TMTPOST — The Beijing Academy of Artificial Intelligence released its AREX autonomous research agent on Tuesday, introducing a dual-loop recursive framework that enables continuous self-correction on long-horizon tasks. AREX alternates between an inner research loop that gathers evidence and forms provisional answers and an outer verification loop that audits constraints, identifies gaps and triggers targeted follow-up inquiry. Model weights have been open-sourced, and a research-experience online system is available in beta, supporting functions such as information tracking, paper screening, podcast summarization, autonomous literature reading, hotspot following and review generation. The agent is designed to move beyond single-pass generation toward recursive improvement, addressing the asymmetry between generating complete answers and verifying partial ones. BAAI positions AREX as a step toward AI systems capable of sustained discovery through experiment, feedback and revision rather than one-shot inference. The release follows earlier demonstrations of related research-agent capabilities at the academy’s annual conference.