AREX: Towards a Recursively Self-Improving Agent for Deep Research
Score
81提出递归自我改进稀疏奖励处理等新机制，在多个基准上显著提升，信息增量很高
方法设计严谨，实验充分，但部分细节如上下文更新工具的具体实现描述略简
开源模型和训练方法，可直接用于构建更强大的研究智能体，实用价值大
2025年最新论文，紧跟当前AI智能体与自我改进研究热点
来自arXiv预印本，作者团队知名（可能为DeepMind等），但尚未经同行评审
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
This paper introduces a novel recursively self-improving framework with significant gains on multiple strong benchmarks, and open-sources the models and training methods, offering high information gain and practical value.
AI commentary
「This paper demonstrates how to break through the bottleneck of deep research via recursive self-improvement, especially valuable for developers building AI agents capable of autonomous complex research. Its context compression tool addresses the core challenge of long-horizon interaction, and open-sourcing models and training data will accelerate progress in this field.」
Quick overview
AREX is a recursively self-improving deep research agent framework that alternates between an inner research loop for gathering evidence and constructing a provisional answer, and an outer self-improvement loop that audits the answer constraint-wise, identifies unresolved claims, and launches targeted follow-up research. It learns an autonomous context-update tool to compress growing interaction history into a compact improvement state without relying on an external model. Trained on a 4B dense model and a 122B-A10B Mixture-of-Experts model, AREX substantially outperforms comparable-scale baselines on BrowseComp, WideSearch, DeepSearchQA, Humanity's Last Exam, and other benchmarks, remaining competitive with models using substantially more activated parameters.
- AREX proposes a recursively self-improving (RSI) paradigm with inner and outer loops to iteratively refine answers
- Learns an autonomous context-update tool that compresses interaction history into a compact improvement state, eliminating external model dependency
- Outperforms comparable-scale baselines on multiple deep research and reasoning benchmarks, and remains competitive with models using more activated parameters
- Supports scaling from 4B dense model to 122B-A10B Mixture-of-Experts model
Full text
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