👥 Multi-Agent¶

🧪 ICML2025 · 7 paper notes

📌 Same area in other venues: 📷 CVPR2026 (2) · 🔬 ICLR2026 (47) · 💬 ACL2026 (40) · 🧪 ICML2026 (24) · 🤖 AAAI2026 (26) · 🧠 NeurIPS2025 (17)

🔥 Top topics: Agents ×6 · LLM ×3 · Reasoning ×2

AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML: This paper proposes AutoML-Agent, a multi-agent LLM collaborative framework for full-pipeline AutoML. It expands the search space using a Retrieval-Augmented Planning strategy, decomposes tasks into parallel subtasks handled by specialized agents, and introduces a multi-stage verification mechanism to guarantee code generation quality, achieving higher automation success rates and model performance across 14 datasets in 7 task categories.
Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination: Proposes the Cross-Environment Cooperation (CEC) paradigm, which trains agents via self-play across a large number of procedurally generated, diverse environments (rather than increasing partner diversity). This enables agents to learn general cooperative norms, achieving zero-shot coordination with unseen partners in unseen environments.
From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium: This paper models multi-LLM coordination as an incomplete information game and proposes the ECON framework. It achieves implicit belief-driven multi-agent coordinated reasoning via Bayesian Nash Equilibrium (BNE) without explicit message passing while providing theoretical convergence guarantees, yielding an average improvement of 11.2% across six reasoning benchmarks.
Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning: This paper proposes WandaPlan, an evaluation environment that systematically assesses the vulnerability of LLM-based multi-agent planning systems to false information by injecting three progressive types of fraud (single-source misinformation, team-coordinated manipulation, and level-escalating attacks) in travel planning scenarios, and designs an Anti-Fraud Agent to mitigate these risks.
MetaAgent: Automatically Constructing Multi-Agent Systems Based on Finite State Machines: Proposes MetaAgent, a framework based on Finite State Machines (FSMs) that automatically designs multi-agent systems given only task descriptions, without requiring external training data. Supporting tool invocation and state backtracking, it outperforms existing automated design methods and approaches the performance of hand-crafted systems across text-based, ML, and software development tasks.
ResearchTown: Simulator of Human Research Community: This paper proposes ResearchTown, a multi-agent framework based on agent-data graphs and TextGNN (text-space message passing), which models human scientific communities as heterogeneous graphs to unify the simulation of three core research activities: literature reading, paper writing, and peer review. A scalable and objective simulation quality evaluation is conducted via a node masking prediction task (ResearchBench).
Theorem-of-Thought: A Multi-Agent Framework for Abductive, Deductive, and Inductive Reasoning in Language Models: The Theorem-of-Thought (ToTh) framework is proposed, where three agents simulating abductive, deductive, and inductive reasoning independently generate reasoning trajectories. These trajectories are constructed into a Formal Reasoning Graph (FRG) and consistently scored using NLI-calibrated Bayesian belief propagation. The terminal node of the highest-scoring graph is selected as the final answer, consistently outperforming CoT, Self-Consistency, and CoT-Decoding on symbolic and numerical reasoning tasks.