AREX-Base

Towards a Recursively Self-Improving Agent for Deep Research Introduction AREX is a family of deep research agents developed by the Beijing Academy of Artificial Intelligence (BAAI). It is designed for long-horizon tasks in which an agent must search across sources, assemble candidate answers, verify multiple constraints, and revise its research plan when the available evidence is incomplete. AREX follows a recursive self-improvement framework consisting of an inner research loop and an outer self-improvement loop. The inner loop conducts deep research, while the outer loop evaluates and improves the research process. This repository contains **AREX-Base**, the larger model in the AREX family. It is built on Qwen3.5-122B-A10B and uses a Mixture-of-Experts architecture with 122B total parameters and 10B activated parameters. **Architecture:** Mixture-of-Experts **Parameters:** 122B **Context length:** 262,144 tokens Key Features** **Recursive self-improvement:** evaluates provisional answers and chooses whether to accept, refine, or restart the research trajectory. **Verification-guided research:** converts unresolved answer constraints into focused follow-up searches. **Autonomous context updates:** retains verified findings, source identifiers, rejected candidates, open constraints, and the next research plan. **Long-horizon tool use:** supports multi-round search, browsing, evidence integration, and answer construction. **Evidence-grounded answers:** maintains supporting evidence and answer-level confidence throughout the research process. Model Family** | Model | Backbone | Context | Positioning | | --- | --- | ---: | --- | | AREX-Base | Qwen3.5-122B-A10B | 256K | Higher-capacity model with the strongest overall AREX results. | | AREX-Turbo | Qwen3.5-4B | 256K | Compact model for lower-cost research and tool-use settings. | Method AREX organizes deep research as two interacting loops supported by autonomous context management. 1. **Inner research loop.** The agen