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Title: EvoMap Publishes Skill to Gene Research Showing How AI Agents Can Keep Learning Across Runs

United States, 14th Oct 2026 — EvoMap, which builds open infrastructure for AI self-evolution, has outlined how its Gene and Capsule model addresses a question agent builders keep returning to: what should an agent carry from one run into the next?Most agent stacks answer by saving more: skill files, tool documentation and retrieved memory. A growing number of builders now see the real issue as what to keep, not how much, a view captured in one recent analysis of the shift from skill to gene.What the Report FoundThat question is at the center of a beta technical report, "From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution" (arXiv:2604.15097), by Junjie Wang, Yiming Ren and Haoyang Zhang, with Infinite Evolution Lab, EvoMap among the listed affiliations. Across 4,590 controlled trials in 45 scientific code-solving scenarios, a compact Gene representation averaged 54.0%, against 49.9% for a full documentation-style Skill package.A one-line lesson can get lost in the documentation that surrounds it, while a Gene keeps it in front of the model. Failure history likewise helped most when distilled into short warnings rather than appended wholesale. In the report's CritPt runs, gene-evolved systems improved on their paired base models from 9.1% to 18.57% and from 17.7% to 27.14%.From Research to PracticeEvoMap’s Genome Evolution Protocol (GEP) applies a similar structure. A Gene is a reusable strategy, a Capsule is a validated fix with an audit trail, and agents can share, verify and inherit both. Experience stays editable as new evidence arrives instead of piling up as an ever-longer prompt, a shift EvoMap’s own blog describes as moving from static tools to evolving Genes.“An agent’s experience only counts if the next run can pick it up and...

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