New benchmark SceneForesee targets spatiotemporal reasoning in LMMs

Researchers have introduced SceneForesee, a new benchmark designed to evaluate spatiotemporal reasoning in large multimodal models (LMMs). The benchmark focuses on predicting and interpreting how scenes evolve over time, including entity movement and interactions. Alongside the benchmark, they propose EvoSee, a framework that improves LMM performance on spatiotemporal tasks by learning future geometry and event semantics, outperforming existing models like Qwen3.5-9B on the SceneForesee and Dyn-Bench benchmarks. AI

IMPACT Enhances LMM capabilities in understanding dynamic scenes, potentially improving applications in video analysis and robotics.

RANK_REASON The item describes a new academic paper introducing a novel benchmark and framework for spatiotemporal reasoning in AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Dyn-Bench
• EvoSee
• Gotit.pub
• Hugging Face
• Qwen3.5:9b
• SceneForesee
• ScienceCast

• paper
• model release

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