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Test-time self-improvement—the ability of an agent to iteratively refine solutions during inference—relies on high-fidelity reflection to surpass current proposals and long-horizon execution to maintain coherent multi-round exploration. Researchers from Renmin University of China, Microsoft Research, and USTC introduce AREX-2, establishing that these capabilities are domain-agnostic and learnable from verifiable environments. Synthesizing long-horizon reflective trajectories across machine learning and algorithmic programming, an AREX-2 agent built on Qwen3.8-27B achieves 81.8 on MLE-bench Lite and 70.7 on Frontier-CS, transferring seamlessly to deep research with 84.0 on BrowseComp, 92.2 on GAIA, and 93.8 on DeepSearchQA while monotonically scaling with compute budget.

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Endowing LLM agents with test-time self-improvement—the ability to iteratively refine solutions—is a vital milestone for autonomous engineering. Researchers from Renmin University and collaborators present AREX-2, an open-source framework leveraging long-horizon reflective trajectories. Decoupling self-improvement into reflection (generating superior alternatives) and long-horizon execution (sustaining iteration over multiple turns), AREX-2 trains on verified machine learning and algorithmic programming trajectories. Built on Qwen3.8-27B, AREX-2 achieves 81.8 on MLE-bench Lite and 70.7 on Frontier-CS, transferring effectively to deep research with 92.2 on GAIA and 93.8 on DeepSearchQA.

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