---
license: cc-by-4.0
language:
- en
pretty_name: Governed Skill Evolution from Persistent Agent Experience
size_categories:
- n<1K
tags:
- ai-agents
- agent-skills
- persistent-memory
- ablation
- cross-model-transfer
- reproducibility
configs:
- config_name: replicate_results
  data_files:
  - split: train
    path: data/replicate_results.jsonl
- config_name: aggregate_results
  data_files:
  - split: train
    path: data/aggregate_results.jsonl
- config_name: skill_impact_ledger
  data_files:
  - split: train
    path: data/skill_impact_ledger.jsonl
---

# 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`](https://github.com/rrrrrredy/governed-skill-evolution/commit/d717c32396cfff1bef2800296541a70e9b4cabb8)
- **Canonical repository:** [rrrrrredy/governed-skill-evolution](https://github.com/rrrrrredy/governed-skill-evolution)
- **Zenodo:** [10.5281/zenodo.22240411](https://doi.org/10.5281/zenodo.22240411)
- **Full study harness:** [Runtime Evolution Workbench](https://github.com/rrrrrredy/runtime-evolution-workbench/tree/main/research/wiki-evolution-study)

## 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`.