SkillCAT: Contrastive Assessment and Topology-Aware Skill Self-Evolution for LLM Agents

arXiv:2606.13317v1 Announce Type: new Abstract: Skill self-evolution methods for LLM agents aim to turn execution trajectories into reusable skill documents, but current pipelines typically learn from one trajectory per task, merge candidate skill patches before checking them, and load the full skill corpus before inference. We propose SkillCAT, a training-free framework that separates this process into three stages. Contrastive Causal Extraction (CCE) samples multiple trajectories for each task and compares same-task success/failure pairs to identify evidence that explains outcome differences

Source: arXiv cs.CL — read the full report at the original publisher.

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