English(EN) SceneForesee: Learning Scene Evolution for Spatiotemporal Reasoning

新基准SceneForesee旨在测试LMM的时空推理能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员推出了SceneForesee，这是一个旨在评估大型多模态模型（LMM）时空推理能力的新基准。该基准侧重于预测和解释场景如何随时间演变，包括实体移动和交互。除基准外，他们还提出了EvoSee，一个通过学习未来几何和事件语义来提高LMM在时空任务上性能的框架，在SceneForesee和Dyn-Bench基准上表现优于Qwen3.5-9B等现有模型。 AI

影响增强LMM理解动态场景的能力，可能改进视频分析和机器人领域的应用。

排序理由该条目描述了一篇介绍用于AI模型时空推理的新基准和框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Chaoyue Li, Shoudong Han, Haoyang Wu, Boxue Yang, Sijia Chen, Yufan Chen, Ruping Zhang · 2026-10-15 04:00

SceneForesee：学习场景演化以进行时空推理

arXiv:2610.14439v1 Announce Type: cross Abstract: Large multimodal models (LMMs) need to understand how entities move and interact as a scene develops. This requires following the same entities over time and relating their observed motion to subsequent changes. We formulate Scene…

本文如何被排名

报道来源 [1]

SceneForesee：学习场景演化以进行时空推理

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