AI Agent

MolAct: An Agentic RL Framework for Molecular Editing and Property Optimization

MolAct：一种用于分子编辑和性质优化的代理强化学习框架

Zhuo Yang, Yeyun Chen, Jiaqing Xie, Ben Gao, Shuaike Shen, Wanhao Liu, Liujia Yang, Beilun Wang, Tianfan Fu, Yuqiang Li

机构 * Shanghai Artificial Intelligence Laboratory（上海人工智能实验室） ； Xidian University（西安电子科技大学） ； Shanghai Innovation Institute（上海创新研究院） ； Wuhan University（武汉大学） ； Shanghai Jiao Tong University（上海交通大学） ； Southeast University（东南大学） ； Nanjing University（南京大学） ； Carnegie Mellon University（卡内基梅隆大学） 纠错

专题命中 工具调用 ：agentic(title,abstract)；agent(abstract)；tool-use(abstract)；分类 cs.AI

AI总结 MolAct框架通过代理强化学习方法，实现了分子编辑和性质优化的多步骤、工具增强过程，提升了分子设计的可靠性和可解释性。

NVIDIA Nemotron 3: Efficient and Open Intelligence

NVIDIA Nemotron 3：高效且开放的智能

NVIDIA, :, Aaron Blakeman, Aaron Grattafiori, Aarti Basant, Abhibha Gupta, Abhinav Khattar, Adi Renduchintala, Aditya Vavre, Akanksha Shukla, Akhiad Bercovich, Aleksander Ficek, Aleksandr Shaposhnikov, Alex Kondratenko, Alexander Bukharin, Alexandre Milesi, Ali Taghibakhshi, Alisa Liu, Amelia Barton, Ameya Sunil Mahabaleshwarkar, Amir Klein, Amit Zuker, Amnon Geifman, Amy Shen, Anahita Bhiwandiwalla, Andrew Tao, Anjulie Agrusa, Ankur Verma, Ann Guan, Anubhav Mandarwal, Arham Mehta, Ashwath Aithal, Ashwin Poojary, Asif Ahamed, Asit Mishra, Asma Kuriparambil Thekkumpate, Ayush Dattagupta, Banghua Zhu, Bardiya Sadeghi, Barnaby Simkin, Ben Lanir, Benedikt Schifferer, Besmira Nushi, Bilal Kartal, Bita Darvish Rouhani, Boris Ginsburg, Brandon Norick, Brandon Soubasis, Branislav Kisacanin, Brian Yu, Bryan Catanzaro, Carlo del Mundo, Chantal Hwang, Charles Wang, Cheng-Ping Hsieh, Chenghao Zhang, Chenhan Yu, Chetan Mungekar, Chintan Patel, Chris Alexiuk, Christopher Parisien, Collin Neale, Cyril Meurillon, Damon Mosk-Aoyama, Dan Su, Dane Corneil, Daniel Afrimi, Daniel Lo, Daniel Rohrer, Daniel Serebrenik, Daria Gitman, Daria Levy, Darko Stosic, David Mosallanezhad, Deepak Narayanan, Dhruv Nathawani, Dima Rekesh, Dina Yared, Divyanshu Kakwani, Dong Ahn, Duncan Riach, Dusan Stosic, Edgar Minasyan, Edward Lin, Eileen Long, Eileen Peters Long, Elad Segal, Elena Lantz, Ellie Evans, Elliott Ning, Eric Chung, Eric Harper, Eric Tramel, Erick Galinkin, Erik Pounds, Evan Briones, Evelina Bakhturina, Evgeny Tsykunov, Faisal Ladhak, Fay Wang, Fei Jia, Felipe Soares, Feng Chen, Ferenc Galko, Frank Sun, Frankie Siino, Gal Hubara Agam, Ganesh Ajjanagadde, Gantavya Bhatt, Gargi Prasad, George Armstrong, Gerald Shen, Gorkem Batmaz, Grigor Nalbandyan, Haifeng Qian, Harsh Sharma, Hayley Ross, Helen Ngo, Herbert Hum, Herman Sahota, Hexin Wang, Himanshu Soni, Hiren Upadhyay, Huizi Mao, Huy C Nguyen, Huy Q Nguyen, Iain Cunningham, Ido Galil, Ido Shahaf, Igor Gitman, Ilya Loshchilov, Itamar Schen, Itay Levy, Ivan Moshkov, Izik Golan, Izzy Putterman, Jan Kautz, Jane Polak Scowcroft, Jared Casper, Jatin Mitra, Jeffrey Glick, Jenny Chen, Jesse Oliver, Jian Zhang, Jiaqi Zeng, Jie Lou, Jimmy Zhang, Jinhang Choi, Jining Huang, Joey Conway, Joey Guman, John Kamalu, Johnny Greco, Jonathan Cohen, Joseph Jennings, Joyjit Daw, Julien Veron Vialard, Junkeun Yi, Jupinder Parmar, Kai Xu, Kan Zhu, Kari Briski, Katherine Cheung, Katherine Luna, Keith Wyss, Keshav Santhanam, Kevin Shih, Kezhi Kong, Khushi Bhardwaj, Kirthi Shankar, Krishna C. Puvvada, Krzysztof Pawelec, Kumar Anik, Lawrence McAfee, Laya Sleiman, Leon Derczynski, Li Ding, Lizzie Wei, Lucas Liebenwein, Luis Vega, Maanu Grover, Maarten Van Segbroeck, Maer Rodrigues de Melo, Mahdi Nazemi, Makesh Narsimhan Sreedhar, Manoj Kilaru, Maor Ashkenazi, Marc Romeijn, Marcin Chochowski, Mark Cai, Markus Kliegl, Maryam Moosaei, Matt Kulka, Matvei Novikov, Mehrzad Samadi, Melissa Corpuz, Mengru Wang, Meredith Price, Michael Andersch, Michael Boone, Michael Evans, Miguel Martinez, Mikail Khona, Mike Chrzanowski, Minseok Lee, Mohammad Dabbah, Mohammad Shoeybi, Mostofa Patwary, Nabin Mulepati, Najeeb Nabwani, Natalie Hereth, Nave Assaf, Negar Habibi, Neta Zmora, Netanel Haber, Nicola Sessions, Nidhi Bhatia, Nikhil Jukar, Nikki Pope, Nikolai Ludwig, Nima Tajbakhsh, Nir Ailon, Nirmal Juluru, Nishant Sharma, Oleksii Hrinchuk, Oleksii Kuchaiev, Olivier Delalleau, Oluwatobi Olabiyi, Omer Ullman Argov, Omri Puny, Oren Tropp, Ouye Xie, Parth Chadha, Pasha Shamis, Paul Gibbons, Pavlo Molchanov, Pawel Morkisz, Peter Dykas, Peter Jin, Pinky Xu, Piotr Januszewski, Pranav Prashant Thombre, Prasoon Varshney, Pritam Gundecha, Przemek Tredak, Qing Miao, Qiyu Wan, Rabeeh Karimi Mahabadi, Rachit Garg, Ran El-Yaniv, Ran Zilberstein, Rasoul Shafipour, Rich Harang, Rick Izzo, Rima Shahbazyan, Rishabh Garg, Ritika Borkar, Ritu Gala, Riyad Islam, Robert Hesse, Roger Waleffe, Rohit Watve, Roi Koren, Ruoxi Zhang, Russell Hewett, Russell J. Hewett, Ryan Prenger, Ryan Timbrook, Sadegh Mahdavi, Sahil Modi, Samuel Kriman, Sangkug Lim, Sanjay Kariyappa, Sanjeev Satheesh, Saori Kaji, Satish Pasumarthi, Saurav Muralidharan, Sean Narentharen, Sean Narenthiran, Seonmyeong Bak, Sergey Kashirsky, Seth Poulos, Shahar Mor, Shanmugam Ramasamy, Shantanu Acharya, Shaona Ghosh, Sharath Turuvekere Sreenivas, Shelby Thomas, Shiqing Fan, Shreya Gopal, Shrimai Prabhumoye, Shubham Pachori, Shubham Toshniwal, Shuoyang Ding, Siddharth Singh, Simeng Sun, Smita Ithape, Somshubra Majumdar, Soumye Singhal, Stas Sergienko, Stefania Alborghetti, Stephen Ge, Sugam Dipak Devare, Sumeet Kumar Barua, Suseella Panguluri, Suyog Gupta, Sweta Priyadarshi, Syeda Nahida Akter, Tan Bui, Teodor-Dumitru Ene, Terry Kong, Thanh Do, Tijmen Blankevoort, Tim Moon, Tom Balough, Tomer Asida, Tomer Bar Natan, Tomer Ronen, Tugrul Konuk, Twinkle Vashishth, Udi Karpas, Ushnish De, Vahid Noorozi, Vahid Noroozi, Venkat Srinivasan, Venmugil Elango, Victor Cui, Vijay Korthikanti, Vinay Rao, Vitaly Kurin, Vitaly Lavrukhin, Vladimir Anisimov, Wanli Jiang, Wasi Uddin Ahmad, Wei Du, Wei Ping, Wenfei Zhou, Will Jennings, William Zhang, Wojciech Prazuch, Xiaowei Ren, Yashaswi Karnati, Yejin Choi, Yev Meyer, Yi-Fu Wu, Yian Zhang, Yigong Qin, Ying Lin, Yonatan Geifman, Yonggan Fu, Yoshi Subara, Yoshi Suhara, Yubo Gao, Zach Moshe, Zhen Dong, Zhongbo Zhu, Zihan Liu, Zijia Chen, Zijie Yan

机构 * NVIDIA 纠错

专题命中 工具调用 ：tool use(abstract)；agentic(abstract)；分类 cs.AI、cs.CL、cs.LG

AI总结 NVIDIA推出的Nemotron 3系列模型通过混合Mamba-Transformer架构实现高效推理与大上下文长度，提供多种规模模型以满足不同应用场景需求。

Scaling Laws for Economic Productivity: Experimental Evidence in LLM-Assisted Consulting, Data Analyst, and Management Tasks

经济生产力的扩展定律：在LLM辅助咨询、数据分析师和管理任务中的实验证据

Ali Merali

专题命中 工具调用 ：tool use(abstract)；agentic(abstract)；分类 cs.AI

AI总结 本文通过实验发现，LLM训练计算量和算法进展可提升专业任务效率，非代理分析任务的生产力增益更大，预计未来十年美国生产力将提升约20%。

Policy-Conditioned Policies for Multi-Agent Task Solving

基于策略条件的多智能体任务解决

Yue Lin, Shuhui Zhu, Wenhao Li, Ang Li, Dan Qiao, Pascal Poupart, Hongyuan Zha, Baoxiang Wang

机构 * The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）） ； University of Waterloo（滑铁卢大学） ； Tongji University（同济大学） ； Vector Institute（向量研究所） 纠错

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

AI总结 本文提出通过程序化表示和大型语言模型实现多智能体任务解决，引入程序均衡概念并提出PIBR算法，有效解决协调博弈和合作环境问题。

Towards Hierarchical Multi-Agent Decision-Making for Uncertainty-Aware EV Charging

面向不确定性感知的电动车充电的分层多智能体决策

Lo Pang-Yun Ting, Ali Şenol, Huan-Yang Wang, Hsu-Chao Lai, Kun-Ta Chuang, Huan Liu

机构 * Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan（国立成功大学计算机科学与信息工程系） ； School of Computing and Augmented Intelligence, Arizona State University, USA（亚利桑那州立大学计算与增强智能学院） 纠错

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

AI总结 本文提出HUCA框架，通过分层多智能体结构和不确定性感知批评者增强机制，实现实时电动车充电控制，有效降低用电成本并满足充电需求。

LLM-Empowered Agentic AI for QoE-Aware Network Slicing Management in Industrial IoT

基于大语言模型的代理AI用于工业物联网中面向QoE的网络切片管理

Xudong Wang, Lei Feng, Ruichen Zhang, Fanqin Zhou, Hongyang Du, Wenjing Li, Dusit Niyato, Abbas Jamalipour, Ping Zhang

专题命中 规划决策 ：agentic(title,abstract)；AI agent(abstract)；planning(abstract)；workflow(abstract)

AI总结 本文提出一种基于大语言模型的代理AI方法，用于工业物联网中面向服务质量的网络切片管理，通过整合推理、规划和适应能力，提升网络切片的延迟、可靠性和成本效率。

Comments 8 pages, 5 figures

TrafficSimAgent: A Hierarchical Agent Framework for Autonomous Traffic Simulation with MCP Control

TrafficSimAgent: 一个用于具有MCP控制的自动驾驶交通模拟的分层代理框架

Yuwei Du, Jun Zhang, Jie Feng, Zhicheng Liu, Jian Yuan, Yong Li

机构 * Department of Electronic Engineering, BNRist, Tsinghua University, Beijing, China（电子工程系、BNRist、清华大学、北京、中国） ； AMAP, Alibaba Group, Beijing, China（AMAP、阿里巴巴集团、北京、中国） 纠错

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

AI总结 TrafficSimAgent通过基于大语言模型的分层代理框架，解决交通模拟中实验设计和决策优化的挑战，实现高效且准确的自动驾驶交通模拟。

Comments The code will be available at: https://github.com/tsinghua-fib-lab/TrafficSimAgent

X-GridAgent: An LLM-Powered Agentic AI System for Assisting Power Grid Analysis

X-GridAgent: 一种基于大语言模型的代理AI系统，用于协助电力 grid 分析

Yihan, Wen, Xin Chen

机构 * Department of Electrical and Computer Engineering, Texas A&M University（电气与计算机工程系，德克萨斯A&M大学） 纠错

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

AI总结 X-GridAgent是一种基于大语言模型的代理AI系统，通过自然语言查询实现电力系统分析的自动化，结合提示优化和混合检索增强生成算法，提升电网分析的效率和准确性。

Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

Nemotron 3 Nano：开放、高效专家混合混合Mamba-Transformer模型用于代理推理

NVIDIA, :, Aaron Blakeman, Aaron Grattafiori, Aarti Basant, Abhibha Gupta, Abhinav Khattar, Adi Renduchintala, Aditya Vavre, Akanksha Shukla, Akhiad Bercovich, Aleksander Ficek, Aleksandr Shaposhnikov, Alex Kondratenko, Alexander Bukharin, Alexandre Milesi, Ali Taghibakhshi, Alisa Liu, Amelia Barton, Ameya Sunil Mahabaleshwarkar, Amir Klein, Amit Zuker, Amnon Geifman, Amy Shen, Anahita Bhiwandiwalla, Andrew Tao, Ann Guan, Anubhav Mandarwal, Arham Mehta, Ashwath Aithal, Ashwin Poojary, Asif Ahamed, Asma Kuriparambil Thekkumpate, Ayush Dattagupta, Banghua Zhu, Bardiya Sadeghi, Barnaby Simkin, Ben Lanir, Benedikt Schifferer, Besmira Nushi, Bilal Kartal, Bita Darvish Rouhani, Boris Ginsburg, Brandon Norick, Brandon Soubasis, Branislav Kisacanin, Brian Yu, Bryan Catanzaro, Carlo del Mundo, Chantal Hwang, Charles Wang, Cheng-Ping Hsieh, Chenghao Zhang, Chenhan Yu, Chetan Mungekar, Chintan Patel, Chris Alexiuk, Christopher Parisien, Collin Neale, Damon Mosk-Aoyama, Dan Su, Dane Corneil, Daniel Afrimi, Daniel Rohrer, Daniel Serebrenik, Daria Gitman, Daria Levy, Darko Stosic, David Mosallanezhad, Deepak Narayanan, Dhruv Nathawani, Dima Rekesh, Dina Yared, Divyanshu Kakwani, Dong Ahn, Duncan Riach, Dusan Stosic, Edgar Minasyan, Edward Lin, Eileen Long, Eileen Peters Long, Elena Lantz, Ellie Evans, Elliott Ning, Eric Chung, Eric Harper, Eric Tramel, Erick Galinkin, Erik Pounds, Evan Briones, Evelina Bakhturina, Faisal Ladhak, Fay Wang, Fei Jia, Felipe Soares, Feng Chen, Ferenc Galko, Frankie Siino, Gal Hubara Agam, Ganesh Ajjanagadde, Gantavya Bhatt, Gargi Prasad, George Armstrong, Gerald Shen, Gorkem Batmaz, Grigor Nalbandyan, Haifeng Qian, Harsh Sharma, Hayley Ross, Helen Ngo, Herman Sahota, Hexin Wang, Himanshu Soni, Hiren Upadhyay, Huizi Mao, Huy C Nguyen, Huy Q Nguyen, Iain Cunningham, Ido Shahaf, Igor Gitman, Ilya Loshchilov, Ivan Moshkov, Izzy Putterman, Jan Kautz, Jane Polak Scowcroft, Jared Casper, Jatin Mitra, Jeffrey Glick, Jenny Chen, Jesse Oliver, Jian Zhang, Jiaqi Zeng, Jie Lou, Jimmy Zhang, Jining Huang, Joey Conway, Joey Guman, John Kamalu, Johnny Greco, Jonathan Cohen, Joseph Jennings, Joyjit Daw, Julien Veron Vialard, Junkeun Yi, Jupinder Parmar, Kai Xu, Kan Zhu, Kari Briski, Katherine Cheung, Katherine Luna, Keshav Santhanam, Kevin Shih, Kezhi Kong, Khushi Bhardwaj, Krishna C. Puvvada, Krzysztof Pawelec, Kumar Anik, Lawrence McAfee, Laya Sleiman, Leon Derczynski, Li Ding, Lucas Liebenwein, Luis Vega, Maanu Grover, Maarten Van Segbroeck, Maer Rodrigues de Melo, Makesh Narsimhan Sreedhar, Manoj Kilaru, Maor Ashkenazi, Marc Romeijn, Mark Cai, Markus Kliegl, Maryam Moosaei, Matvei Novikov, Mehrzad Samadi, Melissa Corpuz, Mengru Wang, Meredith Price, Michael Boone, Michael Evans, Miguel Martinez, Mike Chrzanowski, Mohammad Shoeybi, Mostofa Patwary, Nabin Mulepati, Natalie Hereth, Nave Assaf, Negar Habibi, Neta Zmora, Netanel Haber, Nicola Sessions, Nidhi Bhatia, Nikhil Jukar, Nikki Pope, Nikolai Ludwig, Nima Tajbakhsh, Nirmal Juluru, Oleksii Hrinchuk, Oleksii Kuchaiev, Olivier Delalleau, Oluwatobi Olabiyi, Omer Ullman Argov, Ouye Xie, Parth Chadha, Pasha Shamis, Pavlo Molchanov, Pawel Morkisz, Peter Dykas, Peter Jin, Pinky Xu, Piotr Januszewski, Pranav Prashant Thombre, Prasoon Varshney, Pritam Gundecha, Qing Miao, Rabeeh Karimi Mahabadi, Ran El-Yaniv, Ran Zilberstein, Rasoul Shafipour, Rich Harang, Rick Izzo, Rima Shahbazyan, Rishabh Garg, Ritika Borkar, Ritu Gala, Riyad Islam, Roger Waleffe, Rohit Watve, Roi Koren, Ruoxi Zhang, Russell J. Hewett, Ryan Prenger, Ryan Timbrook, Sadegh Mahdavi, Sahil Modi, Samuel Kriman, Sanjay Kariyappa, Sanjeev Satheesh, Saori Kaji, Satish Pasumarthi, Sean Narentharen, Sean Narenthiran, Seonmyeong Bak, Sergey Kashirsky, Seth Poulos, Shahar Mor, Shanmugam Ramasamy, Shantanu Acharya, Shaona Ghosh, Sharath Turuvekere Sreenivas, Shelby Thomas, Shiqing Fan, Shreya Gopal, Shrimai Prabhumoye, Shubham Pachori, Shubham Toshniwal, Shuoyang Ding, Siddharth Singh, Simeng Sun, Smita Ithape, Somshubra Majumdar, Soumye Singhal, Stefania Alborghetti, Stephen Ge, Sugam Dipak Devare, Sumeet Kumar Barua, Suseella Panguluri, Suyog Gupta, Sweta Priyadarshi, Syeda Nahida Akter, Tan Bui, Teodor-Dumitru Ene, Terry Kong, Thanh Do, Tijmen Blankevoort, Tom Balough, Tomer Asida, Tomer Bar Natan, Tugrul Konuk, Twinkle Vashishth, Udi Karpas, Ushnish De, Vahid Noorozi, Vahid Noroozi, Venkat Srinivasan, Venmugil Elango, Vijay Korthikanti, Vitaly Kurin, Vitaly Lavrukhin, Wanli Jiang, Wasi Uddin Ahmad, Wei Du, Wei Ping, Wenfei Zhou, Will Jennings, William Zhang, Wojciech Prazuch, Xiaowei Ren, Yashaswi Karnati, Yejin Choi, Yev Meyer, Yi-Fu Wu, Yian Zhang, Ying Lin, Yonatan Geifman, Yonggan Fu, Yoshi Subara, Yoshi Suhara, Yubo Gao, Zach Moshe, Zhen Dong, Zihan Liu, Zijia Chen, Zijie Yan

机构 * NVIDIA 纠错

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

AI总结 Nemotron 3 Nano是一种高效混合Mamba-Transformer模型，通过预训练和微调在代理推理任务中实现更高准确性和吞吐量。

Inspection Planning Primitives with Implicit Models

基于隐式模型的检查规划原语

Jingyang You, Hanna Kurniawati, Lashika Medagoda

机构 * School of Computing, The Australian National University（澳大利亚国立大学计算学院） ； Abyss Solutions（Abyss解决方案） 纠错

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

AI总结 本文提出基于隐式模型的检查规划原语，使基于采样的检查规划器能高效利用神经SDFs表示，减少内存使用并提升规划效率。

ETP-R1: Evolving Topological Planning with Reinforcement Fine-tuning for Vision-Language Navigation in Continuous Environments

连续环境中的视觉-语言导航（VLN-CE）需要一个具身体验的智能体在连续环境中导航至目标，遵循自然语言指令。虽然当前基于图的方法通过将环境抽象为拓扑地图并简化动作空间到路径选择，提供了一种高效、结构化的方法，但它们在利用大规模数据和先进训练范式方面落后于基于大视觉-语言模型（LVLMs）的方法。在本文中，我们通过引入ETP-R1框架，将数据扩展和强化微调（RFT）范式应用于基于图的VLN-CE模型，以弥合这一差距。

Shuhao Ye, Sitong Mao, Yuxiang Cui, Xuan Yu, Shichao Zhai, Wen Chen, Shunbo Zhou, Rong Xiong, Yue Wang

机构 * Zhejiang University（浙江大学） ； Huawei Technologies Co., Ltd（华为技术有限公司） ； Zhejiang Humanoid Robot Innovation Center（浙江人形机器人创新中心） 纠错

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

AI总结 ETP-R1通过数据扩展和强化微调范式，提升基于图的连续环境视觉-语言导航性能，实现新状态最先进的表现。

Comments 8 pages, 6 figures

MILP-driven Network Planning Framework for Energy Efficiency and Coverage Maximization in IoT Mesh Networks

基于混合整数线性规划的物联网网状网络能量效率与覆盖最大化的网络规划框架

Ishmal Sohail, Attiq Zeeshan, M. Umar Khan, Syed Zubair, Rana Fayyaz Ahmad, Faizan Hamayat

专题命中 规划决策 ：planning(title,abstract)

AI总结 本文提出基于混合整数线性规划的物联网网状网络网络规划框架，通过优化静态节点放置和移动路径规划，提升覆盖效率并降低能耗。

Reasoning-Driven Amodal Completion: Collaborative Agents and Perceptual Evaluation

基于推理的无遮挡完成：协作代理与感知评估

Hongxing Fan, Shuyu Zhao, Jiayang Ao, Lu Sheng

机构 * School of Computer Science and Engineering, Beihang University（计算机科学与工程学院，北京航空航天大学） ； School of Software, Beihang University（软件学院，北京航空航天大学） ； School of Computing and Information Systems, The University of Melbourne（计算与信息系统学院，墨尔本大学） 纠错

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

AI总结 本文提出一种协作多代理推理框架，通过解耦语义规划与视觉合成，提升无遮挡完成任务的语义一致性和结构完整性。

ClarifyMT-Bench: Benchmarking and Improving Multi-Turn Clarification for Conversational Large Language Models

ClarifyMT-Bench: 会话大语言模型多轮澄清的基准测试与改进

Sichun Luo, Yi Huang, Mukai Li, Shichang Meng, Fengyuan Liu, Zefa Hu, Junlan Feng, Qi Liu

机构 * The University of Hong Kong（香港大学） ； JIUTIAN Research, China Mobile（中移动巨泰研宄院） ； CityUHK（城市大学） 纠错

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

AI总结 ClarifyMT-Bench通过多轮对话基准测试和ClarifyAgent代理方法，解决LLM在多轮对话中澄清不足的问题，提升在模糊情境下的鲁棒性。

Embodied AI-Enhanced IoMT Edge Computing: UAV Trajectory Optimization and Task Offloading with Mobility Prediction

具身AI增强的IoMT边缘计算：基于移动预测的无人机轨迹优化与任务卸载

Siqi Mu, Shuo Wen, Yang Lu, Ruihong Jiang, Bo Ai

机构 * School of Sports Engineering, Beijing Sport University（体育工程学院，北京体育大学） ； School of Computer and Information Technology, Beijing Jiaotong University（计算机与信息学院，北京交通大学） ； State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学） ； School of Electronics and Information Engineering, Beijing Jiaotong University（电子与信息工程学院，北京交通大学） 纠错

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

AI总结 本文提出了一种基于移动预测的无人机轨迹优化与任务卸载方法，利用具身AI增强的IoMT边缘计算框架，通过分层多尺度Transformer模型和预测增强型DRL算法，实现对WBAN用户任务的关键性优化。

The Silent Scholar Problem: A Probabilistic Framework for Breaking Epistemic Asymmetry in LLM Agents

沉默学者问题：一种基于概率的框架，用于在LLM代理中打破知识不对称性

Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng

机构 * School of Science and Technology（科学与技术学院） ； Kwansei Gakuin University（冈山大学） ； School of Engineering & Computer Science（工程与计算机科学学院） ； Victoria University of Wellington（惠灵顿维多利亚大学） 纠错

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

AI总结 本文提出了一种基于概率的框架，用于在LLM代理中打破知识不对称性，通过双向知识交流和不确定性驱动的主动学习策略提升代理的适应性和信息获取能力。

Optimizing Decoding Paths in Masked Diffusion Models by Quantifying Uncertainty

通过量化不确定性优化掩码扩散模型的解码路径

Ziyu Chen, Xinbei Jiang, Peng Sun, Tao Lin

机构 * Zhejiang University（浙江大学） ； Westlake University（西湖大学） ； University of Chicago（芝加哥大学） 纠错

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

AI总结 通过引入去噪熵度量，优化掩码扩散模型的解码路径以提升生成质量与准确性。

LookPlanGraph: Embodied Instruction Following Method with VLM Graph Augmentation

LookPlanGraph: 一种基于视觉语言模型图增强的具身指令跟随方法

Anatoly O. Onishchenko, Alexey K. Kovalev, Aleksandr I. Panov

机构 * MIRAI ； Cognitive AI Systems Lab（认知人工智能系统实验室） 纠错

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

AI总结 LookPlanGraph通过动态更新场景图实现具身指令跟随，利用视觉语言模型增强环境感知，优于静态场景图方法。

NULLBUS: Multimodal Mixed-Supervision for Breast Ultrasound Segmentation via Nullable Global-Local Prompts

NULLBUS：通过可空全局-局部提示实现乳腺超声分割的多模态混合监督

Raja Mallina, Bryar Shareef

机构 * Department of Computer Science, University of Nevada, Las Vegas, NV 89154, USA（计算机科学系，内华达大学拉斯维加斯分校） 纠错

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

AI总结 NULLBUS通过可空全局-局部提示实现乳腺超声分割的多模态混合监督，取得高分割精度和鲁棒性。

Comments 5 pages, 2 figures, and 4 tables

Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomy

命令式学习：一种用于机器人自主性的自监督神经符号学习框架

Chen Wang, Kaiyi Ji, Junyi Geng, Zhongqiang Ren, Taimeng Fu, Fan Yang, Yifan Guo, Haonan He, Xiangyu Chen, Zitong Zhan, Qiwei Du, Shaoshu Su, Bowen Li, Yuheng Qiu, Yi Du, Qihang Li, Yifan Yang, Xiao Lin, Zhipeng Zhao

机构 * University at Buffalo（布法罗大学） ； Pennsylvania State University（宾夕法尼亚州立大学） ； Carnegie Mellon University（卡内基梅隆大学） ； ETH Zürich（苏黎世联邦理工学院） ； Purdue University（普渡大学） 纠错

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

AI总结 命令式学习通过自监督神经符号框架提升机器人自主性，结合神经网络与符号推理，解决数据依赖与泛化能力不足的问题。

Journal ref The International Journal of Robotics Research (IJRR), 2025

Adaptive Financial Sentiment Analysis for NIFTY 50 via Instruction-Tuned LLMs , RAG and Reinforcement Learning Approaches

通过指令调优LLMs、RAG和强化学习方法实现NIFTY 50的自适应金融情绪分析

Chaithra, Kamesh Kadimisetty, Biju R Mohan

机构 * National Institute of Technology Karnataka（印度卡纳塔克国家理工学院） ； Gayatri Vidya Parishad College of Engineering（迦雅利维达帕希拉工程学院） 纠错

专题命中 规划决策 ：agent(abstract)；分类 cs.AI

AI总结 本文提出结合指令调优LLMs、RAG和强化学习的方法，提升NIFTY 50金融情绪分析的准确性和市场适应性。

Comments Accepted in CODS 2025

Tracing Energy Flow: Learning Tactile-based Grasping Force Control to Prevent Slippage in Dynamic Object Interaction

追踪能量流动：学习基于触觉的抓取力控制以防止动态物体交互中的滑动

Cheng-Yu Kuo, Hirofumi Shin, Takamitsu Matsubara

机构 * Graduated School of Science and Technology（科学技术研究生学校） ； Nara Institute of Science and Technology（奈良科学技术大学） ； Honda R&D, Ltd.（本田研发有限公司） 纠错

专题命中 规划决策 ：planning(abstract)

AI总结 本文提出了一种基于触觉的抓取力控制方法，通过能量抽象和模型学习，在动态交互中有效防止滑动，无需外部传感或先验知识。

Comments 8 pages. Accepted by IEEE Robotics and Automation Letters (RA-L)

YCB-Handovers Dataset: Analyzing Object Weight Impact on Human Handovers to Adapt Robotic Handover Motion

YCB-Handovers 数据集：分析物体重量对人类手部交接的影响以适应机器人交接动作

Parag Khanna, Karen Jane Dsouza, Chunyu Wang, Mårten Björkman, Christian Smith

专题命中 规划决策 ：planning(abstract)

AI总结 YCB-Handovers数据集通过分析物体重量对人类交接动作的影响，为机器人运动规划提供数据支持。

Comments Paper presented at the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2025

Closed-Loop Control Law for Low Thrust Orbit Transfer with Guaranteed Stability

闭环控制律用于低推力轨道转移的稳定性保证

Suraj Kumar, Aditya Rallapalli, Nivriti Priyadarshini, Bharat Kumar GVP, Ravi Kumar L

专题命中 规划决策 ：planning(abstract)

AI总结 本文提出了一种基于Lyapunov的改进Q律控制方法，用于实现低推力轨道转移的闭环稳定性，适用于实时 onboard 实施。

Comments 6 pages, 5 figures, 3 tables -- Accepted for publication in Indian Control Conference 2025

Powered Descent Trajectory Optimization of Chandrayaan-3 using Radau Collocation and Controllable Sets

利用拉多求积与可控集的月球车着陆轨迹优化

Suraj Kumar, Aditya Rallapalli, Ashok Kumar Kakula, Bharat Kumar GVP

专题命中 规划决策 ：planning(abstract)

AI总结 本文提出利用拉多求积与可控集优化Chandrayaan-3月球车着陆轨迹，提升轨迹鲁棒性并量化燃料消耗与鲁棒性的权衡。

Comments 6 pages, 6 figure, Accepted for publication in Indian Control Conference 2025

DAO-Agent: Zero Knowledge-Verified Incentives for Decentralized Multi-Agent Coordination

DAO-Agent: 零知识验证的去中心化多智能体协调激励机制

Yihan Xia, Taotao Wang, Wenxin Xu, Shengli Zhang

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；autonomous agent(abstract)

AI总结 DAO-Agent通过零知识证明和混合架构，在去中心化环境中实现可审计的多智能体协调与公平激励分配，显著降低链上计算成本。

Comments 10 pages, 1 figure

Mechanism-Based Intelligence (MBI): Differentiable Incentives for Rational Coordination and Guaranteed Alignment in Multi-Agent Systems

基于机制的智能（MBI）：用于多智能体系统中理性协调和保证对齐的可微激励

Stefano Grassi

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.LG

AI总结 基于机制的智能（MBI）通过可微价格机制实现多智能体系统的理性协调和保证对齐，提供高效、可审计且可扩展的解决方案。

ART: Adaptive Response Tuning Framework -- A Multi-Agent Tournament-Based Approach to LLM Response Optimization

ART：自适应响应调节框架——基于多智能体竞赛的LLM响应优化方法

Omer Jauhar Khan

机构 * Department of Computer Science（计算机科学系） ； National University of Computer and Emerging Sciences (FAST-NUCES)（计算机与新兴科学国立大学（FAST-NUCES）） 纠错

专题命中 多智能体 ：agent(title,abstract)；multi-agent(title,abstract)；分类 cs.AI、cs.CL

AI总结 ART通过多智能体竞赛机制优化LLM响应，提升准确性和一致性，实现8.4%的质量提升和R²超过0.96的收敛效果。

Comments 14 pages, 11 figures, 5 tables. IEEE conference-style paper with appendices

Interaction, Process, Infrastructure: A Unified Framework for Human-Agent Collaboration

交互、过程、基础设施：人机协作的统一框架

Yun Wang, Yan Lu

机构 * Microsoft Research Asia（微软亚洲研究院） 纠错

专题命中 多智能体 ：agent(title,abstract)；分类 cs.AI

AI总结 本文提出了一种统一框架，通过整合交互、过程和基础设施，解决人机协作中结构表示和适应性问题。

Towards Optimal Performance and Action Consistency Guarantees in Dec-POMDPs with Inconsistent Beliefs and Limited Communication

在具有不一致信念和有限通信的Dec-POMDPs中实现最优性能和行动一致性保证

Moshe Rafaeli Shimron, Vadim Indelman

机构 * Technion - Israel Institute of Technology（技术学院-以色列理工学院） 纠错

专题命中 多智能体 ：agent(abstract)；planning(abstract)；multi-agent(abstract)；分类 cs.AI

AI总结 本文提出了一种去中心化框架，用于在具有不一致信念和有限通信的Dec-POMDPs中实现最优联合行动选择，提供行动一致性和性能的概率保证，并在需要时触发通信。

Comments 9 pages, 3 figures, 2 tables