English(EN) From Symbolic Perception to Logical Deduction: A Framework for Guiding Language Models in Geometric Reasoning

LLM框架在几何推理方面可媲美Gemini 2.5 Pro

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员开发了一个新框架，使大型语言模型（LLMs）能够执行复杂的几何推理，这项任务对AI来说历来具有挑战性。该方法集成了几何视觉解析器将图表转换为符号表示，以及符号求解器进行形式化推导，从而减少了幻觉并增强了可解释性。在2025年中国中考的挑战性问题上进行了测试，该框架取得了与Gemini 2.5 Pro相当的性能，同时提供了更透明、更类人的解决方案。 AI

影响该框架可以使LLMs能够处理复杂的视觉和逻辑推理任务，有可能提高它们在需要空间理解和形式化推导领域的效用。

排序理由关于LLM几何推理新框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Weichen Dai, Rafael Medeiros Cabral, Ziyi Shou, Yan Cao, Xin Shen, Dongcai Lu, Yi Zhou · 2026-09-10 04:00

从符号感知到逻辑推理：指导语言模型进行几何推理的框架

arXiv:2609.10335v1 Announce Type: cross Abstract: Plane geometry remains a significant challenge in AI, requiring the integration of visual perception and mathematical reasoning. While Large Multimodal Models (LMMs) naturally handle visuo-linguistic inputs, they are often computa…

本文如何被排名

报道来源 [1]

从符号感知到逻辑推理：指导语言模型进行几何推理的框架

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