Autonomous Repair for Multi-Agent Systems via Monte-Carlo Tree Search

• URL: http://arxiv.org/abs/2607.29055v1
• Date: Fri, 31 Jul 2026 06:18:48 GMT
• Title: Autonomous Repair for Multi-Agent Systems via Monte-Carlo Tree Search
• Abstract summary: We propose a search-based framework that formulates MAS repair as a Monte Carlo Tree Search (MCTS) process.<n>We also introduce StateMAS, a large-scale MAS repair benchmark with 1,310 replayable multi-agent failure trajectories.
• Score: 4.313916831146957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent systems (MAS) are increasingly deployed to solve complex tasks. In case of incorrect or unsatisfactory outputs, users have to manually locate agent mistakes by inspecting agent trajectories (i.e., {\em failure attribution}) and provide feedback to refine the outputs (i.e., {\em repair}). Despite some recent work in MAS failure attribution, automated mechanisms to recover from such mistakes remain largely unexplored. To bridge this gap, we propose MARS, a search-based framework that formulates MAS repair as a Monte Carlo Tree Search (MCTS) process and navigates the vast space of potential repairs via diagnosis-guided expansion with taxonomy-augmented evaluation. Unlike standard MCTS, which evaluates a complete simulation via full rollout, MARS evaluates the agent trajectory using partial rollout to reduce token consumption. Furthermore, we introduce StateMAS, a large-scale MAS repair benchmark with 1,310 replayable multi-agent failure trajectories spanning four types of agent architectures and four LLM backbones. Experiments on StateMAS demonstrate that MARS consistently outperforms state-of-the-art methods, achieving an absolute improvement from 3.0\% to 12.1\% across all settings, while maintaining a comparable token consumption cost. The ablation study further confirms that taxonomy-augmented evaluation and diagnosis-guided expansion are critical to achieving these performance gains.

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