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English(EN) AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials

AtomWorld基准测试LLM在材料科学中的空间推理能力

一个名为AtomWorld的新基准已被开发出来,用于评估大型语言模型(LLMs)在晶体材料背景下的空间推理能力。该基准在四个建模类别中包含十项基本操作,其中Claude Opus 4.6在测试模型中表现最佳。然而,随着复杂性的增加,成功率显著下降,尤其是在涉及复杂空间关系的操作中,这表明LLMs更适合作为材料结构建模的辅助工具,而不是自主代理。 AI

影响 该基准有望推动更复杂、具有空间意识的AI代理在科学发现和材料设计领域的发展。

排序理由 该集群描述了一个用于评估LLM在特定科学领域能力的新的学术基准。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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AtomWorld基准测试LLM在材料科学中的空间推理能力

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该集群描述了一个用于评估LLM在特定科学领域能力的新的学术基准。[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) · Taoyuze Lv, Alexander Chen, Fengyu Xie, Chu Wu, Jeffrey Meng, Dongzhan Zhou, Yingheng Wang, Bram Hoex, Zhicheng Zhong, Tong Xie ·

    AtomWorld:用于评估大型语言模型在晶体材料上空间推理能力的基准测试

    arXiv:2510.04704v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown promising potential in scientific research, enabling tasks ranging from knowledge retrieval to property prediction. Existing science benchmarks mainly focus on perceptual or knowledg…