Overview of the ClinicalSkillQA 2026 Shared Task on Continuous Perception and Procedural Reasoning in Clinical Skill Assessment

Buzhou Tang; Cheng zeng; Jiayi Xiang; Jinyu Chen; Keying Wu; Min Peng; Qianqian Xie; Renxiong Wei; Sophia Ananiadou; Xiyang Huang

arxiv: 2606.02082 · v1 · pith:AYYYFYJEnew · submitted 2026-06-01 · 💻 cs.HC

Overview of the ClinicalSkillQA 2026 Shared Task on Continuous Perception and Procedural Reasoning in Clinical Skill Assessment

Xiyang Huang , Renxiong Wei , Yihuai Xu , Zhiyuan Chen , Keying Wu , Jiayi Xiang , Buzhou Tang , Yanqing Ye

show 5 more authors

Jinyu Chen Cheng Zeng Min Peng Qianqian Xie Sophia Ananiadou

This is my paper

Pith reviewed 2026-06-28 12:54 UTC · model grok-4.3

classification 💻 cs.HC

keywords clinical skill assessmentprocedural reasoningcontinuous perceptionshared tasktemporal orderingclinical videosrationale generationBioNLP

0 comments

The pith

Current models struggle to integrate visual evidence, temporal structure, and clinical workflow knowledge when assessing clinical skills from video.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents an overview of the ClinicalSkillQA 2026 shared task, which requires systems to restore the correct order of shuffled key frames from clinical skill videos and produce expert-aligned rationales. It details a benchmark of 200 test-only instances drawn from three emergency-care procedures, along with the evaluation metrics and the submissions from seven teams. The analysis of results indicates that existing approaches fall short when visual, temporal, and procedural-knowledge elements must be combined. A reader would care because the task directly probes capabilities needed for automated medical training and skill verification.

Core claim

The shared task evaluates continuous perception and procedural reasoning by requiring systems to reconstruct the ground-truth temporal order of shuffled clinical key frames and to generate rationales grounded in clinical workflow knowledge. The benchmark supplies 200 test-only instances sampled from clinical skill videos, each annotated with the correct sequence and an expert-verified rationale. Results from ninety submissions show that current models still struggle with these integrated demands.

What carries the argument

The ClinicalSkillQA benchmark of 200 test-only instances that pairs shuffled clinical key frames with ground-truth temporal orders and expert-verified rationales for three emergency-care procedures.

If this is right

Exact sequence reconstruction is required for full credit on the primary metric.
Local pairwise order consistency provides a secondary measure of temporal understanding.
Rationale quality is scored separately via BERTScore to assess grounding in clinical knowledge.
Successful systems must combine visual evidence, temporal ordering, and workflow knowledge.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same frame-reordering format could be applied to additional clinical procedures to test generality.
Strong performance here might indicate readiness for AI tools that verify trainee skills in real time.
The limited sample of 200 instances leaves open whether rarer procedural variations would expose further weaknesses.

Load-bearing premise

The 200 test-only instances sampled from clinical skill videos together with their expert-verified rationales form a sufficient and unbiased test of continuous perception and procedural reasoning capabilities.

What would settle it

A system that achieves near-perfect Task Accuracy, Pairwise Accuracy, and BERTScore on all 200 instances would falsify the claim that current models struggle with the required integration.

read the original abstract

This paper presents an overview of the ClinicalSkillQA 2026 shared task, which was organized with the BioNLP Workshop at ACL 2026. The goal of this shared task is to evaluate continuous perception and procedural reasoning in clinical skill assessment by requiring systems to reconstruct the correct temporal order of shuffled clinical key frames and generate rationales grounded in clinical workflow knowledge. The benchmark contains 200 test-only instances sampled from clinical skill videos, covering three emergency-care procedures. Each instance is annotated with the ground-truth temporal order and an expert-verified rationale. A total of seven teams participated in the task, collectively making 90 submissions, with four teams providing system description papers. Systems are evaluated using Task Accuracy, Pairwise Accuracy, and BERTScore, which measure exact sequence reconstruction, local temporal consistency, and rationale quality, respectively. In this paper, we describe the task setup, dataset construction, and evaluation criteria. We further summarize the methodologies adopted by participating teams and present a comprehensive analysis of the submitted systems. The official results suggest that current models still struggle with continuous perception and procedural reasoning, especially when they must integrate visual evidence, temporal structure, and clinical workflow knowledge.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a standard shared-task overview paper with no new methods or results, just a report on the task setup and participant scores.

read the letter

This paper is an overview of the ClinicalSkillQA 2026 shared task. It describes a benchmark where systems reorder shuffled key frames from clinical videos and generate expert-style rationales, then reports that the seven participating teams struggled on the 200 test instances.

The task itself combines frame reordering with rationale generation across three emergency procedures. The paper lays out the data construction, the three metrics (exact sequence accuracy, pairwise accuracy, BERTScore), and a high-level summary of the 90 submissions. Four teams also wrote system papers. That documentation is the main value here.

The soft spots are predictable for an overview. The analysis stays at the summary level with no detailed error breakdown or external baselines. The 200-instance test set is presented without much discussion of sampling or potential biases, though the paper does not claim broad generalization beyond this shared task. The central observation about model struggles follows directly from the submitted systems but rests on whatever those teams chose to build.

This is for researchers who track BioNLP shared tasks or work on clinical procedural reasoning. It records the benchmark and initial results in one place, which can be useful for anyone planning to use the data later.

The paper is coherent on its own terms and does the job expected of a shared-task overview. I would send it to peer review for the workshop proceedings rather than desk reject it.

Referee Report

0 major / 0 minor

Summary. This manuscript overviews the ClinicalSkillQA 2026 shared task at the BioNLP Workshop at ACL 2026. The task requires systems to reconstruct the temporal order of shuffled clinical key frames sampled from three emergency-care procedure videos and to generate rationales grounded in clinical workflow knowledge. The benchmark consists of 200 test-only instances, each with ground-truth order and expert-verified rationale. Seven teams made 90 submissions (four with system papers), evaluated by Task Accuracy, Pairwise Accuracy, and BERTScore. The paper describes the task construction, summarizes participating methodologies, and reports that submitted systems continue to struggle with integrating visual evidence, temporal structure, and clinical knowledge.

Significance. If the reported performance gaps hold on this benchmark, the overview documents concrete limitations of current multimodal models on procedural clinical reasoning, supplying a reusable testbed that can focus subsequent research on continuous perception and workflow-aware reasoning in healthcare AI.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the thorough summary and positive recommendation to accept the manuscript. The report correctly captures the task design, participation, and key findings regarding model limitations in continuous perception and procedural reasoning.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper is a purely descriptive overview of a shared task. It reports task setup, dataset construction (200 test-only instances), evaluation metrics (Task Accuracy, Pairwise Accuracy, BERTScore), participant submissions, and observed results without any derivations, equations, fitted parameters, or load-bearing self-citations. No step reduces by construction to its own inputs; the central claim is simply that submitted systems underperformed on the defined benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a descriptive overview paper with no mathematical derivations, fitted parameters, background axioms, or postulated entities.

pith-pipeline@v0.9.1-grok · 5789 in / 1043 out tokens · 31584 ms · 2026-06-28T12:54:49.499724+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

25 extracted references · 9 canonical work pages · 4 internal anchors

[1]

Aho and Jeffrey D

Alfred V. Aho and Jeffrey D. Ullman , title =. 1972

1972
[2]

Publications Manual , year = "1983", publisher =

1983
[3]

Chandra and Dexter C

Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer , year = "1981", title =. doi:10.1145/322234.322243

work page doi:10.1145/322234.322243 1981
[4]

Scalable training of

Andrew, Galen and Gao, Jianfeng , booktitle=. Scalable training of
[5]

Dan Gusfield , title =. 1997

1997
[6]

Tetreault , title =

Mohammad Sadegh Rasooli and Joel R. Tetreault , title =. Computing Research Repository , volume =. 2015 , url =

2015
[7]

A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =

Ando, Rie Kubota and Zhang, Tong , Issn =. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =. Journal of Machine Learning Research , Month = dec, Numpages =
[8]

2025 , eprint=

Continuous Perception Matters: Diagnosing Temporal Integration Failures in Multimodal Models , author=. 2025 , eprint=

2025
[9]

arXiv preprint arXiv:2505.02064 , year=

RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video , author=. arXiv preprint arXiv:2505.02064 , year=

work page arXiv
[10]

Medical education , volume=

Opening the black box of clinical skills assessment via observation: a conceptual model , author=. Medical education , volume=. 2011 , publisher=

2011
[11]

arXiv preprint arXiv:2505.16964 , year=

MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning , author=. arXiv preprint arXiv:2505.16964 , year=

work page arXiv
[12]

MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding

Medxpertqa: Benchmarking expert-level medical reasoning and understanding , author=. arXiv preprint arXiv:2501.18362 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[13]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=
[14]

arXiv preprint arXiv:2511.20937 , year=

ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction , author=. arXiv preprint arXiv:2511.20937 , year=

work page arXiv
[15]

G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

G-eval: NLG evaluation using gpt-4 with better human alignment , author=. arXiv preprint arXiv:2303.16634 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[16]

arXiv preprint arXiv:2305.16739 , year=

AlignScore: Evaluating factual consistency with a unified alignment function , author=. arXiv preprint arXiv:2305.16739 , year=

work page arXiv
[17]

Jama , volume=

Tools for direct observation and assessment of clinical skills of medical trainees: a systematic review , author=. Jama , volume=. 2009 , publisher=

2009
[18]

, author=

Assessment of clinical competence using objective structured examination. , author=. Br Med J , volume=. 1975 , publisher=

1975
[19]

Academic Medicine , volume=

Comparing the psychometric properties of checklists and global rating scales for assessing performance on an OSCE-format examination , author=. Academic Medicine , volume=. 1998 , publisher=

1998
[20]

Academic Medicine , volume=

OSCE checklists do not capture increasing levels of expertise , author=. Academic Medicine , volume=. 1999 , publisher=

1999
[21]

BERTScore: Evaluating Text Generation with BERT

Bertscore: Evaluating text generation with bert , author=. arXiv preprint arXiv:1904.09675 , year=

work page internal anchor Pith review Pith/arXiv arXiv 1904
[22]

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

SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos , author=. arXiv preprint arXiv:2604.09037 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[23]

European conference on computer vision , pages=

Mmbench: Is your multi-modal model an all-around player? , author=. European conference on computer vision , pages=. 2024 , organization=

2024
[24]

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

Mibench: Evaluating multimodal large language models over multiple images , author=. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

2024
[25]

Findings of the Association for Computational Linguistics: ACL 2025 , pages=

Burn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences? , author=. Findings of the Association for Computational Linguistics: ACL 2025 , pages=

2025

[1] [1]

Aho and Jeffrey D

Alfred V. Aho and Jeffrey D. Ullman , title =. 1972

1972

[2] [2]

Publications Manual , year = "1983", publisher =

1983

[3] [3]

Chandra and Dexter C

Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer , year = "1981", title =. doi:10.1145/322234.322243

work page doi:10.1145/322234.322243 1981

[4] [4]

Scalable training of

Andrew, Galen and Gao, Jianfeng , booktitle=. Scalable training of

[5] [5]

Dan Gusfield , title =. 1997

1997

[6] [6]

Tetreault , title =

Mohammad Sadegh Rasooli and Joel R. Tetreault , title =. Computing Research Repository , volume =. 2015 , url =

2015

[7] [7]

A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =

Ando, Rie Kubota and Zhang, Tong , Issn =. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =. Journal of Machine Learning Research , Month = dec, Numpages =

[8] [8]

2025 , eprint=

Continuous Perception Matters: Diagnosing Temporal Integration Failures in Multimodal Models , author=. 2025 , eprint=

2025

[9] [9]

arXiv preprint arXiv:2505.02064 , year=

RTV-Bench: Benchmarking MLLM Continuous Perception, Understanding and Reasoning through Real-Time Video , author=. arXiv preprint arXiv:2505.02064 , year=

work page arXiv

[10] [10]

Medical education , volume=

Opening the black box of clinical skills assessment via observation: a conceptual model , author=. Medical education , volume=. 2011 , publisher=

2011

[11] [11]

arXiv preprint arXiv:2505.16964 , year=

MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning , author=. arXiv preprint arXiv:2505.16964 , year=

work page arXiv

[12] [12]

MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding

Medxpertqa: Benchmarking expert-level medical reasoning and understanding , author=. arXiv preprint arXiv:2501.18362 , year=

work page internal anchor Pith review Pith/arXiv arXiv

[13] [13]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

[14] [14]

arXiv preprint arXiv:2511.20937 , year=

ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction , author=. arXiv preprint arXiv:2511.20937 , year=

work page arXiv

[15] [15]

G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

G-eval: NLG evaluation using gpt-4 with better human alignment , author=. arXiv preprint arXiv:2303.16634 , year=

work page internal anchor Pith review Pith/arXiv arXiv

[16] [16]

arXiv preprint arXiv:2305.16739 , year=

AlignScore: Evaluating factual consistency with a unified alignment function , author=. arXiv preprint arXiv:2305.16739 , year=

work page arXiv

[17] [17]

Jama , volume=

Tools for direct observation and assessment of clinical skills of medical trainees: a systematic review , author=. Jama , volume=. 2009 , publisher=

2009

[18] [18]

, author=

Assessment of clinical competence using objective structured examination. , author=. Br Med J , volume=. 1975 , publisher=

1975

[19] [19]

Academic Medicine , volume=

Comparing the psychometric properties of checklists and global rating scales for assessing performance on an OSCE-format examination , author=. Academic Medicine , volume=. 1998 , publisher=

1998

[20] [20]

Academic Medicine , volume=

OSCE checklists do not capture increasing levels of expertise , author=. Academic Medicine , volume=. 1999 , publisher=

1999

[21] [21]

BERTScore: Evaluating Text Generation with BERT

Bertscore: Evaluating text generation with bert , author=. arXiv preprint arXiv:1904.09675 , year=

work page internal anchor Pith review Pith/arXiv arXiv 1904

[22] [22]

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

SiMing-Bench: Evaluating Procedural Correctness from Continuous Interactions in Clinical Skill Videos , author=. arXiv preprint arXiv:2604.09037 , year=

work page internal anchor Pith review Pith/arXiv arXiv

[23] [23]

European conference on computer vision , pages=

Mmbench: Is your multi-modal model an all-around player? , author=. European conference on computer vision , pages=. 2024 , organization=

2024

[24] [24]

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

Mibench: Evaluating multimodal large language models over multiple images , author=. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages=

2024

[25] [25]

Findings of the Association for Computational Linguistics: ACL 2025 , pages=

Burn After Reading: Do Multimodal Large Language Models Truly Capture Order of Events in Image Sequences? , author=. Findings of the Association for Computational Linguistics: ACL 2025 , pages=

2025