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33 papers in the last four weeks, up 313% on the four weeks before. 0.3% of all new papers.

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Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry
Oct 29, 2025Run Peng, Ziqiao Ma, Amy Pang +5Large Language Model Agents Collaboration
University of Michigan · UNC, Chapel Hill · Georgia Tech +2
Understanding Role Switching in Human-AI Collaboration through Multimodal Behavioral Signals
Sep 25, 2025Avinash Ajit Nargund, Arthur Caetano, Kevin Yang +7Human-Ai Collaboration Collaboration
University of California, Santa Barbara, USA · Washington University in St.Louis, USA
LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
Aug 22, 2025Zirui Li, Stephan Husung, Haoze WangCollaboration Engineering
Product and Systems Engineering Group Technische Universität Ilmenau Ilmenau, Germany
LLM-Driven Collaborative Model for Untangling Commits via Explicit and Implicit Dependency Reasoning
Jul 22, 2025Bo Hou, Xin Tan, Kai Zheng +3Commits Collaboration
State Key Laboratory of Complex & Critical Software Environment (SKLCCSE), School of Computer Science and Engineering, Beihang University, Beijing, China · SKLCCSE, School of Computer Science and Engineering, Beihang University, Beijing, China · School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China
Position: Collaborative Agentic AI Needs Interoperability Across Ecosystems
May 25, 2025Rishi Sharma, Martijn de Vos, Pradyumna Chari +2Interoperability Production Agentic Systems
1EPFL, Lausanne, Switzerland · 2Massachusetts Institute of Technology, Cambridge, MA, USA.
Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks
Apr 24, 2025Yang Liu, Kejia Zhang, Bingjie Yan +11Domain Adaptation Collaboration
The Hong Kong Polytechnic University, China · Institute for AI Industry Research, Tsinghua University, China · Institute for AI Industry Research, Tsinghua University, China and Shanghai Jiao Tong University, China +3
Batch effects can impair federated learning in multi-center omics studies
Dec 8, 2024Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann +5Federated Learning Multi-Omic Integration
Institute for Computational Systems Biomedicine, University of Hamburg, 22761 Hamburg, Germany · Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany · Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark +3
MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis
Date pendingYuqi Zhan, Xinyue Wu, Tianyu Lin +6Clinical Reasoning Training Diagnosis
Hangzhou Dianzi University · Zhejiang University · Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Children and Adolescents’ Health and Diseases
Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems
Date pendingRakibul Hasan Rajib, Mengxin Zheng, Qian LouMulti-Agent Large Language Model Systems Collaboration
University of Central Florida
A2DINOv3: Rethinking Multi-Modal Object Detection via Socialized Collaboration
Date pendingJiekang Feng, Zhihe Fan, Yunqi Zhu +6Cross-Modal Fusion Dinov3
School of Artificial Intelligence, Tianjin University · School of Sports Training, Tianjin University of Sport · School of Computer Science and Engineering, University of New South Wales +2

Oct 29, 2025, cs.CLJ/K move · Enter open · S save

Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry

Run Peng, Ziqiao Ma, Amy Pang, Sikai Li+4

University of Michigan · UNC, Chapel Hill · Georgia Tech+2

Large Language Model Agents Collaboration Information Asymmetry Puzzle

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While Large Language Model (LLM) agents are often approached from the angle of action planning/generation to accomplish a goal (e.g., given by language descriptions), their abilities to collaborate with each other to achieve a joint goal are not well explored. To address this limitation, this paper studies LLM agents in task collaboration, particularly under the condition of information asymmetry, where agents have disparities in their knowledge and skills and need to work together to complete a shared task. We extend Einstein Puzzles, a classical symbolic puzzle, to a table-top game. In this game, two LLM agents must reason, communicate, and act to satisfy spatial and relational constraints required to solve the puzzle. We apply a fine-tuning-plus-verifier framework in which LLM agents are equipped with various communication strategies and verification signals from the environment. Empirical results highlight the critical importance of aligned communication, especially when agents possess both information-seeking and -providing capabilities. Interestingly, agents without communication can still achieve high task performance; however, further analysis reveals a lack of true rule understanding and lower trust from human evaluators. Instead, by integrating an environment-based verifier, we enhance agents' ability to comprehend task rules and complete tasks, promoting both safer and more interpretable collaboration in AI systems. https://github.com/Roihn/EinsteinPuzzles

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