Salvato in:
| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.18373 |
| Tags: |
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Sommario:
- Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-related reasoning involving motion dynamics and spatial interactions. We present a novel approach to address this gap by translating physical-world context cues into interpretable representations aligned with VLM perception, comprehension, and reasoning. We introduce MASS, a model-agnostic approach that injects spatiotemporal signals into the VLM language space via depth-based 3D encoding and visual grounding, coupled with a motion tracker for object dynamics. We also contribute a comprehensive benchmark, MASS-Bench, consisting of 4,350 real-world and AIGC videos and 8,361 free-form video question-answering pairs focused on physics-related comprehension tasks, with detailed annotations including visual detections and grounding over sub-segments, as well as full-sequence 3D motion tracking of entities. To strengthen cross-modal alignment and reasoning, we apply reinforcement fine-tuning to MASS. Experiments and ablations show that our refined VLMs outperform comparable baselines, larger models, and prior state-of-the-art models, achieving performance comparable to closed-source state-of-the-art VLMs, with only a 2\% gap to Gemini-2.5-Flash on physics reasoning and comprehension.