AI 智能体也能「进化」:不换模型，只改「外壳」
大模型智能体的能力不只取决于模型本身，还取决于它的「外壳」——提示词、工具、技能和控制流程。过去让 AI 自我改进，就像单线进化，容易顾此失彼。DarwinX 换了个思路：把多个「外壳」当作一个种群，让它们像生物一样自然选择，只保留那些不牺牲旧能力、又能拓展新能力的变体。结果在四个基准测试上平均提升约 17 个百分点，而且学到的能力能迁移到没见过的任务上。这不是你明天就能用的工具，但它揭示了一个趋势：模型可以冻结，智能却可以继续生长。
📄 原文摘要(英文)
An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow. Self-improvement loops already edit harnesses, yet single-lineage search is path-dependent and local wins often regress other tasks. We introduce DarwinX, which treats self-evolution as selection over a population of harnesses with the model frozen: a preserve-and-extend contract admits only variants that extend coverage without regressing, an archive keeps alternative lineages for recombination, and failure-, teacher-, and self-derived evidence share one edit interface. Fitness comes from each benchmark's own verifier: no gold solutions, no hand-picked winners. Across four benchmarks that progressively separate the evolution signal from the test, one loop adds about 17 points on average: Terminal-Bench 2.1 rises +7.7 to 83.2% on a matched base and to the verified frontier at 84.7% on a stronger one; TerminalWorld's held-out split reaches 68.3%, ahead of every off-the-shelf agent; WebArena-Infinity real-task pass@1 rises from 43.5% to 93.0% audit-clean; and a Terminal-Bench 2.1 harness transfers unchanged to SWE-bench Verified. What evolves is general agent competence, not benchmark-specific patches, so it survives changes of task, verifier, and base model. A frozen model need not be a fixed agent: harness selection turns evaluation compute into durable capability.