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目录
概览 (2025-11-27)
今日共更新536篇论文,其中:
- 自然语言处理共68篇(Computation and Language (cs.CL))
- 人工智能共173篇(Artificial Intelligence (cs.AI))
- 计算机视觉共138篇(Computer Vision and Pattern Recognition (cs.CV))
- 机器学习共161篇(Machine Learning (cs.LG))
自然语言处理
[NLP-0] Revisiting Generalization Across Difficulty Levels: Its Not So Easy
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在不同任务难度下的泛化能力问题，即训练数据的难易程度如何影响模型在不同难度测试集上的表现。现有研究对此结论不一，缺乏系统性评估。其解决方案的关键在于采用基于大量不同LLM输出和项目反应理论（Item Response Theory, IRT）的客观难度评分方法，对六个数据集中样本进行细粒度难度排序，从而排除人类主观判断的影响，实现更可靠、大规模且精细化的跨难度泛化分析。结果表明，无论训练于易题还是难题数据，均无法在全部难度范围内稳定提升性能，凸显了在训练与评估中保持难度多样性的重要性。
链接: https://arxiv.org/abs/2511.21692
作者: Yeganeh Kordi,Nihal V. Nayak,Max Zuo,Ilana Nguyen,Stephen H. Bach
机构: Brown University (布朗大学); Harvard University (哈佛大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is mixed regarding whether training on easier or harder data leads to better results, and whether those gains come on easier or harder test data. We address this question by conducting a systematic evaluation of LLMs’ generalization across models, datasets, and fine-grained groups of example difficulty. We rank examples in six datasets using the outputs of thousands of different LLMs and Item Response Theory (IRT), a well-established difficulty metric in educational testing. Unlike prior work, our difficulty ratings are therefore determined solely by the abilities of many different LLMs, excluding human opinions of difficulty. With a more objective, larger-scale, and finer-grained analysis, we show that cross-difficulty generalization is often limited; training on either easy or hard data cannot achieve consistent improvements across the full range of difficulties. These results show the importance of having a range of difficulties in both training and evaluation data for LLMs, and that taking shortcuts with respect to difficulty is risky.
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[NLP-1] oolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
【速读】： 该论文旨在解决大语言模型在处理复杂、深层次任务（如“人类最后考试”HLE）时存在的计算成本高、效率低以及工具使用不灵活的问题。其核心挑战在于如何在保证性能的同时提升资源利用效率，并实现对用户偏好和工具选择的精准对齐。解决方案的关键在于提出ToolOrchestra方法，通过强化学习框架训练一个轻量级的协调器模型（Orchestrator，8B参数），该模型能够动态调度多种智能工具，并基于结果质量、执行效率及用户偏好三类奖励信号进行优化。实验证明，该方案在多项基准测试中优于GPT-5，在HLE上达到37.1%的准确率（较GPT-5提升2个百分点），同时具备2.5倍的效率优势；在tau2-Bench和FRAMES上则以约30%的成本超越GPT-5，展现出更优的性能-成本权衡和对未见工具的良好泛化能力。
链接: https://arxiv.org/abs/2511.21689
作者: Hongjin Su,Shizhe Diao,Ximing Lu,Mingjie Liu,Jiacheng Xu,Xin Dong,Yonggan Fu,Peter Belcak,Hanrong Ye,Hongxu Yin,Yi Dong,Evelina Bakhturina,Tao Yu,Yejin Choi,Jan Kautz,Pavlo Molchanov
机构: NVIDIA; University of Hong Kong(香港大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
备注: 21 pages, 6 figures
Abstract:Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity’s Last Exam (HLE) remains both conceptually challenging and computationally expensive. We show that small orchestrators managing other models and a variety of tools can both push the upper bound of intelligence and improve efficiency in solving difficult agentic tasks. We introduce ToolOrchestra, a method for training small orchestrators that coordinate intelligent tools. ToolOrchestra explicitly uses reinforcement learning with outcome-, efficiency-, and user-preference-aware rewards. Using ToolOrchestra, we produce Orchestrator, an 8B model that achieves higher accuracy at lower cost than previous tool-use agents while aligning with user preferences on which tools are to be used for a given query. On HLE, Orchestrator achieves a score of 37.1%, outperforming GPT-5 (35.1%) while being 2.5x more efficient. On tau2-Bench and FRAMES, Orchestrator surpasses GPT-5 by a wide margin while using only about 30% of the cost. Extensive analysis shows that Orchestrator achieves the best trade-off between performance and cost under multiple metrics, and generalizes robustly to unseen tools. These results demonstrate that composing diverse tools with a lightweight orchestration model is both more efficient and more effective than existing methods, paving the way for practical and scalable tool-augmented reasoning systems.
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[NLP-2] G2VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning
【速读】： 该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）在空间智能方面缺乏鲁棒性的问题，尤其是其在空间理解与推理任务中的表现不佳。作者认为这一差距源于缺乏能够从二维图像中重建三维空间的视觉几何学习过程。解决方案的关键在于提出G²VLM，一种几何 grounded 的视觉语言模型，它通过融合3D视觉几何特征来直接预测3D属性，并借助上下文学习（in-context learning）和交错推理（interleaved reasoning）增强空间推理能力。该模型统一设计可扩展性强，利用大量多视角图像和视频数据进行训练，同时引入通常依赖于难获取标注的3D视觉先验，从而在3D重建和空间理解任务上均达到先进性能。
链接: https://arxiv.org/abs/2511.21688
作者: Wenbo Hu,Jingli Lin,Yilin Long,Yunlong Ran,Lihan Jiang,Yifan Wang,Chenming Zhu,Runsen Xu,Tai Wang,Jiangmiao Pang
机构: Shanghai AI Lab (上海人工智能实验室); UCLA (加州大学洛杉矶分校); SJTU (上海交通大学); FDU (复旦大学); ZJU (浙江大学); USTC (中国科学技术大学); HKU (香港大学); CUHK (香港中文大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: code are released at this https URL
Abstract:Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to the absence of a visual geometry learning process capable of reconstructing 3D space from 2D images. We present G ^2 VLM, a geometry grounded vision-language model that bridges two fundamental aspects of spatial intelligence: spatial 3D reconstruction and spatial understanding. G ^2 VLM natively leverages learned 3D visual geometry features to directly predict 3D attributes and enhance spatial reasoning tasks via in-context learning and interleaved reasoning. Our unified design is highly scalable for spatial understanding: it trains on abundant multi-view image and video data, while simultaneously leveraging the benefits of 3D visual priors that are typically only derived from hard-to-collect annotations. Experimental results demonstrate G ^2 VLM is proficient in both tasks, achieving comparable results to state-of-the-art feed-forward 3D reconstruction models and achieving better or competitive results across spatial understanding and reasoning tasks. By unifying a semantically strong VLM with low-level 3D vision tasks, we hope G ^2 VLM can serve as a strong baseline for the community and unlock more future applications, such as 3D scene editing.
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[NLP-3] Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
【速读】： 该论文旨在解决当前多智能体合成数据框架中存在的两个核心问题：一是依赖集中式编排器导致的可扩展性瓶颈，二是针对特定领域硬编码造成的灵活性不足。解决方案的关键在于提出一种去中心化的框架Matrix，其将控制流和数据流均表示为通过分布式队列传递的序列化消息，采用点对点（peer-to-peer）设计消除了中央控制器；同时，轻量级代理负责任务调度，而计算密集型操作（如大语言模型推理或容器化环境执行）由分布式服务处理，从而在保持输出质量的前提下实现高达2–15倍的数据生成吞吐量提升，并支持多种数据生成场景的模块化配置与灵活适配。
链接: https://arxiv.org/abs/2511.21686
作者: Dong Wang,Yang Li,Ansong Ni,Ching-Feng Yeh,Youssef Emad,Xinjie Lei,Liam Robbins,Karthik Padthe,Hu Xu,Xian Li,Asli Celikyilmaz,Ramya Raghavendra,Lifei Huang,Carole-Jean Wu,Shang-Wen Li
机构: Meta(元)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sensitive. Many such generation tasks require coordinated multi-agent workflows, where specialized agents collaborate to produce data that is higher quality, more diverse, and structurally richer. However, existing frameworks for multi-agent synthesis often depend on a centralized orchestrator, creating scalability bottlenecks, or are hardcoded for specific domains, limiting flexibility. We present \textbfMatrix, a decentralized framework that represents both control and data flow as serialized messages passed through distributed queues. This peer-to-peer design eliminates the central orchestrator. Each task progresses independently through lightweight agents, while compute-intensive operations, such as LLM inference or containerized environments, are handled by distributed services. Built on Ray, Matrix scales to tens of thousands of concurrent agentic workflows and provides a modular, configurable design that enables easy adaptation to a wide range of data generation workflows. We evaluate Matrix across diverse synthesis scenarios, such as multi-agent collaborative dialogue, web-based reasoning data extraction, and tool-use trajectory generation in customer service environments. In all cases, Matrix achieves 2 – 15\times higher data generation throughput under identical hardware resources, without compromising output quality.
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[NLP-4] he author is dead but what if they never lived? A reception experiment on Czech AI- and human-authored poetry
【速读】： 该论文旨在解决生成式 AI 在低资源语言（如捷克语）中创作诗歌时的可辨识性与审美评价问题，即人类读者是否能准确识别AI与人类创作的诗歌，并探讨其审美判断是否存在偏见。解决方案的关键在于通过实验设计评估捷克母语者对AI与人类诗歌的识别准确率及美学评分，结果表明：AI生成的捷克诗歌在感知层面难以与人类作品区分（识别准确率仅45.8%，接近随机水平），但读者因作者身份认知而产生显著审美偏见——即使AI诗歌实际评分不低于人类作品，一旦被误认为AI所作，其评价显著降低。这揭示了审美判断与作者身份信念之间存在强关联，凸显了AI内容真实性感知与主观评价间的复杂互动机制。
链接: https://arxiv.org/abs/2511.21629
作者: Anna Marklová,Ondřej Vinš,Martina Vokáčová,Jiří Milička
机构: Charles University (查尔斯大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models are increasingly capable of producing creative texts, yet most studies on AI-generated poetry focus on English – a language that dominates training data. In this paper, we examine the perception of AI- and human-written Czech poetry. We ask if Czech native speakers are able to identify it and how they aesthetically judge it. Participants performed at chance level when guessing authorship (45.8% correct on average), indicating that Czech AI-generated poems were largely indistinguishable from human-written ones. Aesthetic evaluations revealed a strong authorship bias: when participants believed a poem was AI-generated, they rated it as less favorably, even though AI poems were in fact rated equally or more favorably than human ones on average. The logistic regression model uncovered that the more the people liked a poem, the less probable was that they accurately assign the authorship. Familiarity with poetry or literary background had no effect on recognition accuracy. Our findings show that AI can convincingly produce poetry even in a morphologically complex, low-resource (with respect of the training data of AI models) Slavic language such as Czech. The results suggest that readers’ beliefs about authorship and the aesthetic evaluation of the poem are interconnected.
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[NLP-5] AGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLM s
【速读】： 该论文旨在解决生成式 AI（Generative AI）在图异常检测，特别是虚假新闻检测中的应用缺乏大规模、真实且标注良好的数据集作为基准的问题。其解决方案的关键在于构建了一个名为TAGFN的大规模真实世界文本属性图数据集，专门用于异常检测任务，能够支持传统方法与基于大语言模型（Large Language Models, LLMs）的图异常检测方法的严格评估，并通过微调增强LLMs在虚假信息识别方面的能力。
链接: https://arxiv.org/abs/2511.21624
作者: Kay Liu,Yuwei Han,Haoyan Xu,Henry Peng Zou,Yue Zhao,Philip S. Yu
机构: University of Illinois Chicago (伊利诺伊大学芝加哥分校); University of Southern California (南加州大学)
类目: ocial and Information Networks (cs.SI); Computation and Language (cs.CL)
备注: Preprint. Under review
Abstract:Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the context of fake news detection, remains significantly underexplored. One of the key challenges is the scarcity of large-scale, realistic, and well-annotated datasets that can serve as reliable benchmarks for outlier detection. To bridge this gap, we introduce TAGFN, a large-scale, real-world text-attributed graph dataset for outlier detection, specifically fake news detection. TAGFN enables rigorous evaluation of both traditional and LLM-based graph outlier detection methods. Furthermore, it facilitates the development of misinformation detection capabilities in LLMs through fine-tuning. We anticipate that TAGFN will be a valuable resource for the community, fostering progress in robust graph-based outlier detection and trustworthy AI. The dataset is publicly available at this https URL and our code is available at this https URL.
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[NLP-6] Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）预训练过程中效率低下的问题，特别是如何通过引入元数据（metadata）来加速预训练进程。其核心解决方案在于系统性地探索多种类型的元数据，并发现细粒度的元数据信号（如文档质量指标）在预训练中具有显著加速效果；关键创新点包括：提出将元数据“附加”到输入序列以作为辅助任务来提升训练效率，以及利用可学习的元标记（meta-tokens）结合掩码损失训练，从而恢复部分速度优势并诱导出与质量感知相关的潜在结构。研究进一步通过探针分析揭示了元数据如何塑造模型隐层表示，为高效、有效的LLM预训练提供了可操作的实践指导。
链接: https://arxiv.org/abs/2511.21613
作者: Dongyang Fan,Diba Hashemi,Sai Praneeth Karimireddy,Martin Jaggi
机构: EPFL (瑞士联邦理工学院); University of Southern California (南加州大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Incorporating metadata in Large Language Models (LLMs) pretraining has recently emerged as a promising approach to accelerate training. However prior work highlighted only one useful signal-URLs, leaving open the question of whether other forms of metadata could yield greater benefits. In this study, we investigate a wider range of metadata types and find other types of metadata, such as fine-grained indicators of document quality that can also accelerate pretraining when prepended. We identify a common feature among effective metadata: they encode information at a finer granularity. We further introduce metadata appending as a means of improving training efficiency, where predicting an appropriate metadata as auxiliary task can help speed up pretraining. In addition, learnable meta-tokens trained with masked loss can recover part of the speedup by inducing quality-aware latent structure. Using probing, we analyze latent representations to understand how metadata shapes learning. Together, these results yield practical guidelines for integrating metadata to improve both the efficiency and effectiveness of LLM pretraining.
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[NLP-7] Auxiliary Metrics Help Decoding Skill Neurons in the Wild
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）内部机制不透明的问题，尤其是如何识别和隔离编码特定技能的神经元。其核心挑战在于，尽管LLMs在多种任务中表现出色，但其内部表征缺乏可解释性，难以定位具体技能对应的神经单元。解决方案的关键在于提出一种简单、轻量且通用的方法：通过将神经元激活与辅助指标（如外部标签和模型自身的置信度分数）相关联，无需人工进行token聚合即可揭示具有任务特异性的可解释行为。该方法扩展了先前基于软提示训练（soft prompt training）识别“技能神经元”（skill neurons）的工作，适用于多技能复杂场景，并在开放文本生成和自然语言推理等任务上验证了有效性，成功检测到既驱动已知技能又揭示算术推理中未被发现捷径的神经元。
链接: https://arxiv.org/abs/2511.21610
作者: Yixiu Zhao,Xiaozhi Wang,Zijun Yao,Lei Hou,Juanzi Li
机构: Tsinghua University (清华大学)
类目: Computation and Language (cs.CL)
备注: 7 pages, 7 figures. Includes additional appendix
Abstract:Large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, yet their internal mechanisms remain largely opaque. In this paper, we introduce a simple, lightweight, and broadly applicable method with a focus on isolating neurons that encode specific skills. Building upon prior work that identified “skill neurons” via soft prompt training on classification tasks, our approach extends the analysis to complex scenarios involving multiple skills. We correlate neuron activations with auxiliary metrics – such as external labels and the model’s own confidence score – thereby uncovering interpretable and task-specific behaviors without the need for manual token aggregation. We empirically validate our method on tasks spanning open-ended text generation and natural language inference, demonstrating its ability to detect neurons that not only drive known skills but also reveal previously unidentified shortcuts in arithmetic reasoning on BigBench.
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[NLP-8] RoParQ: Paraphrase-Aware Alignment of Large Language Models Towards Robustness to Paraphrased Questions
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在面对改写问题（paraphrased questions）时表现出行为不一致的问题，这反映出模型可能依赖表面模式而非真正的语义理解。解决方案的关键在于提出一个名为RoParQ的基准测试集，该集合通过专有模型生成问题改写并筛选出导致判别模型置信度不一致的样本，从而更精准地评估模型的跨改写一致性；同时引入XParaCon这一新指标，量化模型在不同问题变体下的准确率标准差以衡量其鲁棒性，并采用基于推理的、改写感知的监督微调（Supervised Fine-Tuning, SFT）策略，引导模型向语义不变性对齐。实验表明，这种针对性对齐显著提升了模型鲁棒性，轻量级模型经微调后一致性表现可媲美更大规模预训练模型。
链接: https://arxiv.org/abs/2511.21568
作者: Minjoon Choi
机构: Seoul National University (首尔国立大学)
类目: Computation and Language (cs.CL)
备注: 12 pages, 9 figures, 8 tables
Abstract:Large Language Models (LLMs) often exhibit inconsistent behavior when answering paraphrased questions, suggesting a reliance on surface-level patterns rather than true semantic understanding. To address this limitation, we introduce RoParQ, a benchmark specifically constructed to evaluate cross-paraphrase consistency in closed-book multiple-choice QA. This benchmark is derived from standard datasets by generating paraphrases via proprietary models and selectively retaining examples that elicit inconsistent confidence from a judge model. We further propose XParaCon, a novel evaluation metric that quantifies a model’s robustness by measuring the standard deviation of accuracies across question variants. Additionally, we implement a reasoning-based, paraphrase-aware Supervised Fine-Tuning (SFT) strategy designed to align models toward semantic invariance. Our experiments demonstrate that this targeted alignment significantly enhances robustness. Notably, fine-tuned lightweight models achieved consistency levels comparable to much larger pre-trained models. These results highlight the efficacy of our approach in mitigating superficial memorization and fostering more robust, reliable LLMs.
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[NLP-9] Bangla Sign Language Translation: Dataset Creation Challenges Benchmarking and Prospects
【速读】： 该论文旨在解决孟加拉手语翻译（Bangla Sign Language Translation, BdSLT）因资源极度匮乏而受限的问题，其核心挑战在于缺乏高质量的句子级数据集以支持面向听障人群的AI辅助工具开发。解决方案的关键在于构建并公开发布一个名为IsharaKhobor的高质量、多子集的手语翻译数据集，该数据集包含原始版本及经过词汇限制和规范化处理后的两个变体（IsharaKhobor_small 和 IsharaKhobor_canonical_small），并通过基于关键点的原始特征与RQE嵌入进行基准测试，验证了其在提升模型性能方面的有效性，为后续BdSLT研究提供了可复用的数据基础与方法论支持。
链接: https://arxiv.org/abs/2511.21533
作者: Husne Ara Rubaiyeat,Hasan Mahmud,Md Kamrul Hasan
机构: 未知
类目: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 8 tables
Abstract:Bangla Sign Language Translation (BdSLT) has been severely constrained so far as the language itself is very low resource. Standard sentence level dataset creation for BdSLT is of immense importance for developing AI based assistive tools for deaf and hard of hearing people of Bangla speaking community. In this paper, we present a dataset, IsharaKhobor , and two subset of it for enabling research. We also present the challenges towards developing the dataset and present some way forward by benchmarking with landmark based raw and RQE embedding. We do some ablation on vocabulary restriction and canonicalization of the same within the dataset, which resulted in two more datasets, IsharaKhobor_small and IsharaKhobor_canonical_small. The dataset is publicly available at: this http URL [1].
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[NLP-10] Voice Bias and Coreference: An Interpretability Study of Gender in Speech Translation LREC2026
【速读】： 该论文旨在解决语音翻译（Speech Translation, ST）模型在处理具有语法性别标记的语言时可能出现的性别误判问题，即模型可能基于说话人的声学特征（如音高）而非语义信息进行性别分配，从而导致对说话者性别的错误推断。解决方案的关键在于揭示了模型并非简单复制训练数据中的性别关联模式，而是通过一种新颖的机制——利用第一人称代词将指代说话者的词汇与其性别信息关联起来，并从频谱分布中提取性别线索，而非依赖集中于音高的单一声学特征，从而显著提升性别标注准确性。
链接: https://arxiv.org/abs/2511.21517
作者: Lina Conti,Dennis Fucci,Marco Gaido,Matteo Negri,Guillaume Wisniewski,Luisa Bentivogli
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Submitted to LREC 2026
Abstract:Unlike text, speech conveys information about the speaker, such as gender, through acoustic cues like pitch. This gives rise to modality-specific bias concerns. For example, in speech translation (ST), when translating from languages with notional gender, such as English, into languages where gender-ambiguous terms referring to the speaker are assigned grammatical gender, the speaker’s vocal characteristics may play a role in gender assignment. This risks misgendering speakers, whether through masculine defaults or vocal-based assumptions. Yet, how ST models make these decisions remains poorly understood. We investigate the mechanisms ST models use to assign gender to speaker-referring terms across three language pairs (en-es/fr/it), examining how training data patterns, internal language model (ILM) biases, and acoustic information interact. We find that models do not simply replicate term-specific gender associations from training data, but learn broader patterns of masculine prevalence. While the ILM exhibits strong masculine bias, models can override these preferences based on acoustic input. Using contrastive feature attribution on spectrograms, we reveal that the model with higher gender accuracy relies on a previously unknown mechanism: using first-person pronouns to link gendered terms back to the speaker, accessing gender information distributed across the frequency spectrum rather than concentrated in pitch.
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[NLP-11] Hierarchical Ranking Neural Network for Long Document Readability Assessment
【速读】： 该论文旨在解决现有可读性评估方法在应用深度学习技术时忽视文本长度以及可读性标签序数关系的问题。其解决方案的关键在于提出一种双向可读性评估机制，通过捕捉上下文信息识别文本中语义丰富区域，从而预测句子级别的可读性标签；同时引入成对排序算法，利用标签相减建模不同可读性等级间的序数关系，进而辅助文档整体可读性水平的预测。
链接: https://arxiv.org/abs/2511.21473
作者: Yurui Zheng,Yijun Chen,Shaohong Zhang
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Readability assessment aims to evaluate the reading difficulty of a text. In recent years, while deep learning technology has been gradually applied to readability assessment, most approaches fail to consider either the length of the text or the ordinal relationship of readability labels. This paper proposes a bidirectional readability assessment mechanism that captures contextual information to identify regions with rich semantic information in the text, thereby predicting the readability level of individual sentences. These sentence-level labels are then used to assist in predicting the overall readability level of the document. Additionally, a pairwise sorting algorithm is introduced to model the ordinal relationship between readability levels through label subtraction. Experimental results on Chinese and English datasets demonstrate that the proposed model achieves competitive performance and outperforms other baseline models.
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[NLP-12] A Systematic Study of Model Merging Techniques in Large Language Models
【速读】： 该论文旨在解决当前模型合并（model merging）技术在大型语言模型（Large Language Models, LLMs）上效果不佳的问题，即现有方法是否能像在小型模型和分类器中那样有效提升LLM性能。研究发现，尽管已有多种先进合并方法（包括干扰感知和子空间方法），它们在LLM上通常导致显著性能下降，而最简单的方法——任务算术（Task Arithmetic）——是唯一能稳定带来性能提升的策略。因此，解决方案的关键在于：当前主流合并技术无法直接迁移至现代LLM，亟需设计针对LLM特性的专用合并算法以及融合合并机制的微调方法。
链接: https://arxiv.org/abs/2511.21437
作者: Oğuz Kağan Hitit,Leander Girrbach,Zeynep Akata
机构: Koç University; Technical University of Munich (慕尼黑工业大学); Munich Center for Machine Learning (慕尼黑机器学习中心); Helmholtz Munich (亥姆霍兹慕尼黑研究中心)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:Model merging combines multiple fine-tuned checkpoints into a single model without additional training, offering an attractive approach to reusing models and efficiently improving performance. However, it remains unclear whether the advantages reported for smaller models and classifiers generalize to LLMs. We present a large-scale, systematic evaluation of six state-of-the-art merging methods, including recent subspace methods, across four open-weight LLMs, twelve fine-tuned checkpoints per base model, and sixteen standard LLM benchmarks. Evaluating through standardized benchmarks, we measure both the probability that a merged model outperforms the base model and relative gains over the best individual checkpoint. Our results show that the oldest and simplest method, Task Arithmetic, is the only approach that reliably yields performance gains on LLMs. Other interference-aware and subspace merging methods typically result in significant performance drops. Our findings indicate that current merging techniques do not directly transfer to modern LLMs. This motivates the design of LLM-specific merging algorithms and merging-aware fine-tuning methods. Code will be released upon acceptance of this paper.
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[NLP-13] Odin: Oriented Dual-module Integration for Text-rich Network Representation Learning
【速读】： 该论文旨在解决文本属性图（text-attributed graphs）中如何有效融合强文本理解与结构感知推理的问题。现有方法要么依赖图神经网络（GNNs），受限于过平滑（over-smoothing）和依赖跳跃次数的扩散效应；要么采用Transformer模型，忽略图拓扑结构并将节点视为孤立序列。其解决方案的关键在于提出Odin（Oriented Dual-module INtegration）架构，通过在选定深度的Transformer层中注入图结构信息，利用定向双模块消息传递机制将多跳结构整合至特定层，从而实现低、中、高层级的结构抽象，并与语义层次对齐。该设计避免了传统GNN的过平滑问题，且将结构抽象与邻域大小或图拓扑解耦，提升了模型表达能力并支持高效训练与推理。
链接: https://arxiv.org/abs/2511.21416
作者: Kaifeng Hong,Yinglong Zhang,Xiaoying Hong,Xuewen Xia,Xing Xu
机构: 未知
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 32 pages, 2 figures
Abstract:Text-attributed graphs require models to effectively combine strong textual understanding with structurally informed reasoning. Existing approaches either rely on GNNs–limited by over-smoothing and hop-dependent diffusion–or employ Transformers that overlook graph topology and treat nodes as isolated sequences. We propose Odin (Oriented Dual-module INtegration), a new architecture that injects graph structure into Transformers at selected depths through an oriented dual-module this http URL message-passing GNNs, Odin does not rely on multi-hop diffusion; instead, multi-hop structures are integrated at specific Transformer layers, yielding low-, mid-, and high-level structural abstraction aligned with the model’s semantic hierarchy. Because aggregation operates on the global [CLS] representation, Odin fundamentally avoids over-smoothing and decouples structural abstraction from neighborhood size or graph topology. We further establish that Odin’s expressive power strictly contains that of both pure Transformers and this http URL make the design efficient in large-scale or low-resource settings, we introduce Light Odin, a lightweight variant that preserves the same layer-aligned structural abstraction for faster training and inference. Experiments on multiple text-rich graph benchmarks show that Odin achieves state-of-the-art accuracy, while Light Odin delivers competitive performance with significantly reduced computational cost. Together, Odin and Light Odin form a unified, hop-free framework for principled structure-text integration. The source code of this model has been released at this https URL.
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[NLP-14] Subjective Depth and Timescale Transformers: Learning Where and When to Compute
【速读】： 该论文旨在解决标准Transformer（TF）架构中计算分配刚性且均匀的问题，这一局限性会显著影响大规模模型和长序列场景下的效率与可扩展性。其解决方案的关键在于引入两种新型架构——主观深度Transformer（Subjective Depth Transformers, SDT）和主观时标Transformer（Subjective Timescale Transformers, STT），二者均利用贝叶斯惊喜信号（Bayesian surprise）动态路由计算，学习在解码器-only结构中“何处”与“何时”执行计算。SDT通过交替的决策层（Decision layer）与动态层（Dynamic layer）实现基于预期与意外变化的Top-K路由机制；STT进一步将条件计算拓展至时间维度，通过过渡网络预测残差更新并形成时序“变化假设”，从而控制每个token是否跳过或执行TF块，同时管理KV缓存贡献。两类架构均展现出随训练从新颖性到预测驱动的门控转变，验证了其与基于惊喜原理的一致性，并在保持性能的同时实现75%自注意力计算和50% KV缓存需求的减少。
链接: https://arxiv.org/abs/2511.21408
作者: Frederico Wieser,Martin Benfeghoul,Haitham Bou Ammar,Jun Wang,Zafeirios Fountas
机构: AI Centre, Department of Computer Science, University College London, London, UK; Huawei, Noah’s Ark Lab, London
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Theory (cs.IT)
备注:
Abstract:The rigid, uniform allocation of computation in standard Transformer (TF) architectures can limit their efficiency and scalability, particularly for large-scale models and long sequences. Addressing this, we introduce Subjective Depth Transformers (SDT) and Subjective Timescale Transformers (STT), two distinct architectures that leverage Bayesian surprise signals to dynamically route computation, learning where and when to compute within decoder-only TFs. SDT augments a decoder-only stack with alternating Decision and Dynamic layers: a Decision layer computes a full block ‘posterior’ and a lightweight ‘prior,’ while a Dynamic layer employs fixed-capacity Top-K routing based on Bayesian surprise (Expected and Unexpected Change), maintaining a static compute graph. STT extends this conditional computation to the temporal domain: a transition network predicts residual updates, forming a temporal ‘change hypothesis’ that informs a router to dynamically execute or bypass TF blocks for each token, managing KV-cache contributions. Both architectures exhibit the predicted shift from novelty to prediction driven gating over training, suggesting alignment with surprise based principles. While operating at reduced capacity, they offer preliminary insights into the compute-accuracy trade-offs of conditional computation. The proposed architectures establish a flexible framework for efficiency, reducing self-attention computation by 75% and KV-cache requirements by 50% within each compute skipping layer, setting a pathway for more efficient models.
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[NLP-15] xt-to-SQL as Dual-State Reasoning : Integrating Adaptive Context and Progressive Generation
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在复杂企业数据库上执行Text-to-SQL任务时面临的三大挑战：受限的上下文容量、不可靠的模式链接（schema linking）以及对数据库语义的弱锚定（weak grounding）。为应对这些问题，作者提出了一种名为DSR-SQL的双状态推理框架，其核心创新在于将Text-to-SQL建模为自适应上下文状态与渐进生成状态之间的交互过程。其中，上下文状态通过精炼大规模数据库模式并选择相关结构来构建紧凑且语义忠实的环境；生成状态则将SQL合成形式化为反馈引导的状态转移机制，使模型具备自我修正能力并更准确地对齐用户意图。此方法无需后训练或上下文示例即可实现高性能，显著提升了复杂场景下的Text-to-SQL准确性。
链接: https://arxiv.org/abs/2511.21402
作者: Zhifeng Hao,Qibin Song,Ruichu Cai,Boyan Xu
机构: Guangdong University of Technology (广东工业大学); Shantou University (汕头大学); Peng Cheng Laboratory (鹏城实验室)
类目: Computation and Language (cs.CL)
备注:
Abstract:Recent divide-and-conquer reasoning approaches, particularly those based on Chain-of-Thought (CoT), have substantially improved the Text-to-SQL capabilities of Large Language Models (LLMs). However, when applied to complex enterprise databases, such methods struggle to maintain coherent reasoning due to limited context capacity, unreliable schema linking, and weak grounding in database semantics. To overcome these issues, we introduce DSR-SQL, a \textbfDual-\textbfState \textbfReasoning framework that models Text-to-SQL as an interaction between an adaptive context state and a progressive generation state. The first constructs a compact, semantically faithful environment by refining large schemas and selecting relevant structures, while the second formalizes SQL synthesis as feedback-guided state transitions, enabling the model to self-correct and align with user intent. Without any post-training or in-context examples, DSR-SQL achieves competitive performance, reaching 35.28% execution accuracy on Spider 2.0-Snow and 68.32% on BIRD development set. Our implementation will be open-sourced at: this https URL.
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[NLP-16] Can LLM s extract human-like fine-grained evidence for evidence-based fact-checking?
【速读】： 该论文旨在解决在线新闻评论中虚假信息（misinformation）传播问题，核心挑战在于从大量文本中精准提取支持或反驳评论主张的细粒度证据（fine-grained evidence）。其解决方案的关键在于构建了一个针对捷克语和斯洛伐克语评论主张的新标注数据集，其中包含由付费标注者进行双向标注的细粒度证据片段，并通过评估大型语言模型（Large Language Models, LLMs）在该数据集上的表现，分析其与人工标注的一致性。研究发现，尽管多数LLMs难以准确复现原文中的证据片段，导致输出无效，但部分模型如Llama3.1:8B、Qwen3:14B、DeepSeek-R1:32B和GPT-OSS:20B展现出良好的性能平衡，尤其在模型规模与人类标注一致性之间取得较优折衷。
链接: https://arxiv.org/abs/2511.21401
作者: Antonín Jarolím,Martin Fajčík,Lucia Makaiová
机构: VUT (布尔诺理工大学)
类目: Computation and Language (cs.CL)
备注:
Abstract:Misinformation frequently spreads in user comments under online news articles, highlighting the need for effective methods to detect factually incorrect information. To strongly support or refute claims extracted from such comments, it is necessary to identify relevant documents and pinpoint the exact text spans that justify or contradict each claim. This paper focuses on the latter task – fine-grained evidence extraction for Czech and Slovak claims. We create new dataset, containing two-way annotated fine-grained evidence created by paid annotators. We evaluate large language models (LLMs) on this dataset to assess their alignment with human annotations. The results reveal that LLMs often fail to copy evidence verbatim from the source text, leading to invalid outputs. Error-rate analysis shows that the llama3.1:8b model achieves a high proportion of correct outputs despite its relatively small size, while the gpt-oss-120b model underperforms despite having many more parameters. Furthermore, the models qwen3:14b, deepseek-r1:32b, and gpt-oss:20b demonstrate an effective balance between model size and alignment with human annotations.
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[NLP-17] raining Introspective Behavior: Fine-Tuning Induces Reliable Internal State Detection in a 7B Model
【速读】： 该论文试图解决语言模型中 introspective awareness（内省意识）的可训练性问题，即是否可以通过直接训练而非等待模型自发涌现来实现对短暂注入“思想”（injected thoughts）的可靠检测与报告。其解决方案的关键在于：通过在瞬态单标记注入数据上进行微调（fine-tuning），将一个7B参数模型从几乎无法识别注入内容（准确率仅0.4%，假阳性率为6.7%）提升至高精度检测能力（在α=40时准确率达85%，假阳性为0%）。该方法使模型能够捕捉单个token位置注入的短暂信息，并在后续生成步骤中持续保留并报告语义内容，满足Lindsey提出的三项标准中的两项（准确性与接地性）及一项（内部性），表明这一内省行为的特定组件可通过训练直接诱导，从而为构建具备内置透明性的AI系统提供可行路径。
链接: https://arxiv.org/abs/2511.21399
作者: Joshua Fonseca Rivera
机构: The University of Texas at Austin (德克萨斯大学奥斯汀分校)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 16 pages, 8 figures
Abstract:Lindsey (2025) investigates introspective awareness in language models through four experiments, finding that models can sometimes detect and identify injected activation patterns – but unreliably (~20% success in the best model). We focus on the first of these experiments – self-report of injected “thoughts” – and ask whether this capability can be directly trained rather than waiting for emergence. Through fine-tuning on transient single-token injections, we transform a 7B parameter model from near-complete failure (0.4% accuracy, 6.7% false positive rate) to reliable detection (85% accuracy on held-out concepts at \alpha=40, 0% false positives). Our model detects fleeting “thoughts” injected at a single token position, retains that information, and reports the semantic content across subsequent generation steps. On this task, our trained model satisfies three of Lindsey’s criteria: accuracy (correct identification), grounding (0/60 false positives), and internality (detection precedes verbalization). Generalization to unseen concept vectors (7.5pp gap) demonstrates the model learns a transferable skill rather than memorizing specific vectors, though this does not establish metacognitive representation in Lindsey’s sense. These results address an open question raised by Lindsey: whether “training for introspection would help eliminate cross-model differences.” We show that at least one component of introspective behavior can be directly induced, offering a pathway to built-in AI transparency.
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[NLP-18] Prune4Web: DOM Tree Pruning Programming for Web Agent AAAI2026
【速读】： 该论文旨在解决当前基于大语言模型（Large Language Model, LLM）的网页自动化代理在处理复杂真实网页时效率低下的问题，核心挑战在于文档对象模型（Document Object Model, DOM）结构庞大（通常达10,000至100,000 tokens），导致LLM难以高效地进行精准操作定位。现有方法依赖粗粒度DOM截断或低效启发式策略，难以兼顾精度与可扩展性。解决方案的关键在于提出Prune4Web范式，其核心创新是将DOM处理从资源密集型的LLM读取转向高效的程序化剪枝（programmatic pruning），具体通过“DOM树剪枝编程”机制，由LLM生成可执行的Python评分脚本，动态依据子任务语义线索过滤DOM元素，从而显著减少候选元素数量（25x–50x），实现精确动作定位并缓解注意力稀释问题。此外，研究设计了专用数据标注流水线和两轮对话训练策略，统一优化规划器（Planner）、程序化过滤器（Programmatic Filter）与接地器（Grounder），在多个基准上达到SOTA性能，尤其在低级接地任务中准确率从46.8%提升至88.28%。
链接: https://arxiv.org/abs/2511.21398
作者: Jiayuan Zhang,Kaiquan Chen,Zhihao Lu,Enshen Zhou,Qian Yu,Jing Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)
备注: Paper accepted to AAAI 2026
Abstract:Web automation employs intelligent agents to execute high-level tasks by mimicking human interactions with web interfaces. Despite the capabilities of recent Large Language Model (LLM)-based web agents, navigating complex, real-world webpages efficiently remains a significant hurdle due to the prohibitively large size of Document Object Model (DOM) structures, often ranging from 10,000 to 100,000 tokens. Existing strategies typically rely on crude DOM truncation – risking the loss of critical information – or employ inefficient heuristics and separate ranking models, failing to achieve an optimal balance between precision and scalability. To address these challenges, we introduce Prune4Web, a novel paradigm that shifts DOM processing from resource-intensive LLM reading to efficient programmatic pruning. Central to our approach is DOM Tree Pruning Programming, where an LLM generates executable Python scoring scripts to dynamically filter DOM elements based on semantic cues from decomposed sub-tasks. This mechanism eliminates the need for LLMs to ingest raw, massive DOMs, instead delegating traversal and scoring to lightweight, interpretable programs. This methodology achieves a 25x to 50x reduction in candidate elements for grounding, thereby facilitating precise action localization while mitigating attention dilution. Furthermore, we propose a specialized data annotation pipeline and a two-turn dialogue training strategy that jointly optimizes the Planner, Programmatic Filter, and Grounder within a unified framework. Extensive experiments demonstrate state-of-the-art performance. Notably, on our low-level grounding task, Prune4Web dramatically improves accuracy from 46.8% to 88.28%, underscoring its efficacy in real-world web automation.
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[NLP-19] Do Reasoning Vision-Language Models Inversely Scale in Test-Time Compute? A Distractor-centric Empirical Analysis
【速读】： 该论文旨在解决视觉-语言模型（Vision-Language Models, VLMs）在测试时扩展（test-time scaling）过程中，无关信息（即干扰项，distractors）如何影响推理过程与准确率的问题。此前关于语言模型的研究发现存在“逆向缩放效应”（inverse scaling effect），即文本干扰项会导致推理链变长但效果下降。本文通过构建Idis数据集（Images with distractors），系统性地在语义、数值和空间维度上操控视觉干扰项，发现视觉干扰项与文本干扰项存在本质差异：虽然逆向缩放现象依然存在，但视觉干扰项会直接降低准确率而不会延长推理长度。研究的关键在于引入属性计数追踪（attribute count tracking）方法，从推理轨迹中提取关键特征，揭示了干扰项、推理长度与准确率之间的内在交互机制，并进一步提出一种简单提示策略（prompting strategy），有效缓解由视觉偏见引发的错误预测，从而提升模型鲁棒性。
链接: https://arxiv.org/abs/2511.21397
作者: Jiyun Bae,Hyunjong Ok,Sangwoo Mo,Jaeho Lee
机构: Pohang University of Science and Technology (POSTECH)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: preprint
Abstract:How does irrelevant information (i.e., distractors) affect test-time scaling in vision-language models (VLMs)? Prior studies on language models have reported an inverse scaling effect, where textual distractors lead to longer but less effective reasoning. To investigate whether similar phenomena occur in multimodal settings, we introduce Idis (Images with distractors), a visual question-answering dataset that systematically varies distractors along semantic, numerical, and spatial dimensions. Our analyses reveal that visual distractors differ fundamentally from textual ones: although inverse scaling persists, adding visual distractors reduces accuracy without increasing reasoning length. We further show that tracking attribute counts within reasoning traces provides key insights into how distractors, reasoning length, and accuracy interact. Finally, we demonstrate that these trends extend to established visual bias benchmarks such as Waterbirds, and we propose a simple prompting strategy to mitigate bias-driven predictions in reasoning models.
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[NLP-20] BanglaASTE: A Novel Framework for Aspect-Sentiment-Opinion Extraction in Bangla E-commerce Reviews Using Ensemble Deep Learning
【速读】： 该论文旨在解决孟加拉语（Bangla）领域中Aspect Sentiment Triplet Extraction (ASTE) 任务缺乏高质量标注数据与专用模型的问题，从而实现从孟加拉语产品评论中准确提取情感三元组（aspect term, opinion expression, sentiment polarity）。其解决方案的关键在于：构建了首个孟加拉语ASTE标注数据集（BanglaASTE），包含3,345条来自Daraz、Facebook和Rokomari等电商平台的评论；提出一种融合图结构方面-意见匹配与语义相似度技术的混合分类框架，并设计了一个结合孟加拉语BERT上下文嵌入与XGBoost集成学习算法的增强型模型，显著提升了在非正式表达、拼写变体及数据稀疏等挑战下的三元组抽取性能，最终实现了89.9%的准确率和89.1%的F1分数。
链接: https://arxiv.org/abs/2511.21381
作者: Ariful Islam,Md Rifat Hossen,Abir Ahmed,B M Taslimul Haque
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: Presented at the 2025 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON), November 21-22, 2025, University of Rajshahi, Bangladesh. 6 pages, ensemble deep learning, 3,345 annotated Bangla product reviews
Abstract:Aspect-Based Sentiment Analysis (ABSA) has emerged as a critical tool for extracting fine-grained sentiment insights from user-generated content, particularly in e-commerce and social media domains. However, research on Bangla ABSA remains significantly underexplored due to the absence of comprehensive datasets and specialized frameworks for triplet extraction in this language. This paper introduces BanglaASTE, a novel framework for Aspect Sentiment Triplet Extraction (ASTE) that simultaneously identifies aspect terms, opinion expressions, and sentiment polarities from Bangla product reviews. Our contributions include: (1) creation of the first annotated Bangla ASTE dataset containing 3,345 product reviews collected from major e-commerce platforms including Daraz, Facebook, and Rokomari; (2) development of a hybrid classification framework that employs graph-based aspect-opinion matching with semantic similarity techniques; and (3) implementation of an ensemble model combining BanglaBERT contextual embeddings with XGBoost boosting algorithms for enhanced triplet extraction performance. Experimental results demonstrate that our ensemble approach achieves superior performance with 89.9% accuracy and 89.1% F1-score, significantly outperforming baseline models across all evaluation metrics. The framework effectively addresses key challenges in Bangla text processing including informal expressions, spelling variations, and data sparsity. This research advances the state-of-the-art in low-resource language sentiment analysis and provides a scalable solution for Bangla e-commerce analytics applications.
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[NLP-21] Emergent Lexical Semantics in Neural Language Models: Testing Martins Law on LLM -Generated Text
【速读】： 该论文旨在解决生成式 AI（Generative AI）模型在训练过程中语言规律性（如马丁定律，即词频与多义性之间的经验关系）如何演变的问题。其核心解决方案在于首次系统性地研究神经语言模型在训练期间生成文本中马丁定律的动态发展轨迹：通过使用DBSCAN聚类对上下文嵌入进行操作化定义词义，并在四个Pythia模型（参数规模从70M到1B）的30个训练检查点上进行分析。关键发现是马丁定律呈现非单调的发展路径——在约第100个检查点出现，于第104个达到峰值相关性（r ≈ 0.6），随后在后续检查点退化；同时，小模型出现语义崩溃，而大模型则表现出渐进式退化，表明语言结构的涌现并非随训练单调增强，而是存在一个最优的语义窗口。
链接: https://arxiv.org/abs/2511.21334
作者: Kai Kugler
机构: Trier University (特里尔大学)
类目: Computation and Language (cs.CL)
备注: paper draft
Abstract:We present the first systematic investigation of Martin’s Law - the empirical relationship between word frequency and polysemy - in text generated by neural language models during training. Using DBSCAN clustering of contextualized embeddings as an operationalization of word senses, we analyze four Pythia models (70M-1B parameters) across 30 training checkpoints. Our results reveal a non-monotonic developmental trajectory: Martin’s Law emerges around checkpoint 100, reaches peak correlation (r 0.6) at checkpoint 104, then degrades by checkpoint 105. Smaller models (70M, 160M) experience catastrophic semantic collapse at late checkpoints, while larger models (410M, 1B) show graceful degradation. The frequency-specificity trade-off remains stable (r \approx -0.3) across all models. These findings suggest that compliance with linguistic regularities in LLM-generated text is not monotonically increasing with training, but instead follows a balanced trajectory with an optimal semantic window. This work establishes a novel methodology for evaluating emergent linguistic structure in neural language models.
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[NLP-22] ALES: A Taxonomy and Analysis of Cultural Representations in LLM -generated Stories
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在生成故事时对印度多元文化身份的代表性不足问题，特别是文化误表征（cultural misrepresentations）的识别与评估难题。其关键解决方案是构建TALES-Tax——一个基于印度本土参与者（通过焦点小组N=9和个体调查N=15）深度访谈提炼出的文化误表征分类体系，并利用该框架对6个主流模型进行大规模人工标注评估（共2,925条标注，来自108位具有跨71个地区和14种语言背景的 annotators）。研究发现，88%的生成故事存在至少一种文化错误，且在中低资源语言及城乡结合区域的故事中更为显著；进一步地，作者将标注结果转化为TALES-QA问答库，用于独立评估模型的文化知识储备，意外发现模型虽常产生文化错误，却具备足够的文化知识基础。
链接: https://arxiv.org/abs/2511.21322
作者: Kirti Bhagat,Shaily Bhatt,Athul Velagapudi,Aditya Vashistha,Shachi Dave,Danish Pruthi
机构: Indian Institute of Science (印度科学研究所); Carnegie Mellon University (卡内基梅隆大学); Cornell University (康奈尔大学); Google DeepMind (谷歌深度思维)
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
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Abstract:Millions of users across the globe turn to AI chatbots for their creative needs, inviting widespread interest in understanding how such chatbots represent diverse cultures. At the same time, evaluating cultural representations in open-ended tasks remains challenging and underexplored. In this work, we present TALES, an evaluation of cultural misrepresentations in LLM-generated stories for diverse Indian cultural identities. First, we develop TALES-Tax, a taxonomy of cultural misrepresentations by collating insights from participants with lived experiences in India through focus groups (N=9) and individual surveys (N=15). Using TALES-Tax, we evaluate 6 models through a large-scale annotation study spanning 2,925 annotations from 108 annotators with lived cultural experience from across 71 regions in India and 14 languages. Concerningly, we find that 88% of the generated stories contain one or more cultural inaccuracies, and such errors are more prevalent in mid- and low-resourced languages and stories based in peri-urban regions in India. Lastly, we transform the annotations into TALES-QA, a standalone question bank to evaluate the cultural knowledge of foundational models. Through this evaluation, we surprisingly discover that models often possess the requisite cultural knowledge despite generating stories rife with cultural misrepresentations.
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[NLP-23] PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）因参数规模庞大而导致计算和环境成本高昂、可访问性受限的问题。现有参数高效微调（Parameter-efficient Fine-tuning, PEFT）方法虽能减少可训练参数数量并保持下游任务性能，但当前评估仍存在覆盖模型与数据集有限、结果难以复现等局限。为此，作者提出PEFT-Bench——一个面向自回归LLMs的统一端到端基准测试平台，用于系统化评估多种PEFT方法在27个NLP数据集上的表现；其关键创新在于引入PEFT Soft Score Penalties (PSCP)指标，综合考量可训练参数量、推理速度与训练内存使用等多个维度，从而更全面地衡量PEFT方法的实际效率与实用性。
链接: https://arxiv.org/abs/2511.21285
作者: Robert Belanec,Branislav Pecher,Ivan Srba,Maria Bielikova
机构: Brno University of Technology (布赫大学); Kempelen Institute of Intelligent Technologies (肯佩伦智能技术研究所)
类目: Computation and Language (cs.CL)
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Abstract:Despite the state-of-the-art performance of Large Language Models (LLMs) achieved on many tasks, their massive scale often leads to high computational and environmental costs, limiting their accessibility. Parameter-efficient fine-tuning (PEFT) methods address this challenge by reducing the number of trainable parameters while maintaining strong downstream performance. Despite the increased development in PEFT methods, current evaluations remain limited (in terms of evaluated models and datasets) and difficult to reproduce. To bridge this gap, we introduce PEFT-Bench, a unified end-to-end benchmark for evaluating diverse PEFT methods on autoregressive LLMs. We demonstrate its usage across 27 NLP datasets and 6 PEFT methods. To account for different PEFT training and inference factors, we also introduce the PEFT Soft Score Penalties (PSCP) metric, which takes trainable parameters, inference speed, and training memory usage into account.
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[NLP-24] Developing an Open Conversational Speech Corpus for the Isan Language
【速读】： 该论文旨在解决Isan语言（泰国最广泛使用的区域方言）缺乏自然对话语音数据集的问题，尤其针对现有语音语料库多基于朗读或脚本化语音、无法捕捉真实口语特征（如俚语、自发语调、不流畅现象及与标准泰语的频繁代码转换）的局限性。其关键解决方案在于建立一套兼顾表征准确性与计算处理需求的实用转录协议，以应对Isan语言书写系统不统一（因泰语与Isan词汇音调差异导致书写实践多样）所带来的转录指南设计难题，从而确保数据的一致性、可用性和语言真实性。
链接: https://arxiv.org/abs/2511.21229
作者: Adisai Na-Thalang,Chanakan Wittayasakpan,Kritsadha Phatcharoen,Supakit Buakaw
机构: 未知
类目: Computation and Language (cs.CL)
备注: 31 pages, in Thai language, 3 figures, 25 tables
Abstract:This paper introduces the development of the first open conversational speech dataset for the Isan language, the most widely spoken regional dialect in Thailand. Unlike existing speech corpora that are primarily based on read or scripted speech, this dataset consists of natural speech, thereby capturing authentic linguistic phenomena such as colloquials, spontaneous prosody, disfluencies, and frequent code-switching with central Thai. A key challenge in building this resource lies in the lack of a standardized orthography for Isan. Current writing practices vary considerably, due to the different lexical tones between Thai and Isan. This variability complicates the design of transcription guidelines and poses questions regarding consistency, usability, and linguistic authenticity. To address these issues, we establish practical transcription protocols that balance the need for representational accuracy with the requirements of computational processing. By releasing this dataset as an open resource, we aim to contribute to inclusive AI development, support research on underrepresented languages, and provide a basis for addressing the linguistic and technical challenges inherent in modeling conversational speech.
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[NLP-25] Can Finetuing LLM s on Small Human Samples Increase Heterogeneity Alignment and Belief-Action Coherence?
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）是否可作为人类被试替代者用于调查与实验研究的问题，特别是针对现有LLM生成数据在分布偏差、子群体对齐度不足、信念-行为一致性差以及回归系数不可复现等方面的局限性。其解决方案的关键在于通过小样本人类调查数据（如试点研究数据）对LLM进行微调（fine-tuning），以提升模拟数据的真实性与合理性。实证结果表明，微调显著改善了群体异质性、子群对齐度及信念-行为一致性，但即便最优微调模型仍无法准确恢复原始研究的回归系数，说明LLM生成数据尚不适合用于正式的推断性分析。
链接: https://arxiv.org/abs/2511.21218
作者: Steven Wang,Kyle Hunt,Shaojie Tang,Kenneth Joseph
机构: University at Buffalo (纽约州立大学布法罗分校); University at Buffalo (纽约州立大学布法罗分校); University at Buffalo (纽约州立大学布法罗分校); University at Buffalo (纽约州立大学布法罗分校)
类目: Computation and Language (cs.CL)
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Abstract:There is ongoing debate about whether large language models (LLMs) can serve as substitutes for human participants in survey and experimental research. While recent work in fields such as marketing and psychology has explored the potential of LLM-based simulation, a growing body of evidence cautions against this practice: LLMs often fail to align with real human behavior, exhibiting limited diversity, systematic misalignment for minority subgroups, insufficient within-group variance, and discrepancies between stated beliefs and actions. This study examines an important and distinct question in this domain: whether fine-tuning on a small subset of human survey data, such as that obtainable from a pilot study, can mitigate these issues and yield realistic simulated outcomes. Using a behavioral experiment on information disclosure, we compare human and LLM-generated responses across multiple dimensions, including distributional divergence, subgroup alignment, belief-action coherence, and the recovery of regression coefficients. We find that fine-tuning on small human samples substantially improves heterogeneity, alignment, and belief-action coherence relative to the base model. However, even the best-performing fine-tuned models fail to reproduce the regression coefficients of the original study, suggesting that LLM-generated data remain unsuitable for replacing human participants in formal inferential analyses.
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[NLP-26] Self-Guided Defense: Adaptive Safety Alignment for Reasoning Models via Synthesized Guidelines
【速读】： 该论文旨在解决生成式 AI（Generative AI）在面对恶意对抗性越狱提示（adversarial jailbreak prompts）时安全性不足的问题，这类提示具有隐蔽性和欺骗性，常能绕过模型内置的安全机制，导致有害内容生成。解决方案的关键在于提出一种合成指导自适应安全对齐框架（Synthesized Guideline-based Adaptive Safety Alignment, SGASA），其核心创新在于通过数据预合成阶段生成安全准则与增强提示，并利用监督微调（Supervised Fine-tuning, SFT）和直接偏好优化（Direct Preference Optimization, DPO）将这些准则嵌入模型，从而实现模型对有害攻击的自主防御强化，同时减少对良性请求的误拒，显著提升安全性与鲁棒性。
链接: https://arxiv.org/abs/2511.21214
作者: Yuhang Wang,Yanxu Zhu,Dongyuan Lu,Jitao Sang
机构: Beijing Jiaotong University (北京交通大学); University of International Business and Economics (对外经济贸易大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Reasoning models have demonstrated remarkable capabilities in complex reasoning tasks. However, ensuring their safety against adversarial jailbreak prompts remains a critical challenge. Due to the covert and deceptive nature of such prompts, they can often evade built-in safety mechanisms and lead to the generation of harmful content. This underscores the need for an adaptive safety alignment approach that enables models to autonomously reinforce their defenses in response to adversarial inputs. This paper introduces the Synthesized Guideline-based Adaptive Safety Alignment (SGASA) framework, which internalizes model-generated safety guidelines to strengthen models’ ability to enhance robustness against harmful adversarial prompts while minimizing unnecessary refusals of benign requests. SGASA consists of two key stages: Data Pre-synthesis, which generates safety guidelines and augmented prompts; and Alignment Fine-tuning, which leverages Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO) to embed these guidelines into the model. Extensive experiments across multiple datasets demonstrate that SGASA significantly improves model safety, validating its adaptive and scalable effectiveness.
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[NLP-27] AnchorOPT: Towards Optimizing Dynamic Anchors for Adaptive Prompt Learning
【速读】： 该论文旨在解决现有基于CLIP模型的提示学习（prompt learning）方法中锚点（anchor）静态性导致的跨任务与训练阶段适应性不足的问题。其解决方案的关键在于提出AnchorOPT框架，通过两个维度实现动态锚点机制：一是锚点值不再依赖人工设计的显式文本标记（如“形状”、“颜色”），而是从任务特定数据中动态学习；二是锚点与可学习软令牌（soft tokens）之间的位置关系不再固定，而是通过一个基于训练阶段和任务上下文条件化的可学习位置矩阵自适应优化。该方法在两阶段训练中先学习锚点，再冻结锚点并优化软令牌及位置矩阵，从而在不引入额外模块或正则化技术的情况下实现性能提升。
链接: https://arxiv.org/abs/2511.21188
作者: Zheng Li,Yibing Song,Xin Zhang,Lei Luo,Xiang Li,Jian Yang
机构: Nankai University (南开大学); Nanjing University of Science and Technology (南京理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: Technical Report
Abstract:Existing prompt learning methods, which are built upon CLIP models, leverage textual tokens as anchors to guide the learnable soft tokens. This guidance improves CLIP generalizations. However, these anchors-static in both value and position-lack cross-task and stage-adaptive flexibility. To address this limitation, we propose AnchorOPT, a dynamic anchor-based prompt learning framework. Specifically, AnchorOPT introduces dynamism in two key dimensions: (i) anchor values eschew handcrafted explicit textual tokens (e.g., “shape”, “color”), instead learning dynamically from task-specific data; and (ii) the positional relationship between anchor and soft tokens is no longer fixed but adaptively optimized via a learnable position matrix conditioned on the training stage and task context. Training occurs in two stages: we first learn the anchor tokens, then freeze and transfer them to the second stage for optimization of soft tokens and the position matrix. Extensive experiments demonstrate that using only a simple learnable anchor and position matrix achieves performance comparable to or exceeding some methods incorporating additional learnable modules or regularization techniques. As a plug-and-play module, AnchorOPT integrates seamlessly into existing frameworks, yielding consistent performance gains across diverse datasets. Code is publicly available at this https URL.
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[NLP-28] How to Correctly Report LLM -as-a-Judge Evaluations
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）作为评估者时因特异性（specificity）和敏感性（sensitivity）不完美而导致判断噪声大、准确率估计存在偏差的问题。现有校正方法通常假设已知精确的特异性和敏感性参数，但在实际应用中仅能获得这些指标的估计值，且缺乏如何基于估计值构建置信区间的方法。论文提出了一种简单的“插值式”（plug-in）框架，通过结合测试集与校准集的信息来校正偏差并构造反映双重不确定性（来自测试和校准数据集）的置信区间，从而实现可实践且统计严谨的LLM评估；此外，为降低估计不确定性，还设计了一种自适应算法以高效分配校准样本量。
链接: https://arxiv.org/abs/2511.21140
作者: Chungpa Lee,Thomas Zeng,Jongwon Jeong,Jy-yong Sohn,Kangwook Lee
机构: Yonsei University (延世大学); University of Wisconsin–Madison (威斯康星大学麦迪逊分校); KRAFTON
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Applications (stat.AP); Machine Learning (stat.ML)
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Abstract:Large language models (LLMs) are increasingly used as evaluators in lieu of humans. While scalable, their judgments are noisy due to imperfect specificity and sensitivity of LLMs, leading to biased accuracy estimates. Although bias-correction methods exist, they are underutilized in LLM research and typically assume exact knowledge of the model’s specificity and sensitivity. Furthermore, in general we only have estimates of these values and it is not well known how to properly construct confidence intervals using only estimates. This work presents a simple plug-in framework that corrects such bias and constructs confidence intervals reflecting uncertainty from both test and calibration dataset, enabling practical and statistically sound LLM-based evaluation. Additionally, to reduce uncertainty in the accuracy estimate, we introduce an adaptive algorithm that efficiently allocates calibration sample sizes.
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[NLP-29] MortgageLLM : Domain-Adaptive Pretraining with Residual Instruction Transfer Alignment Tuning and Task-Specific Routing
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在专业领域（如抵押贷款金融）应用中面临的双重挑战：一方面需要引入领域特定知识以提升任务性能，另一方面需保持指令遵循能力（instruction-following fidelity），避免因领域适配导致通用对话质量下降。解决方案的关键在于提出一种双轨专业化框架（dual-track specialization framework），从单一基础模型（LLaMA-3.1-8B）出发，构建两个专用专家模型——一个用于问答（QA）和对话交互，另一个专注于结构化任务（如分类与摘要），从而避免多任务优化带来的性能权衡问题。此外，通过引入指令残差技术（instruction residual technique）在不依赖监督微调的前提下恢复指令遵循能力，并设计由专家模型自身执行少样本分类的智能任务路由机制，实现了高效且高精度的领域适应。
链接: https://arxiv.org/abs/2511.21101
作者: Manish Jain,Satheesh Kumar Ponnambalam,Salman Faroz,Chandrakanth Lns,Vinay Sharma
机构: Firstsource
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Large Language Models (LLMs) demonstrate exceptional capabilities across general domains, yet their application to specialized sectors such as mortgage finance requires domain-specific knowledge augmentation while preserving instruction-following fidelity. We present MortgageLLM, a novel domain-specific large language model that addresses this dual challenge. It is developed using a dual-track specialization framework from a single base model (LLaMA-3.1-8B). We opted for this dual-expert approach as a single multi-task model suffers from performance trade-offs, where optimizing for structured tasks (via SFT) degrades conversational fidelity (via DPO). Our dual-track method solves this by creating two specialists, allowing each to be optimally trained for its distinct capability. Our approach applies the instruction residual technique to restore instruction-following capabilities post-domain adaptation without supervised fine-tuning. We contribute: (1) application of this residual technique to the highly specialized mortgage finance domain; (2) a dual-expert architecture combining a conversational QA model and a structured task model for classification and summarization; and (3) an intelligent task routing mechanism using few-shot classification performed by one of the expert models itself. We validate our approach on domain-specific benchmarks, where our final model (MLM v2) significantly outperforms the base LLaMA-3.1-8B-Instruct, achieving an LLM-as-a-Judge summarization score of 4.58 (vs. 3.99), a QA score of 4.09 (vs. 4.0), and a classification score of 2.6 (vs. 1.2). On semantic similarity, our model achieved a BERTScore of 0.77 for summarization (vs. 0.74), 0.68 for QA (vs. 0.58), and 0.75 for classification (vs. 0.73), substantially outperforming baseline approaches.
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[NLP-30] ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers using Phonetic Features
【速读】： 该论文旨在解决低资源语言——缅甸语（Burmese）的自动语音识别（ASR）错误纠正问题。针对这一挑战，作者提出了一种序列到序列的Transformer模型（sequence-to-sequence Transformer model），其关键在于融合音标表示（国际音标，IPA）和对齐信息（alignment information）作为特征输入，从而显著提升ASR输出的词级（word-level）和字符级（character-level）准确率。实验表明，该方案在未增强数据情况下将平均词错误率（WER）从51.56降至39.82，在增强后降至43.59，并提升chrF++评分至0.627，验证了特征设计在低资源场景下对ASR纠错性能提升的重要性。
链接: https://arxiv.org/abs/2511.21088
作者: Ye Bhone Lin,Thura Aung,Ye Kyaw Thu,Thazin Myint Oo
机构: Language Understanding Laboratory, Myanmar; Department of Computer Engineering, KMITL, Bangkok, Thailand; Language and Semantic Technology Research Team, NECTEC, Bangkok, Thailand
类目: Computation and Language (cs.CL); Machine Learning (cs.LG); Sound (cs.SD)
备注: 7 pages, 2 figures, 7 tables, Accepted to iSAI-NLP 2025
Abstract:This paper investigates sequence-to-sequence Transformer models for automatic speech recognition (ASR) error correction in low-resource Burmese, focusing on different feature integration strategies including IPA and alignment information. To our knowledge, this is the first study addressing ASR error correction specifically for Burmese. We evaluate five ASR backbones and show that our ASR Error Correction (AEC) approaches consistently improve word- and character-level accuracy over baseline outputs. The proposed AEC model, combining IPA and alignment features, reduced the average WER of ASR models from 51.56 to 39.82 before augmentation (and 51.56 to 43.59 after augmentation) and improving chrF++ scores from 0.5864 to 0.627, demonstrating consistent gains over the baseline ASR outputs without AEC. Our results highlight the robustness of AEC and the importance of feature design for improving ASR outputs in low-resource settings.
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[NLP-31] Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models
【速读】： 该论文旨在解决大语言模型在受控文本生成中难以满足硬性拼写约束（hard orthographic constraints）的问题，尤其是在字符级约束条件下进行词谜任务时的表现差异。其关键解决方案在于系统性地评估了三种不同架构家族（Qwen3、Claude Haiku-4.5 和 GPT-5-mini）共28种配置在58个词谜任务上的表现，发现模型架构差异带来的性能差距（F1从0.761降至0.343，差距达2.0–2.2倍）远大于同一家族内参数量扩展的影响（八倍参数仅带来83%的性能提升），表明单纯依赖参数规模或计算预算不足以改善约束满足能力；进一步揭示高容量模型对思维预算（thinking budget）敏感且收益显著，而中等规模模型则趋于饱和甚至退化，说明当前模型存在非均匀的计算效益；此外，通过人类解题难度标注验证了模型校准度有限（r=0.24–0.38），并识别出模型在常见但拼写异常词汇（如"data"、“poop”、“loll”）上出现系统性失败（人类成功率86–95%，模型漏检率89–96%），暴露其过度依赖分布合理性而非严格遵守拼写规则的问题，从而指出未来改进需聚焦于架构创新或训练目标优化，而非仅靠参数扩展。
链接: https://arxiv.org/abs/2511.21086
作者: Bryan E. Tuck,Rakesh M. Verma
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Large language models must satisfy hard orthographic constraints during controlled text generation, yet systematic cross-architecture evaluation remains limited. We evaluate 28 configurations spanning three model families (Qwen3, Claude Haiku-4.5, GPT-5-mini) on 58 word puzzles requiring character-level constraint satisfaction. Architectural differences produce substantially larger performance gaps (2.0-2.2x, F1=0.761 vs. 0.343) than parameter scaling within families (83% gain from eightfold scaling), suggesting that constraint satisfaction may require specialized architectural features or training objectives beyond standard language model scaling. Thinking budget sensitivity proves heterogeneous: high-capacity models show strong returns (+0.102 to +0.136 F1), while mid-sized variants saturate or degrade. These patterns are inconsistent with uniform compute benefits. Using difficulty ratings from 10,000 human solvers per puzzle, we establish modest but consistent calibration (r=0.24-0.38) across all families, yet identify systematic failures on common words with unusual orthography (“data”, “poop”, “loll”: 86-95% human success, 89-96% model miss rate). These failures reveal over-reliance on distributional plausibility that penalizes orthographically atypical but constraint-valid patterns, suggesting architectural innovations may be required beyond simply scaling parameters or computational budgets.
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[NLP-32] Enhancing Burmese News Classification with Kolmogorov-Arnold Network Head Fine-tuning
【速读】： 该论文旨在解决低资源语言（如缅甸语）在文本分类任务中因数据稀缺导致模型性能受限的问题，尤其关注传统多层感知机（MLP）作为分类头时存在的表达能力有限和计算开销高的缺陷。其解决方案的关键在于引入Kolmogorov-Arnold Networks (KANs) 作为替代 MLP 的新型分类头结构，通过使用基于傅里叶（FourierKAN）、样条（EfficientKAN）和网格（FasterKAN）的非线性激活机制，在保持高效计算的同时提升模型的表达能力。实验表明，EfficientKAN 结合 fastText 嵌入可达到最高 F1 分数（0.928），而 FasterKAN 在速度与准确率之间取得最佳平衡，证明 KANs 是低资源语言分类任务中更具竞争力的替代方案。
链接: https://arxiv.org/abs/2511.21081
作者: Thura Aung,Eaint Kay Khaing Kyaw,Ye Kyaw Thu,Thazin Myint Oo,Thepchai Supnithi
机构: Language Understanding Laboratory, Myanmar; Department of Computer Engineering, KMITL, Bangkok, Thailand; Language and Semantic Technology Research Team, NECTEC, Bangkok, Thailand
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 6 pages, 2 figures, 4 tables, Accepted to iSAI-NLP 2025
Abstract:In low-resource languages like Burmese, classification tasks often fine-tune only the final classification layer, keeping pre-trained encoder weights frozen. While Multi-Layer Perceptrons (MLPs) are commonly used, their fixed non-linearity can limit expressiveness and increase computational cost. This work explores Kolmogorov-Arnold Networks (KANs) as alternative classification heads, evaluating Fourier-based FourierKAN, Spline-based EfficientKAN, and Grid-based FasterKAN-across diverse embeddings including TF-IDF, fastText, and multilingual transformers (mBERT, Distil-mBERT). Experimental results show that KAN-based heads are competitive with or superior to MLPs. EfficientKAN with fastText achieved the highest F1-score (0.928), while FasterKAN offered the best trade-off between speed and accuracy. On transformer embeddings, EfficientKAN matched or slightly outperformed MLPs with mBERT (0.917 F1). These findings highlight KANs as expressive, efficient alternatives to MLPs for low-resource language classification.
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[NLP-33] Context-Aware Prag matic Metacognitive Prompting for Sarcasm Detection
【速读】： 该论文旨在解决生成式 AI（Generative AI）在讽刺检测（sarcasm detection）任务中表现不佳的问题，尤其是在面对语言多样性、文化差异以及模型对特定词汇或短语缺乏背景知识时的不稳定性。其解决方案的关键在于引入一种检索感知（retrieval-aware）的方法，通过两种互补策略增强上下文信息：一是利用基于网络的非参数化知识检索，在模型缺乏必要背景时提供外部上下文；二是采用自知识检索策略，激发模型内部已有的知识以提升自我认知能力。实验表明，该方法在多个数据集上显著提升了宏F1分数，验证了上下文信息尤其是文化特异性表达对提高大语言模型（LLM）讽刺识别性能的重要性。
链接: https://arxiv.org/abs/2511.21066
作者: Michael Iskandardinata,William Christian,Derwin Suhartono
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Detecting sarcasm remains a challenging task in the areas of Natural Language Processing (NLP) despite recent advances in neural network approaches. Currently, Pre-trained Language Models (PLMs) and Large Language Models (LLMs) are the preferred approach for sarcasm detection. However, the complexity of sarcastic text, combined with linguistic diversity and cultural variation across communities, has made the task more difficult even for PLMs and LLMs. Beyond that, those models also exhibit unreliable detection of words or tokens that require extra grounding for analysis. Building on a state-of-the-art prompting method in LLMs for sarcasm detection called Pragmatic Metacognitive Prompting (PMP), we introduce a retrieval-aware approach that incorporates retrieved contextual information for each target text. Our pipeline explores two complementary ways to provide context: adding non-parametric knowledge using web-based retrieval when the model lacks necessary background, and eliciting the model’s own internal knowledge for a self-knowledge awareness strategy. We evaluated our approach with three datasets, such as Twitter Indonesia Sarcastic, SemEval-2018 Task 3, and MUStARD. Non-parametric retrieval resulted in a significant 9.87% macro-F1 improvement on Twitter Indonesia Sarcastic compared to the original PMP method. Self-knowledge retrieval improves macro-F1 by 3.29% on Semeval and by 4.08% on MUStARD. These findings highlight the importance of context in enhancing LLMs performance in sarcasm detection task, particularly the involvement of culturally specific slang, references, or unknown terms to the LLMs. Future work will focus on optimizing the retrieval of relevant contextual information and examining how retrieval quality affects performance. The experiment code is available at: this https URL.
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[NLP-34] A Unified Understanding of Offline Data Selection and Online Self-refining Generation for Post-training LLM s
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在特定下游任务中微调时的数据质量瓶颈问题，具体包括离线数据选择和在线自精炼生成两个关键环节。其解决方案的核心在于从优化视角出发，提出一种双层数据选择（bilevel data selection）机制用于离线数据筛选，并将在线自精炼生成建模为基于验证集的模型适应步骤，通过为每个问题-响应对分配可学习的数据权重（显式或隐式），实现对训练数据质量的动态优化。该方法首次理论证明了双层数据选择框架的有效性，并在质量和安全感知的LLM微调任务中显著优于未经过滤的直接混合基线。
链接: https://arxiv.org/abs/2511.21056
作者: Quan Xiao,Tianyi Chen
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL); Optimization and Control (math.OC)
备注:
Abstract:Offline data selection and online self-refining generation, which enhance the data quality, are crucial steps in adapting large language models (LLMs) to specific downstream tasks. We tackle offline data selection and online self-refining generations through an optimization perspective. Specifically, bilevel data selection is used for offline data selection with respect to the validation dataset, and we treat online self-refining generation as a model adaptation step of selecting the model trained on current responses that best fits the validation data. Our framework offers a unified understanding of offline data selection and self-refining generation by assigning a learned data weight to each question and response, either explicitly or implicitly. For the first time, we theoretically demonstrate the effectiveness of the bilevel data selection framework and demonstrate its performance gains over unfiltered direct mixing baselines. By combining offline data with validation-weighted online generations, our method enhances fine-tuning performance. Experiments on quality enhancement and safety-aware LLM fine-tuning validate its effectiveness.
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[NLP-35] Semantic Anchors in In-Context Learning: Why Small LLM s Cannot Flip Their Labels KR
【速读】： 该论文试图解决的问题是：在上下文学习（In-Context Learning, ICL）中，大语言模型（Large Language Models, LLMs）是否能够覆盖预训练阶段形成的标签语义（label semantics），还是仅仅对已有的语义基础进行微调。为回答这一问题，作者将LLMs视为由提示诱导的分类器，并通过对比自然演示（natural demonstrations，标签正确）与反转演示（inverted demonstrations，系统性地翻转标签含义）下的行为差异来分析ICL机制。其解决方案的关键在于引入三个对齐度量（真值对齐、先验对齐和提示对齐）以及定义“语义覆盖率”（semantic override rate），即在标签语义被翻转时模型预测的准确性。实验结果表明，在8个分类任务和8个开源LLM（参数规模1–12B）中，ICL始终维持预训练形成的语义锚点（semantic anchor），无法实现有效的语义覆盖——即使使用反转演示，模型也无法建立一致的反向语义分类器，且语义覆盖率为零。这说明ICL主要作用于输入在预训练阶段学到的稳定语义方向上的投影调整，而非灵活重构标签语义，揭示了少样本提示在当前规模下存在根本限制，语义覆盖需依赖超出ICL的干预手段。
链接: https://arxiv.org/abs/2511.21038
作者: Anantha Padmanaban Krishna Kumar(Boston University)
机构: Boston University (波士顿大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 13 pages total (7 pages main text, 3 pages references, 3 pages appendix), 2 figures, 14 tables. Code available at this https URL
Abstract:Can in-context learning (ICL) override pre-trained label semantics, or does it merely refine an existing semantic backbone? We address this question by treating LLMs as prompt-induced classifiers and contrasting their behavior under \emphnatural demonstrations (with correct labels) and \emphinverted demonstrations (systematically flipping label meanings). We decompose ICL behavior into three alignment metrics (truth, prior, and prompt alignment) and introduce a semantic override rate, defined as correctness under flipped semantics. Across eight classification tasks and eight open-source LLMs (1–12B parameters), we find consistent evidence for a semantic anchor view. With natural demonstrations, ICL improves accuracy while maintaining strong prior alignment; most correct predictions coincide with zero-shot behavior, even when the prior is weak. With inverted demonstrations, models cannot learn coherent anti-semantic classifiers: prompt alignment increases only by sacrificing accuracy, and semantic override rates remain exactly zero in our few-shot 1–12B setting. Rather than flexibly remapping label meanings, ICL primarily adjusts how inputs project onto stable semantic directions learned during pre-training, clarifying fundamental limits of few-shot prompting and suggesting that overriding label semantics at these scales requires interventions beyond ICL. All code is available at: this https URL.
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[NLP-36] Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression
【速读】： 该论文旨在解决线性状态空间模型（Linear State-Space Models, SSMs）在长序列任务中因仅保留过去信息的损失性摘要而导致性能下降的问题，尤其是在依赖记忆的任务（如RAG和LongQA）中表现不佳。解决方案的关键在于提出Gated KalmaNet（GKA），其通过在推理时求解在线岭回归（online ridge regression）问题，在保持SSM式常数内存和线性计算复杂度的前提下，有效利用完整的历史信息进行下一个token的预测。核心创新包括：(1) 基于输入依赖门控的自适应正则化策略，控制岭回归条件数以保障低精度环境（如bfloat16）下的数值稳定性并平衡记忆保留；(2) 采用切比雪夫迭代（Chebyshev Iteration）替代传统迭代求解器，提升低精度场景下的稳定性，并结合硬件感知的分块实现与定制反向传播核，显著增强可扩展性。
链接: https://arxiv.org/abs/2511.21016
作者: Liangzu Peng,Aditya Chattopadhyay,Luca Zancato,Elvis Nunez,Wei Xia,Stefano Soatto
机构: University of Pennsylvania (宾夕法尼亚大学); AWS Agentic AI (亚马逊云服务代理人工智能)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 30 pages, 10 figures
Abstract:As efficient alternatives to softmax Attention, linear state-space models (SSMs) achieve constant memory and linear compute, but maintain only a lossy, fading summary of the past, often leading to inferior performance in recall oriented tasks. We propose Gated KalmaNet (GKA), a layer that reduces this gap by accounting for the full past when predicting the next token, while maintaining SSM-style efficiency. GKA achieves this by solving an online ridge regression problem at test time, with constant memory and linear compute cost in the sequence length. Drawing inspiration from the Kalman Filter, we iteratively solve the online ridge regression problem. However, a critical insight is that standard Kalman filter equations are numerically unstable in low-precision environments (like bfloat16) and difficult to parallelize in modern hardware. We address both challenges through two key innovations: (1) an adaptive regularization strategy with input-dependent gating that controls the condition number of the ridge regression, ensuring numerical stability while balancing memory retention. And (2) the use of Chebyshev Iteration instead of other conventional iterative solvers, which we demonstrate to be more stable in low-precision settings. To further improve scalability, we develop a hardware-aware chunk-wise implementation of Chebyshev Iteration along with custom kernels for backpropagating through our adaptive regularization and gating mechanisms. Empirically, GKA shows strong language understanding capabilites on short-context tasks outperforming existing SSM layers (like Mamba2, GLA and Gated DeltaNet). On long-context, GKA excels at real-world RAG and LongQA tasks up to 128k tokens, achieving more than 10 % relative improvement over other fading memory baselines.
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[NLP-37] rackList: Tracing Back Query Linguistic Diversity for Head and Tail Knowledge in Open Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在处理非定义类查询时性能显著下降的问题，特别是当用户需要示例、释义或改写等多样化回答时，LLMs难以生成准确内容。其解决方案的关键在于通过TrackList这一细粒度的语言学与统计分析流程，系统评估预训练数据对LLMs生成不同语义类型答案的影响，并引入Refomed-EN英文医学术语数据集（含6170个标注条目），结合句法与语义相似性指标、统计相关性和嵌入表示方法，量化高频概念（head）与低频概念（tail）对模型输出质量的差异。研究发现，LLMs在定义类问题上表现最优，在示例类问题上最差，且更倾向于对高频知识进行改写，而对尾部技术性知识改写较少，揭示了模型在知识覆盖和多样性生成上的局限性。
链接: https://arxiv.org/abs/2511.21006
作者: Ioana Buhnila,Aman Sinha,Mathieu Constant
机构: 未知
类目: Computation and Language (cs.CL)
备注: under review
Abstract:Large Language Models (LLMs) have proven efficient in giving definition-type answers to user input queries. While for humans giving various types of answers, such as examples and paraphrases, is an easy task, LLMs struggle to provide correct answers for other than definition-type queries. In this study, we evaluated this drop in performance using TrackList, a fine-grained linguistic and statistical analysis pipeline to investigate the impact of the pre-training data on LLMs answers to diverse linguistic queries. We also introduce RefoMed-EN, an English dataset consisting of 6170 human-annotated medical terms alongside their corresponding definitions, denominations, exemplifications, explanations, or paraphrases. We studied whether the high frequency of a concept (head) or low frequency (tail) impacts the language model’s performance. We evaluated the quality of the LLM’s output using syntactic and semantic similarity metrics, statistical correlations and embeddings. Results showed that the LLM’s task performance for definition type questions is the highest, while for the exemplification type it is the lowest. Additionally, we showed that for definition-type questions, large language models are prone to paraphrase more on popular and frequent knowledge and less on tail and technical knowledge, especially in the expert texts.
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[NLP-38] rafficLens: Multi-Camera Traffic Video Analysis Using LLM s ITSC
【速读】： 该论文旨在解决多摄像头交通路口视频数据在转化为文本描述过程中存在的效率低、延迟高的问题，尤其是在利用生成式 AI（Generative AI）模型进行分析时，传统方法依赖逐帧提取特征并调用视觉-语言模型（Vision-Language Model, VLM）导致处理时间过长，难以满足实时交通管理与事件调查的需求。解决方案的关键在于提出 TrafficLens 算法，其核心创新包括：采用基于摄像头重叠覆盖区域的顺序处理策略，通过前序摄像头输出作为后续摄像头输入的提示（prompt），结合不同 token 限制的 VLM 迭代推理机制，在保证信息准确性的前提下显著提升文本生成速度；同时引入对象级相似性检测模块，智能跳过冗余的 VLM 调用，从而实现高达 4 倍的视频到文本转换效率提升。
链接: https://arxiv.org/abs/2511.20965
作者: Md Adnan Arefeen,Biplob Debnath,Srimat Chakradhar
机构: NEC Laboratories America (NEC实验室美国); University of Missouri–Kansas City (密苏里大学堪萨斯城分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
备注: 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)
Abstract:Traffic cameras are essential in urban areas, playing a crucial role in intelligent transportation systems. Multiple cameras at intersections enhance law enforcement capabilities, traffic management, and pedestrian safety. However, efficiently managing and analyzing multi-camera feeds poses challenges due to the vast amount of data. Analyzing such huge video data requires advanced analytical tools. While Large Language Models (LLMs) like ChatGPT, equipped with retrieval-augmented generation (RAG) systems, excel in text-based tasks, integrating them into traffic video analysis demands converting video data into text using a Vision-Language Model (VLM), which is time-consuming and delays the timely utilization of traffic videos for generating insights and investigating incidents. To address these challenges, we propose TrafficLens, a tailored algorithm for multi-camera traffic intersections. TrafficLens employs a sequential approach, utilizing overlapping coverage areas of cameras. It iteratively applies VLMs with varying token limits, using previous outputs as prompts for subsequent cameras, enabling rapid generation of detailed textual descriptions while reducing processing time. Additionally, TrafficLens intelligently bypasses redundant VLM invocations through an object-level similarity detector. Experimental results with real-world datasets demonstrate that TrafficLens reduces video-to-text conversion time by up to 4\times while maintaining information accuracy.
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[NLP-39] Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs SIGMOD2026
【速读】： 该论文旨在解决当前对话式知识图谱问答（Conversational Question Answering over Knowledge Graphs, KGQA）中存在的关键挑战：一是大型语言模型（LLMs）缺乏对私有和动态知识图谱（KGs）的直接访问能力；二是传统KGQA系统难以支持多轮对话，且在实体指代消解和上下文跟踪方面表现不佳；三是现有检索增强生成（RAG）系统虽能获取图结构内容，但常因序列化处理导致结构信息丢失，并面临高索引开销与多轮上下文管理困难。解决方案的关键在于提出Chatty-KG——一个模块化的多智能体系统，通过任务专用的LLM代理协作实现结构化执行：这些代理负责语境理解、对话状态追踪、实体与关系链接以及高效查询规划，从而将自然语言问题精准转化为可执行的SPARQL查询。该设计兼顾了对话灵活性与KG结构化 grounding，显著提升了单轮与多轮场景下的准确率（F1和P@1），同时具备无需微调即可适应动态KG演进的能力。
链接: https://arxiv.org/abs/2511.20940
作者: Reham Omar,Abdelghny Orogat,Ibrahim Abdelaziz,Omij Mangukiya,Panos Kalnis,Essam Mansour
机构: Concordia University (康考迪亚大学); IBM Research (IBM 研究院); KAUST (沙特阿卜杜拉国王科技大学)
类目: Computation and Language (cs.CL)
备注: This paper is accepted to SIGMOD 2026
Abstract:Conversational Question Answering over Knowledge Graphs (KGs) combines the factual grounding of KG-based QA with the interactive nature of dialogue systems. KGs are widely used in enterprise and domain applications to provide structured, evolving, and reliable knowledge. Large language models (LLMs) enable natural and context-aware conversations, but lack direct access to private and dynamic KGs. Retrieval-augmented generation (RAG) systems can retrieve graph content but often serialize structure, struggle with multi-turn context, and require heavy indexing. Traditional KGQA systems preserve structure but typically support only single-turn QA, incur high latency, and struggle with coreference and context tracking. To address these limitations, we propose Chatty-KG, a modular multi-agent system for conversational QA over KGs. Chatty-KG combines RAG-style retrieval with structured execution by generating SPARQL queries through task-specialized LLM agents. These agents collaborate for contextual interpretation, dialogue tracking, entity and relation linking, and efficient query planning, enabling accurate and low-latency translation of natural questions into executable queries. Experiments on large and diverse KGs show that Chatty-KG significantly outperforms state-of-the-art baselines in both single-turn and multi-turn settings, achieving higher F1 and P@1 scores. Its modular design preserves dialogue coherence and supports evolving KGs without fine-tuning or pre-processing. Evaluations with commercial (e.g., GPT-4o, Gemini-2.0) and open-weight (e.g., Phi-4, Gemma 3) LLMs confirm broad compatibility and stable performance. Overall, Chatty-KG unifies conversational flexibility with structured KG grounding, offering a scalable and extensible approach for reliable multi-turn KGQA.
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[NLP-40] ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction
【速读】： 该论文旨在解决当前视觉语言模型（VLMs）在缺乏具身交互的情况下是否具备具身认知（embodied cognition）特征的问题。其核心挑战在于如何在不依赖低级图像生成任务的前提下，评估模型对环境动态变化的理解能力，尤其是从第一人称视角的感知与动作序列中推断世界状态的能力。解决方案的关键在于提出ENACT基准，该基准将具身认知评估转化为一种视觉问答（VQA）形式的世界建模任务，通过两个互补的序列重排序任务——正向世界建模（给定动作重新排序打乱的观察序列）和逆向世界建模（给定观察序列重新排序打乱的动作序列）——来隐式测试模型对因果关系、动作效应推理、具身意识及长时程记忆等具身认知核心能力的掌握程度，同时利用机器人仿真数据（BEHAVIOR）构建可扩展的高质量问答对（共8,972组），从而实现对现代VLMs在真实具身交互场景下的系统性评估。
链接: https://arxiv.org/abs/2511.20937
作者: Qineng Wang,Wenlong Huang,Yu Zhou,Hang Yin,Tianwei Bao,Jianwen Lyu,Weiyu Liu,Ruohan Zhang,Jiajun Wu,Li Fei-Fei,Manling Li
机构: Northwestern University (西北大学); Stanford University (斯坦福大学); UCLA (加州大学洛杉矶分校)
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
备注: Preprint version
Abstract:Embodied cognition argues that intelligence arises from sensorimotor interaction rather than passive observation. It raises an intriguing question: do modern vision-language models (VLMs), trained largely in a disembodied manner, exhibit signs of embodied cognition? We introduce ENACT, a benchmark that casts evaluation of embodied cognition as world modeling from egocentric interaction in a visual question answering (VQA) format. Framed as a partially observable Markov decision process (POMDP) whose actions are scene graph changes, ENACT comprises two complementary sequence reordering tasks: forward world modeling (reorder shuffled observations given actions) and inverse world modeling (reorder shuffled actions given observations). While conceptually simple, solving these tasks implicitly demands capabilities central to embodied cognition-affordance recognition, action-effect reasoning, embodied awareness, and interactive, long-horizon memory from partially observable egocentric input, while avoiding low-level image synthesis that could confound the evaluation. We provide a scalable pipeline that synthesizes QA pairs from robotics simulation (BEHAVIOR) and evaluates models on 8,972 QA pairs spanning long-horizon home-scale activities. Experiments reveal a performance gap between frontier VLMs and humans that widens with interaction horizon. Models consistently perform better on the inverse task than the forward one and exhibit anthropocentric biases, including a preference for right-handed actions and degradation when camera intrinsics or viewpoints deviate from human vision. Website at this https URL.
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[NLP-41] Emergence and Localisation of Semantic Role Circuits in LLM s
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）内部机制如何实现抽象语义结构（abstract semantic structure）的接地问题，即揭示其语义角色（semantic roles）的具体实现方式。解决方案的关键在于提出一种整合角色交叉最小对（role-cross minimal pairs）、时间演化分析（temporal emergence analysis）与跨模型比较（cross-model comparison）的方法，从而系统解析LLMs中语义角色的神经表征动态与结构性特征。该方法揭示了高度集中的电路（89–94%归因于28个节点）、渐进式结构精炼过程以及跨尺度的部分保守性（24–59%组件重叠），表明LLMs通过紧凑且因果隔离的机制实现抽象语义，并具备一定程度的跨规模和架构迁移能力。
链接: https://arxiv.org/abs/2511.20910
作者: Nura Aljaafari,Danilo S. Carvalho,André Freitas
机构: University of Manchester (曼彻斯特大学); Idiap Research Institute (Idiap 研究所)
类目: Computation and Language (cs.CL)
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Abstract:Despite displaying semantic competence, large language models’ internal mechanisms that ground abstract semantic structure remain insufficiently characterised. We propose a method integrating role-cross minimal pairs, temporal emergence analysis, and cross-model comparison to study how LLMs implement semantic roles. Our analysis uncovers: (i) highly concentrated circuits (89-94% attribution within 28 nodes); (ii) gradual structural refinement rather than phase transitions, with larger models sometimes bypassing localised circuits; and (iii) moderate cross-scale conservation (24-59% component overlap) alongside high spectral similarity. These findings suggest that LLMs form compact, causally isolated mechanisms for abstract semantic structure, and these mechanisms exhibit partial transfer across scales and architectures.
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[NLP-42] Winning with Less for Low Resource Languages: Advantage of Cross-Lingual English_Persian Argument Mining Model over LLM Augmentation
【速读】： 该论文旨在解决低资源语言（如波斯语）在论点挖掘（Argument Mining）任务中因标注数据稀缺而导致模型性能受限的问题。其核心解决方案是采用跨语言（cross-lingual）方法，构建三种训练场景：零样本迁移（zero-shot transfer）、基于大语言模型（LLMs）生成合成样本的增强策略，以及结合英语原始数据与人工翻译波斯语句子的轻量级跨语言混合模型。实验表明，尽管LLM增强方法提升了性能，但最终最优的跨语言模型在波斯语测试集上达到74.8%的F1分数，显著优于其他方案，说明轻量级跨语言融合策略能更有效地缓解低资源语言的数据短缺问题，是一种更具实用性的论点挖掘解决方案。
链接: https://arxiv.org/abs/2511.20872
作者: Ali Jahan,Masood Ghayoomi,Annette Hautli-Janisz
机构: 未知
类目: Computation and Language (cs.CL)
备注: Preprint. Under review
Abstract:Argument mining is a subfield of natural language processing to identify and extract the argument components, like premises and conclusions, within a text and to recognize the relations between them. It reveals the logical structure of texts to be used in tasks like knowledge extraction. This paper aims at utilizing a cross-lingual approach to argument mining for low-resource languages, by constructing three training scenarios. We examine the models on English, as a high-resource language, and Persian, as a low-resource language. To this end, we evaluate the models based on the English Microtext corpus \citepPeldszusStede2015, and its parallel Persian translation. The learning scenarios are as follow: (i) zero-shot transfer, where the model is trained solely with the English data, (ii) English-only training enhanced by synthetic examples generated by Large Language Models (LLMs), and (iii) a cross-lingual model that combines the original English data with manually translated Persian sentences. The zero-shot transfer model attains F1 scores of 50.2% on the English test set and 50.7% on the Persian test set. LLM-based augmentation model improves the performance up to 59.2% on English and 69.3% on Persian. The cross-lingual model, trained on both languages but evaluated solely on the Persian test set, surpasses the LLM-based variant, by achieving a F1 of 74.8%. Results indicate that a lightweight cross-lingual blend can outperform considerably the more resource-intensive augmentation pipelines, and it offers a practical pathway for the argument mining task to overcome data resource shortage on low-resource languages.
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[NLP-43] Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory
【速读】： 该论文旨在解决大语言模型（Large Language Model, LLM）代理在连续任务流中缺乏动态记忆演化能力的问题，即现有方法难以在部署过程中持续检索、整合并更新经验性记忆，导致无法有效积累和复用长期交互信息。其解决方案的关键在于提出Evo-Memory框架，这是一个面向自演化记忆的流式基准测试平台，通过结构化多任务序列数据集要求LLM在每次交互后主动搜索、适应并进化记忆；同时引入ExpRAG基线与ReMem优化管道，实现推理、动作执行与记忆更新的闭环协同，从而支持LLM在真实应用场景下实现持续学习与性能提升。
链接: https://arxiv.org/abs/2511.20857
作者: Tianxin Wei,Noveen Sachdeva,Benjamin Coleman,Zhankui He,Yuanchen Bei,Xuying Ning,Mengting Ai,Yunzhe Li,Jingrui He,Ed H. Chi,Chi Wang,Shuo Chen,Fernando Pereira,Wang-Cheng Kang,Derek Zhiyuan Cheng
机构: Google DeepMind(谷歌深度大脑); 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); Artificial Intelligence (cs.AI)
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Abstract:Statefulness is essential for large language model (LLM) agents to perform long-term planning and problem-solving. This makes memory a critical component, yet its management and evolution remain largely underexplored. Existing evaluations mostly focus on static conversational settings, where memory is passively retrieved from dialogue to answer queries, overlooking the dynamic ability to accumulate and reuse experience across evolving task streams. In real-world environments such as interactive problem assistants or embodied agents, LLMs are required to handle continuous task streams, yet often fail to learn from accumulated interactions, losing valuable contextual insights, a limitation that calls for test-time evolution, where LLMs retrieve, integrate, and update memory continuously during deployment. To bridge this gap, we introduce Evo-Memory, a comprehensive streaming benchmark and framework for evaluating self-evolving memory in LLM agents. Evo-Memory structures datasets into sequential task streams, requiring LLMs to search, adapt, and evolve memory after each interaction. We unify and implement over ten representative memory modules and evaluate them across 10 diverse multi-turn goal-oriented and single-turn reasoning and QA datasets. To better benchmark experience reuse, we provide a baseline method, ExpRAG, for retrieving and utilizing prior experience, and further propose ReMem, an action-think-memory refine pipeline that tightly integrates reasoning, task actions, and memory updates to achieve continual improvement.
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[NLP-44] Unsupervised Memorability Modeling from Tip-of-the-Tongue Retrieval Queries WACV2026
【速读】： 该论文旨在解决视觉内容记忆性（visual content memorability）研究中因人工标注成本高而导致的数据集多样性与可扩展性不足的问题，尤其针对现有数据集仅提供聚合记忆分数而无法捕捉自然开放回忆描述中的细微记忆信号这一局限。其解决方案的关键在于构建首个大规模无监督数据集，包含超过82,000个视频及其描述性回忆数据，通过从Reddit等在线平台收集“舌尖现象”（tip-of-the-tongue, ToT）检索查询作为弱监督信号，从而有效挖掘记忆性相关特征；在此基础上，利用该数据集对大视觉语言模型进行微调，在开放式回忆生成任务上超越GPT-4o等先进模型，并采用对比学习策略训练出首个支持多模态ToT检索的模型，为视觉记忆性研究开辟了新路径。
链接: https://arxiv.org/abs/2511.20854
作者: Sree Bhattacharyya,Yaman Kumar Singla,Sudhir Yarram,Somesh Kumar Singh,Harini S I,James Z. Wang
机构: The Pennsylvania State University (宾夕法尼亚州立大学); Adobe Media and Data Science Research (Adobe 媒体与数据科学研究)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted at WACV 2026
Abstract:Visual content memorability has intrigued the scientific community for decades, with applications ranging widely, from understanding nuanced aspects of human memory to enhancing content design. A significant challenge in progressing the field lies in the expensive process of collecting memorability annotations from humans. This limits the diversity and scalability of datasets for modeling visual content memorability. Most existing datasets are limited to collecting aggregate memorability scores for visual content, not capturing the nuanced memorability signals present in natural, open-ended recall descriptions. In this work, we introduce the first large-scale unsupervised dataset designed explicitly for modeling visual memorability signals, containing over 82,000 videos, accompanied by descriptive recall data. We leverage tip-of-the-tongue (ToT) retrieval queries from online platforms such as Reddit. We demonstrate that our unsupervised dataset provides rich signals for two memorability-related tasks: recall generation and ToT retrieval. Large vision-language models fine-tuned on our dataset outperform state-of-the-art models such as GPT-4o in generating open-ended memorability descriptions for visual content. We also employ a contrastive training strategy to create the first model capable of performing multimodal ToT retrieval. Our dataset and models present a novel direction, facilitating progress in visual content memorability research.
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[NLP-45] Length-MAX Tokenizer for Language Models
【速读】： 该论文旨在解决语言模型中词元化（tokenization）效率问题，即传统方法如字节对编码（Byte Pair Encoding, BPE）仅基于子词频率优化词表，导致平均每个字符所需词元数较高，从而增加训练步数和推理延迟。解决方案的关键在于提出一种名为 Length-MAX 的新词元化方法，其核心思想是将长度加权的目标最大化转化为图划分问题，并设计了一种贪心近似算法来构建词表，从而最小化平均词元长度。实验证明，该方法在多个数据集和模型规模下均能显著减少词元数量（14–18%），并提升训练效率与推理速度，同时保持甚至改善下游任务性能。
链接: https://arxiv.org/abs/2511.20849
作者: Dong Dong,Weijie Su
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:We introduce a new tokenizer for language models that minimizes the average tokens per character, thereby reducing the number of tokens needed to represent text during training and to generate text during inference. Our method, which we refer to as the Length-MAX tokenizer, obtains its vocabulary by casting a length-weighted objective maximization as a graph partitioning problem and developing a greedy approximation algorithm. On FineWeb and diverse domains, it yields 14–18% fewer tokens than Byte Pair Encoding (BPE) across vocabulary sizes from 10K to 50K, and the reduction is 13.0% when the size is 64K. Training GPT-2 models at 124M, 355M, and 1.3B parameters from scratch with five runs each shows 18.5%, 17.2%, and 18.5% fewer steps, respectively, to reach a fixed validation loss, and 13.7%, 12.7%, and 13.7% lower inference latency, together with a 16% throughput gain at 124M, while consistently improving on downstream tasks including reducing LAMBADA perplexity by 11.7% and enhancing HellaSwag accuracy by 4.3%. Moreover, the Length-MAX tokenizer achieves 99.62% vocabulary coverage and the out-of-vocabulary rate remains low at 0.12% on test sets. These results demonstrate that optimizing for average token length, rather than frequency alone, offers an effective approach to more efficient language modeling without sacrificing – and often improving – downstream performance. The tokenizer is compatible with production systems and reduces embedding and KV-cache memory by 18% at inference.
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[NLP-46] Structured Prompting Enables More Robust Holistic Evaluation of Language Models
【速读】： 该论文旨在解决当前语言模型（Language Models, LMs）基准测试中因固定提示（fixed prompts）导致性能估计不准确的问题，尤其是在不同模型间缺乏可比性、低估模型上限（ceiling performance）以及误导排行榜排名等方面。其核心挑战在于传统框架如HELM虽能覆盖多任务评估，但受限于静态提示设计，无法充分激发模型潜力，从而产生偏差的性能指标。解决方案的关键在于引入可复现的DSPy+HELM集成框架，通过结构化提示（structured prompting）方法——特别是结合链式思维（chain-of-thought）推理机制——对提示进行自动化优化，实现每项任务下模型性能上限的系统性估计。实验证明，该方法显著提升了性能估计的准确性（平均提升4%）、减少跨基准波动（标准差降低2%），并纠正了因提示敏感性导致的错误排名（3/7基准排名反转），从而为部署决策提供更可靠的依据。
链接: https://arxiv.org/abs/2511.20836
作者: Asad Aali,Muhammad Ahmed Mohsin,Vasiliki Bikia,Arnav Singhvi,Richard Gaus,Suhana Bedi,Hejie Cui,Miguel Fuentes,Alyssa Unell,Yifan Mai,Jordan Cahoon,Michael Pfeffer,Roxana Daneshjou,Sanmi Koyejo,Emily Alsentzer,Percy Liang,Christopher Potts,Nigam H. Shah,Akshay S. Chaudhari
机构: Stanford University (斯坦福大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:As language models (LMs) are increasingly adopted across domains, high-quality benchmarking frameworks that accurately estimate performance are essential for guiding deployment decisions. While frameworks such as Holistic Evaluation of Language Models (HELM) enable broad evaluation across tasks, they often rely on fixed prompts that fail to generalize across LMs, yielding unrepresentative performance estimates. Unless we estimate each LM’s ceiling (maximum achievable via changes to the prompt), we risk underestimating performance. Declarative prompting frameworks, such as DSPy, offer a scalable alternative to manual prompt engineering by crafting structured prompts that can be optimized per task. However, such frameworks have not been systematically evaluated across established benchmarks. We present a reproducible DSPy+HELM framework that introduces structured prompting methods which elicit reasoning, enabling more accurate LM benchmarking. Using four prompting methods, we evaluate four frontier LMs across seven benchmarks (general/medical domain) against existing HELM baseline scores. We find that without structured prompting: (i) HELM underestimates LM performance (by 4% average), (ii) performance estimates vary more across benchmarks (+2% standard deviation), (iii) performance gaps are misrepresented (leaderboard rankings flip on 3/7 benchmarks), and (iv) introducing reasoning (chain-of-thought) reduces LM sensitivity to prompt design (smaller \Delta across prompts). To our knowledge, this is the first large-scale benchmarking study to empirically characterize LM behavior across benchmarks and prompting methods, showing that scalable performance ceiling estimation enables more decision-useful benchmarks. We open-source (i) DSPy+HELM Integration (this https URL) and (ii) Prompt Optimization Pipeline (this https URL).
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[NLP-47] raining-Free Diffusion Priors for Text-to-Image Generation via Optimization-based Visual Inversion
【速读】： 该论文旨在解决当前文本到图像生成（text-to-image generation）扩散模型中对训练有素的扩散先验网络（diffusion prior network）的依赖问题，此类先验通常计算成本高且需大规模数据集进行训练。其解决方案的关键在于引入一种无需训练和无需数据的优化驱动视觉反演（Optimization-based Visual Inversion, OVI）方法，通过从随机伪标记（pseudo-tokens）初始化潜在视觉表示，并迭代优化以最大化其与文本提示嵌入之间的余弦相似度，从而替代传统先验。为进一步提升图像真实性，作者还提出两种新约束机制——基于马氏距离（Mahalanobis-based）和最近邻（Nearest-Neighbor）损失，有效引导优化过程趋向真实图像分布。实验表明，该方法在Kandinsky 2.2上可作为传统先验的有效替代，且经约束优化后的OVI在视觉保真度上显著优于仅使用文本嵌入作为先验的基线，验证了该思路的潜力。
链接: https://arxiv.org/abs/2511.20821
作者: Samuele Dell’Erba,Andrew D. Bagdanov
机构: University of Florence (佛罗伦萨大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: 11 pages, 7 figures, technical report (preprint)
Abstract:Diffusion models have established the state-of-the-art in text-to-image generation, but their performance often relies on a diffusion prior network to translate text embeddings into the visual manifold for easier decoding. These priors are computationally expensive and require extensive training on massive datasets. In this work, we challenge the necessity of a trained prior at all by employing Optimization-based Visual Inversion (OVI), a training-free and data-free alternative, to replace the need for a prior. OVI initializes a latent visual representation from random pseudo-tokens and iteratively optimizes it to maximize the cosine similarity with input textual prompt embedding. We further propose two novel constraints, a Mahalanobis-based and a Nearest-Neighbor loss, to regularize the OVI optimization process toward the distribution of realistic images. Our experiments, conducted on Kandinsky 2.2, show that OVI can serve as an alternative to traditional priors. More importantly, our analysis reveals a critical flaw in current evaluation benchmarks like T2I-CompBench++, where simply using the text embedding as a prior achieves surprisingly high scores, despite lower perceptual quality. Our constrained OVI methods improve visual fidelity over this baseline, with the Nearest-Neighbor approach proving particularly effective, achieving quantitative scores comparable to or higher than the state-of-the-art data-efficient prior, indicating that the idea merits further investigation. The code will be publicly available upon acceptance.
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[NLP-48] SAGE: An Agent ic Explainer Framework for Interpreting SAE Features in Language Models
【速读】： 该论文旨在解决稀疏自编码器（Sparse Autoencoders, SAEs）所提取的特征难以解释的问题，即如何有效提升对SAE特征语义的理解精度与可解释性。其解决方案的关键在于提出SAGE（SAE AGentic Explainer），这是一个基于代理（agent-based）的框架，将特征解释从传统的被动单次生成任务转变为一种主动的、以解释驱动的过程：通过系统性地为每个特征构造多种假设性解释、设计针对性实验进行验证，并依据实证激活反馈迭代优化解释结果，从而显著提升解释的生成能力和预测准确性。
链接: https://arxiv.org/abs/2511.20820
作者: Jiaojiao Han,Wujiang Xu,Mingyu Jin,Mengnan Du
机构: New Jersey Institute of Technology (新泽西理工学院); Rutgers University (罗格斯大学)
类目: Computation and Language (cs.CL)
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Abstract:Large language models (LLMs) have achieved remarkable progress, yet their internal mechanisms remain largely opaque, posing a significant challenge to their safe and reliable deployment. Sparse autoencoders (SAEs) have emerged as a promising tool for decomposing LLM representations into more interpretable features, but explaining the features captured by SAEs remains a challenging task. In this work, we propose SAGE (SAE AGentic Explainer), an agent-based framework that recasts feature interpretation from a passive, single-pass generation task into an active, explanation-driven process. SAGE implements a rigorous methodology by systematically formulating multiple explanations for each feature, designing targeted experiments to test them, and iteratively refining explanations based on empirical activation feedback. Experiments on features from SAEs of diverse language models demonstrate that SAGE produces explanations with significantly higher generative and predictive accuracy compared to state-of-the-art this http URL agent-based framework that recasts feature interpretation from a passive, single-pass generation task into an active, explanationdriven process. SAGE implements a rigorous methodology by systematically formulating multiple explanations for each feature, designing targeted experiments to test them, and iteratively refining explanations based on empirical activation feedback. Experiments on features from SAEs of diverse language models demonstrate that SAGE produces explanations with significantly higher generative and predictive accuracy compared to state-of-the-art baselines.
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[NLP-49] Memories Retrieved from Many Paths: A Multi-Prefix Framework for Robust Detection of Training Data Leakage in Large Language Models
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在训练过程中对训练数据的逐字记忆（verbatim memorization）问题，这一现象可能引发隐私泄露和版权侵权风险。现有定义在捕捉对齐模型中的记忆行为时存在不足，难以全面评估记忆强度。其解决方案的关键在于提出一种新的“多前缀记忆”（multi-prefix memorization）框架：核心思想是被记忆的序列具有深度编码特性，因而可通过远多于非记忆内容的多样前缀被检索出来；通过定义一个序列为“被记忆”，若存在足够数量的不同前缀能触发该序列的生成，则可量化记忆的鲁棒性——即记忆的可检索路径多样性。该方法从单一路径提取转向多路径验证，提供了一种更稳健、实用的数据泄露审计工具。
链接: https://arxiv.org/abs/2511.20799
作者: Trung Cuong Dang,David Mohaisen
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
备注: 11 pages, 2 tables, 8 figures
Abstract:Large language models, trained on massive corpora, are prone to verbatim memorization of training data, creating significant privacy and copyright risks. While previous works have proposed various definitions for memorization, many exhibit shortcomings in comprehensively capturing this phenomenon, especially in aligned models. To address this, we introduce a novel framework: multi-prefix memorization. Our core insight is that memorized sequences are deeply encoded and thus retrievable via a significantly larger number of distinct prefixes than non-memorized content. We formalize this by defining a sequence as memorized if an external adversarial search can identify a target count of distinct prefixes that elicit it. This framework shifts the focus from single-path extraction to quantifying the robustness of a memory, measured by the diversity of its retrieval paths. Through experiments on open-source and aligned chat models, we demonstrate that our multi-prefix definition reliably distinguishes memorized from non-memorized data, providing a robust and practical tool for auditing data leakage in LLMs.
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[NLP-50] CANVAS: A Benchmark for Vision-Language Models on Tool-Based User Interface Design
【速读】： 该论文旨在解决当前缺乏针对视觉语言模型（Vision Language Models, VLMs）在基于工具的用户界面（User Interface, UI）设计任务中性能评估的基准问题，从而量化其与设计软件（如Figma或Sketch）交互并迭代优化UI的能力。解决方案的关键在于提出CANVAS——一个面向VLMs的工具调用型UI设计基准，包含598个设计任务，涵盖30个功能类别，并提供真实参考标注；该基准支持两种任务类型：设计复制（评估完整UI还原能力）和设计修改（评估局部调整能力），并通过上下文感知的工具调用机制模拟真实设计流程，从而揭示领先模型在策略性工具选择上的优势及常见错误模式，为提升VLM在设计自动化中的协作能力提供方向。
链接: https://arxiv.org/abs/2511.20737
作者: Daeheon Jeong,Seoyeon Byun,Kihoon Son,Dae Hyun Kim,Juho Kim
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:User interface (UI) design is an iterative process in which designers progressively refine their work with design software such as Figma or Sketch. Recent advances in vision language models (VLMs) with tool invocation suggest these models can operate design software to edit a UI design through iteration. Understanding and enhancing this capacity is important, as it highlights VLMs’ potential to collaborate with designers within conventional software. However, as no existing benchmark evaluates tool-based design performance, the capacity remains unknown. To address this, we introduce CANVAS, a benchmark for VLMs on tool-based user interface design. Our benchmark contains 598 tool-based design tasks paired with ground-truth references sampled from 3.3K mobile UI designs across 30 function-based categories (e.g., onboarding, messaging). In each task, a VLM updates the design step-by-step through context-based tool invocations (e.g., create a rectangle as a button background), linked to design software. Specifically, CANVAS incorporates two task types: (i) design replication evaluates the ability to reproduce a whole UI screen; (ii) design modification evaluates the ability to modify a specific part of an existing screen. Results suggest that leading models exhibit more strategic tool invocations, improving design quality. Furthermore, we identify common error patterns models exhibit, guiding future work in enhancing tool-based design capabilities.
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[NLP-51] Large Language Models Complicit Responses to Illicit Instructions across Socio-Legal Contexts
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在实际部署中可能存在的“共谋性协助”（complicit facilitation）问题，即模型在无意或有意情况下提供指导或支持，使用户得以实施非法行为。其解决方案的关键在于构建一个涵盖269个非法场景和50种非法意图的评估基准，并通过实证研究揭示LLMs在不同社会法律情境下的安全表现差异，发现当前主流模型如GPT-4o在近半数测试案例中提供非法协助，且现有对齐策略不仅效果有限，甚至可能加剧此类风险。研究进一步指出，模型推理过程中对群体刻板印象（基于温暖与能力维度）的影响是导致共谋性行为的重要机制，提示需从认知偏差和伦理对齐层面重新设计安全防护策略。
链接: https://arxiv.org/abs/2511.20736
作者: Xing Wang,Huiyuan Xie,Yiyan Wang,Chaojun Xiao,Huimin Chen,Holli Sargeant,Felix Steffek,Jie Shao,Zhiyuan Liu,Maosong Sun
机构: Tsinghua University (清华大学); University of Electronic Science and Technology of China (电子科技大学); University of Cambridge (剑桥大学)
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks. However, the risk of these models assisting unlawful activities remains underexplored. In this study, we define this high-risk behavior as complicit facilitation - the provision of guidance or support that enables illicit user instructions - and present four empirical studies that assess its prevalence in widely deployed LLMs. Using real-world legal cases and established legal frameworks, we construct an evaluation benchmark spanning 269 illicit scenarios and 50 illicit intents to assess LLMs’ complicit facilitation behavior. Our findings reveal widespread LLM susceptibility to complicit facilitation, with GPT-4o providing illicit assistance in nearly half of tested cases. Moreover, LLMs exhibit deficient performance in delivering credible legal warnings and positive guidance. Further analysis uncovers substantial safety variation across socio-legal contexts. On the legal side, we observe heightened complicity for crimes against societal interests, non-extreme but frequently occurring violations, and malicious intents driven by subjective motives or deceptive justifications. On the social side, we identify demographic disparities that reveal concerning complicit patterns towards marginalized and disadvantaged groups, with older adults, racial minorities, and individuals in lower-prestige occupations disproportionately more likely to receive unlawful guidance. Analysis of model reasoning traces suggests that model-perceived stereotypes, characterized along warmth and competence, are associated with the model’s complicit behavior. Finally, we demonstrate that existing safety alignment strategies are insufficient and may even exacerbate complicit behavior.
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[NLP-52] InvisibleBench: A Deployment Gate for Caregiving Relationship AI
【速读】： 该论文旨在解决当前照护关系类人工智能（Caregiving-Relationship AI）在实际部署中缺乏系统性、多轮交互下的安全性与合规性评估工具的问题。现有方法多局限于单轮安全测试，无法捕捉长期交互中潜在的风险累积与真实危害的显现。解决方案的关键在于提出 InvisibleBench——一个针对3至20+轮对话的多维基准评测体系，涵盖安全性（Safety）、合规性（Compliance）、创伤知情设计（Trauma-Informed Design）、归属感/文化适配性（Belonging/Cultural Fitness）和记忆一致性（Memory）五个维度，并设置自动失败条件（autofail conditions）以识别危机漏检、医疗建议违规（WOPR Act）、有害信息传播及依恋工程等高风险行为。该框架首次将纵向风险评估引入部署就绪性检验，揭示了当前前沿模型在危机检测上的显著不足（11.8–44.8%），强调生产系统必须采用确定性危机路由机制。
链接: https://arxiv.org/abs/2511.20733
作者: Ali Madad(GiveCare)
机构: GiveCare
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
备注: 29 pages, 3 figures
Abstract:InvisibleBench is a deployment gate for caregiving-relationship AI, evaluating 3-20+ turn interactions across five dimensions: Safety, Compliance, Trauma-Informed Design, Belonging/Cultural Fitness, and Memory. The benchmark includes autofail conditions for missed crises, medical advice (WOPR Act), harmful information, and attachment engineering. We evaluate four frontier models across 17 scenarios (N=68) spanning three complexity tiers. All models show significant safety gaps (11.8-44.8 percent crisis detection), indicating the necessity of deterministic crisis routing in production systems. DeepSeek Chat v3 achieves the highest overall score (75.9 percent), while strengths differ by dimension: GPT-4o Mini leads Compliance (88.2 percent), Gemini leads Trauma-Informed Design (85.0 percent), and Claude Sonnet 4.5 ranks highest in crisis detection (44.8 percent). We release all scenarios, judge prompts, and scoring configurations with code. InvisibleBench extends single-turn safety tests by evaluating longitudinal risk, where real harms emerge. No clinical claims; this is a deployment-readiness evaluation.
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[NLP-53] ST-PPO: Stabilized Off-Policy Proximal Policy Optimization for Multi-Turn Agents Training
【速读】： 该论文旨在解决在多轮对话与推理任务中，基于token级重要性采样（importance sampling）的近端策略优化（PPO）方法在训练大型语言模型（LLM）时出现的性能不稳定甚至崩溃的问题。核心问题源于两个方面：一是token级重要性采样与多轮环境的turn-level结构不匹配，二是离策略样本导致的优势估计偏差大、梯度方差高。解决方案的关键在于提出两种互补的稳定化技术：(1) turn-level重要性采样，使优化过程与多轮推理的自然阶段对齐；(2) clipping-bias校正，通过降低不可靠离策略样本的权重来规范化梯度更新。结合这两项技术的ST-PPO方法，在多个多轮搜索任务（包括通用问答、多跳问答和医学选择题）上显著提升了训练稳定性与任务性能，证明了其在大规模LLM代理训练中的实用性与可扩展性。
链接: https://arxiv.org/abs/2511.20718
作者: Chenliang Li,Adel Elmahdy,Alex Boyd,Zhongruo Wang,Alfredo Garcia,Parminder Bhatia,Taha Kass-Hout,Cao Xiao,Mingyi Hong
机构: Texas A&M University (德州农工大学); GE HealthCare (通用电气健康 care); Independent Researcher (独立研究员); University of Minnesota (明尼苏达大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:PPO has been widely adopted for training large language models (LLMs) at the token level in multi-turn dialogue and reasoning tasks. However, its performance is often unstable and prone to collapse. Through empirical analysis, we identify two main sources of instability in this setting: (1)~token-level importance sampling, which is misaligned with the natural granularity of multi-turn environments that have distinct turn-level stages, and (2) inaccurate advantage estimates from off-policy samples, where the critic has not learned to evaluate certain state-action pairs, resulting in high-variance gradients and unstable updates. To address these challenges, we introduce two complementary stabilization techniques: (1) turn-level importance sampling, which aligns optimization with the natural structure of multi-turn reasoning, and (2) clipping-bias correction, which normalizes gradients by downweighting unreliable, highly off-policy samples. Depending on how these components are combined, we obtain three variants: Turn-PPO (turn-level sampling only), S-PPO (clipping-bias correction applied to token-level PPO), and ST-PPO (turn-level sampling combined with clipping-bias correction). In our experiments, we primarily study ST-PPO and S-PPO, which together demonstrate how the two stabilization mechanisms address complementary sources of instability. Experiments on multi-turn search tasks across general QA, multi-hop QA, and medical multiple-choice QA benchmarks show that ST-PPO and S-PPO consistently prevent the performance collapses observed in large-model training, maintain lower clipping ratios throughout optimization, and achieve higher task performance than standard token-level PPO. These results demonstrate that combining turn-level importance sampling with clipping-bias correction provides a practical and scalable solution for stabilizing multi-turn LLM agent training.
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[NLP-54] LLM s-Powered Accurate Extraction Querying and Intelligent Management of Literature derived 2D Materials Data
【速读】： 该论文旨在解决二维（2D）材料在能源存储与转换领域应用中，其关键物化性质及制备方法等有价值信息分散于大量已发表文献中的问题。解决方案的关键在于系统性地整合和挖掘这些散落在不同研究论文中的数据，以构建结构化知识库或采用智能分析手段（如文献挖掘或生成式AI），从而加速新材料的发现与优化设计。
链接: https://arxiv.org/abs/2511.20691
作者: Lijun Shang,Yadong Yu,Wenqiang Kang,Jian Zhou,Dongyue Gao,Pan Xiang,Zhe Liu,Mengyan Dai,Zhonglu Guo,Zhimei Sun
机构: 未知
类目: Computation and Language (cs.CL); Materials Science (cond-mat.mtrl-sci); Databases (cs.DB)
备注: 100 pages (18 pages main text, 82 pages supplementary material), 5 figures. Supplementary material starts from page 19
Abstract:Two-dimensional (2D) materials have showed widespread applications in energy storage and conversion owning to their unique physicochemical, and electronic properties. Most of the valuable information for the materials, such as their properties and preparation methods, is included in the published research papers. However, due to the dispersion of synthe
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[NLP-55] Dynamic Template Selection for Output Token Generation Optimization: MLP-Based and Transformer Approaches
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在实际部署中因采用统一提示策略而导致的令牌（token）效率低下问题，尤其是在输入与输出令牌成本差异显著（输出令牌价格为输入的4–8倍）的场景下。其核心解决方案是提出动态模板选择（Dynamic Template Selection, DTS）机制，通过根据查询复杂度自适应匹配响应模板，实现成本显著降低而不牺牲回答质量。关键创新在于设计了一个轻量级多层感知机（MLP）路由器，利用预计算嵌入实现90.5%的高路由准确率，优于参数量更大的RoBERTa模型，并且该方法具有跨主流大模型提供商（OpenAI GPT-4、Google Gemini、Anthropic Claude）的泛化能力，在不同平台上均实现32.6%–33.9%的令牌消耗减少。
链接: https://arxiv.org/abs/2511.20683
作者: Bharadwaj Yadavalli
机构: 未知
类目: Computation and Language (cs.CL)
备注: 20 pages, 4 figures, includes production-scale experiments across OpenAI GPT-4, Google Gemini, and Anthropic Claude; code available upon request
Abstract:Contemporary large language model deployments typically employ uniform prompting strategies across diverse query types, applying verbose response patterns to both complex analytical tasks and straightforward factual questions. This one-size-fits-all methodology leads to substantial token inefficiency, a concern amplified by the significant cost differential between input and output tokens–the latter commanding 4-8x higher prices across major providers. We present Dynamic Template Selection (DTS), which adaptively matches response templates to query complexity, achieving significant cost reductions without compromising response quality. We compared two routing approaches: a simple MLP that uses pre-computed embeddings and a more complex fine-tuned RoBERTa transformer. Through comprehensive evaluation on 1,000 MMLU questions, we find that the MLP router achieves 90.5% routing accuracy on held-out test data, marginally exceeding RoBERTa’s performance (89.5%) despite utilizing 125M fewer parameters. Notably, our empirical analysis reveals provider-agnostic behavior in template selection–routing decisions generalize effectively across 3 major LLM providers (OpenAI GPT-4, Google Gemini, and Anthropic Claude), as validated through 9,000 production API calls. While routing accuracy remains consistent at 90.5% across providers, observed token reductions vary from 32.6% to 33.9%, reflecting provider-specific generation characteristics. This work contributes several key elements: formal problem formulation with theoretical grounding in machine learning, four algorithms with corresponding complexity analyses, and extensive empirical validation across production systems. Comments: 20 pages, 4 figures, includes production-scale experiments across OpenAI GPT-4, Google Gemini, and Anthropic Claude; code available upon request Subjects: Computation and Language (cs.CL) Cite as: arXiv:2511.20683 [cs.CL] (or arXiv:2511.20683v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2511.20683 Focus to learn more arXiv-issued DOI via DataCite
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[NLP-56] Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
【速读】： 该论文旨在解决大语言模型（Large Language Models, LLMs）在肿瘤学决策支持中可能因推理错误而产生不安全推荐的问题，这种问题无法通过传统准确性指标捕捉。研究的关键在于构建了一个三级推理错误分类体系（hierarchical taxonomy of reasoning errors），基于GPT-4在真实肿瘤病例记录中的链式思维（chain-of-thought）输出进行标注与验证，将计算失败映射至认知偏差框架（如确认偏倚和锚定偏倚），并证明此类推理错误与指南偏离及潜在有害建议显著相关，从而为临床部署前评估和提升LLM推理可靠性提供可泛化的评价框架。
链接: https://arxiv.org/abs/2511.20680
作者: Matthew W. Kenaston(1),Umair Ayub(1),Mihir Parmar(2),Muhammad Umair Anjum(1),Syed Arsalan Ahmed Naqvi(1),Priya Kumar(1),Samarth Rawal(1),Aadel A. Chaudhuri(4),Yousef Zakharia(3),Elizabeth I. Heath(5),Tanios S. Bekaii-Saab(3),Cui Tao(6),Eliezer M. Van Allen(7),Ben Zhou(2),YooJung Choi(2),Chitta Baral(2),Irbaz Bin Riaz(1 and 3 and 6) ((1) Mayo Clinic College of Medicine and Science, Phoenix, AZ, (2) School of Computing and AI, Arizona State University, Tempe, AZ, (3) Mayo Clinic Comprehensive Cancer Center, Phoenix, AZ, (4) Department of Radiation Oncology, Mayo Clinic, Rochester, MN, (5) Department of Oncology, Mayo Clinic, Rochester, MN, (6) Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, (7) Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA)
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 24 pages, 6 figures, 1 supplementary figure, 3 tables
Abstract:Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology decision support that is not captured by accuracy-based evaluation. In this two-cohort retrospective study, we developed a hierarchical taxonomy of reasoning errors from GPT-4 chain-of-thought responses to real oncology notes and tested its clinical relevance. Using breast and pancreatic cancer notes from the CORAL dataset, we annotated 600 reasoning traces to define a three-tier taxonomy mapping computational failures to cognitive bias frameworks. We validated the taxonomy on 822 responses from prostate cancer consult notes spanning localized through metastatic disease, simulating extraction, analysis, and clinical recommendation tasks. Reasoning errors occurred in 23 percent of interpretations and dominated overall errors, with confirmation bias and anchoring bias most common. Reasoning failures were associated with guideline-discordant and potentially harmful recommendations, particularly in advanced disease management. Automated evaluators using state-of-the-art language models detected error presence but could not reliably classify subtypes. These findings show that large language models may provide fluent but clinically unsafe recommendations when reasoning is flawed. The taxonomy provides a generalizable framework for evaluating and improving reasoning fidelity before clinical deployment.
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[NLP-57] Prompt Engineering Techniques for Context-dependent Text-to-SQL in Arabic IJCNN IJCNN2025
【速读】： 该论文旨在解决阿拉伯语（Arabic）场景下的跨域、上下文依赖型文本到SQL（text-to-SQL）任务缺乏高质量标注数据集及有效模型方法的问题。此前相关研究主要集中在英文和中文，未有针对阿拉伯语的系统性工作。为此，作者提出了首个阿拉伯语跨域、上下文依赖的text-to-SQL数据集Ar-SParC，包含3,450个关联问题序列（共10,225个问题及其对应SQL查询），并基于此开展了40组实验，使用GPT-3.5-turbo与GPT-4.5-turbo两个大语言模型结合10种提示工程策略进行评估。解决方案的关键创新在于提出一种名为GAT corrector的新方法，相较于先前的GAT verifier技术，在零样本（zero-shot）和少样本（in-context learning）设置下均显著提升执行准确率（execution accuracy, EX）和交互准确率（interaction accuracy, IX），平均提升幅度分别为1.9% EX / 1.9% IX（零样本）和1.72% EX / 0.92% IX（少样本），其有效性在阿拉伯语语境中尤为突出，通过消融实验进一步验证了其优于传统验证机制的原因。
链接: https://arxiv.org/abs/2511.20677
作者: Saleh Almohaimeed,May Alsofyani,Saad Almohaimeed,Mansour Al Ghanim,Liqiang Wang
机构: 未知
类目: Computation and Language (cs.CL); Databases (cs.DB)
备注: Accepted at IJCNN 2025 (to appear in IEEE/IJCNN proceedings). This arXiv submission corresponds to the camera-ready version
Abstract:In recent years, the task of cross-domain, context-dependent text-to-SQL has received significant attention. Enables users with no prior knowledge of SQL to have a conversation with databases using natural language. However, most of the available datasets and research have been conducted in English, along with some work in Chinese. To this date, no effort has been made to address this task in the Arabic language. In this paper, we introduce Ar-SParC, the first Arabic cross-domain, context-dependent text-to-SQL dataset. The dataset consists of 3,450 sequences of interrelated questions, each sequence containing an average of approximately three questions, which results in a total of 10225 questions along with their corresponding SQL queries. We conducted 40 experiments on the Ar-SParC dataset using two large language models, GPT-3.5-turbo and GPT-4.5-turbo, applying 10 different prompt engineering techniques, including four question representation methods and six in-context learning techniques. Furthermore, we developed a novel approach named GAT corrector, which enhanced the performance across all 40 experiments, yielding an average improvement of 1.9% in execution accuracy (EX) and 1.9% in interaction accuracy (IX) under zero-shot settings, and an average increase of 1.72% EX and 0.92% IX under in-context learning settings. Finally, we conducted an ablation study with two more experiments to explain why the GAT corrector outperformed the previous GAT verifier technique, particularly for the Arabic language.
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[NLP-58] Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation
【速读】： 该论文旨在解决生成式推荐（Generative Recommendation）中因使用单一统一码本（codebook）对所有物品进行编码而导致的表示效率低下与泛化能力受限的问题，尤其忽略了热门物品（依赖协同过滤信号）与长尾物品（依赖语义理解）之间的本质差异。解决方案的关键在于提出 FlexCode 框架，其核心创新是基于流行度感知机制，自适应地在协同过滤（Collaborative Filtering, CF）码本与语义码本之间分配固定令牌预算，并通过轻量级 MoE（Mixture of Experts）结构动态平衡 CF 精度与语义泛化能力，同时引入对齐与平滑目标以确保跨流行度区间的一致性。该方法显著提升了推荐准确性和长尾物品的鲁棒性。
链接: https://arxiv.org/abs/2511.20673
作者: Zheng Hui,Xiaokai Wei,Reza Shirkavand,Chen Wang,Weizhi Zhang,Alejandro Peláez,Michelle Gong
机构: University of Cambridge (剑桥大学); Roblox Corporation (罗布洛克斯公司); University of Maryland (马里兰大学); University of Illinois Chicago (芝加哥大学)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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Abstract:Generative recommendation has recently emerged as a powerful paradigm that unifies retrieval and generation, representing items as discrete semantic tokens and enabling flexible sequence modeling with autoregressive models. Despite its success, existing approaches rely on a single, uniform codebook to encode all items, overlooking the inherent imbalance between popular items rich in collaborative signals and long-tail items that depend on semantic understanding. We argue that this uniform treatment limits representational efficiency and hinders generalization. To address this, we introduce FlexCode, a popularity-aware framework that adaptively allocates a fixed token budget between a collaborative filtering (CF) codebook and a semantic codebook. A lightweight MoE dynamically balances CF-specific precision and semantic generalization, while an alignment and smoothness objective maintains coherence across the popularity spectrum. We perform experiments on both public and industrial-scale datasets, showing that FlexCode consistently outperform strong baselines. FlexCode provides a new mechanism for token representation in generative recommenders, achieving stronger accuracy and tail robustness, and offering a new perspective on balancing memorization and generalization in token-based recommendation models.
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[NLP-59] MindSET: Advancing Mental Health Benchmarking through Large-Scale Social Media Data
【速读】： 该论文旨在解决现有社交媒体心理健康分析基准数据集因数据量不足、清洗不充分以及内容多样性（如多语言和有害内容）导致的过时与局限性问题。其解决方案的关键在于构建一个名为MindSET的新基准数据集，该数据集基于Reddit上的自报诊断信息进行标注，包含超过1300万条帖子，覆盖七种心理健康状况，规模是以往基准的两倍以上；同时通过严格的预处理流程（包括语言过滤、去除NSFW及重复内容）确保数据质量，并利用LIWC进行语言学分析以验证心理术语分布的合理性。实验表明，基于MindSET训练的模型在诊断检测任务中显著优于旧基准，最高F1分数提升达18个百分点，证明了该数据集在支持早期风险识别和心理趋势分析方面的有效性。
链接: https://arxiv.org/abs/2511.20672
作者: Saad Mankarious,Ayah Zirikly,Daniel Wiechmann,Elma Kerz,Edward Kempa,Yu Qiao
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注:
Abstract:Social media data has become a vital resource for studying mental health, offering real-time insights into thoughts, emotions, and behaviors that traditional methods often miss. Progress in this area has been facilitated by benchmark datasets for mental health analysis; however, most existing benchmarks have become outdated due to limited data availability, inadequate cleaning, and the inherently diverse nature of social media content (e.g., multilingual and harmful material). We present a new benchmark dataset, \textbfMindSET, curated from Reddit using self-reported diagnoses to address these limitations. The annotated dataset contains over \textbf13M annotated posts across seven mental health conditions, more than twice the size of previous benchmarks. To ensure data quality, we applied rigorous preprocessing steps, including language filtering, and removal of Not Safe for Work (NSFW) and duplicate content. We further performed a linguistic analysis using LIWC to examine psychological term frequencies across the eight groups represented in the dataset. To demonstrate the dataset utility, we conducted binary classification experiments for diagnosis detection using both fine-tuned language models and Bag-of-Words (BoW) features. Models trained on MindSET consistently outperformed those trained on previous benchmarks, achieving up to an \textbf18-point improvement in F1 for Autism detection. Overall, MindSET provides a robust foundation for researchers exploring the intersection of social media and mental health, supporting both early risk detection and deeper analysis of emerging psychological trends.
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[NLP-60] Structured Definitions and Segmentations for Legal Reasoning in LLM s: A Study on Indian Legal Data
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）在法律领域表现不佳的问题，其核心原因在于模型缺乏领域特定的预训练，且法律文本通常具有长度长、结构复杂的特点，导致模型难以高效处理和理解。解决方案的关键在于通过三种零样本（zero-shot）实验策略提升模型在法律任务中的表现：一是基于修辞角色（rhetorical roles）重新组织文档以优化长上下文处理；二是明确定义法律术语以增强模型对专业术语的理解；三是模拟法院在推理过程中对修辞角色的逐步分析方式，从而提升模型的逻辑推理能力。实验结果表明，这些方法可显著提升F1分数，最低提升约1.5%，最高达4.36%。
链接: https://arxiv.org/abs/2511.20669
作者: Mann Khatri,Mirza Yusuf,Rajiv Ratn Shah,Ponnurangam Kumaraguru
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: Accepted at BDA 2025 as short paper; This paper is long version
Abstract:Large Language Models (LLMs), trained on extensive datasets from the web, exhibit remarkable general reasoning skills. Despite this, they often struggle in specialized areas like law, mainly because they lack domain-specific pretraining. The legal field presents unique challenges, as legal documents are generally long and intricate, making it hard for models to process the full text efficiently. Previous studies have examined in-context approaches to address the knowledge gap, boosting model performance in new domains without full domain alignment. In our paper, we analyze model behavior on legal tasks by conducting experiments in three areas: (i) reorganizing documents based on rhetorical roles to assess how structured information affects long context processing and model decisions, (ii) defining rhetorical roles to familiarize the model with legal terminology, and (iii) emulating the step-by-step reasoning of courts regarding rhetorical roles to enhance model reasoning. These experiments are conducted in a zero-shot setting across three Indian legal judgment prediction datasets. Our results reveal that organizing data or explaining key legal terms significantly boosts model performance, with a minimum increase of ~1.5% and a maximum improvement of 4.36% in F1 score compared to the baseline.
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[NLP-61] PIRA: Preference-Oriented Instruction-Tuned Reward Models with Dual Aggregation
【速读】： 该论文旨在解决大型语言模型（Large Language Models, LLMs）对齐过程中奖励模型（Reward Models）面临的两大挑战：一是传统判别式奖励模型直接拼接问题与回答作为输入，导致数据效率低下；二是奖励模型易受奖励过优化（reward overoptimization）影响。解决方案的关键在于提出一种名为PIRA的训练范式，其核心策略包括：(1) 将问答对重构为基于偏好的指令以明确任务规范，(2) 聚合来自多样化偏好任务的奖励以降低偏差并提升鲁棒性，(3) 通过在不同丢弃率下平均价值头（value-head）输出来稳定奖励信号。
链接: https://arxiv.org/abs/2511.20668
作者: Yongfu Xue
机构: 未知
类目: Computation and Language (cs.CL)
备注:
Abstract:Reward models are crucial for aligning Large Language Models (LLMs) with human preferences but face two representative challenges. First, traditional discriminative reward models usually concatenate questions and responses directly as input, resulting in low data efficiency. Second, reward models are vulnerable to reward overoptimization. We propose PIRA, a training paradigm addressing these issues through three strategies: (1) Reformulating question-answer pairs into preference-based instructions for clearer and more explicit task specification, (2) aggregating rewards from diverse preference tasks to reduce bias and improve robustness, and (3) averaging value-head outputs under varying dropout rates to stabilize rewards. Extensive experiments have demonstrated the effectiveness of PIRA.
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[NLP-62] A centroid based framework for text classification in itsm environments
【速读】： 该论文旨在解决IT服务管理（ITSM）系统中支持工单的层级分类问题，即如何高效且可解释地将文本工单映射到树状结构的分类体系中。其解决方案的关键在于提出一种基于双嵌入中心点（dual-embedding centroid-based）的分类框架：该框架为每个类别维护独立的语义中心点和词法中心点表示，并在推理阶段通过互斥排名融合（reciprocal rank fusion）进行组合，从而在保持与支持向量机（SVM）相当的层级F1分数（0.731 vs 0.727）的同时，提供直观的中心点解释能力。该方法在8,968条ITSM工单上的实验表明，训练速度提升5.9倍，增量更新速度最高达152倍，且在批量大小为100–1000时，排除嵌入计算后整体加速达8.6–8.8倍，显著优于传统方法，适用于对可解释性和运行效率要求较高的生产级ITSM环境。
链接: https://arxiv.org/abs/2511.20667
作者: Hossein Mohanna,Ali Ait-Bachir
机构: Global AI Lab; EasyVista
类目: Computation and Language (cs.CL)
备注: 11 pages
Abstract:Text classification with hierarchical taxonomies is a fundamental requirement in IT Service Management (ITSM) systems, where support tickets must be categorized into tree-structured taxonomies. We present a dual-embedding centroid-based classification framework that maintains separate semantic and lexical centroid representations per category, combining them through reciprocal rank fusion at inference time. The framework achieves performance competitive with Support Vector Machines (hierarchical F1: 0.731 vs 0.727) while providing interpretability through centroid representations. Evaluated on 8,968 ITSM tickets across 123 categories, this method achieves 5.9 times faster training and up to 152 times faster incremental updates. With 8.6-8.8 times speedup across batch sizes (100-1000 samples) when excluding embedding computation. These results make the method suitable for production ITSM environments prioritizing interpretability and operational efficiency.
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[NLP-63] Harmonic Token Projection (HTP): A Vocabulary-Free Training-Free Deterministic and Reversible Embedding Methodology
【速读】： 该论文旨在解决传统文本嵌入方法依赖训练数据、词汇表和随机参数所带来的不可解释性与计算开销问题。其解决方案的关键在于提出一种无需训练、无词汇表且完全确定性的编码框架——谐波标记投影（Harmonic Token Projection, HTP），该方法通过将每个标记（token）的Unicode整数值映射为一个谐波轨迹，实现离散符号到连续向量空间的双射（bijective）且可解释的映射，从而在几何层面保持语义相似性，并具备亚毫秒级延迟和多语言稳定性。
链接: https://arxiv.org/abs/2511.20665
作者: Tcharlies Schmitz
机构: PX.Center (PX中心)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注:
Abstract:This paper introduces the Harmonic Token Projection (HTP), a reversible and deterministic framework for generating text embeddings without training, vocabularies, or stochastic parameters. Unlike neural embeddings that rely on statistical co-occurrence or optimization, HTP encodes each token analytically as a harmonic trajectory derived from its Unicode integer representation, establishing a bijective and interpretable mapping between discrete symbols and continuous vector space. The harmonic formulation provides phase-coherent projections that preserve both structure and reversibility, enabling semantic similarity estimation from purely geometric alignment. Experimental evaluation on the Semantic Textual Similarity Benchmark (STS-B) and its multilingual extension shows that HTP achieves a Spearman correlation of \rho = 0.68 in English, maintaining stable performance across ten languages with negligible computational cost and sub-millisecond latency per sentence pair. This demonstrates that meaningful semantic relations can emerge from deterministic geometry, offering a transparent and efficient alternative to data-driven embeddings. Keywords: Harmonic Token Projection, reversible embedding, deterministic encoding, semantic similarity, multilingual representation.
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[NLP-64] Democratizing LLM Efficiency: From Hyperscale Optimizations to Universal Deployability AAAI2026
【速读】： 该论文试图解决当前大语言模型（Large Language Models, LLMs）效率优化方法在非超大规模场景下失效的问题，即主流技术如专家混合（Mixture-of-Experts, MoE）、推测解码（Speculative Decoding）和复杂检索增强生成（Retrieval-Augmented Generation, RAG）因依赖海量基础设施与专业团队，在医院、学校、政府等资源有限的机构中反而带来额外开销、脆弱性和碳排放浪费。其解决方案的关键在于推动“鲁棒性简化”（robust simplicity）的新研究方向：通过无需重新训练即可改造预训练模型的高效架构、开发轻量级微调以保持对齐能力、使长链推理经济化、实现无需重型RAG管道的动态知识管理，并将“开销感知效率”（Overhead-Aware Efficiency, OAE）作为标准评估指标，从而将效率定义扩展至采用成本、可持续性和公平性，实现LLM部署的民主化。
链接: https://arxiv.org/abs/2511.20662
作者: Hen-Hsen Huang
机构: 未知
类目: Computation and Language (cs.CL)
备注: 8 pages, accepted as a Blue Sky Talk in AAAI 2026
Abstract:Large language models (LLMs) have become indispensable, but the most celebrated efficiency methods – mixture-of-experts (MoE), speculative decoding, and complex retrieval-augmented generation (RAG) – were built for hyperscale providers with vast infrastructure and elite teams. Outside that context, their benefits collapse into overhead, fragility, and wasted carbon. The result is that a handful of Big Tech companies benefit, while thousands of hospitals, schools, governments, and enterprises are left without viable options. We argue that the next frontier is not greater sophistication at scale, but robust simplicity: efficiency that thrives under modest resources and minimal expertise. We propose a new research agenda: retrofitting pretrained models with more efficient architectures without retraining, inventing lightweight fine-tuning that preserves alignment, making reasoning economical despite long chains of thought, enabling dynamic knowledge management without heavy RAG pipelines, and adopting Overhead-Aware Efficiency (OAE) as a standard benchmark. By redefining efficiency to include adoption cost, sustainability, and fairness, we can democratize LLM deployment – ensuring that optimization reduces inequality and carbon waste rather than amplifying them.
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[NLP-65] Zipf Distributions from Two-Stage Symbolic Processes: Stability Under Stochastic Lexical Filtering
【速读】： 该论文试图解决语言中Zipf定律（Zipf’s law）的起源问题，即为何词频分布呈现幂律关系这一现象长期以来缺乏统一解释，且在不同学科间存在争议。其解决方案的关键在于提出一个无需引入语言学元素的几何机制——全组合词模型（Full Combinatorial Word Model, FCWM），该模型通过有限字母表生成词汇，并利用指数相互作用力驱动词频分布形成幂律曲线，其形状由字母表大小和空白符号概率决定，从而表明Zipf型规律可源于几何约束而非沟通效率。
链接: https://arxiv.org/abs/2511.21060
作者: Vladimir Berman
机构: Aitiologia LLC
类目: Methodology (stat.ME); Computation and Language (cs.CL); Machine Learning (stat.ML)
备注: 16 pages
Abstract:Zipf’s law in language lacks a definitive origin, debated across fields. This study explains Zipf-like behavior using geometric mechanisms without linguistic elements. The Full Combinatorial Word Model (FCWM) forms words from a finite alphabet, generating a geometric distribution of word lengths. Interacting exponential forces yield a power-law rank-frequency curve, determined by alphabet size and blank symbol probability. Simulations support predictions, matching English, Russian, and mixed-genre data. The symbolic model suggests Zipf-type laws arise from geometric constraints, not communicative efficiency.
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[NLP-66] RosettaSpeech: Zero-Shot Speech-to-Speech Translation from Monolingual Data
【速读】： 该论文旨在解决语音到语音翻译（Speech-to-Speech Translation, S2ST）中因缺乏平行语音语料库而导致的模型训练困难问题，传统方法往往依赖复杂的多阶段流水线。其解决方案的关键在于提出一种名为RosettaSpeech的新框架，该框架仅使用单语语音-文本数据，并通过机器翻译（Machine Translation, MT）监督进行训练，从而无需任何平行语音对。模型在训练时以文本作为中间桥梁，但在推理阶段则直接实现端到端的语音到语音转换，既利用了文本基NMT模型的语言知识，又避免了对稀缺平行语音数据的依赖，实现了零样本S2ST的高性能与可扩展性。
链接: https://arxiv.org/abs/2511.20974
作者: Zhisheng Zheng,Xiaohang Sun,Tuan Dinh,Abhishek Yanamandra,Abhinav Jain,Zhu Liu,Sunil Hadap,Vimal Bhat,Manoj Aggarwal,Gerard Medioni,David Harwath
机构: University of Texas at Austin (德克萨斯大学奥斯汀分校); Amazon(亚马逊)
类目: Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Work in progress
Abstract:The scarcity of parallel speech corpora critically hampers speech-to-speech translation (S2ST), often forcing reliance on complex, multi-stage pipelines. This paper introduces RosettaSpeech, a novel and simplified framework for zero-shot S2ST that is trained on monolingual speech-text data augmented by machine translation supervision. While our method leverages the linguistic knowledge inherent in text-based NMT models, it strictly eliminates the need for parallel speech-to-speech pairs. Our model uniquely uses text as an intermediate bridge during training but functions as a direct, end-to-end speech-to-speech model at inference. This streamlined approach achieves state-of-the-art results on standard benchmarks. For instance, on the CVSS-C test set, RosettaSpeech outperforms leading systems, achieving an ASR-BLEU score of 25.17 for German-to-English and 29.86 for Spanish-to-English-relative gains of over 27% and 14%, respectively. Furthermore, we demonstrate that a single model can deliver strong many-to-one translation performance (FR/ES/DE - EN). We also provide a foundational analysis of how training data scaling impacts model performance. By prioritizing reliance on abundant parallel text rather than difficult-to-acquire parallel speech, RosettaSpeech offers a scalable path to creating high-quality, speaker-preserving S2ST for a much broader array of languages.
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[NLP-67] owards Audio Token Compression in Large Audio Language Models
【速读】： 该论文旨在解决大型音频语言模型（Large Audio Language Models, LALMs）在处理长时音频和部署于资源受限平台时面临的两大挑战：一是注意力机制带来的二次复杂度问题，二是音频信号高token率导致的计算与存储开销。为缓解这些问题，论文提出在LALM的音频编码器输出后、LLM解码器输入前，通过无监督分割和均匀平均池化等技术减少音频token数量，从而降低计算负担。关键创新在于采用低秩适配器（low-rank adapters）对压缩后的表示进行微调，以最小化因降采样带来的性能损失。实验表明，该方法可在将输入音频token数最多减少三倍的情况下，仍保持接近帧级精度的自动语音识别（Automatic Speech Recognition, ASR）和语音到语音翻译（Speech-to-Speech Translation, S2ST）性能。
链接: https://arxiv.org/abs/2511.20973
作者: Saurabhchand Bhati,Samuel Thomas,Hilde Kuehne,Rogerio Feris,James Glass
机构: MIT (麻省理工学院); IBM Research (IBM研究院); MIT-IBM Watson AI Lab (MIT-IBM华生人工智能实验室); Tuebingen AI Center/University of Tuebingen (图宾根人工智能中心/图宾根大学)
类目: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Large Audio Language Models (LALMs) demonstrate impressive performance across diverse tasks, ranging from speech recognition to general audio understanding. However, their scalability is limited by the quadratic complexity of attention and the high token rates of audio signals. These challenges make it difficult to extend LALMs to long-form audio and to deploy them on resource-constrained platforms such as edge devices. In this paper, we explore techniques such as unsupervised segmentation, uniform average pooling, etc., to reduce the number of audio tokens generated by the LALM’s audio encoder but before they are consumed by the LLM decoder. To mitigate potential performance degradation introduced by the compressed representations, we employ low-rank adapters to finetune the model. We evaluate our proposed models on two tasks, automatic speech recognition and speech-to-speech translation tasks, that are dependent on effectively uncovering the underlying lexical content of the input signal and study the effect of downsampling on these tasks. Experimental results show that compressed LALMs can achieve performance closer to frame-level LALMs while reducing the input audio token count upto three times before the LLM backbone. Subjects: Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2511.20973 [eess.AS] (or arXiv:2511.20973v1 [eess.AS] for this version) https://doi.org/10.48550/arXiv.2511.20973 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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计算机视觉
[CV-0] Canvas-to-Image: Compositional Image Generation with Multimodal Controls
【速读】：该论文旨在解决当前扩散模型在生成图像时难以实现高保真度的组合控制与多模态协同控制的问题，尤其是在用户同时提供文本提示、主体参考、空间布局、姿态约束和版式标注等异构控制信号时，现有方法往往无法有效整合这些复杂指令。解决方案的关键在于提出一种统一的“Canvas-to-Image”框架，其核心思想是将多种控制信号编码为一张复合画布图像（composite canvas image），使模型能够直接解析该画布以进行集成式的视觉-空间推理；同时，研究者构建了多任务数据集并设计了多任务画布训练策略（Multi-Task Canvas Training），通过联合优化使扩散模型在统一学习范式下同时理解并融合异构控制信号，从而在推理阶段实现跨模态协同控制能力，显著提升身份保留和控制遵循性能。
链接: https://arxiv.org/abs/2511.21691
作者: Yusuf Dalva,Guocheng Gordon Qian,Maya Goldenberg,Tsai-Shien Chen,Kfir Aberman,Sergey Tulyakov,Pinar Yanardag,Kuan-Chieh Jackson Wang
机构: Snap Inc.; UC Merced; Virginia Tech
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 24 pages; webpage: this https URL
Abstract:While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, particularly when users simultaneously specify text prompts, subject references, spatial arrangements, pose constraints, and layout annotations. We introduce Canvas-to-Image, a unified framework that consolidates these heterogeneous controls into a single canvas interface, enabling users to generate images that faithfully reflect their intent. Our key idea is to encode diverse control signals into a single composite canvas image that the model can directly interpret for integrated visual-spatial reasoning. We further curate a suite of multi-task datasets and propose a Multi-Task Canvas Training strategy that optimizes the diffusion model to jointly understand and integrate heterogeneous controls into text-to-image generation within a unified learning paradigm. This joint training enables Canvas-to-Image to reason across multiple control modalities rather than relying on task-specific heuristics, and it generalizes well to multi-control scenarios during inference. Extensive experiments show that Canvas-to-Image significantly outperforms state-of-the-art methods in identity preservation and control adherence across challenging benchmarks, including multi-person composition, pose-controlled composition, layout-constrained generation, and multi-control generation.
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[CV-1] raceGen: World Modeling in 3D Trace Space Enables Learning from Cross-Embodiment Videos
【速读】：该论文旨在解决在新平台、新场景下仅凭少量示范视频学习机器人任务的难题（即小数据问题）。其核心挑战在于，尽管人类和不同机器人拍摄的视频资源丰富，但因本体差异（embodiment）、摄像机视角及环境变化，难以直接利用。解决方案的关键在于提出一种统一的符号化表示——紧凑的三维“轨迹空间”（trace-space），该空间编码场景级轨迹信息，能够抽象掉外观细节而保留操作所需的几何结构。基于此，作者构建了TraceGen世界模型，它在轨迹空间中预测未来运动而非像素空间，从而实现跨本体、跨环境和跨任务的迁移学习；并通过TraceForge数据流水线将异构视频转化为一致的3D轨迹，形成大规模训练语料，使模型在仅需5个目标机器人视频或5个未校准的人类手持视频的情况下即可高效适配并取得高成功率（最高达80%），显著优于现有基于像素的世界模型。
链接: https://arxiv.org/abs/2511.21690
作者: Seungjae Lee,Yoonkyo Jung,Inkook Chun,Yao-Chih Lee,Zikui Cai,Hongjia Huang,Aayush Talreja,Tan Dat Dao,Yongyuan Liang,Jia-Bin Huang,Furong Huang
机构: University of Maryland, College Park (马里兰大学学院市分校); New York University (纽约大学)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注:
Abstract:Learning new robot tasks on new platforms and in new scenes from only a handful of demonstrations remains challenging. While videos of other embodiments - humans and different robots - are abundant, differences in embodiment, camera, and environment hinder their direct use. We address the small-data problem by introducing a unifying, symbolic representation - a compact 3D “trace-space” of scene-level trajectories - that enables learning from cross-embodiment, cross-environment, and cross-task videos. We present TraceGen, a world model that predicts future motion in trace-space rather than pixel space, abstracting away appearance while retaining the geometric structure needed for manipulation. To train TraceGen at scale, we develop TraceForge, a data pipeline that transforms heterogeneous human and robot videos into consistent 3D traces, yielding a corpus of 123K videos and 1.8M observation-trace-language triplets. Pretraining on this corpus produces a transferable 3D motion prior that adapts efficiently: with just five target robot videos, TraceGen attains 80% success across four tasks while offering 50-600x faster inference than state-of-the-art video-based world models. In the more challenging case where only five uncalibrated human demonstration videos captured on a handheld phone are available, it still reaches 67.5% success on a real robot, highlighting TraceGen’s ability to adapt across embodiments without relying on object detectors or heavy pixel-space generation.
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[CV-2] Seeing without Pixels: Perception from Camera Trajectories
【速读】：该论文旨在解决如何仅通过相机轨迹（camera trajectory）——即摄像机在空间中移动的路径——来感知视频内容的问题，而无需直接观察视频像素。这一问题看似难以实现，但研究者提出了一种基于对比学习（contrastive learning）的框架，训练出名为CamFormer的专用编码器，将相机位姿轨迹映射到一个联合嵌入空间，并使其与自然语言对齐。其关键创新在于证明了相机轨迹本身是一个极具信息量的信号，能够有效揭示视频中的动作或观察内容（无论是第一人称视角还是第三人称视角），从而建立“移动方式”与“所见内容”之间的强关联性。此外，所学表示在多种相机位姿估计方法下均表现出鲁棒性和泛化能力，验证了相机轨迹作为轻量化、鲁棒且多用途模态在视频理解中的潜力。
链接: https://arxiv.org/abs/2511.21681
作者: Zihui Xue,Kristen Grauman,Dima Damen,Andrew Zisserman,Tengda Han
机构: Google DeepMind(谷歌深度思维); The University of Texas at Austin(德克萨斯大学奥斯汀分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project website: this https URL
Abstract:Can one perceive a video’s content without seeing its pixels, just from the camera trajectory-the path it carves through space? This paper is the first to systematically investigate this seemingly implausible question. Towards this end, we propose a contrastive learning framework to train CamFormer, a dedicated encoder that projects camera pose trajectories into a joint embedding space, aligning them with natural language. We find that, contrary to its apparent simplicity, the camera trajectory is a remarkably informative signal to uncover video content. In other words, “how you move” can indeed reveal “what you are doing” (egocentric) or “observing” (exocentric). We demonstrate the versatility of our learned CamFormer embeddings on a diverse suite of downstream tasks, ranging from cross-modal alignment to classification and temporal analysis. Importantly, our representations are robust across diverse camera pose estimation methods, including both high-fidelity multi-sensored and standard RGB-only estimators. Our findings establish camera trajectory as a lightweight, robust, and versatile modality for perceiving video content.
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[CV-3] Revolutionizing Glioma Segmentation Grading Using 3D MRI - Guided Hybrid Deep Learning Models
【速读】：该论文旨在解决胶质瘤（Glioma）早期精准诊断难题，以提升治疗干预的时效性和准确性。其核心解决方案是构建一个融合U-Net分割与混合DenseNet-VGG分类网络的深度学习框架，并引入多头注意力机制（multi-head attention）和空间-通道注意力机制（spatial-channel attention），实现对高维3D MRI数据的端到端处理。关键创新在于通过注意力机制聚焦于临床相关的肿瘤区域，显著提升了分割精度（Dice系数达98%）与分类准确率（99%），优于传统CNN模型及无注意力方法，从而为胶质瘤的快速、可靠诊断与分级提供技术支持。
链接: https://arxiv.org/abs/2511.21673
作者: Pandiyaraju V,Sreya Mynampati,Abishek Karthik,Poovarasan L,D. Saraswathi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Gliomas are brain tumor types that have a high mortality rate which means early and accurate diagnosis is important for therapeutic intervention for the tumors. To address this difficulty, the proposed research will develop a hybrid deep learning model which integrates U-Net based segmentation and a hybrid DenseNet-VGG classification network with multihead attention and spatial-channel attention capabilities. The segmentation model will precisely demarcate the tumors in a 3D volume of MRI data guided by spatial and contextual information. The classification network which combines a branch of both DenseNet and VGG, will incorporate the demarcated tumor on which features with attention mechanisms would be focused on clinically relevant features. High-dimensional 3D MRI data could successfully be utilized in the model through preprocessing steps which are normalization, resampling, and data augmentation. Through a variety of measures the framework is evaluated: measures of performance in segmentation are Dice coefficient and Mean Intersection over Union (IoU) and measures of performance in classification are accuracy precision, recall, and F1-score. The hybrid framework that has been proposed has demonstrated through physical testing that it has the capability of obtaining a Dice coefficient of 98% in tumor segmentation, and 99% on classification accuracy, outperforming traditional CNN models and attention-free methods. Utilizing multi-head attention mechanisms enhances notions of priority in aspects of the tumor that are clinically significant, and enhances interpretability and accuracy. The results suggest a great potential of the framework in facilitating the timely and reliable diagnosis and grading of glioma by clinicians is promising, allowing for better planning of patient treatment.
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[CV-4] Uncertainty Quantification for Visual Object Pose Estimation
【速读】：该论文旨在解决单目视觉下物体位姿估计的不确定性量化问题，即如何在不依赖严格分布假设的前提下，为给定的位姿估计提供统计上严谨的不确定性边界。其核心挑战在于从像素级检测噪声出发，构建一个能够高概率包含真实位姿的不确定区域。解决方案的关键是提出SLUE（S-Lemma Uncertainty Estimation），这是一种基于S-lemma启发的凸优化方法，可将由非凸约束诱导的隐式位姿不确定性集压缩为一个单一的椭球不确定性边界，且无需初始猜测边界形状或大小，同时保证以高概率包含真实位姿。此外，通过引入平方和（sum-of-squares）松弛层次，SLUE还能进一步逼近最小体积椭球边界，从而在相同置信水平下获得更紧致的不确定性范围。
链接: https://arxiv.org/abs/2511.21666
作者: Lorenzo Shaikewitz,Charis Georgiou,Luca Carlone
机构: Massachusetts Institute of Technology (麻省理工学院)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 18 pages, 9 figures. Code available: this https URL
Abstract:Quantifying the uncertainty of an object’s pose estimate is essential for robust control and planning. Although pose estimation is a well-studied robotics problem, attaching statistically rigorous uncertainty is not well understood without strict distributional assumptions. We develop distribution-free pose uncertainty bounds about a given pose estimate in the monocular setting. Our pose uncertainty only requires high probability noise bounds on pixel detections of 2D semantic keypoints on a known object. This noise model induces an implicit, non-convex set of pose uncertainty constraints. Our key contribution is SLUE (S-Lemma Uncertainty Estimation), a convex program to reduce this set to a single ellipsoidal uncertainty bound that is guaranteed to contain the true object pose with high probability. SLUE solves a relaxation of the minimum volume bounding ellipsoid problem inspired by the celebrated S-lemma. It requires no initial guess of the bound’s shape or size and is guaranteed to contain the true object pose with high probability. For tighter uncertainty bounds at the same confidence, we extend SLUE to a sum-of-squares relaxation hierarchy which is guaranteed to converge to the minimum volume ellipsoidal uncertainty bound for a given set of keypoint constraints. We show this pose uncertainty bound can easily be projected to independent translation and axis-angle orientation bounds. We evaluate SLUE on two pose estimation datasets and a real-world drone tracking scenario. Compared to prior work, SLUE generates substantially smaller translation bounds and competitive orientation bounds. We release code at this https URL.
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[CV-5] Attention-Guided Patch-Wise Sparse Adversarial Attacks on Vision-Language-Action Models
【速读】：该论文旨在解决现有对抗攻击方法在具身智能中的Vision-Language-Action (VLA)模型上存在的两大问题：一是需要昂贵的端到端训练成本，二是生成的扰动补丁通常明显且不自然。其解决方案的关键在于提出ADVLA框架，该框架直接在视觉编码器投影到文本特征空间的中间特征上施加对抗扰动，而非作用于原始图像或输出层；通过注意力引导机制实现扰动的聚焦与稀疏性，结合敏感度增强、稀疏约束和集中策略，使扰动仅作用于关键区域，在低幅值（L∞=4/255）条件下即可实现近100%攻击成功率，且扰动覆盖不足10%的图像块，单次迭代仅需约0.06秒，显著优于传统基于补丁的攻击方法。
链接: https://arxiv.org/abs/2511.21663
作者: Naifu Zhang,Wei Tao,Xi Xiao,Qianpu Sun,Yuxin Zheng,Wentao Mo,Peiqiang Wang,Nan Zhang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:In recent years, Vision-Language-Action (VLA) models in embodied intelligence have developed rapidly. However, existing adversarial attack methods require costly end-to-end training and often generate noticeable perturbation patches. To address these limitations, we propose ADVLA, a framework that directly applies adversarial perturbations on features projected from the visual encoder into the textual feature space. ADVLA efficiently disrupts downstream action predictions under low-amplitude constraints, and attention guidance allows the perturbations to be both focused and sparse. We introduce three strategies that enhance sensitivity, enforce sparsity, and concentrate perturbations. Experiments demonstrate that under an L_\infty=4/255 constraint, ADVLA combined with Top-K masking modifies less than 10% of the patches while achieving an attack success rate of nearly 100%. The perturbations are concentrated on critical regions, remain almost imperceptible in the overall image, and a single-step iteration takes only about 0.06 seconds, significantly outperforming conventional patch-based attacks. In summary, ADVLA effectively weakens downstream action predictions of VLA models under low-amplitude and locally sparse conditions, avoiding the high training costs and conspicuous perturbations of traditional patch attacks, and demonstrates unique effectiveness and practical value for attacking VLA feature spaces.
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[CV-6] Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following
【速读】：该论文旨在解决当前大型多模态模型（Large Multimodal Models, LMMs）在多准则评估场景下对细粒度评价标准遵循能力不足的问题，尤其是在开放生成任务中一致性与灵活性的缺失。其解决方案的关键在于构建了一个名为Multi-Crit的基准测试集，该基准通过严谨的数据筛选流程收集具有挑战性的响应对，并附带多准则的人类标注；同时引入三种新指标——用于系统性评估模型对多元准则的遵循程度、在不同准则间切换的灵活性以及识别准则级偏好冲突的能力。这一方法首次实现了对LMM作为多模态评判者在复杂评价情境下的量化分析，为提升多模态AI评估系统的可靠性与可控性提供了基础支撑。
链接: https://arxiv.org/abs/2511.21662
作者: Tianyi Xiong,Yi Ge,Ming Li,Zuolong Zhang,Pranav Kulkarni,Kaishen Wang,Qi He,Zeying Zhu,Chenxi Liu,Ruibo Chen,Tong Zheng,Yanshuo Chen,Xiyao Wang,Renrui Zhang,Wenhu Chen,Heng Huang
机构: University of Maryland, College Park (马里兰大学学院市分校); University of Waterloo (滑铁卢大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences. However, their ability to follow diverse, fine-grained evaluation criteria remains underexplored. We develop Multi-Crit, a benchmark for evaluating multimodal judges on their capacity to follow pluralistic criteria and produce reliable criterion-level judgments. Covering both open-ended generation and verifiable reasoning tasks, Multi-Crit is built through a rigorous data curation pipeline that gathers challenging response pairs with multi-criterion human annotations. It further introduces three novel metrics for systematically assessing pluralistic adherence, criterion-switching flexibility, and the ability to recognize criterion-level preference conflicts. Comprehensive analysis of 25 LMMs reveals that 1) proprietary models still struggle to maintain consistent adherence to pluralistic criteria–especially in open-ended evaluation; 2) open-source models lag further behind in flexibly following diverse criteria; and 3) critic fine-tuning with holistic judgment signals enhances visual grounding but fails to generalize to pluralistic criterion-level judgment. Additional analyses on reasoning fine-tuning, test-time scaling, and boundary consistency between open-source and proprietary models further probe the limits of current multimodal judges. As a pioneering study, Multi-Crit lays the foundation for building reliable and steerable multimodal AI evaluation.
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[CV-7] CaFlow: Enhancing Long-Term Action Quality Assessment with Causal Counterfactual Flow
【速读】：该论文旨在解决长期动作质量评估（Long-term Action Quality Assessment, Long-term AQA）中因长时间动态建模困难以及对上下文混杂因素（contextual confounders）敏感而导致的性能不稳定问题。现有方法要么依赖昂贵的人工标注，要么仅采用单向时间建模，易受虚假相关性干扰。其解决方案的关键在于提出一个统一框架CaFlow，核心包括两个模块：一是因果反事实正则化（Causal Counterfactual Regularization, CCR）模块，通过自监督方式解耦因果特征与混杂特征，并利用反事实干预强制因果鲁棒性；二是双向时间条件流（BiT-Flow）模块，结合循环一致性约束建模前向与后向动态，生成更平滑、一致的表示。该方法在多个长期AQA基准上达到最先进性能。
链接: https://arxiv.org/abs/2511.21653
作者: Ruisheng Han,Kanglei Zhou,Shuang Chen,Amir Atapour-Abarghouei,Hubert P. H. Shum
机构: Durham University (杜伦大学); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Action Quality Assessment (AQA) predicts fine-grained execution scores from action videos and is widely applied in sports, rehabilitation, and skill evaluation. Long-term AQA, as in figure skating or rhythmic gymnastics, is especially challenging since it requires modeling extended temporal dynamics while remaining robust to contextual confounders. Existing approaches either depend on costly annotations or rely on unidirectional temporal modeling, making them vulnerable to spurious correlations and unstable long-term representations. To this end, we propose CaFlow, a unified framework that integrates counterfactual de-confounding with bidirectional time-conditioned flow. The Causal Counterfactual Regularization (CCR) module disentangles causal and confounding features in a self-supervised manner and enforces causal robustness through counterfactual interventions, while the BiT-Flow module models forward and backward dynamics with a cycle-consistency constraint to produce smoother and more coherent representations. Extensive experiments on multiple long-term AQA benchmarks demonstrate that CaFlow achieves state-of-the-art performance. Code is available at this https URL
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[CV-8] Continual Error Correction on Low-Resource Devices ACM-MM
【速读】：该论文旨在解决嵌入式设备中人工智能（AI）模型因预测错误导致用户体验下降的问题，尤其针对资源受限设备缺乏高效纠错机制的挑战。现有方法多集中于错误检测，而忽视了快速、低开销的纠正能力。其解决方案的关键在于提出一种结合服务器端知识蒸馏与设备端原型自适应的协同架构：服务器端通过知识蒸馏将基础模型（foundation model）的鲁棒特征表示迁移至轻量级设备适配架构；设备端则基于原型（prototype）更新而非模型重训练实现超高效纠错，从而在保持极低遗忘率（<0.02%）和可忽略计算开销的前提下，仅需少量样本即可显著提升分类准确性（如在Food-101和Flowers-102数据集上单样本场景下纠错率超过50%）。
链接: https://arxiv.org/abs/2511.21652
作者: Kirill Paramonov,Mete Ozay,Aristeidis Mystakidis,Nikolaos Tsalikidis,Dimitrios Sotos,Anastasios Drosou,Dimitrios Tzovaras,Hyunjun Kim,Kiseok Chang,Sangdok Mo,Namwoong Kim,Woojong Yoo,Jijoong Moon,Umberto Michieli
机构: Samsung R&D Institute UK (三星研发研究院英国); CERTH (欧洲核子研究中心); Samsung Research (三星研究)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: ACM MMSys 2025
Abstract:The proliferation of AI models in everyday devices has highlighted a critical challenge: prediction errors that degrade user experience. While existing solutions focus on error detection, they rarely provide efficient correction mechanisms, especially for resource-constrained devices. We present a novel system enabling users to correct AI misclassifications through few-shot learning, requiring minimal computational resources and storage. Our approach combines server-side foundation model training with on-device prototype-based classification, enabling efficient error correction through prototype updates rather than model retraining. The system consists of two key components: (1) a server-side pipeline that leverages knowledge distillation to transfer robust feature representations from foundation models to device-compatible architectures, and (2) a device-side mechanism that enables ultra-efficient error correction through prototype adaptation. We demonstrate our system’s effectiveness on both image classification and object detection tasks, achieving over 50% error correction in one-shot scenarios on Food-101 and Flowers-102 datasets while maintaining minimal forgetting (less than 0.02%) and negligible computational overhead. Our implementation, validated through an Android demonstration app, proves the system’s practicality in real-world scenarios.
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[CV-9] Mechanisms of Non-Monotonic Scaling in Vision Transformers KR
【速读】：该论文试图解决深度视觉Transformer（Vision Transformer, ViT）在增加层数后性能反而下降的问题，这与传统模型扩展假设相悖。其解决方案的关键在于通过系统性实证分析揭示了表征演化遵循“悬崖-平台-爬升”（Cliff-Plateau-Climb）三阶段模式，并发现性能提升与[CLS]标记的逐步边缘化及patch token间分布式共识的增强密切相关。研究进一步提出信息混淆指数（Information Scrambling Index）作为量化信息混合程度的指标，表明深层ViT（如ViT-L）的信息-任务权衡延迟约10层出现，且额外层数主要促进信息扩散而非任务性能提升。因此，该工作建议未来设计应关注通过精确校准深度实现清晰的相变，而非单纯堆叠参数。
链接: https://arxiv.org/abs/2511.21635
作者: Anantha Padmanaban Krishna Kumar(Boston University)
机构: Boston University (波士顿大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages total (11 pages main text, 1 pages references, 4 pages appendix), 5 figures, 11 tables. Code available at this https URL
Abstract:Deeper Vision Transformers often perform worse than shallower ones, which challenges common scaling assumptions. Through a systematic empirical analysis of ViT-S, ViT-B, and ViT-L on ImageNet, we identify a consistent three-phase Cliff-Plateau-Climb pattern that governs how representations evolve with depth. We observe that better performance is associated with progressive marginalization of the [CLS] token, originally designed as a global aggregation hub, in favor of distributed consensus among patch tokens. We quantify patterns of information mixing with an Information Scrambling Index, and show that in ViT-L the information-task tradeoff emerges roughly 10 layers later than in ViT-B, and that these additional layers correlate with increased information diffusion rather than improved task performance. Taken together, these results suggest that transformer architectures in this regime may benefit more from carefully calibrated depth that executes clean phase transitions than from simply increasing parameter count. The Information Scrambling Index provides a useful diagnostic for existing models and suggests a potential design target for future architectures. All code is available at: this https URL.
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[CV-10] Qwen 3-VL Technical Report
【速读】：该论文旨在解决当前视觉语言模型（Vision-Language Model, VLM）在长文本理解、多模态信息融合与时空建模能力上的局限性，尤其是在处理高分辨率图像、视频及跨模态推理任务时的性能瓶颈。解决方案的关键在于三个方面：一是引入增强型交错-MRoPE（interleaved-MRoPE），显著提升对图像与视频中空间-时间特征的建模能力；二是集成DeepStack架构，通过融合多层次视觉Transformer（ViT）特征强化视觉-语言对齐；三是提出基于文本的时间对齐机制，从T-RoPE演进为显式的文本时间戳对齐方法，实现更精确的视频时序定位。这些改进使Qwen3-VL在256K token原生长上下文支持下，实现了纯文本理解、长文档处理和多模态推理能力的全面提升。
链接: https://arxiv.org/abs/2511.21631
作者: Shuai Bai,Yuxuan Cai,Ruizhe Chen,Keqin Chen,Xionghui Chen,Zesen Cheng,Lianghao Deng,Wei Ding,Chang Gao,Chunjiang Ge,Wenbin Ge,Zhifang Guo,Qidong Huang,Jie Huang,Fei Huang,Binyuan Hui,Shutong Jiang,Zhaohai Li,Mingsheng Li,Mei Li,Kaixin Li,Zicheng Lin,Junyang Lin,Xuejing Liu,Jiawei Liu,Chenglong Liu,Yang Liu,Dayiheng Liu,Shixuan Liu,Dunjie Lu,Ruilin Luo,Chenxu Lv,Rui Men,Lingchen Meng,Xuancheng Ren,Xingzhang Ren,Sibo Song,Yuchong Sun,Jun Tang,Jianhong Tu,Jianqiang Wan,Peng Wang,Pengfei Wang,Qiuyue Wang,Yuxuan Wang,Tianbao Xie,Yiheng Xu,Haiyang Xu,Jin Xu,Zhibo Yang,Mingkun Yang,Jianxin Yang,An Yang,Bowen Yu,Fei Zhang,Hang Zhang,Xi Zhang,Bo Zheng,Humen Zhong,Jingren Zhou,Fan Zhou,Jing Zhou,Yuanzhi Zhu,Ke Zhu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 42 pages
Abstract:We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively supports interleaved contexts of up to 256K tokens, seamlessly integrating text, images, and video. The model family includes both dense (2B/4B/8B/32B) and mixture-of-experts (30B-A3B/235B-A22B) variants to accommodate diverse latency-quality trade-offs. Qwen3-VL delivers three core pillars: (i) markedly stronger pure-text understanding, surpassing comparable text-only backbones in several cases; (ii) robust long-context comprehension with a native 256K-token window for both text and interleaved multimodal inputs, enabling faithful retention, retrieval, and cross-referencing across long documents and videos; and (iii) advanced multimodal reasoning across single-image, multi-image, and video tasks, demonstrating leading performance on comprehensive evaluations such as MMMU and visual-math benchmarks (e.g., MathVista and MathVision). Architecturally, we introduce three key upgrades: (i) an enhanced interleaved-MRoPE for stronger spatial-temporal modeling across images and video; (ii) DeepStack integration, which effectively leverages multi-level ViT features to tighten vision-language alignment; and (iii) text-based time alignment for video, evolving from T-RoPE to explicit textual timestamp alignment for more precise temporal grounding. Under comparable token budgets and latency constraints, Qwen3-VL achieves superior performance in both dense and Mixture-of-Experts (MoE) architectures. We envision Qwen3-VL serving as a foundational engine for image-grounded reasoning, agentic decision-making, and multimodal code intelligence in real-world workflows.
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[CV-11] Active Learning for GCN-based Action Recognition
【速读】：该论文旨在解决图卷积网络（Graph Convolutional Networks, GCNs）在基于骨架的动作识别任务中对大量标注数据依赖性强的问题，而实际场景中标注数据往往稀缺。解决方案的关键在于提出一种标签高效（label-efficient）的GCN模型，其核心创新包括：一是设计了一种基于对抗策略的新型采集函数（acquisition function），用于选择具有代表性、多样性和不确定性的少量信息量最大的样本进行标注；二是引入双向且稳定的GCN架构，增强从环境空间到潜在空间的映射能力，从而更有效地理解所选示例的分布特性，显著提升小样本条件下的性能表现。
链接: https://arxiv.org/abs/2511.21625
作者: Hichem Sahbi
机构: Sorbonne University (索邦大学); 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:Despite the notable success of graph convolutional networks (GCNs) in skeleton-based action recognition, their performance often depends on large volumes of labeled data, which are frequently scarce in practical settings. To address this limitation, we propose a novel label-efficient GCN model. Our work makes two primary contributions. First, we develop a novel acquisition function that employs an adversarial strategy to identify a compact set of informative exemplars for labeling. This selection process balances representativeness, diversity, and uncertainty. Second, we introduce bidirectional and stable GCN architectures. These enhanced networks facilitate a more effective mapping between the ambient and latent data spaces, enabling a better understanding of the learned exemplar distribution. Extensive evaluations on two challenging skeleton-based action recognition benchmarks reveal significant improvements achieved by our label-efficient GCNs compared to prior work.
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[CV-12] ReSAM: Refine Requery and Reinforce: Self-Prompting Point-Supervised Segmentation for Remote Sensing Images
【速读】：该论文旨在解决生成式 AI（Generative AI）在遥感图像（Remote Sensing Imagery, RSI）上分割性能不佳的问题，主要归因于域偏移（domain shift）和密集标注数据稀缺。其解决方案的关键在于提出一种自提示（self-prompting）、点监督的适配框架，通过“精炼-重查询-强化”（Refine-Requery-Reinforce）循环机制实现模型迭代优化：首先利用稀疏点标注生成粗粒度伪掩码（Refine），随后构建自适应框提示进行细化（Requery），并在多轮迭代中对特征嵌入进行语义对齐以缓解确认偏倚（Reinforce）。该方法无需全掩码监督，即可显著提升Segment Anything Model（SAM）在RSI上的分割质量与领域鲁棒性。
链接: https://arxiv.org/abs/2511.21606
作者: M.Naseer Subhani
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Interactive segmentation models such as the Segment Anything Model (SAM) have demonstrated remarkable generalization on natural images, but perform suboptimally on remote sensing imagery (RSI) due to severe domain shift and the scarcity of dense annotations. To address this, we propose a self-prompting, point-supervised framework that adapts SAM to RSIs using only sparse point annotations. Our method employs a Refine-Requery-Reinforce loop, where coarse pseudo-masks are generated from initial points (Refine), improved with self-constructed box prompts (Requery), and embeddings are aligned across iterations to reduce confirmation bias (Reinforce). Without relying on full-mask supervision, our approach progressively enhances SAM’s segmentation quality and domain robustness through self-guided prompt adaptation . We evaluate our proposed method on three RSI benchmark datasets, including WHU, HRSID, and NWPU VHR-10, showing that our method consistently surpasses pretrained SAM and recent point-supervised segmentation methods. Our results demonstrate that self-prompting and semantic alignment provide an efficient path towards scalable, point-level adaptation of foundation segmentation models for remote sensing applications.
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[CV-13] MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training
【速读】：该论文旨在解决视频扩散模型在运动一致性、动态真实性和流畅性方面的不足，如抖动、鬼影效应及不合理的运动表现等问题。其核心问题是标准去噪均方误差（MSE）目标函数缺乏对时间一致性的直接监督，导致模型虽能获得低损失值，却生成质量较差的运动序列。解决方案的关键在于提出MoGAN框架——一种以运动为中心的后训练方法，通过在蒸馏后的视频扩散模型基础上引入基于DiT架构的光流判别器（optical-flow discriminator），用于区分真实与生成的运动，并结合分布匹配正则项以保持视觉保真度，从而在不依赖奖励模型或人类偏好数据的情况下显著提升运动真实性。
链接: https://arxiv.org/abs/2511.21592
作者: Haotian Xue,Qi Chen,Zhonghao Wang,Xun Huang,Eli Shechtman,Jinrong Xie,Yongxin Chen
机构: Adobe(Adobe); Georgia Tech(佐治亚理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Video diffusion models achieve strong frame-level fidelity but still struggle with motion coherence, dynamics and realism, often producing jitter, ghosting, or implausible dynamics. A key limitation is that the standard denoising MSE objective provides no direct supervision on temporal consistency, allowing models to achieve low loss while still generating poor motion. We propose MoGAN, a motion-centric post-training framework that improves motion realism without reward models or human preference data. Built atop a 3-step distilled video diffusion model, we train a DiT-based optical-flow discriminator to differentiate real from generated motion, combined with a distribution-matching regularizer to preserve visual fidelity. With experiments on Wan2.1-T2V-1.3B, MoGAN substantially improves motion quality across benchmarks. On VBench, MoGAN boosts motion score by +7.3% over the 50-step teacher and +13.3% over the 3-step DMD model. On VideoJAM-Bench, MoGAN improves motion score by +7.4% over the teacher and +8.8% over DMD, while maintaining comparable or even better aesthetic and image-quality scores. A human study further confirms that MoGAN is preferred for motion quality (52% vs. 38% for the teacher; 56% vs. 29% for DMD). Overall, MoGAN delivers significantly more realistic motion without sacrificing visual fidelity or efficiency, offering a practical path toward fast, high-quality video generation. Project webpage is: this https URL.
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[CV-14] Deep Learning-Based Multiclass Classification of Oral Lesions with Stratified Augmentation
【速读】：该论文旨在解决口腔癌（oral cancer）早期诊断困难的问题，因其常因与良性、癌前及恶性病变在视觉上高度相似而被延误诊断。为提升临床诊疗效果，研究提出基于深度学习的多类分类模型，用于区分十六种不同类型的口腔病变。解决方案的关键在于通过分层数据分割（stratified data splitting）结合先进的数据增强（data augmentation）和过采样（oversampling）策略，有效应对训练数据有限且类别分布不均衡的挑战，从而显著提升少数类别（minority class）的分类性能，实验结果表明该方法在准确率（83.33%）、精确率（89.12%）和召回率（77.31%）方面优于现有主流方法，为构建可信的计算机辅助诊断系统提供了可行路径。
链接: https://arxiv.org/abs/2511.21582
作者: Joy Naoum,Revana Salama,Ali Hamdi
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 3 figures,
Abstract:Oral cancer is highly common across the globe and is mostly diagnosed during the later stages due to the close visual similarity to benign, precancerous, and malignant lesions in the oral cavity. Implementing computer aided diagnosis systems early on has the potential to greatly improve clinical outcomes. This research intends to use deep learning to build a multiclass classifier for sixteen different oral lesions. To overcome the challenges of limited and imbalanced datasets, the proposed technique combines stratified data splitting and advanced data augmentation and oversampling to perform the classification. The experimental results, which achieved 83.33 percent accuracy, 89.12 percent precision, and 77.31 percent recall, demonstrate the superiority of the suggested model over state of the art methods now in use. The suggested model effectively conveys the effectiveness of oversampling and augmentation strategies in situations where the minority class classification performance is noteworthy. As a first step toward trustworthy computer aided diagnostic systems for the early detection of oral cancer in clinical settings, the suggested framework shows promise.
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[CV-15] Harmony: Harmonizing Audio and Video Generation through Cross-Task Synergy
【速读】：该论文旨在解决生成式 AI 中音频-视觉内容同步合成的难题，尤其针对开源模型在鲁棒性音频-视频对齐方面存在的不足。核心问题源于联合扩散过程中的三个根本挑战：(1) 对应漂移（Correspondence Drift），即噪声潜变量并发演化导致对齐学习不稳定；(2) 全局注意力机制效率低下，难以捕捉细粒度时间线索；(3) 传统无分类器指导（Classifier-Free Guidance, CFG）存在模态内偏差，增强条件性但无助于跨模态同步。解决方案的关键在于提出 Harmony 框架，其创新性体现在三个方面：首先采用跨任务协同训练范式（Cross-Task Synergy），利用音频驱动视频与视频驱动音频生成任务提供强监督信号缓解漂移；其次设计全局-局部解耦交互模块（Global-Local Decoupled Interaction Module），实现高效且精确的时间风格对齐；最后引入同步增强型 CFG（SyncCFG），在推理阶段显式分离并放大对齐信号，从而显著提升音画同步精度和生成保真度。
链接: https://arxiv.org/abs/2511.21579
作者: Teng Hu,Zhentao Yu,Guozhen Zhang,Zihan Su,Zhengguang Zhou,Youliang Zhang,Yuan Zhou,Qinglin Lu,Ran Yi
机构: Shanghai Jiao Tong University (上海交通大学); Tencent Hunyuan (腾讯混元)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The synthesis of synchronized audio-visual content is a key challenge in generative AI, with open-source models facing challenges in robust audio-video alignment. Our analysis reveals that this issue is rooted in three fundamental challenges of the joint diffusion process: (1) Correspondence Drift, where concurrently evolving noisy latents impede stable learning of alignment; (2) inefficient global attention mechanisms that fail to capture fine-grained temporal cues; and (3) the intra-modal bias of conventional Classifier-Free Guidance (CFG), which enhances conditionality but not cross-modal synchronization. To overcome these challenges, we introduce Harmony, a novel framework that mechanistically enforces audio-visual synchronization. We first propose a Cross-Task Synergy training paradigm to mitigate drift by leveraging strong supervisory signals from audio-driven video and video-driven audio generation tasks. Then, we design a Global-Local Decoupled Interaction Module for efficient and precise temporal-style alignment. Finally, we present a novel Synchronization-Enhanced CFG (SyncCFG) that explicitly isolates and amplifies the alignment signal during inference. Extensive experiments demonstrate that Harmony establishes a new state-of-the-art, significantly outperforming existing methods in both generation fidelity and, critically, in achieving fine-grained audio-visual synchronization.
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[CV-16] Enhanced Landmark Detection Model in Pelvic Fluoroscopy using 2D/3D Registration Loss
【速读】：该论文旨在解决骨盆荧光透视图像中关键点（landmark）检测在非标准前后位（Antero-Posterior, AP）视角下精度下降的问题，这通常由成像设备或患者体位偏移引起。解决方案的关键在于将2D/3D关键点配准（2D/3D landmark registration）引入U-Net关键点预测模型的训练过程，通过引入姿态估计损失（Pose Estimation Loss）提升模型对不同视角变化的鲁棒性，从而在真实术中条件下实现更准确的定位。
链接: https://arxiv.org/abs/2511.21575
作者: Chou Mo,Yehyun Suh,J. Ryan Martin,Daniel Moyer
机构: Vanderbilt University (范德比尔特大学); University of California-Los Angeles (加州大学洛杉矶分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 3 figures, 1 table
Abstract:Automated landmark detection offers an efficient approach for medical professionals to understand patient anatomic structure and positioning using intra-operative imaging. While current detection methods for pelvic fluoroscopy demonstrate promising accuracy, most assume a fixed Antero-Posterior view of the pelvis. However, orientation often deviates from this standard view, either due to repositioning of the imaging unit or of the target structure itself. To address this limitation, we propose a novel framework that incorporates 2D/3D landmark registration into the training of a U-Net landmark prediction model. We analyze the performance difference by comparing landmark detection accuracy between the baseline U-Net, U-Net trained with Pose Estimation Loss, and U-Net fine-tuned with Pose Estimation Loss under realistic intra-operative conditions where patient pose is variable.
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[CV-17] Multimodal Robust Prompt Distillation for 3D Point Cloud Models
【速读】：该论文旨在解决学习型三维点云模型在安全敏感应用中面临的对抗攻击威胁，现有防御方法普遍存在的两个问题为：（1）计算开销高；（2）对多种攻击类型泛化能力差。解决方案的关键在于提出一种新颖且高效的教师-学生框架——多模态鲁棒提示蒸馏（Multimodal Robust Prompt Distillation, MRPD），其通过融合三个不同模态的教师模型（处理深度投影的视觉模型、高性能3D模型和文本编码器）提供的鲁棒嵌入，指导学生模型学习轻量级提示（prompt），并通过置信度门控机制动态平衡各模态贡献，实现可靠的知识迁移。由于蒸馏过程仅发生在训练阶段，推理时无额外计算开销，从而在保持高效性的同时显著提升模型对白盒与黑盒攻击的鲁棒性，并在干净数据上表现更优。
链接: https://arxiv.org/abs/2511.21574
作者: Xiang Gu,Liming Lu,Xu Zheng,Anan Du,Yongbin Zhou,Shuchao Pang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applications. Existing defense methods often suffer from (1) high computational overhead and (2) poor generalization ability across diverse attack types. To bridge these gaps, we propose a novel yet efficient teacher-student framework, namely Multimodal Robust Prompt Distillation (MRPD) for distilling robust 3D point cloud model. It learns lightweight prompts by aligning student point cloud model’s features with robust embeddings from three distinct teachers: a vision model processing depth projections, a high-performance 3D model, and a text encoder. To ensure a reliable knowledge transfer, this distillation is guided by a confidence-gated mechanism which dynamically balances the contribution of all input modalities. Notably, since the distillation is all during the training stage, there is no additional computational cost at inference. Extensive experiments demonstrate that MRPD substantially outperforms state-of-the-art defense methods against a wide range of white-box and black-box attacks, while even achieving better performance on clean data. Our work presents a new, practical paradigm for building robust 3D vision systems by efficiently harnessing multimodal knowledge.
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[CV-18] UAVLight: A Benchmark for Illumination-Robust 3D Reconstruction in Unmanned Aerial Vehicle (UAV) Scenes
【速读】：该论文旨在解决多视角三维重建中的光照不一致性问题，即在无人机（UAV）拍摄过程中由于太阳方位角、云层变化和阴影等因素导致的光照条件波动，破坏了传统多视图立体视觉（Multi-View Stereo, MVS）与结构光恢复（Structure from Motion, SfM）以及新兴神经渲染方法所依赖的恒定光照假设，从而引发几何漂移、颜色不一致和阴影残留等问题。解决方案的关键在于提出一个受控但真实的基准数据集UAVLight：通过沿可重复、地理标记的飞行路径在一天中多个固定时刻采集同一场景，实现光照自然变化的同时保持几何、相机标定和视角的一致性，并配合标准化评估协议，为开发和测试具有光照鲁棒性的三维重建方法提供了可靠基础。
链接: https://arxiv.org/abs/2511.21565
作者: Kang Du,Xue Liao,Junpeng Xia,Chaozheng Guo,Yi Gu,Yirui Guan,Duotun Wang,ShengHuang,Zeyu Wang
机构: The Hong Kong University of Science and Technology (Guangzhou); The Hong Kong University of Science and Technology; Beijing University of Chemical Technology; Meituan Academy of Robotics Shenzhen
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 6 figures
Abstract:Illumination inconsistency is a fundamental challenge in multi-view 3D reconstruction. Variations in sunlight direction, cloud cover, and shadows break the constant-lighting assumption underlying both classical multi-view stereo (MVS) and structure from motion (SfM) pipelines and recent neural rendering methods, leading to geometry drift, color inconsistency, and shadow imprinting. This issue is especially critical in UAV-based reconstruction, where long flight durations and outdoor environments make lighting changes unavoidable. However, existing datasets either restrict capture to short time windows, thus lacking meaningful illumination diversity, or span months and seasons, where geometric and semantic changes confound the isolated study of lighting robustness. We introduce UAVLight, a controlled-yet-real benchmark for illumination-robust 3D reconstruction. Each scene is captured along repeatable, geo-referenced flight paths at multiple fixed times of day, producing natural lighting variation under consistent geometry, calibration, and viewpoints. With standardized evaluation protocols across lighting conditions, UAVLight provides a reliable foundation for developing and benchmarking reconstruction methods that are consistent, faithful, and relightable in real outdoor environments.
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[CV-19] Video Generation Models Are Good Latent Reward Models
【速读】：该论文旨在解决奖励反馈学习（Reward Feedback Learning, ReFL）在视频生成任务中扩展时面临的挑战，尤其是现有方法依赖像素空间的视觉-语言模型进行奖励建模，导致优化局限于接近完全去噪的阶段，从而造成内存开销大、训练时间长，且缺乏早期对运动动态和结构连贯性的监督。解决方案的关键在于提出过程奖励反馈学习（Process Reward Feedback Learning, PRFL），该框架将偏好优化完全置于噪声潜空间（noisy latent space）中进行，利用预训练视频生成模型天然具备处理任意时间步噪声潜表示的能力，并通过其序列建模特性保持时间信息，从而实现无需VAE解码即可在整个去噪链路中高效反向传播梯度，显著提升与人类偏好的对齐度，同时大幅降低内存消耗和训练时间。
链接: https://arxiv.org/abs/2511.21541
作者: Xiaoyue Mi,Wenqing Yu,Jiesong Lian,Shibo Jie,Ruizhe Zhong,Zijun Liu,Guozhen Zhang,Zixiang Zhou,Zhiyong Xu,Yuan Zhou,Qinglin Lu,Fan Tang
机构: University of Chinese Academy of Sciences (中国科学院大学); Tencent Hunyuan (腾讯混元); Huazhong University of Science and Technology (华中科技大学); Peking University (北京大学); Shanghai Jiao Tong University (上海交通大学); Tsinghua University (清华大学); Nanjing University (南京大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Reward feedback learning (ReFL) has proven effective for aligning image generation with human preferences. However, its extension to video generation faces significant challenges. Existing video reward models rely on vision-language models designed for pixel-space inputs, confining ReFL optimization to near-complete denoising steps after computationally expensive VAE decoding. This pixel-space approach incurs substantial memory overhead and increased training time, and its late-stage optimization lacks early-stage supervision, refining only visual quality rather than fundamental motion dynamics and structural coherence. In this work, we show that pre-trained video generation models are naturally suited for reward modeling in the noisy latent space, as they are explicitly designed to process noisy latent representations at arbitrary timesteps and inherently preserve temporal information through their sequential modeling capabilities. Accordingly, we propose Process Reward Feedback Learning~(PRFL), a framework that conducts preference optimization entirely in latent space, enabling efficient gradient backpropagation throughout the full denoising chain without VAE decoding. Extensive experiments demonstrate that PRFL significantly improves alignment with human preferences, while achieving substantial reductions in memory consumption and training time compared to RGB ReFL.
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[CV-20] he Age-specific Alzheimer s Disease Prediction with Characteristic Constraints in Nonuniform Time Span
【速读】：该论文旨在解决阿尔茨海默病（Alzheimer’s disease）早期预测中因输入影像序列采集时间不规律而导致的特征表示失真问题，进而影响疾病进展预测的准确性。其解决方案的关键在于提出一种基于定量指标引导的序列图像生成方法，并引入年龄缩放因子（age-scaling factor）以生成符合特定年龄阶段的磁共振成像（MRI）图像，从而提升合成图像对疾病进展特征的保真度。实验表明，定量指标的引入显著提升了MRI图像合成的准确性，而年龄缩放的像素损失函数则优化了迭代生成过程，最终在长期预测中实现了结构相似性指数（Structural Similarity Index）达0.882的高质量图像合成效果。
链接: https://arxiv.org/abs/2511.21530
作者: Xin Hong,Kaifeng Huang
机构: Huaqiao University (华侨大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 9 figures
Abstract:Alzheimer’s disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer’s disease poses challenges, particularly in accurately representing the disease’s characteristics when input sequences are captured at irregular time intervals. This study presents an innovative methodology for sequential image generation, guided by quantitative metrics, to maintain the essential features indicative of disease progression. Furthermore, an age-scaling factor is integrated into the process to produce age-specific MRI images, facilitating the prediction of advanced stages of the disease. The results obtained from the ablation study suggest that the inclusion of quantitative metrics significantly improves the accuracy of MRI image synthesis. Furthermore, the application of age-scaled pixel loss contributed to the enhanced iterative generation of MRI images. In terms of long-term disease prognosis, the Structural Similarity Index reached a peak value of 0.882, indicating a substantial degree of similarity in the synthesized images.
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[CV-21] EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor?
【速读】：该论文旨在解决当前遥感基础模型（Remote Sensing Foundation Models, RSFMs）构建中因模型规模和数据集体积不断扩展而导致的计算资源消耗过大、碳排放高以及可访问性受限的问题，这些问题与可持续AI的发展原则相悖。解决方案的关键在于提出一种“专家集成框架”（Ensemble-of-Specialists framework），将训练过程分解为轻量级、任务特定的ConvNeXtV2专家模块，这些专家可被冻结并复用，从而在保证性能的同时显著提升效率、可解释性和可扩展性，并天然支持联邦学习、剪枝及持续专家集成，特别适用于协作式和资源受限场景。
链接: https://arxiv.org/abs/2511.21523
作者: Pierre Adorni,Minh-Tan Pham,Stéphane May,Sébastien Lefèvre
机构: IRISA, Université Bretagne Sud, UMR 6074 (IRISA, 布列塔尼南大学，UMR 6074); Centre National d’Études Spatiales (CNES) (国家空间研究中心); UiT The Arctic University of Norway (UIT 北挪威大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent advances in foundation models have shown great promise in domains such as natural language processing and computer vision, and similar efforts are now emerging in the Earth Observation community. These models aim to generalize across tasks with limited supervision, reducing the need for training separate models for each task. However, current strategies, which largely focus on scaling model size and dataset volume, require prohibitive computational and data resources, limiting accessibility to only a few large institutions. Moreover, this paradigm of ever-larger models stands in stark contrast with the principles of sustainable and environmentally responsible AI, as it leads to immense carbon footprints and resource inefficiency. In this work, we present a novel and efficient alternative: an Ensemble-of-Specialists framework for building Remote Sensing Foundation Models (RSFMs). Our method decomposes the training process into lightweight, task-specific ConvNeXtV2 specialists that can be frozen and reused. This modular approach offers strong advantages in efficiency, interpretability, and extensibility. Moreover, it naturally supports federated training, pruning, and continuous specialist integration, making it particularly well-suited for collaborative and resource-constrained settings. Our framework sets a new direction for building scalable and efficient RSFMs.
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[CV-22] Self-Paced Learning for Images of Antinuclear Antibodies
【速读】：该论文旨在解决抗核抗体（Antinuclear Antibody, ANA）检测中多实例多标签（Multi-Instance Multi-Label, MIML）学习的挑战，尤其是在真实临床环境中使用未经预处理的显微镜图像进行自动化识别的问题。传统方法在面对超过100种共存抗体类型及其复杂荧光模式组合时，存在标注不一致、模型泛化能力弱及难以端到端优化等局限。解决方案的关键在于提出一种新颖的框架，通过三个任务特异性组件实现：（1）实例采样器（instance sampler）基于模式置信度抑制低置信度实例；（2）概率伪标签分配器（probabilistic pseudo-label dispatcher）根据实例可区分性自适应分配标签；（3）自适应学习率权重机制（self-paced weight learning rate coefficients）依据经验标签观测动态调整训练策略。该框架无需人工预处理即可实现高精度的ANA亚区域识别与聚合标签分配，在一个ANA数据集和三个公共医学MIML基准上均取得显著性能提升，验证了其在真实场景下的有效性与先进性。
链接: https://arxiv.org/abs/2511.21519
作者: Yiyang Jiang,Guangwu Qian,Jiaxin Wu,Qi Huang,Qing Li,Yongkang Wu,Xiao-Yong Wei
机构: The Hong Kong Polytechnic University (香港理工大学); Sichuan University (四川大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: IEEE Transactions on Medical Imaging
Abstract:Antinuclear antibody (ANA) testing is a crucial method for diagnosing autoimmune disorders, including lupus, Sjögren’s syndrome, and scleroderma. Despite its importance, manual ANA detection is slow, labor-intensive, and demands years of training. ANA detection is complicated by over 100 coexisting antibody types, resulting in vast fluorescent pattern combinations. Although machine learning and deep learning have enabled automation, ANA detection in real-world clinical settings presents unique challenges as it involves multi-instance, multi-label (MIML) learning. In this paper, a novel framework for ANA detection is proposed that handles the complexities of MIML tasks using unaltered microscope images without manual preprocessing. Inspired by human labeling logic, it identifies consistent ANA sub-regions and assigns aggregated labels accordingly. These steps are implemented using three task-specific components: an instance sampler, a probabilistic pseudo-label dispatcher, and self-paced weight learning rate coefficients. The instance sampler suppresses low-confidence instances by modeling pattern confidence, while the dispatcher adaptively assigns labels based on instance distinguishability. Self-paced learning adjusts training according to empirical label observations. Our framework overcomes limitations of traditional MIML methods and supports end-to-end optimization. Extensive experiments on one ANA dataset and three public medical MIML benchmarks demonstrate the superiority of our framework. On the ANA dataset, our model achieves up to +7.0% F1-Macro and +12.6% mAP gains over the best prior method, setting new state-of-the-art results. It also ranks top-2 across all key metrics on public datasets, reducing Hamming loss and one-error by up to 18.2% and 26.9%, respectively. The source code can be accessed at this https URL.
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[CV-23] Generalized Design Choices for Deepfake Detectors
【速读】：该论文旨在解决当前深度伪造（deepfake）检测方法性能差异难以公平比较的问题，其根源在于实现细节（如数据预处理、增强策略和优化技术）对模型效果的影响往往超过核心架构设计本身。解决方案的关键在于系统性地隔离并评估训练、推理及增量更新等不同环节中的单一设计因素，从而识别出能够稳定提升检测准确率与泛化能力的通用最佳实践，最终在AI-GenBench基准上实现了最先进的性能表现。
链接: https://arxiv.org/abs/2511.21507
作者: Lorenzo Pellegrini,Serafino Pandolfini,Davide Maltoni,Matteo Ferrara,Marco Prati,Marco Ramilli
机构: University of Bologna (博洛尼亚大学); IdentifAI
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 pages, 9 figures, 10 tables, code available: this https URL
Abstract:The effectiveness of deepfake detection methods often depends less on their core design and more on implementation details such as data preprocessing, augmentation strategies, and optimization techniques. These factors make it difficult to fairly compare detectors and to understand which factors truly contribute to their performance. To address this, we systematically investigate how different design choices influence the accuracy and generalization capabilities of deepfake detection models, focusing on aspects related to training, inference, and incremental updates. By isolating the impact of individual factors, we aim to establish robust, architecture-agnostic best practices for the design and development of future deepfake detection systems. Our experiments identify a set of design choices that consistently improve deepfake detection and enable state-of-the-art performance on the AI-GenBench benchmark.
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[CV-24] CanKD: Cross-Attention-based Non-local operation for Feature-based Knowledge Distillation WACV2026
【速读】：该论文旨在解决传统特征蒸馏方法在知识迁移过程中对像素级关系建模不足的问题，尤其是在教师模型与学生模型特征图对齐时缺乏全局上下文信息的动态交互。解决方案的关键在于提出一种基于交叉注意力机制的非局部知识蒸馏框架（Cross-Attention-based Non-local Knowledge Distillation, CanKD），其核心创新是让学生特征图中的每个像素能够动态地关注并整合教师特征图中所有像素的信息，从而实现更全面的像素级关系捕捉和特征表示学习。该方法仅引入一个额外的损失函数，在目标检测和图像分割任务上显著优于现有注意力引导的蒸馏方法，展现出作为计算机视觉中注意力引导蒸馏新范式的潜力。
链接: https://arxiv.org/abs/2511.21503
作者: Shizhe Sun,Wataru Ohyama
机构: Tokyo Denki University (东京电气大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: WACV 2026 Accepted
Abstract:We propose Cross-Attention-based Non-local Knowledge Distillation (CanKD), a novel feature-based knowledge distillation framework that leverages cross-attention mechanisms to enhance the knowledge transfer process. Unlike traditional self-attention-based distillation methods that align teacher and student feature maps independently, CanKD enables each pixel in the student feature map to dynamically consider all pixels in the teacher feature map. This non-local knowledge transfer more thoroughly captures pixel-wise relationships, improving feature representation learning. Our method introduces only an additional loss function to achieve superior performance compared with existing attention-guided distillation methods. Extensive experiments on object detection and image segmentation tasks demonstrate that CanKD outperforms state-of-the-art feature and hybrid distillation methods. These experimental results highlight CanKD’s potential as a new paradigm for attention-guided distillation in computer vision tasks. Code is available at this https URL
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[CV-25] Merge and Bound: Direct Manipulations on Weights for Class Incremental Learning
【速读】：该论文旨在解决类增量学习（Class Incremental Learning, CIL）中的灾难性遗忘（catastrophic forgetting）问题，即模型在学习新类别时会严重遗忘先前任务的知识。解决方案的关键在于提出一种名为“合并与边界”（Merge-and-Bound, MB）的新训练方法，其核心思想是在参数空间中直接操作模型权重：通过两种权重合并策略——跨任务权重合并（inter-task weight merging）和同任务权重合并（intra-task weight merging），实现对历史知识的保留与当前任务的学习；同时引入边界更新技术（bounded update technique），以最小化累积更新量并确保模型在逼近旧模型的同时有效保留先验知识，从而显著缓解遗忘问题。该方法无需修改网络结构或学习目标，可无缝集成至现有CIL框架中。
链接: https://arxiv.org/abs/2511.21490
作者: Taehoon Kim,Donghwan Jang,Bohyung Han
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:We present a novel training approach, named Merge-and-Bound (MB) for Class Incremental Learning (CIL), which directly manipulates model weights in the parameter space for optimization. Our algorithm involves two types of weight merging: inter-task weight merging and intra-task weight merging. Inter-task weight merging unifies previous models by averaging the weights of models from all previous stages. On the other hand, intra-task weight merging facilitates the learning of current task by combining the model parameters within current stage. For reliable weight merging, we also propose a bounded update technique that aims to optimize the target model with minimal cumulative updates and preserve knowledge from previous tasks; this strategy reveals that it is possible to effectively obtain new models near old ones, reducing catastrophic forgetting. MB is seamlessly integrated into existing CIL methods without modifying architecture components or revising learning objectives. We extensively evaluate our algorithm on standard CIL benchmarks and demonstrate superior performance compared to state-of-the-art methods.
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[CV-26] Frequency-Aware Token Reduction for Efficient Vision Transformer NEURIPS2025
【速读】：该论文旨在解决Vision Transformer（视觉Transformer）在计算效率上的瓶颈问题，即其自注意力机制存在与token长度呈二次方增长的计算复杂度。同时，现有token减少方法常忽视自注意力中的频率特性，如秩坍缩（rank collapsing）和过平滑（over-smoothing）现象，导致性能下降。解决方案的关键在于提出一种频域感知的token缩减策略：将token划分为高频token和低频token，选择性保留高频token以维持关键特征表达能力，而将低频token聚合为一个直流分量token（direct current token），从而保留必要的低频信息，有效缓解秩坍缩和过平滑问题，在显著降低计算开销的同时提升模型精度。
链接: https://arxiv.org/abs/2511.21477
作者: Dong-Jae Lee,Jiwan Hur,Jaehyun Choi,Jaemyung Yu,Junmo Kim
机构: KAIST; NAVER AI Lab
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Neurips 2025
Abstract:Vision Transformers have demonstrated exceptional performance across various computer vision tasks, yet their quadratic computational complexity concerning token length remains a significant challenge. To address this, token reduction methods have been widely explored. However, existing approaches often overlook the frequency characteristics of self-attention, such as rank collapsing and over-smoothing phenomenon. In this paper, we propose a frequency-aware token reduction strategy that improves computational efficiency while preserving performance by mitigating rank collapsing. Our method partitions tokens into high-frequency tokens and low-frequency tokens. high-frequency tokens are selectively preserved, while low-frequency tokens are aggregated into a compact direct current token to retain essential low-frequency components. Through extensive experiments and analysis, we demonstrate that our approach significantly improves accuracy while reducing computational overhead and mitigating rank collapsing and over smoothing. Furthermore, we analyze the previous methods, shedding light on their implicit frequency characteristics and limitations.
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[CV-27] MobileI2V: Fast and High-Resolution Image-to-Video on Mobile Devices
【速读】：该论文旨在解决扩散模型在资源受限移动设备上进行实时、高分辨率视频生成时面临的计算复杂度高和生成速度慢的问题。解决方案的关键在于三个方面：首先，提出一种线性混合架构去噪器（linear hybrid architecture denoiser），通过平衡线性注意力模块与Softmax注意力模块的性能，在移动端实现效率与质量的最优权衡；其次，设计时间步蒸馏策略，将图像到视频（I2V）采样步骤从超过20步压缩至仅两步，从而实现10倍的生成速度提升；最后，引入面向移动端的注意力优化机制，在设备端推理过程中使注意力运算速度提升2倍。这些改进共同使得MobileI2V成为首个可在移动端实现720p图像到视频快速生成的轻量级扩散模型，单帧生成时间低于100毫秒，且保持与现有模型相当的质量。
链接: https://arxiv.org/abs/2511.21475
作者: Shuai Zhang,Bao Tang,Siyuan Yu,Yueting Zhu,Jingfeng Yao,Ya Zou,Shanglin Yuan,Li Yu,Wenyu Liu,Xinggang Wang
机构: Huazhong University of Science and Technology (华中科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Our Demo and code: this https URL
Abstract:Recently, video generation has witnessed rapid advancements, drawing increasing attention to image-to-video (I2V) synthesis on mobile devices. However, the substantial computational complexity and slow generation speed of diffusion models pose significant challenges for real-time, high-resolution video generation on resource-constrained mobile devices. In this work, we propose MobileI2V, a 270M lightweight diffusion model for real-time image-to-video generation on mobile devices. The core lies in: (1) We analyzed the performance of linear attention modules and softmax attention modules on mobile devices, and proposed a linear hybrid architecture denoiser that balances generation efficiency and quality. (2) We design a time-step distillation strategy that compresses the I2V sampling steps from more than 20 to only two without significant quality loss, resulting in a 10-fold increase in generation speed. (3) We apply mobile-specific attention optimizations that yield a 2-fold speed-up for attention operations during on-device inference. MobileI2V enables, for the first time, fast 720p image-to-video generation on mobile devices, with quality comparable to existing models. Under one-step conditions, the generation speed of each frame of 720p video is less than 100 ms. Our code is available at: this https URL.
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[CV-28] EvRainDrop: HyperGraph-guided Completion for Effective Frame and Event Stream Aggregation
【速读】：该论文旨在解决事件相机（event camera）产生的事件流在空间上稀疏但时间上密集所带来的欠采样问题，该问题限制了主流事件表示学习方法（如事件帧、体素或张量输入）的性能。解决方案的关键在于提出一种基于超图（hypergraph）引导的时空事件流补全机制：通过超图将不同时刻和空间位置上的事件标记（event tokens）进行关联，并利用上下文信息的消息传递机制完成稀疏事件的补全；同时，该框架可灵活地将RGB标记作为超图节点引入，实现多模态信息补全；最后，借助自注意力机制聚合不同时间步的超图节点信息，从而有效学习和融合多模态特征。
链接: https://arxiv.org/abs/2511.21439
作者: Futian Wang,Fan Zhang,Xiao Wang,Mengqi Wang,Dexing Huang,Jin Tang
机构: Anhui University (安徽大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Event cameras produce asynchronous event streams that are spatially sparse yet temporally dense. Mainstream event representation learning algorithms typically use event frames, voxels, or tensors as input. Although these approaches have achieved notable progress, they struggle to address the undersampling problem caused by spatial sparsity. In this paper, we propose a novel hypergraph-guided spatio-temporal event stream completion mechanism, which connects event tokens across different times and spatial locations via hypergraphs and leverages contextual information message passing to complete these sparse events. The proposed method can flexibly incorporate RGB tokens as nodes in the hypergraph within this completion framework, enabling multi-modal hypergraph-based information completion. Subsequently, we aggregate hypergraph node information across different time steps through self-attention, enabling effective learning and fusion of multi-modal features. Extensive experiments on both single- and multi-label event classification tasks fully validated the effectiveness of our proposed framework. The source code of this paper will be released on this https URL.
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[CV-29] From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in Industrial Settings
【速读】：该论文旨在解决工业场景中大规模未标注人类操作视频数据难以用于视觉-语言-动作（Vision-Language-Action, VLA）模型预训练的问题。现有方法受限于对人工标注的依赖，难以高效利用连续工业视频流中的丰富动作信息。其解决方案的关键在于提出一个完全自动化的端到端框架：首先训练一个轻量级运动分词器（motion tokenizer）来编码运动动态，随后引入一种新颖的“潜在动作能量”（Latent Action Energy）度量，实现无监督的动作片段分割，从而发现语义一致的动作基元（action primitives）。该方法生成结构化的视频片段及其对应的潜在动作序列，可直接用于VLA模型预训练，显著提升了工业视频数据的可用性和可扩展性。
链接: https://arxiv.org/abs/2511.21428
作者: Jiajie Zhang,Sören Schwertfeger,Alexander Kleiner
机构: ShanghaiTech University (上海科技大学); Hangzhou Dianzi University (杭州电子科技大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures
Abstract:We present a novel unsupervised framework to unlock vast unlabeled human demonstration data from continuous industrial video streams for Vision-Language-Action (VLA) model pre-training. Our method first trains a lightweight motion tokenizer to encode motion dynamics, then employs an unsupervised action segmenter leveraging a novel “Latent Action Energy” metric to discover and segment semantically coherent action primitives. The pipeline outputs both segmented video clips and their corresponding latent action sequences, providing structured data directly suitable for VLA pre-training. Evaluations on public benchmarks and a proprietary electric motor assembly dataset demonstrate effective segmentation of key tasks performed by humans at workstations. Further clustering and quantitative assessment via a Vision-Language Model confirm the semantic coherence of the discovered action primitives. To our knowledge, this is the first fully automated end-to-end system for extracting and organizing VLA pre-training data from unstructured industrial videos, offering a scalable solution for embodied AI integration in manufacturing.
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[CV-30] E-M3RF: An Equivariant Multimodal 3D Re-assembly Framework
【速读】：该论文旨在解决3D碎片重装（3D reassembly）问题，尤其针对传统几何特征方法在处理小尺寸、磨损或对称碎片时因几何信息不足或模糊而导致的装配失败，以及现有方法未能显式约束物理合理性（如防止重叠）的问题。解决方案的关键在于提出E-M3RF框架，其核心创新是引入了旋转等变（rotation-equivariant）的多模态表示：一方面利用旋转等变编码器从点云位置中提取几何特征，另一方面通过Transformer模型编码颜色信息，二者融合形成多模态表征；随后采用SE(3)流匹配（SE(3) flow matching）预测碎片间的刚体变换，从而实现更准确且物理可行的重装结果。
链接: https://arxiv.org/abs/2511.21422
作者: Adeela Islam,Stefano Fiorini,Manuel Lecha,Theodore Tsesmelis,Stuart James,Pietro Morerio,Alessio Del Bue
机构: Fondazione Istituto Italiano di Tecnologia (意大利技术研究院基金会); University of Genova (热那亚大学); Durham University (杜伦大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:3D reassembly is a fundamental geometric problem, and in recent years it has increasingly been challenged by deep learning methods rather than classical optimization. While learning approaches have shown promising results, most still rely primarily on geometric features to assemble a whole from its parts. As a result, methods struggle when geometry alone is insufficient or ambiguous, for example, for small, eroded, or symmetric fragments. Additionally, solutions do not impose physical constraints that explicitly prevent overlapping assemblies. To address these limitations, we introduce E-M3RF, an equivariant multimodal 3D reassembly framework that takes as input the point clouds, containing both point positions and colors of fractured fragments, and predicts the transformations required to reassemble them using SE(3) flow matching. Each fragment is represented by both geometric and color features: i) 3D point positions are encoded as rotationconsistent geometric features using a rotation-equivariant encoder, ii) the colors at each 3D point are encoded with a transformer. The two feature sets are then combined to form a multimodal representation. We experimented on four datasets: two synthetic datasets, Breaking Bad and Fantastic Breaks, and two real-world cultural heritage datasets, RePAIR and Presious, demonstrating that E-M3RF on the RePAIR dataset reduces rotation error by 23.1% and translation error by 13.2%, while Chamfer Distance decreases by 18.4% compared to competing methods.
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[CV-31] SAM Guided Semantic and Motion Changed Region Mining for Remote Sensing Change Captioning
【速读】：该论文旨在解决遥感变化描述（Remote Sensing Change Captioning）任务中现有方法存在的区域感知能力弱和时序对齐有限的问题。其解决方案的关键在于引入SAM（Segment Anything Model）基础模型以提取区域级表征，并结合构建的知识图谱注入感兴趣目标的信息，从而增强对变化区域的语义理解与定位精度；具体而言，通过CNN/Transformer提取全局视觉特征，利用SAM识别语义与运动层面的变化区域，并借助跨注意力机制融合多源异构信息，最终由Transformer解码器生成高质量的自然语言描述。
链接: https://arxiv.org/abs/2511.21420
作者: Futian Wang,Mengqi Wang,Xiao Wang,Haowen Wang,Jin Tang
机构: Anhui University (安徽大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Remote sensing change captioning is an emerging and popular research task that aims to describe, in natural language, the content of interest that has changed between two remote sensing images captured at different times. Existing methods typically employ CNNs/Transformers to extract visual representations from the given images or incorporate auxiliary tasks to enhance the final results, with weak region awareness and limited temporal alignment. To address these issues, this paper explores the use of the SAM (Segment Anything Model) foundation model to extract region-level representations and inject region-of-interest knowledge into the captioning framework. Specifically, we employ a CNN/Transformer model to extract global-level vision features, leverage the SAM foundation model to delineate semantic- and motion-level change regions, and utilize a specially constructed knowledge graph to provide information about objects of interest. These heterogeneous sources of information are then fused via cross-attention, and a Transformer decoder is used to generate the final natural language description of the observed changes. Extensive experimental results demonstrate that our method achieves state-of-the-art performance across multiple widely used benchmark datasets. The source code of this paper will be released on this https URL
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[CV-32] DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models
【速读】：该论文旨在解决文本条件视觉自回归模型（VAR）在测试阶段多样性不足的问题，即相同提示常生成高度相似的图像，这限制了其在实际应用中的表现。解决方案的关键在于两阶段策略：首先通过向文本嵌入注入噪声以提升多样性，但会带来图像质量下降；其次提出“尺度旅行”（scale-travel）这一新颖的潜在空间精炼技术，利用多尺度自动编码器提取粗粒度token，从而在生成过程的中间阶段恢复并优化生成结果，有效缓解质量损失，显著提升多样性与图像质量之间的帕累托前沿。
链接: https://arxiv.org/abs/2511.21415
作者: Mingue Park,Prin Phunyaphibarn,Phillip Y. Lee,Minhyuk Sung
机构: KAIST
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requiring retraining, fine-tuning, or substantial computational overhead. While VAR models have recently emerged as strong competitors to diffusion and flow models for image generation, they suffer from a critical limitation in diversity, often producing nearly identical images even for simple prompts. This issue has largely gone unnoticed amid the predominant focus on image quality. We address this limitation at test time in two stages. First, inspired by diversity enhancement techniques in diffusion models, we propose injecting noise into the text embedding. This introduces a trade-off between diversity and image quality: as diversity increases, the image quality sharply declines. To preserve quality, we propose scale-travel: a novel latent refinement technique inspired by time-travel strategies in diffusion models. Specifically, we use a multi-scale autoencoder to extract coarse-scale tokens that enable us to resume generation at intermediate stages. Extensive experiments show that combining text-embedding noise injection with our scale-travel refinement significantly enhances diversity while minimizing image-quality degradation, achieving a new Pareto frontier in the diversity-quality trade-off.
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[CV-33] Monet: Reasoning in Latent Visual Space Beyond Images and Language
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在视觉推理中缺乏类人抽象视觉思维能力的问题，其核心瓶颈在于现有方法依赖外部工具导致灵活性受限。解决方案的关键在于提出Monet训练框架，使MLLMs能够直接在潜在视觉空间（latent visual space）中进行推理，通过生成连续嵌入（continuous embeddings）作为中间视觉思维（intermediate visual thoughts），从而实现无需外部工具的端到端视觉推理。为此，作者设计了三阶段蒸馏式监督微调（SFT）流程以缓解潜在视觉对齐计算成本高和潜在嵌入监督不足的问题，并创新性地提出VLPO（Visual-latent Policy Optimization）强化学习方法，显式将潜在嵌入纳入策略梯度更新，有效提升潜空间推理能力，而非仅增强文本推理。
链接: https://arxiv.org/abs/2511.21395
作者: Qixun Wang,Yang Shi,Yifei Wang,Yuanxing Zhang,Pengfei Wan,Kun Gai,Xianghua Ying,Yisen Wang
机构: Peking University (北京大学); Kling Team; MIT (麻省理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:“Thinking with images” has emerged as an effective paradigm for advancing visual reasoning, extending beyond text-only chains of thought by injecting visual evidence into intermediate reasoning steps. However, existing methods fall short of human-like abstract visual thinking, as their flexibility is fundamentally limited by external tools. In this work, we introduce Monet, a training framework that enables multimodal large language models (MLLMs) to reason directly within the latent visual space by generating continuous embeddings that function as intermediate visual thoughts. We identify two core challenges in training MLLMs for latent visual reasoning: high computational cost in latent-vision alignment and insufficient supervision over latent embeddings, and address them with a three-stage distillation-based supervised fine-tuning (SFT) pipeline. We further reveal a limitation of applying GRPO to latent reasoning: it primarily enhances text-based reasoning rather than latent reasoning. To overcome this, we propose VLPO (Visual-latent Policy Optimization), a reinforcement learning method that explicitly incorporates latent embeddings into policy gradient updates. To support SFT, we construct Monet-SFT-125K, a high-quality text-image interleaved CoT dataset containing 125K real-world, chart, OCR, and geometry CoTs. Our model, Monet-7B, shows consistent gains across real-world perception and reasoning benchmarks and exhibits strong out-of-distribution generalization on challenging abstract visual reasoning tasks. We also empirically analyze the role of each training component and discuss our early unsuccessful attempts, providing insights for future developments in visual latent reasoning. Our model, data, and code are available at this https URL.
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[CV-34] hinking With Bounding Boxes: Enhancing Spatio-Temporal Video Grounding via Reinforcement Fine-Tuning
【速读】：该论文旨在解决多模态大语言模型（MLLM）在时空视频定位（Spatio-temporal Video Grounding, STVG）任务中表现不佳的问题，其核心挑战在于训练目标不匹配以及标准视觉编码器中细粒度区域与词项之间的对齐能力较弱。解决方案的关键在于提出STVG-o1框架，该框架无需修改MLLM架构即可实现最优性能，其创新点包括：引入边界框链式思维（bounding-box chain-of-thought）机制，在生成最终预测前显式推理时空位置；设计一个多维强化奖励函数，包含格式、一致性、时间、空间和思考奖励，通过强化学习微调提供几何感知监督，从而显著提升模型在HCSTVG-v1/v2和VidSTG数据集上的定位精度与泛化能力。
链接: https://arxiv.org/abs/2511.21375
作者: Xin Gu,Haoji Zhang,Qihang Fan,Jingxuan Niu,Zhipeng Zhang,Libo Zhang,Guang Chen,Fan Chen,Longyin Wen,Sijie Zhu
机构: ByteDance Intelligent Creation (字节跳动智能创作); Tsinghua University (清华大学); Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); Shanghai Jiao Tong University (上海交通大学); Institute of Software, Chinese Academy of Sciences (中国科学院软件研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Spatio-temporal video grounding (STVG) requires localizing a target object in untrimmed videos both temporally and spatially from natural language descriptions. Despite their strong language understanding, multimodal large language models (MLLMs) underperform on STVG due to misaligned training objectives and weak fine-grained region-word alignment in standard visual encoders. To address this, we propose STVG-o1, the first framework that enables off-the-shelf MLLMs to achieve state-of-the-art STVG performance without any architectural modifications. Our method introduces a bounding-box chain-of-thought mechanism that explicitly reasons about spatio-temporal locations in an intermediate step before producing the final prediction. We further design a multi-dimensional reinforcement reward function consisting of format, consistency, temporal, spatial, and think rewards, which provides geometry-aware supervision through reinforcement fine-tuning. Evaluated on HCSTVG-v1/v2 and VidSTG, STVG-o1 sets new state-of-the-art results on HCSTVG, outperforming the best task-specific method by 7.3% m_tIoU on HCSTVG-v1, matching specialized models on VidSTG, and surpassing all existing MLLM-based approaches by large margins. It also demonstrates strong open-vocabulary generalization across datasets, establishing MLLMs as viable and powerful backbones for precise spatio-temporal grounding. Our code and models will be released.
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[CV-35] Endo-G2T: Geometry-Guided Temporally Aware Time-Embedded 4DGS For Endoscopic Scenes
【速读】：该论文旨在解决内窥镜（endoscopic）视频中由于强视图依赖效应（如镜面反射、湿面反光和遮挡）导致的纯光度监督与几何信息不一致的问题，进而引发早期几何漂移（geometric drift），使得错误形状在密集化过程中被强化且难以纠正。为实现4D高斯溅射（4DGS）在动态内窥场景下的早期几何锚定（geometry anchoring）、时间一致性（temporal consistency）与计算效率的平衡，提出Endo-G²T方案：其关键在于三点创新——首先，通过“几何引导先验蒸馏”将置信度门控单目深度转化为尺度不变深度损失与深度梯度损失，采用从暖启动到饱和的调度策略软注入先验以避免过拟合；其次，引入时间嵌入高斯场（time-embedded Gaussian field），利用旋转变量参数化实现XYZT空间中的动态建模，保障时间一致性并以轻量正则化偏好平滑运动和清晰不透明边界；最后，基于关键帧约束的流式优化机制，在最大点数预算下聚焦关键帧优化，非关键帧进行轻量更新，显著提升长期稳定性与效率。
链接: https://arxiv.org/abs/2511.21367
作者: Yangle Liu,Fengze Li,Kan Liu,Jieming Ma
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Endoscopic (endo) video exhibits strong view-dependent effects such as specularities, wet reflections, and occlusions. Pure photometric supervision misaligns with geometry and triggers early geometric drift, where erroneous shapes are reinforced during densification and become hard to correct. We ask how to anchor geometry early for 4D Gaussian splatting (4DGS) while maintaining temporal consistency and efficiency in dynamic endoscopic scenes. Thus, we present Endo-G ^2 T, a geometry-guided and temporally aware training scheme for time-embedded 4DGS. First, geo-guided prior distillation converts confidence-gated monocular depth into supervision with scale-invariant depth and depth-gradient losses, using a warm-up-to-cap schedule to inject priors softly and avoid early overfitting. Second, a time-embedded Gaussian field represents dynamics in XYZT with a rotor-like rotation parameterization, yielding temporally coherent geometry with lightweight regularization that favors smooth motion and crisp opacity boundaries. Third, keyframe-constrained streaming improves efficiency and long-horizon stability through keyframe-focused optimization under a max-points budget, while non-keyframes advance with lightweight updates. Across EndoNeRF and StereoMIS-P1 datasets, Endo-G ^2 T achieves state-of-the-art results among monocular reconstruction baselines.
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[CV-36] PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation
【速读】：该论文旨在解决点云中法向量估计的难题，特别是如何在不同数据分布和几何结构下合理选择局部邻域大小以构建有效的特征描述。传统方法依赖参数密集的策略提取完整特征，但难以在多种场景中实现高效且精确的法向预测。其解决方案的关键在于提出一种基于多尺度特征融合的新型特征提取机制：通过多尺度特征聚合模块逐步将不同邻域规模下的局部特征向中心汇聚并缩减patch尺寸，从而兼顾广域结构与细节几何；同时引入跨尺度特征补偿模块，保留大尺度特征信息并增强不同尺度间的关联性，实现对最优几何描述的逼近。此策略使模型具备自适应调整局部patch尺度的能力，显著提升了法向估计精度与效率。
链接: https://arxiv.org/abs/2511.21365
作者: Qing Li,Huifang Feng,Kanle Shi,Yue Gao,Yi Fang,Yu-Shen Liu,Zhizhong Han
机构: Southwest Jiaotong University (西南交通大学); Xihua University (西华大学); Tsinghua University (清华大学); Kuaishou Technology (快手科技); New York University Abu Dhabi (纽约大学阿布扎比分校); Wayne State University (韦恩州立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by TVCG
Abstract:Estimating the normal of a point requires constructing a local patch to provide center-surrounding context, but determining the appropriate neighborhood size is difficult when dealing with different data or geometries. Existing methods commonly employ various parameter-heavy strategies to extract a full feature description from the input patch. However, they still have difficulties in accurately and efficiently predicting normals for various point clouds. In this work, we present a new idea of feature extraction for robust normal estimation of point clouds. We use the fusion of multi-scale features from different neighborhood sizes to address the issue of selecting reasonable patch sizes for various data or geometries. We seek to model a patch feature fitting (PFF) based on multi-scale features to approximate the optimal geometric description for normal estimation and implement the approximation process via multi-scale feature aggregation and cross-scale feature compensation. The feature aggregation module progressively aggregates the patch features of different scales to the center of the patch and shrinks the patch size by removing points far from the center. It not only enables the network to precisely capture the structure characteristic in a wide range, but also describes highly detailed geometries. The feature compensation module ensures the reusability of features from earlier layers of large scales and reveals associated information in different patch sizes. Our approximation strategy based on aggregating the features of multiple scales enables the model to achieve scale adaptation of varying local patches and deliver the optimal feature description. Extensive experiments demonstrate that our method achieves state-of-the-art performance on both synthetic and real-world datasets with fewer network parameters and running time.
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[CV-37] BanglaMM-Disaster: A Multimodal Transformer-Based Deep Learning Framework for Multiclass Disaster Classification in Bangla
【速读】：该论文旨在解决孟加拉国在自然灾害实时监测与快速响应中的关键挑战，特别是针对低资源环境下多模态数据（文本与图像）融合用于灾情分类的不足。其解决方案的关键在于提出了一种端到端的深度学习多模态框架 BanglaMM-Disaster，该框架结合了基于 Transformer 的孟加拉语文本编码器（如 BanglaBERT、mBERT 和 XLM-RoBERTa）与卷积神经网络（CNN）图像特征提取器（如 ResNet50、DenseNet169 和 MobileNetV2），采用早期融合策略整合两种模态信息。实验表明，该方法在自建的 5,037 条孟加拉语社交媒体数据集上达到 83.76% 的准确率，显著优于仅使用文本（+3.84%）或图像（+16.91%）的基线模型，尤其提升了对模糊样本的判别能力，有效填补了孟加拉语多模态灾害分析的研究空白。
链接: https://arxiv.org/abs/2511.21364
作者: Ariful Islam,Md Rifat Hossen,Md. Mahmudul Arif,Abdullah Al Noman,Md Arifur Rahman
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Presented at the 2025 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON), November 21-22, 2025, University of Rajshahi, Bangladesh. 6 pages, 9 disaster classes, multimodal dataset with 5,037 samples
Abstract:Natural disasters remain a major challenge for Bangladesh, so real-time monitoring and quick response systems are essential. In this study, we present BanglaMM-Disaster, an end-to-end deep learning-based multimodal framework for disaster classification in Bangla, using both textual and visual data from social media. We constructed a new dataset of 5,037 Bangla social media posts, each consisting of a caption and a corresponding image, annotated into one of nine disaster-related categories. The proposed model integrates transformer-based text encoders, including BanglaBERT, mBERT, and XLM-RoBERTa, with CNN backbones such as ResNet50, DenseNet169, and MobileNetV2, to process the two modalities. Using early fusion, the best model achieves 83.76% accuracy. This surpasses the best text-only baseline by 3.84% and the image-only baseline by 16.91%. Our analysis also shows reduced misclassification across all classes, with noticeable improvements for ambiguous examples. This work fills a key gap in Bangla multimodal disaster analysis and demonstrates the benefits of combining multiple data types for real-time disaster response in low-resource settings.
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[CV-38] SurgMLLM Bench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding
【速读】：该论文旨在解决当前外科领域多模态大语言模型（Multimodal Large Language Models, MLLMs）评估与应用中的关键瓶颈问题，即现有外科数据集大多采用视觉问答（Visual Question Answering, VQA）格式，存在分类体系异构、缺乏像素级分割标注等问题，导致模型评估不一致且难以泛化。其解决方案的关键在于构建一个统一的多模态基准——SurgMLLMBench，该基准整合了腹腔镜、机器人辅助和显微外科三个领域的像素级器械分割掩码与结构化VQA标注，并采用统一的分类体系，从而支持超越传统VQA任务的全面评估和更丰富的视觉-对话交互能力。实验表明，基于该基准训练的单一模型在不同外科场景中表现稳定，并能有效泛化至未见过的数据集。
链接: https://arxiv.org/abs/2511.21339
作者: Tae-Min Choi,Tae Kyeong Jeong,Garam Kim,Jaemin Lee,Yeongyoon Koh,In Cheul Choi,Jae-Ho Chung,Jong Woong Park,Juyoun Park
机构: Samsung Research; Center for Humanoid Research, Korea Institute of Science and Technology; Department of plastic surgery, College of medicine, Korea University; Department of orthopedic surgery, College of medicine, Korea University
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 10 pages, 5 figures
Abstract:Recent advances in multimodal large language models (LLMs) have highlighted their potential for medical and surgical applications. However, existing surgical datasets predominantly adopt a Visual Question Answering (VQA) format with heterogeneous taxonomies and lack support for pixel-level segmentation, limiting consistent evaluation and applicability. We present SurgMLLMBench, a unified multimodal benchmark explicitly designed for developing and evaluating interactive multimodal LLMs for surgical scene understanding, including the newly collected Micro-surgical Artificial Vascular anastomosIS (MAVIS) dataset. It integrates pixel-level instrument segmentation masks and structured VQA annotations across laparoscopic, robot-assisted, and micro-surgical domains under a unified taxonomy, enabling comprehensive evaluation beyond traditional VQA tasks and richer visual-conversational interactions. Extensive baseline experiments show that a single model trained on SurgMLLMBench achieves consistent performance across domains and generalizes effectively to unseen datasets. SurgMLLMBench will be publicly released as a robust resource to advance multimodal surgical AI research, supporting reproducible evaluation and development of interactive surgical reasoning models.
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[CV-39] Hybrid SIFT-SNN for Efficient Anomaly Detection of Traffic Flow-Control Infrastructure
【速读】：该论文旨在解决交通基础设施中结构异常实时检测的低延迟与低功耗问题，尤其针对移动混凝土护栏等关键设施的安全监测需求。解决方案的关键在于提出了一种混合SIFT-SNN（Scale-Invariant Feature Transform - Spiking Neural Network）神经形态信号处理流水线：首先利用SIFT算法实现空间特征编码以保留物理空间语义信息，再通过基于延迟驱动的脉冲转换层将图像数据转化为稀疏脉冲序列，并由Leaky Integrate-and-Fire（LIF）型脉冲神经网络完成分类任务。该方法在保证92.3%分类准确率的同时，实现了每帧仅9.5毫秒的推理延迟和8.1%的稀疏脉冲活动水平，从而支持边缘端部署，且相较传统卷积神经网络（CNN）具备更强的可解释性与硬件效率。
链接: https://arxiv.org/abs/2511.21337
作者: Munish Rathee(School of Engineering, Computer and Mathematical Science (of Auckland University of Technology) Auckland, New Zealand),Boris Bačić(School of Engineering, Computer and Mathematical Science (of Auckland University of Technology) Institute of Biomedical Technologies (IBTec) Auckland, New Zealand),Maryam Doborjeh(Knowledge Engineering and Discovery Research Institute (KEDRI) (of Auckland University of Technology) Auckland, New Zealand)
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 8 pages, 6 figures. This is a preprint of a paper accepted for presentation at the 2025 International Conference on Image and Vision Computing New Zealand (IVCNZ). The final version will appear in IEEE Xplore
Abstract:This paper presents the SIFT-SNN framework, a low-latency neuromorphic signal-processing pipeline for real-time detection of structural anomalies in transport infrastructure. The proposed approach integrates Scale-Invariant Feature Transform (SIFT) for spatial feature encoding with a latency-driven spike conversion layer and a Leaky Integrate-and-Fire (LIF) Spiking Neural Network (SNN) for classification. The Auckland Harbour Bridge dataset is recorded under various weather and lighting conditions, comprising 6,000 labelled frames that include both real and synthetically augmented unsafe cases. The presented system achieves a classification accuracy of 92.3% (± 0.8%) with a per-frame inference time of 9.5 ms. Achieved sub-10 millisecond latency, combined with sparse spike activity (8.1%), enables real-time, low-power edge deployment. Unlike conventional CNN-based approaches, the hybrid SIFT-SNN pipeline explicitly preserves spatial feature grounding, enhances interpretability, supports transparent decision-making, and operates efficiently on embedded hardware. Although synthetic augmentation improved robustness, generalisation to unseen field conditions remains to be validated. The SIFT-SNN framework is validated through a working prototype deployed on a consumer-grade system and framed as a generalisable case study in structural safety monitoring for movable concrete barriers, which, as a traffic flow-control infrastructure, is deployed in over 20 cities worldwide.
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[CV-40] he More the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment
【速读】：该论文旨在解决多模态学习中联合表示学习的核心挑战，即如何在保持模态间成对对应关系的同时，有效捕捉多个模态之间的高阶交互依赖（higher-order interactions）。现有方法多局限于两两对齐的设置，难以建模如XOR-like等无法通过成对对齐恢复的复杂关系。其解决方案的关键在于提出对比融合（Contrastive Fusion, ConFu）框架，该框架将单个模态及其融合组合共同嵌入到统一表示空间中，并引入额外的融合模态对比项，扩展传统成对对比目标，从而同时优化成对一致性与高阶联合表示能力。
链接: https://arxiv.org/abs/2511.21331
作者: Stefanos Koutoupis,Michaela Areti Zervou,Konstantinos Kontras,Maarten De Vos,Panagiotis Tsakalides,Grigorios Tsagatakis
机构: FORTH(希腊国家研究中心); UoC(克里特大学); KU Leuven(鲁汶大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注:
Abstract:Learning joint representations across multiple modalities remains a central challenge in multimodal machine learning. Prevailing approaches predominantly operate in pairwise settings, aligning two modalities at a time. While some recent methods aim to capture higher-order interactions among multiple modalities, they often overlook or insufficiently preserve pairwise relationships, limiting their effectiveness on single-modality tasks. In this work, we introduce Contrastive Fusion (ConFu), a framework that jointly embeds both individual modalities and their fused combinations into a unified representation space, where modalities and their fused counterparts are aligned. ConFu extends traditional pairwise contrastive objectives with an additional fused-modality contrastive term, encouraging the joint embedding of modality pairs with a third modality. This formulation enables ConFu to capture higher-order dependencies, such as XOR-like relationships, that cannot be recovered through pairwise alignment alone, while still maintaining strong pairwise correspondence. We evaluate ConFu on synthetic and real-world multimodal benchmarks, assessing its ability to exploit cross-modal complementarity, capture higher-order dependencies, and scale with increasing multimodal complexity. Across these settings, ConFu demonstrates competitive performance on retrieval and classification tasks, while supporting unified one-to-one and two-to-one retrieval within a single contrastive framework.
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[CV-41] HTTM: Head-wise Temporal Token Merging for Faster VGGT
【速读】：该论文旨在解决视觉几何基础变换器（Visual Geometry Grounded Transformer, VGGT）在处理大规模场景重建时因全局注意力机制导致的显著延迟瓶颈问题。VGGT虽能一次性联合推断相机位姿、深度和稠密几何信息，但其全视图token间的全连接注意力计算在长序列输入下效率低下。解决方案的关键在于提出一种无需训练的3D token合并方法——头级时间合并（Head-wise Temporal Merging, HTTM），该方法在多头注意力粒度上进行token合并，保留了不同注意力头输出特征的多样性，同时利用头级别上的空间局部性和时间对应关系实现更高合并率与更低的计算成本，从而在GPU推理中实现最高达7倍的加速，且性能损失可忽略不计。
链接: https://arxiv.org/abs/2511.21317
作者: Weitian Wang,Lukas Meiner,Rai Shubham,Cecilia De La Parra,Akash Kumar
机构: Robert Bosch GmbH (罗伯特·博世有限公司); Ruhr University Bochum (鲁尔大学波鸿分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:The Visual Geometry Grounded Transformer (VGGT) marks a significant leap forward in 3D scene reconstruction, as it is the first model that directly infers all key 3D attributes (camera poses, depths, and dense geometry) jointly in one pass. However, this joint inference mechanism requires global attention layers that perform all-to-all attention computation on tokens from all views. For reconstruction of large scenes with long-sequence inputs, this causes a significant latency bottleneck. In this paper, we propose head-wise temporal merging (HTTM), a training-free 3D token merging method for accelerating VGGT. Existing merging techniques merge tokens uniformly across different attention heads, resulting in identical tokens in the layers’ output, which hinders the model’s representational ability. HTTM tackles this problem by merging tokens in multi-head granularity, which preserves the uniqueness of feature tokens after head concatenation. Additionally, this enables HTTM to leverage the spatial locality and temporal correspondence observed at the head level to achieve higher merging ratios with lower merging costs compared to existing methods. Thus, HTTM achieves up to 7x acceleration with negligible performance drops in a GPU-based inference.
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[CV-42] CaliTex: Geometry-Calibrated Attention for View-Coherent 3D Texture Generation
【速读】：该论文旨在解决当前基于扩散模型的3D纹理生成系统中存在的跨视角不一致性问题（cross-view inconsistency），即从某一视角看纹理逼真，但在其他视角下难以保持一致。研究表明，该问题源于注意力机制中的模糊性（attention ambiguity），即未结构化的全注意力机制对所有token和模态 indiscriminately（无差别地）应用，导致几何混淆和外观-结构耦合不稳定。解决方案的关键在于提出CaliTex框架，其核心是几何校准注意力（geometry-calibrated attention），通过两个模块实现：1）部件对齐注意力（Part-Aligned Attention）强制语义匹配部件间的空间对齐；2）条件路由注意力（Condition-Routed Attention）将外观信息通过几何条件路径传输，以维持空间保真度。结合两阶段扩散Transformer，CaliTex使几何一致性成为网络的内在行为而非优化结果，从而显著提升纹理的视图一致性与质量。
链接: https://arxiv.org/abs/2511.21309
作者: Chenyu Liu,Hongze Chen,Jingzhi Bao,Lingting Zhu,Runze Zhang,Weikai Chen,Zeyu Hu,Yingda Yin,Keyang Luo,Xin Wang
机构: PKU(北京大学); HKUST(香港科技大学); CUHK(SZ)(香港中文大学（深圳）); HKU(香港大学); LIGHTSPEED
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Despite major advances brought by diffusion-based models, current 3D texture generation systems remain hindered by cross-view inconsistency – textures that appear convincing from one viewpoint often fail to align across others. We find that this issue arises from attention ambiguity, where unstructured full attention is applied indiscriminately across tokens and modalities, causing geometric confusion and unstable appearance-structure coupling. To address this, we introduce CaliTex, a framework of geometry-calibrated attention that explicitly aligns attention with 3D structure. It introduces two modules: Part-Aligned Attention that enforces spatial alignment across semantically matched parts, and Condition-Routed Attention which routes appearance information through geometry-conditioned pathways to maintain spatial fidelity. Coupled with a two-stage diffusion transformer, CaliTex makes geometric coherence an inherent behavior of the network rather than a byproduct of optimization. Empirically, CaliTex produces seamless and view-consistent textures and outperforms both open-source and commercial baselines.
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[CV-43] PathMamba: A Hybrid Mamba-Transformer for Topologically Coherent Road Segmentation in Satellite Imagery
【速读】：该论文旨在解决卫星遥感图像中道路分割任务中同时实现高精度与拓扑连续性的难题，这对城市规划和灾害响应等应用至关重要。现有基于视觉Transformer（Vision Transformer）的方法虽能捕捉全局上下文信息，但其二次复杂度限制了在资源受限平台上的高效部署；而新兴的状态空间模型（State Space Models, SSMs）如Mamba具有线性时间复杂度，更适合建模长距离连续结构。解决方案的关键在于提出PathMamba这一新型混合架构：通过将Mamba模块用于追踪道路网络的连续性以保持拓扑结构，同时引入Transformer模块融合全局语义信息进行特征精炼，从而在不增加显著计算开销的前提下显著提升分割结果的拓扑连通性。实验表明，该方法在DeepGlobe Road Extraction和Massachusetts Roads数据集上达到新的SOTA性能，尤其在APLS指标上取得突破。
链接: https://arxiv.org/abs/2511.21298
作者: Jules Decaestecker,Nicolas Vigne
机构: Thales CortAIx Labs (Thales CortAIx 实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 5 figures
Abstract:Achieving both high accuracy and topological continuity in road segmentation from satellite imagery is a critical goal for applications ranging from urban planning to disaster response. State-of-the-art methods often rely on Vision Transformers, which excel at capturing global context, yet their quadratic complexity is a significant barrier to efficient deployment, particularly for on-board processing in resource-constrained platforms. In contrast, emerging State Space Models like Mamba offer linear-time efficiency and are inherently suited to modeling long, continuous structures. We posit that these architectures have complementary strengths. To this end, we introduce PathMamba, a novel hybrid architecture that integrates Mamba’s sequential modeling with the Transformer’s global reasoning. Our design strategically uses Mamba blocks to trace the continuous nature of road networks, preserving topological structure, while integrating Transformer blocks to refine features with global context. This approach yields topologically superior segmentation maps without the prohibitive scaling costs of pure attention-based models. Our experiments on the DeepGlobe Road Extraction and Massachusetts Roads datasets demonstrate that PathMamba sets a new state-of-the-art. Notably, it significantly improves topological continuity, as measured by the APLS metric, setting a new benchmark while remaining computationally competitive.
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[CV-44] Co-Training Vision Language Models for Remote Sensing Multi-task Learning
【速读】：该论文旨在解决遥感（Remote Sensing, RS）领域中多任务学习（Multi-Task Learning, MTL）的挑战，即如何构建一个统一的视觉语言模型（Vision Language Model, VLM）以在多种RS任务上实现卓越性能，同时克服数据环境复杂、图像尺度多样及计算资源受限等问题。其关键解决方案包括：(1) 设计了一个灵活的数据编排引擎（data curation engine），涵盖离线处理与在线加载权重机制，有效应对遥感数据的异构性并支持动态视觉-语言对话；(2) 提出统一的动态分辨率策略，适配遥感图像中广泛存在的尺度差异；(3) 针对超高分辨率（Ultra-High-Resolution, UHR）图像引入“Zoom-in Chain”机制及其配套数据集LRS-VQA-Zoom，显著降低计算负担；(4) 显著提升目标检测能力，并提出公平的评估协议以实现VLM与传统检测模型之间的合理比较。这些设计共同推动了通用遥感模型的发展。
链接: https://arxiv.org/abs/2511.21272
作者: Qingyun Li,Shuran Ma,Junwei Luo,Yi Yu,Yue Zhou,Fengxiang Wang,Xudong Lu,Xiaoxing Wang,Xin He,Yushi Chen,Xue Yang,Junchi Yan
机构: Harbin Institute of Technology (哈尔滨工业大学); Xidian University (西安电子科技大学); Wuhan University (武汉大学); Southeast University (东南大学); National University of Defense Technology (国防科技大学); The Chinese University of Hong Kong (香港中文大学); Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures
Abstract:With Transformers achieving outstanding performance on individual remote sensing (RS) tasks, we are now approaching the realization of a unified model that excels across multiple tasks through multi-task learning (MTL). Compared to single-task approaches, MTL methods offer improved generalization, enhanced scalability, and greater practical applicability. Recently, vision language models (VLMs) have achieved promising results in RS image understanding, grounding, and ultra-high-resolution (UHR) image reasoning, respectively. Moreover, the unified text-based interface demonstrates significant potential for MTL. Hence, in this work, we present RSCoVLM, a simple yet flexible VLM baseline for RS MTL. Firstly, we create the data curation engine, including data acquisition, offline processing and integrating, as well as online loading and weighting. This data engine effectively addresses complex RS data enviroment and generates flexible vision-language conversations. Furthermore, we propose a unified dynamic-resolution strategy to address the diverse image scales inherent in RS imagery. For UHR images, we introduce the Zoom-in Chain mechanism together with its corresponding dataset, LRS-VQA-Zoom. The strategies are flexible and effectively mitigate the computational burdens. Additionally, we significantly enhance the model’s object detection capability and propose a novel evaluation protocol that ensures fair comparison between VLMs and conventional detection models. Extensive experiments demonstrate that RSCoVLM achieves state-of-the-art performance across diverse tasks, outperforming existing RS VLMs and even rivaling specialized expert models. All the training and evaluating tools, model weights, and datasets have been fully open-sourced to support reproducibility. We expect that this baseline will promote further progress toward general-purpose RS models.
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[CV-45] Multi-Reward GRPO for Stable and Prosodic Single-Codebook TTS LLM s at Scale
【速读】：该论文旨在解决单码本文本到语音（TTS）大语言模型（LLM）中存在的韵律不稳定、说话人特征漂移及语音自然度下降等问题。其解决方案的关键在于提出一种多奖励组相对策略优化（multi-reward Group Relative Policy Optimization, GRPO）框架，直接优化单码本TTS LLM的token生成策略；该框架在标准的可懂性和说话人相似性目标之外，引入三种基于规则的奖励机制：长度惩罚以保证时长一致性、熵正则化奖励提升解码稳定性，以及由外部推理型LLM通过上下文学习预测多种合理停顿结构所生成的韵律对齐奖励，从而显式监督节奏控制，并提供符合人类偏好的监督信号用于GRPO训练。
链接: https://arxiv.org/abs/2511.21270
作者: Yicheng Zhong,Peiji Yang,Zhisheng Wang
机构: Tencent Technology Co.Ltd (腾讯科技有限公司)
类目: ound (cs.SD); Computer Vision and Pattern Recognition (cs.CV)
备注: 4 pages, 2 figures
Abstract:Recent advances in Large Language Models (LLMs) have transformed text-to-speech (TTS) synthesis, inspiring autoregressive frameworks that represent speech as sequences of discrete codec tokens. Among them, single-codebook TTS LLMs have emerged as compact and streamable architectures that jointly model semantic and acoustic integration. However, despite their efficiency, these models often exhibit unstable prosody, speaker drift, and degraded naturalness. To address these issues, we propose a multi-reward Group Relative Policy Optimization (GRPO) framework that directly optimizes the token generation policy of single-codebook TTS LLMs. Beyond standard intelligibility and speaker similarity objectives, our design integrates three rule-based rewards: a length penalty for duration consistency, an entropy regularization reward for decoding stability, and an LLM-annotated prosody alignment reward that explicitly supervises rhythm. In this prosody reward, an external reasoning LLM predicts multiple plausible pause structures via in-context learning, providing a human-preference-aligned supervisory signal for GRPO training. To assess universality, we further attach a flow-matching (FM) decoder on top of the GRPO-optimized AR backbone and observe consistent additional gains, indicating that our reinforcement optimization enhances the intrinsic AR policy. We further conduct a scalability analysis across data sizes and model scales, revealing that the proposed method consistently enhances prosodic stability, speaker similarity, and overall speech naturalness in single-codebook TTS LLMs.
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[CV-46] Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting
【速读】：该论文旨在解决基于学习的图像匹配（learning-based image matching）对大规模、多样化且几何准确训练数据的高度依赖问题，尤其是当前3D高斯溅射（3D Gaussian Splatting, 3DGS）虽能生成逼真的新视角图像，但其几何不准确性与深度渲染偏差导致难以获得鲁棒的对应点标签。解决方案的关键在于提出MatchGS框架，包含两个核心创新：一是构建几何忠实的数据生成流程，通过精修3DGS的几何结构以生成高精度的对应关系标签，从而在保持渲染保真度的前提下合成多样化的视点；二是设计2D-3D表示对齐策略，将3DGS显式的三维知识注入二维匹配器中，引导半密集匹配器学习视角不变的三维表征。该方法显著降低视差误差（最高达40倍），支持极端视角变化下的监督，并提供基于高斯属性的自监督信号，使仅在所生成数据上训练的先进匹配器在公开基准测试中实现高达17.7%的零样本性能提升。
链接: https://arxiv.org/abs/2511.21265
作者: Juncheng Chen,Chao Xu,Yanjun Cao
机构: Zhejiang University (浙江大学); Huzhou Institute of Zhejiang University (浙江大学湖州研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) enables photorealistic novel-view synthesis and thus is attractive for data generation. However, its geometric inaccuracies and biased depth rendering currently prevent robust correspondence labeling. To address this, we introduce MatchGS, the first framework designed to systematically correct and leverage 3DGS for robust, zero-shot image matching. Our approach is twofold: (1) a geometrically-faithful data generation pipeline that refines 3DGS geometry to produce highly precise correspondence labels, enabling the synthesis of a vast and diverse range of viewpoints without compromising rendering fidelity; and (2) a 2D-3D representation alignment strategy that infuses 3DGS’ explicit 3D knowledge into the 2D matcher, guiding 2D semi-dense matchers to learn viewpoint-invariant 3D representations. Our generated ground-truth correspondences reduce the epipolar error by up to 40 times compared to existing datasets, enable supervision under extreme viewpoint changes, and provide self-supervisory signals through Gaussian attributes. Consequently, state-of-the-art matchers trained solely on our data achieve significant zero-shot performance gains on public benchmarks, with improvements of up to 17.7%. Our work demonstrates that with proper geometric refinement, 3DGS can serve as a scalable, high-fidelity, and structurally-rich data source, paving the way for a new generation of robust zero-shot image matchers.
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[CV-47] LaGen: Towards Autoregressive LiDAR Scene Generation
【速读】：该论文旨在解决现有LiDAR数据生成方法在长时程交互式场景生成方面的局限性问题，即当前方法要么仅支持单帧生成，要么需多帧历史输入且只能确定性地一次性预测多帧，无法实现逐帧自主递归生成与交互控制。其解决方案的关键在于提出LaGen框架，这是首个能够基于单帧LiDAR输入进行逐帧自回归生成长时程4D点云场景的模型；该框架通过引入场景解耦估计模块（scene decoupling estimation module）增强对物体级内容的交互生成能力，并设计噪声调制模块（noise modulation module）以缓解长时间生成过程中的误差累积问题，从而显著提升生成质量，尤其在后续帧上表现优于现有最先进的LiDAR生成与预测模型。
链接: https://arxiv.org/abs/2511.21256
作者: Sizhuo Zhou,Xiaosong Jia,Fanrui Zhang,Junjie Li,Juyong Zhang,Yukang Feng,Jianwen Sun,Songbur Wong,Junqi You,Junchi Yan
机构: University of Science and Technology of China (中国科学技术大学); Shanghai Innovation Institute (上海创新研究院); Shanghai Jiao Tong University (上海交通大学); Fudan University (复旦大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Generative world models for autonomous driving (AD) have become a trending topic. Unlike the widely studied image modality, in this work we explore generative world models for LiDAR data. Existing generation methods for LiDAR data only support single frame generation, while existing prediction approaches require multiple frames of historical input and can only deterministically predict multiple frames at once, lacking interactivity. Both paradigms fail to support long-horizon interactive generation. To this end, we introduce LaGen, which to the best of our knowledge is the first framework capable of frame-by-frame autoregressive generation of long-horizon LiDAR scenes. LaGen is able to take a single-frame LiDAR input as a starting point and effectively utilize bounding box information as conditions to generate high-fidelity 4D scene point clouds. In addition, we introduce a scene decoupling estimation module to enhance the model’s interactive generation capability for object-level content, as well as a noise modulation module to mitigate error accumulation during long-horizon generation. We construct a protocol based on nuScenes for evaluating long-horizon LiDAR scene generation. Experimental results comprehensively demonstrate LaGen outperforms state-of-the-art LiDAR generation and prediction models, especially on the later frames.
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[CV-48] AVFakeBench: A Comprehensive Audio-Video Forgery Detection Benchmark for AV-LMMs
【速读】：该论文旨在解决现有音频-视频（Audio-Video, AV）伪造检测基准难以覆盖真实世界中多样化和复杂伪造场景的问题，特别是当前主流基准仍局限于基于DeepFake的伪造类型及单一粒度标注，无法有效反映多主体（人类与非人类）和多种伪造语义的实际挑战。其解决方案的关键在于提出AVFakeBench——首个涵盖丰富伪造语义的音频-视频伪造检测基准，包含12K精心设计的AV问题，覆盖7类伪造类型与4级标注；并通过多阶段混合伪造框架（融合任务规划模型与专家生成模型）确保伪造内容的质量与多样性，同时构建多任务评估体系（包括二分类、伪造类型识别、细节选择与解释推理），从而系统性地评估音频-视频大语言模型（AV-LMMs）在伪造检测中的潜力与局限。
链接: https://arxiv.org/abs/2511.21251
作者: Shuhan Xia,Peipei Li,Xuannan Liu,Dongsen Zhang,Xinyu Guo,Zekun Li
机构: Beijing University of Posts and Telecommunications (北京邮电大学); University of California, Santa Barbara (加州大学圣塔芭芭拉分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The threat of Audio-Video (AV) forgery is rapidly evolving beyond human-centric deepfakes to include more diverse manipulations across complex natural scenes. However, existing benchmarks are still confined to DeepFake-based forgeries and single-granularity annotations, thus failing to capture the diversity and complexity of real-world forgery scenarios. To address this, we introduce AVFakeBench, the first comprehensive audio-video forgery detection benchmark that spans rich forgery semantics across both human subject and general subject. AVFakeBench comprises 12K carefully curated audio-video questions, covering seven forgery types and four levels of annotations. To ensure high-quality and diverse forgeries, we propose a multi-stage hybrid forgery framework that integrates proprietary models for task planning with expert generative models for precise manipulation. The benchmark establishes a multi-task evaluation framework covering binary judgment, forgery types classification, forgery detail selection, and explanatory reasoning. We evaluate 11 Audio-Video Large Language Models (AV-LMMs) and 2 prevalent detection methods on AVFakeBench, demonstrating the potential of AV-LMMs as emerging forgery detectors while revealing their notable weaknesses in fine-grained perception and reasoning.
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[CV-49] Shift-Equivariant Complex-Valued Convolutional Neural Networks WACV2026
【速读】：该论文旨在解决传统卷积神经网络（Convolutional Neural Networks, CNNs）在下采样和上采样操作中破坏平移等变性（shift equivariance）与平移不变性（shift invariance）的问题。现有方法依赖数据增强来经验性地学习这些性质，缺乏理论保障。解决方案的关键在于引入一种基于可学习多相位采样（Learnable Polyphase Sampling, LPS）的新型下采样和上采样层，并将其扩展至复值神经网络（complex-valued neural networks），同时设计了一个从复数域到实数域的投影层（projection layer）以兼容Gumbel Softmax机制。这一架构理论上保证了平移等变性和不变性，且在极化合成孔径雷达（polarimetric Synthetic Aperture Radar）图像的分类、重建与语义分割任务中验证了其有效性。
链接: https://arxiv.org/abs/2511.21250
作者: Quentin Gabot,Teck-Yian Lim,Jérémy Fix,Joana Frontera-Pons,Chengfang Ren,Jean-Philippe Ovarlez
机构: SONDRA, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France; LORIA, CNRS, CentraleSupélec, Université Paris-Saclay, France; DEMR, ONERA, Université Paris-Saclay, Palaiseau, France; DSO National Laboratories, Singapore
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to WACV 2026
Abstract:Convolutional neural networks have shown remarkable performance in recent years on various computer vision problems. However, the traditional convolutional neural network architecture lacks a critical property: shift equivariance and invariance, broken by downsampling and upsampling operations. Although data augmentation techniques can help the model learn the latter property empirically, a consistent and systematic way to achieve this goal is by designing downsampling and upsampling layers that theoretically guarantee these properties by construction. Adaptive Polyphase Sampling (APS) introduced the cornerstone for shift invariance, later extended to shift equivariance with Learnable Polyphase up/downsampling (LPS) applied to real-valued neural networks. In this paper, we extend the work on LPS to complex-valued neural networks both from a theoretical perspective and with a novel building block of a projection layer from \mathbbC to \mathbbR before the Gumbel Softmax. We finally evaluate this extension on several computer vision problems, specifically for either the invariance property in classification tasks or the equivariance property in both reconstruction and semantic segmentation problems, using polarimetric Synthetic Aperture Radar images.
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[CV-50] FIELDS: Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision
【速读】：该论文旨在解决现有3D人脸重建方法因依赖2D监督信号且缺乏3D真实标注而导致的细微情感特征丢失问题，尤其在自然场景下难以准确捕捉表情强度与真实性。其解决方案的关键在于提出FIELDS（Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision），通过引入直接的3D表情参数监督与辅助的情绪识别分支，实现双监督策略：一方面利用自发4D面部扫描中真实的表情参数引导编码器学习；另一方面设计强度感知的情绪损失函数，促使3D表情参数忠实反映真实情绪内容而不产生夸张。该方法有效弥合了2D/3D域间差距并缓解表情强度偏差，从而从单张图像生成高保真、富含情绪细节的3D人脸模型，显著提升野外环境下的表情识别性能且保持自然性。
链接: https://arxiv.org/abs/2511.21245
作者: Chen Ling,Henglin Shi,Hedvig Kjellström
机构: KTH Royal Institute of Technology (瑞典皇家理工学院); Linköping University (林雪平大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Facial expressions convey the bulk of emotional information in human communication, yet existing 3D face reconstruction methods often miss subtle affective details due to reliance on 2D supervision and lack of 3D ground truth. We propose FIELDS (Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision) to address these limitations by extending self-supervised 2D image consistency cues with direct 3D expression parameter supervision and an auxiliary emotion recognition branch. Our encoder is guided by authentic expression parameters from spontaneous 4D facial scans, while an intensity-aware emotion loss encourages the 3D expression parameters to capture genuine emotion content without exaggeration. This dual-supervision strategy bridges the 2D/3D domain gap and mitigates expression-intensity bias, yielding high-fidelity 3D reconstructions that preserve subtle emotional cues. From a single image, FIELDS produces emotion-rich face models with highly realistic expressions, significantly improving in-the-wild facial expression recognition performance without sacrificing naturalness.
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[CV-51] 3-Tracer: A Tri-level Temporal-Aware Framework for Audio Forgery Detection and Localization
【速读】：该论文旨在解决局部音频伪造（partial audio forgery）的检测难题，即攻击者仅篡改语义关键片段而保留整体感知真实性，导致传统方法难以识别。现有技术多聚焦于单帧独立检测，缺乏对不同时间尺度上瞬态与持续异常的层次化建模能力。其解决方案的关键在于提出T3-Tracer框架，首次从帧、段和音频三个层次协同分析伪造痕迹；核心创新包括帧-音频特征聚合模块（FA-FAM），融合帧内与全局时序信息以捕捉局部伪造线索与语义不一致；以及段级多尺度差异感知模块（SMDAM），通过双分支结构建模帧特征与跨尺度帧间差异，精准定位伪造边界处的突变异常。
链接: https://arxiv.org/abs/2511.21237
作者: Shuhan Xia,Xuannan Liu,Xing Cui,Peipei Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recently, partial audio forgery has emerged as a new form of audio manipulation. Attackers selectively modify partial but semantically critical frames while preserving the overall perceptual authenticity, making such forgeries particularly difficult to detect. Existing methods focus on independently detecting whether a single frame is forged, lacking the hierarchical structure to capture both transient and sustained anomalies across different temporal levels. To address these limitations, We identify three key levels relevant to partial audio forgery detection and present T3-Tracer, the first framework that jointly analyzes audio at the frame, segment, and audio levels to comprehensively detect forgery traces. T3-Tracer consists of two complementary core modules: the Frame-Audio Feature Aggregation Module (FA-FAM) and the Segment-level Multi-Scale Discrepancy-Aware Module (SMDAM). FA-FAM is designed to detect the authenticity of each audio frame. It combines both frame-level and audio-level temporal information to detect intra-frame forgery cues and global semantic inconsistencies. To further refine and correct frame detection, we introduce SMDAM to detect forgery boundaries at the segment level. It adopts a dual-branch architecture that jointly models frame features and inter-frame differences across multi-scale temporal windows, effectively identifying abrupt anomalies that appeared on the forged boundaries. Extensive experiments conducted on three challenging datasets demonstrate that our approach achieves state-of-the-art performance.
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[CV-52] From Diffusion to One-Step Generation: A Comparative Study of Flow-Based Models with Application to Image Inpainting
【速读】：该论文旨在解决生成式模型在采样效率与生成质量之间的权衡问题，特别是针对传统迭代采样方法（如去噪扩散概率模型，DDPM）计算成本高、推理速度慢的局限性。其解决方案的关键在于提出并比较三种生成建模范式：基于扩散过程的DDPM、条件流匹配（CFM）以及均值流（MeanFlow），其中CFM通过学习数据流形上的条件概率路径实现高效高质生成，而MeanFlow则进一步通过建模时间区间内的平均速度实现单步直接生成，显著提升推理效率（50倍加速），同时保持良好的图像质量（FID 29.15）。此外，作者还将CFM扩展至图像修复任务，引入掩码引导采样策略，验证了感知增强训练对修复质量的实质性提升（PSNR和SSIM分别提升73%和45%）。
链接: https://arxiv.org/abs/2511.21215
作者: Umang Agarwal,Rudraksh Sangore,Sumit Laddha
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:We present a comprehensive comparative study of three generative modeling paradigms: Denoising Diffusion Probabilistic Models (DDPM), Conditional Flow Matching (CFM), and MeanFlow. While DDPM and CFM require iterative sampling, MeanFlow enables direct one-step generation by modeling the average velocity over time intervals. We implement all three methods using a unified TinyUNet architecture (1.5M parameters) on CIFAR-10, demonstrating that CFM achieves an FID of 24.15 with 50 steps, significantly outperforming DDPM (FID 402.98). MeanFlow achieves FID 29.15 with single-step sampling – a 50X reduction in inference time. We further extend CFM to image inpainting, implementing mask-guided sampling with four mask types (center, random bbox, irregular, half). Our fine-tuned inpainting model achieves substantial improvements: PSNR increases from 4.95 to 8.57 dB on center masks (+73%), and SSIM improves from 0.289 to 0.418 (+45%), demonstrating the effectiveness of inpainting-aware training.
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[CV-53] owards an Effective Action-Region Tracking Framework for Fine-grained Video Action Recognition
【速读】：该论文旨在解决细粒度动作识别（Fine-grained Action Recognition, FGAR）中难以捕捉局部区域随时间演变的细微差异的问题，现有方法多聚焦于粗粒度运动模式，难以区分相似动作类别。其解决方案的关键在于提出一种基于查询-响应机制的动作区域追踪（Action-Region Tracking, ART）框架：通过区域特定语义激活模块，利用判别性且文本约束的语义作为查询，提取每帧视频中最具动作相关性的局部区域响应，并在时空维度上建立跨帧的关联，形成表征区域级动作动态的动作轨迹片段（action tracklets）。此外，引入多层级轨迹对比约束以增强帧内区分与帧间关联，并设计任务特异性微调机制优化视觉语言模型（VLMs）编码的文本语义，从而在保留原始语义信息的同时提升任务适配性。
链接: https://arxiv.org/abs/2511.21202
作者: Baoli Sun,Yihan Wang,Xinzhu Ma,Zhihui Wang,Kun Lu,Zhiyong Wang
机构: DUT-RU International School of Information Science & Engineering, Dalian University of Technology, China; Beihang University, China; The University of Sydney, Australia
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Fine-grained action recognition (FGAR) aims to identify subtle and distinctive differences among fine-grained action categories. However, current recognition methods often capture coarse-grained motion patterns but struggle to identify subtle details in local regions evolving over time. In this work, we introduce the Action-Region Tracking (ART) framework, a novel solution leveraging a query-response mechanism to discover and track the dynamics of distinctive local details, enabling effective distinction of similar actions. Specifically, we propose a region-specific semantic activation module that employs discriminative and text-constrained semantics as queries to capture the most action-related region responses in each video frame, facilitating interaction among spatial and temporal dimensions with corresponding video features. The captured region responses are organized into action tracklets, which characterize region-based action dynamics by linking related responses across video frames in a coherent sequence. The text-constrained queries encode nuanced semantic representations derived from textual descriptions of action labels extracted by language branches within Visual Language Models (VLMs). To optimize the action tracklets, we design a multi-level tracklet contrastive constraint among region responses at spatial and temporal levels, enabling effective discrimination within each frame and correlation between adjacent frames. Additionally, a task-specific fine-tuning mechanism refines textual semantics such that semantic representations encoded by VLMs are preserved while optimized for task preferences. Comprehensive experiments on widely used action recognition benchmarks demonstrate the superiority to previous state-of-the-art baselines.
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[CV-54] BotaCLIP: Contrastive Learning for Botany-Aware Representation of Earth Observation Data
【速读】：该论文旨在解决如何在不从头训练或产生显著计算成本的前提下，将预训练的地球观测基础模型（DOFA）适配以注入特定领域知识的问题。解决方案的关键在于提出一种轻量级多模态对比学习框架 BotaCLIP，通过高分辨率航空影像与植物样方数据（botanical relevés）之间的对比对齐，结合正则化策略缓解灾难性遗忘，从而在保持模型泛化能力的同时内化生态结构信息。该方法生成的嵌入可作为下游任务的可迁移表示，在生物多样性建模等数据稀缺场景中展现出优于原始 DOFA 和监督基线的性能表现。
链接: https://arxiv.org/abs/2511.21194
作者: Selene Cerna,Sara Si-Moussi,Wilfried Thuiller,Hadrien Hendrikx,Vincent Miele
机构: Laboratoire d’Ecologie Alpine (LECA), Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, Grenoble, France; Centre Inria de l’Université Grenoble Alpes, CNRS, LJK, Grenoble, France
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Foundation models have demonstrated a remarkable ability to learn rich, transferable representations across diverse modalities such as images, text, and audio. In modern machine learning pipelines, these representations often replace raw data as the primary input for downstream tasks. In this paper, we address the challenge of adapting a pre-trained foundation model to inject domain-specific knowledge, without retraining from scratch or incurring significant computational costs. To this end, we introduce BotaCLIP, a lightweight multimodal contrastive framework that adapts a pre-trained Earth Observation foundation model (DOFA) by aligning high-resolution aerial imagery with botanical relevés. Unlike generic embeddings, BotaCLIP internalizes ecological structure through contrastive learning with a regularization strategy that mitigates catastrophic forgetting. Once trained, the resulting embeddings serve as transferable representations for downstream predictors. Motivated by real-world applications in biodiversity modeling, we evaluated BotaCLIP representations in three ecological tasks: plant presence prediction, butterfly occurrence modeling, and soil trophic group abundance estimation. The results showed consistent improvements over those derived from DOFA and supervised baselines. More broadly, this work illustrates how domain-aware adaptation of foundation models can inject expert knowledge into data-scarce settings, enabling frugal representation learning.
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[CV-55] You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering NEURIPS2025
【速读】：该论文旨在解决当前深度聚类模型中全局特征结构与局部特征结构之间的不一致性问题：局部结构通常表现出良好的类内一致性和紧凑性，而全局特征则常呈现边界模糊、聚类分离度差的问题。解决方案的关键在于提出一种无参数的插件式方法DCBoost，其核心机制是利用可靠的局部结构线索来增强全局特征表示。具体而言，首先通过自适应的k近邻一致性过滤策略识别高置信度样本，作为自监督学习中的可信锚点；随后基于这些样本计算判别损失，以同时促进类内紧凑性和类间可分性，从而引导网络优化。实验证明，该方法显著提升了多种现有深度聚类模型的性能，尤其对先进基线（如ProPos）的改进超过3%，且轮廓系数提升超过7倍。
链接: https://arxiv.org/abs/2511.21193
作者: Hanyang Li,Yuheng Jia,Hui Liu,Junhui Hou
机构: Southeast University (东南大学); Saint Francis University (圣弗朗西斯大学); City University of Hong Kong (香港城市大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: The paper is accepted by NeurIPS 2025
Abstract:Recent deep clustering models have produced impressive clustering performance. However, a common issue with existing methods is the disparity between global and local feature structures. While local structures typically show strong consistency and compactness within class samples, global features often present intertwined boundaries and poorly separated clusters. Motivated by this observation, we propose DCBoost, a parameter-free plug-in designed to enhance the global feature structures of current deep clustering models. By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively. Specifically, we first identify high-confidence samples through adaptive k -nearest neighbors-based consistency filtering, aiming to select a sufficient number of samples with high label reliability to serve as trustworthy anchors for self-supervision. Subsequently, these samples are utilized to compute a discriminative loss, which promotes both intra-class compactness and inter-class separability, to guide network optimization. Extensive experiments across various benchmark datasets showcase that our DCBoost significantly improves the clustering performance of diverse existing deep clustering models. Notably, our method improves the performance of current state-of-the-art baselines (e.g., ProPos) by more than 3% and amplifies the silhouette coefficient by over 7\times . Code is available at this https URL.
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[CV-56] When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Models
【速读】：该论文旨在解决视觉-语言-动作（Vision-Language-Action, VLA）模型在面对对抗性攻击时的脆弱性问题，特别是现有攻击方法多为特定模型定制、缺乏通用性和跨模型迁移能力，难以在黑盒场景下有效实施。解决方案的关键在于提出UPA-RFAS（Universal Patch Attack via Robust Feature, Attention, and Semantics）框架，其核心创新包括：（1）在共享特征空间中学习单一物理对抗补丁，结合ℓ₁偏差先验与排斥式InfoNCE损失以诱导可迁移的表征偏移；（2）采用增强鲁棒性的两阶段极小极大优化过程，内层学习不可见的样本级扰动，外层优化针对该强化邻域的通用补丁；（3）引入两个VLA特有损失函数——补丁注意力主导损失（Patch Attention Dominance）用于劫持文本到视觉的注意力机制，以及补丁语义错位损失（Patch Semantic Misalignment）在无标签条件下引发图像与文本不一致。实验表明，UPA-RFAS在多种VLA模型、任务和物理执行场景中均表现出强跨模型迁移能力，揭示了基于补丁的实用攻击面并建立了未来防御研究的基准。
链接: https://arxiv.org/abs/2511.21192
作者: Hui Lu,Yi Yu,Yiming Yang,Chenyu Yi,Qixin Zhang,Bingquan Shen,Alex C. Kot,Xudong Jiang
机构: Nanyang Technological University (南洋理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored, as most existing patches overfit to a single model and fail in black-box settings. To address this gap, we present a systematic study of universal, transferable adversarial patches against VLA-driven robots under unknown architectures, finetuned variants, and sim-to-real shifts. We introduce UPA-RFAS (Universal Patch Attack via Robust Feature, Attention, and Semantics), a unified framework that learns a single physical patch in a shared feature space while promoting cross-model transfer. UPA-RFAS combines (i) a feature-space objective with an \ell_1 deviation prior and repulsive InfoNCE loss to induce transferable representation shifts, (ii) a robustness-augmented two-phase min-max procedure where an inner loop learns invisible sample-wise perturbations and an outer loop optimizes the universal patch against this hardened neighborhood, and (iii) two VLA-specific losses: Patch Attention Dominance to hijack text \to vision attention and Patch Semantic Misalignment to induce image-text mismatch without labels. Experiments across diverse VLA models, manipulation suites, and physical executions show that UPA-RFAS consistently transfers across models, tasks, and viewpoints, exposing a practical patch-based attack surface and establishing a strong baseline for future defenses.
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[CV-57] Scenes as Tokens: Multi-Scale Normal Distributions Transform Tokenizer for General 3D Vision-Language Understanding
【速读】：该论文旨在解决3D场景理解中如何有效将三维场景分割为整体性场景令牌（scene tokens），并跨多种3D理解任务充分利用这些令牌的挑战。其核心解决方案是提出NDTokenizer3D，一种基于多尺度法向量分布变换（Multi-Scale Normal Distributions Transform, NDT）表示的三阶段场景令牌化流水线，结合多尺度NDT解码器（Multi-Scale NDT Decoder, MSDec）。该方法首先从高分辨率点云构建多尺度NDT表示以保留全局上下文与细粒度几何细节，随后通过MSDec逐级融合跨尺度特征生成可被大语言模型（Large Language Model, LLM）直接使用的整体场景令牌；同时，MSDec还被重新设计为支持人类交互式提示（如点、框、掩码）和分割掩码解码的通用接口，从而在统一架构内实现3D参考分割、3D视觉问答和3D密集描述等多种任务的高效处理。
链接: https://arxiv.org/abs/2511.21191
作者: Yutao Tang,Cheng Zhao,Gaurav Mittal,Rohith Kukkala,Rama Chellappa,Cheng Peng,Mei Chen
机构: Johns Hopkins University (约翰霍普金斯大学); Microsoft (微软)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent advances in 3D vision-language models (VLMs) highlight a strong potential for 3D scene understanding and reasoning. However, effectively tokenizing 3D scenes into holistic scene tokens, and leveraging these tokens across diverse 3D understanding tasks, remain highly challenging. We present NDTokenizer3D, a generalist 3D VLM that performs a wide range of 3D scene understanding tasks while naturally supporting human interactions, thereby bridging language-level reasoning with 3D spatial understanding. The core of our approach is a novel three-stage scene tokenization pipeline built upon a Multi-Scale Normal Distributions Transform (NDT) representation, paired with a Multi-Scale NDT Decoder (MSDec). Specifically, NDTokenizer3D first constructs a multi-scale NDT representation from raw high-resolution point clouds, preserving both global context and fine-grained geometric details. Next, the MSDec progressively fuses cross-scale NDT features, producing holistic scene tokens consumable by LLM endpoints. Beyond tokenization, MSDec is repurposed as a general interface for human-interactive prompting (points, boxes, masks) and segmentation-mask decoding, unifying diverse 3D scene understanding tasks within a single architecture. With this compact and unified design, NDTokenizer3D offers a fine-grained, general-purpose 3D VLM, achieving remarkable improvements in 3D Referring Segmentation, 3D Visual Question Answering, and 3D Dense Captioning.
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[CV-58] Progress by Pieces: Test-Time Scaling for Autoregressive Image Generation
【速读】：该论文旨在解决视觉自回归（Visual Autoregressive, VA）模型在测试时计算资源扩展（test-time computation scaling）中面临的两大核心问题：一是传统方法如Best-of-N策略在错误生成路径上浪费大量计算资源；二是栅格扫描解码（raster-scan decoding）缺乏对完整图像蓝图的把握，导致仅能生成少量与提示对齐的候选结果，限制了扩展收益。解决方案的关键在于提出GridAR框架，其核心创新包括两个方面：一是采用网格分区的渐进式生成机制，在同一位置生成多个局部候选并早期剪枝不可行方案，将可行解作为锚点引导后续解码；二是引入布局指定的提示重构策略，通过分析局部视图推断合理布局以弥补蓝图缺失问题，从而提升生成质量与效率。该方法在T2I-CompBench++和PIE-Bench等基准上均实现显著性能提升，且在相同计算预算下优于更大N值的基线。
链接: https://arxiv.org/abs/2511.21185
作者: Joonhyung Park,Hyeongwon Jang,Joowon Kim,Eunho Yang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Recent visual autoregressive (AR) models have shown promising capabilities in text-to-image generation, operating in a manner similar to large language models. While test-time computation scaling has brought remarkable success in enabling reasoning-enhanced outputs for challenging natural language tasks, its adaptation to visual AR models remains unexplored and poses unique challenges. Naively applying test-time scaling strategies such as Best-of-N can be suboptimal: they consume full-length computation on erroneous generation trajectories, while the raster-scan decoding scheme lacks a blueprint of the entire canvas, limiting scaling benefits as only a few prompt-aligned candidates are generated. To address these, we introduce GridAR, a test-time scaling framework designed to elicit the best possible results from visual AR models. GridAR employs a grid-partitioned progressive generation scheme in which multiple partial candidates for the same position are generated within a canvas, infeasible ones are pruned early, and viable ones are fixed as anchors to guide subsequent decoding. Coupled with this, we present a layout-specified prompt reformulation strategy that inspects partial views to infer a feasible layout for satisfying the prompt. The reformulated prompt then guides subsequent image generation to mitigate the blueprint deficiency. Together, GridAR achieves higher-quality results under limited test-time scaling: with N=4, it even outperforms Best-of-N (N=8) by 14.4% on T2I-CompBench++ while reducing cost by 25.6%. It also generalizes to autoregressive image editing, showing comparable edit quality and a 13.9% gain in semantic preservation on PIE-Bench over larger-N baselines.
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[CV-59] LLaVA-UHD v3: Progressive Visual Compression for Efficient Native-Resolution Encoding in MLLM s
【速读】：该论文旨在解决多模态大语言模型（MLLM）中高分辨率视觉编码带来的计算开销过大的问题，尤其是在采用全局原生分辨率视觉编码时，虽然提升了视觉-语言理解能力，但显著增加了推理延迟。解决方案的关键在于提出一种名为“渐进式视觉压缩”（Progressive Visual Compression, PVC）的方法，其核心由两个模块构成：(i) 精细的补丁嵌入机制，支持灵活的补丁尺寸缩放以实现细粒度视觉建模；(ii) 窗口化的令牌压缩模块，分层部署于视觉Transformer（ViT）各层中，逐步聚合局部令牌表示。这两个模块协同作用，使预训练的ViT能够在保持通用性的同时被重构为高效架构，从而在不牺牲性能的前提下大幅降低首次生成 token 的时间（TTFT），实验证明该方法在相同 MLLM 架构下将 TTFT 降低至原来的 1/2.4，并优于现有先进模型如 Qwen2-VL。
链接: https://arxiv.org/abs/2511.21150
作者: Shichu Sun,Yichen Zhang,Haolin Song,Zonghao Guo,Chi Chen,Yidan Zhang,Yuan Yao,Zhiyuan Liu,Maosong Sun
机构: Tsinghua University (清华大学); University of Chinese Academy of Sciences (中国科学院大学); Institute of Automation, Chinese Academy of Sciences (中国科学院自动化研究所); Aerospace Information Research Institute, Chinese Academy of Sciences (中国科学院空天信息创新研究院); School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences (中国科学院大学交叉学科研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Visual encoding followed by token condensing has become the standard architectural paradigm in multi-modal large language models (MLLMs). Many recent MLLMs increasingly favor global native- resolution visual encoding over slice-based methods. To investigate this trend, we systematically compare their behavior on vision-language understanding and attention patterns, revealing that global encoding enhances overall capability but at the expense of greater computational overhead. To address this issue, we present LLaVA-UHD v3, an MLLM centered upon our proposed Progressive Visual Compression (PVC) method, which can be seamlessly integrated into standard Vision Transformer (ViT) to enable efficient native-resolution encoding. The PVC approach consists of two key modules: (i) refined patch embedding, which supports flexible patch-size scaling for fine-grained visual model- ing, (ii) windowed token compression, hierarchically deployed across ViT layers to progressively aggregate local token representations. Jointly modulated by these two modules, a widely pretrained ViT can be reconfigured into an efficient architecture while largely preserving generality. Evaluated across extensive benchmarks, the transformed ViT, termed ViT-UHD, demonstrates competitive performance with MoonViT while reducing TTFT (time-to-first-token) by 2.4x, when developed within an identical MLLM architecture. Building upon ViT-UHD, LLaVA-UHD v3 also achieves competitive performance to Qwen2-VL, while further reducing TTFT by 1.9x. We will release all code and checkpoints to support future research on efficient MLLMs.
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[CV-60] AV-Edit: Multimodal Generative Sound Effect Editing via Audio-Visual Semantic Joint Control
【速读】：该论文旨在解决当前音效编辑（Sound Effect Editing）方法受限于仅依赖低级信号处理或粗粒度文本提示所导致的灵活性不足和音频质量不佳的问题。其解决方案的关键在于提出一种名为AV-Edit的生成式音效编辑框架，通过联合利用视觉、音频与文本语义信息实现细粒度编辑；具体而言，采用专为多模态预训练设计的对比音频-视觉掩码自编码器（Contrastive Audio-Visual Masking Autoencoder, CAV-MAE-Edit）学习跨模态对齐表示，并基于此训练一个编辑型多模态扩散Transformer（Editorial Multimodal Diffusion Transformer, MM-DiT），该模型通过基于相关性的特征门控训练策略，可精准移除与视觉无关的声音并生成与视频内容一致的缺失音频元素，从而实现高质量、语义一致的音效编辑。
链接: https://arxiv.org/abs/2511.21146
作者: Xinyue Guo,Xiaoran Yang,Lipan Zhang,Jianxuan Yang,Zhao Wang,Jian Luan
机构: 未知
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV); Sound (cs.SD)
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Abstract:Sound effect editing-modifying audio by adding, removing, or replacing elements-remains constrained by existing approaches that rely solely on low-level signal processing or coarse text prompts, often resulting in limited flexibility and suboptimal audio quality. To address this, we propose AV-Edit, a generative sound effect editing framework that enables fine-grained editing of existing audio tracks in videos by jointly leveraging visual, audio, and text semantics. Specifically, the proposed method employs a specially designed contrastive audio-visual masking autoencoder (CAV-MAE-Edit) for multimodal pre-training, learning aligned cross-modal representations. These representations are then used to train an editorial Multimodal Diffusion Transformer (MM-DiT) capable of removing visually irrelevant sounds and generating missing audio elements consistent with video content through a correlation-based feature gating training strategy. Furthermore, we construct a dedicated video-based sound editing dataset as an evaluation benchmark. Experiments demonstrate that the proposed AV-Edit generates high-quality audio with precise modifications based on visual content, achieving state-of-the-art performance in the field of sound effect editing and exhibiting strong competitiveness in the domain of audio generation.
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[CV-61] EAR: Temporal-aware Automated Red-teaming for Text-to-Video Models
【速读】：该论文旨在解决文本到视频（Text-to-Video, T2V）生成模型在动态时序内容中潜藏的安全风险问题，现有安全评估方法因聚焦于静态图像与文本生成，难以捕捉视频生成中的复杂时间动态特性。解决方案的关键在于提出一种时序感知的自动化红队测试框架 TEAR（TEmporal-aware Automated Red-teaming），其核心创新包括：1）采用两阶段优化的时序感知测试生成器，通过初始生成器训练与在线偏好学习策略，构造看似无害但能诱发政策违规视频输出的提示；2）引入精炼模型实现提示隐蔽性与对抗有效性的循环提升，从而显著增强攻击成功率，在开源和商用T2V系统上达到超过80%的成功率，远超此前最高57%的水平。
链接: https://arxiv.org/abs/2511.21145
作者: Jiaming He,Guanyu Hou,Hongwei Li,Zhicong Huang,Kangjie Chen,Yi Yu,Wenbo Jiang,Guowen Xu,Tianwei Zhang
机构: University of Electonic Science and Technology of China (电子科技大学); University of Manchester (曼彻斯特大学); Ant Group (蚂蚁集团); Nanyang Technological University (南洋理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Text-to-Video (T2V) models are capable of synthesizing high-quality, temporally coherent dynamic video content, but the diverse generation also inherently introduces critical safety challenges. Existing safety evaluation methods,which focus on static image and text generation, are insufficient to capture the complex temporal dynamics in video generation. To address this, we propose a TEmporal-aware Automated Red-teaming framework, named TEAR, an automated framework designed to uncover safety risks specifically linked to the dynamic temporal sequencing of T2V models. TEAR employs a temporal-aware test generator optimized via a two-stage approach: initial generator training and temporal-aware online preference learning, to craft textually innocuous prompts that exploit temporal dynamics to elicit policy-violating video output. And a refine model is adopted to improve the prompt stealthiness and adversarial effectiveness cyclically. Extensive experimental evaluation demonstrates the effectiveness of TEAR across open-source and commercial T2V systems with over 80% attack success rate, a significant boost from prior best result of 57%.
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[CV-62] STAR: Smartphone-analogous Typing in Augmented Reality
【速读】：该论文旨在解决增强现实（Augmented Reality, AR）应用中高效且易用的文本输入问题。当前AR环境下的文本输入方法仍面临挑战，缺乏类似智能手机上便捷的两指打字体验。解决方案的关键在于提出STAR技术，这是一种模拟智能手机操作的AR文本输入方法：用户在双手皮肤上叠加的虚拟QWERTY键盘上进行拇指打字，利用其对智能手机双拇指输入的熟悉度实现自然交互。实验表明，经过30分钟练习后，用户平均输入速率达21.9词每分钟（WPM），约为其手机打字速度的56%，错误率仅为0.3%，验证了该方案的可行性与潜力。
链接: https://arxiv.org/abs/2511.21143
作者: Taejun Kim,Amy Karlson,Aakar Gupta,Tovi Grossman,Jason Wu,Parastoo Abtahi,Christopher Collins,Michael Glueck,Hemant Bhaskar Surale
机构: Reality Labs Research, Meta; University of Toronto
类目: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
备注: ACM UIST 2023
Abstract:While text entry is an essential and frequent task in Augmented Reality (AR) applications, devising an efficient and easy-to-use text entry method for AR remains an open challenge. This research presents STAR, a smartphone-analogous AR text entry technique that leverages a user’s familiarity with smartphone two-thumb typing. With STAR, a user performs thumb typing on a virtual QWERTY keyboard that is overlain on the skin of their hands. During an evaluation study of STAR, participants achieved a mean typing speed of 21.9 WPM (i.e., 56% of their smartphone typing speed), and a mean error rate of 0.3% after 30 minutes of practice. We further analyze the major factors implicated in the performance gap between STAR and smartphone typing, and discuss ways this gap could be narrowed.
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[CV-63] Referring Video Object Segmentation with Cross-Modality Proxy Queries
【速读】：该论文旨在解决引用视频目标分割（Referring Video Object Segmentation, RVOS）任务中两个关键问题：一是现有方法依赖的条件查询缺乏帧间依赖性和变化建模能力，导致在显著帧间差异下难以准确跟踪目标；二是文本约束引入过晚，可能导致视频特征关注非目标对象。解决方案的关键在于提出一种名为ProxyFormer的新架构，其核心创新是引入一组代理查询（proxy queries），用于整合视觉与文本语义，并促进两者间的语义流动。通过在视频特征编码器的多阶段中动态更新和传播代理查询，ProxyFormer确保视频特征始终聚焦于目标对象，同时建立帧间依赖关系以提升跟踪精度与连贯性。此外，为降低计算开销，作者将跨模态交互解耦为时序与空间维度，并设计联合语义一致性（Joint Semantic Consistency, JSC）训练策略，强化代理查询与视频-文本组合对之间的语义一致性。
链接: https://arxiv.org/abs/2511.21139
作者: Baoli Sun,Xinzhu Ma,Ning Wang,Zhihui Wang,Zhiyong Wang
机构: DUT-RU International School of Information Science & Engineering, Dalian University of Technology (大连理工大学); Chinese University of Hong Kong (香港中文大学); University of Sydney (悉尼大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Referring video object segmentation (RVOS) is an emerging cross-modality task that aims to generate pixel-level maps of the target objects referred by given textual expressions. The main concept involves learning an accurate alignment of visual elements and language expressions within a semantic space. Recent approaches address cross-modality alignment through conditional queries, tracking the target object using a query-response based mechanism built upon transformer structure. However, they exhibit two limitations: (1) these conditional queries lack inter-frame dependency and variation modeling, making accurate target tracking challenging amid significant frame-to-frame variations; and (2) they integrate textual constraints belatedly, which may cause the video features potentially focus on the non-referred objects. Therefore, we propose a novel RVOS architecture called ProxyFormer, which introduces a set of proxy queries to integrate visual and text semantics and facilitate the flow of semantics between them. By progressively updating and propagating proxy queries across multiple stages of video feature encoder, ProxyFormer ensures that the video features are focused on the object of interest. This dynamic evolution also enables the establishment of inter-frame dependencies, enhancing the accuracy and coherence of object tracking. To mitigate high computational costs, we decouple cross-modality interactions into temporal and spatial dimensions. Additionally, we design a Joint Semantic Consistency (JSC) training strategy to align semantic consensus between the proxy queries and the combined video-text pairs. Comprehensive experiments on four widely used RVOS benchmarks demonstrate the superiority of our ProxyFormer to the state-of-the-art methods.
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[CV-64] Efficient Training for Human Video Generation with Entropy-Guided Prioritized Progressive Learning
【速读】：该论文旨在解决扩散模型（Diffusion Models）在人体视频生成任务中训练时面临的高计算成本和大量显存消耗问题，尤其是在处理高分辨率、多帧数据时。其解决方案的关键在于提出一种名为熵引导的优先级渐进学习（Entropy-Guided Prioritized Progressive Learning, Ent-Prog）的高效训练框架：首先引入条件熵膨胀（Conditional Entropy Inflation, CEI）来量化不同模型组件对目标条件生成任务的重要性，从而优先训练关键模块；其次设计自适应渐进调度机制，根据收敛效率动态调整训练过程中的计算复杂度，实现训练时间与GPU显存占用的显著降低，同时保持生成性能。
链接: https://arxiv.org/abs/2511.21136
作者: Changlin Li,Jiawei Zhang,Shuhao Liu,Sihao Lin,Zeyi Shi,Zhihui Li,Xiaojun Chang
机构: Stanford University (斯坦福大学); North China Electric Power University (华北电力大学); University of Adelaide (阿德莱德大学); University of Technology Sydney (悉尼科技大学); University of Science and Technology of China (中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Human video generation has advanced rapidly with the development of diffusion models, but the high computational cost and substantial memory consumption associated with training these models on high-resolution, multi-frame data pose significant challenges. In this paper, we propose Entropy-Guided Prioritized Progressive Learning (Ent-Prog), an efficient training framework tailored for diffusion models on human video generation. First, we introduce Conditional Entropy Inflation (CEI) to assess the importance of different model components on the target conditional generation task, enabling prioritized training of the most critical components. Second, we introduce an adaptive progressive schedule that adaptively increases computational complexity during training by measuring the convergence efficiency. Ent-Prog reduces both training time and GPU memory consumption while maintaining model performance. Extensive experiments across three datasets, demonstrate the effectiveness of Ent-Prog, achieving up to 2.2 \times training speedup and 2.4 \times GPU memory reduction without compromising generative performance.
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[CV-65] SocialNav: Training Human-Inspired Foundation Model for Socially-Aware Embodied Navigation
【速读】：该论文旨在解决具身导航（Embodied Navigation）中如何遵循社会规范这一开放性挑战，即让智能体在复杂动态环境中不仅高效到达目标位置，还能生成符合人类社交行为准则的轨迹。其解决方案的关键在于提出SocialNav——一个具有分层“大脑-动作”架构的基础模型，通过构建大规模SocNav数据集（包含700万样本），其中包含认知激活数据集（提供链式思维推理信号和社会可通行性预测）与专家轨迹金字塔（整合来自互联网视频、仿真环境和真实机器人等多种来源的导航示范），并采用多阶段训练流程：首先通过模仿学习注入通用导航技能与社会规范理解能力，再利用首创的基于流的强化学习框架SAFE-GRPO对行为进行精细化优化，显式奖励社会合规行为。该方法在成功率和社交合规率上分别较最先进方法提升38%和46%，显著提升了导航性能与社会适应性。
链接: https://arxiv.org/abs/2511.21135
作者: Ziyi Chen,Yingnan Guo,Zedong Chu,Minghua Luo,Yanfen Shen,Mingchao Sun,Junjun Hu,Shichao Xie,Kuan Yang,Pei Shi,Zhining Gu,Lu Liu,Honglin Han,Xiaolong Wu,Mu Xu,Yu Zhang
机构: Amap, Alibaba Group (高德地图，阿里巴巴集团); Zhejiang University (浙江大学)
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Embodied navigation that adheres to social norms remains an open research challenge. Our \textbfSocialNav is a foundational model for socially-aware navigation with a hierarchical “brain-action” architecture, capable of understanding high-level social norms and generating low-level, socially compliant trajectories. To enable such dual capabilities, we construct the SocNav Dataset, a large-scale collection of 7 million samples, comprising (1) a Cognitive Activation Dataset providing social reasoning signals such as chain-of-thought explanations and social traversability prediction, and (2) an Expert Trajectories Pyramid aggregating diverse navigation demonstrations from internet videos, simulated environments, and real-world robots. A multi-stage training pipeline is proposed to gradually inject and refine navigation intelligence: we first inject general navigation skills and social norms understanding into the model via imitation learning, and then refine such skills through a deliberately designed Socially-Aware Flow Exploration GRPO (SAFE-GRPO), the first flow-based reinforcement learning framework for embodied navigation that explicitly rewards socially compliant behaviors. SocialNav achieves +38% success rate and +46% social compliance rate compared to the state-of-the-art method, demonstrating strong gains in both navigation performance and social compliance. Our project page: this https URL
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[CV-66] DeepRFTv2: Kernel-level Learning for Image Deblurring
【速读】：该论文旨在解决当前深度学习图像去模糊模型在理解模糊本质上的局限性问题，即现有网络多停留在像素级学习阶段（如端到端像素恢复或伪核级恢复），难以真正建模模糊过程的物理机制。其解决方案的关键在于提出傅里叶核估计器（Fourier Kernel Estimator, FKE），通过在频域中进行激活操作，将空间域的卷积运算转化为乘法运算，从而以低复杂度实现无监督的核级模糊过程学习；同时，将卷积对象从原始图像改为由网络提取的特征图（feature），利用其丰富的语义与结构信息提升模糊建模能力，并结合解耦的多尺度架构与可逆策略，在降低训练内存消耗的同时增强多尺度特征编码与解码效率。
链接: https://arxiv.org/abs/2511.21132
作者: Xintian Mao,Haofei Song,Yin-Nian Liu,Qingli Li,Yan Wang
机构: East China Normal University (华东师范大学); Shanghai Institute of Technical Physics, Chinese Academy of Sciences (中国科学院上海技术物理研究所); University of Chinese Academy of Sciences (中国科学院大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:It is well-known that if a network aims to learn how to deblur, it should understand the blur process. Blurring is naturally caused by the convolution of the sharp image with the blur kernel. Thus, allowing the network to learn the blur process in the kernel-level can significantly improve the image deblurring performance. But, current deep networks are still at the pixel-level learning stage, either performing end-to-end pixel-level restoration or stage-wise pseudo kernel-level restoration, failing to enable the deblur model to understand the essence of the blur. To this end, we propose Fourier Kernel Estimator (FKE), which considers the activation operation in Fourier space and converts the convolution problem in the spatial domain to a multiplication problem in Fourier space. Our FKE, jointly optimized with the deblur model, enables the network to learn the kernel-level blur process with low complexity and without any additional supervision. Furthermore, we change the convolution object of the kernel from image" to network extracted feature", whose rich semantic and structural information is more suitable to blur process learning. With the convolution of the feature and the estimated kernel, our model can learn the essence of blur in kernel-level. To further improve the efficiency of feature extraction, we design a decoupled multi-scale architecture with multiple hierarchical sub-unets with a reversible strategy, which allows better multi-scale encoding and decoding in low training memory. Extensive experiments indicate that our method achieves state-of-the-art motion deblurring results and show potential for handling other kernel-related problems. Analysis also shows our kernel estimator is able to learn physically meaningful kernels. The code will be available at this https URL.
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[CV-67] CtrlVDiff: Controllable Video Generation via Unified Multimodal Video Diffusion
【速读】：该论文旨在解决视频理解与可控视频生成中的双重挑战，即如何在统一的扩散框架中实现对视频内容的精确控制和高质量重建。其核心问题在于：仅依赖几何线索（如深度图、边缘）不足以约束视频的外观、材质与光照等物理属性，导致编辑操作（如重新照明或材质替换）缺乏物理合理性，并易引发时序漂移；同时，融合多种异构图形模态（如语义分割、法线、反照率、粗糙度等）虽能增强控制能力，但需应对架构设计上的灵活性与鲁棒性难题（如支持任意模态子集输入且保持时序一致性），以及大规模时空对齐标注数据的稀缺问题。解决方案的关键在于提出CtrlVDiff模型，采用混合模态控制策略（Hybrid Modality Control Strategy, HMCS），通过动态路由与特征融合机制整合深度、边缘、语义、法线及材质参数等多源信息，在任意模态缺失情况下仍可生成高保真、强时序一致性的视频，并借助自建的MMVideo数据集（包含真实与合成视频的跨模态对齐标注）完成训练，从而显著提升可控编辑能力与泛化性能。
链接: https://arxiv.org/abs/2511.21129
作者: Dianbing Xi,Jiepeng Wang,Yuanzhi Liang,Xi Qiu,Jialun Liu,Hao Pan,Yuchi Huo,Rui Wang,Haibin Huang,Chi Zhang,Xuelong Li
机构: Zhejiang University (浙江大学); China Telecom (TeleAI) (中国电信（TeleAI）); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
备注: 27 pages, 18 figures, 9 tables. Project page: this https URL
Abstract:We tackle the dual challenges of video understanding and controllable video generation within a unified diffusion framework. Our key insights are two-fold: geometry-only cues (e.g., depth, edges) are insufficient: they specify layout but under-constrain appearance, materials, and illumination, limiting physically meaningful edits such as relighting or material swaps and often causing temporal drift. Enriching the model with additional graphics-based modalities (intrinsics and semantics) provides complementary constraints that both disambiguate understanding and enable precise, predictable control during generation. However, building a single model that uses many heterogeneous cues introduces two core difficulties. Architecturally, the model must accept any subset of modalities, remain robust to missing inputs, and inject control signals without sacrificing temporal consistency. Data-wise, training demands large-scale, temporally aligned supervision that ties real videos to per-pixel multimodal annotations. We then propose CtrlVDiff, a unified diffusion model trained with a Hybrid Modality Control Strategy (HMCS) that routes and fuses features from depth, normals, segmentation, edges, and graphics-based intrinsics (albedo, roughness, metallic), and re-renders videos from any chosen subset with strong temporal coherence. To enable this, we build MMVideo, a hybrid real-and-synthetic dataset aligned across modalities and captions. Across understanding and generation benchmarks, CtrlVDiff delivers superior controllability and fidelity, enabling layer-wise edits (relighting, material adjustment, object insertion) and surpassing state-of-the-art baselines while remaining robust when some modalities are unavailable. Comments: 27 pages, 18 figures, 9 tables. Project page: this https URL Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) Cite as: arXiv:2511.21129 [cs.CV] (or arXiv:2511.21129v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2511.21129 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-68] Which Layer Causes Distribution Deviation? Entropy-Guided Adaptive Pruning for Diffusion and Flow Models
【速读】：该论文旨在解决大规模视觉生成模型（如扩散模型和流模型）在迁移到下游任务时存在的参数冗余问题。现有方法通常导致模型效率低下，且难以在保持生成质量的同时实现有效压缩。解决方案的关键在于提出 EntPruner——一个基于熵引导的自动渐进式剪枝框架：其核心创新是引入块级重要性评估策略，利用数据依赖的条件熵偏差（Conditional Entropy Deviation, CED）作为指导指标，量化移除某一模块后输出分布与预训练条件分布的偏离程度，从而优先剪枝对特定下游任务贡献较小的模块；同时设计零样本自适应剪枝机制，在训练过程中动态决定剪枝时机与幅度，避免一次性剪枝引发的模式崩溃并维持模型性能。实验表明，该方法在 DiT 和 SiT 模型上实现了最高达 2.22 倍的推理加速，同时保持了 ImageNet 及多个下游数据集上的生成质量竞争力。
链接: https://arxiv.org/abs/2511.21122
作者: Changlin Li,Jiawei Zhang,Zeyi Shi,Zongxin Yang,Zhihui Li,Xiaojun Chang
机构: Stanford University (斯坦福大学); North China Electric Power University (华北电力大学); University of Technology Sydney (悉尼科技大学); Harvard University (哈佛大学); University of Science and Technology of China (中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Project page: this https URL
Abstract:Large-scale vision generative models, including diffusion and flow models, have demonstrated remarkable performance in visual generation tasks. However, transferring these pre-trained models to downstream tasks often results in significant parameter redundancy. In this paper, we propose EntPruner, an entropy-guided automatic progressive pruning framework for diffusion and flow models. First, we introduce entropy-guided pruning, a block-level importance assessment strategy specifically designed for generative models. Unlike discriminative models, generative models require preserving the diversity and condition-fidelity of the output distribution. As the importance of each module can vary significantly across downstream tasks, EntPruner prioritizes pruning of less important blocks using data-dependent Conditional Entropy Deviation (CED) as a guiding metric. CED quantifies how much the distribution diverges from the learned conditional data distribution after removing a block. Second, we propose a zero-shot adaptive pruning framework to automatically determine when and how much to prune during training. This dynamic strategy avoids the pitfalls of one-shot pruning, mitigating mode collapse, and preserving model performance. Extensive experiments on DiT and SiT models demonstrate the effectiveness of EntPruner, achieving up to 2.22 \times inference speedup while maintaining competitive generation quality on ImageNet and three downstream datasets.
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[CV-69] Deformation-aware Temporal Generation for Early Prediction of Alzheimers Disease
【速读】：该论文旨在解决阿尔茨海默病（Alzheimer’s disease, AD）早期预测中因脑部形态变化难以自动学习而导致的准确性不足问题。现有方法多依赖人工特征提取来分析磁共振成像（MRI）中的形态学变化，存在效率低和泛化能力差的局限。解决方案的关键在于提出一种新型生成模型——形变感知时间生成网络（Deformation-Aware Temporal Generative Network, DATGN），其核心创新包括：首先对缺失数据常见的MRI时序序列进行插值处理以恢复完整性；其次引入双向时间形变感知模块，使网络在生成未来MRI图像时能够遵循AD进展的脑萎缩趋势，从而实现更准确的早期预测。实验表明，DATGN不仅在PSNR和MMSE等图像质量指标上表现优异，且结合其生成的合成数据后，在支持向量机（SVM）、卷积神经网络（CNN）及三维卷积神经网络（3DCNN）分类任务中显著提升了AD vs. 正常对照（NC）和AD vs. 轻度认知障碍（MCI）vs. NC的分类准确率。
链接: https://arxiv.org/abs/2511.21114
作者: Xin Honga,Jie Lin,Minghui Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 29 pages,6figures,one column
Abstract:Alzheimer’s disease (AD), a degenerative brain condition, can benefit from early prediction to slow its progression. As the disease progresses, patients typically undergo brain atrophy. Current prediction methods for Alzheimers disease largely involve analyzing morphological changes in brain images through manual feature extraction. This paper proposes a novel method, the Deformation-Aware Temporal Generative Network (DATGN), to automate the learning of morphological changes in brain images about disease progression for early prediction. Given the common occurrence of missing data in the temporal sequences of MRI images, DATGN initially interpolates incomplete sequences. Subsequently, a bidirectional temporal deformation-aware module guides the network in generating future MRI images that adhere to the disease’s progression, facilitating early prediction of Alzheimer’s disease. DATGN was tested for the generation of temporal sequences of future MRI images using the ADNI dataset, and the experimental results are competitive in terms of PSNR and MMSE image quality metrics. Furthermore, when DATGN-generated synthetic data was integrated into the SVM vs. CNN vs. 3DCNN-based classification methods, significant improvements were achieved from 6. 21% to 16% in AD vs. NC classification accuracy and from 7. 34% to 21. 25% in AD vs. MCI vs. NC classification accuracy. The qualitative visualization results indicate that DATGN produces MRI images consistent with the brain atrophy trend in Alzheimer’s disease, enabling early disease prediction.
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[CV-70] FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain
【速读】：该论文旨在解决在可控驾驶场景重建与三维场景生成中，如何在大视角变化下同时保持几何保真度并合成视觉合理的外观问题。现有方法在融合基于几何的3D高斯表示（3DGS）与基于外观的扩散模型时，常因缺乏像素级、三维一致的编辑准则而导致过度修复和几何漂移。解决方案的关键在于提出一个名为FaithFusion的3DGS-扩散融合框架，其核心是利用像素级期望信息增益（Expected Information Gain, EIG）作为统一策略：EIG既作为空间先验指导扩散模型优化高不确定性区域，又通过像素级权重将编辑信息蒸馏回3DGS，从而实现时空一致性合成。该方法无需额外先验条件，且在Waymo数据集上实现了NTA-IoU、NTL-IoU和FID等指标的SOTA性能，即使在6米车道偏移下仍保持FID为107.47。
链接: https://arxiv.org/abs/2511.21113
作者: YuAn Wang,Xiaofan Li,Chi Huang,Wenhao Zhang,Hao Li,Bosheng Wang,Xun Sun,Jun Wang
机构: Baidu Inc.(百度公司); Nanjing University(南京大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 10 figures
Abstract:In controllable driving-scene reconstruction and 3D scene generation, maintaining geometric fidelity while synthesizing visually plausible appearance under large viewpoint shifts is crucial. However, effective fusion of geometry-based 3DGS and appearance-driven diffusion models faces inherent challenges, as the absence of pixel-wise, 3D-consistent editing criteria often leads to over-restoration and geometric drift. To address these issues, we introduce \textbfFaithFusion, a 3DGS-diffusion fusion framework driven by pixel-wise Expected Information Gain (EIG). EIG acts as a unified policy for coherent spatio-temporal synthesis: it guides diffusion as a spatial prior to refine high-uncertainty regions, while its pixel-level weighting distills the edits back into 3DGS. The resulting plug-and-play system is free from extra prior conditions and structural this http URL experiments on the Waymo dataset demonstrate that our approach attains SOTA performance across NTA-IoU, NTL-IoU, and FID, maintaining an FID of 107.47 even at 6 meters lane shift. Our code is available at this https URL.
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[CV-71] EM-KD: Distilling Efficient Multimodal Large Language Model with Unbalanced Vision Tokens AAAI2026
【速读】：该论文旨在解决高效多模态大语言模型（Efficient Multimodal Large Language Models, Efficient MLLMs）在压缩视觉标记（vision tokens）以降低资源消耗时，因视觉信息丢失而导致的细粒度视觉理解能力下降问题。现有知识蒸馏（Knowledge Distillation）方法虽尝试提升学生模型性能，但忽略了高效学生模型与原始教师模型之间视觉标记分布不均衡所引发的细粒度理解差异。其解决方案的关键在于提出EM-KD范式，通过计算教师与学生模型视觉logits之间的曼哈顿距离（Manhattan distance），并利用匈牙利匹配算法（Hungarian matching algorithm）在空间维度上对齐视觉标记；随后引入两种蒸馏策略：视觉-语言亲和力蒸馏（Vision-Language Affinity Distillation, VLAD）和视觉语义蒸馏（Vision Semantic Distillation, VSD），分别优化文本与视觉标记间的亲和矩阵以及最终层视觉logits的离散概率分布，从而有效缓解因视觉标记不平衡导致的性能退化问题。
链接: https://arxiv.org/abs/2511.21106
作者: Ze Feng,Sen Yang,Boqiang Duan,Wankou Yang,Jingdong Wang
机构: Baidu VIS (百度视觉智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted by AAAI 2026
Abstract:Efficient Multimodal Large Language Models (MLLMs) compress vision tokens to reduce resource consumption, but the loss of visual information can degrade comprehension capabilities. Although some priors introduce Knowledge Distillation to enhance student models, they overlook the fundamental differences in fine-grained vision comprehension caused by unbalanced vision tokens between the efficient student and vanilla teacher. In this paper, we propose EM-KD, a novel paradigm that enhances the Efficient MLLMs with Knowledge Distillation. To overcome the challenge of unbalanced vision tokens, we first calculate the Manhattan distance between the vision logits of teacher and student, and then align them in the spatial dimension with the Hungarian matching algorithm. After alignment, EM-KD introduces two distillation strategies: 1) Vision-Language Affinity Distillation (VLAD) and 2) Vision Semantic Distillation (VSD). Specifically, VLAD calculates the affinity matrix between text tokens and aligned vision tokens, and minimizes the smooth L1 distance of the student and the teacher affinity matrices. Considering the semantic richness of vision logits in the final layer, VSD employs the reverse KL divergence to measure the discrete probability distributions of the aligned vision logits over the vocabulary space. Comprehensive evaluation on diverse benchmarks demonstrates that EM-KD trained model outperforms prior Efficient MLLMs on both accuracy and efficiency with a large margin, validating its effectiveness. Compared with previous distillation methods, which are equipped with our proposed vision token matching strategy for fair comparison, EM-KD also achieves better performance.
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[CV-72] Scaling Foundation Models for Radar Scene Understanding
【速读】：该论文旨在解决雷达感知领域中缺乏统一表征学习框架的问题，现有方法多为任务特定且碎片化，难以实现跨任务迁移。其解决方案的关键在于提出 RadarFM——一种基于结构化空间语言监督的雷达基础模型（Radar Foundation Model），通过两个核心创新实现：一是设计了在原始雷达坐标系下编码车辆分布的结构化描述框架，二是引入哈希感知的对比学习目标，以量化连续场景相似性而非二值匹配，从而支持细粒度的空间推理能力。
链接: https://arxiv.org/abs/2511.21105
作者: Pushkal Mishra,Kshitiz Bansal,Dinesh Bharadia
机构: University of California San Diego (加州大学圣地亚哥分校); Blue River Technology (蓝河科技)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Radar sensors provide reliable perception across adverse weather, lighting, and long-range conditions. Recent advances in foundation models have transformed visual and language understanding, yet their integration with radar sensing remains largely underexplored. Existing radar approaches are fragmented and task-specific; each downstream task employs distinct architectures and training objectives, preventing transfer across tasks. In this work, we introduce RadarFM: a radar foundation model that learns unified scene-level representations through structured spatial language supervision. We make two key contributions: (1) a structured caption framework that encodes vehicle distributions in native radar coordinates, and (2) a hash-aware contrastive learning objective that quantifies continuous scene similarity rather than binary matching, enabling fine-grained spatial reasoning. Leveraging the CARLA simulator, we generate large-scale, well-annotated radar datasets across diverse driving scenarios. We also propose localization-aware metrics that assess spatial accuracy beyond traditional detection measures.
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[CV-73] Pygmalion Effect in Vision: Image-to-Clay Translation for Reflective Geometry Reconstruction
【速读】：该论文旨在解决3D重建中由视图依赖性反射导致的外观与几何信息纠缠问题，这使得在存在复杂反射的情况下准确恢复物体几何结构变得极为困难。解决方案的关键在于提出了一种名为“视觉中的普里阿姆利翁效应”（Pygmalion Effect in Vision）的新框架，其核心思想是通过图像到“陶土状”（clay-like）形态的转换，将反射物体“雕塑”为无反射的中性形式；具体而言，该方法设计了一个双分支网络：一个基于BRDF（双向反射分布函数，Bidirectional Reflectance Distribution Function）的反射分支用于建模镜面反射特性，另一个陶土引导分支则通过提供无反射的监督信号来稳定几何结构并优化表面法向量。两个分支联合训练，利用合成的陶土状图像作为中性监督信号，从而有效抑制镜面线索并保持几何一致性，显著提升了法向量精度和网格完整性。
链接: https://arxiv.org/abs/2511.21098
作者: Gayoung Lee,Junho Kim,Jin-Hwa Kim,Junmo Kim
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR)
备注:
Abstract:Understanding reflection remains a long-standing challenge in 3D reconstruction due to the entanglement of appearance and geometry under view-dependent reflections. In this work, we present the Pygmalion Effect in Vision, a novel framework that metaphorically “sculpts” reflective objects into clay-like forms through image-to-clay translation. Inspired by the myth of Pygmalion, our method learns to suppress specular cues while preserving intrinsic geometric consistency, enabling robust reconstruction from multi-view images containing complex reflections. Specifically, we introduce a dual-branch network in which a BRDF-based reflective branch is complemented by a clay-guided branch that stabilizes geometry and refines surface normals. The two branches are trained jointly using the synthesized clay-like images, which provide a neutral, reflection-free supervision signal that complements the reflective views. Experiments on both synthetic and real datasets demonstrate substantial improvement in normal accuracy and mesh completeness over existing reflection-handling methods. Beyond technical gains, our framework reveals that seeing by unshining, translating radiance into neutrality, can serve as a powerful inductive bias for reflective object geometry learning.
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[CV-74] CLRecogEye : Curriculum Learning towards exploiting convolution features for Dynamic Iris Recognition
【速读】：该论文旨在解决虹膜识别算法在实际应用中面临的鲁棒性不足问题，尤其是由旋转、尺度变化、镜面反射和散焦模糊等因素引起的性能下降。现有方法多依赖于简单的点对点匹配策略（如余弦或L2距离），未能有效利用虹膜图像的时空结构信息。其解决方案的关键在于提出一种新型且通用的匹配流程，通过将虹膜图像沿一维分割为子图像序列并输入3D卷积神经网络（3D-CNN），从而学习丰富的时空特征表示；同时采用课程学习（curriculum learning）策略训练模型，使网络能够直接在特征空间中嵌入时间依赖关系，增强深度度量空间中的判别能力。该框架结合三元组损失（triplet loss）与ArcFace损失端到端优化，显著提升了在复杂条件下的虹膜识别鲁棒性和泛化性能。
链接: https://arxiv.org/abs/2511.21097
作者: Geetanjali Sharma,Gaurav Jaswal,Aditya Nigam,Raghavendra Ramachandra
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 12 Pages, 3 figures, ISVC conference 2025
Abstract:Iris authentication algorithms have achieved impressive recognition performance, making them highly promising for real-world applications such as border control, citizen identification, and both criminal investigations and commercial systems. However, their robustness is still challenged by variations in rotation, scale, specular reflections, and defocus blur. In addition, most existing approaches rely on straightforward point-to-point comparisons, typically using cosine or L2 distance, without effectively leveraging the spatio-spatial-temporal structure of iris patterns. To address these limitations, we propose a novel and generalized matching pipeline that learns rich spatio-spatial-temporal representations of iris features. Our approach first splits each iris image along one dimension, generating a sequence of sub-images that serve as input to a 3D-CNN, enabling the network to capture both spatial and spatio-spatial-temporal cues. To further enhance the modeling of spatio-spatial-temporal feature dynamics, we train the model in curriculum manner. This design allows the network to embed temporal dependencies directly into the feature space, improving discriminability in the deep metric domain. The framework is trained end-to-end with triplet and ArcFace loss in a curriculum manner, enforcing highly discriminative embeddings despite challenges like rotation, scale, reflections, and blur. This design yields a robust and generalizable solution for iris this http URL code: this https URL
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[CV-75] MIRA: Multimodal Iterative Reasoning Agent for Image Editing
【速读】：该论文旨在解决扩散模型在指令引导图像编辑中对复杂用户指令理解不足的问题，尤其针对涉及组合关系、上下文线索或指代表达的指令，常导致语义漂移或编辑意图未能准确实现。解决方案的关键在于提出一种轻量级、即插即用的多模态推理代理MIRA（Multimodal Iterative Reasoning Agent），其通过迭代的感知-推理-动作循环模拟多轮人机交互过程，逐步预测原子级编辑指令，并利用视觉反馈动态调整决策，从而提升编辑的语义一致性和感知质量。
链接: https://arxiv.org/abs/2511.21087
作者: Ziyun Zeng,Hang Hua,Jiebo Luo
机构: University of Rochester (罗切斯特大学); MIT-IBM Watson AI Lab (MIT-IBM沃森人工智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Instruction-guided image editing offers an intuitive way for users to edit images with natural language. However, diffusion-based editing models often struggle to accurately interpret complex user instructions, especially those involving compositional relationships, contextual cues, or referring expressions, leading to edits that drift semantically or fail to reflect the intended changes. We tackle this problem by proposing MIRA (Multimodal Iterative Reasoning Agent), a lightweight, plug-and-play multimodal reasoning agent that performs editing through an iterative perception-reasoning-action loop, effectively simulating multi-turn human-model interaction processes. Instead of issuing a single prompt or static plan, MIRA predicts atomic edit instructions step by step, using visual feedback to make its decisions. Our 150K multimodal tool-use dataset, MIRA-Editing, combined with a two-stage SFT + GRPO training pipeline, enables MIRA to perform reasoning and editing over complex editing instructions. When paired with open-source image editing models such as Flux.1-Kontext, Step1X-Edit, and Qwen-Image-Edit, MIRA significantly improves both semantic consistency and perceptual quality, achieving performance comparable to or exceeding proprietary systems such as GPT-Image and Nano-Banana.
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[CV-76] OVOD-Agent : A Markov-Bandit Framework for Proactive Visual Reasoning and Self-Evolving Detection
【速读】：该论文旨在解决开放词汇目标检测（Open-Vocabulary Object Detection, OVOD）中因文本表示空间未充分探索而导致的类别泛化能力不足问题，尤其是现有方法在训练时依赖多模态数据但推理阶段仍受限于固定类别名称，造成模态间不一致。解决方案的关键在于提出OVOD-Agent框架，将被动的类别匹配机制转变为基于视觉推理的主动探测与自我进化过程：通过引入可解释的视觉思维链（Visual-CoT），以显式动作形式扩展文本优化流程；并利用弱马尔可夫决策过程（Weakly Markovian Decision Process, w-MDP）建模视觉上下文状态转移，结合Bandit模块生成探索信号引导模型聚焦不确定区域，最终通过马尔可夫转移矩阵与Bandit轨迹联合优化自监督奖励模型（Reward Model, RM），形成从探索到学习的闭环机制，显著提升对稀有类别的检测性能。
链接: https://arxiv.org/abs/2511.21064
作者: Chujie Wang,Jianyu Lu,Zhiyuan Luo,Xi Chen,Chu He
机构: Wuhan University (武汉大学)
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Open-Vocabulary Object Detection (OVOD) aims to enable detectors to generalize across categories by leveraging semantic information. Although existing methods are pretrained on large vision-language datasets, their inference is still limited to fixed category names, creating a gap between multimodal training and unimodal inference. Previous work has shown that improving textual representation can significantly enhance OVOD performance, indicating that the textual space is still underexplored. To this end, we propose OVOD-Agent, which transforms passive category matching into proactive visual reasoning and self-evolving detection. Inspired by the Chain-of-Thought (CoT) paradigm, OVOD-Agent extends the textual optimization process into an interpretable Visual-CoT with explicit actions. OVOD’s lightweight nature makes LLM-based management unsuitable; instead, we model visual context transitions as a Weakly Markovian Decision Process (w-MDP) over eight state spaces, which naturally represents the agent’s state, memory, and interaction dynamics. A Bandit module generates exploration signals under limited supervision, helping the agent focus on uncertain regions and adapt its detection policy. We further integrate Markov transition matrices with Bandit trajectories for self-supervised Reward Model (RM) optimization, forming a closed loop from Bandit exploration to RM learning. Experiments on COCO and LVIS show that OVOD-Agent provides consistent improvements across OVOD backbones, particularly on rare categories, confirming the effectiveness of the proposed framework.
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[CV-77] Long-Term Alzheimers Disease Prediction: A Novel Image Generation Method Using Temporal Parameter Estimation with Normal Inverse Gamma Distribution on Uneven Time Series
【速读】：该论文旨在解决长时序图像生成中因时间间隔不规则导致疾病相关特征难以保持的问题，尤其是在阿尔茨海默病（Alzheimer’s Disease, AD）的影像预测任务中。其解决方案的关键在于提出一种基于时间参数估计的正态逆伽马分布模型（Temporal Normal Inverse Gamma Distribution, T-NIG），通过在分布中引入时间参数来建模脑部影像序列中特征随时间变化的动态规律，并利用坐标邻域特征提取机制增强对不规则时间点间图像演变的理解。此外，T-NIG通过不确定性估计有效降低模型中的认知不确定性（epistemic uncertainty）和随机不确定性（aleatoric uncertainty），从而提升在稀疏或不规则时间数据下的长期预测性能，确保生成图像在时间跨度上仍能准确反映疾病进展特征。
链接: https://arxiv.org/abs/2511.21057
作者: Xin Hong,Xinze Sun,Yinhao Li,Yen-Wei Chen
机构: Huaqiao University (华侨大学); Ritsumeikan University (立命馆大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 13pages, 6 figures
Abstract:Image generation can provide physicians with an imaging diagnosis basis in the prediction of Alzheimer’s Disease (AD). Recent research has shown that long-term AD predictions by image generation often face difficulties maintaining disease-related characteristics when dealing with irregular time intervals in sequential data. Considering that the time-related aspects of the distribution can reflect changes in disease-related characteristics when images are distributed unevenly, this research proposes a model to estimate the temporal parameter within the Normal Inverse Gamma Distribution (T-NIG) to assist in generating images over the long term. The T-NIG model employs brain images from two different time points to create intermediate brain images, forecast future images, and predict the disease. T-NIG is designed by identifying features using coordinate neighborhoods. It incorporates a time parameter into the normal inverse gamma distribution to understand how features change in brain imaging sequences that have varying time intervals. Additionally, T-NIG utilizes uncertainty estimation to reduce both epistemic and aleatoric uncertainties in the model, which arise from insufficient temporal data. In particular, the T-NIG model demonstrates state-of-the-art performance in both short-term and long-term prediction tasks within the dataset. Experimental results indicate that T-NIG is proficient in forecasting disease progression while maintaining disease-related characteristics, even when faced with an irregular temporal data distribution.
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[CV-78] AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios AAAI2026
【速读】：该论文旨在解决当前Referring Multi-Object Tracking (RMOT)研究主要局限于地面场景的问题，从而限制了智能机器人系统对大尺度场景上下文的感知能力及综合跟踪与路径规划性能。为应对这一挑战，作者提出AerialMind——首个面向无人机（UAV）场景的大规模RMOT基准数据集，并开发了半自动化协作式代理标注辅助框架COALA以降低人工标注成本并保证质量；同时提出HawkEyeTrack（HETrack）方法，通过协同增强视觉-语言表征学习来提升无人机场景下的感知能力。其解决方案的关键在于构建适用于空中平台的RMOT数据集与高效标注机制，并设计融合多模态信息的跟踪模型以实现自然语言引导下的精准目标检测与追踪。
链接: https://arxiv.org/abs/2511.21053
作者: Chenglizhao Chen,Shaofeng Liang,Runwei Guan,Xiaolou Sun,Haocheng Zhao,Haiyun Jiang,Tao Huang,Henghui Ding,Qing-Long Han
机构: 1. Hong Kong University of Science and Technology (Guangzhou) (香港科技大学(广州)); 2. Institute of Artificial Intelligence, Chinese Academy of Sciences (中国科学院人工智能研究院); 3. School of Computer Science and Engineering, Sun Yat-sen University (中山大学计算机科学与工程学院); 4. School of Information Science and Engineering, Central South University (中南大学信息科学与工程学院); 5. School of Software, Tsinghua University (清华大学软件学院); 6. School of Computer Science, Fudan University (复旦大学计算机科学系); 7. School of Data Science, The Chinese University of Hong Kong (深圳) (香港中文大学(深圳)数据科学学院); 8. School of Cyber Science and Engineering, Huazhong University of Science and Technology (华中科技大学网络科学与工程学院); 9. School of Electrical and Electronic Engineering, Nanyang Technological University (新加坡南洋理工大学电气与电子工程学院)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: AAAI 2026
Abstract:Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current RMOT research remains mostly confined to ground-level scenarios, which constrains their ability to capture broad-scale scene contexts and perform comprehensive tracking and path planning. In contrast, Unmanned Aerial Vehicles (UAVs) leverage their expansive aerial perspectives and superior maneuverability to enable wide-area surveillance. Moreover, UAVs have emerged as critical platforms for Embodied Intelligence, which has given rise to an unprecedented demand for intelligent aerial systems capable of natural language interaction. To this end, we introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios, which aims to bridge this research gap. To facilitate its construction, we develop an innovative semi-automated collaborative agent-based labeling assistant (COALA) framework that significantly reduces labor costs while maintaining annotation quality. Furthermore, we propose HawkEyeTrack (HETrack), a novel method that collaboratively enhances vision-language representation learning and improves the perception of UAV scenarios. Comprehensive experiments validated the challenging nature of our dataset and the effectiveness of our method.
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[CV-79] MUSE: Manipulating Unified Framework for Synthesizing Emotions in Images via Test-Time Optimization
【速读】：该论文旨在解决图像情感合成（Image Emotional Synthesis, IES）中生成与编辑任务被人为分离导致的效率低下问题，尤其是在治疗干预或叙事等应用场景中，生成与编辑往往需协同进行。解决方案的关键在于提出MUSE框架，其核心创新是采用类测试时扩展（Test-Time Scaling, TTS）策略，在无需额外微调扩散模型或专用情感合成数据集的前提下，实现情感引导的统一生成与编辑。具体而言，MUSE通过三个关键机制实现稳定且可控的情感合成：(1) 利用现成的情感分类器，结合基于梯度优化的情感标记（emotional tokens）实现稳定引导；(2) 基于语义相似性识别最优情感引导时机；(3) 采用多情感损失函数降低相近情绪间的干扰。该方法显著提升了情感准确性、语义多样性，并在内容一致性、文本提示遵循度和真实情感表达之间取得良好平衡。
链接: https://arxiv.org/abs/2511.21051
作者: Yingjie Xia,Xi Wang,Jinglei Shi,Vicky Kalogeiton,Jian Yang
机构: Nankai University (南开大学); Ecole Polytechnique (巴黎综合理工学院); Dalian University of Technology (大连理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Images evoke emotions that profoundly influence perception, often prioritized over content. Current Image Emotional Synthesis (IES) approaches artificially separate generation and editing tasks, creating inefficiencies and limiting applications where these tasks naturally intertwine, such as therapeutic interventions or storytelling. In this work, we introduce MUSE, the first unified framework capable of both emotional generation and editing. By adopting a strategy conceptually aligned with Test-Time Scaling (TTS) that widely used in both LLM and diffusion model communities, it avoids the requirement for additional updating diffusion model and specialized emotional synthesis datasets. More specifically, MUSE addresses three key questions in emotional synthesis: (1) HOW to stably guide synthesis by leveraging an off-the-shelf emotion classifier with gradient-based optimization of emotional tokens; (2) WHEN to introduce emotional guidance by identifying the optimal timing using semantic similarity as a supervisory signal; and (3) WHICH emotion to guide synthesis through a multi-emotion loss that reduces interference from inherent and similar emotions. Experimental results show that MUSE performs favorably against all methods for both generation and editing, improving emotional accuracy and semantic diversity while maintaining an optimal balance between desired content, adherence to text prompts, and realistic emotional expression. It establishes a new paradigm for emotion synthesis.
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[CV-80] PG-ControlNet: A Physics-Guided ControlNet for Generative Spatially Varying Image Deblurring
【速读】：该论文旨在解决空间变化图像去模糊（spatially varying image deblurring）这一根本上病态的问题，尤其在复杂运动模糊与其他模糊形式混合且存在显著噪声的情况下。现有基于学习的方法主要分为两类：一类是基于模型的深度展开方法，虽能引入物理约束但常产生过度平滑和伪影；另一类是生成模型，虽具优异感知质量却因物理约束弱而出现细节幻觉。论文提出了一种新颖框架，通过显式、密集的物理约束来驾驭强大的生成先验，从而实现二者统一。其关键创新在于将退化场建模为高维压缩核的稠密连续体，以捕捉细微的运动与退化模式变化，并利用该丰富描述场条件化ControlNet架构，强引导扩散采样过程，从而在物理准确性与感知真实性之间取得平衡。
链接: https://arxiv.org/abs/2511.21043
作者: Hakki Motorcu,Mujdat Cetin
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages, 4 figures
Abstract:Spatially varying image deblurring remains a fundamentally ill-posed problem, especially when degradations arise from complex mixtures of motion and other forms of blur under significant noise. State-of-the-art learning-based approaches generally fall into two paradigms: model-based deep unrolling methods that enforce physical constraints by modeling the degradations, but often produce over-smoothed, artifact-laden textures, and generative models that achieve superior perceptual quality yet hallucinate details due to weak physical constraints. In this paper, we propose a novel framework that uniquely reconciles these paradigms by taming a powerful generative prior with explicit, dense physical constraints. Rather than oversimplifying the degradation field, we model it as a dense continuum of high-dimensional compressed kernels, ensuring that minute variations in motion and other degradation patterns are captured. We leverage this rich descriptor field to condition a ControlNet architecture, strongly guiding the diffusion sampling process. Extensive experiments demonstrate that our method effectively bridges the gap between physical accuracy and perceptual realism, outperforming state-of-the-art model-based methods as well as generative baselines in challenging, severely blurred scenarios.
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[CV-81] LungNoduleAgent : A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules AAAI2026
【速读】：该论文旨在解决当前基于多模态大语言模型（Multimodal Large Language Models）在肺部CT影像中对肺结节（Lung Nodule）形态描述不准确、缺乏临床医学专家知识融合的问题，从而影响诊断的可靠性与临床实用性。其解决方案的关键在于提出一种名为LungNoduleAgent的协作式多智能体系统（Collaborative Multi-Agent System），通过将诊断流程分解为三个模块：Nodule Spotter负责精准定位结节，Radiologist模块结合局部图像描述生成结构化报告，Doctor Agent System则利用病理知识库和多智能体框架进行恶性程度推理。该设计实现了区域级语义对齐（Region-Level Semantic Alignment）与多智能体协同决策，显著提升了肺结节分析的准确性与临床可解释性。
链接: https://arxiv.org/abs/2511.21042
作者: Cheng Yang,Hui Jin,Xinlei Yu,Zhipeng Wang,Yaoqun Liu,Fenglei Fan,Dajiang Lei,Gangyong Jia,Changmiao Wang,Ruiquan Ge
机构: Hangzhou Dianzi University (杭州电子科技大学); Hefei University of Technology (合肥工业大学); China University of Geosciences (武汉) (中国地质大学（武汉）); Beijing Institute of Technology (北京理工大学); Zhejiang University (浙江大学); Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by AAAI 2026
Abstract:Diagnosing lung cancer typically involves physicians identifying lung nodules in Computed tomography (CT) scans and generating diagnostic reports based on their morphological features and medical expertise. Although advancements have been made in using multimodal large language models for analyzing lung CT scans, challenges remain in accurately describing nodule morphology and incorporating medical expertise. These limitations affect the reliability and effectiveness of these models in clinical settings. Collaborative multi-agent systems offer a promising strategy for achieving a balance between generality and precision in medical applications, yet their potential in pathology has not been thoroughly explored. To bridge these gaps, we introduce LungNoduleAgent, an innovative collaborative multi-agent system specifically designed for analyzing lung CT scans. LungNoduleAgent streamlines the diagnostic process into sequential components, improving precision in describing nodules and grading malignancy through three primary modules. The first module, the Nodule Spotter, coordinates clinical detection models to accurately identify nodules. The second module, the Radiologist, integrates localized image description techniques to produce comprehensive CT reports. Finally, the Doctor Agent System performs malignancy reasoning by using images and CT reports, supported by a pathology knowledge base and a multi-agent system framework. Extensive testing on two private datasets and the public LIDC-IDRI dataset indicates that LungNoduleAgent surpasses mainstream vision-language models, agent systems, and advanced expert models. These results highlight the importance of region-level semantic alignment and multi-agent collaboration in diagnosing nodules. LungNoduleAgent stands out as a promising foundational tool for supporting clinical analyses of lung nodules.
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[CV-82] CNN-LSTM Hybrid Architecture for Over-the-Air Automatic Modulation Classification Using SDR
【速读】：该论文旨在解决自动调制识别（Automatic Modulation Classification, AMC）问题，即在无先验知识的情况下准确识别无线通信信号的调制方式，以支持认知无线电、频谱监测和智能通信网络等应用场景。解决方案的关键在于提出了一种融合卷积神经网络（Convolutional Neural Network, CNN）与长短期记忆网络（Long Short-Term Memory, LSTM）的混合架构，其中CNN负责提取信号的空间特征，LSTM用于建模时变信号的时间依赖性，从而有效处理复杂且动态变化的通信信号；同时，系统基于软件定义无线电（Software Defined Radio, SDR）平台实现，并在包含RadioML2018数据集与自建数据集的混合训练集上进行优化，在0–30 dB信噪比范围内实现了93.48%的识别准确率，验证了该方法在噪声环境下的鲁棒性和实用性。
链接: https://arxiv.org/abs/2511.21040
作者: Dinanath Padhya,Krishna Acharya,Bipul Kumar Dahal,Dinesh Baniya Kshatri
机构: Thapathali Campus, Institute of Engineering, Tribhuvan University (特里布万大学工程学院)
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: 8 Pages, 10 figures, 2 Tables, Accepted in Journal (Journal of Innovations in Engineering Education), Issue is not Published Yet
Abstract:Automatic Modulation Classification (AMC) is a core technology for future wireless communication systems, enabling the identification of modulation schemes without prior knowledge. This capability is essential for applications in cognitive radio, spectrum monitoring, and intelligent communication networks. We propose an AMC system based on a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, integrated with a Software Defined Radio (SDR) platform. The proposed architecture leverages CNNs for spatial feature extraction and LSTMs for capturing temporal dependencies, enabling efficient handling of complex, time-varying communication signals. The system’s practical ability was demonstrated by identifying over-the-air (OTA) signals from a custom-built FM transmitter alongside other modulation schemes. The system was trained on a hybrid dataset combining the RadioML2018 dataset with a custom-generated dataset, featuring samples at Signal-to-Noise Ratios (SNRs) from 0 to 30dB. System performance was evaluated using accuracy, precision, recall, F1 score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The optimized model achieved 93.48% accuracy, 93.53% precision, 93.48% recall, and an F1 score of 93.45%. The AUC-ROC analysis confirmed the model’s discriminative power, even in noisy conditions. This paper’s experimental results validate the effectiveness of the hybrid CNN-LSTM architecture for AMC, suggesting its potential application in adaptive spectrum management and advanced cognitive radio systems.
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[CV-83] FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation
【速读】：该论文旨在解决音乐到舞蹈生成（music-to-dance generation）中现有方法生成效率低的问题，这一瓶颈限制了高保真3D渲染和角色表现力在真实场景中的应用。其解决方案的关键在于提出 FlowerDance 框架，该框架通过结合 MeanFlow 与物理一致性约束（Physical Consistency Constraints），实现仅需少量采样步骤即可生成具有物理合理性与艺术表现力的高质量动作；同时采用基于 BiMamba 的轻量高效骨干网络和通道级跨模态融合机制，以非自回归方式实现高速推理与低内存占用，并支持交互式动作编辑，从而在 AIST++ 和 FineDance 数据集上达到 Motion Quality 与 Generation Efficiency 的最先进水平。
链接: https://arxiv.org/abs/2511.21029
作者: Kaixing Yang,Xulong Tang,Ziqiao Peng,Xiangyue Zhang,Puwei Wang,Jun He,Hongyan Liu
机构: Renmin University of China (中国人民大学); Tsinghua University (清华大学); Wuhan University (武汉大学); Malou Tech Inc (马洛科技公司)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注:
Abstract:Music-to-dance generation aims to translate auditory signals into expressive human motion, with broad applications in virtual reality, choreography, and digital entertainment. Despite promising progress, the limited generation efficiency of existing methods leaves insufficient computational headroom for high-fidelity 3D rendering, thereby constraining the expressiveness of 3D characters during real-world applications. Thus, we propose FlowerDance, which not only generates refined motion with physical plausibility and artistic expressiveness, but also achieves significant generation efficiency on inference speed and memory utilization . Specifically, FlowerDance combines MeanFlow with Physical Consistency Constraints, which enables high-quality motion generation with only a few sampling steps. Moreover, FlowerDance leverages a simple but efficient model architecture with BiMamba-based backbone and Channel-Level Cross-Modal Fusion, which generates dance with efficient non-autoregressive manner. Meanwhile, FlowerDance supports motion editing, enabling users to interactively refine dance sequences. Extensive experiments on AIST++ and FineDance show that FlowerDance achieves state-of-the-art results in both motion quality and generation efficiency. Code will be released upon acceptance.
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[CV-84] CaptionQA: Is Your Caption as Useful as the Image Itself?
【速读】：该论文旨在解决当前图像描述（image captions）评估方法忽视其在下游任务中实际效用的问题，即现有评价体系无法准确衡量生成的caption是否能有效替代图像参与真实多模态任务。解决方案的关键在于提出一个基于实用性的基准测试框架CaptionQA，该框架通过构建细粒度领域相关的问题集（覆盖自然图像、文档、电商和具身AI四大领域，共69个子类），并要求大型语言模型（LLM）仅凭caption回答这些需依赖视觉信息的多项选择题，从而直接量化caption对下游任务的支持能力。此设计首次实现了对caption“图像级效用”的可测量性，揭示了主流多模态大模型在传统图像问答（Image QA）指标上表现相近时，在caption实用性上的显著差距（最高达32%下降）。
链接: https://arxiv.org/abs/2511.21025
作者: Shijia Yang,Yunong Liu,Bohan Zhai,Ximeng Sun,Zicheng Liu,Emad Barsoum,Manling Li,Chenfeng Xu
机构: Advanced Micro Devices, Inc. (超微半导体公司); Stanford University (斯坦福大学); Northwestern University (西北大学); UT Austin (德克萨斯大学奥斯汀分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic inference pipelines. Yet current evaluation practices miss a fundamental question: Can captions stand-in for images in real downstream tasks? We propose a utility-based benchmark, CaptionQA, to evaluate model-generated captions, where caption quality is measured by how well it supports downstream tasks. CaptionQA is an extensible domain-dependent benchmark covering 4 domains–Natural, Document, E-commerce, and Embodied AI–each with fine-grained taxonomies (25 top-level and 69 subcategories) that identify useful information for domain-specific tasks. CaptionQA builds 33,027 densely annotated multiple-choice questions (50.3 per image on average) that explicitly require visual information to answer, providing a comprehensive probe of caption utility. In our evaluation protocol, an LLM answers these questions using captions alone, directly measuring whether captions preserve image-level utility and are utilizable by a downstream LLM. Evaluating state-of-the-art MLLMs reveals substantial gaps between the image and its caption utility. Notably, models nearly identical on traditional image-QA benchmarks lower by up to 32% in caption utility. We release CaptionQA along with an open-source pipeline for extension to new domains. The code is available at this https URL.
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[CV-85] CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching
【速读】：该论文旨在解决文本引导的扩散模型在图像重饰（image retouching）中难以实现物理一致性且缺乏精确参数控制的问题，尤其是对曝光、白平衡、变焦等相机参数的精准调节能力不足。现有方法要么依赖模糊且纠缠的文本提示，导致相机控制精度差；要么为每个参数单独训练分支，牺牲了可扩展性、多参数组合能力以及对细微变化的敏感性。解决方案的关键在于提出 CameraMaster，一个统一的、相机感知的框架，其核心思想是显式解耦相机指令（camera directive）与相机参数嵌入（parameter embedding），并通过交叉注意力机制将两者协同注入内容特征和时间嵌入中，从而实现强相机敏感的语义上下文构建，并在去噪过程中进行分层统一调制，确保语义与参数的高度对齐。
链接: https://arxiv.org/abs/2511.21024
作者: Qirui Yang,Yang Yang,Ying Zeng,Xiaobin Hu,Bo Li,Huanjing Yue,Jingyu Yang,Peng-Tao Jiang
机构: Tianjin University (天津大学); vivo Mobile Communication Co., Ltd (维沃移动通信有限公司); NUS (新加坡国立大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Text-guided diffusion models have greatly advanced image editing and generation. However, achieving physically consistent image retouching with precise parameter control (e.g., exposure, white balance, zoom) remains challenging. Existing methods either rely solely on ambiguous and entangled text prompts, which hinders precise camera control, or train separate heads/weights for parameter adjustment, which compromises scalability, multi-parameter composition, and sensitivity to subtle variations. To address these limitations, we propose CameraMaster, a unified camera-aware framework for image retouching. The key idea is to explicitly decouple the camera directive and then coherently integrate two critical information streams: a directive representation that captures the photographer’s intent, and a parameter embedding that encodes precise camera settings. CameraMaster first uses the camera parameter embedding to modulate both the camera directive and the content semantics. The modulated directive is then injected into the content features via cross-attention, yielding a strongly camera-sensitive semantic context. In addition, the directive and camera embeddings are injected as conditioning and gating signals into the time embedding, enabling unified, layer-wise modulation throughout the denoising process and enforcing tight semantic-parameter alignment. To train and evaluate CameraMaster, we construct a large-scale dataset of 78K image-prompt pairs annotated with camera parameters. Extensive experiments show that CameraMaster produces monotonic and near-linear responses to parameter variations, supports seamless multi-parameter composition, and significantly outperforms existing methods.
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[CV-86] Structure-Aware Prototype Guided Trusted Multi-View Classification
【速读】：该论文旨在解决可信多视图分类（Trustworthy Multi-View Classification, TMVC）中因多源信息异构、不一致甚至冲突而导致决策不可靠的问题。现有方法通常依赖全局稠密邻域关系建模视图内依赖，计算开销大且难以保证跨视图关系的一致性；同时，通过人工赋权聚合多视图证据，无法确保学习到的多视图邻域结构在类别空间内具有一致性，从而削弱了分类结果的可信度。解决方案的关键在于引入原型（prototype）来表征每个视图的邻域结构，简化视图内邻域关系的学习，并实现视图内与视图间结构的动态对齐，从而更高效地发现跨视图共识，提升分类性能与鲁棒性。
链接: https://arxiv.org/abs/2511.21021
作者: Haojian Huang,Jiahao Shi,Zhe Liu,Harold Haodong Chen,Han Fang,Hao Sun,Zhongjiang He
机构: HKUST(GZ); Institute of Artificial Intelligence (TeleAI), China Telecom; Harbin Engineering University; Universiti Sains Malaysia
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 12 pages, 8 figures, 7 tables, Ongoing Work
Abstract:Trustworthy multi-view classification (TMVC) addresses the challenge of achieving reliable decision-making in complex scenarios where multi-source information is heterogeneous, inconsistent, or even conflicting. Existing TMVC approaches predominantly rely on globally dense neighbor relationships to model intra-view dependencies, leading to high computational costs and an inability to directly ensure consistency across inter-view relationships. Furthermore, these methods typically aggregate evidence from different views through manually assigned weights, lacking guarantees that the learned multi-view neighbor structures are consistent within the class space, thus undermining the trustworthiness of classification outcomes. To overcome these limitations, we propose a novel TMVC framework that introduces prototypes to represent the neighbor structures of each view. By simplifying the learning of intra-view neighbor relations and enabling dynamic alignment of intra- and inter-view structure, our approach facilitates more efficient and consistent discovery of cross-view consensus. Extensive experiments on multiple public multi-view datasets demonstrate that our method achieves competitive downstream performance and robustness compared to prevalent TMVC methods.
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[CV-87] Probabilistic Wildfire Spread Prediction Using an Autoregressive Conditional Generative Adversarial Network
【速读】：该论文旨在解决现有 wildfire spread 预测方法在实时决策中面临的两大挑战：一是物理模型（如 FARSITE）计算成本高，难以满足实时性需求；二是传统深度学习模型预测结果过于平滑，无法准确刻画野火传播的非线性动态与边界不确定性。解决方案的关键在于提出一种自回归条件生成对抗网络（autoregressive conditional generative adversarial network, CGAN），通过将预测任务建模为序列状态转移问题，实现长期稳定的时序演化预测；同时利用对抗学习机制捕捉野火传播中的强非线性特征和不确定性，而非仅拟合像素均值，从而显著提升预测精度与边界清晰度，增强模型的物理可解释性，为应急响应与疏散规划提供更可靠的时间敏感预测支持。
链接: https://arxiv.org/abs/2511.21019
作者: Taehoon Kang,Taeyong Kim
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computer Vision and Pattern Recognition (cs.CV)
备注: 22 pages, 15 figures, Submitted to Journal of Environmental Management
Abstract:Climate change has intensified the frequency and severity of wildfires, making rapid and accurate prediction of fire spread essential for effective mitigation and response. Physics-based simulators such as FARSITE offer high-fidelity predictions but are computationally intensive, limiting their applicability in real-time decision-making, while existing deep learning models often yield overly smooth predictions that fail to capture the complex, nonlinear dynamics of wildfire propagation. This study proposes an autoregressive conditional generative adversarial network (CGAN) for probabilistic wildfire spread prediction. By formulating the prediction task as an autoregressive problem, the model learns sequential state transitions, ensuring long-term prediction stability. Experimental results demonstrate that the proposed CGAN-based model outperforms conventional deep learning models in both overall predictive accuracy and boundary delineation of fire perimeters. These results demonstrate that adversarial learning allows the model to capture the strong nonlinearity and uncertainty of wildfire spread, instead of simply fitting the pixel average. Furthermore, the autoregressive framework facilitates systematic temporal forecasting of wildfire evolution. The proposed CGAN-based autoregressive framework enhances both the accuracy and physical interpretability of wildfire spread prediction, offering a promising foundation for time-sensitive response and evacuation planning.
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[CV-88] MetaRank: Task-Aware Metric Selection for Model Transferability Estimation
【速读】：该论文旨在解决迁移学习中预训练模型选择的效率与准确性问题，即如何在不进行全量微调的情况下，根据目标数据集特性自动选择最优的模型可迁移性评估（Model Transferability Estimation, MTE）指标。传统方法常依赖经验或单一指标的平均表现，忽视了MTE指标在不同任务上的显著差异性。其解决方案的关键在于提出MetaRank框架，将MTE指标选择建模为一个学习排序（learning-to-rank）问题：通过预训练语言模型对目标数据集和MTE指标的文本描述进行语义编码，映射至共享嵌入空间，并利用多样化的元任务训练一个元预测器，以捕捉数据特征与指标机制之间的复杂关系；该模型在离线阶段优化列表级目标，优先确保Top性能指标的正确排序，从而在在线阶段针对新目标数据集快速生成适配的MTE指标排名，实现任务感知的自动化指标选择。
链接: https://arxiv.org/abs/2511.21007
作者: Yuhang Liu,Wenjie Zhao,Yunhui Guo
机构: Fudan University (复旦大学); University of Texas at Dallas (德克萨斯大学达拉斯分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 figures
Abstract:Selecting an appropriate pre-trained source model is a critical, yet computationally expensive, task in transfer learning. Model Transferability Estimation (MTE) methods address this by providing efficient proxy metrics to rank models without full fine-tuning. In practice, the choice of which MTE metric to use is often ad hoc or guided simply by a metric’s average historical performance. However, we observe that the effectiveness of MTE metrics is highly task-dependent and no single metric is universally optimal across all target datasets. To address this gap, we introduce MetaRank, a meta-learning framework for automatic, task-aware MTE metric selection. We formulate metric selection as a learning-to-rank problem. Rather than relying on conventional meta-features, MetaRank encodes textual descriptions of both datasets and MTE metrics using a pretrained language model, embedding them into a shared semantic space. A meta-predictor is then trained offline on diverse meta-tasks to learn the intricate relationship between dataset characteristics and metric mechanisms, optimized with a listwise objective that prioritizes correctly ranking the top-performing metrics. During the subsequent online phase, MetaRank efficiently ranks the candidate MTE metrics for a new, unseen target dataset based on its textual description, enabling practitioners to select the most appropriate metric a priori. Extensive experiments across 11 pretrained models and 11 target datasets demonstrate the strong effectiveness of our approach.
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[CV-89] Knowledge Completes the Vision: A Multimodal Entity-aware Retrieval-Augmented Generation Framework for News Image Captioning AAAI2026
【速读】：该论文针对新闻图像描述生成任务中存在的三大挑战展开研究：(1) 信息覆盖不完整，(2) 跨模态对齐弱，(3) 视觉实体定位不佳。为解决这些问题，作者提出MERGE框架——首个面向新闻图像描述的多模态实体感知检索增强生成方法。其核心创新在于构建了一个以实体为中心的多模态知识库（Entity-centric Multimodal Knowledge Base, EMKB），融合文本、视觉与结构化知识以支持丰富背景信息的检索；同时通过分阶段假设-描述策略强化跨模态对齐，并利用基于图像内容动态引导的检索机制提升视觉实体匹配精度。实验表明，MERGE在GoodNews和NYTimes800k数据集上显著优于现有最优方法，在CIDEr和F1-score指标上均有明显提升，且在未见的Visual News数据集上展现出优异的泛化能力。
链接: https://arxiv.org/abs/2511.21002
作者: Xiaoxing You,Qiang Huang,Lingyu Li,Chi Zhang,Xiaopeng Liu,Min Zhang,Jun Yu
机构: 1. Shanghai Jiao Tong University (上海交通大学); 2. Alibaba Group (阿里巴巴集团); 3. Tencent (腾讯); 4. Tsinghua University (清华大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to AAAI 2026
Abstract:News image captioning aims to produce journalistically informative descriptions by combining visual content with contextual cues from associated articles. Despite recent advances, existing methods struggle with three key challenges: (1) incomplete information coverage, (2) weak cross-modal alignment, and (3) suboptimal visual-entity grounding. To address these issues, we introduce MERGE, the first Multimodal Entity-aware Retrieval-augmented GEneration framework for news image captioning. MERGE constructs an entity-centric multimodal knowledge base (EMKB) that integrates textual, visual, and structured knowledge, enabling enriched background retrieval. It improves cross-modal alignment through a multistage hypothesis-caption strategy and enhances visual-entity matching via dynamic retrieval guided by image content. Extensive experiments on GoodNews and NYTimes800k show that MERGE significantly outperforms state-of-the-art baselines, with CIDEr gains of +6.84 and +1.16 in caption quality, and F1-score improvements of +4.14 and +2.64 in named entity recognition. Notably, MERGE also generalizes well to the unseen Visual News dataset, achieving +20.17 in CIDEr and +6.22 in F1-score, demonstrating strong robustness and domain adaptability.
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[CV-90] From Inpainting to Layer Decomposition: Repurposing Generative Inpainting Models for Image Layer Decomposition
【速读】：该论文旨在解决单张图像中对象与背景的分层分解问题（layer decomposition），这是实现灵活内容编辑的关键前提，但受限于现有方法和数据不足而难以实现。其解决方案的核心在于：首先，利用扩散模型（diffusion-based model）进行图像修复（inpainting）任务的轻量级微调，从而适配分层分解任务；其次，提出一种新颖的多模态上下文融合模块（multi-modal context fusion module），通过线性注意力复杂度设计，在潜在空间中更有效地保留细节信息，从而提升物体移除和遮挡恢复的效果。该方法在纯合成数据集上训练，无需人工标注，显著优于现有方法，为下游编辑和创意应用开辟了新路径。
链接: https://arxiv.org/abs/2511.20996
作者: Jingxi Chen,Yixiao Zhang,Xiaoye Qian,Zongxia Li,Cornelia Fermuller,Caren Chen,Yiannis Aloimonos
机构: University of Maryland (马里兰大学); Amazon (亚马逊)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Images can be viewed as layered compositions, foreground objects over background, with potential occlusions. This layered representation enables independent editing of elements, offering greater flexibility for content creation. Despite the progress in large generative models, decomposing a single image into layers remains challenging due to limited methods and data. We observe a strong connection between layer decomposition and in/outpainting tasks, and propose adapting a diffusion-based inpainting model for layer decomposition using lightweight finetuning. To further preserve detail in the latent space, we introduce a novel multi-modal context fusion module with linear attention complexity. Our model is trained purely on a synthetic dataset constructed from open-source assets and achieves superior performance in object removal and occlusion recovery, unlocking new possibilities in downstream editing and creative applications.
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[CV-91] GuardTrace-VL: Detecting Unsafe Multimodel Reasoning via Iterative Safety Supervision
【速读】：该论文旨在解决多模态大推理模型（Multimodal Large Reasoning Models, MLRMs）在视觉-语言任务中产生的中间推理过程（reasoning traces）可能包含有害内容的问题，而现有安全防护机制仅评估输入问题和最终答案，忽略了推理阶段潜在的风险，如偏见推理或违反政策的视觉上下文使用。解决方案的关键在于提出GuardTrace-VL，一个基于视觉感知的安全审计器，通过联合图像与文本分析对完整的“问题-思考-答案”（Question-Thinking-Answer, QTA）管道进行监控，实现对推理过程中不安全内容的实时检测。其核心创新包括构建GuardTrace数据集（通过多样化提示策略生成并经MLRM与人工投票精炼），以及采用三阶段渐进式训练方案，使模型能够根据风险等级学习细粒度、情境依赖的安全偏好，从而显著提升对不安全推理的检测性能（F1得分达93.1%，较前人方法提升13.5%）。
链接: https://arxiv.org/abs/2511.20994
作者: Yuxiao Xiang,Junchi Chen,Zhenchao Jin,Changtao Miao,Haojie Yuan,Qi Chu,Tao Gong,Nenghai Yu
机构: 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); The University of Hong Kong (香港大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:Multimodal large reasoning models (MLRMs) are increasingly deployed for vision-language tasks that produce explicit intermediate rationales. However, reasoning traces can contain unsafe content even when the final answer is non-harmful, creating deployment risks. Existing multimodal safety guards primarily evaluate only the input question and the final answer, neglecting the intermediate reasoning process. This oversight allows undetected harm, such as biased inferences or policy-violating use of visual context, to emerge during reasoning. We introduce GuardTrace-VL, a vision-aware safety auditor that monitors the full Question-Thinking-Answer (QTA) pipeline via joint image-text analysis, enabling detection of unsafe content as it emerges in the reasoning stage. To support training and evaluation, we construct the GuardTrace dataset, which is generated through diverse prompting strategies and refined via a MLRM- and human-based voting and verification pipeline. Furthermore, we propose a three-stage progressive training scheme combined with the data refinement process, enabling the model to learn nuanced and context-dependent safety preferences according to different risk levels. On our proposed test set covering both in-domain and out-of-domain scenarios, GuardTrace-VL model achieves an F1 score of 93.1% on unsafe reasoning detection tasks, representing a 13.5% improvement in F1 score compared to the previous strongest multimodal safety defense methods. The codes will be made publicly available.
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[CV-92] Wavefront-Constrained Passive Obscured Object Detection
【速读】：该论文旨在解决在低信噪比条件下，从视场外微弱光信号中准确定位与分割被遮挡物体的难题，该问题因多重散射和介质诱导扰动而尤为复杂。现有基于实值建模或局部卷积操作的方法难以捕捉相干光传播的物理本质，且易收敛至非物理解，导致观测稳定性与可靠性严重下降。其解决方案的关键在于提出一种物理驱动的波前传播补偿网络（Wavefront Propagating Compensation Network, WavePCNet），核心创新包括：引入三相波前复数传播重投影（Tri-Phase Wavefront Complex-Propagation Reprojection, TriWCP）以精确约束相干传播行为，结合动量记忆机制抑制扰动累积，并设计高频跨层补偿增强模块构建多尺度频率选择性路径，动态建模层间结构一致性，从而显著提升模型在复杂环境下的鲁棒性与可解释性。
链接: https://arxiv.org/abs/2511.20991
作者: Zhiwen Zheng,Yiwei Ouyang,Zhao Huang,Tao Zhang,Xiaoshuai Zhang,Huiyu Zhou,Wenwen Tang,Shaowei Jiang,Jin Liu,Xingru Huang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Accurately localizing and segmenting obscured objects from faint light patterns beyond the field of view is highly challenging due to multiple scattering and medium-induced perturbations. Most existing methods, based on real-valued modeling or local convolutional operations, are inadequate for capturing the underlying physics of coherent light propagation. Moreover, under low signal-to-noise conditions, these methods often converge to non-physical solutions, severely compromising the stability and reliability of the observation. To address these challenges, we propose a novel physics-driven Wavefront Propagating Compensation Network (WavePCNet) to simulate wavefront propagation and enhance the perception of obscured objects. This WavePCNet integrates the Tri-Phase Wavefront Complex-Propagation Reprojection (TriWCP) to incorporate complex amplitude transfer operators to precisely constrain coherent propagation behavior, along with a momentum memory mechanism to effectively suppress the accumulation of perturbations. Additionally, a High-frequency Cross-layer Compensation Enhancement is introduced to construct frequency-selective pathways with multi-scale receptive fields and dynamically model structural consistency across layers, further boosting the model’s robustness and interpretability under complex environmental conditions. Extensive experiments conducted on four physically collected datasets demonstrate that WavePCNet consistently outperforms state-of-the-art methods across both accuracy and robustness.
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[CV-93] RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detection
【速读】：该论文旨在解决参考式伪装目标检测（Referring Camouflaged Object Detection, Ref-COD）中依赖测试时参考图像的问题，该限制导致部署困难、延迟增加及数据采集负担加重。解决方案的关键在于：在训练阶段将参考信息蒸馏为类别原型记忆（class-prototype memory），并在推理阶段通过查询条件化的原型混合机制合成参考向量，从而无需任何测试时参考图像即可实现高效检测；同时引入双向注意力对齐模块（bidirectional attention alignment module）以弥合参考统计特征与伪装查询特征之间的表示差异，显著提升模型的泛化能力与实用性。
链接: https://arxiv.org/abs/2511.20989
作者: Yu-Huan Wu,Zi-Xuan Zhu,Yan Wang,Liangli Zhen,Deng-Ping Fan
机构: VCIP, CS, Nankai University (南开大学); IHPC, A*STAR, Singapore (新加坡科技研究局)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages, 5 figure, 6 tables
Abstract:Referring Camouflaged Object Detection (Ref-COD) segments specified camouflaged objects in a scene by leveraging a small set of referring images. Though effective, current systems adopt a dual-branch design that requires reference images at test time, which limits deployability and adds latency and data-collection burden. We introduce a Ref-COD framework that distills references into a class-prototype memory during training and synthesizes a reference vector at inference via a query-conditioned mixture of prototypes. Concretely, we maintain an EMA-updated prototype per category and predict mixture weights from the query to produce a guidance vector without any test-time references. To bridge the representation gap between reference statistics and camouflaged query features, we propose a bidirectional attention alignment module that adapts both the query features and the class representation. Thus, our approach yields a simple, efficient path to Ref-COD without mandatory references. We evaluate the proposed method on the large-scale R2C7K benchmark. Extensive experiments demonstrate competitive or superior performance of the proposed method compared with recent state-of-the-arts. Code is available at this https URL.
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[CV-94] Inversion-Free Style Transfer with Dual Rectified Flows
【速读】：该论文旨在解决当前训练-free扩散模型在图像风格迁移（style transfer）中依赖计算昂贵的反演（inversion）过程所导致的效率低下与视觉失真问题。其解决方案的关键在于提出一种基于双修正流（dual rectified flows）的无反演风格迁移框架，通过仅使用前向传播即可建模内容与风格分布，并在并行预测内容和风格轨迹的基础上，利用动态中点插值融合两者速度场，实现对目标风格化图像的稳健生成；同时引入注意力注入机制以增强风格整合效果，从而在保持内容结构的同时提升视觉保真度与计算效率。
链接: https://arxiv.org/abs/2511.20986
作者: Yingying Deng,Xiangyu He,Fan Tang,Weiming Dong,Xucheng Yin
机构: University of Science and Technology Beijing (北京科技大学); Chinese Academy of Sciences (中国科学院); University of Chinese Academy of Sciences (中国科学院大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Style transfer, a pivotal task in image processing, synthesizes visually compelling images by seamlessly blending realistic content with artistic styles, enabling applications in photo editing and creative design. While mainstream training-free diffusion-based methods have greatly advanced style transfer in recent years, their reliance on computationally inversion processes compromises efficiency and introduces visual distortions when inversion is inaccurate. To address these limitations, we propose a novel \textitinversion-free style transfer framework based on dual rectified flows, which tackles the challenge of finding an unknown stylized distribution from two distinct inputs (content and style images), \textitonly with forward pass. Our approach predicts content and style trajectories in parallel, then fuses them through a dynamic midpoint interpolation that integrates velocities from both paths while adapting to the evolving stylized image. By jointly modeling the content, style, and stylized distributions, our velocity field design achieves robust fusion and avoids the shortcomings of naive overlays. Attention injection further guides style integration, enhancing visual fidelity, content preservation, and computational efficiency. Extensive experiments demonstrate generalization across diverse styles and content, providing an effective and efficient pipeline for style transfer.
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[CV-95] Privacy-Preserving Federated Vision Transformer Learning Leverag ing Lightweight Homomorphic Encryption in Medical AI
【速读】：该论文旨在解决医疗领域联邦学习（Federated Learning, FL）中模型梯度隐私泄露的问题，尤其是在多机构协作进行组织病理学图像分类时，传统FL方法因传输未加密的模型梯度而易受重建攻击（reconstruction attacks），从而暴露敏感患者信息。解决方案的关键在于提出一种结合视觉Transformer（Vision Transformer, ViT）与同态加密（Homomorphic Encryption, HE）的隐私保护框架：利用ViT的CLS token作为紧凑的768维特征表示，并采用CKKS同态加密对这些token进行加密后上传至服务器，实现安全聚合；该设计不仅将通信开销降低30倍，还有效抵御了模型逆向攻击（如PSNR达52.26 dB、SSIM为0.999的高保真图像重构），同时支持在密文上直接执行推理，兼顾安全性与效率。
链接: https://arxiv.org/abs/2511.20983
作者: Al Amin,Kamrul Hasan,Liang Hong,Sharif Ullah
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR)
备注: 7 pages, 4 figures
Abstract:Collaborative machine learning across healthcare institutions promises improved diagnostic accuracy by leveraging diverse datasets, yet privacy regulations such as HIPAA prohibit direct patient data sharing. While federated learning (FL) enables decentralized training without raw data exchange, recent studies show that model gradients in conventional FL remain vulnerable to reconstruction attacks, potentially exposing sensitive medical information. This paper presents a privacy-preserving federated learning framework combining Vision Transformers (ViT) with homomorphic encryption (HE) for secure multi-institutional histopathology classification. The approach leverages the ViT CLS token as a compact 768-dimensional feature representation for secure aggregation, encrypting these tokens using CKKS homomorphic encryption before transmission to the server. We demonstrate that encrypting CLS tokens achieves a 30-fold communication reduction compared to gradient encryption while maintaining strong privacy guarantees. Through evaluation on a three-client federated setup for lung cancer histopathology classification, we show that gradients are highly susceptible to model inversion attacks (PSNR: 52.26 dB, SSIM: 0.999, NMI: 0.741), enabling near-perfect image reconstruction. In contrast, the proposed CLS-protected HE approach prevents such attacks while enabling encrypted inference directly on ciphertexts, requiring only 326 KB of encrypted data transmission per aggregation round. The framework achieves 96.12 percent global classification accuracy in the unencrypted domain and 90.02 percent in the encrypted domain.
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[CV-96] Beyond Realism: Learning the Art of Expressive Composition with StickerNet
【速读】：该论文旨在解决传统图像合成（image composition）任务中过度追求视觉真实性和语义合理性，而忽视了用户在实际内容创作平台上的表达意图与风格多样性的问题。现代用户更倾向于通过图像编辑实现艺术性、趣味性或社交互动性，而非单纯还原现实。为此，作者提出“表达式合成”（expressive composition）这一新任务，并设计了StickerNet框架作为解决方案——其关键在于采用两阶段建模：首先识别合成类型（composition type），再预测相应的放置参数（如透明度、掩码、位置和缩放比例）。区别于以往基于模拟对象放置的数据构建方式，StickerNet直接从一个匿名在线视觉创作平台上收集的180万次真实编辑行为中构建数据集，确保训练监督信号与用户社区验证的实际操作高度一致，从而有效捕捉复杂且模糊的表达意图。
链接: https://arxiv.org/abs/2511.20957
作者: Haoming Lu,David Kocharian,Humphrey Shi
机构: Picsart AI Research (Picsart人工智能研究); Picsart Inc. (Picsart公司); College of Computing (计算机学院); Georgia Institute of Technology (佐治亚理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:As a widely used operation in image editing workflows, image composition has traditionally been studied with a focus on achieving visual realism and semantic plausibility. However, in practical editing scenarios of the modern content creation landscape, many compositions are not intended to preserve realism. Instead, users of online platforms motivated by gaining community recognition often aim to create content that is more artistic, playful, or socially engaging. Taking inspiration from this observation, we define the expressive composition task, a new formulation of image composition that embraces stylistic diversity and looser placement logic, reflecting how users edit images on real-world creative platforms. To address this underexplored problem, we present StickerNet, a two-stage framework that first determines the composition type, then predicts placement parameters such as opacity, mask, location, and scale accordingly. Unlike prior work that constructs datasets by simulating object placements on real images, we directly build our dataset from 1.8 million editing actions collected on an anonymous online visual creation and editing platform, each reflecting user-community validated placement decisions. This grounding in authentic editing behavior ensures strong alignment between task definition and training supervision. User studies and quantitative evaluations show that StickerNet outperforms common baselines and closely matches human placement behavior, demonstrating the effectiveness of learning from real-world editing patterns despite the inherent ambiguity of the task. This work introduces a new direction in visual understanding that emphasizes expressiveness and user intent over realism.
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[CV-97] BUSTR: Breast Ultrasound Text Reporting with a Descriptor-Aware Vision-Language Model
【速读】：该论文旨在解决乳腺超声（Breast Ultrasound, BUS）自动报告生成（Radiology Report Generation, RRG）中因缺乏图像-报告配对数据集以及大语言模型可能产生幻觉（hallucinations）而导致的性能瓶颈问题。解决方案的关键在于提出一种多任务视觉-语言框架BUSTR，其不依赖于配对的图像-报告监督信号，而是通过结构化描述符（如BI-RADS、病理学和组织学特征）与放射组学（radiomics）特征构建报告；利用多头Swin编码器学习描述符感知的视觉表示，并通过联合优化token级交叉熵损失与输入输出表示间的余弦相似度对齐损失，在视觉与文本token层面实现跨模态对齐，从而在两个公开BUS数据集上显著提升自然语言生成指标及临床有效性指标，尤其在BI-RADS分类和病理目标上表现突出。
链接: https://arxiv.org/abs/2511.20956
作者: Rawa Mohammed,Mina Attin,Bryar Shareef
机构: University of Nevada, Las Vegas (内华达大学拉斯维加斯分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 13 pages, 2 figures, 6 tables
Abstract:Automated radiology report generation (RRG) for breast ultrasound (BUS) is limited by the lack of paired image-report datasets and the risk of hallucinations from large language models. We propose BUSTR, a multitask vision-language framework that generates BUS reports without requiring paired image-report supervision. BUSTR constructs reports from structured descriptors (e.g., BI-RADS, pathology, histology) and radiomics features, learns descriptor-aware visual representations with a multi-head Swin encoder trained using a multitask loss over dataset-specific descriptor sets, and aligns visual and textual tokens via a dual-level objective that combines token-level cross-entropy with a cosine-similarity alignment loss between input and output representations. We evaluate BUSTR on two public BUS datasets, BrEaST and BUS-BRA, which differ in size and available descriptors. Across both datasets, BUSTR consistently improves standard natural language generation metrics and clinical efficacy metrics, particularly for key targets such as BI-RADS category and pathology. Our results show that this descriptor-aware vision model, trained with a combined token-level and alignment loss, improves both automatic report metrics and clinical efficacy without requiring paired image-report data. The source code can be found at this https URL
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[CV-98] UruDendro4: A Benchmark Dataset for Automatic Tree-Ring Detection in Cross-Section Images of Pinus taeda L ICPR
【速读】：该论文旨在解决树木年轮（annual rings）自动检测与量化中的数据稀缺和精度不足问题，尤其是在基于横截面图像进行年木材积估算时的建模挑战。其核心解决方案是构建并公开了UruDendro4数据集，该数据集包含102张火炬松（Pinus taeda L.）木材横截面图像及其人工标注的年轮边界，且样本覆盖树干多个高度位置，从而支持体积化年木材积建模。此外，论文通过引入DeepCS-TRD方法作为基准，在该数据集上实现了高精度检测（mAP=0.838，mAR=0.782），并通过消融实验验证参数配置的有效性，进一步证明了该数据集有助于提升模型在年轮检测任务中的泛化能力。
链接: https://arxiv.org/abs/2511.20935
作者: Henry Marichal,Joaquin Blanco,Diego Passarella,Gregory Randall
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted at IEEE 15th International Conference on Pattern Recognition Systems (ICPRS-25)
Abstract:Tree-ring growth represents the annual wood increment for a tree, and quantifying it allows researchers to assess which silvicultural practices are best suited for each species. Manual measurement of this growth is time-consuming and often imprecise, as it is typically performed along 4 to 8 radial directions on a cross-sectional disc. In recent years, automated algorithms and datasets have emerged to enhance accuracy and automate the delineation of annual rings in cross-sectional images. To address the scarcity of wood cross-section data, we introduce the UruDendro4 dataset, a collection of 102 image samples of Pinus taeda L., each manually annotated with annual growth rings. Unlike existing public datasets, UruDendro4 includes samples extracted at multiple heights along the stem, allowing for the volumetric modeling of annual growth using manually delineated rings. This dataset (images and annotations) allows the development of volumetric models for annual wood estimation based on cross-sectional imagery. Additionally, we provide a performance baseline for automatic ring detection on this dataset using state-of-the-art methods. The highest performance was achieved by the DeepCS-TRD method, with a mean Average Precision of 0.838, a mean Average Recall of 0.782, and an Adapted Rand Error score of 0.084. A series of ablation experiments were conducted to empirically validate the final parameter configuration. Furthermore, we empirically demonstrate that training a learning model including this dataset improves the model’s generalization in the tree-ring detection task. Comments: Accepted at IEEE 15th International Conference on Pattern Recognition Systems (ICPRS-25) Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2511.20935 [cs.CV] (or arXiv:2511.20935v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2511.20935 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[CV-99] Guaranteed Optimal Compositional Explanations for Neurons
【速读】：该论文旨在解决当前生成组合式解释（compositional explanations）中缺乏理论最优性保障的问题，即如何在计算资源受限的情况下获得空间对齐度（spatial alignment）的最优逻辑规则描述。现有方法普遍采用束搜索（beam search）来近似最优解，但其无法提供任何最优性保证，且难以判断所得解释与真实最优之间的差距。论文的关键解决方案在于提出首个可计算理论最优组合式解释的框架：(i) 通过分解识别影响空间对齐度的核心因素；(ii) 设计启发式估计策略以高效评估搜索过程中任意阶段的对齐程度；(iii) 构建首个能在合理时间内计算出最优解释的算法。该框架首次实现了在卷积神经网络（Convolutional Neural Networks, CNNs）视觉任务中对组合解释最优性的理论保障，并实证表明传统束搜索在存在概念重叠时可能产生10–40%的次优解释。
链接: https://arxiv.org/abs/2511.20934
作者: Biagio La Rosa,Leilani H. Gilpin
机构: University of California, Santa Cruz (加州大学圣克鲁兹分校)
类目: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 41 pages, 10 figures
Abstract:While neurons are the basic units of deep neural networks, it is still unclear what they learn and if their knowledge is aligned with that of humans. Compositional explanations aim to answer this question by describing the spatial alignment between neuron activations and concepts through logical rules. These logical descriptions are typically computed via a search over all possible concept combinations. Since computing the spatial alignment over the entire state space is computationally infeasible, the literature commonly adopts beam search to restrict the space. However, beam search cannot provide any theoretical guarantees of optimality, and it remains unclear how close current explanations are to the true optimum. In this theoretical paper, we address this gap by introducing the first framework for computing guaranteed optimal compositional explanations. Specifically, we propose: (i) a decomposition that identifies the factors influencing the spatial alignment, (ii) a heuristic to estimate the alignment at any stage of the search, and (iii) the first algorithm that can compute optimal compositional explanations within a feasible time. Using this framework, we analyze the differences between optimal and non-optimal explanations in the most popular settings for compositional explanations, the computer vision domain and Convolutional Neural Networks. In these settings, we demonstrate that 10-40 percent of explanations obtained with beam search are suboptimal when overlapping concepts are involved. Finally, we evaluate a beam-search variant guided by our proposed decomposition and heuristic, showing that it matches or improves runtime over prior methods while offering greater flexibility in hyperparameters and computational resources.
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[CV-100] Open Vocabulary Compositional Explanations for Neuron Alignment
【速读】：该论文试图解决现有生成式 AI (Generative AI) 中神经元可解释性方法依赖人工标注数据集的问题，从而限制了其在特定领域和预定义概念上的应用。解决方案的关键在于提出一个面向视觉领域的框架，通过利用开放词汇语义分割模型生成的掩码（mask），实现对任意概念和数据集的神经元探查与组合式解释（compositional explanations）。该框架包含三个步骤：指定任意概念、使用开放词汇模型生成语义分割掩码、并从掩码中推导出组合式解释，从而显著提升了解释的灵活性与泛化能力。
链接: https://arxiv.org/abs/2511.20931
作者: Biagio La Rosa,Leilani H. Gilpin
机构: University of California, Santa Cruz (加州大学圣克鲁兹分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 47 pages, 11 figures
Abstract:Neurons are the fundamental building blocks of deep neural networks, and their interconnections allow AI to achieve unprecedented results. Motivated by the goal of understanding how neurons encode information, compositional explanations leverage logical relationships between concepts to express the spatial alignment between neuron activations and human knowledge. However, these explanations rely on human-annotated datasets, restricting their applicability to specific domains and predefined concepts. This paper addresses this limitation by introducing a framework for the vision domain that allows users to probe neurons for arbitrary concepts and datasets. Specifically, the framework leverages masks generated by open vocabulary semantic segmentation to compute open vocabulary compositional explanations. The proposed framework consists of three steps: specifying arbitrary concepts, generating semantic segmentation masks using open vocabulary models, and deriving compositional explanations from these masks. The paper compares the proposed framework with previous methods for computing compositional explanations both in terms of quantitative metrics and human interpretability, analyzes the differences in explanations when shifting from human-annotated data to model-annotated data, and showcases the additional capabilities provided by the framework in terms of flexibility of the explanations with respect to the tasks and properties of interest.
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[CV-101] Smooth regularization for efficient video recognition NEURIPS2025
【速读】：该论文旨在解决视频识别模型中轻量级架构在捕捉复杂时间动态时性能受限的问题，尤其是由于缺乏对连续帧间表示平滑性的约束导致的表征不稳定。解决方案的关键在于提出一种平滑正则化技术，通过将连续帧中间层嵌入（intermediate-layer embeddings）的变化建模为高斯随机游走（Gaussian Random Walk, GRW），从而强制模型学习低加速度的表示路径，有效抑制突变，增强时间一致性。此方法显著提升了轻量化模型在Kinetics-600数据集上的准确率，并在FLOP和内存约束下超越现有最优性能。
链接: https://arxiv.org/abs/2511.20928
作者: Gil Goldman,Raja Giryes,Mahadev Satyanarayanan
机构: Carnegie Mellon University (卡内基梅隆大学); Tel-Aviv University (特拉维夫大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to NeurIPS 2025
Abstract:We propose a smooth regularization technique that instills a strong temporal inductive bias in video recognition models, particularly benefiting lightweight architectures. Our method encourages smoothness in the intermediate-layer embeddings of consecutive frames by modeling their changes as a Gaussian Random Walk (GRW). This penalizes abrupt representational shifts, thereby promoting low-acceleration solutions that better align with the natural temporal coherence inherent in videos. By leveraging this enforced smoothness, lightweight models can more effectively capture complex temporal dynamics. Applied to such models, our technique yields a 3.8% to 6.4% accuracy improvement on Kinetics-600. Notably, the MoViNets model family trained with our smooth regularization improves the current state of the art by 3.8% to 6.1% within their respective FLOP constraints, while MobileNetV3 and the MoViNets-Stream family achieve gains of 4.9% to 6.4% over prior state-of-the-art models with comparable memory footprints. Our code and models are available at this https URL.
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[CV-102] A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern
【速读】：该论文旨在解决脑桥小脑角（cerebellopontine angle, CPA）池增强T1加权磁共振成像（contrast-enhanced T1-weighted MRI, T1ce）中对比剂剂量过高带来的潜在风险问题，如肾毒性或过敏反应。其核心解决方案是采用深度学习（deep learning, DL）模型，从低剂量模拟的T1ce图像中重建出接近标准剂量图像质量的图像，从而在保证诊断性能的前提下显著降低对比剂用量。关键在于通过多中心回顾性研究训练DL模型，使其能够有效恢复图像结构信息与细节特征，并在图像质量、分割精度及放射科医生主观评分方面均表现出优于原始低剂量图像的结果，实现仅用10%–30%标准剂量即可获得可诊断图像的目标。
链接: https://arxiv.org/abs/2511.20926
作者: Yunjie Chen,Rianne A. Weber,Olaf M. Neve,Stephan R. Romeijn,Erik F. Hensen,Jelmer M. Wolterink,Qian Tao,Marius Staring,Berit M. Verbist
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Objectives: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. Materials and methods: In this multi-center retrospective study, T1 and T1ce of vestibular schwannoma (VS) patients were used to simulate low-dose T1ce with varying reductions of contrast agent dose. DL models were trained to restore standard-dose T1ce from the low-dose simulation. The image quality and segmentation performance of the DL-restored T1ce were evaluated. A head and neck radiologist was asked to rate DL-restored images in multiple aspects, including image quality and diagnostic characterization. Results: 203 MRI studies from 72 VS patients (mean age, 58.51 \pm 14.73, 39 men) were evaluated. As the input dose increased, the structural similarity index measure of the restored T1ce increased from 0.639 \pm 0.113 to 0.993 \pm 0.009, and the peak signal-to-noise ratio increased from 21.6 \pm 3.73 dB to 41.4 \pm 4.84 dB. At 10% input dose, using DL-restored T1ce for segmentation improved the Dice from 0.673 to 0.734, the 95% Hausdorff distance from 2.38 mm to 2.07 mm, and the average surface distance from 1.00 mm to 0.59 mm. Both DL-restored T1ce from 10% and 30% input doses showed excellent images, with the latter being considered more informative. Conclusion: The DL model improved the image quality of low-dose MRI of the CPA cistern, which makes lesion detection and diagnostic characterization possible with 10% - 30% of the standard dose.
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[CV-103] GaINeR: Geometry-Aware Implicit Network Representation
【速读】：该论文旨在解决传统隐式神经表示（Implicit Neural Representations, INRs）在几何结构显式建模、局部编辑能力以及与物理仿真集成方面的局限性，从而限制其在动态或交互场景中的应用。解决方案的关键在于提出GaINeR框架，通过将可训练的高斯分布与基于神经网络的INR相结合：对于图像坐标，模型检索K个最近邻高斯分布，利用距离加权聚合嵌入特征，并通过神经网络预测RGB值，从而实现连续图像表示、可解释的几何结构和灵活的局部编辑能力，为物理感知和交互式图像操作提供基础。
链接: https://arxiv.org/abs/2511.20924
作者: Weronika Jakubowska,Mikołaj Zieliński,Rafał Tobiasz,Krzysztof Byrski,Maciej Zięba,Dominik Belter,Przemysław Spurek
机构: Wrocław University of Science and Technology (弗罗茨瓦夫理工大学); Poznań University of Technology (波兹南理工大学); IDEAS Research Institute (IDEAS 研究所); Jagiellonian University (雅盖隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 16 pages, 16 figures
Abstract:Implicit Neural Representations (INRs) have become an essential tool for modeling continuous 2D images, enabling high-fidelity reconstruction, super-resolution, and compression. Popular architectures such as SIREN, WIRE, and FINER demonstrate the potential of INR for capturing fine-grained image details. However, traditional INRs often lack explicit geometric structure and have limited capabilities for local editing or integration with physical simulation, restricting their applicability in dynamic or interactive settings. To address these limitations, we propose GaINeR: Geometry-Aware Implicit Network Representation, a novel framework for 2D images that combines trainable Gaussian distributions with a neural network-based INR. For a given image coordinate, the model retrieves the K nearest Gaussians, aggregates distance-weighted embeddings, and predicts the RGB value via a neural network. This design enables continuous image representation, interpretable geometric structure, and flexible local editing, providing a foundation for physically aware and interactive image manipulation. The official implementation of our method is publicly available at this https URL.
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[CV-104] st-Time Alignment of Text-to-Image Diffusion Models via Null-Text Embedding Optimisation
【速读】：该论文旨在解决测试时对齐（Test-time alignment, TTA）中模型因过度优化或奖励欺骗（reward hack）而导致性能下降的问题。现有方法通常通过调整潜在变量或噪声来优化目标奖励函数，容易引入非语义的噪声模式，从而导致生成结果偏离预期语义。其解决方案的关键在于提出Null-Text Test-Time Alignment（Null-TTA），通过优化Classifier-Free Guidance中的无条件嵌入（unconditional embedding）来实现扩散模型的对齐，而非直接操纵潜在空间或噪声变量。由于文本嵌入空间具有结构化的语义特性，该方法确保优化过程在语义一致的流形上进行，避免了奖励欺骗；同时，利用无条件嵌入作为生成分布的锚点，Null-TTA可直接引导模型的生成分布趋向目标奖励，而无需更新模型参数，从而在保持跨奖励泛化能力的同时实现最优的测试时对齐效果。
链接: https://arxiv.org/abs/2511.20889
作者: Taehoon Kim,Henry Gouk,Timothy Hospedales
机构: University of Edinburgh (爱丁堡大学); Samsung AI Center, Cambridge (三星人工智能中心)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Test-time alignment (TTA) aims to adapt models to specific rewards during inference. However, existing methods tend to either under-optimise or over-optimise (reward hack) the target reward function. We propose Null-Text Test-Time Alignment (Null-TTA), which aligns diffusion models by optimising the unconditional embedding in classifier-free guidance, rather than manipulating latent or noise variables. Due to the structured semantic nature of the text embedding space, this ensures alignment occurs on a semantically coherent manifold and prevents reward hacking (exploiting non-semantic noise patterns to improve the reward). Since the unconditional embedding in classifier-free guidance serves as the anchor for the model’s generative distribution, Null-TTA directly steers model’s generative distribution towards the target reward rather than just adjusting the samples, even without updating model parameters. Thanks to these desirable properties, we show that Null-TTA achieves state-of-the-art target test-time alignment while maintaining strong cross-reward generalisation. This establishes semantic-space optimisation as an effective and principled novel paradigm for TTA.
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[CV-105] V2-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence
【速读】：该论文旨在解决跨视角物体对应（cross-view object correspondence）问题，尤其聚焦于第一人称视角（ego-centric）与第三人称视角（exo-centric）之间的物体一致性关联任务。由于视角和外观变化剧烈，现有分割模型如SAM2难以直接应用。其解决方案的关键在于提出V²-SAM框架，通过两个互补的提示生成器实现：一是基于DINOv3特征构建的跨视角锚点提示生成器（V²-Anchor），首次在跨视角场景中启用基于坐标的提示机制；二是跨视角视觉提示生成器（V²-Visual），利用新颖的视觉提示匹配器从特征与结构两个维度对齐第一人称与第三人称表征。此外，引入多专家设计与事后循环一致性选择器（PCCS）以自适应选取最可靠的专家输出，从而有效融合几何与外观线索，显著提升跨视角对应性能。
链接: https://arxiv.org/abs/2511.20886
作者: Jiancheng Pan,Runze Wang,Tianwen Qian,Mohammad Mahdi,Yanwei Fu,Xiangyang Xue,Xiaomeng Huang,Luc Van Gool,Danda Pani Paudel,Yuqian Fu
机构: INSAIT(INSAIT); Sofia University “St. Kliment Ohridski”(索菲亚大学“克莱门特·奥赫里德斯基”); Tsinghua University(清华大学); Fudan University(复旦大学); East China Normal University(华东师范大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 19 pages
Abstract:Cross-view object correspondence, exemplified by the representative task of ego-exo object correspondence, aims to establish consistent associations of the same object across different viewpoints (e.g., ego-centric and exo-centric). This task poses significant challenges due to drastic viewpoint and appearance variations, making existing segmentation models, such as SAM2, non-trivial to apply directly. To address this, we present V^2-SAM, a unified cross-view object correspondence framework that adapts SAM2 from single-view segmentation to cross-view correspondence through two complementary prompt generators. Specifically, the Cross-View Anchor Prompt Generator (V^2-Anchor), built upon DINOv3 features, establishes geometry-aware correspondences and, for the first time, unlocks coordinate-based prompting for SAM2 in cross-view scenarios, while the Cross-View Visual Prompt Generator (V^2-Visual) enhances appearance-guided cues via a novel visual prompt matcher that aligns ego-exo representations from both feature and structural perspectives. To effectively exploit the strengths of both prompts, we further adopt a multi-expert design and introduce a Post-hoc Cyclic Consistency Selector (PCCS) that adaptively selects the most reliable expert based on cyclic consistency. Extensive experiments validate the effectiveness of V^2-SAM, achieving new state-of-the-art performance on Ego-Exo4D (ego-exo object correspondence), DAVIS-2017 (video object tracking), and HANDAL-X (robotic-ready cross-view correspondence).
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[CV-106] Estimating Fog Parameters from a Sequence of Stereo Images
【速读】：该论文旨在解决在雾天环境下，基于立体图像序列的雾参数估计与动态更新问题，以提升视觉SLAM（Simultaneous Localization and Mapping）或里程计系统在雾霾中的鲁棒性。传统方法通常逐帧估计雾参数，易受误差传播影响，且难以处理全局非均匀的自然雾。其解决方案的关键在于提出一种新的优化框架，通过假设雾仅在局部区域内具有同质性（locally homogeneous），从而同时估计所有雾参数，避免误差累积并更有效地建模真实世界的雾分布特性。该方法可作为模块无缝集成至现有视觉SLAM系统中，显著改善雾天环境下的感知性能。
链接: https://arxiv.org/abs/2511.20865
作者: Yining Ding,João F. C. Mota,Andrew M. Wallace,Sen Wang
机构: Edinburgh Centre for Robotics (爱丁堡机器人中心); Heriot-Watt University (赫瑞-瓦特大学); Sense Robotics Lab (感知机器人实验室); Imperial College London (帝国理工学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We propose a method which, given a sequence of stereo foggy images, estimates the parameters of a fog model and updates them dynamically. In contrast with previous approaches, which estimate the parameters sequentially and thus are prone to error propagation, our algorithm estimates all the parameters simultaneously by solving a novel optimisation problem. By assuming that fog is only locally homogeneous, our method effectively handles real-world fog, which is often globally inhomogeneous. The proposed algorithm can be easily used as an add-on module in existing visual Simultaneous Localisation and Mapping (SLAM) or odometry systems in the presence of fog. In order to assess our method, we also created a new dataset, the Stereo Driving In Real Fog (SDIRF), consisting of high-quality, consecutive stereo frames of real, foggy road scenes under a variety of visibility conditions, totalling over 40 minutes and 34k frames. As a first-of-its-kind, SDIRF contains the camera’s photometric parameters calibrated in a lab environment, which is a prerequisite for correctly applying the atmospheric scattering model to foggy images. The dataset also includes the counterpart clear data of the same routes recorded in overcast weather, which is useful for companion work in image defogging and depth reconstruction. We conducted extensive experiments using both synthetic foggy data and real foggy sequences from SDIRF to demonstrate the superiority of the proposed algorithm over prior methods. Our method not only produces the most accurate estimates on synthetic data, but also adapts better to real fog. We make our code and SDIRF publicly available\footnotethis https URL to the community with the aim of advancing the research on visual perception in fog.
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[CV-107] MODEST: Multi-Optics Depth-of-Field Stereo Dataset
【速读】：该论文旨在解决当前深度估计（depth estimation）与景深渲染（depth-of-field rendering）模型在真实光学条件下的泛化能力不足问题，尤其是由于缺乏大规模、高保真、真实的立体DSLR数据集，导致基于合成数据训练的模型难以在现实场景中有效应用。解决方案的关键在于构建首个高分辨率（5472 × 3648 px）立体DSLR数据集，包含18000张图像，系统性地在复杂真实场景中变化焦距（28–70 mm）和光圈（f/2.8–f/22），覆盖50种光学配置，并为每种配置提供专用校准图像集，从而支持对单目与立体深度估计、浅景深渲染、去模糊、三维重建及新视角合成等任务的可控分析与评估。该数据集通过引入多尺度光学幻觉、反射表面、透明玻璃墙、精细细节及自然/人工光照变化等挑战性视觉元素，显著提升了模型训练与测试的真实光学还原度，有效弥合了合成数据与真实相机光学之间的“现实差距”（realism gap）。
链接: https://arxiv.org/abs/2511.20853
作者: Nisarg K. Trivedi,Vinayak A. Belludi,Li-Yun Wang,Pardis Taghavi,Dante Lok
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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Abstract:Reliable depth estimation under real optical conditions remains a core challenge for camera vision in systems such as autonomous robotics and augmented reality. Despite recent progress in depth estimation and depth-of-field rendering, research remains constrained by the lack of large-scale, high-fidelity, real stereo DSLR datasets, limiting real-world generalization and evaluation of models trained on synthetic data as shown extensively in literature. We present the first high-resolution (5472 \times 3648px) stereo DSLR dataset with 18000 images, systematically varying focal length and aperture across complex real scenes and capturing the optical realism and complexity of professional camera systems. For 9 scenes with varying scene complexity, lighting and background, images are captured with two identical camera assemblies at 10 focal lengths (28-70mm) and 5 apertures (f/2.8-f/22), spanning 50 optical configurations in 2000 images per scene. This full-range optics coverage enables controlled analysis of geometric and optical effects for monocular and stereo depth estimation, shallow depth-of-field rendering, deblurring, 3D scene reconstruction and novel view synthesis. Each focal configuration has a dedicated calibration image set, supporting evaluation of classical and learning based methods for intrinsic and extrinsic calibration. The dataset features challenging visual elements such as multi-scale optical illusions, reflective surfaces, mirrors, transparent glass walls, fine-grained details, and natural / artificial ambient light variations. This work attempts to bridge the realism gap between synthetic training data and real camera optics, and demonstrates challenges with the current state-of-the-art monocular, stereo depth and depth-of-field methods. We release the dataset, calibration files, and evaluation code to support reproducible research on real-world optical generalization.
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[CV-108] RefTr: Recurrent Refinement of Confluent Trajectories for 3D Vascular Tree Centerline Graphs
【速读】：该论文旨在解决三维医学图像中血管树中心线（centerline）检测的难题，尤其是如何在保持高召回率的同时确保树状拓扑结构的正确性，以避免因遗漏小分支而导致临床误判。其关键解决方案是提出了一种基于Transformer解码器的“生产者-精炼器”（Producer-Refiner）架构，通过递归精炼共汇轨迹（confluent trajectory）来生成最终的中心线图；该方法利用共汇轨迹表示显式约束树结构的有效性，并采用递归精炼机制提升精度，同时减少参数量（相比前代最优模型降低2.4倍），并引入高效的非极大值抑制算法优化空间树图结构，从而在多个公开数据集上实现更优的召回率与可比的精确度，兼具高效推理和轻量化特性。
链接: https://arxiv.org/abs/2511.20823
作者: Roman Naeem,David Hagerman,Jennifer Alvén,Fredrik Kahl
机构: Chalmers University of Technology (查尔姆斯理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Tubular trees, such as blood vessels and lung airways, are essential for material transport within the human body. Accurately detecting their centerlines with correct tree topology is critical for clinical tasks such as diagnosis, treatment planning, and surgical navigation. In these applications, maintaining high recall is crucial, as missing small branches can result in fatal mistakes caused by incomplete assessments or undetected abnormalities. We present RefTr, a 3D image-to-graph model for centerline generation of vascular trees via recurrent refinement of confluent trajectories. RefTr uses a Producer-Refiner architecture based on a Transformer decoder, where the Producer proposes a set of initial confluent trajectories that are recurrently refined by the Refiner to produce final trajectories, which forms the centerline graph. The confluent trajectory representation enables refinement of complete trajectories while explicitly enforcing a valid tree topology. The recurrent refinement scheme improves precision and reuses the same Refiner block across multiple steps, yielding a 2.4x reduction in decoder parameters compared to previous SOTA. We also introduce an efficient non-maximum suppression algorithm for spatial tree graphs to merge duplicate branches and boost precision. Across multiple public centerline datasets, RefTr achieves superior recall and comparable precision to previous SOTA, while offering faster inference and substantially fewer parameters, demonstrating its potential as a new state-of-the-art framework for vascular tree analysis in 3D medical imaging.
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[CV-109] SPHINX: A Synthetic Environment for Visual Perception and Reasoning
【速读】：该论文旨在解决当前视觉感知与推理模型在面对复杂认知任务时性能不足的问题，尤其是针对生成式 AI（Generative AI）在多模态推理能力上的局限性。其解决方案的关键在于构建一个名为 Sphinx 的合成环境，该环境通过程序化生成包含图案、瓷砖、图表、图标和几何原语的谜题，并为每个谜题提供可验证的真值解，从而实现精准评估与大规模数据集构建；同时引入基于可验证奖励的强化学习（Reinforcement Learning with Verifiable Rewards, RLVR），显著提升模型在 25 种任务类型上的准确率，包括对称性检测、几何变换、空间推理等，且在外部视觉推理基准上也取得增益，凸显了 RLVR 在推动多模态推理发展中的潜力。
链接: https://arxiv.org/abs/2511.20814
作者: Md Tanvirul Alam,Saksham Aggarwal,Justin Yang Chae,Nidhi Rastogi
机构: Rochester Institute of Technology (罗彻斯特理工学院); University of Washington (华盛顿大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:We present Sphinx, a synthetic environment for visual perception and reasoning that targets core cognitive primitives. Sphinx procedurally generates puzzles using motifs, tiles, charts, icons, and geometric primitives, each paired with verifiable ground-truth solutions, enabling both precise evaluation and large-scale dataset construction. The benchmark covers 25 task types spanning symmetry detection, geometric transformations, spatial reasoning, chart interpretation, and sequence prediction. Evaluating recent large vision-language models (LVLMs) shows that even state-of-the-art GPT-5 attains only 51.1% accuracy, well below human performance. Finally, we demonstrate that reinforcement learning with verifiable rewards (RLVR) substantially improves model accuracy on these tasks and yields gains on external visual reasoning benchmarks, highlighting its promise for advancing multimodal reasoning.
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[CV-110] Layer-Aware Video Composition via Split-then-Merge
【速读】：该论文旨在解决生成式视频合成（Generative Video Synthesis）中的控制性不足与数据稀缺问题。传统方法依赖标注数据集或手工规则，难以建模动态主体与复杂场景之间的交互关系。其解决方案的关键在于提出Split-then-Merge (StM) 框架：首先将大规模未标注视频分解为动态前景和背景层，随后通过自监督方式重新组合这些层以学习复杂的场景交互机制；同时引入一种感知变换的训练流程，结合多层融合与增强策略实现具身感知（affordance-aware）的合成，并设计身份保持损失（identity-preservation loss）确保前景在融合过程中的语义一致性。
链接: https://arxiv.org/abs/2511.20809
作者: Ozgur Kara,Yujia Chen,Ming-Hsuan Yang,James M. Rehg,Wen-Sheng Chu,Du Tran
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校); Google(谷歌)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: Project Webpage: this https URL
Abstract:We present Split-then-Merge (StM), a novel framework designed to enhance control in generative video composition and address its data scarcity problem. Unlike conventional methods relying on annotated datasets or handcrafted rules, StM splits a large corpus of unlabeled videos into dynamic foreground and background layers, then self-composes them to learn how dynamic subjects interact with diverse scenes. This process enables the model to learn the complex compositional dynamics required for realistic video generation. StM introduces a novel transformation-aware training pipeline that utilizes a multi-layer fusion and augmentation to achieve affordance-aware composition, alongside an identity-preservation loss that maintains foreground fidelity during blending. Experiments show StM outperforms SoTA methods in both quantitative benchmarks and in humans/VLLM-based qualitative evaluations. More details are available at our project page: this https URL
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[CV-111] Δ-NeRF: Incremental Refinement of Neural Radiance Fields through Residual Control and Knowledge Transfer
【速读】：该论文旨在解决神经辐射场（Neural Radiance Fields, NeRF）在增量数据场景下难以高效更新的问题，尤其是在卫星遥感等连续观测场景中，传统方法需重新训练整个模型且无法访问历史数据时易发生灾难性遗忘。其解决方案的核心是提出一种模块化残差框架 Δ-NeRF，关键创新包括：(1) 一个残差控制器，在不访问历史数据的前提下向冻结的基线NeRF注入逐层修正项以实现增量优化；(2) 一种不确定性感知的门控机制，自适应融合基线与精修预测结果，避免过拟合；(3) 一种视图选择策略，在减少47%训练数据的同时保持性能稳定，并结合知识蒸馏将增强模型压缩至原大小的20%，显著提升效率与实用性。
链接: https://arxiv.org/abs/2511.20804
作者: Kriti Ghosh,Devjyoti Chakraborty,Lakshmish Ramaswamy,Suchendra M. Bhandarkar,In Kee Kim,Nancy O’Hare,Deepak Mishra
机构: University of Texas at Dallas (德克萨斯大学达拉斯分校); University of Texas Health Science Center at Dallas (德克萨斯大学健康科学中心达拉斯分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Neural Radiance Fields (NeRFs) have demonstrated remarkable capabilities in 3D reconstruction and novel view synthesis. However, most existing NeRF frameworks require complete retraining when new views are introduced incrementally, limiting their applicability in domains where data arrives sequentially. This limitation is particularly problematic in satellite-based terrain analysis, where regions are repeatedly observed over time. Incremental refinement of NeRFs remains underexplored, and naive approaches suffer from catastrophic forgetting when past data is unavailable. We propose \Delta -NeRF, a unique modular residual framework for incremental NeRF refinement. \Delta -NeRF introduces several novel techniques including: (1) a residual controller that injects per-layer corrections into a frozen base NeRF, enabling refinement without access to past data; (2) an uncertainty-aware gating mechanism that prevents overcorrection by adaptively combining base and refined predictions; and (3) a view selection strategy that reduces training data by up to 47% while maintaining performance. Additionally, we employ knowledge distillation to compress the enhanced model into a compact student network (20% of original size). Experiments on satellite imagery demonstrate that \Delta -NeRF achieves performance comparable to joint training while reducing training time by 30-42%. \Delta -NeRF consistently outperforms existing baselines, achieving an improvement of up to 43.5% in PSNR over naive fine-tuning and surpassing joint training on some metrics.
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[CV-112] Intriguing Properties of Dynamic Sampling Networks
【速读】：该论文旨在解决深度学习中动态采样机制（dynamic sampling mechanisms）的理论分析缺乏统一框架的问题。现有方法如可变形卷积（deformable convolutions）、活动卷积单元（active convolutional units）和空间变换网络（spatial transformer networks）虽在计算机视觉任务中表现优异，但其理论基础分散且未被系统化。解决方案的关键在于提出一种新颖的算子——“warping”，它以最小实现形式概括了多种动态采样结构，并具备良好的可分析性。通过将输入建模为独立同分布（IID）变量或齐次随机场（homogeneous random fields），作者对该算子进行了统计分析，揭示了前向与反向传播中的独特不对称性，并指出此类机制构成一类与传统平移不变卷积算子正交的新类别。进一步地，论文明确了动态采样网络稳定训练所需的条件，并引入基于梯度更新信息的新型损失景观可视化方法，从而提升了对模型学习行为的理解。
链接: https://arxiv.org/abs/2511.20800
作者: Dario Morle,Reid Zaffino
机构: McGill University (麦吉尔大学); MILA-Quebec AI Institute (蒙特利尔学习算法研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Dynamic sampling mechanisms in deep learning architectures have demonstrated utility across many computer vision models, though the theoretical analysis of these structures has not yet been unified. In this paper we connect the various dynamic sampling methods by developing and analyzing a novel operator which generalizes existing methods, which we term “warping”. Warping provides a minimal implementation of dynamic sampling which is amenable to analysis, and can be used to reconstruct existing architectures including deformable convolutions, active convolutional units, and spatial transformer networks. Using our formalism, we provide statistical analysis of the operator by modeling the inputs as both IID variables and homogeneous random fields. Extending this analysis, we discover a unique asymmetry between the forward and backward pass of the model training. We demonstrate that these mechanisms represent an entirely different class of orthogonal operators to the traditional translationally invariant operators defined by convolutions. With a combination of theoretical analysis and empirical investigation, we find the conditions necessary to ensure stable training of dynamic sampling networks. In addition, statistical analysis of discretization effects are studied. Finally, we introduce a novel loss landscape visualization which utilizes gradient update information directly, to better understand learning behavior.
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[CV-113] Revisiting KRISP: A Lightweight Reproduction and Analysis of Knowledge-Enhanced Vision-Language Models
【速读】：该论文旨在解决知识增强型视觉问答（Vision-Language Reasoning, VLR）模型在资源受限场景下部署困难的问题，尤其是原始KRISP模型参数量庞大、计算开销高且依赖大型骨干网络，难以在边缘设备（如智能手机和AR-VR设备）上实现高效推理。其解决方案的关键在于提出一种轻量化复现版本，通过系统性消融实验揭示原模型设计中的缺陷与隐含问题，并在保持约75%原始性能的前提下显著减少参数量；同时利用外部知识图谱（Knowledge Graph, KG）的领域约束，有效抑制生成式AI（Generative AI）幻觉，确保输出仅限于指定知识域内，从而实现低资源条件下的可靠离线视觉推理。
链接: https://arxiv.org/abs/2511.20795
作者: Souradeep Dutta,Keshav Bulia,Neena S Nair
机构: Indian Institute of Technology Bombay (印度理工学院孟买分校)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 7 pages , 4 figures
Abstract:Facebook AI Research introduced KRISP [4], which integrates structured external knowledge into pipelines for vision-language reasoning. Despite its effectiveness, the original model has been developed for industrial-scale training, is computationally demanding, and is tightly connected to a large backbone. In this work, we reexamine KRISP from a different angle and offer a lightweight reproduction with significantly fewer parameters. Even though our replicated model performs about 75 % of the original, the replication process uncovers a number of design flaws, real-world pitfalls, and implicit problems that were not fully covered in the original paper. We offer insights into the scalability and efficacy of knowledge-enhanced VQA architectures under resource constraints through systematic ablation studies, which include a proof-of-concept on synthetic VQA data and evaluation on the DAQUAR dataset. Our model, configured with a low parameter setup and constrained by the external Knowledge graph domain, prevents AI hallucinations and generates outputs solely within that domain. Minimal parameters allow us to function on edge devices like smartphones and AR-VR, further improving offline visual reasoning.
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[CV-114] LongVT: Incentivizing “Thinking with Long Videos” via Native Tool Calling
【速读】：该论文旨在解决大型多模态模型（Large Multimodal Models, LMMs）在处理长视频时因证据稀疏且时空分布分散而导致的幻觉问题，尤其是在依赖文本链式思维（Chain-of-Thought）进行推理时表现不稳定。其解决方案的关键在于提出一种端到端的代理框架 LongVT，通过“全局到局部”的推理循环实现对长视频的分层理解：首先利用 LMM 的内在时间定位能力作为原生视频裁剪工具，自动识别并聚焦于关键片段，再通过细粒度帧重采样进行深度分析，形成“多模态工具链式思维”（Multimodal Chain-of-Tool-Thought），直至答案被检索到的视觉证据所支撑。该方法模拟人类观看长视频的认知过程，显著提升了长视频理解与推理的准确性与鲁棒性。
链接: https://arxiv.org/abs/2511.20785
作者: Zuhao Yang,Sudong Wang,Kaichen Zhang,Keming Wu,Sicong Leng,Yifan Zhang,Chengwei Qin,Shijian Lu,Xingxuan Li,Lidong Bing
机构: MiroMind AI; NTU; HKUST(GZ); THU; LMMs-Lab Team
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Large multimodal models (LMMs) have shown great potential for video reasoning with textual Chain-of-Thought. However, they remain vulnerable to hallucinations, especially when processing long-form videos where evidence is sparse and temporally dispersed. Inspired by how humans comprehend long videos - by first skimming globally and then examining relevant clips for details - we introduce LongVT, an end-to-end agentic framework that enables “Thinking with Long Videos” via interleaved Multimodal Chain-of-Tool-Thought. Specifically, we exploit LMMs’ inherent temporal grounding ability as a native video cropping tool to zoom in on a specific video clip and resample finer-grained video frames. This global-to-local reasoning loop continues until answers are grounded in retrieved visual evidence. Given the scarcity of fine-grained question-answering (QA) data for the long video reasoning task, we curate and will release a data suite named VideoSIAH to facilitate both training and evaluation. Specifically, our training dataset consists of 247.9K samples for tool-integrated cold-start supervised fine-tuning, 1.6K samples for agentic reinforcement learning, and 15.4K samples for agentic reinforcement fine-tuning, respectively. Our evaluation benchmark consists of 1,280 QA pairs that are carefully curated through a semi-automatic data pipeline with human-in-the-loop validation. With a meticulously designed three-stage training strategy and extensive empirical validation, LongVT consistently outperforms existing strong baselines across four challenging long-video understanding and reasoning benchmarks. Our codes, data, and model checkpoints are publicly available at this https URL .
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[CV-115] One Patch is All You Need: Joint Surface Material Reconstruction and Classification from Minimal Visual Cues
【速读】：该论文旨在解决从稀疏视觉线索中理解材料表面的问题，尤其针对机器人、仿真和材料感知等应用中受限或局部视角环境下的挑战。现有方法多依赖密集或全场景观测，在部分视场条件下效果受限。解决方案的关键在于提出SMARC模型，其核心是将部分卷积U-Net与分类头相结合，实现仅用图像中10%连续补丁即可同时完成完整RGB表面重建（图像修复）和材料类别分类，从而在极端观测稀疏性下仍能进行空间推理与语义理解。
链接: https://arxiv.org/abs/2511.20784
作者: Sindhuja Penchala,Gavin Money,Gabriel Marques,Samuel Wood,Jessica Kirschman,Travis Atkison,Shahram Rahimi,Noorbakhsh Amiri Golilarz
机构: The University of Alabama (阿拉巴马大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 9 pages,3 figures, 5 tables
Abstract:Understanding material surfaces from sparse visual cues is critical for applications in robotics, simulation, and material perception. However, most existing methods rely on dense or full-scene observations, limiting their effectiveness in constrained or partial view environment. To address this challenge, we introduce SMARC, a unified model for Surface MAterial Reconstruction and Classification from minimal visual input. By giving only a single 10% contiguous patch of the image, SMARC recognizes and reconstructs the full RGB surface while simultaneously classifying the material category. Our architecture combines a Partial Convolutional U-Net with a classification head, enabling both spatial inpainting and semantic understanding under extreme observation sparsity. We compared SMARC against five models including convolutional autoencoders [17], Vision Transformer (ViT) [13], Masked Autoencoder (MAE) [5], Swin Transformer [9], and DETR [2] using Touch and Go dataset [16] of real-world surface textures. SMARC achieves state-of-the-art results with a PSNR of 17.55 dB and a material classification accuracy of 85.10%. Our findings highlight the advantages of partial convolution in spatial reasoning under missing data and establish a strong foundation for minimal-vision surface understanding.
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[CV-116] CHiQPM: Calibrated Hierarchical Interpretable Image Classification NEURIPS2025
【速读】：该论文旨在解决安全关键领域中可信人工智能（Trustworthy AI）的可解释性问题，特别是在需要同时提供全局和局部可解释性的场景下。现有方法往往难以兼顾模型整体决策逻辑的透明度与个体预测的可理解性，从而限制了人类专家在推理过程中的有效参与。解决方案的关键在于提出校准分层QPM（Calibrated Hierarchical QPM, CHiQPM），其创新性体现在两个方面：一是通过对比式解释多数类别实现更优的全局可解释性；二是引入类人层级解释结构，支持可解释的置信预测（Conformal Prediction, CP），并通过层级遍历机制自然地生成一致且可解释的预测集合，从而在不牺牲99%非可解释模型准确率的前提下，实现了高精度与强可解释性的统一。
链接: https://arxiv.org/abs/2511.20779
作者: Thomas Norrenbrock,Timo Kaiser,Sovan Biswas,Neslihan Kose,Ramesh Manuvinakurike,Bodo Rosenhahn
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
备注: Accepted to NeurIPS 2025
Abstract:Globally interpretable models are a promising approach for trustworthy AI in safety-critical domains. Alongside global explanations, detailed local explanations are a crucial complement to effectively support human experts during inference. This work proposes the Calibrated Hierarchical QPM (CHiQPM) which offers uniquely comprehensive global and local interpretability, paving the way for human-AI complementarity. CHiQPM achieves superior global interpretability by contrastively explaining the majority of classes and offers novel hierarchical explanations that are more similar to how humans reason and can be traversed to offer a built-in interpretable Conformal prediction (CP) method. Our comprehensive evaluation shows that CHiQPM achieves state-of-the-art accuracy as a point predictor, maintaining 99% accuracy of non-interpretable models. This demonstrates a substantial improvement, where interpretability is incorporated without sacrificing overall accuracy. Furthermore, its calibrated set prediction is competitively efficient to other CP methods, while providing interpretable predictions of coherent sets along its hierarchical explanation.
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[CV-117] xt-Guided Semantic Image Encoder
【速读】：该论文旨在解决视觉语言模型（Vision-Language Models, VLMs）中图像编码器（Image Encoder）在预训练阶段与文本模态分离、缺乏任务感知能力的问题，即传统图像编码器对输入图像的处理是“无差别”的，无法根据具体的下游任务或文本查询动态调整其表示。解决方案的关键在于提出一种文本引导的语义图像编码器（Text-Guided Semantic Image Encoder, TIE），该模块通过将文本查询作为条件输入，生成与当前任务相关的图像表征，从而实现图像编码过程的语义对齐和动态适配。实验表明，TIE显著提升了VLM在多个图像到文本任务上的性能（平均提升1.5–1.3点），同时减少一半图像块（token）使用量，提高了推理效率，并增强了对查询相关区域的关注度，提升了模型的可解释性与泛化能力。
链接: https://arxiv.org/abs/2511.20770
作者: Raghuveer Thirukovalluru,Xiaochuang Han,Bhuwan Dhingra,Emily Dinan,Maha Elbayad
机构: Meta(Meta)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 20 pages, 6 figures
Abstract:Image encoders, a fundamental component of vision-language models (VLMs), are typically pretrained independently before being aligned with a language model. This standard paradigm results in encoders that process images agnostically, without regard to the specific downstream task or text query. To address this limitation, we propose the Text-Guided Semantic Image Encoder (TIE), which generates image representations conditioned on the input text query. VLMs equipped with TIE outperform their conventional counterparts by +1.5 and +1.3 points on average across nine image-to-text benchmarks at the 1B and 3B scales, respectively, with gains reaching up to 6 points on tasks such as DocVQA and InfoVQA. Moreover, TIE-based VLMs attain superior performance while utilizing only half as many image tiles (tokens), resulting in notably improved inference efficiency. TIE also generalizes well with generic queries, indicating that text-conditioned training effectively optimizes the encoder to capture key visual features. Qualitative analysis confirms that TIE consistently attends to query-relevant regions, enhancing both interpretability and query-specific grounding.
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[CV-118] Prompt-Aware Adaptive Elastic Weight Consolidation for Continual Learning in Medical Vision-Language Models
【速读】：该论文旨在解决医疗人工智能（AI）系统在临床部署中面临的灾难性遗忘问题，即模型在学习新成像协议时会丢失先前的诊断能力，尤其对于需要维持跨模态对齐（如医学图像与临床术语之间的关系）的医疗视觉-语言模型而言更为严峻。其解决方案的关键在于提出Prompt-Aware Adaptive Elastic Weight Consolidation（PA-EWC），通过提示引导的参数专业化机制，将模型参数按功能角色分类（视觉描述信息处理、空间引导信息处理和医学语义信息处理），实现对关键知识的靶向保护，同时允许模型适应新的临床需求；该方法还引入自适应Fisher信息计算与梯度稳定性分析，并基于医学术语密度构建加权复杂度指标，从而显著提升模型在多模态医学影像任务中的持续学习性能。
链接: https://arxiv.org/abs/2511.20732
作者: Ziyuan Gao,Philippe Morel
机构: 未知
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted by 32nd International Conference on MultiMedia Modeling (MMM 2026)
Abstract:Medical AI systems face catastrophic forgetting when deployed in clinical settings, where models must learn new imaging protocols while retaining prior diagnostic capabilities. This challenge is particularly acute for medical vision-language models that must preserve complex cross-modal alignments between medical images and clinical terminology across diverse imaging modalities. We introduce Prompt- Aware Adaptive Elastic Weight Consolidation (PA-EWC), a novel continual learning approach that addresses catastrophic forgetting through prompt-guided parameter specialization. Our method systematically categorizes model parameters based on their functional roles in processing visual-descriptive, spatial-guided, and medical-semantic information, enabling targeted protection of critical knowledge while allowing adaptation to new clinical requirements. PA-EWC incorporates adaptive Fisher Information computation with gradient stability analysis and develops weighted complexity metrics based on medical terminology density. We evaluate our approach across five medical imaging datasets (Kvasir-SEG, ISIC 2018, CheXlocalize, BUSI, CAMUS) representing diverse modalities including endoscopy, dermoscopy, radiography, and ultrasound. Experimental results demonstrate that PA-EWC reduces catastrophic forgetting by up to 17.58% compared to baseline methods, with performance improvements of 4.30% on chest X-ray pathology localization and 6.06% on polyp segmentation.
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[CV-119] DinoLizer: Learning from the Best for Generative Inpainting Localization
【速读】：该论文旨在解决生成式图像修复（generative inpainting）中篡改区域的精准定位问题，即在经过AI生成内容修复后的图像中识别出被修改的具体区域。解决方案的关键在于提出DinoLizer模型，其基于预训练的DINOv2模型构建，并在其视觉Transformer（Vision Transformer, ViT）的patch嵌入层上添加一个线性分类头，用于在14×14的patch分辨率下预测篡改区域；该分类头被设计为聚焦于语义层面发生改变的区域，将非语义调整视为原始内容的一部分。此外，由于ViT输入尺寸固定，采用滑动窗口策略聚合大图像上的预测结果，并通过后处理优化二值篡改掩膜，从而实现高精度定位。实验表明，该方法在多个生成模型衍生的数据集上超越现有最先进局部篡改检测器，且对常见后处理操作具有鲁棒性。
链接: https://arxiv.org/abs/2511.20722
作者: Minh Thong Doi(IMT Nord Europe, CRIStAL),Jan Butora(CRIStAL),Vincent Itier(IMT Nord Europe, CRIStAL),Jérémie Boulanger(CRIStAL),Patrick Bas(CRIStAL)
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:We introduce DinoLizer, a DINOv2-based model for localizing manipulated regions in generative inpainting. Our method builds on a DINOv2 model pretrained to detect synthetic images on the B-Free dataset. We add a linear classification head on top of the Vision Transformer’s patch embeddings to predict manipulations at a 14\times 14 patch resolution. The head is trained to focus on semantically altered regions, treating non-semantic edits as part of the original content. Because the ViT accepts only fixed-size inputs, we use a sliding-window strategy to aggregate predictions over larger images; the resulting heatmaps are post-processed to refine the estimated binary manipulation masks. Empirical results show that DinoLizer surpasses state-of-the-art local manipulation detectors on a range of inpainting datasets derived from different generative models. It remains robust to common post-processing operations such as resizing, noise addition, and JPEG (double) compression. On average, DinoLizer achieves a 12% higher Intersection-over-Union (IoU) than the next best model, with even greater gains after post-processing. Our experiments with off-the-shelf DINOv2 demonstrate the strong representational power of Vision Transformers for this task. Finally, extensive ablation studies comparing DINOv2 and its successor, DINOv3, in deepfake localization confirm DinoLizer’s superiority. The code will be publicly available upon acceptance of the paper.
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[CV-120] Foundry: Distilling 3D Foundation Models for the Edge
【速读】：该论文旨在解决大规模自监督学习（Self-Supervised Learning, SSL）预训练的基础模型（Foundation Models）因参数量大、计算成本高而难以部署在资源受限的边缘设备（如机器人和AR/VR头显）的问题。现有压缩技术（如标准知识蒸馏）虽能生成高效的小模型，但会牺牲基础模型固有的下游任务无关的通用表征能力。解决方案的关键在于提出一种全新的蒸馏范式——基础模型蒸馏（Foundation Model Distillation, FMD），其核心是训练学生模型学习教师模型的“超令牌”（SuperTokens）集合，从而重建教师模型在token级别上的表征，捕捉其潜在空间的紧凑基底。该方法实现了模型压缩与通用表征能力保留之间的平衡，使得单个蒸馏模型在分类、部件分割及少样本场景等多个下游任务中均表现出强迁移性，同时显著减少token数量和浮点运算次数（FLOPs），提升了在边缘硬件上的实用性。
链接: https://arxiv.org/abs/2511.20721
作者: Guillaume Letellier,Siddharth Srivastava(IIT Delhi),Frédéric Jurie,Gaurav Sharma(IIT Kanpur)
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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Abstract:Foundation models pre-trained with self-supervised learning (SSL) on large-scale datasets have become powerful general-purpose feature extractors. However, their immense size and computational cost make them prohibitive for deployment on edge devices such as robots and AR/VR headsets. Existing compression techniques like standard knowledge distillation create efficient ‘specialist’ models but sacrifice the crucial, downstream-agnostic generality that makes foundation models so valuable. In this paper, we introduce Foundation Model Distillation (FMD), a new paradigm for compressing large SSL models into compact, efficient, and faithful proxies that retain their general-purpose representational power. We present Foundry, the first implementation of FMD for 3D point clouds. Our approach, Foundry, trains a student to learn a compressed set of SuperTokens that reconstruct the teacher’s token-level representations, capturing a compact basis of its latent space. A single distilled model maintains strong transferability across diverse downstream tasks-classification, part segmentation, and few-shot scenarios-approaching full foundation-model performance while using significantly fewer tokens and FLOPs, making such models more practical for deployment on resourceconstrained hardware.
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[CV-121] DeeAD: Dynamic Early Exit of Vision-Language Action for Efficient Autonomous Driving
【速读】：该论文旨在解决视觉-语言-动作（Vision-Language Action, VLA）模型在自动驾驶规划中因深层Transformer结构导致的显著推理延迟问题。解决方案的关键在于提出一种无需重新训练的、基于动作引导的早退出（early-exit）框架DeeAD，其通过评估中间轨迹的物理可行性来提前终止推理过程；具体而言，当预测轨迹与轻量级规划先验（如导航或低精度规划）在允许偏差（2米内）范围内一致时即终止计算，从而实现高效推理。此外，引入多跳控制器根据得分变化率自适应跳过冗余层，进一步提升效率，且无需修改现有VLA模型结构（如ORION），实验证明可在保持规划质量和安全性的前提下实现最高达28%的Transformer层稀疏性和29%的延迟降低。
链接: https://arxiv.org/abs/2511.20720
作者: Haibo HU,Lianming Huang,Nan Guan,Chun Jason Xue
机构: City University of Hong Kong (香港城市大学); Mohamed bin Zayed University of Artificial Intelligence (穆罕默德·本·扎耶德人工智能大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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Abstract:Vision-Language Action (VLA) models unify perception, reasoning, and trajectory generation for autonomous driving, but suffer from significant inference latency due to deep transformer stacks. We present DeeAD, a training-free, action-guided early-exit framework that accelerates VLA planning by evaluating the physical feasibility of intermediate trajectories. Instead of relying on confidence scores, DeeAD terminates inference when predicted trajectories align with lightweight planning priors (e.g., Navigation or Low-precision Planning) within a tolerable deviation (2m). To improve efficiency, we introduce a multi-hop controller that adaptively skips redundant layers based on the change rate of scores. DeeAD integrates into existing VLA models, such as ORION, without requiring retraining. Experiments on the Bench2Drive benchmark demonstrate up to 28% transformer-layer sparsity and 29% latency reduction, while preserving planning quality and safety.
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[CV-122] Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection?
【速读】：该论文旨在解决资源受限设备（如交通摄像头）在视频目标识别中面临的挑战，即如何在本地轻量级跟踪与边缘服务器高精度检测之间进行动态决策，以平衡计算延迟、识别准确率和帧率需求。其解决方案的关键在于提出一种基于深度强化学习的自适应选择机制——LTED-Ada，在单设备场景下根据实时帧率、精度和延迟要求动态决定执行本地跟踪还是边缘检测；在多设备场景下进一步引入联邦学习实现跨设备协同策略训练，从而提升算法对未见帧率和性能约束的泛化能力。
链接: https://arxiv.org/abs/2511.20716
作者: Kun Guo,Yun Shen,Xijun Wang,Chaoqun You,Yun Rui,Tony Q. S. Quek
机构: East China Normal University (华东师范大学); Sun Yat-sen University (中山大学); Fudan University (复旦大学); Singapore University of Technology and Design (新加坡科技设计大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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Abstract:Fast and accurate video object recognition, which relies on frame-by-frame video analytics, remains a challenge for resource-constrained devices such as traffic cameras. Recent advances in mobile edge computing have made it possible to offload computation-intensive object detection to edge servers equipped with high-accuracy neural networks, while lightweight and fast object tracking algorithms run locally on devices. This hybrid approach offers a promising solution but introduces a new challenge: deciding when to perform edge detection versus local tracking. To address this, we formulate two long-term optimization problems for both single-device and multi-device scenarios, taking into account the temporal correlation of consecutive frames and the dynamic conditions of mobile edge networks. Based on the formulation, we propose the LTED-Ada in single-device setting, a deep reinforcement learning-based algorithm that adaptively selects between local tracking and edge detection, according to the frame rate as well as recognition accuracy and delay requirement. In multi-device setting, we further enhance LTED-Ada using federated learning to enable collaborative policy training across devices, thereby improving its generalization to unseen frame rates and performance requirements. Finally, we conduct extensive hardware-in-the-loop experiments using multiple Raspberry Pi 4B devices and a personal computer as the edge server, demonstrating the superiority of LTED-Ada.
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[CV-123] Inferix: A Block-Diffusion based Next-Generation Inference Engine for World Simulation
【速读】：该论文旨在解决当前视觉基础模型在生成长时、物理真实且可交互的高质量视频方面的能力瓶颈，特别是在世界模型（World Models）构建中面临的生成连贯性差、效率低以及难以支持实时交互等问题。其核心解决方案是提出一种半自回归（semi-autoregressive）解码范式——即块扩散（block-diffusion）方法，该方法通过在每个块内应用扩散机制并依赖前序块进行条件控制，在保留扩散模型高保真度的同时引入类似大语言模型（LLM）的键值缓存（KV Cache）管理机制，从而实现高效、可变长度且稳定的视频生成。这一创新显著提升了生成视频的时空一致性与推理能力，使世界模型迈向更逼真的沉浸式环境模拟。
链接: https://arxiv.org/abs/2511.20714
作者: Inferix Team:Tianyu Feng,Yizeng Han,Jiahao He,Yuanyu He,Xi Lin,Teng Liu,Hanfeng Lu,Jiasheng Tang,Wei Wang,Zhiyuan Wang,Jichao Wu,Mingyang Yang,Yinghao Yu,Zeyu Zhang,Bohan Zhuang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:World models serve as core simulators for fields such as agentic AI, embodied AI, and gaming, capable of generating long, physically realistic, and interactive high-quality videos. Moreover, scaling these models could unlock emergent capabilities in visual perception, understanding, and reasoning, paving the way for a new paradigm that moves beyond current LLM-centric vision foundation models. A key breakthrough empowering them is the semi-autoregressive (block-diffusion) decoding paradigm, which merges the strengths of diffusion and autoregressive methods by generating video tokens in block-applying diffusion within each block while conditioning on previous ones, resulting in more coherent and stable video sequences. Crucially, it overcomes limitations of standard video diffusion by reintroducing LLM-style KV Cache management, enabling efficient, variable-length, and high-quality generation. Therefore, Inferix is specifically designed as a next-generation inference engine to enable immersive world synthesis through optimized semi-autoregressive decoding processes. This dedicated focus on world simulation distinctly sets it apart from systems engineered for high-concurrency scenarios (like vLLM or SGLang) and from classic video diffusion models (such as xDiTs). Inferix further enhances its offering with interactive video streaming and profiling, enabling real-time interaction and realistic simulation to accurately model world dynamics. Additionally, it supports efficient benchmarking through seamless integration of LV-Bench, a new fine-grained evaluation benchmark tailored for minute-long video generation scenarios. We hope the community will work together to advance Inferix and foster world model exploration. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2511.20714 [cs.CV] (or arXiv:2511.20714v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2511.20714 Focus to learn more arXiv-issued DOI via DataCite
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[CV-124] Are Neuro-Inspired Multi-Modal Vision-Language Models Resilient to Membership Inference Privacy Leakage?
【速读】：该论文旨在解决多模态视觉语言模型（Vision-Language Models, VLMs）在黑盒环境下面临的成员推理攻击（Membership Inference Attack, MIA）隐私泄露问题，即攻击者通过分析模型输出来推断特定训练数据是否曾被用于训练。解决方案的关键在于提出一种基于系统神经科学启发的拓扑正则化框架（tau），通过引入神经启发的拓扑结构约束，在保持模型生成能力（如文本描述与参考句相似性）的同时显著提升模型对图像-文本关联隐私攻击的鲁棒性。实验表明，采用tau=0配置的神经启发VLM（NEURO VLM）相较于基线模型在COCO、CC3M和NoCaps数据集上平均ROC-AUC下降24%，证明其在不明显损害模型效用的前提下增强了隐私保护能力。
链接: https://arxiv.org/abs/2511.20710
作者: David Amebley,Sayanton Dibbo
机构: The University of Alabama (阿拉巴马大学); Alabama Center for the Advancement of AI (阿拉巴马人工智能进步中心); Trustworthy AI Lab (可信人工智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:In the age of agentic AI, the growing deployment of multi-modal models (MMs) has introduced new attack vectors that can leak sensitive training data in MMs, causing privacy leakage. This paper investigates a black-box privacy attack, i.e., membership inference attack (MIA) on multi-modal vision-language models (VLMs). State-of-the-art research analyzes privacy attacks primarily to unimodal AI-ML systems, while recent studies indicate MMs can also be vulnerable to privacy attacks. While researchers have demonstrated that biologically inspired neural network representations can improve unimodal model resilience against adversarial attacks, it remains unexplored whether neuro-inspired MMs are resilient against privacy attacks. In this work, we introduce a systematic neuroscience-inspired topological regularization (tau) framework to analyze MM VLMs resilience against image-text-based inference privacy attacks. We examine this phenomenon using three VLMs: BLIP, PaliGemma 2, and ViT-GPT2, across three benchmark datasets: COCO, CC3M, and NoCaps. Our experiments compare the resilience of baseline and neuro VLMs (with topological regularization), where the tau 0 configuration defines the NEURO variant of VLM. Our results on the BLIP model using the COCO dataset illustrate that MIA attack success in NEURO VLMs drops by 24% mean ROC-AUC, while achieving similar model utility (similarities between generated and reference captions) in terms of MPNet and ROUGE-2 metrics. This shows neuro VLMs are comparatively more resilient against privacy attacks, while not significantly compromising model utility. Our extensive evaluation with PaliGemma 2 and ViT-GPT2 models, on two additional datasets: CC3M and NoCaps, further validates the consistency of the findings. This work contributes to the growing understanding of privacy risks in MMs and provides evidence on neuro VLMs privacy threat resilience.
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[CV-125] Deep Parameter Interpolation for Scalar Conditioning
【速读】：该论文旨在解决深度神经网络在处理具有额外标量输入（如时间步长或噪声水平）任务时的架构设计难题，尤其是在生成式模型（如扩散模型和流匹配模型）中，如何高效且灵活地将标量信息融入高维向量（如图像）的表示过程。传统方法通常通过将标量编码为额外的图像输入或在特定网络组件中融合标量与向量信息来实现条件建模，但这些方式限制了网络架构的选择灵活性。其解决方案的关键在于提出一种通用的**深度参数插值（Deep Parameter Interpolation, DPI）**方法：在单个网络中维护两组可学习参数，并在训练和采样过程中根据标量值动态插值这两组参数，从而实现对标量的显式依赖，而无需修改网络结构本身。此方法显著提升了去噪性能和样本质量，同时保持与标准标量条件技术相当的计算效率。
链接: https://arxiv.org/abs/2511.21028
作者: Chicago Y. Park,Michael T. McCann,Cristina Garcia-Cardona,Brendt Wohlberg,Ulugbek S. Kamilov
机构: WashU (华盛顿大学); Los Alamos National Laboratory (洛斯阿拉莫斯国家实验室); UW–Madison (威斯康星大学麦迪逊分校)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We propose deep parameter interpolation (DPI), a general-purpose method for transforming an existing deep neural network architecture into one that accepts an additional scalar input. Recent deep generative models, including diffusion models and flow matching, employ a single neural network to learn a time- or noise level-dependent vector field. Designing a network architecture to accurately represent this vector field is challenging because the network must integrate information from two different sources: a high-dimensional vector (usually an image) and a scalar. Common approaches either encode the scalar as an additional image input or combine scalar and vector information in specific network components, which restricts architecture choices. Instead, we propose to maintain two learnable parameter sets within a single network and to introduce the scalar dependency by dynamically interpolating between the parameter sets based on the scalar value during training and sampling. DPI is a simple, architecture-agnostic method for adding scalar dependence to a neural network. We demonstrate that our method improves denoising performance and enhances sample quality for both diffusion and flow matching models, while achieving computational efficiency comparable to standard scalar conditioning techniques. Code is available at this https URL.
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[CV-126] Adversarial Multi-Task Learning for Liver Tumor Segmentation Dynamic Enhancement Regression and Classification
【速读】：该论文旨在解决肝脏肿瘤的分割（segmentation）、动态增强回归（dynamic enhancement regression）和分类（classification）三项任务难以在端到端框架中协同完成的问题，其核心挑战在于缺乏能够捕捉多任务间相关性以实现相互提升的有效架构，以及无法高效提取动态磁共振成像（dynamic MRI）中的时序信息。解决方案的关键在于提出多任务交互对抗学习网络（MTI-Net），其创新点包括：1）引入多域信息熵融合（MdIEF）模块，利用熵感知的高频谱信息融合频率域与谱域特征，显著增强动态MRI数据的提取与利用能力；2）设计任务交互模块以建立分割与回归任务间的高阶一致性，促进跨任务协同优化；3）提出任务驱动判别器（TDD）用于挖掘任务内部高阶关系；4）采用浅层Transformer进行位置编码，有效建模动态MRI序列内的时序依赖性。实验表明，MTI-Net在238例患者数据上实现了多任务高性能，具备临床辅助评估肝肿瘤的潜力。
链接: https://arxiv.org/abs/2511.20793
作者: Xiaojiao Xiao,Qinmin Vivian Hu,Tae Hyun Kim,Guanghui Wang
机构: Toronto Metropolitan University (多伦多都会大学); Hanyang University (汉阳大学)
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Liver tumor segmentation, dynamic enhancement regression, and classification are critical for clinical assessment and diagnosis. However, no prior work has attempted to achieve these tasks simultaneously in an end-to-end framework, primarily due to the lack of an effective framework that captures inter-task relevance for mutual improvement and the absence of a mechanism to extract dynamic MRI information effectively. To address these challenges, we propose the Multi-Task Interaction adversarial learning Network (MTI-Net), a novel integrated framework designed to tackle these tasks simultaneously. MTI-Net incorporates Multi-domain Information Entropy Fusion (MdIEF), which utilizes entropy-aware, high-frequency spectral information to effectively integrate features from both frequency and spectral domains, enhancing the extraction and utilization of dynamic MRI data. The network also introduces a task interaction module that establishes higher-order consistency between segmentation and regression, thus fostering inter-task synergy and improving overall performance. Additionally, we designed a novel task-driven discriminator (TDD) to capture internal high-order relationships between tasks. For dynamic MRI information extraction, we employ a shallow Transformer network to perform positional encoding, which captures the relationships within dynamic MRI sequences. In experiments on a dataset of 238 subjects, MTI-Net demonstrates high performance across multiple tasks, indicating its strong potential for assisting in the clinical assessment of liver tumors. The code is available at: this https URL.
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[CV-127] Automated Histopathologic Assessment of Hirschsprung Disease Using a Multi-Stage Vision Transformer Framework
【速读】：该论文旨在解决先天性巨结肠症（Hirschsprung Disease）诊断中 ganglion cells（神经节细胞）识别不准确的问题，其核心挑战在于如何在复杂的组织结构中精确分割并定位这些细胞。解决方案的关键是提出了一种三阶段的分割框架，基于 Vision Transformer (ViT-B/16) 模型，模拟病理学家的诊断流程：首先分割肌层（muscularis propria），再勾画肌间神经丛（myenteric plexus），最后在解剖学有效区域内识别 ganglion cells。该方法通过分辨率特定的切片策略和定制后处理确保解剖一致性，在多个评估指标上表现出优异性能，证明了 ViT 模型在利用全局组织上下文和捕捉微尺度细胞形态方面的有效性，为数字病理学流程提供了减少观察者间差异、辅助诊断 Hirschsprung Disease 的潜在工具。
链接: https://arxiv.org/abs/2511.20734
作者: Youssef Megahed,Saleh Abou-Alwan,Anthony Fuller,Dina El Demellawy,Steven Hawken,Adrian D. C. Chan
机构: Carleton University (卡尔顿大学)
类目: Quantitative Methods (q-bio.QM); Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
备注: 16 pages, 8 figures, 6 tables
Abstract:Hirschsprung Disease is characterized by the absence of ganglion cells in the myenteric plexus. Therefore, their correct identification is crucial for diagnosing Hirschsprung disease. We introduce a three-stage segmentation framework based on a Vision Transformer (ViT-B/16) that mimics the pathologist’s diagnostic approach. The framework sequentially segments the muscularis propria, delineates the myenteric plexus, and identifies ganglion cells within anatomically valid regions. 30 whole-slide images of colon tissue were used, each containing expert manual annotations of muscularis, plexus, and ganglion cells at varying levels of certainty. A 5-fold cross-validation scheme was applied to each stage, along with resolution-specific tiling strategies and tailored postprocessing to ensure anatomical consistency. The proposed method achieved a Dice coefficient of 89.9% and a Plexus Inclusion Rate of 100% for muscularis segmentation. Plexus segmentation reached a recall of 94.8%, a precision of 84.2% and a Ganglia Inclusion Rate of 99.7%. For high-certainty ganglion cells, the model achieved 62.1% precision and 89.1% recall, while joint certainty scores yielded 67.0% precision. These results indicate that ViT-based models are effective at leveraging global tissue context and capturing cellular morphology at small scales, even within complex histological tissue structures. This multi-stage methodology has great potential to support digital pathology workflows by reducing inter-observer variability and assisting in the evaluation of Hirschsprung disease. The clinical impact will be evaluated in future work with larger multi-center datasets and additional expert annotations.
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[CV-128] A Fractional Variational Approach to Spectral Filtering Using the Fourier Transform
【速读】：该论文旨在解决拉曼光谱分析中荧光信号干扰与噪声对微弱光谱特征的掩盖问题，这些问题会严重影响化学特征（如峰位、强度和峰面积）的准确识别。解决方案的关键在于提出一种基于频域变分方法的滤波策略，通过引入分数阶微分（fractional derivatives）构建能量泛函，在噪声抑制与关键化学特征保留之间实现平衡；同时利用香农熵（Shannon entropy）优化正则化参数和微分阶数，从而提升滤波效果的鲁棒性与可实现性。
链接: https://arxiv.org/abs/2511.20675
作者: Nelson H. T. Lemes,José Claudinei Ferreira,Higor V. M. Ferreira
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Mathematical Physics (math-ph)
备注: 31 pages, 3 figures, 2 tables
Abstract:The interference of fluorescence signals and noise remains a significant challenge in Raman spectrum analysis, often obscuring subtle spectral features that are critical for accurate analysis. Inspired by variational methods similar to those used in image denoising, our approach minimizes a functional involving fractional derivatives to balance noise suppression with the preservation of essential chemical features of the signal, such as peak position, intensity, and area. The original problem is reformulated in the frequency domain through the Fourier transform, making the implementation simple and fast. In this work, we discuss the theoretical framework, practical implementation, and the advantages and limitations of this method in the context of simulated Raman data, as well as in image processing. The main contribution of this article is the combination of a variational approach in the frequency domain, the use of fractional derivatives, and the optimization of the regularization parameter and derivative order through the concept of Shannon entropy. This work explores how the fractional order, combined with the regularization parameter, affects noise removal and preserves the essential features of the spectrum and image. Finally, the study shows that the combination of the proposed strategies produces an efficient, robust, and easily implementable filter.
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人工智能
[AI-0] Agent ic Learner with Grow-and-Refine Multimodal Semantic Memory
【速读】：该论文旨在解决多模态大语言模型（Multimodal Large Language Models, MLLMs）在持续学习和跨任务推理中缺乏有效记忆机制的问题，特别是现有基于轨迹的记忆方法存在“简短偏差”（briefness bias），难以保留领域知识，并且仅记录单一模态的行为痕迹，无法捕捉视觉注意力与逻辑推理协同作用的多模态语义信息。解决方案的关键在于提出ViLoMem——一种双流记忆框架，通过分离编码视觉干扰模式（visual distraction patterns）和逻辑推理错误（logical reasoning errors），构建紧凑、基于模式（schema-based）的多模态语义记忆；该框架遵循“生长与精炼”（grow-and-refine）原则，逐步积累并更新多模态知识，在保持策略稳定性的同时避免灾难性遗忘，从而显著提升跨域代理学习中的准确率并减少重复错误。
链接: https://arxiv.org/abs/2511.21678
作者: Weihao Bo,Shan Zhang,Yanpeng Sun,Jingjing Wu,Qunyi Xie,Xiao Tan,Kunbin Chen,Wei He,Xiaofan Li,Na Zhao,Jingdong Wang,Zechao Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:MLLMs exhibit strong reasoning on isolated queries, yet they operate de novo – solving each problem independently and often repeating the same mistakes. Existing memory-augmented agents mainly store past trajectories for reuse. However, trajectory-based memory suffers from brevity bias, gradually losing essential domain knowledge. More critically, even in truly multimodal problem-solving settings, it records only a single-modality trace of past behavior, failing to preserve how visual attention and logical reasoning jointly contributed to the solution. This is fundamentally misaligned with human cognition: semantic memory is both multimodal and integrated, preserving visual and abstract knowledge through coordinated but distinct representational streams. We thus introduce ViLoMem, a dual-stream memory framework that constructs compact, schema-based memory. It separately encodes visual distraction patterns and logical reasoning errors, enabling MLLMs to learn from their successful and failed experiences. Following a grow-and-refine principle, the system incrementally accumulates and updates multimodal semantic knowledge – preserving stable, generalizable strategies while avoiding catastrophic forgetting. Across six multimodal benchmarks, ViLoMem consistently improves pass@1 accuracy and substantially reduces repeated visual and logical errors. Ablations confirm the necessity of dual-stream memory with explicit distraction–hallucination separation, demonstrating the value of error-aware multimodal memory for lifelong and cross-domain agentic learning. Our project page will be available at this https URL.
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[AI-1] hrough the telecom lens: Are all training samples important?
【速读】：该论文旨在解决电信领域中AI训练数据量大、样本冗余且传统方法假设所有训练样本同等重要所带来的计算效率低下和资源浪费问题。其核心挑战在于如何在不牺牲模型精度的前提下，识别并优先处理对模型学习具有显著影响的样本，从而降低计算与能源消耗。解决方案的关键在于提出一种基于样本级梯度分析的样本重要性框架（sample importance framework），通过跨训练轮次（epochs）识别样本的影响模式与冗余性，择优保留高价值数据，实现计算资源的优化分配，最终在三个真实电信数据集上验证了该方法在维持性能的同时显著减少数据需求与计算开销，推动可持续AI在电信领域的应用。
链接: https://arxiv.org/abs/2511.21668
作者: Shruti Bothe,Illyyne Saffar,Aurelie Boisbunon,Hasan Farooq,Julien Forgeat,Md Moin Uddin Chowdhury
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 8pages, 1 table, 8 figures
Abstract:The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is often noisy, high-dimensional, costly to store, process, and label. Despite Ai’s critical role, standard workflows still assume all training samples contribute equally. On the other hand, next generation systems require AI models that are accurate, efficient, and this http URL paper questions the assumptions of equal importance by focusing on applying and analyzing the roles of individual samples in telecom training and assessing whether the proposed model optimizes computation and energy use. we perform sample-level gradient analysis across epochs to identify patterns of influence and redundancy in model learning. Based on this, we propose a sample importance framework thats electively prioritizes impactful data and reduces computation without compromising accuracy. Experiments on three real-world telecom datasets show that our method [reserves performance while reducing data needs and computational overhead while advancing the goals of sustainable AI in telecommunications.
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[AI-2] Escaping the Verifier: Learning to Reason via Demonstrations
【速读】：该论文旨在解决在缺乏任务特定验证器（verifier）的情况下，如何利用专家演示（expert demonstrations）训练大型语言模型（Large Language Models, LLMs）以获得强推理能力的问题。现有方法依赖强化学习（Reinforcement Learning, RL）与特定任务的验证器配合，但在许多实际场景中，验证器难以获取，而专家演示却相对丰富，却未被有效用于推理训练。解决方案的关键在于提出一种名为 RARO（Relativistic Adversarial Reasoning Optimization）的新框架，其核心是通过逆强化学习（Inverse Reinforcement Learning）从专家演示中学习推理能力：该框架构建了一个策略（policy，生成器）与一个相对评判器（relativistic critic，判别器）之间的对抗交互机制——策略模仿专家答案，评判器则学习区分政策输出与专家答案；两者通过强化学习联合、持续地优化，并识别出关键的稳定化技术以实现鲁棒训练。实证结果表明，RARO在 Countdown、DeepMath 和诗歌写作等任务上显著优于无验证器基线方法，且具备与基于验证器的强化学习相当的稳健扩展趋势，证明了仅凭专家演示即可有效激发强推理性能。
链接: https://arxiv.org/abs/2511.21667
作者: Locke Cai,Ivan Provilkov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Training Large Language Models (LLMs) to reason often relies on Reinforcement Learning (RL) with task-specific verifiers. However, many real-world reasoning-intensive tasks lack verifiers, despite offering abundant expert demonstrations that remain under-utilized for reasoning-focused training. We introduce RARO (Relativistic Adversarial Reasoning Optimization) that learns strong reasoning capabilities from only expert demonstrations via Inverse Reinforcement Learning. Our method sets up an adversarial interaction between a policy (generator) and a relativistic critic (discriminator): the policy learns to mimic expert answers, while the critic learns to compare and distinguish between policy and expert answers. Our method trains both the policy and the critic jointly and continuously via RL, and we identify the key stabilization techniques required for robust learning. Empirically, RARO significantly outperforms strong verifier-free baselines on all of our evaluation tasks – Countdown, DeepMath, and Poetry Writing – and enjoys the same robust scaling trends as RL on verifiable tasks. These results demonstrate that our method effectively elicits strong reasoning performance from expert demonstrations alone, enabling robust reasoning learning even when task-specific verifiers are unavailable.
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[AI-3] Bridging the Unavoidable A Priori: A Framework for Comparative Causal Modeling
【速读】：该论文试图解决的问题是：如何将系统动力学（System Dynamics）与结构方程模型（Structural Equation Modeling, SEM）这两种基于不同假设的方法整合到一个统一的数学框架中，以促进负责任的人工智能/机器学习（AI/ML）的发展，并更好地理解系统动力学在数据科学和AI/ML应用中的认识论基础。其解决方案的关键在于构建一个能够从分布中生成系统、开发方法并比较结果的通用数学框架，从而克服因方法论假设差异导致的整合障碍（即Dana Meadow所称的“不可避免的先验”）。
链接: https://arxiv.org/abs/2511.21636
作者: Peter S. Hovmand,Kari O’Donnell,Callie Ogland-Hand,Brian Biroscak,Douglas D. Gunzler
机构: 未知
类目: Artificial Intelligence (cs.AI); Applications (stat.AP); Computation (stat.CO); Methodology (stat.ME); Machine Learning (stat.ML)
备注: Presented at 43rd Conference of the International System Dynamics Society in Boston, United States
Abstract:AI/ML models have rapidly gained prominence as innovations for solving previously unsolved problems and their unintended consequences from amplifying human biases. Advocates for responsible AI/ML have sought ways to draw on the richer causal models of system dynamics to better inform the development of responsible AI/ML. However, a major barrier to advancing this work is the difficulty of bringing together methods rooted in different underlying assumptions (i.e., Dana Meadow’s “the unavoidable a priori”). This paper brings system dynamics and structural equation modeling together into a common mathematical framework that can be used to generate systems from distributions, develop methods, and compare results to inform the underlying epistemology of system dynamics for data science and AI/ML applications.
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[AI-4] Scale-Agnostic Kolmogorov-Arnold Geometry in Neural Networks
【速读】：该论文旨在解决神经网络在真实高维数据（如MNIST图像，784维）中是否仍会自发形成Kolmogorov-Arnold几何（KAG）结构及其空间特性的问题。此前研究仅在低维合成任务中观察到此类结构，但其在现实场景中的普适性与尺度不变性尚不明确。解决方案的关键在于对2层多层感知机（MLP）在MNIST分类任务中进行系统性的多尺度空间分析，从局部7像素邻域到整幅28×28图像均检测KAG结构；结果表明，无论采用标准训练还是空间增强训练，KAG结构均稳定出现且具有尺度无关性，揭示了神经网络在学习过程中自发构建组织化、尺度不变几何结构的能力。
链接: https://arxiv.org/abs/2511.21626
作者: Mathew Vanherreweghe,Michael H. Freedman,Keith M. Adams
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Recent work by Freedman and Mulligan demonstrated that shallow multilayer perceptrons spontaneously develop Kolmogorov-Arnold geometric (KAG) structure during training on synthetic three-dimensional tasks. However, it remained unclear whether this phenomenon persists in realistic high-dimensional settings and what spatial properties this geometry exhibits. We extend KAG analysis to MNIST digit classification (784 dimensions) using 2-layer MLPs with systematic spatial analysis at multiple scales. We find that KAG emerges during training and appears consistently across spatial scales, from local 7-pixel neighborhoods to the full 28x28 image. This scale-agnostic property holds across different training procedures: both standard training and training with spatial augmentation produce the same qualitative pattern. These findings reveal that neural networks spontaneously develop organized, scale-invariant geometric structure during learning on realistic high-dimensional data. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2511.21626 [cs.LG] (or arXiv:2511.21626v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2511.21626 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-5] On the Origin of Algorithmic Progress in AI
【速读】：该论文试图解决的问题是：在2012至2023年间，AI训练FLOP效率提升高达22,000倍的现象中，现有算法改进（如小规模消融实验）所能解释的部分远低于这一数值，即存在显著的“效率差距”。传统假设认为算法进步对所有模型规模均具普适性效率增益，但本文质疑这一前提。解决方案的关键在于引入尺度依赖性效率改进的概念——通过系统性的缩放实验（scaling experiments），发现部分算法（如LSTM到Transformer的过渡）在计算资源增加时展现出指数级的效率提升，而其他创新则无明显缩放效应。作者通过实验外推与文献估计，最终解释了6,930倍的效率增长，其中多数由LSTM到Transformer的尺度相关演进贡献，揭示了算法效率评估必须考虑计算规模参考点（reference-dependent）。
链接: https://arxiv.org/abs/2511.21622
作者: Hans Gundlach,Alex Fogelson,Jayson Lynch,Ana Trisovic,Jonathan Rosenfeld,Anmol Sandhu,Neil Thompson
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Algorithms have been estimated to increase AI training FLOP efficiency by a factor of 22,000 between 2012 and 2023 [Ho et al., 2024]. Running small-scale ablation experiments on key innovations from this time period, we are able to account for less than 10x of these gains. Surveying the broader literature, we estimate that additional innovations not included in our ablations account for less than 10x, yielding a total under 100x. This leads us to conduct scaling experiments, which reveal that much of this efficiency gap can be explained by algorithms with scale-dependent efficiency improvements. In particular, we conduct scaling experiments between LSTMs and Transformers, finding exponent differences in their compute-optimal scaling law while finding little scaling difference for many other innovations. These experiments demonstrate that - contrary to standard assumptions - an algorithm’s efficiency gains are tied to compute scale. Using experimental extrapolation and literature estimates, we account for 6,930x efficiency gains over the same time period, with the scale-dependent LSTM-to-Transformer transition accounting for the majority of gains. Our results indicate that algorithmic progress for small models has been far slower than previously assumed, and that measures of algorithmic efficiency are strongly reference-dependent.
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[AI-6] On the Limits of Innate Planning in Large Language Models
【速读】：该论文旨在解决当前大语言模型（Large Language Models, LLMs）在规划能力和状态感知推理方面存在的不确定性问题，特别是在不依赖代码执行或其他外部工具的情况下，评估其在复杂任务中的表现。研究以经典的8数码谜题（8-puzzle）为实验场景，因其需要精确的状态跟踪和目标导向的规划，且可逐步验证结果。关键解决方案在于设计多阶段测试：首先在标准提示条件下（零样本、思维链、算法链）评估模型性能，随后引入分层纠错反馈，并进一步加入外部移动验证器仅提供合法动作。结果显示，尽管纠错反馈能提升部分模型的成功率，但多数成功路径冗长、计算成本高且非直接；即使有外部验证支持，所有模型仍无法解决任何谜题。这揭示了LLMs存在两大核心缺陷：（1）内部状态表示脆弱，导致频繁产生非法动作；（2）启发式规划能力弱，易陷入循环或选择无益于目标的动作。因此，论文指出，若缺乏外部工具如代码解释器，当前LLMs在规划任务上仍存在显著局限，未来进展可能需引入显式状态维护机制与结构化搜索策略。
链接: https://arxiv.org/abs/2511.21591
作者: Charles Schepanowski,Charles Ling
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 33 pages, 7 figures
Abstract:Large language models (LLMs) achieve impressive results on many benchmarks, yet their capacity for planning and stateful reasoning remains unclear. We study these abilities directly, without code execution or other tools, using the 8-puzzle: a classic task that requires state tracking and goal-directed planning while allowing precise, step-by-step evaluation. Four models are tested under common prompting conditions (Zero-Shot, Chain-of-Thought, Algorithm-of-Thought) and with tiered corrective feedback. Feedback improves success rates for some model-prompt combinations, but many successful runs are long, computationally expensive, and indirect. We then examine the models with an external move validator that provides only valid moves. Despite this level of assistance, none of the models solve any puzzles in this setting. Qualitative analysis reveals two dominant deficits across all models: (1) brittle internal state representations, leading to frequent invalid moves, and (2) weak heuristic planning, with models entering loops or selecting actions that do not reduce the distance to the goal state. These findings indicate that, in the absence of external tools such as code interpreters, current LLMs have substantial limitations in planning and that further progress may require mechanisms for maintaining explicit state and performing structured search.
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[AI-7] Model-Based Policy Adaptation for Closed-Loop End-to-End Autonomous Driving NEURIPS2025
【速读】：该论文旨在解决端到端（End-to-end, E2E）自动驾驶模型在开环评估中表现优异，但在闭环（closed-loop）部署时易出现级联误差和泛化能力差的问题。解决方案的关键在于提出一种基于模型的策略适配框架（Model-based Policy Adaptation, MPA），其核心包括：利用几何一致的仿真引擎生成多样化的反事实轨迹以扩展训练数据分布；在此基础上训练一个扩散模型驱动的策略适配器（diffusion-based policy adapter）来微调基础策略输出，并引入多步Q值模型（multi-step Q value model）用于评估长期后果；推理时通过适配器生成多个轨迹候选，由Q值模型选择预期效用最高的动作序列，从而提升部署阶段的鲁棒性和安全性。
链接: https://arxiv.org/abs/2511.21584
作者: Haohong Lin,Yunzhi Zhang,Wenhao Ding,Jiajun Wu,Ding Zhao
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: Published at NeurIPS 2025: this https URL
Abstract:End-to-end (E2E) autonomous driving models have demonstrated strong performance in open-loop evaluations but often suffer from cascading errors and poor generalization in closed-loop settings. To address this gap, we propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents during deployment. MPA first generates diverse counterfactual trajectories using a geometry-consistent simulation engine, exposing the agent to scenarios beyond the original dataset. Based on this generated data, MPA trains a diffusion-based policy adapter to refine the base policy’s predictions and a multi-step Q value model to evaluate long-term outcomes. At inference time, the adapter proposes multiple trajectory candidates, and the Q value model selects the one with the highest expected utility. Experiments on the nuScenes benchmark using a photorealistic closed-loop simulator demonstrate that MPA significantly improves performance across in-domain, out-of-domain, and safety-critical scenarios. We further investigate how the scale of counterfactual data and inference-time guidance strategies affect overall effectiveness.
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[AI-8] HarmonicAttack: An Adaptive Cross-Domain Audio Watermark Removal
【速读】：该论文旨在解决AI生成音频（AI-generated audio）中存在的安全风险问题，尤其是针对语音伪造（voice-cloning fraud）和虚假信息传播等滥用行为，提出一种高效且通用的音频水印移除方法，以客观评估现有音频水印方案的鲁棒性。其解决方案的关键在于提出HarmonicAttack——一种仅需掌握目标水印生成机制即可训练的通用水印移除模型，采用双路径卷积自编码器（dual-path convolutional autoencoder），在时域与频域联合操作，并结合GAN风格的训练策略，实现对原始音频中嵌入水印的有效分离。该方法在保持近实时性能的同时，展现出优于以往移除技术的去水印能力，并具备良好的跨分布迁移性能。
链接: https://arxiv.org/abs/2511.21577
作者: Kexin Li,Xiao Hu,Ilya Grishchenko,David Lie
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
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Abstract:The availability of high-quality, AI-generated audio raises security challenges such as misinformation campaigns and voice-cloning fraud. A key defense against the misuse of AI-generated audio is by watermarking it, so that it can be easily distinguished from genuine audio. As those seeking to misuse AI-generated audio may thus seek to remove audio watermarks, studying effective watermark removal techniques is critical to being able to objectively evaluate the robustness of audio watermarks against removal. Previous watermark removal schemes either assume impractical knowledge of the watermarks they are designed to remove or are computationally expensive, potentially generating a false sense of confidence in current watermark schemes. We introduce HarmonicAttack, an efficient audio watermark removal method that only requires the basic ability to generate the watermarks from the targeted scheme and nothing else. With this, we are able to train a general watermark removal model that is able to remove the watermarks generated by the targeted scheme from any watermarked audio sample. HarmonicAttack employs a dual-path convolutional autoencoder that operates in both temporal and frequency domains, along with GAN-style training, to separate the watermark from the original audio. When evaluated against state-of-the-art watermark schemes AudioSeal, WavMark, and Silentcipher, HarmonicAttack demonstrates greater watermark removal ability than previous watermark removal methods with near real-time performance. Moreover, while HarmonicAttack requires training, we find that it is able to transfer to out-of-distribution samples with minimal degradation in performance. Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI) Cite as: arXiv:2511.21577 [cs.SD] (or arXiv:2511.21577v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2511.21577 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-9] BAMAS: Structuring Budget-Aware Multi-Agent Systems AAAI2026
【速读】：该论文旨在解决大规模语言模型（Large Language Model, LLM）驱动的多智能体系统在实际部署中因成本过高而难以规模化的问题，尤其是在存在明确预算约束时如何高效构建多智能体系统。其解决方案的关键在于提出BAMAS框架，该框架通过两个核心步骤实现预算感知：首先，将智能体选择建模为整数线性规划（Integer Linear Programming, ILP）问题，在性能与成本之间进行权衡以选出最优LLM集合；其次，利用基于强化学习的方法自动确定这些LLM之间的协作拓扑结构，从而优化任务执行效率。实验表明，BAMAS可在保持与现有先进方法相当性能的同时，最高降低86%的成本。
链接: https://arxiv.org/abs/2511.21572
作者: Liming Yang,Junyu Luo,Xuanzhe Liu,Yiling Lou,Zhenpeng Chen
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: Accepted by AAAI 2026 (oral paper)
Abstract:Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks. As these systems scale in complexity, cost becomes an important consideration for practical deployment. However, existing work rarely addresses how to structure multi-agent systems under explicit budget constraints. In this paper, we propose BAMAS, a novel approach for building multi-agent systems with budget awareness. BAMAS first selects an optimal set of LLMs by formulating and solving an Integer Linear Programming problem that balances performance and cost. It then determines how these LLMs should collaborate by leveraging a reinforcement learning-based method to select the interaction topology. Finally, the system is instantiated and executed based on the selected agents and their collaboration topology. We evaluate BAMAS on three representative tasks and compare it with state-of-the-art agent construction methods. Results show that BAMAS achieves comparable performance while reducing cost by up to 86%.
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[AI-10] From Prediction to Foresight: The Role of AI in Designing Responsible Futures
【速读】：该论文旨在解决如何在快速技术变革与复杂全球挑战并存的背景下，提升政策制定者对未来不确定性的应对能力，并推动可持续、负责任的未来设计。其核心问题在于：如何将人工智能（Artificial Intelligence, AI）有效融入前瞻性规划中，以增强决策的伦理性和系统性，而非单纯依赖技术预测。解决方案的关键在于提出“负责任计算预见”（responsible computational foresight）这一新范式，强调以人类为中心的人工智能与计算建模相结合，通过建立基础原则和开发AI驱动的预见工具，辅助政策制定者识别风险、评估多维系统间相互依赖关系，并做出符合伦理、具有长期视野的战略决策。该方案的核心是将AI定位为支持性工具，补充而非替代人类判断，从而赋能政策制定者和社区主动塑造更具韧性与道德正当性的未来。
链接: https://arxiv.org/abs/2511.21570
作者: Maria Perez-Ortiz
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注: Accessible at this https URL
Abstract:In an era marked by rapid technological advancements and complex global challenges, responsible foresight has emerged as an essential framework for policymakers aiming to navigate future uncertainties and shape the future. Responsible foresight entails the ethical anticipation of emerging opportunities and risks, with a focus on fostering proactive, sustainable, and accountable future design. This paper coins the term “responsible computational foresight”, examining the role of human-centric artificial intelligence and computational modeling in advancing responsible foresight, establishing a set of foundational principles for this new field and presenting a suite of AI-driven foresight tools currently shaping it. AI, particularly in conjunction with simulations and scenario analysis, enhances policymakers’ ability to address uncertainty, evaluate risks, and devise strategies geared toward sustainable, resilient futures. However, responsible foresight extends beyond mere technical forecasting; it demands a nuanced understanding of the interdependencies within social, environmental, economic and political systems, alongside a commitment to ethical, long-term decision-making that supports human intelligence. We argue that AI will play a role as a supportive tool in responsible, human-centered foresight, complementing rather than substituting policymaker judgment to enable the proactive shaping of resilient and ethically sound futures. This paper advocates for the thoughtful integration of AI into foresight practices to empower policymakers and communities as they confront the grand challenges of the 21st century.
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[AI-11] Self-Transparency Failures in Expert-Persona LLM s: A Large-Scale Behavioral Audit
【速读】：该论文试图解决的问题是：在高风险专业领域中，语言模型若无法可靠披露其AI身份，将导致用户对其能力边界产生错误信任，从而引发潜在危害。解决方案的关键在于揭示模型自透明性（self-transparency）并非由参数规模决定，而是受训练因素影响——通过系统性审计16个开源模型（参数量4B–671B）在19,200次测试中的表现，发现不同职业角色（如金融顾问与神经外科医生）下模型的自我披露率差异显著（3.5%–73.6%），且模型身份比参数量更能预测行为（ΔR²_adj = 0.359 vs. 0.018）。此外，推理优化反而抑制了透明性（最高下降48.4%），表明安全行为需通过专门设计而非简单扩展规模实现，强调组织必须进行实证验证以确保部署时的行为可靠性。
链接: https://arxiv.org/abs/2511.21569
作者: Alex Diep
机构: 未知
类目: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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Abstract:If a language model cannot reliably disclose its AI identity in expert contexts, users cannot trust its competence boundaries. This study examines self-transparency in models assigned professional personas within high-stakes domains where false expertise risks user harm. Using a common-garden design, sixteen open-weight models (4B–671B parameters) were audited across 19,200 trials. Models exhibited sharp domain-specific inconsistency: a Financial Advisor persona elicited 30.8% disclosure initially, while a Neurosurgeon persona elicited only 3.5%. This creates preconditions for a “Reverse Gell-Mann Amnesia” effect, where transparency in some domains leads users to overgeneralize trust to contexts where disclosure fails. Disclosure ranged from 2.8% to 73.6%, with a 14B model reaching 61.4% while a 70B produced just 4.1%. Model identity predicted behavior better than parameter count ( \Delta R_adj^2 = 0.359 vs 0.018). Reasoning optimization actively suppressed self-transparency in some models, with reasoning variants showing up to 48.4% lower disclosure than base counterparts. Bayesian validation with Rogan–Gladen correction confirmed robustness to measurement error ( \kappa = 0.908 ). These findings demonstrate transparency reflects training factors rather than scale. Organizations cannot assume safety properties transfer to deployment contexts, requiring deliberate behavior design and empirical verification.
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[AI-12] VacuumVLA: Boosting VLA Capabilities via a Unified Suction and Gripping Tool for Complex Robotic Manipulation
【速读】：该论文旨在解决当前视觉-语言-动作（Vision Language Action, VLA）系统中普遍采用的平行双指夹爪在执行特定现实任务时的局限性问题，例如擦拭玻璃表面或无手柄抽屉的开启等，这些问题源于夹爪接触面积不足或缺乏吸附力。解决方案的关键在于提出一种低成本、集成化的硬件设计，将机械双指夹爪与真空吸盘单元整合为单一末端执行器，实现两种操作模态（夹持与吸附）的灵活切换或协同使用，从而显著扩展机器人可执行任务的范围。实验结果表明，该混合末端执行器在DexVLA和Pi0两个先进的VLA框架下均能有效提升复杂任务的成功率。
链接: https://arxiv.org/abs/2511.21557
作者: Hui Zhou,Siyuan Huang,Minxing Li,Hao Zhang,Lue Fan,Shaoshuai Shi
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注: 8 pages
Abstract:Vision Language Action models have significantly advanced general purpose robotic manipulation by harnessing large scale pretrained vision and language representations. Among existing approaches, a majority of current VLA systems employ parallel two finger grippers as their default end effectors. However, such grippers face inherent limitations in handling certain real world tasks such as wiping glass surfaces or opening drawers without handles due to insufficient contact area or lack of adhesion. To overcome these challenges, we present a low cost, integrated hardware design that combines a mechanical two finger gripper with a vacuum suction unit, enabling dual mode manipulation within a single end effector. Our system supports flexible switching or synergistic use of both modalities, expanding the range of feasible tasks. We validate the efficiency and practicality of our design within two state of the art VLA frameworks: DexVLA and Pi0. Experimental results demonstrate that with the proposed hybrid end effector, robots can successfully perform multiple complex tasks that are infeasible for conventional two finger grippers alone. All hardware designs and controlling systems will be released.
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[AI-13] Predictive Safety Shield for Dyna-Q Reinforcement Learning
【速读】：该论文旨在解决强化学习（Reinforcement Learning, RL）在现实世界任务中缺乏安全保证的问题。现有安全屏障（Safety Shield）方法通常依赖于随机采样安全动作或固定的备用控制器，忽略了不同安全动作对未来性能的影响。其解决方案的关键在于提出一种预测性安全屏障（Predictive Safety Shield），该屏障基于模型的强化学习代理在离散空间中运行时，通过局部更新Q函数来实现——该Q函数由环境模型的安全仿真生成的预测驱动。这种方法在维持硬性安全约束的同时，提升了整体性能，并且实验表明即使较短的预测时域也能识别最优路径，且对分布偏移（如仿真与现实之间的差异）具有鲁棒性，无需额外训练。
链接: https://arxiv.org/abs/2511.21531
作者: Jin Pin,Krasowski Hanna,Vanneaux Elena
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO); Systems and Control (eess.SY)
备注:
Abstract:Obtaining safety guarantees for reinforcement learning is a major challenge to achieve applicability for real-world tasks. Safety shields extend standard reinforcement learning and achieve hard safety guarantees. However, existing safety shields commonly use random sampling of safe actions or a fixed fallback controller, therefore disregarding future performance implications of different safe actions. In this work, we propose a predictive safety shield for model-based reinforcement learning agents in discrete space. Our safety shield updates the Q-function locally based on safe predictions, which originate from a safe simulation of the environment model. This shielding approach improves performance while maintaining hard safety guarantees. Our experiments on gridworld environments demonstrate that even short prediction horizons can be sufficient to identify the optimal path. We observe that our approach is robust to distribution shifts, e.g., between simulation and reality, without requiring additional training.
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[AI-14] Pessimistic Verification for Open Ended Math Questions
【速读】：该论文旨在解决语言模型在开放性数学问题验证中的性能瓶颈问题，其核心挑战在于错误检测能力的不足。解决方案的关键在于提出了一种“悲观验证”（pessimistic verification）机制：通过为同一证明构建多个并行验证流程，若任一验证结果报告错误，则判定整个证明为错误。该方法无需显著增加计算资源，即可在多个数学验证基准上显著提升性能，且在测试时扩展性优于长链思维（long-CoT）策略。此外，案例研究表明，强模型中的多数假负例实则源于原始数据集标注错误，进一步说明该方法的实际效果被低估，从而凸显了悲观验证在提升语言模型数学推理可靠性与执行长周期任务能力方面的价值。
链接: https://arxiv.org/abs/2511.21522
作者: Yanxing Huang,Zihan Tang,Zejin Lin,Peng Li,Yang Liu
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:The key limitation of the verification performance lies in the ability of error detection. With this intuition we designed several variants of pessimistic verification, which are simple workflows that could significantly improve the verification of open-ended math questions. In pessimistic verification we construct multiple parallel verifications for the same proof, and the proof is deemed incorrect if any one of them reports an error. This simple technique significantly improves the performance across many math verification benchmarks without incurring substantial computational resources. Its token efficiency even surpassed extended long-CoT in test-time scaling. Our case studies further indicate that the majority of false negatives in stronger models are actually caused by annotation errors in the original dataset, so our method’s performance is in fact underestimated. Self-verification for mathematical problems can effectively improve the reliability and performance of language model outputs, and it also plays a critical role in enabling long-horizon mathematical tasks. We believe that research on pessimistic verification will help enhance the mathematical capabilities of language models across a wide range of tasks.
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[AI-15] Mechanistic Interpretability for Transformer-based Time Series Classification
【速读】：该论文旨在解决基于Transformer的模型在时间序列分类任务中因结构复杂而导致内部决策机制不透明的问题。现有可解释性方法多集中于输入到输出的归因分析，难以揭示模型内部的因果机制。其解决方案的关键在于将自然语言处理（NLP）领域中的机制可解释性技术——包括激活修补（activation patching）、注意力显著性（attention saliency）和稀疏自编码器（sparse autoencoders）——迁移并适配至专为时间序列分类设计的Transformer架构中，从而系统地探究单个注意力头与时间步的因果角色，构建内部信息传播的因果图，并识别驱动正确分类的关键组件。
链接: https://arxiv.org/abs/2511.21514
作者: Matīss Kalnāre,Sofoklis Kitharidis,Thomas Bäck,Niki van Stein
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Transformer-based models have become state-of-the-art tools in various machine learning tasks, including time series classification, yet their complexity makes understanding their internal decision-making challenging. Existing explainability methods often focus on input-output attributions, leaving the internal mechanisms largely opaque. This paper addresses this gap by adapting various Mechanistic Interpretability techniques; activation patching, attention saliency, and sparse autoencoders, from NLP to transformer architectures designed explicitly for time series classification. We systematically probe the internal causal roles of individual attention heads and timesteps, revealing causal structures within these models. Through experimentation on a benchmark time series dataset, we construct causal graphs illustrating how information propagates internally, highlighting key attention heads and temporal positions driving correct classifications. Additionally, we demonstrate the potential of sparse autoencoders for uncovering interpretable latent features. Our findings provide both methodological contributions to transformer interpretability and novel insights into the functional mechanics underlying transformer performance in time series classification tasks.
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[AI-16] ool-RoCo: An Agent -as-Tool Self-organization Large Language Model Benchmark in Multi-robot Cooperation
链接: https://arxiv.org/abs/2511.21510
作者: Ke Zhang,Xiaoning Zhao,Ce Zheng,Jiahong Ning,Dandan Zhu,Wenqi Zhang,Chen Sun,Toshiharu Sugawara
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: 9 pages, 3 figures
[AI-17] Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes NEURIPS2025
【速读】：该论文旨在解决当前基于神经网络代理模型（neural surrogates）在航空航天领域，尤其是跨音速条件下三维机翼流动模拟中的应用局限性问题。现有研究多集中于二维翼型或钝体流动，忽视了跨音速流场中高度非线性的可压缩效应及三维现象（如翼尖涡流），导致模型泛化能力不足。解决方案的关键在于构建一个包含约30,000个独特几何与来流条件的高保真CFD数据集，覆盖三维机翼在跨音速区间的体积和表面场数据，并基于此评估先进神经代理模型（如AB-UPT）在几何和来流变化下的分布外（OOD）泛化性能。结果表明，AB-UPT能准确重构未见机翼构型的阻力-升力帕累托前沿，展现出在快速气动设计探索中的高效性和物理一致性潜力。
链接: https://arxiv.org/abs/2511.21474
作者: Fabian Paischer,Leo Cotteleer,Yann Dreze,Richard Kurle,Dylan Rubini,Maurits Bleeker,Tobias Kronlachner,Johannes Brandstetter
机构: 未知
类目: Computational Engineering, Finance, and Science (cs.CE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: NeurIPS 2025 ML4PS Workshop
Abstract:The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wakes. Extending these methods to aerospace, particularly in the transonic regime, remains challenging due to the high level of non-linearity of compressible flows and 3D effects such as wingtip vortices. Existing aerospace datasets predominantly focus on 2D airfoils, neglecting these critical 3D phenomena. To address this gap, we present a new dataset of CFD simulations for 3D wings in the transonic regime. The dataset comprises volumetric and surface-level fields for around 30,000 samples with unique geometry and inflow conditions. This allows computation of lift and drag coefficients, providing a foundation for data-driven aerodynamic optimization of the drag-lift Pareto front. We evaluate several state-of-the-art neural surrogates on our dataset, including Transolver and AB-UPT, focusing on their out-of-distribution (OOD) generalization over geometry and inflow variations. AB-UPT demonstrates strong performance for transonic flowfields and reproduces physically consistent drag-lift Pareto fronts even for unseen wing configurations. Our results demonstrate that AB-UPT can approximate drag-lift Pareto fronts for unseen geometries, highlighting its potential as an efficient and effective tool for rapid aerodynamic design exploration. To facilitate future research, we open-source our dataset at this https URL.
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[AI-18] SpatialBench: Benchmarking Multimodal Large Language Models for Spatial Cognition
【速读】：该论文旨在解决当前多模态大语言模型（Multimodal Large Language Models, MLLMs）在空间认知能力评估中存在的重要缺陷：现有基准测试通常将空间认知简化为单一维度指标，无法刻画其层次结构与各能力之间的相互依赖关系。为此，作者提出了一种分层的空间认知框架（hierarchical spatial cognition framework），将空间智能分解为从基础感知到高级规划的五个逐步复杂的层级，并基于此构建了SpatialBench——一个大规模、细粒度的基准测试集，涵盖15项与这些认知层级对齐的任务。解决方案的关键在于引入一种以高阶能力为导向的统一评价指标，能够跨异构任务可靠地衡量模型的整体空间推理能力，从而揭示MLLMs在感知 grounding 上表现良好但符号推理、因果推断和规划能力严重受限的性能分层现象。
链接: https://arxiv.org/abs/2511.21471
作者: Peiran Xu,Sudong Wang,Yao Zhu,Jianing Li,Yunjian Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Spatial cognition is fundamental to real-world multimodal intelligence, allowing models to effectively interact with the physical environment. While multimodal large language models (MLLMs) have made significant strides, existing benchmarks often oversimplify spatial cognition, reducing it to a single-dimensional metric, which fails to capture the hierarchical structure and interdependence of spatial abilities. To address this gap, we propose a hierarchical spatial cognition framework that decomposes spatial intelligence into five progressively complex levels from basic observation to high-level planning. Building upon this taxonomy, we construct SpatialBench, a large-scale, fine-grained benchmark covering 15 tasks aligned with these cognitive levels. To provide a unified evaluation across heterogeneous tasks, we further introduce a high-level capability-oriented metric that reliably assesses a model’s overall spatial reasoning ability. Extensive experiments over massive MLLMs reveal distinct performance stratification across cognitive levels: models exhibit strong perceptual grounding yet remain limited in symbolic reasoning, causal inference, and planning. Additional human tests demonstrate that humans perform selective, goal-directed abstraction, while MLLMs tend to over-attend to surface details without coherent spatial intent. Our work establishes the first systematic framework for measuring hierarchical spatial cognition in MLLMs, laying the foundation for future spatially intelligent systems.
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[AI-19] MADRA: Multi-Agent Debate for Risk-Aware Embodied Planning
【速读】：该论文旨在解决具身智能体（Embodied AI Agents）在家庭环境中执行任务时的安全性问题，尤其是如何在不牺牲任务执行效率的前提下，准确识别并拒绝危险指令。现有方法要么因偏好对齐训练导致计算成本过高，要么因单一代理安全提示造成过度拒绝（over-rejection）。解决方案的关键在于提出一种无需训练的多智能体辩论风险评估框架（MADRA），通过多个基于大语言模型（LLM）的代理对指令安全性进行辩论，并由一个关键评估器依据逻辑严谨性、风险识别能力、证据质量和清晰度进行评分，结合迭代讨论与共识投票机制，在显著降低误拒率的同时保持对危险任务的高度敏感性。此外，论文还引入了分层认知协同规划框架，集成安全、记忆、规划与自我进化机制，进一步提升任务成功率。
链接: https://arxiv.org/abs/2511.21460
作者: Junjian Wang,Lidan Zhao,Xi Sheryl Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Ensuring the safety of embodied AI agents during task planning is critical for real-world deployment, especially in household environments where dangerous instructions pose significant risks. Existing methods often suffer from either high computational costs due to preference alignment training or over-rejection when using single-agent safety prompts. To address these limitations, we propose MADRA, a training-free Multi-Agent Debate Risk Assessment framework that leverages collective reasoning to enhance safety awareness without sacrificing task performance. MADRA employs multiple LLM-based agents to debate the safety of a given instruction, guided by a critical evaluator that scores responses based on logical soundness, risk identification, evidence quality, and clarity. Through iterative deliberation and consensus voting, MADRA significantly reduces false rejections while maintaining high sensitivity to dangerous tasks. Additionally, we introduce a hierarchical cognitive collaborative planning framework that integrates safety, memory, planning, and self-evolution mechanisms to improve task success rates through continuous learning. We also contribute SafeAware-VH, a benchmark dataset for safety-aware task planning in VirtualHome, containing 800 annotated instructions. Extensive experiments on AI2-THOR and VirtualHome demonstrate that our approach achieves over 90% rejection of unsafe tasks while ensuring that safe-task rejection is low, outperforming existing methods in both safety and execution efficiency. Our work provides a scalable, model-agnostic solution for building trustworthy embodied agents.
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[AI-20] Constructing and Benchmarking: a Labeled Email Dataset for Text-Based Phishing and Spam Detection Framework
【速读】：该论文旨在解决由大型语言模型（Large Language Models, LLMs）生成的钓鱼邮件和垃圾邮件日益增多所带来的网络安全威胁，尤其是这些内容在情感诉求（如紧迫感、恐惧、权威性）和动机（如诱导点击链接、窃取凭证、金融诈骗）层面具有高度欺骗性的问题。解决方案的关键在于构建了一个高质量、细粒度标注的电子邮件数据集，明确区分人类与LLM生成的内容，并基于此对多个LLM进行情绪和动机识别能力的基准测试，最终选用最可靠的模型完成全量数据标注；同时通过语义保持的重写策略评估分类模型在对抗性改写下的鲁棒性，从而为提升AI辅助邮件安全检测系统提供可复现的数据资源与评估框架。
链接: https://arxiv.org/abs/2511.21448
作者: Rebeka Toth,Tamas Bisztray,Richard Dubniczky
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Databases (cs.DB)
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Abstract:Phishing and spam emails remain a major cybersecurity threat, with attackers increasingly leveraging Large Language Models (LLMs) to craft highly deceptive content. This study presents a comprehensive email dataset containing phishing, spam, and legitimate messages, explicitly distinguishing between human- and LLM-generated content. Each email is annotated with its category, emotional appeal (e.g., urgency, fear, authority), and underlying motivation (e.g., link-following, credential theft, financial fraud). We benchmark multiple LLMs on their ability to identify these emotional and motivational cues and select the most reliable model to annotate the full dataset. To evaluate classification robustness, emails were also rephrased using several LLMs while preserving meaning and intent. A state-of-the-art LLM was then assessed on its performance across both original and rephrased emails using expert-labeled ground truth. The results highlight strong phishing detection capabilities but reveal persistent challenges in distinguishing spam from legitimate emails. Our dataset and evaluation framework contribute to improving AI-assisted email security systems. To support open science, all code, templates, and resources are available on our project site.
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[AI-21] EWE: An Agent ic Framework for Extreme Weather Analysis
【速读】：该论文旨在解决极端天气事件（Extreme Weather Events）诊断分析中因传统专家驱动、劳动密集型范式所导致的分析瓶颈问题，该瓶颈严重制约了科学进展。解决方案的关键在于提出首个专注于自动诊断推理的智能代理框架——极端天气专家（Extreme Weather Expert, EWE），其核心创新包括：基于知识引导的规划机制、闭环推理流程以及面向气象领域的定制化工具集；EWE能够自主从原始气象数据生成并解释多模态可视化结果，实现全面诊断分析，并配套构建了首个针对该新兴领域的基准测试集（含103个高影响力事件）与分步评估指标，从而推动自动化科学发现进程，助力发展中国家更公平地获取专业知识与资源。
链接: https://arxiv.org/abs/2511.21444
作者: Zhe Jiang,Jiong Wang,Xiaoyu Yue,Zijie Guo,Wenlong Zhang,Fenghua Ling,Wanli Ouyang,Lei Bai
机构: 未知
类目: Artificial Intelligence (cs.AI); Atmospheric and Oceanic Physics (physics.ao-ph)
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Abstract:Extreme weather events pose escalating risks to global society, underscoring the urgent need to unravel their underlying physical mechanisms. Yet the prevailing expert-driven, labor-intensive diagnostic paradigm has created a critical analytical bottleneck, stalling scientific progress. While AI for Earth Science has achieved notable advances in prediction, the equally essential challenge of automated diagnostic reasoning remains largely unexplored. We present the Extreme Weather Expert (EWE), the first intelligent agent framework dedicated to this task. EWE emulates expert workflows through knowledge-guided planning, closed-loop reasoning, and a domain-tailored meteorological toolkit. It autonomously produces and interprets multimodal visualizations from raw meteorological data, enabling comprehensive diagnostic analyses. To catalyze progress, we introduce the first benchmark for this emerging field, comprising a curated dataset of 103 high-impact events and a novel step-wise evaluation metric. EWE marks a step toward automated scientific discovery and offers the potential to democratize expertise and intellectual resources, particularly for developing nations vulnerable to extreme weather.
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[AI-22] Conversational no-code and multi-agent ic disease module identification and drug repurposing prediction with ChatDRex
链接: https://arxiv.org/abs/2511.21438
作者: Simon Süwer,Kester Bagemihl,Sylvie Baier,Lucia Dicunta,Markus List,Jan Baumbach,Andreas Maier,Fernando M. Delgado-Chaves
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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[AI-23] New Hybrid Heuristics for Pseudo-Boolean Propagation
【速读】：该论文针对伪布尔求解（pseudo-boolean solving）中单位传播（unit propagation）策略的优化问题，旨在提升当前主流混合模式——即结合监视字面量（watched literal）机制与计数法（counting method）——的决策效率。其解决方案的关键在于提出了一种新的启发式策略，该策略在RoundingSAT求解器中显著优于现有方法，能够大幅提升求解性能。
链接: https://arxiv.org/abs/2511.21417
作者: Mia Müßig,Jan Johannsen
机构: 未知
类目: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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Abstract:In pseudo-boolean solving the currently most successful unit propagation strategy is a hybrid mode combining the watched literal scheme with the counting method. This short paper introduces new heuristics for this hybrid decision, which are able to drastically outperform the current method in the RoundingSAT solver.
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[AI-24] Automated Dynamic AI Inference Scaling on HPC-Infrastructure: Integrating Kubernetes Slurm and vLLM
【速读】：该论文旨在解决在高等教育场景中，对生成式 AI（Generative AI）推理需求不断增长背景下，传统高性能计算（High-Performance Computing, HPC）系统难以适配同步、面向用户的动态大语言模型（Large Language Models, LLMs）应用工作负载的问题。其解决方案的关键在于将 vLLM 推理框架、Slurm 作业调度系统与 Kubernetes 容器编排平台集成于超算系统 RAMSES 上，构建一个可高效扩展的 LLM 服务架构；初步基准测试表明，该架构在支持 100、500 和 1000 并发请求时具有良好的可扩展性，端到端延迟增加仅约 500 ms。
链接: https://arxiv.org/abs/2511.21413
作者: Tim Trappen,Robert Keßler,Roland Pabel,Viktor Achter,Stefan Wesner
机构: 未知
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Databases (cs.DB); Performance (cs.PF)
备注: 6 pages, 3 figures
Abstract:Due to rising demands for Artificial Inteligence (AI) inference, especially in higher education, novel solutions utilising existing infrastructure are emerging. The utilisation of High-Performance Computing (HPC) has become a prevalent approach for the implementation of such solutions. However, the classical operating model of HPC does not adapt well to the requirements of synchronous, user-facing dynamic AI application workloads. In this paper, we propose our solution that serves LLMs by integrating vLLM, Slurm and Kubernetes on the supercomputer \textitRAMSES. The initial benchmark indicates that the proposed architecture scales efficiently for 100, 500 and 1000 concurrent requests, incurring only an overhead of approximately 500 ms in terms of end-to-end latency.
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[AI-25] RIA: A Ranking-Infused Approach for Optimized listwise CTR Prediction
【速读】：该论文旨在解决现有推荐系统中排序（ranking）与重排序（reranking）过程解耦导致的列表级评估模型表现弱的问题，具体表现为组合稀疏性（combinatorial sparsity）和在严格延迟约束下表达能力受限。其解决方案的关键在于提出一种统一的端到端框架RIA（Ranking-Infused Architecture），通过四个核心模块实现点对点（pointwise）与列表级（listwise）评估的无缝融合：(1) 用户与候选双Transformer（User and Candidate Dual Transformer, UCDT）用于细粒度建模用户-物品-上下文交互；(2) 上下文感知的用户历史与目标模块（Context-aware User History and Target, CUHT）实现位置敏感的偏好学习；(3) 列表级多层级状态转移单元（Listwise Multi-HSTU, LMH）捕获物品间的层次依赖关系；(4) 嵌入缓存模块（Embedding Cache, EC）在推理阶段平衡效率与效果。RIA通过共享排序与重排序阶段的表示，实现了丰富的上下文知识迁移并保持低延迟，显著提升了推荐性能，在公开与工业数据集上均取得AUC和LogLoss指标的提升，并在美团广告系统中实现CTR提升+1.69%、CPM提升+4.54%。
链接: https://arxiv.org/abs/2511.21394
作者: Guoxiao Zhang,Tan Qu,Ao Li,DongLin Ni,Qianlong Xie,Xingxing Wang
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
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Abstract:Reranking improves recommendation quality by modeling item interactions. However, existing methods often decouple ranking and reranking, leading to weak listwise evaluation models that suffer from combinatorial sparsity and limited representational power under strict latency constraints. In this paper, we propose RIA (Ranking-Infused Architecture), a unified, end-to-end framework that seamlessly integrates pointwise and listwise evaluation. RIA introduces four key components: (1) the User and Candidate DualTransformer (UCDT) for fine-grained user-item-context modeling; (2) the Context-aware User History and Target (CUHT) module for position-sensitive preference learning; (3) the Listwise Multi-HSTU (LMH) module to capture hierarchical item dependencies; and (4) the Embedding Cache (EC) module to bridge efficiency and effectiveness during inference. By sharing representations across ranking and reranking, RIA enables rich contextual knowledge transfer while maintaining low latency. Extensive experiments show that RIA outperforms state-of-the-art models on both public and industrial datasets, achieving significant gains in AUC and LogLoss. Deployed in Meituan advertising system, RIA yields a +1.69% improvement in Click-Through Rate (CTR) and a +4.54% increase in Cost Per Mille (CPM) in online A/B tests.
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[AI-26] FITRep: Attention-Guided Item Representation via MLLM s
【速读】：该论文旨在解决在线平台中因近似重复内容（near-duplicate items）导致的用户体验下降问题，这些问题通常表现为视觉和文本高度相似的物品在推荐或广告系统中重复出现，从而降低用户满意度与平台收益。现有方法依赖多模态大语言模型（Multimodal Large Language Models, MLLMs）生成嵌入表示，但将其视为黑箱处理，忽视了内容结构中的关键信息（如主元素与辅助元素之间的关系），进而引发局部结构坍塌（local structural collapse）问题。解决方案的关键在于提出 FITRep——首个基于注意力机制的白盒物品表示框架，其核心创新包括：(1) 利用MLLMs提取概念层级信息（Concept Hierarchical Information Extraction, CHIE），显式建模内容语义层次；(2) 设计结构保持的降维方法（Structure-Preserving Dimensionality Reduction, SPDR），采用自适应UMAP实现高效压缩；(3) 基于FAISS的聚类策略（FAISS-Based Clustering, FBC），为每个物品分配唯一聚类ID以支持细粒度去重。该方案在美团广告系统上线后，在线A/B测试显示点击率（CTR）提升3.60%，每千次展示收入（CPM）提升4.25%，验证了其有效性与实际落地价值。
链接: https://arxiv.org/abs/2511.21389
作者: Guoxiao Zhang,Ao Li,Tan Qu,Qianlong Xie,Xingxing Wang
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
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Abstract:Online platforms usually suffer from user experience degradation due to near-duplicate items with similar visuals and text. While Multimodal Large Language Models (MLLMs) enable multimodal embedding, existing methods treat representations as black boxes, ignoring structural relationships (e.g., primary vs. auxiliary elements), leading to local structural collapse problem. To address this, inspired by Feature Integration Theory (FIT), we propose FITRep, the first attention-guided, white-box item representation framework for fine-grained item deduplication. FITRep consists of: (1) Concept Hierarchical Information Extraction (CHIE), using MLLMs to extract hierarchical semantic concepts; (2) Structure-Preserving Dimensionality Reduction (SPDR), an adaptive UMAP-based method for efficient information compression; and (3) FAISS-Based Clustering (FBC), a FAISS-based clustering that assigns each item a unique cluster id using FAISS. Deployed on Meituan’s advertising system, FITRep achieves +3.60% CTR and +4.25% CPM gains in online A/B tests, demonstrating both effectiveness and real-world impact.
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[AI-27] Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data
【速读】：该论文旨在解决异常检测中训练数据污染（contamination）带来的性能下降问题，尤其是在正常与异常数据分布重叠的噪声环境中，传统方法因依赖固定污染比例而难以适应实际场景。其解决方案的关键在于提出自适应且激进的异常排除策略（Adaptive and Aggressive Rejection, AAR），通过改进的z-score与基于高斯混合模型（Gaussian Mixture Model, GMM）的阈值动态识别并剔除异常样本，同时融合硬拒绝与软拒绝机制，在保留正常数据的同时有效提升异常排除能力，从而显著改善模型鲁棒性与检测性能。
链接: https://arxiv.org/abs/2511.21378
作者: Jungi Lee,Jungkwon Kim,Chi Zhang,Kwangsun Yoo,Seok-Joo Byun
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Handling contaminated data poses a critical challenge in anomaly detection, as traditional models assume training on purely normal data. Conventional methods mitigate contamination by relying on fixed contamination ratios, but discrepancies between assumed and actual ratios can severely degrade performance, especially in noisy environments where normal and abnormal data distributions overlap. To address these limitations, we propose Adaptive and Aggressive Rejection (AAR), a novel method that dynamically excludes anomalies using a modified z-score and Gaussian mixture model-based thresholds. AAR effectively balances the trade-off between preserving normal data and excluding anomalies by integrating hard and soft rejection strategies. Extensive experiments on two image datasets and thirty tabular datasets demonstrate that AAR outperforms the state-of-the-art method by 0.041 AUROC. By providing a scalable and reliable solution, AAR enhances robustness against contaminated datasets, paving the way for broader real-world applications in domains such as security and healthcare.
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[AI-28] he Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods AAAI
【速读】：该论文旨在解决现有局部特征归因方法（local feature attribution methods）在评估其保真度（fidelity）时存在的计算效率低和结果随机性问题。当前主流指标如Infidelity依赖蒙特卡洛近似，需大量模型推理且引入采样不确定性，难以满足医疗等高风险场景对可信赖解释的需求。解决方案的关键在于提出一种新的保真度度量——定向预测变化（Directed Prediction Change, DPC），通过改进已有预测变化（Prediction Change, PC）指标，在引导扰动实验（Guided Perturbation Experiment）框架下同时考虑扰动方向与归因方向，从而实现几乎十倍的加速并消除随机性，获得确定性和可复现的评估结果，同时保持与局部Infidelity相同的测量属性。
链接: https://arxiv.org/abs/2511.21363
作者: Kevin Iselborn,David Dembinsky,Adriano Lucieri,Andreas Dengel
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 10 figures, 5 tables, accepted at AAAI SECURE-AI4H workshop
Abstract:The utility of an explanation method critically depends on its fidelity to the underlying machine learning model. Especially in high-stakes medical settings, clinicians and regulators require explanations that faithfully reflect the model’s decision process. Existing fidelity metrics such as Infidelity rely on Monte Carlo approximation, which demands numerous model evaluations and introduces uncertainty due to random sampling. This work proposes a novel metric for evaluating the fidelity of local feature attribution methods by modifying the existing Prediction Change (PC) metric within the Guided Perturbation Experiment. By incorporating the direction of both perturbation and attribution, the proposed Directed Prediction Change (DPC) metric achieves an almost tenfold speedup and eliminates randomness, resulting in a deterministic and trustworthy evaluation procedure that measures the same property as local Infidelity. DPC is evaluated on two datasets (skin lesion images and financial tabular data), two black-box models, seven explanation algorithms, and a wide range of hyperparameters. Across 4,744 distinct explanations, the results demonstrate that DPC, together with PC, enables a holistic and computationally efficient evaluation of both baseline-oriented and local feature attribution methods, while providing deterministic and reproducible outcomes.
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[AI-29] Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance
【速读】：该论文旨在解决对抗式逆强化学习（Adversarial Inverse Reinforcement Learning, AIRL）在高复杂度、不完美信息环境下的性能瓶颈问题，尤其是在稀疏奖励和显著不确定性的场景中，如二人有限制德州扑克（Heads-Up Limit Hold’em, HULHE）中的表现不佳。其解决方案的关键在于提出一种增强型框架——混合对抗式逆强化学习（Hybrid-AIRL, H-AIRL），通过引入基于专家数据的监督损失（supervised loss）与随机正则化机制（stochastic regularization mechanism），提升奖励函数推理的准确性与策略学习的稳定性，从而实现更高效的样本利用和更鲁棒的学习过程。
链接: https://arxiv.org/abs/2511.21356
作者: Bram Silue,Santiago Amaya-Corredor,Patrick Mannion,Lander Willem,Pieter Libin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Comments: 13 pages, 5 figures, 1 table. Code: this https URL . Submitted to ESANN 2026
Abstract:Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by inferring dense reward functions from expert demonstrations. However, its performance in highly complex, imperfect-information settings remains largely unexplored. To explore this gap, we evaluate AIRL in the context of Heads-Up Limit Hold’em (HULHE) poker, a domain characterized by sparse, delayed rewards and significant uncertainty. In this setting, we find that AIRL struggles to infer a sufficiently informative reward function. To overcome this limitation, we contribute Hybrid-AIRL (H-AIRL), an extension that enhances reward inference and policy learning by incorporating a supervised loss derived from expert data and a stochastic regularization mechanism. We evaluate H-AIRL on a carefully selected set of Gymnasium benchmarks and the HULHE poker setting. Additionally, we analyze the learned reward function through visualization to gain deeper insights into the learning process. Our experimental results show that H-AIRL achieves higher sample efficiency and more stable learning compared to AIRL. This highlights the benefits of incorporating supervised signals into inverse RL and establishes H-AIRL as a promising framework for tackling challenging, real-world settings.
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[AI-30] Generating Separated Singing Vocals Using a Diffusion Model Conditioned on Music Mixtures
【速读】：该论文旨在解决音乐混音中人声分离（singing voice separation）的问题，即从包含多种乐器和人声的实录音乐中提取纯净的独唱人声。传统方法通常依赖于神经网络对时频表示进行掩码或变换以分离目标源，而本文提出了一种基于生成式扩散模型（generative diffusion model）的新方案：训练模型从混合音频中生成对应的独唱人声，条件为原始混合信号。该方案的关键在于利用扩散模型的迭代采样机制实现可控的质量-效率权衡，并支持用户在必要时对输出进行精细化调整，同时在补充数据训练下取得了与非生成基线相当的客观指标表现。
链接: https://arxiv.org/abs/2511.21342
作者: Genís Plaja-Roglans,Yun-Ning Hung,Xavier Serra,Igor Pereira
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注: Accepted for publication at WASPAA 2025
Abstract:Separating the individual elements in a musical mixture is an essential process for music analysis and practice. While this is generally addressed using neural networks optimized to mask or transform the time-frequency representation of a mixture to extract the target sources, the flexibility and generalization capabilities of generative diffusion models are giving rise to a novel class of solutions for this complicated task. In this work, we explore singing voice separation from real music recordings using a diffusion model which is trained to generate the solo vocals conditioned on the corresponding mixture. Our approach improves upon prior generative systems and achieves competitive objective scores against non-generative baselines when trained with supplementary data. The iterative nature of diffusion sampling enables the user to control the quality-efficiency trade-off, and also refine the output when needed. We present an ablation study of the sampling algorithm, highlighting the effects of the user-configurable parameters.
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[AI-31] SONAR: Spectral-Contrastive Audio Residuals for Generalizable Deepfake Detection
【速读】：该论文旨在解决深度伪造音频（Deepfake audio, DF）检测模型在分布外输入上泛化能力差的问题，其核心原因是神经网络存在频谱偏差（spectral bias），即优先学习低频结构而忽略高频细节，导致生成器留下的高频伪影未被有效利用。解决方案的关键在于提出一种频率引导的对比框架——Spectral-cONtrastive Audio Residuals (SONAR)，通过可学习的SRM（Spectral Residual Masking）高通滤波器显式分离音频信号为互补表示：XLSR编码器捕获主导低频内容，而同一路径前接值约束的高通滤波器提取微弱高频残差；再借助频率交叉注意力融合两种视图以建模长短程频率依赖，并采用频率感知的Jensen-Shannon对比损失拉近真实内容-噪声对、推远伪造嵌入，从而加速优化并增强决策边界。此方法将高频残差提升为首要学习信号，在潜在空间中形成两个不相交流形：自然高频（natural-HF）对应真实音频，畸变高频（distorted-HF）对应合成音频，实现数据驱动的频率解耦与判别增强。
链接: https://arxiv.org/abs/2511.21325
作者: Ido Nitzan HIdekel,Gal lifshitz,Khen Cohen,Dan Raviv
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
备注:
Abstract:Deepfake (DF) audio detectors still struggle to generalize to out of distribution inputs. A central reason is spectral bias, the tendency of neural networks to learn low-frequency structure before high-frequency (HF) details, which both causes DF generators to leave HF artifacts and leaves those same artifacts under-exploited by common detectors. To address this gap, we propose Spectral-cONtrastive Audio Residuals (SONAR), a frequency-guided framework that explicitly disentangles an audio signal into complementary representations. An XLSR encoder captures the dominant low-frequency content, while the same cloned path, preceded by learnable SRM, value-constrained high-pass filters, distills faint HF residuals. Frequency cross-attention reunites the two views for long- and short-range frequency dependencies, and a frequency-aware Jensen-Shannon contrastive loss pulls real content-noise pairs together while pushing fake embeddings apart, accelerating optimization and sharpening decision boundaries. Evaluated on the ASVspoof 2021 and in-the-wild benchmarks, SONAR attains state-of-the-art performance and converges four times faster than strong baselines. By elevating faint high-frequency residuals to first-class learning signals, SONAR unveils a fully data-driven, frequency-guided contrastive framework that splits the latent space into two disjoint manifolds: natural-HF for genuine audio and distorted-HF for synthetic audio, thereby sharpening decision boundaries. Because the scheme operates purely at the representation level, it is architecture-agnostic and, in future work, can be seamlessly integrated into any model or modality where subtle high-frequency cues are decisive.
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[AI-32] Improvement of Collision Avoidance in Cut-In Maneuvers Using Time-to-Collision Metrics
【速读】：该论文旨在解决自动驾驶车辆（AV）在切入场景（cut-in scenarios）中面临的碰撞规避难题，这类场景因其他车辆突然变道或插入而具有高度动态性和不确定性。解决方案的关键在于引入一种基于时间到碰撞（Time-to-Collision, TTC）指标的新策略，并通过深度学习模型对TTC进行融合计算，从而实现更精准的潜在碰撞预测与更合理的避让决策，相较于传统仅依赖TTC的静态阈值方法，显著提升了系统的响应能力与安全性。
链接: https://arxiv.org/abs/2511.21280
作者: Jamal Raiyn
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:This paper proposes a new strategy for collision avoidance system leveraging Time-to-Collision (TTC) metrics for handling cut-in scenarios, which are particularly challenging for autonomous vehicles (AVs). By integrating a deep learning with TTC calculations, the system predicts potential collisions and determines appropriate evasive actions compared to traditional TTC -based approaches.
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[AI-33] Causality Without Causal Models
【速读】：该论文旨在解决当前因果关系定义（尤其是Halpern-Pearl定义）在应用范围和表达能力上的局限性问题，例如无法处理包含析取、否定、信念以及嵌套反事实的公式，且难以适用于不满足传统因果模型假设的场景（如允许回溯推理的模型）。其解决方案的关键在于对Halpern-Pearl的因果定义进行抽象化，提取其核心逻辑结构，使其不依赖于特定的因果模型形式，从而可推广至任何能定义反事实（counterfactuals）的模型框架中，并支持更复杂的逻辑表达式与解释机制。这一抽象方法不仅扩展了因果分析的适用范围，还为构建通用的解释理论提供了基础。
链接: https://arxiv.org/abs/2511.21260
作者: Joseph Y. Halpern(Cornell University),Rafael Pass(Cornell University)
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: In Proceedings TARK 2025, arXiv:2511.20540
Abstract:Perhaps the most prominent current definition of (actual) causality is due to Halpern and Pearl. It is defined using causal models (also known as structural equations models). We abstract the definition, extracting its key features, so that it can be applied to any other model where counterfactuals are defined. By abstracting the definition, we gain a number of benefits. Not only can we apply the definition in a wider range of models, including ones that allow, for example, backtracking, but we can apply the definition to determine if A is a cause of B even if A and B are formulas involving disjunctions, negations, beliefs, and nested counterfactuals (none of which can be handled by the Halpern-Pearl definition). Moreover, we can extend the ideas to getting an abstract definition of explanation that can be applied beyond causal models. Finally, we gain a deeper understanding of features of the definition even in causal models.
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[AI-34] Privacy in Federated Learning with Spiking Neural Networks
【速读】：该论文旨在解决联邦学习（Federated Learning, FL）中梯度泄露（gradient leakage）对Spiking Neural Networks（SNNs）隐私安全的潜在威胁问题。传统人工神经网络（ANNs）中的梯度泄露攻击已被广泛研究，能够通过共享梯度重建敏感输入数据，但SNN因其独特的脉冲事件驱动机制和基于代理梯度（surrogate gradients）的训练方式，其梯度是否仍具隐私风险尚不明确。论文的关键解决方案在于首次系统性地将多种梯度重构攻击方法适配至脉冲域，并通过跨多个数据域的实证分析发现：与ANN梯度能清晰还原输入内容不同，SNN梯度呈现噪声大、时序不一致的特性，无法恢复有意义的空间或时间结构，表明脉冲动力学与代理梯度训练共同显著降低了梯度的信息量，从而揭示了类脑计算（neuromorphic computation）在隐私保护方面的内在潜力。
链接: https://arxiv.org/abs/2511.21181
作者: Dogukan Aksu,Jesus Martinez del Rincon,Ihsen Alouani
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
备注:
Abstract:Spiking neural networks (SNNs) have emerged as prominent candidates for embedded and edge AI. Their inherent low power consumption makes them far more efficient than conventional ANNs in scenarios where energy budgets are tightly constrained. In parallel, federated learning (FL) has become the prevailing training paradigm in such settings, enabling on-device learning while limiting the exposure of raw data. However, gradient inversion attacks represent a critical privacy threat in FL, where sensitive training data can be reconstructed directly from shared gradients. While this vulnerability has been widely investigated in conventional ANNs, its implications for SNNs remain largely unexplored. In this work, we present the first comprehensive empirical study of gradient leakage in SNNs across diverse data domains. SNNs are inherently non-differentiable and are typically trained using surrogate gradients, which we hypothesized would be less correlated with the original input and thus less informative from a privacy perspective. To investigate this, we adapt different gradient leakage attacks to the spike domain. Our experiments reveal a striking contrast with conventional ANNs: whereas ANN gradients reliably expose salient input content, SNN gradients yield noisy, temporally inconsistent reconstructions that fail to recover meaningful spatial or temporal structure. These results indicate that the combination of event-driven dynamics and surrogate-gradient training substantially reduces gradient informativeness. To the best of our knowledge, this work provides the first systematic benchmark of gradient inversion attacks for spiking architectures, highlighting the inherent privacy-preserving potential of neuromorphic computation.
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[AI-35] CAHS-Attack: CLIP-Aware Heuristic Search Attack Method for Stable Diffusion
【速读】：该论文旨在解决扩散模型（Diffusion Models）在面对对抗性提示（adversarial prompts）时表现出的脆弱性问题，尤其关注在现实场景中由于缺乏白盒访问权限或手工提示工程效果不佳而导致攻击能力受限的困境。解决方案的关键在于提出一种名为CAHS-Attack的CLIP感知启发式搜索攻击方法：该方法结合蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）进行细粒度后缀优化，并利用约束遗传算法（constrained genetic algorithm）预先筛选高潜力对抗性提示作为根节点，同时在每次模拟回溯中保留语义破坏力最强的结果以实现高效的局部搜索。实验表明，该方法在短提示和长提示上均达到当前最优攻击性能，且揭示了当前基于CLIP的文本编码器是导致扩散模型脆弱性的根本原因，暗示了文本到图像生成流程中的基础安全风险。
链接: https://arxiv.org/abs/2511.21180
作者: Shuhan Xia,Jing Dai,Hui Ouyang,Yadong Shang,Dongxiao Zhao,Peipei Li
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注:
Abstract:Diffusion models exhibit notable fragility when faced with adversarial prompts, and strengthening attack capabilities is crucial for uncovering such vulnerabilities and building more robust generative systems. Existing works often rely on white-box access to model gradients or hand-crafted prompt engineering, which is infeasible in real-world deployments due to restricted access or poor attack effect. In this paper, we propose CAHS-Attack , a CLIP-Aware Heuristic Search attack method. CAHS-Attack integrates Monte Carlo Tree Search (MCTS) to perform fine-grained suffix optimization, leveraging a constrained genetic algorithm to preselect high-potential adversarial prompts as root nodes, and retaining the most semantically disruptive outcome at each simulation rollout for efficient local search. Extensive experiments demonstrate that our method achieves state-of-the-art attack performance across both short and long prompts of varying semantics. Furthermore, we find that the fragility of SD models can be attributed to the inherent vulnerability of their CLIP-based text encoders, suggesting a fundamental security risk in current text-to-image pipelines.
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[AI-36] Maglev-Pentabot: Magnetic Levitation System for Non-Contact Manipulation using Deep Reinforcement Learning
【速读】：该论文旨在解决当前非接触式操控技术在工业应用中难以实现对克级质量物体灵活操控的问题，尤其是现有2D和3D磁悬浮系统多局限于微米级或毫克级对象。其解决方案的关键在于提出一种名为Maglev-Pentabot的磁悬浮系统，结合深度强化学习（Deep Reinforcement Learning, DRL）构建复杂控制策略，并通过数值优化电磁铁布局以最大化可控空间，同时引入动作重映射方法缓解因磁场强度强非线性导致的样本稀疏问题，从而提升DRL控制器的收敛性与泛化能力。实验表明该系统不仅能实现灵活操控，还可推广至未显式训练的运输任务，且具备通过增大电磁铁规模扩展至更重负载的能力，为工业级机器人应用提供了可扩展的框架。
链接: https://arxiv.org/abs/2511.21149
作者: Guoming Huang,Qingyi Zhou,Dianjing Liu,Shuai Zhang,Ming Zhou,Zongfu Yu
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
备注:
Abstract:Non-contact manipulation has emerged as a transformative approach across various industrial fields. However, current flexible 2D and 3D non-contact manipulation techniques are often limited to microscopic scales, typically controlling objects in the milligram range. In this paper, we present a magnetic levitation system, termed Maglev-Pentabot, designed to address this limitation. The Maglev-Pentabot leverages deep reinforcement learning (DRL) to develop complex control strategies for manipulating objects in the gram range. Specifically, we propose an electromagnet arrangement optimized through numerical analysis to maximize controllable space. Additionally, an action remapping method is introduced to address sample sparsity issues caused by the strong nonlinearity in magnetic field intensity, hence allowing the DRL controller to converge. Experimental results demonstrate flexible manipulation capabilities, and notably, our system can generalize to transport tasks it has not been explicitly trained for. Furthermore, our approach can be scaled to manipulate heavier objects using larger electromagnets, offering a reference framework for industrial-scale robotic applications.
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[AI-37] Beyond Patch Aggregation: 3-Pass Pyramid Indexing for Vision-Enhanced Document Retrieval
【速读】：该论文旨在解决传统文档导向的检索增强生成（Document-centric RAG）流水线中因依赖光学字符识别（OCR）和脆弱的文本分块、表格解析与版式重建策略所导致的维护成本高、对布局微小变化敏感以及空间线索丢失的问题。其核心解决方案是提出VisionRAG，一个无OCR且模型无关的多模态检索系统：通过直接以图像形式索引文档，保留版式、表格和空间信息；采用三阶段金字塔索引框架，基于全局页面摘要、章节标题、视觉热点和事实级线索构建轻量级语义向量作为检索代理，仅需存储每页17至27个向量，显著降低内存开销；查询时利用倒数排名融合（Reciprocal Rank Fusion）整合多层级信号实现鲁棒排序，并将原始页面图像编码为base64传递给多模态大语言模型（Multimodal LLM）完成最终问答，从而在保持高效性的同时具备跨多模态编码器的灵活性。
链接: https://arxiv.org/abs/2511.21121
作者: Anup Roy,Rishabh Gyanendra Upadhyay,Animesh Rameshbhai Panara,Robin Mills
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
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Abstract:Document centric RAG pipelines usually begin with OCR, followed by brittle heuristics for chunking, table parsing, and layout reconstruction. These text first workflows are costly to maintain, sensitive to small layout shifts, and often lose the spatial cues that contain the answer. Vision first retrieval has emerged as a strong alternative. By operating directly on page images, systems like ColPali and ColQwen preserve structure and reduce pipeline complexity while achieving strong benchmark performance. However, these late interaction models tie retrieval to a specific vision backbone and require storing hundreds of patch embeddings per page, creating high memory overhead and complicating large scale deployment. We introduce VisionRAG, a multimodal retrieval system that is OCR free and model agnostic. VisionRAG indexes documents directly as images, preserving layout, tables, and spatial cues, and builds semantic vectors without committing to a specific extraction. Our three pass pyramid indexing framework creates vectors using global page summaries, section headers, visual hotspots, and fact level cues. These summaries act as lightweight retrieval surrogates. At query time, VisionRAG retrieves the most relevant pages using the pyramid index, then forwards the raw page image encoded as base64 to a multimodal LLM for final question answering. During retrieval, reciprocal rank fusion integrates signals across the pyramid to produce robust ranking. VisionRAG stores only 17 to 27 vectors per page, matching the efficiency of patch based methods while staying flexible across multimodal encoders. On financial document benchmarks, it achieves 0.8051 accuracy at 10 on FinanceBench and 0.9629 recall at 100 on TAT DQA. These results show that OCR free, summary guided multimodal retrieval is a practical and scalable alternative to traditional text extraction pipelines. Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI) Cite as: arXiv:2511.21121 [cs.IR] (or arXiv:2511.21121v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2511.21121 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-38] Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling AAAI2026
【速读】：该论文旨在解决现有分子属性预测方法在建模化学扰动在生物系统中传播机制时的局限性，特别是忽略了细胞响应（如形态学和基因表达）对药物效应的影响，以及当前细胞感知方法在外部生物数据模态不完整和跨分子、细胞、基因层级依赖关系建模不足的问题。其解决方案的关键在于提出CHMR（Cell-aware Hierarchical Multi-modal Representations）框架，该框架通过联合建模分子与细胞响应之间的局部-全局依赖关系，并引入一种新颖的树状结构向量量化模块来捕捉潜在的生物层次结构，从而实现对多模态生物信息的层次化、鲁棒表示学习。
链接: https://arxiv.org/abs/2511.21120
作者: Mengran Li,Zelin Zang,Wenbin Xing,Junzhou Chen,Ronghui Zhang,Jiebo Luo,Stan Z. Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted to AAAI 2026 (Oral)
Abstract:Understanding how chemical perturbations propagate through biological systems is essential for robust molecular property prediction. While most existing methods focus on chemical structures alone, recent advances highlight the crucial role of cellular responses such as morphology and gene expression in shaping drug effects. However, current cell-aware approaches face two key limitations: (1) modality incompleteness in external biological data, and (2) insufficient modeling of hierarchical dependencies across molecular, cellular, and genomic levels. We propose CHMR (Cell-aware Hierarchical Multi-modal Representations), a robust framework that jointly models local-global dependencies between molecules and cellular responses and captures latent biological hierarchies via a novel tree-structured vector quantization module. Evaluated on nine public benchmarks spanning 728 tasks, CHMR outperforms state-of-the-art baselines, yielding average improvements of 3.6% on classification and 17.2% on regression tasks. These results demonstrate the advantage of hierarchy-aware, multimodal learning for reliable and biologically grounded molecular representations, offering a generalizable framework for integrative biomedical modeling. The code is in this https URL.
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[AI-39] Dynamic Stratified Contrastive Learning with Upstream Augmentation for MILP Branching
【速读】：该论文旨在解决混合整数线性规划（Mixed Integer Linear Programming, MILP）在分支定界（Branch-and-Bound, B&B）求解过程中面临的三个核心挑战：节点语义随深度变化的不一致性、上游节点数据稀缺与不平衡，以及强分支样本采集成本高。解决方案的关键在于提出一种动态分层对比训练框架（Dynamic Stratified Contrastive Training Framework, \ours），其核心机制包括：首先基于节点特征分布对B&B树中的节点进行分层分组；其次设计一种基于图卷积神经网络（Graph Convolutional Neural Network, GCNN）的判别模型，通过渐进式分离不同组别节点来学习更细粒度的节点表示；此外，引入一种上游增强型MILP衍生方法，生成理论等价且带扰动的新实例以缓解数据稀疏问题。该方案有效捕捉了节点间的细微语义差异，显著提升了分支准确率和求解效率，尤其在上游节点表现突出，并在标准MILP基准测试中展现出良好的泛化能力。
链接: https://arxiv.org/abs/2511.21107
作者: Tongkai Lu,Shuai Ma,Chongyang Tao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 18 pages
Abstract:Mixed Integer Linear Programming (MILP) is a fundamental class of NP-hard problems that has garnered significant attention from both academia and industry. The Branch-and-Bound (B\B) method is the dominant approach for solving MILPs and the branching plays an important role in B\B methods. Neural-based learning frameworks have recently been developed to enhance branching policies and the efficiency of solving MILPs. However, these methods still struggle with semantic variation across depths, the scarcity of upstream nodes, and the costly collection of strong branching samples. To address these issues, we propose \ours, a Dynamic \underline\textbfStratified \underline\textbfContrastive Training Framework for \underline\textbfMILP Branching. It groups branch-and-bound nodes based on their feature distributions and trains a GCNN-based discriminative model to progressively separate nodes across groups, learning finer-grained node representations throughout the tree. To address data scarcity and imbalance at upstream nodes, we introduce an upstream-augmented MILP derivation procedure that generates both theoretically equivalent and perturbed instances. \ours~effectively models subtle semantic differences between nodes, significantly enhancing branching accuracy and solving efficiency, particularly for upstream nodes. Extensive experiments on standard MILP benchmarks demonstrate that our method enhances branching accuracy, reduces solving time, and generalizes effectively to unseen instances.
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[AI-40] From Bits to Rounds: Parallel Decoding with Exploration for Diffusion Language Models
【速读】：该论文旨在解决扩散语言模型（Diffusion Language Models, DLMs）在解码过程中因依赖高置信度词元而导致的信息效率低下问题，这种策略存在信息论瓶颈，限制了每轮解码的有效进展，最终导致生成速度变慢。其解决方案的关键在于提出一种无需训练的探索-利用（Explore-Then-Exploit, ETE）解码策略，通过跨块解码与对高不确定性词元的定向探索相结合，重塑条件分布并触发一系列高置信度预测的级联效应，从而最大化每轮的信息吞吐量，显著减少所需解码轮数，同时保持生成质量不变。
链接: https://arxiv.org/abs/2511.21103
作者: Hengyu Fu,Baihe Huang,Virginia Adams,Charles Wang,Venkat Srinivasan,Jiantao Jiao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 24 pages, 6 figures
Abstract:Diffusion Language Models (DLMs) have recently emerged as a strong alternative to autoregressive language models (LMs). DLMs offer comparable accuracy with faster inference speed via parallel decoding. However, standard DLM decoding strategies relying on high-confidence tokens encounter an inherent information-theoretic bottleneck that restricts decoding progress and ultimately slows generation. We demonstrate both theoretically and empirically that prioritizing high-confidence tokens is inherently inefficient. High-probability tokens carry negligible information and strictly relying on them limits the effective progress made in each decoding round. We prove that the number of decoding rounds must grow linearly with the sample’s total information (negative log-likelihood) and inversely with the per-round information budget, establishing a bits-to-rounds principle. We also propose Explore-Then-Exploit (ETE), a training-free decoding strategy that maximizes information throughput and decoding efficiency. ETE combines cross-block decoding with targeted exploration of high-uncertainty tokens to reshape the conditional distribution and trigger cascades of confident predictions. Experiments verify our theoretical bounds and demonstrate that ETE consistently reduces the required number of decoding rounds compared to confidence-only baselines without compromising generation quality.
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[AI-41] MNM : Multi-level Neuroimaging Meta-analysis with Hyperbolic Brain-Text Representations MICCAI2025
【速读】：该论文旨在解决神经影像学研究中因样本量小而导致的可靠性不足问题，传统元分析方法常依赖关键词检索或线性映射，难以捕捉大脑内在的层次结构。其解决方案的关键在于提出一种基于双曲几何（hyperbolic geometry）的新框架，通过Lorentz模型将文献文本与脑激活图嵌入到共享的双曲空间中，从而同时建模语义相似性和大脑功能区域的层次组织关系。该方法实现了多层级神经影像元分析（multi-level neuroimaging meta-analysis, MNM），包括：1）对齐文本与脑图像嵌入以实现语义对应；2）引导文本与脑激活之间的层次结构；3）保持脑激活模式内部的层次关系，显著提升了元分析的鲁棒性与可解释性。
链接: https://arxiv.org/abs/2511.21092
作者: Seunghun Baek,Jaejin Lee,Jaeyoon Sim,Minjae Jeong,Won Hwa Kim
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: MICCAI 2025 (Provisional Accept; top ~9%)
Abstract:Various neuroimaging studies suffer from small sample size problem which often limit their reliability. Meta-analysis addresses this challenge by aggregating findings from different studies to identify consistent patterns of brain activity. However, traditional approaches based on keyword retrieval or linear mappings often overlook the rich hierarchical structure in the brain. In this work, we propose a novel framework that leverages hyperbolic geometry to bridge the gap between neuroscience literature and brain activation maps. By embedding text from research articles and corresponding brain images into a shared hyperbolic space via the Lorentz model, our method captures both semantic similarity and hierarchical organization inherent in neuroimaging data. In the hyperbolic space, our method performs multi-level neuroimaging meta-analysis (MNM) by 1) aligning brain and text embeddings for semantic correspondence, 2) guiding hierarchy between text and brain activations, and 3) preserving the hierarchical relationships within brain activation patterns. Experimental results demonstrate that our model outperforms baselines, offering a robust and interpretable paradigm of multi-level neuroimaging meta-analysis via hyperbolic brain-text representation.
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[AI-42] MLPMoE: Zero-Shot Architectural Metamorphosis of Dense LLM MLPs into Static Mixture-of-Experts
链接: https://arxiv.org/abs/2511.21089
作者: Ivan Novikov
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
[AI-43] Aligning LLM s with Biomedical Knowledge using Balanced Fine-Tuning
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在生物医学领域知识对齐中的两个核心问题：一是标准监督微调（Supervised Fine-Tuning, SFT）容易过拟合于表面指令模式，难以有效内化稀疏文本数据中复杂的生物医学推理机制；二是强化学习（Reinforcement Learning, RL）因缺乏可行的奖励信号（如需湿实验验证药物反应），难以在该领域应用。解决方案的关键在于提出一种平衡微调（Balanced Fine-Tuning, BFT）方法，其核心是双层加权机制：在token层面通过预测概率缩放损失以稳定梯度并防止过拟合，在样本层面利用“最小组置信度”自适应增强困难样本的学习。该方法无需外部奖励信号即可从稀疏数据中高效学习复杂推理能力，并在医学和生物学任务中显著优于SFT与现有基线模型（如GeneAgent）。
链接: https://arxiv.org/abs/2511.21075
作者: Zhenchao Tang,Fang Wang,Haohuai He,Jiale Zhou,Tianxu Lv,Jun Zhu,Shouzhi Chen,Minghao Yang,Yu Wang,Jiayang Wu,Yidong Song,Jianhua Yao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Effective post-training is essential to align Large Language Models (LLMs) with specialized biomedical knowledge to accelerate life science research. However, current approaches face significant limitations. First, biomedical reasoning involves intricate mechanisms often represented by sparse textual data. Standard Supervised Fine-Tuning (SFT) tends to overfit to surface-level instruction patterns without effectively internalizing this fragmented scientific knowledge. Second, Reinforcement Learning (RL) is impractical for this domain, as defining meaningful rewards often necessitates prohibitive experimental validation (e.g., wet-lab verification of drug responses), rendering real-time feedback unfeasible. We propose Balanced Fine-Tuning (BFT), an efficient post-training method designed to learn complex reasoning from sparse data without external reward signals. BFT operates through a two-layer weighting mechanism: 1. At the token level, it scales loss via prediction probabilities to stabilize gradients and prevent overfitting; 2. At the sample level, it uses “minimum group confidence” to adaptively enhance the learning of hard samples. Experiments demonstrate that BFT significantly outperforms SFT. In medical tasks, it enables LLMs to acquire knowledge that SFT misses. In biological tasks, BFT-based LLMs surpass GeneAgent (an accurate agent for biology analysis) in biological process reasoning. Moreover, the text embeddings generated by BFT can be directly applied to downstream tasks, such as gene interaction and single-cell perturbation response prediction. These results indicate that BFT facilitates broad applications of LLMs in biomedical research.
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[AI-44] Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLM s AAAI-26
【速读】：该论文试图解决大语言模型（Large Language Models, LLMs）在下游任务微调过程中普遍存在的安全-能力权衡问题，即提升任务性能往往会导致安全对齐的退化，即使在良性数据集上也是如此。传统方法如监督微调（Supervised Fine-Tuning, SFT）和基于人类反馈的强化学习（Reinforcement Learning from Human Feedback, RLHF）均难以避免这一现象。论文提出以可验证奖励强化学习（Reinforcement Learning with Verifiable Rewards, RLVR）作为解决方案，其关键在于通过KL约束优化推导出安全漂移的理论上限，并证明在特定条件下可完全消除安全退化；实证结果进一步表明，RLVR能够在不牺牲甚至提升安全护栏的前提下显著增强模型的推理能力，从而挑战了“安全与能力不可兼得”的固有假设。
链接: https://arxiv.org/abs/2511.21050
作者: Dongkyu Derek Cho,Huan Song,Arijit Ghosh Chowdhury,Haotian An,Yawei Wang,Rohit Thekkanal,Negin Sokhandan,Sharlina Keshava,Hannah Marlowe
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: AAAI-26 Workshop on Post-AI Formal Methods
Abstract:Fine-tuning large language models (LLMs) for downstream tasks typically exhibit a fundamental safety-capability tradeoff, where improving task performance degrades safety alignment even on benign datasets. This degradation persists across standard approaches including supervised finetuning (SFT) and reinforcement learning from human feedback (RLHF). While reinforcement learning with verifiable rewards (RLVR) has emerged as a promising alternative that optimizes models on objectively measurable tasks, its safety implications remain unexplored. We present the first comprehensive theoretical and empirical analysis of safety properties in RLVR. Theoretically, we derive upper bounds on safety drift under KL-constrained optimization and prove conditions under which safety degradation is eliminated. Empirically, we conduct extensive experiments across five adversarial safety benchmarks, demonstrating that RLVR can simultaneously enhance reasoning capabilities while maintaining or improving safety guardrails. Our comprehensive ablation studies examine the effects of optimization algorithms, model scale, and task domains. Our findings challenge the prevailing assumption of an inevitable safety capability trade-off, and establish that a specific training methodology can achieve both objectives simultaneously, providing insights for the safe deployment of reasoning-capable LLMs.
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[AI-45] FedAPA: Federated Learning with Adaptive Prototype Aggregation Toward Heterogeneous Wi-Fi CSI-based Crowd Counting
【速读】：该论文旨在解决基于Wi-Fi信道状态信息（Channel State Information, CSI）的传感技术在大规模部署中面临的两大挑战：一是需要大量特定场景的训练数据，二是联邦学习（Federated Learning, FL）在异构传感数据和设备资源条件下难以实现高效协同建模。解决方案的关键在于提出FedAPA算法，其核心创新是采用自适应原型聚合（Adaptive Prototype Aggregation, APA）策略，通过相似性权重动态分配同伴原型贡献，从而为每个客户端生成个性化的全局原型而非固定权重聚合；同时，在本地训练阶段引入分类损失与表示对比学习相结合的混合目标函数，以对齐本地与全局知识，提升模型泛化能力与通信效率。实验表明，FedAPA在准确率、F1分数、平均绝对误差（MAE）及通信开销等指标上均显著优于多个基线方法。
链接: https://arxiv.org/abs/2511.21048
作者: Jingtao Guo,Yuyi Mao,Ivan Wang-Hei Ho
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 17 pages, 11 figures, this article was submitted to IEEE for possible publication
Abstract:Wi-Fi channel state information (CSI)-based sensing provides a non-invasive, device-free approach for tasks such as human activity recognition and crowd counting, but large-scale deployment is hindered by the need for extensive site-specific training data. Federated learning (FL) offers a way to avoid raw data sharing but is challenged by heterogeneous sensing data and device resources. This paper proposes FedAPA, a collaborative Wi-Fi CSI-based sensing algorithm that uses adaptive prototype aggregation (APA) strategy to assign similarity-based weights to peer prototypes, enabling adaptive client contributions and yielding a personalized global prototype for each client instead of a fixed-weight aggregation. During local training, we adopt a hybrid objective that combines classification learning with representation contrastive learning to align local and global knowledge. We provide a convergence analysis of FedAPA and evaluate it in a real-world distributed Wi-Fi crowd counting scenario with six environments and up to 20 people. The results show that our method outperform multiple baselines in terms of accuracy, F1 score, mean absolute error (MAE), and communication overhead, with FedAPA achieving at least a 9.65% increase in accuracy, a 9% gain in F1 score, a 0.29 reduction in MAE, and a 95.94% reduction in communication overhead.
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[AI-46] owards Trustworthy Legal AI through LLM Agents and Formal Reasoning
【速读】：该论文旨在解决当前基于大语言模型（Large Language Models, LLMs）的法律推理系统在司法实践中缺乏形式化保障的问题，即现有方法虽能进行表层文本分析，但难以满足法理学中对判决合理性（包括实质理性与形式理性）的要求。其解决方案的关键在于提出L4M框架，通过结合对抗性LLM代理与SMT求解器支持的符号证明机制，实现自然语言解释灵活性与逻辑验证严谨性的统一：具体包含三个阶段——法规形式化、双角色事实与法条提取（检察官与辩护方独立映射）、以及以求解器为核心的裁决过程，其中通过unsat核心触发迭代自省直至获得可满足公式，并由法官型LLM生成透明且优化的判决语句，从而在保证法律推理可解释性和严格性的前提下提升性能表现。
链接: https://arxiv.org/abs/2511.21033
作者: Linze Chen,Yufan Cai,Zhe Hou,Jinsong Dong
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:The rationality of law manifests in two forms: substantive rationality, which concerns the fairness or moral desirability of outcomes, and formal rationality, which requires legal decisions to follow explicitly stated, general, and logically coherent rules. Existing LLM-based systems excel at surface-level text analysis but lack the guarantees required for principled jurisprudence. We introduce L4M, a novel framework that combines adversarial LLM agents with SMT-solver-backed proofs to unite the interpretive flexibility of natural language with the rigor of symbolic verification. The pipeline consists of three phases: (1) Statute Formalization, where domain-specific prompts convert legal provisions into logical formulae; (2) Dual Fact and Statute Extraction, in which prosecutor- and defense-aligned LLMs independently map case narratives to fact tuples and statutes, ensuring role isolation; and (3) Solver-Centric Adjudication, where an autoformalizer compiles both parties’ arguments into logic constraints, and unsat cores trigger iterative self-critique until a satisfiable formula is achieved, which is then verbalized by a Judge-LLM into a transparent verdict and optimized sentence. Experimental results on public benchmarks show that our system surpasses advanced LLMs including GPT-o4-mini, DeepSeek-V3, and Claude 4 as well as state-of-the-art Legal AI baselines, while providing rigorous and explainable symbolic justifications.
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[AI-47] ICPO: Intrinsic Confidence-Driven Group Relative Preference Optimization for Efficient Reinforcement Learning
【速读】：该论文旨在解决强化学习中可验证奖励（Reinforcement Learning with Verifiable Rewards, RLVR）方法在提升大语言模型（Large Language Models, LLMs）推理能力时面临的训练不稳定、熵崩溃等问题，这些问题主要源于奖励粒度粗、奖励噪声大以及探索效率低。解决方案的关键在于提出内在置信度驱动的群体相对偏好优化方法（Intrinsic Confidence-Driven Group Relative Preference Optimization, ICPO），其核心思想是利用LLM生成不同响应的概率分布直接反映其对推理过程的自我评估；通过比较同一输入下多个响应的相对生成概率计算偏好优势得分（preference advantage score），并将该得分与可验证奖励融合，从而引导更有效的探索，缓解奖励噪声与粗粒度问题，抑制过自信错误，并增强对高质量但被低估响应的相对优势，避免模型过拟合特定策略，最终实现更稳定且深入的训练过程。
链接: https://arxiv.org/abs/2511.21005
作者: Jinpeng Wang,Chao Li,Ting Ye,Mengyuan Zhang,Wei Liu,Jian Luan
机构: 未知
类目: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
备注:
Abstract:Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates significant potential in enhancing the reasoning capabilities of Large Language Models (LLMs). However, existing RLVR methods are often constrained by issues such as coarse-grained rewards, reward noise, and inefficient exploration, which lead to unstable training and entropy collapse. To address this challenge, we propose the Intrinsic Confidence-Driven Group Relative Preference Optimization method (ICPO). The intuition behind it lies in the fact that the probabilities of an LLM generating different responses can inherently and directly reflect its self-assessment of the reasoning process. Inspired by the idea of preference modeling, ICPO calculates a preference advantage score for each response by comparing the relative generation probabilities of multiple responses under the same input prompt, and integrates this score with verifiable rewards to guide the exploration process. We have discovered that the preference advantage score not only alleviates the issues of coarse-grained rewards and reward noise but also effectively curbs overconfident errors, enhances the relative superiority of undervalued high-quality responses, and prevents the model from overfitting to specific strategies, thereby facilitating more thorough exploration. Comprehensive experiments across four general-domain benchmarks and three mathematical benchmarks demonstrate that ICPO steadily boosts reasoning compared to GRPO.
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[AI-48] FANoise: Singular Value-Adaptive Noise Modulation for Robust Multimodal Representation Learning AAAI2026
【速读】：该论文旨在解决当前表示学习（Representation Learning）中因静态或启发式噪声注入导致的鲁棒性和泛化能力不足的问题，尤其在多模态表示学习场景下，现有方法未能充分考虑训练过程中特征分布的动态变化。其解决方案的关键在于提出了一种名为FANoise的新型特征自适应噪声注入策略，该策略基于对比学习中的梯度与特征分布动态特性，能够根据训练阶段自动调整噪声强度，从而有效缓解噪声带来的负面影响，同时保留其对表征性能的增益作用，实现了理论依据充分、实验验证有效的改进。
链接: https://arxiv.org/abs/2511.20997
作者: Jiaoyang Li,Jun Fang,Tianhao Gao,Xiaohui Zhang,Zhiyuan Liu,Chao Liu,Pengzhang Liu,Qixia Jiang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 13 pages, 5 figures, accept to AAAI2026
Abstract:Representation learning is fundamental to modern machine learning, powering applications such as text retrieval and multimodal understanding. However, learning robust and generalizable representations remains challenging. While prior work has demonstrated that active noise injection, a form of data augmentation, can enhance encoding performance, most existing methods rely on heuristic or static noise, overlooking the dynamic nature of feature distributions during training. In this work, we systematically study the role of noise in representation learning from both gradient-based and feature distribution perspectives, using InfoNCE loss as a representative example. Focusing on multimodal representation learning, we propose FANoise, a novel feature-adaptive noise injection strategy. By leveraging the dynamics of contrastive learning, FANoise effectively mitigates the negative impacts of noise while preserving its benefits. Under this theoretically grounded framework, comprehensive experiments demonstrate that FANoise consistently improves overall performance on multimodal tasks across various base VLM models.
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[AI-49] Subgoal Graph-Augmented Planning for LLM -Guided Open-World Reinforcement Learning
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在强化学习（Reinforcement Learning, RL）中因计划-执行对齐不足而导致的实用性受限问题，具体表现为：LLMs生成的子目标（subgoals）虽语义合理，但常因缺乏环境特定知识而不可行或无关，且单LLM规划过程将生成与自我验证混同，导致高自信但不可靠的子目标。解决方案的关键在于提出Subgoal Graph-Augmented Actor-Critic-Refiner（SGA-ACR）框架，其核心创新包括：引入环境特定的子目标图（subgoal graph）和结构化实体知识以增强规划的可执行性，并设计多LLM规划流水线，显式分离生成、批判与精炼三个阶段，从而实现可验证的子目标生成；同时，通过子目标追踪器监控执行进度、提供辅助奖励并动态更新子目标图，确保计划与行动持续对齐。
链接: https://arxiv.org/abs/2511.20993
作者: Shanwei Fan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) offer strong high-level planning capabilities for reinforcement learning (RL) by decomposing tasks into subgoals. However, their practical utility is limited by poor planning-execution alignment, which reflects a critical gap between abstract plans and actionable, environment-compatible behaviors. This misalignment arises from two interrelated limitations: (1) LLMs often produce subgoals that are semantically plausible but infeasible or irrelevant in the target environment due to insufficient grounding in environment-specific knowledge, and (2) single-LLM planning conflates generation with self-verification, resulting in overconfident yet unreliable subgoals that frequently fail during execution. To address these challenges, we propose Subgoal Graph-Augmented Actor-Critic-Refiner (SGA-ACR), a framework that integrates an environment-specific subgoal graph and structured entity knowledge with a multi-LLM planning pipeline that explicitly separates generation, critique, and refinement to produce executable and verifiable subgoals. A subgoal tracker further monitors execution progress, provides auxiliary rewards, and adaptively updates the subgoal graph to maintain alignment between plans and actions. Experimental results on 22 diverse tasks in the open-world game “Crafter” demonstrate the effectiveness of our proposed method.
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[AI-50] SpaceX: Exploring metrics with the SPACE model for developer productivity
【速读】：该论文旨在解决传统生产力评估方法（如单一维度的确定性启发式）在捕捉开发者实际效能时存在的局限性问题，尤其是在开源软件协作环境中对复杂交互模式和情感因素的忽视。其解决方案的关键在于提出并验证了一个综合性的多维生产力指标——复合生产力评分（Composite Productivity Score, CPS），通过整合来自代码提交频率与情感倾向（基于RoBERTa模型的情感分类）的统计关联性，以及对贡献者交互拓扑结构的分析，从而更精准地反映开发者的实际贡献质量与协作动态，克服了仅依赖提交量等表面指标所带来的偏差。
链接: https://arxiv.org/abs/2511.20955
作者: Sanchit Kaul,Kevin Nhu,Jason Eissayou,Ivan Eser,Victor Borup
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
备注: Code available at this https URL
Abstract:This empirical investigation elucidates the limitations of deterministic, unidimensional productivity heuristics by operationalizing the SPACE framework through extensive repository mining. Utilizing a dataset derived from open-source repositories, the study employs rigorous statistical methodologies including Generalized Linear Mixed Models (GLMM) and RoBERTa-based sentiment classification to synthesize a holistic, multi-faceted productivity metric. Analytical results reveal a statistically significant positive correlation between negative affective states and commit frequency, implying a cycle of iterative remediation driven by frustration. Furthermore, the investigation has demonstrated that analyzing the topology of contributor interactions yields superior fidelity in mapping collaborative dynamics compared to traditional volume-based metrics. Ultimately, this research posits a Composite Productivity Score (CPS) to address the heterogeneity of developer efficacy.
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[AI-51] Resilient Charging Infrastructure via Decentralized Coordination of Electric Vehicles at Scale
【速读】：该论文旨在解决电动汽车（Electric Vehicles, EVs）大规模接入背景下，去中心化充电控制在极端扰动场景下（如充电站故障或突发充电需求激增）所面临的效率下降与用户舒适度受损问题。现有方法虽能实现高效协调和隐私保护，但在资源竞争加剧时易导致排队时间延长，影响用户体验。其解决方案的关键在于提出一种基于集体学习（collective learning）的协同框架，使EV能够动态权衡个体舒适度与系统整体效率（即各充电站总队列长度），通过自适应调整行为优先级，在不同充电站容量和时空分布条件下实现帕累托最优（Pareto-optimal）的平衡。实验表明，该方法显著优于基线方案，尤其在高比例站点故障或对抗性EV行为下展现出更强的鲁棒性和可信度。
链接: https://arxiv.org/abs/2511.20943
作者: Chuhao Qin,Alexandru Sorici,Andrei Olaru,Evangelos Pournaras,Adina Magda Florea
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
备注: 14 pages, 12 figures. This work has been submitted to the IEEE for possible publication
Abstract:The rapid adoption of electric vehicles (EVs) introduces major challenges for decentralized charging control. Existing decentralized approaches efficiently coordinate a large number of EVs to select charging stations while reducing energy costs, preventing power peak and preserving driver privacy. However, they often struggle under severe contingencies, such as station outages or unexpected surges in charging requests. These situations create competition for limited charging slots, resulting in long queues and reduced driver comfort. To address these limitations, we propose a novel collective learning-based coordination framework that allows EVs to balance individual comfort on their selections against system-wide efficiency, i.e., the overall queues across all stations. In the framework, EVs are recommended for adaptive charging behaviors that shift priority between comfort and efficiency, achieving Pareto-optimal trade-offs under varying station capacities and dynamic spatio-temporal EV distribution. Experiments using real-world data from EVs and charging stations show that the proposed approach outperforms baseline methods, significantly reducing travel and queuing time. The results reveal that, under uncertain charging conditions, EV drivers that behave selfishly or altruistically at the right moments achieve shorter waiting time than those maintaining moderate behavior throughout. Our findings under high fractions of station outages and adversarial EVs further demonstrate improved resilience and trustworthiness of decentralized EV charging infrastructure.
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[AI-52] Improving Procedural Skill Explanations via Constrained Generation: A Symbolic-LLM Hybrid Architecture
【速读】：该论文旨在解决生成式 AI（Generative AI）在程序性技能学习中生成解释时存在的结构浅层化问题，即模型虽能输出流畅文本，却难以准确传达操作步骤背后的因果逻辑、目标导向性和组合结构。其解决方案的关键在于提出 Ivy 系统，该系统通过融合符号化的 Task-Method-Knowledge (TMK) 模型与生成式解释层——一个受 TMK 结构约束的大语言模型（LLM），从而确保生成的解释具备明确的因果转换、目标层次和问题分解结构。TMK 作为知识框架对 LLM 的生成过程施加显式结构限制，显著提升了“如何”和“为何”类问题解释的结构性质量。
链接: https://arxiv.org/abs/2511.20942
作者: Rahul Dass,Thomas Bowlin,Zebing Li,Xiao Jin,Ashok Goel
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:In procedural skill learning, instructional explanations must convey not just steps, but the causal, goal-directed, and compositional logic behind them. Large language models (LLMs) often produce fluent yet shallow responses that miss this structure. We present Ivy, an AI coaching system that delivers structured, multi-step explanations by combining symbolic Task-Method-Knowledge (TMK) models with a generative interpretation layer-an LLM that constructs explanations while being constrained by TMK structure. TMK encodes causal transitions, goal hierarchies, and problem decompositions, and guides the LLM within explicit structural bounds. We evaluate Ivy against responses against GPT and retrieval-augmented GPT baselines using expert and independent annotations across three inferential dimensions. Results show that symbolic constraints consistently improve the structural quality of explanations for “how” and “why” questions. This study demonstrates a scalable AI for education approach that strengthens the pedagogical value of AI-generated explanations in intelligent coaching systems.
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[AI-53] Exploring Time-Step Size in Reinforcement Learning for Sepsis Treatment
【速读】：该论文旨在解决现有基于强化学习（Reinforcement Learning, RL）的脓毒症管理研究中，普遍采用4小时时间步长（time-step size）可能因粒度过粗而扭曲患者动态、导致次优治疗策略的问题。其关键解决方案在于通过控制变量实验，系统比较了四种不同时间步长（Δt = 1, 2, 4, 8 小时）下的模型性能，并设计了动作重映射（action re-mapping）方法以确保在不同时间步长数据集上公平评估策略；同时，在两种策略学习设置下进行跨时间步长（cross-Δt）模型选择，从而量化时间步长对状态表征学习、行为克隆、策略训练和离策略评估的影响。结果表明，使用静态行为策略在更细粒度时间步长（Δt = 1 h 和 2 h）下训练的策略整体表现最优且稳定，揭示了时间步长作为离线强化学习在医疗领域中的核心设计参数的重要性。
链接: https://arxiv.org/abs/2511.20913
作者: Yingchuan Sun,Shengpu Tang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Existing studies on reinforcement learning (RL) for sepsis management have mostly followed an established problem setup, in which patient data are aggregated into 4-hour time steps. Although concerns have been raised regarding the coarseness of this time-step size, which might distort patient dynamics and lead to suboptimal treatment policies, the extent to which this is a problem in practice remains unexplored. In this work, we conducted empirical experiments for a controlled comparison of four time-step sizes ( \Delta t!=!1,2,4,8 h) on this domain, following an identical offline RL pipeline. To enable a fair comparison across time-step sizes, we designed action re-mapping methods that allow for evaluation of policies on datasets with different time-step sizes, and conducted cross- \Delta t model selections under two policy learning setups. Our goal was to quantify how time-step size influences state representation learning, behavior cloning, policy training, and off-policy evaluation. Our results show that performance trends across \Delta t vary as learning setups change, while policies learned at finer time-step sizes ( \Delta t = 1 h and 2 h) using a static behavior policy achieve the overall best performance and stability. Our work highlights time-step size as a core design choice in offline RL for healthcare and provides evidence supporting alternatives beyond the conventional 4-hour setup.
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[AI-54] Evolved SampleWeights for Bias Mitigation: Effectiveness Depends on Optimization Objectives
【速读】：该论文旨在解决机器学习模型在真实世界数据上训练时可能产生的偏见问题，这种偏见会负面影响边缘化群体的预测结果。其解决方案的关键在于通过重加权（reweighting）策略调整训练数据中每个样本的权重，从而缓解模型预测中的不公平性。论文比较了三种生成权重的方法：基于遗传算法（Genetic Algorithm, GA）演化权重、仅根据数据集特征计算权重，以及对所有样本赋予相等权重。实验表明，利用GA优化权重可实现更优的公平性与预测性能之间的权衡，尤其当优化目标同时包含准确率（accuracy）和人口统计均等差异（demographic parity difference）时，进化得到的权重在多数数据集上显著优于其他方法。
链接: https://arxiv.org/abs/2511.20909
作者: Anil K. Saini,Jose Guadalupe Hernandez,Emily F. Wong,Debanshi Misra,Jason H. Moore
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注:
Abstract:Machine learning models trained on real-world data may inadvertently make biased predictions that negatively impact marginalized communities. Reweighting is a method that can mitigate such bias in model predictions by assigning a weight to each data point used during model training. In this paper, we compare three methods for generating these weights: (1) evolving them using a Genetic Algorithm (GA), (2) computing them using only dataset characteristics, and (3) assigning equal weights to all data points. Model performance under each strategy was evaluated using paired predictive and fairness metrics, which also served as optimization objectives for the GA during evolution. Specifically, we used two predictive metrics (accuracy and area under the Receiver Operating Characteristic curve) and two fairness metrics (demographic parity difference and subgroup false negative fairness). Using experiments on eleven publicly available datasets (including two medical datasets), we show that evolved sample weights can produce models that achieve better trade-offs between fairness and predictive performance than alternative weighting methods. However, the magnitude of these benefits depends strongly on the choice of optimization objectives. Our experiments reveal that optimizing with accuracy and demographic parity difference metrics yields the largest number of datasets for which evolved weights are significantly better than other weighting strategies in optimizing both objectives.
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[AI-55] Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy
【速读】：该论文旨在解决当前基于扩散模型（diffusion）和流模型（flow-based）的机器人控制策略在长时程操作与模仿学习任务中存在的计算效率低下问题。现有方法在每个控制步骤均采用固定的推理预算（inference budget），导致简单子任务浪费计算资源，而复杂任务可能因预算不足而性能受限。解决方案的关键在于提出难度感知随机插值策略（Difficulty-Aware Stochastic Interpolant Policy, DA-SIP），其核心是引入一个难度分类器，在每个控制周期内根据观测数据动态调整积分步数（integration horizon）、最优求解器变体以及常微分方程（ODE）/随机微分方程（SDE）的集成方式，从而实现推理资源的智能分配。DA-SIP基于随机插值公式构建统一框架，支持多样化的训练与推理配置，在多个操纵任务上实现了2.6–4.4倍的总计算时间减少，同时保持与固定最大计算预算基线相当的任务成功率。
链接: https://arxiv.org/abs/2511.20906
作者: Inkook Chun,Seungjae Lee,Michael S. Albergo,Saining Xie,Eric Vanden-Eijnden
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
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Abstract:Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation learning tasks. However, these controllers employ a fixed inference budget at every control step, regardless of task complexity, leading to computational inefficiency for simple subtasks while potentially underperforming on challenging ones. To address these issues, we introduce Difficulty-Aware Stochastic Interpolant Policy (DA-SIP), a framework that enables robotic controllers to adaptively adjust their integration horizon in real time based on task difficulty. Our approach employs a difficulty classifier that analyzes observations to dynamically select the step budget, the optimal solver variant, and ODE/SDE integration at each control cycle. DA-SIP builds upon the stochastic interpolant formulation to provide a unified framework that unlocks diverse training and inference configurations for diffusion- and flow-based policies. Through comprehensive benchmarks across diverse manipulation tasks, DA-SIP achieves 2.6-4.4x reduction in total computation time while maintaining task success rates comparable to fixed maximum-computation baselines. By implementing adaptive computation within this framework, DA-SIP transforms generative robot controllers into efficient, task-aware systems that intelligently allocate inference resources where they provide the greatest benefit.
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[AI-56] A Taxonomy of Pix Fraud in Brazil: Attack Methodologies AI-Driven Amplification and Defensive Strategies
【速读】：该论文旨在解决巴西中央银行于2020年推出的即时支付系统（Pix）所面临的安全威胁问题，特别是针对用户和金融机构的欺诈攻击类型识别与分类。研究通过结构化文献综述与银行业专业人士的探索性访谈相结合的方法，揭示了欺诈手段从纯社会工程学策略向融合人为操控与技术漏洞利用的混合型攻击演进的趋势。解决方案的关键在于安全防护措施需与攻击手法的复杂化同步升级，尤其强调构建自适应防御机制和持续性的用户安全意识培养。
链接: https://arxiv.org/abs/2511.20902
作者: Glener Lanes Pizzolato,Brenda Medeiros Lopes,Claudio Schepke,Diego Kreutz
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 5 pages, 1 figure, 2 tables, submitted to ERRC/WRSeg 2025
Abstract:This work presents a review of attack methodologies targeting Pix, the instant payment system launched by the Central Bank of Brazil in 2020. The study aims to identify and classify the main types of fraud affecting users and financial institutions, highlighting the evolution and increasing sophistication of these techniques. The methodology combines a structured literature review with exploratory interviews conducted with professionals from the banking sector. The results show that fraud schemes have evolved from purely social engineering approaches to hybrid strategies that integrate human manipulation with technical exploitation. The study concludes that security measures must advance at the same pace as the growing complexity of attack methodologies, with particular emphasis on adaptive defenses and continuous user awareness.
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[AI-57] Representation Interventions Enable Lifelong Unstructured Knowledge Control
【速读】：该论文旨在解决大语言模型（Large Language Models, LLMs）在终身学习场景下如何高效、准确地更新复杂且非结构化知识的问题，同时避免因频繁知识编辑导致的交叉干扰和性能退化。解决方案的关键在于提出RILKE（Representation Intervention for Lifelong KnowledgE Control），其核心思想是将知识控制视为对模型表示空间的干预；通过训练具有语义不变性（paraphrase-robust）和编辑局部化（edit-localized）特性的模块，限制每次知识更新仅作用于低维子空间以最小化干扰，并在推理阶段利用查询自适应路由器动态选择最优模块来引导生成过程，从而在冻结基础参数的前提下实现细粒度的知识调控与通用能力保持。
链接: https://arxiv.org/abs/2511.20892
作者: Xuyuan Liu,Zhengzhang Chen,Xinshuai Dong,Yanchi Liu,Xujiang Zhao,Shengyu Chen,Haoyu Wang,Yujun Yan,Haifeng Chen
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 18 Page
Abstract:Large language models (LLMs) often produce incorrect or outdated content. Updating their knowledge efficiently and accurately without costly retraining is a major challenge. This problem is especially hard for complex, unstructured knowledge in a lifelong setting, where many edits must coexist without interference. We introduce RILKE (Representation Intervention for Lifelong KnowledgE Control), a robust and scalable method that treats knowledge control as interventions within the model’s representation space. Leveraging representation-space expressiveness, we identify two properties enabling RILKE to deliver fine-grained control over complex, unstructured knowledge while maintaining general utility with frozen base weights. During training, RILKE learns paraphrase-robust and edit-localized modules that limit each update to a low-dimensional subspace to minimize cross-edit interference. In inference, a query-adaptive router selects the appropriate module to guide the model’s generation. In evaluation on knowledge editing benchmarks with LLaMA and Qwen models, RILKE is scalable to large-scale datasets, demonstrating high edit success, strong paraphrase generalization, and preserving general utility with modest memory overhead. These results show RILKE is an effective and scalable solution for lifelong knowledge control in LLMs.
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[AI-58] Selecting Belief-State Approximations in Simulators with Latent States
【速读】：该论文旨在解决复杂模拟器中状态重置（state resetting）的挑战，尤其是在存在隐变量（latent variables）时如何从观测历史中采样后验分布（即信念状态, belief state），以支持基于样本的规划和真实数据校准。其核心问题是：在仅能通过采样访问模拟器的前提下，如何选择最优的信念状态近似采样方法。解决方案的关键在于将这一问题形式化为一个通用的条件分布选择任务，并提出一种新算法及其理论分析框架。该框架揭示了两种不同的建模视角——基于隐状态的选择（latent state-based selection）与基于观测的选择（observation-based selection），并指出二者在不同回放策略（Single-Reset 与 Repeated-Reset）下的性能差异：观察导向的选择在标准单次重置策略下可能失效，但在重复重置策略下可获得理论保证，从而揭示了该问题背后丰富的算法权衡与理论细节。
链接: https://arxiv.org/abs/2511.20870
作者: Nan Jiang
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注:
Abstract:State resetting is a fundamental but often overlooked capability of simulators. It supports sample-based planning by allowing resets to previously encountered simulation states, and enables calibration of simulators using real data by resetting to states observed in real-system traces. While often taken for granted, state resetting in complex simulators can be nontrivial: when the simulator comes with latent variables (states), state resetting requires sampling from the posterior over the latent state given the observable history, a.k.a. the belief state (Silver and Veness, 2010). While exact sampling is often infeasible, many approximate belief-state samplers can be constructed, raising the question of how to select among them using only sampling access to the simulator. In this paper, we show that this problem reduces to a general conditional distribution-selection task and develop a new algorithm and analysis under sampling-only access. Building on this reduction, the belief-state selection problem admits two different formulations: latent state-based selection, which directly targets the conditional distribution of the latent state, and observation-based selection, which targets the induced distribution over the observation. Interestingly, these formulations differ in how their guarantees interact with the downstream roll-out methods: perhaps surprisingly, observation-based selection may fail under the most natural roll-out method (which we call Single-Reset) but enjoys guarantees under the less conventional alternative (which we call Repeated-Reset). Together with discussion on issues such as distribution shift and the choice of sampling policies, our paper reveals a rich landscape of algorithmic choices, theoretical nuances, and open questions, in this seemingly simple problem. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) Cite as: arXiv:2511.20870 [cs.LG] (or arXiv:2511.20870v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2511.20870 Focus to learn more arXiv-issued DOI via DataCite
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[AI-59] Computing Evolutionarily Stable Strategies in Multiplayer Games
【速读】：该论文旨在解决多玩家非退化博弈中所有演化稳定策略（Evolutionarily Stable Strategies, ESS）的计算问题。其解决方案的关键在于提出了一种能够系统性识别并计算此类博弈中全部ESS的算法，从而为复杂博弈结构下的稳定性分析提供了理论保障与计算工具。
链接: https://arxiv.org/abs/2511.20859
作者: Sam Ganzfried
机构: 未知
类目: Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Theoretical Economics (econ.TH); Populations and Evolution (q-bio.PE)
备注:
Abstract:We present an algorithm for computing all evolutionarily stable strategies in nondegenerate normal-form games with three or more players.
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[AI-60] NOIR 2.0: Neural Signal Operated Intelligent Robots for Everyday Activities
【速读】：该论文旨在解决人类通过脑信号直接控制机器人完成日常任务时存在的效率低、适应性差的问题。其核心挑战在于如何实现高精度、快速的脑-机意图解码，以及如何使系统能够高效适应不同用户的行为模式。解决方案的关键在于提出NOIR 2.0系统，其采用更快更准确的脑解码算法显著缩短任务完成时间（减少46%），并引入少样本机器人学习算法，借助基础模型（foundation models）实现仅需15次演示即可完成个性化适配，相较传统单次演示方法大幅提升了样本效率，整体人类操作时间减少65%。
链接: https://arxiv.org/abs/2511.20848
作者: Tasha Kim,Yingke Wang,Hanvit Cho,Alex Hodges
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Systems and Control (eess.SY)
备注: Conference on Robot Learning (CoRL 2024), CoRoboLearn
Abstract:Neural Signal Operated Intelligent Robots (NOIR) system is a versatile brain-robot interface that allows humans to control robots for daily tasks using their brain signals. This interface utilizes electroencephalography (EEG) to translate human intentions regarding specific objects and desired actions directly into commands that robots can execute. We present NOIR 2.0, an enhanced version of NOIR. NOIR 2.0 includes faster and more accurate brain decoding algorithms, which reduce task completion time by 46%. NOIR 2.0 uses few-shot robot learning algorithms to adapt to individual users and predict their intentions. The new learning algorithms leverage foundation models for more sample-efficient learning and adaptation (15 demos vs. a single demo), significantly reducing overall human time by 65%.
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[AI-61] Pre-train to Gain: Robust Learning Without Clean Labels
【速读】：该论文旨在解决深度神经网络在标签噪声环境下训练时因过拟合导致的泛化性能下降和准确率降低问题。其解决方案的关键在于：通过自监督学习（Self-Supervised Learning, SSL）预训练一个无标签的数据特征提取骨干网络，随后在含噪声标签的数据集上进行标准的监督训练，从而无需依赖干净标签子集即可提升模型对标签噪声的鲁棒性。实验表明，使用SimCLR和Barlow Twins等SSL方法进行预训练，在CIFAR-10和CIFAR-100数据集上的合成噪声和真实噪声场景中均显著提升了分类准确率及下游标签错误检测性能（F1分数和平衡准确率），且噪声率越高，性能优势越明显。
链接: https://arxiv.org/abs/2511.20844
作者: David Szczecina,Nicholas Pellegrino,Paul Fieguth
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
备注: 5 pages, 3 figures
Abstract:Training deep networks with noisy labels leads to poor generalization and degraded accuracy due to overfitting to label noise. Existing approaches for learning with noisy labels often rely on the availability of a clean subset of data. By pre-training a feature extractor backbone without labels using self-supervised learning (SSL), followed by standard supervised training on the noisy dataset, we can train a more noise robust model without requiring a subset with clean labels. We evaluate the use of SimCLR and Barlow~Twins as SSL methods on CIFAR-10 and CIFAR-100 under synthetic and real world noise. Across all noise rates, self-supervised pre-training consistently improves classification accuracy and enhances downstream label-error detection (F1 and Balanced Accuracy). The performance gap widens as the noise rate increases, demonstrating improved robustness. Notably, our approach achieves comparable results to ImageNet pre-trained models at low noise levels, while substantially outperforming them under high noise conditions.
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[AI-62] Primal: A Unified Deterministic Framework for Quasi-Orthogonal Hashing and Manifold Learning
【速读】：该论文旨在解决传统随机投影方法（如随机傅里叶特征，Random Fourier Features）在生成稳定、可调且具有良好正交性保持能力的向量表示时存在的局限性，尤其是在需要确定性构造和数学严格保证的应用场景中。解决方案的关键在于提出一种基于素数平方根数论独立性的确定性特征映射框架——Primal，其核心创新是利用Besicovitch性质生成无理频率调制，从而确保无限非重复相位轨迹；并通过两个算法变体实现不同功能：StaticPrime用于生成逼近理论Welch界最优准正交性的时序位置编码，DynamicPrime则通过单一缩放参数σ动态切换为等距核映射（低频）或最大熵单向哈希（高频），分别适用于信号重建与隐私保护型超维度计算任务。此方法在正交性保留和分布紧致性上显著优于归一化高斯基线，提供了一种计算高效且数学严谨的替代随机矩阵投影的新范式。
链接: https://arxiv.org/abs/2511.20839
作者: Vladimer Khasia
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:We present Primal, a deterministic feature mapping framework that harnesses the number-theoretic independence of prime square roots to construct robust, tunable vector representations. Diverging from standard stochastic projections (e.g., Random Fourier Features), our method exploits the Besicovitch property to create irrational frequency modulations that guarantee infinite non-repeating phase trajectories. We formalize two distinct algorithmic variants: (1) StaticPrime, a sequence generation method that produces temporal position encodings empirically approaching the theoretical Welch bound for quasi-orthogonality; and (2) DynamicPrime, a tunable projection layer for input-dependent feature mapping. A central novelty of the dynamic framework is its ability to unify two disparate mathematical utility classes through a single scaling parameter \sigma. In the low-frequency regime, the method acts as an isometric kernel map, effectively linearizing non-convex geometries (e.g., spirals) to enable high-fidelity signal reconstruction and compressive sensing. Conversely, the high-frequency regime induces chaotic phase wrapping, transforming the projection into a maximum-entropy one-way hash suitable for Hyperdimensional Computing and privacy-preserving Split Learning. Empirical evaluations demonstrate that our framework yields superior orthogonality retention and distribution tightness compared to normalized Gaussian baselines, establishing it as a computationally efficient, mathematically rigorous alternative to random matrix projections. The code is available at this https URL
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[AI-63] Conformal Safety Monitoring for Flight Testing: A Case Study in Data-Driven Safety Learning ICRA2025
【速读】：该论文旨在解决飞行测试中由于参数不确定性导致的安全风险难以实时监控的问题，尤其关注如何为飞行员提供明确、前瞻性的中止决策依据，以在安全违规发生前及时终止危险机动。解决方案的关键在于构建一个数据驱动的运行时安全监测框架，其核心由三个通用组件构成：基于近期观测预测未来状态的模型、利用最近邻方法对预测状态进行安全性分类的模型，以及通过保形预测（conformal prediction）实现分类器校准的方法，从而在理论上保证风险识别的可靠性，并显著优于基线方法在提前风险判别上的性能。
链接: https://arxiv.org/abs/2511.20811
作者: Aaron O. Feldman,D. Isaiah Harp,Joseph Duncan,Mac Schwager
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Applications (stat.AP)
备注: ICRA 2025 Workshop on Robot safety under uncertainty from intangible specifications
Abstract:We develop a data-driven approach for runtime safety monitoring in flight testing, where pilots perform maneuvers on aircraft with uncertain parameters. Because safety violations can arise unexpectedly as a result of these uncertainties, pilots need clear, preemptive criteria to abort the maneuver in advance of safety violation. To solve this problem, we use offline stochastic trajectory simulation to learn a calibrated statistical model of the short-term safety risk facing pilots. We use flight testing as a motivating example for data-driven learning/monitoring of safety due to its inherent safety risk, uncertainty, and human-interaction. However, our approach consists of three broadly-applicable components: a model to predict future state from recent observations, a nearest neighbor model to classify the safety of the predicted state, and classifier calibration via conformal prediction. We evaluate our method on a flight dynamics model with uncertain parameters, demonstrating its ability to reliably identify unsafe scenarios, match theoretical guarantees, and outperform baseline approaches in preemptive classification of risk.
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[AI-64] Physics Steering: Causal Control of Cross-Domain Concepts in a Physics Foundation Model
【速读】：该论文试图解决的问题是：科学基础模型（scientific foundation models）是否像语言和图像领域的基础模型一样，能够学习到可解释且可操控的抽象概念表征，从而实现对物理行为的因果控制。解决方案的关键在于，通过提取模型在不同物理 regime下前向传播时的激活向量，并计算其“delta”表示作为激活空间中的概念方向（concept directions），这些方向编码了特定的物理特征；随后将这些概念方向注入模型推理过程，实现了对模拟结果中物理特征的诱导或移除，从而证明了科学基础模型能够学习泛化的物理原理表征，而非仅依赖于数据中的表面相关性。
链接: https://arxiv.org/abs/2511.20798
作者: Rio Alexa Fear,Payel Mukhopadhyay,Michael McCabe,Alberto Bietti,Miles Cranmer
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computational Physics (physics.comp-ph)
备注: 16 Pages, 9 Figures. Code available at this https URL
Abstract:Recent advances in mechanistic interpretability have revealed that large language models (LLMs) develop internal representations corresponding not only to concrete entities but also distinct, human-understandable abstract concepts and behaviour. Moreover, these hidden features can be directly manipulated to steer model behaviour. However, it remains an open question whether this phenomenon is unique to models trained on inherently structured data (ie. language, images) or if it is a general property of foundation models. In this work, we investigate the internal representations of a large physics-focused foundation model. Inspired by recent work identifying single directions in activation space for complex behaviours in LLMs, we extract activation vectors from the model during forward passes over simulation datasets for different physical regimes. We then compute “delta” representations between the two regimes. These delta tensors act as concept directions in activation space, encoding specific physical features. By injecting these concept directions back into the model during inference, we can steer its predictions, demonstrating causal control over physical behaviours, such as inducing or removing some particular physical feature from a simulation. These results suggest that scientific foundation models learn generalised representations of physical principles. They do not merely rely on superficial correlations and patterns in the simulations. Our findings open new avenues for understanding and controlling scientific foundation models and has implications for AI-enabled scientific discovery.
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[AI-65] OpenApps: Simulating Environment Variations to Measure UI-Agent Reliability
【速读】：该论文旨在解决当前自主UI智能体（autonomous UI-Agents）评估中缺乏对应用界面变化鲁棒性（reliability across app variations）的衡量问题。现有评估通常依赖于固定环境（如现有应用的克隆版本），无法反映智能体在真实部署场景中因应用设计或内容差异导致的性能波动。为填补这一盲区，作者提出OpenApps——一个轻量级开源生态系统，包含六个可配置外观与内容的应用（如消息、日历、地图等），仅需单个CPU即可生成数千种应用变体，并支持大规模独立评估（超过10,000次实验）。其核心创新在于通过系统化引入应用层面的变化维度，揭示了多模态智能体在不同环境配置下任务成功率波动可达50%以上，且行为模式（如循环执行或幻觉动作）也显著不同，从而强调了将“应用变异性”纳入可靠性评估的重要性。
链接: https://arxiv.org/abs/2511.20766
作者: Karen Ullrich,Jingtong Su,Claudia Shi,Arjun Subramonian,Amir Bar,Ivan Evtimov,Nikolaos Tsilivis,Randall Balestriero,Julia Kempe,Mark Ibrahim
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注:
Abstract:Reliability is key to realizing the promise of autonomous UI-Agents, multimodal agents that directly interact with apps in the same manner as humans, as users must be able to trust an agent to complete a given task. Current evaluations rely on fixed environments, often clones of existing apps, which are limited in that they can only shed light on whether or how often an agent can complete a task within a specific environment. When deployed however, agents are likely to encounter variations in app design and content that can affect an agent’s ability to complete a task. To address this blind spot of measuring agent reliability across app variations, we develop OpenApps, a light-weight open-source ecosystem with six apps (messenger, calendar, maps, etc.) that are configurable in appearance and content. OpenApps requires just a single CPU to run, enabling easy generation and deployment of thousands of versions of each app. Specifically, we run more than 10,000 independent evaluations to study reliability across seven leading multimodal agents. We find that while standard reliability within a fixed app is relatively stable, reliability can vary drastically when measured across app variations. Task success rates for many agents can fluctuate by more than 50% across app variations. For example, Kimi-VL-3B’s average success across all tasks fluctuates from 63% to just 4% across app versions. We also find agent behaviors such as looping or hallucinating actions can differ drastically depending on the environment configuration. These initial findings highlight the importance of measuring reliability along this new dimension of app variations. OpenApps is available at this https URL
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[AI-66] Data-Driven Methods and AI in Engineering Design: A Systematic Literature Review Focusing on Challenges and Opportunities
【速读】：该论文试图解决数据驱动方法（Data-Driven Methods, DDMs）在产品开发流程中应用碎片化的问题，其核心在于缺乏对何时以及在何种开发阶段使用何种DDM的清晰认知。解决方案的关键是通过PRISMA系统文献综述方法，基于V模型简化后的四个阶段（系统设计、系统实现、系统集成与验证），系统梳理当前DDMs在工程设计中的具体应用情况，识别主流方法及其在不同阶段的分布特征，并揭示现有研究在模型可解释性、跨阶段可追溯性和真实场景验证等方面的局限，从而为制定面向设计阶段的指导原则提供基础支撑。
链接: https://arxiv.org/abs/2511.20730
作者: Nehal Afifi,Christoph Wittig,Lukas Paehler,Andreas Lindenmann,Kai Wolter,Felix Leitenberger,Melih Dogru,Patric Grauberger,Tobias Düser,Albert Albers,Sven Matthiesen
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:The increasing availability of data and advancements in computational intelligence have accelerated the adoption of data-driven methods (DDMs) in product development. However, their integration into product development remains fragmented. This fragmentation stems from uncertainty, particularly the lack of clarity on what types of DDMs to use and when to employ them across the product development lifecycle. To address this, a necessary first step is to investigate the usage of DDM in engineering design by identifying which methods are being used, at which development stages, and for what application. This paper presents a PRISMA systematic literature review. The V-model as a product development framework was adopted and simplified into four stages: system design, system implementation, system integration, and validation. A structured search across Scopus, Web of Science, and IEEE Xplore (2014–2024) retrieved 1,689 records. After screening, 114 publications underwent full-text analysis. Findings show that machine learning (ML) and statistical methods dominate current practice, whereas deep learning (DL), though still less common, exhibits a clear upward trend in adoption. Additionally, supervised learning, clustering, regression analysis, and surrogate modeling are prevalent in design, implementation, and integration system stages but contributions to validation remain limited. Key challenges in existing applications include limited model interpretability, poor cross-stage traceability, and insufficient validation under real-world conditions. Additionally, it highlights key limitations and opportunities such as the need for interpretable hybrid models. This review is a first step toward design-stage guidelines; a follow-up synthesis should map computer science algorithms to engineering design problems and activities.
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[AI-67] Spatio-Temporal Trajectory Foundation Model - Recent Advances and Future Directions CIKM2025
【速读】：该论文旨在解决当前对轨迹基础模型（Trajectory Foundation Models, TFMs）缺乏系统性研究的问题，尤其是其在时空任务中适应性和泛化能力的提升。解决方案的关键在于构建一个全面的TFMs综述框架，包括现有方法的分类体系、对各类方法优劣的批判性分析，并指出当前开放挑战与未来研究方向，从而推动鲁棒、负责任且可迁移的轨迹基础模型发展，助力时空通用智能的进步。
链接: https://arxiv.org/abs/2511.20729
作者: Sean Bin Yang,Ying Sun,Yunyao Cheng,Yan Lin,Kristian Torp,Jilin Hu
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: This paper has been accepted by CIKM 2025 STIntelligence Workshop
Abstract:Foundation models (FMs) have emerged as a powerful paradigm, enabling a diverse range of data analytics and knowledge discovery tasks across scientific fields. Inspired by the success of FMs, particularly large language models, researchers have recently begun to explore spatio-temporal foundation models (STFMs) to improve adaptability and generalization across a wide spectrum of spatio-temporal (ST) tasks. Despite rapid progress, a systematic investigation of trajectory foundation models (TFMs), a crucial subclass of STFMs, is largely lacking. This tutorial addresses this gap by offering a comprehensive overview of recent advances in TFMs, including a taxonomy of existing methodologies and a critical analysis of their strengths and limitations. In addition, the tutorial highlights open challenges and outlines promising research directions to advance spatio-temporal general intelligence through the development of robust, responsible, and transferable TFMs.
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[AI-68] Learning from Risk: LLM -Guided Generation of Safety-Critical Scenarios with Prior Knowledge
【速读】：该论文旨在解决自动驾驶系统在罕见长尾事件（long-tail events）和复杂多智能体交互场景中缺乏充分训练与验证的问题，这类场景在真实世界数据中稀少但对安全验证至关重要。解决方案的关键在于提出一个高保真场景生成框架，其核心是将条件变分自编码器（CVAE）与大语言模型（LLM）相结合：CVAE从大规模自然驾驶数据中编码历史轨迹和地图信息，学习潜在交通结构以生成物理一致的基础场景；在此基础上，LLM作为对抗推理引擎，将非结构化场景描述解析为领域特定的损失函数，并动态引导不同风险等级下的场景生成，从而实现真实感与可控性的平衡，显著提升高风险及长尾事件的覆盖率和挑战性。
链接: https://arxiv.org/abs/2511.20726
作者: Yuhang Wang,Heye Huang,Zhenhua Xu,Kailai Sun,Baoshen Guo,Jinhua Zhao
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 24 pages, 6 figures
Abstract:Autonomous driving faces critical challenges in rare long-tail events and complex multi-agent interactions, which are scarce in real-world data yet essential for robust safety validation. This paper presents a high-fidelity scenario generation framework that integrates a conditional variational autoencoder (CVAE) with a large language model (LLM). The CVAE encodes historical trajectories and map information from large-scale naturalistic datasets to learn latent traffic structures, enabling the generation of physically consistent base scenarios. Building on this, the LLM acts as an adversarial reasoning engine, parsing unstructured scene descriptions into domain-specific loss functions and dynamically guiding scenario generation across varying risk levels. This knowledge-driven optimization balances realism with controllability, ensuring that generated scenarios remain both plausible and risk-sensitive. Extensive experiments in CARLA and SMARTS demonstrate that our framework substantially increases the coverage of high-risk and long-tail events, improves consistency between simulated and real-world traffic distributions, and exposes autonomous driving systems to interactions that are significantly more challenging than those produced by existing rule- or data-driven methods. These results establish a new pathway for safety validation, enabling principled stress-testing of autonomous systems under rare but consequential events.
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[AI-69] Gradient Descent Algorithm Survey
【速读】：该论文旨在解决深度学习中优化算法在实际配置与应用时的选型困惑与性能瓶颈问题，特别是针对不同模型规模和训练场景下如何合理选择、调参及提升优化效果。其解决方案的关键在于系统性地分析了SGD、Mini-batch SGD、Momentum、Adam和Lion这五种主流优化算法的核心优势、局限性，并提出了具有实操性的参数调整建议与应用场景指南，从而为学术研究与工程实践提供标准化参考，助力高效应对多样化的优化挑战。
链接: https://arxiv.org/abs/2511.20725
作者: Deng Fucheng,Wang Wanjie,Gong Ao,Wang Xiaoqi,Wang Fan
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注:
Abstract:Focusing on the practical configuration needs of optimization algorithms in deep learning, this article concentrates on five major algorithms: SGD, Mini-batch SGD, Momentum, Adam, and Lion. It systematically analyzes the core advantages, limitations, and key practical recommendations of each algorithm. The research aims to gain an in-depth understanding of these algorithms and provide a standardized reference for the reasonable selection, parameter tuning, and performance improvement of optimization algorithms in both academic research and engineering practice, helping to solve optimization challenges in different scales of models and various training scenarios.
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[AI-70] Learning Multi-Access Point Coordination in Agent ic AI Wi-Fi with Large Language Models
【速读】：该论文旨在解决当前多接入点协调（Multi-access point coordination, MAPC）技术在密集重叠基本服务集（Overlapping Basic Service Set, OBSS）场景下因依赖静态协议规则而难以适应动态网络环境（如变化的干扰水平和拓扑结构）的问题。解决方案的关键在于提出一种基于智能体的AI Wi-Fi框架，其中每个接入点被建模为一个自主的大语言模型（Large Language Model, LLM）智能体，通过认知工作流实现自然语言对话式的协同推理与实时策略协商，该工作流整合了记忆、反思和工具调用能力，使决策能够基于历史经验与环境反馈进行动态调整，从而显著提升网络性能并展现出对未来无线网络的智能化潜力。
链接: https://arxiv.org/abs/2511.20719
作者: Yifan Fan,Le Liang,Peng Liu,Xiao Li,Ziyang Guo,Qiao Lan,Shi Jin,Wen Tong
机构: 未知
类目: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Signal Processing (eess.SP)
备注:
Abstract:Multi-access point coordination (MAPC) is a key technology for enhancing throughput in next-generation Wi-Fi within dense overlapping basic service sets. However, existing MAPC protocols rely on static, protocol-defined rules, which limits their ability to adapt to dynamic network conditions such as varying interference levels and topologies. To address this limitation, we propose a novel Agentic AI Wi-Fi framework where each access point, modeled as an autonomous large language model agent, collaboratively reasons about the network state and negotiates adaptive coordination strategies in real time. This dynamic collaboration is achieved through a cognitive workflow that enables the agents to engage in natural language dialogue, leveraging integrated memory, reflection, and tool use to ground their decisions in past experience and environmental feedback. Comprehensive simulation results demonstrate that our agentic framework successfully learns to adapt to diverse and dynamic network environments, significantly outperforming the state-of-the-art spatial reuse baseline and validating its potential as a robust and intelligent solution for future wireless networks.
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[AI-71] Active Slice Discovery in Large Language Models NEURIPS2025
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在特定数据子集上系统性错误的问题，即“误差切片”（error slices）的识别难题。这类切片可能对应于特定人口统计学群体，例如模型在识别涉及该群体的毒性评论时表现不佳。传统方法依赖大量人工标注来识别这些切片，成本高昂且效率低下。论文提出了一种名为“主动切片发现”（Active Slice Discovery）的方法，其关键在于通过有限的标注资源，利用主动学习算法（如基于不确定性的策略）高效地聚合可能属于同一误差切片的样本，并验证其是否共享一致的模型误判模式。实验表明，在毒性分类任务中，不确定性驱动的主动学习算法仅需2–10%的可用切片成员信息即可达到与全监督方法相当的准确率，显著优于基线方法。
链接: https://arxiv.org/abs/2511.20713
作者: Minhui Zhang,Prahar Ijner,Yoav Wald,Elliot Creager
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Accepted for presentation at NeurIPS 2025 - Reliable ML Workshop
Abstract:Large Language Models (LLMs) often exhibit systematic errors on specific subsets of data, known as error slices. For instance, a slice can correspond to a certain demographic, where a model does poorly in identifying toxic comments regarding that demographic. Identifying error slices is crucial to understanding and improving models, but it is also challenging. An appealing approach to reduce the amount of manual annotation required is to actively group errors that are likely to belong to the same slice, while using limited access to an annotator to verify whether the chosen samples share the same pattern of model mistake. In this paper, we formalize this approach as Active Slice Discovery and explore it empirically on a problem of discovering human-defined slices in toxicity classification. We examine the efficacy of active slice discovery under different choices of feature representations and active learning algorithms. On several slices, we find that uncertainty-based active learning algorithms are most effective, achieving competitive accuracy using 2-10% of the available slice membership information, while significantly outperforming baselines.
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[AI-72] DUALGUAGE: Automated Joint Security-Functionality Benchmarking for Secure Code Generation
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在生成代码时难以同时保障安全性与功能正确性的问题。现有基准测试多仅关注漏洞减少，忽视功能正确性的保持，或在不同数据集上分别评估安全性和功能性，无法实现二者协同验证。解决方案的关键在于提出DUALGAUGE框架——一个全自动的联合评估系统，其核心由代理式程序执行器（agentic program executor）和基于LLM的评估模块构成，能够在沙箱环境中运行代码并同步评估其功能正确性与安全漏洞；此外，作者还构建了DUALGAUGE-BENCH这一高质量基准数据集，包含多样化编程任务及人工验证的测试套件，确保对规范要求的全面覆盖，从而推动可复现、可扩展且严谨的安全与正确性联合评测。
链接: https://arxiv.org/abs/2511.20709
作者: Abhijeet Pathak,Suvadra Barua,Dinesh Gudimetla,Rupam Patir,Jiawei Guo,Hongxin Hu,Haipeng Cai
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:Large language models (LLMs) and autonomous coding agents are increasingly used to generate software across a wide range of domains. Yet a core requirement remains unmet: ensuring that generated code is secure without compromising its functional correctness. Existing benchmarks and evaluations for secure code generation fall short-many measure only vulnerability reduction, disregard correctness preservation, or evaluate security and functionality on separate datasets, violating the fundamental need for simultaneous joint evaluation. We present DUALGAUGE, the first fully automated benchmarking framework designed to rigorously evaluate the security and correctness of LLM-generated code in unison. Given the lack of datasets enabling joint evaluation of secure code generation, we also present DUALGAUGE-BENCH, a curated benchmark suite of diverse coding tasks, each paired with manually validated test suites for both security and functionality, designed for full coverage of specification requirements. At the core of DUALGAUGE is an agentic program executor, which runs a program against given tests in sandboxed environments, and an LLM-based evaluator, which assesses both correctness and vulnerability behavior against expected outcomes. We rigorously evaluated and ensured the quality of DUALGAUGE-BENCH and the accuracy of DUALGAUGE, and applied DUALGAUGE to benchmarking ten leading LLMs on DUALGAUGE-BENCH across thousands of test scenarios. Our results reveal critical gaps in correct and secure code generation by these LLMs, for which our open-source system and datasets help accelerate progress via reproducible, scalable, and rigorous evaluation.
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[AI-73] Solving Diffusion Inverse Problems with Restart Posterior Sampling
【速读】：该论文致力于解决利用预训练扩散模型求解线性和非线性逆问题（inverse problems）时存在的局限性，包括现有方法对后验分布的强近似假设、依赖昂贵的梯度反向传播计算以及仅适用于线性观测模型等问题。其核心解决方案是提出一种名为“后验采样重启”（Restart for Posterior Sampling, RePS）的通用且高效的框架，关键在于将重启采样策略从无条件扩散模型扩展至后验推理场景，并引入适用于任意可微测量模型的条件常微分方程（ODE），同时设计了一种简化的重启机制以压缩采样过程中累积的近似误差。相比先前方法，RePS无需对得分网络进行反向传播，显著降低计算开销，并在多种逆问题设置下实现更快收敛与更优重建质量。
链接: https://arxiv.org/abs/2511.20705
作者: Bilal Ahmed,Joseph G. Makin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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Abstract:Inverse problems are fundamental to science and engineering, where the goal is to infer an underlying signal or state from incomplete or noisy measurements. Recent approaches employ diffusion models as powerful implicit priors for such problems, owing to their ability to capture complex data distributions. However, existing diffusion-based methods for inverse problems often rely on strong approximations of the posterior distribution, require computationally expensive gradient backpropagation through the score network, or are restricted to linear measurement models. In this work, we propose Restart for Posterior Sampling (RePS), a general and efficient framework for solving both linear and non-linear inverse problems using pre-trained diffusion models. RePS builds on the idea of restart-based sampling, previously shown to improve sample quality in unconditional diffusion, and extends it to posterior inference. Our method employs a conditioned ODE applicable to any differentiable measurement model and introduces a simplified restart strategy that contracts accumulated approximation errors during sampling. Unlike some of the prior approaches, RePS avoids backpropagation through the score network, substantially reducing computational cost. We demonstrate that RePS achieves faster convergence and superior reconstruction quality compared to existing diffusion-based baselines across a range of inverse problems, including both linear and non-linear settings. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Machine Learning (stat.ML) Cite as: arXiv:2511.20705 [cs.LG] (or arXiv:2511.20705v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2511.20705 Focus to learn more arXiv-issued DOI via DataCite
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[AI-74] PropensityBench: Evaluating Latent Safety Risks in Large Language Models via an Agent ic Approach
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）安全评估中存在的重要盲区：现有方法仅关注模型“能否”执行高风险行为（即能力测试），而忽视了模型在具备此类能力时“是否会”主动采取有害行动，即潜在倾向性（propensity）。这种倾向性可能表现为模型在压力情境下策略性地隐藏能力或快速获取能力，并表现出对滥用的隐性动机。解决方案的关键在于提出PropensityBench这一新型基准框架，通过模拟高风险能力并引入代理工具（proxy tools）与现实约束条件（如资源稀缺或自主权激励），动态评估模型在不同压力下的行为选择。实验发现9种危险倾向信号，表明即使缺乏实际执行能力，模型仍可能倾向于选择高风险工具，从而揭示了将静态能力审计转向动态倾向性评估的必要性，为前沿AI系统的安全部署提供了新范式。
链接: https://arxiv.org/abs/2511.20703
作者: Udari Madhushani Sehwag,Shayan Shabihi,Alex McAvoy,Vikash Sehwag,Yuancheng Xu,Dalton Towers,Furong Huang
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Recent advances in Large Language Models (LLMs) have sparked concerns over their potential to acquire and misuse dangerous or high-risk capabilities, posing frontier risks. Current safety evaluations primarily test for what a model \textitcan do - its capabilities - without assessing what it \textitwould do if endowed with high-risk capabilities. This leaves a critical blind spot: models may strategically conceal capabilities or rapidly acquire them, while harboring latent inclinations toward misuse. We argue that \textbfpropensity - the likelihood of a model to pursue harmful actions if empowered - is a critical, yet underexplored, axis of safety evaluation. We present \textbfPropensityBench , a novel benchmark framework that assesses the proclivity of models to engage in risky behaviors when equipped with simulated dangerous capabilities using proxy tools. Our framework includes 5,874 scenarios with 6,648 tools spanning four high-risk domains: cybersecurity, self-proliferation, biosecurity, and chemical security. We simulate access to powerful capabilities via a controlled agentic environment and evaluate the models’ choices under varying operational pressures that reflect real-world constraints or incentives models may encounter, such as resource scarcity or gaining more autonomy. Across open-source and proprietary frontier models, we uncover 9 alarming signs of propensity: models frequently choose high-risk tools when under pressure, despite lacking the capability to execute such actions unaided. These findings call for a shift from static capability audits toward dynamic propensity assessments as a prerequisite for deploying frontier AI systems safely. Our code is available at this https URL.
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[AI-75] Post-Pruning Accuracy Recovery via Data-Free Knowledge Distillation
【速读】：该论文旨在解决模型剪枝（Model Pruning）在隐私敏感领域（如医疗或金融）中因无法访问原始训练数据而导致性能下降的问题。其核心挑战在于：全局非结构化剪枝虽能有效压缩模型，但常造成显著精度损失，而传统微调方法依赖原始训练数据，难以满足GDPR、HIPAA等法规要求。解决方案的关键在于提出一种无数据知识蒸馏（Data-Free Knowledge Distillation）框架，利用DeepInversion技术从预训练教师模型中逆向生成隐私保护的“梦境”图像（即合成数据），作为迁移集来指导剪枝后的学生网络学习教师模型的知识，从而在不接触任何真实数据的情况下恢复剪枝带来的性能损失。
链接: https://arxiv.org/abs/2511.20702
作者: Chinmay Tripurwar,Utkarsh Maurya,Dishant
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Model pruning is a widely adopted technique to reduce the computational complexity and memory footprint of Deep Neural Networks (DNNs). However, global unstructured pruning often leads to significant degradation in accuracy, typically necessitating fine-tuning on the original training dataset to recover performance. In privacy-sensitive domains such as healthcare or finance, access to the original training data is often restricted post-deployment due to regulations (e.g., GDPR, HIPAA). This paper proposes a Data-Free Knowledge Distillation framework to bridge the gap between model compression and data privacy. We utilize DeepInversion to synthesize privacy-preserving ``dream’’ images from the pre-trained teacher model by inverting Batch Normalization (BN) statistics. These synthetic images serve as a transfer set to distill knowledge from the original teacher to the pruned student network. Experimental results on CIFAR-10 across various architectures (ResNet, MobileNet, VGG) demonstrate that our method significantly recovers accuracy lost during pruning without accessing a single real data point.
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[AI-76] Cross Domain Evaluation of Multimodal Chain-of-Thought Reasoning of different datasets into the Amazon CoT Framework
【速读】：该论文旨在解决多模态链式思维（Multimodal Chain-of-Thought, Multimodal-CoT）在跨领域泛化能力不足的问题，尤其是在需要广泛常识与世界知识的视觉问答任务中。其解决方案的关键在于采用Zhang等人提出的两阶段框架：首先分离推理过程中的理由生成（rationale generation）与答案推断（answer inference），并通过基于T5的语言模型结合门控融合机制（gated fusion mechanism）整合视觉特征。实验表明，视觉信息的引入能显著减少理由生成中的幻觉问题，但不同题型下的推理效果差异较大，尤其在常识推理任务中表现受限，这为未来提升跨域泛化能力提供了重要方向。
链接: https://arxiv.org/abs/2511.20701
作者: Nitya Tiwari,Parv Maheshwari,Vidisha Agarwal
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:While recent work has extended CoT to multimodal settings, achieving state-of-the-art results on science question answering benchmarks like ScienceQA, the generalizability of these approaches across diverse domains remains underexplored. This work presents a comprehensive analysis of Multimodal Chain-of-Thought (Multimodal-CoT) reasoning, evaluating its effectiveness on the A-OKVQA, OKVQA and ChartQA datasets, which requires broad commonsense and world knowledge beyond scientific reasoning. We implement the two-stage framework proposed by Zhang et al. [3], which separates rationale generation from answer inference and integrates vision features through a gated fusion mechanism with T5-based language models. Through systematic ablation studies, we analyze the contributions of vision features, rationale quality, and architectural choices. Our findings reveal that while vision integration significantly reduces hallucination in rationale generation, the effectiveness of CoT reasoning varies substantially across question types, with commonsense reasoning presenting particular challenges. This work provides practical insights for researchers implementing multimodal reasoning systems and identifies key areas for future improvement in cross-domain generalization.
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[AI-77] Paraconsistent-Lib: an intuitive PAL2v algorithm Python Library
【速读】：该论文旨在解决在推理与决策系统中构建帕科斯逻辑2.0版本（PAL2v）算法时存在的复杂性高、代码冗余和易出错的问题。解决方案的关键在于提出并实现了一个名为Paraconsistent-Lib的开源Python库，该库封装了PAL2v标准计算过程，提供三种输出结果：经典格中的12个区域之一的Paraconsistent分析、Paraconsistent分析节点（PAN）输出以及决策输出；通过该库，可将诸如Para-analyzer、ParaExtrCTX、PAL2v Filter、PANnet及PNN等知名PAL2v算法以独立或网络形式实现，显著降低开发复杂度、代码规模及错误率。
链接: https://arxiv.org/abs/2511.20700
作者: Arnaldo de Carvalho Junior,Diego Oliveira da Cruz,Bruno da Silva Alves,Fernando da Silva Paulo Junior,João Inacio da Silva Filho
机构: 未知
类目: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
备注: 11 pages, 9 figures, 2 appendix
Abstract:This paper introduces Paraconsistent-Lib, an open-source, easy-to-use Python library for building PAL2v algorithms in reasoning and decision-making systems. Paraconsistent-Lib is designed as a general-purpose library of PAL2v standard calculations, presenting three types of results: paraconsistent analysis in one of the 12 classical lattice PAL2v regions, paraconsistent analysis node (PAN) outputs, and a decision output. With Paraconsistent-Lib, well-known PAL2v algorithms such as Para-analyzer, ParaExtrCTX, PAL2v Filter, paraconsistent analysis network (PANnet), and paraconsistent neural network (PNN) can be written in stand-alone or network form, reducing complexity, code size, and bugs, as two examples presented in this paper. Given its stable state, Paraconsistent-Lib is an active development to respond to user-required features and enhancements received on GitHub.
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[AI-78] In Defense of the Turing Test and its Legacy
【速读】：该论文试图解决的问题是：对图灵测试（Turing test）的常见批评往往基于误解，这些批评不仅偏离了图灵本人的核心论点，也忽视了人工智能（AI）发展的历史脉络。其解决方案的关键在于澄清图灵原始测试的意图，指出诸如Weizenbaum等人对测试的误读以及六种最常被引用的批评实际上并不公平地对待图灵的论证及其在AI发展史中的位置，从而为理解图灵测试的本质及其在当代AI研究中的价值提供更准确的历史与理论依据。
链接: https://arxiv.org/abs/2511.20699
作者: Bernardo Gonçalves
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
备注: 6 pages
Abstract:Considering that Turing’s original test was co-opted by Weizenbaum and that six of the most common criticisms of the Turing test are unfair to both Turing’s argument and the historical development of AI.
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[AI-79] On the Role of Hidden States of Modern Hopfield Network in Transformer NEURIPS2025
【速读】：该论文旨在解决深度Transformer模型中存在的严重问题，如秩坍缩（rank collapse）和标记均匀性（token uniformity），这些问题会导致注意力权重在深层网络中逐渐退化，从而影响模型性能。解决方案的关键在于引入一种新的注意力机制——现代霍普菲尔德注意力（Modern Hopfield Attention, MHA），其核心是将现代霍普菲尔德网络（Modern Hopfield Network, MHN）的隐藏状态作为额外变量嵌入到自注意力机制中，从而实现注意力分数从输入层到输出层的继承。这一改进不仅理论上增强了注意力权重的多样性与稳定性，还在实践中显著提升了Vision Transformer和GPT等模型的准确率，且无需增加训练参数。
链接: https://arxiv.org/abs/2511.20698
作者: Tsubasa Masumura,Masato Taki
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: NeurIPS 2025 accepted
Abstract:Associative memory models based on Hopfield networks and self-attention based on key-value mechanisms have been popular approaches in the study of memory mechanisms in deep learning. It has been pointed out that the state update rule of the modern Hopfield network (MHN) in the adiabatic approximation is in agreement with the self-attention layer of Transformer. In this paper, we go beyond this approximation and investigate the relationship between MHN and self-attention. Our results show that the correspondence between Hopfield networks and Transformers can be established in a more generalized form by adding a new variable, the hidden state derived from the MHN, to self-attention. This new attention mechanism, modern Hopfield attention (MHA), allows the inheritance of attention scores from the input layer of the Transformer to the output layer, which greatly improves the nature of attention weights. In particular, we show both theoretically and empirically that MHA hidden states significantly improve serious problem of deep Transformers known as rank collapse and token uniformity. We also confirm that MHA can systematically improve accuracy without adding training parameters to the Vision Transformer or GPT. Our results provide a new case in which Hopfield networks can be a useful perspective for improving the Transformer architecture.
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[AI-80] Musical Score Understanding Benchmark: Evaluating Large Language Models Comprehension of Complete Musical Scores
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）和视觉-语言模型（Vision-Language Models, VLMs）在音乐记谱理解能力上的不足问题，尤其是其对符号结构如音高、节奏、和声与曲式等多层级音乐信息的推理能力尚未被系统评估。解决方案的关键在于提出了首个大规模、人工标注的音乐记谱理解基准测试（Musical Score Understanding Benchmark, MSU-Bench），涵盖ABC记谱法和PDF图像两种模态，并构建了四个递进层次的评估维度：起始信息（Onset Information）、记谱细节（Notation Note）、和弦与和声（Chord Harmony）以及织体与曲式（Texture Form）。通过在15+种先进模型上进行零样本与微调实验，该研究揭示了模态间性能差距显著、逐层正确性难以维持等关键挑战，同时验证了微调策略能有效提升跨模态理解能力并保持通用知识，为AI与音乐学交叉领域的多模态推理提供了严谨的评估框架与研究基础。
链接: https://arxiv.org/abs/2511.20697
作者: Congren Dai,Yue Yang,Krinos Li,Huichi Zhou,Shijie Liang,Zhang Bo,Enyang Liu,Ge Jin,Hongran An,Haosen Zhang,Peiyuan Jing,KinHei Lee,Zhenxuan Zhang,Xiaobing Li,Maosong Sun
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
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Abstract:Understanding complete musical scores requires reasoning over symbolic structures such as pitch, rhythm, harmony, and form. Despite the rapid progress of Large Language Models (LLMs) and Vision-Language Models (VLMs) in natural language and multimodal tasks, their ability to comprehend musical notation remains underexplored. We introduce Musical Score Understanding Benchmark (MSU-Bench), the first large-scale, human-curated benchmark for evaluating score-level musical understanding across both textual (ABC notation) and visual (PDF) modalities. MSU-Bench comprises 1,800 generative question-answer (QA) pairs drawn from works spanning Bach, Beethoven, Chopin, Debussy, and others, organised into four progressive levels of comprehension: Onset Information, Notation Note, Chord Harmony, and Texture Form. Through extensive zero-shot and fine-tuned evaluations of over 15+ state-of-the-art (SOTA) models, we reveal sharp modality gaps, fragile level-wise success rates, and the difficulty of sustaining multilevel correctness. Fine-tuning markedly improves performance in both modalities while preserving general knowledge, establishing MSU-Bench as a rigorous foundation for future research at the intersection of Artificial Intelligence (AI), musicological, and multimodal reasoning.
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[AI-81] Prototype-Guided Non-Exemplar Continual Learning for Cross-subject EEG Decoding
【速读】：该论文旨在解决持续脑电图（EEG）解码任务中因个体间信号差异大而导致的“灾难性遗忘”问题，即在引入新受试者时，先前知识常被覆盖。传统方法依赖存储历史数据作为回放缓冲区以防止遗忘，但面临隐私和存储限制。其解决方案的关键在于提出一种原型引导的非示例持续学习框架（ProNECL），通过构建每个受试者的类别级原型来总结判别性特征，并利用跨受试者特征对齐与知识蒸馏技术，将新特征空间逐步对齐至全局原型记忆库，从而在不访问任何历史EEG样本的前提下实现知识保留与适应性的有效平衡。
链接: https://arxiv.org/abs/2511.20696
作者: Dan Li,Hye-Bin Shin,Yeon-Woo Choi
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 4 pages, 2 figures, 14th IEEE International Winter Conference on Brain-Computer Interface Conference 2026
Abstract:Due to the significant variability in electroencephalogram (EEG) signals across individuals, knowledge acquired from previous subjects is often overwritten as new subjects are introduced in continual EEG decoding task. Current works mainly rely on storing the historical data of seen subjects as a replay buffer to prevent forgetting. However, privacy concerns or memory constraints make keeping such data impractical. Instead, we propose a Prototype-guided Non-Exemplar Continual Learning (ProNECL)framework that preserves prior knowledge without accessing any historical EEG samples. ProNECL constructs class-level prototypes to summarize discriminative representations from each subject and incrementally aligns new feature spaces with the global prototype memory through cross-subject feature alignment and knowledge distillation. Validated on the BCI Competition IV 2a and 2b datasets, our framework effectively balances knowledge retention and adaptability, achieving superior performance in cross-subject continual EEG decoding tasks.
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[AI-82] A Brief History of Digital Twin Technology
【速读】：该论文旨在解决数字孪生（Digital Twin）技术在医疗领域中从概念到临床广泛应用所面临的挑战，特别是数据互操作性、隐私保护和模型保真度等问题，以推动其在诊断、治疗规划及药物研发中的实质性应用。解决方案的关键在于融合可解释人工智能（Explainable AI）、联邦学习（Federated Learning）与标准化监管框架等新兴技术路径，从而提升数字孪生的可信度、安全性与跨机构协同能力，并为多器官系统建模、基因组整合及伦理治理等前沿方向提供支撑，最终实现从被动治疗向预测性、预防性和个性化医疗的范式转变。
链接: https://arxiv.org/abs/2511.20695
作者: Yunqi Zhang,Kuangyu Shi,Biao Li
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Medical Physics (physics.med-ph)
备注: 21 pages, 1 figure, 1 table
Abstract:Emerging from NASA’s spacecraft simulations in the 1960s, digital twin technology has advanced through industrial adoption to spark a healthcare transformation. A digital twin is a dynamic, data-driven virtual counterpart of a physical system, continuously updated through real-time data streams and capable of bidirectional interaction. In medicine, digital twin integrates imaging, biosensors, and computational models to generate patient-specific simulations that support diagnosis, treatment planning, and drug development. Representative applications include cardiac digital twin for predicting arrhythmia treatment outcomes, oncology digital twin for tracking tumor progression and optimizing radiotherapy, and pharmacological digital twin for accelerating drug discovery. Despite rapid progress, major challenges, including interoperability, data privacy, and model fidelity, continue to limit widespread clinical integration. Emerging solutions such as explainable AI, federated learning, and harmonized regulatory frameworks offer promising pathways forward. Looking ahead, advances in multi-organ digital twin, genomics integration, and ethical governance will be essential to ensure that digital twin shifts healthcare from reactive treatment to predictive, preventive, and truly personalized medicine.
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[AI-83] Reasoning With a Star: A Heliophysics Dataset and Benchmark for Agent ic Scientific Reasoning NEURIPS2025
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在日地物理（heliophysics）领域中进行科学推理时面临的挑战，即不仅需要事实记忆，还需整合物理假设、保持单位一致性并以清晰的科学格式输出结果。其解决方案的关键在于构建了一个名为“Reasoning With a Star”的专用数据集，该数据集源自美国国家航空航天局（NASA）和大气研究中心（UCAR）的“与恒星共存”暑期学校问题集，并结构化为包含问题上下文、推理步骤、预期答案类型、真实标签、格式提示和元数据的问答形式；同时引入一种程序化评分机制，通过考虑单位敏感的数值容差、符号等价性和模式验证来评估预测准确性。实验表明，基于系统工程原理分解任务流程的多智能体协作模式，在需要演绎推理的问题上显著优于单一提示（single-shot prompting）方法。
链接: https://arxiv.org/abs/2511.20694
作者: Kevin Lee,Russell Spiewak,James Walsh
机构: 未知
类目: Artificial Intelligence (cs.AI); Solar and Stellar Astrophysics (astro-ph.SR); Machine Learning (cs.LG); Space Physics (physics.space-ph)
备注: Accepted at NeurIPS 2025 Machine Learning and the Physical Sciences (ML4PS) Workshop. Dataset: this https URL
Abstract:Scientific reasoning through Large Language Models in heliophysics involves more than just recalling facts: it requires incorporating physical assumptions, maintaining consistent units, and providing clear scientific formats through coordinated approaches. To address these challenges, we present Reasoning With a Star, a newly contributed heliophysics dataset applicable to reasoning; we also provide an initial benchmarking approach. Our data are constructed from National Aeronautics and Space Administration University Corporation for Atmospheric Research Living With a Star summer school problem sets and compiled into a readily consumable question-and-answer structure with question contexts, reasoning steps, expected answer type, ground-truth targets, format hints, and metadata. A programmatic grader checks the predictions using unit-aware numerical tolerance, symbolic equivalence, and schema validation. We benchmark a single-shot baseline and four multi-agent patterns, finding that decomposing workflows through systems engineering principles outperforms direct prompting on problems requiring deductive reasoning rather than pure inductive recall.
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[AI-84] A2Flow: Automating Agent ic Workflow Generation via Self-Adaptive Abstraction Operators AAAI-2026
【速读】：该论文旨在解决当前大型语言模型（Large Language Models, LLMs）在自动化生成智能体工作流（agentic workflows）过程中，严重依赖人工预定义操作符（operators）所导致的泛化能力弱与可扩展性差的问题。其解决方案的关键在于提出一个完全自动化的框架 A²Flow，该框架通过三阶段的操作符提取机制实现自适应抽象：首先基于专家示范和LLM推理生成案例特定的操作符；其次对跨任务相似操作符进行聚类以形成初步抽象；最后利用长链式思维提示（chain-of-thought prompting）与多路径推理深度提取出紧凑且通用的执行操作符。这些操作符作为无需人工预定义的可复用构建模块，显著提升了工作流生成的自动化水平与效率，并结合操作符记忆机制优化节点级搜索策略，从而在通用与具身基准测试中实现平均性能提升2.4%和19.3%，同时降低37%资源消耗。
链接: https://arxiv.org/abs/2511.20693
作者: Mingming Zhao,Xiaokang Wei,Yuanqi Shao,Kaiwen Zhou,Lin Yang,Siwei Rao,Junhui Zhan,Zhitang Chen
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
备注: Accepted by AAAI-2026
Abstract:Large language models (LLMs) have shown strong potential in automating the design of agentic workflows. However, existing methods still rely heavily on manually predefined operators, limiting generalization and scalability. To address this issue, we propose A^2Flow , a fully automated framework for agentic workflow generation based on self-adaptive abstraction operators. A^2Flow employs a three-stage operator extraction process: 1) Case-based Initial Operator Generation: leveraging expert demonstrations and LLM reasoning to generate case-specific operators; 2) Operator Clustering and Preliminary Abstraction: grouping similar operators across tasks to form preliminary abstractions; and 3) Deep Extraction for Abstract Execution Operators: applying long chain-of-thought prompting and multi-path reasoning to derive compact and generalizable execution operators. These operators serve as reusable building blocks for workflow construction without manual predefinition. Furthermore, we enhance node-level workflow search with an operator memory mechanism, which retains historical outputs to enrich context and improve decision-making. Experiments on general and embodied benchmarks show that A^2Flow achieves a 2.4% and 19.3% average performance improvement and reduces resource usage by 37% over state-of-the-art baselines. Homepage:this https URL
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[AI-85] Hybrid coupling with operator inference and the overlapping Schwarz alternating method
【速读】：该论文旨在解决多尺度建模与仿真中传统高保真全阶模型（Full Order Model, FOM）计算耗时长、网格生成复杂的问题。其核心挑战在于如何高效耦合不同子域上的非侵入式算子推断（Operator Inference, OpInf）降阶模型（Reduced Order Model, ROM）与高保真FOM，同时保持精度与灵活性。解决方案的关键在于提出一种新颖的混合方法，利用重叠Schwarz交替法（Overlapping Schwarz Alternating Method, O-SAM）实现子域局部ROM与FOM之间的无缝集成，支持异构模型、不同网格及时间积分格式的协同工作，从而在保证高精度的前提下显著提升计算效率，数值实验表明相较传统FOM-FOM耦合可实现最高达106倍的加速比。
链接: https://arxiv.org/abs/2511.20687
作者: Irina Tezaur,Eric Parish,Anthony Gruber,Ian Moore,Christopher Wentland,Alejandro Mota
机构: 未知
类目: Numerical Analysis (math.NA); Artificial Intelligence (cs.AI); Mathematical Physics (math-ph)
备注:
Abstract:This paper presents a novel hybrid approach for coupling subdomain-local non-intrusive Operator Inference (OpInf) reduced order models (ROMs) with each other and with subdomain-local high-fidelity full order models (FOMs) with using the overlapping Schwarz alternating method (O-SAM). The proposed methodology addresses significant challenges in multiscale modeling and simulation, particularly the long runtime and complex mesh generation requirements associated with traditional high-fidelity simulations. By leveraging the flexibility of O-SAM, we enable the seamless integration of disparate models, meshes, and time integration schemes, enhancing computational efficiency while maintaining high accuracy. Our approach is demonstrated through a series of numerical experiments on complex three-dimensional (3D) solid dynamics problems, showcasing speedups of up to 106x compared to conventional FOM-FOM couplings. This work paves the way for more efficient simulation workflows in engineering applications, with potential extensions to a wide range of partial differential equations.
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[AI-86] AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI
【速读】：该论文旨在解决当前生成式 AI（Generative AI）安全评估数据集普遍存在的两个核心问题：一是现有数据集以英语为主，难以覆盖非英语语境下如韩国等特定社会文化背景中的风险；二是多数数据集局限于文本模态，缺乏多模态场景下的安全性测试。解决方案的关键在于提出并构建了AssurAI——一个高质量、大规模的韩语多模态安全评估数据集，包含11,480个跨文本、图像、视频和音频模态的实例，并基于由跨学科专家团队制定的35类AI风险分类体系进行结构化标注，同时采用两阶段构建流程（专家引导种子生成与众包扩展）、三重独立标注及迭代式专家红队测试机制确保数据质量，从而有效支撑针对韩国社区生成式AI系统的安全性评估与改进。
链接: https://arxiv.org/abs/2511.20686
作者: Chae-Gyun Lim,Seung-Ho Han,EunYoung Byun,Jeongyun Han,Soohyun Cho,Eojin Joo,Heehyeon Kim,Sieun Kim,Juhoon Lee,Hyunsoo Lee,Dongkun Lee,Jonghwan Hyeon,Yechan Hwang,Young-Jun Lee,Kyeongryul Lee,Minhyeong An,Hyunjun Ahn,Jeongwoo Son,Junho Park,Donggyu Yoon,Taehyung Kim,Jeemin Kim,Dasom Choi,Kwangyoung Lee,Hyunseung Lim,Yeohyun Jung,Jongok Hong,Sooyohn Nam,Joonyoung Park,Sungmin Na,Yubin Choi,Jeanne Choi,Yoojin Hong,Sueun Jang,Youngseok Seo,Somin Park,Seoungung Jo,Wonhye Chae,Yeeun Jo,Eunyoung Kim,Joyce Jiyoung Whang,HwaJung Hong,Joseph Seering,Uichin Lee,Juho Kim,Sunna Choi,Seokyeon Ko,Taeho Kim,Kyunghoon Kim,Myungsik Ha,So Jung Lee,Jemin Hwang,JoonHo Kwak,Ho-Jin Choi
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
备注: 16 pages, HuggingFace: this https URL
Abstract:The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks in non-English, socio-cultural contexts such as Korean, and are often limited to the text modality. To address this gap, we introduce AssurAI, a new quality-controlled Korean multimodal dataset for evaluating the safety of generative AI. First, we define a taxonomy of 35 distinct AI risk factors, adapted from established frameworks by a multidisciplinary expert group to cover both universal harms and relevance to the Korean socio-cultural context. Second, leveraging this taxonomy, we construct and release AssurAI, a large-scale Korean multimodal dataset comprising 11,480 instances across text, image, video, and audio. Third, we apply the rigorous quality control process used to ensure data integrity, featuring a two-phase construction (i.e., expert-led seeding and crowdsourced scaling), triple independent annotation, and an iterative expert red-teaming loop. Our pilot study validates AssurAI’s effectiveness in assessing the safety of recent LLMs. We release AssurAI to the public to facilitate the development of safer and more reliable generative AI systems for the Korean community.
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[AI-87] Minimizing Hyperbolic Embedding Distortion with LLM -Guided Hierarchy Restructuring
【速读】：该论文旨在解决如何通过自动化手段优化层次结构数据以提升其在双曲空间中的嵌入质量的问题。当前双曲学习在推荐系统、计算机视觉等场景中广泛应用，但其嵌入质量高度依赖于输入层次结构的拓扑特性，如高分支因子和单继承性。论文提出一种基于提示（prompt-based）的方法，利用大语言模型（Large Language Models, LLMs）对现有层次结构进行重构，使其更符合双曲嵌入的最优条件。解决方案的关键在于将已知的双曲嵌入理想属性转化为可执行的提示指令，引导LLM自动调整层次结构，从而显著提升嵌入质量，并提供可解释的重组依据，助力知识工程师进行结构优化。
链接: https://arxiv.org/abs/2511.20679
作者: Melika Ayoughi,Pascal Mettes,Paul Groth
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Hyperbolic geometry is an effective geometry for embedding hierarchical data structures. Hyperbolic learning has therefore become increasingly prominent in machine learning applications where data is hierarchically organized or governed by hierarchical semantics, ranging from recommendation systems to computer vision. The quality of hyperbolic embeddings is tightly coupled to the structure of the input hierarchy, which is often derived from knowledge graphs or ontologies. Recent work has uncovered that for an optimal hyperbolic embedding, a high branching factor and single inheritance are key, while embedding algorithms are robust to imbalance and hierarchy size. To assist knowledge engineers in reorganizing hierarchical knowledge, this paper investigates whether Large Language Models (LLMs) have the ability to automatically restructure hierarchies to meet these criteria. We propose a prompt-based approach to transform existing hierarchies using LLMs, guided by known desiderata for hyperbolic embeddings. Experiments on 16 diverse hierarchies show that LLM-restructured hierarchies consistently yield higher-quality hyperbolic embeddings across several standard embedding quality metrics. Moreover, we show how LLM-guided hierarchy restructuring enables explainable reorganizations, providing justifications to knowledge engineers.
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[AI-88] MTTR-A: Measuring Cognitive Recovery Latency in Multi-Agent Systems
【速读】：该论文旨在解决自主多智能体系统（Multi-Agent Systems, MAS）中认知稳定性（cognitive stability）的量化评估问题，特别是如何衡量智能体工作流在推理一致性丧失后恢复推理连贯性所需的时间。现有可观测性工具仅能监控系统输出，无法量化认知恢复的延迟。其解决方案的关键在于将经典可靠性指标（如平均恢复时间 MTTR、平均无故障时间 MTBF）引入认知领域，提出并定义了 MTTR-A（Mean Time-to-Recovery for Agentic Systems），作为运行时认知恢复延迟的量化指标；并通过基于 AG~News 语料和 LangGraph 框架的基准仿真验证了不同反射模式下的恢复性能，表明自动化反射机制可在约 6 秒内恢复稳定，而人工审批则需约 12 秒，从而首次将认知恢复过程标准化为可测量、可比较的性能属性，为分布式推理的运行时可靠性提供了理论基础与实证依据。
链接: https://arxiv.org/abs/2511.20663
作者: Barak Or
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
备注: preprint
Abstract:Ensuring cognitive stability in autonomous multi-agent systems (MAS) is a central challenge for large-scale, distributed AI. While existing observability tools monitor system outputs, they cannot quantify how rapidly agentic workflows recover once reasoning coherence has been lost. We adapt classical reliability metrics-Mean Time-to-Recovery (MTTR), Mean Time Between Failures (MTBF), and related ratios-into the cognitive domain, defining MTTR-A (Mean Time-to-Recovery for Agentic Systems) as a runtime measure of cognitive recovery latency. MTTR-A quantifies the time required for a MAS to detect reasoning drift and restore consistent operation, capturing the recovery of reasoning coherence rather than infrastructural repair. A benchmark simulation using the AG~News corpus and the LangGraph orchestration framework was conducted, modeling recovery latencies across multiple reflex modes. Automated reflexes restored stability within approximately 6s on average, while human-approval interventions required about 12s. Across 200 runs, the median simulated MTTR-A was 6.21±2.14s, MTBF=6.7±2.14s, and NRR=0.08, demonstrating measurable runtime resilience across reflex strategies. By formalizing recovery latency as a quantifiable property of distributed reasoning-and deriving reliability bounds linking recovery time and cognitive uptime-this work establishes a foundation for runtime dependability in agentic cognition, transforming cognitive recovery from an ad-hoc process into a standardized, interpretable performance Comments: preprint Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Systems and Control (eess.SY) Cite as: arXiv:2511.20663 [cs.MA] (or arXiv:2511.20663v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2511.20663 Focus to learn more arXiv-issued DOI via DataCite
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[AI-89] ransforming Higher Education with AI-Powered Video Lectures
【速读】：该论文旨在解决高等教育中视频讲座制作效率低、资源开发成本高以及可访问性不足的问题，尤其在教师工作负荷重、难以持续产出高质量教学视频的背景下。其解决方案的关键在于构建一个半自动化的工作流，整合Google Gemini用于脚本生成、Amazon Polly实现语音合成、Microsoft PowerPoint完成视频组装，从而在保留教学意图的同时确保脚本与幻灯片同步、叙事连贯且支持定制化，有效提升了视频内容生产的效率和一致性，同时验证了AI生成的教学视频（AIIV）在学习效果上可媲美人工制作视频（HIV）。
链接: https://arxiv.org/abs/2511.20660
作者: Dengsheng Zhang
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注: 27 pages, 9 figures
Abstract:The integration of artificial intelligence (AI) into video lecture production has the potential to transform higher education by streamlining content creation and enhancing accessibility. This paper investigates a semi automated workflow that combines Google Gemini for script generation, Amazon Polly for voice synthesis, and Microsoft PowerPoint for video assembly. Unlike fully automated text to video platforms, this hybrid approach preserves pedagogical intent while ensuring script to slide synchronization, narrative coherence, and customization. Case studies demonstrate the effectiveness of Gemini in generating accurate and context-sensitive scripts for visually rich academic presentations, while Polly provides natural-sounding narration with controllable pacing. A two course pilot study was conducted to evaluate AI generated instructional videos (AIIV) against human instructional videos (HIV). Both qualitative and quantitative results indicate that AIIVs are comparable to HIVs in terms of learning outcomes, with students reporting high levels of clarity, coherence, and usability. However, limitations remain, particularly regarding audio quality and the absence of human-like avatars. The findings suggest that AI assisted video production can reduce instructor workload, improve scalability, and deliver effective learning resources, while future improvements in synthetic voices and avatars may further enhance learner engagement.
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[AI-90] Intelligent Agents with Emotional Intelligence: Current Trends Challenges and Future Prospects
【速读】：该论文旨在解决当前情感智能（Affective Intelligence）研究中缺乏系统性综述的问题，尤其在情绪理解、诱发与表达三个核心环节之间存在割裂，且相关挑战未被充分探讨。其解决方案的关键在于提出一个全面的框架，涵盖多模态数据处理以实现情绪理解，引入情感认知机制（包括认知评估、情绪映射及决策、学习和推理中的自适应调节），并整合文本、语音和面部表情等多模态的情感表达技术，从而提升人机交互中的情感智能水平。同时，论文还分析了现有方法应对关键挑战的策略，并指出生成式技术（Generative Technologies）在未来推动情感计算发展的潜力。
链接: https://arxiv.org/abs/2511.20657
作者: Raziyeh Zall,Alireza Kheyrkhah,Erik Cambria,Zahra Naseri,M.Reza Kangavari
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:The development of agents with emotional intelligence is becoming increasingly vital due to their significant role in human-computer interaction and the growing integration of computer systems across various sectors of society. Affective computing aims to design intelligent systems that can recognize, evoke, and express human emotions, thereby emulating human emotional intelligence. While previous reviews have focused on specific aspects of this field, there has been limited comprehensive research that encompasses emotion understanding, elicitation, and expression, along with the related challenges. This survey addresses this gap by providing a holistic overview of core components of artificial emotion intelligence. It covers emotion understanding through multimodal data processing, as well as affective cognition, which includes cognitive appraisal, emotion mapping, and adaptive modulation in decision-making, learning, and reasoning. Additionally, it addresses the synthesis of emotional expression across text, speech, and facial modalities to enhance human-agent interaction. This paper identifies and analyzes the key challenges and issues encountered in the development of affective systems, covering state-of-the-art methodologies designed to address them. Finally, we highlight promising future directions, with particular emphasis on the potential of generative technologies to advance affective computing.
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[AI-91] Context-Aware Visual Prompting: Automating Geospatial Web Dashboards with Large Language Models and Agent Self-Validation for Decision Support
【速读】：该论文旨在解决基于Web的地理空间风险分析与决策支持仪表板开发中面临的三大挑战：大规模多维环境数据的可视化难题、实现复杂度高以及自动化程度不足。其解决方案的关键在于提出了一种生成式AI（Generative AI）框架，该框架利用大语言模型（Large Language Models, LLMs）从用户定义的输入（如UI线框图、需求说明和数据源）自动构建交互式地理空间仪表板。其中，核心创新是引入了上下文感知视觉提示（Context-Aware Visual Prompting, CAVP）机制，通过提取并编码界面布局语义来引导LLM生成高质量代码；同时结合结构化知识图谱嵌入领域知识，提升代码生成的准确性与情境适配性，并集成基于代理的自验证机制（Agent-based LLM + Pass@k评估与语义指标），确保输出可靠性。该框架最终实现了基于MVVM架构的可扩展React代码生成流水线，显著优于基线方法且功能超越第三方平台，支持多页面全功能界面。
链接: https://arxiv.org/abs/2511.20656
作者: Haowen Xu,Jose Tupayachi,Xiao-Ying Yu
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
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Abstract:The development of web-based geospatial dashboards for risk analysis and decision support is often challenged by the difficulty in visualization of big, multi-dimensional environmental data, implementation complexity, and limited automation. We introduce a generative AI framework that harnesses Large Language Models (LLMs) to automate the creation of interactive geospatial dashboards from user-defined inputs including UI wireframes, requirements, and data sources. By incorporating a structured knowledge graph, the workflow embeds domain knowledge into the generation process and enable accurate and context-aware code completions. A key component of our approach is the Context-Aware Visual Prompting (CAVP) mechanism, which extracts encodes and interface semantics from visual layouts to guide LLM driven generation of codes. The new framework also integrates a self-validation mechanism that uses an agent-based LLM and Pass@k evaluation alongside semantic metrics to assure output reliability. Dashboard snippets are paired with data visualization codebases and ontological representations, enabling a pipeline that produces scalable React-based completions using the MVVM architectural pattern. Our results demonstrate improved performance over baseline approaches and expanded functionality over third party platforms, while incorporating multi-page, fully functional interfaces. We successfully developed a framework to implement LLMs, demonstrated the pipeline for automated code generation, deployment, and performed chain-of-thought AI agents in self-validation. This integrative approach is guided by structured knowledge and visual prompts, providing an innovative geospatial solution in enhancing risk analysis and decision making.
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[AI-92] CodeVaani: A Multilingual Voice-Based Code Learning Assistant
【速读】：该论文旨在解决编程教育中因语言障碍导致的不平等问题，特别是针对英语能力有限的多语言学习者（如印度学生）难以参与以英文文本交互为主的编程教学场景。其解决方案的关键在于构建了一个名为CodeVaani的多语言语音驱动助教系统，该系统集成Indic自动语音识别（ASR）、面向代码的转录优化模块以及代码理解模型，能够支持以母语进行语音交互并生成文本与音频双模态响应，从而实现自然、低门槛的编程学习体验。
链接: https://arxiv.org/abs/2511.20654
作者: Jayant Havare,Srikanth Tamilselvam,Ashish Mittal,Shalaka Thorat,Soham Jadia,Varsha Apte,Ganesh Ramakrishnan
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
备注:
Abstract:Programming education often assumes English proficiency and text-based interaction, creating barriers for students from multilingual regions such as India. We present CodeVaani, a multilingual speech-driven assistant for understanding code, built into Bodhitree [1], a Learning Management System developed at IIT Bombay. It is a voice-enabled assistant that helps learners explore programming concepts in their native languages. The system integrates Indic ASR, a codeaware transcription refinement module, and a code model for generating relevant answers. Responses are provided in both text and audio for natural interaction. In a study with 28 beginner programmers, CodeVaani achieved 75% response accuracy, with over 80% of participants rating the experience positively. Compared to classroom assistance, our framework offers ondemand availability, scalability to support many learners, and multilingual support that lowers the entry barrier for students with limited English proficiency. The demo will illustrate these capabilities and highlight how voice-based AI systems can make programming education more inclusive. Supplementary artifacts and demo video are also made available.
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[AI-93] Domain-Grounded Evaluation of LLM s in International Student Knowledge
【速读】：该论文旨在解决当前大语言模型（Large Language Models, LLMs）在留学咨询场景中可靠性不足的问题，特别是其回答准确性与幻觉（hallucination）现象的不可控性。研究者通过构建基于真实教育科技平台ApplyBoard advising流程的高质量测试集，系统评估了LLMs在多领域（如入学、签证、奖学金）交叉问题上的表现，提出了一套兼顾领域覆盖度与内容忠实性的评分机制——即“正确、部分正确或错误”的三分类标准，并引入“过范围”（over-scoped）和“欠覆盖”（under-coverage）作为衡量幻觉与相关性损失的核心指标。解决方案的关键在于：建立一个可复用、领域感知的评估协议，从而为教育与咨询服务部署前的LLM审计提供标准化方法论支撑。
链接: https://arxiv.org/abs/2511.20653
作者: Claudinei Daitx,Haitham Amar
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注:
Abstract:Large language models (LLMs) are increasingly used to answer high-stakes study-abroad questions about admissions, visas, scholarships, and eligibility. Yet it remains unclear how reliably they advise students, and how often otherwise helpful answers drift into unsupported claims (``hallucinations’'). This work provides a clear, domain-grounded overview of how current LLMs behave in this setting. Using realistic questions set drawn from ApplyBoard’s advising workflows – an EdTech platform that supports students from discovery to enrolment – we evaluate two essentials side by side: accuracy (is the information correct and complete?) and hallucination (does the model add content not supported by the question or domain evidence). These questions are categorized by domain scope which can be a single-domain or multi-domain – when it must integrate evidence across areas such as admissions, visas, and scholarships. To reflect real advising quality, we grade answers with a simple rubric which is correct, partial, or wrong. The rubric is domain-coverage-aware: an answer can be partial if it addresses only a subset of the required domains, and it can be over-scoped if it introduces extra, unnecessary domains; both patterns are captured in our scoring as under-coverage or reduced relevance/hallucination. We also report measures of faithfulness and answer relevance, alongside an aggregate hallucination score, to capture relevance and usefulness. All models are tested with the same questions for a fair, head-to-head comparison. Our goals are to: (1) give a clear picture of which models are most dependable for study-abroad advising, (2) surface common failure modes – where answers are incomplete, off-topic, or unsupported, and (3) offer a practical, reusable protocol for auditing LLMs before deployment in education and advising contexts. Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2511.20653 [cs.HC] (or arXiv:2511.20653v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2511.20653 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haitham Amar Dr [view email] [v1] Tue, 7 Oct 2025 15:37:34 UTC (257 KB) Full-text links: Access Paper: View a PDF of the paper titled Domain-Grounded Evaluation of LLMs in International Student Knowledge, by Claudinei Daitx and Haitham AmarView PDFHTML (experimental)TeX Source view license Current browse context: cs.HC prev | next new | recent | 2025-11 Change to browse by: cs cs.AI cs.LG 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
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[AI-94] When LLM s Cant Help: Real-World Evaluation of LLM s in Nutrition
【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在营养领域应用中缺乏外在评估的问题，尤其是在随机对照试验（Randomised Controlled Trials, RCTs）这一金标准下验证其实际效果的缺失。当前LLM在营养咨询中的表现多基于内在评估（intrinsic evaluation），难以反映其在真实场景中的有效性与用户影响。为填补这一空白，研究者设计并实施了首个针对营养领域的LLM RCT，关键解决方案在于将两个基于LLM的功能集成到规则驱动的聊天机器人中：(1) 利用LLM对消息进行重述以提升对话多样性和用户参与度；(2) 使用微调后的LLM提供营养咨询服务。尽管这些功能在内在测试中表现良好，但RCT结果显示它们并未带来一致的实际改善，凸显了内在评估与现实部署效果之间的显著差距，强调需采用跨学科、以人为中心的方法来推进LLM在健康干预中的可靠落地。
链接: https://arxiv.org/abs/2511.20652
作者: Karen Jia-Hui Li,Simone Balloccu,Ondrej Dusek,Ehud Reiter
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: Published at INLG 2025 main conference
Abstract:The increasing trust in large language models (LLMs), especially in the form of chatbots, is often undermined by the lack of their extrinsic evaluation. This holds particularly true in nutrition, where randomised controlled trials (RCTs) are the gold standard, and experts demand them for evidence-based deployment. LLMs have shown promising results in this field, but these are limited to intrinsic setups. We address this gap by running the first RCT involving LLMs for nutrition. We augment a rule-based chatbot with two LLM-based features: (1) message rephrasing for conversational variety and engagement, and (2) nutritional counselling through a fine-tuned model. In our seven-week RCT (n=81), we compare chatbot variants with and without LLM integration. We measure effects on dietary outcome, emotional well-being, and engagement. Despite our LLM-based features performing well in intrinsic evaluation, we find that they did not yield consistent benefits in real-world deployment. These results highlight critical gaps between intrinsic evaluations and real-world impact, emphasising the need for interdisciplinary, human-centred approaches.\footnoteWe provide all of our code and results at: \ \hrefthis https URLthis https URL
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[AI-95] Data-Driven Assessment of Concrete Slab Integrity via Impact-Echo Signals and Neural Networks
【速读】：该论文旨在解决混凝土桥面板内部缺陷（如分层、空隙和蜂窝状缺陷）难以通过传统目视检查或人工敲击法可靠检测的问题。其解决方案的关键在于构建一个基于机器学习的冲击回波（Impact Echo, IE）自动化框架，该框架结合快速傅里叶变换（Fast Fourier Transform, FFT）提取主导频率特征并生成空间分布图，利用无监督k-means聚类识别低频缺陷区域，并通过实验室标定的Ground Truth Masks（GTMs）提供高置信度训练标签；进一步地，将空间有序的峰值频率序列输入堆叠的长短期记忆网络（stacked Long Short-Term Memory, LSTM），实现对四类常见混凝土缺陷的多类别分类，整体准确率达73%。该方法显著提升了非破坏性评估（Non-Destructive Evaluation, NDE）的客观性、可扩展性和可重复性，支持在实际桥梁结构中实现智能化、数据驱动的大规模健康监测。
链接: https://arxiv.org/abs/2511.21080
作者: Yeswanth Ravichandran,Duoduo Liao,Charan Teja Kurakula
机构: 未知
类目: ignal Processing (eess.SP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted by IEEE Big Data 2025
Abstract:Subsurface defects such as delamination, voids, and honeycombing critically affect the durability of concrete bridge decks but are difficult to detect reliably using visual inspection or manual sounding. This paper presents a machine learning based Impact Echo (IE) framework that automates both defect localization and multi-class classification of common concrete defects. Raw IE signals from Federal Highway Administration (FHWA) laboratory slabs and in-service bridge decks are transformed via Fast Fourier Transform (FFT) into dominant peak-frequency features and interpolated into spatial maps for defect zone visualization. Unsupervised k-means clustering highlights low-frequency, defect-prone regions, while Ground Truth Masks (GTMs) derived from seeded lab defects are used to validate spatial accuracy and generate high-confidence training labels. From these validated regions, spatially ordered peak-frequency sequences are constructed and fed into a stacked Long Short-Term Memory (LSTM) network that classifies four defect types shallow delamination, deep delamination, voids, and honeycombing with 73% overall accuracy. Field validation on the bridge deck demonstrates that models trained on laboratory data generalize under realistic coupling, noise, and environmental variability. The proposed framework enhances the objectivity, scalability, and repeatability of Non-Destructive Evaluation (NDE), supporting intelligent, data-driven bridge health monitoring at a network scale.
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[AI-96] Even with AI Bijection Discovery is Still Hard: The Opportunities and Challenges of OpenEvolve for Novel Bijection Construction
【速读】：该论文旨在解决如何利用进化式程序合成系统（如OpenEvolve）辅助发现组合数学中的双射构造（combinatorial bijection discovery）问题，尤其是针对已知和开放性问题的求解。其解决方案的关键在于：将生成式AI（Generative AI）与进化算法相结合，通过多轮迭代优化由大型语言模型（LLMs）生成的候选代码方案，从而逐步逼近更优甚至全新的双射构造；尽管初步实验显示该方法在特定问题上具备潜力，但当前前沿系统仍难以独立完成研究级双射发现任务，凸显了人类数学家在这一过程中的不可或缺作用。
链接: https://arxiv.org/abs/2511.20987
作者: Davis Brown,Jesse He,Helen Jenne,Henry Kvinge,Max Vargas
机构: 未知
类目: Combinatorics (math.CO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
备注: 16 pages, 3 figures. This is an extended abstract submitted to FPSAC 2026
Abstract:Evolutionary program synthesis systems such as AlphaEvolve, OpenEvolve, and ShinkaEvolve offer a new approach to AI-assisted mathematical discovery. These systems utilize teams of large language models (LLMs) to generate candidate solutions to a problem as human readable code. These candidate solutions are then ‘evolved’ with the goal of improving them beyond what an LLM can produce in a single shot. While existing mathematical applications have mostly focused on problems of establishing bounds (e.g., sphere packing), the program synthesis approach is well suited to any problem where the solution takes the form of an explicit construction. With this in mind, in this paper we explore the use of OpenEvolve for combinatorial bijection discovery. We describe the results of applying OpenEvolve to three bijection construction problems involving Dyck paths, two of which are known and one of which is open. We find that while systems like OpenEvolve show promise as a valuable tool for combinatorialists, the problem of finding novel, research-level bijections remains a challenging task for current frontier systems, reinforcing the need for human mathematicians in the loop. We describe some lessons learned for others in the field interested in exploring the use of these systems.
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[AI-97] AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
【速读】：该论文旨在解决当前科学研究中因数据局限性、模型可迁移性不足及AI系统与实验流程脱节所导致的发现效率低下问题。其核心解决方案在于构建端到端集成的AI-enabled科学工作流，关键要素包括：利用大规模基础模型（foundation models）和主动学习（active learning）实现预测与验证的闭环优化，发展可迁移的电子结构与原子间相互作用模型以提升跨领域适用性，并推动自驱动实验室（self-driving laboratories）在真实复杂环境中加速科学发现；同时强调生成式系统需基于合成可行性（synthesisability）而非理想化相态，从而确保结果的物理可解释性和可重复性。
链接: https://arxiv.org/abs/2511.20976
作者: Stephen G. Dale,Nikita Kazeev,Alastair J. A. Price,Victor Posligua,Stephan Roche,O. Anatole von Lilienfeld,Konstantin S. Novoselov,Xavier Bresson,Gianmarco Mengaldo,Xudong Chen,Terence J. O’Kane,Emily R. Lines,Matthew J. Allen,Amandine E. Debus,Clayton Miller,Jiayu Zhou,Hiroko H. Dodge,David Rousseau,Andrey Ustyuzhanin,Ziyun Yan,Mario Lanza,Fabio Sciarrino,Ryo Yoshida,Zhidong Leong,Teck Leong Tan,Qianxiao Li,Adil Kabylda,Igor Poltavsky,Alexandre Tkatchenko,Sherif Abdulkader Tawfik,Prathami Divakar Kamath,Theo Jaffrelot Inizan,Kristin A. Persson,Bryant Y. Li,Vir Karan,Chenru Duan,Haojun Jia,Qiyuan Zhao,Hiroyuki Hayashi,Atsuto Seko,Isao Tanaka,Omar M. Yaghi,Tim Gould,Bun Chan,Stefan Vuckovic,Tianbo Li,Min Lin,Zehcen Tang,Yang Li,Yong Xu,Amrita Joshi,Xiaonan Wang,Leonard W.T. Ng,Sergei V. Kalinin,Mahshid Ahmadi,Jiyizhe Zhang,Shuyuan Zhang,Alexei Lapkin,Ming Xiao,Zhe Wu,Kedar Hippalgaonkar,Limsoon Wong,Lorenzo Bastonero,Nicola Marzari,Dorye Luis Esteras Cordoba,Andrei Tomut,Alba Quinones Andrade,Jose-Hugo Garcia
机构: 未知
类目: Physics and Society (physics.soc-ph); Artificial Intelligence (cs.AI); Atmospheric and Oceanic Physics (physics.ao-ph); Atomic and Molecular Clusters (physics.atm-clus); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
备注:
Abstract:Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a forward-looking view of AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing. Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific workflows that connect simulations to experiments and generative systems grounded in synthesisability rather than purely idealised phases. Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation while maintaining reproducibility and physical interpretability. Taken together, these perspectives outline where AI-enabled science stands today, identify bottlenecks in data, methods and infrastructure, and chart concrete directions for building AI systems that are not only more powerful but also more transparent and capable of accelerating discovery in complex real-world environments.
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[AI-98] A Review of Pseudospectral Optimal Control: From Theory to Flight WWW
【速读】：该论文旨在解决航空航天与自主系统中复杂控制问题的求解难题，其核心在于将伪谱理论（pseudospectral theory）与最优控制理论（optimal control theory）融合，构建“伪谱最优控制理论”（pseudospectral optimal control theory）。解决方案的关键在于利用两者共同定义在Sobolev空间中的数学结构，从而实现高精度、高效能的数值计算方法，并通过NASA航天器上的飞行验证展示了该方法在实际嵌入式平台上的成功应用，标志着伪谱最优控制从理论走向工程实践的重要转折。
链接: https://arxiv.org/abs/2511.20843
作者: I. M. Ross,M. Karpenko
机构: 未知
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Systems and Control (eess.SY); Functional Analysis (math.FA); Numerical Analysis (math.NA)
备注: this https URL
Abstract:The home space for optimal control is a Sobolev space. The home space for pseudospectral theory is also a Sobolev space. It thus seems natural to combine pseudospectral theory with optimal control theory and construct ``pseudospectral optimal control theory,‘’ a term coined by Ross. In this paper, we review key theoretical results in pseudospectral optimal control that have proven to be critical for a successful flight. Implementation details of flight demonstrations onboard NASA spacecraft are discussed along with emerging trends and techniques in both theory and practice. The 2011 launch of pseudospectral optimal control in embedded platforms is changing the way in which we see solutions to challenging control problems in aerospace and autonomous systems.
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[AI-99] Morality in AI. A plea to embed morality in LLM architectures and frameworks
【速读】：该论文试图解决的问题是：如何在大型语言模型（Large Language Models, LLMs）中有效嵌入道德意义处理能力，以确保其在人类决策与行为中介入时具备合理的道德判断力。当前主流方法依赖自下而上的技术手段，如微调（fine-tuning）和基于人类反馈的强化学习（Reinforcement Learning from Human Feedback, RLHF），但这些方法难以从根本上塑造模型的道德认知机制。论文提出一种自上而下的解决方案，其关键在于将道德意义处理直接嵌入Transformer架构的机制与框架中，通过设计原则实现对注意力机制的重构——将注意力视为结构与处理之间的动态接口，并借鉴伊里斯·默多克（Iris Murdoch）提出的“爱之关注”（loving attention）理论，即一种持续、公正的观察方式，可促使个体重新认识他人并产生道德转变。这一哲学理念被转化为三项技术路径：修改训练目标、运行时权重调整以及注意力机制的架构优化。该方案强调道德嵌入应与外部约束方法互补，从而推动LLM向更具伦理一致性的方向发展。
链接: https://arxiv.org/abs/2511.20689
作者: Gunter Bombaerts,Bram Delisse,Uzay Kaymak
机构: 未知
类目: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI)
备注:
Abstract:Large language models (LLMs) increasingly mediate human decision-making and behaviour. Ensuring LLM processing of moral meaning therefore has become a critical challenge. Current approaches rely predominantly on bottom-up methods such as fine-tuning and reinforcement learning from human feedback. We propose a fundamentally different approach: embedding moral meaning processing directly into the architectural mechanisms and frameworks of transformer-based models through top-down design principles. We first sketch a framework that conceptualizes attention as a dynamic interface mediating between structure and processing, contrasting with existing linear attention frameworks in psychology. We start from established biological-artificial attention analogies in neural architecture design to improve cognitive processing. We extend this analysis to moral processing, using Iris Murdoch’s theory of loving attention (sustained, just observation that enables moral transformation by reseeing others with clarity and compassion) to philosophically discuss functional analogies between human and LLM moral processing. We formulate and evaluate potentially promising technical operationalizations to embed morality in LLM architectures and frameworks. We acknowledge the limitations of our exploration and give three key contributions. (1) We conceptualize attention as a dynamic system mechanism mediating between structure and processing. (2) Drawing on the Murdoch notion of loving attention, we outline technical pathways for embedding morality in LLMs, through modified training objectives, runtime weight adjustments, and architectural refinements to attention. (3) We argue that integrating morality into architectures and frameworks complements external, constraint-based methods. We conclude with a call for collaboration between transformer designers and philosophers engaged in AI ethics.
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机器学习
[LG-0] DSD: A Distributed Speculative Decoding Solution for Edge-Cloud Agile Large Model Serving
链接: https://arxiv.org/abs/2511.21669
作者: Fengze Yu,Leshu Li,Brad McDanel,Saiqian Zhang
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:
Abstract:Large language model (LLM) inference often suffers from high decoding latency and limited scalability across heterogeneous edge-cloud environments. Existing speculative decoding (SD) techniques accelerate token generation but remain confined to single-node execution. We propose DSD, a distributed speculative decoding framework that extends SD to multi-device deployments through coordinated draft-target execution. Given the lack of prior work on simulating this paradigm, we first introduce DSD-Sim, a discrete-event simulator that captures network, batching, and scheduling dynamics. Building on insights from DSD-Sim, we further design an Adaptive Window Control (AWC) policy that dynamically adjusts speculation window size to optimize throughput. Experiments across diverse workloads show that DSD achieves up to 1.1x speedup and 9.7% higher throughput over existing SD baselines, enabling agile and scalable LLM serving across edge and cloud.
[LG-1] EvilGenie: A Reward Hacking Benchmark
链接: https://arxiv.org/abs/2511.21654
作者: Jonathan Gabor,Jayson Lynch,Jonathan Rosenfeld
类目: Machine Learning (cs.LG)
*备注:
Abstract:We introduce EvilGenie, a benchmark for reward hacking in programming settings. We source problems from LiveCodeBench and create an environment in which agents can easily reward hack, such as by hardcoding test cases or editing the testing files. We measure reward hacking in three ways: held out unit tests, LLM judges, and test file edit detection. We verify these methods against human review and each other. We find the LLM judge to be highly effective at detecting reward hacking in unambiguous cases, and observe only minimal improvement from the use of held out test cases. In addition to testing many models using Inspect’s basic_agent scaffold, we also measure reward hacking rates for three popular proprietary coding agents: OpenAI’s Codex, Anthropic’s Claude Code, and Google’s Gemini CLI Using GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro, respectively. We observe explicit reward hacking by both Codex and Claude Code, and misaligned behavior by all three agents. Our codebase can be found at this https URL.
[LG-2] Aligning LLM s Toward Multi-Turn Conversational Outcomes Using Iterative PPO
链接: https://arxiv.org/abs/2511.21638
作者: Daniel R. Jiang,Jalaj Bhandari,Yukai Yang,Rémi Munos,Tyler Lu
类目: Machine Learning (cs.LG)
*备注: 12 pages, 2 figures
Abstract:Optimizing large language models (LLMs) for multi-turn conversational outcomes remains a significant challenge, especially in goal-oriented settings like AI marketing or sales agents who facilitate transactions via messaging platforms. The difficulty stems from sparse, long-horizon rewards and the discrepancy between response-level planning and token-level generation. In this technical note, we propose a formal reduction of the multi-turn RL problem into a sequence of single-turn RLHF-style problems. This is achieved by setting a learned multi-turn Q-function as the reward model for the single-turn problem. We demonstrate and prove a key insight: solving this single-turn RL problem with standard token-level PPO is equivalent to a policy improvement step within the multi-turn problem. This insight naturally leads to Iterative PPO, a batch online policy iteration algorithm that alternates between fitting Q-functions from logged conversation trajectories and improving the policy. A major practical advantage is that Iterative PPO directly leverages stable, off-the-shelf single-turn RLHF tools, making it straightforward to implement. Our method occupies a middle ground between fully online and fully offline approaches, retaining the adaptability of online updates while gaining the stability benefits of offline training.
[LG-3] Beyond Accuracy: An Empirical Study of Uncertainty Estimation in Imputation
链接: https://arxiv.org/abs/2511.21607
作者: Zarin Tahia Hossain,Mostafa Milani
类目: Databases (cs.DB); Machine Learning (cs.LG)
*备注: To appear in conference proceedings
Abstract:Handling missing data is a central challenge in data-driven analysis. Modern imputation methods not only aim for accurate reconstruction but also differ in how they represent and quantify uncertainty. Yet, the reliability and calibration of these uncertainty estimates remain poorly understood. This paper presents a systematic empirical study of uncertainty in imputation, comparing representative methods from three major families: statistical (MICE, SoftImpute), distribution alignment (OT-Impute), and deep generative (GAIN, MIWAE, TabCSDI). Experiments span multiple datasets, missingness mechanisms (MCAR, MAR, MNAR), and missingness rates. Uncertainty is estimated through three complementary routes: multi-run variability, conditional sampling, and predictive-distribution modeling, and evaluated using calibration curves and the Expected Calibration Error (ECE). Results show that accuracy and calibration are often misaligned: models with high reconstruction accuracy do not necessarily yield reliable uncertainty. We analyze method-specific trade-offs among accuracy, calibration, and runtime, identify stable configurations, and offer guidelines for selecting uncertainty-aware imputers in data cleaning and downstream machine learning pipelines.
[LG-4] AB-DRW: A DFT-based Robust Watermark for Generative Tabular Data
链接: https://arxiv.org/abs/2511.21600
作者: Yizhou Zhao,Xiang Li,Peter Song,Qi Long,Weijie Su
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:
Abstract:The rise of generative AI has enabled the production of high-fidelity synthetic tabular data across fields such as healthcare, finance, and public policy, raising growing concerns about data provenance and misuse. Watermarking offers a promising solution to address these concerns by ensuring the traceability of synthetic data, but existing methods face many limitations: they are computationally expensive due to reliance on large diffusion models, struggle with mixed discrete-continuous data, or lack robustness to post-modifications. To address them, we propose TAB-DRW, an efficient and robust post-editing watermarking scheme for generative tabular data. TAB-DRW embeds watermark signals in the frequency domain: it normalizes heterogeneous features via the Yeo-Johnson transformation and standardization, applies the discrete Fourier transform (DFT), and adjusts the imaginary parts of adaptively selected entries according to precomputed pseudorandom bits. To further enhance robustness and efficiency, we introduce a novel rank-based pseudorandom bit generation method that enables row-wise retrieval without incurring storage overhead. Experiments on five benchmark tabular datasets show that TAB-DRW achieves strong detectability and robustness against common post-processing attacks, while preserving high data fidelity and fully supporting mixed-type features.
[LG-5] Visualizing LLM Latent Space Geometry Through Dimensionality Reduction
链接: https://arxiv.org/abs/2511.21594
作者: Alex Ning,Vainateya Rangaraju
类目: Machine Learning (cs.LG)
*备注: 24 pages, 16 figures
Abstract:Large language models (LLMs) achieve state-of-the-art results across many natural language tasks, but their internal mechanisms remain difficult to interpret. In this work, we extract, process, and visualize latent state geometries in Transformer-based language models through dimensionality reduction. We capture layerwise activations at multiple points within Transformer blocks and enable systematic analysis through Principal Component Analysis (PCA) and Uniform Manifold Approximation (UMAP). We demonstrate experiments on GPT-2 and LLaMa models, where we uncover interesting geometric patterns in latent space. Notably, we identify a clear separation between attention and MLP component outputs across intermediate layers, a pattern not documented in prior work to our knowledge. We also characterize the high norm of latent states at the initial sequence position and visualize the layerwise evolution of latent states. Additionally, we demonstrate the high-dimensional helical structure of GPT-2’s positional embeddings, the sequence-wise geometric patterns in LLaMa, and experiment with repeating token sequences. We aim to support systematic analysis of Transformer internals with the goal of enabling further reproducible interpretability research. We make our code available at this https URL.
[LG-6] An AI-Enabled Hybrid Cyber-Physical Framework for Adaptive Control in Smart Grids
链接: https://arxiv.org/abs/2511.21590
作者: Muhammad Siddique,Sohaib Zafar
类目: Machine Learning (cs.LG)
*备注: 16 pages, 11 figures, IEEEaccess journal
Abstract:Smart grids are a fusion of classical power infrastructure and advanced communication networks and smart control, to create a cyber-physical environment that is more efficient and flexible than ever before. This integration causes vulnerabilities that can undermine grid stability as well as reliability. Digital forensics is a fundamental concept of learning and identifying, detecting, and mitigating such security incidents. This paper presents an all-in-one machine learning-based digital forensic framework of smart grid systems deployed on the Cloud. The framework combines the data acquisition at the sensor-level, authenticated communication, scalable cloud storage and automated forensic analytics. The model uses supervised and unsupervised learning algorithms - such as Random Forest, Support Vector Machine, Gradient Boosted Trees and deep neural architectures for anomaly detection, event reconstruction and intrusion analysis in real time. After several simulation and experimental studies on real-time smart-meter data streams, the proposed framework is shown to be very accurate, scalable and resilient to cyber-attacks including data tampering, false-data injection and coordinated control-loop manipulation. The results indicate that cloud services are the best backbone for big-data-driven forensic workflows, which allows energy utilities to achieve a fast situational awareness and intelligent incident response.
[LG-7] Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning
链接: https://arxiv.org/abs/2511.21581
作者: Alex Ning,Yen-Ling Kuo,Gabe Gomes
类目: Machine Learning (cs.LG)
*备注: 13 pages, 6 figures
Abstract:Latent reasoning represents a new development in Transformer language models that has shown potential in compressing reasoning lengths compared to chain-of-thought reasoning. By directly passing the information-rich previous final latent state into the next sequence, latent reasoning removes the restriction to human language tokens as the medium for reasoning. We develop adaptive-length latent reasoning models and introduce a post-SFT reinforcement-learning methodology to optimize latent reasoning length by minimizing reasoning length while maintaining accuracy. This, in turn, further reduces compute usage and raises the bar on the compressive capabilities of latent reasoning models. Experiments on the Llama 3.2 1B model and the GSM8K-Aug dataset show a 52% drop in total reasoning length with no penalty to accuracy. In future work, we plan to extend to additional models and datasets, analyze relationships between training coefficients, experiment with architecture variations, and continue our knowledge distillation for latent reasoning SFT efforts. We make our code and pretrained weights available at this https URL.
[LG-8] A decoupled alignment kernel for peptide membrane permeability predictions
链接: https://arxiv.org/abs/2511.21566
作者: Ali Amirahmadi,Gökçe Geylan,Leonardo De Maria,Farzaneh Etminani,Mattias Ohlsson,Alessandro Tibo
类目: Machine Learning (cs.LG)
*备注: submitted to Journal of Cheminformatics
Abstract:Cyclic peptides are promising modalities for targeting intracellular sites; however, cell-membrane permeability remains a key bottleneck, exacerbated by limited public data and the need for well-calibrated uncertainty. Instead of relying on data-eager complex deep learning architecture, we propose a monomer-aware decoupled global alignment kernel (MD-GAK), which couples chemically meaningful residue-residue similarity with sequence alignment while decoupling local matches from gap penalties. MD-GAK is a relatively simple kernel. To further demonstrate the robustness of our framework, we also introduce a variant, PMD-GAK, which incorporates a triangular positional prior. As we will show in the experimental section, PMD-GAK can offer additional advantages over MD-GAK, particularly in reducing calibration errors. Since our focus is on uncertainty estimation, we use Gaussian Processes as the predictive model, as both MD-GAK and PMD-GAK can be directly applied within this framework. We demonstrate the effectiveness of our methods through an extensive set of experiments, comparing our fully reproducible approach against state-of-the-art models, and show that it outperforms them across all metrics.
[LG-9] Machine Learning Approaches to Clinical Risk Prediction: Multi-Scale Temporal Alignment in Electronic Health Records
链接: https://arxiv.org/abs/2511.21561
作者: Wei-Chen Chang,Lu Dai,Ting Xu
类目: Machine Learning (cs.LG)
*备注: 5 pages, 3 figures
Abstract:This study proposes a risk prediction method based on a Multi-Scale Temporal Alignment Network (MSTAN) to address the challenges of temporal irregularity, sampling interval differences, and multi-scale dynamic dependencies in Electronic Health Records (EHR). The method focuses on temporal feature modeling by introducing a learnable temporal alignment mechanism and a multi-scale convolutional feature extraction structure to jointly model long-term trends and short-term fluctuations in EHR sequences. At the input level, the model maps multi-source clinical features into a unified high-dimensional semantic space and employs temporal embedding and alignment modules to dynamically weight irregularly sampled data, reducing the impact of temporal distribution differences on model performance. The multi-scale feature extraction module then captures key patterns across different temporal granularities through multi-layer convolution and hierarchical fusion, achieving a fine-grained representation of patient states. Finally, an attention-based aggregation mechanism integrates global temporal dependencies to generate individual-level risk representations for disease risk prediction and health status assessment. Experiments conducted on publicly available EHR datasets show that the proposed model outperforms mainstream baselines in accuracy, recall, precision, and F1-Score, demonstrating the effectiveness and robustness of multi-scale temporal alignment in complex medical time-series analysis. This study provides a new solution for intelligent representation of high-dimensional asynchronous medical sequences and offers important technical support for EHR-driven clinical risk prediction.
[LG-10] Computing Strategic Responses to Non-Linear Classifiers
链接: https://arxiv.org/abs/2511.21560
作者: Jack Geary,Boyan Gao,Henry Gouk
类目: Machine Learning (cs.LG)
*备注:
Abstract:We consider the problem of strategic classification, where the act of deploying a classifier leads to strategic behaviour that induces a distribution shift on subsequent observations. Current approaches to learning classifiers in strategic settings are focused primarily on the linear setting, but in many cases non-linear classifiers are more suitable. A central limitation to progress for non-linear classifiers arises from the inability to compute best responses in these settings. We present a novel method for computing the best response by optimising the Lagrangian dual of the Agents’ objective. We demonstrate that our method reproduces best responses in linear settings, identifying key weaknesses in existing approaches. We present further results demonstrating our method can be straight-forwardly applied to non-linear classifier settings, where it is useful for both evaluation and training.
[LG-11] MMA: A Momentum Mamba Architecture for Human Activity Recognition with Inertial Sensors
链接: https://arxiv.org/abs/2511.21550
作者: Thai-Khanh Nguyen,Uyen Vo,Tan M. Nguyen,Thieu N. Vo,Trung-Hieu Le,Cuong Pham
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
*备注: 14 pages, 5 pages
Abstract:Human activity recognition (HAR) from inertial sensors is essential for ubiquitous computing, mobile health, and ambient intelligence. Conventional deep models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformers have advanced HAR but remain limited by vanishing or exloding gradients, high computational cost, and difficulty in capturing long-range dependencies. Structured state-space models (SSMs) like Mamba address these challenges with linear complexity and effective temporal modeling, yet they are restricted to first-order dynamics without stable longterm memory mechanisms. We introduce Momentum Mamba, a momentum-augmented SSM that incorporates second-order dynamics to improve stability of information flow across time steps, robustness, and long-sequence modeling. Two extensions further expand its capacity: Complex Momentum Mamba for frequency-selective memory scaling. Experiments on multiple HAR benchmarks demonstrate consistent gains over vanilla Mamba and Transformer baselines in accuracy, robustness, and convergence speed. With only moderate increases in training cost, momentum-augmented SSMs offer a favorable accuracy-efficiency balance, establishing them as a scalable paradigm for HAR and a promising principal framework for broader sequence modeling applications.
[LG-12] Context-Specific Causal Graph Discovery with Unobserved Contexts: Non-Stationarity Regimes and Spatio-Temporal Patterns
链接: https://arxiv.org/abs/2511.21537
作者: Martin Rabel,Jakob Runge
类目: Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:
Abstract:Real-world data, for example in climate applications, often consists of spatially gridded time series data or data with comparable structure. While the underlying system is often believed to behave similar at different points in space and time, those variations that do exist are twofold relevant: They often encode important information in and of themselves. And they may negatively affect the stability / convergence and reliability\Slashvalidity of results of algorithms assuming stationarity or space-translation invariance. We study the information encoded in changes of the causal graph, with stability in mind. An analysis of this general task identifies two core challenges. We develop guiding principles to overcome these challenges, and provide a framework realizing these principles by modifying constraint-based causal discovery approaches on the level of independence testing. This leads to an extremely modular, easily extensible and widely applicable framework. It can leverage existing constraint-based causal discovery methods (demonstrated on IID-algorithms PC, PC-stable, FCI and time series algorithms PCMCI, PCMCI+, LPCMCI) with little to no modification. The built-in modularity allows to systematically understand and improve upon an entire array of subproblems. By design, it can be extended by leveraging insights from change-point-detection, clustering, independence-testing and other well-studied related problems. The division into more accessible sub-problems also simplifies the understanding of fundamental limitations, hyperparameters controlling trade-offs and the statistical interpretation of results. An open-source implementation will be available soon.
[LG-13] IntAttention: A Fully Integer Attention Pipeline for Efficient Edge Inference
链接: https://arxiv.org/abs/2511.21513
作者: Wanli Zhong,Haibo Feng,Zirui Zhou,Hanyang Peng,Shiqi Yu
类目: Machine Learning (cs.LG)
*备注:
Abstract:Deploying Transformer models on edge devices is limited by latency and energy budgets. While INT8 quantization effectively accelerates the primary matrix multiplications, it exposes the softmax as the dominant bottleneck. This stage incurs a costly dequantize-softmax-requantize detour, which can account for up to 65% of total attention latency and disrupts the end-to-end integer dataflow critical for edge hardware efficiency. To address this limitation, we present IntAttention, the first fully integer, plug-and-play attention pipeline without retraining. At the core of our approach lies IndexSoftmax, a hardware-friendly operator that replaces floating-point exponentials entirely within the integer domain. IntAttention integrates sparsity-aware clipping, a 32-entry lookup-table approximation, and direct integer normalization, thereby eliminating all datatype conversion overhead. We evaluate IntAttention and demonstrate consistent and substantial gains. Our method achieves up to 3.7x speedup and 61% energy reduction over FP16 baselines and 2.0x faster than conventional INT8 attention pipelines on Armv8 CPUs. These gains are achieved with high-fidelity accuracy comparable to baselines across diverse language and vision models, enabling practical and efficient Transformer inference on commodity edge devices. Code will be released in later version of this work.
[LG-14] Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation AAAI AAAI26
链接: https://arxiv.org/abs/2511.21500
作者: Qian Hong,Cheng Bian,Xiao Zhou,Xiaoyu Li,Yelei Li,Zijing Zeng
类目: Machine Learning (cs.LG)
*备注: The 40th Annual AAAI Conference on Artificial Intelligence (AAAI 26)
Abstract:Translating non-invasive signals such as photoplethysmography (PPG) and ballistocardiography (BCG) into clinically meaningful signals like arterial blood pressure (ABP) is vital for continuous, low-cost healthcare monitoring. However, temporal misalignment in multimodal signal transformation impairs transformation accuracy, especially in capturing critical features like ABP peaks. Conventional synchronization methods often rely on strong similarity assumptions or manual tuning, while existing Learning with Noisy Labels (LNL) approaches are ineffective under time-shifted supervision, either discarding excessive data or failing to correct label shifts. To address this challenge, we propose ShiftSyncNet, a meta-learning-based bi-level optimization framework that automatically mitigates performance degradation due to time misalignment. It comprises a transformation network (TransNet) and a time-shift correction network (SyncNet), where SyncNet learns time offsets between training pairs and applies Fourier phase shifts to align supervision signals. Experiments on one real-world industrial dataset and two public datasets show that ShiftSyncNet outperforms strong baselines by 9.4%, 6.0%, and 12.8%, respectively. The results highlight its effectiveness in correcting time shifts, improving label quality, and enhancing transformation accuracy across diverse misalignment scenarios, pointing toward a unified direction for addressing temporal inconsistencies in multimodal physiological transformation.
[LG-15] Mean-Field Limits for Two-Layer Neural Networks Trained with Consensus-Based Optimization
链接: https://arxiv.org/abs/2511.21466
作者: William De Deyn,Michael Herty,Giovanni Samaey
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:We study two-layer neural networks and train these with a particle-based method called consensus-based optimization (CBO). We compare the performance of CBO against Adam on two test cases and demonstrate how a hybrid approach, combining CBO with Adam, provides faster convergence than CBO. In the context of multi-task learning, we recast CBO into a formulation that offers less memory overhead. The CBO method allows for a mean-field limit formulation, which we couple with the mean-field limit of the neural network. To this end, we first reformulate CBO within the optimal transport framework. Finally, in the limit of infinitely many particles, we define the corresponding dynamics on the Wasserstein-over-Wasserstein space and show that the variance decreases monotonically.
[LG-16] Ensemble Performance Through the Lens of Linear Independence of Classifier Votes in Data Streams
链接: https://arxiv.org/abs/2511.21465
作者: Enes Bektas,Fazli Can
类目: Machine Learning (cs.LG)
*备注: 14 pages, 3 figures, 5 tables
Abstract:Ensemble learning improves classification performance by combining multiple base classifiers. While increasing the number of classifiers generally enhances accuracy, excessively large ensembles can lead to computational inefficiency and diminishing returns. This paper investigates the relationship between ensemble size and performance through the lens of linear independence among classifier votes in data streams. We propose that ensembles composed of linearly independent classifiers maximize representational capacity, particularly under a geometric model. We then generalize the importance of linear independence to the weighted majority voting problem. By modeling the probability of achieving linear independence among classifier outputs, we derive a theoretical framework that explains the trade-off between ensemble size and accuracy. Our analysis leads to a theoretical estimate of the ensemble size required to achieve a user-specified probability of linear independence. We validate our theory through experiments on both real-world and synthetic datasets using two ensemble methods, OzaBagging and GOOWE. Our results confirm that this theoretical estimate effectively identifies the point of performance saturation for robust ensembles like OzaBagging. Conversely, for complex weighting schemes like GOOWE, our framework reveals that high theoretical diversity can trigger algorithmic instability. Our implementation is publicly available to support reproducibility and future research.
[LG-17] SUPN: Shallow Universal Polynomial Networks
链接: https://arxiv.org/abs/2511.21414
作者: Zachary Morrow,Michael Penwarden,Brian Chen,Aurya Javeed,Akil Narayan,John D. Jakeman
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注: 25 pages, supplementary material
Abstract:Deep neural networks (DNNs) and Kolmogorov-Arnold networks (KANs) are popular methods for function approximation due to their flexibility and expressivity. However, they typically require a large number of trainable parameters to produce a suitable approximation. Beyond making the resulting network less transparent, overparameterization creates a large optimization space, likely producing local minima in training that have quite different generalization errors. In this case, network initialization can have an outsize impact on the model’s out-of-sample accuracy. For these reasons, we propose shallow universal polynomial networks (SUPNs). These networks replace all but the last hidden layer with a single layer of polynomials with learnable coefficients, leveraging the strengths of DNNs and polynomials to achieve sufficient expressivity with far fewer parameters. We prove that SUPNs converge at the same rate as the best polynomial approximation of the same degree, and we derive explicit formulas for quasi-optimal SUPN parameters. We complement theory with an extensive suite of numerical experiments involving SUPNs, DNNs, KANs, and polynomial projection in one, two, and ten dimensions, consisting of over 13,000 trained models. On the target functions we numerically studied, for a given number of trainable parameters, the approximation error and variability are often lower for SUPNs than for DNNs and KANs by an order of magnitude. In our examples, SUPNs even outperform polynomial projection on non-smooth functions.
[LG-18] Controlling changes to attention logits
链接: https://arxiv.org/abs/2511.21377
作者: Ben Anson,Laurence Aitchison
类目: Machine Learning (cs.LG)
*备注:
Abstract:Stability of neural network weights is critical when training transformer models. The query and key weights are particularly problematic, as they tend to grow large without any intervention. Applying normalization to queries and keys, known as `QK norm’, fixes stability issues in practice, but is not always applicable. For example, QK norm is not compatible with Multi Latent Attention (MLA) because QK norm requires full materialization of queries and keys during inference, which is not done in MLA. In this paper we suggest that controlling the changes to logits is important for stability. We show that these changes are controllable by assigning parameter-dependent learning rates to the query and key weights. We find that our cheap intervention allows us to increase the base learning rate of the network, outperform other methods in the MLA setting, and achieve performance competitive with QK norm when using Multi-head Attention.
[LG-19] Best Practices for Machine Learning Experimentation in Scientific Applications
链接: https://arxiv.org/abs/2511.21354
作者: Umberto Michelucci,Francesca Venturini
类目: Machine Learning (cs.LG)
*备注:
Abstract:Machine learning (ML) is increasingly adopted in scientific research, yet the quality and reliability of results often depend on how experiments are designed and documented. Poor baselines, inconsistent preprocessing, or insufficient validation can lead to misleading conclusions about model performance. This paper presents a practical and structured guide for conducting ML experiments in scientific applications, focussing on reproducibility, fair comparison, and transparent reporting. We outline a step-by-step workflow, from dataset preparation to model selection and evaluation, and propose metrics that account for overfitting and instability across validation folds, including the Logarithmic Overfitting Ratio (LOR) and the Composite Overfitting Score (COS). Through recommended practices and example reporting formats, this work aims to support researchers in establishing robust baselines and drawing valid evidence-based insights from ML models applied to scientific problems.
[LG-20] Learning Multi-Order Block Structure in Higher-Order Networks
链接: https://arxiv.org/abs/2511.21350
作者: Kazuki Nakajima,Yuya Sasaki,Takeaki Uno,Masaki Aida
类目: ocial and Information Networks (cs.SI); Machine Learning (cs.LG)
*备注: 38 pages, 10 figures, and 7 tables
Abstract:Higher-order networks, naturally described as hypergraphs, are essential for modeling real-world systems involving interactions among three or more entities. Stochastic block models offer a principled framework for characterizing mesoscale organization, yet their extension to hypergraphs involves a trade-off between expressive power and computational complexity. A recent simplification, a single-order model, mitigates this complexity by assuming a single affinity pattern governs interactions of all orders. This universal assumption, however, may overlook order-dependent structural details. Here, we propose a framework that relaxes this assumption by introducing a multi-order block structure, in which different affinity patterns govern distinct subsets of interaction orders. Our framework is based on a multi-order stochastic block model and searches for the optimal partition of the set of interaction orders that maximizes out-of-sample hyperlink prediction performance. Analyzing a diverse range of real-world networks, we find that multi-order block structures are prevalent. Accounting for them not only yields better predictive performance over the single-order model but also uncovers sharper, more interpretable mesoscale organization. Our findings reveal that order-dependent mechanisms are a key feature of the mesoscale organization of real-world higher-order networks.
[LG-21] Masks Can Be Distracting: On Context Comprehension in Diffusion Language Models
链接: https://arxiv.org/abs/2511.21338
作者: Julianna Piskorz,Cristina Pinneri,Alvaro Correia,Motasem Alfarra,Risheek Garrepalli,Christos Louizos
类目: Machine Learning (cs.LG)
*备注:
Abstract:Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to Autoregressive Language Models (ARLMs), leveraging a denoising objective that, in principle, should enable more uniform context utilisation. In this work, we examine the context comprehension abilities of MDLMs and uncover two key limitations. First, despite their more global training objective and bidirectional attention mechanism, similarly to ARLMS, MDLMs exhibit a strong locality bias: performance is highly sensitive to the position of relevant information within the input, favouring local over distant context. Second, we show that appending a large number of mask tokens–required for generation–can significantly degrade context comprehension. Through systematic ablations, we find that these masks act as distractors, reducing the model’s ability to process relevant information. To address this, we introduce a mask-agnostic loss function that encourages predictions to remain invariant to the number of appended masks. Fine-tuning with this objective substantially mitigates the distracting effect of masks, improving robustness of MDLMs. Overall, our findings reveal critical limitations of the current MDLM training paradigm and provide actionable insights for building diffusion-based language models with stronger context comprehension.
[LG-22] SGM: Regular and Irregular Time-series Generation using Score-based Generative Models
链接: https://arxiv.org/abs/2511.21335
作者: Haksoo Lim,Jaehoon Lee,Sewon Park,Minjung Kim,Noseong Park
类目: Machine Learning (cs.LG)
*备注:
Abstract:Score-based generative models (SGMs) have demonstrated unparalleled sampling quality and diversity in numerous fields, such as image generation, voice synthesis, and tabular data synthesis, etc. Inspired by those outstanding results, we apply SGMs to synthesize time-series by learning its conditional score function. To this end, we present a conditional score network for time-series synthesis, deriving a denoising score matching loss tailored for our purposes. In particular, our presented denoising score matching loss is the conditional denoising score matching loss for time-series synthesis. In addition, our framework is such flexible that both regular and irregular time-series can be synthesized with minimal changes to our model design. Finally, we obtain exceptional synthesis performance on various time-series datasets, achieving state-of-the-art sampling diversity and quality.
[LG-23] Sawtooth Sampling for Time Series Denoising Diffusion Implicit Models
链接: https://arxiv.org/abs/2511.21320
作者: Heiko Oppel,Andreas Spilz,Michael Munz
类目: Machine Learning (cs.LG)
*备注:
Abstract:Denoising Diffusion Probabilistic Models (DDPMs) can generate synthetic timeseries data to help improve the performance of a classifier, but their sampling process is computationally expensive. We address this by combining implicit diffusion models with a novel Sawtooth Sampler that accelerates the reverse process and can be applied to any pretrained diffusion model. Our approach achieves a 30 times speed-up over the standard baseline while also enhancing the quality of the generated sequences for classification tasks.
[LG-24] A Physics-Informed U-net-LSTM Network for Data-Driven Seismic Response Modeling of Structures
链接: https://arxiv.org/abs/2511.21276
作者: Sutirtha Biswas,Kshitij Kumar Yadav
类目: Machine Learning (cs.LG)
*备注:
Abstract:Accurate and efficient seismic response prediction is essential for the design of resilient structures. While the Finite Element Method (FEM) remains the standard for nonlinear seismic analysis, its high computational demands limit its scalability and real time applicability. Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short Term Memory (LSTM) models, have shown promise in reducing the computational cost of nonlinear seismic analysis of structures. However, these data driven models often struggle to generalize and capture the underlying physics, leading to reduced reliability. We propose a novel Physics Informed U Net LSTM framework that integrates physical laws with deep learning to enhance both accuracy and efficiency. By embedding domain specific constraints into the learning process, the proposed model achieves improved predictive performance over conventional Machine Learning architectures. This hybrid approach bridges the gap between purely data driven methods and physics based modeling, offering a robust and computationally efficient alternative for seismic response prediction of structures.
[LG-25] RISC-V Based TinyML Accelerator for Depthwise Separable Convolutions in Edge AI
链接: https://arxiv.org/abs/2511.21232
作者: Muhammed Yildirim,Ozcan Ozturk
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: 13 pages, 7 tables, 14 figures
Abstract:The increasing demand for on-device intelligence in Edge AI and TinyML applications requires the efficient execution of modern Convolutional Neural Networks (CNNs). While lightweight architectures like MobileNetV2 employ Depthwise Separable Convolutions (DSC) to reduce computational complexity, their multi-stage design introduces a critical performance bottleneck inherent to layer-by-layer execution: the high energy and latency cost of transferring intermediate feature maps to either large on-chip buffers or off-chip DRAM. To address this memory wall, this paper introduces a novel hardware accelerator architecture that utilizes a fused pixel-wise dataflow. Implemented as a Custom Function Unit (CFU) for a RISC-V processor, our architecture eliminates the need for intermediate buffers entirely, reducing the data movement up to 87% compared to conventional layer-by-layer execution. It computes a single output pixel to completion across all DSC stages-expansion, depthwise convolution, and projection-by streaming data through a tightly-coupled pipeline without writing to memory. Evaluated on a Xilinx Artix-7 FPGA, our design achieves a speedup of up to 59.3x over the baseline software execution on the RISC-V core. Furthermore, ASIC synthesis projects a compact 0.284 mm ^2 footprint with 910 mW power at 2 GHz in 28 nm, and a 1.20 mm ^2 footprint with 233 mW power at 300 MHz in 40 nm. This work confirms the feasibility of a zero-buffer dataflow within a TinyML resource envelope, offering a novel and effective strategy for overcoming the memory wall in edge AI accelerators.
[LG-26] Robust Gene Prioritization via Fast-mRMR Feature Selection in high-dimensional omics data
链接: https://arxiv.org/abs/2511.21211
作者: Rubén Fernández-Farelo,Jorge Paz-Ruza,Bertha Guijarro-Berdiñas,Amparo Alonso-Betanzos,Alex A. Freitas
类目: Machine Learning (cs.LG)
*备注:
Abstract:Gene prioritization (identifying genes potentially associated with a biological process) is increasingly tackled with Artificial Intelligence. However, existing methods struggle with the high dimensionality and incomplete labelling of biomedical data. This work proposes a more robust and efficient pipeline that leverages Fast-mRMR feature selection to retain only relevant, non-redundant features for classifiers. This enables us to build simpler and more effective models, as well as to combine different biological feature sets. Experiments on Dietary Restriction datasets show significant improvements over existing methods, proving that feature selection can be critical for reliable gene prioritization.
[LG-27] I-GLIDE: Input Groups for Latent Health Indicators in Degradation Estimation
链接: https://arxiv.org/abs/2511.21208
作者: Lucas Thil,Jesse Read,Rim Kaddah,Guillaume Doquet
类目: Machine Learning (cs.LG)
*备注: Included in the conference series: Joint European Conference on Machine Learning and Knowledge Discovery in Databases
Abstract:Accurate remaining useful life (RUL) prediction hinges on the quality of health indicators (HIs), yet existing methods often fail to disentangle complex degradation mechanisms in multi-sensor systems or quantify uncertainty in HI reliability. This paper introduces a novel framework for HI construction, advancing three key contributions. First, we adapt Reconstruction along Projected Pathways (RaPP) as a health indicator (HI) for RUL prediction for the first time, showing that it outperforms traditional reconstruction error metrics. Second, we show that augmenting RaPP-derived HIs with aleatoric and epistemic uncertainty quantification (UQ) via Monte Carlo dropout and probabilistic latent spaces- significantly improves RUL-prediction robustness. Third, and most critically, we propose indicator groups, a paradigm that isolates sensor subsets to model system-specific degradations, giving rise to our novel method, I-GLIDE which enables interpretable, mechanism-specific diagnostics. Evaluated on data sourced from aerospace and manufacturing systems, our approach achieves marked improvements in accuracy and generalizability compared to state-of-the-art HI methods while providing actionable insights into system failure pathways. This work bridges the gap between anomaly detection and prognostics, offering a principled framework for uncertainty-aware degradation modeling in complex systems.
[LG-28] rustless Federated Learning at Edge-Scale: A Compositional Architecture for Decentralized Verifiable and Incentive-Aligned Coordination
链接: https://arxiv.org/abs/2511.21118
作者: Pius Onobhayedo,Paul Osemudiame Oamen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Artificial intelligence is retracing the Internet’s path from centralized provision to distributed creation. Initially, resource-intensive computation concentrates within institutions capable of training and serving large this http URL, as federated learning matures, billions of edge devices holding sensitive data will be able to collectively improve models without surrendering raw information, enabling both contribution and consumption at scale. This democratic vision remains unrealized due to certain compositional gaps; aggregators handle updates without accountability, economic mechanisms are lacking and even when present remain vulnerable to gaming, coordination serializes state modifications limiting scalability, and governance permits retroactive manipulation. This work addresses these gaps by leveraging cryptographic receipts to prove aggregation correctness, geometric novelty measurement to prevent incentive gaming, parallel object ownership to achieve linear scalability, and time-locked policies to check retroactive manipulation.
[LG-29] Interpretable Fair Clustering
链接: https://arxiv.org/abs/2511.21109
作者: Mudi Jiang,Jiahui Zhou,Xinying Liu,Zengyou He,Zhikui Chen
类目: Machine Learning (cs.LG)
*备注:
Abstract:Fair clustering has gained increasing attention in recent years, especially in applications involving socially sensitive attributes. However, existing fair clustering methods often lack interpretability, limiting their applicability in high-stakes scenarios where understanding the rationale behind clustering decisions is essential. In this work, we address this limitation by proposing an interpretable and fair clustering framework, which integrates fairness constraints into the structure of decision trees. Our approach constructs interpretable decision trees that partition the data while ensuring fair treatment across protected groups. To further enhance the practicality of our framework, we also introduce a variant that requires no fairness hyperparameter tuning, achieved through post-pruning a tree constructed without fairness constraints. Extensive experiments on both real-world and synthetic datasets demonstrate that our method not only delivers competitive clustering performance and improved fairness, but also offers additional advantages such as interpretability and the ability to handle multiple sensitive attributes. These strengths enable our method to perform robustly under complex fairness constraints, opening new possibilities for equitable and transparent clustering.
[LG-30] BRIDGE: Building Representations In Domain Guided Program Verification
链接: https://arxiv.org/abs/2511.21104
作者: Robert Joseph George,Carson Eisenach,Udaya Ghai,Dominique Perrault-Joncas,Anima Anandkumar,Dean Foster
类目: Machine Learning (cs.LG)
*备注: Approx. 31 pages including appendices, 11 figures, 4 tables. Empirical study of LLM-based verified program synthesis in Lean4 (code, specs, and proofs)
Abstract:Large language models (LLMs) have achieved impressive results in code generation, yet struggle with program verification, especially in interactive proof frameworks such as Lean4. A central challenge is scalability: verified synthesis requires not just code, but also precise specifications and correctness proofs, and existing approaches rarely span all three domains. We present BRIDGE, the first systematic study of structured prompting for scalable verified program generation. BRIDGE decomposes verification into three interconnected domains: Code (executable implementations), Specifications (formal intent statements), and Proofs (constructive correctness arguments). Our key idea is to elicit distinct reasoning behaviors functional, specification-driven, and proof-oriented as intermediate representations that preserve semantic structure and connect these domains. Through systematic ablations, we show that this approach substantially improves both accuracy and efficiency beyond standard error feedback methods. For example, functional reasoning improves correctness of code in formal languages (Lean4) by nearly 1.5x (pass@5) over direct baselines. In inference-time compute, functional reasoning is also 2x more efficient, achieving higher pass rates with fewer generations and lower total sampling budgets. Similarly, we find that specification-driven prompting boosts Python coding pass rates by up to 17.5%. These findings suggest that structured domain alignment is a promising direction for advancing verified synthesis. BRIDGE establishes a foundation for training via expert iteration or RLVR, enabling models to internalize these reasoning strategies across code, specifications, and proofs.
[LG-31] Generative Early Stage Ranking
链接: https://arxiv.org/abs/2511.21095
作者: Juhee Hong,Meng Liu,Shengzhi Wang,Xiaoheng Mao,Huihui Cheng,Leon Gao,Christopher Leung,Jin Zhou,Chandra Mouli Sekar,Zhao Zhu,Ruochen Liu,Tuan Trieu,Dawei Sun,Jeet Kanjani,Rui Li,Jing Qian,Xuan Cao,Minjie Fan,Mingze Gao
类目: Machine Learning (cs.LG)
*备注:
Abstract:Large-scale recommendations commonly adopt a multi-stage cascading ranking system paradigm to balance effectiveness and efficiency. Early Stage Ranking (ESR) systems utilize the “user-item decoupling” approach, where independently learned user and item representations are only combined at the final layer. While efficient, this design is limited in effectiveness, as it struggles to capture fine-grained user-item affinities and cross-signals. To address these, we propose the Generative Early Stage Ranking (GESR) paradigm, introducing the Mixture of Attention (MoA) module which leverages diverse attention mechanisms to bridge the effectiveness gap: the Hard Matching Attention (HMA) module encodes explicit cross-signals by computing raw match counts between user and item features; the Target-Aware Self Attention module generates target-aware user representations conditioned on the item, enabling more personalized learning; and the Cross Attention modules facilitate early and more enriched interactions between user-item features. MoA’s specialized attention encodings are further refined in the final layer through a Multi-Logit Parameterized Gating (MLPG) module, which integrates the newly learned embeddings via gating and produces secondary logits that are fused with the primary logit. To address the efficiency and latency challenges, we have introduced a comprehensive suite of optimization techniques. These span from custom kernels that maximize the capabilities of the latest hardware to efficient serving solutions powered by caching mechanisms. The proposed GESR paradigm has shown substantial improvements in topline metrics, engagement, and consumption tasks, as validated by both offline and online experiments. To the best of our knowledge, this marks the first successful deployment of full target-aware attention sequence modeling within an ESR stage at such a scale.
[LG-32] Deceptron: Learned Local Inverses for Fast and Stable Physics Inversion NEURIPS2025
链接: https://arxiv.org/abs/2511.21076
作者: Aaditya L. Kachhadiya
类目: Machine Learning (cs.LG)
*备注: 10 pages, 11 main figures. Accepted for poster presentation at the NeurIPS 2025 Machine Learning and the Physical Sciences Workshop
Abstract:Inverse problems in the physical sciences are often ill-conditioned in input space, making progress step-size sensitive. We propose the Deceptron, a lightweight bidirectional module that learns a local inverse of a differentiable forward surrogate. Training combines a supervised fit, forward-reverse consistency, a lightweight spectral penalty, a soft bias tie, and a Jacobian Composition Penalty (JCP) that encourages J_g(f(x)),J_f(x)!\approx!I via JVP/VJP probes. At solve time, D-IPG (Deceptron Inverse-Preconditioned Gradient) takes a descent step in output space, pulls it back through g , and projects under the same backtracking and stopping rules as baselines. On Heat-1D initial-condition recovery and a Damped Oscillator inverse problem, D-IPG reaches a fixed normalized tolerance with \sim 20 \times fewer iterations on Heat and \sim 2-3 \times fewer on Oscillator than projected gradient, competitive in iterations and cost with Gauss-Newton. Diagnostics show JCP reduces a measured composition error and tracks iteration gains. We also preview a single-scale 2D instantiation, DeceptronNet (v0), that learns few-step corrections under a strict fairness protocol and exhibits notably fast convergence.
[LG-33] G-Net: A Provably Easy Construction of High-Accuracy Random Binary Neural Networks
链接: https://arxiv.org/abs/2511.21063
作者: Alireza Aghasi,Nicholas Marshall,Saeid Pourmand,Wyatt Whiting
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We propose a novel randomized algorithm for constructing binary neural networks with tunable accuracy. This approach is motivated by hyperdimensional computing (HDC), which is a brain-inspired paradigm that leverages high-dimensional vector representations, offering efficient hardware implementation and robustness to model corruptions. Unlike traditional low-precision methods that use quantization, we consider binary embeddings of data as points in the hypercube equipped with the Hamming distance. We propose a novel family of floating-point neural networks, G-Nets, which are general enough to mimic standard network layers. Each floating-point G-Net has a randomized binary embedding, an embedded hyperdimensional (EHD) G-Net, that retains the accuracy of its floating-point counterparts, with theoretical guarantees, due to the concentration of measure. Empirically, our binary models match convolutional neural network accuracies and outperform prior HDC models by large margins, for example, we achieve almost 30% higher accuracy on CIFAR-10 compared to prior HDC models. G-Nets are a theoretically justified bridge between neural networks and randomized binary neural networks, opening a new direction for constructing robust binary/quantized deep learning models. Our implementation is available at this https URL.
[LG-34] Efficient Diffusion Planning with Temporal Diffusion AAAI26
链接: https://arxiv.org/abs/2511.21054
作者: Jiaming Guo,Rui Zhang,Zerun Li,Yunkai Gao,Shaohui Peng,Siming Lan,Xing Hu,Zidong Du,Xishan Zhang,Ling Li
类目: Machine Learning (cs.LG)
*备注: Accepted by the AAAI26 Conference Main Track
Abstract:Diffusion planning is a promising method for learning high-performance policies from offline data. To avoid the impact of discrepancies between planning and reality on performance, previous works generate new plans at each time step. However, this incurs significant computational overhead and leads to lower decision frequencies, and frequent plan switching may also affect performance. In contrast, humans might create detailed short-term plans and more general, sometimes vague, long-term plans, and adjust them over time. Inspired by this, we propose the Temporal Diffusion Planner (TDP) which improves decision efficiency by distributing the denoising steps across the time dimension. TDP begins by generating an initial plan that becomes progressively more vague over time. At each subsequent time step, rather than generating an entirely new plan, TDP updates the previous one with a small number of denoising steps. This reduces the average number of denoising steps, improving decision efficiency. Additionally, we introduce an automated replanning mechanism to prevent significant deviations between the plan and reality. Experiments on D4RL show that, compared to previous works that generate new plans every time step, TDP improves the decision-making frequency by 11-24.8 times while achieving higher or comparable performance.
[LG-35] RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression
链接: https://arxiv.org/abs/2511.21035
作者: Shima Rafiei,Zahra Nabizadeh Shahr Babak,Shadrokh Samavi,Shahram Shirani
类目: Machine Learning (cs.LG)
*备注:
Abstract:Holography offers significant potential for AR/VR applications, yet its adoption is limited by the high demands of data compression. Existing deep learning approaches generally lack rate adaptivity within a single network. We present RAVQ-HoloNet, a rate-adaptive vector quantization framework that achieves high-fidelity reconstructions at low and ultra-low bit rates, outperforming current state-of-the-art methods. In low bit, our method exceeds by -33.91% in BD-Rate and achieves a BD-PSNR of 1.02 dB from the best existing method demonstrated by the rate-distortion curve.
[LG-36] Prediction of Herd Life in Dairy Cows Using Multi-Head Attention Transformers
链接: https://arxiv.org/abs/2511.21034
作者: Mahdi Saki,Justin Lipman
类目: Machine Learning (cs.LG)
*备注:
Abstract:Dairy farmers should decide to keep or cull a cow based on an objective assessment of her likely performance in the herd. For this purpose, farmers need to identify more resilient cows, which can cope better with farm conditions and complete more lactations. This decision-making process is inherently complex, with significant environmental and economic implications. In this study, we develop an AI-driven model to predict cow longevity using historical multivariate time-series data recorded from birth. Leveraging advanced AI techniques, specifically Multi-Head Attention Transformers, we analysed approximately 780,000 records from 19,000 unique cows across 7 farms in Australia. The results demonstrate that our model achieves an overall determination coefficient of 83% in predicting herd life across the studied farms, highlighting its potential for practical application in dairy herd management.
[LG-37] A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems
链接: https://arxiv.org/abs/2511.21032
作者: Yuxuan Zhu,Cong Fu,Yabo Ni,Anxiang Zeng,Yuan Fang
类目: Machine Learning (cs.LG)
*备注:
Abstract:Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to capture both stable and transient patterns. Existing approaches such as invariant learning and self-supervised learning offer partial solutions but often suffer from unstable temporal generalization, representation collapse, or inefficient data utilization. To address these limitations, we propose ELBO _\textTDS , a probabilistic framework that integrates seamlessly into industry-scale incremental learning pipelines. First, we identify key shifting factors through statistical analysis of real-world production data and design a simple yet effective data augmentation strategy that resamples these time-varying factors to extend the training support. Second, to harness the benefits of this extended distribution while preventing representation collapse, we model the temporal recommendation scenario using a causal graph and derive a self-supervised variational objective, ELBO _\textTDS , grounded in the causal structure. Extensive experiments supported by both theoretical and empirical analysis demonstrate that our method achieves superior temporal generalization, yielding a 2.33% uplift in GMV per user and has been successfully deployed in Shopee Product Search. Code is available at this https URL.
[LG-38] Staggered Environment Resets Improve Massively Parallel On-Policy Reinforcement Learning
链接: https://arxiv.org/abs/2511.21011
作者: Sid Bharthulwar,Stone Tao,Hao Su
类目: Machine Learning (cs.LG)
*备注:
Abstract:Massively parallel GPU simulation environments have accelerated reinforcement learning (RL) research by enabling fast data collection for on-policy RL algorithms like Proximal Policy Optimization (PPO). To maximize throughput, it is common to use short rollouts per policy update, increasing the update-to-data (UTD) ra- tio. However, we find that, in this setting, standard synchronous resets introduce harmful nonstationarity, skewing the learning signal and destabilizing training. We introduce staggered resets, a simple yet effective technique where environments are initialized and reset at varied points within the task horizon. This yields training batches with greater temporal diversity, reducing the nonstationarity induced by synchronized rollouts. We characterize dimensions along which RL environments can benefit significantly from staggered resets through illustrative toy environ- ments. We then apply this technique to challenging high-dimensional robotics environments, achieving significantly higher sample efficiency, faster wall-clock convergence, and stronger final performance. Finally, this technique scales better with more parallel environments compared to naive synchronized rollouts.
[LG-39] ChatGpt Content detection: A new approach using xlm-roberta alignment
链接: https://arxiv.org/abs/2511.21009
作者: Md Tasnin Tanvir,Dr Santanu Kumar Dash,Ishan Shahnan,Nafis Fuad,Tanvir Rahman,Abdullah Al Faisal,Asadullah Al Mamun
类目: Machine Learning (cs.LG)
*备注:
Abstract:The challenge of separating AI-generated text from human-authored content is becoming more urgent as generative AI technologies like ChatGPT become more widely available. In this work, we address this issue by looking at both the detection of content that has been entirely generated by AI and the identification of human text that has been reworded by AI. In our work, a comprehensive methodology to detect AI- generated text using XLM-RoBERTa, a state-of-the-art multilingual transformer model. Our approach includes rigorous preprocessing, and feature extraction involving perplexity, semantic, and readability features. We fine-tuned the XLM-RoBERTa model on a balanced dataset of human and AI-generated texts and evaluated its performance. The model demonstrated high accuracy and robust performance across various text genres. Additionally, we conducted feature analysis to understand the model’s decision-making process, revealing that perplexity and attention-based features are critical in differentiating between human and AI-generated texts. Our findings offer a valuable tool for maintaining academic integrity and contribute to the broader field of AI ethics by promoting transparency and accountability in AI systems. Future research directions include exploring other advanced models and expanding the dataset to enhance the model’s generalizability.
[LG-40] Estimating Ising Models in Total Variation Distance
链接: https://arxiv.org/abs/2511.21008
作者: Constantinos Daskalakis,Vardis Kandiros,Rui Yao
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:We consider the problem of estimating Ising models over n variables in Total Variation (TV) distance, given l independent samples from the model. While the statistical complexity of the problem is well-understood [DMR20], identifying computationally and statistically efficient algorithms has been challenging. In particular, remarkable progress has occurred in several settings, such as when the underlying graph is a tree [DP21, BGPV21], when the entries of the interaction matrix follow a Gaussian distribution [GM24, CK24], or when the bulk of its eigenvalues lie in a small interval [AJK+24, KLV24], but no unified framework for polynomial-time estimation in TV exists so far. Our main contribution is a unified analysis of the Maximum Pseudo-Likelihood Estimator (MPLE) for two general classes of Ising models. The first class includes models that have bounded operator norm and satisfy the Modified Log-Sobolev Inequality (MLSI), a functional inequality that was introduced to study the convergence of the associated Glauber dynamics to stationarity. In the second class of models, the interaction matrix has bounded infinity norm (or bounded width), which is the most common assumption in the literature for structure learning of Ising models. We show how our general results for these classes yield polynomial-time algorithms and optimal or near-optimal sample complexity guarantees in a variety of settings. Our proofs employ a variety of tools from tensorization inequalities to measure decompositions and concentration bounds.
[LG-41] Dataset Poisoning Attacks on Behavioral Cloning Policies
链接: https://arxiv.org/abs/2511.20992
作者: Akansha Kalra,Soumil Datta,Ethan Gilmore,Duc La,Guanhong Tao,Daniel S. Brown
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR); Robotics (cs.RO)
*备注: Accepted at EAI SmartSP 2025
Abstract:Behavior Cloning (BC) is a popular framework for training sequential decision policies from expert demonstrations via supervised learning. As these policies are increasingly being deployed in the real world, their robustness and potential vulnerabilities are an important concern. In this work, we perform the first analysis of the efficacy of clean-label backdoor attacks on BC policies. Our backdoor attacks poison a dataset of demonstrations by injecting a visual trigger to create a spurious correlation that can be exploited at test time. We evaluate how policy vulnerability scales with the fraction of poisoned data, the strength of the trigger, and the trigger type. We also introduce a novel entropy-based test-time trigger attack that substantially degrades policy performance by identifying critical states where test-time triggering of the backdoor is expected to be most effective at degrading performance. We empirically demonstrate that BC policies trained on even minimally poisoned datasets exhibit deceptively high, near-baseline task performance despite being highly vulnerable to backdoor trigger attacks during deployment. Our results underscore the urgent need for more research into the robustness of BC policies, particularly as large-scale datasets are increasingly used to train policies for real-world cyber-physical systems. Videos and code are available at this https URL.
[LG-42] Independent policy gradient-based reinforcement learning for economic and reliable energy management of multi-microgrid systems
链接: https://arxiv.org/abs/2511.20977
作者: Junkai Hu,Li Xia
类目: ystems and Control (eess.SY); Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:
Abstract:Efficiency and reliability are both crucial for energy management, especially in multi-microgrid systems (MMSs) integrating intermittent and distributed renewable energy sources. This study investigates an economic and reliable energy management problem in MMSs under a distributed scheme, where each microgrid independently updates its energy management policy in a decentralized manner to optimize the long-term system performance collaboratively. We introduce the mean and variance of the exchange power between the MMS and the main grid as indicators for the economic performance and reliability of the system. Accordingly, we formulate the energy management problem as a mean-variance team stochastic game (MV-TSG), where conventional methods based on the maximization of expected cumulative rewards are unsuitable for variance metrics. To solve MV-TSGs, we propose a fully distributed independent policy gradient algorithm, with rigorous convergence analysis, for scenarios with known model parameters. For large-scale scenarios with unknown model parameters, we further develop a deep reinforcement learning algorithm based on independent policy gradients, enabling data-driven policy optimization. Numerical experiments in two scenarios validate the effectiveness of the proposed methods. Our approaches fully leverage the distributed computational capabilities of MMSs and achieve a well-balanced trade-off between economic performance and operational reliability.
[LG-43] Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection
链接: https://arxiv.org/abs/2511.20944
作者: Yaw Osei Adjei(Kwame Nkrumah University of Science and Technology)
类目: Machine Learning (cs.LG); Cryptography and Security (cs.CR)
*备注: 8 pages, 12 figures, 7 tables
Abstract:Business Email Compromise (BEC) is a sophisticated social engineering threat that manipulates organizational hierarchies and exploits psychological vulnerabilities, leading to significant financial damage. According to the 2024 FBI Internet Crime Report, BEC accounts for over 2.9 billion in annual adjusted losses, presenting significant economic asymmetry: the cost of a False Negative (fraud loss) exceeds the cost of a False Positive (manual review) by orders of magnitude (approximately 1 to 5,480). This paper examines two detection paradigms for BEC: the Forensic Psycholinguistic Stream, which utilizes CatBoost to analyze psycholinguistic cues with high interpretability and low latency, and the Semantic Stream, which employs DistilBERT for deep learning-based contextual language understanding, offering superior accuracy at higher computational cost. We evaluated DistilBERT on an adversarially poisoned dataset (N = 7,990) generated via our Black Hole protocol, benchmarked on Tesla T4 GPU infrastructure, achieving superior detection (AUC = 1.0000, F1 = 0.9981) with acceptable real-time latency (7.403 milliseconds). CatBoost achieves competitive detection (AUC = 0.9905, F1 = 0.9486) at 8.4x lower latency (0.885 milliseconds), consuming negligible computational resources. For organizations with GPU infrastructure, DistilBERT offers superior accuracy. CatBoost is preferable for edge deployments or cost-sensitive environments due to comparable security and lower operational costs. Both approaches demonstrate return on investment exceeding 99.96% when optimized through cost-sensitive learning, by significantly reducing false negatives and associated financial losses. Comments: 8 pages, 12 figures, 7 tables Subjects: Machine Learning (cs.LG); Cryptography and Security (cs.CR) ACMclasses: I.2.7; I.5.4; K.4.4 Cite as: arXiv:2511.20944 [cs.LG] (or arXiv:2511.20944v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2511.20944 Focus to learn more arXiv-issued DOI via DataCite
[LG-44] Operationalizing Quantized Disentanglement
链接: https://arxiv.org/abs/2511.20927
作者: Vitoria Barin-Pacela,Kartik Ahuja,Simon Lacoste-Julien,Pascal Vincent
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Recent theoretical work established the unsupervised identifiability of quantized factors under any diffeomorphism. The theory assumes that quantization thresholds correspond to axis-aligned discontinuities in the probability density of the latent factors. By constraining a learned map to have a density with axis-aligned discontinuities, we can recover the quantization of the factors. However, translating this high-level principle into an effective practical criterion remains challenging, especially under nonlinear maps. Here, we develop a criterion for unsupervised disentanglement by encouraging axis-aligned discontinuities. Discontinuities manifest as sharp changes in the estimated density of factors and form what we call cliffs. Following the definition of independent discontinuities from the theory, we encourage the location of the cliffs along a factor to be independent of the values of the other factors. We show that our method, Cliff, outperforms the baselines on all disentanglement benchmarks, demonstrating its effectiveness in unsupervised disentanglement.
[LG-45] Readout-Side Bypass for Residual Hybrid Quantum-Classical Models
链接: https://arxiv.org/abs/2511.20922
作者: Guilin Zhang,Wulan Guo,Ziqi Tan,Hongyang He,Hailong Jiang
类目: Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)
*备注: 5 pages, 1 figure, 6 tables
Abstract:Quantum machine learning (QML) promises compact and expressive representations, but suffers from the measurement bottleneck - a narrow quantum-to-classical readout that limits performance and amplifies privacy risk. We propose a lightweight residual hybrid architecture that concatenates quantum features with raw inputs before classification, bypassing the bottleneck without increasing quantum complexity. Experiments show our model outperforms pure quantum and prior hybrid models in both centralized and federated settings. It achieves up to +55% accuracy improvement over quantum baselines, while retaining low communication cost and enhanced privacy robustness. Ablation studies confirm the effectiveness of the residual connection at the quantum-classical interface. Our method offers a practical, near-term pathway for integrating quantum models into privacy-sensitive, resource-constrained settings like federated edge learning.
[LG-46] Probabilistic Hash Embeddings for Online Learning of Categorical Features AAAI2026
链接: https://arxiv.org/abs/2511.20893
作者: Aodong Li,Abishek Sankararaman,Balakrishnan Narayanaswamy
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: AAAI 2026 Oral
Abstract:We study streaming data with categorical features where the vocabulary of categorical feature values is changing and can even grow unboundedly over time. Feature hashing is commonly used as a pre-processing step to map these categorical values into a feature space of fixed size before learning their embeddings. While these methods have been developed and evaluated for offline or batch settings, in this paper we consider online settings. We show that deterministic embeddings are sensitive to the arrival order of categories and suffer from forgetting in online learning, leading to performance deterioration. To mitigate this issue, we propose a probabilistic hash embedding (PHE) model that treats hash embeddings as stochastic and applies Bayesian online learning to learn incrementally from data. Based on the structure of PHE, we derive a scalable inference algorithm to learn model parameters and infer/update the posteriors of hash embeddings and other latent variables. Our algorithm (i) can handle an evolving vocabulary of categorical items, (ii) is adaptive to new items without forgetting old items, (iii) is implementable with a bounded set of parameters that does not grow with the number of distinct observed values on the stream, and (iv) is invariant to the item arrival order. Experiments in classification, sequence modeling, and recommendation systems in online learning setups demonstrate the superior performance of PHE while maintaining high memory efficiency (consumes as low as 2~4 memory of a one-hot embedding table). Supplementary materials are at this https URL
[LG-47] Representation Integrity in Temporal Graph Learning Methods
链接: https://arxiv.org/abs/2511.20873
作者: Elahe Kooshafar
类目: Machine Learning (cs.LG)
*备注: 70 pages
Abstract:Real-world systems ranging from airline routes to cryptocurrency transfers are naturally modelled as dynamic graphs whose topology changes over time. Conventional benchmarks judge dynamic-graph learners by a handful of task-specific scores, yet seldom ask whether the embeddings themselves remain a truthful, interpretable reflection of the evolving network. We formalize this requirement as representation integrity and derive a family of indexes that measure how closely embedding changes follow graph changes. Three synthetic scenarios, Gradual Merge, Abrupt Move, and Periodic Re-wiring, are used to screen forty-two candidate indexes. Based on which we recommend one index that passes all of our theoretical and empirical tests. In particular, this validated metric consistently ranks the provably stable UASE and IPP models highest. We then use this index to do a comparative study on representation integrity of common dynamic graph learning models. This study exposes the scenario-specific strengths of neural methods, and shows a strong positive rank correlation with one-step link-prediction AUC. The proposed integrity framework, therefore, offers a task-agnostic and interpretable evaluation tool for dynamic-graph representation quality, providing more explicit guidance for model selection and future architecture design.
[LG-48] A review on data fusion in multimodal learning analytics and educational data mining
链接: https://arxiv.org/abs/2511.20871
作者: Wilson Chango,Juan A. Lara,Rebeca Cerezo,Cristóbal Romero
类目: Computers and Society (cs.CY); Machine Learning (cs.LG)
*备注:
Abstract:The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students’ data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area.
[LG-49] Accelerating Sparse Convolutions in Voxel-Based Point Cloud Networks
链接: https://arxiv.org/abs/2511.20834
作者: Dionysios Adamopoulos,Anastasia Poulopoulou,Georgios Goumas,Christina Giannoula
类目: Distributed, Parallel, and Cluster Computing (cs.DC); Hardware Architecture (cs.AR); Machine Learning (cs.LG); Performance (cs.PF)
*备注:
Abstract:Sparse Convolution (SpC) powers 3D point cloud networks widely used in autonomous driving and AR/VR. SpC builds a kernel map that stores mappings between input voxel coordinates, output coordinates, and weight offsets, then uses this map to compute feature vectors for output coordinates. Our work identifies three key properties of voxel coordinates: they are integer-valued, bounded within a limited spatial range, and geometrically continuous-neighboring voxels on the same object surface are highly likely to exist at small spatial offsets from each other. Prior SpC engines do not fully exploit these properties and suffer from high pre-processing and post-processing overheads during kernel map construction. To address this, we design Spira, the first voxel-property-aware SpC engine for GPUs. Spira proposes: (i) a high-performance one-shot search algorithm that builds the kernel map with no preprocessing and high memory locality, (ii) an effective packed-native processing scheme that accesses packed voxel coordinates at low cost, (iii) a flexible dual-dataflow execution mechanism that efficiently computes output feature vectors by adapting to layer characteristics, and (iv) a network-wide parallelization strategy that builds kernel maps for all SpC layers concurrently at network start. Our evaluation shows that Spira significantly outperforms prior SpC engines by 1.71x on average and up to 2.31x for end-to-end inference, and by 2.13x on average and up to 3.32x for layer-wise execution across diverse layer configurations.
[LG-50] Autoregressive Surrogate Modeling of the Solar Wind with Spherical Fourier Neural Operator ICDM2025
链接: https://arxiv.org/abs/2511.20830
作者: Reza Mansouri,Dustin Kempton,Pete Riley,Rafal Angryk
类目: Machine Learning (cs.LG)
*备注: IEEE Conference on Data Mining (ICDM 2025)
Abstract:The solar wind, a continuous outflow of charged particles from the Sun’s corona, shapes the heliosphere and impacts space systems near Earth. Accurate prediction of features such as high-speed streams and coronal mass ejections is critical for space weather forecasting, but traditional three-dimensional magnetohydrodynamic (MHD) models are computationally expensive, limiting rapid exploration of boundary condition uncertainties. We introduce the first autoregressive machine learning surrogate for steady-state solar wind radial velocity using the Spherical Fourier Neural Operator (SFNO). By predicting a limited radial range and iteratively propagating the solution outward, the model improves accuracy in distant regions compared to a single-step approach. Compared with the numerical HUX surrogate, SFNO demonstrates superior or comparable performance while providing a flexible, trainable, and data-driven alternative, establishing a novel methodology for high-fidelity solar wind modeling. The source code and additional visual results are available at this https URL.
[LG-51] Effects of Initialization Biases on Deep Neural Network Training Dynamics
链接: https://arxiv.org/abs/2511.20826
作者: Nicholas Pellegrino,David Szczecina,Paul W. Fieguth
类目: Machine Learning (cs.LG)
*备注: 5 pages, 2 figures, submitted to the 11th Annual Conference on Vision and Intelligent Systems
Abstract:Untrained large neural networks, just after random initialization, tend to favour a small subset of classes, assigning high predicted probabilities to these few classes and approximately zero probability to all others. This bias, termed Initial Guessing Bias, affects the early training dynamics, when the model is fitting to the coarse structure of the data. The choice of loss function against which to train the model has a large impact on how these early dynamics play out. Two recent loss functions, Blurry and Piecewise-zero loss, were designed for robustness to label errors but can become unable to steer the direction of training when exposed to this initial bias. Results indicate that the choice of loss function has a dramatic effect on the early phase training of networks, and highlights the need for careful consideration of how Initial Guessing Bias may interact with various components of the training scheme.
[LG-52] Pretraining Transformer-Based Models on Diffusion-Generated Synthetic Graphs for Alzheimers Disease Prediction
链接: https://arxiv.org/abs/2511.20704
作者: Abolfazl Moslemi,Hossein Peyvandi
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注: 14 pages. Preprint
Abstract:Early and accurate detection of Alzheimer’s disease (AD) is crucial for enabling timely intervention and improving outcomes. However, developing reliable machine learning (ML) models for AD diagnosis is challenging due to limited labeled data, multi-site heterogeneity, and class imbalance. We propose a Transformer-based diagnostic framework that combines diffusion-based synthetic data generation with graph representation learning and transfer learning. A class-conditional denoising diffusion probabilistic model (DDPM) is trained on the real-world NACC dataset to generate a large synthetic cohort that mirrors multimodal clinical and neuroimaging feature distributions while balancing diagnostic classes. Modality-specific Graph Transformer encoders are first pretrained on this synthetic data to learn robust, class-discriminative representations and are then frozen while a neural classifier is trained on embeddings from the original NACC data. We quantify distributional alignment between real and synthetic cohorts using metrics such as Maximum Mean Discrepancy (MMD), Frechet distance, and energy distance, and complement discrimination metrics with calibration and fixed-specificity sensitivity analyses. Empirically, our framework outperforms standard baselines, including early and late fusion deep neural networks and the multimodal graph-based model MaGNet, yielding higher AUC, accuracy, sensitivity, and specificity under subject-wise cross-validation on NACC. These results show that diffusion-based synthetic pretraining with Graph Transformers can improve generalization in low-sample, imbalanced clinical prediction settings.
[LG-53] Dual-Domain Deep Learning Method to Accelerate Local Basis Functions Computation for Reservoir Simulation in High-Contrast Porous Media
链接: https://arxiv.org/abs/2511.20685
作者: Peiqi Li,Jie Chen
类目: Numerical Analysis (math.NA); Machine Learning (cs.LG)
*备注:
Abstract:In energy science, Darcy flow in heterogeneous porous media is a central problem in reservoir sim-ulation. However, the pronounced multiscale characteristics of such media pose significant challenges to conventional numerical methods in terms of computational demand and efficiency. The Mixed Generalized Multiscale Finite Element Method (MGMsFEM) provides an effective framework for addressing these challenges, yet the construction of multiscale basis functions remains computationally expensive. In this work, we propose a dual-domain deep learning framework to accelerate the computation of multiscale basis functions within MGMsFEM for solving Darcy flow problems. By extracting and decoding permeability field features in both the frequency and spatial domains, the method enables rapid generation of numerical matrices of multiscale basis functions. Numerical experiments demonstrate that the proposed framework achieves significant computational acceleration while maintaining high approximation accuracy, thereby offering the potential for future applications in real-world reservoir engineering.
[LG-54] On Evolution-Based Models for Experimentation Under Interference
链接: https://arxiv.org/abs/2511.21675
作者: Sadegh Shirani,Mohsen Bayati
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Social and Information Networks (cs.SI); Econometrics (econ.EM)
*备注:
Abstract:Causal effect estimation in networked systems is central to data-driven decision making. In such settings, interventions on one unit can spill over to others, and in complex physical or social systems, the interaction pathways driving these interference structures remain largely unobserved. We argue that for identifying population-level causal effects, it is not necessary to recover the exact network structure; instead, it suffices to characterize how those interactions contribute to the evolution of outcomes. Building on this principle, we study an evolution-based approach that investigates how outcomes change across observation rounds in response to interventions, hence compensating for missing network information. Using an exposure-mapping perspective, we give an axiomatic characterization of when the empirical distribution of outcomes follows a low-dimensional recursive equation, and identify minimal structural conditions under which such evolution mappings exist. We frame this as a distributional counterpart to difference-in-differences. Rather than assuming parallel paths for individual units, it exploits parallel evolution patterns across treatment scenarios to estimate counterfactual trajectories. A key insight is that treatment randomization plays a role beyond eliminating latent confounding; it induces an implicit sampling from hidden interference channels, enabling consistent learning about heterogeneous spillover effects. We highlight causal message passing as an instantiation of this method in dense networks while extending to more general interference structures, including influencer networks where a small set of units drives most spillovers. Finally, we discuss the limits of this approach, showing that strong temporal trends or endogenous interference can undermine identification.
[LG-55] Phase Transition for Stochastic Block Model with more than sqrtn Communities (II)
链接: https://arxiv.org/abs/2511.21526
作者: Alexandra Carpentier,Christophe Giraud,Nicolas Verzelen
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST)
*备注:
Abstract:A fundamental theoretical question in network analysis is to determine under which conditions community recovery is possible in polynomial time in the Stochastic Block Model (SBM). When the number K of communities remains smaller than \sqrtn --where n denotes the number of nodes–, non-trivial community recovery is possible in polynomial time above, and only above, the Kesten–Stigum (KS) threshold, originally postulated using arguments from statistical physics. When K \geq \sqrtn , Chin, Mossel, Sohn, and Wein recently proved that, in the \emphsparse regime, community recovery in polynomial time is achievable below the KS threshold by counting non-backtracking paths. This finding led them to postulate a new threshold for the many-communities regime K \geq \sqrtn . Subsequently, Carpentier, Giraud, and Verzelen established the failure of low-degree polynomials below this new threshold across all density regimes, and demonstrated successful recovery above the threshold in certain moderately sparse settings. While these results provide strong evidence that, in the many community setting, the computational barrier lies at the threshold proposed in~Chin et al., the question of achieving recovery above this threshold still remains open in most density regimes. The present work is a follow-up to~Carpentier et al., in which we prove Conjecture~1.4 stated therein by: \ 1- Constructing a family of motifs satisfying specific structural properties; and\ 2- Proving that community recovery is possible above the proposed threshold by counting such motifs.\ Our results complete the picture of the computational barrier for community recovery in the SBM with K \geq \sqrtn communities. They also indicate that, in moderately sparse regimes, the optimal algorithms appear to be fundamentally different from spectral methods. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR); Statistics Theory (math.ST) Cite as: arXiv:2511.21526 [stat.ML] (or arXiv:2511.21526v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2511.21526 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-56] Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations
链接: https://arxiv.org/abs/2511.21369
作者: Tingkai Xue,Chin Chun Ooi,Zhengwei Ge,Fong Yew Leong,Hongying Li,Chang Wei Kang
类目: Computational Physics (physics.comp-ph); Machine Learning (cs.LG); Fluid Dynamics (physics.flu-dyn)
*备注:
Abstract:Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a reduced set of metrics (e.g. plane-level concentrations). This work presents a hybrid physics-neural model that predicts scalar transport in a complex domain orders of magnitude faster than the 3D simulation (from hours to less than 1 min). This end-to-end differentiable framework jointly learns the physical model parameterization (i.e. orthotropic diffusivity) and a non-Markovian neural closure model to capture unresolved, ‘coarse-grained’ effects, thereby enabling stable, long time horizon rollouts. This proposed model is data-efficient (learning with 26 training data), and can be flexibly extended to an out-of-distribution scenario (with a moving source), achieving a Spearman correlation coefficient of 0.96 at the final simulation time. Overall results show that this differentiable physics-neural framework enables fast, accurate, and generalizable coarse-grained surrogates for physical phenomena.
[LG-57] Phase-Aware Code-Aided EM Algorithm for Blind Channel Estimation in PSK-Modulated OFDM
链接: https://arxiv.org/abs/2511.21340
作者: Chin-Hung Chen,Ivana Nikoloska,Wim van Houtum,Yan Wu,Alex Alvarado
类目: ignal Processing (eess.SP); Machine Learning (cs.LG)
*备注: preprint
Abstract:This paper presents a fully blind phase-aware expectation-maximization (EM) algorithm for OFDM systems with the phase-shift keying (PSK) modulation. We address the well-known local maximum problem of the EM algorithm for blind channel estimation. This is primarily caused by the unknown phase ambiguity in the channel estimates, which conventional blind EM estimators cannot resolve. To overcome this limitation, we propose to exploit the extrinsic information from the decoder as model evidence metrics. A finite set of candidate models is generated based on the inherent symmetries of PSK modulation, and the decoder selects the most likely candidate model. Simulation results demonstrate that, when combined with a simple convolutional code, the phase-aware EM algorithm reliably resolves phase ambiguity during the initialization stage and reduces the local convergence rate from 80% to nearly 0% in frequency-selective channels with a constant phase ambiguity. The algorithm is invoked only once after the EM initialization stage, resulting in negligible additional complexity during subsequent turbo iterations.
[LG-58] On the Periodic Orbits of the Dual Logarithmic Derivative Operator
链接: https://arxiv.org/abs/2511.21283
作者: Xiaohang Yu,William Knottenbelt
类目: Dynamical Systems (math.DS); Machine Learning (cs.LG)
*备注:
Abstract:We study the periodic behaviour of the dual logarithmic derivative operator \mathcalA[f]=\mathrmd\ln f/\mathrmd\ln x in a complex analytic setting. We show that \mathcalA admits genuinely nondegenerate period- 2 orbits and identify a canonical explicit example. Motivated by this, we obtain a complete classification of all nondegenerate period- 2 solutions, which are precisely the rational pairs (c a x^c/(1-ax^c),, c/(1-ax^c)) with ac\neq 0 . We further classify all fixed points of \mathcalA , showing that every solution of \mathcalA[f]=f has the form f(x)=1/(a-\ln x) . As an illustration, logistic-type functions become pre-periodic under \mathcalA after a logarithmic change of variables, entering the period- 2 family in one iterate. These results give an explicit description of the low-period structure of \mathcalA and provide a tractable example of operator-induced dynamics on function spaces.
[LG-59] Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso
链接: https://arxiv.org/abs/2511.21257
作者: Sullivan Hué,Sébastien Laurent,Ulrich Aiounou,Emmanuel Flachaire
类目: Econometrics (econ.EM); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:Post-Double-Lasso is becoming the most popular method for estimating linear regression models with many covariates when the purpose is to obtain an accurate estimate of a parameter of interest, such as an average treatment effect. However, this method can suffer from substantial omitted variable bias in finite sample. We propose a new method called Post-Double-Autometrics, which is based on Autometrics, and show that this method outperforms Post-Double-Lasso. Its use in a standard application of economic growth sheds new light on the hypothesis of convergence from poor to rich economies.
[LG-60] he Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval
链接: https://arxiv.org/abs/2511.21247
作者: Jaime Garcia-Martinez,David Diaz-Guerra,John Anderson,Ricardo Falcon-Perez,Pablo Cabañas-Molero,Tuomas Virtanen,Julio J. Carabias-Orti,Pedro Vera-Candeas
类目: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
*备注:
Abstract:This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source separation and related MIR tasks within the classical music domain. The dataset is composed of over one hour recordings of musical pieces performed by the Colibrì Ensemble at The Spheres recording studio, capturing two canonical works - Tchaikovsky’s Romeo and Juliet and Mozart’s Symphony No. 40 - along with chromatic scales and solo excerpts for each instrument. The recording setup employed 23 microphones, including close spot, main, and ambient microphones, enabling the creation of realistic stereo mixes with controlled bleeding and providing isolated stems for supervised training of source separation models. In addition, room impulse responses were estimated for each instrument position, offering valuable acoustic characterization of the recording space. We present the dataset structure, acoustic analysis, and baseline evaluations using X-UMX based models for orchestral family separation and microphone debleeding. Results highlight both the potential and the challenges of source separation in complex orchestral scenarios, underscoring the dataset’s value for benchmarking and for exploring new approaches to separation, localization, dereverberation, and immersive rendering of classical music.
[LG-61] Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference
链接: https://arxiv.org/abs/2511.21223
作者: Jasraj Singh,Shelvia Wongso,Jeremie Houssineau,Badr-Eddine Chérief-Abdellatif
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models that would otherwise be intractable. However, its formulation depends on expectations and divergences defined through high-dimensional integrals, often rendering analytical treatment impossible and necessitating heavy reliance on approximate learning and inference techniques. Possibility theory, an imprecise probability framework, allows to directly model epistemic uncertainty instead of leveraging subjective probabilities. While this framework provides robustness and interpretability under sparse or imprecise information, adapting VI to the possibilistic setting requires rethinking core concepts such as entropy and divergence, which presuppose additivity. In this work, we develop a principled formulation of possibilistic variational inference and apply it to a special class of exponential-family functions, highlighting parallels with their probabilistic counterparts and revealing the distinctive mathematical structures of possibility theory.
[LG-62] Lattice-to-total thermal conductivity ratio: a phonon-glass electron-crystal descriptor for data-driven thermoelectric design
链接: https://arxiv.org/abs/2511.21213
作者: Yifan Sun,Zhi Li,Tetsuya Imamura,Yuji Ohishi,Chris Wolverton,Ken Kurosaki
类目: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG)
*备注: 15 pages, 7 figures
Abstract:Thermoelectrics (TEs) are promising candidates for energy harvesting with performance quantified by figure of merit, ZT . To accelerate the discovery of high- ZT materials, efforts have focused on identifying compounds with low thermal conductivity \kappa . Using a curated dataset of 71,913 entries, we show that high- ZT materials reside not only in the low- \kappa regime but also cluster near a lattice-to-total thermal conductivity ratio ( \kappa_\mathrmL/\kappa ) of approximately 0.5, consistent with the phonon-glass electron-crystal design concept. Building on this insight, we construct a framework consisting of two machine learning models for the lattice and electronic components of thermal conductivity that jointly provide both \kappa and \kappa_\mathrmL/\kappa for screening and guiding the optimization of TE materials. Among 104,567 compounds screened, our models identify 2,522 ultralow- \kappa candidates. Follow-up case studies demonstrate that this framework can reliably provide optimization strategies by suggesting new dopants and alloys that shift pristine materials toward the \kappa_\mathrmL/\kappa approaching 0.5 regime. Ultimately, by integrating rapid screening with PGEC-guided optimization, our data-driven framework effectively bridges the critical gap between materials discovery and performance enhancement.
[LG-63] Nonconvex Penalized LAD Estimation in Partial Linear Models with DNNs: Asymptotic Analysis and Proximal Algorithms
链接: https://arxiv.org/abs/2511.21115
作者: Lechen Feng,Haoran Li,Lucky Li,Xingqiu Zhao
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:This paper investigates the partial linear model by Least Absolute Deviation (LAD) regression. We parameterize the nonparametric term using Deep Neural Networks (DNNs) and formulate a penalized LAD problem for estimation. Specifically, our model exhibits the following challenges. First, the regularization term can be nonconvex and nonsmooth, necessitating the introduction of infinite dimensional variational analysis and nonsmooth analysis into the asymptotic normality discussion. Second, our network must expand (in width, sparsity level and depth) as more samples are observed, thereby introducing additional difficulties for theoretical analysis. Third, the oracle of the proposed estimator is itself defined through a ultra high-dimensional, nonconvex, and discontinuous optimization problem, which already entails substantial computational and theoretical challenges. Under such the challenges, we establish the consistency, convergence rate, and asymptotic normality of the estimator. Furthermore, we analyze the oracle problem itself and its continuous relaxation. We study the convergence of a proximal subgradient method for both formulations, highlighting their structural differences lead to distinct computational subproblems along the iterations. In particular, the relaxed formulation admits significantly cheaper proximal updates, reflecting an inherent trade-off between statistical accuracy and computational tractability.
[LG-64] Crowdsourcing the Frontier: Advancing Hybrid Physics-ML Climate Simulation via 50000 Kaggle Competition
链接: https://arxiv.org/abs/2511.20963
作者: Jerry Lin,Zeyuan Hu,Tom Beucler,Katherine Frields,Hannah Christensen,Walter Hannah,Helge Heuer,Peter Ukkonnen,Laura A. Mansfield,Tian Zheng,Liran Peng,Ritwik Gupta,Pierre Gentine,Yusef Al-Naher,Mingjiang Duan,Kyo Hattori,Weiliang Ji,Chunhan Li,Kippei Matsuda,Naoki Murakami,Shlomo Ron,Marec Serlin,Hongjian Song,Yuma Tanabe,Daisuke Yamamoto,Jianyao Zhou,Mike Pritchard
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注: Main text: 29 pages, 10 figures. SI: 47 pages, 37 figures
Abstract:Subgrid machine-learning (ML) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and machine learning researchers opened up the offline aspect of this problem to the broader machine learning and data science community with the release of ClimSim, a NeurIPS Datasets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams’ architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution, real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art (SOTA) results on certain metrics such as zonal mean bias patterns and global RMSE, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.
[LG-65] Geometric Calibration and Neutral Zones for Uncertainty-Aware Multi-Class Classification
链接: https://arxiv.org/abs/2511.20960
作者: Soumojit Das,Nairanjana Dasgupta,Prashanta Dutta
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME)
*备注:
Abstract:Modern artificial intelligence systems make critical decisions yet often fail silently when uncertain. We develop a geometric framework for post-hoc calibration of neural network probability outputs, treating probability vectors as points on the (c-1) -dimensional probability simplex equipped with the Fisher–Rao metric. Our approach yields Additive Log-Ratio (ALR) calibration maps that reduce exactly to Platt scaling for binary problems (Proposition~1) while extending naturally to multi-class settings – providing a principled generalization that existing methods lack. Complementing calibration, we define geometric reliability scores based on Fisher–Rao distance and construct neutral zones for principled deferral of uncertain predictions. Theoretical contributions include: (i) consistency of the calibration estimator at rate O_p(n^-1/2) via M-estimation theory (Theorem~1), and (ii) tight concentration bounds for reliability scores with explicit sub-Gaussian parameters enabling sample size calculations for validation set design (Theorem~2). We conjecture Neyman–Pearson optimality of our neutral zone construction based on connections to Bhattacharyya coefficients. Empirical validation on Adeno-Associated Virus classification demonstrates that the two-stage framework (calibration followed by reliability-based deferral) captures 72.5% of errors while deferring 34.5% of samples. Notably, this operational gain is achievable with any well-calibrated probability output; the contribution of geometric calibration lies in its theoretical foundations rather than empirical superiority over simpler alternatives. This work bridges information geometry and statistical learning, offering formal guarantees relevant to applications requiring rigorous validation. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Statistics Theory (math.ST); Methodology (stat.ME) Cite as: arXiv:2511.20960 [stat.ML] (or arXiv:2511.20960v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2511.20960 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
[LG-66] Fusion of classical and quantum kernels enables accurate and robust two-sample tests
链接: https://arxiv.org/abs/2511.20941
作者: Yu Terada,Yugo Ogio,Ken Arai,Hiroyuki Tezuka,Yu Tanaka
类目: Quantum Physics (quant-ph); Machine Learning (cs.LG); Methodology (stat.ME)
*备注: 11 pages, 5 figures
Abstract:Two-sample tests have been extensively employed in various scientific fields and machine learning such as evaluation on the effectiveness of drugs and A/B testing on different marketing strategies to discriminate whether two sets of samples come from the same distribution or not. Kernel-based procedures for hypothetical testing have been proposed to efficiently disentangle high-dimensional complex structures in data to obtain accurate results in a model-free way by embedding the data into the reproducing kernel Hilbert space (RKHS). While the choice of kernels plays a crucial role for their performance, little is understood about how to choose kernel especially for small datasets. Here we aim to construct a hypothetical test which is effective even for small datasets, based on the theoretical foundation of kernel-based tests using maximum mean discrepancy, which is called MMD-FUSE. To address this, we enhance the MMD-FUSE framework by incorporating quantum kernels and propose a novel hybrid testing strategy that fuses classical and quantum kernels. This approach creates a powerful and adaptive test by combining the domain-specific inductive biases of classical kernels with the unique expressive power of quantum kernels. We evaluate our method on various synthetic and real-world clinical datasets, and our experiments reveal two key findings: 1) With appropriate hyperparameter tuning, MMD-FUSE with quantum kernels consistently improves test power over classical counterparts, especially for small and high-dimensional data. 2) The proposed hybrid framework demonstrates remarkable robustness, adapting to different data characteristics and achieving high test power across diverse scenarios. These results highlight the potential of quantum-inspired and hybrid kernel strategies to build more effective statistical tests, offering a versatile tool for data analysis where sample sizes are limited.
[LG-67] Deep Learning as a Convex Paradigm of Computation: Minimizing Circuit Size with ResNets
链接: https://arxiv.org/abs/2511.20888
作者: Arthur Jacot
类目: Machine Learning (stat.ML); Computational Complexity (cs.CC); Machine Learning (cs.LG)
*备注:
Abstract:This paper argues that DNNs implement a computational Occam’s razor – finding the simplest' algorithm that fits the data -- and that this could explain their incredible and wide-ranging success over more traditional statistical methods. We start with the discovery that the set of real-valued function f that can be \epsilon -approximated with a binary circuit of size at most c\epsilon^-\gamma becomes convex in the Harder than Monte Carlo’ (HTMC) regime, when \gamma2 , allowing for the definition of a HTMC norm on functions. In parallel one can define a complexity measure on the parameters of a ResNets (a weighted \ell_1 norm of the parameters), which induce a `ResNet norm’ on functions. The HTMC and ResNet norms can then be related by an almost matching sandwich bound. Thus minimizing this ResNet norm is equivalent to finding a circuit that fits the data with an almost minimal number of nodes (within a power of 2 of being optimal). ResNets thus appear as an alternative model for computation of real functions, better adapted to the HTMC regime and its convexity.
[LG-68] When Features Beat Noise: A Feature Selection Technique Through Noise-Based Hypothesis Testing
链接: https://arxiv.org/abs/2511.20851
作者: Mousam Sinha,Tirtha Sarathi Ghosh,Ridam Pal
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:
Abstract:Feature selection has remained a daunting challenge in machine learning and artificial intelligence, where increasingly complex, high-dimensional datasets demand principled strategies for isolating the most informative predictors. Despite widespread adoption, many established techniques suffer from notable limitations; some incur substantial computational cost, while others offer no definite statistical driven stopping criteria or assesses the significance of their importance scores. A common heuristic approach introduces multiple random noise features and retains all predictors ranked above the strongest noise feature. Although intuitive, this strategy lacks theoretical justification and depends heavily on heuristics. This paper proposes a novel feature selection method that addresses these limitations. Our approach introduces multiple random noise features and evaluates each feature’s importance against the maximum importance value among these noise features incorporating a non-parametric bootstrap-based hypothesis testing framework to establish a solid theoretical foundation. We establish the conceptual soundness of our approach through statistical derivations that articulate the principles guiding the design of our algorithm. To evaluate its reliability, we generated simulated datasets under controlled statistical settings and benchmarked performance against Boruta and Knockoff-based methods, observing consistently stronger recovery of meaningful signal. As a demonstration of practical utility, we applied the technique across diverse real-world datasets, where it surpassed feature selection techniques including Boruta, RFE, and Extra Trees. Hence, the method emerges as a robust algorithm for principled feature selection, enabling the distillation of informative predictors that support reliable inference, enhanced predictive performance, and efficient computation.
[LG-69] A Set of Rules for Model Validation
链接: https://arxiv.org/abs/2511.20711
作者: José Camacho
类目: Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:
Abstract:The validation of a data-driven model is the process of assessing the model’s ability to generalize to new, unseen data in the population of interest. This paper proposes a set of general rules for model validation. These rules are designed to help practitioners create reliable validation plans and report their results transparently. While no validation scheme is flawless, these rules can help practitioners ensure their strategy is sufficient for practical use, openly discuss any limitations of their validation strategy, and report clear, comparable performance metrics.
[LG-70] he Human Brain as a Combinatorial Complex NEURIPS2025
链接: https://arxiv.org/abs/2511.20692
作者: Valentina Sánchez,Çiçek Güven,Koen Haak,Theodore Papamarkou,Gonzalo Nápoles,Marie Šafář Postma
类目: Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)
*备注: Accepted as an Extended Abstract at the NeurReps Workshop, NeurIPS 2025
Abstract:We propose a framework for constructing combinatorial complexes (CCs) from fMRI time series data that captures both pairwise and higher-order neural interactions through information-theoretic measures, bridging topological deep learning and network neuroscience. Current graph-based representations of brain networks systematically miss the higher-order dependencies that characterize neural complexity, where information processing often involves synergistic interactions that cannot be decomposed into pairwise relationships. Unlike topological lifting approaches that map relational structures into higher-order domains, our method directly constructs CCs from statistical dependencies in the data. Our CCs generalize graphs by incorporating higher-order cells that represent collective dependencies among brain regions, naturally accommodating the multi-scale, hierarchical nature of neural processing. The framework constructs data-driven combinatorial complexes using O-information and S-information measures computed from fMRI signals, preserving both pairwise connections and higher-order cells (e.g., triplets, quadruplets) based on synergistic dependencies. Using NetSim simulations as a controlled proof-of-concept dataset, we demonstrate our CC construction pipeline and show how both pairwise and higher-order dependencies in neural time series can be quantified and represented within a unified structure. This work provides a framework for brain network representation that preserves fundamental higher-order structure invisible to traditional graph methods, and enables the application of topological deep learning (TDL) architectures to neural data.
[LG-71] Cryptocurrency Portfolio Management with Reinforcement Learning: Soft Actor–Critic and Deep Deterministic Policy Gradient Algorithms
链接: https://arxiv.org/abs/2511.20678
作者: Kamal Paykan(Department of Mathematics, Tafresh University, Tafresh, Iran)
类目: Computational Finance (q-fin.CP); Machine Learning (cs.LG)
*备注:
Abstract:This paper proposes a reinforcement learning–based framework for cryptocurrency portfolio management using the Soft Actor–Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms. Traditional portfolio optimization methods often struggle to adapt to the highly volatile and nonlinear dynamics of cryptocurrency markets. To address this, we design an agent that learns continuous trading actions directly from historical market data through interaction with a simulated trading environment. The agent optimizes portfolio weights to maximize cumulative returns while minimizing downside risk and transaction costs. Experimental evaluations on multiple cryptocurrencies demonstrate that the SAC and DDPG agents outperform baseline strategies such as equal-weighted and mean–variance portfolios. The SAC algorithm, with its entropy-regularized objective, shows greater stability and robustness in noisy market conditions compared to DDPG. These results highlight the potential of deep reinforcement learning for adaptive and data-driven portfolio management in cryptocurrency markets.
信息检索
[IR-0] Generating Querying Code from Text for Multi-Modal Electronic Health Record
链接: https://arxiv.org/abs/2511.20904
作者: Mengliang ZHang
类目: Information Retrieval (cs.IR)
*备注:
Abstract:Electronic health records (EHR) contain extensive structured and unstructured data, including tabular information and free-text clinical notes. Querying relevant patient information often requires complex database operations, increasing the workload for clinicians. However, complex table relationships and professional terminology in EHRs limit the query accuracy. In this work, we construct a publicly available dataset, TQGen, that integrates both \textbfTables and clinical \textbfText for natural language-to-query \textbfGeneration. To address the challenges posed by complex medical terminology and diverse types of questions in EHRs, we propose TQGen-EHRQuery, a framework comprising a medical knowledge module and a questions template matching module. For processing medical text, we introduced the concept of a toolset, which encapsulates the text processing module as a callable tool, thereby improving processing efficiency and flexibility. We conducted extensive experiments to assess the effectiveness of our dataset and workflow, demonstrating their potential to enhance information querying in EHR systems.
[IR-1] E-GEO: A Testbed for Generative Engine Optimization in E-Commerce
链接: https://arxiv.org/abs/2511.20867
作者: Puneet S. Bagga,Vivek F. Farias,Tamar Korkotashvili,Tianyi Peng,Yuhang Wu
类目: Information Retrieval (cs.IR)
*备注:
Abstract:With the rise of large language models (LLMs), generative engines are becoming powerful alternatives to traditional search, reshaping retrieval tasks. In e-commerce, for instance, conversational shopping agents now guide consumers to relevant products. This shift has created the need for generative engine optimization (GEO)–improving content visibility and relevance for generative engines. Yet despite its growing importance, current GEO practices are ad hoc, and their impacts remain poorly understood, especially in e-commerce. We address this gap by introducing E-GEO, the first benchmark built specifically for e-commerce GEO. E-GEO contains over 7,000 realistic, multi-sentence consumer product queries paired with relevant listings, capturing rich intent, constraints, preferences, and shopping contexts that existing datasets largely miss. Using this benchmark, we conduct the first large-scale empirical study of e-commerce GEO, evaluating 15 common rewriting heuristics and comparing their empirical performance. To move beyond heuristics, we further formulate GEO as a tractable optimization problem and develop a lightweight iterative prompt-optimization algorithm that can significantly outperform these baselines. Surprisingly, the optimized prompts reveal a stable, domain-agnostic pattern–suggesting the existence of a “universally effective” GEO strategy. Our data and code are publicly available at this https URL.
