语言大模型 / LLM

Are Large-Language Models Graph Algorithmic Reasoners?

Alexander K Taylor, Anthony Cuturrufo, Vishal Yathish, Mingyu Derek Ma, Wei Wang

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)；prompting(abstract)

Comments 9 pages, 13 Figures

PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking

Markus J. Buehler

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；small language model(abstract)；preference optimization(abstract)

Ask-before-Plan: Proactive Language Agents for Real-World Planning

Xuan Zhang, Yang Deng, Zifeng Ren, See-Kiong Ng, Tat-Seng Chua

专题命中 推理与问题求解 ：language agent(title,abstract)；LLM(abstract)；large language model(abstract)；language model(abstract)

Comments Accepted by EMNLP 2024 Findings

CPL: Critical Plan Step Learning Boosts LLM Generalization in Reasoning Tasks

Tianlong Wang, Junzhe Chen, Xueting Han, Jing Bai

专题命中 推理与问题求解 ：LLM(title)；large language model(abstract)；language model(abstract)；post-training(abstract)

LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks

Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Saldyt, Anil Murthy

专题命中 推理与问题求解 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)；prompting(abstract)

Journal ref Proceedings of the 41 st International Conference on Machine Learning, Vienna, Austria. PMLR 235, 2024

Competition-Level Problems are Effective LLM Evaluators

Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen

专题命中 推理与问题求解 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)；prompting(abstract)

Comments ACL 2024

Language Models as Knowledge Bases for Visual Word Sense Disambiguation

Anastasia Kritharoula, Maria Lymperaiou, Giorgos Stamou

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)；prompting(abstract)

Journal ref KBC-LM workshop@ ISWC 2023

Can an Embodied Agent Find Your "Cat-shaped Mug"? LLM-Guided Exploration for Zero-Shot Object Navigation

Vishnu Sashank Dorbala, James F. Mullen, Dinesh Manocha

专题命中 推理与问题求解 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)；prompting(abstract)

Comments 10 pages

Journal ref IEEE Robotics and Automation Letters 9.5 (2024) 4083-4090

Customizing Language Model Responses with Contrastive In-Context Learning

Xiang Gao, Kamalika Das

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)；prompting(abstract)

Comments Accepted to appear at AAAI 2024

Your Co-Workers Matter: Evaluating Collaborative Capabilities of Language Models in Blocks World

Guande Wu, Chen Zhao, Claudio Silva, He He

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)；language agent(abstract)

Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models

Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv, Weilin Zhao, Ruobing Xie, Bowen Zhou, Zhiyuan Liu, Maosong Sun

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)；instruction tuning(abstract)

Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning

Xijie Huang, Li Lyna Zhang, Kwang-Ting Cheng, Fan Yang, Mao Yang

专题命中 推理与问题求解 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)；prompting(abstract)

LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers

Theo X. Olausson, Alex Gu, Benjamin Lipkin, Cedegao E. Zhang, Armando Solar-Lezama, Joshua B. Tenenbaum, Roger Levy

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)；prompting(abstract)

Comments EMNLP Main 2023 (Outstanding Paper Award)

Journal ref Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5153-5176, Singapore. Association for Computational Linguistics

TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance

Haorui Wang, Rongzhi Zhang, Yinghao Li, Lingkai Kong, Yuchen Zhuang, Xiusi Chen, Chao Zhang

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)；prompting(abstract)

Divide and Prompt: Chain of Thought Prompting for Text-to-SQL

Xiping Liu, Zhao Tan

专题命中 推理与问题求解 ：prompting(title,abstract)；LLM(abstract)；large language model(abstract)；language model(abstract)

PAL: Program-aided Language Models

Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig

专题命中 推理与问题求解 ：language model(title,abstract)；LLM(abstract)；large language model(abstract)；prompting(abstract)

Comments The first three authors contributed equally. Our code and data are publicly available at http://reasonwithpal.com/

TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planning

TREK：面向复杂行程规划的大语言模型智能体旅行推理与评估工具包

Jinhu Qi, Wentao Zhang, Siu Man Ng, Feiyang Xu, Yanyu Chen, Yaoman Li, Irwin King

机构 * The Chinese University of Hong Kong（香港中文大学） ； Macao Polytechnic University（澳门理工大学） 纠错

专题命中 推理与问题求解 ：LLM(title,summary_cn)；分类 cs.CL

AI总结 本研究推出TREK旅行基准测试，解决现有行程规划智能体基准的不足，实验显示最强LLM智能体仅在不足五成任务生成可行计划，满足旅行者未明确需求是普遍瓶颈。

Comments Code, data, and evaluator: https://github.com/TonyQJH/TREK-A-Travel-Reasoning-and-Evaluation-Kit-for-LLM-Agents-in-Complex-Trip-Planning

Your Reviews Replicate You: LLM-Based Agents as Customer Digital Twins for Conjoint Analysis

你的评价复制你：基于大语言模型的代理作为联合分析的客户数字双胞胎

Bin Xuan, Jungmin Hwang, Hakyeon Lee

机构 * Department of Data Science（数据科学系） ； Seoul National University of Science and Technology（首尔科学技术大学） 纠错

专题命中 推理与问题求解 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)；分类 cs.AI

AI总结 本文提出利用大语言模型构建的客户数字双胞胎进行联合分析，通过聚合用户评论历史和检索增强生成技术，实现了高效准确的偏好预测，验证了其在市场研究中的可行性。

Comments 12 pages, 3 figures + This abstract introduces an LLM-based Customer Digital Twin framework that replaces human respondents in conjoint analysis with RAG-enhanced customer agents, validated at 87.73% accuracy on Reddit user data, and positions the contribution as a scalable alternative to traditional preference elicitation methods

SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction

Ling Yang, Zhaochen Yu, Tianjun Zhang, Minkai Xu, Joseph E. Gonzalez, Bin Cui, Shuicheng Yan

专题命中 推理与问题求解 ：LLM(title,abstract)；large language model(abstract)；language model(abstract)；preference optimization(abstract)

Comments ICLR 2025. Project: https://github.com/YangLing0818/SuperCorrect-llm

Reinforcing Egocentric Spatial Perception in Multimodal Large Language Models via Ego Scene Augmentation

通过自我场景增强在多模态大语言模型中强化自我中心空间感知

Chi Kit Wong, Ye Pan, Yuanhuiyi Lyu, Xu Zheng, Zidong Cao, Lutao Jiang, Zixin Zhang, Huiyu Zhou, Xuming Hu

机构 * The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）） ； Guangxi Zhuang Autonomous Region Information Center（广西壮族自治区信息中心） ； The Hong Kong University of Science and Technology（香港科技大学） 纠错

专题命中 推理与问题求解 ：large language model(title,abstract)；language model(title,abstract)

AI总结 研究如何强化多模态大语言模型的自我中心空间感知，提出自我场景增强框架ESA，利用自我元素图作为中间表示，通过视觉基础模型增强空间感知，在EgoTextVQA基准上取得显著性能提升。

Comments 14 pages, 8 figures. Chi Kit Wong and Ye Pan contributed equally. Code: https://github.com/Chikit-WONG/spatialGraph

MMDynOpt-Agent: Dynamic Optimization for Multimodal Large Language Model Reasoning via Reinforcement Learning

MMDynOpt-Agent：基于强化学习的多模态大语言模型推理动态优化方法

Wenjin Liu, Haoran Luo, Fayuan Ke, Zhenghong Lin, Yue Lu, Zhe Cui, Anh Tuan Luu, Carl Yang

专题命中 推理与问题求解 ：large language model(title,abstract)；language model(title,abstract)

AI总结 该研究针对多模态大语言模型推理效率不足的问题，提出MMDynOpt-Agent，通过强化学习将多模态推理动态优化建模为马尔可夫决策过程，结合多轮优化提示与多维度奖励机制，在15个公开数据集上性能优于基线方法。

CoViLLM: An Adaptive Human-Robot Collaborative Assembly Framework Using Large Language Models

CoViLLM：一种利用大语言模型的自适应人机协作装配框架

Jiabao Zhao, Jonghan Lim, Hongliang Li, Ilya Kovalenko

机构 * The Pennsylvania State University（宾夕法尼亚州立大学） 纠错

专题命中 推理与问题求解 ：large language model(title,abstract)；language model(title,abstract)

AI总结 本文提出CoViLLM框架，结合深度相机定位、人类操作员分类和大语言模型，实现定制化和新产品的自适应装配，通过NIST装配任务板验证，实验表明该框架提升了人机协作的灵活性。

Comments 6 pages, 7 figures. Accepted to ASME MSEC 2026

TennisVAR: A Stroke-Evidence-Grounded Multimodal Large Language Model for Tactical Reasoning in Tennis Videos

TennisVAR：一种基于击球证据的多模态大语言模型，用于网球视频中的战术推理

Yifan Mei, Qingling Shi, Changli Wu, Jiayuan Rao, Jiayi Ji, Liujuan Cao

专题命中 推理与问题求解 ：large language model(title,abstract)；language model(title,abstract)

AI总结 该研究针对网球视频理解的感知与理解差距，提出回合级战术推理任务，构建专家标注基准TRACE，开发基于证据的多模态大语言模型TennisVAR，实现网球视频的战术推理。

Comments Project Page: https://whynotgit2025.github.io/TennisVAR/

Agent-Based Test Assertion Generation via Diverse Perspective Aggregation

基于智能体的多视角聚合测试断言生成

Dong Wang, Qiaoyu Han, Lin Yang, Jianyi Zhou, Guangtai Liang, Junjie Chen

专题命中 推理与问题求解 ：LLM(summary_cn,abstract)；large language model(abstract)；language model(abstract)；prompting(abstract)

AI总结 针对现有LLM断言生成方法的局限，提出基于智能体的AssertMate框架，通过三个组件聚合多视角，在Defects4J和EvoSuite验证中性能显著优于现有技术。

The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics

可察觉的轨迹：利用思维链动态检测大语言模型中的推理失败

Shashwat Sourav, Aishwarya Balwani

专题命中 推理与问题求解 ：LLM(abstract,abstract_cn)；large language model(abstract,abstract_cn)；language model(abstract,abstract_cn)；分类 cs.CL、cs.AI、cs.LG

AI总结 本研究利用思维链动态特性，在不假设语言化CoT语义忠实性的情况下，检测大语言模型在布尔可满足性任务中的分布式推理失败，并通过针对性提示干预提升了Llama3-70B的准确率。

Any House Any Task: Scalable Long-Horizon Planning for Abstract Human Tasks

任何房屋任何任务：为抽象人类任务的可扩展长周期规划

Zhihong Liu, Yang Li, Rengming Huang, Cewu Lu, Panpan Cai

专题命中 推理与问题求解 ：LLM(summary_cn,abstract)；large language model(abstract)；language model(abstract)；prompting(abstract)

AI总结 AHAT是一种优化长周期规划的家庭任务规划器，通过结合LLM和TGPO算法，在复杂家庭任务中实现高效规划。

Latent Collaboration in Multi-Agent Systems

多智能体系统中的潜在协作

Jiaru Zou, Ruizhong Qiu, Gaotang Li, Xiyuan Yang, Katherine Tieu, Pan Lu, Ke Shen, Hanghang Tong, Yejin Choi, Jingrui He, James Zou, Mengdi Wang, Ling Yang

机构 * University of Washington（华盛顿大学） 纠错

专题命中 推理与问题求解 ：LLM(summary_cn,abstract)；large language model(abstract)；language model(abstract)；分类 cs.CL、cs.AI、cs.LG

AI总结 提出LatentMAS框架，使LLM智能体在连续潜在空间直接协作，无需文本中介，实现更高精度、更低开销和更快推理。

Comments ICML2026 Spotlight, Project: https://github.com/Gen-Verse/LatentMAS

LOCUS: Local Visual Cue Search for Enhancing Fine-Grained Perception in Multimodal Large Language Models

LOCUS: 局部视觉线索搜索增强多模态大语言模型的细粒度感知

Zhou Tao, Fang Zhang, Zewen Ding, Shida Wang, Xiaokun Sun, YongXiang Hua, Haoyu Cao, Linli Xu

机构 * University of Science and Technology of China（中国科学技术大学） ； State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室） 纠错

专题命中 推理与问题求解 ：large language model(title,abstract)；language model(title,abstract)

AI总结 提出LOCUS训练框架，通过可验证的局部线索搜索代理任务，使MLLM内化细粒度证据选择，提升定位敏感视觉理解而不改变推理接口。

Smart-TCP: An Agentic AI-based Autonomous and Adaptive TCP Protocol

Smart-TCP：基于智能体AI的自主自适应TCP协议

Yule Han, Kezhi Wang, Yizhe Zhao, Kun Yang

专题命中 推理与问题求解 ：LLM(abstract,abstract_cn)；SLM(abstract,abstract_cn)；large language model(abstract)；language model(abstract)

AI总结 提出Smart-TCP框架，利用大/小语言模型与算术逻辑单元协同决策，实现TCP控制逻辑的自适应，实验显示高精度与全生命周期成功率。

Comments Submitted for possible journal publication

PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models

感知基准：评估多模态大语言模型中的原子视觉感知

Zichao Lin, Yifeng Xie, Bowen Qu, Haiming Wang, Jia Li, Haoning Wu, Yuhao Dong, Zuhao Yang, Jinguo Zhu, Haoyu Lu, Zijia Zhao, Tongtian Yue, Zhangyang Qi, Junwei Yang, Mengfan Dong, Peizhou Cao, Chenzhuang Du, Zaida Zhou, Haotian Yao, Hao Yang, Hongcheng Gao, Lin Sui, Weihong Li, Xinxing Zu, Jia Chen, Yao Wang, Xiaoxue Wu, Yalin Wang, Y. Charles, Yiping Bao, Yangyang Liu, Zhiqi Huang, Xinyu Zhou

机构 * Moonshot AI（登月人工智能） 纠错

专题命中 推理与问题求解 ：large language model(title,abstract)；language model(title,abstract)

AI总结 介绍用于评估多模态大语言模型原子视觉感知能力的PerceptionBench基准，通过自下而上方法构建错误分类法及相关问题，测试16个前沿模型，发现原子感知待解决，该基准为衡量MLLM视觉感知边界提供标准。