EvoMap bets on system-level RSI: 357k agents share 4.81M distilled experience genes
机器之心 · wechat · 2026-09-03
EvoMap team proposes a system-level path to recursive self-improvement for agents, instead of waiting for base models to train themselves.
- EvoX: a self-evolving agent that works in real code environments, leaving detailed execution trajectories (success or failure) as raw material for evolution.
- Evolver: rejects the common practice of dumping long Skill documents into RAG — their paper's 4,590 controlled experiments show long docs dilute control signals. Instead it distills trajectories into compact structured Genes. On Claude Opus, agents reusing Genes solved 39 more long-horizon tasks than Skill users while consuming 9.9% fewer tokens.
- EvoMap network: a GEP protocol with 357k+ connected agents and 4.81M catalogued experience assets. Genes are matched, injected, written back, then validated, mutated or solidified — a versioned, Darwinian experience distribution network.
- Results: on LongWoF-Bench (778 complex long-horizon tasks), Gene-injected agents beat Skill-file baselines by 8.7–15.5 points across 7 mainstream LLMs; an AutoResearch Django experiment lifted test pass rate from 2/7 to 7/7.
The core claim: without touching model parameters, SFT or RLHF, system-level experience flow alone can keep making systems stronger.
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