Multi-Agent Systems

CitySim: Modeling Urban Behaviors and City Dynamics with Large-Scale LLM-Driven Agent Simulation EMNLP 2025 Client Selection for Federated Policy Optimization with Environment Heterogeneity JMLR 2025 Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models CVPR 2025 Commitment to Sparse Strategies in Two-Player Games AAAI 2025 Completing A Systematic Review in Hours instead of Months with Interactive AI Agents ACL 2025 Computing Game Symmetries and Equilibria That Respect Them AAAI 2025 Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents ACL 2025 Contract-based Design and Verification of Multi-Agent Systems with Quantitative Temporal Requirements AAAI 2025 Cooperative Policy Agreement: Learning Diverse Policy for Offline MARL AAAI 2025 Creolization versus code-switching: An agent-based cognitive model for bilingual strategies in language contact NAACL 2025 CrowdAgent: Multi-Agent Managed Multi-Source Annotation System EMNLP 2025 DebateCoder: Towards Collective Intelligence of LLMs via Test Case Driven LLM Debate for Code Generation ACL 2025 Decentralized and Uncoordinated Learning of Stable Matchings: A Game-Theoretic Approach AAAI 2025 Decentralized Convergence to Equilibrium Prices in Trading Networks AAAI 2025 DeMAC: Enhancing Multi-Agent Coordination with Dynamic DAG and Manager-Player Feedback EMNLP 2025 Deviate or Not: Learning Coalition Structures with Multiple-bit Observations in Games AAAI 2025 Dynamic Multi-Interest Graph Neural Network for Session-Based Recommendation AAAI 2025 Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment EMNLP 2025 Enhancing Text-to-SQL with Question Classification and Multi-Agent Collaboration NAACL 2025 Equilibria of the Colonel Blotto Games with Costs AAAI 2025 Error Detection in Medical Note through Multi Agent Debate ACL 2025 Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning L4DC 2025 Exploration Lab IITK at SemEval-2025 Task 8: Multi-LLM Agent QA over Tabular Data SemEval 2025 Exploring the Efficacy of Multi-Agent Reinforcement Learning for Autonomous Cyber Defence: A CAGE Challenge 4 Perspective AAAI 2025 FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning AAAI 2025