Improving Spatial-Temporal Reasoning in Video-Language Models with Structured Video Prompting
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...
| Authors | Sadegh Mohammadian |
|---|---|
| Primary category | cs.CV |
| Published | 23 August 2026 |
| Last updated at source | 5 September 2026 |
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