@inproceedings{huang-etal-2026-siming,
title = "{S}i{M}ing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos",
author = "Huang, Xiyang and
Lin, Jiawei and
Wu, Keying and
Huang, Jiaxin and
Yang, Kailai and
Wei, Renxiong and
Zeng, Cheng and
Xiang, Jiayi and
Kuang, Ziyan and
Peng, Min and
Xie, Qianqian and
Ananiadou, Sophia",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2203/",
pages = "50070--50103",
ISBN = "979-8-89176-459-0",
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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<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.</abstract>
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%0 Conference Proceedings
%T SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos
%A Huang, Xiyang
%A Lin, Jiawei
%A Wu, Keying
%A Huang, Jiaxin
%A Yang, Kailai
%A Wei, Renxiong
%A Zeng, Cheng
%A Xiang, Jiayi
%A Kuang, Ziyan
%A Peng, Min
%A Xie, Qianqian
%A Ananiadou, Sophia
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F huang-etal-2026-siming
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2203/
%P 50070-50103
Markdown (Informal)
[SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos](https://aclanthology.org/2026.emnlp-main.2203/) (Huang et al., EMNLP 2026)
ACL
- Xiyang Huang, Jiawei Lin, Keying Wu, Jiaxin Huang, Kailai Yang, Renxiong Wei, Cheng Zeng, Jiayi Xiang, Ziyan Kuang, Min Peng, Qianqian Xie, and Sophia Ananiadou. 2026. SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 50070–50103, Budapest, Hungary. Association for Computational Linguistics.