pith:RSLHGCOW
EvoSkill: Automated Skill Discovery for Multi-Agent Systems
EvoSkill automatically discovers reusable agent skills through failure analysis to improve performance without changing the model.
arxiv:2603.02766 v1 · 2026-03-03 · cs.AI · cs.MA
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Claims
EvoSkill analyzes execution failures, proposes new skills or edits to existing ones, and materializes them into structured, reusable skill folders. A Pareto frontier of agent programs governs selection, retaining only skills that improve held-out validation performance while the underlying model remains frozen. We evaluate EvoSkill on two benchmarks: OfficeQA... improves exact-match accuracy by 7.3% (60.6% → 67.9%); and SealQA... yields a 12.1% gain (26.6% → 38.7%). ... skills evolved from SealQA transfers zero-shot to BrowseComp, improving accuracy by 5.3%.
That iterative failure analysis can reliably generate skills whose benefits on held-out validation reflect genuine generalization rather than benchmark-specific artifacts or unstated implementation choices in the skill proposal step.
EvoSkill evolves agent skills via failure analysis and Pareto frontier selection, raising exact-match accuracy 7.3% on OfficeQA and 12.1% on SealQA with 5.3% zero-shot transfer to BrowseComp.
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| First computed | 2026-05-17T23:38:15.400988Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
8c967309d6b875abef0037a4ca135f3812be2599ee232b74f19d79d336018128
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· · · · ·Agent API
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Canonical record JSON
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