English(EN) Grid-Bench: Can LLMs Predict How Discrete Worlds Change?

新基准Grid-Bench测试大型语言模型预测空间序列变化的能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了Grid-Bench，这是一个旨在评估大型语言模型（LLMs）预测顺序空间操作结果能力的新基准。该基准涉及转换由网格表示的离散世界，模型根据初始配置、规则和基于文本的事件历史来预测最终状态。对六个LLMs的评估显示，随着事件历史的延长，完整状态的准确性急剧下降，即使在精确匹配分数相似的模型之间也突显了模型性能和错误模式的显著差异。 AI

影响该基准可能会揭示LLMs在处理顺序推理和状态跟踪方面的关键局限性，影响其在需要复杂环境交互的任务中的开发。

排序理由该集群包含一篇详细介绍用于评估LLMs的新基准的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Zixiang Xu, Zirui Song, Wanrong Zheng, Chiyu Zhang, Yue Huang, Yuyang Peng, Kexin Zhang, Jinkun Chen, Xiuying Chen, Xiangliang Zhang, Yan Liu, Laurent Itti, Frank Shyu, Xiaoqiang Luo, Sandeep Pandey, Xi Liu, Luke Simon, Xiyang Hu, Yue Zhao · 2026-10-16 04:00

Grid-Bench：LLM能否预测离散世界如何变化？

arXiv:2610.22312v1 Announce Type: new Abstract: Spatial instructions describe how to move objects, transform regions, or modify their surroundings. When several operations are applied in sequence, each acts on a configuration shaped by earlier changes. How accurately can large la…

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报道来源 [1]

Grid-Bench：LLM能否预测离散世界如何变化？

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