Tag
StepFinder is a lightweight framework that uses LLMs only in the feature construction phase to encode execution logs into temporal semantic sequences, then applies parameter-efficient temporal and attention modules for failure attribution in multi-agent systems. It reduces inference time by 79% compared to the fastest LLM-based method on the Who&When benchmark.
SkillAdaptor is a training-free step-level skill adaptation framework with explicit failure attribution for LLM agents, improving performance on WebShop, PinchBench, and Claw-Eval.
This survey paper provides a unified review of LLM-based multi-agent systems, focusing on collaboration, failure attribution, and self-evolution through the LIFE framework, identifying open challenges and proposing a cross-stage research agenda.