AI Agent

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

Rushang Karia, Pulkit Verma, Alberto Speranzon, Siddharth Srivastava

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

Comments Ontology, Automated Planning, Planner Improvement

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

A Reinforcement Learning-based Adaptive Control Model for Future Street Planning, An Algorithm and A Case Study

Qiming Ye, Yuxiang Feng, Jing Han, Marc Stettler, Panagiotis Angeloudis

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

Comments Proceeding for 57th ISOCARP World Planning Congress, Nov 8-11, 2021, Doha, Qatar

Guiding Robot Exploration in Reinforcement Learning via Automated Planning

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

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

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

Adaptive Informative Path Planning with Multimodal Sensing

Shushman Choudhury, Nate Gruver, Mykel J. Kochenderfer

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

Comments First two authors contributed equally; International Conference on Automated Planning and Scheduling (ICAPS) 2020

Planning Robot Motion using Deep Visual Prediction

Meenakshi Sarkar, Prabhu Pradhan, Debasish Ghose

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

Comments Article at AAAI-18 Workshop on Planning and Inference

Mixed Integer Linear Programming For Exact Finite-Horizon Planning In Decentralized Pomdps

Raghav Aras, Alain Dutech, François Charpillet

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

Journal ref Dans The International Conference on Automated Planning and Scheduling (2007)

AgenticER: the next frontier in Entity Resolution

AgenticER：实体解析的下一个前沿领域

George Papadakis, Panos Korovesis, Manolis Koubarakis, Themis Palpanas

专题命中 规划决策 ：agentic(summary_cn,abstract)；autonomous agent(abstract)

AI总结 针对现有实体解析被动范式无法适配异构流数据场景的问题，提出Agentic ER新范式，将其建模为自主智能体的序列决策过程，构建参考架构并明确研究挑战，开拓数据管理与智能体交叉的新研究方向。

Comments Vision paper submitted to VLDB, 6 pages

HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency

HEPTAPOD：编排高能物理工作流程以实现自主智能体

Tony Menzo, Alexander Roman, Sergei Gleyzer, Konstantin Matchev, George T. Fleming, Stefan Höche, Stephen Mrenna, Prasanth Shyamsundar

专题命中 规划决策 ：agent(abstract)；tool use(abstract)；planning(abstract)；workflow(abstract)

AI总结 研究针对高能物理工作流程，介绍HEPTAPOD编排框架，能集成外部大语言模型，使其与特定领域工具交互构建管理HEP管道，通过案例展示其能力，为高能物理中人工参与及智能体辅助工作流程奠定基础。

Comments 47 pages, 6 figures

VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct

VeriEvol：通过可验证的进化指令扩展多模态数学推理

Haoling Li, Kai Zheng, Jie Wu, Can Xu, Qingfeng Sun, Han Hu, Yujiu Yang

机构 * Tsinghua University（清华大学） ； Tencent Hunyuan（腾讯混元） 纠错

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

AI总结 提出VeriEvol框架，通过类型感知进化模块生成更难的问题，并利用HTV-Agent验证器确保答案可靠性，从而在强化学习中扩展多模态数学推理数据，提升模型性能。

IPIBench: Evaluating Interactive Proactive Intelligence of MLLMs under Continuous Streams

IPIBench: 在连续流下评估多模态大模型的交互式主动智能

Jinzhao Li, Yinuo Chen, Wenxuan Song, Yijia Lei, Yichi Zhang, Honglei Yan, Panwang Pan, Miao Liu

机构 * College of AI, Tsinghua University（清华大学人工智能学院） ； ByteDance（字节跳动） 纠错

专题命中 规划决策 ：agent(summary_cn,abstract)；agentic(abstract)

AI总结 提出IPIBench基准，用于评估多模态大模型在流式视频场景中的交互式主动智能，并设计IPI-Agent框架以改善主动触发和交互协调。

Heterogeneous Scientific Foundation Model Collaboration

异质科学基础模型协作

Zihao Li, Jiaru Zou, Feihao Fang, Xuying Ning, Mengting Ai, Tianxin Wei, Sirui Chen, Xiyuan Yang, Jingrui He

机构 * University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） 纠错

专题命中 规划决策 ：agent(abstract)；planning(abstract)；agentic(abstract)；multi-agent(abstract)

AI总结 本文提出Eywa框架，通过将语言模型与领域特定基础模型结合，提升科学领域多模态任务的推理能力，实验显示其在结构化数据任务中表现更优。

Comments Preprint. 57 Pages

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

Learning Multi-Agent Local Collision-Avoidance for Collaborative Carrying tasks with Coupled Quadrupedal Robots

学习多智能体局部避障以实现协作搬运任务 with 耦合四足机器人

Francesca Bray, Simone Tolomei, Andrei Cramariuc, Cesar Cadena, Marco Hutter

机构 * Robotic Systems Lab, ETH Zurich（埃因斯坦理工大学机器人系统实验室） 纠错

专题命中 规划决策 ：agent(title)；multi-agent(title)

AI总结 本文提出基于强化学习的策略，使两台连接物体的四足机器人在未知环境中自主避障，无需预设路径或地图。

DBF-MA: A Differential Bayesian Filtering Planner for Multi-Agent Autonomous Racing Overtakes

Trent Weiss, Amar Kulkarni, Madhur Behl

机构 * Trent Weiss 1 ； Amar Kulkarni 2 ； Madhur Behl 2 纠错

专题命中 规划决策 ：agent(title)；multi-agent(title)

Comments This work has been submitted to the IEEE for possible publication

Multi-agent Robust and Optimal Policy Learning for Data Harvesting

Shili Wu, Yancheng Zhu, Aniruddha Datta, Sean B. Andersson

专题命中 规划决策 ：agent(title)；multi-agent(title)

ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges

Cheng Qian, Hongyi Du, Hongru Wang, Xiusi Chen, Yuji Zhang, Avirup Sil, Chengxiang Zhai, Kathleen McKeown, Heng Ji

机构 * University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） ； IBM Research AI（IBM人工智能研究） ； Columbia University（哥伦比亚大学） 纠错

专题命中 规划决策 ：agent(abstract)；tool use(abstract)；planning(abstract)；multi-agent(abstract)

Comments 36 Pages, 26 Figures, 5 Tables

AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in Dynamic Environments by Leveraging Object Detection

Roohan Ahmed Khan, Valerii Serpiva, Demetros Aschalew, Aleksey Fedoseev, Dzmitry Tsetserukou

机构 * Intelligent Space Robotics Laboratory（智能空间机器人实验室） ； Center for Digital Engineering（数字工程中心） ； Skolkovo Institute of Science and Technology（斯克尔科夫科学与技术研究所） 纠错

专题命中 规划决策 ：agent(title)；planning(title)

Comments Manuscript has been accepted at 2025 INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS (ICUAS)

D-HAL: Distributed Hierarchical Adversarial Learning for Multi-Agent Interaction in Autonomous Intersection Management

Guanzhou Li, Jianping Wu, Yujing He

专题命中 规划决策 ：agent(title)；multi-agent(title)

On global convergence of area-constrained formations of hierarchical multi-agent systems

Toshiharu Sugie, Fei Tong, Brian D. O. Anderson, Zhiyong Sun

专题命中 规划决策 ：agent(title)；multi-agent(title)

Comments Accepted in the 59th IEEE Conference on Decision and Control (CDC 2020). arXiv admin note: text overlap with arXiv:1808.00312

Empathic Autonomous Agents

Timotheus Kampik, Juan Carlos Nieves, Helena Lindgren

专题命中 规划决策 ：autonomous agent(title,abstract)；agent(abstract,comments)；multi-agent(comments)

Comments Accepted for: Engineering Multi-Agent Systems, 6th International Workshop, EMAS 2018, Stockholm, Sweden, 14-15 July, 2018, Revised, Selected, and Invited Papers

AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis

AdaLens：用于监控和引导长时间运行的智能体数据分析的交互式故事情节

Yangtian Liu, Yan Miao, Shuhan Liu, Yunfan Zhou, Dae Hyun Kim, Di Weng, Yingcai Wu

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

AI总结 针对长时间智能体数据分析的传统界面无法满足可观测性与可引导性需求，本文提出交互式系统AdaLens，结合故事情节表示与引导交互，经案例及用户研究验证其能有效支持分析师监控与引导此类分析。

PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance

PACE：用于去中心化金融中安全AI智能体的策略验证合约执行

Rabimba Karanjai, Yang Lu, Richard Williamson, Hemanth Hm, Prakhar Mehrotra, Lei Xu, Weidong, Shi

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

AI总结 该研究提出PACE框架，通过引入类型化交易意图等机制，实现去中心化金融中AI智能体的安全交易授权，在确定性沙箱中实现0.00不安全执行率和0.00误报率，提升了DeFi场景下AI智能体的安全性。

VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning

VISOR: 通过迭代搜索和超视距推理实现基于视觉检索的增强生成

Yucheng Shen, Jiulong Wu, Jizhou Huang, Dawei Yin, Lingyong Yan, Min Cao

机构 * Soochow University（苏州大学） ； Baidu Inc.（百度公司） 纠错

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

AI总结 VISOR通过结构化证据空间和动态轨迹机制解决多步推理中的视觉证据稀疏和搜索漂移问题，实现高效长视距视觉推理。

Comments Accepted by ACM Multimedia 2026 (MM '26). 8 pages, 3 figures. Code: https://github.com/syc1336/VISOR

AstronOS: A Unified Execution Model and Runtime for Long-Horizon Agentic Systems

AstronOS：面向长周期智能体系统的统一执行模型与运行时

Zhenhang Nie, Gui Zheng, Xudong Sun, Tailong Zhu, Bin Zhang

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

AI总结 AstronOS是面向长周期智能体系统的统一执行模型与运行时，其介导的交接策略在三阶段任务中通过率远高于其他策略，虽时间成本更高但端到端表现更优。

Comments 23 pages, 2 figures, 13 tables. Zhenhang Nie and Gui Zheng contributed equally; Gui Zheng and Bin Zhang are corresponding authors

Agentic Data Cleaning Without a Clean Reference: An Experimental Study of Capabilities and Trade-offs

无干净参考的智能体数据清洗：能力与权衡的实验研究

Hadi Fadlallah

机构 * Faculty of Arts and Sciences（文理学院） ； University of Sciences and Arts in Lebanon（黎巴嫩科学与艺术大学） 纠错

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

AI总结 本研究提出整合多组件的基于证据的无参考智能体数据清洗框架，经多数据集实验发现，额外能力会在多指标间引入权衡，无配置在所有标准上最优。

Comments 21 pages, 3 figures, Submitted to New Generation Computing

Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration

基于期望自由能的火星探测机器人信息路径规划

Ajith Anil Meera, Pablo Lanillos, Wouter Kouw

机构 * TU Eindhoven（埃因霍温理工大学） ； Cajal Centre for Neuroscience, Spanish National Research Council（西班牙国家研究委员会卡哈尔神经科学中心） 纠错

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

AI总结 该研究提出将主动推理的期望自由能作为统一准则，用于预算约束下的机器人信息路径规划，其规划方法在火星探测场景中可同时实现精准地图构建与高价值区域定位，性能优于信息论基准方法。

Comments accepted for IWAI 2026

Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Tasks

代理聚合用于长周期代理任务的并行扩展

Yoonsang Lee, Howard Yen, Xi Ye, Danqi Chen

机构 * Princeton Language and Intelligence, Princeton University（普林斯顿大学语言与智能实验室） 纠错

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

AI总结 本文提出AggAgent，通过将并行轨迹视为环境，有效解决长周期代理任务中轨迹信息聚合问题，提升性能并降低开销。

Comments COLM 2026. Code is available at https://github.com/princeton-pli/AggAgent