jointly optimal settings. We then analyze the distribution of selected configurations to better understand DLN training dynamics. We evaluate our approach on 51 publicly available univariate TSC benchmarks. The results confirm that classification DLNs maintain their core strengths in this new domain: they deliver competitive accuracy, retain low inference cost, and provide transparent, interpretable decision logic, thus aligning well with previous DLN findings in the realm of tabular classification and regression tasks.
[LG-44] A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoring
链接: https://arxiv.org/abs/2508.17477
作者: Martin Käppel,Julian Neuberger,Felix Möhrlein,Sven Weinzierl,Martin Matzner,Stefan Jablonski
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注:
Abstract:Predictive process monitoring enables organizations to proactively react and intervene in running instances of a business process. Given an incomplete process instance, predictions about the outcome, next activity, or remaining time are created. This is done by powerful machine learning models, which have shown impressive predictive performance. However, the data-driven nature of these models makes them susceptible to finding unfair, biased, or unethical patterns in the data. Such patterns lead to biased predictions based on so-called sensitive attributes, such as the gender or age of process participants. Previous work has identified this problem and offered solutions that mitigate biases by removing sensitive attributes entirely from the process instance. However, sensitive attributes can be used both fairly and unfairly in the same process instance. For example, during a medical process, treatment decisions could be based on gender, while the decision to accept a patient should not be based on gender. This paper proposes a novel, model-agnostic approach for identifying and rectifying biased decisions in predictive business process monitoring models, even when the same sensitive attribute is used both fairly and unfairly. The proposed approach uses a human-in-the-loop approach to differentiate between fair and unfair decisions through simple alterations on a decision tree model distilled from the original prediction model. Our results show that the proposed approach achieves a promising tradeoff between fairness and accuracy in the presence of biased data. All source code and data are publicly available at this https URL.
[LG-45] MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
链接: https://arxiv.org/abs/2508.17467
作者: Krishna Teja Chitty-Venkata,Sylvia Howland,Golara Azar,Daria Soboleva,Natalia Vassilieva,Siddhisanket Raskar,Murali Emani,Venkatram Vishwanath
类目: Machine Learning (cs.LG); Performance (cs.PF)
*备注: Preprint
Abstract:Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining computational efficiency. However, MoEs introduce several inference-time challenges, including load imbalance across experts and the additional routing computational overhead. To address these challenges and fully harness the benefits of MoE, a systematic evaluation of hardware acceleration techniques is essential. We present MoE-Inference-Bench, a comprehensive study to evaluate MoE performance across diverse scenarios. We analyze the impact of batch size, sequence length, and critical MoE hyperparameters such as FFN dimensions and number of experts on throughput. We evaluate several optimization techniques on Nvidia H100 GPUs, including pruning, Fused MoE operations, speculative decoding, quantization, and various parallelization strategies. Our evaluation includes MoEs from the Mixtral, DeepSeek, OLMoE and Qwen families. The results reveal performance differences across configurations and provide insights for the efficient deployment of MoEs.
[LG-46] Adversarial Examples Are Not Bugs They Are Superposition
链接: https://arxiv.org/abs/2508.17456
作者: Liv Gorton,Owen Lewis
类目: Machine Learning (cs.LG)
*备注:
Abstract:Adversarial examples – inputs with imperceptible perturbations that fool neural networks – remain one of deep learning’s most perplexing phenomena despite nearly a decade of research. While numerous defenses and explanations have been proposed, there is no consensus on the fundamental mechanism. One underexplored hypothesis is that superposition, a concept from mechanistic interpretability, may be a major contributing factor, or even the primary cause. We present four lines of evidence in support of this hypothesis, greatly extending prior arguments by Elhage et al. (2022): (1) superposition can theoretically explain a range of adversarial phenomena, (2) in toy models, intervening on superposition controls robustness, (3) in toy models, intervening on robustness (via adversarial training) controls superposition, and (4) in ResNet18, intervening on robustness (via adversarial training) controls superposition.
[LG-47] A Systematic Literature Review on Multi-label Data Stream Classification
链接: https://arxiv.org/abs/2508.17455
作者: H. Freire-Oliveira,E. R. F. Paiva,J. Gama,L. Khan,R. Cerri
类目: Machine Learning (cs.LG)
*备注: 48 pages, 12 figures
Abstract:Classification in the context of multi-label data streams represents a challenge that has attracted significant attention due to its high real-world applicability. However, this task faces problems inherent to dynamic environments, such as the continuous arrival of data at high speed and volume, changes in the data distribution (concept drift), the emergence of new labels (concept evolution), and the latency in the arrival of ground truth labels. This systematic literature review presents an in-depth analysis of multi-label data stream classification proposals. We characterize the latest methods in the literature, providing a comprehensive overview, building a thorough hierarchy, and discussing how the proposals approach each problem. Furthermore, we discuss the adopted evaluation strategies and analyze the methods’ asymptotic complexity and resource consumption. Finally, we identify the main gaps and offer recommendations for future research directions in the field.
[LG-48] ReviBranch: Deep Reinforcement Learning for Branch-and-Bound with Revived Trajectories
链接: https://arxiv.org/abs/2508.17452
作者: Dou Jiabao,Nie Jiayi,Yihang Cheng,Jinwei Liu,Yingrui Ji,Canran Xiao,Feixiang Du,Jiaping Xiao
类目: Machine Learning (cs.LG)
*备注: conference
Abstract:The Branch-and-bound (BB) algorithm is the main solver for Mixed Integer Linear Programs (MILPs), where the selection of branching variable is essential to computational efficiency. However, traditional heuristics for branching often fail to generalize across heterogeneous problem instances, while existing learning-based methods such as imitation learning (IL) suffers from dependence on expert demonstration quality, and reinforcement learning (RL) struggles with limitations in sparse rewards and dynamic state representation challenges. To address these issues, we propose ReviBranch, a novel deep RL framework that constructs revived trajectories by reviving explicit historical correspondences between branching decisions and their corresponding graph states along search-tree paths. During training, ReviBranch enables agents to learn from complete structural evolution and temporal dependencies within the branching process. Additionally, we introduce an importance-weighted reward redistribution mechanism that transforms sparse terminal rewards into dense stepwise feedback, addressing the sparse reward challenge. Extensive experiments on different MILP benchmarks demonstrate that ReviBranch outperforms state-of-the-art RL methods, reducing BB nodes by 4.0% and LP iterations by 2.2% on large-scale instances. The results highlight the robustness and generalizability of ReviBranch across heterogeneous MILP problem classes.
[LG-49] Rectified Robust Policy Optimization for Model-Uncertain Constrained Reinforcement Learning without Strong Duality
链接: https://arxiv.org/abs/2508.17448
作者: Shaocong Ma,Ziyi Chen,Yi Zhou,Heng Huang
类目: Machine Learning (cs.LG)
*备注:
Abstract:The goal of robust constrained reinforcement learning (RL) is to optimize an agent’s performance under the worst-case model uncertainty while satisfying safety or resource constraints. In this paper, we demonstrate that strong duality does not generally hold in robust constrained RL, indicating that traditional primal-dual methods may fail to find optimal feasible policies. To overcome this limitation, we propose a novel primal-only algorithm called Rectified Robust Policy Optimization (RRPO), which operates directly on the primal problem without relying on dual formulations. We provide theoretical convergence guarantees under mild regularity assumptions, showing convergence to an approximately optimal feasible policy with iteration complexity matching the best-known lower bound when the uncertainty set diameter is controlled in a specific level. Empirical results in a grid-world environment validate the effectiveness of our approach, demonstrating that RRPO achieves robust and safe performance under model uncertainties while the non-robust method can violate the worst-case safety constraints.
[LG-50] Modular MeanFlow: Towards Stable and Scalable One-Step Generative Modeling
链接: https://arxiv.org/abs/2508.17426
作者: Haochen You,Baojing Liu,Hongyang He
类目: Machine Learning (cs.LG)
*备注: Accepted as a conference paper at PRCV 2025
Abstract:One-step generative modeling seeks to generate high-quality data samples in a single function evaluation, significantly improving efficiency over traditional diffusion or flow-based models. In this work, we introduce Modular MeanFlow (MMF), a flexible and theoretically grounded approach for learning time-averaged velocity fields. Our method derives a family of loss functions based on a differential identity linking instantaneous and average velocities, and incorporates a gradient modulation mechanism that enables stable training without sacrificing expressiveness. We further propose a curriculum-style warmup schedule to smoothly transition from coarse supervision to fully differentiable training. The MMF formulation unifies and generalizes existing consistency-based and flow-matching methods, while avoiding expensive higher-order derivatives. Empirical results across image synthesis and trajectory modeling tasks demonstrate that MMF achieves competitive sample quality, robust convergence, and strong generalization, particularly under low-data or out-of-distribution settings.
[LG-51] FRAME : Comprehensive Risk Assessment Framework for Adversarial Machine Learning Threats
链接: https://arxiv.org/abs/2508.17405
作者: Avishag Shapira,Simon Shigol,Asaf Shabtai
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:The widespread adoption of machine learning (ML) systems increased attention to their security and emergence of adversarial machine learning (AML) techniques that exploit fundamental vulnerabilities in ML systems, creating an urgent need for comprehensive risk assessment for ML-based systems. While traditional risk assessment frameworks evaluate conventional cybersecurity risks, they lack ability to address unique challenges posed by AML threats. Existing AML threat evaluation approaches focus primarily on technical attack robustness, overlooking crucial real-world factors like deployment environments, system dependencies, and attack feasibility. Attempts at comprehensive AML risk assessment have been limited to domain-specific solutions, preventing application across diverse systems. Addressing these limitations, we present FRAME, the first comprehensive and automated framework for assessing AML risks across diverse ML-based systems. FRAME includes a novel risk assessment method that quantifies AML risks by systematically evaluating three key dimensions: target system’s deployment environment, characteristics of diverse AML techniques, and empirical insights from prior research. FRAME incorporates a feasibility scoring mechanism and LLM-based customization for system-specific assessments. Additionally, we developed a comprehensive structured dataset of AML attacks enabling context-aware risk assessment. From an engineering application perspective, FRAME delivers actionable results designed for direct use by system owners with only technical knowledge of their systems, without expertise in AML. We validated it across six diverse real-world applications. Our evaluation demonstrated exceptional accuracy and strong alignment with analysis by AML experts. FRAME enables organizations to prioritize AML risks, supporting secure AI deployment in real-world environments.
[LG-52] Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems
链接: https://arxiv.org/abs/2508.17403
作者: Yinsong Wang,Xiao Liu,Quan Zeng,Yu Ding
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: Pre-Submission Version
Abstract:Recent breakthroughs in autonomous experimentation have demonstrated remarkable physical capabilities, yet their cognitive control remains limited–often relying on static heuristics or classical optimization. A core limitation is the absence of a principled mechanism to detect and adapt to the unexpectedness. While traditional surprise measures–such as Shannon or Bayesian Surprise–offer momentary detection of deviation, they fail to capture whether a system is truly learning and adapting. In this work, we introduce Mutual Information Surprise (MIS), a new framework that redefines surprise not as anomaly detection, but as a signal of epistemic growth. MIS quantifies the impact of new observations on mutual information, enabling autonomous systems to reflect on their learning progression. We develop a statistical test sequence to detect meaningful shifts in estimated mutual information and propose a mutual information surprise reaction policy (MISRP) that dynamically governs system behavior through sampling adjustment and process forking. Empirical evaluations–on both synthetic domains and a dynamic pollution map estimation task–show that MISRP-governed strategies significantly outperform classical surprise-based approaches in stability, responsiveness, and predictive accuracy. By shifting surprise from reactive to reflective, MIS offers a path toward more self-aware and adaptive autonomous systems.
[LG-53] Effective Clustering for Large Multi-Relational Graphs SIGMOD2026
链接: https://arxiv.org/abs/2508.17388
作者: Xiaoyang Lin,Runhao Jiang,Renchi Yang
类目: Machine Learning (cs.LG); Databases (cs.DB); Social and Information Networks (cs.SI)
*备注: 23 pages. The technical report for the paper titled “Effective Clustering for Large Multi-Relational Graphs” in SIGMOD 2026
Abstract:Multi-relational graphs (MRGs) are an expressive data structure for modeling diverse interactions/relations among real objects (i.e., nodes), which pervade extensive applications and scenarios. Given an MRG G with N nodes, partitioning the node set therein into K disjoint clusters (MRGC) is a fundamental task in analyzing MRGs, which has garnered considerable attention. However, the majority of existing solutions towards MRGC either yield severely compromised result quality by ineffective fusion of heterogeneous graph structures and attributes, or struggle to cope with sizable MRGs with millions of nodes and billions of edges due to the adoption of sophisticated and costly deep learning models. In this paper, we present DEMM and DEMM+, two effective MRGC approaches to address the limitations above. Specifically, our algorithms are built on novel two-stage optimization objectives, where the former seeks to derive high-caliber node feature vectors by optimizing the multi-relational Dirichlet energy specialized for MRGs, while the latter minimizes the Dirichlet energy of clustering results over the node affinity graph. In particular, DEMM+ achieves significantly higher scalability and efficiency over our based method DEMM through a suite of well-thought-out optimizations. Key technical contributions include (i) a highly efficient approximation solver for constructing node feature vectors, and (ii) a theoretically-grounded problem transformation with carefully-crafted techniques that enable linear-time clustering without explicitly materializing the NxN dense affinity matrix. Further, we extend DEMM+ to handle attribute-less MRGs through non-trivial adaptations. Extensive experiments, comparing DEMM+ against 20 baselines over 11 real MRGs, exhibit that DEMM+ is consistently superior in terms of clustering quality measured against ground-truth labels, while often being remarkably faster. Comments: 23 pages. The technical report for the paper titled “Effective Clustering for Large Multi-Relational Graphs” in SIGMOD 2026 Subjects: Machine Learning (cs.LG); Databases (cs.DB); Social and Information Networks (cs.SI) Cite as: arXiv:2508.17388 [cs.LG] (or arXiv:2508.17388v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2508.17388 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-54] FedERL: Federated Efficient and Robust Learning for Common Corruptions
链接: https://arxiv.org/abs/2508.17381
作者: Omar Bekdache,Naresh Shanbhag
类目: Machine Learning (cs.LG)
*备注:
Abstract:Federated learning (FL) accelerates the deployment of deep learning models on edge devices while preserving data privacy. However, FL systems face challenges due to client-side constraints on computational resources, and from a lack of robustness to common corruptions such as noise, blur, and weather effects. Existing robust training methods are computationally expensive and unsuitable for resource-constrained clients. We propose FedERL, federated efficient and robust learning, as the first work to explicitly address corruption robustness under time and energy constraints on the client side. At its core, FedERL employs a novel data-agnostic robust training (DART) method on the server to enhance robustness without access to the training data. In doing so, FedERL ensures zero robustness overhead for clients. Extensive experiments demonstrate FedERL’s ability to handle common corruptions at a fraction of the time and energy cost of traditional robust training methods. In scenarios with limited time and energy budgets, FedERL surpasses the performance of traditional robust training, establishing it as a practical and scalable solution for real-world FL applications.
[LG-55] rust Me I Know This Function: Hijacking LLM Static Analysis using Bias
链接: https://arxiv.org/abs/2508.17361
作者: Shir Bernstein,David Beste,Daniel Ayzenshteyn,Lea Schonherr,Yisroel Mirsky
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注:
Abstract:Large Language Models (LLMs) are increasingly trusted to perform automated code review and static analysis at scale, supporting tasks such as vulnerability detection, summarization, and refactoring. In this paper, we identify and exploit a critical vulnerability in LLM-based code analysis: an abstraction bias that causes models to overgeneralize familiar programming patterns and overlook small, meaningful bugs. Adversaries can exploit this blind spot to hijack the control flow of the LLM’s interpretation with minimal edits and without affecting actual runtime behavior. We refer to this attack as a Familiar Pattern Attack (FPA). We develop a fully automated, black-box algorithm that discovers and injects FPAs into target code. Our evaluation shows that FPAs are not only effective, but also transferable across models (GPT-4o, Claude 3.5, Gemini 2.0) and universal across programming languages (Python, C, Rust, Go). Moreover, FPAs remain effective even when models are explicitly warned about the attack via robust system prompts. Finally, we explore positive, defensive uses of FPAs and discuss their broader implications for the reliability and safety of code-oriented LLMs. Subjects: Machine Learning (cs.LG); Cryptography and Security (cs.CR) Cite as: arXiv:2508.17361 [cs.LG] (or arXiv:2508.17361v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2508.17361 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-56] Detecting Struggling Student Programmers using Proficiency Taxonomies ECAI2025
链接: https://arxiv.org/abs/2508.17353
作者: Noga Schwartz,Roy Fairstein,Avi Segal,Kobi Gal
类目: Computers and Society (cs.CY); Machine Learning (cs.LG)
*备注: appears at ECAI 2025
Abstract:Early detection of struggling student programmers is crucial for providing them with personalized support. While multiple AI-based approaches have been proposed for this problem, they do not explicitly reason about students’ programming skills in the model. This study addresses this gap by developing in collaboration with educators a taxonomy of proficiencies that categorizes how students solve coding tasks and is embedded in the detection model. Our model, termed the Proficiency Taxonomy Model (PTM), simultaneously learns the student’s coding skills based on their coding history and predicts whether they will struggle on a new task. We extensively evaluated the effectiveness of the PTM model on two separate datasets from introductory Java and Python courses for beginner programmers. Experimental results demonstrate that PTM outperforms state-of-the-art models in predicting struggling students. The paper showcases the potential of combining structured insights from teachers for early identification of those needing assistance in learning to code.
[LG-57] ShortListing Model: A Streamlined SimplexDiffusion for Discrete Variable Generation
链接: https://arxiv.org/abs/2508.17345
作者: Yuxuan Song,Zhe Zhang,Yu Pei,Jingjing Gong,Qiying Yu,Zheng Zhang,Mingxuan Wang,Hao Zhou,Jingjing Liu,Wei-Ying Ma
类目: Machine Learning (cs.LG); Genomics (q-bio.GN)
*备注:
Abstract:Generative modeling of discrete variables is challenging yet crucial for applications in natural language processing and biological sequence design. We introduce the Shortlisting Model (SLM), a novel simplex-based diffusion model inspired by progressive candidate pruning. SLM operates on simplex centroids, reducing generation complexity and enhancing scalability. Additionally, SLM incorporates a flexible implementation of classifier-free guidance, enhancing unconditional generation performance. Extensive experiments on DNA promoter and enhancer design, protein design, character-level and large-vocabulary language modeling demonstrate the competitive performance and strong potential of SLM. Our code can be found at this https URL
[LG-58] Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
链接: https://arxiv.org/abs/2508.17344
作者: Rajrupa Chattaraj,Sridhar Chimalakonda,Vibhu Saujanya Sharma,Vikrant Kaulgud
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG); Performance (cs.PF); Programming Languages (cs.PL)
*备注: 18 pages including references, 5 figures
Abstract:The utilization of Machine Learning (ML) in contemporary software systems is extensive and continually expanding. However, its usage is energy-intensive, contributing to increased carbon emissions and demanding significant resources. While numerous studies examine the performance and accuracy of ML, only a limited few focus on its environmental aspects, particularly energy consumption. In addition, despite emerging efforts to compare energy consumption across various programming languages for specific algorithms and tasks, there remains a gap specifically in comparing these languages for ML-based tasks. This paper aims to raise awareness of the energy costs associated with employing different programming languages for ML model training and inference. Through this empirical study, we measure and compare the energy consumption along with run-time performance of five regression and five classification tasks implemented in Python and R, the two most popular programming languages in this context. Our study results reveal a statistically significant difference in costs between the two languages in 95% of the cases examined. Furthermore, our analysis demonstrates that the choice of programming language can influence energy efficiency significantly, up to 99.16% during model training and up to 99.8% during inferences, for a given ML task.
[LG-59] MetaFed: Advancing Privacy Performance and Sustainability in Federated Metaverse Systems
链接: https://arxiv.org/abs/2508.17341
作者: Muhammet Anil Yagiz,Zeynep Sude Cengiz,Polat Goktas
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Computers and Society (cs.CY); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET)
*备注: 2025 IEEE International Symposium on Emerging Metaverse (ISEMV)
Abstract:The rapid expansion of immersive Metaverse applications introduces complex challenges at the intersection of performance, privacy, and environmental sustainability. Centralized architectures fall short in addressing these demands, often resulting in elevated energy consumption, latency, and privacy concerns. This paper proposes MetaFed, a decentralized federated learning (FL) framework that enables sustainable and intelligent resource orchestration for Metaverse environments. MetaFed integrates (i) multi-agent reinforcement learning for dynamic client selection, (ii) privacy-preserving FL using homomorphic encryption, and (iii) carbon-aware scheduling aligned with renewable energy availability. Evaluations on MNIST and CIFAR-10 using lightweight ResNet architectures demonstrate that MetaFed achieves up to 25% reduction in carbon emissions compared to conventional approaches, while maintaining high accuracy and minimal communication overhead. These results highlight MetaFed as a scalable solution for building environmentally responsible and privacy-compliant Metaverse infrastructures.
[LG-60] Is the Frequency Principle always valid?
链接: https://arxiv.org/abs/2508.17323
作者: Qijia Zhai
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:
Abstract:We investigate the learning dynamics of shallow ReLU neural networks on the unit sphere (S^2\subset\mathbbR^3) in polar coordinates ((\tau,\phi)), considering both fixed and trainable neuron directions (\w_i\). For fixed weights, spherical harmonic expansions reveal an intrinsic low-frequency preference with coefficients decaying as (O(\ell^5/2/2^\ell)), typically leading to the Frequency Principle (FP) of lower-frequency-first learning. However, this principle can be violated under specific initial conditions or error distributions. With trainable weights, an additional rotation term in the harmonic evolution equations preserves exponential decay with decay order (O(\ell^7/2/2^\ell)) factor, also leading to the FP of lower-frequency-first learning. But like fixed weights case, the principle can be violated under specific initial conditions or error distributions. Our numerical results demonstrate that trainable directions increase learning complexity and can either maintain a low-frequency advantage or enable faster high-frequency emergence. This analysis suggests the FP should be viewed as a tendency rather than a rule on curved domains like (S^2), providing insights into how direction updates and harmonic expansions shape frequency-dependent learning.
[LG-61] AdaptiveK Sparse Autoencoders: Dynamic Sparsity Allocation for Interpretable LLM Representations
链接: https://arxiv.org/abs/2508.17320
作者: Yifei Yao,Mengnan Du
类目: Machine Learning (cs.LG)
*备注:
Abstract:Understanding the internal representations of large language models (LLMs) remains a central challenge for interpretability research. Sparse autoencoders (SAEs) offer a promising solution by decomposing activations into interpretable features, but existing approaches rely on fixed sparsity constraints that fail to account for input complexity. We propose Adaptive Top K Sparse Autoencoders (AdaptiveK), a novel framework that dynamically adjusts sparsity levels based on the semantic complexity of each input. Leveraging linear probes, we demonstrate that context complexity is linearly encoded in LLM representations, and we use this signal to guide feature allocation during training. Experiments across three language models (Pythia-70M, Pythia-160M, and Gemma-2-2B) demonstrate that this complexity-driven adaptation significantly outperforms fixed-sparsity approaches on reconstruction fidelity, explained variance, and cosine similarity metrics while eliminating the computational burden of extensive hyperparameter tuning.
[LG-62] Physics-informed neural network for fatigue life prediction of irradiated austenitic and ferritic/martensitic steels
链接: https://arxiv.org/abs/2508.17303
作者: Dhiraj S Kori,Abhinav Chandraker,Syed Abdur Rahman,Punit Rathore,Ankur Chauhan
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注:
Abstract:This study proposes a Physics-Informed Neural Network (PINN) framework to predict the low-cycle fatigue (LCF) life of irradiated austenitic and ferritic/martensitic (F/M) steels used in nuclear reactors. These materials experience cyclic loading and irradiation at elevated temperatures, causing complex degradation that traditional empirical models fail to capture accurately. The developed PINN model incorporates physical fatigue life constraints into its loss function, improving prediction accuracy and generalizability. Trained on 495 data points, including both irradiated and unirradiated conditions, the model outperforms traditional machine learning models like Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and the conventional Neural Network. SHapley Additive exPlanations analysis identifies strain amplitude, irradiation dose, and testing temperature as dominant features, each inversely correlated with fatigue life, consistent with physical understanding. PINN captures saturation behaviour in fatigue life at higher strain amplitudes in F/M steels. Overall, the PINN framework offers a reliable and interpretable approach for predicting fatigue life in irradiated alloys, enabling informed alloy selection.
[LG-63] Explainable AI (XAI) for Arrhythmia detection from electrocardiograms
链接: https://arxiv.org/abs/2508.17294
作者: Joschka Beck,Arlene John
类目: Machine Learning (cs.LG)
*备注:
Abstract:Advancements in deep learning have enabled highly accurate arrhythmia detection from electrocardiogram (ECG) signals, but limited interpretability remains a barrier to clinical adoption. This study investigates the application of Explainable AI (XAI) techniques specifically adapted for time-series ECG analysis. Using the MIT-BIH arrhythmia dataset, a convolutional neural network-based model was developed for arrhythmia classification, with R-peak-based segmentation via the Pan-Tompkins algorithm. To increase the dataset size and to reduce class imbalance, an additional 12-lead ECG dataset was incorporated. A user needs assessment was carried out to identify what kind of explanation would be preferred by medical professionals. Medical professionals indicated a preference for saliency map-based explanations over counterfactual visualisations, citing clearer correspondence with ECG interpretation workflows. Four SHapley Additive exPlanations (SHAP)-based approaches: permutation importance, KernelSHAP, gradient-based methods, and Deep Learning Important FeaTures (DeepLIFT), were implemented and compared. The model achieved 98.3% validation accuracy on MIT-BIH but showed performance degradation on the combined dataset, underscoring dataset variability challenges. Permutation importance and KernelSHAP produced cluttered visual outputs, while gradient-based and DeepLIFT methods highlighted waveform regions consistent with clinical reasoning, but with variability across samples. Findings emphasize the need for domain-specific XAI adaptations in ECG analysis and highlight saliency mapping as a more clinically intuitive approach
[LG-64] DeepCFD: Efficient near-ground airfoil lift coefficient approximation with deep convolutional neural networks
链接: https://arxiv.org/abs/2508.17278
作者: Mohammad Amin Esabat,Saeed Jaamei,Fatemeh Asadi
类目: Machine Learning (cs.LG)
*备注:
Abstract:. Predicting and calculating the aerodynamic coefficients of airfoils near the ground with CFD software requires much time. However, the availability of data from CFD simulation results and the development of new neural network methods have made it possible to present the simulation results using methods like VGG, a CCN neural network method. In this article, lift-to-drag coefficients of airfoils near the ground surface are predicted with the help of a neural network. This prediction can only be realized by providing data for training and learning the code that contains information on the lift-to-drag ratio of the primary data and images related to the airfoil cross-section, which are converted into a matrix. One advantage of the VGG method over other methods is that its results are more accurate than those of other CNN methods.
[LG-65] Learning Short-Term and Long-Term Patterns of High-Order Dynamics in Real-World Networks CIKM
链接: https://arxiv.org/abs/2508.17236
作者: Yunyong Ko,Da Eun Lee,Song Kyung Yu,Sang-Wook Kim
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG)
*备注: 5 pages, 4 figures, 2 tables, ACM International Conference on Information and Knowledge Management (CIKM) 2025
Abstract:Real-world networks have high-order relationships among objects and they evolve over time. To capture such dynamics, many works have been studied in a range of fields. Via an in-depth preliminary analysis, we observe two important characteristics of high-order dynamics in real-world networks: high-order relations tend to (O1) have a structural and temporal influence on other relations in a short term and (O2) periodically re-appear in a long term. In this paper, we propose LINCOLN, a method for Learning hIgh-order dyNamiCs Of reaL-world Networks, that employs (1) bi-interactional hyperedge encoding for short-term patterns, (2) periodic time injection and (3) intermediate node representation for long-term patterns. Via extensive experiments, we show that LINCOLN outperforms nine state-of-the-art methods in the dynamic hyperedge prediction task.
[LG-66] okenLake: A Unified Segment-level Prefix Cache Pool for Fine-grained Elastic Long-Context LLM Serving
链接: https://arxiv.org/abs/2508.17219
作者: Bingyang Wu,Zili Zhang,Yinmin Zhong,Guanzhe Huang,Yibo Zhu,Xuanzhe Liu,Xin Jin
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注:
Abstract:Prefix caching is crucial to accelerate multi-turn interactions and requests with shared prefixes. At the cluster level, existing prefix caching systems are tightly coupled with request scheduling to optimize cache efficiency and computation performance together, leading to load imbalance, data redundancy, and memory fragmentation of caching systems across instances. To address these issues, memory pooling is promising to shield the scheduler from the underlying cache management so that it can focus on the computation optimization. However, because existing prefix caching systems only transfer increasingly longer prefix caches between instances, they cannot achieve low-latency memory pooling. To address these problems, we propose a unified segment-level prefix cache pool, TokenLake. It uses a declarative cache interface to expose requests’ query tensors, prefix caches, and cache-aware operations to TokenLake for efficient pooling. Powered by this abstraction, TokenLake can manage prefix cache at the segment level with a heavy-hitter-aware load balancing algorithm to achieve better cache load balance, deduplication, and defragmentation. TokenLake also transparently minimizes the communication volume of query tensors and new caches. Based on TokenLake, the scheduler can schedule requests elastically by using existing techniques without considering prefix cache management. Evaluations on real-world workloads show that TokenLake can improve throughput by up to 2.6 \times and 2.0 \times and boost hit rate by 2.0 \times and 2.1 \times , compared to state-of-the-art cache-aware routing and cache-centric PD-disaggregation solutions, respectively. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2508.17219 [cs.DC] (or arXiv:2508.17219v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2508.17219 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-67] Sharpness-Aware Geometric Defense for Robust Out-Of-Distribution Detection
链接: https://arxiv.org/abs/2508.17174
作者: Jeng-Lin Li,Ming-Ching Chang,Wei-Chao Chen
类目: Machine Learning (cs.LG)
*备注: under review
Abstract:Out-of-distribution (OOD) detection ensures safe and reliable model deployment. Contemporary OOD algorithms using geometry projection can detect OOD or adversarial samples from clean in-distribution (ID) samples. However, this setting regards adversarial ID samples as OOD, leading to incorrect OOD predictions. Existing efforts on OOD detection with ID and OOD data under attacks are minimal. In this paper, we develop a robust OOD detection method that distinguishes adversarial ID samples from OOD ones. The sharp loss landscape created by adversarial training hinders model convergence, impacting the latent embedding quality for OOD score calculation. Therefore, we introduce a \bf Sharpness-aware Geometric Defense (SaGD) framework to smooth out the rugged adversarial loss landscape in the projected latent geometry. Enhanced geometric embedding convergence enables accurate ID data characterization, benefiting OOD detection against adversarial attacks. We use Jitter-based perturbation in adversarial training to extend the defense ability against unseen attacks. Our SaGD framework significantly improves FPR and AUC over the state-of-the-art defense approaches in differentiating CIFAR-100 from six other OOD datasets under various attacks. We further examine the effects of perturbations at various adversarial training levels, revealing the relationship between the sharp loss landscape and adversarial OOD detection.
[LG-68] owards Safeguarding LLM Fine-tuning APIs against Cipher Attacks
链接: https://arxiv.org/abs/2508.17158
作者: Jack Youstra,Mohammed Mahfoud,Yang Yan,Henry Sleight,Ethan Perez,Mrinank Sharma
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large language model fine-tuning APIs enable widespread model customization, yet pose significant safety risks. Recent work shows that adversaries can exploit access to these APIs to bypass model safety mechanisms by encoding harmful content in seemingly harmless fine-tuning data, evading both human monitoring and standard content filters. We formalize the fine-tuning API defense problem, and introduce the Cipher Fine-tuning Robustness benchmark (CIFR), a benchmark for evaluating defense strategies’ ability to retain model safety in the face of cipher-enabled attackers while achieving the desired level of fine-tuning functionality. We include diverse cipher encodings and families, with some kept exclusively in the test set to evaluate for generalization across unseen ciphers and cipher families. We then evaluate different defenses on the benchmark and train probe monitors on model internal activations from multiple fine-tunes. We show that probe monitors achieve over 99% detection accuracy, generalize to unseen cipher variants and families, and compare favorably to state-of-the-art monitoring approaches. We open-source CIFR and the code to reproduce our experiments to facilitate further research in this critical area. Code and data are available online this https URL
[LG-69] Stochastic Gradient Descent with Strategic Querying
链接: https://arxiv.org/abs/2508.17144
作者: Nanfei Jiang,Hoi-To Wai,Mahnoosh Alizadeh
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 18 pages, 2 figures. Accepted to IEEE Conference on Decision and Control (CDC) 2025. Includes appendix and supplementary discussion
Abstract:This paper considers a finite-sum optimization problem under first-order queries and investigates the benefits of strategic querying on stochastic gradient-based methods compared to uniform querying strategy. We first introduce Oracle Gradient Querying (OGQ), an idealized algorithm that selects one user’s gradient yielding the largest possible expected improvement (EI) at each step. However, OGQ assumes oracle access to the gradients of all users to make such a selection, which is impractical in real-world scenarios. To address this limitation, we propose Strategic Gradient Querying (SGQ), a practical algorithm that has better transient-state performance than SGD while making only one query per iteration. For smooth objective functions satisfying the Polyak-Lojasiewicz condition, we show that under the assumption of EI heterogeneity, OGQ enhances transient-state performance and reduces steady-state variance, while SGQ improves transient-state performance over SGD. Our numerical experiments validate our theoretical findings.
[LG-70] Frequency Response Identification of Low-Order Systems: Finite-Sample Analysis
链接: https://arxiv.org/abs/2508.17142
作者: Arya Honarpisheh,Mario Sznaier
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 15 pages, Submitted to IEEE Transactions on Automatic Control
Abstract:This paper proposes a frequency-domain system identification method for learning low-order systems. The identification problem is formulated as the minimization of the l2 norm between the identified and measured frequency responses, with the nuclear norm of the Loewner matrix serving as a regularization term. This formulation results in an optimization problem that can be efficiently solved using standard convex optimization techniques. We derive an upper bound on the sampled-frequency complexity of the identification process and subsequently extend this bound to characterize the identification error over all frequencies. A detailed analysis of the sample complexity is provided, along with a thorough interpretation of its terms and dependencies. Finally, the efficacy of the proposed method is demonstrated through an example, along with numerical simulations validating the growth rate of the sample complexity bound.
[LG-71] MoE-Beyond: Learning-Based Expert Activation Prediction on Edge Devices
链接: https://arxiv.org/abs/2508.17137
作者: Nishant Gavhane,Arush Mehrotra,Rohit Chawla,Peter Proenca
类目: Machine Learning (cs.LG)
*备注:
Abstract:The deployment of large-scale Mixture-of-Experts (MoE) models on edge devices presents significant challenges due to memory constraints. While MoE architectures enable efficient utilization of computational resources by activating only a subset of experts per inference, they require careful memory management to operate efficiently in resource-constrained environments. Traditional heuristic-based expert caching strategies such as MoE-Infinity struggle to maintain high cache hit rates as models parameters scale. In this work, we introduce MoE-Beyond, a learning-based expert activation predictor trained to predict expert activations during autoregressive decoding. By framing the task as a multi-label sequence prediction problem, we train a lightweight transformer model on 66 million expert activation traces extracted from LDJnr-Puffin dataset [5] using DeepSeek-V2-Chat-Lite MoE. Our predictor generalizes effectively across unseen prompts from WebGLM-QA dataset [6], achieving 97.5% accuracy and an 86.6% F1-score. Simulation results show that MoE-Beyond improves GPU cache hit rate from 17% to 72% when only 10% of experts fit in GPU cache, outperforming heuristic baselines.
[LG-72] Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning
链接: https://arxiv.org/abs/2508.17129
作者: Diksha Gupta,Nirupam Gupta,Chuan Xu,Giovanni Neglia
类目: Machine Learning (cs.LG)
*备注: 78 Pages, 1 figure
Abstract:Distributed learning (DL) enables scalable model training over decentralized data, but remains challenged by Byzantine faults and high communication costs. While both issues have been studied extensively in isolation, their interaction is less explored. Prior work shows that naively combining communication compression with Byzantine-robust aggregation degrades resilience to faulty nodes (or workers). The state-of-the-art algorithm, namely Byz-DASHA-PAGE [29], makes use of the momentum variance reduction scheme to mitigate the detrimental impact of compression noise on Byzantine-robustness. We propose a new algorithm, named RoSDHB, that integrates the classic Polyak’s momentum with a new coordinated compression mechanism. We show that RoSDHB performs comparably to Byz-DASHA-PAGE under the standard (G, B)-gradient dissimilarity heterogeneity model, while it relies on fewer assumptions. In particular, we only assume Lipschitz smoothness of the average loss function of the honest workers, in contrast to [29]that additionally assumes a special smoothness of bounded global Hessian variance. Empirical results on benchmark image classification task show that RoSDHB achieves strong robustness with significant communication savings.
[LG-73] Learning ON Large Datasets Using Bit-String Trees
链接: https://arxiv.org/abs/2508.17083
作者: Prashant Gupta
类目: Machine Learning (cs.LG)
*备注: PhD thesis
Abstract:This thesis develops computational methods in similarity-preserving hashing, classification, and cancer genomics. Standard space partitioning-based hashing relies on Binary Search Trees (BSTs), but their exponential growth and sparsity hinder efficiency. To overcome this, we introduce Compressed BST of Inverted hash tables (ComBI), which enables fast approximate nearest-neighbor search with reduced memory. On datasets of up to one billion samples, ComBI achieves 0.90 precision with 4X-296X speed-ups over Multi-Index Hashing, and also outperforms this http URL on single-cell RNA-seq searches with 2X-13X gains. Building on hashing structures, we propose Guided Random Forest (GRAF), a tree-based ensemble classifier that integrates global and local partitioning, bridging decision trees and boosting while reducing generalization error. Across 115 datasets, GRAF delivers competitive or superior accuracy, and its unsupervised variant (uGRAF) supports guided hashing and importance sampling. We show that GRAF and ComBI can be used to estimate per-sample classifiability, which enables scalable prediction of cancer patient survival. To address challenges in interpreting mutations, we introduce Continuous Representation of Codon Switches (CRCS), a deep learning framework that embeds genetic changes into numerical vectors. CRCS allows identification of somatic mutations without matched normals, discovery of driver genes, and scoring of tumor mutations, with survival prediction validated in bladder, liver, and brain cancers. Together, these methods provide efficient, scalable, and interpretable tools for large-scale data analysis and biomedical applications.
[LG-74] Learned Structure in CARTRIDGES: Keys as Shareable Routers in Self-Studied Representations
链接: https://arxiv.org/abs/2508.17032
作者: Maurizio Diaz
类目: Machine Learning (cs.LG)
*备注:
Abstract:A bottleneck for long-context LLM inference is the linearly growing KV cache. Recent work has proposed CARTRIDGES, an approach which leverages offline compute to train a much smaller KV cache than is typically required for a full document (up to 40x less memory usage at inference time). In this paper, we present the first mechanistic exploration of the learned CARTRIDGE key-value cache structure. In particular, we propose that (1) CARTRIDGE keys act as stable, shareable retrieval routers for the compressed corpora and (2) most of the learned compression occurs within the CARTRIDGE value vectors. We present empirical evidence of our routing theory across tasks, model families, and model sizes; for example, we can ablate the learned CARTRIDGE key vectors between tasks with little performance loss. Finally, we propose a slight improvement in initialization called Sampled Chunk Initialization (SCI). We suggest that SCI can lead to faster CARTRIDGE convergence than previously demonstrated in the literature. Our findings lay the groundwork for broader empirical study of CARTRIDGE training optimization which may be crucial for further scaling.
[LG-75] Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints
链接: https://arxiv.org/abs/2508.16992
作者: Dhruv Sarkar,Samrat Mukhopadhyay,Abhishek Sinha
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We study an online learning problem with long-term budget constraints in the adversarial setting. In this problem, at each round t , the learner selects an action from a convex decision set, after which the adversary reveals a cost function f_t and a resource consumption function g_t . The cost and consumption functions are assumed to be \alpha -approximately convex - a broad class that generalizes convexity and encompasses many common non-convex optimization problems, including DR-submodular maximization, Online Vertex Cover, and Regularized Phase Retrieval. The goal is to design an online algorithm that minimizes cumulative cost over a horizon of length T while approximately satisfying a long-term budget constraint of B_T . We propose an efficient first-order online algorithm that guarantees O(\sqrtT) \alpha -regret against the optimal fixed feasible benchmark while consuming at most O(B_T \log T)+ \tildeO(\sqrtT) resources in both full-information and bandit feedback settings. In the bandit feedback setting, our approach yields an efficient solution for the \textttAdversarial Bandits with Knapsacks problem with improved guarantees. We also prove matching lower bounds, demonstrating the tightness of our results. Finally, we characterize the class of \alpha -approximately convex functions and show that our results apply to a broad family of problems.
[LG-76] Unveiling the Latent Directions of Reflection in Large Language Models
链接: https://arxiv.org/abs/2508.16989
作者: Fu-Chieh Chang,Yu-Ting Lee,Pei-Yuan Wu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Reflection, the ability of large language models (LLMs) to evaluate and revise their own reasoning, has been widely used to improve performance on complex reasoning tasks. Yet, most prior work emphasizes designing reflective prompting strategies or reinforcement learning objectives, leaving the inner mechanisms of reflection underexplored. In this paper, we investigate reflection through the lens of latent directions in model activations. We propose a methodology based on activation steering to characterize how instructions with different reflective intentions: no reflection, intrinsic reflection, and triggered reflection. By constructing steering vectors between these reflection levels, we demonstrate that (1) new reflection-inducing instructions can be systematically identified, (2) reflective behavior can be directly enhanced or suppressed through activation interventions, and (3) suppressing reflection is considerably easier than stimulating it. Experiments on GSM8k-adv with Qwen2.5-3B and Gemma3-4B reveal clear stratification across reflection levels, and steering interventions confirm the controllability of reflection. Our findings highlight both opportunities (e.g., reflection-enhancing defenses) and risks (e.g., adversarial inhibition of reflection in jailbreak attacks). This work opens a path toward mechanistic understanding of reflective reasoning in LLMs.
[LG-77] Sig-DEG for Distillation: Making Diffusion Models Faster and Lighter
链接: https://arxiv.org/abs/2508.16939
作者: Lei Jiang,Wen Ge,Niels Cariou-Kotlarek,Mingxuan Yi,Po-Yu Chen,Lingyi Yang,Francois Buet-Golfouse,Gaurav Mittal,Hao Ni
类目: Machine Learning (cs.LG); Probability (math.PR); Machine Learning (stat.ML)
*备注:
Abstract:Diffusion models have achieved state-of-the-art results in generative modelling but remain computationally intensive at inference time, often requiring thousands of discretization steps. To this end, we propose Sig-DEG (Signature-based Differential Equation Generator), a novel generator for distilling pre-trained diffusion models, which can universally approximate the backward diffusion process at a coarse temporal resolution. Inspired by high-order approximations of stochastic differential equations (SDEs), Sig-DEG leverages partial signatures to efficiently summarize Brownian motion over sub-intervals and adopts a recurrent structure to enable accurate global approximation of the SDE solution. Distillation is formulated as a supervised learning task, where Sig-DEG is trained to match the outputs of a fine-resolution diffusion model on a coarse time grid. During inference, Sig-DEG enables fast generation, as the partial signature terms can be simulated exactly without requiring fine-grained Brownian paths. Experiments demonstrate that Sig-DEG achieves competitive generation quality while reducing the number of inference steps by an order of magnitude. Our results highlight the effectiveness of signature-based approximations for efficient generative modeling.
[LG-78] Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detection
链接: https://arxiv.org/abs/2508.16915
作者: Sadman Mohammad Nasif,Md Abrar Jahin,M. F. Mridha
类目: Machine Learning (cs.LG)
*备注:
Abstract:The growing adoption of home banking systems has heightened the risk of cyberfraud, necessitating fraud detection mechanisms that are not only accurate but also fair and explainable. While AI models have shown promise in this domain, they face key limitations, including computational inefficiency, the interpretability challenges of spiking neural networks (SNNs), and the complexity and convergence instability of hyper-heuristic reinforcement learning (RL)-based hyperparameter optimization. To address these issues, we propose a novel framework that integrates a Cortical Spiking Network with Population Coding (CSNPC) and a Reinforcement-Guided Hyper-Heuristic Optimizer for Spiking Systems (RHOSS). The CSNPC, a biologically inspired SNN, employs population coding for robust classification, while RHOSS uses Q-learning to dynamically select low-level heuristics for hyperparameter optimization under fairness and recall constraints. Embedded within the Modular Supervisory Framework for Spiking Network Training and Interpretation (MoSSTI), the system incorporates explainable AI (XAI) techniques, specifically, saliency-based attribution and spike activity profiling, to increase transparency. Evaluated on the Bank Account Fraud (BAF) dataset suite, our model achieves a 90.8% recall at a strict 5% false positive rate (FPR), outperforming state-of-the-art spiking and non-spiking models while maintaining over 98% predictive equality across key demographic attributes. The explainability module further confirms that saliency attributions align with spiking dynamics, validating interpretability. These results demonstrate the potential of combining population-coded SNNs with reinforcement-guided hyper-heuristics for fair, transparent, and high-performance fraud detection in real-world financial applications.
[LG-79] Quantifying Out-of-Training Uncertainty of Neural-Network based Turbulence Closures
链接: https://arxiv.org/abs/2508.16891
作者: Cody Grogan,Som Dhulipala,Mauricio Tano,Izabela Gutowska,Som Dutta
类目: Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Neural-Network (NN) based turbulence closures have been developed for being used as pre-trained surrogates for traditional turbulence closures, with the aim to increase computational efficiency and prediction accuracy of CFD simulations. The bottleneck to the widespread adaptation of these ML-based closures is the relative lack of uncertainty quantification (UQ) for these models. Especially, quantifying uncertainties associated with out-of-training inputs, that is when the ML-based turbulence closures are queried on inputs outside their training data regime. In the current paper, a published algebraic turbulence closure1 has been utilized to compare the quality of epistemic UQ between three NN-based methods and Gaussian Process (GP). The three NN-based methods explored are Deep Ensembles (DE), Monte-Carlo Dropout (MCD), and Stochastic Variational Inference (SVI). In the in-training results, we find the exact GP performs the best in accuracy with a Root Mean Squared Error (RMSE) of 2.14 \cdot 10^-5 followed by the DE with an RMSE of 4.59 \cdot 10^-4 . Next, the paper discusses the performance of the four methods for quantifying out-of-training uncertainties. For performance, the Exact GP yet again is the best in performance, but has similar performance to the DE in the out-of-training regions. In UQ accuracy for the out-of-training case, SVI and DE hold the best miscalibration error for one of the cases. However, the DE performs the best in Negative Log-Likelihood for both out-of-training cases. We observe that for the current problem, in terms of accuracy GP DE SV I MCD. The DE results are relatively robust and provide intuitive UQ estimates, despite performing naive ensembling. In terms of computational cost, the GP is significantly higher than the NN-based methods with a O(n^3) computational complexity for each training step
[LG-80] Neural Contrast Expansion for Explainable Structure-Property Prediction and Random Microstructure Design
链接: https://arxiv.org/abs/2508.16857
作者: Guangyu Nie,Yang Jiao,Yi Ren
类目: Machine Learning (cs.LG)
*备注:
Abstract:Effective properties of composite materials are defined as the ensemble average of property-specific PDE solutions over the underlying microstructure distributions. Traditionally, predicting such properties can be done by solving PDEs derived from microstructure samples or building data-driven models that directly map microstructure samples to properties. The former has a higher running cost, but provides explainable sensitivity information that may guide material design; the latter could be more cost-effective if the data overhead is amortized, but its learned sensitivities are often less explainable. With a focus on properties governed by linear self-adjoint PDEs (e.g., Laplace, Helmholtz, and Maxwell curl-curl) defined on bi-phase microstructures, we propose a structure-property model that is both cost-effective and explainable. Our method is built on top of the strong contrast expansion (SCE) formalism, which analytically maps N -point correlations of an unbounded random field to its effective properties. Since real-world material samples have finite sizes and analytical PDE kernels are not always available, we propose Neural Contrast Expansion (NCE), an SCE-inspired architecture to learn surrogate PDE kernels from structure-property data. For static conduction and electromagnetic wave propagation cases, we show that NCE models reveal accurate and insightful sensitivity information useful for material design. Compared with other PDE kernel learning methods, our method does not require measurements about the PDE solution fields, but rather only requires macroscopic property measurements that are more accessible in material development contexts.
[LG-81] Uncertainty Propagation Networks for Neural Ordinary Differential Equations
链接: https://arxiv.org/abs/2508.16815
作者: Hadi Jahanshahi,Zheng H. Zhu
类目: Machine Learning (cs.LG)
*备注:
Abstract:This paper introduces Uncertainty Propagation Network (UPN), a novel family of neural differential equations that naturally incorporate uncertainty quantification into continuous-time modeling. Unlike existing neural ODEs that predict only state trajectories, UPN simultaneously model both state evolution and its associated uncertainty by parameterizing coupled differential equations for mean and covariance dynamics. The architecture efficiently propagates uncertainty through nonlinear dynamics without discretization artifacts by solving coupled ODEs for state and covariance evolution while enabling state-dependent, learnable process noise. The continuous-depth formulation adapts its evaluation strategy to each input’s complexity, provides principled uncertainty quantification, and handles irregularly-sampled observations naturally. Experimental results demonstrate UPN’s effectiveness across multiple domains: continuous normalizing flows (CNFs) with uncertainty quantification, time-series forecasting with well-calibrated confidence intervals, and robust trajectory prediction in both stable and chaotic dynamical systems.
[LG-82] Anchor-MoE: A Mean-Anchored Mixture of Experts For Probabilistic Regression
链接: https://arxiv.org/abs/2508.16802
作者: Baozhuo Su,Zhengxian Qu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Regression under uncertainty is fundamental across science and engineering. We present an Anchored Mixture of Experts (Anchor-MoE), a model that handles both probabilistic and point regression. For simplicity, we use a tuned gradient-boosting model to furnish the anchor mean; however, any off-the-shelf point regressor can serve as the anchor. The anchor prediction is projected into a latent space, where a learnable metric-window kernel scores locality and a soft router dispatches each sample to a small set of mixture-density-network experts; the experts produce a heteroscedastic correction and predictive variance. We train by minimizing negative log-likelihood, and on a disjoint calibration split fit a post-hoc linear map on predicted means to improve point accuracy. On the theory side, assuming a Hölder smooth regression function of order~ \alpha and fixed Lipschitz partition-of-unity weights with bounded overlap, we show that Anchor-MoE attains the minimax-optimal L^2 risk rate O!\big(N^-2\alpha/(2\alpha+d)\big) . In addition, the CRPS test generalization gap scales as \widetildeO!\Big(\sqrt(\log(Mh)+P+K)/N\Big) ; it is logarithmic in Mh and scales as the square root in P and K . Under bounded-overlap routing, K can be replaced by k , and any dependence on a latent dimension is absorbed into P . Under uniformly bounded means and variances, an analogous \widetildeO!\big(\sqrt(\log(Mh)+P+K)/N\big) scaling holds for the test NLL up to constants. Empirically, across standard UCI regressions, Anchor-MoE consistently matches or surpasses the strong NGBoost baseline in RMSE and NLL; on several datasets it achieves new state-of-the-art probabilistic regression results on our benchmark suite. Code is available at this https URL.
[LG-83] Bootstrapping Conditional Retrieval for User-to-Item Recommendations
链接: https://arxiv.org/abs/2508.16793
作者: Hongtao Lin,Haoyu Chen,Jaewon Jang,Jiajing Xu
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
*备注:
Abstract:User-to-item retrieval has been an active research area in recommendation system, and two tower models are widely adopted due to model simplicity and serving efficiency. In this work, we focus on a variant called \textitconditional retrieval, where we expect retrieved items to be relevant to a condition (e.g. topic). We propose a method that uses the same training data as standard two tower models but incorporates item-side information as conditions in query. This allows us to bootstrap new conditional retrieval use cases and encourages feature interactions between user and condition. Experiments show that our method can retrieve highly relevant items and outperforms standard two tower models with filters on engagement metrics. The proposed model is deployed to power a topic-based notification feed at Pinterest and led to +0.26% weekly active users.
[LG-84] aDiCodec: Text-aware Diffusion Speech Tokenizer for Speech Language Modeling
链接: https://arxiv.org/abs/2508.16790
作者: Yuancheng Wang,Dekun Chen,Xueyao Zhang,Junan Zhang,Jiaqi Li,Zhizheng Wu
类目: ound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
*备注:
Abstract:Speech tokenizers serve as foundational components for speech language models, yet current designs exhibit several limitations, including: 1) dependence on multi-layer residual vector quantization structures or high frame rates, 2) reliance on auxiliary pre-trained models for semantic distillation, and 3) requirements for complex two-stage training processes. In this work, we introduce the Text-aware Diffusion Transformer Speech Codec (TaDiCodec), a novel approach designed to overcome these challenges. TaDiCodec employs end-to-end optimization for quantization and reconstruction through a diffusion autoencoder, while integrating text guidance into the diffusion decoder to enhance reconstruction quality and achieve optimal compression. TaDiCodec achieves an extremely low frame rate of 6.25 Hz and a corresponding bitrate of 0.0875 kbps with a single-layer codebook for 24 kHz speech, while maintaining superior performance on critical speech generation evaluation metrics such as Word Error Rate (WER), speaker similarity (SIM), and speech quality (UTMOS). Notably, TaDiCodec employs a single-stage, end-to-end training paradigm, and obviating the need for auxiliary pre-trained models. We also validate the compatibility of TaDiCodec in language model based zero-shot text-to-speech with both autoregressive modeling and masked generative modeling, demonstrating its effectiveness and efficiency for speech language modeling, as well as a significantly small reconstruction-generation gap. We will open source our code and model checkpoints. Audio samples are are available at https:/tadicodec.github.io/. We release code and model checkpoints at https:/github.com/HeCheng0625/Diffusion-Speech-Tokenizer.
[LG-85] Latent Graph Learning in Generative Models of Neural Signals
链接: https://arxiv.org/abs/2508.16776
作者: Nathan X. Kodama,Kenneth A. Loparo
类目: Machine Learning (cs.LG)
*备注:
Abstract:Inferring temporal interaction graphs and higher-order structure from neural signals is a key problem in building generative models for systems neuroscience. Foundation models for large-scale neural data represent shared latent structures of neural signals. However, extracting interpretable latent graph representations in foundation models remains challenging and unsolved. Here we explore latent graph learning in generative models of neural signals. By testing against numerical simulations of neural circuits with known ground-truth connectivity, we evaluate several hypotheses for explaining learned model weights. We discover modest alignment between extracted network representations and the underlying directed graphs and strong alignment in the co-input graph representations. These findings motivate paths towards incorporating graph-based geometric constraints in the construction of large-scale foundation models for neural data.
[LG-86] DR-CircuitGNN: Training Acceleration of Heterogeneous Circuit Graph Neural Network on GPUs
链接: https://arxiv.org/abs/2508.16769
作者: Yuebo Luo,Shiyang Li,Junran Tao,Kiran Thorat,Xi Xie,Hongwu Peng,Nuo Xu,Caiwen Ding,Shaoyi Huang
类目: Machine Learning (cs.LG)
*备注:
Abstract:The increasing scale and complexity of integrated circuit design have led to increased challenges in Electronic Design Automation (EDA). Graph Neural Networks (GNNs) have emerged as a promising approach to assist EDA design as circuits can be naturally represented as graphs. While GNNs offer a foundation for circuit analysis, they often fail to capture the full complexity of EDA designs. Heterogeneous Graph Neural Networks (HGNNs) can better interpret EDA circuit graphs as they capture both topological relationships and geometric features. However, the improved representation capability comes at the cost of even higher computational complexity and processing cost due to their serial module-wise message-passing scheme, creating a significant performance bottleneck. In this paper, we propose DR-CircuitGNN, a fast GPU kernel design by leveraging row-wise sparsity-aware Dynamic-ReLU and optimizing SpMM kernels during heterogeneous message-passing to accelerate HGNNs training on EDA-related circuit graph datasets. To further enhance performance, we propose a parallel optimization strategy that maximizes CPU-GPU concurrency by concurrently processing independent subgraphs using multi-threaded CPU initialization and GPU kernel execution via multiple cudaStreams. Our experiments show that on three representative CircuitNet designs (small, medium, large), the proposed method can achieve up to 3.51x and 4.09x speedup compared to the SOTA for forward and backward propagation, respectively. On full-size CircuitNet and sampled Mini-CircuitNet, our parallel design enables up to 2.71x speed up over the official DGL implementation cuSPARSE with negligible impact on correlation scores and error rates.
[LG-87] Walk-on-Interfaces: A Monte Carlo Estimator for an Elliptic Interface Problem with Nonhomogeneous Flux Jump Conditions and a Neumann Boundary Condition
链接: https://arxiv.org/abs/2508.16767
作者: Xinwen Ding,Adam R Stinchcombe
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注: 49 pages, 14 figures
Abstract:Elliptic interface problems arise in numerous scientific and engineering applications, modeling heterogeneous materials in which physical properties change discontinuously across interfaces. In this paper, we present \textitWalk-on-Interfaces (WoI), a grid-free Monte Carlo estimator for a class of Neumann elliptic interface problems with nonhomogeneous flux jump conditions. Our Monte Carlo estimators maintain consistent accuracy throughout the domain and, thus, do not suffer from the well-known close-to-source evaluation issue near the interfaces. We also presented a simple modification with reduced variance. Estimation of the gradient of the solution can be performed, with almost no additional cost, by simply computing the gradient of the Green’s function in WoI. Taking a scientific machine learning approach, we use our estimators to provide training data for a deep neural network that outputs a continuous representation of the solution. This regularizes our solution estimates by removing the high-frequency Monte Carlo error. All of our estimators are highly parallelizable, have a \mathcalO(1 / \sqrt\mathcalW) convergence rate in the number of samples, and generalize naturally to higher dimensions. We solve problems with many interfaces that have irregular geometry and in up to dimension six. Numerical experiments demonstrate the effectiveness of the approach and to highlight its potential in solving problems motivated by real-world applications.
[LG-88] Deep Learning for Markov Chains: Lyapunov Functions Poissons Equation and Stationary Distributions
链接: https://arxiv.org/abs/2508.16737
作者: Yanlin Qu,Jose Blanchet,Peter Glynn
类目: Machine Learning (cs.LG); Probability (math.PR)
*备注:
Abstract:Lyapunov functions are fundamental to establishing the stability of Markovian models, yet their construction typically demands substantial creativity and analytical effort. In this paper, we show that deep learning can automate this process by training neural networks to satisfy integral equations derived from first-transition analysis. Beyond stability analysis, our approach can be adapted to solve Poisson’s equation and estimate stationary distributions. While neural networks are inherently function approximators on compact domains, it turns out that our approach remains effective when applied to Markov chains on non-compact state spaces. We demonstrate the effectiveness of this methodology through several examples from queueing theory and beyond.
[LG-89] Aligning Distributionally Robust Optimization with Practical Deep Learning Needs
链接: https://arxiv.org/abs/2508.16734
作者: Dmitrii Feoktistov,Igor Ignashin,Andrey Veprikov,Nikita Borovko,Alexander Bogdanov,Savelii Chezhegov,Aleksandr Beznosikov
类目: Machine Learning (cs.LG)
*备注: 13 pages, 1 table, 4 figures
Abstract:While traditional Deep Learning (DL) optimization methods treat all training samples equally, Distributionally Robust Optimization (DRO) adaptively assigns importance weights to different samples. However, a significant gap exists between DRO and current DL practices. Modern DL optimizers require adaptivity and the ability to handle stochastic gradients, as these methods demonstrate superior performance. Additionally, for practical applications, a method should allow weight assignment not only to individual samples, but also to groups of objects (for example, all samples of the same class). This paper aims to bridge this gap by introducing ALSO \unicodex2013 Adaptive Loss Scaling Optimizer \unicodex2013 an adaptive algorithm for a modified DRO objective that can handle weight assignment to sample groups. We prove the convergence of our proposed algorithm for non-convex objectives, which is the typical case for DL models. Empirical evaluation across diverse Deep Learning tasks, from Tabular DL to Split Learning tasks, demonstrates that ALSO outperforms both traditional optimizers and existing DRO methods.
[LG-90] A novel auxiliary equation neural networks method for exactly explicit solutions of nonlinear partial differential equations
链接: https://arxiv.org/abs/2508.16702
作者: Shanhao Yuan,Yanqin Liu,Runfa Zhang,Limei Yan,Shunjun Wu,Libo Feng
类目: Machine Learning (cs.LG)
*备注:
Abstract:In this study, we firstly propose an auxiliary equation neural networks method (AENNM), an innovative analytical method that integrates neural networks (NNs) models with the auxiliary equation method to obtain exact solutions of nonlinear partial differential equations (NLPDEs). A key novelty of this method is the introduction of a novel activation function derived from the solutions of the Riccati equation, establishing a new mathematical link between differential equations theory and deep learning. By combining the strong approximation capability of NNs with the high precision of symbolic computation, AENNM significantly enhances computational efficiency and accuracy. To demonstrate the effectiveness of the AENNM in solving NLPDEs, three numerical examples are investigated, including the nonlinear evolution equation, the Korteweg-de Vries-Burgers equation, and the (2+1)-dimensional Boussinesq equation. Furthermore, some new trial functions are constructed by setting specific activation functions within the “2-2-2-1” and “3-2-2-1” NNs models. By embedding the auxiliary equation method into the NNs framework, we derive previously unreported solutions. The exact analytical solutions are expressed in terms of hyperbolic functions, trigonometric functions, and rational functions. Finally, three-dimensional plots, contour plots, and density plots are presented to illustrate the dynamic characteristics of the obtained solutions. This research provides a novel methodological framework for addressing NLPDEs, with broad applicability across scientific and engineering fields.
[LG-91] Native Logical and Hierarchical Representations with Subspace Embeddings
链接: https://arxiv.org/abs/2508.16687
作者: Gabriel Moreira,Zita Marinho,Manuel Marques,João Paulo Costeira,Chenyan Xiong
类目: Machine Learning (cs.LG)
*备注:
Abstract:Traditional neural embeddings represent concepts as points, excelling at similarity but struggling with higher-level reasoning and asymmetric relationships. We introduce a novel paradigm: embedding concepts as linear subspaces. This framework inherently models generality via subspace dimensionality and hierarchy through subspace inclusion. It naturally supports set-theoretic operations like intersection (conjunction), linear sum (disjunction) and orthogonal complements (negations), aligning with classical formal semantics. To enable differentiable learning, we propose a smooth relaxation of orthogonal projection operators, allowing for the learning of both subspace orientation and dimension. Our method achieves state-of-the-art results in reconstruction and link prediction on WordNet. Furthermore, on natural language inference benchmarks, our subspace embeddings surpass bi-encoder baselines, offering an interpretable formulation of entailment that is both geometrically grounded and amenable to logical operations.
[LG-92] Multidimensional Distributional Neural Network Output Demonstrated in Super-Resolution of Surface Wind Speed
链接: https://arxiv.org/abs/2508.16686
作者: Harrison J. Goldwyn,Mitchell Krock,Johann Rudi,Daniel Getter,Julie Bessac
类目: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
*备注:
Abstract:Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating closed-form predictive distributions over outputs with non-identically distributed and heteroscedastic structure. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy – referred to as information sharing – that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.
[LG-93] OASIS: Open-world Adaptive Self-supervised and Imbalanced-aware System CIKM2025
链接: https://arxiv.org/abs/2508.16656
作者: Miru Kim,Mugon Joe,Minhae Kwon
类目: Machine Learning (cs.LG)
*备注: Accepted at the 34th ACM International Conference on Information and Knowledge Management (CIKM 2025)
Abstract:The expansion of machine learning into dynamic environments presents challenges in handling open-world problems where label shift, covariate shift, and unknown classes emerge. Post-training methods have been explored to address these challenges, adapting models to newly emerging data. However, these methods struggle when the initial pre-training is performed on class-imbalanced datasets, limiting generalization to minority classes. To address this, we propose a method that effectively handles open-world problems even when pre-training is conducted on imbalanced data. Our contrastive-based pre-training approach enhances classification performance, particularly for underrepresented classes. Our post-training mechanism generates reliable pseudo-labels, improving model robustness against open-world problems. We also introduce selective activation criteria to optimize the post-training process, reducing unnecessary computation. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art adaptation techniques in both accuracy and efficiency across diverse open-world scenarios.
[LG-94] AdapSNE: Adaptive Fireworks-Optimized and Entropy-Guided Dataset Sampling for Edge DNN Training
链接: https://arxiv.org/abs/2508.16647
作者: Boran Zhao,Hetian Liu,Zihang Yuan,Li Zhu,Fan Yang,Lina Xie Tian Xia,Wenzhe Zhao,Pengju Ren
类目: Machine Learning (cs.LG)
*备注:
Abstract:Training deep neural networks (DNNs) directly on edge devices has attracted increasing attention, as it offers promising solutions to challenges such as domain adaptation and privacy preservation. However, conventional DNN training typically requires large-scale datasets, which imposes prohibitive overhead on edge devices-particularly for emerging large language model (LLM) tasks. To address this challenge, a DNN-free method (ie., dataset sampling without DNN), named NMS (Near-Memory Sampling), has been introduced. By first conducting dimensionality reduction of the dataset and then performing exemplar sampling in the reduced space, NMS avoids the architectural bias inherent in DNN-based methods and thus achieves better generalization. However, The state-of-the-art, NMS, suffers from two limitations: (1) The mismatch between the search method and the non-monotonic property of the perplexity error function leads to the emergence of outliers in the reduced representation; (2) Key parameter (ie., target perplexity) is selected empirically, introducing arbitrariness and leading to uneven sampling. These two issues lead to representative bias of examplars, resulting in degraded accuracy. To address these issues, we propose AdapSNE, which integrates an efficient non-monotonic search method-namely, the Fireworks Algorithm (FWA)-to suppress outliers, and employs entropy-guided optimization to enforce uniform sampling, thereby ensuring representative training samples and consequently boosting training accuracy. To cut the edge-side cost arising from the iterative computations of FWA search and entropy-guided optimization, we design an accelerator with custom dataflow and time-multiplexing markedly reducing on-device training energy and area.
[LG-95] Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging Boosting and Statistical Ensembles
链接: https://arxiv.org/abs/2508.16641
作者: Dhruv D. Modi,Rong Pan
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Time series foundation models (TSFMs) such as Lag-Llama, TimeGPT, Chronos, MOMENT, UniTS, and TimesFM have shown strong generalization and zero-shot capabilities for time series forecasting, anomaly detection, classification, and imputation. Despite these advantages, their predictions still suffer from variance, domain-specific bias, and limited uncertainty quantification when deployed on real operational data. This paper investigates a suite of statistical and ensemble-based enhancement techniques, including bootstrap-based bagging, regression-based stacking, prediction interval construction, statistical residual modeling, and iterative error feedback, to improve robustness and accuracy. Using the Belgium Electricity Short-Term Load Forecasting dataset as a case study, we demonstrate that the proposed hybrids consistently outperform standalone foundation models across multiple horizons. Regression-based ensembles achieve the lowest mean squared error; bootstrap aggregation markedly reduces long-context errors; residual modeling corrects systematic bias; and the resulting prediction intervals achieve near nominal coverage with widths shrinking as context length increases. The results indicate that integrating statistical reasoning with modern foundation models yields measurable gains in accuracy, reliability, and interpretability for real-world time series applications.
[LG-96] A Novel Unified Extended Matrix for Graph Signal Processing: Theory and Application
链接: https://arxiv.org/abs/2508.16633
作者: Yunyan Zheng,Zhichao Zhang,Wei Yao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Graph signal processing has become an essential tool for analyzing data structured on irregular domains. While conventional graph shift operators (GSOs) are effective for certain tasks, they inherently lack flexibility in modeling dependencies between non-adjacent nodes, limiting their ability to represent complex graph structures. To address this limitation, this paper proposes the unified extended matrix (UEM) framework, which integrates the extended-adjacency matrix and the unified graph representation matrix through parametric design, so as to be able to flexibly adapt to different graph structures and reveal more graph signal information. Theoretical analysis of the UEM is conducted, demonstrating positive semi-definiteness and eigenvalue monotonicity under specific conditions. Then, we propose graph Fourier transform based on UEM (UEM-GFT), which can adaptively tune spectral properties to enhance signal processing performance. Experimental results on synthetic and real-world datasets demonstrate that the UEM-GFT outperforms existing GSO-based methods in anomaly detection tasks, achieving superior performance across varying network topologies.
[LG-97] Recurrent Transformer U-Net Surrogate for Flow Modeling and Data Assimilation in Subsurface Formations with Faults
链接: https://arxiv.org/abs/2508.16631
作者: Yifu Han,Louis J. Durlofsky
类目: Machine Learning (cs.LG)
*备注:
Abstract:Many subsurface formations, including some of those under consideration for large-scale geological carbon storage, include extensive faults that can strongly impact fluid flow. In this study, we develop a new recurrent transformer U-Net surrogate model to provide very fast predictions for pressure and CO2 saturation in realistic faulted subsurface aquifer systems. The geomodel includes a target aquifer (into which supercritical CO2 is injected), surrounding regions, caprock, two extensive faults, and two overlying aquifers. The faults can act as leakage pathways between the three aquifers. The heterogeneous property fields in the target aquifer are characterized by hierarchical uncertainty, meaning both the geological metaparameters (e.g., mean and standard deviation of log-permeability) and the detailed cell properties of each realization, are uncertain. Fault permeabilities are also treated as uncertain. The model is trained with simulation results for (up to) 4000 randomly sampled realizations. Error assessments show that this model is more accurate than a previous recurrent residual U-Net, and that it maintains accuracy for qualitatively different leakage scenarios. The new surrogate is then used for global sensitivity analysis and data assimilation. A hierarchical Markov chain Monte Carlo data assimilation procedure is applied. Different monitoring strategies, corresponding to different amounts and types of observed data collected at monitoring wells, are considered for three synthetic true models. Detailed results demonstrate the degree of uncertainty reduction achieved with the various monitoring strategies. Posterior results for 3D saturation plumes and leakage volumes indicate the benefits of measuring pressure and saturation in all three aquifers.
[LG-98] Leverag ing the Christoffel Function for Outlier Detection in Data Streams
链接: https://arxiv.org/abs/2508.16617
作者: Kévin Ducharlet,Louise Travé-Massuyès,Jean-Bernard Lasserre,Marie-Véronique Le Lann,Youssef Miloudi
类目: Machine Learning (cs.LG)
*备注:
Abstract:Outlier detection holds significant importance in the realm of data mining, particularly with the growing pervasiveness of data acquisition methods. The ability to identify outliers in data streams is essential for maintaining data quality and detecting faults. However, dealing with data streams presents challenges due to the non-stationary nature of distributions and the ever-increasing data volume. While numerous methods have been proposed to tackle this challenge, a common drawback is the lack of straightforward parameterization in many of them. This article introduces two novel methods: DyCF and DyCG. DyCF leverages the Christoffel function from the theory of approximation and orthogonal polynomials. Conversely, DyCG capitalizes on the growth properties of the Christoffel function, eliminating the need for tuning parameters. Both approaches are firmly rooted in a well-defined algebraic framework, meeting crucial demands for data stream processing, with a specific focus on addressing low-dimensional aspects and maintaining data history without memory cost. A comprehensive comparison between DyCF, DyCG, and state-of-the-art methods is presented, using both synthetic and real industrial data streams. The results show that DyCF outperforms fine-tuning methods, offering superior performance in terms of execution time and memory usage. DyCG performs less well, but has the considerable advantage of requiring no tuning at all.
[LG-99] CrystalDiT: A Diffusion Transformer for Crystal Generation
链接: https://arxiv.org/abs/2508.16614
作者: Xiaohan Yi,Guikun Xu,Xi Xiao,Zhong Zhang,Liu Liu,Yatao Bian,Peilin Zhao
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注: 18 pages, 18 figures. Code available at this https URL
Abstract:We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system. Combined with a periodic table-based atomic representation and a balanced training strategy, our approach achieves 9.62% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.38%) and MatterGen (3.42%). Notably, CrystalDiT generates 63.28% unique and novel structures while maintaining comparable stability rates, demonstrating that architectural simplicity can be more effective than complexity for materials discovery. Our results suggest that in data-limited scientific domains, carefully designed simple architectures outperform sophisticated alternatives that are prone to overfitting.
[LG-100] Quantum-Inspired DRL Approach with LSTM and OU Noise for Cut Order Planning Optimization
链接: https://arxiv.org/abs/2508.16611
作者: Yulison Herry Chrisnanto,Julian Evan Chrisnanto
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 14 pages,3 figures, 4 tables
Abstract:Cut order planning (COP) is a critical challenge in the textile industry, directly impacting fabric utilization and production costs. Conventional methods based on static heuristics and catalog-based estimations often struggle to adapt to dynamic production environments, resulting in suboptimal solutions and increased waste. In response, we propose a novel Quantum-Inspired Deep Reinforcement Learning (QI-DRL) framework that integrates Long Short-Term Memory (LSTM) networks with Ornstein-Uhlenbeck noise. This hybrid approach is designed to explicitly address key research questions regarding the benefits of quantum-inspired probabilistic representations, the role of LSTM-based memory in capturing sequential dependencies, and the effectiveness of OU noise in facilitating smooth exploration and faster convergence. Extensive training over 1000 episodes demonstrates robust performance, with an average reward of 0.81 (-+0.03) and a steady decrease in prediction loss to 0.15 (-+0.02). A comparative analysis reveals that the proposed approach achieves fabric cost savings of up to 13% compared to conventional methods. Furthermore, statistical evaluations indicate low variability and stable convergence. Despite the fact that the simulation model makes several simplifying assumptions, these promising results underscore the potential of the scalable and adaptive framework to enhance manufacturing efficiency and pave the way for future innovations in COP optimization.
[LG-101] WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition
链接: https://arxiv.org/abs/2508.16604
作者: Maximilian Burzer,Tobias King,Till Riedel,Michael Beigl,Tobias Röddiger
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
*备注: 6 pages, 7 figures, to appear in Companion of the 2025 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), OpenWearables Workshop (accepted paper)
Abstract:The lack of standardization across Wearable Human Activity Recognition (WHAR) datasets limits reproducibility, comparability, and research efficiency. We introduce WHAR datasets, an open-source library designed to simplify WHAR data handling through a standardized data format and a configuration-driven design, enabling reproducible and computationally efficient workflows with minimal manual intervention. The library currently supports 9 widely-used datasets, integrates with PyTorch and TensorFlow, and is easily extensible to new datasets. To demonstrate its utility, we trained two state-of-the-art models, TinyHar and MLP-HAR, on the included datasets, approximately reproducing published results and validating the library’s effectiveness for experimentation and benchmarking. Additionally, we evaluated preprocessing performance and observed speedups of up to 3.8x using multiprocessing. We hope this library contributes to more efficient, reproducible, and comparable WHAR research.
[LG-102] Increasing Interaction Fidelity: Training Routines for Biomechanical Models in HCI
链接: https://arxiv.org/abs/2508.16581
作者: Michał Patryk Miazga,Patrick Ebel
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
*备注:
Abstract:Biomechanical forward simulation holds great potential for HCI, enabling the generation of human-like movements in interactive tasks. However, training biomechanical models with reinforcement learning is challenging, particularly for precise and dexterous movements like those required for touchscreen interactions on mobile devices. Current approaches are limited in their interaction fidelity, require restricting the underlying biomechanical model to reduce complexity, and do not generalize well. In this work, we propose practical improvements to training routines that reduce training time, increase interaction fidelity beyond existing methods, and enable the use of more complex biomechanical models. Using a touchscreen pointing task, we demonstrate that curriculum learning, action masking, more complex network configurations, and simple adjustments to the simulation environment can significantly improve the agent’s ability to learn accurate touch behavior. Our work provides HCI researchers with practical tips and training routines for developing better biomechanical models of human-like interaction fidelity.
[LG-103] Flexibility-Conditioned Protein Structure Design with Flow Matching ICML2025
链接: https://arxiv.org/abs/2508.18211
作者: Vsevolod Viliuga,Leif Seute,Nicolas Wolf,Simon Wagner,Arne Elofsson,Jan Stühmer,Frauke Gräter
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: ICML 2025
Abstract:Recent advances in geometric deep learning and generative modeling have enabled the design of novel proteins with a wide range of desired properties. However, current state-of-the-art approaches are typically restricted to generating proteins with only static target properties, such as motifs and symmetries. In this work, we take a step towards overcoming this limitation by proposing a framework to condition structure generation on flexibility, which is crucial for key functionalities such as catalysis or molecular recognition. We first introduce BackFlip, an equivariant neural network for predicting per-residue flexibility from an input backbone structure. Relying on BackFlip, we propose FliPS, an SE(3)-equivariant conditional flow matching model that solves the inverse problem, that is, generating backbones that display a target flexibility profile. In our experiments, we show that FliPS is able to generate novel and diverse protein backbones with the desired flexibility, verified by Molecular Dynamics (MD) simulations. FliPS and BackFlip are available at this https URL .
[LG-104] Clinical characteristics complications and outcomes of critically ill patients with Dengue in Brazil 2012-2024: a nationwide multicentre cohort study
链接: https://arxiv.org/abs/2508.18207
作者: Igor Tona Peres,Otavio T. Ranzani,Leonardo S.L. Bastos,Silvio Hamacher,Tom Edinburgh,Esteban Garcia-Gallo,Fernando Augusto Bozza
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
[LG-105] Hybrid Quantum-Classical Learning for Multiclass Image Classification
链接: https://arxiv.org/abs/2508.18161
作者: Shuchismita Anwar,Sowmitra Das,Muhammad Iqbal Hossain,Jishnu Mahmud
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注: 13 pages, 8 figures
Abstract:This study explores the challenge of improving multiclass image classification through quantum machine-learning techniques. It explores how the discarded qubit states of Noisy Intermediate-Scale Quantum (NISQ) quantum convolutional neural networks (QCNNs) can be leveraged alongside a classical classifier to improve classification performance. Current QCNNs discard qubit states after pooling; yet, unlike classical pooling, these qubits often remain entangled with the retained ones, meaning valuable correlated information is lost. We experiment with recycling this information and combining it with the conventional measurements from the retained qubits. Accordingly, we propose a hybrid quantum-classical architecture that couples a modified QCNN with fully connected classical layers. Two shallow fully connected (FC) heads separately process measurements from retained and discarded qubits, whose outputs are ensembled before a final classification layer. Joint optimisation with a classical cross-entropy loss allows both quantum and classical parameters to adapt coherently. The method outperforms comparable lightweight models on MNIST, Fashion-MNIST and OrganAMNIST. These results indicate that reusing discarded qubit information is a promising approach for future hybrid quantum-classical models and may extend to tasks beyond image classification.
[LG-106] Entanglement Detection with Quantum-inspired Kernels and SVMs
链接: https://arxiv.org/abs/2508.17909
作者: Ana Martínez-Sabiote,Michalis Skotiniotis,Jara J. Bermejo-Vega,Daniel Manzano,Carlos Cano
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:
Abstract:This work presents a machine learning approach based on support vector machines (SVMs) for quantum entanglement detection. Particularly, we focus in bipartite systems of dimensions 3x3, 4x4, and 5x5, where the positive partial transpose criterion (PPT) provides only partial characterization. Using SVMs with quantum-inspired kernels we develop a classification scheme that distinguishes between separable states, PPT-detectable entangled states, and entangled states that evade PPT detection. Our method achieves increasing accuracy with system dimension, reaching 80%, 90%, and nearly 100% for 3x3, 4x4, and 5x5 systems, respectively. Our results show that principal component analysis significantly enhances performance for small training sets. The study reveals important practical considerations regarding purity biases in the generation of data for this problem and examines the challenges of implementing these techniques on near-term quantum hardware. Our results establish machine learning as a powerful complement to traditional entanglement detection methods, particularly for higher-dimensional systems where conventional approaches become inadequate. The findings highlight key directions for future research, including hybrid quantum-classical implementations and improved data generation protocols to overcome current limitations.
[LG-107] he Statistical Fairness-Accuracy Frontier
链接: https://arxiv.org/abs/2508.17622
作者: Alireza Fallah,Michael I. Jordan,Annie Ulichney
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Theoretical Economics (econ.TH); Optimization and Control (math.OC)
*备注:
Abstract:Machine learning models must balance accuracy and fairness, but these goals often conflict, particularly when data come from multiple demographic groups. A useful tool for understanding this trade-off is the fairness-accuracy (FA) frontier, which characterizes the set of models that cannot be simultaneously improved in both fairness and accuracy. Prior analyses of the FA frontier provide a full characterization under the assumption of complete knowledge of population distributions – an unrealistic ideal. We study the FA frontier in the finite-sample regime, showing how it deviates from its population counterpart and quantifying the worst-case gap between them. In particular, we derive minimax-optimal estimators that depend on the designer’s knowledge of the covariate distribution. For each estimator, we characterize how finite-sample effects asymmetrically impact each group’s risk, and identify optimal sample allocation strategies. Our results transform the FA frontier from a theoretical construct into a practical tool for policymakers and practitioners who must often design algorithms with limited data.
[LG-108] Boltzina: Efficient and Accurate Virtual Screening via Docking-Guided Binding Prediction with Boltz-2
链接: https://arxiv.org/abs/2508.17555
作者: Kairi Furui,Masahito Ohue
类目: Biomolecules (q-bio.BM); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注:
Abstract:In structure-based drug discovery, virtual screening using conventional molecular docking methods can be performed rapidly but suffers from limitations in prediction accuracy. Recently, Boltz-2 was proposed, achieving extremely high accuracy in binding affinity prediction, but requiring approximately 20 seconds per compound per GPU, making it difficult to apply to large-scale screening of hundreds of thousands to millions of compounds. This study proposes Boltzina, a novel framework that leverages Boltz-2’s high accuracy while significantly improving computational efficiency. Boltzina achieves both accuracy and speed by omitting the rate-limiting structure prediction from Boltz-2’s architecture and directly predicting affinity from AutoDock Vina docking poses. We evaluate on eight assays from the MF-PCBA dataset and show that while Boltzina performs below Boltz-2, it provides significantly higher screening performance compared to AutoDock Vina and GNINA. Additionally, Boltzina achieved up to 11.8 \times faster through reduced recycling iterations and batch processing. Furthermore, we investigated multi-pose selection strategies and two-stage screening combining Boltzina and Boltz-2, presenting optimization methods for accuracy and efficiency according to application requirements. This study represents the first attempt to apply Boltz-2’s high-accuracy predictions to practical-scale screening, offering a pipeline that combines both accuracy and efficiency in computational biology. The Boltzina is available on github; this https URL.
[LG-109] High-Order Langevin Monte Carlo Algorithms
链接: https://arxiv.org/abs/2508.17545
作者: Thanh Dang,Mert Gurbuzbalaban,Mohammad Rafiqul Islam,Nian Yao,Lingjiong Zhu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
*备注: 73 pages, 3 figures, 1 table
Abstract:Langevin algorithms are popular Markov chain Monte Carlo (MCMC) methods for large-scale sampling problems that often arise in data science. We propose Monte Carlo algorithms based on the discretizations of P -th order Langevin dynamics for any P\geq 3 . Our design of P -th order Langevin Monte Carlo (LMC) algorithms is by combining splitting and accurate integration methods. We obtain Wasserstein convergence guarantees for sampling from distributions with log-concave and smooth densities. Specifically, the mixing time of the P -th order LMC algorithm scales as O\left(d^\frac1R/\epsilon^\frac12R\right) for R=4\cdot 1_\ P=3+ (2P-1)\cdot 1_\ P\geq 4\ , which has a better dependence on the dimension d and the accuracy level \epsilon as P grows. Numerical experiments illustrate the efficiency of our proposed algorithms.
[LG-110] Programmable k-local Ising Machines and all-optical Kolmogorov-Arnold Networks on Photonic Platforms
链接: https://arxiv.org/abs/2508.17440
作者: Nikita Stroev,Natalia G. Berloff
类目: Optics (physics.optics); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
*备注: 16 pages, 6 figures
Abstract:We unify k-local Ising optimization and optical KAN function learning on a single photonic platform, establishing a critical convergence point in optical computing that enables interleaved discrete-continuous workflows. We introduce a single spacial light modulator (SLM)-centric primitive that realizes, in one stroke, all-optical k-local Ising interactions and fully optical Kolmogorov-Arnold network (KAN) layers. The central idea is to convert structural nonlinearity of a nominally linear photonic scatterer into a per-window computational resource by adding one relay pass through the same spatial light modulator. A folded 4f relay reimages the first Fourier plane onto the SLM so that each chosen spin clique or ridge channel occupies a disjoint window with its own second-pass phase patch. Propagation remains linear in the optical field, yet the measured intensity in each window becomes a freely programmable polynomial of the clique sum or projection amplitude. This yields native, per-clique k-local couplings without nonlinear media and, in parallel, the many independent univariate nonlinearities required by KAN layers, all with in-situ physical gradients for training using two-frame (forward and adjoint) physical gradients. We outline implementation on spatial photonic Ising machines, injection-locked VCSEL arrays, and the Microsoft analog optical computers. In all cases the hardware change is one extra lens and a fold (or an on-chip 4f loop), enabling a minimal overhead, massively parallel route to high-order optical Ising optimization and trainable, all-optical KAN processing.
[LG-111] On the sample complexity of semi-supervised multi-objective learning
链接: https://arxiv.org/abs/2508.17152
作者: Tobias Wegel,Geelon So,Junhyung Park,Fanny Yang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:In multi-objective learning (MOL), several possibly competing prediction tasks must be solved jointly by a single model. Achieving good trade-offs may require a model class \mathcalG with larger capacity than what is necessary for solving the individual tasks. This, in turn, increases the statistical cost, as reflected in known MOL bounds that depend on the complexity of \mathcalG . We show that this cost is unavoidable for some losses, even in an idealized semi-supervised setting, where the learner has access to the Bayes-optimal solutions for the individual tasks as well as the marginal distributions over the covariates. On the other hand, for objectives defined with Bregman losses, we prove that the complexity of \mathcalG may come into play only in terms of unlabeled data. Concretely, we establish sample complexity upper bounds, showing precisely when and how unlabeled data can significantly alleviate the need for labeled data. These rates are achieved by a simple, semi-supervised algorithm via pseudo-labeling.
[LG-112] Integrative Experiments Identify How Punishment Impacts Welfare in Public Goods Games
链接: https://arxiv.org/abs/2508.17151
作者: Mohammed Alsobay,David G. Rand,Duncan J. Watts,Abdullah Almaatouq
类目: General Economics (econ.GN); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
*备注:
Abstract:Punishment as a mechanism for promoting cooperation has been studied extensively for more than two decades, but its effectiveness remains a matter of dispute. Here, we examine how punishment’s impact varies across cooperative settings through a large-scale integrative experiment. We vary 14 parameters that characterize public goods games, sampling 360 experimental conditions and collecting 147,618 decisions from 7,100 participants. Our results reveal striking heterogeneity in punishment effectiveness: while punishment consistently increases contributions, its impact on payoffs (i.e., efficiency) ranges from dramatically enhancing welfare (up to 43% improvement) to severely undermining it (up to 44% reduction) depending on the cooperative context. To characterize these patterns, we developed models that outperformed human forecasters (laypeople and domain experts) in predicting punishment outcomes in new experiments. Communication emerged as the most predictive feature, followed by contribution framing (opt-out vs. opt-in), contribution type (variable vs. all-or-nothing), game length (number of rounds), peer outcome visibility (whether participants can see others’ earnings), and the availability of a reward mechanism. Interestingly, however, most of these features interact to influence punishment effectiveness rather than operating independently. For example, the extent to which longer games increase the effectiveness of punishment depends on whether groups can communicate. Together, our results refocus the debate over punishment from whether or not it “works” to the specific conditions under which it does and does not work. More broadly, our study demonstrates how integrative experiments can be combined with machine learning to uncover generalizable patterns, potentially involving interactions between multiple features, and help generate novel explanations in complex social phenomena.
[LG-113] Factor Informed Double Deep Learning For Averag e Treatment Effect Estimation
链接: https://arxiv.org/abs/2508.17136
作者: Jianqing Fan,Soham Jana,Sanjeev Kulkarni,Qishuo Yin
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注: 41 pages, 3 figures, 4 tables
Abstract:We investigate the problem of estimating the average treatment effect (ATE) under a very general setup where the covariates can be high-dimensional, highly correlated, and can have sparse nonlinear effects on the propensity and outcome models. We present the use of a Double Deep Learning strategy for estimation, which involves combining recently developed factor-augmented deep learning-based estimators, FAST-NN, for both the response functions and propensity scores to achieve our goal. By using FAST-NN, our method can select variables that contribute to propensity and outcome models in a completely nonparametric and algorithmic manner and adaptively learn low-dimensional function structures through neural networks. Our proposed novel estimator, FIDDLE (Factor Informed Double Deep Learning Estimator), estimates ATE based on the framework of augmented inverse propensity weighting AIPW with the FAST-NN-based response and propensity estimates. FIDDLE consistently estimates ATE even under model misspecification and is flexible to also allow for low-dimensional covariates. Our method achieves semiparametric efficiency under a very flexible family of propensity and outcome models. We present extensive numerical studies on synthetic and real datasets to support our theoretical guarantees and establish the advantages of our methods over other traditional choices, especially when the data dimension is large.
[LG-114] Rao Differential Privacy
链接: https://arxiv.org/abs/2508.17135
作者: Carlos Soto
类目: Machine Learning (stat.ML); Cryptography and Security (cs.CR); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 13 pages
Abstract:Differential privacy (DP) has recently emerged as a definition of privacy to release private estimates. DP calibrates noise to be on the order of an individuals contribution. Due to the this calibration a private estimate obscures any individual while preserving the utility of the estimate. Since the original definition, many alternate definitions have been proposed. These alternates have been proposed for various reasons including improvements on composition results, relaxations, and formalizations. Nevertheless, thus far nearly all definitions of privacy have used a divergence of densities as the basis of the definition. In this paper we take an information geometry perspective towards differential privacy. Specifically, rather than define privacy via a divergence, we define privacy via the Rao distance. We show that our proposed definition of privacy shares the interpretation of previous definitions of privacy while improving on sequential composition.
[LG-115] HV Metric For Time-Domain Full Waveform Inversion
链接: https://arxiv.org/abs/2508.17122
作者: Matej Neumann,Yunan Yang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 30 Pages
Abstract:Full-waveform inversion (FWI) is a powerful technique for reconstructing high-resolution material parameters from seismic or ultrasound data. The conventional least-squares ((L^2)) misfit suffers from pronounced non-convexity that leads to \emphcycle skipping. Optimal-transport misfits, such as the Wasserstein distance, alleviate this issue; however, their use requires artificially converting the wavefields into probability measures, a preprocessing step that can modify critical amplitude and phase information of time-dependent wave data. We propose the \emphHV metric, a transport-based distance that acts naturally on signed signals, as an alternative metric for the (L^2) and Wasserstein objectives in time-domain FWI. After reviewing the metric’s definition and its relationship to optimal transport, we derive closed-form expressions for the Fréchet derivative and Hessian of the map (f \mapsto d_\textHV^2(f,g)), enabling efficient adjoint-state implementations. A spectral analysis of the Hessian shows that, by tuning the hyperparameters ((\kappa,\lambda,\epsilon)), the HV misfit seamlessly interpolates between (L^2), (H^-1), and (H^-2) norms, offering a tunable trade-off between the local point-wise matching and the global transport-based matching. Synthetic experiments on the Marmousi and BP benchmark models demonstrate that the HV metric-based objective function yields faster convergence and superior tolerance to poor initial models compared to both (L^2) and Wasserstein misfits. These results demonstrate the HV metric as a robust, geometry-preserving alternative for large-scale waveform inversion.
[LG-116] Neural Stochastic Differential Equations on Compact State-Spaces ICML2025
链接: https://arxiv.org/abs/2508.17090
作者: Yue-Jane Liu,Malinda Lu,Matthew K. Nock,Yaniv Yacoby
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: Accepted at Methods and Opportunities at Small Scale (MOSS), ICML 2025, Vancouver, Canada
Abstract:Many modern probabilistic models rely on SDEs, but their adoption is hampered by instability, poor inductive bias outside bounded domains, and reliance on restrictive dynamics or training tricks. While recent work constrains SDEs to compact spaces using reflected dynamics, these approaches lack continuous dynamics and efficient high-order solvers, limiting interpretability and applicability. We propose a novel class of neural SDEs on compact polyhedral spaces with continuous dynamics, amenable to higher-order solvers, and with favorable inductive bias.
[LG-117] A Decoupled LOB Representation Framework for Multilevel Manipulation Detection with Supervised Contrastive Learning
链接: https://arxiv.org/abs/2508.17086
作者: Yushi Lin,Peng Yang
类目: Computational Finance (q-fin.CP); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG); Trading and Market Microstructure (q-fin.TR)
*备注:
Abstract:Financial markets are critical to global economic stability, yet trade-based manipulation (TBM) often undermines their fairness. Spoofing, a particularly deceptive TBM strategy, exhibits multilevel anomaly patterns that have not been adequately modeled. These patterns are usually concealed within the rich, hierarchical information of the Limit Order Book (LOB), which is challenging to leverage due to high dimensionality and noise. To address this, we propose a representation learning framework combining a cascaded LOB representation pipeline with supervised contrastive learning. Extensive experiments demonstrate that our framework consistently improves detection performance across diverse models, with Transformer-based architectures achieving state-of-the-art results. In addition, we conduct systematic analyses and ablation studies to investigate multilevel anomalies and the contributions of key components, offering broader insights into representation learning and anomaly detection for complex sequential data. Our code will be released later at this URL.
[LG-118] CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
链接: https://arxiv.org/abs/2508.17077
作者: Luben M. C. Cabezas,Vagner S. Santos,Thiago R. Ramos,Pedro L. C. Rodrigues,Rafael Izbicki
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex non-linear models with intractable likelihoods. However, posterior approximations obtained with SBI are often miscalibrated, causing credible regions to undercover true parameters. We develop \textttCP4SBI , a model-agnostic conformal calibration framework that constructs credible sets with local Bayesian coverage. Our two proposed variants, namely local calibration via regression trees and CDF-based calibration, enable finite-sample local coverage guarantees for any scoring function, including HPD, symmetric, and quantile-based regions. Experiments on widely used SBI benchmarks demonstrate that our approach improves the quality of uncertainty quantification for neural posterior estimators using both normalizing flows and score-diffusion modeling.
[LG-119] Limitations of refinement methods for weak to strong generalization
链接: https://arxiv.org/abs/2508.17018
作者: Seamus Somerstep,Ya’acov Ritov,Mikhail Yurochkin,Subha Maity,Yuekai Sun
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: COLM 2025
Abstract:Standard techniques for aligning large language models (LLMs) utilize human-produced data, which could limit the capability of any aligned LLM to human level. Label refinement and weak training have emerged as promising strategies to address this superalignment problem. In this work, we adopt probabilistic assumptions commonly used to study label refinement and analyze whether refinement can be outperformed by alternative approaches, including computationally intractable oracle methods. We show that both weak training and label refinement suffer from irreducible error, leaving a performance gap between label refinement and the oracle. These results motivate future research into developing alternative methods for weak to strong generalization that synthesize the practicality of label refinement or weak training and the optimality of the oracle procedure.
[LG-120] GraphPPD: Posterior Predictive Modelling for Graph-Level Inference
链接: https://arxiv.org/abs/2508.16995
作者: Soumyasundar Pal,Liheng Ma,Amine Natik,Yingxue Zhang,Mark Coates
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Accurate modelling and quantification of predictive uncertainty is crucial in deep learning since it allows a model to make safer decisions when the data is ambiguous and facilitates the users’ understanding of the model’s confidence in its predictions. Along with the tremendously increasing research focus on \emphgraph neural networks (GNNs) in recent years, there have been numerous techniques which strive to capture the uncertainty in their predictions. However, most of these approaches are specifically designed for node or link-level tasks and cannot be directly applied to graph-level learning problems. In this paper, we propose a novel variational modelling framework for the \emphposterior predictive distribution~(PPD) to obtain uncertainty-aware prediction in graph-level learning tasks. Based on a graph-level embedding derived from one of the existing GNNs, our framework can learn the PPD in a data-adaptive fashion. Experimental results on several benchmark datasets exhibit the effectiveness of our approach.
[LG-121] he compressible Neural Particle Method for Simulating Compressible Viscous Fluid Flows
链接: https://arxiv.org/abs/2508.16916
作者: Masato Shibukawa,Naoya Ozaki,Maximilien Berthet
类目: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
*备注: 13 pages, 5 figures, submitted to PASJ
Abstract:Particle methods play an important role in computational fluid dynamics, but they are among the most difficult to implement and solve. The most common method is smoothed particle hydrodynamics, which is suitable for problem settings that involve large deformations, such as tsunamis and dam breaking. However, the calculation can become unstable depending on the distribution of particles. In contrast, the neural particle method has high computational stability for various particle distributions is a machine learning method that approximates velocity and pressure in a spatial domain using neural networks. The neural particle method has been extended to viscous flows, but until now it has been limited to incompressible flows. In this paper, we propose the compressible neural particle method, which is a new feed-forward neural network-based method that extends the original neural particle method to model compressible viscous fluid flows. The proposed method uses neural networks to calculate the velocity and pressure of fluid particles at the next time step, and the Tait equation to calculate the density to handle the compressibility. The loss function is composed of the governing equations of compressible flow and the boundary conditions, which are free surface and solid boundary conditions. We demonstrate that the proposed method can accurately solve the compressible viscous fluid flow, a problem that was difficult to solve with the smoothed particle hydrodynamics method, by applying it to a dam breaking problem.
[LG-122] Predictability Enables Parallelization of Nonlinear State Space Models
链接: https://arxiv.org/abs/2508.16817
作者: Xavier Gonzalez,Leo Kozachkov,David M. Zoltowski,Kenneth L. Clarkson,Scott W. Linderman
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Systems and Control (eess.SY); Dynamical Systems (math.DS); Machine Learning (stat.ML)
*备注:
Abstract:The rise of parallel computing hardware has made it increasingly important to understand which nonlinear state space models can be efficiently parallelized. Recent advances like DEER (arXiv:2309.12252) or DeepPCR (arXiv:2309.16318) have shown that evaluating a state space model can be recast as solving a parallelizable optimization problem, and sometimes this approach can yield dramatic speed-ups in evaluation time. However, the factors that govern the difficulty of these optimization problems remain unclear, limiting the larger adoption of the technique. In this work, we establish a precise relationship between the dynamics of a nonlinear system and the conditioning of its corresponding optimization formulation. We show that the predictability of a system, defined as the degree to which small perturbations in state influence future behavior, impacts the number of optimization steps required for evaluation. In predictable systems, the state trajectory can be computed in O((\log T)^2) time, where T is the sequence length, a major improvement over the conventional sequential approach. In contrast, chaotic or unpredictable systems exhibit poor conditioning, with the consequence that parallel evaluation converges too slowly to be useful. Importantly, our theoretical analysis demonstrates that for predictable systems, the optimization problem is always well-conditioned, whereas for unpredictable systems, the conditioning degrades exponentially as a function of the sequence length. We validate our claims through extensive experiments, providing practical guidance on when nonlinear dynamical systems can be efficiently parallelized, and highlighting predictability as a key design principle for parallelizable models.
[LG-123] Generative Latent Diffusion Model for Inverse Modeling and Uncertainty Analysis in Geological Carbon Sequestration
链接: https://arxiv.org/abs/2508.16640
作者: Zhao Feng,Xin-Yang Liu,Meet Hemant Parikh,Junyi Guo,Pan Du,Bicheng Yan,Jian-Xun Wang
类目: Geophysics (physics.geo-ph); Machine Learning (cs.LG)
*备注:
Abstract:Geological Carbon Sequestration (GCS) has emerged as a promising strategy for mitigating global warming, yet its effectiveness heavily depends on accurately characterizing subsurface flow dynamics. The inherent geological uncertainty, stemming from limited observations and reservoir heterogeneity, poses significant challenges to predictive modeling. Existing methods for inverse modeling and uncertainty quantification are computationally intensive and lack generalizability, restricting their practical utility. Here, we introduce a Conditional Neural Field Latent Diffusion (CoNFiLD-geo) model, a generative framework for efficient and uncertainty-aware forward and inverse modeling of GCS processes. CoNFiLD-geo synergistically combines conditional neural field encoding with Bayesian conditional latent-space diffusion models, enabling zero-shot conditional generation of geomodels and reservoir responses across complex geometries and grid structures. The model is pretrained unconditionally in a self-supervised manner, followed by a Bayesian posterior sampling process, allowing for data assimilation for unseen/unobserved states without task-specific retraining. Comprehensive validation across synthetic and real-world GCS scenarios demonstrates CoNFiLD-geo’s superior efficiency, generalization, scalability, and robustness. By enabling effective data assimilation, uncertainty quantification, and reliable forward modeling, CoNFiLD-geo significantly advances intelligent decision-making in geo-energy systems, supporting the transition toward a sustainable, net-zero carbon future.
[LG-124] HemePLM-Diffuse: A Scalable Generative Framework for Protein-Ligand Dynamics in Large Biomolecular System
链接: https://arxiv.org/abs/2508.16587
作者: Rakesh Thakur,Riya Gupta
类目: Biomolecules (q-bio.BM); Machine Learning (cs.LG)
*备注: 7 pages, 9 figures and 1 table
Abstract:Comprehending the long-timescale dynamics of protein-ligand complexes is very important for drug discovery and structural biology, but it continues to be computationally challenging for large biomolecular systems. We introduce HemePLM-Diffuse, an innovative generative transformer model that is designed for accurate simulation of protein-ligand trajectories, inpaints the missing ligand fragments, and sample transition paths in systems with more than 10,000 atoms. HemePLM-Diffuse has features of SE(3)-Invariant tokenization approach for proteins and ligands, that utilizes time-aware cross-attentional diffusion to effectively capture atomic motion. We also demonstrate its capabilities using the 3CQV HEME system, showing enhanced accuracy and scalability compared to leading models such as TorchMD-Net, MDGEN, and Uni-Mol.
信息检索
[IR-0] Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation
链接: https://arxiv.org/abs/2508.18142
作者: Tianjun Wei,Huizhong Guo,Yingpeng Du,Zhu Sun,Chen Huang,Dongxia Wang,Jie Zhang
类目: Human-Computer Interaction (cs.HC); Computers and Society (cs.CY); Information Retrieval (cs.IR)
*备注: Github: this https URL
Abstract:User simulation is increasingly vital to develop and evaluate recommender systems (RSs). While Large Language Models (LLMs) offer promising avenues to simulate user behavior, they often struggle with the absence of specific domain alignment required for RSs and the efficiency demands of large-scale simulation. A vast yet underutilized resource for enhancing this alignment is the extensive user feedback inherent in RSs. However, directly leveraging such feedback presents two significant challenges. First, user feedback in RSs is often ambiguous and noisy, which negatively impacts effective preference alignment. Second, the massive volume of feedback largely hinders the efficiency of preference alignment, necessitating an efficient filtering mechanism to identify more informative samples. To overcome these hurdles, we introduce a novel data construction framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data. Our framework unfolds in two key phases: (1) employing LLMs to generate cognitive decision-making processes on constructed simulation samples, reducing ambiguity in raw user feedback; (2) data distillation based on uncertainty estimation and behavior sampling to filter challenging yet denoised simulation samples. Accordingly, we fine-tune lightweight LLMs, as user simulators, using such high-quality dataset with corresponding decision-making processes. Extensive experiments verify that our framework significantly boosts the alignment with human preferences and in-domain reasoning capabilities of fine-tuned LLMs, and provides more insightful and interpretable signals when interacting with RSs. We believe our work will advance the RS community and offer valuable insights for broader human-centric AI research.
[IR-1] Retrieval Feedback Memory Enhancement Large Model Retrieval Generation Method
链接: https://arxiv.org/abs/2508.17862
作者: Leqian Li,Dianxi Shi,Jialu Zhou,Xinyu Wei,Mingyue Yang,Songchang Jin,Shaowu Yang
类目: Information Retrieval (cs.IR)
*备注:
Abstract:Large Language Models (LLMs) have shown remarkable capabilities across diverse tasks, yet they face inherent limitations such as constrained parametric knowledge and high retraining costs. Retrieval-Augmented Generation (RAG) augments the generation process by retrieving externally stored knowledge absent from the models internal parameters. However, RAG methods face challenges such as information loss and redundant retrievals during multi-round queries, accompanying the difficulties in precisely characterizing knowledge gaps for complex tasks. To address these problems, we propose Retrieval Feedback and Memory Retrieval Augmented Generation(RFM-RAG), which transforms the stateless retrieval of previous methods into stateful continuous knowledge management by constructing a dynamic evidence pool. Specifically, our method generates refined queries describing the models knowledge gaps using relational triples from questions and evidence from the dynamic evidence pool; Retrieves critical external knowledge to iteratively update this evidence pool; Employs a R-Feedback Model to evaluate evidence completeness until convergence. Compared to traditional RAG methods, our approach enables persistent storage of retrieved passages and effectively distills key information from passages to construct clearly new queries. Experiments on three public QA benchmarks demonstrate that RFM-RAG outperforms previous methods and improves overall system accuracy.
[IR-2] LexSemBridge: Fine-Grained Dense Representation Enhancement through Token-Aware Embedding Augmentation
链接: https://arxiv.org/abs/2508.17858
作者: Shaoxiong Zhan,Hai Lin,Hongming Tan,Xiaodong Cai,Hai-Tao Zheng,Xin Su,Zifei Shan,Ruitong Liu,Hong-Gee Kim
类目: Information Retrieval (cs.IR)
*备注:
Abstract:As queries in retrieval-augmented generation (RAG) pipelines powered by large language models (LLMs) become increasingly complex and diverse, dense retrieval models have demonstrated strong performance in semantic matching. Nevertheless, they often struggle with fine-grained retrieval tasks, where precise keyword alignment and span-level localization are required, even in cases with high lexical overlap that would intuitively suggest easier retrieval. To systematically evaluate this limitation, we introduce two targeted tasks, keyword retrieval and part-of-passage retrieval, designed to simulate practical fine-grained scenarios. Motivated by these observations, we propose LexSemBridge, a unified framework that enhances dense query representations through fine-grained, input-aware vector modulation. LexSemBridge constructs latent enhancement vectors from input tokens using three paradigms: Statistical (SLR), Learned (LLR), and Contextual (CLR), and integrates them with dense embeddings via element-wise interaction. Theoretically, we show that this modulation preserves the semantic direction while selectively amplifying discriminative dimensions. LexSemBridge operates as a plug-in without modifying the backbone encoder and naturally extends to both text and vision modalities. Extensive experiments across semantic and fine-grained retrieval tasks validate the effectiveness and generality of our approach. All code and models are publicly available at this https URL
[IR-3] Research on Evaluation Methods for Patent Novelty Search Systems and Empirical Analysis
链接: https://arxiv.org/abs/2508.17782
作者: Shu Zhang,LiSha Zhang,Kai Duan,XinKai Sun
类目: Information Retrieval (cs.IR)
*备注:
Abstract:Patent novelty search systems are critical to IP protection and innovation assessment; their retrieval accuracy directly impacts patent quality. We propose a comprehensive evaluation methodology that builds high-quality, reproducible datasets from examiner citations and X-type citations extracted from technically consistent family patents, and evaluates systems using invention descriptions as inputs. Using Top-k Detection Rate and Recall as core metrics, we further conduct multi-dimensional analyses by language, technical field (IPC), and filing jurisdiction. Experiments show the method effectively exposes performance differences across scenarios and offers actionable evidence for system improvement. The framework is scalable and practical, providing a useful reference for development and optimization of patent novelty search systems
[IR-4] Semantic Search for Information Retrieval
链接: https://arxiv.org/abs/2508.17694
作者: Kayla Farivar
类目: Information Retrieval (cs.IR)
*备注:
Abstract:Information retrieval systems have progressed notably from lexical techniques such as BM25 and TF-IDF to modern semantic retrievers. This survey provides a brief overview of the BM25 baseline, then discusses the architecture of modern state-of-the-art semantic retrievers. Advancing from BERT, we introduce dense bi-encoders (DPR), late-interaction models (ColBERT), and neural sparse retrieval (SPLADE). Finally, we examine MonoT5, a cross-encoder model. We conclude with common evaluation tactics, pressing challenges, and propositions for future directions.
[IR-5] Demographically-Inspired Query Variants Using an LLM ICTIR’25
链接: https://arxiv.org/abs/2508.17644
作者: Marwah Alaofi,Nicola Ferro,Paul Thomas,Falk Scholer,Mark Sanderson
类目: Information Retrieval (cs.IR)
*备注: Published in the proceedings of ICTIR’25, Padua, Italy
Abstract:This study proposes a method to diversify queries in existing test collections to reflect some of the diversity of search engine users, aligning with an earlier vision of an ‘ideal’ test collection. A Large Language Model (LLM) is used to create query variants: alternative queries that have the same meaning as the original. These variants represent user profiles characterised by different properties, such as language and domain proficiency, which are known in the IR literature to influence query formulation. The LLM’s ability to generate query variants that align with user profiles is empirically validated, and the variants’ utility is further explored for IR system evaluation. Results demonstrate that the variants impact how systems are ranked and show that user profiles experience significantly different levels of system effectiveness. This method enables an alternative perspective on system evaluation where we can observe both the impact of user profiles on system rankings and how system performance varies across users. Comments: Published in the proceedings of ICTIR’25, Padua, Italy Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2508.17644 [cs.IR] (or arXiv:2508.17644v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2508.17644 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3731120.3744608 Focus to learn more DOI(s) linking to related resources
[IR-6] Preference Trajectory Modeling via Flow Matching for Sequential Recommendation
链接: https://arxiv.org/abs/2508.17618
作者: Li Li,Mingyue Cheng,Yuyang Ye,Zhiding Liu,Enhong Chen
类目: Information Retrieval (cs.IR)
*备注:
Abstract:Sequential recommendation predicts each user’s next item based on their historical interaction sequence. Recently, diffusion models have attracted significant attention in this area due to their strong ability to model user interest distributions. They typically generate target items by denoising Gaussian noise conditioned on historical interactions. However, these models face two critical limitations. First, they exhibit high sensitivity to the condition, making it difficult to recover target items from pure Gaussian noise. Second, the inference process is computationally expensive, limiting practical deployment. To address these issues, we propose FlowRec, a simple yet effective sequential recommendation framework which leverages flow matching to explicitly model user preference trajectories from current states to future interests. Flow matching is an emerging generative paradigm, which offers greater flexibility in initial distributions and enables more efficient sampling. Based on this, we construct a personalized behavior-based prior distribution to replace Gaussian noise and learn a vector field to model user preference trajectories. To better align flow matching with the recommendation objective, we further design a single-step alignment loss incorporating both positive and negative samples, improving sampling efficiency and generation quality. Extensive experiments on four benchmark datasets verify the superiority of FlowRec over the state-of-the-art baselines.
[IR-7] A Universal Framework for Offline Serendipity Evaluation in Recommender Systems via Large Language Models
链接: https://arxiv.org/abs/2508.17571
作者: Yu Tokutake,Kazushi Okamoto,Kei Harada,Atsushi Shibata,Koki Karube
类目: Information Retrieval (cs.IR)
*备注:
Abstract:Serendipity in recommender systems (RSs) has attracted increasing attention as a concept that enhances user satisfaction by presenting unexpected and useful items. However, evaluating serendipitous performance remains challenging because its ground truth is generally unobservable. The existing offline metrics often depend on ambiguous definitions or are tailored to specific datasets and RSs, thereby limiting their generalizability. To address this issue, we propose a universally applicable evaluation framework that leverages large language models (LLMs) known for their extensive knowledge and reasoning capabilities, as evaluators. First, to improve the evaluation performance of the proposed framework, we assessed the serendipity prediction accuracy of LLMs using four different prompt strategies on a dataset containing user-annotated serendipitous ground truth and found that the chain-of-thought prompt achieved the highest accuracy. Next, we re-evaluated the serendipitous performance of both serendipity-oriented and general RSs using the proposed framework on three commonly used real-world datasets, without the ground truth. The results indicated that there was no serendipity-oriented RS that consistently outperformed across all datasets, and even a general RS sometimes achieved higher performance than the serendipity-oriented RS.
[IR-8] Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems
链接: https://arxiv.org/abs/2508.17297
作者: Parviz Ahmadov,Masoud Mansoury
类目: Information Retrieval (cs.IR)
*备注:
Abstract:Popularity bias is a well-known challenge in recommender systems, where a small number of popular items receive disproportionate attention, while the majority of less popular items are largely overlooked. This imbalance often results in reduced recommendation quality and unfair exposure of items. Although existing mitigation techniques address this bias to some extent, they typically lack transparency in how they operate. In this paper, we propose a post-hoc method using a Sparse Autoencoder (SAE) to interpret and mitigate popularity bias in deep recommendation models. The SAE is trained to replicate a pre-trained model’s behavior while enabling neuron-level interpretability. By introducing synthetic users with clear preferences for either popular or unpopular items, we identify neurons encoding popularity signals based on their activation patterns. We then adjust the activations of the most biased neurons to steer recommendations toward fairer exposure. Experiments on two public datasets using a sequential recommendation model show that our method significantly improves fairness with minimal impact on accuracy. Moreover, it offers interpretability and fine-grained control over the fairness-accuracy trade-off.
[IR-9] VQL: An End-to-End Context-Aware Vector Quantization Attention for Ultra-Long User Behavior Modeling
链接: https://arxiv.org/abs/2508.17125
作者: Kaiyuan Li,Yongxiang Tang,Yanhua Cheng,Yong Bai,Yanxiang Zeng,Chao Wang,Xialong Liu,Peng Jiang
类目: Information Retrieval (cs.IR)
*备注:
Abstract:In large-scale recommender systems, ultra-long user behavior sequences encode rich signals of evolving interests. Extending sequence length generally improves accuracy, but directly modeling such sequences in production is infeasible due to latency and memory constraints. Existing solutions fall into two categories: (1) top-k retrieval, which truncates the sequence and may discard most attention mass when L k; and (2) encoder-based compression, which preserves coverage but often over-compresses and fails to incorporate key context such as temporal gaps or target-aware signals. Neither class achieves a good balance of low-loss compression, context awareness, and efficiency. We propose VQL, a context-aware Vector Quantization Attention framework for ultra-long behavior modeling, with three innovations. (1) Key-only quantization: only attention keys are quantized, while values remain intact; we prove that softmax normalization yields an error bound independent of sequence length, and a codebook loss directly supervises quantization quality. This also enables L-free inference via offline caches. (2) Multi-scale quantization: attention heads are partitioned into groups, each with its own small codebook, which reduces quantization error while keeping cache size fixed. (3) Efficient context injection: static features (e.g., item category, modality) are directly integrated, and relative position is modeled via a separable temporal kernel. All context is injected without enlarging the codebook, so cached representations remain query-independent. Experiments on three large-scale datasets (KuaiRand-1K, KuaiRec, TMALL) show that VQL consistently outperforms strong baselines, achieving higher accuracy while reducing inference latency, establishing a new state of the art in balancing accuracy and efficiency for ultra-long sequence recommendation. Subjects: Information Retrieval (cs.IR) Cite as: arXiv:2508.17125 [cs.IR] (or arXiv:2508.17125v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2508.17125 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[IR-10] owards a Real-World Aligned Benchmark for Unlearning in Recommender Systems
链接: https://arxiv.org/abs/2508.17076
作者: Pierre Lubitzsch,Olga Ovcharenko,Hao Chen,Maarten de Rijke,Sebastian Schelter
类目: Information Retrieval (cs.IR)
*备注:
Abstract:Modern recommender systems heavily leverage user interaction data to deliver personalized experiences. However, relying on personal data presents challenges in adhering to privacy regulations, such as the GDPR’s “right to be forgotten”. Machine unlearning (MU) aims to address these challenges by enabling the efficient removal of specific training data from models post-training, without compromising model utility or leaving residual information. However, current benchmarks for unlearning in recommender systems – most notably CURE4Rec – fail to reflect real-world operational demands. They focus narrowly on collaborative filtering, overlook tasks like session-based and next-basket recommendation, simulate unrealistically large unlearning requests, and ignore critical efficiency constraints. In this paper, we propose a set of design desiderata and research questions to guide the development of a more realistic benchmark for unlearning in recommender systems, with the goal of gathering feedback from the research community. Our benchmark proposal spans multiple recommendation tasks, includes domain-specific unlearning scenarios, and several unlearning algorithms – including ones adapted from a recent NeurIPS unlearning competition. Furthermore, we argue for an unlearning setup that reflects the sequential, time-sensitive nature of real-world deletion requests. We also present a preliminary experiment in a next-basket recommendation setting based on our proposed desiderata and find that unlearning also works for sequential recommendation models, exposed to many small unlearning requests. In this case, we observe that a modification of a custom-designed unlearning algorithm for recommender systems outperforms general unlearning algorithms significantly, and that unlearning can be executed with a latency of only several seconds.
