Lightweight Structured Multimodal Reasoning for Clinical Scene Understanding in Robotics
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912608072761344 |
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| author | Jha, Saurav Ehrlich, Stefan K. |
| author_facet | Jha, Saurav Ehrlich, Stefan K. |
| contents | Healthcare robotics requires robust multimodal perception and reasoning to ensure safety in dynamic clinical environments. Current Vision-Language Models (VLMs) demonstrate strong general-purpose capabilities but remain limited in temporal reasoning, uncertainty estimation, and structured outputs needed for robotic planning. We present a lightweight agentic multimodal framework for video-based scene understanding. Combining the Qwen2.5-VL-3B-Instruct model with a SmolAgent-based orchestration layer, it supports chain-of-thought reasoning, speech-vision fusion, and dynamic tool invocation. The framework generates structured scene graphs and leverages a hybrid retrieval module for interpretable and adaptive reasoning. Evaluations on the Video-MME benchmark and a custom clinical dataset show competitive accuracy and improved robustness compared to state-of-the-art VLMs, demonstrating its potential for applications in robot-assisted surgery, patient monitoring, and decision support. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_22014 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightweight Structured Multimodal Reasoning for Clinical Scene Understanding in Robotics Jha, Saurav Ehrlich, Stefan K. Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Robotics Healthcare robotics requires robust multimodal perception and reasoning to ensure safety in dynamic clinical environments. Current Vision-Language Models (VLMs) demonstrate strong general-purpose capabilities but remain limited in temporal reasoning, uncertainty estimation, and structured outputs needed for robotic planning. We present a lightweight agentic multimodal framework for video-based scene understanding. Combining the Qwen2.5-VL-3B-Instruct model with a SmolAgent-based orchestration layer, it supports chain-of-thought reasoning, speech-vision fusion, and dynamic tool invocation. The framework generates structured scene graphs and leverages a hybrid retrieval module for interpretable and adaptive reasoning. Evaluations on the Video-MME benchmark and a custom clinical dataset show competitive accuracy and improved robustness compared to state-of-the-art VLMs, demonstrating its potential for applications in robot-assisted surgery, patient monitoring, and decision support. |
| title | Lightweight Structured Multimodal Reasoning for Clinical Scene Understanding in Robotics |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2509.22014 |