大模型推理能力

Arranging Test Tubes in Racks Using Combined Task and Motion Planning

Weiwei Wan, Takeyuki Kotaka, Kensuke Harada

专题命中 规划推理 ：planning(title,abstract)；reasoning(abstract)

Enabling Topological Planning with Monocular Vision

Gregory J. Stein, Christopher Bradley, Victoria Preston, Nicholas Roy

专题命中 规划推理 ：planning(title,abstract)；reasoning(abstract)

Comments 7 pages (6 for content + 1 for references), 5 figures. Accepted to the 2020 IEEE International Conference on Robotics and Automation

Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems

Adam Eck, Maulik Shah, Prashant Doshi, Leen-Kiat Soh

专题命中 规划推理 ：planning(title,abstract)；reasoning(abstract)

Comments Pre-print with appendices for AAAI 2020

Collaborative Policy Learning for Open Knowledge Graph Reasoning

Cong Fu, Tong Chen, Meng Qu, Woojeong Jin, Xiang Ren

专题命中 规划推理 ：reasoning(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Automated Process Planning for Turning: A Feature-Free Approach

Morad Behandish, Saigopal Nelaturi, Chaman Singh Verma, Mats Allard

专题命中 规划推理 ：planning(title,abstract)；reasoning(abstract)

Comments A shorter version of the paper was presented at the International Conference on Manufacturing Research (ICMR'2018), Skövde, Sweden

Journal ref Journal of Production and Manufacturing Research, 2019

Locomotion Planning through a Hybrid Bayesian Trajectory Optimization

Tim Seyde, Jan Carius, Ruben Grandia, Farbod Farshidian, Marco Hutter

专题命中 规划推理 ：planning(title,abstract)；reasoning(abstract)

Comments Accepted for publication at the IEEE International Conference on Robotics and Automation (ICRA) 2019

Multi-Hop Knowledge Graph Reasoning with Reward Shaping

Xi Victoria Lin, Richard Socher, Caiming Xiong

专题命中 规划推理 ：reasoning(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments Accepted to EMNLP 2018, 12 pages

Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

Shang-Yu Su, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen

专题命中 规划推理 ：planning(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 11 pages, 10 figures, EMNLP 2018 long paper

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Kam-Fai Wong, Shang-Yu Su

专题命中 规划推理 ：planning(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Comments 11 pages, 8 figures, Accepted in ACL 2018

Order-Planning Neural Text Generation From Structured Data

Lei Sha, Lili Mou, Tianyu Liu, Pascal Poupart, Sujian Li, Baobao Chang, Zhifang Sui

专题命中 规划推理 ：planning(title,abstract)；分类 cs.CL、cs.AI、cs.LG

Planning Problems for Sophisticated Agents with Present Bias

Jon Kleinberg, Sigal Oren, Manish Raghavan

专题命中 规划推理 ：planning(title,abstract)；reasoning(abstract)

SafeRun: Enabling Determinism in LLM Planning for Running

SafeRun：在跑步规划中实现LLM的确定性

Meilin Chen, Zepeng Zhai, Jiaxuan Zhao, Yuan Lu

机构 * University of California, Berkeley（加州大学伯克利分校） ； Stanford University（斯坦福大学） 纠错

专题命中 规划推理 ：planning(title,abstract)；分类 cs.CL、cs.AI

AI总结 针对LLM在跑步规划中因概率性导致安全违规的问题，提出SafeRun框架，通过解耦架构将LLM的软解释与确定性求解器的硬约束分离，实现100%安全评分。

Comments Workshop on Planning in the Era of LLMs (LM4Plan) at ICML 2026

Constructing the Umwelt: Cognitive Planning through Belief-Intent Co-Evolution

构建环境：通过信念-意图共演的认知规划

Shiyao Sang

机构 * Shiyao Sang（桑世尧） 纠错

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

AI总结 本文提出基于信念-意图共演的认知规划方法，通过MBCWM和TIWM模型实现环境与现实的认知一致性，提升自动驾驶规划性能并展现类人认知行为。

Comments 12 pages, 8 figures. A paradigm shift from reconstructing the world to understanding it: planning through Belief-Intent Co-Evolution

Dynamic Intelligence Ceilings: Measuring Long-Horizon Limits of Planning and Creativity in Artificial Systems

动态智能上限：测量人工智能系统长期规划和创造力的长期限制

Truong Xuan Khanh, Truong Quynh Hoa

机构 * H&K Research Studio, Clevix LLC（Clevix LLC 研究室） 纠错

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

AI总结 本文提出动态智能上限概念，通过轨迹导向评估框架量化人工智能系统长期规划与创造力的限制，揭示智能上限的动态性与轨迹依赖性。

Comments This paper introduces a trajectory-centric evaluation framework for analyzing long-horizon intelligence limits in artificial systems, focusing on developmental behavior, planning, and structural creativity rather than proposing new learning algorithms. 11 pages, 2 figures

Chopping Trees: Semantic Similarity Based Dynamic Pruning for Tree-of-Thought Reasoning

砍树：基于语义相似性的动态剪枝用于树状思维推理

Joongho Kim, Xirui Huang, Zarreen Reza, Gabriel Grand

机构 * Massachusetts Institute of Technology (MIT)（麻省理工学院） 纠错

专题命中 规划推理 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

AI总结 SSDP通过动态剪枝技术提升树状思维推理效率，实现2.3倍速度提升且保持高准确性。

Comments 39th Conference on Neural Information Processing Systems (NeurIPS 2025) Workshop on Efficient Reasoning

LLaVul: A Multimodal LLM for Interpretable Vulnerability Reasoning about Source Code

Ala Jararweh, Michael Adams, Avinash Sahu, Abdullah Mueen, Afsah Anwar

机构 * Department of Computer Science, The University of New Mexico（计算机科学系，新墨西哥大学） ； Comprehensive Cancer Center, The University of New Mexico（综合癌症中心，新墨西哥大学） 纠错

专题命中 规划推理 ：reasoning(title,abstract)；分类 cs.CL、cs.AI

Journal ref A. Jararweh, M. Adams, A. Sahu, A. Mueen and A. Anwar, "LLaVul: A Multimodal LLM for Interpretable Vulnerability Reasoning about Source Code," 2025 5th Intelligent Cybersecurity Conference (ICSC), Tampa, FL, USA, 2025, pp. 232-241

Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework

Neel P. Bhatt, Yunhao Yang, Rohan Siva, Daniel Milan, Ufuk Topcu, Zhangyang Wang

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

Comments Fine-tuned models, code, and datasets are available at https://uncertainty-in-planning.github.io/

Video Language Planning

Yilun Du, Mengjiao Yang, Pete Florence, Fei Xia, Ayzaan Wahid, Brian Ichter, Pierre Sermanet, Tianhe Yu, Pieter Abbeel, Joshua B. Tenenbaum, Leslie Kaelbling, Andy Zeng, Jonathan Tompson

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

Comments https://video-language-planning.github.io/

World Value Functions: Knowledge Representation for Learning and Planning

Geraud Nangue Tasse, Benjamin Rosman, Steven James

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

Comments Accepted at the Planning and Reinforcement Learning Workshop at ICAPS 2022. arXiv admin note: text overlap with arXiv:2205.08827

Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators

Clement Gehring, Masataro Asai, Rohan Chitnis, Tom Silver, Leslie Pack Kaelbling, Shirin Sohrabi, Michael Katz

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

Comments Equal contributions by the first two authors. This manuscript is a camera-ready version accepted in ICAPS-2022. It is significantly updated from past versions (e.g., in the ICAPS PRL (Planning and RL) workshop) with additional experiments comparing existing work (STRIPS-HGN (Shen, Trevizan, and Thiebaux 2020) and GBFS-GNN (Rivlin, Hazan, and Karpas 2019))

Differentiable Spatial Planning using Transformers

Devendra Singh Chaplot, Deepak Pathak, Jitendra Malik

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

Comments Published at ICML 2021. See project webpage at https://devendrachaplot.github.io/projects/spatial-planning-transformers

Learning-based Preference Prediction for Constrained Multi-Criteria Path-Planning

Kevin Osanlou, Christophe Guettier, Andrei Bursuc, Tristan Cazenave, Eric Jacopin

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

Comments arXiv admin note: text overlap with arXiv:2108.00978

Journal ref International Conference on Automated Planning and Scheduling 2019, Workshop SPARK

E-PDDL: A Standardized Way of Defining Epistemic Planning Problems

Francesco Fabiano, Biplav Srivastava, Jonathan Lenchner, Lior Horesh, Francesca Rossi, Marianna Bergamaschi Ganapini

专题命中 规划推理 ：planning(title,abstract)；分类 cs.CL、cs.AI

Comments 9 pages, Knowledge Engineering for Planning and Scheduling - ICAPS 2021

Cognitive Mapping and Planning for Visual Navigation

Saurabh Gupta, Varun Tolani, James Davidson, Sergey Levine, Rahul Sukthankar, Jitendra Malik

专题命中 规划推理 ：planning(title,abstract)；分类 cs.AI、cs.LG

Comments Extended IJCV Version of the original paper at CVPR17. Project website with code, models, simulation environment and videos: https://sites.google.com/view/cognitive-mapping-and-planning/

Representation Signatures and Risk-Feedback Alignment in LLM Trading Agents

表示签名与LLM交易智能体中的风险反馈对齐

Weicheng Xue

机构 * Virginia Tech（弗吉尼亚理工大学） 纠错

专题命中 规划推理 ：CoT(abstract,abstract_cn)；reasoning(abstract)；planning(abstract)；分类 cs.LG

AI总结 通过TradeArena测试平台研究LLM交易智能体在金融决策中的行为对齐与表示动态，发现故障前表示签名（规划嵌入漂移、流形有效秩收缩）并验证风险反馈作为外部对齐信号的有效性。

AgentSLABench: Evaluating and Benchmarking Agentic Systems Under Resource Constraints

AgentSLABench：资源约束下智能体系统的评估与基准测试

Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju

专题命中 规划推理 ：CoT(abstract,abstract_cn)；planning(abstract,abstract_cn)；分类 cs.AI

AI总结 本研究提出AgentSLABench框架，在资源约束下评估自主AI智能体，通过多维度指标分析发现专用智能体表现优于通用基线，验证了效率调整成功率的重要性并开放相关资源。

Comments 7 pages, 2 figures, 9 tables. Code, sealed test sets, and profiling artifacts available at: https://github.com/MeherBhaskar/agentslabench

Advancing Multimodal Judge Models through a Capability-Oriented Benchmark and MCTS-Driven Data Generation

通过能力导向的基准和MCTS驱动的数据生成推进多模态评判模型

Zeyu Chen, Huanjin Yao, Ziwang Zhao, Min Yang

机构 * Tsinghua University（清华大学） ； ByteDance（字节跳动） 纠错

专题命中 规划推理 ：CoT(abstract,abstract_cn)；reasoning(abstract)；chain-of-thought(abstract)；分类 cs.AI

AI总结 本文提出M-JudgeBench和Judge-MCTS框架，通过能力导向的基准和MCTS驱动的数据生成提升多模态评判模型的评估能力。

LLM-Powered Multi-Agent System for Automated Crypto Portfolio Management

基于LLM的多智能体系统实现自动化加密货币投资组合管理

Yichen Luo, Yebo Feng, Jiahua Xu, Paolo Tasca, Yang Liu

机构 * University College London（伦敦大学学院） ； Nanyang Technological University（南洋理工大学） ； Exponential Science（指数科学） 纠错

专题命中 规划推理 ：CoT(abstract,abstract_cn)；reasoning(abstract)；chain-of-thought(abstract)；分类 cs.AI

AI总结 提出一个三智能体系统（市场、新闻、交易），通过分层、协作和辩论架构融合多模态信号，在2025年回测中实现133.52%累计收益和1.502夏普比率，优于单智能体和深度学习基线。

When Hard Negatives Hurt: Bridging the Generative-Discriminative Gap in Hard Negative Synthesis for Retrieval

当硬负例有害时：弥合检索中硬负例生成的生成-判别鸿沟

Zhicheng Zhang, Jiwei Tang, Kuicai Dong, Xiaopeng Li, Jieming Zhu, Jingyu Li, Qianhui Zhu, Fengyuan Lu, Wang Jiaheng, Gang Wang, Hai-Tao Zheng, Zhaocheng Du

机构 * Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院） ； Huawei Technologies Co., Ltd.（华为技术有限公司） ； City University of Hong Kong（香港城市大学） ； School of Cyber Science and Technology, Sun Yat-sen University（中山大学信息科学与技术学院） ； School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院） ； The Hong Kong University of Science and Technology（香港科学与技术大学） ； Huawei Noah’s Ark Lab（华为诺亚实验室） 纠错

专题命中 规划推理 ：CoT(summary_cn,abstract)；分类 cs.LG

AI总结 针对检索中硬负例生成存在的生成-判别鸿沟问题，提出CausalNeg方法，通过CoT引导的反事实扰动和查询视角熵最大化来提升检索性能。

Comments Accepted at KDD 2026

EvoEmo: Towards Evolved Emotional Policies for Adversarial LLM Agents in Multi-Turn Price Negotiation

EvoEmo：面向多轮价格谈判中对抗性LLM智能体的进化情感策略

Yunbo Long, Liming Xu, Lukas Beckenbauer, Yuhan Liu, Alexandra Brintrup

机构 * Department of Engineering, University of Cambridge（剑桥大学工程系） ； Rotman School of Management, University of Toronto（多伦多大学罗特曼管理学院） ； TUM School of Management, Technical University of Munich（慕尼黑技术大学管理学院） ； The Alan Turing Institute, London, UK（伦敦阿尔安·图灵研究院） 纠错

专题命中 规划推理 ：CoT(abstract,abstract_cn)；reasoning(abstract)；chain-of-thought(abstract)；分类 cs.AI

AI总结 提出EvoEmo进化强化学习框架，通过将情感状态转移建模为马尔可夫决策过程并采用种群遗传优化，动态优化多轮谈判中的情感表达，显著提升LLM智能体的谈判成功率、效率和买家节省。