大模型推理能力

NL2Plan: Robust LLM-Driven Planning from Minimal Text Descriptions

Elliot Gestrin, Marco Kuhlmann, Jendrik Seipp

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

Comments Accepted for the ICAPS 2024 Workshop on Human-Aware and Explainable Planning

Urban Comfort Assessment in the Era of Digital Planning: A Multidimensional, Data-driven, and AI-assisted Framework

Sijie Yang, Binyu Lei, Filip Biljecki

机构 * Department of Architecture, National University of Singapore（新加坡国立大学建筑系） ； School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院） ； Department of Real Estate, National University of Singapore（新加坡国立大学房地产系） 纠错

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

Comments Presented at 19th International Conference on Computational Urban Planning and Urban Management (CUPUM 2025)

Planning with Adaptive World Models for Autonomous Driving

Arun Balajee Vasudevan, Neehar Peri, Jeff Schneider, Deva Ramanan

机构 * Carnegie Mellon University（卡内基梅隆大学） 纠错

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

Comments This project has been accepted to the International Conference on Robotics and Automation (ICRA) 2025. Project Page: https://arunbalajeev.github.io/world_models_planning/world_model_paper.html

AAAI Workshop on AI Planning for Cyber-Physical Systems -- CAIPI24

Oliver Niggemann, Gautam Biswas, Alexander Diedrich, Jonas Ehrhardt, René Heesch, Niklas Widulle

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

Comments This is the Proceedings of the AAAI Workshop on AI Planning for Cyber-Physical Systems - CAIPI24, which was held in Vancouver, CA, February 26, 2024

On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)

Vishal Pallagani, Kaushik Roy, Bharath Muppasani, Francesco Fabiano, Andrea Loreggia, Keerthiram Murugesan, Biplav Srivastava, Francesca Rossi, Lior Horesh, Amit Sheth

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

Journal ref Proceedings of the International Conference on Automated Planning and Scheduling, 34(1), 432-444 (2024)

Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Settings

Rushang Karia, Pulkit Verma, Alberto Speranzon, Siddharth Srivastava

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

Comments To appear at ICAPS-24

Journal ref Proceedings of the International Conference on Automated Planning and Scheduling, 34(1), 310-318, 2024

A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency

Bharath Muppasani, Vishal Pallagani, Biplav Srivastava, Raghava Mutharaju, Michael N. Huhns, Vignesh Narayanan

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

Comments Ontology, Automated Planning, Planner Improvement

Human-Centric Goal Reasoning with Ripple-Down Rules

Kenji Brameld, Germán Castro, Claude Sammut, Mark Roberts, David W. Aha

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

Comments Proceedings of the Ninth Goal Reasoning Workshop (Advances in Cognitive Systems, 2021)

Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?

Nils Wilken, Lea Cohausz, Christian Bartelt, Heiner Stuckenschmidt

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

Comments Full publication: Wilken, N., Cohausz, L., Bartelt, C., Stuckenschmidt, H. (2023). Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?. In: Seipel, D., Steen, A. (eds) KI 2023: Advances in Artificial Intelligence. KI 2023. Lecture Notes in Computer Science(), vol 14236. Springer, Cham. arXiv admin note: text overlap with arXiv:2301.10571

Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How

Dhruv Saxena, Maxim Likhachev

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

Comments Accepted for publication at the International Conference on Automated Planning and Scheduling (ICAPS), 2023

Deliberative Acting, Online Planning and Learning with Hierarchical Operational Models

Sunandita Patra, James Mason, Malik Ghallab, Dana Nau, Paolo Traverso

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

Comments Published in Artificial Intelligence (AIJ). Please cite as: Sunandita Patra, James Mason, Malik Ghallab, Dana Nau, Paolo Traverso. Deliberative Acting, Planning and Learning with Hierarchical Operational Models. Artificial Intelligence, Elsevier, 2021, 299, pp.103523. 10.1016/j.artint.2021.103523. arXiv admin note: text overlap with arXiv:2003.03932

Journal ref Artificial Intelligence, Elsevier, 2021, 299, pp.103523

Approximate Computing for Robotic path planning -- Experimentation, Case Study and Practical Implications

Hrishav Bakul Barua

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

Comments Approximate Computing, Multi-robot Systems, Multi-agent Systems, Good Enough Computing, Green Computing, Robot Path Planning, Energy-Efficient Computing

Encoding Compositionality in Classical Planning Solutions

Angeline Aguinaldo, William Regli

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

Comments IJCAI Generalization in Planning Workshop 2021

Guiding Robot Exploration in Reinforcement Learning via Automated Planning

Yohei Hayamizu, Saeid Amiri, Kishan Chandan, Keiki Takadama, Shiqi Zhang

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

Comments Accepted in International Conference of Planning and Scheduling (ICAPS-21)

Iterative Planning with Plan-Space Explanations: A Tool and User Study

Rebecca Eifler, Jörg Hoffmann

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

Comments Proceedings of the International Workshop of Explainable AI Planning (XAIP'20), at ICAPS'20

On the Importance of Domain Model Configuration for Automated Planning Engines

Mauro Vallati, Lukas Chrpa, Thomas L. McCluskey, Frank Hutter

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

Comments Under consideration in Journal of Automated Reasoning

Towards Explainable AI Planning as a Service

Michael Cashmore, Anna Collins, Benjamin Krarup, Senka Krivic, Daniele Magazzeni, David Smith

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

Comments 2nd ICAPS Workshop on Explainable Planning (XAIP-2019)

Planning Robot Motion using Deep Visual Prediction

Meenakshi Sarkar, Prabhu Pradhan, Debasish Ghose

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

Comments 7th ICAPS Workshop on Planning and Robotics (PlanRob), 2019

Probabilistic Planning by Probabilistic Programming

Vaishak Belle

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

Comments Article at AAAI-18 Workshop on Planning and Inference

Addendum to: Summary Information for Reasoning About Hierarchical Plans

Lavindra de Silva, Sebastian Sardina, Lin Padgham

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

Comments This paper is a more detailed version of the following publication: Lavindra de Silva, Sebastian Sardina, Lin Padgham: Summary Information for Reasoning About Hierarchical Plans. ECAI 2016: 1300-1308

Knowledge Engineering for Planning-Based Hypothesis Generation

Shirin Sohrabi, Octavian Udrea, Anton V. Riabov

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

Comments This paper appears in the Proceedings of the Automated Planning and Scheduling (ICAPS) Workshop on Knowledge Engineering for Planning and Scheduling (KEPS)

Infer Human's Intentions Before Following Natural Language Instructions

在遵循自然语言指令前推断人类的意图

Yanming Wan, Yue Wu, Yiping Wang, Jiayuan Mao, Natasha Jaques

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

AI总结 该研究针对人类指令的歧义问题，提出FISER框架，通过显式推断人类意图改进协作具身任务的指令遵循，在HandMeThat基准上达到最优性能。

Understanding Inference Scaling for LLMs: Bottlenecks, Trade-offs, and Performance Principles

理解大语言模型的推理扩展：瓶颈、权衡与性能原则

Moiz Arif, Avinash Maurya, Sudharshan Vazhkudai, Bogdan Nicolae

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

AI总结 本文研究了大语言模型推理扩展中的瓶颈、权衡和性能原则，通过在GPU集群上评估从8B到671B参数的模型，系统探讨了数据并行、张量并行和流水线并行之间的相互作用，揭示了推理工作负载中数据并行的容量陷阱以及张量并行和稀疏MoE模型的性能限制。

Comments ISCA'26: The 53rd International Symposium on Computer Architecture, Industry Track

Learning to Trust: Dynamic Utilization of Retrieval-Augmented Generation for E-commerce Search Relevance

学习信任：动态利用检索增强生成用于电子商务搜索相关性

Tingqiao Xu, Shaowei Yao, Chenhe Dong, Yiming Jin, Zerui Huang, Dan Ou, Haihong Tang, Bo Zheng

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

AI总结 本文提出DyKnow-RAG框架，通过动态利用外部知识使LLM学会信任，提升电子商务搜索相关性评估的准确性和效率。

Graph-GRPO: Dependency-Aware Credit Assignment for Generative E-commerce Search Relevance

Graph-GRPO：面向生成式电商搜索相关性的依赖感知信用分配

Jiarui Che, Yifei Chen, Zhixing Tian, Chenyang Wang, Ziguang Cheng

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

AI总结 提出Graph-GRPO，一种基于图结构的GRPO扩展，通过构建推理依赖图并传播结果级奖励实现细粒度信用分配，提升电商搜索相关性建模。

Comments 11 pages, 2 figures, 2 tables. Submitted to CIKM 2026

Personalized Embodied Navigation for Portable Object Finding

面向便携物品寻找的个性化具身导航

Vishnu Sashank Dorbala, Bhrij Patel, Amrit Singh Bedi, Dinesh Manocha

机构 * University of Maryland, College Park（马里兰大学学院公园分校） ； University of Central Florida（中央佛罗里达大学） 纠错

专题命中 规划推理 ：planning(summary_cn,abstract)

AI总结 本文提出了一种面向动态环境的个性化习惯学习方法，通过引入Transit-Aware Planning算法提升便携物品寻找的性能，在模拟和现实环境中均取得显著效果。

Comments 10 pages

Demystifying Chains, Trees, and Graphs of Thoughts

解开思维链、树和图的谜团

Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Guangyuan Piao, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Aidan O'Mahony, Onur Mutlu, Torsten Hoefler

机构 * ETH Zurich（苏黎世联邦理工学院） ； Dell（戴尔） ； Cledar ； BASF SE（巴斯夫欧洲公司） 纠错

专题命中 规划推理 ：reasoning(abstract)；chain-of-thought(abstract)；planning(abstract)；分类 cs.CL、cs.AI、cs.LG

AI总结 本文通过分析提示执行流程，构建了首个结构增强的大语言模型推理方案分类法，揭示了不同结构对推理性能和成本的影响，并探讨了提示工程与语言模型生态系统的理论基础。

Journal ref IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 47, Issue 12, pages 10967-10989 (December 2025)

Understanding by Reconstruction: Reversing the Software Development Process for LLM Pretraining

通过重构理解：为LLM预训练反转软件开发过程

Zhiyuan Zeng, Yichi Zhang, Yong Shan, Kai Hua, Siyuan Fang, Zhaiyu Liu, Jiaheng Liu, Haozhe Wang, Yining Zheng, Ming Ding, Ke Shen, Ge Zhang, Wenhao Huang, Xipeng Qiu

专题命中 规划推理 ：reasoning(abstract)；chain-of-thought(abstract)；CoT(abstract)；planning(abstract)

AI总结 本文提出通过重构软件开发过程来提升LLM性能，通过模拟多智能体轨迹生成更丰富的监督信号，实验显示在多个基准测试中效果显著。

Tree Search for Language Model Agents

语言模型代理的树搜索

Jing Yu Koh, Stephen McAleer, Daniel Fried, Ruslan Salakhutdinov

机构 * Carnegie Mellon University（卡内基梅隆大学） 纠错

专题命中 规划推理 ：reasoning(abstract)；planning(abstract)；test-time compute(abstract)；分类 cs.CL、cs.AI、cs.LG

AI总结 本文提出一种树搜索算法，用于提升语言模型代理在现实网页任务中的表现，实验显示其在成功率上显著优于基线方法。

Comments 13 pages. Models and code available at https://jykoh.com/search-agents

LORE: A Large Generative Model for Search Relevance

LORE：一种大规模生成模型用于搜索相关性

Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, Ruicong Xu, Haoran Li, Songyan Liu, Pengjie Wang, Jian Xu, Bo Zheng

机构 * Alibaba Group（阿里巴巴集团） 纠错

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

AI总结 LORE提出了一种基于大规模生成模型的搜索相关性框架，通过两阶段训练和分层部署策略提升搜索效果，为垂直领域提供方法参考。