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English(EN) Intern-S1-MO: Long-horizon Reasoning Agent for Olympiad?Level Mathematical Problem Solving

AI代理Intern-S1-MO攻克奥赛级别数学难题

研究人员开发了Intern-S1-MO,这是一种新颖的长时域推理代理,旨在解决奥赛级别的复杂数学问题。该代理采用多轮、分层推理方法,使用相互连接的大型推理模型(LRM)系统进行推理、摘要和验证。通过维护一个紧凑的引理记忆,Intern-S1-MO可以在多个阶段探索广泛的推理空间,克服了标准LRM的上下文长度限制。该系统使用一种新的强化学习框架OREAL-H进行训练,该框架同时增强了LRM的推理能力和代理的整体性能。实验表明,Intern-S1-MO取得了显著成果,在IMO2025非几何问题上可媲美银牌选手,并在CMO2025竞赛中达到金牌水平。 AI

影响 这项研究展示了一种新颖的AI长时域推理方法,有望在复杂问题求解领域提升能力。

排序理由 该项目是一篇研究论文,详细介绍了一种用于数学问题求解的新型AI代理和训练框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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AI代理Intern-S1-MO攻克奥赛级别数学难题

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该项目是一篇研究论文,详细介绍了一种用于数学问题求解的新型AI代理和训练框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yuzhe Gu, Songyang Gao, Zijian Wu, Lingkai Kong, Wenwei Zhang, Zhongrui Cai, Fan Zheng, Tianyou Ma, Junhao Shen, Haiteng Zhao, Duanyang Zhang, Huilun Zhang, Kuikun Liu, Chengqi Lyu, Yanhui Duan, Chiyu Chen, Ningsheng Ma, Jianfei Gao, Han Lyu, Dahua Lin, … ·

    Intern-S1-MO:面向奥赛级数学问题解决的长时域推理代理

    arXiv:2512.10739v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have expanded the mathematical reasoning frontier through Chain-of-Thought (CoT) techniques and Reinforcement Learning with Verifiable Rewards (RLVR), capable of solving AIME-level problems. However…