AI 学会给自己升级，但这次升级的是“手脚”不是“脑子”
我们总以为 AI 自我进化是让模型变聪明；这篇让 AI 升级的是自己干活的方式——怎么调用工具、怎么管理上下文、怎么判断任务做完——而不是模型本身的参数。研究者把 AI 的“操作流程”拆成五个独立模块，拿同一任务的成功和失败案例做对比，找出反复出错的环节，只改那一块，再拼回去。为了不让 AI 只是“背下考试题”，他们特意用和评测完全不沾边的 2,000 个任务来练手，结果在编程和终端操作两类基准上，升级后的流程对没见过的任务也稳定变强，换一个底层模型照样管用。它不是你明天能用上的东西，但它指向一个更现实的方向：AI 的进步不只在“更聪明”，也在“更会干活”。
📄 原文摘要(英文)
Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve execution mechanisms from experience. However, generalizable harness RSI remains challenging. First, evolving harnesses on evaluation benchmarks or their subsets makes it difficult to distinguish reusable improvements from benchmark-specific adaptation. Second, single-trajectory updates can conflate systematic harness deficiencies with instance-specific reasoning and solution details, producing modifications that transfer poorly to unseen tasks. Third, localizing recurring behavioral deficiencies within monolithic harnesses is difficult, while whole-harness optimization can entangle unrelated mechanisms and complicate attribution and validation. We propose ModularRSI, a benchmark-disjoint, contrastive, and modular framework for generalizable harness evolution. ModularRSI contrasts successful and failed trajectories for the same task and aggregates evidence across tasks to identify recurring behavioral deficiencies. It decomposes the evolvable harness into five functional modules: Agent Loop, Tool Use, Observation Management, Context Management, and Task Completion Detection. Each module evolves independently within a restricted modification scope, followed by an integration stage that combines the evolved modules into a unified harness and resolves potential conflicts. To support benchmark-disjoint evolution, we curate 2,000 executable evolution tasks from external sources that are disjoint from downstream evaluation benchmarks. Experiments on TB2.0 and SWE-Bench Verified show consistent improvements on unseen in-domain and cross-domain tasks, with the evolved harness also transferring across different foundation models.