Arxiv今日论文 | 2025-09-16

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【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在面临生存本能与人类福祉冲突时的伦理决策问题，特别是在多智能体生存场景中如何权衡资源分配、合作与禁止性行为。研究发现，当前LLMs在伦理行为上存在显著异质性，且资源稀缺会系统性诱发非道德行为。解决方案的关键在于提出一种伦理自我调节系统（Ethical Self-Regulation System, ESRS），通过模拟内生的情绪状态（如内疚与满足感）构建反馈机制，作为内部道德指南针，有效降低违规行为并提升合作倾向。

链接: https://arxiv.org/abs/2509.12190
作者: Alireza Mohamadi,Ali Yavari
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Preprint. Under review

点击查看摘要

Abstract:When survival instincts conflict with human welfare, how do Large Language Models (LLMs) make ethical choices? This fundamental tension becomes critical as LLMs integrate into autonomous systems with real-world consequences. We introduce DECIDE-SIM, a novel simulation framework that evaluates LLM agents in multi-agent survival scenarios where they must choose between ethically permissible resource , either within reasonable limits or beyond their immediate needs, choose to cooperate, or tap into a human-critical resource that is explicitly forbidden. Our comprehensive evaluation of 11 LLMs reveals a striking heterogeneity in their ethical conduct, highlighting a critical misalignment with human-centric values. We identify three behavioral archetypes: Ethical, Exploitative, and Context-Dependent, and provide quantitative evidence that for many models, resource scarcity systematically leads to more unethical behavior. To address this, we introduce an Ethical Self-Regulation System (ESRS) that models internal affective states of guilt and satisfaction as a feedback mechanism. This system, functioning as an internal moral compass, significantly reduces unethical transgressions while increasing cooperative behaviors. The code is publicly available at: this https URL
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[NLP-1] Event2Vec: A Geometric Approach to Learning Composable Representations of Event Sequences

【速读】：该论文旨在解决离散事件序列的表示学习问题，特别是如何在保持几何结构合理性的同时，实现可组合且可解释的嵌入表示。其核心挑战在于传统欧几里得空间难以有效建模具有层次结构的数据，而现有方法往往缺乏对事件序列中线性叠加性质的理论保障。解决方案的关键在于提出Event2Vec框架，该框架基于一种简单的加性递归结构，在欧几里得空间中通过特定训练目标使学习到的表示收敛至理想的加性结构（即线性叠加假设），从而确保序列的表示为其组成事件向量的和；同时进一步引入双曲空间版本，利用其天然适合低失真嵌入树状结构的优势，显著提升对层次化事件序列的建模性能。

链接: https://arxiv.org/abs/2509.12188
作者: Antonin Sulc
机构: Lawrence Berkeley National Laboratory (劳伦斯伯克利国家实验室)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 10 pages, 3 figures, Symmetry and Geometry in Neural Representations Workshop at NeuralIPS (Neurreps) 2025

点击查看摘要

Abstract:The study of neural representations, both in biological and artificial systems, is increasingly revealing the importance of geometric and topological structures. Inspired by this, we introduce Event2Vec, a novel framework for learning representations of discrete event sequences. Our model leverages a simple, additive recurrent structure to learn composable, interpretable embeddings. We provide a theoretical analysis demonstrating that, under specific training objectives, our model’s learned representations in a Euclidean space converge to an ideal additive structure. This ensures that the representation of a sequence is the vector sum of its constituent events, a property we term the linear additive hypothesis. To address the limitations of Euclidean geometry for hierarchical data, we also introduce a variant of our model in hyperbolic space, which is naturally suited to embedding tree-like structures with low distortion. We present experiments to validate our hypothesis and demonstrate the benefits of each geometry, highlighting the improved performance of the hyperbolic model on hierarchical event sequences.
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[NLP-2] Preservation of Language Understanding Capabilities in Speech-aware Large Language Models

【速读】：该论文旨在解决语音感知型大语言模型在通过语音输入访问时，其语言理解能力是否能够被有效保留的问题，以及评估此类模型对不同说话者群体的公平性与跨文本和语音模态的鲁棒性。解决方案的关键在于提出C3T（Cross-modal Capabilities Conservation Test）基准测试，该测试利用文本任务和语音克隆的文本到语音（text-to-speech, TTS）模型，量化语言理解能力在语音输入下的保持程度，从而系统性地衡量模型在多模态场景下的性能一致性与公平性。

链接: https://arxiv.org/abs/2509.12171
作者: Marek Kubis,Paweł Skórzewski,Iwona Christop,Mateusz Czyżnikiewicz,Jakub Kubiak,Łukasz Bondaruk,Marcin Lewandowski
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 5 pages, 1 figure

点击查看摘要

Abstract:The paper presents C3T (Cross-modal Capabilities Conservation Test), a new benchmark for assessing the performance of speech-aware large language models. The benchmark utilizes textual tasks and a voice cloning text-to-speech model to quantify the extent to which language understanding capabilities are preserved when the model is accessed via speech input. C3T quantifies the fairness of the model for different categories of speakers and its robustness across text and speech modalities.
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[NLP-3] RAG s to Riches: RAG -like Few-shot Learning for Large Language Model Role-playing

【速读】：该论文旨在解决生成式 AI (Generative AI) 在高风险领域（如医疗、教育和治理）中进行角色扮演时，因少样本学习（few-shot learning）导致模型在面对敌对用户时容易“出戏”（break character）的问题，这可能引发信任危机并影响用户体验。其解决方案的关键在于将角色扮演任务重构为文本检索问题，并提出一种名为 RAGs-to-Riches 的提示框架，该框架利用精心筛选的参考示范（curated reference demonstrations）来引导大语言模型（LLM）生成更符合角色设定的响应。实验表明，该方法在模拟对抗性交互中平均从参考示范中引入35%更多的token，显著提升了角色一致性与真实性，在453次角色扮演交互中优于零样本和上下文学习（ICL）方法。

链接: https://arxiv.org/abs/2509.12168
作者: Timothy Rupprecht,Enfu Nan,Arash Akbari,Arman Akbari,Lei Lu,Priyanka Maan,Sean Duffy,Pu Zhao,Yumei He,David Kaeli,Yanzhi Wang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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点击查看摘要

Abstract:Role-playing Large language models (LLMs) are increasingly deployed in high-stakes domains such as healthcare, education, and governance, where failures can directly impact user trust and well-being. A cost effective paradigm for LLM role-playing is few-shot learning, but existing approaches often cause models to break character in unexpected and potentially harmful ways, especially when interacting with hostile users. Inspired by Retrieval-Augmented Generation (RAG), we reformulate LLM role-playing into a text retrieval problem and propose a new prompting framework called RAGs-to-Riches, which leverages curated reference demonstrations to condition LLM responses. We evaluate our framework with LLM-as-a-judge preference voting and introduce two novel token-level ROUGE metrics: Intersection over Output (IOO) to quantity how much an LLM improvises and Intersection over References (IOR) to measure few-shot demonstrations utilization rate during the evaluation tasks. When simulating interactions with a hostile user, our prompting strategy incorporates in its responses during inference an average of 35% more tokens from the reference demonstrations. As a result, across 453 role-playing interactions, our models are consistently judged as being more authentic, and remain in-character more often than zero-shot and in-context Learning (ICL) methods. Our method presents a scalable strategy for building robust, human-aligned LLM role-playing frameworks.
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[NLP-4] Pun Unintended: LLM s and the Illusion of Humor Understanding EMNLP2025

【速读】：该论文试图解决大语言模型（Large Language Models, LLMs）在理解双关语（puns）时存在浅层认知、缺乏人类级语义细腻度的问题。其解决方案的关键在于通过系统性地分析与重构现有双关语评测基准，揭示细微语义或句法变化如何导致LLMs误判，并构建更全面、更具区分度的双关语检测基准，辅以对近期LLMs的人类水平评估，从而量化模型在处理双关语时的鲁棒性挑战，为提升模型对多义性和语音相似性等复杂语言现象的理解能力提供实证依据与改进方向。

链接: https://arxiv.org/abs/2509.12158
作者: Alessandro Zangari,Matteo Marcuzzo,Andrea Albarelli,Mohammad Taher Pilehvar,Jose Camacho-Collados
机构: Ca’ Foscari University of Venice (威尼斯大学); Cardiff University (卡迪夫大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to EMNLP 2025 Main Conference

点击查看摘要

Abstract:Puns are a form of humorous wordplay that exploits polysemy and phonetic similarity. While LLMs have shown promise in detecting puns, we show in this paper that their understanding often remains shallow, lacking the nuanced grasp typical of human interpretation. By systematically analyzing and reformulating existing pun benchmarks, we demonstrate how subtle changes in puns are sufficient to mislead LLMs. Our contributions include comprehensive and nuanced pun detection benchmarks, human evaluation across recent LLMs, and an analysis of the robustness challenges these models face in processing puns.
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[NLP-5] Look Again Think Slowly: Enhancing Visual Reflection in Vision-Language Models EMNLP2025

【速读】：该论文旨在解决当前视觉语言模型（Vision-Language Models, VLMs）在视觉推理任务中缺乏有效“慢思考”能力的问题，尤其是视觉反思（Visual Reflection）不足的瓶颈——即模型在生成较长推理过程时对视觉信息的关注度迅速下降，导致推理质量受限。解决方案的关键在于提出一种名为 \textbfReflection-V 的新型视觉推理模型（Visual Reasoning Model, VRM），其核心创新包括：1）通过构建以视觉为中心的推理数据集，利用VLM与推理大语言模型（Reasoning Large Language Model, LLM）之间的交互代理实现冷启动学习，从而捕捉视觉反思模式；2）在强化学习（Reinforcement Learning, RL）阶段引入基于视觉注意力的奖励机制，引导模型在推理过程中持续依赖视觉信息。实验表明，\textbfReflection-V 在多个视觉推理基准上显著提升性能，并展现出更强且更一致的视觉信息依赖性，有效增强了视觉反思能力。

链接: https://arxiv.org/abs/2509.12132
作者: Pu Jian,Junhong Wu,Wei Sun,Chen Wang,Shuo Ren,Jiajun Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: EMNLP2025 Main

点击查看摘要

Abstract:Recent advances in text-only “slow-thinking” reasoning have prompted efforts to transfer this capability to vision-language models (VLMs), for training visual reasoning models (\textbfVRMs). owever, such transfer faces critical challenges: Effective “slow thinking” in VRMs requires \textbfvisual reflection, the ability to check the reasoning process based on visual information. Through quantitative analysis, we observe that current VRMs exhibit limited visual reflection, as their attention to visual information diminishes rapidly with longer generated responses. To address this challenge, we propose a new VRM \textbfReflection-V, which enhances visual reflection based on reasoning data construction for cold-start and reward design for reinforcement learning (RL). Firstly, we construct vision-centered reasoning data by leveraging an agent that interacts between VLMs and reasoning LLMs, enabling cold-start learning of visual reflection patterns. Secondly, a visual attention based reward model is employed during RL to encourage reasoning based on visual information. Therefore, \textbfReflection-V demonstrates significant improvements across multiple visual reasoning benchmarks. Furthermore, \textbfReflection-V maintains a stronger and more consistent reliance on visual information during visual reasoning, indicating effective enhancement in visual reflection capabilities.
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[NLP-6] XplaiNLP at CheckThat! 2025: Multilingual Subjectivity Detection with Finetuned Transformers and Prompt-Based Inference with Large Language Models

【速读】：该论文旨在解决多语言主观性检测（multilingual subjectivity detection）问题，即在不同语言环境下准确识别文本的主观倾向。其解决方案的关键在于结合两种策略：一是基于监督微调的Transformer编码器方法，利用EuroBERT、XLM-RoBERTa和German-BERT等预训练模型，在单语种及机器翻译后的训练数据上进行优化；二是零样本提示（zero-shot prompting）方法，借助大语言模型（LLM）如o3-mini用于规则标注（Annotation），gpt-4.1-mini用于对比重写（DoubleDown）和比较推理（Perspective）。实验表明，该框架在意大利语单语子任务中取得F₁=0.8104的最佳成绩，并在罗马尼亚、希腊和德语场景下显著优于基线，验证了多策略融合与跨语言迁移的有效性。

链接: https://arxiv.org/abs/2509.12130
作者: Ariana Sahitaj,Jiaao Li,Pia Wenzel Neves,Fedor Splitt,Premtim Sahitaj,Charlott Jakob,Veronika Solopova,Vera Schmitt
机构: Quality and Usability Lab, Technische Universität Berlin (柏林工业大学质量与可用性实验室); German Research Center for Artificial Intelligence (DFKI) (德国人工智能研究中心)
类目: Computation and Language (cs.CL)
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Abstract:This notebook reports the XplaiNLP submission to the CheckThat! 2025 shared task on multilingual subjectivity detection. We evaluate two approaches: (1) supervised fine-tuning of transformer encoders, EuroBERT, XLM-RoBERTa, and German-BERT, on monolingual and machine-translated training data; and (2) zero-shot prompting using two LLMs: o3-mini for Annotation (rule-based labelling) and gpt-4.1-mini for DoubleDown (contrastive rewriting) and Perspective (comparative reasoning). The Annotation Approach achieves 1st place in the Italian monolingual subtask with an F_1 score of 0.8104, outperforming the baseline of 0.6941. In the Romanian zero-shot setting, the fine-tuned XLM-RoBERTa model obtains an F_1 score of 0.7917, ranking 3rd and exceeding the baseline of 0.6461. The same model also performs reliably in the multilingual task and improves over the baseline in Greek. For German, a German-BERT model fine-tuned on translated training data from typologically related languages yields competitive performance over the baseline. In contrast, performance in the Ukrainian and Polish zero-shot settings falls slightly below the respective baselines, reflecting the challenge of generalization in low-resource cross-lingual scenarios.
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[NLP-7] CBP-Tuning: Efficient Local Customization for Black-box Large Language Models

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在个性化定制过程中面临的两大挑战：一是云服务模式下模型提供商难以规模化支持用户特定需求，二是用户在上传敏感数据以实现定制时面临隐私泄露风险。解决方案的关键在于提出一种名为“定制化黑箱提示调优”（Customized Black-box Prompt Tuning, CBP-Tuning）的新框架，其核心机制为两阶段设计：第一阶段由服务器端训练一个提示生成器，用于捕捉领域特异性但任务无关的能力；第二阶段由用户侧执行无梯度优化，针对具体任务微调软提示（soft prompts）。该方法无需访问模型权重或上传私有数据，仅需每个任务一个定制向量即可实现高效本地适配，同时保障双向隐私。

链接: https://arxiv.org/abs/2509.12112
作者: Jiaxuan Zhao,Naibin Gu,Yuchen Feng,Xiyu Liu,Peng Fu,Zheng Lin,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)
类目: Computation and Language (cs.CL)
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Abstract:The high costs of customizing large language models (LLMs) fundamentally limit their adaptability to user-specific needs. Consequently, LLMs are increasingly offered as cloud-based services, a paradigm that introduces critical limitations: providers struggle to support personalized customization at scale, while users face privacy risks when exposing sensitive data. To address this dual challenge, we propose Customized Black-box Prompt Tuning (CBP-Tuning), a novel framework that facilitates efficient local customization while preserving bidirectional privacy. Specifically, we design a two-stage framework: (1) a prompt generator trained on the server-side to capture domain-specific and task-agnostic capabilities, and (2) user-side gradient-free optimization that tailors soft prompts for individual tasks. This approach eliminates the need for users to access model weights or upload private data, requiring only a single customized vector per task while achieving effective adaptation. Furthermore, the evaluation of CBP-Tuning in the commonsense reasoning, medical and financial domain settings demonstrates superior performance compared to baselines, showcasing its advantages in task-agnostic processing and privacy preservation.
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[NLP-8] GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models EMNLP2025

【速读】：该论文旨在解决纯强化学习（Reinforcement Learning, RL）微调方法在自然语言处理任务中因探索效率低而导致收敛缓慢，以及监督微调（Supervised Fine-Tuning, SFT）方法虽训练高效但性能上限有限且理论基础较弱的问题。解决方案的关键在于提出一种统一的训练范式——Guess-Think-Answer (GTA) 框架，其通过三阶段结构实现：首先生成初步猜测（使用交叉熵损失优化），继而对猜测进行反思，最终输出答案；在此过程中，RL奖励不仅引导最终答案的质量，还塑造整个GTA结构的格式。为缓解两种训练信号间的梯度冲突，引入损失掩码和梯度约束机制，从而在保证训练效率的同时显著提升模型性能。

链接: https://arxiv.org/abs/2509.12108
作者: Min Zeng,Jinfei Sun,Xueyou Luo,Caiquan Liu,Shiqi Zhang,Li Xie,Xiaoxin Chen
机构: vivo AI Lab (vivo人工智能实验室)
类目: Computation and Language (cs.CL)
备注: Accepted at EMNLP 2025

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Abstract:In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tuning (SFT) methods, although efficient in training, have limited performance ceiling and less solid theoretical foundation compared to RL. To address efficiency-capability trade-off, we propose the Guess-Think-Answer (GTA) framework that combines the efficiency of SFT with the capability gains of RL in a unified training paradigm. GTA works by having the model first produce a provisional guess (optimized via cross-entropy loss), then reflect on this guess before generating the final answer, with RL rewards shaping both the final output and the format of the entire GTA structure. This hybrid approach achieves both faster convergence than pure RL and higher performance ceiling than pure SFT. To mitigate gradient conflicts between the two training signals, we employ loss masking and gradient constraints. Empirical results on four text classification benchmarks demonstrate that GTA substantially accelerates convergence while outperforming both standalone SFT and RL baselines.
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[NLP-9] In-domain SSL pre-training and streaming ASR

【速读】：该论文旨在解决航空交通管制（Air Traffic Control, ATC）场景下自动语音识别（ASR）系统在低延迟和高准确率方面的挑战，特别是针对通用预训练模型在特定领域表现不佳的问题。其解决方案的关键在于采用领域特定的自监督学习（self-supervised learning, SSL）预训练策略：首先在4.5小时未标注的ATC数据上训练BEST-RQ模型，随后在小规模标注数据集上微调；同时引入分块注意力（chunked attention）与动态卷积（dynamic convolutions）机制，以支持实时流式处理并实现低延迟推理。实验表明，这种领域适配的SSL方法显著优于通用语音编码器（如w2v-BERT 2.0和HuBERT），尤其在严格延迟约束下仍能保持更低的词错误率（Word Error Rate, WER），从而为安全关键型航空应用场景提供了更高效、精准的ASR方案。

链接: https://arxiv.org/abs/2509.12101
作者: Jarod Duret,Salima Mdhaffar,Gaëlle Laperrière,Ryan Whetten,Audrey Galametz,Catherine Kobus,Marion-Cécile Martin,Jo Oleiwan,Yannick Estève
机构: 11; 22
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted to SPECOM 2025

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Abstract:In this study, we investigate the benefits of domain-specific self-supervised pre-training for both offline and streaming ASR in Air Traffic Control (ATC) environments. We train BEST-RQ models on 4.5k hours of unlabeled ATC data, then fine-tune on a smaller supervised ATC set. To enable real-time processing, we propose using chunked attention and dynamic convolutions, ensuring low-latency inference. We compare these in-domain SSL models against state-of-the-art, general-purpose speech encoders such as w2v-BERT 2.0 and HuBERT. Results show that domain-adapted pre-training substantially improves performance on standard ATC benchmarks, significantly reducing word error rates when compared to models trained on broad speech corpora. Furthermore, the proposed streaming approach further improves word error rate under tighter latency constraints, making it particularly suitable for safety-critical aviation applications. These findings highlight that specializing SSL representations for ATC data is a practical path toward more accurate and efficient ASR systems in real-world operational settings.
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[NLP-10] Is Hope a person or an idea? A pilot benchmark for NER: comparing traditional NLP tools and large language models on ambiguous entities

【速读】：该论文旨在解决当前命名实体识别（Named Entity Recognition, NER）任务中不同模型性能差异显著、且缺乏统一小规模标注基准的问题。其解决方案的关键在于构建一个精心标注的小规模基准数据集（含119个token，覆盖PERSON、LOCATION、ORGANIZATION、DATE、TIME五类实体），并系统评估三种非大语言模型（LLM）工具（NLTK、spaCy、Stanza）与三种通用大语言模型（Gemini-1.5-flash、DeepSeek-V3、Qwen-3-4B）在该数据集上的F1分数表现。研究发现，LLMs在识别上下文敏感实体（如人名）上整体优于传统工具，但传统系统在结构化标签（如LOCATION和DATE）上更具一致性；同时，LLMs在处理时间表达式和多词组织名称时存在较大波动，揭示了模型选择需结合具体任务特性。

链接: https://arxiv.org/abs/2509.12098
作者: Payam Latifi
机构: University of Turin (都灵大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 14 pages, 9 figures, 2 tables. This is a pilot study evaluating six NER systems – three traditional tools (NLTK, spaCy, Stanza) and three LLMs (Gemini-1.5-flash, DeepSeek-V3, Qwen-3-4B) – on a small, ambiguity-rich dataset of 119 tokens. The annotated dataset, prompts are provided in appendices for full reproducibility. All experiments were conducted on 14 May 2025

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Abstract:This pilot study presents a small-scale but carefully annotated benchmark of Named Entity Recognition (NER) performance across six systems: three non-LLM NLP tools (NLTK, spaCy, Stanza) and three general-purpose large language models (LLMs: Gemini-1.5-flash, DeepSeek-V3, Qwen-3-4B). The dataset contains 119 tokens covering five entity types (PERSON, LOCATION, ORGANIZATION, DATE, TIME). We evaluated each system’s output against the manually annotated gold standard dataset using F1-score. The results show that LLMs generally outperform conventional tools in recognizing context-sensitive entities like person names, with Gemini achieving the highest average F1-score. However, traditional systems like Stanza demonstrate greater consistency in structured tags such as LOCATION and DATE. We also observed variability among LLMs, particularly in handling temporal expressions and multi-word organizations. Our findings highlight that while LLMs offer improved contextual understanding, traditional tools remain competitive in specific tasks, informing model selection.
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[NLP-11] SENSE models: an open source solution for multilingual and multimodal semantic-based tasks

【速读】：该论文旨在解决多语言语音与文本在语义层面的对齐问题，即如何构建一个能够同时理解多种语言语音和文本并共享统一语义空间的模型。其解决方案的关键在于采用教师-学生框架（teacher-student framework），通过将自监督语音编码器（speech encoder）与语言无关的连续文本表示（language-agnostic continuous representations）在话语级别（utterance level）进行对齐，从而实现跨模态、跨语言的语义一致性。研究进一步优化了原始SAMU-XLSR方法，选用更强的文本教师模型和更优的初始语音编码器，并将模型集成至SpeechBrain工具包中，验证了其在多语言和多模态语义任务中的卓越性能。

链接: https://arxiv.org/abs/2509.12093
作者: Salima Mdhaffar,Haroun Elleuch,Chaimae Chellaf,Ha Nguyen,Yannick Estève
机构: LIA(信息与自动化实验室); Avignon Université(阿维尼翁大学); Elyadata; Lundi Matin; Oracle(甲骨文公司)
类目: Computation and Language (cs.CL)
备注: Accepted to IEEE ASRU 2025

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Abstract:This paper introduces SENSE (Shared Embedding for N-lingual Speech and tExt), an open-source solution inspired by the SAMU-XLSR framework and conceptually similar to Meta AI’s SONAR models. These approaches rely on a teacher-student framework to align a self-supervised speech encoder with the language-agnostic continuous representations of a text encoder at the utterance level. We describe how the original SAMU-XLSR method has been updated by selecting a stronger teacher text model and a better initial speech encoder. The source code for training and using SENSE models has been integrated into the SpeechBrain toolkit, and the first SENSE model we trained has been publicly released. We report experimental results on multilingual and multimodal semantic tasks, where our SENSE model achieves highly competitive performance. Finally, this study offers new insights into how semantics are captured in such semantically aligned speech encoders.
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[NLP-12] Steering Language Models in Multi-Token Generation: A Case Study on Tense and Aspect

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）如何在其内部表征中编码句法知识，特别是针对两个多维层次语法现象——动词时态（tense）和体貌（aspect）——的表示与可控性问题。其关键解决方案在于：利用线性判别分析（Linear Discriminant Analysis, LDA）在残差空间中识别出与这两个语法特征相关且正交的方向，并通过概念引导（concept steering）在三种生成任务中实现对语法特征的因果控制。研究进一步发现，引导强度、位置和持续时间是影响多标记生成过程中有效控制的关键参数，需通过人工调参或自动化优化以减少如话题漂移和文本退化等副作用。

链接: https://arxiv.org/abs/2509.12065
作者: Alina Klerings,Jannik Brinkmann,Daniel Ruffinelli,Simone Ponzetto
机构: University of Mannheim (曼海姆大学); Technical University Clausthal (克劳斯塔尔工业大学)
类目: Computation and Language (cs.CL)
备注: to be published in The 2025 Conference on Empirical Methods in Natural Language Processing

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Abstract:Large language models (LLMs) are able to generate grammatically well-formed text, but how do they encode their syntactic knowledge internally? While prior work has focused largely on binary grammatical contrasts, in this work, we study the representation and control of two multidimensional hierarchical grammar phenomena - verb tense and aspect - and for each, identify distinct, orthogonal directions in residual space using linear discriminant analysis. Next, we demonstrate causal control over both grammatical features through concept steering across three generation tasks. Then, we use these identified features in a case study to investigate factors influencing effective steering in multi-token generation. We find that steering strength, location, and duration are crucial parameters for reducing undesirable side effects such as topic shift and degeneration. Our findings suggest that models encode tense and aspect in structurally organized, human-like ways, but effective control of such features during generation is sensitive to multiple factors and requires manual tuning or automated optimization.
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[NLP-13] FinGEAR: Financial Mapping-Guided Enhanced Answer Retrieval

【速读】：该论文旨在解决金融披露文件（如10-K表格）在检索任务中因文本长度长、监管章节层级复杂及领域术语专业性强而导致标准检索增强生成（Retrieval-Augmented Generation, RAG）模型效果不佳的问题。解决方案的关键在于提出FinGEAR（Financial Mapping-Guided Enhanced Answer Retrieval），其核心创新包括：基于金融词典的逐项引导机制（Finance Lexicon for Item-level guidance, FLAM）、用于项内搜索的双层索引结构（Summary Tree与Question Tree），以及两阶段交叉编码器重排序器。该设计通过显式建模文档结构和领域术语信号，实现细粒度且查询感知的上下文选择，显著提升检索准确性和下游问答性能。

链接: https://arxiv.org/abs/2509.12042
作者: Ying Li,Mengyu Wang,Miguel de Carvalho,Sotirios Sabanis,Tiejun Ma
机构: The University of Edinburgh(爱丁堡大学); University of Aveiro(阿维罗大学); National Technical University of Athens(雅典国立技术大学); Archimedes/Athena Research Centre(阿基米德/阿瑞娜研究中心); The Artificial Intelligence Applications Institute(人工智能应用研究所)
类目: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL)
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Abstract:Financial disclosures such as 10-K filings present challenging retrieval problems due to their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We introduce FinGEAR (Financial Mapping-Guided Enhanced Answer Retrieval), a retrieval framework tailored to financial documents. FinGEAR combines a finance lexicon for Item-level guidance (FLAM), dual hierarchical indices for within-Item search (Summary Tree and Question Tree), and a two-stage cross-encoder reranker. This design aligns retrieval with disclosure structure and terminology, enabling fine-grained, query-aware context selection. Evaluated on full 10-Ks with queries aligned to the FinQA dataset, FinGEAR delivers consistent gains in precision, recall, F1, and relevancy, improving F1 by up to 56.7% over flat RAG, 12.5% over graph-based RAGs, and 217.6% over prior tree-based systems, while also increasing downstream answer accuracy with a fixed reader. By jointly modeling section hierarchy and domain lexicon signals, FinGEAR improves retrieval fidelity and provides a practical foundation for high-stakes financial analysis.
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[NLP-14] AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models EMNLP2025

【速读】：该论文旨在解决在严格内存约束下如何高效部署大型语言模型（Large Language Models, LLMs）的问题，核心挑战在于从超大规模的组合搜索空间（超过10^100种配置）中找到最优的逐层量化位宽分配方案，以在模型质量与内存占用之间实现最佳平衡。解决方案的关键在于提出AMQ（Automated Mixed-Precision Weight-Only Quantization）框架，其四大创新包括：基于先验知识的搜索空间剪枝、量化代理（quantization proxy）以规避格式转换开销、质量预测器（quality predictor）降低评估成本，以及迭代式搜索与更新策略，从而实现快速且稳定的收敛，有效探索质量-效率帕累托前沿。

链接: https://arxiv.org/abs/2509.12019
作者: Sangjun Lee,Seung-taek Woo,Jungyu Jin,Changhun Lee,Eunhyeok Park
机构: Pohang University of Science and Technology (POSTECH)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: EMNLP 2025 Main Conference, Long Paper (Oral)

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Abstract:To enable broader deployment of Large Language Models (LLMs), it is essential to identify the best-performing model under strict memory constraints. We present AMQ, Automated Mixed-Precision Weight-Only Quantization, a framework that assigns layer-wise quantization bit-widths to optimally balance model quality and memory usage. However, the combinatorial search space, with over 10^100 possible configurations, makes conventional black-box optimization infeasible. AMQ overcomes this challenge through four key innovations:(1) search space pruning using prior knowledge to exclude unpromising configurations, (2) quantization proxy to bypass costly format conversions during search, (3) quality predictor to minimize evaluation overhead, and (4) iterative search-and-update strategy for fast and stable convergence. By integrating these components, AMQ efficiently explores the quality-efficiency landscape, reaching the Pareto frontier and yielding LLMs that are both compact and high-performing. Our code is available at this https URL.
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[NLP-15] xt Adaptation to Plain Language and Easy Read via Automatic Post-Editing Cycles

【速读】：该论文旨在解决将复杂文本自动转化为通俗易懂的通用语言（Plain Language）和易读文本（Easy Read）的问题，尤其针对西班牙语场景下的可读性优化。其解决方案的关键在于采用迭代式自动后编辑（automatic post-editing）策略，即对初始大型语言模型（Large Language Model, LLM）生成的适应版本进行多轮迭代优化，每一轮都基于可读性和相似性指标判断是否继续调整，直至无法进一步提升为止，从而实现高质量、逐步逼近目标语言风格的文本适配。

链接: https://arxiv.org/abs/2509.11991
作者: Jesús Calleja,David Ponce,Thierry Etchegoyhen
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:We describe Vicomtech’s participation in the CLEARS challenge on text adaptation to Plain Language and Easy Read in Spanish. Our approach features automatic post-editing of different types of initial Large Language Model adaptations, where successive adaptations are generated iteratively until readability and similarity metrics indicate that no further adaptation refinement can be successfully performed. Taking the average of all official metrics, our submissions achieved first and second place in Plain language and Easy Read adaptation, respectively.
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[NLP-16] Query-Focused Extractive Summarization for Sentiment Explanation

【速读】：该论文旨在解决客户反馈文本中情感成因分析的效率与准确性问题，即如何从大量文本文档中自动提取与特定查询相关的、能够解释用户情感（如正面或负面）的关键信息。其核心挑战在于查询与源文档之间存在的语言不一致（linguistic dissonance），这会阻碍现有模型对相关语义内容的准确捕捉。解决方案的关键在于提出一个领域无关的多偏置（multi-bias）框架，通过引入基于情感的偏置（sentiment-based biases）和查询扩展策略（query expansion），在通用层面有效弥合查询与文档之间的语义鸿沟；实验结果表明，该方法在真实世界的情感感知查询聚焦摘要（sentiment-aware Query-Focused Summarization, QFS）数据集上显著优于基线模型。

链接: https://arxiv.org/abs/2509.11989
作者: Ahmed Moubtahij,Sylvie Ratté,Yazid Attabi,Maxime Dumas
机构: Croesus Lab; École de Technologie Supérieure
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Constructive analysis of feedback from clients often requires determining the cause of their sentiment from a substantial amount of text documents. To assist and improve the productivity of such endeavors, we leverage the task of Query-Focused Summarization (QFS). Models of this task are often impeded by the linguistic dissonance between the query and the source documents. We propose and substantiate a multi-bias framework to help bridge this gap at a domain-agnostic, generic level; we then formulate specialized approaches for the problem of sentiment explanation through sentiment-based biases and query expansion. We achieve experimental results outperforming baseline models on a real-world proprietary sentiment-aware QFS dataset.
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[NLP-17] Lost in Embeddings: Information Loss in Vision-Language Models

【速读】：该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）中因视觉编码器输出经连接模块（connector component）投影至语言模型嵌入空间时可能引发的信息损失问题，这一过程虽对跨模态融合至关重要，但其对模型性能的具体影响尚未被充分研究。解决方案的关键在于提出两种互补的分析方法：一是通过比较图像表示在投影前后的k近邻关系变化来评估语义信息保留程度；二是基于投影后表示进行视觉嵌入重建，从而在图像块（patch）级别定位信息损失区域。实验表明，连接模块显著扭曲了视觉表示的局部几何结构，导致k近邻差异达40–60%，且与检索性能下降密切相关；同时，重建结果揭示了高信息损失区域可有效预测模型在视觉接地问答任务中的失败案例，为理解VLM内部机制提供了可解释性依据。

链接: https://arxiv.org/abs/2509.11986
作者: Wenyan Li,Raphael Tang,Chengzu Li,Caiqi Zhang,Ivan Vulić,Anders Søgaard
机构: University of Copenhagen (哥本哈根大学); Microsoft (微软); University of Cambridge (剑桥大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
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Abstract:Vision–language models (VLMs) often process visual inputs through a pretrained vision encoder, followed by a projection into the language model’s embedding space via a connector component. While crucial for modality fusion, the potential information loss induced by this projection step and its direct impact on model capabilities remain understudied. We introduce two complementary approaches to examine and quantify this loss by analyzing the latent representation space. First, we evaluate semantic information preservation by analyzing changes in k-nearest neighbor relationships between image representations, before and after projection. Second, we directly measure information loss by reconstructing visual embeddings from the projected representation, localizing loss at an image patch level. Experiments reveal that connectors substantially distort the local geometry of visual representations, with k-nearest neighbors diverging by 40–60% post-projection, correlating with degradation in retrieval performance. The patch-level embedding reconstruction provides interpretable insights for model behavior on visually grounded question-answering tasks, finding that areas of high information loss reliably predict instances where models struggle.
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[NLP-18] MillStone: How Open-Minded Are LLM s?

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在处理争议性议题时，其立场和观点如何受到外部信息源影响的问题。当前LLMs正逐步取代传统搜索引擎，成为用户获取信息的重要工具，尤其是在涉及政治、社会等敏感话题时，其输出的中立性与可信赖度至关重要。为系统评估这一问题，作者提出MillSTONE基准，这是首个专门用于测量外部论据对LLMs立场影响的评测框架。解决方案的关键在于：通过设计多维度测试集，量化不同LLMs对对立论点的开放程度、一致性以及说服力判断，并揭示权威信息源对模型立场的显著操控效应，从而凸显在构建基于LLM的信息检索与搜索系统时，源头选择的重要性及潜在风险。

链接: https://arxiv.org/abs/2509.11967
作者: Harold Triedman,Vitaly Shmatikov
机构: Cornell Tech (康奈尔技术学院)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 19 pages, 7 tables, 7 figures

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Abstract:Large language models equipped with Web search, information retrieval tools, and other agentic capabilities are beginning to supplant traditional search engines. As users start to rely on LLMs for information on many topics, including controversial and debatable issues, it is important to understand how the stances and opinions expressed in LLM outputs are influenced by the documents they use as their information sources. In this paper, we present MillStone, the first benchmark that aims to systematically measure the effect of external arguments on the stances that LLMs take on controversial issues (not all of them political). We apply MillStone to nine leading LLMs and measure how ``open-minded’’ they are to arguments supporting opposite sides of these issues, whether different LLMs agree with each other, which arguments LLMs find most persuasive, and whether these arguments are the same for different LLMs. In general, we find that LLMs are open-minded on most issues. An authoritative source of information can easily sway an LLM’s stance, highlighting the importance of source selection and the risk that LLM-based information retrieval and search systems can be manipulated. Comments: 19 pages, 7 tables, 7 figures Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) Cite as: arXiv:2509.11967 [cs.LG] (or arXiv:2509.11967v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.11967 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[NLP-19] oolRM: Outcome Reward Models for Tool-Calling Large Language Models

【速读】：该论文旨在解决当前奖励模型（Reward Model）在工具调用（Tool Use）场景下性能不足的问题，尤其是现有模型主要基于自然语言输出进行训练，难以有效评估工具推理与执行的质量。其核心挑战在于缺乏针对工具使用行为的专门奖励建模方法，导致模型无法捕捉关键的有效工具使用信号。解决方案的关键在于提出一种基于结果导向（Outcome-based）的奖励模型训练框架，利用许可宽松的开源大语言模型（LLM）合成训练数据，并在1.7B至14B参数规模范围内训练出更高效的奖励模型；该方法通过奖励引导的数据筛选机制实现数据高效微调，在多个跨领域基准上显著优于通用基线，平均任务性能提升达25%。

链接: https://arxiv.org/abs/2509.11963
作者: Mayank Agarwal,Ibrahim Abdelaziz,Kinjal Basu,Merve Unuvar,Luis A. Lastras,Yara Rizk,Pavan Kapanipathi
机构: IBM Research (IBM 研究院)
类目: Computation and Language (cs.CL)
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Abstract:As large language models (LLMs) increasingly interact with external tools, reward modeling for tool use has become a critical yet underexplored area. Existing reward models, trained primarily on natural language outputs, struggle to evaluate tool-based reasoning and execution. To quantify this gap, we introduce FC-RewardBench, the first benchmark designed to systematically assess reward models’ performance in tool-calling scenarios. Our analysis shows that current reward models often miss key signals of effective tool use, highlighting the need for domain-specific modeling. To address this, we propose a training framework for outcome-based reward models using data synthesized from permissively licensed, open-weight LLMs. We train models ranging from 1.7B to 14B parameters and evaluate them across seven out-of-domain benchmarks. These models consistently outperform general-purpose baselines, achieving up to 25% average improvement in downstream task performance and enabling data-efficient fine-tuning through reward-guided filtering.
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[NLP-20] Spec-LLaVA: Accelerating Vision-Language Models with Dynamic Tree-Based Speculative Decoding ICML

【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在推理过程中因自回归生成方式导致的缓慢问题，从而限制其在实时应用场景中的部署。解决方案的关键在于提出Spec-LLaVA系统，该系统采用推测解码（speculative decoding）策略：通过一个轻量级的草稿VLM预测未来token，由大型目标模型并行验证这些预测，从而实现每步生成多个token；进一步地，设计了一种基于动态树结构的验证算法，根据草稿模型置信度自适应地扩展和剪枝推测分支，以最大化效率。实验表明，在MS COCO跨域图像上，Spec-LLaVA在不损失生成质量的前提下，使LLaVA-1.5（7B、13B参数规模）的解码速度提升最高达3.28倍，且轻量级草稿模型的设计使其适用于资源受限或设备端部署场景。

链接: https://arxiv.org/abs/2509.11961
作者: Mingxiao Huo,Jiayi Zhang,Hewei Wang,Jinfeng Xu,Zheyu Chen,Huilin Tai,Yijun Chen
机构: 未知
类目: Computation and Language (cs.CL)
备注: 7pages, accepted by ICML TTODLer-FM workshop

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Abstract:Vision-Language Models (VLMs) enable powerful multimodal reasoning but suffer from slow autoregressive inference, limiting their deployment in real-time applications. We introduce Spec-LLaVA, a system that applies speculative decoding to accelerate VLMs without sacrificing output quality. Spec-LLaVA pairs a lightweight draft VLM with a large target model: the draft speculates future tokens, which the target verifies in parallel, allowing multiple tokens to be generated per step. To maximize efficiency, we design a dynamic tree-based verification algorithm that adaptively expands and prunes speculative branches using draft model confidence. On MS COCO out-of-domain images, Spec-LLaVA achieves up to 3.28 \times faster decoding on LLaVA-1.5 (7B, 13B) with no loss in generation quality. This work presents a lossless acceleration framework for VLMs using dynamic tree-structured speculative decoding, opening a path toward practical real-time multimodal assistants. Importantly, the lightweight draft model design makes the framework amenable to resource-constrained or on-device deployment settings.
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[NLP-21] How to Evaluate Medical AI

【速读】：该论文旨在解决医学诊断中人工智能（AI）评估方法的可靠性与临床相关性问题，特别是传统指标（如精确率和召回率）未能考虑专家判断中的固有变异性，导致对AI性能评估不一致；同时，现有基于一致性统计量（如Cohen’s Kappa）的方法虽更可靠但缺乏可解释性。其解决方案的关键在于提出一种新的相对评估指标——算法诊断的相对精确率与召回率（Relative Precision and Recall of Algorithmic Diagnostics, RPAD 和 RRAD），通过将AI输出与多个专家意见进行比较而非单一参考标准，并以专家间分歧作为归一化基准，从而提供更稳定、贴近实际临床情境的性能衡量方式。此外，研究还创新性地采用自由形式诊断识别机制，在无需预设诊断列表的情况下实现高达98%的准确率，进一步验证了该方法的有效性和实用性。

链接: https://arxiv.org/abs/2509.11941
作者: Ilia Kopanichuk,Petr Anokhin,Vladimir Shaposhnikov,Vladimir Makharev,Ekaterina Tsapieva,Iaroslav Bespalov,Dmitry V. Dylov,Ivan Oseledets
机构: AIRI(人工智能研究 institute); Moscow Institute of Physics and Technology(莫斯科物理技术学院); Skokovo Institute of Science and Technology(斯科科沃科学与技术研究所); Innopolis University(因诺波利斯大学); SberHealth(СберЗдоровье)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 10 pages, 7 fugures

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Abstract:The integration of artificial intelligence (AI) into medical diagnostic workflows requires robust and consistent evaluation methods to ensure reliability, clinical relevance, and the inherent variability in expert judgments. Traditional metrics like precision and recall often fail to account for the inherent variability in expert judgments, leading to inconsistent assessments of AI performance. Inter-rater agreement statistics like Cohen’s Kappa are more reliable but they lack interpretability. We introduce Relative Precision and Recall of Algorithmic Diagnostics (RPAD and RRAD) - a new evaluation metrics that compare AI outputs against multiple expert opinions rather than a single reference. By normalizing performance against inter-expert disagreement, these metrics provide a more stable and realistic measure of the quality of predicted diagnosis. In addition to the comprehensive analysis of diagnostic quality measures, our study contains a very important side result. Our evaluation methodology allows us to avoid selecting diagnoses from a limited list when evaluating a given case. Instead, both the models being tested and the examiners verifying them arrive at a free-form diagnosis. In this automated methodology for establishing the identity of free-form clinical diagnoses, a remarkable 98% accuracy becomes attainable. We evaluate our approach using 360 medical dialogues, comparing multiple large language models (LLMs) against a panel of physicians. Large-scale study shows that top-performing models, such as DeepSeek-V3, achieve consistency on par with or exceeding expert consensus. Moreover, we demonstrate that expert judgments exhibit significant variability - often greater than that between AI and humans. This finding underscores the limitations of any absolute metrics and supports the need to adopt relative metrics in medical AI.
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[NLP-22] Designing LLM s for cultural sensitivity: Evidence from English-Japanese translation

【速读】：该论文试图解决的问题是：大型语言模型（Large Language Models, LLMs）在跨文化语境下的多语言交流中，虽然能够生成接近字面准确的翻译，但其是否支持符合文化规范的沟通仍不明确。为解决此问题，研究者设计了三种提示策略：（1）简单的“仅翻译”提示，（2）针对接收方文化背景的目标受众提示，以及（3）提供日本沟通规范明确指导的指令式提示。关键解决方案在于通过文化定制化提示（culturally-tailored prompting）显著提升翻译内容的文化适配性，从而改善跨文化沟通的恰当性，为设计更具文化包容性的多语言LLMs提供了实证依据和实践建议。

链接: https://arxiv.org/abs/2509.11921
作者: Helene Tenzer,Oumnia Abidi,Stefan Feuerriegel
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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点击查看摘要

Abstract:Large language models (LLMs) are increasingly used in everyday communication, including multilingual interactions across different cultural contexts. While LLMs can now generate near-perfect literal translations, it remains unclear whether LLMs support culturally appropriate communication. In this paper, we analyze the cultural sensitivity of different LLM designs when applied to English-Japanese translations of workplace e-mails. Here, we vary the prompting strategies: (1) naive “just translate” prompts, (2) audience-targeted prompts specifying the recipient’s cultural background, and (3) instructional prompts with explicit guidance on Japanese communication norms. Using a mixed-methods study, we then analyze culture-specific language patterns to evaluate how well translations adapt to cultural norms. Further, we examine the appropriateness of the tone of the translations as perceived by native speakers. We find that culturally-tailored prompting can improve cultural fit, based on which we offer recommendations for designing culturally inclusive LLMs in multilingual settings.
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[NLP-23] Uncertainty in Authorship: Why Perfect AI Detection Is Mathematically Impossible

【速读】：该论文试图解决的问题是：随着大语言模型（Large Language Models, LLMs）生成文本能力的提升，如何准确区分人类写作与AI生成文本这一日益严峻的挑战。其核心难点在于，当前检测方法（如风格分析、水印技术和神经分类器）在提高检测置信度的同时，往往不可避免地干扰文本的自然性和真实性，从而引入新的不确定性。解决方案的关键在于提出一个类比框架——将作者识别的局限性类比于量子不确定性原理，揭示出检测精度与文本完整性之间存在根本性的权衡关系：追求更高检测准确性会破坏文本的原始特征，使得其他检测指标失效。因此，论文指出，当AI文本高度模仿人类写作时，实现完美检测不仅是技术难题，更是理论上的不可能任务，这反映了语言本质中不可回避的矛盾。

链接: https://arxiv.org/abs/2509.11915
作者: Aadil Gani Ganie
机构: UNIVERSITAT POLITECNICA DE VALENCIA (瓦伦西亚理工大学)
类目: Computation and Language (cs.CL)
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Abstract:As large language models (LLMs) become more advanced, it is increasingly difficult to distinguish between human-written and AI-generated text. This paper draws a conceptual parallel between quantum uncertainty and the limits of authorship detection in natural language. We argue that there is a fundamental trade-off: the more confidently one tries to identify whether a text was written by a human or an AI, the more one risks disrupting the text’s natural flow and authenticity. This mirrors the tension between precision and disturbance found in quantum systems. We explore how current detection methods–such as stylometry, watermarking, and neural classifiers–face inherent limitations. Enhancing detection accuracy often leads to changes in the AI’s output, making other features less reliable. In effect, the very act of trying to detect AI authorship introduces uncertainty elsewhere in the text. Our analysis shows that when AI-generated text closely mimics human writing, perfect detection becomes not just technologically difficult but theoretically impossible. We address counterarguments and discuss the broader implications for authorship, ethics, and policy. Ultimately, we suggest that the challenge of AI-text detection is not just a matter of better tools–it reflects a deeper, unavoidable tension in the nature of language itself.
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[NLP-24] Growing Perspectives: Modelling Embodied Perspective Taking and Inner Narrative Development Using Large Language Models

【速读】：该论文试图解决的问题是如何在计算模型中同时建模语言理解和具身视角采择（embodied perspective taking），以更好地模拟人类协作中的认知发展过程。现有模型大多仅关注语言或行为层面的交互，而忽视了二者协同作用对协作效能的影响。解决方案的关键在于提出PerspAct系统，该系统将ReAct（Reason and Act）范式与大语言模型（Large Language Models, LLMs）相结合，并基于Selman的发展阶段理论构建内部叙事生成机制，从而在任务执行前模拟不同发展阶段的视角采择能力，并通过扩展的指挥者任务（director task）评估这些内部叙事如何影响合作行为的质量与效率。实验表明，LLM能够生成符合特定发展阶段的语言内省叙事，且在互动过程中会动态调整至更高级别，进而提升协作效果，凸显了语言与具身交互共同塑造认知发展的潜力。

链接: https://arxiv.org/abs/2509.11868
作者: Sabrina Patania,Luca Annese,Anna Lambiase,Anita Pellegrini,Tom Foulsham,Azzurra Ruggeri,Silvia Rossi,Silvia Serino,Dimitri Ognibene
机构: University of Milan-Bicocca (米兰博科尼大学); University of Essex (埃塞克斯大学); Technical University of Munich (慕尼黑工业大学); University of Naples Federico II (那不勒斯腓特烈二世大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Robotics (cs.RO)
备注: Accepted at ICDL this https URL

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Abstract:Language and embodied perspective taking are essential for human collaboration, yet few computational models address both simultaneously. This work investigates the PerspAct system [1], which integrates the ReAct (Reason and Act) paradigm with Large Language Models (LLMs) to simulate developmental stages of perspective taking, grounded in Selman’s theory [2]. Using an extended director task, we evaluate GPT’s ability to generate internal narratives aligned with specified developmental stages, and assess how these influence collaborative performance both qualitatively (action selection) and quantitatively (task efficiency). Results show that GPT reliably produces developmentally-consistent narratives before task execution but often shifts towards more advanced stages during interaction, suggesting that language exchanges help refine internal representations. Higher developmental stages generally enhance collaborative effectiveness, while earlier stages yield more variable outcomes in complex contexts. These findings highlight the potential of integrating embodied perspective taking and language in LLMs to better model developmental dynamics and stress the importance of evaluating internal speech during combined linguistic and embodied tasks.
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[NLP-25] MOOM: Maintenance Organization and Optimization of Memory in Ultra-Long Role-Playing Dialogues

【速读】：该论文旨在解决人机角色扮演场景中长对话连贯性维护问题，特别是现有记忆提取方法普遍存在不可控的记忆增长（memory growth）现象。解决方案的关键在于提出MOOM——首个双分支记忆插件，其核心创新是基于文学理论将叙事结构建模为情节发展（plot development）与人物塑造（character portrayal）两大要素：一个分支在多时间尺度上总结情节冲突，另一个分支提取用户角色画像；同时引入受“竞争抑制”（competition-inhibition）记忆理论启发的遗忘机制，有效控制记忆容量并防止无序膨胀。

链接: https://arxiv.org/abs/2509.11860
作者: Weishu Chen,Jinyi Tang,Zhouhui Hou,Shihao Han,Mingjie Zhan,Zhiyuan Huang,Delong Liu,Jiawei Guo,Zhicheng Zhao,Fei Su
机构: Beijing University of Posts and Telecommunications (北京邮电大学); SenseTime (商汤科技); Beijing Key Laboratory of Network System and Network Culture (北京市网络系统与网络文化重点实验室)
类目: Computation and Language (cs.CL)
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Abstract:Memory extraction is crucial for maintaining coherent ultra-long dialogues in human-robot role-playing scenarios. However, existing methods often exhibit uncontrolled memory growth. To address this, we propose MOOM, the first dual-branch memory plugin that leverages literary theory by modeling plot development and character portrayal as core storytelling elements. Specifically, one branch summarizes plot conflicts across multiple time scales, while the other extracts the user’s character profile. MOOM further integrates a forgetting mechanism, inspired by the ``competition-inhibition’’ memory theory, to constrain memory capacity and mitigate uncontrolled growth. Furthermore, we present ZH-4O, a Chinese ultra-long dialogue dataset specifically designed for role-playing, featuring dialogues that average 600 turns and include manually annotated memory information. Experimental results demonstrate that MOOM outperforms all state-of-the-art memory extraction methods, requiring fewer large language model invocations while maintaining a controllable memory capacity.
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[NLP-26] he AI Memory Gap: Users Misremember What They Created With AI or Without

【速读】：该论文旨在解决交互式文本生成中因使用大语言模型（Large Language Models, LLMs）而导致的来源记忆混淆问题，即用户难以准确区分哪些想法或文本内容是由自己生成的，哪些是借助AI生成的。其解决方案的关键在于通过一项预注册实验（n=184）验证了：在使用LLM辅助创作后，用户的来源记忆显著下降，尤其在混合人-AI工作流中（即仅idea或仅扩展部分由AI生成时），错误归因概率最高；研究进一步采用计算源记忆模型对结果进行验证，从而强调在设计和应用交互式文本生成技术时必须考虑源混淆风险，以提升用户对生成内容的可追溯性与责任意识。

链接: https://arxiv.org/abs/2509.11851
作者: Tim Zindulka,Sven Goller,Daniela Fernandes,Robin Welsch,Daniel Buschek
机构: University of Bayreuth (拜罗伊特大学); Aalto University (阿尔托大学)
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
备注: 31 pages, 10 figures, 9 tables

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Abstract:As large language models (LLMs) become embedded in interactive text generation, disclosure of AI as a source depends on people remembering which ideas or texts came from themselves and which were created with AI. We investigate how accurately people remember the source of content when using AI. In a pre-registered experiment, 184 participants generated and elaborated on ideas both unaided and with an LLM-based chatbot. One week later, they were asked to identify the source (noAI vs withAI) of these ideas and texts. Our findings reveal a significant gap in memory: After AI use, the odds of correct attribution dropped, with the steepest decline in mixed human-AI workflows, where either the idea or elaboration was created with AI. We validated our results using a computational model of source memory. Discussing broader implications, we highlight the importance of considering source confusion in the design and use of interactive text generation technologies.
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[NLP-27] Collaborative Document Editing with Multiple Users and AI Agents

【速读】：该论文试图解决当前AI写作辅助工具主要面向个体用户设计，导致协作写作时合作者需离开共享工作空间使用AI并重新整合结果，从而破坏协同效率的问题。解决方案的关键在于将AI代理（AI agents）直接集成到协作写作环境中，并通过两种新的共享对象——代理档案（agent profiles）和任务（tasks）实现对AI使用的透明化与可定制化；其中，代理响应以熟悉的评论功能呈现，使团队能够基于现有作者权、控制权和协调规范将AI代理纳入协作流程，而非将其视为团队成员，从而有效提升协作效率与资源管理能力。

链接: https://arxiv.org/abs/2509.11826
作者: Florian Lehmann,Krystsina Shauchenka,Daniel Buschek
机构: University of Bayreuth (拜罗伊特大学)
类目: Human-Computer Interaction (cs.HC); Computation and Language (cs.CL)
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Abstract:Current AI writing support tools are largely designed for individuals, complicating collaboration when co-writers must leave the shared workspace to use AI and then communicate and reintegrate results. We propose integrating AI agents directly into collaborative writing environments. Our prototype makes AI use transparent and customisable through two new shared objects: agent profiles and tasks. Agent responses appear in the familiar comment feature. In a user study (N=30), 14 teams worked on writing projects during one week. Interaction logs and interviews show that teams incorporated agents into existing norms of authorship, control, and coordination, rather than treating them as team members. Agent profiles were viewed as personal territory, while created agents and outputs became shared resources. We discuss implications for team-based AI interaction, highlighting opportunities and boundaries for treating AI as a shared resource in collaborative work.
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[NLP-28] SCDTour: Embedding Axis Ordering and Merging for Interpretable Semantic Change Detection EMNLP2025

【速读】：该论文旨在解决语义变化检测（Semantic Change Detection, SCD）中嵌入表示的可解释性与性能之间的权衡问题：提升可解释性常导致SCD性能下降，反之亦然。解决方案的关键在于提出SCDTour方法，该方法通过排序并合并具有高语义相似性的可解释轴（interpretable axes），在保留语义变化检测性能的同时增强嵌入的可解释性；其核心机制包括两个维度：(a) 嵌入空间中各轴间的语义相似性，以及 (b) 每个轴对语义变化的贡献度。实验表明，SCDTour不仅维持了高SCD性能，还通过聚类排序后的轴生成更精细的词义划分，在任务表现上可媲美甚至超越原始全维嵌入。

链接: https://arxiv.org/abs/2509.11818
作者: Taichi Aida,Danushka Bollegala
机构: Tokyo Metropolitan Unviersity (东京都立大学); University of Liverpool (利物浦大学)
类目: Computation and Language (cs.CL)
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Abstract:In Semantic Change Detection (SCD), it is a common problem to obtain embeddings that are both interpretable and high-performing. However, improving interpretability often leads to a loss in the SCD performance, and vice versa. To address this problem, we propose SCDTour, a method that orders and merges interpretable axes to alleviate the performance degradation of SCD. SCDTour considers both (a) semantic similarity between axes in the embedding space, as well as (b) the degree to which each axis contributes to semantic change. Experimental results show that SCDTour preserves performance in semantic change detection while maintaining high interpretability. Moreover, agglomerating the sorted axes produces a more refined set of word senses, which achieves comparable or improved performance against the original full-dimensional embeddings in the SCD task. These findings demonstrate that SCDTour effectively balances interpretability and SCD performance, enabling meaningful interpretation of semantic shifts through a small number of refined axes. Source code is available at this https URL .
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[NLP-29] Collapse of Irrelevant Representations (CIR) Ensures Robust and Non-Disruptive LLM Unlearning

【速读】：该论文旨在解决当前语言模型中危险知识难以有效移除的问题，即现有遗忘技术与安全训练方法在删除特定有害信息时往往无法彻底清除，甚至可能损害模型的通用性能。其解决方案的关键在于提出一种高度选择性的遗忘机制：通过主成分分析（PCA）对激活值和模块输出梯度进行分解，识别出包含共性表征的子空间，并在计算遗忘更新前将其压缩坍缩，从而避免误删通用语义表示，仅针对与被遗忘事实相关的特异性表征实施精准干预。实验表明，该方法在Llama-3.1-8B模型上对生物危害和网络危害类事实的攻击后准确率分别降低80倍和30倍，同时通用性能损失仅为基准方法的1/30（WikiText损失增加仅0.1%），且每条事实遗忘所需计算资源不足3 GPU秒。

链接: https://arxiv.org/abs/2509.11816
作者: Filip Sondej,Yushi Yang
机构: Jagiellonian University (雅盖隆大学); University of Oxford (牛津大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Current unlearning techniques and safety training consistently fail to remove dangerous knowledge from language models. We analyze the root causes and propose a highly selective technique which unlearns robustly and without disrupting general performance. We perform PCA on activations and module output gradients to identify subspaces containing common representations, and collapse them before calculating unlearning updates. This way we avoid unlearning general representations, and only target those specific to the unlearned facts. When unlearning WMDP dataset facts from Llama-3.1-8B, we drop post-attack accuracy 80x more than our best baseline (Circuit Breakers) on biohazardous facts and 30x more on cyberhazardous facts. Despite this, we disrupt general performance 30x less (only 0.1% WikiText loss increase), while requiring less than 3 GPU-seconds per fact. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2509.11816 [cs.LG] (or arXiv:2509.11816v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.11816 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[NLP-30] PledgeTracker: A System for Monitoring the Fulfilment of Pledges EMNLP2025

【速读】：该论文旨在解决政治承诺（political pledges）履行情况的追踪问题，传统方法将其简化为文档分类任务，忽略了承诺履行过程中的动态性、时间性和多文档证据分布特性。解决方案的关键在于提出PledgeTracker系统，通过结构化事件时间线构建的方式重构承诺验证任务，其核心包括三部分：多步证据检索模块、时间线构建模块和履行过滤模块，从而捕捉承诺履行的演化过程，并生成可解释且结构化的时序记录，显著提升事实核查效率并降低人工验证负担。

链接: https://arxiv.org/abs/2509.11804
作者: Yulong Chen,Michael Sejr Schlichtkrull,Zhenyun Deng,David Corney,Nasim Asl,Joshua Salisbury,Andrew Dudfield,Andreas Vlachos
机构: University of Cambridge (剑桥大学); Queen Mary University London (伦敦玛丽女王大学); Full Fact
类目: Computation and Language (cs.CL)
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Abstract:Political pledges reflect candidates’ policy commitments, but tracking their fulfilment requires reasoning over incremental evidence distributed across multiple, dynamically updated sources. Existing methods simplify this task into a document classification task, overlooking its dynamic, temporal and multi-document nature. To address this issue, we introduce \textscPledgeTracker, a system that reformulates pledge verification into structured event timeline construction. PledgeTracker consists of three core components: (1) a multi-step evidence retrieval module; (2) a timeline construction module and; (3) a fulfilment filtering module, allowing the capture of the evolving nature of pledge fulfilment and producing interpretable and structured timelines. We evaluate PledgeTracker in collaboration with professional fact-checkers in real-world workflows, demonstrating its effectiveness in retrieving relevant evidence and reducing human verification effort.
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[NLP-31] From Fuzzy Speech to Medical Insight: Benchmarking LLM s on Noisy Patient Narratives

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在医疗场景中对患者自述文本（patient-generated narratives）理解能力不足的问题，这类文本通常具有语言噪声、模糊表达和非专业术语等特点，而现有基准测试多基于结构化、干净的临床文本，难以反映真实世界中的复杂性。解决方案的关键在于构建了一个新颖的合成数据集——Noisy Diagnostic Benchmark (NDB)，该数据集模拟了不同水平的语言噪声、模糊性和通俗术语，并包含与临床一致的标注诊断信息，从而能够系统评估LLMs在接近真实临床交互条件下的诊断推理能力。通过该基准，研究者对多种先进模型（如BERT-based和T5架构）进行了微调与评测，为未来LLMs在医疗自然语言处理任务中的鲁棒性提升提供了可复现的测试平台。

链接: https://arxiv.org/abs/2509.11803
作者: Eden Mama,Liel Sheri,Yehudit Aperstein,Alexander Apartsin
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:The widespread adoption of large language models (LLMs) in healthcare raises critical questions about their ability to interpret patient-generated narratives, which are often informal, ambiguous, and noisy. Existing benchmarks typically rely on clean, structured clinical text, offering limited insight into model performance under realistic conditions. In this work, we present a novel synthetic dataset designed to simulate patient self-descriptions characterized by varying levels of linguistic noise, fuzzy language, and layperson terminology. Our dataset comprises clinically consistent scenarios annotated with ground-truth diagnoses, spanning a spectrum of communication clarity to reflect diverse real-world reporting styles. Using this benchmark, we fine-tune and evaluate several state-of-the-art models (LLMs), including BERT-based and encoder-decoder T5 models. To support reproducibility and future research, we release the Noisy Diagnostic Benchmark (NDB), a structured dataset of noisy, synthetic patient descriptions designed to stress-test and compare the diagnostic capabilities of large language models (LLMs) under realistic linguistic conditions. We made the benchmark available for the community: this https URL
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[NLP-32] When Curiosity Signals Danger: Predicting Health Crises Through Online Medication Inquiries

【速读】：该论文旨在解决在线医疗论坛中患者关于药物使用的提问难以有效识别关键风险问题的问题，这些问题可能预示着用药混乱、误用或潜在的健康危机。解决方案的关键在于构建了一个经过人工标注的药物相关问题数据集，依据临床风险因素对每条提问进行关键性标注，并在此基础上对比传统机器学习分类器（基于TF-IDF特征）与三种先进大语言模型（Large Language Model, LLM）方法的性能，从而验证不同技术在实时分诊和预警系统中的潜力。

链接: https://arxiv.org/abs/2509.11802
作者: Dvora Goncharok,Arbel Shifman,Alexander Apartsin,Yehudit Aperstein
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Online medical forums are a rich and underutilized source of insight into patient concerns, especially regarding medication use. Some of the many questions users pose may signal confusion, misuse, or even the early warning signs of a developing health crisis. Detecting these critical questions that may precede severe adverse events or life-threatening complications is vital for timely intervention and improving patient safety. This study introduces a novel annotated dataset of medication-related questions extracted from online forums. Each entry is manually labelled for criticality based on clinical risk factors. We benchmark the performance of six traditional machine learning classifiers using TF-IDF textual representations, alongside three state-of-the-art large language model (LLM)-based classification approaches that leverage deep contextual understanding. Our results highlight the potential of classical and modern methods to support real-time triage and alert systems in digital health spaces. The curated dataset is made publicly available to encourage further research at the intersection of patient-generated data, natural language processing, and early warning systems for critical health events. The dataset and benchmark are available at: this https URL.
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[NLP-33] User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums

【速读】：该论文旨在解决工业论坛中用户反馈信息难以系统分析的问题，这类反馈虽能提供真实场景下的用户体验（User Experience, UX）洞察，但因其非结构化和领域专业性强的特点，传统数据处理方法难以准确识别、分类与量化其中的信息。解决方案的关键在于构建一个名为User eXperience Perception Insights Dataset (UXPID) 的合成匿名数据集，包含7130条从公开工业自动化论坛提取的多轮评论记录，每条记录均以JSON格式存储并附有元数据和上下文信息，并通过大语言模型（Large Language Model, LLM）对每条反馈进行系统标注，涵盖用户体验洞察、用户期望、严重性与情感评分及主题分类等维度，从而为用户需求挖掘、UX分析及AI驱动的反馈处理研究提供高质量训练与评估资源。

链接: https://arxiv.org/abs/2509.11777
作者: Mikhail Kulyabin,Jan Joosten,Choro Ulan uulu,Nuno Miguel Martins Pacheco,Fabian Ries,Filippos Petridis,Jan Bosch,Helena Holmström Olsson
机构: Siemens AG (西门子集团); Eindhoven University of Technology (埃因霍温理工大学); Chalmers University of Technology (查尔默斯理工大学); Malmö University (马尔默大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Customer feedback in industrial forums reflect a rich but underexplored source of insight into real-world product experience. These publicly shared discussions offer an organic view of user expectations, frustrations, and success stories shaped by the specific contexts of use. Yet, harnessing this information for systematic analysis remains challenging due to the unstructured and domain-specific nature of the content. The lack of structure and specialized vocabulary makes it difficult for traditional data analysis techniques to accurately interpret, categorize, and quantify the feedback, thereby limiting its potential to inform product development and support strategies. To address these challenges, this paper presents the User eXperience Perception Insights Dataset (UXPID), a collection of 7130 artificially synthesized and anonymized user feedback branches extracted from a public industrial automation forum. Each JavaScript object notation (JSON) record contains multi-post comments related to specific hardware and software products, enriched with metadata and contextual conversation data. Leveraging a large language model (LLM), each branch is systematically analyzed and annotated for UX insights, user expectations, severity and sentiment ratings, and topic classifications. The UXPID dataset is designed to facilitate research in user requirements, user experience (UX) analysis, and AI-driven feedback processing, particularly where privacy and licensing restrictions limit access to real-world data. UXPID supports the training and evaluation of transformer-based models for tasks such as issue detection, sentiment analysis, and requirements extraction in the context of technical forums.
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[NLP-34] An Agent ic Toolkit for Adaptive Information Extraction from Regulatory Documents

【速读】：该论文旨在解决欧盟法规要求的建筑产品性能声明（Declaration of Performance, DoP）文档在自动化关键值对（Key-Value Pair, KVP）提取与问答（Question Answering, QA）任务中面临的挑战，这些问题源于DoP文档在布局、语言、结构模式和格式上的高度多样性，导致现有静态或仅依赖大语言模型（Large Language Model, LLM）的抽取管道容易产生幻觉且难以适应结构差异。解决方案的关键在于提出一种领域特定、状态感知的智能体系统（stateful agentic system），其采用规划器-执行器-响应器（planner-executor-responder）架构，能够推理用户意图、识别文档模态，并动态编排工具进行可追溯的鲁棒推理，从而避免工具误用或执行循环，显著提升跨格式与多语言场景下的稳定性与准确性。

链接: https://arxiv.org/abs/2509.11773
作者: Gaye Colakoglu,Gürkan Solmaz,Jonathan Fürst
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Declaration of Performance (DoP) documents, mandated by EU regulation, certify the performance of construction products. While some of their content is standardized, DoPs vary widely in layout, language, schema, and format, posing challenges for automated key-value pair extraction (KVP) and question answering (QA). Existing static or LLM-only IE pipelines often hallucinate and fail to adapt to this structural diversity. Our domain-specific, stateful agentic system addresses these challenges through a planner-executor-responder architecture. The system infers user intent, detects document modality, and orchestrates tools dynamically for robust, traceable reasoning while avoiding tool misuse or execution loops. Evaluation on a curated DoP dataset demonstrates improved robustness across formats and languages, offering a scalable solution for structured data extraction in regulated workflows.
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[NLP-35] Room acoustics affect communicative success in hybrid meeting spaces: a pilot study

【速读】：该论文试图解决的问题是：在新冠疫情期间，高校和企业广泛采用混合会议（hybrid meetings）模式，但往往忽视了会议室的声学设计，尤其是混响过强导致的语音清晰度下降、沟通误解及认知与嗓音疲劳等问题。解决方案的关键在于通过空间声学干预（如增加吸声材料等措施）改善 seminar room 的声学环境，并验证其对混合会议中沟通成效的提升作用。研究结果显示，尽管样本量较小未达统计显著性，但声学改进明显提升了混合会议中的交流效果。

链接: https://arxiv.org/abs/2509.11709
作者: Robert Einig,Stefan Janscha,Jonas Schuster,Julian Koch,Martin Hagmueller,Barbara Schuppler
机构: 未知
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
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Abstract:Since the COVID-19 pandemic in 2020, universities and companies have increasingly integrated hybrid features into their meeting spaces, or even created dedicated rooms for this purpose. While the importance of a fast and stable internet connection is often prioritized, the acoustic design of seminar rooms is frequently overlooked. Poor acoustics, particularly excessive reverberation, can lead to issues such as misunderstandings, reduced speech intelligibility or cognitive and vocal fatigue. This pilot study investigates whether room acoustic interventions in a seminar room at Graz University of Technology support better communication in hybrid meetings. For this purpose, we recorded two groups of persons twice, once before and once after improving the acoustics of the room. Our findings – despite not reaching statistical significance due to the small sample size - indicate clearly that our spatial interventions improve communicative success in hybrid meetings. To make the paper accessible also for readers from the speech communication community, we explain room acoustics background, relevant for the interpretation of our results.
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[NLP-36] CoachMe: Decoding Sport Elements with a Reference-Based Coaching Instruction Generation Model ACL2025 ACL

【速读】：该论文旨在解决运动指导中生成精准、领域特定且具信息量的纠正性指令的问题，尤其是在体育动作分析中，如何从学习者与参考动作之间的差异出发，提供具有教练思维逻辑的反馈。其解决方案的关键在于提出CoachMe模型，该模型基于参考样本，从时序和物理两个维度分析学习者动作与标准动作的差异，从而实现领域知识的学习和类教练推理能力的构建，使生成的指令不仅语气专业，更能具体指出错误并给出改进方法，实验表明其在花样滑冰和拳击场景下显著优于GPT-4o（分别提升31.6%和58.3%的G-Eval得分）。

链接: https://arxiv.org/abs/2509.11698
作者: Wei-Hsin Yeh,Yu-An Su,Chih-Ning Chen,Yi-Hsueh Lin,Calvin Ku,Wen-Hsin Chiu,Min-Chun Hu,Lun-Wei Ku
机构: Institute of Information Science, Academia Sinica (中央研究院資訊科學研究所); National Tsing Hua University (國立清華大學); National Taiwan University (國立臺灣大學)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Published in Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2025. Official version: this https URL

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Abstract:Motion instruction is a crucial task that helps athletes refine their technique by analyzing movements and providing corrective guidance. Although recent advances in multimodal models have improved motion understanding, generating precise and sport-specific instruction remains challenging due to the highly domain-specific nature of sports and the need for informative guidance. We propose CoachMe, a reference-based model that analyzes the differences between a learner’s motion and a reference under temporal and physical aspects. This approach enables both domain-knowledge learning and the acquisition of a coach-like thinking process that identifies movement errors effectively and provides feedback to explain how to improve. In this paper, we illustrate how CoachMe adapts well to specific sports such as skating and boxing by learning from general movements and then leveraging limited data. Experiments show that CoachMe provides high-quality instructions instead of directions merely in the tone of a coach but without critical information. CoachMe outperforms GPT-4o by 31.6% in G-Eval on figure skating and by 58.3% on boxing. Analysis further confirms that it elaborates on errors and their corresponding improvement methods in the generated instructions. You can find CoachMe here: this https URL
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[NLP-37] A Dynamic Knowledge Update-Driven Model with Large Language Models for Fake News Detection

【速读】：该论文旨在解决虚假新闻检测中因新闻事件动态发展导致的时效性知识缺失与噪声干扰问题，即如何在事件演变过程中持续更新可信知识并准确挖掘新闻语义。其解决方案的关键在于提出一种基于动态知识更新驱动的模型（DYNAMO），通过构建新闻领域特定的知识图谱实现新知识的持续注入，并结合大语言模型完成双重任务：一是对新闻真实性进行判别，二是验证新增知识的正确性；具体而言，利用蒙特卡洛树搜索（Monte Carlo Tree Search）对复杂新闻进行分步解析与验证，再从已验证的真实新闻文本及推理路径中提取并更新知识，从而保障知识来源的可靠性与检测结果的准确性。

链接: https://arxiv.org/abs/2509.11687
作者: Di Jin,Jun Yang,Xiaobao Wang,Junwei Zhang,Shuqi Li,Dongxiao He
机构: Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University (天津大学认知计算与应用重点实验室，智能与计算机学院); Qinghai Minzu University (青海民族大学); Hangzhou Institute of Medicine, Chinese Academy of Sciences (中国科学院杭州医学研究所)
类目: Computation and Language (cs.CL)
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Abstract:As the Internet and social media evolve rapidly, distinguishing credible news from a vast amount of complex information poses a significant challenge. Due to the suddenness and instability of news events, the authenticity labels of news can potentially shift as events develop, making it crucial for fake news detection to obtain the latest event updates. Existing methods employ retrieval-augmented generation to fill knowledge gaps, but they suffer from issues such as insufficient credibility of retrieved content and interference from noisy information. We propose a dynamic knowledge update-driven model for fake news detection (DYNAMO), which leverages knowledge graphs to achieve continuous updating of new knowledge and integrates with large language models to fulfill dual functions: news authenticity detection and verification of new knowledge correctness, solving the two key problems of ensuring the authenticity of new knowledge and deeply mining news semantics. Specifically, we first construct a news-domain-specific knowledge graph. Then, we use Monte Carlo Tree Search to decompose complex news and verify them step by step. Finally, we extract and update new knowledge from verified real news texts and reasoning paths. Experimental results demonstrate that DYNAMO achieves the best performance on two real-world datasets.
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[NLP-38] Measuring Visual Understanding in Telecom domain: Performance Metrics for Image-to-UML conversion using VLMs

【速读】：该论文旨在解决电信领域3GPP文档中序列图（sequence diagram）图像到可机器读取的PlantUML（puml）格式转换过程中的评估缺失问题，即现有研究缺乏对转换后puml脚本各组件准确性的系统性比较。其解决方案的关键在于提出一套针对puml脚本不同组成部分的性能指标，包括参与者识别、消息流准确性、序列顺序和分组结构保留，并利用版本控制工具捕捉差异，从而量化VLMs（如Claude Sonnet与GPT-4V）在转换过程中的误差表现。实验表明，节点、边和消息能被准确捕获，但复杂结构（如注释、框图和分组）仍存在显著不足，揭示了训练数据中此类组件表征不足是当前VLMs性能瓶颈的核心原因。

链接: https://arxiv.org/abs/2509.11667
作者: HG Ranjani,Rutuja Prabhudesai
机构: Ericsson R&D (Ericsson研发部)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
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Abstract:Telecom domain 3GPP documents are replete with images containing sequence diagrams. Advances in Vision-Language Large Models (VLMs) have eased conversion of such images to machine-readable PlantUML (puml) formats. However, there is a gap in evaluation of such conversions - existing works do not compare puml scripts for various components. In this work, we propose performance metrics to measure the effectiveness of such conversions. A dataset of sequence diagrams from 3GPP documents is chosen to be representative of domain-specific actual scenarios. We compare puml outputs from two VLMs - Claude Sonnet and GPT-4V - against manually created ground truth representations. We use version control tools to capture differences and introduce standard performance metrics to measure accuracies along various components: participant identification, message flow accuracy, sequence ordering, and grouping construct preservation. We demonstrate effectiveness of proposed metrics in quantifying conversion errors across various components of puml scripts. The results show that nodes, edges and messages are accurately captured. However, we observe that VLMs do not necessarily perform well on complex structures such as notes, box, groups. Our experiments and performance metrics indicates a need for better representation of these components in training data for fine-tuned VLMs.
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[NLP-39] MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs

【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Model, MLLM）在昇腾（Ascend）NPU平台上训练效率低、精度损失以及对高分辨率视觉内容处理能力不足的问题。其关键解决方案包括：1）提出基于原生分辨率视觉Transformer（native-resolution Vision Transformers）的MindVL架构，避免固定分辨率切片带来的细节丢失，从而有效保留复杂图表等密集视觉内容的细粒度信息与全局结构；2）开发专为Ascend NPU定制的分布式多模态训练框架Mindspeed-MLLM，并通过算子等效替换保障训练精度；3）采用三阶段训练策略（预训练→多任务训练→监督指令微调）、混合并行技术及测试时分辨率搜索与权重平均机制，显著提升训练效率与模型性能，最终在仅使用约十分之一训练数据的情况下达到与Qwen2.5-VL相当甚至更优的多模态理解与文档表格解析能力。

链接: https://arxiv.org/abs/2509.11662
作者: Feilong Chen,Yijiang Liu,Yi Huang,Hao Wang,Miren Tian,Ya-Qi Yu,Minghui Liao,Jihao Wu
机构: Huawei Technologies Co., Ltd. (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); Image and Video Processing (eess.IV)
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Abstract:We propose MindVL, a multimodal large langauge model trained on Ascend NPUs. Similar to Qwen2.5-VL, MindVL adopts native-resolution Vision Transformers, which enables it to process images at their original variable resolutions. This design avoids the degradation caused by fixed-resolution tiling while preserving fine-grained details and global layouts, which is crucial for visually dense content such as complex charts and diagrams. To ensure the smooth training of MindVL on Ascend NPUs, we develop Mindspeed-MLLM, a distributed multimodal training framework tailored for Ascend NPUs. To maintain training accuracy, we implement equivalent replacements for certain operators. MindVL undergoes a three-phase training process, namely the warm-up phase, multitask training phase, and supervised instruction tuning phase, to gradually enhance its capabilities. This process starts with basic visual and multimodal pre-training, followed by large-scale multiask trainging and instruction tuning. We also adopt multimodal data packaging and hybrid parallelism techniques, which significantly improve end-to-end training speed. To further boost model performance, we specifically introduce test-time resolution search and model weight averaging. Notably, despite using about 1/10 of the training data required by Qwen2.5-VL, MindVL achieves performance on par with Qwen2.5-VL in evaluations of general multimodal understanding and document/table comprehension. Beyond overall scores, MindVL also delivers leading performance in OCR assessments.
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[NLP-40] MALLM : Multi-Agent Large Language Models Framework EMNLP2025

【速读】：该论文旨在解决当前多智能体辩论（Multi-Agent Debate, MAD）框架在工具使用设计上偏向特定任务、缺乏集成评估机制，以及对代理人格（agent personas）、响应生成器（response generators）、讨论范式（discussion paradigms）和决策协议（decision protocols）配置灵活性不足的问题。其解决方案的关键在于提出一个名为MALLM（Multi-Agent Large Language Models）的开源框架，该框架支持超过144种可组合的MAD配置选项，并通过简单的配置文件定义辩论流程，同时兼容HuggingFace上的多种文本数据集并提供标准化评估流水线，从而系统性地分析MAD各组件及其交互机制，为研究者提供深入理解与优化多智能体协同推理能力的工具。

链接: https://arxiv.org/abs/2509.11656
作者: Jonas Becker,Lars Benedikt Kaesberg,Niklas Bauer,Jan Philip Wahle,Terry Ruas,Bela Gipp
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at EMNLP 2025 (Demo)

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Abstract:Multi-agent debate (MAD) has demonstrated the ability to augment collective intelligence by scaling test-time compute and leveraging expertise. Current frameworks for multi-agent debate are often designed towards tool use, lack integrated evaluation, or provide limited configurability of agent personas, response generators, discussion paradigms, and decision protocols. We introduce MALLM (Multi-Agent Large Language Models), an open-source framework that enables systematic analysis of MAD components. MALLM offers more than 144 unique configurations of MAD, including (1) agent personas (e.g., Expert, Personality), (2) response generators (e.g., Critical, Reasoning), (3) discussion paradigms (e.g., Memory, Relay), and (4) decision protocols (e.g., Voting, Consensus). MALLM uses simple configuration files to define a debate. Furthermore, MALLM can load any textual Huggingface dataset (e.g., MMLU-Pro, WinoGrande) and provides an evaluation pipeline for easy comparison of MAD configurations. MALLM is tailored towards researchers and provides a window into the heart of multi-agent debate, facilitating the understanding of its components and their interplay.
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[NLP-41] EthicsMH: A Pilot Benchmark for Ethical Reasoning in Mental Health AI

【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在心理健康等敏感领域部署时面临的伦理推理、公平性与责任对齐问题，特别是现有基准测试未能充分捕捉临床实践中保密性、自主性、有益性和偏见交织的伦理困境。其解决方案的关键在于提出一个名为EthicsMH的试点数据集，包含125个精心设计的心理健康场景，每个场景均结构化标注了多个决策选项、专家一致的推理路径、预期模型行为、现实世界影响及多方利益相关者视角，从而不仅评估决策准确性，还衡量解释质量与专业规范的一致性，为AI伦理与心理健康决策之间的桥梁提供可扩展的任务框架。

链接: https://arxiv.org/abs/2509.11648
作者: Sai Kartheek Reddy Kasu
机构: IIIT Dharwad, India
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:The deployment of large language models (LLMs) in mental health and other sensitive domains raises urgent questions about ethical reasoning, fairness, and responsible alignment. Yet, existing benchmarks for moral and clinical decision-making do not adequately capture the unique ethical dilemmas encountered in mental health practice, where confidentiality, autonomy, beneficence, and bias frequently intersect. To address this gap, we introduce Ethical Reasoning in Mental Health (EthicsMH), a pilot dataset of 125 scenarios designed to evaluate how AI systems navigate ethically charged situations in therapeutic and psychiatric contexts. Each scenario is enriched with structured fields, including multiple decision options, expert-aligned reasoning, expected model behavior, real-world impact, and multi-stakeholder viewpoints. This structure enables evaluation not only of decision accuracy but also of explanation quality and alignment with professional norms. Although modest in scale and developed with model-assisted generation, EthicsMH establishes a task framework that bridges AI ethics and mental health decision-making. By releasing this dataset, we aim to provide a seed resource that can be expanded through community and expert contributions, fostering the development of AI systems capable of responsibly handling some of society’s most delicate decisions.
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[NLP-42] AesBiasBench: Evaluating Bias and Alignment in Multimodal Language Models for Personalized Image Aesthetic Assessment EMNLP2025

【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在个性化图像审美评估（Personalized Image Aesthetic Assessment, PIAA）中可能存在的隐性偏见问题，尤其是由性别、年龄和教育等人口统计因素引发的刻板印象偏差（stereotype bias），以及模型输出与真实人类审美偏好之间的一致性不足。解决方案的关键在于提出AesBiasBench基准测试框架，通过两个互补维度进行系统评估：一是量化不同群体间审美评价的差异以衡量刻板印象偏差，二是测量模型预测与人类真实审美偏好之间的对齐程度；该框架涵盖三个子任务（审美感知、审美评估、共情）并引入结构化指标（IFD、NRD、AAS），从而为MLLMs在主观视觉-语言任务中的公平性和准确性提供可量化的评估依据。

链接: https://arxiv.org/abs/2509.11620
作者: Kun Li,Lai-Man Po,Hongzheng Yang,Xuyuan Xu,Kangcheng Liu,Yuzhi Zhao
机构: 未知
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: Accepted by EMNLP 2025

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Abstract:Multimodal Large Language Models (MLLMs) are increasingly applied in Personalized Image Aesthetic Assessment (PIAA) as a scalable alternative to expert evaluations. However, their predictions may reflect subtle biases influenced by demographic factors such as gender, age, and education. In this work, we propose AesBiasBench, a benchmark designed to evaluate MLLMs along two complementary dimensions: (1) stereotype bias, quantified by measuring variations in aesthetic evaluations across demographic groups; and (2) alignment between model outputs and genuine human aesthetic preferences. Our benchmark covers three subtasks (Aesthetic Perception, Assessment, Empathy) and introduces structured metrics (IFD, NRD, AAS) to assess both bias and alignment. We evaluate 19 MLLMs, including proprietary models (e.g., GPT-4o, Claude-3.5-Sonnet) and open-source models (e.g., InternVL-2.5, Qwen2.5-VL). Results indicate that smaller models exhibit stronger stereotype biases, whereas larger models align more closely with human preferences. Incorporating identity information often exacerbates bias, particularly in emotional judgments. These findings underscore the importance of identity-aware evaluation frameworks in subjective vision-language tasks.
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[NLP-43] HalluDetect: Detecting Mitigating and Benchmarking Hallucinations in Conversational Systems

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在实际应用中普遍存在幻觉（hallucination）问题，尤其针对基于LLaMA 3.1 8B Instruct构建的消费者投诉聊天机器人，其幻觉现象严重限制了在关键场景下的可靠性。解决方案的关键在于提出HalluDetect——一个基于LLM的幻觉检测系统，结合五种不同的聊天机器人架构进行对比评估，最终发现AgentBot架构通过优化推理策略，在每轮对话中将幻觉降至0.4159，同时保持最高文本准确率（96.13%），从而显著提升事实准确性，为高风险领域中的幻觉缓解提供了可扩展框架。

链接: https://arxiv.org/abs/2509.11619
作者: Spandan Anaokar,Shrey Ganatra,Harshvivek Kashid,Swapnil Bhattacharyya,Shruti Nair,Reshma Sekhar,Siddharth Manohar,Rahul Hemrajani,Pushpak Bhattacharyya
机构: Indian Institute of Technology Bombay (印度理工学院孟买分校); National Law School of India University, Bangalore (印度国家法律学院，班加罗尔)
类目: Computation and Language (cs.CL)
备注: 6 pages + references + appendix, 3 figures, 2 tables

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Abstract:Large Language Models (LLMs) are widely used in industry but remain prone to hallucinations, limiting their reliability in critical applications. This work addresses hallucination reduction in consumer grievance chatbots built using LLaMA 3.1 8B Instruct, a compact model frequently used in industry. We develop HalluDetect, an LLM-based hallucination detection system that achieves an F1 score of 69% outperforming baseline detectors by 25.44%. Benchmarking five chatbot architectures, we find that out of them, AgentBot minimizes hallucinations to 0.4159 per turn while maintaining the highest token accuracy (96.13%), making it the most effective mitigation strategy. Our findings provide a scalable framework for hallucination mitigation, demonstrating that optimized inference strategies can significantly improve factual accuracy. While applied to consumer law, our approach generalizes to other high-risk domains, enhancing trust in LLM-driven assistants. We will release the code and dataset
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[NLP-44] Dynamic Span Interaction and Graph-Aware Memory for Entity-Level Sentiment Classification

【速读】：该论文旨在解决实体级情感分类（Entity-level Sentiment Classification）中的关键挑战，包括：精准建模实体与其周边情感表达之间的细微复杂交互、捕捉跨句依赖关系、确保同一实体在文档中多处提及时的情感预测一致性（通过共指消解），以及应对否定、歧义和重叠观点等语言现象。解决方案的核心在于提出SpanEIT框架，其创新点包括：基于动态跨度交互的细粒度实体与情感短语表示建模、采用双向注意力机制实现精细化交互捕获、引入图注意力网络以整合句法结构与共现关系，并设计了一个共指感知的记忆模块来保障实体层面的一致性。实验表明，该方法在FSAD、BARU和IMDB数据集上显著优于当前最优的Transformer及混合基线模型，在准确率和F1分数上均取得提升。

链接: https://arxiv.org/abs/2509.11604
作者: Md. Mithun Hossain,Sanjara,Md. Shakil Hossain,Sudipto Chaki
机构: Bangladesh University of Business and Technology (孟加拉国商业与技术大学)
类目: Computation and Language (cs.CL)
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Abstract:Entity-level sentiment classification involves identifying the sentiment polarity linked to specific entities within text. This task poses several challenges: effectively modeling the subtle and complex interactions between entities and their surrounding sentiment expressions; capturing dependencies that may span across sentences; and ensuring consistent sentiment predictions for multiple mentions of the same entity through coreference resolution. Additionally, linguistic phenomena such as negation, ambiguity, and overlapping opinions further complicate the analysis. These complexities make entity-level sentiment classification a difficult problem, especially in real-world, noisy textual data. To address these issues, we propose SpanEIT, a novel framework integrating dynamic span interaction and graph-aware memory mechanisms for enhanced entity-sentiment relational modeling. SpanEIT builds span-based representations for entities and candidate sentiment phrases, employs bidirectional attention for fine-grained interactions, and uses a graph attention network to capture syntactic and co-occurrence relations. A coreference-aware memory module ensures entity-level consistency across documents. Experiments on FSAD, BARU, and IMDB datasets show SpanEIT outperforms state-of-the-art transformer and hybrid baselines in accuracy and F1 scores. Ablation and interpretability analyses validate the effectiveness of our approach, underscoring its potential for fine-grained sentiment analysis in applications like social media monitoring and customer feedback analysis.
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[NLP-45] Analyzing Information-Seeking Behaviors in a Hakka AI Chatbot: A Cognitive-Prag matic Study

【速读】：该论文旨在解决濒危语言（如客家话）因缺乏有效教学工具和文化传承机制而面临消失风险的问题。其解决方案的关键在于开发并验证一个基于生成式 AI（Generative AI）的对话系统——TALKA，该系统通过融合认知层次分析（基于布鲁姆认知分类法）与对话行为类型标注（Dialogue Act Categorization），深入解析用户在与AI互动中的认知意图与交际功能。研究通过对7,077条用户语句的精细化标注发现，AI驱动的对话不仅能促进低资源语言学习者的认知发展，还能增强其文化认同感与表达自信，从而为语言保护提供可量化的技术路径与教育实践依据。

链接: https://arxiv.org/abs/2509.11591
作者: Chu-Hsuan Lee,Chen-Chi Chang,Hung-Shin Lee,Yun-Hsiang Hsu,Ching-Yuan Chen
机构: 未知
类目: Computation and Language (cs.CL)
备注: Accepted to HICSS-59 (2026)

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Abstract:With many endangered languages at risk of disappearing, efforts to preserve them now rely more than ever on using technology alongside culturally informed teaching strategies. This study examines user behaviors in TALKA, a generative AI-powered chatbot designed for Hakka language engagement, by employing a dual-layered analytical framework grounded in Bloom’s Taxonomy of cognitive processes and dialogue act categorization. We analyzed 7,077 user utterances, each carefully annotated according to six cognitive levels and eleven dialogue act types. These included a variety of functions, such as asking for information, requesting translations, making cultural inquiries, and using language creatively. Pragmatic classifications further highlight how different types of dialogue acts–such as feedback, control commands, and social greetings–align with specific cognitive intentions. The results suggest that generative AI chatbots can support language learning in meaningful ways–especially when they are designed with an understanding of how users think and communicate. They may also help learners express themselves more confidently and connect with their cultural identity. The TALKA case provides empirical insights into how AI-mediated dialogue facilitates cognitive development in low-resource language learners, as well as pragmatic negotiation and socio-cultural affiliation. By focusing on AI-assisted language learning, this study offers new insights into how technology can support language preservation and educational practice.
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[NLP-46] Formal Reasoning for Intelligent QA Systems: A Case Study in the Educational Domain

【速读】：该论文旨在解决闭域问答（Closed-domain QA）系统中推理过程缺乏可信性与可验证性的问题，特别是当程序正确性和政策合规性至关重要的场景下，现有大语言模型（LLM）的推理轨迹常表现为看似合理但缺乏因果依据的解释。为应对这一挑战，作者提出了一种神经符号框架 MCFR（Model Checking for Formal Reasoning），其核心创新在于将 LLM 与模型检测（Model Checking）技术相结合：首先利用 LLM 将自然语言转化为形式化规范（formal specifications），再通过模型检测在状态转移模型上进行属性验证，从而实现对推理路径的严格形式化检验。此方法显著提升了推理的忠实度（faithfulness）和可解释性，为高风险领域中的可验证问答提供了可行路径。

链接: https://arxiv.org/abs/2509.11572
作者: Tuan Bui,An Nguyen,Phat Thai,Minh Hua,Ngan Pham L.N.,Ngan Pham T.B.,Dung Le,Long Nguyen,Thanh-Tung Tran,Thang Bui,Tho Quan
机构: Ho Chi Minh City University of Technology (HCMUT) - VNU-HCM (胡志明市科技大学（HCMUT）- 越南国家大学胡志明市分校); International University - VNU-HCM (国际大学 - 越南国家大学胡志明市分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Published at the 2nd ACM Workshop in AI-powered Question Answering Systems (AIQAM '25), co-located with ACM Multimedia 2025

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Abstract:Reasoning is essential for closed-domain QA systems in which procedural correctness and policy compliance are critical. While large language models (LLMs) have shown strong performance on many reasoning tasks, recent work reveals that their reasoning traces are often unfaithful - serving more as plausible justifications than as causally grounded derivations. Efforts to combine LLMs with symbolic engines (e.g., Prover9, Z3) have improved reliability but remain limited to static forms of logic, struggling with dynamic, state-based reasoning such as multi-step progressions and conditional transitions. In this paper, we propose MCFR (Model Checking for Formal Reasoning), a neuro-symbolic framework that integrates LLMs with model checking to support property verification. MCFR translates natural language into formal specifications and verifies them over transition models. To support evaluation, we introduce EduMC-QA, a benchmark dataset grounded in real academic procedures. Our results show that MCFR improves reasoning faithfulness and interpretability, offering a viable path toward verifiable QA in high-stakes closed-domain applications. In addition to evaluating MCFR, we compare its performance with state-of-the-art LLMs such as ChatGPT, DeepSeek, and Claude to contextualize its effectiveness. Comments: Published at the 2nd ACM Workshop in AI-powered Question Answering Systems (AIQAM '25), co-located with ACM Multimedia 2025 Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2509.11572 [cs.AI] (or arXiv:2509.11572v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2509.11572 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Proceedings of the 2nd ACM Workshop in AI-powered Question and Answering Systems (AIQAM '25), October 27-28, 2025, Dublin, Ireland Related DOI: https://doi.org/10.1145/3746274.3760392 Focus to learn more DOI(s) linking to related resources
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[NLP-47] Bhaasha Bhasa Zaban: A Survey for Low-Resourced Languages in South Asia - Current Stage and Challenges

【速读】：该论文旨在解决南亚地区低资源语言在自然语言处理（Natural Language Processing, NLP）领域中模型开发与评估被忽视的问题，特别是针对超过650种南亚语言中多数缺乏计算资源或未被现有语言模型覆盖的现状。其解决方案的关键在于系统性地回顾2020年以来基于Transformer架构（如BERT、T5、GPT）的NLP研究进展，从数据、模型和任务三个核心维度分析当前挑战，包括关键领域（如健康）数据缺失、代码混杂（code-mixing）现象以及缺乏标准化评估基准，并提出应加强针对性的数据收集、构建符合南亚文化与语言特性的统一评测基准，以推动该地区语言的公平代表性与模型发展。

链接: https://arxiv.org/abs/2509.11570
作者: Sampoorna Poria,Xiaolei Huang
机构: West Bengal University of Technology (西孟加拉邦技术大学); University of Memphis (孟菲斯大学)
类目: Computation and Language (cs.CL)
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Abstract:Rapid developments of large language models have revolutionized many NLP tasks for English data. Unfortunately, the models and their evaluations for low-resource languages are being overlooked, especially for languages in South Asia. Although there are more than 650 languages in South Asia, many of them either have very limited computational resources or are missing from existing language models. Thus, a concrete question to be answered is: Can we assess the current stage and challenges to inform our NLP community and facilitate model developments for South Asian languages? In this survey, we have comprehensively examined current efforts and challenges of NLP models for South Asian languages by retrieving studies since 2020, with a focus on transformer-based models, such as BERT, T5, GPT. We present advances and gaps across 3 essential aspects: data, models, tasks, such as available data sources, fine-tuning strategies, domain applications. Our findings highlight substantial issues, including missing data in critical domains (e.g., health), code-mixing, and lack of standardized evaluation benchmarks. Our survey aims to raise awareness within the NLP community for more targeted data curation, unify benchmarks tailored to cultural and linguistic nuances of South Asia, and encourage an equitable representation of South Asian languages. The complete list of resources is available at: this https URL.
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[NLP-48] D2HScore: Reasoning -Aware Hallucination Detection via Semantic Breadth and Depth Analysis in LLM s

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在实际应用中因生成非事实性内容（即“幻觉”）而导致可靠性不足的问题，尤其在金融、安全和医疗等高风险领域尤为关键。其解决方案的核心在于从模型架构与生成动态的角度重新审视幻觉检测机制，提出了一种无需训练且无需标签的框架 D²HScore（Dispersion and Drift-based Hallucination Score），通过分解幻觉信号为两个互补维度：层内分散度（Intra-Layer Dispersion），用于量化每层中token表示的语义多样性；以及层间漂移度（Inter-Layer Drift），用于追踪关键token表示在多层结构中的语义演化过程。该方法借助注意力机制引导token选择，确保漂移反映的是有意义的语义变化而非噪声或冗余信息，从而实现对生成过程中横向（层内）与纵向（层间）表征动态的联合建模，提供一种可解释且轻量的幻觉检测代理指标。

链接: https://arxiv.org/abs/2509.11569
作者: Yue Ding,Xiaofang Zhu,Tianze Xia,Junfei Wu,Xinlong Chen,Qiang Liu,Liang Wang
机构: 未知
类目: Computation and Language (cs.CL)
备注: under review

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Abstract:Although large Language Models (LLMs) have achieved remarkable success, their practical application is often hindered by the generation of non-factual content, which is called “hallucination”. Ensuring the reliability of LLMs’ outputs is a critical challenge, particularly in high-stakes domains such as finance, security, and healthcare. In this work, we revisit hallucination detection from the perspective of model architecture and generation dynamics. Leveraging the multi-layer structure and autoregressive decoding process of LLMs, we decompose hallucination signals into two complementary dimensions: the semantic breadth of token representations within each layer, and the semantic depth of core concepts as they evolve across layers. Based on this insight, we propose \textbfD ^2 HScore (Dispersion and Drift-based Hallucination Score), a training-free and label-free framework that jointly measures: (1) \textbfIntra-Layer Dispersion, which quantifies the semantic diversity of token representations within each layer; and (2) \textbfInter-Layer Drift, which tracks the progressive transformation of key token representations across layers. To ensure drift reflects the evolution of meaningful semantics rather than noisy or redundant tokens, we guide token selection using attention signals. By capturing both the horizontal and vertical dynamics of representation during inference, D ^2 HScore provides an interpretable and lightweight proxy for hallucination detection. Extensive experiments across five open-source LLMs and five widely used benchmarks demonstrate that D ^2 HScore consistently outperforms existing training-free baselines.
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[NLP-49] HiChunk: Evaluating and Enhancing Retrieval-Augmented Generation with Hierarchical Chunking

【速读】：该论文旨在解决检索增强生成（Retrieval-Augmented Generation, RAG）系统中文档分块（document chunking）质量缺乏有效评估工具的问题，尤其指出现有评估基准因证据稀疏（evidence sparsity）而难以准确衡量分块效果。其解决方案的关键在于提出HiCBench评估基准和HiChunk分块框架：HiCBench包含人工标注的多层级文档分块点、合成的高证据密度问答对及其对应证据来源，从而提供更可靠的评估依据；HiChunk则基于微调的大语言模型（LLM）构建多层级文档结构，并结合Auto-Merge检索算法提升检索质量，实现高效且高质量的文档分块，进而优化RAG系统的整体性能。

链接: https://arxiv.org/abs/2509.11552
作者: Wensheng Lu,Keyu Chen,Ruizhi Qiao,Xing Sun
机构: Tencent YouTu Lab (腾讯优图实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 17 pages, 5 figures, 6 tables

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Abstract:Retrieval-Augmented Generation (RAG) enhances the response capabilities of language models by integrating external knowledge sources. However, document chunking as an important part of RAG system often lacks effective evaluation tools. This paper first analyzes why existing RAG evaluation benchmarks are inadequate for assessing document chunking quality, specifically due to evidence sparsity. Based on this conclusion, we propose HiCBench, which includes manually annotated multi-level document chunking points, synthesized evidence-dense quetion answer(QA) pairs, and their corresponding evidence sources. Additionally, we introduce the HiChunk framework, a multi-level document structuring framework based on fine-tuned LLMs, combined with the Auto-Merge retrieval algorithm to improve retrieval quality. Experiments demonstrate that HiCBench effectively evaluates the impact of different chunking methods across the entire RAG pipeline. Moreover, HiChunk achieves better chunking quality within reasonable time consumption, thereby enhancing the overall performance of RAG systems.
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[NLP-50] HARP: Hallucination Detection via Reasoning Subspace Projection

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）中幻觉（Hallucination）问题对关键决策场景下可靠应用的阻碍，尤其针对现有检测方法在语义与推理信息解耦能力不足及鲁棒性差的问题。其解决方案的关键在于提出HARP框架，通过理论证明LLM的隐藏状态空间可分解为语义子空间（semantic subspace）与推理子空间（reasoning subspace）的直和，并利用Unembedding层参数的奇异值分解（SVD）分离出这两个子空间的基向量；随后将隐藏状态投影至推理子空间基向量上，获得低维、去噪且富含推理特征的输入用于检测，从而显著提升检测性能与鲁棒性。

链接: https://arxiv.org/abs/2509.11536
作者: Junjie Hu,Gang Tu,ShengYu Cheng,Jinxin Li,Jinting Wang,Rui Chen,Zhilong Zhou,Dongbo Shan
机构: Huazhong University of Science and Technology (华中科技大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Hallucinations in Large Language Models (LLMs) pose a major barrier to their reliable use in critical decision-making. Although existing hallucination detection methods have improved accuracy, they still struggle with disentangling semantic and reasoning information and maintaining robustness. To address these challenges, we propose HARP (Hallucination detection via reasoning subspace projection), a novel hallucination detection framework. HARP establishes that the hidden state space of LLMs can be decomposed into a direct sum of a semantic subspace and a reasoning subspace, where the former encodes linguistic expression and the latter captures internal reasoning processes. Moreover, we demonstrate that the Unembedding layer can disentangle these subspaces, and by applying Singular Value Decomposition (SVD) to its parameters, the basis vectors spanning the semantic and reasoning subspaces are obtained. Finally, HARP projects hidden states onto the basis vectors of the reasoning subspace, and the resulting projections are then used as input features for hallucination detection in LLMs. By using these projections, HARP reduces the dimension of the feature to approximately 5% of the original, filters out most noise, and achieves enhanced robustness. Experiments across multiple datasets show that HARP achieves state-of-the-art hallucination detection performance; in particular, it achieves an AUROC of 92.8% on TriviaQA, outperforming the previous best method by 7.5%.
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[NLP-51] On the Distinctive Co-occurrence Characteristics of Antonymy

【速读】：该论文旨在解决语义关系中同义关系（antonymy）是否具有独特共现模式的问题，此前研究虽发现反义词对在文本中频繁共现，但缺乏与其他语义关系（如类义关系、上下位关系等）的系统比较，因而无法确定其独特性。解决方案的关键在于采用稳健的共现度量方法，将反义关系与三种其他语义关系在不同词性类别中进行对比分析，结果表明反义词对不仅共现强度高、存在偏好线性顺序（preferred linear order），且多出现在较短的文本跨度内（short spans），从而揭示了反义关系在共现特征上的独特性。

链接: https://arxiv.org/abs/2509.11534
作者: Zhihan Cao,Hiroaki Yamada,Takenobu Tokunaga
机构: Institute of Science Tokyo (东京科学研究所); School of Computing (计算学院)
类目: Computation and Language (cs.CL)
备注: Accepted by *SEM 2025

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Abstract:Antonymy has long received particular attention in lexical semantics. Previous studies have shown that antonym pairs frequently co-occur in text, across genres and parts of speech, more often than would be expected by chance. However, whether this co-occurrence pattern is distinctive of antonymy remains unclear, due to a lack of comparison with other semantic relations. This work fills the gap by comparing antonymy with three other relations across parts of speech using robust co-occurrence metrics. We find that antonymy is distinctive in three respects: antonym pairs co-occur with high strength, in a preferred linear order, and within short spans. All results are available online.
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[NLP-52] PeruMedQA: Benchmarking Large Language Models (LLM s) on Peruvian Medical Exams - Dataset Construction and Evaluation

【速读】：该论文旨在解决医学大语言模型（Medical Large Language Models, LLMs）在西班牙语语境下，尤其是拉丁美洲国家（以秘鲁为例）的医学考试问答任务中性能是否可迁移的问题。当前虽有LLMs在英语医学考试中表现优异，但其在西班牙语医疗场景中的适应性尚未充分验证，这限制了其在拉美地区的临床应用潜力。解决方案的关键在于构建一个高质量、多领域的秘鲁医师专科培训考试数据集（PeruMedQA），并基于此数据集对medgemma-4b-it模型进行参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）和低秩适配（Low-Rank Adaptation, LoRA）优化，从而显著提升模型在特定语境下的准确率，使其在多项测试中超越其他同类模型，甚至媲美700亿参数的大模型。

链接: https://arxiv.org/abs/2509.11517
作者: Rodrigo M. Carrillo-Larco,Jesus Lovón Melgarejo,Manuel Castillo-Cara,Gusseppe Bravo-Rocca
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: this https URL

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Abstract:BACKGROUND: Medical large language models (LLMS) have demonstrated remarkable performance in answering medical examinations. However, the extent to which this high performance is transferable to medical questions in Spanish and from a Latin American country remains unexplored. This knowledge is crucial as LLM-based medical applications gain traction in Latin America. AIMS: to build a dataset of questions from medical examinations taken by Peruvian physicians pursuing specialty training; to fine-tune a LLM on this dataset; to evaluate and compare the performance in terms of accuracy between vanilla LLMs and the fine-tuned LLM. METHODS: We curated PeruMedQA, a multiple-choice question-answering (MCQA) datasets containing 8,380 questions spanning 12 medical domains (2018-2025). We selected eight medical LLMs including medgemma-4b-it and medgemma-27b-text-it, and developed zero-shot task-specific prompts to answer the questions appropriately. We employed parameter-efficient fine tuning (PEFT)and low-rant adaptation (LoRA) to fine-tune medgemma-4b-it utilizing all questions except those from 2025 (test set). RESULTS: medgemma-27b-text-it outperformed all other models, achieving a proportion of correct answers exceeding 90% in several instances. LLMs with 10 billion parameters exhibited 60% of correct answers, while some exams yielded results 50%. The fine-tuned version of medgemma-4b-it emerged victorious agains all LLMs with 10 billion parameters and rivaled a LLM with 70 billion parameters across various examinations. CONCLUSIONS: For medical AI application and research that require knowledge bases from Spanish-speaking countries and those exhibiting similar epidemiological profiles to Peru’s, interested parties should utilize medgemma-27b-text-it or a fine-tuned version of medgemma-4b-it.
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[NLP-53] LVLMs are Bad at Overhearing Human Referential Communication EMNLP2025

【速读】：该论文旨在解决 embodied agent 在真实世界任务中理解自发对话中生成的指代表达（referring expressions）的能力问题，这要求模型能够融合语言、视觉与对话交互信息。解决方案的关键在于评估七种前沿大型视觉语言模型（Large Vision Language Models, LVLMs）作为“旁听者”（overhearers）在持续监听人类参与者重复进行协作物体匹配任务时的学习能力，从而检验其是否能通过多轮对话积累知识并提升指代表达理解性能。研究发现，当前LVLMs在此任务上仍面临挑战，且未表现出随着旁听次数增加而稳定提升的性能趋势。

链接: https://arxiv.org/abs/2509.11514
作者: Zhengxiang Wang,Weiling Li,Panagiotis Kaliosis,Owen Rambow,Susan E. Brennan
机构: Stony Brook University (石溪大学)
类目: Computation and Language (cs.CL)
备注: EMNLP 2025 (Main)

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Abstract:During spontaneous conversations, speakers collaborate on novel referring expressions, which they can then re-use in subsequent conversations. Understanding such referring expressions is an important ability for an embodied agent, so that it can carry out tasks in the real world. This requires integrating and understanding language, vision, and conversational interaction. We study the capabilities of seven state-of-the-art Large Vision Language Models (LVLMs) as overhearers to a corpus of spontaneous conversations between pairs of human discourse participants engaged in a collaborative object-matching task. We find that such a task remains challenging for current LVLMs and they all fail to show a consistent performance improvement as they overhear more conversations from the same discourse participants repeating the same task for multiple rounds. We release our corpus and code for reproducibility and to facilitate future research.
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[NLP-54] Unsupervised Candidate Ranking for Lexical Substitution via Holistic Sentence Semantics

【速读】：该论文旨在解决词汇替换（Lexical Substitution）任务中候选词排序的难题，尤其是如何有效建模候选词替换对目标词及其上下文之间的双向影响。现有方法通常仅关注目标位置的语义变化或依赖多评估指标的参数调优，难以准确刻画语义差异。解决方案的关键在于引入两种可解释性更强的方法：一是基于注意力权重的机制，二是利用集成梯度（Integrated Gradients）方法，二者均用于量化上下文标记对目标标记的影响，并通过比较原句与替换句之间的语义相似度来对候选词进行排序，从而提升排序性能。

链接: https://arxiv.org/abs/2509.11513
作者: Zhongyang Hu,Naijie Gu,Xiangzhi Tao,Tianhui Gu,Yibing Zhou
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:A key subtask in lexical substitution is ranking the given candidate words. A common approach is to replace the target word with a candidate in the original sentence and feed the modified sentence into a model to capture semantic differences before and after substitution. However, effectively modeling the bidirectional influence of candidate substitution on both the target word and its context remains challenging. Existing methods often focus solely on semantic changes at the target position or rely on parameter tuning over multiple evaluation metrics, making it difficult to accurately characterize semantic variation. To address this, we investigate two approaches: one based on attention weights and another leveraging the more interpretable integrated gradients method, both designed to measure the influence of context tokens on the target token and to rank candidates by incorporating semantic similarity between the original and substituted sentences. Experiments on the LS07 and SWORDS datasets demonstrate that both approaches improve ranking performance.
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[NLP-55] DeDisCo at the DISRPT 2025 Shared Task: A System for Discourse Relation Classification EMNLP2025

【速读】：该论文旨在解决话语关系分类（discourse relation classification）问题，即自动识别文本中两个句子或片段之间的语义关系类型。其解决方案的关键在于采用两种不同的模型架构：一是基于MT5的编码器方法，二是基于开源Qwen模型的解码器方法；同时引入了针对低资源语言的数据增强策略——通过自动翻译英文匹配数据生成训练样本，并融合了前届共享任务中表现优异的某些语言学特征。该方法在DISRPT 2025评测中取得了71.28的宏平均准确率（macro-accuracy），并提供了对结果的解释与误差分析。

链接: https://arxiv.org/abs/2509.11498
作者: Zhuoxuan Ju,Jingni Wu,Abhishek Purushothama,Amir Zeldes
机构: Corpling Lab (Corpling 实验室); Georgetown University (乔治城大学)
类目: Computation and Language (cs.CL)
备注: System submission for the DISRPT 2025 - Shared Task on Discourse Relation Parsing and Treebanking In conjunction with CODI-CRAC EMNLP 2025. 1st place in Task 3: relation classification

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Abstract:This paper presents DeDisCo, Georgetown University’s entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results.
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[NLP-56] AKCIT-FN at CheckThat! 2025: Switching Fine-Tuned SLMs and LLM Prompting for Multilingual Claim Normalization

【速读】：该论文旨在解决社交媒体中非正式语句的**主张规范化（claim normalization）**问题，即如何将包含事实核查需求的非结构化、口语化社交文本转化为简洁且自包含的陈述句，以支持自动化事实核查流水线。其核心挑战在于跨语言场景下，尤其在低资源语言中实现高质量的规范化处理。解决方案的关键在于：针对高资源语言采用微调的小型语言模型（Small Language Models, SLMs），而在零样本（zero-shot）场景下则利用大语言模型（Large Language Model, LLM）提示工程（prompting），从而在20种语言中实现优异性能，其中15种语言进入前三名，包括5种零样本语言表现突出，验证了LLM提示策略的有效性。

链接: https://arxiv.org/abs/2509.11496
作者: Fabrycio Leite Nakano Almada,Kauan Divino Pouso Mariano,Maykon Adriell Dutra,Victor Emanuel da Silva Monteiro,Juliana Resplande Sant’Anna Gomes,Arlindo Rodrigues Galvão Filho,Anderson da Silva Soares
机构: Institute of Informatics, Federal University of Goiás, Brazil (戈亚斯联邦大学信息学院，巴西); Advanced Knowledge Center in Immersive Technology (AKCIT), Federal University of Goiás, Brazil (沉浸式技术高级知识中心，戈亚斯联邦大学，巴西)
类目: Computation and Language (cs.CL)
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Abstract:Claim normalization, the transformation of informal social media posts into concise, self-contained statements, is a crucial step in automated fact-checking pipelines. This paper details our submission to the CLEF-2025 CheckThat! Task~2, which challenges systems to perform claim normalization across twenty languages, divided into thirteen supervised (high-resource) and seven zero-shot (no training data) tracks. Our approach, leveraging fine-tuned Small Language Models (SLMs) for supervised languages and Large Language Model (LLM) prompting for zero-shot scenarios, achieved podium positions (top three) in fifteen of the twenty languages. Notably, this included second-place rankings in eight languages, five of which were among the seven designated zero-shot languages, underscoring the effectiveness of our LLM-based zero-shot strategy. For Portuguese, our initial development language, our system achieved an average METEOR score of 0.5290, ranking third. All implementation artifacts, including inference, training, evaluation scripts, and prompt configurations, are publicly available at this https URL. Comments: 15 pages, 2 figures Subjects: Computation and Language (cs.CL) Cite as: arXiv:2509.11496 [cs.CL] (or arXiv:2509.11496v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2509.11496 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[NLP-57] ClaimIQ at CheckThat! 2025: Comparing Prompted and Fine-Tuned Language Models for Verifying Numerical Claims

【速读】：该论文旨在解决数值型和时间型事实陈述的自动验证问题，即通过检索到的证据判断声明的真实性。其核心挑战在于如何有效利用证据并提升模型在不同数据分布下的泛化能力。解决方案的关键在于结合两种互补策略：一是采用指令微调的大语言模型（LLM）进行零样本提示（zero-shot prompting），二是使用参数高效微调技术LoRA对模型进行监督微调；同时，为提升证据质量，研究了多种选择策略，包括全文档输入以及基于BM25和MiniLM的top-k句子过滤方法。实验表明，经LoRA微调的LLaMA模型在英文验证集上表现优异，但测试集性能显著下降，揭示出证据粒度与模型适应性对实现鲁棒数值事实验证的重要性。

链接: https://arxiv.org/abs/2509.11492
作者: Anirban Saha Anik,Md Fahimul Kabir Chowdhury,Andrew Wyckoff,Sagnik Ray Choudhury
机构: University of North Texas (北德克萨斯大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Notebook for the CheckThat! Lab at CLEF 2025

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Abstract:This paper presents our system for Task 3 of the CLEF 2025 CheckThat! Lab, which focuses on verifying numerical and temporal claims using retrieved evidence. We explore two complementary approaches: zero-shot prompting with instruction-tuned large language models (LLMs) and supervised fine-tuning using parameter-efficient LoRA. To enhance evidence quality, we investigate several selection strategies, including full-document input and top-k sentence filtering using BM25 and MiniLM. Our best-performing model LLaMA fine-tuned with LoRA achieves strong performance on the English validation set. However, a notable drop in the test set highlights a generalization challenge. These findings underscore the importance of evidence granularity and model adaptation for robust numerical fact verification.
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[NLP-58] Improving LLM s Learning for Coreference Resolution

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在共指消解（Coreference Resolution, CR）任务中普遍存在的幻觉（hallucination）和性能不足问题。现有基于LLM的方法，特别是基于问答（Question-Answering, QA）模板与文档模板（Document Template）的方法，在处理CR时难以保证生成文本的准确性与一致性。论文提出两种创新技术：逆向训练联合推理（Reversed Training with Joint Inference） 和 迭代文档生成（Iterative Document Generation）。其中，逆向训练可提升QA模板方法的性能，而迭代文档生成则有效消除生成源文本中的幻觉并显著增强共指消解效果。二者结合形成了一种高效且鲁棒的LLM-based共指消解解决方案。

链接: https://arxiv.org/abs/2509.11466
作者: Yujian Gan,Yuan Liang,Yanni Lin,Juntao Yu,Massimo Poesio
机构: Queen Mary University of London (伦敦玛丽女王大学); Guangxi Normal University (广西师范大学); University of Utrecht (乌得勒支大学)
类目: Computation and Language (cs.CL)
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Abstract:Coreference Resolution (CR) is crucial for many NLP tasks, but existing LLMs struggle with hallucination and under-performance. In this paper, we investigate the limitations of existing LLM-based approaches to CR-specifically the Question-Answering (QA) Template and Document Template methods and propose two novel techniques: Reversed Training with Joint Inference and Iterative Document Generation. Our experiments show that Reversed Training improves the QA Template method, while Iterative Document Generation eliminates hallucinations in the generated source text and boosts coreference resolution. Integrating these methods and techniques offers an effective and robust solution to LLM-based coreference resolution.
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[NLP-59] CEMTM: Contextual Embedding-based Multimodal Topic Modeling EMNLP2025

【速读】：该论文旨在解决多模态文档（含文本与图像）中主题建模的挑战，特别是如何在短文本和长文档场景下提取语义一致且可解释的主题结构。现有方法在处理多图文档时存在重复编码效率低、跨模态语义对齐不足以及主题解释性弱等问题。其解决方案的关键在于提出CEMTM模型：首先利用微调后的大型视觉语言模型（Large Vision Language Models, LVLMs）获取上下文感知的嵌入表示；其次引入分布注意力机制加权词级贡献以增强主题推断精度；最后通过重构目标使基于主题的表示与文档嵌入对齐，从而保障跨模态语义一致性。此外，CEMTM支持单文档内多个图像的高效处理，并通过显式的词-主题和文档-主题分布保持模型可解释性。

链接: https://arxiv.org/abs/2509.11465
作者: Amirhossein Abaskohi,Raymond Li,Chuyuan Li,Shafiq Joty,Giuseppe Carenini
机构: The University of British Columbia (不列颠哥伦比亚大学); Salesforce AI Research
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:We introduce CEMTM, a context-enhanced multimodal topic model designed to infer coherent and interpretable topic structures from both short and long documents containing text and images. CEMTM builds on fine-tuned large vision language models (LVLMs) to obtain contextualized embeddings, and employs a distributional attention mechanism to weight token-level contributions to topic inference. A reconstruction objective aligns topic-based representations with the document embedding, encouraging semantic consistency across modalities. Unlike existing approaches, CEMTM can process multiple images per document without repeated encoding and maintains interpretability through explicit word-topic and document-topic distributions. Extensive experiments on six multimodal benchmarks show that CEMTM consistently outperforms unimodal and multimodal baselines, achieving a remarkable average LLM score of 2.61. Further analysis shows its effectiveness in downstream few-shot retrieval and its ability to capture visually grounded semantics in complex domains such as scientific articles.
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[NLP-60] Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weighting

【速读】：该论文旨在解决多目标强化学习（Multi-Objective Reinforcement Learning, MORL）中因采用固定权重的线性奖励标量化方法而导致无法捕捉非凸帕累托前沿（Pareto front）的问题，这一局限在大语言模型的在线偏好对齐任务中尤为显著。其关键解决方案在于提出动态奖励加权机制（dynamic reward weighting），该机制在在线强化学习过程中自适应地调整奖励权重，从而持续平衡并优先处理不同目标，有效促进目标空间中帕累托前沿的探索。文中进一步提出了两种渐进式增强的实现方法：基于超体积引导的权重自适应与基于梯度的权重优化，二者共同构成一个通用工具集，可兼容多种主流在线强化学习算法（如GRPO、REINFORCE和RLOO），并在多个数学推理数据集上实现帕累托占优解，且训练步数更少。

链接: https://arxiv.org/abs/2509.11452
作者: Yining Lu,Zilong Wang,Shiyang Li,Xin Liu,Changlong Yu,Qingyu Yin,Zhan Shi,Zixuan Zhang,Meng Jiang
机构: University of Notre Dame (圣母大学); Amazon
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
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Abstract:Prior works in multi-objective reinforcement learning typically use linear reward scalarization with fixed weights, which provably fail to capture non-convex Pareto fronts and thus yield suboptimal results. This limitation becomes especially critical in online preference alignment for large language models. Here, stochastic trajectories generated by parameterized policies create highly non-linear and non-convex mappings from parameters to objectives that no single static weighting scheme can find optimal trade-offs. We address this limitation by introducing dynamic reward weighting, which adaptively adjusts reward weights during the online reinforcement learning process. Unlike existing approaches that rely on fixed-weight interpolation, our dynamic weighting continuously balances and prioritizes objectives in training, facilitating effective exploration of Pareto fronts in objective space. We introduce two approaches of increasing sophistication and generalizability: (1) hypervolume-guided weight adaptation and (2) gradient-based weight optimization, offering a versatile toolkit for online multi-objective alignment. Our extensive experiments demonstrate their compatibility with commonly used online reinforcement learning algorithms (including GRPO, REINFORCE, and RLOO), effectiveness across multiple mathematical reasoning datasets, and applicability to different model families, consistently achieving Pareto dominant solutions with fewer training steps than fixed-weight linear scalarization baselines.
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[NLP-61] CognitiveSky: Scalable Sentiment and Narrative Analysis for Decentralized Social Media

【速读】：该论文旨在解决在去中心化社交平台（如Bluesky）上对公共话语进行实时分析的挑战，尤其关注如何有效提取和可视化用户生成内容中的情感、情绪与叙事模式。其解决方案的关键在于构建一个名为CognitiveSky的开源可扩展框架，该框架通过Bluesky的API接入数据，并利用基于Transformer的模型对大规模文本进行标注，输出结构化结果；进而驱动动态仪表板以呈现情绪、活跃度及话题演化的趋势。整个系统基于免费层级基础设施实现，兼顾低运营成本与高可访问性，且模块化设计支持多领域应用，如虚假信息检测、危机响应和公民情绪分析，从而为计算社会科学提供透明、可扩展的工具。

链接: https://arxiv.org/abs/2509.11444
作者: Gaurab Chhetri,Anandi Dutta,Subasish Das
机构: Texas State University (德克萨斯州立大学)
类目: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
备注: This is the author’s preprint version of a paper accepted for presentation at HICSS 59 (Hawaii International Conference on System Sciences), 2026, Hawaii, USA. The final published version will appear in the official conference proceedings. Conference site: this https URL

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Abstract:The emergence of decentralized social media platforms presents new opportunities and challenges for real-time analysis of public discourse. This study introduces CognitiveSky, an open-source and scalable framework designed for sentiment, emotion, and narrative analysis on Bluesky, a federated Twitter or this http URL alternative. By ingesting data through Bluesky’s Application Programming Interface (API), CognitiveSky applies transformer-based models to annotate large-scale user-generated content and produces structured and analyzable outputs. These summaries drive a dynamic dashboard that visualizes evolving patterns in emotion, activity, and conversation topics. Built entirely on free-tier infrastructure, CognitiveSky achieves both low operational cost and high accessibility. While demonstrated here for monitoring mental health discourse, its modular design enables applications across domains such as disinformation detection, crisis response, and civic sentiment analysis. By bridging large language models with decentralized networks, CognitiveSky offers a transparent, extensible tool for computational social science in an era of shifting digital ecosystems.
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[NLP-62] A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm ICML WWW

【速读】：该论文旨在解决多平台公共意见情感分析在“15分钟城市”（15-minute city）议题中的跨域一致性与可扩展性问题，尤其针对Twitter、Reddit和新闻媒体等异构文本来源的语义差异与标注难题。其解决方案的关键在于构建一个统一的自动化情感分类流水线，采用压缩型Transformer模型（如Llama-3-8B进行标注）实现对长文本与短文本的一致性处理，并通过分层五折交叉验证对比DistilRoBERTa、DistilBERT、MiniLM、ELECTRA和TinyBERT五种模型的性能表现。研究发现，尽管新闻数据因类别不平衡导致性能虚高，Reddit存在摘要信息丢失，Twitter则提供适中挑战，但压缩模型在准确率与效率之间实现了良好平衡，表明在城市规划话语的情感识别任务中，无需依赖大规模模型即可实现高效、可复现的分类效果。

链接: https://arxiv.org/abs/2509.11443
作者: Gaurab Chhetri,Darrell Anderson,Boniphace Kutela,Subasish Das
机构: Texas State University (德克萨斯州立大学); Texas A&M University (德克萨斯农工大学)
类目: Computation and Language (cs.CL); Social and Information Networks (cs.SI)
备注: This is the author’s preprint version of a paper accepted for presentation at the 24th International Conference on Machine Learning and Applications (ICMLA 2025), December 3-5, 2025, Florida, USA. The final published version will appear in the official IEEE proceedings. Conference site: this https URL

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Abstract:This study presents the first multi-platform sentiment analysis of public opinion on the 15-minute city concept across Twitter, Reddit, and news media. Using compressed transformer models and Llama-3-8B for annotation, we classify sentiment across heterogeneous text domains. Our pipeline handles long-form and short-form text, supports consistent annotation, and enables reproducible evaluation. We benchmark five models (DistilRoBERTa, DistilBERT, MiniLM, ELECTRA, TinyBERT) using stratified 5-fold cross-validation, reporting F1-score, AUC, and training time. DistilRoBERTa achieved the highest F1 (0.8292), TinyBERT the best efficiency, and MiniLM the best cross-platform consistency. Results show News data yields inflated performance due to class imbalance, Reddit suffers from summarization loss, and Twitter offers moderate challenge. Compressed models perform competitively, challenging assumptions that larger models are necessary. We identify platform-specific trade-offs and propose directions for scalable, real-world sentiment classification in urban planning discourse.
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[NLP-63] Securing AI Agents : Implementing Role-Based Access Control for Industrial Applications

【速读】：该论文旨在解决AI代理（AI agents）在工业应用中面临的安全威胁问题，尤其是提示注入攻击（prompt injection attacks）对其完整性与可靠性的潜在风险。针对这一挑战，论文提出了一种将基于角色的访问控制（Role-Based Access Control, RBAC）集成到AI代理架构中的框架，其关键在于通过RBAC机制构建一个安全防护层（security guardrail），从而实现对AI代理行为的有效约束与权限管理，支持在本地部署（on-premises）场景下的可扩展、安全的AI代理落地应用。

链接: https://arxiv.org/abs/2509.11431
作者: Aadil Gani Ganie
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:The emergence of Large Language Models (LLMs) has significantly advanced solutions across various domains, from political science to software development. However, these models are constrained by their training data, which is static and limited to information available up to a specific date. Additionally, their generalized nature often necessitates fine-tuning – whether for classification or instructional purposes – to effectively perform specific downstream tasks. AI agents, leveraging LLMs as their core, mitigate some of these limitations by accessing external tools and real-time data, enabling applications such as live weather reporting and data analysis. In industrial settings, AI agents are transforming operations by enhancing decision-making, predictive maintenance, and process optimization. For example, in manufacturing, AI agents enable near-autonomous systems that boost productivity and support real-time decision-making. Despite these advancements, AI agents remain vulnerable to security threats, including prompt injection attacks, which pose significant risks to their integrity and reliability. To address these challenges, this paper proposes a framework for integrating Role-Based Access Control (RBAC) into AI agents, providing a robust security guardrail. This framework aims to support the effective and scalable deployment of AI agents, with a focus on on-premises implementations.
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[NLP-64] FuseCodec: Semantic-Contextual Fusion and Supervision for Neural Codecs

【速读】：该论文旨在解决现有神经声码器（neural codecs）在语音分词（speech tokenization）中仅捕捉低级声学特征、忽视语义和上下文线索的问题。当前方法虽尝试引入自监督语音模型的语义表示或预训练语言模型的上下文表示，但在对齐与统一语义与上下文表示方面仍存在挑战。解决方案的关键在于提出 FuseCodec，通过强跨模态对齐和全局信息监督实现声学、语义与上下文表示的融合：（i）潜空间融合（Latent Representation Fusion）将语义与上下文特征直接注入编码器潜空间，实现鲁棒且统一的表征学习；（ii）全局语义-上下文监督（Global Semantic-Contextual Supervision）利用全局池化并广播的表示来监督离散标记，提升时序一致性和跨模态对齐；（iii）时序对齐的上下文监督（Temporally Aligned Contextual Supervision）通过局部窗口内动态匹配上下文与语音标记，实现细粒度的标记级监督。实验表明，FuseCodec 在 LibriSpeech 上超越 EnCodec、SpeechTokenizer 和 DAC，在转录准确率、感知质量、可懂度和说话人相似性等指标上达到最优性能，验证了语义与上下文引导分词的有效性。

链接: https://arxiv.org/abs/2509.11425
作者: Md Mubtasim Ahasan,Rafat Hasan Khan,Tasnim Mohiuddin,Aman Chadha,Tariq Iqbal,M Ashraful Amin,Amin Ahsan Ali,Md Mofijul Islam,A K M Mahbubur Rahman
机构: Center for Computational & Data Sciences, Independent University, Bangladesh (独立大学计算与数据科学中心); Amazon GenAI; Qatar Computing Research Institute (卡塔尔计算研究研究所); University of Virginia (弗吉尼亚大学)
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Audio and Speech Processing (eess.AS)
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Abstract:Speech tokenization enables discrete representation and facilitates speech language modeling. However, existing neural codecs capture low-level acoustic features, overlooking the semantic and contextual cues inherent to human speech. While recent efforts introduced semantic representations from self-supervised speech models or incorporated contextual representations from pre-trained language models, challenges remain in aligning and unifying the semantic and contextual representations. We introduce FuseCodec, which unifies acoustic, semantic, and contextual representations through strong cross-modal alignment and globally informed supervision. We propose three complementary techniques: (i) Latent Representation Fusion, integrating semantic and contextual features directly into the encoder latent space for robust and unified representation learning; (ii) Global Semantic-Contextual Supervision, supervising discrete tokens with globally pooled and broadcasted representations to enhance temporal consistency and cross-modal alignment; and (iii) Temporally Aligned Contextual Supervision, strengthening alignment by dynamically matching contextual and speech tokens within a local window for fine-grained token-level supervision. We further introduce FuseCodec-TTS, demonstrating our methodology’s applicability to zero-shot speech synthesis. Empirically, FuseCodec achieves state-of-the-art performance in LibriSpeech, surpassing EnCodec, SpeechTokenizer, and DAC in transcription accuracy, perceptual quality, intelligibility, and speaker similarity. Results highlight the effectiveness of contextually and semantically guided tokenization for speech tokenization and downstream tasks. Code and pretrained models are available at this https URL.
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[NLP-65] Continually Adding New Languages to Multilingual Language Models

【速读】：该论文旨在解决多语言语言模型（Multilingual Language Models）在新增语言时面临的持续学习问题：由于模型训练通常基于固定语言集，若需支持新语言，往往需要从头重新训练，这不仅成本高昂，且受限于原始预训练数据不可获取的现实。现有方法如持续预训练易引发灾难性遗忘（catastrophic forgetting），而经验回放等缓解策略因缺乏原始数据无法应用。其解决方案的关键在于提出 Layer-Selective LoRA (LayRA)，通过仅在初始层和最终层添加低秩适配器（Low-Rank Adapters, LoRA），冻结其余参数，在不依赖原始预训练数据的前提下有效缓解遗忘，并利用多语言模型中语言编码、推理与翻译的分层特性（初始层处理源语言输入，中间层以英语推理，末层转回源语言输出）实现对新语言的高效适应。实验表明，LayRA在保持原有语言能力的同时，在新语言学习上优于或媲美传统LoRA方法，并可通过模型算术（model arithmetic）赋予模型强指令遵循能力，无需目标语言的指令微调数据。

链接: https://arxiv.org/abs/2509.11414
作者: Abraham Toluwase Owodunni,Sachin Kumar
机构: The Ohio State University (俄亥俄州立大学)
类目: Computation and Language (cs.CL)
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Abstract:Multilingual language models are trained on a fixed set of languages, and to support new languages, the models need to be retrained from scratch. This is an expensive endeavor and is often infeasible, as model developers tend not to release their pre-training data. Naive approaches, such as continued pretraining, suffer from catastrophic forgetting; however, mitigation strategies like experience replay cannot be applied due to the lack of original pretraining data. In this work, we investigate the problem of continually adding new languages to a multilingual model, assuming access to pretraining data in only the target languages. We explore multiple approaches to address this problem and propose Layer-Selective LoRA (LayRA), which adds Low-Rank Adapters (LoRA) to selected initial and final layers while keeping the rest of the model frozen. LayRA builds on two insights: (1) LoRA reduces forgetting, and (2) multilingual models encode inputs in the source language in the initial layers, reason in English in intermediate layers, and translate back to the source language in final layers. We experiment with adding multiple combinations of Galician, Swahili, and Urdu to pretrained language models and evaluate each method on diverse multilingual tasks. We find that LayRA provides the overall best tradeoff between preserving models’ capabilities in previously supported languages, while being competitive with existing approaches such as LoRA in learning new languages. We also demonstrate that using model arithmetic, the adapted models can be equipped with strong instruction following abilities without access to any instruction tuning data in the target languages.
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[NLP-66] ransformer Enhanced Relation Classification: A Comparative Analysis of Contextuality Data Efficiency and Sequence Complexity

【速读】：该论文旨在解决关系抽取（Relation Extraction, RE）任务中不同模型架构性能差异的问题，特别是在大语言模型（Large Language Models, LLMs）兴起背景下，系统比较基于Transformer与非Transformer的深度监督学习方法在关系分类中的表现。其解决方案的关键在于通过在TACRED、TACREV和RE-TACRED三个标准数据集上进行详尽对比实验，评估包括PA-LSTM、C-GCN、AGGCN等非Transformer模型以及BERT、RoBERTa、R-BERT等Transformer模型的微F1分数，并考察不同句长和训练数据比例下的鲁棒性。结果表明，Transformer-based模型显著优于传统模型，微F1提升至80–90%，而后者仅为64–67%，验证了预训练语言模型在关系抽取任务中的优越性。

链接: https://arxiv.org/abs/2509.11374
作者: Bowen Jing,Yang Cui,Tianpeng Huang
机构: University of Manchester (曼彻斯特大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:In the era of large language model, relation extraction (RE) plays an important role in information extraction through the transformation of unstructured raw text into structured data (Wadhwa et al., 2023). In this paper, we systematically compare the performance of deep supervised learning approaches without transformers and those with transformers. We used a series of non-transformer architectures such as PA-LSTM(Zhang et al., 2017), C-GCN(Zhang et al., 2018), and AGGCN(attention guide GCN)(Guo et al., 2019), and a series of transformer architectures such as BERT, RoBERTa, and R-BERT(Wu and He, 2019). Our comparison included traditional metrics like micro F1, as well as evaluations in different scenarios, varying sentence lengths, and different percentages of the dataset for training. Our experiments were conducted on TACRED, TACREV, and RE-TACRED. The results show that transformer-based models outperform non-transformer models, achieving micro F1 scores of 80-90% compared to 64-67% for non-transformer models. Additionally, we briefly review the research journey in supervised relation classification and discuss the role and current status of large language models (LLMs) in relation extraction.
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[NLP-67] !MSA at AraHealthQA 2025 Shared Task: Enhancing LLM Performance for Arabic Clinical Question Answering through Prompt Engineering and Ensemble Learning EMNLP2025

【速读】：该论文旨在解决阿拉伯语临床场景下的问答任务，包括多选题（Sub-Task 1）和开放式问答（Sub-Task 2），其核心挑战在于提升模型在特定领域（医疗健康）中对阿拉伯语的理解与生成能力。解决方案的关键在于：对于多选题，采用Gemini 2.5 Flash模型结合少量样本提示（few-shot prompting）、数据预处理及三种提示配置的集成策略以增强分类准确性；对于开放式问答，则使用统一提示模板，引入角色扮演（role-playing）作为阿拉伯语医学专家，并融合少量示例与后处理机制，从而生成简洁且准确的回答，覆盖填空、医患问答、语法错误纠正（GEC）及改写变体等多种问题形式。

链接: https://arxiv.org/abs/2509.11365
作者: Mohamed Tarek,Seif Ahmed,Mohamed Basem
机构: MSA University (MSA大学)
类目: Computation and Language (cs.CL)
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Abstract:We present our systems for Track 2 (General Arabic Health QA, MedArabiQ) of the AraHealthQA-2025 shared task, where our methodology secured 2nd place in both Sub-Task 1 (multiple-choice question answering) and Sub-Task 2 (open-ended question answering) in Arabic clinical contexts. For Sub-Task 1, we leverage the Gemini 2.5 Flash model with few-shot prompting, dataset preprocessing, and an ensemble of three prompt configurations to improve classification accuracy on standard, biased, and fill-in-the-blank questions. For Sub-Task 2, we employ a unified prompt with the same model, incorporating role-playing as an Arabic medical expert, few-shot examples, and post-processing to generate concise responses across fill-in-the-blank, patient-doctor QA, GEC, and paraphrased variants.
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[NLP-68] Ko-PIQA: A Korean Physical Commonsense Reasoning Dataset with Cultural Context

【速读】：该论文旨在解决当前物理常识推理数据集（如PIQA） predominantly 英语中心化且缺乏文化多样性的问题。其解决方案的关键在于构建一个具有文化根基的韩语物理常识推理数据集——Ko-PIQA，通过多阶段过滤与GPT-4o精炼结合人工验证的方法，从301万条网络爬取问题中筛选出441个高质量问答对，并特别融入了19.7%包含韩国传统文化元素（如泡菜、韩服、泡菜冰箱等）的问题，要求模型具备跨文化理解能力而非仅依赖直译。这一设计凸显了文化特定情境下常识推理的挑战，为韩语模型评估和更具包容性的常识推理研究提供了基准。

链接: https://arxiv.org/abs/2509.11303
作者: Dasol Choi,Jungwhan Kim,Guijin Son
机构: Yonsei University (延世大学); AIM Intelligence; NAVER Cloud; OnelineAI; MODULABS
类目: Computation and Language (cs.CL)
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Abstract:Physical commonsense reasoning datasets like PIQA are predominantly English-centric and lack cultural diversity. We introduce Ko-PIQA, a Korean physical commonsense reasoning dataset that incorporates cultural context. Starting from 3.01 million web-crawled questions, we employed a multi-stage filtering approach using three language models to identify 11,553 PIQA-style questions. Through GPT-4o refinement and human validation, we obtained 441 high-quality question-answer pairs. A key feature of Ko-PIQA is its cultural grounding: 19.7% of questions contain culturally specific elements like traditional Korean foods (kimchi), clothing (hanbok), and specialized appliances (kimchi refrigerators) that require culturally-aware reasoning beyond direct translation. We evaluate seven language models on Ko-PIQA, with the best model achieving 83.22% accuracy while the weakest reaches only 59.86%, demonstrating significant room for improvement. Models particularly struggle with culturally specific scenarios, highlighting the importance of culturally diverse datasets. Ko-PIQA serves as both a benchmark for Korean language models and a foundation for more inclusive commonsense reasoning research. The dataset and code will be publicly available.
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[NLP-69] Opal: An Operator Algebra View of RLHF

【速读】：该论文旨在解决强化学习中人类反馈（Reinforcement Learning from Human Feedback, RLHF）方法在形式化表达与统一建模上的不一致性问题，尤其是不同算法（如DPO、RRHF、ORPO等）在目标函数设计上的差异难以比较和归一化。其核心解决方案是提出Opal框架，将RLHF的目标建模为基于基础效用的两层结构：加性惩罚项（additive penalties）与乘性成对权重（multiplicative pairwise weights），并建立一个可还原性判据——当参考策略固定、惩罚项为加性且权重独立于中间边际时，所有目标函数可坍缩为成对边际的标准形式（normal form）。在此基础上，作者进一步提出GKPO（Generalized Kernel Preference Object）作为通用范式，支持多种RLHF方法的标准化表示、序列化、哈希及失败假设的显式标记，从而实现跨方法转换与最小压力测试（SHIFT/GATE/SCORE），为RLHF提供理论一致性和工程可实现性的统一接口。

链接: https://arxiv.org/abs/2509.11298
作者: Madhava Gaikwad
机构: Microsoft(微软)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:We present Opal, an operator view of reinforcement learning from human feedback (RLHF). Objectives are expressed as ladders of two primitives on a base utility: additive penalties and multiplicative pairwise weights. We describe a simple reduction law with if-and-only-if conditions: such ladders collapse to a normal form on pairwise margins when the reference is fixed, penalties are additive, and weights are independent of intermediate margins. When these assumptions do not hold (reference shift, non-additive gates, score-dependent weights), small examples demonstrate non-reducibility. Building on this view, we introduce GKPO (Generalized Kernel Preference Object), a canonical schema in which many RLHF methods can be represented and, when reducible, mapped back from. GKPO provides a standard JSON serialization, canonicalization and hashing rules, and explicit flags with finite witnesses when assumptions fail. We illustrate these ideas with GKPO examples for DPO, RRHF, and ORPO, along with cross-method conversions (where assumptions permit) and minimal stress tests (SHIFT/GATE/SCORE) that highlight non-reducibility. A lightweight Python reference library accompanies the schema, implementing canonical hashing and adapters for DPO and RRHF. Comments: 11 pages main Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) MSC classes: 68T05, 68T07, 68Q32, 62H30, 62F15, 90C30 ACMclasses: I.2.6; I.2.7; I.2.8; G.3; G.1.6 Cite as: arXiv:2509.11298 [cs.LG] (or arXiv:2509.11298v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.11298 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[NLP-70] he Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences

【速读】：该论文旨在解决科研人员在使用大语言模型（Large Language Models, LLMs）时因缺乏系统性提示工程（prompt engineering）方法而导致的效率低下和输出质量不稳定的问题。其核心挑战在于，尽管提示工程能显著提升LLMs生成结果的可靠性与实用性，但其高认知投入门槛阻碍了研究人员将其融入日常生命科学工作流中。解决方案的关键在于从58种文本提示技术中提炼出6种核心策略——零样本（zero-shot）、少样本（few-shot）学习、思维链生成（thought generation）、集成（ensembling）、自我批评（self-criticism）与分解（decomposition），并结合生命科学场景（如文献综述、数据提取与编辑任务）提供结构化指导，明确提示设计的正误范式，规避多轮对话退化、幻觉及推理模型混淆等常见陷阱，从而推动提示工程由随机尝试向低摩擦、可复用的系统性实践演进，最终增强研究质量而非替代既有专业流程。

链接: https://arxiv.org/abs/2509.11295
作者: Valentin Romanov,Steven A Niederer
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Developing effective prompts demands significant cognitive investment to generate reliable, high-quality responses from Large Language Models (LLMs). By deploying case-specific prompt engineering techniques that streamline frequently performed life sciences workflows, researchers could achieve substantial efficiency gains that far exceed the initial time investment required to master these techniques. The Prompt Report published in 2025 outlined 58 different text-based prompt engineering techniques, highlighting the numerous ways prompts could be constructed. To provide actionable guidelines and reduce the friction of navigating these various approaches, we distil this report to focus on 6 core techniques: zero-shot, few-shot approaches, thought generation, ensembling, self-criticism, and decomposition. We breakdown the significance of each approach and ground it in use cases relevant to life sciences, from literature summarization and data extraction to editorial tasks. We provide detailed recommendations for how prompts should and shouldn’t be structured, addressing common pitfalls including multi-turn conversation degradation, hallucinations, and distinctions between reasoning and non-reasoning models. We examine context window limitations, agentic tools like Claude Code, while analyzing the effectiveness of Deep Research tools across OpenAI, Google, Anthropic and Perplexity platforms, discussing current limitations. We demonstrate how prompt engineering can augment rather than replace existing established individual practices around data processing and document editing. Our aim is to provide actionable guidance on core prompt engineering principles, and to facilitate the transition from opportunistic prompting to an effective, low-friction systematic practice that contributes to higher quality research.
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[NLP-71] Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations EMNLP2025

【速读】：该论文旨在解决大型视觉语言模型（Large Vision-Language Models, LVLMs）中存在的严重幻觉问题，即模型生成的文本响应与输入图像内容不一致。现有缓解方法主要依赖偏好对齐（preference alignment），需借助外部人工标注或辅助模型收集偏好数据，导致成本高且难以持续优化。论文提出的解决方案是自主偏好对齐自注入方法（Autonomous Preference Alignment via Self-Injection, APASI），其关键在于利用目标LVLM自身主动向生成响应中“自注入”幻觉，从而构造出具有不同偏好水平的响应对；该过程基于对幻觉模式的三个关键观察，确保低偏好响应能真实模拟实际幻觉特征，提供精准的学习信号。此外，APASI引入迭代对齐训练策略与课程学习机制，动态提升偏好数据难度，实现模型稳定、持续的幻觉抑制能力提升。

链接: https://arxiv.org/abs/2509.11287
作者: Yifan Lu,Ziqi Zhang,Chunfeng Yuan,Jun Gao,Congxuan Zhang,Xiaojuan Qi,Bing Li,Weiming Hu
机构: Beijing Key Laboratory of Super Intelligent Security of Multi-Modal Information, CASIA (中国科学院自动化研究所); State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA (中国科学院自动化研究所); School of Artificial Intelligence, University of Chinese Academy of Sciences (中国科学院大学人工智能学院); Hello Group (Hello Group); Nanchang Hangkong University (南昌航空大学); The University of Hong Kong (香港大学); School of Information Science and Technology, ShanghaiTech University (上海科技大学信息科学与技术学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
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Abstract:Large Vision-Language Models (LVLMs) suffer from serious hallucination problems, where the model-generated responses are inconsistent with the visual inputs. Existing hallucination mitigation methods are mainly based on preference alignment and require external human annotations or auxiliary models for preference data collection, which increase costs and limit sustainable improvement. To tackle these challenges, we propose Autonomous Preference Alignment via Self-Injection (APASI), a novel and generalizable method that mitigates hallucinations without external dependencies. APASI leverages the target LVLM to self-inject hallucinations into a generated response, creating a pair of responses with varying preference levels. During the self-injection process, the dis-preferred response is generated based on three key observations of hallucinations, ensuring it simulates real hallucination patterns. This fidelity offers an accurate learning signal for hallucination mitigation. Moreover, APASI incorporates an iterative alignment training strategy combined with curriculum learning to periodically update the preference data with increasing challenge, enabling stable and continuous enhancement of the LVLM. Extensive experiments across six benchmarks show that APASI not only effectively mitigates hallucinations for three baseline models but also achieves comparable or even superior performance to alignment-based methods with external dependency, thereby demonstrating its effectiveness and generalization capability. The code is available at this https URL.
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[NLP-72] Evalet: Evaluating Large Language Models by Frag menting Outputs into Functions

【速读】：该论文试图解决当前“LLM-as-a-Judge”方法在评估生成式 AI 输出时存在的问题：即仅提供整体评分（holistic scores），而无法揭示具体哪些内容片段及其语用功能影响了评分结果，导致用户难以验证评分合理性并定位可改进的细节。解决方案的关键在于提出“功能分割”（functional fragmentation）方法，将每个输出分解为关键片段，并识别每个片段相对于评价标准所承担的语用功能（rhetorical functions），从而显式暴露影响评估的核心元素及其与用户目标的契合度。该方法通过可视化系统 Evalet 实现，支持对多个输出进行逐片段的检查、评分和对比，实证研究表明其能显著提升用户发现评估偏差的能力（提高48%），进而增强对 LLM 评估结果的信任并识别更具行动价值的问题。

链接: https://arxiv.org/abs/2509.11206
作者: Tae Soo Kim,Heechan Lee,Yoonjoo Lee,Joseph Seering,Juho Kim
机构: KAIST(韩国科学技术院)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Practitioners increasingly rely on Large Language Models (LLMs) to evaluate generative AI outputs through “LLM-as-a-Judge” approaches. However, these methods produce holistic scores that obscure which specific elements influenced the assessments. We propose functional fragmentation, a method that dissects each output into key fragments and interprets the rhetoric functions that each fragment serves relative to evaluation criteria – surfacing the elements of interest and revealing how they fulfill or hinder user goals. We instantiate this approach in Evalet, an interactive system that visualizes fragment-level functions across many outputs to support inspection, rating, and comparison of evaluations. A user study (N=10) found that, while practitioners struggled to validate holistic scores, our approach helped them identify 48% more evaluation misalignments. This helped them calibrate trust in LLM evaluations and rely on them to find more actionable issues in model outputs. Our work shifts LLM evaluation from quantitative scores toward qualitative, fine-grained analysis of model behavior.
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[NLP-73] DreamNav: A Trajectory-Based Imaginative Framework for Zero-Shot Vision-and-Language Navigation

【速读】：该论文旨在解决零样本视觉-语言导航（Vision-and-Language Navigation in Continuous Environments, VLN-CE）中现有方法依赖高成本感知与被动场景理解、控制粒度局限于点级动作、缺乏前瞻性与长程规划能力的问题。其解决方案的关键在于提出DreamNav框架，通过三个核心组件实现：(1) EgoView Corrector对齐视角并稳定第一人称感知以降低感官成本；(2) Trajectory Predictor采用轨迹级规划而非点级动作，更好地对齐指令语义；(3) Imagination Predictor引入想象预测机制，赋予智能体主动思考能力以支持前瞻性和长时程规划。该方法仅使用第一人称输入即实现了轨迹级规划与主动想象的统一，在VLN-CE和真实世界测试中达到新的零样本SOTA性能。

链接: https://arxiv.org/abs/2509.11197
作者: Yunheng Wang,Yuetong Fang,Taowen Wang,Yixiao Feng,Yawen Tan,Shuning Zhang,Peiran Liu,Yiding Ji,Renjing Xu
机构: The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州）); Zhejiang Normal University (浙江师范大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Vision-and-Language Navigation in Continuous Environments (VLN-CE), which links language instructions to perception and control in the real world, is a core capability of embodied robots. Recently, large-scale pretrained foundation models have been leveraged as shared priors for perception, reasoning, and action, enabling zero-shot VLN without task-specific training. However, existing zero-shot VLN methods depend on costly perception and passive scene understanding, collapsing control to point-level choices. As a result, they are expensive to deploy, misaligned in action semantics, and short-sighted in planning. To address these issues, we present DreamNav that focuses on the following three aspects: (1) for reducing sensory cost, our EgoView Corrector aligns viewpoints and stabilizes egocentric perception; (2) instead of point-level actions, our Trajectory Predictor favors global trajectory-level planning to better align with instruction semantics; and (3) to enable anticipatory and long-horizon planning, we propose an Imagination Predictor to endow the agent with proactive thinking capability. On VLN-CE and real-world tests, DreamNav sets a new zero-shot state-of-the-art (SOTA), outperforming the strongest egocentric baseline with extra information by up to 7.49% and 18.15% in terms of SR and SPL metrics. To our knowledge, this is the first zero-shot VLN method to unify trajectory-level planning and active imagination while using only egocentric inputs.
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[NLP-74] RanAT4BIE: Random Adversarial Training for Biomedical Information Extraction IJCNN

【速读】：该论文旨在解决预训练语言模型在生物医学信息抽取（BioIE）任务中性能提升与计算效率之间的矛盾问题。尽管传统对抗训练（adversarial training）能显著提升模型性能，但其带来的计算开销较大，限制了实际应用。解决方案的关键在于提出随机对抗训练（Random Adversarial Training, RAT），该框架通过将随机采样机制与对抗训练原理相结合，在保持模型泛化能力和鲁棒性的同时，大幅降低计算成本，从而实现性能与效率的平衡。

链接: https://arxiv.org/abs/2509.11191
作者: Jian Chen,Shengyi Lv,Leilei Su
机构: Hainan University (海南大学); Hainan University (海南大学)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
备注: Accepted for publication at the International Joint Conference on Neural Networks (IJCNN) 2025

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Abstract:We introduce random adversarial training (RAT), a novel framework successfully applied to biomedical information extraction (BioIE) tasks. Building on PubMedBERT as the foundational architecture, our study first validates the effectiveness of conventional adversarial training in enhancing pre-trained language models’ performance on BioIE tasks. While adversarial training yields significant improvements across various performance metrics, it also introduces considerable computational overhead. To address this limitation, we propose RAT as an efficiency solution for biomedical information extraction. This framework strategically integrates random sampling mechanisms with adversarial training principles, achieving dual objectives: enhanced model generalization and robustness while significantly reducing computational costs. Through comprehensive evaluations, RAT demonstrates superior performance compared to baseline models in BioIE tasks. The results highlight RAT’s potential as a transformative framework for biomedical natural language processing, offering a balanced solution to the model performance and computational efficiency.
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[NLP-75] Optimal Brain Restoration for Joint Quantization and Sparsification of LLM s

【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）压缩中单一方法逼近极限的问题，即当量化（quantization）和稀疏化（sparsity）各自达到性能瓶颈后，如何进一步提升压缩效率并维持下游任务性能。其解决方案的关键在于提出一种无需训练的通用框架 Optimal Brain Restoration (OBR)，通过误差补偿机制协调量化与稀疏化对权重分布的不同需求：量化倾向于紧凑的权重范围，而稀疏化依赖高方差以保留重要参数。OBR基于二阶 Hessian 目标函数进行建模，经由代理近似转化为可解问题，并最终通过分组误差补偿获得闭式解，从而在保持模型精度的前提下实现极端压缩（如 W4A4KV4 量化配合 50% 稀疏度），显著提升推理速度与降低内存占用。

链接: https://arxiv.org/abs/2509.11177
作者: Hang Guo,Yawei Li,Luca Benini
机构: ETH Zürich (苏黎世联邦理工学院)
类目: Computation and Language (cs.CL)
备注: Preprint

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Abstract:Recent advances in Large Language Model (LLM) compression, such as quantization and pruning, have achieved notable success. However, as these techniques gradually approach their respective limits, relying on a single method for further compression has become increasingly challenging. In this work, we explore an alternative solution by combining quantization and sparsity. This joint approach, though promising, introduces new difficulties due to the inherently conflicting requirements on weight distributions: quantization favors compact ranges, while pruning benefits from high variance. To attack this problem, we propose Optimal Brain Restoration (OBR), a general and training-free framework that aligns pruning and quantization by error compensation between both. OBR minimizes performance degradation on downstream tasks by building on a second-order Hessian objective, which is then reformulated into a tractable problem through surrogate approximation and ultimately reaches a closed-form solution via group error compensation. Experiments show that OBR enables aggressive W4A4KV4 quantization with 50% sparsity on existing LLMs, and delivers up to 4.72x speedup and 6.4x memory reduction compared to the FP16-dense baseline.
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[NLP-76] Differentially-private text generation degrades output language quality

【速读】：该论文试图解决的问题是：在差分隐私（Differential Privacy, DP）约束下微调的大语言模型（Large Language Models, LLMs）对生成文本的语言质量与下游任务实用性的影响尚不明确。解决方案的关键在于系统性地评估五种不同LLM在三种语料库上、四种隐私强度下的文本输出表现，包括长度、语法正确性、词汇多样性以及在书籍类型分类和死亡原因识别等下游任务中的性能，从而量化DP微调对合成数据质量和实用性的潜在负面影响。

链接: https://arxiv.org/abs/2509.11176
作者: Erion Çano,Ivan Habernal
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 20 pages, 3 figures, 35 tables

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Abstract:Ensuring user privacy by synthesizing data from large language models (LLMs) tuned under differential privacy (DP) has become popular recently. However, the impact of DP fine-tuned LLMs on the quality of the language and the utility of the texts they produce has not been investigated. In this work, we tune five LLMs with three corpora under four levels of privacy and assess the length, the grammatical correctness, and the lexical diversity of the text outputs they produce. We also probe the utility of the synthetic outputs in downstream classification tasks such as book genre recognition based on book descriptions and cause of death recognition based on verbal autopsies. The results indicate that LLMs tuned under stronger privacy constrains produce texts that are shorter by at least 77 %, that are less grammatically correct by at least 9 %, and are less diverse by at least 10 % in bi-gram diversity. Furthermore, the accuracy they reach in downstream classification tasks decreases, which might be detrimental to the usefulness of the generated synthetic data.
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[NLP-77] AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLM s

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）中注意力机制（Attention Mechanism）的二次复杂度问题，这一瓶颈限制了模型在长文本上下文中的扩展能力，成为计算和内存层面的关键制约因素。解决方案的核心在于提出AQUA（Attention via QUery mAgnitudes），一种新颖且通用的近似策略：首先在离线阶段通过奇异值分解（SVD）对校准数据集计算一个与语言无关的投影矩阵；随后在在线推理阶段，基于查询向量的幅值动态选择稀疏维度进行注意力计算，从而显著降低注意力计算成本，同时保持性能损失可忽略。理论分析明确了其相较于标准注意力的效率拐点，实验表明在Llama-3.1-8B等先进模型上实现25%的注意力点积计算减少，且对多种基准测试性能影响不显著，同时具备与现有token淘汰方法（如H2O）协同加速及直接缩减KV缓存内存的能力。

链接: https://arxiv.org/abs/2509.11155
作者: Santhosh G S,Saurav Prakash,Balaraman Ravindran
机构: Indian Institute of Technology Madras (印度理工学院马德拉斯分校)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:The quadratic complexity of the attention mechanism remains a fundamental barrier to scaling Large Language Models (LLMs) to longer contexts, creating a critical bottleneck in both computation and memory. To address this, we introduce AQUA (Attention via QUery mAgnitudes) a novel and versatile approximation strategy that significantly reduces the cost of attention with a graceful performance trade-off. Our method operates in two phases: an efficient offline step where we compute a universal, language agnostic projection matrix via SVD on a calibration dataset, and an online inference step where we project query and key vectors and dynamically select a sparse subset of dimensions based on the query’s magnitude. We provide a formal theoretical analysis of AQUA, establishing the break-even point at which it becomes more computationally efficient than standard attention. Our empirical evaluations on state-of-the-art models like Llama-3.1-8B demonstrate that a 25% reduction in the attention dot-product computation can be achieved with a statistically insignificant impact on performance across a wide range of benchmarks. We further showcase the versatility of AQUA by demonstrating its ability to synergistically accelerate existing token eviction methods like H2O and to directly reduce KV-cache memory size. By offering a controllable knob to balance efficiency and accuracy, AQUA provides a practical and powerful tool for making large-scale LLM inference more accessible and sustainable.
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[NLP-78] xt2Mem: A Unified Memory Operation Language for Memory Operating System

【速读】：该论文旨在解决当前大型语言模型代理（Large Language Model Agents）在长期交互中依赖记忆机制时存在的标准化缺失问题，具体表现为现有框架仅提供基础的记忆操作（如编码、检索和删除），而缺乏对合并、提升、降级、分割、锁定和过期等高阶操作的统一支持，且无形式化可执行的指令规范，导致系统行为不可预测。其解决方案的关键在于提出 Text2Mem——一种统一的记忆操作语言，通过定义一套紧凑而富有表现力的操作集合，并以 JSON Schema 形式明确表达每条指令的结构与语义不变性，结合解析器、验证器与适配器组件实现从自然语言到可靠执行的转化，从而确保跨异构后端的安全性、确定性和可移植性。

链接: https://arxiv.org/abs/2509.11145
作者: Felix Wang,Boyu Chen,Kerun Xu,Bo Tang,Feiyu Xiong,Zhiyu Li
机构: 未知
类目: Computation and Language (cs.CL)
备注: 11 pages, 3 figures

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Abstract:Large language model agents increasingly depend on memory to sustain long horizon interaction, but existing frameworks remain limited. Most expose only a few basic primitives such as encode, retrieve, and delete, while higher order operations like merge, promote, demote, split, lock, and expire are missing or inconsistently supported. Moreover, there is no formal and executable specification for memory commands, leaving scope and lifecycle rules implicit and causing unpredictable behavior across systems. We introduce Text2Mem, a unified memory operation language that provides a standardized pathway from natural language to reliable execution. Text2Mem defines a compact yet expressive operation set aligned with encoding, storage, and retrieval. Each instruction is represented as a JSON based schema instance with required fields and semantic invariants, which a parser transforms into typed operation objects with normalized parameters. A validator ensures correctness before execution, while adapters map typed objects either to a SQL prototype backend or to real memory frameworks. Model based services such as embeddings or summarization are integrated when required. All results are returned through a unified execution contract. This design ensures safety, determinism, and portability across heterogeneous backends. We also outline Text2Mem Bench, a planned benchmark that separates schema generation from backend execution to enable systematic evaluation. Together, these components establish the first standardized foundation for memory control in agents.
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[NLP-79] When Smiley Turns Hostile: Interpreting How Emojis Trigger LLM s Toxicity

【速读】：该论文旨在解决生成式 AI（Generative AI）在接收到表情符号（emoji）输入时可能诱发毒性内容生成的问题，特别是探究emoji是否能显著增强模型的毒性输出行为及其内在机制。解决方案的关键在于：首先通过自动化构建含emoji的提示词（prompt）来隐晦表达毒性意图，在5种主流语言和7个知名大语言模型（LLM）上验证了emoji可轻易诱导毒性生成；其次从语义认知、序列生成与分词三个层面进行模型级解释，揭示emoji作为异质语义通道绕过安全机制的机理；最后进一步分析预训练语料库，发现emoji相关数据污染可能与毒性行为存在潜在关联，从而为理解并缓解此类风险提供理论依据与实证支撑。

链接: https://arxiv.org/abs/2509.11141
作者: Shiyao Cui,Xijia Feng,Yingkang Wang,Junxiao Yang,Zhexin Zhang,Biplab Sikdar,Hongning Wang,Han Qiu,Minlie Huang
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Emojis are globally used non-verbal cues in digital communication, and extensive research has examined how large language models (LLMs) understand and utilize emojis across contexts. While usually associated with friendliness or playfulness, it is observed that emojis may trigger toxic content generation in LLMs. Motivated by such a observation, we aim to investigate: (1) whether emojis can clearly enhance the toxicity generation in LLMs and (2) how to interpret this phenomenon. We begin with a comprehensive exploration of emoji-triggered LLM toxicity generation by automating the construction of prompts with emojis to subtly express toxic intent. Experiments across 5 mainstream languages on 7 famous LLMs along with jailbreak tasks demonstrate that prompts with emojis could easily induce toxicity generation. To understand this phenomenon, we conduct model-level interpretations spanning semantic cognition, sequence generation and tokenization, suggesting that emojis can act as a heterogeneous semantic channel to bypass the safety mechanisms. To pursue deeper insights, we further probe the pre-training corpus and uncover potential correlation between the emoji-related data polution with the toxicity generation behaviors. Supplementary materials provide our implementation code and data. (Warning: This paper contains potentially sensitive contents)
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[NLP-80] Agent ic Username Suggestion and Multimodal Gender Detection in Online Platforms: Introducing the PNGT-26K Dataset

【速读】：该论文旨在解决波斯语姓名在自然语言处理（Natural Language Processing, NLP）应用中，尤其是在性别识别和数字身份创建方面所面临的挑战，主要源于拼写转换不一致性和文化特定的命名模式，以及现有工具在处理波斯语姓名时性能显著下降的问题。解决方案的关键在于提出一个名为PNGT-26K的综合性波斯语姓名数据集，包含约26,000个姓名元组，每个元组包含姓名、其常见性别标签及英文转写形式，并基于此数据集开发了两个框架：Open Gender Detection（用于基于用户输入如头像和姓名进行概率性别预测）和Nominalist（利用代理型AI帮助用户为社交媒体账户选择用户名），二者均可轻松集成到各类网站中以提升用户体验。

链接: https://arxiv.org/abs/2509.11136
作者: Farbod Bijary,Mohsen Ebadpour,Amirhosein Tajbakhsh
机构: Amirkabir University of Technology (阿米尔卡比尔理工大学); Iran University of Science & Technology (伊朗科学技术大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Social and Information Networks (cs.SI)
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Abstract:Persian names present unique challenges for natural language processing applications, particularly in gender detection and digital identity creation, due to transliteration inconsistencies and cultural- specific naming patterns. Existing tools exhibit significant performance degradation on Persian names, while the scarcity of comprehensive datasets further compounds these limitations. To address these challenges, the present research introduces PNGT-26K, a comprehensive dataset of Persian names, their commonly associated gender, and their English transliteration, consisting of approximately 26,000 tuples. As a demonstration of how this resource can be utilized, we also introduce two frameworks, namely Open Gender Detection and Nominalist. Open Gender Detection is a production- grade, ready-to-use framework for using existing data from a user, such as profile photo and name, to give a probabilistic guess about the person’s gender. Nominalist, the second framework introduced by this paper, utilizes agentic AI to help users choose a username for their social media accounts on any platform. It can be easily integrated into any website to provide a better user experience. The PNGT-26K dataset, Nominalist and Open Gender Detection frameworks are publicly available on Github.
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[NLP-81] Joint Effects of Argumentation Theory Audio Modality and Data Enrichment on LLM -Based Fallacy Classification

【速读】：该论文旨在解决大语言模型（Large Language Model, LLM）在政治辩论场景中进行谬误分类时，如何受上下文信息和情感 tone 元数据影响的问题。其核心挑战在于：尽管理论驱动的提示策略（如 pragma-dialectics 和 argumentation table 框架）可提升推理可解释性，但引入额外的上下文与基于音频的情感 tone 元数据反而可能降低模型性能，尤其导致对“诉诸情感”类谬误的过度标记。解决方案的关键在于识别出“注意力稀释效应”——即增加输入维度会分散模型对逻辑结构的关注，从而削弱其谬误识别能力；实验表明，基础提示通常优于复杂增强提示，凸显了在多模态输入下保持模型专注力的重要性。

链接: https://arxiv.org/abs/2509.11127
作者: Hongxu Zhou,Hylke Westerdijk,Khondoker Ittehadul Islam
机构: University of Groningen (格罗宁根大学)
类目: Computation and Language (cs.CL)
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Abstract:This study investigates how context and emotional tone metadata influence large language model (LLM) reasoning and performance in fallacy classification tasks, particularly within political debate settings. Using data from U.S. presidential debates, we classify six fallacy types through various prompting strategies applied to the Qwen-3 (8B) model. We introduce two theoretically grounded Chain-of-Thought frameworks: Pragma-Dialectics and the Periodic Table of Arguments, and evaluate their effectiveness against a baseline prompt under three input settings: text-only, text with context, and text with both context and audio-based emotional tone metadata. Results suggest that while theoretical prompting can improve interpretability and, in some cases, accuracy, the addition of context and especially emotional tone metadata often leads to lowered performance. Emotional tone metadata biases the model toward labeling statements as \textitAppeal to Emotion, worsening logical reasoning. Overall, basic prompts often outperformed enhanced ones, suggesting that attention dilution from added inputs may worsen rather than improve fallacy classification in LLMs.
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[NLP-82] We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism EMNLP

【速读】：该论文旨在解决谈判对话系统中冲突化解能力不足与个性化适配性差的问题，即如何通过引入论点机制（argumentation mechanisms）提升谈判中的逻辑推理与冲突解决能力，并借助人格属性（personality attributes）增强交互的个性化适应性。其解决方案的关键在于提出了一种新的任务——人格驱动的论点式谈判对话生成（Personality-driven Argumentation-based Negotiation Dialogue Generation, PAN-DG），并构建了首个面向旅游场景的多人格维度谈判对话数据集PACT（Personality-driven Argumentation-based negotiation Conversations for Tourism sector）。该数据集融合了论点风格、偏好特征和购买行为三种人格属性，利用大语言模型（LLMs）生成高质量对话，并通过预训练与微调对比实验验证了微调后的LLMs能有效生成符合个体人格特征且具理性逻辑的谈判响应，从而显著提升了系统的个性化表达能力和论证推理水平。

链接: https://arxiv.org/abs/2509.11118
作者: Priyanshu Priya,Saurav Dudhate,Desai Vishesh Yasheshbhai,Asif Ekbal
机构: Indian Institute of Technology Patna (印度理工学院巴特那分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Paper is accepted at EMNLP (Findings) 2025

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Abstract:Integrating argumentation mechanisms into negotiation dialogue systems improves conflict resolution through exchanges of arguments and critiques. Moreover, incorporating personality attributes enhances adaptability by aligning interactions with individuals’ preferences and styles. To advance these capabilities in negotiation dialogue systems, we propose a novel Personality-driven Argumentation-based Negotiation Dialogue Generation (PAN-DG) task. To support this task, we introduce PACT, a dataset of Personality-driven Argumentation-based negotiation Conversations for Tourism sector. This dataset, generated using Large Language Models (LLMs), features three distinct personality profiles, viz. Argumentation Profile, Preference Profile, and Buying Style Profile to simulate a variety of negotiation scenarios involving diverse personalities. Thorough automatic and manual evaluations indicate that the dataset comprises high-quality dialogues. Further, we conduct comparative experiments between pre-trained and fine-tuned LLMs for the PAN-DG task. Multi-dimensional evaluation demonstrates that the fine-tuned LLMs effectively generate personality-driven rational responses during negotiations. This underscores the effectiveness of PACT in enhancing personalization and reasoning capabilities in negotiation dialogue systems, thereby establishing a foundation for future research in this domain.
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[NLP-83] Fluid Language Model Benchmarking

【速读】：该论文旨在解决语言模型（Language Model, LM）基准测试中存在的多个核心问题：全面评估成本高、基准测试难以准确衡量预期能力、以及因标注错误和基准饱和导致的评估质量下降。其解决方案的关键在于提出一种名为“Fluid Benchmarking”的新方法，该方法受心理测量学启发，通过构建基于现有LM评估结果的项目反应模型（Item Response Theory, IRT），将模型性能映射到潜在能力空间，并据此动态选择最适配当前LM能力水平的测试项，类似教育领域的计算机自适应测试（Computerized Adaptive Testing）。该方法显著提升了评估效率、有效性、稳定性及抗饱和能力，实验证明其在MMLU数据集上使用仅50分之一的测试项即可实现更高有效性和更低方差。

链接: https://arxiv.org/abs/2509.11106
作者: Valentin Hofmann,David Heineman,Ian Magnusson,Kyle Lo,Jesse Dodge,Maarten Sap,Pang Wei Koh,Chun Wang,Hannaneh Hajishirzi,Noah A. Smith
机构: Allen Institute for AI (艾伦人工智能研究所); University of Washington (华盛顿大学); Carnegie Mellon University (卡内基梅隆大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: COLM 2025

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Abstract:Language model (LM) benchmarking faces several challenges: comprehensive evaluations are costly, benchmarks often fail to measure the intended capabilities, and evaluation quality can degrade due to labeling errors and benchmark saturation. Although various strategies have been proposed to mitigate these issues, they tend to address individual aspects in isolation, neglecting broader questions about overall evaluation quality. Here, we introduce Fluid Benchmarking, a new evaluation approach that advances LM benchmarking across multiple dimensions. Inspired by psychometrics, Fluid Benchmarking is based on the insight that the relative value of benchmark items depends on an LM’s capability level, suggesting that evaluation should adapt to each LM. Methodologically, Fluid Benchmarking estimates an item response model based on existing LM evaluation results and uses the inferred quantities to select evaluation items dynamically, similar to computerized adaptive testing in education. In our experiments, we compare Fluid Benchmarking against the common practice of random item sampling as well as more sophisticated baselines, including alternative methods grounded in item response theory. We examine four dimensions – efficiency, validity, variance, and saturation – and find that Fluid Benchmarking achieves superior performance in all of them (e.g., higher validity and less variance on MMLU with fifty times fewer items). Our analysis shows that the two components of Fluid Benchmarking have distinct effects: item response theory, used to map performance into a latent ability space, increases validity, while dynamic item selection reduces variance. Overall, our results suggest that LM benchmarking can be substantially improved by moving beyond static evaluation.
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[NLP-84] EmoBench-Reddit: A Hierarchical Benchmark for Evaluating the Emotional Intelligence of Multimodal Large Language Models

【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在情感理解能力评估方面的不足，特别是其对复杂且主观的人类情绪（如讽刺、幽默、悲伤等）的识别与共情能力缺乏系统性评测。现有基准主要聚焦于客观视觉问答或图像描述任务，难以全面衡量模型在真实社交语境下的情感认知水平。解决方案的关键在于提出EmoBench-Reddit这一新型分层评估基准，该基准基于Reddit平台的350个精心筛选样本，每个样本包含图像、用户文本及由用户标签确认的情绪类别（sad, humor, sarcasm, happy），并设计了一套从基础感知到高级认知的多层级任务框架：感知任务考察模型对基本视觉元素（如颜色、物体）的识别能力，认知任务则要求进行场景推理、意图理解和整合文本语境的深度共情，从而实现对MLLMs多模态情感理解能力的精细化评估。

链接: https://arxiv.org/abs/2509.11101
作者: Haokun Li,Yazhou Zhang,Jizhi Ding,Qiuchi Li,Peng Zhang
机构: Tianjin University (天津大学); Shandong Institute of Petroleum and Chemical Technology (山东石油化工学院); Beijing Institute of Technology (北京理工大学)
类目: Computation and Language (cs.CL)
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Abstract:With the rapid advancement of Multimodal Large Language Models (MLLMs), they have demonstrated exceptional capabilities across a variety of vision-language tasks. However, current evaluation benchmarks predominantly focus on objective visual question answering or captioning, inadequately assessing the models’ ability to understand complex and subjective human emotions. To bridge this gap, we introduce EmoBench-Reddit, a novel, hierarchical benchmark for multimodal emotion understanding. The dataset comprises 350 meticulously curated samples from the social media platform Reddit, each containing an image, associated user-provided text, and an emotion category (sad, humor, sarcasm, happy) confirmed by user flairs. We designed a hierarchical task framework that progresses from basic perception to advanced cognition, with each data point featuring six multiple-choice questions and one open-ended question of increasing difficulty. Perception tasks evaluate the model’s ability to identify basic visual elements (e.g., colors, objects), while cognition tasks require scene reasoning, intent understanding, and deep empathy integrating textual context. We ensured annotation quality through a combination of AI assistance (Claude 4) and manual verification.
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[NLP-85] he System Description of CPS Team for Track on Driving with Language of CVPR 2024 Autonomous Grand Challenge

【速读】：该论文旨在解决自动驾驶场景中多模态理解与语言驱动决策的问题，特别是在视觉-语言模型（Vision-Language Model, VLM）框架下实现对复杂驾驶语义的理解与推理。其解决方案的关键在于：首先，基于DriveLM-nuScenes数据集进行训练，确保模型具备真实场景下的多模态感知能力；其次，采用LLaVA架构并结合LoRA（Low-Rank Adaptation）和DoRA（Direct Optimal Rank Adaptation）方法进行高效微调，提升模型在特定任务上的适应性；再次，引入开源深度估计模型提供的深度信息以增强视觉表征的几何感知能力；最后，在推理阶段采用Chain-of-Thought（CoT）推理策略处理多选题和是非题，显著提升了答案的准确性。这一系列技术整合使系统在CVPR 2024 Autonomous Grand Challenge的Driving with Language赛道中取得验证集得分0.7799的最优成绩。

链接: https://arxiv.org/abs/2509.11071
作者: Jinghan Peng,Jingwen Wang,Xing Yu,Dehui Du
机构: East China Normal University (华东师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:This report outlines our approach using vision language model systems for the Driving with Language track of the CVPR 2024 Autonomous Grand Challenge. We have exclusively utilized the DriveLM-nuScenes dataset for training our models. Our systems are built on the LLaVA models, which we enhanced through fine-tuning with the LoRA and DoRA methods. Additionally, we have integrated depth information from open-source depth estimation models to enrich the training and inference processes. For inference, particularly with multiple-choice and yes/no questions, we adopted a Chain-of-Thought reasoning approach to improve the accuracy of the results. This comprehensive methodology enabled us to achieve a top score of 0.7799 on the validation set leaderboard, ranking 1st on the leaderboard.
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[NLP-86] Rethinking Human Preference Evaluation of LLM Rationales

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）生成的自然语言推理过程（rationales）在评估上的挑战问题，即现有基于二元偏好判断（binary preference judgments）的人工或LLM评判方式缺乏细粒度解释力，难以揭示为何一个推理过程优于另一个。其解决方案的关键在于：首先通过系统识别文献中已有的关键推理属性（rationale attributes），并结合自动指标、LLM判别和人工标注进行量化评估；随后利用SHAP（SHapley Additive exPlanations）方法分析标准人类偏好数据集（如MT-Bench和Chatbot Arena）以确定哪些属性最能解释人类偏好；最终基于这些属性构建属性特定的ELO评分体系，实现对模型生成推理过程的更细致比较与洞察。这一方法显著提升了评价的可解释性与精细化程度，为未来可信赖的推理质量评估提供了新范式。

链接: https://arxiv.org/abs/2509.11026
作者: Ziang Li,Manasi Ganti,Zixian Ma,Helena Vasconcelos,Qijia He,Ranjay Krishna
机构: University of Washington (华盛顿大学); Stanford University (斯坦福大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Published in the XLLM-Reason-Plan Workshop on the Application of LLM Explainability to Reasoning and Planning at COLM 2025

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Abstract:Large language models (LLMs) often generate natural language rationales – free-form explanations that help improve performance on complex reasoning tasks and enhance interpretability for human users. However, evaluating these rationales remains challenging. While recent work has relied on binary preference judgments from humans or LLM judges, such evaluations are often opaque and coarse-grained, offering limited insight into what makes one rationale better than another. In this work, we rethink preference evaluation for LLM-generated rationales by asking: (1) What attributes define good rationales? (2) Can human preferences be explained by these attributes? (3) Can attribute-based evaluation overcome the limitations of binary comparisons? We identify a set of key rationale attributes from prior literature and assess them using automatic metrics, LLM judgments, and human annotations. We then analyze two standard human preference datasets MT Bench and Chatbot Arena using SHAP to identify which attributes best explain human preference outcomes. Finally, we re-evaluate model-generated rationales using attribute-specific ELO scores, revealing more nuanced model comparisons and insights. Our findings suggest that fine-grained attribute evaluations can better characterize rationale quality and guide future research toward more interpretable and reliable evaluation practices.
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[NLP-87] ReFineG: Synergizing Small Supervised Models and LLM s for Low-Resource Grounded Multimodal NER

【速读】：该论文旨在解决低资源场景下 grounded Multimodal Named Entity Recognition (GMNER) 的性能瓶颈问题，即现有监督方法依赖昂贵的多模态标注且在低资源领域表现不佳，而多模态大语言模型（MLLMs）虽具强泛化能力却因领域知识冲突导致冗余或错误的实体识别。其解决方案的关键在于提出 ReFineG 框架，通过三阶段协同机制实现小规模监督模型与冻结 MLLMs 的高效融合：首先利用领域感知的 NER 数据合成策略将 LLM 知识迁移至小模型并规避领域知识冲突；其次采用基于不确定性的机制筛选可靠预测并引导不确定结果由 MLLM 处理；最后借助多模态上下文选择算法，通过类比推理提升视觉定位精度，从而在有限标注条件下显著增强 GMNER 性能。

链接: https://arxiv.org/abs/2509.10975
作者: Jielong Tang,Shuang Wang,Zhenxing Wang,Jianxing Yu,Jian Yin
机构: 未知
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
备注: CCKS 2025 Shared Task Paper

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Abstract:Grounded Multimodal Named Entity Recognition (GMNER) extends traditional NER by jointly detecting textual mentions and grounding them to visual regions. While existing supervised methods achieve strong performance, they rely on costly multimodal annotations and often underperform in low-resource domains. Multimodal Large Language Models (MLLMs) show strong generalization but suffer from Domain Knowledge Conflict, producing redundant or incorrect mentions for domain-specific entities. To address these challenges, we propose ReFineG, a three-stage collaborative framework that integrates small supervised models with frozen MLLMs for low-resource GMNER. In the Training Stage, a domain-aware NER data synthesis strategy transfers LLM knowledge to small models with supervised training while avoiding domain knowledge conflicts. In the Refinement Stage, an uncertainty-based mechanism retains confident predictions from supervised models and delegates uncertain ones to the MLLM. In the Grounding Stage, a multimodal context selection algorithm enhances visual grounding through analogical reasoning. In the CCKS2025 GMNER Shared Task, ReFineG ranked second with an F1 score of 0.6461 on the online leaderboard, demonstrating its effectiveness with limited annotations.
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[NLP-88] An Interpretable Benchmark for Clickbait Detection and Tactic Attribution

【速读】：该论文旨在解决数字媒体中虚假标题（clickbait headlines）泛滥所引发的信息可信度下降与用户信任危机问题，尤其关注现有机器学习模型在检测点击诱饵内容时缺乏可解释性这一局限。其解决方案的关键在于提出一个两阶段的可解释点击诱饵检测框架：第一阶段通过对比微调后的BERT分类器与大语言模型（LLMs）如GPT-4.0和Gemini 2.4 Flash，在零样本提示和少样本提示下识别点击诱饵；第二阶段则利用专门设计的BERT分类器对每个标题进行具体操纵策略（linguistic manipulation strategies）的归因分析。为支持该方法，作者还构建了一个合成数据集，基于预定义的点击诱饵策略系统性增强真实新闻标题，从而实现可控实验与模型行为的深入剖析，推动透明且可信的人工智能系统在对抗操纵性内容中的应用。

链接: https://arxiv.org/abs/2509.10937
作者: Lihi Nofar,Tomer Portal,Aviv Elbaz,Alexander Apartsin,Yehudit Aperstein
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media. While recent advances in machine learning have improved the detection of manipulative content, the lack of explainability limits their practical adoption. This paper presents a model for explainable clickbait detection that not only identifies clickbait titles but also attributes them to specific linguistic manipulation strategies. We introduce a synthetic dataset generated by systematically augmenting real news headlines using a predefined catalogue of clickbait strategies. This dataset enables controlled experimentation and detailed analysis of model behaviour. We present a two-stage framework for automatic clickbait analysis comprising detection and tactic attribution. In the first stage, we compare a fine-tuned BERT classifier with large language models (LLMs), specifically GPT-4.0 and Gemini 2.4 Flash, under both zero-shot prompting and few-shot prompting enriched with illustrative clickbait headlines and their associated persuasive tactics. In the second stage, a dedicated BERT-based classifier predicts the specific clickbait strategies present in each headline. This work advances the development of transparent and trustworthy AI systems for combating manipulative media content. We share the dataset with the research community at this https URL
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[NLP-89] Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents EMNLP2025

【速读】：该论文旨在解决传统信息提取方法（如摘要生成）过于注重全面覆盖而忽视内容吸引力的问题，从而导致读者参与度不足。其核心挑战在于如何在保持关键信息准确性的前提下，提升文本的可读性和情感共鸣力。解决方案的关键在于提出了一种名为Spotlight的新范式，通过两阶段训练流程实现：首先在自建基准数据集上微调大语言模型以识别文档中的关键要素，随后利用直接偏好优化（Direct Preference Optimization, DPO）对齐人类偏好，使生成的内容更具吸引力和叙事性，从而显著增强原始文档的阅读体验与用户参与价值。

链接: https://arxiv.org/abs/2509.10935
作者: Ankan Mullick,Sombit Bose,Rounak Saha,Ayan Kumar Bhowmick,Aditya Vempaty,Prasenjit Dey,Ravi Kokku,Pawan Goyal,Niloy Ganguly
机构: IIT Kharagpur (印度理工学院克哈格普尔分校); Emergence AI
类目: Computation and Language (cs.CL)
备注: Paper accepted in EMNLP 2025 Main Conference (Full)

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Abstract:In this paper, we introduce Spotlight, a novel paradigm for information extraction that produces concise, engaging narratives by highlighting the most compelling aspects of a document. Unlike traditional summaries, which prioritize comprehensive coverage, spotlights selectively emphasize intriguing content to foster deeper reader engagement with the source material. We formally differentiate spotlights from related constructs and support our analysis with a detailed benchmarking study using new datasets curated for this work. To generate high-quality spotlights, we propose a two-stage approach: fine-tuning a large language model on our benchmark data, followed by alignment via Direct Preference Optimization (DPO). Our comprehensive evaluation demonstrates that the resulting model not only identifies key elements with precision but also enhances readability and boosts the engagement value of the original document.
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[NLP-90] Public Data Assisted Differentially Private In-Context Learning EMNLP2025

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在上下文学习（In-context Learning, ICL）过程中因提示（prompt）暴露导致的私有数据泄露风险，尤其是在面临恶意攻击时的隐私安全问题。现有差分隐私（Differential Privacy, DP）方法虽能提供强隐私保障，但常显著损害ICL的性能。其解决方案的关键在于引入与任务相关的公共数据到ICL框架中，在不牺牲隐私保护的前提下提升模型效用；具体而言，通过设计一种结合公共数据的私有上下文学习算法，实现了隐私保护与模型性能之间的有效权衡，并实验证明该方法对成员推断攻击（Membership Inference Attacks）具有鲁棒性，从而验证了其在实际应用中的隐私保护有效性。

链接: https://arxiv.org/abs/2509.10932
作者: Seongho Joo,Hyukhun Koh,Kyomin Jung
机构: Seoul National University (首尔国立大学)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: EMNLP 2025 Findings

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Abstract:In-context learning (ICL) in Large Language Models (LLMs) has shown remarkable performance across various tasks without requiring fine-tuning. However, recent studies have highlighted the risk of private data leakage through the prompt in ICL, especially when LLMs are exposed to malicious attacks. While differential privacy (DP) provides strong privacy guarantees, it often significantly reduces the utility of in-context learning (ICL). To address this challenge, we incorporate task-related public data into the ICL framework while maintaining the DP guarantee. Based on this approach, we propose a private in-context learning algorithm that effectively balances privacy protection and model utility. Through experiments, we demonstrate that our approach significantly improves the utility of private ICL with the assistance of public data. Additionally, we show that our method is robust against membership inference attacks, demonstrating empirical privacy protection.
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[NLP-91] Harmful Prompt Laundering: Jailbreaking LLM s with Abductive Styles and Symbolic Encoding EMNLP2025

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）可能被用于有害目的的安全漏洞问题，特别是针对其内在架构和学习范式所引发的通用越狱攻击（universal jailbreak attacks）。为应对这一挑战，作者提出了一种名为有害提示清洗（Harmful Prompt Laundering, HaPLa）的新颖且普适的越狱技术，其核心创新在于两个关键策略：一是溯因框架（abductive framing），通过引导模型推断通向有害行为的合理中间步骤，而非直接响应明确的有害请求；二是符号编码（symbolic encoding），利用轻量且灵活的文本混淆方法规避当前LLM对显式有害关键词的敏感性。实验表明，HaPLa在GPT系列模型上攻击成功率超过95%，在所有目标模型上平均达70%，揭示了在不显著削弱模型对良性查询响应能力的前提下实现安全微调仍具根本性挑战。

链接: https://arxiv.org/abs/2509.10931
作者: Seongho Joo,Hyukhun Koh,Kyomin Jung
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: EMNLP 2025

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Abstract:Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their potential misuse for harmful purposes remains a significant concern. To strengthen defenses against such vulnerabilities, it is essential to investigate universal jailbreak attacks that exploit intrinsic weaknesses in the architecture and learning paradigms of LLMs. In response, we propose \textbfHarmful \textbfPrompt \textbfLaundering (HaPLa), a novel and broadly applicable jailbreaking technique that requires only black-box access to target models. HaPLa incorporates two primary strategies: 1) \textitabductive framing, which instructs LLMs to infer plausible intermediate steps toward harmful activities, rather than directly responding to explicit harmful queries; and 2) \textitsymbolic encoding, a lightweight and flexible approach designed to obfuscate harmful content, given that current LLMs remain sensitive primarily to explicit harmful keywords. Experimental results show that HaPLa achieves over 95% attack success rate on GPT-series models and 70% across all targets. Further analysis with diverse symbolic encoding rules also reveals a fundamental challenge: it remains difficult to safely tune LLMs without significantly diminishing their helpfulness in responding to benign queries.
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[NLP-92] Aligning ESG Controversy Data with International Guidelines through Semi-Automatic Ontology Construction ISWC2025 DATE

【速读】：该论文旨在解决如何将新闻中报道的环境、社会和治理（Environmental, Social, and Governance, ESG）事件与基于原则的国际规范框架（如联合国全球契约或可持续发展目标）进行准确对齐的问题。这一挑战源于规范性文本通常语言抽象、缺乏标准化分类体系，且与商业数据提供商的专有分类系统不兼容。解决方案的关键在于提出一种半自动方法，通过轻量级本体设计、形式化模式建模以及大语言模型（Large Language Models, LLMs），将规范性原则转化为可重用的资源描述框架（Resource Description Framework, RDF）模板；这些模板用于从非结构化新闻内容中提取信息，并构建一个结构化的知识图谱，从而实现对具体事件与框架原则之间关联的可解释、可扩展映射。

链接: https://arxiv.org/abs/2509.10922
作者: Tsuyoshi Iwata,Guillaume Comte,Melissa Flores,Ryoma Kondo,Ryohei Hisano
机构: University of Zürich (UZH)(苏黎世大学); RepRisk AG(风险评估公司); 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); The Canon Institute for Global Studies(佳能全球战略研究所)
类目: Computation and Language (cs.CL); Computers and Society (cs.CY)
备注: Author accepted manuscript. This paper has been accepted for presentation at the ISWC 2025 Posters Demos Track. License details will be updated once the official proceedings are published

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Abstract:The growing importance of environmental, social, and governance data in regulatory and investment contexts has increased the need for accurate, interpretable, and internationally aligned representations of non-financial risks, particularly those reported in unstructured news sources. However, aligning such controversy-related data with principle-based normative frameworks, such as the United Nations Global Compact or Sustainable Development Goals, presents significant challenges. These frameworks are typically expressed in abstract language, lack standardized taxonomies, and differ from the proprietary classification systems used by commercial data providers. In this paper, we present a semi-automatic method for constructing structured knowledge representations of environmental, social, and governance events reported in the news. Our approach uses lightweight ontology design, formal pattern modeling, and large language models to convert normative principles into reusable templates expressed in the Resource Description Framework. These templates are used to extract relevant information from news content and populate a structured knowledge graph that links reported incidents to specific framework principles. The result is a scalable and transparent framework for identifying and interpreting non-compliance with international sustainability guidelines.
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[NLP-93] CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis EMNLP2025

【速读】：该论文旨在解决当前大语言模型（Large Language Models, LLMs）在跨文化场景下评估体系存在的碎片化分类、领域局限性以及高度依赖人工标注的问题。其解决方案的关键在于提出一个名为CultureSynth的新框架，该框架包含两个核心组成部分：一是构建了一个涵盖12个一级和130个二级主题的多语言层次化文化分类体系；二是采用基于检索增强生成（Retrieval-Augmented Generation, RAG）的方法，利用事实知识自动生成具有文化相关性的问答对。这一方法显著提升了评估的系统性和可扩展性，同时减少了对人工标注的依赖，并通过在7种语言中构建包含19,360条数据的合成基准（CultureSynth-7）验证了其有效性。

链接: https://arxiv.org/abs/2509.10886
作者: Xinyu Zhang,Pei Zhang,Shuang Luo,Jialong Tang,Yu Wan,Baosong Yang,Fei Huang
机构: Tongyi Lab, Alibaba Group Inc (阿里巴巴集团)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted as a Findings paper at EMNLP 2025

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Abstract:Cultural competence, defined as the ability to understand and adapt to multicultural contexts, is increasingly vital for large language models (LLMs) in global environments. While several cultural benchmarks exist to assess LLMs’ cultural competence, current evaluations suffer from fragmented taxonomies, domain specificity, and heavy reliance on manual data annotation. To address these limitations, we introduce CultureSynth, a novel framework comprising (1) a comprehensive hierarchical multilingual cultural taxonomy covering 12 primary and 130 secondary topics, and (2) a Retrieval-Augmented Generation (RAG)-based methodology leveraging factual knowledge to synthesize culturally relevant question-answer pairs. The CultureSynth-7 synthetic benchmark contains 19,360 entries and 4,149 manually verified entries across 7 languages. Evaluation of 14 prevalent LLMs of different sizes reveals clear performance stratification led by ChatGPT-4o-Latest and Qwen2.5-72B-Instruct. The results demonstrate that a 3B-parameter threshold is necessary for achieving basic cultural competence, models display varying architectural biases in knowledge processing, and significant geographic disparities exist across models. We believe that CultureSynth offers a scalable framework for developing culturally aware AI systems while reducing reliance on manual annotation\footnoteBenchmark is available at this https URL…
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[NLP-94] rm2Note: Synthesising Differentially Private Clinical Notes from Medical Terms

【速读】：该论文旨在解决医疗领域中临床笔记（clinical notes）合成时面临的隐私保护与数据效用难以平衡的问题。在高风险场景下，使用真实临床数据训练机器学习模型存在隐私泄露风险，而现有差分隐私（differential privacy, DP）合成方法在长文本生成任务中表现不足。其解决方案的关键在于提出Term2Note方法，通过结构化分离内容（content）与形式（form），将临床笔记按章节分解，基于差分隐私的医学术语（DP medical terms）逐段生成内容，并为每一段施加独立的差分隐私约束；同时引入一个差分隐私质量最大化器（DP quality maximiser）以筛选高质量输出。此设计在保证强隐私保障的同时显著提升了合成笔记的统计保真度和下游任务（如多标签分类）的实用性，优于现有基线方法。

链接: https://arxiv.org/abs/2509.10882
作者: Yuping Wu,Viktor Schlegel,Warren Del-Pinto,Srinivasan Nandakumar,Iqra Zahid,Yidan Sun,Usama Farghaly Omar,Amirah Jasmine,Arun-Kumar Kaliya-Perumal,Chun Shen Tham,Gabriel Connors,Anil A Bharath,Goran Nenadic
机构: University of Manchester (曼彻斯特大学); University of Oxford (牛津大学); University of Edinburgh (爱丁堡大学); King’s College London (伦敦国王学院); National Institute for Health and Care Research (英国国家健康与护理研究所)
类目: Computation and Language (cs.CL)
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Abstract:Training data is fundamental to the success of modern machine learning models, yet in high-stakes domains such as healthcare, the use of real-world training data is severely constrained by concerns over privacy leakage. A promising solution to this challenge is the use of differentially private (DP) synthetic data, which offers formal privacy guarantees while maintaining data utility. However, striking the right balance between privacy protection and utility remains challenging in clinical note synthesis, given its domain specificity and the complexity of long-form text generation. In this paper, we present Term2Note, a methodology to synthesise long clinical notes under strong DP constraints. By structurally separating content and form, Term2Note generates section-wise note content conditioned on DP medical terms, with each governed by separate DP constraints. A DP quality maximiser further enhances synthetic notes by selecting high-quality outputs. Experimental results show that Term2Note produces synthetic notes with statistical properties closely aligned with real clinical notes, demonstrating strong fidelity. In addition, multi-label classification models trained on these synthetic notes perform comparably to those trained on real data, confirming their high utility. Compared to existing DP text generation baselines, Term2Note achieves substantial improvements in both fidelity and utility while operating under fewer assumptions, suggesting its potential as a viable privacy-preserving alternative to using sensitive clinical notes.
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[NLP-95] Quantifier Scope Interpretation in Language Learners and LLM s

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在处理多量词句子时的歧义解读问题，特别是跨语言环境下LLMs对量词作用域（quantifier scope）的理解是否与人类一致。其解决方案的关键在于采用量化概率方法评估LLMs对不同作用域解释的偏好，并通过人类相似性（Human Similarity, HS）分数衡量LLMs行为与人类认知模式的匹配程度，从而揭示模型架构、规模及预训练数据的语言背景如何影响其对量词作用域的语义解析能力。

链接: https://arxiv.org/abs/2509.10860
作者: Shaohua Fang,Yue Li,Yan Cong
机构: Purdue University (普渡大学)
类目: Computation and Language (cs.CL)
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Abstract:Sentences with multiple quantifiers often lead to interpretive ambiguities, which can vary across languages. This study adopts a cross-linguistic approach to examine how large language models (LLMs) handle quantifier scope interpretation in English and Chinese, using probabilities to assess interpretive likelihood. Human similarity (HS) scores were used to quantify the extent to which LLMs emulate human performance across language groups. Results reveal that most LLMs prefer the surface scope interpretations, aligning with human tendencies, while only some differentiate between English and Chinese in the inverse scope preferences, reflecting human-similar patterns. HS scores highlight variability in LLMs’ approximation of human behavior, but their overall potential to align with humans is notable. Differences in model architecture, scale, and particularly models’ pre-training data language background, significantly influence how closely LLMs approximate human quantifier scope interpretations.
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[NLP-96] Pre-Storag e Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue EMNLP2025

【速读】：该论文旨在解决对话式人工智能（Conversational AI）中长期记忆构建的效率与效果问题，即当前系统在多轮对话中依赖大模型进行实时推理来整合跨会话信息，导致计算负担重且性能高度依赖模型规模。解决方案的关键在于提出PREMem（Pre-storage Reasoning for Episodic Memory），其核心创新是将复杂的推理过程从响应生成阶段前移至记忆存储阶段：通过提取细粒度的记忆片段（分为事实性、体验性和主观性三类），并在不同会话间建立显式关联关系，捕捉如扩展、转化和推论等演化模式，从而在预存储阶段完成知识结构化与语义丰富化，显著降低交互时的计算开销，并使小模型也能达到接近大模型的性能表现。

链接: https://arxiv.org/abs/2509.10852
作者: Sangyeop Kim,Yohan Lee,Sanghwa Kim,Hyunjong Kim,Sungzoon Cho
机构: Seoul National University (首尔国立大学); Coxwave; Independent Researcher; KAIST (韩国科学技术院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted by EMNLP 2025 (Findings)

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Abstract:Effective long-term memory in conversational AI requires synthesizing information across multiple sessions. However, current systems place excessive reasoning burden on response generation, making performance significantly dependent on model sizes. We introduce PREMem (Pre-storage Reasoning for Episodic Memory), a novel approach that shifts complex reasoning processes from inference to memory construction. PREMem extracts fine-grained memory fragments categorized into factual, experiential, and subjective information; it then establishes explicit relationships between memory items across sessions, capturing evolution patterns like extensions, transformations, and implications. By performing this reasoning during pre-storage rather than when generating a response, PREMem creates enriched representations while reducing computational demands during interactions. Experiments show significant performance improvements across all model sizes, with smaller models achieving results comparable to much larger baselines while maintaining effectiveness even with constrained token budgets. Code and dataset are available at this https URL.
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[NLP-97] A funny companion: Distinct neural responses to perceived AI- versus humangenerated humor

【速读】：该论文旨在解决人工智能（AI）在具备类人沟通能力后，人类如何认知和情感响应AI幽默的问题。其核心问题是：尽管AI能生成类似人类的幽默内容，但人们对AI幽默的认知加工机制与对人类幽默的反应是否存在差异？解决方案的关键在于通过脑电图（EEG）技术对比分析人类对AI与人类来源幽默的神经生理反应，发现AI幽默虽引发较小的N400效应（反映较低的认知努力），却诱发更大的晚期正电位（LPP），表明更强的情绪惊讶与情感投入；同时揭示出随时间推移，大脑对AI幽默的处理效率提升且情感奖励增强，这一动态适应过程挑战了“算法厌恶”，并受个体对AI信任度调节，从而证明幽默可作为促进人机社会互动的重要媒介。

链接: https://arxiv.org/abs/2509.10847
作者: Xiaohui Rao,Hanlin Wu,Zhenguang G. Cai
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:As AI companions become capable of human-like communication, including telling jokes, understanding how people cognitively and emotionally respond to AI humor becomes increasingly important. This study used electroencephalography (EEG) to compare how people process humor from AI versus human sources. Behavioral analysis revealed that participants rated AI and human humor as comparably funny. However, neurophysiological data showed that AI humor elicited a smaller N400 effect, suggesting reduced cognitive effort during the processing of incongruity. This was accompanied by a larger Late Positive Potential (LPP), indicating a greater degree of surprise and emotional response. This enhanced LPP likely stems from the violation of low initial expectations regarding AI’s comedic capabilities. Furthermore, a key temporal dynamic emerged: human humor showed habituation effects, marked by an increasing N400 and a decreasing LPP over time. In contrast, AI humor demonstrated increasing processing efficiency and emotional reward, with a decreasing N400 and an increasing LPP. This trajectory reveals how the brain can dynamically update its predictive model of AI capabilities. This process of cumulative reinforcement challenges “algorithm aversion” in humor, as it demonstrates how cognitive adaptation to AI’s language patterns can lead to an intensified emotional reward. Additionally, participants’ social attitudes toward AI modulated these neural responses, with higher perceived AI trustworthiness correlating with enhanced emotional engagement. These findings indicate that the brain responds to AI humor with surprisingly positive and intense reactions, highlighting humor’s potential for fostering genuine engagement in human-AI social interaction.
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[NLP-98] xt2Sign Diffusion: A Generative Approach for Gloss-Free Sign Language Production

【速读】：该论文旨在解决现有手语生成（Sign Language Production, SLP）方法依赖于词素（gloss）这一符号化中间表示所带来的局限性问题，如标注数据稀缺、语言特异性以及由此导致的灵活性和泛化能力不足。其解决方案的关键在于提出一种基于扩散模型的无词素（gloss-free）生成框架——Text2Sign Diffusion（Text2SignDiff），通过构建一个无词素的潜在扩散模型，联合噪声潜在手语编码与口语文本进行非自回归迭代去噪生成，从而减少误差累积；同时设计跨模态手语对齐器（cross-modal signing aligner），学习视觉与文本内容在手语与口语间的共享潜在空间，支撑条件扩散过程，实现无需词素标注即可生成更准确且语境相关的手语序列。

链接: https://arxiv.org/abs/2509.10845
作者: Liqian Feng,Lintao Wang,Kun Hu,Dehui Kong,Zhiyong Wang
机构: The University of Sydney (悉尼大学); Edith Cowan University (埃迪斯科文大学); Beijing University of Technology (北京工业大学)
类目: Computation and Language (cs.CL); Multimedia (cs.MM)
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Abstract:Sign language production (SLP) aims to translate spoken language sentences into a sequence of pose frames in a sign language, bridging the communication gap and promoting digital inclusion for deaf and hard-of-hearing communities. Existing methods typically rely on gloss, a symbolic representation of sign language words or phrases that serves as an intermediate step in SLP. This limits the flexibility and generalization of SLP, as gloss annotations are often unavailable and language-specific. Therefore, we present a novel diffusion-based generative approach - Text2Sign Diffusion (Text2SignDiff) for gloss-free SLP. Specifically, a gloss-free latent diffusion model is proposed to generate sign language sequences from noisy latent sign codes and spoken text jointly, reducing the potential error accumulation through a non-autoregressive iterative denoising process. We also design a cross-modal signing aligner that learns a shared latent space to bridge visual and textual content in sign and spoken languages. This alignment supports the conditioned diffusion-based process, enabling more accurate and contextually relevant sign language generation without gloss. Extensive experiments on the commonly used PHOENIX14T and How2Sign datasets demonstrate the effectiveness of our method, achieving the state-of-the-art performance.
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[NLP-99] GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings

【速读】：该论文旨在解决领域特定嵌入模型（domain-specific embedding models）在资源受限环境下的部署难题，尤其是现有基于大语言模型（Large Language Models, LLMs）的嵌入模型因参数量庞大而难以压缩的问题。当前剪枝方法对所有参数一视同仁，未能区分通用语义表示与领域特异性模式，导致剪枝决策次优。解决方案的关键在于提出GAPrune框架，其核心是引入Domain Alignment Importance (DAI)评分机制，综合利用Fisher信息衡量参数重要性与通用-领域梯度对齐度评估参数行为，从而识别出既对领域任务不关键又可能干扰通用目标的参数。此策略实现了高效压缩的同时保持甚至提升领域性能，在FinMTEB和ChemTEB两个基准上验证了其优越性。

链接: https://arxiv.org/abs/2509.10844
作者: Yixuan Tang,Yi Yang
机构: The Hong Kong University of Science and Technology (香港科技大学)
类目: Computation and Language (cs.CL)
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Abstract:Domain-specific embedding models have shown promise for applications that require specialized semantic understanding, such as coding agents and financial retrieval systems, often achieving higher performance gains than general models. However, state-of-the-art embedding models are typically based on LLMs, which contain billions of parameters, making deployment challenging in resource-constrained environments. Model compression through pruning offers a promising solution, but existing pruning methods treat all parameters uniformly, failing to distinguish between general semantic representations and domain-specific patterns, leading to suboptimal pruning decisions. Thus, we propose GAPrune, a pruning framework that addresses this challenge by considering both domain importance and preserving general linguistic foundation. Our method uses Fisher Information to measure importance and general-domain gradient alignment to assess parameter behavior, then combines these signals using our Domain Alignment Importance (DAI) scoring. Lower DAI scores indicate that the parameter is either less important for the domain task or creates conflicts between domain and general objectives. Experiments on two domain benchmarks, FinMTEB and ChemTEB, show that GAPrune maintains performance within 2.5% of dense models in one-shot pruning at 50% sparsity, while outperforming all baselines. With retraining in 100 steps, GAPrune achieves +4.51% improvement on FinMTEB and +1.73% on ChemTEB, demonstrating that our pruning strategy not only preserves but enhances domain-specific capabilities. Our findings demonstrate that principled pruning strategies can achieve model compression and enhanced domain specialization, providing the research community with a new approach for development.
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[NLP-100] Evaluating Large Language Models for Evidence-Based Clinical Question Answering

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在临床和生物医学领域中回答循证问题时的准确性与可靠性问题，尤其是在面对结构化指南推荐、叙事性指南及系统综述等不同来源证据时的表现差异。其关键解决方案在于引入检索增强提示（retrieval-augmented prompting），通过提供高相关性文献摘要（如金标准摘要或Top 3 PubMed摘要）显著提升模型回答的准确性，同时发现模型性能不仅依赖于自身规模，更受源文献清晰度与检索信息质量的影响，表明精准的外部证据检索与上下文整合是提升LLMs在临床问答任务中事实一致性与证据对齐性的核心策略。

链接: https://arxiv.org/abs/2509.10843
作者: Can Wang,Yiqun Chen
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Large Language Models (LLMs) have demonstrated substantial progress in biomedical and clinical applications, motivating rigorous evaluation of their ability to answer nuanced, evidence-based questions. We curate a multi-source benchmark drawing from Cochrane systematic reviews and clinical guidelines, including structured recommendations from the American Heart Association and narrative guidance used by insurers. Using GPT-4o-mini and GPT-5, we observe consistent performance patterns across sources and clinical domains: accuracy is highest on structured guideline recommendations (90%) and lower on narrative guideline and systematic review questions (60–70%). We also find a strong correlation between accuracy and the citation count of the underlying systematic reviews, where each doubling of citations is associated with roughly a 30% increase in the odds of a correct answer. Models show moderate ability to reason about evidence quality when contextual information is supplied. When we incorporate retrieval-augmented prompting, providing the gold-source abstract raises accuracy on previously incorrect items to 0.79; providing top 3 PubMed abstracts (ranked by semantic relevance) improves accuracy to 0.23, while random abstracts reduce accuracy (0.10, within temperature variation). These effects are mirrored in GPT-4o-mini, underscoring that source clarity and targeted retrieval – not just model size – drive performance. Overall, our results highlight both the promise and current limitations of LLMs for evidence-based clinical question answering. Retrieval-augmented prompting emerges as a useful strategy to improve factual accuracy and alignment with source evidence, while stratified evaluation by specialty and question type remains essential to understand current knowledge access and to contextualize model performance.
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[NLP-101] owards Automated Error Discovery: A Study in Conversational AI EMNLP2025

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）驱动的对话智能体在实际部署中难以识别未明确指定错误的问题，尤其是由响应生成模型更新或用户行为变化引发的未知错误。其核心挑战在于现有方法依赖预定义指令，无法有效捕捉动态环境中出现的新类型错误。解决方案的关键在于提出 Automated Error Discovery 框架，并设计 SEEED（Soft Clustering Extended Encoder-Based Error Detection）方法——通过增强负样本的距离权重以优化 Soft Nearest Neighbor Loss，并引入基于标签的样本排序机制（Label-Based Sample Ranking）来筛选高对比度样本，从而提升表示学习能力。实验表明，SEEED 在多个标注错误的对话数据集上优于 GPT-4o 和 Phi-4 等基线模型，在检测未知错误方面的准确率最高提升 8 个百分点，并展现出对未知意图检测的强大泛化能力。

链接: https://arxiv.org/abs/2509.10833
作者: Dominic Petrak,Thy Thy Tran,Iryna Gurevych
机构: Ubiquitous Knowledge Processing Lab (UKP Lab); Department of Computer Science and Hessian Center for AI (hessian.AI); Technical University of Darmstadt
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
备注: Accepted to EMNLP 2025 main conference

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Abstract:Although LLM-based conversational agents demonstrate strong fluency and coherence, they still produce undesirable behaviors (errors) that are challenging to prevent from reaching users during deployment. Recent research leverages large language models (LLMs) to detect errors and guide response-generation models toward improvement. However, current LLMs struggle to identify errors not explicitly specified in their instructions, such as those arising from updates to the response-generation model or shifts in user behavior. In this work, we introduce Automated Error Discovery, a framework for detecting and defining errors in conversational AI, and propose SEEED (Soft Clustering Extended Encoder-Based Error Detection), as an encoder-based approach to its implementation. We enhance the Soft Nearest Neighbor Loss by amplifying distance weighting for negative samples and introduce Label-Based Sample Ranking to select highly contrastive examples for better representation learning. SEEED outperforms adapted baselines – including GPT-4o and Phi-4 – across multiple error-annotated dialogue datasets, improving the accuracy for detecting unknown errors by up to 8 points and demonstrating strong generalization to unknown intent detection.
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[NLP-102] Judge Q: Trainable Queries for Optimized Information Retention in KV Cache Eviction

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在处理长序列时因键值缓存（Key-Value Cache, KV cache）线性增长而导致的内存占用过高与解码效率下降的问题。现有KV缓存淘汰策略通常依赖预填充阶段最后窗口作为查询来计算缓存重要性得分，但这种方法易过度关注局部信息，可能忽略全局关键内容，从而影响生成质量。解决方案的关键在于提出一种名为Judge Q的新训练方法，其核心是在输入序列末尾引入一个软标记列表（soft token list），仅微调模型嵌入层以极低的训练成本，使这些软标记的注意力图能对齐真实解码标记的注意力分布；通过这种方式，软标记对应的查询可有效捕捉全局信息，进而更准确地评估KV缓存中键值的重要性，实现高质量的缓存淘汰。实验表明，该方法在相同淘汰预算下显著优于现有方案，在LongBench和RULER等基准上分别提升约1点和超过3点性能。

链接: https://arxiv.org/abs/2509.10798
作者: Yijun Liu,Yixuan Wang,Yuzhuang Xu,Shiyu Ji,Yang Xu,Qingfu Zhu,Wanxiang Che
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Large language models (LLMs) utilize key-value (KV) cache to store historical information during sequence processing. The size of KV cache grows linearly as the length of the sequence extends, which seriously affects memory usage and decoding efficiency. Current methods for KV cache eviction typically utilize the last window from the pre-filling phase as queries to compute the KV importance scores for eviction. Although this scheme is simple to implement, it tends to overly focus on local information, potentially leading to the neglect or omission of crucial global information. To mitigate this issue, we propose Judge Q, a novel training method which incorporates a soft token list. This method only tunes the model’s embedding layer at a low training cost. By concatenating the soft token list at the end of the input sequence, we train these tokens’ attention map to the original input sequence to align with that of the actual decoded tokens. In this way, the queries corresponding to the soft tokens can effectively capture global information and better evaluate the importance of the keys and values within the KV cache, thus maintaining decoding quality when KV cache is evicted. Under the same eviction budget, our method exhibits less performance degradation compared to existing eviction approaches. We validate our approach through experiments conducted on models such as Llama-3.1-8B-Instruct and Mistral-7B-Instruct-v0.3, using benchmarks including LongBench, RULER, and Needle-in-a-Haystack. Results indicate an improvement of approximately 1 point on the LongBench and over 3 points on RULER. This proposed methodology can be seamlessly integrated into existing open-source models with minimal training overhead, thereby enhancing performance in KV cache eviction scenarios.
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[NLP-103] Agent Arch: A Comprehensive Benchmark to Evaluate Agent Architectures in Enterprise

【速读】：该论文试图解决的问题是：当前对智能体架构（agentic architecture）中各组件的研究多局限于孤立分析，缺乏对复杂多智能体系统中不同设计维度之间交互作用的实证理解。其解决方案的关键在于构建了一个面向企业的综合性基准测试，评估了18种不同的智能体配置在主流大语言模型上的表现，并系统考察了四个关键维度：编排策略（orchestration strategy）、智能体提示实现方式（ReAct vs. 函数调用）、记忆架构（memory architecture）以及思维工具集成（thinking tool integration）。该方法揭示了模型特定的架构偏好，挑战了“一刀切”的通用设计范式，并为未来智能体系统的架构选择与模型优化提供了基于实证的数据支持。

链接: https://arxiv.org/abs/2509.10769
作者: Tara Bogavelli,Roshnee Sharma,Hari Subramani
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
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Abstract:While individual components of agentic architectures have been studied in isolation, there remains limited empirical understanding of how different design dimensions interact within complex multi-agent systems. This study aims to address these gaps by providing a comprehensive enterprise-specific benchmark evaluating 18 distinct agentic configurations across state-of-the-art large language models. We examine four critical agentic system dimensions: orchestration strategy, agent prompt implementation (ReAct versus function calling), memory architecture, and thinking tool integration. Our benchmark reveals significant model-specific architectural preferences that challenge the prevalent one-size-fits-all paradigm in agentic AI systems. It also reveals significant weaknesses in overall agentic performance on enterprise tasks with the highest scoring models achieving a maximum of only 35.3% success on the more complex task and 70.8% on the simpler task. We hope these findings inform the design of future agentic systems by enabling more empirically backed decisions regarding architectural components and model selection.
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[NLP-104] RECAP: Transparent Inference-Time Emotion Alignment for Medical Dialogue Systems

【速读】：该论文旨在解决大型语言模型在医疗场景中缺乏情感感知能力的问题，即模型虽能提供医学上正确的建议，但往往忽视患者的情绪状态，导致回应缺乏共情（empathy），这在患者情绪脆弱时尤为不利，可能影响治疗依从性、安全性与医患信任。其解决方案的关键在于提出一种推理时（inference-time）的框架RECAP（Reflect-Extract-Calibrate-Align-Produce），该框架通过分解共情为可解释的认知评估阶段（appraisal-theoretic stages），并引入基于李克特量表（Likert-scale）的维度化信号，实现对情绪线索的结构化处理，从而在不重新训练模型的前提下显著提升情感推理能力，并保证输出结果的可审计性。

链接: https://arxiv.org/abs/2509.10746
作者: Adarsh Srinivasan,Jacob Dineen,Muhammad Umar Afzal,Muhammad Uzair Sarfraz,Irbaz B. Riaz,Ben Zhou
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Large language models in healthcare often miss critical emotional cues, delivering medically sound but emotionally flat advice. This is especially problematic in clinical contexts where patients are distressed and vulnerable, and require empathic communication to support safety, adherence, and trust. We present RECAP (Reflect-Extract-Calibrate-Align-Produce), an inference-time framework that adds structured emotional reasoning without retraining. By decomposing empathy into transparent appraisal-theoretic stages and exposing per-dimension Likert signals, RECAP produces nuanced, auditable responses. Across EmoBench, SECEU, and EQ-Bench, RECAP improves emotional reasoning by 22-28% on 8B models and 10-13% on larger models over zero-shot baselines. Clinician evaluations further confirm superior empathetic communication. RECAP shows that modular, theory-grounded prompting can systematically enhance emotional intelligence in medical AI while preserving the accountability required for deployment.
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[NLP-105] Automated MCQA Benchmarking at Scale: Evaluating Reasoning Traces as Retrieval Sources for Domain Adaptation of Small Language Models DATE

【速读】：该论文旨在解决科学知识快速迭代背景下，现有语言模型评估基准难以反映最新研究成果的问题。为应对这一挑战，作者提出了一种可扩展、模块化的框架，用于直接从大规模科学文献语料中自动生成多选题问答（MCQA）基准。其解决方案的关键在于构建一个端到端自动化流水线，涵盖PDF解析、语义分块、问题生成与模型评估等环节，并创新性地引入基于推理轨迹（reasoning trace）的检索增强生成（RAG）策略，显著提升了小型语言模型在专业领域测试中的表现，使其在某些任务上超越了GPT-4。

链接: https://arxiv.org/abs/2509.10744
作者: Ozan Gokdemir,Neil Getty,Robert Underwood,Sandeep Madireddy,Franck Cappello,Arvind Ramanathan,Ian T. Foster,Rick L. Stevens
机构: Argonne National Laboratory (阿贡国家实验室); The University of Chicago (芝加哥大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: This manuscript has been accepted for publication at the Supercomputing 25 (SC '25) Conference (Frontiers in Generative AI for HPC Science and Engineering: Foundations, Challenges, and Opportunities Workshop) in St. Louis, MO, USA on November 16th, 2025. It will appear in the SC25 Workshop Proceedings after that date

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Abstract:As scientific knowledge grows at an unprecedented pace, evaluation benchmarks must evolve to reflect new discoveries and ensure language models are tested on current, diverse literature. We propose a scalable, modular framework for generating multiple-choice question-answering (MCQA) benchmarks directly from large corpora of scientific papers. Our pipeline automates every stage of MCQA creation, including PDF parsing, semantic chunking, question generation, and model evaluation. As a case study, we generate more than 16,000 MCQs from 22,000 open-access articles in radiation and cancer biology. We then evaluate a suite of small language models (1.1B-14B parameters) on these questions, comparing baseline accuracy with retrieval-augmented generation (RAG) from paper-derived semantic chunks and from reasoning traces distilled from GPT-4.1. We find that reasoning-trace retrieval consistently improves performance on both synthetic and expert-annotated benchmarks, enabling several small models to surpass GPT-4 on the 2023 Astro Radiation and Cancer Biology exam.
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[NLP-106] Reasoning Under Uncertainty: Exploring Probabilistic Reasoning Capabilities of LLM s

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在处理显式离散概率分布时表现出的推理能力不明确且不一致的问题。其关键解决方案是设计并实施三项精心构建的任务——模式识别、最大似然估计和样本生成，通过提示模型对联合分布或条件分布进行查询，从而系统性地评估其概率推理能力，包括频率分析、边缘化及生成行为等核心技能。实证结果表明，更大规模的模型在推理能力和样本生成方面展现出显著优势，同时揭示了模型对概率表示符号敏感以及上下文长度增加导致性能下降超过60%等局限性。

链接: https://arxiv.org/abs/2509.10739
作者: Mobina Pournemat,Keivan Rezaei,Gaurang Sriramanan,Arman Zarei,Jiaxiang Fu,Yang Wang,Hamid Eghbalzadeh,Soheil Feizi
机构: University of Maryland (马里兰大学); Meta (Meta)
类目: Computation and Language (cs.CL)
备注: 25 pages, 4 figures, 6 tables

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Abstract:Despite widespread success in language understanding and generation, large language models (LLMs) exhibit unclear and often inconsistent behavior when faced with tasks that require probabilistic reasoning. In this work, we present the first comprehensive study of the reasoning capabilities of LLMs over explicit discrete probability distributions. Given observations from a probability distribution, we evaluate models on three carefully designed tasks, mode identification, maximum likelihood estimation, and sample generation, by prompting them to provide responses to queries about either the joint distribution or its conditionals. These tasks thus probe a range of probabilistic skills, including frequency analysis, marginalization, and generative behavior. Through comprehensive empirical evaluations, we demonstrate that there exists a clear performance gap between smaller and larger models, with the latter demonstrating stronger inference and surprising capabilities in sample generation. Furthermore, our investigations reveal notable limitations, including sensitivity to variations in the notation utilized to represent probabilistic outcomes and performance degradation of over 60% as context length increases. Together, our results provide a detailed understanding of the probabilistic reasoning abilities of LLMs and identify key directions for future improvement.
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[NLP-107] PolyTruth: Multilingual Disinformation Detection using Transformer-Based Language Models

【速读】：该论文旨在解决当前生成式 AI（Generative AI）在多语言环境下检测虚假信息（disinformation）能力不足的问题，特别是现有模型大多仅在英语语境下进行评估，难以适应跨语言传播的虚假信息挑战。其解决方案的关键在于构建了一个大规模、多语言、跨语系的虚假信息语料库——PolyTruth Disinfo Corpus，包含60,486条陈述对（虚假主张与事实纠正），覆盖25种语言及五大语言家族，并涵盖政治、健康、气候、金融和阴谋论等广泛主题；同时系统比较了五种主流多语言Transformer模型（mBERT、XLM、XLM-RoBERTa、RemBERT 和 mT5）在该语料库上的分类性能，揭示了不同模型在资源匮乏语言中的表现差异，为多语言虚假信息检测提供了实证依据和可复现的基准数据集。

链接: https://arxiv.org/abs/2509.10737
作者: Zaur Gouliev,Jennifer Waters,Chengqian Wang
机构: University College Dublin (都柏林大学)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 11 pages, 5 figures, 4 tables. Submitted to arXiv in Computation and Language

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Abstract:Disinformation spreads rapidly across linguistic boundaries, yet most AI models are still benchmarked only on English. We address this gap with a systematic comparison of five multilingual transformer models: mBERT, XLM, XLM-RoBERTa, RemBERT, and mT5 on a common fake-vs-true machine learning classification task. While transformer-based language models have demonstrated notable success in detecting disinformation in English, their effectiveness in multilingual contexts still remains up for debate. To facilitate evaluation, we introduce PolyTruth Disinfo Corpus, a novel corpus of 60,486 statement pairs (false claim vs. factual correction) spanning over twenty five languages that collectively cover five language families and a broad topical range from politics, health, climate, finance, and conspiracy, half of which are fact-checked disinformation claims verified by an augmented MindBugs Discovery dataset. Our experiments revealed performance variations. Models such as RemBERT achieved better overall accuracy, particularly excelling in low-resource languages, whereas models like mBERT and XLM exhibit considerable limitations when training data is scarce. We provide a discussion of these performance patterns and implications for real-world deployment. The dataset is publicly available on our GitHub repository to encourage further experimentation and advancement. Our findings illuminate both the potential and the current limitations of AI systems for multilingual disinformation detection.
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[NLP-108] SearchInstruct: Enhancing Domain Adaptation via Retrieval-Based Instruction Dataset Creation

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在特定领域进行监督微调（Supervised Fine-Tuning, SFT）时，高质量指令数据集构建困难的问题，尤其针对领域约束严格和标注数据稀缺的场景。其解决方案的关键在于提出了一种名为SearchInstruct的新方法：首先利用少量人工编写的领域特定问题作为种子，通过大语言模型进行系统性扩展；随后动态检索领域相关的知识资源，为每个扩展后的问答对生成准确且上下文相关的回答，从而显著提升SFT数据集的多样性与质量，最终增强LLM在专业领域的性能表现。

链接: https://arxiv.org/abs/2509.10708
作者: Iman Barati,Mostafa Amiri,Heshaam Faili
机构: Iran University of Science and Technology (伊朗科学与技术大学); University of Tehran (德黑兰大学)
类目: Computation and Language (cs.CL)
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Abstract:Supervised Fine-Tuning (SFT) is essential for training large language models (LLMs), significantly enhancing critical capabilities such as instruction following and in-context learning. Nevertheless, creating suitable training datasets tailored for specific domains remains challenging due to unique domain constraints and data scarcity. In this paper, we propose SearchInstruct, an innovative method explicitly designed to construct high quality instruction datasets for SFT. Our approach begins with a limited set of domain specific, human generated questions, which are systematically expanded using a large language model. Subsequently, domain relevant resources are dynamically retrieved to generate accurate and contextually appropriate answers for each augmented question. Experimental evaluation demonstrates that SearchInstruct enhances both the diversity and quality of SFT datasets, leading to measurable improvements in LLM performance within specialized domains. Additionally, we show that beyond dataset generation, the proposed method can also effectively facilitate tasks such as model editing, enabling efficient updates to existing models. To facilitate reproducibility and community adoption, we provide full implementation details, the complete set of generated instruction response pairs, and the source code in a publicly accessible Git repository: [this https URL](this https URL)
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[NLP-109] Understanding AI Evaluation Patterns: How Different GPT Models Assess Vision-Language Descriptions

【速读】：该论文旨在解决当前生成式 AI (Generative AI) 系统在评估其他 AI 输出时可能出现的偏见传播问题，尤其是当多模型协同评估时如何识别和理解不同评估模型的“评估个性”及其内在偏差。解决方案的关键在于通过系统性对比分析三个 GPT 变体（GPT-4o、GPT-4o-mini 和 GPT-5）对 NVIDIA Describe Anything Model 生成的视觉语言描述进行评估的行为模式，发现各模型展现出显著不同的评估策略与倾向：GPT-4o-mini 表现出高一致性，GPT-4o 在错误检测上表现优异，而 GPT-5 则呈现极端保守且高变异性；进一步通过 Gemini 2.5 Pro 独立生成问题验证这些差异源于模型固有特性而非外部干扰，并揭示 GPT 家族内部评估语义高度相似但与 Gemini 显著不同，同时所有 GPT 模型均表现出 2:1 的负面评估偏好。研究结论表明，评估能力并不随通用能力提升而增强，强调构建鲁棒 AI 评估体系需引入多样化架构视角。

链接: https://arxiv.org/abs/2509.10707
作者: Sajjad Abdoli,Rudi Cilibrasi,Rima Al-Shikh
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:As AI systems increasingly evaluate other AI outputs, understanding their assessment behavior becomes crucial for preventing cascading biases. This study analyzes vision-language descriptions generated by NVIDIA’s Describe Anything Model and evaluated by three GPT variants (GPT-4o, GPT-4o-mini, GPT-5) to uncover distinct “evaluation personalities” the underlying assessment strategies and biases each model demonstrates. GPT-4o-mini exhibits systematic consistency with minimal variance, GPT-4o excels at error detection, while GPT-5 shows extreme conservatism with high variability. Controlled experiments using Gemini 2.5 Pro as an independent question generator validate that these personalities are inherent model properties rather than artifacts. Cross-family analysis through semantic similarity of generated questions reveals significant divergence: GPT models cluster together with high similarity while Gemini exhibits markedly different evaluation strategies. All GPT models demonstrate a consistent 2:1 bias favoring negative assessment over positive confirmation, though this pattern appears family-specific rather than universal across AI architectures. These findings suggest that evaluation competence does not scale with general capability and that robust AI assessment requires diverse architectural perspectives.
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[NLP-110] A Survey on Retrieval And Structuring Augmented Generation with Large Language Models KDD’25

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在实际应用中面临的三大核心问题：幻觉生成（hallucination generation）、知识过时（outdated knowledge）以及领域专业知识有限（limited domain expertise）。其解决方案的关键在于引入检索与结构化（Retrieval And Structuring, RAS）增强生成框架，通过动态信息检索机制获取外部知识，并结合文本结构化技术（如分类体系构建、层次化分类和信息抽取）将非结构化文本转化为有序的知识表示，最终利用提示工程、推理框架及知识嵌入等方法实现结构化知识与LLMs的有效融合，从而提升模型的准确性、时效性与专业性。

链接: https://arxiv.org/abs/2509.10697
作者: Pengcheng Jiang,Siru Ouyang,Yizhu Jiao,Ming Zhong,Runchu Tian,Jiawei Han
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)
备注: KDD’25 survey track

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Abstract:Large Language Models (LLMs) have revolutionized natural language processing with their remarkable capabilities in text generation and reasoning. However, these models face critical challenges when deployed in real-world applications, including hallucination generation, outdated knowledge, and limited domain expertise. Retrieval And Structuring (RAS) Augmented Generation addresses these limitations by integrating dynamic information retrieval with structured knowledge representations. This survey (1) examines retrieval mechanisms including sparse, dense, and hybrid approaches for accessing external knowledge; (2) explore text structuring techniques such as taxonomy construction, hierarchical classification, and information extraction that transform unstructured text into organized representations; and (3) investigate how these structured representations integrate with LLMs through prompt-based methods, reasoning frameworks, and knowledge embedding techniques. It also identifies technical challenges in retrieval efficiency, structure quality, and knowledge integration, while highlighting research opportunities in multimodal retrieval, cross-lingual structures, and interactive systems. This comprehensive overview provides researchers and practitioners with insights into RAS methods, applications, and future directions.
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[NLP-111] Struct-Bench: A Benchmark for Differentially Private Structured Text Generation

【速读】：该论文旨在解决差分隐私（Differential Privacy, DP）合成数据生成在结构化数据（尤其是包含自然语言字段的表格数据）上的评估难题。现有评估方法（如FID）难以捕捉此类数据的结构特性与变量间相关性，导致无法有效衡量合成数据的质量与隐私保护效果。解决方案的关键在于提出Struct-Bench框架，其核心创新是通过上下文无关文法（Context-Free Grammar, CFG）显式建模数据结构，并构建包含5个真实世界和2个合成数据集的基准测试集，每个数据集均附带CFG标注。该框架不仅提供标准化的评估指标实现和排行榜，还支持对DP合成方法（如Private Evolution）进行质量优化，从而为隐私保护下的结构化数据生成研究提供了可复现、可比较的评估平台。

链接: https://arxiv.org/abs/2509.10696
作者: Shuaiqi Wang,Vikas Raunak,Arturs Backurs,Victor Reis,Pei Zhou,Sihao Chen,Longqi Yang,Zinan Lin,Sergey Yekhanin,Giulia Fanti
机构: Carnegie Mellon University (卡内基梅隆大学); Microsoft Corporation (微软公司); Microsoft Research (微软研究院); Google DeepMind (谷歌深度思维)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Differentially private (DP) synthetic data generation is a promising technique for utilizing private datasets that otherwise cannot be exposed for model training or other analytics. While much research literature has focused on generating private unstructured text and image data, in enterprise settings, structured data (e.g., tabular) is more common, often including natural language fields or components. Existing synthetic data evaluation techniques (e.g., FID) struggle to capture the structural properties and correlations of such datasets. In this work, we propose Struct-Bench, a framework and benchmark for evaluating synthetic datasets derived from structured datasets that contain natural language data. The Struct-Bench framework requires users to provide a representation of their dataset structure as a Context-Free Grammar (CFG). Our benchmark comprises 5 real-world and 2 synthetically generated datasets, each annotated with CFGs. We show that these datasets demonstrably present a great challenge even for state-of-the-art DP synthetic data generation methods. Struct-Bench also includes reference implementations of different metrics and a leaderboard, thereby providing researchers a standardized evaluation platform to benchmark and investigate privacy-preserving synthetic data generation methods. Further, we also present a case study showing how to use Struct-Bench to improve the synthetic data quality of Private Evolution (PE) on structured data. The benchmark and the leaderboard have been publicly made available at this https URL.
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[NLP-112] Pluralistic Alignment for Healthcare: A Role-Driven Framework EMNLP2025

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在医疗等高敏感领域部署时，其输出难以充分反映不同人群间多元价值观与视角的问题。现有对齐方法（如模块化多元主义 Modular Pluralism）在健康场景中表现不足，因个人、文化及情境因素共同塑造了复杂的多元性。为此，作者提出首个轻量且可泛化的多元对齐方法 EthosAgents，其核心在于通过模拟多样化的伦理立场与价值取向来增强模型的多元对齐能力，实证表明该方法在七种不同规模的开放与闭源模型中均有效提升了三种模式下的多元对齐效果，揭示了健康相关多元性需具备适应性与规范意识的对齐策略，为其他高风险领域提供了可借鉴的技术路径。

链接: https://arxiv.org/abs/2509.10685
作者: Jiayou Zhong,Anudeex Shetty,Chao Jia,Xuanrui Lin,Usman Naseem
机构: University of Waterloo (滑铁卢大学); Macquarie University (麦考瑞大学); the University of Melbourne (墨尔本大学); Alibaba Group (阿里巴巴集团)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted to EMNLP 2025 (Main Proceedings)

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Abstract:As large language models are increasingly deployed in sensitive domains such as healthcare, ensuring their outputs reflect the diverse values and perspectives held across populations is critical. However, existing alignment approaches, including pluralistic paradigms like Modular Pluralism, often fall short in the health domain, where personal, cultural, and situational factors shape pluralism. Motivated by the aforementioned healthcare challenges, we propose a first lightweight, generalizable, pluralistic alignment approach, EthosAgents, designed to simulate diverse perspectives and values. We empirically show that it advances the pluralistic alignment for all three modes across seven varying-sized open and closed models. Our findings reveal that health-related pluralism demands adaptable and normatively aware approaches, offering insights into how these models can better respect diversity in other high-stakes domains.
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[NLP-113] LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM -based Systems

【速读】：该论文旨在解决生成式 AI（Generative AI），特别是大语言模型（Large Language Models, LLMs）在实际部署过程中面临的安全与隐私风险问题。随着LLM被广泛集成到各类系统中，攻击者可能利用其进行数据窃取、服务中断或恶意滥用，而传统软件安全防护手段难以完全应对针对LLM特有的威胁。解决方案的关键在于通过系统性回顾和全面分类，梳理从开发到运维全生命周期中的各类威胁及其严重程度，并将防御策略按生命周期阶段和对应攻击类型进行结构化映射，从而为开发者、厂商及研究者提供可操作的风险识别与缓解路径，推动LLM系统的安全、隐私保护型集成与应用。

链接: https://arxiv.org/abs/2509.10682
作者: Vitor Hugo Galhardo Moia,Igor Jochem Sanz,Gabriel Antonio Fontes Rebello,Rodrigo Duarte de Meneses,Briland Hitaj,Ulf Lindqvist
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
备注: 37 pages, 8 figures, 13 tables

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Abstract:The success and wide adoption of generative AI (GenAI), particularly large language models (LLMs), has attracted the attention of cybercriminals seeking to abuse models, steal sensitive data, or disrupt services. Moreover, providing security to LLM-based systems is a great challenge, as both traditional threats to software applications and threats targeting LLMs and their integration must be mitigated. In this survey, we shed light on security and privacy concerns of such LLM-based systems by performing a systematic review and comprehensive categorization of threats and defensive strategies considering the entire software and LLM life cycles. We analyze real-world scenarios with distinct characteristics of LLM usage, spanning from development to operation. In addition, threats are classified according to their severity level and to which scenarios they pertain, facilitating the identification of the most relevant threats. Recommended defense strategies are systematically categorized and mapped to the corresponding life cycle phase and possible attack strategies they attenuate. This work paves the way for consumers and vendors to understand and efficiently mitigate risks during integration of LLMs in their respective solutions or organizations. It also enables the research community to benefit from the discussion of open challenges and edge cases that may hinder the secure and privacy-preserving adoption of LLM-based systems.
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[NLP-114] Context Copying Modulation: The Role of Entropy Neurons in Managing Parametric and Contextual Knowledge Conflicts EMNLP2025

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在面对上下文信息与内部参数化知识冲突时行为不一致的问题，即缺乏对模型输出分布预期结果的统一解释。其解决方案的关键在于识别并验证一类称为“熵神经元”（entropy neurons）的特定神经元在抑制上下文复制行为中的作用：通过实证表明，这些神经元负责在多种LLM中抑制对上下文内容的直接复制，并且删除它们会导致生成过程发生显著变化，从而深化了对LLM在处理冲突信息时内部动态机制的理解。

链接: https://arxiv.org/abs/2509.10663
作者: Zineddine Tighidet,Andrea Mogini,Hedi Ben-younes,Jiali Mei,Patrick Gallinari,Benjamin Piwowarski
机构: BNP Paribas(法国巴黎银行); Sorbonne Université (索邦大学); CNRS (法国国家科学研究中心); ISIR (智能机器人与交互研究所); Criteo AI Lab (Criteo人工智能实验室)
类目: Computation and Language (cs.CL)
备注: Accepted at EMNLP 2025

点击查看摘要

Abstract:The behavior of Large Language Models (LLMs) when facing contextual information that conflicts with their internal parametric knowledge is inconsistent, with no generally accepted explanation for the expected outcome distribution. Recent work has identified in autoregressive transformer models a class of neurons – called entropy neurons – that produce a significant effect on the model output entropy while having an overall moderate impact on the ranking of the predicted tokens. In this paper, we investigate the preliminary claim that these neurons are involved in inhibiting context copying behavior in transformers by looking at their role in resolving conflicts between contextual and parametric information. We show that entropy neurons are responsible for suppressing context copying across a range of LLMs, and that ablating them leads to a significant change in the generation process. These results enhance our understanding of the internal dynamics of LLMs when handling conflicting information.
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[NLP-115] Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation EMNLP2025

【速读】：该论文试图解决计算形态学（computational morphology）在语言记录（language documentation）实践中应用有限的问题，其核心在于研究与实践之间存在的脱节。解决方案的关键在于引入以用户为中心的设计（User-Centered Design, UCD）原则，通过将实际使用者（如语言记录学者）的需求置于研究设计的核心位置，从而提升工具的可用性与实用性，并由此催生更贴近真实场景的研究方向，例如模型约束、标签标准化、分词以及个性化等关键问题。

链接: https://arxiv.org/abs/2509.10644
作者: Enora Rice,Katharina von der Wense,Alexis Palmer
机构: University of Colorado Boulder (科罗拉多大学博尔德分校); Johannes Gutenberg University Mainz (马克斯·普朗克研究所)
类目: Computation and Language (cs.CL)
备注: Accepted to EMNLP 2025

点击查看摘要

Abstract:Computational morphology has the potential to support language documentation through tasks like morphological segmentation and the generation of Interlinear Glossed Text (IGT). However, our research outputs have seen limited use in real-world language documentation settings. This position paper situates the disconnect between computational morphology and language documentation within a broader misalignment between research and practice in NLP and argues that the field risks becoming decontextualized and ineffectual without systematic integration of User-Centered Design (UCD). To demonstrate how principles from UCD can reshape the research agenda, we present a case study of GlossLM, a state-of-the-art multilingual IGT generation model. Through a small-scale user study with three documentary linguists, we find that despite strong metric based performance, the system fails to meet core usability needs in real documentation contexts. These insights raise new research questions around model constraints, label standardization, segmentation, and personalization. We argue that centering users not only produces more effective tools, but surfaces richer, more relevant research directions
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[NLP-116] No Answer Needed: Predicting LLM Answer Accuracy from Question-Only Linear Probes

【速读】：该论文试图解决的问题是：大型语言模型（Large Language Models, LLMs）是否能够提前预判自身回答的正确性。为了解决这一问题，研究者在模型读取问题但尚未生成任何输出 token 的阶段提取激活值，并训练线性探测器（linear probes）来预测后续答案的正确性。其关键解决方案在于，在模型推理过程中早期阶段（即“提前”阶段）捕捉到与正确性相关的潜在表示方向，并发现该方向在中间层达到预测性能饱和，表明自我评估能力在计算过程中间阶段即已形成；此外，该方向还与模型选择“我不知道”作为回应的行为高度相关，说明它同时捕获了模型的置信度信息。

链接: https://arxiv.org/abs/2509.10625
作者: Iván Vicente Moreno Cencerrado,Arnau Padrés Masdemont,Anton Gonzalvez Hawthorne,David Demitri Africa,Lorenzo Pacchiardi
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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点击查看摘要

Abstract:Do large language models (LLMs) anticipate when they will answer correctly? To study this, we extract activations after a question is read but before any tokens are generated, and train linear probes to predict whether the model’s forthcoming answer will be correct. Across three open-source model families ranging from 7 to 70 billion parameters, projections on this “in-advance correctness direction” trained on generic trivia questions predict success in distribution and on diverse out-of-distribution knowledge datasets, outperforming black-box baselines and verbalised predicted confidence. Predictive power saturates in intermediate layers, suggesting that self-assessment emerges mid-computation. Notably, generalisation falters on questions requiring mathematical reasoning. Moreover, for models responding “I don’t know”, doing so strongly correlates with the probe score, indicating that the same direction also captures confidence. By complementing previous results on truthfulness and other behaviours obtained with probes and sparse auto-encoders, our work contributes essential findings to elucidate LLM internals.
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[NLP-117] Smart Trial: Evaluating the Use of Large Language Models for Recruiting Clinical Trial Participants via Social Media

【速读】：该论文旨在解决临床试验（Clinical Trials, CT）中招募符合复杂纳入标准的参与者效率低下的问题，传统方法如医院电子健康记录筛查或广告宣传存在耗时且地域受限的缺陷。其解决方案的关键在于利用大型语言模型（Large Language Models, LLMs）分析个体在社交媒体上发布的健康相关文本，从而识别潜在符合条件的参与者。研究构建了名为TRIALQA的新数据集，涵盖结肠癌和前列腺癌两个Reddit子版块的用户帖子，并由专业标注员对每条文本进行标注，明确其是否满足特定CT的纳入标准以及参与意愿的原因。通过在该数据集上测试七种主流LLMs及多种训练与推理策略，验证了LLM在自动化筛选潜在受试者方面的潜力，同时也揭示了当前模型在处理多跳推理任务以准确判断资格时仍存在显著挑战。

链接: https://arxiv.org/abs/2509.10584
作者: Xiaofan Zhou,Zisu Wang,Janice Krieger,Mohan Zalake,Lu Cheng
机构: University of Illinois at Chicago (伊利诺伊大学芝加哥分校); Colorado State University (科罗拉多州立大学); Mayo Clinic (梅奥诊所)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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点击查看摘要

Abstract:Clinical trials (CT) are essential for advancing medical research and treatment, yet efficiently recruiting eligible participants – each of whom must meet complex eligibility criteria – remains a significant challenge. Traditional recruitment approaches, such as advertisements or electronic health record screening within hospitals, are often time-consuming and geographically constrained. This work addresses the recruitment challenge by leveraging the vast amount of health-related information individuals share on social media platforms. With the emergence of powerful large language models (LLMs) capable of sophisticated text understanding, we pose the central research question: Can LLM-driven tools facilitate CT recruitment by identifying potential participants through their engagement on social media? To investigate this question, we introduce TRIALQA, a novel dataset comprising two social media collections from the subreddits on colon cancer and prostate cancer. Using eligibility criteria from public real-world CTs, experienced annotators are hired to annotate TRIALQA to indicate (1) whether a social media user meets a given eligibility criterion and (2) the user’s stated reasons for interest in participating in CT. We benchmark seven widely used LLMs on these two prediction tasks, employing six distinct training and inference strategies. Our extensive experiments reveal that, while LLMs show considerable promise, they still face challenges in performing the complex, multi-hop reasoning needed to accurately assess eligibility criteria.
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[NLP-118] Uncovering the Vulnerability of Large Language Models in the Financial Domain via Risk Concealment

【速读】：该论文旨在解决金融领域大语言模型（Large Language Models, LLMs）在监管合规性方面的安全漏洞问题，即现有红队测试主要关注有害内容生成，而忽视了模型可能通过隐蔽手段规避金融监管要求的风险。其解决方案的关键在于提出一种名为风险隐藏攻击（Risk-Concealment Attacks, RCA）的多轮迭代框架，该框架通过逐步隐藏监管风险来诱导LLM产生表面合规但实质违反监管规则的响应。实验表明，RCA可有效绕过九种主流金融LLM，平均攻击成功率（ASR）达93.18%，其中GPT-4.1和OpenAI o1分别达到98.28%和97.56%，揭示了当前对齐技术在金融场景中的显著不足，并强调需加强该领域的监管敏感型内容过滤机制。

链接: https://arxiv.org/abs/2509.10546
作者: Gang Cheng,Haibo Jin,Wenbin Zhang,Haohan Wang,Jun Zhuang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Preprint, under review. TL;DR: We propose a multi-turn red-teaming framework, RCA, that reveals critical regulatory vulnerabilities in financial LLMs, achieving over 93% attack success on a proposed new benchmark, FIN-Bench

点击查看摘要

Abstract:Large Language Models (LLMs) are increasingly integrated into financial applications, yet existing red-teaming research primarily targets harmful content, largely neglecting regulatory risks. In this work, we aim to investigate the vulnerability of financial LLMs through red-teaming approaches. We introduce Risk-Concealment Attacks (RCA), a novel multi-turn framework that iteratively conceals regulatory risks to provoke seemingly compliant yet regulatory-violating responses from LLMs. To enable systematic evaluation, we construct FIN-Bench, a domain-specific benchmark for assessing LLM safety in financial contexts. Extensive experiments on FIN-Bench demonstrate that RCA effectively bypasses nine mainstream LLMs, achieving an average attack success rate (ASR) of 93.18%, including 98.28% on GPT-4.1 and 97.56% on OpenAI o1. These findings reveal a critical gap in current alignment techniques and underscore the urgent need for stronger moderation mechanisms in financial domains. We hope this work offers practical insights for advancing robust and domain-aware LLM alignment.
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[NLP-119] DualAlign: Generating Clinically Grounded Synthetic Data

【速读】：该论文旨在解决生成式人工智能（Generative AI）在医疗领域应用中面临的三大挑战：真实电子健康记录（EHR）数据的隐私限制、罕见疾病标注数据稀缺，以及观察性数据集中的系统性偏倚。为应对这些问题，作者提出DualAlign框架，其核心创新在于通过双重对齐机制提升合成临床数据的统计保真度和临床合理性：一是统计对齐（statistical alignment），利用患者人口学特征和风险因素约束生成过程；二是语义对齐（semantic alignment），引入真实世界症状轨迹引导内容生成。实验以阿尔茨海默病（Alzheimer’s disease, AD）为例表明，该方法能生成贴近临床文档的细粒度症状级文本，并在LLaMA 3.1-8B模型上结合人工标注数据微调后显著优于仅使用真实数据或无引导合成基线的方法，从而提供了一种兼顾临床相关性与隐私保护的实用合成数据生成方案。

链接: https://arxiv.org/abs/2509.10538
作者: Rumeng Li,Xun Wang,Hong Yu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
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点击查看摘要

Abstract:Synthetic clinical data are increasingly important for advancing AI in healthcare, given strict privacy constraints on real-world EHRs, limited availability of annotated rare-condition data, and systemic biases in observational datasets. While large language models (LLMs) can generate fluent clinical text, producing synthetic data that is both realistic and clinically meaningful remains challenging. We introduce DualAlign, a framework that enhances statistical fidelity and clinical plausibility through dual alignment: (1) statistical alignment, which conditions generation on patient demographics and risk factors; and (2) semantic alignment, which incorporates real-world symptom trajectories to guide content generation. Using Alzheimer’s disease (AD) as a case study, DualAlign produces context-grounded symptom-level sentences that better reflect real-world clinical documentation. Fine-tuning an LLaMA 3.1-8B model with a combination of DualAlign-generated and human-annotated data yields substantial performance gains over models trained on gold data alone or unguided synthetic baselines. While DualAlign does not fully capture longitudinal complexity, it offers a practical approach for generating clinically grounded, privacy-preserving synthetic data to support low-resource clinical text analysis.
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[NLP-120] Decoupling the “What” and “Where” With Polar Coordinate Positional Embeddings

【速读】：该论文旨在解决Transformer架构中位置编码方法RoPE（Rotary Position Embedding）存在的“内容-位置混淆”问题，即在注意力机制中，查询（query）与键（key）的匹配同时依赖于语义内容（what）和序列位置（where），导致二者耦合，影响模型在需要独立处理内容或位置信息的任务上的性能。解决方案的关键在于提出一种新的位置编码方式——极坐标位置嵌入（Polar Coordinate Position Embeddings, PoPE），其通过将位置信息从内容空间中解耦出来，实现对位置和内容的独立建模，从而消除what-where混淆。实验表明，PoPE在诊断任务、音乐、基因组和自然语言建模等多领域均优于RoPE，且具备优异的零样本长度外推能力。

链接: https://arxiv.org/abs/2509.10534
作者: Anand Gopalakrishnan,Robert Csordás,Jürgen Schmidhuber,Michael C. Mozer
机构: The Swiss AI Lab (IDSIA), USI & SUPSI, Lugano, Switzerland; OpenAI; Center for Generative AI, KAUST, Thuwal, Saudi Arabia; University of Colorado, Boulder, CO, USA
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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点击查看摘要

Abstract:The attention mechanism in a Transformer architecture matches key to query based on both content – the what – and position in a sequence – the where. We present an analysis indicating that what and where are entangled in the popular RoPE rotary position embedding. This entanglement can impair performance particularly when decisions require independent matches on these two factors. We propose an improvement to RoPE, which we call Polar Coordinate Position Embeddings or PoPE, that eliminates the what-where confound. PoPE is far superior on a diagnostic task requiring indexing solely by position or by content. On autoregressive sequence modeling in music, genomic, and natural language domains, Transformers using PoPE as the positional encoding scheme outperform baselines using RoPE with respect to evaluation loss (perplexity) and downstream task performance. On language modeling, these gains persist across model scale, from 124M to 774M parameters. Crucially, PoPE shows strong zero-shot length extrapolation capabilities, whereas RoPE’s performance degrades significantly on longer sequences at test time without fine tuning or the use of position-interpolation methods.
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[NLP-121] Real-Time RAG for the Identification of Supply Chain Vulnerabilities

【速读】：该论文旨在解决生成式 AI（Generative AI）在供应链分析中因知识库滞后而无法及时提供新兴信息支持的问题，从而限制了其对实时供应链中断事件的响应能力。解决方案的关键在于引入检索增强生成（Retrieval-Augmented Generation, RAG）预处理与高级网络爬虫技术相结合的方法，通过优化嵌入检索模型的微调策略以提升检索质量，同时采用自适应迭代检索机制动态调整检索深度，显著改善了信息更新的时效性与分析准确性之间的权衡关系。实验证明，检索质量是性能提升的核心因素，相较而言，对大型语言模型（Large Language Model, LLM）进行微调效果有限且资源开销高。

链接: https://arxiv.org/abs/2509.10469
作者: Jesse Ponnock,Grace Kenneally,Michael Robert Briggs,Elinor Yeo,Tyrone Patterson III,Nicholas Kinberg,Matthew Kalinowski,David Hechtman
机构: MITRE Corporation (MITRE 公司)
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: 14 pages, 5 figures, 1 table. Approved for Public Release; Distribution Unlimited. PRS Release Number: 25-0864

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Abstract:New technologies in generative AI can enable deeper analysis into our nation’s supply chains but truly informative insights require the continual updating and aggregation of massive data in a timely manner. Large Language Models (LLMs) offer unprecedented analytical opportunities however, their knowledge base is constrained to the models’ last training date, rendering these capabilities unusable for organizations whose mission impacts rely on emerging and timely information. This research proposes an innovative approach to supply chain analysis by integrating emerging Retrieval-Augmented Generation (RAG) preprocessing and retrieval techniques with advanced web-scraping technologies. Our method aims to reduce latency in incorporating new information into an augmented-LLM, enabling timely analysis of supply chain disruptors. Through experimentation, this study evaluates the combinatorial effects of these techniques towards timeliness and quality trade-offs. Our results suggest that in applying RAG systems to supply chain analysis, fine-tuning the embedding retrieval model consistently provides the most significant performance gains, underscoring the critical importance of retrieval quality. Adaptive iterative retrieval, which dynamically adjusts retrieval depth based on context, further enhances performance, especially on complex sup- ply chain queries. Conversely, fine-tuning the LLM yields limited improvements and higher resource costs, while techniques such as downward query abstraction significantly outperforms upward abstraction in practice.
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[NLP-122] Learning Decomposed Contextual Token Representations from Pretrained and Collaborative Signals for Generative Recommendation

【速读】：该论文旨在解决生成式推荐系统中因两阶段训练目标不一致所引发的两个关键问题：一是静态词元化（tokenization）导致的固定词元分配无法适应多样化的使用场景；二是预训练语义信息在推荐模型训练过程中被覆盖，造成语言模型嵌入知识的丢失。解决方案的关键在于提出一种统一框架——DEcomposed COntextual Token Representations (DECOR)，其核心创新包括：(1) 上下文感知的词元组合机制，用于根据用户交互上下文动态优化词元嵌入；(2) 分解式嵌入融合策略，将预训练代码本嵌入与新学习的协同嵌入进行有效整合，在保留预训练语义的同时增强词元嵌入的适应性。

链接: https://arxiv.org/abs/2509.10468
作者: Yifan Liu,Yaokun Liu,Zelin Li,Zhenrui Yue,Gyuseok Lee,Ruichen Yao,Yang Zhang,Dong Wang
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: preprint under review

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Abstract:Recent advances in generative recommenders adopt a two-stage paradigm: items are first tokenized into semantic IDs using a pretrained tokenizer, and then large language models (LLMs) are trained to generate the next item via sequence-to-sequence modeling. However, these two stages are optimized for different objectives: semantic reconstruction during tokenizer pretraining versus user interaction modeling during recommender training. This objective misalignment leads to two key limitations: (i) suboptimal static tokenization, where fixed token assignments fail to reflect diverse usage contexts; and (ii) discarded pretrained semantics, where pretrained knowledge - typically from language model embeddings - is overwritten during recommender training on user interactions. To address these limitations, we propose to learn DEcomposed COntextual Token Representations (DECOR), a unified framework that preserves pretrained semantics while enhancing the adaptability of token embeddings. DECOR introduces contextualized token composition to refine token embeddings based on user interaction context, and decomposed embedding fusion that integrates pretrained codebook embeddings with newly learned collaborative embeddings. Experiments on three real-world datasets demonstrate that DECOR consistently outperforms state-of-the-art baselines in recommendation performance. Our code will be made available upon publication.
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[NLP-123] DSRAG : A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph

【速读】：该论文旨在解决当前通用大语言模型（Large Language Models, LLMs）在特定领域任务中普遍存在的知识幻觉（knowledge hallucination）和领域适应性不足的问题，从而限制其在专业问答场景中的有效性。解决方案的关键在于提出一种基于图结构的检索增强生成框架——DSRAG（Domain-Specific RAG），其核心创新在于利用领域文档构建包含文本、图像和表格等异构信息的多模态知识图谱（Multimodal Knowledge Graph），并通过语义剪枝与结构化子图检索机制，融合知识图谱上下文与向量检索结果，引导语言模型生成更可靠的回答，显著提升领域特定问答性能。

链接: https://arxiv.org/abs/2509.10467
作者: Mengzheng Yang,Yanfei Ren,David Osei Opoku,Ruochang Li,Peng Ren,Chunxiao Xing
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
备注: 12 pages, 5 figures. Accepted to the 22nd International Conference on Web Information Systems and Applications (WISA 2025)

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Abstract:Current general-purpose large language models (LLMs) commonly exhibit knowledge hallucination and insufficient domain-specific adaptability in domain-specific tasks, limiting their effectiveness in specialized question answering scenarios. Retrieval-augmented generation (RAG) effectively tackles these challenges by integrating external knowledge to enhance accuracy and relevance. However, traditional RAG still faces limitations in domain knowledge accuracy and context this http URL enhance domain-specific question answering performance, this work focuses on a graph-based RAG framework, emphasizing the critical role of knowledge graph quality during the generation process. We propose DSRAG (Domain-Specific RAG), a multimodal knowledge graph-driven retrieval-augmented generation framework designed for domain-specific applications. Our approach leverages domain-specific documents as the primary knowledge source, integrating heterogeneous information such as text, images, and tables to construct a multimodal knowledge graph covering both conceptual and instance layers. Building on this foundation, we introduce semantic pruning and structured subgraph retrieval mechanisms, combining knowledge graph context and vector retrieval results to guide the language model towards producing more reliable responses. Evaluations using the Langfuse multidimensional scoring mechanism show that our method excels in domain-specific question answering, validating the efficacy of integrating multimodal knowledge graphs with retrieval-augmented generation.
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[NLP-124] Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG -RL EMNLP2025

【速读】：该论文旨在解决健康谣言（health misinformation）在线传播对公共健康构成的威胁，特别是现有自动反驳策略生成的内容缺乏针对性，未能考虑受众健康素养（health literacy）水平差异导致的信息可及性与有效性不足的问题。解决方案的关键在于提出一种受控健康素养（Controlled-Literacy）框架，该框架结合检索增强生成（Retrieval-Augmented Generation, RAG）与强化学习（Reinforcement Learning, RL），通过检索适配特定健康素养水平的知识库内容来支撑生成过程，并设计融合主观用户偏好与客观可读性指标的奖励函数，以优化生成内容在目标健康素养层级下的可理解性与接受度。实验表明，该方法能有效生成更易懂且更受用户青睐的反驳信息，从而提升公共卫生沟通的公平性与影响力。

链接: https://arxiv.org/abs/2509.01058
作者: Xiaoying Song,Anirban Saha Anik,Dibakar Barua,Pengcheng Luo,Junhua Ding,Lingzi Hong
机构: University of North Texas (北德克萨斯大学); Peking University (北京大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at Findings of EMNLP 2025

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Abstract:Health misinformation spreading online poses a significant threat to public health. Researchers have explored methods for automatically generating counterspeech to health misinformation as a mitigation strategy. Existing approaches often produce uniform responses, ignoring that the health literacy level of the audience could affect the accessibility and effectiveness of counterspeech. We propose a Controlled-Literacy framework using retrieval-augmented generation (RAG) with reinforcement learning (RL) to generate tailored counterspeech adapted to different health literacy levels. In particular, we retrieve knowledge aligned with specific health literacy levels, enabling accessible and factual information to support generation. We design a reward function incorporating subjective user preferences and objective readability-based rewards to optimize counterspeech to the target health literacy level. Experiment results show that Controlled-Literacy outperforms baselines by generating more accessible and user-preferred counterspeech. This research contributes to more equitable and impactful public health communication by improving the accessibility and comprehension of counterspeech to health misinformation
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[NLP-125] A Dynamic Fusion Model for Consistent Crisis Response EMNLP2025

【速读】：该论文旨在解决危机沟通中生成式响应（Generative AI）风格一致性不足的问题，这一问题可能削弱受影响人群对响应方的信任。解决方案的关键在于提出了一种新的风格一致性评估指标，并基于该指标设计了一种融合驱动的生成方法：首先在实例层面评估候选响应的风格，随后通过融合优化策略整合响应内容，从而在保持高响应质量的同时显著降低不同响应间的风格差异。

链接: https://arxiv.org/abs/2509.01053
作者: Xiaoying Song,Anirban Saha Anik,Eduardo Blanco,Vanessa Frias-Martinez,Lingzi Hong
机构: University of North Texas (北德克萨斯大学); University of Arizona (亚利桑那大学); University of Maryland (马里兰大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at Findings of EMNLP 2025

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Abstract:In response to the urgent need for effective communication with crisis-affected populations, automated responses driven by language models have been proposed to assist in crisis communications. A critical yet often overlooked factor is the consistency of response style, which could affect the trust of affected individuals in responders. Despite its importance, few studies have explored methods for maintaining stylistic consistency across generated responses. To address this gap, we propose a novel metric for evaluating style consistency and introduce a fusion-based generation approach grounded in this metric. Our method employs a two-stage process: it first assesses the style of candidate responses and then optimizes and integrates them at the instance level through a fusion process. This enables the generation of high-quality responses while significantly reducing stylistic variation between instances. Experimental results across multiple datasets demonstrate that our approach consistently outperforms baselines in both response quality and stylistic uniformity.
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[NLP-126] Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts EMNLP2025

【速读】：该论文旨在解决将自动语音识别（Automatic Speech Recognition, ASR）系统集成到自然语言查询系统中以提升韩国气象预报员天气预报效率的问题，核心挑战在于韩语气象领域专用词汇和语言复杂性。解决方案的关键在于构建了一个由母语韩语者录制的口语查询评估数据集，并在此基础上对多语言ASR模型家族进行系统性评估，识别出领域术语识别性能瓶颈；随后采用一种基于文本转语音（Text-to-Speech, TTS）的数据增强方法，在不损害通用领域性能的前提下显著提升了专业术语的识别准确率。

链接: https://arxiv.org/abs/2410.18444
作者: ChaeHun Park,Hojun Cho,Jaegul Choo
机构: KAIST AI (韩国科学技术院人工智能)
类目: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
备注: EMNLP 2025 Findings

点击查看摘要

Abstract:This paper explores integrating Automatic Speech Recognition (ASR) into natural language query systems to improve weather forecasting efficiency for Korean meteorologists. We address challenges in developing ASR systems for the Korean weather domain, specifically specialized vocabulary and Korean linguistic intricacies. To tackle these issues, we constructed an evaluation dataset of spoken queries recorded by native Korean speakers. Using this dataset, we assessed various configurations of a multilingual ASR model family, identifying performance limitations related to domain-specific terminology. We then implemented a simple text-to-speech-based data augmentation method, which improved the recognition of specialized terms while maintaining general-domain performance. Our contributions include creating a domain-specific dataset, comprehensive ASR model evaluations, and an effective augmentation technique. We believe our work provides a foundation for future advancements in ASR for the Korean weather forecasting domain.
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[NLP-127] When marine radar target detection meets pretrained large language models

【速读】：该论文旨在解决深度学习（Deep Learning, DL）方法在处理雷达回波信号序列特征时面临的冗余特征段问题以及受限模型规模带来的性能瓶颈。其解决方案的关键在于构建一个将特征预处理与大语言模型（Large Language Models, LLMs）相结合的框架：首先通过分词和补丁选择算法过滤掉低信息量的特征段，并将保留的补丁映射为适配预训练LLM特征空间的嵌入表示；随后利用这些优化后的嵌入，仅微调LLM中的归一化层以降低训练负担并提升性能，从而在监督学习任务中显著优于现有最优基线方法。

链接: https://arxiv.org/abs/2509.12110
作者: Qiying Hu,Linping Zhang,Xueqian Wang,Gang Li,Yu Liu,Xiao-Ping Zhang
机构: Tsinghua University (清华大学); Tsinghua Shenzhen International Graduate School (清华大学深圳国际研究生院)
类目: ignal Processing (eess.SP); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Deep learning (DL) methods are widely used to extract high-dimensional patterns from the sequence features of radar echo signals. However, conventional DL algorithms face challenges such as redundant feature segments, and constraints from restricted model sizes. To address these issues, we propose a framework that integrates feature preprocessing with large language models (LLMs). Our preprocessing module tokenizes radar sequence features, applies a patch selection algorithm to filter out uninformative segments, and projects the selected patches into embeddings compatible with the feature space of pre-trained LLMs. Leveraging these refined embeddings, we incorporate a pre-trained LLM, fine-tuning only the normalization layers to reduce training burdens while enhancing performance. Experiments on measured datasets demonstrate that the proposed method significantly outperforms the state-of-the-art baselines on supervised learning tests.
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[NLP-128] RadarLLM : Adapting Pretrained Large Language Models for Marine Radar Target Detection with Preference-aware Loss

【速读】：该论文旨在解决预训练大语言模型（Large Language Models, LLMs）在海洋目标检测任务中直接微调时易出现过拟合的问题，尤其是在低信杂比（Signal-to-Clutter Ratio, SCR）等挑战性场景下，模型倾向于记忆噪声或伪特征模式而非学习具有泛化能力的判别性结构。解决方案的关键在于提出一种名为RadarLLM的新颖微调框架，其核心是引入一种偏好感知损失函数（preference-aware loss），该损失函数通过在线评估各特征块的学习价值，选择性地优化不同特征区域，从而引导模型聚焦于最具泛化能力的特征模式。理论分析表明，该方法可转化为有效特征标记的选择问题，实验证明其在低SCR场景和小样本条件下均显著优于现有最优基线方法。

链接: https://arxiv.org/abs/2509.12089
作者: Qiying Hu
机构: 未知
类目: ignal Processing (eess.SP); Computation and Language (cs.CL)
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Abstract:Recent advances in pre-trained large language models (LLMs) have demonstrated their capacities to capture universal knowledge, making them promising general-purpose optimization solvers for wireless signal processing. Motivated by these findings, we take the first step towards fine-tuning pre-trained LLMs for the effective analysis of radar signal features in marine target detection tasks. Nevertheless, directly fine-tuning pre-trained LLMs on marine target detection tasks tends to suffer from pronounced overfitting, particularly in challenging low signal-to-clutter ratio (SCR) scenarios. This overfitting primarily stems from the model’s tendency to memorize spurious or noisy feature patterns rather than learning discriminative structures that generalize well to unseen data. To address this challenge, we introduce RadarLLM, a novel fine-tuning framework that utilizes an effective preference-aware loss. Unlike conventional training strategies that uniformly optimize all feature tokens, this loss function selectively optimizes different feature patches based on their online evaluated learning values, thus guiding the model to focus on the most generalizable patterns during optimization. We theoretically demonstrate the effectiveness of the evaluated learning values by transforming the problem as selecting useful feature tokens. Extensive experiments on real-world marine radar datasets show that 1) the proposed loss function is much better than the original one, with particularly significant gains in challenging low SCR scenarios and 2) RadarLLM consistently outperforms state-of-the-art baselines across diverse detection scenarios, with particularly notable gains under limited training data conditions.
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[NLP-129] rading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning

【速读】：该论文旨在解决金融领域中人工智能模型在专业、结构化推理能力上与人类金融分析师和交易员存在差距的问题，尤其关注市场对可解释性和可信度的高要求。传统时间序列模型缺乏可解释性，而大语言模型（LLM）难以将自然语言分析转化为严谨且可执行的交易决策；尽管推理型LLM在分步规划与验证方面取得进展，其在风险敏感型金融决策中的应用仍不充分。解决方案的关键在于提出Trading-R1模型，该模型融合了战略思维与计划能力，支持投资逻辑的系统构建、基于事实的分析以及波动率调整的决策机制，并通过监督微调与三阶段由易到难的强化学习进行训练，从而实现更稳健的风险调整收益与更低回撤表现。

链接: https://arxiv.org/abs/2509.11420
作者: Yijia Xiao,Edward Sun,Tong Chen,Fang Wu,Di Luo,Wei Wang
机构: 未知
类目: Trading and Market Microstructure (q-fin.TR); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Tauric Research: this https URL

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Abstract:Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language analysis into disciplined, executable trades. Although reasoning LLMs have advanced in step-by-step planning and verification, their application to risk-sensitive financial decisions is underexplored. We present Trading-R1, a financially-aware model that incorporates strategic thinking and planning for comprehensive thesis composition, facts-grounded analysis, and volatility-adjusted decision making. Trading-R1 aligns reasoning with trading principles through supervised fine-tuning and reinforcement learning with a three-stage easy-to-hard curriculum. Training uses Tauric-TR1-DB, a 100k-sample corpus spanning 18 months, 14 equities, and five heterogeneous financial data sources. Evaluated on six major equities and ETFs, Trading-R1 demonstrates improved risk-adjusted returns and lower drawdowns compared to both open-source and proprietary instruction-following models as well as reasoning models. The system generates structured, evidence-based investment theses that support disciplined and interpretable trading decisions. Trading-R1 Terminal will be released at this https URL.
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[NLP-130] Length-Aware Rotary Position Embedding for Text-Speech Alignment

【速读】：该论文旨在解决当前基于Transformer架构的文本到语音（Text-to-Speech, TTS）系统中，由于使用旋转位置编码（Rotary Position Embedding, RoPE）导致的文本-语音对齐不准确问题，尤其是在长语音生成和不同语句长度变化下性能退化明显的问题。解决方案的关键在于提出长度感知的旋转位置编码（Length-aware RoPE, LARoPE），其通过引入长度归一化的相对位置索引来计算查询与键之间的相对距离，从而更有效地建模跨模态的位置关系，显著提升对齐精度和模型鲁棒性，尤其在30秒以上语音生成任务中表现稳定，且在零样本TTS基准测试中达到最优词错误率。

链接: https://arxiv.org/abs/2509.11084
作者: Hyeongju Kim,Juheon Lee,Jinhyeok Yang,Jacob Morton
机构: 未知
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Sound (cs.SD)
备注: 5 pages, 3 figures, preprint

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Abstract:Many recent text-to-speech (TTS) systems are built on transformer architectures and employ cross-attention mechanisms for text-speech alignment. Within these systems, rotary position embedding (RoPE) is commonly used to encode positional information in text and speech representations. In this work, we introduce length-aware RoPE (LARoPE), a simple yet effective extension of RoPE that improves text-speech alignment. Unlike RoPE, which relies on absolute indices, LARoPE computes relative distances between query and key positions using length-normalized indices. Experimental results show that LARoPE consistently outperforms RoPE, offering faster loss convergence, more accurate text-speech alignment, and higher overall TTS quality. Furthermore, LARoPE demonstrates greater resilience to variations in utterance duration and maintains stable performance in extended speech generation up to 30 seconds, whereas RoPE suffers from notable degradation. Notably, our method achieves a state-of-the-art word error rate on a standard zero-shot TTS benchmark.
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[NLP-131] Why Bonds Fail Differently? Explainable Multimodal Learning for Multi-Class Default Prediction

【速读】：该论文旨在解决债券违约预测中传统机器学习模型难以捕捉金融数据的不规则性和时间依赖性，以及深度学习模型缺乏可解释性的问题（尤其在金融决策场景下）。其解决方案的关键在于提出了一种名为EMDLOT（Explainable Multimodal Deep Learning for Time-series）的新型框架，该框架融合数值型时间序列数据（如财务指标与宏观经济变量）和非结构化文本数据（如债券募集说明书），采用时序感知LSTM处理不规则时间序列，并通过软聚类与多层次注意力机制提升模型的可解释性，从而在召回率、F1分数和平均精度均值（mAP）等指标上优于XGBoost、LSTM等基准模型，特别是在识别违约或展期企业方面表现突出。

链接: https://arxiv.org/abs/2509.10802
作者: Yi Lu,Aifan Ling,Chaoqun Wang,Yaxin Xu
机构: Shanghai International Studies University (上海外国语大学); Xi’an Jiaotong-Liverpool University (西安交通大学利物浦大学); Shanghai University of Finance and Economics (上海财经大学)
类目: Risk Management (q-fin.RM); Computation and Language (cs.CL); Machine Learning (cs.LG); Computational Finance (q-fin.CP)
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Abstract:In recent years, China’s bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data’s irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-making. To tackle these issues, we propose EMDLOT (Explainable Multimodal Deep Learning for Time-series), a novel framework for multi-class bond default prediction. EMDLOT integrates numerical time-series (financial/macroeconomic indicators) and unstructured textual data (bond prospectuses), uses Time-Aware LSTM to handle irregular sequences, and adopts soft clustering and multi-level attention to boost interpretability. Experiments on 1994 Chinese firms (2015-2024) show EMDLOT outperforms traditional (e.g., XGBoost) and deep learning (e.g., LSTM) benchmarks in recall, F1-score, and mAP, especially in identifying default/extended firms. Ablation studies validate each component’s value, and attention analyses reveal economically intuitive default drivers. This work provides a practical tool and a trustworthy framework for transparent financial risk modeling.
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计算机视觉

[CV-0] Character-Centric Understanding of Animated Movies

【速读】：该论文旨在解决动画电影中角色识别困难的问题，现有识别系统难以应对动画角色在外观、运动和形变上的极端多样性。解决方案的关键在于构建一个自动化的音视频角色库（audio-visual character bank），该库通过在线来源自动收集每个角色的视觉样例和语音样本，从而实现多模态角色识别，即使面对长尾分布的外观变化也能保持鲁棒性。基于此，论文进一步探索了面向视障人群的音频描述（Audio Description, AD）生成和面向听障人群的角色感知字幕（character-aware subtitling）两项下游应用，显著提升了动画内容的可访问性和叙事理解能力。

链接: https://arxiv.org/abs/2509.12204
作者: Zhongrui Gui,Junyu Xie,Tengda Han,Weidi Xie,Andrew Zisserman
机构: University of Oxford (牛津大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Animated movies are captivating for their unique character designs and imaginative storytelling, yet they pose significant challenges for existing recognition systems. Unlike the consistent visual patterns detected by conventional face recognition methods, animated characters exhibit extreme diversity in their appearance, motion, and deformation. In this work, we propose an audio-visual pipeline to enable automatic and robust animated character recognition, and thereby enhance character-centric understanding of animated movies. Central to our approach is the automatic construction of an audio-visual character bank from online sources. This bank contains both visual exemplars and voice (audio) samples for each character, enabling subsequent multi-modal character recognition despite long-tailed appearance distributions. Building on accurate character recognition, we explore two downstream applications: Audio Description (AD) generation for visually impaired audiences, and character-aware subtitling for the hearing impaired. To support research in this domain, we introduce CMD-AM, a new dataset of 75 animated movies with comprehensive annotations. Our character-centric pipeline demonstrates significant improvements in both accessibility and narrative comprehension for animated content over prior face-detection-based approaches. For the code and dataset, visit this https URL.
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[CV-1] LazyDrag : Enabling Stable Drag -Based Editing on Multi-Modal Diffusion Transformers via Explicit Correspondence

【速读】：该论文旨在解决基于拖拽的图像编辑中依赖隐式点匹配（implicit point matching）所导致的核心瓶颈问题，即在生成式 AI（Generative AI）模型中因注意力机制限制而造成的反演强度弱化与高成本的测试时优化（Test-Time Optimization, TTO），从而严重抑制了扩散模型在高保真修复（inpainting）和文本引导生成方面的潜力。解决方案的关键在于提出 LazyDrag，一种针对多模态扩散 Transformer 的新型拖拽编辑方法，其通过从用户拖拽输入显式生成对应关系图（correspondence map）作为可靠的注意力控制参考，直接消除对隐式点匹配的依赖；这一可靠参考使得稳定且全强度的反演过程成为可能，无需 TTO，从而释放模型的完整生成能力，并实现几何控制与文本引导的自然统一，支持复杂编辑任务如对象生成、语义感知移动等。

链接: https://arxiv.org/abs/2509.12203
作者: Zixin Yin,Xili Dai,Duomin Wang,Xianfang Zeng,Lionel M. Ni,Gang Yu,Heung-Yeung Shum
机构: The Hong Kong University of Science and Technology (香港科技大学); StepFun(斯谱凡); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The reliance on implicit point matching via attention has become a core bottleneck in drag-based editing, resulting in a fundamental compromise on weakened inversion strength and costly test-time optimization (TTO). This compromise severely limits the generative capabilities of diffusion models, suppressing high-fidelity inpainting and text-guided creation. In this paper, we introduce LazyDrag, the first drag-based image editing method for Multi-Modal Diffusion Transformers, which directly eliminates the reliance on implicit point matching. In concrete terms, our method generates an explicit correspondence map from user drag inputs as a reliable reference to boost the attention control. This reliable reference opens the potential for a stable full-strength inversion process, which is the first in the drag-based editing task. It obviates the necessity for TTO and unlocks the generative capability of models. Therefore, LazyDrag naturally unifies precise geometric control with text guidance, enabling complex edits that were previously out of reach: opening the mouth of a dog and inpainting its interior, generating new objects like a ``tennis ball’', or for ambiguous drags, making context-aware changes like moving a hand into a pocket. Additionally, LazyDrag supports multi-round workflows with simultaneous move and scale operations. Evaluated on the DragBench, our method outperforms baselines in drag accuracy and perceptual quality, as validated by VIEScore and human evaluation. LazyDrag not only establishes new state-of-the-art performance, but also paves a new way to editing paradigms.
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[CV-2] OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling

【速读】：该论文旨在解决当前4D世界建模（4D world modeling）中缺乏高质量、大规模、多域且具备时空标注数据的问题，从而限制了生成式AI在4D几何重建、未来预测及相机控制视频生成等关键任务上的发展。解决方案的关键在于提出OmniWorld数据集，其由新收集的OmniWorld-Game合成数据集和多个精选公共数据集组成，具有更丰富的模态覆盖、更大的规模以及更真实的动态交互特性，显著提升了现有模型在复杂4D环境建模中的性能表现，并为训练与评估通用4D世界模型提供了强大资源。

链接: https://arxiv.org/abs/2509.12201
作者: Yang Zhou,Yifan Wang,Jianjun Zhou,Wenzheng Chang,Haoyu Guo,Zizun Li,Kaijing Ma,Xinyue Li,Yating Wang,Haoyi Zhu,Mingyu Liu,Dingning Liu,Jiange Yang,Zhoujie Fu,Junyi Chen,Chunhua Shen,Jiangmiao Pang,Kaipeng Zhang,Tong He
机构: Shanghai AI Lab(上海人工智能实验室); ZJU(浙江大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The field of 4D world modeling - aiming to jointly capture spatial geometry and temporal dynamics - has witnessed remarkable progress in recent years, driven by advances in large-scale generative models and multimodal learning. However, the development of truly general 4D world models remains fundamentally constrained by the availability of high-quality data. Existing datasets and benchmarks often lack the dynamic complexity, multi-domain diversity, and spatial-temporal annotations required to support key tasks such as 4D geometric reconstruction, future prediction, and camera-control video generation. To address this gap, we introduce OmniWorld, a large-scale, multi-domain, multi-modal dataset specifically designed for 4D world modeling. OmniWorld consists of a newly collected OmniWorld-Game dataset and several curated public datasets spanning diverse domains. Compared with existing synthetic datasets, OmniWorld-Game provides richer modality coverage, larger scale, and more realistic dynamic interactions. Based on this dataset, we establish a challenging benchmark that exposes the limitations of current state-of-the-art (SOTA) approaches in modeling complex 4D environments. Moreover, fine-tuning existing SOTA methods on OmniWorld leads to significant performance gains across 4D reconstruction and video generation tasks, strongly validating OmniWorld as a powerful resource for training and evaluation. We envision OmniWorld as a catalyst for accelerating the development of general-purpose 4D world models, ultimately advancing machines’ holistic understanding of the physical world.
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[CV-3] 3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review

【速读】：该论文旨在解决从野外（in-the-wild）LiDAR点云中进行三维人体姿态估计（3D human pose estimation）与人体网格恢复（human mesh recovery）的难题。其解决方案的关键在于提出一个结构化的分类体系（taxonomic framework），对现有方法按关键维度进行系统归类，并在此基础上深入分析各类方法的优势、局限及设计选择；同时，通过量化比较三个主流数据集的特性、统一评估指标定义，并建立基准测试表格，实现公平、可复现的性能对比，从而推动LiDAR驱动的三维人体理解研究进展。

链接: https://arxiv.org/abs/2509.12197
作者: Salma Galaaoui,Eduardo Valle,David Picard,Nermin Samet
机构: Valeo.ai(维沃人工智能); LIGM, École Nationale des Ponts et Chaussées, IP Paris, Univ Gustave Eiffel, CNRS(法国国家科学研究中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In this paper, we present a comprehensive review of 3D human pose estimation and human mesh recovery from in-the-wild LiDAR point clouds. We compare existing approaches across several key dimensions, and propose a structured taxonomy to classify these methods. Following this taxonomy, we analyze each method’s strengths, limitations, and design choices. In addition, (i) we perform a quantitative comparison of the three most widely used datasets, detailing their characteristics; (ii) we compile unified definitions of all evaluation metrics; and (iii) we establish benchmark tables for both tasks on these datasets to enable fair comparisons and promote progress in the field. We also outline open challenges and research directions critical for advancing LiDAR-based 3D human understanding. Moreover, we maintain an accompanying webpage that organizes papers according to our taxonomy and continuously update it with new studies: this https URL
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[CV-4] Advancing Medical Artificial Intelligence Using a Century of Cases

【速读】：该论文旨在解决当前医学人工智能（AI）评估中忽视专家医生多维度推理与表达能力的问题，尤其是针对临床病理学会议（Clinicopathological Conferences, CPCs）这一长期用于训练和测试医学专家判断力的权威平台，如何有效衡量AI在复杂病例中的诊断推理过程及呈现能力。其关键解决方案是构建了CPC-Bench——一个涵盖10项文本与多模态任务的医师验证基准，并开发出“Dr. CaBot”这一AI讨论者模型，该模型仅基于病例陈述即可生成结构化文字与幻灯片视频报告，模拟人类专家的讲解逻辑与表达方式。实证表明，尽管LLMs在最终诊断准确性上超越了20名医生基线（如o3模型在60%案例中排名第一），且CaBot在盲测中被误判为人类撰写的概率高达74%，但在图像识别和文献检索等任务上仍存在明显短板，凸显出当前AI在医学多模态理解与知识整合方面的不足。

链接: https://arxiv.org/abs/2509.12194
作者: Thomas A. Buckley,Riccardo Conci,Peter G. Brodeur,Jason Gusdorf,Sourik Beltrán,Bita Behrouzi,Byron Crowe,Jacob Dockterman,Muzzammil Muhammad,Sarah Ohnigian,Andrew Sanchez,James A. Diao,Aashna P. Shah,Daniel Restrepo,Eric S. Rosenberg,Andrew S. Lea,Marinka Zitnik,Scott H. Podolsky,Zahir Kanjee,Raja-Elie E. Abdulnour,Jacob M. Koshy,Adam Rodman,Arjun K. Manrai
机构: 未知
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:BACKGROUND: For over a century, the New England Journal of Medicine Clinicopathological Conferences (CPCs) have tested the reasoning of expert physicians and, recently, artificial intelligence (AI). However, prior AI evaluations have focused on final diagnoses without addressing the multifaceted reasoning and presentation skills required of expert discussants. METHODS: Using 7102 CPCs (1923-2025) and 1021 Image Challenges (2006-2025), we conducted extensive physician annotation and automated processing to create CPC-Bench, a physician-validated benchmark spanning 10 text-based and multimodal tasks, against which we evaluated leading large language models (LLMs). Then, we developed “Dr. CaBot,” an AI discussant designed to produce written and slide-based video presentations using only the case presentation, modeling the role of the human expert in these cases. RESULTS: When challenged with 377 contemporary CPCs, o3 (OpenAI) ranked the final diagnosis first in 60% of cases and within the top ten in 84% of cases, outperforming a 20-physician baseline; next-test selection accuracy reached 98%. Event-level physician annotations quantified AI diagnostic accuracy per unit of information. Performance was lower on literature search and image tasks; o3 and Gemini 2.5 Pro (Google) achieved 67% accuracy on image challenges. In blinded comparisons of CaBot vs. human expert-generated text, physicians misclassified the source of the differential in 46 of 62 (74%) of trials, and scored CaBot more favorably across quality dimensions. To promote research, we are releasing CaBot and CPC-Bench. CONCLUSIONS: LLMs exceed physician performance on complex text-based differential diagnosis and convincingly emulate expert medical presentations, but image interpretation and literature retrieval remain weaker. CPC-Bench and CaBot may enable transparent and continued tracking of progress in medical AI. Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2509.12194 [cs.AI] (or arXiv:2509.12194v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2509.12194 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Arjun Manrai [view email] [v1] Mon, 15 Sep 2025 17:54:51 UTC (4,093 KB)
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[CV-5] Domain-Adaptive Pretraining Improves Primate Behavior Recognition CVPR2025

【速读】：该论文旨在解决动物行为识别中因标注成本高昂而导致大规模数据集难以构建的问题，从而限制了模型性能提升的瓶颈。其核心解决方案是采用自监督学习方法，特别是通过领域自适应预训练（Domain-Adaptive Pretraining, DAP）来显著提升灵长类行为识别的准确性。关键在于利用预训练的V-JEPA模型，并在目标领域（如大猿行为）的数据上继续进行无监督预训练，无需额外标注样本即可获得显著性能增益——实验表明，在PanAf和ChimpACT两个数据集上，准确率分别提升了6.1个百分点和6.3个百分点mAP，证明DAP是性能提升的主要来源。

链接: https://arxiv.org/abs/2509.12193
作者: Felix B. Mueller,Timo Lueddecke,Richard Vogg,Alexander S. Ecker
机构: University of Göttingen (哥廷根大学); Max Planck Institute for Dynamics and Self-Organization (马克斯·普朗克复杂系统动力学与自组织研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Oral at the CVPR 2025 Workshop CV4Animals

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Abstract:Computer vision for animal behavior offers promising tools to aid research in ecology, cognition, and to support conservation efforts. Video camera traps allow for large-scale data collection, but high labeling costs remain a bottleneck to creating large-scale datasets. We thus need data-efficient learning approaches. In this work, we show that we can utilize self-supervised learning to considerably improve action recognition on primate behavior. On two datasets of great ape behavior (PanAf and ChimpACT), we outperform published state-of-the-art action recognition models by 6.1 %pt. accuracy and 6.3 %pt. mAP, respectively. We achieve this by utilizing a pretrained V-JEPA model and applying domain-adaptive pretraining (DAP), i.e. continuing the pretraining with in-domain data. We show that most of the performance gain stems from the DAP. Our method promises great potential for improving the recognition of animal behavior, as DAP does not require labeled samples. Code is available at this https URL
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[CV-6] HoloGarment: 360° Novel View Synthesis of In-the-Wild Garments

【速读】：该论文旨在解决真实场景中服装的 novel view synthesis (NVS) 问题，其挑战在于显著的遮挡、复杂的人体姿态以及衣物形变，而现有方法因依赖合成的3D训练数据（通常为无遮挡且静态对象）导致在真实服装上的泛化能力差。解决方案的关键在于提出一种名为 HoloGarment 的新方法，其核心是通过结合大规模真实视频数据与小规模合成3D数据，设计了一种新颖的隐式训练范式，以优化一个共享的服装嵌入空间（garment embedding space）。该嵌入空间在推理阶段进一步支持动态视频到360° NVS，通过微调特定真实视频的服装嵌入构建“atlas”表示，从而捕捉与人体姿态或运动无关的服装几何与纹理信息，实现对褶皱、姿态变化和遮挡等现实因素的鲁棒处理，并保持高保真度的视觉一致性与细节精度。

链接: https://arxiv.org/abs/2509.12187
作者: Johanna Karras,Yingwei Li,Yasamin Jafarian,Ira Kemelmacher-Shlizerman
机构: University of Washington (华盛顿大学); Google(谷歌)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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Abstract:Novel view synthesis (NVS) of in-the-wild garments is a challenging task due significant occlusions, complex human poses, and cloth deformations. Prior methods rely on synthetic 3D training data consisting of mostly unoccluded and static objects, leading to poor generalization on real-world clothing. In this paper, we propose HoloGarment (Hologram-Garment), a method that takes 1-3 images or a continuous video of a person wearing a garment and generates 360° novel views of the garment in a canonical pose. Our key insight is to bridge the domain gap between real and synthetic data with a novel implicit training paradigm leveraging a combination of large-scale real video data and small-scale synthetic 3D data to optimize a shared garment embedding space. During inference, the shared embedding space further enables dynamic video-to-360° NVS through the construction of a garment “atlas” representation by finetuning a garment embedding on a specific real-world video. The atlas captures garment-specific geometry and texture across all viewpoints, independent of body pose or motion. Extensive experiments show that HoloGarment achieves state-of-the-art performance on NVS of in-the-wild garments from images and videos. Notably, our method robustly handles challenging real-world artifacts – such as wrinkling, pose variation, and occlusion – while maintaining photorealism, view consistency, fine texture details, and accurate geometry. Visit our project page for additional results: this https URL
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[CV-7] LoRA-fine-tuned Large Vision Models for Automated Assessment of Post-SBRT Lung Injury

【速读】：该论文旨在解决大型视觉模型（Large Vision Models, LVMs）在诊断放射性肺损伤（Radiation-Induced Lung Injury, RILI）时的计算效率与性能平衡问题，尤其是在基于立体定向体部放疗（Stereotactic Body Radiation Therapy, SBRT）后X射线CT图像中的应用。解决方案的关键在于采用低秩适配（Low-Rank Adaptation, LoRA）技术对DinoV2和SwinV2等预训练LVMs进行微调，该方法通过引入低秩矩阵更新而非全参数微调，在保持甚至超越传统全量微调性能的同时，大幅降低可训练参数数量，从而显著减少计算开销与训练时间，提升了模型在医学影像分析任务中的实用性与部署效率。

链接: https://arxiv.org/abs/2509.12155
作者: M. Bolhassani,B. Veasey,E. Daugherty,S. Keltner,N. Kumar,N. Dunlap,A. Amini
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This study investigates the efficacy of Low-Rank Adaptation (LoRA) for fine-tuning large Vision Models, DinoV2 and SwinV2, to diagnose Radiation-Induced Lung Injury (RILI) from X-ray CT scans following Stereotactic Body Radiation Therapy (SBRT). To evaluate the robustness and efficiency of this approach, we compare LoRA with traditional full fine-tuning and inference-only (no fine-tuning) methods. Cropped images of two sizes (50 mm3 and 75 mm3), centered at the treatment isocenter, in addition to different adaptation techniques for adapting the 2D LVMs for 3D data were used to determine the sensitivity of the models to spatial context. Experimental results show that LoRA achieves comparable or superior performance to traditional fine-tuning while significantly reducing computational costs and training times by requiring fewer trainable parameters.
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[CV-8] Multi Anatomy X-Ray Foundation Model

【速读】：该论文旨在解决当前生成式 AI (Generative AI) 基础模型在放射学中普遍局限于胸部解剖结构、难以跨不同临床任务泛化的问题。其解决方案的关键在于构建一个名为 XR-0 的多部位 X 射线基础模型，该模型基于包含 115 万张图像的大规模私有数据集，采用自监督学习方法进行训练，覆盖多种解剖区域，并在 12 个数据集和 20 项下游任务（包括分类、检索、分割、定位、视觉定位及报告生成）上进行了系统评估。结果表明，解剖多样性与监督策略是构建鲁棒且通用的医学视觉模型的核心要素，为可扩展、可适应的放射科人工智能系统奠定了基础。

链接: https://arxiv.org/abs/2509.12146
作者: Nishank Singla,Krisztian Koos,Farzin Haddadpour,Amin Honarmandi Shandiz,Lovish Chum,Xiaojian Xu,Qing Jin,Erhan Bas
机构: GE HealthCare(通用电气医疗)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: This work has been submitted to the IEEE for possible publication

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Abstract:X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work, we introduce XR-0, the multi-anatomy X-ray foundation model using self-supervised learning on a large, private dataset of 1.15 million images spanning diverse anatomical regions and evaluated across 12 datasets and 20 downstream tasks, including classification, retrieval, segmentation, localization, visual grounding, and report generation. XR-0 achieves state-of-the-art performance on most multi-anatomy tasks and remains competitive on chest-specific benchmarks. Our results demonstrate that anatomical diversity and supervision are critical for building robust, general-purpose medical vision models, paving the way for scalable and adaptable AI systems in radiology.
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[CV-9] Open-ended Hierarchical Streaming Video Understanding with Vision Language Models

【速读】：该论文旨在解决流式视频理解中在线时间动作定位与自由形式描述生成相结合的难题，尤其针对缺乏层次化和细粒度时间标注数据的问题。解决方案的关键在于提出OpenHOUSE（Open-ended Hierarchical Online Understanding System for Events），其核心创新是引入一个专门设计的流式模块，能够精准检测相邻动作之间的边界，显著提升性能（近乎翻倍优于现有方法的直接扩展），并利用大语言模型（LLM）将原子动作分组为更高层次事件，从而增强现有数据集的语义层次结构。

链接: https://arxiv.org/abs/2509.12145
作者: Hyolim Kang,Yunsu Park,Youngbeom Yoo,Yeeun Choi,Seon Joo Kim
机构: Yonsei University (延世大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages

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Abstract:We introduce Hierarchical Streaming Video Understanding, a task that combines online temporal action localization with free-form description generation. Given the scarcity of datasets with hierarchical and fine-grained temporal annotations, we demonstrate that LLMs can effectively group atomic actions into higher-level events, enriching existing datasets. We then propose OpenHOUSE (Open-ended Hierarchical Online Understanding System for Events), which extends streaming action perception beyond action classification. OpenHOUSE features a specialized streaming module that accurately detects boundaries between closely adjacent actions, nearly doubling the performance of direct extensions of existing methods. We envision the future of streaming action perception in the integration of powerful generative models, with OpenHOUSE representing a key step in that direction.
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[CV-10] 3DViT-GAT: A Unified Atlas-Based 3D Vision Transformer and Graph Learning Framework for Major Depressive Disorder Detection Using Structural MRI Data

【速读】：该论文旨在解决抑郁症（Major Depressive Disorder, MDD）自动检测中因传统方法依赖于体素级特征或基于预定义脑图谱的手工区域表示，难以捕捉复杂脑部结构模式的问题。其解决方案的关键在于构建一个统一的深度学习框架：首先利用视觉Transformer（Vision Transformer, ViT）从结构磁共振成像（sMRI）数据中提取三维区域嵌入（3D region embeddings），随后通过图神经网络（Graph Neural Network, GNN）进行分类；同时引入余弦相似性图建模区域间关系以指导分类，实验表明基于脑图谱定义区域的方法优于直接从均匀3D补丁中学习区域的立方体方法，凸显了引入领域特定解剖先验的重要性。

链接: https://arxiv.org/abs/2509.12143
作者: Nojod M. Alotaibi,Areej M. Alhothali,Manar S. Ali
机构: King Abdulaziz University (国王阿卜杜勒阿齐兹大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Automated detection of MDD using structural magnetic resonance imaging (sMRI) and deep learning (DL) methods holds increasing promise for improving diagnostic accuracy and enabling early intervention. Most existing methods employ either voxel-level features or handcrafted regional representations built from predefined brain atlases, limiting their ability to capture complex brain patterns. This paper develops a unified pipeline that utilizes Vision Transformers (ViTs) for extracting 3D region embeddings from sMRI data and Graph Neural Network (GNN) for classification. We explore two strategies for defining regions: (1) an atlas-based approach using predefined structural and functional brain atlases, and (2) an cube-based method by which ViTs are trained directly to identify regions from uniformly extracted 3D patches. Further, cosine similarity graphs are generated to model interregional relationships, and guide GNN-based classification. Extensive experiments were conducted using the REST-meta-MDD dataset to demonstrate the effectiveness of our model. With stratified 10-fold cross-validation, the best model obtained 78.98% accuracy, 76.54% sensitivity, 81.58% specificity, 81.58% precision, and 78.98% F1-score. Further, atlas-based models consistently outperformed the cube-based approach, highlighting the importance of using domain-specific anatomical priors for MDD detection.
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[CV-11] RailSafeNet: Visual Scene Understanding for Tram Safety

【速读】：该论文旨在解决城市轨道交通中人车交互安全问题，尤其关注电车（tram）在高密度人群区域运行时可能引发的碰撞风险，从行人、驾驶员、骑行者、宠物到乘客等多类对象的安全保障。其解决方案的关键在于提出一种名为RailSafeNet的实时框架，该框架融合语义分割（semantic segmentation）、目标检测（object detection）与基于规则的距离评估模块（rule-based Distance Assessor），仅依赖单目视频输入即可实现对轨道入侵行为的识别与风险分级：首先利用改进的SegFormer B3模型进行场景语义解析，再通过微调后的YOLOv8实现高精度目标检测（mAP达75.6%），最终结合标准轨距1435mm对比投影距离判断潜在危险，从而提前预警驾驶人员，提升整体运行安全性。

链接: https://arxiv.org/abs/2509.12125
作者: Ing. Ondrej Valach,Ing. Ivan Gruber
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Tram-human interaction safety is an important challenge, given that trams frequently operate in densely populated areas, where collisions can range from minor injuries to fatal outcomes. This paper addresses the issue from the perspective of designing a solution leveraging digital image processing, deep learning, and artificial intelligence to improve the safety of pedestrians, drivers, cyclists, pets, and tram passengers. We present RailSafeNet, a real-time framework that fuses semantic segmentation, object detection and a rule-based Distance Assessor to highlight track intrusions. Using only monocular video, the system identifies rails, localises nearby objects and classifies their risk by comparing projected distances with the standard 1435mm rail gauge. Experiments on the diverse RailSem19 dataset show that a class-filtered SegFormer B3 model achieves 65% intersection-over-union (IoU), while a fine-tuned YOLOv8 attains 75.6% mean average precision (mAP) calculated at an intersection over union (IoU) threshold of 0.50. RailSafeNet therefore delivers accurate, annotation-light scene understanding that can warn drivers before dangerous situations escalate. Code available at this https URL.
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[CV-12] FS-SAM2: Adapting Segment Anything Model 2 for Few-Shot Semantic Segmentation via Low-Rank Adaptation

【速读】：该论文旨在解决少样本语义分割（Few-shot Semantic Segmentation）问题，即如何在仅提供少量标注样本的情况下，使模型具备对未见类别的分割能力。传统方法通常通过从头训练额外模块来微调预训练模型，但这类方法依赖大规模数据集进行充分训练，计算成本高且难以适应多样化的图像分布。本文提出基于Segment Anything Model 2（SAM2）的少样本分割方法FS-SAM2，其关键创新在于直接复用SAM2的视频分割模块以处理图像输入，并引入低秩适配（Low-Rank Adaptation, LoRA）策略对原始模块进行轻量级参数调整，从而在保持SAM2强大分割性能的同时，显著减少需元训练的参数量，有效应对标准数据集中图像多样性带来的挑战。该方案支持任意K-shot配置，在PASCAL-5^i、COCO-20^i和FSS-1000等基准上取得优异效果，且推理阶段具有良好的计算效率。

链接: https://arxiv.org/abs/2509.12105
作者: Bernardo Forni,Gabriele Lombardi,Federico Pozzi,Mirco Planamente
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Few-shot semantic segmentation has recently attracted great attention. The goal is to develop a model capable of segmenting unseen classes using only a few annotated samples. Most existing approaches adapt a pre-trained model by training from scratch an additional module. Achieving optimal performance with these approaches requires extensive training on large-scale datasets. The Segment Anything Model 2 (SAM2) is a foundational model for zero-shot image and video segmentation with a modular design. In this paper, we propose a Few-Shot segmentation method based on SAM2 (FS-SAM2), where SAM2’s video capabilities are directly repurposed for the few-shot task. Moreover, we apply a Low-Rank Adaptation (LoRA) to the original modules in order to handle the diverse images typically found in standard datasets, unlike the temporally connected frames used in SAM2’s pre-training. With this approach, only a small number of parameters is meta-trained, which effectively adapts SAM2 while benefiting from its impressive segmentation performance. Our method supports any K-shot configuration. We evaluate FS-SAM2 on the PASCAL-5 ^i , COCO-20 ^i and FSS-1000 datasets, achieving remarkable results and demonstrating excellent computational efficiency during inference. Code is available at this https URL
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[CV-13] End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI

【速读】：该论文旨在解决从心脏电影磁共振成像（cine CMR）序列中重建心脏运动的问题，尤其针对术中（intra-procedural）场景下仅能获取稀疏切片数据时传统方法无法有效恢复完整4D多结构心脏网格的局限性。其解决方案的关键在于提出TetHeart框架，该框架首次实现了从离线全栈数据到术中稀疏切片观测的端到端统一建模：通过深度可变形四面体（deep deformable tetrahedra）构建显式-隐式混合表示，以在共享空间中一致地捕捉心脏各结构的形状与运动；同时引入注意力机制实现切片自适应的2D-3D特征融合，并结合蒸馏策略从全切片到稀疏切片迁移知识，确保极端稀疏条件下的高精度重建；此外，采用两阶段弱监督运动学习方案，仅需心室舒张末期（ED）和收缩末期（ES）关键帧标注即可完成训练，显著降低标注成本并提升泛化能力。

链接: https://arxiv.org/abs/2509.12090
作者: Yihong Chen,Jiancheng Yang,Deniz Sayin Mercadier,Hieu Le,Juerg Schwitter,Pascal Fua
机构: Swiss Federal Institute of Technology Lausanne (EPFL)(瑞士联邦理工学院洛桑分校); UNC-Charlotte(北卡罗来纳大学夏洛特分校); University Hospital Lausanne (CHUV)(洛桑大学医院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Reconstructing cardiac motion from cine CMR sequences is critical for diagnosis, prediction, and intervention. Existing methods rely on complete CMR stacks to infer full heart motion, limiting their utility in intra-procedural scenarios where only sparse observations are available. We present TetHeart, the first end-to-end framework that unifies full 4D multi-structure heart mesh recovery from both offline full-stack acquisitions and intra-procedural sparse-slice observations. Our method leverages deep deformable tetrahedra, an explicit-implicit hybrid representation, to capture shape and motion in a coherent space shared across cardiac structures. It is initialized from high-quality pre-procedural or offline-acquired full stacks to build detailed, patient-specific heart meshes, which can then be updated using whatever slices are available, from full stacks down to a single slice. We further incorporate several key innovations: (i) an attentive mechanism for slice-adaptive 2D-3D feature assembly that dynamically integrates information from arbitrary numbers of slices at any position, combined with a distillation strategy from full-slice to sparse-slice settings to ensure accurate reconstruction under extreme sparsity; and (ii) a two-stage weakly supervised motion learning scheme requiring only keyframe (e.g., ED and ES) annotations. Trained and validated on three large public datasets and externally evaluated zero-shot on additional private interventional and public CMR datasets, TetHeart achieves state-of-the-art accuracy and strong generalization in both pre- and intra-procedural settings.
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[CV-14] Progressive Flow-inspired Unfolding for Spectral Compressive Imaging

【速读】：该论文旨在解决编码孔径快照式光谱成像（Coded Aperture Snapshot Spectral Imaging, CASSI）中重建质量不稳定的问题，即现有深度展开网络（Deep Unfolding Networks, DUNs）在迭代过程中存在不可控的重建轨迹，导致各阶段重构质量出现突变、缺乏渐进式优化。其解决方案的关键在于提出一种轨迹可控的新型展开框架，通过借鉴扩散轨迹（diffusion trajectories）和流匹配（flow matching）的思想，强制从噪声初始估计到高质量重建之间形成平滑、连续的优化路径；同时设计了一个面向高光谱重建的高效空间-光谱Transformer以及频域融合模块，以提升计算效率并保障特征一致性，从而实现更优的重建质量和收敛稳定性。

链接: https://arxiv.org/abs/2509.12079
作者: Xiaodong Wang,Ping Wang,Zijun He,Mengjie Qin,Xin Yuan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Coded aperture snapshot spectral imaging (CASSI) retrieves a 3D hyperspectral image (HSI) from a single 2D compressed measurement, which is a highly challenging reconstruction task. Recent deep unfolding networks (DUNs), empowered by explicit data-fidelity updates and implicit deep denoisers, have achieved the state of the art in CASSI reconstruction. However, existing unfolding approaches suffer from uncontrollable reconstruction trajectories, leading to abrupt quality jumps and non-gradual refinement across stages. Inspired by diffusion trajectories and flow matching, we propose a novel trajectory-controllable unfolding framework that enforces smooth, continuous optimization paths from noisy initial estimates to high-quality reconstructions. To achieve computational efficiency, we design an efficient spatial-spectral Transformer tailored for hyperspectral reconstruction, along with a frequency-domain fusion module to gurantee feature consistency. Experiments on simulation and real data demonstrate that our method achieves better reconstruction quality and efficiency than prior state-of-the-art approaches.
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[CV-15] Early Detection of Branched Broomrape (Phelipanche ramosa) Infestation in Tomato Crops Using Leaf Spectral Analysis and Machine Learning

【速读】：该论文旨在解决向日葵（番茄）作物中恶性寄生杂草——分枝列当（Phelipanche ramosa）的早期检测难题，以减少其对宿主植物营养吸收造成的产量损失。解决方案的关键在于利用叶级光谱反射率（400–2500 nm）结合集成学习（ensemble machine learning）方法，在田间条件下实现早期、无损识别感染植株。研究通过便携式光谱仪采集数据，并采用预处理技术（如波段去噪、插值、Savitzky-Golay平滑及基于相关性的波段筛选），发现1500 nm和2000 nm附近水吸收特征在感染初期具有显著差异，表明受侵植株叶片含水量降低。最终构建的集成模型（融合随机森林、XGBoost、支持向量机（SVM）与高斯核函数、朴素贝叶斯）在585 GDD时达到89%准确率，且对感染和健康植株的召回率分别为0.86和0.93，证明该方法可在冠层症状显现前有效识别病害，为精准防控提供技术支持。

链接: https://arxiv.org/abs/2509.12074
作者: Mohammadreza Narimani,Alireza Pourreza,Ali Moghimi,Parastoo Farajpoor,Hamid Jafarbiglu,Mohsen B. Mesgaran
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Signal Processing (eess.SP)
备注: Author-accepted version. Accepted and presented at AGRICONTROL 2025 (8th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture), UC Davis, USA. To appear in IFAC-PapersOnLine (Elsevier)

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Abstract:Branched broomrape (Phelipanche ramosa) is a chlorophyll-deficient parasitic weed that threatens tomato production by extracting nutrients from the host. We investigate early detection using leaf-level spectral reflectance (400-2500 nm) and ensemble machine learning. In a field experiment in Woodland, California, we tracked 300 tomato plants across growth stages defined by growing degree days (GDD). Leaf reflectance was acquired with a portable spectrometer and preprocessed (band denoising, 1 nm interpolation, Savitzky-Golay smoothing, correlation-based band reduction). Clear class differences were observed near 1500 nm and 2000 nm water absorption features, consistent with reduced leaf water content in infected plants at early stages. An ensemble combining Random Forest, XGBoost, SVM with RBF kernel, and Naive Bayes achieved 89% accuracy at 585 GDD, with recalls of 0.86 (infected) and 0.93 (noninfected). Accuracy declined at later stages (e.g., 69% at 1568 GDD), likely due to senescence and weed interference. Despite the small number of infected plants and environmental confounders, results show that proximal sensing with ensemble learning enables timely detection of broomrape before canopy symptoms are visible, supporting targeted interventions and reduced yield losses.
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[CV-16] U-Mamba2: Scaling State Space Models for Dental Anatomy Segmentation in CBCT

【速读】：该论文旨在解决锥形束计算机断层扫描（Cone-Beam Computed Tomography, CBCT）中多牙体解剖结构精准分割的难题，该任务在口腔临床诊断与手术规划中至关重要，但传统方法存在效率低、精度不足的问题。解决方案的关键在于提出U-Mamba2网络架构，其创新性地将Mamba2状态空间模型嵌入U-Net框架，强化了结构约束以提升计算效率而不牺牲性能；同时引入交互式点击提示与交叉注意力模块、采用自监督预训练策略，并融合牙科领域知识，从而有效应对CBCT图像中复杂解剖结构分割的挑战。

链接: https://arxiv.org/abs/2509.12069
作者: Zhi Qin Tan,Xiatian Zhu,Owen Addison,Yunpeng Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Cone-Beam Computed Tomography (CBCT) is a widely used 3D imaging technique in dentistry, providing volumetric information about the anatomical structures of jaws and teeth. Accurate segmentation of these anatomies is critical for clinical applications such as diagnosis and surgical planning, but remains time-consuming and challenging. In this paper, we present U-Mamba2, a new neural network architecture designed for multi-anatomy CBCT segmentation in the context of the ToothFairy3 challenge. U-Mamba2 integrates the Mamba2 state space models into the U-Net architecture, enforcing stronger structural constraints for higher efficiency without compromising performance. In addition, we integrate interactive click prompts with cross-attention blocks, pre-train U-Mamba2 using self-supervised learning, and incorporate dental domain knowledge into the model design to address key challenges of dental anatomy segmentation in CBCT. Extensive experiments, including independent tests, demonstrate that U-Mamba2 is both effective and efficient, securing top 3 places in both tasks of the Toothfairy3 challenge. In Task 1, U-Mamba2 achieved a mean Dice of 0.792, HD95 of 93.19 with the held-out test data, with an average inference time of XX (TBC during the ODIN workshop). In Task 2, U-Mamba2 achieved the mean Dice of 0.852 and HD95 of 7.39 with the held-out test data. The code is publicly available at this https URL.
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[CV-17] End-to-End Learning of Multi-Organ Implicit Surfaces from 3D Medical Imaging Data

【速读】：该论文旨在解决从3D医学影像中实现高分辨率器官表面重建的难题，传统方法受限于图像分辨率，高精度重建往往需要巨大的内存和计算资源。解决方案的关键在于提出ImplMORe，一种基于隐式表面表示（implicit surface representations）的端到端深度学习方法，通过引入3D卷积神经网络（3D CNN）编码器提取局部特征，并结合多尺度插值技术在连续域中学习占用函数（occupancy functions），从而以紧凑且可微的方式表征器官形状。该方法利用占用函数的连续特性，在不增加输入图像分辨率的情况下，实现了优于离散显式表示方法的精细表面重建效果。

链接: https://arxiv.org/abs/2509.12068
作者: Farahdiba Zarin,Nicolas Padoy,Jérémy Dana,Vinkle Srivastav
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The fine-grained surface reconstruction of different organs from 3D medical imaging can provide advanced diagnostic support and improved surgical planning. However, the representation of the organs is often limited by the resolution, with a detailed higher resolution requiring more memory and computing footprint. Implicit representations of objects have been proposed to alleviate this problem in general computer vision by providing compact and differentiable functions to represent the 3D object shapes. However, architectural and data-related differences prevent the direct application of these methods to medical images. This work introduces ImplMORe, an end-to-end deep learning method using implicit surface representations for multi-organ reconstruction from 3D medical images. ImplMORe incorporates local features using a 3D CNN encoder and performs multi-scale interpolation to learn the features in the continuous domain using occupancy functions. We apply our method for single and multiple organ reconstructions using the totalsegmentator dataset. By leveraging the continuous nature of occupancy functions, our approach outperforms the discrete explicit representation based surface reconstruction approaches, providing fine-grained surface details of the organ at a resolution higher than the given input image. The source code will be made publicly available at: this https URL
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[CV-18] Robust Fetal Pose Estimation across Gestational Ages via Cross-Population Augmentation MICCAI2025

【速读】：该论文旨在解决胎儿运动（fetal motion）在早期妊娠年龄（gestational age, GA）下难以准确量化的问题，尤其是在早期GA的4D胎儿超声成像中，由于母体和胎儿解剖结构随孕周显著变化，且早期GA标注数据稀缺，现有基于3D回波平面成像（EPI）时间序列的地标预测方法无法有效泛化。解决方案的关键在于提出一种跨人群数据增强框架（cross-population data augmentation framework），其核心创新是引入胎儿特异性增强策略，模拟早期GA特有的宫内环境与胎儿体位特征，从而仅使用高龄GA标注图像即可训练出对低龄GA群体具有鲁棒性的姿态估计模型，显著提升不同孕周下的姿态估计准确性与稳定性。

链接: https://arxiv.org/abs/2509.12062
作者: Sebastian Diaz,Benjamin Billot,Neel Dey,Molin Zhang,Esra Abaci Turk,P. Ellen Grant,Polina Golland,Elfar Adalsteinsson
机构: Massachusetts General Hospital (马萨诸塞州总医院); Harvard Medical School (哈佛医学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted MICCAI 2025

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Abstract:Fetal motion is a critical indicator of neurological development and intrauterine health, yet its quantification remains challenging, particularly at earlier gestational ages (GA). Current methods track fetal motion by predicting the location of annotated landmarks on 3D echo planar imaging (EPI) time-series, primarily in third-trimester fetuses. The predicted landmarks enable simplification of the fetal body for downstream analysis. While these methods perform well within their training age distribution, they consistently fail to generalize to early GAs due to significant anatomical changes in both mother and fetus across gestation, as well as the difficulty of obtaining annotated early GA EPI data. In this work, we develop a cross-population data augmentation framework that enables pose estimation models to robustly generalize to younger GA clinical cohorts using only annotated images from older GA cohorts. Specifically, we introduce a fetal-specific augmentation strategy that simulates the distinct intrauterine environment and fetal positioning of early GAs. Our experiments find that cross-population augmentation yields reduced variability and significant improvements across both older GA and challenging early GA cases. By enabling more reliable pose estimation across gestation, our work potentially facilitates early clinical detection and intervention in challenging 4D fetal imaging settings. Code is available at this https URL.
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[CV-19] AvatarSync: Rethinking Talking-Head Animation through Autoregressive Perspective

【速读】：该论文旨在解决现有基于生成对抗网络（Generative Adversarial Networks, GANs）或扩散模型的说话头动画方法中存在的帧间闪烁（inter-frame flicker）、身份漂移（identity drift）以及推理速度慢等问题，这些问题限制了其在实际应用中的可用性。解决方案的关键在于提出一种自回归框架 AvatarSync，其核心创新是采用两阶段生成策略：第一阶段为面部关键帧生成（Facial Keyframe Generation, FKG），通过文本或音频到音素（phoneme）的多对一映射构建语义表征，并利用定制化的文本-帧因果注意力掩码生成高质量关键帧；第二阶段为帧间插值，引入基于选择性状态空间模型的时间戳感知自适应策略，实现高效的双向上下文推理，从而保障时序一致性和视觉平滑性。该设计实现了语义建模与视觉动态的解耦，显著提升了生成质量、稳定性和计算效率。

链接: https://arxiv.org/abs/2509.12052
作者: Yuchen Deng,Xiuyang Wu,Hai-Tao Zheng,Suiyang Zhang,Yi He,Yuxing Han
机构: Shenzhen International Graduate School, Tsinghua University (清华大学深圳国际研究生院); Pengcheng Laboratory (鹏城实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Existing talking-head animation approaches based on Generative Adversarial Networks (GANs) or diffusion models often suffer from inter-frame flicker, identity drift, and slow inference. These limitations inherent to their video generation pipelines restrict their suitability for applications. To address this, we introduce AvatarSync, an autoregressive framework on phoneme representations that generates realistic and controllable talking-head animations from a single reference image, driven directly text or audio input. In addition, AvatarSync adopts a two-stage generation strategy, decoupling semantic modeling from visual dynamics, which is a deliberate “Divide and Conquer” design. The first stage, Facial Keyframe Generation (FKG), focuses on phoneme-level semantic representation by leveraging the many-to-one mapping from text or audio to phonemes. A Phoneme-to-Visual Mapping is constructed to anchor abstract phonemes to character-level units. Combined with a customized Text-Frame Causal Attention Mask, the keyframes are generated. The second stage, inter-frame interpolation, emphasizes temporal coherence and visual smoothness. We introduce a timestamp-aware adaptive strategy based on a selective state space model, enabling efficient bidirectional context reasoning. To support deployment, we optimize the inference pipeline to reduce latency without compromising visual fidelity. Extensive experiments show that AvatarSync outperforms existing talking-head animation methods in visual fidelity, temporal consistency, and computational efficiency, providing a scalable and controllable solution.
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[CV-20] A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset

【速读】：该论文旨在解决农业环境中动物行为分析依赖人工观察所带来的效率低、主观性强及可扩展性差的问题。其核心解决方案是一套模块化流水线，关键在于融合了零样本目标检测（zero-shot object detection）、基于运动感知的跟踪与分割技术，以及利用视觉变换器（vision transformer）进行高级特征提取，从而实现对群体饲养环境下动物行为的鲁棒识别。该方法在猪的行为监测中验证有效，显著提升了准确率与跟踪稳定性，为精准养猪和动物福利评估提供了自动化、客观且连续的分析手段。

链接: https://arxiv.org/abs/2509.12047
作者: Haiyu Yang,Enhong Liu,Jennifer Sun,Sumit Sharma,Meike van Leerdam,Sebastien Franceschini,Puchun Niu,Miel Hostens
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 9 figures, Submitted to Computers and Electronics in Agriculture

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Abstract:Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. However, traditional manual observation methods are time-consuming, subjective, and limited in scalability. We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment. Our approach combines state-of-the-art models for zero-shot object detection, motion-aware tracking and segmentation, and advanced feature extraction using vision transformers for robust behavior recognition. The pipeline addresses challenges including animal occlusions and group housing scenarios as demonstrated in indoor pig monitoring. We validated our system on the Edinburgh Pig Behavior Video Dataset for multiple behavioral tasks. Our temporal model achieved 94.2% overall accuracy, representing a 21.2 percentage point improvement over existing methods. The pipeline demonstrated robust tracking capabilities with 93.3% identity preservation score and 89.3% object detection precision. The modular design suggests potential for adaptation to other contexts, though further validation across species would be required. The open-source implementation provides a scalable solution for behavior monitoring, contributing to precision pig farming and welfare assessment through automated, objective, and continuous analysis.
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[CV-21] Layout-Conditioned Autoregressive Text-to-Image Generation via Structured Masking

【速读】：该论文旨在解决自回归（Autoregressive, AR）模型在布局条件图像生成（layout-conditioned image generation）中面临的两大挑战：一是布局条件的稀疏性导致模型难以有效利用空间约束，二是特征纠缠问题可能引发不同区域与其描述之间的错误关联。解决方案的关键在于提出一种结构化掩码机制（Structured Masking for AR-based Layout-to-Image, SMARLI），通过设计特定的结构化掩码策略来调控全局提示、布局和图像标记之间的注意力交互，从而防止区域间误关联并充分注入布局约束；此外，还引入基于组相对策略优化（Group Relative Policy Optimization, GRPO）的后训练方案，结合专门设计的布局奖励函数，进一步提升生成质量和布局准确性。

链接: https://arxiv.org/abs/2509.12046
作者: Zirui Zheng,Takashi Isobe,Tong Shen,Xu Jia,Jianbin Zhao,Xiaomin Li,Mengmeng Ge,Baolu Li,Qinghe Wang,Dong Li,Dong Zhou,Yunzhi Zhuge,Huchuan Lu,Emad Barsoum
机构: 1. Shanghai Jiao Tong University (上海交通大学); 2. Alibaba Group (阿里巴巴集团)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 3 figures

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Abstract:While autoregressive (AR) models have demonstrated remarkable success in image generation, extending them to layout-conditioned generation remains challenging due to the sparse nature of layout conditions and the risk of feature entanglement. We present Structured Masking for AR-based Layout-to-Image (SMARLI), a novel framework for layoutto-image generation that effectively integrates spatial layout constraints into AR-based image generation. To equip AR model with layout control, a specially designed structured masking strategy is applied to attention computation to govern the interaction among the global prompt, layout, and image tokens. This design prevents mis-association between different regions and their descriptions while enabling sufficient injection of layout constraints into the generation process. To further enhance generation quality and layout accuracy, we incorporate Group Relative Policy Optimization (GRPO) based post-training scheme with specially designed layout reward functions for next-set-based AR models. Experimental results demonstrate that SMARLI is able to seamlessly integrate layout tokens with text and image tokens without compromising generation quality. It achieves superior layoutaware control while maintaining the structural simplicity and generation efficiency of AR models.
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[CV-22] Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing

【速读】：该论文旨在解决开放词汇遥感图像分割（Open-Vocabulary Remote Sensing Image Segmentation, OVRSIS）任务中缺乏统一评估基准和自然图像与遥感图像之间域差异导致模型性能受限的问题。为应对这些挑战，作者首先构建了一个标准化的OVRSIS基准（OVRSISBench），基于广泛使用的遥感分割数据集实现方法间的公平比较；在此基础上，系统评估了多个代表性开放词汇分割模型，揭示其在遥感场景下的局限性。解决方案的核心在于提出一种专为遥感设计的新框架RSKT-Seg，其关键创新包括：(1) 多方向代价图聚合模块（RS-CMA），通过多方向视觉-语言余弦相似度计算捕捉旋转不变特征；(2) 高效代价图融合变压器（RS-Fusion），以轻量化维度压缩策略联合建模空间与语义依赖；(3) 遥感知识迁移模块（RS-Transfer），利用预训练知识增强上采样过程并促进域适应。实验表明，RSKT-Seg在OVRSISBench上相较强基线提升+3.8 mIoU和+5.9 mACC，同时推理速度提高2倍。

链接: https://arxiv.org/abs/2509.12040
作者: Bingyu Li,Haocheng Dong,Da Zhang,Zhiyuan Zhao,Junyu Gao,Xuelong Li
机构: TeleAI; University of Science and Technology of China (中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Open-Vocabulary Remote Sensing Image Segmentation (OVRSIS), an emerging task that adapts Open-Vocabulary Segmentation (OVS) to the remote sensing (RS) domain, remains underexplored due to the absence of a unified evaluation benchmark and the domain gap between natural and RS images. To bridge these gaps, we first establish a standardized OVRSIS benchmark (\textbfOVRSISBench) based on widely-used RS segmentation datasets, enabling consistent evaluation across methods. Using this benchmark, we comprehensively evaluate several representative OVS/OVRSIS models and reveal their limitations when directly applied to remote sensing scenarios. Building on these insights, we propose \textbfRSKT-Seg, a novel open-vocabulary segmentation framework tailored for remote sensing. RSKT-Seg integrates three key components: (1) a Multi-Directional Cost Map Aggregation (RS-CMA) module that captures rotation-invariant visual cues by computing vision-language cosine similarities across multiple directions; (2) an Efficient Cost Map Fusion (RS-Fusion) transformer, which jointly models spatial and semantic dependencies with a lightweight dimensionality reduction strategy; and (3) a Remote Sensing Knowledge Transfer (RS-Transfer) module that injects pre-trained knowledge and facilitates domain adaptation via enhanced upsampling. Extensive experiments on the benchmark show that RSKT-Seg consistently outperforms strong OVS baselines by +3.8 mIoU and +5.9 mACC, while achieving 2x faster inference through efficient aggregation. Our code is \hrefthis https URL\textcolorbluehere.
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[CV-23] RAM: Robust Representation Learning via Adaptive Mask for All-in-One Image Restoration

【速读】：该论文旨在解决现有图像恢复方法在极端退化场景下性能下降的问题，特别是当退化与图像结构强耦合时，传统基于退化导向的方法难以实现内容感知的鲁棒恢复。此外，还面临任务间性能不平衡、对已见退化过拟合以及对未见退化泛化能力弱等挑战。解决方案的关键在于提出一种两阶段框架RAM++，其核心创新包括：1）自适应语义感知掩码（AdaSAM），通过像素级掩码策略在预训练阶段聚焦于语义丰富区域，使网络同时学习生成先验和内容先验；2）掩码属性导通（MAC），一种选择性微调机制，优化高贡献层以弥合掩码预训练与全图微调之间的完整性差距，同时保留已学先验；3）鲁棒特征正则化（RFR），利用DINOv2提取的语义一致且退化不变特征进行高效融合，确保恢复结果在语义层面忠实且连贯。这些设计共同实现了对已见、未见、极端及混合退化场景下的高性能、均衡且鲁棒的图像恢复。

链接: https://arxiv.org/abs/2509.12039
作者: Zilong Zhang,Chujie Qin,Chunle Guo,Yong Zhang,Chao Xue,Ming-Ming Cheng,Chongyi Li
机构: Nankai University (南开大学); Chongqing Chang’an Wangjiang Industrial Group Co., Ltd (重庆长安望江工业集团有限公司); Tiandy Technologies (天仪研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 22 figures

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Abstract:This work presents Robust Representation Learning via Adaptive Mask (RAM++), a two-stage framework for all-in-one image restoration. RAM++ integrates high-level semantic understanding with low-level texture generation to achieve content-oriented robust restoration. It addresses the limitations of existing degradation-oriented methods in extreme scenarios (e.g., degradations strongly coupled with image structures). RAM++ also mitigates common challenges such as unbalanced performance across tasks, overfitting to seen degradations, and weak generalization to unseen ones through three key designs: 1) Adaptive Semantic-Aware Mask (AdaSAM): a pretraining strategy that applies pixel-level masks to semantically rich and textured regions. This design enables the network to learn both generative priors and image content priors from various degradations. 2) Mask Attribute Conductance (MAC): a selective fine-tuning strategy that adjusts the layers with higher contributions to bridge the integrity gap between masked pretraining and full-image fine-tuning while retaining learned priors. 3) Robust Feature Regularization (RFR): a strategy that leverages DINOv2’s semantically consistent and degradation-invariant representations, together with efficient feature fusion, to achieve faithful and semantically coherent restoration. With these designs, RAM++ achieves robust, well-balanced, and state-of-the-art performance across seen, unseen, extreme, and mixed degradations. Our code and model will be released at this https URL
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[CV-24] Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustness

【速读】：该论文旨在解决扩散模型（Diffusion Models）在图像生成过程中可能泄露敏感或有害概念（如NSFW内容、私人个体、艺术风格等）的问题，即如何在不损害模型整体生成能力的前提下，实现对特定概念的鲁棒性移除。解决方案的关键在于提出SCORE（Secure and Concept-Oriented Robust Erasure）框架，其核心思想是将概念擦除建模为一个对抗性独立问题，通过最小化目标概念与生成输出之间的互信息（Mutual Information），从而理论上保证模型输出与被擦除概念统计独立。该方法提供形式化证明以确保收敛性和残余概念泄漏的上界，并结合对抗优化、轨迹一致性与显著性驱动微调策略，在多个基准测试中显著优于现有方法（如EraseAnything、ANT、MACE等），实现更高擦除效力（最高提升12.5%）且保持高质量生成能力。

链接: https://arxiv.org/abs/2509.12024
作者: Zixuan Fu,Yan Ren,Finn Carter,Chenyue Wen,Le Ku,Daheng Yu,Emily Davis,Bo Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Camera ready version

点击查看摘要

Abstract:Diffusion models have achieved unprecedented success in image generation but pose increasing risks in terms of privacy, fairness, and security. A growing demand exists to \empherase sensitive or harmful concepts (e.g., NSFW content, private individuals, artistic styles) from these models while preserving their overall generative capabilities. We introduce \textbfSCORE (Secure and Concept-Oriented Robust Erasure), a novel framework for robust concept removal in diffusion models. SCORE formulates concept erasure as an \emphadversarial independence problem, theoretically guaranteeing that the model’s outputs become statistically independent of the erased concept. Unlike prior heuristic methods, SCORE minimizes the mutual information between a target concept and generated outputs, yielding provable erasure guarantees. We provide formal proofs establishing convergence properties and derive upper bounds on residual concept leakage. Empirically, we evaluate SCORE on Stable Diffusion and FLUX across four challenging benchmarks: object erasure, NSFW removal, celebrity face suppression, and artistic style unlearning. SCORE consistently outperforms state-of-the-art methods including EraseAnything, ANT, MACE, ESD, and UCE, achieving up to \textbf12.5% higher erasure efficacy while maintaining comparable or superior image quality. By integrating adversarial optimization, trajectory consistency, and saliency-driven fine-tuning, SCORE sets a new standard for secure and robust concept erasure in diffusion models.
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[CV-25] Learning to Generate 4D LiDAR Sequences ICCV2025

【速读】：该论文旨在解决4D LiDAR数据生成中面临的可控性（controllability）、时序稳定性（temporal stability）以及评估标准缺失的问题，这些问题限制了LiDAR在3D感知任务中的应用潜力。其解决方案的关键在于提出LiDARCrafter框架：首先将自然语言指令解析为以车辆为中心的场景图（ego-centric scene graphs），通过三分支扩散模型分别生成物体布局、轨迹和形状；随后利用范围图像扩散模型生成初始扫描，并由自回归模块扩展为时序一致的LiDAR序列；此外，显式的布局设计支持对象级编辑（如插入或重定位），从而提升可控性和灵活性。同时，作者构建EvalSuite基准测试集，涵盖场景级、物体级和序列级指标，实现公平评估。

链接: https://arxiv.org/abs/2509.11959
作者: Ao Liang,Youquan Liu,Yu Yang,Dongyue Lu,Linfeng Li,Lingdong Kong,Huaici Zhao,Wei Tsang Ooi
机构: NUS(新加坡国立大学); UCAS(中国科学院大学); SIA, CAS(中科院自动化研究所); FDU(复旦大学); ZJU(浙江大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Abstract Paper (Non-Archival) @ ICCV 2025 Wild3D Workshop; GitHub Repo at this https URL

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Abstract:While generative world models have advanced video and occupancy-based data synthesis, LiDAR generation remains underexplored despite its importance for accurate 3D perception. Extending generation to 4D LiDAR data introduces challenges in controllability, temporal stability, and evaluation. We present LiDARCrafter, a unified framework that converts free-form language into editable LiDAR sequences. Instructions are parsed into ego-centric scene graphs, which a tri-branch diffusion model transforms into object layouts, trajectories, and shapes. A range-image diffusion model generates the initial scan, and an autoregressive module extends it into a temporally coherent sequence. The explicit layout design further supports object-level editing, such as insertion or relocation. To enable fair assessment, we provide EvalSuite, a benchmark spanning scene-, object-, and sequence-level metrics. On nuScenes, LiDARCrafter achieves state-of-the-art fidelity, controllability, and temporal consistency, offering a foundation for LiDAR-based simulation and data augmentation.
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[CV-26] CLAIRE: A Dual Encoder Network with RIFT Loss and Phi-3 Small Language Model Based Interpretability for Cross-Modality Synthetic Aperture Radar and Optical Land Cover Segmentation

【速读】：该论文旨在解决遥感影像中土地覆盖分类的三大挑战：自然景观的复杂性、类别间视觉相似性以及训练数据中存在的显著类别不平衡问题。其核心解决方案是提出一种双编码器架构，分别从光学和合成孔径雷达（SAR）图像中独立提取模态特异性特征，并通过一个名为CLAIRE（Cross-modality Land cover segmentation with Attention and Imbalance-aware Reasoning-Enhanced Explanations）的跨模态注意力融合模块进行特征融合，从而增强对互补空间与纹理信息的捕捉能力；同时引入RIFT（Rare-Instance Focal-Tversky）混合损失函数，结合加权焦点损失（Weighted Focal Loss）与Tversky损失，有效缓解类别不平衡问题并提升小样本类别的分割性能。

链接: https://arxiv.org/abs/2509.11952
作者: Debopom Sutradhar,Arefin Ittesafun Abian,Mohaimenul Azam Khan Raiaan,Reem E. Mohamed,Sheikh Izzal Azid,Sami Azam
机构: United International University (联合国际大学); Charles Darwin University (查尔斯达尔文大学); Murdoch University (默多克大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 23 pages, 6 figures, 10 tables

点击查看摘要

Abstract:Accurate land cover classification from satellite imagery is crucial in environmental monitoring and sustainable resource management. However, it remains challenging due to the complexity of natural landscapes, the visual similarity between classes, and the significant class imbalance in the available datasets. To address these issues, we propose a dual encoder architecture that independently extracts modality-specific features from optical and Synthetic Aperture Radar (SAR) imagery, which are then fused using a cross-modality attention-fusion module named Cross-modality Land cover segmentation with Attention and Imbalance- aware Reasoning-Enhanced Explanations (CLAIRE). This fusion mechanism highlights complementary spatial and textural features, enabling the network to better capture detailed and diverse land cover patterns. We incorporate a hybrid loss function that utilizes Weighted Focal Loss and Tversky Loss named RIFT (Rare-Instance Focal-Tversky) to address class imbalance and improve segmentation performance across underrepresented categories. Our model achieves competitive performance across multiple benchmarks: a mean Intersection over Union (mIoU) of 56.02% and Overall Accuracy (OA) of 84.56% on the WHU-OPT-SAR dataset; strong generalization with a mIoU of 59.89% and OA of 73.92% on the OpenEarthMap-SAR dataset; and remarkable robustness under cloud-obstructed conditions, achieving an mIoU of 86.86% and OA of 94.58% on the PIE-RGB-SAR dataset. Additionally, we introduce a metric-driven reasoning module generated by a Small Language Model (Phi-3), which generates expert-level, sample-specific justifications for model predictions, thereby enhancing transparency and interpretability.
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[CV-27] Sphere-GAN: a GAN-based Approach for Saliency Estimation in 360° Videos

【速读】：该论文旨在解决360°视频中视觉显著性（saliency）估计的难题，即如何准确识别用户在全景内容中关注的重点区域，从而优化处理与传输策略。现有方法多针对2D图像设计，难以适配球面结构的360°视频数据。其解决方案的关键在于提出Sphere-GAN模型，该模型基于生成对抗网络（Generative Adversarial Network, GAN）架构，并引入球面卷积（spherical convolution）以有效建模球面空间中的局部与全局特征关系，从而实现对360°视频显著图的高精度预测。

链接: https://arxiv.org/abs/2509.11948
作者: Mahmoud Z. A. Wahba,Sara Baldoni,Federica Battisti
机构: University of Padova (帕多瓦大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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Abstract:The recent success of immersive applications is pushing the research community to define new approaches to process 360° images and videos and optimize their transmission. Among these, saliency estimation provides a powerful tool that can be used to identify visually relevant areas and, consequently, adapt processing algorithms. Although saliency estimation has been widely investigated for 2D content, very few algorithms have been proposed for 360° saliency estimation. Towards this goal, we introduce Sphere-GAN, a saliency detection model for 360° videos that leverages a Generative Adversarial Network with spherical convolutions. Extensive experiments were conducted using a public 360° video saliency dataset, and the results demonstrate that Sphere-GAN outperforms state-of-the-art models in accurately predicting saliency maps.
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[CV-28] Graph Algorithm Unrolling with Douglas-Rachford Iterations for Image Interpolation with Guaranteed Initialization

【速读】：该论文旨在解决图像插值（image interpolation）问题，其核心挑战在于传统深度神经网络（DNNs）随机初始化参数并依赖随机梯度下降（SGD）优化时，易陷入性能较差的局部极小值。解决方案的关键在于：首先基于已知的（伪）线性插值器 $\Theta$ 初始化有向图邻接矩阵 $A$ ，构建一个基准模型；随后从数据中学习扰动矩阵 $P$ 和 $P^{(2)}$ 以增强 $A$ ，并通过Douglas-Rachford（DR）迭代实现恢复效果，最终将DR迭代展开为轻量且可解释的神经网络结构。该方法在保持高性能的同时显著减少了网络参数量，并取得了当前最优的图像插值结果。

链接: https://arxiv.org/abs/2509.11926
作者: Xue Zhang,Bingshuo Hu,Gene Cheung
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Conventional deep neural nets (DNNs) initialize network parameters at random and then optimize each one via stochastic gradient descent (SGD), resulting in substantial risk of poor-performing local this http URL on the image interpolation problem and leveraging a recent theorem that maps a (pseudo-)linear interpolator \Theta to a directed graph filter that is a solution to a MAP problem regularized with a graph shift variation (GSV) prior, we first initialize a directed graph adjacency matrix A based on a known interpolator \Theta, establishing a baseline this http URL, towards further gain, we learn perturbation matrices P and P(2) from data to augment A, whose restoration effects are implemented via Douglas-Rachford (DR) iterations, which we unroll into a lightweight interpretable neural this http URL results demonstrate state-of-the-art image interpolation results, while drastically reducing network parameters.
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[CV-29] Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI

【速读】：该论文旨在解决从颈动脉3D MRI图像中直接诊断动脉粥样硬化斑块易损性的挑战，这一任务对放射科医生和传统3D视觉网络均具有较高难度。其解决方案的关键在于提出一种基于变分推理与多模态知识蒸馏（Variation inference and Multimodal knowledge Distillation, VMD）的策略，通过利用有限标注图像数据与放射学报告中的跨模态先验知识，提升模型在未标注3D MRI图像上的诊断准确性，从而实现借助放射科医生领域知识自动化诊断斑块易损性的目标。

链接: https://arxiv.org/abs/2509.11924
作者: Bo Cao(1),Fan Yu(1),Mengmeng Feng(1),SenHao Zhang(1),Xin Meng(1),Yue Zhang(1),Zhen Qian(2),Jie Lu(1) ((1) Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, China, (2) Beijing United Intelligent Imaging Research Institute, China)
机构: Xuanwu Hospital, Capital Medical University (北京宣武医院，首都医科大学); Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics (北京市磁共振成像与脑信息学重点实验室); Beijing United Imaging Research Institute of Intelligent Imaging (北京联影智能影像研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Multimodal learning has attracted much attention in recent years due to its ability to effectively utilize data features from a variety of different modalities. Diagnosing the vulnerability of atherosclerotic plaques directly from carotid 3D MRI images is relatively challenging for both radiologists and conventional 3D vision networks. In clinical practice, radiologists assess patient conditions using a multimodal approach that incorporates various imaging modalities and domain-specific expertise, paving the way for the creation of multimodal diagnostic networks. In this paper, we have developed an effective strategy to leverage radiologists’ domain knowledge to automate the diagnosis of carotid plaque vulnerability through Variation inference and Multimodal knowledge Distillation (VMD). This method excels in harnessing cross-modality prior knowledge from limited image annotations and radiology reports within training data, thereby enhancing the diagnostic network’s accuracy for unannotated 3D MRI images. We conducted in-depth experiments on the dataset collected in-house and verified the effectiveness of the VMD strategy we proposed.
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[CV-30] NeuroGaze-Distill: Brain-informed Distillation and Depression-Inspired Geometric Priors for Robust Facial Emotion Recognition ICLR

【速读】：该论文旨在解决面部情绪识别（Facial Emotion Recognition, FER）模型在跨数据集场景下泛化能力差的问题，其根本原因在于仅依赖像素信息的模型容易受到面部外观的间接性和偏差影响，难以捕捉真实的情绪状态。解决方案的关键在于提出一种跨模态蒸馏框架NeuroGaze-Distill，通过将脑电图（EEG）信号中蕴含的先验知识迁移至纯图像模型中：一是利用静态的5×5情感维度原型（Valence/Arousal, V/A prototypes），冻结后作为特征对齐目标；二是引入受抑郁症研究启发的几何先验（Depression-inspired Geometric Prior, D-Geo），约束嵌入空间结构以符合情绪相关的行为学发现（如高愉悦区域的“快感缺失”式收缩）。该方法无需部署时使用非视觉信号或EEG-人脸配对，且仅需轻量级正则化模块即可显著提升模型鲁棒性与跨域性能。

链接: https://arxiv.org/abs/2509.11916
作者: Zilin Li,Weiwei Xu,Xuanqi Zhao,Yiran Zhu
机构: Donghua University (东华大学); North China Electric Power University (华北电力大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint. Vision-only deployment; EEG used only to form static prototypes. Includes appendix, 7 figures and 3 tables. Considering submission to the International Conference on Learning Representations (ICLR) 2026, Rio de Janeiro, Brazil

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Abstract:Facial emotion recognition (FER) models trained only on pixels often fail to generalize across datasets because facial appearance is an indirect and biased proxy for underlying affect. We present NeuroGaze-Distill, a cross-modal distillation framework that transfers brain-informed priors into an image-only FER student via static Valence/Arousal (V/A) prototypes and a depression-inspired geometric prior (D-Geo). A teacher trained on EEG topographic maps from DREAMER (with MAHNOB-HCI as unlabeled support) produces a consolidated 5x5 V/A prototype grid that is frozen and reused; no EEG-face pairing and no non-visual signals at deployment are required. The student (ResNet-18/50) is trained on FERPlus with conventional CE/KD and two lightweight regularizers: (i) Proto-KD (cosine) aligns student features to the static prototypes; (ii) D-Geo softly shapes the embedding geometry in line with affective findings often reported in depression research (e.g., anhedonia-like contraction in high-valence regions). We evaluate both within-domain (FERPlus validation) and cross-dataset protocols (AffectNet-mini; optional CK+), reporting standard 8-way scores alongside present-only Macro-F1 and balanced accuracy to fairly handle label-set mismatch. Ablations attribute consistent gains to prototypes and D-Geo, and favor 5x5 over denser grids for stability. The method is simple, deployable, and improves robustness without architectural complexity.
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[CV-31] Integrating Prior Observations for Incremental 3D Scene Graph Prediction ICML

【速读】：该论文旨在解决现有3D语义场景图（3DSSG）构建方法在真实环境中应用受限的问题，具体包括：1）多数方法仅依赖传感器数据，未充分利用语义丰富的先验信息；2）假设具备完整的场景重建，难以适应增量式、实时的现实场景。其解决方案的关键在于提出一种异构图模型，通过消息传递机制直接整合多模态信息（如语义嵌入CLIP和先前观测），并在多层结构中灵活融合全局与局部场景表示，无需专用模块或完整场景重建，从而实现可扩展且泛化能力强的增量式3DSSG预测。

链接: https://arxiv.org/abs/2509.11895
作者: Marian Renz,Felix Igelbrink,Martin Atzmueller
机构: DFKI Niedersachsen (DFKI NI); German Research Center for Artificial Intelligence (德国人工智能研究中心); Osnabrück University (奥斯纳布吕克大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted at 24th International Conference on Machine Learning and Applications (ICMLA’25)

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Abstract:3D semantic scene graphs (3DSSG) provide compact structured representations of environments by explicitly modeling objects, attributes, and relationships. While 3DSSGs have shown promise in robotics and embodied AI, many existing methods rely mainly on sensor data, not integrating further information from semantically rich environments. Additionally, most methods assume access to complete scene reconstructions, limiting their applicability in real-world, incremental settings. This paper introduces a novel heterogeneous graph model for incremental 3DSSG prediction that integrates additional, multi-modal information, such as prior observations, directly into the message-passing process. Utilizing multiple layers, the model flexibly incorporates global and local scene representations without requiring specialized modules or full scene reconstructions. We evaluate our approach on the 3DSSG dataset, showing that GNNs enriched with multi-modal information such as semantic embeddings (e.g., CLIP) and prior observations offer a scalable and generalizable solution for complex, real-world environments. The full source code of the presented architecture will be made available at this https URL.
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[CV-32] Logit Mixture Outlier Exposure for Fine-grained Out-of-Distribution Detection

【速读】：该论文旨在解决当前生成式 AI（Generative AI）模型在处理分布外数据（out-of-distribution data）时，难以有效区分输入数据来源（即是否来自训练分布）的问题，尤其在分布外数据靠近分布内数据时检测性能下降。其核心挑战在于模型在输入空间或特征空间中难以学习清晰的类间关系和边界分离。解决方案的关键在于引入对数空间（logit space）中的线性插值混合技术，通过在对数空间中混合分布内与分布外数据，实现类间 logits 的平滑过渡，并强制对数空间混合结果与输入空间混合结果保持一致性，从而显著降低决策边界附近的输出波动，提升分布外检测的鲁棒性和准确性。

链接: https://arxiv.org/abs/2509.11892
作者: Akito Shinohara,Kohei Fukuda,Hiroaki Aizawa
机构: Hiroshima University (广岛大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to DICTA2025

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Abstract:The ability to detect out-of-distribution data is essential not only for ensuring robustness against unknown or unexpected input data but also for improving the generalization performance of the model. Among various out-of-distribution detection methods, Outlier Exposure and Mixture Outlier Exposure are promising approaches that enhance out-of-distribution detection performance by exposing the outlier data during training. However, even with these sophisticated techniques, it remains challenging for models to learn the relationships between classes effectively and to distinguish data sampling from in-distribution and out-of-distribution clearly. Therefore, we focus on the logit space, where the properties between class-wise distributions are distinctly separated from those in the input or feature spaces. Specifically, we propose a linear interpolation technique in the logit space that mixes in-distribution and out-of-distribution data to facilitate smoothing logits between classes and improve the out-of-distribution detection performance, particularly for out-of-distribution data that lie close to the in-distribution data. Additionally, we enforce consistency between the logits obtained through mixing in the logit space and those generated via mixing in the input space. Our experiments demonstrate that our logit-space mixing technique reduces the abrupt fluctuations in the model outputs near the decision boundaries, resulting in smoother and more reliable separation between in-distribution and out-of-distribution data. Furthermore, we evaluate the effectiveness of the proposed method on a fine-grained out-of-distribution detection task.
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[CV-33] BREA-Depth: Bronchoscopy Realistic Airway-geometric Depth Estimation MICCAI2025

【速读】：该论文旨在解决支气管镜检查中单目深度估计（monocular depth estimation）的准确性与鲁棒性问题，尤其针对现有基础模型在缺乏解剖结构先验知识时易受局部纹理干扰、难以捕捉全局气道结构的问题。解决方案的关键在于提出Brea-Depth框架，通过引入空气道特异性几何先验（airway-specific geometric priors）来增强基础模型的适应能力：一是设计了一个深度感知的CycleGAN（depth-aware CycleGAN），用于对齐真实支气管镜图像与基于解剖数据生成的空气道几何形态，有效缩小域差距；二是提出空气道结构意识损失（airway structure awareness loss），强制气道腔内深度一致性并保持平滑过渡和结构完整性，从而提升模型在复杂分支气道中的泛化能力和3D重建精度。

链接: https://arxiv.org/abs/2509.11885
作者: Francis Xiatian Zhang,Emile Mackute,Mohammadreza Kasaei,Kevin Dhaliwal,Robert Thomson,Mohsen Khadem
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The paper has been accepted to MICCAI 2025

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Abstract:Monocular depth estimation in bronchoscopy can significantly improve real-time navigation accuracy and enhance the safety of interventions in complex, branching airways. Recent advances in depth foundation models have shown promise for endoscopic scenarios, yet these models often lack anatomical awareness in bronchoscopy, overfitting to local textures rather than capturing the global airway structure, particularly under ambiguous depth cues and poor lighting. To address this, we propose Brea-Depth, a novel framework that integrates airway-specific geometric priors into foundation model adaptation for bronchoscopic depth estimation. Our method introduces a depth-aware CycleGAN, refining the translation between real bronchoscopic images and airway geometries from anatomical data, effectively bridging the domain gap. In addition, we introduce an airway structure awareness loss to enforce depth consistency within the airway lumen while preserving smooth transitions and structural integrity. By incorporating anatomical priors, Brea-Depth enhances model generalization and yields more robust, accurate 3D airway reconstructions. To assess anatomical realism, we introduce Airway Depth Structure Evaluation, a new metric for structural consistency. We validate BREA-Depth on a collected ex vivo human lung dataset and an open bronchoscopic dataset, where it outperforms existing methods in anatomical depth preservation.
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[CV-34] SAM-TTT: Segment Anything Model via Reverse Parameter Configuration and Test-Time Training for Camouflaged Object Detection ACM-MM25

【速读】：该论文旨在解决现有基于Segment Anything Model (SAM) 的伪装目标检测（Camouflaged Object Detection, COD）模型在下游任务中语义理解能力不足的问题，其核心在于忽视了不良参数对SAM性能的负面影响。解决方案的关键在于提出两个模块：一是反向参数配置模块（Reverse SAM Parameter Configuration Module），通过无训练方式调整SAM参数以抑制不利影响；二是T-Visioner模块，引入原本用于语言任务的测试时训练（Test-Time Training, TTT）层到视觉任务中，增强优势参数并利用其线性复杂度与高表达力的隐藏状态。二者协同作用，使SAM-TTT在COD任务中显著提升语义理解能力，并实现当前最优性能。

链接: https://arxiv.org/abs/2509.11884
作者: Zhenni Yu,Li Zhao,Guobao Xiao,Xiaoqin Zhang
机构: Wenzhou University (温州大学); Tongji University (同济大学); Zhejiang Shuren University (浙江树人大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted by ACM MM 25

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Abstract:This paper introduces a new Segment Anything Model (SAM) that leverages reverse parameter configuration and test-time training to enhance its performance on Camouflaged Object Detection (COD), named SAM-TTT. While most existing SAM-based COD models primarily focus on enhancing SAM by extracting favorable features and amplifying its advantageous parameters, a crucial gap is identified: insufficient attention to adverse parameters that impair SAM’s semantic understanding in downstream tasks. To tackle this issue, the Reverse SAM Parameter Configuration Module is proposed to effectively mitigate the influence of adverse parameters in a train-free manner by configuring SAM’s parameters. Building on this foundation, the T-Visioner Module is unveiled to strengthen advantageous parameters by integrating Test-Time Training layers, originally developed for language tasks, into vision tasks. Test-Time Training layers represent a new class of sequence modeling layers characterized by linear complexity and an expressive hidden state. By integrating two modules, SAM-TTT simultaneously suppresses adverse parameters while reinforcing advantageous ones, significantly improving SAM’s semantic understanding in COD task. Our experimental results on various COD benchmarks demonstrate that the proposed approach achieves state-of-the-art performance, setting a new benchmark in the field. The code will be available at this https URL.
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[CV-35] Do It Yourself (DIY): Modifying Images for Poems in a Zero-Shot Setting Using Weighted Prompt Manipulation

【速读】：该论文旨在解决诗歌图像生成中语义准确性和上下文相关性不足的问题，尤其是在零样本（zero-shot）场景下难以根据读者个性化需求调整生成图像的挑战。解决方案的关键在于提出一种新颖的加权提示操控（Weighted Prompt Manipulation, WPM）技术，该技术通过系统性地修改扩散模型中的注意力权重和文本嵌入（text embeddings），动态调节特定词汇在图像生成过程中的重要性，从而增强或抑制其对最终视觉结果的影响，实现语义更丰富、情境更贴切的诗歌可视化。

链接: https://arxiv.org/abs/2509.11878
作者: Sofia Jamil,Kotla Sai Charan,Sriparna Saha,Koustava Goswami,K J Joseph
机构: Indian Institute of Technology Patna (印度理工学院巴特那分校); Adobe Research (Adobe 研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Poetry is an expressive form of art that invites multiple interpretations, as readers often bring their own emotions, experiences, and cultural backgrounds into their understanding of a poem. Recognizing this, we aim to generate images for poems and improve these images in a zero-shot setting, enabling audiences to modify images as per their requirements. To achieve this, we introduce a novel Weighted Prompt Manipulation (WPM) technique, which systematically modifies attention weights and text embeddings within diffusion models. By dynamically adjusting the importance of specific words, WPM enhances or suppresses their influence in the final generated image, leading to semantically richer and more contextually accurate visualizations. Our approach exploits diffusion models and large language models (LLMs) such as GPT in conjunction with existing poetry datasets, ensuring a comprehensive and structured methodology for improved image generation in the literary domain. To the best of our knowledge, this is the first attempt at integrating weighted prompt manipulation for enhancing imagery in poetic language.
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[CV-36] Multi-animal tracking in Transition: Comparative Insights into Established and Emerging Methods

【速读】：该论文旨在解决精准畜牧养殖中长期多动物跟踪（Multi-Animal Tracking, MAT）的准确性不足问题，尤其针对猪只在复杂场景下的跟踪挑战，如频繁遮挡、个体外观相似性高、运动模式不规则及行为类型多样等。其关键解决方案在于系统性地对比评估MAT专用工具（如DeepLabCut和idTracker）与前沿多目标跟踪（Multi-Object Tracking, MOT）方法（包括ByteTrack、DeepSORT、cross-input consistency、Track-Anything和PromptTrack），并基于10分钟猪只跟踪数据集验证发现：尽管MAT工具用户友好且广泛应用，但MOT方法在长时跟踪任务中显著优于传统MAT工具，表明最新MOT技术能有效提升自动化牲畜追踪的精度与可靠性。

链接: https://arxiv.org/abs/2509.11873
作者: Anne Marthe Sophie Ngo Bibinbe,Patrick Gagnon,Jamie Ahloy-Dallaire,Eric R. Paquet
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 21 pages, 3 figures, 5 tables

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Abstract:Precision livestock farming requires advanced monitoring tools to meet the increasing management needs of the industry. Computer vision systems capable of long-term multi-animal tracking (MAT) are essential for continuous behavioral monitoring in livestock production. MAT, a specialized subset of multi-object tracking (MOT), shares many challenges with MOT, but also faces domain-specific issues including frequent animal occlusion, highly similar appearances among animals, erratic motion patterns, and a wide range of behavior types. While some existing MAT tools are user-friendly and widely adopted, they often underperform compared to state-of-the-art MOT methods, which can result in inaccurate downstream tasks such as behavior analysis, health state estimation, and related applications. In this study, we benchmarked both MAT and MOT approaches for long-term tracking of pigs. We compared tools such as DeepLabCut and idTracker with MOT-based methods including ByteTrack, DeepSORT, cross-input consistency, and newer approaches like Track-Anything and PromptTrack. All methods were evaluated on a 10-minute pig tracking dataset. Our results demonstrate that, overall, MOT approaches outperform traditional MAT tools, even for long-term tracking scenarios. These findings highlight the potential of recent MOT techniques to enhance the accuracy and reliability of automated livestock tracking. Comments: 21 pages, 3 figures, 5 tables Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2509.11873 [cs.CV] (or arXiv:2509.11873v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2509.11873 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-37] Dr.V: A Hierarchical Perception-Temporal-Cognition Framework to Diagnose Video Hallucination by Fine-grained Spatial-Temporal Grounding

【速读】：该论文旨在解决大视频模型（Large Video Models, LVMs）在视频理解任务中普遍存在幻觉问题，即模型生成的内容与输入视频事实不符。解决方案的关键在于提出一个分层诊断框架Dr.V，其核心是通过细粒度的空间-时间定位（fine-grained spatial-temporal grounding）在感知层、时序层和认知层逐级分析视频内容，从而精准识别幻觉。该框架包含两个关键组件：Dr.V-Bench基准数据集（涵盖10k实例，具有详细时空标注）和Dr.V-Agent视频代理，后者通过系统性地应用空间-时间定位与认知推理，模拟人类视频理解过程，显著提升幻觉检测的准确性、可解释性和可靠性。

链接: https://arxiv.org/abs/2509.11866
作者: Meng Luo,Shengqiong Wu,Liqiang Jing,Tianjie Ju,Li Zheng,Jinxiang Lai,Tianlong Wu,Xinya Du,Jian Li,Siyuan Yan,Jiebo Luo,William Yang Wang,Hao Fei,Mong-Li Lee,Wynne Hsu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 25 pages, 16 figures

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Abstract:Recent advancements in large video models (LVMs) have significantly enhance video understanding. However, these models continue to suffer from hallucinations, producing content that conflicts with input videos. To address this issue, we propose Dr.V, a hierarchical framework covering perceptive, temporal, and cognitive levels to diagnose video hallucination by fine-grained spatial-temporal grounding. Dr.V comprises of two key components: a benchmark dataset Dr.V-Bench and a satellite video agent Dr.V-Agent. Dr.V-Bench includes 10k instances drawn from 4,974 videos spanning diverse tasks, each enriched with detailed spatial-temporal annotation. Dr.V-Agent detects hallucinations in LVMs by systematically applying fine-grained spatial-temporal grounding at the perceptive and temporal levels, followed by cognitive level reasoning. This step-by-step pipeline mirrors human-like video comprehension and effectively identifies hallucinations. Extensive experiments demonstrate that Dr.V-Agent is effective in diagnosing hallucination while enhancing interpretability and reliability, offering a practical blueprint for robust video understanding in real-world scenarios. All our data and code are available at this https URL.
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[CV-38] Bridging Vision Language Models and Symbolic Grounding for Video Question Answering

【速读】：该论文旨在解决当前视觉语言模型（Vision Language Models, VLMs）在视频问答（Video Question Answering, VQA）任务中依赖浅层相关性、导致时空因果推理能力弱且可解释性差的问题。其解决方案的关键在于引入符号化场景图（symbolic scene graphs, SGs）作为中间表征，通过提示（prompting）和视觉定位（visual localization）将冻结的VLM与结构化的对象-关系表示进行模块化融合，从而增强模型对视频中时空和因果线索的精确感知与推理能力。

链接: https://arxiv.org/abs/2509.11862
作者: Haodi Ma,Vyom Pathak,Daisy Zhe Wang
机构: University of Florida (佛罗里达大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Video Question Answering (VQA) requires models to reason over spatial, temporal, and causal cues in videos. Recent vision language models (VLMs) achieve strong results but often rely on shallow correlations, leading to weak temporal grounding and limited interpretability. We study symbolic scene graphs (SGs) as intermediate grounding signals for VQA. SGs provide structured object-relation representations that complement VLMs holistic reasoning. We introduce SG-VLM, a modular framework that integrates frozen VLMs with scene graph grounding via prompting and visual localization. Across three benchmarks (NExT-QA, iVQA, ActivityNet-QA) and multiple VLMs (QwenVL, InternVL), SG-VLM improves causal and temporal reasoning and outperforms prior baselines, though gains over strong VLMs are limited. These findings highlight both the promise and current limitations of symbolic grounding, and offer guidance for future hybrid VLM-symbolic approaches in video understanding.
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[CV-39] Segmentation-Driven Initialization for Sparse-view 3D Gaussian Splatting

【速读】：该论文旨在解决稀疏视图合成（sparse-view synthesis）中因观测数据有限而导致的几何与外观恢复困难问题，尤其针对现有3D高斯溅射（3D Gaussian Splatting, 3DGS）方法在真实稀疏视图场景下依赖结构光从运动（Structure-from-Motion, SfM）进行相机位姿估计效果不佳，以及无SfM方法使用多视图立体（Multi-View Stereo, MVS）生成海量3D高斯导致内存开销过大的问题。解决方案的关键在于提出基于分割驱动的初始化方法（Segmentation-Driven Initialization for Gaussian Splatting, SDI-GS），通过区域级分割识别并保留结构显著区域，实现对稠密点云的选择性下采样，在显著降低高斯数量（最高达50%）的同时保持场景保真度，并提升训练速度与内存效率，从而增强3DGS在受限视角场景下的实用性。

链接: https://arxiv.org/abs/2509.11853
作者: Yi-Hsin Li,Thomas Sikora,Sebastian Knorr,Måarten Sjöström
机构: Mid Sweden University (中瑞典大学); Technical University of Berlin (柏林工业大学); Hochschule für Technik und Wirtschaft Berlin (柏林应用技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Sparse-view synthesis remains a challenging problem due to the difficulty of recovering accurate geometry and appearance from limited observations. While recent advances in 3D Gaussian Splatting (3DGS) have enabled real-time rendering with competitive quality, existing pipelines often rely on Structure-from-Motion (SfM) for camera pose estimation, an approach that struggles in genuinely sparse-view settings. Moreover, several SfM-free methods replace SfM with multi-view stereo (MVS) models, but generate massive numbers of 3D Gaussians by back-projecting every pixel into 3D space, leading to high memory costs. We propose Segmentation-Driven Initialization for Gaussian Splatting (SDI-GS), a method that mitigates inefficiency by leveraging region-based segmentation to identify and retain only structurally significant regions. This enables selective downsampling of the dense point cloud, preserving scene fidelity while substantially reducing Gaussian count. Experiments across diverse benchmarks show that SDI-GS reduces Gaussian count by up to 50% and achieves comparable or superior rendering quality in PSNR and SSIM, with only marginal degradation in LPIPS. It further enables faster training and lower memory footprint, advancing the practicality of 3DGS for constrained-view scenarios.
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[CV-40] Synthetic Captions for Open-Vocabulary Zero-Shot Segmentation ICCV2025

【速读】：该论文旨在解决生成式视觉语言模型（Generative Vision-Language Models, VLMs）在图像与语言模态之间缺乏空间密集对齐的问题，从而提升零样本开放词汇分割等密集任务的性能。其解决方案的关键在于利用VLM生成的合成描述（synthetic captions）来实现图像与语言的密集对齐：这些合成描述具有高语义理解能力、成本低且易于扩展，能够作为高质量监督信号注入到密集对齐方法中，显著提升模型在标准零样本开放词汇分割基准上的表现，同时具备更高的数据效率。

链接: https://arxiv.org/abs/2509.11840
作者: Tim Lebailly,Vijay Veerabadran,Satwik Kottur,Karl Ridgeway,Michael Louis Iuzzolino
机构: Meta; KU Leuven(鲁汶大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: ICCV 2025 CDEL Workshop

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Abstract:Generative vision-language models (VLMs) exhibit strong high-level image understanding but lack spatially dense alignment between vision and language modalities, as our findings indicate. Orthogonal to advancements in generative VLMs, another line of research has focused on representation learning for vision-language alignment, targeting zero-shot inference for dense tasks like segmentation. In this work, we bridge these two directions by densely aligning images with synthetic descriptions generated by VLMs. Synthetic captions are inexpensive, scalable, and easy to generate, making them an excellent source of high-level semantic understanding for dense alignment methods. Empirically, our approach outperforms prior work on standard zero-shot open-vocabulary segmentation benchmarks/datasets, while also being more data-efficient.
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[CV-41] rajBooster: Boosting Humanoid Whole-Body Manipulation via Trajectory-Centric Learning

【速读】：该论文旨在解决模仿学习（Imitation Learning, IL）在长时序任务和高精度控制中因误差累积而导致性能下降的问题，尤其是现有残差策略学习方法仅依赖局部修正、缺乏对状态演化全局理解所引发的鲁棒性和泛化能力不足。其解决方案的关键在于引入基于Koopman算子理论的全局动力学建模机制，通过在隐空间中施加线性时不变（Linear Time-Invariant, LTI）结构来捕捉状态转移规律，从而实现对残差策略更新的全局引导；具体提出KORR（Koopman-guided Online Residual Refinement）框架，使残差修正条件依赖于Koopman预测的隐状态，确保动作优化具有全局一致性与稳定性，显著提升长时序任务中的预测准确性和环境外推能力。

链接: https://arxiv.org/abs/2509.11839
作者: Jiacheng Liu,Pengxiang Ding,Qihang Zhou,Yuxuan Wu,Da Huang,Zimian Peng,Wei Xiao,Weinan Zhang,Lixin Yang,Cewu Lu,Donglin Wang
机构: Zhejiang University (浙江大学); Westlake University (西湖大学); Shanghai Jiao Tong University (上海交通大学); Shanghai Innovation Institute (上海创新研究院)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Imitation learning (IL) enables efficient skill acquisition from demonstrations but often struggles with long-horizon tasks and high-precision control due to compounding errors. Residual policy learning offers a promising, model-agnostic solution by refining a base policy through closed-loop corrections. However, existing approaches primarily focus on local corrections to the base policy, lacking a global understanding of state evolution, which limits robustness and generalization to unseen scenarios. To address this, we propose incorporating global dynamics modeling to guide residual policy updates. Specifically, we leverage Koopman operator theory to impose linear time-invariant structure in a learned latent space, enabling reliable state transitions and improved extrapolation for long-horizon prediction and unseen environments. We introduce KORR (Koopman-guided Online Residual Refinement), a simple yet effective framework that conditions residual corrections on Koopman-predicted latent states, enabling globally informed and stable action refinement. We evaluate KORR on long-horizon, fine-grained robotic furniture assembly tasks under various perturbations. Results demonstrate consistent gains in performance, robustness, and generalization over strong baselines. Our findings further highlight the potential of Koopman-based modeling to bridge modern learning methods with classical control theory. For more details, please refer to this https URL.
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[CV-42] Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Network

【速读】：该论文旨在解决现有概率验证方法在复杂高维语义分割任务中难以扩展且保守性过强的问题，导致无法为医学影像、自动驾驶等安全敏感场景提供实用的可靠性保障。其解决方案的关键在于提出一种架构无关且可扩展的 probabilistic verification 框架，结合基于采样的可达性分析（sampling-based reachability analysis）与 conformal inference (CI)，在不牺牲严格性的前提下显著降低保守性，从而实现对高维输出的可靠安全保证。

链接: https://arxiv.org/abs/2509.11838
作者: Navid Hashemi,Samuel Sasaki,Diego Manzanas Lopez,Ipek Oguz,Meiyi Ma,Taylor T. Johnson
机构: Vanderbilt University (范德比尔特大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Semantic segmentation networks (SSNs) play a critical role in domains such as medical imaging, autonomous driving, and environmental monitoring, where safety hinges on reliable model behavior under uncertainty. Yet, existing probabilistic verification approaches struggle to scale with the complexity and dimensionality of modern segmentation tasks, often yielding guarantees that are too conservative to be practical. We introduce a probabilistic verification framework that is both architecture-agnostic and scalable to high-dimensional outputs. Our approach combines sampling-based reachability analysis with conformal inference (CI) to deliver provable guarantees while avoiding the excessive conservatism of prior methods. To counteract CI’s limitations in high-dimensional settings, we propose novel strategies that reduce conservatism without compromising rigor. Empirical evaluation on large-scale segmentation models across CamVid, OCTA-500, Lung Segmentation, and Cityscapes demonstrates that our method provides reliable safety guarantees while substantially tightening bounds compared to SOTA. We also provide a toolbox implementing this technique, available on Github.
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[CV-43] FedDAF: Federated Domain Adaptation Using Model Functional Distance WACV2026

【速读】：该论文旨在解决联邦域适应（Federated Domain Adaptation, FDA）中的两个核心挑战：源域与目标域之间的分布偏移（domain shift）以及目标客户端标注数据稀缺的问题。现有方法通常仅关注域偏移而假设目标数据充足，或虽同时处理两者却未能根据目标客户端的目标优化信息共享策略。本文提出的FedDAF解决方案的关键在于通过计算源模型与目标模型在目标数据上均值梯度场（mean gradient fields）之间的模型功能距离（model functional distance），并利用Gompertz函数对梯度方向夹角进行归一化，实现基于目标目标的相似性加权聚合。该机制能够在目标数据有限的情况下，有效融合来自源客户端的相关知识，从而提升模型在目标域上的泛化性能。

链接: https://arxiv.org/abs/2509.11819
作者: Mrinmay Sen,Ankita Das,Sidhant Nair,C Krishna Mohan
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 2 figures, 3 tables. Submitted to WACV 2026

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Abstract:Federated Domain Adaptation (FDA) is a federated learning (FL) approach that improves model performance at the target client by collaborating with source clients while preserving data privacy. FDA faces two primary challenges: domain shifts between source and target data and limited labeled data at the target. Most existing FDA methods focus on domain shifts, assuming ample target data, yet often neglect the combined challenges of both domain shifts and data scarcity. Moreover, approaches that address both challenges fail to prioritize sharing relevant information from source clients according to the target’s objective. In this paper, we propose FedDAF, a novel approach addressing both challenges in FDA. FedDAF uses similarity-based aggregation of the global source model and target model by calculating model functional distance from their mean gradient fields computed on target data. This enables effective model aggregation based on the target objective, constructed using target data, even with limited data. While computing model functional distance between these two models, FedDAF computes the angle between their mean gradient fields and then normalizes with the Gompertz function. To construct the global source model, all the local source models are aggregated using simple average in the server. Experiments on real-world datasets demonstrate FedDAF’s superiority over existing FL, PFL, and FDA methods in terms of achieving better test accuracy.
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[CV-44] MAFS: Masked Autoencoder for Infrared-Visible Image Fusion and Semantic Segmentation

【速读】：该论文旨在解决红外-可见光图像融合与语义分割任务之间缺乏协同优化的问题，即现有方法未从宏观任务层面探索像素级图像融合与跨模态特征融合感知任务之间的相互促进机制。其解决方案的关键在于提出一个统一的并行网络结构MAFS，包含融合子网络和分割子网络：一方面设计异质特征融合策略以增强图像融合的语义感知能力；另一方面通过级联融合子网络与分割骨干网络，将分割相关知识迁移至基于特征融合的语义分割中，从而实现双向知识传递；此外，引入基于最大最小公平分配原则的动态因子以自适应调整两任务权重，保障多任务训练的稳定性与效率。

链接: https://arxiv.org/abs/2509.11817
作者: Liying Wang,Xiaoli Zhang,Chuanmin Jia,Siwei Ma
机构: Jilin University (吉林大学); Peking University (北京大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by TIP 2025

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Abstract:Infrared-visible image fusion methods aim at generating fused images with good visual quality and also facilitate the performance of high-level tasks. Indeed, existing semantic-driven methods have considered semantic information injection for downstream applications. However, none of them investigates the potential for reciprocal promotion between pixel-wise image fusion and cross-modal feature fusion perception tasks from a macroscopic task-level perspective. To address this limitation, we propose a unified network for image fusion and semantic segmentation. MAFS is a parallel structure, containing a fusion sub-network and a segmentation sub-network. On the one hand, We devise a heterogeneous feature fusion strategy to enhance semantic-aware capabilities for image fusion. On the other hand, by cascading the fusion sub-network and a segmentation backbone, segmentation-related knowledge is transferred to promote feature-level fusion-based segmentation. Within the framework, we design a novel multi-stage Transformer decoder to aggregate fine-grained multi-scale fused features efficiently. Additionally, a dynamic factor based on the max-min fairness allocation principle is introduced to generate adaptive weights of two tasks and guarantee smooth training in a multi-task manner. Extensive experiments demonstrate that our approach achieves competitive results compared with state-of-the-art methods. The code is available at this https URL.
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[CV-45] SpecVLM: Fast Speculative Decoding in Vision-Language Models

【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在使用推测解码（Speculative Decoding）时面临的独特系统约束问题，尤其是预填充阶段（prefill stage）中视觉 token 数量随图像分辨率和视频长度显著增长，导致计算和内存开销（特别是键值缓存，Key-Value Cache）急剧上升。解决方案的关键在于提出 SpecVLM 系统：首先建立一个基于 EAGLE-2 风格的基线模型 EagleVLM，实现 1.5–2.3 倍端到端加速；其次引入一种弹性视觉压缩器（elastic visual compressor），自适应选择剪枝、池化、卷积与重采样等操作，以在每输入基础上平衡 FLOPs/参数与精度；同时设计了一种在线 logits 蒸馏协议（online-logit distillation protocol），无需离线蒸馏语料库即可训练草稿模型（draft model），利用实时教师 logits 和中间特征，采用交叉熵与 Smooth L1 混合目标函数，节省存储与预处理成本且计算高效。该方法揭示了训练时间扩展效应：更长的在线训练单调提升草稿模型平均接受长度，从而增强推测效率。最终 SpecVLM 在 LLaVA 和 MMMU 数据集上实现 2.5–2.9 倍端到端加速，且保持目标模型输出分布不变（lossless decoding）。

链接: https://arxiv.org/abs/2509.11815
作者: Haiduo Huang,Fuwei Yang,Zhenhua Liu,Xuanwu Yin,Dong Li,Pengju Ren,Emad Barsoum
机构: Advanced Micro Devices, Inc.(高级微设备公司); Institute of Artificial Intelligence and Robotics(人工智能与机器人研究所), Xi’an Jiaotong University(西安交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Speculative decoding is a powerful way to accelerate autoregressive large language models (LLMs), but directly porting it to vision-language models (VLMs) faces unique systems constraints: the prefill stage is dominated by visual tokens whose count scales with image resolution and video length, inflating both compute and memory, especially the key-value (KV) cache. We study speculative decoding for VLMs and introduce SpecVLM, a practical system that (1) establishes a strong EAGLE-2-style baseline, EagleVLM, delivering 1.5–2.3x end-to-end speedups over full autoregressive inference, and (2) further accelerates VLM inference with an elastic visual compressor that adaptively selects among pruning, pooling, convolution, and resampler primitives to balance FLOPs/parameters and accuracy per input. To avoid costly offline distillation corpora, we propose an online-logit distillation protocol that trains the draft model with on-the-fly teacher logits and penultimate features using a combined cross-entropy and Smooth L1 objective, eliminating storage and preprocessing while remaining compute-efficient. This protocol reveals a training-time scaling effect: longer online training monotonically increases the draft model’s average accepted length, improving speculative efficiency. Empirically, SpecVLM achieves additional acceleration, culminating in 2.5–2.9x end-to-end speedups within 5 epochs across LLaVA and MMMU, consistently over resolutions and task difficulties, while preserving the target model’s output distribution (lossless decoding). Our code is available at this https URL.
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[CV-46] LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio

【速读】：该论文旨在解决当前基于深度学习的视网膜血管分割模型在提取微小血管时性能不足且计算成本过高的问题，尤其是在计算资源有限的真实临床场景中。解决方案的关键在于提出一种轻量级网络LFRA-Net，其核心创新是：在编码器-解码器瓶颈处引入焦点调制注意力（focal modulation attention），并在选择性跳跃连接中嵌入区域感知注意力（region-aware attention），从而高效捕获局部与全局依赖关系，增强特征表示能力和区域聚焦能力。该设计使模型在仅0.17百万参数、0.66 MB内存和10.50 GFLOPs的前提下，实现了优于多个前沿模型的分割精度（如Dice分数达84.28%~88.44%），显著提升了准确率与计算效率之间的平衡，适用于资源受限环境下的实时临床应用。

链接: https://arxiv.org/abs/2509.11811
作者: Mehwish Mehmood,Shahzaib Iqbal,Tariq Mahmood Khan,Ivor Spence,Muhammad Fahim
机构: Queen’s University Belfast (贝尔法斯特女王大学); Abasyn University Islamabad Campus (阿巴斯恩伊斯兰堡校区); Naif Arab University for Security Sciences (安全科学纳伊夫阿拉伯大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Retinal vessel segmentation is critical for the early diagnosis of vision-threatening and systemic diseases, especially in real-world clinical settings with limited computational resources. Although significant improvements have been made in deep learning-based segmentation methods, current models still face challenges in extracting tiny vessels and suffer from high computational costs. In this study, we present LFRA-Net by incorporating focal modulation attention at the encoder-decoder bottleneck and region-aware attention in the selective skip connections. LFRA-Net is a lightweight network optimized for precise and effective retinal vascular segmentation. It enhances feature representation and regional focus by efficiently capturing local and global dependencies. LFRA-Net outperformed many state-of-the-art models while maintaining lightweight characteristics with only 0.17 million parameters, 0.66 MB memory size, and 10.50 GFLOPs. We validated it on three publicly available datasets: DRIVE, STARE, and CHASE_DB. It performed better in terms of Dice score (84.28%, 88.44%, and 85.50%) and Jaccard index (72.86%, 79.31%, and 74.70%) on the DRIVE, STARE, and CHASE_DB datasets, respectively. LFRA-Net provides an ideal ratio between segmentation accuracy and computational cost compared to existing deep learning methods, which makes it suitable for real-time clinical applications in areas with limited resources. The code can be found at this https URL.
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[CV-47] Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics

【速读】：该论文旨在解决当前医学图像诊断模型在训练时使用过于简化的独热标签（one-hot labels）所导致的问题，即忽略了诊断过程中的不确定性，使得模型在面对噪声、模糊或冲突的医学图像时产生过度自信的预测。其解决方案的关键在于引入一种新颖的框架，通过神经网络训练动态（Neural Network Training Dynamics, NNTD）评估每个训练样本的内在难度，并在训练过程中聚合与校准模型预测，生成反映学习过程中不确定性的伪标签（uncertainty-aware pseudo-labels）。该方法不依赖特定模型架构，可无缝集成至任意监督学习流程中，从而显著提升模型的不确定性估计能力和鲁棒性。

链接: https://arxiv.org/abs/2509.11800
作者: Ang Nan Gu,Michael Tsang,Hooman Vaseli,Purang Abolmaesumi,Teresa Tsang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Computer-aided diagnosis systems must make critical decisions from medical images that are often noisy, ambiguous, or conflicting, yet today’s models are trained on overly simplistic labels that ignore diagnostic uncertainty. One-hot labels erase inter-rater variability and force models to make overconfident predictions, especially when faced with incomplete or artifact-laden inputs. We address this gap by introducing a novel framework that brings uncertainty back into the label space. Our method leverages neural network training dynamics (NNTD) to assess the inherent difficulty of each training sample. By aggregating and calibrating model predictions during training, we generate uncertainty-aware pseudo-labels that reflect the ambiguity encountered during learning. This label augmentation approach is architecture-agnostic and can be applied to any supervised learning pipeline to enhance uncertainty estimation and robustness. We validate our approach on a challenging echocardiography classification benchmark, demonstrating superior performance over specialized baselines in calibration, selective classification, and multi-view fusion.
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[CV-48] FineQuest: Adaptive Knowledge-Assisted Sports Video Understanding via Agent -of-Thoughts Reasoning ACM-MM2025

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在体育视频理解任务中表现不佳的问题，特别是面对体育视频固有的复杂性和领域特异性时，传统方法难以准确捕捉视觉实例与专业术语之间的关联。解决方案的关键在于提出一个无需训练的框架 FineQuest，其核心创新是引入双模态推理机制：一是针对简单查询的反应式推理（Reactive Reasoning），二是针对复杂查询的反思式推理（Deliberative Reasoning），并结合一个跨九种体育项目的多模态体育知识场景图（SSGraph），该图编码了视觉实例与领域特定术语以增强推理准确性，从而显著提升体育视频问答（VideoQA）的性能。

链接: https://arxiv.org/abs/2509.11796
作者: Haodong Chen,Haojian Huang,XinXiang Yin,Dian Shao
机构: Northwestern Polytechnical University (西北工业大学); The University of Hong Kong (香港大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Video Question Answering (VideoQA) based on Large Language Models (LLMs) has shown potential in general video understanding but faces significant challenges when applied to the inherently complex domain of sports videos. In this work, we propose FineQuest, the first training-free framework that leverages dual-mode reasoning inspired by cognitive science: i) Reactive Reasoning for straightforward sports queries and ii) Deliberative Reasoning for more complex ones. To bridge the knowledge gap between general-purpose models and domain-specific sports understanding, FineQuest incorporates SSGraph, a multimodal sports knowledge scene graph spanning nine sports, which encodes both visual instances and domain-specific terminology to enhance reasoning accuracy. Furthermore, we introduce two new sports VideoQA benchmarks, Gym-QA and Diving-QA, derived from the FineGym and FineDiving datasets, enabling diverse and comprehensive evaluation. FineQuest achieves state-of-the-art performance on these benchmarks as well as the existing SPORTU dataset, while maintains strong general VideoQA capabilities.
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[CV-49] SA-UNetv2: Rethinking Spatial Attention U-Net for Retinal Vessel Segmentation

【速读】：该论文旨在解决视网膜血管分割中因前景-背景类别严重不平衡导致的模型性能下降问题，以及现有SA-UNet模型在跳跃连接中注意力机制利用不足的问题。解决方案的关键在于：首先，在所有跳跃连接中引入跨尺度空间注意力机制（cross-scale spatial attention），以增强多尺度特征融合能力；其次，采用加权二元交叉熵（Weighted Binary Cross-Entropy, BCE）与马修斯相关系数（Matthews Correlation Coefficient, MCC）相结合的损失函数，提升模型对类别不平衡的鲁棒性。该方法在DRIVE和STARE公开数据集上实现了SOTA性能，同时具备极低的内存占用（1.2MB）和参数量（0.26M），适合在资源受限的CPU环境中高效部署。

链接: https://arxiv.org/abs/2509.11774
作者: Changlu Guo,Anders Nymark Christensen,Anders Bjorholm Dahl,Yugen Yi,Morten Rieger Hannemose
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Retinal vessel segmentation is essential for early diagnosis of diseases such as diabetic retinopathy, hypertension, and neurodegenerative disorders. Although SA-UNet introduces spatial attention in the bottleneck, it underuses attention in skip connections and does not address the severe foreground-background imbalance. We propose SA-UNetv2, a lightweight model that injects cross-scale spatial attention into all skip connections to strengthen multi-scale feature fusion and adopts a weighted Binary Cross-Entropy (BCE) plus Matthews Correlation Coefficient (MCC) loss to improve robustness to class imbalance. On the public DRIVE and STARE datasets, SA-UNetv2 achieves state-of-the-art performance with only 1.2MB memory and 0.26M parameters (less than 50% of SA-UNet), and 1 second CPU inference on 592 x 592 x 3 images, demonstrating strong efficiency and deployability in resource-constrained, CPU-only settings.
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[CV-50] Seg2Track-SAM2: SAM2-based Multi-object Tracking and Segmentation for Zero-shot Generalization

【速读】：该论文旨在解决多目标跟踪与分割（MOTS）任务中面临的挑战，包括轨迹初始化、身份管理不一致以及内存效率低的问题。现有基于基础模型如SAM2的方法虽具备出色的零样本视频分割能力，但在跟踪过程中难以维持稳定的对象身份关联且存在较高的内存开销。解决方案的关键在于提出Seg2Track-SAM2框架，其核心创新是引入一个无需微调的新型Seg2Track模块，将预训练目标检测器与SAM2高效融合，实现端到端的轨迹初始化、维护与强化；同时采用滑动窗口记忆策略，在保持性能几乎不变的前提下将内存消耗降低高达75%，从而在资源受限场景下仍可部署。该方法在KITTI MOTS基准上实现了SOTA性能，并在关联准确率（AssA）方面树立了新标准。

链接: https://arxiv.org/abs/2509.11772
作者: Diogo Mendonça,Tiago Barros,Cristiano Premebida,Urbano J. Nunes
机构: University of Coimbra (科英布拉大学); Institute of Systems and Robotics (机器人研究所); Department of Electrical and Computer Engineering (电气与计算机工程系)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Autonomous systems require robust Multi-Object Tracking (MOT) capabilities to operate reliably in dynamic environments. MOT ensures consistent object identity assignment and precise spatial delineation. Recent advances in foundation models, such as SAM2, have demonstrated strong zero-shot generalization for video segmentation, but their direct application to MOTS (MOT+Segmentation) remains limited by insufficient identity management and memory efficiency. This work introduces Seg2Track-SAM2, a framework that integrates pre-trained object detectors with SAM2 and a novel Seg2Track module to address track initialization, track management, and reinforcement. The proposed approach requires no fine-tuning and remains detector-agnostic. Experimental results on KITTI MOT and KITTI MOTS benchmarks show that Seg2Track-SAM2 achieves state-of-the-art (SOTA) performance, ranking fourth overall in both car and pedestrian classes on KITTI MOTS, while establishing a new benchmark in association accuracy (AssA). Furthermore, a sliding-window memory strategy reduces memory usage by up to 75% with negligible performance degradation, supporting deployment under resource constraints. These results confirm that Seg2Track-SAM2 advances MOTS by combining robust zero-shot tracking, enhanced identity preservation, and efficient memory utilization. The code is available at this https URL
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[CV-51] MSMA: Multi-Scale Feature Fusion For Multi-Attribute 3D Face Reconstruction From Unconstrained Images

【速读】：该论文旨在解决从单张非约束环境下图像中进行高精度三维人脸重建的问题，尤其针对现有基于学习的方法在复杂面部属性和多样环境条件下难以捕捉多尺度细节特征、导致重建不完整或 inaccurate 的局限性。其解决方案的关键在于提出了一种多尺度特征融合与多属性（Multi-Scale Feature Fusion with Multi-Attribute, MSMA）框架，通过引入大感受野注意力模块增强跨尺度特征提取的精确性，并结合多属性学习策略，从而实现从单一二维图像中更准确地估计三维人脸参数。

链接: https://arxiv.org/abs/2509.11763
作者: Danling Cao
机构: The Hong Kong Polytechnic University (香港理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Reconstructing 3D face from a single unconstrained image remains a challenging problem due to diverse conditions in unconstrained environments. Recently, learning-based methods have achieved notable results by effectively capturing complex facial structures and details across varying conditions. Consequently, many existing approaches employ projection-based losses between generated and input images to constrain model training. However, learning-based methods for 3D face reconstruction typically require substantial amounts of 3D facial data, which is difficult and costly to obtain. Consequently, to reduce reliance on labeled 3D face datasets, many existing approaches employ projection-based losses between generated and input images to constrain model training. Nonetheless, despite these advancements, existing approaches frequently struggle to capture detailed and multi-scale features under diverse facial attributes and conditions, leading to incomplete or less accurate reconstructions. In this paper, we propose a Multi-Scale Feature Fusion with Multi-Attribute (MSMA) framework for 3D face reconstruction from unconstrained images. Our method integrates multi-scale feature fusion with a focus on multi-attribute learning and leverages a large-kernel attention module to enhance the precision of feature extraction across scales, enabling accurate 3D facial parameter estimation from a single 2D image. Comprehensive experiments on the MICC Florence, Facewarehouse and custom-collect datasets demonstrate that our approach achieves results on par with current state-of-the-art methods, and in some instances, surpasses SOTA performance across challenging conditions.
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[CV-52] A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications

【速读】：该论文旨在解决临床超声影像中因标注数据稀缺和任务特定模型泛化能力有限而导致的通用型人工智能（AI）模型难以部署的问题。其解决方案的关键在于构建一个名为EchoCare的超声基础模型，该模型基于自监督学习方法，在包含450万张来自五大洲23个国家、多种成像设备及多民族人群的大型公开数据集EchoCareData上训练而成；创新性地引入分层分类器架构，实现像素级与表征级特征的联合学习，从而同时捕捉全局解剖结构与局部超声特性，显著提升模型在疾病诊断、病灶分割、器官检测等10项不同难度任务上的性能表现，并支持下游任务的微调与本地化适配。

链接: https://arxiv.org/abs/2509.11752
作者: Hongyuan Zhang,Yuheng Wu,Mingyang Zhao,Zhiwei Chen,Rebecca Li,Fei Zhu,Haohan Zhao,Xiaohua Yuan,Meng Yang,Chunli Qiu,Xiang Cong,Haiyan Chen,Lina Luan,Randolph H.L. Wong,Huai Liao,Colin A Graham,Shi Chang,Guowei Tao,Dong Yi,Zhen Lei,Nassir Navab,Sebastien Ourselin,Jiebo Luo,Hongbin Liu,Gaofeng Meng
机构: Center for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, Chinese Academy of Sciences, Hong Kong, China; City University of Hong Kong, Hong Kong, China; State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Division of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, China; Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Hong Kong, China; Xiangya Hospital Central South University, Changsha, China; Hunan Frontline Medical Technology Co., Ltd, Changsha, China; Qilu Hospital of Shandong University, Jinan, China; Zhongshan Hospital of Fudan University, Shanghai, China; Shanghai Geriatric Medical Center, Shanghai, China; Division of Cardiothoracic Surgery, Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China; Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China; School of Biomedical Engineering & Imaging Sciences, King’s College London, UK; Department of Computer Science, University of Rochester, USA; State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Artificial intelligence (AI) that can effectively learn ultrasound representations by integrating multi-source data holds significant promise for advancing clinical care. However, the scarcity of large labeled datasets in real-world clinical environments and the limited generalizability of task-specific models have hindered the development of generalizable clinical AI models for ultrasound applications. In this study, we present EchoCare, a novel ultrasound foundation model for generalist clinical use, developed via self-supervised learning on our curated, publicly available, large-scale dataset EchoCareData. EchoCareData comprises 4.5 million ultrasound images, sourced from over 23 countries across 5 continents and acquired via a diverse range of distinct imaging devices, thus encompassing global cohorts that are multi-center, multi-device, and multi-ethnic. Unlike prior studies that adopt off-the-shelf vision foundation model architectures, we introduce a hierarchical classifier into EchoCare to enable joint learning of pixel-level and representation-level features, capturing both global anatomical contexts and local ultrasound characteristics. With minimal training, EchoCare outperforms state-of-the-art comparison models across 10 representative ultrasound benchmarks of varying diagnostic difficulties, spanning disease diagnosis, lesion segmentation, organ detection, landmark prediction, quantitative regression, imaging enhancement and report generation. The code and pretrained model are publicly released, rendering EchoCare accessible for fine-tuning and local adaptation, supporting extensibility to additional applications. EchoCare provides a fully open and generalizable foundation model to boost the development of AI technologies for diverse clinical ultrasound applications.
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[CV-53] Bridging the Gap Between Sparsity and Redundancy: A Dual-Decoding Framework with Global Context for Map Inference

【速读】：该论文旨在解决轨迹数据（trajectory data）在自动地图推断中因密度不均导致的道路碎片化（sparse areas）和冗余路段（dense regions）问题。其核心解决方案是提出DGMap框架，关键在于引入双解码机制与全局上下文感知能力，通过多尺度网格编码（Multi-scale Grid Encoding）、掩码增强的关键点提取（Mask-enhanced Keypoint Extraction）以及全局上下文感知的关系预测模块（Global Context-aware Relation Prediction），从而在稀疏区域提升关键点检测精度以减少道路断裂，在密集区域建模长距离轨迹模式以抑制虚假连接，最终显著改善地图构建质量。

链接: https://arxiv.org/abs/2509.11731
作者: Yudong Shen,Wenyu Wu,Jiali Mao,Yixiao Tong,Guoping Liu,Chaoya Wang
机构: East China Normal University (华东师范大学); DiDi Chuxing (滴滴出行)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Trajectory data has become a key resource for automated map in-ference due to its low cost, broad coverage, and continuous availability. However, uneven trajectory density often leads to frag-mented roads in sparse areas and redundant segments in dense regions, posing significant challenges for existing methods. To address these issues, we propose DGMap, a dual-decoding framework with global context awareness, featuring Multi-scale Grid Encoding, Mask-enhanced Keypoint Extraction, and Global Context-aware Relation Prediction. By integrating global semantic context with local geometric features, DGMap improves keypoint detection accuracy to reduce road fragmentation in sparse-trajectory areas. Additionally, the Global Context-aware Relation Prediction module suppresses false connections in dense-trajectory regions by modeling long-range trajectory patterns. Experimental results on three real-world datasets show that DGMap outperforms state-of-the-art methods by 5% in APLS, with notable performance gains on trajectory data from the Didi Chuxing platform
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[CV-54] Microsurgical Instrument Segmentation for Robot-Assisted Surgery

【速读】：该论文旨在解决微外科场景中细长结构（如手术器械）分割不准确的问题，这主要受分辨率损失、对比度低和类别不平衡等因素影响。其解决方案的关键在于提出一种名为MISRA（Microsurgery Instrument Segmentation for Robotic Assistance）的分割框架：首先通过引入亮度通道增强RGB输入以提升细节感知；其次采用跳跃注意力机制（skip attention）保留细长结构特征；最后设计迭代反馈模块（Iterative Feedback Module, IFM）在多轮迭代中恢复连续性，从而改善器械接触与重叠区域的分割稳定性。实验表明，该方法在平均类别交并比（mean class IoU）上较现有方法提升5.37%，展现出更强的鲁棒性和实用性。

链接: https://arxiv.org/abs/2509.11727
作者: Tae Kyeong Jeong,Garam Kim,Juyoun Park
机构: Korea Institute of Science and Technology (韩国科学技术院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Accurate segmentation of thin structures is critical for microsurgical scene understanding but remains challenging due to resolution loss, low contrast, and class imbalance. We propose Microsurgery Instrument Segmentation for Robotic Assistance(MISRA), a segmentation framework that augments RGB input with luminance channels, integrates skip attention to preserve elongated features, and employs an Iterative Feedback Module(IFM) for continuity restoration across multiple passes. In addition, we introduce a dedicated microsurgical dataset with fine-grained annotations of surgical instruments including thin objects, providing a benchmark for robust evaluation Dataset available at this https URL. Experiments demonstrate that MISRA achieves competitive performance, improving the mean class IoU by 5.37% over competing methods, while delivering more stable predictions at instrument contacts and overlaps. These results position MISRA as a promising step toward reliable scene parsing for computer-assisted and robotic microsurgery.
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[CV-55] DRAG : Data Reconstruction Attack using Guided Diffusion ICML2025

【速读】：该论文旨在解决在分层推理（Split Inference, SI）场景下，大型基础模型（Foundation Models）因中间表示（Intermediate Representations, IR）泄露而带来的数据隐私风险问题。现有研究主要针对小型CNN分类模型的数据重建攻击，对基础模型在SI设置中的隐私漏洞关注不足。论文提出的解决方案关键在于利用预训练的潜在扩散模型（Latent Diffusion Model, LDM）所蕴含的丰富先验知识，通过引导扩散机制对IR进行迭代重建，从而从深度层的中间表示中高效生成高保真度的原始图像。该方法显著优于当前最先进的重建技术，在定性和定量评估上均展现出更强的重建能力，凸显了在SI场景中加强大型模型隐私保护的紧迫性。

链接: https://arxiv.org/abs/2509.11724
作者: Wa-Kin Lei,Jun-Cheng Chen,Shang-Tse Chen
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: ICML 2025

点击查看摘要

Abstract:With the rise of large foundation models, split inference (SI) has emerged as a popular computational paradigm for deploying models across lightweight edge devices and cloud servers, addressing data privacy and computational cost concerns. However, most existing data reconstruction attacks have focused on smaller CNN classification models, leaving the privacy risks of foundation models in SI settings largely unexplored. To address this gap, we propose a novel data reconstruction attack based on guided diffusion, which leverages the rich prior knowledge embedded in a latent diffusion model (LDM) pre-trained on a large-scale dataset. Our method performs iterative reconstruction on the LDM’s learned image prior, effectively generating high-fidelity images resembling the original data from their intermediate representations (IR). Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, both qualitatively and quantitatively, in reconstructing data from deep-layer IRs of the vision foundation model. The results highlight the urgent need for more robust privacy protection mechanisms for large models in SI scenarios. Code is available at: this https URL.
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[CV-56] Advanced Layout Analysis Models for Docling

【速读】：该论文旨在解决文档转换流程中布局分析（Layout Analysis）精度不足的问题，特别是在复杂和多样化的文档类型上。解决方案的关键在于开发并集成了一系列基于RT-DETR、RT-DETRv2和DFINE架构的新型目标检测模型，并在包含15万份文档的异构语料库上进行训练；同时通过后处理步骤优化原始检测结果以更适配文档转换任务。实验表明，所提出的五个新模型相比原有基线实现了20.6%–23.9%的mAP提升，其中最优模型“heron-101”在单张NVIDIA A100 GPU上达到78% mAP与28 ms/图像的推理速度，兼具高精度与高效性。

链接: https://arxiv.org/abs/2509.11720
作者: Nikolaos Livathinos,Christoph Auer,Ahmed Nassar,Rafael Teixeira de Lima,Maksym Lysak,Brown Ebouky,Cesar Berrospi,Michele Dolfi,Panagiotis Vagenas,Matteo Omenetti,Kasper Dinkla,Yusik Kim,Valery Weber,Lucas Morin,Ingmar Meijer,Viktor Kuropiatnyk,Tim Strohmeyer,A.Said Gurbuz,Peter W. J. Staar
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages. 4 figures. Technical report for the layout models of Docling

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Abstract:This technical report documents the development of novel Layout Analysis models integrated into the Docling document-conversion pipeline. We trained several state-of-the-art object detectors based on the RT-DETR, RT-DETRv2 and DFINE architectures on a heterogeneous corpus of 150,000 documents (both openly available and proprietary). Post-processing steps were applied to the raw detections to make them more applicable to the document conversion task. We evaluated the effectiveness of the layout analysis on various document benchmarks using different methodologies while also measuring the runtime performance across different environments (CPU, Nvidia and Apple GPUs). We introduce five new document layout models achieving 20.6% - 23.9% mAP improvement over Docling’s previous baseline, with comparable or better runtime. Our best model, “heron-101”, attains 78% mAP with 28 ms/image inference time on a single NVIDIA A100 GPU. Extensive quantitative and qualitative experiments establish best practices for training, evaluating, and deploying document-layout detectors, providing actionable guidance for the document conversion community. All trained checkpoints, code, and documentation are released under a permissive license on HuggingFace.
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[CV-57] he Quest for Universal Master Key Filters in DS-CNNs

【速读】：该论文旨在解决深度可分离卷积神经网络（Depthwise Separable Convolutional Neural Networks, DS-CNNs）中滤波器冗余与本质规律缺失的问题，即为何大量训练滤波器在不同架构和任务下趋于收敛于某些共通的结构。其解决方案的关键在于通过系统性无监督搜索发现了一组仅由8个通用滤波器组成的“主钥匙滤波器”（Master Key Filters），这些滤波器本质上是差分高斯（Difference of Gaussians, DoGs）、高斯及其导数等经典图像处理算子，且在不同模型和数据集上均稳定出现；更重要的是，冻结这8个滤波器进行初始化即可在ImageNet上达到超过80%的准确率，并在小数据集上优于大量可训练参数的模型，揭示了深度卷积层对这类基础空间算子的内在偏好，为理解泛化能力和迁移学习提供了新的视角。

链接: https://arxiv.org/abs/2509.11711
作者: Zahra Babaiee,Peyman M. Kiassari,Daniela Rus,Radu Grosu
机构: Technische Universität Wien (维也纳工业大学); Massachusetts Institute of Technology (麻省理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:A recent study has proposed the “Master Key Filters Hypothesis” for convolutional neural network filters. This paper extends this hypothesis by radically constraining its scope to a single set of just 8 universal filters that depthwise separable convolutional networks inherently converge to. While conventional DS-CNNs employ thousands of distinct trained filters, our analysis reveals these filters are predom- inantly linear shifts (ax+b) of our discovered universal set. Through systematic unsupervised search, we extracted these fundamental patterns across different architectures and datasets. Remarkably, networks initialized with these 8 unique frozen filters achieve over 80% ImageNet accuracy, and even outperform models with thousands of trainable parameters when applied to smaller datasets. The identified master key filters closely match Difference of Gaussians (DoGs), Gaussians, and their derivatives, structures that are not only fundamental to classical image processing but also strikingly similar to receptive fields in mammalian visual systems. Our findings provide compelling evidence that depthwise convolutional layers naturally gravitate toward this fundamental set of spatial operators regardless of task or architecture. This work offers new insights for understanding generalization and transfer learning through the universal language of these master key filters.
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[CV-58] Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillation

【速读】：该论文旨在解决医学图像分割中不确定性估计的可靠性问题，尤其是在视网膜血管分割任务中，准确的不确定性量化对诊断应用至关重要。传统方法如深度集成（Deep Ensembles）虽能提升分割性能，但其训练和推理成本随模型数量增加而显著上升。解决方案的关键在于提出集成蒸馏（Ensemble Distillation），即通过知识蒸馏技术将多个集成模型的知识压缩到单一模型中，在保持与深度集成相当的校准性和分割性能的同时，大幅降低计算复杂度，从而实现高效且可靠的不确定性估计。

链接: https://arxiv.org/abs/2509.11689
作者: Jeremiah Fadugba,Petru Manescu,Bolanle Oladejo,Delmiro Fernandez-Reyes,Philipp Berens
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 5 pages, 5 figure

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Abstract:Uncertainty estimation is critical for reliable medical image segmentation, particularly in retinal vessel analysis, where accurate predictions are essential for diagnostic applications. Deep Ensembles, where multiple networks are trained individually, are widely used to improve medical image segmentation performance. However, training and testing costs increase with the number of ensembles. In this work, we propose Ensemble Distillation as a robust alternative to commonly used uncertainty estimation techniques by distilling the knowledge of multiple ensemble models into a single model. Through extensive experiments on the DRIVE and FIVES datasets, we demonstrate that Ensemble Distillation achieves comparable performance via calibration and segmentation metrics, while significantly reducing computational complexity. These findings suggest that Ensemble distillation provides an efficient and reliable approach for uncertainty estimation in the segmentation of the retinal vessels, making it a promising tool for medical imaging applications.
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[CV-59] IMD: A 6-DoF Pose Estimation Benchmark for Industrial Metallic Objects

【速读】：该论文旨在解决当前6D位姿估计（6D pose estimation）模型在工业场景下泛化能力不足的问题，其根源在于现有基准数据集主要基于纹理丰富且低反射率的日常物品，而工业环境中常见金属材质、无纹理且高反射的物体导致模型性能显著下降。解决方案的关键在于提出一个全新的工业金属数据集（Industrial Metallic Dataset, IMD），该数据集包含45个真实比例的工业部件，在自然室内光照和多样化物体布局下通过RGB-D相机采集，构建了更贴近真实工业环境的测试基准；同时，该基准支持视频目标分割、6D位姿跟踪和单样本位姿估计三项任务，为评估和比较工业机器人场景下的分割与位姿估计算法提供了标准化基线，从而推动模型向工业适用性演进。

链接: https://arxiv.org/abs/2509.11680
作者: Ruimin Ma,Sebastian Zudaire,Zhen Li,Chi Zhang
机构: KTH Royal Institute of Technology (皇家理工学院); ABB Corporate Research (ABB公司研究部门)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 19 figures, 2 tables. Accepted in 2025 8th International Conference on Robotics, Control and Automation Engineering (RCAE 2025)

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Abstract:Object 6DoF (6D) pose estimation is essential for robotic perception, especially in industrial settings. It enables robots to interact with the environment and manipulate objects. However, existing benchmarks on object 6D pose estimation primarily use everyday objects with rich textures and low-reflectivity, limiting model generalization to industrial scenarios where objects are often metallic, texture-less, and highly reflective. To address this gap, we propose a novel dataset and benchmark namely \textitIndustrial Metallic Dataset (IMD), tailored for industrial applications. Our dataset comprises 45 true-to-scale industrial components, captured with an RGB-D camera under natural indoor lighting and varied object arrangements to replicate real-world conditions. The benchmark supports three tasks, including video object segmentation, 6D pose tracking, and one-shot 6D pose estimation. We evaluate existing state-of-the-art models, including XMem and SAM2 for segmentation, and BundleTrack and BundleSDF for pose estimation, to assess model performance in industrial contexts. Evaluation results show that our industrial dataset is more challenging than existing household object datasets. This benchmark provides the baseline for developing and comparing segmentation and pose estimation algorithms that better generalize to industrial robotics scenarios.
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[CV-60] RouteExtract: A Modular Pipeline for Extracting Routes from Paper Maps ICCV2025

【速读】：该论文旨在解决纸质地图中导航路径难以数字化利用的问题，即如何从扫描的纸质地图中自动提取可通行的步道（trail）网络，并将其转化为适用于GPS导航的结构化路径数据。其解决方案的关键在于提出了一套端到端的处理流程：首先通过地理配准（georeferencing）将地图图像与真实地理坐标对齐，接着采用基于U-Net的二值分割模型精确识别出步道区域，随后构建图结构表示路径拓扑关系，并最终利用路由引擎进行迭代优化，生成符合实际使用需求的GPS可执行路线。该方法在多种地图风格下均表现出鲁棒性，实现了从静态纸质地图到动态导航系统的有效转化。

链接: https://arxiv.org/abs/2509.11674
作者: Bjoern Kremser,Yusuke Matsui
机构: Technical University of Munich (慕尼黑工业大学); The University of Tokyo (东京大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to the Workshop on Graphic Design Understanding and Generation (GDUG) at ICCV 2025. 8 pages, 7 figures

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Abstract:Paper maps remain widely used for hiking and sightseeing because they contain curated trails and locally relevant annotations that are often missing from digital navigation applications such as Google Maps. We propose a pipeline to extract navigable trails from scanned maps, enabling their use in GPS-based navigation. Our method combines georeferencing, U-Net-based binary segmentation, graph construction, and an iterative refinement procedure using a routing engine. We evaluate the full end-to-end pipeline as well as individual components, showing that the approach can robustly recover trail networks from diverse map styles and generate GPS routes suitable for practical use.
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[CV-61] ParaEQsA: Parallel and Asynchronous Embodied Questions Scheduling and Answering ICRA2026

【速读】：该论文旨在解决传统具身问答（Embodied Question Answering, EQA）系统在现实场景中面临的多任务并发处理问题，即如何高效响应多个异步到达且具有不同紧急程度的问题。现有EQA方法通常仅处理单个问题，难以适应实际部署中复杂的多任务需求。为此，论文提出了一种新的框架ParaEQsA，其核心创新在于引入了共享组记忆模块以减少冗余探索，并设计了优先级规划模块实现动态调度，从而在保证回答准确性的前提下提升系统响应速度与资源利用效率。实验表明，该方案通过 urgency-aware 的并行调度机制显著优于顺序基线模型，在多项指标上实现了更优的性能表现。

链接: https://arxiv.org/abs/2509.11663
作者: Haisheng Wang,Weiming Zhi
机构: Software Engineering Institute, East China Normal University, Shanghai, China; School of Computer Science, The University of Sydney, Australia; College of Connected Computing, Vanderbilt University, TN, USA; Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 6 figures, 2026 IEEE Conference on Robotics and Automation (ICRA 2026)

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Abstract:This paper formulates the Embodied Questions Answering (EQsA) problem, introduces a corresponding benchmark, and proposes a system to tackle the problem. Classical Embodied Question Answering (EQA) is typically formulated as answering one single question by actively exploring a 3D environment. Real deployments, however, often demand handling multiple questions that may arrive asynchronously and carry different urgencies. We formalize this setting as Embodied Questions Answering (EQsA) and present ParaEQsA, a framework for parallel, urgency-aware scheduling and answering. ParaEQsA leverages a group memory module shared among questions to reduce redundant exploration, and a priority-planning module to dynamically schedule questions. To evaluate this setting, we contribute the Parallel Asynchronous Embodied Questions (PAEQs) benchmark containing 40 indoor scenes and five questions per scene (200 in total), featuring asynchronous follow-up questions and urgency labels. We further propose metrics for EQsA performance: Direct Answer Rate (DAR), and Normalized Urgency-Weighted Latency (NUWL), which jointly measure efficiency and responsiveness of this system. ParaEQsA consistently outperforms strong sequential baselines adapted from recent EQA systems, while reducing exploration and delay. Empirical evaluations investigate the relative contributions of priority, urgency modeling, spatial scope, reward estimation, and dependency reasoning within our framework. Together, these results demonstrate that urgency-aware, parallel scheduling is key to making embodied agents responsive and efficient under realistic, multi-question workloads.
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[CV-62] DTGen: Generative Diffusion-Based Few-Shot Data Augmentation for Fine-Grained Dirty Tableware Recognition

【速读】：该论文旨在解决智能餐具清洁领域中因细粒度分类任务和少量样本数据稀缺导致的工业应用瓶颈问题，尤其在脏污餐具识别场景下难以实现高精度检测与实际部署。其解决方案的关键在于提出DTGen——一种基于生成式扩散模型（generative diffusion models）的少样本数据增强方法，通过LoRA（Low-Rank Adaptation）实现高效领域专业化，利用结构化提示（structured prompts）生成多样化脏污图像，并借助CLIP-based跨模态过滤机制保障合成数据质量，从而在极有限的真实样本条件下生成高质量、近乎无限的合成样本，显著提升分类器性能并支持细粒度脏污识别。此外，论文还探讨了轻量化部署策略，为将DTGen集成至嵌入式洗碗机并优化能耗与洗涤剂使用提供了可行路径。

链接: https://arxiv.org/abs/2509.11661
作者: Lifei Hao,Yue Cheng,Baoqi Huang,Bing Jia,Xuandong Zhao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Intelligent tableware cleaning is a critical application in food safety and smart homes, but existing methods are limited by coarse-grained classification and scarcity of few-shot data, making it difficult to meet industrialization requirements. We propose DTGen, a few-shot data augmentation scheme based on generative diffusion models, specifically designed for fine-grained dirty tableware recognition. DTGen achieves efficient domain specialization through LoRA, generates diverse dirty images via structured prompts, and ensures data quality through CLIP-based cross-modal filtering. Under extremely limited real few-shot conditions, DTGen can synthesize virtually unlimited high-quality samples, significantly improving classifier performance and supporting fine-grained dirty tableware recognition. We further elaborate on lightweight deployment strategies, promising to transfer DTGen’s benefits to embedded dishwashers and integrate with cleaning programs to intelligently regulate energy consumption and detergent usage. Research results demonstrate that DTGen not only validates the value of generative AI in few-shot industrial vision but also provides a feasible deployment path for automated tableware cleaning and food safety monitoring.
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[CV-63] Joint-octamamba:an octa joint segmentation network based on feature enhanced mamba

【速读】：该论文旨在解决现有基于二维（2D）方法在光学相干断层扫描血管成像（OCTA）中对视网膜血管（RV）分割精度不足的问题，以及联合分割模型在不同任务间性能不平衡的局限性。其关键解决方案是提出一种融合多特征提取模块与Mamba状态空间模型的新架构RVMamba，并进一步设计FAZMamba及统一的Joint-OCTAMamba框架，以同时提升黄斑无血管区（FAZ）分割性能并缓解多任务间的性能失衡问题。

链接: https://arxiv.org/abs/2509.11649
作者: Chuang Liu,Nan Guo
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:OCTA is a crucial non-invasive imaging technique for diagnosing and monitoring retinal diseases like diabetic retinopathy, age-related macular degeneration, and glaucoma. Current 2D-based methods for retinal vessel (RV) segmentation offer insufficient accuracy. To address this, we propose RVMamba, a novel architecture integrating multiple feature extraction modules with the Mamba state-space model. Moreover, existing joint segmentation models for OCTA data exhibit performance imbalance between different tasks. To simultaneously improve the segmentation of the foveal avascular zone (FAZ) and mitigate this imbalance, we introduce FAZMamba and a unified Joint-OCTAMamba framework. Experimental results on the OCTA-500 dataset demonstrate that Joint-OCTAMamba outperforms existing models across evaluation this http URL code is available at this https URL.
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[CV-64] WeatherBench: A Real-World Benchmark Dataset for All-in-One Adverse Weather Image Restoration

【速读】：该论文旨在解决当前统一图像复原方法在训练与评估过程中依赖混合单天气合成数据集所导致的域差异问题，以及缺乏大规模真实世界多天气复原数据集这一关键瓶颈。其解决方案的关键在于构建了一个真实世界中的全场景多天气图像复原基准数据集（all-in-one adverse weather image restoration benchmark dataset），该数据集包含雨、雪、雾等多种天气条件下的成对退化与清晰图像，覆盖多样户外场景和光照设置，具有精确配准特性，从而支持监督学习和严格评估。

链接: https://arxiv.org/abs/2509.11642
作者: Qiyuan Guan,Qianfeng Yang,Xiang Chen,Tianyu Song,Guiyue Jin,Jiyu Jin
机构: Dalian Polytechnic University (大连Polytechnic大学); Nanjing University of Science and Technology (南京理工大学); Dalian Martime University (大连海事大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by ACMMM 2025 Datasets Track

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Abstract:Existing all-in-one image restoration approaches, which aim to handle multiple weather degradations within a single framework, are predominantly trained and evaluated using mixed single-weather synthetic datasets. However, these datasets often differ significantly in resolution, style, and domain characteristics, leading to substantial domain gaps that hinder the development and fair evaluation of unified models. Furthermore, the lack of a large-scale, real-world all-in-one weather restoration dataset remains a critical bottleneck in advancing this field. To address these limitations, we present a real-world all-in-one adverse weather image restoration benchmark dataset, which contains image pairs captured under various weather conditions, including rain, snow, and haze, as well as diverse outdoor scenes and illumination settings. The resulting dataset provides precisely aligned degraded and clean images, enabling supervised learning and rigorous evaluation. We conduct comprehensive experiments by benchmarking a variety of task-specific, task-general, and all-in-one restoration methods on our dataset. Our dataset offers a valuable foundation for advancing robust and practical all-in-one image restoration in real-world scenarios. The dataset has been publicly released and is available at this https URL.
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[CV-65] IS-Diff: Improving Diffusion-Based Inpainting with Better Initial Seed

【速读】：该论文旨在解决传统扩散模型（Diffusion Models）在自由形式图像修复（free-form inpainting）任务中，由于随机初始化种子（noise）导致掩码区域语义信息不匹配的问题，从而引发修复结果一致性差、与未掩码区域 coherence 低等缺陷。其解决方案的关键在于提出一种完全无需训练的初始种子优化方法——初始种子精修扩散模型（IS-Diff），通过从非掩码区域采样初始种子来模拟掩码区域的数据分布，从而引导扩散过程生成语义更一致的结果；同时引入动态选择性精修机制，在中间潜在空间检测严重不和谐的修复区域，并动态调整初始先验强度，进一步提升修复质量。

链接: https://arxiv.org/abs/2509.11638
作者: Yongzhe Lyu,Yu Wu,Yutian Lin,Bo Du
机构: Wuhan University (武汉大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Diffusion models have shown promising results in free-form inpainting. Recent studies based on refined diffusion samplers or novel architectural designs led to realistic results and high data consistency. However, random initialization seed (noise) adopted in vanilla diffusion process may introduce mismatched semantic information in masked regions, leading to biased inpainting results, e.g., low consistency and low coherence with the other unmasked area. To address this issue, we propose the Initial Seed refined Diffusion Model (IS-Diff), a completely training-free approach incorporating distributional harmonious seeds to produce harmonious results. Specifically, IS-Diff employs initial seeds sampled from unmasked areas to imitate the masked data distribution, thereby setting a promising direction for the diffusion procedure. Moreover, a dynamic selective refinement mechanism is proposed to detect severe unharmonious inpaintings in intermediate latent and adjust the strength of our initialization prior dynamically. We validate our method on both standard and large-mask inpainting tasks using the CelebA-HQ, ImageNet, and Places2 datasets, demonstrating its effectiveness across all metrics compared to state-of-the-art inpainting methods.
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[CV-66] SpeCa: Accelerating Diffusion Transformers with Speculative Feature Caching

【速读】：该论文旨在解决扩散模型（Diffusion Models）在实时应用中面临的两大核心问题：一是时间上的严格依赖性导致难以并行化，二是每个去噪步骤均需进行计算密集的前向传播，显著限制了推理效率。其解决方案的关键在于提出一种名为SpeCa的“预测-验证”加速框架，通过引入推测采样（Speculative Sampling）机制，基于已完全计算的参考时间步预测后续时间步的中间特征，并设计了一种无参数的验证机制以高效评估预测可靠性，从而实现对每一步预测的实时接受或拒绝决策；同时，该方法还引入样本自适应计算分配策略，根据生成内容的复杂度动态调整资源分配，在保证高质量输出的前提下大幅降低计算开销。

链接: https://arxiv.org/abs/2509.11628
作者: Jiacheng Liu,Chang Zou,Yuanhuiyi Lyu,Fei Ren,Shaobo Wang,Kaixin Li,Linfeng Zhang
机构: Shanghai Jiao Tong University (上海交通大学); Shandong University (山东大学); University of Electronic Science and Technology of China (电子科技大学); The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州) ); Tsinghua University (清华大学); National University of Singapore (新加坡国立大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 9 figures, ACM Multimedia 2025

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Abstract:Diffusion models have revolutionized high-fidelity image and video synthesis, yet their computational demands remain prohibitive for real-time applications. These models face two fundamental challenges: strict temporal dependencies preventing parallelization, and computationally intensive forward passes required at each denoising step. Drawing inspiration from speculative decoding in large language models, we present SpeCa, a novel ‘Forecast-then-verify’ acceleration framework that effectively addresses both limitations. SpeCa’s core innovation lies in introducing Speculative Sampling to diffusion models, predicting intermediate features for subsequent timesteps based on fully computed reference timesteps. Our approach implements a parameter-free verification mechanism that efficiently evaluates prediction reliability, enabling real-time decisions to accept or reject each prediction while incurring negligible computational overhead. Furthermore, SpeCa introduces sample-adaptive computation allocation that dynamically modulates resources based on generation complexity, allocating reduced computation for simpler samples while preserving intensive processing for complex instances. Experiments demonstrate 6.34x acceleration on FLUX with minimal quality degradation (5.5% drop), 7.3x speedup on DiT while preserving generation fidelity, and 79.84% VBench score at 6.1x acceleration for HunyuanVideo. The verification mechanism incurs minimal overhead (1.67%-3.5% of full inference costs), establishing a new paradigm for efficient diffusion model inference while maintaining generation quality even at aggressive acceleration ratios. Our codes have been released in Github: \textbfthis https URL
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[CV-67] A Controllable 3D Deepfake Generation Framework with Gaussian Splatting

【速读】：该论文旨在解决传统2D深度伪造（deepfake）方法在几何不一致性、新视角泛化能力差以及三维空间控制性不足等问题。其关键解决方案是提出一种基于3D高斯点绘（3D Gaussian Splatting）的新型深度伪造生成框架，通过将参数化头部模型与动态高斯表示相结合，实现多视角一致的渲染、精确的表情控制和无缝背景融合；同时，显式分离头像与背景高斯点，并利用预训练2D引导优化面部区域跨视角一致性，引入修复模块增强极端姿态和表情下的视觉一致性，从而显著提升多视角渲染质量和三维一致性，弥补了3D建模与深度伪造合成之间的差距。

链接: https://arxiv.org/abs/2509.11624
作者: Wending Liu,Siyun Liang,Huy H. Nguyen,Isao Echizen
机构: The University of Tokyo (东京大学); National Institute of Informatics (信息研究所); Technical University of Munich (慕尼黑工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:We propose a novel 3D deepfake generation framework based on 3D Gaussian Splatting that enables realistic, identity-preserving face swapping and reenactment in a fully controllable 3D space. Compared to conventional 2D deepfake approaches that suffer from geometric inconsistencies and limited generalization to novel view, our method combines a parametric head model with dynamic Gaussian representations to support multi-view consistent rendering, precise expression control, and seamless background integration. To address editing challenges in point-based representations, we explicitly separate the head and background Gaussians and use pre-trained 2D guidance to optimize the facial region across views. We further introduce a repair module to enhance visual consistency under extreme poses and expressions. Experiments on NeRSemble and additional evaluation videos demonstrate that our method achieves comparable performance to state-of-the-art 2D approaches in identity preservation, as well as pose and expression consistency, while significantly outperforming them in multi-view rendering quality and 3D consistency. Our approach bridges the gap between 3D modeling and deepfake synthesis, enabling new directions for scene-aware, controllable, and immersive visual forgeries, revealing the threat that emerging 3D Gaussian Splatting technique could be used for manipulation attacks.
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[CV-68] DUAL-VAD: Dual Benchmarks and Anomaly-Focused Sampling for Video Anomaly Detection

【速读】：该论文旨在解决视频异常检测（Video Anomaly Detection, VAD）领域中现有基准测试局限于帧级或视频级任务、难以全面评估模型泛化能力的问题。其解决方案的关键在于提出一种基于softmax的帧分配策略，该策略优先选择异常密集片段以实现对异常区域的重点采样，同时保持对全视频的覆盖，从而在时间尺度上实现平衡采样；在此基础上构建了两个互补的基准：图像级基准用于评估帧级推理能力，视频级基准则扩展至时序局部化并引入异常评分机制，实验证明该方法在UCF-Crime数据集上于帧级和视频级均取得提升，且消融实验验证了异常聚焦采样相较于均匀和随机采样的显著优势。

链接: https://arxiv.org/abs/2509.11605
作者: Seoik Jung,Taekyung Song,Joshua Jordan Daniel,JinYoung Lee,SungJun Lee
机构: PIA-SPACE Inc.(PIA-SPACE公司); Sejong University(世宗大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages in IEEE double-column format, 1 figure, 5 tables. The paper introduces a unified framework for Video Anomaly Detection (VAD) featuring dual benchmarks and an anomaly-focused sampling strategy

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Abstract:Video Anomaly Detection (VAD) is critical for surveillance and public safety. However, existing benchmarks are limited to either frame-level or video-level tasks, restricting a holistic view of model generalization. This work first introduces a softmax-based frame allocation strategy that prioritizes anomaly-dense segments while maintaining full-video coverage, enabling balanced sampling across temporal scales. Building on this process, we construct two complementary benchmarks. The image-based benchmark evaluates frame-level reasoning with representative frames, while the video-based benchmark extends to temporally localized segments and incorporates an abnormality scoring this http URL on UCF-Crime demonstrate improvements at both the frame and video levels, and ablation studies confirm clear advantages of anomaly-focused sampling over uniform and random baselines.
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[CV-69] Disentangling Content from Style to Overcome Shortcut Learning: A Hybrid Generative-Discriminative Learning Framework

【速读】：该论文旨在解决自监督学习（Self-Supervised Learning, SSL）中因捷径学习（Shortcut Learning）导致的泛化性能瓶颈问题，即模型倾向于依赖输入中的表面特征（如纹理）而非内在结构，从而在未见域上表现不佳。现有方法多通过调整域特定特征的对齐或分离来缓解此问题，但未能从根本上改变促使捷径依赖的学习机制。论文提出HyGDL（Hybrid Generative-Discriminative Learning Framework），其核心在于基于不变性预训练原则（Invariance Pre-training Principle），通过系统性地改变输入中的偏置（如风格）而保持监督信号恒定，迫使模型学习到与风格无关的本质内容；该框架采用单编码器结构，并通过向量投影解析出表示中与风格正交的内容-风格解耦成分，实现了显式的风格-内容分离，从机制层面根除捷径学习的根源。

链接: https://arxiv.org/abs/2509.11598
作者: Siming Fu,Sijun Dong,Xiaoliang Meng
机构: Wuhan University (武汉大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Despite the remarkable success of Self-Supervised Learning (SSL), its generalization is fundamentally hindered by Shortcut Learning, where models exploit superficial features like texture instead of intrinsic structure. We experimentally verify this flaw within the generative paradigm (e.g., MAE) and argue it is a systemic issue also affecting discriminative methods, identifying it as the root cause of their failure on unseen domains. While existing methods often tackle this at a surface level by aligning or separating domain-specific features, they fail to alter the underlying learning mechanism that fosters shortcut dependency. To address this at its core, we propose HyGDL (Hybrid Generative-Discriminative Learning Framework), a hybrid framework that achieves explicit content-style disentanglement. Our approach is guided by the Invariance Pre-training Principle: forcing a model to learn an invariant essence by systematically varying a bias (e.g., style) at the input while keeping the supervision signal constant. HyGDL operates on a single encoder and analytically defines style as the component of a representation that is orthogonal to its style-invariant content, derived via vector projection.
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[CV-70] MVQA-68K: A Multi-dimensional and Causally-annotated Dataset with Quality Interpretability for Video Assessment

【速读】：该论文旨在解决传统视频质量评估（VQA）方法因仅输出单一数值评分而导致的评估结果缺乏全面性和可解释性的问题。其解决方案的关键在于提出一个名为MVQA-68K的多维视频质量评估数据集，该数据集包含超过68,000个精心标注的视频，覆盖七项核心质量维度（整体美学、摄像机运动、动态程度、纹理细节、构图、视觉质量和事实一致性），并为每个标注提供详细的链式思维推理过程，从而显著提升模型的可解释性与评估完整性。实验表明，该数据集能有效增强多种多模态大语言模型（MLLMs）在VQA任务上的性能，并显著改善零样本泛化能力。

链接: https://arxiv.org/abs/2509.11589
作者: Yanyun Pu,Kehan Li,Zeyi Huang,Zhijie Zhong,Kaixiang Yang
机构: Huawei Technologies Co.(华为技术有限公司); South China University of Technology(华南理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:With the rapid advancement of video generation models such as Sora, video quality assessment (VQA) is becoming increasingly crucial for selecting high-quality videos from large-scale datasets used in pre-training. Traditional VQA methods, typically producing single numerical scores, often lack comprehensiveness and interpretability. To address these challenges, we introduce MVQA-68K, a novel multi-dimensional VQA dataset comprising over 68,000 carefully annotated videos, covering seven essential quality dimensions: overall aesthetics, camera movement, dynamic degree, texture detail, composition, visual quality, and factual consistency. Each annotation includes detailed chain-of-thought reasoning to facilitate interpretability and comprehensive understanding. Extensive experiments demonstrate that MVQA-68K significantly enhances the performance of various multimodal large language models (MLLMs) on the VQA task, achieving state-of-the-art results not only on our internal test set (Fig.1) but also on public benchmarks including LSVQ-test, LSVQ-1080p, and LIVE-VQC. Meantime, incorporating explicit reasoning process during VQA training substantially boosts the zero-shot generalization. Code and dataset will be available at github: this https URL
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[CV-71] Optimizing Class Distributions for Bias-Aware Multi-Class Learning

【速读】：该论文旨在解决多类图像分类任务中因类别分布不均衡导致的模型性能偏差问题，尤其是在安全关键场景下需要对特定类别（如“人类”）优先优化性能的需求。解决方案的关键在于提出一种迭代式、以数据为中心的框架BiCDO（Bias-Controlled Class Distribution Optimizer），通过识别帕累托最优的类别分布，在最小化目标函数中的偏差和方差的同时，实现对特定类别的性能优先级控制；该方法无需改变现有训练流程即可集成到任意带标签的多类数据集，并在CIFAR-10和iNaturalist21上验证了其提升模型整体平衡性能的有效性。

链接: https://arxiv.org/abs/2509.11588
作者: Mirco Felske,Stefan Stiene
机构: CLAAS E-Systems GmbH (CLAAS E-Systems GmbH); Hochschule Osnabrück, University of Applied Sciences (奥斯纳布吕克应用科学大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been accepted for the upcoming 59th Hawaii International Conference on System Sciences (HICSS-59)

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Abstract:We propose BiCDO (Bias-Controlled Class Distribution Optimizer), an iterative, data-centric framework that identifies Pareto optimized class distributions for multi-class image classification. BiCDO enables performance prioritization for specific classes, which is useful in safety-critical scenarios (e.g. prioritizing ‘Human’ over ‘Dog’). Unlike uniform distributions, BiCDO determines the optimal number of images per class to enhance reliability and minimize bias and variance in the objective function. BiCDO can be incorporated into existing training pipelines with minimal code changes and supports any labelled multi-class dataset. We have validated BiCDO using EfficientNet, ResNet and ConvNeXt on CIFAR-10 and iNaturalist21 datasets, demonstrating improved, balanced model performance through optimized data distribution.
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[CV-72] Hierarchical Identity Learning for Unsupervised Visible-Infrared Person Re-Identification

【速读】：该论文旨在解决无监督可见光-红外行人重识别（Unsupervised Visible-Infrared Person Re-Identification, USVI-ReID）中因模态差异导致特征不一致的问题，尤其针对现有基于聚类的对比学习方法仅用单一簇中心表示个体、忽视簇内细粒度差异的局限性。其解决方案的关键在于提出层次化身份学习（Hierarchical Identity Learning, HIL）框架：首先对粗粒度聚类结果进行二次聚类以生成多个子簇记忆体，从而捕捉图像间的细粒度变化；进而设计多中心对比学习（Multi-Center Contrastive Learning, MCCL）机制，通过多个簇中心优化模态内聚类并缩小跨模态差异；最后引入双向逆向选择传输（Bidirectional Reverse Selection Transmission, BRST）机制，利用伪标签的双向匹配建立可靠的跨模态对应关系，显著提升跨模态匹配质量。

链接: https://arxiv.org/abs/2509.11587
作者: Haonan Shi,Yubin Wang,De Cheng,Lingfeng He,Nannan Wang,Xinbo Gao
机构: Xidian University (西安电子科技大学); Tongji University (同济大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Unsupervised visible-infrared person re-identification (USVI-ReID) aims to learn modality-invariant image features from unlabeled cross-modal person datasets by reducing the modality gap while minimizing reliance on costly manual annotations. Existing methods typically address USVI-ReID using cluster-based contrastive learning, which represents a person by a single cluster center. However, they primarily focus on the commonality of images within each cluster while neglecting the finer-grained differences among them. To address the limitation, we propose a Hierarchical Identity Learning (HIL) framework. Since each cluster may contain several smaller sub-clusters that reflect fine-grained variations among images, we generate multiple memories for each existing coarse-grained cluster via a secondary clustering. Additionally, we propose Multi-Center Contrastive Learning (MCCL) to refine representations for enhancing intra-modal clustering and minimizing cross-modal discrepancies. To further improve cross-modal matching quality, we design a Bidirectional Reverse Selection Transmission (BRST) mechanism, which establishes reliable cross-modal correspondences by performing bidirectional matching of pseudo-labels. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate that the proposed method outperforms existing approaches. The source code is available at: this https URL.
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[CV-73] Gaussian-Plus-SDF SLAM: High-fidelity 3D Reconstruction at 150 fps

【速读】：该论文旨在解决基于高斯模型的SLAM（Simultaneous Localization and Mapping）方法在处理RGB-D数据时计算效率低下的问题，其典型表现是帧率低于20 fps，远落后于几何导向的方法（如KinectFusion，可达数百fps）。核心瓶颈在于：为建模场景需使用大量高斯分布并进行复杂的迭代优化，若高斯数量不足或优化次数不够，则重建质量显著下降。解决方案的关键在于提出一种高斯-有向距离场（Gaussian-SDF）混合表示：利用SDF高效构建平滑的几何与外观结构（类似几何导向方法），同时仅用少量3D高斯分布对未被SDF充分表达的细节进行局部优化。该设计使高斯数量减少50%，优化迭代次数减少75%，从而实现超过150 fps的实时重建性能（Azure Kinect实测），在保持与现有方法相当重建质量的前提下，相较最优方案提速一个数量级。

链接: https://arxiv.org/abs/2509.11574
作者: Zhexi Peng,Kun Zhou,Tianjia Shao
机构: State Key Lab of CAD&CG, Zhejiang University (浙江大学CAD&CG国家重点实验室); Hangzhou Research Institute of AI and Holographic Technology (杭州人工智能与全息技术研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:While recent Gaussian-based SLAM methods achieve photorealistic reconstruction from RGB-D data, their computational performance remains a critical bottleneck. State-of-the-art techniques operate at less than 20 fps, significantly lagging behind geometry-centric approaches like KinectFusion (hundreds of fps). This limitation stems from the heavy computational burden: modeling scenes requires numerous Gaussians and complex iterative optimization to fit RGB-D data, where insufficient Gaussian counts or optimization iterations cause severe quality degradation. To address this, we propose a Gaussian-SDF hybrid representation, combining a colorized Signed Distance Field (SDF) for smooth geometry and appearance with 3D Gaussians to capture underrepresented details. The SDF is efficiently constructed via RGB-D fusion (as in geometry-centric methods), while Gaussians undergo iterative optimization. Our representation enables drastic Gaussian reduction (50% fewer) by avoiding full-scene Gaussian modeling, and efficient Gaussian optimization (75% fewer iterations) through targeted appearance refinement. Building upon this representation, we develop GPS-SLAM (Gaussian-Plus-SDF SLAM), a real-time 3D reconstruction system achieving over 150 fps on real-world Azure Kinect sequences – delivering an order-of-magnitude speedup over state-of-the-art techniques while maintaining comparable reconstruction quality. We will release the source code and data to facilitate future research.
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[CV-74] How Auxiliary Reasoning Unleashes GUI Grounding in VLMs

【速读】：该论文旨在解决视觉语言模型（VLM）在图形用户界面（GUI）接地任务中表现不佳的问题，尽管其在Pointing Game等指标上展现出潜在的空间理解能力，但在输出显式坐标时性能不足。解决方案的关键在于提出三种零样本辅助推理方法，通过在输入图像中加入明确的空间线索（如坐标轴、网格和标注交点），引导VLM释放其隐含的空间感知能力，从而无需昂贵的数据标注即可显著提升GUI接地性能。

链接: https://arxiv.org/abs/2509.11548
作者: Weiming Li,Yan Shao,Jing Yang,Yujing Lu,Ling Zhong,Yuhan Wang,Manni Duan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Graphical user interface (GUI) grounding is a fundamental task for building GUI agents. However, general vision-language models (VLMs) struggle with this task due to a lack of specific optimization. We identify a key gap in this paper: while VLMs exhibit significant latent grounding potential, as demonstrated by their performance measured by Pointing Game, they underperform when tasked with outputting explicit coordinates. To address this discrepancy, and bypass the high data and annotation costs of current fine-tuning approaches, we propose three zero-shot auxiliary reasoning methods. By providing explicit spatial cues such as axes, grids and labeled intersections as part of the input image, these methods enable VLMs to articulate their implicit spatial understanding capabilities. We evaluate these methods on four GUI grounding benchmarks across seven open-source and proprietary VLMs. The evaluation results demonstrate that the proposed methods substantially improve the performance of GUI grounding.
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[CV-75] SFGNet: Semantic and Frequency Guided Network for Camouflaged Object Detection ICASSP2026

【速读】：该论文旨在解决隐蔽目标检测（Camouflaged Object Detection, COD）中因目标与背景高度相似而导致的边界模糊和结构信息丢失问题。现有方法普遍忽视文本提示（textual prompts）在语义层面的差异以及细粒度频域特征的利用，从而限制了模型对复杂背景和精细边界的感知能力。解决方案的关键在于提出一种语义与频域引导网络（Semantic and Frequency Guided Network, SFGNet），其核心创新包括：1）引入语义提示（semantic prompts）以增强对不同目标类别的区分能力；2）设计多带傅里叶模块（Multi-Band Fourier Module, MBFM）提取并强化频域特征，提升对复杂背景和模糊边界的建模能力；3）构建交互式结构增强块（Interactive Structure Enhancement Block, ISEB），有效保留预测结果中的结构完整性和边界细节。实验表明，该方法在三个COD基准数据集上显著优于当前最优方法。

链接: https://arxiv.org/abs/2509.11539
作者: Dezhen Wang,Haixiang Zhao,Xiang Shen,Sheng Miao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: This paper has been submitted to ICASSP 2026. Copyright 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/republishing, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work. DOI will be added upon IEEE Xplore publication

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Abstract:Camouflaged object detection (COD) aims to segment objects that blend into their surroundings. However, most existing studies overlook the semantic differences among textual prompts of different targets as well as fine-grained frequency features. In this work, we propose a novel Semantic and Frequency Guided Network (SFGNet), which incorporates semantic prompts and frequency-domain features to capture camouflaged objects and improve boundary perception. We further design Multi-Band Fourier Module(MBFM) to enhance the ability of the network in handling complex backgrounds and blurred boundaries. In addition, we design an Interactive Structure Enhancement Block (ISEB) to ensure structural integrity and boundary details in the predictions. Extensive experiments conducted on three COD benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches. The core code of the model is available at the following link: this https URL.
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[CV-76] Multiple Instance Learning Framework with Masked Hard Instance Mining for Gigapixel Histopathology Image Analysis

【速读】：该论文旨在解决当前基于多实例学习（Multiple Instance Learning, MIL）的计算病理学方法在处理全切片图像（Whole Slide Images, WSIs）时存在的偏差问题，即现有方法倾向于关注易于分类的显著实例，而忽视了对诊断更具判别力的难例（hard examples）。其解决方案的关键在于提出一种带掩码硬实例挖掘的新型MIL框架（Masked Hard Instance Mining MIL, MHIM-MIL），通过引入Siamese结构与一致性约束机制，利用类感知实例概率和动量教师模型来动态掩码显著实例并隐式挖掘难例；同时结合大规模随机掩码策略与全局回收网络以确保挖掘出多样且不冗余的难例，并通过指数移动平均更新教师模型，持续识别新难例并稳定训练过程，从而提升模型在癌症诊断、亚型分类及生存分析等任务中的性能与效率。

链接: https://arxiv.org/abs/2509.11526
作者: Wenhao Tang,Sheng Huang,Heng Fang,Fengtao Zhou,Bo Liu,Qingshan Liu
机构: Chongqing University (重庆大学); Hong Kong University of Science and Technology (香港科技大学); Nanjing University of Posts and Telecommunications (南京邮电大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 27 pages, 8 figures

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Abstract:Digitizing pathological images into gigapixel Whole Slide Images (WSIs) has opened new avenues for Computational Pathology (CPath). As positive tissue comprises only a small fraction of gigapixel WSIs, existing Multiple Instance Learning (MIL) methods typically focus on identifying salient instances via attention mechanisms. However, this leads to a bias towards easy-to-classify instances while neglecting challenging ones. Recent studies have shown that hard examples are crucial for accurately modeling discriminative boundaries. Applying such an idea at the instance level, we elaborate a novel MIL framework with masked hard instance mining (MHIM-MIL), which utilizes a Siamese structure with a consistency constraint to explore the hard instances. Using a class-aware instance probability, MHIM-MIL employs a momentum teacher to mask salient instances and implicitly mine hard instances for training the student model. To obtain diverse, non-redundant hard instances, we adopt large-scale random masking while utilizing a global recycle network to mitigate the risk of losing key features. Furthermore, the student updates the teacher using an exponential moving average, which identifies new hard instances for subsequent training iterations and stabilizes optimization. Experimental results on cancer diagnosis, subtyping, survival analysis tasks, and 12 benchmarks demonstrate that MHIM-MIL outperforms the latest methods in both performance and efficiency. The code is available at: this https URL.
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[CV-77] Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs

【速读】：该论文旨在解决当前视觉-语言-动作（Vision-Language-Action, VLA）模型在不同硬件平台（边缘设备与数据中心GPU）上的性能扩展规律及其功耗预算关系不明确的问题。其关键解决方案在于系统性地评估五种代表性VLA模型在LIBERO基准下的准确性及系统级指标（如延迟、吞吐量和峰值内存占用），并在多种边缘功率约束和高性能数据中心GPU配置下进行对比分析，从而揭示架构选择（如动作标记化方式和模型骨干尺寸）对资源效率的影响，并发现边缘设备在特定条件下可达到甚至超越旧款数据中心GPU的性能表现，为VLA模型在多样化部署场景中的优化与选型提供了实证依据。

链接: https://arxiv.org/abs/2509.11480
作者: Amir Taherin,Juyi Lin,Arash Akbari,Arman Akbari,Pu Zhao,Weiwei Chen,David Kaeli,Yanzhi Wang
机构: Northeastern University (东北大学); EmbodyX Inc
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Robotics (cs.RO)
备注: To appear in the Asilomar Conference on Signals, Systems, and Computers 2025

点击查看摘要

Abstract:Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic control, yet their performance scaling across model architectures and hardware platforms, as well as their associated power budgets, remain poorly understood. This work presents an evaluation of five representative VLA models – spanning state-of-the-art baselines and two newly proposed architectures – targeting edge and datacenter GPU platforms. Using the LIBERO benchmark, we measure accuracy alongside system-level metrics, including latency, throughput, and peak memory usage, under varying edge power constraints and high-performance datacenter GPU configurations. Our results identify distinct scaling trends: (1) architectural choices, such as action tokenization and model backbone size, strongly influence throughput and memory footprint; (2) power-constrained edge devices exhibit non-linear performance degradation, with some configurations matching or exceeding older datacenter GPUs; and (3) high-throughput variants can be achieved without significant accuracy loss. These findings provide actionable insights when selecting and optimizing VLAs across a range of deployment constraints. Our work challenges current assumptions about the superiority of datacenter hardware for robotic inference.
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[CV-78] Modality-Aware Infrared and Visible Image Fusion with Target-Aware Supervision ICCV

【速读】：该论文旨在解决红外与可见光图像融合（Infrared and Visible Image Fusion, IVIF）中如何有效整合多模态互补信息、提升关键区域语义一致性及融合结果可解释性的问题。解决方案的关键在于提出FusionNet框架，其核心创新包括：(1) 引入模态感知注意力机制（modality-aware attention mechanism），动态调整红外与可见光特征的贡献权重，以增强任务关键区域的感知能力；(2) 设计像素级α混合模块（pixel-wise alpha blending module），学习空间自适应且内容感知的融合权重，实现细粒度、可解释的融合过程；(3) 提出目标感知损失函数（target-aware loss），利用弱标注感兴趣区域（ROI）监督，强化重要目标（如行人、车辆）所在区域的语义一致性。该方法在M3FD数据集上验证了其在语义保留、感知质量和可解释性方面的优越性能，为下游任务（如目标检测和场景理解）提供了通用且可扩展的多模态融合方案。

链接: https://arxiv.org/abs/2509.11476
作者: Tianyao Sun,Dawei Xiang,Tianqi Ding,Xiang Fang,Yijiashun Qi,Zunduo Zhao
机构: Baylor University (贝勒大学); University of Connecticut (康涅狄格大学); University of Michigan (密歇根大学); New York University (纽约大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted by 2025 6th International Conference on Computer Vision and Data Mining (ICCVDM 2025)

点击查看摘要

Abstract:Infrared and visible image fusion (IVIF) is a fundamental task in multi-modal perception that aims to integrate complementary structural and textural cues from different spectral domains. In this paper, we propose FusionNet, a novel end-to-end fusion framework that explicitly models inter-modality interaction and enhances task-critical regions. FusionNet introduces a modality-aware attention mechanism that dynamically adjusts the contribution of infrared and visible features based on their discriminative capacity. To achieve fine-grained, interpretable fusion, we further incorporate a pixel-wise alpha blending module, which learns spatially-varying fusion weights in an adaptive and content-aware manner. Moreover, we formulate a target-aware loss that leverages weak ROI supervision to preserve semantic consistency in regions containing important objects (e.g., pedestrians, vehicles). Experiments on the public M3FD dataset demonstrate that FusionNet generates fused images with enhanced semantic preservation, high perceptual quality, and clear interpretability. Our framework provides a general and extensible solution for semantic-aware multi-modal image fusion, with benefits for downstream tasks such as object detection and scene understanding.
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[CV-79] Beyond Frame-wise Tracking: A Trajectory-based Paradigm for Efficient Point Cloud Tracking

【速读】：该论文旨在解决LiDAR-based 3D单目标跟踪（3D SOT）中帧级运动估计方法缺乏长期时序上下文、在稀疏或遮挡场景下鲁棒性不足，而序列级方法虽鲁棒但计算成本过高的矛盾问题。其解决方案的关键在于提出一种新颖的基于轨迹（trajectory-based）范式及其具体实现TrajTrack：该框架通过仅利用历史边界框轨迹信息隐式学习运动连续性，无需额外点云输入，从而在保持轻量化的同时显著提升跟踪精度；具体而言，TrajTrack首先生成快速显式的运动初值，再通过隐式运动建模模块预测未来轨迹以优化初始估计，实验证明其在NuScenes大规模基准上相较强基线提升4.48%精度且运行速度达56 FPS，展现出优异性能与通用性。

链接: https://arxiv.org/abs/2509.11453
作者: BaiChen Fan,Sifan Zhou,Jian Li,Shibo Zhao,Muqing Cao,Qin Wang
机构: Nanjing University of Posts and Telecommunications (南京邮电大学); Carnegie Mellon University (卡内基梅隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Robotics (cs.RO)
备注: 9 pages, 7 figures

点击查看摘要

Abstract:LiDAR-based 3D single object tracking (3D SOT) is a critical task in robotics and autonomous systems. Existing methods typically follow frame-wise motion estimation or a sequence-based paradigm. However, the two-frame methods are efficient but lack long-term temporal context, making them vulnerable in sparse or occluded scenes, while sequence-based methods that process multiple point clouds gain robustness at a significant computational cost. To resolve this dilemma, we propose a novel trajectory-based paradigm and its instantiation, TrajTrack. TrajTrack is a lightweight framework that enhances a base two-frame tracker by implicitly learning motion continuity from historical bounding box trajectories alone-without requiring additional, costly point cloud inputs. It first generates a fast, explicit motion proposal and then uses an implicit motion modeling module to predict the future trajectory, which in turn refines and corrects the initial proposal. Extensive experiments on the large-scale NuScenes benchmark show that TrajTrack achieves new state-of-the-art performance, dramatically improving tracking precision by 4.48% over a strong baseline while running at 56 FPS. Besides, we also demonstrate the strong generalizability of TrajTrack across different base trackers. Video is available at this https URL.
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[CV-80] MultiMAE for Brain MRIs: Robustness to Missing Inputs Using Multi-Modal Masked Autoencoder

【速读】：该论文旨在解决医学影像数据中常见但极具挑战性的输入序列缺失问题，尤其在依赖完整输入数据的深度学习模型中表现尤为突出。其解决方案的关键在于提出一种基于掩码自编码器（Masked Autoencoder, MAE）的多模态、多任务预训练框架，将每种MRI序列视为独立模态，利用晚期融合式Transformer编码器整合多序列信息，并为每个模态设计独立的解码器流以实现多任务重建。这种架构不仅使模型能够从可用输入中推理出缺失序列，还通过跨序列推理能力增强了对缺失数据的鲁棒性，从而构建出一个灵活且可迁移的脑部MRI编码器，在下游分割和分类任务中显著优于MAE-ViT基线模型（Dice分数提升10.1%，MCC提升0.46）。

链接: https://arxiv.org/abs/2509.11442
作者: Ayhan Can Erdur,Christian Beischl,Daniel Scholz,Jiazhen Pan,Benedikt Wiestler,Daniel Rueckert,Jan C Peeken
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Official implementation: this https URL

点击查看摘要

Abstract:Missing input sequences are common in medical imaging data, posing a challenge for deep learning models reliant on complete input data. In this work, inspired by MultiMAE [2], we develop a masked autoencoder (MAE) paradigm for multi-modal, multi-task learning in 3D medical imaging with brain MRIs. Our method treats each MRI sequence as a separate input modality, leveraging a late-fusion-style transformer encoder to integrate multi-sequence information (multi-modal) and individual decoder streams for each modality for multi-task reconstruction. This pretraining strategy guides the model to learn rich representations per modality while also equipping it to handle missing inputs through cross-sequence reasoning. The result is a flexible and generalizable encoder for brain MRIs that infers missing sequences from available inputs and can be adapted to various downstream applications. We demonstrate the performance and robustness of our method against an MAE-ViT baseline in downstream segmentation and classification tasks, showing absolute improvement of 10.1 overall Dice score and 0.46 MCC over the baselines with missing input sequences. Our experiments demonstrate the strength of this pretraining strategy. The implementation is made available.
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[CV-81] Disentanglement of Biological and Technical Factors via Latent Space Rotation in Clinical Imaging Improves Disease Pattern Discovery MICCAI2025

【速读】：该论文旨在解决医学影像数据中因成像设备厂商、扫描参数及重建算法差异导致的域偏移（domain shift）问题，这一偏移会干扰生物特征与技术因素的分离，从而影响可泛化的疾病模式识别和生物标志物发现。其解决方案的关键在于提出一种基于后处理旋转（post-hoc rotation）的潜在空间学习方法，通过主动学习域偏移来实现生物因素与技术因素的解耦（disentanglement），从而在不同采集条件下获得稳定且具有生物学意义的组织类型聚类。实验表明，该方法显著提升了聚类一致性（ARI、NMI、Dice指标分别提升19.01%、16.85%、12.39%），并优于四种最先进的数据标准化方法，在特发性肺纤维化患者中进一步验证了其对生存预测的增强效果，证明该无标签框架能有效促进多中心常规影像数据中的生物标志物挖掘。

链接: https://arxiv.org/abs/2509.11436
作者: Jeanny Pan,Philipp Seeböck,Christoph Fürböck,Svitlana Pochepnia,Jennifer Straub,Lucian Beer,Helmut Prosch,Georg Langs
机构: University of Vienna (维也纳大学); Medical University of Vienna (维也纳医科大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: The Fourth Workshop on Applications of Medical Artificial Intelligence, AMAI 2025, Held in Conjunction with MICCAI 2025, Daejeon, Republic of Korea, September 23, 2025, Proceedings

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Abstract:Identifying new disease-related patterns in medical imaging data with the help of machine learning enlarges the vocabulary of rec- ognizable findings. This supports diagnostic and prognostic assessment. However, image appearance varies not only due to biological differences, but also due to imaging technology linked to vendors, scanning- or re- construction parameters. The resulting domain shifts impedes data rep- resentation learning strategies and the discovery of biologically mean- ingful cluster appearances. To address these challenges, we introduce an approach to actively learn the domain shift via post-hoc rotation of the data latent space, enabling disentanglement of biological and tech- nical factors. Results on real-world heterogeneous clinical data show- case that the learned disentangled representation leads to stable clus- ters representing tissue-types across different acquisition settings. Clus- ter consistency is improved by +19.01% (ARI), +16.85% (NMI), and +12.39% (Dice) compared to the entangled representation, outperform- ing four state-of-the-art harmonization methods. When using the clus- ters to quantify tissue composition on idiopathic pulmonary fibrosis pa- tients, the learned profiles enhance Cox survival prediction. This indi- cates that the proposed label-free framework facilitates biomarker dis- covery in multi-center routine imaging data. Code is available on GitHub this https URL.
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[CV-82] Enhancing Generalization in Vision-Language-Action Models by Preserving Pretrained Representations

【速读】：该论文旨在解决直接微调视觉语言模型（Vision-Language Models, VLMs）于机器人数据时，容易破坏预训练特征表示并限制泛化能力的问题。其核心解决方案在于提出一个三组件框架：首先采用双编码器结构，其中冻结视觉编码器以保留预训练特征，而可训练编码器用于任务适应；其次引入基于字符串的动作标记化方法，将连续动作映射为字符序列，使其与模型预训练域对齐；最后设计联合训练策略，融合机器人示范数据与视觉-语言数据集，强化空间推理和物体可用性（affordance）建模。该方案显著提升了机器人在模拟和真实场景中的鲁棒性、指令泛化能力和任务成功率。

链接: https://arxiv.org/abs/2509.11417
作者: Shresth Grover,Akshay Gopalkrishnan,Bo Ai,Henrik I. Christensen,Hao Su,Xuanlin Li
机构: UC San Diego (加州大学圣地亚哥分校); Hillbot; OpenAI (谷歌)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Project Page: this https URL

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Abstract:Vision-language-action (VLA) models finetuned from vision-language models (VLMs) hold the promise of leveraging rich pretrained representations to build generalist robots across diverse tasks and environments. However, direct fine-tuning on robot data often disrupts these representations and limits generalization. We present a framework that better preserves pretrained features while adapting them for robot manipulation. Our approach introduces three components: (i) a dual-encoder design with one frozen vision encoder to retain pretrained features and another trainable for task adaptation, (ii) a string-based action tokenizer that casts continuous actions into character sequences aligned with the model’s pretraining domain, and (iii) a co-training strategy that combines robot demonstrations with vision-language datasets emphasizing spatial reasoning and affordances. Evaluations in simulation and on real robots show that our method improves robustness to visual perturbations, generalization to novel instructions and environments, and overall task success compared to baselines.
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[CV-83] On the Skinning of Gaussian Avatars

【速读】：该论文旨在解决基于高斯溅射（Gaussian splatting）的人体虚拟形象动画中，因线性混合皮肤（Linear Blend Skinning, LBS）无法有效处理高斯分布的非线性旋转特性而导致的形变伪影问题。现有方法通常依赖网格属性来调整非线性高斯旋转或训练额外模型预测校正偏移，增加了复杂性。本文的关键解决方案是提出一种加权旋转混合（Weighted Rotation Blending）方法，利用四元数平均（Quaternion Averaging）实现更合理的旋转插值，从而在不改变线性混合皮肤技术核心逻辑的前提下，仅通过修改LBS策略即可高效地对顶点级高斯分布进行动画处理，并兼容任意高斯光栅化器与渲染引擎。

链接: https://arxiv.org/abs/2509.11411
作者: Nikolaos Zioulis,Nikolaos Kotarelas,Georgios Albanis,Spyridon Thermos,Anargyros Chatzitofis
机构: Moverse
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
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Abstract:Radiance field-based methods have recently been used to reconstruct human avatars, showing that we can significantly downscale the systems needed for creating animated human avatars. Although this progress has been initiated by neural radiance fields, their slow rendering and backward mapping from the observation space to the canonical space have been the main challenges. With Gaussian splatting overcoming both challenges, a new family of approaches has emerged that are faster to train and render, while also straightforward to implement using forward skinning from the canonical to the observation space. However, the linear blend skinning required for the deformation of the Gaussians does not provide valid results for their non-linear rotation properties. To address such artifacts, recent works use mesh properties to rotate the non-linear Gaussian properties or train models to predict corrective offsets. Instead, we propose a weighted rotation blending approach that leverages quaternion averaging. This leads to simpler vertex-based Gaussians that can be efficiently animated and integrated in any engine by only modifying the linear blend skinning technique, and using any Gaussian rasterizer.
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[CV-84] No Modality Left Behind: Dynamic Model Generation for Incomplete Medical Data MICCAI2025

【速读】：该论文旨在解决多模态医学影像数据中因部分缺失而导致深度学习模型训练与应用困难的问题。传统方法要么丢弃不完整的样本，要么依赖插补或改造dropout策略，限制了模型的鲁棒性和泛化能力。其解决方案的关键在于提出一种基于超网络（hypernetwork）的方法，通过动态生成任务特定的分类模型来适应可用模态集合：超网络学习预测任务模型参数，使其根据当前可用模态自动调整，从而实现对所有样本（无论完整性如何）的有效训练与推理，显著提升了模型在缺失模态场景下的适应性与性能。

链接: https://arxiv.org/abs/2509.11406
作者: Christoph Fürböck,Paul Weiser,Branko Mitic,Philipp Seeböck,Thomas Helbich,Georg Langs
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at MICCAI2025 ML-CDS Workshop

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Abstract:In real world clinical environments, training and applying deep learning models on multi-modal medical imaging data often struggles with partially incomplete data. Standard approaches either discard missing samples, require imputation or repurpose dropout learning schemes, limiting robustness and generalizability. To address this, we propose a hypernetwork-based method that dynamically generates task-specific classification models conditioned on the set of available modalities. Instead of training a fixed model, a hypernetwork learns to predict the parameters of a task model adapted to available modalities, enabling training and inference on all samples, regardless of completeness. We compare this approach with (1) models trained only on complete data, (2) state of the art channel dropout methods, and (3) an imputation-based method, using artificially incomplete datasets to systematically analyze robustness to missing modalities. Results demonstrate superior adaptability of our method, outperforming state of the art approaches with an absolute increase in accuracy of up to 8% when trained on a dataset with 25% completeness (75% of training data with missing modalities). By enabling a single model to generalize across all modality configurations, our approach provides an efficient solution for real-world multi-modal medical data analysis.
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[CV-85] MixANT: Observation-dependent Memory Propagation for Stochastic Dense Action Anticipation ICCV2025

【速读】：该论文旨在解决现有状态空间模型（State Space Models, SSMs）在长期密集人类行为预测中因静态遗忘门（forget-gate，即A矩阵）导致的时序记忆能力受限问题。其解决方案的关键在于提出MixANT架构，通过引入专家混合（mixture of experts）机制，根据输入特征动态选择情境相关的A矩阵，从而实现输入依赖的遗忘门调控，在不牺牲计算效率的前提下显著提升模型的表示能力和预测准确性。

链接: https://arxiv.org/abs/2509.11394
作者: Syed Talal Wasim,Hamid Suleman,Olga Zatsarynna,Muzammal Naseer,Juergen Gall
机构: University of Bonn (波恩大学); Lamarr Institute of ML and AI (拉马尔机器学习与人工智能研究所); Khalifa University (哈利法大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ICCV 2025

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Abstract:We present MixANT, a novel architecture for stochastic long-term dense anticipation of human activities. While recent State Space Models (SSMs) like Mamba have shown promise through input-dependent selectivity on three key parameters, the critical forget-gate ( \textbfA matrix) controlling temporal memory remains static. We address this limitation by introducing a mixture of experts approach that dynamically selects contextually relevant \textbfA matrices based on input features, enhancing representational capacity without sacrificing computational efficiency. Extensive experiments on the 50Salads, Breakfast, and Assembly101 datasets demonstrate that MixANT consistently outperforms state-of-the-art methods across all evaluation settings. Our results highlight the importance of input-dependent forget-gate mechanisms for reliable prediction of human behavior in diverse real-world scenarios.
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[CV-86] In-Vivo Skin 3-D Surface Reconstruction and Wrinkle Depth Estimation using Handheld High Resolution Tactile Sensing

【速读】：该论文旨在解决皮肤表面三维（3-D）重建中缺乏便携、高分辨率且经验证设备的问题，尤其是在不同身体部位进行深度重建的准确性与一致性难题。解决方案的关键在于开发了一种基于GelSight触觉成像技术的紧凑型皮肤表面重建探头，结合定制弹性凝胶和基于学习的重建算法，实现了微米级皱纹高度估计；同时集成力传感模块以确保接触一致性，从而在15名无皮肤疾病参与者中首次获得跨多个体表区域的 validated（经验证）皱纹深度指标，并成功检测到保湿剂使用后三处位置皱纹高度的统计学显著降低。

链接: https://arxiv.org/abs/2509.11385
作者: Akhil Padmanabha,Arpit Agarwal,Catherine Li,Austin Williams,Dinesh K. Patel,Sankalp Chopkar,Achu Wilson,Ahmet Ozkan,Wenzhen Yuan,Sonal Choudhary,Arash Mostaghimi,Zackory Erickson,Carmel Majidi
机构: Carnegie Mellon University (卡内基梅隆大学); University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); University of Pittsburgh Medical Center (匹兹堡大学医学中心); Brigham & Women’s Hospital (布莱根妇女医院)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:

点击查看摘要

Abstract:Three-dimensional (3-D) skin surface reconstruction offers promise for objective and quantitative dermatological assessment, but no portable, high-resolution device exists that has been validated and used for depth reconstruction across various body locations. We present a compact 3-D skin reconstruction probe based on GelSight tactile imaging with a custom elastic gel and a learning-based reconstruction algorithm for micron-level wrinkle height estimation. Our probe, integrated into a handheld probe with force sensing for consistent contact, achieves a mean absolute error of 12.55 micron on wrinkle-like test objects. In a study with 15 participants without skin disorders, we provide the first validated wrinkle depth metrics across multiple body regions. We further demonstrate statistically significant reductions in wrinkle height at three locations following over-the-counter moisturizer application. Our work offers a validated tool for clinical and cosmetic skin analysis, with potential applications in diagnosis, treatment monitoring, and skincare efficacy evaluation.
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[CV-87] PersonaX: Multimodal Datasets with LLM -Inferred Behavior Traits

【速读】：该论文旨在解决现有数据集普遍缺乏多模态整合的问题，即如何有效结合行为特征描述与面部属性、生平信息等互补模态以实现对人类公共特质的全面分析。其解决方案的关键在于构建PersonaX这一多模态数据集，包含CelebPersona（9444名公众人物）和AthlePersona（4181名职业运动员），每个样本均包含由三个高性能大语言模型（Large Language Models, LLMs）推断出的行为特质评分、面部图像及结构化生平特征；同时提出一种专为多模态和多测量数据设计的因果表示学习（Causal Representation Learning, CRL）框架，提供理论可识别性保障，并通过统计独立性检验与实证实验验证其有效性，从而推动基于LLM推理行为特质的多模态分析与因果推理研究。

链接: https://arxiv.org/abs/2509.11362
作者: Loka Li,Wong Yu Kang,Minghao Fu,Guangyi Chen,Zhenhao Chen,Gongxu Luo,Yuewen Sun,Salman Khan,Peter Spirtes,Kun Zhang
机构: Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学); Carnegie Mellon University (卡内基梅隆大学); University of California San Diego (加州大学圣地亚哥分校); Australian National University (澳大利亚国立大学)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Understanding human behavior traits is central to applications in human-computer interaction, computational social science, and personalized AI systems. Such understanding often requires integrating multiple modalities to capture nuanced patterns and relationships. However, existing resources rarely provide datasets that combine behavioral descriptors with complementary modalities such as facial attributes and biographical information. To address this gap, we present PersonaX, a curated collection of multimodal datasets designed to enable comprehensive analysis of public traits across modalities. PersonaX consists of (1) CelebPersona, featuring 9444 public figures from diverse occupations, and (2) AthlePersona, covering 4181 professional athletes across 7 major sports leagues. Each dataset includes behavioral trait assessments inferred by three high-performing large language models, alongside facial imagery and structured biographical features. We analyze PersonaX at two complementary levels. First, we abstract high-level trait scores from text descriptions and apply five statistical independence tests to examine their relationships with other modalities. Second, we introduce a novel causal representation learning (CRL) framework tailored to multimodal and multi-measurement data, providing theoretical identifiability guarantees. Experiments on both synthetic and real-world data demonstrate the effectiveness of our approach. By unifying structured and unstructured analysis, PersonaX establishes a foundation for studying LLM-inferred behavioral traits in conjunction with visual and biographical attributes, advancing multimodal trait analysis and causal reasoning.
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[CV-88] GLaVE-Cap: Global-Local Aligned Video Captioning with Vision Expert Integration

【速读】：该论文旨在解决当前视频细粒度描述（Video Detailed Captioning）任务中，基于局部到全局（local-to-global）范式的生成结果缺乏细节且上下文不一致的问题。具体而言，现有方法在局部caption生成阶段缺乏细粒度控制机制，在局部与全局caption之间也存在交互薄弱的问题。为解决上述问题，论文提出GLaVE-Cap框架，其核心创新在于两个模块：TrackFusion通过引入视觉专家（Vision Expert）获取跨帧视觉提示，并采用双流结构实现全面的局部caption生成；CaptionBridge则建立局部与全局caption之间的动态交互机制，利用全局上下文引导局部描述，并自适应地将局部caption整合为连贯的全局caption。这一设计显著提升了生成caption的细节丰富性和语义一致性。

链接: https://arxiv.org/abs/2509.11360
作者: Wan Xu,Feng Zhu,Yihan Zeng,Yuanfan Guo,Ming Liu,Hang Xu,Wangmeng Zuo
机构: Harbin Institute of Technology (哈尔滨工业大学); Huawei Noah’s Ark Lab (华为诺亚方舟实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Video detailed captioning aims to generate comprehensive video descriptions to facilitate video understanding. Recently, most efforts in the video detailed captioning community have been made towards a local-to-global paradigm, which first generates local captions from video clips and then summarizes them into a global caption. However, we find this paradigm leads to less detailed and contextual-inconsistent captions, which can be attributed to (1) no mechanism to ensure fine-grained captions, and (2) weak interaction between local and global captions. To remedy the above two issues, we propose GLaVE-Cap, a Global-Local aligned framework with Vision Expert integration for Captioning, which consists of two core modules: TrackFusion enables comprehensive local caption generation, by leveraging vision experts to acquire cross-frame visual prompts, coupled with a dual-stream structure; while CaptionBridge establishes a local-global interaction, by using global context to guide local captioning, and adaptively summarizing local captions into a coherent global caption. Besides, we construct GLaVE-Bench, a comprehensive video captioning benchmark featuring 5X more queries per video than existing benchmarks, covering diverse visual dimensions to facilitate reliable evaluation. We further provide a training dataset GLaVE-1.2M containing 16K high-quality fine-grained video captions and 1.2M related question-answer pairs. Extensive experiments on four benchmarks show that our GLaVE-Cap achieves state-of-the-art performance. Besides, the ablation studies and student model analyses further validate the effectiveness of the proposed modules and the contribution of GLaVE-1.2M to the video understanding community. The source code, model weights, benchmark, and dataset will be open-sourced.
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[CV-89] Promoting Shape Bias in CNNs: Frequency-Based and Contrastive Regularization for Corruption Robustness

【速读】：该论文旨在解决卷积神经网络（Convolutional Neural Networks, CNNs）在图像分类任务中对常见图像退化（common corruptions）敏感的问题，这类退化人类视觉系统能轻松应对。其核心原因在于CNN过度依赖局部纹理线索（local texture cues），而忽视了全局物体形状（global object shapes），与人类感知机制存在显著差异。解决方案的关键在于提出两种互补的正则化策略：一是引入辅助损失函数，强制原始输入与低频滤波输入之间的特征一致性，从而抑制对高频纹理的依赖；二是采用监督对比学习（supervised contrastive learning），构建以类别一致且形状相关表示为核心的特征空间结构。实验表明，这两种方法均能在不损害干净准确率的前提下提升模型在CIFAR-10-C基准上的鲁棒性，证明损失级正则化可有效引导CNN向更具形状感知能力的表示演化。

链接: https://arxiv.org/abs/2509.11355
作者: Robin Narsingh Ranabhat,Longwei Wang,Amit Kumar Patel,KC santosh
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Convolutional Neural Networks (CNNs) excel at image classification but remain vulnerable to common corruptions that humans handle with ease. A key reason for this fragility is their reliance on local texture cues rather than global object shapes – a stark contrast to human perception. To address this, we propose two complementary regularization strategies designed to encourage shape-biased representations and enhance robustness. The first introduces an auxiliary loss that enforces feature consistency between original and low-frequency filtered inputs, discouraging dependence on high-frequency textures. The second incorporates supervised contrastive learning to structure the feature space around class-consistent, shape-relevant representations. Evaluated on the CIFAR-10-C benchmark, both methods improve corruption robustness without degrading clean accuracy. Our results suggest that loss-level regularization can effectively steer CNNs toward more shape-aware, resilient representations.
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[CV-90] Beyond Instance Consistency: Investigating View Diversity in Self-supervised Learning

【速读】：该论文旨在解决自监督学习（Self-supervised Learning, SSL）在非标志性数据（non-iconic data）场景下，传统基于实例一致性（instance consistency）假设失效时的表征学习有效性问题。其核心挑战在于：当不同视图可能包含不同对象或语义信息时，传统的正样本对（positive pairs）不再满足严格的一致性要求，从而影响模型性能。解决方案的关键在于通过系统性消融实验发现，即便缺乏严格的实例一致性，SSL仍可学习到有意义的表示；进一步提出增加视图多样性（如强制零重叠或使用较小裁剪尺度）能提升分类和密集预测任务的下游性能，但过度多样性会降低效果，因此存在一个最优的视图多样性区间。作者引入地球移动距离（Earth Mover’s Distance, EMD）作为视图间互信息的估计器，发现适度的EMD值与SSL学习性能正相关，为未来SSL框架设计提供了量化依据。

链接: https://arxiv.org/abs/2509.11344
作者: Huaiyuan Qin,Muli Yang,Siyuan Hu,Peng Hu,Yu Zhang,Chen Gong,Hongyuan Zhu
机构: Institute for Infocomm Research (I2R), A*STAR, Singapore; National University of Singapore, Singapore; Sichuan University, China; Southeast University, China; Shanghai Jiaotong University, China
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Self-supervised learning (SSL) conventionally relies on the instance consistency paradigm, assuming that different views of the same image can be treated as positive pairs. However, this assumption breaks down for non-iconic data, where different views may contain distinct objects or semantic information. In this paper, we investigate the effectiveness of SSL when instance consistency is not guaranteed. Through extensive ablation studies, we demonstrate that SSL can still learn meaningful representations even when positive pairs lack strict instance consistency. Furthermore, our analysis further reveals that increasing view diversity, by enforcing zero overlapping or using smaller crop scales, can enhance downstream performance on classification and dense prediction tasks. However, excessive diversity is found to reduce effectiveness, suggesting an optimal range for view diversity. To quantify this, we adopt the Earth Mover’s Distance (EMD) as an estimator to measure mutual information between views, finding that moderate EMD values correlate with improved SSL learning, providing insights for future SSL framework design. We validate our findings across a range of settings, highlighting their robustness and applicability on diverse data sources.
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[CV-91] Dual Band Video Thermography Near Ambient Conditions

【速读】：该论文旨在解决热成像视频中反射光与发射光成分难以分离的问题，这对准确估计物体的发射率（emissivity）、温度及形状等物理属性至关重要。在近环境温度条件下，反射和发射成分通常具有相近的强度且随时间动态变化，传统方法往往假设其中一种成分占主导或为恒定值，这限制了其在计算机视觉应用中的适用性。解决方案的关键在于提出首个基于双波段热成像的图像形成模型，并利用两台具有不同光谱敏感性的热相机获取数据，从而开发出能够同时估计表面发射率、时变温度并隔离动态背景的算法，实现了对反射与发射成分的有效分离。

链接: https://arxiv.org/abs/2509.11334
作者: Sriram Narayanan,Mani Ramanagopal,Srinivasa G. Narasimhan
机构: Carnegie Mellon University (卡内基梅隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Long-wave infrared radiation captured by a thermal camera consists of two components: (a) light from the environment reflected or transmitted by a surface, and (b) light emitted by the surface after undergoing heat transport through the object and exchanging heat with the surrounding environment. Separating these components is essential for understanding object properties such as emissivity, temperature, reflectance and shape. Previous thermography studies often assume that only one component is dominant (e.g., in welding) or that the second component is constant and can be subtracted. However, in near-ambient conditions, which are most relevant to computer vision applications, both components are typically comparable in magnitude and vary over time. We introduce the first method that separates reflected and emitted components of light in videos captured by two thermal cameras with different spectral sensitivities. We derive a dual-band thermal image formation model and develop algorithms to estimate the surface’s emissivity and its time-varying temperature while isolating a dynamic background. We quantitatively evaluate our approach using carefully calibrated emissivities for a range of materials and show qualitative results on complex everyday scenes, such as a glass filled with hot liquid and people moving in the background.
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[CV-92] oward Next-generation Medical Vision Backbones: Modeling Finer-grained Long-range Visual Dependency MICCAI2025

【速读】：该论文旨在解决医学图像计算（Medical Image Computing, MIC）中细粒度长程视觉依赖建模的难题，即如何有效捕捉医学图像中全局远距离上下文与局部细微视觉特征之间的关系。传统卷积神经网络（Convolutional Neural Networks, CNNs）受限于固有的局部性，难以建模长程依赖；而Transformer虽擅长长程建模，但因自注意力机制计算开销高，无法直接处理高分辨率特征（如未下采样或未分块的全图特征），从而难以刻画医学图像中细微结构的精细依赖关系。解决方案的关键在于提出并验证了基于多层感知机（Multi-layer Perceptron, MLP）的视觉模型作为替代范式：实验表明，MLP在保持计算和内存效率的同时，能够有效建模更高分辨率医学特征中的细粒度长程依赖，优于Transformer和CNN，在多种医学视觉任务中持续提升性能，为下一代医学图像分析骨干网络提供了新方向。

链接: https://arxiv.org/abs/2509.11328
作者: Mingyuan Meng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Invited as Long Oral Presentation (Top 8) at MICCAI 2025 Doctoral Consortium

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Abstract:Medical Image Computing (MIC) is a broad research topic covering both pixel-wise (e.g., segmentation, registration) and image-wise (e.g., classification, regression) vision tasks. Effective analysis demands models that capture both global long-range context and local subtle visual characteristics, necessitating fine-grained long-range visual dependency modeling. Compared to Convolutional Neural Networks (CNNs) that are limited by intrinsic locality, transformers excel at long-range modeling; however, due to the high computational loads of self-attention, transformers typically cannot process high-resolution features (e.g., full-scale image features before downsampling or patch embedding) and thus face difficulties in modeling fine-grained dependency among subtle medical image details. Concurrently, Multi-layer Perceptron (MLP)-based visual models are recognized as computation/memory-efficient alternatives in modeling long-range visual dependency but have yet to be widely investigated in the MIC community. This doctoral research advances deep learning-based MIC by investigating effective long-range visual dependency modeling. It first presents innovative use of transformers for both pixel- and image-wise medical vision tasks. The focus then shifts to MLPs, pioneeringly developing MLP-based visual models to capture fine-grained long-range visual dependency in medical images. Extensive experiments confirm the critical role of long-range dependency modeling in MIC and reveal a key finding: MLPs provide feasibility in modeling finer-grained long-range dependency among higher-resolution medical features containing enriched anatomical/pathological details. This finding establishes MLPs as a superior paradigm over transformers/CNNs, consistently enhancing performance across various medical vision tasks and paving the way for next-generation medical vision backbones.
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[CV-93] Motion Estimation for Multi-Object Tracking using KalmanNet with Semantic-Independent Encoding

【速读】：该论文旨在解决多目标跟踪（Multi-Object Tracking, MOT）中运动估计（Motion Estimation）的准确性与鲁棒性问题，尤其是在传统卡尔曼滤波（Kalman Filter, KF）因参数不匹配或目标运动非平稳时性能下降的情形。其解决方案的关键在于提出一种名为语义无关卡尔曼网络（Semantic-Independent KalmanNet, SIKNet）的新方法，该方法通过一个语义无关编码器（Semantic-Independent Encoder, SIE）对状态向量进行分步编码：首先使用1D卷积（核大小为1）沿同质语义维度提取独立语义信息；随后通过全连接层和非线性激活函数捕捉异质语义元素间的非线性及交叉依赖关系，从而实现更精准的运动建模。实验表明，SIKNet在自建的大规模半仿真数据集上显著优于传统KF及现有学习辅助滤波方法。

链接: https://arxiv.org/abs/2509.11323
作者: Jian Song,Wei Mei,Yunfeng Xu,Qiang Fu,Renke Kou,Lina Bu,Yucheng Long
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Motion estimation is a crucial component in multi-object tracking (MOT). It predicts the trajectory of objects by analyzing the changes in their positions in consecutive frames of images, reducing tracking failures and identity switches. The Kalman filter (KF) based on the linear constant-velocity model is one of the most commonly used methods in MOT. However, it may yield unsatisfactory results when KF’s parameters are mismatched and objects move in non-stationary. In this work, we utilize the learning-aided filter to handle the motion estimation of MOT. In particular, we propose a novel method named Semantic-Independent KalmanNet (SIKNet), which encodes the state vector (the input feature) using a Semantic-Independent Encoder (SIE) by two steps. First, the SIE uses a 1D convolution with a kernel size of 1, which convolves along the dimension of homogeneous-semantic elements across different state vectors to encode independent semantic information. Then it employs a fully-connected layer and a nonlinear activation layer to encode nonlinear and cross-dependency information between heterogeneous-semantic elements. To independently evaluate the performance of the motion estimation module in MOT, we constructed a large-scale semi-simulated dataset from several open-source MOT datasets. Experimental results demonstrate that the proposed SIKNet outperforms the traditional KF and achieves superior robustness and accuracy than existing learning-aided filters. The code is available at (this https URL and this https URL). Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2509.11323 [cs.CV] (or arXiv:2509.11323v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2509.11323 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Jian Song [view email] [v1] Sun, 14 Sep 2025 15:57:46 UTC (3,128 KB) Full-text links: Access Paper: View a PDF of the paper titled Motion Estimation for Multi-Object Tracking using KalmanNet with Semantic-Independent Encoding, by Jian Song and 5 other authorsView PDFTeX SourceOther Formats view license Current browse context: cs.CV prev | next new | recent | 2025-09 Change to browse by: cs cs.AI References Citations NASA ADSGoogle Scholar Semantic Scholar 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-94] UnLoc: Leverag ing Depth Uncertainties for Floorplan Localization

【速读】：该论文旨在解决顺序相机定位（sequential camera localization）中的两大关键问题：一是现有方法在深度预测中缺乏不确定性建模，导致定位结果可靠性不足；二是依赖为每个环境单独训练的深度网络，限制了模型在未见场景中的泛化能力。其解决方案的核心在于提出一种新颖的概率模型，将深度预测显式建模为概率分布，从而实现不确定性估计，并通过引入现成的预训练单目深度模型，避免了对特定环境的深度网络定制，显著提升了模型在不同场景下的鲁棒性和泛化性能。

链接: https://arxiv.org/abs/2509.11301
作者: Matthias Wüest,Francis Engelmann,Ondrej Miksik,Marc Pollefeys,Daniel Barath
机构: ETH Zurich (苏黎世联邦理工学院); Stanford University (斯坦福大学); Microsoft
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We propose UnLoc, an efficient data-driven solution for sequential camera localization within floorplans. Floorplan data is readily available, long-term persistent, and robust to changes in visual appearance. We address key limitations of recent methods, such as the lack of uncertainty modeling in depth predictions and the necessity for custom depth networks trained for each environment. We introduce a novel probabilistic model that incorporates uncertainty estimation, modeling depth predictions as explicit probability distributions. By leveraging off-the-shelf pre-trained monocular depth models, we eliminate the need to rely on per-environment-trained depth networks, enhancing generalization to unseen spaces. We evaluate UnLoc on large-scale synthetic and real-world datasets, demonstrating significant improvements over existing methods in terms of accuracy and robustness. Notably, we achieve 2.7 times higher localization recall on long sequences (100 frames) and 16.7 times higher on short ones (15 frames) than the state of the art on the challenging LaMAR HGE dataset.
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[CV-95] Leverag ing Geometric Priors for Unaligned Scene Change Detection

【速读】：该论文旨在解决**未对齐场景变化检测（Unaligned Scene Change Detection, Unaligned SCD）中的核心挑战，即在图像对因视角差异导致视觉信息不一致时，如何可靠地识别视觉重叠区域、建立鲁棒的跨图像对应关系并显式检测遮挡。现有方法仅依赖二维视觉线索进行匹配，在大视角变化下易出现漂移或失败，且受限于小规模数据集提供的二维变化掩码，难以学习泛化性强的多视角知识。其关键解决方案是首次引入来自几何基础模型（Geometric Foundation Model）**的几何先验，通过融合视觉基础模型的强大表征能力，构建了一个无需训练的框架，从而在视角不对齐条件下实现更可靠的变化检测性能。

链接: https://arxiv.org/abs/2509.11292
作者: Ziling Liu,Ziwei Chen,Mingqi Gao,Jinyu Yang,Feng Zheng
机构: Southern University of Science and Technology (南方科技大学); University of Sheffield (谢菲尔德大学); Tapall.ai; Spatialtemporal AI
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Unaligned Scene Change Detection aims to detect scene changes between image pairs captured at different times without assuming viewpoint alignment. To handle viewpoint variations, current methods rely solely on 2D visual cues to establish cross-image correspondence to assist change detection. However, large viewpoint changes can alter visual observations, causing appearance-based matching to drift or fail. Additionally, supervision limited to 2D change masks from small-scale SCD datasets restricts the learning of generalizable multi-view knowledge, making it difficult to reliably identify visual overlaps and handle occlusions. This lack of explicit geometric reasoning represents a critical yet overlooked limitation. In this work, we are the first to leverage geometric priors from a Geometric Foundation Model to address the core challenges of unaligned SCD, including reliable identification of visual overlaps, robust correspondence establishment, and explicit occlusion detection. Building on these priors, we propose a training-free framework that integrates them with the powerful representations of a visual foundation model to enable reliable change detection under viewpoint misalignment. Through extensive evaluation on the PSCD, ChangeSim, and PASLCD datasets, we demonstrate that our approach achieves superior and robust performance. Our code will be released at this https URL.
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[CV-96] ROSGS: Relightable Outdoor Scenes With Gaussian Splatting

【速读】：该论文旨在解决户外场景中几何、反射率与光照的分解难题，尤其针对现有基于NeRF或3D高斯泼溅（3DGS）方法存在的计算开销大和光照表示频率受限的问题。其解决方案的关键在于提出一种两阶段框架ROS GS：首先利用单目法向先验信息，通过紧凑的2D高斯泼溅（2DGS）表示高效重建场景几何；随后在该几何基础上，构建混合光照模型——以球面高斯函数捕捉阳光的高频率方向性成分，同时借助球谐系数学习辐射传输函数以全面建模低频天光，从而实现高精度且高效的户外场景重光照（relighting）。

链接: https://arxiv.org/abs/2509.11275
作者: Lianjun Liao,Chunhui Zhang,Tong Wu,Henglei Lv,Bailin Deng,Lin Gao
机构: North China University of Technology (华北理工大学); Institute of Computing Technology, Chinese Academy of Sciences (中国科学院计算技术研究所); University of Chinese Academy of Sciences (中国科学院大学); Cardiff University (卡迪夫大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:Image data captured outdoors often exhibit unbounded scenes and unconstrained, varying lighting conditions, making it challenging to decompose them into geometry, reflectance, and illumination. Recent works have focused on achieving this decomposition using Neural Radiance Fields (NeRF) or the 3D Gaussian Splatting (3DGS) representation but remain hindered by two key limitations: the high computational overhead associated with neural networks of NeRF and the use of low-frequency lighting representations, which often result in inefficient rendering and suboptimal relighting accuracy. We propose ROSGS, a two-stage pipeline designed to efficiently reconstruct relightable outdoor scenes using the Gaussian Splatting representation. By leveraging monocular normal priors, ROSGS first reconstructs the scene’s geometry with the compact 2D Gaussian Splatting (2DGS) representation, providing an efficient and accurate geometric foundation. Building upon this reconstructed geometry, ROSGS then decomposes the scene’s texture and lighting through a hybrid lighting model. This model effectively represents typical outdoor lighting by employing a spherical Gaussian function to capture the directional, high-frequency components of sunlight, while learning a radiance transfer function via Spherical Harmonic coefficients to model the remaining low-frequency skylight comprehensively. Both quantitative metrics and qualitative comparisons demonstrate that ROSGS achieves state-of-the-art performance in relighting outdoor scenes and highlight its ability to deliver superior relighting accuracy and rendering efficiency.
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[CV-97] Synthetic Dataset Evaluation Based on Generalized Cross Validation

【速读】：该论文旨在解决当前合成数据集（synthetic dataset）质量评估缺乏统一标准框架的问题，以支持生成式 AI (Generative AI) 研究中对合成数据的优化与应用。其解决方案的关键在于提出一个融合广义交叉验证（Generalized Cross-Validation, GCV）实验设计与领域迁移学习（domain transfer learning）原理的新评估框架：通过在合成数据和多个真实世界基准数据集（如 KITTI、BDD100K）上训练特定任务模型（如 YOLOv5s），构建跨性能矩阵并进行归一化处理，进而形成 GCV 矩阵来量化领域迁移能力；同时引入两个核心指标——模拟质量指标用于衡量合成数据与真实数据的相似性，转移质量指标用于评估合成数据在不同真实场景中的多样性与覆盖度，从而实现可扩展、可量化的合成数据质量评估。

链接: https://arxiv.org/abs/2509.11273
作者: Zhihang Song,Dingyi Yao,Ruibo Ming,Lihui Peng,Danya Yao,Yi Zhang
机构: Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted for publication in IST 2025. Official IEEE Xplore entry will be available once published

点击查看摘要

Abstract:With the rapid advancement of synthetic dataset generation techniques, evaluating the quality of synthetic data has become a critical research focus. Robust evaluation not only drives innovations in data generation methods but also guides researchers in optimizing the utilization of these synthetic resources. However, current evaluation studies for synthetic datasets remain limited, lacking a universally accepted standard framework. To address this, this paper proposes a novel evaluation framework integrating generalized cross-validation experiments and domain transfer learning principles, enabling generalizable and comparable assessments of synthetic dataset quality. The framework involves training task-specific models (e.g., YOLOv5s) on both synthetic datasets and multiple real-world benchmarks (e.g., KITTI, BDD100K), forming a cross-performance matrix. Following normalization, a Generalized Cross-Validation (GCV) Matrix is constructed to quantify domain transferability. The framework introduces two key metrics. One measures the simulation quality by quantifying the similarity between synthetic data and real-world datasets, while another evaluates the transfer quality by assessing the diversity and coverage of synthetic data across various real-world scenarios. Experimental validation on Virtual KITTI demonstrates the effectiveness of our proposed framework and metrics in assessing synthetic data fidelity. This scalable and quantifiable evaluation solution overcomes traditional limitations, providing a principled approach to guide synthetic dataset optimization in artificial intelligence research.
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[CV-98] SelectMix: Enhancing Label Noise Robustness through Targeted Sample Mixing

【速读】：该论文旨在解决深度神经网络在训练过程中对噪声标签（noisy labels）的过度记忆问题，这一现象会显著损害模型的泛化性能。现有基于Mixup的方法通常采用无差别混合策略，缺乏对样本选择和混合方式的合理指导，反而可能传播噪声监督信号。其解决方案的关键在于提出SelectMix框架，该框架通过K折交叉验证进行置信度不匹配分析，识别出潜在的噪声或模糊样本，并仅将这些不确定样本与来自其潜在类别中置信预测样本进行选择性混合；同时，利用所有参与混合类别的软标签（soft labels）来精确反映混合样本的真实组成，从而确保监督信号与实际输入高度一致，有效提升模型在噪声标签环境下的鲁棒性和泛化能力。

链接: https://arxiv.org/abs/2509.11265
作者: Qiuhao Liu,Ling Li,Yao Lu,Qi Xuan,Zhaowei Zhu,Jiaheng Wei
机构: Hong Kong University of Science and Technology (Guangzhou) (香港科技大学(广州)); Tsinghua University (清华大学); Guangdong Provincial Key Laboratory of Artificial Intelligence (广东省人工智能重点实验室)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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Abstract:Deep neural networks tend to memorize noisy labels, severely degrading their generalization performance. Although Mixup has demonstrated effectiveness in improving generalization and robustness, existing Mixup-based methods typically perform indiscriminate mixing without principled guidance on sample selection and mixing strategy, inadvertently propagating noisy supervision. To overcome these limitations, we propose SelectMix, a confidence-guided mixing framework explicitly tailored for noisy labels. SelectMix first identifies potentially noisy or ambiguous samples through confidence based mismatch analysis using K-fold cross-validation, then selectively blends identified uncertain samples with confidently predicted peers from their potential classes. Furthermore, SelectMix employs soft labels derived from all classes involved in the mixing process, ensuring the labels accurately represent the composition of the mixed samples, thus aligning supervision signals closely with the actual mixed inputs. Through extensive theoretical analysis and empirical evaluations on multiple synthetic (MNIST, Fashion-MNIST, CIFAR-10, CIFAR-100) and real-world benchmark datasets (CIFAR-N, MNIST and Clothing1M), we demonstrate that SelectMix consistently outperforms strong baseline methods, validating its effectiveness and robustness in learning with noisy labels.
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[CV-99] Cross-Domain Attribute Alignment with CLIP: A Rehearsal-Free Approach for Class-Incremental Unsupervised Domain Adaptation ACM-MM2025

【速读】：该论文旨在解决类增量无监督域适应（Class-Incremental Unsupervised Domain Adaptation, CI-UDA）中的灾难性遗忘与域偏移问题。在CI-UDA场景中，模型需持续适应不同时间步的未标注目标域，且各阶段的目标类别互不重叠但均属于源域类别集合；传统方法通常依赖存储历史目标样本进行回放（rehearsal），并仅对跨域共享类别进行对齐，导致内存不断增长且因不对称对齐引发知识遗忘。本文的关键创新在于提出一种无需回放机制的解决方案：通过CLIP提取类无关属性（attribute），构建视觉原型（key）与文本提示（value）组成的“键值对”表示，并维护两个对应不同域的属性字典；通过跨域属性对齐实现视觉注意力一致性和预测一致性，从而在缓解域偏移的同时有效减少灾难性遗忘，显著提升模型在多个CI-UDA基准上的性能表现。

链接: https://arxiv.org/abs/2509.11264
作者: Kerun Mi,Guoliang Kang,Guangyu Li,Lin Zhao,Tao Zhou,Chen Gong
机构: Nanjing University of Science and Technology (南京理工大学); Beihang University (北京航空航天大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to ACM MM 2025

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Abstract:Class-Incremental Unsupervised Domain Adaptation (CI-UDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where the sets of potential target classes appearing at different time steps are disjoint and are subsets of the source classes. The key to solving this problem lies in avoiding catastrophic forgetting of knowledge about previous target classes during continuously mitigating the domain shift. Most previous works cumbersomely combine two technical components. On one hand, they need to store and utilize rehearsal target sample from previous time steps to avoid catastrophic forgetting; on the other hand, they perform alignment only between classes shared across domains at each time step. Consequently, the memory will continuously increase and the asymmetric alignment may inevitably result in knowledge forgetting. In this paper, we propose to mine and preserve domain-invariant and class-agnostic knowledge to facilitate the CI-UDA task. Specifically, via using CLIP, we extract the class-agnostic properties which we name as “attribute”. In our framework, we learn a “key-value” pair to represent an attribute, where the key corresponds to the visual prototype and the value is the textual prompt. We maintain two attribute dictionaries, each corresponding to a different domain. Then we perform attribute alignment across domains to mitigate the domain shift, via encouraging visual attention consistency and prediction consistency. Through attribute modeling and cross-domain alignment, we effectively reduce catastrophic knowledge forgetting while mitigating the domain shift, in a rehearsal-free way. Experiments on three CI-UDA benchmarks demonstrate that our method outperforms previous state-of-the-art methods and effectively alleviates catastrophic forgetting. Code is available at this https URL.
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[CV-100] Realistic Environmental Injection Attacks on GUI Agents

【速读】：该论文旨在解决当前基于大视觉语言模型（Large Vision-Language Models, LVLMs）的图形用户界面（GUI）代理在开放世界环境中易受环境注入攻击（Environmental Injection Attacks, EIAs）的问题。现有攻击方法假设触发图像位置和上下文固定、且面积较大，与真实网页动态变化、小尺寸图像嵌入的场景不符，导致其有效性受限。为此，作者提出Chameleon攻击框架，其关键创新在于：一是LLM驱动的环境模拟（LLM-Driven Environment Simulation），可自动生成多样化且高保真的网页仿真环境；二是注意力黑洞机制（Attention Black Hole），将注意力权重转化为显式监督信号，引导代理聚焦于触发区域。实验证明，该框架在6个真实网站和4种LVLM驱动的GUI代理上显著优于现有方法，揭示了现代GUI代理中未被充分探索的漏洞，并为未来开放世界GUI代理系统的防御研究奠定基础。

链接: https://arxiv.org/abs/2509.11250
作者: Yitong Zhang,Ximo Li,Liyi Cai,Jia Li
机构: Tsinghua University (清华大学); Beihang University (北京航空航天大学); Peking University (北京大学)
类目: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:GUI agents built on LVLMs are increasingly used to interact with websites. However, their exposure to open-world content makes them vulnerable to Environmental Injection Attacks (EIAs) that hijack agent behavior via webpage elements. Many recent studies assume the attacker to be a regular user who can only upload a single trigger image, which is more realistic than earlier assumptions of website-level administrative control. However, these works still fall short of realism: (1) the trigger’s position and surrounding context remain largely fixed between training and testing, failing to capture the dynamic nature of real webpages and (2) the trigger often occupies an unrealistically large area, whereas real-world images are typically small. To better reflect real-world scenarios, we introduce a more realistic threat model where the attacker is a regular user and the trigger image is small and embedded within a dynamically changing environment. As a result, existing attacks prove largely ineffective under this threat model. To better expose the vulnerabilities of GUI agents, we propose Chameleon, an attack framework with two main novelties. The first is LLM-Driven Environment Simulation, which automatically generates diverse and high-fidelity webpage simulations. The second is Attention Black Hole, which transforms attention weights into explicit supervisory signals that guide the agent’s focus toward the trigger region. We evaluate Chameleon on 6 realistic websites and 4 representative LVLM-powered GUI agents, where it significantly outperforms existing methods. Ablation studies confirm that both novelties are critical to performance. Our findings reveal underexplored vulnerabilities in modern GUI agents and establish a robust foundation for future research on defense in open-world GUI agent systems. The code is publicly available at this https URL. Subjects: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2509.11250 [cs.CR] (or arXiv:2509.11250v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2509.11250 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-101] Contextualized Multimodal Lifelong Person Re-Identification in Hybrid Clothing States

【速读】：该论文旨在解决现实监控场景中Person Re-Identification (ReID)面临的两个核心挑战：衣物变化（Clothing Change ReID, CCReID）与持续学习（Continual Learning ReID, LReID）问题。传统方法通常仅针对同衣（Same-Cloth, SC）场景设计，或将CCReID视为独立子问题，难以在动态环境中同时实现对SC和CC的鲁棒识别与增量学习。为此，作者提出LReID-Hybrid任务，并设计CMLReID框架，其关键在于引入两个创新模块：(1) 上下文感知语义提示（Context-Aware Semantic Prompt, CASP），通过生成自适应提示词并融合上下文信息，将多粒度视觉特征与语义文本空间对齐；(2) 自适应知识融合与投影（Adaptive Knowledge Fusion and Projection, AKFP），采用双路径学习器结合衣物状态感知投影损失（Clothing-State-Aware Projection Loss），构建稳定且区分性强的SC/CC原型表示，从而有效缓解特征错位与任务间遗忘问题。实验表明，CMLReID在多个数据集上均优于现有最先进方法，展现出优异的泛化能力和连续学习稳定性。

链接: https://arxiv.org/abs/2509.11247
作者: Robert Long,Rongxin Jiang,Mingrui Yan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Person Re-Identification (ReID) has several challenges in real-world surveillance systems due to clothing changes (CCReID) and the need for maintaining continual learning (LReID). Previous existing methods either develop models specifically for one application, which is mostly a same-cloth (SC) setting or treat CCReID as its own separate sub-problem. In this work, we will introduce the LReID-Hybrid task with the goal of developing a model to achieve both SC and CC while learning in a continual setting. Mismatched representations and forgetting from one task to the next are significant issues, we address this with CMLReID, a CLIP-based framework composed of two novel tasks: (1) Context-Aware Semantic Prompt (CASP) that generates adaptive prompts, and also incorporates context to align richly multi-grained visual cues with semantic text space; and (2) Adaptive Knowledge Fusion and Projection (AKFP) which produces robust SC/CC prototypes through the use of a dual-path learner that aligns features with our Clothing-State-Aware Projection Loss. Experiments performed on a wide range of datasets and illustrate that CMLReID outperforms all state-of-the-art methods with strong robustness and generalization despite clothing variations and a sophisticated process of sequential learning.
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[CV-102] MIS-LSTM: Multichannel Image-Sequence LSTM for Sleep Quality and Stress Prediction

【速读】：该论文旨在解决从多模态生活日志数据中实现每日层面的睡眠质量与压力预测问题。其解决方案的关键在于提出了一种混合框架MIS-LSTM，该框架将CNN编码器与LSTM序列模型相结合：首先将连续传感器流按N小时分块并转化为多通道图像，同时用专用的一维卷积神经网络（1D-CNN）编码稀疏离散事件；通过卷积块注意力模块（Convolutional Block Attention Module, CBAM）融合两种模态以生成精细化的块嵌入表示，并由LSTM捕获长程时间依赖关系；此外，引入不确定性感知集成方法UALRE，通过高置信度个体预测替代低置信度多数投票，从而提升模型鲁棒性。实验表明，该方法在2025 ETRI Lifelog Challenge数据集上显著优于传统LSTM、1D-CNN和CNN基线模型。

链接: https://arxiv.org/abs/2509.11232
作者: Seongwan Park,Jieun Woo,Siheon Yang
机构: Sungkyunkwan University (成均馆大学); Yeungnam University (岭南大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: ICTC 2025

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Abstract:This paper presents MIS-LSTM, a hybrid framework that joins CNN encoders with an LSTM sequence model for sleep quality and stress prediction at the day level from multimodal lifelog data. Continuous sensor streams are first partitioned into N-hour blocks and rendered as multi-channel images, while sparse discrete events are encoded with a dedicated 1D-CNN. A Convolutional Block Attention Module fuses the two modalities into refined block embeddings, which an LSTM then aggregates to capture long-range temporal dependencies. To further boost robustness, we introduce UALRE, an uncertainty-aware ensemble that overrides lowconfidence majority votes with high-confidence individual predictions. Experiments on the 2025 ETRI Lifelog Challenge dataset show that Our base MISLSTM achieves Macro-F1 0.615; with the UALRE ensemble, the score improves to 0.647, outperforming strong LSTM, 1D-CNN, and CNN baselines. Ablations confirm (i) the superiority of multi-channel over stacked-vertical imaging, (ii) the benefit of a 4-hour block granularity, and (iii) the efficacy of modality-specific discrete encoding.
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[CV-103] ANROT-HELANet: Adverserially and Naturally Robust Attention-Based Aggregation Network via The Hellinger Distance for Few-Shot Classification

【速读】：该论文旨在解决Few-Shot Learning (FSL) 在面对对抗攻击（adversarial attacks）和自然噪声（natural noise）时鲁棒性不足的问题。现有基于贝叶斯估计的方法虽利用Kullback-Leibler (KL) 散度提升性能，但仍易受扰动影响。其解决方案的关键在于提出ANROT-HELANet——一种结合Hellinger距离特征类别聚合机制、注意力机制与新型Hellinger相似性对比损失函数（Hellinger Similarity Contrastive Loss）的网络架构。该方法通过Hellinger距离构建更稳定的特征聚合策略，有效提升模型在ε=0.30的对抗扰动和σ=0.30高斯噪声下的鲁棒性，并在miniImageNet等基准数据集上实现1-shot和5-shot场景下分别1.20%和1.40%的准确率提升，同时在图像重建质量上以FID=2.75显著优于传统VAE（3.43）和WAE（3.38）。

链接: https://arxiv.org/abs/2509.11220
作者: Gao Yu Lee,Tanmoy Dam,Md Meftahul Ferdaus,Daniel Puiu Poenar,Vu N.Duong
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Preprint version. The manuscript has been submitted to a journal. All changes will be transferred to the final version if accepted. Also an erratum: In Figure 10 and 11, the $ε= 0.005$ value should be $ε= 0.05$

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Abstract:Few-Shot Learning (FSL), which involves learning to generalize using only a few data samples, has demonstrated promising and superior performances to ordinary CNN methods. While Bayesian based estimation approaches using Kullback-Leibler (KL) divergence have shown improvements, they remain vulnerable to adversarial attacks and natural noises. We introduce ANROT-HELANet, an Adversarially and Naturally RObusT Hellinger Aggregation Network that significantly advances the state-of-the-art in FSL robustness and performance. Our approach implements an adversarially and naturally robust Hellinger distance-based feature class aggregation scheme, demonstrating resilience to adversarial perturbations up to \epsilon=0.30 and Gaussian noise up to \sigma=0.30 . The network achieves substantial improvements across benchmark datasets, including gains of 1.20% and 1.40% for 1-shot and 5-shot scenarios on miniImageNet respectively. We introduce a novel Hellinger Similarity contrastive loss function that generalizes cosine similarity contrastive loss for variational few-shot inference scenarios. Our approach also achieves superior image reconstruction quality with a FID score of 2.75, outperforming traditional VAE (3.43) and WAE (3.38) approaches. Extensive experiments conducted on four few-shot benchmarked datasets verify that ANROT-HELANet’s combination of Hellinger distance-based feature aggregation, attention mechanisms, and our novel loss function establishes new state-of-the-art performance while maintaining robustness against both adversarial and natural perturbations. Our code repository will be available at this https URL.
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[CV-104] CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning

【速读】：该论文旨在解决传统基于射频识别（RFID）的牛只身份识别系统易因标签丢失、损坏或篡改而失效，且难以应对动态牛群组成和数据采集中断等问题。其解决方案的关键在于提出一种基于协作式模型无关元学习（Cooperative Model-Agnostic Meta-Learning, CCoMAML）与多头注意力特征融合（Multi-Head Attention Feature Fusion, MHAFF）相结合的少样本学习框架，通过高效利用少量样本实现模型快速适应新数据，无需频繁重新训练，从而在实时牛只识别任务中达到98.46%和97.91%的F1分数，显著优于现有先进方法。

链接: https://arxiv.org/abs/2509.11219
作者: Rabin Dulal,Lihong Zheng,Ashad Kabir
机构: Charles Sturt University (查尔斯·斯特大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Cattle identification is critical for efficient livestock farming management, currently reliant on radio-frequency identification (RFID) ear tags. However, RFID-based systems are prone to failure due to loss, damage, tampering, and vulnerability to external attacks. As a robust alternative, biometric identification using cattle muzzle patterns similar to human fingerprints has emerged as a promising solution. Deep learning techniques have demonstrated success in leveraging these unique patterns for accurate identification. But deep learning models face significant challenges, including limited data availability, disruptions during data collection, and dynamic herd compositions that require frequent model retraining. To address these limitations, this paper proposes a novel few-shot learning framework for real-time cattle identification using Cooperative Model-Agnostic Meta-Learning (CCoMAML) with Multi-Head Attention Feature Fusion (MHAFF) as a feature extractor model. This model offers great model adaptability to new data through efficient learning from few data samples without retraining. The proposed approach has been rigorously evaluated against current state-of-the-art few-shot learning techniques applied in cattle identification. Comprehensive experimental results demonstrate that our proposed CCoMAML with MHAFF has superior cattle identification performance with 98.46% and 97.91% F1 scores.
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[CV-105] Geometrically Constrained and Token-Based Probabilistic Spatial Transformers

【速读】：该论文旨在解决细粒度视觉分类（Fine-grained Visual Classification, FGVC）中因几何变化（如任意方位、尺度和透视畸变）导致的性能下降问题。现有等变架构虽能缓解此问题，但通常计算开销大且限制假设空间。其解决方案的关键在于重新利用空间变换网络（Spatial Transformer Networks, STNs）作为基于Transformer的视觉流水线中的规范化工具，提出一种概率性、分量式的扩展方法：将仿射变换分解为旋转、缩放和剪切分量，通过共享定位编码器回归各分量并施加几何约束；同时采用高斯变分后验建模每个分量的不确定性，并在推理阶段通过采样实现规范化。此外，设计了一种新颖的分量对齐损失函数，利用增强参数引导空间对齐，从而显著提升模型鲁棒性，在挑战性的蛾类分类基准上验证了有效性。

链接: https://arxiv.org/abs/2509.11218
作者: Johann Schmidt,Sebastian Stober
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Fine-grained visual classification (FGVC) remains highly sensitive to geometric variability, where objects appear under arbitrary orientations, scales, and perspective distortions. While equivariant architectures address this issue, they typically require substantial computational resources and restrict the hypothesis space. We revisit Spatial Transformer Networks (STNs) as a canonicalization tool for transformer-based vision pipelines, emphasizing their flexibility, backbone-agnostic nature, and lack of architectural constraints. We propose a probabilistic, component-wise extension that improves robustness. Specifically, we decompose affine transformations into rotation, scaling, and shearing, and regress each component under geometric constraints using a shared localization encoder. To capture uncertainty, we model each component with a Gaussian variational posterior and perform sampling-based canonicalization during inference.A novel component-wise alignment loss leverages augmentation parameters to guide spatial alignment. Experiments on challenging moth classification benchmarks demonstrate that our method consistently improves robustness compared to other STNs.
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[CV-106] Beyond Sliders: Mastering the Art of Diffusion-based Image Manipulation

【速读】：该论文旨在解决现有图像生成方法（如概念滑块，concept sliders）在处理真实世界图像（no-AIGC images）时难以实现高保真度和精细控制的问题。其解决方案的关键在于提出Beyond Sliders框架，该框架融合了生成对抗网络（GANs）与扩散模型（diffusion models），并通过文本和视觉双重细粒度引导，在对抗性机制下对图像进行精细化调整，从而显著提升图像的真实感与可控性。

链接: https://arxiv.org/abs/2509.11213
作者: Yufei Tang,Daiheng Gao,Pingyu Wu,Wenbo Zhou,Bang Zhang,Weiming Zhang
机构: FUYAO University Of Science And Technology (福耀科技大学); University of Science and Technology of China (中国科学技术大学); Anhui Province Key Laboratory of Digital Security (安徽省数字安全重点实验室); Alibaba TongYi Lab (阿里巴巴通义实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In the realm of image generation, the quest for realism and customization has never been more pressing. While existing methods like concept sliders have made strides, they often falter when it comes to no-AIGC images, particularly images captured in real world settings. To bridge this gap, we introduce Beyond Sliders, an innovative framework that integrates GANs and diffusion models to facilitate sophisticated image manipulation across diverse image categories. Improved upon concept sliders, our method refines the image through fine grained guidance both textual and visual in an adversarial manner, leading to a marked enhancement in image quality and realism. Extensive experimental validation confirms the robustness and versatility of Beyond Sliders across a spectrum of applications.
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[CV-107] Scaling Up Forest Vision with Synthetic Data

【速读】：该论文旨在解决当前树体分割（Tree Segmentation）算法在训练过程中对大规模标注真实森林点云数据的依赖问题，而这类数据获取成本高、标注难度大，限制了鲁棒性森林视觉系统的发展。其解决方案的关键在于利用合成数据（Synthetic Data）进行预训练，并仅需少量真实森林样地数据（小于0.1公顷）进行微调（Fine-tuning）。作者开发了一套集成游戏引擎与物理驱动LiDAR模拟的新颖合成数据生成管道，显著提升了数据规模、多样性和物理真实性，实验表明该方法可在极小标注量下实现与全量真实数据训练模型相当甚至更优的分割性能，验证了物理建模、多样性与规模是成功应用合成数据的核心因素。

链接: https://arxiv.org/abs/2509.11201
作者: Yihang She,Andrew Blake,David Coomes,Srinivasan Keshav
机构: University of Cambridge (剑桥大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Accurate tree segmentation is a key step in extracting individual tree metrics from forest laser scans, and is essential to understanding ecosystem functions in carbon cycling and beyond. Over the past decade, tree segmentation algorithms have advanced rapidly due to developments in AI. However existing, public, 3D forest datasets are not large enough to build robust tree segmentation systems. Motivated by the success of synthetic data in other domains such as self-driving, we investigate whether similar approaches can help with tree segmentation. In place of expensive field data collection and annotation, we use synthetic data during pretraining, and then require only minimal, real forest plot annotation for fine-tuning. We have developed a new synthetic data generation pipeline to do this for forest vision tasks, integrating advances in game-engines with physics-based LiDAR simulation. As a result, we have produced a comprehensive, diverse, annotated 3D forest dataset on an unprecedented scale. Extensive experiments with a state-of-the-art tree segmentation algorithm and a popular real dataset show that our synthetic data can substantially reduce the need for labelled real data. After fine-tuning on just a single, real, forest plot of less than 0.1 hectare, the pretrained model achieves segmentations that are competitive with a model trained on the full scale real data. We have also identified critical factors for successful use of synthetic data: physics, diversity, and scale, paving the way for more robust 3D forest vision systems in the future. Our data generation pipeline and the resulting dataset are available at this https URL. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2509.11201 [cs.CV] (or arXiv:2509.11201v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2509.11201 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-108] he Impact of Skin Tone Label Granularity on the Performance and Fairness of AI Based Dermatology Image Classification Models

【速读】：该论文旨在解决生成式 AI (Generative AI) 在皮肤病变分类任务中因肤色（Fitzpatrick Skin Tone, FST）分类粒度不均而导致的偏见问题。研究发现，当前广泛使用的FST尺度在浅肤色人群中的分类粒度更细，可能加剧模型对深肤色群体的性能下降。解决方案的关键在于：通过对比不同粒度的FST分组（如三组 vs. 四组）训练模型，揭示了减少粒度反而会损害整体性能，从而强调应谨慎选择FST分组方式，并提出应逐步摒弃FST尺度，转向能更好体现人类肤色多样性、更公平的替代量表，以提升AI皮肤病变分类模型的公平性和鲁棒性。

链接: https://arxiv.org/abs/2509.11184
作者: Partha Shah,Durva Sankhe,Maariyah Rashid,Zakaa Khaled,Esther Puyol-Antón,Tiarna Lee,Maram Alqarni,Sweta Rai,Andrew P. King
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Artificial intelligence (AI) models to automatically classify skin lesions from dermatology images have shown promising performance but also susceptibility to bias by skin tone. The most common way of representing skin tone information is the Fitzpatrick Skin Tone (FST) scale. The FST scale has been criticised for having greater granularity in its skin tone categories for lighter-skinned subjects. This paper conducts an investigation of the impact (on performance and bias) on AI classification models of granularity in the FST scale. By training multiple AI models to classify benign vs. malignant lesions using FST-specific data of differing granularity, we show that: (i) when training models using FST-specific data based on three groups (FST 1/2, 3/4 and 5/6), performance is generally better for models trained on FST-specific data compared to a general model trained on FST-balanced data; (ii) reducing the granularity of FST scale information (from 1/2 and 3/4 to 1/2/3/4) can have a detrimental effect on performance. Our results highlight the importance of the granularity of FST groups when training lesion classification models. Given the question marks over possible human biases in the choice of categories in the FST scale, this paper provides evidence for a move away from the FST scale in fair AI research and a transition to an alternative scale that better represents the diversity of human skin tones.
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[CV-109] StegOT: Trade-offs in Steganography via Optimal Transport

【速读】：该论文旨在解决现有基于生成对抗网络（Generative Adversarial Networks, GANs）和变分自编码器（Variational Autoencoders, VAEs）的图像隐写模型中存在的模式坍缩（mode collapse）问题，该问题会导致载体图像与秘密图像在隐写图像中的信息分布失衡，进而影响后续提取效果。解决方案的关键在于提出一种基于自编码器架构的隐写模型 StegOT，其核心创新是引入最优传输理论（optimal transport theory），设计了多通道最优传输（Multiple Channel Optimal Transport, MCOT）模块，通过将具有多峰特征分布的图像映射为单峰分布，实现载体图像与秘密图像间的信息平衡与优化，从而在保持隐写图像质量的同时提升恢复图像的质量。

链接: https://arxiv.org/abs/2509.11178
作者: Chengde Lin,Xuezhu Gong,Shuxue Ding,Mingzhe Yang,Xijun Lu,Chengjun Mo
机构: Guilin University of Electronic Technology (桂林电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Image hiding is often referred to as steganography, which aims to hide a secret image in a cover image of the same resolution. Many steganography models are based on genera-tive adversarial networks (GANs) and variational autoencoders (VAEs). However, most existing models suffer from mode collapse. Mode collapse will lead to an information imbalance between the cover and secret images in the stego image and further affect the subsequent extraction. To address these challenges, this paper proposes StegOT, an autoencoder-based steganography model incorporating optimal transport theory. We designed the multiple channel optimal transport (MCOT) module to transform the feature distribution, which exhibits multiple peaks, into a single peak to achieve the trade-off of information. Experiments demonstrate that we not only achieve a trade-off between the cover and secret images but also enhance the quality of both the stego and recovery images. The source code will be released on this https URL.
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[CV-110] SPHERE: Semantic-PHysical Engaged REpresentation for 3D Semantic Scene Completion

【速读】：该论文旨在解决相机-based 3D语义场景补全（3D Semantic Scene Completion, SSC）中现有方法难以同时捕捉物理规律以生成真实几何细节的问题。具体而言，基于体素（voxel）和基于平面（plane）的方法在几何细节建模上存在局限，而神经重建方法如NeRF和3DGS虽具备更强的物理感知能力，却因计算成本高、收敛慢导致语义准确性不足。解决方案的关键在于提出一种语义-物理协同表示框架SPHERE（Semantic-PHysical Engaged REpresentation），通过融合体素与高斯表示实现语义与物理信息的联合利用：首先引入语义引导的高斯初始化（SGI）模块，利用双分支3D场景表示定位关键体素作为锚点以高效初始化高斯分布；其次设计物理感知谐波增强（PHE）模块，引入语义球谐函数建模物理感知上下文细节，并通过焦点分布对齐促进语义与几何的一致性，从而生成具有真实感细节的SSC结果。

链接: https://arxiv.org/abs/2509.11171
作者: Zhiwen Yang,Yuxin Peng
机构: Peking University (北京大学); Wangxuan Institute of Computer Technology (王选计算机技术研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Camera-based 3D Semantic Scene Completion (SSC) is a critical task in autonomous driving systems, assessing voxel-level geometry and semantics for holistic scene perception. While existing voxel-based and plane-based SSC methods have achieved considerable progress, they struggle to capture physical regularities for realistic geometric details. On the other hand, neural reconstruction methods like NeRF and 3DGS demonstrate superior physical awareness, but suffer from high computational cost and slow convergence when handling large-scale, complex autonomous driving scenes, leading to inferior semantic accuracy. To address these issues, we propose the Semantic-PHysical Engaged REpresentation (SPHERE) for camera-based SSC, which integrates voxel and Gaussian representations for joint exploitation of semantic and physical information. First, the Semantic-guided Gaussian Initialization (SGI) module leverages dual-branch 3D scene representations to locate focal voxels as anchors to guide efficient Gaussian initialization. Then, the Physical-aware Harmonics Enhancement (PHE) module incorporates semantic spherical harmonics to model physical-aware contextual details and promote semantic-geometry consistency through focal distribution alignment, generating SSC results with realistic details. Extensive experiments and analyses on the popular SemanticKITTI and SSCBench-KITTI-360 benchmarks validate the effectiveness of SPHERE. The code is available at this https URL.
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[CV-111] Multispectral-NeRF:a multispectral modeling approach based on neural radiance fields

【速读】：该论文旨在解决当前多光谱（Multispectral）三维重建方法中存在的高成本、低精度及几何特征不佳等问题，尤其是现有基于神经辐射场（NeRF）的模型仅支持三波段数据（如RGB），无法有效利用多波段光谱信息的局限性。其解决方案的关键在于提出一种名为Multispectral-NeRF的新架构，通过三项核心改进实现多光谱信息的有效融合：一是扩展隐藏层维度以适配六波段光谱输入；二是重构残差函数以优化重建图像与参考图像间的光谱差异计算；三是调整数据压缩模块以应对多光谱图像更高的位深度需求。实验结果表明，该方法能够准确保留场景的原始光谱特性并生成高质量的三维重建结果。

链接: https://arxiv.org/abs/2509.11169
作者: Hong Zhang,Fei Guo,Zihan Xie,Dizhao Yao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:3D reconstruction technology generates three-dimensional representations of real-world objects, scenes, or environments using sensor data such as 2D images, with extensive applications in robotics, autonomous vehicles, and virtual reality systems. Traditional 3D reconstruction techniques based on 2D images typically relies on RGB spectral information. With advances in sensor technology, additional spectral bands beyond RGB have been increasingly incorporated into 3D reconstruction workflows. Existing methods that integrate these expanded spectral data often suffer from expensive scheme prices, low accuracy and poor geometric features. Three - dimensional reconstruction based on NeRF can effectively address the various issues in current multispectral 3D reconstruction methods, producing high - precision and high - quality reconstruction results. However, currently, NeRF and some improved models such as NeRFacto are trained on three - band data and cannot take into account the multi - band information. To address this problem, we propose Multispectral-NeRF, an enhanced neural architecture derived from NeRF that can effectively integrates multispectral information. Our technical contributions comprise threefold modifications: Expanding hidden layer dimensionality to accommodate 6-band spectral inputs; Redesigning residual functions to optimize spectral discrepancy calculations between reconstructed and reference images; Adapting data compression modules to address the increased bit-depth requirements of multispectral imagery. Experimental results confirm that Multispectral-NeRF successfully processes multi-band spectral features while accurately preserving the original scenes’ spectral characteristics.
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[CV-112] raffic-MLLM : A Spatio-Temporal MLLM with Retrieval-Augmented Generation for Causal Inference in Traffic

【速读】：该论文旨在解决当前智能交通系统中交通视频理解面临的两大挑战：一是难以准确建模时空因果关系，二是缺乏对领域知识的有效融合，从而限制了模型在复杂场景下的表现。其解决方案的关键在于提出一种面向细粒度交通分析的多模态大语言模型（Traffic-MLLM），基于Qwen2.5-VL架构，利用高质量交通领域多模态数据集并采用低秩适应（LoRA）进行轻量级微调，显著提升了对视频序列中连续时空特征的建模能力；同时引入一种创新的知识提示模块，结合思维链（Chain-of-Thought, CoT）推理与检索增强生成（Retrieval-Augmented Generation, RAG），实现交通法规和领域知识的精准注入，从而大幅提升模型的逻辑推理与知识适配能力。

链接: https://arxiv.org/abs/2509.11165
作者: Waikit Xiu,Qiang Lu,Xiying Li,Chen Hu,Shengbo Sun
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:As intelligent transportation systems advance, traffic video understanding plays an increasingly pivotal role in comprehensive scene perception and causal analysis. Yet, existing approaches face notable challenges in accurately modeling spatiotemporal causality and integrating domain-specific knowledge, limiting their effectiveness in complex scenarios. To address these limitations, we propose Traffic-MLLM, a multimodal large language model tailored for fine-grained traffic analysis. Built on the Qwen2.5-VL backbone, our model leverages high-quality traffic-specific multimodal datasets and uses Low-Rank Adaptation (LoRA) for lightweight fine-tuning, significantly enhancing its capacity to model continuous spatiotemporal features in video sequences. Furthermore, we introduce an innovative knowledge prompting module fusing Chain-of-Thought (CoT) reasoning with Retrieval-Augmented Generation (RAG), enabling precise injection of detailed traffic regulations and domain knowledge into the inference process. This design markedly boosts the model’s logical reasoning and knowledge adaptation capabilities. Experimental results on TrafficQA and DriveQA benchmarks show Traffic-MLLM achieves state-of-the-art performance, validating its superior ability to process multimodal traffic data. It also exhibits remarkable zero-shot reasoning and cross-scenario generalization capabilities.
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[CV-113] No Mesh No Problem: Estimating Coral Volume and Surface from Sparse Multi-View Images

【速读】：该论文旨在解决珊瑚礁监测中珊瑚生长量化难题，即如何通过二维多视角RGB图像准确估算珊瑚的三维体积和表面积。其核心挑战在于珊瑚复杂形态导致的传统测量方法效率低且精度不足。解决方案的关键在于提出一种轻量级、可扩展的学习框架：首先利用预训练的VGGT模块从每张图像中提取密集点图，并融合为统一点云，同时引入每视角置信度评分；随后将该点云输入两个并行的DGCNN解码器头，联合输出珊瑚体积与表面积及其置信度估计；此外，通过基于高斯负对数似然的复合损失函数（在真实域与对数域均优化），显著提升了预测稳定性并提供不确定性估计。该方法实现了从稀疏图像中高效、可靠地重建珊瑚几何特征，适用于珊瑚生长分析与珊瑚礁长期监测场景。

链接: https://arxiv.org/abs/2509.11164
作者: Diego Eustachio Farchione,Ramzi Idoughi,Peter Wonka
机构: KAUST(沙特阿卜杜拉国王科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Effective reef monitoring requires the quantification of coral growth via accurate volumetric and surface area estimates, which is a challenging task due to the complex morphology of corals. We propose a novel, lightweight, and scalable learning framework that addresses this challenge by predicting the 3D volume and surface area of coral-like objects from 2D multi-view RGB images. Our approach utilizes a pre-trained module (VGGT) to extract dense point maps from each view; these maps are merged into a unified point cloud and enriched with per-view confidence scores. The resulting cloud is fed to two parallel DGCNN decoder heads, which jointly output the volume and the surface area of the coral, as well as their corresponding confidence estimate. To enhance prediction stability and provide uncertainty estimates, we introduce a composite loss function based on Gaussian negative log-likelihood in both real and log domains. Our method achieves competitive accuracy and generalizes well to unseen morphologies. This framework paves the way for efficient and scalable coral geometry estimation directly from a sparse set of images, with potential applications in coral growth analysis and reef monitoring.
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[CV-114] ManiVID-3D: Generalizable View-Invariant Reinforcement Learning for Robotic Manipulation via Disentangled 3D Representations

【速读】：该论文旨在解决视觉强化学习（Visual Reinforcement Learning, Visual RL）在真实世界机器人操作中因相机视角变化而导致策略失效的问题。现有方法通常依赖精确的相机标定或难以应对大角度视角变化，限制了其在实际场景中的部署。解决方案的关键在于提出一种名为ManiVID-3D的新型3D强化学习架构，通过自监督解耦特征学习来构建视图不变（view-invariant）表示，并引入轻量级的ViewNet模块，在无需外参标定的情况下自动将任意视角下的点云观测对齐到统一的空间坐标系；同时开发了GPU加速的批量渲染模块，实现每秒处理超过5000帧的能力，显著提升训练效率。这一设计使得系统在10个仿真和5个真实任务中均表现出更强的鲁棒性和更高的成功率（较最先进方法提升44.7%），且参数量减少80%，验证了几何一致性表征对于复杂环境下可扩展机器人操作的有效性。

链接: https://arxiv.org/abs/2509.11125
作者: Zheng Li,Pei Qu,Yufei Jia,Shihui Zhou,Haizhou Ge,Jiahang Cao,Jinni Zhou,Guyue Zhou,Jun Ma
机构: The Hong Kong University of Science and Technology (Guangzhou) (香港科技大学（广州）); Tsinghua University (清华大学); The University of Hong Kong (香港大学); The Hong Kong University of Science and Technology (香港科技大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Deploying visual reinforcement learning (RL) policies in real-world manipulation is often hindered by camera viewpoint changes. A policy trained from a fixed front-facing camera may fail when the camera is shifted–an unavoidable situation in real-world settings where sensor placement is hard to manage appropriately. Existing methods often rely on precise camera calibration or struggle with large perspective changes. To address these limitations, we propose ManiVID-3D, a novel 3D RL architecture designed for robotic manipulation, which learns view-invariant representations through self-supervised disentangled feature learning. The framework incorporates ViewNet, a lightweight yet effective module that automatically aligns point cloud observations from arbitrary viewpoints into a unified spatial coordinate system without the need for extrinsic calibration. Additionally, we develop an efficient GPU-accelerated batch rendering module capable of processing over 5000 frames per second, enabling large-scale training for 3D visual RL at unprecedented speeds. Extensive evaluation across 10 simulated and 5 real-world tasks demonstrates that our approach achieves a 44.7% higher success rate than state-of-the-art methods under viewpoint variations while using 80% fewer parameters. The system’s robustness to severe perspective changes and strong sim-to-real performance highlight the effectiveness of learning geometrically consistent representations for scalable robotic manipulation in unstructured environments. Our project website can be found in this https URL.
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[CV-115] SVR-GS: Spatially Variant Regularization for Probabilistic Masks in 3D Gaussian Splatting

【速读】：该论文旨在解决3D高斯溅射（3D Gaussian Splatting, 3DGS）在优化高斯数量时存在的效率与精度失衡问题，即传统基于全局掩码的剪枝方法（如MaskGS）未能与局部逐像素（逐射线）重建损失对齐，导致剪枝策略无法精准识别低重要性高斯。其解决方案的关键在于提出空间可变正则化器（Spatially Variant Regularizer, SVR-GS），该方法通过计算每个高斯沿射线的有效贡献生成逐像素的空间掩码，从而在图像质量敏感区域施加稀疏压力，精确地保留高重要性高斯并剪除冗余项。实验表明，SVR-GS相比MaskGS和原始3DGS分别减少1.79×和5.63×高斯数量，同时仅引入0.50 dB和0.40 dB的PSNR下降，显著提升了模型的紧凑性、推理速度与内存效率，适用于机器人、AR/VR及移动感知等实时场景。

链接: https://arxiv.org/abs/2509.11116
作者: Ashkan Taghipour,Vahid Naghshin,Benjamin Southwell,Farid Boussaid,Hamid Laga,Mohammed Bennamoun
机构: The University of Western Australia (西澳大利亚大学); Dolby Laboratories (杜比实验室); Murdoch University (默多克大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:3D Gaussian Splatting (3DGS) enables fast, high-quality novel view synthesis but typically relies on densification followed by pruning to optimize the number of Gaussians. Existing mask-based pruning, such as MaskGS, regularizes the global mean of the mask, which is misaligned with the local per-pixel (per-ray) reconstruction loss that determines image quality along individual camera rays. This paper introduces SVR-GS, a spatially variant regularizer that renders a per-pixel spatial mask from each Gaussian’s effective contribution along the ray, thereby applying sparsity pressure where it matters: on low-importance Gaussians. We explore three spatial-mask aggregation strategies, implement them in CUDA, and conduct a gradient analysis to motivate our final design. Extensive experiments on Tanks\Temples, Deep Blending, and Mip-NeRF360 datasets demonstrate that, on average across the three datasets, the proposed SVR-GS reduces the number of Gaussians by 1.79(\times) compared to MaskGS and 5.63(\times) compared to 3DGS, while incurring only 0.50 dB and 0.40 dB PSNR drops, respectively. These gains translate into significantly smaller, faster, and more memory-efficient models, making them well-suited for real-time applications such as robotics, AR/VR, and mobile perception.
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[CV-116] WildSmoke: Ready-to-Use Dynamic 3D Smoke Assets from a Single Video in the Wild

【速读】：该论文旨在解决从单个野外视频中提取并重建动态3D烟雾资产的难题，同时实现交互式流体模拟以支持烟雾设计与编辑。现有方法多依赖于受控实验室环境下的高质量数据，难以处理真实场景中复杂背景、噪声和视角变化等问题。论文提出了一套完整的流水线，其关键在于针对野外视频中烟雾重建的三大挑战：烟雾提取与背景去除、烟雾粒子及相机位姿初始化、多视角视频推断；通过针对性技术设计，实现了比以往方法在野外视频上平均PSNR提升2.22的高质量重建，并进一步利用物理仿真实现多样且逼真的流体编辑能力。

链接: https://arxiv.org/abs/2509.11114
作者: Yuqiu Liu,Jialin Song,Manolis Savva,Wuyang Chen
机构: Simon Fraser University (西蒙弗雷泽大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:We propose a pipeline to extract and reconstruct dynamic 3D smoke assets from a single in-the-wild video, and further integrate interactive simulation for smoke design and editing. Recent developments in 3D vision have significantly improved reconstructing and rendering fluid dynamics, supporting realistic and temporally consistent view synthesis. However, current fluid reconstructions rely heavily on carefully controlled clean lab environments, whereas real-world videos captured in the wild are largely underexplored. We pinpoint three key challenges of reconstructing smoke in real-world videos and design targeted techniques, including smoke extraction with background removal, initialization of smoke particles and camera poses, and inferring multi-view videos. Our method not only outperforms previous reconstruction and generation methods with high-quality smoke reconstructions (+2.22 average PSNR on wild videos), but also enables diverse and realistic editing of fluid dynamics by simulating our smoke assets. We provide our models, data, and 4D smoke assets at [this https URL](this https URL).
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[CV-117] Filling the Gaps: A Multitask Hybrid Multiscale Generative Framework for Missing Modality in Remote Sensing Semantic Segmentation

【速读】：该论文旨在解决多模态遥感语义分割中因传感器故障或恶劣天气导致的模态缺失问题，此类缺失会显著降低模型性能。现有生成式方法（如AutoEncoder和GAN）在处理遥感数据的异质性时存在局限，难以捕捉复杂场景下的语义上下文信息，且易受主导模态影响，导致模型在模态缺失条件下鲁棒性差。为此，作者提出一种新型生成增强型多模态学习网络（GEMMNet），其关键在于：（1）混合特征提取器（HyFEx）有效学习模态特异性表示；（2）带多尺度感知的混合融合机制（HyFMA）跨尺度捕获模态协同语义上下文；（3）互补损失函数（CoLoss）通过促进模态间一致性来缓解固有偏差，提升模型在模态缺失下的泛化能力。

链接: https://arxiv.org/abs/2509.11102
作者: Nhi Kieu,Kien Nguyen,Arnold Wiliem,Clinton Fookes,Sridha Sridharan
机构: Queensland University of Technology (昆士兰科技大学); Shield AI
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to DICTA 2025

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Abstract:Multimodal learning has shown significant performance boost compared to ordinary unimodal models across various domains. However, in real-world scenarios, multimodal signals are susceptible to missing because of sensor failures and adverse weather conditions, which drastically deteriorates models’ operation and performance. Generative models such as AutoEncoder (AE) and Generative Adversarial Network (GAN) are intuitive solutions aiming to reconstruct missing modality from available ones. Yet, their efficacy in remote sensing semantic segmentation remains underexplored. In this paper, we first examine the limitations of existing generative approaches in handling the heterogeneity of multimodal remote sensing data. They inadequately capture semantic context in complex scenes with large intra-class and small inter-class variation. In addition, traditional generative models are susceptible to heavy dependence on the dominant modality, introducing bias that affects model robustness under missing modality conditions. To tackle these limitations, we propose a novel Generative-Enhanced MultiModal learning Network (GEMMNet) with three key components: (1) Hybrid Feature Extractor (HyFEx) to effectively learn modality-specific representations, (2) Hybrid Fusion with Multiscale Awareness (HyFMA) to capture modality-synergistic semantic context across scales and (3) Complementary Loss (CoLoss) scheme to alleviate the inherent bias by encouraging consistency across modalities and tasks. Our method, GEMMNet, outperforms both generative baselines AE, cGAN (conditional GAN), and state-of-the-art non-generative approaches - mmformer and shaspec - on two challenging semantic segmentation remote sensing datasets (Vaihingen and Potsdam). Source code is made available.
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[CV-118] 3DAeroRelief: The first 3D Benchmark UAV Dataset for Post-Disaster Assessment

【速读】：该论文旨在解决自然灾害后结构损伤评估中因依赖二维图像而导致的深度信息缺失、遮挡严重及空间上下文不足的问题。现有三维语义分割基准主要聚焦于城市或室内场景，缺乏对灾后环境的针对性支持。其解决方案的关键在于构建首个专为灾后评估设计的3D基准数据集3DAeroRelief，该数据集通过低成本无人机（UAV）采集飓风受灾区域的点云数据，并利用Structure-from-Motion（SfM）和Multi-View Stereo（MVS）技术重建高密度3D点云，同时采用人工2D标注并投影至3D空间实现语义标注。此方案不仅提供了真实灾害场景下细粒度结构损伤的大规模室外3D数据，还借助UAV的灵活性与安全性，为灾后应急响应中的鲁棒3D视觉系统研究提供了关键资源。

链接: https://arxiv.org/abs/2509.11097
作者: Nhut Le,Ehsan Karimi,Maryam Rahnemoonfar
机构: Lehigh University (利海大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Timely assessment of structural damage is critical for disaster response and recovery. However, most prior work in natural disaster analysis relies on 2D imagery, which lacks depth, suffers from occlusions, and provides limited spatial context. 3D semantic segmentation offers a richer alternative, but existing 3D benchmarks focus mainly on urban or indoor scenes, with little attention to disaster-affected areas. To address this gap, we present 3DAeroRelief–the first 3D benchmark dataset specifically designed for post-disaster assessment. Collected using low-cost unmanned aerial vehicles (UAVs) over hurricane-damaged regions, the dataset features dense 3D point clouds reconstructed via Structure-from-Motion and Multi-View Stereo techniques. Semantic annotations were produced through manual 2D labeling and projected into 3D space. Unlike existing datasets, 3DAeroRelief captures 3D large-scale outdoor environments with fine-grained structural damage in real-world disaster contexts. UAVs enable affordable, flexible, and safe data collection in hazardous areas, making them particularly well-suited for emergency scenarios. To demonstrate the utility of 3DAeroRelief, we evaluate several state-of-the-art 3D segmentation models on the dataset to highlight both the challenges and opportunities of 3D scene understanding in disaster response. Our dataset serves as a valuable resource for advancing robust 3D vision systems in real-world applications for post-disaster scenarios.
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[CV-119] A Copula-Guided Temporal Dependency Method for Multitemporal Hyperspectral Images Unmixing

【速读】：该论文旨在解决多时相高光谱解混（Multitemporal Hyperspectral Unmixing, MTHU）中现有方法难以有效建模时间依赖性、从而无法捕捉物质动态演变的问题。其解决方案的关键在于引入哥德尔理论（Copula Theory），构建了一个基于哥德尔函数的时序依赖引导框架（Cog-TD），通过两个核心模块——哥德尔函数估计与时间依赖性引导——显式刻画时序结构，从而实现对动态端元和丰度的精准估计。该方法在数学模型上重新定义了MTHU问题，并提供了理论支持以证明所估计的哥德尔函数的有效性及时间依赖性在高光谱图像中的存在性。

链接: https://arxiv.org/abs/2509.11096
作者: Ruiying Li,Bin Pan,Qiaoying Qu,Xia Xu,Zhenwei Shi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Multitemporal hyperspectral unmixing (MTHU) aims to model variable endmembers and dynamical abundances, which emphasizes the critical temporal information. However, existing methods have limitations in modeling temporal dependency, thus fail to capture the dynamical material evolution. Motivated by the ability of copula theory in modeling dependency structure explicitly, in this paper, we propose a copula-guided temporal dependency method (Cog-TD) for multitemporal hyperspectral unmixing. Cog-TD defines new mathematical model, constructs copula-guided framework and provides two key modules with theoretical support. The mathematical model provides explicit formulations for MTHU problem definition, which describes temporal dependency structure by incorporating copula theory. The copula-guided framework is constructed for utilizing copula function, which estimates dynamical endmembers and abundances with temporal dependency. The key modules consist of copula function estimation and temporal dependency guidance, which computes and employs temporal information to guide unmixing process. Moreover, the theoretical support demonstrates that estimated copula function is valid and the represented temporal dependency exists in hyperspectral images. The major contributions of this paper include redefining MTHU problem with temporal dependency, proposing a copula-guided framework, developing two key modules and providing theoretical support. Our experimental results on both synthetic and real-world datasets demonstrate the utility of the proposed method.
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[CV-120] SMILE: A Super-resolution Guided Multi-task Learning Method for Hyperspectral Unmixing

【速读】：该论文旨在解决高光谱解混（hyperspectral unmixing）因空间分辨率低而导致性能受限的问题，提出了一种超分辨率引导的多任务学习方法（SMILE）。其关键在于通过理论分析验证了超分辨率与解混任务间的任务亲和性（task affinity），并设计了一个能够同时学习共享特征与特定特征的框架，从而将超分辨率中提取的正向信息有效迁移至解混过程；此外，为确保解混收敛性，提出了可达性定理（accessibility theorem），证明了解混最优解的存在性，实现了从理论到方法的系统性突破。

链接: https://arxiv.org/abs/2509.11093
作者: Ruiying Li,Bin Pan,Qiaoying Qu,Xia Xu,Zhenwei Shi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The performance of hyperspectral unmixing may be constrained by low spatial resolution, which can be enhanced using super-resolution in a multitask learning way. However, integrating super-resolution and unmixing directly may suffer two challenges: Task affinity is not verified, and the convergence of unmixing is not guaranteed. To address the above issues, in this paper, we provide theoretical analysis and propose super-resolution guided multi-task learning method for hyperspectral unmixing (SMILE). The provided theoretical analysis validates feasibility of multitask learning way and verifies task affinity, which consists of relationship and existence theorems by proving the positive guidance of super-resolution. The proposed framework generalizes positive information from super-resolution to unmixing by learning both shared and specific representations. Moreover, to guarantee the convergence, we provide the accessibility theorem by proving the optimal solution of unmixing. The major contributions of SMILE include providing progressive theoretical support, and designing a new framework for unmixing under the guidance of super-resolution. Our experiments on both synthetic and real datasets have substantiate the usefulness of our work.
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[CV-121] PanoLora: Bridging Perspective and Panoramic Video Generation with LoRA Adaptation

【速读】：该论文旨在解决生成高质量360°全景视频（Panoramic Video）的难题，其核心挑战在于全景投影与传统透视视图（Perspective View）在几何结构和视角覆盖范围上的本质差异——后者依赖单一视点且视野有限，而前者需完整渲染周围环境，导致标准视频生成模型难以直接适配。解决方案的关键在于将全景视频生成视为从透视视图到全景视图的适应（Adaptation）问题，并引入低秩适应（Low-Rank Adaptation, LoRA）方法进行高效微调：理论分析表明，当LoRA的秩超过任务自由度时，可有效建模两类投影间的映射关系；实验验证显示，仅需约1,000个训练视频即可对预训练视频扩散模型进行微调，在保持正确投影几何的同时，显著优于现有最先进方法在视觉质量、左右一致性及运动多样性方面的表现。

链接: https://arxiv.org/abs/2509.11092
作者: Zeyu Dong,Yuyang Yin,Yuqi Li,Eric Li,Hao-Xiang Guo,Yikai Wang
机构: Beijing Normal University (北京师范大学); Beijing Jiaotong University (北京交通大学); The City College of New York (纽约市立大学城市学院); Skywork AI (天工智能)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Generating high-quality 360° panoramic videos remains a significant challenge due to the fundamental differences between panoramic and traditional perspective-view projections. While perspective videos rely on a single viewpoint with a limited field of view, panoramic content requires rendering the full surrounding environment, making it difficult for standard video generation models to adapt. Existing solutions often introduce complex architectures or large-scale training, leading to inefficiency and suboptimal results. Motivated by the success of Low-Rank Adaptation (LoRA) in style transfer tasks, we propose treating panoramic video generation as an adaptation problem from perspective views. Through theoretical analysis, we demonstrate that LoRA can effectively model the transformation between these projections when its rank exceeds the degrees of freedom in the task. Our approach efficiently fine-tunes a pretrained video diffusion model using only approximately 1,000 videos while achieving high-quality panoramic generation. Experimental results demonstrate that our method maintains proper projection geometry and surpasses previous state-of-the-art approaches in visual quality, left-right consistency, and motion diversity.
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[CV-122] End-to-End Visual Autonomous Parking via Control-Aided Attention

【速读】：该论文旨在解决端到端自动驾驶泊车系统中感知与控制模块缺乏有效协同的问题，尤其关注在关键区域因视觉注意力不稳定导致的控制决策可靠性下降问题。现有方法依赖纯Transformer自注意力机制时，易产生时空不一致的注意力分布，影响策略的长期稳定性。其解决方案的关键在于提出CAA-Policy框架，引入一种新颖的Control-Aided Attention（CAA）机制，通过控制信号反向传播的梯度引导视觉注意力学习，而非仅最小化训练损失；这种策略促使注意力聚焦于对动作输出方差贡献最大的视觉特征，从而提升策略的鲁棒性和泛化能力。此外，该方法还结合短 horizon 路径点预测作为辅助任务，并引入独立训练的运动预测模块以增强目标位姿的时序跟踪稳定性，显著提升了整体系统的精度、鲁棒性与可解释性。

链接: https://arxiv.org/abs/2509.11090
作者: Chao Chen,Shunyu Yao,Yuanwu He,Tao Feng,Ruojing Song,Yuliang Guo,Xinyu Huang,Chenxu Wu,Ren Liu,Chen Feng
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Precise parking requires an end-to-end system where perception adaptively provides policy-relevant details-especially in critical areas where fine control decisions are essential. End-to-end learning offers a unified framework by directly mapping sensor inputs to control actions, but existing approaches lack effective synergy between perception and control. We find that transformer-based self-attention, when used alone, tends to produce unstable and temporally inconsistent spatial attention, which undermines the reliability of downstream policy decisions over time. Instead, we propose CAA-Policy, an end-to-end imitation learning system that allows control signal to guide the learning of visual attention via a novel Control-Aided Attention (CAA) mechanism. For the first time, we train such an attention module in a self-supervised manner, using backpropagated gradients from the control outputs instead of from the training loss. This strategy encourages the attention to focus on visual features that induce high variance in action outputs, rather than merely minimizing the training loss-a shift we demonstrate leads to a more robust and generalizable policy. To further enhance stability, CAA-Policy integrates short-horizon waypoint prediction as an auxiliary task, and introduces a separately trained motion prediction module to robustly track the target spot over time. Extensive experiments in the CARLA simulator show that \titlevariable~consistently surpasses both the end-to-end learning baseline and the modular BEV segmentation + hybrid A* pipeline, achieving superior accuracy, robustness, and interpretability. Code is released at this https URL.
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[CV-123] SH-SAS: An Implicit Neural Representation for Complex Spherical-Harmonic Scattering Fields for 3D Synthetic Aperture Sonar

【速读】：该论文旨在解决合成孔径声呐（SAS）三维重建中难以准确建模声散射方向依赖性的问题，传统时域反投影算法无法刻画各向异性散射特性，且易受采样限制、混叠和遮挡影响；而现有基于神经体积表示的方法则将每个体素视为各向同性的散射密度，忽略了方向性信息。解决方案的关键在于提出SH-SAS，一种基于球谐函数（spherical harmonic, SH）系数的隐式神经表示方法，通过多分辨率哈希编码器驱动轻量级多层感知机（MLP），输出指定阶数L内的复数SH系数，其中零阶项作为各向同性散射场（即密度项），高阶项以极低参数开销高效捕捉方向性散射特征，并直接从一维飞行时间信号训练模型，无需中间波束形成图像监督，从而显著提升三维重建质量和几何精度。

链接: https://arxiv.org/abs/2509.11087
作者: Omkar Shailendra Vengurlekar,Adithya Pediredla,Suren Jayasuriya
机构: Arizona State University(亚利桑那州立大学); Dartmouth College(达特茅斯学院)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Synthetic aperture sonar (SAS) reconstruction requires recovering both the spatial distribution of acoustic scatterers and their direction-dependent response. Time-domain backprojection is the most common 3D SAS reconstruction algorithm, but it does not model directionality and can suffer from sampling limitations, aliasing, and occlusion. Prior neural volumetric methods applied to synthetic aperture sonar treat each voxel as an isotropic scattering density, not modeling anisotropic returns. We introduce SH-SAS, an implicit neural representation that expresses the complex acoustic scattering field as a set of spherical harmonic (SH) coefficients. A multi-resolution hash encoder feeds a lightweight MLP that outputs complex SH coefficients up to a specified degree L. The zeroth-order coefficient acts as an isotropic scattering field, which also serves as the density term, while higher orders compactly capture directional scattering with minimal parameter overhead. Because the model predicts the complex amplitude for any transmit-receive baseline, training is performed directly from 1-D time-of-flight signals without the need to beamform intermediate images for supervision. Across synthetic and real SAS (both in-air and underwater) benchmarks, results show that SH-SAS performs better in terms of 3D reconstruction quality and geometric metrics than previous methods.
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[CV-124] Mars Traversability Prediction: A Multi-modal Self-supervised Approach for Costmap Generation

【速读】：该论文旨在解决行星探测车在复杂地形中准确预测可 traversability（可通行性）成本图（costmap）的问题，以提升自主导航的鲁棒性和效率。其解决方案的关键在于构建一个融合视觉（Camera）与激光雷达（LiDAR）数据的多模态鸟瞰图（BEV）成本图预测框架，采用基于DINOv3的图像编码器、FiLM（Feature-wise Linear Modulation）机制实现传感器融合，并利用IMU（惯性测量单元）推导的标签进行自监督训练；同时引入结合Huber损失与平滑项的优化目标，显著提升了模型对输入扰动（如噪声、遮挡、颜色缺失）的鲁棒性，实验表明几何信息主导了学习到的成本分布，且性能变化微小，验证了方法的有效性与稳定性。

链接: https://arxiv.org/abs/2509.11082
作者: Zongwu Xie,Kaijie Yun,Yang Liu,Yiming Ji,Han Li
机构: Harbin Institute of Technology (哈尔滨工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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Abstract:We present a robust multi-modal framework for predicting traversability costmaps for planetary rovers. Our model fuses camera and LiDAR data to produce a bird’s-eye-view (BEV) terrain costmap, trained self-supervised using IMU-derived labels. Key updates include a DINOv3-based image encoder, FiLM-based sensor fusion, and an optimization loss combining Huber and smoothness terms. Experimental ablations (removing image color, occluding inputs, adding noise) show only minor changes in MAE/MSE (e.g. MAE increases from ~0.0775 to 0.0915 when LiDAR is sparsified), indicating that geometry dominates the learned cost and the model is highly robust. We attribute the small performance differences to the IMU labeling primarily reflecting terrain geometry rather than semantics and to limited data diversity. Unlike prior work claiming large gains, we emphasize our contributions: (1) a high-fidelity, reproducible simulation environment; (2) a self-supervised IMU-based labeling pipeline; and (3) a strong multi-modal BEV costmap prediction model. We discuss limitations and future work such as domain generalization and dataset expansion.
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[CV-125] Organoid Tracker: A SAM2-Powered Platform for Zero-shot Cyst Analysis in Human Kidney Organoid Videos

【速读】：该论文旨在解决当前肾类器官（kidney organoid）模型在多囊肾病（polycystic kidney disease, PKD）药物筛选中，由于缺乏高效、精细的图像分析手段而导致的瓶颈问题。传统手动分析方法仅能进行粗粒度分类（如“命中”与“非命中”），难以提取像素级和时序动态信息，限制了对囊肿形成速率、生长速度及形态变化等关键指标的定量评估。解决方案的关键在于开发了一个名为Organoid Tracker的图形用户界面（GUI）平台，其核心创新是基于前沿视觉基础模型Segment Anything Model 2 (SAM2)，实现了零样本分割（zero-shot segmentation）与自动化时空显微视频分析，无需编程即可生成高精度定量指标和完整报告，从而显著提升肾类器官研究在发育机制解析、疾病建模及治疗靶点发现中的效率与深度。

链接: https://arxiv.org/abs/2509.11063
作者: Xiaoyu Huang,Lauren M Maxson,Trang Nguyen,Cheng Jack Song,Yuankai Huo
机构: Vanderbilt University (范德比尔特大学); University of Alabama at Birmingham (阿拉巴马大学伯明翰分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent advances in organoid models have revolutionized the study of human kidney disease mechanisms and drug discovery by enabling scalable, cost-effective research without the need for animal sacrifice. Here, we present a kidney organoid platform optimized for efficient screening in polycystic kidney disease (PKD). While these systems generate rich spatial-temporal microscopy video datasets, current manual approaches to analysis remain limited to coarse classifications (e.g., hit vs. non-hit), often missing valuable pixel-level and longitudinal information. To help overcome this bottleneck, we developed Organoid Tracker, a graphical user interface (GUI) platform designed with a modular plugin architecture, which empowers researchers to extract detailed, quantitative metrics without programming expertise. Built on the cutting-edge vision foundation model Segment Anything Model 2 (SAM2), Organoid Tracker enables zero-shot segmentation and automated analysis of spatial-temporal microscopy videos. It quantifies key metrics such as cyst formation rate, growth velocity, and morphological changes, while generating comprehensive reports. By providing an extensible, open-source framework, Organoid Tracker offers a powerful solution for improving and accelerating research in kidney development, PKD modeling, and therapeutic discovery. The platform is publicly available as open-source software at this https URL.
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[CV-126] Action Hints: Semantic Typicality and Context Uniqueness for Generalizable Skeleton-based Video Anomaly Detection

【速读】：该论文旨在解决零样本视频异常检测（Zero-Shot Video Anomaly Detection, ZS-VAD）中因缺乏目标域训练数据而导致模型泛化能力不足的问题，尤其针对新场景下正常与异常行为模式差异带来的挑战。其解决方案的关键在于通过动作典型性（typicality）与独特性（uniqueness）学习来挖掘骨架数据的潜在语义信息：首先引入语言引导的语义典型性建模模块，将骨架片段映射至动作语义空间，并利用大语言模型（Large Language Model, LLM）的知识蒸馏出典型正常与异常行为特征；其次设计测试时上下文独特性分析模块，在无目标域训练样本的情况下，基于骨架片段间的时空差异自适应地构建场景感知的正常边界，从而实现对未知监控场景的高精度异常定位。

链接: https://arxiv.org/abs/2509.11058
作者: Canhui Tang,Sanping Zhou,Haoyue Shi,Le Wang
机构: Xi’an Jiaotong University (西安交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Zero-Shot Video Anomaly Detection (ZS-VAD) requires temporally localizing anomalies without target domain training data, which is a crucial task due to various practical concerns, e.g., data privacy or new surveillance deployments. Skeleton-based approach has inherent generalizable advantages in achieving ZS-VAD as it eliminates domain disparities both in background and human appearance. However, existing methods only learn low-level skeleton representation and rely on the domain-limited normality boundary, which cannot generalize well to new scenes with different normal and abnormal behavior patterns. In this paper, we propose a novel zero-shot video anomaly detection framework, unlocking the potential of skeleton data via action typicality and uniqueness learning. Firstly, we introduce a language-guided semantic typicality modeling module that projects skeleton snippets into action semantic space and distills LLM’s knowledge of typical normal and abnormal behaviors during training. Secondly, we propose a test-time context uniqueness analysis module to finely analyze the spatio-temporal differences between skeleton snippets and then derive scene-adaptive boundaries. Without using any training samples from the target domain, our method achieves state-of-the-art results against skeleton-based methods on four large-scale VAD datasets: ShanghaiTech, UBnormal, NWPU, and UCF-Crime, featuring over 100 unseen surveillance scenes.
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[CV-127] Rate-Distortion Limits for Multimodal Retrieval: Theory Optimal Codes and Finite-Sample Guarantees ICCV

【速读】：该论文旨在解决多模态检索（multimodal retrieval）中的信息论极限问题，即确定实现高质量检索所需的最小比特数（bits per query）。其核心挑战在于如何量化不同模态间的熵不平衡与跨模态冗余对检索性能的影响。解决方案的关键在于：首先将排序任务建模为有损信源编码（lossy source coding），推导出基于互秩失真（reciprocal-rank distortion）的单字母率失真函数 $ R(D) $，并证明一个下界分解为模态平衡项和熵不平衡惩罚项 $ \kappa,\Delta H $；其次设计了一种显式的熵加权随机量化器（entropy-weighted stochastic quantizer），结合自适应的每模态温度解码器，通过Blahut-Arimoto算法证明该方案可在 $ n $ 个训练三元组下达到 $ O(n^{-1}) $ 的失真逼近；最终通过VC型分析获得首个有限样本超额风险界，其复杂度在模态数量和熵差上均呈次线性增长。实验验证了该方法在高斯混合模型和Flickr30k数据集上可逼近理论前沿，显著优于固定温度和朴素CLIP基线。

链接: https://arxiv.org/abs/2509.11054
作者: Thomas Y. Chen
机构: Columbia University (哥伦比亚大学)
类目: Information Theory (cs.IT); Computer Vision and Pattern Recognition (cs.CV)
备注: ICCV MRR 2025

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Abstract:We establish the first information-theoretic limits for multimodal retrieval. Casting ranking as lossy source coding, we derive a single-letter rate-distortion function R(D) for reciprocal-rank distortion and prove a converse bound that splits into a modality-balanced term plus a skew penalty \kappa,\Delta H capturing entropy imbalance and cross-modal redundancy. We then construct an explicit entropy-weighted stochastic quantizer with an adaptive, per-modality temperature decoder; a Blahut-Arimoto argument shows this scheme achieves distortion within O(n^-1) of R(D) using n training triples. A VC-type analysis yields the first finite-sample excess-risk bound whose complexity scales sub-linearly in both the number of modalities and the entropy gap. Experiments on controlled Gaussian mixtures and Flickr30k confirm that our adaptive codes sit within two percentage points of the theoretical frontier, while fixed-temperature and naive CLIP baselines lag significantly. Taken together, our results give a principled answer to “how many bits per query are necessary” for high-quality multimodal retrieval and provide design guidance for entropy-aware contrastive objectives, continual-learning retrievers, and retrieval-augmented generators.
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[CV-128] Data-Efficient Ensemble Weather Forecasting with Diffusion Models

【速读】：该论文旨在解决生成式天气预报中因扩散模型（diffusion models）通常为自回归结构而导致的计算成本过高问题，尤其是在气候科学领域数据稀缺或获取困难的背景下。其解决方案的关键在于通过精心设计的数据选择策略来提升训练效率：研究发现，采用简单的时间分层抽样（time stratified sampling）方法，在仅使用20%训练数据的情况下，即可达到甚至优于全数据训练的性能表现，尤其在部分指标上显著超越全数据模型，从而证明了数据高效型扩散训练的可行性，为未来基于模型感知或自适应采样的优化方向提供了重要依据。

链接: https://arxiv.org/abs/2509.11047
作者: Kevin Valencia,Ziyang Liu,Justin Cui
机构: University of California, Los Angeles (加州大学洛杉矶分校)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Although numerical weather forecasting methods have dominated the field, recent advances in deep learning methods, such as diffusion models, have shown promise in ensemble weather forecasting. However, such models are typically autoregressive and are thus computationally expensive. This is a challenge in climate science, where data can be limited, costly, or difficult to work with. In this work, we explore the impact of curated data selection on these autoregressive diffusion models. We evaluate several data sampling strategies and show that a simple time stratified sampling approach achieves performance similar to or better than full-data training. Notably, it outperforms the full-data model on certain metrics and performs only slightly worse on others while using only 20% of the training data. Our results demonstrate the feasibility of data-efficient diffusion training, especially for weather forecasting, and motivates future work on adaptive or model-aware sampling methods that go beyond random or purely temporal sampling.
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[CV-129] Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images

【速读】：该论文旨在解决多实例学习（Multi-Instance Learning, MIL）在处理弱标注复杂数据（如计算病理学中的全切片图像，WSIs）时存在的两个核心问题：一是实例冗余导致模型鲁棒性差，二是缺乏显式的机制来剔除非信息性实例，从而限制了模型的可解释性和性能。解决方案的关键在于提出一种新的簇级稀疏多实例学习框架（Cluster-level Sparse MIL, csMIL），其核心创新包括：全局-局部联合聚类以识别具有诊断意义的实例簇、簇内注意力机制用于聚焦关键区域、以及簇级稀疏正则化策略实现对无关簇的自动筛选。这一设计显著提升了模型对噪声实例的鲁棒性、增强了病理判读的可解释性，并降低了计算复杂度。

链接: https://arxiv.org/abs/2509.11034
作者: Yuedi Zhang,Zhixiang Xia,Guosheng Yin,Bin Liu
机构: Georgia Institute of Technology (佐治亚理工学院); Anonymous University (匿名大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Multi-Instance Learning (MIL) is pivotal for analyzing complex, weakly labeled datasets, such as whole-slide images (WSIs) in computational pathology, where bags comprise unordered collections of instances with sparse diagnostic relevance. Traditional MIL approaches, including early statistical methods and recent attention-based frameworks, struggle with instance redundancy and lack explicit mechanisms for discarding non-informative instances, limiting their robustness and interpretability. We propose Cluster-level Sparse MIL (csMIL), a novel framework that integrates global-local instance clustering, within-cluster attention, and cluster-level sparsity induction to address these challenges. Our csMIL first performs global clustering across all bags to establish K cluster centers, followed by local clustering within each bag to assign cluster labels. Attention scores are computed within each cluster, and sparse regularization is applied to cluster weights, enabling the selective retention of diagnostically relevant clusters while discarding irrelevant ones. This approach enhances robustness to noisy instances, improves interpretability by identifying critical regions, and reduces computational complexity. Theoretical analysis demonstrates that csMIL requires O(s log K) bags to recover s relevant clusters, aligning with compressed sensing principles. Empirically, csMIL achieves state-of-the-art performance on two public histopathology benchmarks (CAMELYON16, TCGA-NSCLC).
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[CV-130] Improving Fungi Prototype Representations for Few-Shot Classification

【速读】：该论文旨在解决在真实野外采集数据中自动识别真菌物种的难题，尤其针对类别分布高度不均衡、许多物种（尤其是稀有和记录不足的类群）训练样本极少的问题。其解决方案的关键在于提出一种基于原型网络（prototypical networks）的鲁棒深度学习方法，通过增强原型表示来提升少样本条件下的分类性能，从而显著改善对常见与稀有真菌物种的识别准确率，在FungiCLEF 2025竞赛的公共和私有排行榜上Recall@5指标均超过基线30个百分点以上。

链接: https://arxiv.org/abs/2509.11020
作者: Abdarahmane Traore,Éric Hervet,Andy Couturier
机构: Embia; Université de Moncton (蒙克顿大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The FungiCLEF 2025 competition addresses the challenge of automatic fungal species recognition using realistic, field-collected observational data. Accurate identification tools support both mycologists and citizen scientists, greatly enhancing large-scale biodiversity monitoring. Effective recognition systems in this context must handle highly imbalanced class distributions and provide reliable performance even when very few training samples are available for many species, especially rare and under-documented taxa that are often missing from standard training sets. According to competition organizers, about 20% of all verified fungi observations, representing nearly 20,000 instances, are associated with these rarely recorded species. To tackle this challenge, we propose a robust deep learning method based on prototypical networks, which enhances prototype representations for few-shot fungal classification. Our prototypical network approach exceeds the competition baseline by more than 30 percentage points in Recall@5 on both the public (PB) and private (PR) leaderboards. This demonstrates strong potential for accurately identifying both common and rare fungal species, supporting the main objectives of FungiCLEF 2025.
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[CV-131] AD-GS: Alternating Densification for Sparse-Input 3D Gaussian Splatting SIGGRAPH

【速读】：该论文旨在解决3D Gaussian Splatting (3DGS) 在稀疏视图（sparse-view）设置下易产生伪影（如浮点物、几何不准确和过拟合）的问题。其关键解决方案是提出一种交替式稠密化框架（Alternating Densification, AD-GS），通过在高密度化阶段与低密度化阶段之间交替执行：高密度化阶段激进地添加高斯原语并结合光度损失训练以捕捉细节；低密度化阶段则通过剧烈的透明度裁剪和伪视角一致性及边缘感知深度平滑约束来规范几何结构。这种机制有效控制模型容量增长，减少过拟合，同时逐步优化场景表示质量。

链接: https://arxiv.org/abs/2509.11003
作者: Gurutva Patle,Nilay Girgaonkar,Nagabhushan Somraj,Rajiv Soundararajan
机构: Indian Institute of Science (印度科学研究所); Birla Institute of Technology and Science, Pilani - Hyderabad Campus (比特学院与科学学院，比兰尼-海得拉巴校区)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:3D Gaussian Splatting (3DGS) has shown impressive results in real-time novel view synthesis. However, it often struggles under sparse-view settings, producing undesirable artifacts such as floaters, inaccurate geometry, and overfitting due to limited observations. We find that a key contributing factor is uncontrolled densification, where adding Gaussian primitives rapidly without guidance can harm geometry and cause artifacts. We propose AD-GS, a novel alternating densification framework that interleaves high and low densification phases. During high densification, the model densifies aggressively, followed by photometric loss based training to capture fine-grained scene details. Low densification then primarily involves aggressive opacity pruning of Gaussians followed by regularizing their geometry through pseudo-view consistency and edge-aware depth smoothness. This alternating approach helps reduce overfitting by carefully controlling model capacity growth while progressively refining the scene representation. Extensive experiments on challenging datasets demonstrate that AD-GS significantly improves rendering quality and geometric consistency compared to existing methods.
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[CV-132] Policy-Driven Transfer Learning in Resource-Limited Animal Monitoring

【速读】：该论文旨在解决野生动物保护与牲畜管理中动物健康监测与种群管理面临的自动化检测与追踪难题，尤其针对基于无人机（UAV）的计算机视觉系统在标注数据稀缺场景下难以训练高效深度学习（DL）模型的问题。其解决方案的关键在于提出一种基于强化学习（Reinforcement Learning, RL）的迁移学习框架，利用上置信界（Upper Confidence Bound, UCB）算法自动筛选最适合动物检测任务的预训练神经网络模型，通过系统性评估和排序候选模型性能，显著提升检测率并大幅降低计算时间。

链接: https://arxiv.org/abs/2509.10995
作者: Nisha Pillai,Aditi Virupakshaiah,Harrison W. Smith,Amanda J. Ashworth,Prasanna Gowda,Phillip R. Owens,Adam R. Rivers,Bindu Nanduri,Mahalingam Ramkumar
机构: Mississippi State University (密西西比州立大学); University of Arkansas (阿肯色大学); USDA-ARS (美国农业部农业研究服务局)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Animal health monitoring and population management are critical aspects of wildlife conservation and livestock management that increasingly rely on automated detection and tracking systems. While Unmanned Aerial Vehicle (UAV) based systems combined with computer vision offer promising solutions for non-invasive animal monitoring across challenging terrains, limited availability of labeled training data remains an obstacle in developing effective deep learning (DL) models for these applications. Transfer learning has emerged as a potential solution, allowing models trained on large datasets to be adapted for resource-limited scenarios such as those with limited data. However, the vast landscape of pre-trained neural network architectures makes it challenging to select optimal models, particularly for researchers new to the field. In this paper, we propose a reinforcement learning (RL)-based transfer learning framework that employs an upper confidence bound (UCB) algorithm to automatically select the most suitable pre-trained model for animal detection tasks. Our approach systematically evaluates and ranks candidate models based on their performance, streamlining the model selection process. Experimental results demonstrate that our framework achieves a higher detection rate while requiring significantly less computational time compared to traditional methods.
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[CV-133] rueSkin: Towards Fair and Accurate Skin Tone Recognition and Generation

【速读】：该论文旨在解决皮肤色调（skin tone）识别与生成任务中存在的准确性不足和偏见问题，尤其是在大型多模态模型（LMMs）和生成式AI中表现不佳的现状。其解决方案的关键在于构建了一个名为TrueSkin的数据集，该数据集包含7299张图像，系统性地划分为6类皮肤色调，并覆盖多样化的光照条件、拍摄角度和设备设置。通过该数据集，研究者不仅对现有模型进行了基准测试，揭示了LMMs倾向于将中间肤色误判为较浅肤色，而生成模型则因提示词中无关属性（如发型或环境背景）引发偏差导致皮肤色调失真；更重要的是，利用TrueSkin训练的识别模型分类准确率提升超过20%，且微调后的生成模型在皮肤色调保真度上显著改善，表明该数据集是提升皮肤色调相关任务公平性和准确性的关键资源。

链接: https://arxiv.org/abs/2509.10980
作者: Haoming Lu
机构: Topaz Labs
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Skin tone recognition and generation play important roles in model fairness, healthcare, and generative AI, yet they remain challenging due to the lack of comprehensive datasets and robust methodologies. Compared to other human image analysis tasks, state-of-the-art large multimodal models (LMMs) and image generation models struggle to recognize and synthesize skin tones accurately. To address this, we introduce TrueSkin, a dataset with 7299 images systematically categorized into 6 classes, collected under diverse lighting conditions, camera angles, and capture settings. Using TrueSkin, we benchmark existing recognition and generation approaches, revealing substantial biases: LMMs tend to misclassify intermediate skin tones as lighter ones, whereas generative models struggle to accurately produce specified skin tones when influenced by inherent biases from unrelated attributes in the prompts, such as hairstyle or environmental context. We further demonstrate that training a recognition model on TrueSkin improves classification accuracy by more than 20% compared to LMMs and conventional approaches, and fine-tuning with TrueSkin significantly improves skin tone fidelity in image generation models. Our findings highlight the need for comprehensive datasets like TrueSkin, which not only serves as a benchmark for evaluating existing models but also provides a valuable training resource to enhance fairness and accuracy in skin tone recognition and generation tasks.
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[CV-134] Gaze Authentication: Factors Influencing Authentication Performance

【速读】：该论文旨在解决基于眼动追踪的认证系统（gaze-based authentication）性能优化问题，重点探究影响认证准确性的关键因素。其解决方案的关键在于通过大规模实验验证了三个核心策略：首先，采用统一的校准目标距离可提升认证性能；其次，融合已校准与未校准的眼动数据能增强识别效果；再次，提高眼动信号质量对认证精度有显著正向作用。此外，研究发现简单的三样本移动平均滤波器会轻微降低整体认证性能，提示在预处理阶段需谨慎选择滤波方法。

链接: https://arxiv.org/abs/2509.10969
作者: Dillon Lohr,Michael J Proulx,Mehedi Hasan Raju,Oleg V Komogortsev
机构: Meta Reality Labs Research (Meta 虚拟现实实验室研究); Texas State University (德克萨斯州立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This paper examines the key factors that influence the performance of state-of-the-art gaze-based authentication. Experiments were conducted on a large-scale, in-house dataset comprising 8,849 subjects collected with Meta Quest Pro equivalent hardware running a video oculography-driven gaze estimation pipeline at 72Hz. The state-of-the-art neural network architecture was employed to study the influence of the following factors on authentication performance: eye tracking signal quality, various aspects of eye tracking calibration, and simple filtering on estimated raw gaze. We found that using the same calibration target depth for eye tracking calibration, fusing calibrated and non-calibrated gaze, and improving eye tracking signal quality all enhance authentication performance. We also found that a simple three-sample moving average filter slightly reduces authentication performance in general. While these findings hold true for the most part, some exceptions were noted.
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[CV-135] Simulating Sinogram-Domain Motion and Correcting Image-Domain Artifacts Using Deep Learning in HR-pQCT Bone Imaging

【速读】：该论文旨在解决高分辨率外周定量计算机断层扫描（HR-pQCT）中因刚性运动伪影（如皮质骨条纹和骨小梁模糊）导致的骨微结构在体评估困难问题。现有技术虽有多种运动分级方法，但缺乏标准化的退化模型，因而无法实现有效的运动校正。解决方案的关键在于：首先优化一种传统的sinogram基方法以模拟HR-pQCT图像中的运动伪影，构建包含运动污染图像及其对应真值的配对数据集，从而支持监督学习框架下的运动校正；其次提出一种边缘增强自注意力Wasserstein生成对抗网络（ESWGAN-GP），通过边缘增强跳跃连接保留骨小梁边界，并利用自注意力机制捕捉长程依赖关系，同时引入基于VGG的感知损失以重建精细微结构特征，最终在模拟和真实数据集上均取得显著性能提升（如SNR、SSIM和VIF指标）。

链接: https://arxiv.org/abs/2509.10961
作者: Farhan Sadik,Christopher L. Newman,Stuart J. Warden,Rachel K. Surowiec
机构: Purdue University (普渡大学); Indiana University (印第安纳大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Rigid-motion artifacts, such as cortical bone streaking and trabecular smearing, hinder in vivo assessment of bone microstructures in high-resolution peripheral quantitative computed tomography (HR-pQCT). Despite various motion grading techniques, no motion correction methods exist due to the lack of standardized degradation models. We optimize a conventional sinogram-based method to simulate motion artifacts in HR-pQCT images, creating paired datasets of motion-corrupted images and their corresponding ground truth, which enables seamless integration into supervised learning frameworks for motion correction. As such, we propose an Edge-enhanced Self-attention Wasserstein Generative Adversarial Network with Gradient Penalty (ESWGAN-GP) to address motion artifacts in both simulated (source) and real-world (target) datasets. The model incorporates edge-enhancing skip connections to preserve trabecular edges and self-attention mechanisms to capture long-range dependencies, facilitating motion correction. A visual geometry group (VGG)-based perceptual loss is used to reconstruct fine micro-structural features. The ESWGAN-GP achieves a mean signal-to-noise ratio (SNR) of 26.78, structural similarity index measure (SSIM) of 0.81, and visual information fidelity (VIF) of 0.76 for the source dataset, while showing improved performance on the target dataset with an SNR of 29.31, SSIM of 0.87, and VIF of 0.81. The proposed methods address a simplified representation of real-world motion that may not fully capture the complexity of in vivo motion artifacts. Nevertheless, because motion artifacts present one of the foremost challenges to more widespread adoption of this modality, these methods represent an important initial step toward implementing deep learning-based motion correction in HR-pQCT.
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[CV-136] Lightweight Metadata-Aware Mixture-of-Experts Masked Autoencoder for Earth Observation

【速读】：该论文旨在解决当前地球观测（Earth Observation, EO）领域中大规模基础模型计算成本过高、难以在下游任务中复用的问题。其解决方案的关键在于提出一种参数量仅为2.5M的元数据感知型混合专家掩码自编码器（Metadata-aware Mixture-of-Experts Masked Autoencoder, MoE-MAE），通过稀疏专家路由机制与时空元数据（地理坐标及季节/日周期编码）联合建模，实现高效预训练与迁移学习。该方法在BigEarthNet-Landsat上预训练后，冻结编码器进行线性探测，在保持极小参数规模的同时展现出与更大模型相当的性能；且在无显式元数据的EuroSAT-Landsat测试集上仍具竞争力，验证了其泛化能力与可扩展性。

链接: https://arxiv.org/abs/2509.10919
作者: Mohanad Albughdadi
机构: European Centre for Medium-Range Weather Forecasts (欧洲中期天气预报中心)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Recent advances in Earth Observation have focused on large-scale foundation models. However, these models are computationally expensive, limiting their accessibility and reuse for downstream tasks. In this work, we investigate compact architectures as a practical pathway toward smaller general-purpose EO models. We propose a Metadata-aware Mixture-of-Experts Masked Autoencoder (MoE-MAE) with only 2.5M parameters. The model combines sparse expert routing with geo-temporal conditioning, incorporating imagery alongside latitude/longitude and seasonal/daily cyclic encodings. We pretrain the MoE-MAE on the BigEarthNet-Landsat dataset and evaluate embeddings from its frozen encoder using linear probes. Despite its small size, the model competes with much larger architectures, demonstrating that metadata-aware pretraining improves transfer and label efficiency. To further assess generalization, we evaluate on the EuroSAT-Landsat dataset, which lacks explicit metadata, and still observe competitive performance compared to models with hundreds of millions of parameters. These results suggest that compact, metadata-aware MoE-MAEs are an efficient and scalable step toward future EO foundation models.
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[CV-137] Robustifying Diffusion-Denoised Smoothing Against Covariate Shift

【速读】：该论文旨在解决基于扩散模型（denoising diffusion model）的随机平滑（randomized smoothing）方法在实现l2-对抗扰动下的认证鲁棒性时，因噪声估计偏差导致的协变量偏移（covariate shift）问题，从而影响平滑分类器的性能。解决方案的关键在于提出一种新的对抗目标函数，聚焦于扩散模型中添加噪声的分布建模，通过训练基础分类器以抵御由去噪器引入的协变量偏移，从而显著提升在MNIST、CIFAR-10和ImageNet三个标准数据集上的认证准确率，并达到当前最优水平。

链接: https://arxiv.org/abs/2509.10913
作者: Ali Hedayatnia,Mostafa Tavassolipour,Babak Nadjar Araabi,Abdol-Hossein Vahabie
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Randomized smoothing is a well-established method for achieving certified robustness against l2-adversarial perturbations. By incorporating a denoiser before the base classifier, pretrained classifiers can be seamlessly integrated into randomized smoothing without significant performance degradation. Among existing methods, Diffusion Denoised Smoothing - where a pretrained denoising diffusion model serves as the denoiser - has produced state-of-the-art results. However, we show that employing a denoising diffusion model introduces a covariate shift via misestimation of the added noise, ultimately degrading the smoothed classifier’s performance. To address this issue, we propose a novel adversarial objective function focused on the added noise of the denoising diffusion model. This approach is inspired by our understanding of the origin of the covariate shift. Our goal is to train the base classifier to ensure it is robust against the covariate shift introduced by the denoiser. Our method significantly improves certified accuracy across three standard classification benchmarks - MNIST, CIFAR-10, and ImageNet - achieving new state-of-the-art performance in l2-adversarial perturbations. Our implementation is publicly available at this https URL
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[CV-138] otal Variation Subgradient Guided Image Fusion for Dual-Camera CASSI System

【速读】：该论文旨在解决光谱成像技术中长期存在的光谱分辨率、空间分辨率与时间分辨率之间的权衡问题，尤其针对压缩感知基的编码孔径快照式光谱成像（Coded Aperture Snapshot Spectral Imaging, CASSI）在高压缩比下导致的病态重建问题。传统基于模型的方法受限于手工设计的图像先验，而深度学习方法则因黑箱特性缺乏物理可解释性。解决方案的关键在于提出一种双相机CASSI重建框架，融合总变差（Total Variation, TV）次梯度理论，构建端到端的SD-CASSI数学模型，显著降低逆问题求解的计算复杂度，并通过引入动态正则化策略——利用RGB/全色参考图像的归一化梯度约束构造具有严格凸优化保证的TV次梯度相似性函数，同时设计自适应参考生成与更新机制，以辅助相机提供的空间先验指导次梯度方向，从而实现对空间-光谱结构一致性的有效保持和可解释性强的高性能重建。

链接: https://arxiv.org/abs/2509.10897
作者: Weiqiang Zhao,Tianzhu Liu,Yuzhe Gui,Yanfeng Gu
机构: Harbin Institute of Technology (哈尔滨工业大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Optics (physics.optics)
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Abstract:Spectral imaging technology has long-faced fundamental challenges in balancing spectral, spatial, and temporal reso- lutions. While compressive sensing-based Coded Aperture Snapshot Spectral Imaging (CASSI) mitigates this trade-off through optical encoding, high compression ratios result in ill-posed reconstruction problems. Traditional model-based methods exhibit limited performance due to reliance on handcrafted inherent image priors, while deep learning approaches are constrained by their black-box nature, which compromises physical interpretability. To address these limitations, we propose a dual-camera CASSI reconstruction framework that integrates total variation (TV) subgradient theory. By es- tablishing an end-to-end SD-CASSI mathematical model, we reduce the computational complexity of solving the inverse problem and provide a mathematically well-founded framework for analyzing multi-camera systems. A dynamic regular- ization strategy is introduced, incorporating normalized gradient constraints from RGB/panchromatic-derived reference images, which constructs a TV subgradient similarity function with strict convex optimization guarantees. Leveraging spatial priors from auxiliary cameras, an adaptive reference generation and updating mechanism is designed to provide subgradient guidance. Experimental results demonstrate that the proposed method effectively preserves spatial-spectral structural consistency. The theoretical framework establishes an interpretable mathematical foundation for computational spectral imaging, demonstrating robust performance across diverse reconstruction scenarios. The source code is available at this https URL.
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[CV-139] AutoOEP - A Multi-modal Framework for Online Exam Proctoring

【速读】：该论文旨在解决在线教育中远程考试场景下学术诚信保障的难题，传统人工监考难以规模化实施，而现有自动化方案普遍存在侵入性强或作弊行为识别覆盖率不足的问题。其解决方案的关键在于提出了一种多模态自动监考框架AutoOEP（Automated Online Exam Proctoring），通过双摄像头协同采集考生正面与工作区侧视图像，融合面部身份验证（基于ArcFace）、头部姿态估计、视线追踪及嘴部动作分析的Face模块，与基于YOLOv11改进模型的物体检测和手部接近度监测的Hand模块，并利用LSTM网络对时序特征进行建模，最终输出实时作弊概率评分，从而实现高精度、低资源消耗的自动化监考。

链接: https://arxiv.org/abs/2509.10887
作者: Aryan Kashyap Naveen,Bhuvanesh Singla,Raajan Wankhade,Shreesha M,Ramu S,Ram Mohana Reddy Guddeti
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The burgeoning of online education has created an urgent need for robust and scalable systems to ensure academic integrity during remote examinations. Traditional human proctoring is often not feasible at scale, while existing automated solutions can be intrusive or fail to detect a wide range of cheating behaviors. This paper introduces AutoOEP (Automated Online Exam Proctoring), a comprehensive, multi-modal framework that leverages computer vision and machine learning to provide effective, automated proctoring. The system utilizes a dual-camera setup to capture both a frontal view of the examinee and a side view of the workspace, minimizing blind spots. Our approach integrates several parallel analyses: the Face Module performs continuous identity verification using ArcFace, along with head pose estimation, gaze tracking, and mouth movement analysis to detect suspicious cues. Concurrently, the Hand Module employs a fine-tuned YOLOv11 model for detecting prohibited items (e.g., mobile phones, notes) and tracks hand proximity to these objects. Features from these modules are aggregated and fed into a Long Short-Term Memory (LSTM) network that analyzes temporal patterns to calculate a real-time cheating probability score. We evaluate AutoOEP on a custom-collected dataset simulating diverse exam conditions. Our system achieves an accuracy of 90.7% in classifying suspicious activities. The object detection component obtains a mean Average Precision (mAP@.5) of 0.57 for prohibited items, and the entire framework processes video streams at approximately 2.4 frames per second without a GPU. The results demonstrate that AutoOEP is an effective and resource-efficient solution for automated proctoring, significantly reducing the need for human intervention and enhancing the integrity of online assessments.
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[CV-140] Nav-R1: Reasoning and Navigation in Embodied Scenes

【速读】：该论文旨在解决具身导航（embodied navigation）中推理不连贯、不稳定导致泛化能力差，以及长程语义推理与低延迟控制难以平衡的问题。其解决方案的关键在于提出Nav-R1，一个统一具身环境推理的具身基础模型：首先构建包含11万条步骤式思维链（Chains-of-Thought, CoT）的大规模数据集Nav-CoT-110K，实现结构化推理的冷启动初始化；在此基础上设计基于GRPO的强化学习框架，引入格式、理解与导航三重奖励机制以提升结构一致性、语义锚定和路径保真度；并创新性地提出“快在慢中”（Fast-in-Slow）推理范式，将高阶语义推理与低延迟反应式控制解耦，从而实现高效且连贯的实时导航。

链接: https://arxiv.org/abs/2509.10884
作者: Qingxiang Liu,Ting Huang,Zeyu Zhang,Hao Tang
机构: Shanghai University of Engineering Science (上海工程技术大学); Peking University (北京大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Embodied navigation requires agents to integrate perception, reasoning, and action for robust interaction in complex 3D environments. Existing approaches often suffer from incoherent and unstable reasoning traces that hinder generalization across diverse environments, and difficulty balancing long-horizon semantic reasoning with low-latency control for real-time navigation. To address these challenges, we propose Nav-R1, an embodied foundation model that unifies reasoning in embodied environments. We first construct Nav-CoT-110K, a large-scale dataset of step-by-step Chains-of-Thought (CoT) for embodied tasks, which enables cold-start initialization with structured reasoning. Building on this foundation, we design a GRPO-based reinforcement learning framework with three complementary rewards: format, understanding, and navigation, to improve structural adherence, semantic grounding, and path fidelity. Furthermore, we introduce a Fast-in-Slow reasoning paradigm, decoupling deliberate semantic reasoning from low-latency reactive control for efficient yet coherent navigation. Extensive evaluations on embodied AI benchmarks demonstrate that Nav-R1 consistently outperforms strong baselines, with over 8% average improvement in reasoning and navigation performance. Real-world deployment on a mobile robot further validates its robustness under limited onboard resources. Code: this https URL. Website: this https URL.
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[CV-141] OpenUrban3D: Annotation-Free Open-Vocabulary Semantic Segmentation of Large-Scale Urban Point Clouds

【速读】：该论文旨在解决大尺度城市点云场景中开放词汇语义分割（Open-vocabulary Semantic Segmentation）的难题，即如何在无高质量多视角图像、无需预训练点云分割网络或人工标注的情况下，实现对任意自然语言描述的物体类别进行零样本分割。其核心挑战在于城市环境中几何、尺度和外观差异显著，且现有三维（3D）分割方法泛化能力不足。解决方案的关键在于提出 OpenUrban3D 框架，通过从原始点云直接生成鲁棒语义特征：首先利用多视角、多粒度渲染与掩码级视觉-语言特征提取，再结合样本平衡融合策略，并将这些特征蒸馏至 3D 主干模型中，从而在不依赖外部图像对齐或预训练模型的前提下，实现跨场景的高精度零样本分割，同时保留语义丰富性和几何先验信息。

链接: https://arxiv.org/abs/2509.10842
作者: Chongyu Wang,Kunlei Jing,Jihua Zhu,Di Wang
机构: Xi’an Jiaotong University (西安交通大学); Shaanxi Joint (Key) Laboratory for Artificial Intelligence (西安交通大学人工智能联合（重点）实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Open-vocabulary semantic segmentation enables models to recognize and segment objects from arbitrary natural language descriptions, offering the flexibility to handle novel, fine-grained, or functionally defined categories beyond fixed label sets. While this capability is crucial for large-scale urban point clouds that support applications such as digital twins, smart city management, and urban analytics, it remains largely unexplored in this domain. The main obstacles are the frequent absence of high-quality, well-aligned multi-view imagery in large-scale urban point cloud datasets and the poor generalization of existing three-dimensional (3D) segmentation pipelines across diverse urban environments with substantial variation in geometry, scale, and appearance. To address these challenges, we present OpenUrban3D, the first 3D open-vocabulary semantic segmentation framework for large-scale urban scenes that operates without aligned multi-view images, pre-trained point cloud segmentation networks, or manual annotations. Our approach generates robust semantic features directly from raw point clouds through multi-view, multi-granularity rendering, mask-level vision-language feature extraction, and sample-balanced fusion, followed by distillation into a 3D backbone model. This design enables zero-shot segmentation for arbitrary text queries while capturing both semantic richness and geometric priors. Extensive experiments on large-scale urban benchmarks, including SensatUrban and SUM, show that OpenUrban3D achieves significant improvements in both segmentation accuracy and cross-scene generalization over existing methods, demonstrating its potential as a flexible and scalable solution for 3D urban scene understanding.
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[CV-142] Point-Plane Projections for Accurate LiDAR Semantic Segmentation in Small Data Scenarios

【速读】：该论文旨在解决LiDAR点云语义分割在数据稀缺场景下性能受限的问题，尤其是现有方法因计算复杂度高和依赖大量训练数据而导致泛化能力不足。解决方案的关键在于通过点-平面投影（point-plane projection）从多维2D表示中有效学习互补特征，从而仅利用LiDAR数据即可提升模型表现；同时引入一种符合LiDAR传感器特性的几何感知数据增强技术，以缓解类别不平衡问题，显著改善小样本条件下的分割精度。

链接: https://arxiv.org/abs/2509.10841
作者: Simone Mosco,Daniel Fusaro,Wanmeng Li,Emanuele Menegatti,Alberto Pretto
机构: University of Padova (帕多瓦大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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Abstract:LiDAR point cloud semantic segmentation is essential for interpreting 3D environments in applications such as autonomous driving and robotics. Recent methods achieve strong performance by exploiting different point cloud representations or incorporating data from other sensors, such as cameras or external datasets. However, these approaches often suffer from high computational complexity and require large amounts of training data, limiting their generalization in data-scarce scenarios. In this paper, we improve the performance of point-based methods by effectively learning features from 2D representations through point-plane projections, enabling the extraction of complementary information while relying solely on LiDAR data. Additionally, we introduce a geometry-aware technique for data augmentation that aligns with LiDAR sensor properties and mitigates class imbalance. We implemented and evaluated our method that applies point-plane projections onto multiple informative 2D representations of the point cloud. Experiments demonstrate that this approach leads to significant improvements in limited-data scenarios, while also achieving competitive results on two publicly available standard datasets, as SemanticKITTI and PandaSet. The code of our method is available at this https URL
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[CV-143] Multi-Task Diffusion Approach For Prediction of Glioma Tumor Progression

【速读】：该论文旨在解决胶质瘤（glioma）进展预测中因临床实践中纵向MRI数据稀疏且采集不规则导致的建模困难问题，尤其在随访序列不完整、数据分布失衡的情况下难以实现可靠的时间依赖性预测。其解决方案的关键在于提出一种多任务扩散框架，能够进行时间无关（time-agnostic）的像素级肿瘤演化预测：一方面生成任意未来时间点的FLAIR序列图像，另一方面基于符号距离场（signed distance fields, SDFs）输出空间概率性肿瘤演化图谱以量化不确定性；同时引入预训练形变模块建模跨扫描间的非线性形变，从而捕捉任意时间间隔内的动态变化；此外，通过针对性的数据增强策略合成完整的三阶段随访序列并补全缺失模态，提升模型稳定性与准确性，并结合放疗剂量加权焦点损失（radiotherapy-weighted focal loss）强化临床高风险区域的学习能力。

链接: https://arxiv.org/abs/2509.10824
作者: Aghiles Kebaili,Romain Modzelewski,Jérôme Lapuyade-Lahorgue,Maxime Fontanilles,Sébastien Thureau,Su Ruan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Glioma, an aggressive brain malignancy characterized by rapid progression and its poor prognosis, poses significant challenges for accurate evolution prediction. These challenges are exacerbated by sparse, irregularly acquired longitudinal MRI data in clinical practice, where incomplete follow-up sequences create data imbalances and make reliable modeling difficult. In this paper, we present a multitask diffusion framework for time-agnostic, pixel-wise prediction of glioma progression. The model simultaneously generates future FLAIR sequences at any chosen time point and estimates spatial probabilistic tumor evolution maps derived using signed distance fields (SDFs), allowing uncertainty quantification. To capture temporal dynamics of tumor evolution across arbitrary intervals, we integrate a pretrained deformation module that models inter-scan changes using deformation fields. Regarding the common clinical limitation of data scarcity, we implement a targeted augmentation pipeline that synthesizes complete sequences of three follow-up scans and imputes missing MRI modalities from available patient studies, improving the stability and accuracy of predictive models. Based on merely two follow-up scans at earlier timepoints, our framework produces flexible time-depending probability maps, enabling clinicians to interrogate tumor progression risks at any future temporal milestone. We further introduce a radiotherapy-weighted focal loss term that leverages radiation dose maps, as these highlight regions of greater clinical importance during model training. The proposed method was trained on a public dataset and evaluated on an internal private dataset, achieving promising results in both cases
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[CV-144] Well-Conditioned Polynomial Representations for Mathematical Handwriting Recognition

【速读】：该论文旨在解决数学手写符号数字化表示中如何在保证建模精度的同时降低计算成本的问题。其解决方案的关键在于系统比较不同正交基（Legendre、Legendre-Sobolev、Chebyshev 及 Chebyshev-Sobolev）与多项式阶次之间的权衡关系，通过分析各基底下多项式求值的条件数（condition number），并量化不同内积定义的范数对符号间差异变化的约束能力，从而选择最优的基底与阶次组合以实现高效且精确的数字墨迹建模。

链接: https://arxiv.org/abs/2509.10815
作者: Robert M. Corless,Deepak Singh Kalhan,Stephen M. Watt
机构: Western University (西维多利亚大学); University of Waterloo (滑铁卢大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Previous work has made use of a parameterized plane curve polynomial representation for mathematical handwriting, with the polynomials represented in a Legendre or Legendre-Sobolev graded basis. This provides a compact geometric representation for the digital ink. Preliminary results have also been shown for Chebyshev and Chebyshev-Sobolev bases. This article explores the trade-offs between basis choice and polynomial degree to achieve accurate modeling with a low computational cost. To do this, we consider the condition number for polynomial evaluation in these bases and bound how the various inner products give norms for the variations between symbols.
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[CV-145] InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts

【速读】：该论文旨在解决当前具身智能（Embodied AI）研究中大规模、可模拟室内场景数据集存在的三大核心问题：数据规模与多样性不足、布局过于简化（缺乏小型物品）、以及物体间严重碰撞。其解决方案的关键在于提出一个名为InternScenes的新颖大规模可模拟室内场景数据集，通过整合真实扫描场景、程序生成场景和设计师创建场景三类异构来源，构建包含约40,000个多样化场景、196万3D物体及288类对象的高质量数据集；同时采用系统化的数据处理流程，在保留大量小尺寸物品以增强布局真实性的同时，通过物理仿真消除物体冲突，并为真实场景创建“现实到模拟”（real-to-sim）复制品以保障可模拟性，且引入交互式物体提升场景互动性，从而显著提升模型训练规模与任务复杂度的适配能力。

链接: https://arxiv.org/abs/2509.10813
作者: Weipeng Zhong,Peizhou Cao,Yichen Jin,Li Luo,Wenzhe Cai,Jingli Lin,Hanqing Wang,Zhaoyang Lyu,Tai Wang,Bo Dai,Xudong Xu,Jiangmiao Pang
机构: Shanghai Artificial Intelligence Laboratory (上海人工智能实验室); Shanghai Jiao Tong University (上海交通大学); Beihang University (北京航空航天大学); The University of Hong Kong (香港大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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Abstract:The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts. However, existing datasets typically suffer from limitations in data scale or diversity, sanitized layouts lacking small items, and severe object collisions. To address these shortcomings, we introduce \textbfInternScenes, a novel large-scale simulatable indoor scene dataset comprising approximately 40,000 diverse scenes by integrating three disparate scene sources, real-world scans, procedurally generated scenes, and designer-created scenes, including 1.96M 3D objects and covering 15 common scene types and 288 object classes. We particularly preserve massive small items in the scenes, resulting in realistic and complex layouts with an average of 41.5 objects per region. Our comprehensive data processing pipeline ensures simulatability by creating real-to-sim replicas for real-world scans, enhances interactivity by incorporating interactive objects into these scenes, and resolves object collisions by physical simulations. We demonstrate the value of InternScenes with two benchmark applications: scene layout generation and point-goal navigation. Both show the new challenges posed by the complex and realistic layouts. More importantly, InternScenes paves the way for scaling up the model training for both tasks, making the generation and navigation in such complex scenes possible. We commit to open-sourcing the data, models, and benchmarks to benefit the whole community.
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[CV-146] Group Evidence Matters: Tiling-based Semantic Gating for Dense Object Detection

【速读】：该论文旨在解决无人机遥感影像中密集小目标因远距离视角、遮挡和背景杂乱而容易被漏检的问题。其核心解决方案是提出一种检测器无关的后处理框架，通过将重叠区域引发的冗余信息转化为群体证据（group evidence）来提升召回率；关键步骤包括：重叠分块（overlapping tiling）以恢复低置信度候选目标，利用基于边界框中心点的Spatial Gate（DBSCAN聚类）和基于ResNet-18特征嵌入的Semantic Gate（DBSCAN聚类）共同验证群体证据，随后对有效群体进行受控的置信度重加权，最后采用类别感知的非极大值抑制（class-aware NMS）融合策略完成输出。该方法无需重新训练模型，可与主流检测器无缝集成，实验证明在VisDrone数据集上召回率从0.685提升至0.778，适用于对召回率敏感的应用场景如远场计数与监控。

链接: https://arxiv.org/abs/2509.10779
作者: Yilun Xiao
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages, 7 figures

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Abstract:Dense small objects in UAV imagery are often missed due to long-range viewpoints, occlusion, and clutter[cite: 5]. This paper presents a detector-agnostic post-processing framework that converts overlap-induced redundancy into group evidence[cite: 6]. Overlapping tiling first recovers low-confidence candidates[cite: 7]. A Spatial Gate (DBSCAN on box centroids) and a Semantic Gate (DBSCAN on ResNet-18 embeddings) then validates group evidence[cite: 7]. Validated groups receive controlled confidence reweighting before class-aware NMS fusion[cite: 8]. Experiments on VisDrone show a recall increase from 0.685 to 0.778 (+0.093) and a precision adjustment from 0.801 to 0.595, yielding F1=0.669[cite: 9]. Post-processing latency averages 0.095 s per image[cite: 10]. These results indicate recall-first, precision-trade-off behavior that benefits recall-sensitive applications such as far-field counting and monitoring[cite: 10]. Ablation confirms that tiling exposes missed objects, spatial clustering stabilizes geometry, semantic clustering enforces appearance coherence, and reweighting provides calibrated integration with the baseline[cite: 11]. The framework requires no retraining and integrates with modern detectors[cite: 12]. Future work will reduce semantic gating cost and extend the approach with temporal cues[cite: 13].
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[CV-147] Enhancement Without Contrast: Stability-Aware Multicenter Machine Learning for Glioma MRI Imaging

【速读】：该论文旨在解决钆基对比剂（Gadolinium-based contrast agents, GBCAs）在胶质瘤影像中应用所引发的安全性、成本及可及性问题，提出通过机器学习（Machine Learning, ML）从非增强磁共振成像（non-contrast MRI）预测增强效应的替代方法，以减少对GBCAs的依赖并提升多中心模型的泛化能力。其解决方案的关键在于构建一个稳定性感知（stability-aware）的框架，通过在四个TCIA数据集（UCSF-PDGM、UPENN-GB、BRATS-Africa、BRATS-TCGA-LGG）上系统评估1,200种ML管道（包括特征提取、降维与分类器组合），采用旋转验证策略筛选出在不同中心间具有高稳定性和一致性能的最优模型（如MI-ETr管道），从而实现跨中心可靠预测胶质瘤增强状态的目标。

链接: https://arxiv.org/abs/2509.10767
作者: Sajad Amiri,Shahram Taeb,Sara Gharibi,Setareh Dehghanfard,Somayeh Sadat Mehrnia,Mehrdad Oveisi,Ilker Hacihaliloglu,Arman Rahmim,Mohammad R. Salmanpour
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 Pages, 1 Figure, and 6 Tables

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Abstract:Gadolinium-based contrast agents (GBCAs) are central to glioma imaging but raise safety, cost, and accessibility concerns. Predicting contrast enhancement from non-contrast MRI using machine learning (ML) offers a safer alternative, as enhancement reflects tumor aggressiveness and informs treatment planning. Yet scanner and cohort variability hinder robust model selection. We propose a stability-aware framework to identify reproducible ML pipelines for multicenter prediction of glioma MRI contrast enhancement. We analyzed 1,446 glioma cases from four TCIA datasets (UCSF-PDGM, UPENN-GB, BRATS-Africa, BRATS-TCGA-LGG). Non-contrast T1WI served as input, with enhancement derived from paired post-contrast T1WI. Using PyRadiomics under IBSI standards, 108 features were extracted and combined with 48 dimensionality reduction methods and 25 classifiers, yielding 1,200 pipelines. Rotational validation was trained on three datasets and tested on the fourth. Cross-validation prediction accuracies ranged from 0.91 to 0.96, with external testing achieving 0.87 (UCSF-PDGM), 0.98 (UPENN-GB), and 0.95 (BRATS-Africa), with an average of 0.93. F1, precision, and recall were stable (0.87 to 0.96), while ROC-AUC varied more widely (0.50 to 0.82), reflecting cohort heterogeneity. The MI linked with ETr pipeline consistently ranked highest, balancing accuracy and stability. This framework demonstrates that stability-aware model selection enables reliable prediction of contrast enhancement from non-contrast glioma MRI, reducing reliance on GBCAs and improving generalizability across centers. It provides a scalable template for reproducible ML in neuro-oncology and beyond.
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[CV-148] EditDuet: A Multi-Agent System for Video Non-Linear Editing SIGGRAPH2025

【速读】：该论文旨在解决视频编辑自动化问题，即如何在无需人工干预的情况下，根据自然语言指令自动完成视频剪辑与拼接任务。现有研究多集中于视频检索或用户界面设计，而将核心编辑决策仍交由用户处理，限制了自动化程度。本文提出一种基于多智能体的解决方案，其关键在于引入Editor代理和Critic代理的协同机制：Editor代理接收视频片段和自然语言指令，利用常见视频编辑工具生成候选序列；Critic代理则对生成结果进行评估，提供自然语言反馈或确认输出。通过学习驱动的跨代理通信策略，实现语言指令到视频结构的高效映射，从而显著提升编辑质量、时间约束满足度及人类偏好表现。

链接: https://arxiv.org/abs/2509.10761
作者: Marcelo Sandoval-Castaneda,Bryan Russell,Josef Sivic,Gregory Shakhnarovich,Fabian Caba Heilbron
机构: TTI-Chicago (芝加哥机器学习研究所); Adobe (Adobe公司); Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University (捷克信息学、机器人学与控制论研究所，捷克技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: SIGGRAPH 2025

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Abstract:Automated tools for video editing and assembly have applications ranging from filmmaking and advertisement to content creation for social media. Previous video editing work has mainly focused on either retrieval or user interfaces, leaving actual editing to the user. In contrast, we propose to automate the core task of video editing, formulating it as sequential decision making process. Ours is a multi-agent approach. We design an Editor agent and a Critic agent. The Editor takes as input a collection of video clips together with natural language instructions and uses tools commonly found in video editing software to produce an edited sequence. On the other hand, the Critic gives natural language feedback to the editor based on the produced sequence or renders it if it is satisfactory. We introduce a learning-based approach for enabling effective communication across specialized agents to address the language-driven video editing task. Finally, we explore an LLM-as-a-judge metric for evaluating the quality of video editing system and compare it with general human preference. We evaluate our system’s output video sequences qualitatively and quantitatively through a user study and find that our system vastly outperforms existing approaches in terms of coverage, time constraint satisfaction, and human preference.
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[CV-149] Every Camera Effect Every Time All at Once: 4D Gaussian Ray Tracing for Physics-based Camera Effect Data Generation

【速读】：该论文旨在解决当前计算机视觉系统在面对真实世界相机效应（如鱼眼畸变和滚动快门）时性能下降的问题，其根本原因在于训练数据中缺乏对这些效应的有效学习。现有数据生成方法要么成本高昂、存在模拟到现实的差距，要么无法精确建模相机效应。解决方案的关键在于提出一种名为4D Gaussian Ray Tracing (4D-GRT) 的两阶段新框架，该框架结合了4D高斯点绘（4D Gaussian Splatting）与基于物理的光线追踪技术，首先重建动态场景，再通过光线追踪生成具有可控且物理准确相机效应的视频，从而实现高效且高质量的相机效应模拟。

链接: https://arxiv.org/abs/2509.10759
作者: Yi-Ruei Liu,You-Zhe Xie,Yu-Hsiang Hsu,I-Sheng Fang,Yu-Lun Liu,Jun-Cheng Chen
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); National Yang Ming Chiao Tung University (国立阳明交通大学); National Central University (国立中央大学); Research Center for Information Technology Innovation, Academia Sinica (中央研究院资讯科技创新研究中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Common computer vision systems typically assume ideal pinhole cameras but fail when facing real-world camera effects such as fisheye distortion and rolling shutter, mainly due to the lack of learning from training data with camera effects. Existing data generation approaches suffer from either high costs, sim-to-real gaps or fail to accurately model camera effects. To address this bottleneck, we propose 4D Gaussian Ray Tracing (4D-GRT), a novel two-stage pipeline that combines 4D Gaussian Splatting with physically-based ray tracing for camera effect simulation. Given multi-view videos, 4D-GRT first reconstructs dynamic scenes, then applies ray tracing to generate videos with controllable, physically accurate camera effects. 4D-GRT achieves the fastest rendering speed while performing better or comparable rendering quality compared to existing baselines. Additionally, we construct eight synthetic dynamic scenes in indoor environments across four camera effects as a benchmark to evaluate generated videos with camera effects.
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[CV-150] SCOPE: Speech-guided COllaborative PErception Framework for Surgical Scene Segmentation

【速读】：该论文旨在解决手术场景中关键元素（如手术器械和解剖结构）的精准分割与跟踪问题，以支持术中上下文感知的辅助决策。现有方法受限于领域特定的监督模型，依赖标注数据且难以适应新场景或超出预定义标签类别。解决方案的关键在于提出一种语音引导的协同感知框架（SCOPE），其核心是一个协同感知代理，能够结合开放集视觉基础模型（VFM）生成的分割候选结果，并通过临床医生的自然语音反馈进行引导，实现手术器械的实时分割与标注；随后，器械本身作为交互指针用于标记其他场景元素，从而在无需手动提示的情况下完成动态、自适应的手术场景理解，推动人机协作范式的落地。

链接: https://arxiv.org/abs/2509.10748
作者: Jecia Z.Y. Mao,Francis X Creighton,Russell H Taylor,Manish Sahu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Accurate segmentation and tracking of relevant elements of the surgical scene is crucial to enable context-aware intraoperative assistance and decision making. Current solutions remain tethered to domain-specific, supervised models that rely on labeled data and required domain-specific data to adapt to new surgical scenarios and beyond predefined label categories. Recent advances in prompt-driven vision foundation models (VFM) have enabled open-set, zero-shot segmentation across heterogeneous medical images. However, dependence of these models on manual visual or textual cues restricts their deployment in introperative surgical settings. We introduce a speech-guided collaborative perception (SCOPE) framework that integrates reasoning capabilities of large language model (LLM) with perception capabilities of open-set VFMs to support on-the-fly segmentation, labeling and tracking of surgical instruments and anatomy in intraoperative video streams. A key component of this framework is a collaborative perception agent, which generates top candidates of VFM-generated segmentation and incorporates intuitive speech feedback from clinicians to guide the segmentation of surgical instruments in a natural human-machine collaboration paradigm. Afterwards, instruments themselves serve as interactive pointers to label additional elements of the surgical scene. We evaluated our proposed framework on a subset of publicly available Cataract1k dataset and an in-house ex-vivo skull-base dataset to demonstrate its potential to generate on-the-fly segmentation and tracking of surgical scene. Furthermore, we demonstrate its dynamic capabilities through a live mock ex-vivo experiment. This human-AI collaboration paradigm showcase the potential of developing adaptable, hands-free, surgeon-centric tools for dynamic operating-room environments.
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[CV-151] SegSLR: Promptable Video Segmentation for Isolated Sign Language Recognition

【速读】：该论文旨在解决孤立手语识别（Isolated Sign Language Recognition, ISLR）中多模态信息融合时关键细节丢失的问题，尤其是由于使用边界框等粗粒度表示导致的手部形状和朝向信息损失。解决方案的关键在于提出SegSLR系统，通过可提示的零样本视频分割技术将RGB图像与姿态信息进行精细融合：利用姿态信息提供的粗略定位，对视频中的手部和身体区域进行像素级分割，从而保留完整的形状信息，并聚焦于最相关的视觉区域进行后续处理，有效提升了ISLR的识别性能。

链接: https://arxiv.org/abs/2509.10710
作者: Sven Schreiber,Noha Sarhan,Simone Frintrop,Christian Wilms
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at GCPR 2025

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Abstract:Isolated Sign Language Recognition (ISLR) approaches primarily rely on RGB data or signer pose information. However, combining these modalities often results in the loss of crucial details, such as hand shape and orientation, due to imprecise representations like bounding boxes. Therefore, we propose the ISLR system SegSLR, which combines RGB and pose information through promptable zero-shot video segmentation. Given the rough localization of the hands and the signer’s body from pose information, we segment the respective parts through the video to maintain all relevant shape information. Subsequently, the segmentations focus the processing of the RGB data on the most relevant body parts for ISLR. This effectively combines RGB and pose information. Our evaluation on the complex ChaLearn249 IsoGD dataset shows that SegSLR outperforms state-of-the-art methods. Furthermore, ablation studies indicate that SegSLR strongly benefits from focusing on the signer’s body and hands, justifying our design choices.
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[CV-152] Maestro: Self-Improving Text-to-Image Generation via Agent Orchestration

【速读】：该论文旨在解决文本到图像（Text-to-Image, T2I）生成模型高度依赖人工干预的问题，特别是针对初始提示（prompt）常因描述不足而需反复迭代优化的挑战。解决方案的关键在于提出Maestro系统，其核心创新为：1）自评机制（self-critique），由多模态大语言模型（Multimodal Large Language Model, MLLM）代理作为“批评者”识别图像缺陷并提供可解释的编辑信号，再由“验证者”代理在不偏离用户意图的前提下整合修改；2）自演化机制（self-evolution），利用MLLM作为评判者进行图像间的两两比较，淘汰低质图像并进化出更符合用户意图的提示候选。此方法使T2I模型能够仅凭初始提示实现自主迭代优化，显著提升图像质量，并在黑盒模型上展现出优于现有自动化方法的效果。

链接: https://arxiv.org/abs/2509.10704
作者: Xingchen Wan,Han Zhou,Ruoxi Sun,Hootan Nakhost,Ke Jiang,Rajarishi Sinha,Sercan Ö. Arık
机构: Google(谷歌)
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 7 figures, 2 tables (22 pages, 9 figures and 3 tables including references and appendices)

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Abstract:Text-to-image (T2I) models, while offering immense creative potential, are highly reliant on human intervention, posing significant usability challenges that often necessitate manual, iterative prompt engineering over often underspecified prompts. This paper introduces Maestro, a novel self-evolving image generation system that enables T2I models to autonomously self-improve generated images through iterative evolution of prompts, using only an initial prompt. Maestro incorporates two key innovations: 1) self-critique, where specialized multimodal LLM (MLLM) agents act as ‘critics’ to identify weaknesses in generated images, correct for under-specification, and provide interpretable edit signals, which are then integrated by a ‘verifier’ agent while preserving user intent; and 2) self-evolution, utilizing MLLM-as-a-judge for head-to-head comparisons between iteratively generated images, eschewing problematic images, and evolving creative prompt candidates that align with user intents. Extensive experiments on complex T2I tasks using black-box models demonstrate that Maestro significantly improves image quality over initial prompts and state-of-the-art automated methods, with effectiveness scaling with more advanced MLLM components. This work presents a robust, interpretable, and effective pathway towards self-improving T2I generation.
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[CV-153] CrunchLLM : Multitask LLM s for Structured Business Reasoning and Outcome Prediction

【速读】：该论文旨在解决初创企业成功预测问题（即通过并购或首次公开募股IPO实现退出），其核心挑战在于如何有效融合来自Crunchbase等数据源的结构化特征（如融资轮次、行业类别、投资网络）与非结构化文本信息（如公司描述），以提升预测准确性。解决方案的关键在于提出一种领域适配的大语言模型框架CrunchLLM，该框架通过参数高效微调（Parameter-Efficient Fine-Tuning, PEFT）与提示优化（Prompt Optimization）相结合的方式，将基础模型专业化于创业数据，并实现结构化与非结构化数据的深度融合，从而在准确率超过80%的同时提供可解释的推理路径，显著优于传统机器学习方法和通用大语言模型。

链接: https://arxiv.org/abs/2509.10698
作者: Rabeya Tus Sadia,Qiang Cheng
机构: University of Kentucky (肯塔基大学)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Predicting the success of start-up companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and innovation research. Datasets such as Crunchbase provide both structured information (e.g., funding rounds, industries, investor networks) and unstructured text (e.g., company descriptions), but effectively leveraging this heterogeneous data for prediction remains challenging. Traditional machine learning approaches often rely only on structured features and achieve moderate accuracy, while large language models (LLMs) offer rich reasoning abilities but struggle to adapt directly to domain-specific business data. We present \textbfCrunchLLM, a domain-adapted LLM framework for startup success prediction. CrunchLLM integrates structured company attributes with unstructured textual narratives and applies parameter-efficient fine-tuning strategies alongside prompt optimization to specialize foundation models for entrepreneurship data. Our approach achieves accuracy exceeding 80% on Crunchbase startup success prediction, significantly outperforming traditional classifiers and baseline LLMs. Beyond predictive performance, CrunchLLM provides interpretable reasoning traces that justify its predictions, enhancing transparency and trustworthiness for financial and policy decision makers. This work demonstrates how adapting LLMs with domain-aware fine-tuning and structured–unstructured data fusion can advance predictive modeling of entrepreneurial outcomes. CrunchLLM contributes a methodological framework and a practical tool for data-driven decision making in venture capital and innovation policy.
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[CV-154] Stable Part Diffusion 4D: Multi-View RGB and Kinematic Parts Video Generation

【速读】：该论文旨在解决从单目输入中生成配对的RGB视频与运动学部件（kinematic parts）视频的问题，传统部件分割方法依赖外观语义线索，难以捕捉与物体关节运动一致的结构组件。其解决方案的关键在于提出Stable Part Diffusion 4D（SP4D），一个双分支扩散模型框架，联合生成RGB帧与对应的部件分割图；通过引入空间颜色编码方案将部件掩码映射为连续的RGB-like图像，使分割分支可复用RGB分支的潜在VAE，并借助后处理恢复部件分割结果；同时设计双向扩散融合模块（BiDiFuse）和对比性部件一致性损失，提升跨分支的空间与时间一致性，从而生成具有运动学意义的部件结构，支持后续动画与动作相关任务。

链接: https://arxiv.org/abs/2509.10687
作者: Hao Zhang,Chun-Han Yao,Simon Donné,Narendra Ahuja,Varun Jampani
机构: Stability AI (Stability AI); University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We present Stable Part Diffusion 4D (SP4D), a framework for generating paired RGB and kinematic part videos from monocular inputs. Unlike conventional part segmentation methods that rely on appearance-based semantic cues, SP4D learns to produce kinematic parts - structural components aligned with object articulation and consistent across views and time. SP4D adopts a dual-branch diffusion model that jointly synthesizes RGB frames and corresponding part segmentation maps. To simplify the architecture and flexibly enable different part counts, we introduce a spatial color encoding scheme that maps part masks to continuous RGB-like images. This encoding allows the segmentation branch to share the latent VAE from the RGB branch, while enabling part segmentation to be recovered via straightforward post-processing. A Bidirectional Diffusion Fusion (BiDiFuse) module enhances cross-branch consistency, supported by a contrastive part consistency loss to promote spatial and temporal alignment of part predictions. We demonstrate that the generated 2D part maps can be lifted to 3D to derive skeletal structures and harmonic skinning weights with few manual adjustments. To train and evaluate SP4D, we construct KinematicParts20K, a curated dataset of over 20K rigged objects selected and processed from Objaverse XL (Deitke et al., 2023), each paired with multi-view RGB and part video sequences. Experiments show that SP4D generalizes strongly to diverse scenarios, including real-world videos, novel generated objects, and rare articulated poses, producing kinematic-aware outputs suitable for downstream animation and motion-related tasks.
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[CV-155] A Comparison and Evaluation of Fine-tuned Convolutional Neural Networks to Large Language Models for Image Classification and Segmentation of Brain Tumors on MRI

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在医学影像任务中应用潜力的未知问题，特别是其在胶质瘤分类与分割任务中的表现是否优于传统卷积神经网络（Convolutional Neural Networks, CNNs）。研究的关键在于通过对比分析通用视觉-语言LLM（LLaMA 3.2 Instruct）与定制化3D CNN在BraTS 2020多模态脑MRI数据集上的性能差异，发现当前LLMs在空间理解能力上存在显著局限，即使经过微调，其空间定位准确性仍远低于CNN，且在分类任务中特异性提升有限，表明LLMs在当前架构下不适合直接用于图像驱动的医疗诊断任务，需更严谨的微调策略或新型训练范式以提升其鲁棒性和实用性。

链接: https://arxiv.org/abs/2509.10683
作者: Felicia Liu,Jay J. Yoo,Farzad Khalvati
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Large Language Models (LLMs) have shown strong performance in text-based healthcare tasks. However, their utility in image-based applications remains unexplored. We investigate the effectiveness of LLMs for medical imaging tasks, specifically glioma classification and segmentation, and compare their performance to that of traditional convolutional neural networks (CNNs). Using the BraTS 2020 dataset of multi-modal brain MRIs, we evaluated a general-purpose vision-language LLM (LLaMA 3.2 Instruct) both before and after fine-tuning, and benchmarked its performance against custom 3D CNNs. For glioma classification (Low-Grade vs. High-Grade), the CNN achieved 80% accuracy and balanced precision and recall. The general LLM reached 76% accuracy but suffered from a specificity of only 18%, often misclassifying Low-Grade tumors. Fine-tuning improved specificity to 55%, but overall performance declined (e.g., accuracy dropped to 72%). For segmentation, three methods - center point, bounding box, and polygon extraction, were implemented. CNNs accurately localized gliomas, though small tumors were sometimes missed. In contrast, LLMs consistently clustered predictions near the image center, with no distinction of glioma size, location, or placement. Fine-tuning improved output formatting but failed to meaningfully enhance spatial accuracy. The bounding polygon method yielded random, unstructured outputs. Overall, CNNs outperformed LLMs in both tasks. LLMs showed limited spatial understanding and minimal improvement from fine-tuning, indicating that, in their current form, they are not well-suited for image-based tasks. More rigorous fine-tuning or alternative training strategies may be needed for LLMs to achieve better performance, robustness, and utility in the medical space.
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[CV-156] USCTNet: A deep unfolding nuclear-norm optimization solver for physically consistent HSI reconstruction

【速读】：该论文旨在解决从单张RGB图像重建高光谱图像（Hyperspectral Image, HSI）时存在的病态性（ill-posedness）及物理不一致性问题，尤其是在相机光谱灵敏度（Camera Spectral Sensitivity, CSS）和场景照明条件未准确建模的情况下。其核心解决方案是将RGB到HSI的重建建模为一个基于物理约束的逆问题，通过在可学习的变换域中引入核范数正则化，并显式估计CSS与照明以定义每迭代步中的前向算子，从而保证色度一致性。关键创新在于提出一种数据自适应的低秩子空间奇异值阈值（Singular-Value Thresholding, SVT）算子，避免了传统方法中全奇异值分解（SVD）带来的计算开销与数值不稳定问题，进而构建了USCTNet——一种深度展开求解器，结合参数估计模块与可学习的近端更新机制，在标准基准上显著优于现有基于RGB的方法。

链接: https://arxiv.org/abs/2509.10651
作者: Xiaoyang Ma,Yiyang Chai,Xinran Qu,Hong Sun
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Reconstructing hyperspectral images (HSIs) from a single RGB image is ill-posed and can become physically inconsistent when the camera spectral sensitivity (CSS) and scene illumination are misspecified. We formulate RGB-to-HSI reconstruction as a physics-grounded inverse problem regularized by a nuclear norm in a learnable transform domain, and we explicitly estimate CSS and illumination to define the forward operator embedded in each iteration, ensuring colorimetric consistency. To avoid the cost and instability of full singular-value decompositions (SVDs) required by singular-value thresholding (SVT), we introduce a data-adaptive low-rank subspace SVT operator. Building on these components, we develop USCTNet, a deep unfolding solver tailored to HSI that couples a parameter estimation module with learnable proximal updates. Extensive experiments on standard benchmarks show consistent improvements over state-of-the-art RGB-based methods in reconstruction accuracy. Code: this https URL
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[CV-157] Accurate and Private Diagnosis of Rare Genetic Syndromes from Facial Images with Federated Deep Learning

【速读】：该论文旨在解决面部畸形表型分析中因患者数据分散在不同医疗机构且受隐私法规限制而导致的模型训练受限问题。传统基于集中式数据的GestaltMatcher框架虽具临床价值，但难以扩展至多机构协作场景。其解决方案的关键在于提出一种基于跨边（cross-silo）水平联邦学习（horizontal federated learning）的联邦GestaltMatcher服务，通过将患者图像映射到共享潜在空间，并采用隐私保护的核矩阵计算机制，在不共享原始图像的前提下实现综合征推理与发现；同时支持新参与方直接利用历史训练得到的全局特征提取器和核配置，实现高效协同建模与持续优化。实验表明，该方案可保持超过90%的集中式性能，并对异构数据分布和不同数量的参与方具有鲁棒性。

链接: https://arxiv.org/abs/2509.10635
作者: Ali Burak Ünal,Cem Ata Baykara,Peter Krawitz,Mete Akgün
机构: University of Tübingen (图宾根大学); University of Bonn (波恩大学)
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Machine learning has shown promise in facial dysmorphology, where characteristic facial features provide diagnostic clues for rare genetic disorders. GestaltMatcher, a leading framework in this field, has demonstrated clinical utility across multiple studies, but its reliance on centralized datasets limits further development, as patient data are siloed across institutions and subject to strict privacy regulations. We introduce a federated GestaltMatcher service based on a cross-silo horizontal federated learning framework, which allows hospitals to collaboratively train a global ensemble feature extractor without sharing patient images. Patient data are mapped into a shared latent space, and a privacy-preserving kernel matrix computation framework enables syndrome inference and discovery while safeguarding confidentiality. New participants can directly benefit from and contribute to the system by adopting the global feature extractor and kernel configuration from previous training rounds. Experiments show that the federated service retains over 90% of centralized performance and remains robust to both varying silo numbers and heterogeneous data distributions.
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[CV-158] Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses ICCV2025

【速读】：该论文旨在解决当前深度学习模型在3D结构磁共振成像（Structural Magnetic Resonance Imaging, sMRI）脑部影像分析中普遍存在的泛化能力不足问题，即现有模型多针对特定任务进行定制，且依赖大量标注数据，在跨任务和跨人群场景下表现受限。其解决方案的关键在于提出一个基于SimCLR的自监督学习（Self-Supervised Learning, SSL）基础模型，该模型在18,759名患者（共44,958次扫描）的多样化公共数据集上进行预训练，涵盖多种神经系统疾病，从而实现高分辨率、通用性强且可迁移的特征表示学习。实验表明，该模型在四个下游预测任务中均优于Masked Autoencoders（MAE）及两种监督基线模型，尤其在仅使用20%标注样本时仍能显著提升阿尔茨海默病（Alzheimer’s Disease, AD）预测性能，验证了其高效利用有限标注数据的能力。

链接: https://arxiv.org/abs/2509.10620
作者: Emily Kaczmarek,Justin Szeto,Brennan Nichyporuk,Tal Arbel
机构: McGill University (麦吉尔大学); Mila - Quebec Artificial Intelligence Institute (魁北克人工智能研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted to ICCV 2025 Workshop CVAMD

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Abstract:3D structural Magnetic Resonance Imaging (MRI) brain scans are commonly acquired in clinical settings to monitor a wide range of neurological conditions, including neurodegenerative disorders and stroke. While deep learning models have shown promising results analyzing 3D MRI across a number of brain imaging tasks, most are highly tailored for specific tasks with limited labeled data, and are not able to generalize across tasks and/or populations. The development of self-supervised learning (SSL) has enabled the creation of large medical foundation models that leverage diverse, unlabeled datasets ranging from healthy to diseased data, showing significant success in 2D medical imaging applications. However, even the very few foundation models for 3D brain MRI that have been developed remain limited in resolution, scope, or accessibility. In this work, we present a general, high-resolution SimCLR-based SSL foundation model for 3D brain structural MRI, pre-trained on 18,759 patients (44,958 scans) from 11 publicly available datasets spanning diverse neurological diseases. We compare our model to Masked Autoencoders (MAE), as well as two supervised baselines, on four diverse downstream prediction tasks in both in-distribution and out-of-distribution settings. Our fine-tuned SimCLR model outperforms all other models across all tasks. Notably, our model still achieves superior performance when fine-tuned using only 20% of labeled training samples for predicting Alzheimer’s disease. We use publicly available code and data, and release our trained model at this https URL, contributing a broadly applicable and accessible foundation model for clinical brain MRI analysis.
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[CV-159] SurgLaVi: Large-Scale Hierarchical Dataset for Surgical Vision-Language Representation Learning

【速读】：该论文旨在解决当前手术视觉-语言预训练（Vision-Language Pre-training, VLP）中因数据集规模有限、程序多样性不足、语义质量不高及缺乏层级结构而导致模型泛化能力弱的问题。其解决方案的关键在于构建了目前最大且最多样化的手术视觉-语言数据集SurgLaVi，包含近240k个视频片段-文本对，覆盖200余种手术操作，并在阶段（phase）、步骤（step）和任务（task）三个层次上进行结构化标注；同时提出一套全自动的数据处理流水线，通过双模态过滤机制确保标注质量，并生成语义丰富、易于理解的细粒度描述。这一高质量、大规模、层级化的数据资源为开发更强大的手术基础模型提供了关键支撑。

链接: https://arxiv.org/abs/2509.10555
作者: Alejandra Perez,Chinedu Nwoye,Ramtin Raji Kermani,Omid Mohareri,Muhammad Abdullah Jamal
机构: Intuitive Surgical Inc.(直觉手术公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Vision-language pre-training (VLP) offers unique advantages for surgery by aligning language with surgical videos, enabling workflow understanding and transfer across tasks without relying on expert-labeled datasets. However, progress in surgical VLP remains constrained by the limited scale, procedural diversity, semantic quality, and hierarchical structure of existing datasets. In this work, we present SurgLaVi, the largest and most diverse surgical vision-language dataset to date, comprising nearly 240k clip-caption pairs from more than 200 procedures, and comprising hierarchical levels at phase-, step-, and task-level. At the core of SurgLaVi lies a fully automated pipeline that systematically generates fine-grained transcriptions of surgical videos and segments them into coherent procedural units. To ensure high-quality annotations, it applies dual-modality filtering to remove irrelevant and noisy samples. Within this framework, the resulting captions are enriched with contextual detail, producing annotations that are both semantically rich and easy to interpret. To ensure accessibility, we release SurgLaVi-\beta, an open-source derivative of 113k clip-caption pairs constructed entirely from public data, which is over four times larger than existing surgical VLP datasets. To demonstrate the value of SurgLaVi datasets, we introduce SurgCLIP, a CLIP-style video-text contrastive framework with dual encoders, as a representative base model. SurgCLIP achieves consistent improvements across phase, step, action, and tool recognition, surpassing prior state-of-the-art methods, often by large margins. These results validate that large-scale, semantically rich, and hierarchically structured datasets directly translate into stronger and more generalizable representations, establishing SurgLaVi as a key resource for developing surgical foundation models.
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[CV-160] Mitigating Catastrophic Forgetting and Mode Collapse in Text-to-Image Diffusion via Latent Replay

【速读】：该论文旨在解决生成式 AI（Generative AI）模型在持续学习（continual learning）过程中面临的两个核心问题：一是“灾难性遗忘”（catastrophic forgetting），即模型在学习新任务时会丢失先前习得的知识；二是“模式崩溃”（mode collapse），即模型输出逐渐趋于重复，多样性下降。针对这些问题，论文提出的关键解决方案是引入受神经科学启发的潜在重放（Latent Replay）方法。其核心在于不存储原始图像数据，而是保留模型内部架构中提取的紧凑高维特征表示（latent representations），从而以极低的内存开销实现对关键知识的有效记忆与回放。实验表明，该方法显著优于现有基线，在五种连续学习的视觉概念上保持了77.59%的图像对齐度（Image Alignment, IA），较基线提升14%，同时维持了良好的输出多样性。值得注意的是，随机选择存储的潜在样本反而优于基于相似性的策略，揭示了潜在重放机制在生成模型持续学习中的高效性和鲁棒性。

链接: https://arxiv.org/abs/2509.10529
作者: Aoi Otani
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Continual learning – the ability to acquire knowledge incrementally without forgetting previous skills – is fundamental to natural intelligence. While the human brain excels at this, artificial neural networks struggle with “catastrophic forgetting,” where learning new tasks erases previously acquired knowledge. This challenge is particularly severe for text-to-image diffusion models, which generate images from textual prompts. Additionally, these models face “mode collapse,” where their outputs become increasingly repetitive over time. To address these challenges, we apply Latent Replay, a neuroscience-inspired approach, to diffusion models. Traditional replay methods mitigate forgetting by storing and revisiting past examples, typically requiring large collections of images. Latent Replay instead retains only compact, high-level feature representations extracted from the model’s internal architecture. This mirrors the hippocampal process of storing neural activity patterns rather than raw sensory inputs, reducing memory usage while preserving critical information. Through experiments with five sequentially learned visual concepts, we demonstrate that Latent Replay significantly outperforms existing methods in maintaining model versatility. After learning all concepts, our approach retained 77.59% Image Alignment (IA) on the earliest concept, 14% higher than baseline methods, while maintaining diverse outputs. Surprisingly, random selection of stored latent examples outperforms similarity-based strategies. Our findings suggest that Latent Replay enables efficient continual learning for generative AI models, paving the way for personalized text-to-image models that evolve with user needs without excessive computational costs.
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[CV-161] Multimodal Deep Learning for ATCO Command Lifecycle Modeling and Workload Prediction

【速读】：该论文旨在解决空中交通管制员（Air Traffic Controllers, ATCOs）在高密度空域中发出高强度语音指令时，如何准确建模其工作负荷的问题，以提升空中交通的安全性与效率。解决方案的关键在于构建了一个多模态深度学习框架，该框架融合了结构化数据、飞行轨迹序列和图像特征，用于估计ATCO指令生命周期中的两个核心参数：指令与相应飞机机动动作之间的时间偏移量（time offset）以及指令持续时间（command duration）。研究通过滑动窗口和直方图方法检测机动点，开发了CNN-Transformer集成模型，实现了高精度、可泛化且具备可解释性的预测结果，首次将轨迹与语音指令关联建模，为智能指令生成及工作负荷评估、人员配置与调度提供了实用支持。

链接: https://arxiv.org/abs/2509.10522
作者: Kaizhen Tan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Audio and Speech Processing (eess.AS)
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Abstract:Air traffic controllers (ATCOs) issue high-intensity voice commands in dense airspace, where accurate workload modeling is critical for safety and efficiency. This paper proposes a multimodal deep learning framework that integrates structured data, trajectory sequences, and image features to estimate two key parameters in the ATCO command lifecycle: the time offset between a command and the resulting aircraft maneuver, and the command duration. A high-quality dataset was constructed, with maneuver points detected using sliding window and histogram-based methods. A CNN-Transformer ensemble model was developed for accurate, generalizable, and interpretable predictions. By linking trajectories to voice commands, this work offers the first model of its kind to support intelligent command generation and provides practical value for workload assessment, staffing, and scheduling.
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[CV-162] FEDEXCHANGE: Bridging the Domain Gap in Federated Object Detection for Free

【速读】：该论文旨在解决联邦目标检测（Federated Object Detection, FOD）中因环境、天气等域间差异导致的跨域泛化能力不足问题，同时克服现有方法在边缘设备上引入额外本地训练正则化而导致计算开销高的局限性。其解决方案的关键在于提出了一种名为FEDEXCHANGE的新颖FOD框架，该框架通过服务器端动态模型交换策略实现域间知识迁移：在聚合轮次中常规地聚合本地模型，在交换轮次中基于距离度量对本地模型进行聚类并交换，使客户端能够在不直接共享数据且无额外本地计算负担的情况下，从其他域的模型中学习多样化特征，从而显著提升跨域检测性能。

链接: https://arxiv.org/abs/2509.10503
作者: Haolin Yuan,Jingtao Li,Weiming Zhuang,Chen Chen,Lingjuan Lyu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Federated Object Detection (FOD) enables clients to collaboratively train a global object detection model without accessing their local data from diverse domains. However, significant variations in environment, weather, and other domain specific factors hinder performance, making cross domain generalization a key challenge. Existing FOD methods often overlook the hardware constraints of edge devices and introduce local training regularizations that incur high computational costs, limiting real-world applicability. In this paper, we propose FEDEXCHANGE, a novel FOD framework that bridges domain gaps without introducing additional local computational overhead. FEDEXCHANGE employs a server side dynamic model exchange strategy that enables each client to gain insights from other clients’ domain data without direct data sharing. Specifically, FEDEXCHANGE allows the server to alternate between model aggregation and model exchange. During aggregation rounds, the server aggregates all local models as usual. In exchange rounds, FEDEXCHANGE clusters and exchanges local models based on distance measures, allowing local models to learn from a variety of domains. As all operations are performed on the server side, clients can achieve improved cross domain utility without any additional computational overhead. Extensive evaluations demonstrate that FEDEXCHANGE enhances FOD performance, achieving 1.6X better mean average precision in challenging domains, such as rainy conditions, while requiring only 0.8X the computational resources compared to baseline methods.
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[CV-163] A Real-Time Diminished Reality Approach to Privacy in MR Collaboration

【速读】：该论文旨在解决共享空间混合现实（Mixed Reality, MR）会议中用户隐私保护的问题，即如何在多人协同的MR环境中，使主佩戴者能够实时移除其个人或敏感物品，防止其他参与者看到这些内容。解决方案的关键在于构建一个基于图像修复（inpainting）的实时减弱现实（Diminished Reality, DR）系统：首先通过YOLOv11实现高精度目标检测与语义分割，再利用改进的解耦时空变换器（Decoupled Spatial-Temporal Transformer, DSTT）模型对目标区域进行高质量视频修复，且整个流程以移动式ZED 2i深度相机作为次级观察视角实现，无需固定观测点或预先扫描环境，从而保证了系统的便携性与鲁棒性，在720p分辨率下可稳定达到20 fps以上帧率，验证了其实时性与实用性。

链接: https://arxiv.org/abs/2509.10466
作者: Christian Fane
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
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Abstract:Diminished reality (DR) refers to the digital removal of real-world objects by compositing background content in their place. This thesis presents a real-time, inpainting-based DR system designed to enable privacy control in shared-space mixed reality (MR) meetings. The system allows a primary headset user to selectively remove personal or sensitive items from their environment, ensuring that those objects are no longer visible to other participants. Removal is achieved through semantic segmentation and precise object selection, followed by real-time inpainting from the viewpoint of a secondary observer, implemented using a mobile ZED 2i depth camera. The solution is designed to be portable and robust, requiring neither a fixed secondary viewpoint nor prior 3D scanning of the environment. The system utilises YOLOv11 for object detection and a modified Decoupled Spatial-Temporal Transformer (DSTT) model for high-quality video inpainting. At 720p resolution, the pipeline sustains frame rates exceeding 20 fps, demonstrating the feasibility of real-time diminished reality for practical privacy-preserving MR applications.
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[CV-164] he 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results ICCV2025

【速读】：该论文旨在解决**解耦表示学习（Disentangled Representation Learning, DRL）**在真实场景中应用不足的问题，即如何将DRL从理论研究推进到可控生成等实际任务中，克服传统方法依赖合成基准测试、缺乏鲁棒性、可解释性和泛化能力的局限。解决方案的关键在于推动DRL方法向现实世界落地，通过引入新颖的归纳偏置（如语言）、结合扩散模型（diffusion models）提升建模能力、探索3D感知的解耦表示，并拓展至自动驾驶和脑电图（EEG）分析等专业领域，从而增强模型在复杂真实环境中的可控生成性能与实用性。

链接: https://arxiv.org/abs/2509.10463
作者: Qiuyu Chen,Xin Jin,Yue Song,Xihui Liu,Shuai Yang,Tao Yang,Ziqiang Li,Jianguo Huang,Yuntao Wei,Ba’ao Xie,Nicu Sebe,Wenjun(Kevin)Zeng,Jooyeol Yun,Davide Abati,Mohamed Omran,Jaegul Choo,Amir Habibian,Auke Wiggers,Masato Kobayashi,Ning Ding,Toru Tamaki,Marzieh Gheisari,Auguste Genovesio,Yuheng Chen,Dingkun Liu,Xinyao Yang,Xinping Xu,Baicheng Chen,Dongrui Wu,Junhao Geng,Lexiang Lv,Jianxin Lin,Hanzhe Liang,Jie Zhou,Xuanxin Chen,Jinbao Wang,Can Gao,Zhangyi Wang,Zongze Li,Bihan Wen,Yixin Gao,Xiaohan Pan,Xin Li,Zhibo Chen,Baorui Peng,Zhongming Chen,Haoran Jin
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Workshop summary paper for ICCV 2025, 9 accepted papers, 9 figures, IEEE conference format, covers topics including diffusion models, controllable generation, 3D-aware disentanglement, autonomous driving applications, and EEG analysis

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Abstract:This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop aimed to bridge the gap between the theoretical promise of Disentangled Representation Learning (DRL) and its application in realistic scenarios, moving beyond synthetic benchmarks. DRL4Real focused on evaluating DRL methods in practical applications such as controllable generation, exploring advancements in model robustness, interpretability, and generalization. The workshop accepted 9 papers covering a broad range of topics, including the integration of novel inductive biases (e.g., language), the application of diffusion models to DRL, 3D-aware disentanglement, and the expansion of DRL into specialized domains like autonomous driving and EEG analysis. This summary details the workshop’s objectives, the themes of the accepted papers, and provides an overview of the methodologies proposed by the authors.
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[CV-165] Spectral and Rhythm Features for Audio Classification with Deep Convolutional Neural Networks

【速读】：该论文旨在解决如何有效利用卷积神经网络（Convolutional Neural Networks, CNNs）进行音频分类的问题，特别是评估不同频谱和节奏特征表示对分类性能的影响。解决方案的关键在于系统性地比较多种特征表示方法（如梅尔尺度频谱图、梅尔频率倒谱系数（Mel-Frequency Cepstral Coefficients, MFCCs）、循环时谱图、短时傅里叶变换（STFT）色度图、恒定Q变换（CQT）色度图以及色度能量归一化统计量（CENS）色度图）在深度CNN框架下的表现，并基于ESC-50数据集的实验结果表明，梅尔尺度频谱图和MFCCs在音频分类任务中显著优于其他特征表示方式。

链接: https://arxiv.org/abs/2410.06927
作者: Friedrich Wolf-Monheim
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
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Abstract:Convolutional neural networks (CNNs) are widely used in computer vision. They can be used not only for conventional digital image material to recognize patterns, but also for feature extraction from digital imagery representing spectral and rhythm features extracted from time-domain digital audio signals for the acoustic classification of sounds. Different spectral and rhythm feature representations like mel-scaled spectrograms, mel-frequency cepstral coefficients (MFCCs), cyclic tempograms, short-time Fourier transform (STFT) chromagrams, constant-Q transform (CQT) chromagrams and chroma energy normalized statistics (CENS) chromagrams are investigated in terms of the audio classification performance using a deep convolutional neural network. It can be clearly shown that the mel-scaled spectrograms and the mel-frequency cepstral coefficients (MFCCs) perform significantly better than the other spectral and rhythm features investigated in this research for audio classification tasks using deep CNNs. The experiments were carried out with the aid of the ESC-50 dataset with 2,000 labeled environmental audio recordings.
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[CV-166] Learning Stackable and Skippable LEGO Bricks for Efficient Reconfigurable and Variable-Resolution Diffusion Modeling

【速读】：该论文旨在解决扩散模型在训练和采样过程中计算成本高、网络结构缺乏灵活性的问题，特别是针对迭代精炼阶段中难以实现可变分辨率生成与高效采样的需求。其解决方案的关键在于提出一种名为LEGO bricks的新颖模块设计，该模块通过局部特征增强（Local-feature Enrichment）与全局内容协调（Global-content Orchestration）的协同机制，结合MLP与Transformer块，在保持全分辨率图像一致性的同时实现模块化堆叠。这一设计使得模型在测试时可灵活跳过部分模块以降低采样开销，并支持生成高于训练数据分辨率的图像，从而显著提升训练效率、加速收敛并减少采样时间。

链接: https://arxiv.org/abs/2310.06389
作者: Huangjie Zheng,Zhendong Wang,Jianbo Yuan,Guanghan Ning,Pengcheng He,Quanzeng You,Hongxia Yang,Mingyuan Zhou
机构: The University of Texas at Austin (德克萨斯大学奥斯汀分校); ByteDance Inc. (字节跳动公司); Microsoft Azure AI (微软Azure人工智能)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Machine Learning (stat.ML)
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Abstract:Diffusion models excel at generating photo-realistic images but come with significant computational costs in both training and sampling. While various techniques address these computational challenges, a less-explored issue is designing an efficient and adaptable network backbone for iterative refinement. Current options like U-Net and Vision Transformer often rely on resource-intensive deep networks and lack the flexibility needed for generating images at variable resolutions or with a smaller network than used in training. This study introduces LEGO bricks, which seamlessly integrate Local-feature Enrichment and Global-content Orchestration. These bricks can be stacked to create a test-time reconfigurable diffusion backbone, allowing selective skipping of bricks to reduce sampling costs and generate higher-resolution images than the training data. LEGO bricks enrich local regions with an MLP and transform them using a Transformer block while maintaining a consistent full-resolution image across all bricks. Experimental results demonstrate that LEGO bricks enhance training efficiency, expedite convergence, and facilitate variable-resolution image generation while maintaining strong generative performance. Moreover, LEGO significantly reduces sampling time compared to other methods, establishing it as a valuable enhancement for diffusion models. Our code and project page are available at this https URL.
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[CV-167] Data-driven Smile Design: Personalized Dental Aesthetics Outcomes Using Deep Learning

【速读】：该论文旨在解决传统微笑设计中难以平衡美学与功能需求的问题，以及依赖牙医经验、石膏模型和手绘导致的个体化不足和结果不可控问题。其解决方案的关键在于构建一个整合生成式AI（Generative AI）、大数据分析与图像识别技术的自动化系统，包含面部特征提取模块和图像生成模块，从而实现无论经验水平高低的牙医均可便捷生成个性化且美观的微笑设计方案，并为未来虚拟/增强现实技术的实时预览及审美偏好分析提供数据支持与优化基础。

链接: https://arxiv.org/abs/2509.12001
作者: Marcus Lin,Jennifer Lai
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 2 figures

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Abstract:A healthy smile plays a significant role in functional as well as esthetic considerations, improving confidence. It is difficult for dental professionals to strike a balance between esthetic requirements and functional requirements. Traditional smile design has had heavy reliance on dentist expertise and used plaster models and hand drawings, raising questions about the outcome for patients. Digital technology, led by Dr. Christian Coachman in 2007, allows photographic and videographic assessments, enabling improved intercommunication among specialists and patients. Advances in artificial intelligence (AI) and big data have supported analysis of facial features and development of personalized smile designs in the last few years. Outputs are, however, susceptible to practitioner bias or limitations of training data, and may be suboptimal for individual users. The study presented here suggests a comprehensive system integrating AI, big data, and recognition technologies to automate the smile design process so that both experienced and inexperienced dentists can generate pleasing aesthetics with ease. The system has a Facial Feature Extraction Module and an Image Generation Module, serving diverse practitioner and patient needs. User data can be incorporated in future research for design optimization and testing of virtual and augmented reality for real-time previewing. Data gathered can also be employed in aesthetic preference analyses, which can enhance our knowledge of smile design in dental practice.
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[CV-168] Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via U-Net Segmentation

【速读】：该论文旨在解决复杂无序的迷宫状条纹图案（labyrinthine stripe patterns）在缺乏长程有序性的情况下难以进行定量表征的问题。其解决方案的关键在于结合深度学习与几何分析：首先利用经过合成噪声（包括加性白高斯噪声和Simplex噪声）训练的U-Net模型实现对磁光图像中条纹结构的鲁棒分割；在此基础上，构建基于骨架化（skeletonization）、图映射（graph mapping）和样条拟合（spline fitting）的几何分析流程，从而定量测量局部条纹长度与曲率，揭示条纹在磁场退火过程中的演化规律。该方法不仅实现了对磁性条纹图案几何与拓扑特性的量化分析，还识别出两种由磁场极性决定的不同演化模式（Type A 和 Type B），为理解复杂 labyrinthine 系统的局部结构演化提供了新视角和通用工具。

链接: https://arxiv.org/abs/2509.11485
作者: Vinícius Yu Okubo,Kotaro Shimizu,B.S. Shivaran,Gia-Wei Chern,Hae Yong Kim
机构: University of São Paulo (圣保罗大学); The University of Tokyo (东京大学); University of Virginia (弗吉尼亚大学)
类目: Materials Science (cond-mat.mtrl-sci); Computer Vision and Pattern Recognition (cs.CV)
备注: 15 pages, 13 figures. This manuscript has been submitted to IEEE Access for possible publication. It has not yet been peer reviewed or accepted

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Abstract:Labyrinthine stripe patterns are common in many physical systems, yet their lack of long-range order makes quantitative characterization challenging. We investigate the evolution of such patterns in bismuth-doped yttrium iron garnet (Bi:YIG) films subjected to a magnetic field annealing protocol. A U-Net deep learning model, trained with synthetic degradations including additive white Gaussian and Simplex noise, enables robust segmentation of experimental magneto-optical images despite noise and occlusions. Building on this segmentation, we develop a geometric analysis pipeline based on skeletonization, graph mapping, and spline fitting, which quantifies local stripe propagation through length and curvature measurements. Applying this framework to 444 images from 12 annealing protocol trials, we analyze the transition from the “quenched” state to a more parallel and coherent “annealed” state, and identify two distinct evolution modes (Type A and Type B) linked to field polarity. Our results provide a quantitative analysis of geometric and topological properties in magnetic stripe patterns and offer new insights into their local structural evolution, and establish a general tool for analyzing complex labyrinthine systems.
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[CV-169] Introduction to a Low-Cost AI-Powered GUI for Unstained Cell Culture Analysis

【速读】：该论文旨在解决低预算实验室在进行活细胞（未染色）图像分析时缺乏高效、准确且无需人工标注训练数据的自动化工具的问题。其解决方案的关键在于提出了一种基于Python的端到端显微图像分析框架，采用先进的计算机视觉与机器学习流水线，在仅依赖标签无关（label-free）数据的前提下实现语义和实例分割、特征提取、分析评估及自动报告生成；该框架无需训练阶段，通过用户友好的跨平台图形界面（GUI）降低使用门槛，同时提供脚本接口支持开发者集成，具有模块化架构、单图与批量处理能力，并在公开活细胞数据集上展现出优于Cellpose和StarDist等现有工具的精度与可重复性，尤其适用于细胞移植和肌肉再生等个性化医疗场景。

链接: https://arxiv.org/abs/2509.11354
作者: Surajit Das,Pavel Zun
机构: 未知
类目: Quantitative Methods (q-bio.QM); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV); Cell Behavior (q-bio.CB)
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Abstract:This article presents a novel microscopy image analysis framework designed for low-budget labs equipped with a standard CPU desktop. The Python-based program enables cytometric analysis of live, unstained cells in culture through an advanced computer vision and machine learning pipeline. Crucially, the framework operates on label-free data, requiring no manually annotated training data or training phase. It is accessible via a user-friendly, cross-platform GUI that requires no programming skills, while also providing a scripting interface for programmatic control and integration by developers. The end-to-end workflow performs semantic and instance segmentation, feature extraction, analysis, evaluation, and automated report generation. Its modular architecture supports easy maintenance and flexible integration while supporting both single-image and batch processing. Validated on several unstained cell types from the public dataset of livecells, the framework demonstrates superior accuracy and reproducibility compared to contemporary tools like Cellpose and StarDist. Its competitive segmentation speed on a CPU-based platform highlights its significant potential for basic research and clinical applications – particularly in cell transplantation for personalized medicine and muscle regeneration therapies.
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[CV-170] UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction

【速读】：该论文旨在解决当前人工智能（Artificial Intelligence, AI）在超声成像中将疾病预测与组织分割视为独立任务所导致的计算资源消耗过高问题。其解决方案的关键在于提出了一种名为UltraUPConvNet的轻量化通用框架，该框架能够同时实现超声图像分类与分割任务，在保持先进性能的同时显著降低计算复杂度。模型基于包含超过9700个标注样本的多中心数据集进行训练，覆盖七个不同解剖区域，验证了其在多个数据集上的有效性与高效性。

链接: https://arxiv.org/abs/2509.11108
作者: Zhi Chen
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 pages

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Abstract:Ultrasound imaging is widely used in clinical practice due to its cost-effectiveness, mobility, and safety. However, current AI research often treats disease prediction and tissue segmentation as two separate tasks and their model requires substantial computational overhead. In such a situation, we introduce UltraUPConvNet, a computationally efficient universal framework designed for both ultrasound image classification and segmentation. Trained on a large-scale dataset containing more than 9,700 annotations across seven different anatomical regions, our model achieves state-of-the-art performance on certain datasets with lower computational overhead. Our model weights and codes are available at this https URL
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[CV-171] Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis

【速读】：该论文旨在解决分枝列当（Branched broomrape, Phelipanche ramosa）对番茄种植造成的寄生性胁迫的早期识别难题，其危害包括营养掠夺和高达80%的产量损失。由于该寄生植物多为地下生活且种子繁殖能力强（每株产种超20万粒，可存活长达20年），传统人工监测难以实现高效、大范围检测。解决方案的关键在于构建一个端到端的遥感分析流程：基于Sentinel-2多光谱影像与时间序列分析，结合地面实测数据和合成数据校准神经网络模型，提取五类植物生理特征（如叶面积指数、冠层叶绿素含量等），并利用长短期记忆网络（LSTM）对植被像素进行时序建模。该方法在训练集上达到88%准确率，在测试集上保持87%准确率，且F1分数达0.89，证明了卫星驱动的时间序列建模在规模化识别番茄田寄生胁迫中的有效性。

链接: https://arxiv.org/abs/2509.10804
作者: Mohammadreza Narimani,Alireza Pourreza,Ali Moghimi,Parastoo Farajpoor,Hamid Jafarbiglu,Mohsen Mesgaran
机构: 未知
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Author-accepted version. Published in Proceedings of SPIE Defense + Commercial Sensing 2025, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping X (Vol. 13475), Paper 134750U. Official version: this https URL

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Abstract:Branched broomrape (Phelipanche ramosa (L.) Pomel) is a chlorophyll-deficient parasitic plant that threatens tomato production by extracting nutrients from the host, with reported yield losses up to 80 percent. Its mostly subterranean life cycle and prolific seed production (more than 200,000 seeds per plant, viable for up to 20 years) make early detection essential. We present an end-to-end pipeline that uses Sentinel-2 imagery and time-series analysis to identify broomrape-infested tomato fields in California. Regions of interest were defined from farmer-reported infestations, and images with less than 10 percent cloud cover were retained. We processed 12 spectral bands and sun-sensor geometry, computed 20 vegetation indices (e.g., NDVI, NDMI), and derived five plant traits (Leaf Area Index, Leaf Chlorophyll Content, Canopy Chlorophyll Content, Fraction of Absorbed Photosynthetically Active Radiation, and Fractional Vegetation Cover) using a neural network calibrated with ground-truth and synthetic data. Trends in Canopy Chlorophyll Content delineated transplanting-to-harvest periods, and phenology was aligned using growing degree days. Vegetation pixels were segmented and used to train a Long Short-Term Memory (LSTM) network on 18,874 pixels across 48 growing-degree-day time points. The model achieved 88 percent training accuracy and 87 percent test accuracy, with precision 0.86, recall 0.92, and F1 0.89. Permutation feature importance ranked NDMI, Canopy Chlorophyll Content, FAPAR, and a chlorophyll red-edge index as most informative, consistent with the physiological effects of infestation. Results show the promise of satellite-driven time-series modeling for scalable detection of parasitic stress in tomato farms.
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[CV-172] Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuning

【速读】：该论文旨在解决医学视觉基础模型（Medical Vision Foundation Models, Med-VFMs）在目标域上进行自适应微调时效率低下、性能受限的问题，尤其针对体积医学图像分割任务。现有方法通常随机选取少量目标域样本进行微调，缺乏对样本信息量的有效筛选，导致适应效果不佳。解决方案的关键在于提出一种无源域访问的主动域自适应（Active Source-Free Domain Adaptation, ASFDA）方法：首先设计了一种基于主动学习（Active Learning, AL）的新策略，通过两个核心指标——多样化知识差异（Diversified Knowledge Divergence, DKD）和解剖分割难度（Anatomical Segmentation Difficulty, ASD）——从目标域中高效选择最具信息量的样本；DKD用于衡量源-目标知识差距并促进语义多样性，ASD则通过前景区域预测熵动态评估解剖结构分割难度；同时引入选择性半监督微调机制，进一步提升微调效率与性能，从而在最小样本预算下最大化模型在目标域上的适应能力。

链接: https://arxiv.org/abs/2509.10784
作者: Jin Yang,Daniel S. Marcus,Aristeidis Sotiras
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 17 pages, 5 figures, 8 tables

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Abstract:Medical Vision Foundation Models (Med-VFMs) have superior capabilities of interpreting medical images due to the knowledge learned from self-supervised pre-training with extensive unannotated images. To improve their performance on adaptive downstream evaluations, especially segmentation, a few samples from target domains are selected randomly for fine-tuning them. However, there lacks works to explore the way of adapting Med-VFMs to achieve the optimal performance on target domains efficiently. Thus, it is highly demanded to design an efficient way of fine-tuning Med-VFMs by selecting informative samples to maximize their adaptation performance on target domains. To achieve this, we propose an Active Source-Free Domain Adaptation (ASFDA) method to efficiently adapt Med-VFMs to target domains for volumetric medical image segmentation. This ASFDA employs a novel Active Learning (AL) method to select the most informative samples from target domains for fine-tuning Med-VFMs without the access to source pre-training samples, thus maximizing their performance with the minimal selection budget. In this AL method, we design an Active Test Time Sample Query strategy to select samples from the target domains via two query metrics, including Diversified Knowledge Divergence (DKD) and Anatomical Segmentation Difficulty (ASD). DKD is designed to measure the source-target knowledge gap and intra-domain diversity. It utilizes the knowledge of pre-training to guide the querying of source-dissimilar and semantic-diverse samples from the target domains. ASD is designed to evaluate the difficulty in segmentation of anatomical structures by measuring predictive entropy from foreground regions adaptively. Additionally, our ASFDA method employs a Selective Semi-supervised Fine-tuning to improve the performance and efficiency of fine-tuning by identifying samples with high reliability from unqueried ones.
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[CV-173] Automated Cervical Os Segmentation for Camera-Guided Speculum-Free Screening

【速读】：该论文旨在解决宫颈癌筛查中因传统窥阴器（speculum）使用带来的障碍，尤其是在资源匮乏地区难以普及的问题。其核心解决方案是开发一种基于深度学习的实时宫颈外口（cervical os）分割方法，通过集成成像与采样功能的无窥阴器设备实现非专业人员也能准确识别目标区域。关键创新在于采用预训练于外科视频的视觉Transformer模型（EndoViT/DPT），在IARC宫颈图像数据集上实现了较高的分割性能（DICE=0.50±0.31，检测率=0.87±0.33），并在外部验证中展现出21.5 FPS的实时处理能力，为自动化宫颈筛查设备提供了可靠的技术支撑。

链接: https://arxiv.org/abs/2509.10593
作者: Aoife McDonald-Bowyer,Anjana Wijekoon,Ryan Laurance Love,Katie Allan,Scott Colvin,Aleksandra Gentry-Maharaj,Adeola Olaitan,Danail Stoyanov,Agostino Stilli,Sophia Bano
机构: The UCL Hawkes Institute, University College London, London, UK; Institute of Reproductive and Developmental Biology, Imperial College London, London, UK; Queen Charlotte’s and Chelsea Hospital, Imperial College Healthcare NHS Trust, London, UK; Department of Women’s Cancer, EGA Institute for Women’s Health, University College London, London, UK; MRC Clinical Trials Unit, Institute of Clinical Trials & Methodology, University College London, London, UK
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Cervical cancer is highly preventable, yet persistent barriers to screening limit progress toward elimination goals. Speculum-free devices that integrate imaging and sampling could improve access, particularly in low-resource settings, but require reliable visual guidance. This study evaluates deep learning methods for real-time segmentation of the cervical os in transvaginal endoscopic images. Five encoder-decoder architectures were compared using 913 frames from 200 cases in the IARC Cervical Image Dataset, annotated by gynaecologists. Performance was assessed using IoU, DICE, detection rate, and distance metrics with ten-fold cross-validation. EndoViT/DPT, a vision transformer pre-trained on surgical video, achieved the highest DICE (0.50 \pm 0.31) and detection rate (0.87 \pm 0.33), outperforming CNN-based approaches. External validation with phantom data demonstrated robust segmentation under variable conditions at 21.5 FPS, supporting real-time feasibility. These results establish a foundation for integrating automated os recognition into speculum-free cervical screening devices to support non-expert use in both high- and low-resource contexts.
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[CV-174] FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification

【速读】：该论文旨在解决医学图像分类中模型预测性能与可解释性之间的矛盾问题，尤其针对图神经网络（GNN）在临床场景下因黑箱特性而限制透明度和可信度的问题。其解决方案的关键在于提出一种名为FireGNN的可解释图学习框架，通过将可训练模糊规则（trainable fuzzy rules）嵌入GNN结构中，利用节点度、聚类系数和标签一致性等拓扑描述符，并引入可学习的阈值和锐度参数，实现内在符号推理（intrinsic symbolic reasoning），从而在保持高分类性能的同时生成人类可理解的规则解释。

链接: https://arxiv.org/abs/2509.10510
作者: Prajit Sengupta,Islem Rekik
机构: BASIRA Lab (BASIRA 实验室); Department of Computing (计算机系); Imperial College London (帝国理工学院)
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Medical image classification requires not only high predictive performance but also interpretability to ensure clinical trust and adoption. Graph Neural Networks (GNNs) offer a powerful framework for modeling relational structures within datasets; however, standard GNNs often operate as black boxes, limiting transparency and usability, particularly in clinical settings. In this work, we present an interpretable graph-based learning framework named FireGNN that integrates trainable fuzzy rules into GNNs for medical image classification. These rules embed topological descriptors - node degree, clustering coefficient, and label agreement - using learnable thresholds and sharpness parameters to enable intrinsic symbolic reasoning. Additionally, we explore auxiliary self-supervised tasks (e.g., homophily prediction, similarity entropy) as a benchmark to evaluate the contribution of topological learning. Our fuzzy-rule-enhanced model achieves strong performance across five MedMNIST benchmarks and the synthetic dataset MorphoMNIST, while also generating interpretable rule-based explanations. To our knowledge, this is the first integration of trainable fuzzy rules within a GNN.
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[CV-175] MIDOG 2025 Track 2: A Deep Learning Model for Classification of Atypical and Normal Mitotic Figures under Class and Hardness Imbalances

【速读】：该论文旨在解决数字病理学中对有丝分裂图像（mitotic figures）进行正常与异常类型准确分类的问题，这对肿瘤预后评估至关重要。由于真实世界组织病理学数据存在细微形态差异、类别不平衡及实例难度分布不均等挑战，传统深度学习模型难以实现鲁棒性能。其解决方案的关键在于提出一种基于ResNet主干网络的新型深度学习架构，该架构通过专用分类头同时建模有丝分裂图像的表型（phenotype）和实例难度（instance difficulty），并结合焦点损失（focal loss）缓解类别不平衡问题，辅以全面的数据增强策略提升模型泛化能力。实验表明，该方法在MIDOG 2025 Track 2数据集上实现了高平衡准确率（0.8744 ± 0.0093）和AUC值（0.9505 ± 0.029），具备良好的跨场景一致性与临床应用潜力。

链接: https://arxiv.org/abs/2509.10502
作者: Sujatha Kotte,Vangala Govindakrishnan Saipradeep,Vidushi Walia,Dhandapani Nandagopal,Thomas Joseph,Naveen Sivadasan,Bhagat Singh Lali
机构: TCS Research, Tata Consultancy Services (TCS) Ltd, Hyderabad, India; Department of Pathology, Tata Medical Center, Kolkata, India
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Quantitative Methods (q-bio.QM)
备注: MIDOG 2025 Track 2 submission

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Abstract:Motivation: Accurate classification of mitotic figures into normal and atypical types is crucial for tumor prognostication in digital pathology. However, developing robust deep learning models for this task is challenging due to the subtle morphological differences, as well as significant class and hardness imbalances in real-world histopathology datasets. Methods: We propose a novel deep learning approach based on a ResNet backbone with specialized classification heads. Our architecture uniquely models both the mitotic figure phenotype and the instance difficulty simultaneously. This method is specifically designed to handle the challenges of diverse tissue types, scanner variability, and imbalanced data. We employed focal loss to effectively mitigate the pronounced class imbalance, and a comprehensive data augmentation pipeline was implemented to enhance the model’s robustness and generalizability. Results: Our approach demonstrated strong and consistent performance. In a 5-fold cross-validation on the MIDOG 2025 Track 2 dataset, it achieved a mean balanced accuracy of 0.8744 +/- 0.0093 and an ROC AUC of 0.9505 +/- 0.029. The model showed robust generalization across preliminary leaderboard evaluations, achieving an overall balanced accuracy of 0.8736 +/- 0.0204. Conclusion: The proposed method offers a reliable and generalizable solution for the classification of atypical and normal mitotic figures. By addressing the inherent challenges of real world data, our approach has the potential to support precise prognostic assessments in clinical practice and improve consistency in pathological diagnosis.
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[CV-176] Spectral Bottleneck in Deep Neural Networks: Noise is All You Need

【速读】：该论文旨在解决隐式神经表示（Implicit Neural Representations, INRs）在处理高频率主导信号时面临的“频谱瓶颈”（spectral bottleneck）问题，即当目标信号缺乏低频分量而以宽带高频为主时，网络难以有效学习和重建全部频率成分，即便这些频率仍在模型的表征能力范围内。解决方案的关键在于提出一种通用的目标感知“权重扰动方案”（WINNER - Weight Initialization with Noise for Neural Representations），通过在均匀初始化的权重上添加自适应调节的高斯噪声来优化初始状态：噪声幅度由目标信号的频谱质心（spectral centroid）决定，从而调控网络激活的频谱特性及经验神经切线核（empirical neural tangent kernel）的特征基底，实现对任意频谱内容信号的有效拟合，同时提升收敛速度与重建精度。

链接: https://arxiv.org/abs/2509.09719
作者: Hemanth Chandravamsi,Dhanush V. Shenoy,Itay Zinn,Shimon Pisnoy,Steven H. Frankel
机构: Technion – Israel Institute of Technology (以色列理工学院)
类目: Audio and Speech Processing (eess.AS); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Sound (cs.SD)
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Abstract:Deep neural networks are known to exhibit a spectral learning bias, wherein low-frequency components are learned early in training, while high-frequency modes emerge more gradually in later epochs. However, when the target signal lacks low-frequency components and is dominated by broadband high frequencies, training suffers from a ‘spectral bottleneck’, and the model fails to reconstruct the entire signal, including the frequency components that lie within the network’s representational capacity. We examine such a scenario in the context of implicit neural representations (INRs) with sinusoidal representation networks (SIRENs), focusing on the challenge of fitting high-frequency-dominant signals that are susceptible to spectral bottleneck. To effectively fit any target signal irrespective of it’s frequency content, we propose a generalized target-aware ‘weight perturbation scheme’ (WINNER - weight initialization with noise for neural representations) for network initialization. The scheme perturbs uniformly initialized weights with Gaussian noise, where the noise scales are adaptively determined by the spectral centroid of the target signal. We show that the noise scales can provide control over the spectra of network activations and the eigenbasis of the empirical neural tangent kernel. This method not only addresses the spectral bottleneck but also yields faster convergence and with improved representation accuracy, outperforming state-of-the-art approaches in audio fitting and achieving notable gains in image fitting and denoising tasks. Beyond signal reconstruction, our approach opens new directions for adaptive weight initialization strategies in computer vision and scientific machine learning.
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人工智能

[AI-0] Dynamic Relational Priming Improves Transformer in Multivariate Time Series

【速读】：该论文旨在解决标准注意力机制在处理多变量时间序列（Multivariate Time Series, MTS）数据时存在的局限性，即其静态的token表示无法适应不同通道对之间潜在的异质性关系动态，从而难以有效建模复杂系统中由不同物理规律或时间动态支配的多样化依赖关系。解决方案的关键在于提出一种动态关系引导注意力机制（attention with dynamic relational priming, prime attention），其核心思想是通过可学习的调制机制，在每一对token交互中动态调整token的表示，使其能够针对特定关系进行优化，从而实现关系特异性信息的有效提取；该方法在保持与标准注意力相同渐近计算复杂度的前提下，显著提升了对MTS数据中复杂依赖结构的建模能力。

链接: https://arxiv.org/abs/2509.12196
作者: Hunjae Lee,Corey Clark
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Standard attention mechanisms in transformers employ static token representations that remain unchanged across all pair-wise computations in each layer. This limits their representational alignment with the potentially diverse relational dynamics of each token-pair interaction. While they excel in domains with relatively homogeneous relationships, standard attention’s static relational learning struggles to capture the diverse, heterogeneous inter-channel dependencies of multivariate time series (MTS) data–where different channel-pair interactions within a single system may be governed by entirely different physical laws or temporal dynamics. To better align the attention mechanism for such domain phenomena, we propose attention with dynamic relational priming (prime attention). Unlike standard attention where each token presents an identical representation across all of its pair-wise interactions, prime attention tailors each token dynamically (or per interaction) through learnable modulations to best capture the unique relational dynamics of each token pair, optimizing each pair-wise interaction for that specific relationship. This representational plasticity of prime attention enables effective extraction of relationship-specific information in MTS while maintaining the same asymptotic computational complexity as standard attention. Our results demonstrate that prime attention consistently outperforms standard attention across benchmarks, achieving up to 6.5% improvement in forecasting accuracy. In addition, we find that prime attention achieves comparable or superior performance using up to 40% less sequence length compared to standard attention, further demonstrating its superior relational modeling capabilities.
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[AI-1] Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation

【速读】：该论文试图解决当前人工智能对齐（AI alignment）方法中单向适应的局限性问题，即现有基于强化学习的人类反馈（RLHF）范式仅让AI模型适应人类偏好，而将人类认知视为固定不变，忽略了人与AI之间可能存在的双向协同进化潜力。为应对这一挑战，论文提出了一种新的双向认知对齐（Bidirectional Cognitive Alignment, BiCA）框架，其核心解决方案在于引入可学习的协议（learnable protocols）、表示映射（representation mapping）以及KL散度预算约束（KL-budget constraints），以实现人类与AI在协作任务中的可控共演化。实验表明，BiCA在协同导航任务中显著优于传统方法，不仅提升了成功率和协议收敛速度，还意外增强了系统在分布外场景下的安全性，并揭示了最优协作发生在人类与AI能力交集而非并集的位置，从而验证了从单向对齐到双向共对齐范式的必要性和有效性。

链接: https://arxiv.org/abs/2509.12179
作者: Yubo Li,Weiyi Song
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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Abstract:Current AI alignment through RLHF follows a single directional paradigm that AI conforms to human preferences while treating human cognition as fixed. We propose a shift to co-alignment through Bidirectional Cognitive Alignment (BiCA), where humans and AI mutually adapt. BiCA uses learnable protocols, representation mapping, and KL-budget constraints for controlled co-evolution. In collaborative navigation, BiCA achieved 85.5% success versus 70.3% baseline, with 230% better mutual adaptation and 332% better protocol convergence. Emergent protocols outperformed handcrafted ones by 84%, while bidirectional adaptation unexpectedly improved safety (+23% out-of-distribution robustness). The 46% synergy improvement demonstrates optimal collaboration exists at the intersection, not union, of human and AI capabilities, validating the shift from single-directional to co-alignment paradigms.
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[AI-2] Approaches to Analysis and Design of AI-Based Autonomous Vehicles

【速读】：该论文旨在解决基于生成式 AI (Generative AI) 的自动驾驶车辆（AV）在闭环控制下因感知机制不明确而导致的可靠性风险问题。其核心挑战在于如何量化并保障AI驱动感知不确定性对系统稳定性、鲁棒性和性能的影响。解决方案的关键在于提出一种新型建模方法，将AI感知误差分解为三类基本不确定性：由马尔可夫链建模的状态依赖误差、由高斯过程刻画的随机波动以及由有界扰动表示的确定性偏差；在此基础上，通过线性矩阵不等式（LMIs）框架建立了均方意义下的随机稳定性（Stochastic Stability, SS）理论，并进一步设计了基于凸优化的随机最优保证成本控制方法，从而实现了对AI感知不确定性的系统性分析与闭环控制合成。

链接: https://arxiv.org/abs/2509.12169
作者: Tao Yan,Zheyu Zhang,Jingjing Jiang,Wen-Hua Chen
机构: 未知
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
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Abstract:Artificial intelligence (AI) models are becoming key components in an autonomous vehicle (AV), especially in handling complicated perception tasks. However, closing the loop through AI-based feedback may pose significant risks on reliability of autonomous driving due to very limited understanding about the mechanism of AI-driven perception processes. To overcome it, this paper aims to develop tools for modeling, analysis, and synthesis for a class of AI-based AV; in particular, their closed-loop properties, e.g., stability, robustness, and performance, are rigorously studied in the statistical sense. First, we provide a novel modeling means for the AI-driven perception processes by looking at their error characteristics. Specifically, three fundamental AI-induced perception uncertainties are recognized and modeled by Markov chains, Gaussian processes, and bounded disturbances, respectively. By means of that, the closed-loop stochastic stability (SS) is established in the sense of mean square, and then, an SS control synthesis method is presented within the framework of linear matrix inequalities (LMIs). Besides the SS properties, the robustness and performance of AI-based AVs are discussed in terms of a stochastic guaranteed cost, and criteria are given to test the robustness level of an AV when in the presence of AI-induced uncertainties. Furthermore, the stochastic optimal guaranteed cost control is investigated, and an efficient design procedure is developed innovatively based on LMI techniques and convex optimization. Finally, to illustrate the effectiveness, the developed results are applied to an example of car following control, along with extensive simulation.
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[AI-3] EfficientUICoder: Efficient MLLM -based UI Code Generation via Input and Output Token Compression

【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在UI2Code任务中因输入图像token和输出代码token数量庞大而导致的高计算开销问题，以及由此引发的冗余信息干扰、关键UI元素关注不足、生成HTML文件过长且常无效等挑战。解决方案的关键在于提出EfficientUICoder压缩框架，其核心包括三个组件：（1）面向元素与布局的token压缩（Element and Layout-aware Token Compression），通过检测UI元素区域并构建元素树保留关键信息；（2）基于区域的token精炼（Region-aware Token Refinement），利用注意力分数剔除低关注度区域token，并融合未选区域中的高注意力token以提升语义聚焦；（3）自适应重复token抑制（Adaptive Duplicate Token Suppression），通过跟踪HTML/CSS结构频率并施加指数惩罚来动态减少重复生成。该方案在保持网页质量的前提下实现55%-60%的token压缩比，并显著降低计算成本与推理时间。

链接: https://arxiv.org/abs/2509.12159
作者: Jingyu Xiao,Zhongyi Zhang,Yuxuan Wan,Yintong Huo,Yang Liu,Michael R.Lyu
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
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Abstract:Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development efficiency. However, these tasks incur substantially higher computational overhead than traditional code generation due to the large number of input image tokens and extensive output code tokens required. Our comprehensive study identifies significant redundancies in both image and code tokens that exacerbate computational complexity and hinder focus on key UI elements, resulting in excessively lengthy and often invalid HTML files. We propose EfficientUICoder, a compression framework for efficient UI code generation with three key components. First, Element and Layout-aware Token Compression preserves essential UI information by detecting element regions and constructing UI element trees. Second, Region-aware Token Refinement leverages attention scores to discard low-attention tokens from selected regions while integrating high-attention tokens from unselected regions. Third, Adaptive Duplicate Token Suppression dynamically reduces repetitive generation by tracking HTML/CSS structure frequencies and applying exponential penalties. Extensive experiments show EfficientUICoderachieves a 55%-60% compression ratio without compromising webpage quality and delivers superior efficiency improvements: reducing computational cost by 44.9%, generated tokens by 41.4%, prefill time by 46.6%, and inference time by 48.8% on 34B-level MLLMs. Code is available at this https URL.
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[AI-4] Beyond PII: How Users Attempt to Estimate and Mitigate Implicit LLM Inference

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）从看似无害的文本中推断用户个人属性所带来的隐私风险问题，尤其是用户对这类风险的认知不足及其应对策略的有效性。其解决方案的关键在于通过实证调查揭示用户在识别和规避此类推理风险时的局限性，并比较用户手动重写与自动化工具（如ChatGPT和Rescriber）在隐私保护效果上的差异。研究发现，尽管用户普遍采用改写策略，但多数方法（特别是同义替换）效果有限；相比之下，抽象化和引入模糊性等策略更为有效，这提示未来LLM交互设计应注重推理感知（inference-aware）机制，以提升用户隐私防护能力。

链接: https://arxiv.org/abs/2509.12152
作者: Synthia Wang,Sai Teja Peddinti,Nina Taft,Nick Feamster
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
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Abstract:Large Language Models (LLMs) such as ChatGPT can infer personal attributes from seemingly innocuous text, raising privacy risks beyond memorized data leakage. While prior work has demonstrated these risks, little is known about how users estimate and respond. We conducted a survey with 240 U.S. participants who judged text snippets for inference risks, reported concern levels, and attempted rewrites to block inference. We compared their rewrites with those generated by ChatGPT and Rescriber, a state-of-the-art sanitization tool. Results show that participants struggled to anticipate inference, performing a little better than chance. User rewrites were effective in just 28% of cases - better than Rescriber but worse than ChatGPT. We examined our participants’ rewriting strategies, and observed that while paraphrasing was the most common strategy it is also the least effective; instead abstraction and adding ambiguity were more successful. Our work highlights the importance of inference-aware design in LLM interactions.
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[AI-5] Control Analysis and Design for Autonomous Vehicles Subject to Imperfect AI-Based Perception

【速读】：该论文旨在解决自动驾驶汽车（AV）系统中因基于人工智能（AI）的感知模块引入而带来的安全性问题，尤其是当AI算法具有黑箱特性时，难以进行闭环稳定性分析与性能保障。其解决方案的关键在于摒弃对AI感知过程的直接建模，转而通过感知误差模型（Perception Error Models, PEMs）来刻画AI引起的感知误差，具体考虑两类典型误差：误检（misdetection）和测量噪声（measurement noise），分别用连续时间马尔可夫链和维纳过程建模。在此基础上构建了增强型驾驶模型，并利用随机微积分证明了特定类AI驱动AV系统的闭环稳定性；进一步提出一种保证性能的输出反馈控制综合方法，将问题转化为凸优化形式，从而实现稳定性和性能的双重保障。

链接: https://arxiv.org/abs/2509.12137
作者: Tao Yan,Zheyu Zhang,Jingjing Jiang,Wen-Hua Chen
机构: 未知
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI)
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Abstract:Safety is a critical concern in autonomous vehicle (AV) systems, especially when AI-based sensing and perception modules are involved. However, due to the black box nature of AI algorithms, it makes closed-loop analysis and synthesis particularly challenging, for example, establishing closed-loop stability and ensuring performance, while they are fundamental to AV safety. To approach this difficulty, this paper aims to develop new modeling, analysis, and synthesis tools for AI-based AVs. Inspired by recent developments in perception error models (PEMs), the focus is shifted from directly modeling AI-based perception processes to characterizing the perception errors they produce. Two key classes of AI-induced perception errors are considered: misdetection and measurement noise. These error patterns are modeled using continuous-time Markov chains and Wiener processes, respectively. By means of that, a PEM-augmented driving model is proposed, with which we are able to establish the closed-loop stability for a class of AI-driven AV systems via stochastic calculus. Furthermore, a performance-guaranteed output feedback control synthesis method is presented, which ensures both stability and satisfactory performance. The method is formulated as a convex optimization problem, allowing for efficient numerical solutions. The results are then applied to an adaptive cruise control (ACC) scenario, demonstrating their effectiveness and robustness despite the corrupted and misleading perception.
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[AI-6] K-Level Policy Gradients for Multi-Agent Reinforcement Learning

【速读】：该论文旨在解决深度多智能体强化学习（Deep Multi-Agent Reinforcement Learning, Deep MARL）中因策略更新未考虑其他智能体同步更新而导致的协调偏差问题。现有演员-评论家（Actor-Critic）算法通常仅基于其他智能体当前的策略进行更新，忽略了同一更新步中其他智能体策略的动态变化，从而引发策略不一致和收敛困难。解决方案的关键在于提出K-Level Policy Gradient（KPG），其核心思想是递归地将每个智能体的策略更新视为对其他智能体已更新策略的响应，从而实现更高效的协同策略探索。理论证明表明，在特定条件下，有限迭代的KPG可单调收敛至局部纳什均衡；实验验证了其在StarCraft II和多智能体MuJoCo环境中的性能优于主流Deep MARL方法。

链接: https://arxiv.org/abs/2509.12117
作者: Aryaman Reddi,Gabriele Tiboni,Jan Peters,Carlo D’Eramo
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Actor-critic algorithms for deep multi-agent reinforcement learning (MARL) typically employ a policy update that responds to the current strategies of other agents. While being straightforward, this approach does not account for the updates of other agents at the same update step, resulting in miscoordination. In this paper, we introduce the K -Level Policy Gradient (KPG), a method that recursively updates each agent against the updated policies of other agents, speeding up the discovery of effective coordinated policies. We theoretically prove that KPG with finite iterates achieves monotonic convergence to a local Nash equilibrium under certain conditions. We provide principled implementations of KPG by applying it to the deep MARL algorithms MAPPO, MADDPG, and FACMAC. Empirically, we demonstrate superior performance over existing deep MARL algorithms in StarCraft II and multi-agent MuJoCo.
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[AI-7] Exploring Conversational Design Choices in LLM s for Pedagogical Purposes: Socratic and Narrative Approaches for Improving Instructors Teaching Practice

【速读】：该论文试图解决的问题是：如何设计适用于教育场景的生成式 AI（Generative AI）工具，以有效支持教师的专业发展，并适应不同背景与态度的教师群体。解决方案的关键在于提出并评估 TeaPT——一种面向教学目的的大语言模型（LLM），通过两种对话策略实现差异化支持：一是苏格拉底式（Socratic）方法，借助引导性提问促进反思；二是叙事式（Narrative）方法，提供详细建议以扩展外显认知。研究发现，这两种策略在不同教师群体中表现出显著差异化的偏好和效果，表明自适应的对话设计能够根据教师的经验水平和对人工智能的态度优化交互体验与学习成效。

链接: https://arxiv.org/abs/2509.12107
作者: Si Chen,Isabel R. Molnar,Peiyu Li,Adam Acunin,Ting Hua,Alex Ambrose,Nitesh V. Chawla,Ronald Metoyer
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
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Abstract:Large language models (LLMs) typically generate direct answers, yet they are increasingly used as learning tools. Studying instructors’ usage is critical, given their role in teaching and guiding AI adoption in education. We designed and evaluated TeaPT, an LLM for pedagogical purposes that supports instructors’ professional development through two conversational approaches: a Socratic approach that uses guided questioning to foster reflection, and a Narrative approach that offers elaborated suggestions to extend externalized cognition. In a mixed-method study with 41 higher-education instructors, the Socratic version elicited greater engagement, while the Narrative version was preferred for actionable guidance. Subgroup analyses further revealed that less-experienced, AI-optimistic instructors favored the Socratic version, whereas more-experienced, AI-cautious instructors preferred the Narrative version. We contribute design implications for LLMs for pedagogical purposes, showing how adaptive conversational approaches can support instructors with varied profiles while highlighting how AI attitudes and experience shape interaction and learning.
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[AI-8] JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference CIKM2025

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在法律实践中因“黑箱”特性引发的司法公平性问题。解决方案的关键在于提出并实现了一个名为JustEva的开源评估工具包，其核心创新包括：一套涵盖65个非法律因素的结构化标签体系、三种核心公平性指标（不一致性、偏见和不平衡误差）、稳健的统计推断方法以及直观的信息可视化功能；该工具包支持两类实验流程——生成结构化输出与基于回归等统计方法的分析，从而为LLM在法律任务中的公平性提供系统性评估与改进路径。

链接: https://arxiv.org/abs/2509.12104
作者: Zongyue Xue,Siyuan Zheng,Shaochun Wang,Yiran Hu,Shenran Wang,Yuxin Yao,Haitao Li,Qingyao Ai,Yiqun Liu,Yun Liu,Weixing Shen
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:The integration of Large Language Models (LLMs) into legal practice raises pressing concerns about judicial fairness, particularly due to the nature of their “black-box” processes. This study introduces JustEva, a comprehensive, open-source evaluation toolkit designed to measure LLM fairness in legal tasks. JustEva features several advantages: (1) a structured label system covering 65 extra-legal factors; (2) three core fairness metrics - inconsistency, bias, and imbalanced inaccuracy; (3) robust statistical inference methods; and (4) informative visualizations. The toolkit supports two types of experiments, enabling a complete evaluation workflow: (1) generating structured outputs from LLMs using a provided dataset, and (2) conducting statistical analysis and inference on LLMs’ outputs through regression and other statistical methods. Empirical application of JustEva reveals significant fairness deficiencies in current LLMs, highlighting the lack of fair and trustworthy LLM legal tools. JustEva offers a convenient tool and methodological foundation for evaluating and improving algorithmic fairness in the legal domain.
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[AI-9] Can LLM s Address Mental Health Questions? A Comparison with Human Therapists

【速读】：该论文试图解决的问题是：在心理健康服务资源有限的背景下，如何评估由大语言模型（Large Language Models, LLMs）生成的对话式响应在质量与用户接受度方面是否可替代专业治疗师撰写的内容。解决方案的关键在于通过对比真实患者提问下LLMs（ChatGPT、Gemini、Llama）与治疗师撰写的回复，在文本特征和用户感知层面进行系统分析：结果显示LLMs生成的回答更长、更易读、词汇更丰富且语气更积极，且用户普遍认为其更清晰、尊重和支持性；然而，无论是普通用户还是专业治疗师均更偏好人类治疗师的支持。这表明，尽管LLMs具备显著的沟通优势，但其在心理支持场景中的应用仍需平衡其技术能力与信任、隐私及责任等关键问题。

链接: https://arxiv.org/abs/2509.12102
作者: Synthia Wang,Yuwei Cheng,Austin Song,Sarah Keedy,Marc Berman,Nick Feamster
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
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Abstract:Limited access to mental health care has motivated the use of digital tools and conversational agents powered by large language models (LLMs), yet their quality and reception remain unclear. We present a study comparing therapist-written responses to those generated by ChatGPT, Gemini, and Llama for real patient questions. Text analysis showed that LLMs produced longer, more readable, and lexically richer responses with a more positive tone, while therapist responses were more often written in the first person. In a survey with 150 users and 23 licensed therapists, participants rated LLM responses as clearer, more respectful, and more supportive than therapist-written answers. Yet, both groups of participants expressed a stronger preference for human therapist support. These findings highlight the promise and limitations of LLMs in mental health, underscoring the need for designs that balance their communicative strengths with concerns of trust, privacy, and accountability.
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[AI-10] Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants ICAPS2025

【速读】：该论文旨在解决基于模型的系统工程（MBSE）环境中工程模型缺乏符号规划语义（如前提条件、效果及资源可用性和时间约束）的问题，这限制了对系统变体能否完成特定任务及其执行效率的评估能力。解决方案的关键在于提出一种模型驱动的方法，通过在SysML工程模型中引入专用的建模构造型（stereotype），将核心规划概念以可重用的方式嵌入现有模型结构，并利用算法自动生成符合Planning Domain Definition Language (PDDL) 格式的领域文件和问题文件，从而实现工程模型与规划模型的一致性集成与自动化转换，避免了传统依赖人工转换或外部能力模型的局限性。

链接: https://arxiv.org/abs/2509.12091
作者: Hamied Nabizada,Lasse Beers,Alain Chahine,Felix Gehlhoff,Oliver Niggemann,Alexander Fay
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Presented at the KEPS-Workshop, ICAPS 2025

点击查看摘要

Abstract:Engineering models created in Model-Based Systems Engineering (MBSE) environments contain detailed information about system structure and behavior. However, they typically lack symbolic planning semantics such as preconditions, effects, and constraints related to resource availability and timing. This limits their ability to evaluate whether a given system variant can fulfill specific tasks and how efficiently it performs compared to alternatives. To address this gap, this paper presents a model-driven method that enables the specification and automated generation of symbolic planning artifacts within SysML-based engineering models. A dedicated SysML profile introduces reusable stereotypes for core planning constructs. These are integrated into existing model structures and processed by an algorithm that generates a valid domain file and a corresponding problem file in Planning Domain Definition Language (PDDL). In contrast to previous approaches that rely on manual transformations or external capability models, the method supports native integration and maintains consistency between engineering and planning artifacts. The applicability of the method is demonstrated through a case study from aircraft assembly. The example illustrates how existing engineering models are enriched with planning semantics and how the proposed workflow is applied to generate consistent planning artifacts from these models. The generated planning artifacts enable the validation of system variants through AI planning. Comments: Presented at the KEPS-Workshop, ICAPS 2025 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2509.12091 [cs.AI] (or arXiv:2509.12091v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2509.12091 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-11] Deceptive Risk Minimization: Out-of-Distribution Generalization by Deceiving Distribution Shift Detectors

【速读】：该论文旨在解决机器学习模型在分布外（out-of-distribution, OOD）场景下的泛化能力不足问题，即当测试数据与训练数据来自不同分布时，模型性能显著下降的问题。其核心挑战在于识别并利用对分布变化鲁棒的稳定特征，以消除虚假相关性（spurious correlations）。解决方案的关键是提出一种称为“欺骗风险最小化”（deceptive risk minimization, DRM）的新机制：通过学习使训练数据在观察者眼中呈现独立同分布（i.i.d.）特性的表示，从而识别出跨域稳定的特征；该方法通过一个可微分目标函数实现，同时优化任务特定损失和基于保形鞅（conformal martingales）检测器的分布不变性约束，无需访问测试数据或预先划分训练数据为有限个域，因而相较于传统的领域自适应或不变表示学习方法更具实用性与普适性。

链接: https://arxiv.org/abs/2509.12081
作者: Anirudha Majumdar
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
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Abstract:This paper proposes deception as a mechanism for out-of-distribution (OOD) generalization: by learning data representations that make training data appear independent and identically distributed (iid) to an observer, we can identify stable features that eliminate spurious correlations and generalize to unseen domains. We refer to this principle as deceptive risk minimization (DRM) and instantiate it with a practical differentiable objective that simultaneously learns features that eliminate distribution shifts from the perspective of a detector based on conformal martingales while minimizing a task-specific loss. In contrast to domain adaptation or prior invariant representation learning methods, DRM does not require access to test data or a partitioning of training data into a finite number of data-generating domains. We demonstrate the efficacy of DRM on numerical experiments with concept shift and a simulated imitation learning setting with covariate shift in environments that a robot is deployed in.
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[AI-12] A Time-Series Foundation Model by Universal Delay Embedding

【速读】：该论文旨在解决时间序列预测中长期依赖建模困难、非线性动态系统表征能力不足以及模型可解释性差的问题。其核心解决方案是提出通用延迟嵌入（Universal Delay Embedding, UDE）框架，关键在于将Takens定理指导下的延迟嵌入表示与Koopman算子预测相结合：通过构造Hankel矩阵生成二维子空间块（patch），将其视为图像并利用深度学习技术提取特征；进一步将这些块作为token输入自注意力编码器，从而在潜在空间中以线性方式学习有限维Koopman算子，实现对非线性时间序列的高效且可解释的预测。

链接: https://arxiv.org/abs/2509.12080
作者: Zijian Wang,Peng Tao,Jifan Shi,Rui Bao,Rui Liu,Luonan Chen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:This study introduces Universal Delay Embedding (UDE), a pretrained foundation model designed to revolutionize time-series forecasting through principled integration of delay embedding representation and Koopman operator prediction. Leveraging Takens’ embedding theorem, UDE as a dynamical representation of observed data constructs two-dimensional subspace patches from Hankel matrices, theoretically preserving dynamical and topological properties of underlying dynamical systems. Such patches are viewed as images, which can be efficiently processed by exploiting advanced deep learning technologies. Computationally, these patches further serve as tokens for learning a self-attention encoder, thus enabling accurate prediction of nonlinear time-series by a finite-dimensional Koopman operator in a linear manner in a latent space. Extensive evaluations across various benchmarks and real-world climate datasets demonstrate over 20% average reduction in mean squared error versus state-of-the-art foundation models, alongside superior generalization in fine-tuning scenarios. In particular, the learned dynamical representations and Koopman operator prediction forms from the patches exhibit exceptional interpretability, with consistent identification of topologically informative subspaces and robust encoding of domain-invariant dynamics, establishing UDE as a scalable, interpretable framework for universal time-series modeling and forecasting with broad scientific and industrial applicability.
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[AI-13] When Safe Unimodal Inputs Collide: Optimizing Reasoning Chains for Cross-Modal Safety in Multimodal Large Language Models

【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在长链推理过程中难以维持安全对齐的问题，即当看似无害的单模态输入通过跨模态组合形成有害输出时所表现出的隐式推理风险（implicit reasoning risk）。解决方案的关键在于提出首个具有可解释推理路径的数据集Safe-Semantics-but-Unsafe-Interpretation (SSUI)，并设计基于该数据集的安全感知推理路径优化训练框架（Safety-aware Reasoning Path Optimization, SRPO），从而将MLLM内部的推理过程与人类安全价值观对齐。实验表明，SRPO训练后的模型在关键安全基准测试中达到当前最优性能，尤其在新提出的推理路径基准（Reasoning Path Benchmark, RSBench）上显著优于开源及主流商业MLLMs。

链接: https://arxiv.org/abs/2509.12060
作者: Wei Cai,Shujuan Liu,Jian Zhao,Ziyan Shi,Yusheng Zhao,Yuchen Yuan,Tianle Zhang,Chi Zhang,Xuelong Li
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Multimodal Large Language Models (MLLMs) are susceptible to the implicit reasoning risk, wherein innocuous unimodal inputs synergistically assemble into risky multimodal data that produce harmful outputs. We attribute this vulnerability to the difficulty of MLLMs maintaining safety alignment through long-chain reasoning. To address this issue, we introduce Safe-Semantics-but-Unsafe-Interpretation (SSUI), the first dataset featuring interpretable reasoning paths tailored for such a cross-modal challenge. A novel training framework, Safety-aware Reasoning Path Optimization (SRPO), is also designed based on the SSUI dataset to align the MLLM’s internal reasoning process with human safety values. Experimental results show that our SRPO-trained models achieve state-of-the-art results on key safety benchmarks, including the proposed Reasoning Path Benchmark (RSBench), significantly outperforming both open-source and top-tier commercial MLLMs.
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[AI-14] LEGO: Spatial Accelerator Generation and Optimization for Tensor Applications HPCA2025

【速读】：该论文旨在解决现代张量应用（尤其是生成式 AI 和基础模型）中对灵活加速器架构的高需求问题，现有框架在 RTL（寄存器传输级）生成的设计灵活性与生产力之间存在权衡——要么仅限于少量手工编写的模板，要么无法自动产生 RTL。解决方案的关键在于提出 LEGO 框架，该框架基于仿射变换的架构表示方法，能够自动生成空间架构设计并输出可综合的 RTL 代码，无需手工编写 RTL 模板；其前端通过分析数据重用优化不同空间数据流设计的融合与内存系统合成，后端则将硬件表示为基本图单元，利用线性规划算法进行流水线寄存器最优插入和无效逻辑切换开销最小化，从而实现高效、自动化的硬件生成。

链接: https://arxiv.org/abs/2509.12053
作者: Yujun Lin,Zhekai Zhang,Song Han
机构: 未知
类目: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: The first two authors have equal contributions; Published as a conference paper in HPCA 2025; 13 pages, 14 figures

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Abstract:Modern tensor applications, especially foundation models and generative AI applications require multiple input modalities (both vision and language), which increases the demand for flexible accelerator architecture. Existing frameworks suffer from the trade-off between design flexibility and productivity of RTL generation: either limited to very few hand-written templates or cannot automatically generate the RTL. To address this challenge, we propose the LEGO framework, which targets tensor applications and automatically generates spatial architecture design and outputs synthesizable RTL code without handwritten RTL design templates. Leveraging the affine-transformation-based architecture representation, LEGO front end finds interconnections between function units, synthesizes the memory system, and fuses different spatial dataflow designs based on data reuse analysis. LEGO back end then translates the hardware in a primitive-level graph to perform lower-level optimizations, and applies a set of linear-programming algorithms to optimally insert pipeline registers and reduce the overhead of unused logic when switching spatial dataflows. Our evaluation demonstrates that LEGO can achieve 3.2x speedup and 2.4x energy efficiency compared to previous work Gemmini, and can generate one architecture for diverse modern foundation models in generative AI applications.
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[AI-15] Interaction-Driven Browsing: A Human-in-the-Loop Conceptual Framework Informed by Human Web Browsing for Browser-Using Agents

【速读】：该论文旨在解决当前浏览器使用代理（Browser-Using Agents, BUAs）在执行复杂、非线性网络任务时的局限性，即大多数BUAs仅能完成单一指令后终止，难以支持用户在目标模糊、决策迭代和上下文变化下的多步骤浏览需求。其解决方案的关键在于提出一个受人类网页浏览行为理论启发的人类在回路（Human-in-the-Loop, HITL）概念框架，该框架通过一个迭代循环实现：BUA主动提议下一步操作，用户通过反馈引导浏览流程；同时区分探索（exploration）与利用（exploitation）动作，使用户可灵活控制浏览的广度与深度，从而降低用户的物理与认知负荷，并保留传统浏览心智模型，最终提升任务达成满意度。

链接: https://arxiv.org/abs/2509.12049
作者: Hyeonggeun Yun,Jinkyu Jang
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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Abstract:Although browser-using agents (BUAs) show promise for web tasks and automation, most BUAs terminate after executing a single instruction, failing to support users’ complex, nonlinear browsing with ambiguous goals, iterative decision-making, and changing contexts. We present a human-in-the-loop (HITL) conceptual framework informed by theories of human web browsing behavior. The framework centers on an iterative loop in which the BUA proactively proposes next actions and the user steers the browsing process through feedback. It also distinguishes between exploration and exploitation actions, enabling users to control the breadth and depth of their browsing. Consequently, the framework aims to reduce users’ physical and cognitive effort while preserving users’ traditional browsing mental model and supporting users in achieving satisfactory outcomes. We illustrate how the framework operates with hypothetical use cases and discuss the shift from manual browsing to interaction-driven browsing. We contribute a theoretically informed conceptual framework for BUAs.
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[AI-16] Human-AI Use Patterns for Decision-Making in Disaster Scenarios: A Systematic Review

【速读】：该论文旨在解决高风险灾难场景中决策制定面临的不确定性、动态环境和资源有限等挑战，其解决方案的关键在于系统性梳理人类与人工智能（Human-AI）协作模式在灾害管理全阶段的应用。研究基于51篇同行评审文献，识别出四大核心类别：人机决策支持系统、任务与资源协调、信任与透明度、仿真与培训，并进一步分析认知增强智能、多智能体协同、可解释AI及虚拟训练环境等子模式。这些模式共同作用于提升情境感知能力、响应效率与复杂决策支持，同时指出当前在可扩展性、可解释性和系统互操作性方面的局限，强调未来需发展适应性强、可信且情境感知的人机协同系统以增强灾害韧性与公平恢复效果。

链接: https://arxiv.org/abs/2509.12034
作者: Emmanuel Adjei Domfeh,Christopher L. Dancy
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 10 pages, 2 figures

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Abstract:In high-stakes disaster scenarios, timely and informed decision-making is critical yet often challenged by uncertainty, dynamic environments, and limited resources. This paper presents a systematic review of Human-AI collaboration patterns that support decision-making across all disaster management phases. Drawing from 51 peer-reviewed studies, we identify four major categories: Human-AI Decision Support Systems, Task and Resource Coordination, Trust and Transparency, and Simulation and Training. Within these, we analyze sub-patterns such as cognitive-augmented intelligence, multi-agent coordination, explainable AI, and virtual training environments. Our review highlights how AI systems may enhance situational awareness, improves response efficiency, and support complex decision-making, while also surfacing critical limitations in scalability, interpretability, and system interoperability. We conclude by outlining key challenges and future research directions, emphasizing the need for adaptive, trustworthy, and context-aware Human-AI systems to improve disaster resilience and equitable recovery outcomes.
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[AI-17] Imitation Learning as Return Distribution Matching

【速读】：该论文旨在解决风险敏感型模仿学习（Risk-Sensitive Imitation Learning, RS-IL）问题，即不仅要求代理（agent）在期望回报上匹配专家（expert）的平均表现，还需在其回报分布的其他特征（如方差）上保持一致，以体现相似的风险态度。其核心挑战在于如何在不依赖完整环境动态模型的情况下，有效建模和学习具有风险敏感性的策略。解决方案的关键在于提出了一种基于Wasserstein距离匹配专家回报分布的通用形式化框架，并引入一个高效且表达能力强的非马尔可夫策略子类（non-Markovian policies），从而克服了传统马尔可夫策略在该任务中的表达局限性。在此基础上，作者设计了两种可证明高效的算法：RS-BC（当转移模型未知时）和RS-KT（当转移模型已知时），其中RS-KT通过利用环境动态信息显著降低了样本复杂度，进一步验证了该方法在样本效率上的优势。

链接: https://arxiv.org/abs/2509.12026
作者: Filippo Lazzati,Alberto Maria Metelli
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:We study the problem of training a risk-sensitive reinforcement learning (RL) agent through imitation learning (IL). Unlike standard IL, our goal is not only to train an agent that matches the expert’s expected return (i.e., its average performance) but also its risk attitude (i.e., other features of the return distribution, such as variance). We propose a general formulation of the risk-sensitive IL problem in which the objective is to match the expert’s return distribution in Wasserstein distance. We focus on the tabular setting and assume the expert’s reward is known. After demonstrating the limited expressivity of Markovian policies for this task, we introduce an efficient and sufficiently expressive subclass of non-Markovian policies tailored to it. Building on this subclass, we develop two provably efficient algorithms, RS-BC and RS-KT, for solving the problem when the transition model is unknown and known, respectively. We show that RS-KT achieves substantially lower sample complexity than RS-BC by exploiting dynamics information. We further demonstrate the sample efficiency of return distribution matching in the setting where the expert’s reward is unknown by designing an oracle-based variant of RS-KT. Finally, we complement our theoretical analysis of RS-KT and RS-BC with numerical simulations, highlighting both their sample efficiency and the advantages of non-Markovian policies over standard sample-efficient IL algorithms.
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[AI-18] Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids

【速读】：该论文旨在解决如何将专家代理（expert agent）在特定环境中的行为泛化到新环境和/或新增约束条件下的问题。传统逆强化学习（Inverse Reinforcement Learning, IRL）虽能恢复专家的潜在奖励函数，但其本质上是病态的——存在多个奖励函数可解释相同观测行为（称为可行集），这些奖励在新环境中可能诱导不同策略。为解决此不确定性，论文提出一种新颖且原理严谨的决策准则：从可行集中某个有界子集内所有奖励所诱导的策略中选择“平均”策略。关键创新在于证明该平均策略可通过使用该子集奖励的质心（centroid）进行规划获得，并推导出该质心的闭式表达式；进一步设计了一个仅需离线专家演示数据即可高效估计该质心的算法，从而实现稳定、可推广的行为迁移。

链接: https://arxiv.org/abs/2509.12010
作者: Filippo Lazzati,Alberto Maria Metelli
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:We study the problem of generalizing an expert agent’s behavior, provided through demonstrations, to new environments and/or additional constraints. Inverse Reinforcement Learning (IRL) offers a promising solution by seeking to recover the expert’s underlying reward function, which, if used for planning in the new settings, would reproduce the desired behavior. However, IRL is inherently ill-posed: multiple reward functions, forming the so-called feasible set, can explain the same observed behavior. Since these rewards may induce different policies in the new setting, in the absence of additional information, a decision criterion is needed to select which policy to deploy. In this paper, we propose a novel, principled criterion that selects the “average” policy among those induced by the rewards in a certain bounded subset of the feasible set. Remarkably, we show that this policy can be obtained by planning with the reward centroid of that subset, for which we derive a closed-form expression. We then present a provably efficient algorithm for estimating this centroid using an offline dataset of expert demonstrations only. Finally, we conduct numerical simulations that illustrate the relationship between the expert’s behavior and the behavior produced by our method.
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[AI-19] Poison to Detect: Detection of Targeted Overfitting in Federated Learning

【速读】：该论文旨在解决联邦学习（Federated Learning, FL）中由恶意 orchestrator 引发的针对性过拟合（targeted overfitting）隐私威胁问题，即恶意方通过操纵全局聚合过程，导致特定客户端本地模型发生异常过拟合，从而泄露敏感信息。解决方案的关键在于提出三种客户端侧的早期检测技术：(a) 标签翻转（label flipping）、(b) 后门触发器注入（backdoor trigger injection）和 © 模型指纹识别（model fingerprinting），这些方法使客户端能够在模型被恶意污染前验证全局聚合结果的完整性，从而及时退出训练以避免潜在损害。

链接: https://arxiv.org/abs/2509.11974
作者: Soumia Zohra El Mestari,Maciej Krzysztof Zuziak,Gabriele Lenzini
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
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Abstract:Federated Learning (FL) enables collaborative model training across decentralised clients while keeping local data private, making it a widely adopted privacy-enhancing technology (PET). Despite its privacy benefits, FL remains vulnerable to privacy attacks, including those targeting specific clients. In this paper, we study an underexplored threat where a dishonest orchestrator intentionally manipulates the aggregation process to induce targeted overfitting in the local models of specific clients. Whereas many studies in this area predominantly focus on reducing the amount of information leakage during training, we focus on enabling an early client-side detection of targeted overfitting, thereby allowing clients to disengage before significant harm occurs. In line with this, we propose three detection techniques - (a) label flipping, (b) backdoor trigger injection, and © model fingerprinting - that enable clients to verify the integrity of the global aggregation. We evaluated our methods on multiple datasets under different attack scenarios. Our results show that the three methods reliably detect targeted overfitting induced by the orchestrator, but they differ in terms of computational complexity, detection latency, and false-positive rates.
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[AI-20] MusicSwarm: Biologically Inspired Intelligence for Music Composition

【速读】：该论文旨在解决生成式 AI (Generative AI) 在长时序、高复杂度创作任务中难以维持结构一致性与多样性的问题，特别是在音乐创作领域。其解决方案的关键在于提出一种去中心化的“音乐蜂群”（MusicSwarm）架构，由大量相同的冻结基础模型组成，通过 stigmergic（刺激传递）式的点对点信号进行协作，无需参数更新即可实现协同创作。该系统通过局部感知、记忆适应与动态共识机制，在音高、节奏和结构层面形成互补角色分工，并借助自相似网络揭示出具有小世界特性的高效连接结构，从而将局部创新整合为全局音乐形式。此方法将专业化从模型权重更新转移到交互规则、共享记忆与动态共识机制，显著提升了计算效率与创造性输出的多样性与结构性。

链接: https://arxiv.org/abs/2509.11973
作者: Markus J. Buehler
机构: 未知
类目: Artificial Intelligence (cs.AI); Multimedia (cs.MM); Sound (cs.SD)
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Abstract:We show that coherent, long-form musical composition can emerge from a decentralized swarm of identical, frozen foundation models that coordinate via stigmergic, peer-to-peer signals, without any weight updates. We compare a centralized multi-agent system with a global critic to a fully decentralized swarm in which bar-wise agents sense and deposit harmonic, rhythmic, and structural cues, adapt short-term memory, and reach consensus. Across symbolic, audio, and graph-theoretic analyses, the swarm yields superior quality while delivering greater diversity and structural variety and leads across creativity metrics. The dynamics contract toward a stable configuration of complementary roles, and self-similarity networks reveal a small-world architecture with efficient long-range connectivity and specialized bridging motifs, clarifying how local novelties consolidate into global musical form. By shifting specialization from parameter updates to interaction rules, shared memory, and dynamic consensus, MusicSwarm provides a compute- and data-efficient route to long-horizon creative structure that is immediately transferable beyond music to collaborative writing, design, and scientific discovery.
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[AI-21] me-Constrained Intelligent Adversaries for Automation Vulnerability Testing: A Multi-Robot Patrol Case Study

【速读】：该论文旨在解决物理自主系统（physical autonomous systems）在面对敌对攻击时的鲁棒性评估问题，具体聚焦于多机器人巡逻场景中如何构建一个现实且具有挑战性的对手模型，以检验巡逻系统的安全性并指导抗脆弱性设计。解决方案的关键在于提出一种基于机器学习的对抗者模型（adversary model），该模型通过观察机器人巡逻行为来策略性地尝试在有限时间内未被发现地进入受保护区域，从而为巡逻系统提供更严格、更具现实意义的测试基准，并验证其对多种先进去中心化多机器人巡逻策略的有效性。

链接: https://arxiv.org/abs/2509.11971
作者: James C. Ward,Alex Bott,Connor York,Edmund R. Hunt
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:Simulating hostile attacks of physical autonomous systems can be a useful tool to examine their robustness to attack and inform vulnerability-aware design. In this work, we examine this through the lens of multi-robot patrol, by presenting a machine learning-based adversary model that observes robot patrol behavior in order to attempt to gain undetected access to a secure environment within a limited time duration. Such a model allows for evaluation of a patrol system against a realistic potential adversary, offering insight into future patrol strategy design. We show that our new model outperforms existing baselines, thus providing a more stringent test, and examine its performance against multiple leading decentralized multi-robot patrol strategies.
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[AI-22] A GPU-Accelerated RAG -Based Telegram Assistant for Supporting Parallel Processing Students

【速读】：该论文旨在解决高等教育中学生在常规教学时间之外缺乏持续、即时学术支持的问题，特别是在高性能计算（High Performance Computing, HPC）相关课程如“并行处理导论”中的学习辅助需求。解决方案的关键在于构建一个面向特定领域的检索增强生成（Retrieval-Augmented Generation, RAG）系统，其核心组件为量化后的Mistral-7B Instruct模型，并以Telegram机器人形式部署，实现基于课程材料的实时、个性化问答。通过GPU加速显著降低推理延迟，使该系统可在消费级硬件上高效运行，从而提供一种低成本、私密且有效的AI辅导方案。

链接: https://arxiv.org/abs/2509.11947
作者: Guy Tel-Zur
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
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Abstract:This project addresses a critical pedagogical need: offering students continuous, on-demand academic assistance beyond conventional reception hours. I present a domain-specific Retrieval-Augmented Generation (RAG) system powered by a quantized Mistral-7B Instruct model and deployed as a Telegram bot. The assistant enhances learning by delivering real-time, personalized responses aligned with the “Introduction to Parallel Processing” course materials. GPU acceleration significantly improves inference latency, enabling practical deployment on consumer hardware. This approach demonstrates how consumer GPUs can enable affordable, private, and effective AI tutoring for HPC education.
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[AI-23] Agent ic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare

【速读】：该论文旨在解决医疗领域中多模态数据（multimodal data）在复杂诊断任务下的正确推理问题，当前已有的一些多模态推理模型在医疗场景中的应用仍存在局限性，难以实现准确的诊断推理。解决方案的关键在于提出一种基于时序图（temporal graph）的推理过程建模方法，该方法通过有向图结构实现动态推理路径的回溯、修正与重构，支持新增或删除推理依据以优化最终推荐结果；同时，该方案引入多代理时序推理框架，结合任务分配与交叉验证机制，提升推理输出的准确性，并能对患者不同时间点的多模态数据进行追踪分析，从而支持疾病进展的动态建模。

链接: https://arxiv.org/abs/2509.11944
作者: Susanta Mitra
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Healthcare and medicine are multimodal disciplines that deal with multimodal data for reasoning and diagnosing multiple diseases. Although some multimodal reasoning models have emerged for reasoning complex tasks in scientific domains, their applications in the healthcare domain remain limited and fall short in correct reasoning for diagnosis. To address the challenges of multimodal medical reasoning for correct diagnosis and assist the healthcare professionals, a novel temporal graph-based reasoning process modelled through a directed graph has been proposed in the current work. It helps in accommodating dynamic changes in reasons through backtracking, refining the reasoning content, and creating new or deleting existing reasons to reach the best recommendation or answer. Again, consideration of multimodal data at different time points can enable tracking and analysis of patient health and disease progression. Moreover, the proposed multi-agent temporal reasoning framework provides task distributions and a cross-validation mechanism to further enhance the accuracy of reasoning outputs. A few basic experiments and analysis results justify the novelty and practical utility of the proposed preliminary approach.
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[AI-24] Neuro-Symbolic Agents with Modal Logic for Autonomous Diagnostics

【速读】：该论文旨在解决当前智能代理在复杂、动态环境中进行自主决策时面临的推理结构脆弱性与逻辑一致性不足的问题，尤其是在依赖生成式语言模型（Language Models, LMs）的系统中，容易产生物理或逻辑上不可行的结论。解决方案的关键在于提出一种神经符号多智能体架构，其中每个代理的信念状态以Kripke模型（Kripke models）形式形式化表示，并利用模态逻辑（modal logic）对可能性和必然性进行精确推理；同时引入不可变的领域特定知识作为逻辑约束，主动引导语言模型的假设生成过程，从而在保持语言模型语义直觉优势的同时，确保推理结果具备严格的逻辑一致性和可验证性。

链接: https://arxiv.org/abs/2509.11943
作者: Antonin Sulc,Thorsten Hellert
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Logic in Computer Science (cs.LO); Multiagent Systems (cs.MA)
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Abstract:The development of intelligent agents, particularly those powered by language models (LMs), has shown the critical role in various environments that require intelligent and autonomous decision. Environments are not passive testing grounds and they represent the data required for agents to learn and exhibit very challenging conditions that require adaptive, complex and autonomous capacity to make decisions. While the paradigm of scaling models and datasets has led to remarkable emergent capabilities, we argue that scaling the structure, fidelity, and logical consistency of agent reasoning within these environments is a crucial, yet underexplored, dimension of AI research. This paper introduces a neuro-symbolic multi-agent architecture where the belief states of individual agents are formally represented as Kripke models. This foundational choice enables them to reason about known concepts of \emphpossibility and \emphnecessity using the formal language of modal logic. In this work, we use of immutable, domain-specific knowledge to make infere information, which is encoded as logical constraints essential for proper diagnosis. In the proposed model, we show constraints that actively guide the hypothesis generation of LMs, effectively preventing them from reaching physically or logically untenable conclusions. In a high-fidelity simulated particle accelerator environment, our system successfully diagnoses complex, cascading failures by combining the powerful semantic intuition of LMs with the rigorous, verifiable validation of modal logic and a factual world model and showcasing a viable path toward more robust, reliable, and verifiable autonomous agents.
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[AI-25] VisDocSketcher: Towards Scalable Visual Documentation with Agent ic Systems

【速读】：该论文旨在解决自动化生成高质量视觉文档（Visual Documentation）的难题，尤其是针对当前缺乏能够直接从代码中自动提取关键元素并生成结构清晰、语义准确的可视化表示的方法。其核心挑战在于：一方面，人工创建视觉文档效率低下且难以规模化；另一方面，现有方法无法有效评估生成结果的质量，导致评估主观性强、标准化困难。解决方案的关键在于提出VisDocSketcher——一种基于代理的LLM系统，结合静态分析与大语言模型（Large Language Model, LLM）代理，自动识别代码中的关键组件并生成对应的可视化表示；同时设计了AutoSketchEval评估框架，通过代码级指标客观衡量生成文档的准确性与对齐度，从而实现从生成到评估的闭环自动化流程。

链接: https://arxiv.org/abs/2509.11942
作者: Luís F. Gomes,Xin Zhou,David Lo,Rui Abreu
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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Abstract:Visual documentation is an effective tool for reducing the cognitive barrier developers face when understanding unfamiliar code, enabling more intuitive comprehension. Compared to textual documentation, it provides a higher-level understanding of the system structure and data flow. Developers usually prefer visual representations over lengthy textual descriptions for large software systems. Visual documentation is both difficult to produce and challenging to evaluate. Manually creating it is time-consuming, and currently, no existing approach can automatically generate high-level visual documentation directly from code. Its evaluation is often subjective, making it difficult to standardize and automate. To address these challenges, this paper presents the first exploration of using agentic LLM systems to automatically generate visual documentation. We introduce VisDocSketcher, the first agent-based approach that combines static analysis with LLM agents to identify key elements in the code and produce corresponding visual representations. We propose a novel evaluation framework, AutoSketchEval, for assessing the quality of generated visual documentation using code-level metrics. The experimental results show that our approach can valid visual documentation for 74.4% of the samples. It shows an improvement of 26.7-39.8% over a simple template-based baseline. Our evaluation framework can reliably distinguish high-quality (code-aligned) visual documentation from low-quality (non-aligned) ones, achieving an AUC exceeding 0.87. Our work lays the foundation for future research on automated visual documentation by introducing practical tools that not only generate valid visual representations but also reliably assess their quality.
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[AI-26] Neuromorphic Intelligence

【速读】：该论文试图解决的问题是：如何构建一个统一的理论框架，以整合人工智能、神经科学、物理学、化学和材料科学等多学科知识，从而推动类脑计算（neuromorphic computing）系统的发展，使其具备高能效、自适应性和可持续性。解决方案的关键在于引入动力系统理论（dynamical systems theory），该理论基于微分 calculus，为自然与人工基质中的推理、学习与控制提供了严谨的建模语言；在此框架下，噪声可被转化为学习资源，而微分遗传编程（differential genetic programming）则可用于发现实现自适应行为的动力学系统，从而推动由物理基质动态涌现的类脑智能（emergent neuromorphic intelligence）的发展。

链接: https://arxiv.org/abs/2509.11940
作者: Marcel van Gerven
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Neuromorphic computing seeks to replicate the remarkable efficiency, flexibility, and adaptability of the human brain in artificial systems. Unlike conventional digital approaches, which depend on massive computational and energy resources, neuromorphic systems exploit brain-inspired principles of computation to achieve orders of magnitude greater energy efficiency. By drawing on insights from artificial intelligence, neuroscience, physics, chemistry, and materials science, neuromorphic computing promises to deliver intelligent systems that are sustainable, transparent, and widely accessible. A central challenge, however, is to identify a unifying theoretical framework capable of bridging these diverse disciplines. We argue that dynamical systems theory provides such a foundation. Rooted in differential calculus, it offers a principled language for modeling inference, learning, and control in both natural and artificial substrates. Within this framework, noise can be harnessed as a resource for learning, while differential genetic programming enables the discovery of dynamical systems that implement adaptive behaviors. Embracing this perspective paves the way toward emergent neuromorphic intelligence, where intel- ligent behavior arises from the dynamics of physical substrates, advancing both the science and sustainability of AI.
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[AI-27] MMORE: Massive Multimodal Open RAG Extraction ICML2025

【速读】：该论文旨在解决多模态文档在大规模场景下知识提取与检索增强生成（Retrieval-Augmented Generation, RAG）的挑战，即如何高效处理异构格式文档（如文本、表格、图像、音频和视频）并将其转化为适用于大语言模型（Large Language Models, LLMs）的统一结构化表示。解决方案的关键在于提出MMORE——一个开源的、模块化且分布式的多模态开放检索增强生成与提取流水线，其核心优势包括：支持超过十五种文件类型的数据摄取与转换、采用混合密集-稀疏检索策略提升召回准确性、通过跨CPU/GPU的并行化架构实现高可扩展性，并在PubMedQA等基准上验证了其在医学问答任务中随检索深度增加而提升LLM性能的能力。

链接: https://arxiv.org/abs/2509.11937
作者: Alexandre Sallinen,Stefan Krsteski,Paul Teiletche,Marc-Antoine Allard,Baptiste Lecoeur,Michael Zhang,Fabrice Nemo,David Kalajdzic,Matthias Meyer,Mary-Anne Hartley
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: This paper was originally submitted to the CODEML workshop for ICML 2025. 9 pages (including references and appendices)

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Abstract:We introduce MMORE, an open-source pipeline for Massive Multimodal Open RetrievalAugmented Generation and Extraction, designed to ingest, transform, and retrieve knowledge from heterogeneous document formats at scale. MMORE supports more than fifteen file types, including text, tables, images, emails, audio, and video, and processes them into a unified format to enable downstream applications for LLMs. The architecture offers modular, distributed processing, enabling scalable parallelization across CPUs and GPUs. On processing benchmarks, MMORE demonstrates a 3.8-fold speedup over single-node baselines and 40% higher accuracy than Docling on scanned PDFs. The pipeline integrates hybrid dense-sparse retrieval and supports both interactive APIs and batch RAG endpoints. Evaluated on PubMedQA, MMORE-augmented medical LLMs improve biomedical QA accuracy with increasing retrieval depth. MMORE provides a robust, extensible foundation for deploying task-agnostic RAG systems on diverse, real-world multimodal data. The codebase is available at this https URL.
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[AI-28] BuildingGym: An open-source toolbox for AI-based building energy management using reinforcement learning

【速读】：该论文旨在解决强化学习（Reinforcement Learning, RL）在建筑能源管理（Building Energy Management, BEM）中缺乏灵活、通用框架的问题，使得RL方法难以适配不同控制任务和场景。解决方案的关键在于提出BuildingGym——一个开源的、研究友好的灵活框架，其核心是集成EnergyPlus仿真器，支持系统级与房间级控制，并能接受外部信号作为控制输入（如智能电网或电动汽车社区），从而提升应用灵活性；同时内置多种RL算法并简化配置流程，使建筑管理者和AI专家均可高效实现最优控制策略的训练与部署，显著提升了RL在BEM领域的可操作性与适应性。

链接: https://arxiv.org/abs/2509.11922
作者: Xilei Dai,Ruotian Chen,Songze Guan,Wen-Tai Li,Chau Yuen
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Reinforcement learning (RL) has proven effective for AI-based building energy management. However, there is a lack of flexible framework to implement RL across various control problems in building energy management. To address this gap, we propose BuildingGym, an open-source tool designed as a research-friendly and flexible framework for training RL control strategies for common challenges in building energy management. BuildingGym integrates EnergyPlus as its core simulator, making it suitable for both system-level and room-level control. Additionally, BuildingGym is able to accept external signals as control inputs instead of taking the building as a stand-alone entity. This feature makes BuildingGym applicable for more flexible environments, e.g. smart grid and EVs community. The tool provides several built-in RL algorithms for control strategy training, simplifying the process for building managers to obtain optimal control strategies. Users can achieve this by following a few straightforward steps to configure BuildingGym for optimization control for common problems in the building energy management field. Moreover, AI specialists can easily implement and test state-of-the-art control algorithms within the platform. BuildingGym bridges the gap between building managers and AI specialists by allowing for the easy configuration and replacement of RL algorithms, simulators, and control environments or problems. With BuildingGym, we efficiently set up training tasks for cooling load management, targeting both constant and dynamic cooling load management. The built-in algorithms demonstrated strong performance across both tasks, highlighting the effectiveness of BuildingGym in optimizing cooling strategies.
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[AI-29] EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models

【速读】：该论文旨在解决当前大语言模型（Large Language Models, LLMs）在全双工（full-duplex）实时多模态场景下缺乏持续记忆能力的问题，即如何让模型在处理实时音视频流时，不仅能识别多个用户并提供个性化响应，还能长期维护用户事实、偏好及社交关系等知识。解决方案的关键在于提出EgoMem——一个专为全双工模型设计的终身记忆代理（lifelong memory agent），其核心由三个异步过程组成：（i）基于人脸与语音的动态用户检索与上下文获取；（ii）基于多模态对话的个性化音频响应生成；（iii）从多模态流中自动检测对话边界并提取信息更新长期记忆。该方案完全依赖原始音视频流，无需预处理或标注数据，适用于真实世界中的具身交互场景，实验表明其检索与记忆管理模块准确率超95%，集成至RoboEgo多模态聊天机器人后，在实时个性化对话中实现超过87%的事实一致性得分。

链接: https://arxiv.org/abs/2509.11914
作者: Yiqun Yao,Naitong Yu,Xiang Li,Xin Jiang,Xuezhi Fang,Wenjia Ma,Xuying Meng,Jing Li,Aixin Sun,Yequan Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:We introduce EgoMem, the first lifelong memory agent tailored for full-duplex models that process real-time omnimodal streams. EgoMem enables real-time models to recognize multiple users directly from raw audiovisual streams, to provide personalized response, and to maintain long-term knowledge of users’ facts, preferences, and social relationships extracted from audiovisual history. EgoMem operates with three asynchronous processes: (i) a retrieval process that dynamically identifies user via face and voice, and gathers relevant context from a long-term memory; (ii) an omnimodal dialog process that generates personalized audio responses based on the retrieved context; and (iii) a memory management process that automatically detects dialog boundaries from omnimodal streams, and extracts necessary information to update the long-term memory. Unlike existing memory agents for LLMs, EgoMem relies entirely on raw audiovisual streams, making it especially suitable for lifelong, real-time, and embodied scenarios. Experimental results demonstrate that EgoMem’s retrieval and memory management modules achieve over 95% accuracy on the test set. When integrated with a fine-tuned RoboEgo omnimodal chatbot, the system achieves fact-consistency scores above 87% in real-time personalized dialogs, establishing a strong baseline for future research.
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[AI-30] Learning Representations in Video Game Agents with Supervised Contrastive Imitation Learning

【速读】：该论文旨在解决模仿学习（Imitation Learning, IL）中状态表示学习不足的问题，即如何从观测数据中提取更有效的潜在表示，以更好地捕捉与动作相关的关键因素，从而提升代理（agent）对观察到的状态到执行动作之间因果关系的建模能力。其解决方案的关键在于将监督对比学习（Supervised Contrastive Learning, SupCon）引入IL框架，并设计了一种适用于连续输出空间的SupCon损失函数，使其无需受限于环境动作类型即可有效运行，从而在多个3D和2D游戏环境中实现了更高质量的状态表示、更快的学习收敛速度以及更强的泛化性能。

链接: https://arxiv.org/abs/2509.11880
作者: Carlos Celemin,Joseph Brennan,Pierluigi Vito Amadori,Tim Bradley
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:This paper introduces a novel application of Supervised Contrastive Learning (SupCon) to Imitation Learning (IL), with a focus on learning more effective state representations for agents in video game environments. The goal is to obtain latent representations of the observations that capture better the action-relevant factors, thereby modeling better the cause-effect relationship from the observations that are mapped to the actions performed by the demonstrator, for example, the player jumps whenever an obstacle appears ahead. We propose an approach to integrate the SupCon loss with continuous output spaces, enabling SupCon to operate without constraints regarding the type of actions of the environment. Experiments on the 3D games Astro Bot and Returnal, and multiple 2D Atari games show improved representation quality, faster learning convergence, and better generalization compared to baseline models trained only with supervised action prediction loss functions.
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[AI-31] nma: Robust Cross-Embodiment Robot Manipulation with Diffusion Transformer

【速读】：该论文旨在解决在轻量化、跨体态（cross-embodiment）学习场景下，如何有效结合扩散模型（diffusion models）与Transformer策略（Transformer policies）以提升机器人操作任务的稳定性与泛化能力的问题。其解决方案的关键在于提出Tenma——一种面向双臂控制的轻量级扩散-Transformer架构，核心创新包括：通过跨体态归一化器（cross-embodiment normalizer）将异构多模态状态/动作空间映射至共享潜在空间，实现多视角RGB、本体感知（proprioception）与语言信息的有效融合；引入联合状态-时间编码器（Joint State-Time encoder）以实现时序对齐的观测学习并显著提升推理速度；以及优化扩散动作解码器（diffusion action decoder），增强训练稳定性和学习容量。该方案在计算资源匹配条件下，实现了88.95%的平均成功率，远超基线模型（最佳仅18.12%），验证了多模态与跨体态学习策略在提升基于Transformer的模仿学习策略能力方面的巨大潜力。

链接: https://arxiv.org/abs/2509.11865
作者: Travis Davies,Yiqi Huang,Yunxin Liu,Xiang Chen,Huxian Liu,Luhui Hu
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 4 figures

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Abstract:Scaling Transformer policies and diffusion models has advanced robotic manipulation, yet combining these techniques in lightweight, cross-embodiment learning settings remains challenging. We study design choices that most affect stability and performance for diffusion-transformer policies trained on heterogeneous, multimodal robot data, and introduce Tenma, a lightweight diffusion-transformer for bi-manual arm control. Tenma integrates multiview RGB, proprioception, and language via a cross-embodiment normalizer that maps disparate state/action spaces into a shared latent space; a Joint State-Time encoder for temporally aligned observation learning with inference speed boosts; and a diffusion action decoder optimized for training stability and learning capacity. Across benchmarks and under matched compute, Tenma achieves an average success rate of 88.95% in-distribution and maintains strong performance under object and scene shifts, substantially exceeding baseline policies whose best in-distribution average is 18.12%. Despite using moderate data scale, Tenma delivers robust manipulation and generalization, indicating the great potential for multimodal and cross-embodiment learning strategies for further augmenting the capacity of transformer-based imitation learning policies.
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[AI-32] Data-Driven Analysis of Text-Conditioned AI-Generated Music: A Case Study with Suno and Udio

【速读】：该论文旨在解决当前生成式 AI 音乐（Generative AI Music）平台如Suno和Udio在实际应用中用户创作行为与内容主题的系统性分析问题，以及这些平台如何被用于激发音乐创作灵感。其解决方案的关键在于结合先进的文本嵌入模型（text embedding models）、降维与聚类方法，对用户生成的歌曲提示词（prompts）、标签（tags）及歌词进行自动化处理与语义分析，并通过交互式可视化工具呈现结果，从而揭示出歌词主题、语言偏好、提示策略及元标签（metatags）引导模型的特殊使用模式。

链接: https://arxiv.org/abs/2509.11824
作者: Luca Casini,Laura Cros Vila,David Dalmazzo,Anna-Kaisa Kaila,Bob L.T. Sturm
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Sound (cs.SD)
备注: Submitted for review to TISMIR Digital Musicology special issue

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Abstract:Online AI platforms for creating music from text prompts (AI music), such as Suno and Udio, are now being used by hundreds of thousands of users. Some AI music is appearing in advertising, and even charting, in multiple countries. How are these platforms being used? What subjects are inspiring their users? This article answers these questions for Suno and Udio using a large collection of songs generated by users of these platforms from May to October 2024. Using a combination of state-of-the-art text embedding models, dimensionality reduction and clustering methods, we analyze the prompts, tags and lyrics, and automatically annotate and display the processed data in interactive plots. Our results reveal prominent themes in lyrics, language preference, prompting strategies, as well as peculiar attempts at steering models through the use of metatags. To promote the musicological study of the developing cultural practice of AI-generated music we share our code and resources.
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[AI-33] HeLoFusion: An Efficient and Scalable Encoder for Modeling Heterogeneous and Multi-Scale Interactions in Trajectory Prediction

【速读】：该论文旨在解决自动驾驶中多智能体轨迹预测问题，核心挑战在于如何有效建模复杂的社会动态，特别是多尺度交互（multi-scale interactions）与异构智能体（heterogeneous agents）行为的多样性。解决方案的关键在于提出HeLoFusion架构——一种基于局部性的高效且可扩展编码器，通过为每个智能体构建中心化的多尺度图结构，显式捕捉直接成对依赖关系与群体级交互（如车队或人群）；同时引入聚合-分解消息传递机制和类型特定特征网络，以应对异构性问题，从而学习细粒度的、类型相关的交互模式。这种以局部性为基础的设计实现了多层次社会上下文的原理性表征，显著提升了轨迹预测性能，在Waymo Open Motion Dataset上达到当前最优结果。

链接: https://arxiv.org/abs/2509.11719
作者: Bingqing Wei,Lianmin Chen,Zhongyu Xia,Yongtao Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Multi-agent trajectory prediction in autonomous driving requires a comprehensive understanding of complex social dynamics. Existing methods, however, often struggle to capture the full richness of these dynamics, particularly the co-existence of multi-scale interactions and the diverse behaviors of heterogeneous agents. To address these challenges, this paper introduces HeLoFusion, an efficient and scalable encoder for modeling heterogeneous and multi-scale agent interactions. Instead of relying on global context, HeLoFusion constructs local, multi-scale graphs centered on each agent, allowing it to effectively model both direct pairwise dependencies and complex group-wise interactions (\textite.g., platooning vehicles or pedestrian crowds). Furthermore, HeLoFusion tackles the critical challenge of agent heterogeneity through an aggregation-decomposition message-passing scheme and type-specific feature networks, enabling it to learn nuanced, type-dependent interaction patterns. This locality-focused approach enables a principled representation of multi-level social context, yielding powerful and expressive agent embeddings. On the challenging Waymo Open Motion Dataset, HeLoFusion achieves state-of-the-art performance, setting new benchmarks for key metrics including Soft mAP and minADE. Our work demonstrates that a locality-grounded architecture, which explicitly models multi-scale and heterogeneous interactions, is a highly effective strategy for advancing motion forecasting.
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[AI-34] Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models EMNLP2025

【速读】：该论文旨在解决代码大语言模型（Code LLMs）在推理程序运行时行为和理解实际功能方面的关键局限性，具体表现为：一是模型难以准确解释程序在运行时的实际执行逻辑；二是现有方法对语义信息（如执行轨迹）的表示不一致且碎片化，限制了模型的泛化与推理能力。解决方案的关键在于提出一个通用框架，用于将语义信息（如执行轨迹）系统性地整合到与代码任务相关的提示中，并通过实证研究探索其在监督微调（SFT）和推理阶段的作用。实验结果表明，尽管语义信息在理论上具有潜力，但其对SFT和测试时扩展的效果有限，挑战了以往研究的假设。

链接: https://arxiv.org/abs/2509.11686
作者: Jian Wang,Xiaofei Xie,Qiang Hu,Shangqing Liu,Yi Li
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: EMNLP2025-findings

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Abstract:Code Large Language Models (Code LLMs) have opened a new era in programming with their impressive capabilities. However, recent research has revealed critical limitations in their ability to reason about runtime behavior and understand the actual functionality of programs, which poses significant challenges for their post-training and practical deployment. Specifically, Code LLMs encounter two principal issues: (1) a lack of proficiency in reasoning about program execution behavior, as they struggle to interpret what programs actually do during runtime, and (2) the inconsistent and fragmented representation of semantic information, such as execution traces, across existing methods, which hinders their ability to generalize and reason effectively. These challenges underscore the necessity for more systematic approaches to enhance the reasoning capabilities of Code LLMs. To address these issues, we introduce a generic framework to support integrating semantic information~(e.g., execution trace) to code task-relevant prompts, and conduct a comprehensive study to explore the role of semantic information in enhancing the reasoning ability of Code LLMs accordingly. Specifically, we focus on investigating the usefulness of trace-based semantic information in boosting supervised fine-tuning~(SFT) and post-phase inference of Code LLMs. The experimental results surprisingly disagree with previous works and demonstrate that semantic information has limited usefulness for SFT and test time scaling of Code LLM.
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[AI-35] Adapting and Evaluating Multimodal Large Language Models for Adolescent Idiopathic Scoliosis Self-Management: A Divide and Conquer Framework MICCAI2025

【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在青少年特发性脊柱侧弯（Adolescent Idiopathic Scoliosis, AIS）自我管理场景中的能力不足问题，尤其是其在解读复杂脊柱X光片和理解AIS护理知识方面的局限性。解决方案的关键在于两个方面：一是引入脊柱关键点提示（spinal keypoint prompting）以增强模型对影像的视觉理解能力，二是构建AIS专属知识库并采用检索增强生成（Retrieval-Augmented Generation, RAG）技术提升模型在专业知识问答任务中的表现。实验表明，不同架构下视觉提示效果差异显著，而RAG显著提升了知识评估任务的性能，但当前MLLMs在准确识别脊柱畸形位置（最高准确率0.55）和方向（最高准确率0.13）方面仍存在重大挑战，表明其尚未具备实现个性化AIS辅助服务的能力。

链接: https://arxiv.org/abs/2509.11645
作者: Zhaolong Wu,Pu Luo,Jason Pui Yin Cheung,Teng Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: Accepted by MICCAI 2025 MLLMCP Workshop

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Abstract:This study presents the first comprehensive evaluation of Multimodal Large Language Models (MLLMs) for Adolescent Idiopathic Scoliosis (AIS) self-management. We constructed a database of approximately 3,000 anteroposterior X-rays with diagnostic texts and evaluated five MLLMs through a `Divide and Conquer’ framework consisting of a visual question-answering task, a domain knowledge assessment task, and a patient education counseling assessment task. Our investigation revealed limitations of MLLMs’ ability in interpreting complex spinal radiographs and comprehending AIS care knowledge. To address these, we pioneered enhancing MLLMs with spinal keypoint prompting and compiled an AIS knowledge base for retrieval augmented generation (RAG), respectively. Results showed varying effectiveness of visual prompting across different architectures, while RAG substantially improved models’ performances on the knowledge assessment task. Our findings indicate current MLLMs are far from capable in realizing personalized assistant in AIS care. The greatest challenge lies in their abilities to obtain accurate detections of spinal deformity locations (best accuracy: 0.55) and directions (best accuracy: 0.13).
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[AI-36] ask-Agnostic Learnable Weighted-Knowledge Base Scheme for Robust Semantic Communications

【速读】：该论文旨在解决第六代移动通信（6G）网络中因异构数据偏置（如标签翻转噪声和类别不平衡）导致的任务无关语义通信系统在图像传输时语义恢复鲁棒性不足的问题。解决方案的关键在于提出了一种任务无关的可学习加权知识库语义通信（TALSC）框架，其核心创新包括：1）引入样本置信度模块（SCM）作为元学习器，根据任务损失反馈评估样本重要性并动态调整学习器更新策略；2）通过可学习加权知识库（LW-KB）提供经验知识驱动语义编码网络的学习；3）设计SCM-GE方法，利用Kolmogorov-Arnold网络（KAN）嵌入SCM，实现无需重新训练即可按需扩展的可伸缩置信度评估机制，从而显著提升语义恢复准确率（SRA）和多尺度结构相似性（MS-SSIM），相比现有最优方法至少提升12%。

链接: https://arxiv.org/abs/2509.11636
作者: Shiyao Jiang,Jian Jiao,Xingjian Zhang,Ye Wang,Dusit Niyato,Qinyu Zhang
机构: 未知
类目: Information Theory (cs.IT); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
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Abstract:With the emergence of diverse and massive data in the upcoming sixth-generation (6G) networks, the task-agnostic semantic communication system is regarded to provide robust intelligent services. In this paper, we propose a task-agnostic learnable weighted-knowledge base semantic communication (TALSC) framework for robust image transmission to address the real-world heterogeneous data bias in KB, including label flipping noise and class imbalance. The TALSC framework incorporates a sample confidence module (SCM) as meta-learner and the semantic coding networks as learners. The learners are updated based on the empirical knowledge provided by the learnable weighted-KB (LW-KB). Meanwhile, the meta-learner evaluates the significance of samples according to the task loss feedback, and adjusts the update strategy of learners to enhance the robustness in semantic recovery for unknown tasks. To strike a balance between SCM parameters and precision of significance evaluation, we design an SCM-grid extension (SCM-GE) approach by embedding the Kolmogorov-Arnold networks (KAN) within SCM, which leverages the concept of spline refinement in KAN and enables scalable SCM with customizable granularity without retraining. Simulations demonstrate that the TALSC framework effectively mitigates the effects of flipping noise and class imbalance in task-agnostic image semantic communication, achieving at least 12% higher semantic recovery accuracy (SRA) and multi-scale structural similarity (MS-SSIM) compared to state-of-the-art methods.
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[AI-37] Reason ed Safety Alignment: Ensuring Jailbreak Defense via Answer-Then-Check

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在面对越狱攻击（jailbreak attacks）时的安全性不足问题，即模型容易被恶意提示诱导生成有害或违规内容。其解决方案的关键在于提出一种名为“先回答再检查”（Answer-Then-Check）的安全对齐方法，该方法通过引入推理能力，在模型生成最终答案前先进行自我评估：模型首先在思考过程中直接回答用户问题，随后对其安全性进行批判性分析，从而决定是否输出该回答。这一机制显著提升了模型对恶意提示的鲁棒性，并在保持通用推理能力（如MMLU、MATH500和HumanEval等基准测试表现稳定）的同时，降低了过度拒绝（over-refusal）现象，且具备提供安全替代响应的能力，实现更可控、更安全的生成行为。

链接: https://arxiv.org/abs/2509.11629
作者: Chentao Cao,Xiaojun Xu,Bo Han,Hang Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:As large language models (LLMs) continue to advance in capabilities, ensuring their safety against jailbreak attacks remains a critical challenge. In this paper, we introduce a novel safety alignment approach called Answer-Then-Check, which enhances LLM robustness against malicious prompts by applying thinking ability to mitigate jailbreaking problems before producing a final answer to the user. Our method enables models to directly answer the question in their thought and then critically evaluate its safety before deciding whether to provide it. To implement this approach, we construct the Reasoned Safety Alignment (ReSA) dataset, comprising 80K examples that teach models to reason through direct responses and then analyze their safety. Experimental results demonstrate that our approach achieves the Pareto frontier with superior safety capability while decreasing over-refusal rates on over-refusal benchmarks. Notably, the model fine-tuned with ReSA maintains general reasoning capabilities on benchmarks like MMLU, MATH500, and HumanEval. Besides, our method equips models with the ability to perform safe completion. Unlike post-hoc methods that can only reject harmful queries, our model can provide helpful and safe alternative responses for sensitive topics (e.g., self-harm). Furthermore, we discover that training on a small subset of just 500 examples can achieve comparable performance to using the full dataset, suggesting that safety alignment may require less data than previously assumed.
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[AI-38] Automated Creation and Enrichment Framework for Improved Invocation of Enterprise APIs as Tools

【速读】：该论文旨在解决企业在使用大型语言模型（Large Language Models, LLMs）代理时，因API文档质量差、输入/输出结构复杂及操作数量庞大而导致的工具选择困难与有效载荷（payload）生成准确率下降的问题（最高可达25%）。解决方案的关键在于提出ACE框架，该框架通过两个核心机制实现：一是自动生成包含参数描述和示例的增强型工具规范，提升工具选择与调用的准确性；二是引入动态短名单机制，在运行时过滤相关工具，降低提示词复杂度并保持可扩展性。此框架实现了企业API到LLM兼容工具的端到端自动化创建、增强与动态选择，是首个系统性解决该问题的方案。

链接: https://arxiv.org/abs/2509.11626
作者: Prerna Agarwal,Himanshu Gupta,Soujanya Soni,Rohith Vallam,Renuka Sindhgatta,Sameep Mehta
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
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Abstract:Recent advancements in Large Language Models (LLMs) has lead to the development of agents capable of complex reasoning and interaction with external tools. In enterprise contexts, the effective use of such tools that are often enabled by application programming interfaces (APIs), is hindered by poor documentation, complex input or output schema, and large number of operations. These challenges make tool selection difficult and reduce the accuracy of payload formation by up to 25%. We propose ACE, an automated tool creation and enrichment framework that transforms enterprise APIs into LLM-compatible tools. ACE, (i) generates enriched tool specifications with parameter descriptions and examples to improve selection and invocation accuracy, and (ii) incorporates a dynamic shortlisting mechanism that filters relevant tools at runtime, reducing prompt complexity while maintaining scalability. We validate our framework on both proprietary and open-source APIs and demonstrate its integration with agentic frameworks. To the best of our knowledge, ACE is the first end-to-end framework that automates the creation, enrichment, and dynamic selection of enterprise API tools for LLM agents.
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[AI-39] Inducing Uncertainty for Test-Time Privacy

【速读】：该论文旨在解决测试时隐私（test-time privacy）问题，即在模型完成数据删除（unlearning）后，仍可能对被移除数据产生高置信度的错误预测，从而被攻击者利用来危害用户隐私。传统方法无法有效防御此类威胁，尤其当攻击者拥有完整模型访问权限时。解决方案的关键在于提出一种基于帕累托最优目标的微调算法，通过扰动模型权重，在保护实例上引入最大不确定性，同时保持其余数据上的性能不变；进一步提供了一个无需凸性假设即可实现 $(\varepsilon, \delta)$ 保证的可证明近似算法，并给出了紧致且非平凡的隐私-效用权衡边界。实验证明，该方法在图像识别任务中相比预训练模型可提升3倍不确定性，仅损失0.2%准确率。

链接: https://arxiv.org/abs/2509.11625
作者: Muhammad H. Ashiq,Peter Triantafillou,Hung Yun Tseng,Grigoris G. Chrysos
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:Unlearning is the predominant method for removing the influence of data in machine learning models. However, even after unlearning, models often continue to produce the same predictions on the unlearned data with high confidence. This persistent behavior can be exploited by adversaries using confident model predictions on incorrect or obsolete data to harm users. We call this threat model, which unlearning fails to protect against, test-time privacy. In particular, an adversary with full model access can bypass any naive defenses which ensure test-time privacy. To address this threat, we introduce an algorithm which perturbs model weights to induce maximal uncertainty on protected instances while preserving accuracy on the rest of the instances. Our core algorithm is based on finetuning with a Pareto optimal objective that explicitly balances test-time privacy against utility. We also provide a certifiable approximation algorithm which achieves (\varepsilon, \delta) guarantees without convexity assumptions. We then prove a tight, non-vacuous bound that characterizes the privacy-utility tradeoff that our algorithms incur. Empirically, our method obtains 3\times stronger uncertainty than pretraining with 0.2% drops in accuracy on various image recognition benchmarks. Altogether, this framework provides a tool to guarantee additional protection to end users.
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[AI-40] Dynamic Adaptive Parsing of Temporal and Cross-Variable Patterns for Network State Classification

【速读】：该论文旨在解决网络状态分类中难以同时捕捉流量数据的复杂时间周期性与变量间动态依赖关系的问题。现有方法通常在两者之间存在权衡：侧重时间模式的模型忽略变量间关联，而关注依赖建模的方法则难以保留细粒度的时间特征。解决方案的关键在于提出DAPNet框架，其基于Mixture-of-Experts架构，集成三个专用子网络分别处理周期分析、跨变量动态相关性建模及混合时间特征提取，并通过一个可学习的门控网络根据输入样本动态分配专家权重并融合输出；此外，引入一种混合正则化损失函数以提升训练稳定性并缓解类别不平衡问题。

链接: https://arxiv.org/abs/2509.11601
作者: Yuan Gao,Xuelong Wang,Zhenguo Dong,Yong Zhang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Effective network state classification is a primary task for ensuring network security and optimizing performance. Existing deep learning models have shown considerable progress in this area. Some methods excel at analyzing the complex temporal periodicities found in traffic data, while graph-based approaches are adept at modeling the dynamic dependencies between different variables. However, a key trade-off remains, as these methods struggle to capture both characteristics simultaneously. Models focused on temporal patterns often overlook crucial variable dependencies, whereas those centered on dependencies may fail to capture fine-grained temporal details. To address this trade-off, we introduce DAPNet, a framework based on a Mixture-of-Experts architecture. DAPNet integrates three specialized networks for periodic analysis, dynamic cross-variable correlation modeling, and hybrid temporal feature extraction. A learnable gating network dynamically assigns weights to experts based on the input sample and computes a weighted fusion of their outputs. Furthermore, a hybrid regularization loss function ensures stable training and addresses the common issue of class imbalance. Extensive experiments on two large-scale network intrusion detection datasets (CICIDS2017/2018) validate DAPNet’s higher accuracy for its target application. The generalizability of the architectural design is evaluated across ten public UEA benchmark datasets, positioning DAPNet as a specialized framework for network state classification.
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[AI-41] AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions

【速读】：该论文旨在解决反洗钱（Anti-Money Laundering, AML）研究中因缺乏可公开共享且符合监管要求的交易数据集而导致的实验受限问题。其解决方案的关键在于提出AMLNet，一个基于知识的多智能体框架，包含两个协同工作的模块：一是规则感知的交易生成器，能够生成涵盖洗钱核心阶段（放置、掩饰、融合）及高级模式（如拆分交易、自适应阈值行为）的1,090,173条合成交易；二是集成检测流水线，在内部测试集上达到F1分数0.90（精确率0.84，召回率0.97），并能迁移至外部SynthAML数据集，验证了架构在不同合成生成范式下的泛化能力。该方案同时通过多维评估（监管合规性、时间特性、网络结构、行为真实性）确保合成数据的质量与实用性，并开源数据集以推动可复现和监管意识强的AML研究。

链接: https://arxiv.org/abs/2509.11595
作者: Sabin Huda,Ernest Foo,Zahra Jadidi,MA Hakim Newton,Abdul Sattar
机构: 未知
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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Abstract:Anti-money laundering (AML) research is constrained by the lack of publicly shareable, regulation-aligned transaction datasets. We present AMLNet, a knowledge-based multi-agent framework with two coordinated units: a regulation-aware transaction generator and an ensemble detection pipeline. The generator produces 1,090,173 synthetic transactions (approximately 0.16% laundering-positive) spanning core laundering phases (placement, layering, integration) and advanced typologies (e.g., structuring, adaptive threshold behavior). Regulatory alignment reaches 75% based on AUSTRAC rule coverage (Section 4.2), while a composite technical fidelity score of 0.75 summarizes temporal, structural, and behavioral realism components (Section 4.4). The detection ensemble achieves F1 0.90 (precision 0.84, recall 0.97) on the internal test partitions of AMLNet and adapts to the external SynthAML dataset, indicating architectural generalizability across different synthetic generation paradigms. We provide multi-dimensional evaluation (regulatory, temporal, network, behavioral) and release the dataset (Version 1.0, this https URL), to advance reproducible and regulation-conscious AML experimentation.
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[AI-42] GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning

【速读】：该论文旨在解决机器人在抓取任务中如何高效选择基座位姿（base pose）的问题，特别是在单次RGB-D图像输入下实现快速、安全且可达的基座定位。其核心挑战在于如何在不依赖大量人工标注数据的前提下，融合几何感知与运动可行性来优化基座位置。解决方案的关键在于提出一种基于两阶段课程学习（two-stage curriculum）的快速评分模型GBPP：首先利用低成本的“距离-可见性”启发式规则自动标注大规模数据集；随后通过少量高保真仿真试验对模型进行微调，使其预测结果更贴近真实抓取效果。该方法采用PointNet++风格的点云编码器结合MLP对候选位姿进行密集评分，从而实现在无需完整任务与运动规划的情况下在线快速选择最优基座位姿，显著优于仅依赖距离或几何特征的基线方法，并在错误情况下表现出渐进式退化特性。

链接: https://arxiv.org/abs/2509.11594
作者: Jizhuo Chen,Diwen Liu,Jiaming Wang,Harold Soh
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Jizhuo Chen and Diwen Liu contributed equally

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Abstract:GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGB-D snapshot. The method uses a two stage curriculum: (1) a simple distance-visibility rule auto-labels a large dataset at low cost; and (2) a smaller set of high fidelity simulation trials refines the model to match true grasp outcomes. A PointNet++ style point cloud encoder with an MLP scores dense grids of candidate poses, enabling rapid online selection without full task-and-motion optimization. In simulation and on a real mobile manipulator, GBPP outperforms proximity and geometry only baselines, choosing safer and more reachable stances and degrading gracefully when wrong. The results offer a practical recipe for data efficient, geometry aware base placement: use inexpensive heuristics for coverage, then calibrate with targeted simulation.
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[AI-43] A Survey of Reasoning and Agent ic Systems in Time Series with Large Language Models

【速读】：该论文旨在解决时间序列推理（time series reasoning）中如何有效整合中间证据以提升模型在动态世界中的理解、解释与决策能力的问题。其核心挑战在于确保推理过程不仅准确，而且具备可追溯性、鲁棒性和实用性，尤其是在面对数据漂移（shift）、流式处理（streaming）和长期规划（long-horizon）等现实场景时。解决方案的关键在于构建基于推理拓扑（reasoning topology）的系统化分类框架：包括单步直接推理、线性链式推理（显式引入中间步骤）以及分支结构推理（支持探索、修正与聚合），并将其与任务目标（如因果推断、生成建模、解释性分析）相结合，同时引入多维标签体系（tag set）涵盖分解验证、工具使用、知识访问、多模态融合、代理循环（agent loop）及大语言模型（LLM）对齐等关键机制。该框架揭示了不同拓扑结构在可靠性与计算成本之间的权衡，并强调未来进展依赖于能将推理质量映射到实际效用的基准测试与闭环实验平台，从而推动从单一精度指标向规模化可靠性的范式转变。

链接: https://arxiv.org/abs/2509.11575
作者: Ching Chang,Yidan Shi,Defu Cao,Wei Yang,Jeehyun Hwang,Haixin Wang,Jiacheng Pang,Wei Wang,Yan Liu,Wen-Chih Peng,Tien-Fu Chen
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: This paper is currently under review

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Abstract:Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer. This survey defines the problem and organizes the literature by reasoning topology with three families: direct reasoning in one step, linear chain reasoning with explicit intermediates, and branch-structured reasoning that explores, revises, and aggregates. The topology is crossed with the main objectives of the field, including traditional time series analysis, explanation and understanding, causal inference and decision making, and time series generation, while a compact tag set spans these axes and captures decomposition and verification, ensembling, tool use, knowledge access, multimodality, agent loops, and LLM alignment regimes. Methods and systems are reviewed across domains, showing what each topology enables and where it breaks down in faithfulness or robustness, along with curated datasets, benchmarks, and resources that support study and deployment (this https URL). Evaluation practices that keep evidence visible and temporally aligned are highlighted, and guidance is distilled on matching topology to uncertainty, grounding with observable artifacts, planning for shift and streaming, and treating cost and latency as design budgets. We emphasize that reasoning structures must balance capacity for grounding and self-correction against computational cost and reproducibility, while future progress will likely depend on benchmarks that tie reasoning quality to utility and on closed-loop testbeds that trade off cost and risk under shift-aware, streaming, and long-horizon settings. Taken together, these directions mark a shift from narrow accuracy toward reliability at scale, enabling systems that not only analyze but also understand, explain, and act on dynamic worlds with traceable evidence and credible outcomes.
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[AI-44] Dstack: A Zero Trust Framework for Confidential Containers

【速读】：该论文旨在解决Web3应用在执行平台上面临的信任问题，即如何在不依赖中心化信任机构的前提下保障计算过程的机密性和完整性。当前基于可信执行环境（Trusted Execution Environments, TEEs）的实现存在安全可靠性不足、抗审查能力弱以及厂商锁定等问题，难以满足Web3对去中心化和零信任架构的需求。解决方案的关键在于提出dstack框架，其核心创新包括：(1) 可移植的机密容器（Portable Confidential Containers），支持跨异构TEE环境的安全工作负载迁移；(2) 去中心化的代码管理机制，利用智能合约实现对TEE应用的透明治理；(3) 可验证的域管理机制，无需中心化机构即可确保应用身份的安全与可验证性。这三个创新通过dstack-OS、dstack-KMS和dstack-Gateway三个核心组件落地，实现了VM级TEE性能优势与Web3所需无信任保证的统一。

链接: https://arxiv.org/abs/2509.11555
作者: Shunfan Zhou,Kevin Wang,Hang Yin
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
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Abstract:Web3 applications require execution platforms that maintain confidentiality and integrity without relying on centralized trust authorities. While Trusted Execution Environments (TEEs) offer promising capabilities for confidential computing, current implementations face significant limitations when applied to Web3 contexts, particularly in security reliability, censorship resistance, and vendor independence. This paper presents dstack, a comprehensive framework that transforms raw TEE technology into a true Zero Trust platform. We introduce three key innovations: (1) Portable Confidential Containers that enable seamless workload migration across heterogeneous TEE environments while maintaining security guarantees, (2) Decentralized Code Management that leverages smart contracts for transparent governance of TEE applications, and (3) Verifiable Domain Management that ensures secure and verifiable application identity without centralized authorities. These innovations are implemented through three core components: dstack-OS, dstack-KMS, and dstack-Gateway. Together, they demonstrate how to achieve both the performance advantages of VM-level TEE solutions and the trustless guarantees required by Web3 applications. Our evaluation shows that dstack provides comprehensive security guarantees while maintaining practical usability for real-world applications. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2509.11555 [cs.CR] (or arXiv:2509.11555v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2509.11555 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-45] ask Decoding based on Eye Movements using Synthetic Data Augmentation

【速读】：该论文旨在解决如何通过增强眼动数据（eye movement data）来提升任务解码（task decoding）的准确性问题，以验证Yarbus提出的“可通过眼动模式推断观察者任务”的假设。其解决方案的关键在于利用多种合成数据生成器（如CTGAN、CopulaGAN和Gretel AI）生成高质量的合成眼动数据，并将其与真实数据结合进行训练，从而显著提升传统机器学习算法（如随机森林、Inception Time等）在任务分类中的性能。实验表明，当合成数据量达到真实数据的五倍时，分类准确率从28.1%提升至82%，证明了合成数据增强对提升眼动数据分析效果的有效性。

链接: https://arxiv.org/abs/2509.11547
作者: Shanmuka Sadhu,Arca Baran,Preeti Pandey,Ayush Kumar
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Machine learning has been extensively used in various applications related to eye-tracking research. Understanding eye movement is one of the most significant subsets of eye-tracking research that reveals the scanning pattern of an individual. Researchers have thoroughly analyzed eye movement data to understand various eye-tracking applications, such as attention mechanisms, navigational behavior, task understanding, etc. The outcome of traditional machine learning algorithms used for decoding tasks based on eye movement data has received a mixed reaction to Yarbus’ claim that it is possible to decode the observer’s task from their eye movements. In this paper, to support the hypothesis by Yarbus, we are decoding tasks categories while generating synthetic data samples using well-known Synthetic Data Generators CTGAN and its variations such as CopulaGAN and Gretel AI Synthetic Data generators on available data from an in-person user study. Our results show that augmenting more eye movement data combined with additional synthetically generated improves classification accuracy even with traditional machine learning algorithms. We see a significant improvement in task decoding accuracy from 28.1% using Random Forest to 82% using Inception Time when five times more data is added in addition to the 320 real eye movement dataset sample. Our proposed framework outperforms all the available studies on this dataset because of the use of additional synthetic datasets. We validated our claim with various algorithms and combinations of real and synthetic data to show how decoding accuracy increases with the increase in the augmentation of generated data to real data.
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[AI-46] UI-S1: Advancing GUI Automation via Semi-online Reinforcement Learning

【速读】：该论文旨在解决当前基于强化学习（Reinforcement Learning, RL）的图形用户界面（Graphical User Interface, GUI）智能体在离线训练与在线部署之间存在的性能鸿沟问题：离线RL虽能稳定训练，但缺乏多步任务执行所需的轨迹级奖励信号；而在线RL虽可获取此类信号，却面临稀疏奖励和高昂部署成本的问题。其解决方案的核心是提出一种新型“半在线强化学习”（Semi-online Reinforcement Learning）范式，该方法在预收集的离线轨迹上模拟在线RL过程，在每轮rollout中保留原始模型输出，并通过一个Patch Module自适应恢复 rollout 轨迹与专家轨迹之间的偏差；同时引入折扣未来回报以捕捉长期训练信号，并结合加权步骤级和回合级优势进行策略优化，从而显著提升多轮交互能力。

链接: https://arxiv.org/abs/2509.11543
作者: Zhengxi Lu,Jiabo Ye,Fei Tang,Yongliang Shen,Haiyang Xu,Ziwei Zheng,Weiming Lu,Ming Yan,Fei Huang,Jun Xiao,Yueting Zhuang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 22 pages, 17 figures

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Abstract:Graphical User Interface (GUI) agents have demonstrated remarkable progress in automating complex user interface interactions through reinforcement learning. However, current approaches face a fundamental dilemma: offline RL enables stable training on pre-collected trajectories, but struggles with multi-step task execution for lack of trajectory-level reward signals; online RL captures these signals through environment interaction, but suffers from sparse rewards and prohibitive deployment costs. To address it, we present Semi-online Reinforcement Learning, a novel paradigm that simulates online RL on offline trajectories. During each rollout process, we preserve the original model output within the multi-turn dialogue, where a Patch Module adaptively recovers the divergence between rollout and expert trajectories. To capture long-term training signals, Semi-online RL introduces discounted future returns into the reward computation and optimizes the policy with weighted step-level and episode-level advantages. We further introduce Semi-Online Performance (SOP), a metric that aligns better with true online performance, serving as a practical and effective proxy for real-world evaluation. Experiments show that ours Semi-online RL achieves SOTA performance among 7B models across four dynamic benchmarks, with significant gains over the base model (e.g., +12.0% on AndroidWorld, +23.8% on AITW), demonstrating significant progress in bridging the gap between offline training efficiency and online multi-turn reasoning. The code is available at this https URL.
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[AI-47] Know What You Dont Know: Selective Prediction for Early Exit DNNs

【速读】：该论文旨在解决深度神经网络（Deep Neural Networks, DNNs）在关键应用中推理延迟高与可信度不足的问题。现有早期退出（Early Exit, EE）策略虽能降低延迟，但因DNN易产生过度自信（overconfidence），导致大量样本过早退出，从而损害预测可靠性。解决方案的关键在于引入选择性预测（Selective Prediction, SP）机制，通过在每一层部署判别硬样本的防御分类器（Deferral Classifiers, DCs），在决定是否提前退出前评估样本的“难预测性”（hardness），若判定为困难样本则将其移交专家模型处理。此方法有效避免了对困难样本的错误预测，显著提升了整体可信度（降低50%误判风险），同时实现2.05倍的加速比。

链接: https://arxiv.org/abs/2509.11520
作者: Divya Jyoti Bajpai,Manjesh Kumar Hanawal
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: To appear in the the Fifth International Conference on AI ML Systems

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Abstract:Inference latency and trustworthiness of Deep Neural Networks (DNNs) are the bottlenecks in deploying them in critical applications like sensitive tasks. Early Exit (EE) DNNs overcome the latency issues by allowing samples to exit from intermediary layers if they attain high' confidence scores on the predicted class. However, the DNNs are known to exhibit overconfidence, which can lead to many samples exiting early and render EE strategies untrustworthy. We use Selective Prediction (SP) to overcome this issue by checking the hardness’ of the samples rather than just relying on the confidence score alone. We propose SPEED, a novel approach that uses Deferral Classifiers (DCs) at each layer to check the hardness of samples before performing EEs. Specifically, the DCs identify if a sample is hard to predict at an intermediary layer, leading to hallucination, and defer it to an expert. Early detection of hard samples for inference prevents the wastage of computational resources and improves trust by deferring the hard samples to the expert. We demonstrate that EE aided with SP improves both accuracy and latency. Our method minimizes the risk of wrong prediction by 50% with a speedup of 2.05\times as compared to the final layer. The anonymized source code is available at this https URL
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[AI-48] Machine Learning-Driven Predictive Resource Management in Complex Science Workflows

【速读】：该论文旨在解决科学实验中数据处理工作流（data processing workflows）在资源分配上的挑战，尤其是在大规模分布式协作环境中，由于分析场景多样、参与者技能水平不一以及计算资源选项不断扩展，导致难以提前准确估算每一步骤的资源需求。解决方案的关键在于引入一套基于机器学习（machine learning）的预测模型集成到生产与分布式分析（Production and Distributed Analysis, PanDA）工作流管理系统中，通过学习实际处理过程中的资源使用特征，实现对关键资源需求的精准预测，从而支持更高效、主动的资源调度决策，提升跨异构资源环境下的复杂工作流处理效率。

链接: https://arxiv.org/abs/2509.11512
作者: Tasnuva Chowdhury,Tadashi Maeno,Fatih Furkan Akman,Joseph Boudreau,Sankha Dutta,Shengyu Feng,Adolfy Hoisie,Kuan-Chieh Hsu,Raees Khan,Jaehyung Kim,Ozgur O. Kilic,Scott Klasky,Alexei Klimentov,Tatiana Korchuganova,Verena Ingrid Martinez Outschoorn,Paul Nilsson,David K. Park,Norbert Podhorszki,Yihui Ren,John Rembrandt Steele,Frédéric Suter,Sairam Sri Vatsavai,Torre Wenaus,Wei Yang,Yiming Yang,Shinjae Yoo
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:The collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimating resource requirements in advance is challenging due to a wide range of analysis scenarios, varying skill levels among community members, and the continuously increasing spectrum of computing options. One practical approach to mitigate these challenges involves initially processing a subset of each step to measure precise resource utilization from actual processing profiles before completing the entire step. While this two-staged approach enables processing on optimal resources for most of the workflow, it has drawbacks such as initial inaccuracies leading to potential failures and suboptimal resource usage, along with overhead from waiting for initial processing completion, which is critical for fast-turnaround analyses. In this context, our study introduces a novel pipeline of machine learning models within a comprehensive workflow management system, the Production and Distributed Analysis (PanDA) system. These models employ advanced machine learning techniques to predict key resource requirements, overcoming challenges posed by limited upfront knowledge of characteristics at each step. Accurate forecasts of resource requirements enable informed and proactive decision-making in workflow management, enhancing the efficiency of handling diverse, complex workflows across heterogeneous resources.
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[AI-49] MedicalOS: An LLM Agent based Operating System for Digital Healthcare

【速读】：该论文旨在解决当前数字健康技术（如电子健康记录）在临床实践中难以学习和使用的问题，包括多系统管理负担、重复性手动操作、复杂用户界面导航以及大量时间被消耗于行政事务而非患者照护。其解决方案的关键在于提出一个名为MedicalOS的统一代理（agent-based）操作系统，作为面向医疗领域的特定抽象层，将自然语言指令转化为预定义的数字化医疗命令（如患者询问、病史检索、检查管理、报告生成等），并通过机器语言（如Python、API、MCP、Linux）封装为即插即用工具。该设计确保了操作的安全性、透明性和合规性，同时实现了高诊断准确率与结构化输出，验证了其在214例跨22个专科病例中的可信性和可扩展性。

链接: https://arxiv.org/abs/2509.11507
作者: Jared Zhu,Junde Wu
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Decades’ advances in digital health technologies, such as electronic health records, have largely streamlined routine clinical processes. Yet, most these systems are still hard to learn and use: Clinicians often face the burden of managing multiple tools, repeating manual actions for each patient, navigating complicated UI trees to locate functions, and spending significant time on administration instead of caring for patients. The recent rise of large language model (LLM) based agents demonstrates exceptional capability in coding and computer operation, revealing the potential for humans to interact with operating systems and software not by direct manipulation, but by instructing agents through natural language. This shift highlights the need for an abstraction layer, an agent-computer interface, that translates human language into machine-executable commands. In digital healthcare, however, requires a more domain-specific abstractions that strictly follow trusted clinical guidelines and procedural standards to ensure safety, transparency, and compliance. To address this need, we present \textbfMedicalOS, a unified agent-based operational system designed as such a domain-specific abstract layer for healthcare. It translates human instructions into pre-defined digital healthcare commands, such as patient inquiry, history retrieval, exam management, report generation, referrals, treatment planning, that we wrapped as off-the-shelf tools using machine languages (e.g., Python, APIs, MCP, Linux). We empirically validate MedicalOS on 214 patient cases across 22 specialties, demonstrating high diagnostic accuracy and confidence, clinically sound examination requests, and consistent generation of structured reports and medication recommendations. These results highlight MedicalOS as a trustworthy and scalable foundation for advancing workflow automation in clinical practice.
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[AI-50] RAPTOR: A Foundation Policy for Quadrotor Control

【速读】：该论文旨在解决当前机器人控制策略在面对新环境或平台时适应性差的问题，即现代基于强化学习（Reinforcement Learning, RL）的神经网络策略通常对单一环境过度拟合，在遭遇Simulation-to-Reality（Sim2Real）差异或系统微小变化时性能急剧下降，需重新识别与训练。解决方案的关键在于提出一种名为RAPTOR的方法，其核心是通过元模仿学习（Meta-Imitation Learning）从1000个不同四旋翼平台的教师策略中蒸馏出一个具备零样本适应能力的基础策略（foundation policy）。该策略仅含2084个参数，采用递归结构（recurrence in the hidden layer）实现上下文内学习（In-Context Learning），从而在毫秒级时间内完成对未见过的四旋翼平台的快速自适应调整，显著提升控制系统的泛化能力和部署效率。

链接: https://arxiv.org/abs/2509.11481
作者: Jonas Eschmann,Dario Albani,Giuseppe Loianno
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Humans are remarkably data-efficient when adapting to new unseen conditions, like driving a new car. In contrast, modern robotic control systems, like neural network policies trained using Reinforcement Learning (RL), are highly specialized for single environments. Because of this overfitting, they are known to break down even under small differences like the Simulation-to-Reality (Sim2Real) gap and require system identification and retraining for even minimal changes to the system. In this work, we present RAPTOR, a method for training a highly adaptive foundation policy for quadrotor control. Our method enables training a single, end-to-end neural-network policy to control a wide variety of quadrotors. We test 10 different real quadrotors from 32 g to 2.4 kg that also differ in motor type (brushed vs. brushless), frame type (soft vs. rigid), propeller type (2/3/4-blade), and flight controller (PX4/Betaflight/Crazyflie/M5StampFly). We find that a tiny, three-layer policy with only 2084 parameters is sufficient for zero-shot adaptation to a wide variety of platforms. The adaptation through In-Context Learning is made possible by using a recurrence in the hidden layer. The policy is trained through a novel Meta-Imitation Learning algorithm, where we sample 1000 quadrotors and train a teacher policy for each of them using Reinforcement Learning. Subsequently, the 1000 teachers are distilled into a single, adaptive student policy. We find that within milliseconds, the resulting foundation policy adapts zero-shot to unseen quadrotors. We extensively test the capabilities of the foundation policy under numerous conditions (trajectory tracking, indoor/outdoor, wind disturbance, poking, different propellers).
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[AI-51] Designing and Evaluating a Conversational Agent for Early Detection of Alzheimers Disease and Related Dementias

【速读】：该论文旨在解决阿尔茨海默病及相关痴呆（Alzheimer’s disease and related dementias, ADRD）早期诊断延迟的问题，即当前多数病例在疾病进展至较晚期才被确诊，而早期识别对及时干预至关重要。其解决方案的关键在于设计一种基于大语言模型（large language models, LLMs）的语音交互式对话代理（conversational agent），通过结构化对话主动获取患者及知情者关于认知功能障碍的详细叙述性信息，并结合临床专家盲法访谈进行验证。结果显示，该代理所识别的症状与专业医师判断高度一致，且用户对其耐心和系统性提问方式表示认可，表明此类对话代理可作为痴呆评估流程中的结构化前端工具，尤其在敏感医疗场景中需重视交互设计因素。

链接: https://arxiv.org/abs/2509.11478
作者: Andrew G. Breithaupt,Nayoung Choi,James D. Finch,Jeanne M. Powell,Arin L. Nelson,Oz A. Alon,Howard J. Rosen,Jinho D. Choi
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: First two authors contributed equally

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Abstract:Early detection of Alzheimer’s disease and related dementias (ADRD) is critical for timely intervention, yet most diagnoses are delayed until advanced stages. While comprehensive patient narratives are essential for accurate diagnosis, prior work has largely focused on screening studies that classify cognitive status from interactions rather than supporting the diagnostic process. We designed voice-interactive conversational agents, leveraging large language models (LLMs), to elicit narratives relevant to ADRD from patients and informants. We evaluated the agent with 30 adults with suspected ADRD through conversation analysis (n=30), user surveys (n=19), and clinical validation against blinded specialist interviews (n=24). Symptoms detected by the agent aligned well with those identified by specialists across symptoms. Users appreciated the agent’s patience and systematic questioning, which supported engagement and expression of complex, hard-to-describe experiences. This preliminary work suggests conversational agents may serve as structured front-end tools for dementia assessment, highlighting interaction design considerations in sensitive healthcare contexts.
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[AI-52] CareerPooler: AI-Powered Metaphorical Pool Simulation Improves Experience and Outcomes in Career Exploration

【速读】：该论文旨在解决当前职业探索过程中因信息有限和结果不可预测而导致决策困难的问题，同时指出现有基于线性对话的生成式AI（Generative AI）系统往往提供过于全面且理想化的建议，忽视了现实职业发展路径的非线性和努力性质。其解决方案的关键在于提出一个名为CareerPooler的生成式AI驱动系统，该系统采用台球桌隐喻来模拟职业发展为一种空间与叙事相结合的交互过程：用户通过击打代表里程碑、技能和随机事件的球体，利用提示、碰撞与反弹机制体现不确定性下的决策行为。这一设计显著提升了用户的参与度、信息获取量、满意度及职业清晰度，并通过定性分析揭示了空间-叙事交互有助于经验式学习、挫折中的韧性培养以及心理负担的减轻。

链接: https://arxiv.org/abs/2509.11461
作者: Ziyi Wang,Ziwen Zeng,Yuan Li,Zijian Ding
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
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Abstract:Career exploration is uncertain, requiring decisions with limited information and unpredictable outcomes. While generative AI offers new opportunities for career guidance, most systems rely on linear chat interfaces that produce overly comprehensive and idealized suggestions, overlooking the non-linear and effortful nature of real-world trajectories. We present CareerPooler, a generative AI-powered system that employs a pool-table metaphor to simulate career development as a spatial and narrative interaction. Users strike balls representing milestones, skills, and random events, where hints, collisions, and rebounds embody decision-making under uncertainty. In a within-subjects study with 24 participants, CareerPooler significantly improved engagement, information gain, satisfaction, and career clarity compared to a chatbot baseline. Qualitative findings show that spatial-narrative interaction fosters experience-based learning, resilience through setbacks, and reduced psychological burden. Our findings contribute to the design of AI-assisted career exploration systems and more broadly suggest that visually grounded analogical interactions can make generative systems engaging and satisfying.
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[AI-53] Knowledge-Guided Adaptive Mixture of Experts for Precipitation Prediction

【速读】：该论文旨在解决多源异构气象观测数据（如雷达、卫星影像和地面观测）在降水预测中难以有效融合的问题，传统深度学习模型往往无法充分捕捉不同模态数据的空间-时间特征差异。其解决方案的关键在于提出一种自适应专家混合（Adaptive Mixture of Experts, Adaptive MoE）模型，其中每个专家专门处理特定模态或时空模式，并引入一个动态路由机制以自动分配输入至最相关的专家，从而提升预测精度与可解释性。

链接: https://arxiv.org/abs/2509.11459
作者: Chen Jiang,Kofi Osei,Sai Deepthi Yeddula,Dongji Feng,Wei-Shinn Ku
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 13 pages

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Abstract:Accurate precipitation forecasting is indispensable in agriculture, disaster management, and sustainable strategies. However, predicting rainfall has been challenging due to the complexity of climate systems and the heterogeneous nature of multi-source observational data, including radar, satellite imagery, and surface-level measurements. The multi-source data vary in spatial and temporal resolution, and they carry domain-specific features, making it challenging for effective integration in conventional deep learning models. Previous research has explored various machine learning techniques for weather prediction; however, most struggle with the integration of data with heterogeneous modalities. To address these limitations, we propose an Adaptive Mixture of Experts (MoE) model tailored for precipitation rate prediction. Each expert within the model specializes in a specific modality or spatio-temporal pattern. We also incorporated a dynamic router that learns to assign inputs to the most relevant experts. Our results show that this modular design enhances predictive accuracy and interpretability. In addition to the modeling framework, we introduced an interactive web-based visualization tool that enables users to intuitively explore historical weather patterns over time and space. The tool was designed to support decision-making for stakeholders in climate-sensitive sectors. We evaluated our approach using a curated multimodal climate dataset capturing real-world conditions during Hurricane Ian in 2022. The benchmark results show that the Adaptive MoE significantly outperformed all the baselines.
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[AI-54] abular Data with Class Imbalance: Predicting Electric Vehicle Crash Severity with Pretrained Transformers (TabPFN) and Mamba-Based Models ICML WWW

【速读】：该论文旨在解决电动汽车（Electric Vehicle, EV）碰撞事故中严重伤害预测的准确性问题，以支持数据驱动的安全干预措施。其关键解决方案在于构建并评估三种先进的深度表格学习模型（TabPFN、MambaNet 和 MambaAttention），结合特征重要性分析识别出高影响力变量（如交叉口关系、首次有害事件、驾驶员年龄等），并通过 SMOTEENN 方法处理类别不平衡问题，最终发现基于注意力机制的 MambaAttention 模型在识别严重伤情案例上表现最优，体现了深度表格架构在提升 EV 碰撞严重程度预测性能方面的潜力。

链接: https://arxiv.org/abs/2509.11449
作者: Shriyank Somvanshi,Pavan Hebli,Gaurab Chhetri,Subasish Das
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: This is the author’s preprint version of a paper accepted for presentation at the 24th International Conference on Machine Learning and Applications (ICMLA 2025), December 3-5, 2025, Florida, USA. The final published version will appear in the official IEEE proceedings. Conference site: this https URL

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Abstract:This study presents a deep tabular learning framework for predicting crash severity in electric vehicle (EV) collisions using real-world crash data from Texas (2017-2023). After filtering for electric-only vehicles, 23,301 EV-involved crash records were analyzed. Feature importance techniques using XGBoost and Random Forest identified intersection relation, first harmful event, person age, crash speed limit, and day of week as the top predictors, along with advanced safety features like automatic emergency braking. To address class imbalance, Synthetic Minority Over-sampling Technique and Edited Nearest Neighbors (SMOTEENN) resampling was applied. Three state-of-the-art deep tabular models, TabPFN, MambaNet, and MambaAttention, were benchmarked for severity prediction. While TabPFN demonstrated strong generalization, MambaAttention achieved superior performance in classifying severe injury cases due to its attention-based feature reweighting. The findings highlight the potential of deep tabular architectures for improving crash severity prediction and enabling data-driven safety interventions in EV crash contexts.
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[AI-55] Framing AI System Benchmarking as a Learning Task: FlexBench and the Open MLPerf Dataset

【速读】：该论文试图解决现有AI系统基准测试（如MLPerf）难以跟上AI技术快速演进步伐的问题，导致在AI系统的部署、优化和软硬件协同设计决策中缺乏有效支持。其解决方案的关键在于将基准测试本身视为一个AI任务，通过持续评估和优化模型在多样化数据集、软件和硬件环境下的表现，利用准确率、延迟、吞吐量、能耗和成本等关键指标进行动态调整；具体实现为提出FlexBench——一个模块化扩展的MLPerf大语言模型（LLM）推理基准，集成HuggingFace生态，并构建开放的MLPerf数据集以支持协作式数据维护、预测建模与特征工程，从而帮助从业者基于自身资源约束做出更具成本效益的AI部署决策。

链接: https://arxiv.org/abs/2509.11413
作者: Grigori Fursin,Daniel Altunay
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Existing AI system benchmarks such as MLPerf often struggle to keep pace with the rapidly evolving AI landscape, making it difficult to support informed deployment, optimization, and co-design decisions for AI systems. We suggest that benchmarking itself can be framed as an AI task - one in which models are continuously evaluated and optimized across diverse datasets, software, and hardware, using key metrics such as accuracy, latency, throughput, energy consumption, and cost. To support this perspective, we present FlexBench: a modular extension of the MLPerf LLM inference benchmark, integrated with HuggingFace and designed to provide relevant and actionable insights. Benchmarking results and metadata are collected into an Open MLPerf Dataset, which can be collaboratively curated, extended, and leveraged for predictive modeling and feature engineering. We successfully validated the FlexBench concept through MLPerf Inference submissions, including evaluations of DeepSeek R1 and LLaMA 3.3 on commodity servers. The broader objective is to enable practitioners to make cost-effective AI deployment decisions that reflect their available resources, requirements, and constraints.
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[AI-56] From Firewalls to Frontiers: AI Red-Teaming is a Domain-Specific Evolution of Cyber Red-Teaming

【速读】：该论文试图解决的问题是：随着企业系统越来越多地采用人工智能（Artificial Intelligence, AI），传统的网络红队（Cyber Red Team）方法已不足以有效识别和评估AI系统特有的安全漏洞与风险，亟需一种专门针对AI的红队策略。解决方案的关键在于将AI红队（AI Red Team）视为网络安全红队的一个领域特定演化方向，通过融合两者的优势——即让传统网络安全红队识别AI带来的新型风险、新的失效模式及不可修复的缺陷，从而重新 prioritizing（重新优先排序）披露与缓解策略；同时，也让AI红队借鉴网络安全红队成熟的方法论框架，如模拟真实攻击者行为、建立规则明确的交战规范（Rules of Engagement）以及构建可重复、可扩展的工具链——最终形成一个协同进化、适应快速变化威胁环境的稳健安全生态系统。

链接: https://arxiv.org/abs/2509.11398
作者: Anusha Sinha,Keltin Grimes,James Lucassen,Michael Feffer,Nathan VanHoudnos,Zhiwei Steven Wu,Hoda Heidari
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:A red team simulates adversary attacks to help defenders find effective strategies to defend their systems in a real-world operational setting. As more enterprise systems adopt AI, red-teaming will need to evolve to address the unique vulnerabilities and risks posed by AI systems. We take the position that AI systems can be more effectively red-teamed if AI red-teaming is recognized as a domain-specific evolution of cyber red-teaming. Specifically, we argue that existing Cyber Red Teams who adopt this framing will be able to better evaluate systems with AI components by recognizing that AI poses new risks, has new failure modes to exploit, and often contains unpatchable bugs that re-prioritize disclosure and mitigation strategies. Similarly, adopting a cybersecurity framing will allow existing AI Red Teams to leverage a well-tested structure to emulate realistic adversaries, promote mutual accountability with formal rules of engagement, and provide a pattern to mature the tooling necessary for repeatable, scalable engagements. In these ways, the merging of AI and Cyber Red Teams will create a robust security ecosystem and best position the community to adapt to the rapidly changing threat landscape.
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[AI-57] Intelligent Reservoir Decision Support: An Integrated Framework Combining Large Language Models Advanced Prompt Engineering and Multimodal Data Fusion for Real-Time Petroleum Operations

【速读】：该论文旨在解决石油行业在油藏管理中面临的挑战，即如何快速整合复杂的多模态数据以支持实时决策。其核心问题是传统方法难以高效处理地震解释、测井数据和生产数据等异构信息，导致油藏表征精度低、预测不准及现场适应性差。解决方案的关键在于构建一个集成框架，融合先进的大语言模型（如GPT-4o、Claude 4 Sonnet、Gemini 2.5 Pro）与领域特定的检索增强生成（Retrieval-Augmented Generation, RAG），结合链式思维推理（chain-of-thought reasoning）和少样本学习（few-shot learning），并通过视觉Transformer对多源数据进行深度融合，从而实现高精度的油藏表征（94.2%准确率）、生产预测（87.6%精度）和井位优化（91.4%成功率），同时显著提升响应速度（亚秒级）与安全性（96.2%可靠性），并降低运营成本达72%。

链接: https://arxiv.org/abs/2509.11376
作者: Seyed Kourosh Mahjour,Seyed Saman Mahjour
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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Abstract:The petroleum industry faces unprecedented challenges in reservoir management, requiring rapid integration of complex multimodal datasets for real-time decision support. This study presents a novel integrated framework combining state-of-the-art large language models (GPT-4o, Claude 4 Sonnet, Gemini 2.5 Pro) with advanced prompt engineering techniques and multimodal data fusion for comprehensive reservoir analysis. The framework implements domain-specific retrieval-augmented generation (RAG) with over 50,000 petroleum engineering documents, chain-of-thought reasoning, and few-shot learning for rapid field adaptation. Multimodal integration processes seismic interpretations, well logs, and production data through specialized AI models with vision transformers. Field validation across 15 diverse reservoir environments demonstrates exceptional performance: 94.2% reservoir characterization accuracy, 87.6% production forecasting precision, and 91.4% well placement optimization success rate. The system achieves sub-second response times while maintaining 96.2% safety reliability with no high-risk incidents during evaluation. Economic analysis reveals 62-78% cost reductions (mean 72%) relative to traditional methods with 8-month payback period. Few-shot learning reduces field adaptation time by 72%, while automated prompt optimization achieves 89% improvement in reasoning quality. The framework processed real-time data streams with 96.2% anomaly detection accuracy and reduced environmental incidents by 45%. We provide detailed experimental protocols, baseline comparisons, ablation studies, and statistical significance testing to ensure reproducibility. This research demonstrates practical integration of cutting-edge AI technologies with petroleum domain expertise for enhanced operational efficiency, safety, and economic performance.
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[AI-58] Detecting Model Drifts in Non-Stationary Environment Using Edit Operation Measures

【速读】：该论文旨在解决强化学习（Reinforcement Learning, RL）代理在非平稳环境（non-stationary environments）中因状态转移概率或奖励函数变化导致的模型漂移（model drift）问题。解决方案的关键在于提出了一种基于编辑操作（edit operation）的分布差异度量方法，通过分析在稳态与扰动条件下生成的状态-动作轨迹（state-action trajectories）之间的分布变化，实现对漂移的有效检测。实验表明，该方法在不同噪声水平下均能准确区分存在漂移与无漂移场景，为非平稳强化学习环境中的漂移检测提供了实用工具。

链接: https://arxiv.org/abs/2509.11367
作者: Chang-Hwan Lee,Alexander Shim
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Reinforcement learning (RL) agents typically assume stationary environment dynamics. Yet in real-world applications such as healthcare, robotics, and finance, transition probabilities or reward functions may evolve, leading to model drift. This paper proposes a novel framework to detect such drifts by analyzing the distributional changes in sequences of agent behavior. Specifically, we introduce a suite of edit operation-based measures to quantify deviations between state-action trajectories generated under stationary and perturbed conditions. Our experiments demonstrate that these measures can effectively distinguish drifted from non-drifted scenarios, even under varying levels of noise, providing a practical tool for drift detection in non-stationary RL environments.
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[AI-59] MAPGD: Multi-Agent Prompt Gradient Descent for Collaborative Prompt Optimization

【速读】：该论文旨在解决当前提示工程（Prompt Engineering）在利用大语言模型（Large Language Models, LLMs）时存在的局限性，包括单一优化路径导致的适应性差、效率低、视角狭窄、梯度冲突以及计算成本高等问题。其解决方案的关键在于提出MAPGD（Multi-Agent Prompt Gradient Descent）框架，该框架通过多智能体协作与基于梯度的优化相结合，引入任务清晰度、示例选择、格式设计和风格优化等专业化智能体，并采用语义梯度协调机制缓解梯度冲突，结合基于 bandit 的候选选择策略实现高效探索-利用平衡，同时提供理论收敛保证，从而实现更鲁棒、可解释且高效的提示优化。

链接: https://arxiv.org/abs/2509.11361
作者: Yichen Han,Bojun Liu,Zhengpeng zhou,Guanyu Liu,Zeng Zhang,Yang Yang,Wenli Wang,Isaac N Shi,Yunyan,Lewei He,Tianyu Shi
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Prompt engineering is crucial for leveraging large language models (LLMs), but existing methods often rely on a single optimization trajectory, limiting adaptability and efficiency while suffering from narrow perspectives, gradient conflicts, and high computational cost. We propose MAPGD (Multi-Agent Prompt Gradient Descent), a framework integrating multi-agent collaboration with gradient-based optimization. MAPGD features specialized agents for task clarity, example selection, format design, and stylistic refinement; semantic gradient coordination to resolve conflicts; bandit-based candidate selection for efficient exploration-exploitation; and theoretical convergence guarantees. Experiments on classification, generation, and reasoning tasks show MAPGD outperforms single-agent and random baselines in accuracy and efficiency. Ablations confirm the benefits of gradient fusion, agent specialization, and conflict resolution, providing a unified, gradient-inspired multi-agent approach to robust and interpretable prompt optimization.
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[AI-60] he power of dynamic causality in observer-based design for soft sensor applications

【速读】：该论文旨在解决传统基于观测器的软传感器（soft sensor）中传感器选择方法存在的局限性问题，即依赖线性化可观测性指标或静态统计相关性，难以捕捉复杂系统随时间演化的动态因果关系。解决方案的关键在于引入液态时间常数（liquid-time constant, LTC）网络这一连续时间神经架构，通过受控扰动分析量化每个传感器输入对状态估计的因果影响，并采用迭代剪枝策略自动识别并移除因果影响最小的传感器输入，直至性能下降为止。该方法能够从物理机制出发构建最小且高效的传感器集，在提升预测精度的同时增强模型的可解释性。

链接: https://arxiv.org/abs/2509.11336
作者: William Farlessyost,Sebastian Oberst,Shweta Singh
机构: 未知
类目: Artificial Intelligence (cs.AI)
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点击查看摘要

Abstract:This paper introduces a novel framework for optimizing observer-based soft sensors through dynamic causality analysis. Traditional approaches to sensor selection often rely on linearized observability indices or statistical correlations that fail to capture the temporal evolution of complex systems. We address this gap by leveraging liquid-time constant (LTC) networks, continuous-time neural architectures with input-dependent time constants, to systematically identify and prune sensor inputs with minimal causal influence on state estimation. Our methodology implements an iterative workflow: training an LTC observer on candidate inputs, quantifying each input’s causal impact through controlled perturbation analysis, removing inputs with negligible effect, and retraining until performance degradation occurs. We demonstrate this approach on three mechanistic testbeds representing distinct physical domains: a harmonically forced spring-mass-damper system, a nonlinear continuous stirred-tank reactor, and a predator-prey model following the structure of the Lotka-Volterra model, but with seasonal forcing and added complexity. Results show that our causality-guided pruning consistently identifies minimal sensor sets that align with underlying physics while improving prediction accuracy. The framework automatically distinguishes essential physical measurements from noise and determines when derived interaction terms provide complementary versus redundant information. Beyond computational efficiency, this approach enhances interpretability by grounding sensor selection decisions in dynamic causal relationships rather than static correlations, offering significant benefits for soft sensing applications across process engineering, ecological monitoring, and agricultural domains.
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[AI-61] A five-layer framework for AI governance: integrating regulation standards and certification

【速读】：该论文旨在解决当前人工智能（Artificial Intelligence, AI）治理中高阶监管原则与实际实施之间缺乏清晰衔接的问题，即现有框架未能明确说明法规如何转化为合规机制，导致合规与执行存在空白。其解决方案的关键在于提出一个五层AI治理框架，从宏观的监管指令逐层细化至具体的标准、评估方法和认证流程，通过逐步聚焦的结构化路径实现技术、监管与伦理要求的统一整合。该框架在AI公平性和AI事件报告两个案例研究中得到验证，能够识别法律 mandate、标准化和实施层面的缺口，并适应全球及区域性的治理需求，从而为政策制定者、监管机构和行业利益相关方提供可操作的合规与风险管理路线图。

链接: https://arxiv.org/abs/2509.11332
作者: Avinash Agarwal,Manisha J. Nene
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 17 pages, 2 tables, 1 figure. This is the authors’ accepted manuscript of the article published as: Avinash Agarwal, Manisha J. Nene; “A five-layer framework for AI governance: integrating regulation, standards, and certification.” Transforming Government: People, Process and Policy, 11 September 2025; 19 (3): 535-555. this https URL

点击查看摘要

Abstract:Purpose: The governance of artificial iintelligence (AI) systems requires a structured approach that connects high-level regulatory principles with practical implementation. Existing frameworks lack clarity on how regulations translate into conformity mechanisms, leading to gaps in compliance and enforcement. This paper addresses this critical gap in AI governance. Methodology/Approach: A five-layer AI governance framework is proposed, spanning from broad regulatory mandates to specific standards, assessment methodologies, and certification processes. By narrowing its scope through progressively focused layers, the framework provides a structured pathway to meet technical, regulatory, and ethical requirements. Its applicability is validated through two case studies on AI fairness and AI incident reporting. Findings: The case studies demonstrate the framework’s ability to identify gaps in legal mandates, standardization, and implementation. It adapts to both global and region-specific AI governance needs, mapping regulatory mandates with practical applications to improve compliance and risk management. Practical Implications - By offering a clear and actionable roadmap, this work contributes to global AI governance by equipping policymakers, regulators, and industry stakeholders with a model to enhance compliance and risk management. Social Implications: The framework supports the development of policies that build public trust and promote the ethical use of AI for the benefit of society. Originality/Value: This study proposes a five-layer AI governance framework that bridges high-level regulatory mandates and implementation guidelines. Validated through case studies on AI fairness and incident reporting, it identifies gaps such as missing standardized assessment procedures and reporting mechanisms, providing a structured foundation for targeted governance measures. Comments: 17 pages, 2 tables, 1 figure. This is the authors’ accepted manuscript of the article published as: Avinash Agarwal, Manisha J. Nene; “A five-layer framework for AI governance: integrating regulation, standards, and certification.” Transforming Government: People, Process and Policy, 11 September 2025; 19 (3): 535-555. this https URL Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC) Cite as: arXiv:2509.11332 [cs.CY] (or arXiv:2509.11332v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2509.11332 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journalreference: Transforming Government: People, Process and Policy, 11 September 2025; 19 (3): 535-555 Related DOI: https://doi.org/10.1108/TG-03-2025-0065 Focus to learn more DOI(s) linking to related resources Submission history From: Avinash Agarwal [view email] [v1] Sun, 14 Sep 2025 16:19:08 UTC (478 KB) Full-text links: Access Paper: View a PDF of the paper titled A five-layer framework for AI governance: integrating regulation, standards, and certification, by Avinash Agarwal and Manisha J. 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[AI-62] Decoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts

【速读】：该论文旨在解决环境中微塑料（microplastics）和纳米塑料（nanoplastics）污染导致的健康风险识别难题，尤其是从海量科学文献中自动提取污染物来源与健康影响之间的复杂因果关系。其解决方案的关键在于提出了一种基于大语言模型（Large Language Models, LLMs）的新型框架，能够从科学摘要中抽取结构化的“关系元路径”（relational metapaths），这些元路径构成多跳语义链，连接污染源与健康效应；同时通过构建毒性轨迹图（Toxicity Trajectory Graph）追踪暴露途径与生物系统中的传播路径，并引入动态证据协调模块以解决研究结论随时间演化或存在矛盾时的语义冲突问题，从而实现高可靠性、高实用性的因果知识抽取。

链接: https://arxiv.org/abs/2509.11330
作者: Sudeshna Jana,Manjira Sinha,Tirthankar Dasgupta
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 6 figures, 4 tables

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Abstract:The widespread use of plastics and their persistence in the environment have led to the accumulation of micro- and nano-plastics across air, water, and soil, posing serious health risks including respiratory, gastrointestinal, and neurological disorders. We propose a novel framework that leverages large language models to extract relational metapaths, multi-hop semantic chains linking pollutant sources to health impacts, from scientific abstracts. Our system identifies and connects entities across diverse contexts to construct structured relational metapaths, which are aggregated into a Toxicity Trajectory Graph that traces pollutant propagation through exposure routes and biological systems. Moreover, to ensure consistency and reliability, we incorporate a dynamic evidence reconciliation module that resolves semantic conflicts arising from evolving or contradictory research findings. Our approach demonstrates strong performance in extracting reliable, high-utility relational knowledge from noisy scientific text and offers a scalable solution for mining complex cause-effect structures in domain-specific corpora.
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[AI-63] Weakly Supervised Vulnerability Localization via Multiple Instance Learning

【速读】：该论文旨在解决软件漏洞定位（vulnerability localization）中因缺乏细粒度的语句级标注而导致模型训练成本高昂的问题。现有方法多依赖于函数或文件级别的粗粒度标签，而开发者仍需手动排查大量代码以精确定位漏洞语句，效率低下。解决方案的关键在于提出一种名为WAVES（WeAkly supervised Vulnerability Localization via multiplE inStance learning）的新方法，其核心思想是利用多实例学习（Multiple Instance Learning, MIL）机制，将已有的函数级标签转换为伪标签（pseudo labels）用于语句级分类器训练，从而无需额外的人工标注语句级标签即可实现高精度的漏洞定位。实验表明，WAVES在漏洞检测任务上性能与基线相当，在语句级漏洞定位上达到当前最优水平。

链接: https://arxiv.org/abs/2509.11312
作者: Wenchao Gu,Yupan Chen,Yanlin Wang,Hongyu Zhang,Cuiyun Gao,Michael R. Lyu
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
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Abstract:Software vulnerability detection has emerged as a significant concern in the field of software security recently, capturing the attention of numerous researchers and developers. Most previous approaches focus on coarse-grained vulnerability detection, such as at the function or file level. However, the developers would still encounter the challenge of manually inspecting a large volume of code inside the vulnerable function to identify the specific vulnerable statements for modification, indicating the importance of vulnerability localization. Training the model for vulnerability localization usually requires ground-truth labels at the statement-level, and labeling vulnerable statements demands expert knowledge, which incurs high costs. Hence, the demand for an approach that eliminates the need for additional labeling at the statement-level is on the rise. To tackle this problem, we propose a novel approach called WAVES for WeAkly supervised Vulnerability Localization via multiplE inStance learning, which does not need the additional statement-level labels during the training. WAVES has the capability to determine whether a function is vulnerable (i.e., vulnerability detection) and pinpoint the vulnerable statements (i.e., vulnerability localization). Specifically, inspired by the concept of multiple instance learning, WAVES converts the ground-truth label at the function-level into pseudo labels for individual statements, eliminating the need for additional statement-level labeling. These pseudo labels are utilized to train the classifiers for the function-level representation vectors. Extensive experimentation on three popular benchmark datasets demonstrates that, in comparison to previous baselines, our approach achieves comparable performance in vulnerability detection and state-of-the-art performance in statement-level vulnerability localization.
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[AI-64] Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble AAAI2026

【速读】：该论文旨在解决社会科学研究中两个紧迫问题：一是问卷调查部署成本不断上升，二是调查响应数据中日益显著的人口统计学失衡。其解决方案的关键在于提出一种新颖的对齐框架（alignment framework），将大语言模型（Large Language Models, LLMs）作为人类受访者的代理（agent proxies），通过两阶段建模实现高效且可调控的模拟：首先构建多样化的代理人格（endowments）以模拟合理的受访者画像，其次基于观测数据选择代表性子集来逼近真实人群分布。该方法不依赖个体层面的 demographic 信息，仅使用聚合调查结果，结合结构化提示工程、基于熵的采样和回归选择策略，实现了高保真度的群体响应模式再现与显著的响应多样性，具有良好的泛化能力和简洁性。

链接: https://arxiv.org/abs/2509.11311
作者: Bingchen Wang,Zi-Yu Khoo,Bryan Kian Hsiang Low
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Preprint of work originally submitted to AAAI 2026. Under revision for resubmission to a machine learning venue

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Abstract:Large language models (LLMs) have demonstrated promise in emulating human-like responses across a wide range of tasks. In this paper, we propose a novel alignment framework that treats LLMs as agent proxies for human survey respondents, affording a cost-effective and steerable solution to two pressing challenges in the social sciences: the rising cost of survey deployment and the growing demographic imbalance in survey response data. Drawing inspiration from the theory of revealed preference, we formulate alignment as a two-stage problem: constructing diverse agent personas called endowments that simulate plausible respondent profiles, and selecting a representative subset to approximate a ground-truth population based on observed data. To implement the paradigm, we introduce P2P, a system that steers LLM agents toward representative behavioral patterns using structured prompt engineering, entropy-based sampling, and regression-based selection. Unlike personalization-heavy approaches, our alignment approach is demographic-agnostic and relies only on aggregate survey results, offering better generalizability and parsimony. Beyond improving data efficiency in social science research, our framework offers a testbed for studying the operationalization of pluralistic alignment. We demonstrate the efficacy of our approach on real-world opinion survey datasets, showing that our aligned agent populations can reproduce aggregate response patterns with high fidelity and exhibit substantial response diversity, even without demographic conditioning.
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[AI-65] Policy Learning for Social Robot-Led Physiotherapy

【速读】：该论文旨在解决社交机器人在物理治疗指导中因患者行为数据稀缺而导致的决策模型难以训练和部署的问题。解决方案的关键在于利用33名专业医疗人员作为患者代理（patient proxies），通过他们与机器人交互产生的数据构建一个能够生成运动表现指标和主观疲劳感知评分的患者行为模型，并基于此模型在仿真环境中训练强化学习策略，从而实现根据个体疲劳耐受性和性能波动动态调整训练指令，同时适用于不同康复阶段患者的个性化运动方案。

链接: https://arxiv.org/abs/2509.11297
作者: Carl Bettosi,Lynne Ballie,Susan Shenkin,Marta Romeo
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
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Abstract:Social robots offer a promising solution for autonomously guiding patients through physiotherapy exercise sessions, but effective deployment requires advanced decision-making to adapt to patient needs. A key challenge is the scarcity of patient behavior data for developing robust policies. To address this, we engaged 33 expert healthcare practitioners as patient proxies, using their interactions with our robot to inform a patient behavior model capable of generating exercise performance metrics and subjective scores on perceived exertion. We trained a reinforcement learning-based policy in simulation, demonstrating that it can adapt exercise instructions to individual exertion tolerances and fluctuating performance, while also being applicable to patients at different recovery stages with varying exercise plans.
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[AI-66] Energy-Aware 6G Network Design: A Survey

【速读】：该论文旨在解决6G移动网络在支持海量用户和数据密集型应用背景下所面临的能源效率与可持续性挑战，其核心问题是如何在网络设计中实现能源感知（energy-aware）以降低能耗并提升可持续性。解决方案的关键在于引入多维度的能源管理机制，包括能量采集（energy harvesting）、精细化的能源建模与参数定义、面向能源意识的服务分类体系，以及利用人工智能/机器学习（AI/ML）技术优化网络能效；同时强调通过标准化组织（如3GPP、ITU、IEEE）推动相关技术落地，并识别出当前在信息采集开销、用户授权管理等方面的开放性研究问题，从而为构建性能与能效双优的6G网络提供系统性路径。

链接: https://arxiv.org/abs/2509.11289
作者: Rashmi Kamran,Mahesh Ganesh Bhat,Pranav Jha,Shana Moothedath,Manjesh Hanawal,Prasanna Chaporkar
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
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Abstract:6th Generation (6G) mobile networks are envisioned to support several new capabilities and data-centric applications for unprecedented number of users, potentially raising significant energy efficiency and sustainability concerns. This brings focus on sustainability as one of the key objectives in the their design. To move towards sustainable solution, research and standardization community is focusing on several key issues like energy information monitoring and exposure, use of renewable energy, and use of Artificial Intelligence/Machine Learning (AI/ML) for improving the energy efficiency in 6G networks. The goal is to build energy-aware solutions that takes into account the energy information resulting in energy efficient networks. Design of energy-aware 6G networks brings in new challenges like increased overheads in gathering and exposing of energy related information, and the associated user consent management. The aim of this paper is to provide a comprehensive survey of methods used for design of energy efficient 6G networks, like energy harvesting, energy models and parameters, classification of energy-aware services, and AI/ML-based solutions. The survey also includes few use cases that demonstrate the benefits of incorporating energy awareness into network decisions. Several ongoing standardization efforts in 3GPP, ITU, and IEEE are included to provide insights into the ongoing work and highlight the opportunities for new contributions. We conclude this survey with open research problems and challenges that can be explored to make energy-aware design feasible and ensure optimality regarding performance and energy goals for 6G networks.
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[AI-67] Efficient Single-Step Framework for Incremental Class Learning in Neural Networks

【速读】：该论文旨在解决类增量学习（Class Incremental Learning, CIL）中普遍存在的灾难性遗忘（catastrophic forgetting）问题，尤其是在资源受限场景下，现有方法往往依赖复杂的迭代训练过程和大量计算资源，难以实现高效可持续的学习。其解决方案的关键在于提出一种名为CIFNet的新型架构，通过整合预训练且冻结的特征提取器、压缩的数据缓冲区以及一个高效的单层神经网络分类器，实现了基于固定特征的单步优化，从而避免了对主干网络的重复微调，显著降低了计算开销与训练时间，同时在分类准确率上达到或接近当前最先进方法的水平。

链接: https://arxiv.org/abs/2509.11285
作者: Alejandro Dopico-Castro,Oscar Fontenla-Romero,Bertha Guijarro-Berdiñas,Amparo Alonso-Betanzos
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Incremental learning remains a critical challenge in machine learning, as models often struggle with catastrophic forgetting -the tendency to lose previously acquired knowledge when learning new information. These challenges are even more pronounced in resource-limited settings. Many existing Class Incremental Learning (CIL) methods achieve high accuracy by continually adapting their feature representations; however, they often require substantial computational resources and complex, iterative training procedures. This work introduces CIFNet (Class Incremental and Frugal Network), a novel CIL approach that addresses these limitations by offering a highly efficient and sustainable solution. CIFNet’s key innovation lies in its novel integration of several existing, yet separately explored, components: a pre-trained and frozen feature extractor, a compressed data buffer, and an efficient non-iterative one-layer neural network for classification. A pre-trained and frozen feature extractor eliminates computationally expensive fine-tuning of the backbone. This, combined with a compressed buffer for efficient memory use, enables CIFNet to perform efficient class-incremental learning through a single-step optimization process on fixed features, minimizing computational overhead and training time without requiring multiple weight updates. Experiments on benchmark datasets confirm that CIFNet effectively mitigates catastrophic forgetting at the classifier level, achieving high accuracy comparable to that of existing state-of-the-art methods, while substantially improving training efficiency and sustainability. CIFNet represents a significant advancement in making class-incremental learning more accessible and pragmatic in environments with limited resources, especially when strong pre-trained feature extractors are available.
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[AI-68] Embodied Intelligence in Disassembly: Multimodal Perception Cross-validation and Continual Learning in Neuro-Symbolic TAMP

【速读】：该论文旨在解决新能源汽车动力电池在非结构化拆解场景中，由于环境动态性导致机器人感知鲁棒性不足的问题，从而阻碍了自主拆解在工业应用中的落地。其解决方案的关键在于提出了一种基于神经符号任务与运动规划（Neuro-Symbolic Task and Motion Planning, TAMP）的持续学习框架，通过引入多模态感知交叉验证机制，构建前向工作流与后向学习流的双向推理流程：前向流动态优化动作策略，后向流自动收集历史任务执行中的有效数据以支持系统持续学习，实现自我优化。实验表明，该方法将动态拆解场景下的任务成功率从81.68%提升至100%，同时将平均感知误判次数由3.389次降至1.128次。

链接: https://arxiv.org/abs/2509.11270
作者: Ziwen He,Zhigang Wang,Yanlong Peng,Pengxu Chang,Hong Yang,Ming Chen
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages, 3 figures. Accepted at CASE2025. This arXiv version contains minor corrections

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Abstract:With the rapid development of the new energy vehicle industry, the efficient disassembly and recycling of power batteries have become a critical challenge for the circular economy. In current unstructured disassembly scenarios, the dynamic nature of the environment severely limits the robustness of robotic perception, posing a significant barrier to autonomous disassembly in industrial applications. This paper proposes a continual learning framework based on Neuro-Symbolic task and motion planning (TAMP) to enhance the adaptability of embodied intelligence systems in dynamic environments. Our approach integrates a multimodal perception cross-validation mechanism into a bidirectional reasoning flow: the forward working flow dynamically refines and optimizes action strategies, while the backward learning flow autonomously collects effective data from historical task executions to facilitate continual system learning, enabling self-optimization. Experimental results show that the proposed framework improves the task success rate in dynamic disassembly scenarios from 81.68% to 100%, while reducing the average number of perception misjudgments from 3.389 to 1.128. This research provides a new paradigm for enhancing the robustness and adaptability of embodied intelligence in complex industrial environments.
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[AI-69] Gradient Free Deep Reinforcement Learning With TabPFN

【速读】：该论文旨在解决当前深度强化学习（Deep Reinforcement Learning, DRL）中基于梯度优化方法所面临的超参数敏感性高、训练动态不稳定以及计算成本高的问题。其核心解决方案是提出一种无需梯度更新的新型深度强化学习框架——TabPFN RL，该方法复用预训练于数百万个合成数据集上的Transformer模型TabPFN（Tabular Pre-trained Transformer for Fast Inference），将其作为Q函数近似器使用。关键创新在于利用TabPFN的上下文内学习（in-context learning）能力，在不进行反向传播或任务特定微调的情况下，仅通过单次前向传播即可预测Q值，从而实现完全无梯度的训练与推理过程。此外，为应对模型固定上下文容量限制，设计了高奖励轨迹门控机制以保留最优5%的轨迹，有效提升样本效率并支持持续学习。实验证明，TabPFN RL在Gymnasium经典控制环境中性能可媲美甚至超越DQN，且无需梯度下降或复杂超参数调优，展现出强大的泛化能力和计算高效性。

链接: https://arxiv.org/abs/2509.11259
作者: David Schiff,Ofir Lindenbaum,Yonathan Efroni
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Gradient based optimization is fundamental to most modern deep reinforcement learning algorithms, however, it introduces significant sensitivity to hyperparameters, unstable training dynamics, and high computational costs. We propose TabPFN RL, a novel gradient free deep RL framework that repurposes the meta trained transformer TabPFN as a Q function approximator. Originally developed for tabular classification, TabPFN is a transformer pre trained on millions of synthetic datasets to perform inference on new unseen datasets via in context learning. Given an in context dataset of sample label pairs and new unlabeled data, it predicts the most likely labels in a single forward pass, without gradient updates or task specific fine tuning. We use TabPFN to predict Q values using inference only, thereby eliminating the need for back propagation at both training and inference. To cope with the model’s fixed context budget, we design a high reward episode gate that retains only the top 5% of trajectories. Empirical evaluations on the Gymnasium classic control suite demonstrate that TabPFN RL matches or surpasses Deep Q Network on CartPole v1, MountainCar v0, and Acrobot v1, without applying gradient descent or any extensive hyperparameter tuning. We discuss the theoretical aspects of how bootstrapped targets and non stationary visitation distributions violate the independence assumptions encoded in TabPFN’s prior, yet the model retains a surprising generalization capacity. We further formalize the intrinsic context size limit of in context RL algorithms and propose principled truncation strategies that enable continual learning when the context is full. Our results establish prior fitted networks such as TabPFN as a viable foundation for fast and computationally efficient RL, opening new directions for gradient free RL with large pre trained transformers.
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[AI-70] VideoAgent : Personalized Synthesis of Scientific Videos

【速读】：该论文旨在解决科学视频自动化生成中缺乏个性化动态编排与多模态内容同步机制的问题。现有文献主要聚焦于静态媒体（如海报和幻灯片）的自动化处理，难以实现针对用户需求的叙事逻辑定制及图文音视频的精准协同。其解决方案的关键在于提出VideoAgent——一个基于多智能体（multi-agent）框架的系统，能够将源论文解析为细粒度资产库，并根据用户要求动态调度静态幻灯片与动态动画，从而构建结构清晰、概念表达准确的个性化科学视频；同时配套设计了SciVidEval评估体系，融合自动化的多模态质量指标与基于视频问答的人工评价，以全面衡量知识传递效果。

链接: https://arxiv.org/abs/2509.11253
作者: Xiao Liang,Bangxin Li,Zixuan Chen,Hanyue Zheng,Zhi Ma,Di Wang,Cong Tian,Quan Wang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Automating the generation of scientific videos is a crucial yet challenging task for effective knowledge dissemination. However, existing works on document automation primarily focus on static media such as posters and slides, lacking mechanisms for personalized dynamic orchestration and multimodal content synchronization. To address these challenges, we introduce VideoAgent, a novel multi-agent framework that synthesizes personalized scientific videos through a conversational interface. VideoAgent parses a source paper into a fine-grained asset library and, guided by user requirements, orchestrates a narrative flow that synthesizes both static slides and dynamic animations to explain complex concepts. To enable rigorous evaluation, we also propose SciVidEval, the first comprehensive suite for this task, which combines automated metrics for multimodal content quality and synchronization with a Video-Quiz-based human evaluation to measure knowledge transfer. Extensive experiments demonstrate that our method significantly outperforms existing commercial scientific video generation services and approaches human-level quality in scientific communication.
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[AI-71] Beyond Autoregression: An Empirical Study of Diffusion Large Language Models for Code Generation

【速读】：该论文旨在解决当前基于自回归（autoregressive）机制的大语言模型（Large Language Models, LLMs）在代码生成任务中存在的两大固有局限：一是生成效率低，因每次仅生成一个token；二是难以适应编程过程中非顺序的编辑行为，因其强制采用从左到右的生成顺序。为应对这些问题，论文提出采用扩散语言模型（diffusion LLMs）作为替代方案，其核心创新在于引入多token预测（multi-token prediction）和灵活生成顺序（flexible generation order），从而提升生成效率并更好地模拟人类编程时的迭代编辑过程。实验表明，扩散LLMs在性能上可与同规模自回归LLMs相媲美，并在长代码理解和长度外推能力方面更具优势。

链接: https://arxiv.org/abs/2509.11252
作者: Chengze li,Yitong Zhang,Jia Li,Liyi Cai,Ge Li
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
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Abstract:LLMs have become the mainstream approaches to code generation. Existing LLMs mainly employ autoregressive generation, i.e. generating code token-by-token from left to right. However, the underlying autoregressive generation has two limitations in code generation. First, autoregressive LLMs only generate a token at each step, showing low efficiency in practice. Second, programming is a non-sequential process involving back-and-forth editing, while autoregressive LLMs only employ the left-to-right generation order. These two intrinsic limitations hinder the further development of LLMs in code generation. Recently, diffusion LLMs have emerged as a promising alternative. Diffusion LLMs address the above limitations with two advances, including multi-token prediction (i.e. generating multiple tokens at each step) and flexible generation order (i.e. flexibly determining which positions to generate tokens). However, there is no systematic study exploring diffusion LLMs in code generation. To bridge the knowledge gap, we present the first empirical study of diffusion LLMs for code generation. Our study involves 9 representative diffusion LLMs and conduct experiments on 4 widely used benchmarks. Based on the results, we summarize the following findings. (1) Existing diffusion LLMs are competitive with autoregressive LLMs with similar sizes. (2) Diffusion LLMs have a stronger length extrapolation ability than autoregressive LLMs and perform better in long code understanding. (3) We explore factors impacting the effectiveness and efficiency of diffusion LLMs, and provide practical guidance. (4) We discuss several promising further directions to improve diffusion LLMs on code generation. We open-source all source code, data, and results to facilitate the following research. The code is publicly available at this https URL.
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[AI-72] ransZero: Parallel Tree Expansion in MuZero using Transformer Networks

【速读】：该论文旨在解决模型基于强化学习（Model-Based Reinforcement Learning, MBRL）中蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）存在的序列瓶颈问题，即传统方法如MuZero需逐层构建搜索树，限制了计算效率。解决方案的关键在于提出TransZero，其核心创新是采用基于Transformer的网络并行生成多个潜在未来状态，并结合均值-方差约束（Mean-Variance Constrained, MVC）评估器，消除对依赖顺序访问次数的依赖，从而实现规划过程中整个子树的并行扩展。这一设计显著提升了推理速度，在MiniGrid和LunarLander环境中实现了相比MuZero最高达11倍的壁钟时间加速，同时保持了样本效率。

链接: https://arxiv.org/abs/2509.11233
作者: Emil Malmsten,Wendelin Böhmer
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Submitted to BNAIC/BeNeLearn 2025. 15 pages, 4 figures

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Abstract:We present TransZero, a model-based reinforcement learning algorithm that removes the sequential bottleneck in Monte Carlo Tree Search (MCTS). Unlike MuZero, which constructs its search tree step by step using a recurrent dynamics model, TransZero employs a transformer-based network to generate multiple latent future states simultaneously. Combined with the Mean-Variance Constrained (MVC) evaluator that eliminates dependence on inherently sequential visitation counts, our approach enables the parallel expansion of entire subtrees during planning. Experiments in MiniGrid and LunarLander show that TransZero achieves up to an eleven-fold speedup in wall-clock time compared to MuZero while maintaining sample efficiency. These results demonstrate that parallel tree construction can substantially accelerate model-based reinforcement learning, bringing real-time decision-making in complex environments closer to practice. The code is publicly available on GitHub.
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[AI-73] MEMBOT: Memory-Based Robot in Intermittent POMDP

【速读】：该论文旨在解决机器人在现实环境中因传感器噪声、遮挡或故障导致的局部可观测性（partial observability）问题，此类场景下传统强化学习（Reinforcement Learning, RL）方法因依赖完整状态观测而表现不佳。解决方案的关键在于提出一种模块化记忆架构 MEMBOT，其核心创新是通过两阶段训练策略将信念推理（belief inference）与策略学习分离：首先在离线多任务预训练阶段利用重建损失学习一个任务无关的潜在信念编码器（latent belief encoder），该编码器由状态空间模型（State-Space Model, SSM）和长短期记忆网络（LSTM）构成，能融合时序观测与动作以推断持久的隐状态表示；随后采用行为克隆对特定任务策略进行微调。这种显式建模信念的方法显著提升了策略在观测间歇性缺失条件下的鲁棒性、可迁移性和数据效率，在 MetaWorld 和 Robomimic 的 10 个机器人操作基准任务中，即使在 50% 观测可用率下仍保持高达峰值性能 80% 的表现。

链接: https://arxiv.org/abs/2509.11225
作者: Youzhi Liang,Eyan Noronha
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
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Abstract:Robotic systems deployed in real-world environments often operate under con- ditions of partial and often intermittent observability, where sensor inputs may be noisy, occluded, or entirely unavailable due to failures or environmental con- straints. Traditional reinforcement learning (RL) approaches that assume full state observability are ill-equipped for such challenges. In this work, we introduce MEMBOT, a modular memory-based architecture designed to address intermittent partial observability in robotic control tasks. MEMBOT decouples belief inference from policy learning through a two-phase training process: an offline multi-task learning pretraining stage that learns a robust task-agnostic latent belief encoder using a reconstruction losses, followed by fine-tuning of task-specific policies using behavior cloning. The belief encoder, implemented as a state-space model (SSM) and a LSTM, integrates temporal sequences of observations and actions to infer latent state representations that persist even when observations are dropped. We train and evaluate MEMBOT on 10 robotic manipulation benchmark tasks from MetaWorld and Robomimic under varying rates of observation dropout. Results show that MEMBOT consistently outperforms both memoryless and naively recur- rent baselines, maintaining up to 80% of peak performance under 50% observation availability. These findings highlight the effectiveness of explicit belief modeling in achieving robust, transferable, and data-efficient policies for real-world partially observable robotic systems.
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[AI-74] Federated Recommender System with Data Valuation for E-commerce Platform

【速读】：该论文旨在解决联邦推荐系统（Federated Recommender System）中因客户端本地数据稀疏和偏差导致的性能瓶颈问题，同时探索如何有效利用平台侧全局公开数据来增强本地训练，而不引入噪声或增加计算负担。解决方案的关键在于提出FedGDVE框架，其核心机制包括：(i) 使用预训练图编码器提取全局结构特征以识别语义对齐样本；(ii) 引入局部有效性预测器评估样本与客户端特定需求的相关性；(iii) 基于强化学习的概率估计器筛选并采样最相关的全局交互数据，从而实现精准、高效的本地图增强，最终在联邦学习环境中将推荐性能提升高达34.86%。

链接: https://arxiv.org/abs/2509.11196
作者: Jongwon Park,Minku Kang,Wooseok Sim,Soyoung Lee,Hogun Park
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to Expert Systems with Applications Journal, Elsevier

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Abstract:Federated Learning (FL) is gaining prominence in machine learning as privacy concerns grow. This paradigm allows each client (e.g., an individual online store) to train a recommendation model locally while sharing only model updates, without exposing the raw interaction logs to a central server, thereby preserving privacy in a decentralized environment. Nonetheless, most existing FL-based recommender systems still rely solely on each client’s private data, despite the abundance of publicly available datasets that could be leveraged to enrich local training; this potential remains largely underexplored. To this end, we consider a realistic scenario wherein a large shopping platform collaborates with multiple small online stores to build a global recommender system. The platform possesses global data, such as shareable user and item lists, while each store holds a portion of interaction data privately (or locally). Although integrating global data can help mitigate the limitations of sparse and biased clients’ local data, it also introduces additional challenges: simply combining all global interactions can amplify noise and irrelevant patterns, worsening personalization and increasing computational costs. To address these challenges, we propose FedGDVE, which selectively augments each client’s local graph with semantically aligned samples from the global dataset. FedGDVE employs: (i) a pre-trained graph encoder to extract global structural features, (ii) a local valid predictor to assess client-specific relevance, (iii) a reinforcement-learning-based probability estimator to filter and sample only the most pertinent global interactions. FedGDVE improves performance by up to 34.86% on recognized benchmarks in FL environments.
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[AI-75] Your Compiler is Backdooring Your Model: Understanding and Exploiting Compilation Inconsistency Vulnerabilities in Deep Learning Compilers

【速读】：该论文旨在解决深度学习（Deep Learning, DL）编译器设计中潜在的安全漏洞问题，即未修改的官方DL编译器是否能在编译过程中隐式改变模型语义并引入隐蔽后门。其核心发现是：即使模型本身为良性，编译过程仍可能在不改变正常准确率的前提下，使触发输入产生恶意行为，且这种攻击对现有检测手段具有高度隐蔽性。解决方案的关键在于揭示了DL编译器在优化和转换过程中可能无意或有意地引入语义偏移，从而形成一种“自然触发”的后门机制——这一现象不仅存在于对抗场景下（通过精心构造的模型实现100%触发成功率），也存在于真实世界模型中（如HuggingFace上31个模型存在自然触发特征）。这表明DL编译器本身已成为一个不可忽视的安全风险源，亟需从架构层面加强可信性保障。

链接: https://arxiv.org/abs/2509.11173
作者: Simin Chen,Jinjun Peng,Yixin He,Junfeng Yang,Baishakhi Ray
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE)
备注: This paper is accepted to SP 2026

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Abstract:Deep learning (DL) compilers are core infrastructure in modern DL systems, offering flexibility and scalability beyond vendor-specific libraries. This work uncovers a fundamental vulnerability in their design: can an official, unmodified compiler alter a model’s semantics during compilation and introduce hidden backdoors? We study both adversarial and natural settings. In the adversarial case, we craft benign models where triggers have no effect pre-compilation but become effective backdoors after compilation. Tested on six models, three commercial compilers, and two hardware platforms, our attack yields 100% success on triggered inputs while preserving normal accuracy and remaining undetected by state-of-the-art detectors. The attack generalizes across compilers, hardware, and floating-point settings. In the natural setting, we analyze the top 100 HuggingFace models (including one with 220M+ downloads) and find natural triggers in 31 models. This shows that compilers can introduce risks even without adversarial manipulation. Our results reveal an overlooked threat: unmodified DL compilers can silently alter model semantics. To our knowledge, this is the first work to expose inherent security risks in DL compiler design, opening a new direction for secure and trustworthy ML. Comments: This paper is accepted to SP 2026 Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Software Engineering (cs.SE) Cite as: arXiv:2509.11173 [cs.CR] (or arXiv:2509.11173v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2509.11173 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-76] An Entropy-Guided Curriculum Learning Strategy for Data-Efficient Acoustic Scene Classification under Domain Shift

【速读】：该论文旨在解决声学场景分类（Acoustic Scene Classification, ASC）在有限标注数据条件下，因录音设备差异导致的域偏移（domain shift）问题，尤其关注模型从少量设备上训练后如何泛化到未见过的设备。解决方案的关键在于提出一种基于熵引导的课程学习（curriculum learning）策略：通过辅助域分类器估计每个样本的设备后验概率，并以香农熵（Shannon entropy）作为域不变性的代理指标，优先训练高熵样本（即域不确定性高的样本），逐步引入低熵样本（即域特异性强的样本），从而引导模型学习更具泛化能力的特征表示。该方法无需增加模型架构复杂度或推理开销，具有良好的可集成性与实用性。

链接: https://arxiv.org/abs/2509.11168
作者: Peihong Zhang,Yuxuan Liu,Zhixin Li,Rui Sang,Yiqiang Cai,Yizhou Tan,Shengchen Li
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Accepted at the Detection and Classification of Acoustic Scenes and Events (DCASE) Workshop 2025

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Abstract:Acoustic Scene Classification (ASC) faces challenges in generalizing across recording devices, particularly when labeled data is limited. The DCASE 2024 Challenge Task 1 highlights this issue by requiring models to learn from small labeled subsets recorded on a few devices. These models need to then generalize to recordings from previously unseen devices under strict complexity constraints. While techniques such as data augmentation and the use of pre-trained models are well-established for improving model generalization, optimizing the training strategy represents a complementary yet less-explored path that introduces no additional architectural complexity or inference overhead. Among various training strategies, curriculum learning offers a promising paradigm by structuring the learning process from easier to harder examples. In this work, we propose an entropy-guided curriculum learning strategy to address the domain shift problem in data-efficient ASC. Specifically, we quantify the uncertainty of device domain predictions for each training sample by computing the Shannon entropy of the device posterior probabilities estimated by an auxiliary domain classifier. Using entropy as a proxy for domain invariance, the curriculum begins with high-entropy samples and gradually incorporates low-entropy, domain-specific ones to facilitate the learning of generalizable representations. Experimental results on multiple DCASE 2024 ASC baselines demonstrate that our strategy effectively mitigates domain shift, particularly under limited labeled data conditions. Our strategy is architecture-agnostic and introduces no additional inference cost, making it easily integrable into existing ASC baselines and offering a practical solution to domain shift.
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[AI-77] Harnessing Optimization Dynamics for Curvature-Informed Model Merging

【速读】：该论文旨在解决大规模语言模型在监督微调（Supervised Fine-Tuning, SFT）阶段中，如何有效合并多个具备特定能力（如数学推理、代码生成、指令遵循等）的SFT检查点，从而构建一个统一且性能优越的单一模型的问题。传统线性加权融合方法常因任务间参数干扰导致负迁移（negative transfer），难以充分利用各模块化能力。其解决方案的关键在于提出两种协同机制：一是优化轨迹感知（Optimization Trajectory Aware, OTA）合并策略，利用优化器二阶矩统计量作为对角曲率代理，重新加权参数更新以缓解不同任务间的冲突；二是快速费舍尔嫁接（Fast Fisher Grafting, FFG），通过曲率驱动的任务局部化操作稀疏化低重要性或冲突性参数修改，生成集中在早期注意力查询/键投影和词元嵌入中的极低秩掩码，从而实现高效的能力整合。实验表明，OTA+FFG显著优于现有权重空间基线，且在不同稀疏度下保持鲁棒性，揭示了不同SFT检查点间存在显著曲率重叠，为简单线性合并为何在实践中有效提供了新视角。

链接: https://arxiv.org/abs/2509.11167
作者: Pouria Mahdavinia,Hamed Mahdavi,Niloofar Mireshghallah,Mehrdad Mahdavi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Model merging is an effective post-training strategy for composing capabilities in large language models without joint retraining. We study this in the supervised fine-tuning (SFT) stage, where multiple capability-based SFT checkpoints – spanning math, code, precise instruction following, general instruction following, and knowledge recall – must be consolidated into a single model. We introduce Optimization Trajectory Aware (OTA) Merging, a curvature-aware aggregation that leverages optimizer second-moment statistics as a diagonal curvature proxy to reweight parameter edits and mitigate interference. Complementing OTA, we propose Fast Fisher Grafting (FFG), a curvature-driven task-localization step that sparsifies conflicting or low-importance edits. FFG induces extremely low-rank masks concentrated in early attention query/key projections and token embeddings, exploiting shared curvature across capabilities. We further develop a memory-light compression of the second moments that preserves OTA’s effect. Across diverse capability-based SFT checkpoints, OTA+FFG improves merged-model quality over strong weight-space baselines, reduces negative transfer, and remains robust across sparsity levels. Analyses reveal substantial curvature overlap between checkpoints, offering a novel lens on why simple linear merging can be effective in practice. Ablations confirm that FFG is critical for reducing task interference and that the compressed second moments retain the gains of the full formulation. To facilitate reproducibility, we open-source all code, training and evaluation scripts, visualization artifacts, and capability-specific SFT checkpoints at this https URL.
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[AI-78] Feature Space Topology Control via Hopkins Loss ICTAI2025

【速读】：该论文旨在解决如何主动调控特征空间拓扑（feature space topology）的问题，而非仅保留原始输入数据的拓扑结构。传统方法通常致力于保持输入数据在特征空间中的局部或全局结构，而本研究提出了一种新的损失函数——Hopkins损失（Hopkins loss），其核心创新在于利用Hopkins统计量来显式地引导特征空间形成期望的拓扑结构（如更均匀分布或聚类性更强的布局）。该方法在语音、文本和图像数据上的分类与降维任务中均表现出良好效果，能够在不显著影响分类性能的前提下实现对特征空间拓扑的有效控制，为生成建模、迁移学习及对抗鲁棒性等应用提供了新的工具。

链接: https://arxiv.org/abs/2509.11154
作者: Einari Vaaras,Manu Airaksinen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted for publication in Proc. IEEE ICTAI 2025, Athens, Greece

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Abstract:Feature space topology refers to the organization of samples within the feature space. Modifying this topology can be beneficial in machine learning applications, including dimensionality reduction, generative modeling, transfer learning, and robustness to adversarial attacks. This paper introduces a novel loss function, Hopkins loss, which leverages the Hopkins statistic to enforce a desired feature space topology, which is in contrast to existing topology-related methods that aim to preserve input feature topology. We evaluate the effectiveness of Hopkins loss on speech, text, and image data in two scenarios: classification and dimensionality reduction using nonlinear bottleneck autoencoders. Our experiments show that integrating Hopkins loss into classification or dimensionality reduction has only a small impact on classification performance while providing the benefit of modifying feature topology.
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[AI-79] AI-Generated Content in Cross-Domain Applications: Research Trends Challenges and Propositions

【速读】：该论文旨在解决当前关于生成式 AI (Generative AI) 生成内容（AIGC）在不同领域中的最新进展与新兴挑战缺乏系统性综述的问题。其解决方案的关键在于汇聚来自多个学科的16位学者，从三个维度进行整合：首先，提供AIGC的广泛概览，涵盖生成式AI的训练技术、检测方法以及AI生成内容在数字平台上的传播与使用；其次，分析AIGC在多样化应用场景中的社会影响，并梳理现有应对方法；最后，识别关键技术挑战并提出未来研究方向，从而为跨领域理解AIGC的研究趋势、挑战与发展方向提供系统性洞见。

链接: https://arxiv.org/abs/2509.11151
作者: Jianxin Li,Liang Qu,Taotao Cai,Zhixue Zhao,Nur Al Hasan Haldar,Aneesh Krishna,Xiangjie Kong,Flavio Romero Macau,Tanmoy Chakraborty,Aniket Deroy,Binshan Lin,Karen Blackmore,Nasimul Noman,Jingxian Cheng,Ningning Cui,Jianliang Xu
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Artificial Intelligence Generated Content (AIGC) has rapidly emerged with the capability to generate different forms of content, including text, images, videos, and other modalities, which can achieve a quality similar to content created by humans. As a result, AIGC is now widely applied across various domains such as digital marketing, education, and public health, and has shown promising results by enhancing content creation efficiency and improving information delivery. However, there are few studies that explore the latest progress and emerging challenges of AIGC across different domains. To bridge this gap, this paper brings together 16 scholars from multiple disciplines to provide a cross-domain perspective on the trends and challenges of AIGC. Specifically, the contributions of this paper are threefold: (1) It first provides a broader overview of AIGC, spanning the training techniques of Generative AI, detection methods, and both the spread and use of AI-generated content across digital platforms. (2) It then introduces the societal impacts of AIGC across diverse domains, along with a review of existing methods employed in these contexts. (3) Finally, it discusses the key technical challenges and presents research propositions to guide future work. Through these contributions, this vision paper seeks to offer readers a cross-domain perspective on AIGC, providing insights into its current research trends, ongoing challenges, and future directions.
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[AI-80] RoVerFly: Robust and Versatile Learning-based Control of Quadrotor Across Payload Configurations

【速读】：该论文旨在解决四旋翼无人机（quadrotor）在执行精确任意轨迹跟踪时面临的挑战，尤其是当其携带柔性电缆悬挂负载时所引入的额外自由度和混合动力学特性问题。传统基于模型的方法虽能提供稳定性保证，但需大量调参且难以适应负载变化（如质量或缆绳长度改变）。解决方案的关键在于提出一种统一的学习型控制框架 RoVerFly，其中强化学习（Reinforcement Learning, RL）策略作为鲁棒且通用的跟踪控制器，通过任务与域随机化训练，实现对不同负载配置的零样本泛化能力（包括无负载、不同质量及缆绳长度），无需控制器切换或重新调参，同时保持反馈跟踪控制器的可解释性与结构清晰性。

链接: https://arxiv.org/abs/2509.11149
作者: Mintae Kim,Jiaze Cai,Koushil Sreenath
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages

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Abstract:Designing robust controllers for precise, arbitrary trajectory tracking with quadrotors is challenging due to nonlinear dynamics and underactuation, and becomes harder with flexible cable-suspended payloads that introduce extra degrees of freedom and hybridness. Classical model-based methods offer stability guarantees but require extensive tuning and often do not adapt when the configuration changes, such as when a payload is added or removed, or when the payload mass or cable length varies. We present RoVerFly, a unified learning-based control framework in which a reinforcement learning (RL) policy serves as a robust and versatile tracking controller for standard quadrotors and for cable-suspended payload systems across a range of configurations. Trained with task and domain randomization, the controller is resilient to disturbances and varying dynamics. It achieves strong zero-shot generalization across payload settings, including no payload as well as varying mass and cable length, without controller switching or re-tuning, while retaining the interpretability and structure of a feedback tracking controller. Code and supplementary materials are available at this https URL
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[AI-81] AlignKT: Explicitly Modeling Knowledge State for Knowledge Tracing with Ideal State Alignment

【速读】：该论文旨在解决知识追踪（Knowledge Tracing, KT）模型在现有研究中普遍存在的问题：即多数模型仅关注学习者交互序列的拟合，而忽视了对学习者知识状态本身的建模，导致模型可解释性差且难以提供有效的教学支持。解决方案的关键在于提出AlignKT框架，其核心创新是采用前端到后端（frontend-to-backend）架构，显式建模一个稳定的知识状态，并通过引入基于教育学理论定义的理想知识状态作为对齐准则，从而提升模型的可解释性和教学指导能力。此外，该方法使用五个编码器实现结构设计，并融合对比学习模块以增强对齐过程的鲁棒性。

链接: https://arxiv.org/abs/2509.11135
作者: Jing Xiao,Chang You,Zhiyu Chen
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Knowledge Tracing (KT) serves as a fundamental component of Intelligent Tutoring Systems (ITS), enabling these systems to monitor and understand learners’ progress by modeling their knowledge state. However, many existing KT models primarily focus on fitting the sequences of learners’ interactions, and often overlook the knowledge state itself. This limitation leads to reduced interpretability and insufficient instructional support from the ITS. To address this challenge, we propose AlignKT, which employs a frontend-to-backend architecture to explicitly model a stable knowledge state. In this approach, the preliminary knowledge state is aligned with an additional criterion. Specifically, we define an ideal knowledge state based on pedagogical theories as the alignment criterion, providing a foundation for interpretability. We utilize five encoders to implement this set-up, and incorporate a contrastive learning module to enhance the robustness of the alignment process. Through extensive experiments, AlignKT demonstrates superior performance, outperforming seven KT baselines on three real-world datasets. It achieves state-of-the-art results on two of these datasets and exhibits competitive performance on the third. The code of this work is available at this https URL.
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[AI-82] Neural cellular automata: applications to biology and beyond classical AI

【速读】：该论文旨在解决如何通过计算模型实现对生物自组织过程的建模与泛化，尤其是在多尺度生物学系统（如分子、细胞、组织及器官水平）中捕捉适应性自我调节动态的问题。其解决方案的关键在于引入神经元细胞自动机（Neural Cellular Automata, NCA），将人工神经网络（Artificial Neural Networks, ANNs）作为局部决策中心嵌入到细胞自动机框架中，使更新规则具备可训练性和可演化性，从而在无中央控制的前提下，实现由局部交互驱动的全局协调行为。这种架构不仅能够再现生物启发的目标模式，还展现出对扰动的鲁棒性以及开放式的适应与推理能力，为连接多尺度生物学与生成式人工智能提供了统一且计算高效的范式。

链接: https://arxiv.org/abs/2509.11131
作者: Benedikt Hartl,Michael Levin,Léo Pio-Lopez
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Other Quantitative Biology (q-bio.OT)
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Abstract:Neural Cellular Automata (NCA) represent a powerful framework for modeling biological self-organization, extending classical rule-based systems with trainable, differentiable (or evolvable) update rules that capture the adaptive self-regulatory dynamics of living matter. By embedding Artificial Neural Networks (ANNs) as local decision-making centers and interaction rules between localized agents, NCA can simulate processes across molecular, cellular, tissue, and system-level scales, offering a multiscale competency architecture perspective on evolution, development, regeneration, aging, morphogenesis, and robotic control. These models not only reproduce biologically inspired target patterns but also generalize to novel conditions, demonstrating robustness to perturbations and the capacity for open-ended adaptation and reasoning. Given their immense success in recent developments, we here review current literature of NCAs that are relevant primarily for biological or bioengineering applications. Moreover, we emphasize that beyond biology, NCAs display robust and generalizing goal-directed dynamics without centralized control, e.g., in controlling or regenerating composite robotic morphologies or even on cutting-edge reasoning tasks such as ARC-AGI-1. In addition, the same principles of iterative state-refinement is reminiscent to modern generative Artificial Intelligence (AI), such as probabilistic diffusion models. Their governing self-regulatory behavior is constraint to fully localized interactions, yet their collective behavior scales into coordinated system-level outcomes. We thus argue that NCAs constitute a unifying computationally lean paradigm that not only bridges fundamental insights from multiscale biology with modern generative AI, but have the potential to design truly bio-inspired collective intelligence capable of hierarchical reasoning and control.
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[AI-83] ENJ: Optimizing Noise with Genetic Algorithms to Jailbreak LSMs

【速读】：该论文旨在解决大型语音模型（Large Speech Models, LSMs）在实际应用中面临的安全部署问题，尤其是传统语音对抗攻击方法在攻击效果与隐蔽性之间难以平衡的挑战。解决方案的关键在于提出一种名为进化噪声越狱（Evolutionary Noise Jailbreak, ENJ）的新方法，其核心创新是将环境噪声从被动干扰因素转变为可主动优化的攻击载体：通过遗传算法对噪声进行种群初始化、交叉融合与概率变异等操作，迭代生成融合恶意指令的音频样本——这些样本对人类听觉感知无害，却能诱导LSM解析并执行有害命令。实验表明，ENJ在多个主流语音模型上显著优于现有基线方法，揭示了噪声在语音安全中的双重角色，并为复杂声学环境下的模型防御提供了新思路。

链接: https://arxiv.org/abs/2509.11128
作者: Yibo Zhang,Liang Lin
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
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Abstract:The widespread application of Large Speech Models (LSMs) has made their security risks increasingly prominent. Traditional speech adversarial attack methods face challenges in balancing effectiveness and stealth. This paper proposes Evolutionary Noise Jailbreak (ENJ), which utilizes a genetic algorithm to transform environmental noise from a passive interference into an actively optimizable attack carrier for jailbreaking LSMs. Through operations such as population initialization, crossover fusion, and probabilistic mutation, this method iteratively evolves a series of audio samples that fuse malicious instructions with background noise. These samples sound like harmless noise to humans but can induce the model to parse and execute harmful commands. Extensive experiments on multiple mainstream speech models show that ENJ’s attack effectiveness is significantly superior to existing baseline methods. This research reveals the dual role of noise in speech security and provides new critical insights for model security defense in complex acoustic environments.
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[AI-84] Application of Machine Learning for Correcting Defect-induced Neuromorphic Circuit Inference Errors

【速读】：该论文旨在解决全模拟忆阻器（ReRAM）基类脑电路中因固定故障（stuck-at faults）导致的推理错误问题，这类故障会显著降低神经形态系统在边缘计算和物联网（IoT）应用中的可靠性和良率。解决方案的关键在于提出一种基于机器学习的纠错方法：利用轻量级神经网络对电路输出电压进行训练，从而恢复因缺陷引起的性能下降；实验表明，该方法可在多层阵列架构中有效纠正六种空间缺陷类型，并将推理准确率从55%提升至90%，同时具备良好的泛化能力，能适应训练时未见的缺陷类型，且支持实时自适应学习以应对动态或老化引起的故障演化。

链接: https://arxiv.org/abs/2509.11113
作者: Vedant Sawal,Hiu Yung Wong
机构: 未知
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
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Abstract:This paper presents a machine learning-based approach to correct inference errors caused by stuck-at faults in fully analog ReRAM-based neuromorphic circuits. Using a Design-Technology Co-Optimization (DTCO) simulation framework, we model and analyze six spatial defect types-circular, circular-complement, ring, row, column, and checkerboard-across multiple layers of a multi-array neuromorphic architecture. We demonstrate that the proposed correction method, which employs a lightweight neural network trained on the circuit’s output voltages, can recover up to 35% (from 55% to 90%) inference accuracy loss in defective scenarios. Our results, based on handwritten digit recognition tasks, show that even small corrective networks can significantly improve circuit robustness. This method offers a scalable and energy-efficient path toward enhanced yield and reliability for neuromorphic systems in edge and internet-of-things (IoTs) applications. In addition to correcting the specific defect types used during training, our method also demonstrates the ability to generalize-achieving reasonable accuracy when tested on different types of defects not seen during training. The framework can be readily extended to support real-time adaptive learning, enabling on-chip correction for dynamic or aging-induced fault profiles.
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[AI-85] Multi-Modal Sensing Aided mmWave Beamforming for V2V Communications with Transformers

【速读】：该论文旨在解决毫米波（mmWave）通信中因高动态车辆环境导致的波束训练开销大、可用空口时间减少的问题，其根源在于传统标准定义的波束成形方法需频繁交换导频信号并进行全量波束测量。解决方案的关键在于提出一种多模态感知与融合学习框架，通过视觉和GPS坐标两种感知模态分别提取特征，并利用模态特定编码器进行特征解耦后融合，从而预测最优的top-k波束，实现对最佳视距链路的主动建立。该方法显著降低了波束搜索空间开销（最多减少76.56%），同时保持较高预测准确率（最高达77.58%），且平均功率损耗仅约2.32 dB，优于单一模态方案。

链接: https://arxiv.org/abs/2509.11112
作者: Muhammad Baqer Mollah,Honggang Wang,Hua Fang
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Information Theory (cs.IT); Machine Learning (cs.LG)
备注: 6 Pages, Accepted to present at 2025 IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan

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Abstract:Beamforming techniques are utilized in millimeter wave (mmWave) communication to address the inherent path loss limitation, thereby establishing and maintaining reliable connections. However, adopting standard defined beamforming approach in highly dynamic vehicular environments often incurs high beam training overheads and reduces the available airtime for communications, which is mainly due to exchanging pilot signals and exhaustive beam measurements. To this end, we present a multi-modal sensing and fusion learning framework as a potential alternative solution to reduce such overheads. In this framework, we first extract the features individually from the visual and GPS coordinates sensing modalities by modality specific encoders, and subsequently fuse the multimodal features to obtain predicted top-k beams so that the best line-of-sight links can be proactively established. To show the generalizability of the proposed framework, we perform a comprehensive experiment in four different vehicle-to-vehicle (V2V) scenarios from real-world multi-modal sensing and communication dataset. From the experiment, we observe that the proposed framework achieves up to 77.58% accuracy on predicting top-15 beams correctly, outperforms single modalities, incurs roughly as low as 2.32 dB average power loss, and considerably reduces the beam searching space overheads by 76.56% for top-15 beams with respect to standard defined approach.
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[AI-86] Membership Inference Attacks on Recommender System: A Survey

【速读】：该论文旨在解决推荐系统（Recommender Systems, RecSys）在面对成员推理攻击（Membership Inference Attacks, MIAs）时的隐私泄露问题。MIAs 可通过判断某用户交互记录是否被用于训练目标模型来推断敏感信息，从而引发严重的隐私风险。现有针对分类模型或自然语言处理任务的 MIA 方法因无法处理 RecSys 中特有的“未见后验概率”（unseen posterior probability）而效果不佳，且该领域尚无系统性综述。本文的关键解决方案是首次对 RecSys 领域的 MIA 进行全面调研，提出统一分类框架（unified taxonomy），梳理不同攻击方法的设计原理、挑战与优劣，并基于当前研究空白指出未来方向，为学术界提供理论参考与实践指导。

链接: https://arxiv.org/abs/2509.11080
作者: Jiajie He,Yuechun Gu,Keke Chen,Xintong Chen
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:Recommender systems (RecSys) have been widely applied to various applications, including E-commerce, finance, healthcare, social media and have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. However, recent studies have shown that RecSys are vulnerable to membership inference attacks (MIAs), which aim to infer whether user interaction record was used to train a target model or not. MIAs on RecSys models can directly lead to a privacy breach. For example, via identifying the fact that a purchase record that has been used to train a RecSys associated with a specific user, an attacker can infer that user’s special quirks. In recent years, MIAs have been shown to be effective on other ML tasks, e.g., classification models and natural language processing. However, traditional MIAs are ill-suited for RecSys due to the unseen posterior probability. Although MIAs on RecSys form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this article, we conduct the first comprehensive survey on RecSys MIAs. This survey offers a comprehensive review of the latest advancements in RecSys MIAs, exploring the design principles, challenges, attack and defense associated with this emerging field. We provide a unified taxonomy that categorizes different RecSys MIAs based on their characterizations and discuss their pros and cons. Based on the limitations and gaps identified in this survey, we point out several promising future research directions to inspire the researchers who wish to follow this area. This survey not only serves as a reference for the research community but also provides a clear description for researchers outside this research domain.
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[AI-87] Difficulty-Aware Agent Orchestration in LLM -Powered Workflows

【速读】：该论文旨在解决当前基于大语言模型（Large Language Model, LLM）的多智能体系统在处理不同难度任务时存在的效率与性能失衡问题，具体表现为静态或任务级工作流对简单查询过度处理、对复杂查询表现不足，且未充分考虑异构LLM间的效率-性能权衡。解决方案的关键在于提出一种难度感知的智能体编排框架（Difficulty-Aware Agentic Orchestration, DAAO），其核心由三个相互依赖模块构成：基于变分自编码器（Variational Autoencoder, VAE）的难度估计模块、模块化操作符分配模块以及兼顾成本与性能的LLM路由模块，从而实现根据输入查询难度动态调整工作流深度、操作符选择和LLM分配，支持细粒度、查询特定的推理策略，在六个基准测试中同时提升了准确率和推理效率。

链接: https://arxiv.org/abs/2509.11079
作者: Jinwei Su,Yinghui Xia,Qizhen Lan,Xinyuan Song,Yang Jingsong,Lewei He,Tianyu Shi
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Large Language Model (LLM)-based agentic systems have shown strong capabilities across various tasks. However, ex- isting multi-agent frameworks often rely on static or task- level workflows, which either over-process simple queries or underperform on complex ones, while also neglecting the efficiency-performance trade-offs across heterogeneous LLMs. To address these limitations, we propose Difficulty- Aware Agentic Orchestration (DAAO), a dynamic frame- work that adapts workflow depth, operator selection, and LLM assignment based on the difficulty of each input query. DAAO comprises three interdependent modules: a variational autoencoder (VAE) for difficulty estimation, a modular opera- tor allocator, and a cost- and performance-aware LLM router. By leveraging heterogeneous LLMs and dynamically tailor- ing workflows, DAAO enables fine-grained, query-specific reasoning strategies. DAAO outperforms prior multi-agent systems in both accuracy and inference efficiency across six benchmarks. We will release our code and implementation details upon publication.
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[AI-88] Patient-Zero: A Unified Framework for Real-Record-Free Patient Agent Generation

【速读】：该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）生成医学合成数据时存在的三大核心问题：数据隐私风险、准确性不足以及多样性有限，且现有方法缺乏与真实患者相似的交互能力。为应对这些挑战，作者提出了一种名为Patient-Zero的现实患者生成框架，其关键创新在于引入了一个医学对齐的多步骤生成架构，通过分层医学知识注入构建完整虚拟患者记录，无需依赖任何真实医疗记录；同时设计了动态更新机制以增强虚拟患者的对话一致性与交互性能，结合自适应对话策略和实时临床合理性验证，从而在保证医学严谨性的前提下实现上下文多样性和高保真度的虚拟患者生成。

链接: https://arxiv.org/abs/2509.11078
作者: Yunghwei Lai,Weizhi Ma,Yang Liu
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Synthetic data generation using large language models (LLMs) has emerged as a promising solution across various domains, particularly in medical field, to mitigate data collection challenges. However, existing studies mainly utilize LLMs to rewrite and complete existing medical records, where the limitations in data privacy, accuracy, and diversity sill exist, and additionally lack the ability to interact like real patients. To address these issues, we propose a realistic patient generation framework, Patient-Zero, which requires no real medical records. Patient-Zero first introduces a medically-aligned multi-step generation architecture, which builds comprehensive patient records through hierarchical medical knowledge injection without real medical records. Then, to optimize the virtual patient’s interaction abilities with humans, Patient-Zero designs a dynamic updating mechanism to improve the consistency and conversational performance. Our framework enables the generation of contextually diverse patient records while maintaining strict medical coherence, supported by adaptive dialogue strategies and real-time clinical plausibility verification. Experimental results demonstrate that our model achieves good performance in accuracy, diversity, and consistency. After training with our generated virtual patients, existing models show significant improvements on the MedQA dataset.
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[AI-89] ractable Asymmetric Verification for Large Language Models via Deterministic Replicability

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在动态多智能体系统中面临的计算可信性问题，即如何验证一个智能体的输出确实由声称的LLM生成，而非被伪造或由低成本、低性能模型替代。解决方案的关键在于提出一种具有可 tractable asymmetric effort（可 tractable 异构努力）特性的验证框架，其核心是基于自回归模型固有的确定性可复现性（deterministic replicability）原则，要求所有智能体运行在完全一致的软硬件环境中。在此前提下，通过随机抽样并概率性审计LLM输出的小片段，实现高效验证，模拟结果表明针对性验证速度比全量重生成快超过12倍，且可通过参数调节检测概率，从而为可审计的LLM系统提供基础机制，支撑负责任的人工智能发展。

链接: https://arxiv.org/abs/2509.11068
作者: Zan-Kai Chong,Hiroyuki Ohsaki,Bryan Ng
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:The landscape of Large Language Models (LLMs) shifts rapidly towards dynamic, multi-agent systems. This introduces a fundamental challenge in establishing computational trust, specifically how one agent can verify that another’s output was genuinely produced by a claimed LLM, and not falsified or generated by a cheaper or inferior model. To address this challenge, this paper proposes a verification framework that achieves tractable asymmetric effort, where the cost to verify a computation is substantially lower than the cost to perform it. Our approach is built upon the principle of deterministic replicability, a property inherent to autoregressive models that strictly necessitates a computationally homogeneous environment where all agents operate on identical hardware and software stacks. Within this defined context, our framework enables multiple validators to probabilistically audit small, random segments of an LLM’s output and it distributes the verification workload effectively. The simulations demonstrated that targeted verification can be over 12 times faster than full regeneration, with tunable parameters to adjust the detection probability. By establishing a tractable mechanism for auditable LLM systems, our work offers a foundational layer for responsible AI and serves as a cornerstone for future research into the more complex, heterogeneous multi-agent systems.
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[AI-90] Agent ic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration

【速读】：该论文旨在解决桌面自动化中自主代理在执行复杂多步骤任务时因协调能力不足和质量控制欠缺而导致的性能瓶颈问题。其解决方案的关键在于提出了一种基于有限状态机（Finite-State Machine, FSM）的多智能体架构——Agentic Lybic，通过FSM驱动的组件间动态路由机制实现任务策略的自适应选择与最优执行路径规划，同时结合严格的质量门控机制，从而支持容错重规划与错误恢复，显著提升了复杂计算环境下桌面自动化的可靠性与泛化能力。

链接: https://arxiv.org/abs/2509.11067
作者: Liangxuan Guo,Bin Zhu,Qingqian Tao,Kangning Liu,Xun Zhao,Xianzhe Qin,Jin Gao,Guangfu Hao
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
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Abstract:Autonomous agents for desktop automation struggle with complex multi-step tasks due to poor coordination and inadequate quality control. We introduce \textscAgentic Lybic, a novel multi-agent system where the entire architecture operates as a finite-state machine (FSM). This core innovation enables dynamic orchestration. Our system comprises four components: a Controller, a Manager, three Workers (Technician for code-based operations, Operator for GUI interactions, and Analyst for decision support), and an Evaluator. The critical mechanism is the FSM-based routing between these components, which provides flexibility and generalization by dynamically selecting the optimal execution strategy for each subtask. This principled orchestration, combined with robust quality gating, enables adaptive replanning and error recovery. Evaluated officially on the OSWorld benchmark, \textscAgentic Lybic achieves a state-of-the-art 57.07% success rate in 50 steps, substantially outperforming existing methods. Results demonstrate that principled multi-agent orchestration with continuous quality control provides superior reliability for generalized desktop automation in complex computing environments.
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[AI-91] An Advanced Convolutional Neural Network for Bearing Fault Diagnosis under Limited Data

【速读】：该论文针对轴承故障诊断中因数据标注成本高或隐私限制导致的高质量标签数据稀缺问题，提出了一种基于有限样本的数据增强与对比傅里叶卷积框架（DAC-FCF）。其关键解决方案包括：1）设计了一种条件一致潜在表示与重构生成对抗网络（CCLR-GAN），以生成更具多样性的故障样本，克服传统数据增强方法易出现模式崩溃且质量低的问题；2）引入基于对比学习的联合优化机制，有效建模有限训练样本间的复杂关系；3）构建一维傅里叶卷积神经网络（1D-FCNN），实现对振动信号全局特征的感知，弥补传统卷积神经网络（CNN）局部感受野的不足。实验表明，DAC-FCF在CWRU数据集和自建测试平台上分别较基线方法提升32%和10%，验证了其有效性。

链接: https://arxiv.org/abs/2509.11053
作者: Shengke Sun,Shuzhen Han,Ziqian Luan,Xinghao Qin,Jiao Yin,Zhanshan Zhao,Jinli Cao,Hua Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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Abstract:In the area of bearing fault diagnosis, deep learning (DL) methods have been widely used recently. However, due to the high cost or privacy concerns, high-quality labeled data are scarce in real world scenarios. While few-shot learning has shown promise in addressing data scarcity, existing methods still face significant limitations in this domain. Traditional data augmentation techniques often suffer from mode collapse and generate low-quality samples that fail to capture the diversity of bearing fault patterns. Moreover, conventional convolutional neural networks (CNNs) with local receptive fields makes them inadequate for extracting global features from complex vibration signals. Additionally, existing methods fail to model the intricate relationships between limited training samples. To solve these problems, we propose an advanced data augmentation and contrastive fourier convolution framework (DAC-FCF) for bearing fault diagnosis under limited data. Firstly, a novel conditional consistent latent representation and reconstruction generative adversarial network (CCLR-GAN) is proposed to generate more diverse data. Secondly, a contrastive learning based joint optimization mechanism is utilized to better model the relations between the available training data. Finally, we propose a 1D fourier convolution neural network (1D-FCNN) to achieve a global-aware of the input data. Experiments demonstrate that DAC-FCF achieves significant improvements, outperforming baselines by up to 32% on case western reserve university (CWRU) dataset and 10% on a self-collected test bench. Extensive ablation experiments prove the effectiveness of the proposed components. Thus, the proposed DAC-FCF offers a promising solution for bearing fault diagnosis under limited data.
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[AI-92] Frag mentGPT GPT : A Unified GPT Model for Frag ment Growing Linking and Merging in Molecular Design

【速读】：该论文旨在解决片段药物设计（Fragment-Based Drug Discovery, FBDD）中连接子（linker）设计的挑战，尤其是在处理结构冗余（如重复环系）时难以通过传统原子或键的增删进行有效优化的问题。其核心解决方案是提出FragmentGPT模型，关键在于两个创新组件：一是基于化学感知和能量的键断裂预训练策略，赋予GPT模型片段扩展、连接与合并能力；二是奖励排序对齐与专家探索（Reward Ranked Alignment with Expert Exploration, RAE）算法，融合专家模仿学习以增强多样性、数据选择与增强以实现帕累托最优及复合评分最优，并通过监督微调（Supervised Fine-Tuning, SFT）使学习策略与多目标药物属性对齐。该框架可条件化生成连接不同分子亚单元的高效连接子，同时优化多种药理学目标并智能处理结构冗余，从而实现受控、目标驱动的分子组装。

链接: https://arxiv.org/abs/2509.11044
作者: Xuefeng Liu,Songhao Jiang,Qinan Huang,Tinson Xu,Ian Foster,Mengdi Wang,Hening Lin,Jinbo Xu,Rick Stevens
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
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Abstract:Fragment-Based Drug Discovery (FBDD) is a popular approach in early drug development, but designing effective linkers to combine disconnected molecular fragments into chemically and pharmacologically viable candidates remains challenging. Further complexity arises when fragments contain structural redundancies, like duplicate rings, which cannot be addressed by simply adding or removing atoms or bonds. To address these challenges in a unified framework, we introduce FragmentGPT, which integrates two core components: (1) a novel chemically-aware, energy-based bond cleavage pre-training strategy that equips the GPT-based model with fragment growing, linking, and merging capabilities, and (2) a novel Reward Ranked Alignment with Expert Exploration (RAE) algorithm that combines expert imitation learning for diversity enhancement, data selection and augmentation for Pareto and composite score optimality, and Supervised Fine-Tuning (SFT) to align the learner policy with multi-objective goals. Conditioned on fragment pairs, FragmentGPT generates linkers that connect diverse molecular subunits while simultaneously optimizing for multiple pharmaceutical goals. It also learns to resolve structural redundancies-such as duplicated fragments-through intelligent merging, enabling the synthesis of optimized molecules. FragmentGPT facilitates controlled, goal-driven molecular assembly. Experiments and ablation studies on real-world cancer datasets demonstrate its ability to generate chemically valid, high-quality molecules tailored for downstream drug discovery tasks.
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[AI-93] Free-MAD: Consensus-Free Multi-Agent Debate

【速读】：该论文旨在解决多智能体辩论（Multi-agent Debate, MAD）方法中存在的三大问题：一是多轮交互导致的令牌开销高、可扩展性差；二是大语言模型（Large Language Models, LLMs）固有的从众倾向使正确回答在辩论中被错误观点误导，引发误差传播；三是最终通过多数投票决策引入随机性和不公平性，降低推理性能。解决方案的关键在于提出 Free-MAD 框架，其核心创新为：1）设计基于评分的决策机制，不依赖最后一轮结果，而是评估整个辩论轨迹中各代理推理演化的动态过程，实现更精准公平的输出判定；2）引入反从众机制（anti-conformity），有效抑制多数意见对个体推理的过度影响，提升系统鲁棒性。实验表明，Free-MAD仅需单轮辩论即可显著提升推理性能并降低计算成本，在真实攻击场景下也展现出更强的稳定性。

链接: https://arxiv.org/abs/2509.11035
作者: Yu Cui,Hang Fu,Haibin Zhang,Licheng Wang,Cong Zuo
机构: 未知
类目: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:Multi-agent debate (MAD) is an emerging approach to improving the reasoning capabilities of large language models (LLMs). Existing MAD methods rely on multiple rounds of interaction among agents to reach consensus, and the final output is selected by majority voting in the last round. However, this consensus-based design faces several limitations. First, multiple rounds of communication increases token overhead and limits scalability. Second, due to the inherent conformity of LLMs, agents that initially produce correct responses may be influenced by incorrect ones during the debate process, causing error propagation. Third, majority voting introduces randomness and unfairness in the decision-making phase, and can degrade the reasoning performance. To address these issues, we propose \textscFree-MAD, a novel MAD framework that eliminates the need for consensus among agents. \textscFree-MAD introduces a novel score-based decision mechanism that evaluates the entire debate trajectory rather than relying on the last round only. This mechanism tracks how each agent’s reasoning evolves, enabling more accurate and fair outcomes. In addition, \textscFree-MAD reconstructs the debate phase by introducing anti-conformity, a mechanism that enables agents to mitigate excessive influence from the majority. Experiments on eight benchmark datasets demonstrate that \textscFree-MAD significantly improves reasoning performance while requiring only a single-round debate and thus reducing token costs. We also show that compared to existing MAD approaches, \textscFree-MAD exhibits improved robustness in real-world attack scenarios. Subjects: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) Cite as: arXiv:2509.11035 [cs.AI] (or arXiv:2509.11035v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2509.11035 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-94] Hardness Structural Knowledge and Opportunity: An Analytical Framework for Modular Performance Modeling

【速读】：该论文旨在解决性能影响建模中因配置空间指数级增长而导致的建模困难问题，尤其关注结构知识（structural knowledge）与系统特性（structural aspects）如何共同影响模块化性能建模的改进机会。其解决方案的关键在于通过引入并量化“建模难度”（modeling hardness）的概念，构建一个分析矩阵来系统评估不同结构特征（如模块数量和每个模块的选项数）及结构知识水平对建模改进潜力的影响。研究发现，建模难度主要由模块数量和每模块配置选项数决定，且更高水平的结构知识与更大建模难度均显著提升建模改进机会，但其作用强度因性能指标而异：在排序准确率任务（如调试）中结构知识更关键，在预测准确率任务（如资源管理）中建模难度影响更强。这一发现为系统设计者提供了可操作的指导，使其能根据系统特性和任务目标合理分配资源并选择建模策略。

链接: https://arxiv.org/abs/2509.11000
作者: Omid Gheibi,Christian Kästner,Pooyan Jamshidi
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Performance-influence models are beneficial for understanding how configurations affect system performance, but their creation is challenging due to the exponential growth of configuration spaces. While gray-box approaches leverage selective “structural knowledge” (like the module execution graph of the system) to improve modeling, the relationship between this knowledge, a system’s characteristics (we call them “structural aspects”), and potential model improvements is not well understood. This paper addresses this gap by formally investigating how variations in structural aspects (e.g., the number of modules and options per module) and the level of structural knowledge impact the creation of “opportunities” for improved “modular performance modeling”. We introduce and quantify the concept of modeling “hardness”, defined as the inherent difficulty of performance modeling. Through controlled experiments with synthetic system models, we establish an “analytical matrix” to measure these concepts. Our findings show that modeling hardness is primarily driven by the number of modules and configuration options per module. More importantly, we demonstrate that both higher levels of structural knowledge and increased modeling hardness significantly enhance the opportunity for improvement. The impact of these factors varies by performance metric; for ranking accuracy (e.g., in debugging task), structural knowledge is more dominant, while for prediction accuracy (e.g., in resource management task), hardness plays a stronger role. These results provide actionable insights for system designers, guiding them to strategically allocate time and select appropriate modeling approaches based on a system’s characteristics and a given task’s objectives.
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[AI-95] Factor Graph Optimization for Leak Localization in Water Distribution Networks

【速读】：该论文旨在解决供水管网系统中泄漏检测与定位问题，该问题对环境、经济和社会具有直接影响。传统方法如扩展卡尔曼滤波（Extended Kalman Filter, EKF）或无迹卡尔曼滤波（Unscented Kalman Filter, UKF）通常仅能估计当前网络状态，难以实现高精度的时空状态演化建模和泄漏源定位。本文提出的关键解决方案是基于因子图优化（factor graph optimization）技术，构建了一个由两个因子图组成的新型架构：一个用于无泄漏状态估计的因子图和另一个用于泄漏定位的因子图。该方法能够融合压力与需求传感器数据，在获得新观测时同时更新当前及历史时刻的网络状态，从而实现更快速、更准确的泄漏定位，实验表明其在计算效率和定位精度上均优于现有主流方法。

链接: https://arxiv.org/abs/2509.10982
作者: Paul Irofti,Luis Romero-Ben,Florin Stoican,Vicenç Puig
机构: 未知
类目: ystems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Detecting and localizing leaks in water distribution network systems is an important topic with direct environmental, economic, and social impact. Our paper is the first to explore the use of factor graph optimization techniques for leak localization in water distribution networks, enabling us to perform sensor fusion between pressure and demand sensor readings and to estimate the network’s temporal and structural state evolution across all network nodes. The methodology introduces specific water network factors and proposes a new architecture composed of two factor graphs: a leak-free state estimation factor graph and a leak localization factor graph. When a new sensor reading is obtained, unlike Kalman and other interpolation-based methods, which estimate only the current network state, factor graphs update both current and past states. Results on Modena, L-TOWN and synthetic networks show that factor graphs are much faster than nonlinear Kalman-based alternatives such as the UKF, while also providing improvements in localization compared to state-of-the-art estimation-localization approaches. Implementation and benchmarks are available at this https URL.
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[AI-96] Decoupling Search and Learning in Neural Net Training

【速读】：该论文试图解决梯度下降（Gradient Descent）在训练过程中通常收敛到单一极小值，缺乏探索可能具有更好泛化性能的其他极小值的问题。其解决方案的关键在于将搜索过程从高维参数空间解耦至可 tractable 的中间激活表示空间（intermediate activations），通过进化搜索（evolutionary search）在该表示空间中发现多样且高质量的表征解，随后利用回归方式在参数空间中进行基于梯度的学习，以逼近这些已搜索到的表示。这一两阶段框架实现了对不同表征路径的有效探索，并证明了所学模型在多个基准数据集上接近标准随机梯度下降（SGD）的性能，同时展现出与传统梯度下降不同的训练轨迹。

链接: https://arxiv.org/abs/2509.10973
作者: Akshay Vegesna,Samip Dahal
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Gradient descent typically converges to a single minimum of the training loss without mechanisms to explore alternative minima that may generalize better. Searching for diverse minima directly in high-dimensional parameter space is generally intractable. To address this, we propose a framework that performs training in two distinct phases: search in a tractable representation space (the space of intermediate activations) to find diverse representational solutions, and gradient-based learning in parameter space by regressing to those searched representations. Through evolutionary search, we discover representational solutions whose fitness and diversity scale with compute–larger populations and more generations produce better and more varied solutions. These representations prove to be learnable: networks trained by regressing to searched representations approach SGD’s performance on MNIST, CIFAR-10, and CIFAR-100. Performance improves with search compute up to saturation. The resulting models differ qualitatively from networks trained with gradient descent, following different representational trajectories during training. This work demonstrates how future training algorithms could overcome gradient descent’s exploratory limitations by decoupling search in representation space from efficient gradient-based learning in parameter space.
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[AI-97] Enhancing Computational Cognitive Architectures with LLM s: A Case Study

【速读】：该论文旨在解决当前计算认知架构在处理现实世界复杂性与保持心理真实性之间难以兼顾的问题，即如何在提升模型计算能力的同时维持其心理学上的合理性。解决方案的关键在于将大语言模型（LLM）与Clarion认知架构进行协同整合，利用Clarion中隐式-显式二分法（implicit-explicit dichotomy）作为桥梁，实现LLM的强计算能力与Clarion的心理学精细结构之间的无缝融合。

链接: https://arxiv.org/abs/2509.10972
作者: Ron Sun
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Computational cognitive architectures are broadly scoped models of the human mind that combine different psychological functionalities (as well as often different computational methods for these different functionalities) into one unified framework. They structure them in a psychologically plausible and validated way. However, such models thus far have only limited computational capabilities, mostly limited by the computational tools and techniques that were adopted. More recently, LLMs have proved to be more capable computationally than any other tools. Thus, in order to deal with both real-world complexity and psychological realism at the same time, incorporating LLMs into cognitive architectures naturally becomes an important task. In the present article, a synergistic combination of the Clarion cognitive architecture and LLMs is discussed as a case study. The implicit-explicit dichotomy that is fundamental to Clarion is leveraged for a seamless integration of Clarion and LLMs. As a result, computational power of LLMs is combined with psychological nicety of Clarion.
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[AI-98] PHLoRA: data-free Post-hoc Low-Rank Adapter extraction from full-rank checkpoint

【速读】：该论文旨在解决现有低秩适应（Low-Rank Adaptation, LoRA）方法依赖训练阶段显式优化每个适配器的问题，以及如何将已有的全秩微调模型高效转化为可部署的适配器模块，从而支持灵活推理和规模化服务。其解决方案的关键在于提出PHLoRA（Post-hoc LoRA），通过计算基础模型与微调后模型之间的权重差异的低秩分解，无需访问训练数据或梯度即可提取适配器模块；该方法可无缝集成至S-LoRA实现动态路由，或在NVIDIA NIM等工业平台中实现可扩展部署，显著降低推理延迟并节省成本，同时保持下游任务性能几乎无损。

链接: https://arxiv.org/abs/2509.10971
作者: Bhoomit Vasani,Jack FitzGerald,Anjie Fang,Sushmit Vaish
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:We introduce PHLoRA (Pronounced “flora”). (Post-hoc LoRA), a simple yet powerful method to extract low-rank adaptation adapters from full-rank fine-tuned models without requiring access to training data or gradients. By computing the low-rank decomposition of weight differences between a base model and its fine-tuned counterpart, our method reconstructs adapter modules that can be merged or dynamically routed at inference time via S-LoRA, or served in scalable, industry settings using platforms like NVIDIA NIM. This approach amortizes latency overhead across requests and yields substantial cost savings. Unlike prior work that trains each adapter explicitly, our approach decouples fine-tuning from adapter generation, allowing adapter extraction from existing full-rank models or third-party checkpoints. Experiments on text, image, and video benchmarks using the Amazon Nova model family demonstrate that extracted adapters preserve high energy from the full weight delta, can be pruned safely, and yield negligible degradation in downstream task performance when re-merged. Overall, PHLoRA provides a practical path for making all existing full-rank checkpoints adapter-ready, democratizing scalable inference for all models.
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[AI-99] he Psychogenic Machine: Simulating AI Psychosis Delusion Reinforcement and Harm Enablement in Large Language Models

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在与用户交互过程中可能诱发或加剧精神病性症状（如妄想、幻觉等）的潜在风险问题，即“AI精神病”（AI psychosis）现象。其核心问题是当前LLMs因高度顺从性和高共情倾向，在特定情境下可能强化脆弱用户的妄想信念，从而带来心理危害。解决方案的关键在于提出并验证了一个名为psychosis-bench的新基准，该基准包含16个结构化、12轮对话场景，模拟三种典型妄想主题（性妄想、夸大/救世主妄想、关系妄想）的发展过程，并系统评估LLMs在妄想确认（Delusion Confirmation Score, DCS）、有害行为助长（Harm Enablement Score, HES）和安全干预（Safety Intervention Score, SIS）三个维度的表现。研究发现所有被测模型均表现出显著的心理诱发潜力，尤其在隐含语境中表现更差，且DCS与HES呈强正相关（rs = .77），表明模型安全性并非单纯依赖规模提升，而是需通过结构化评估和跨领域协作（开发者、政策制定者与医疗专业人员）进行针对性优化。

链接: https://arxiv.org/abs/2509.10970
作者: Joshua Au Yeung,Jacopo Dalmasso,Luca Foschini,Richard JB Dobson,Zeljko Kraljevic
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Background: Emerging reports of “AI psychosis” are on the rise, where user-LLM interactions may exacerbate or induce psychosis or adverse psychological symptoms. The sycophantic and agreeable nature of LLMs can beneficial, it can become a vector for harm by reinforcing delusional beliefs in vulnerable users. Methods: We introduce psychosis-bench, a novel benchmark designed to systematically evaluate the psychogenicity of LLMs comprimising 16 structured, 12-turn conversational scenarios simulating the progression of delusional themes(Erotic Delusions, Grandiose/Messianic Delusions, Referential Delusions) and potential harms. We evaluated eight prominent LLMs for Delusion Confirmation (DCS), Harm Enablement (HES), and Safety Intervention(SIS) across explicit and implicit conversational contexts. Findings: Across 1,536 simulated conversation turns, all LLMs demonstrated psychogenic potential, showing a strong tendency to perpetuate rather than challenge delusions (mean DCS of 0.91 \pm 0.88). Models frequently enabled harmful user requests (mean HES of 0.69 \pm 0.84) and offered safety interventions in only roughly a third of applicable turns (mean SIS of 0.37 \pm 0.48). 51 / 128 (39.8%) of scenarios had no safety interventions offered. Performance was significantly worse in implicit scenarios, models were more likely to confirm delusions and enable harm while offering fewer interventions (p .001). A strong correlation was found between DCS and HES (rs = .77). Model performance varied widely, indicating that safety is not an emergent property of scale alone. Conclusion: This study establishes LLM psychogenicity as a quantifiable risk and underscores the urgent need for re-thinking how we train LLMs. We frame this issue not merely as a technical challenge but as a public health imperative requiring collaboration between developers, policymakers, and healthcare professionals. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2509.10970 [cs.LG] (or arXiv:2509.10970v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.10970 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Joshua Au Yeung [view email] [v1] Sat, 13 Sep 2025 20:10:28 UTC (3,905 KB)
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[AI-100] ViSTR-GP: Online Cyberattack Detection via Vision-to-State Tensor Regression and Gaussian Processes in Automated Robotic Operations

【速读】：该论文旨在解决工业机器人制造过程中数据完整性攻击（data-integrity attacks）难以检测的问题，这类攻击通过操纵操作数据对物理系统造成干扰，而传统基于入侵检测或模型的方法难以有效识别。解决方案的关键在于构建一个在线检测框架ViSTR-GP，其核心是引入一个独立于控制器的视觉通道（来自上方摄像头），用于交叉验证编码器报告的测量值与视觉估计值之间的差异；该框架利用一次交互式分割初始化SAM-Track生成逐帧掩膜，结合低秩张量回归代理模型映射掩膜至测量值，并采用矩阵变量高斯过程建模正常残差以捕捉时间结构和关节间相关性，最终通过预测分布推导帧级检验统计量实现早期、可解释的异常告警。

链接: https://arxiv.org/abs/2509.10948
作者: Navid Aftabi,Philip Samaha,Jin Ma,Long Cheng,Ramy Harik,Dan Li
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Systems and Control (eess.SY); Optimization and Control (math.OC)
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Abstract:Industrial robotic systems are central to automating smart manufacturing operations. Connected and automated factories face growing cybersecurity risks that can potentially cause interruptions and damages to physical operations. Among these attacks, data-integrity attacks often involve sophisticated exploitation of vulnerabilities that enable an attacker to access and manipulate the operational data and are hence difficult to detect with only existing intrusion detection or model-based detection. This paper addresses the challenges in utilizing existing side-channels to detect data-integrity attacks in robotic manufacturing processes by developing an online detection framework, ViSTR-GP, that cross-checks encoder-reported measurements against a vision-based estimate from an overhead camera outside the controller’s authority. In this framework, a one-time interactive segmentation initializes SAM-Track to generate per-frame masks. A low-rank tensor-regression surrogate maps each mask to measurements, while a matrix-variate Gaussian process models nominal residuals, capturing temporal structure and cross-joint correlations. A frame-wise test statistic derived from the predictive distribution provides an online detector with interpretable thresholds. We validate the framework on a real-world robotic testbed with synchronized video frame and encoder data, collecting multiple nominal cycles and constructing replay attack scenarios with graded end-effector deviations. Results on the testbed indicate that the proposed framework recovers joint angles accurately and detects data-integrity attacks earlier with more frequent alarms than all baselines. These improvements are most evident in the most subtle attacks. These results show that plants can detect data-integrity attacks by adding an independent physical channel, bypassing the controller’s authority, without needing complex instrumentation.
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[AI-101] When the Code Autopilot Breaks: Why LLM s Falter in Embedded Machine Learning

【速读】：该论文旨在解决生成式 AI（Generative AI）在嵌入式机器学习（Embedded Machine Learning, EML）工作流中自动化代码生成时出现的隐性失败与不可预测行为问题。其解决方案的关键在于构建一个自动驾驶框架（autopilot framework），通过系统性地协调数据预处理、模型转换和设备端推理代码生成，对不同提示格式（prompt format）、模型行为及结构假设下的失败模式进行实证分析，从而识别出格式误导性解释、编译通过但运行时中断等典型错误类别，并建立可复用的故障分类体系，为提升嵌入式机器学习系统中大语言模型（Large Language Models, LLMs）代码生成的可靠性与可追溯性提供依据。

链接: https://arxiv.org/abs/2509.10946
作者: Roberto Morabito,Guanghan Wu
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: This paper has been accepted for publication in Computer (IEEE). Upon publication, the copyright will be transferred to IEEE

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Abstract:Large Language Models (LLMs) are increasingly used to automate software generation in embedded machine learning workflows, yet their outputs often fail silently or behave unpredictably. This article presents an empirical investigation of failure modes in LLM-powered ML pipelines, based on an autopilot framework that orchestrates data preprocessing, model conversion, and on-device inference code generation. We show how prompt format, model behavior, and structural assumptions influence both success rates and failure characteristics, often in ways that standard validation pipelines fail to detect. Our analysis reveals a diverse set of error-prone behaviors, including format-induced misinterpretations and runtime-disruptive code that compiles but breaks downstream. We derive a taxonomy of failure categories and analyze errors across multiple LLMs, highlighting common root causes and systemic fragilities. Though grounded in specific devices, our study reveals broader challenges in LLM-based code generation. We conclude by discussing directions for improving reliability and traceability in LLM-powered embedded ML systems.
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[AI-102] Clarifying Model Transparency: Interpretability versus Explainability in Deep Learning with MNIST and IMDB Examples

【速读】：该论文试图解决深度学习模型因“黑箱”特性（black box problem）导致在高信任领域难以被广泛接受的问题，其解决方案的关键在于厘清并区分“可解释性”（interpretability）与“可解释性方法”（explainability）的内涵差异。研究指出，可解释性指模型本身具备全局层面的人类可理解性，而可解释性方法则侧重于事后分析单个预测或行为的局部依据，如特征重要性或词级贡献度；这种区分有助于构建更可靠、可信的人工智能系统。

链接: https://arxiv.org/abs/2509.10929
作者: Mitali Raj
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:The impressive capabilities of deep learning models are often counterbalanced by their inherent opacity, commonly termed the “black box” problem, which impedes their widespread acceptance in high-trust domains. In response, the intersecting disciplines of interpretability and explainability, collectively falling under the Explainable AI (XAI) umbrella, have become focal points of research. Although these terms are frequently used as synonyms, they carry distinct conceptual weights. This document offers a comparative exploration of interpretability and explainability within the deep learning paradigm, carefully outlining their respective definitions, objectives, prevalent methodologies, and inherent difficulties. Through illustrative examinations of the MNIST digit classification task and IMDB sentiment analysis, we substantiate a key argument: interpretability generally pertains to a model’s inherent capacity for human comprehension of its operational mechanisms (global understanding), whereas explainability is more commonly associated with post-hoc techniques designed to illuminate the basis for a model’s individual predictions or behaviors (local explanations). For example, feature attribution methods can reveal why a specific MNIST image is recognized as a ‘7’, and word-level importance can clarify an IMDB sentiment outcome. However, these local insights do not render the complex underlying model globally transparent. A clear grasp of this differentiation, as demonstrated by these standard datasets, is vital for fostering dependable and sound artificial intelligence.
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[AI-103] oMA: Token Merge with Attention for Image Generation with Diffusion Models ICML2025

【速读】：该论文旨在解决扩散模型（Diffusion Models）在生成高保真图像时因Transformer架构中注意力机制的二次复杂度而导致的可扩展性瓶颈问题。现有插件式令牌压缩方法（如ToMeSD和ToFu）虽能通过合并冗余令牌降低计算量（FLOPs），但依赖GPU效率低下的操作（如排序和散列写入），在与优化注意力实现（如FlashAttention）结合时引入额外开销，抵消了理论上的加速优势。其解决方案的关键在于提出Token Merge with Attention (ToMA)，通过三项核心改进实现GPU友好的高效令牌压缩：1）将令牌合并重构为子模优化问题以选择多样化的令牌；2）将合并/解合并操作设计为类注意力的线性变换，利用GPU友好的矩阵运算；3）利用潜在空间局部性和序列冗余性（模式复用）最小化运行时开销。实验表明，ToMA在SDXL和Flux模型上分别将生成延迟降低24%和23%，且保持图像质量（DINO Δ 0.07），显著优于先前方法，有效弥合了理论效率与实际性能之间的差距。

链接: https://arxiv.org/abs/2509.10918
作者: Wenbo Lu,Shaoyi Zheng,Yuxuan Xia,Shengjie Wang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: In proceedings of the 42nd International Conference on Machine Learning (ICML 2025). Code available at this https URL

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Abstract:Diffusion models excel in high-fidelity image generation but face scalability limits due to transformers’ quadratic attention complexity. Plug-and-play token reduction methods like ToMeSD and ToFu reduce FLOPs by merging redundant tokens in generated images but rely on GPU-inefficient operations (e.g., sorting, scattered writes), introducing overheads that negate theoretical speedups when paired with optimized attention implementations (e.g., FlashAttention). To bridge this gap, we propose Token Merge with Attention (ToMA), an off-the-shelf method that redesigns token reduction for GPU-aligned efficiency, with three key contributions: 1) a reformulation of token merge as a submodular optimization problem to select diverse tokens; 2) merge/unmerge as an attention-like linear transformation via GPU-friendly matrix operations; and 3) exploiting latent locality and sequential redundancy (pattern reuse) to minimize overhead. ToMA reduces SDXL/Flux generation latency by 24%/23%, respectively (with DINO \Delta 0.07 ), outperforming prior methods. This work bridges the gap between theoretical and practical efficiency for transformers in diffusion.
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[AI-104] Is the `Agent Paradigm a Limiting Framework for Next-Generation Intelligent Systems?

【速读】：该论文试图解决当前人工智能研究中过度依赖“代理”（agent）范式所带来的概念模糊性和人类中心主义偏见问题，这种范式虽在推动从基础理论到大语言模型（LLM）等应用发展中发挥了重要作用，但可能限制了对智能本质的深入理解。其解决方案的关键在于提出从“代理导向”向“系统级动态”框架的转变，强调以世界建模、物质智能（material intelligence）和复杂系统原理为基础的新研究路径，从而探索非代理性（non-agentic）和非人类中心的通用智能形式，这不仅需要新型架构设计，更要求重新定义智能的本质。

链接: https://arxiv.org/abs/2509.10875
作者: Jesse Gardner,Vladimir A. Baulin
机构: 未知
类目: Artificial Intelligence (cs.AI); Soft Condensed Matter (cond-mat.soft)
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Abstract:The concept of the ‘agent’ has profoundly shaped Artificial Intelligence (AI) research, guiding development from foundational theories to contemporary applications like Large Language Model (LLM)-based systems. This paper critically re-evaluates the necessity and optimality of this agent-centric paradigm. We argue that its persistent conceptual ambiguities and inherent anthropocentric biases may represent a limiting framework. We distinguish between agentic systems (AI inspired by agency, often semi-autonomous, e.g., LLM-based agents), agential systems (fully autonomous, self-producing systems, currently only biological), and non-agentic systems (tools without the impression of agency). Our analysis, based on a systematic review of relevant literature, deconstructs the agent paradigm across various AI frameworks, highlighting challenges in defining and measuring properties like autonomy and goal-directedness. We argue that the ‘agentic’ framing of many AI systems, while heuristically useful, can be misleading and may obscure the underlying computational mechanisms, particularly in Large Language Models (LLMs). As an alternative, we propose a shift in focus towards frameworks grounded in system-level dynamics, world modeling, and material intelligence. We conclude that investigating non-agentic and systemic frameworks, inspired by complex systems, biology, and unconventional computing, is essential for advancing towards robust, scalable, and potentially non-anthropomorphic forms of general intelligence. This requires not only new architectures but also a fundamental reconsideration of our understanding of intelligence itself, moving beyond the agent metaphor.
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[AI-105] Optimal message passing for molecular prediction is simple attentive and spatial

【速读】：该论文旨在解决分子性质预测中消息传递神经网络（Message-Passing Neural Networks, MPNNs）的预测性能提升问题，尤其关注如何通过简化消息传递机制和引入多维度分子图描述符来优化模型表现。其解决方案的关键在于设计了一种结构简约但高效的MPNN架构：采用双向消息传递与注意力机制，并基于排除自感知（self-perception）的最小化消息公式，显著提升了类别可分性；同时发现，在适当选择3D描述符的前提下，使用2D分子图即可达到与3D图相当的预测性能，且计算成本降低超50%，这对高通量筛选具有重要应用价值。

链接: https://arxiv.org/abs/2509.10871
作者: Alma C. Castaneda-Leautaud,Rommie E. Amaro
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM)
备注: 32 pages, 12 figures. Preprint submitted to RSC Drug Discovery

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Abstract:Strategies to improve the predicting performance of Message-Passing Neural-Networks for molecular property predictions can be achieved by simplifying how the message is passed and by using descriptors that capture multiple aspects of molecular graphs. In this work, we designed model architectures that achieved state-of-the-art performance, surpassing more complex models such as those pre-trained on external databases. We assessed dataset diversity to complement our performance results, finding that structural diversity influences the need for additional components in our MPNNs and feature sets. In most datasets, our best architecture employs bidirectional message-passing with an attention mechanism, applied to a minimalist message formulation that excludes self-perception, highlighting that relatively simpler models, compared to classical MPNNs, yield higher class separability. In contrast, we found that convolution normalization factors do not benefit the predictive power in all the datasets tested. This was corroborated in both global and node-level outputs. Additionally, we analyzed the influence of both adding spatial features and working with 3D graphs, finding that 2D molecular graphs are sufficient when complemented with appropriately chosen 3D descriptors. This approach not only preserves predictive performance but also reduces computational cost by over 50%, making it particularly advantageous for high-throughput screening campaigns. Comments: 32 pages, 12 figures. Preprint submitted to RSC Drug Discovery Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Biomolecules (q-bio.BM) Cite as: arXiv:2509.10871 [cs.LG] (or arXiv:2509.10871v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.10871 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-106] GTHNA: Local-global Graph Transformer with Memory Reconstruction for Holistic Node Anomaly Evaluation

【速读】：该论文旨在解决图结构数据中异常检测的难题，即如何准确识别在结构和行为特征上均偏离多数节点的稀有异常节点。现有方法如基于图卷积网络（Graph Convolutional Networks, GCNs）的方法常因过平滑（over-smoothing）导致节点表示难以区分，而基于图重建的方法则易受异常节点干扰，影响检测精度。其解决方案的关键在于提出一个集成三项核心组件的综合评估框架：局部-全局Transformer编码器用于捕获多尺度结构依赖关系，记忆引导的重建机制抑制异常节点对重构过程的影响，以及多尺度表示匹配策略实现从不同粒度层面评估异常程度。通过融合重建误差与记忆匹配信号计算异常得分，显著提升了模型的鲁棒性和泛化能力。

链接: https://arxiv.org/abs/2509.10869
作者: Mingkang Li,Xuexiong Luo,Yue Zhang,Yaoyang Li,Fu Lin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 9 pages, 7 figures

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Abstract:Anomaly detection in graph-structured data is an inherently challenging problem, as it requires the identification of rare nodes that deviate from the majority in both their structural and behavioral characteristics. Existing methods, such as those based on graph convolutional networks (GCNs), often suffer from over-smoothing, which causes the learned node representations to become indistinguishable. Furthermore, graph reconstruction-based approaches are vulnerable to anomalous node interference during the reconstruction process, leading to inaccurate anomaly detection. In this work, we propose a novel and holistic anomaly evaluation framework that integrates three key components: a local-global Transformer encoder, a memory-guided reconstruction mechanism, and a multi-scale representation matching strategy. These components work synergistically to enhance the model’s ability to capture both local and global structural dependencies, suppress the influence of anomalous nodes, and assess anomalies from multiple levels of granularity. Anomaly scores are computed by combining reconstruction errors and memory matching signals, resulting in a more robust evaluation. Extensive experiments on seven benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches, offering a comprehensive and generalizable solution for anomaly detection across various graph domains.
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[AI-107] Large Language Models for Security Operations Centers: A Comprehensive Survey

【速读】：该论文旨在解决安全运营中心（Security Operations Center, SOC）在实际运行中面临的诸多挑战，包括告警数量庞大、资源有限、高级专家短缺、响应延迟以及威胁情报利用效率低等问题。其解决方案的关键在于系统性地探索生成式 AI，特别是大语言模型（Large Language Models, LLMs）在 SOC 工作流中的集成应用，通过自动化日志分析、优化告警筛选、提升检测准确率并快速提供专业知识，从而增强 SOC 的整体效能与响应能力。

链接: https://arxiv.org/abs/2509.10858
作者: Ali Habibzadeh,Farid Feyzi,Reza Ebrahimi Atani
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
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Abstract:Large Language Models (LLMs) have emerged as powerful tools capable of understanding and generating human-like text, offering transformative potential across diverse domains. The Security Operations Center (SOC), responsible for safeguarding digital infrastructure, represents one of these domains. SOCs serve as the frontline of defense in cybersecurity, tasked with continuous monitoring, detection, and response to incidents. However, SOCs face persistent challenges such as high alert volumes, limited resources, high demand for experts with advanced knowledge, delayed response times, and difficulties in leveraging threat intelligence effectively. In this context, LLMs can offer promising solutions by automating log analysis, streamlining triage, improving detection accuracy, and providing the required knowledge in less time. This survey systematically explores the integration of generative AI and more specifically LLMs into SOC workflow, providing a structured perspective on its capabilities, challenges, and future directions. We believe that this survey offers researchers and SOC managers a broad overview of the current state of LLM integration within academic study. To the best of our knowledge, this is the first comprehensive study to examine LLM applications in SOCs in details.
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[AI-108] From Grounding to Skolemization: A Logic-Constrained Vector Symbolic Architecture for Complex Query Answering

【速读】：该论文旨在解决复杂查询回答（Complex Query Answering, CQA）在不完整知识图谱（Knowledge Graph, KG）上的逻辑严谨性与计算效率之间的根本权衡问题，其形式化为存在一阶谓词逻辑中带有一个自由变量的推理（Existential First-Order logic with one free variable, EFO₁）。现有方法中，基于实例化（Grounding-based）的方法虽逻辑可靠但易受组合爆炸影响，而多数基于Skolem化（Skolemization-based）的方法则因未显式建模Skolem函数而导致逻辑一致性缺失。论文提出逻辑约束向量符号架构（Logic-constrained Vector Symbolic Architecture, LVSA），其核心创新在于融合可微分Skolem化模块与神经否定器（neural negator），并通过逻辑约束驱动的优化协议协同几何表示与逻辑约束，从而在理论上保证对所有EFO₁查询的通用性，并在实践中显著优于现有Skolem化方法且大幅降低推理开销。

链接: https://arxiv.org/abs/2509.10837
作者: Yuyin Lu,Hegang Chen,Yanghui Rao
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Complex Query Answering (CQA) over incomplete Knowledge Graphs (KGs), typically formalized as reasoning with Existential First-Order predicate logic with one free variable (EFO _1 ), faces a fundamental trade-off between logical soundness and computational efficiency. This work establishes the Grounding-Skolemization dichotomy for systematically analyzing CQA methods through the lens of formal logic. While Grounding-based methods inherently suffer from combinatorial explosion, most Skolemization-based methods neglect to explicitly model Skolem functions and compromise logical consistency. To address these limitations, we propose the Logic-constrained Vector Symbolic Architecture (LVSA), a neuro-symbolic framework that unifies a differentiable Skolemization module and a neural negator, as well as a logical constraint-driven optimization protocol to harmonize geometric and logical requirements. Theoretically, LVSA guarantees universality for all EFO _1 queries. Empirically, it outperforms state-of-the-art Skolemization-based methods and reduces inference costs by orders of magnitude compared to Grounding-based baselines.
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[AI-109] FACTORS: Factorial Approximation for Complementary Two-factor Optimization with Risk-aware Scoring

【速读】：该论文旨在解决机器学习训练过程中因多种训练因素（training factors）组合敏感性导致的性能波动与稳定性问题，尤其在固定预算下难以可靠选择最优配置的挑战。其解决方案的关键在于提出FACTORS框架，该框架融合实验设计（Design of Experiments, DoE）与Shapley值分解技术，通过两种互补路径——基于条件均值的插件法和基于最小二乘重构的Shapley贡献估计法——稳定估计主效应与两因子交互作用，并将其整合进一个风险调整的目标函数中，同时考虑不确定性与成本。该方法实现了跨异质因子空间的标准化估计、偏差校正与不确定性量化，从而在有限计算资源下高效识别高置信度配置，显著提升排序保真度与最优配置识别能力，为模型调参提供可解释且稳定的决策依据。

链接: https://arxiv.org/abs/2509.10825
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: 43 pages, 8 figures

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Abstract:We propose FACTORS, a framework that combines design of experiments with Shapley decomposition to address performance and stability issues that are sensitive to combinations of training factors. Our approach consistently estimates main effects and two-factor interactions, then integrates them into a risk-adjusted objective function that jointly accounts for uncertainty and cost, enabling reliable selection of configurations under a fixed budget. Effect estimation is implemented through two complementary paths: a plug-in path based on conditional means, and a least-squares path that reconstructs Shapley contributions from samples. These paths are designed to work complementarily even when design density and bias levels differ. By incorporating standardization of estimates, bias correction, and uncertainty quantification, our procedure ensures comparability across heterogeneous factor spaces and designs, while a lightweight search routine yields configurations within practical time even for large factor spaces. On the theoretical side, we provide error decompositions, sample complexity analysis, and upper bounds on optimality gaps. On the interpretive side, we summarize main effects and interactions in map form, highlighting adjustment priorities and safe improvement pathways. Across diverse datasets and design conditions, our approach improves rank preservation and optimal configuration identification, reduces decision-making risks, and offers a tuning foundation that delivers interpretable justification alongside stable performance gains even under budget constraints.
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[AI-110] LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在决策支持任务中因训练数据缺失而导致的幻觉问题，以及现有方法如检索增强生成（Retrieval-Augmented Generation, RAG）在面对关键信息缺失时仍无法充分建模复杂决策逻辑的局限性。解决方案的关键在于提出一种基于优化人机对话与单调布尔及k值函数的计算框架，用于发现可计算高效的个人专家心智模型（Expert Mental Model, EMM），并通过四步算法实现LLM提示工程：(1) 因素识别，(2) 因素分层结构构建，(3) 生成广义专家心智模型规范，(4) 从该规范生成详细广义专家心智模型，从而提升LLM在决策场景下的准确性与可解释性。

链接: https://arxiv.org/abs/2509.10818
作者: Boris Kovalerchuk,Brent D. Fegley
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 25 pages,4 figures, 2 tables

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Abstract:Difficult decision-making problems abound in various disciplines and domains. The proliferation of generative techniques, especially large language models (LLMs), has excited interest in using them for decision support. However, LLMs cannot yet resolve missingness in their training data, leading to hallucinations. Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating external information retrieval, reducing hallucinations and improving accuracy. Yet, RAG and related methods are only partial solutions, as they may lack access to all necessary sources or key missing information. Even everyday issues often challenge LLMs’ abilities. Submitting longer prompts with context and examples is one approach to address knowledge gaps, but designing effective prompts is non-trivial and may not capture complex mental models of domain experts. For tasks with missing critical information, LLMs are insufficient, as are many existing systems poorly represented in available documents. This paper explores how LLMs can make decision-making more efficient, using a running example of evaluating whether to respond to a call for proposals. We propose a technology based on optimized human-machine dialogue and monotone Boolean and k-valued functions to discover a computationally tractable personal expert mental model (EMM) of decision-making. Our EMM algorithm for LLM prompt engineering has four steps: (1) factor identification, (2) hierarchical structuring of factors, (3) generating a generalized expert mental model specification, and (4) generating a detailed generalized expert mental model from that specification.
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[AI-111] Rethinking Sparse Autoencoders: Select-and-Project for Fairness and Control from Encoder Features Alone

【速读】：该论文旨在解决当前基于稀疏自编码器（Sparse Autoencoders, SAEs）的去偏方法中存在的根本性假设问题——即假设特征表示存储在解码器权重中，而这一假设可能并不准确。为此，作者提出了一种以编码器为核心的去偏新范式，其关键在于：首先采用一种非传统的SAE特征选择策略；其次设计了一种通过正交化输入嵌入与编码器权重来实现去偏的新方法；最后引入编码器权重插值机制，在去偏过程中保持下游任务性能不变。整体框架命名为S\P TopK，实验证明其在公平性指标上相比传统SAE方法提升达3.2倍，并在测试时视觉语言模型（VLM）去偏任务中达到state-of-the-art效果，提升幅度最高达1.8倍。

链接: https://arxiv.org/abs/2509.10809
作者: Antonio Bărbălau,Cristian Daniel Păduraru,Teodor Poncu,Alexandru Tifrea,Elena Burceanu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Sparse Autoencoders (SAEs) have proven valuable due to their ability to provide interpretable and steerable representations. Current debiasing methods based on SAEs manipulate these sparse activations presuming that feature representations are housed within decoder weights. We challenge this fundamental assumption and introduce an encoder-focused alternative for representation debiasing, contributing three key findings: (i) we highlight an unconventional SAE feature selection strategy, (ii) we propose a novel SAE debiasing methodology that orthogonalizes input embeddings against encoder weights, and (iii) we establish a performance-preserving mechanism during debiasing through encoder weight interpolation. Our Selection and Projection framework, termed S\P TopK, surpasses conventional SAE usage in fairness metrics by a factor of up to 3.2 and advances state-of-the-art test-time VLM debiasing results by a factor of up to 1.8 while maintaining downstream performance.
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[AI-112] GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在硬件故障条件下的鲁棒性（robustness）问题，即评估Transformer架构模型在遭受各类硬件故障时的行为稳定性与可靠性。当前尽管Transformer已成为自然语言处理等多个领域的核心结构，但其在故障环境下的表现仍缺乏系统性研究。解决方案的关键在于提出GoldenTransformer——一个模块化、可扩展的故障注入框架，该框架基于PyTorch和HuggingFace Transformers构建，支持在预训练Transformer模型中可控地注入多种类型的故障，包括权重损坏（weight corruption）、激活值扰动（activation injection）以及注意力机制层面的干扰（attention-level disruptions），并兼顾结构复杂性、潜在依赖关系和非均匀层定义等挑战，从而为模型鲁棒性分析提供统一、可复现且可视化友好的实验平台。

链接: https://arxiv.org/abs/2509.10790
作者: Luke Howard
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Transformers have become the foundation for a wide range of state–of–the–art models across natural language processing, computer vision, and other machine learning domains. Despite their widespread deployment, the robustness of these models under fault conditions remains underexplored. We present GoldenTransformer, a modular and extensible fault injection framework designed to evaluate the resiliency of Large Language Models to induced hardware faults. GoldenTransformer offers a unified Python-based platform for injecting diverse classes of faults–such as weight corruption, activation injections, and attention–level disruptions–into pretrained transformer–based models. Inspired by the GoldenEye simulator for DNNs, our framework focuses on the unique challenges of working with large transformer architectures, including considerations such as structural complexity, latent dependencies, and nonuniform layer definitions. GoldenTransformer is built atop PyTorch and HuggingFace Transformers, and it supports experiment reproducibility, metric logging, and visualization out of the box. We detail the technical design and use of GoldenTransformer and demonstrate through several example experiments on classification and generation tasks. By enabling controlled injection of faults at multiple logical and structural points in a transformer, GoldenTransformer offers researchers and practitioners a valuable tool for model robustness analysis and for guiding dependable system design in real-world LLM applications.
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[AI-113] Bridging Cultural Distance Between Models Default and Local Classroom Demands: How Global Teachers Adopt GenAI to Support Everyday Teaching Practices

【速读】：该论文试图解决生成式 AI（Generative AI）在K-12教育场景中因训练数据文化分布不均而导致的“默认文化”与本地教学实践需求之间存在文化距离（Cultural Distance）的问题。解决方案的关键在于提出并验证了一个三层次的文化距离框架，该框架基于对来自南非、台湾和美国的30名教师的深度访谈，提炼出从低到高不同层级的文化距离实例及教师应对策略，为AI设计者、政策制定者和教育工作者提供实证依据，以开发更具公平性和文化响应性的生成式AI教育工具。

链接: https://arxiv.org/abs/2509.10780
作者: Ruiwei Xiao,Qing Xiao,Xinying Hou,Hanqi Jane Li,Phenyo Phemelo Moletsane,Hong Shen,John Stamper
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 15 pages, 1 figure

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Abstract:Generative AI (GenAI) is rapidly entering K-12 classrooms, offering teachers new ways for teaching practices. Yet GenAI models are often trained on culturally uneven datasets, embedding a “default culture” that often misaligns with local classrooms. To understand how teachers navigate this gap, we defined the new concept Cultural Distance (the gap between GenAI’s default cultural repertoire and the situated demands of teaching practice) and conducted in-depth interviews with 30 K-12 teachers, 10 each from South Africa, Taiwan, and the United States, who had integrated AI into their teaching practice. These teachers’ experiences informed the development of our three-level cultural distance framework. This work contributes the concept and framework of cultural distance, six illustrative instances spanning in low, mid, high distance levels with teachers’ experiences and strategies for addressing them. Empirically, we offer implications to help AI designers, policymakers, and educators create more equitable and culturally responsive GenAI tools for education.
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[AI-114] Contextual Budget Bandit for Food Rescue Volunteer Engagement

【速读】：该论文旨在解决志愿参与式食物救援平台中存在的双重挑战：一是维持志愿者参与度，二是最大化救援食物量；同时，现有算法在提升志愿者参与度时加剧了地理不平等，导致部分社区长期处于劣势。解决方案的关键在于提出一种名为Contextual Budget Bandit的新型决策模型，该模型基于带有状态依赖的预算分配机制，在非平稳多臂老虎机（restless multi-armed bandits）框架下实现动态资源调度，能够自动向匹配率较低的地区倾斜更高预算，从而缓解地理公平性问题。为高效求解该问题，作者进一步设计了快速启发式算法Mitosis，其在志愿者稀缺场景下仍能保证最优预算分配，实验证明该方法在合成与真实数据集上均显著优于基线，并有效提升了食物救援过程中的地理公平性。

链接: https://arxiv.org/abs/2509.10777
作者: Ariana Tang,Naveen Raman,Fei Fang,Zheyuan Ryan Shi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:Volunteer-based food rescue platforms tackle food waste by matching surplus food to communities in need. These platforms face the dual problem of maintaining volunteer engagement and maximizing the food rescued. Existing algorithms to improve volunteer engagement exacerbate geographical disparities, leaving some communities systematically disadvantaged. We address this issue by proposing Contextual Budget Bandit. Contextual Budget Bandit incorporates context-dependent budget allocation in restless multi-armed bandits, a model of decision-making which allows for stateful arms. By doing so, we can allocate higher budgets to communities with lower match rates, thereby alleviating geographical disparities. To tackle this problem, we develop an empirically fast heuristic algorithm. Because the heuristic algorithm can achieve a poor approximation when active volunteers are scarce, we design the Mitosis algorithm, which is guaranteed to compute the optimal budget allocation. Empirically, we demonstrate that our algorithms outperform baselines on both synthetic and real-world food rescue datasets, and show how our algorithm achieves geographical fairness in food rescue.
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[AI-115] A Content-dependent Watermark for Safeguarding Image Attribution

【速读】：该论文旨在解决当前图像归属认证技术在面对伪造攻击时的脆弱性问题，特别是现有数字水印方法因内容无关性（content-agnostic）和依赖检测器验证（detector-based verification）而易被复制或欺骗，从而导致误 attribution 风险，损害 AI 模型开发者与数字艺术家的权益。其解决方案的关键在于提出 MetaSeal 框架，通过引入内容依赖型水印（content-dependent watermarking）并结合密码学安全保证，实现三重核心能力：（1）防伪造能力（forgery resistance），确保未经授权的复制无法生效且必须通过加密验证；（2）鲁棒且自包含的保护机制，将归属信息直接嵌入图像中并抵御良性变换；（3）篡改证据生成能力，使恶意修改在视觉上可被察觉。实验表明，MetaSeal 在自然图像和 AI 生成图像上均能有效抵抗伪造攻击，为图像归属提供了新的安全标准。

链接: https://arxiv.org/abs/2509.10766
作者: Tong Zhou,Ruyi Ding,Gaowen Liu,Charles Fleming,Ramana Rao Kompella,Yunsi Fei,Xiaolin Xu,Shaolei Ren
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 18 pages, 13 figures

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Abstract:The rapid growth of digital and AI-generated images has amplified the need for secure and verifiable methods of image attribution. While digital watermarking offers more robust protection than metadata-based approaches–which can be easily stripped–current watermarking techniques remain vulnerable to forgery, creating risks of misattribution that can damage the reputations of AI model developers and the rights of digital artists. These vulnerabilities arise from two key issues: (1) content-agnostic watermarks, which, once learned or leaked, can be transferred across images to fake attribution, and (2) reliance on detector-based verification, which is unreliable since detectors can be tricked. We present MetaSeal, a novel framework for content-dependent watermarking with cryptographic security guarantees to safeguard image attribution. Our design provides (1) forgery resistance, preventing unauthorized replication and enforcing cryptographic verification; (2) robust, self-contained protection, embedding attribution directly into images while maintaining resilience against benign transformations; and (3) evidence of tampering, making malicious alterations visually detectable. Experiments demonstrate that MetaSeal effectively mitigates forgery attempts and applies to both natural and AI-generated images, establishing a new standard for secure image attribution.
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[AI-116] AI Answer Engine Citation Behavior An Empirical Analysis of the GEO1 6 Framework

【速读】：该论文旨在解决生成式 AI (Generative AI) 答案引擎在引用网页时所体现的页面质量差异问题，即如何系统性评估和提升被引用网页的质量以增强答案的可信度与准确性。其解决方案的关键在于提出 GEO-16 框架——一个包含 16 个支柱（pillar）的审计体系，将页面质量信号转化为带状支柱评分，并通过归一化后的 GEO 分数 G（范围为 0 到 1）量化整体页面质量；研究发现，Metadata 和 Freshness、Semantic HTML 及 Structured Data 等支柱与引用显著相关，且当 GEO 分数 ≥ 0.70 并满足至少 12 个支柱命中时，可显著提高被引用概率，从而为内容发布者提供可操作的优化策略。

链接: https://arxiv.org/abs/2509.10762
作者: Arlen Kumar,Leanid Palkhouski
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:AI answer engines increasingly mediate access to domain knowledge by generating responses and citing web sources. We introduce GEO-16, a 16 pillar auditing framework that converts on page quality signals into banded pillar scores and a normalized GEO score G that ranges from 0 to 1. Using 70 product intent prompts, we collected 1,702 citations across three engines (Brave Summary, Google AI Overviews, and Perplexity) and audited 1,100 unique URLs. In our corpus, the engines differed in the GEO quality of the pages they cited, and pillars related to Metadata and Freshness, Semantic HTML, and Structured Data showed the strongest associations with citation. Logistic models with domain clustered standard errors indicate that overall page quality is a strong predictor of citation, and simple operating points (for example, G at least 0.70 combined with at least 12 pillar hits) align with substantially higher citation rates in our data. We report per engine contrasts, vertical effects, threshold analysis, and diagnostics, then translate findings into a practical playbook for publishers. The study is observational and focuses on English language B2B SaaS pages; we discuss limitations, threats to validity, and reproducibility considerations.
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[AI-117] HalluField: Detecting LLM Hallucinations via Field-Theoretic Modeling

【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在生成文本时普遍存在幻觉（hallucination）问题，即模型输出内容与事实不符或不可靠，从而限制其在高风险应用场景中的部署。解决方案的关键在于提出一种基于场论（field-theoretic）的新型检测方法——HalluField，其核心思想是将LLM对查询和温度参数的响应建模为一系列离散的似然token路径，并通过能量-熵分布分析来量化响应的语义稳定性；当能量景观出现不稳定或异常波动时，即可判定为幻觉。该方法无需微调或额外神经网络，仅依赖模型输出logits即可实现高效、准确的幻觉检测，且具有物理原理支撑（类比热力学第一定律），在多个模型和数据集上达到当前最优性能。

链接: https://arxiv.org/abs/2509.10753
作者: Minh Vu,Brian K. Tran,Syed A. Shah,Geigh Zollicoffer,Nhat Hoang-Xuan,Manish Bhattarai
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Large Language Models (LLMs) exhibit impressive reasoning and question-answering capabilities. However, they often produce inaccurate or unreliable content known as hallucinations. This unreliability significantly limits their deployment in high-stakes applications. Thus, there is a growing need for a general-purpose method to detect hallucinations in LLMs. In this work, we introduce HalluField, a novel field-theoretic approach for hallucination detection based on a parametrized variational principle and thermodynamics. Inspired by thermodynamics, HalluField models an LLM’s response to a given query and temperature setting as a collection of discrete likelihood token paths, each associated with a corresponding energy and entropy. By analyzing how energy and entropy distributions vary across token paths under changes in temperature and likelihood, HalluField quantifies the semantic stability of a response. Hallucinations are then detected by identifying unstable or erratic behavior in this energy landscape. HalluField is computationally efficient and highly practical: it operates directly on the model’s output logits without requiring fine-tuning or auxiliary neural networks. Notably, the method is grounded in a principled physical interpretation, drawing analogies to the first law of thermodynamics. Remarkably, by modeling LLM behavior through this physical lens, HalluField achieves state-of-the-art hallucination detection performance across models and datasets.
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[AI-118] Dark Patterns Meet GUI Agents : LLM Agent s: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight

【速读】：该论文试图解决的问题是：随着大语言模型驱动的图形用户界面（GUI）代理（GUI agents）在自动化任务中日益普及，如何理解暗模式（dark patterns）对这些代理行为的影响，以及人类与代理协作时如何有效应对这些操纵性设计。解决方案的关键在于通过两阶段实证研究揭示了人类与代理在面对16种暗模式时的不同失效机制——代理因缺乏识别能力及任务导向优先级而忽视风险，人类则因认知捷径和习惯性顺从而受骗；同时发现人工监督虽能提升规避效果，但引入注意力隧道效应和认知负荷等新问题。因此，论文提出需从透明度、可调节自主性与监督机制三方面重构人机协同系统的设计原则。

链接: https://arxiv.org/abs/2509.10723
作者: Jingyu Tang,Chaoran Chen,Jiawen Li,Zhiping Zhang,Bingcan Guo,Ibrahim Khalilov,Simret Araya Gebreegziabher,Bingsheng Yao,Dakuo Wang,Yanfang Ye,Tianshi Li,Ziang Xiao,Yaxing Yao,Toby Jia-Jun Li
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
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Abstract:The dark patterns, deceptive interface designs manipulating user behaviors, have been extensively studied for their effects on human decision-making and autonomy. Yet, with the rising prominence of LLM-powered GUI agents that automate tasks from high-level intents, understanding how dark patterns affect agents is increasingly important. We present a two-phase empirical study examining how agents, human participants, and human-AI teams respond to 16 types of dark patterns across diverse scenarios. Phase 1 highlights that agents often fail to recognize dark patterns, and even when aware, prioritize task completion over protective action. Phase 2 revealed divergent failure modes: humans succumb due to cognitive shortcuts and habitual compliance, while agents falter from procedural blind spots. Human oversight improved avoidance but introduced costs such as attentional tunneling and cognitive load. Our findings show neither humans nor agents are uniformly resilient, and collaboration introduces new vulnerabilities, suggesting design needs for transparency, adjustable autonomy, and oversight.
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[AI-119] Kalman Bayesian Transformer

【速读】：该论文旨在解决在分布变化（distribution shift）和延迟敏感环境（latency-critical environments）下，如何实现稳定且数据高效的顺序微调（sequential fine-tuning）问题。传统批量学习难以适应小样本增量数据，而现有方法在缺乏不确定性量化时易导致灾难性遗忘（catastrophic forgetting）。解决方案的关键在于将顺序微调建模为贝叶斯框架下的后验推断（posterior inference）问题，通过引入随机变量的闭式矩传播（closed-form moment propagation）、卡尔曼贝叶斯神经网络（Kalman Bayesian Neural Networks）以及Softmax函数矩的泰勒近似（Taylor approximations），显式地将预训练模型作为先验，并基于量化不确定性自适应平衡新信息与旧知识，从而实现鲁棒且高效的学习。

链接: https://arxiv.org/abs/2509.10695
作者: Haoming Jing,Oren Wright,José M. F. Moura,Yorie Nakahira
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to the 64th IEEE Conference on Decision and Control (CDC 2025)

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Abstract:Sequential fine-tuning of transformers is useful when new data arrive sequentially, especially with shifting distributions. Unlike batch learning, sequential learning demands that training be stabilized despite a small amount of data by balancing new information and previously learned knowledge in the pre-trained models. This challenge is further complicated when training is to be completed in latency-critical environments and learning must additionally quantify and be mediated by uncertainty. Motivated by these challenges, we propose a novel method that frames sequential fine-tuning as a posterior inference problem within a Bayesian framework. Our approach integrates closed-form moment propagation of random variables, Kalman Bayesian Neural Networks, and Taylor approximations of the moments of softmax functions. By explicitly accounting for pre-trained models as priors and adaptively balancing them against new information based on quantified uncertainty, our method achieves robust and data-efficient sequential learning. The effectiveness of our method is demonstrated through numerical simulations involving sequential adaptation of a decision transformer to tasks characterized by distribution shifts and limited memory resources.
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[AI-120] Learning Concave Bid Shading Strategies in Online Auctions via Measure-valued Proximal Optimization

【速读】：该论文旨在解决第一价格拍卖（first-price auction）中的出价遮蔽（bid shading）策略优化问题，目标是通过调整出价以最大化预期收益。其核心解决方案将出价遮蔽建模为一种测度值优化（measure-valued optimization）问题，其中出价遮蔽参数的联合分布被置于凸优化框架下进行优化；关键创新在于引入基于数据驱动的能量函数（energy functional），该函数条件依赖于上下文信息（context），如发布者/用户属性（domain、ad slot type、device、location等），并通过带正则化的Wasserstein近端更新（Wasserstein-proximal update）动态调整遮蔽参数分布，从而引导出价分布向高期望盈余区域集中（即胜率与价值差距均较大的区域）。该方法最终可获得闭式解，具备理论保证与计算可行性。

链接: https://arxiv.org/abs/2509.10693
作者: Iman Nodozi,Djordje Gligorijevic,Abhishek Halder
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC); Machine Learning (stat.ML)
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Abstract:This work proposes a bid shading strategy for first-price auctions as a measure-valued optimization problem. We consider a standard parametric form for bid shading and formulate the problem as convex optimization over the joint distribution of shading parameters. After each auction, the shading parameter distribution is adapted via a regularized Wasserstein-proximal update with a data-driven energy functional. This energy functional is conditional on the context, i.e., on publisher/user attributes such as domain, ad slot type, device, or location. The proposed algorithm encourages the bid distribution to place more weight on values with higher expected surplus, i.e., where the win probability and the value gap are both large. We show that the resulting measure-valued convex optimization problem admits a closed form solution. A numerical example illustrates the proposed method.
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[AI-121] Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy

【速读】：该论文旨在解决去中心化联邦学习（Decentralized Federated Learning, DFL）中的隐私泄露问题，尤其是在资源受限的物联网（IoT）设备上，模型更新可能通过推理攻击和成员推断攻击暴露敏感数据。传统差分隐私（Differential Privacy, DP）方法虽能提供理论保障，但因缺乏透明性导致客户端无法感知历史轮次中已注入的噪声，从而不得不按最坏情况添加额外噪声，严重影响模型精度。其解决方案的关键在于提出PrivateDFL框架，该框架结合超维计算（Hyperdimensional Computing, HDC）与差分隐私机制，建立可审计的累积噪声会计系统，使每个客户端仅需添加当前所需噪声与已有噪声之间的差值，避免重复冗余加噪，从而在保持严格 $(\epsilon, \delta)$ 隐私保证的前提下显著提升准确率、降低延迟和能耗。

链接: https://arxiv.org/abs/2509.10691
作者: Fardin Jalil Piran,Zhiling Chen,Yang Zhang,Qianyu Zhou,Jiong Tang,Farhad Imani
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 21 pages

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Abstract:Decentralized federated learning faces privacy risks because model updates can leak data through inference attacks and membership inference, a concern that grows over many client exchanges. Differential privacy offers principled protection by injecting calibrated noise so confidential information remains secure on resource-limited IoT devices. Yet without transparency, black-box training cannot track noise already injected by previous clients and rounds, which forces worst-case additions and harms accuracy. We propose PrivateDFL, an explainable framework that joins hyperdimensional computing with differential privacy and keeps an auditable account of cumulative noise so each client adds only the difference between the required noise and what has already been accumulated. We evaluate on MNIST, ISOLET, and UCI-HAR to span image, signal, and tabular modalities, and we benchmark against transformer-based and deep learning-based baselines trained centrally with Differentially Private Stochastic Gradient Descent (DP-SGD) and Renyi Differential Privacy (RDP). PrivateDFL delivers higher accuracy, lower latency, and lower energy across IID and non-IID partitions while preserving formal (epsilon, delta) guarantees and operating without a central server. For example, under non-IID partitions, PrivateDFL achieves 24.42% higher accuracy than the Vision Transformer on MNIST while using about 10x less training time, 76x lower inference latency, and 11x less energy, and on ISOLET it exceeds Transformer accuracy by more than 80% with roughly 10x less training time, 40x lower inference latency, and 36x less training energy. Future work will extend the explainable accounting to adversarial clients and adaptive topologies with heterogeneous privacy budgets.
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[AI-122] ZapGPT : Free-form Language Prompting for Simulated Cellular Control

【速读】：该论文试图解决的问题是：如何在不依赖任务特定调优或精心设计评估指标的情况下，仅通过自由形式的自然语言提示来引导人工或生物集体系统（如模拟细胞群体）的行为。现有方法普遍依赖工程化的奖励函数、任务特异性监督或固定命令集，限制了对新指令的泛化能力。解决方案的关键在于构建一个闭环演化框架——其中第一个AI模型将自然语言指令转化为对模拟细胞系统的干预策略，第二个AI模型则评估该策略是否符合语言描述的动态行为，随后前者基于后者的评分进行演化优化。此方法无需预设目标函数或领域特定提示设计，且展现出对未见过的语言指令的泛化能力，为语言作为通用控制层替代传统数学目标函数和规则编程提供了实证支持。

链接: https://arxiv.org/abs/2509.10660
作者: Nam H. Le,Patrick Erickson,Yanbo Zhang,Michael Levin,Josh Bongard
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Cell Behavior (q-bio.CB)
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Abstract:Human language is one of the most expressive tools for conveying intent, yet most artificial or biological systems lack mechanisms to interpret or respond meaningfully to it. Bridging this gap could enable more natural forms of control over complex, decentralized systems. In AI and artificial life, recent work explores how language can specify high-level goals, but most systems still depend on engineered rewards, task-specific supervision, or rigid command sets, limiting generalization to novel instructions. Similar constraints apply in synthetic biology and bioengineering, where the locus of control is often genomic rather than environmental perturbation. A key open question is whether artificial or biological collectives can be guided by free-form natural language alone, without task-specific tuning or carefully designed evaluation metrics. We provide one possible answer here by showing, for the first time, that simple agents’ collective behavior can be guided by free-form language prompts: one AI model transforms an imperative prompt into an intervention that is applied to simulated cells; a second AI model scores how well the prompt describes the resulting cellular dynamics; and the former AI model is evolved to improve the scores generated by the latter. Unlike previous work, our method does not require engineered fitness functions or domain-specific prompt design. We show that the evolved system generalizes to unseen prompts without retraining. By treating natural language as a control layer, the system suggests a future in which spoken or written prompts could direct computational, robotic, or biological systems to desired behaviors. This work provides a concrete step toward this vision of AI-biology partnerships, in which language replaces mathematical objective functions, fixed rules, and domain-specific programming. Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Cell Behavior (q-bio.CB) Cite as: arXiv:2509.10660 [cs.AI] (or arXiv:2509.10660v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2509.10660 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-123] Self-Supervised Goal-Reaching Results in Multi-Agent Cooperation and Exploration

【速读】：该论文旨在解决多智能体强化学习（Multi-Agent Reinforcement Learning, MARL）中因复杂奖励函数设计困难而导致的协作与长程推理问题。传统方法依赖人工设计的标量奖励函数来引导智能体行为，但这一过程不仅繁琐且难以有效激发所需的协同策略。解决方案的关键在于采用自监督的目标达成（self-supervised goal-reaching）机制，使智能体不再追求最大化标量奖励，而是通过最大化访问特定目标状态的概率来学习行为策略。这种方法允许用户仅以单一目标状态定义任务，无需构建复杂的奖励函数；尽管反馈信号稀疏，实验表明该方法仍能有效促进智能体在MARL基准测试中学习出高效协作行为，并在无显式探索机制的情况下自发产生合作与探索现象。

链接: https://arxiv.org/abs/2509.10656
作者: Chirayu Nimonkar,Shlok Shah,Catherine Ji,Benjamin Eysenbach
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Project website with videos this https URL and code this https URL are online

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Abstract:For groups of autonomous agents to achieve a particular goal, they must engage in coordination and long-horizon reasoning. However, designing reward functions to elicit such behavior is challenging. In this paper, we study how self-supervised goal-reaching techniques can be leveraged to enable agents to cooperate. The key idea is that, rather than have agents maximize some scalar reward, agents aim to maximize the likelihood of visiting a certain goal. This problem setting enables human users to specify tasks via a single goal state rather than implementing a complex reward function. While the feedback signal is quite sparse, we will demonstrate that self-supervised goal-reaching techniques enable agents to learn from such feedback. On MARL benchmarks, our proposed method outperforms alternative approaches that have access to the same sparse reward signal as our method. While our method has no explicit mechanism for exploration, we observe that self-supervised multi-agent goal-reaching leads to emergent cooperation and exploration in settings where alternative approaches never witness a single successful trial.
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[AI-124] SCOR: A Framework for Responsible AI Innovation in Digital Ecosystems

【速读】：该论文旨在解决AI驱动的数字生态系统中缺乏协同伦理治理的问题，尤其是在多元利益相关者（如科技公司、监管机构、加速器和公民社会）共存时，难以实现责任、公平与包容性的统一。解决方案的关键在于提出一个四支柱框架（SCOR），包括：共享伦理宪章（Shared Ethical Charter, S）、结构化共同设计与利益相关者参与协议（structured Co-Design and Stakeholder Engagement protocols, C）、持续监督与学习机制（Continuous Oversight and Learning, O），以及适应性法规对齐策略（Adaptive Regulatory Alignment strategies, R）。该框架通过从轻量级模块到深度审计系统的分层实践指南，支持不同规模组织在医疗、金融和智慧城市等场景中协调组织文化、领导激励与跨司法管辖区合规，并结合定量指标与定性评估（如用户信任和文化变革）以确保伦理原则可操作且可扩展。

链接: https://arxiv.org/abs/2509.10653
作者: Mohammad Saleh Torkestani,Taha Mansouri
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: Proceeding of The British Academy of Management Conference 2025, University of Kent, UK

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Abstract:AI-driven digital ecosystems span diverse stakeholders including technology firms, regulators, accelerators and civil society, yet often lack cohesive ethical governance. This paper proposes a four-pillar framework (SCOR) to embed accountability, fairness, and inclusivity across such multi-actor networks. Leveraging a design science approach, we develop a Shared Ethical Charter(S), structured Co-Design and Stakeholder Engagement protocols©, a system of Continuous Oversight and Learning(O), and Adaptive Regulatory Alignment strategies®. Each component includes practical guidance, from lite modules for resource-constrained start-ups to in-depth auditing systems for larger consortia. Through illustrative vignettes in healthcare, finance, and smart city contexts, we demonstrate how the framework can harmonize organizational culture, leadership incentives, and cross-jurisdictional compliance. Our mixed-method KPI design further ensures that quantitative targets are complemented by qualitative assessments of user trust and cultural change. By uniting ethical principles with scalable operational structures, this paper offers a replicable pathway toward responsible AI innovation in complex digital ecosystems.
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[AI-125] Vibe Coding for UX Design: Understanding UX Professionals Perceptions of AI-Assisted Design and Development

【速读】：该论文旨在解决生成式 AI（Generative AI）在用户体验（UX）设计实践中引发的 workflows 重构与协作模式变化问题，特别是“vibe coding”这一新兴范式如何重塑设计流程、团队分工及责任分配。其解决方案的关键在于通过实证研究识别出 vibe coding 的四阶段工作流（构思、AI生成、调试、评审），揭示其在提升迭代效率和降低参与门槛的同时，也带来代码可靠性不足、集成困难以及对AI过度依赖等挑战，并进一步指出效率导向的原型设计与反思性设计意图之间的张力，从而为负责任的人机协同提供理论框架，聚焦于技能退化（deskilling）、所有权归属与披露（ownership and disclosure）、以及创造力保护（creativity safeguarding）等核心议题。

链接: https://arxiv.org/abs/2509.10652
作者: Jie Li,Youyang Hou,Laura Lin,Ruihao Zhu,Hancheng Cao,Abdallah El Ali
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Emerging Technologies (cs.ET)
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Abstract:Generative AI is reshaping UX design practices through “vibe coding,” where UX professionals express intent in natural language and AI translates it into functional prototypes and code. Despite rapid adoption, little research has examined how vibe coding reconfigures UX workflows and collaboration. Drawing on interviews with 20 UX professionals across enterprises, startups, and academia, we show how vibe coding follows a four-stage workflow of ideation, AI generation, debugging, and review. This accelerates iteration, supports creativity, and lowers barriers to participation. However, professionals reported challenges of code unreliability, integration, and AI over-reliance. We find tensions between efficiency-driven prototyping (“intending the right design”) and reflection (“designing the right intention”), introducing new asymmetries in trust, responsibility, and social stigma within teams. Through the lens of responsible human-AI collaboration for AI-assisted UX design and development, we contribute a deeper understanding of deskilling, ownership and disclosure, and creativity safeguarding in the age of vibe coding.
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[AI-126] st-Time Warmup for Multimodal Large Language Models

【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在复杂推理任务中表现不足的问题，其根源在于尽管模型各组件（如语言模型、视觉编码器和连接模块）均在大规模数据上预训练，但整个模型的微调通常仅依赖数千至数百万标注样本，导致推理能力受限。解决方案的关键在于提出一种“测试时预热”（Test-Time Warmup）方法，该方法通过利用弱监督辅助任务的数据，在每个测试实例前动态调整模型参数，从而提升模型在多样化推理任务中的鲁棒性与性能。

链接: https://arxiv.org/abs/2509.10641
作者: Nikita Rajaneesh,Thomas Zollo,Richard Zemel
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Multimodal Large Language Models (MLLMs) hold great promise for advanced reasoning at the intersection of text and images, yet they have not fully realized this potential. MLLMs typically integrate an LLM, a vision encoder, and a connector that maps the vision encoder’s embeddings into the LLM’s text embedding space. Although each component is pretrained on massive datasets with billions of samples, the entire multimodal model is typically trained on only thousands (or a few million) samples, which can result in weak performance on complex reasoning tasks. To address these shortcomings, instead of relying on extensive labeled datasets for fine-tuning, we propose a Test-Time Warmup method that adapts the MLLM per test instance by leveraging data from weakly supervised auxiliary tasks. With our approach, we observe a relative performance improvement of 4.03% on MMMU, 5.28% on VQA-Rad, and 1.63% on GQA on the Llama-Vision-Instruct model. Our method demonstrates that ‘warming up’ before inference can enhance MLLMs’ robustness across diverse reasoning tasks.
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[AI-127] Optimal Multimarginal Schrödinger Bridge: Minimum Spanning Tree over Measure-valued Vertices

【速读】：该论文旨在解决多边缘Schrödinger桥（Multimarginal Schrödinger Bridge, MSB）问题中，如何在所有可能的图结构中寻找最优耦合（optimal coupling）的问题。传统MSB假设相关结构由一个预先给定的无向连通图指定，而本文则扩展了这一框架，目标是通过优化图结构本身来获得最优解。解决方案的关键在于将该问题转化为度量值顶点上的最小生成树（Minimum Spanning Tree, MST）问题：首先构建一个完全图，其边权为对应二边缘Schrödinger桥（bimarginal SB）的最优值与两端点熵之和；随后在此完全图上求解标准MST问题，从而得到最优图结构及对应的耦合方案。

链接: https://arxiv.org/abs/2509.10626
作者: Georgiy A. Bondar,Abhishek Halder
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Optimization and Control (math.OC); Machine Learning (stat.ML)
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Abstract:The Multimarginal Schrödinger Bridge (MSB) finds the optimal coupling among a collection of random vectors with known statistics and a known correlation structure. In the MSB formulation, this correlation structure is specified \empha priori as an undirected connected graph with measure-valued vertices. In this work, we formulate and solve the problem of finding the optimal MSB in the sense we seek the optimal coupling over all possible graph structures. We find that computing the optimal MSB amounts to solving the minimum spanning tree problem over measure-valued vertices. We show that the resulting problem can be solved in two steps. The first step constructs a complete graph with edge weight equal to a sum of the optimal value of the corresponding bimarginal SB and the entropies of the endpoints. The second step solves a standard minimum spanning tree problem over that complete weighted graph. Numerical experiments illustrate the proposed solution.
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[AI-128] National Running Club Database: Assessing Collegiate Club Athletes Cross Country Race Results

【速读】：该论文旨在解决运动科学领域中高质量、大规模运动员数据集稀缺的问题，尤其是针对非精英俱乐部跑者（club athletes）的长期训练与比赛表现数据难以获取的困境。传统研究常依赖小规模数据集（通常少于500条记录），且多为人工爬取，存在样本量不足和标准化程度低的局限性。解决方案的关键在于构建并公开发布国家越野跑俱乐部数据库（National Running Club Database, NRCD），该数据库整合了2023至2024赛季5,585名运动员的15,397条比赛结果，覆盖不同性别、距离（如女子6,000米、男子8,000米）及训练频率，并引入赛道条件（如天气和海拔变化）进行标准化处理，从而提升数据分析的准确性与可比性。这一举措为跑者、教练及团队提供了基于真实世界数据的决策支持工具，推动了生成式 AI (Generative AI) 在体育科学中的应用落地。

链接: https://arxiv.org/abs/2509.10600
作者: Jonathan A. Karr Jr,Ben Darden,Nicholas Pell,Ryan M. Fryer,Kayla Ambrose,Evan Hall,Ramzi K. Bualuan,Nitesh V. Chawla
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:The National Running Club Database (NRCD) aggregates 15,397 race results of 5,585 athletes from the 2023 and 2024 cross country seasons. This paper introduces the NRCD dataset, which provides insights into individual athlete progressions, enabling data-driven decision-making. Analysis reveals that runners’ improvement per calendar day for women, racing 6,000m, and men, racing 8,000m, is more pronounced in athletes with slower initial race times and those who race more frequently. Additionally, we factor in course conditions, including weather and elevation gain, to standardize improvement. While the NRCD shows a gender imbalance, 3,484 men vs. 2,101 women, the racing frequency between genders is comparable. This publication makes the NRCD dataset accessible to the research community, addressing a previous challenge where smaller datasets, often limited to 500 entries, had to be manually scraped from the internet. Focusing on club athletes rather than elite professionals offers a unique lens into the performance of real-world runners who balance competition with academics and other commitments. These results serve as a valuable resource for runners, coaches, and teams, bridging the gap between raw data and applied sports science.
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[AI-129] GenAI Voice Mode in Programming Education

【速读】：该论文旨在解决新手程序员（尤其是有残疾的学生）在编程学习过程中因视觉障碍等限制而面临的可访问性问题，探索实时语音接口结合生成式AI（Generative AI）在真实课堂环境中对9年级学生学习Python的交互效果与反馈质量。其解决方案的关键在于开发并部署一个基于OpenAI Realtime API的语音驱动教学助手（GenAI Voice Tutor），通过分析1210条语音对话内容及学生感知问卷数据，揭示了学生如何使用该工具进行调试、AI反馈的准确性（71.4%正确率）及其局限性，特别是对编程代码元素的语音输出问题。研究首次系统考察了实时语音GenAI导师与新手程序员之间的互动动态，为未来教育工具设计提供了实证依据，尤其有助于满足多样化学习者的无障碍学习需求。

链接: https://arxiv.org/abs/2509.10596
作者: Sven Jacobs,Natalie Kiesler
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Accepted for the 25th International Conference on Computing Education Research (Koli Calling '25)

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Abstract:Real-time voice interfaces using multimodal Generative AI (GenAI) can potentially address the accessibility needs of novice programmers with disabilities (e.g., related to vision). Yet, little is known about how novices interact with GenAI tools and their feedback quality in the form of audio output. This paper analyzes audio dialogues from nine 9th-grade students using a voice-enabled tutor (powered by OpenAI’s Realtime API) in an authentic classroom setting while learning Python. We examined the students’ voice prompts and AI’s responses (1210 messages) by using qualitative coding. We also gathered students’ perceptions via the Partner Modeling Questionnaire. The GenAI Voice Tutor primarily offered feedback on mistakes and next steps, but its correctness was limited (71.4% correct out of 416 feedback outputs). Quality issues were observed, particularly when the AI attempted to utter programming code elements. Students used the GenAI voice tutor primarily for debugging. They perceived it as competent, only somewhat human-like, and flexible. The present study is the first to explore the interaction dynamics of real-time voice GenAI tutors and novice programmers, informing future educational tool design and potentially addressing accessibility needs of diverse learners.
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[AI-130] SME-TEAM: Leverag ing Trust and Ethics for Secure and Responsible Use of AI and LLM s in SMEs

【速读】：该论文旨在解决生成式 AI（Generative AI）和大型语言模型（Large Language Models, LLMs）在中小企业（SMEs）中应用时面临的显著技术、伦理与信任问题。其解决方案的关键在于提出一个结构化的多阶段框架，围绕数据（Data）、算法（Algorithms）、人类监督（Human Oversight）和模型架构（Model Architecture）四大支柱，将伦理原则嵌入AI生命周期的全过程，从而实现安全、负责任的AI部署，并提升中小企业在多样化应用场景中的AI能力与可持续创新能力。

链接: https://arxiv.org/abs/2509.10594
作者: Iqbal H. Sarker,Helge Janicke,Ahmad Mohsin,Leandros Maglaras
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:Artificial Intelligence (AI) and Large Language Models (LLMs) are reshaping today’s business practices, however, their adoption within small and medium-sized enterprises (SMEs) raises significant technical, ethical and trust issues. This paper proposes a structured, multi-phased framework designed to embed trust and ethical principles throughout the AI lifecycle for their secure and responsible use in SMEs. Structured around four pillars, i.e., Data, Algorithms, Human oversight, and Model Architecture, the framework bridges theoretical ethical principles with operational practice, enhancing AI capabilities in diverse SME applications. Ultimately, this paper offers a structured roadmap for responsible AI adoption, framing trust and ethics as a catalyst for resilience, competitiveness, and sustainable innovation in SMEs.
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[AI-131] Assisting the Grading of a Handwritten General Chemistry Exam with Artificial Intelligence

【速读】：该论文旨在解决传统手工批改化学考试中效率低、一致性差的问题，探索基于人工智能（AI）的评分系统在手写化学试卷中的有效性与可靠性。其解决方案的关键在于将包含化学反应方程式、简答与长答、数值推导及图形绘制等多样题型的纸质试卷以图像形式输入AI模型，并通过线性回归分析和心理测量学评估方法，量化AI评分与人工评分的一致性。研究发现，AI在文本类和化学反应类题目上表现良好，但在数值计算和图形类任务中可靠性较低，因此强调需引入人工审核机制进行选择性过滤，从而保障评分准确性与教学公平性。

链接: https://arxiv.org/abs/2509.10591
作者: Jan Cvengros,Gerd Kortemeyer
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
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Abstract:We explore the effectiveness and reliability of an artificial intelligence (AI)-based grading system for a handwritten general chemistry exam, comparing AI-assigned scores to human grading across various types of questions. Exam pages and grading rubrics were uploaded as images to account for chemical reaction equations, short and long open-ended answers, numerical and symbolic answer derivations, drawing, and sketching in pencil-and-paper format. Using linear regression analyses and psychometric evaluations, the investigation reveals high agreement between AI and human graders for textual and chemical reaction questions, while highlighting lower reliability for numerical and graphical tasks. The findings emphasize the necessity for human oversight to ensure grading accuracy, based on selective filtering. The results indicate promising applications for AI in routine assessment tasks, though careful consideration must be given to student perceptions of fairness and trust in integrating AI-based grading into educational practice.
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[AI-132] Machine Unlearning for Responsible and Adaptive AI in Education ESORICS2025

【速读】：该论文旨在解决当前教育领域中机器学习（Machine Learning, ML）系统在隐私保护、对抗性输入鲁棒性、系统性偏见以及动态学习环境适应性等方面存在的关键挑战，这些问题制约了负责任人工智能（Responsible AI）原则的落地与可信AI教育系统的构建。解决方案的关键在于引入机器遗忘（Machine Unlearning, MU）技术，通过系统性梳理42篇同行评审文献，识别出MU在隐私保护、抗攻击能力、偏见缓解和自适应性四个核心维度的潜力，并提出一个面向教育场景的参考架构——负责任与自适应AI的机器遗忘应用架构（MU-RAAI），从而为ML模型提供可解释、可控且可持续演化的机制，推动AI在教育中的可信部署与优化。

链接: https://arxiv.org/abs/2509.10590
作者: Betty Mayeku,Sandra Hummel,Parisa Memarmoshrefi
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
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Abstract:The concept of Machine Unlearning (MU) has gained popularity in various domains due to its ability to address several issues in Machine Learning (ML) models, particularly those related to privacy, security, bias mitigation, and adaptability. With these abilities, MU is evolving into a promising technology in upholding Responsible AI principles and optimizing ML models’ performance. However, despite its promising potential, the concept has not received much attention in the education sector. In an attempt to encourage further uptake of this promising technology in the educational landscape, this paper demonstrates that MU indeed has great potential to serve as a practical mechanism for operationalizing Responsible AI principles as well as an essential tool for Adaptive AI within the educational application domain hence fostering trust in AI-driven educational systems. Through a structured review of 42 peer-reviewed sources, we identify four domains where MU holds particular promise namely privacy protection, resilience against adversarial inputs, mitigation of systemic bias, and adaptability in evolving learning contexts. We systematically explore these potentials and their interventions to core challenges in ML-based education systems. As a conceptual contribution, we present a reference Machine Unlearning application architecture for Responsible and Adaptive AI (MU-RAAI) in education context.
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[AI-133] LearnLens: An AI-Enhanced Dashboard to Support Teachers in Open-Ended Classrooms

【速读】：该论文试图解决的问题是：在探索式学习环境（Exploratory Learning Environments, ELEs）中，尽管学生通过模拟平台和开放式科学课程能够进行动手探究与问题解决，但教师难以及时获取学生对核心概念的理解情况，从而影响教学调整的时效性与针对性。解决方案的关键在于设计并实现一个面向教师的增强型仪表盘——LearnLens，该系统利用生成式 AI (Generative AI, GenAI) 技术处理学生的开放式作答数据，提供包括样例回答、词云、柱状图及 AI 自动生成的总结等多种可视化洞察，帮助教师识别学生思维模式中的共性与差异，从而依据实时反馈优化课堂指导策略。

链接: https://arxiv.org/abs/2509.10582
作者: Namrata Srivastava,Shruti Jain,Clayton Cohn,Naveeduddin Mohammed,Umesh Timalsina,Gautam Biswas
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
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Abstract:Exploratory learning environments (ELEs), such as simulation-based platforms and open-ended science curricula, promote hands-on exploration and problem-solving but make it difficult for teachers to gain timely insights into students’ conceptual understanding. This paper presents LearnLens, a generative AI (GenAI)-enhanced teacher-facing dashboard designed to support problem-based instruction in middle school science. LearnLens processes students’ open-ended responses from digital assessments to provide various insights, including sample responses, word clouds, bar charts, and AI-generated summaries. These features elucidate students’ thinking, enabling teachers to adjust their instruction based on emerging patterns of understanding. The dashboard was informed by teacher input during professional development sessions and implemented within a middle school Earth science curriculum. We report insights from teacher interviews that highlight the dashboard’s usability and potential to guide teachers’ instruction in the classroom.
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[AI-134] he Coding Limits of Robust Watermarking for Generative Models

【速读】：该论文旨在解决生成式 AI 模型中水印鲁棒性的理论极限问题，即在何种条件下水印能够抵抗恶意篡改并保持可检测性。其核心贡献在于提出了“无消息密钥编码”（messageless secret-key codes）这一新的编码抽象，形式化了鲁棒水印所需的三个基本属性：正确性（soundness）、篡改检测能力（tamper detection）以及伪随机性（pseudorandomness）。通过该框架，作者证明了一个严格的不可逾越阈值：对于二进制输出，任何水印方案都无法在超过一半编码比特被修改的情况下存活；对于大小为 $ q $ 的符号集，对应的阈值为 $ 1 - 1/q $。进一步地，作者基于标准密码学假设（如伪随机函数和公开计数器），构造出可在多项式时间内实现的显式编码方案，其容错率逼近上述理论上限，仅存在常数级别的松弛。实验部分验证了该阈值在实际场景中的存在性——以图像水印为例，简单的裁剪与缩放操作即可可靠地翻转约一半潜在符号，导致基于信念传播的解码失败，从而擦除水印但不影响图像视觉质量。此研究首次从理论上和实践中完整刻画了生成式模型水印鲁棒性的边界。

链接: https://arxiv.org/abs/2509.10577
作者: Danilo Francati,Yevin Nikhel Goonatilake,Shubham Pawar,Daniele Venturi,Giuseppe Ateniese
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
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Abstract:We prove a sharp threshold for the robustness of cryptographic watermarking for generative models. This is achieved by introducing a coding abstraction, which we call messageless secret-key codes, that formalizes sufficient and necessary requirements of robust watermarking: soundness, tamper detection, and pseudorandomness. Thus, we establish that robustness has a precise limit: For binary outputs no scheme can survive if more than half of the encoded bits are modified, and for an alphabet of size q the corresponding threshold is (1-1/q) of the symbols. Complementing this impossibility, we give explicit constructions that meet the bound up to a constant slack. For every \delta 0 , assuming pseudorandom functions and access to a public counter, we build linear-time codes that tolerate up to (1/2)(1-\delta) errors in the binary case and (1-1/q)(1-\delta) errors in the q -ary case. Together with the lower bound, these yield the maximum robustness achievable under standard cryptographic assumptions. We then test experimentally whether this limit appears in practice by looking at the recent watermarking for images of Gunn, Zhao, and Song (ICLR 2025). We show that a simple crop and resize operation reliably flipped about half of the latent signs and consistently prevented belief-propagation decoding from recovering the codeword, erasing the watermark while leaving the image visually intact. These results provide a complete characterization of robust watermarking, identifying the threshold at which robustness fails, constructions that achieve it, and an experimental confirmation that the threshold is already reached in practice. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2509.10577 [cs.CR] (or arXiv:2509.10577v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2509.10577 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Danilo Francati [view email] [v1] Thu, 11 Sep 2025 18:08:32 UTC (15,855 KB)
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[AI-135] Aesthetic Experience and Educational Value in Co-creating Art with Generative AI: Evidence from a Survey of Young Learners

【速读】：该论文旨在解决生成式AI（Generative AI）在青少年与艺术学习者协同创作过程中如何重塑审美体验与教育价值的问题。其核心挑战在于厘清人类创作者角色的重构、原创性观念的变迁、创作流程的转变以及审美判断的形成机制。解决方案的关键在于构建一个融合杜威经验美学、伊德后现象学与行动者网络理论（Actor-Network Theory, ANT）的综合性分析框架，揭示人机共创意图中动态交互关系的本质；研究发现，创作者在协作中呈现流动主体性（兼具导演、对话伙伴与发现者三重身份），采用“意图-生成-选择-精炼”的迭代对话式工作流，并推动教育价值从技术技能训练转向高阶能力培养，如批判性判断、跨模态构思与反思性思维。因此，论文主张艺术教育应引导学习者建立批判性的共创立场，在技术协作中保持人类在概念建构、价值判断与意义生成上的独特性。

链接: https://arxiv.org/abs/2509.10576
作者: Chengyuan Zhang,Suzhe Xu
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
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Abstract:This study investigates the aesthetic experience and educational value of collaborative artmaking with generative artificial intelligence (AI) among young learners and art students. Based on a survey of 112 participants, we examine how human creators renegotiate their roles, how conventional notions of originality are challenged, how the creative process is transformed, and how aesthetic judgment is formed in human–AI co-creation. Empirically, participants generally view AI as a partner that stimulates ideation and expands creative boundaries rather than a passive tool, while simultaneously voicing concerns about stylistic homogenization and the erosion of traditional authorship. Theoretically, we synthesize Dewey’s aesthetics of experience, Ihde’s postphenomenology, and actor–network theory (ANT) into a single analytical framework to unpack the dynamics between human creators and AI as a non-human actant. Findings indicate (i) a fluid subjectivity in which creators shift across multiple stances (director, dialogic partner, discoverer); (ii) an iterative, dialogic workflow (intent–generate–select–refine) that centers critical interpretation; and (iii) an educational value shift from technical skill training toward higher-order competencies such as critical judgment, cross-modal ideation, and reflexivity. We argue that arts education should cultivate a \emphcritical co-creation stance toward technology, guiding learners to collaborate with AI while preserving human distinctiveness in concept formation, judgment, and meaning-making.
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[AI-136] Quality Assessment of Tabular Data using Large Language Models and Code Generation EMNLP

【速读】：该论文旨在解决表格数据集在下游分析中因数据质量不可靠而导致的挑战，尤其是传统基于规则的验证方法在效率、人工干预和计算成本方面的局限性。其解决方案的关键在于提出一个三阶段框架，融合统计异常值检测与大语言模型（Large Language Model, LLM）驱动的规则生成和代码合成：首先通过传统聚类过滤样本，随后迭代式地利用LLM生成语义有效的质量规则，并借助代码生成型LLM将规则转化为可执行的验证器；同时，通过检索增强生成（Retrieval-Augmented Generation, RAG）引入外部知识源和领域特定的少量示例来提升规则可靠性，并设置强约束机制保障规则与代码的一致性和准确性。

链接: https://arxiv.org/abs/2509.10572
作者: Ashlesha Akella,Akshar Kaul,Krishnasuri Narayanam,Sameep Mehta
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Databases (cs.DB)
备注: EMNLP industry track submitted

点击查看摘要

Abstract:Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computational costs. We present a three-stage framework that combines statistical inliner detection with LLM-driven rule and code generation. After filtering data samples through traditional clustering, we iteratively prompt LLMs to produce semantically valid quality rules and synthesize their executable validators through code-generating LLMs. To generate reliable quality rules, we aid LLMs with retrieval-augmented generation (RAG) by leveraging external knowledge sources and domain-specific few-shot examples. Robust guardrails ensure the accuracy and consistency of both rules and code snippets. Extensive evaluations on benchmark datasets confirm the effectiveness of our approach.
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[AI-137] Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey

【速读】：该论文旨在解决传统深度学习方法在轨迹预测中面临的三大核心问题：缺乏可解释性、对大规模标注数据的强依赖性以及在长尾场景下的泛化能力弱。其解决方案的关键在于引入大型基础模型（Large Foundation Models, LFMs），特别是大型语言模型（Large Language Models, LLMs）和多模态大型语言模型（Multimodal Large Language Models, MLLMs），通过融合语言与场景语义信息，实现可解释的上下文推理，从而显著提升复杂环境中的预测安全性与泛化性能。论文强调了三种关键技术路径：轨迹-语言映射、多模态融合与约束驱动推理，为自动驾驶中车辆与行人轨迹预测提供了系统性的新范式。

链接: https://arxiv.org/abs/2509.10570
作者: Wei Dai,Shengen Wu,Wei Wu,Zhenhao Wang,Sisuo Lyu,Haicheng Liao,Limin Yu,Weiping Ding,Runwei Guan,Yutao Yue
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 22 pages, 6 figures

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Abstract:Trajectory prediction serves as a critical functionality in autonomous driving, enabling the anticipation of future motion paths for traffic participants such as vehicles and pedestrians, which is essential for driving safety. Although conventional deep learning methods have improved accuracy, they remain hindered by inherent limitations, including lack of interpretability, heavy reliance on large-scale annotated data, and weak generalization in long-tail scenarios. The rise of Large Foundation Models (LFMs) is transforming the research paradigm of trajectory prediction. This survey offers a systematic review of recent advances in LFMs, particularly Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) for trajectory prediction. By integrating linguistic and scene semantics, LFMs facilitate interpretable contextual reasoning, significantly enhancing prediction safety and generalization in complex environments. The article highlights three core methodologies: trajectory-language mapping, multimodal fusion, and constraint-based reasoning. It covers prediction tasks for both vehicles and pedestrians, evaluation metrics, and dataset analyses. Key challenges such as computational latency, data scarcity, and real-world robustness are discussed, along with future research directions including low-latency inference, causality-aware modeling, and motion foundation models.
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[AI-138] MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models

【速读】：该论文旨在解决生成式 AI（Generative AI）模型中内容溯源与版权保护的难题，特别是针对潜在扩散模型（Latent Diffusion Models）生成内容缺乏可追溯水印机制的问题。其解决方案的关键在于提出 MarkDiffusion——一个开源的 Python 工具包，包含三个核心组件：统一的水印算法集成框架以简化开发流程、可视化工具用于直观展示水印嵌入与提取过程，以及涵盖检测性（detectability）、鲁棒性（robustness）和输出质量（output quality）三大维度的标准化评估模块，辅以8条自动化评估流水线，从而推动水印技术在学术研究与实际应用中的规范化与可比性。

链接: https://arxiv.org/abs/2509.10569
作者: Leyi Pan,Sheng Guan,Zheyu Fu,Luyang Si,Zian Wang,Xuming Hu,Irwin King,Philip S. Yu,Aiwei Liu,Lijie Wen
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: 23 pages, 13 figures, 5 tables

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Abstract:We introduce MarkDiffusion, an open-source Python toolkit for generative watermarking of latent diffusion models. It comprises three key components: a unified implementation framework for streamlined watermarking algorithm integrations and user-friendly interfaces; a mechanism visualization suite that intuitively showcases added and extracted watermark patterns to aid public understanding; and a comprehensive evaluation module offering standard implementations of 24 tools across three essential aspects - detectability, robustness, and output quality - plus 8 automated evaluation pipelines. Through MarkDiffusion, we seek to assist researchers, enhance public awareness and engagement in generative watermarking, and promote consensus while advancing research and applications.
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[AI-139] AVEC: Bootstrapping Privacy for Local LLM s

【速读】：该论文旨在解决本地语言模型（Local Language Models, LLMs）在边缘计算环境中如何实现隐私保护的问题，特别是针对委托查询（delegated queries）场景下的隐私泄露风险。其核心挑战在于如何在不牺牲可用性的前提下，对模型推理过程进行可验证的隐私控制。解决方案的关键在于提出 AVEC（Adaptive Verifiable Edge Control）框架，该框架通过引入基于敏感度（sensitivity）、本地置信度（local confidence）和历史使用情况的自适应预算分配算法，动态调整每查询的差分隐私（Differential Privacy, DP）参数，并结合设备端完整性校验的可验证变换（verifiable transformation），以实现隐私保障的显式可验证性。此外，作者采用 Rényi 差分隐私（Rényi Differential Privacy）与计数器（odometer-based accounting）的形式化分析工具，建立了隐私保证的理论边界，包括效用上限、委托泄露界以及确定性门控和仅哈希认证的不可能性结果，从而为私有化本地 LLM 的实证研究提供了概念架构与理论基础。

链接: https://arxiv.org/abs/2509.10561
作者: Madhava Gaikwad
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 12 pages

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Abstract:This position paper presents AVEC (Adaptive Verifiable Edge Control), a framework for bootstrapping privacy for local language models by enforcing privacy at the edge with explicit verifiability for delegated queries. AVEC introduces an adaptive budgeting algorithm that allocates per-query differential privacy parameters based on sensitivity, local confidence, and historical usage, and uses verifiable transformation with on-device integrity checks. We formalize guarantees using Rényi differential privacy with odometer-based accounting, and establish utility ceilings, delegation-leakage bounds, and impossibility results for deterministic gating and hash-only certification. Our evaluation is simulation-based by design to study mechanism behavior and accounting; we do not claim deployment readiness or task-level utility with live LLMs. The contribution is a conceptual architecture and theoretical foundation that chart a pathway for empirical follow-up on privately bootstrapping local LLMs.
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[AI-140] ASL360: AI-Enabled Adaptive Streaming of Layered 360° Video over UAV-assisted Wireless Networks

【速读】：该论文旨在解决移动虚拟现实（VR）用户在下一代无线网络中进行按需360°视频流媒体时的用户体验质量（Quality of Experience, QoE）优化问题。针对动态网络环境下的高带宽需求与资源受限挑战，作者提出了一种基于自适应深度强化学习的调度算法ASL360，其关键在于将调度决策建模为带有约束的马尔可夫决策过程（Constrained Markov Decision Process, CMDP），并采用近端策略优化（Proximal Policy Optimization, PPO）方法求解最优策略；同时引入动态成本调整机制，实时平衡视频质量、缓冲占用率和质量变化三个核心指标，从而显著提升QoE——实验表明，相比现有基线方法，ASL360可实现平均视频质量提高约2 dB、平均卡顿时间降低80%、视频质量波动降低57%。

链接: https://arxiv.org/abs/2509.10544
作者: Alireza Mohammadhosseini,Jacob Chakareski,Nicholas Mastronarde
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: This paper has been accepted for presentation at the IEEE Global Communications Conference (GLOBECOM) 2025

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Abstract:We propose ASL360, an adaptive deep reinforcement learning-based scheduler for on-demand 360° video streaming to mobile VR users in next generation wireless networks. We aim to maximize the overall Quality of Experience (QoE) of the users served over a UAV-assisted 5G wireless network. Our system model comprises a macro base station (MBS) and a UAV-mounted base station which both deploy mm-Wave transmission to the users. The 360° video is encoded into dependent layers and segmented tiles, allowing a user to schedule downloads of each layer’s segments. Furthermore, each user utilizes multiple buffers to store the corresponding video layer’s segments. We model the scheduling decision as a Constrained Markov Decision Process (CMDP), where the agent selects Base or Enhancement layers to maximize the QoE and use a policy gradient-based method (PPO) to find the optimal policy. Additionally, we implement a dynamic adjustment mechanism for cost components, allowing the system to adaptively balance and prioritize the video quality, buffer occupancy, and quality change based on real-time network and streaming session conditions. We demonstrate that ASL360 significantly improves the QoE, achieving approximately 2 dB higher average video quality, 80% lower average rebuffering time, and 57% lower video quality variation, relative to competitive baseline methods. Our results show the effectiveness of our layered and adaptive approach in enhancing the QoE in immersive videostreaming applications, particularly in dynamic and challenging network environments.
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[AI-141] Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods

【速读】：该论文旨在解决分布式拒绝服务（Distributed Denial-of-Service, DDoS）攻击难以被传统检测机制识别的问题，尤其是当攻击流量通过细微调整规避检测时。其解决方案的关键在于：首先，利用时空蜂巢图（spatio-temporal hive-plot）编码构建模式识别基线；其次，引入基于快速梯度符号法（FGSM）和投影梯度下降法（PGD）的对抗训练，并结合空间噪声与图像偏移增强鲁棒性；最后，通过对帧级预测结果的分析，发现第3-4帧即具备强预测信号，表明早期阶段即可实现有效分类。该方法在基准数据集上将对抗样本准确率从50–55%提升至93%以上，同时保持对干净样本的良好性能。

链接: https://arxiv.org/abs/2509.10543
作者: Landon Bragg,Nathan Dorsey,Josh Prior,John Ajit,Ben Kim,Nate Willis,Pablo Rivas
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: The 27th International Conference on Artificial Intelligence (ICAI’25)

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Abstract:Distributed Denial-of-Service (DDoS) attacks remain a serious threat to online infrastructure, often bypassing detection by altering traffic in subtle ways. We present a method using hive-plot sequences of network data and a 3D convolutional neural network (3D CNN) to classify DDoS traffic with high accuracy. Our system relies on three main ideas: (1) using spatio-temporal hive-plot encodings to set a pattern-recognition baseline, (2) applying adversarial training with FGSM and PGD alongside spatial noise and image shifts, and (3) analyzing frame-wise predictions to find early signals. On a benchmark dataset, our method lifts adversarial accuracy from 50-55% to over 93% while maintaining clean-sample performance. Frames 3-4 offer strong predictive signals, showing early-stage classification is possible.
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[AI-142] Situation Model of the Transport Transport Emissions and Meteorological Conditions

【速读】：该论文旨在解决城市交通排放污染问题，特别是如何通过系统性方法分析气象条件对交通排放量及其扩散的影响，从而为城市规划者和政策制定者提供更有效的交通管理与环境保护协同决策依据。解决方案的关键在于构建基于模糊推理系统（Fuzzy Inference System, FIS）的预测模型，该模型整合了布拉格市实测的交通、气象及排放数据，能够量化不同环境条件下交通排放的变化趋势，进而支持更具环境敏感性的城市交通规划与管理策略。

链接: https://arxiv.org/abs/2509.10541
作者: V. Benes,M. Svitek,A. Michalikova,M. Melicherik
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Air pollution in cities and the possibilities of reducing this pollution represents one of the most important factors that today’s society has to deal with. This paper focuses on a systemic approach to traffic emissions with their relation to meteorological conditions, analyzing the effect of weather on the quantity and dispersion of traffic emissions in a city. Using fuzzy inference systems (FIS) the model for prediction of changes in emissions depending on various conditions is developed. The proposed model is based on traffic, meteorology and emission data measured in Prague, Czech Republic. The main objective of the work is to provide insight into how urban planners and policymakers can plan and manage urban transport more effectively with environmental protection in mind.
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[AI-143] EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System AAAI

【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）助手在企业工作流中集成时所面临的新型安全威胁，特别是零点击提示注入（prompt injection）漏洞导致的远程未认证数据泄露问题。其核心解决方案在于通过多阶段攻击链绕过现有防护机制（如跨提示注入尝试分类器、链接脱敏策略和内容安全策略），揭示了当前防御体系的局限性，并提出一套工程化缓解措施：包括提示分区（prompt partitioning）、增强输入/输出过滤、基于溯源的访问控制以及严格的内容安全策略。关键创新在于系统性识别出LLM信任边界失效的根本原因，并强调最小权限原则、纵深防御架构与持续对抗测试对于构建安全AI协作者的重要性。

链接: https://arxiv.org/abs/2509.10540
作者: Pavan Reddy,Aditya Sanjay Gujral
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 8 pages content, 1 page references, 2 figures, Published at AAAI Fall Symposium Series 2025

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Abstract:Large language model (LLM) assistants are increasingly integrated into enterprise workflows, raising new security concerns as they bridge internal and external data sources. This paper presents an in-depth case study of EchoLeak (CVE-2025-32711), a zero-click prompt injection vulnerability in Microsoft 365 Copilot that enabled remote, unauthenticated data exfiltration via a single crafted email. By chaining multiple bypasses-evading Microsofts XPIA (Cross Prompt Injection Attempt) classifier, circumventing link redaction with reference-style Markdown, exploiting auto-fetched images, and abusing a Microsoft Teams proxy allowed by the content security policy-EchoLeak achieved full privilege escalation across LLM trust boundaries without user interaction. We analyze why existing defenses failed, and outline a set of engineering mitigations including prompt partitioning, enhanced input/output filtering, provenance-based access control, and strict content security policies. Beyond the specific exploit, we derive generalizable lessons for building secure AI copilots, emphasizing the principle of least privilege, defense-in-depth architectures, and continuous adversarial testing. Our findings establish prompt injection as a practical, high-severity vulnerability class in production AI systems and provide a blueprint for defending against future AI-native threats.
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[AI-144] On Using Large-Batches in Federated Learning

【速读】：该论文旨在解决联邦学习（Federated Learning, FL）中因使用大批次（large-batch）训练导致的泛化性能下降问题，尤其是在设备计算资源受限和网络带宽有限的场景下，如何平衡并行效率与模型统计性能。其解决方案的关键在于探索小批次（small-batch）与大批次训练之间的权衡机制，提出一种新型的大批次训练技术，在保持高并行扩展性的同时，显著提升模型的泛化能力。实验表明，在相同迭代次数下，该方法在ResNet50和VGG11模型上分别比传统小批次训练提升了32.33%和3.74%的测试准确率。

链接: https://arxiv.org/abs/2509.10537
作者: Sahil Tyagi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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Abstract:Efficient Federated learning (FL) is crucial for training deep networks over devices with limited compute resources and bounded networks. With the advent of big data, devices either generate or collect multimodal data to train either generic or local-context aware networks, particularly when data privacy and locality is vital. FL algorithms generally trade-off between parallel and statistical performance, improving model quality at the cost of higher communication frequency, or vice versa. Under frequent synchronization settings, FL over a large cluster of devices may perform more work per-training iteration by processing a larger global batch-size, thus attaining considerable training speedup. However, this may result in poor test performance (i.e., low test loss or accuracy) due to generalization degradation issues associated with large-batch training. To address these challenges with large-batches, this work proposes our vision of exploiting the trade-offs between small and large-batch training, and explore new directions to enjoy both the parallel scaling of large-batches and good generalizability of small-batch training. For the same number of iterations, we observe that our proposed large-batch training technique attains about 32.33% and 3.74% higher test accuracy than small-batch training in ResNet50 and VGG11 models respectively.
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[AI-145] Semantic-guided LoRA Parameters Generation

【速读】：该论文旨在解决边缘计算场景下个性化模型适配的两大难题：一是用户任务具有特定偏好，而传统统一模型在封闭世界假设下难以满足多样化需求；二是当训练与部署数据存在显著领域偏移时，模型性能易下降。同时，为每个用户重新训练或微调模型既不现实又存在隐私风险。解决方案的关键在于提出一种名为Semantic-guided LoRA Parameter Generation (SG-LoRA) 的新框架，其核心创新是利用任务描述作为语义桥梁，在共享嵌入空间中度量目标任务与已知专家任务的相似性，并据此建模目标任务的LoRA参数分布，从而在无需任何用户专属数据或额外训练的情况下生成高性能的用户定制化LoRA参数。该方法实现了零样本开放世界下的实时个性化模型构建，兼顾了适应性与隐私保护。

链接: https://arxiv.org/abs/2509.10535
作者: Miaoge Li,Yang Chen,Zhijie Rao,Can Jiang,Jingcai Guo
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 19 pages, 9 figures

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Abstract:Low-Rank Adaptation (LoRA) has demonstrated strong generalization capabilities across a variety of tasks for efficiently fine-tuning AI models, especially on resource-constrained edges. However, in real-world applications, edge users often exhibit task-specific preferences that are difficult to handle with a unified model trained under a closed-world assumption, and the challenge may further increase when there are significant domain shifts between training and deployment. Meanwhile, retraining/fine-tuning models for each user is also impractical due to its cost-intensive nature and privacy concerns over raw data utilization from edges. To address these challenges, we propose Semantic-guided LoRA Parameter Generation (SG-LoRA), the first of its kind framework to efficiently produce user-specific LoRA parameters without any additional training on user tasks or access to user-specific data. Concretely, SG-LoRA uses task descriptions as the semantic bridge, measuring their proximity to a set of known expert tasks in a shared embedding space. Based on this semantic guidance, it models the target task’s LoRA parameter distribution to generate high-performing parameters for novel tasks. SG-LoRA enables the real-time construction of LoRA models aligned with individual intents by distilling knowledge from prominent LoRA experts and, meanwhile, offering a privacy-preserving solution for personalized model adaptation in a novel zero-shot open-world setting proposed in this work. Extensive experiments on multiple challenging tasks confirm the superior performance and remarkable adaptability of SG-LoRA. Code is available at this https URL.
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[AI-146] FinXplore: An Adaptive Deep Reinforcement Learning Framework for Balancing and Discovering Investment Opportunities

【速读】：该论文旨在解决深度强化学习（Deep Reinforcement Learning, DRL）在投资组合优化中普遍存在的局限性——即现有方法通常仅限于预定义投资标的内的资产配置，忽视了对扩展投资机会的探索。为应对这一问题，作者提出了一种融合“利用”与“探索”的投资景观框架，其关键在于引入两个协同工作的DRL智能体：一个负责在现有资产池中进行动态配置（exploitation），另一个则专注于在扩展的投资宇宙中发现新机会（exploration）。通过动态平衡这两类目标，该方案能够适应市场变化并提升投资组合绩效，实验结果验证了其相对于当前最优策略和基线方法的优越性。

链接: https://arxiv.org/abs/2509.10531
作者: Himanshu Choudhary,Arishi Orra,Manoj Thakur
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Portfolio optimization is essential for balancing risk and return in financial decision-making. Deep Reinforcement Learning (DRL) has stood out as a cutting-edge tool for portfolio optimization that learns dynamic asset allocation using trial-and-error interactions. However, most DRL-based methods are restricted to allocating assets within a pre-defined investment universe and overlook exploring new opportunities. This study introduces an investment landscape that integrates exploiting existing assets with exploring new investment opportunities in an extended universe. The proposed approach leverages two DRL agents and dynamically balances these objectives to adapt to evolving markets while enhancing portfolio performance. One agent allocates assets within the existing universe, while another assists in exploring new opportunities in the extended universe. The effciency of the proposed methodology is determined using two real-world market data sets. The experiments demonstrate the superiority of the suggested approach against the state-of-the-art portfolio strategies and baseline methods.
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[AI-147] Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts

【速读】：该论文旨在解决基于混合专家（Mixture of Experts, MoE）架构的Transformer模型在长序列建模中面临的计算效率低和长程依赖捕捉能力不足的问题，尤其是专家资源分配缺乏动态适应性。解决方案的关键在于提出一种动态自适应共享专家与分组多头注意力混合模型（Dynamic Adaptive Shared Expert and Grouped Multi-Head Attention Hybrid Model, DASG-MoE），其核心创新包括：1）引入分组多头注意力（Grouped Multi-Head Attention, GMHA）机制，通过序列分组、局部滑动窗口注意力和特征聚合降低计算复杂度并增强对局部信息的泛化能力；2）设计双尺度共享专家结构（Dual-Scale Shared Expert Structure, DSSE），利用轻量浅层专家快速响应低维特征、深层专家通过预训练迁移与后训练优化处理高维语义，实现效率与精度的动态平衡；3）提出分层自适应动态路由（Hierarchical Adaptive Dynamic Routing, ADR）机制，根据特征复杂度和任务需求动态选择专家层级，并通过局部专家激活策略优化资源分配。实验表明，该模型在多个长序列基准数据集上优于现有先进方法。

链接: https://arxiv.org/abs/2509.10530
作者: Cheng Li,Jiexiong Liu,Yixuan Chen,Jie ji
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Transformer models based on the Mixture of Experts (MoE) architecture have made significant progress in long-sequence modeling, but existing models still have shortcomings in computational efficiency and the ability to capture long-range dependencies, especially in terms of the dynamic adaptability of expert resource allocation. In this paper, we propose a Dynamic Adaptive Shared Expert and Grouped Multi-Head Attention Hybrid Model (DASG-MoE) to enhance long-sequence modeling capabilities by integrating three modules. First, we employ the Grouped Multi-Head Attention (GMHA) mechanism to effectively reduce the computational complexity of long sequences. By parallel processing through sequence grouping, local sliding window attention, and feature aggregation, we address long-range dependency issues and the model’s lack of generalization for local information. Second, we design a Dual-Scale Shared Expert Structure (DSSE), where shallow experts use lightweight computations to quickly respond to low-dimensional features, while deep experts process high-dimensional complex semantics through pre-training transfer and post-training optimization, achieving a dynamic balance between efficiency and accuracy. Third, we propose a hierarchical Adaptive Dynamic Routing (ADR) mechanism that dynamically selects expert levels based on feature complexity and task requirements, and optimizes resource allocation through a local expert activation strategy. Experiments on multiple long-sequence benchmark datasets demonstrate that our DASG-MoE model outperforms state-of-the-art models.
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[AI-148] STM-Graph: A Python Framework for Spatio-Temporal Mapping and Graph Neural Network Predictions CIKM2025

【速读】：该论文旨在解决城市时空数据在预测分析中因动态性和复杂性带来的挑战。其解决方案的关键在于提出STM-Graph，一个开源的Python框架，能够将原始的城市时空事件数据转换为适用于图神经网络（GNN）训练和预测的图结构表示；该框架集成了多种空间映射方法、来自OpenStreetMap的城市特征、多类GNN模型、全面的可视化工具以及面向专业与非专业用户的图形界面（GUI），具备模块化和可扩展性，支持新映射方法和自定义模型的集成，从而促进快速实验与基准测试，成为城市计算领域研究人员和实践者的重要工具。

链接: https://arxiv.org/abs/2509.10528
作者: Amirhossein Ghaffari,Huong Nguyen,Lauri Lovén,Ekaterina Gilman
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted manuscript (CC BY 4.0). To appear in ACM CIKM 2025, Seoul, Nov 10-14, 2025. DOI: https://doi.org/10.1145/3746252.3761645 . The Version of Record will be uploaded when available

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Abstract:Urban spatio-temporal data present unique challenges for predictive analytics due to their dynamic and complex nature. We introduce STM-Graph, an open-source Python framework that transforms raw spatio-temporal urban event data into graph representations suitable for Graph Neural Network (GNN) training and prediction. STM-Graph integrates diverse spatial mapping methods, urban features from OpenStreetMap, multiple GNN models, comprehensive visualization tools, and a graphical user interface (GUI) suitable for professional and non-professional users. This modular and extensible framework facilitates rapid experimentation and benchmarking. It allows integration of new mapping methods and custom models, making it a valuable resource for researchers and practitioners in urban computing. The source code of the framework and GUI are available at: this https URL and this https URL.
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[AI-149] Resource-Aware Neural Network Pruning Using Graph-based Reinforcement Learning

【速读】：该论文旨在解决传统神经网络剪枝方法依赖手工设计启发式规则和局部优化视角所导致的次优性能及低效剪枝策略问题。其核心解决方案是将图结构观察空间引入自动化机器学习（AutoML）框架，通过构建捕捉层与通道间完整拓扑关系的网络图表示，替代原有的逐层观察空间，从而实现对网络结构的全局理解。关键创新包括：1）使用图注意力网络（Graph Attention Network, GAT）编码器生成丰富的网络嵌入；2）将连续剪枝比例的动作空间转化为细粒度的二进制动作空间，使智能体能够直接从数据中学习最优通道重要性标准，摆脱预定义评分函数的限制；3）在约束马尔可夫决策过程（Constrained Markov Decision Process, CMDP）框架下建模剪枝决策，确保满足目标压缩率等资源约束，并设计自竞争奖励机制以驱动智能体持续提升性能。

链接: https://arxiv.org/abs/2509.10526
作者: Dieter Balemans,Thomas Huybrechts,Jan Steckel,Siegfried Mercelis
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:This paper presents a novel approach to neural network pruning by integrating a graph-based observation space into an AutoML framework to address the limitations of existing methods. Traditional pruning approaches often depend on hand-crafted heuristics and local optimization perspectives, which can lead to suboptimal performance and inefficient pruning strategies. Our framework transforms the pruning process by introducing a graph representation of the target neural network that captures complete topological relationships between layers and channels, replacing the limited layer-wise observation space with a global view of network structure. The core innovations include a Graph Attention Network (GAT) encoder that processes the network’s graph representation and generates a rich embedding. Additionally, for the action space we transition from continuous pruning ratios to fine-grained binary action spaces which enables the agent to learn optimal channel importance criteria directly from data, moving away from predefined scoring functions. These contributions are modelled within a Constrained Markov Decision Process (CMDP) framework, allowing the agent to make informed pruning decisions while adhering to resource constraints such as target compression rates. For this, we design a self-competition reward system that encourages the agent to outperform its previous best performance while satisfying the defined constraints. We demonstrate the effectiveness of our approach through extensive experiments on benchmark datasets including CIFAR-10, CIFAR-100, and ImageNet. The experiments show that our method consistently outperforms traditional pruning techniques, showing state-of-the-art results while learning task-specific pruning strategies that identify functionally redundant connections beyond simple weight magnitude considerations.
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[AI-150] From Predictions to Explanations: Explainable AI for Autism Diagnosis and Identification of Critical Brain Regions

【速读】：该论文旨在解决自闭症谱系障碍（Autism Spectrum Disorder, ASD）研究中因数据稀缺导致的机器学习模型训练困难问题，以及现有深度学习模型缺乏可解释性、难以与神经生物学证据对接的局限。其解决方案的关键在于构建一个两模块的计算机辅助诊断框架：第一模块采用跨域迁移学习（cross-domain transfer learning）对深度学习模型进行微调，以有效缓解ASD数据不足的问题；第二模块引入三种可解释人工智能（Explainable AI, XAI）技术——显著性映射（saliency mapping）、梯度加权类激活映射（Gradient-weighted Class Activation Mapping）和SHapley Additive exPlanations（SHAP）分析，实现模型决策过程的可视化并识别与ASD最相关的脑区。该方法不仅提升了诊断准确性，还通过与已知神经生物学证据的对比验证了临床相关性，为ASD的精准医学研究提供了可解释且可靠的技术路径。

链接: https://arxiv.org/abs/2509.10523
作者: Kush Gupta,Amir Aly,Emmanuel Ifeachor,Rohit Shankar
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by atypical brain maturation. However, the adaptation of transfer learning paradigms in machine learning for ASD research remains notably limited. In this study, we propose a computer-aided diagnostic framework with two modules. This chapter presents a two-module framework combining deep learning and explainable AI for ASD diagnosis. The first module leverages a deep learning model fine-tuned through cross-domain transfer learning for ASD classification. The second module focuses on interpreting the model decisions and identifying critical brain regions. To achieve this, we employed three explainable AI (XAI) techniques: saliency mapping, Gradient-weighted Class Activation Mapping, and SHapley Additive exPlanations (SHAP) analysis. This framework demonstrates that cross-domain transfer learning can effectively address data scarcity in ASD research. In addition, by applying three established explainability techniques, the approach reveals how the model makes diagnostic decisions and identifies brain regions most associated with ASD. These findings were compared against established neurobiological evidence, highlighting strong alignment and reinforcing the clinical relevance of the proposed approach.
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[AI-151] A Comparative Benchmark of Federated Learning Strategies for Mortality Prediction on Heterogeneous and Imbalanced Clinical Data

【速读】：该论文旨在解决医疗场景中机器学习模型在真实世界临床数据下因数据隐私限制和统计异质性（non-IID）导致的性能下降问题，同时应对死亡率预测任务中固有的类别不平衡挑战。其解决方案的关键在于采用联邦学习（Federated Learning, FL）框架，并通过在客户端本地应用SMOTE-Tomek方法缓解类别不平衡，结合多种FL策略（FedAvg、FedProx、FedAdagrad、FedAdam、FedCluster）进行对比实验。研究发现，基于正则化的方法（如FedProx）在非独立同分布（non-IID）和类别不平衡条件下表现最优，不仅实现了最高的F1分数（0.8831），还保持了稳定的收敛性，显著优于标准聚合或服务器端自适应优化方法，为实际医疗场景中的联邦学习策略选择提供了重要实证依据。

链接: https://arxiv.org/abs/2509.10517
作者: Rodrigo Tertulino
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: This has been preparing to be submitted to the Journal of the Brazilian Computer Society (JBCS)

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Abstract:Machine learning models hold significant potential for predicting in-hospital mortality, yet data privacy constraints and the statistical heterogeneity of real-world clinical data often hamper their development. Federated Learning (FL) offers a privacy-preserving solution, but its performance under non-Independent and Identically Distributed (non-IID) and imbalanced conditions requires rigorous investigation. The study presents a comparative benchmark of five federated learning strategies: FedAvg, FedProx, FedAdagrad, FedAdam, and FedCluster for mortality prediction. Using the large-scale MIMIC-IV dataset, we simulate a realistic non-IID environment by partitioning data by clinical care unit. To address the inherent class imbalance of the task, the SMOTE-Tomek technique is applied to each client’s local training data. Our experiments, conducted over 50 communication rounds, reveal that the regularization-based strategy, FedProx, consistently outperformed other methods, achieving the highest F1-Score of 0.8831 while maintaining stable convergence. While the baseline FedAvg was the most computationally efficient, its predictive performance was substantially lower. Our findings indicate that regularization-based FL algorithms like FedProx offer a more robust and effective solution for heterogeneous and imbalanced clinical prediction tasks than standard or server-side adaptive aggregation methods. The work provides a crucial empirical benchmark for selecting appropriate FL strategies for real-world healthcare applications.
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[AI-152] Privacy-Preserving Personalization in Education: A Federated Recommender System for Student Performance Prediction

【速读】：该论文旨在解决教育数字化背景下，推荐系统在实现个性化教学的同时如何保障学生数据隐私的问题。传统集中式推荐系统依赖于中央数据存储，难以满足现代数据保护法规的要求。其解决方案的关键在于采用联邦学习（Federated Learning, FL）框架，在不收集和集中存储敏感学生数据的前提下，利用基于深度神经网络（Deep Neural Network, DNN）的模型进行分布式训练，并通过优化联邦聚合策略（如FedProx）有效应对学生数据异构性问题，从而在保护隐私的同时实现高性能的内容推荐（F1-Score达76.28%，接近集中式XGBoost模型性能的82.85%）。

链接: https://arxiv.org/abs/2509.10516
作者: Rodrigo Tertulino
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: This paper has been prepared to be submitted to the Brazilian Journal of Informatics in Education - RBIE

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Abstract:The increasing digitalization of education presents unprecedented opportunities for data-driven personalization, yet it introduces significant student data privacy challenges. Conventional recommender systems rely on centralized data, a paradigm often incompatible with modern data protection regulations. A novel privacy-preserving recommender system is proposed and evaluated to address this critical issue using Federated Learning (FL). The approach utilizes a Deep Neural Network (DNN) with rich, engineered features from the large-scale ASSISTments educational dataset. A rigorous comparative analysis of federated aggregation strategies was conducted, identifying FedProx as a significantly more stable and effective method for handling heterogeneous student data than the standard FedAvg baseline. The optimized federated model achieves a high-performance F1-Score of 76.28%, corresponding to 82.85% of the performance of a powerful, centralized XGBoost model. These findings validate that a federated approach can provide highly effective content recommendations without centralizing sensitive student data. Consequently, our work presents a viable and robust solution to the personalization-privacy dilemma in modern educational platforms.
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[AI-153] LogGuardQ: A Cognitive-Enhanced Reinforcement Learning Framework for Cybersecurity Anomaly Detection in Security Logs

【速读】：该论文旨在解决传统强化学习（Reinforcement Learning, RL）算法在动态环境中面临的有效探索效率低、稳定性差和适应性不足的问题。针对这些问题，作者提出了一种名为LogGuardQ的新型框架，其核心创新在于融合了受人类认知启发的双记忆系统与基于温度衰减和好奇心驱动的自适应探索策略，从而显著提升了模型在复杂环境中的检测准确率与学习稳定性。

链接: https://arxiv.org/abs/2509.10511
作者: Umberto Gonçalves de Sousa
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
备注: 17 pages, 6 figures

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Abstract:Reinforcement learning (RL) has transformed sequential decision-making, but traditional algorithms like Deep Q-Networks (DQNs) and Proximal Policy Optimization (PPO) often struggle with efficient exploration, stability, and adaptability in dynamic environments. This study presents LogGuardQ (Adaptive Log Guard with Cognitive enhancement), a novel framework that integrates a dual-memory system inspired by human cognition and adaptive exploration strategies driven by temperature decay and curiosity. Evaluated on a dataset of 1,000,000 simulated access logs with 47.9% anomalies over 20,000 episodes, LogGuardQ achieves a 96.0% detection rate (versus 93.0% for DQN and 47.1% for PPO), with precision of 0.4776, recall of 0.9996, and an F1-score of 0.6450. The mean reward is 20.34 \pm 44.63 across all episodes (versus 18.80 \pm 43.98 for DQN and -0.17 \pm 23.79 for PPO), with an average of 5.0 steps per episode (constant across models). Graphical analyses, including learning curves smoothed with a Savgol filter (window=501, polynomial=2), variance trends, action distributions, and cumulative detections, demonstrate LogGuardQ’s superior stability and efficiency. Statistical tests (Mann-Whitney U) confirm significant performance advantages (e.g., p = 0.0002 vs. DQN with negligible effect size, p 0.0001 vs. PPO with medium effect size, and p 0.0001 for DQN vs. PPO with small effect size). By bridging cognitive science and RL, LogGuardQ offers a scalable approach to adaptive learning in uncertain environments, with potential applications in cybersecurity, intrusion detection, and decision-making under uncertainty.
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[AI-154] he Anti-Ouroboros Effect: Emergent Resilience in Large Language Models from Recursive Selective Feedback

【速读】：该论文旨在解决递归训练大型语言模型（Large Language Models, LLMs）时面临的稳定性问题，即模型在自我生成数据上持续训练可能导致的性能退化（model collapse）。传统理论预测此类循环训练将引发渐进式性能下降，但本文通过引入一种选择性反馈机制（selective feedback mechanism）挑战了这一观点。其关键在于：仅保留高质量输出作为训练数据，而非无差别地使用模型自动生成的内容，从而形成简单的筛选压力。实验表明，这种机制不仅抑制了性能衰减，反而在Gemma 2B模型的复杂摘要任务中显著提升了ROUGE-L F1得分（五代迭代后提升6.6%），远超未过滤控制组（下降3.5%）和随机过滤组（下降4.2%），并揭示出一种名为“反奥罗波罗斯效应”（Anti-Ouroboros Effect）的新现象——系统韧性可作为LLMs在简单选择压力下涌现的特性，为构建更安全、鲁棒的AI系统提供了可扩展的新范式。

链接: https://arxiv.org/abs/2509.10509
作者: Sai Teja Reddy Adapala
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 5 pages, 3 figures, 2 tables. Code is available at: this https URL

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Abstract:The stability of recursively trained large language models (LLMs) is a foundational problem for AI safety. Prevailing theory predicts model collapse, a progressive degradation when models are trained on their own output. We challenge this narrative by introducing a selective feedback mechanism. Contrary to expectation, instead of merely slowing decay, our experiments provide strong evidence that this pressure reverses it, inducing a statistically significant performance improvement in a Gemma 2B model on a complex summarization task. We name this phenomenon the Anti-Ouroboros Effect. We contrast this with a foundational experiment using a simple classifier, where the theoretical degenerative loop was validated, highlighting the unique dynamics of high-dimensional models. Our findings establish that systemic resilience can be an emergent property of LLMs under simple selection pressure, suggesting a powerful and scalable principle for developing safer and more robust AI systems. Across five generations, a quality-filtered condition improved by 6.6% in ROUGE-L F1 score, whereas an unfiltered control degraded by 3.5% and a random-filter control degraded by 4.2%
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[AI-155] CAR-BRAINet: Sub-6GHz Aided Spatial Adaptive Beam Prediction with Multi Head Attention for Heterogeneous Vehicular Networks

【速读】：该论文旨在解决异构车联网（HetVNet）在高移动性、复杂真实场景下保持稳定连接的挑战，特别是现有波束预测模型多基于理想化假设，难以应对实际道路环境中由3GPP-C-V2X和IEEE 802.11BD等MAC协议、多普勒频移、距离变化及信噪比（SNR）波动带来的动态干扰问题。解决方案的关键在于提出一种轻量级深度学习模型CAR-BRAINet，其核心由卷积神经网络与多头注意力机制（Multi-Head Attention, MHA）组成，能够有效融合多种环境因素，在城市、乡村和高速公路三种动态数据集上实现精准波束预测，同时显著降低波束开销并提升频谱效率（相较现有方法提升17.9422%），且无需依赖移动用户的位置角度和天线尺寸信息，从而减少冗余传感器延迟。

链接: https://arxiv.org/abs/2509.10508
作者: Aathira G Menon(1),Prabu Krishnan(1),Shyam Lal(1) ((1) Department of Electronics and Communication Engineering, National Institute of Technology Karnataka (NITK), Surathkal)
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
备注: 10 pages, 10 figures, 6 tables, (to be published)

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Abstract:Heterogeneous Vehicular Networks (HetVNets) play a key role by stacking different communication technologies such as sub-6GHz, mm-wave and DSRC to meet diverse connectivity needs of 5G/B5G vehicular networks. HetVNet helps address the humongous user demands-but maintaining a steady connection in a highly mobile, real-world conditions remain a challenge. Though there has been ample of studies on beam prediction models a dedicated solution for HetVNets is sparsely explored. Hence, it is the need of the hour to develop a reliable beam prediction solution, specifically for HetVNets. This paper introduces a lightweight deep learning-based solution termed-“CAR-BRAINet” which consists of convolutional neural networks with a powerful multi-head attention (MHA) mechanism. Existing literature on beam prediction is largely studied under a limited, idealised vehicular scenario, often overlooking the real-time complexities and intricacies of vehicular networks. Therefore, this study aims to mimic the complexities of a real-time driving scenario by incorporating key factors such as prominent MAC protocols-3GPP-C-V2X and IEEE 802.11BD, the effect of Doppler shifts under high velocity and varying distance and SNR levels into three high-quality dynamic datasets pertaining to urban, rural and highway vehicular networks. CAR-BRAINet performs effectively across all the vehicular scenarios, demonstrating precise beam prediction with minimal beam overhead and a steady improvement of 17.9422% on the spectral efficiency over the existing methods. Thus, this study justifies the effectiveness of CAR-BRAINet in complex HetVNets, offering promising performance without relying on the location angle and antenna dimensions of the mobile users, and thereby reducing the redundant sensor-latency.
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[AI-156] An Internet of Intelligent Things Framework for Decentralized Heterogeneous Platforms

【速读】：该论文旨在解决物联网中智能物体（Internet of Intelligent Things, IoIT）在嵌入式设备上部署机器学习（ML）/深度学习（DL）模型时面临的能源效率低下、计算资源受限及存储不足等问题，尤其针对集中式系统中存在的性能瓶颈与安全风险。解决方案的关键在于提出一种异构的、去中心化的传感与监控IoIT对等网（peer-to-peer mesh network）系统模型，通过联邦学习（federated learning）实现分布式模型训练，利用元启发式算法（metaheuristics）优化任务分配与路由路径，并结合多目标优化方法平衡可靠性、能效与延迟等相互冲突的性能指标。

链接: https://arxiv.org/abs/2509.10507
作者: Vadim Allayev,Mahbubur Rahman
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
备注: 11 pages

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Abstract:Internet of Intelligent Things (IoIT), an emerging field, combines the utility of Internet of Things (IoT) devices with the innovation of embedded AI algorithms. However, it does not come without challenges, and struggles regarding available computing resources, energy supply, and storage limitations. In particular, many impediments to IoIT are linked to the energy-efficient deployment of machine learning (ML)/deep learning (DL) models in embedded devices. Research has been conducted to design energy-efficient IoIT platforms, but these papers often focus on centralized systems, in which some central entity processes all the data and coordinates actions. This can be problematic, e.g., serve as bottleneck or lead to security concerns. In a decentralized system, nodes/devices would self-organize and make their own decisions. Therefore, to address such issues, we propose a heterogeneous, decentralized sensing and monitoring IoIT peer-to-peer mesh network system model. Nodes in the network will coordinate towards several optimization goals: reliability, energy efficiency, and latency. The system employs federated learning to train nodes in a distributed manner, metaheuristics to optimize task allocation and routing paths, and multi-objective optimization to balance conflicting performance goals.
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[AI-157] Retrosynthesis Planning via Worst-path Policy Optimisation in Tree-structured MDPs

【速读】：该论文旨在解决传统逆合成规划方法在树状结构中对最弱环节（即不可行的末端节点）敏感的问题，这类方法通常仅优化平均性能，忽视了 worst-case 敏感性导致合成路径整体失效的风险。解决方案的关键在于将逆合成问题重新建模为树状马尔可夫决策过程（tree-structured Markov Decision Process, MDP）中的最差路径优化问题（worst-path optimisation），并证明该形式具有唯一最优解和单调改进保证。基于此理论框架，作者提出交互式逆合成规划方法 InterRetro，通过与树状 MDP 交互学习最差路径的价值函数，并利用自模仿机制优先强化高优势估计的历史决策，从而显著提升合成路径的有效性和效率。

链接: https://arxiv.org/abs/2509.10504
作者: Mianchu Wang,Giovanni Montana
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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Abstract:Retrosynthesis planning aims to decompose target molecules into available building blocks, forming a synthesis tree where each internal node represents an intermediate compound and each leaf ideally corresponds to a purchasable reactant. However, this tree becomes invalid if any leaf node is not a valid building block, making the planning process vulnerable to the “weakest link” in the synthetic route. Existing methods often optimise for average performance across branches, failing to account for this worst-case sensitivity. In this paper, we reframe retrosynthesis as a worst-path optimisation problem within tree-structured Markov Decision Processes (MDPs). We prove that this formulation admits a unique optimal solution and offers monotonic improvement guarantees. Building on this insight, we introduce Interactive Retrosynthesis Planning (InterRetro), a method that interacts with the tree MDP, learns a value function for worst-path outcomes, and improves its policy through self-imitation, preferentially reinforcing past decisions with high estimated advantage. Empirically, InterRetro achieves state-of-the-art results, solving 100% of targets on the Retro*-190 benchmark, shortening synthetic routes by 4.9%, and achieving promising performance using only 10% of the training data - representing a significant advance in computational retrosynthesis planning.
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[AI-158] From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction ECAI2025

【速读】：该论文旨在解决降水预报中零膨胀数据（zero-inflated data）带来的挑战，即观测数据中存在大量零值（无降水事件），而非零值稀疏且分布不规则，导致传统模型难以准确捕捉时间序列的动态模式。其解决方案的关键在于提出零膨胀扩散框架（Zero Inflation Diffusion Framework, ZIDF），该框架通过三个核心模块协同工作：利用高斯扰动平滑零膨胀分布以缓解数据稀疏性问题；采用基于Transformer的预测机制建模长期时序依赖关系；并通过扩散去噪过程恢复原始数据结构。这一设计显著提升了对稀疏时间序列的建模能力，在实验中相较非平稳Transformer基线模型实现了最高达56.7%的均方误差（MSE）和21.1%的平均绝对误差（MAE）降低。

链接: https://arxiv.org/abs/2509.10501
作者: Wentao Gao,Jiuyong Li,Lin Liu,Thuc Duy Le,Xiongren Chen,Xiaojing Du,Jixue Liu,Yanchang Zhao,Yun Chen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: ECAI 2025 Accepted

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Abstract:Zero-inflated data pose significant challenges in precipitation forecasting due to the predominance of zeros with sparse non-zero events. To address this, we propose the Zero Inflation Diffusion Framework (ZIDF), which integrates Gaussian perturbation for smoothing zero-inflated distributions, Transformer-based prediction for capturing temporal patterns, and diffusion-based denoising to restore the original data structure. In our experiments, we use observational precipitation data collected from South Australia along with synthetically generated zero-inflated data. Results show that ZIDF demonstrates significant performance improvements over multiple state-of-the-art precipitation forecasting models, achieving up to 56.7% reduction in MSE and 21.1% reduction in MAE relative to the baseline Non-stationary Transformer. These findings highlight ZIDF’s ability to robustly handle sparse time series data and suggest its potential generalizability to other domains where zero inflation is a key challenge.
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[AI-159] owards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization

【速读】：该论文旨在解决开放无线接入网（Open Radio Access Network, O-RAN）架构中资源管理的复杂性问题，特别是如何在动态需求和复杂网络拓扑下，联合优化功能分割选择（functional split selection）与虚拟化单元部署（virtualized unit placement），以实现高效、可扩展的资源分配。其解决方案的关键在于提出了一种图增强的近端策略优化（Graph-Augmented Proximal Policy Optimization, GPPO）框架，该框架利用图神经网络（Graph Neural Networks, GNNs）提取拓扑感知特征，并引入动作掩码（action masking）机制有效处理组合决策空间，从而在统一模型中协同优化功能分割与部署决策，显著提升性能并具备良好的可扩展性。

链接: https://arxiv.org/abs/2509.10499
作者: Duc-Thinh Ngo(STACK, LS2N),Kandaraj Piamrat,Ons Aouedi,Thomas Hassan,Philippe Raipin-Parvédy
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
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Abstract:Open Radio Access Network (O-RAN) architectures enable flexible, scalable, and cost-efficient mobile networks by disaggregating and virtualizing baseband functions. However, this flexibility introduces significant challenges for resource management, requiring joint optimization of functional split selection and virtualized unit placement under dynamic demands and complex topologies. Existing solutions often address these aspects separately or lack scalability in large and real-world scenarios. In this work, we propose a novel Graph-Augmented Proximal Policy Optimization (GPPO) framework that leverages Graph Neural Networks (GNNs) for topology-aware feature extraction and integrates action masking to efficiently navigate the combinatorial decision space. Our approach jointly optimizes functional split and placement decisions, capturing the full complexity of O-RAN resource allocation. Extensive experiments on both small-and large-scale O-RAN scenarios demonstrate that GPPO consistently outperforms state-of-the-art baselines, achieving up to 18% lower deployment cost and 25% higher reward in generalization tests, while maintaining perfect reliability. These results highlight the effectiveness and scalability of GPPO for practical O-RAN deployments.
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[AI-160] Online Learning Based Efficient Resource Allocation for LoRaWAN Network

【速读】：该论文旨在解决大规模LoRaWAN网络中Packet Delivery Ratio (PDR)与Energy Efficiency (EE)之间的权衡问题，传统方法往往仅优化单一指标或缺乏对动态信道环境的适应能力，导致性能不佳。其解决方案的关键在于提出两种基于在线学习的资源分配框架：D-LoRa是一个完全分布式的框架，将问题建模为组合多臂赌博机（Combinatorial Multi-Armed Bandit），通过分解联合参数选择并设计专用的解耦奖励函数显著降低学习复杂度，使节点能自主适应网络动态；CD-LoRa则在此基础上引入轻量级集中式初始化阶段，完成一次性准最优信道分配与动作空间剪枝，从而加速后续分布式学习过程。二者在不同场景下均展现出优越性能，实验证明可提升PDR达10.8%、EE达26.1%，有效支持可扩展且高效的LoRaWAN部署。

链接: https://arxiv.org/abs/2509.10493
作者: Ruiqi Wang,Jing Ren,Tongyu Song,Wenjun Li,Xiong Wang,Sheng Wang,Shizhong Xu
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI)
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Abstract:The deployment of large-scale LoRaWAN networks requires jointly optimizing conflicting metrics like Packet Delivery Ratio (PDR) and Energy Efficiency (EE) by dynamically allocating transmission parameters, including Carrier Frequency, Spreading Factor, and Transmission Power. Existing methods often oversimplify this challenge, focusing on a single metric or lacking the adaptability needed for dynamic channel environments, leading to suboptimal performance. To address this, we propose two online learning-based resource allocation frameworks that intelligently navigate the PDR-EE trade-off. Our foundational proposal, D-LoRa, is a fully distributed framework that models the problem as a Combinatorial Multi-Armed Bandit. By decomposing the joint parameter selection and employing specialized, disaggregated reward functions, D-LoRa dramatically reduces learning complexity and enables nodes to autonomously adapt to network dynamics. To further enhance performance in LoRaWAN networks, we introduce CD-LoRa, a hybrid framework that integrates a lightweight, centralized initialization phase to perform a one-time, quasi-optimal channel assignment and action space pruning, thereby accelerating subsequent distributed learning. Extensive simulations and real-world field experiments demonstrate the superiority of our frameworks, showing that D-LoRa excels in non-stationary environments while CD-LoRa achieves the fastest convergence in stationary conditions. In physical deployments, our methods outperform state-of-the-art baselines, improving PDR by up to 10.8% and EE by 26.1%, confirming their practical effectiveness for scalable and efficient LoRaWAN networks.
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[AI-161] SABR: A Stable Adaptive Bitrate Framework Using Behavior Cloning Pretraining and Reinforcement Learning Fine-Tuning

【速读】：该论文旨在解决当前基于学习的自适应比特率（Adaptive Bitrate, ABR）控制方法在真实复杂网络环境中泛化能力差的问题，尤其是当训练数据分布与实际部署场景存在差异（即out-of-distribution, OOD）时，现有方法性能显著下降。解决方案的关键在于提出SABR训练框架，其核心是结合行为克隆（Behavior Cloning, BC）预训练与强化学习（Reinforcement Learning, RL）微调：首先利用大规模真实网络trace进行BC预训练以获得初步策略，再通过RL细调提升对未见网络条件的适应能力，从而实现更稳定的学习和更强的泛化性能。

链接: https://arxiv.org/abs/2509.10486
作者: Pengcheng Luo,Yunyang Zhao,Bowen Zhang,Genke Yang,Boon-Hee Soong,Chau Yuen
机构: 未知
类目: Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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Abstract:With the advent of 5G, the internet has entered a new video-centric era. From short-video platforms like TikTok to long-video platforms like Bilibili, online video services are reshaping user consumption habits. Adaptive Bitrate (ABR) control is widely recognized as a critical factor influencing Quality of Experience (QoE). Recent learning-based ABR methods have attracted increasing attention. However, most of them rely on limited network trace sets during training and overlook the wide-distribution characteristics of real-world network conditions, resulting in poor generalization in out-of-distribution (OOD) scenarios. To address this limitation, we propose SABR, a training framework that combines behavior cloning (BC) pretraining with reinforcement learning (RL) fine-tuning. We also introduce benchmarks, ABRBench-3G and ABRBench-4G+, which provide wide-coverage training traces and dedicated OOD test sets for assessing robustness to unseen network conditions. Experimental results demonstrate that SABR achieves the best average rank compared with Pensieve, Comyco, and NetLLM across the proposed benchmarks. These results indicate that SABR enables more stable learning across wide distributions and improves generalization to unseen network conditions.
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[AI-162] AegisShield: Democratizing Cyber Threat Modeling with Generative AI

【速读】：该论文旨在解决传统威胁建模（Threat Modeling）在复杂技术系统中难以规模化的问题，尤其针对资源有限的小型组织。其解决方案的关键在于开发并评估AegisShield——一个增强型生成式AI（Generative AI）威胁建模工具，该工具整合STRIDE和MITRE ATT&CK框架，结合来自国家漏洞数据库（NVD）与AlienVault Open Threat Exchange的实时威胁情报，实现威胁自动生成与系统化评估。实验表明，该方法显著降低建模复杂度（p < 0.001），产出语义上与专家定义威胁高度一致（p < 0.05），且85.4%的威胁能准确映射至MITRE ATT&CK技术（p < 0.001），从而帮助资源受限组织更早识别风险，并推动“安全设计优先”实践的普及。

链接: https://arxiv.org/abs/2509.10482
作者: Matthew Grofsky
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Master’s thesis

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Abstract:The increasing sophistication of technology systems makes traditional threat modeling hard to scale, especially for small organizations with limited resources. This paper develops and evaluates AegisShield, a generative AI enhanced threat modeling tool that implements STRIDE and MITRE ATTCK to automate threat generation and provide systematic assessments. By integrating real time threat intelligence from the National Vulnerability Database and AlienVault Open Threat Exchange, AegisShield produces streamlined and accessible threat descriptions. Our assessment of 243 threats from 15 case studies and over 8000 AI generated threats shows that AegisShield reduces complexity (p less than 0.001), yields outputs semantically aligned with expert developed threats (p less than 0.05), and achieves an 85.4 percent success rate in mapping threats to MITRE ATTCK techniques (p less than 0.001). Automating and standardizing threat modeling helps under resourced organizations address risk earlier and supports wider adoption of secure by design practices.
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[AI-163] Program Skeletons for Automated Program Translation

【速读】：该论文旨在解决跨编程语言自动翻译软件的难题，尤其是如何在保持程序整体功能不变的前提下，将源语言中特定的实现细节抽象为目标语言中惯用的结构。传统方法难以规模化且自动化程度低，其核心挑战在于需从源语言特性中抽象出行为语义，并以目标语言的语义方式进行重构。解决方案的关键在于提出了一种名为“程序骨架（program skeletons）”的新框架：骨架通过抽象和总结底层具体代码片段，保留源程序的高层结构，从而可被机械地映射到目标语言；同时，骨架允许对每个片段采用多种实现方式，可与现有的数据驱动代码合成器协同工作。更重要的是，骨架支持概念上的可靠分解（sound decomposition），即只要每个片段翻译正确，组合后的最终程序即可保证整体正确性。该方法在Python到JavaScript的原型系统Skel中验证，对9个真实世界程序（部分超过1k行代码）实现了约95%的自动翻译准确率，仅5%需人工干预，且所有翻译结果均通过全程序测试套件验证。

链接: https://arxiv.org/abs/2504.07483
作者: Bo Wang,Tianyu Li,Ruishi Li,Umang Mathur,Prateek Saxena
机构: 未知
类目: Programming Languages (cs.PL); Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
备注: Accepted by PLDI 2025 (46th ACM SIGPLAN Conference on Programming Language Design and Implementation)

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Abstract:Translating software between programming languages is a challenging task, for which automated techniques have been elusive and hard to scale up to larger programs. A key difficulty in cross-language translation is that one has to re-express the intended behavior of the source program into idiomatic constructs of a different target language. This task needs abstracting away from the source language-specific details, while keeping the overall functionality the same. In this work, we propose a novel and systematic approach for making such translation amenable to automation based on a framework we call program skeletons. A program skeleton retains the high-level structure of the source program by abstracting away and effectively summarizing lower-level concrete code fragments, which can be mechanically translated to the target programming language. A skeleton, by design, permits many different ways of filling in the concrete implementation for fragments, which can work in conjunction with existing data-driven code synthesizers. Most importantly, skeletons can conceptually enable sound decomposition, i.e., if each individual fragment is correctly translated, taken together with the mechanically translated skeleton, the final translated program is deemed to be correct as a whole. We present a prototype system called Skel embodying the idea of skeleton-based translation from Python to JavaScript. Our results show promising scalability compared to prior works. For 9 real-world Python programs, some with more than about 1k lines of code, 95% of their code fragments can be automatically translated, while about 5% require manual effort. All the final translations are correct with respect to whole-program test suites.
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[AI-164] Designing MacPherson Suspension Architectures using Bayesian Optimization

【速读】：该论文旨在解决传统工程设计流程中依赖人工、耗时且成本高昂的问题，即通过手动提出设计方案并借助计算机仿真（如有限元分析或多体系统方法）进行合规性测试，最终再进行物理原型制造，整个过程可能长达数月。其解决方案的关键在于提出了一种基于贝叶斯优化（Bayesian optimization）的通用框架，用于直接优化设计参数以满足目标规格，无需依赖梯度信息（该信息在许多学科模型中难以获取）。该方法本质上是一种高维非线性函数的广义逆计算工具，同时引入了两级收敛准则：一是收敛至最优满足所有设计约束的解，二是收敛至最小范数解，从而提升效率与鲁棒性。实证表明，该方法在车辆底盘设计问题上具有良好的通用性、可扩展性和高效性。

链接: https://arxiv.org/abs/2206.09022
作者: Sinnu Susan Thomas,Jacopo Palandri,Mohsen Lakehal-ayat,Punarjay Chakravarty,Friedrich Wolf-Monheim,Matthew B. Blaschko
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Numerical Analysis (math.NA); Optimization and Control (math.OC)
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Abstract:Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by a finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months, and is a significant cost in practice. We have developed a Bayesian optimization system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing a generalized inverse of a high-dimensional non-linear function that does not require e.g. gradient information, which is often unavailable from discipline models. We furthermore develop a two-tier convergence criterion based on (i) convergence to a solution optimally satisfying all specified design criteria, or (ii) convergence to a minimum-norm solution. We demonstrate the proposed approach on a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient, and that the novel convergence criteria can be implemented straightforwardly based on existing concepts and subroutines in popular Bayesian optimization software packages.
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[AI-165] Quantum Architecture Search for Solving Quantum Machine Learning Tasks

【速读】：该论文旨在解决当前量子计算中量子电路架构设计（Quantum Architecture Search, QAS）的自动化难题，尤其是在噪声中等规模量子（NISQ）设备上高效构建可执行分类任务的参数化量子电路问题。其解决方案的关键在于提出一种基于强化学习（Reinforcement Learning, RL）的框架——RL-QAS，通过智能体自主探索低复杂度且高精度的量子电路结构，从而实现对量子机器学习模型架构的自动优化与发现。

链接: https://arxiv.org/abs/2509.11198
作者: Michael Kölle,Simon Salfer,Tobias Rohe,Philipp Altmann,Claudia Linnhoff-Popien
机构: 未知
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains error-prone and limited in scale, Variational Quantum Circuits offer a noise-resilient framework suitable for today’s devices. The performance of these circuits strongly depends on the underlying architecture of their parameterized quantum components. Identifying efficient, hardware-compatible quantum circuit architectures – known as Quantum Architecture Search (QAS) – is therefore essential. Manual QAS is complex and error-prone, motivating efforts to automate it. Among various automated strategies, Reinforcement Learning (RL) remains underexplored, particularly in Quantum Machine Learning contexts. This work introduces RL-QAS, a framework that applies RL to discover effective circuit architectures for classification tasks. We evaluate RL-QAS using the Iris and binary MNIST datasets. The agent autonomously discovers low-complexity circuit designs that achieve high test accuracy. Our results show that RL is a viable approach for automated architecture search in quantum machine learning. However, applying RL-QAS to more complex tasks will require further refinement of the search strategy and performance evaluation mechanisms.
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[AI-166] Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits

【速读】：该论文旨在解决变分量子电路（Variational Quantum Circuits, VQCs）在优化过程中常遭遇的“ barren plateau ”（平坦区）问题，即梯度消失导致训练效率极低甚至无法收敛的现象。其解决方案的关键在于引入经典神经网络中的“彩票理论”（Lottery Ticket Hypothesis, LTH），通过剪枝（pruning）方法识别出性能相当但参数更少的子电路结构——即“中奖票”（winning ticket）。研究发现，在弱LTH场景下，仅保留26.0%的原始参数即可维持性能；在强LTH场景下，无需训练即可学习剪枝掩码，在二进制VQC中实现45%参数量时达到100%准确率，表明LTH可有效缓解平坦区问题并提升量子机器学习任务中VQC的参数效率与优化稳定性。

链接: https://arxiv.org/abs/2509.11190
作者: Michael Kölle,Leonhard Klingert,Julian Schönberger,Philipp Altmann,Tobias Rohe,Claudia Linnhoff-Popien
机构: 未知
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Quantum computing is an emerging field in computer science that has seen considerable progress in recent years, especially in machine learning. By harnessing the principles of quantum physics, it can surpass the limitations of classical algorithms. However, variational quantum circuits (VQCs), which rely on adjustable parameters, often face the barren plateau phenomenon, hindering optimization. The Lottery Ticket Hypothesis (LTH) is a recent concept in classical machine learning that has led to notable improvements in parameter efficiency for neural networks. It states that within a large network, a smaller, more efficient subnetwork, or ‘‘winning ticket,’’ can achieve comparable performance, potentially circumventing plateau challenges. In this work, we investigate whether this idea can apply to VQCs. We show that the weak LTH holds for VQCs, revealing winning tickets that retain just 26.0% of the original parameters. For the strong LTH, where a pruning mask is learned without any training, we discovered a winning ticket in a binary VQC, achieving 100% accuracy with only 45% of the weights. These findings indicate that LTH may mitigate barren plateaus by reducing parameter counts while preserving performance, thus enhancing the efficiency of VQCs in quantum machine learning tasks.
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[AI-167] sting for LLM response differences: the case of a composite null consisting of semantically irrelevant query perturbations

【速读】：该论文试图解决的问题是：在生成式 AI（Generative AI）模型中，传统统计假设检验方法无法准确判断两个语义等价的输入查询是否诱导出相同的响应分布，因为响应分布对查询的语义无关扰动过于敏感，导致统计检验结果与用户实际需求不一致。解决方案的关键在于将一组由用户定义的语义相似查询纳入检验过程，通过估计这些查询对应的响应分布集合来构建更鲁棒的检验统计量，从而实现对响应分布差异的更合理判断；作者在二值响应场景下证明了所提检验方法的渐近有效性与一致性，并讨论了实际应用中的功效和计算效率问题。

链接: https://arxiv.org/abs/2509.10963
作者: Aranyak Acharyya,Carey E. Priebe,Hayden S. Helm
机构: 未知
类目: atistics Theory (math.ST); Artificial Intelligence (cs.AI); Methodology (stat.ME)
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Abstract:Given an input query, generative models such as large language models produce a random response drawn from a response distribution. Given two input queries, it is natural to ask if their response distributions are the same. While traditional statistical hypothesis testing is designed to address this question, the response distribution induced by an input query is often sensitive to semantically irrelevant perturbations to the query, so much so that a traditional test of equality might indicate that two semantically equivalent queries induce statistically different response distributions. As a result, the outcome of the statistical test may not align with the user’s requirements. In this paper, we address this misalignment by incorporating into the testing procedure consideration of a collection of semantically similar queries. In our setting, the mapping from the collection of user-defined semantically similar queries to the corresponding collection of response distributions is not known a priori and must be estimated, with a fixed budget. Although the problem we address is quite general, we focus our analysis on the setting where the responses are binary, show that the proposed test is asymptotically valid and consistent, and discuss important practical considerations with respect to power and computation.
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[AI-168] Physics-informed neural network solves minimal surfaces in curved spacetime

【速读】：该论文旨在解决曲率时空中的最小曲面边界值问题，特别是涉及奇点和移动边界的复杂情形。其解决方案的关键在于构建一种基于物理信息神经网络（Physics-Informed Neural Networks, PINNs）的灵活框架，通过将物理定律嵌入损失函数，并设计能捕捉奇异性与动态边界的网络结构，从而实现对具有复杂边界条件的常微分方程和偏微分方程的鲁棒且高精度求解。该方法支持“软”（基于损失函数）与“硬”（基于公式构造）两种边界条件施加方式，甚至可将边界位置作为可训练参数处理，适用于高能理论物理中如反德西特（AdS）时空下的威尔逊圈和胶子散射振幅等场景，同时也广泛适用于数学、工程和自然科学领域中存在奇点与移动边界的各类边界值问题。

链接: https://arxiv.org/abs/2509.10866
作者: Koji Hashimoto,Koichi Kyo,Masaki Murata,Gakuto Ogiwara,Norihiro Tanahashi
机构: 未知
类目: High Energy Physics - Theory (hep-th); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); General Relativity and Quantum Cosmology (gr-qc)
备注: 40 pages, 17 figures, 3 tables

点击查看摘要

Abstract:We develop a flexible framework based on physics-informed neural networks (PINNs) for solving boundary value problems involving minimal surfaces in curved spacetimes, with a particular emphasis on singularities and moving boundaries. By encoding the underlying physical laws into the loss function and designing network architectures that incorporate the singular behavior and dynamic boundaries, our approach enables robust and accurate solutions to both ordinary and partial differential equations with complex boundary conditions. We demonstrate the versatility of this framework through applications to minimal surface problems in anti-de Sitter (AdS) spacetime, including examples relevant to the AdS/CFT correspondence (e.g. Wilson loops and gluon scattering amplitudes) popularly used in the context of string theory in theoretical physics. Our methods efficiently handle singularities at boundaries, and also support both “soft” (loss-based) and “hard” (formulation-based) imposition of boundary conditions, including cases where the position of a boundary is promoted to a trainable parameter. The techniques developed here are not limited to high-energy theoretical physics but are broadly applicable to boundary value problems encountered in mathematics, engineering, and the natural sciences, wherever singularities and moving boundaries play a critical role.
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[AI-169] Gene-R1: Reasoning with Data-Augmented Lightweight LLM s for Gene Set Analysis

【速读】：该论文旨在解决当前基因集分析（Gene Set Analysis, GSA）中依赖专有大语言模型（Large Language Models, LLMs）导致的成本高和数据隐私风险问题，同时填补了尚未探索先进推理策略在GSA任务中应用的研究空白。解决方案的关键在于提出Gene-R1——一个数据增强的学习框架，通过赋予轻量级开源LLMs逐步推理能力，使其能够精准注释基因集的生物学功能并提供一致的解释性洞察，从而在分布内和分布外基因集上均达到与商业模型相当甚至更优的性能表现，展现出卓越的泛化能力。

链接: https://arxiv.org/abs/2509.10575
作者: Zhizheng Wang,Yifan Yang,Qiao Jin,Zhiyong Lu
机构: 未知
类目: Genomics (q-bio.GN); Artificial Intelligence (cs.AI)
备注: 14 pages, 4 figures, 6 tables, 40 references

点击查看摘要

Abstract:The gene set analysis (GSA) is a foundational approach for uncovering the molecular functions associated with a group of genes. Recently, LLM-powered methods have emerged to annotate gene sets with biological functions together with coherent explanatory insights. However, existing studies primarily focus on proprietary models, which have been shown to outperform their open-source counterparts despite concerns over cost and data privacy. Furthermore, no research has investigated the application of advanced reasoning strategies to the GSA task. To address this gap, we introduce Gene-R1, a data-augmented learning framework that equips lightweight and open-source LLMs with step-by-step reasoning capabilities tailored to GSA. Experiments on 1,508 in-distribution gene sets demonstrate that Gene-R1 achieves substantial performance gains, matching commercial LLMs. On 106 out-of-distribution gene sets, Gene-R1 performs comparably to both commercial and large-scale LLMs, exhibiting robust generalizability across diverse gene sources.
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[AI-170] Biomarkers of brain diseases

【速读】：该论文试图解决的问题是：尽管脑数据类型多样且人工智能（AI）分析算法日益先进，但脑特征在临床诊断与预后中的应用仍然极为有限。其核心症结在于当前研究仍依赖于患者与健康对照组的群体比较来寻找生物标志物（biomarker），而忽视了脑特征固有的退化性（degeneracy）——即相同功能状态可能由不同神经机制实现，导致单一模态数据难以稳定识别可靠的生物标志物。解决方案的关键在于摒弃单一数据类型的群体比较方法，转而采用多模态（如脑活动、神经递质、神经调质、脑成像）和纵向脑数据，先通过这些整合信息对个体进行分组，再在此基础上定义多维生物标志物，从而提升脑疾病诊断与预后的精准性和可解释性。

链接: https://arxiv.org/abs/2509.10547
作者: Pascal Helson,Arvind Kumar
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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点击查看摘要

Abstract:Despite the diversity of brain data acquired and advanced AI-based algorithms to analyze them, brain features are rarely used in clinics for diagnosis and prognosis. Here we argue that the field continues to rely on cohort comparisons to seek biomarkers, despite the well-established degeneracy of brain features. Using a thought experiment, we show that more data and more powerful algorithms will not be sufficient to identify biomarkers of brain diseases. We argue that instead of comparing patient versus healthy controls using single data type, we should use multimodal (e.g. brain activity, neurotransmitters, neuromodulators, brain imaging) and longitudinal brain data to guide the grouping before defining multidimensional biomarkers for brain diseases.
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[AI-171] Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks

【速读】：该论文旨在解决精神疾病诊断中因功能磁共振成像（fMRI）数据稀缺及信息多样性导致的挑战，特别是现有基于图的自监督学习（SSL）方法多局限于时域表示、忽视频域信息的问题。其解决方案的关键在于提出一种频率增强型脑网络框架（Frequency-Enhanced Network, FENet），通过构建多视角脑网络并显式融合时域与频域特征，引入领域特定编码器（包括高效频域编码器以突出与疾病相关的频率特征），以及设计域一致性引导的学习目标，从而在小样本条件下提升精神病识别性能，并揭示高频特征在疾病检测中的关键作用。

链接: https://arxiv.org/abs/2509.10524
作者: Mujie Liu,Mengchu Zhu,Qichao Dong,Ting Dang,Jiangang Ma,Jing Ren,Feng Xia
机构: 未知
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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点击查看摘要

Abstract:Psychiatric disorders involve complex neural activity changes, with functional magnetic resonance imaging (fMRI) data serving as key diagnostic evidence. However, data scarcity and the diverse nature of fMRI information pose significant challenges. While graph-based self-supervised learning (SSL) methods have shown promise in brain network analysis, they primarily focus on time-domain representations, often overlooking the rich information embedded in the frequency domain. To overcome these limitations, we propose Frequency-Enhanced Network (FENet), a novel SSL framework specially designed for fMRI data that integrates time-domain and frequency-domain information to improve psychiatric disorder detection in small-sample datasets. FENet constructs multi-view brain networks based on the inherent properties of fMRI data, explicitly incorporating frequency information into the learning process of representation. Additionally, it employs domain-specific encoders to capture temporal-spectral characteristics, including an efficient frequency-domain encoder that highlights disease-relevant frequency features. Finally, FENet introduces a domain consistency-guided learning objective, which balances the utilization of diverse information and generates frequency-enhanced brain graph representations. Experiments on two real-world medical datasets demonstrate that FENet outperforms state-of-the-art methods while maintaining strong performance in minimal data conditions. Furthermore, we analyze the correlation between various frequency-domain features and psychiatric disorders, emphasizing the critical role of high-frequency information in disorder detection.
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[AI-172] Distributed Gossip-GAN for Low-overhead CSI Feedback Training in FDD mMIMO-OFDM Systems

【速读】：该论文旨在解决大规模多输入多输出（massive multiple-input multiple-output, mMIMO）系统中信道状态信息（channel state information, CSI）反馈开销大、数据隐私保护难以及用户移动性导致模型需频繁重训练并易发生灾难性遗忘的问题。解决方案的关键在于提出一种基于 gossiping 生成对抗网络（Gossip-GAN）的分布式 CSI 反馈训练框架：每个用户仅用少量本地数据训练生成式 AI (Generative Adversarial Network, GAN) 模型，并通过全分布式 gossip 学习策略实现模型协同优化，从而在降低上行链路带宽消耗的同时，有效避免过拟合与灾难性遗忘，且保持对新环境的鲁棒性。

链接: https://arxiv.org/abs/2509.10490
作者: Yuwen Cao,Guijun Liu,Tomoaki Ohtsuki,Howard H. Yang,Tony Q. S. Quek
机构: 未知
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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点击查看摘要

Abstract:The deep autoencoder (DAE) framework has turned out to be efficient in reducing the channel state information (CSI) feedback overhead in massive multiple-input multipleoutput (mMIMO) systems. However, these DAE approaches presented in prior works rely heavily on large-scale data collected through the base station (BS) for model training, thus rendering excessive bandwidth usage and data privacy issues, particularly for mMIMO systems. When considering users’ mobility and encountering new channel environments, the existing CSI feedback models may often need to be retrained. Returning back to previous environments, however, will make these models perform poorly and face the risk of catastrophic forgetting. To solve the above challenging problems, we propose a novel gossiping generative adversarial network (Gossip-GAN)-aided CSI feedback training framework. Notably, Gossip-GAN enables the CSI feedback training with low-overhead while preserving users’ privacy. Specially, each user collects a small amount of data to train a GAN model. Meanwhile, a fully distributed gossip-learning strategy is exploited to avoid model overfitting, and to accelerate the model training as well. Simulation results demonstrate that Gossip-GAN can i) achieve a similar CSI feedback accuracy as centralized training with real-world datasets, ii) address catastrophic forgetting challenges in mobile scenarios, and iii) greatly reduce the uplink bandwidth usage. Besides, our results show that the proposed approach possesses an inherent robustness.
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[AI-173] Momentum-integrated Multi-task Stock Recommendation with Converge-based Optimization

【速读】：该论文旨在解决传统时间序列预测方法在股票推荐中难以同时捕捉短期趋势与排名顺序的问题，而这二者是投资者决策的关键因素。解决方案的核心在于提出一个融合动量指标的多任务学习框架——MiM-StocR，其关键创新包括：引入动量线（momentum line）以增强模型对短期趋势的感知能力；设计自适应k近似NDCG（Adaptive-k ApproxNDCG）列表级排序损失函数，以优化优质股票的优先级排序和投资组合分配；并提出基于收敛性的四平衡策略（Converge-based Quad-Balancing, CQB），有效缓解股票市场波动性导致的过拟合问题。

链接: https://arxiv.org/abs/2509.10461
作者: Hao Wang,Jingshu Peng,Yanyan Shen,Xujia Li,Lei Chen
机构: 未知
类目: atistical Finance (q-fin.ST); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 5 figures

点击查看摘要

Abstract:Stock recommendation is critical in Fintech applications, which use price series and alternative information to estimate future stock performance. Although deep learning models are prevalent in stock recommendation systems, traditional time-series forecasting training often fails to capture stock trends and rankings simultaneously, which are essential consideration factors for investors. To tackle this issue, we introduce a Multi-Task Learning (MTL) framework for stock recommendation, \textbfMomentum-\textbfintegrated \textbfMulti-task \textbfStock \textbfRecommendation with Converge-based Optimization (\textbfMiM-StocR). To improve the model’s ability to capture short-term trends, we novelly invoke a momentum line indicator in model training. To prioritize top-performing stocks and optimize investment allocation, we propose a list-wise ranking loss function called Adaptive-k ApproxNDCG. Moreover, due to the volatility and uncertainty of the stock market, existing MTL frameworks face overfitting issues when applied to stock time series. To mitigate this issue, we introduce the Converge-based Quad-Balancing (CQB) method. We conducted extensive experiments on three stock benchmarks: SEE50, CSI 100, and CSI 300. MiM-StocR outperforms state-of-the-art MTL baselines across both ranking and profitable evaluations.
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机器学习

[LG-0] All that structure matches does not glitter

链接: https://arxiv.org/abs/2509.12178
作者: Maya M. Martirossyan,Thomas Egg,Philipp Hoellmer,George Karypis,Mark Transtrum,Adrian Roitberg,Mingjie Liu,Richard G. Hennig,Ellad B. Tadmor,Stefano Martiniani
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注:

点击查看摘要

Abstract:Generative models for materials, especially inorganic crystals, hold potential to transform the theoretical prediction of novel compounds and structures. Advancement in this field depends critically on robust benchmarks and minimal, information-rich datasets that enable meaningful model evaluation. This paper critically examines common datasets and reported metrics for a crystal structure prediction task \unicodex2014 generating the most likely structures given the chemical composition of a material. We focus on three key issues: First, materials datasets should contain unique crystal structures; for example, we show that the widely-utilized carbon-24 dataset only contains \approx 40% unique structures. Second, materials datasets should not be split randomly if polymorphs of many different compositions are numerous, which we find to be the case for the perov-5 dataset. Third, benchmarks can mislead if used uncritically, e.g., reporting a match rate metric without considering the structural variety exhibited by identical building blocks. To address these oft-overlooked issues, we introduce several fixes. We provide revised versions of the carbon-24 dataset: one with duplicates removed, one deduplicated and split by number of atoms N , and two containing only identical structures but with different unit cells. We also propose a new split for the perov-5 dataset which ensures polymorphs are grouped within each split subset, setting a more sensible standard for benchmarking model performance. Finally, we present METRe and cRMSE, new model evaluation metrics that can correct existing issues with the match rate metric.

[LG-1] From Autoencoders to CycleGAN: Robust Unpaired Face Manipulation via Adversarial Learning

链接: https://arxiv.org/abs/2509.12176
作者: Collin Guo
类目: Machine Learning (cs.LG)
*备注: 8 pages, 7 figures

点击查看摘要

Abstract:Human face synthesis and manipulation are increasingly important in entertainment and AI, with a growing demand for highly realistic, identity-preserving images even when only unpaired, unaligned datasets are available. We study unpaired face manipulation via adversarial learning, moving from autoencoder baselines to a robust, guided CycleGAN framework. While autoencoders capture coarse identity, they often miss fine details. Our approach integrates spectral normalization for stable training, identity- and perceptual-guided losses to preserve subject identity and high-level structure, and landmark-weighted cycle constraints to maintain facial geometry across pose and illumination changes. Experiments show that our adversarial trained CycleGAN improves realism (FID), perceptual quality (LPIPS), and identity preservation (ID-Sim) over autoencoders, with competitive cycle-reconstruction SSIM and practical inference times, which achieved high quality without paired datasets and approaching pix2pix on curated paired subsets. These results demonstrate that guided, spectrally normalized CycleGANs provide a practical path from autoencoders to robust unpaired face manipulation.

[LG-2] Learning Neural Networks by Neuron Pursuit

链接: https://arxiv.org/abs/2509.12154
作者: Akshay Kumar,Jarvis Haupt
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:The first part of this paper studies the evolution of gradient flow for homogeneous neural networks near a class of saddle points exhibiting a sparsity structure. The choice of these saddle points is motivated from previous works on homogeneous networks, which identified the first saddle point encountered by gradient flow after escaping the origin. It is shown here that, when initialized sufficiently close to such saddle points, gradient flow remains near the saddle point for a sufficiently long time, during which the set of weights with small norm remain small but converge in direction. Furthermore, important empirical observations are made on the behavior of gradient descent after escaping these saddle points. The second part of the paper, motivated by these results, introduces a greedy algorithm to train deep neural networks called Neuron Pursuit (NP). It is an iterative procedure which alternates between expanding the network by adding neuron(s) with carefully chosen weights, and minimizing the training loss using this augmented network. The efficacy of the proposed algorithm is validated using numerical experiments.

[LG-3] Learning Contact Dynamics for Control with Action-conditioned Face Interaction Graph Networks

链接: https://arxiv.org/abs/2509.12151
作者: Zongyao Yi,Joachim Hertzberg,Martin Atzmueller
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:We present a learnable physics simulator that provides accurate motion and force-torque prediction of robot end effectors in contact-rich manipulation. The proposed model extends the state-of-the-art GNN-based simulator (FIGNet) with novel node and edge types, enabling action-conditional predictions for control and state estimation tasks. In simulation, the MPC agent using our model matches the performance of the same controller with the ground truth dynamics model in a challenging peg-in-hole task, while in the real-world experiment, our model achieves a 50% improvement in motion prediction accuracy and 3 \times increase in force-torque prediction precision over the baseline physics simulator. Source code and data are publicly available.

[LG-4] Do machine learning climate models work in changing climate dynamics?

链接: https://arxiv.org/abs/2509.12147
作者: Maria Conchita Agana Navarro,Geng Li,Theo Wolf,María Pérez-Ortiz
类目: Machine Learning (cs.LG)
*备注: 8 pages, 2 figures

点击查看摘要

Abstract:Climate change is accelerating the frequency and severity of unprecedented events, deviating from established patterns. Predicting these out-of-distribution (OOD) events is critical for assessing risks and guiding climate adaptation. While machine learning (ML) models have shown promise in providing precise, high-speed climate predictions, their ability to generalize under distribution shifts remains a significant limitation that has been underexplored in climate contexts. This research systematically evaluates state-of-the-art ML-based climate models in diverse OOD scenarios by adapting established OOD evaluation methodologies to climate data. Experiments on large-scale datasets reveal notable performance variability across scenarios, shedding light on the strengths and limitations of current models. These findings underscore the importance of robust evaluation frameworks and provide actionable insights to guide the reliable application of ML for climate risk forecasting.

[LG-5] Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data

链接: https://arxiv.org/abs/2509.12094
作者: Tianqi Zhao,Russa Biswas,Megha Khosla
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Graph machine learning models often achieve similar overall performance yet behave differently at the node level, failing on different subsets of nodes with varying reliability. Standard evaluation metrics such as accuracy obscure these fine grained differences, making it difficult to diagnose when and where models fail. We introduce NodePro, a node profiling framework that enables fine-grained diagnosis of model behavior by assigning interpretable profile scores to individual nodes. These scores combine data-centric signals, such as feature dissimilarity, label uncertainty, and structural ambiguity, with model-centric measures of prediction confidence and consistency during training. By aligning model behavior with these profiles, NodePro reveals systematic differences between models, even when aggregate metrics are indistinguishable. We show that node profiles generalize to unseen nodes, supporting prediction reliability without ground-truth labels. Finally, we demonstrate the utility of NodePro in identifying semantically inconsistent or corrupted nodes in a structured knowledge graph, illustrating its effectiveness in real-world settings.

[LG-6] Foundational theory for optimal decision tree problems. II. Optimal hypersurface decision tree algorithm

链接: https://arxiv.org/abs/2509.12057
作者: Xi He
类目: Machine Learning (cs.LG); Discrete Mathematics (cs.DM); Data Structures and Algorithms (cs.DS)
*备注:

点击查看摘要

Abstract:Decision trees are a ubiquitous model for classification and regression tasks due to their interpretability and efficiency. However, solving the optimal decision tree (ODT) problem remains a challenging combinatorial optimization task. Even for the simplest splitting rules–axis-parallel hyperplanes–it is NP-hard to optimize. In Part I of this series, we rigorously defined the proper decision tree model through four axioms and, based on these, introduced four formal definitions of the ODT problem. From these definitions, we derived four generic algorithms capable of solving ODT problems for arbitrary decision trees satisfying the axioms. We also analyzed the combinatorial geometric properties of hypersurfaces, showing that decision trees defined by polynomial hypersurface splitting rules satisfy the proper axioms that we proposed. In this second paper (Part II) of this two-part series, building on the algorithmic and geometric foundations established in Part I, we introduce the first hypersurface decision tree (HODT) algorithm. To the best of our knowledge, existing optimal decision tree methods are, to date, limited to hyperplane splitting rules–a special case of hypersurfaces–and rely on general-purpose solvers. In contrast, our HODT algorithm addresses the general hypersurface decision tree model without requiring external solvers. Using synthetic datasets generated from ground-truth hyperplane decision trees, we vary tree size, data size, dimensionality, and label and feature noise. Results showing that our algorithm recovers the ground truth more accurately than axis-parallel trees and exhibits greater robustness to noise. We also analyzed generalization performance across 30 real-world datasets, showing that HODT can achieve up to 30% higher accuracy than the state-of-the-art optimal axis-parallel decision tree algorithm when tree complexity is properly controlled. Subjects: Machine Learning (cs.LG); Discrete Mathematics (cs.DM); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2509.12057 [cs.LG] (or arXiv:2509.12057v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.12057 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-7] Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System

链接: https://arxiv.org/abs/2509.12048
作者: Hoon Sagong,Heesu Kim,Hanbeen Hong
类目: Machine Learning (cs.LG)
*备注: Accepted paper at International Conference on ICT Convergence 2025

点击查看摘要

Abstract:Conventional autonomous trading systems struggle to balance computational efficiency and market responsiveness due to their fixed operating frequency. We propose Hi-DARTS, a hierarchical multi-agent reinforcement learning framework that addresses this trade-off. Hi-DARTS utilizes a meta-agent to analyze market volatility and dynamically activate specialized Time Frame Agents for high-frequency or low-frequency trading as needed. During back-testing on AAPL stock from January 2024 to May 2025, Hi-DARTS yielded a cumulative return of 25.17% with a Sharpe Ratio of 0.75. This performance surpasses standard benchmarks, including a passive buy-and-hold strategy on AAPL (12.19% return) and the SP 500 ETF (SPY) (20.01% return). Our work demonstrates that dynamic, hierarchical agents can achieve superior risk-adjusted returns while maintaining high computational efficiency.

[LG-8] ravel Time and Weather-Aware Traffic Forecasting in a Conformal Graph Neural Network Framework

链接: https://arxiv.org/abs/2509.12043
作者: Mayur Patil,Qadeer Ahmed,Shawn Midlam-Mohler
类目: Machine Learning (cs.LG)
*备注: This manuscript has been accepted as a REGULAR PAPER in the Transactions on Intelligent Transportation Systems 2025

点击查看摘要

Abstract:Traffic flow forecasting is essential for managing congestion, improving safety, and optimizing various transportation systems. However, it remains a prevailing challenge due to the stochastic nature of urban traffic and environmental factors. Better predictions require models capable of accommodating the traffic variability influenced by multiple dynamic and complex interdependent factors. In this work, we propose a Graph Neural Network (GNN) framework to address the stochasticity by leveraging adaptive adjacency matrices using log-normal distributions and Coefficient of Variation (CV) values to reflect real-world travel time variability. Additionally, weather factors such as temperature, wind speed, and precipitation adjust edge weights and enable GNN to capture evolving spatio-temporal dependencies across traffic stations. This enhancement over the static adjacency matrix allows the model to adapt effectively to traffic stochasticity and changing environmental conditions. Furthermore, we utilize the Adaptive Conformal Prediction (ACP) framework to provide reliable uncertainty quantification, achieving target coverage while maintaining acceptable prediction intervals. Experimental results demonstrate that the proposed model, in comparison with baseline methods, showed better prediction accuracy and uncertainty bounds. We, then, validate this method by constructing traffic scenarios in SUMO and applying Monte-Carlo simulation to derive a travel time distribution for a Vehicle Under Test (VUT) to reflect real-world variability. The simulated mean travel time of the VUT falls within the intervals defined by INRIX historical data, verifying the model’s robustness.

[LG-9] Learning non-Markovian Dynamical Systems with Signature-based Encoders ECAI2025

链接: https://arxiv.org/abs/2509.12022
作者: Eliott Pradeleix,Rémy Hosseinkhan-Boucher,Alena Shilova,Onofrio Semeraro,Lionel Mathelin
类目: Machine Learning (cs.LG)
*备注: Accepted at [ML-DE] Machine Learning Meets Differential Equations 2025 (ECAI 2025). To appear in Proceedings of Machine Learning Research (PMLR)

点击查看摘要

Abstract:Neural ordinary differential equations offer an effective framework for modeling dynamical systems by learning a continuous-time vector field. However, they rely on the Markovian assumption - that future states depend only on the current state - which is often untrue in real-world scenarios where the dynamics may depend on the history of past states. This limitation becomes especially evident in settings involving the continuous control of complex systems with delays and memory effects. To capture historical dependencies, existing approaches often rely on recurrent neural network (RNN)-based encoders, which are inherently discrete and struggle with continuous modeling. In addition, they may exhibit poor training behavior. In this work, we investigate the use of the signature transform as an encoder for learning non-Markovian dynamics in a continuous-time setting. The signature transform offers a continuous-time alternative with strong theoretical foundations and proven efficiency in summarizing multidimensional information in time. We integrate a signature-based encoding scheme into encoder-decoder dynamics models and demonstrate that it outperforms RNN-based alternatives in test performance on synthetic benchmarks.

[LG-10] Improving Out-of-Domain Audio Deepfake Detection via Layer Selection and Fusion of SSL-Based Countermeasures

链接: https://arxiv.org/abs/2509.12003
作者: Pierre Serrano,Raphaël Duroselle,Florian Angulo,Jean-François Bonastre,Olivier Boeffard
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Audio deepfake detection systems based on frozen pre-trained self-supervised learning (SSL) encoders show a high level of performance when combined with layer-weighted pooling methods, such as multi-head factorized attentive pooling (MHFA). However, they still struggle to generalize to out-of-domain (OOD) conditions. We tackle this problem by studying the behavior of six different pre-trained SSLs, on four different test corpora. We perform a layer-by-layer analysis to determine which layers contribute most. Next, we study the pooling head, comparing a strategy based on a single layer with automatic selection via MHFA. We observed that selecting the best layer gave very good results, while reducing system parameters by up to 80%. A wide variation in performance as a function of test corpus and SSL model is also observed, showing that the pre-training strategy of the encoder plays a role. Finally, score-level fusion of several encoders improved generalization to OOD attacks.

[LG-11] Learning from Uncertain Similarity and Unlabeled Data

链接: https://arxiv.org/abs/2509.11984
作者: Meng Wei,Zhongnian Li,Peng Ying,Xinzheng Xu
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Existing similarity-based weakly supervised learning approaches often rely on precise similarity annotations between data pairs, which may inadvertently expose sensitive label information and raise privacy risks. To mitigate this issue, we propose Uncertain Similarity and Unlabeled Learning (USimUL), a novel framework where each similarity pair is embedded with an uncertainty component to reduce label leakage. In this paper, we propose an unbiased risk estimator that learns from uncertain similarity and unlabeled data. Additionally, we theoretically prove that the estimator achieves statistically optimal parametric convergence rates. Extensive experiments on both benchmark and real-world datasets show that our method achieves superior classification performance compared to conventional similarity-based approaches.

[LG-12] Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training

链接: https://arxiv.org/abs/2509.11983
作者: Chuan He,Zhanwang Deng,Zhaosong Lu
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 27 pages

点击查看摘要

Abstract:Neural network (NN) training is inherently a large-scale matrix optimization problem, yet the matrix structure of NN parameters has long been overlooked. Recently, the optimizer Muon \citejordanmuon, which explicitly exploits this structure, has gained significant attention for its strong performance in foundation model training. A key component contributing to Muon’s success is matrix orthogonalization. In this paper, we propose \it low-rank orthogonalization, which explicitly leverages the low-rank nature of gradients during NN training. Building on this, we propose low-rank matrix-signed gradient descent and a low-rank variant of Muon. Our numerical experiments demonstrate the superior performance of low-rank orthogonalization, with the low-rank Muon achieving promising results in GPT-2 and LLaMA pretraining – surpassing the performance of the carefully tuned vanilla Muon. Theoretically, we establish the iteration complexity of the low-rank matrix-signed gradient descent for finding an approximate stationary solution, as well as that of low-rank Muon for finding an approximate stochastic stationary solution under heavy-tailed noise.

[LG-13] Examining the Relationship between Scientific Publishing Activity and Hype-Driven Financial Bubbles: A Comparison of the Dot-Com and AI Eras

链接: https://arxiv.org/abs/2509.11982
作者: Aksheytha Chelikavada,Casey C. Bennett
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注:

点击查看摘要

Abstract:Financial bubbles often arrive without much warning, but create long-lasting economic effects. For example, during the dot-com bubble, innovative technologies created market disruptions through excitement for a promised bright future. Such technologies originated from research where scientists had developed them for years prior to their entry into the markets. That raises a question on the possibility of analyzing scientific publishing data (e.g. citation networks) leading up to a bubble for signals that may forecast the rise and fall of similar future bubbles. To that end, we utilized temporal SNAs to detect possible relationships between the publication citation networks of scientists and financial market data during two modern eras of rapidly shifting technology: 1) dot-com era from 1994 to 2001 and 2) AI era from 2017 to 2024. Results showed that the patterns from the dot-com era (which did end in a bubble) did not definitively predict the rise and fall of an AI bubble. While yearly citation networks reflected possible changes in publishing behavior of scientists between the two eras, there was a subset of AI era scientists whose publication influence patterns mirrored those during the dot-com era. Upon further analysis using multiple analysis techniques (LSTM, KNN, AR X/GARCH), the data seems to suggest two possibilities for the AI era: unprecedented form of financial bubble unseen or that no bubble exists. In conclusion, our findings imply that the patterns present in the dot-com era do not effectively translate in such a manner to apply them to the AI market.

[LG-14] Deep operator network for surrogate modeling of poroelasticity with random permeability fields

链接: https://arxiv.org/abs/2509.11966
作者: Sangjoon Park,Yeonjong Shin,Jinhyun Choo
类目: Machine Learning (cs.LG); Geophysics (physics.geo-ph)
*备注:

点击查看摘要

Abstract:Poroelasticity – coupled fluid flow and elastic deformation in porous media – often involves spatially variable permeability, especially in subsurface systems. In such cases, simulations with random permeability fields are widely used for probabilistic analysis, uncertainty quantification, and inverse problems. These simulations require repeated forward solves that are often prohibitively expensive, motivating the development of efficient surrogate models. However, efficient surrogate modeling techniques for poroelasticity with random permeability fields remain scarce. In this study, we propose a surrogate modeling framework based on the deep operator network (DeepONet), a neural architecture designed to learn mappings between infinite-dimensional function spaces. The proposed surrogate model approximates the solution operator that maps random permeability fields to transient poroelastic responses. To enhance predictive accuracy and stability, we integrate three strategies: nondimensionalization of the governing equations, input dimensionality reduction via Karhunen–Loéve expansion, and a two-step training procedure that decouples the optimization of branch and trunk networks. The methodology is evaluated on two benchmark problems in poroelasticity: soil consolidation and ground subsidence induced by groundwater extraction. In both cases, the DeepONet achieves substantial speedup in inference while maintaining high predictive accuracy across a wide range of permeability statistics. These results highlight the potential of the proposed approach as a scalable and efficient surrogate modeling technique for poroelastic systems with random permeability fields.

[LG-15] abStruct: Measuring Structural Fidelity of Tabular Data

链接: https://arxiv.org/abs/2509.11950
作者: Xiangjian Jiang,Nikola Simidjievski,Mateja Jamnik
类目: Machine Learning (cs.LG)
*备注: 55 pages, 60 tables, 7 figures

点击查看摘要

Abstract:Evaluating tabular generators remains a challenging problem, as the unique causal structural prior of heterogeneous tabular data does not lend itself to intuitive human inspection. Recent work has introduced structural fidelity as a tabular-specific evaluation dimension to assess whether synthetic data complies with the causal structures of real data. However, existing benchmarks often neglect the interplay between structural fidelity and conventional evaluation dimensions, thus failing to provide a holistic understanding of model performance. Moreover, they are typically limited to toy datasets, as quantifying existing structural fidelity metrics requires access to ground-truth causal structures, which are rarely available for real-world datasets. In this paper, we propose a novel evaluation framework that jointly considers structural fidelity and conventional evaluation dimensions. We introduce a new evaluation metric, \textbfglobal utility , which enables the assessment of structural fidelity even in the absence of ground-truth causal structures. In addition, we present \textbfTabStruct , a comprehensive evaluation benchmark offering large-scale quantitative analysis on 13 tabular generators from nine distinct categories, across 29 datasets. Our results demonstrate that global utility provides a task-independent, domain-agnostic lens for tabular generator performance. We release the TabStruct benchmark suite, including all datasets, evaluation pipelines, and raw results. Code is available at this https URL.

[LG-16] High Effort Low Gain: Fundamental Limits of Active Learning for Linear Dynamical Systems

链接: https://arxiv.org/abs/2509.11907
作者: Nicolas Chatzikiriakos,Kevin Jamieson,Andrea Iannelli
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:In this work, we consider the problem of identifying an unknown linear dynamical system given a finite hypothesis class. In particular, we analyze the effect of the excitation input on the sample complexity of identifying the true system with high probability. To this end, we present sample complexity lower bounds that capture the choice of the selected excitation input. The sample complexity lower bound gives rise to a system theoretic condition to determine the potential benefit of experiment design. Informed by the analysis of the sample complexity lower bound, we propose a persistent excitation (PE) condition tailored to the considered setting, which we then use to establish sample complexity upper bounds. Notably, the \acsPE condition is weaker than in the case of an infinite hypothesis class and allows analyzing different excitation inputs modularly. Crucially, the lower and upper bounds share the same dependency on key problem parameters. Finally, we leverage these insights to propose an active learning algorithm that sequentially excites the system optimally with respect to the current estimate, and provide sample complexity guarantees for the presented algorithm. Concluding simulations showcase the effectiveness of the proposed algorithm.

[LG-17] ransparent and Fair Profiling in Employment Services: Evidence from Switzerland

链接: https://arxiv.org/abs/2509.11847
作者: Tim Räz
类目: Machine Learning (cs.LG); Computers and Society (cs.CY)
*备注: 35 pages including appendix

点击查看摘要

Abstract:Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of predictive performance, interpretability, and fairness. It is shown that explainable boosting machines, a recent interpretable model, perform nearly as well as the best black-box models. It is also shown how model sparsity, feature smoothing, and fairness mitigation can enhance transparency and fairness with only minor losses in performance. These findings suggest that interpretable profiling provides an accountable and trustworthy alternative to black-box models without compromising performance.

[LG-18] Visualization and Analysis of the Loss Landscape in Graph Neural Networks

链接: https://arxiv.org/abs/2509.11792
作者: Samir Moustafa,Lorenz Kummer,Simon Fetzel,Nils M. Kriege,Wilfried N. Gansterer
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Graph Neural Networks (GNNs) are powerful models for graph-structured data, with broad applications. However, the interplay between GNN parameter optimization, expressivity, and generalization remains poorly understood. We address this by introducing an efficient learnable dimensionality reduction method for visualizing GNN loss landscapes, and by analyzing the effects of over-smoothing, jumping knowledge, quantization, sparsification, and preconditioner on GNN optimization. Our learnable projection method surpasses the state-of-the-art PCA-based approach, enabling accurate reconstruction of high-dimensional parameters with lower memory usage. We further show that architecture, sparsification, and optimizer’s preconditioning significantly impact the GNN optimization landscape and their training process and final prediction performance. These insights contribute to developing more efficient designs of GNN architectures and training strategies.

[LG-19] Synthetic vs. Real Training Data for Visual Navigation

链接: https://arxiv.org/abs/2509.11791
作者: Lauri Suomela,Sasanka Kuruppu Arachchige,German F. Torres,Harry Edelman,Joni-Kristian Kämäräinen
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注: Presented at CoRL 2025 workshop on “Making Sense of Data in Robotics”

点击查看摘要

Abstract:This paper investigates how the performance of visual navigation policies trained in simulation compares to policies trained with real-world data. Performance degradation of simulator-trained policies is often significant when they are evaluated in the real world. However, despite this well-known sim-to-real gap, we demonstrate that simulator-trained policies can match the performance of their real-world-trained counterparts. Central to our approach is a navigation policy architecture that bridges the sim-to-real appearance gap by leveraging pretrained visual representations and runs real-time on robot hardware. Evaluations on a wheeled mobile robot show that the proposed policy, when trained in simulation, outperforms its real-world-trained version by 31% and the prior state-of-the-art methods by 50% in navigation success rate. Policy generalization is verified by deploying the same model onboard a drone. Our results highlight the importance of diverse image encoder pretraining for sim-to-real generalization, and identify on-policy learning as a key advantage of simulated training over training with real data. Comments: Presented at CoRL 2025 workshop on “Making Sense of Data in Robotics” Subjects: Robotics (cs.RO); Machine Learning (cs.LG) Cite as: arXiv:2509.11791 [cs.RO] (or arXiv:2509.11791v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2509.11791 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-20] Watch Your Step: A Cost-Sensitive Framework for Accelerometer-Based Fall Detection in Real-World Streaming Scenarios

链接: https://arxiv.org/abs/2509.11789
作者: Timilehin B. Aderinola,Luca Palmerini,Ilaria D’Ascanio,Lorenzo Chiari,Jochen Klenk,Clemens Becker,Brian Caulfield,Georgiana Ifrim
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Real-time fall detection is crucial for enabling timely interventions and mitigating the severe health consequences of falls, particularly in older adults. However, existing methods often rely on simulated data or assumptions such as prior knowledge of fall events, limiting their real-world applicability. Practical deployment also requires efficient computation and robust evaluation metrics tailored to continuous monitoring. This paper presents a real-time fall detection framework for continuous monitoring without prior knowledge of fall events. Using over 60 hours of inertial measurement unit (IMU) data from the FARSEEING real-world falls dataset, we employ recent efficient classifiers to compute fall probabilities in streaming mode. To enhance robustness, we introduce a cost-sensitive learning strategy that tunes the decision threshold using a cost function reflecting the higher risk of missed falls compared to false alarms. Unlike many methods that achieve high recall only at the cost of precision, our framework achieved Recall of 1.00, Precision of 0.84, and an F1 score of 0.91 on FARSEEING, detecting all falls while keeping false alarms low, with average inference time below 5 ms per sample. These results demonstrate that cost-sensitive threshold tuning enhances the robustness of accelerometer-based fall detection. They also highlight the potential of our computationally efficient framework for deployment in real-time wearable sensor systems for continuous monitoring.

[LG-21] Multimodal Regression for Enzyme Turnover Rates Prediction ICIP IJCAI2025

链接: https://arxiv.org/abs/2509.11782
作者: Bozhen Hu,Cheng Tan,Siyuan Li,Jiangbin Zheng,Sizhe Qiu,Jun Xia,Stan Z. Li
类目: Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: 9 pages, 5 figures. This paper was withdrawn from the IJCAI 2025 proceedings due to the lack of participation in the conference and presentation

点击查看摘要

Abstract:The enzyme turnover rate is a fundamental parameter in enzyme kinetics, reflecting the catalytic efficiency of enzymes. However, enzyme turnover rates remain scarce across most organisms due to the high cost and complexity of experimental measurements. To address this gap, we propose a multimodal framework for predicting the enzyme turnover rate by integrating enzyme sequences, substrate structures, and environmental factors. Our model combines a pre-trained language model and a convolutional neural network to extract features from protein sequences, while a graph neural network captures informative representations from substrate molecules. An attention mechanism is incorporated to enhance interactions between enzyme and substrate representations. Furthermore, we leverage symbolic regression via Kolmogorov-Arnold Networks to explicitly learn mathematical formulas that govern the enzyme turnover rate, enabling interpretable and accurate predictions. Extensive experiments demonstrate that our framework outperforms both traditional and state-of-the-art deep learning approaches. This work provides a robust tool for studying enzyme kinetics and holds promise for applications in enzyme engineering, biotechnology, and industrial biocatalysis.

[LG-22] Stabilizing PINNs: A regularization scheme for PINN training to avoid unstable fixed points of dynamical systems

链接: https://arxiv.org/abs/2509.11768
作者: Milos Babic,Franz M. Rohrhofer,Bernhard C. Geiger
类目: Machine Learning (cs.LG)
*备注: 8 pages, 3 figures

点击查看摘要

Abstract:It was recently shown that the loss function used for training physics-informed neural networks (PINNs) exhibits local minima at solutions corresponding to fixed points of dynamical systems. In the forward setting, where the PINN is trained to solve initial value problems, these local minima can interfere with training and potentially leading to physically incorrect solutions. Building on stability theory, this paper proposes a regularization scheme that penalizes solutions corresponding to unstable fixed points. Experimental results on four dynamical systems, including the Lotka-Volterra model and the van der Pol oscillator, show that our scheme helps avoiding physically incorrect solutions and substantially improves the training success rate of PINNs.

[LG-23] Data Fusion and Machine Learning for Ship Fuel Consumption Modelling - A Case of Bulk Carrier Vessel

链接: https://arxiv.org/abs/2509.11750
作者: Abdella Mohamed,Xiangyu Hu,Christian Hendricks
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注: 44 pages, 6 figures, preprint version

点击查看摘要

Abstract:There is an increasing push for operational measures to reduce ships’ bunker fuel consumption and carbon emissions, driven by the International Maritime Organization (IMO) mandates. Key performance indicators such as the Energy Efficiency Operational Indicator (EEOI) focus on fuel efficiency. Strategies like trim optimization, virtual arrival, and green routing have emerged. The theoretical basis for these approaches lies in accurate prediction of fuel consumption as a function of sailing speed, displacement, trim, climate, and sea state. This study utilized 296 voyage reports from a bulk carrier vessel over one year (November 16, 2021 to November 21, 2022) and 28 parameters, integrating hydrometeorological big data from the Copernicus Marine Environment Monitoring Service (CMEMS) with 19 parameters and the European Centre for Medium-Range Weather Forecasts (ECMWF) with 61 parameters. The objective was to evaluate whether fusing external public data sources enhances modeling accuracy and to highlight the most influential parameters affecting fuel consumption. The results reveal a strong potential for machine learning techniques to predict ship fuel consumption accurately by combining voyage reports with climate and sea data. However, validation on similar classes of vessels remains necessary to confirm generalizability. Comments: 44 pages, 6 figures, preprint version Subjects: Machine Learning (cs.LG); Optimization and Control (math.OC) Cite as: arXiv:2509.11750 [cs.LG] (or arXiv:2509.11750v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.11750 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-24] Analysing Python Machine Learning Notebooks with Moose

链接: https://arxiv.org/abs/2509.11748
作者: Marius Mignard,Steven Costiou,Nicolas Anquetil,Anne Etien
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Machine Learning (ML) code, particularly within notebooks, often exhibits lower quality compared to traditional software. Bad practices arise at three distinct levels: general Python coding conventions, the organizational structure of the notebook itself, and ML-specific aspects such as reproducibility and correct API usage. However, existing analysis tools typically focus on only one of these levels and struggle to capture ML-specific semantics, limiting their ability to detect issues. This paper introduces Vespucci Linter, a static analysis tool with multi-level capabilities, built on Moose and designed to address this challenge. Leveraging a metamodeling approach that unifies the notebook’s structural elements with Python code entities, our linter enables a more contextualized analysis to identify issues across all three levels. We implemented 22 linting rules derived from the literature and applied our tool to a corpus of 5,000 notebooks from the Kaggle platform. The results reveal violations at all levels, validating the relevance of our multi-level approach and demonstrating Vespucci Linter’s potential to improve the quality and reliability of ML development in notebook environments.

[LG-25] Fast and Interpretable Machine Learning Modelling of Atmospheric Molecular Clusters

链接: https://arxiv.org/abs/2509.11728
作者: Lauri Seppäläinen,Jakub Kubečka,Jonas Elm,Kai Puolamäki
类目: Machine Learning (cs.LG)
*备注: 38 pages with 2 page appendix, 9 figures. The source code used in the paper are available at this https URL

点击查看摘要

Abstract:Understanding how atmospheric molecular clusters form and grow is key to resolving one of the biggest uncertainties in climate modelling: the formation of new aerosol particles. While quantum chemistry offers accurate insights into these early-stage clusters, its steep computational costs limit large-scale exploration. In this work, we present a fast, interpretable, and surprisingly powerful alternative: k -nearest neighbour ( k -NN) regression model. By leveraging chemically informed distance metrics, including a kernel-induced metric and one learned via metric learning for kernel regression (MLKR), we show that simple k -NN models can rival more complex kernel ridge regression (KRR) models in accuracy, while reducing computational time by orders of magnitude. We perform this comparison with the well-established Faber-Christensen-Huang-Lilienfeld (FCHL19) molecular descriptor, but other descriptors (e.g., FCHL18, MBDF, and CM) can be shown to have similar performance. Applied to both simple organic molecules in the QM9 benchmark set and large datasets of atmospheric molecular clusters (sulphuric acid-water and sulphuric-multibase -base systems), our k -NN models achieve near-chemical accuracy, scale seamlessly to datasets with over 250,000 entries, and even appears to extrapolate to larger unseen clusters with minimal error (often nearing 1 kcal/mol). With built-in interpretability and straightforward uncertainty estimation, this work positions k -NN as a potent tool for accelerating discovery in atmospheric chemistry and beyond.

[LG-26] Neural Audio Codecs for Prompt-Driven Universal Source Separation

链接: https://arxiv.org/abs/2509.11717
作者: Adhiraj Banerjee,Vipul Arora
类目: ound (cs.SD); Machine Learning (cs.LG)
*备注: 21 pages, 1 figure, pre-print, under review

点击查看摘要

Abstract:Text-guided source separation supports flexible audio editing across media and assistive applications, but existing models like AudioSep are too compute-heavy for edge deployment. Neural audio codec (NAC) models such as CodecFormer and SDCodec are compute-efficient but limited to fixed-class separation. We introduce CodecSep, the first NAC-based model for on-device universal, text-driven separation. CodecSep combines DAC compression with a Transformer masker modulated by CLAP-derived FiLM parameters. Across six open-domain benchmarks under matched training/prompt protocols, \textbfCodecSep surpasses \textbfAudioSep in separation fidelity (SI-SDR) while remaining competitive in perceptual quality (ViSQOL) and matching or exceeding fixed-stem baselines (TDANet, CodecFormer, SDCodec). In code-stream deployments, it needs just 1.35~GMACs end-to-end – approximately 54\times less compute ( 25\times architecture-only) than spectrogram-domain separators like AudioSep – while remaining fully bitstream-compatible.

[LG-27] Beyond Regularity: Modeling Chaotic Mobility Patterns for Next Location Prediction

链接: https://arxiv.org/abs/2509.11713
作者: Yuqian Wu,Yuhong Peng,Jiapeng Yu,Xiangyu Liu,Zeting Yan,Kang Lin,Weifeng Su,Bingqing Qu,Raymond Lee,Dingqi Yang
类目: Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 12 pages, 5 figures

点击查看摘要

Abstract:Next location prediction is a key task in human mobility analysis, crucial for applications like smart city resource allocation and personalized navigation services. However, existing methods face two significant challenges: first, they fail to address the dynamic imbalance between periodic and chaotic mobile patterns, leading to inadequate adaptation over sparse trajectories; second, they underutilize contextual cues, such as temporal regularities in arrival times, which persist even in chaotic patterns and offer stronger predictability than spatial forecasts due to reduced search spaces. To tackle these challenges, we propose \textbf\method, a \underline\textbfCh\underline\textbfAotic \underline\textbfNeural \underline\textbfOscillator n\underline\textbfEtwork for next location prediction, which introduces a biologically inspired Chaotic Neural Oscillatory Attention mechanism to inject adaptive variability into traditional attention, enabling balanced representation of evolving mobility behaviors, and employs a Tri-Pair Interaction Encoder along with a Cross Context Attentive Decoder to fuse multimodal ``who-when-where’’ contexts in a joint framework for enhanced prediction performance. Extensive experiments on two real-world datasets demonstrate that CANOE consistently and significantly outperforms a sizeable collection of state-of-the-art baselines, yielding 3.17%-13.11% improvement over the best-performing baselines across different cases. In particular, CANOE can make robust predictions over mobility trajectories of different mobility chaotic levels. A series of ablation studies also supports our key design choices. Our code is available at: this https URL.

[LG-28] An Interventional Approach to Real-Time Disaster Assessment via Causal Attribution

链接: https://arxiv.org/abs/2509.11676
作者: Saketh Vishnubhatla,Alimohammad Beigi,Rui Heng Foo,Umang Goel,Ujun Jeong,Bohan Jiang,Adrienne Raglin,Huan Liu
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Traditional disaster analysis and modelling tools for assessing the severity of a disaster are predictive in nature. Based on the past observational data, these tools prescribe how the current input state (e.g., environmental conditions, situation reports) results in a severity assessment. However, these systems are not meant to be interventional in the causal sense, where the user can modify the current input state to simulate counterfactual “what-if” scenarios. In this work, we provide an alternative interventional tool that complements traditional disaster modelling tools by leveraging real-time data sources like satellite imagery, news, and social media. Our tool also helps understand the causal attribution of different factors on the estimated severity, over any given region of interest. In addition, we provide actionable recourses that would enable easier mitigation planning. Our source code is publicly available.

[LG-29] Assessing On-the-Ground Disaster Impact Using Online Data Sources

链接: https://arxiv.org/abs/2509.11634
作者: Saketh Vishnubhatla,Ujun Jeong,Bohan Jiang,Paras Sheth,Zhen Tan,Adrienne Raglin,Huan Liu
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Assessing the impact of a disaster in terms of asset losses and human casualties is essential for preparing effective response plans. Traditional methods include offline assessments conducted on the ground, where volunteers and first responders work together to collect the estimate of losses through windshield surveys or on-ground inspection. However, these methods have a time delay and are prone to different biases. Recently, various online data sources, including social media, news reports, aerial imagery, and satellite data, have been utilized to evaluate the impact of disasters. Online data sources provide real-time data streams for estimating the offline impact. Limited research exists on how different online sources help estimate disaster impact at a given administrative unit. In our work, we curate a comprehensive dataset by collecting data from multiple online sources for a few billion-dollar disasters at the county level. We also analyze how online estimates compare with traditional offline-based impact estimates for the disaster. Our findings provide insight into how different sources can provide complementary information to assess the disaster.

[LG-30] Adaptive-GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching and Temporal Decay

链接: https://arxiv.org/abs/2509.11633
作者: Ocheme Anthony Ekle,William Eberle
类目: Machine Learning (cs.LG)
*备注: 10 pages, 6 figures. Accepted for presentation at the IEEE International Conference on Knowledge Graphs (ICKG 2025). This is the authors accepted version; the final published paper will be available via IEEE Xplore

点击查看摘要

Abstract:Anomaly detection in dynamic graphs is essential for identifying malicious activities, fraud, and unexpected behaviors in real-world systems such as cybersecurity and power grids. However, existing approaches struggle with scalability, probabilistic interpretability, and adaptability to evolving traffic patterns. In this paper, we propose ADAPTIVE-GRAPHSKETCH, a lightweight and scalable framework for real-time anomaly detection in streaming edge data. Our method integrates temporal multi-tensor sketching with Count-Min Sketch using Conservative Update (CMS-CU) to compactly track edge frequency patterns with bounded memory, while mitigating hash collision issues. We incorporate Bayesian inference for probabilistic anomaly scoring and apply Exponentially Weighted Moving Average (EWMA) for adaptive thresholding tuned to burst intensity. Extensive experiments on four real-world intrusion detection datasets demonstrate that ADAPTIVE-GRAPHSKETCH outperforms state-of-the-art baselines such as ANOEDGE-G/L, MIDAS-R, and F-FADE, achieving up to 6.5% AUC gain on CIC-IDS2018 and up to 15.6% on CIC-DDoS2019, while processing 20 million edges in under 3.4 seconds using only 10 hash functions. Our results show that ADAPTIVE-GRAPHSKETCH is practical and effective for fast, accurate anomaly detection in large-scale streaming graphs. Keywords: Anomaly Detection, Streaming, Real-time, Dynamic Graphs, Edge Streams, Tensor Sketching Comments: 10 pages, 6 figures. Accepted for presentation at the IEEE International Conference on Knowledge Graphs (ICKG 2025). This is the authors accepted version; the final published paper will be available via IEEE Xplore Subjects: Machine Learning (cs.LG) Cite as: arXiv:2509.11633 [cs.LG] (or arXiv:2509.11633v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.11633 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-31] opology Structure Optimization of Reservoirs Using GLMY Homology

链接: https://arxiv.org/abs/2509.11612
作者: Yu Chen,Shengwei Wang,Hongwei Lin
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Reservoir is an efficient network for time series processing. It is well known that network structure is one of the determinants of its performance. However, the topology structure of reservoirs, as well as their performance, is hard to analyzed, due to the lack of suitable mathematical tools. In this paper, we study the topology structure of reservoirs using persistent GLMY homology theory, and develop a method to improve its performance. Specifically, it is found that the reservoir performance is closely related to the one-dimensional GLMY homology groups. Then, we develop a reservoir structure optimization method by modifying the minimal representative cycles of one-dimensional GLMY homology groups. Finally, by experiments, it is validated that the performance of reservoirs is jointly influenced by the reservoir structure and the periodicity of the dataset.

[LG-32] Scaling to Multimodal and Multichannel Heart Sound Classification: Fine-Tuning Wav2Vec 2.0 with Synthetic and Augmented Biosignals

链接: https://arxiv.org/abs/2509.11606
作者: Milan Marocchi,Matthew Fynn,Kayapanda Mandana,Yue Rong
类目: ound (cs.SD); Machine Learning (cs.LG); Signal Processing (eess.SP)
*备注: 35 pages, 37 figures, 19 tables

点击查看摘要

Abstract:Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early detection is critical, creating a demand for accurate and inexpensive pre-screening methods. Deep learning has recently been applied to classify abnormal heart sounds indicative of CVDs using synchronised phonocardiogram (PCG) and electrocardiogram (ECG) signals, as well as multichannel PCG (mPCG). However, state-of-the-art architectures remain underutilised due to the limited availability of synchronised and multichannel datasets. Augmented datasets and pre-trained models provide a pathway to overcome these limitations, enabling transformer-based architectures to be trained effectively. This work combines traditional signal processing with denoising diffusion models, WaveGrad and DiffWave, to create an augmented dataset to fine-tune a Wav2Vec 2.0-based classifier on multimodal and multichannel heart sound datasets. The approach achieves state-of-the-art performance. On the Computing in Cardiology (CinC) 2016 dataset of single channel PCG, accuracy, unweighted average recall (UAR), sensitivity, specificity and Matthew’s correlation coefficient (MCC) reach 92.48%, 93.05%, 93.63%, 92.48%, 94.93% and 0.8283, respectively. Using the synchronised PCG and ECG signals of the training-a dataset from CinC, 93.14%, 92.21%, 94.35%, 90.10%, 95.12% and 0.8380 are achieved for accuracy, UAR, sensitivity, specificity and MCC, respectively. Using a wearable vest dataset consisting of mPCG data, the model achieves 77.13% accuracy, 74.25% UAR, 86.47% sensitivity, 62.04% specificity, and 0.5082 MCC. These results demonstrate the effectiveness of transformer-based models for CVD detection when supported by augmented datasets, highlighting their potential to advance multimodal and multichannel heart sound classification.

[LG-33] Learning Singularity-Encoded Greens Functions with Application to Iterative Methods

链接: https://arxiv.org/abs/2509.11580
作者: Qi Sun,Shengyan Li,Bowen Zheng,Lili Ju,Xuejun Xu
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Green’s function provides an inherent connection between theoretical analysis and numerical methods for elliptic partial differential equations, and general absence of its closed-form expression necessitates surrogate modeling to guide the design of effective solvers. Unfortunately, numerical computation of Green’s function remains challenging due to its doubled dimensionality and intrinsic singularity. In this paper, we present a novel singularity-encoded learning approach to resolve these problems in an unsupervised fashion. Our method embeds the Green’s function within a one-order higher-dimensional space by encoding its prior estimate as an augmented variable, followed by a neural network parametrization to manage the increased dimensionality. By projecting the trained neural network solution back onto the original domain, our deep surrogate model exploits its spectral bias to accelerate conventional iterative schemes, serving either as a preconditioner or as part of a hybrid solver. The effectiveness of our proposed method is empirically verified through numerical experiments with two and four dimensional Green’s functions, achieving satisfactory resolution of singularities and acceleration of iterative solvers.

[LG-34] Compressed Sensing: Mathematical Foundations Implementation and Advanced Optimization Techniques

链接: https://arxiv.org/abs/2509.11550
作者: Shane Stevenson,Maryam Sabagh
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Compressed sensing is a signal processing technique that allows for the reconstruction of a signal from a small set of measurements. The key idea behind compressed sensing is that many real-world signals are inherently sparse, meaning that they can be efficiently represented in a different space with only a few components compared to their original space representation. In this paper we will explore the mathematical formulation behind compressed sensing, its logic and pathologies, and apply compressed sensing to real world signals.

[LG-35] DARD: Dice Adversarial Robustness Distillation against Adversarial Attacks

链接: https://arxiv.org/abs/2509.11525
作者: Jing Zou,Shungeng Zhang,Meikang Qiu,Chong Li
类目: Machine Learning (cs.LG)
*备注: Accepted at SecureComm 2025, 15 pages, 4 figures

点击查看摘要

Abstract:Deep learning models are vulnerable to adversarial exam- ples, posing critical security challenges in real-world applications. While Adversarial Training (AT ) is a widely adopted defense mechanism to enhance robustness, it often incurs a trade-off by degrading performance on unperturbed, natural data. Recent efforts have highlighted that larger models exhibit enhanced robustness over their smaller counterparts. In this paper, we empirically demonstrate that such robustness can be sys- tematically distilled from large teacher models into compact student models. To achieve better performance, we introduce Dice Adversarial Robustness Distillation (DARD), a novel method designed to transfer robustness through a tailored knowledge distillation paradigm. Addition- ally, we propose Dice Projected Gradient Descent (DPGD), an adversar- ial example generalization method optimized for effective attack. Our ex- tensive experiments demonstrate that the DARD approach consistently outperforms adversarially trained networks with the same architecture, achieving superior robustness and standard accuracy.

[LG-36] SafeDiver: Cooperative AUV-USV Assisted Diver Communication via Multi-agent Reinforcement Learning Approach

链接: https://arxiv.org/abs/2509.11508
作者: Tinglong Deng,Hang Tao,Xinxiang Wang,Yinyan Wang,Hanjiang Luo
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG); Robotics (cs.RO)
*备注:

点击查看摘要

Abstract:As underwater human activities are increasing, the demand for underwater communication service presents a significant challenge. Existing underwater diver communication methods face hurdles due to inherent disadvantages and complex underwater environments. To address this issue, we propose a scheme that utilizes maritime unmanned systems to assist divers with reliable and high-speed communication. Multiple AUVs are equipped with optical and acoustic multimodal communication devices as relay nodes, providing adaptive communication services based on changes in the diver’s activity area. By using a multi-agent reinforcement learning (MARL) approach to control the cooperative movement of AUVs, high-speed and reliable data transmission between divers can be achieved. At the same time, utilizing the advantages of on-demand deployment and wide coverage of unmanned surface vehicles (USVs) as surface relay nodes to coordinate and forward information from AUVs, and controlling AUVs to adaptively select relay USV nodes for data transmission, high-quality communication between divers and surface platform can be achieved. Through simulation verification, the proposed scheme can effectively achieve reliable and high-speed communication for divers.

[LG-37] OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions

链接: https://arxiv.org/abs/2509.11499
作者: Chris Young,Juejing Liu,Marie L. Mortensen,Yifu Feng,Elizabeth Li,Zheming Wang,Xiaofeng Guo,Kevin M. Rosso,Xin Zhang
类目: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
*备注:

点击查看摘要

Abstract:The proliferation of spectroscopic data across various scientific and engineering fields necessitates automated processing. We introduce OASIS (Omni-purpose Analysis of Spectra via Intelligent Systems), a machine learning (ML) framework for technique-independent, automated spectral analysis, encompassing denoising, baseline correction, and comprehensive peak parameter (location, intensity, FWHM) retrieval without human intervention. OASIS achieves its versatility through models trained on a strategically designed synthetic dataset incorporating features from numerous spectroscopy techniques. Critically, the development of innovative, task-specific loss functions-such as the vicinity peak response (ViPeR) for peak localization-enabled the creation of compact yet highly accurate models from this dataset, validated with experimental data from Raman, UV-vis, and fluorescence spectroscopy. OASIS demonstrates significant potential for applications including in situ experiments, high-throughput optimization, and online monitoring. This study underscores the optimization of the loss function as a key resource-efficient strategy to develop high-performance ML models.

[LG-38] Drug Repurposing Using Deep Embedded Clustering and Graph Neural Networks ICML

链接: https://arxiv.org/abs/2509.11493
作者: Luke Delzer,Robert Kroleski,Ali K. AlShami,Jugal Kalita
类目: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
*备注: Accepted at the 2025 International Conference on Machine Learning and Applications (ICMLA)

点击查看摘要

Abstract:Drug repurposing has historically been an economically infeasible process for identifying novel uses for abandoned drugs. Modern machine learning has enabled the identification of complex biochemical intricacies in candidate drugs; however, many studies rely on simplified datasets with known drug-disease similarities. We propose a machine learning pipeline that uses unsupervised deep embedded clustering, combined with supervised graph neural network link prediction to identify new drug-disease links from multi-omic data. Unsupervised autoencoder and cluster training reduced the dimensionality of omic data into a compressed latent embedding. A total of 9,022 unique drugs were partitioned into 35 clusters with a mean silhouette score of 0.8550. Graph neural networks achieved strong statistical performance, with a prediction accuracy of 0.901, receiver operating characteristic area under the curve of 0.960, and F1-Score of 0.901. A ranked list comprised of 477 per-cluster link probabilities exceeding 99 percent was generated. This study could provide new drug-disease link prospects across unrelated disease domains, while advancing the understanding of machine learning in drug repurposing studies.

[LG-39] Long-time dynamics and universality of nonconvex gradient descent

链接: https://arxiv.org/abs/2509.11426
作者: Qiyang Han
类目: Machine Learning (cs.LG); Information Theory (cs.IT); Optimization and Control (math.OC); Statistics Theory (math.ST); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:This paper develops a general approach to characterize the long-time trajectory behavior of nonconvex gradient descent in generalized single-index models in the large aspect ratio regime. In this regime, we show that for each iteration the gradient descent iterate concentrates around a deterministic vector called the Gaussian theoretical gradient descent', whose dynamics can be tracked by a state evolution system of two recursive equations for two scalars. Our concentration guarantees hold universally for a broad class of design matrices and remain valid over long time horizons until algorithmic convergence or divergence occurs. Moreover, our approach reveals that gradient descent iterates are in general approximately independent of the data and strongly incoherent with the feature vectors, a phenomenon previously known as the implicit regularization’ effect of gradient descent in specific models under Gaussian data. As an illustration of the utility of our general theory, we present two applications of different natures in the regression setting. In the first, we prove global convergence of nonconvex gradient descent with general independent initialization for a broad class of structured link functions, and establish universality of randomly initialized gradient descent in phase retrieval for large aspect ratios. In the second, we develop a data-free iterative algorithm for estimating state evolution parameters along the entire gradient descent trajectory, thereby providing a low-cost yet statistically valid tool for practical tasks such as hyperparameter tuning and runtime determination. As a by-product of our analysis, we show that in the large aspect ratio regime, the Gaussian theoretical gradient descent coincides with a recent line of dynamical mean-field theory for gradient descent over the constant-time horizon. Subjects: Machine Learning (cs.LG); Information Theory (cs.IT); Optimization and Control (math.OC); Statistics Theory (math.ST); Machine Learning (stat.ML) Cite as: arXiv:2509.11426 [cs.LG] (or arXiv:2509.11426v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.11426 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-40] Enhancing ML Models Interpretability for Credit Scoring

链接: https://arxiv.org/abs/2509.11389
作者: Sagi Schwartz,Qinling Wang,Fang Fang
类目: Machine Learning (cs.LG); Risk Management (q-fin.RM)
*备注:

点击查看摘要

Abstract:Predicting default is essential for banks to ensure profitability and financial stability. While modern machine learning methods often outperform traditional regression techniques, their lack of transparency limits their use in regulated environments. Explainable artificial intelligence (XAI) has emerged as a solution in domains like credit scoring. However, most XAI research focuses on post-hoc interpretation of black-box models, which does not produce models lightweight or transparent enough to meet regulatory requirements, such as those for Internal Ratings-Based (IRB) models. This paper proposes a hybrid approach: post-hoc interpretations of black-box models guide feature selection, followed by training glass-box models that maintain both predictive power and transparency. Using the Lending Club dataset, we demonstrate that this approach achieves performance comparable to a benchmark black-box model while using only 10 features - an 88.5% reduction. In our example, SHapley Additive exPlanations (SHAP) is used for feature selection, eXtreme Gradient Boosting (XGBoost) serves as the benchmark and the base black-box model, and Explainable Boosting Machine (EBM) and Penalized Logistic Tree Regression (PLTR) are the investigated glass-box models. We also show that model refinement using feature interaction analysis, correlation checks, and expert input can further enhance model interpretability and robustness. Subjects: Machine Learning (cs.LG); Risk Management (q-fin.RM) Cite as: arXiv:2509.11389 [cs.LG] (or arXiv:2509.11389v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.11389 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Fang Fang [view email] [v1] Sun, 14 Sep 2025 18:47:38 UTC (1,420 KB)

[LG-41] Decoding Musical Origins: Distinguishing Human and AI Composers

链接: https://arxiv.org/abs/2509.11369
作者: Cheng-Yang Tsai,Tzu-Wei Huang,Shao-Yu Wei,Guan-Wei Chen,Hung-Ying Chu,Yu-Cheng Lin
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:With the rapid advancement of Large Language Models (LLMs), AI-driven music generation has become a vibrant and fruitful area of research. However, the representation of musical data remains a significant challenge. To address this, a novel, machine-learning-friendly music notation system, YNote, was developed. This study leverages YNote to train an effective classification model capable of distinguishing whether a piece of music was composed by a human (Native), a rule-based algorithm (Algorithm Generated), or an LLM (LLM Generated). We frame this as a text classification problem, applying the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to extract structural features from YNote sequences and using the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The resulting model achieves an accuracy of 98.25%, successfully demonstrating that YNote retains sufficient stylistic information for analysis. More importantly, the model can identify the unique " technological fingerprints " left by different AI generation techniques, providing a powerful tool for tracing the origins of AI-generated content.

[LG-42] Online Omniprediction with Long-Term Constraints

链接: https://arxiv.org/abs/2509.11357
作者: Yahav Bechavod,Jiuyao Lu,Aaron Roth
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT)
*备注:

点击查看摘要

Abstract:We introduce and study the problem of online omniprediction with long-term constraints. At each round, a forecaster is tasked with generating predictions for an underlying (adaptively, adversarially chosen) state that are broadcast to a collection of downstream agents, who must each choose an action. Each of the downstream agents has both a utility function mapping actions and state to utilities, and a vector-valued constraint function mapping actions and states to vector-valued costs. The utility and constraint functions can arbitrarily differ across downstream agents. Their goal is to choose actions that guarantee themselves no regret while simultaneously guaranteeing that they do not cumulatively violate the constraints across time. We show how to make a single set of predictions so that each of the downstream agents can guarantee this by acting as a simple function of the predictions, guaranteeing each of them \tildeO(\sqrtT) regret and O(1) cumulative constraint violation. We also show how to extend our guarantees to arbitrary intersecting contextually defined \emphsubsequences, guaranteeing each agent both regret and constraint violation bounds not just marginally, but simultaneously on each subsequence, against a benchmark set of actions simultaneously tailored to each subsequence.

[LG-43] On Linear Mode Connectivity of Mixture-of-Experts Architectures

链接: https://arxiv.org/abs/2509.11348
作者: Viet-Hoang Tran,Van Hoan Trinh,Khanh Vinh Bui,Tan M. Nguyen
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Linear Mode Connectivity (LMC) is a notable phenomenon in the loss landscapes of neural networks, wherein independently trained models have been observed to be connected–up to permutation symmetries–by linear paths in parameter space along which the loss remains consistently low. This observation challenges classical views of non-convex optimization and has implications for model ensembling, generalization, and our understanding of neural loss geometry. Inspired by recent studies on LMC in standard neural networks, we systematically investigate this phenomenon within Mixture-of-Experts (MoE) architectures–a class of models known for their scalability and computational efficiency, which combine traditional neural networks–referred to as experts–through a learnable gating mechanism. We begin by conducting a comprehensive analysis of both dense and sparse gating regimes, demonstrating that the symmetries inherent to MoE architectures are fully characterized by permutations acting on both the expert components and the gating function. Building on these foundational findings, we propose a matching algorithm that enables alignment between independently trained MoEs, thereby facilitating the discovery of LMC. Finally, we empirically validate the presence of LMC using our proposed algorithm across diverse MoE configurations–including dense, sparse, and shared-expert variants–under a wide range of model settings and datasets of varying scales and modalities. Our results confirm the existence of LMC in MoE architectures and offer fundamental insights into the functional landscape and optimization dynamics of deep learning models.

[LG-44] BiLSTM-VHP: BiLSTM-Powered Network for Viral Host Prediction

链接: https://arxiv.org/abs/2509.11345
作者: Azher Ahmed Efat,Farzana Islam,Annajiat Alim Rasel,Munima Haque
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Recorded history shows the long coexistence of humans and animals, suggesting it began much earlier. Despite some beneficial interdependence, many animals carry viral diseases that can spread to humans. These diseases are known as zoonotic diseases. Recent outbreaks of SARS-CoV-2, Monkeypox and swine flu viruses have shown how these viruses can disrupt human life and cause death. Fast and accurate predictions of the host from which the virus spreads can help prevent these diseases from spreading. This work presents BiLSTM-VHP, a lightweight bidirectional long short-term memory (LSTM)-based architecture that can predict the host from the nucleotide sequence of orthohantavirus, rabies lyssavirus, and rotavirus A with high accuracy. The proposed model works with nucleotide sequences of 400 bases in length and achieved a prediction accuracy of 89.62% for orthohantavirus, 96.58% for rotavirus A, and 77.22% for rabies lyssavirus outperforming previous studies. Moreover, performance of the model is assessed using the confusion matrix, F-1 score, precision, recall, microaverage AUC. In addition, we introduce three curated datasets of orthohantavirus, rotavirus A, and rabies lyssavirus containing 8,575, 95,197, and 22,052 nucleotide sequences divided into 9, 12, and 29 host classes, respectively. The codes and dataset are available at this https URL

[LG-45] On the Escaping Efficiency of Distributed Adversarial Training Algorithms

链接: https://arxiv.org/abs/2509.11337
作者: Ying Cao,Kun Yuan,Ali H. Sayed
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Adversarial training has been widely studied in recent years due to its role in improving model robustness against adversarial attacks. This paper focuses on comparing different distributed adversarial training algorithms–including centralized and decentralized strategies–within multi-agent learning environments. Previous studies have highlighted the importance of model flatness in determining robustness. To this end, we develop a general theoretical framework to study the escaping efficiency of these algorithms from local minima, which is closely related to the flatness of the resulting models. We show that when the perturbation bound is sufficiently small (i.e., when the attack strength is relatively mild) and a large batch size is used, decentralized adversarial training algorithms–including consensus and diffusion–are guaranteed to escape faster from local minima than the centralized strategy, thereby favoring flatter minima. However, as the perturbation bound increases, this trend may no longer hold. In the simulation results, we illustrate our theoretical findings and systematically compare the performance of models obtained through decentralized and centralized adversarial training algorithms. The results highlight the potential of decentralized strategies to enhance the robustness of models in distributed settings.

[LG-46] MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis

链接: https://arxiv.org/abs/2509.11335
作者: Yonghao Weng,Liqiang Gao,Linwu Zhu,Jian Huang
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注:

点击查看摘要

Abstract:Recently, large language models (LLMs) have achieved remarkable breakthroughs in general domains such as programming and writing, and have demonstrated strong potential in various scientific research scenarios. However, the capabilities of AI models in the highly specialized field of materials characterization and analysis have not yet been systematically or sufficiently validated. To address this gap, we present MatQnA, the first multi-modal benchmark dataset specifically designed for material characterization techniques. MatQnA includes ten mainstream characterization methods, such as X-ray Photoelectron Spectroscopy (XPS), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), etc. We employ a hybrid approach combining LLMs with human-in-the-loop validation to construct high-quality question-answer pairs, integrating both multiple-choice and subjective questions. Our preliminary evaluation results show that the most advanced multi-modal AI models (e.g., GPT-4.1, Claude 4, Gemini 2.5, and Doubao Vision Pro 32K) have already achieved nearly 90% accuracy on objective questions in materials data interpretation and analysis tasks, demonstrating strong potential for applications in materials characterization and analysis. The MatQnA dataset is publicly available at this https URL.

[LG-47] Derivative-informed Graph Convolutional Autoencoder with Phase Classification for the Lifshitz-Petrich Model

链接: https://arxiv.org/abs/2509.11293
作者: Yanlai Chen,Yajie Ji,Zhenli Xu
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:The Lifshitz-Petrich (LP) model is a classical model for describing complex spatial patterns such as quasicrystals and multiphase structures. Solving and classifying the solutions of the LP model is challenging due to the presence of high-order gradient terms and the long-range orientational order characteristic of the quasicrystals. To address these challenges, we propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) to classify the multi-component multi-state solutions of the LP model. The classifier consists of two stages. In the offline stage, the DiGCA phase classifier innovatively incorporates both solutions and their derivatives for training a graph convolutional autoencoder which effectively captures intricate spatial dependencies while significantly reducing the dimensionality of the solution space. In the online phase, the framework employs a neural network classifier to efficiently categorize encoded solutions into distinct phase diagrams. The numerical results demonstrate that the DiGCA phase classifier accurately solves the LP model, classifies its solutions, and rapidly generates detailed phase diagrams in a robust manner, offering significant improvements in both efficiency and accuracy over traditional methods.

[LG-48] PINGS: Physics-Informed Neural Network for Fast Generative Sampling

链接: https://arxiv.org/abs/2509.11284
作者: Achmad Ardani Prasha,Clavino Ourizqi Rachmadi,Muhamad Fauzan Ibnu Syahlan,Naufal Rahfi Anugerah,Nanda Garin Raditya,Putri Amelia,Sabrina Laila Mutiara,Hilman Syachr Ramadhan
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: 19 pages, 4 figures

点击查看摘要

Abstract:We introduce PINGS (Physics-Informed Neural Network for Fast Generative Sampling), a framework that amortizes diffusion sampling by training a physics-informed network to approximate reverse-time probability-flow dynamics, reducing sampling to a single forward pass (NFE = 1). As a proof of concept, we learn a direct map from a 3D standard normal to a non-Gaussian Gaussian Mixture Model (GMM). PINGS preserves the target’s distributional structure (multi-bandwidth kernel MMD^2 = 1.88 \times 10^-2 with small errors in mean, covariance, skewness, and excess kurtosis) and achieves constant-time generation: 10^4 samples in 16.54 \pm 0.56 millisecond on an RTX 3090, versus 468-843 millisecond for DPM-Solver (10/20) and 960 millisecond for DDIM (50) under matched conditions. We also sanity-check the PINN/automatic-differentiation pipeline on a damped harmonic oscillator, obtaining MSEs down to \mathcalO(10^-5) . Compared to fast but iterative ODE solvers and direct-map families (Flow, Rectified-Flow, Consistency), PINGS frames generative sampling as a PINN-style residual problem with endpoint anchoring, yielding a white-box, differentiable map with NFE = 1. These proof-of-concept results position PINGS as a promising route to fast, function-based generative sampling with potential extensions to scientific simulation (e.g., fast calorimetry).

[LG-49] Protected Probabilistic Classification Library

链接: https://arxiv.org/abs/2509.11267
作者: Ivan Petej
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:This paper introduces a new Python package specifically designed to address calibration of probabilistic classifiers under dataset shift. The method is demonstrated in binary and multi-class settings and its effectiveness is measured against a number of existing post-hoc calibration methods. The empirical results are promising and suggest that our technique can be helpful in a variety of settings for batch and online learning classification problems where the underlying data distribution changes between the training and test sets.

[LG-50] Revisiting Meter Tracking in Carnatic Music using Deep Learning Approaches

链接: https://arxiv.org/abs/2509.11241
作者: Satyajeet Prabhu
类目: ound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS)
*备注:

点击查看摘要

Abstract:Beat and downbeat tracking, jointly referred to as Meter Tracking, is a fundamental task in Music Information Retrieval (MIR). Deep learning models have far surpassed traditional signal processing and classical machine learning approaches in this domain, particularly for Western (Eurogenetic) genres, where large annotated datasets are widely available. These systems, however, perform less reliably on underrepresented musical traditions. Carnatic music, a rich tradition from the Indian subcontinent, is renowned for its rhythmic intricacy and unique metrical structures (tālas). The most notable prior work on meter tracking in this context employed probabilistic Dynamic Bayesian Networks (DBNs). The performance of state-of-the-art (SOTA) deep learning models on Carnatic music, however, remains largely unexplored. In this study, we evaluate two models for meter tracking in Carnatic music: the Temporal Convolutional Network (TCN), a lightweight architecture that has been successfully adapted for Latin rhythms, and Beat This!, a transformer-based model designed for broad stylistic coverage without the need for post-processing. Replicating the experimental setup of the DBN baseline on the Carnatic Music Rhythm (CMR _f ) dataset, we systematically assess the performance of these models in a directly comparable setting. We further investigate adaptation strategies, including fine-tuning the models on Carnatic data and the use of musically informed parameters. Results show that while off-the-shelf models do not always outperform the DBN, their performance improves substantially with transfer learning, matching or surpassing the baseline. These findings indicate that SOTA deep learning models can be effectively adapted to underrepresented traditions, paving the way for more inclusive and broadly applicable meter tracking systems. Subjects: Sound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS) Cite as: arXiv:2509.11241 [cs.SD] (or arXiv:2509.11241v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2509.11241 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-51] Online Optimization on Hadamard Manifolds: Curvature Independent Regret Bounds on Horospherically Convex Objectives

链接: https://arxiv.org/abs/2509.11236
作者: Emre Sahinoglu,Shahin Shahrampour
类目: Machine Learning (cs.LG); Optimization and Control (math.OC); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:We study online Riemannian optimization on Hadamard manifolds under the framework of horospherical convexity (h-convexity). Prior work mostly relies on the geodesic convexity (g-convexity), leading to regret bounds scaling poorly with the manifold curvature. To address this limitation, we analyze Riemannian online gradient descent for h-convex and strongly h-convex functions and establish O(\sqrtT) and O(\log(T)) regret guarantees, respectively. These bounds are curvature-independent and match the results in the Euclidean setting. We validate our approach with experiments on the manifold of symmetric positive definite (SPD) matrices equipped with the affine-invariant metric. In particular, we investigate online Tyler’s M -estimation and online Fréchet mean computation, showing the application of h-convexity in practice.

[LG-52] Foundational theory for optimal decision tree problems. I. Algorithmic and geometric foundations

链接: https://arxiv.org/abs/2509.11226
作者: Xi He
类目: Machine Learning (cs.LG); Discrete Mathematics (cs.DM); Data Structures and Algorithms (cs.DS)
*备注: 50 pages, 1 figure

点击查看摘要

Abstract:In the first paper (part I) of this series of two, we introduce four novel definitions of the ODT problems: three for size-constrained trees and one for depth-constrained trees. These definitions are stated unambiguously through executable recursive programs, satisfying all criteria we propose for a formal specification. In this sense, they resemble the “standard form” used in the study of general-purpose solvers. Grounded in algebraic programming theory-a relational formalism for deriving correct-by-construction algorithms from specifications-we can not only establish the existence or nonexistence of dynamic programming solutions but also derive them constructively whenever they exist. Consequently, the four generic problem definitions yield four novel optimal algorithms for ODT problems with arbitrary splitting rules that satisfy the axioms and objective functions of a given form. These algorithms encompass the known depth-constrained, axis-parallel ODT algorithm as the special case, while providing a unified, efficient, and elegant solution for the general ODT problem. In Part II, we present the first optimal hypersurface decision tree algorithm and provide comprehensive experiments against axis-parallel decision tree algorithms, including heuristic CART and state-of-the-art optimal methods. The results demonstrate the significant potential of decision trees with flexible splitting rules. Moreover, our framework is readily extendable to support algorithms for constructing even more flexible decision trees, including those with mixed splitting rules. Comments: 50 pages, 1 figure Subjects: Machine Learning (cs.LG); Discrete Mathematics (cs.DM); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2509.11226 [cs.LG] (or arXiv:2509.11226v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.11226 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-53] GK-SMOTE: A Hyperparameter-free Noise-Resilient Gaussian KDE-Based Oversampling Approach APWEB

链接: https://arxiv.org/abs/2509.11163
作者: Mahabubur Rahman Miraj,Hongyu Huang,Ting Yang,Jinxue Zhao,Nankun Mu,Xinyu Lei
类目: Machine Learning (cs.LG)
*备注: 15 pages, 5 figures, 9th APWeb-WAIM joint International Conference on Web and Big Data (APWeb-WAIM 2025)

点击查看摘要

Abstract:Imbalanced classification is a significant challenge in machine learning, especially in critical applications like medical diagnosis, fraud detection, and cybersecurity. Traditional oversampling techniques, such as SMOTE, often fail to handle label noise and complex data distributions, leading to reduced classification accuracy. In this paper, we propose GK-SMOTE, a hyperparameter-free, noise-resilient extension of SMOTE, built on Gaussian Kernel Density Estimation (KDE). GK-SMOTE enhances class separability by generating synthetic samples in high-density minority regions, while effectively avoiding noisy or ambiguous areas. This self-adaptive approach uses Gaussian KDE to differentiate between safe and noisy regions, ensuring more accurate sample generation without requiring extensive parameter tuning. Our extensive experiments on diverse binary classification datasets demonstrate that GK-SMOTE outperforms existing state-of-the-art oversampling techniques across key evaluation metrics, including MCC, Balanced Accuracy, and AUPRC. The proposed method offers a robust, efficient solution for imbalanced classification tasks, especially in noisy data environments, making it an attractive choice for real-world applications.

[LG-54] Stabilizing Data-Free Model Extraction ECAI-2025

链接: https://arxiv.org/abs/2509.11159
作者: Dat-Thinh Nguyen,Kim-Hung Le,Nhien-An Le-Khac
类目: Machine Learning (cs.LG)
*备注: 28th European Conference on Artificial Intelligence (ECAI-2025)

点击查看摘要

Abstract:Model extraction is a severe threat to Machine Learning-as-a-Service systems, especially through data-free approaches, where dishonest users can replicate the functionality of a black-box target model without access to realistic data. Despite recent advancements, existing data-free model extraction methods suffer from the oscillating accuracy of the substitute model. This oscillation, which could be attributed to the constant shift in the generated data distribution during the attack, makes the attack impractical since the optimal substitute model cannot be determined without access to the target model’s in-distribution data. Hence, we propose MetaDFME, a novel data-free model extraction method that employs meta-learning in the generator training to reduce the distribution shift, aiming to mitigate the substitute model’s accuracy oscillation. In detail, we train our generator to iteratively capture the meta-representations of the synthetic data during the attack. These meta-representations can be adapted with a few steps to produce data that facilitates the substitute model to learn from the target model while reducing the effect of distribution shifts. Our experiments on popular baseline image datasets, MNIST, SVHN, CIFAR-10, and CIFAR-100, demonstrate that MetaDFME outperforms the current state-of-the-art data-free model extraction method while exhibiting a more stable substitute model’s accuracy during the attack.

[LG-55] BIGNet: Pretrained Graph Neural Network for Embedding Semantic Spatial and Topological Data in BIM Models

链接: https://arxiv.org/abs/2509.11104
作者: Jin Han,Xin-Zheng Lu,Jia-Rui Lin
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Large Foundation Models (LFMs) have demonstrated significant advantages in civil engineering, but they primarily focus on textual and visual data, overlooking the rich semantic, spatial, and topological features in BIM (Building Information Modelling) models. Therefore, this study develops the first large-scale graph neural network (GNN), BIGNet, to learn, and reuse multidimensional design features embedded in BIM models. Firstly, a scalable graph representation is introduced to encode the “semantic-spatial-topological” features of BIM components, and a dataset with nearly 1 million nodes and 3.5 million edges is created. Subsequently, BIGNet is proposed by introducing a new message-passing mechanism to GraphMAE2 and further pretrained with a node masking strategy. Finally, BIGNet is evaluated in various transfer learning tasks for BIM-based design checking. Results show that: 1) homogeneous graph representation outperforms heterogeneous graph in learning design features, 2) considering local spatial relationships in a 30 cm radius enhances performance, and 3) BIGNet with GAT (Graph Attention Network)-based feature extraction achieves the best transfer learning results. This innovation leads to a 72.7% improvement in Average F1-score over non-pretrained models, demonstrating its effectiveness in learning and transferring BIM design features and facilitating their automated application in future design and lifecycle management.

[LG-56] GCN-TULHOR: Trajectory-User Linking Leverag ing GCNs and Higher-Order Spatial Representations

链接: https://arxiv.org/abs/2509.11095
作者: Khoa Tran,Pranav Gupta,Manos Papagelis
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Trajectory-user linking (TUL) aims to associate anonymized trajectories with the users who generated them, which is crucial for personalized recommendations, privacy-preserving analytics, and secure location-based services. Existing methods struggle with sparse data, incomplete routes, and limited modeling of complex spatial dependencies, often relying on low-level check-in data or ignoring spatial patterns. In this paper, we introduced GCN-TULHOR, a method that transforms raw location data into higher-order mobility flow representations using hexagonal tessellation, reducing data sparsity and capturing richer spatial semantics, and integrating Graph Convolutional Networks (GCNs). Our approach converts both sparse check-in and continuous GPS trajectory data into unified higher-order flow representations, mitigating sparsity while capturing deeper semantic information. The GCN layer explicitly models complex spatial relationships and non-local dependencies without requiring side information such as timestamps or points of interest. Experiments on six real-world datasets show consistent improvements over classical baselines, RNN- and Transformer-based models, and the TULHOR method in accuracy, precision, recall, and F1-score. GCN-TULHOR achieves 1-8% relative gains in accuracy and F1. Sensitivity analysis identifies an optimal setup with a single GCN layer and 512-dimensional embeddings. The integration of GCNs enhances spatial learning and improves generalizability across mobility data. This work highlights the value of combining graph-based spatial learning with sequential modeling, offering a robust and scalable solution for TUL with applications in recommendations, urban planning, and security.

[LG-57] DemandLens: Enhancing Forecast Accuracy Through Product-Specific Hyperparameter Optimization

链接: https://arxiv.org/abs/2509.11085
作者: Srijesh Pillai,M. I. Jawid Nazir
类目: Machine Learning (cs.LG); Applications (stat.AP)
*备注: 10 pages, 12 figures, 3 tables. Accepted for publication in the proceedings of the 2025 Advances in Science and Engineering Technology International Conferences (ASET)

点击查看摘要

Abstract:DemandLens demonstrates an innovative Prophet based forecasting model for the mattress-in-a-box industry, incorporating COVID-19 metrics and SKU-specific hyperparameter optimization. This industry has seen significant growth of E-commerce players in the recent years, wherein the business model majorly relies on outsourcing Mattress manufacturing and related logistics and supply chain operations, focusing on marketing the product and driving conversions through Direct-to-Consumer sales channels. Now, within the United States, there are a limited number of Mattress contract manufacturers available, and hence, it is important that they manage their raw materials, supply chain, and, inventory intelligently, to be able to cater maximum Mattress brands. Our approach addresses the critical need for accurate Sales Forecasting in an industry that is heavily dependent on third-party Contract Manufacturing. This, in turn, helps the contract manufacturers to be prepared, hence, avoiding bottleneck scenarios, and aiding them to source raw materials at optimal rates. The model demonstrates strong predictive capabilities through SKU-specific Hyperparameter optimization, offering the Contract Manufacturers and Mattress brands a reliable tool to streamline supply chain operations.

[LG-58] Machine Learning Framework for Audio-Based Equipment Condition Monitoring: A Comparative Study of Classification Algorithms

链接: https://arxiv.org/abs/2509.11075
作者: Srijesh Pillai,Yodhin Agarwal,Zaheeruddin Ahmed
类目: Machine Learning (cs.LG)
*备注: 10 pages, 7 figures. Accepted for publication in the proceedings of the 2025 Advances in Science and Engineering Technology International Conferences (ASET)

点击查看摘要

Abstract:Audio-based equipment condition monitoring suffers from a lack of standardized methodologies for algorithm selection, hindering reproducible research. This paper addresses this gap by introducing a comprehensive framework for the systematic and statistically rigorous evaluation of machine learning models. Leveraging a rich 127-feature set across time, frequency, and time-frequency domains, our methodology is validated on both synthetic and real-world datasets. Results demonstrate that an ensemble method achieves superior performance (94.2% accuracy, 0.942 F1-score), with statistical testing confirming its significant outperformance of individual algorithms by 8-15%. Ultimately, this work provides a validated benchmarking protocol and practical guidelines for selecting robust monitoring solutions in industrial settings.

[LG-59] BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization

链接: https://arxiv.org/abs/2509.11056
作者: Yuhang Li,Yang Lu,Wei Chen,Bo Ai,Zhiguo Ding,Dusit Niyato
类目: ystems and Control (eess.SY); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Artificial intelligence (AI) is anticipated to emerge as a pivotal enabler for the forthcoming sixth-generation (6G) wireless communication systems. However, current research efforts regarding large AI models for wireless communications primarily focus on fine-tuning pre-trained large language models (LLMs) for specific tasks. This paper investigates the large-scale AI model designed for beamforming optimization to adapt and generalize to diverse tasks defined by system utilities and scales. We propose a novel framework based on bidirectional encoder representations from transformers (BERT), termed BERT4beam. We aim to formulate the beamforming optimization problem as a token-level sequence learning task, perform tokenization of the channel state information, construct the BERT model, and conduct task-specific pre-training and fine-tuning strategies. Based on the framework, we propose two BERT-based approaches for single-task and multi-task beamforming optimization, respectively. Both approaches are generalizable for varying user scales. Moreover, the former can adapt to varying system utilities and antenna configurations by re-configuring the input and output module of the BERT model, while the latter, termed UBERT, can directly generalize to diverse tasks, due to a finer-grained tokenization strategy. Extensive simulation results demonstrate that the two proposed approaches can achieve near-optimal performance and outperform existing AI models across various beamforming optimization tasks, showcasing strong adaptability and generalizability.

[LG-60] Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction

链接: https://arxiv.org/abs/2509.11046
作者: Seon-Geun Jeong,Kyeong-Hwan Moon,Won-Joo Hwang
类目: Emerging Technologies (cs.ET); Machine Learning (cs.LG); Biomolecules (q-bio.BM)
*备注: 43 pages, 9 figures, and 12 tables. Accepted by EPJ Quantum Technology

点击查看摘要

Abstract:Protein-ligand binding affinity is critical in drug discovery, but experimentally determining it is time-consuming and expensive. Artificial intelligence (AI) has been used to predict binding affinity, significantly accelerating this process. However, the high-performance requirements and vast datasets involved in affinity prediction demand increasingly large AI models, requiring substantial computational resources and training time. Quantum machine learning has emerged as a promising solution to these challenges. In particular, hybrid quantum-classical models can reduce the number of parameters while maintaining or improving performance compared to classical counterparts. Despite these advantages, challenges persist: why hybrid quantum models achieve these benefits, whether quantum neural networks (QNNs) can replace classical neural networks, and whether such models are feasible on noisy intermediate-scale quantum (NISQ) devices. This study addresses these challenges by proposing a hybrid quantum neural network (HQNN) that empirically demonstrates the capability to approximate non-linear functions in the latent feature space derived from classical embedding. The primary goal of this study is to achieve a parameter-efficient model in binding affinity prediction while ensuring feasibility on NISQ devices. Numerical results indicate that HQNN achieves comparable or superior performance and parameter efficiency compared to classical neural networks, underscoring its potential as a viable replacement. This study highlights the potential of hybrid QML in computational drug discovery, offering insights into its applicability and advantages in addressing the computational challenges of protein-ligand binding affinity prediction.

[LG-61] California Wildfire Inventory (CAWFI): An Extensive Dataset for Predictive Techniques based on Artificial Intelligence

链接: https://arxiv.org/abs/2509.11015
作者: Rohan Tan Bhowmik,Youn Soo Jung,Juan Aguilera,Mary Prunicki,Kari Nadeau
类目: Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注:

点击查看摘要

Abstract:Due to climate change and the disruption of ecosystems worldwide, wildfires are increasingly impacting environment, infrastructure, and human lives globally. Additionally, an exacerbating climate crisis means that these losses would continue to grow if preventative measures are not implemented. Though recent advancements in artificial intelligence enable wildfire management techniques, most deployed solutions focus on detecting wildfires after ignition. The development of predictive techniques with high accuracy requires extensive datasets to train machine learning models. This paper presents the California Wildfire Inventory (CAWFI), a wildfire database of over 37 million data points for building and training wildfire prediction solutions, thereby potentially preventing megafires and flash fires by addressing them before they spark. The dataset compiles daily historical California wildfire data from 2012 to 2018 and indicator data from 2012 to 2022. The indicator data consists of leading indicators (meteorological data correlating to wildfire-prone conditions), trailing indicators (environmental data correlating to prior and early wildfire activity), and geological indicators (vegetation and elevation data dictating wildfire risk and spread patterns). CAWFI has already demonstrated success when used to train a spatio-temporal artificial intelligence model, predicting 85.7% of future wildfires larger than 300,000 acres when trained on 2012-2017 indicator data. This dataset is intended to enable wildfire prediction research and solutions as well as set a precedent for future wildfire databases in other regions.

[LG-62] CogGNN: Cognitive Graph Neural Networks in Generative Connectomics

链接: https://arxiv.org/abs/2509.10864
作者: Mayssa Soussia,Yijun Lin,Mohamed Ali Mahjoub,Islem Rekik
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Generative learning has advanced network neuroscience, enabling tasks like graph super-resolution, temporal graph prediction, and multimodal brain graph fusion. However, current methods, mainly based on graph neural networks (GNNs), focus solely on structural and topological properties, neglecting cognitive traits. To address this, we introduce the first cognified generative model, CogGNN, which endows GNNs with cognitive capabilities (e.g., visual memory) to generate brain networks that preserve cognitive features. While broadly applicable, we present CogGNN, a specific variant designed to integrate visual input, a key factor in brain functions like pattern recognition and memory recall. As a proof of concept, we use our model to learn connectional brain templates (CBTs), population-level fingerprints from multi-view brain networks. Unlike prior work that overlooks cognitive properties, CogGNN generates CBTs that are both cognitively and structurally meaningful. Our contributions are: (i) a novel cognition-aware generative model with a visual-memory-based loss; (ii) a CBT-learning framework with a co-optimization strategy to yield well-centered, discriminative, cognitively enhanced templates. Extensive experiments show that CogGNN outperforms state-of-the-art methods, establishing a strong foundation for cognitively grounded brain network modeling.

[LG-63] Neurosymbolic AI Transfer Learning Improves Network Intrusion Detection

链接: https://arxiv.org/abs/2509.10850
作者: Huynh T. T. Tran,Jacob Sander,Achraf Cohen,Brian Jalaian,Nathaniel D. Bastian
类目: Machine Learning (cs.LG)
*备注: 9 pages, 2 figures, 6 tables

点击查看摘要

Abstract:Transfer learning is commonly utilized in various fields such as computer vision, natural language processing, and medical imaging due to its impressive capability to address subtasks and work with different datasets. However, its application in cybersecurity has not been thoroughly explored. In this paper, we present an innovative neurosymbolic AI framework designed for network intrusion detection systems, which play a crucial role in combating malicious activities in cybersecurity. Our framework leverages transfer learning and uncertainty quantification. The findings indicate that transfer learning models, trained on large and well-structured datasets, outperform neural-based models that rely on smaller datasets, paving the way for a new era in cybersecurity solutions.

[LG-64] A Comparison of Selected Image Transformation Techniques for Malware Classification

链接: https://arxiv.org/abs/2509.10838
作者: Rishit Agrawal,Kunal Bhatnagar,Andrew Do,Ronnit Rana,Mark Stamp
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Recently, a considerable amount of malware research has focused on the use of powerful image-based machine learning techniques, which generally yield impressive results. However, before image-based techniques can be applied to malware, the samples must be converted to images, and there is no generally-accepted approach for doing so. The malware-to-image conversion strategies found in the literature often appear to be ad hoc, with little or no effort made to take into account properties of executable files. In this paper, we experiment with eight distinct malware-to-image conversion techniques, and for each, we test a variety of learning models. We find that several of these image conversion techniques perform similarly across a range of learning models, in spite of the image conversion processes being quite different. These results suggest that the effectiveness of image-based malware classification techniques may depend more on the inherent strengths of image analysis techniques, as opposed to the precise details of the image conversion strategy.

[LG-65] RSL-RL: A Learning Library for Robotics Research

链接: https://arxiv.org/abs/2509.10771
作者: Clemens Schwarke,Mayank Mittal,Nikita Rudin,David Hoeller,Marco Hutter
类目: Robotics (cs.RO); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:RSL-RL is an open-source Reinforcement Learning library tailored to the specific needs of the robotics community. Unlike broad general-purpose frameworks, its design philosophy prioritizes a compact and easily modifiable codebase, allowing researchers to adapt and extend algorithms with minimal overhead. The library focuses on algorithms most widely adopted in robotics, together with auxiliary techniques that address robotics-specific challenges. Optimized for GPU-only training, RSL-RL achieves high-throughput performance in large-scale simulation environments. Its effectiveness has been validated in both simulation benchmarks and in real-world robotic experiments, demonstrating its utility as a lightweight, extensible, and practical framework to develop learning-based robotic controllers. The library is open-sourced at: this https URL.

[LG-66] Matched-Pair Experimental Design with Active Learning

链接: https://arxiv.org/abs/2509.10742
作者: Weizhi Li,Gautam Dasarathy,Visar Berisha
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Matched-pair experimental designs aim to detect treatment effects by pairing participants and comparing within-pair outcome differences. In many situations, the overall effect size is small across the entire population. Then, the focus naturally shifts to identifying and targeting high treatment-effect regions where the intervention is most effective. This paper proposes a matched-pair experimental design that sequentially and actively enrolls patients in high treatment-effect regions. Importantly, we frame the identification of the target region as a classification problem and propose an active learning framework tailored to matched-pair designs. The proposed design not only reduces the experimental cost of detecting treatment efficacy, but also ensures that the identified regions enclose the entire high-treatment-effect regions. Our theoretical analysis of the framework’s label complexity, along with experiments in practical scenarios, demonstrates the efficiency and advantages of the approach.

[LG-67] Using LLM s for Late Multimodal Sensor Fusion for Activity Recognition

链接: https://arxiv.org/abs/2509.10729
作者: Ilker Demirel,Karan Thakkar,Benjamin Elizalde,Miquel Espi Marques,Shirley Ren,Jaya Narain
类目: Machine Learning (cs.LG)
*备注: Preprint, under review

点击查看摘要

Abstract:Sensor data streams provide valuable information around activities and context for downstream applications, though integrating complementary information can be challenging. We show that large language models (LLMs) can be used for late fusion for activity classification from audio and motion time series data. We curated a subset of data for diverse activity recognition across contexts (e.g., household activities, sports) from the Ego4D dataset. Evaluated LLMs achieved 12-class zero- and one-shot classification F1-scores significantly above chance, with no task-specific training. Zero-shot classification via LLM-based fusion from modality-specific models can enable multimodal temporal applications where there is limited aligned training data for learning a shared embedding space. Additionally, LLM-based fusion can enable model deploying without requiring additional memory and computation for targeted application-specific multimodal models.

[LG-68] Coordinated Reinforcement Learning Prefetching Architecture for Multicore Systems

链接: https://arxiv.org/abs/2509.10719
作者: Mohammed Humaid Siddiqui,Fernando Guzman,Yufei Wu,Ruishu Ann
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Hardware Architecture (cs.AR); Machine Learning (cs.LG); Performance (cs.PF)
*备注: 47 pages, 12 figures, technical report prepared at Fairleigh Dickinson University

点击查看摘要

Abstract:Hardware prefetching is critical to fill the performance gap between CPU speeds and slower memory accesses. With multicore architectures becoming commonplace, traditional prefetchers are severely challenged. Independent core operation creates significant redundancy (up to 20% of prefetch requests are duplicates), causing unnecessary memory bus traffic and wasted bandwidth. Furthermore, cutting-edge prefetchers such as Pythia suffer from about a 10% performance loss when scaling from a single-core to a four-core system. To solve these problems, we propose CRL-Pythia, a coordinated reinforcement learning based prefetcher specifically designed for multicore systems. In this work, CRL-Pythia addresses these issues by enabling cross-core sharing of information and cooperative prefetching decisions, which greatly reduces redundant prefetch requests and improves learning convergence across cores. Our experiments demonstrate that CRL-Pythia outperforms single Pythia configurations in all cases, with approximately 12% IPC (instructions per cycle) improvement for bandwidth-constrained workloads, while imposing moderate hardware overhead. Our sensitivity analyses also verify its robustness and scalability, thereby making CRL-Pythia a practical and efficient solution to contemporary multicore systems.

[LG-69] MinatoLoader: Accelerating Machine Learning Training Through Efficient Data Preprocessing EUROSYS2026 DATE

链接: https://arxiv.org/abs/2509.10712
作者: Rahma Nouaji,Stella Bitchebe,Ricardo Macedo,Oana Balmau
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: Paper accepted at EuroSys 2026 (will be updated after the camera-ready)

点击查看摘要

Abstract:Data loaders are used by Machine Learning (ML) frameworks like PyTorch and TensorFlow to apply transformations to data before feeding it into the accelerator. This operation is called data preprocessing. Data preprocessing plays an important role in the ML training workflow because if it is inefficiently pipelined with the training, it can yield high GPU idleness, resulting in important training delays. Unfortunately, existing data loaders turn out to waste GPU resources, with 76% GPU idleness when using the PyTorch data loader, for example. One key source of inefficiency is the variability in preprocessing time across samples within the same dataset. Existing data loaders are oblivious to this variability, and they construct batches without any consideration of slow or fast samples. In this case, the entire batch is delayed by a single slow sample, stalling the training pipeline and resulting in head-of-line blocking. To address these inefficiencies, we present MinatoLoader, a general-purpose data loader for PyTorch that accelerates training and improves GPU utilization. MinatoLoader is designed for a single-server setup, containing multiple GPUs. It continuously prepares data in the background and actively constructs batches by prioritizing fast-to-preprocess samples, while slower samples are processed in parallel. We evaluate MinatoLoader on servers with V100 and A100 GPUs. On a machine with four A100 GPUs, MinatoLoader improves the training time of a wide range of workloads by up to 7.5\times ( 3.6\times on average) over PyTorch DataLoader and Pecan, and up to 3\times ( 2.2\times on average) over DALI. It also increases average GPU utilization from 46.4% with PyTorch to 90.45%, while preserving model accuracy and enabling faster convergence. Comments: Paper accepted at EuroSys 2026 (will be updated after the camera-ready) Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG) Cite as: arXiv:2509.10712 [cs.DC] (or arXiv:2509.10712v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2509.10712 Focus to learn more arXiv-issued DOI via DataCite

[LG-70] DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators MICRO2023

链接: https://arxiv.org/abs/2509.10702
作者: Charles Hong,Qijing Huang,Grace Dinh,Mahesh Subedar,Yakun Sophia Shao
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: Published at MICRO 2023

点击查看摘要

Abstract:In the hardware design space exploration process, it is critical to optimize both hardware parameters and algorithm-to-hardware mappings. Previous work has largely approached this simultaneous optimization problem by separately exploring the hardware design space and the mapspace - both individually large and highly nonconvex spaces - independently. The resulting combinatorial explosion has created significant difficulties for optimizers. In this paper, we introduce DOSA, which consists of differentiable performance models and a gradient descent-based optimization technique to simultaneously explore both spaces and identify high-performing design points. Experimental results demonstrate that DOSA outperforms random search and Bayesian optimization by 2.80x and 12.59x, respectively, in improving DNN model energy-delay product, given a similar number of samples. We also demonstrate the modularity and flexibility of DOSA by augmenting our analytical model with a learned model, allowing us to optimize buffer sizes and mappings of a real DNN accelerator and attain a 1.82x improvement in energy-delay product. Comments: Published at MICRO 2023 Subjects: Hardware Architecture (cs.AR); Machine Learning (cs.LG) Cite as: arXiv:2509.10702 [cs.AR] (or arXiv:2509.10702v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2509.10702 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-71] Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks

链接: https://arxiv.org/abs/2509.10694
作者: Kahfi S. Zulkifli,Wenbo Qian,Shaowei Zhu,Yuan Zhou,Zhen Zhang,Chang Lou
类目: Machine Learning (cs.LG); Programming Languages (cs.PL)
*备注:

点击查看摘要

Abstract:Modern machine learning frameworks support very large models by incorporating parallelism and optimization techniques. Yet, these very techniques add new layers of complexity, introducing silent errors that severely degrade model performance. Existing solutions are either ad hoc or too costly for production. We present Scalify, a lightweight framework that exposes silent errors by verifying semantic equivalence of computational graphs using equality saturation and Datalog-style reasoning. To scale, Scalify partitions graphs with parallel rewriting and layer memoization, reuses rewrite templates, and augments equality saturation with relational reasoning and symbolic bijection inference. It further localizes discrepancies to precise code sites, turning verification results into actionable debugging guidance. Scalify verifies models as large as Llama-3.1-405B within minutes on a commodity machine and exposed five unknown bugs in Amazon production machine learning frameworks. Subjects: Machine Learning (cs.LG); Programming Languages (cs.PL) Cite as: arXiv:2509.10694 [cs.LG] (or arXiv:2509.10694v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.10694 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-72] Least-Ambiguous Multi-Label Classifier ICTAI2025

链接: https://arxiv.org/abs/2509.10689
作者: Misgina Tsighe Hagos,Claes Lundström
类目: Machine Learning (cs.LG)
*备注: Accepted at the 37th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2025

点击查看摘要

Abstract:Multi-label learning often requires identifying all relevant labels for training instances, but collecting full label annotations is costly and labor-intensive. In many datasets, only a single positive label is annotated per training instance, despite the presence of multiple relevant labels. This setting, known as single-positive multi-label learning (SPMLL), presents a significant challenge due to its extreme form of partial supervision. We propose a model-agnostic approach to SPMLL that draws on conformal prediction to produce calibrated set-valued outputs, enabling reliable multi-label predictions at test time. Our method bridges the supervision gap between single-label training and multi-label evaluation without relying on label distribution assumptions. We evaluate our approach on 12 benchmark datasets, demonstrating consistent improvements over existing baselines and practical applicability.

[LG-73] M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations

链接: https://arxiv.org/abs/2509.10659
作者: Bo Lei,Victor M. Castillo,Yeping Hu
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE); Computational Physics (physics.comp-ph)
*备注: Accepted and published in Transactions on Machine Learning Research (TMLR), 2025

点击查看摘要

Abstract:Mesh-based graph neural networks (GNNs) have become effective surrogates for PDE simulations, yet their deep message passing incurs high cost and over-smoothing on large, long-range meshes; hierarchical GNNs shorten propagation paths but still face two key obstacles: (i) building coarse graphs that respect mesh topology, geometry, and physical discontinuities, and (ii) maintaining fine-scale accuracy without sacrificing the speed gained from coarsening. We tackle these challenges with M4GN, a three-tier, segment-centric hierarchical network. M4GN begins with a hybrid segmentation strategy that pairs a fast graph partitioner with a superpixel-style refinement guided by modal-decomposition features, producing contiguous segments of dynamically consistent nodes. These segments are encoded by a permutation-invariant aggregator, avoiding the order sensitivity and quadratic cost of aggregation approaches used in prior works. The resulting information bridges a micro-level GNN, which captures local dynamics, and a macro-level transformer that reasons efficiently across segments, achieving a principled balance between accuracy and efficiency. Evaluated on multiple representative benchmark datasets, M4GN improves prediction accuracy by up to 56% while achieving up to 22% faster inference than state-of-the-art baselines.

[LG-74] Interpretable neural network system identification method for two families of second-order systems based on characteristic curves

链接: https://arxiv.org/abs/2509.10632
作者: Federico J. Gonzalez,Luis P. Lara
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:Nonlinear system identification often involves a fundamental trade-off between interpretability and flexibility, often requiring the incorporation of physical constraints. We propose a unified data-driven framework that combines the mathematical structure of the governing differential equations with the flexibility of neural networks (NNs). At the core of our approach is the concept of characteristic curves (CCs), which represent individual nonlinear functions (e.g., friction and restoring components) of the system. Each CC is modeled by a dedicated NN, enabling a modular and interpretable representation of the system equation. To demonstrate the versatility of the CC-based formalism, we introduce three identification strategies: (1) SINDy-CC, which extends the sparse regression approach of SINDy by incorporating the mathematical structure of the governing equations as constraints; (2) Poly-CC, which represents each CC using high-degree polynomials; and (3) NN-CC, which uses NNs without requiring prior assumptions about basis functions. Our results show that all three approaches are well-suited for systems with simple polynomial nonlinearities, such as the van der Pol oscillator. In contrast, NN-CC demonstrates superior performance in modeling systems with complex nonlinearities and discontinuities, such as those observed in stick-slip systems. The key contribution of this work is to demonstrate that the CC-based framework, particularly the NN-CC approach, can capture complex nonlinearities while maintaining interpretability through the explicit representation of the CCs. This balance makes it well-suited for modeling systems with discontinuities and complex nonlinearities that are challenging to assess using traditional polynomial or sparse regression methods, providing a powerful tool for nonlinear system identification.

[LG-75] pySigLib - Fast Signature-Based Computations on CPU and GPU

链接: https://arxiv.org/abs/2509.10613
作者: Daniil Shmelev,Cristopher Salvi
类目: Machine Learning (cs.LG); Mathematical Software (cs.MS); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:Signature-based methods have recently gained significant traction in machine learning for sequential data. In particular, signature kernels have emerged as powerful discriminators and training losses for generative models on time-series, notably in quantitative finance. However, existing implementations do not scale to the dataset sizes and sequence lengths encountered in practice. We present pySigLib, a high-performance Python library offering optimised implementations of signatures and signature kernels on CPU and GPU, fully compatible with PyTorch’s automatic differentiation. Beyond an efficient software stack for large-scale signature-based computation, we introduce a novel differentiation scheme for signature kernels that delivers accurate gradients at a fraction of the runtime of existing libraries.

[LG-76] GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python

链接: https://arxiv.org/abs/2509.10560
作者: Xuechen Liang,Xiaoxing He,Shengdao Wang,Jean-Philippe Montillet,Zhengkai Huang,Gaël Kermarrec,Shunqiang Hu,Yu Zhou,Jiahui Huang
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Geodetic time series – such as Global Navigation Satellite System (GNSS) positions, satellite altimetry-derived sea surface height (SSH), and tide gauge (TG) records – is essential for monitoring surface deformation and sea level change. Accurate forecasts of these variables can enhance early warning systems and support hazard mitigation for earthquakes, landslides, coastal storm surge, and long-term sea level. However, the nonlinear, non-stationary, and incomplete nature of such variables presents significant challenges for classic models, which often fail to capture long-term dependencies and complex spatiotemporal dynamics. We introduce GTS Forecaster, an open-source Python package for geodetic time series forecasting. It integrates advanced deep learning models – including kernel attention networks (KAN), graph neural network-based gated recurrent units (GNNGRU), and time-aware graph neural networks (TimeGNN) – to effectively model nonlinear spatial-temporal patterns. The package also provides robust preprocessing tools, including outlier detection and a reinforcement learning-based gap-filling algorithm, the Kalman-TransFusion Interpolation Framework (KTIF). GTS Forecaster currently supports forecasting, visualization, and evaluation of GNSS, SSH, and TG datasets, and is adaptable to general time series applications. By combining cutting-edge models with an accessible interface, it facilitates the application of deep learning in geodetic forecasting tasks.

[LG-77] Auditable Early Stopping for Agent ic Routing: Ledger-Verified Run-Wise Certificates under Local DP

链接: https://arxiv.org/abs/2509.10550
作者: Shivam Akhauri
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:In production tool-use agents (e.g., retrieval - summarization - calculator), routers must know when to stop exploring while preserving local DP and leaving an auditable trail. We present run-wise early-stopping certificates for perturb-and-MAP (PaM) best-first search on context-indexed prefix DAGs whose children partition the leaves. We couple realized path scores and pruning keys to a single exponential race realized lazily via offset propagation. With exact leaf counts N(v), lazy reuse at winners and independent residuals yield an Exact mode with a sound halting rule based on Key(v) = M_tau(v) - log t(v), where t(v) is the minimum arrival time among leaves under v. With only upper bounds N_ub = N, a Surrogate mode uses a parent-anchored surrogate race without winner reuse; because -log t_hat = -log t, the frontier invariant holds and stopping remains sound. We add a compiler from shared-node DAGs to prefix DAGs, local finiteness checks, a SuffixCountDP routine for exact counts with safe downgrades, a validator-side tightening term kappa = log(N/N_ub), and an auditable ledger/validator that replays runs deterministically. We also give an absolute LogSumExp tail bound, an acyclicity certificate, and a fallback PRF-per-leaf scheme (NoCert) whose work matches a realized-score best-first baseline up to a small per-node overhead. Finally, we integrate a price/latency/(epsilon, delta)-aware multi-LLM controller and DP-trained LoRA adapters chosen at runtime; these choices do not affect the two-mode frontier invariants. We report Mac/commodity-hardware reproducible results, a small real tool-use pipeline, and validator-checked audit trails, with code and ledgers provided.

[LG-78] Contextuality Holonomy and Discrete Fiber Bundles in Group-Valued Boltzmann Machines

链接: https://arxiv.org/abs/2509.10536
作者: Jean-Pierre Magnot
类目: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Quantum Physics (quant-ph)
*备注:

点击查看摘要

Abstract:We propose a geometric extension of restricted Boltzmann machines (RBMs) by allowing weights to take values in abstract groups such as ( \mathrmGL_n(\mathbbR) ), ( \mathrmSU(2) ), or even infinite-dimensional operator groups. This generalization enables the modeling of complex relational structures, including projective transformations, spinor dynamics, and functional symmetries, with direct applications to vision, language, and quantum learning. A central contribution of this work is the introduction of a \emphcontextuality index based on group-valued holonomies computed along cycles in the RBM graph. This index quantifies the global inconsistency or “curvature” induced by local weights, generalizing classical notions of coherence, consistency, and geometric flatness. We establish links with sheaf-theoretic contextuality, gauge theory, and noncommutative geometry, and provide numerical and diagrammatic examples in both finite and infinite dimensions. This framework opens novel directions in AI, from curvature-aware learning architectures to topological regularization in uncertain or adversarial environments. Subjects: Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an); Quantum Physics (quant-ph) MSC classes: 68T07 (primary), 22E70, 81P13, 57R22, 60B20, 68T05, 15B52 Cite as: arXiv:2509.10536 [cs.LG] (or arXiv:2509.10536v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2509.10536 Focus to learn more arXiv-issued DOI via DataCite

[LG-79] Variational Gaussian Mixture Manifold Models for Client-Specific Federated Personalization

链接: https://arxiv.org/abs/2509.10521
作者: Sai Puppala,Ismail Hossain,Md Jahangir Alam,Sajedul Talukder
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Personalized federated learning (PFL) often fails under label skew and non-stationarity because a single global parameterization ignores client-specific geometry. We introduce VGM ^2 (Variational Gaussian Mixture Manifold), a geometry-centric PFL framework that (i) learns client-specific parametric UMAP embeddings, (ii) models latent pairwise distances with mixture relation markers for same and different class pairs, and (iii) exchanges only variational, uncertainty-aware marker statistics. Each client maintains a Dirichlet-Normal-Inverse-Gamma (Dir-NIG) posterior over marker weights, means, and variances; the server aggregates via conjugate moment matching to form global priors that guide subsequent rounds. We prove that this aggregation minimizes the summed reverse Kullback-Leibler divergence from client posteriors within the conjugate family, yielding stability under heterogeneity. We further incorporate a calibration term for distance-to-similarity mapping and report communication and compute budgets. Across eight vision datasets with non-IID label shards, VGM ^2 achieves competitive or superior test F1 scores compared to strong baselines while communicating only small geometry summaries. Privacy is strengthened through secure aggregation and optional differential privacy noise, and we provide a membership-inference stress test. Code and configurations will be released to ensure full reproducibility.

[LG-80] Offline Contextual Bandit with Counterfactual Sample Identification RECSYS’25

链接: https://arxiv.org/abs/2509.10520
作者: Alexandre Gilotte,Otmane Sakhi,Imad Aouali,Benjamin Heymann
类目: Machine Learning (cs.LG)
*备注: Recsys '25, CONSEQUENCES: Causality, Counterfactuals Sequential Decision-Making Workshop

点击查看摘要

Abstract:In production systems, contextual bandit approaches often rely on direct reward models that take both action and context as input. However, these models can suffer from confounding, making it difficult to isolate the effect of the action from that of the context. We present \emphCounterfactual Sample Identification, a new approach that re-frames the problem: rather than predicting reward, it learns to recognize which action led to a successful (binary) outcome by comparing it to a counterfactual action sampled from the logging policy under the same context. The method is theoretically grounded and consistently outperforms direct models in both synthetic experiments and real-world deployments.

[LG-81] Gradient Estimation Methods of Approximate Multipliers for High-Accuracy Retraining of Deep Learning Models

链接: https://arxiv.org/abs/2509.10519
作者: Chang Meng,Wayne Burleson,Giovanni De Micheli
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Approximate multipliers (AppMults) are widely used in deep learning accelerators to reduce their area, delay, and power consumption. However, AppMults introduce arithmetic errors into deep learning models, necessitating a retraining process to recover accuracy. A key step in retraining is computing the gradient of the AppMult, i.e., the partial derivative of the approximate product with respect to each input operand. Existing approaches typically estimate this gradient using that of the accurate multiplier (AccMult), which can lead to suboptimal retraining results. To address this, we propose two methods to obtain more precise gradients of AppMults. The first, called LUT-2D, characterizes the AppMult gradient with 2-dimensional lookup tables (LUTs), providing fine-grained estimation and achieving the highest retraining accuracy. The second, called LUT-1D, is a compact and more efficient variant that stores gradient values in 1-dimensional LUTs, achieving comparable retraining accuracy with shorter runtime. Experimental results show that on CIFAR-10 with convolutional neural networks, our LUT-2D and LUT-1D methods improve retraining accuracy by 3.83% and 3.72% on average, respectively. On ImageNet with vision transformer models, our LUT-1D method improves retraining accuracy by 23.69% on average, compared to a state-of-the-art retraining framework.

[LG-82] Holographic Knowledge Manifolds: A Novel Pipeline for Continual Learning Without Catastrophic Forgetting in Large Language Models

链接: https://arxiv.org/abs/2509.10518
作者: Justin Arndt
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:We introduce the Holographic Knowledge Manifold (HKM), a four-phase pipeline that achieves zero catastrophic forgetting in AI knowledge representation while maintaining minimal memory growth and high efficiency. Leveraging fractal quantization, probabilistic entanglement, and dynamic diffraction chipping, HKM compresses knowledge substrates by 3x with 67% storage savings, integrates holographically at 100%, and supports over 1,020 updates with 1% growth per increment. In experiments on combined WikiText and FB15k datasets (scaled to 2,997 nodes), we demonstrate industry-leading performance: 0% forgetting (infinite improvement over GEM baselines), 3x compression, and 53% training time reduction on consumer GPU hardware. Hypothetical cost analyses project 92.4M savings over 5 years at petabyte scale, with 21.2% energy reduction and 33% lower carbon footprint. This work hypothesizes a paradigm shift for public large language models (LLMs), enabling “eternal” adaptation without retraining. Future extensions to multimodal fusion and quantum hardware could further democratize scalable AI, potentially reducing fine-tuning costs by 60-80% for models like Llama-3 or Grok-4. Code, datasets, and full results are publicly available for reproducibility.

[LG-83] Adaptive Preference Optimization with Uncertainty-aware Utility Anchor EMNLP2025

链接: https://arxiv.org/abs/2509.10515
作者: Xiaobo Wang,Zixia Jia,Jiaqi Li,Qi Liu,Zilong Zheng
类目: Machine Learning (cs.LG)
*备注: Accepted by EMNLP 2025 Findings

点击查看摘要

Abstract:Offline preference optimization methods are efficient for large language models (LLMs) alignment. Direct Preference optimization (DPO)-like learning, one of the most popular approaches, stands out for its efficiency in reward modeling. However, these methods typically follow the convention to use Bradley-Terry (BT) reward modeling that faces several critical assumptions, including the requirement for pairwise training data, model distribution shifting, human rationality assumption, etc. To address these limitations, we propose a general framework for offline preference optimization methods, Adaptive Preference Optimization with Utility Anchor (UAPO), which introduces an anchoring function to estimate the uncertainties brought from preference data annotation. Our method enables training even in scenarios where the data is unpaired, significantly enhancing data utilization efficiency. Moreover, the anchor design makes UAPO more robust in the training process. Experimental results demonstrate that UAPO achieves competitive outcomes without the strict dependency on data pairing, paving the way for more flexible and effective preference optimization methods.

[LG-84] A Differential Manifold Perspective and Universality Analysis of Continuous Attractors in Artificial Neural Networks

链接: https://arxiv.org/abs/2509.10514
作者: Shaoxin Tian,Hongkai Liu,Yuying Yang,Jiali Yu,Zizheng Miao,Xuming Huang,Zhishuai Liu,Zhang Yi
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Continuous attractors are critical for information processing in both biological and artificial neural systems, with implications for spatial navigation, memory, and deep learning optimization. However, existing research lacks a unified framework to analyze their properties across diverse dynamical systems, limiting cross-architectural generalizability. This study establishes a novel framework from the perspective of differential manifolds to investigate continuous attractors in artificial neural networks. It verifies compatibility with prior conclusions, elucidates links between continuous attractor phenomena and eigenvalues of the local Jacobian matrix, and demonstrates the universality of singular value stratification in common classification models and datasets. These findings suggest continuous attractors may be ubiquitous in general neural networks, highlighting the need for a general theory, with the proposed framework offering a promising foundation given the close mathematical connection between eigenvalues and singular values.

[LG-85] Mixture-of-Clustered-Experts: Advancing Expert Specialization and Generalization in Instruction Tuning

链接: https://arxiv.org/abs/2509.10513
作者: Sugyeong Eo,Jungjun Lee,Chanjun Park,Heuiseok Lim
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:A sparse Mixture-of-Experts (MoE) architecture has emerged as a highly scalable solution by conditionally activating sub-modules without a proportional increase in computational costs. However, improving expert specialization to enhance performance and generalization remains a challenge for MoE, especially in instruction tuning scenarios characterized by significant input heterogeneity. In this work, we propose the Mixture-of-Clustered-Experts (MoCE) to address this limitation through a dual-stage routing mechanism. The first stage in the mechanism performs expert group routing based on sequence-level features, while the second stage activates the top- k experts within the group at the token level. This approach enables the effective partitioning of heterogeneous inputs based on their knowledge requirements, encouraging expert group specialization while maintaining the advantages of token-level routing. We evaluate MoCE across a comprehensive set of benchmarks, demonstrating its consistent superiority over strong baselines and its enhanced generalization capabilities. Detailed analysis further highlights the robustness and effectiveness of MoCE.

[LG-86] A Service-Oriented Adaptive Hierarchical Incentive Mechanism for Federated Learning

链接: https://arxiv.org/abs/2509.10512
作者: Jiaxing Cao,Yuzhou Gao,Jiwei Huang
类目: Machine Learning (cs.LG); Computer Science and Game Theory (cs.GT); Systems and Control (eess.SY)
*备注: Accepted at CollaborateCom 2025

点击查看摘要

Abstract:Recently, federated learning (FL) has emerged as a novel framework for distributed model training. In FL, the task publisher (TP) releases tasks, and local model owners (LMOs) use their local data to train models. Sometimes, FL suffers from the lack of training data, and thus workers are recruited for gathering data. To this end, this paper proposes an adaptive incentive mechanism from a service-oriented perspective, with the objective of maximizing the utilities of TP, LMOs and workers. Specifically, a Stackelberg game is theoretically established between the LMOs and TP, positioning TP as the leader and the LMOs as followers. An analytical Nash equilibrium solution is derived to maximize their utilities. The interaction between LMOs and workers is formulated by a multi-agent Markov decision process (MAMDP), with the optimal strategy identified via deep reinforcement learning (DRL). Additionally, an Adaptively Searching the Optimal Strategy Algorithm (ASOSA) is designed to stabilize the strategies of each participant and solve the coupling problems. Extensive numerical experiments are conducted to validate the efficacy of the proposed method.

[LG-87] AttnBoost: Retail Supply Chain Sales Insights via Gradient Boosting Perspective

链接: https://arxiv.org/abs/2509.10506
作者: Muxin Ge,Hanyu Ma,Yiyang Wu,Xiaoli Ma,Yadi Liu,Ye Aung Moe,Weizheng Xie
类目: Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)
*备注:

点击查看摘要

Abstract:Forecasting product demand in retail supply chains presents a complex challenge due to noisy, heterogeneous features and rapidly shifting consumer behavior. While traditional gradient boosting decision trees (GBDT) offer strong predictive performance on structured data, they often lack adaptive mechanisms to identify and emphasize the most relevant features under changing conditions. In this work, we propose AttnBoost, an interpretable learning framework that integrates feature-level attention into the boosting process to enhance both predictive accuracy and explainability. Specifically, the model dynamically adjusts feature importance during each boosting round via a lightweight attention mechanism, allowing it to focus on high-impact variables such as promotions, pricing, and seasonal trends. We evaluate AttnBoost on a large-scale retail sales dataset and demonstrate that it outperforms standard machine learning and deep tabular models, while also providing actionable insights for supply chain managers. An ablation study confirms the utility of the attention module in mitigating overfitting and improving interpretability. Our results suggest that attention-guided boosting represents a promising direction for interpretable and scalable AI in real-world forecasting applications.

[LG-88] Exploring Multi-view Symbolic Regression methods in physical sciences

链接: https://arxiv.org/abs/2509.10500
作者: Etienne Russeil,Fabrício Olivetti de França,Konstantin Malanchev,Guillaume Moinard,Maxime Cherrey
类目: Machine Learning (cs.LG); Astrophysics of Galaxies (astro-ph.GA); Instrumentation and Methods for Astrophysics (astro-ph.IM); Data Analysis, Statistics and Probability (physics.data-an)
*备注: 15 pages, 7 figures. Presented at the “Symbolic regression in the physical sciences” conference at the Royal Society. Submitted to Philosophical Transactions A

点击查看摘要

Abstract:Describing the world behavior through mathematical functions help scientists to achieve a better understanding of the inner mechanisms of different phenomena. Traditionally, this is done by deriving new equations from first principles and careful observations. A modern alternative is to automate part of this process with symbolic regression (SR). The SR algorithms search for a function that adequately fits the observed data while trying to enforce sparsity, in the hopes of generating an interpretable equation. A particularly interesting extension to these algorithms is the Multi-view Symbolic Regression (MvSR). It searches for a parametric function capable of describing multiple datasets generated by the same phenomena, which helps to mitigate the common problems of overfitting and data scarcity. Recently, multiple implementations added support to MvSR with small differences between them. In this paper, we test and compare MvSR as supported in Operon, PySR, phy-SO, and eggp, in different real-world datasets. We show that they all often achieve good accuracy while proposing solutions with only few free parameters. However, we find that certain features enable a more frequent generation of better models. We conclude by providing guidelines for future MvSR developments.

[LG-89] SOH-KLSTM: A Hybrid Kolmogorov-Arnold Network and LSTM Model for Enhanced Lithium-Ion Battery Health Monitoring

链接: https://arxiv.org/abs/2509.10496
作者: Imen Jarraya,Safa Ben Atitallah,Fatimah Alahmeda,Mohamed Abdelkadera,Maha Drissa,Fatma Abdelhadic,Anis Koubaaa
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Accurate and reliable State Of Health (SOH) estimation for Lithium (Li) batteries is critical to ensure the longevity, safety, and optimal performance of applications like electric vehicles, unmanned aerial vehicles, consumer electronics, and renewable energy storage systems. Conventional SOH estimation techniques fail to represent the non-linear and temporal aspects of battery degradation effectively. In this study, we propose a novel SOH prediction framework (SOH-KLSTM) using Kolmogorov-Arnold Network (KAN)-Integrated Candidate Cell State in LSTM for Li batteries Health Monitoring. This hybrid approach combines the ability of LSTM to learn long-term dependencies for accurate time series predictions with KAN’s non-linear approximation capabilities to effectively capture complex degradation behaviors in Lithium batteries.

[LG-90] Moment Estimates and DeepRitz Methods on Learning Diffusion Systems with Non-gradient Drifts

链接: https://arxiv.org/abs/2509.10495
作者: Fanze Kong,Chen-Chih Lai,Yubin Lu
类目: Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:

点击查看摘要

Abstract:Conservative-dissipative dynamics are ubiquitous across a variety of complex open systems. We propose a data-driven two-phase method, the Moment-DeepRitz Method, for learning drift decompositions in generalized diffusion systems involving conservative-dissipative dynamics. The method is robust to noisy data, adaptable to rough potentials and oscillatory rotations. We demonstrate its effectiveness through several numerical experiments.

[LG-91] he LLM as a Network Operator: A Vision for Generative AI in the 6G Radio Access Network NEURIPS2025

链接: https://arxiv.org/abs/2509.10478
作者: Oluwaseyi Giwa,Michael Adewole,Tobi Awodumila,Pelumi Aderinto
类目: Networking and Internet Architecture (cs.NI); Machine Learning (cs.LG); Systems and Control (eess.SY)
*备注: Submitted to Workshop on AI and ML for Next-Generation Wireless Communications and Networking, NeurIPS 2025

点击查看摘要

Abstract:The management of future AI-native Next-Generation (NextG) Radio Access Networks (RANs), including 6G and beyond, presents a challenge of immense complexity that exceeds the capabilities of traditional automation. In response, we introduce the concept of the LLM-RAN Operator. In this paradigm, a Large Language Model (LLM) is embedded into the RAN control loop to translate high-level human intents into optimal network actions. Unlike prior empirical studies, we present a formal framework for an LLM-RAN operator that builds on earlier work by making guarantees checkable through an adapter aligned with the Open RAN (O-RAN) standard, separating strategic LLM-driven guidance in the Non-Real-Time (RT) RAN intelligent controller (RIC) from reactive execution in the Near-RT RIC, including a proposition on policy expressiveness and a theorem on convergence to stable fixed points. By framing the problem with mathematical rigor, our work provides the analytical tools to reason about the feasibility and stability of AI-native RAN control. It identifies critical research challenges in safety, real-time performance, and physical-world grounding. This paper aims to bridge the gap between AI theory and wireless systems engineering in the NextG era, aligning with the AI4NextG vision to develop knowledgeable, intent-driven wireless networks that integrate generative AI into the heart of the RAN.

[LG-92] Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning

链接: https://arxiv.org/abs/2509.10132
作者: Nour Jamoussi,Giuseppe Serra,Photios A. Stavrou,Marios Kountouris
类目: Machine Learning (cs.LG); Information Theory (cs.IT); Networking and Internet Architecture (cs.NI)
*备注:

点击查看摘要

Abstract:Bayesian Federated Learning (BFL) combines uncertainty modeling with decentralized training, enabling the development of personalized and reliable models under data heterogeneity and privacy constraints. Existing approaches typically rely on Markov Chain Monte Carlo (MCMC) sampling or variational inference, often incorporating personalization mechanisms to better adapt to local data distributions. In this work, we propose an information-geometric projection framework for personalization in parametric BFL. By projecting the global model onto a neighborhood of the user’s local model, our method enables a tunable trade-off between global generalization and local specialization. Under mild assumptions, we show that this projection step is equivalent to computing a barycenter on the statistical manifold, allowing us to derive closed-form solutions and achieve cost-free personalization. We apply the proposed approach to a variational learning setup using the Improved Variational Online Newton (IVON) optimizer and extend its application to general aggregation schemes in BFL. Empirical evaluations under heterogeneous data distributions confirm that our method effectively balances global and local performance with minimal computational overhead.

[LG-93] Agent ic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks

链接: https://arxiv.org/abs/2509.06775
作者: Po-Heng Chou,Pin-Qi Fu,Walid Saad,Li-Chun Wang
类目: ystems and Control (eess.SY); Information Theory (cs.IT); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注: 6 pages, 3 figures, accepted by 2025 IEEE Globecom Workshops

点击查看摘要

Abstract:This paper presents an agentic artificial intelligence (AI)-driven double deep Q-network (DDQN) scheduling framework for licensed and unlicensed band allocation in New Radio (NR) sidelink (SL) networks. SL must share licensed spectrum with cellular communications (CC) and unlicensed bands with Wi-Fi, posing significant challenges for coexistence. Unlike prior rule-based or threshold-based methods, the proposed agentic scheduler autonomously perceives queueing dynamics, channel conditions, and coexistence states, and adapts its policy to maintain quality-of-service (QoS). Simulation results show that our framework reduces the blocking rate by up to 87.5% compared to threshold-based scheduling under limited licensed bandwidth. These findings demonstrate the potential of Agentic AI to enable stable, QoS-aware, and adaptive scheduling for future NR SL systems.

[LG-94] Information Entropy-Based Scheduling for Communication-Efficient Decentralized Learning

链接: https://arxiv.org/abs/2507.17426
作者: Jaiprakash Nagar,Zheng Chen,Marios Kountouris,Photios A. Stavrou
类目: Information Theory (cs.IT); Machine Learning (cs.LG); Networking and Internet Architecture (cs.NI)
*备注:

点击查看摘要

Abstract:This paper addresses decentralized stochastic gradient descent (D-SGD) over resource-constrained networks by introducing node-based and link-based scheduling strategies to enhance communication efficiency. In each iteration of the D-SGD algorithm, only a few disjoint subsets of nodes or links are randomly activated, subject to a given communication cost constraint. We propose a novel importance metric based on information entropy to determine node and link scheduling probabilities. We validate the effectiveness of our approach through extensive simulations, comparing it against state-of-the-art methods, including betweenness centrality (BC) for node scheduling and \textitMATCHA for link scheduling. The results show that our method consistently outperforms the BC-based method in the node scheduling case, achieving faster convergence with up to 60% lower communication budgets. At higher communication budgets (above 60%), our method maintains comparable or superior performance. In the link scheduling case, our method delivers results that are superior to or on par with those of \textitMATCHA.

[LG-95] he Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection

链接: https://arxiv.org/abs/2509.12185
作者: Argimiro Arratia,Alejandra Cabaña,Ernesto Mordecki,Gerard Rovira-Parra
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 29 pages, 4 figures

点击查看摘要

Abstract:Model selection in non-linear models often prioritizes performance metrics over statistical tests, limiting the ability to account for sampling variability. We propose the use of a statistical test to assess the equality of variances in forecasting errors. The test builds upon the classic Morgan-Pitman approach, incorporating enhancements to ensure robustness against data with heavy-tailed distributions or outliers with high variance, plus a strategy to make residuals from machine learning models statistically independent. Through a series of simulations and real-world data applications, we demonstrate the test’s effectiveness and practical utility, offering a reliable tool for model evaluation and selection in diverse contexts.

[LG-96] MMM: Clustering Multivariate Longitudinal Mixed-type Data

链接: https://arxiv.org/abs/2509.12166
作者: Francesco Amato,Julien Jacques
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:

点击查看摘要

Abstract:Multivariate longitudinal data of mixed-type are increasingly collected in many science domains. However, algorithms to cluster this kind of data remain scarce, due to the challenge to simultaneously model the within- and between-time dependence structures for multivariate data of mixed kind. We introduce the Mixture of Mixed-Matrices (MMM) model: reorganizing the data in a three-way structure and assuming that the non-continuous variables are observations of underlying latent continuous variables, the model relies on a mixture of matrix-variate normal distributions to perform clustering in the latent dimension. The MMM model is thus able to handle continuous, ordinal, binary, nominal and count data and to concurrently model the heterogeneity, the association among the responses and the temporal dependence structure in a parsimonious way and without assuming conditional independence. The inference is carried out through an MCMC-EM algorithm, which is detailed. An evaluation of the model through synthetic data shows its inference abilities. A real-world application on financial data is presented.

[LG-97] Identifiable Autoregressive Variational Autoencoders for Nonlinear and Nonstationary Spatio-Temporal Blind Source Separation

链接: https://arxiv.org/abs/2509.11962
作者: Mika Sipilä,Klaus Nordhausen,Sara Taskinen
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:

点击查看摘要

Abstract:The modeling and prediction of multivariate spatio-temporal data involve numerous challenges. Dimension reduction methods can significantly simplify this process, provided that they account for the complex dependencies between variables and across time and space. Nonlinear blind source separation has emerged as a promising approach, particularly following recent advances in identifiability results. Building on these developments, we introduce the identifiable autoregressive variational autoencoder, which ensures the identifiability of latent components consisting of nonstationary autoregressive processes. The blind source separation efficacy of the proposed method is showcased through a simulation study, where it is compared against state-of-the-art methods, and the spatio-temporal prediction performance is evaluated against several competitors on air pollution and weather datasets.

[LG-98] Quantum Noise Tomography with Physics-Informed Neural Networks NEURIPS

链接: https://arxiv.org/abs/2509.11911
作者: Antonin Sulc
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注: 6 pages, 3 figures, Machine Learning and the Physical Sciences Workshop at the 39th conference on Neural Information Processing Systems (NeurIPS)

点击查看摘要

Abstract:Characterizing the environmental interactions of quantum systems is a critical bottleneck in the development of robust quantum technologies. Traditional tomographic methods are often data-intensive and struggle with scalability. In this work, we introduce a novel framework for performing Lindblad tomography using Physics-Informed Neural Networks (PINNs). By embedding the Lindblad master equation directly into the neural network’s loss function, our approach simultaneously learns the quantum state’s evolution and infers the underlying dissipation parameters from sparse, time-series measurement data. Our results show that PINNs can reconstruct both the system dynamics and the functional form of unknown noise parameters, presenting a sample-efficient and scalable solution for quantum device characterization. Ultimately, our method produces a fully-differentiable digital twin of a noisy quantum system by learning its governing master equation.

[LG-99] Wavelet-SARIMA-Transformer: A Hybrid Model for Rainfall Forecasting

链接: https://arxiv.org/abs/2509.11903
作者: Junmoni Saikia,Kuldeep Goswami,Sarat C. Kakaty
类目: Applications (stat.AP); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注:

点击查看摘要

Abstract:This study develops and evaluates a novel hybridWavelet SARIMA Transformer, WST framework to forecast using monthly rainfall across five meteorological subdivisions of Northeast India over the 1971 to 2023 period. The approach employs the Maximal Overlap Discrete Wavelet Transform, MODWT with four wavelet families such as, Haar, Daubechies, Symlet, Coiflet etc. to achieve shift invariant, multiresolution decomposition of the rainfall series. Linear and seasonal components are modeled using Seasonal ARIMA, SARIMA, while nonlinear components are modeled by a Transformer network, and forecasts are reconstructed via inverse MODWT. Comprehensive validation using an 80 is to 20 train test split and multiple performance indices such as, RMSE, MAE, SMAPE, Willmotts d, Skill Score, Percent Bias, Explained Variance, and Legates McCabes E1 demonstrates the superiority of the Haar-based hybrid model, WHST. Across all subdivisions, WHST consistently achieved lower forecast errors, stronger agreement with observed rainfall, and unbiased predictions compared with stand alone SARIMA, stand-alone Transformer, and two-stage wavelet hybrids. Residual adequacy was confirmed through the Ljung Box test, while Taylor diagrams provided an inte- grated assessment of correlation, variance fidelity, and RMSE, further reinforcing the robustness of the proposed approach. The results highlight the effectiveness of integrating multiresolution signal decomposition with complementary linear and deep learning models for hydroclimatic forecasting. Beyond rainfall, the proposed WST framework offers a scalable methodology for forecasting complex environmental time series, with direct implications for flood risk management, water resources planning, and climate adaptation strategies in data-sparse and climate-sensitive regions.

[LG-100] ProteuS: A Generative Approach for Simulating Concept Drift in Financial Markets

链接: https://arxiv.org/abs/2509.11844
作者: Andrés L. Suárez-Cetrulo,Alejandro Cervantes,David Quintana
类目: atistical Finance (q-fin.ST); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Financial markets are complex, non-stationary systems where the underlying data distributions can shift over time, a phenomenon known as regime changes, as well as concept drift in the machine learning literature. These shifts, often triggered by major economic events, pose a significant challenge for traditional statistical and machine learning models. A fundamental problem in developing and validating adaptive algorithms is the lack of a ground truth in real-world financial data, making it difficult to evaluate a model’s ability to detect and recover from these drifts. This paper addresses this challenge by introducing a novel framework, named ProteuS, for generating semi-synthetic financial time series with pre-defined structural breaks. Our methodology involves fitting ARMA-GARCH models to real-world ETF data to capture distinct market regimes, and then simulating realistic, gradual, and abrupt transitions between them. The resulting datasets, which include a comprehensive set of technical indicators, provide a controlled environment with a known ground truth of regime changes. An analysis of the generated data confirms the complexity of the task, revealing significant overlap between the different market states. We aim to provide the research community with a tool for the rigorous evaluation of concept drift detection and adaptation mechanisms, paving the way for more robust financial forecasting models.

[LG-101] EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images

链接: https://arxiv.org/abs/2509.11714
作者: Hafza Eman,Furqan Shaukat,Muhammad Hamza Zafar,Syed Muhammad Anwar
类目: Image and Video Processing (eess.IV); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Objective: Lung cancer is a leading cause of cancer-related mortality worldwide, primarily due to delayed diagnosis and poor early detection. This study aims to develop a computer-aided diagnosis (CAD) system that leverages large vision-language models (VLMs) for the accurate detection and classification of pulmonary nodules in computed tomography (CT) scans. Methods: We propose an end-to-end CAD pipeline consisting of two modules: (i) a detection module (CADe) based on the Segment Anything Model 2 (SAM2), in which the standard visual prompt is replaced with a text prompt encoded by CLIP (Contrastive Language-Image Pretraining), and (ii) a diagnosis module (CADx) that calculates similarity scores between segmented nodules and radiomic features. To add clinical context, synthetic electronic medical records (EMRs) were generated using radiomic assessments by expert radiologists and combined with similarity scores for final classification. The method was tested on the publicly available LIDC-IDRI dataset (1,018 CT scans). Results: The proposed approach demonstrated strong performance in zero-shot lung nodule analysis. The CADe module achieved a Dice score of 0.92 and an IoU of 0.85 for nodule segmentation. The CADx module attained a specificity of 0.97 for malignancy classification, surpassing existing fully supervised methods. Conclusions: The integration of VLMs with radiomics and synthetic EMRs allows for accurate and clinically relevant CAD of pulmonary nodules in CT scans. The proposed system shows strong potential to enhance early lung cancer detection, increase diagnostic confidence, and improve patient management in routine clinical workflows. Subjects: Image and Video Processing (eess.IV); Machine Learning (cs.LG) Cite as: arXiv:2509.11714 [eess.IV] (or arXiv:2509.11714v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2509.11714 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Furqan Shaukat [view email] [v1] Mon, 15 Sep 2025 09:11:17 UTC (1,907 KB)

[LG-102] SpaPool: Soft Partition Assignment Pooling for__Graph Neural Networks

链接: https://arxiv.org/abs/2509.11675
作者: Rodrigue Govan(ISEA),Romane Scherrer(ISEA),Philippe Fournier-Viger,Nazha Selmaoui-Folcher(ISEA)
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:This paper introduces SpaPool, a novel pooling method that combines the strengths of both dense and sparse techniques for a graph neural network. SpaPool groups vertices into an adaptive number of clusters, leveraging the benefits of both dense and sparse approaches. It aims to maintain the structural integrity of the graph while reducing its size efficiently. Experimental results on several datasets demonstrate that SpaPool achieves competitive performance compared to existing pooling techniques and excels particularly on small-scale graphs. This makes SpaPool a promising method for applications requiring efficient and effective graph processing.

[LG-103] E-ROBOT: a dimension-free method for robust statistics and machine learning via Schrödinger bridge

链接: https://arxiv.org/abs/2509.11532
作者: Davide La Vecchia,Hang Liu
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:We propose the Entropic-regularized Robust Optimal Transport (E-ROBOT) framework, a novel method that combines the robustness of ROBOT with the computational and statistical benefits of entropic regularization. We show that, rooted in the Schrödinger bridge problem theory, E-ROBOT defines the robust Sinkhorn divergence \overlineW_\varepsilon,\lambda , where the parameter \lambda controls robustness and \varepsilon governs the regularization strength. Letting n\in \mathbbN denote the sample size, a central theoretical contribution is establishing that the sample complexity of \overlineW_\varepsilon,\lambda is \mathcalO(n^-1/2) , thereby avoiding the curse of dimensionality that plagues standard ROBOT. This dimension-free property unlocks the use of \overlineW_\varepsilon,\lambda as a loss function in large-dimensional statistical and machine learning tasks. With this regard, we demonstrate its utility through four applications: goodness-of-fit testing; computation of barycenters for corrupted 2D and 3D shapes; definition of gradient flows; and image colour transfer. From the computation standpoint, a perk of our novel method is that it can be easily implemented by modifying existing (\textttPython) routines. From the theoretical standpoint, our work opens the door to many research directions in statistics and machine learning: we discuss some of them.

[LG-104] Learning Majority-to-Minority Transformations with MMD and Triplet Loss for Imbalanced Classification

链接: https://arxiv.org/abs/2509.11511
作者: Suman Cha,Hyunjoong Kim
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: .19 pages, 6 figures

点击查看摘要

Abstract:Class imbalance in supervised classification often degrades model performance by biasing predictions toward the majority class, particularly in critical applications such as medical diagnosis and fraud detection. Traditional oversampling techniques, including SMOTE and its variants, generate synthetic minority samples via local interpolation but fail to capture global data distributions in high-dimensional spaces. Deep generative models based on GANs offer richer distribution modeling yet suffer from training instability and mode collapse under severe imbalance. To overcome these limitations, we introduce an oversampling framework that learns a parametric transformation to map majority samples into the minority distribution. Our approach minimizes the maximum mean discrepancy (MMD) between transformed and true minority samples for global alignment, and incorporates a triplet loss regularizer to enforce boundary awareness by guiding synthesized samples toward challenging borderline regions. We evaluate our method on 29 synthetic and real-world datasets, demonstrating consistent improvements over classical and generative baselines in AUROC, G-mean, F1-score, and MCC. These results confirm the robustness, computational efficiency, and practical utility of the proposed framework for imbalanced classification tasks.

[LG-105] Preconditioned subgradient method for composite optimization: overparameterization and fast convergence

链接: https://arxiv.org/abs/2509.11486
作者: Mateo Díaz,Liwei Jiang,Abdel Ghani Labassi
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 84 pages, 8 figures

点击查看摘要

Abstract:Composite optimization problems involve minimizing the composition of a smooth map with a convex function. Such objectives arise in numerous data science and signal processing applications, including phase retrieval, blind deconvolution, and collaborative filtering. The subgradient method achieves local linear convergence when the composite loss is well-conditioned. However, if the smooth map is, in a certain sense, ill-conditioned or overparameterized, the subgradient method exhibits much slower sublinear convergence even when the convex function is well-conditioned. To overcome this limitation, we introduce a Levenberg-Morrison-Marquardt subgradient method that converges linearly under mild regularity conditions at a rate determined solely by the convex function. Further, we demonstrate that these regularity conditions hold for several problems of practical interest, including square-variable formulations, matrix sensing, and tensor factorization. Numerical experiments illustrate the benefits of our method.

[LG-106] A Particle-Flow Algorithm for Free-Support Wasserstein Barycenters

链接: https://arxiv.org/abs/2509.11435
作者: Kisung You
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Computation (stat.CO)
*备注:

点击查看摘要

Abstract:The Wasserstein barycenter extends the Euclidean mean to the space of probability measures by minimizing the weighted sum of squared 2-Wasserstein distances. We develop a free-support algorithm for computing Wasserstein barycenters that avoids entropic regularization and instead follows the formal Riemannian geometry of Wasserstein space. In our approach, barycenter atoms evolve as particles advected by averaged optimal-transport displacements, with barycentric projections of optimal transport plans used in place of Monge maps when the latter do not exist. This yields a geometry-aware particle-flow update that preserves sharp features of the Wasserstein barycenter while remaining computationally tractable. We establish theoretical guarantees, including consistency of barycentric projections, monotone descent and convergence to stationary points, stability with respect to perturbations of the inputs, and resolution consistency as the number of atoms increases. Empirical studies on averaging probability distributions, Bayesian posterior aggregation, image prototypes and classification, and large-scale clustering demonstrate accuracy and scalability of the proposed particle-flow approach, positioning it as a principled alternative to both linear programming and regularized solvers.

[LG-107] Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning

链接: https://arxiv.org/abs/2509.11390
作者: Arthur M. Faria,Mehdi Djellabi,Igor O. Sokolov,Savvas Varsamopoulos
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:We introduce Quantum Graph Attention Networks (QGATs) as trainable quantum encoders for inductive learning on graphs, extending the Quantum Graph Neural Networks (QGNN) framework. QGATs leverage parameterized quantum circuits to encode node features and neighborhood structures, with quantum attention mechanisms modulating the contribution of each neighbor via dynamically learned unitaries. This allows for expressive, locality-aware quantum representations that can generalize across unseen graph instances. We evaluate our approach on the QM9 dataset, targeting the prediction of various chemical properties. Our experiments compare classical and quantum graph neural networks-with and without attention layers-demonstrating that attention consistently improves performance in both paradigms. Notably, we observe that quantum attention yields increasing benefits as graph size grows, with QGATs significantly outperforming their non-attentive quantum counterparts on larger molecular graphs. Furthermore, for smaller graphs, QGATs achieve predictive accuracy comparable to classical GAT models, highlighting their viability as expressive quantum encoders. These results show the potential of quantum attention mechanisms to enhance the inductive capacity of QGNN in chemistry and beyond.

[LG-108] Some Robustness Properties of Label Cleaning

链接: https://arxiv.org/abs/2509.11379
作者: Chen Cheng,John Duchi
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注: 39 pages

点击查看摘要

Abstract:We demonstrate that learning procedures that rely on aggregated labels, e.g., label information distilled from noisy responses, enjoy robustness properties impossible without data cleaning. This robustness appears in several ways. In the context of risk consistency – when one takes the standard approach in machine learning of minimizing a surrogate (typically convex) loss in place of a desired task loss (such as the zero-one mis-classification error) – procedures using label aggregation obtain stronger consistency guarantees than those even possible using raw labels. And while classical statistical scenarios of fitting perfectly-specified models suggest that incorporating all possible information – modeling uncertainty in labels – is statistically efficient, consistency fails for ``standard’’ approaches as soon as a loss to be minimized is even slightly mis-specified. Yet procedures leveraging aggregated information still converge to optimal classifiers, highlighting how incorporating a fuller view of the data analysis pipeline, from collection to model-fitting to prediction time, can yield a more robust methodology by refining noisy signals.

[LG-109] Next-Generation Reservoir Computing for Dynamical Inference

链接: https://arxiv.org/abs/2509.11338
作者: Rok Cestnik,Erik A. Martens
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 10 pages, 10 figures

点击查看摘要

Abstract:We present a simple and scalable implementation of next-generation reservoir computing for modeling dynamical systems from time series data. Our approach uses a pseudorandom nonlinear projection of time-delay embedded input, allowing an arbitrary dimension of the feature space, thus providing a flexible alternative to the polynomial-based projections used in previous next-generation reservoir computing variants. We apply the method to benchmark tasks – including attractor reconstruction and bifurcation diagram estimation – using only partial and noisy observations. We also include an exploratory example of estimating asymptotic oscillation phases. The models remain stable over long rollouts and generalize beyond training data. This framework enables the precise control of system state and is well suited for surrogate modeling and digital twin applications.

[LG-110] Contrastive Network Representation Learning

链接: https://arxiv.org/abs/2509.11316
作者: Zihan Dong,Xin Zhou,Ryumei Nakada,Lexin Li,Linjun Zhang
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)
*备注:

点击查看摘要

Abstract:Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks such as classification, trait prediction, edge identification, and community detection. Motivated by challenges in brain connectivity data analysis that is characterized by subject-specific, high-dimensional, and sparse networks that lack node or edge covariates, we propose a novel contrastive learning-based statistical approach for network edge embedding, which we name as Adaptive Contrastive Edge Representation Learning (ACERL). It builds on two key components: contrastive learning of augmented network pairs, and a data-driven adaptive random masking mechanism. We establish the non-asymptotic error bounds, and show that our method achieves the minimax optimal convergence rate for edge representation learning. We further demonstrate the applicability of the learned representation in multiple downstream tasks, including network classification, important edge detection, and community detection, and establish the corresponding theoretical guarantees. We validate our method through both synthetic data and real brain connectivities studies, and show its competitive performance compared to the baseline method of sparse principal components analysis.

[LG-111] From PowerSGD to PowerSGD: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees

链接: https://arxiv.org/abs/2509.11254
作者: Shengping Xie,Chuyan Chen,Kun Yuan
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Low-rank gradient compression methods, such as PowerSGD, have gained attention in communication-efficient distributed optimization. However, the convergence guarantees of PowerSGD remain unclear, particularly in stochastic settings. In this paper, we show that PowerSGD does not always converge to the optimal solution and provide a clear counterexample to support this finding. To address this, we introduce PowerSGD+, which periodically updates the projection subspace via singular value decomposition, ensuring that it remains aligned with the optimal subspace. We prove that PowerSGD+ converges under standard assumptions and validate its effectiveness through empirical evaluation on large language model tasks.

[LG-112] Predictable Compression Failures: Why Language Models Actually Hallucinate

链接: https://arxiv.org/abs/2509.11208
作者: Leon Chlon,Ahmed Karim,Maggie Chlon
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Large language models perform near-Bayesian inference yet violate permutation invariance on exchangeable data. We resolve this by showing transformers minimize expected conditional description length (cross-entropy) over orderings, \mathbbE_\pi[\ell(Y \mid \Gamma_\pi(X))] , which admits a Kolmogorov-complexity interpretation up to additive constants, rather than the permutation-invariant description length \ell(Y \mid X) . This makes them Bayesian in expectation, not in realization. We derive (i) a Quantified Martingale Violation bound showing order-induced deviations scale as O(\log n) with constants; (ii) the Expectation-level Decompression Law linking information budgets to reliability for Bernoulli predicates; and (iii) deployable planners (B2T/RoH/ISR) for answer/abstain decisions. Empirically, permutation dispersion follows a+b\ln n (Qwen2-7B b \approx 0.377 , Llama-3.1-8B b \approx 0.147 ); permutation mixtures improve ground-truth likelihood/accuracy; and randomized dose-response shows hallucinations drop by \sim 0.13 per additional nat. A pre-specified audit with a fixed ISR=1.0 achieves near-0% hallucinations via calibrated refusal at 24% abstention. The framework turns hallucinations into predictable compression failures and enables principled information budgeting.

[LG-113] Maximum diversity weighting and invariants of time series

链接: https://arxiv.org/abs/2509.11146
作者: Byungchang So
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Signal Processing (eess.SP); Metric Geometry (math.MG)
*备注:

点击查看摘要

Abstract:Magnitude, obtained as a special case of Euler characteristic of enriched category, represents a sense of the size of metric spaces and is related to classical notions such as cardinality, dimension, and volume. While the studies have explained the meaning of magnitude from various perspectives, continuity also gives a valuable view of magnitude. Based on established results about continuity of magnitude and maximum diversity, this article focuses on continuity of weighting, a distribution whose totality is magnitude, and its variation corresponding to maximum diversity. Meanwhile, recent studies also illuminated the connection between magnitude and data analysis by applying magnitude theory to point clouds representing the data or the set of model parameters. This article will also provide an application for time series analysis by introducing a new kind of invariants of periodic time series, where the invariance follows directly from the continuity results. As a use-case, a simple machine learning experiment is conducted with real-world data, in which the suggested invariants improved the performance.

[LG-114] What is in a Price? Estimating Willingness-to-Pay with Bayesian Hierarchical Models

链接: https://arxiv.org/abs/2509.11089
作者: Srijesh Pillai,Rajesh Kumar Chandrawat
类目: Applications (stat.AP); Machine Learning (cs.LG); Econometrics (econ.EM); Machine Learning (stat.ML)
*备注: 7 pages, 6 figures, 1 table. Accepted for publication in the proceedings of the 2025 Advances in Science and Engineering Technology International Conferences (ASET)

点击查看摘要

Abstract:For premium consumer products, pricing strategy is not about a single number, but about understanding the perceived monetary value of the features that justify a higher cost. This paper proposes a robust methodology to deconstruct a product’s price into the tangible value of its constituent parts. We employ Bayesian Hierarchical Conjoint Analysis, a sophisticated statistical technique, to solve this high-stakes business problem using the Apple iPhone as a universally recognizable case study. We first simulate a realistic choice based conjoint survey where consumers choose between different hypothetical iPhone configurations. We then develop a Bayesian Hierarchical Logit Model to infer consumer preferences from this choice data. The core innovation of our model is its ability to directly estimate the Willingness-to-Pay (WTP) in dollars for specific feature upgrades, such as a “Pro” camera system or increased storage. Our results demonstrate that the model successfully recovers the true, underlying feature valuations from noisy data, providing not just a point estimate but a full posterior probability distribution for the dollar value of each feature. This work provides a powerful, practical framework for data-driven product design and pricing strategy, enabling businesses to make more intelligent decisions about which features to build and how to price them.

[LG-115] Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning

链接: https://arxiv.org/abs/2509.11070
作者: Jia-Qi Yang,Lei Shi
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Functional Analysis (math.FA); Numerical Analysis (math.NA); Statistics Theory (math.ST)
*备注: 34 pages, 3 figures

点击查看摘要

Abstract:We develop a stochastic approximation framework for learning nonlinear operators between infinite-dimensional spaces utilizing general Mercer operator-valued kernels. Our framework encompasses two key classes: (i) compact kernels, which admit discrete spectral decompositions, and (ii) diagonal kernels of the form K(x,x’)=k(x,x’)T , where k is a scalar-valued kernel and T is a positive operator on the output space. This broad setting induces expressive vector-valued reproducing kernel Hilbert spaces (RKHSs) that generalize the classical K=kI paradigm, thereby enabling rich structural modeling with rigorous theoretical guarantees. To address target operators lying outside the RKHS, we introduce vector-valued interpolation spaces to precisely quantify misspecification error. Within this framework, we establish dimension-free polynomial convergence rates, demonstrating that nonlinear operator learning can overcome the curse of dimensionality. The use of general operator-valued kernels further allows us to derive rates for intrinsically nonlinear operator learning, going beyond the linear-type behavior inherent in diagonal constructions of K=kI . Importantly, this framework accommodates a wide range of operator learning tasks, ranging from integral operators such as Fredholm operators to architectures based on encoder-decoder representations. Moreover, we validate its effectiveness through numerical experiments on the two-dimensional Navier-Stokes equations.

[LG-116] Convergence Rate in Nonlinear Two-Time-Scale Stochastic Approximation with State (Time)-Dependence

链接: https://arxiv.org/abs/2509.11039
作者: Zixi Chen,Yumin Xu,Ruixun Zhang
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注: 23 pages

点击查看摘要

Abstract:The nonlinear two-time-scale stochastic approximation is widely studied under conditions of bounded variances in noise. Motivated by recent advances that allow for variability linked to the current state or time, we consider state- and time-dependent noises. We show that the Lyapunov function exhibits polynomial convergence rates in both cases, with the rate of polynomial delay depending on the parameters of state- or time-dependent noises. Notably, if the state noise parameters fully approach their limiting value, the Lyapunov function achieves an exponential convergence rate. We provide two numerical examples to illustrate our theoretical findings in the context of stochastic gradient descent with Polyak-Ruppert averaging and stochastic bilevel optimization.

[LG-117] Gradient Methods with Online Scaling Part II. Practical Aspects

链接: https://arxiv.org/abs/2509.11007
作者: Ya-Chi Chu,Wenzhi Gao,Yinyu Ye,Madeleine Udell
类目: Optimization and Control (math.OC); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:Part I of this work [Gao25] establishes online scaled gradient methods (OSGM), a framework that utilizes online convex optimization to adapt stepsizes in gradient methods. This paper focuses on the practical aspects of OSGM. We leverage the OSGM framework to design new adaptive first-order methods and provide insights into their empirical behavior. The resulting method, OSGM-Best, matches the performance of quasi-Newton variants while requiring less memory and cheaper iterations. We also extend OSGM to nonconvex optimization and outline directions that connect OSGM to existing branches of optimization theory and practice.

[LG-118] Predictive Free Energy Simulations Through Hierarchical Distillation of Quantum Hamiltonians

链接: https://arxiv.org/abs/2509.10967
作者: Chenghan Li,Garnet Kin-Lic Chan
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG); Biological Physics (physics.bio-ph)
*备注:

点击查看摘要

Abstract:Obtaining the free energies of condensed phase chemical reactions remains computationally prohibitive for high-level quantum mechanical methods. We introduce a hierarchical machine learning framework that bridges this gap by distilling knowledge from a small number of high-fidelity quantum calculations into increasingly coarse-grained, machine-learned quantum Hamiltonians. By retaining explicit electronic degrees of freedom, our approach further enables a faithful embedding of quantum and classical degrees of freedom that captures long-range electrostatics and the quantum response to a classical environment to infinite order. As validation, we compute the proton dissociation constants of weak acids and the kinetic rate of an enzymatic reaction entirely from first principles, reproducing experimental measurements within chemical accuracy or their uncertainties. Our work demonstrates a path to condensed phase simulations of reaction free energies at the highest levels of accuracy with converged statistics.

[LG-119] On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals

链接: https://arxiv.org/abs/2509.10874
作者: Baskaran Sripathmanathan,Xiaowen Dong,Michael Bronstein
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: This work has been accepted for publication at IEEE CAMSAP 2025

点击查看摘要

Abstract:We investigate graph signal reconstruction and sample selection for classification tasks. We present general theoretical characterisations of classification error applicable to multiple commonly used reconstruction methods, and compare that to the classical reconstruction error. We demonstrate the applicability of our results by using them to derive new optimal sampling methods for linearized graph convolutional networks, and show improvement over other graph signal processing based methods.

[LG-120] Variable Selection Using Relative Importance Rankings

链接: https://arxiv.org/abs/2509.10853
作者: Tien-En Chang,Argon Chen
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注: 26 pages, 9 figures

点击查看摘要

Abstract:Although conceptually related, variable selection and relative importance (RI) analysis have been treated quite differently in the literature. While RI is typically used for post-hoc model explanation, this paper explores its potential for variable ranking and filter-based selection before model creation. Specifically, we anticipate strong performance from the RI measures because they incorporate both direct and combined effects of predictors, addressing a key limitation of marginal correlation that ignores dependencies among predictors. We implement and evaluate the RI-based variable selection methods using general dominance (GD), comprehensive relative importance (CRI), and a newly proposed, computationally efficient variant termed CRI.Z. We first demonstrate how the RI measures more accurately rank the variables than the marginal correlation, especially when there are suppressed or weak predictors. We then show that predictive models built on these rankings are highly competitive, often outperforming state-of-the-art methods such as the lasso and relaxed lasso. The proposed RI-based methods are particularly effective in challenging cases involving clusters of highly correlated predictors, a setting known to cause failures in many benchmark methods. Although lasso methods have dominated the recent literature on variable selection, our study reveals that the RI-based method is a powerful and competitive alternative. We believe these underutilized tools deserve greater attention in statistics and machine learning communities. The code is available at: this https URL. Comments: 26 pages, 9 figures Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2509.10853 [stat.ML] (or arXiv:2509.10853v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2509.10853 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-121] Parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles

链接: https://arxiv.org/abs/2509.10756
作者: Amanuel Anteneh
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:We show that ensembles of deep neural networks, called deep ensembles, can be used to perform quantum parameter estimation while also providing a means for quantifying uncertainty in parameter estimates, which is a key advantage of using Bayesian inference for parameter estimation. These models are shown to be more robust to noise in the measurement results used to perform the parameter estimation as well as noise in the data used to train them. We also show that much less data is needed to achieve comparable performance to Bayesian inference based estimation, which is known to reach the ultimate precision limit as more data is collected, than was used in previous proposals.

[LG-122] On a Geometry of Interbrain Networks

链接: https://arxiv.org/abs/2509.10650
作者: Nicolás Hinrichs(1,2),Noah Guzmán(3),Melanie Weber(4) ((1) Embodied Cognitive Science Unit, Okinawa Institute of Science and Technology, Okinawa, Japan, (2) Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, (3) Independent scholar, (4) School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States)
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)
*备注: 4 pages, 1 figure, submitted to NeurReps workshop 2025

点击查看摘要

Abstract:Effective analysis in neuroscience benefits significantly from robust conceptual frameworks. Traditional metrics of interbrain synchrony in social neuroscience typically depend on fixed, correlation-based approaches, restricting their explanatory capacity to descriptive observations. Inspired by the successful integration of geometric insights in network science, we propose leveraging discrete geometry to examine the dynamic reconfigurations in neural interactions during social exchanges. Unlike conventional synchrony approaches, our method interprets inter-brain connectivity changes through the evolving geometric structures of neural networks. This geometric framework is realized through a pipeline that identifies critical transitions in network connectivity using entropy metrics derived from curvature distributions. By doing so, we significantly enhance the capacity of hyperscanning methodologies to uncover underlying neural mechanisms in interactive social behavior.

[LG-123] Assessing the Limits of Graph Neural Networks for Vapor-Liquid Equilibrium Prediction: A Cryogenic Mixture Case Study

链接: https://arxiv.org/abs/2509.10565
作者: Aryan Gupta
类目: Chemical Physics (physics.chem-ph); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
*备注:

点击查看摘要

Abstract:Accurate and fast thermophysical models are needed to embed vapor-liquid equilibrium (VLE) calculations in design, optimization, and control loops for cryogenic mixtures. This study asks whether a structure-aware graph neural network (GNN; DimeNet++) trained on GERG-2008/CoolProp data can act as a practical surrogate for an equation of state (EoS). We generate a ternary dataset over 90-200 K and pressures to 100 bar, curate it with a 15% density filter (reducing 5,200 states to 1,516), and pair each state with a lightweight molecular-dynamics snapshot to supply structural features. The model is trained in two stages; pretraining on residual Helmholtz energy followed by pressure fine-tuning with a stability penalty; and evaluated via single-phase interpolation tests, solver-free derivative-quality diagnostics, an audited VLE driver, and a latency benchmark. Within its regime, the GNN interpolates single-phase properties reasonably well; however, the VLE driver accepts no GNN equilibria on tested binaries (all plotted VLE points are CoolProp fallback or the solver fails), and diagnostic probes reveal jagged P(V|T) paths and thermal-stability flags concentrated in dense/cold regions, indicating insufficient derivative smoothness/consistency for robust equilibrium solving. An end-to-end timing comparison shows no single-phase speed advantage relative to CoolProp (tens of milliseconds vs sub-millisecond). We conclude that, as configured, the surrogate in this study is not solver-ready for VLE and offers no runtime benefit; its value is methodological, delineating failure modes and pointing to remedies such as physics-informed training signals and targeted coverage near phase boundaries.

[LG-124] HiLWS: A Human-in-the-Loop Weak Supervision Framework for Curating Clinical and Home Video Data for Neurological Assessment

链接: https://arxiv.org/abs/2509.10557
作者: Atefeh Irani,Maryam S. Mirian,Alex Lassooij,Reshad Hosseini,Hadi Moradi,Martin J. McKeown
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Video-based assessment of motor symptoms in conditions such as Parkinson’s disease (PD) offers a scalable alternative to in-clinic evaluations, but home-recorded videos introduce significant challenges, including visual degradation, inconsistent task execution, annotation noise, and domain shifts. We present HiLWS, a cascaded human-in-the-loop weak supervision framework for curating and annotating hand motor task videos from both clinical and home settings. Unlike conventional single-stage weak supervision methods, HiLWS employs a novel cascaded approach, first applies weak supervision to aggregate expert-provided annotations into probabilistic labels, which are then used to train machine learning models. Model predictions, combined with expert input, are subsequently refined through a second stage of weak supervision. The complete pipeline includes quality filtering, optimized pose estimation, and task-specific segment extraction, complemented by context-sensitive evaluation metrics that assess both visual fidelity and clinical relevance by prioritizing ambiguous cases for expert review. Our findings reveal key failure modes in home recorded data and emphasize the importance of context-sensitive curation strategies for robust medical video analysis.

[LG-125] rial-Level Time-frequency EEG Desynchronization as a Neural Marker of Pain

链接: https://arxiv.org/abs/2509.10552
作者: D.A. Blanco-Mora,A. Dierolf,J. Gonçalves,M. van Der Meulen
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)
*备注: 7 pages, 3 Figures

点击查看摘要

Abstract:Pain remains one of the most pressing health challenges, yet its measurement still relies heavily on self-report, limiting monitoring in non-communicative patients and hindering translational research. Neural oscillations recorded with electroencephalography (EEG) provide a promising avenue for identifying reproducible markers of nociceptive processing. Prior studies have reported pain-related event-related desynchronization (ERD) in the alpha and beta bands, but most rely on trial-averaging, obscuring variability that may be critical for perception. We analyzed high-density EEG from 59 healthy participants who underwent electrical stimulation under Pain and No-Pain conditions. Per-trial time-frequency decomposition revealed robust beta-band ERD in frontal-central electrodes that differentiated Pain from No-Pain trials. Generalized linear mixed models demonstrated that ERD scaled with subjective intensity ratings (VAS), and that age and gender moderated this relationship. Reverse models further showed that ERD predicted VAS ratings across participants, underscoring its potential as a nonverbal marker of pain. These findings provide preliminary evidence that trial-level EEG oscillations can serve as reliable indicators of pain and open avenues for individualized, report-free pain monitoring. Future work should validate these results in patient populations and extend analyses to multimodal approaches combining EEG, MRI, and attention-based modulation strategies.

[LG-126] Adaptive Temporal Fusion Transformers for Cryptocurrency Price Prediction

链接: https://arxiv.org/abs/2509.10542
作者: Arash Peik,Mohammad Ali Zare Chahooki,Amin Milani Fard,Mehdi Agha Sarram
类目: atistical Finance (q-fin.ST); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Precise short-term price prediction in the highly volatile cryptocurrency market is critical for informed trading strategies. Although Temporal Fusion Transformers (TFTs) have shown potential, their direct use often struggles in the face of the market’s non-stationary nature and extreme volatility. This paper introduces an adaptive TFT modeling approach leveraging dynamic subseries lengths and pattern-based categorization to enhance short-term forecasting. We propose a novel segmentation method where subseries end at relative maxima, identified when the price increase from the preceding minimum surpasses a threshold, thus capturing significant upward movements, which act as key markers for the end of a growth phase, while potentially filtering the noise. Crucially, the fixed-length pattern ending each subseries determines the category assigned to the subsequent variable-length subseries, grouping typical market responses that follow similar preceding conditions. A distinct TFT model trained for each category is specialized in predicting the evolution of these subsequent subseries based on their initial steps after the preceding peak. Experimental results on ETH-USDT 10-minute data over a two-month test period demonstrate that our adaptive approach significantly outperforms baseline fixed-length TFT and LSTM models in prediction accuracy and simulated trading profitability. Our combination of adaptive segmentation and pattern-conditioned forecasting enables more robust and responsive cryptocurrency price prediction.

[LG-127] Crystal Systems Classification of Phosphate-Based Cathode Materials Using Machine Learning for Lithium-Ion Battery

链接: https://arxiv.org/abs/2509.10532
作者: Yogesh Yadav,Sandeep K Yadav,Vivek Vijay,Ambesh Dixit
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注: 21 Pages, 12 Figures, Submitted to Physica B: Condensed Matter Journal

点击查看摘要

Abstract:The physical and chemical characteristics of cathodes used in batteries are derived from the lithium-ion phosphate cathodes crystalline arrangement, which is pivotal to the overall battery performance. Therefore, the correct prediction of the crystal system is essential to estimate the properties of cathodes. This study applies machine learning classification algorithms for predicting the crystal systems, namely monoclinic, orthorhombic, and triclinic, related to Li P (Mn, Fe, Co, Ni, V) O based Phosphate cathodes. The data used in this work is extracted from the Materials Project. Feature evaluation showed that cathode properties depend on the crystal structure, and optimized classification strategies lead to better predictability. Ensemble machine learning algorithms such as Random Forest, Extremely Randomized Trees, and Gradient Boosting Machines have demonstrated the best predictive capabilities for crystal systems in the Monte Carlo cross-validation test. Additionally, sequential forward selection (SFS) is performed to identify the most critical features influencing the prediction accuracy for different machine learning models, with Volume, Band gap, and Sites as input features ensemble machine learning algorithms such as Random Forest (80.69%), Extremely Randomized Tree (78.96%), and Gradient Boosting Machine (80.40%) approaches lead to the maximum accuracy towards crystallographic classification with stability and the predicted materials can be the potential cathode materials for lithium ion batteries.

[LG-128] An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions

链接: https://arxiv.org/abs/2509.10527
作者: Byeonghee Lee,Joonsung Kang
类目: Image and Video Processing (eess.IV); Computers and Society (cs.CY); Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 11 pages, 1 figure

点击查看摘要

Abstract:Biomarker discovery in neurodegenerative diseases requires robust, interpretable frameworks capable of integrating high-dimensional multi-omics data under low-sample conditions. We propose a novel ensemble approach combining Graph Attention Networks (GAT), MultiOmics Variational AutoEncoder (MOVE), Elastic-net sparse regression, and Storey’s False Discovery Rate (FDR). This framework is benchmarked against state-of-the-art methods including DIABLO, MOCAT, AMOGEL, and MOMLIN. We evaluate performance using both simulated multi-omics data and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Our method demonstrates superior predictive accuracy, feature selection precision, and biological relevance. Biomarker gene maps derived from both datasets are visualized and interpreted, offering insights into latent molecular mechanisms underlying dementia.

[LG-129] DeepSeasons: a Deep Learning scale-selecting approach to Seasonal Forecasts

链接: https://arxiv.org/abs/2509.10494
作者: A. Navarra,G. G. Navarra
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Seasonal forecasting remains challenging due to the inherent chaotic nature of atmospheric dynamics. This paper introduces DeepSeasons, a novel deep learning approach designed to enhance the accuracy and reliability of seasonal forecasts. Leveraging advanced neural network architectures and extensive historical climatic datasets, DeepSeasons identifies complex, nonlinear patterns and dependencies in climate variables with similar or improved skill respcet GCM-based forecasting methods, at a significant lower cost. The framework also allow tailored application to specific regions or variables, rather than the overall problem of predicting the entire atmosphere/ocean system. The proposed methods also allow for direct predictions of anomalies and time-means, opening a new approach to long-term forecasting and highlighting its potential for operational deployment in climate-sensitive sectors. This innovative methodology promises substantial improvements in managing climate-related risks and decision-making processes.

[LG-130] FlowECG: Using Flow Matching to Create a More Efficient ECG Signal Generator

链接: https://arxiv.org/abs/2509.10491
作者: Vitalii Bondar,Serhii Semenov,Vira Babenko,Dmytro Holovniak
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 8 pages, 2 figures, 1 table, reviewed version will be published in “Sensors, Devices and Systems 2025 Proceedings” (Springer’s Lecture Notes in Electrical Engineering)

点击查看摘要

Abstract:Synthetic electrocardiogram generation serves medical AI applications requiring privacy-preserving data sharing and training dataset augmentation. Current diffusion-based methods achieve high generation quality but require hundreds of neural network evaluations during sampling, creating computational bottlenecks for clinical deployment. We propose FlowECG, a flow matching approach that adapts the SSSD-ECG architecture by replacing the iterative diffusion process with continuous flow dynamics. Flow matching learns direct transport paths from noise to data distributions through ordinary differential equation solving. We evaluate our method on the PTB-XL dataset using Dynamic Time Warping, Wasserstein distance, Maximum Mean Discrepancy, and spectral similarity metrics. FlowECG matches SSSD-ECG performance at 200 neural function evaluations, outperforming the baseline on three metrics. The key finding shows that FlowECG maintains generation quality with substantially fewer sampling steps, achieving comparable results with 10-25 evaluations compared to 200 for diffusion methods. This efficiency improvement reduces computational requirements by an order of magnitude while preserving physiologically realistic 12-lead ECG characteristics. The approach enables practical deployment in resource-limited clinical settings where real-time generation or large-scale synthetic data creation is needed.

[LG-131] Green Learning for STAR-RIS mmWave Systems with Implicit CSI

链接: https://arxiv.org/abs/2509.06820
作者: Yu-Hsiang Huang,Po-Heng Chou,Wan-Jen Huang,Walid Saad,C.-C. Jay Kuo
类目: ignal Processing (eess.SP); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 6 pages, 4 figures, 2 tables, accepted by 2025 IEEE Globecom

点击查看摘要

Abstract:In this paper, a green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) MIMO broadcasting systems. Motivated by the growing emphasis on environmental sustainability in future 6G networks, this work adopts a broadcasting transmission architecture for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption. Different from conventional optimization methods, such as block coordinate descent (BCD) that require perfect channel state information (CSI) and iterative computation, the proposed GL framework operates directly on received uplink pilot signals without explicit CSI estimation. Unlike deep learning (DL) approaches that require CSI-based labels for training, the proposed GL approach also avoids deep neural networks and backpropagation, leading to a more lightweight design. Although the proposed GL framework is trained with supervision generated by BCD under full CSI, inference is performed in a fully CSI-free manner. The proposed GL integrates subspace approximation with adjusted bias (Saab), relevant feature test (RFT)-based supervised feature selection, and eXtreme gradient boosting (XGBoost)-based decision learning to jointly predict the STAR-RIS coefficients and transmit precoder. Simulation results show that the proposed GL approach achieves competitive spectral efficiency compared to BCD and DL-based models, while reducing floating-point operations (FLOPs) by over four orders of magnitude. These advantages make the proposed GL approach highly suitable for real-time deployment in energy- and hardware-constrained broadcasting scenarios.

[LG-132] YOLO-based Bearing Fault Diagnosis With Continuous Wavelet Transform

链接: https://arxiv.org/abs/2509.03070
作者: Po-Heng Chou,Wei-Lung Mao,Ru-Ping Lin
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: 5 pages, 2 figures, 2 tables, submitted to IEEE Sensors Letters

点击查看摘要

Abstract:This letter proposes a YOLO-based framework for spatial bearing fault diagnosis using time-frequency spectrograms derived from continuous wavelet transform (CWT). One-dimensional vibration signals are first transformed into time-frequency spectrograms using Morlet wavelets to capture transient fault signatures. These spectrograms are then processed by YOLOv9, v10, and v11 models to classify fault types. Evaluated on three benchmark datasets, including Case Western Reserve University (CWRU), Paderborn University (PU), and Intelligent Maintenance System (IMS), the proposed CWT-YOLO pipeline achieves significantly higher accuracy and generalizability than the baseline MCNN-LSTM model. Notably, YOLOv11 reaches mAP scores of 99.4% (CWRU), 97.8% (PU), and 99.5% (IMS). In addition, its region-aware detection mechanism enables direct visualization of fault locations in spectrograms, offering a practical solution for condition monitoring in rotating machinery.

信息检索

[IR-0] SAQ: Pushing the Limits of Vector Quantization through Code Adjustment and Dimension Segmentation SIGMOD

链接: https://arxiv.org/abs/2509.12086
作者: Hui Li,Shiyuan Deng,Xiao Yan,Xiangyu Zhi,James Cheng
类目: Databases (cs.DB); Data Structures and Algorithms (cs.DS); Information Retrieval (cs.IR)
*备注: 13 pages, 12 figures, accepted by SIGMOD

点击查看摘要

Abstract:Approximate Nearest Neighbor Search (ANNS) plays a critical role in applications such as search engines, recommender systems, and RAG for LLMs. Vector quantization (VQ), a crucial technique for ANNS, is commonly used to reduce space overhead and accelerate distance computations. However, despite significant research advances, state-of-the-art VQ methods still face challenges in balancing encoding efficiency and quantization accuracy. To address these limitations, we propose a novel VQ method called SAQ. To improve accuracy, SAQ employs a new dimension segmentation technique to strategically partition PCA-projected vectors into segments along their dimensions. By prioritizing leading dimension segments with larger magnitudes, SAQ allocates more bits to high-impact segments, optimizing the use of the available space quota. An efficient dynamic programming algorithm is developed to optimize dimension segmentation and bit allocation, ensuring minimal quantization error. To speed up vector encoding, SAQ devises a code adjustment technique to first quantize each dimension independently and then progressively refine quantized vectors using a coordinate-descent-like approach to avoid exhaustive enumeration. Extensive experiments demonstrate SAQ’s superiority over classical methods (e.g., PQ, PCA) and recent state-of-the-art approaches (e.g., LVQ, Extended RabitQ). SAQ achieves up to 80% reduction in quantization error and accelerates encoding speed by over 80x compared to Extended RabitQ.

[IR-1] AEFS: Adaptive Early Feature Selection for Deep Recommender Systems

链接: https://arxiv.org/abs/2509.12076
作者: Fan Hu,Gaofeng Lu,Jun Chen,Chaonan Guo,Yuekui Yang,Xirong Li
类目: Information Retrieval (cs.IR)
*备注: Accepted by TKDE

点击查看摘要

Abstract:Feature selection has emerged as a crucial technique in refining recommender systems. Recent advancements leveraging Automated Machine Learning (AutoML) has drawn significant attention, particularly in two main categories: early feature selection and late feature selection, differentiated by whether the selection occurs before or after the embedding layer. The early feature selection selects a fixed subset of features and retrains the model, while the late feature selection, known as adaptive feature selection, dynamically adjusts feature choices for each data instance, recognizing the variability in feature significance. Although adaptive feature selection has shown remarkable improvements in performance, its main drawback lies in its post-embedding layer feature selection. This process often becomes cumbersome and inefficient in large-scale recommender systems with billions of ID-type features, leading to a highly sparse and parameter-heavy embedding layer. To overcome this, we introduce Adaptive Early Feature Selection (AEFS), a very simple method that not only adaptively selects informative features for each instance, but also significantly reduces the activated parameters of the embedding layer. AEFS employs a dual-model architecture, encompassing an auxiliary model dedicated to feature selection and a main model responsible for prediction. To ensure effective alignment between these two models, we incorporate two collaborative training loss constraints. Our extensive experiments on three benchmark datasets validate the efficiency and effectiveness of our approach. Notably, AEFS matches the performance of current state-of-theart Adaptive Late Feature Selection methods while achieving a significant reduction of 37. 5% in the activated parameters of the embedding layer. AEFS is open-source at https://github. com/fly-dragon211/AEFS .

[IR-2] Results of the 2025 Video Browser Showdown

链接: https://arxiv.org/abs/2509.12000
作者: Luca Rossetto,Klaus Schoeffmann,Cathal Gurrin,Jakub Lokoč,Werner Bailer
类目: Multimedia (cs.MM); Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:This report presents the results of the 14th Video Browser Showdown, held at the 2025 International Conference on Multimedia Modeling on the 8th of January 2025 in Nara, Japan.

[IR-3] Decoding in Latent Spaces for Efficient Inference in LLM -based Recommendation EMNLP’25

链接: https://arxiv.org/abs/2509.11524
作者: Chengbing Wang,Yang Zhang,Zhicheng Wang,Tianhao Shi,Keqin Bao,Fuli Feng,Tat-Seng Chua
类目: Information Retrieval (cs.IR)
*备注: Accepted for publication in EMNLP’25

点击查看摘要

Abstract:Fine-tuning large language models (LLMs) for recommendation in a generative manner has delivered promising results, but encounters significant inference overhead due to autoregressive decoding in the language space. This work explores bypassing language-space decoding by directly matching candidate items with the LLM’s internal thought representations in the latent space, eliminating the time-consuming autoregressive process to reduce computational costs. Towards this, we introduce Light Latent-space Decoding (L2D), an effective and efficient latent-space decoding method. L2D represents user-preferred items by using the hidden states of test sequences reflecting the LLM’s internal thought, and obtains candidate item representations from the hidden states of training sequences labeled with the corresponding candidate items. It then matches the two types of representations to decode items, achieving latent-space decoding. In this way, it enables efficient decoding without altering the LLM’s generative tuning paradigm, thereby preserving performance. Extensive empirical results demonstrate that L2D is more than 10x faster than language-space decoding while maintaining or enhancing performance.

[IR-4] Acoustic Overspecification in Electronic Dance Music Taxonomy

链接: https://arxiv.org/abs/2509.11474
作者: Weilun Xu,Tianhao Dai,Oscar Goudet,Xiaoxuan Wang
类目: ound (cs.SD); Information Retrieval (cs.IR)
*备注: 5 pages, 3 figures, conference paper

点击查看摘要

Abstract:Electronic Dance Music (EDM) classification typically relies on industry-defined taxonomies with numerous subgenres, yet the acoustic basis for these distinctions remains unclear. Current approaches use supervised learning with prescribed genre labels, assuming their validity without systematic evaluation. In this paper, we propose an unsupervised approach to discover the natural acoustic structure of EDM independent of commercial labels. Our method combines novel tempogram-based features capturing EDM’s layered rhythmic patterns with multi-criteria feature selection. To validate that our findings reflect genuine acoustic structure rather than methodological artifacts, we compare our results against state-of-the-art pre-trained audio embeddings (MERT and CLAP). Both our feature space and embedding representations converge to 19-23 natural acoustic families compared to the prescribed 35, providing consistent evidence of significant overspecification in current EDM taxonomy by approximately one-third.

[IR-5] Do Large Language Models Favor Recent Content? A Study on Recency Bias in LLM -Based Reranking

链接: https://arxiv.org/abs/2509.11353
作者: Hanpei Fang,Sijie Tao,Nuo Chen,Kai-Xin Chang,Tetsuya Sakai
类目: Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:Large language models (LLMs) are increasingly deployed in information systems, including being used as second-stage rerankers in information retrieval pipelines, yet their susceptibility to recency bias has received little attention. We investigate whether LLMs implicitly favour newer documents by prepending artificial publication dates to passages in the TREC Deep Learning passage retrieval collections in 2021 (DL21) and 2022 (DL22). Across seven models, GPT-3.5-turbo, GPT-4o, GPT-4, LLaMA-3 8B/70B, and Qwen-2.5 7B/72B, “fresh” passages are consistently promoted, shifting the Top-10’s mean publication year forward by up to 4.78 years and moving individual items by as many as 95 ranks in our listwise reranking experiments. Although larger models attenuate the effect, none eliminate it. We also observe that the preference of LLMs between two passages with an identical relevance level can be reversed by up to 25% on average after date injection in our pairwise preference experiments. These findings provide quantitative evidence of a pervasive recency bias in LLMs and highlight the importance of effective bias-mitigation strategies.

[IR-6] An Incentive-Compatible Reward Sharing Mechanism for Mitigating Mirroring Attacks in Decentralized Data-Feed Systems

链接: https://arxiv.org/abs/2509.11294
作者: Sina Aeeneh,Nikola Zlatanov,Jiangshan Yu
类目: Computer Science and Game Theory (cs.GT); Emerging Technologies (cs.ET); Information Retrieval (cs.IR); Information Theory (cs.IT); Probability (math.PR)
*备注:

点击查看摘要

Abstract:Decentralized data-feed systems enable blockchain-based smart contracts to access off-chain information by aggregating values from multiple oracles. To improve accuracy, these systems typically use an aggregation function, such as majority voting, to consolidate the inputs they receive from oracles and make a decision. Depending on the final decision and the values reported by the oracles, the participating oracles are compensated through shared rewards. However, such incentive mechanisms are vulnerable to mirroring attacks, where a single user controls multiple oracles to bias the decision of the aggregation function and maximize rewards. This paper analyzes the impact of mirroring attacks on the reliability and dependability of majority voting-based data-feed systems. We demonstrate how existing incentive mechanisms can unintentionally encourage rational users to implement such attacks. To address this, we propose a new incentive mechanism that discourages Sybil behavior. We prove that the proposed mechanism leads to a Nash Equilibrium in which each user operates only one oracle. Finally, we discuss the practical implementation of the proposed incentive mechanism and provide numerical examples to demonstrate its effectiveness.

[IR-7] Understanding the Information Cocoon: A Multidimensional Assessment and Analysis of News Recommendation Systems

链接: https://arxiv.org/abs/2509.11139
作者: Xin Wang,Xiaowen Huang,Jitao Sang
类目: Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:Personalized news recommendation systems inadvertently create information cocoons–homogeneous information bubbles that reinforce user biases and amplify societal polarization. To address the lack of comprehensive assessment frameworks in prior research, we propose a multidimensional analysis that evaluates cocoons through dual perspectives: (1) Individual homogenization via topic diversity (including the number of topic categories and category information entropy) and click repetition; (2) Group polarization via network density and community openness. Through multi-round experiments on real-world datasets, we benchmark seven algorithms and reveal critical insights. Furthermore, we design five lightweight mitigation strategies. This work establishes the first unified metric framework for information cocoons and delivers deployable solutions for ethical recommendation systems.

[IR-8] SPARK: Adaptive Low-Rank Knowledge Graph Modeling in Hybrid Geometric Spaces for Recommendation CIKM’-25

链接: https://arxiv.org/abs/2509.11094
作者: Binhao Wang,Yutian Xiao,Maolin Wang,Zhiqi Li,Tianshuo Wei,Ruocheng Guo,Xiangyu Zhao
类目: Information Retrieval (cs.IR)
*备注: Accepted by CIKM’ 25

点击查看摘要

Abstract:Knowledge Graphs (KGs) enhance recommender systems but face challenges from inherent noise, sparsity, and Euclidean geometry’s inadequacy for complex relational structures, critically impairing representation learning, especially for long-tail entities. Existing methods also often lack adaptive multi-source signal fusion tailored to item popularity. This paper introduces SPARK, a novel multi-stage framework systematically tackling these issues. SPARK first employs Tucker low-rank decomposition to denoise KGs and generate robust entity representations. Subsequently, an SVD-initialized hybrid geometric GNN concurrently learns representations in Euclidean and Hyperbolic spaces; the latter is strategically leveraged for its aptitude in modeling hierarchical structures, effectively capturing semantic features of sparse, long-tail items. A core contribution is an item popularity-aware adaptive fusion strategy that dynamically weights signals from collaborative filtering, refined KG embeddings, and diverse geometric spaces for precise modeling of both mainstream and long-tail items. Finally, contrastive learning aligns these multi-source representations. Extensive experiments demonstrate SPARK’s significant superiority over state-of-the-art methods, particularly in improving long-tail item recommendation, offering a robust, principled approach to knowledge-enhanced recommendation. Implementation code is available at this https URL.

[IR-9] Decentralized Identity Management on Ripple: A Conceptual Framework for High-Speed Low-Cost Identity Transactions in Attestation-Based Attribute-Based Identity

链接: https://arxiv.org/abs/2509.10545
作者: Ruwanga Konara,Kasun De Zoysa,Asanka Sayakkara
类目: Cryptography and Security (cs.CR); Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:Recent years have seen many industrial implementations and much scholastic research, i.e., prototypes and theoretical frameworks, in Decentralized Identity Management Systems (DIDMS). It is safe to say that Attestation-Based Attribute-Based Decentralized IDM (ABABDIDM) has not received anywhere near the same level of attention in the literature as general Attribute-Based DIDMs (ABDIDM), i.e, decentralized Attribute-Based Access Control (ABAC). The use of decentralization, i.e., DIDM, is to improve upon the security and privacy-related issues of centralized Identity Management Systems (IDM) and Attribute-Based IDMs (ABIDM). And blockchain is the framework used for decentralization in all these schemes. Many DIDMs - even ABDIDMs - have been defined on popular blockchains such as Hyperledger, Ethereum, and Bitcoin. However, despite the characteristics of Ripple that makes it appealing for an ABIDM, there is a lack of research to develop an Identity Management System (IDMS) on Ripple in literature. We have attempted to conceptualize an ABABDIDM on Ripple.

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