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Recent evaluation confirms the symptom (LLM-authored skills deliver +0.0pp gain while human-curated ones deliver +16.2pp (SkillsBench)), yet the underlying mechanism has not been isolated. We provide (1) a \\textbf{reproducible trigger}: ablations that isolate drift: one disables skill injection (flat floor, +0.002), one imposes premature retirement (active harm, $-$0.019); (2) \\textbf{trace-level diagnostics}: an append-only evidence log with per-skill contribution scores, attribution verdicts, and router engagement metrics that make the failure visible before it reaches end-task scores; and (3) a \\textbf{verified fix}: a minimal governance recipe (outcome-driven retirement + bounded active-ca","title":"Library Drift: Diagnosing and Fixing a Silent Failure Mode in Self-Evolving LLM Skill Libraries","url":"https://arxiv.org/abs/2605.19576","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.19576v2 Announce Type: replace \nAbstract: Self-evolving skill libraries face a silent failure mode we term \\emph{library drift}: unbounded skill accumulation without outcome-driven lifecycle management causes retrieval degradation, false-positive injections, and performance stagnation. Recent evaluation confirms the symptom (LLM-authored skills deliver +0.0pp gain while human-curated ones deliver +16.2pp (SkillsBench)), yet the underlying mechanism has not been isolated. 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