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English(EN) PhysMent: An Interactive Approach For LLM Reasoning In Physics Problems

新的基准测试通过交互式物理模拟来测试LLM的物理推理能力

研究人员推出PhysMent,这是一个旨在通过交互式实验来评估大型语言模型(LLM)物理推理能力的新基准。与静态基准不同,PhysMent要求LLM通过施加力、查询状态和修改环境来积极与MuJoCo物理模拟器互动,然后再回答问题。虽然当前模型在定性任务上表现出能力,但在需要精确、多步实验程序的定量问题上,它们的性能显著下降,大多数模型在具有挑战性的单一概念任务上的得分低于30%。 AI

影响 该基准测试有望推动LLM在更强大的物理推理和交互能力方面的发展。

排序理由 该项目描述了一个用于物理推理的LLM的新基准和评估,属于研究范畴。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的基准测试通过交互式物理模拟来测试LLM的物理推理能力

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该项目描述了一个用于物理推理的LLM的新基准和评估,属于研究范畴。[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) · Joseph Chan, Utkarsh Jha, Xiyin Yang, Abhinav Jarajapu, Anik Sahai, Eddie Hu, Robin Jeshua Deepak, Stefano Saravalle, Aditya Shah ·

    PhysMent:物理问题中用于LLM推理的交互式方法

    arXiv:2609.13152v1 Announce Type: new Abstract: Large language models (LLMs) perform strongly on static science benchmarks, yet their ability to reason about the physical world through active experimentation remains poorly understood. We introduce PhysMent, a benchmark that evalu…