AI Agent

Long-Term Planning and Situational Awareness in OpenAI Five

Jonathan Raiman, Susan Zhang, Filip Wolski

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.CL、cs.LG

Learning Latent State Spaces for Planning through Reward Prediction

Aaron Havens, Yi Ouyang, Prabhat Nagarajan, Yasuhiro Fujita

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments Deep RL Workshop, Neurips 2019, Vancouver

From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving

Manfred Eppe, Phuong D. H. Nguyen, Stefan Wermter

专题命中 规划决策 ：planning(title,abstract)；tool use(abstract)；分类 cs.AI、cs.LG

Engaging in Dialogue about an Agent's Norms and Behaviors

Daniel Kasenberg, Antonio Roque, Ravenna Thielstrom, Matthias Scheutz

专题命中 规划决策 ：agent(title,abstract)；planning(abstract)；分类 cs.AI、cs.CL

Comments Accepted to the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI)

Learning Compact Models for Planning with Exogenous Processes

Rohan Chitnis, Tomás Lozano-Pérez

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments CoRL 2019 final version

Deep Learned Path Planning via Randomized Reward-Linked-Goals and Potential Space Applications

Tamir Blum, William Jones, Kazuya Yoshida

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments 8 pages, 3 tables, 5 figures

Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning

Arthur Juliani, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius, Danny Lange

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments IJCAI 2019

Search on the Replay Buffer: Bridging Planning and Reinforcement Learning

Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments Run our algorithm in your browser: http://bit.ly/rl_search

Learning Latent Dynamics for Planning from Pixels

Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments 20 pages, 12 figures, 1 table

Learning Portable Representations for High-Level Planning

Steven James, Benjamin Rosman, George Konidaris

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

An investigation of model-free planning

Arthur Guez, Mehdi Mirza, Karol Gregor, Rishabh Kabra, Sébastien Racanière, Théophane Weber, David Raposo, Adam Santoro, Laurent Orseau, Tom Eccles, Greg Wayne, David Silver, Timothy Lillicrap

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Dynamic Planning Networks

Norman Tasfi, Miriam Capretz

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

PRM-RL: Long-range Robotic Navigation Tasks by Combining Reinforcement Learning and Sampling-based Planning

Aleksandra Faust, Oscar Ramirez, Marek Fiser, Kenneth Oslund, Anthony Francis, James Davidson, Lydia Tapia

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments 9 pages, 7 figures

Journal ref IEEE International Conference on Robotics and Automation (ICRA), 2018

Robust and Adaptive Planning under Model Uncertainty

Apoorva Sharma, James Harrison, Matthew Tsao, Marco Pavone

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Interactive Agent Modeling by Learning to Probe

Tianmin Shu, Caiming Xiong, Ying Nian Wu, Song-Chun Zhu

专题命中 规划决策 ：agent(title,abstract)；planning(abstract)；分类 cs.AI、cs.LG

Comments 14 pages

Learning and Planning with a Semantic Model

Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Comments submitted to ICLR 2019

Learning model-based planning from scratch

Razvan Pascanu, Yujia Li, Oriol Vinyals, Nicolas Heess, Lars Buesing, Sebastien Racanière, David Reichert, Théophane Weber, Daan Wierstra, Peter Battaglia

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Simple Regret Optimization in Online Planning for Markov Decision Processes

Zohar Feldman, Carmel Domshlak

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI、cs.LG

Weather- and Location-Aware Agentic Dining Recommendation: Leveraging LLM World Knowledge for Region-Sensitive Contextual Reasoning

感知天气与位置的智能体餐饮推荐：利用大语言模型世界知识开展区域敏感语境推理

Kadharmoideen Fadurudeen

专题命中 规划决策 ：agentic(title,abstract)；agent(abstract)；分类 cs.AI

AI总结 该研究提出感知天气与位置的智能体餐饮推荐系统，利用LLM的世界知识生成区域敏感的适配天气的餐饮推荐，为智能体推荐提供了可扩展的架构模式。

Comments 5 pages. An agentic LLM system that reasons over combined location and weather context for region-sensitive dining recommendation. Working prototype implemented and briefly deployed end-to-end

AIvilization v0: Toward Large-Scale Artificial Social Simulation with a Unified Agent Architecture and Adaptive Agent Profiles

AIvilization v0：迈向大规模人工社会模拟的统一代理架构与自适应代理档案

Wenkai Fan, Shurui Zhang, Xiaolong Wang, Haowei Yang, Tsz Wai Chan, Xingyan Chen, Junquan Bi, Zirui Zhou, Jia Liu, Kani Chen

机构 * The Hong Kong University of Science and Technology（香港科学与技术大学） ； Bauhinia AI 纠错

专题命中 规划决策 ：agent(title,abstract)；planning(abstract)；分类 cs.AI

AI总结 AIvilization v0通过统一代理架构和自适应代理档案，实现大规模人工社会模拟，解决目标稳定性与反应正确性矛盾，支持长周期自主性和多目标环境下的稳健性。

Comments v2: major revision. Agent architecture and environment consolidated into self-contained sections; new problem-setting section formalizing the asynchronous event model; evaluation reorganized by experiment with results reported alongside each protocol; added action-simulator audit, and human-steering analyses; other sections rewritten

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 规划决策 ：planning(title,abstract)；agent(abstract)；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

Exploring Agentic Workflows for Generating High Quality Math Visual Aids

探索用于生成高质量数学视觉辅助工具的智能工作流程

Rizwaan Malik, Ashna Khetan, Isabel Sieh, Samin Khan

机构 * Stanford Graduate School of Education（斯坦福大学教育研究生院） ； Department of Computer Science（计算机科学系） 纠错

专题命中 规划决策 ：agentic(title,abstract)；workflow(abstract)；分类 cs.AI

AI总结 研究针对中学数学图表可靠生成难的问题，引入智能工作流程，让 LLM 智能体评估并迭代改进生成的视觉效果，通过探索性评估确定改进关键领域，初步证明可提高 AI 生成数学图表的可靠性和教育价值。

Comments 13 pages, 9 figures. Exploratory course project on agentic workflows for generating and evaluating K 12 mathematical diagrams

FocalAD: Local Motion Planning for End-to-End Autonomous Driving

FocalAD：端到端自动驾驶中的局部运动规划

Bin Sun, Boao Zhang, Jiayi Lu, Xinjie Feng, Jiachen Shang, Rui Cao, Mengchao Zheng, Chuanye Wang, Shichun Yang, Yaoguang Cao, Ziying Song

机构 * School of Transportation Science and Engineering（交通科学与工程学院） ； State Key Lab of Intelligent Transportation System（智能交通运输系统国家重点实验室） ； Hangzhou International Innovation Institute（杭州国际创新研究院） ； School of Computer Science and Technolog（计算机科学与技术学院） ； Beijing Jiaotong University（北京交通大学） 纠错

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI

AI总结 本文提出FocalAD框架，通过增强局部运动表示和关注关键局部邻居，提升自动驾驶的规划可靠性与鲁棒性，在nuScenes和Bench2Drive等数据集上表现优异。

Journal ref FocalAD: Local Motion Planning for End-to-End Autonomous Driving. Automot. Innov. (2026)

VeriGraph: Scene Graphs for Execution Verifiable Robot Planning

VeriGraph：用于可验证机器人规划的场景图

Daniel Ekpo, Mara Levy, Saksham Suri, Chuong Huynh, Archana Swaminathan, Abhinav Shrivastava

机构 * University of Maryland, College Park, MD USA（马里兰大学学院公园分校） 纠错

专题命中 规划决策 ：planning(title,abstract)；agent(abstract,comments)；分类 cs.AI

AI总结 VeriGraph通过整合视觉语言模型和场景图，提升机器人任务规划的可行性验证与修正，显著提高任务完成率。

Comments Accepted to ICRA 2026. Project website: https://verigraph-agent.github.io

WORKSWORLD: A Domain for Integrated Numeric Planning and Scheduling of Distributed Pipelined Workflows

WORKSWORLD：一个用于分布式流水线工作流集成数值规划和调度的领域

Taylor Paul, William Regli

专题命中 规划决策 ：planning(title,abstract)；workflow(abstract)；分类 cs.AI

AI总结 本文提出WORKSWORLD领域，用于永久调度工作流的数值无关规划器，通过定义数据源、组件和目标，解决联合规划与调度问题，实验证明在普通硬件上可处理最多14个组件的线性链工作流。

Comments To be published in Proceedings of the International Conference on Automated Planning and Scheduling Volume 36 (2026)

Interpretable Responsibility Sharing as a Heuristic for Task and Motion Planning

可解释的责任共享作为任务与运动规划的启发式方法

Arda Sarp Yenicesu, Sepehr Nourmohammadi, Berk Cicek, Ozgur S. Oguz

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.AI

AI总结 本文提出了一种名为可解释责任共享（IRS）的新型启发式方法，用于提升家用机器人任务与运动规划的效率，通过利用人类构建的环境和固有偏见，结合辅助物体简化任务执行。

Comments Accepted for the Special Issue "Planning and Learning for Autonomous Robotics" in Robotics and Autonomous Systems

Socio-technical aspects of Agentic AI

群体技术视角下的代理AI

Praveen Kumar Donta, Alaa Saleh, Ying Li, Shubham Vaishnav, Kai Fang, Hailin Feng, Yuchao Xia, Thippa Reddy Gadekallu, Qiyang Zhang, Xiaodan Shi, Ali Beikmohammadi, Sindri Magnússon, Ilir Murturi, Chinmaya Kumar Dehury, Marcin Paprzycki, Lauri Loven, Sasu Tarkoma, Schahram Dustdar

机构 * Department of Computer and Systems Sciences, Stockholm University（斯德哥尔摩大学计算机与系统科学系） ； Center for Ubiquitous Computing, University of Oulu（奥卢大学无处不在计算中心） ； College of Computer Science and Engineering, Northeastern University（东北大学计算机科学与工程学院） ； Zhejiang A\&F University, Hangzhou（浙江工业大学之江学院） ； School of Computer Science, Peking University（北京大学计算机科学学院） ； Department of Mechatronics, University of Prishtina（普里什蒂纳大学机电系） ； Department of Computer Science, IISER Berhampur（伯尔哈普尔IISER计算机科学系） ； Systems Research Institute Polish Academy of Sciences（波兰科学院系统研究所） ； Department of Computer Science, University of Helsinki（赫尔辛基大学计算机科学系） 纠错

专题命中 规划决策 ：agentic(title,abstract)；planning(abstract)；分类 cs.AI

AI总结 本文从社会技术视角探讨代理AI，分析其技术组件与社会背景的关联，揭示伦理挑战及未来研究方向。

Comments Dear Reviewer, please note that this is not survey/review or position paper. This paper introduced new framework (MAD-BAD-SAD Framework) for Socio-technical aspects of Agentic AI, Ethical considerations, which is very important to consider beside technical development

Trustworthy AI in the Agentic Lakehouse: from Concurrency to Governance

可信AI在代理湖仓中的实现：从并发到治理

Jacopo Tagliabue, Federico Bianchi, Ciro Greco

专题命中 规划决策 ：agentic(title,abstract)；agent(abstract)；分类 cs.AI

AI总结 本文提出Bauplan设计，通过事务机制实现湖仓中的数据和计算隔离，解决代理工作流的可信性问题，并提供自修复管道的实现。

Comments AAAI26, pre-print of paper accepted at the Trustworthy Agentic AI Workshop

ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning

Binrong Zhu, Guiran Liu, Nina Jiang

机构 * Department of Computer Science（计算机科学系） ； San Francisco State University（旧金山州立大学） ； Department of Mechanical & Industrial Engineering（机械与工业工程系） ； Louisiana State University（路易斯安那州立大学） 纠错

专题命中 规划决策 ：planning(title,abstract)；agent(abstract,comments)；分类 cs.AI

Comments Manuscript previously submitted to the NeurIPS 2025 Workshop on Bridging Language, Agent, and World Models (LAW 2025)

Towards Zero-Shot, Controllable Dialog Planning with LLMs

Dirk Väth, Ngoc Thang Vu

专题命中 规划决策 ：planning(title,abstract)；agent(abstract)；分类 cs.CL

Comments This paper has been accepted for publication at the AAAI 2022 Workshop on Planning in the Era of LLMs