ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915941622743040 |
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| author | Wang, Xucheng Zhang, Xiaoman Kim, Sung Eun Pal, Ankit Rajpurkar, Pranav |
| author_facet | Wang, Xucheng Zhang, Xiaoman Kim, Sung Eun Pal, Ankit Rajpurkar, Pranav |
| contents | Ultrasound acquisition requires skilled probe manipulation and real-time adjustments. Vision-language models (VLMs) could enable autonomous ultrasound systems, but existing benchmarks evaluate only static images, not dynamic procedural understanding. We introduce ReXSonoVQA, a video QA benchmark with 514 video clips and 514 questions (249 MCQ, 265 free-response) targeting three competencies: Action-Goal Reasoning, Artifact Resolution & Optimization, and Procedure Context & Planning. Zero-shot evaluation of Gemini 3 Pro, Qwen3.5-397B, LLaVA-Video-72B, and Seed 2.0 Pro shows VLMs can extract some procedural information, but troubleshooting questions remain challenging with minimal gains over text-only baselines, exposing limitations in causal reasoning. ReXSonoVQA enables developing perception systems for ultrasound training, guidance, and robotic automation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10916 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding Wang, Xucheng Zhang, Xiaoman Kim, Sung Eun Pal, Ankit Rajpurkar, Pranav Computer Vision and Pattern Recognition Artificial Intelligence Ultrasound acquisition requires skilled probe manipulation and real-time adjustments. Vision-language models (VLMs) could enable autonomous ultrasound systems, but existing benchmarks evaluate only static images, not dynamic procedural understanding. We introduce ReXSonoVQA, a video QA benchmark with 514 video clips and 514 questions (249 MCQ, 265 free-response) targeting three competencies: Action-Goal Reasoning, Artifact Resolution & Optimization, and Procedure Context & Planning. Zero-shot evaluation of Gemini 3 Pro, Qwen3.5-397B, LLaVA-Video-72B, and Seed 2.0 Pro shows VLMs can extract some procedural information, but troubleshooting questions remain challenging with minimal gains over text-only baselines, exposing limitations in causal reasoning. ReXSonoVQA enables developing perception systems for ultrasound training, guidance, and robotic automation. |
| title | ReXSonoVQA: A Video QA Benchmark for Procedure-Centric Ultrasound Understanding |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2604.10916 |