Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting
arXiv:2608.28666v1 Announce Type: new Abstract: Video-language models (VLMs) remain brittle on tasks that require tracking events over time and grounding answers in specific spatial regions. We propose that part of this limitation can be addressed through better organization of visual evidence at inference time. We introduce structured video prompting, a training-free inference-time method that augments the input video with lightweight spatial structure and temporal structure, providing explicit anchors for organizing evidence across space and time without changing model weights or decoding and without altering the question prompt in the main comparison. We evaluate this approach on two complementary video benchmarks and two open video-language models. Across these settings, structured inputs improve performance in several cases, with gains varying by model and task. Our findings suggest that some failures of VLMs arise not only from reasoning capacity, but also from how video evidence is presented at inference time. These results highlight structured video prompting as a simple and practical direction for improving video understanding.
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[Submitted on 23 Aug 2026]
Title:Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting
View a PDF of the paper titled Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting, by Sadegh Mohammadian
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Abstract:Video-language models (VLMs) remain brittle on tasks that require tracking events over time and grounding answers in specific spatial regions. We propose that part of this limitation can be addressed through better organization of visual evidence at inference time. We introduce structured video prompting, a training-free inference-time method that augments the input video with lightweight spatial structure and temporal structure, providing explicit anchors for organizing evidence across space and time without changing model weights or decoding and without altering the question prompt in the main comparison. We evaluate this approach on two complementary video benchmarks and two open video-language models. Across these settings, structured inputs improve performance in several cases, with gains varying by model and task. Our findings suggest that some failures of VLMs arise not only from reasoning capacity, but also from how video evidence is presented at inference time. These results highlight structured video prompting as a simple and practical direction for improving video understanding.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.28666 [cs.CV]
(or arXiv:2608.28666v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.28666
arXiv-issued DOI via DataCite (pending registration)
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From: Sadegh Mohammadian [view email] [v1] Sun, 23 Aug 2026 20:16:09 UTC (3,196 KB)
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