AI 自己教自己，新领域从 30% 提到 55%
大模型遇到没学过的领域，通常得靠人喂数据或找个更强的老师。这篇让模型自己当自己的老师：它把做过的任务、踩过的坑、总结出的技巧存进一个「知识库」，然后反复执行任务、验证改进、把有效的经验合并回去，循环迭代。在律所知识测试上，全对率从 30% 提到 55%。它不是你明天能用上的东西，但「没有老师也能自学」是让 AI 走出实验室、进入真实世界的关键一步。
📄 原文摘要(英文)
Foundation models in changing environments can encounter unfamiliar domain knowledge that their original training did not cover. However, new domains rarely provide large amounts of expert-created training data or reliable supervisory signals for further training. We introduce Recursa, a foundation model system that adapts to new domains by learning from its own experience without human experts or a stronger teacher model. Recursa uses a knowledge repository to store what it learns: domain-specific skills and instructions on when to apply them, along with memories of past task experiences and reflections on what to improve. Recursa bootstraps the repository from domain materials and then refines it through recursive self-improvement. It uses parallel learners to execute tasks and synthesize local improvements, then validates and merges the improvements into the knowledge repository. On Harvey LAB's Firm Knowledge benchmark, Recursa raises the all-pass rate on held-out test tasks from 30.0% to 55.0% with DeepSeek-V4.1-Flash and from 35.0% to 46.0% with Qwen3.8-Flash.