pith:3UMECFYB
From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution
A compact Gene representation for reusable experience outperforms documentation-heavy Skill packages in guiding AI code solvers and enabling iterative evolution.
arxiv:2604.15097 v2 · 2026-04-16 · cs.SE · cs.CL
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Claims
A compact Gene representation yields the strongest overall average, remains competitive under substantial structural perturbations, and outperforms matched-budget Skill fragments, while reattaching documentation-oriented material usually weakens rather than improves it. On CritPt, gene-evolved systems improve over their paired base models from 9.1% to 18.57% and from 17.7% to 27.14%.
That the 45 scientific code-solving scenarios and the specific definition of Gene versus Skill fragments are representative of broader experience-reuse needs, and that observed gains stem primarily from representation format rather than unstated differences in prompt construction or model behavior.
Compact Gene representations of experience outperform documentation-oriented Skill packages for test-time control and iterative evolution in code-solving tasks, with measured gains on CritPt from 9.1% to 18.57% and 17.7% to 27.14%.
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| First computed | 2026-06-03T01:05:13.711959Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
dd1841170111212ac16bc75baa79cf565fd3694940ff1b6aba21b99eb48d3887
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· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/3UMECFYBCEQSVQLLY5N2U6OPKZ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: dd1841170111212ac16bc75baa79cf565fd3694940ff1b6aba21b99eb48d3887
Canonical record JSON
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