Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion
Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos. We introduce pause-and-think-T, a reasoning-centric training dataset that encourages models to pause, reason over visual evidence, and produce concise, actionable responses. The dataset promotes structured reasoning prior to answer generation, guiding models toward human-like, scene-grounded assistance. We fine-tune a compact 4B-parameter model and evaluate it on our pause-and-think-B benchmark targeting contextual understanding and goal planning tasks. The model achieves 58.0% accuracy at 59x fewer parameters than Qwen3-VL-235B (58.9%), matching GPT-5.2 on sc...
| Authors | Shivam Singh, Saptarshi Majumder, Pratik Prabhanjan Brahma et al. |
|---|---|
| Primary category | cs.CV |
| Published | 30 May 2026 |
| Last updated at source | 29 June 2026 |
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