RedinGhost/governed-skill-evolution

Governed Skill Evolution from Persistent Agent Experience

Prospective ablation and cross-model transfer study of three experience-retention conditions for governed Agent Skill evolution: no persistent history, flat chronological history, and a persistent Pattern Registry with a forward-chained Skill Impact Ledger.

• —Author: Song Luo
• —Version: 1.0.0
• —Source snapshot: `d717c32396cfff1bef2800296541a70e9b4cabb8`
• —Canonical repository: rrrrrredy/governed-skill-evolution
• —Zenodo: 10.5281/zenodo.22240411
• —Full study harness: Runtime Evolution Workbench

Viewer subsets

• —replicate_results: 9 rows, one per condition and replicate.
• —aggregate_results: 3 condition-level descriptive summaries.
• —skill_impact_ledger: 36 compact ledger entries. The full verified ledger, including digest material, remains under results/.

Main result

Persistent Wiki did not outperform flat history on task quality in this setting. It used fewer input tokens and produced a larger target-model Skill gain, but had lower final task quality and more rollbacks. The release therefore preserves negative and mixed results rather than treating persistent state as automatically beneficial.

Evidence boundary

This is prospective descriptive evidence for one synthetic deterministic grader, three replicate sequences, and two Codex model versions in one model family. It is not a causal provider comparison, safety certification, or broad claim about Agent performance.

Source code and machine-readable protocols are Apache-2.0. The manuscript, documentation, result tables, figures, and release metadata are CC BY 4.0; see CONTENT-LICENSE.md.