AREX: Towards a Recursively Self-Improving Agent for Deep Research

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Shuqi Lu, Chaofan Li, Kun Luo, Zhang Zhang, Hui Wang, Hongwang Xiao, Zheng Liu, Lei Xiong, Jiahao Wang, Sen Wang, Xiyan Jiang, Wanli Li, Yuyang Hu, Hongjin Qian, Bingyu Yan, Ziyi Xia, Yingxia Shao, Kang Liu, Zhicheng Dou, Di He, Chaozhuo Li, Qiwei Ye, Zhongyuan Wang, Zheng Liu
• Venue: arXiv: cs.AI
• Published: 2026-07-23
• Collected: 2026-07-24
• URL: http://arxiv.org/abs/2607.21461v1
• PDF: https://arxiv.org/pdf/2607.21461v1
• DOI: UNKNOWN

Plain-Language Brief

The paper introduces a new family of agents called AREX for deep research. These agents are designed to recursively improve their answers by verifying intermediate results, guiding subsequent refinement, and learning an autonomous context-update tool to sustain improvement over long horizons. The authors train AREX on synthetic tasks and high-quality trajectories through agentic mid-training and long-horizon reinforcement learning.

What the Document Says

• The paper discusses the need for deep research agents that can find answers satisfying multiple constraints
• The discovery--verification asymmetry in deep research suggests the need for recursive improvement of the current answer by verifying intermediate results and guiding subsequent refinement
• AREX is introduced as a family of Recursively Self-Improving (RSI) agents that alternate between an inner research loop and an outer self-improvement loop
• The inner research loop gathers evidence and constructs a provisional answer
• The outer self-improvement loop audits the answer constraint-wise, identifies unresolved claims, and launches targeted follow-up research
• AREX learns an autonomous context-update tool to compress interaction history into a compact improvement state
• Training of AREX is done on verified synthetic tasks and high-quality trajectories through agentic mid-training and long-horizon reinforcement learning
• The paper mentions emphasizing key steps where decisive evidence is acquired or erroneous research directions are corrected during long horizon learning
• Instantiation of a dense 4B model and a 122B-A10B Mixture-of-Experts model is mentioned
• Across various reasoning, tool-use benchmarks, AREX outperforms comparable-scale baselines and remains competitive with models using substantially more activated parameters

Key Concepts Mentioned

• Deep research agents
• Recursively Self-Improving (RSI) agents
• AREX
• inner research loop
• outer self-improvement loop
• autonomous context-update tool
• agentic mid-training
• long-horizon reinforcement learning
• decisive evidence
• erroneous research directions

Claims Made by the Document

• AREX substantially outperforms comparable-scale baselines and remains competitive with models using substantially more activated parameters

Evidence Used

• Across BrowseComp, WideSearch, DeepSearchQA, Humanity's Last Exam (HLE), and other reasoning and tool-use benchmarks

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

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Raw Abstract / Excerpt

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.