给AI换个脚手架，它自己变强了
同一个AI，换一套「外挂工具」去练，练完再换回原来那套，它反而更强了。研究者发现，从同一个基础系统演化出来的不同工具变体，各自擅长修补不同的失败——有的补这类错，有的补那类错，而且随着模型变强，哪个变体有用也在变。所以他们不追求「一套万能工具」，而是养一池子专攻不同方向的兄弟工具：只有当模型在原工具下全军覆没时，才叫这些兄弟上场重试，把成功的轨迹拿去训练，再让模型回到原工具下干活。在 AppWorld、SWE-Gym、DBBench 三个环境里，这样练出来的模型回到原始工具下，明显强过只练模型本身。它不是你明天能用上的东西——但它是「AI 靠换环境练出通用能力」这条路上一个具体、可复用的做法。
📄 原文摘要(英文)
Harness evolution from a common base exhibits Locality, Complementarity, and Nonstationarity: variants repair different failures, and their utility changes as the policy learns. Rather than seeking a universally superior harness, we introduce Harness-S to selectively rescue failures of a base execution system through sibling harnesses and transfer the resulting capabilities into a single policy that remains effective under the original harness. Directional Harness Evolution develops specialized sibling harnesses along distinct repair directions, forming a reusable Sibling Harness Pool that remains fixed during policy training. Complementary Harness Rescue Training invokes this pool only when all initial attempts under the base harness fail. The same policy generates fresh rollouts under selected auxiliary harnesses, and these trajectories replace the failed group for reinforcement learning. An Adaptive Harness Router selects the Top-k variants using Harness Behavior Profiles and voting across multiple LLMs. Policy-aware profile re-anchoring refreshes this evidence as the policy learns. Across AppWorld, SWE-Gym, and DBBench, the trained policy substantially outperforms policy-only training when returned to the original base harness. Behavioral analyses and held-out cases support the transfer of harness-elicited behaviors into the policy itself. Reusing the router for Rank-1 inference provides additional gains at the same executor rollout count.