SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos

Xiyang Huang; Jiawei Lin; Keying Wu; Jiaxin Huang; Kailai Yang; Renxiong Wei; Cheng zeng; Jiayi Xiang; Ziyan Kuang; Min Peng; Qianqian Xie; Sophia Ananiadou

arXiv · 2604.09037

SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos

Xiyang Huang·Jiawei Lin·Keying Wu·Jiaxin Huang·Kailai Yang·Renxiong Wei·Cheng zeng·Jiayi Xiang·Ziyan Kuang·Min Peng·Qianqian Xie·Sophia Ananiadou

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Abstract

Current video benchmarks for multimodal large language models (MLLMs) focus on event recognition, temporal ordering, and long-context recall, but overlook a harder capability required for expert procedural judgment: tracking how ongoing interactions update the procedural state and thereby determine the correctness of later actions. We introduce SiMing-Bench, the first benchmark for evaluating this capability from full-length clinical skill videos. It targets rubric-grounded process-level judgment of whether interaction-driven state updates preserve procedural correctness across an entire workflow. SiMing-Bench is instantiated with SiMing-Score, a physician-annotated dataset of real clinical skill examination videos spanning cardiopulmonary resuscitation, automated external defibrillator operation, and bag-mask ventilation, each paired with a standardized step-wise rubric and dual-expert labels. Across diverse open- and closed-source MLLMs, we observe consistently weak agreement with physician judgments. Moreover, weak performance on rubric-defined intermediate steps persists even when overall procedure-level correlation appears acceptable, suggesting that coarse global assessment substantially overestimates current models' procedural judgment ability. Additional analyses with binary step judgment and step-aligned clips indicate that the bottleneck is not merely fine-grained scoring or temporal localization, but modeling how continuous interactions update procedural state over time.

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Xiyang Huang, Jiawei Lin, Keying Wu, Jiaxin Huang, Kailai Yang, Renxiong Wei, Cheng zeng, Jiayi Xiang, Ziyan Kuang, Min Peng, Qianqian Xie, Sophia Ananiadou. 2026-04-10. SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos. https://arxiv.org/abs/2604.09037

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