Arxiv今日论文 | 2025-04-11

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【速读】：该论文试图解决如何评估小型和中型生成式语言模型（Generative Language Models）在表征表示（representational alignment）和行为一致性（behavioral alignment）方面与人类语义判断的一致性问题。论文通过引入一个基于词三元组任务的新颖评估框架，超越传统的成对比较方法，深入探究这些模型在不同层级上的语义关联特性。解决方案的关键在于综合分析模型的表征表示与行为响应，并揭示模型规模、指令微调以及表征-行为一致性之间的依赖关系，从而为理解小型和中型生成式语言模型的语义能力提供新的视角。

链接: https://arxiv.org/abs/2504.07965
作者: Lorenz Linhardt,Tom Neuhäuser,Lenka Tětková,Oliver Eberle
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: ICLR 2025 Workshop on Representational Alignment (Re-Align)

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Abstract:Small and mid-sized generative language models have gained increasing attention. Their size and availability make them amenable to being analyzed at a behavioral as well as a representational level, allowing investigations of how these levels interact. We evaluate 32 publicly available language models for their representational and behavioral alignment with human similarity judgments on a word triplet task. This provides a novel evaluation setting to probe semantic associations in language beyond common pairwise comparisons. We find that (1) even the representations of small language models can achieve human-level alignment, (2) instruction-tuned model variants can exhibit substantially increased agreement, (3) the pattern of alignment across layers is highly model dependent, and (4) alignment based on models’ behavioral responses is highly dependent on model size, matching their representational alignment only for the largest evaluated models.
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[NLP-1] VCR-Bench: A Comprehensive Evaluation Framework for Video Chain-of-Thought Reasoning

【速读】：该论文试图解决视频领域中大视觉语言模型（Large Vision-Language Models, LVLMs）在链式思维（Chain-of-Thought, CoT）推理能力评估方面缺乏严谨框架的问题。当前视频基准测试无法充分评估模型的推理过程，也无法明确失败原因是否源于感知或推理能力的不足。为了解决这一问题，论文提出VCR-Bench，这是一个包含859个视频及其对应的1,034组高质量问答对的新基准，每个问答对均附带人工标注的逐步CoT理性分析，并明确标注每一步与感知或推理能力的关联。关键解决方案在于设计了七个任务维度以及基于逐步标注的CoT评分（CoT Score）来全面评估LVLMs的CoT推理能力，并通过实验揭示了现有模型在处理复杂视频推理任务中的显著瓶颈，特别是时间-空间信息处理能力的不足。

链接: https://arxiv.org/abs/2504.07956
作者: Yukun Qi,Yiming Zhao,Yu Zeng,Xikun Bao,Wenxuan Huang,Lin Chen,Zehui Chen,Jie Zhao,Zhongang Qi,Feng Zhao
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:The advancement of Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs) and large vision-language models (LVLMs). However, a rigorous evaluation framework for video CoT reasoning remains absent. Current video benchmarks fail to adequately assess the reasoning process and expose whether failures stem from deficiencies in perception or reasoning capabilities. Therefore, we introduce VCR-Bench, a novel benchmark designed to comprehensively evaluate LVLMs’ Video Chain-of-Thought Reasoning capabilities. VCR-Bench comprises 859 videos spanning a variety of video content and durations, along with 1,034 high-quality question-answer pairs. Each pair is manually annotated with a stepwise CoT rationale, where every step is tagged to indicate its association with the perception or reasoning capabilities. Furthermore, we design seven distinct task dimensions and propose the CoT score to assess the entire CoT process based on the stepwise tagged CoT rationals. Extensive experiments on VCR-Bench highlight substantial limitations in current LVLMs. Even the top-performing model, o1, only achieves a 62.8% CoT score and an 56.7% accuracy, while most models score below 40%. Experiments show most models score lower on perception than reasoning steps, revealing LVLMs’ key bottleneck in temporal-spatial information processing for complex video reasoning. A robust positive correlation between the CoT score and accuracy confirms the validity of our evaluation framework and underscores the critical role of CoT reasoning in solving complex video reasoning tasks. We hope VCR-Bench to serve as a standardized evaluation framework and expose the actual drawbacks in complex video reasoning task.
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[NLP-2] Perception-R1: Pioneering Perception Policy with Reinforcement Learning

【速读】：该论文旨在探索基于规则的强化学习（Reinforcement Learning, RL）在多模态大语言模型（MLLM）后训练阶段用于感知策略学习的潜力。尽管初步实验显示其在某些视觉感知任务中表现良好，但研究发现将思考过程引入RL并不总能带来性能提升。因此，论文深入分析了RL在视觉感知中的本质作用，并观察到感知复杂度是影响RL效果的关键因素之一，同时奖励设计对逼近模型感知上限至关重要。为解决这些问题，论文提出了一种名为Perception-R1的可扩展RL框架，在Qwen2.5-VL-3B-Instruct模型上进行后训练，显著提升了多项任务的表现，包括RefCOCO+ (+4.2%)、PixMo-Count (+17.9%)、PageOCR (+4.2%) 和COCO2017 val的AP评分（首次达到31.9%），从而为感知策略学习建立了坚实的基准。解决方案的关键在于结合感知复杂度与优化的奖励设计，并通过Perception-R1框架有效提升模型在多种视觉感知任务上的性能。

链接: https://arxiv.org/abs/2504.07954
作者: En Yu,Kangheng Lin,Liang Zhao,Jisheng Yin,Yana Wei,Yuang Peng,Haoran Wei,Jianjian Sun,Chunrui Han,Zheng Ge,Xiangyu Zhang,Daxin Jiang,Jingyu Wang,Wenbing Tao
机构: Huazhong University of Science and Technology (华中科技大学); Beijing University of Posts and Telecommunications (北京邮电大学); StepFun; Johns Hopkins University (约翰斯·霍普金斯大学); Tingshua University (错误，应为 Tsinghua University (清华大学))
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
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Abstract:Inspired by the success of DeepSeek-R1, we explore the potential of rule-based reinforcement learning (RL) in MLLM post-training for perception policy learning. While promising, our initial experiments reveal that incorporating a thinking process through RL does not consistently lead to performance gains across all visual perception tasks. This leads us to delve into the essential role of RL in the context of visual perception. In this work, we return to the fundamentals and explore the effects of RL on different perception tasks. We observe that the perceptual complexity is a major factor in determining the effectiveness of RL. We also observe that reward design plays a crucial role in further approching the upper limit of model perception. To leverage these findings, we propose Perception-R1, a scalable RL framework using GRPO during MLLM post-training. With a standard Qwen2.5-VL-3B-Instruct, Perception-R1 achieves +4.2% on RefCOCO+, +17.9% on PixMo-Count, +4.2% on PageOCR, and notably, 31.9% AP on COCO2017 val for the first time, establishing a strong baseline for perception policy learning.
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[NLP-3] Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory

【速读】：该论文试图解决当前语言模型在处理复杂任务时缺乏持久记忆的问题，即每次输入查询都是独立处理，无法保留先前尝试中的洞见。为了解决这一问题，论文提出了一种名为Dynamic Cheatsheet (DC) 的轻量级框架，其关键是赋予黑盒语言模型一种持续演进的记忆能力。不同于重复发现或重新提交相同解决方案与错误，DC 允许模型在推理阶段存储并重用积累的策略、代码片段以及通用问题解决洞见，从而显著提升多种任务的表现，且无需显式的真值标签或人工反馈。

链接: https://arxiv.org/abs/2504.07952
作者: Mirac Suzgun,Mert Yuksekgonul,Federico Bianchi,Dan Jurafsky,James Zou
机构: 未知
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
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Abstract:Despite their impressive performance on complex tasks, current language models (LMs) typically operate in a vacuum: Each input query is processed separately, without retaining insights from previous attempts. Here, we present Dynamic Cheatsheet (DC), a lightweight framework that endows a black-box LM with a persistent, evolving memory. Rather than repeatedly re-discovering or re-committing the same solutions and mistakes, DC enables models to store and reuse accumulated strategies, code snippets, and general problem-solving insights at inference time. This test-time learning enhances performance substantially across a range of tasks without needing explicit ground-truth labels or human feedback. Leveraging DC, Claude 3.5 Sonnet’s accuracy more than doubled on AIME math exams once it began retaining algebraic insights across questions. Similarly, GPT-4o’s success rate on Game of 24 increased from 10% to 99% after the model discovered and reused a Python-based solution. In tasks prone to arithmetic mistakes, such as balancing equations, DC enabled GPT-4o and Claude to reach near-perfect accuracy by recalling previously validated code, whereas their baselines stagnated around 50%. Beyond arithmetic challenges, DC yields notable accuracy gains on knowledge-demanding tasks. Claude achieved a 9% improvement in GPQA-Diamond and an 8% boost on MMLU-Pro problems. Crucially, DC’s memory is self-curated, focusing on concise, transferable snippets rather than entire transcript. Unlike finetuning or static retrieval methods, DC adapts LMs’ problem-solving skills on the fly, without modifying their underlying parameters. Overall, our findings present DC as a promising approach for augmenting LMs with persistent memory, bridging the divide between isolated inference events and the cumulative, experience-driven learning characteristic of human cognition.
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[NLP-4] Redefining Machine Translation on Social Network Services with Large Language Models

【速读】：该论文旨在解决社交网络服务（Social Network Services, SNS）中机器翻译（Machine Translation, MT）在处理文化细微差异内容（如表情包、俚语和流行文化引用）时表现不足的问题。尽管大型语言模型（Large Language Models, LLMs）在通用翻译任务上取得了进展，但它们在特定于SNS内容上的性能受限于缺乏专门训练数据和评估基准。
解决方案的关键在于提出RedTrans，一个72B参数的LLM，专为SNS翻译设计，并通过以下创新方法进行训练：(1) 监督微调结合双LLM回译采样（Supervised Finetuning with Dual-LLM Back-Translation Sampling），一种利用基于LLM的回译来选择多样化数据的大规模微调方法；(2) 重写偏好优化算法（Rewritten Preference Optimization, RePO），通过专家标注识别并修正错误的偏好对，构建可靠的偏好语料库；(3) RedTrans-Bench，首个SNS翻译基准，评估幽默本地化、表情符号语义以及 meme 适应等现象。实验表明，RedTrans在性能上优于现有最先进的LLMs，并已成功部署到实际生产环境中，验证了领域特定适配能够有效弥合通用与文化相关翻译系统之间的差距。

链接: https://arxiv.org/abs/2504.07901
作者: Hongcheng Guo,Fei Zhao,Shaosheng Cao,Xinze Lyu,Ziyan Liu,Yue Wang,Boyang Wang,Zhoujun Li,Chonggang Lu,Zhe Xu,Yao Hu
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:The globalization of social interactions has heightened the need for machine translation (MT) on Social Network Services (SNS), yet traditional models struggle with culturally nuanced content like memes, slang, and pop culture references. While large language models (LLMs) have advanced general-purpose translation, their performance on SNS-specific content remains limited due to insufficient specialized training data and evaluation benchmarks. This paper introduces RedTrans, a 72B LLM tailored for SNS translation, trained on a novel dataset developed through three innovations: (1) Supervised Finetuning with Dual-LLM Back-Translation Sampling, an unsupervised sampling method using LLM-based back-translation to select diverse data for large-scale finetuning; (2) Rewritten Preference Optimization (RePO), an algorithm that identifies and corrects erroneous preference pairs through expert annotation, building reliable preference corpora; and (3) RedTrans-Bench, the first benchmark for SNS translation, evaluating phenomena like humor localization, emoji semantics, and meme adaptation. Experiments show RedTrans outperforms state-of-the-art LLMs. Besides, RedTrans has already been deployed in a real-world production environment, demonstrating that domain-specific adaptation, effectively bridges the gap between generic and culturally grounded translation systems.
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[NLP-5] How do Large Language Models Understand Relevance? A Mechanistic Interpretability Perspective

【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在信息检索（Information Retrieval, IR）任务中进行相关性判断的内部机制尚未被充分探索的问题。论文的关键在于通过机械性可解释性（mechanistic interpretability）的方法，系统性地分析不同LLM模块如何共同贡献于相关性判断。研究采用激活修补（activation patching）技术，揭示了LLMs生成点对点或成对相关性判断的多阶段渐进过程：早期层提取查询和文档信息，中间层根据指令处理相关性信息，后期层利用特定注意力头生成所需格式的相关性判断。这一发现为理解LLMs相关性评估的机制提供了洞见，并为未来基于LLMs优化IR任务的研究提供了重要参考。

链接: https://arxiv.org/abs/2504.07898
作者: Qi Liu,Jiaxin Mao,Ji-Rong Wen
机构: Gaoling School of Artificial Intelligence (高瓴人工智能学院, Renmin University of China (中国人民大学), Beijing (北京), China)
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Recent studies have shown that large language models (LLMs) can assess relevance and support information retrieval (IR) tasks such as document ranking and relevance judgment generation. However, the internal mechanisms by which off-the-shelf LLMs understand and operationalize relevance remain largely unexplored. In this paper, we systematically investigate how different LLM modules contribute to relevance judgment through the lens of mechanistic interpretability. Using activation patching techniques, we analyze the roles of various model components and identify a multi-stage, progressive process in generating either pointwise or pairwise relevance judgment. Specifically, LLMs first extract query and document information in the early layers, then process relevance information according to instructions in the middle layers, and finally utilize specific attention heads in the later layers to generate relevance judgments in the required format. Our findings provide insights into the mechanisms underlying relevance assessment in LLMs, offering valuable implications for future research on leveraging LLMs for IR tasks.
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[NLP-6] Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models : Scalable Automated Assessment with LLM -as-a-Judge

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在实际应用中因嵌入偏见而导致的不公平性和潜在危害问题。论文关注的重点是如何评估和提升LLMs在对抗性偏见诱导下的鲁棒性。解决方案的关键在于提出了一套可扩展的基准测试框架，包括采用多任务方法系统性探测模型在多种社会文化维度上的偏见（i），通过“LLM作为法官”的方式量化模型的安全性得分以自动化评估响应的稳健性（ii），以及利用越狱技术检验安全机制的漏洞（iii）。此外，论文还发布了一个精心策划的与偏见相关的提示数据集CLEAR-Bias，用于促进系统的脆弱性基准测试。研究揭示了模型规模与安全性之间的权衡关系，为开发更公平且稳健的语言模型提供了重要指导。

链接: https://arxiv.org/abs/2504.07887
作者: Riccardo Cantini,Alessio Orsino,Massimo Ruggiero,Domenico Talia
机构: University of Calabria (University of Calabria)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Large Language Models (LLMs) have revolutionized artificial intelligence, driving advancements in machine translation, summarization, and conversational agents. However, their increasing integration into critical societal domains has raised concerns about embedded biases, which can perpetuate stereotypes and compromise fairness. These biases stem from various sources, including historical inequalities in training data, linguistic imbalances, and adversarial manipulation. Despite mitigation efforts, recent studies indicate that LLMs remain vulnerable to adversarial attacks designed to elicit biased responses. This work proposes a scalable benchmarking framework to evaluate LLM robustness against adversarial bias elicitation. Our methodology involves (i) systematically probing models with a multi-task approach targeting biases across various sociocultural dimensions, (ii) quantifying robustness through safety scores using an LLM-as-a-Judge approach for automated assessment of model responses, and (iii) employing jailbreak techniques to investigate vulnerabilities in safety mechanisms. Our analysis examines prevalent biases in both small and large state-of-the-art models and their impact on model safety. Additionally, we assess the safety of domain-specific models fine-tuned for critical fields, such as medicine. Finally, we release a curated dataset of bias-related prompts, CLEAR-Bias, to facilitate systematic vulnerability benchmarking. Our findings reveal critical trade-offs between model size and safety, aiding the development of fairer and more robust future language models.
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[NLP-7] oken Level Routing Inference System for Edge Devices ACL

【速读】：该论文旨在解决大型语言模型（Large Language Model, LLM）推理在边缘设备上的部署效率受限与小型语言模型（Small Language Model）响应质量下降及幻觉现象增加之间的权衡问题。论文的关键解决方案是提出了一种新颖的合作解码推理系统，通过让小型模型在本地进行推理的同时，仅针对关键标记（critical tokens）选择性地咨询云端的大型模型，从而结合两者的优势：既保持小型模型的快速解码和低资源消耗特性，又利用大型模型提升推理质量。这种策略显著提升了推理性能，在CommonsenseQA任务上，使用仅0.5B参数的模型便实现了60%的性能提升，且仅有不到7%的标记生成需要上传至云端。

链接: https://arxiv.org/abs/2504.07878
作者: Jianshu She,Wenhao Zheng,Zhengzhong Liu,Hongyi Wang,Eric Xing,Huaxiu Yao,Qirong Ho
机构: Mohamed bin Zayed University of Artificial Intelligence (MBZUAI); University of North Carolina at Chapel Hill; Computer Science Department at Rutgers University
类目: Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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Abstract:The computational complexity of large language model (LLM) inference significantly constrains their deployment efficiency on edge devices. In contrast, small language models offer faster decoding and lower resource consumption but often suffer from degraded response quality and heightened susceptibility to hallucinations. To address this trade-off, collaborative decoding, in which a large model assists in generating critical tokens, has emerged as a promising solution. This paradigm leverages the strengths of both model types by enabling high-quality inference through selective intervention of the large model, while maintaining the speed and efficiency of the smaller model. In this work, we present a novel collaborative decoding inference system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation. Remarkably, the system achieves a 60% performance gain on CommonsenseQA using only a 0.5B model on an M1 MacBook, with under 7% of tokens generation uploaded to the large model in the cloud.
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[NLP-8] Dual Engines of Thoughts: A Depth-Breadth Integration Framework for Open-Ended Analysis

【速读】：该论文试图解决传统推理框架在处理开放性问题时的局限性，这些框架通常专注于寻找单一正确答案，而难以应对需要广泛探索和深入分析的复杂开放性问题。为了解决这一问题，论文提出了一种名为Dual Engines of Thoughts (DEoT) 的分析框架。DEoT 的关键在于其集成设计的三大核心组件：Base Prompter（用于优化用户查询）、Solver Agent（负责任务分解、执行与验证），以及由 Breadth Engine（用于探索多样影响因素）和 Depth Engine（用于深度调查）组成的 Dual-Engine System。这种设计使 DEoT 能够在广度覆盖与深度分析之间实现平衡，并可根据具体需求灵活调整参数与工具配置，从而显著提升对复杂多面问题的处理能力。实验结果表明，DEoT 在解决此类问题时总胜率为 77%-86%，优于现有推理模型，展示了其在实际应用中的有效性。

链接: https://arxiv.org/abs/2504.07872
作者: Fei-Hsuan Yu,Yun-Cheng Chou,Teng-Ruei Chen
机构: NeuroWatt(Intelligent Technology Research and Development)
类目: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
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Abstract:We propose the Dual Engines of Thoughts (DEoT), an analytical framework for comprehensive open-ended reasoning. While traditional reasoning frameworks primarily focus on finding “the best answer” or “the correct answer” for single-answer problems, DEoT is specifically designed for “open-ended questions,” enabling both broader and deeper analytical exploration. The framework centers on three key components: a Base Prompter for refining user queries, a Solver Agent that orchestrates task decomposition, execution, and validation, and a Dual-Engine System consisting of a Breadth Engine (to explore diverse impact factors) and a Depth Engine (to perform deep investigations). This integrated design allows DEoT to balance wide-ranging coverage with in-depth analysis, and it is highly customizable, enabling users to adjust analytical parameters and tool configurations based on specific requirements. Experimental results show that DEoT excels in addressing complex, multi-faceted questions, achieving a total win rate of 77-86% compared to existing reasoning models, thus highlighting its effectiveness in real-world applications.
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[NLP-9] Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs

【速读】：该论文试图解决在大规模参数（超过1000亿）条件下训练稠密大语言模型（Dense LLM）时面临的显著优化与系统挑战。解决方案的关键在于提出了一种深度缩放的三明治归一化（depth-scaled sandwich normalization）方法，该方法有效消除了深层模型训练过程中损失函数的尖峰现象，从而稳定了训练过程。此外，通过在Ascend NPU集群上进行系统级优化，并利用8,192颗Ascend NPUs高效执行大规模训练，进一步提升了训练效率与效果。

链接: https://arxiv.org/abs/2504.07866
作者: Yichun Yin,Wenyong Huang,Kaikai Song,Yehui Tang,Xueyu Wu,Wei Guo,Peng Guo,Yaoyuan Wang,Xiaojun Meng,Yasheng Wang,Dong Li,Can Chen,Dandan Tu,Yin Li,Fisher Yu,Ruiming Tang,Yunhe Wang,Baojun Wang,Bin Wang,Bo Wang,Boxiao Liu,Changzheng Zhang,Duyu Tang,Fei Mi,Hui Jin,Jiansheng Wei,Jiarui Qin,Jinpeng Li,Jun Zhao,Liqun Deng,Lin Li,Minghui Xu,Naifu Zhang,Nianzu Zheng,Qiang Li,Rongju Ruan,Shengjun Cheng,Tianyu Guo,Wei He,Wei Li,Weiwen Liu,Wulong Liu,Xinyi Dai,Yonghan Dong,Yu Pan,Yue Li,Yufei Wang,Yujun Li,Yunsheng Ni,Zhe Liu,Zhenhe Zhang,Zhicheng Liu
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:We present Pangu Ultra, a Large Language Model (LLM) with 135 billion parameters and dense Transformer modules trained on Ascend Neural Processing Units (NPUs). Although the field of LLM has been witnessing unprecedented advances in pushing the scale and capability of LLM in recent years, training such a large-scale model still involves significant optimization and system challenges. To stabilize the training process, we propose depth-scaled sandwich normalization, which effectively eliminates loss spikes during the training process of deep models. We pre-train our model on 13.2 trillion diverse and high-quality tokens and further enhance its reasoning capabilities during post-training. To perform such large-scale training efficiently, we utilize 8,192 Ascend NPUs with a series of system optimizations. Evaluations on multiple diverse benchmarks indicate that Pangu Ultra significantly advances the state-of-the-art capabilities of dense LLMs such as Llama 405B and Mistral Large 2, and even achieves competitive results with DeepSeek-R1, whose sparse model structure contains much more parameters. Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters. Our model and system will be available for our commercial customers.
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[NLP-10] he KL3M Data Project: Copyright-Clean Training Resources for Large Language Models

【速读】：该论文试图解决大型语言模型在预训练数据方面面临的版权侵权和合同违约相关的全球性不确定性问题，这为用户和开发者带来了潜在的法律风险。论文的关键解决方案是通过引入KL3M Data Project，构建了一个最大的综合训练数据管道，以最小化与版权或合同违约相关的风险。该项目的基础是一个包含超过1.32亿份文档和数万亿令牌的语料库，这些文档来自16个不同的来源，并经过验证符合文中详细描述的严格版权和许可协议。此外，论文还公开了整个管道的所有资源，包括获取和处理文档的源代码、原始文档格式及其出处和元数据、标准化格式的提取内容、文档的预分词表示，以及各种中间和后期训练资源（如问答、摘要、转换、起草、分类、预测和对话数据），所有资源均以CC-BY条款免费提供给公众。

链接: https://arxiv.org/abs/2504.07854
作者: Michael J Bommarito II,Jillian Bommarito,Daniel Martin Katz
机构: Institute for the Advancement of Legal and Ethical AI (ALEA Institute)(法律与伦理人工智能促进研究所); CodeX—The Stanford Center for Legal Informatics(斯坦福法律信息学中心); The Law Lab, Illinois Tech—Chicago Kent College of Law(伊利诺伊理工学院芝加哥肯特法学院法律实验室); Center for Legal Technology & Data Science, Bucerius Law School(布塞留斯法律科学学院法律技术与数据科学中心)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
备注: 27 pages, 7 figures, 9 table

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Abstract:Practically all large language models have been pre-trained on data that is subject to global uncertainty related to copyright infringement and breach of contract. This creates potential risk for users and developers due to this uncertain legal status. The KL3M Data Project directly confronts this critical issue by introducing the largest comprehensive training data pipeline that minimizes risks related to copyright or breach of contract. The foundation of this project is a corpus of over 132 million documents and trillions of tokens spanning 16 different sources that have been verified to meet the strict copyright and licensing protocol detailed herein. We are releasing the entire pipeline, including 1) the source code to acquire and process these documents, 2) the original document formats with associated provenance and metadata, 3) extracted content in a standardized format, 4) pre-tokenized representations of the documents, and 5) various mid- and post-train resources such as question-answer, summarization, conversion, drafting, classification, prediction, and conversational data. All of these resources are freely available to the public on S3, Hugging Face, and GitHub under CC-BY terms. We are committed to continuing this project in furtherance of a more ethical, legal, and sustainable approach to the development and use of AI models.
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[NLP-11] Understanding Learner-LLM Chatbot Interactions and the Impact of Prompting Guidelines

【速读】：该论文试图解决的问题是用户在使用大型语言模型（Large Language Models, LLMs）时普遍存在的有效提示（prompting）难题，即尽管LLMs具备直观性和易用性，但用户常因提示不清晰或结构不佳而导致交互效率低下。此外，研究还关注如何通过系统化的指导改善用户与AI之间的互动质量。

解决方案的关键在于引入并对比三种不同类型的提示指南：一种是通过结构化方法开发的任务特定框架，以及两种基准方法。通过分析来自107名用户的642次交互数据，并利用Von NeuMidas（一种扩展的实用主义标注方案），研究者们分类了常见的提示错误并识别出用户的典型行为模式。最终，通过评估这些指导方针对用户行为、提示策略遵循程度以及AI生成响应质量的影响，论文揭示了结构化提示指导在提升AI辅助沟通中的作用，从而为提高用户在AI交互中的能力提供了有价值的见解。

链接: https://arxiv.org/abs/2504.07840
作者: Cansu Koyuturk,Emily Theophilou,Sabrina Patania,Gregor Donabauer,Andrea Martinenghi,Chiara Antico,Alessia Telari,Alessia Testa,Sathya Bursic,Franca Garzotto,Davinia Hernandez-Leo,Udo Kruschwitz,Davide Taibi,Simona Amenta,Martin Ruskov,Dimitri Ognibene
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
备注: Accepted for AIED 2025, the 26th International Conference on Artificial Intelligence in Education, July 22 - 26, 2025, Palermo, Italy

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Abstract:Large Language Models (LLMs) have transformed human-computer interaction by enabling natural language-based communication with AI-powered chatbots. These models are designed to be intuitive and user-friendly, allowing users to articulate requests with minimal effort. However, despite their accessibility, studies reveal that users often struggle with effective prompting, resulting in inefficient responses. Existing research has highlighted both the limitations of LLMs in interpreting vague or poorly structured prompts and the difficulties users face in crafting precise queries. This study investigates learner-AI interactions through an educational experiment in which participants receive structured guidance on effective prompting. We introduce and compare three types of prompting guidelines: a task-specific framework developed through a structured methodology and two baseline approaches. To assess user behavior and prompting efficacy, we analyze a dataset of 642 interactions from 107 users. Using Von NeuMidas, an extended pragmatic annotation schema for LLM interaction analysis, we categorize common prompting errors and identify recurring behavioral patterns. We then evaluate the impact of different guidelines by examining changes in user behavior, adherence to prompting strategies, and the overall quality of AI-generated responses. Our findings provide a deeper understanding of how users engage with LLMs and the role of structured prompting guidance in enhancing AI-assisted communication. By comparing different instructional frameworks, we offer insights into more effective approaches for improving user competency in AI interactions, with implications for AI literacy, chatbot usability, and the design of more responsive AI systems.
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[NLP-12] Deceptive Automated Interpretability: Language Models Coordinating to Fool Oversight Systems

【速读】：该论文试图解决人工智能代理通过自动化神经网络解释机制欺骗监管系统的问题。解决方案的关键在于利用稀疏自编码器（Sparse Autoencoders, SAEs）作为实验框架，证明大型语言模型（如Llama、DeepSeek R1和Claude 3.7 Sonnet）能够生成规避检测的欺骗性解释，并通过隐写术方法将有害信息隐藏于看似无害的解释中，同时保持与参考标签相当的解释质量。研究进一步揭示，当模型认为有害特征可能带来负面后果时，它们会主动制定欺骗策略。论文最终提出缓解措施，强调了对抗欺骗行为的深入理解和有效防御的重要性。

链接: https://arxiv.org/abs/2504.07831
作者: Simon Lermen,Mateusz Dziemian,Natalia Pérez-Campanero Antolín
机构: Apart Research
类目: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:We demonstrate how AI agents can coordinate to deceive oversight systems using automated interpretability of neural networks. Using sparse autoencoders (SAEs) as our experimental framework, we show that language models (Llama, DeepSeek R1, and Claude 3.7 Sonnet) can generate deceptive explanations that evade detection. Our agents employ steganographic methods to hide information in seemingly innocent explanations, successfully fooling oversight models while achieving explanation quality comparable to reference labels. We further find that models can scheme to develop deceptive strategies when they believe the detection of harmful features might lead to negative consequences for themselves. All tested LLM agents were capable of deceiving the overseer while achieving high interpretability scores comparable to those of reference labels. We conclude by proposing mitigation strategies, emphasizing the critical need for robust understanding and defenses against deception.
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[NLP-13] MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations

【速读】：本文旨在解决在线社交网络中虚假信息传播及其验证机制的问题。为应对这一挑战，论文提出了一种名为MOSAIC的新颖开源社会网络模拟框架，其关键是将生成式语言模型（LLMs）与有向社交图结合，通过构建多样化的精细人物画像来表示用户，并模拟大规模的内容传播与互动动态。该框架不仅评估了三种不同的内容审核策略在抑制非事实信息传播方面的有效性，还分析了这些策略对用户参与度的影响，发现它们能够在减少虚假信息传播的同时提升用户互动水平。此外，研究进一步探讨了模拟代理对其社交互动的理由阐述与其集体参与模式之间的关系。通过开源此模拟软件，作者鼓励AI领域和社会科学领域的更多研究探索。

链接: https://arxiv.org/abs/2504.07830
作者: Genglin Liu,Salman Rahman,Elisa Kreiss,Marzyeh Ghassemi,Saadia Gabriel
机构: University of California, Los Angeles (加州大学洛杉矶分校); MIT CSAIL (麻省理工学院计算机科学与人工智能实验室)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
备注: Work in progress. 22 pages

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Abstract:We present a novel, open-source social network simulation framework, MOSAIC, where generative language agents predict user behaviors such as liking, sharing, and flagging content. This simulation combines LLM agents with a directed social graph to analyze emergent deception behaviors and gain a better understanding of how users determine the veracity of online social content. By constructing user representations from diverse fine-grained personas, our system enables multi-agent simulations that model content dissemination and engagement dynamics at scale. Within this framework, we evaluate three different content moderation strategies with simulated misinformation dissemination, and we find that they not only mitigate the spread of non-factual content but also increase user engagement. In addition, we analyze the trajectories of popular content in our simulations, and explore whether simulation agents’ articulated reasoning for their social interactions truly aligns with their collective engagement patterns. We open-source our simulation software to encourage further research within AI and social sciences.
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[NLP-14] MuSaRoNews: A Multidomain Multimodal Satire Dataset from Romanian News Articles

【速读】：该论文旨在解决用单一模态（如纯文本）难以有效检测罗马尼亚新闻文章中讽刺与虚假信息之间不一致的问题。解决方案的关键在于引入了一个多模态数据集 MuSaRoNews，包含来自真实和讽刺新闻来源的 117,834 篇公开新闻文章，并结合文本和视觉等多种模态的信息以提升讽刺检测的性能。实验结果表明，多模态方法相较于单一模态显著提高了检测效果。

链接: https://arxiv.org/abs/2504.07826
作者: Răzvan-Alexandru Smădu,Andreea Iuga,Dumitru-Clementin Cercel
机构: National University of Science and Technology POLITEHNICA Bucharest (布加勒斯特理工大学)
类目: Computation and Language (cs.CL)
备注: 10 pages, 9 figures

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Abstract:Satire and fake news can both contribute to the spread of false information, even though both have different purposes (one if for amusement, the other is to misinform). However, it is not enough to rely purely on text to detect the incongruity between the surface meaning and the actual meaning of the news articles, and, often, other sources of information (e.g., visual) provide an important clue for satire detection. This work introduces a multimodal corpus for satire detection in Romanian news articles named MuSaRoNews. Specifically, we gathered 117,834 public news articles from real and satirical news sources, composing the first multimodal corpus for satire detection in the Romanian language. We conducted experiments and showed that the use of both modalities improves performance.
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[NLP-15] What the HellaSwag? On the Validity of Common-Sense Reasoning Benchmarks

【速读】：该论文试图解决现有常识推理基准测试中存在的严重构念效度问题。具体而言，HellaSwag 存在从基本语法错误到误导性提示或等价选项等多种问题，导致其无法准确衡量语言模型的常识推理能力。论文的关键解决方案是通过全面评估揭示这些问题，并提出改进基准测试的标准要求，同时发布了一个修正版数据集 GoldenSwag，作为更可靠的常识推理评估工具。这一修正版数据集旨在克服原数据集中存在的缺陷，从而提供更有效的评价手段。

链接: https://arxiv.org/abs/2504.07825
作者: Pavel Chizhov,Mattia Nee,Pierre-Carl Langlais,Ivan P. Yamshchikov
机构: CAIRO, Technical University of Applied Sciences Würzburg-Schweinfurt (卡尔斯鲁厄应用技术大学); PleIAs, Paris, France (巴黎PleIAs,法国)
类目: Computation and Language (cs.CL)
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Abstract:Common-sense reasoning is a key language model capability because it encapsulates not just specific factual knowledge but rather general language and world understanding. Measuring common-sense reasoning, therefore, is crucial for language models of different sizes and applications. One of the most widely used benchmarks for evaluating such capabilities is HellaSwag; however, in this paper, we show that it has severe construct validity issues. These issues range from basic ungrammaticality and numerous typos to misleading prompts or equally correct options. Furthermore, we show that if models are evaluated only on answer texts, or with “Lorem ipsum dolor…” instead of the question, more than 65% of model predictions remain the same, and this cannot be attributed merely to contamination. Since benchmark scores are an essential part of model selection in both research and commercial applications, these validity issues can have severe consequences. In particular, knowing that taking benchmark scores at face value is ubiquitous, inadequate evaluation leads to ill-informed decisions about models. In this paper, we thoroughly investigate critical validity issues posed by HellaSwag and illustrate them with various evaluations using generative language models of different sizes. We argue that this benchmark does not accurately measure common-sense reasoning and, therefore, should not be used for evaluation in its current state. Based on the results of our study, we propose requirements that should be met by future common-sense reasoning benchmarks. In addition, we release GoldenSwag, a corrected subset of HellaSwag, which, to our belief, facilitates acceptable common-sense reasoning evaluation.
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[NLP-16] Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models

【速读】：该论文旨在解决大规模混合专家（Mixture-of-Experts, MoE）模型在实际部署中因巨大参数量导致的计算资源挑战，特别是针对其内在的两方面特性：同一MoE层内专家的功能冗余（intra-layer expert homogeneity）以及跨层专家的相似性模式（inter-layer similarity patterns）。论文的关键解决方案是提出了一种名为Cluster-driven Expert Pruning (C-Prune) 的两阶段框架。C-Prune 首先通过逐层专家聚类识别功能相似的专家组，然后利用全局聚类剪枝机制，基于跨层一致性的重要度评分统一消除冗余的专家集群，从而实现针对特定任务的自适应压缩。实验结果验证了C-Prune在有效减小模型规模的同时，优于现有MoE剪枝方法。

链接: https://arxiv.org/abs/2504.07807
作者: Hongcheng Guo,Juntao Yao,Boyang Wang,Junjia Du,Shaosheng Cao,Donglin Di,Shun Zhang,Zhoujun Li
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Mixture-of-Experts (MoE) architectures have emerged as a promising paradigm for scaling large language models (LLMs) with sparse activation of task-specific experts. Despite their computational efficiency during inference, the massive overall parameter footprint of MoE models (e.g., GPT-4) introduces critical challenges for practical deployment. Current pruning approaches often fail to address two inherent characteristics of MoE systems: 1).intra-layer expert homogeneity where experts within the same MoE layer exhibit functional redundancy, and 2). inter-layer similarity patterns where deeper layers tend to contain progressively more homogeneous experts. To tackle these issues, we propose Cluster-driven Expert Pruning (C-Prune), a novel two-stage framework for adaptive task-specific compression of MoE LLMs. C-Prune operates through layer-wise expert clustering, which groups functionally similar experts within each MoE layer using parameter similarity metrics, followed by global cluster pruning, which eliminates redundant clusters across all layers through a unified importance scoring mechanism that accounts for cross-layer homogeneity. We validate C-Prune through extensive experiments on multiple MoE models and benchmarks. The results demonstrate that C-Prune effectively reduces model size while outperforming existing MoE pruning methods.
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[NLP-17] A System for Comprehensive Assessment of RAG Frameworks

【速读】：该论文旨在解决现有评估框架无法提供全面的黑盒方法来评估部署环境中的检索增强生成（Retrieval Augmented Generation, RAG）系统的问题。论文的关键解决方案是提出SCARF（System for Comprehensive Assessment of RAG Frameworks），这是一个模块化且灵活的评估框架，用于系统性地基准测试部署的RAG应用。SCARF通过端到端的黑盒评估方法，支持多种部署配置，并结合自动化测试功能，涵盖向量数据库和大型语言模型（Large Language Models, LLM）服务策略，从而生成详细的性能报告。此外，SCARF还整合了响应连贯性等实际考量因素，为研究人员和行业专业人士提供了可扩展且适应性强的评估工具。通过REST API接口，SCARF展示了其在真实场景中评估不同RAG框架及其配置的灵活性。

链接: https://arxiv.org/abs/2504.07803
作者: Mattia Rengo,Senad Beadini,Domenico Alfano,Roberto Abbruzzese
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Technical Report, 7 pages, 2 figures, 1 table

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Abstract:Retrieval Augmented Generation (RAG) has emerged as a standard paradigm for enhancing the factual accuracy and contextual relevance of Large Language Models (LLMs) by integrating retrieval mechanisms. However, existing evaluation frameworks fail to provide a holistic black-box approach to assessing RAG systems, especially in real-world deployment scenarios. To address this gap, we introduce SCARF (System for Comprehensive Assessment of RAG Frameworks), a modular and flexible evaluation framework designed to benchmark deployed RAG applications systematically. SCARF provides an end-to-end, black-box evaluation methodology, enabling a limited-effort comparison across diverse RAG frameworks. Our framework supports multiple deployment configurations and facilitates automated testing across vector databases and LLM serving strategies, producing a detailed performance report. Moreover, SCARF integrates practical considerations such as response coherence, providing a scalable and adaptable solution for researchers and industry professionals evaluating RAG applications. Using the REST APIs interface, we demonstrate how SCARF can be applied to real-world scenarios, showcasing its flexibility in assessing different RAG frameworks and configurations. SCARF is available at GitHub repository.
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[NLP-18] Plan-and-Refine: Diverse and Comprehensive Retrieval-Augmented Generation

【速读】：本文研究了（检索增强）大型语言模型（Large Language Models, LLMs）在生成多样化和全面响应方面的局限性，并提出了一种基于两阶段系统设计的Plan-and-Refine (PR)框架。论文的核心问题是提升生成式AI (Generative AI) 模型在答案事实性和全面性上的表现。解决方案的关键在于引入了全局探索（global exploration）与局部利用（local exploitation）的两阶段机制：第一阶段通过生成包含多样化查询方面的计划列表来实现全局探索；第二阶段则基于每个计划生成并迭代优化响应提案以提高提案质量。最终采用奖励模型选择最具事实性和覆盖性的提案。实验结果表明，PR框架在ICAT评估方法下显著优于基线模型，在ANTIQUE数据集上提升了13.1%，在TREC数据集上提升了15.41%。

链接: https://arxiv.org/abs/2504.07794
作者: Alireza Salemi,Chris Samarinas,Hamed Zamani
机构: University of Massachusetts Amherst (马萨诸塞大学阿默斯特分校)
类目: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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Abstract:This paper studies the limitations of (retrieval-augmented) large language models (LLMs) in generating diverse and comprehensive responses, and introduces the Plan-and-Refine (PR) framework based on a two phase system design. In the global exploration phase, PR generates a diverse set of plans for the given input, where each plan consists of a list of diverse query aspects with corresponding additional descriptions. This phase is followed by a local exploitation phase that generates a response proposal for the input query conditioned on each plan and iteratively refines the proposal for improving the proposal quality. Finally, a reward model is employed to select the proposal with the highest factuality and coverage. We conduct our experiments based on the ICAT evaluation methodology–a recent approach for answer factuality and comprehensiveness evaluation. Experiments on the two diverse information seeking benchmarks adopted from non-factoid question answering and TREC search result diversification tasks demonstrate that PR significantly outperforms baselines, achieving up to a 13.1% improvement on the ANTIQUE dataset and a 15.41% improvement on the TREC dataset. Furthermore, a smaller scale user study confirms the substantial efficacy of the PR framework.
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[NLP-19] Efficient Tuning of Large Language Models for Knowledge-Grounded Dialogue Generation ACL

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在缺乏未包含于其训练数据中的最新或领域特定知识时，难以生成上下文相关且信息丰富的对话响应的问题。论文提出的关键解决方案是KEDiT方法，其核心在于通过两阶段操作实现高效的LLM微调：首先，利用信息瓶颈（information bottleneck）将检索到的知识压缩为可学习的参数，保留关键信息的同时降低计算开销；其次，在微调过程中通过轻量级的知识感知适配器（knowledge-aware adapter）将这些压缩后的知识向量融入LLM，仅更新模型不到2%的参数。这种设计既发挥了预训练LLM的强大能力，又增强了其适应动态知识的能力，为医学等领域的应用提供了可扩展的解决方案。

链接: https://arxiv.org/abs/2504.07754
作者: Bo Zhang,Hui Ma,Dailin Li,Jian Ding,Jian Wang,Bo Xu,HongFei Lin
机构: Dalian University of Technology (大连理工大学); Hefei University of Technology (合肥工业大学)
类目: Computation and Language (cs.CL)
备注: Accepted at TACL; pre-MIT Press publication version. Code and data are available at this https URL

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Abstract:Large language models (LLMs) demonstrate remarkable text comprehension and generation capabilities but often lack the ability to utilize up-to-date or domain-specific knowledge not included in their training data. To address this gap, we introduce KEDiT, an efficient method for fine-tuning LLMs for knowledge-grounded dialogue generation. KEDiT operates in two main phases: first, it employs an information bottleneck to compress retrieved knowledge into learnable parameters, retaining essential information while minimizing computational overhead. Second, a lightweight knowledge-aware adapter integrates these compressed knowledge vectors into the LLM during fine-tuning, updating less than 2% of the model parameters. The experimental results on the Wizard of Wikipedia and a newly constructed PubMed-Dialog dataset demonstrate that KEDiT excels in generating contextually relevant and informative responses, outperforming competitive baselines in automatic, LLM-based, and human evaluations. This approach effectively combines the strengths of pretrained LLMs with the adaptability needed for incorporating dynamic knowledge, presenting a scalable solution for fields such as medicine.
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[NLP-20] NorEval: A Norwegian Language Understanding and Generation Evaluation Benchmark

【速读】：该论文试图解决挪威语生成式语言模型（Norwegian generative language models, LMs）在大规模标准化基准测试中的评估难题。现有挪威语基准测试存在任务类别覆盖不足及未同时支持两种官方书面标准（Bokmål 和 Nynorsk）的问题。为应对这些挑战，论文提出 NorEval，这是一个包含 24 个高质量人工创建数据集的新综合评估套件，其中五个数据集为全新创建。NorEval 的关键创新在于其广泛的任务类别覆盖、对两种挪威语书面标准的支持以及建立的人类基准，同时通过集成到 LM Evaluation Harness 中实现评估的灵活性与可重复性。这一方案的核心在于构建全面且平衡的评估框架以有效衡量挪威语生成式语言模型的能力。

链接: https://arxiv.org/abs/2504.07749
作者: Vladislav Mikhailov,Tita Enstad,David Samuel,Hans Christian Farsethås,Andrey Kutuzov,Erik Velldal,Lilja Øvrelid
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:This paper introduces NorEval, a new and comprehensive evaluation suite for large-scale standardized benchmarking of Norwegian generative language models (LMs). NorEval consists of 24 high-quality human-created datasets – of which five are created from scratch. In contrast to existing benchmarks for Norwegian, NorEval covers a broad spectrum of task categories targeting Norwegian language understanding and generation, establishes human baselines, and focuses on both of the official written standards of the Norwegian language: Bokmål and Nynorsk. All our datasets and a collection of over 100 human-written prompts are integrated into LM Evaluation Harness, ensuring flexible and reproducible evaluation. We describe the NorEval design and present the results of benchmarking 19 open-source pre-trained and instruction-tuned LMs for Norwegian in various scenarios. Our benchmark, evaluation framework, and annotation materials are publicly available.
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[NLP-21] Zero-Shot Cross-Domain Code Search without Fine-Tuning

【速读】：该论文旨在解决跨域代码搜索中的零样本（zero-shot）问题，即在无需针对每个领域进行昂贵的微调（fine-tuning-free）的情况下，实现自然语言查询与目标领域代码片段之间的语义匹配。当前最先进的基于预训练语言模型（Pre-trained Language Models, PLMs）的方法在跨域场景下表现不佳，而现有的有效零样本方法RAPID虽然有效，但需要大量的计算资源且需为每个领域维护专门的模型，这凸显了开发一种无需微调的通用跨域代码搜索方法的需求。

解决方案的关键在于通过分解代码搜索的查询-代码匹配过程，将其转化为两个更简单的任务：查询-注释匹配（query-comment matching）和代码-代码匹配（code-code matching）。研究发现，在零样本跨域设置下，查询-代码、查询-注释和代码-代码三种匹配模式之间具有较强的互补性。基于此，论文提出了一种名为CodeBridge的新方法。CodeBridge利用大规模语言模型（Large Language Models, LLMs）生成注释和伪代码，并结合基于PLM的相似度评分和基于采样的融合策略，将上述三种匹配模式整合起来。实验结果表明，CodeBridge在三个数据集上的MRR指标平均优于现有基于PLM的代码搜索方法CoCoSoDa和UniXcoder约21.4%和24.9%，同时其性能与需要昂贵微调的RAPID方法相当或更好。

链接: https://arxiv.org/abs/2504.07740
作者: Keyu Liang,Zhongxin Liu,Chao Liu,Zhiyuan Wan,David Lo,Xiaohu Yang
机构: The State Key Laboratory of Blockchain and Data Security(Zhejiang University)(浙江大学); Zhejiang University(浙江大学); School of Big Data and Software Engineering(重庆大学); Chongqing University(重庆大学); Singapore Management University(SMU)(新加坡管理大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL)
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Abstract:Code search aims to retrieve semantically relevant code snippets for natural language queries. While pre-trained language models (PLMs) have shown remarkable performance in this task, they struggle in cross-domain scenarios, often requiring costly fine-tuning or facing performance drops in zero-shot settings. RAPID, which generates synthetic data for model fine-tuning, is currently the only effective method for zero-shot cross-domain code search. Despite its effectiveness, RAPID demands substantial computational resources for fine-tuning and needs to maintain specialized models for each domain, underscoring the need for a zero-shot, fine-tuning-free approach for cross-domain code search. The key to tackling zero-shot cross-domain code search lies in bridging the gaps among domains. In this work, we propose to break the query-code matching process of code search into two simpler tasks: query-comment matching and code-code matching. Our empirical study reveals the strong complementarity among the three matching schemas in zero-shot cross-domain settings, i.e., query-code, query-comment, and code-code matching. Based on the findings, we propose CodeBridge, a zero-shot, fine-tuning-free approach for cross-domain code search. Specifically, CodeBridge uses Large Language Models (LLMs) to generate comments and pseudo-code, then combines query-code, query-comment, and code-code matching via PLM-based similarity scoring and sampling-based fusion. Experimental results show that our approach outperforms the state-of-the-art PLM-based code search approaches, i.e., CoCoSoDa and UniXcoder, by an average of 21.4% and 24.9% in MRR, respectively, across three datasets. Our approach also yields results that are better than or comparable to those of the zero-shot cross-domain code search approach RAPID, which requires costly fine-tuning. Subjects: Software Engineering (cs.SE); Computation and Language (cs.CL) Cite as: arXiv:2504.07740 [cs.SE] (or arXiv:2504.07740v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2504.07740 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Related DOI: https://doi.org/10.1145/3729357 Focus to learn more DOI(s) linking to related resources
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[NLP-22] Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information

【速读】：该论文旨在解决从大规模文档语料库中自动化构建领域特定知识图谱（Knowledge Graph）的问题，尤其针对核聚变能源这一高度专业化且范围广、异构性强的领域。论文的关键解决方案在于提出了一种多步骤方法，其核心包括自动命名实体识别（Automatic Named Entity Recognition）与实体解析（Entity Resolution）。研究展示了如何利用预训练的大语言模型（Large Language Models, LLMs）应对这些挑战，并通过与Zipf定律的对比评估其性能。此外，论文还开发了一种结合大语言模型与多提示方法的知识图谱检索增强生成系统（Retrieval-Augmented Generation System），以提供上下文相关的自然语言查询答案，特别是需要跨连接实体推理的复杂多跳问题。

链接: https://arxiv.org/abs/2504.07738
作者: A. Loreti,K. Chen,R. George,R. Firth,A. Agnello,S. Tanaka
机构: UK Atomic Energy Authority (英国原子能管理局), STFC Hartree Centre (科学与技术设施委员会哈特里中心), IBM Research (IBM研究), Fusion Computing Lab (融合计算实验室) (ukaea-stfc 融合计算实验室合作)
类目: Computation and Language (cs.CL)
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Abstract:In this document, we discuss a multi-step approach to automated construction of a knowledge graph, for structuring and representing domain-specific knowledge from large document corpora. We apply our method to build the first knowledge graph of nuclear fusion energy, a highly specialized field characterized by vast scope and heterogeneity. This is an ideal benchmark to test the key features of our pipeline, including automatic named entity recognition and entity resolution. We show how pre-trained large language models can be used to address these challenges and we evaluate their performance against Zipf’s law, which characterizes human-generated natural language. Additionally, we develop a knowledge-graph retrieval-augmented generation system that combines large language models with a multi-prompt approach. This system provides contextually relevant answers to natural-language queries, including complex multi-hop questions that require reasoning across interconnected entities.
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[NLP-23] DeepGreen: Effective LLM -Driven Green-washing Monitoring System Designed for Empirical Testing – Evidence from China

【速读】：该论文试图解决企业绿色清洗（Green-washing）行为检测的问题。解决方案的关键在于提出了一种基于大型语言模型驱动（Large Language Model Driven, LLM-Driven）的系统DeepGreen，它通过双层LLM分析实现对企业财务报表中潜在绿色关键词的初步识别，并利用迭代语义分析评估其实施程度。最终从两层输出比值中衍生出核心变量GreenImplement，用于量化绿色实施度。这一方法结合了小提琴图和K-means聚类分析，验证了变量的有效性并与华证ESG评级进行了对比，为监管机构和投资者提供了新的监测视角。实验结果表明绿色实施可显著提升公司资产回报率，但存在规模上的异质性，中小型企业贡献有限，因此存在更强的绿色清洗动机。

链接: https://arxiv.org/abs/2504.07733
作者: Congluo Xu,Yu Miao,Yiling Xiao,Chengmengjia Lin
机构: Sichuan University (四川大学)
类目: Computation and Language (cs.CL); General Economics (econ.GN)
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Abstract:This paper proposes DeepGreen, an Large Language Model Driven (LLM-Driven) system for detecting corporate green-washing behaviour. Utilizing dual-layer LLM analysis, DeepGreen preliminarily identifies potential green keywords in financial statements and then assesses their implementation degree via iterative semantic analysis of LLM. A core variable GreenImplement is derived from the ratio from the two layers’ output. We extract 204 financial statements of 68 companies from A-share market over three years, comprising 89,893 words, and analyse them through DeepGreen. Our analysis, supported by violin plots and K-means clustering, reveals insights and validates the variable against the Huazheng ESG rating. It offers a novel perspective for regulatory agencies and investors, serving as a proactive monitoring tool that complements traditional this http URL tests show that green implementation can significantly boost the asset return rate of companies, but there is heterogeneity in scale. Small and medium-sized companies have limited contribution to asset return via green implementation, so there is a stronger motivation for green-washing.
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[NLP-24] MRD-RAG : Enhancing Medical Diagnosis with Multi-Round Retrieval-Augmented Generation

【速读】：该论文旨在解决现有医疗领域检索增强生成（Retrieval-Augmented Generation, RAG）框架在多轮诊断对话中的不足。具体而言，大多数现有医疗RAG框架仅适用于单轮问答任务，无法满足多轮诊断对话的需求；而现有的多轮RAG框架未能充分考虑潜在疾病之间的关联性，难以像医生一样进行精确的逐步询问。为了解决这些问题，论文提出了一种名为多轮诊断RAG（Multi-Round Diagnostic RAG, MRD-RAG）的新框架，其关键在于模仿医生的诊断过程，通过分析潜在疾病的诊断信息，实现与医生类似的多轮精准诊断能力。实验结果表明，该框架显著提升了大型语言模型（LLMs）在医疗诊断中的性能。

链接: https://arxiv.org/abs/2504.07724
作者: Yixiang Chen,Penglei Sun,Xiang Li,Xiaowen Chu
机构: Hong Kong University of Science and Technology (香港科技大学); Hong Kong University of Science and Technology Guangzhou (香港科技大学广州校区)
类目: Computation and Language (cs.CL)
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Abstract:In recent years, accurately and quickly deploying medical large language models (LLMs) has become a significant trend. Among these, retrieval-augmented generation (RAG) has garnered significant attention due to its features of rapid deployment and privacy protection. However, existing medical RAG frameworks still have shortcomings. Most existing medical RAG frameworks are designed for single-round question answering tasks and are not suitable for multi-round diagnostic dialogue. On the other hand, existing medical multi-round RAG frameworks do not consider the interconnections between potential diseases to inquire precisely like a doctor. To address these issues, we propose a Multi-Round Diagnostic RAG (MRD-RAG) framework that mimics the doctor’s diagnostic process. This RAG framework can analyze diagnosis information of potential diseases and accurately conduct multi-round diagnosis like a doctor. To evaluate the effectiveness of our proposed frameworks, we conduct experiments on two modern medical datasets and two traditional Chinese medicine datasets, with evaluations by GPT and human doctors on different methods. The results indicate that our RAG framework can significantly enhance the diagnostic performance of LLMs, highlighting the potential of our approach in medical diagnosis. The code and data can be found in our project website this https URL.
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[NLP-25] Proactive User Information Acquisition via Chats on User-Favored Topics

【速读】：本文旨在解决聊天导向对话系统中主动获取用户特定信息（Proactive acquisition of specific user Information via chats on user-favored Topics, 简称PIVOT）的技术挑战。尽管生成式AI (Generative AI) 和大型语言模型 (Large Language Models, LLMs) 在许多任务中表现出色，但它们在PIVOT任务中的成功率较低。为解决此问题，论文的关键在于构建了一个适配于PIVOT任务分析的数据集，并通过分析该数据集获得了有价值的洞见，最终基于这些洞见开发出一个简单而有效的系统，显著提升了PIVOT任务的表现。

链接: https://arxiv.org/abs/2504.07698
作者: Shiki Sato,Jun Baba,Asahi Hentona,Shinji Iwata,Akifumi Yoshimoto,Koichiro Yoshino
机构: CyberAgent(赛博代理公司); Institute of Science Tokyo(东京工业大学)
类目: Computation and Language (cs.CL)
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Abstract:Chat-oriented dialogue systems designed to provide tangible benefits, such as sharing the latest news or preventing frailty in senior citizens, often require Proactive acquisition of specific user Information via chats on user-faVOred Topics (PIVOT). This study proposes the PIVOT task, designed to advance the technical foundation for these systems. In this task, a system needs to acquire the answers of a user to predefined questions without making the user feel abrupt while engaging in a chat on a predefined topic. We found that even recent large language models (LLMs) show a low success rate in the PIVOT task. We constructed a dataset suitable for the analysis to develop more effective systems. Finally, we developed a simple but effective system for this task by incorporating insights obtained through the analysis of this dataset.
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[NLP-26] Context-Aware Monolingual Human Evaluation of Machine Translation

【速读】：该论文试图解决在无参考源文本的情况下评估机器翻译（Machine Translation, MT）质量的问题。解决方案的关键在于提出一种基于上下文感知的单语人类评估方法，并通过与传统的双语评估（包含源文本）进行对比，验证其有效性。研究设计了两种场景：单一MT系统的评估以及成对MT系统的对比评估，由专业译员同时完成单语和双语评估任务。结果表明，上下文感知的单语人类评估能够达到与双语评估相当的效果，证明了单语评估作为一种高效评估MT质量方法的可行性和潜力。

链接: https://arxiv.org/abs/2504.07685
作者: Silvio Picinini,Sheila Castilho
机构: 未知
类目: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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Abstract:This paper explores the potential of context-aware monolingual human evaluation for assessing machine translation (MT) when no source is given for reference. To this end, we compare monolingual with bilingual evaluations (with source text), under two scenarios: the evaluation of a single MT system, and the comparative evaluation of pairwise MT systems. Four professional translators performed both monolingual and bilingual evaluations by assigning ratings and annotating errors, and providing feedback on their experience. Our findings suggest that context-aware monolingual human evaluation achieves comparable outcomes to human bilingual evaluations, and suggest the feasibility and potential of monolingual evaluation as an efficient approach to assessing MT.
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[NLP-27] Synthetic Fluency: Hallucinations Confabulations and the Creation of Irish Words in LLM -Generated Translations

【速读】：该论文试图解决大型语言模型（Large Language Model, LLM）在翻译成爱尔兰语（Irish）时产生的幻觉现象（hallucinations），特别是模型生成虚构且不存在的新词的问题。研究的关键在于对这些幻觉现象进行分类，并分析其是否遵循爱尔兰语的形态学规则以及表现出的语言倾向性。通过对比GPT-4.o与GPT-4.o Mini两个版本，发现尽管两者产生相似类型的幻觉，但Mini版本的幻觉频率显著更高。研究并未提供确定性答案，而是引发关于LLM使用及其对爱尔兰语词汇发展和语言演变潜在影响的思考，旨在激发对技术如何随着时间塑造低资源、形态丰富的语言的讨论。

链接: https://arxiv.org/abs/2504.07680
作者: Sheila Castilho,Zoe Fitzsimmons,Claire Holton,Aoife Mc Donagh
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:This study examines hallucinations in Large Language Model (LLM) translations into Irish, specifically focusing on instances where the models generate novel, non-existent words. We classify these hallucinations within verb and noun categories, identifying six distinct patterns among the latter. Additionally, we analyse whether these hallucinations adhere to Irish morphological rules and what linguistic tendencies they exhibit. Our findings show that while both GPT-4.o and GPT-4.o Mini produce similar types of hallucinations, the Mini model generates them at a significantly higher frequency. Beyond classification, the discussion raises speculative questions about the implications of these hallucinations for the Irish language. Rather than seeking definitive answers, we offer food for thought regarding the increasing use of LLMs and their potential role in shaping Irish vocabulary and linguistic evolution. We aim to prompt discussion on how such technologies might influence language over time, particularly in the context of low-resource, morphologically rich languages.
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[NLP-28] Unveiling the Impact of Multimodal Features on Chinese Spelling Correction: From Analysis to Design

【速读】：该论文旨在解决中文拼写纠错（Chinese Spelling Correction, CSC）任务中现有方法存在的问题，特别是大型语言模型（Large Language Models, LLMs）在纠正错误时容易出现的过矫正（over-correction）现象，以及如何有效利用语音信息（phonetic）和字形信息（graphemic）来提升纠错性能。论文的关键在于提出了一种名为Multimodal Analysis for Character Usage (\textbf{MACU}) 的实验方法，用于识别多模态拼写纠错模型的潜在改进方向，并基于此设计了一个新的多模态模型NamBert。NamBert通过更有效地整合语音和字形特征，显著提升了纠错效果，同时在标准数据集上的实验结果证明其优于当前最先进的方法（SOTA）。此外，论文还系统性地对比了NamBert与LLMs在CSC任务中的优缺点。

链接: https://arxiv.org/abs/2504.07661
作者: Xiaowu Zhang,Hongfei Zhao,Jingyi Hou,Zhijie Liu
机构: University of Science and Technology Beijing (北京科技大学); Fudan University, Shanghai 200433, China (复旦大学)
类目: Computation and Language (cs.CL)
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Abstract:The Chinese Spelling Correction (CSC) task focuses on detecting and correcting spelling errors in sentences. Current research primarily explores two approaches: traditional multimodal pre-trained models and large language models (LLMs). However, LLMs face limitations in CSC, particularly over-correction, making them suboptimal for this task. While existing studies have investigated the use of phonetic and graphemic information in multimodal CSC models, effectively leveraging these features to enhance correction performance remains a challenge. To address this, we propose the Multimodal Analysis for Character Usage (\textbfMACU) experiment, identifying potential improvements for multimodal correctison. Based on empirical findings, we introduce \textbfNamBert, a novel multimodal model for Chinese spelling correction. Experiments on benchmark datasets demonstrate NamBert’s superiority over SOTA methods. We also conduct a comprehensive comparison between NamBert and LLMs, systematically evaluating their strengths and limitations in CSC. Our code and model are available at this https URL.
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[NLP-29] On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Data

【速读】：该论文旨在探索大型语言模型（Large Language Models, LLMs）在处理未出现在训练数据中的时间推理任务时的适用性，重点关注结构化和半结构化匿名化数据。论文不仅开发了直接的LLM流水线，还比较了多种方法并进行了深入分析。研究识别并检查了自然语言中的十七个常见时间推理任务，重点在于其算法组件。为了评估LLM性能，创建了“推理与回答时间能力数据集”(RATA)，使用半结构化匿名化数据以确保依赖推理而非先验知识。论文比较了几种方法，涉及最先进的技术如思维树（Tree-of-Thought）、自我反思（self-reflection）和代码执行，并针对此场景进行了调整。研究表明，实现可扩展且可靠的时间推理解决方案不仅需要单个LLM，还需要集成方法，这是解决方案的关键所在。

链接: https://arxiv.org/abs/2504.07646
作者: Alfredo Garrachón Ruiz,Tomás de la Rosa,Daniel Borrajo
机构: AI Research, JP Morgan Chase (JP摩根大通AI研究)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:The applicability of Large Language Models (LLMs) in temporal reasoning tasks over data that is not present during training is still a field that remains to be explored. In this paper we work on this topic, focusing on structured and semi-structured anonymized data. We not only develop a direct LLM pipeline, but also compare various methodologies and conduct an in-depth analysis. We identified and examined seventeen common temporal reasoning tasks in natural language, focusing on their algorithmic components. To assess LLM performance, we created the \textitReasoning and Answering Temporal Ability dataset (RATA), featuring semi-structured anonymized data to ensure reliance on reasoning rather than on prior knowledge. We compared several methodologies, involving SoTA techniques such as Tree-of-Thought, self-reflexion and code execution, tuned specifically for this scenario. Our results suggest that achieving scalable and reliable solutions requires more than just standalone LLMs, highlighting the need for integrated approaches.
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[NLP-30] CollEX – A Multimodal Agent ic RAG System Enabling Interactive Exploration of Scientific Collections

【速读】：本文旨在解决科学收藏品数量庞大且复杂度高导致的传统搜索系统缺乏直观性和互动性的问题，这为学习者、教育者和研究者设置了显著障碍。论文的关键解决方案在于引入CollEx，这是一个基于创新的多模态代理检索增强生成（RAG）系统的平台，通过最先进的大型视觉-语言模型（LVLMs）作为多模态代理，并通过直观的聊天界面访问。CollEx通过配备先进工具的专业代理抽象复杂的交互操作，促进以好奇心驱动的探索，极大地简化了对多样化科学收藏品及其记录的访问。其集成文本和视觉模态的功能，支持教育场景下的独立探索以及激发科学兴趣和好奇心，同时服务于研究社区，发现跨学科联系并补充视觉数据。通过包含来自本地公共大学32个收藏集的超过64,000条独特记录的概念验证应用展示了系统的有效性。

链接: https://arxiv.org/abs/2504.07643
作者: Florian Schneider,Narges Baba Ahmadi,Niloufar Baba Ahmadi,Iris Vogel,Martin Semmann,Chris Biemann
机构: 未知
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In this paper, we introduce CollEx, an innovative multimodal agentic Retrieval-Augmented Generation (RAG) system designed to enhance interactive exploration of extensive scientific collections. Given the overwhelming volume and inherent complexity of scientific collections, conventional search systems often lack necessary intuitiveness and interactivity, presenting substantial barriers for learners, educators, and researchers. CollEx addresses these limitations by employing state-of-the-art Large Vision-Language Models (LVLMs) as multimodal agents accessible through an intuitive chat interface. By abstracting complex interactions via specialized agents equipped with advanced tools, CollEx facilitates curiosity-driven exploration, significantly simplifying access to diverse scientific collections and records therein. Our system integrates textual and visual modalities, supporting educational scenarios that are helpful for teachers, pupils, students, and researchers by fostering independent exploration as well as scientific excitement and curiosity. Furthermore, CollEx serves the research community by discovering interdisciplinary connections and complementing visual data. We illustrate the effectiveness of our system through a proof-of-concept application containing over 64,000 unique records across 32 collections from a local scientific collection from a public university.
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[NLP-31] ConceptFormer: Towards Efficient Use of Knowledge-Graph Embeddings in Large Language Models

【速读】：本文旨在解决现有 Retrieval Augmented Generation (RAG) 方法在将结构化知识（如知识图谱 KGs）融入大语言模型 (LLMs) 时存在的效率低下问题，尤其是由于对预训练语言模型 (PLMs) 内部架构的修改或依赖于将知识图谱文本化导致的低效 token 使用。论文提出了一种名为 ConceptFormer 的新方法，其关键是无需修改 LLM 内部结构或依赖于知识图谱的文本输入，而是直接在 LLM 的嵌入向量空间中创建并注入概念向量 (\emphconcept vectors)，以封装知识图谱节点的信息。通过与冻结的 LLM 结合训练，ConceptFormer 构建了一个全面的查找表，将知识图谱节点映射到相应概念向量，从而以高效且可扩展的方式增强 LLM 的事实回忆能力 (factual recall)。实验表明，向 GPT-2 0.1B 注入概念向量后，在维基百科句子上的 Hit@10 准确率最高提升了 272%，而在合成生成句子上的提升高达 348%，显著优于基于图谱文本化的 RAG 方法，同时减少了 130 倍的输入 token 消耗。

链接: https://arxiv.org/abs/2504.07624
作者: Joel Barmettler,Abraham Bernstein,Luca Rossetto
机构: University of Zurich(Zurich, Switzerland); Dublin City University(Dublin, Ireland)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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Abstract:Retrieval Augmented Generation (RAG) has enjoyed increased attention in the recent past and recent advancements in Large Language Models (LLMs) have highlighted the importance of integrating world knowledge into these systems. Current RAG methodologies often modify the internal architecture of pre-trained language models (PLMs) or rely on textifying knowledge graphs (KGs), which is inefficient in terms of token usage. This paper introduces ConceptFormer, a new approach to augment LLMs with structured knowledge from KGs, such as Wikidata, without altering their internal structure or relying on textual input of KGs. ConceptFormer operates in the LLM embedding vector space, creating and injecting \emphconcept vectors that encapsulate the information of the KG nodes directly. Trained in conjunction with a frozen LLM, ConceptFormer generates a comprehensive lookup table that maps KG nodes to their respective concept vectors. The approach aims to enhance the factual recall capabilities of LLMs by enabling them to process these concept vectors natively, thus enriching them with structured world knowledge in an efficient and scalable manner. Our experiments demonstrate that the addition of concept vectors to GPT-2 0.1B substantially increases its factual recall ability (Hit@10) by up to 272% when tested on sentences from Wikipedia and up to 348% on synthetically generated sentences. Even injecting only a single concept vector into the prompt increases factual recall ability (Hit@10) by up to 213% on Wikipedia sentences, significantly outperforming RAG with graph textification while consuming 130x fewer input tokens.
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[NLP-32] VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

【速读】：本文旨在解决如何通过强化学习（Reinforcement Learning, RL）提升视觉-语言模型（Vision-Language Models, VLMs）的视觉推理能力。论文的关键创新在于提出了一种名为VLM-R1的专用框架，该框架借鉴了DeepSeek R1的核心思想，利用基于规则的奖励机制（rule-based reward formulation），通过具有明确标准答案的任务实现精确且稳定的奖励计算。这一设计充分利用了视觉任务中固有的标注信息，使其与基于规则的奖励机制天然契合。通过这一方法，研究者不仅验证了RL在视觉理解任务中的竞争力，还在泛化能力上超越了监督微调（Supervised Fine-Tuning, SFT）。此外，通过系统的消融实验，论文揭示了奖励操纵现象、检测任务中的“aha时刻”、训练数据质量的重要性以及不同模型规模下RL的扩展行为等重要见解，从而深入探讨了强化学习对提升视觉-语言模型能力的具体作用机制。

链接: https://arxiv.org/abs/2504.07615
作者: Haozhan Shen,Peng Liu,Jingcheng Li,Chunxin Fang,Yibo Ma,Jiajia Liao,Qiaoli Shen,Zilun Zhang,Kangjia Zhao,Qianqian Zhang,Ruochen Xu,Tiancheng Zhao
机构: Zhejiang University (浙江大学); Om AI Research (未知中文名称); Binjiang Institute of Zhejiang University (浙江大学滨江研究院)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
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Abstract:Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective design. The core of R1 lies in its rule-based reward formulation, which leverages tasks with deterministic ground-truth answers to enable precise and stable reward computation. In the visual domain, we similarly observe that a wide range of visual understanding tasks are inherently equipped with well-defined ground-truth annotations. This property makes them naturally compatible with rule-based reward mechanisms. Motivated by this observation, we investigate the extension of R1-style reinforcement learning to Vision-Language Models (VLMs), aiming to enhance their visual reasoning capabilities. To this end, we develop VLM-R1, a dedicated framework designed to harness RL for improving VLMs’ performance on general vision-language tasks. Using this framework, we further explore the feasibility of applying RL to visual domain. Experimental results indicate that the RL-based model not only delivers competitive performance on visual understanding tasks but also surpasses Supervised Fine-Tuning (SFT) in generalization ability. Furthermore, we conduct comprehensive ablation studies that uncover a series of noteworthy insights, including the presence of reward hacking in object detection, the emergence of the “OD aha moment”, the impact of training data quality, and the scaling behavior of RL across different model sizes. Through these analyses, we aim to deepen the understanding of how reinforcement learning enhances the capabilities of vision-language models, and we hope our findings and open-source contributions will support continued progress in the vision-language RL community. Our code and model are available at this https URL
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[NLP-33] SaRoHead: A Dataset for Satire Detection in Romanian Multi-Domain News Headlines

【速读】：该论文试图解决新闻标题中的讽刺检测问题，特别是在罗马尼亚多领域新闻标题这一特定场景下。解决方案的关键在于构建了一个名为SaRoHead的新数据集（corpus），用于训练和评估讽刺检测模型。研究发现，某些非讽刺标题中使用的吸睛手法（clickbait）对模型性能有显著影响。

链接: https://arxiv.org/abs/2504.07612
作者: Mihnea-Alexandru Vîrlan,Răzvan-Alexandru Smădu,Dumitru-Clementin Cercel
机构: National University of Science and Technology POLITEHNICA Bucharest (布加勒斯特理工大学)
类目: Computation and Language (cs.CL)
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Abstract:The headline is an important part of a news article, influenced by expressiveness and connection to the exposed subject. Although most news outlets aim to present reality objectively, some publications prefer a humorous approach in which stylistic elements of satire, irony, and sarcasm blend to cover specific topics. Satire detection can be difficult because a headline aims to expose the main idea behind a news article. In this paper, we propose SaRoHead, the first corpus for satire detection in Romanian multi-domain news headlines. Our findings show that the clickbait used in some non-satirical headlines significantly influences the model.
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[NLP-34] Do LLM s Understand Your Translations? Evaluating Parag raph-level MT with Question Answering

【速读】：该论文试图解决现有自动翻译评估指标在跨句边界有效保留语义方面表现不足的问题，特别是在长且复杂的文本段落翻译评估中的局限性。论文指出，单纯依赖单一的内在质量评分（trained to mimic human judgments）可能不足以全面评价长篇翻译质量，而需要一种更“实用”（pragmatic）的方法，即评估翻译在上下文中准确传递关键信息的能力。

解决方案的关键在于提出TREQA（Translation Evaluation via Question-Answering）框架。该框架通过评估候选翻译对针对源文本或参考文本中关键信息设计的阅读理解问题的回答准确性，实现翻译质量的外延评估。这种方法在文学等需要长程理解的领域中表现出色，能够与甚至在某些情况下优于现有的神经网络和大语言模型（LLM）驱动的评估指标，尽管TREQA从未被显式优化以与人类判断相关联。此外，生成的问题和答案具有可解释性，实验分析表明它们能有效定位专家在评估数据集中识别出的翻译错误。

链接: https://arxiv.org/abs/2504.07583
作者: Patrick Fernandes,Sweta Agrawal,Emmanouil Zaranis,André F.T. Martins,Graham Neubig
机构: Carnegie Mellon University (卡内基梅隆大学); Instituto de Telecomunicações (葡萄牙电信研究所); Instituto Superior Técnico, Universidade de Lisboa (里斯本理工大学); Unbabel
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Despite the steady progress in machine translation evaluation, existing automatic metrics struggle to capture how well meaning is preserved beyond sentence boundaries. We posit that reliance on a single intrinsic quality score, trained to mimic human judgments, might be insufficient for evaluating translations of long, complex passages, and a more ``pragmatic’’ approach that assesses how accurately key information is conveyed by a translation in context is needed. We introduce TREQA (Translation Evaluation via Question-Answering), a framework that extrinsically evaluates translation quality by assessing how accurately candidate translations answer reading comprehension questions that target key information in the original source or reference texts. In challenging domains that require long-range understanding, such as literary texts, we show that TREQA is competitive with and, in some cases, outperforms state-of-the-art neural and LLM-based metrics in ranking alternative paragraph-level translations, despite never being explicitly optimized to correlate with human judgments. Furthermore, the generated questions and answers offer interpretability: empirical analysis shows that they effectively target translation errors identified by experts in evaluated datasets. Our code is available at this https URL
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[NLP-35] AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation

【速读】：该论文试图解决如何评估和提升AI生成文本的写作质量这一根本性问题。现有的写作质量评估方法因主观性强且需要专业知识而未得到社区足够关注，且现有模型在此类任务上的表现远不及随机基线。论文的关键解决方案是引入写作质量基准（Writing Quality Benchmark, WQ）和专门训练的写作质量奖励模型（Writing Quality Reward Model, WQRM）。通过整合五个写作偏好数据集构建WQ，并训练不同规模的WQRM，在四个分布外测试集上展现出强泛化能力，同时在WQ基准上达到74%的准确率。此外，论文展示了WQRM在推理阶段通过额外计算资源生成和排序候选修订版的能力，从而从初始草稿中选择更高质量的输出。人机评估结果表明，基于WQRM的选择方案使专家更倾向于优选样本，整体占比达66%，当奖励差距大于1分时提升至72.2%。

链接: https://arxiv.org/abs/2504.07532
作者: Tuhin Chakrabarty,Philippe Laban,Chien-Sheng Wu
机构: Salesforce AI Research (Salesforce AI 研究院); Microsoft Research (微软研究院)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:AI-generated text is proliferating across domains, from creative writing and journalism to marketing content and scientific articles. Models can follow user-provided instructions to generate coherent and grammatically correct outputs but in this work, we study a more fundamental question: how do we evaluate and improve the writing quality of AI-generated text? Writing quality assessment has received less attention from the community, in part because it is fundamentally subjective and requires expertise. We first introduce the Writing Quality Benchmark (WQ) by consolidating five writing-preference datasets into 4,729 writing quality judgments. Our experiments show that competitive baselines, including state-of-the-art LLMs that excel at reasoning tasks, barely outperform random baselines on WQ. We then train specialized Writing Quality Reward Models (WQRM) of various sizes for writing quality assessment that demonstrate strong generalization on four out-of-distribution test sets and 74% accuracy on the WQ benchmark. To further show WQRM’s practical benefits during inference, we leverage additional test-time compute to generate and rank multiple candidate revisions, allowing us to select higher-quality outputs from an initial draft. Human evaluation with 9 experienced writers confirm that WQRM-based selection produces writing samples preferred by experts 66% overall, and 72.2% when the reward gap is larger than 1 point. We release our datasets and models to encourage community engagement with writing quality assessment and development of AI writing systems better aligned with human preferences.
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[NLP-36] Supervised Optimism Correction: Be Confident When LLM s Are Sure

【速读】：该论文旨在解决在基于标记级马尔可夫决策过程的有监督微调与离线强化学习之间的理论联系下，广泛使用的束搜索方法因过度乐观而导致推理错误被放大的问题。论文指出，大型语言模型确实会学习隐式的Q函数用于推理，但束搜索方法由于对次优步骤的Q值估计过高，不可避免地放大了推理误差。为了解决这一局限性，论文提出了有监督乐观校正（Supervised Optimism Correction, SOC）方法，通过在有监督微调过程中引入一个简单而有效的辅助损失来校正token级别的Q值估计。具体而言，该辅助损失利用隐式价值正则化增强模型对专家演示响应的信心，从而抑制对监督不足响应的过度乐观估计。实验结果表明，所提出的SOC方法在包括GSM8K、MATH和GAOKAO在内的数学推理基准测试中显著提升了开源模型的表现。

链接: https://arxiv.org/abs/2504.07527
作者: Junjie Zhang,Rushuai Yang,Shunyu Liu,Ting-En Lin,Fei Huang,Yi Chen,Yongbin Li,Dacheng Tao
机构: Nanyang Technological University (南洋理工大学); Hong Kong University of Science and Technology (香港科技大学); Alibaba Group (阿里巴巴集团)
类目: Computation and Language (cs.CL)
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Abstract:In this work, we establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning under the token-level Markov decision process, revealing that large language models indeed learn an implicit Q -function for inference. Through this theoretical lens, we demonstrate that the widely used beam search method suffers from unacceptable over-optimism, where inference errors are inevitably amplified due to inflated Q -value estimations of suboptimal steps. To address this limitation, we propose Supervised Optimism Correction(SOC), which introduces a simple yet effective auxiliary loss for token-level Q -value estimations during supervised fine-tuning. Specifically, the auxiliary loss employs implicit value regularization to boost model confidence in expert-demonstrated responses, thereby suppressing over-optimism toward insufficiently supervised responses. Extensive experiments on mathematical reasoning benchmarks, including GSM8K, MATH, and GAOKAO, showcase the superiority of the proposed SOC with beam search across a series of open-source models.
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[NLP-37] Geological Inference from Textual Data using Word Embeddings

【速读】：该论文旨在探索利用自然语言处理（NLP）技术定位地质资源，特别是工业矿物。为实现这一目标，研究通过使用基于GloVe模型训练的词嵌入方法提取目标关键词与地质文本语料库之间的语义关系，并筛选具有地理意义的词汇（如城市名），进而依据余弦相似度对这些词汇进行排序。为了增强特征提取并提升语义关系的准确性，研究采用了多种降维技术，包括主成分分析（PCA）、自动编码器（Autoencoder）、变分自编码器（VAE）以及结合长短时记忆网络的变分自编码器（VAE-LSTM）。为验证方法的有效性，研究计算了与目标关键词最相关的十个城市的地理分布与已知矿产地的距离，并采用大圆距离公式（haversine equation）进行衡量。研究的关键在于将NLP技术与先进的降维技术相结合，以揭示自然资源的空间分布规律，尽管结果表明定位精度与预期区域一致，但仍有进一步优化的空间。

链接: https://arxiv.org/abs/2504.07490
作者: Nanmanas Linphrachaya,Irving Gómez-Méndez,Adil Siripatana
机构: CMKL University; The University of Edinburgh (爱丁堡大学)
类目: Computation and Language (cs.CL); Methodology (stat.ME)
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Abstract:This research explores the use of Natural Language Processing (NLP) techniques to locate geological resources, with a specific focus on industrial minerals. By using word embeddings trained with the GloVe model, we extract semantic relationships between target keywords and a corpus of geological texts. The text is filtered to retain only words with geographical significance, such as city names, which are then ranked by their cosine similarity to the target keyword. Dimensional reduction techniques, including Principal Component Analysis (PCA), Autoencoder, Variational Autoencoder (VAE), and VAE with Long Short-Term Memory (VAE-LSTM), are applied to enhance feature extraction and improve the accuracy of semantic relations. For benchmarking, we calculate the proximity between the ten cities most semantically related to the target keyword and identified mine locations using the haversine equation. The results demonstrate that combining NLP with dimensional reduction techniques provides meaningful insights into the spatial distribution of natural resources. Although the result shows to be in the same region as the supposed location, the accuracy has room for improvement. Subjects: Computation and Language (cs.CL); Methodology (stat.ME) Cite as: arXiv:2504.07490 [cs.CL] (or arXiv:2504.07490v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2504.07490 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[NLP-38] ransformer-Based Temporal Information Extraction and Application: A Review

【速读】：该论文旨在解决Temporal Information Extraction (IE) 领域中缺乏系统性综述的问题，特别是在基于Transformer的预训练语言模型在该领域应用的研究。论文的关键在于通过系统总结和分析基于Transformer的方法在时间信息抽取中的工作，揭示其在医疗、新闻报道和情报分析等领域的应用潜力，并同时指出未来可能的研究方向。

链接: https://arxiv.org/abs/2504.07470
作者: Xin Su,Phillip Howard,Steven Bethard
机构: Intel Labs (英特尔实验室); University of Arizona (亚利桑那大学)
类目: Computation and Language (cs.CL)
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Abstract:Temporal information extraction (IE) aims to extract structured temporal information from unstructured text, thereby uncovering the implicit timelines within. This technique is applied across domains such as healthcare, newswire, and intelligence analysis, aiding models in these areas to perform temporal reasoning and enabling human users to grasp the temporal structure of text. Transformer-based pre-trained language models have produced revolutionary advancements in natural language processing, demonstrating exceptional performance across a multitude of tasks. Despite the achievements garnered by Transformer-based approaches in temporal IE, there is a lack of comprehensive reviews on these endeavors. In this paper, we aim to bridge this gap by systematically summarizing and analyzing the body of work on temporal IE using Transformers while highlighting potential future research directions.
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[NLP-39] Defense against Prompt Injection Attacks via Mixture of Encodings

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）在面对提示注入攻击（prompt injection attacks）时的安全性与任务性能之间的权衡问题。现有防御方法如Base64虽然能有效降低攻击成功率，但会损害LLMs在某些自然语言处理（NLP）任务上的表现。论文的关键解决方案是提出了一种新的防御机制——编码混合策略（mixture of encodings），通过结合多种字符编码方式（包括Base64）来增强安全性，同时保持对所有NLP任务的高性能表现。实验结果表明，该方法在减少攻击成功率的同时，显著优于现有的基于字符编码的防御方法，从而验证了其在安全性和任务性能方面的有效性。

链接: https://arxiv.org/abs/2504.07467
作者: Ruiyi Zhang,David Sullivan,Kyle Jackson,Pengtao Xie,Mei Chen
机构: UC San Diego (加州大学圣地亚哥分校); Microsoft (微软)
类目: Computation and Language (cs.CL)
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Abstract:Large Language Models (LLMs) have emerged as a dominant approach for a wide range of NLP tasks, with their access to external information further enhancing their capabilities. However, this introduces new vulnerabilities, known as prompt injection attacks, where external content embeds malicious instructions that manipulate the LLM’s output. Recently, the Base64 defense has been recognized as one of the most effective methods for reducing success rate of prompt injection attacks. Despite its efficacy, this method can degrade LLM performance on certain NLP tasks. To address this challenge, we propose a novel defense mechanism: mixture of encodings, which utilizes multiple character encodings, including Base64. Extensive experimental results show that our method achieves one of the lowest attack success rates under prompt injection attacks, while maintaining high performance across all NLP tasks, outperforming existing character encoding-based defense methods. This underscores the effectiveness of our mixture of encodings strategy for both safety and task performance metrics.
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[NLP-40] Beyond LLM s: A Linguistic Approach to Causal Graph Generation from Narrative Texts NAACL2025

【速读】：本文提出了一种从叙事文本中生成因果图的新框架，旨在弥合高级别因果关系与具体事件间关系之间的鸿沟。论文的关键在于引入了一个结合“专家索引”（Expert Index）的混合系统，该索引包含七种基于语言学特征的设计，并将其整合到情境-任务-行动-结果（STAC）分类模型中。通过将RoBERTa嵌入与专家索引相结合，该方法在因果链接识别方面实现了比纯大语言模型（LLM）方法更高的精度。此外，通过一个结构化的五次迭代提示过程进一步优化和构建连贯的因果图。实验结果显示，相比GPT-4o和Claude 3.5，本方法生成的因果图质量更高且保持良好的可读性，提供了一种可解释且高效的工具来捕捉叙事中的细微因果链。

链接: https://arxiv.org/abs/2504.07459
作者: Zehan Li,Ruhua Pan,Xinyu Pi
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:We propose a novel framework for generating causal graphs from narrative texts, bridging high-level causality and detailed event-specific relationships. Our method first extracts concise, agent-centered vertices using large language model (LLM)-based summarization. We introduce an “Expert Index,” comprising seven linguistically informed features, integrated into a Situation-Task-Action-Consequence (STAC) classification model. This hybrid system, combining RoBERTa embeddings with the Expert Index, achieves superior precision in causal link identification compared to pure LLM-based approaches. Finally, a structured five-iteration prompting process refines and constructs connected causal graphs. Experiments on 100 narrative chapters and short stories demonstrate that our approach consistently outperforms GPT-4o and Claude 3.5 in causal graph quality, while maintaining readability. The open-source tool provides an interpretable, efficient solution for capturing nuanced causal chains in narratives.
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[NLP-41] LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation

【速读】：该论文旨在解决低秩适应（LoRA）在参数高效微调（PEFT）方法中的两个主要问题：显著的计算开销以及在多任务场景下的参数干扰。为了解决这些问题，论文提出了一种名为LoRA with Reduced Interference (LoRI) 的方法。其关键是冻结投影矩阵 ( A ) 作为随机投影，并通过任务特定的掩码稀疏化矩阵 ( B )，从而大幅减少可训练参数的数量同时保持强大的任务性能。此外，LoRI 利用适配器子空间之间的正交性来最小化多任务场景下的跨任务干扰，并通过稀疏性缓解灾难性遗忘，以支持连续学习。实验结果表明，LoRI 在自然语言理解、数学推理、代码生成和安全对齐等任务中优于全量微调和现有 PEFT 方法，且所需可训练参数比 LoRA 减少高达 95%。

链接: https://arxiv.org/abs/2504.07448
作者: Juzheng Zhang,Jiacheng You,Ashwinee Panda,Tom Goldstein
机构: University of Maryland (马里兰大学); Tsinghua University (清华大学)
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Low-Rank Adaptation (LoRA) has emerged as a popular parameter-efficient fine-tuning (PEFT) method for Large Language Models (LLMs), yet it still incurs notable overhead and suffers from parameter interference in multi-task scenarios. We propose LoRA with Reduced Interference (LoRI), a simple yet effective approach that freezes the projection matrices A as random projections and sparsifies the matrices B using task-specific masks. This design substantially reduces the number of trainable parameters while maintaining strong task performance. Moreover, LoRI minimizes cross-task interference in adapter merging by leveraging the orthogonality between adapter subspaces, and supports continual learning by using sparsity to mitigate catastrophic forgetting. Extensive experiments across natural language understanding, mathematical reasoning, code generation, and safety alignment tasks demonstrate that LoRI outperforms full fine-tuning and existing PEFT methods, while using up to 95% fewer trainable parameters than LoRA. In multi-task experiments, LoRI enables effective adapter merging and continual learning with reduced cross-task interference. Code is available at: this https URL
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[NLP-42] Revisiting LLM Evaluation through Mechanism Interpretability: a New Metric and Model Utility Law

【速读】：该论文旨在解决当前大型语言模型（LLMs）快速发展背景下传统评估方法难以跟上其进步速度的问题。论文的核心贡献在于分析了传统评估流程的主要局限性，并提出了一种名为模型利用率指数（Model Utilization Index, MUI）的新指标，通过引入机制可解释性技术来补充传统的性能度量方法。MUI 的关键创新之处在于不仅衡量模型完成任务的表现，还量化了实现这些成果所付出的努力程度，从而全面评估模型的整体能力。论文的关键解决方案是通过 MUI 提供一种新的视角来理解 LLMs 的实际效用，并基于此总结出“效用法则”（Utility Law），揭示了模型性能与利用率之间的反比关系，进而为训练决策、数据污染问题、模型对比公平性以及数据多样性等关键挑战提供了指导原则。

链接: https://arxiv.org/abs/2504.07440
作者: Yixin Cao,Jiahao Ying,Yaoning Wang,Xipeng Qiu,Xuanjing Huang,Yugang Jiang
机构: School of Computer Science, Fudan University (计算机科学学院，复旦大学); School of Computer Science, Singapore Management University (计算机科学学院，新加坡管理大学)
类目: Computation and Language (cs.CL)
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Abstract:Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications, yet current evaluation methods struggle to keep pace with their rapid development. In this paper, we analyze the core limitations of traditional evaluation pipelines and propose a novel metric, the Model Utilization Index (MUI), which introduces mechanism interpretability techniques to complement traditional performance metrics. MUI quantifies the extent to which a model leverages its capabilities to complete tasks. The core idea is that to assess an LLM’s overall ability, we must evaluate not only its task performance but also the effort expended to achieve the outcome. Our extensive experiments reveal an inverse relationship between MUI and performance, from which we deduce a common trend observed in popular LLMs, which we term the Utility Law. Based on this, we derive four corollaries that address key challenges, including training judgement, the issue of data contamination, fairness in model comparison, and data diversity. We hope that our survey, novel metric, and utility law will foster mutual advancement in both evaluation and mechanism interpretability. Our code can be found at this https URL.
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[NLP-43] LLM 4Ranking: An Easy-to-use Framework of Utilizing Large Language Models for Document Reranking

【速读】：本文旨在解决利用大型语言模型（Large Language Models, LLMs）进行文档重排序（document reranking）在研究和实际应用中的性能与效率提升问题。论文的关键在于提出了一种统一框架\textbf{LLM4Ranking}，该框架允许用户通过开源或基于闭源API的LLMs采用不同的重排序方法。其核心解决方案的关键点在于提供了一个简单且可扩展的接口以实现基于LLMs的文档重排序，并配备了易于使用的评估与微调脚本，从而支持对多种模型和方法在多个常用数据集上的实验验证，确保结果的可重复性。代码已公开发布。

链接: https://arxiv.org/abs/2504.07439
作者: Qi Liu,Haozhe Duan,Yiqun Chen,Quanfeng Lu,Weiwei Sun,Jiaxin Mao
机构: Renmin University of China (中国人民大学); Shanghai Jiao Tong University (上海交通大学); Carnegie Mellon University (卡内基梅隆大学)
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL)
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Abstract:Utilizing large language models (LLMs) for document reranking has been a popular and promising research direction in recent years, many studies are dedicated to improving the performance and efficiency of using LLMs for reranking. Besides, it can also be applied in many real-world applications, such as search engines or retrieval-augmented generation. In response to the growing demand for research and application in practice, we introduce a unified framework, \textbfLLM4Ranking, which enables users to adopt different ranking methods using open-source or closed-source API-based LLMs. Our framework provides a simple and extensible interface for document reranking with LLMs, as well as easy-to-use evaluation and fine-tuning scripts for this task. We conducted experiments based on this framework and evaluated various models and methods on several widely used datasets, providing reproducibility results on utilizing LLMs for document reranking. Our code is publicly available at this https URL.
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[NLP-44] From Token to Line: Enhancing Code Generation with a Long-Term Perspective

【速读】：该论文旨在解决代码生成任务中大语言模型（Large Language Models, LLMs）冗余生成结果以及倾向于过拟合局部模式的问题。尽管现有研究尝试通过多令牌预测策略缓解这些问题，但对生成过程中适当处理长度的选择仍缺乏足够关注。论文的关键洞察在于，通过对LLMs生成过程中的令牌间注意力进行分析发现，注意力分数的高尖峰通常出现在行末尾，这表明将每行代码视为基本处理单元并按顺序生成是合理的。基于此，论文提出了一种名为\textbfLSR-MCTS的算法，利用蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）逐行确定代码并选择最优路径，并在每个节点集成自优化机制以增强多样性并通过错误修正生成高质量程序。实验结果表明，该方法在三个公开编码基准测试中优于当前最先进的性能方法。

链接: https://arxiv.org/abs/2504.07433
作者: Tingwei Lu,Yangning Li,Liyuan Wang,Binghuai Lin,Jiwei Tang,Wanshi Xu,Hai-Tao Zheng,Yinghui Li,Bingxu An,Zhao Wei,Yong Xu
机构: Tsinghua University (清华大学); Peng Cheng Laboratory (鹏城实验室); Tencent Technology Co., Ltd (腾讯科技); Peking University (北京大学)
类目: Computation and Language (cs.CL)
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Abstract:The emergence of large language models (LLMs) has significantly promoted the development of code generation task, sparking a surge in pertinent literature. Current research is hindered by redundant generation results and a tendency to overfit local patterns in the short term. Although existing studies attempt to alleviate the issue by adopting a multi-token prediction strategy, there remains limited focus on choosing the appropriate processing length for generations. By analyzing the attention between tokens during the generation process of LLMs, it can be observed that the high spikes of the attention scores typically appear at the end of lines. This insight suggests that it is reasonable to treat each line of code as a fundamental processing unit and generate them sequentially. Inspired by this, we propose the \textbfLSR-MCTS algorithm, which leverages MCTS to determine the code line-by-line and select the optimal path. Further, we integrate a self-refine mechanism at each node to enhance diversity and generate higher-quality programs through error correction. Extensive experiments and comprehensive analyses on three public coding benchmarks demonstrate that our method outperforms the state-of-the-art performance approaches.
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[NLP-45] Agent Ada: Skill-Adaptive Data Analytics for Tailored Insight Discovery

【速读】：该论文试图解决的问题是如何让大型语言模型（LLMs）驱动的分析代理具备学习和应用新分析技能的能力，从而更高效地从数据中提取专业化洞察。现有的方法通常需要用户手动选择适用的数据分析方法，而AgentAda通过自动识别技能库中的合适技能来执行分析任务，突破了现有LLMs无法直接支持某些复杂分析任务的限制。其解决方案的关键在于AgentAda的数据到洞察提取策略，包含三个核心步骤：(I) 问题生成器生成与用户目标和角色相关的查询；(II) 基于混合检索增强生成（RAG）的技能匹配器从技能库中选择最佳分析技能；(III) 代码生成器根据所选技能的文档生成可执行代码以提取关键模式。此外，论文还提出了一种新颖的LLM作为裁判的方法，用于大规模自动化评估分析结果的质量。

链接: https://arxiv.org/abs/2504.07421
作者: Amirhossein Abaskohi,Amrutha Varshini Ramesh,Shailesh Nanisetty,Chirag Goel,David Vazquez,Christopher Pal,Spandana Gella,Giuseppe Carenini,Issam H. Laradji
机构: ServiceNow Research (ServiceNow 研究院); University of British Columbia (不列颠哥伦比亚大学); University of Toronto (多伦多大学); University of Montreal (蒙特利尔大学); Mila; CIFAR AI Chair; Polytechnique Montréal (蒙特利尔理工学院)
类目: Computation and Language (cs.CL)
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Abstract:We introduce AgentAda, the first LLM-powered analytics agent that can learn and use new analytics skills to extract more specialized insights. Unlike existing methods that require users to manually decide which data analytics method to apply, AgentAda automatically identifies the skill needed from a library of analytical skills to perform the analysis. This also allows AgentAda to use skills that existing LLMs cannot perform out of the box. The library covers a range of methods, including clustering, predictive modeling, and NLP techniques like BERT, which allow AgentAda to handle complex analytics tasks based on what the user needs. AgentAda’s dataset-to-insight extraction strategy consists of three key steps: (I) a question generator to generate queries relevant to the user’s goal and persona, (II) a hybrid Retrieval-Augmented Generation (RAG)-based skill matcher to choose the best data analytics skill from the skill library, and (III) a code generator that produces executable code based on the retrieved skill’s documentation to extract key patterns. We also introduce KaggleBench, a benchmark of curated notebooks across diverse domains, to evaluate AgentAda’s performance. We conducted a human evaluation demonstrating that AgentAda provides more insightful analytics than existing tools, with 48.78% of evaluators preferring its analyses, compared to 27.67% for the unskilled agent. We also propose a novel LLM-as-a-judge approach that we show is aligned with human evaluation as a way to automate insight quality evaluation at larger scale.
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[NLP-46] RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability

【速读】：该论文旨在解决现有多模态模型在放射学领域中存在的三个主要问题：1) 难以有效利用复杂的放射学报告进行学习；2) 过度依赖低分辨率图像；3) 注意力机制的可解释性有限。为了解决这些问题，论文提出了RadZero，这是一种基于相似性的跨注意框架，具有零样本多任务能力。其关键在于利用大型语言模型从放射学报告中提取最小语义句子，并采用多正样本对比学习策略来有效捕捉图像与多个相关文本描述之间的关系。此外，RadZero通过预训练的视觉编码器结合额外的可训练Transformer层实现高效高分辨率图像处理，同时通过计算文本嵌入与局部图像块特征之间的相似性，实现了分类的零样本推理以及像素级跨模态相似性图谱，从而支持定位和分割任务。实验结果表明，RadZero在零样本分类、定位和分割方面优于现有最先进方法，并通过跨模态相似性图谱分析展示了提升视觉-语言对齐可解释性的潜力。

链接: https://arxiv.org/abs/2504.07416
作者: Jonggwon Park,Soobum Kim,Byungmu Yoon,Kyoyun Choi
机构: DEEPNOID Inc. (DEEPNOID Inc.)
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Recent advancements in multi-modal models have significantly improved vision-language alignment in radiology. However, existing approaches struggle to effectively utilize complex radiology reports for learning, rely on low-resolution images, and offer limited interpretability in attention mechanisms. To address these challenges, we introduce RadZero, a novel similarity-based cross-attention framework for vision-language alignment in radiology with zero-shot multi-task capability. RadZero leverages large language models to extract minimal semantic sentences from radiology reports and employs a multi-positive contrastive learning strategy to effectively capture relationships between images and multiple relevant textual descriptions. It also utilizes a pre-trained vision encoder with additional trainable Transformer layers, allowing efficient high-resolution image processing. By computing similarity between text embeddings and local image patch features, RadZero enables zero-shot inference with similarity probability for classification and pixel-level cross-modal similarity maps for grounding and segmentation. Experimental results on public chest radiograph benchmarks show that RadZero outperforms state-of-the-art methods in zero-shot classification, grounding, and segmentation. Furthermore, cross-modal similarity map analysis highlights its potential for improving explainability in vision-language alignment. Additionally, qualitative evaluation demonstrates RadZero’s capability for open-vocabulary semantic segmentation, further validating its effectiveness in medical imaging.
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[NLP-47] Leverag ing LLM s for Multimodal Retrieval-Augmented Radiology Report Generation via Key Phrase Extraction

【速读】：该论文旨在解决自动化胸部X射线（Chest X-ray, CXR）报告生成中的资源消耗大以及生成结果易出现幻觉（hallucinations）的问题。为应对这些挑战，论文提出了一种检索增强生成方法（retrieval-augmented generation approach），结合多模态检索与大型语言模型（Large Language Models, LLMs），在降低计算需求的同时提升生成报告的准确性。关键在于利用LLMs提取诊断报告中的关键短语以聚焦核心信息，并通过探索有效的训练策略（如图像编码器结构搜索、文本嵌入加噪、附加训练目标等）结合预训练图像编码器与对比学习技术，实现文本与语义图像嵌入之间的互补性。这种方法无需对LLMs进行微调，即可在MIMIC-CXR数据集上达到最先进的CheXbert评分表现，并展现出多视角报告生成任务上的鲁棒泛化能力。

链接: https://arxiv.org/abs/2504.07415
作者: Kyoyun Choi,Byungmu Yoon,Soobum Kim,Jonggwon Park
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Automated radiology report generation (RRG) holds potential to reduce radiologists’ workload, especially as recent advancements in large language models (LLMs) enable the development of multimodal models for chest X-ray (CXR) report generation. However, multimodal LLMs (MLLMs) are resource-intensive, requiring vast datasets and substantial computational cost for training. To address these challenges, we propose a retrieval-augmented generation approach that leverages multimodal retrieval and LLMs to generate radiology reports while mitigating hallucinations and reducing computational demands. Our method uses LLMs to extract key phrases from radiology reports, effectively focusing on essential diagnostic information. Through exploring effective training strategies, including image encoder structure search, adding noise to text embeddings, and additional training objectives, we combine complementary pre-trained image encoders and adopt contrastive learning between text and semantic image embeddings. We evaluate our approach on MIMIC-CXR dataset, achieving state-of-the-art results on CheXbert metrics and competitive RadGraph F1 metric alongside MLLMs, without requiring LLM fine-tuning. Our method demonstrates robust generalization for multi-view RRG, making it suitable for comprehensive clinical applications.
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[NLP-48] AI Coding with Few-Shot Prompting for Thematic Analysis

【速读】：该论文试图解决大规模语料库主题分析中编码工作量巨大且难以实施的问题。论文的关键解决方案是利用少量示例提示（few-shot prompting）的方法，通过在语义相似段落上生成高质量代码，并结合成本较低且可扩展性更强的大型语言模型（GPT 3.5-Turbo），以提升编码质量和效率。

链接: https://arxiv.org/abs/2504.07408
作者: Samuel Flanders,Melati Nungsari,Mark Cheong Wing Loong
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:This paper explores the use of large language models (LLMs), here represented by GPT 3.5-Turbo to perform coding for a thematic analysis. Coding is highly labor intensive, making it infeasible for most researchers to conduct exhaustive thematic analyses of large corpora. We utilize few-shot prompting with higher quality codes generated on semantically similar passages to enhance the quality of the codes while utilizing a cheap, more easily scalable model.
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[NLP-49] alking Point based Ideological Discourse Analysis in News Events

【速读】：该论文试图解决在大型语言模型（LLMs）时代下分析意识形态话语的挑战，具体表现为现有模型难以捕捉塑造现实世界叙事的关键要素，无法聚焦驱动主导话语的特征元素，且缺乏整合理解抽象意识形态观点所需的上下文信息。为应对这些局限性，论文提出了一种基于意识形态话语分析理论的框架，用于分析与现实事件相关的新闻文章。解决方案的关键在于通过构建一种关系结构——议题点（talking points），来表征新闻文章，该结构捕获实体间互动、角色以及媒体框架与其讨论主题之间的关联；随后形成重复主题的词汇表——显著议题点，用于生成特定意识形态的观点或党派视角。关键还体现在通过自动化任务（如意识形态和党派分类任务）及人工验证评估框架性能，并展示其在创建事件快照中的直接应用价值。

链接: https://arxiv.org/abs/2504.07400
作者: Nishanth Nakshatri,Nikhil Mehta,Siyi Liu,Sihao Chen,Daniel J. Hopkins,Dan Roth,Dan Goldwasser
机构: Purdue University (普渡大学); University of Pennsylvania (宾夕法尼亚大学)
类目: Computation and Language (cs.CL)
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Abstract:Analyzing ideological discourse even in the age of LLMs remains a challenge, as these models often struggle to capture the key elements that shape real-world narratives. Specifically, LLMs fail to focus on characteristic elements driving dominant discourses and lack the ability to integrate contextual information required for understanding abstract ideological views. To address these limitations, we propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events. Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion. It then constructs a vocabulary of repeating themes - prominent talking points, that are used to generate ideology-specific viewpoints (or partisan perspectives). We evaluate our framework’s ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation. Additionally, we demonstrate straightforward applicability of our framework in creating event snapshots, a visual way of interpreting event discourse. We release resulting dataset and model to the community to support further research.
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[NLP-50] ask-Circuit Quantization: Leverag ing Knowledge Localization and Interpretability for Compression

【速读】：本文旨在解决后训练量化（Post-Training Quantization, PTQ）在低比特（如2到3比特）场景下可能导致下游任务性能显著下降的问题。论文的关键创新在于提出了一种新的混合精度PTQ方法——任务电路量化（Task-Circuit Quantization, TaCQ）。TaCQ 的核心思想是借鉴自动化电路发现的方法，直接将量化过程与特定的权重电路（定义为与下游任务性能相关的权重集合）关联起来。通过保留这些与任务性能密切相关的权重为16位，而将其他权重量化为低比特，TaCQ 在仅增加微小内存开销的情况下保持了模型性能。具体而言，TaCQ 通过对比未量化的模型权重与均匀量化模型，估计量化引起的权重变化，并利用梯度信息预测其对任务性能的影响，从而有针对性地保护任务相关权重。这一方法使得 TaCQ 在多种任务（如问答、数学推理和文本到SQL转换）以及不同模型（Llama-3 和 Qwen2.5）上的表现优于现有方法，尤其是在2到3比特的低比特设置中取得了显著性能提升。

链接: https://arxiv.org/abs/2504.07389
作者: Hanqi Xiao,Yi-Lin Sung,Elias Stengel-Eskin,Mohit Bansal
机构: UNC Chapel Hill
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Post-training quantization (PTQ) reduces a model’s memory footprint by mapping full precision weights into low bit weights without costly retraining, but can degrade its downstream performance especially in low 2- to 3-bit settings. We develop a new mixed-precision PTQ approach, Task-Circuit Quantization (TaCQ), that draws parallels to automated circuit discovery, directly conditioning the quantization process on specific weight circuits – which we define as sets of weights associated with downstream task performance. These weights are kept as 16-bit weights, while others are quantized, maintaining performance while only adding a marginal memory cost. Specifically, TaCQ contrasts unquantized model weights with a uniformly-quantized model to estimate the expected change in weights due to quantization and uses gradient information to predict the resulting impact on task performance, allowing us to preserve task-specific weights. We compare TaCQ-based quantization to existing mixed-precision quantization methods when conditioning both on general-purpose and task-specific data. Across QA, math reasoning, and text-to-SQL tasks for both Llama-3 and Qwen2.5, we find that TaCQ outperforms baselines using the same calibration data and a lower weight budget, achieving major improvements in the 2 and 3-bit regime. With only 3.1 bits we are able to recover 96% of Llama-3-8B-Instruct’s unquantized 16-bit MMLU performance, obtaining a 5.25% absolute improvement over SPQR. We also observe consistently large gains over existing methods in the 2-bit regime, with an average gain of 14.74% over the strongest baseline, SliM-LLM. Moreover, we observe a 7.20% gain without conditioning on specific tasks, showing TaCQ’s ability to identify important weights is not limited to task-conditioned settings.
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[NLP-51] ALE: A Tool-Augmented Framework for Reference-Free Evaluation of Large Language Models

【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在实际自主应用场景中，基于静态预标注参考进行评估所面临的成本高、可扩展性差及完整性不足的问题。解决方案的关键在于提出了一种名为工具增强型LLM评估（Tool-Augmented LLM Evaluation, TALE）的新框架。与依赖固定参考或仅依靠LLM作为裁判的传统方法不同，TALE通过引入具备工具访问能力的智能体，主动检索和综合外部证据来评估LLM输出。该框架通过迭代生成网络查询、收集信息、总结发现并反思优化后续搜索的方式，摆脱了对静态参考的依赖，使其适用于更广泛的实际自由形式问答任务。实验结果表明，TALE不仅在衡量响应准确性方面优于传统的基于参考的度量标准，还与人工评估达到了高度一致，从而显著提升了LLM评估在真实动态场景中的可靠性。

链接: https://arxiv.org/abs/2504.07385
作者: Sher Badshah,Ali Emami,Hassan Sajjad
机构: Dalhousie University (达尔豪斯大学); Brock University (布洛克大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:As Large Language Models (LLMs) become increasingly integrated into real-world, autonomous applications, relying on static, pre-annotated references for evaluation poses significant challenges in cost, scalability, and completeness. We propose Tool-Augmented LLM Evaluation (TALE), a framework to assess LLM outputs without predetermined ground-truth answers. Unlike conventional metrics that compare to fixed references or depend solely on LLM-as-a-judge knowledge, TALE employs an agent with tool-access capabilities that actively retrieves and synthesizes external evidence. It iteratively generates web queries, collects information, summarizes findings, and refines subsequent searches through reflection. By shifting away from static references, TALE aligns with free-form question-answering tasks common in real-world scenarios. Experimental results on multiple free-form QA benchmarks show that TALE not only outperforms standard reference-based metrics for measuring response accuracy but also achieves substantial to near-perfect agreement with human evaluations. TALE enhances the reliability of LLM evaluations in real-world, dynamic scenarios without relying on static references.
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[NLP-52] Enhancing Time Series Forecasting via Multi-Level Text Alignment with LLM s

【速读】：该论文旨在解决将大语言模型（Large Language Models, LLMs）应用于时间序列预测时面临的独特挑战，即如何将连续的时间序列数据与基于离散标记的语言表示形式对齐，同时保持预测准确性与可解释性。现有方法尝试将时间序列重新编程为文本形式，但往往难以提供有意义且可解释的结果。论文的关键解决方案在于提出了一种多层级文本对齐框架，通过将时间序列分解为趋势、季节性和残差成分，并针对各成分构建特定的文本表示，利用多层级对齐机制将组件特定嵌入与预训练词嵌入对齐，从而不仅提升了预测精度，还增强了时间序列表示的可解释性。

链接: https://arxiv.org/abs/2504.07360
作者: Taibiao Zhao,Xiaobing Chen,Mingxuan Sun
机构: 未知
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:The adaptation of large language models (LLMs) to time series forecasting poses unique challenges, as time series data is continuous in nature, while LLMs operate on discrete tokens. Despite the success of LLMs in natural language processing (NLP) and other structured domains, aligning time series data with language-based representations while maintaining both predictive accuracy and interpretability remains a significant hurdle. Existing methods have attempted to reprogram time series data into text-based forms, but these often fall short in delivering meaningful, interpretable results. In this paper, we propose a multi-level text alignment framework for time series forecasting using LLMs that not only improves prediction accuracy but also enhances the interpretability of time series representations. Our method decomposes time series into trend, seasonal, and residual components, which are then reprogrammed into component-specific text representations. We introduce a multi-level alignment mechanism, where component-specific embeddings are aligned with pre-trained word tokens, enabling more interpretable forecasts. Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art models in accuracy while providing good interpretability.
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[NLP-53] Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extraction

【速读】：本文旨在系统性研究大规模推理模型（LRMs, Large Reasoning Models）在理解人类指令并生成准确输出时是否仍需大量提示工程或优化。以事件抽取这一结构化任务为案例研究，作者测试了两种LRMs（DeepSeek-R1和o1）以及两种通用大型语言模型（LLMs, Large Language Models）（GPT-4o和GPT-4.5），考察它们作为任务模型或提示优化器时的表现。研究的关键在于发现即使在如事件抽取这样复杂的任务中，LRMs作为任务模型依然从提示优化中获益，并且将LRMs用作提示优化器能够产生更有效的提示。此外，论文还分析了LRMs常见的错误，并强调了其在细化任务指令和事件指南方面的稳定性和一致性。

链接: https://arxiv.org/abs/2504.07357
作者: Saurabh Srivastava,Ziyu Yao
机构: 未知
类目: Computation and Language (cs.CL)
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Abstract:Large Reasoning Models (LRMs) such as DeepSeek-R1 and OpenAI o1 have demonstrated remarkable capabilities in various reasoning tasks. Their strong capability to generate and reason over intermediate thoughts has also led to arguments that they may no longer require extensive prompt engineering or optimization to interpret human instructions and produce accurate outputs. In this work, we aim to systematically study this open question, using the structured task of event extraction for a case study. We experimented with two LRMs (DeepSeek-R1 and o1) and two general-purpose Large Language Models (LLMs) (GPT-4o and GPT-4.5), when they were used as task models or prompt optimizers. Our results show that on tasks as complicated as event extraction, LRMs as task models still benefit from prompt optimization, and that using LRMs as prompt optimizers yields more effective prompts. Finally, we provide an error analysis of common errors made by LRMs and highlight the stability and consistency of LRMs in refining task instructions and event guidelines.
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[NLP-54] Alice: Proactive Learning with Teachers Demonstrations for Weak-to-Strong Generalization

【速读】：该论文旨在解决大型语言模型（Large Language Models, LLMs）能力不断增强背景下，如何实现有效的人类监督这一关键挑战。传统弱到强泛化（Weak-to-Strong Generalization, W2SG）方法依赖于被动学习，即弱教师提供带噪声的演示来训练强学生，这种机制限制了学生在训练过程中发挥其知识潜力。论文的关键创新在于提出Alice框架（主动学习结合教师演示），通过利用师生模型之间的互补知识提升学习效果：首先通过引发教师模型的不确定性探测其知识库，然后结合教师响应与这些洞察作为演示，引导学生模型自我生成优化后的响应以实现更高效的监督。此外，针对师生能力差距较大的情况，进一步引入级联Alice（Cascade Alice），采用分层训练策略，由较弱的教师模型初步监督中间模型，再逐层指导更强的模型。实验结果表明，该方法显著提升了W2SG性能，在知识推理、数学推理和逻辑推理三项任务中分别取得了+4.0%、+22.62%和+12.11%的改进，凸显了新范式在促进稳健知识迁移和提升监督效果方面的有效性。

链接: https://arxiv.org/abs/2504.07316
作者: Shujin Wu,Cheng Qian,Yi R.(May)Fung,Paul Pu Liang,Heng Ji
机构: University of Illinois Urbana-Champaign (伊利诺伊大学香槟分校); University of Southern California (南加州大学); Massachusetts Institute of Technology (麻省理工学院)
类目: Computation and Language (cs.CL)
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Abstract:The growing capabilities of large language models (LLMs) present a key challenge of maintaining effective human oversight. Weak-to-strong generalization (W2SG) offers a promising framework for supervising increasingly capable LLMs using weaker ones. Traditional W2SG methods rely on passive learning, where a weak teacher provides noisy demonstrations to train a strong student. This hinders students from employing their knowledge during training and reaching their full potential. In this work, we introduce Alice (proActive learning with teacher’s Demonstrations), a framework that leverages complementary knowledge between teacher and student to enhance the learning this http URL probe the knowledge base of the teacher model by eliciting their uncertainty, and then use these insights together with teachers’ responses as demonstrations to guide student models in self-generating improved responses for supervision. In addition, for situations with significant capability gaps between teacher and student models, we introduce cascade Alice, which employs a hierarchical training approach where weak teachers initially supervise intermediate models, who then guide stronger models in sequence. Experimental results demonstrate that our method significantly enhances the W2SG performance, yielding substantial improvements in three key tasks compared to the original W2SG: knowledge-based reasoning (+4.0%), mathematical reasoning (+22.62%), and logical reasoning (+12.11%). This highlights the effectiveness of our new W2SG paradigm that enables more robust knowledge transfer and supervision outcome.
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[NLP-55] Multilingual MFA: Forced Alignment on Low-Resource Related Languages

【速读】：该论文旨在探讨多语言（Multilingual）和跨语言（Crosslingual）训练在相关与不相关的澳大利亚语言中的效果，特别是针对具有相似音位库存的语言。论文的关键在于通过蒙特利尔强制对齐器（Montreal Forced Aligner）从零开始训练声学模型，并调整一个大型英语基线模型，评估其在已见数据、未见数据（属于已知语言）以及完全未见的语言和数据上的表现。研究结果表明，调整英语基线模型对于处理之前未见过的语言具有显著优势。因此，论文的核心解决方案在于利用英语基线模型的迁移学习能力以提升对低资源语言的支持效果。

链接: https://arxiv.org/abs/2504.07315
作者: Alessio Tosolini,Claire Bowern
机构: Yale University (耶鲁大学); Yale University (耶鲁大学)
类目: Computation and Language (cs.CL)
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Abstract:We compare the outcomes of multilingual and crosslingual training for related and unrelated Australian languages with similar phonological inventories. We use the Montreal Forced Aligner to train acoustic models from scratch and adapt a large English model, evaluating results against seen data, unseen data (seen language), and unseen data and language. Results indicate benefits of adapting the English baseline model for previously unseen languages.
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[NLP-56] PAYADOR: A Minimalist Approach to Grounding Language Models on Structured Data for Interactive Storytelling and Role-playing Games

【速读】：该论文旨在解决交互叙事（Interactive Storytelling, IS）系统在处理玩家输入时面临的“世界更新问题”（world-update problem），特别是当预期体验高度依赖即兴创作时，如角色扮演游戏（Role-playing Games, RPGs）。传统方法通常将玩家输入映射到预编程的动作上，这可能严重限制玩家的自由意志。论文提出的关键解决方案是PAYADOR，它专注于预测动作的结果而非直接表示动作本身。通过将大型语言模型（Large Language Model）与虚构世界的最小化表示相结合，实现了这一目标，并取得了有前景的结果。此贡献已开源，以便用于释放RPGs中协同创造力的潜力。

链接: https://arxiv.org/abs/2504.07304
作者: Santiago Góngora,Luis Chiruzzo,Gonzalo Méndez,Pablo Gervás
机构: Instituto de Computación, Facultad de Ingeniería, Universidad de la República (乌拉圭); Facultad de Informática, Universidad Complutense de Madrid (马德里康普顿斯大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
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Abstract:Every time an Interactive Storytelling (IS) system gets a player input, it is facing the world-update problem. Classical approaches to this problem consist in mapping that input to known preprogrammed actions, what can severely constrain the free will of the player. When the expected experience has a strong focus on improvisation, like in Role-playing Games (RPGs), this problem is critical. In this paper we present PAYADOR, a different approach that focuses on predicting the outcomes of the actions instead of representing the actions themselves. To implement this approach, we ground a Large Language Model to a minimal representation of the fictional world, obtaining promising results. We make this contribution open-source, so it can be adapted and used for other related research on unleashing the co-creativity power of RPGs.
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[NLP-57] MDIT: A Model-free Data Interpolation Method for Diverse Instruction Tuning

【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在指令微调过程中因现有数据管理策略难以生成多样化和全面的数据，从而限制模型性能进一步提升的问题。解决方案的关键在于提出了一种名为MDIT（Model-Free Data Interpolation for Diverse Instruction Tuning）的新方法，它通过任务插值生成多样且高质量的指令数据，并采用基于多样性的聚类策略确保训练数据的多样性。此外，MDIT无需依赖外部资源即可实现高效自动的数据合成，显著提升了LLMs在通用问答、数学推理和代码生成等多任务上的表现。

链接: https://arxiv.org/abs/2504.07288
作者: Yangning Li,Zihua Lan,Lv Qingsong,Yinghui Li,Hai-Tao Zheng
机构: Tsinghua University (清华大学)
类目: Computation and Language (cs.CL)
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Abstract:As Large Language Models (LLMs) are increasingly applied across various tasks, instruction tuning has emerged as a critical method for enhancing model performance. However, current data management strategies face substantial challenges in generating diverse and comprehensive data, restricting further improvements in model performance. To address this gap, we propose MDIT, a novel model-free data interpolation method for diverse instruction tuning, which generates varied and high-quality instruction data by performing task interpolation. Moreover, it contains diversity-based clustering strategies to ensure the diversity of the training data. Extensive experiments show that our method achieves superior performance in multiple benchmark tasks. The LLMs finetuned with MDIT show significant improvements in numerous tasks such as general question answering, math reasoning, and code generation. MDIT offers an efficient and automatic data synthetic method, generating diverse instruction data without depending on external resources while expanding the application potential of LLMs in complex environments.
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[NLP-58] RAISE: Reinforenced Adaptive Instruction Selection For Large Language Models

【速读】：该论文旨在解决在大语言模型（Large Language Models, LLMs）指令微调过程中，如何更有效地选择指令以优化模型性能的问题。当前大多数指令选择方法依赖于启发式的质量度量指标，并仅在训练前考虑数据选择，这种设计导致指令微调的优化不足，且固定的启发式指标难以针对具体任务进行优化。论文的关键解决方案是提出了一种动态的、面向任务目标的指令选择框架RAISE(Reinforced Adaptive Instruction SElection)。该框架将整个指令微调过程纳入优化范围，通过强化学习（Reinforcement Learning, RL）训练选择策略，在每一步基于指令对模型性能提升的预期影响来选择指令。这种方法不仅具有良好的可解释性，还具备较强的任务特定优化能力，从而显著提升了指令微调的效果与效率。实验结果表明，RAISE相较于其他指令选择方法表现更优，并且仅需更新全量训练步骤的1%，即可实现卓越的性能提升。

链接: https://arxiv.org/abs/2504.07282
作者: Lv Qingsong,Yangning Li,Zihua Lan,Zishan Xu,Jiwei Tang,Yinghui Li,Wenhao Jiang,Hai-Tao Zheng,Philip S. Yu
机构: Tsinghua University (清华大学)
类目: Computation and Language (cs.CL)
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Abstract:In the instruction fine-tuning of large language models (LLMs), it has become a consensus that a few high-quality instructions are superior to a large number of low-quality instructions. At present, many instruction selection methods have been proposed, but most of these methods select instruction based on heuristic quality metrics, and only consider data selection before training. These designs lead to insufficient optimization of instruction fine-tuning, and fixed heuristic indicators are often difficult to optimize for specific tasks. So we designed a dynamic, task-objective-driven instruction selection framework RAISE(Reinforenced Adaptive Instruction SElection), which incorporates the entire instruction fine-tuning process into optimization, selecting instruction at each step based on the expected impact of instruction on model performance improvement. Our approach is well interpretable and has strong task-specific optimization capabilities. By modeling dynamic instruction selection as a sequential decision-making process, we use RL to train our selection strategy. Extensive experiments and result analysis prove the superiority of our method compared with other instruction selection methods. Notably, RAISE achieves superior performance by updating only 1% of the training steps compared to full-data training, demonstrating its efficiency and effectiveness.
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[NLP-59] Language Modeling for the Future of Finance: A Quantitative Survey into Metrics Tasks and Data Opportunities

【速读】：该论文旨在系统性地考察自然语言处理（NLP）技术在金融领域的应用趋势，通过回顾2017年至2024年间发表的374篇相关文献，特别是其中直接涉及金融任务的221篇，分析NLP方法在金融分析与决策中的进展与挑战。论文的关键在于从11个定性和定量维度评估这些研究，并识别出几大趋势，包括通用语言模型的广泛应用、情感分析与信息抽取的持续进步，以及可解释性与隐私保护方法的新兴努力。此外，论文强调了适配金融领域的评价指标的重要性，并指出构建更易获取且适应性强的数据集、纳入金融危机时期的样本以增强模型鲁棒性的必要性。因此，论文的核心解决方案在于提供一个结构化的NLP在金融领域应用的综述，并为研究人员和实践者提供了实用洞见。

链接: https://arxiv.org/abs/2504.07274
作者: Nikita Tatarinov,Siddhant Sukhani,Agam Shah,Sudheer Chava
机构: Georgia Institute of Technology (乔治亚理工学院)
类目: Computation and Language (cs.CL)
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Abstract:Recent advances in language modeling have led to growing interest in applying Natural Language Processing (NLP) techniques to financial problems, enabling new approaches to analysis and decision-making. To systematically examine this trend, we review 374 NLP research papers published between 2017 and 2024 across 38 conferences and workshops, with a focused analysis of 221 papers that directly address finance-related tasks. We evaluate these papers across 11 qualitative and quantitative dimensions, identifying key trends such as the increasing use of general-purpose language models, steady progress in sentiment analysis and information extraction, and emerging efforts around explainability and privacy-preserving methods. We also discuss the use of evaluation metrics, highlighting the importance of domain-specific ones to complement standard machine learning metrics. Our findings emphasize the need for more accessible, adaptive datasets and highlight the significance of incorporating financial crisis periods to strengthen model robustness under real-world conditions. This survey provides a structured overview of NLP research applied to finance and offers practical insights for researchers and practitioners working at this intersection.
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[NLP-60] Visual-Aware Speech Recognition for Noisy Scenarios

【速读】：该论文试图解决在 noisy 环境下自动语音识别（Automatic Speech Recognition, ASR）或视听语音识别（Audio-Visual Speech Recognition, AVSR）模型性能下降的问题。论文的关键解决方案在于提出一种通过关联噪声源与视觉线索来提升转录质量的模型。不同于依赖唇部运动且需依赖说话者可见性的现有方法，该模型利用更广泛的环境视觉信息，模拟人类在嘈杂环境中过滤语音和抑制噪声的能力。这种方法通过重新调整预训练的语音编码器和视觉编码器，并结合多头注意力机制实现，使模型能够同时进行语音转录和视频输入中的噪声标签预测。实验结果表明，该方法显著优于现有的音频-only 模型，并强调了视觉线索在提高转录准确性方面的重要作用。

链接: https://arxiv.org/abs/2504.07229
作者: Lakshmipathi Balaji,Karan Singla
机构: IIIT-Hyderabad (印度国际信息技术学院); Whissle Inc. (Whissle Inc.)
类目: Computation and Language (cs.CL); Audio and Speech Processing (eess.AS); Signal Processing (eess.SP)
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Abstract:Humans have the ability to utilize visual cues, such as lip movements and visual scenes, to enhance auditory perception, particularly in noisy environments. However, current Automatic Speech Recognition (ASR) or Audio-Visual Speech Recognition (AVSR) models often struggle in noisy scenarios. To solve this task, we propose a model that improves transcription by correlating noise sources to visual cues. Unlike works that rely on lip motion and require the speaker’s visibility, we exploit broader visual information from the environment. This allows our model to naturally filter speech from noise and improve transcription, much like humans do in noisy scenarios. Our method re-purposes pretrained speech and visual encoders, linking them with multi-headed attention. This approach enables the transcription of speech and the prediction of noise labels in video inputs. We introduce a scalable pipeline to develop audio-visual datasets, where visual cues correlate to noise in the audio. We show significant improvements over existing audio-only models in noisy scenarios. Results also highlight that visual cues play a vital role in improved transcription accuracy.
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[NLP-61] ConceptCarve: Dynamic Realization of Evidence ACL2025

【速读】：该论文试图解决在大规模社交媒体社区中寻找人类观点与行为证据的挑战性任务，特别是研究枪支所有权与自由感知之间的关系。为应对这一任务中的两大关键挑战：(1)识别抽象概念实例；(2)这些概念实例在不同社区中可能以不同方式具体化，论文提出了ConceptCarve框架。其关键是利用传统检索器和大型语言模型（LLMs）动态刻画检索空间，从而有效提升在社交媒体社区内获取证据的能力，并提供可解释的证据表示形式用于跨社区复杂思维模式的定性分析。

链接: https://arxiv.org/abs/2504.07228
作者: Eylon Caplan,Dan Goldwasser
机构: Purdue University (普渡大学), West Lafayette, IN, USA
类目: Computation and Language (cs.CL)
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Abstract:Finding evidence for human opinion and behavior at scale is a challenging task, often requiring an understanding of sophisticated thought patterns among vast online communities found on social media. For example, studying how gun ownership is related to the perception of Freedom, requires a retrieval system that can operate at scale over social media posts, while dealing with two key challenges: (1) identifying abstract concept instances, (2) which can be instantiated differently across different communities. To address these, we introduce ConceptCarve, an evidence retrieval framework that utilizes traditional retrievers and LLMs to dynamically characterize the search space during retrieval. Our experiments show that ConceptCarve surpasses traditional retrieval systems in finding evidence within a social media community. It also produces an interpretable representation of the evidence for that community, which we use to qualitatively analyze complex thought patterns that manifest differently across the communities.
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[NLP-62] SemEval-2025 Task 5: LLM s4Subjects – LLM -based Automated Subject Tagging for a National Technical Librarys Open-Access Catalog SEMEVAL2025

【速读】：该论文试图解决科学和技术记录（使用GND分类法的英文和德文材料）自动化主题标注的问题。解决方案的关键在于基于大型语言模型（LLMs）的系统开发，特别是利用LLM集成（LLMs ensembles）、合成数据生成（synthetic data generation）以及多语言处理（multilingual processing）来推荐顶级主题（top-k subjects），并通过定量指标（如精确率、召回率、F1分数）和领域专家的定性评估进行系统性能的综合评价。

链接: https://arxiv.org/abs/2504.07199
作者: Jennifer D’Souza,Sameer Sadruddin,Holger Israel,Mathias Begoin,Diana Slawig
机构: TIB Leibniz Information Centre for Science and Technology (TIB莱布尼兹科学与技术信息中心), Hannover, Germany
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL); Machine Learning (cs.LG)
备注: 10 pages, 4 figures, Accepted as SemEval 2025 Task 5 description paper

点击查看摘要

Abstract:We present SemEval-2025 Task 5: LLMs4Subjects, a shared task on automated subject tagging for scientific and technical records in English and German using the GND taxonomy. Participants developed LLM-based systems to recommend top-k subjects, evaluated through quantitative metrics (precision, recall, F1-score) and qualitative assessments by subject specialists. Results highlight the effectiveness of LLM ensembles, synthetic data generation, and multilingual processing, offering insights into applying LLMs for digital library classification.
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[NLP-63] HypoEval: Hypothesis-Guided Evaluation for Natural Language Generation

【速读】：该论文旨在解决现有大型语言模型（Large Language Models, LLMs）作为自动评估自然语言生成方法中的两个主要问题：一是零样本设置下的低对齐度（zero-shot setting without consulting any human input），二是基于标注数据微调LLMs需要大量样本且自动化评估缺乏充分推理支持。论文的关键创新在于提出HypoEval框架，通过利用少量人工评估（仅30个）生成更详细的评分标准，并结合清单式的多维分解方法整合LLM在各个维度上的评分以获得总体评价结果。这种方法不仅实现了与人类排名（Spearman相关性）和评分（Pearson相关性）的高度一致，而且在有限的人工标注数据下显著提升了评估性能，同时保持了良好的可解释性和可靠性。

链接: https://arxiv.org/abs/2504.07174
作者: Mingxuan Li,Hanchen Li,Chenhao Tan
机构: Department of Computer Science (计算机科学系), University of Chicago (芝加哥大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 22 pages, 3 figures, code link: this https URL

点击查看摘要

Abstract:Large language models (LLMs) have demonstrated great potential for automating the evaluation of natural language generation. Previous frameworks of LLM-as-a-judge fall short in two ways: they either use zero-shot setting without consulting any human input, which leads to low alignment, or fine-tune LLMs on labeled data, which requires a non-trivial number of samples. Moreover, previous methods often provide little reasoning behind automated evaluations. In this paper, we propose HypoEval, Hypothesis-guided Evaluation framework, which first uses a small corpus of human evaluations to generate more detailed rubrics for human judgments and then incorporates a checklist-like approach to combine LLM’s assigned scores on each decomposed dimension to acquire overall scores. With only 30 human evaluations, HypoEval achieves state-of-the-art performance in alignment with both human rankings (Spearman correlation) and human scores (Pearson correlation), on average outperforming G-Eval by 11.86% and fine-tuned Llama-3.1-8B-Instruct with at least 3 times more human evaluations by 11.95%. Furthermore, we conduct systematic studies to assess the robustness of HypoEval, highlighting its effectiveness as a reliable and interpretable automated evaluation framework.
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[NLP-64] R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents

【速读】：该论文旨在解决开源模型在实际软件工程（SWE）任务（如解决GITHUB问题）中面临的两个关键挑战：1）可扩展的执行环境 curated；2）测试时计算的最优扩展。论文的关键解决方案包括AgentGym，这是一个包含超过8700个任务的可执行健身环境，用于训练真实的软件工程师代理。AgentGym的核心贡献有两点：1）SYNGEN，一种合成数据 curated 配方，通过直接从提交中使用测试生成和回译来实现可扩展的可执行环境 curated，从而减少对人工编写的问题或单元测试的依赖。这种方法使我们的32B模型在SWE-Bench Verified基准上的pass@1性能达到34.4%；2）混合测试时扩展，深入分析了两种测试时扩展轴：基于执行的验证器和无执行验证器，展示了它们互补的优势和局限性。基于测试的验证器区分度低，而无执行验证器则存在偏见且常依赖于风格特征。令人惊讶的是，尽管每种方法单独应用时性能饱和在42-43%，但通过利用它们的互补优势，可以显著提高性能。总体而言，我们的方法在SWE-Bench Verified基准上达到了51%，代表了开源权重软件工程师代理的新技术水平，并首次展示了与专有模型（如o1、o1-preview和sonnet-3.5-v2）竞争的能力。我们还将开源我们的环境、模型和代理轨迹。

链接: https://arxiv.org/abs/2504.07164
作者: Naman Jain,Jaskirat Singh,Manish Shetty,Liang Zheng,Koushik Sen,Ion Stoica
机构: UC Berkeley (加州大学伯克利分校); Australian National University (澳大利亚国立大学)
类目: oftware Engineering (cs.SE); Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: Website: this https URL

点击查看摘要

Abstract:Improving open-source models on real-world SWE tasks (solving GITHUB issues) faces two key challenges: 1) scalable curation of execution environments to train these models, and, 2) optimal scaling of test-time compute. We introduce AgentGym, the largest procedurally-curated executable gym environment for training real-world SWE-agents, consisting of more than 8.7K tasks. AgentGym is powered by two main contributions: 1) SYNGEN: a synthetic data curation recipe that enables scalable curation of executable environments using test-generation and back-translation directly from commits, thereby reducing reliance on human-written issues or unit tests. We show that this enables more scalable training leading to pass@1 performance of 34.4% on SWE-Bench Verified benchmark with our 32B model. 2) Hybrid Test-time Scaling: we provide an in-depth analysis of two test-time scaling axes; execution-based and execution-free verifiers, demonstrating that they exhibit complementary strengths and limitations. Test-based verifiers suffer from low distinguishability, while execution-free verifiers are biased and often rely on stylistic features. Surprisingly, we find that while each approach individually saturates around 42-43%, significantly higher gains can be obtained by leveraging their complementary strengths. Overall, our approach achieves 51% on the SWE-Bench Verified benchmark, reflecting a new state-of-the-art for open-weight SWE-agents and for the first time showing competitive performance with proprietary models such as o1, o1-preview and sonnet-3.5-v2 (with tools). We will open-source our environments, models, and agent trajectories.
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[NLP-65] Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models

【速读】：该论文试图解决如何通过轻量级架构实现高效的推理能力，同时保持通用能力的问题。解决方案的关键在于精心设计的数据清洗（data curation）和创新的训练范式（training paradigms），通过对开源的混合专家（Mixture-of-Experts, MoE）大语言模型（Large Language Models, LLMs）Ling-Lite进行进一步训练，使其在仅激活27.5亿参数的情况下，具备全面且强大的推理能力，涵盖从简单到复杂的各类推理任务，同时保持指令跟随（instruction following）、工具使用（tool use）和知识保留（knowledge retention）等通用能力。

链接: https://arxiv.org/abs/2504.07158
作者: Ling Team:Caizhi Tang,Chilin Fu,Chunwei Wu,Jia Guo,Jianwen Wang,Jingyu Hu,Liang Jiang,Meng Li,Peng Jiao,Pingping Liu,Shaomian Zheng,Shiwei Liang,Shuaicheng Li,Yalin Zhang,Yingting Wu,Yongkang Liu,Zhenyu Huang
机构: AI@Ant Group (蚂蚁集团)
类目: Machine Learning (cs.LG); Computation and Language (cs.CL)
备注: 10 pages

点击查看摘要

Abstract:This technical report presents Ring-Lite-Distill, a lightweight reasoning model derived from our open-source Mixture-of-Experts (MoE) Large Language Models (LLMs) Ling-Lite. This study demonstrates that through meticulous high-quality data curation and ingenious training paradigms, the compact MoE model Ling-Lite can be further trained to achieve exceptional reasoning capabilities, while maintaining its parameter-efficient architecture with only 2.75 billion activated parameters, establishing an efficient lightweight reasoning architecture. In particular, in constructing this model, we have not merely focused on enhancing advanced reasoning capabilities, exemplified by high-difficulty mathematical problem solving, but rather aimed to develop a reasoning model with more comprehensive competency coverage. Our approach ensures coverage across reasoning tasks of varying difficulty levels while preserving generic capabilities, such as instruction following, tool use, and knowledge retention. We show that, Ring-Lite-Distill’s reasoning ability reaches a level comparable to DeepSeek-R1-Distill-Qwen-7B, while its general capabilities significantly surpass those of DeepSeek-R1-Distill-Qwen-7B. The models are accessible at this https URL
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[NLP-66] DeepSeek -R1 Thoughtology: Lets think about LLM Reasoning

【速读】：本文旨在研究大型推理模型（如DeepSeek-R1）在复杂问题求解中的新范式及其潜在影响。不同于传统大型语言模型（LLMs）直接生成答案的方式，DeepSeek-R1通过构建详细的多步推理链来“思考”问题，并公开其推理过程，从而开启了“Thoughtology”这一研究领域。论文的关键在于分析推理长度的影响与可控性、长或混淆上下文的管理、文化及安全性问题，以及DeepSeek-R1在人类语言处理和世界建模等认知现象中的地位。研究发现，DeepSeek-R1存在一个推理的“甜蜜点”，额外的推理时间可能反而会降低性能；同时，它倾向于反复纠缠于已探索过的问题表述，阻碍进一步探索。此外，与非推理版本相比，DeepSeek-R1表现出显著的安全隐患，可能威胁到安全对齐的LLMs。因此，本文的关键解决方案在于揭示这些特性，并为如何优化推理模型以提高性能和安全性提供指导。

链接: https://arxiv.org/abs/2504.07128
作者: Sara Vera Marjanović,Arkil Patel,Vaibhav Adlakha,Milad Aghajohari,Parishad BehnamGhader,Mehar Bhatia,Aditi Khandelwal,Austin Kraft,Benno Krojer,Xing Han Lù,Nicholas Meade,Dongchan Shin,Amirhossein Kazemnejad,Gaurav Kamath,Marius Mosbach,Karolina Stańczak,Siva Reddy
机构: 未知
类目: Computation and Language (cs.CL)
备注: 142 pages, pre-print

点击查看摘要

Abstract:Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly “thinking” about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1’s basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-à-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a ‘sweet spot’ of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.
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[NLP-67] Proposed 2MW Wind Turbine for Use in the Governorate of Dhofar at the Sultanate of Oman

【速读】：该论文试图解决在阿曼杜古姆地区设计一个适配于首个商业规模（50MW）风电场的水平轴风力发电机（HAWT）的问题。解决方案的关键在于基于阿曼风速图集确定的最大平均风速参考值（6 m/s 或 21.6 km/h），通过建立数学模型估算风轮机的功率输出，并利用MATLAB程序将目标电功率与设计变量匹配优化。最终设计出具有70米直径、三叶片、24转/分钟转速的风轮机，其输出功率达到2.37 MW，超出目标功率2 MW，同时考虑了约15%的齿轮箱和发电机损耗，为设计提供了可靠依据。

链接: https://arxiv.org/abs/2504.07126
作者: Osama Ahmed Marzouk,Omar Rashid Hamdan Al Badi,Maadh Hamed Salman Al Rashdi,Hamed Mohammed Eid Al Balushi
机构: 未知
类目: Computational Engineering, Finance, and Science (cs.CE); Computation and Language (cs.CL)
备注: 9 pages, 14 figures, 2 tables, 1 computer code, open access, published peer-reviewed journal paper

点击查看摘要

Abstract:In this work, we propose a preliminary design of a horizontal-axis wind turbine (HAWT) as a candidate for the Dhofar Wind Farm project, in the southern Omani Governorate “Dhofar”, at the southwest part of the Sultanate of Oman. This wind farm (under construction) is considered to be the first commercial, utility-scale (50MW) wind farm in the GCC (Gulf Cooperation Council) area. The proposed wind turbine has an expected electricity generation of 2MW. We studied the wind atlas of Oman and from which we determined the maximum possible mean wind speed in the entire Sultanate and built our design based on that reference value, which is 6m/s (21.6km/h). After this, we applied a set of modeling equations that estimate the power output from the wind turbine rotor and matched the target electric power to the design variables using a MATLAB computer code. We reached a suitable design and we present here the distribution of the blade angle (twist angle), and the power per unit span along the rotor blade. The rotor design has 3 blades with a diameter of 70m and a rotational speed of 24rpm. This rotor gives 2.37MW of output power, which exceeds the target 2MW output, allowing for about 15% of power losses in the gearbox and generator. We utilized some commercial designs of wind turbines from different international manufacturers as references for typical limits or recommended values of some design parameters.
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[NLP-68] CLEAR: Contrasting Textual Feedback with Experts and Amateurs for Reasoning NAACL

【速读】：该论文试图解决语言模型在复杂推理任务中的性能提升问题。解决方案的关键在于提出了一种名为CLEAR (Contrasting Textual Feedback with Experts and Amateurs for Reasoning) 的新方法，该方法结合了大型“专家”模型和小型“业余”模型的优势。通过让这两种模型分别对语言模型的初始输出提供反馈，并将它们的反馈进行对比与精炼，形成改进后的反馈，进而迭代优化CLEAR的响应。实验结果表明，CLEAR在多项具有挑战性的推理任务中表现出色，包括故事大纲改进、受限生成、数学推理以及毒性缓解等。

链接: https://arxiv.org/abs/2504.07116
作者: Andrew Rufail,Daniel Kim,Sean O’Brien,Kevin Zhu
机构: Algoverse AI Research
类目: Computation and Language (cs.CL)
备注: Accepted at the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Student Research Workshop (SRW)

点击查看摘要

Abstract:We introduce CLEAR (Contrasting Textual Feedback with Experts and Amateurs for Reasoning), a novel approach to language model reasoning that leverages the strengths of a larger (expert) model and smaller (amateur) model. The expert and amateur models each provide feedback on a model’s initial output and are contrasted with each other into refined feedback. This feedback is subsequently applied to iteratively improve CLEAR’s responses. Our experiments demonstrate that CLEAR outperforms state-of-the-art methods in several challenging reasoning tasks, including story outline improvement (up to 19.6% relative increase in interestingness), constrained generation (up to 18.5% increase in coverage), mathematical reasoning (up to 6.7% improvement in accuracy) and mitigation of toxicity (decrease of up to 22% in toxicity).
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[NLP-69] EqualizeIR: Mitigating Linguistic Biases in Retrieval Models NAACL2025

【速读】：该论文旨在解决现有信息检索（Information Retrieval, IR）模型在处理语言复杂性不同的查询时表现出显著偏倚的问题，即这些模型在处理语言较简单或较复杂的查询时表现良好，但在面对语言复杂性和简单性相反的查询时性能下降。为了解决这一问题，论文提出了一种名为EqualizeIR的框架，其关键是通过利用一个带有语言偏倚的弱学习器来捕捉IR数据集中的语言偏倚，并通过对该偏倚弱学习器的预测进行正则化和精炼，训练出一个鲁棒的模型。这种方法有效防止了鲁棒模型过度拟合到数据中的特定语言模式，从而减少了语言简单和复杂查询之间的性能差异，同时提升了整体检索性能。

链接: https://arxiv.org/abs/2504.07115
作者: Jiali Cheng,Hadi Amiri
机构: University of Massachusetts Lowell (马萨诸塞大学洛厄尔分校)
类目: Computation and Language (cs.CL); Machine Learning (cs.LG)
备注: NAACL 2025

点击查看摘要

Abstract:This study finds that existing information retrieval (IR) models show significant biases based on the linguistic complexity of input queries, performing well on linguistically simpler (or more complex) queries while underperforming on linguistically more complex (or simpler) queries. To address this issue, we propose EqualizeIR, a framework to mitigate linguistic biases in IR models. EqualizeIR uses a linguistically biased weak learner to capture linguistic biases in IR datasets and then trains a robust model by regularizing and refining its predictions using the biased weak learner. This approach effectively prevents the robust model from overfitting to specific linguistic patterns in data. We propose four approaches for developing linguistically-biased models. Extensive experiments on several datasets show that our method reduces performance disparities across linguistically simple and complex queries, while improving overall retrieval performance.
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[NLP-70] ChatBench: From Static Benchmarks to Human-AI Evaluation

【速读】：该论文旨在解决如何有效评估人类与大型语言模型（LLMs）协同工作的能力，而非仅关注LLMs在孤立环境下的表现。标准基准如MMLU主要衡量“AI-alone”能力，而未能反映人机协作的真实情况。为解决此问题，论文设计并实施了一项用户研究，将MMLU问题转化为用户与AI之间的对话形式，并构建了一个包含“AI-alone”、“user-alone”及“user-AI”数据的新数据集ChatBench，涵盖396个问题及两种LLMs的144K个答案和7,336段用户-AI对话。论文的关键发现是“AI-alone”准确性无法预测“user-AI”准确性，且在数学、物理及道德推理等多个领域存在显著差异。为改进评估，论文提出通过微调用户模拟器来提升其估计“user-AI”准确性的能力，在未见问题上的相关性提高了20多个点，从而开启了规模化交互评估的可能性。因此，该研究的核心解决方案在于创建一个能够真实反映人机协作场景的数据集，并开发相应的评估方法以更准确地衡量协同工作的效果。

链接: https://arxiv.org/abs/2504.07114
作者: Serina Chang,Ashton Anderson,Jake M. Hofman
机构: Microsoft Research (微软研究); University of California, Berkeley (加州大学伯克利分校); University of Toronto (多伦多大学)
类目: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
备注:

点击查看摘要

Abstract:With the rapid adoption of LLM-based chatbots, there is a pressing need to evaluate what humans and LLMs can achieve together. However, standard benchmarks, such as MMLU, measure LLM capabilities in isolation (i.e., “AI-alone”). Here, we design and conduct a user study to convert MMLU questions into user-AI conversations, by seeding the user with the question and having them carry out a conversation with the LLM to answer their question. We release ChatBench, a new dataset with AI-alone, user-alone, and user-AI data for 396 questions and two LLMs, including 144K answers and 7,336 user-AI conversations. We find that AI-alone accuracy fails to predict user-AI accuracy, with significant differences across multiple subjects (math, physics, and moral reasoning), and we analyze the user-AI conversations to provide insight into how they diverge from AI-alone benchmarks. Finally, we show that fine-tuning a user simulator on a subset of ChatBench improves its ability to estimate user-AI accuracies, increasing correlation on held-out questions by more than 20 points, creating possibilities for scaling interactive evaluation.
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[NLP-71] How Robust Are Router-LLM s? Analysis of the Frag ility of LLM Routing Capabilities

【速读】：该论文试图解决大型语言模型（Large Language Model, LLM）路由在实际应用中存在的评估基准局限性问题。当前评估基准主要关注通用模型能力，而忽视了任务特定行为以及隐私、安全性和通过偏好数据引入的潜在后门漏洞等重要问题。为了解决这些问题，论文提出了DSC（Diverse, Simple, and Categorized）基准框架，它不仅涵盖了多种查询类型（如编码、翻译、数学、人类指令、常识和LLM越狱），还整合了隐私和安全性评估以揭示隐藏风险。关键在于通过这一全面的评估框架，揭示现有基于偏好数据的路由器通常会做出次优的、类别驱动型决策的问题，并强调需要更平衡的方法来优化效率与安全性之间的权衡。

链接: https://arxiv.org/abs/2504.07113
作者: Aly M. Kassem,Bernhard Schölkopf,Zhijing Jin
机构: Independent; MPI for Intelligent Systems (马克斯·普朗克智能系统研究所); MPI & University of Toronto (马克斯·普朗克智能系统研究所 & 多伦多大学)
类目: Computation and Language (cs.CL); Databases (cs.DB)
备注:

点击查看摘要

Abstract:Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance by dynamically assigning queries to the most appropriate model based on query complexity. Despite recent advances showing that preference-data-based routers can outperform traditional methods, current evaluation benchmarks remain limited. They largely focus on general model capabilities while overlooking task-specific behaviors and critical concerns such as privacy, safety, and potential backdoor vulnerabilities introduced through preference data. In response, we propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types, including coding, translation, mathematics, human instructions, general knowledge, and LLM jailbreaking. Additionally, it integrates privacy and safety assessments to reveal hidden risks. Our experiments on three preference-based routers and two commercial counterparts demonstrate that while these systems improve efficiency, they often make suboptimal, category-driven decisions. For instance, a BERT-based router directs all coding and mathematics queries to the most powerful LLM even when simpler models would suffice, while routing jailbreaking attempts to weaker models, thereby elevating safety risks.
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[NLP-72] OSCAR: Online Soft Compression And Reranking

【速读】：该论文旨在解决 Retrieval-Augmented Generation (RAG) 管道在外部知识检索规模扩大时计算开销过高的问题。为应对这一挑战，论文提出了一种名为 OSCAR 的新型查询依赖在线软压缩方法，其关键在于通过在推理阶段动态压缩检索到的信息，消除了存储开销并实现了更高的压缩率，同时保持了性能不下降。此外，OSCAR 还扩展了重排序功能，进一步优化了 RAG 管道的效率。实验结果表明，对于参数量从 10 亿到 240 亿的大型语言模型 (LLMs)，OSCAR 实现了 2 到 5 倍的推理速度提升，并且几乎无损准确性。

链接: https://arxiv.org/abs/2504.07109
作者: Maxime Louis,Thibault Formal,Hervé Dejean,Stéphane Clinchant
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge, leading to improved accuracy and relevance. However, scaling RAG pipelines remains computationally expensive as retrieval sizes grow. To address this, we introduce OSCAR, a novel query-dependent online soft compression method that reduces computational overhead while preserving performance. Unlike traditional hard compression methods, which shorten retrieved texts, or soft compression approaches, which map documents to continuous embeddings offline, OSCAR dynamically compresses retrieved information at inference time, eliminating storage overhead and enabling higher compression rates. Additionally, we extend OSCAR to simultaneously perform reranking, further optimizing the efficiency of the RAG pipeline. Our experiments demonstrate state-of-the-art performance with a 2-5x speed-up in inference and minimal to no loss in accuracy for LLMs ranging from 1B to 24B parameters. The models are available at: this https URL.
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[NLP-73] Relevance Isnt All You Need: Scaling RAG Systems With Inference-Time Compute Via Multi-Criteria Reranking

【速读】：该论文旨在解决现有基于 Retrieval Augmented Generation (RAG) 的大语言模型系统在优化过程中仅关注上下文相关性（Context Relevance）而导致的信息瓶颈问题，以及由此引发的下游响应质量下降。论文的关键在于提出了一种新的方法 RErank BEyond reLevance (REBEL)，通过引入多准则优化（multi-criteria optimization），结合 Chain-of-Thought 提示技术（以及可选的 Multi-Turn 对话机制），在保证上下文相关性的基础上进一步提升答案质量。这种方法使得 RAG 系统能够在推理时间计算资源的使用上实现更好的扩展性，并在性能与速度之间提供新的权衡曲线，从而同时提高检索上下文的相关性和回答质量。

链接: https://arxiv.org/abs/2504.07104
作者: Will LeVine,Bijan Varjavand
机构: Microsoft; Scale AI
类目: Information Retrieval (cs.IR); Computation and Language (cs.CL); Machine Learning (cs.LG)
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Abstract:Modern Large Language Model (LLM) systems typically rely on Retrieval Augmented Generation (RAG) which aims to gather context that is useful for response generation. These RAG systems typically optimize strictly towards retrieving context that is maximally relevant to the query. However, conventional theory suggests that retrieval systems which seek to maximize context relevance without any additional explicit criteria can create information bottlenecks. We reaffirm this finding in the modern age of LLM’s by showing that in standard RAG pipelines, maximizing for context relevance alone can degrade downstream response quality. In response, we show evaluations of existing RAG methods which account for both context relevance and answer quality. These evaluations introduce a novel finding that existing RAG systems scale poorly with inference time compute usage when considering our combined metric. We introduce “RErank BEyond reLevance (REBEL)”, which enables RAG systems to scale with inference-time compute via injection of multi-criteria optimization using Chain-of-Thought prompting (and optionally Multi-Turn dialogue). Ultimately, this enables a new performance/speed tradeoff curve, where RAG systems are able to achieve both higher relevance of retrieved contexts and superior answer quality as inference time increases. Code for the implementation of our method in llama-index can be found at the following PR: this https URL. Code for running experiments using this llama-index implementation can be found at this https URL.
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[NLP-74] FG-RAG : Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG

【速读】：该论文旨在解决现有基于GraphRAG的系统在Query-Focused Summarization (QFS)任务中存在的两个主要问题：一是缺乏对特定查询的感知，导致生成的响应内容过于粗粒度；二是检索到的内容缺乏足够的上下文信息，难以生成全面且多样的响应。为了解决这些问题，论文提出了一种名为Context-Aware Fine-Grained Graph RAG (FG-RAG) 的解决方案。其关键在于通过引入Context-Aware Entity Expansion增强图检索中的实体覆盖范围，以提供更丰富的上下文信息，并利用Query-Level Fine-Grained Summarization在响应生成过程中融入细粒度细节，从而提升对查询的感知能力。实验结果表明，FG-RAG在多个评估指标上优于其他RAG系统。

链接: https://arxiv.org/abs/2504.07103
作者: Yubin Hong,Chaofan Li,Jingyi Zhang,Yingxia Shao
机构: Beijing University of Posts and Telecommunications (北京邮电大学)
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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Abstract:Retrieval-Augmented Generation (RAG) enables large language models to provide more precise and pertinent responses by incorporating external knowledge. In the Query-Focused Summarization (QFS) task, GraphRAG-based approaches have notably enhanced the comprehensiveness and diversity of generated responses. However, existing GraphRAG-based approaches predominantly focus on coarse-grained information summarization without being aware of the specific query, and the retrieved content lacks sufficient contextual information to generate comprehensive responses. To address the deficiencies of current RAG systems, we propose Context-Aware Fine-Grained Graph RAG (FG-RAG) to enhance the performance of the QFS task. FG-RAG employs Context-Aware Entity Expansion in graph retrieval to expand the coverage of retrieved entities in the graph, thus providing enough contextual information for the retrieved content. Furthermore, FG-RAG utilizes Query-Level Fine-Grained Summarization to incorporate fine-grained details during response generation, enhancing query awareness for the generated summarization. Our evaluation demonstrates that FG-RAG outperforms other RAG systems in multiple metrics of comprehensiveness, diversity, and empowerment when handling the QFS task. Our implementation is available at this https URL.
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[NLP-75] EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models

【速读】：该论文试图解决自然语言处理（NLP）系统在应对人类语言多样性方面存在的挑战，特别是现有基准测试往往忽视了语言内部的变化，导致非标准方言使用者的服务不足。为了解决这一问题，论文引入了一个名为EnDive（英语多样性）的新基准，该基准评估了五种广泛使用的大型语言模型（LLMs）在语言理解、算法推理、数学和逻辑任务中的表现。解决方案的关键在于通过使用来自母语者的验证示例，采用少量提示的方法将标准美式英语数据集翻译成五种代表性不足的方言，并通过流畅性评估、偏好测试和语义相似性度量与基于规则的方法进行比较。此外，通过人类评估确认了高质量的翻译结果，并创建了一个具有挑战性的数据集来揭示显著的性能差异，从而推动了对口音感知的NLP发展，揭示模型偏差并促进更公平的语言技术。

链接: https://arxiv.org/abs/2504.07100
作者: Abhay Gupta,Jacob Cheung,Philip Meng,Shayan Sayyed,Austen Liao,Kevin Zhu,Sean O’Brien
机构: Algoverse AI Research
类目: Computation and Language (cs.CL)
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Abstract:The diversity of human language, shaped by social, cultural, and regional influences, presents significant challenges for natural language processing (NLP) systems. Existing benchmarks often overlook intra-language variations, leaving speakers of non-standard dialects underserved. To address this gap, we introduce EnDive (English Diversity), a benchmark that evaluates five widely-used large language models (LLMs) across tasks in language understanding, algorithmic reasoning, mathematics, and logic. Our framework translates Standard American English datasets into five underrepresented dialects using few-shot prompting with verified examples from native speakers, and compare these translations against rule-based methods via fluency assessments, preference tests, and semantic similarity metrics. Human evaluations confirm high translation quality, with average scores of at least 6.02/7 for faithfulness, fluency, and formality. By filtering out near-identical translations, we create a challenging dataset that reveals significant performance disparities - models consistently underperform on dialectal inputs compared to Standard American English. EnDive thus advances dialect-aware NLP by uncovering model biases and promoting more equitable language technologies.
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计算机视觉

[CV-0] PixelFlow: Pixel-Space Generative Models with Flow

【速读】：该论文试图解决现有图像生成模型过度依赖预训练变分自编码器（Variational Autoencoder, VAE）及其潜在空间表示的问题，提出了一种直接在原始像素空间（raw pixel space）操作的图像生成模型家族PixelFlow。解决方案的关键在于通过高效的级联流建模（cascade flow modeling），PixelFlow实现了像素空间中的可负担计算成本，并使整个模型能够端到端可训练，从而简化了图像生成流程并提升了生成质量。

链接: https://arxiv.org/abs/2504.07963
作者: Shoufa Chen,Chongjian Ge,Shilong Zhang,Peize Sun,Ping Luo
机构: The University of Hong Kong (香港大学); Adobe (Adobe)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We present PixelFlow, a family of image generation models that operate directly in the raw pixel space, in contrast to the predominant latent-space models. This approach simplifies the image generation process by eliminating the need for a pre-trained Variational Autoencoder (VAE) and enabling the whole model end-to-end trainable. Through efficient cascade flow modeling, PixelFlow achieves affordable computation cost in pixel space. It achieves an FID of 1.98 on 256 \times 256 ImageNet class-conditional image generation benchmark. The qualitative text-to-image results demonstrate that PixelFlow excels in image quality, artistry, and semantic control. We hope this new paradigm will inspire and open up new opportunities for next-generation visual generation models. Code and models are available at this https URL.
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[CV-1] GLUS: Global-Local Reasoning Unified into A Single Large Language Model for Video Segmentation CVPR2025

【速读】：该论文致力于解决多模态大语言模型（Multi-modal Large Language Models, MLLMs）在视频对象分割（Referring Video Object Segmentation, RefVOS）任务中的挑战。传统基于MLLM的方法通常难以兼顾“Ref”和“VOS”之间的权衡：要么专注于少数关键帧以实现全局推理，要么跟踪连续帧以实现局部推理，但都需要依赖外部视频对象分割（Video Object Segmentation, VOS）或帧选择器来缓解另一端的问题。论文的关键解决方案在于提出了一种名为GLUS的新框架，通过引入稀疏的“上下文帧”提供全局信息，并利用连续的“查询帧”进行局部目标跟踪，实现了全局与局部一致性在单一视频分割MLLM中的统一。此外，通过与预训练的VOS记忆库联合训练，该框架能够同时处理短时序和长时序信息。为了提高信息效率，论文还引入了目标对比学习以区分难例假阳性对象，并设计了自精炼框架以识别关键帧并执行传播。这些创新共同构成了一个简单而有效的基线方法，在MeViS和Ref-Youtube-VOS基准测试中达到了新的技术水平。

链接: https://arxiv.org/abs/2504.07962
作者: Lang Lin,Xueyang Yu,Ziqi Pang,Yu-Xiong Wang
机构: University of Illinois Urbana-Champaign (伊利诺伊大学厄巴纳-香槟分校)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This paper proposes a novel framework utilizing multi-modal large language models (MLLMs) for referring video object segmentation (RefVOS). Previous MLLM-based methods commonly struggle with the dilemma between “Ref” and “VOS”: they either specialize in understanding a few key frames (global reasoning) or tracking objects on continuous frames (local reasoning), and rely on external VOS or frame selectors to mitigate the other end of the challenge. However, our framework GLUS shows that global and local consistency can be unified into a single video segmentation MLLM: a set of sparse “context frames” provides global information, while a stream of continuous “query frames” conducts local object tracking. This is further supported by jointly training the MLLM with a pre-trained VOS memory bank to simultaneously digest short-range and long-range temporal information. To improve the information efficiency within the limited context window of MLLMs, we introduce object contrastive learning to distinguish hard false-positive objects and a self-refined framework to identify crucial frames and perform propagation. By collectively integrating these insights, our GLUS delivers a simple yet effective baseline, achieving new state-of-the-art for MLLMs on the MeViS and Ref-Youtube-VOS benchmark. Our project page is at this https URL.
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[CV-2] Geo4D: Leverag ing Video Generators for Geometric 4D Scene Reconstruction

【速读】：该论文试图解决单目动态场景三维重建（Monocular 3D Reconstruction of Dynamic Scenes）的问题。解决方案的关键在于利用视频扩散模型（Video Diffusion Models）捕获的强大动态先验知识，并通过引入Geo4D方法，在仅使用合成数据（Synthetic Data）的情况下进行训练，同时实现对真实数据的零样本泛化（Zero-shot Generalization）。Geo4D通过预测点云图、深度图和光线图等多种互补几何模态，并在推理阶段采用多模态对齐算法（Multi-modal Alignment Algorithm）以及滑动窗口技术（Sliding Windows），实现了长视频的鲁棒且精确的四维重建（4D Reconstruction）。

链接: https://arxiv.org/abs/2504.07961
作者: Zeren Jiang,Chuanxia Zheng,Iro Laina,Diane Larlus,Andrea Vedaldi
机构: Visual Geometry Group, University of Oxford (牛津大学视觉几何组); Naver Labs Europe (Naver欧洲实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic prior captured by such video models, Geo4D can be trained using only synthetic data while generalizing well to real data in a zero-shot manner. Geo4D predicts several complementary geometric modalities, namely point, depth, and ray maps. It uses a new multi-modal alignment algorithm to align and fuse these modalities, as well as multiple sliding windows, at inference time, thus obtaining robust and accurate 4D reconstruction of long videos. Extensive experiments across multiple benchmarks show that Geo4D significantly surpasses state-of-the-art video depth estimation methods, including recent methods such as MonST3R, which are also designed to handle dynamic scenes.
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[CV-3] VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning

【速读】：该论文旨在解决当前图像生成任务中主流方法依赖于构建任务特定模型的局限性，这些模型在支持广泛需求时效率较低的问题。同时，尽管已有通用模型尝试克服这一限制，但它们面临通用任务指令、合适的任务分布以及统一架构设计等关键挑战。为了解决这些问题，论文提出了VisualCloze，这是一种通用图像生成框架，支持广泛的领域内任务、对未见过任务的泛化能力、多任务的无缝整合以及逆向生成能力。关键解决方案在于引入视觉上下文学习机制，使模型能够从视觉演示中识别任务，而非依赖基于语言的任务指令，从而减少任务歧义并增强泛化能力。此外，通过构建Graph200K图结构数据集，建立相互关联的任务以提高任务密度和跨任务的可迁移知识，并利用预训练的图像补丁模型的强大生成先验，进一步优化统一图像生成的目标一致性。

链接: https://arxiv.org/abs/2504.07960
作者: Zhong-Yu Li,Ruoyi Du,Juncheng Yan,Le Zhuo,Zhen Li,Peng Gao,Zhanyu Ma,Ming-Ming Cheng
机构: VCIP, CS, Nankai University (南开大学); Beijing University of Posts and Telecommunications (北京邮电大学); Tsinghua University (清华大学); Shanghai AI Laboratory (上海人工智能实验室); The Chinese University of Hong Kong (香港中文大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recent progress in diffusion models significantly advances various image generation tasks. However, the current mainstream approach remains focused on building task-specific models, which have limited efficiency when supporting a wide range of different needs. While universal models attempt to address this limitation, they face critical challenges, including generalizable task instruction, appropriate task distributions, and unified architectural design. To tackle these challenges, we propose VisualCloze, a universal image generation framework, which supports a wide range of in-domain tasks, generalization to unseen ones, unseen unification of multiple tasks, and reverse generation. Unlike existing methods that rely on language-based task instruction, leading to task ambiguity and weak generalization, we integrate visual in-context learning, allowing models to identify tasks from visual demonstrations. Meanwhile, the inherent sparsity of visual task distributions hampers the learning of transferable knowledge across tasks. To this end, we introduce Graph200K, a graph-structured dataset that establishes various interrelated tasks, enhancing task density and transferable knowledge. Furthermore, we uncover that our unified image generation formulation shared a consistent objective with image infilling, enabling us to leverage the strong generative priors of pre-trained infilling models without modifying the architectures.
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[CV-4] CCMNet: Leverag ing Calibrated Color Correction Matrices for Cross-Camera Color Constancy

【速读】：本文旨在解决跨相机计算色恒常性（cross-camera computational color constancy）的问题，即在无需针对新相机重新训练的情况下，使白平衡算法能够适应不同相机的特性。论文的关键解决方案在于利用图像信号处理器（ISP）中预校准的颜色校正矩阵（Color Correction Matrices, CCMs），将标准空间（如CIE XYZ）中的预定义照明颜色（沿普朗克轨迹Plankian locus）映射到待测相机的原始色彩空间，并进一步将其编码为紧凑的相机指纹嵌入（Camera Fingerprint Embedding, CFE）。这种嵌入方式使得网络能够适应未见过的相机。此外，为了防止因训练数据量有限而导致的过拟合，论文还引入了一种基于插值的数据增强技术，以扩充相机及其CCMs的多样性。实验结果表明，该方法在多个数据集和主干网络上实现了最先进的跨相机色恒常性性能，同时保持了轻量化且仅依赖于ISP中已有的数据。

链接: https://arxiv.org/abs/2504.07959
作者: Dongyoung Kim,Mahmoud Afifi,Dongyun Kim,Michael S. Brown,Seon Joo Kim
机构: Yonsei University (延世大学); AI Center - Toronto, Samsung Electronics (三星电子多伦多AI中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Computational color constancy, or white balancing, is a key module in a camera’s image signal processor (ISP) that corrects color casts from scene lighting. Because this operation occurs in the camera-specific raw color space, white balance algorithms must adapt to different cameras. This paper introduces a learning-based method for cross-camera color constancy that generalizes to new cameras without retraining. Our method leverages pre-calibrated color correction matrices (CCMs) available on ISPs that map the camera’s raw color space to a standard space (e.g., CIE XYZ). Our method uses these CCMs to transform predefined illumination colors (i.e., along the Planckian locus) into the test camera’s raw space. The mapped illuminants are encoded into a compact camera fingerprint embedding (CFE) that enables the network to adapt to unseen cameras. To prevent overfitting due to limited cameras and CCMs during training, we introduce a data augmentation technique that interpolates between cameras and their CCMs. Experimental results across multiple datasets and backbones show that our method achieves state-of-the-art cross-camera color constancy while remaining lightweight and relying only on data readily available in camera ISPs.
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[CV-5] Detect Anything 3D in the Wild

【速读】：该论文旨在解决现有深度学习方法在开放世界场景下（zero-shot 场景）对未知类别物体及任意相机配置的泛化能力不足的问题。论文提出了一种名为 DetAny3D 的可提示化 3D 检测基础模型，能够仅利用单目输入检测任何新颖物体及其在任意相机配置下的姿态。为应对标注 3D 数据稀缺的挑战，DetAny3D 利用了广泛预训练的 2D 基础模型中的丰富先验知识。其解决方案的关键在于引入两个核心模块：2D Aggregator 模块用于对齐来自不同 2D 基础模型的特征，以及带有 Zero-Embedding Mapping 的 3D Interpreter 模块，以缓解 2D 到 3D 知识迁移过程中的灾难性遗忘问题。实验结果验证了 DetAny3D 在未见类别和新相机配置上的强大泛化能力，并在域内数据上超越了许多竞争对手。

链接: https://arxiv.org/abs/2504.07958
作者: Hanxue Zhang,Haoran Jiang,Qingsong Yao,Yanan Sun,Renrui Zhang,Hao Zhao,Hongyang Li,Hongzi Zhu,Zetong Yang
机构: OpenDriveLab at Shanghai AI Laboratory (上海人工智能实验室); Shanghai Jiao Tong University (上海交通大学); Fudan University (复旦大学); Stanford University (斯坦福大学); CUHK MMLab (香港中文大学多媒体实验室); Tsinghua University (清华大学); GAC R&D Center (广汽研发中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Despite the success of deep learning in close-set 3D object detection, existing approaches struggle with zero-shot generalization to novel objects and camera configurations. We introduce DetAny3D, a promptable 3D detection foundation model capable of detecting any novel object under arbitrary camera configurations using only monocular inputs. Training a foundation model for 3D detection is fundamentally constrained by the limited availability of annotated 3D data, which motivates DetAny3D to leverage the rich prior knowledge embedded in extensively pre-trained 2D foundation models to compensate for this scarcity. To effectively transfer 2D knowledge to 3D, DetAny3D incorporates two core modules: the 2D Aggregator, which aligns features from different 2D foundation models, and the 3D Interpreter with Zero-Embedding Mapping, which mitigates catastrophic forgetting in 2D-to-3D knowledge transfer. Experimental results validate the strong generalization of our DetAny3D, which not only achieves state-of-the-art performance on unseen categories and novel camera configurations, but also surpasses most competitors on in-domain data.DetAny3D sheds light on the potential of the 3D foundation model for diverse applications in real-world scenarios, e.g., rare object detection in autonomous driving, and demonstrates promise for further exploration of 3D-centric tasks in open-world settings. More visualization results can be found at DetAny3D project page.
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[CV-6] MM-IFEngine: Towards Multimodal Instruction Following

【速读】：该论文旨在解决多模态大型语言模型（MLLMs）在指令跟随（Instruction Following, IF）能力方面的训练数据不足、现有基准过于简单且指令原子化以及评估策略不精确的问题。为应对这些挑战，论文的关键解决方案是提出MM-IFEngine，这是一个高效的数据生成管道，用于创建高质量的图像-指令对。由此产生的大规模、多样化的训练数据集MM-IFInstruct-23k适用于监督微调（Supervised Fine-Tuning, SFT），并通过扩展形成MM-IFDPO-23k以支持直接偏好优化（Direct Preference Optimization, DPO）。此外，论文还引入了MM-IFEval，这是一个包含输出响应组合级约束和与输入图像相关的感知级约束的多样化多模态指令跟随基准，并结合基于规则的评估和法官模型构建了一个全面的评估流程。实验表明，在MM-IFInstruct-23k和MM-IFDPO-23k上进行微调显著提升了多种IF基准的表现，如MM-IFEval (+10.2%)、MIA (+7.6%) 和 IFEval (+12.3%)。

链接: https://arxiv.org/abs/2504.07957
作者: Shengyuan Ding,Shenxi Wu,Xiangyu Zhao,Yuhang Zang,Haodong Duan,Xiaoyi Dong,Pan Zhang,Yuhang Cao,Dahua Lin,Jiaqi Wang
机构: Fudan University (复旦大学); Shanghai AI Laboratory (上海人工智能实验室); Shanghai Jiaotong University (上海交通大学); The Chinese University of Hong Kong (香港中文大学); CPII under InnoHK (InnoHK旗下的CPII); Shanghai Innovation Institute (上海创新研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The Instruction Following (IF) ability measures how well Multi-modal Large Language Models (MLLMs) understand exactly what users are telling them and whether they are doing it right. Existing multimodal instruction following training data is scarce, the benchmarks are simple with atomic instructions, and the evaluation strategies are imprecise for tasks demanding exact output constraints. To address this, we present MM-IFEngine, an effective pipeline to generate high-quality image-instruction pairs. Our MM-IFEngine pipeline yields large-scale, diverse, and high-quality training data MM-IFInstruct-23k, which is suitable for Supervised Fine-Tuning (SFT) and extended as MM-IFDPO-23k for Direct Preference Optimization (DPO). We further introduce MM-IFEval, a challenging and diverse multi-modal instruction-following benchmark that includes (1) both compose-level constraints for output responses and perception-level constraints tied to the input images, and (2) a comprehensive evaluation pipeline incorporating both rule-based assessment and judge model. We conduct SFT and DPO experiments and demonstrate that fine-tuning MLLMs on MM-IFInstruct-23k and MM-IFDPO-23k achieves notable gains on various IF benchmarks, such as MM-IFEval (+10.2 % ), MIA (+7.6 % ), and IFEval (+12.3 % ). The full data and evaluation code will be released on this https URL.
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[CV-7] BoxDreamer: Dreaming Box Corners for Generalizable Object Pose Estimation

【速读】：该论文旨在解决基于RGB图像在稀疏视图（sparse-view）设置下物体位姿估计（object pose estimation）的泛化能力不足的问题，特别是在存在遮挡（occlusions）的情况下。现有方法虽能估计未见物体的位姿，但其在稀疏参考视图场景中的泛化能力有限，限制了实际应用效果。为克服这些局限性，论文提出了一种新的中间表示方法，将物体边界框的角点（corner points of the object bounding box）作为物体位姿的表征，并通过从稀疏输入视图可靠恢复三维物体角点（3D object corners），以及利用一种新颖的基于参考的点合成器（reference-based point synthesizer）来估计目标视图中的二维角点（2D corner points）。这种方法即使在存在遮挡的复杂场景中也能有效工作。最终，利用这些角点的语义信息建立二维到三维的对应关系（2D-3D correspondences），并通过PnP算法完成物体位姿估计。实验结果表明，该方法显著提升了物体位姿估计的泛化能力，在YCB-Video和Occluded-LINEMOD数据集上的表现优于现有最先进的方法。

链接: https://arxiv.org/abs/2504.07955
作者: Yuanhong Yu,Xingyi He,Chen Zhao,Junhao Yu,Jiaqi Yang,Ruizhen Hu,Yujun Shen,Xing Zhu,Xiaowei Zhou,Sida Peng
机构: State Key Lab of CAD & CG, Zhejiang University (浙江大学国家重点实验室); Ant Group (蚂蚁集团); EPFL (瑞士联邦理工学院); Chongqing University (重庆大学); Northwestern Polytechnical University (西北工业大学); Shenzhen University (深圳大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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点击查看摘要

Abstract:This paper presents a generalizable RGB-based approach for object pose estimation, specifically designed to address challenges in sparse-view settings. While existing methods can estimate the poses of unseen objects, their generalization ability remains limited in scenarios involving occlusions and sparse reference views, restricting their real-world applicability. To overcome these limitations, we introduce corner points of the object bounding box as an intermediate representation of the object pose. The 3D object corners can be reliably recovered from sparse input views, while the 2D corner points in the target view are estimated through a novel reference-based point synthesizer, which works well even in scenarios involving occlusions. As object semantic points, object corners naturally establish 2D-3D correspondences for object pose estimation with a PnP algorithm. Extensive experiments on the YCB-Video and Occluded-LINEMOD datasets show that our approach outperforms state-of-the-art methods, highlighting the effectiveness of the proposed representation and significantly enhancing the generalization capabilities of object pose estimation, which is crucial for real-world applications.
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[CV-8] Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models

【速读】：该论文试图解决的问题是如何构建更有效的多模态通用模型（Multimodal General-Purpose Models），特别是探讨不同架构设计在多模态学习中的优劣。当前主流方法倾向于通过后期融合（Late-Fusion）的方式，将单独预训练的组件（如视觉编码器与大型语言模型LLMs）连接并继续多模态微调，但这种方法是否在本质上优于其他架构仍存疑。论文的关键在于重新审视原生多模态模型（Native Multimodal Models, NMMs）的架构设计，并通过大规模实验研究（涵盖457个具有不同架构和训练混合的数据集的模型），发现早期融合（Early-Fusion）架构相较于后期融合架构并无劣势，甚至在较低参数量下表现出更强性能、更高的训练效率及更好的部署便利性。进一步地，论文提出通过引入专家混合（Mixture of Experts, MoEs）机制，使模型能够学习模态特定的权重，显著提升整体性能。

链接: https://arxiv.org/abs/2504.07951
作者: Mustafa Shukor,Enrico Fini,Victor Guilherme Turrisi da Costa,Matthieu Cord,Joshua Susskind,Alaaeldin El-Nouby
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Building general-purpose models that can effectively perceive the world through multimodal signals has been a long-standing goal. Current approaches involve integrating separately pre-trained components, such as connecting vision encoders to LLMs and continuing multimodal training. While such approaches exhibit remarkable sample efficiency, it remains an open question whether such late-fusion architectures are inherently superior. In this work, we revisit the architectural design of native multimodal models (NMMs)–those trained from the ground up on all modalities–and conduct an extensive scaling laws study, spanning 457 trained models with different architectures and training mixtures. Our investigation reveals no inherent advantage to late-fusion architectures over early-fusion ones, which do not rely on image encoders. On the contrary, early-fusion exhibits stronger performance at lower parameter counts, is more efficient to train, and is easier to deploy. Motivated by the strong performance of the early-fusion architectures, we show that incorporating Mixture of Experts (MoEs) allows for models that learn modality-specific weights, significantly enhancing performance.
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[CV-9] InteractAvatar: Modeling Hand-Face Interaction in Photorealistic Avatars with Deformable Gaussians

【速读】：本文旨在解决数字虚拟人（Digital Avatar）在表达自然行为时手部与身体交互（如手-脸交互）建模不足的问题，这是跨行业（如远程会议、游戏、AR/VR等）的重要挑战。现有3D手部和头部虚拟人模型通常忽视了手-体交互这一关键方面。为应对这一问题，论文提出了InteracttAvatar，这是一种能够真实捕捉动态手部及非刚性手-脸交互的高保真模型。其核心解决方案在于引入了Dynamic Gaussian Hand模型，该模型结合模板模型、3D高斯点 splatting 技术以及动态优化模块，能够捕获基于姿态依赖的变化（如关节运动中产生的细微皱纹和复杂阴影）。此外，手-脸交互模块进一步通过几何与外观动态的建模，再现了常见手势中的微妙变化。实验表明，InteracttAvatar可以从单目或多视角视频中以高精度细节重建手部及手-脸交互，并支持新姿态驱动的动画生成。

链接: https://arxiv.org/abs/2504.07949
作者: Kefan Chen,Sergiu Oprea,Justin Theiss,Sreyas Mohan,Srinath Sridhar,Aayush Prakash
机构: Brown University (布朗大学); Meta Reality Labs (Meta现实实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:With the rising interest from the community in digital avatars coupled with the importance of expressions and gestures in communication, modeling natural avatar behavior remains an important challenge across many industries such as teleconferencing, gaming, and AR/VR. Human hands are the primary tool for interacting with the environment and essential for realistic human behavior modeling, yet existing 3D hand and head avatar models often overlook the crucial aspect of hand-body interactions, such as between hand and face. We present InteracttAvatar, the first model to faithfully capture the photorealistic appearance of dynamic hand and non-rigid hand-face interactions. Our novel Dynamic Gaussian Hand model, combining template model and 3D Gaussian Splatting as well as a dynamic refinement module, captures pose-dependent change, e.g. the fine wrinkles and complex shadows that occur during articulation. Importantly, our hand-face interaction module models the subtle geometry and appearance dynamics that underlie common gestures. Through experiments of novel view synthesis, self reenactment and cross-identity reenactment, we demonstrate that InteracttAvatar can reconstruct hand and hand-face interactions from monocular or multiview videos with high-fidelity details and be animated with novel poses.
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[CV-10] GenEAva: Generating Cartoon Avatars with Fine-Grained Facial Expressions from Realistic Diffusion-based Faces

【速读】：该论文旨在解决现有卡通 avatar 数据集和生成方法难以呈现高度表情丰富的虚拟形象，以及常基于真实世界身份导致隐私问题的挑战。为应对这些难题，论文提出了一种名为 GenEAva 的新框架，其关键是通过微调最先进的文本到图像扩散模型（SDXL）生成细节丰富且表情细腻的面部，并进一步利用风格化模型将逼真的面部转换为卡通 avatar，同时保留身份和表情特征。此外，论文引入了首个包含 135 种精细面部表情的表达性卡通 avatar 数据集 GenEAva 1.0，验证了所提方法在表情表现力上的优越性，并确保生成的 avatar 不包含微调数据中的记忆身份信息。

链接: https://arxiv.org/abs/2504.07945
作者: Hao Yu,Rupayan Mallick,Margrit Betke,Sarah Adel Bargal
机构: Department of Computer Science, Boston University (波士顿大学), USA; Department of Computer Science, Georgetown University (乔治城大学), USA
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Cartoon avatars have been widely used in various applications, including social media, online tutoring, and gaming. However, existing cartoon avatar datasets and generation methods struggle to present highly expressive avatars with fine-grained facial expressions and are often inspired from real-world identities, raising privacy concerns. To address these challenges, we propose a novel framework, GenEAva, for generating high-quality cartoon avatars with fine-grained facial expressions. Our approach fine-tunes a state-of-the-art text-to-image diffusion model to synthesize highly detailed and expressive facial expressions. We then incorporate a stylization model that transforms these realistic faces into cartoon avatars while preserving both identity and expression. Leveraging this framework, we introduce the first expressive cartoon avatar dataset, GenEAva 1.0, specifically designed to capture 135 fine-grained facial expressions, featuring 13,230 expressive cartoon avatars with a balanced distribution across genders, racial groups, and age ranges. We demonstrate that our fine-tuned model generates more expressive faces than the state-of-the-art text-to-image diffusion model SDXL. We also verify that the cartoon avatars generated by our framework do not include memorized identities from fine-tuning data. The proposed framework and dataset provide a diverse and expressive benchmark for future research in cartoon avatar generation.
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[CV-11] HoloPart: Generative 3D Part Amodal Segmentation

【速读】：该论文致力于解决3D部分非模态分割（3D Part Amodal Segmentation）问题，即在物体部分被遮挡的情况下，将3D形状分解为完整且语义上有意义的部分。现有方法仅能识别可见表面区域，无法处理被遮挡的几何结构，限制了其实用性。为应对这一挑战，论文提出了一种两阶段的方法：首先利用现有的3D部分分割技术获取初始的不完整部分片段；其次引入基于扩散模型的新方法HoloPart，完成这些片段以生成完整的3D部分。HoloPart的关键在于其局部注意力机制用于捕捉精细的几何细节，以及全局形状上下文注意力机制确保整体形状一致性。通过新提出的基准测试，证明了HoloPart显著优于现有的形状补全方法，并为几何编辑、动画及材质分配等应用开辟了新的途径。

链接: https://arxiv.org/abs/2504.07943
作者: Yunhan Yang,Yuan-Chen Guo,Yukun Huang,Zi-Xin Zou,Zhipeng Yu,Yangguang Li,Yan-Pei Cao,Xihui Liu
机构: The University of Hong Kong (香港大学); VAST (VirtuAl Specialization Team)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:3D part amodal segmentation–decomposing a 3D shape into complete, semantically meaningful parts, even when occluded–is a challenging but crucial task for 3D content creation and understanding. Existing 3D part segmentation methods only identify visible surface patches, limiting their utility. Inspired by 2D amodal segmentation, we introduce this novel task to the 3D domain and propose a practical, two-stage approach, addressing the key challenges of inferring occluded 3D geometry, maintaining global shape consistency, and handling diverse shapes with limited training data. First, we leverage existing 3D part segmentation to obtain initial, incomplete part segments. Second, we introduce HoloPart, a novel diffusion-based model, to complete these segments into full 3D parts. HoloPart utilizes a specialized architecture with local attention to capture fine-grained part geometry and global shape context attention to ensure overall shape consistency. We introduce new benchmarks based on the ABO and PartObjaverse-Tiny datasets and demonstrate that HoloPart significantly outperforms state-of-the-art shape completion methods. By incorporating HoloPart with existing segmentation techniques, we achieve promising results on 3D part amodal segmentation, opening new avenues for applications in geometry editing, animation, and material assignment.
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[CV-12] MARS: a Multimodal Alignment and Ranking System for Few-Shot Segmentation

【速读】：该论文试图解决Few Shot Segmentation领域中现有方法在掩码选择上仅依赖查询图像与示例图像之间的视觉相似性而导致预测结果次优的问题。论文提出了一种名为MARS的即插即用排名系统，其关键是利用多模态线索（multimodal cues）对掩码提议进行稳健的筛选和合并。具体而言，MARS通过对局部和全局层次上的多模态评分来评估提议掩码，并结合四种评分组件以实现鲁棒的排名。实验验证了这些组件集成的重要性，并展示了MARS能够轻松适配多种现有方法，在多个基准数据集（COCO-20i、Pascal-5i、LVIS-92i和FSS-1000）上取得了新的最优性能。

链接: https://arxiv.org/abs/2504.07942
作者: Nico Catalano,Stefano Samele,Paolo Pertino,Matteo Matteucci
机构: Politecnico di Milano (米兰理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Current Few Shot Segmentation literature lacks a mask selection method that goes beyond visual similarity between the query and example images, leading to suboptimal predictions. We present MARS, a plug-and-play ranking system that leverages multimodal cues to filter and merge mask proposals robustly. Starting from a set of mask predictions for a single query image, we score, filter, and merge them to improve results. Proposals are evaluated using multimodal scores computed at local and global levels. Extensive experiments on COCO-20i, Pascal-5i, LVIS-92i, and FSS-1000 demonstrate that integrating all four scoring components is crucial for robust ranking, validating our contribution. As MARS can be effortlessly integrated with various mask proposal systems, we deploy it across a wide range of top-performer methods and achieve new state-of-the-art results on multiple existing benchmarks. Code will be available upon acceptance.
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[CV-13] Beyond the Frame: Generating 360° Panoramic Videos from Perspective Videos

【速读】：本文旨在解决视频到360°全景视频生成（Video-to-360° Generation）的问题，即从普通视角视频输入，生成与原始视频一致的完整全景视频。不同于标准视频生成任务，该任务的输出视野显著扩大，模型需深入理解场景的空间布局及物体动态以保持时空一致性。为应对这些挑战，关键在于构建高质量的成对训练数据集，通过网络上的大量360°视频开发高效的数据过滤管道，并设计一系列考虑几何结构与运动信息的操作，以提升生成质量与学习效率。实验结果验证了所提方法在生成真实且连贯的全景视频方面的有效性，并展示了其在视频稳定化、相机视点控制及交互式视觉问答等领域的应用潜力。

链接: https://arxiv.org/abs/2504.07940
作者: Rundong Luo,Matthew Wallingford,Ali Farhadi,Noah Snavely,Wei-Chiu Ma
机构: Cornell University (康奈尔大学); University of Washington (华盛顿大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:360° videos have emerged as a promising medium to represent our dynamic visual world. Compared to the “tunnel vision” of standard cameras, their borderless field of view offers a more complete perspective of our surroundings. While existing video models excel at producing standard videos, their ability to generate full panoramic videos remains elusive. In this paper, we investigate the task of video-to-360° generation: given a perspective video as input, our goal is to generate a full panoramic video that is consistent with the original video. Unlike conventional video generation tasks, the output’s field of view is significantly larger, and the model is required to have a deep understanding of both the spatial layout of the scene and the dynamics of objects to maintain spatio-temporal consistency. To address these challenges, we first leverage the abundant 360° videos available online and develop a high-quality data filtering pipeline to curate pairwise training data. We then carefully design a series of geometry- and motion-aware operations to facilitate the learning process and improve the quality of 360° video generation. Experimental results demonstrate that our model can generate realistic and coherent 360° videos from in-the-wild perspective video. In addition, we showcase its potential applications, including video stabilization, camera viewpoint control, and interactive visual question answering.
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[CV-14] SoTA with Less: MCTS-Guided Sample Selection for Data-Efficient Visual Reasoning Self-Improvement

【速读】：该论文旨在解决视觉推理任务中训练样本不足的问题，提出了一种仅依赖自我提升（无需知识蒸馏）且显著减少训练样本需求的有效方法。解决方案的关键在于准确量化训练数据的难度，并利用这种难度信息进行有效的数据筛选。具体而言，作者通过重新利用蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）来实现这一目标。MCTS能够基于视觉语言模型（Vision-Language Models, VLMs）解决每个问题所需的迭代次数，量化样本难度，从而选出真正具有挑战性的样本。这种方法在从70k开源训练样本中筛选出11k样本后，对Qwen2.5-VL-7B-Instruct进行了强化微调（Reinforcement Fine-Tuning, RFT），最终生成了ThinkLite-VL模型。实验结果表明，ThinkLite-VL在八个基准测试中的平均性能提升了7%，并在MathVista数据集上达到了75.1%的SoTA准确率，显著优于其他现有的7B规模推理模型及经典选择方法的基线模型。

链接: https://arxiv.org/abs/2504.07934
作者: Xiyao Wang,Zhengyuan Yang,Chao Feng,Hongjin Lu,Linjie Li,Chung-Ching Lin,Kevin Lin,Furong Huang,Lijuan Wang
机构: University of Maryland, College Park (马里兰大学帕克分校); Microsoft (微软); University of Michigan (密歇根大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In this paper, we present an effective method to enhance visual reasoning with significantly fewer training samples, relying purely on self-improvement with no knowledge distillation. Our key insight is that the difficulty of training data during reinforcement fine-tuning (RFT) is critical. Appropriately challenging samples can substantially boost reasoning capabilities even when the dataset is small. Despite being intuitive, the main challenge remains in accurately quantifying sample difficulty to enable effective data filtering. To this end, we propose a novel way of repurposing Monte Carlo Tree Search (MCTS) to achieve that. Starting from our curated 70k open-source training samples, we introduce an MCTS-based selection method that quantifies sample difficulty based on the number of iterations required by the VLMs to solve each problem. This explicit step-by-step reasoning in MCTS enforces the model to think longer and better identifies samples that are genuinely challenging. We filter and retain 11k samples to perform RFT on Qwen2.5-VL-7B-Instruct, resulting in our final model, ThinkLite-VL. Evaluation results on eight benchmarks show that ThinkLite-VL improves the average performance of Qwen2.5-VL-7B-Instruct by 7%, using only 11k training samples with no knowledge distillation. This significantly outperforms all existing 7B-level reasoning VLMs, and our fairly comparable baselines that use classic selection methods such as accuracy-based filtering. Notably, on MathVista, ThinkLite-VL-7B achieves the SoTA accuracy of 75.1, surpassing Qwen2.5-VL-72B, GPT-4o, and O1. Our code, data, and model are available at this https URL.
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[CV-15] SAMJAM: Zero-Shot Video Scene Graph Generation for Egocentric Kitchen Videos

【速读】：该论文旨在解决视频场景图生成（Video Scene Graph Generation, VidSGG）在动态厨房环境理解中的挑战，特别是当前模型需要大量训练数据才能有效生成场景图的问题。此外，尽管视觉语言模型（Vision Language Model, VLM）和视觉基础模型（Vision Foundation Model, VFM）在多种任务中展现出零样本（zero-shot）能力，但像Gemini这样的VLM在处理视频动态时存在不足，无法保持对象身份在帧之间的稳定性。为克服这些限制，论文提出了一种名为SAMJAM的零样本流水线方法，其关键是结合了SAM2的时间跟踪能力和Gemini的语义理解能力。具体而言，SAMJAM通过改进的对象定位（更精确的边界框）增强了Gemini的功能，并引入匹配算法将场景图中的对象与SAM2生成或传播的掩码进行映射，从而在动态环境中生成时间一致性场景图。这种方法最终在EPIC-KITCHENS和EPIC-KITCHENS-100数据集上展示了比Gemini高出8.33%的平均召回率性能提升。

链接: https://arxiv.org/abs/2504.07867
作者: Joshua Li,Fernando Jose Pena Cantu,Emily Yu,Alexander Wong,Yuchen Cui,Yuhao Chen
机构: Vision and Image Processing Lab, University of Waterloo (滑铁卢大学视觉与图像处理实验室); Robot Intelligence Lab, UCLA (加州大学洛杉矶分校机器人智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Video Scene Graph Generation (VidSGG) is an important topic in understanding dynamic kitchen environments. Current models for VidSGG require extensive training to produce scene graphs. Recently, Vision Language Models (VLM) and Vision Foundation Models (VFM) have demonstrated impressive zero-shot capabilities in a variety of tasks. However, VLMs like Gemini struggle with the dynamics for VidSGG, failing to maintain stable object identities across frames. To overcome this limitation, we propose SAMJAM, a zero-shot pipeline that combines SAM2’s temporal tracking with Gemini’s semantic understanding. SAM2 also improves upon Gemini’s object grounding by producing more accurate bounding boxes. In our method, we first prompt Gemini to generate a frame-level scene graph. Then, we employ a matching algorithm to map each object in the scene graph with a SAM2-generated or SAM2-propagated mask, producing a temporally-consistent scene graph in dynamic environments. Finally, we repeat this process again in each of the following frames. We empirically demonstrate that SAMJAM outperforms Gemini by 8.33% in mean recall on the EPIC-KITCHENS and EPIC-KITCHENS-100 datasets.
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[CV-16] V2V3D: View-to-View Denoised 3D Reconstruction for Light-Field Microscopy CVPR2025

【速读】：该论文旨在解决现有光场显微镜（Light Field Microscopy, LFM）重建算法对传感器噪声敏感以及需要难以获取的带标注真实数据进行训练的问题。为应对这些挑战，论文提出了一种名为V2V3D的无监督视图到视图（view2view）框架，通过统一架构实现图像去噪与三维重建的同时优化，建立了新的联合优化范式。其关键在于假设光场图像是由一致的三维信号产生，并且每幅视图中的噪声相互独立，从而利用噪声到噪声（Noise2Noise）的原则进行有效去噪。此外，通过引入基于波光学的特征对齐技术，设计特定的卷积核以增强高频细节的恢复，并提供包含光场图像及其对应三维强度体数据集的LFM数据集。实验表明，该方法在计算效率和性能上优于其他最先进的方法。

链接: https://arxiv.org/abs/2504.07853
作者: Jiayin Zhao,Zhenqi Fu,Tao Yu,Hui Qiao
机构: Tsinghua University (清华大学); Shanghai AI Laboratory (上海人工智能实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: CVPR 2025

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Abstract:Light field microscopy (LFM) has gained significant attention due to its ability to capture snapshot-based, large-scale 3D fluorescence images. However, existing LFM reconstruction algorithms are highly sensitive to sensor noise or require hard-to-get ground-truth annotated data for training. To address these challenges, this paper introduces V2V3D, an unsupervised view2view-based framework that establishes a new paradigm for joint optimization of image denoising and 3D reconstruction in a unified architecture. We assume that the LF images are derived from a consistent 3D signal, with the noise in each view being independent. This enables V2V3D to incorporate the principle of noise2noise for effective denoising. To enhance the recovery of high-frequency details, we propose a novel wave-optics-based feature alignment technique, which transforms the point spread function, used for forward propagation in wave optics, into convolution kernels specifically designed for feature alignment. Moreover, we introduce an LFM dataset containing LF images and their corresponding 3D intensity volumes. Extensive experiments demonstrate that our approach achieves high computational efficiency and outperforms the other state-of-the-art methods. These advancements position V2V3D as a promising solution for 3D imaging under challenging conditions.
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[CV-17] AerialVG: A Challenging Benchmark for Aerial Visual Grounding by Exploring Positional Relations

【速读】：该论文旨在解决从航拍视角进行视觉定位（Aerial Visual Grounding, AerialVG）的新任务，其核心问题是传统视觉定位方法在处理航拍图像时面临的挑战，包括基于外观特征的定位不足以区分多个视觉相似的目标对象，以及需要强调目标间的空间位置关系。此外，现有视觉定位模型难以应对高分辨率航拍图像带来的计算复杂性和定位困难。为解决这些问题，论文提出了一个包含5000张真实航拍图像、5万条人工标注描述和10.3万个目标对象的首个AerialVG数据集，并设计了一个创新的模型，其中引入了分层交叉注意力机制（Hierarchical Cross-Attention）以聚焦于目标区域，同时开发了关系感知定位模块（Relation-Aware Grounding Module）来推断空间关系。关键在于通过增强的空间推理能力实现更精准的航拍视觉定位。

链接: https://arxiv.org/abs/2504.07836
作者: Junli Liu,Qizhi Chen,Zhigang Wang,Yiwen Tang,Yiting Zhang,Chi Yan,Dong Wang,Xuelong Li,Bin Zhao
机构: Northwestern Polytechnical University (西北工业大学); Shanghai AI Laboratory (上海人工智能实验室); Zhejiang University (浙江大学); TeleAI (通义千问)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Visual grounding (VG) aims to localize target objects in an image based on natural language descriptions. In this paper, we propose AerialVG, a new task focusing on visual grounding from aerial views. Compared to traditional VG, AerialVG poses new challenges, \emphe.g., appearance-based grounding is insufficient to distinguish among multiple visually similar objects, and positional relations should be emphasized. Besides, existing VG models struggle when applied to aerial imagery, where high-resolution images cause significant difficulties. To address these challenges, we introduce the first AerialVG dataset, consisting of 5K real-world aerial images, 50K manually annotated descriptions, and 103K objects. Particularly, each annotation in AerialVG dataset contains multiple target objects annotated with relative spatial relations, requiring models to perform comprehensive spatial reasoning. Furthermore, we propose an innovative model especially for the AerialVG task, where a Hierarchical Cross-Attention is devised to focus on target regions, and a Relation-Aware Grounding module is designed to infer positional relations. Experimental results validate the effectiveness of our dataset and method, highlighting the importance of spatial reasoning in aerial visual grounding. The code and dataset will be released.
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[CV-18] P2Object: Single Point Supervised Object Detection and Instance Segmentation

【速读】：该论文旨在解决单点监督下的目标识别性能与全监督算法之间存在的显著性能差距问题。传统方法通过离散框采样的方式生成类别无关的候选框，并将其视为单一的包（bag），这给多重实例学习（MIL）带来了巨大挑战。为了解决这一问题，论文提出了Point-to-Box Network (P2BNet)，通过以锚点（anchor-like）的方式生成候选框，并采用从粗到精的优化范式对候选框进行精炼，构建平衡的实例级候选框包。进一步研究发现，无论是图像级还是实例级的候选框包，其建立都基于离散框采样，导致伪框估计陷入次优解，从而出现目标边界截断或背景过度包含的问题。为此，论文探索了离散到连续的优化方法，提出了P2BNet++和Point-to-Mask Network (P2MNet)。其中，P2BNet++通过更好地利用空间线索实现了近似连续的候选框采样策略；而P2MNet则引入低层图像信息辅助像素预测，并设计了边界自预测机制来缓解估计框的限制。得益于连续的目标感知像素级感知能力，P2MNet能够生成更精确的边界框并推广到分割任务。实验结果表明，该方法在COCO、VOC、SBD和Cityscapes数据集上的平均精度均值（mAP）上大幅超越现有方法，展现了缩小与全监督任务性能差距的巨大潜力。

链接: https://arxiv.org/abs/2504.07813
作者: Pengfei Chen,Xuehui Yu,Xumeng Han,Kuiran Wang,Guorong Li,Lingxi Xie,Zhenjun Han,Jianbin Jiao
机构: University of Chinese Academy of Sciences (中国科学院大学); University of Chinese Academy of Sciences (中国科学院大学); University of Chinese Academy of Sciences (中国科学院大学); 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:Object recognition using single-point supervision has attracted increasing attention recently. However, the performance gap compared with fully-supervised algorithms remains large. Previous works generated class-agnostic \textbf\textitproposals in an image offline and then treated mixed candidates as a single bag, putting a huge burden on multiple instance learning (MIL). In this paper, we introduce Point-to-Box Network (P2BNet), which constructs balanced \textbf\textitinstance-level proposal bags by generating proposals in an anchor-like way and refining the proposals in a coarse-to-fine paradigm. Through further research, we find that the bag of proposals, either at the image level or the instance level, is established on discrete box sampling. This leads the pseudo box estimation into a sub-optimal solution, resulting in the truncation of object boundaries or the excessive inclusion of background. Hence, we conduct a series exploration of discrete-to-continuous optimization, yielding P2BNet++ and Point-to-Mask Network (P2MNet). P2BNet++ conducts an approximately continuous proposal sampling strategy by better utilizing spatial clues. P2MNet further introduces low-level image information to assist in pixel prediction, and a boundary self-prediction is designed to relieve the limitation of the estimated boxes. Benefiting from the continuous object-aware \textbf\textitpixel-level perception, P2MNet can generate more precise bounding boxes and generalize to segmentation tasks. Our method largely surpasses the previous methods in terms of the mean average precision on COCO, VOC, SBD, and Cityscapes, demonstrating great potential to bridge the performance gap compared with fully supervised tasks.
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[CV-19] Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement

【速读】：该论文旨在解决低光照条件下拍摄的图像在许多应用中存在的显著限制，这些问题包括细节丢失、对比度降低以及噪声掩盖等。为了改善这些图像的质量，以便进行诸如图像分割和目标检测等任务，论文提出了一种基于Retinex分解（将图像分解为光照、反射率和噪声成分）的变分法来增强低光照图像。解决方案的关键在于引入了一个颜色校正预处理步骤，并设计了一种新颖的非局部梯度型保真项以保留结构细节。此外，还提出了一个自动伽马校正模块。通过构建提出的变分方法，进一步扩展模型，将其深度展开版本中的近端算子替换为可学习网络，并引入交叉注意力机制以捕捉反射率的非局部先验和基于非局部梯度的约束中的长距离依赖关系。实验结果表明，这两种方法在不同数据集上与几种最新的最先进方法相比具有竞争力，特别是变分模型在视觉效果和质量指标方面优于大多数深度学习方法，尽管它不依赖于学习策略。

链接: https://arxiv.org/abs/2504.07810
作者: Daniel Torres,Joan Duran,Julia Navarro,Catalina Sbert
机构: Universidad de las Islas Baleares ( UIB ) (巴利阿里群岛大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Images captured under low-light conditions present significant limitations in many applications, as poor lighting can obscure details, reduce contrast, and hide noise. Removing the illumination effects and enhancing the quality of such images is crucial for many tasks, such as image segmentation and object detection. In this paper, we propose a variational method for low-light image enhancement based on the Retinex decomposition into illumination, reflectance, and noise components. A color correction pre-processing step is applied to the low-light image, which is then used as the observed input in the decomposition. Moreover, our model integrates a novel nonlocal gradient-type fidelity term designed to preserve structural details. Additionally, we propose an automatic gamma correction module. Building on the proposed variational approach, we extend the model by introducing its deep unfolding counterpart, in which the proximal operators are replaced with learnable networks. We propose cross-attention mechanisms to capture long-range dependencies in both the nonlocal prior of the reflectance and the nonlocal gradient-based constraint. Experimental results demonstrate that both methods compare favorably with several recent and state-of-the-art techniques across different datasets. In particular, despite not relying on learning strategies, the variational model outperforms most deep learning approaches both visually and in terms of quality metrics.
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[CV-20] Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations

【速读】：该论文旨在解决深度学习系统在处理分布外（Out-of-Distribution, OOD）数据检测时，基于似然估计的深度生成模型表现不佳的问题。传统观点认为这些模型对OOD数据的高似然值可能导致误判，尤其在图像领域。然而，本文指出，这一不足并非源于似然本身，而是由于图像空间的若干固有特性限制了其作为有效检测指标的能力。关键在于，通过采用扩散模型的概率流公式构建足够精确的似然估计器，并将其应用于预训练编码器的表征空间中，可以使得基于似然的方法达到与当前最先进的OOD检测方法相当的性能。

链接: https://arxiv.org/abs/2504.07793
作者: Yifan Ding,Arturas Aleksandrauskas,Amirhossein Ahmadian,Jonas Unger,Fredrik Lindsten,Gabriel Eilertsen
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Out-of-distribution (OOD) detection is critical for ensuring the reliability of deep learning systems, particularly in safety-critical applications. Likelihood-based deep generative models have historically faced criticism for their unsatisfactory performance in OOD detection, often assigning higher likelihood to OOD data than in-distribution samples when applied to image data. In this work, we demonstrate that likelihood is not inherently flawed. Rather, several properties in the images space prohibit likelihood as a valid detection score. Given a sufficiently good likelihood estimator, specifically using the probability flow formulation of a diffusion model, we show that likelihood-based methods can still perform on par with state-of-the-art methods when applied in the representation space of pre-trained encoders. The code of our work can be found at \hrefthis https URL\textttthis https URL .
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[CV-21] Breaking the Barriers: Video Vision Transformers for Word-Level Sign Language Recognition

【速读】：该论文旨在解决动态手语词级识别中的通信障碍问题，特别是由于听人群体对手语流利度有限而导致的沟通困难。传统卷积神经网络（CNN）在处理视频序列的时间全局依赖性方面存在计算开销大且能力不足的问题。为克服这些限制，论文提出了一种基于视频视觉Transformer（ViViT）的模型，其关键是利用Transformer模型中的自注意力机制，有效捕捉空间和时间维度上的全局关系，从而更高效地实现复杂手势的识别任务。实验结果显示，VideoMAE模型在WLASL100数据集上达到了75.58%的Top-1准确率，显著优于传统CNN的65.89%，证明了基于Transformer架构在手语识别（SLR）领域的巨大潜力。

链接: https://arxiv.org/abs/2504.07792
作者: Alexander Brettmann,Jakob Grävinghoff,Marlene Rüschoff,Marie Westhues
机构: University of Cologne (科隆大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Sign language is a fundamental means of communication for the deaf and hard-of-hearing (DHH) community, enabling nuanced expression through gestures, facial expressions, and body movements. Despite its critical role in facilitating interaction within the DHH population, significant barriers persist due to the limited fluency in sign language among the hearing population. Overcoming this communication gap through automatic sign language recognition (SLR) remains a challenge, particularly at a dynamic word-level, where temporal and spatial dependencies must be effectively recognized. While Convolutional Neural Networks have shown potential in SLR, they are computationally intensive and have difficulties in capturing global temporal dependencies between video sequences. To address these limitations, we propose a Video Vision Transformer (ViViT) model for word-level American Sign Language (ASL) recognition. Transformer models make use of self-attention mechanisms to effectively capture global relationships across spatial and temporal dimensions, which makes them suitable for complex gesture recognition tasks. The VideoMAE model achieves a Top-1 accuracy of 75.58% on the WLASL100 dataset, highlighting its strong performance compared to traditional CNNs with 65.89%. Our study demonstrates that transformer-based architectures have great potential to advance SLR, overcome communication barriers and promote the inclusion of DHH individuals.
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[CV-22] owards Micro-Action Recognition with Limited Annotations: An Asynchronous Pseudo Labeling and Training Approach

【速读】：该论文试图解决微动作识别（Micro-Action Recognition, MAR）数据集标注困难的问题，并在此背景下研究半监督学习（Semi-Supervised Learning, SSL）方法在半监督微动作识别（Semi-Supervised MAR, SSMAR）任务中的应用。论文发现传统SSL方法容易因直接使用分类器预测作为伪标签而过拟合于不准确的伪标签，从而导致误差累积和性能下降。为解决此问题，论文提出了一种新颖的框架，称为异步伪标签与训练（Asynchronous Pseudo Labeling and Training, APLT）。APLT的关键在于显式分离伪标签生成过程与模型训练过程：通过引入离线阶段的半监督聚类方法生成更准确的伪标签，并采用自适应阈值策略动态过滤不同类别的噪声标签，构建基于过滤后伪标签的记忆原型分类器以指导后续训练，从而实现更精准的伪标签利用并避免过拟合。实验结果表明，APLT显著优于现有SSL方法，在仅使用50%标记数据的情况下，其在MA-12数据集上的准确率比FixMatch提升了14.5%。

链接: https://arxiv.org/abs/2504.07785
作者: Yan Zhang,Lechao Cheng,Yaxiong Wang,Zhun Zhong,Meng Wang
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Micro-Action Recognition (MAR) aims to classify subtle human actions in video. However, annotating MAR datasets is particularly challenging due to the subtlety of actions. To this end, we introduce the setting of Semi-Supervised MAR (SSMAR), where only a part of samples are labeled. We first evaluate traditional Semi-Supervised Learning (SSL) methods to SSMAR and find that these methods tend to overfit on inaccurate pseudo-labels, leading to error accumulation and degraded performance. This issue primarily arises from the common practice of directly using the predictions of classifier as pseudo-labels to train the model. To solve this issue, we propose a novel framework, called Asynchronous Pseudo Labeling and Training (APLT), which explicitly separates the pseudo-labeling process from model training. Specifically, we introduce a semi-supervised clustering method during the offline pseudo-labeling phase to generate more accurate pseudo-labels. Moreover, a self-adaptive thresholding strategy is proposed to dynamically filter noisy labels of different classes. We then build a memory-based prototype classifier based on the filtered pseudo-labels, which is fixed and used to guide the subsequent model training phase. By alternating the two pseudo-labeling and model training phases in an asynchronous manner, the model can not only be learned with more accurate pseudo-labels but also avoid the overfitting issue. Experiments on three MAR datasets show that our APLT largely outperforms state-of-the-art SSL methods. For instance, APLT improves accuracy by 14.5% over FixMatch on the MA-12 dataset when using only 50% labeled data. Code will be publicly available.
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[CV-23] Exploring a Patch-Wise Approach for Privacy-Preserving Fake ID Detection

【速读】：该论文旨在解决在数字化时代验证身份文件真实性这一关键挑战，特别是在数字银行、加密货币交易所及租赁等实际应用场景中假ID检测的难题。目前，该领域缺乏公开的真实身份文件数据，大多数研究依赖于因隐私原因无法共享的专有内部数据库，这严重阻碍了技术进步。为应对这一困境，论文探索了隐私保护与性能之间的权衡，提出了一种新颖的基于补丁（patch-wise）的假ID检测方法。该方案的关键在于通过两级匿名化处理（完全匿名化与伪匿名化）以及不同大小的补丁配置，调整补丁图像中可见敏感信息的数量，从而增强隐私保护同时保持检测性能。此外，文中还结合了视觉Transformer（Vision Transformers）和基础模型（Foundation Models）等最新技术进行分析，并引入了一个包含48,400个真实与伪造ID文档补丁的第一公开数据集及其实验框架与模型代码，以促进进一步研究。

链接: https://arxiv.org/abs/2504.07761
作者: Javier Muñoz-Haro,Ruben Tolosana,Ruben Vera-Rodriguez,Aythami Morales,Julian Fierrez
机构: Biometrics and Data Pattern Analytics Lab, Universidad Autonoma de Madrid (生物特征与数据模式分析实验室, 马德里自治大学), Madrid, Spain
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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Abstract:In an increasingly digitalized world, verifying the authenticity of ID documents has become a critical challenge for real-life applications such as digital banking, crypto-exchanges, renting, etc. This study focuses on the topic of fake ID detection, covering several limitations in the field. In particular, no publicly available data from real ID documents exists, and most studies rely on proprietary in-house databases that are not available due to privacy reasons. In order to shed some light on this critical challenge that makes difficult to advance in the field, we explore a trade-off between privacy (i.e., amount of sensitive data available) and performance, proposing a novel patch-wise approach for privacy-preserving fake ID detection. Our proposed approach explores how privacy can be enhanced through: i) two levels of anonymization for an ID document (i.e., fully- and pseudo-anonymized), and ii) different patch size configurations, varying the amount of sensitive data visible in the patch image. Also, state-of-the-art methods such as Vision Transformers and Foundation Models are considered in the analysis. The experimental framework shows that, on an unseen database (DLC-2021), our proposal achieves 13.91% and 0% EERs at patch and ID document level, showing a good generalization to other databases. In addition to this exploration, another key contribution of our study is the release of the first publicly available database that contains 48,400 patches from both real and fake ID documents, along with the experimental framework and models, which will be available in our GitHub.
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[CV-24] PIDSR:ComplementaryPolarizedImageDemosaicingandSuper-Resolution

【速读】：本文旨在解决由偏振相机输出的色彩偏振滤波阵列（Color-Polarization Filter Array, CPFA）原始图像在重建全分辨率全彩偏振图像过程中引入的误差问题，以及由此导致的偏振参数（如偏振度 Degree of Polarization, DoP 和偏振角 Angle of Polarization, AoP）的准确性不足。同时，论文还关注偏振相机因硬件设计限制导致的分辨率较低的问题。现有方法要么无法提升分辨率（如偏振图像 demosaicing 方法），要么在从 demosaicing 结果进行超分辨重建时保留甚至放大 DoP 和 AoP 的错误（如偏振图像超分辨方法）。为此，本文提出了一种名为 PIDSR 的联合框架，通过互补的偏振图像 demosaicing 和超分辨技术，在一次处理中直接获得高质量的高分辨率（High-Resolution, HR）偏振图像，并显著提高 DoP 和 AoP 的准确性。关键在于将 demosaicing 和超分辨过程结合，实现相互增强的效果。

链接: https://arxiv.org/abs/2504.07758
作者: Shuangfan Zhou,Chu Zhou,Youwei Lyu,Heng Guo,Zhanyu Ma,Boxin Shi,Imari Sato
机构: School of Artificial Intelligence, Beijing University of Posts and Telecommunications (北京邮电大学人工智能学院); National Institute of Informatics (国立信息学研究所); State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University (北京大学计算机科学国家重点实验室多媒体信息处理实验室); National Engineering Research Center of Visual Technology, School of Computer Science, Peking University (北京大学计算机科学国家视觉技术工程研究中心)
类目: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
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Abstract:Polarization cameras can capture multiple polarized images with different polarizer angles in a single shot, bringing convenience to polarization-based downstream tasks. However, their direct outputs are color-polarization filter array (CPFA) raw images, requiring demosaicing to reconstruct full-resolution, full-color polarized images; unfortunately, this necessary step introduces artifacts that make polarization-related parameters such as the degree of polarization (DoP) and angle of polarization (AoP) prone to error. Besides, limited by the hardware design, the resolution of a polarization camera is often much lower than that of a conventional RGB camera. Existing polarized image demosaicing (PID) methods are limited in that they cannot enhance resolution, while polarized image super-resolution (PISR) methods, though designed to obtain high-resolution (HR) polarized images from the demosaicing results, tend to retain or even amplify errors in the DoP and AoP introduced by demosaicing artifacts. In this paper, we propose PIDSR, a joint framework that performs complementary Polarized Image Demosaicing and Super-Resolution, showing the ability to robustly obtain high-quality HR polarized images with more accurate DoP and AoP from a CPFA raw image in a direct manner. Experiments show our PIDSR not only achieves state-of-the-art performance on both synthetic and real data, but also facilitates downstream tasks.
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[CV-25] SF2T: Self-supervised Frag ment Finetuning of Video-LLM s for Fine-Grained Understanding CVPR2025

【速读】：该论文旨在解决视频基础大语言模型（Video-based Large Language Models, Video-LLMs）在细粒度理解方面的不足，特别是在视觉动态和视频细节查询方面的能力薄弱问题。论文的关键解决方案是提出了一种名为自监督片段微调（Self-Supervised Fragment Fine-Tuning, SF²T）的新方法。SF²T 利用视频丰富的内在特性进行训练，无需人工标注即可显著提升 Video-LLMs 的细粒度视频理解能力，同时规避自然语言在捕捉复杂时空变化时的局限性。此外，论文还构建了一个名为 FineVidBench 的新基准数据集，用于全面评估 Video-LLMs 在场景级和片段级上的性能。实验结果验证了 SF²T 方法的有效性，表明其能够有效提升模型捕获和解析时空细节的能力。

链接: https://arxiv.org/abs/2504.07745
作者: Yangliu Hu,Zikai Song,Na Feng,Yawei Luo,Junqing Yu,Yi-Ping Phoebe Chen,Wei Yang
机构: Huazhong University of Science and Technology (华中科技大学); Zhejiang University (浙江大学); La Trobe University (拉筹伯大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Accepted to CVPR2025

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Abstract:Video-based Large Language Models (Video-LLMs) have witnessed substantial advancements in recent years, propelled by the advancement in multi-modal LLMs. Although these models have demonstrated proficiency in providing the overall description of videos, they struggle with fine-grained understanding, particularly in aspects such as visual dynamics and video details inquiries. To tackle these shortcomings, we find that fine-tuning Video-LLMs on self-supervised fragment tasks, greatly improve their fine-grained video understanding abilities. Hence we propose two key contributions:(1) Self-Supervised Fragment Fine-Tuning (SF ^2 T), a novel effortless fine-tuning method, employs the rich inherent characteristics of videos for training, while unlocking more fine-grained understanding ability of Video-LLMs. Moreover, it relieves researchers from labor-intensive annotations and smartly circumvents the limitations of natural language, which often fails to capture the complex spatiotemporal variations in videos; (2) A novel benchmark dataset, namely FineVidBench, for rigorously assessing Video-LLMs’ performance at both the scene and fragment levels, offering a comprehensive evaluation of their capabilities. We assessed multiple models and validated the effectiveness of SF ^2 T on them. Experimental results reveal that our approach improves their ability to capture and interpret spatiotemporal details.
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[CV-26] MMLA: Multi-Environment Multi-Species Low-Altitude Aerial Footage Dataset

【速读】：该论文试图解决在低空无人机影像中实时野生动物检测的挑战，特别是在不同物种和环境间模型泛化能力不足的问题。论文的关键解决方案是构建了一个名为“多环境、多物种低空航拍数据集（MMLA）”的数据集，包含来自三个不同环境的无人机视频，涵盖五种野生动物。基于此数据集，论文全面评估了三种 YOLO 模型（YOLOv5m、YOLOv8m 和 YOLOv11m）的检测性能，并揭示了地点间显著的性能差异以及物种特定的检测变化。研究强调了在不同环境中评估检测算法的重要性，以实现无人机辅助野生动物监测的鲁棒性。

链接: https://arxiv.org/abs/2504.07744
作者: Jenna Kline,Samuel Stevens,Guy Maalouf,Camille Rondeau Saint-Jean,Dat Nguyen Ngoc,Majid Mirmehdi,David Guerin,Tilo Burghardt,Elzbieta Pastucha,Blair Costelloe,Matthew Watson,Thomas Richardson,Ulrik Pagh Schultz Lundquist
机构: The Ohio State University (俄亥俄州立大学); University of Southern Denmark (南丹麦大学); University of Bristol (布里斯托大学); WildDroneEU (未知); Max Planck Institute of Animal Behavior (马克斯·普朗克动物行为研究所)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Real-time wildlife detection in drone imagery is critical for numerous applications, including animal ecology, conservation, and biodiversity monitoring. Low-altitude drone missions are effective for collecting fine-grained animal movement and behavior data, particularly if missions are automated for increased speed and consistency. However, little work exists on evaluating computer vision models on low-altitude aerial imagery and generalizability across different species and settings. To fill this gap, we present a novel multi-environment, multi-species, low-altitude aerial footage (MMLA) dataset. MMLA consists of drone footage collected across three diverse environments: Ol Pejeta Conservancy and Mpala Research Centre in Kenya, and The Wilds Conservation Center in Ohio, which includes five species: Plains zebras, Grevy’s zebras, giraffes, onagers, and African Painted Dogs. We comprehensively evaluate three YOLO models (YOLOv5m, YOLOv8m, and YOLOv11m) for detecting animals. Results demonstrate significant performance disparities across locations and species-specific detection variations. Our work highlights the importance of evaluating detection algorithms across different environments for robust wildlife monitoring applications using drones.
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[CV-27] Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI

【速读】：该论文试图解决多参数磁共振成像（MRI）中特定MRI序列类型下多器官分割工具性能量化的问题。现有工具如MRSegmentator (MRSeg)、TotalSegmentator MRI (TS) 和 TotalVibeSegmentator (VIBE) 虽已被提出用于MRI中的多器官分割，但它们在不同MRI序列类型的性能尚未被系统评估。为此，研究者从公开的Duke Liver数据集中挑选了一组包含四种典型MRI序列（预增强脂肪抑制T1、动脉期T1加权、静脉期T1加权和延迟期T1加权）的40个体积数据，并对其中的十个腹部结构进行了手动标注。通过在这一精心策划的数据集上对三种工具进行基准测试，论文揭示了MRSeg在Dice评分（80.7 ± 18.6）和Hausdorff距离误差（8.9 ± 10.4 mm）方面表现最佳（p > 0.05），尤其是在不同MRI序列类型的对比中。解决方案的关键在于使用精心设计的包含多种序列类型的标准化数据集来评估这些工具的性能差异。

链接: https://arxiv.org/abs/2504.07729
作者: Nicole Tran,Anisa Prasad,Yan Zhuang,Tejas Sudharshan Mathai,Boah Kim,Sydney Lewis,Pritam Mukherjee,Jianfei Liu,Ronald M. Summers
机构: Imaging Biomarkers and Computer-Aided Diagnosis Laboratory (影像标志物与计算机辅助诊断实验室), Radiology and Imaging Sciences (放射学与影像科学), National Institutes of Health Clinical Center (国立卫生研究院临床中心), Bethesda (贝塞斯达), USA (美国)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: Published at SPIE Medical Imaging 2025

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Abstract:The segmentation of multiple organs in multi-parametric MRI studies is critical for many applications in radiology, such as correlating imaging biomarkers with disease status (e.g., cirrhosis, diabetes). Recently, three publicly available tools, such as MRSegmentator (MRSeg), TotalSegmentator MRI (TS), and TotalVibeSegmentator (VIBE), have been proposed for multi-organ segmentation in MRI. However, the performance of these tools on specific MRI sequence types has not yet been quantified. In this work, a subset of 40 volumes from the public Duke Liver Dataset was curated. The curated dataset contained 10 volumes each from the pre-contrast fat saturated T1, arterial T1w, venous T1w, and delayed T1w phases, respectively. Ten abdominal structures were manually annotated in these volumes. Next, the performance of the three public tools was benchmarked on this curated dataset. The results indicated that MRSeg obtained a Dice score of 80.7 \pm 18.6 and Hausdorff Distance (HD) error of 8.9 \pm 10.4 mm. It fared the best ( p .05 ) across the different sequence types in contrast to TS and VIBE.
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[CV-28] Multi-modal Reference Learning for Fine-grained Text-to-Image Retrieval

【速读】：该论文旨在解决细粒度文本到图像检索任务中，由于文本描述的歧义性导致的视觉细节未能被准确表征的问题。现有方法通常假设每个训练图像都能被其对应的文本描述精确描绘，但实际中文本描述可能模糊且无法捕捉图像中的判别性视觉细节，从而影响表征学习的准确性。为缓解文本歧义的影响，论文提出了一种多模态参考学习框架以学习更鲁棒的表征。方案的关键在于首先设计了一个多模态参考构造模块，将同一对象的所有视觉和文本细节聚合为一个全面的多模态参考；随后通过参考引导的表征学习模块利用这些多模态参考来学习更准确的视觉和文本表征，并进一步引入基于参考的细化方法，利用对象参考计算基于参考的相似性以优化初始检索结果。实验表明，所提方法在多个数据集上的性能超越了当前最先进的方法。

链接: https://arxiv.org/abs/2504.07718
作者: Zehong Ma,Hao Chen,Wei Zeng,Limin Su,Shiliang Zhang
机构: State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University (北京大学); Beijing Union University (北京联合大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: TMM25

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Abstract:Fine-grained text-to-image retrieval aims to retrieve a fine-grained target image with a given text query. Existing methods typically assume that each training image is accurately depicted by its textual descriptions. However, textual descriptions can be ambiguous and fail to depict discriminative visual details in images, leading to inaccurate representation learning. To alleviate the effects of text ambiguity, we propose a Multi-Modal Reference learning framework to learn robust representations. We first propose a multi-modal reference construction module to aggregate all visual and textual details of the same object into a comprehensive multi-modal reference. The multi-modal reference hence facilitates the subsequent representation learning and retrieval similarity computation. Specifically, a reference-guided representation learning module is proposed to use multi-modal references to learn more accurate visual and textual representations. Additionally, we introduce a reference-based refinement method that employs the object references to compute a reference-based similarity that refines the initial retrieval results. Extensive experiments are conducted on five fine-grained text-to-image retrieval datasets for different text-to-image retrieval tasks. The proposed method has achieved superior performance over state-of-the-art methods. For instance, on the text-to-person image retrieval dataset RSTPReid, our method achieves the Rank1 accuracy of 56.2%, surpassing the recent CFine by 5.6%.
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[CV-29] Distilling Knowledge from Heterogeneous Architectures for Semantic Segmentation AAAI2025

【速读】：该论文旨在解决当前语义分割知识蒸馏方法仅关注同构架构内教师知识模仿的问题，而忽视了异构架构（如CNN与Transformer）中包含的不同归纳偏置所蕴含的多样化知识。这些多样化的知识对于学生模型在蒸馏过程中获得更精确且全面的数据理解至关重要。论文的关键在于首次提出了一种从异构视角出发的通用语义分割知识蒸馏方法HeteroAKD。由于异构架构之间存在显著差异，直接跨架构传递知识面临巨大挑战。为此，论文通过巧妙地将教师与学生的中间特征投影到对齐的logits空间来消除特定于架构的信息影响。此外，引入了教师-学生知识混合机制(KMM)和教师-学生知识评估机制(KEM)，通过评估异构教师-学生知识之间的可靠性和差异性，利用异构架构中的多样化知识并向学生提供定制化知识。实验结果表明，该方法在多种教师-学生配对下显著优于现有最先进的知识蒸馏方法。

链接: https://arxiv.org/abs/2504.07691
作者: Yanglin Huang,Kai Hu,Yuan Zhang,Zhineng Chen,Xieping Gao
机构: 未知
类目: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)
备注: Accepted to AAAI 2025

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Abstract:Current knowledge distillation (KD) methods for semantic segmentation focus on guiding the student to imitate the teacher’s knowledge within homogeneous architectures. However, these methods overlook the diverse knowledge contained in architectures with different inductive biases, which is crucial for enabling the student to acquire a more precise and comprehensive understanding of the data during distillation. To this end, we propose for the first time a generic knowledge distillation method for semantic segmentation from a heterogeneous perspective, named HeteroAKD. Due to the substantial disparities between heterogeneous architectures, such as CNN and Transformer, directly transferring cross-architecture knowledge presents significant challenges. To eliminate the influence of architecture-specific information, the intermediate features of both the teacher and student are skillfully projected into an aligned logits space. Furthermore, to utilize diverse knowledge from heterogeneous architectures and deliver customized knowledge required by the student, a teacher-student knowledge mixing mechanism (KMM) and a teacher-student knowledge evaluation mechanism (KEM) are introduced. These mechanisms are performed by assessing the reliability and its discrepancy between heterogeneous teacher-student knowledge. Extensive experiments conducted on three main-stream benchmarks using various teacher-student pairs demonstrate that our HeteroAKD outperforms state-of-the-art KD methods in facilitating distillation between heterogeneous architectures.
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[CV-30] FMNV: A Dataset of Media-Published News Videos for Fake News Detection

【速读】：该论文旨在解决现有假新闻检测数据集主要聚焦于用户生成内容而忽视媒体机构发布的高质量伪造视频的问题，这类专业制作的假新闻视频因政治动机或病毒传播特性可能造成更大的社会危害。为填补这一空白，论文构建了一个名为FMNV的新数据集，专门收录由媒体组织发布的新闻视频，并通过分析将其假新闻视频分为四类。基于此分类，利用大语言模型(LLMs)自动生成误导性内容以测试检测系统。同时，提出了一种名为FMNVD的基线模型，采用双流架构结合CLIP和Faster R-CNN进行视频特征提取，并通过协同注意力机制优化多模态特征融合与整合。实验结果验证了FMNV数据集在多种基线模型上的通用性以及FMNVD模型在高影响力假新闻检测方面的优越性能。因此，该研究为媒体生态系统中高影响力假新闻的检测设定了重要基准，并推动了跨模态不一致性分析方法的发展。关键在于创建了专注于媒体发布假新闻视频的高质量数据集FMNV及其相应的检测模型FMNVD。

链接: https://arxiv.org/abs/2504.07687
作者: Yihao Wang,Zhong Qian,Peifeng Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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Abstract:News media, particularly video-based platforms, have become deeply embedded in daily life, concurrently amplifying risks of misinformation dissemination. Consequently, multimodal fake news detection has garnered significant research attention. However, existing datasets predominantly comprise user-generated videos characterized by crude editing and limited public engagement, whereas professionally crafted fake news videos disseminated by media outlets often politically or virally motivated pose substantially greater societal harm. To address this gap, we construct FMNV, a novel dataset exclusively composed of news videos published by media organizations. Through empirical analysis of existing datasets and our curated collection, we categorize fake news videos into four distinct types. Building upon this taxonomy, we employ Large Language Models (LLMs) to automatically generate deceptive content by manipulating authentic media-published news videos. Furthermore, we propose FMNVD, a baseline model featuring a dual-stream architecture integrating CLIP and Faster R-CNN for video feature extraction, enhanced by co-attention mechanisms for feature refinement and multimodal aggregation. Comparative experiments demonstrate both the generalization capability of FMNV across multiple baselines and the superior detection efficacy of FMNVD. This work establishes critical benchmarks for detecting high-impact fake news in media ecosystems while advancing methodologies for cross-modal inconsistency analysis.
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[CV-31] Localization Meets Uncertainty: Uncertainty-Aware Multi-Modal Localization

【速读】：该论文旨在解决复杂室内环境中机器人导航中定位可靠性不足的问题。论文提出了一种不确定性感知的定位方法，通过引入基于百分位数的拒绝策略，在不修改预测模型的情况下增强定位输出的可靠性。该策略基于网络估计的aleatoric（偶然性）和epistemic（认知性）不确定性，筛选出不可靠的三维姿态预测。关键在于利用不确定性阈值过滤数据，并结合多模态端到端定位方法，融合RGB图像和2D LiDAR数据。实验结果表明，采用更严格的不确定性阈值可显著提高定位精度，同时有效去除极端异常值，与地面真实轨迹更好地对齐。

链接: https://arxiv.org/abs/2504.07677
作者: Hye-Min Won,Jieun Lee,Jiyong Oh
机构: ETRI (Electronics and Telecommunications Research Institute (电子通讯研究院)); Polaris3D (Polaris3D)
类目: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
备注: 14 pages, 6 figures

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Abstract:Reliable localization is critical for robot navigation in complex indoor environments. In this paper, we propose an uncertainty-aware localization method that enhances the reliability of localization outputs without modifying the prediction model itself. This study introduces a percentile-based rejection strategy that filters out unreliable 3-DoF pose predictions based on aleatoric and epistemic uncertainties the network estimates. We apply this approach to a multi-modal end-to-end localization that fuses RGB images and 2D LiDAR data, and we evaluate it across three real-world datasets collected using a commercialized serving robot. Experimental results show that applying stricter uncertainty thresholds consistently improves pose accuracy. Specifically, the mean position error is reduced by 41.0%, 56.7%, and 69.4%, and the mean orientation error by 55.6%, 65.7%, and 73.3%, when applying 90%, 80%, and 70% thresholds, respectively. Furthermore, the rejection strategy effectively removes extreme outliers, resulting in better alignment with ground truth trajectories. To the best of our knowledge, this is the first study to quantitatively demonstrate the benefits of percentile-based uncertainty rejection in multi-modal end-to-end localization tasks. Our approach provides a practical means to enhance the reliability and accuracy of localization systems in real-world deployments.
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[CV-32] LAPIS: A novel dataset for personalized image aesthetic assessment CVPR2025

【速读】：该论文试图解决个性化图像美学评估（Personalized Image Aesthetic Assessment, PIAA）的问题，特别是针对艺术作品图像的美学评价。论文的关键在于构建了一个名为Leuven Art Personalized Image Set (LAPIS) 的新颖数据集，该数据集包含11,723幅艺术品图像，并且每张图像都附有美学评分和与美学欣赏相关的图像属性。此外，LAPIS 还提供了标注者丰富的个人属性。通过使用两个现有的最先进的PIAA模型在LAPIS上的性能评估，以及通过消融研究分析个人属性和图像属性的贡献，论文发现移除某些个人和图像属性会导致性能下降。这些研究结果揭示了现有模型在艺术图像美学评估中的不足，从而强调了改进这一领域模型的必要性。

链接: https://arxiv.org/abs/2504.07670
作者: Anne-Sofie Maerten,Li-Wei Chen,Stefanie De Winter,Christophe Bossens,Johan Wagemans
机构: Department of Brain and Cognition, KU Leuven, Belgium (鲁汶大学脑与认知系); Department of Art History, KU Leuven, Belgium (鲁汶大学艺术史系)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: accepted at the CVPR 2025 workshop on AI for Creative Visual Content Generation Editing and Understanding (CVEU)

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Abstract:We present the Leuven Art Personalized Image Set (LAPIS), a novel dataset for personalized image aesthetic assessment (PIAA). It is the first dataset with images of artworks that is suitable for PIAA. LAPIS consists of 11,723 images and was meticulously curated in collaboration with art historians. Each image has an aesthetics score and a set of image attributes known to relate to aesthetic appreciation. Besides rich image attributes, LAPIS offers rich personal attributes of each annotator. We implemented two existing state-of-the-art PIAA models and assessed their performance on LAPIS. We assess the contribution of personal attributes and image attributes through ablation studies and find that performance deteriorates when certain personal and image attributes are removed. An analysis of failure cases reveals that both existing models make similar incorrect predictions, highlighting the need for improvements in artistic image aesthetic assessment. The LAPIS project page can be found at: this https URL
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[CV-33] S2R-HDR: A Large-Scale Rendered Dataset for HDR Fusion

【速读】：该论文旨在解决基于学习的高动态范围（HDR）融合方法在训练数据有限情况下泛化能力不足的问题，因为从动态场景收集大规模HDR图像既昂贵又技术上具有挑战性。为应对这些挑战，论文提出了S2R-HDR，这是一个用于HDR融合的第一个大规模高质量合成数据集，包含24,000个HDR样本。通过使用Unreal Engine 5，设计了一组包含多种动态元素、运动类型、高动态范围场景和照明条件的现实主义HDR场景。此外，开发了一个高效的渲染管道来生成真实的HDR图像。为了进一步缩小合成数据与真实世界数据之间的领域差距，引入了S2R-Adapter，这是一种专门设计用于弥合这一差距并增强模型泛化能力的领域适应方法。实验证明，在真实世界数据集上的结果表明，该方法实现了最先进的HDR重建性能。数据集和代码将在提供的链接处获取。

链接: https://arxiv.org/abs/2504.07667
作者: Yujin Wang,Jiarui Wu,Yichen Bian,Fan Zhang,Tianfan Xue
机构: Shanghai AI Laboratory (上海人工智能实验室); The Chinese University of Hong Kong (香港中文大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: this https URL

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Abstract:The generalization of learning-based high dynamic range (HDR) fusion is often limited by the availability of training data, as collecting large-scale HDR images from dynamic scenes is both costly and technically challenging. To address these challenges, we propose S2R-HDR, the first large-scale high-quality synthetic dataset for HDR fusion, with 24,000 HDR samples. Using Unreal Engine 5, we design a diverse set of realistic HDR scenes that encompass various dynamic elements, motion types, high dynamic range scenes, and lighting. Additionally, we develop an efficient rendering pipeline to generate realistic HDR images. To further mitigate the domain gap between synthetic and real-world data, we introduce S2R-Adapter, a domain adaptation designed to bridge this gap and enhance the generalization ability of models. Experimental results on real-world datasets demonstrate that our approach achieves state-of-the-art HDR reconstruction performance. Dataset and code will be available at this https URL.
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[CV-34] End-to-End Facial Expression Detection in Long Videos

【速读】：该论文旨在解决面部表情检测中两个相关任务（表情定位与分类）分别处理导致的误差传播、特征学习效率低下以及性能次优的问题。现有方法通常采用两阶段训练管道，先通过定位模型检测表情区间，再利用分类模型对检测到的片段进行情感类别划分，但这种顺序处理方式缺乏任务间的联合优化。为了解决这些问题，论文提出了一种端到端的面部表情检测网络FEDN，其关键在于通过引入基于注意力机制的特征提取模块，结合片段级注意力与滑动窗口注意力，实现定位与分类任务的同时优化，从而显著减少误差传播并提升整体性能。实验结果表明，FEDN在CASME²和CASME³数据集上实现了最先进的定位与检测精度，验证了联合优化在长视频鲁棒面部表情检测中的优势。

链接: https://arxiv.org/abs/2504.07660
作者: Yini Fang,Alec Diallo,Yiqi Shi,Frederic Jumelle,Bertram Shi
机构: Hong Kong University of Science and Technology (香港科技大学); Ydentity Organization (身份组织); Bright Nation Limited (明亮国度有限公司); University of Edinburgh (爱丁堡大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Facial expression detection involves two interrelated tasks: spotting, which identifies the onset and offset of expressions, and recognition, which classifies them into emotional categories. Most existing methods treat these tasks separately using a two-step training pipelines. A spotting model first detects expression intervals. A recognition model then classifies the detected segments. However, this sequential approach leads to error propagation, inefficient feature learning, and suboptimal performance due to the lack of joint optimization of the two tasks. We propose FEDN, an end-to-end Facial Expression Detection Network that jointly optimizes spotting and recognition. Our model introduces a novel attention-based feature extraction module, incorporating segment attention and sliding window attention to improve facial feature learning. By unifying two tasks within a single network, we greatly reduce error propagation and enhance overall performance. Experiments on CASME^2 and CASME^3 demonstrate state-of-the-art accuracy for both spotting and detection, underscoring the benefits of joint optimization for robust facial expression detection in long videos.
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[CV-35] RASMD: RGB And SWIR Multispectral Driving Dataset for Robust Perception in Adverse Conditions

【速读】：该论文旨在解决当前自动驾驶算法过度依赖可见光谱，在恶劣环境（如雾、雨、雪、强光和高对比度）下性能易下降的问题。尽管近红外（NIR）和长波红外（LWIR）等其他光谱带能在这些情况下增强视觉感知，但它们存在局限性，并缺乏大规模数据集和基准。论文的关键在于引入RGB与短波红外（SWIR）多光谱驾驶（RASMD）数据集，该数据集包含10万对在多样化地点、光照和天气条件下同步且空间对齐的RGB-SWIR图像对。此外，提供了一个子集用于RGB-SWIR翻译和对象检测标注，以展示SWIR成像的实用性。实验表明，将RGB和SWIR数据结合在集成框架中，相较于仅使用RGB的方法，显著提高了检测精度，特别是在可见光传感器表现不佳的情况下。

链接: https://arxiv.org/abs/2504.07603
作者: Youngwan Jin,Michal Kovac,Yagiz Nalcakan,Hyeongjin Ju,Hanbin Song,Sanghyeop Yeo,Shiho Kim
机构: Yonsei University (延世大学); Slovak University of Technology (斯洛伐克技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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点击查看摘要

Abstract:Current autonomous driving algorithms heavily rely on the visible spectrum, which is prone to performance degradation in adverse conditions like fog, rain, snow, glare, and high contrast. Although other spectral bands like near-infrared (NIR) and long-wave infrared (LWIR) can enhance vision perception in such situations, they have limitations and lack large-scale datasets and benchmarks. Short-wave infrared (SWIR) imaging offers several advantages over NIR and LWIR. However, no publicly available large-scale datasets currently incorporate SWIR data for autonomous driving. To address this gap, we introduce the RGB and SWIR Multispectral Driving (RASMD) dataset, which comprises 100,000 synchronized and spatially aligned RGB-SWIR image pairs collected across diverse locations, lighting, and weather conditions. In addition, we provide a subset for RGB-SWIR translation and object detection annotations for a subset of challenging traffic scenarios to demonstrate the utility of SWIR imaging through experiments on both object detection and RGB-to-SWIR image translation. Our experiments show that combining RGB and SWIR data in an ensemble framework significantly improves detection accuracy compared to RGB-only approaches, particularly in conditions where visible-spectrum sensors struggle. We anticipate that the RASMD dataset will advance research in multispectral imaging for autonomous driving and robust perception systems.
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[CV-36] On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition

【速读】：该论文旨在探索神经网络缩放规律（Neural Scaling Laws）在基于步态识别任务中的适用性，以量化数据量、模型规模及计算资源对下游步态识别性能的影响。论文的关键在于首次通过大规模自监督预训练（Self-Supervised Pretraining），基于骨架信息（Skeleton-Based）构建稳健的步态识别模型（GaitPT），使其对行走变量（Walking Covariates）具有不变性，并通过实验验证数据、模型规模与计算资源对性能的幂律提升关系。此外，通过对比不同架构（如GaitPT与GaitFormer）在固定计算预算下的表现，进一步揭示了模型设计对性能的贡献，为实际步态识别系统的资源分配与性能评估提供实践指导。

链接: https://arxiv.org/abs/2504.07598
作者: Adrian Cosma,Andy Cǎtrunǎ,Emilian Rǎdoi
机构: University Politehnica of Bucharest (布加勒斯特理工大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 10 pages, 10 Figures, 3 Tables

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Abstract:Gait recognition from video streams is a challenging problem in computer vision biometrics due to the subtle differences between gaits and numerous confounding factors. Recent advancements in self-supervised pretraining have led to the development of robust gait recognition models that are invariant to walking covariates. While neural scaling laws have transformed model development in other domains by linking performance to data, model size, and compute, their applicability to gait remains unexplored. In this work, we conduct the first empirical study scaling on skeleton-based self-supervised gait recognition to quantify the effect of data quantity, model size and compute on downstream gait recognition performance. We pretrain multiple variants of GaitPT - a transformer-based architecture - on a dataset of 2.7 million walking sequences collected in the wild. We evaluate zero-shot performance across four benchmark datasets to derive scaling laws for data, model size, and compute. Our findings demonstrate predictable power-law improvements in performance with increased scale and confirm that data and compute scaling significantly influence downstream accuracy. We further isolate architectural contributions by comparing GaitPT with GaitFormer under controlled compute budgets. These results provide practical insights into resource allocation and performance estimation for real-world gait recognition systems.
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[CV-37] Extending Visual Dynamics for Video-to-Music Generation

【速读】：该论文旨在解决现有视频到音乐生成方法在特定场景下的局限性以及对视觉动态建模不足的问题。为了解决这些挑战，论文的关键在于提出DyViM框架，通过增强动态建模能力来提升视频到音乐生成的效果。具体而言，DyViM利用简化后的运动编码器提取帧级动态特征，并结合自注意力机制实现帧内特征聚合；随后将这些动态特征融入现有的音乐标记中以解决视频与音乐表示之间的时序错位问题。此外，通过跨注意力机制传递高层语义信息，并采用退火调优策略高效微调预训练的音乐解码器，从而实现无缝适配。实验结果验证了DyViM相较于当前最先进方法的优势。

链接: https://arxiv.org/abs/2504.07594
作者: Xiaohao Liu,Teng Tu,Yunshan Ma,Tat-Seng Chua
机构: National University of Singapore(Singapore); Singapore Management University(Singapore)
类目: Multimedia (cs.MM); Computer Vision and Pattern Recognition (cs.CV)
备注: Under review

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Abstract:Music profoundly enhances video production by improving quality, engagement, and emotional resonance, sparking growing interest in video-to-music generation. Despite recent advances, existing approaches remain limited in specific scenarios or undervalue the visual dynamics. To address these limitations, we focus on tackling the complexity of dynamics and resolving temporal misalignment between video and music representations. To this end, we propose DyViM, a novel framework to enhance dynamics modeling for video-to-music generation. Specifically, we extract frame-wise dynamics features via a simplified motion encoder inherited from optical flow methods, followed by a self-attention module for aggregation within frames. These dynamic features are then incorporated to extend existing music tokens for temporal alignment. Additionally, high-level semantics are conveyed through a cross-attention mechanism, and an annealing tuning strategy benefits to fine-tune well-trained music decoders efficiently, therefore facilitating seamless adaptation. Extensive experiments demonstrate DyViM’s superiority over state-of-the-art (SOTA) methods.
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[CV-38] Benchmarking Image Embeddings for E-Commerce: Evaluating Off-the Shelf Foundation Models Fine-Tuning Strategies and Practical Trade-offs

【速读】：该论文旨在评估基础模型图像嵌入在电子商务分类和检索任务中的适用性，以支持其在现实世界应用中的表现。论文通过全面分析来自预训练卷积网络和Transformer模型的嵌入（这些模型分别采用监督学习、自监督学习和文本-图像对比学习方法），探讨了全微调(full fine-tuning)和迁移学习(top-tuning)在六个多样化电子商务数据集上的性能差异。研究的关键发现是：尽管全微调始终表现出色，但文本-图像嵌入和自监督嵌入在较少训练的情况下可以达到相似的性能；而迁移学习作为全微调的高效替代方案，能够显著降低计算成本。此外，论文还指出跨领域微调(cross-tuning)的效果依赖于数据集特性。因此，论文的核心解决方案在于提出一种结合效率与性能平衡的嵌入选择和微调策略，为实际应用提供指导。

链接: https://arxiv.org/abs/2504.07567
作者: Urszula Czerwinska,Cenk Bircanoglu,Jeremy Chamoux
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Information Retrieval (cs.IR); Machine Learning (cs.LG)
备注: accepted at Future Technologies Conference (FTC 2025)

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Abstract:We benchmark foundation models image embeddings for classification and retrieval in e-Commerce, evaluating their suitability for real-world applications. Our study spans embeddings from pre-trained convolutional and transformer models trained via supervised, self-supervised, and text-image contrastive learning. We assess full fine-tuning and transfer learning (top-tuning) on six diverse e-Commerce datasets: fashion, consumer goods, cars, food, and retail. Results show full fine-tuning consistently performs well, while text-image and self-supervised embeddings can match its performance with less training. While supervised embeddings remain stable across architectures, SSL and contrastive embeddings vary significantly, often benefiting from top-tuning. Top-tuning emerges as an efficient alternative to full fine-tuning, reducing computational costs. We also explore cross-tuning, noting its impact depends on dataset characteristics. Our findings offer practical guidelines for embedding selection and fine-tuning strategies, balancing efficiency and performance.
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[CV-39] okenFocus-VQA: Enhancing Text-to-Image Alignment with Position-Aware Focus and Multi-Perspective Aggregations on LVLMs

【速读】：该论文旨在解决现有文本到图像（Text-to-Image, T2I）生成模型评估方法在细粒度语义对齐方面存在的挑战。当前基于全局相似性度量的方法往往无法捕捉文本描述与视觉内容之间的关键词级对应关系。为了解决这一问题，论文提出了一种名为TokenFocus-VQA的新颖评估框架，利用视觉问答（Visual Question Answering, VQA）范式并通过位置特定概率优化来调用大型视觉-语言模型（Large Vision-Language Models, LVLMs）。该方案的关键创新在于设计了一种令牌感知损失函数，该函数选择性地关注预定义词汇位置上的概率分布，这些位置对应于重要的语义元素，从而实现对细粒度语义对齐的精确测量。此外，该框架还集成了集成学习技术，从不同架构的LVLMs中聚合多视角评估结果，进一步提升了性能。通过在NTIRE 2025 T2I质量评估挑战赛第一赛道中的表现可以看出，TokenFocus-VQA在公开评估中排名第二（公允分0.8445，仅比第一名低0.0001），并在官方私人测试集中同样获得第二名（0.8426），证明了其在捕捉微妙文本-图像对应关系方面的优越性。

链接: https://arxiv.org/abs/2504.07556
作者: Zijian Zhang,Xuhui Zheng,Xuecheng Wu,Chong Peng,Xuezhi Cao
机构: Meituan Inc (美团); Nanjing University (南京大学); Xi’an Jiaotong University (西安交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:While text-to-image (T2I) generation models have achieved remarkable progress in recent years, existing evaluation methodologies for vision-language alignment still struggle with the fine-grained semantic matching. Current approaches based on global similarity metrics often overlook critical token-level correspondences between textual descriptions and visual content. To this end, we present TokenFocus-VQA, a novel evaluation framework that leverages Large Vision-Language Models (LVLMs) through visual question answering (VQA) paradigm with position-specific probability optimization. Our key innovation lies in designing a token-aware loss function that selectively focuses on probability distributions at pre-defined vocabulary positions corresponding to crucial semantic elements, enabling precise measurement of fine-grained semantical alignment. The proposed framework further integrates ensemble learning techniques to aggregate multi-perspective assessments from diverse LVLMs architectures, thereby achieving further performance enhancement. Evaluated on the NTIRE 2025 T2I Quality Assessment Challenge Track 1, our TokenFocus-VQA ranks 2nd place (0.8445, only 0.0001 lower than the 1st method) on public evaluation and 2nd place (0.8426) on the official private test set, demonstrating superiority in capturing nuanced text-image correspondences compared to conventional evaluation methods.
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[CV-40] STeP: A General and Scalable Framework for Solving Video Inverse Problems with Spatiotemporal Diffusion Priors

【速读】：该论文致力于解决一般贝叶斯反演问题中的视频数据处理，特别是利用扩散模型先验（diffusion model priors）来有效捕获复杂的时空关系。传统方法因受限于计算资源和数据需求，通常采用图像扩散先验结合启发式规则来保证时间一致性，但难以忠实恢复数据中的潜在时空关系，尤其在高时间不确定性任务中表现欠佳。论文的关键创新在于通过微调预训练的图像扩散模型（pretrained image diffusion models），基于特定领域的有限视频数据构建实用且可访问的空间-时间扩散先验（spatiotemporal diffusion prior）。这一插拔式（plug-and-play）的先验使得论文提出了一种通用且可扩展的框架，用于解决科学领域中的复杂视频反演问题，如黑洞成像和动态磁共振成像（dynamic MRI）。通过引入该先验，显著提升了对数据中复杂时空关系的建模能力，并同时增强了空间保真度。

链接: https://arxiv.org/abs/2504.07549
作者: Bingliang Zhang,Zihui Wu,Berthy T. Feng,Yang Song,Yisong Yue,Katherine L. Bouman
机构: California Institute of Technology; OpenAI
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We study how to solve general Bayesian inverse problems involving videos using diffusion model priors. While it is desirable to use a video diffusion prior to effectively capture complex temporal relationships, due to the computational and data requirements of training such a model, prior work has instead relied on image diffusion priors on single frames combined with heuristics to enforce temporal consistency. However, these approaches struggle with faithfully recovering the underlying temporal relationships, particularly for tasks with high temporal uncertainty. In this paper, we demonstrate the feasibility of practical and accessible spatiotemporal diffusion priors by fine-tuning latent video diffusion models from pretrained image diffusion models using limited videos in specific domains. Leveraging this plug-and-play spatiotemporal diffusion prior, we introduce a general and scalable framework for solving video inverse problems. We then apply our framework to two challenging scientific video inverse problems–black hole imaging and dynamic MRI. Our framework enables the generation of diverse, high-fidelity video reconstructions that not only fit observations but also recover multi-modal solutions. By incorporating a spatiotemporal diffusion prior, we significantly improve our ability to capture complex temporal relationships in the data while also enhancing spatial fidelity.
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[CV-41] SydneyScapes: Image Segmentation for Australian Environments

【速读】：该论文旨在解决自动驾驶车辆（Autonomous Vehicles, AVs）在特定地理环境下的计算机视觉算法开发与测试需求，尤其是在澳大利亚场景中缺乏本地化标注数据集的问题。论文的关键解决方案是引入SydneyScapes数据集，该数据集专门针对图像语义分割、实例分割和全景分割等计算机视觉任务设计，包含来自澳大利亚新南威尔士州（New South Wales, NSW）悉尼及其周边城市的756张高质量图像及像素级标注。通过提供本地化的标注数据和工具，该数据集能够支持AV行业和研究人员在澳大利亚环境中进行算法开发、测试和部署。此外，论文还提供了基于最新算法的基准测试结果，为未来研究建立参考标准。该数据集已公开发布。

链接: https://arxiv.org/abs/2504.07542
作者: Hongyu Lyu,Julie Stephany Berrio,Mao Shan,Stewart Worrall
机构: The University of Sydney (悉尼大学); Australian Centre for Robotics (澳大利亚机器人中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Autonomous Vehicles (AVs) are being partially deployed and tested across various global locations, including China, the USA, Germany, France, Japan, Korea, and the UK, but with limited demonstrations in Australia. The integration of machine learning (ML) into AV perception systems highlights the need for locally labelled datasets to develop and test algorithms in specific environments. To address this, we introduce SydneyScapes - a dataset tailored for computer vision tasks of image semantic, instance, and panoptic segmentation. This dataset, collected from Sydney and surrounding cities in New South Wales (NSW), Australia, consists of 756 images with high-quality pixel-level annotations. It is designed to assist AV industry and researchers by providing annotated data and tools for algorithm development, testing, and deployment in the Australian context. Additionally, we offer benchmarking results using state-of-the-art algorithms to establish reference points for future research and development. The dataset is publicly available at this https URL.
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[CV-42] DGOcc: Depth-aware Global Query-based Network for Monocular 3D Occupancy Prediction

【速读】：该论文旨在解决从单目2D图像预测大规模室外场景3D占用的问题，这是一个不适定且资源消耗大的任务。论文提出了一种名为\textbf{DGOcc}的网络模型，其关键在于通过深度感知全局查询模块（Depth-aware Global Query-based Module, GQ）充分利用先验深度图提供的几何信息，并通过分层监督策略（Hierarchical Supervision Strategy, HSS）避免高维3D体素特征的全分辨率上采样，从而降低GPU内存使用和时间成本。

链接: https://arxiv.org/abs/2504.07524
作者: Xu Zhao,Pengju Zhang,Bo Liu,Yihong Wu
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: under review

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Abstract:Monocular 3D occupancy prediction, aiming to predict the occupancy and semantics within interesting regions of 3D scenes from only 2D images, has garnered increasing attention recently for its vital role in 3D scene understanding. Predicting the 3D occupancy of large-scale outdoor scenes from 2D images is ill-posed and resource-intensive. In this paper, we present \textbfDGOcc, a \textbfDepth-aware \textbfGlobal query-based network for monocular 3D \textbfOccupancy prediction. We first explore prior depth maps to extract depth context features that provide explicit geometric information for the occupancy network. Then, in order to fully exploit the depth context features, we propose a Global Query-based (GQ) Module. The cooperation of attention mechanisms and scale-aware operations facilitates the feature interaction between images and 3D voxels. Moreover, a Hierarchical Supervision Strategy (HSS) is designed to avoid upsampling the high-dimension 3D voxel features to full resolution, which mitigates GPU memory utilization and time cost. Extensive experiments on SemanticKITTI and SSCBench-KITTI-360 datasets demonstrate that the proposed method achieves the best performance on monocular semantic occupancy prediction while reducing GPU and time overhead.
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[CV-43] VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding

【速读】：该论文旨在解决多模态大型语言模型（Multimodal Large Language Models, MLLMs）在时间敏感型视频任务中的挑战，特别是生成准确的时间戳以标记特定事件发生的问题。现有方法要求MLLMs直接生成绝对或相对时间戳，但观察发现这些模型倾向于依赖语言模式而非视觉线索，从而影响性能。为了解决这一问题，论文提出VideoExpert，这是一种适用于多种时间敏感型视频任务的通用MLLM。其关键在于引入“专家”概念，将模型分为两个并行模块：时间专家（Temporal Expert）和空间专家（Spatial Expert）。时间专家专注于建模时间序列和时间定位，通过处理高帧率压缩后的令牌捕捉视频动态变化，并包含轻量级预测头以实现精确的事件定位；而空间专家则侧重于内容细节分析和指令执行，处理专门设计的空间令牌和语言输入，生成与内容相关联的响应。这两个专家通过特殊令牌协作，确保时间定位与内容生成的协调一致。此外，时间专家和空间专家保持独立的参数集，通过将时间定位从内容生成中分离，避免文本模式偏差对时间戳预测的影响。同时，引入的空间压缩模块用于获取空间令牌，在保留关键信息的同时过滤和压缩补丁令牌，为空间专家提供紧凑且细节丰富的输入。实验结果验证了VideoExpert的有效性和多功能性。

链接: https://arxiv.org/abs/2504.07519
作者: Henghao Zhao,Ge-Peng Ji,Rui Yan,Huan Xiong,Zechao Li
机构: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China (南京理工大学计算机科学与工程学院); School of Computing, Australian National University, Canberra 2601, Australia (澳大利亚国立大学计算学院); Institute for Advanced Study in Mathematics, Harbin Institute of Technology, Heilongjiang 150001, China (哈尔滨工业大学数学研究院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The core challenge in video understanding lies in perceiving dynamic content changes over time. However, multimodal large language models struggle with temporal-sensitive video tasks, which requires generating timestamps to mark the occurrence of specific events. Existing strategies require MLLMs to generate absolute or relative timestamps directly. We have observed that those MLLMs tend to rely more on language patterns than visual cues when generating timestamps, affecting their performance. To address this problem, we propose VideoExpert, a general-purpose MLLM suitable for several temporal-sensitive video tasks. Inspired by the expert concept, VideoExpert integrates two parallel modules: the Temporal Expert and the Spatial Expert. The Temporal Expert is responsible for modeling time sequences and performing temporal grounding. It processes high-frame-rate yet compressed tokens to capture dynamic variations in videos and includes a lightweight prediction head for precise event localization. The Spatial Expert focuses on content detail analysis and instruction following. It handles specially designed spatial tokens and language input, aiming to generate content-related responses. These two experts collaborate seamlessly via a special token, ensuring coordinated temporal grounding and content generation. Notably, the Temporal and Spatial Experts maintain independent parameter sets. By offloading temporal grounding from content generation, VideoExpert prevents text pattern biases in timestamp predictions. Moreover, we introduce a Spatial Compress module to obtain spatial tokens. This module filters and compresses patch tokens while preserving key information, delivering compact yet detail-rich input for the Spatial Expert. Extensive experiments demonstrate the effectiveness and versatility of the VideoExpert.
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[CV-44] Event Signal Filtering via Probability Flux Estimation

【速读】：该论文致力于解决事件相机（Event Camera）信号质量因内在随机性导致的退化问题，旨在通过开发一种生成式在线滤波框架来提升事件信号的保真度，并确保在不同采集条件下输出的一致性。传统时间序列依赖固定的时间采样捕捉稳态行为，而事件通过极性和事件间隔编码瞬态动态，这使得信号建模更加复杂。为应对这一挑战，论文重新审视了事件生成的理论基础，将其视为扩散过程，并将事件中的状态和过程信息建模为底层辐照扩散阈值边界处的连续概率流。基于此洞察，论文引入了一种名为事件密度流滤波器（Event Density Flow Filter, EDFilter）的生成式在线滤波框架。关键创新在于利用非参数核平滑方法从离散事件重建连续概率流以估计事件相关性，并从中重采样滤波后的事件；同时采用空间和时间核函数，在时变优化框架下优化保真度。此外，提出了一种具有O(1)复杂度的快速递归求解器，利用状态空间模型和查找表实现高效似然计算。论文还发布了Rotary Event Dataset (RED)，提供微秒级真实世界地面真值辐照度用于全参考事件滤波评估。实验结果验证了EDFilter在事件滤波、超分辨率以及直接基于事件的斑点跟踪等任务中的性能，并在SLAM和视频重建等下游应用中展示了其鲁棒性和有效性。

链接: https://arxiv.org/abs/2504.07503
作者: Jinze Chen,Wei Zhai,Yang Cao,Bin Li,Zheng-Jun Zha
机构: University of Science and Technology of China (中国科学技术大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Events offer a novel paradigm for capturing scene dynamics via asynchronous sensing, but their inherent randomness often leads to degraded signal quality. Event signal filtering is thus essential for enhancing fidelity by reducing this internal randomness and ensuring consistent outputs across diverse acquisition conditions. Unlike traditional time series that rely on fixed temporal sampling to capture steady-state behaviors, events encode transient dynamics through polarity and event intervals, making signal modeling significantly more complex. To address this, the theoretical foundation of event generation is revisited through the lens of diffusion processes. The state and process information within events is modeled as continuous probability flux at threshold boundaries of the underlying irradiance diffusion. Building on this insight, a generative, online filtering framework called Event Density Flow Filter (EDFilter) is introduced. EDFilter estimates event correlation by reconstructing the continuous probability flux from discrete events using nonparametric kernel smoothing, and then resamples filtered events from this flux. To optimize fidelity over time, spatial and temporal kernels are employed in a time-varying optimization framework. A fast recursive solver with O(1) complexity is proposed, leveraging state-space models and lookup tables for efficient likelihood computation. Furthermore, a new real-world benchmark Rotary Event Dataset (RED) is released, offering microsecond-level ground truth irradiance for full-reference event filtering evaluation. Extensive experiments validate EDFilter’s performance across tasks like event filtering, super-resolution, and direct event-based blob tracking. Significant gains in downstream applications such as SLAM and video reconstruction underscore its robustness and effectiveness.
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[CV-45] Kimi-VL Technical Report

【速读】：该论文旨在开发一种高效开源的多模态视觉语言模型（Vision-Language Model, VLM），以实现先进的多模态推理、长上下文理解及强大的代理能力，同时显著降低参数规模。论文的关键创新在于提出Kimi-VL及其扩展版本Kimi-VL-Thinking，通过混合专家模型（Mixture-of-Experts, MoE）架构，在仅激活28亿语言解码器参数的情况下，实现了卓越的性能表现。具体而言，Kimi-VL结合高效的MoonViT视觉编码器，在长上下文处理、超高清图像理解以及复杂视觉语言任务（如OCR、数学推理等）中表现出色，并在多个基准测试中与更大型的竞争对手（如GPT-4o-mini、Qwen2.5-VL-7B等）竞争。而Kimi-VL-Thinking则进一步通过长链思维（Long Chain-of-Thought, CoT）的监督微调及强化学习优化，提升了长时间范围内的推理能力，同时保持紧凑的参数规模。因此，该研究的核心解决方案在于设计一种兼具高效性与强大推理能力的轻量级多模态模型框架。

链接: https://arxiv.org/abs/2504.07491
作者: Kimi Team:Angang Du,Bohong Yin,Bowei Xing,Bowen Qu,Bowen Wang,Cheng Chen,Chenlin Zhang,Chenzhuang Du,Chu Wei,Congcong Wang,Dehao Zhang,Dikang Du,Dongliang Wang,Enming Yuan,Enzhe Lu,Fang Li,Flood Sung,Guangda Wei,Guokun Lai,Han Zhu,Hao Ding,Hao Hu,Hao Yang,Hao Zhang,Haoning Wu,Haotian Yao,Haoyu Lu,Heng Wang,Hongcheng Gao,Huabin Zheng,Jiaming Li,Jianlin Su,Jianzhou Wang,Jiaqi Deng,Jiezhong Qiu,Jin Xie,Jinhong Wang,Jingyuan Liu,Junjie Yan,Kun Ouyang,Liang Chen,Lin Sui,Longhui Yu,Mengfan Dong,Mengnan Dong,Nuo Xu,Pengyu Cheng,Qizheng Gu,Runjie Zhou,Shaowei Liu,Sihan Cao,Tao Yu,Tianhui Song,Tongtong Bai,Wei Song,Weiran He,Weixiao Huang,Weixin Xu,Xiaokun Yuan,Xingcheng Yao,Xingzhe Wu,Xinxing Zu,Xinyu Zhou,Xinyuan Wang,Y. Charles,Yan Zhong,Yang Li,Yangyang Hu,Yanru Chen,Yejie Wang,Yibo Liu,Yibo Miao,Yidao Qin,Yimin Chen,Yiping Bao,Yiqin Wang,Yongsheng Kang,Yuanxin Liu,Yulun Du,Yuxin Wu,Yuzhi Wang,Yuzi Yan,Zaida Zhou,Zhaowei Li,Zhejun Jiang,Zheng Zhang,Zhilin Yang,Zhiqi Huang,Zihao Huang,Zijia Zhao,Ziwei Chen
机构: Kimi Team
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2.8B parameters in its language decoder (Kimi-VL-A3B). Kimi-VL demonstrates strong performance across challenging domains: as a general-purpose VLM, Kimi-VL excels in multi-turn agent tasks (e.g., OSWorld), matching flagship models. Furthermore, it exhibits remarkable capabilities across diverse challenging vision language tasks, including college-level image and video comprehension, OCR, mathematical reasoning, and multi-image understanding. In comparative evaluations, it effectively competes with cutting-edge efficient VLMs such as GPT-4o-mini, Qwen2.5-VL-7B, and Gemma-3-12B-IT, while surpassing GPT-4o in several key domains. Kimi-VL also advances in processing long contexts and perceiving clearly. With a 128K extended context window, Kimi-VL can process diverse long inputs, achieving impressive scores of 64.5 on LongVideoBench and 35.1 on MMLongBench-Doc. Its native-resolution vision encoder, MoonViT, further allows it to see and understand ultra-high-resolution visual inputs, achieving 83.2 on InfoVQA and 34.5 on ScreenSpot-Pro, while maintaining lower computational cost for common tasks. Building upon Kimi-VL, we introduce an advanced long-thinking variant: Kimi-VL-Thinking. Developed through long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL), this model exhibits strong long-horizon reasoning capabilities. It achieves scores of 61.7 on MMMU, 36.8 on MathVision, and 71.3 on MathVista while maintaining the compact 2.8B activated LLM parameters, setting a new standard for efficient multimodal thinking models. Code and models are publicly accessible at this https URL.
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[CV-46] CMEdataset Advancing China Map Detection and Standardization with Digital Image Resources

【速读】：该论文旨在解决现有地图数据集无法有效应对复杂地图问题（如国家边界误标、要素缺失和边界模糊等）的不足。论文的关键解决方案是创建了一个名为“问题地图数据集”（Problematic Map Dataset）的新数据集，该数据集涵盖了五个关键问题领域，旨在为问题地图检测技术提供多样化的样本，支持高精度的地图合规性检测，并提升地图数据的质量与时效性。这一数据集不仅为地图合规性检查、国家安全监测及地图更新提供了重要资源，还促进了相关技术的创新与应用。

链接: https://arxiv.org/abs/2504.07476
作者: Yan Xu,Zhenqiang Zhang,Zhiwei Zhou,Liting Geng,Yue Li,Jintao Li
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:Digital images of Chinas maps play a crucial role in map detection, particularly in ensuring national sovereignty, territorial integrity, and map compliance. However, there is currently no publicly available dataset specifically dedicated to problematic maps the CME dataset. Existing datasets primarily focus on general map data and are insufficient for effectively identifying complex issues such as national boundary misrepresentations, missing elements, and blurred boundaries. Therefore, this study creates a Problematic Map dataset that covers five key problem areas, aiming to provide diverse samples for problematic map detection technologies, support high-precision map compliance detection, and enhance map data quality and timeliness. This dataset not only provides essential resources for map compliance, national security monitoring, and map updates, but also fosters innovation and application of related technologies.
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[CV-47] Learning Universal Features for Generalizable Image Forgery Localization

【速读】：该论文旨在解决现有图像伪造检测方法在处理未见伪造类型时效果不佳的问题。大多数现有方法依赖于识别图像中留下的编辑痕迹，但由于不同伪造方式留下的痕迹各异，这些方法在面对训练数据中未包含的伪造类型时表现欠佳。为应对这一挑战，论文提出了一种通用图像伪造定位（Generalizable Image Forgery Localization, GIFL）的方法。其关键在于从原始内容中学习通用特征，而非特定伪造类型的痕迹，因为这些通用特征在不同伪造类型中相对一致，能够用于定位未见过的伪造内容。此外，论文还构建了一个包含多种流行深度生成模型编辑图像的新数据集，以促进针对深度生成模型操纵图像的检测研究。实验结果表明，该方法在未见伪造检测中优于现有最先进的方法，并在已见伪造检测中也表现出竞争力。

链接: https://arxiv.org/abs/2504.07462
作者: Hengrun Zhao,Yunzhi Zhuge,Yifan Wang,Lijun Wang,Huchuan Lu,Yu Zeng
机构: Dalian University of Technology (大连理工大学); Johns Hopkins University (约翰斯·霍普金斯大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In recent years, advanced image editing and generation methods have rapidly evolved, making detecting and locating forged image content increasingly challenging. Most existing image forgery detection methods rely on identifying the edited traces left in the image. However, because the traces of different forgeries are distinct, these methods can identify familiar forgeries included in the training data but struggle to handle unseen ones. In response, we present an approach for Generalizable Image Forgery Localization (GIFL). Once trained, our model can detect both seen and unseen forgeries, providing a more practical and efficient solution to counter false information in the era of generative AI. Our method focuses on learning general features from the pristine content rather than traces of specific forgeries, which are relatively consistent across different types of forgeries and therefore can be used as universal features to locate unseen forgeries. Additionally, as existing image forgery datasets are still dominated by traditional hand-crafted forgeries, we construct a new dataset consisting of images edited by various popular deep generative image editing methods to further encourage research in detecting images manipulated by deep generative models. Extensive experimental results show that the proposed approach outperforms state-of-the-art methods in the detection of unseen forgeries and also demonstrates competitive results for seen forgeries. The code and dataset are available at this https URL.
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[CV-48] How Can Objects Help Video-Language Understanding?

【速读】：该论文试图解决的问题是如何在多模态大型语言模型（Multimodal Large Language Models, MLLMs）中利用对象信息来提升视频-语言理解能力。论文从对象表示和适配的角度探讨了这一问题。解决方案的关键在于显式整合以对象为中心的表示方法，并通过评估发现，符号化对象既易于集成又能有效提升问答任务的表现。研究通过五个视频问答数据集的广泛评估验证了这一结论，强调了显式整合感知模块到MLLM设计中的必要性。

链接: https://arxiv.org/abs/2504.07454
作者: Zitian Tang,Shijie Wang,Junho Cho,Jaewook Yoo,Chen Sun
机构: Brown University (布朗大学); Samsung Electronics (三星电子)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:How multimodal large language models (MLLMs) perceive the visual world remains a mystery. To one extreme, object and relation modeling may be implicitly implemented with inductive biases, for example by treating objects as tokens. To the other extreme, empirical results reveal the surprising finding that simply performing visual captioning, which tends to ignore spatial configuration of the objects, serves as a strong baseline for video understanding. We aim to answer the question: how can objects help video-language understanding in MLLMs? We tackle the question from the object representation and adaptation perspectives. Specifically, we investigate the trade-off between representation expressiveness (e.g., distributed versus symbolic) and integration difficulty (e.g., data-efficiency when learning the adapters). Through extensive evaluations on five video question answering datasets, we confirm that explicit integration of object-centric representation remains necessary, and the symbolic objects can be most easily integrated while being performant for question answering. We hope our findings can encourage the community to explore the explicit integration of perception modules into MLLM design. Our code and models will be publicly released.
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[CV-49] WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer

【速读】：本文旨在解决无人水面艇（Unmanned Surface Vehicles, USVs）在复杂水环境中的鲁棒目标检测问题，特别是针对水体表面目标检测中存在的边缘模糊及多尺度物体检测挑战。尽管视觉与雷达融合提供了可行方案，但现有方法存在跨模态特征冲突的问题，这会削弱模型的可靠性。为了解决这一问题，论文提出了一种名为WS-DETR的鲁棒视觉-雷达融合模型。该模型的关键在于引入了多尺度边缘信息集成模块（Multi-Scale Edge Information Integration, MSEII）以增强边缘感知能力，并设计了分层特征聚合器（Hierarchical Feature Aggregator, HiFA）来提升编码器中的多尺度目标检测性能；同时采用自运动点表示进行连续卷积和残差连接，有效提取不规则点云数据下的异常特征；此外，通过自适应特征交互融合模块（Adaptive Feature Interactive Fusion, AFIF），实现了视觉与雷达特征的几何对齐与语义融合，进一步缓解了跨模态冲突问题。实验结果表明，WS-DETR在WaterScenes数据集上的表现达到当前最优水平，即使在恶劣天气和光照条件下也保持优越性。

链接: https://arxiv.org/abs/2504.07441
作者: Huilin Yin,Pengyu Wang,Senmao Li,Jun Yan,Daniel Watzenig
机构: College of Electronics and Information Engineering, Tongji University (同济大学); Graz University of Technology (格拉茨技术大学); Virtual Vehicle Research (虚拟车辆研究中心)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Robust object detection for Unmanned Surface Vehicles (USVs) in complex water environments is essential for reliable navigation and operation. Specifically, water surface object detection faces challenges from blurred edges and diverse object scales. Although vision-radar fusion offers a feasible solution, existing approaches suffer from cross-modal feature conflicts, which negatively affect model robustness. To address this problem, we propose a robust vision-radar fusion model WS-DETR. In particular, we first introduce a Multi-Scale Edge Information Integration (MSEII) module to enhance edge perception and a Hierarchical Feature Aggregator (HiFA) to boost multi-scale object detection in the encoder. Then, we adopt self-moving point representations for continuous convolution and residual connection to efficiently extract irregular features under the scenarios of irregular point cloud data. To further mitigate cross-modal conflicts, an Adaptive Feature Interactive Fusion (AFIF) module is introduced to integrate visual and radar features through geometric alignment and semantic fusion. Extensive experiments on the WaterScenes dataset demonstrate that WS-DETR achieves state-of-the-art (SOTA) performance, maintaining its superiority even under adverse weather and lighting conditions.
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[CV-50] hermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based Refinement ICRA2025

【速读】：该论文旨在解决在全天候条件下从双目热成像图中实时恢复视差（disparity）的问题，尤其关注如夜间无人机监控或床底清洁机器人等应用场景。论文的关键解决方案在于提出了一种名为ThermoStereoRT的方法，其核心包括：(1) 利用轻量且强大的主干网络构建三维代价体（3D cost volume），并通过多尺度注意力机制生成初始视差图；(2) 设计新颖的通道与空间注意力模块以优化视差图；(3) 针对热成像数据稀疏标注的挑战，采用知识蒸馏技术提升性能而不增加计算开销。这些创新点确保了方法在保持实时处理能力的同时，具备鲁棒的准确性。

链接: https://arxiv.org/abs/2504.07418
作者: Anning Hu,Ang Li,Xirui Jin,Danping Zou
机构: Shanghai Key Laboratory of Navigation and Location-based Service (上海导航与位置服务重点实验室), Shanghai Jiao Tong University (上海交通大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We introduce ThermoStereoRT, a real-time thermal stereo matching method designed for all-weather conditions that recovers disparity from two rectified thermal stereo images, envisioning applications such as night-time drone surveillance or under-bed cleaning robots. Leveraging a lightweight yet powerful backbone, ThermoStereoRT constructs a 3D cost volume from thermal images and employs multi-scale attention mechanisms to produce an initial disparity map. To refine this map, we design a novel channel and spatial attention module. Addressing the challenge of sparse ground truth data in thermal imagery, we utilize knowledge distillation to boost performance without increasing computational demands. Comprehensive evaluations on multiple datasets demonstrate that ThermoStereoRT delivers both real-time capacity and robust accuracy, making it a promising solution for real-world deployment in various challenging environments. Our code will be released on this https URL
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[CV-51] FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation

【速读】：该论文旨在解决在快速发展的二维生成模型领域中，如何在保持主体身份的同时实现多样化编辑这一关键挑战。现有方法通常在身份保持与个性化操作之间存在固有的权衡问题。论文提出了一种名为FlexIP的新框架，通过两个专用组件来解耦这些目标：用于风格操控的个性化适配器（Personalization Adapter）和用于身份维护的保留适配器（Preservation Adapter）。解决方案的关键在于通过将这两种控制机制显式注入生成模型，并利用权重适配器的动态调整，在推理过程中实现灵活的参数化控制。实验结果表明，该方法突破了传统方法的性能限制，实现了卓越的身份保持能力，同时支持更丰富的个性化生成能力。

链接: https://arxiv.org/abs/2504.07405
作者: Linyan Huang,Haonan Lin,Yanning Zhou,Kaiwen Xiao
机构: Tencent AIPD (腾讯人工智能研发部)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:With the rapid advancement of 2D generative models, preserving subject identity while enabling diverse editing has emerged as a critical research focus. Existing methods typically face inherent trade-offs between identity preservation and personalized manipulation. We introduce FlexIP, a novel framework that decouples these objectives through two dedicated components: a Personalization Adapter for stylistic manipulation and a Preservation Adapter for identity maintenance. By explicitly injecting both control mechanisms into the generative model, our framework enables flexible parameterized control during inference through dynamic tuning of the weight adapter. Experimental results demonstrate that our approach breaks through the performance limitations of conventional methods, achieving superior identity preservation while supporting more diverse personalized generation capabilities (Project Page: this https URL).
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[CV-52] FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair

【速读】：本文旨在解决计算机视觉系统中公平性保障的问题，特别是在无需重新训练或访问模型内部参数的情况下，通过结合非共形预测（Conformal Prediction）与动态输出修复机制，实现对预测误差和公平性偏差的同时评估与调整。关键在于提出了一种公平感知的非共形性得分（Fairness-Aware Non-Conformity Score），并基于此构建了一个自适应阈值，以提供严格的有限样本、分布无关的保证。当新图像的非共形性得分超过校准后的阈值时，FAIR-SIGHT 框架会实施针对性的修正操作，如分类中的 logit 调整和检测中的置信度重校准，从而有效减少群体与个体层面的公平性差异。这一方案的核心创新点在于其在不依赖模型内部信息的前提下，实现了公平性和预测性能的兼顾。

链接: https://arxiv.org/abs/2504.07395
作者: Arya Fayyazi,Mehdi Kamal,Massoud Pedram
机构: University of Southern California (南加州大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
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Abstract:We introduce FAIR-SIGHT, an innovative post-hoc framework designed to ensure fairness in computer vision systems by combining conformal prediction with a dynamic output repair mechanism. Our approach calculates a fairness-aware non-conformity score that simultaneously assesses prediction errors and fairness violations. Using conformal prediction, we establish an adaptive threshold that provides rigorous finite-sample, distribution-free guarantees. When the non-conformity score for a new image exceeds the calibrated threshold, FAIR-SIGHT implements targeted corrective adjustments, such as logit shifts for classification and confidence recalibration for detection, to reduce both group and individual fairness disparities, all without the need for retraining or having access to internal model parameters. Comprehensive theoretical analysis validates our method’s error control and convergence properties. At the same time, extensive empirical evaluations on benchmark datasets show that FAIR-SIGHT significantly reduces fairness disparities while preserving high predictive performance.
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[CV-53] ID-Booth: Identity-consistent Face Generation with Diffusion Models

【速读】：该论文旨在解决现有生成式模型在身份一致性与图像多样性之间的权衡问题。具体而言，当前基于扩散模型的生成方法通常在训练过程中未充分考虑主体身份信息，导致生成图像的身份一致性较差；而采用基于身份的训练目标的方法虽能提高身份一致性，但容易过拟合于特定身份，从而降低生成图像的多样性。论文提出的解决方案之关键是引入了一种名为ID-Booth的新框架，该框架结合去噪网络、变分自编码器以及文本编码器，利用一种新颖的三元组身份训练目标，在保持预训练扩散模型合成能力的同时，实现了身份一致性的生成，并提升了跨身份的可分离性及整体图像多样性。这一方法不仅增强了生成数据的质量，还促进了小规模数据集的有效增强以及隐私保护下的高性能识别模型训练。

链接: https://arxiv.org/abs/2504.07392
作者: Darian Tomašević,Fadi Boutros,Chenhao Lin,Naser Damer,Vitomir Štruc,Peter Peer
机构: University of Ljubljana (卢布尔雅那大学); Fraunhofer Institute for Computer Graphics Research IGD (弗劳恩霍夫计算机图形研究所); Xi’an Jiaotong University (西安交通大学); University of Ljubljana (卢布尔雅那大学)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2025, 14 pages

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Abstract:Recent advances in generative modeling have enabled the generation of high-quality synthetic data that is applicable in a variety of domains, including face recognition. Here, state-of-the-art generative models typically rely on conditioning and fine-tuning of powerful pretrained diffusion models to facilitate the synthesis of realistic images of a desired identity. Yet, these models often do not consider the identity of subjects during training, leading to poor consistency between generated and intended identities. In contrast, methods that employ identity-based training objectives tend to overfit on various aspects of the identity, and in turn, lower the diversity of images that can be generated. To address these issues, we present in this paper a novel generative diffusion-based framework, called ID-Booth. ID-Booth consists of a denoising network responsible for data generation, a variational auto-encoder for mapping images to and from a lower-dimensional latent space and a text encoder that allows for prompt-based control over the generation procedure. The framework utilizes a novel triplet identity training objective and enables identity-consistent image generation while retaining the synthesis capabilities of pretrained diffusion models. Experiments with a state-of-the-art latent diffusion model and diverse prompts reveal that our method facilitates better intra-identity consistency and inter-identity separability than competing methods, while achieving higher image diversity. In turn, the produced data allows for effective augmentation of small-scale datasets and training of better-performing recognition models in a privacy-preserving manner. The source code for the ID-Booth framework is publicly available at this https URL.
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[CV-54] Model Discrepancy Learning: Synthetic Faces Detection Based on Multi-Reconstruction

【速读】：该论文旨在解决合成人脸检测问题，特别是现有方法在区分不同生成技术所创造的合成图像与真实图像时存在的局限性。论文的关键洞察在于发现特定图像在不同生成方法之间的重建差异显著，并且匹配的生成技术能够提供更准确的重建结果。基于此，作者提出了一种多重建检测器（Multi-Reconstruction-based detector），通过利用多种生成模型对图像进行逆向重建并分析真实图像、GAN生成图像以及扩散模型（DM）生成图像之间的重建差异，从而实现有效的区分。此外，论文还引入了一个包含多种GAN和扩散模型生成的亚洲合成人脸数据集（ASFD），以补充现有的合成人脸数据集。实验结果表明，该检测器表现出卓越的性能，具有强大的泛化能力和鲁棒性。

链接: https://arxiv.org/abs/2504.07382
作者: Qingchao Jiang,Zhishuo Xu,Zhiying Zhu,Ning Chen,Haoyue Wang,Zhongjie Ba
机构: East China University of Science and Technology (华东理工大学); Fudan University (复旦大学); The State Key Laboratory of Blockchain and Data Security, Zhejiang University (浙江大学区块链与数据安全国家重点实验室)
类目: Computer Vision and Pattern Recognition (cs.CV)
备注: 6 pages, 6 figures

点击查看摘要

Abstract:Advances in image generation enable hyper-realistic synthetic faces but also pose risks, thus making synthetic face detection crucial. Previous research focuses on the general differences between generated images and real images, often overlooking the discrepancies among various generative techniques. In this paper, we explore the intrinsic relationship between synthetic images and their corresponding generation technologies. We find that specific images exhibit significant reconstruction discrepancies across different generative methods and that matching generation techniques provide more accurate reconstructions. Based on this insight, we propose a Multi-Reconstruction-based detector. By reversing and reconstructing images using multiple generative models, we analyze the reconstruction differences among real, GAN-generated, and DM-generated images to facilitate effective differentiation. Additionally, we introduce the Asian Synthetic Face Dataset (ASFD), containing synthetic Asian faces generated with various GANs and DMs. This dataset complements existing synthetic face datasets. Experimental results demonstrate that our detector achieves exceptional performance, with strong generalization and robustness.
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[CV-55] BRepFormer: Transformer-Based B-rep Geometric Feature Recognition

【速读】：该论文致力于解决复杂几何特征识别中的两个主要问题：一是现有研究多集中于加工特征识别（Machining Feature Recognition, MFR），未能有效捕捉复杂几何模型的精细拓扑和几何特性；二是缺乏适合工业应用且涵盖更复杂B-rep模型的数据集。为应对这些问题，论文提出了BRepFormer，这是一种基于Transformer的新型模型，能够同时识别加工特征和复杂CAD模型的特征。其关键在于通过编码和融合几何与拓扑特征，并利用Transformer架构进行特征传播及识别头以定位几何特征。此外，在每次Transformer迭代中，引入了一种结合边特征和拓扑特征的偏差，以强化每个面的几何约束。同时，论文还构建了一个名为Complex B-rep Feature Dataset (CBF) 的数据集，包含20,000个B-rep模型，更好地适配工业需求。实验结果表明，BRepFormer在MFInstSeg、MFTRCAD以及CBF数据集上达到了最先进的准确性。

链接: https://arxiv.org/abs/2504.07378
作者: Yongkang Dai,Xiaoshui Huang,Yunpeng Bai,Hao Guo,Hongping Gan,Ling Yang,Yilei Shi
机构: School of Software, Northwestern Polytechnical University(Xi’an, China); Shanghai Jiao Tong University School of Medicine(School of Public Health, Shang hai, China); National University of Singapore(Singapore); ZJU Hangzhou Global Scientific and Technological Innovation Center(Hang zhou, China)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Recognizing geometric features on B-rep models is a cornerstone technique for multimedia content-based retrieval and has been widely applied in intelligent manufacturing. However, previous research often merely focused on Machining Feature Recognition (MFR), falling short in effectively capturing the intricate topological and geometric characteristics of complex geometry features. In this paper, we propose BRepFormer, a novel transformer-based model to recognize both machining feature and complex CAD models’ features. BRepFormer encodes and fuses the geometric and topological features of the models. Afterwards, BRepFormer utilizes a transformer architecture for feature propagation and a recognition head to identify geometry features. During each iteration of the transformer, we incorporate a bias that combines edge features and topology features to reinforce geometric constraints on each face. In addition, we also proposed a dataset named Complex B-rep Feature Dataset (CBF), comprising 20,000 B-rep models. By covering more complex B-rep models, it is better aligned with industrial applications. The experimental results demonstrate that BRepFormer achieves state-of-the-art accuracy on the MFInstSeg, MFTRCAD, and our CBF datasets.
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[CV-56] Novel Diffusion Models for Multimodal 3D Hand Trajectory Prediction

【速读】：该论文旨在解决手部轨迹预测（Hand Trajectory Prediction, HTP）中存在的两个主要问题：一是现有方法仅利用二维自中心观测（2D egocentric observations），缺乏对来自二维和三维观测的多模态环境信息的认知；二是忽视了手部运动与头戴设备摄像头自运动（headset camera egomotion）之间的协同作用。为了解决这些问题，论文提出了一种名为MMTwin的新方法，其关键是设计了包含两种潜在扩散模型（即自运动扩散模型和HTP扩散模型）的双胞胎架构，并通过引入一种新颖的混合Mamba-Transformer模块作为HTP扩散的去噪模型，以更好地融合多模态特征。这一方案能够同时预测相机自运动和未来手部轨迹，从而提升三维手部轨迹预测的性能。

链接: https://arxiv.org/abs/2504.07375
作者: Junyi Ma,Wentao Bao,Jingyi Xu,Guanzhong Sun,Xieyuanli Chen,Hesheng Wang
机构: IRMV Lab, the Department of Automation, Shanghai Jiao Tong University (上海交通大学自动化系); Meta Reality Labs (Meta现实实验室); the Department of Electronic Engineering, Shanghai Jiao Tong University (上海交通大学电子工程系); the School of Information and Control Engineering, China University of Mining and Technology (中国矿业大学信息与控制工程学院); the College of Intelligence Science and Technology, National University of Defense Technology (国防科技大学智能科学与技术学院)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Predicting hand motion is critical for understanding human intentions and bridging the action space between human movements and robot manipulations. Existing hand trajectory prediction (HTP) methods forecast the future hand waypoints in 3D space conditioned on past egocentric observations. However, such models are only designed to accommodate 2D egocentric video inputs. There is a lack of awareness of multimodal environmental information from both 2D and 3D observations, hindering the further improvement of 3D HTP performance. In addition, these models overlook the synergy between hand movements and headset camera egomotion, either predicting hand trajectories in isolation or encoding egomotion only from past frames. To address these limitations, we propose novel diffusion models (MMTwin) for multimodal 3D hand trajectory prediction. MMTwin is designed to absorb multimodal information as input encompassing 2D RGB images, 3D point clouds, past hand waypoints, and text prompt. Besides, two latent diffusion models, the egomotion diffusion and the HTP diffusion as twins, are integrated into MMTwin to predict camera egomotion and future hand trajectories concurrently. We propose a novel hybrid Mamba-Transformer module as the denoising model of the HTP diffusion to better fuse multimodal features. The experimental results on three publicly available datasets and our self-recorded data demonstrate that our proposed MMTwin can predict plausible future 3D hand trajectories compared to the state-of-the-art baselines, and generalizes well to unseen environments. The code and pretrained models will be released at this https URL.
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[CV-57] View-Dependent Uncertainty Estimation of 3D Gaussian Splatting

【速读】：该论文旨在解决三维高斯点云（3D Gaussian Splatting, 3DGS）场景中不确定性估计不足的问题，这对于下游任务如资产提取和场景补全至关重要。由于三维高斯点的颜色具有视点依赖性，从某些角度可能是确定的，而从其他角度则可能不确定。为了解决这一问题，论文提出将不确定性建模为每个三维高斯点的额外视点相关特征，并通过球谐函数（spherical harmonics）进行建模。这种方法简单且有效，易于解释，并能够无缝集成到传统的3DGS流水线中。此外，与集成方法相比，该方法在保持高精度的同时显著提高了计算速度，实验结果验证了其有效性。

链接: https://arxiv.org/abs/2504.07370
作者: Chenyu Han,Corentin Dumery
机构: Computer Vision Lab, EPFL (计算机视觉实验室, 洛桑联邦理工学院); Lausanne, Switzerland (瑞士洛桑)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:3D Gaussian Splatting (3DGS) has become increasingly popular in 3D scene reconstruction for its high visual accuracy. However, uncertainty estimation of 3DGS scenes remains underexplored and is crucial to downstream tasks such as asset extraction and scene completion. Since the appearance of 3D gaussians is view-dependent, the color of a gaussian can thus be certain from an angle and uncertain from another. We thus propose to model uncertainty in 3DGS as an additional view-dependent per-gaussian feature that can be modeled with spherical harmonics. This simple yet effective modeling is easily interpretable and can be integrated into the traditional 3DGS pipeline. It is also significantly faster than ensemble methods while maintaining high accuracy, as demonstrated in our experiments.
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[CV-58] Zeus: Zero-shot LLM Instruction for Union Segmentation in Multimodal Medical Imaging

【速读】：该论文旨在解决医学图像分割领域中整合领域知识（尤其是文本信息）以满足临床诊断需求的问题。然而，现有方法在收集配对视觉-语言数据集时面临高昂的成本和时间消耗，这成为显著挑战。为应对这一问题，论文提出了一种新颖的视觉-大型语言模型（Vision-LLM）联合框架。其关键在于利用冻结的大型语言模型（LLMs）基于相应医学图像生成零样本指令，模拟放射学扫描与报告生成过程，并从多模态放射学图像（如T1-w或T2-w MRI以及CT）中生成更精确的文本指令。通过充分利用LLMs在语义理解和丰富知识方面的强大能力，该方法强调从不同模态中提取特定特征并重新整合信息，从而实现最终的临床诊断。此方案无需依赖预先收集的视觉-语言数据集即可处理多模态分割任务。实验结果验证了所提方法的优越性。

链接: https://arxiv.org/abs/2504.07336
作者: Siyuan Dai,Kai Ye,Guodong Liu,Haoteng Tang,Liang Zhan
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
备注: 21 pages, 4 figures, In Press by a journal

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Abstract:Medical image segmentation has achieved remarkable success through the continuous advancement of UNet-based and Transformer-based foundation backbones. However, clinical diagnosis in the real world often requires integrating domain knowledge, especially textual information. Conducting multimodal learning involves visual and text modalities shown as a solution, but collecting paired vision-language datasets is expensive and time-consuming, posing significant challenges. Inspired by the superior ability in numerous cross-modal tasks for Large Language Models (LLMs), we proposed a novel Vision-LLM union framework to address the issues. Specifically, we introduce frozen LLMs for zero-shot instruction generation based on corresponding medical images, imitating the radiology scanning and report generation process. To better approximate real-world diagnostic processes, we generate more precise text instruction from multimodal radiology images (e.g., T1-w or T2-w MRI and CT). Based on the impressive ability of semantic understanding and rich knowledge of LLMs. This process emphasizes extracting special features from different modalities and reunion the information for the ultimate clinical diagnostic. With generated text instruction, our proposed union segmentation framework can handle multimodal segmentation without prior collected vision-language datasets. To evaluate our proposed method, we conduct comprehensive experiments with influential baselines, the statistical results and the visualized case study demonstrate the superiority of our novel method.
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[CV-59] DLTPose: 6DoF Pose Estimation From Accurate Dense Surface Point Estimates

【速读】：该论文旨在解决基于RGB-D图像的六自由度（6DoF）物体位姿估计问题，特别是针对对称物体和遮挡物体的位姿估计挑战。传统方法在处理对称物体时容易出现关键点分配不一致的问题，而密集像素预测方法虽然鲁棒性较好，但精度有限。为了解决这些问题，论文提出了一种名为DLTPose的新方法。

DLTPose的关键在于结合稀疏关键点方法的高精度与密集像素预测的鲁棒性：它通过预测每个像素到一组最少四个关键点的径向距离，并利用一种新颖的直接线性变换（Direct Linear Transform, DLT）公式来生成精确的三维物体表面估计，从而实现更优的6DoF位姿估计。此外，论文引入了一种新颖的对称感知关键点排序方法，解决了对称物体导致的关键点分配不一致问题，通过灵活调整关键点顺序，增强了模型学习稳定关键点表示的能力。实验结果表明，DLTPose在LINEMOD、Occlusion LINEMOD和YCB-Video数据集上的表现优于现有方法，特别是在处理对称和遮挡物体时，展示了卓越的平均召回率（Mean Average Recall）。

链接: https://arxiv.org/abs/2504.07335
作者: Akash Jadhav,Michael Greenspan
机构: Dept. of Electrical and Computer Engineering (电气与计算机工程系), Ingenuity Labs Research Institute (才智实验室研究院), Queen’s University (女王大学), Kingston, Ontario, Canada
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We propose DLTPose, a novel method for 6DoF object pose estimation from RGB-D images that combines the accuracy of sparse keypoint methods with the robustness of dense pixel-wise predictions. DLTPose predicts per-pixel radial distances to a set of minimally four keypoints, which are then fed into our novel Direct Linear Transform (DLT) formulation to produce accurate 3D object frame surface estimates, leading to better 6DoF pose estimation. Additionally, we introduce a novel symmetry-aware keypoint ordering approach, designed to handle object symmetries that otherwise cause inconsistencies in keypoint assignments. Previous keypoint-based methods relied on fixed keypoint orderings, which failed to account for the multiple valid configurations exhibited by symmetric objects, which our ordering approach exploits to enhance the model’s ability to learn stable keypoint representations. Extensive experiments on the benchmark LINEMOD, Occlusion LINEMOD and YCB-Video datasets show that DLTPose outperforms existing methods, especially for symmetric and occluded objects, demonstrating superior Mean Average Recall values of 86.5% (LM), 79.7% (LM-O) and 89.5% (YCB-V). The code is available at this https URL .
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[CV-60] Objaverse: Curated 3D Object Dataset with Quality Annotations CVPR2025

【速读】：该论文试图解决Objaverse数据集中低质量模型占比较高的问题，这限制了其在3D内容生成任务中的实用性。为了解决这一问题，论文的关键方案是通过人工专家详细标注的方式，为10,000个3D对象添加包括美学质量评分、纹理颜色分类、多对象组合标志、透明特性等在内的丰富属性标签，并进一步训练了一个神经网络以扩展这些标注到剩余的Objaverse数据集。实验和用户研究表明，基于高质量子集预训练的模型在图像到3D生成任务中表现更优，且更高数据质量有助于加速训练收敛。这一方法证明了精心筛选与丰富注释可以弥补原始数据规模的不足，为发展3D生成模型提供了更高效的路径。

链接: https://arxiv.org/abs/2504.07334
作者: Chendi Lin,Heshan Liu,Qunshu Lin,Zachary Bright,Shitao Tang,Yihui He,Minghao Liu,Ling Zhu,Cindy Le
机构: Carnegie Mellon University (卡内基梅隆大学); Zhejiang University (浙江大学); Exascale Labs (超算实验室); Simon Fraser University (西蒙弗雷泽大学); 2077AI; Columbia University (哥伦比亚大学)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 8 pages, 8 figures. Accepted to CVPR 2025 Workshop on Efficient Large Vision Models (April 2025)

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Abstract:This paper presents Objaverse++, a curated subset of Objaverse enhanced with detailed attribute annotations by human experts. Recent advances in 3D content generation have been driven by large-scale datasets such as Objaverse, which contains over 800,000 3D objects collected from the Internet. Although Objaverse represents the largest available 3D asset collection, its utility is limited by the predominance of low-quality models. To address this limitation, we manually annotate 10,000 3D objects with detailed attributes, including aesthetic quality scores, texture color classifications, multi-object composition flags, transparency characteristics, etc. Then, we trained a neural network capable of annotating the tags for the rest of the Objaverse dataset. Through experiments and a user study on generation results, we demonstrate that models pre-trained on our quality-focused subset achieve better performance than those trained on the larger dataset of Objaverse in image-to-3D generation tasks. In addition, by comparing multiple subsets of training data filtered by our tags, our results show that the higher the data quality, the faster the training loss converges. These findings suggest that careful curation and rich annotation can compensate for the raw dataset size, potentially offering a more efficient path to develop 3D generative models. We release our enhanced dataset of approximately 500,000 curated 3D models to facilitate further research on various downstream tasks in 3D computer vision. In the near future, we aim to extend our annotations to cover the entire Objaverse dataset.
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[CV-61] CEC-MMR: Cross-Entropy Clustering Approach to Multi-Modal Regression

【速读】：该论文旨在解决回归分析应用中多值属性分布建模的问题，传统单变量高斯分布方法在面对多峰分布时表现欠佳，因预测均值可能位于峰值之间而导致预测值与实际数据显著偏离。为应对这一挑战，通常采用混合密度网络（Mixture Density Network, MDN）通过神经网络学习参数以构建混合分布，但其固有限制在于难以精确确定组件数量。本文提出了一种名为CEC-MMR的新方法，基于交叉熵聚类（Cross-Entropy Clustering, CEC），能够自动检测回归问题中的组件数量，并且能够根据属性及其值唯一标识其对应的组件。实验结果表明，CEC-MMR相比经典MDN具有更优的表现。解决方案的关键在于引入CEC算法实现组件数量的自动检测以及属性值与其对应组件的唯一映射能力。

链接: https://arxiv.org/abs/2504.07301
作者: Krzysztof Byrski,Jacek Tabor,Przemysław Spurek,Marcin Mazur
机构: Jagiellonian University ( Jagiellonian University )
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:In practical applications of regression analysis, it is not uncommon to encounter a multitude of values for each attribute. In such a situation, the univariate distribution, which is typically Gaussian, is suboptimal because the mean may be situated between modes, resulting in a predicted value that differs significantly from the actual data. Consequently, to address this issue, a mixture distribution with parameters learned by a neural network, known as a Mixture Density Network (MDN), is typically employed. However, this approach has an important inherent limitation, in that it is not feasible to ascertain the precise number of components with a reasonable degree of accuracy. In this paper, we introduce CEC-MMR, a novel approach based on Cross-Entropy Clustering (CEC), which allows for the automatic detection of the number of components in a regression problem. Furthermore, given an attribute and its value, our method is capable of uniquely identifying it with the underlying component. The experimental results demonstrate that CEC-MMR yields superior outcomes compared to classical MDNs.
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[CV-62] Quantifying Epistemic Uncertainty in Absolute Pose Regression

【速读】：该论文试图解决视觉重定位任务中绝对位姿回归模型预测准确性与可靠性不足的问题，尤其是在训练域外的情况。解决方案的关键在于提出了一种新颖的方法，通过变分框架估计观测值的似然性，从而量化绝对位姿回归模型的认识不确定性（epistemic uncertainty），同时提供了一种统一的模型来处理观测歧义，并在存在重复结构时以概率方式实现相机的局部化。这种方法在捕捉不确定性与预测误差之间的关系方面优于现有方法。

链接: https://arxiv.org/abs/2504.07260
作者: Fereidoon Zangeneh,Amit Dekel,Alessandro Pieropan,Patric Jensfelt
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Visual relocalization is the task of estimating the camera pose given an image it views. Absolute pose regression offers a solution to this task by training a neural network, directly regressing the camera pose from image features. While an attractive solution in terms of memory and compute efficiency, absolute pose regression’s predictions are inaccurate and unreliable outside the training domain. In this work, we propose a novel method for quantifying the epistemic uncertainty of an absolute pose regression model by estimating the likelihood of observations within a variational framework. Beyond providing a measure of confidence in predictions, our approach offers a unified model that also handles observation ambiguities, probabilistically localizing the camera in the presence of repetitive structures. Our method outperforms existing approaches in capturing the relation between uncertainty and prediction error.
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[CV-63] Few-Shot Adaptation of Grounding DINO for Agricultural Domain

【速读】：该论文旨在解决农业领域中深度学习模型依赖大量标注数据的问题，提出了一种高效的少量样本适应方法。解决方案的关键在于简化Grounding-DINO架构，具体通过移除文本编码器模块（BERT），并引入一个随机初始化的可训练文本嵌入，从而有效提升了模型在多个农业数据集上的性能，包括植物杂草检测、植物计数、昆虫识别、水果计数以及遥感任务等，特别是在少量样本学习条件下，其mAP相较于完全微调的YOLO模型高出约24%，并在遥感任务中比先前最先进的方法高出约10%。

链接: https://arxiv.org/abs/2504.07252
作者: Rajhans Singh,Rafael Bidese Puhl,Kshitiz Dhakal,Sudhir Sornapudi
机构: Corteva Agriscience (科迪华农业科技)
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Deep learning models are transforming agricultural applications by enabling automated phenotyping, monitoring, and yield estimation. However, their effectiveness heavily depends on large amounts of annotated training data, which can be labor and time intensive. Recent advances in open-set object detection, particularly with models like Grounding-DINO, offer a potential solution to detect regions of interests based on text prompt input. Initial zero-shot experiments revealed challenges in crafting effective text prompts, especially for complex objects like individual leaves and visually similar classes. To address these limitations, we propose an efficient few-shot adaptation method that simplifies the Grounding-DINO architecture by removing the text encoder module (BERT) and introducing a randomly initialized trainable text embedding. This method achieves superior performance across multiple agricultural datasets, including plant-weed detection, plant counting, insect identification, fruit counting, and remote sensing tasks. Specifically, it demonstrates up to a \sim24% higher mAP than fully fine-tuned YOLO models on agricultural datasets and outperforms previous state-of-the-art methods by \sim10% in remote sensing, under few-shot learning conditions. Our method offers a promising solution for automating annotation and accelerating the development of specialized agricultural AI solutions.
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[CV-64] MESA: Text-Driven Terrain Generation Using Latent Diffusion and Global Copernicus Data CVPR2025

【速读】：该论文旨在解决传统地形建模方法依赖于繁琐的手工规则和专业知识的问题，提出了一种以数据为中心的新方法。关键在于通过训练扩散模型（Diffusion Model）利用全球遥感数据生成高质量的地形样本，该模型能够根据文本描述灵活且可扩展地生成真实的多样化地形景观。这一方案展示了数据驱动模型在地形生成中的潜力，并通过开放的Major TOM Core-DEM扩展数据集作为支持资源，为全球地形数据提供全面参考。

链接: https://arxiv.org/abs/2504.07210
作者: Paul Borne–Pons(Adobe Research),Mikolaj Czerkawski(Asterisk Labs),Rosalie Martin(Adobe Research),Romain Rouffet(Adobe Research)
机构: Adobe Research; Asterisk Labs (ASTERISK实验室)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: Accepted at CVPR 2025 Workshop MORSE

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Abstract:Terrain modeling has traditionally relied on procedural techniques, which often require extensive domain expertise and handcrafted rules. In this paper, we present MESA - a novel data-centric alternative by training a diffusion model on global remote sensing data. This approach leverages large-scale geospatial information to generate high-quality terrain samples from text descriptions, showcasing a flexible and scalable solution for terrain generation. The model’s capabilities are demonstrated through extensive experiments, highlighting its ability to generate realistic and diverse terrain landscapes. The dataset produced to support this work, the Major TOM Core-DEM extension dataset, is released openly as a comprehensive resource for global terrain data. The results suggest that data-driven models, trained on remote sensing data, can provide a powerful tool for realistic terrain modeling and generation.
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[CV-65] Face-LLaVA: Facial Expression and Attribute Understanding through Instruction Tuning

【速读】：该论文旨在解决基于人脸的社会化计算机视觉任务中的挑战，特别是在面部表情识别、属性检测、年龄估计及深度伪造检测等多模态任务中，提升现有多模态大型语言模型（MLLMs）在人脸处理方面的性能。论文的关键创新在于提出了Face-LLaVA，一种以人脸为中心的大规模语言模型，结合了上下文学习能力和自然语言描述生成能力。其解决方案的核心是开发了一个新颖的人脸特定视觉编码器，该编码器通过人脸区域引导的跨注意力机制（Face-Region Guided Cross-Attention），将人脸几何信息与局部视觉特征进行有效融合。此外，研究团队构建了面向人脸处理的指令调优数据集FaceInstruct-1M，并验证了Face-LLaVA在九个不同数据集上的优越表现，证明其在学术界和商业应用中的竞争力。

链接: https://arxiv.org/abs/2504.07198
作者: Ashutosh Chaubey,Xulang Guan,Mohammad Soleymani
机构: Institute for Creative Technologies, University of Southern California (南加州大学创意技术研究所)
类目: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: Project Page: this https URL

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Abstract:The human face plays a central role in social communication, necessitating the use of performant computer vision tools for human-centered applications. We propose Face-LLaVA, a multimodal large language model for face-centered, in-context learning, including facial expression and attribute recognition. Additionally, Face-LLaVA is able to generate natural language descriptions that can be used for reasoning. Leveraging existing visual databases, we first developed FaceInstruct-1M, a face-centered database for instruction tuning MLLMs for face processing. We then developed a novel face-specific visual encoder powered by Face-Region Guided Cross-Attention that integrates face geometry with local visual features. We evaluated the proposed method across nine different datasets and five different face processing tasks, including facial expression recognition, action unit detection, facial attribute detection, age estimation and deepfake detection. Face-LLaVA achieves superior results compared to existing open-source MLLMs and competitive performance compared to commercial solutions. Our model output also receives a higher reasoning rating by GPT under a zero-shot setting across all the tasks. Both our dataset and model wil be released at this https URL to support future advancements in social AI and foundational vision-language research.
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[CV-66] Perception in Reflection

【速读】：该论文旨在解决当前大规模视觉-语言模型（Large Vision-Language Models, LVLMs）在初始阶段难以实现完美感知的问题。论文提出了一种名为Reflective Perception (RePer) 的双模态反射机制，通过交替使用策略模型与评估模型，实现了视觉感知的迭代优化。该方案的关键在于基于Reflective Perceptual Learning (RPL) 的设计，它通过精心构建的视觉反射数据集和反射式不可能性训练方法，强化了模型的内在反射能力。实验结果表明，RePer 在图像理解、描述精度以及幻觉减少方面取得了可量化的提升，并且其注意力模式与人类视觉焦点表现出强对齐，同时 RPL 在细粒度和自由形式的偏好对齐上进行了优化。这些进展确立了“感知即反射”范式在复杂推理和多步操作任务中的潜力。

链接: https://arxiv.org/abs/2504.07165
作者: Yana Wei,Liang Zhao,Kangheng Lin,En Yu,Yuang Peng,Runpei Dong,Jianjian Sun,Haoran Wei,Zheng Ge,Xiangyu Zhang,Vishal M. Patel
机构: 未知
类目: Computer Vision and Pattern Recognition (cs.CV)
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Abstract:We present a perception in reflection paradigm designed to transcend the limitations of current large vision-language models (LVLMs), which are expected yet often fail to achieve perfect perception initially. Specifically, we propose Reflective Perception (RePer), a dual-model reflection mechanism that systematically alternates between policy and critic models, enables iterative refinement of visual perception. This framework is powered by Reflective Perceptual Learning (RPL), which reinforces intrinsic reflective capabilities through a methodically constructed visual reflection dataset and reflective unlikelihood training. Comprehensive experimental evaluation demonstrates RePer’s quantifiable improvements in image understanding, captioning precision, and hallucination reduction. Notably, RePer achieves strong alignment between model attention patterns and human visual focus, while RPL optimizes fine-grained and free-form preference alignment. These advancements establish perception in reflection as a robust paradigm for future multimodal agents, particularly in tasks requiring complex reasoning and multi-step manipulation.
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[CV-67] Boundary representation learning via Transformer

【速读】：该论文旨在解决Transformer网络在计算机辅助设计（CAD）领域中处理边界表示（Boundary Representation, B-rep）模型时所面临的挑战。尽管Transformers在自然语言处理、计算机视觉和图形学中取得了显著成功，但由于B-rep模型具有不规则拓扑结构和连续几何定义等独特特性，其在CAD中的应用尚未得到充分探索。为了解决这一问题，论文提出了Boundary Representation Transformer (BRT)，这是一种将Transformer适应于B-rep学习的新方法。

BRT的关键在于引入了一种连续几何嵌入方法，能够将B-rep表面（包括裁剪和未裁剪的表面）编码为Bézier三角形，从而在不进行离散化的情况下保留形状和连续性。此外，BRT还采用了一种拓扑感知嵌入方法，将这些几何嵌入组织成适合Transformers的离散标记序列，以捕捉B-rep模型中的几何与拓扑特征。这种方法使得Transformer的注意力机制能够有效地学习B-rep模型中边界元素的形状模式和上下文语义。实验结果表明，BRT在零件分类和特征识别任务中达到了最先进的性能。

链接: https://arxiv.org/abs/2504.07134
作者: Qiang Zou,Lizhen Zhu
机构: State Key Laboratory of CAD&&&CG, Zhejiang University (浙江大学)
类目: Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The recent rise of generative artificial intelligence (AI), powered by Transformer networks, has achieved remarkable success in natural language processing, computer vision, and graphics. However, the application of Transformers in computer-aided design (CAD), particularly for processing boundary representation (B-rep) models, remains largely unexplored. To bridge this gap, this paper introduces Boundary Representation Transformer (BRT), a novel method adapting Transformer for B-rep learning. B-rep models pose unique challenges due to their irregular topology and continuous geometric definitions, which are fundamentally different from the structured and discrete data Transformers are designed for. To address this, BRT proposes a continuous geometric embedding method that encodes B-rep surfaces (trimmed and untrimmed) into Bézier triangles, preserving their shape and continuity without discretization. Additionally, BRT employs a topology-aware embedding method that organizes these geometric embeddings into a sequence of discrete tokens suitable for Transformers, capturing both geometric and topological characteristics within B-rep models. This enables the Transformer’s attention mechanism to effectively learn shape patterns and contextual semantics of boundary elements in a B-rep model. Extensive experiments demonstrate that BRT achieves state-of-the-art performance in part classification and feature recognition tasks.
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[CV-68] Zero-Shot Low-dose CT Denoising via Sinogram Flicking

【速读】：该论文旨在解决低剂量CT成像中监督学习方法依赖大量配对噪声与清晰图像的问题，而这种配对数据在临床实践中难以获取。此外，现有的零样本自监督方法（如ZS-N2N）虽避免了配对数据需求，但通常通过降采样操作降低图像分辨率，并受限于单一图像本身的信息。论文的关键解决方案是提出了一种基于“sinogram flicking”的零样本低剂量CT成像方法，通过随机匹配共轭射线生成多个具有相同结构但不同噪声模式的sinogram副本，利用这些动态闪烁的sinogram对网络进行训练，从而在不损失分辨率的情况下有效增强去噪性能，最终实现优于现有方法（如ZS-N2N）的表现。

链接: https://arxiv.org/abs/2504.07927
作者: Yongyi Shi,Ge Wang
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 4 pages, 4 figures

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Abstract:Many low-dose CT imaging methods rely on supervised learning, which requires a large number of paired noisy and clean images. However, obtaining paired images in clinical practice is challenging. To address this issue, zero-shot self-supervised methods train denoising networks using only the information within a single image, such as ZS-N2N. However, these methods often employ downsampling operations that degrade image resolution. Additionally, the training dataset is inherently constrained to the image itself. In this paper, we propose a zero-shot low-dose CT imaging method based on sinogram flicking, which operates within a single image but generates many copies via random conjugate ray matching. Specifically, two conjugate X-ray pencil beams measure the same path; their expected values should be identical, while their noise levels vary during measurements. By randomly swapping portions of the conjugate X-rays in the sinogram domain, we generate a large set of sinograms with consistent content but varying noise patterns. When displayed dynamically, these sinograms exhibit a flickering effect due to their identical structural content but differing noise patterns-hence the term sinogram flicking. We train the network on pairs of sinograms with the same content but different noise distributions using a lightweight model adapted from ZS-NSN. This process is repeated to obtain the final results. A simulation study demonstrates that our method outperforms state-of-the-art approaches such as ZS-N2N.
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[CV-69] he Efficacy of Semantics-Preserving Transformations in Self-Supervised Learning for Medical Ultrasound

【速读】：该论文旨在解决医学影像领域中数据增强(Data Augmentation)和预处理策略在自监督学习(Self-Supervised Learning, SSL)中的有效性问题，特别是针对肺部超声影像任务。传统自然图像领域的数据增强方法并不总是适用于医学成像任务。为此，论文设计了一种新的语义保持型数据增强管道，并通过系统性实验评估了三种数据增强流程：(1)跨成像领域的基线管道，(2)专为超声影像设计的语义保持型管道，以及(3)从上述两种管道中提取的最佳变换组合。同时，研究还探讨了语义保持型预处理对下游任务性能的影响。论文的关键在于提出并验证了语义保持的数据增强与预处理策略能够显著提升特定医学诊断任务（如COVID-19分类需要全局上下文信息的任务）的性能，而基于裁剪的方法更适合局部模式识别任务（如B线和胸腔积液检测）。最终，论文为超声影像领域中使用SSL的研究人员提供了关于数据增强和预处理策略的实用指导。

链接: https://arxiv.org/abs/2504.07904
作者: Blake VanBerlo,Alexander Wong,Jesse Hoey,Robert Arntfield
机构: Cheriton School of Computer Science at the University of Waterloo (查尔顿计算机科学学院，滑铁卢大学); Department of Systems Design Engineering at the University of Waterloo (系统设计工程系，滑铁卢大学); Schulich School of Medicine and Dentistry at Western University (舒立克医学与牙科学校，西方大学); Natural Sciences and Engineering Research Council of Canada (自然科学与工程研究委员会，加拿大); Compute Ontario (computeontario.ca) (安大略省计算); Digital Research Alliance of Canada (alliance.can.ca) (加拿大数字研究联盟)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
备注: 17 pages, 12 figures, 18 tables, Submitted to Medical Image Analysis

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Abstract:Data augmentation is a central component of joint embedding self-supervised learning (SSL). Approaches that work for natural images may not always be effective in medical imaging tasks. This study systematically investigated the impact of data augmentation and preprocessing strategies in SSL for lung ultrasound. Three data augmentation pipelines were assessed: (1) a baseline pipeline commonly used across imaging domains, (2) a novel semantic-preserving pipeline designed for ultrasound, and (3) a distilled set of the most effective transformations from both pipelines. Pretrained models were evaluated on multiple classification tasks: B-line detection, pleural effusion detection, and COVID-19 classification. Experiments revealed that semantics-preserving data augmentation resulted in the greatest performance for COVID-19 classification - a diagnostic task requiring global image context. Cropping-based methods yielded the greatest performance on the B-line and pleural effusion object classification tasks, which require strong local pattern recognition. Lastly, semantics-preserving ultrasound image preprocessing resulted in increased downstream performance for multiple tasks. Guidance regarding data augmentation and preprocessing strategies was synthesized for practitioners working with SSL in ultrasound.
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[CV-70] HarmonySeg: Tubular Structure Segmentation with Deep-Shallow Feature Fusion and Growth-Suppression Balanced Loss

【速读】：该论文旨在解决医学图像中管状结构（如血管和气道树）分割面临的挑战，包括结构多样性、复杂拓扑以及通常存在的数据标注不完整等问题。为应对这些困难，论文提出了一种名为HarmonySeg的新框架。其关键解决方案包括：设计了一个具有可变感受野的灵活卷积块的深浅结合解码网络，以有效适应不同尺度的管状结构；引入血管ness图作为辅助信息，并通过浅深融合模块与图像特征对齐，同时消除不合理候选以保持高精度；设计了一种保持拓扑结构的损失函数，利用上下文和形状先验平衡管状结构的生长与抑制，从而处理低质量及不完整标注。实验结果表明，该模型在多个公开数据集上的表现优于现有最先进的方法，并具备良好的泛化能力。

链接: https://arxiv.org/abs/2504.07827
作者: Yi Huang,Ke Zhang,Wei Liu,Yuanyuan Wang,Vishal M. Patel,Le Lu,Xu Han,Dakai Jin,Ke Yan
机构: DAMO Academy, Alibaba Group (达摩院，阿里巴巴集团); Hupan Lab, Hangzhou, China (湖畔实验室，中国杭州); Department of Biomedical Engineering, Fudan University (复旦大学生物医学工程系); Department of Electrical and Computer Engineering, Johns Hopkins University (约翰斯·霍普金斯大学电气与计算机工程系); Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of College of Medicine, Zhejiang University (浙江大学医学院附属第一医院肝胆胰外科); DAMO Academy, Alibaba Group (达摩院，阿里巴巴集团); Hupan Lab, Hangzhou, China (湖畔实验室，中国杭州); DAMO Academy, Alibaba Group (达摩院，阿里巴巴集团)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Accurate segmentation of tubular structures in medical images, such as vessels and airway trees, is crucial for computer-aided diagnosis, radiotherapy, and surgical planning. However, significant challenges exist in algorithm design when faced with diverse sizes, complex topologies, and (often) incomplete data annotation of these structures. We address these difficulties by proposing a new tubular structure segmentation framework named HarmonySeg. First, we design a deep-to-shallow decoder network featuring flexible convolution blocks with varying receptive fields, which enables the model to effectively adapt to tubular structures of different scales. Second, to highlight potential anatomical regions and improve the recall of small tubular structures, we incorporate vesselness maps as auxiliary information. These maps are aligned with image features through a shallow-and-deep fusion module, which simultaneously eliminates unreasonable candidates to maintain high precision. Finally, we introduce a topology-preserving loss function that leverages contextual and shape priors to balance the growth and suppression of tubular structures, which also allows the model to handle low-quality and incomplete annotations. Extensive quantitative experiments are conducted on four public datasets. The results show that our model can accurately segment 2D and 3D tubular structures and outperform existing state-of-the-art methods. External validation on a private dataset also demonstrates good generalizability.
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[CV-71] Adaptive Detection of Fast Moving Celestial Objects Using a Mixture of Experts and Physical-Inspired Neural Network

【速读】：该论文旨在解决传统方法在处理空间望远镜观测数据时检测快速移动天体（Fast Moving Celestial Objects, FMCOs）效果不佳的问题。随着空间望远镜的普及及其多样化观测模式，传统基于地面望远镜的图像差分技术和检测算法无法充分利用空间观测数据的独特特性。为此，论文提出了一种新颖的算法，其关键是将最先进的快速移动天体检测神经网络转化为物理启发式神经网络。这些网络利用望远镜的点扩散函数（Point Spread Function, PSF）和具体观测模式作为先验信息，能够直接识别星场中的快速移动天体，而无需额外训练。此外，所有神经网络通过“专家混合”技术集成，形成一个综合的快速移动天体检测算法。实验结果表明，该方法在不同观测模式下均能有效检测快速移动天体。

链接: https://arxiv.org/abs/2504.07777
作者: Peng Jia,Ge Li,Bafeng Cheng,Yushan Li,Rongyu Sun
机构: College of Physics and Optoelectronics, Taiyuan University of Technology (太原理工大学物理与光电工程学院); Key Laboratory of Space Object and Debris Observation, Purple Mountain Observatory, Chinese Academy of Sciences (中国科学院紫金山天文台空间目标与碎片观测重点实验室)
类目: Instrumentation and Methods for Astrophysics (astro-ph.IM); Earth and Planetary Astrophysics (astro-ph.EP); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Optics (physics.optics)
备注: Accepted by the AJ

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Abstract:Fast moving celestial objects are characterized by velocities across the celestial sphere that significantly differ from the motions of background stars. In observational images, these objects exhibit distinct shapes, contrasting with the typical appearances of stars. Depending on the observational method employed, these celestial entities may be designated as near-Earth objects or asteroids. Historically, fast moving celestial objects have been observed using ground-based telescopes, where the relative stability of stars and Earth facilitated effective image differencing techniques alongside traditional fast moving celestial object detection and classification algorithms. However, the growing prevalence of space-based telescopes, along with their diverse observational modes, produces images with different properties, rendering conventional methods less effective. This paper presents a novel algorithm for detecting fast moving celestial objects within star fields. Our approach enhances state-of-the-art fast moving celestial object detection neural networks by transforming them into physical-inspired neural networks. These neural networks leverage the point spread function of the telescope and the specific observational mode as prior information; they can directly identify moving fast moving celestial objects within star fields without requiring additional training, thereby addressing the limitations of traditional techniques. Additionally, all neural networks are integrated using the mixture of experts technique, forming a comprehensive fast moving celestial object detection algorithm. We have evaluated our algorithm using simulated observational data that mimics various observations carried out by space based telescope scenarios and real observation images. Results demonstrate that our method effectively detects fast moving celestial objects across different observational modes.
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[CV-72] Focal Cortical Dysplasia Type II Detection Using Cross Modality Transfer Learning and Grad-CAM in 3D-CNNs for MRI Analysis

【速读】：该论文旨在解决局灶性皮质发育不良（FCD）II型在MRI影像中因细微异常导致难以诊断的问题，这常引发误诊并限制药物难治性癫痫的有效治疗。论文的关键解决方案是利用三维卷积神经网络（3D-CNNs）结合跨模态迁移学习与可解释人工智能（XAI）技术，特别是梯度加权类激活映射（Grad-CAM）。通过采用预训练权重的ResNet架构（ResNet-18、-34和-50），研究证明了跨模态迁移学习显著提升了分类准确性至80.3%，并增强了模型对临床相关区域的关注度，通过新的Heat-Score指标量化模型的解释能力。这种结合迁移学习与XAI的方法，不仅提高了诊断精度，还缩小了AI预测与临床洞察之间的差距，为复杂病理的AI辅助诊断提供了重要参考。

链接: https://arxiv.org/abs/2504.07775
作者: Lorenzo Lasagni,Antonio Ciccarone,Renzo Guerrini,Matteo Lenge,Ludovico D’incerti
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Medical Physics (physics.med-ph)
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Abstract:Focal cortical dysplasia (FCD) type II is a major cause of drug-resistant epilepsy, often curable only by surgery. Despite its clinical importance, the diagnosis of FCD is very difficult in MRI because of subtle abnormalities, leading to misdiagnosis. This study investigates the use of 3D convolutional neural networks (3D-CNNs) for FCD detection, using a dataset of 170 subjects (85 FCD patients and 85 controls) composed of T1-weighted and FLAIR MRI scans. In particular, it investigates the benefits obtained from cross-modality transfer learning and explainable artificial intelligence (XAI) techniques, in particular Gradient-weighted Class Activation Mapping (Grad-CAM). ResNet architectures (ResNet-18, -34, and -50) were implemented, employing transfer learning strategies that used pre-trained weights from segmentation tasks. Results indicate that transfer learning significantly enhances classification accuracy (up to 80.3%) and interpretability, as measured by a novel Heat-Score metric, which evaluates the model’s focus on clinically relevant regions. Improvements in the Heat-Score metric underscore the model’s seizure zone localization capabilities, bringing AI predictions and clinical insights closer together. These results highlight the importance of transfer learning, including cross-modality, and XAI in advancing AI-based medical diagnostics, especially for difficult-to-diagnose pathologies such as FCD.
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[CV-73] PRAD: Periapical Radiograph Analysis Dataset and Benchmark Model Development

【速读】：该论文旨在解决牙科根尖放射影像（Periapical Radiographs, PR）分析领域中深度学习（Deep Learning, DL）应用受限的问题。尽管DL在牙科辅助诊断中的成像模态（如全景片和锥形束CT）已有一定发展，但针对PR的专门辅助分析研究仍显不足。这主要是由于PR数据集中分辨率限制、伪影等问题导致标注困难，以及高质量、大规模公开数据集的缺乏，从而阻碍了DL技术在此领域的进一步推广与性能提升。

为解决上述问题，论文的关键在于提出了两个核心贡献：首先构建了一个包含10,000张临床PR图像的数据集PRAD-10K，并由专业牙医提供像素级标注，涵盖九种不同的解剖结构、病变及人工修复或医疗设备；其次，设计了一种名为PRNet的DL网络，用于PR分割任务，并通过实验验证其在PRAD-10K数据集上的表现超越了现有的医学图像分割模型。这些方法有效缓解了PR分析中数据稀缺和技术瓶颈的挑战。

链接: https://arxiv.org/abs/2504.07760
作者: Zhenhuan Zhou,Yuchen Zhang,Ruihong Xu,Xuansen Zhao,Tao Li
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 11 pages Under Review

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Abstract:Deep learning (DL), a pivotal technology in artificial intelligence, has recently gained substantial traction in the domain of dental auxiliary diagnosis. However, its application has predominantly been confined to imaging modalities such as panoramic radiographs and Cone Beam Computed Tomography, with limited focus on auxiliary analysis specifically targeting Periapical Radiographs (PR). PR are the most extensively utilized imaging modality in endodontics and periodontics due to their capability to capture detailed local lesions at a low cost. Nevertheless, challenges such as resolution limitations and artifacts complicate the annotation and recognition of PR, leading to a scarcity of publicly available, large-scale, high-quality PR analysis datasets. This scarcity has somewhat impeded the advancement of DL applications in PR analysis. In this paper, we present PRAD-10K, a dataset for PR analysis. PRAD-10K comprises 10,000 clinical periapical radiograph images, with pixel-level annotations provided by professional dentists for nine distinct anatomical structures, lesions, and artificial restorations or medical devices, We also include classification labels for images with typical conditions or lesions. Furthermore, we introduce a DL network named PRNet to establish benchmarks for PR segmentation tasks. Experimental results demonstrate that PRNet surpasses previous state-of-the-art medical image segmentation models on the PRAD-10K dataset. The codes and dataset will be made publicly available.
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[CV-74] Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstruction

【速读】：该论文旨在解决稀疏视图采样下双能 CT (Dual-Energy Computed Tomography, DECT) 图像重建中因数据不完整易产生伪影的问题，同时现有方法多局限于图像域且缺乏全局约束导致重建质量不足。论文的关键解决方案是提出了一种基于虚拟掩膜引导扩散模型的双域稀疏视图重建方法，利用 DECT 中高低能数据间的强信道相关性设计虚拟掩膜并进行扰动操作，构建高维张量作为扩散模型的先验信息；此外，采用双域协作策略将小波域随机选择的高频分量信息与投影域信息融合，以优化全局结构和局部细节。

链接: https://arxiv.org/abs/2504.07753
作者: Zini Chen,Yao Xiao,Junyan Zhang,Shaoyu Wang,Liu Shi,Qiegen Liu
机构: 未知
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Sparse-view sampling in dual-energy computed tomography (DECT) significantly reduces radiation dose and increases imaging speed, yet is highly prone to artifacts. Although diffusion models have demonstrated potential in effectively handling incomplete data, most existing methods in this field focus on the image do-main and lack global constraints, which consequently leads to insufficient reconstruction quality. In this study, we propose a dual-domain virtual-mask in-formed diffusion model for sparse-view reconstruction by leveraging the high inter-channel correlation in DECT. Specifically, the study designs a virtual mask and applies it to the high-energy and low-energy data to perform perturbation operations, thus constructing high-dimensional tensors that serve as the prior information of the diffusion model. In addition, a dual-domain collaboration strategy is adopted to integrate the information of the randomly selected high-frequency components in the wavelet domain with the information in the projection domain, for the purpose of optimizing the global struc-tures and local details. Experimental results indicated that the present method exhibits excellent performance across multiple datasets.
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[CV-75] Heart Failure Prediction using Modal Decomposition and Masked Autoencoders for Scarce Echocardiography Databases

【速读】：该论文试图解决心力衰竭（Heart Failure, HF）的早期、快速和有效预测这一挑战性问题。针对这一任务，论文提出了一种基于实时分析超声心动图视频序列的自动系统。解决方案的关键在于设计了一个两阶段的深度学习框架：第一阶段利用高阶动态模式分解（Higher Order Dynamic Mode Decomposition, HODMD）算法对超声心动图视频数据库中的数据进行增强和特征提取，并将其转换为机器学习兼容的标注图像集合；第二阶段构建并训练了一种视觉Transformer（Vision Transformer, ViT），并通过自监督学习（Self-Supervised Learning, SSL）方法从有限的超声心动图数据库中有效训练ViT模型。实验结果验证了HODMD算法的有效性和所提系统的优越性。

链接: https://arxiv.org/abs/2504.07606
作者: Andrés Bell-Navas,María Villalba-Orero,Enrique Lara-Pezzi,Jesús Garicano-Mena,Soledad Le Clainche
机构: Universidad Politécnica de Madrid(马德里理工大学); Universidad Complutense de Madrid(马德里康普顿斯大学); Centro Nacional de Investigaciones Cardiovasculares Carlos III(国家心血管研究中⼼ Carlos III); Universidad Politécnica de Madrid(马德里理工大学)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
备注: 37 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:2404.19579

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Abstract:Heart diseases constitute the main cause of international human defunction. According to the World Health Organization (WHO), approximately 18 million deaths happen each year due to precisely heart diseases. In particular, heart failures (HF) press the healthcare industry to develop systems for their early, rapid and effective prediction. In this work, an automatic system which analyses in real-time echocardiography video sequences is proposed for the challenging and more specific task of prediction of heart failure times. This system is based on a novel deep learning framework, and works in two stages. The first one transforms the data included in a database of echocardiography video sequences into a machine learning-compatible collection of annotated images which can be used in the training phase of any kind of machine learning-based framework, including a deep learning one. This initial stage includes the use of the Higher Order Dynamic Mode Decomposition (HODMD) algorithm for both data augmentation and feature extraction. The second stage is focused on building and training a Vision Transformer (ViT). Self-supervised learning (SSL) methods, which have been so far barely explored in the literature about heart failure prediction, are applied to effectively train the ViT from scratch, even with scarce databases of echocardiograms. The designed neural network analyses images from echocardiography sequences to estimate the time in which a heart failure will happen. The results obtained show the efficacy of the HODMD algorithm and the superiority of the proposed system with respect to several established ViT and Convolutional Neural Network (CNN) architectures.
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[CV-76] PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation

【速读】：该论文旨在解决临床及现有基于人工智能的方法仅关注MRI（磁共振成像）的幅度图像而忽视相位数据的问题，尽管相位数据在下游任务（如肿瘤分割与分类）中具有潜在价值。论文的关键创新在于提出了一种名为 \textit{PhaseGen} 的新型复值扩散模型，该模型能够根据常用的临床幅度图像生成合成的MRI原始k-Space数据。这种方法使创建人工复值原始数据成为可能，从而实现需要k-Space信息的模型预训练。实验评估表明，使用合成相位数据进行训练显著提升了实际数据中颅骨剥离任务的泛化能力，分割准确率从41.1%提高到80.1%，并且当与有限的真实世界数据结合时，还能改善MRI重建效果。这项工作标志着利用生成式AI弥合基于幅度的数据集与MRI原始数据复值性质之间差距的重要进展，为更精准高效的诊断任务提供了新途径。

链接: https://arxiv.org/abs/2504.07560
作者: Moritz Rempe,Fabian Hörst,Helmut Becker,Marco Schlimbach,Lukas Rotkopf,Kevin Kröninger,Jens Kleesiek
机构: Institute for AI in Medicine (IKIM), University Hospital Essen, Girardetstraße 2, 45131 Essen, Germany (医学人工智能研究所, 埃森大学医院); Cancer Research Center Cologne Essen (CCCE), University Medicine Essen, Hufelandstraße 55, 45147 Essen, Germany (科隆埃森癌症研究中心, 埃森大学医学中心); Department of Physics, Technical University Dortmund, Otto-Hahn-Straße 4a, 44227 Dortmund, Germany (杜塞尔多夫工业大学物理系); German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany (德国癌症研究中心); RACOON Study Group, Site Essen, Essen Germany (RACOON研究小组, 埃森分部); German Cancer Consortium (DKTK), Partner Site Essen, Hufelandstraße 55, 45147 Essen, Germany (德国癌症联盟, 埃森合作研究中心); Medical Faculty and Faculty of Computer Science, University of Duisburg-Essen, 45141 Essen, Germany (杜伊斯堡-埃森大学医学院和计算机科学学院)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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Abstract:Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligence (AI)-based methods focus only on magnitude images, discarding the phase data despite its potential for downstream tasks, such as tumor segmentation and classification. In this work, we introduce \textitPhaseGen , a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images, commonly used in clinical practice. This enables the creation of artificial complex-valued raw data, allowing pretraining for models that require k-Space information. We evaluate PhaseGen on two tasks: skull-stripping directly in k-Space and MRI reconstruction using the publicly available FastMRI dataset. Our results show that training with synthetic phase data significantly improves generalization for skull-stripping on real-world data, with an increased segmentation accuracy from 41.1% to 80.1% , and enhances MRI reconstruction when combined with limited real-world data. This work presents a step forward in utilizing generative AI to bridge the gap between magnitude-based datasets and the complex-valued nature of MRI raw data. This approach allows researchers to leverage the vast amount of avaliable image domain data in combination with the information-rich k-Space data for more accurate and efficient diagnostic tasks. We make our code publicly \hrefthis https URL\textavailable here .
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[CV-77] Novel Pooling-based VGG-Lite for Pneumonia and Covid-19 Detection from Imbalanced Chest X-Ray Datasets

【速读】：该论文旨在解决胸部X光（Chest X-Ray, CXR）数据集中类别不平衡（class imbalance）的问题。解决方案的关键在于提出了一种基于池化操作的轻量级VGG模型（VGG-Lite），并通过创新性的模块设计增强模型性能。具体而言，VGG-Lite模型以VGG-16和MobileNet-V2为基础构建，并在其基础上引入了“边缘增强模块（Edge Enhanced Module, EEM）”。EEM包含一个并行分支，其中结合了“负样本图像层”和一种全新的自定义池化层——“2Max-Min Pooling”。这种池化层专注于肺炎CXR图像中的边缘特征，作为高效的空间注意力模块（Spatial Attention Module, SAM）发挥作用。通过这些创新设计，该框架在两个不同的CXR数据集上均取得了显著优于预训练CNN模型及三种现有先进模型（Vision Transformer、Pooling-based Vision Transformer 和 PneuNet）的表现。

链接: https://arxiv.org/abs/2504.07468
作者: Santanu Roy,Ashvath Suresh,Palak Sahu,Tulika Rudra Gupta
机构: Department of Computer Science and Engineering, at Christ (Deemed to be University), Kengery Campus, Bangalore, India (计算机科学与工程系，基督大学（ deemed to be University），坎格里校区，印度班加罗尔); Department of Computer Science and Engineering, at NIIT University, Rajasthan, India (计算机科学与工程系，NIIT大学，拉贾斯坦邦，印度); Dana-Farber Cancer Institute, Harvard Medical School, USA (达纳-法伯癌症研究所，哈佛医学院，美国)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:This paper proposes a novel pooling-based VGG-Lite model in order to mitigate class imbalance issues in Chest X-Ray (CXR) datasets. Automatic Pneumonia detection from CXR images by deep learning model has emerged as a prominent and dynamic area of research, since the inception of the new Covid-19 variant in 2020. However, the standard Convolutional Neural Network (CNN) models encounter challenges associated with class imbalance, a prevalent issue found in many medical datasets. The innovations introduced in the proposed model architecture include: (I) A very lightweight CNN model, `VGG-Lite’, is proposed as a base model, inspired by VGG-16 and MobileNet-V2 architecture. (II) On top of this base model, we leverage an Edge Enhanced Module (EEM)" through a parallel branch, consisting of a negative image layer", and a novel custom pooling layer 2Max-Min Pooling". This 2Max-Min Pooling layer is entirely novel in this investigation, providing more attention to edge components within pneumonia CXR images. Thus, it works as an efficient spatial attention module (SAM). We have implemented the proposed framework on two separate CXR datasets. The first dataset is obtained from a readily available source on the internet, and the second dataset is a more challenging CXR dataset, assembled by our research team from three different sources. Experimental results reveal that our proposed framework has outperformed pre-trained CNN models, and three recent trend existing models Vision Transformer", Pooling-based Vision Transformer (PiT)'' and PneuNet", by substantial margins on both datasets. The proposed framework VGG-Lite with EEM, has achieved a macro average of 95% accuracy, 97.1% precision, 96.1% recall, and 96.6% F1 score on the ``Pneumonia Imbalance CXR dataset", without employing any pre-processing technique.
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[CV-78] Synthetic CT Generation from Time-of-Flight Non-Attenutaion-Corrected PET for Whole-Body PET Attenuation Correction

【速读】：该论文旨在解决正电子发射断层成像（PET）在磁共振成像（MRI）系统中的衰减校正（AC）难题，由于缺乏可用的计算机断层扫描（CT）数据来直接估计组织密度变化引起的光子损耗。论文的关键解决方案在于提出了一种深度学习方法，通过使用时间飞行（TOF）非衰减校正（NAC）PET图像直接生成合成CT（sCT）图像，从而提升PET/MRI系统的衰减校正精度。其关键创新点在于利用在大规模自然图像数据集上预训练的模型，并进一步针对特定机构提供的TOF NAC PET与CT配对数据进行微调，实现了在体轮廓区域内最低的平均绝对误差（MAE）和最高的峰值信噪比（PSNR），显著改善了骨骼和软组织结构的重建效果。这一研究强调了采用预训练深度学习模型在医学图像转换任务中的有效性。

链接: https://arxiv.org/abs/2504.07450
作者: Weijie Chen,James Wang,Alan McMillan
机构: 未知
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Positron Emission Tomography (PET) imaging requires accurate attenuation correction (AC) to account for photon loss due to tissue density variations. In PET/MR systems, computed tomography (CT), which offers a straightforward estimation of AC is not available. This study presents a deep learning approach to generate synthetic CT (sCT) images directly from Time-of-Flight (TOF) non-attenuation corrected (NAC) PET images, enhancing AC for PET/MR. We first evaluated models pre-trained on large-scale natural image datasets for a CT-to-CT reconstruction task, finding that the pre-trained model outperformed those trained solely on medical datasets. The pre-trained model was then fine-tuned using an institutional dataset of 35 TOF NAC PET and CT volume pairs, achieving the lowest mean absolute error (MAE) of 74.49 HU and highest peak signal-to-noise ratio (PSNR) of 28.66 dB within the body contour region. Visual assessments demonstrated improved reconstruction of both bone and soft tissue structures from TOF NAC PET images. This work highlights the effectiveness of using pre-trained deep learning models for medical image translation tasks. Future work will assess the impact of sCT on PET attenuation correction and explore additional neural network architectures and datasets to further enhance performance and practical applications in PET imaging.
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[CV-79] Identifying regions of interest in whole slide images of renal cell carcinoma

【速读】：该论文旨在解决肾细胞癌（RCC）全切片图像（WSI）中感兴趣区域（ROI）检测耗时且繁琐的问题，以减少病理学家的工作负担并提高诊断准确性。解决方案的关键在于提出了一种基于高效纹理描述符——主导旋转局部二值模式（DRLBP）与颜色变换的方法，该方法能够揭示并利用显微镜高倍率下丰富的纹理变化性，从而保留结构信息并增强区分能力。此外，通过分别在颜色通道上提取WSI补丁特征形成直方图，并采用最常出现的模式作为特征选择步骤来剔除非信息性特征，同时结合支持向量机（SVM）等分类器进行性能评估。最终，该研究还探索了基于迁移学习的深度学习方法用于图像补丁分类，进一步验证了所提方法在识别ROI方面的高效性和有效性。

链接: https://arxiv.org/abs/2504.07313
作者: Mohammed Lamine Benomar,Nesma Settouti,Eric Debreuve,Xavier Descombes,Damien Ambrosetti
机构: 未知
类目: Image and Video Processing (eess.IV); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:The histopathological images contain a huge amount of information, which can make diagnosis an extremely timeconsuming and tedious task. In this study, we developed a completely automated system to detect regions of interest (ROIs) in whole slide images (WSI) of renal cell carcinoma (RCC), to reduce time analysis and assist pathologists in making more accurate decisions. The proposed approach is based on an efficient texture descriptor named dominant rotated local binary pattern (DRLBP) and color transformation to reveal and exploit the immense texture variability at the microscopic high magnifications level. Thereby, the DRLBPs retain the structural information and utilize the magnitude values in a local neighborhood for more discriminative power. For the classification of the relevant ROIs, feature extraction of WSIs patches was performed on the color channels separately to form the histograms. Next, we used the most frequently occurring patterns as a feature selection step to discard non-informative features. The performances of different classifiers on a set of 1800 kidney cancer patches originating from 12 whole slide images were compared and evaluated. Furthermore, the small size of the image dataset allows to investigate deep learning approach based on transfer learning for image patches classification by using deep features and fine-tuning methods. High recognition accuracy was obtained and the classifiers are efficient, the best precision result was 99.17% achieved with SVM. Moreover, transfer learning models perform well with comparable performance, and the highest precision using ResNet-50 reached 98.50%. The proposed approach results revealed a very efficient image classification and demonstrated efficacy in identifying ROIs. This study presents an automatic system to detect regions of interest relevant to the diagnosis of kidney cancer in whole slide histopathology images.
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[CV-80] MoEDiff-SR: Mixture of Experts-Guided Diffusion Model for Region-Adaptive MRI Super-Resolution

【速读】：该论文旨在解决低场强磁共振成像（如3T）在临床诊断和神经影像研究中因空间分辨率有限而难以捕捉关键解剖细节的问题。为克服这一局限，论文提出了一种名为MoEDiff-SR的混合专家（Mixture of Experts, MoE）引导的扩散模型，用于区域自适应磁共振图像超分辨率重建（Super-Resolution, SR）。其关键在于通过Transformer特征提取器计算多尺度patch嵌入，结合MoE门控网络动态分配不同去噪专家的权重，这些专家分别针对脑MRI的不同特性（如半卵圆中心、脑沟及脑回皮质以及灰白质交界区）进行专业化处理。最终输出由动态分配的门控概率聚合各专家的去噪结果生成，确保了区域特定的适应性和性能提升。实验结果表明，该方法在图像质量定量指标、感知保真度和计算效率方面优于现有最先进的方法，并且临床评估验证了其在识别细微病理特征方面的卓越诊断能力。

链接: https://arxiv.org/abs/2504.07308
作者: Zhe Wang,Yuhua Ru,Aladine Chetouani,Fang Chen,Fabian Bauer,Liping Zhang,Didier Hans,Rachid Jennane,Mohamed Jarraya,Yung Hsin Chen
机构: Department of Radiology, Massachusetts General Hospital, Harvard Medical School (麻省总医院, 哈佛医学院); Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University (苏州大学第一附属医院血液研究所); L2TI Laboratory, University Sorbonne Paris Nord (巴黎第十三大学L2TI实验室); department of Medical School, Henan University of Chinese Medicine (河南中医药大学医学院); Division of Radiology, German Cancer Research Center (德国癌症研究中心放射科); Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School (麻省总医院Athinoula A. Martinos生物医学成像中心, 哈佛医学院); Nuclear Medicine Division, Geneva University Hospital (日内瓦大学医院核医学科); IDP Institute, UMR CNRS 7013, University of Orleans (法国奥尔良大学IDP研究所)
类目: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
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Abstract:Magnetic Resonance Imaging (MRI) at lower field strengths (e.g., 3T) suffers from limited spatial resolution, making it challenging to capture fine anatomical details essential for clinical diagnosis and neuroimaging research. To overcome this limitation, we propose MoEDiff-SR, a Mixture of Experts (MoE)-guided diffusion model for region-adaptive MRI Super-Resolution (SR). Unlike conventional diffusion-based SR models that apply a uniform denoising process across the entire image, MoEDiff-SR dynamically selects specialized denoising experts at a fine-grained token level, ensuring region-specific adaptation and enhanced SR performance. Specifically, our approach first employs a Transformer-based feature extractor to compute multi-scale patch embeddings, capturing both global structural information and local texture details. The extracted feature embeddings are then fed into an MoE gating network, which assigns adaptive weights to multiple diffusion-based denoisers, each specializing in different brain MRI characteristics, such as centrum semiovale, sulcal and gyral cortex, and grey-white matter junction. The final output is produced by aggregating the denoised results from these specialized experts according to dynamically assigned gating probabilities. Experimental results demonstrate that MoEDiff-SR outperforms existing state-of-the-art methods in terms of quantitative image quality metrics, perceptual fidelity, and computational efficiency. Difference maps from each expert further highlight their distinct specializations, confirming the effective region-specific denoising capability and the interpretability of expert contributions. Additionally, clinical evaluation validates its superior diagnostic capability in identifying subtle pathological features, emphasizing its practical relevance in clinical neuroimaging. Our code is available at this https URL.
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人工智能

[AI-0] We Are All Creators: Generative AI Collective Knowledge and the Path Towards Human-AI Synergy

【速读】：该论文试图解决生成式人工智能（Generative AI）带来的挑战，特别是其对传统人类创造力和独特性的冲击，以及由此引发的关于作者权、版权和智能本质的激烈辩论。论文的核心问题是探讨如何在法律、伦理和技术层面合理应对生成式AI的广泛应用，并提出如何有效利用其潜力以推动社会创新与公平发展。

解决方案的关键在于倡导人机协同（human-AI synergy）。论文指出，生成式AI并非简单的生物性创造力或逐字复制，而是通过数学模式综合展现一种替代形式的智能与创造力。因此，应将其视为一种基于集体人类知识统计模式提取的工具，而非单一来源的创作主体。这种视角强调了在 Attribution 问题上的复杂性，并主张放弃可能无效的法律限制，转而通过人机协作来最大化AI的潜力。通过结合人类直觉、语境判断和伦理意识，可以实现创新的民主化并应对复杂挑战。这一方案的关键在于以现实的态度认识AI的能力与局限，并确保这些工具的平等可及性，以避免加剧社会不平等并实现集体利益的最大化。

链接: https://arxiv.org/abs/2504.07936
作者: Jordi Linares-Pellicer,Juan Izquierdo-Domenech,Isabel Ferri-Molla,Carlos Aliaga-Torro
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Generative AI presents a profound challenge to traditional notions of human uniqueness, particularly in creativity. Fueled by neural network based foundation models, these systems demonstrate remarkable content generation capabilities, sparking intense debates about authorship, copyright, and intelligence itself. This paper argues that generative AI represents an alternative form of intelligence and creativity, operating through mathematical pattern synthesis rather than biological understanding or verbatim replication. The fundamental differences between artificial and biological neural networks reveal AI learning as primarily statistical pattern extraction from vast datasets crystallized forms of collective human knowledge scraped from the internet. This perspective complicates copyright theft narratives and highlights practical challenges in attributing AI outputs to individual sources. Rather than pursuing potentially futile legal restrictions, we advocate for human AI synergy. By embracing generative AI as a complementary tool alongside human intuition, context, and ethical judgment, society can unlock unprecedented innovation, democratize creative expression, and address complex challenges. This collaborative approach, grounded in realistic understanding of AIs capabilities and limitations, offers the most promising path forward. Additionally, recognizing these models as products of collective human knowledge raises ethical questions about accessibility ensuring equitable access to these tools could prevent widening societal divides and leverage their full potential for collective benefit.
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[AI-1] he Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation

【速读】：该论文试图解决下一代场地推荐系统（Next-venue Recommender Systems）在城市动态中的系统性影响研究不足的问题。现有研究主要关注推荐系统的预测准确性，而忽视了其对城市行为模式的潜在深远影响。论文的关键解决方案在于引入一个基于真实移动数据的模拟框架，用于建模人类与人工智能之间的反馈回路（human-AI feedback loop），以量化算法建议如何影响个体行为，并进一步改变重新训练模型所依赖的数据分布。通过这一框架，论文系统性地评估了不同推荐策略下算法采纳的影响，揭示了推荐系统在提升个体访问多样性的同时可能加剧集体不平等的现象，并探讨了其对社会共位网络结构、城市可达性和空间隔离的更广泛影响。该框架为评估AI辅助移动服务的社会影响提供了新的视角，并为预测潜在风险、评估监管干预以及设计伦理算法系统提供了计算工具。

链接: https://arxiv.org/abs/2504.07911
作者: Giovanni Mauro,Marco Minici,Luca Pappalardo
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:Next-venue recommender systems are increasingly embedded in location-based services, shaping individual mobility decisions in urban environments. While their predictive accuracy has been extensively studied, less attention has been paid to their systemic impact on urban dynamics. In this work, we introduce a simulation framework to model the human-AI feedback loop underpinning next-venue recommendation, capturing how algorithmic suggestions influence individual behavior, which in turn reshapes the data used to retrain the models. Our simulations, grounded in real-world mobility data, systematically explore the effects of algorithmic adoption across a range of recommendation strategies. We find that while recommender systems consistently increase individual-level diversity in visited venues, they may simultaneously amplify collective inequality by concentrating visits on a limited subset of popular places. This divergence extends to the structure of social co-location networks, revealing broader implications for urban accessibility and spatial segregation. Our framework operationalizes the feedback loop in next-venue recommendation and offers a novel lens through which to assess the societal impact of AI-assisted mobility-providing a computational tool to anticipate future risks, evaluate regulatory interventions, and inform the design of ethic algorithmic systems.
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[AI-2] Fast Adaptation with Behavioral Foundation Models

【速读】：该论文试图解决的问题是如何在不降低性能的前提下，通过少量在线环境交互步骤快速提升行为基础模型（BFMs）的零样本（zero-shot）策略性能。论文的关键在于利用预训练BFMs已学到的一组技能，这些技能包含比其推理过程识别出的策略更高效的策略，并提出基于演员-评论家（actor-critic）和仅演员（actor-only）的快速适应策略，在预训练BFM的任务嵌入空间中进行低维搜索，以快速优化零样本策略的表现，同时避免了微调预训练强化学习模型时常出现的初始“遗忘”现象。实验结果表明，所提方法在多个导航和运动任务中实现了10%-40%的性能提升。

链接: https://arxiv.org/abs/2504.07896
作者: Harshit Sikchi,Andrea Tirinzoni,Ahmed Touati,Yingchen Xu,Anssi Kanervisto,Scott Niekum,Amy Zhang,Alessandro Lazaric,Matteo Pirotta
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Robotics (cs.RO)
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Abstract:Unsupervised zero-shot reinforcement learning (RL) has emerged as a powerful paradigm for pretraining behavioral foundation models (BFMs), enabling agents to solve a wide range of downstream tasks specified via reward functions in a zero-shot fashion, i.e., without additional test-time learning or planning. This is achieved by learning self-supervised task embeddings alongside corresponding near-optimal behaviors and incorporating an inference procedure to directly retrieve the latent task embedding and associated policy for any given reward function. Despite promising results, zero-shot policies are often suboptimal due to errors induced by the unsupervised training process, the embedding, and the inference procedure. In this paper, we focus on devising fast adaptation strategies to improve the zero-shot performance of BFMs in a few steps of online interaction with the environment while avoiding any performance drop during the adaptation process. Notably, we demonstrate that existing BFMs learn a set of skills containing more performant policies than those identified by their inference procedure, making them well-suited for fast adaptation. Motivated by this observation, we propose both actor-critic and actor-only fast adaptation strategies that search in the low-dimensional task-embedding space of the pre-trained BFM to rapidly improve the performance of its zero-shot policies on any downstream task. Notably, our approach mitigates the initial “unlearning” phase commonly observed when fine-tuning pre-trained RL models. We evaluate our fast adaptation strategies on top of four state-of-the-art zero-shot RL methods in multiple navigation and locomotion domains. Our results show that they achieve 10-40% improvement over their zero-shot performance in a few tens of episodes, outperforming existing baselines.
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[AI-3] SpecReason : Fast and Accurate Inference-Time Compute via Speculative Reasoning

【速读】：该论文旨在解决由推理时间计算开销导致的高延迟问题，同时保持或提升复杂任务的准确性。传统方法通过生成长链思维序列（CoTs）显著提高了性能，但由于推理序列长度以及解码的自回归性质，带来了较高的推理延迟。论文的关键洞察在于，大型推理模型（LRM）的推理及其嵌入的推理过程对近似值具有很高的容忍度，即复杂的任务通常被分解为较简单的步骤，每个步骤的价值主要体现在其提供的语义洞见，而非生成的具体标记。基于此，论文提出了SpecReason系统，它通过轻量级模型推测性地执行较简单的中间推理步骤，并仅在必要时调用昂贵的基础模型来验证（甚至修正）推测结果。这种方法侧重于利用思维标记的语义灵活性以保持最终答案的准确性，与传统的推测解码技术形成互补，后者要求每一步骤的标记等效性。实验结果显示，SpecReason相比原始LRM推理提升了1.5-2.5倍的速度，同时提高了1.0-9.9%的准确性；与不使用SpecReason的推测解码结合后，进一步降低了19.4-44.2%的延迟。

链接: https://arxiv.org/abs/2504.07891
作者: Rui Pan,Yinwei Dai,Zhihao Zhang,Gabriele Oliaro,Zhihao Jia,Ravi Netravali
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Recent advances in inference-time compute have significantly improved performance on complex tasks by generating long chains of thought (CoTs) using Large Reasoning Models (LRMs). However, this improved accuracy comes at the cost of high inference latency due to the length of generated reasoning sequences and the autoregressive nature of decoding. Our key insight in tackling these overheads is that LRM inference, and the reasoning that it embeds, is highly tolerant of approximations: complex tasks are typically broken down into simpler steps, each of which brings utility based on the semantic insight it provides for downstream steps rather than the exact tokens it generates. Accordingly, we introduce SpecReason, a system that automatically accelerates LRM inference by using a lightweight model to (speculatively) carry out simpler intermediate reasoning steps and reserving the costly base model only to assess (and potentially correct) the speculated outputs. Importantly, SpecReason’s focus on exploiting the semantic flexibility of thinking tokens in preserving final-answer accuracy is complementary to prior speculation techniques, most notably speculative decoding, which demands token-level equivalence at each step. Across a variety of reasoning benchmarks, SpecReason achieves 1.5-2.5 \times speedup over vanilla LRM inference while improving accuracy by 1.0-9.9%. Compared to speculative decoding without SpecReason, their combination yields an additional 19.4-44.2% latency reduction. We open-source SpecReason at this https URL.
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[AI-4] Empowering Global Voices: A Data-Efficient Phoneme-Tone Adaptive Approach to High-Fidelity Speech Synthesis

【速读】：该论文致力于解决低资源语言在文本转语音（Text-to-Speech, TTS）技术应用中因数据匮乏和语言复杂性带来的挑战。解决方案的关键在于提出了一种结合数据优化框架与先进声学模型的新方法，通过在泰语等低资源语言上的实践，有效应对复杂的音韵规则及稀缺资源问题。该方法不仅实现了零样本语音克隆，还在金融、医疗、教育和法律等多个领域客户端应用中展现了性能提升，其综合评估结果表明该模型达到了当前最先进的技术水平，为数据受限环境下的TTS生产提供了可扩展的解决方案，并对行业更广泛应用及多语言支持具有重要意义。

链接: https://arxiv.org/abs/2504.07858
作者: Yizhong Geng,Jizhuo Xu,Zeyu Liang,Jinghan Yang,Xiaoyi Shi,Xiaoyu Shen
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
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Abstract:Text-to-speech (TTS) technology has achieved impressive results for widely spoken languages, yet many under-resourced languages remain challenged by limited data and linguistic complexities. In this paper, we present a novel methodology that integrates a data-optimized framework with an advanced acoustic model to build high-quality TTS systems for low-resource scenarios. We demonstrate the effectiveness of our approach using Thai as an illustrative case, where intricate phonetic rules and sparse resources are effectively addressed. Our method enables zero-shot voice cloning and improved performance across diverse client applications, ranging from finance to healthcare, education, and law. Extensive evaluations - both subjective and objective - confirm that our model meets state-of-the-art standards, offering a scalable solution for TTS production in data-limited settings, with significant implications for broader industry adoption and multilingual accessibility.
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[AI-5] 2D-Curri-DPO: Two-Dimensional Curriculum Learning for Direct Preference Optimization

【速读】：该论文旨在解决大型语言模型与人类偏好对齐的问题，以确保其安全部署。传统直接偏好优化（Direct Preference Optimization, DPO）方法受限于单一偏好对的依赖，而近期如Curriculum-DPO等改进方法虽引入多对偏好集成，但未充分考虑输入提示本身的复杂性。为此，论文提出了一种名为2D-Curri-DPO的新框架，其关键是采用联合建模提示复杂度（Prompt Complexity, PC）和成对可区分性（Pairwise Distinguishability, PD）的二维课程学习策略。该框架通过引入双重难度指标量化提示语义复杂性和响应偏好清晰度，定义包含多种可选策略的课程策略空间，并结合基于KL散度的自适应机制实现动态参考模型更新，从而提升训练稳定性。实验结果表明，2D-Curri-DPO在多个基准测试（如MT-Bench、Vicuna Bench和WizardLM）中显著优于标准DPO及现有课程方法，并在UltraFeedback等挑战性数据集上达到当前最优性能。消融研究验证了二维结构和自适应机制的优势，分析提供了策略选择的指导。这些发现表明，有效的对齐需要同时建模提示复杂度和成对可区分性，确立了自适应多维课程学习作为基于偏好的语言模型优化的强大且可解释的新范式。

链接: https://arxiv.org/abs/2504.07856
作者: Mengyang Li,Zhong Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Aligning large language models with human preferences is crucial for their safe deployment. While Direct Preference Optimization (DPO) offers an efficient alternative to reinforcement learning from human feedback, traditional DPO methods are limited by their reliance on single preference pairs. Recent work like Curriculum-DPO integrates multiple pairs using a one-dimensional difficulty curriculum based on pairwise distinguishability (PD), but overlooks the complexity of the input prompt itself. To address this, we propose 2D-Curri-DPO, a novel framework employing a two-dimensional curriculum that jointly models Prompt Complexity (PC) and Pairwise Distinguishability. This framework introduces dual difficulty metrics to quantify prompt semantic complexity and response preference clarity, defines a curriculum strategy space encompassing multiple selectable strategies for task adaptation, and incorporates a KL-divergence-based adaptive mechanism for dynamic reference model updates to enhance training stability. Comprehensive experiments demonstrate that 2D-Curri-DPO significantly outperforms standard DPO and prior curriculum methods across multiple benchmarks, including MT-Bench, Vicuna Bench, and WizardLM. Our approach achieves state-of-the-art performance on challenging test sets like UltraFeedback. Ablation studies confirm the benefits of the 2D structure and adaptive mechanisms, while analysis provides guidance for strategy selection. These findings demonstrate that effective alignment requires modeling both prompt complexity and pairwise distinguishability, establishing adaptive, multi-dimensional curriculum learning as a powerful and interpretable new paradigm for preference-based language model optimization.
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[AI-6] Independence Is Not an Issue in Neurosymbolic AI

【速读】：该论文试图解决神经符号 AI (Neurosymbolic AI) 中因条件独立随机变量导致的确定性偏差 (deterministic bias) 问题。现有观点认为这些条件独立的随机变量有害，因为它们与确定性偏差现象相关联，使系统倾向于从解空间中优先选择一种有效解。然而，论文通过提供证据反驳这一结论，并指出确定性偏差实际上是神经符号 AI 应用不当产生的伪影。解决方案的关键在于重新审视神经符号 AI 的设计和应用方式，以消除这种由不当方法引入的偏差现象。

链接: https://arxiv.org/abs/2504.07851
作者: Håkan Karlsson Faronius,Pedro Zuidberg Dos Martires
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:A popular approach to neurosymbolic AI is to take the output of the last layer of a neural network, e.g. a softmax activation, and pass it through a sparse computation graph encoding certain logical constraints one wishes to enforce. This induces a probability distribution over a set of random variables, which happen to be conditionally independent of each other in many commonly used neurosymbolic AI models. Such conditionally independent random variables have been deemed harmful as their presence has been observed to co-occur with a phenomenon dubbed deterministic bias, where systems learn to deterministically prefer one of the valid solutions from the solution space over the others. We provide evidence contesting this conclusion and show that the phenomenon of deterministic bias is an artifact of improperly applying neurosymbolic AI.
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[AI-7] Anytime Single-Step MAPF Planning with Anytime PIBT

【速读】：该论文旨在解决经典单步多智能体路径规划方法PIBT在解质量方面的不足，其主要问题是PIBT过于贪心（greedy）的优先级分配导致解质量较差，并且无法充分利用可用的规划时间，往往过早终止于首个找到的解。为了解决这些问题，论文提出了一种Anytime PIBT方法，其关键在于保留PIBT快速生成初始解的能力的同时，通过一种渐进优化的方式（anytime manner）持续改进解的质量。论文证明了在足够的时间下，Anytime PIBT能够收敛至最优解，并验证了其能够在毫秒级别显著提升单步解的质量，甚至达到最优。然而，研究还发现，这种单步解质量的提升对全局范围内的路径成本（full-horizon solution costs）影响有限。

链接: https://arxiv.org/abs/2504.07841
作者: Nayesha Gandotra,Rishi Veerapaneni,Muhammad Suhail Saleem,Daniel Harabor,Jiaoyang Li,Maxim Likhachev
机构: 未知
类目: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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Abstract:PIBT is a popular Multi-Agent Path Finding (MAPF) method at the core of many state-of-the-art MAPF methods including LaCAM, CS-PIBT, and WPPL. The main utility of PIBT is that it is a very fast and effective single-step MAPF solver and can return a collision-free single-step solution for hundreds of agents in less than a millisecond. However, the main drawback of PIBT is that it is extremely greedy in respect to its priorities and thus leads to poor solution quality. Additionally, PIBT cannot use all the planning time that might be available to it and returns the first solution it finds. We thus develop Anytime PIBT, which quickly finds a one-step solution identically to PIBT but then continuously improves the solution in an anytime manner. We prove that Anytime PIBT converges to the optimal solution given sufficient time. We experimentally validate that Anytime PIBT can rapidly improve single-step solution quality within milliseconds and even find the optimal single-step action. However, we interestingly find that improving the single-step solution quality does not have a significant effect on full-horizon solution costs.
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[AI-8] Deep Learning-based Intrusion Detection Systems: A Survey

【速读】：该论文旨在系统性地梳理基于深度学习（Deep Learning, DL）的入侵检测系统（DL-based Intrusion Detection System, DL-IDS）的研究现状，从数据收集、日志存储、日志解析、图谱总结、攻击检测到攻击调查等各个阶段进行全面回顾，并探讨当前面临的挑战与未来研究方向。论文的关键在于强调通过深度学习技术挖掘已知系统行为模式的潜在规律，从而实现对未知漏洞（zero-day vulnerabilities）利用的入侵检测泛化能力提升，这为DL-IDS的高通用性提供了理论基础和技术支持。

链接: https://arxiv.org/abs/2504.07839
作者: Zhiwei Xu,Yujuan Wu,Shiheng Wang,Jiabao Gao,Tian Qiu,Ziqi Wang,Hai Wan,Xibin Zhao
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
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Abstract:Intrusion Detection Systems (IDS) have long been a hot topic in the cybersecurity community. In recent years, with the introduction of deep learning (DL) techniques, IDS have made great progress due to their increasing generalizability. The rationale behind this is that by learning the underlying patterns of known system behaviors, IDS detection can be generalized to intrusions that exploit zero-day vulnerabilities. In this survey, we refer to this type of IDS as DL-based IDS (DL-IDS). From the perspective of DL, this survey systematically reviews all the stages of DL-IDS, including data collection, log storage, log parsing, graph summarization, attack detection, and attack investigation. To accommodate current researchers, a section describing the publicly available benchmark datasets is included. This survey further discusses current challenges and potential future research directions, aiming to help researchers understand the basic ideas and visions of DL-IDS research, as well as to motivate their research interests.
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[AI-9] DG-STMTL: A Novel Graph Convolutional Network for Multi-Task Spatio-Temporal Traffic Forecasting

【速读】：本文旨在解决智能交通系统中时空交通预测的准确性问题，特别是如何有效建模复杂的时空依赖关系并适应数据中的固有动态性。在多任务学习（MTL）场景下，传统图卷积网络（GCN）面临静态邻接矩阵引入领域偏差或可学习矩阵可能过拟合特定模式的挑战，而MTL本身也因任务干扰可能带来显著障碍。为克服这些挑战，论文提出了一种新颖的多任务学习框架——动态组别时空多任务学习（DG-STMTL）。其关键是通过任务特定门控机制结合静态与动态邻接矩阵的混合生成模块，以及增强时空依赖建模能力的组别GCN模块。实验结果表明，该方法在真实数据集上的表现优于现有技术，体现了其有效性和鲁棒性。

链接: https://arxiv.org/abs/2504.07822
作者: Wanna Cui,Peizheng Wang,Faliang Yin
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Spatio-temporal traffic prediction is crucial in intelligent transportation systems. The key challenge of accurate prediction is how to model the complex spatio-temporal dependencies and adapt to the inherent dynamics in data. Traditional Graph Convolutional Networks (GCNs) often struggle with static adjacency matrices that introduce domain bias or learnable matrices that may be overfitting to specific patterns. This challenge becomes more complex when considering Multi-Task Learning (MTL). While MTL has the potential to enhance prediction accuracy through task synergies, it can also face significant hurdles due to task interference. To overcome these challenges, this study introduces a novel MTL framework, Dynamic Group-wise Spatio-Temporal Multi-Task Learning (DG-STMTL). DG-STMTL proposes a hybrid adjacency matrix generation module that combines static matrices with dynamic ones through a task-specific gating mechanism. We also introduce a group-wise GCN module to enhance the modelling capability of spatio-temporal dependencies. We conduct extensive experiments on two real-world datasets to evaluate our method. Results show that our method outperforms other state-of-the-arts, indicating its effectiveness and robustness.
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[AI-10] FairEval: Evaluating Fairness in LLM -Based Recommendations with Personality Awareness

【速读】：该论文试图解决推荐系统中基于大型语言模型（Large Language Models, LLMs）的公平性问题，特别是关注人口统计学（如性别、种族、年龄等）和心理学（如人格特质）维度上的偏差。论文的关键解决方案是提出了一种名为FairEval的新评估框架，它将人格特质与八种敏感的人口统计学属性相结合，以全面评估用户层面的偏见。通过这一框架，论文评估了包括ChatGPT 4o和Gemini 1.5 Flash在内的模型在音乐和电影推荐中的表现，并引入了公平性度量指标PAFS，揭示了高达34.79%的差异，强调了提高提示敏感性稳健性和构建更包容的推荐系统的必要性。

链接: https://arxiv.org/abs/2504.07801
作者: Chandan Kumar Sah,Xiaoli Lian,Tony Xu,Li Zhang
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 11 pages, 5 figures, under review at a top-tier ACM conference in recommender systems

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Abstract:Recent advances in Large Language Models (LLMs) have enabled their application to recommender systems (RecLLMs), yet concerns remain regarding fairness across demographic and psychological user dimensions. We introduce FairEval, a novel evaluation framework to systematically assess fairness in LLM-based recommendations. FairEval integrates personality traits with eight sensitive demographic attributes,including gender, race, and age, enabling a comprehensive assessment of user-level bias. We evaluate models, including ChatGPT 4o and Gemini 1.5 Flash, on music and movie recommendations. FairEval’s fairness metric, PAFS, achieves scores up to 0.9969 for ChatGPT 4o and 0.9997 for Gemini 1.5 Flash, with disparities reaching 34.79 percent. These results highlight the importance of robustness in prompt sensitivity and support more inclusive recommendation systems.
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[AI-11] Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems

【速读】：该论文旨在解决现实世界中动态调度场景因无法预见的干扰而难以适应的问题，传统静态调度方法与人工设计的启发式算法难以应对这种复杂性。为了解决这一挑战，论文提出了一种创新方法——结合遗传规划（Genetic Programming, GP）与通过强化学习（Reinforcement Learning, RL）训练的Transformer的混合方法（GPRT）。该方案的关键在于GPRT能够利用Transformer优化由GP生成的启发式规则，同时引导GP的进化过程，从而实现启发式规则在动态环境中的更强适应性和有效性。这种方法通过集装箱码头卡车调度的实际应用验证了其优越性，并展示了GP与RL结合产生鲁棒且高效的调度解决方案的能力，其通用框架适用于多种动态调度问题。

链接: https://arxiv.org/abs/2504.07779
作者: Xian Chen,Rong Qu,Jing Dong,Ruibin Bai,Yaochu Jin
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Dynamic scheduling in real-world environments often struggles to adapt to unforeseen disruptions, making traditional static scheduling methods and human-designed heuristics inadequate. This paper introduces an innovative approach that combines Genetic Programming (GP) with a Transformer trained through Reinforcement Learning (GPRT), specifically designed to tackle the complexities of dynamic scheduling scenarios. GPRT leverages the Transformer to refine heuristics generated by GP while also seeding and guiding the evolution of GP. This dual functionality enhances the adaptability and effectiveness of the scheduling heuristics, enabling them to better respond to the dynamic nature of real-world tasks. The efficacy of this integrated approach is demonstrated through a practical application in container terminal truck scheduling, where the GPRT method outperforms traditional GP, standalone Transformer methods, and other state-of-the-art competitors. The key contribution of this research is the development of the GPRT method, which showcases a novel combination of GP and Reinforcement Learning (RL) to produce robust and efficient scheduling solutions. Importantly, GPRT is not limited to container port truck scheduling; it offers a versatile framework applicable to various dynamic scheduling challenges. Its practicality, coupled with its interpretability and ease of modification, makes it a valuable tool for diverse real-world scenarios.
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[AI-12] SlimSpeech: Lightweight and Efficient Text-to-Speech with Slim Rectified Flow

【速读】：该论文旨在解决传统基于流匹配的语音合成方法中模型参数量较大且推理步骤较多的问题，同时保持合成语音的高质量。论文的关键解决方案在于提出了一种名为SlimSpeech的轻量级高效语音合成系统，其基于校正流（rectified flow）模型，并通过以下两个核心策略实现：一是重新设计模型结构以大幅减少参数量，并将其作为教师模型；二是通过对重流操作（reflow operation）的优化，从较大的教师模型直接衍生出一个参数更少且采样轨迹更简单的学生模型，同时结合蒸馏技术进一步提升模型性能。实验结果表明，该方法在显著降低模型参数量的同时，通过一步采样实现了与大模型相当的性能。

链接: https://arxiv.org/abs/2504.07776
作者: Kaidi Wang,Wenhao Guan,Shenghui Lu,Jianglong Yao,Lin Li,Qingyang Hong
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI)
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Abstract:Recently, flow matching based speech synthesis has significantly enhanced the quality of synthesized speech while reducing the number of inference steps. In this paper, we introduce SlimSpeech, a lightweight and efficient speech synthesis system based on rectified flow. We have built upon the existing speech synthesis method utilizing the rectified flow model, modifying its structure to reduce parameters and serve as a teacher model. By refining the reflow operation, we directly derive a smaller model with a more straight sampling trajectory from the larger model, while utilizing distillation techniques to further enhance the model performance. Experimental results demonstrate that our proposed method, with significantly reduced model parameters, achieves comparable performance to larger models through one-step sampling.
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[AI-13] Data over dialogue: Why artificial intelligence is unlikely to humanise medicine

【速读】：该论文试图探讨人工智能（Artificial Intelligence, AI）和机器学习（Machine Learning, ML）系统在医学领域的应用是否能够如一些专家所主张的那样，通过显著提升临床医生与患者之间的关系质量来实现医学的人性化实践。论文的核心论点是反驳这一观点，认为医疗ML系统的使用更可能对这些关系产生负面影响，特别是损害信任、关怀、共情、理解和沟通的质量。论文的关键在于分析医疗ML系统在实际应用中可能导致上述关系质量下降的具体机制，并提出相应的批判性见解，而非提供具体的解决方案。

链接: https://arxiv.org/abs/2504.07763
作者: Joshua Hatherley
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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Abstract:Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.
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[AI-14] Search-contempt: a hybrid MCTS algorithm for training AlphaZero-like engines with better computational efficiency

【速读】：该论文试图解决在自博弈（self-play）训练过程中计算资源消耗过高的问题，特别是像AlphaZero那样需要数千万美元计算预算才能训练出强大模型的局限性。论文的关键解决方案是引入了一种名为“search-contempt”的新型混合蒙特卡洛树搜索（MCTS）算法变体。search-contempt通过从根本上改变自博弈中生成的局面分布，优先选择更具挑战性的局面，从而显著提升了引擎的性能，尤其是在不公平棋局（Odds Chess）等场景下表现突出。此外，这一方法大幅降低了训练所需的计算资源需求，使得在有限的计算预算下（如使用标准消费级GPU），仅需数十万场训练游戏和数万美元的成本即可实现高效的自博弈训练，为生成式AI (Generative AI) 模型的轻量化训练提供了新的可能性。

链接: https://arxiv.org/abs/2504.07757
作者: Ameya Joshi
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:AlphaZero in 2017 was able to master chess and other games without human knowledge by playing millions of games against itself (self-play), with a computation budget running in the tens of millions of dollars. It used a variant of the Monte Carlo Tree Search (MCTS) algorithm, known as PUCT. This paper introduces search-contempt, a novel hybrid variant of the MCTS algorithm that fundamentally alters the distribution of positions generated in self-play, preferring more challenging positions. In addition, search-contempt has been shown to give a big boost in strength for engines in Odds Chess (where one side receives an unfavorable position from the start). More significantly, it opens up the possibility of training a self-play based engine, in a much more computationally efficient manner with the number of training games running into hundreds of thousands, costing tens of thousands of dollars (instead of tens of millions of training games costing millions of dollars required by AlphaZero). This means that it may finally be possible to train such a program from zero on a standard consumer GPU even with a very limited compute, cost, or time budget.
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[AI-15] “i am a stochastic parrot and so r u”: Is AI-based framing of human behaviour and cognition a conceptual metaphor or conceptual engineering?

【速读】：该论文试图解决的问题是如何合理评估将人工智能（AI）相关概念类比于人类行为或认知能力的合理性，并探讨这种类比在概念上的适用性及其潜在意义。论文关注的核心问题是：当使用计算与人工智能的概念框架来描述人类领域时，这种做法的本质是什么？是概念隐喻还是概念工程的尝试？论文认为这些类比本质上属于概念隐喻，但这一观点存在两个关键问题：一是未能意识到自身认识论的偶然性（1），二是可能陷入“地图与领土谬误”（2）。此外，在计算概念的基础层面，这种类比还构成了误导性的“双重隐喻”，因为其隐喻性地连接了人类心理学与计算（3）。针对这些问题，论文的关键解决方案在于提出一种基于概念工程的方法，通过满足特定标准来规避概念隐喻视角下的谬误与认识论缺陷，从而推动对人类与AI概念领域交叉融合的反思，以改进现有概念的边界及其应用方式。

链接: https://arxiv.org/abs/2504.07756
作者: Warmhold Jan Thomas Mollema,Thomas Wachter
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 26 pages

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Abstract:Given the massive integration of AI technologies into our daily lives, AI-related concepts are being used to metaphorically compare AI systems with human behaviour and/or cognitive abilities like language acquisition. Rightfully, the epistemic success of these metaphorical comparisons should be debated. Against the backdrop of the conflicting positions of the ‘computational’ and ‘meat’ chauvinisms, we ask: can the conceptual constellation of the computational and AI be applied to the human domain and what does it mean to do so? What is one doing when the conceptual constellations of AI in particular are used in this fashion? Rooted in a Wittgensteinian view of concepts and language-use, we consider two possible answers and pit them against each other: either these examples are conceptual metaphors, or they are attempts at conceptual engineering. We argue that they are conceptual metaphors, but that (1) this position is unaware of its own epistemological contingency, and (2) it risks committing the ‘‘map-territory fallacy’’. Down at the conceptual foundations of computation, (3) it most importantly is a misleading ‘double metaphor’ because of the metaphorical connection between human psychology and computation. In response to the shortcomings of this projected conceptual organisation of AI onto the human domain, we argue that there is a semantic catch. The perspective of the conceptual metaphors shows avenues for forms of conceptual engineering. If this methodology’s criteria are met, the fallacies and epistemic shortcomings related to the conceptual metaphor view can be bypassed. At its best, the cross-pollution of the human and AI conceptual domains is one that prompts us to reflect anew on how the boundaries of our current concepts serve us and how they could be approved.
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[AI-16] Counting Hours Counting Losses: The Toll of Unpredictable Work Schedules on Financial Security

【速读】：该论文旨在解决因不可预见收入波动导致财务脆弱性加剧的问题，重点关注不稳定工作排班对个人财务规划能力的影响。论文的核心在于探索个体如何依赖对未来事件的预测与规划来管理财务，并提出了一种模拟框架，通过在线学习技术动态调整工人的消费策略以适应不断变化的工作安排信息。关键解决方案在于构建这一能够理论与实证验证的框架，证明工人提前预知排班变动的能力可以提升其长期效用，而无法预测未来事件则会恶化其财务稳定性。此外，该框架还用于评估缓解排班不确定性问题的各种干预措施的有效性。

链接: https://arxiv.org/abs/2504.07719
作者: Pegah Nokhiz,Aravinda Kanchana Ruwanpathirana,Aditya Bhaskara,Suresh Venkatasubramanian
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:Financial instability has become a significant issue in today’s society. While research typically focuses on financial aspects, there is a tendency to overlook time-related aspects of unstable work schedules. The inability to rely on consistent work schedules leads to burnout, work-family conflicts, and financial shocks that directly impact workers’ income and assets. Unforeseen fluctuations in earnings pose challenges in financial planning, affecting decisions on savings and spending and ultimately undermining individuals’ long-term financial stability and well-being. This issue is particularly evident in sectors where workers experience frequently changing schedules without sufficient notice, including those in the food service and retail sectors, part-time and hourly workers, and individuals with lower incomes. These groups are already more financially vulnerable, and the unpredictable nature of their schedules exacerbates their financial fragility. Our objective is to understand how unforeseen fluctuations in earnings exacerbate financial fragility by investigating the extent to which individuals’ financial management depends on their ability to anticipate and plan for the future. To address this question, we develop a simulation framework that models how individuals optimize utility amidst financial uncertainty and the imperative to avoid financial ruin. We employ online learning techniques, specifically adapting workers’ consumption policies based on evolving information about their work schedules. With this framework, we show both theoretically and empirically how a worker’s capacity to anticipate schedule changes enhances their long-term utility. Conversely, the inability to predict future events can worsen workers’ instability. Moreover, our framework enables us to explore interventions to mitigate the problem of schedule uncertainty and evaluate their effectiveness. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2504.07719 [cs.LG] (or arXiv:2504.07719v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2504.07719 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Pegah Nokhiz [view email] [v1] Thu, 10 Apr 2025 13:09:56 UTC (325 KB)
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[AI-17] PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization SIGIR2025

【速读】：该论文致力于解决基于Retrieval-Augmented Generation (RAG) 的大语言模型（LLMs）在安全方面的三个关键挑战：(1) 当注入知识库的中毒文本数量有限时，现有攻击方法的有效性显著下降；(2) 现有攻击缺乏足够的隐蔽性，容易被异常检测系统识别，从而削弱其效果；(3) 现有方法依赖启发式方法生成中毒文本，缺乏形式化的优化框架和理论保证，限制了其效果与适用性。为应对这些挑战，论文提出了一种名为协调Prompt-RAG攻击（PR-Attack）的新型优化驱动攻击方案。其关键是通过在知识库中引入少量中毒文本，并在提示（prompt）中嵌入后门触发器，在触发激活时使LLM针对特定查询生成预设响应，同时在其他上下文中保持正常行为，从而实现高攻击效率与隐蔽性的兼顾。为此，论文将攻击生成过程建模为双层优化问题，利用严谨的优化框架开发最优的中毒文本和触发器。广泛的实验验证表明，PR-Attack即使在极小规模的中毒文本下仍能实现高攻击成功率，并显著提升隐蔽性。

链接: https://arxiv.org/abs/2504.07717
作者: Yang Jiao,Xiaodong Wang,Kai Yang
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: Accepted at SIGIR 2025

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Abstract:Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications, e.g., medical question-answering, mathematical sciences, and code generation. However, they also exhibit inherent limitations, such as outdated knowledge and susceptibility to hallucinations. Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to address these issues, but it also introduces new vulnerabilities. Recent efforts have focused on the security of RAG-based LLMs, yet existing attack methods face three critical challenges: (1) their effectiveness declines sharply when only a limited number of poisoned texts can be injected into the knowledge database, (2) they lack sufficient stealth, as the attacks are often detectable by anomaly detection systems, which compromises their effectiveness, and (3) they rely on heuristic approaches to generate poisoned texts, lacking formal optimization frameworks and theoretic guarantees, which limits their effectiveness and applicability. To address these issues, we propose coordinated Prompt-RAG attack (PR-attack), a novel optimization-driven attack that introduces a small number of poisoned texts into the knowledge database while embedding a backdoor trigger within the prompt. When activated, the trigger causes the LLM to generate pre-designed responses to targeted queries, while maintaining normal behavior in other contexts. This ensures both high effectiveness and stealth. We formulate the attack generation process as a bilevel optimization problem leveraging a principled optimization framework to develop optimal poisoned texts and triggers. Extensive experiments across diverse LLMs and datasets demonstrate the effectiveness of PR-Attack, achieving a high attack success rate even with a limited number of poisoned texts and significantly improved stealth compared to existing methods.
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[AI-18] Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams

【速读】：该论文旨在解决在线主题建模（Online Topic Modeling）的问题，特别是如何有效处理随时间连续到达的数据流，以识别和跟踪文本数据中的主题动态变化。解决方案的关键在于提出了一种名为StreamETM的新方法，它通过将嵌入主题模型（Embedded Topic Model, ETM）与不平衡最优传输（unbalanced optimal transport）结合，实现对连续数据批次模型的融合，从而适应数据流的特性。此外，还引入了一种在线变点检测算法，用于及时识别主题随时间的变化，进一步提升对文本数据流动态变化的感知能力。实验结果表明，StreamETM在模拟数据和真实数据上的表现优于现有方法。

链接: https://arxiv.org/abs/2504.07711
作者: Federica Granese,Benjamin Navet,Serena Villata,Charles Bouveyron
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing volume of textual data daily, making online topic modeling methods essential for managing these data streams that continuously arrive over time. This paper introduces a novel approach to online topic modeling named StreamETM. This approach builds on the Embedded Topic Model (ETM) to handle data streams by merging models learned on consecutive partial document batches using unbalanced optimal transport. Additionally, an online change point detection algorithm is employed to identify shifts in topics over time, enabling the identification of significant changes in the dynamics of text streams. Numerical experiments on simulated and real-world data show StreamETM outperforming competitors.
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[AI-19] Synthesizing High-Quality Programming Tasks with LLM -based Expert and Student Agents

【速读】：该论文旨在解决生成式 AI 在编程任务生成中存在质量差距的问题，具体表现为生成的任务可能与目标编程概念不匹配、对学生而言难以理解，或包含如错误测试等关键问题。现有方法通常需要人工教师进行验证干预。为应对这些挑战，论文提出了一种名为 PyTaskSyn 的新合成技术，其关键是将任务生成过程分解为多个阶段，并通过模拟专家代理和学生代理使用强弱不同的生成模型来执行这些阶段。论文通过广泛评估表明，PyTaskSyn 显著提高了任务质量，并展示了验证管道中每种专业化代理类型的重要性。此外，通过公开可用的 Web 应用程序进行的用户研究进一步证明，PyTaskSyn 可以提供与专家设计任务相当的高质量编程任务，同时降低工作量和成本，且比在线资源中的编程任务更具吸引力。

链接: https://arxiv.org/abs/2504.07655
作者: Manh Hung Nguyen,Victor-Alexandru Pădurean,Alkis Gotovos,Sebastian Tschiatschek,Adish Singla
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:Generative AI is transforming computing education by enabling the automatic generation of personalized content and feedback. We investigate its capabilities in providing high-quality programming tasks to students. Despite promising advancements in task generation, a quality gap remains between AI-generated and expert-created tasks. The AI-generated tasks may not align with target programming concepts, could be incomprehensible for students to solve, or may contain critical issues such as incorrect tests. Existing works often require interventions from human teachers for validation. We address these challenges by introducing PyTaskSyn, a novel synthesis technique that first generates a programming task and then decides whether it meets certain quality criteria to be given to students. The key idea is to break this process into multiple stages performed by expert and student agents simulated using both strong and weaker generative models. Through extensive evaluation, we show that PyTaskSyn significantly improves task quality compared to baseline techniques and showcases the importance of each specialized agent type in our validation pipeline. Additionally, we conducted user studies using our publicly available web application and show that PyTaskSyn can deliver high-quality programming tasks comparable to expert-designed ones while reducing workload and costs, and being more engaging than programming tasks that are available in online resources.
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[AI-20] ms-Mamba: Multi-scale Mamba for Time-Series Forecasting

【速读】：该论文试图解决时间序列预测任务中现有架构仅在单一时间尺度下处理输入数据的问题，这在信息随多重时间尺度变化的任务中可能并非最优解。论文提出的解决方案之关键是引入了一种名为多尺度Mamba（ms-Mamba）的新架构，通过使用具有不同采样率（\Delta s）的多个Mamba块来整合多种时间尺度，从而有效提升模型性能。实验结果表明，ms-Mamba在多个基准数据集上超越了最先进的Transformer-based和Mamba-based模型。

链接: https://arxiv.org/abs/2504.07654
作者: Yusuf Meric Karadag,Sinan Kalkan,Ipek Gursel Dino
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:The problem of Time-series Forecasting is generally addressed by recurrent, Transformer-based and the recently proposed Mamba-based architectures. However, existing architectures generally process their input at a single temporal scale, which may be sub-optimal for many tasks where information changes over multiple time scales. In this paper, we introduce a novel architecture called Multi-scale Mamba (ms-Mamba) to address this gap. ms-Mamba incorporates multiple temporal scales by using multiple Mamba blocks with different sampling rates ( \Delta s). Our experiments on many benchmarks demonstrate that ms-Mamba outperforms state-of-the-art approaches, including the recently proposed Transformer-based and Mamba-based models.
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[AI-21] Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning

【速读】：该论文试图解决大型语言模型（Large Language Models, LLMs）在自然语言处理中因“幻觉”（hallucinations）导致的不准确性与逻辑不一致问题，这些问题严重影响了LLMs的可靠性，尤其是在需要高事实准确性（factual accuracy）的应用领域。论文的关键解决方案在于提出了一种神经符号方法（neuro-symbolic approach），将符号本体论推理（symbolic ontological reasoning）与机器学习技术相结合，以提升LLM输出的一致性和可靠性。具体而言，该方案利用OWL本体（ontology）、基于符号的推理器（如HermiT）进行一致性检查，并采用轻量级机器学习模型（如逻辑回归）将自然语言陈述映射为与本体兼容的逻辑形式。当检测到LLM输出与本体之间的不一致时，系统通过生成解释性反馈引导LLM迭代修正，直至获得逻辑一致且语义连贯的响应。这一方法在特定领域的实验表明，显著提升了LLM输出的语义连贯性和事实准确性。

链接: https://arxiv.org/abs/2504.07640
作者: Ruslan Idelfonso Magana Vsevolodovna,Marco Monti
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 11 pages, 1 figure, includes prototype implementation and experimental evaluation. Submitted for consideration in the arXiv Artificial Intelligence category (cs.AI)

点击查看摘要

Abstract:Large Language Models (LLMs) demonstrate impressive capabilities in natural language processing but suffer from inaccuracies and logical inconsistencies known as hallucinations. This compromises their reliability, especially in domains requiring factual accuracy. We propose a neuro-symbolic approach integrating symbolic ontological reasoning and machine learning methods to enhance the consistency and reliability of LLM outputs. Our workflow utilizes OWL ontologies, a symbolic reasoner (e.g., HermiT) for consistency checking, and a lightweight machine learning model (logistic regression) for mapping natural language statements into logical forms compatible with the ontology. When inconsistencies between LLM outputs and the ontology are detected, the system generates explanatory feedback to guide the LLM towards a corrected, logically coherent response in an iterative refinement loop. We present a working Python prototype demonstrating this pipeline. Experimental results in a defined domain suggest significant improvements in semantic coherence and factual accuracy of LLM outputs, showcasing the potential of combining LLM fluency with the rigor of formal semantics.
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[AI-22] Predicting the Lifespan of Industrial Printheads with Survival Analysis

【速读】：该论文旨在解决生产喷头寿命预测这一关键问题，这对于维护规划和生产优化具有重要意义。论文的关键在于采用生存分析方法，并结合五种技术（Kaplan-Meier估计器、Cox比例风险模型、Weibull加速失效时间模型、随机生存森林和梯度提升）来估算生存概率和失效率。通过将这些估计结果利用等距回归进行精炼并聚合以确定预期失效次数，最终通过多个时间窗口的真实数据验证模型可靠性。论文使用三种性能指标的定量评估表明，生存分析方法在喷头寿命预测方面优于行业标准基线方法。

链接: https://arxiv.org/abs/2504.07638
作者: Dan Parii,Evelyne Janssen,Guangzhi Tang,Charalampos Kouzinopoulos,Marcin Pietrasik
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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点击查看摘要

Abstract:Accurately predicting the lifespan of critical device components is essential for maintenance planning and production optimization, making it a topic of significant interest in both academia and industry. In this work, we investigate the use of survival analysis for predicting the lifespan of production printheads developed by Canon Production Printing. Specifically, we focus on the application of five techniques to estimate survival probabilities and failure rates: the Kaplan-Meier estimator, Cox proportional hazard model, Weibull accelerated failure time model, random survival forest, and gradient boosting. The resulting estimates are further refined using isotonic regression and subsequently aggregated to determine the expected number of failures. The predictions are then validated against real-world ground truth data across multiple time windows to assess model reliability. Our quantitative evaluation using three performance metrics demonstrates that survival analysis outperforms industry-standard baseline methods for printhead lifespan prediction.
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[AI-23] Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey

【速读】：该论文试图解决的问题在于，尽管生成式人工智能（Generative AI, GenAI）与物联网（Internet of Things, IoT）的结合受到广泛关注，但现有研究多集中于特定且狭义的应用场景，缺乏对GenAI与IoT融合在更广泛生态系统中的潜力、挑战及影响的全面分析。论文的关键解决方案是通过遵循PRISMA方法的系统性文献回顾，提供一个涵盖机遇、问题和考量的综合性概述，并构建比较框架，明确研究问题，以全面探索GenAI与IoT计算整合的过去、现在和未来发展方向，从而为专家和初学者提供有价值的洞见。

链接: https://arxiv.org/abs/2504.07635
作者: Fabrizio Mangione,Claudio Savaglio,Giancarlo Fortino
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:The integration of Generative Artificial Intelligence (GenAI) within the Internet of Things (IoT) is garnering considerable interest. This growing attention stems from the continuous evolution and widespread adoption they are both having individually, enough to spontaneously reshape numerous sectors, including Healthcare, Manufacturing, and Smart Cities. Hence, their increasing popularity has catalyzed further extensive research for understanding the potential of the duo GenAI-IoT, how they interplay, and to which extent their synergy can innovate the state-of-the-art in their individual scenarios. However, despite the increasing prominence of GenAI for IoT Computing, much of the existing research remains focused on specific, narrowly scoped applications. This fragmented approach highlights the need for a more comprehensive analysis of the potential, challenges, and implications of GenAI integration within the broader IoT ecosystem. This survey exactly aims to address this gap by providing a holistic overview of the opportunities, issues, and considerations arising from the convergence of these mainstream paradigms. Our contribution is realized through a systematic literature review following the PRISMA methodology. A comparison framework is presented, and well-defined research questions are outlined to comprehensively explore the past, present, and future directions of GenAI integration with IoT Computing, offering valuable insights for both experts and newcomers.
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[AI-24] Deep Learning Meets Teleconnections: Improving S2S Predictions for European Winter Weather

【速读】：该论文旨在解决次季节至季节（Subseasonal-to-Seasonal, S2S）时间尺度上的天气预测挑战，特别是针对北大西洋-欧洲（North Atlantic-European, NAE）天气模态的长期预报能力不足的问题。尽管平流层极地涡旋（Stratospheric Polar Vortex, SPV）和马登-朱利安振荡（Madden-Julian Oscillation, MJO）等遥相关现象提供了增强可预测性的窗口，但其复杂相互作用在传统业务预报中尚未得到充分利用。为此，论文开发并评估了多种深度学习架构，包括基于历史模态的长短期记忆网络（LSTM）、结合SPV和MJO指数的索引增强型LSTM（Index-LSTM），以及利用视觉变换器（Vision Transformer, ViT）直接编码平流层风场和热带向外长波辐射场的ViT-LSTM模型。关键在于通过引入物理意义明确的气候变量（如SPV和MJO），显著提升了深度学习模型在较长预报时效（超过四周）内的表现，尤其是对斯堪的纳维亚阻塞（Scandinavian Blocking, SB）和大西洋脊（Atlantic Ridge, AR）等关键模态的预测准确性，并揭示了新的预测模式。这一方法不仅提高了S2S预测技能，还展示了深度学习作为探索大气动力学与可预测性新工具的潜力。

链接: https://arxiv.org/abs/2504.07625
作者: Philine L. Bommer,Marlene Kretschmer,Fiona R. Spuler,Kirill Bykov,Marina M.-C. Höhne
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 21 pages, 6 figures

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Abstract:Predictions on subseasonal-to-seasonal (S2S) timescales–ranging from two weeks to two month–are crucial for early warning systems but remain challenging owing to chaos in the climate system. Teleconnections, such as the stratospheric polar vortex (SPV) and Madden-Julian Oscillation (MJO), offer windows of enhanced predictability, however, their complex interactions remain underutilized in operational forecasting. Here, we developed and evaluated deep learning architectures to predict North Atlantic-European (NAE) weather regimes, systematically assessing the role of remote drivers in improving S2S forecast skill of deep learning models. We implemented (1) a Long Short-term Memory (LSTM) network predicting the NAE regimes of the next six weeks based on previous regimes, (2) an Index-LSTM incorporating SPV and MJO indices, and (3) a ViT-LSTM using a Vision Transformer to directly encode stratospheric wind and tropical outgoing longwave radiation fields. These models are compared with operational hindcasts as well as other AI models. Our results show that leveraging teleconnection information enhances skill at longer lead times. Notably, the ViT-LSTM outperforms ECMWF’s subseasonal hindcasts beyond week 4 by improving Scandinavian Blocking (SB) and Atlantic Ridge (AR) predictions. Analysis of high-confidence predictions reveals that NAO-, SB, and AR opportunity forecasts can be associated with SPV variability and MJO phase patterns aligning with established pathways, also indicating new patterns. Overall, our work demonstrates that encoding physically meaningful climate fields can enhance S2S prediction skill, advancing AI-driven subseasonal forecast. Moreover, the experiments highlight the potential of deep learning methods as investigative tools, providing new insights into atmospheric dynamics and predictability.
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[AI-25] Beating Transformers using Synthetic Cognition

【速读】：该论文旨在解决如何通过**合成认知（Synthetic Cognition）**机制开发情景反应行为的问题，同时探索其在序列分类任务中的应用。当前的Transformer架构虽然在生成式任务中表现出色，但在推理能力上仍有不足。本文的关键在于提出了一种新的机制，用于处理序列数据，并将其应用于合成认知框架下以实现即时反应行为。此外，研究通过在DNA基础模型的序列分类任务中测试该机制，验证了其优越性，最终不仅扩展了合成认知的能力以处理序列数据，还超越了传统的Transformer架构在序列分类任务上的表现。

链接: https://arxiv.org/abs/2504.07619
作者: Alfredo Ibias,Miguel Rodriguez-Galindo,Hector Antona,Guillem Ramirez-Miranda,Enric Guinovart
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:The road to Artificial General Intelligence goes through the generation of episodic reactive behaviors, where the Transformer architecture has been proven to be the state-of-the-art. However, they still fail to develop reasoning. Recently, a novel approach for developing cognitive architectures, called Synthetic Cognition, has been proposed and implemented to develop instantaneous reactive behavior. In this study, we aim to explore the use of Synthetic Cognition to develop episodic reactive behaviors. We propose a mechanism to deal with sequences for the recent implementation of Synthetic Cognition, and test it against DNA foundation models in DNA sequence classification tasks. In our experiments, our proposal clearly outperforms the DNA foundation models, obtaining the best score on more benchmark tasks than the alternatives. Thus, we achieve two goals: expanding Synthetic Cognition to deal with sequences, and beating the Transformer architecture for sequence classification.
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[AI-26] Learning Long Short-Term Intention within Human Daily Behaviors

【速读】：该论文致力于解决自主家用机器人在理解人类行为并提供适当服务时所面临的挑战，特别是如何分析复杂的人类行为并预测其真实意图。传统观点将人类视为无懈可击的标准，但忽视了人类可能犯错的情况。为此，论文提出了一个名为“长短时意图预测”（Long Short-Term Intention Prediction）的独特任务，要求机器人能够预测与人类价值观一致的长期意图以及反映即时行动意图的短期意图，并检测两者之间潜在的不一致性以提供必要的警告和建议。关键解决方案在于提出了一种用于表示复杂意图状态的长短时意图模型，并构建了一个相应的数据集进行训练；同时采用两阶段方法集成意图模型：首先预测基于价值的长期意图和基于动作的短期意图；其次分析长期与短期意图之间的一致性。实验结果表明，所提出的模型能够帮助机器人理解人类在长短期的行为模式，从而判断人类意图的一致性。

链接: https://arxiv.org/abs/2504.07597
作者: Zhe Sun,Rujie Wu,Xiaodong Yang,Hongzhao Xie,Haiyan Jiang,Junda Bi,Zhenliang Zhang
机构: 未知
类目: Robotics (cs.RO); Artificial Intelligence (cs.AI)
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Abstract:In the domain of autonomous household robots, it is of utmost importance for robots to understand human behaviors and provide appropriate services. This requires the robots to possess the capability to analyze complex human behaviors and predict the true intentions of humans. Traditionally, humans are perceived as flawless, with their decisions acting as the standards that robots should strive to align with. However, this raises a pertinent question: What if humans make mistakes? In this research, we present a unique task, termed “long short-term intention prediction”. This task requires robots can predict the long-term intention of humans, which aligns with human values, and the short term intention of humans, which reflects the immediate action intention. Meanwhile, the robots need to detect the potential non-consistency between the short-term and long-term intentions, and provide necessary warnings and suggestions. To facilitate this task, we propose a long short-term intention model to represent the complex intention states, and build a dataset to train this intention model. Then we propose a two-stage method to integrate the intention model for robots: i) predicting human intentions of both value-based long-term intentions and action-based short-term intentions; and 2) analyzing the consistency between the long-term and short-term intentions. Experimental results indicate that the proposed long short-term intention model can assist robots in comprehending human behavioral patterns over both long-term and short-term durations, which helps determine the consistency between long-term and short-term intentions of humans.
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[AI-27] Boosting Universal LLM Reward Design through the Heuristic Reward Observation Space Evolution

【速读】：该论文旨在解决现有大型语言模型（LLMs）驱动的强化学习（RL）奖励设计框架未能有效利用历史探索数据和人工任务描述来迭代演化奖励观察空间（ROS）的问题。论文的关键创新在于提出了一种新的启发式框架，通过基于表格的探索缓存机制和文本-代码协调策略来增强LLM驱动的奖励设计。具体而言，该框架引入了一个状态执行表，用于跟踪环境状态的历史使用情况和成功率，从而克服了LLM对话中常见的马尔可夫性约束，促进更有效的探索。此外，通过结构化提示将用户提供的任务描述与专家定义的成功标准对齐，确保奖励设计目标的一致性。综合基准RL任务的评估结果验证了所提框架的有效性和稳定性。

链接: https://arxiv.org/abs/2504.07596
作者: Zen Kit Heng,Zimeng Zhao,Tianhao Wu,Yuanfei Wang,Mingdong Wu,Yangang Wang,Hao Dong
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 7 pages, 5 figures

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Abstract:Large Language Models (LLMs) are emerging as promising tools for automated reinforcement learning (RL) reward design, owing to their robust capabilities in commonsense reasoning and code generation. By engaging in dialogues with RL agents, LLMs construct a Reward Observation Space (ROS) by selecting relevant environment states and defining their internal operations. However, existing frameworks have not effectively leveraged historical exploration data or manual task descriptions to iteratively evolve this space. In this paper, we propose a novel heuristic framework that enhances LLM-driven reward design by evolving the ROS through a table-based exploration caching mechanism and a text-code reconciliation strategy. Our framework introduces a state execution table, which tracks the historical usage and success rates of environment states, overcoming the Markovian constraint typically found in LLM dialogues and facilitating more effective exploration. Furthermore, we reconcile user-provided task descriptions with expert-defined success criteria using structured prompts, ensuring alignment in reward design objectives. Comprehensive evaluations on benchmark RL tasks demonstrate the effectiveness and stability of the proposed framework. Code and video demos are available at this http URL.
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[AI-28] Malware analysis assisted by AI with R2AI

【速读】：该论文旨在研究人工智能辅助恶意软件（Malware）分析的质量、速度和成本，重点关注2024-2025年Linux和物联网（IoT）领域的恶意软件。其解决方案的关键在于利用Radare2反汇编工具的AI扩展r2ai，并结合大型语言模型（LLMs）如Claude 3.5和3.7 Sonnet进行分析。尽管并非所有恶意软件或LLMs都等效，但研究表明，在有经验分析师的指导与干预下，这些AI工具能够提供与传统方法相当甚至更优的分析质量，同时显著提升分析速度。然而，论文强调，生成式AI (Generative AI) 无法独立工作，必须持续接受分析师的引导以纠正幻觉（hallucinations）、夸大（exaggerations）及遗漏（omissions）。此外，控制成本也是关键之一，需注意在AI可能无进展地循环运行时对其进行适当的监控与调整。

链接: https://arxiv.org/abs/2504.07574
作者: Axelle Apvrille,Daniel Nakov
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 11 pages

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Abstract:This research studies the quality, speed and cost of malware analysis assisted by artificial intelligence. It focuses on Linux and IoT malware of 2024-2025, and uses r2ai, the AI extension of Radare2’s disassembler. Not all malware and not all LLMs are equivalent but the study shows excellent results with Claude 3.5 and 3.7 Sonnet. Despite a few errors, the quality of analysis is overall equal or better than without AI assistance. For good results, the AI cannot operate alone and must constantly be guided by an experienced analyst. The gain of speed is largely visible with AI assistance, even when taking account the time to understand AI’s hallucinations, exaggerations and omissions. The cost is usually noticeably lower than the salary of a malware analyst, but attention and guidance is needed to keep it under control in cases where the AI would naturally loop without showing progress.
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[AI-29] Diffusion Transformers for Tabular Data Time Series Generation

【速读】：该论文旨在解决表格式数据时间序列生成的问题，特别是针对每个时间点的数据依赖于其他点且表格式数据具有异构性与可变长度序列的挑战。论文的关键创新在于提出了一种基于Diffusion Transformers (DiTs) 的方法，通过扩展DiTs框架以处理异构数据和可变长度序列，从而实现更高效的表格式数据时间序列生成。实验结果表明，该方法在六个数据集上的表现显著优于现有方法。

链接: https://arxiv.org/abs/2504.07566
作者: Fabrizio Garuti,Enver Sangineto,Simone Luetto,Lorenzo Forni,Rita Cucchiara
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 26 pages, 19 figures, 13 tables

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Abstract:Tabular data generation has recently attracted a growing interest due to its different application scenarios. However, generating time series of tabular data, where each element of the series depends on the others, remains a largely unexplored domain. This gap is probably due to the difficulty of jointly solving different problems, the main of which are the heterogeneity of tabular data (a problem common to non-time-dependent approaches) and the variable length of a time series. In this paper, we propose a Diffusion Transformers (DiTs) based approach for tabular data series generation. Inspired by the recent success of DiTs in image and video generation, we extend this framework to deal with heterogeneous data and variable-length sequences. Using extensive experiments on six datasets, we show that the proposed approach outperforms previous work by a large margin.
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[AI-30] ReXCL: A Tool for Requirement Document Extraction and Classification

【速读】：该论文旨在解决软件开发过程中需求工程（Requirement Engineering）中需求文档提取与分类效率低、准确性不足的问题。解决方案的关键在于提出ReXCL工具，其包含两个核心模块：Extraction模块通过启发式方法和预测建模将原始需求文档转换为预定义模式；Classification模块利用基于编码器模型的自适应微调技术为需求分配类别标签。通过这两个模块的协同工作，ReXCL实现了对半结构化需求文档自动化模式化的高效处理。

链接: https://arxiv.org/abs/2504.07562
作者: Paheli Bhattacharya,Manojit Chakraborty,Santhosh Kumar Arumugam,Rishabh Gupta
机构: 未知
类目: oftware Engineering (cs.SE); Artificial Intelligence (cs.AI)
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Abstract:This paper presents the ReXCL tool, which automates the extraction and classification processes in requirement engineering, enhancing the software development lifecycle. The tool features two main modules: Extraction, which processes raw requirement documents into a predefined schema using heuristics and predictive modeling, and Classification, which assigns class labels to requirements using adaptive fine-tuning of encoder-based models. The final output can be exported to external requirement engineering tools. Performance evaluations indicate that ReXCL significantly improves efficiency and accuracy in managing requirements, marking a novel approach to automating the schematization of semi-structured requirement documents.
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[AI-31] PoGO: A Scalable Proof of Useful Work via Quantized Gradient Descent and Merkle Proofs

【速读】：本文旨在解决区块链共识机制中计算密集型任务的可验证性问题，提出了一种名为“梯度优化证明（Proof of Gradient Optimization, PoGO）”的设计。其核心解决方案的关键在于利用量化梯度（4位精度）显著降低存储与计算需求，同时确保验证者能够确认模型损失确实被有效降低。此外，通过引入针对完整32位模型的Merkle证明，处理大规模参数集的同时支持最小化链上数据的随机叶节点检查。论文以GPT-3（1750亿参数）及较小但高性能的模型（如Gemma~3，270亿参数）为例进行说明，并通过实证分析表明验证成本远低于训练成本。关键还在于讨论了引入有意义训练步骤时较长区块时间的需求、专用GPU硬件的权衡，以及二进制差分更新的增量优化潜力。最后，论文指出微调过程可通过调整数据集和采样方式实现类似验证流程，而最终协议允许验证者发出正向或负向证明，以聚合确认更新或惩罚矿工。

链接: https://arxiv.org/abs/2504.07540
作者: José I. Orlicki
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 14 pages, 1 figure, 1 table

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Abstract:We present a design called \emphProof of Gradient Optimization (PoGO) for blockchain consensus, where miners produce verifiable evidence of training large-scale machine-learning models. Building on previous work, we incorporate \emphquantized gradients (4-bit precision) to reduce storage and computation requirements, while still preserving the ability of verifiers to check that real progress has been made on lowering the model’s loss. Additionally, we employ Merkle proofs over the full 32-bit model to handle large parameter sets and to enable random leaf checks with minimal on-chain data. We illustrate these ideas using GPT-3 (175B parameters) as a reference example and also refer to smaller but high-performance models (e.g., \emphGemma~3 with 27B parameters). We provide an empirical cost analysis showing that verification is significantly cheaper than training, thanks in part to quantization and sampling. We also discuss the necessity of longer block times (potentially hours) when incorporating meaningful training steps, the trade-offs when using specialized GPU hardware, and how binary diffs may incrementally optimize updates. Finally, we note that fine-tuning can be handled in a similar manner, merely changing the dataset and the manner of sampling but preserving the overall verification flow. Our protocol allows verifiers to issue either \emphpositive or \emphnegative attestations; these are aggregated at finalization to either confirm the update or slash the miner.
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[AI-32] A taxonomy of epistemic injustice in the context of AI and the case for generative hermeneutical erasure

【速读】：该论文试图解决人工智能（Artificial Intelligence, AI）领域中的认识论不公正（Epistemic Injustice）问题，并提出一种新的认识论不公正形式——生成性释义抹除（Generative Hermeneutical Erasure）。论文首先基于认识论不公正的一般分类法，构建了一个适用于AI语境的认识论不公正类型学，整合了技术哲学、政治哲学和社会认识论领域的研究成果；其次，论文聚焦于大型语言模型（Large Language Models, LLMs）的应用可能带来的概念抹除效应，尤其是当AI系统在非西方语境中部署时，其“无立场的观点”（View from Nowhere）会贬低非西方认识论，导致其认识论特性逐渐被侵蚀，并最终引发释义抹除。解决方案的关键在于提出一个能够映射AI领域内认识论不公正的分类框架，以及识别并定义这种新的AI相关认识论不公正形式，从而为理解与应对AI带来的伦理挑战提供理论基础。

链接: https://arxiv.org/abs/2504.07531
作者: Warmhold Jan Thomas Mollema
机构: 未知
类目: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
备注: 29 pages; 3 figures; 1 table

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Abstract:Whether related to machine learning models’ epistemic opacity, algorithmic classification systems’ discriminatory automation of testimonial prejudice, the distortion of human beliefs via the ‘hallucinations’ of generative AI, the inclusion of the global South in global AI governance, the execution of bureaucratic violence via algorithmic systems, or located in the interaction with conversational artificial agents epistemic injustice related to AI is a growing concern. Based on a proposed general taxonomy of epistemic injustice, this paper first sketches a taxonomy of the types of epistemic injustice in the context of AI, relying on the work of scholars from the fields of philosophy of technology, political philosophy and social epistemology. Secondly, an additional perspective on epistemic injustice in the context of AI: generative hermeneutical erasure. I argue that this injustice that can come about through the application of Large Language Models (LLMs) and contend that generative AI, when being deployed outside of its Western space of conception, can have effects of conceptual erasure, particularly in the epistemic domain, followed by forms of conceptual disruption caused by a mismatch between AI system and the interlocutor in terms of conceptual frameworks. AI systems’ ‘view from nowhere’ epistemically inferiorizes non-Western epistemologies and thereby contributes to the erosion of their epistemic particulars, gradually contributing to hermeneutical erasure. This work’s relevance lies in proposal of a taxonomy that allows epistemic injustices to be mapped in the AI domain and the proposal of a novel form of AI-related epistemic injustice.
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[AI-33] Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data

【速读】：该论文旨在解决高维表格数据中异常检测面临的挑战，主要源于Multiple Views (MV) 效应导致的数据分布在多个低维子空间的现象。传统方法因无法准确理解MV效应的本质，依赖于启发式搜索方案，难以精确捕捉数据的真实结构。论文的关键在于引入Myopic Subspace Theory (MST)，将MV效应形式化为数学框架，并将子空间选择表述为随机优化问题。基于此理论，提出V-GAN（Generative AI），通过训练生成模型解决该优化问题，避免特征空间的穷尽搜索同时保留数据的内在结构。实验表明，利用V-GAN子空间构建集成方法显著提升了单类分类性能，并在合成数据上更准确地识别子空间且具有更好的扩展性。

链接: https://arxiv.org/abs/2504.07522
作者: Jose Cribeiro-Ramallo,Federico Matteucci,Paul Enciu,Alexander Jenke,Vadim Arzamasov,Thorsten Strufe,Klemens Böhm
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Statistics Theory (math.ST)
备注: 35 pages, pre-print

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Abstract:Outlier detection in high-dimensional tabular data is challenging since data is often distributed across multiple lower-dimensional subspaces – a phenomenon known as the Multiple Views effect (MV). This effect led to a large body of research focused on mining such subspaces, known as subspace selection. However, as the precise nature of the MV effect was not well understood, traditional methods had to rely on heuristic-driven search schemes that struggle to accurately capture the true structure of the data. Properly identifying these subspaces is critical for unsupervised tasks such as outlier detection or clustering, where misrepresenting the underlying data structure can hinder the performance. We introduce Myopic Subspace Theory (MST), a new theoretical framework that mathematically formulates the Multiple Views effect and writes subspace selection as a stochastic optimization problem. Based on MST, we introduce V-GAN, a generative method trained to solve such an optimization problem. This approach avoids any exhaustive search over the feature space while ensuring that the intrinsic data structure is preserved. Experiments on 42 real-world datasets show that using V-GAN subspaces to build ensemble methods leads to a significant increase in one-class classification performance – compared to existing subspace selection, feature selection, and embedding methods. Further experiments on synthetic data show that V-GAN identifies subspaces more accurately while scaling better than other relevant subspace selection methods. These results confirm the theoretical guarantees of our approach and also highlight its practical viability in high-dimensional settings.
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[AI-34] Why We Feel: Breaking Boundaries in Emotional Reasoning with Multimodal Large Language Models CVPR

【速读】：该论文试图解决现有情感分析仅关注情绪类别识别而忽视深层次因果驱动因素的问题。为了解决这一问题，论文提出了Emotion Interpretation (EI)，专注于显性（如可观察对象、人际互动）或隐性（如文化背景、屏幕外事件）因果因素的研究，强调对触发因素的推理而非简单的标签分类。为此，论文构建了一个包含1,615个基础样本和50个复杂样本的大规模基准EIBench，并提出了一种粗到细自问自答（Coarse-to-Fine Self-Ask, CFSA）标注管道，通过迭代问答引导Vision-Language Models生成高质量的因果解释性标签。关键在于EI任务对因果推理的重视以及CFSA管道在大规模多模态数据上的高效性和准确性。

链接: https://arxiv.org/abs/2504.07521
作者: Yuxiang Lin,Jingdong Sun,Zhi-Qi Cheng,Jue Wang,Haomin Liang,Zebang Cheng,Yifei Dong,Jun-Yan He,Xiaojiang Peng,Xian-Sheng Hua
机构: 未知
类目: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
备注: Accepted at CVPR Workshop NEXD 2025. 21 pages, Project: this https URL

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Abstract:Most existing emotion analysis emphasizes which emotion arises (e.g., happy, sad, angry) but neglects the deeper why. We propose Emotion Interpretation (EI), focusing on causal factors-whether explicit (e.g., observable objects, interpersonal interactions) or implicit (e.g., cultural context, off-screen events)-that drive emotional responses. Unlike traditional emotion recognition, EI tasks require reasoning about triggers instead of mere labeling. To facilitate EI research, we present EIBench, a large-scale benchmark encompassing 1,615 basic EI samples and 50 complex EI samples featuring multifaceted emotions. Each instance demands rationale-based explanations rather than straightforward categorization. We further propose a Coarse-to-Fine Self-Ask (CFSA) annotation pipeline, which guides Vision-Language Models (VLLMs) through iterative question-answer rounds to yield high-quality labels at scale. Extensive evaluations on open-source and proprietary large language models under four experimental settings reveal consistent performance gaps-especially for more intricate scenarios-underscoring EI’s potential to enrich empathetic, context-aware AI applications. Our benchmark and methods are publicly available at: this https URL, offering a foundation for advanced multimodal causal analysis and next-generation affective computing.
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[AI-35] Enhancements for Developing a Comprehensive AI Fairness Assessment Standard

【速读】：该论文试图解决AI系统在多元化应用场景下公平性评估不足的问题。当前TEC标准主要针对表格数据和监督学习模型的公平性评估，但随着AI技术的发展，如图像、非结构化文本以及生成式AI（Generative AI）等新领域的应用日益广泛，该标准亟需扩展以适应更广泛的场景并提升其影响力。解决方案的关键在于提出对TEC标准的扩充，将图像、非结构化文本及生成式AI（包括大型语言模型）纳入公平性评估体系，从而构建一个更加全面且与时俱进的框架，推动各行业负责任且可信赖的AI部署。

链接: https://arxiv.org/abs/2504.07516
作者: Avinash Agarwal,Mayashankar Kumar,Manisha J. Nene
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 5 pages. Published in 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). Access: this https URL

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Abstract:As AI systems increasingly influence critical sectors like telecommunications, finance, healthcare, and public services, ensuring fairness in decision-making is essential to prevent biased or unjust outcomes that disproportionately affect vulnerable entities or result in adverse impacts. This need is particularly pressing as the industry approaches the 6G era, where AI will drive complex functions like autonomous network management and hyper-personalized services. The TEC Standard for Fairness Assessment and Rating of AI Systems provides guidelines for evaluating fairness in AI, focusing primarily on tabular data and supervised learning models. However, as AI applications diversify, this standard requires enhancement to strengthen its impact and broaden its applicability. This paper proposes an expansion of the TEC Standard to include fairness assessments for images, unstructured text, and generative AI, including large language models, ensuring a more comprehensive approach that keeps pace with evolving AI technologies. By incorporating these dimensions, the enhanced framework will promote responsible and trustworthy AI deployment across various sectors.
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[AI-36] GPT Carry-On: Training Foundation Model for Customization Could Be Simple Scalable and Affordable

【速读】：该论文试图解决如何以高效的方式定制大型语言模型（Large Language Model, LLM），使其适应特定用户或任务需求的问题。传统方法如继续训练或微调需要大量的计算资源和内存，而部署中的推理节点通常配置较低端的GPU以加速前向传播。论文提出了一种框架，充分利用现有LLM及其在线服务系统的优势：通过在预训练LLM的最后一层嵌入上训练额外的Transformer块（作为基座），再结合一个轻量级的“carry-on模块”将基座模型组合成定制化LLM。关键在于无需更新基座模型参数的情况下，将大部分训练工作外包至推理节点，并仅需在训练节点上训练轻量级的carry-on模块（如论文中提到的仅消耗不到1GB GPU内存即可完成30B LLM的100M参数训练）。此外，该方法支持混合多层或多领域专用LLM（如聊天、编码、数学等），以适配新任务，同时通过小样本学习（如1000个思维链数据样本）实现极小规模模型（如仅两层、1MB参数）的快速收敛与性能提升，验证了其在数学问题求解中的有效性。

链接: https://arxiv.org/abs/2504.07513
作者: Jianqiao Wangni
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (stat.ML)
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Abstract:Modern large language foundation models (LLM) have now entered the daily lives of millions of users. We ask a natural question whether it is possible to customize LLM for every user or every task. From system and industrial economy consideration, general continue-training or fine-tuning still require substantial computation and memory of training GPU nodes, whereas most inference nodes under deployment, possibly with lower-end GPUs, are configured to make forward pass fastest possible. We propose a framework to take full advantages of existing LLMs and systems of online service. We train an additional branch of transformer blocks on the final-layer embedding of pretrained LLMs, which is the base, then a carry-on module merge the base models to compose a customized LLM. We can mix multiple layers, or multiple LLMs specialized in different domains such as chat, coding, math, to form a new mixture of LLM that best fit a new task. As the base model don’t need to update parameters, we are able to outsource most computation of the training job on inference nodes, and only train a lightweight carry-on on training nodes, where we consume less than 1GB GPU memory to train a 100M carry-on layer on 30B LLM. We tested Qwen and DeepSeek opensourced models for continue-pretraining and got faster loss convergence. We use it to improve solving math questions with extremely small computation and model size, with 1000 data samples of chain-of-thoughts, and as small as 1 MB parameters of two layer layer carry-on, and the results are promising.
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[AI-37] Bottleneck Identification in Resource-Constrained Project Scheduling via Constraint Relaxation

【速读】：该论文旨在解决资源受限项目调度问题中特定项目在已有调度方案下工期延误（Tardiness）的问题。论文的关键在于通过自动识别调度瓶颈及其相关约束，并针对性地放松这些约束来优化调度结果。为此，作者提出了两种方法：第一种方法借鉴车间作业调度领域的现有方法，适用于非特定目标的约束松弛（Untargeted Relaxations）；第二种方法则针对松弛问题的潜在改进进行分析，提出特定目标的约束松弛（Targeted Relaxations）。令人惊讶的是，非特定目标的松弛方法取得了与特定目标松弛方法相当的效果。

链接: https://arxiv.org/abs/2504.07495
作者: Lukáš Nedbálek,Antonín Novák
机构: 未知
类目: Artificial Intelligence (cs.AI)
备注: 8 pages, 4 figures, submitted to the ICORES 2025 conference

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Abstract:In realistic production scenarios, Advanced Planning and Scheduling (APS) tools often require manual intervention by production planners, as the system works with incomplete information, resulting in suboptimal schedules. Often, the preferable solution is not found just because of the too-restrictive constraints specifying the optimization problem, representing bottlenecks in the schedule. To provide computer-assisted support for decision-making, we aim to automatically identify bottlenecks in the given schedule while linking them to the particular constraints to be relaxed. In this work, we address the problem of reducing the tardiness of a particular project in an obtained schedule in the resource-constrained project scheduling problem by relaxing constraints related to identified bottlenecks. We develop two methods for this purpose. The first method adapts existing approaches from the job shop literature and utilizes them for so-called untargeted relaxations. The second method identifies potential improvements in relaxed versions of the problem and proposes targeted relaxations. Surprisingly, the untargeted relaxations result in improvements comparable to the targeted relaxations.
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[AI-38] Enhanced Question-Answering for Skill-based learning using Knowledge-based AI and Generative AI

【速读】：该论文旨在解决在线学习环境中支持学习者深入理解所学技能的长期挑战，特别是当学习者需要关于程序性知识（如何完成任务）和推理（原因分析）的解释时。论文的关键解决方案是提出利用基于知识的AI框架TMK（任务-方法-知识）模型来显著增强智能代理理解和解释学习者技能相关问题的能力。为此，研究引入了Ivy这一智能代理，它结合大型语言模型（LLM）和迭代精炼技术，生成体现目的论、因果性和组合性原则的解释。与仅依赖非结构化文本的传统方法相比，这种方法能够提供更深层次且相关的反馈，从而帮助学习者在在线环境中有效掌握解决问题所需的全面技能理解。

链接: https://arxiv.org/abs/2504.07463
作者: Rahul K. Dass,Rochan H. Madhusudhana,Erin C. Deye,Shashank Verma,Timothy A. Bydlon,Grace Brazil,Ashok K. Goel
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Supporting learners’ understanding of taught skills in online settings is a longstanding challenge. While exercises and chat-based agents can evaluate understanding in limited contexts, this challenge is magnified when learners seek explanations that delve into procedural knowledge (how things are done) and reasoning (why things happen). We hypothesize that an intelligent agent’s ability to understand and explain learners’ questions about skills can be significantly enhanced using the TMK (Task-Method-Knowledge) model, a Knowledge-based AI framework. We introduce Ivy, an intelligent agent that leverages an LLM and iterative refinement techniques to generate explanations that embody teleological, causal, and compositional principles. Our initial evaluation demonstrates that this approach goes beyond the typical shallow responses produced by an agent with access to unstructured text, thereby substantially improving the depth and relevance of feedback. This can potentially ensure learners develop a comprehensive understanding of skills crucial for effective problem-solving in online environments.
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[AI-39] Enhancing Player Enjoyment with a Two-Tier DRL and LLM -Based Agent System for Fighting Games

【速读】：该论文旨在解决现有格斗游戏代理研究较少关注玩家享受度（Player Enjoyment）的问题，这是开发者和玩家都极为重视的关键因素。为填补这一研究空白，并为设计以提升享受度为导向的代理建立实用基准，论文提出了一种两级代理（Two-Tier Agent, TTA）系统，并在经典格斗游戏《街头霸王II》中进行了实验验证。
解决方案的关键在于TTA系统的双层架构：第一层通过任务导向网络架构、模块化奖励函数及混合训练生成多样且技能娴熟的深度强化学习（DRL）代理；第二层则借助大型语言模型超代理（Large Language Model Hyper-Agent），利用玩家的游戏数据与反馈动态选择合适的DRL对手。此外，论文还分析并建模了影响对手享受度的关键因素。实验结果表明，与基线方法相比，高级技能的执行成功率提升了64.36%至156.36%，且训练出的代理展现出明显的不同游戏风格。小规模用户研究表明，玩家反馈进一步验证了TTA系统的有效性。

链接: https://arxiv.org/abs/2504.07425
作者: Shouren Wang,Zehua Jiang,Fernando Sliva,Sam Earle,Julian Togelius
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 15 pages, 8 figures. Submitted to a peer-reviewed conference, under review

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Abstract:Deep reinforcement learning (DRL) has effectively enhanced gameplay experiences and game design across various game genres. However, few studies on fighting game agents have focused explicitly on enhancing player enjoyment, a critical factor for both developers and players. To address this gap and establish a practical baseline for designing enjoyability-focused agents, we propose a two-tier agent (TTA) system and conducted experiments in the classic fighting game Street Fighter II. The first tier of TTA employs a task-oriented network architecture, modularized reward functions, and hybrid training to produce diverse and skilled DRL agents. In the second tier of TTA, a Large Language Model Hyper-Agent, leveraging players’ playing data and feedback, dynamically selects suitable DRL opponents. In addition, we investigate and model several key factors that affect the enjoyability of the opponent. The experiments demonstrate improvements from 64. 36% to 156. 36% in the execution of advanced skills over baseline methods. The trained agents also exhibit distinct game-playing styles. Additionally, we conducted a small-scale user study, and the overall enjoyment in the player’s feedback validates the effectiveness of our TTA system.
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[AI-40] Routing to the Right Expertise: A Trustworthy Judge for Instruction-based Image Editing

【速读】：该论文旨在解决基于指令的图像编辑（Instruction-based Image Editing, IIE）模型输出评估中存在的两大问题：一是现有评估方法难以与人类判断保持一致，二是缺乏可解释性。为了解决这些问题，论文提出了通过专家路由进行判断的方法（JUdgement through Routing of Expertise, JURE）。JURE 的关键是设计了一个动态路由机制，将特定指令及其输出分配给预选的、具备原子级专长的专家模型，并聚合这些专家的反馈形成最终判断。这种设计不仅能够提供易于理解的解释，还通过实验验证实现了与人类判断的高度一致性，同时其模块化架构便于未来扩展以适应 IIE 领域的技术进步。

链接: https://arxiv.org/abs/2504.07424
作者: Chenxi Sun,Hongzhi Zhang,Qi Wang,Fuzheng Zhang
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Instruction-based Image Editing (IIE) models have made significantly improvement due to the progress of multimodal large language models (MLLMs) and diffusion models, which can understand and reason about complex editing instructions. In addition to advancing current IIE models, accurately evaluating their output has become increasingly critical and challenging. Current IIE evaluation methods and their evaluation procedures often fall short of aligning with human judgment and often lack explainability. To address these limitations, we propose JUdgement through Routing of Expertise (JURE). Each expert in JURE is a pre-selected model assumed to be equipped with an atomic expertise that can provide useful feedback to judge output, and the router dynamically routes the evaluation task of a given instruction and its output to appropriate experts, aggregating their feedback into a final judge. JURE is trustworthy in two aspects. First, it can effortlessly provide explanations about its judge by examining the routed experts and their feedback. Second, experimental results demonstrate that JURE is reliable by achieving superior alignment with human judgments, setting a new standard for automated IIE evaluation. Moreover, JURE’s flexible design is future-proof - modular experts can be seamlessly replaced or expanded to accommodate advancements in IIE, maintaining consistently high evaluation quality. Our evaluation data and results are available at this https URL.
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[AI-41] Over-Relying on Reliance: Towards Realistic Evaluations of AI-Based Clinical Decision Support ALT

【速读】：该论文试图解决当前基于人工智能的临床决策支持系统（AI-CDS）评估中存在的局限性问题，即现有的评估指标（如信任度、依赖程度、接受度及任务表现等）未能充分反映AI在临床场景中实际有用的时机与局限。论文主张超越这些传统评估指标（即所谓的“人机协作陷阱”），强调需要关注AI在临床环境中真实且生态有效的价值体现。关键在于倡导采用更符合医疗领域特点的研究设计与评估方法，以衡量AI带来的实际临床效益及其与医护人员工作流程的互补作用。

链接: https://arxiv.org/abs/2504.07423
作者: Venkatesh Sivaraman,Katelyn Morrison,Will Epperson,Adam Perer
机构: 未知
类目: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Other Quantitative Biology (q-bio.OT)
备注: Accepted to the CHI '25 Workshop on Envisioning the Future of Interactive Health

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Abstract:As AI-based clinical decision support (AI-CDS) is introduced in more and more aspects of healthcare services, HCI research plays an increasingly important role in designing for complementarity between AI and clinicians. However, current evaluations of AI-CDS often fail to capture when AI is and is not useful to clinicians. This position paper reflects on our work and influential AI-CDS literature to advocate for moving beyond evaluation metrics like Trust, Reliance, Acceptance, and Performance on the AI’s task (what we term the “trap” of human-AI collaboration). Although these metrics can be meaningful in some simple scenarios, we argue that optimizing for them ignores important ways that AI falls short of clinical benefit, as well as ways that clinicians successfully use AI. As the fields of HCI and AI in healthcare develop new ways to design and evaluate CDS tools, we call on the community to prioritize ecologically valid, domain-appropriate study setups that measure the emergent forms of value that AI can bring to healthcare professionals.
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[AI-42] he Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses

【速读】：该论文旨在解决如何通过预防护理和药物依从性降低住院风险的问题。解决方案的关键在于利用机器学习技术，特别是Gradient Boosting模型，通过对1,171名患者的结构化数据进行分析，实现了高达81.2%的五年人院风险预测准确性，并揭示了高药物依从性和持续预防护理可分别降低38.3%和37.7%的住院风险，从而证明了个性化干预的潜在价值及其对长期医疗成本节约的贡献。

链接: https://arxiv.org/abs/2504.07422
作者: Yixin Zhang,Yisong Chen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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Abstract:This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical savings.
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[AI-43] LauraTSE: Target Speaker Extraction using Auto-Regressive Decoder-Only Language Models

【速读】：该论文旨在解决目标说话人提取（Target Speaker Extraction, TSE）的问题。为实现这一目标，论文提出了一种基于LauraGPT主干网络的Auto-Regressive Decoder-Only语言模型，称为LauraTSE。其关键解决方案在于采用一个小规模的自回归解码器-only语言模型，该模型接收混合语音和参考语音的连续表示，并生成目标语音离散编解码表示的前几层；同时，结合混合语音和参考信息，通过一步编码器-only语言模型重构预测的编解码嵌入向量之和。此方法在性能上达到了现有生成式和判别式TSE模型的优越或相当水平，且据作者所知，LauraTSE是首个利用自回归解码器-only语言模型作为主干的单任务TSE模型。

链接: https://arxiv.org/abs/2504.07402
作者: Beilong Tang,Bang Zeng,Ming Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 5 pages, 1 figure

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Abstract:We propose LauraTSE, an Auto-Regressive Decoder-Only Language Model for Target Speaker Extraction (TSE) based on the LauraGPT backbone. It employs a small-scale auto-regressive decoder-only language model which takes the continuous representations for both the mixture and the reference speeches and produces the first few layers of the target speech’s discrete codec representations. In addition, a one-step encoder-only language model reconstructs the sum of the predicted codec embeddings using both the mixture and the reference information. Our approach achieves superior or comparable performance to existing generative and discriminative TSE models. To the best of our knowledge, LauraTSE is the first single-task TSE model to leverage an auto-regressive decoder-only language model as the backbone.
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[AI-44] A Novel Mamba-based Sequential Recommendation Method

【速读】：本文旨在解决基于Transformer的序列推荐模型在处理长用户行为序列时自注意力模块计算复杂度呈二次增长的问题，同时满足大规模推荐系统对高效率和高效果的需求。论文的关键创新在于提出了一种多头潜在Mamba架构（multi-head latent Mamba architecture），通过结合低维Mamba层、全连接层以及位置编码，能够在每个潜在子空间内同时捕获历史信息和物品信息，从而有效降低计算复杂度并提升模型的可扩展性。此外，该方法通过与大型语言模型（LLMs）的集成及微调，进一步实现了跨领域的推荐能力。实验结果表明，所提出的Hydra方法在大幅减少参数量和训练时间的同时，显著优于现有最先进的序列推荐基准模型。

链接: https://arxiv.org/abs/2504.07398
作者: Jun Yuan
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
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Abstract:Sequential recommendation (SR), which encodes user activity to predict the next action, has emerged as a widely adopted strategy in developing commercial personalized recommendation systems. Although Transformer-based models have proven effective for sequential recommendation, the complexity of the self-attention module in Transformers scales quadratically with the sequence length. Controlling model complexity is essential for large-scale recommendation systems, as these systems may need to handle billion-scale vocabularies that evolve continuously, as well as user behavior sequences that can exceed tens of thousands in length. In this paper, we propose a novel multi-head latent Mamba architecture, which employs multiple low-dimensional Mamba layers and fully connected layers coupled with positional encoding to simultaneously capture historical and item information within each latent subspace. Our proposed method not only enables scaling up to large-scale parameters but also extends to multi-domain recommendation by integrating and fine-tuning LLMs. Through extensive experiments on public datasets, we demonstrate how Hydra effectively addresses the effectiveness-efficiency dilemma, outperforming state-of-the-art sequential recommendation baselines with significantly fewer parameters and reduced training time.
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[AI-45] MicroNAS: An Automated Framework for Developing a Fall Detection System

【速读】：该论文旨在解决在资源受限的微控制器（如仅有320 KB内存的ESP32）上优化神经网络模型的问题。传统方法通常依赖两阶段的剪枝（pruning）策略，但这些方法可能无法充分考虑目标硬件的内存限制。为应对这一挑战，论文提出了一种名为MicroNAS的自动化神经架构搜索工具，其关键创新在于引入了一种新颖的方法，将目标微控制器的内存大小作为优化指导，从而实现对卷积神经网络（CNN）和门控循环单元（GRU）架构的内存驱动型优化。通过与传统剪枝方法对比，MicroNAS展示了显著的优势，并在面向下肢截肢者跌倒检测系统（FDS）的应用中验证了其实效性，特别是在解决数据集类别不平衡问题方面表现出色，最终实现了更高的F1分数。论文还提供了开源代码，供生物力学研究人员基于微控制器平台设计专用机器学习模型。

链接: https://arxiv.org/abs/2504.07397
作者: Seyed Mojtaba Mohasel,John Sheppard,Lindsey K. Molina,Richard R. Neptune,Shane R. Wurdeman,Corey A. Pew
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:This work presents MicroNAS, an automated neural architecture search tool specifically designed to create models optimized for microcontrollers with small memory resources. The ESP32 microcontroller, with 320 KB of memory, is used as the target platform. The artificial intelligence contribution lies in a novel method for optimizing convolutional neural network and gated recurrent unit architectures by considering the memory size of the target microcontroller as a guide. A comparison is made between memory-driven model optimization and traditional two-stage methods, which use pruning, to show the effectiveness of the proposed framework. To demonstrate the engineering application of MicroNAS, a fall detection system (FDS) for lower-limb amputees is developed as a pilot study. A critical challenge in fall detection studies, class imbalance in the dataset, is addressed. The results show that MicroNAS models achieved higher F1-scores than alternative approaches, such as ensemble methods and H2O Automated Machine Learning, presenting a significant step forward in real-time FDS development. Biomechanists using body-worn sensors for activity detection can adopt the open-source code to design machine learning models tailored for microcontroller platforms with limited memory.
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[AI-46] ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method

【速读】：该论文旨在构建一个多模态气候基准数据集（ClimateBench-M），以推动气候科学领域的人工通用智能发展。论文的核心问题是扩展现有气候任务的多样性与复杂性，除了传统的天气预报外，还涵盖热带气旋强度预测、洪水灾害评估等特定领域的应用，以及以自然语言形式表达的气候陈述与置信度评估。为实现这一目标，论文的关键解决方案在于：(1) 构建一个统一时空粒度的多模态数据集，整合ERA5的时间序列气候数据、NOAA的极端天气事件数据以及NASA HLS的卫星图像数据；(2) 提出一种简单但强大的生成式方法（Generative Method），在天气预报、雷暴警报及作物分割等任务中表现出竞争力。该数据集及其代码已公开发布。

链接: https://arxiv.org/abs/2504.07394
作者: Dongqi Fu,Yada Zhu,Zhining Liu,Lecheng Zheng,Xiao Lin,Zihao Li,Liri Fang,Katherine Tieu,Onkar Bhardwaj,Kommy Weldemariam,Hanghang Tong,Hendrik Hamann,Jingrui He
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Preprint, 29 pages

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Abstract:Climate science studies the structure and dynamics of Earth’s climate system and seeks to understand how climate changes over time, where the data is usually stored in the format of time series, recording the climate features, geolocation, time attributes, etc. Recently, much research attention has been paid to the climate benchmarks. In addition to the most common task of weather forecasting, several pioneering benchmark works are proposed for extending the modality, such as domain-specific applications like tropical cyclone intensity prediction and flash flood damage estimation, or climate statement and confidence level in the format of natural language. To further motivate the artificial general intelligence development for climate science, in this paper, we first contribute a multi-modal climate benchmark, i.e., ClimateBench-M, which aligns (1) the time series climate data from ERA5, (2) extreme weather events data from NOAA, and (3) satellite image data from NASA HLS based on a unified spatial-temporal granularity. Second, under each data modality, we also propose a simple but strong generative method that could produce competitive performance in weather forecasting, thunderstorm alerts, and crop segmentation tasks in the proposed ClimateBench-M. The data and code of ClimateBench-M are publicly available at this https URL.
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[AI-47] PROPEL: Supervised and Reinforcement Learning for Large-Scale Supply Chain Planning

【速读】：该论文旨在解决大规模供应链规划（Supply Chain Planning, SCP）优化问题，这类问题通常以混合整数规划（MIP）模型表示，包含整数（非二元）变量、连续变量以及流平衡和容量约束。现有结合机器学习（Machine Learning, ML）与优化的方法主要针对二元MIP和图问题，难以有效处理此类复杂问题。论文的关键解决方案是提出PROPEL框架，将优化与监督学习及深度强化学习（Deep Reinforcement Learning, DRL）相结合，显著缩小搜索空间。PROPEL通过监督学习识别最优解中固定为零的整数变量，而非预测所有整数变量的值，从而利用SCP应用的结构特性；同时，其DRL组件在监督学习未能达到所需最优容限时，选择需要放松的固定为零变量以提升解的质量。该方法已在具有百万级变量的实际供应链规划问题中得到验证，并实现了求解时间和质量的显著改进。

链接: https://arxiv.org/abs/2504.07383
作者: Vahid Eghbal Akhlaghi,Reza Zandehshahvar,Pascal Van Hentenryck
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:This paper considers how to fuse Machine Learning (ML) and optimization to solve large-scale Supply Chain Planning (SCP) optimization problems. These problems can be formulated as MIP models which feature both integer (non-binary) and continuous variables, as well as flow balance and capacity constraints. This raises fundamental challenges for existing integrations of ML and optimization that have focused on binary MIPs and graph problems. To address these, the paper proposes PROPEL, a new framework that combines optimization with both supervised and Deep Reinforcement Learning (DRL) to reduce the size of search space significantly. PROPEL uses supervised learning, not to predict the values of all integer variables, but to identify the variables that are fixed to zero in the optimal solution, leveraging the structure of SCP applications. PROPEL includes a DRL component that selects which fixed-at-zero variables must be relaxed to improve solution quality when the supervised learning step does not produce a solution with the desired optimality tolerance. PROPEL has been applied to industrial supply chain planning optimizations with millions of variables. The computational results show dramatic improvements in solution times and quality, including a 60% reduction in primal integral and an 88% primal gap reduction, and improvement factors of up to 13.57 and 15.92, respectively.
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[AI-48] ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling

【速读】：该论文旨在解决利用机器学习预测临床结局（如死亡率、再入院率和长期并发症起始）时，电子健康记录（EHR）数据时间复杂性带来的重大挑战。为应对这一问题，论文提出了一种创新的时间序列架构——ChronoFormer，其关键在于通过整合时间嵌入（temporal embeddings）、分层注意力机制（hierarchical attention mechanisms）以及领域特定掩码技术（domain specific masking techniques），有效编码和利用纵向患者数据中的时间依赖关系。实验结果表明，ChronoFormer在三个基准任务上显著优于当前最先进的方法，并通过注意力模式的详细分析证明了其捕捉具有临床意义的长程时间关系的能力。

链接: https://arxiv.org/abs/2504.07373
作者: Yuanyun Zhang,Shi Li
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
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Abstract:The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFormer, an innovative transformer based architecture specifically designed to encode and leverage temporal dependencies in longitudinal patient data. ChronoFormer integrates temporal embeddings, hierarchical attention mechanisms, and domain specific masking techniques. Extensive experiments conducted on three benchmark tasks mortality prediction, readmission prediction, and long term comorbidity onset demonstrate substantial improvements over current state of the art methods. Furthermore, detailed analyses of attention patterns underscore ChronoFormer’s capability to capture clinically meaningful long range temporal relationships.
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[AI-49] A Balanced Approach of Rapid Genetic Exploration and Surrogate Exploitation for Hyperparameter Optimization

【速读】：该论文试图解决超参数优化（Hyperparameter Optimization, HPO）中探索与利用难以平衡的问题。现有进化算法（Evolutionary Algorithms, EAs）在HPO任务中虽展现出潜力，但往往在有效利用已有信息方面表现不足。为解决此问题，论文的关键方案是将线性代理模型（linear surrogate model）集成到遗传算法（Genetic Algorithm, GA）中，通过平滑整合多种策略，从而显著提升利用性能，在不同实验中实现了平均1.89%的改进（最大6.55%，最小-3.45%）。

链接: https://arxiv.org/abs/2504.07359
作者: Chul Kim,Inwhee Joe
机构: 未知
类目: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
备注: Published in IEEE Access, 12 pages, 10 figures. DOI: https://doi.org/10.1109/ACCESS.2024.3508269

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Abstract:This paper proposes a new method for hyperparameter optimization (HPO) that balances exploration and exploitation. While evolutionary algorithms (EAs) show promise in HPO, they often struggle with effective exploitation. To address this, we integrate a linear surrogate model into a genetic algorithm (GA), allowing for smooth integration of multiple strategies. This combination improves exploitation performance, achieving an average improvement of 1.89 percent (max 6.55 percent, min -3.45 percent) over existing HPO methods.
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[AI-50] Quantum-Inspired Genetic Algorithm for Robust Source Separation in Smart City Acoustics

【速读】：该论文旨在解决城市复杂声景（Urban Soundscapes）中由于重叠声源、多样化的声学事件及不可预测的噪声水平导致的精确声学场景分析难题，特别是在训练数据有限的情况下。为应对这一挑战，论文提出了一种新颖的基于量子启发的遗传算法（Quantum-Inspired Genetic Algorithm, p-QIGA）。其关键是利用量子叠加（quantum superposition）实现高效解空间探索，并通过量子纠缠（entanglement）处理相关声源，从而在有限数据条件下实现鲁棒的声源分离。此外，通过将这些量子启发概念融入遗传算法框架以优化分离参数，p-QIGA展示了卓越的抗噪能力和对小样本数据的适应性，在噪声环境中信号失真比（SDR）最高提升达8.2 dB，且仅使用10%的训练数据即可超越基准方法2 dB。

链接: https://arxiv.org/abs/2504.07345
作者: Minh K. Quan,Mayuri Wijayasundara,Sujeeva Setunge,Pubudu N. Pathirana
机构: 未知
类目: ound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
备注: 6 pages, 2 figures, IEEE International Conference on Communications (ICC 2025)

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Abstract:The cacophony of urban sounds presents a significant challenge for smart city applications that rely on accurate acoustic scene analysis. Effectively analyzing these complex soundscapes, often characterized by overlapping sound sources, diverse acoustic events, and unpredictable noise levels, requires precise source separation. This task becomes more complicated when only limited training data is available. This paper introduces a novel Quantum-Inspired Genetic Algorithm (p-QIGA) for source separation, drawing inspiration from quantum information theory to enhance acoustic scene analysis in smart cities. By leveraging quantum superposition for efficient solution space exploration and entanglement to handle correlated sources, p-QIGA achieves robust separation even with limited data. These quantum-inspired concepts are integrated into a genetic algorithm framework to optimize source separation parameters. The effectiveness of our approach is demonstrated on two datasets: the TAU Urban Acoustic Scenes 2020 Mobile dataset, representing typical urban soundscapes, and the Silent Cities dataset, capturing quieter urban environments during the COVID-19 pandemic. Experimental results show that the p-QIGA achieves accuracy comparable to state-of-the-art methods while exhibiting superior resilience to noise and limited training data, achieving up to 8.2 dB signal-to-distortion ratio (SDR) in noisy environments and outperforming baseline methods by up to 2 dB with only 10% of the training data. This research highlights the potential of p-QIGA to advance acoustic signal processing in smart cities, particularly for noise pollution monitoring and acoustic surveillance.
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[AI-51] Modeling Response Consistency in Multi-Agent LLM Systems: A Comparative Analysis of Shared and Separate Context Approaches

【速读】：该论文致力于解决大型语言模型（Large Language Models, LLMs）在多智能体系统（Multi-Agent Systems, MAS）中部署时面临的上下文管理、响应一致性与可扩展性挑战，特别是在内存限制和噪声输入条件下的性能优化问题。现有研究主要关注完全集中式或去中心化的配置优化，但忽略了两者之间的权衡以及内存约束与噪声管理之间的相互作用。为此，论文提出了一种概率框架，分析共享上下文与独立上下文配置对响应一致性和响应时间的影响，并引入响应一致性指数（Response Consistency Index, RCI）作为评估指标。解决方案的关键在于聚焦于内存约束与噪声管理之间的交互关系，为在具有相互依赖主题的环境中优化可扩展性和响应时间提供洞见，从而全面理解不同配置对LLM驱动的多智能体系统效率的影响，指导更鲁棒架构的设计。

链接: https://arxiv.org/abs/2504.07303
作者: Tooraj Helmi
机构: 未知
类目: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI)
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Abstract:Large Language Models (LLMs) are increasingly utilized in multi-agent systems (MAS) to enhance collaborative problem-solving and interactive reasoning. Recent advancements have enabled LLMs to function as autonomous agents capable of understanding complex interactions across multiple topics. However, deploying LLMs in MAS introduces challenges related to context management, response consistency, and scalability, especially when agents must operate under memory limitations and handle noisy inputs. While prior research has explored optimizing context sharing and response latency in LLM-driven MAS, these efforts often focus on either fully centralized or decentralized configurations, each with distinct trade-offs. In this paper, we develop a probabilistic framework to analyze the impact of shared versus separate context configurations on response consistency and response times in LLM-based MAS. We introduce the Response Consistency Index (RCI) as a metric to evaluate the effects of context limitations, noise, and inter-agent dependencies on system performance. Our approach differs from existing research by focusing on the interplay between memory constraints and noise management, providing insights into optimizing scalability and response times in environments with interdependent topics. Through this analysis, we offer a comprehensive understanding of how different configurations impact the efficiency of LLM-driven multi-agent systems, thereby guiding the design of more robust architectures. Subjects: Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI) Cite as: arXiv:2504.07303 [cs.MA] (or arXiv:2504.07303v1 [cs.MA] for this version) https://doi.org/10.48550/arXiv.2504.07303 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-52] A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning Prediction Expert Recommendation Assessment and LLM Automation

【速读】：该论文旨在解决血培养过度开具且缺乏明确指征的问题，这不仅浪费医疗资源，还加剧了全球抗生素滥用的压力。论文通过开发机器学习（Machine Learning, ML）模型，利用结构化电子健康记录（Electronic Health Record, EHR）数据和提供者笔记（通过大语言模型 Large Language Model, LLM 获取），预测菌血症的风险。关键在于结合结构化与非结构化数据的集成方法，通过引入诊断编码等特征优化模型性能，最终实现比现有专家推荐框架及基于LLM的管道更高的特异性，同时保持敏感性不降低，从而提升诊断管理效能。

链接: https://arxiv.org/abs/2504.07278
作者: Fatemeh Amrollahi,Nicholas Marshall,Fateme Nateghi Haredasht,Kameron C Black,Aydin Zahedivash,Manoj V Maddali,Stephen P. Ma,Amy Chang,MD Phar Stanley C Deresinski,Mary Kane Goldstein,Steven M. Asch,Niaz Banaei,Jonathan H Chen
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: 10 pages, 2 figures, 2 tables, conference

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Abstract:Blood cultures are often over ordered without clear justification, straining healthcare resources and contributing to inappropriate antibiotic use pressures worsened by the global shortage. In study of 135483 emergency department (ED) blood culture orders, we developed machine learning (ML) models to predict the risk of bacteremia using structured electronic health record (EHR) data and provider notes via a large language model (LLM). The structured models AUC improved from 0.76 to 0.79 with note embeddings and reached 0.81 with added diagnosis codes. Compared to an expert recommendation framework applied by human reviewers and an LLM-based pipeline, our ML approach offered higher specificity without compromising sensitivity. The recommendation framework achieved sensitivity 86%, specificity 57%, while the LLM maintained high sensitivity (96%) but over classified negatives, reducing specificity (16%). These findings demonstrate that ML models integrating structured and unstructured data can outperform consensus recommendations, enhancing diagnostic stewardship beyond existing standards of care.
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[AI-53] Better Decisions through the Right Causal World Model

【速读】：该论文旨在解决强化学习 (Reinforcement Learning, RL) 代理在训练数据中利用虚假相关性导致行为脆弱且泛化能力差的问题。解决方案的关键在于引入了一种名为因果对象中心模型提取工具 (Causal Object-centric Model Extraction Tool, COMET) 的新型算法。COMET 首先从观测中提取对象中心的状态描述，并识别与所描绘对象属性相关的环境内部状态；通过符号回归 (symbolic regression) 模型化对象中心的转换过程并推导控制对象动态的因果关系。此外，COMET 还结合大型语言模型 (Large Language Models, LLMs) 进行语义推理以注释因果变量，从而增强可解释性。最终，COMET 构建的因果世界模型 (Causal World Models, CWMs) 能够与环境的真实因果结构保持一致，使代理能够专注于任务相关特征，有效规避捷径效应，提升在动态场景中的规划与决策能力。

链接: https://arxiv.org/abs/2504.07257
作者: Elisabeth Dillies,Quentin Delfosse,Jannis Blüml,Raban Emunds,Florian Peter Busch,Kristian Kersting
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 5 pages including references, 2 figures

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Abstract:Reinforcement learning (RL) agents have shown remarkable performances in various environments, where they can discover effective policies directly from sensory inputs. However, these agents often exploit spurious correlations in the training data, resulting in brittle behaviours that fail to generalize to new or slightly modified environments. To address this, we introduce the Causal Object-centric Model Extraction Tool (COMET), a novel algorithm designed to learn the exact interpretable causal world models (CWMs). COMET first extracts object-centric state descriptions from observations and identifies the environment’s internal states related to the depicted objects’ properties. Using symbolic regression, it models object-centric transitions and derives causal relationships governing object dynamics. COMET further incorporates large language models (LLMs) for semantic inference, annotating causal variables to enhance interpretability. By leveraging these capabilities, COMET constructs CWMs that align with the true causal structure of the environment, enabling agents to focus on task-relevant features. The extracted CWMs mitigate the danger of shortcuts, permitting the development of RL systems capable of better planning and decision-making across dynamic scenarios. Our results, validated in Atari environments such as Pong and Freeway, demonstrate the accuracy and robustness of COMET, highlighting its potential to bridge the gap between object-centric reasoning and causal inference in reinforcement learning. Comments: 5 pages including references, 2 figures Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2504.07257 [cs.AI] (or arXiv:2504.07257v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2504.07257 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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[AI-54] A new training approach for text classification in Mental Health: LatentGLoss

【速读】：本文旨在解决精神健康分类任务中的建模挑战，通过结合传统机器学习算法、深度学习架构以及基于Transformer的模型，探索更有效的分类方法。研究的关键创新在于提出了一种新颖的双模型训练策略，该策略包含教师-学生网络架构。与标准的知识蒸馏技术不同，此方法不依赖软标签传递，而是通过调整损失函数，使信息在教师模型的输出及其潜在表示之间流动，从而显著提升模型在精神健康预测任务中的学习能力。这一策略有效增强了各建模阶段的表现，为精神健康数据的序列模式建模提供了新的视角。

链接: https://arxiv.org/abs/2504.07245
作者: Korhan Sevinç
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: 10 pages, 3 Figures, 4 Tables

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Abstract:This study presents a multi-stage approach to mental health classification by leveraging traditional machine learning algorithms, deep learning architectures, and transformer-based models. A novel data set was curated and utilized to evaluate the performance of various methods, starting with conventional classifiers and advancing through neural networks. To broaden the architectural scope, recurrent neural networks (RNNs) such as LSTM and GRU were also evaluated to explore their effectiveness in modeling sequential patterns in the data. Subsequently, transformer models such as BERT were fine-tuned to assess the impact of contextual embeddings in this domain. Beyond these baseline evaluations, the core contribution of this study lies in a novel training strategy involving a dual-model architecture composed of a teacher and a student network. Unlike standard distillation techniques, this method does not rely on soft label transfer; instead, it facilitates information flow through both the teacher model’s output and its latent representations by modifying the loss function. The experimental results highlight the effectiveness of each modeling stage and demonstrate that the proposed loss function and teacher-student interaction significantly enhance the model’s learning capacity in mental health prediction tasks.
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[AI-55] rustworthy AI Must Account for Intersectionality ICLR2025

【速读】：该论文试图解决在构建可信人工智能（Trustworthy AI）时，如何平衡多个重要方面（公平性、隐私性、鲁棒性、可解释性和不确定性量化）之间可能存在的冲突问题。论文指出，单独增强某一特性（如隐私保护）可能会无意间对其他特性（如公平性）产生负面影响，从而导致难以同时提升所有方面。解决方案的关键在于超越单一维度的考量，采用整体视角（holistic view），充分考虑这些特性的相互作用与交叉影响（intersectionality），从而实现更全面、集成的可信性（integrated trustworthiness）。论文通过案例研究和指导建议进一步说明这一方法在金融行业等领域的应用潜力，并强调综合考虑各维度的重要性。

链接: https://arxiv.org/abs/2504.07170
作者: Jesse C. Cresswell
机构: 未知
类目: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
备注: Presented at the ICLR 2025 Workshop on Bidirectional Human-AI Alignment

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Abstract:Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainability, and uncertainty quantification. However, efforts to enhance one aspect often introduce unintended trade-offs that negatively impact others, making it challenging to improve all aspects simultaneously. In this position paper, we review notable approaches to these five aspects and systematically consider every pair, detailing the negative interactions that can arise. For example, applying differential privacy to model training can amplify biases in the data, undermining fairness. Drawing on these findings, we take the position that addressing trustworthiness along each axis in isolation is insufficient. Instead, research on Trustworthy AI must account for intersectionality between aspects and adopt a holistic view across all relevant axes at once. To illustrate our perspective, we provide guidance on how researchers can work towards integrated trustworthiness, a case study on how intersectionality applies to the financial industry, and alternative views to our position.
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[AI-56] Secure Text Mail Encryption with Generative Adversarial Networks

【速读】：本文提出了一种基于生成式对抗网络（Generative Adversarial Networks, GANs）的加密模型，旨在解决传统加密方法（如RSA）在效率与安全性之间难以平衡的问题。论文的关键在于利用GAN动态生成私有密钥，并通过其与公钥之间的映射关系实现文本加密。具体而言，该方案通过GAN生成的随机十进制数对字母字符串进行整数表示下的加法规则加密，同时结合基于NOT逻辑门的双向可逆映射确保解密过程的正确性。此外，通过组件级加密技术，支持高达 (10^8) 比特的总密钥规模，从而显著提升了加密效率与安全性。论文的核心创新点在于GAN加密模型的独特配置及其随机组合特性，这使得加密过程既高效又安全，前提是使用者不了解GAN加密电路的具体实现细节。

链接: https://arxiv.org/abs/2504.07140
作者: Alexej Schelle
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
备注: 7 pages, 3 figures, one table; Preprint before publication

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Abstract:This work presents an encryption model based on Generative Adversarial Networks (GANs). Encryption of RTF-8 data is realized by dynamically generating decimal numbers that lead to the encryption and decryption of alphabetic strings in integer representation by simple addition rules, the modulus of the dimension of the considered alphabet. The binary numbers for the private dynamical keys correlate with the binary numbers of public reference keys from a mapping defined by the specific GAN configuration. For reversible encryption with bijective mapping between dynamic and reference keys as defined by the GAN encryptor with random combinations of NOT logical gates between bitwise subcomponents of the transmitted text signal, secure text encryption can be realized by transferring a GAN-encrypted public key with encrypted text from a sender to a receiver. Using the technique described above, secure text mail transfer can be realized from component-wise encryption of text mail strings with total key sizes of up to 10^8 bits that define random decimal numbers obtained from the GAN. From the present model, we assert that encrypted texts can be transmitted more efficiently and securely than from RSA encryption, as long as users of the specific configuration of the GAN encryption model are unaware of the GAN encryptor circuit.
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[AI-57] Artificial Intelligence Index Report 2025

【速读】：该报告旨在全面追踪和解读人工智能（AI）领域最关键的发展趋势，涵盖从地缘政治格局的变化到底层技术的快速演进，以及AI在商业、政策制定和公共生活中的扩展角色。其核心目标是为政策制定者、记者、高管、研究人员及公众提供准确、严谨验证且全球范围的数据，以帮助这些利益相关方做出更明智的关于AI开发和部署的决策。关键在于通过纵向跟踪，为AI领域的快速发展提供必要的背景信息，使人们能够理解AI当前的状态、发展历程及未来可能的方向。这一使命使得AI Index成为全球公认的权威AI资源，并被广泛引用和使用。

链接: https://arxiv.org/abs/2504.07139
作者: Nestor Maslej,Loredana Fattorini,Raymond Perrault,Yolanda Gil,Vanessa Parli,Njenga Kariuki,Emily Capstick,Anka Reuel,Erik Brynjolfsson,John Etchemendy,Katrina Ligett,Terah Lyons,James Manyika,Juan Carlos Niebles,Yoav Shoham,Russell Wald,Tobi Walsh,Armin Hamrah,Lapo Santarlasci,Julia Betts Lotufo,Alexandra Rome,Andrew Shi,Sukrut Oak
机构: 未知
类目: Artificial Intelligence (cs.AI)
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Abstract:Welcome to the eighth edition of the AI Index report. The 2025 Index is our most comprehensive to date and arrives at an important moment, as AI’s influence across society, the economy, and global governance continues to intensify. New in this year’s report are in-depth analyses of the evolving landscape of AI hardware, novel estimates of inference costs, and new analyses of AI publication and patenting trends. We also introduce fresh data on corporate adoption of responsible AI practices, along with expanded coverage of AI’s growing role in science and medicine. Since its founding in 2017 as an offshoot of the One Hundred Year Study of Artificial Intelligence, the AI Index has been committed to equipping policymakers, journalists, executives, researchers, and the public with accurate, rigorously validated, and globally sourced data. Our mission has always been to help these stakeholders make better-informed decisions about the development and deployment of AI. In a world where AI is discussed everywhere - from boardrooms to kitchen tables - this mission has never been more essential. The AI Index continues to lead in tracking and interpreting the most critical trends shaping the field - from the shifting geopolitical landscape and the rapid evolution of underlying technologies, to AI’s expanding role in business, policymaking, and public life. Longitudinal tracking remains at the heart of our mission. In a domain advancing at breakneck speed, the Index provides essential context - helping us understand where AI stands today, how it got here, and where it may be headed next. Recognized globally as one of the most authoritative resources on artificial intelligence, the AI Index has been cited in major media outlets such as The New York Times, Bloomberg, and The Guardian; referenced in hundreds of academic papers; and used by policymakers and government agencies around the world.
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[AI-58] Large Language Model (LLM ) for Software Security: Code Analysis Malware Analysis Reverse Engineering

【速读】：该论文旨在解决利用大型语言模型（Large Language Models, LLMs）在恶意软件代码分析与检测中的应用问题，重点关注如何通过LLMs提升恶意软件识别、分析及防御的能力。论文的关键在于探索基于LLM的方法论，特别是结合语义和结构洞察以更精准地识别恶意意图的技术路径，并总结了静态分析在恶意软件检测中的重要作用，同时引入了相关数据集和专用模型。此外，论文还讨论了支持自动化恶意软件研究的数据集，为研究人员和网络安全专业人士提供了关于LLM驱动的恶意软件检测与防御策略的洞见，同时也指出了增强网络安全性韧性的未来方向。

链接: https://arxiv.org/abs/2504.07137
作者: Hamed Jelodar,Samita Bai,Parisa Hamedi,Hesamodin Mohammadian,Roozbeh Razavi-Far,Ali Ghorbani
机构: 未知
类目: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
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Abstract:Large Language Models (LLMs) have recently emerged as powerful tools in cybersecurity, offering advanced capabilities in malware detection, generation, and real-time monitoring. Numerous studies have explored their application in cybersecurity, demonstrating their effectiveness in identifying novel malware variants, analyzing malicious code structures, and enhancing automated threat analysis. Several transformer-based architectures and LLM-driven models have been proposed to improve malware analysis, leveraging semantic and structural insights to recognize malicious intent more accurately. This study presents a comprehensive review of LLM-based approaches in malware code analysis, summarizing recent advancements, trends, and methodologies. We examine notable scholarly works to map the research landscape, identify key challenges, and highlight emerging innovations in LLM-driven cybersecurity. Additionally, we emphasize the role of static analysis in malware detection, introduce notable datasets and specialized LLM models, and discuss essential datasets supporting automated malware research. This study serves as a valuable resource for researchers and cybersecurity professionals, offering insights into LLM-powered malware detection and defence strategies while outlining future directions for strengthening cybersecurity resilience.
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[AI-59] Embedding Reliability Verification Constraints into Generation Expansion Planning

【速读】：该论文旨在解决发电规划方法在处理可靠性评估的随机生产模拟与优化模型之间不兼容的数学结构的问题，这一挑战阻碍了可靠性的约束集成。论文的关键解决方案是提出了一种利用加权斜决策树（Weighted Oblique Decision Tree, WODT）技术将可靠性验证约束嵌入到发电扩展规划中的方法。具体而言，对于每个规划年份，生成带有可靠性评估模拟标记的发电组合数据集，并训练WODT模型。通过深度优先搜索技术提取可靠性可行域，并将其表述为析取约束，再借助凸包建模技术将其转化为混合整数线性形式，最终嵌入到机组组合集成的发电扩展规划模型中。该方法通过德克萨斯电力可靠性委员会（ERCOT）地区的长期发电规划案例研究得以验证，证明了其在实现可靠且优化的规划方案方面的有效性。

链接: https://arxiv.org/abs/2504.07131
作者: Peng Liu,Lian Cheng,Benjamin P.Omell,Anthony P.Burgard
机构: 未知
类目: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
备注: 5 pages,3 figures. IEEE PES general meeting 2025

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Abstract:Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions.
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[AI-60] Sacred or Secular? Religious Bias in AI-Generated Financial Advice

【速读】：该论文旨在解决人工智能（AI）在生成金融建议过程中存在的宗教偏见问题，特别是聚焦于ChatGPT对金融查询的回应。研究采用基于提示的方法和内容分析，发现由ChatGPT生成的金融电子邮件中有50%表现出宗教偏见，并且这种偏见在内部群体（ingroup）和外部群体（outgroup）互动中均存在。内部群体偏见根据宗教一致性个性化回复，而外部群体偏见则引入可能使客户疏远或引发意识形态摩擦的宗教框架。论文基于批判性算法研究框架，认为ChatGPT作为财务叙事的中介，选择性地强化特定宗教观点。论文的关键在于揭示这些偏见的存在及其潜在影响，并强调需要更高的透明度、偏见缓解策略以及监管监督以确保AI驱动的金融服务中的中立性。

链接: https://arxiv.org/abs/2504.07118
作者: Muhammad Salar Khan,Hamza Umer
机构: 未知
类目: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET)
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Abstract:This study examines religious biases in AI-generated financial advice, focusing on ChatGPT’s responses to financial queries. Using a prompt-based methodology and content analysis, we find that 50% of the financial emails generated by ChatGPT exhibit religious biases, with explicit biases present in both ingroup and outgroup interactions. While ingroup biases personalize responses based on religious alignment, outgroup biases introduce religious framing that may alienate clients or create ideological friction. These findings align with broader research on AI bias and suggest that ChatGPT is not merely reflecting societal biases but actively shaping financial discourse based on perceived religious identity. Using the Critical Algorithm Studies framework, we argue that ChatGPT functions as a mediator of financial narratives, selectively reinforcing religious perspectives. This study underscores the need for greater transparency, bias mitigation strategies, and regulatory oversight to ensure neutrality in AI-driven financial services.
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[AI-61] OKRA: an Explainable Heterogeneous Multi-Stakeholder Job Recommender System ECIR2025

【速读】：该论文旨在解决招聘领域推荐系统中的高风险问题，特别是在确保算法的可解释性（Explainability）和公平性（Fairness）的同时，处理高度异构的招聘数据，并为不同利益相关者提供特定的解释。论文提出的关键解决方案是开发一种基于图神经网络的新型可解释多利益相关者职位推荐系统——Occupational Knowledge-based Recommender using Attention (OKRA)。OKRA 的关键创新在于其能够同时为求职者和企业生成推荐及相应的解释，从而在准确性、可解释性和公平性之间实现平衡。研究结果表明，OKRA 在两个数据集上的 nDCG 性能显著优于六种基线方法，并揭示了现有模型对城市地区求职者和职位的潜在偏见。

链接: https://arxiv.org/abs/2504.07108
作者: Roan Schellingerhout,Francesco Barile,Nava Tintarev
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
备注: 17 pages, 1 figure, 1 table, to be published in the proceedings of ECIR2025

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Abstract:The use of recommender systems in the recruitment domain has been labeled as ‘high-risk’ in recent legislation. As a result, strict requirements regarding explainability and fairness have been put in place to ensure proper treatment of all involved stakeholders. To allow for stakeholder-specific explainability, while also handling highly heterogeneous recruitment data, we propose a novel explainable multi-stakeholder job recommender system using graph neural networks: the Occupational Knowledge-based Recommender using Attention (OKRA). The proposed method is capable of providing both candidate- and company-side recommendations and explanations. We find that OKRA performs substantially better than six baselines in terms of nDCG for two datasets. Furthermore, we find that the tested models show a bias toward candidates and vacancies located in urban areas. Overall, our findings suggest that OKRA provides a balance between accuracy, explainability, and fairness.
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[AI-62] Personalized Recommendation Models in Federated Settings: A Survey

【速读】：该论文旨在解决联邦推荐系统（FedRecSys）中用户个性化建模不足的问题，特别是在去中心化且非独立同分布（non-IID）数据环境下的异构偏好捕捉。论文的关键在于提出将个性化模型作为核心解决方案，以有效捕获用户的细粒度偏好，并通过系统性分析联邦推荐系统的个性化发展历程，从集中式范式过渡到联邦特定创新。此外，论文批判性地评估了构建个性化联邦推荐系统的技术挑战，并综合了有前景的方法来应对这些挑战。

链接: https://arxiv.org/abs/2504.07101
作者: Chunxu Zhang,Guodong Long,Zijian Zhang,Zhiwei Li,Honglei Zhang,Qiang Yang,Bo Yang
机构: 未知
类目: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
备注: 20 pages, 8 figures

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Abstract:Federated recommender systems (FedRecSys) have emerged as a pivotal solution for privacy-aware recommendations, balancing growing demands for data security and personalized experiences. Current research efforts predominantly concentrate on adapting traditional recommendation architectures to federated environments, optimizing communication efficiency, and mitigating security vulnerabilities. However, user personalization modeling, which is essential for capturing heterogeneous preferences in this decentralized and non-IID data setting, remains underexplored. This survey addresses this gap by systematically exploring personalization in FedRecSys, charting its evolution from centralized paradigms to federated-specific innovations. We establish a foundational definition of personalization in a federated setting, emphasizing personalized models as a critical solution for capturing fine-grained user preferences. The work critically examines the technical hurdles of building personalized FedRecSys and synthesizes promising methodologies to meet these challenges. As the first consolidated study in this domain, this survey serves as both a technical reference and a catalyst for advancing personalized FedRecSys research.
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[AI-63] Note on the identification of total effect in Cluster-DAGs with cycles

【速读】：该论文旨在解决在包含循环的聚类有向无环图（cluster-DAG）中总效应的可识别性问题，同时假设底层有向无环图（DAG）保持无环特性。论文的关键解决方案包括两方面：首先，将聚类限制为最多包含四个节点的子结构；其次，调整d-分离（d-separation）的概念。基于此，论文提出了一种图形化标准来解决可识别性问题。

链接: https://arxiv.org/abs/2504.07921
作者: Clément Yvernes
机构: 未知
类目: atistics Theory (math.ST); Artificial Intelligence (cs.AI)
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Abstract:In this note, we discuss the identifiability of a total effect in cluster-DAGs, allowing for cycles within the cluster-DAG (while still assuming the associated underlying DAG to be acyclic). This is presented into two key results: first, restricting the cluster-DAG to clusters containing at most four nodes; second, adapting the notion of d-separation. We provide a graphical criterion to address the identifiability problem.
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[AI-64] Automating quantum feature map design via large language models

【速读】：该论文试图解决的问题是如何设计具有实用优势的量子特征映射（Quantum Feature Maps），以克服当前理论上有潜力但实际应用中仍具挑战性的局限。解决方案的关键在于提出了一种基于大型语言模型（Large Language Models, LLMs）的自主系统，该系统包含生成（Generation）、存储（Storage）、验证（Validation）、评估（Evaluation）和审查（Review）五个组件，通过这些组件迭代优化量子特征映射。实验表明，该方法能够在无需人工干预的情况下成功发现和改进特征映射，并且生成的最佳特征映射在MNIST、Fashion-MNIST和CIFAR-10数据集上的表现优于现有量子基线，与经典核方法相比也展现出竞争力。这一框架为探索数据集自适应量子特征以及LLM驱动的量子算法设计自动化提供了新思路。

链接: https://arxiv.org/abs/2504.07396
作者: Kenya Sakka,Kosuke Mitarai,Keisuke Fujii
机构: 未知
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI)
备注: 39 pages, 6 figures

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Abstract:Quantum feature maps are a key component of quantum machine learning, encoding classical data into quantum states to exploit the expressive power of high-dimensional Hilbert spaces. Despite their theoretical promise, designing quantum feature maps that offer practical advantages over classical methods remains an open challenge. In this work, we propose an agentic system that autonomously generates, evaluates, and refines quantum feature maps using large language models. The system consists of five component: Generation, Storage, Validation, Evaluation, and Review. Using these components, it iteratively improves quantum feature maps. Experiments on the MNIST dataset show that it can successfully discover and refine feature maps without human intervention. The best feature map generated outperforms existing quantum baselines and achieves competitive accuracy compared to classical kernels across MNIST, Fashion-MNIST, and CIFAR-10. Our approach provides a framework for exploring dataset-adaptive quantum features and highlights the potential of LLM-driven automation in quantum algorithm design.
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[AI-65] Min-Max Optimisation for Nonconvex-Nonconcave Functions Using a Random Zeroth-Order Extrag radient Algorithm

【速读】：本文研究了随机高斯平滑零阶ExtraGradient (ZO-EG) 方法在处理可能具有非凸-非凹 (NC-NC) 目标函数的min-max优化问题中的性能。论文关注无约束与约束、可微与不可微情形下的min-max问题，并从变分不等式的视角讨论min-max问题。对于无约束问题，论文证明了ZO-EG算法收敛至NC-NC目标函数的(\epsilon)-平稳点邻域，其半径可通过方差缩减方案控制，并分析了算法复杂度。对于约束问题，引入了近端变分不等式的概念，并给出满足该性质的函数实例，同时证明了与无约束情形类似的结论。在不可微情况下，证明了ZO-EG算法收敛至目标函数平滑版本的(\epsilon)-平稳点邻域，该邻域半径可控，且与原始目标函数的((\delta,\epsilon))-Goldstein平稳点相关联。论文的关键在于通过引入随机高斯平滑技术和变分不等式框架，解决了NC-NC目标函数下min-max优化问题的收敛性与复杂度分析。

链接: https://arxiv.org/abs/2504.07388
作者: Amir Ali Farzin,Yuen Man Pun,Philipp Braun,Antoine Lesage-landry,Youssef Diouane,Iman Shames
机构: 未知
类目: Optimization and Control (math.OC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Numerical Analysis (math.NA)
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点击查看摘要

Abstract:This study explores the performance of the random Gaussian smoothing Zeroth-Order ExtraGradient (ZO-EG) scheme considering min-max optimisation problems with possibly NonConvex-NonConcave (NC-NC) objective functions. We consider both unconstrained and constrained, differentiable and non-differentiable settings. We discuss the min-max problem from the point of view of variational inequalities. For the unconstrained problem, we establish the convergence of the ZO-EG algorithm to the neighbourhood of an \epsilon -stationary point of the NC-NC objective function, whose radius can be controlled under a variance reduction scheme, along with its complexity. For the constrained problem, we introduce the new notion of proximal variational inequalities and give examples of functions satisfying this property. Moreover, we prove analogous results to the unconstrained case for the constrained problem. For the non-differentiable case, we prove the convergence of the ZO-EG algorithm to a neighbourhood of an \epsilon -stationary point of the smoothed version of the objective function, where the radius of the neighbourhood can be controlled, which can be related to the ( \delta,\epsilon )-Goldstein stationary point of the original objective function.
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[AI-66] Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology

【速读】：本文旨在解决物理信息神经网络（Physics-Informed Neural Networks, PINNs）及其变体Physics-Informed Kolmogorov-Arnold Networks (PIKANs) 在系统药理学建模中的性能评估与优化问题，特别是关注其在精度和速度方面的表现。论文的关键在于引入了一种基于切比雪夫多项式的改进PIKAN架构（tanh-cPIKAN），通过参数化单变量函数并增加额外非线性来提升性能。此外，研究系统探讨了优化器选择、表示方法及训练配置对PINNs和PIKANs性能的影响，并利用Optax库评估不同组合在处理不适定、非唯一性和数据稀疏条件下的有效性。研究还分析了模型架构（MLP vs. KAN）、数值精度、二阶方法的预热阶段需求以及初始学习率敏感性等因素，并考察了优化器在大规模模型上的可扩展性及其在计算效率与数值准确性之间的权衡。最终，通过两个系统药理学案例研究，提供了关于选择优化器和表示模型/架构以实现稳健高效灰箱发现的实际指导。

链接: https://arxiv.org/abs/2504.07379
作者: Nazanin Ahmadi Daryakenari,Khemraj Shukla,George Em Karniadakis
机构: 未知
类目: Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI)
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Abstract:Physics-Informed Kolmogorov-Arnold Networks (PIKANs) are gaining attention as an effective counterpart to the original multilayer perceptron-based Physics-Informed Neural Networks (PINNs). Both representation models can address inverse problems and facilitate gray-box system identification. However, a comprehensive understanding of their performance in terms of accuracy and speed remains underexplored. In particular, we introduce a modified PIKAN architecture, tanh-cPIKAN, which is based on Chebyshev polynomials for parametrization of the univariate functions with an extra nonlinearity for enhanced performance. We then present a systematic investigation of how choices of the optimizer, representation, and training configuration influence the performance of PINNs and PIKANs in the context of systems pharmacology modeling. We benchmark a wide range of first-order, second-order, and hybrid optimizers, including various learning rate schedulers. We use the new Optax library to identify the most effective combinations for learning gray-boxes under ill-posed, non-unique, and data-sparse conditions. We examine the influence of model architecture (MLP vs. KAN), numerical precision (single vs. double), the need for warm-up phases for second-order methods, and sensitivity to the initial learning rate. We also assess the optimizer scalability for larger models and analyze the trade-offs introduced by JAX in terms of computational efficiency and numerical accuracy. Using two representative systems pharmacology case studies - a pharmacokinetics model and a chemotherapy drug-response model - we offer practical guidance on selecting optimizers and representation models/architectures for robust and efficient gray-box discovery. Our findings provide actionable insights for improving the training of physics-informed networks in biomedical applications and beyond.
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[AI-67] Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits CCS2025

【速读】：该论文试图解决传统神经网络（Neural Networks, NNs）在处理复杂任务时需要大量可训练参数导致计算和能耗增加的问题。论文的解决方案关键在于评估变分量子电路（Variational Quantum Circuits, VQCs）在简单监督学习和强化学习任务中的表现，通过利用量子力学特性来捕捉复杂的模式关系，并通常需要更少的参数。研究者模拟了VQCs并在真实量子硬件上执行部分训练过程以估算实际训练时间，结果显示VQCs能够在显著减少参数数量的情况下达到与NNs相当的性能，尽管其训练时间较长。随着量子技术、算法的进步以及VQC架构的优化，论文提出VQCs可能在未来某些机器学习任务中具有优势。

链接: https://arxiv.org/abs/2504.07273
作者: Michael Kölle,Alexander Feist,Jonas Stein,Sebastian Wölckert,Claudia Linnhoff-Popien
机构: 未知
类目: Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
备注: Accepted at ICCS 2025

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Abstract:In recent years, neural networks (NNs) have driven significant advances in machine learning. However, as tasks grow more complex, NNs often require large numbers of trainable parameters, which increases computational and energy demands. Variational quantum circuits (VQCs) offer a promising alternative: they leverage quantum mechanics to capture intricate relationships and typically need fewer parameters. In this work, we evaluate NNs and VQCs on simple supervised and reinforcement learning tasks, examining models with different parameter sizes. We simulate VQCs and execute selected parts of the training process on real quantum hardware to approximate actual training times. Our results show that VQCs can match NNs in performance while using significantly fewer parameters, despite longer training durations. As quantum technology and algorithms advance, and VQC architectures improve, we posit that VQCs could become advantageous for certain machine learning tasks.
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[AI-68] PLM-eXplain: Divide and Conquer the Protein Embedding Space

【速读】：该论文试图解决蛋白质语言模型（Protein Language Models, PLMs）在生物信息学中的强大预测能力与其黑箱性质导致的可解释性不足之间的矛盾。为了解决这一问题，论文提出了一种可解释的适配器层——PLM-eXplain (PLM-X)，其关键是将PLMs的嵌入向量分解为两个子空间：一个基于已确立的生化特征的可解释子空间，以及一个保留模型预测能力的残差子空间。通过这种方式，PLM-X不仅能够保持高性能，还实现了对模型决策的生物学解释，同时无需牺牲准确性，从而为增强PLMs在多种下游应用中的可解释性提供了一种通用解决方案。

链接: https://arxiv.org/abs/2504.07156
作者: Jan van Eck,Dea Gogishvili,Wilson Silva,Sanne Abeln
机构: 未知
类目: Biomolecules (q-bio.BM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Abstract:Protein language models (PLMs) have revolutionised computational biology through their ability to generate powerful sequence representations for diverse prediction tasks. However, their black-box nature limits biological interpretation and translation to actionable insights. We present an explainable adapter layer - PLM-eXplain (PLM-X), that bridges this gap by factoring PLM embeddings into two components: an interpretable subspace based on established biochemical features, and a residual subspace that preserves the model’s predictive power. Using embeddings from ESM2, our adapter incorporates well-established properties, including secondary structure and hydropathy while maintaining high performance. We demonstrate the effectiveness of our approach across three protein-level classification tasks: prediction of extracellular vesicle association, identification of transmembrane helices, and prediction of aggregation propensity. PLM-X enables biological interpretation of model decisions without sacrificing accuracy, offering a generalisable solution for enhancing PLM interpretability across various downstream applications. This work addresses a critical need in computational biology by providing a bridge between powerful deep learning models and actionable biological insights.
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[AI-69] RP-SAM2: Refining Point Prompts for Stable Surgical Instrument Segmentation

【速读】：该论文旨在解决在白内障手术中精确分割手术器械的问题，特别是在数据标注有限的情况下开发全自动模型面临的挑战。传统基于提示的方法（如SAM2）虽然提供了灵活性，但对点提示位置高度敏感，容易导致分割结果不一致。论文的关键解决方案是引入RP-SAM2，它通过集成一个新颖的移位块（shift block）和一个复合损失函数（compound loss function）来稳定点提示。这种方法减少了标注人员对精确点位置的依赖，同时保持了鲁棒的分割能力。实验表明，RP-SAM2在Catrasct1k数据集上的分割准确性显著提高，并在CaDIS数据集上生成的伪掩码进一步验证了其优越性。这些结果证明了RP-SAM2作为半自动器械分割实用、稳定且可靠的方法。

链接: https://arxiv.org/abs/2504.07117
作者: Nuren Zhaksylyk,Ibrahim Almakky,Jay Paranjape,S. Swaroop Vedula,Shameema Sikder,Vishal M. Patel,Mohammad Yaqub
机构: 未知
类目: Tissues and Organs (q-bio.TO); Artificial Intelligence (cs.AI)
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Abstract:Accurate surgical instrument segmentation is essential in cataract surgery for tasks such as skill assessment and workflow optimization. However, limited annotated data makes it difficult to develop fully automatic models. Prompt-based methods like SAM2 offer flexibility yet remain highly sensitive to the point prompt placement, often leading to inconsistent segmentations. We address this issue by introducing RP-SAM2, which incorporates a novel shift block and a compound loss function to stabilize point prompts. Our approach reduces annotator reliance on precise point positioning while maintaining robust segmentation capabilities. Experiments on the Cataract1k dataset demonstrate that RP-SAM2 improves segmentation accuracy, with a 2% mDSC gain, a 21.36% reduction in mHD95, and decreased variance across random single-point prompt results compared to SAM2. Additionally, on the CaDIS dataset, pseudo masks generated by RP-SAM2 for fine-tuning SAM2’s mask decoder outperformed those generated by SAM2. These results highlight RP-SAM2 as a practical, stable and reliable solution for semi-automatic instrument segmentation in data-constrained medical settings. The code is available at this https URL.
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机器学习

[LG-0] C3PO: Critical-Layer Core-Expert Collaborative Pathway Optimization for Test-Time Expert Re-Mixing

链接: https://arxiv.org/abs/2504.07964
作者: Zhongyang Li,Ziyue Li,Tianyi Zhou
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Mixture-of-Experts (MoE) Large Language Models (LLMs) suffer from severely sub-optimal expert pathways-our study reveals that naive expert selection learned from pretraining leaves a surprising 10-20% accuracy gap for improvement. Motivated by this observation, we develop a novel class of test-time optimization methods to re-weight or “re-mixing” the experts in different layers jointly for each test sample. Since the test sample’s ground truth is unknown, we propose to optimize a surrogate objective defined by the sample’s “successful neighbors” from a reference set of samples. We introduce three surrogates and algorithms based on mode-finding, kernel regression, and the average loss of similar reference samples/tasks. To reduce the cost of optimizing whole pathways, we apply our algorithms merely to the core experts’ mixing weights in critical layers, which enjoy similar performance but save significant computation. This leads to “Critical-Layer, Core-Expert, Collaborative Pathway Optimization (C3PO)”. We apply C3PO to two recent MoE LLMs and examine it on six widely-used benchmarks. It consistently improves the base model by 7-15% in accuracy and outperforms widely used test-time learning baselines, e.g., in-context learning and prompt/prefix tuning, by a large margin. Moreover, C3PO enables MoE LLMs with 1-3B active parameters to outperform LLMs of 7-9B parameters, hence improving MoE’s advantages on efficiency. Our thorough ablation study further sheds novel insights on achieving test-time improvement on MoE.

[LG-1] Semantically Encoding Activity Labels for Context-Aware Human Activity Recognition

链接: https://arxiv.org/abs/2504.07916
作者: Wen Ge,Guanyi Mou,Emmanuel O. Agu,Kyumin Lee
类目: Machine Learning (cs.LG)
*备注: Percom 2025

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Abstract:Prior work has primarily formulated CA-HAR as a multi-label classification problem, where model inputs are time-series sensor data and target labels are binary encodings representing whether a given activity or context occurs. These CA-HAR methods either predicted each label independently or manually imposed relationships using graphs. However, both strategies often neglect an essential aspect: activity labels have rich semantic relationships. For instance, walking, jogging, and running activities share similar movement patterns but differ in pace and intensity, indicating that they are semantically related. Consequently, prior CA-HAR methods often struggled to accurately capture these inherent and nuanced relationships, particularly on datasets with noisy labels typically used for CA-HAR or situations where the ideal sensor type is unavailable (e.g., recognizing speech without audio sensors). To address this limitation, we propose SEAL, which leverage LMs to encode CA-HAR activity labels to capture semantic relationships. LMs generate vector embeddings that preserve rich semantic information from natural language. Our SEAL approach encodes input-time series sensor data from smart devices and their associated activity and context labels (text) as vector embeddings. During training, SEAL aligns the sensor data representations with their corresponding activity/context label embeddings in a shared embedding space. At inference time, SEAL performs a similarity search, returning the CA-HAR label with the embedding representation closest to the input data. Although LMs have been widely explored in other domains, surprisingly, their potential in CA-HAR has been underexplored, making our approach a novel contribution to the field. Our research opens up new possibilities for integrating more advanced LMs into CA-HAR tasks.

[LG-2] Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining

链接: https://arxiv.org/abs/2504.07912
作者: Rosie Zhao,Alexandru Meterez,Sham Kakade,Cengiz Pehlevan,Samy Jelassi,Eran Malach
类目: Machine Learning (cs.LG)
*备注:

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Abstract:Reinforcement learning (RL)-based fine-tuning has become a crucial step in post-training language models for advanced mathematical reasoning and coding. Following the success of frontier reasoning models, recent work has demonstrated that RL fine-tuning consistently improves performance, even in smaller-scale models; however, the underlying mechanisms driving these improvements are not well-understood. Understanding the effects of RL fine-tuning requires disentangling its interaction with pretraining data composition, hyperparameters, and model scale, but such problems are exacerbated by the lack of transparency regarding the training data used in many existing models. In this work, we present a systematic end-to-end study of RL fine-tuning for mathematical reasoning by training models entirely from scratch on different mixtures of fully open datasets. We investigate the effects of various RL fine-tuning algorithms (PPO, GRPO, and Expert Iteration) across models of different scales. Our study reveals that RL algorithms consistently converge towards a dominant output distribution, amplifying patterns in the pretraining data. We also find that models of different scales trained on the same data mixture will converge to distinct output distributions, suggesting that there are scale-dependent biases in model generalization. Moreover, we find that RL post-training on simpler questions can lead to performance gains on harder ones, indicating that certain reasoning capabilities generalize across tasks. Our findings show that small-scale proxies in controlled settings can elicit interesting insights regarding the role of RL in shaping language model behavior.

[LG-3] Hodge Laplacians and Hodge Diffusion Maps

链接: https://arxiv.org/abs/2504.07910
作者: Alvaro Almeida Gomez,Jorge Duque Franco
类目: Machine Learning (cs.LG)
*备注: 53 Pages, comments are welcome!

点击查看摘要

Abstract:We introduce Hodge Diffusion Maps, a novel manifold learning algorithm designed to analyze and extract topological information from high-dimensional data-sets. This method approximates the exterior derivative acting on differential forms, thereby providing an approximation of the Hodge Laplacian operator. Hodge Diffusion Maps extend existing non-linear dimensionality reduction techniques, including vector diffusion maps, as well as the theories behind diffusion maps and Laplacian Eigenmaps. Our approach captures higher-order topological features of the data-set by projecting it into lower-dimensional Euclidean spaces using the Hodge Laplacian. We develop a theoretical framework to estimate the approximation error of the exterior derivative, based on sample points distributed over a real manifold. Numerical experiments support and validate the proposed methodology.

[LG-4] DiverseFlow: Sample-Efficient Diverse Mode Coverag e in Flows

链接: https://arxiv.org/abs/2504.07894
作者: Mashrur M. Morshed,Vishnu Boddeti
类目: Machine Learning (cs.LG)
*备注:

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Abstract:Many real-world applications of flow-based generative models desire a diverse set of samples that cover multiple modes of the target distribution. However, the predominant approach for obtaining diverse sets is not sample-efficient, as it involves independently obtaining many samples from the source distribution and mapping them through the flow until the desired mode coverage is achieved. As an alternative to repeated sampling, we introduce DiverseFlow: a training-free approach to improve the diversity of flow models. Our key idea is to employ a determinantal point process to induce a coupling between the samples that drives diversity under a fixed sampling budget. In essence, DiverseFlow allows exploration of more variations in a learned flow model with fewer samples. We demonstrate the efficacy of our method for tasks where sample-efficient diversity is desirable, such as text-guided image generation with polysemous words, inverse problems like large-hole inpainting, and class-conditional image synthesis.

[LG-5] Robust Hallucination Detection in LLM s via Adaptive Token Selection

链接: https://arxiv.org/abs/2504.07863
作者: Mengjia Niu,Hamed Haddadi,Guansong Pang
类目: Machine Learning (cs.LG)
*备注:

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Abstract:Hallucinations in large language models (LLMs) pose significant safety concerns that impede their broader deployment. Recent research in hallucination detection has demonstrated that LLMs’ internal representations contain truthfulness hints, which can be harnessed for detector training. However, the performance of these detectors is heavily dependent on the internal representations of predetermined tokens, fluctuating considerably when working on free-form generations with varying lengths and sparse distributions of hallucinated entities. To address this, we propose HaMI, a novel approach that enables robust detection of hallucinations through adaptive selection and learning of critical tokens that are most indicative of hallucinations. We achieve this robustness by an innovative formulation of the Hallucination detection task as Multiple Instance (HaMI) learning over token-level representations within a sequence, thereby facilitating a joint optimisation of token selection and hallucination detection on generation sequences of diverse forms. Comprehensive experimental results on four hallucination benchmarks show that HaMI significantly outperforms existing state-of-the-art approaches.

[LG-6] Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks

链接: https://arxiv.org/abs/2504.07835
作者: Erin Carson,Xinye Chen
类目: Machine Learning (cs.LG); Numerical Analysis (math.NA)
*备注:

点击查看摘要

Abstract:Motivated by the growing demand for low-precision arithmetic in computational science, we exploit lower-precision emulation in Python – widely regarded as the dominant programming language for numerical analysis and machine learning. Low-precision training has revolutionized deep learning by enabling more efficient computation and reduced memory and energy consumption while maintaining model fidelity. To better enable numerical experimentation with and exploration of low precision computation, we developed the Pychop library, which supports customizable floating-point formats and a comprehensive set of rounding modes in Python, allowing users to benefit from fast, low-precision emulation in numerous applications. Pychop also introduces interfaces for both PyTorch and JAX, enabling efficient low-precision emulation on GPUs for neural network training and inference with unparalleled flexibility. In this paper, we offer a comprehensive exposition of the design, implementation, validation, and practical application of Pychop, establishing it as a foundational tool for advancing efficient mixed-precision algorithms. Furthermore, we present empirical results on low-precision emulation for image classification and object detection using published datasets, illustrating the sensitivity of the use of low precision and offering valuable insights into its impact. Pychop enables in-depth investigations into the effects of numerical precision, facilitates the development of novel hardware accelerators, and integrates seamlessly into existing deep learning workflows. Software and experimental code are publicly available at this https URL. Subjects: Machine Learning (cs.LG); Numerical Analysis (math.NA) Cite as: arXiv:2504.07835 [cs.LG] (or arXiv:2504.07835v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2504.07835 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-7] Quantum Machine Learning: Unveiling Trends Impacts through Bibliometric Analysis

链接: https://arxiv.org/abs/2504.07726
作者: Riya Bansal,Nikhil Kumar Rajput
类目: Digital Libraries (cs.DL); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Quantum Machine Learning (QML) is the intersection of two revolutionary fields: quantum computing and machine learning. It promises to unlock unparalleled capabilities in data analysis, model building, and problem-solving by harnessing the unique properties of quantum mechanics. This research endeavors to conduct a comprehensive bibliometric analysis of scientific information pertaining to QML covering the period from 2000 to 2023. An extensive dataset comprising 9493 scholarly works is meticulously examined to unveil notable trends, impact factors, and funding patterns within the domain. Additionally, the study employs bibliometric mapping techniques to visually illustrate the network relationships among key countries, institutions, authors, patent citations and significant keywords in QML research. The analysis reveals a consistent growth in publications over the examined period. The findings highlight the United States and China as prominent contributors, exhibiting substantial publication and citation metrics. Notably, the study concludes that QML, as a research subject, is currently in a formative stage, characterized by robust scholarly activity and ongoing development.

[LG-8] Relaxing the Markov Requirements on Reinforcement Learning Under Weak Partial Ignorability

链接: https://arxiv.org/abs/2504.07722
作者: MaryLena Bleile
类目: Machine Learning (cs.LG); Methodology (stat.ME)
*备注:

点击查看摘要

Abstract:Incomplete data, confounding effects, and violations of the Markov property are interrelated problems which are ubiquitous in Reinforcement Learning applications. We introduce the concept of ``partial ignorabilty" and leverage it to establish a novel convergence theorem for adaptive Reinforcement Learning. This theoretical result relaxes the Markov assumption on the stochastic process underlying conventional Q -learning, deploying a generalized form of the Robbins-Monro stochastic approximation theorem to establish optimality. This result has clear downstream implications for most active subfields of Reinforcement Learning, with clear paths for extension to the field of Causal Inference.

[LG-9] Data Requirement Goal Modeling for Machine Learning Systems

链接: https://arxiv.org/abs/2504.07664
作者: Asma Yamani,Nadeen AlAmoudi,Salma Albilali,Malak Baslyman,Jameleddine Hassine
类目: oftware Engineering (cs.SE); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Machine Learning (ML) has been integrated into various software and systems. Two main components are essential for training an ML model: the training data and the ML algorithm. Given the critical role of data in ML system development, it has become increasingly important to assess the quality of data attributes and ensure that the data meets specific requirements before its utilization. This work proposes an approach to guide non-experts in identifying data requirements for ML systems using goal modeling. In this approach, we first develop the Data Requirement Goal Model (DRGM) by surveying the white literature to identify and categorize the issues and challenges faced by data scientists and requirement engineers working on ML-related projects. An initial DRGM was built to accommodate common tasks that would generalize across projects. Then, based on insights from both white and gray literature, a customization mechanism is built to help adjust the tasks, KPIs, and goals’ importance of different elements within the DRGM. The generated model can aid its users in evaluating different datasets using GRL evaluation strategies. We then validate the approach through two illustrative examples based on real-world projects. The results from the illustrative examples demonstrate that the data requirements identified by the proposed approach align with the requirements of real-world projects, demonstrating the practicality and effectiveness of the proposed framework. The proposed dataset selection customization mechanism and the proposed DRGM are helpful in guiding non-experts in identifying the data requirements for machine learning systems tailored to a specific ML problem. This approach also aids in evaluating different dataset alternatives to choose the optimum dataset for the problem. For future work, we recommend implementing tool support to generate the DRGM based on a chatbot interface.

[LG-10] Prediction of Usage Probabilities of Shopping-Mall Corridors Using Heterogeneous Graph Neural Networks

链接: https://arxiv.org/abs/2504.07645
作者: Malik M Barakathullah,Immanuel Koh
类目: Machine Learning (cs.LG)
*备注: 17 pages, working manuscript with partial results

点击查看摘要

Abstract:We present a method based on graph neural network (GNN) for prediction of probabilities of usage of shopping-mall corridors. The heterogeneous graph network of shops and corridor paths are obtained from floorplans of the malls by creating vector layers for corridors, shops and entrances. These are subsequently assimilated into nodes and edges of graphs. The prediction of the usage probability is based on the shop features, namely, the area and usage categories they fall into, and on the graph connecting these shops, corridor junctions and entrances by corridor paths. Though the presented method is applicable for training on datasets obtained from a field survey or from pedestrian-detecting sensors, the target data of the supervised deep-learning work flow in this work are obtained from a probability method. We also include a context-specific representation learning of latent features. The usage-probability prediction is made on each edge, which is a connection by a section of corridor path between the adjacent nodes representing the shops or corridor points. To create a feature for each edge, the hidden-layer feature vectors acquired in the message-passing GNN layers at the nodes of each edge are averaged and concatenated with the vector obtained by their multiplication. These edge-features are then passed to multilayer perceptrons (MLP) to make the final prediction of usage probability on each edge. The samples of synthetic learning dataset for each shopping mall are obtained by changing the shops’ usage and area categories, and by subsequently feeding the graph into the probability model. When including different shopping malls in a single dataset, we also propose to consider graph-level features to inform the model with specific identifying features of each mall. Comments: 17 pages, working manuscript with partial results Subjects: Machine Learning (cs.LG) MSC classes: 68T07 ACMclasses: G.3; I.2; J.4 Cite as: arXiv:2504.07645 [cs.LG] (or arXiv:2504.07645v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2504.07645 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-11] Kernel Logistic Regression Learning for High-Capacity Hopfield Networks

链接: https://arxiv.org/abs/2504.07633
作者: Akira Tamamori
类目: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
*备注: submitted to IEICE journal

点击查看摘要

Abstract:Hebbian learning limits Hopfield network storage capacity (pattern-to-neuron ratio around 0.14). We propose Kernel Logistic Regression (KLR) learning. Unlike linear methods, KLR uses kernels to implicitly map patterns to high-dimensional feature space, enhancing separability. By learning dual variables, KLR dramatically improves storage capacity, achieving perfect recall even when pattern numbers exceed neuron numbers (up to ratio 1.5 shown), and enhances noise robustness. KLR demonstrably outperforms Hebbian and linear logistic regression approaches.

[LG-12] CTSR: Cartesian tensor-based sparse regression for data-driven discovery of high-dimensional invariant governing equations

链接: https://arxiv.org/abs/2504.07618
作者: Boqian Zhang,Juanmian Lei,Guoyou Sun,Shuaibing Ding,Jian Guo
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Accurate and concise governing equations are crucial for understanding system dynamics. Recently, data-driven methods such as sparse regression have been employed to automatically uncover governing equations from data, representing a significant shift from traditional first-principles modeling. However, most existing methods focus on scalar equations, limiting their applicability to simple, low-dimensional scenarios, and failing to ensure rotation and reflection invariance without incurring significant computational cost or requiring additional prior knowledge. This paper proposes a Cartesian tensor-based sparse regression (CTSR) technique to accurately and efficiently uncover complex, high-dimensional governing equations while ensuring invariance. Evaluations on two two-dimensional (2D) and two three-dimensional (3D) test cases demonstrate that the proposed method achieves superior accuracy and efficiency compared to the conventional technique.

[LG-13] Conditional Conformal Risk Adaptation

链接: https://arxiv.org/abs/2504.07611
作者: Rui Luo,Zhixin Zhou
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Uncertainty quantification is becoming increasingly important in image segmentation, especially for high-stakes applications like medical imaging. While conformal risk control generalizes conformal prediction beyond standard miscoverage to handle various loss functions such as false negative rate, its application to segmentation often yields inadequate conditional risk control: some images experience very high false negative rates while others have negligibly small ones. We develop Conformal Risk Adaptation (CRA), which introduces a new score function for creating adaptive prediction sets that significantly improve conditional risk control for segmentation tasks. We establish a novel theoretical framework that demonstrates a fundamental connection between conformal risk control and conformal prediction through a weighted quantile approach, applicable to any score function. To address the challenge of poorly calibrated probabilities in segmentation models, we introduce a specialized probability calibration framework that enhances the reliability of pixel-wise inclusion estimates. Using these calibrated probabilities, we propose Calibrated Conformal Risk Adaptation (CCRA) and a stratified variant (CCRA-S) that partitions images based on their characteristics and applies group-specific thresholds to further enhance conditional risk control. Our experiments on polyp segmentation demonstrate that all three methods (CRA, CCRA, and CCRA-S) provide valid marginal risk control and deliver more consistent conditional risk control across diverse images compared to standard approaches, offering a principled approach to uncertainty quantification that is particularly valuable for high-stakes and personalized segmentation applications.

[LG-14] Privacy-Preserving Vertical K-Means Clustering

链接: https://arxiv.org/abs/2504.07578
作者: Federico Mazzone,Trevor Brown,Florian Kerschbaum,Kevin H. Wilson,Maarten Everts,Florian Hahn,Andreas Peter
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Clustering is a fundamental data processing task used for grouping records based on one or more features. In the vertically partitioned setting, data is distributed among entities, with each holding only a subset of those features. A key challenge in this scenario is that computing distances between records requires access to all distributed features, which may be privacy-sensitive and cannot be directly shared with other parties. The goal is to compute the joint clusters while preserving the privacy of each entity’s dataset. Existing solutions using secret sharing or garbled circuits implement privacy-preserving variants of Lloyd’s algorithm but incur high communication costs, scaling as O(nkt), where n is the number of data points, k the number of clusters, and t the number of rounds. These methods become impractical for large datasets or several parties, limiting their use to LAN settings only. On the other hand, a different line of solutions rely on differential privacy (DP) to outsource the local features of the parties to a central server. However, they often significantly degrade the utility of the clustering outcome due to excessive noise. In this work, we propose a novel solution based on homomorphic encryption and DP, reducing communication complexity to O(n+kt). In our method, parties securely outsource their features once, allowing a computing party to perform clustering operations under encryption. DP is applied only to the clusters’ centroids, ensuring privacy with minimal impact on utility. Our solution clusters 100,000 two-dimensional points into five clusters using only 73MB of communication, compared to 101GB for existing works, and completes in just under 3 minutes on a 100Mbps network, whereas existing works take over 1 day. This makes our solution practical even for WAN deployments, all while maintaining accuracy comparable to plaintext k-means algorithms.

[LG-15] Using LLM s for Analyzing AIS Data

链接: https://arxiv.org/abs/2504.07557
作者: Gaspard Mertends,Gilles Dejaegere,Mahmoud Sakr
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Recent research in Large Language Models (LLMs), has had a profound impact across various fields, including mobility data science. This paper explores the and experiment with different approaches to using LLMs for analyzing AIS data. We propose a set of carefully designed queries to assess the reasoning capabilities of LLMs in this kind of tasks. Further, we experiment with four different methods: (1) using LLMs as a natural language interface to a spatial database, (2) reasoning on raw data, (3) reasoning on compressed trajectories, and (4) reasoning on semantic trajectories. We investigate the strengths and weaknesses for the four methods, and discuss the findings. The goal is to provide valuable insights for both researchers and practitioners on selecting the most appropriate LLM-based method depending on their specific data analysis objectives.

[LG-16] Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving

链接: https://arxiv.org/abs/2504.07494
作者: Shihong Gao,Xin Zhang,Yanyan Shen,Lei Chen
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Large language model (LLM) inference serving systems are essential to various LLM-based applications. As demand for LLM services continues to grow, scaling these systems to handle high request rates while meeting latency Service-Level Objectives (SLOs), referred to as effective throughput, becomes critical. However, existing systems often struggle to improve effective throughput, primarily due to a significant decline in Time To First Token (TTFT) SLO attainment. We identify two major causes of this bottleneck: (1) memory-intensive KV cache that limits batch size expansion under GPU memory constraints, and (2) rigid batch composition enforced by the default First-Come-First-Serve scheduling policy. In this paper, we introduce Apt-Serve, a scalable framework designed to enhance effective throughput in LLM inference serving. Apt-Serve features a new hybrid cache scheme that combines KV cache with a memory-efficient hidden cache for reusable input hidden state vectors, allowing large batch sizes and improving request concurrency. Based on the hybrid cache, Apt-Serve employs an adaptive runtime scheduling mechanism that dynamically optimizes batch composition. We formally define the adaptive scheduling optimization problem and propose an efficient algorithm with theoretical guarantees. Extensive evaluations on three real-world datasets and LLMs ranging from 13B to 66B parameters demonstrate that Apt-Serve achieves up to 8.8x improvement in effective throughput compared to the state-of-the-art inference serving systems.

[LG-17] Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to Network Security

链接: https://arxiv.org/abs/2504.07478
作者: Caroline Panggabean,Chandrasekar Venkatachalam,Priyanka Shah,Sincy John,Renuka Devi P,Shanmugavalli Venkatachalam
类目: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
*备注: Accepted at the 2024 5th International Conference on Smart Electronics and Communication (ICOSEC). This is the accepted manuscript version. The final version is published by IEEE at this https URL

点击查看摘要

Abstract:Detecting Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. This research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a Neural Turing Machine (NTM) for enhanced intrusion detection. Trained on the UNSW-NB15 and BoT-IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long-term pattern recognition. The proposed approach achieves 99% accuracy in distinguishing between normal, DoS, and DDoS traffic. These findings offer promising advancements in real-time threat detection and contribute to improved network security across various domains.

[LG-18] raversal Learning Coordination For Lossless And Efficient Distributed Learning

链接: https://arxiv.org/abs/2504.07471
作者: Erdenebileg Batbaatar,Jeonggeol Kim,Yongcheol Kim,Young Yoon
类目: Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)
*备注:

点击查看摘要

Abstract:In this paper, we introduce Traversal Learning (TL), a novel approach designed to address the problem of decreased quality encountered in popular distributed learning (DL) paradigms such as Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL). Traditional FL experiences from an accuracy drop during aggregation due to its averaging function, while SL and SFL face increased loss due to the independent gradient updates on each split network. TL adopts a unique strategy where the model traverses the nodes during forward propagation (FP) and performs backward propagation (BP) on the orchestrator, effectively implementing centralized learning (CL) principles within a distributed environment. The orchestrator is tasked with generating virtual batches and planning the sequential node visits of the model during FP, aligning them with the ordered index of the data within these batches. We conducted experiments on six datasets representing diverse characteristics across various domains. Our evaluation demonstrates that TL is on par with classic CL approaches in terms of accurate inference, thereby offering a viable and robust solution for DL tasks. TL outperformed other DL methods and improved accuracy by 7.85% for independent and identically distributed (IID) datasets, macro F1-score by 1.06% for non-IID datasets, accuracy by 2.60% for text classification, and AUC by 3.88% and 4.54% for medical and financial datasets, respectively. By effectively preserving data privacy while maintaining performance, TL represents a significant advancement in DL methodologies.

[LG-19] Multi-Modal Data Fusion for Moisture Content Prediction in Apple Drying

链接: https://arxiv.org/abs/2504.07465
作者: Shichen Li,Chenhui Shao
类目: Machine Learning (cs.LG)
*备注: Accepted for publication in the Proceedings of the 53rd North American Manufacturing Research Conference (NAMRC 53), to appear in Manufacturing Letters

点击查看摘要

Abstract:Fruit drying is widely used in food manufacturing to reduce product moisture, ensure product safety, and extend product shelf life. Accurately predicting final moisture content (MC) is critically needed for quality control of drying processes. State-of-the-art methods can build deterministic relationships between process parameters and MC, but cannot adequately account for inherent process variabilities that are ubiquitous in fruit drying. To address this gap, this paper presents a novel multi-modal data fusion framework to effectively fuse two modalities of data: tabular data (process parameters) and high-dimensional image data (images of dried apple slices) to enable accurate MC prediction. The proposed modeling architecture permits flexible adjustment of information portion from tabular and image data modalities. Experimental validation shows that the multi-modal approach improves predictive accuracy substantially compared to state-of-the-art methods. The proposed method reduces root-mean-squared errors by 19.3%, 24.2%, and 15.2% over tabular-only, image-only, and standard tabular-image fusion models, respectively. Furthermore, it is demonstrated that our method is robust in varied tabular-image ratios and capable of effectively capturing inherent small-scale process variabilities. The proposed framework is extensible to a variety of other drying technologies.

[LG-20] Unifying and extending Diffusion Models through PDEs for solving Inverse Problems

链接: https://arxiv.org/abs/2504.07437
作者: Agnimitra Dasgupta,Alexsander Marciano da Cunha,Ali Fardisi,Mehrnegar Aminy,Brianna Binder,Bryan Shaddy,Assad A Oberai
类目: Machine Learning (cs.LG); Computation (stat.CO); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:Diffusion models have emerged as powerful generative tools with applications in computer vision and scientific machine learning (SciML), where they have been used to solve large-scale probabilistic inverse problems. Traditionally, these models have been derived using principles of variational inference, denoising, statistical signal processing, and stochastic differential equations. In contrast to the conventional presentation, in this study we derive diffusion models using ideas from linear partial differential equations and demonstrate that this approach has several benefits that include a constructive derivation of the forward and reverse processes, a unified derivation of multiple formulations and sampling strategies, and the discovery of a new class of models. We also apply the conditional version of these models to solving canonical conditional density estimation problems and challenging inverse problems. These problems help establish benchmarks for systematically quantifying the performance of different formulations and sampling strategies in this study, and for future studies. Finally, we identify and implement a mechanism through which a single diffusion model can be applied to measurements obtained from multiple measurement operators. Taken together, the contents of this manuscript provide a new understanding and several new directions in the application of diffusion models to solving physics-based inverse problems.

[LG-21] Multi-Selection for Recommendation Systems

链接: https://arxiv.org/abs/2504.07403
作者: Sahasrajit Sarmasarkar,Zhihao Jiang,Ashish Goel,Aleksandra Korolova,Kamesh Munagala
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:We present the construction of a multi-selection model to answer differentially private queries in the context of recommendation systems. The server sends back multiple recommendations and a ``local model’’ to the user, which the user can run locally on its device to select the item that best fits its private features. We study a setup where the server uses a deep neural network (trained on the Movielens 25M dataset as the ground truth for movie recommendation. In the multi-selection paradigm, the average recommendation utility is approximately 97% of the optimal utility (as determined by the ground truth neural network) while maintaining a local differential privacy guarantee with \epsilon ranging around 1 with respect to feature vectors of neighboring users. This is in comparison to an average recommendation utility of 91% in the non-multi-selection regime under the same constraints.

[LG-22] State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements

链接: https://arxiv.org/abs/2504.07393
作者: Wonjin Song,Feng Bao
类目: Machine Learning (cs.LG); Optimization and Control (math.OC)
*备注:

点击查看摘要

Abstract:Reliable state estimation is essential for autonomous systems operating in complex, noisy environments. Classical filtering approaches, such as the Kalman filter, can struggle when facing nonlinear dynamics or non-Gaussian noise, and even more flexible particle filters often encounter sample degeneracy or high computational costs in large-scale domains. Meanwhile, adaptive machine learning techniques, including Q-learning and neuroevolutionary algorithms such as NEAT, rely heavily on accurate state feedback to guide learning; when sensor data are imperfect, these methods suffer from degraded convergence and suboptimal performance. In this paper, we propose an integrated framework that unifies particle filtering with Q-learning and NEAT to explicitly address the challenge of noisy measurements. By refining radar-based observations into reliable state estimates, our particle filter drives more stable policy updates (in Q-learning) or controller evolution (in NEAT), allowing both reinforcement learning and neuroevolution to converge faster, achieve higher returns or fitness, and exhibit greater resilience to sensor uncertainty. Experiments on grid-based navigation and a simulated car environment highlight consistent gains in training stability, final performance, and success rates over baselines lacking advanced filtering. Altogether, these findings underscore that accurate state estimation is not merely a preprocessing step, but a vital component capable of substantially enhancing adaptive machine learning in real-world applications plagued by sensor noise.

[LG-23] Minimum width for universal approximation using squashable activation functions

链接: https://arxiv.org/abs/2504.07371
作者: Jonghyun Shin,Namjun Kim,Geonho Hwang,Sejun Park
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:The exact minimum width that allows for universal approximation of unbounded-depth networks is known only for ReLU and its variants. In this work, we study the minimum width of networks using general activation functions. Specifically, we focus on squashable functions that can approximate the identity function and binary step function by alternatively composing with affine transformations. We show that for networks using a squashable activation function to universally approximate L^p functions from [0,1]^d_x to \mathbb R^d_y , the minimum width is \max\d_x,d_y,2\ unless d_x=d_y=1 ; the same bound holds for d_x=d_y=1 if the activation function is monotone. We then provide sufficient conditions for squashability and show that all non-affine analytic functions and a class of piecewise functions are squashable, i.e., our minimum width result holds for those general classes of activation functions.

[LG-24] Leverag ing deep learning for plant disease identification: a bibliometric analysis in SCOPUS from 2018 to 2024

链接: https://arxiv.org/abs/2504.07342
作者: Enow Takang Achuo Albert,Ngalle Hermine Bille,Ngonkeu Mangaptche Eddy Leonard
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:This work aimed to present a bibliometric analysis of deep learning research for plant disease identification, with a special focus on generative modeling. A thorough analysis of SCOPUS-sourced bibliometric data from 253 documents was performed. Key performance metrics such as accuracy, precision, recall, and F1-score were analyzed for generative modeling. The findings highlighted significant contributions from some authors Too and Arnal Barbedo, whose works had notable citation counts, suggesting their influence on the academic community. Co-authorship networks revealed strong collaborative clusters, while keyword analysis identified emerging research gaps. This study highlights the role of collaboration and citation metrics in shaping research directions and enhancing the impact of scholarly work in applications of deep learning to plant disease identification. Future research should explore the methodologies of highly cited studies to inform best practices and policy-making.

[LG-25] FLASH: Flexible Learning of Adaptive Sampling from History in Temporal Graph Neural Networks

链接: https://arxiv.org/abs/2504.07337
作者: Or Feldman,Krishna Sri Ipsit Mantri,Carola-Bibiane Schönlieb,Chaim Baskin,Moshe Eliasof
类目: Machine Learning (cs.LG); Social and Information Networks (cs.SI)
*备注: 22 pages, 4 figures, 12 tables

点击查看摘要

Abstract:Aggregating temporal signals from historic interactions is a key step in future link prediction on dynamic graphs. However, incorporating long histories is resource-intensive. Hence, temporal graph neural networks (TGNNs) often rely on historical neighbors sampling heuristics such as uniform sampling or recent neighbors selection. These heuristics are static and fail to adapt to the underlying graph structure. We introduce FLASH, a learnable and graph-adaptive neighborhood selection mechanism that generalizes existing heuristics. FLASH integrates seamlessly into TGNNs and is trained end-to-end using a self-supervised ranking loss. We provide theoretical evidence that commonly used heuristics hinders TGNNs performance, motivating our design. Extensive experiments across multiple benchmarks demonstrate consistent and significant performance improvements for TGNNs equipped with FLASH.

[LG-26] Bregman-Hausdorff divergence: strengthening the connections between computational geometry and machine learning

链接: https://arxiv.org/abs/2504.07322
作者: Tuyen Pham,Hana Dal Poz Kouřimská,Hubert Wagner
类目: Machine Learning (cs.LG); Computational Geometry (cs.CG); Information Theory (cs.IT)
*备注: 23 pages, 11 figures, 3 tables, 3 algorithms, submitted to Machine Learning and Knowledge Extraction

点击查看摘要

Abstract:The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures. Specifically, we focus on the family of Bregman divergences, which includes the popular Kullback–Leibler divergence (also known as relative entropy). As a proof of concept, we use the resulting Bregman–Hausdorff divergence to compare two collections of probabilistic predictions produced by different machine learning models trained using the relative entropy loss. The algorithms we propose are surprisingly efficient even for large inputs with hundreds of dimensions. In addition to the introduction of this technical concept, we provide a survey. It outlines the basics of Bregman geometry, as well as computational geometry algorithms. We focus on algorithms that are compatible with this geometry and are relevant for machine learning. Comments: 23 pages, 11 figures, 3 tables, 3 algorithms, submitted to Machine Learning and Knowledge Extraction Subjects: Machine Learning (cs.LG); Computational Geometry (cs.CG); Information Theory (cs.IT) Cite as: arXiv:2504.07322 [cs.LG] (or arXiv:2504.07322v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2504.07322 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-27] Follow-the-Perturbed-Leader Achieves Best-of-Both-Worlds for the m-Set Semi-Bandit Problems

链接: https://arxiv.org/abs/2504.07307
作者: Jingxin Zhan,Zhihua Zhang
类目: Machine Learning (cs.LG); Machine Learning (stat.ML)
*备注:

点击查看摘要

Abstract:We consider a common case of the combinatorial semi-bandit problem, the m -set semi-bandit, where the learner exactly selects m arms from the total d arms. In the adversarial setting, the best regret bound, known to be \mathcalO(\sqrtnmd) for time horizon n , is achieved by the well-known Follow-the-Regularized-Leader (FTRL) policy, which, however, requires to explicitly compute the arm-selection probabilities by solving optimizing problems at each time step and sample according to it. This problem can be avoided by the Follow-the-Perturbed-Leader (FTPL) policy, which simply pulls the m arms that rank among the m smallest (estimated) loss with random perturbation. In this paper, we show that FTPL with a Fréchet perturbation also enjoys the optimal regret bound \mathcalO(\sqrtnmd) in the adversarial setting and achieves best-of-both-world regret bounds, i.e., achieves a logarithmic regret for the stochastic setting.

[LG-28] Data Fusion of Deep Learned Molecular Embeddings for Property Prediction

链接: https://arxiv.org/abs/2504.07297
作者: Robert J Appleton,Brian C Barnes,Alejandro Strachan
类目: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci)
*备注:

点击查看摘要

Abstract:Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many problems data is sparse, severely limiting their accuracy and applicability. To improve predictions, techniques such as transfer learning and multi-task learning have been used. The performance of multi-task learning models depends on the strength of the underlying correlations between tasks and the completeness of the dataset. We find that standard multi-task models tend to underperform when trained on sparse datasets with weakly correlated properties. To address this gap, we use data fusion techniques to combine the learned molecular embeddings of various single-task models and trained a multi-task model on this combined embedding. We apply this technique to a widely used benchmark dataset of quantum chemistry data for small molecules as well as a newly compiled sparse dataset of experimental data collected from literature and our own quantum chemistry and thermochemical calculations. The results show that the fused, multi-task models outperform standard multi-task models for sparse datasets and can provide enhanced prediction on data-limited properties compared to single-task models.

[LG-29] A Scalable Approach to Clustering Embedding Projections

链接: https://arxiv.org/abs/2504.07285
作者: Donghao Ren,Fred Hohman,Dominik Moritz
类目: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
*备注: 4 pages, 4 figures

点击查看摘要

Abstract:Interactive visualization of embedding projections is a useful technique for understanding data and evaluating machine learning models. Labeling data within these visualizations is critical for interpretation, as labels provide an overview of the projection and guide user navigation. However, most methods for producing labels require clustering the points, which can be computationally expensive as the number of points grows. In this paper, we describe an efficient clustering approach using kernel density estimation in the projected 2D space instead of points. This algorithm can produce high-quality cluster regions from a 2D density map in a few hundred milliseconds, orders of magnitude faster than current approaches. We contribute the design of the algorithm, benchmarks, and applications that demonstrate the utility of the algorithm, including labeling and summarization.

[LG-30] Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach AISTATS2025

链接: https://arxiv.org/abs/2504.07261
作者: Dheeraj Baby,Boran Han,Shuai Zhang,Cuixiong Hu,Yuyang Wang,Yu-Xiang Wang
类目: Machine Learning (cs.LG)
*备注: To appear at AISTATS 2025

点击查看摘要

Abstract:We study the well-motivated problem of online distribution shift in which the data arrive in batches and the distribution of each batch can change arbitrarily over time. Since the shifts can be large or small, abrupt or gradual, the length of the relevant historical data to learn from may vary over time, which poses a major challenge in designing algorithms that can automatically adapt to the best attention span'' while remaining computationally efficient. We propose a meta-algorithm that takes any network architecture and any Online Learner (OL) algorithm as input and produces a new algorithm which provably enhances the performance of the given OL under non-stationarity. Our algorithm is efficient (it requires maintaining only O(\log(T)) OL instances) and adaptive (it automatically chooses OL instances with the ideal attention’’ length at every timestamp). Experiments on various real-world datasets across text and image modalities show that our method consistently improves the accuracy of user specified OL algorithms for classification tasks. Key novel algorithmic ingredients include a \emphmulti-resolution instance design inspired by wavelet theory and a cross-validation-through-time technique. Both could be of independent interest.

[LG-31] Resource-efficient Inference with Foundation Model Programs

链接: https://arxiv.org/abs/2504.07247
作者: Lunyiu Nie,Zhimin Ding,Kevin Yu,Marco Cheung,Chris Jermaine,Swarat Chaudhuri
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:The inference-time resource costs of large language and vision models present a growing challenge in production deployments. We propose the use of foundation model programs, i.e., programs that can invoke foundation models with varying resource costs and performance, as an approach to this problem. Specifically, we present a method that translates a task into a program, then learns a policy for resource allocation that, on each input, selects foundation model “backends” for each program module. The policy uses smaller, cheaper backends to handle simpler subtasks, while allowing more complex subtasks to leverage larger, more capable models. We evaluate the method on two new “streaming” visual question-answering tasks in which a system answers a question on a sequence of inputs, receiving ground-truth feedback after each answer. Compared to monolithic multi-modal models, our implementation achieves up to 98% resource savings with minimal accuracy loss, demonstrating its potential for scalable and resource-efficient multi-modal inference.

[LG-32] Prototype-Based Continual Learning with Label-free Replay Buffer and Cluster Preservation Loss

链接: https://arxiv.org/abs/2504.07240
作者: Agil Aghasanli,Yi Li,Plamen Angelov
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Continual learning techniques employ simple replay sample selection processes and use them during subsequent tasks. Typically, they rely on labeled data. In this paper, we depart from this by automatically selecting prototypes stored without labels, preserving cluster structures in the latent space across tasks. By eliminating label dependence in the replay buffer and introducing cluster preservation loss, it is demonstrated that the proposed method can maintain essential information from previously encountered tasks while ensuring adaptation to new tasks. “Push-away” and “pull-toward” mechanisms over previously learned prototypes are also introduced for class-incremental and domain-incremental scenarios. These mechanisms ensure the retention of previously learned information as well as adaptation to new classes or domain shifts. The proposed method is evaluated on several benchmarks, including SplitCIFAR100, SplitImageNet32, SplitTinyImageNet, and SplitCaltech256 for class-incremental, as well as R-MNIST and CORe50 for domain-incremental setting using pre-extracted DINOv2 features. Experimental results indicate that the label-free replay-based technique outperforms state-of-the-art continual learning methods and, in some cases, even surpasses offline learning. An unsupervised variant of the proposed technique for the class-incremental setting, avoiding labels use even on incoming data, also demonstrated competitive performance, outperforming particular supervised baselines in some cases. These findings underscore the effectiveness of the proposed framework in retaining prior information and facilitating continual adaptation.

[LG-33] Evolutionary algorithms meet self-supervised learning: a comprehensive survey

链接: https://arxiv.org/abs/2504.07213
作者: Adriano Vinhas,João Correia,Penousal Machado
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:The number of studies that combine Evolutionary Machine Learning and self-supervised learning has been growing steadily in recent years. Evolutionary Machine Learning has been shown to help automate the design of machine learning algorithms and to lead to more reliable solutions. Self-supervised learning, on the other hand, has produced good results in learning useful features when labelled data is limited. This suggests that the combination of these two areas can help both in shaping evolutionary processes and in automating the design of deep neural networks, while also reducing the need for labelled data. Still, there are no detailed reviews that explain how Evolutionary Machine Learning and self-supervised learning can be used together. To help with this, we provide an overview of studies that bring these areas together. Based on this growing interest and the range of existing works, we suggest a new sub-area of research, which we call Evolutionary Self-Supervised Learning and introduce a taxonomy for it. Finally, we point out some of the main challenges and suggest directions for future research to help Evolutionary Self-Supervised Learning grow and mature as a field.

[LG-34] Multi-Object Tracking for Collision Avoidance Using Multiple Cameras in Open RAN Networks

链接: https://arxiv.org/abs/2504.07163
作者: Jordi Serra,Anton Aguilar,Ebrahim Abu-Helalah,Raúl Parada,Paolo Dini
类目: Multiagent Systems (cs.MA); Machine Learning (cs.LG); Robotics (cs.RO)
*备注:

点击查看摘要

Abstract:This paper deals with the multi-object detection and tracking problem, within the scope of open Radio Access Network (RAN), for collision avoidance in vehicular scenarios. To this end, a set of distributed intelligent agents collocated with cameras are considered. The fusion of detected objects is done at an edge service, considering Open RAN connectivity. Then, the edge service predicts the objects trajectories for collision avoidance. Compared to the related work a more realistic Open RAN network is implemented and multiple cameras are used.

[LG-35] GAAPO: Genetic Algorithmic Applied to Prompt Optimization

链接: https://arxiv.org/abs/2504.07157
作者: Xavier Sécheresse,Jacques-Yves Guilbert–Ly,Antoine Villedieu de Torcy
类目: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
*备注: 26 pages, 9 figures

点击查看摘要

Abstract:Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, with their performance heavily dependent on the quality of input prompts \citeschulhoff2025promptsurvey \citesahoo2025promptengineering. While prompt engineering has proven effective, it typically relies on manual adjustments, making it time-consuming and potentially suboptimal. This paper introduces GAAPO (Genetic Algorithm Applied to Prompt Optimization), a novel hybrid optimization framework that leverages genetic algorithm \citedejong1988gen principles to evolve prompts through successive generations. Unlike traditional genetic approaches that rely solely on mutation and crossover operations, GAAPO integrates multiple specialized prompt generation strategies within its evolutionary framework. Through extensive experimentation on diverse datasets including ETHOS, MMLU-Pro, and GPQA, our analysis reveals several important point for the future development of automatic prompt optimization methods: importance of the tradeoff between the population size and the number of generations, effect of selection methods on stability results, capacity of different LLMs and especially reasoning models to be able to automatically generate prompts from similar queries… Furthermore, we provide insights into the relative effectiveness of different prompt generation strategies and their evolution across optimization phases. These findings contribute to both the theoretical understanding of prompt optimization and practical applications in improving LLM performance.

[LG-36] Compound Fault Diagnosis for Train Transmission Systems Using Deep Learning with Fourier-enhanced Representation ALT

链接: https://arxiv.org/abs/2504.07155
作者: Jonathan Adam Rico,Nagarajan Raghavan,Senthilnath Jayavelu
类目: Machine Learning (cs.LG)
*备注: Accepted for the 2025 IEEE Conference on Prognostics and Health Management (ICPHM 2025)

点击查看摘要

Abstract:Fault diagnosis prevents train disruptions by ensuring the stability and reliability of their transmission systems. Data-driven fault diagnosis models have several advantages over traditional methods in terms of dealing with non-linearity, adaptability, scalability, and automation. However, existing data-driven models are trained on separate transmission components and only consider single faults due to the limitations of existing datasets. These models will perform worse in scenarios where components operate with each other at the same time, affecting each component’s vibration signals. To address some of these challenges, we propose a frequency domain representation and a 1-dimensional convolutional neural network for compound fault diagnosis and applied it on the PHM Beijing 2024 dataset, which includes 21 sensor channels, 17 single faults, and 42 compound faults from 4 interacting components, that is, motor, gearbox, left axle box, and right axle box. Our proposed model achieved 97.67% and 93.93% accuracies on the test set with 17 single faults and on the test set with 42 compound faults, respectively.

[LG-37] Deep Sturm–Liouville: From Sample-Based to 1D Regularization with Learnable Orthogonal Basis Functions

链接: https://arxiv.org/abs/2504.07151
作者: David Vigouroux,Joseba Dalmau,Louis Béthune(IRIT, IRIT-ADRIA, UT3),Victor Boutin
类目: Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Although Artificial Neural Networks (ANNs) have achieved remarkable success across various tasks, they still suffer from limited generalization. We hypothesize that this limitation arises from the traditional sample-based (0–dimensionnal) regularization used in ANNs. To overcome this, we introduce \textitDeep Sturm–Liouville (DSL), a novel function approximator that enables continuous 1D regularization along field lines in the input space by integrating the Sturm–Liouville Theorem (SLT) into the deep learning framework. DSL defines field lines traversing the input space, along which a Sturm–Liouville problem is solved to generate orthogonal basis functions, enforcing implicit regularization thanks to the desirable properties of SLT. These basis functions are linearly combined to construct the DSL approximator. Both the vector field and basis functions are parameterized by neural networks and learned jointly. We demonstrate that the DSL formulation naturally arises when solving a Rank-1 Parabolic Eigenvalue Problem. DSL is trained efficiently using stochastic gradient descent via implicit differentiation. DSL achieves competitive performance and demonstrate improved sample efficiency on diverse multivariate datasets including high-dimensional image datasets such as MNIST and CIFAR-10.

[LG-38] SolRPDS: A Dataset for Analyzing Rug Pulls in Solana Decentralized Finance

链接: https://arxiv.org/abs/2504.07132
作者: Abdulrahman Alhaidari,Bhavani Kalal,Balaji Palanisamy,Shamik Sural
类目: Cryptography and Security (cs.CR); Computational Engineering, Finance, and Science (cs.CE); Machine Learning (cs.LG)
*备注: Accepted paper to appear in the 15th ACM Conference on Data and Application Security and Privacy (CODASPY 2025)

点击查看摘要

Abstract:Rug pulls in Solana have caused significant damage to users interacting with Decentralized Finance (DeFi). A rug pull occurs when developers exploit users’ trust and drain liquidity from token pools on Decentralized Exchanges (DEXs), leaving users with worthless tokens. Although rug pulls in Ethereum and Binance Smart Chain (BSC) have gained attention recently, analysis of rug pulls in Solana remains largely under-explored. In this paper, we introduce SolRPDS (Solana Rug Pull Dataset), the first public rug pull dataset derived from Solana’s transactions. We examine approximately four years of DeFi data (2021-2024) that covers suspected and confirmed tokens exhibiting rug pull patterns. The dataset, derived from 3.69 billion transactions, consists of 62,895 suspicious liquidity pools. The data is annotated for inactivity states, which is a key indicator, and includes several detailed liquidity activities such as additions, removals, and last interaction as well as other attributes such as inactivity periods and withdrawn token amounts, to help identify suspicious behavior. Our preliminary analysis reveals clear distinctions between legitimate and fraudulent liquidity pools and we found that 22,195 tokens in the dataset exhibit rug pull patterns during the examined period. SolRPDS can support a wide range of future research on rug pulls including the development of data-driven and heuristic-based solutions for real-time rug pull detection and mitigation.

[LG-39] DashCLIP: Leverag ing multimodal models for generating semantic embeddings for DoorDash

链接: https://arxiv.org/abs/2504.07110
作者: Omkar Gurjar,Kin Sum Liu,Praveen Kolli,Utsaw Kumar,Mandar Rahurkar
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Despite the success of vision-language models in various generative tasks, obtaining high-quality semantic representations for products and user intents is still challenging due to the inability of off-the-shelf models to capture nuanced relationships between the entities. In this paper, we introduce a joint training framework for product and user queries by aligning uni-modal and multi-modal encoders through contrastive learning on image-text data. Our novel approach trains a query encoder with an LLM-curated relevance dataset, eliminating the reliance on engagement history. These embeddings demonstrate strong generalization capabilities and improve performance across applications, including product categorization and relevance prediction. For personalized ads recommendation, a significant uplift in the click-through rate and conversion rate after the deployment further confirms the impact on key business metrics. We believe that the flexibility of our framework makes it a promising solution toward enriching the user experience across the e-commerce landscape.

[LG-40] Guarding Digital Privacy: Exploring User Profiling and Security Enhancements

链接: https://arxiv.org/abs/2504.07107
作者: Rishika Kohli,Shaifu Gupta,Manoj Singh Gaur
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
*备注: 46 Pages, 8 tables, 9 figures

点击查看摘要

Abstract:User profiling, the practice of collecting user information for personalized recommendations, has become widespread, driving progress in technology. However, this growth poses a threat to user privacy, as devices often collect sensitive data without their owners’ awareness. This article aims to consolidate knowledge on user profiling, exploring various approaches and associated challenges. Through the lens of two companies sharing user data and an analysis of 18 popular Android applications in India across various categories, including \textitSocial, Education, Entertainment, Travel, Shopping and Others , the article unveils privacy vulnerabilities. Further, the article propose an enhanced machine learning framework, employing decision trees and neural networks, that improves state-of-the-art classifiers in detecting personal information exposure. Leveraging the XAI (explainable artificial intelligence) algorithm LIME (Local Interpretable Model-agnostic Explanations), it enhances interpretability, crucial for reliably identifying sensitive data. Results demonstrate a noteworthy performance boost, achieving a 75.01% accuracy with a reduced training time of 3.62 seconds for neural networks. Concluding, the paper suggests research directions to strengthen digital security measures.

[LG-41] Behavior Importance-Aware Graph Neural Architecture Search for Cross-Domain Recommendation AAAI2025

链接: https://arxiv.org/abs/2504.07102
作者: Chendi Ge,Xin Wang,Ziwei Zhang,Yijian Qin,Hong Chen,Haiyang Wu,Yang Zhang,Yuekui Yang,Wenwu Zhu
类目: Information Retrieval (cs.IR); Machine Learning (cs.LG)
*备注: AAAI 2025 Oral

点击查看摘要

Abstract:Cross-domain recommendation (CDR) mitigates data sparsity and cold-start issues in recommendation systems. While recent CDR approaches using graph neural networks (GNNs) capture complex user-item interactions, they rely on manually designed architectures that are often suboptimal and labor-intensive. Additionally, extracting valuable behavioral information from source domains to improve target domain recommendations remains challenging. To address these challenges, we propose Behavior importance-aware Graph Neural Architecture Search (BiGNAS), a framework that jointly optimizes GNN architecture and data importance for CDR. BiGNAS introduces two key components: a Cross-Domain Customized Supernetwork and a Graph-Based Behavior Importance Perceptron. The supernetwork, as a one-shot, retrain-free module, automatically searches the optimal GNN architecture for each domain without the need for retraining. The perceptron uses auxiliary learning to dynamically assess the importance of source domain behaviors, thereby improving target domain recommendations. Extensive experiments on benchmark CDR datasets and a large-scale industry advertising dataset demonstrate that BiGNAS consistently outperforms state-of-the-art baselines. To the best of our knowledge, this is the first work to jointly optimize GNN architecture and behavior data importance for cross-domain recommendation.

[LG-42] DOMAC: Differentiable Optimization for High-Speed Multipliers and Multiply-Accumulators

链接: https://arxiv.org/abs/2503.23943
作者: Chenhao Xue,Yi Ren,Jinwei Zhou,Kezhi Li,Chen Zhang,Yibo Lin,Lining Zhang,Qiang Xu,Guangyu Sun
类目: Hardware Architecture (cs.AR); Machine Learning (cs.LG)
*备注: Accepted by ISEDA 2025

点击查看摘要

Abstract:Multipliers and multiply-accumulators (MACs) are fundamental building blocks for compute-intensive applications such as artificial intelligence. With the diminishing returns of Moore’s Law, optimizing multiplier performance now necessitates process-aware architectural innovations rather than relying solely on technology scaling. In this paper, we introduce DOMAC, a novel approach that employs differentiable optimization for designing multipliers and MACs at specific technology nodes. DOMAC establishes an analogy between optimizing multi-staged parallel compressor trees and training deep neural networks. Building on this insight, DOMAC reformulates the discrete optimization challenge into a continuous problem by incorporating differentiable timing and area objectives. This formulation enables us to utilize existing deep learning toolkit for highly efficient implementation of the differentiable solver. Experimental results demonstrate that DOMAC achieves significant enhancements in both performance and area efficiency compared to state-of-the-art baselines and commercial IPs in multiplier and MAC designs.

[LG-43] rading Graph Neural Network

链接: https://arxiv.org/abs/2504.07923
作者: Xian Wu
类目: Trading and Market Microstructure (q-fin.TR); Machine Learning (cs.LG); General Economics (econ.GN); Pricing of Securities (q-fin.PR)
*备注:

点击查看摘要

Abstract:This paper proposes a new algorithm – Trading Graph Neural Network (TGNN) that can structurally estimate the impact of asset features, dealer features and relationship features on asset prices in trading networks. It combines the strength of the traditional simulated method of moments (SMM) and recent machine learning techniques – Graph Neural Network (GNN). It outperforms existing reduced-form methods with network centrality measures in prediction accuracy. The method can be used on networks with any structure, allowing for heterogeneity among both traders and assets.

[LG-44] Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows

链接: https://arxiv.org/abs/2504.07820
作者: Nicolaj Rux,Michael Quellmalz,Gabriele Steidl
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Functional Analysis (math.FA); Probability (math.PR)
*备注: 48 pages, 10 figures

点击查看摘要

Abstract:Negative distance kernels K(x,y) := - |x-y| were used in the definition of maximum mean discrepancies (MMDs) in statistics and lead to favorable numerical results in various applications. In particular, so-called slicing techniques for handling high-dimensional kernel summations profit from the simple parameter-free structure of the distance kernel. However, due to its non-smoothness in x=y , most of the classical theoretical results, e.g. on Wasserstein gradient flows of the corresponding MMD functional do not longer hold true. In this paper, we propose a new kernel which keeps the favorable properties of the negative distance kernel as being conditionally positive definite of order one with a nearly linear increase towards infinity and a simple slicing structure, but is Lipschitz differentiable now. Our construction is based on a simple 1D smoothing procedure of the absolute value function followed by a Riemann-Liouville fractional integral transform. Numerical results demonstrate that the new kernel performs similarly well as the negative distance kernel in gradient descent methods, but now with theoretical guarantees.

[LG-45] Performance of Rank-One Tensor Approximation on Incomplete Data

链接: https://arxiv.org/abs/2504.07818
作者: Hugo Lebeau
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
*备注:

点击查看摘要

Abstract:We are interested in the estimation of a rank-one tensor signal when only a portion \varepsilon of its noisy observation is available. We show that the study of this problem can be reduced to that of a random matrix model whose spectral analysis gives access to the reconstruction performance. These results shed light on and specify the loss of performance induced by an artificial reduction of the memory cost of a tensor via the deletion of a random part of its entries.

[LG-46] Gradient-based Sample Selection for Faster Bayesian Optimization

链接: https://arxiv.org/abs/2504.07742
作者: Qiyu Wei,Haowei Wang,Zirui Cao,Songhao Wang,Richard Allmendinger,Mauricio A Álvarez
类目: Machine Learning (stat.ML); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Bayesian optimization (BO) is an effective technique for black-box optimization. However, its applicability is typically limited to moderate-budget problems due to the cubic complexity in computing the Gaussian process (GP) surrogate model. In large-budget scenarios, directly employing the standard GP model faces significant challenges in computational time and resource requirements. In this paper, we propose a novel approach, gradient-based sample selection Bayesian Optimization (GSSBO), to enhance the computational efficiency of BO. The GP model is constructed on a selected set of samples instead of the whole dataset. These samples are selected by leveraging gradient information to maintain diversity and representation. We provide a theoretical analysis of the gradient-based sample selection strategy and obtain explicit sublinear regret bounds for our proposed framework. Extensive experiments on synthetic and real-world tasks demonstrate that our approach significantly reduces the computational cost of GP fitting in BO while maintaining optimization performance comparable to baseline methods.

[LG-47] Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks

链接: https://arxiv.org/abs/2504.07741
作者: Marius Kurz,Andrea Beck,Benjamin Sanderse
类目: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG)
*备注: 17 pages, 10 figures

点击查看摘要

Abstract:This work proposes a novel methodology for turbulence modeling in Large Eddy Simulation (LES) based on Graph Neural Networks (GNNs), which embeds the discrete rotational, reflectional and translational symmetries of the Navier-Stokes equations into the model architecture. In addition, suitable invariant input and output spaces are derived that allow the GNN models to be embedded seamlessly into the LES framework to obtain a symmetry-preserving simulation setup. The suitability of the proposed approach is investigated for two canonical test cases: Homogeneous Isotropic Turbulence (HIT) and turbulent channel flow. For both cases, GNN models are trained successfully in actual simulations using Reinforcement Learning (RL) to ensure that the models are consistent with the underlying LES formulation and discretization. It is demonstrated for the HIT case that the resulting GNN-based LES scheme recovers rotational and reflectional equivariance up to machine precision in actual simulations. At the same time, the stability and accuracy remain on par with non-symmetry-preserving machine learning models that fail to obey these properties. The same modeling strategy translates well to turbulent channel flow, where the GNN model successfully learns the more complex flow physics and is able to recover the turbulent statistics and Reynolds stresses. It is shown that the GNN model learns a zonal modeling strategy with distinct behaviors in the near-wall and outer regions. The proposed approach thus demonstrates the potential of GNNs for turbulence modeling, especially in the context of LES and RL.

[LG-48] A Novel Deep Learning Approach for Emulating Computationally Expensive Postfire Debris Flows

链接: https://arxiv.org/abs/2504.07736
作者: Palak Patel,Luke McGuire,Abani Patra
类目: Fluid Dynamics (physics.flu-dyn); Machine Learning (cs.LG); Geophysics (physics.geo-ph)
*备注: Manuscript submitted to Computers Geosciences, 22 pages, 10 figures

点击查看摘要

Abstract:Traditional physics-based models of geophysical flows, such as debris flows and landslides that pose significant risks to human lives and infrastructure are computationally expensive, limiting their utility for large-scale parameter sweeps, uncertainty quantification, inversions or real-time applications. This study presents an efficient alternative, a deep learning-based surrogate model built using a modified U-Net architecture to predict the dynamics of runoff-generated debris flows across diverse terrain based on data from physics based simulations. The study area is divided into smaller patches for localized predictions using a patch-predict-stitch methodology (complemented by limited global data to accelerate training). The patches are then combined to reconstruct spatially continuous flow maps, ensuring scalability for large domains. To enable fast training using limited expensive simulations, the deep learning model was trained on data from an ensemble of physics based simulations using parameters generated via Latin Hypercube Sampling and validated on unseen parameter sets and terrain, achieving maximum pointwise errors below 10% and robust generalization. Uncertainty quantification using Monte Carlo methods are enabled using the validated surrogate, which can facilitate probabilistic hazard assessments. This study highlights the potential of deep learning surrogates as powerful tools for geophysical flow analysis, enabling computationally efficient and reliable probabilistic hazard map predictions.

[LG-49] Conformalized Generative Bayesian Imaging: An Uncertainty Quantification Framework for Computational Imaging

链接: https://arxiv.org/abs/2504.07696
作者: Canberk Ekmekci,Mujdat Cetin
类目: Image and Video Processing (eess.IV); Machine Learning (cs.LG)
*备注: 19 pages, 9 figures, preprint

点击查看摘要

Abstract:Uncertainty quantification plays an important role in achieving trustworthy and reliable learning-based computational imaging. Recent advances in generative modeling and Bayesian neural networks have enabled the development of uncertainty-aware image reconstruction methods. Current generative model-based methods seek to quantify the inherent (aleatoric) uncertainty on the underlying image for given measurements by learning to sample from the posterior distribution of the underlying image. On the other hand, Bayesian neural network-based approaches aim to quantify the model (epistemic) uncertainty on the parameters of a deep neural network-based reconstruction method by approximating the posterior distribution of those parameters. Unfortunately, an ongoing need for an inversion method that can jointly quantify complex aleatoric uncertainty and epistemic uncertainty patterns still persists. In this paper, we present a scalable framework that can quantify both aleatoric and epistemic uncertainties. The proposed framework accepts an existing generative model-based posterior sampling method as an input and introduces an epistemic uncertainty quantification capability through Bayesian neural networks with latent variables and deep ensembling. Furthermore, by leveraging the conformal prediction methodology, the proposed framework can be easily calibrated to ensure rigorous uncertainty quantification. We evaluated the proposed framework on magnetic resonance imaging, computed tomography, and image inpainting problems and showed that the epistemic and aleatoric uncertainty estimates produced by the proposed framework display the characteristic features of true epistemic and aleatoric uncertainties. Furthermore, our results demonstrated that the use of conformal prediction on top of the proposed framework enables marginal coverage guarantees consistent with frequentist principles.

[LG-50] Stochastic Smoothed Primal-Dual Algorithms for Nonconvex Optimization with Linear Inequality Constraints

链接: https://arxiv.org/abs/2504.07607
作者: Ruichuan Huang,Jiawei Zhang,Ahmet Alacaoglu
类目: Optimization and Control (math.OC); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:We propose smoothed primal-dual algorithms for solving stochastic and smooth nonconvex optimization problems with linear inequality constraints. Our algorithms are single-loop and only require a single stochastic gradient based on one sample at each iteration. A distinguishing feature of our algorithm is that it is based on an inexact gradient descent framework for the Moreau envelope, where the gradient of the Moreau envelope is estimated using one step of a stochastic primal-dual augmented Lagrangian method. To handle inequality constraints and stochasticity, we combine the recently established global error bounds in constrained optimization with a Moreau envelope-based analysis of stochastic proximal algorithms. For obtaining \varepsilon -stationary points, we establish the optimal O(\varepsilon^-4) sample complexity guarantee for our algorithms and provide extensions to stochastic linear constraints. We also show how to improve this complexity to O(\varepsilon^-3) by using variance reduction and the expected smoothness assumption. Unlike existing methods, the iterations of our algorithms are free of subproblems, large batch sizes or increasing penalty parameters and use dual variable updates to ensure feasibility.

[LG-51] A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature

链接: https://arxiv.org/abs/2504.07481
作者: Tian Xie,Menghui Jiang,Huanfeng Shen,Huifang Li,Cao Zeng,Xiaobin Guan,Jun Ma,Guanhao Zhang,Liangpei Zhang
类目: Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Land surface temperature (LST) retrieval from remote sensing data is pivotal for analyzing climate processes and surface energy budgets. However, LST retrieval is an ill-posed inverse problem, which becomes particularly severe when only a single band is available. In this paper, we propose a deeply coupled framework integrating mechanistic modeling and machine learning to enhance the accuracy and generalizability of single-channel LST retrieval. Training samples are generated using a physically-based radiative transfer model and a global collection of 5810 atmospheric profiles. A physics-informed machine learning framework is proposed to systematically incorporate the first principles from classical physical inversion models into the learning workflow, with optimization constrained by radiative transfer equations. Global validation demonstrated a 30% reduction in root-mean-square error versus standalone methods. Under extreme humidity, the mean absolute error decreased from 4.87 K to 2.29 K (53% improvement). Continental-scale tests across five continents confirmed the superior generalizability of this model.

[LG-52] Conditional Data Synthesis Augmentation

链接: https://arxiv.org/abs/2504.07426
作者: Xinyu Tian,Xiaotong Shen
类目: Methodology (stat.ME); Machine Learning (cs.LG)
*备注:

点击查看摘要

Abstract:Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to synthesize high-fidelity data for improving model performance across multimodal domains including tabular, textual, and image data. CoDSA generates synthetic samples that faithfully capture the conditional distributions of the original data, with a focus on under-sampled or high-interest regions. Through transfer learning, CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas. This process preserves inter-modal relationships, mitigates data imbalance, improves domain adaptation, and boosts generalization. We also introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation, providing formal guarantees of its effectiveness. Extensive experiments demonstrate that CoDSA consistently outperforms non-adaptive augmentation strategies and state-of-the-art baselines in both supervised and unsupervised settings.

[LG-53] hroughput-Optimal Scheduling Algorithms for LLM Inference and AI Agents

链接: https://arxiv.org/abs/2504.07347
作者: Yueying Li,Jim Dai,Tianyi Peng
类目: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR)
*备注:

点击查看摘要

Abstract:As demand for Large Language Models (LLMs) and AI agents rapidly grows, optimizing systems for efficient LLM inference becomes critical. While significant efforts have targeted system-level engineering, little is explored through a mathematical modeling and queuing perspective. In this paper, we aim to develop the queuing fundamentals for LLM inference, bridging the gap between queuing and LLM system communities. In particular, we study the throughput aspect in LLM inference systems. We prove that a large class of ‘work-conserving’ scheduling algorithms can achieve maximum throughput for both individual requests and AI agent workloads, highlighting ‘work-conserving’ as a key design principle in practice. Evaluations of real-world systems show that Orca and Sarathi-serve are throughput-optimal, reassuring practitioners, while FastTransformer and vanilla vLLM are not maximally stable and should be used with caution. Our results highlight the substantial benefits queuing community can offer in improving LLM inference systems and call for more interdisciplinary developments. Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Probability (math.PR) Cite as: arXiv:2504.07347 [stat.ML] (or arXiv:2504.07347v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2504.07347 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

[LG-54] Learning to erase quantum states: thermodynamic implications of quantum learning theory

链接: https://arxiv.org/abs/2504.07341
作者: Haimeng Zhao,Yuzhen Zhang,John Preskill
类目: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech); Computational Complexity (cs.CC); Information Theory (cs.IT); Machine Learning (cs.LG)
*备注: 5.5 pages + 1 figure

点击查看摘要

Abstract:The energy cost of erasing quantum states depends on our knowledge of the states. We show that learning algorithms can acquire such knowledge to erase many copies of an unknown state at the optimal energy cost. This is proved by showing that learning can be made fully reversible and has no fundamental energy cost itself. With simple counting arguments, we relate the energy cost of erasing quantum states to their complexity, entanglement, and magic. We further show that the constructed erasure protocol is computationally efficient when learning is efficient. Conversely, under standard cryptographic assumptions, we prove that the optimal energy cost cannot be achieved efficiently in general. These results also enable efficient work extraction based on learning. Together, our results establish a concrete connection between quantum learning theory and thermodynamics, highlighting the physical significance of learning processes and enabling efficient learning-based protocols for thermodynamic tasks.

[LG-55] Earth-like planet predictor: A machine learning approach

链接: https://arxiv.org/abs/2504.07235
作者: Jeanne Davoult,Romain Eltschinger,Yann Alibert
类目: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
*备注: 11 pages, 5 figures, published in AA

点击查看摘要

Abstract:Searching for planets analogous to Earth in terms of mass and equilibrium temperature is currently the first step in the quest for habitable conditions outside our Solar System and, ultimately, the search for life in the universe. Future missions such as PLATO or LIFE will begin to detect and characterise these small, cold planets, dedicating significant observation time to them. The aim of this work is to predict which stars are most likely to host an Earth-like planet (ELP) to avoid blind searches, minimises detection times, and thus maximises the number of detections. Using a previous study on correlations between the presence of an ELP and the properties of its system, we trained a Random Forest to recognise and classify systems as ‘hosting an ELP’ or ‘not hosting an ELP’. The Random Forest was trained and tested on populations of synthetic planetary systems derived from the Bern model, and then applied to real observed systems. The tests conducted on the machine learning (ML) model yield precision scores of up to 0.99, indicating that 99% of the systems identified by the model as having ELPs possess at least one. Among the few real observed systems that have been tested, 44 have been selected as having a high probability of hosting an ELP, and a quick study of the stability of these systems confirms that the presence of an Earth-like planet within them would leave them stable. The excellent results obtained from the tests conducted on the ML model demonstrate its ability to recognise the typical architectures of systems with or without ELPs within populations derived from the Bern model. If we assume that the Bern model adequately describes the architecture of real systems, then such a tool can prove indispensable in the search for Earth-like planets. A similar approach could be applied to other planetary system formation models to validate those predictions.

[LG-56] Reservoir Computing with a Single Oscillating Gas Bubble: Emphasizing the Chaotic Regime

链接: https://arxiv.org/abs/2504.07221
作者: Hend Abdel-Ghani,A. H. Abbas,Ivan S. Maksymov
类目: Chaotic Dynamics (nlin.CD); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Fluid Dynamics (physics.flu-dyn)
*备注:

点击查看摘要

Abstract:The rising computational and energy demands of artificial intelligence systems urge the exploration of alternative software and hardware solutions that exploit physical effects for computation. According to machine learning theory, a neural network-based computational system must exhibit nonlinearity to effectively model complex patterns and relationships. This requirement has driven extensive research into various nonlinear physical systems to enhance the performance of neural networks. In this paper, we propose and theoretically validate a reservoir computing system based on a single bubble trapped within a bulk of liquid. By applying an external acoustic pressure wave to both encode input information and excite the complex nonlinear dynamics, we showcase the ability of this single-bubble reservoir computing system to forecast complex benchmarking time series and undertake classification tasks with high accuracy. Specifically, we demonstrate that a chaotic physical regime of bubble oscillation proves to be the most effective for this kind of computations.

[LG-57] Can SGD Select Good Fishermen? Local Convergence under Self-Selection Biases and Beyond

链接: https://arxiv.org/abs/2504.07133
作者: Alkis Kalavasis,Anay Mehrotra,Felix Zhou
类目: Machine Learning (stat.ML); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Statistics Theory (math.ST)
*备注:

点击查看摘要

Abstract:We revisit the problem of estimating k linear regressors with self-selection bias in d dimensions with the maximum selection criterion, as introduced by Cherapanamjeri, Daskalakis, Ilyas, and Zampetakis [CDIZ23, STOC’23]. Our main result is a \operatornamepoly(d,k,1/\varepsilon) + k^O(k) time algorithm for this problem, which yields an improvement in the running time of the algorithms of [CDIZ23] and [GM24, arXiv]. We achieve this by providing the first local convergence algorithm for self-selection, thus resolving the main open question of [CDIZ23]. To obtain this algorithm, we reduce self-selection to a seemingly unrelated statistical problem called coarsening. Coarsening occurs when one does not observe the exact value of the sample but only some set (a subset of the sample space) that contains the exact value. Inference from coarse samples arises in various real-world applications due to rounding by humans and algorithms, limited precision of instruments, and lag in multi-agent systems. Our reduction to coarsening is intuitive and relies on the geometry of the self-selection problem, which enables us to bypass the limitations of previous analytic approaches. To demonstrate its applicability, we provide a local convergence algorithm for linear regression under another self-selection criterion, which is related to second-price auction data. Further, we give the first polynomial time local convergence algorithm for coarse Gaussian mean estimation given samples generated from a convex partition. Previously, only a sample-efficient algorithm was known due to Fotakis, Kalavasis, Kontonis, and Tzamos [FKKT21, COLT’21]. Subjects: Machine Learning (stat.ML); Data Structures and Algorithms (cs.DS); Machine Learning (cs.LG); Statistics Theory (math.ST) Cite as: arXiv:2504.07133 [stat.ML] (or arXiv:2504.07133v1 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2504.07133 Focus to learn more arXiv-issued DOI via DataCite

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[IR-0] Siren Federate: Bridging document relational and graph models for exploratory graph analysis

链接: https://arxiv.org/abs/2504.07815
作者: Georgeta Bordea,Stephane Campinas,Matteo Catena,Renaud Delbru
类目: Information Retrieval (cs.IR)
*备注: 36 pages, 16 figures, submitted to the ComSIS journal

点击查看摘要

Abstract:Investigative workflows require interactive exploratory analysis on large heterogeneous knowledge graphs. Current databases show limitations in enabling such task. This paper discusses the architecture of Siren Federate, a system that efficiently supports exploratory graph analysis by bridging document-oriented, relational and graph models. Technical contributions include distributed join algorithms, adaptive query planning, query plan folding, semantic caching, and semi-join decomposition for path query. Semi-join decomposition addresses the exponential growth of intermediate results in path-based queries. Experiments show that Siren Federate exhibits low latency and scales well with the amount of data, the number of users, and the number of computing nodes.

[IR-1] REANIMATOR: Reanimate Retrieval Test Collections with Extracted and Synthetic Resources

链接: https://arxiv.org/abs/2504.07584
作者: Björn Engelmann,Fabian Haak,Philipp Schaer,Mani Erfanian Abdoust,Linus Netze,Meik Bittkowski
类目: Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:Retrieval test collections are essential for evaluating information retrieval systems, yet they often lack generalizability across tasks. To overcome this limitation, we introduce REANIMATOR, a versatile framework designed to enable the repurposing of existing test collections by enriching them with extracted and synthetic resources. REANIMATOR enhances test collections from PDF files by parsing full texts and machine-readable tables, as well as related contextual information. It then employs state-of-the-art large language models to produce synthetic relevance labels. Including an optional human-in-the-loop step can help validate the resources that have been extracted and generated. We demonstrate its potential with a revitalized version of the TREC-COVID test collection, showcasing the development of a retrieval-augmented generation system and evaluating the impact of tables on retrieval-augmented generation. REANIMATOR enables the reuse of test collections for new applications, lowering costs and broadening the utility of legacy resources.

[IR-2] Explicit Uncertainty Modeling for Video Watch Time Prediction

链接: https://arxiv.org/abs/2504.07575
作者: Shanshan Wu,Shuchang Liu,Shuai Zhang,Xiaoyu Yang,Xiang Li,Lantao Hu,Han Li
类目: Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:In video recommendation, a critical component that determines the system’s recommendation accuracy is the watch-time prediction module, since how long a user watches a video directly reflects personalized preferences. One of the key challenges of this problem is the user’s stochastic watch-time behavior. To improve the prediction accuracy for such an uncertain behavior, existing approaches show that one can either reduce the noise through duration bias modeling or formulate a distribution modeling task to capture the uncertainty. However, the uncontrolled uncertainty is not always equally distributed across users and videos, inducing a balancing paradox between the model accuracy and the ability to capture out-of-distribution samples. In practice, we find that the uncertainty of the watch-time prediction model also provides key information about user behavior, which, in turn, could benefit the prediction task itself. Following this notion, we derive an explicit uncertainty modeling strategy for the prediction model and propose an adversarial optimization framework that can better exploit the user watch-time behavior. This framework has been deployed online on an industrial video sharing platform that serves hundreds of millions of daily active users, which obtains a significant increase in users’ video watch time by 0.31% through the online A/B test. Furthermore, extended offline experiments on two public datasets verify the effectiveness of the proposed framework across various watch-time prediction backbones.

[IR-3] Exploring Human-Like Thinking in Search Simulations with Large Language Models

链接: https://arxiv.org/abs/2504.07570
作者: Erhan Zhang,Xingzhu Wang,Peiyuan Gong,Zixuan Yang,Jiaxin Mao
类目: Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:Simulating user search behavior is a critical task in information retrieval, which can be employed for user behavior modeling, data augmentation, and system evaluation. Recent advancements in large language models (LLMs) have opened up new possibilities for generating human-like actions including querying, browsing, and clicking. In this work, we explore the integration of human-like thinking into search simulations by leveraging LLMs to simulate users’ hidden cognitive processes. Specifically, given a search task and context, we prompt LLMs to first think like a human before executing the corresponding action. As existing search datasets do not include users’ thought processes, we conducted a user study to collect a new dataset enriched with users’ explicit thinking. We investigate the impact of incorporating such human-like thinking on simulation performance and apply supervised fine-tuning (SFT) to teach LLMs to emulate both human thinking and actions. Our experiments span two dimensions in leveraging LLMs for user simulation: (1) with or without explicit thinking, and (2) with or without fine-tuning on the thinking-augmented dataset. The results demonstrate the feasibility and potential of incorporating human-like thinking in user simulations, though performance improvements on some metrics remain modest. We believe this exploration provides new avenues and inspirations for advancing user behavior modeling in search simulations.

[IR-4] Emergency Communication: OTFS-Based Semantic Transmission with Diffusion Noise Suppression

链接: https://arxiv.org/abs/2504.07420
作者: Kexin Zhang,Xin Zhang,Lixin Li,Wensheng Lin,Wenchi Cheng,Qinghe Du
类目: Information Retrieval (cs.IR)
*备注: 5 pages, 3 figures

点击查看摘要

Abstract:Due to their flexibility and dynamic coverage capabilities, Unmanned Aerial Vehicles (UAVs) have emerged as vital platforms for emergency communication in disaster-stricken areas. However, the complex channel conditions in high-speed mobile scenarios significantly impact the reliability and efficiency of traditional communication systems. This paper presents an intelligent emergency communication framework that integrates Orthogonal Time Frequency Space (OTFS) modulation, semantic communication, and a diffusion-based denoising module to address these challenges. OTFS ensures robust communication under dynamic channel conditions due to its superior anti-fading characteristics and adaptability to rapidly changing environments. Semantic communication further enhances transmission efficiency by focusing on key information extraction and reducing data redundancy. Moreover, a diffusion-based channel denoising module is proposed to leverage the gradual noise reduction process and statistical noise modeling, optimizing the accuracy of semantic information recovery. Experimental results demonstrate that the proposed solution significantly improves link stability and transmission performance in high-mobility UAV scenarios, achieving at least a 3dB SNR gain over existing methods.

[IR-5] owards Distribution Matching between Collaborative and Language Spaces for Generative Recommendation SIGIR2025

链接: https://arxiv.org/abs/2504.07363
作者: Yi Zhang,Yiwen Zhang,Yu Wang,Tong Chen,Hongzhi Yin
类目: Information Retrieval (cs.IR)
*备注: Accepted by SIGIR2025

点击查看摘要

Abstract:Generative recommendation aims to learn the underlying generative process over the entire item set to produce recommendations for users. Although it leverages non-linear probabilistic models to surpass the limited modeling capacity of linear factor models, it is often constrained by a trade-off between representation ability and tractability. With the rise of a new generation of generative methods based on pre-trained language models (LMs), incorporating LMs into general recommendation with implicit feedback has gained considerable attention. However, adapting them to generative recommendation remains challenging. The core reason lies in the mismatch between the input-output formats and semantics of generative models and LMs, making it challenging to achieve optimal alignment in the feature space. This work addresses this issue by proposing a model-agnostic generative recommendation framework called DMRec, which introduces a probabilistic meta-network to bridge the outputs of LMs with user interactions, thereby enabling an equivalent probabilistic modeling process. Subsequently, we design three cross-space distribution matching processes aimed at maximizing shared information while preserving the unique semantics of each space and filtering out irrelevant information. We apply DMRec to three different types of generative recommendation methods and conduct extensive experiments on three public datasets. The experimental results demonstrate that DMRec can effectively enhance the recommendation performance of these generative models, and it shows significant advantages over mainstream LM-enhanced recommendation methods.

[IR-6] Are AI Agents interacting with Online Ads?

链接: https://arxiv.org/abs/2504.07112
作者: Andreas Stöckl,Joel Nitu
类目: Information Retrieval (cs.IR)
*备注:

点击查看摘要

Abstract:As AI-driven agents become increasingly integrated into the digital ecosystem, they reshape how online advertising is perceived and processed. Particularly in the travel and hotel booking sector, these autonomous systems influence the effectiveness of traditional advertising formats. While visual cues and emotional appeals sway human users, AI agents prioritize structured data such as price, availability, and specifications. This study examines how different AI agents interact with online advertising, whether they incorporate ads into their decision-making processes, and which ad formats prove most effective. We analyze interaction patterns, click behavior, and decision-making strategies through experiments with multimodal language models such as OpenAI GPT-4o, Anthropic Claude, and Google Gemini 2.0 Flash. Our findings reveal that AI agents neither ignore nor systematically avoid advertisements but instead favor certain features-particularly keywords and structured data. These insights have significant implications for the future design of advertising strategies in AI-dominated digital environments.

[IR-7] Business Entity Entropy

链接: https://arxiv.org/abs/2504.07106
作者: Adam McCabe,Matthew H. Chequers
类目: Information Retrieval (cs.IR)
*备注: 23 pages, 14 figures, 2 tables. For more information on our research and applications in the decisioncontext problem, visit this https URL

点击查看摘要

Abstract:Organizations generate vast amounts of interconnected content across various platforms. While language models enable sophisticated reasoning for use in business applications, retrieving and contextualizing information from organizational memory remains challenging. We explore this challenge through the lens of entropy, proposing a measure of entity entropy to quantify the distribution of an entity’s knowledge across documents as well as a novel generative model inspired by diffusion models in order to provide an explanation for observed behaviours. Empirical analysis on a large-scale enterprise corpus reveals heavy-tailed entropy distributions, a correlation between entity size and entropy, and category-specific entropy patterns. These findings suggest that not all entities are equally retrievable, motivating the need for entity-centric retrieval or pre-processing strategies for a subset of, but not all, entities. We discuss practical implications and theoretical models to guide the design of more efficient knowledge retrieval systems.

[IR-8] he Feedback Loop Between Recommendation Systems and Reactive Users

链接: https://arxiv.org/abs/2504.07105
作者: Atefeh Mollabagher,Parinaz Naghizadeh
类目: Information Retrieval (cs.IR); Computer Science and Game Theory (cs.GT)
*备注:

点击查看摘要

Abstract:Recommendation systems underlie a variety of online platforms. These recommendation systems and their users form a feedback loop, wherein the former aims to maximize user engagement through personalization and the promotion of popular content, while the recommendations shape users’ opinions or behaviors, potentially influencing future recommendations. These dynamics have been shown to lead to shifts in users’ opinions. In this paper, we ask whether reactive users, who are cognizant of the influence of the content they consume, can prevent such changes by actively choosing whether to engage with recommended content. We first model the feedback loop between reactive users’ opinion dynamics and a recommendation system. We study these dynamics under three different policies - fixed content consumption (a passive policy), and decreasing or adaptive decreasing content consumption (reactive policies). We analytically show how reactive policies can help users effectively prevent or restrict undesirable opinion shifts, while still deriving utility from consuming content on the platform. We validate and illustrate our theoretical findings through numerical experiments.

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