AI自己给自己写说明书，效率反超人类调校
调教AI一直靠人肉试错：改提示词、换工具、调参数，模型一换全得重来。这篇让AI自己进化出那套“操作手册”——不是改单个零件，而是把整套执行流程当活物来养：多个“岛屿”并行试错，被淘汰的变异当反面教材，再让专门的变异体重写整本手册，最后有个总指挥负责嫁接好用的分支、换掉不行的写手。结果在三个真实任务上，它自己搜出来的配置全面超过八种人工设计的顶级方案，在终端操作测试里甚至超过官方榜单第一名，还省了26%的token。更意外的是，这套方法顺手解决了两个数学难题的已知最优界。它不是你明天能直接用的工具，但“让AI自己调自己”这条路，可能比人继续手动调参更接近真正的智能。
📄 原文摘要(英文)
Modern agentic systems combine an AI model with a harness that controls execution and environmental interactions. Harness design strongly affects long-horizon performance, yet its combinatorial search space demands substantial human effort that must be repeated as models change. Existing automated methods explore this space narrowly, optimizing only components such as prompts or skills or becoming trapped by fixed, exploitative search strategies. We introduce MILO (Meta-evolutionary Island Orchestration), a framework that co-evolves agent harnesses and the strategy used to discover them. MILO combines: (i) hierarchical lineage memory over island-based trees, using rejected mutations as negative evidence; (ii) per-island mutator agents that rewrite complete harnesses using global search history and parent-specific feedback; and (iii) an orchestrator that adapts search through lineage grafting and speciation, mutator reassignment and curriculum revision. Across Terminal-Bench 2.1, PaperBench, and DeepSWE, MILO-discovered harnesses outperform eight state-of-the-art harnesses and six search methods using frontier (Opus 4.8) and open-weight (gpt-oss-120b) models. With Opus 4.8, MILO improves resolution over its initial harness by +12.0%, +28.3%, and +10.3%, respectively, compared with best prior-search gains of +4.5%, +18.3%, and 0%. On Terminal-Bench 2.1, it achieves 86.1 pm 2.0%, exceeding the official leaderboard's top entry (83.8 pm 2.3%) while using 26\% fewer tokens than its initial harness. On EinsteinArena open problems, MILO improves best-known upper bounds for Erdős minimum-overlap (0.3808586 to 0.3808568) and the first and third autocorrelation inequalities (1.50274365 to 1.50274360; 1.45081 to 1.44889).