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.

Introduction. Deep research is challenging not only because relevant evidence is difficult to locate, but because a valid answer must often satisfy multiple coupled constraints simultaneously. An agent must discover viable candidates, integrate distributed and potentially conflicting evidence, and verify that each required condition is adequately supported (Yao et al., 2023; Nakano et al., 2021; Huang et al., 2025). Many existing systems address this challenge by extending a single search trajectory with additional reasoning, tool interactions, or context (OpenAI, 2025; Jin et al., 2025; Wu et al., 2025a; Gao et al., 2026). Although greater inference-time computation can broaden exploration, it does not guarantee systematic progress: early errors may persist, exhausted directions may be revisited, and partially valid candidates may be accepted prematurely. The key challenge is therefore not merely to search longer, but to identify which constraints remain unresolved and use that diagnosis to formulate a more targeted next research problem.

Discussion / Conclusion. In this work, we introduced AREX, a recursively self-improving agent for deep research. AREX exploits the discovery– verification asymmetry in deep research to transform provisional answers into partially verified research states, preserving supported evidence while isolating unresolved claims for subsequent targeted investigation. Coupled with autonomous context updating and step-aware training, AREX achieves strong performance across deep search, wide search, agentic reasoning, and tool-use benchmarks. Our preliminary training analysis further highlights the importance of identifying and reinforcing decision-critical steps in long-horizon trajectories, rather than treating all intermediate actions uniformly. Future work will investigate more general and autonomous mechanisms for estimating step utility and assigning fine-grained training signals.