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English(EN) Achieving Olympiad-Level Geometry Large Language Model Agent via Complexity Boosting Reinforcement Learning

大语言模型智能体InternGeometry达到奥赛级几何问题解决能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了InternGeometry，一个能够解决国际数学奥林匹克（IMO）级别几何问题的LLM智能体。该智能体通过迭代地提出辅助构造、使用符号引擎进行验证以及从反馈中学习，克服了在提出辅助构造方面的局限性。InternGeometry在2000-2024年的IMO几何问题上取得了50分中的44分，超过了金牌选手的平均得分，并且比AlphaGeometry 2等之前的专家模型使用了显著更少量的训练数据。 AI

影响 展示了LLM智能体能够以显著更少的数据在专业、复杂的任务上达到专家级性能。

排序理由 该集群描述了一篇研究论文，详细介绍了一种LLM智能体解决复杂几何问题的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

大语言模型智能体InternGeometry达到奥赛级几何问题解决能力

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该集群描述了一篇研究论文，详细介绍了一种LLM智能体解决复杂几何问题的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Haiteng Zhao, Junhao Shen, Yiming Zhang, Songyang Gao, Kuikun Liu, Tianyou Ma, Fan Zheng, Dahua Lin, Wenwei Zhang, Kai Chen ·

    通过复杂性增强强化学习实现奥赛级几何大语言模型代理

    arXiv:2512.10534v4 Announce Type: replace Abstract: Large language model (LLM) agents exhibit strong mathematical problem-solving abilities and can even solve International Mathematical Olympiad (IMO) level problems with the assistance of formal proof systems. However, due to wea…

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