{"ID":2837961,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18373","arxiv_id":"2511.18373","title":"MASS: Motion-Aware Spatial-Temporal Grounding for Physics Reasoning and Comprehension in Vision-Language Models","abstract":"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.","short_abstract":"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, co...","url_abs":"https://arxiv.org/abs/2511.18373","url_pdf":"https://arxiv.org/pdf/2511.18373v2","authors":"[\"Xiyang Wu\",\"Zongxia Li\",\"Jihui Jin\",\"Guangyao Shi\",\"Gouthaman KV\",\"Vishnu Raj\",\"Nilotpal Sinha\",\"Jingxi Chen\",\"Fan Du\",\"Dinesh Manocha\"]","published":"2025-11-23T09:43:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Language Model\"]","has_code":false}