AI学会自我纠错：像人一样反复查漏补缺
现在的AI做深度研究，往往一条路走到黑，找到答案就停。但真实研究需要同时满足多个约束条件，比如找一篇论文既要引用高、又要方法新、还得代码开源。这篇论文发现一个不对称：验证答案比发现答案容易得多——检查一个候选答案是否满足某个条件，比从头搜索要快。于是他们让AI学会“自我改进”：先快速给出一个初步答案，然后像审稿人一样逐条检查，发现哪个条件没满足，就专门去补查，再更新答案，如此循环。为了支撑长时间循环，AI还学会自动压缩对话历史，只保留已验证的证据和未解决的约束，不依赖外部模型。最终，一个40亿参数的模型在多个深度搜索和推理测试中，击败了比自己大几十倍的模型。它不是你明天就能用的工具，但揭示了AI研究能力的一个新方向：不是搜得更久，而是搜得更聪明。
📄 原文摘要(英文)
Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a research agent should do more than simply search longer: it should recursively improve its current answer by verifying intermediate results and using the partially verified state to guide subsequent refinement. We introduce AREX, a family of Recursively Self-Improving (RSI) deep research agents. AREX alternates between an inner research loop that gathers evidence and constructs a provisional answer, and an outer self-improvement loop that audits the answer constraint-wise, identifies unresolved claims, and launches targeted follow-up research. To sustain RSI over long horizons, AREX learns an autonomous context-update tool that compresses growing interaction history into a compact improvement state preserving verified evidence and unresolved constraints, without relying on an external model. We train AREX on verified synthetic tasks and high-quality trajectories through agentic mid-training and long-horizon reinforcement learning. To mitigate sparse final rewards during long horizon learning, we emphasize key steps where decisive evidence is acquired or erroneous research directions are corrected. We instantiate a dense 4B model and a 122B-A10B Mixture-of-Experts model. Across BrowseComp, WideSearch, DeepSearchQA, Humanity's Last Exam (HLE), and other reasoning and tool-use benchmarks, AREX substantially outperforms comparable-scale baselines and remains competitive with models using substantially more activated parameters.