AI RESEARCH
StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems
arXiv CS.AI
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ArXi:2606.03467v1 Announce Type: new LLM-based multi-agent systems exhibit remarkable collaborative capabilities in complex multi-step tasks. However, these systems are highly sensitive to single-step execution errors that can propagate through agent interactions and lead to cascading failures. To understand the causes of failure and improve system reliability, failure attribution has been