Autonomous Repair for Multi-Agent Systems via Monte-Carlo Tree Search
3.60T1 sourcearXiv cs.MA
Source record
Published by arXiv cs.MA (T1 source). The original is at https://arxiv.org/abs/2607.29055.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryIntroduces MARS, a Monte Carlo Tree Search framework that automates repair of multi-agent LLM system failures by navigating repair options via diagnosis-guided expansion and taxonomy-augmented evaluation. Releases StateMAS benchmark with 1,310 replayable failure trajectories; reports 3.0–12.1% absolute gains over prior methods at comparable token cost.
Why it mattersFills a noted gap in automated multi-agent failure recovery and ships a sizeable public benchmark; useful reference for anyone running multi-agent pipelines that need self-correction loops.
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