AI学会自我迭代：像科学家一样反复验证答案
现在的AI做研究，往往是一次性搜索然后给出答案，错了就重来。这篇论文让AI学会像人类研究者一样：先给出一个初步答案，然后逐条检查每个约束条件是否满足，发现漏洞后，针对性地补充搜索，再检查，再改进——循环往复，直到所有条件都通过。关键在于，它不依赖外部模型来总结历史，而是自己学会压缩对话历史，只保留已验证的证据和未解决的问题，从而在超长推理中保持高效。在多个需要深度搜索和推理的测试中，这个4B参数的模型表现超过了同规模模型，甚至接近更大模型。它不是你明天能用上的，但它展示了AI从“搜索答案”走向“验证答案”的关键一步。
📄 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.