AREX这篇论文让AI自己递归改进答案，在多个搜索和推理基准上超越了同体量模型，想了解自我优化怎么做可以看看。
AREX是一种递归自我改进(RSI)的深度研究代理，设计用于解决需要满足多重约束的复杂检索任务。AREX通过内循环收集证据构建临时答案，外循环逐约束审计答案并启动针对性的后续研究。其核心创新是学习一个自主上下文更新工具，将交互历史压缩为紧凑的改进状态，无需外部模型。AREX在4B密集模型和122B-A10B混合专家模型上实例化，在BrowseComp、WideSearch、DeepSearchQA、HLE等基准上显著优于可比规模基线，与使用更多激活参数的模型竞争。
AREX: Towards a Recursively Self-Improving Agent for Deep Research
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.