Ratchet: A Minimal Hygiene Recipe for Self-Evolving LLM Agents

arXiv:2605.22148v1 Announce Type: cross Abstract: Self-evolving skill libraries, pioneered by Voyager, let frozen LLM agents accumulate reusable knowledge without weight updates, yet recent evaluation shows that LLM-authored skills deliver +0.0pp over no-skill baselines while human-curated ones deliver +16.2pp: the bottleneck is not skill authoring but lifecycle management. We introduce extbf{Ratchet}, a single-agent loop in which a frozen LLM writes, retrieves, curates, and retires its own natural-language skills. Ratchet integrates four candidate hygiene mechanisms: outcome-driven retirement, a bounded active-cap, meta-skill authoring guidance, and pattern canonicalisation. On MBPP+ hard-100 with Claude Opus 4.7, Ratchet lifts held-out pass@1 from a 0.258 pm 0.047 baseline to a late-window rolling mean of 0.584 (peak 0.658 pm 0.042) across 100 rounds and 3 seeds, a +0.328 pm 0.018 rolling-mean gain where the no-skill control drifts at +0.002 pm 0.005; the same recipe transfers to an agentic solver on SWE-bench Verified (+0.22 peak lift over 20 rounds). Eight ablations (A1--A8) reveal that the minimal working recipe is smaller than our design suggests: retirement and the meta-skill authoring prior are load-bearing, while explicit deduplication (canonicalisation, cover-guard) is subsumed by the meta-skill itself. A non-divergence proposition shows that bounded cap and retirement threshold together prevent expected performance from drifting below the no-skills floor.

Source: arXiv cs.CL | 2026-05-22