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\title{DLNLP at ClinicalSkillQA: EvidenceFlow for Structured Zero-Shot Clinical Keyframe Ordering}

\author{
Kexin Li, Zhekun Wang, Yiran Wang, Di Zhao\thanks{Corresponding author.} \\
Dalian Minzu University \\
\texttt{zhaodi@dlnu.edu.cn}
}

\begin{document}
\maketitle
\begin{abstract}
The ClinSkill QA shared task requires models to recover the temporal order of scrambled clinical keyframes and generate explanations. We propose EvidenceFlow, a structured zero-shot framework based on Qwen2.5-VL that decomposes the task into global overview, local evidence modeling, and ordering decision, with two variants: model-led EvidenceFlow-M and rule-guided EvidenceFlow-R. On the official test set, EvidenceFlow-R achieves better ordering performance, while EvidenceFlow-M produces better explanation quality, revealing a trade-off between ordering stability and rationale generation. EvidenceFlow provides an interpretable zero-shot baseline for clinical keyframe ordering.
\end{abstract}

\section{Introduction}
Clinical skill assessment is crucial in medical education. The ClinSkill QA task requires models to reorder scrambled clinical keyframes and provide a rationale for the predicted sequence, offering a fine-grained testbed for multimodal understanding of clinical processes in realistic teaching scenarios.

Recent multimodal LLMs have advanced visual understanding and zero-shot reasoning, yet clinical keyframe ordering remains challenging for three reasons. First, the input consists of only a few scrambled static images, requiring the model to infer temporal progression from subtle local differences. Second, multiple visual cues may evolve in parallel, such as chest exposure and hand positioning, which makes global ordering more difficult. Third, the task requires readable explanations grounded in clear evidence.

To address these challenges, we propose a structured zero-shot framework based on Qwen2.5-VL~\citep{bai2025qwen25vl} that decomposes clinical keyframe ordering into three stages: global overview, local evidence modeling, and ordering decision. The framework explores two ordering approaches: model-led reasoning and rule-guided correction with explicit evidence constraints. This design is motivated by a practical tension in the task: flexible reasoning may support better explanation generation, whereas stronger evidence constraints may improve ordering stability.

Our contributions are threefold:

\begin{enumerate}
    \item We propose \textbf{EvidenceFlow}, a structured zero-shot framework for clinical keyframe ordering and rationale generation.
    \item We design a three-stage pipeline with two ordering variants: model-led \textbf{EvidenceFlow-M} and rule-guided \textbf{EvidenceFlow-R}.
    \item We show a trade-off between ordering stability and explanation quality, clarifying current MLLM limitations on this task.    
\end{enumerate}

\section{Related Work}
In recent years, multimodal research in medical and clinical scenarios has mainly focused on medical visual question answering, image dialogue, and multi-image understanding. Representative studies such as PMC-VQA~\citep{zhang2023pmcvqa}, LLaVA-Med~\citep{li2023llavamed}, and Med-MIM~\citep{yang2025medmim} demonstrate the potential of multimodal models in medical image understanding, open-ended QA, and multi-image reasoning.

For structured multimodal reasoning, Multimodal-CoT~\citep{zhang2023multimodalcot} shows that making intermediate reasoning explicit can facilitate complex vision-language tasks. For temporal and multi-image understanding, MuirBench~\citep{wang2025muirbench}, Mementos~\citep{wang2024mementos}, TempCompass~\citep{liu2024tempcompass}, and TempVS~\citep{song2025burnafterreading} indicate that robust cross-image or cross-frame temporal reasoning remains challenging for current MLLMs. In the zero-shot setting, Socratic Models~\citep{zeng2022socraticmodels} further suggest that organizing intermediate evidence through language can improve interpretability without task-specific training.

Our work differs from these studies by focusing on structured zero-shot ordering in clinical keyframe sequences, where both temporal recovery and evidence-grounded explanation are required.


\section{Method}
\subsection{Task Definition and Overall Framework}
Given a set of clinical keyframes \(X=\{x_1, x_2, \ldots, x_n\}\) (typically \(n \in [4,6]\)), the model must output the correct temporal order \(Y = [y_1, y_2, \dots, y_n]\) and a natural language explanation. Unlike classification, the core task is to recover the full procedural order from subtle cross-image differences.

\begin{figure*}[t]
    \centering
    \includegraphics[width=0.8\textwidth]{paper/img/framework.pdf}
    \caption{Overview of the EvidenceFlow framework for clinical keyframe ordering.}
    \label{fig:framework}
\end{figure*}

We propose EvidenceFlow, a structured zero-shot framework that decomposes clinical keyframe ordering into three stages—global overview, local evidence modeling, and ordering decision—with two variants: model-led EvidenceFlow-M and rule-guided EvidenceFlow-R, as shown in Figure~\ref{fig:framework}.

\subsection{Global Overview and Local Evidence Modeling}
In the global overview stage, all keyframes in the same sample are first concatenated into a grid image according to their labels, and then input into the multimodal model for holistic analysis. The model needs to extract main change cues, early or late candidate anchors, and corresponding uncertainty information at the group level, denoted as \(G = \{a, s^{early}, s^{late}, u\}\), where \(a\) denotes the dominant axis of change; \(s^{early}\) and \(s^{late}\) denote the confidence that an image serves as an early or late anchor, respectively; and \(u\) denotes global uncertainty information. Subsequently, the axis of change with the highest confidence is extracted as the main reference axis for subsequent sorting, and candidate anchors are extracted for subsequent ordering decisions.

In the local evidence stage, each image is independently analyzed along six dimensions (chest exposure, hand positioning, hand stability, airway, ventilation, AED), producing a vector \(e_i = [chest_i, hand_i, stab_i, airway_i, vent_i, aed_i, c_i, z_i]\), where the first six encode local states, \(c_i\) denotes confidence, and \(z_i\) denotes a coarse stage label. This stage complements the global overview by providing fine-grained cues for ordering.

\subsection{EvidenceFlow Ordering Decision} 
Given the global overview and local evidence, the framework proceeds to the ordering decision stage. This stage requires further integrating the overall change trends at the image-group level with the fine-grained clinical cues at the single-image level to generate a consistent global order.

Under this framework, we instantiate two ordering variants: EvidenceFlow-M emphasizes model-led reasoning, whereas EvidenceFlow-R relies more on explicit evidence constraints and rule-based correction. By comparing these two variants, we analyze how different control strategies affect ordering stability and explanation generation.

\subsubsection{EvidenceFlow-M: Model-Led Ordering Implementation}
This stage first determines whether stable start/end anchors exist to select an ordering mode. To do so, it computes a pairwise relation score for each image from pairwise comparisons:
\begin{equation}
R_{\text{pair}}(x) = \frac{1}{|X| - 1} \sum_{y \neq x} c_{x,y} \delta_{x,y}
\end{equation}

The resulting score characterizes the relative earliness or lateness tendency of each image within the set, where \(c_{x,y}\) denotes the pairwise comparison confidence between images \(x\) and \(y\), and \(\delta_{x,y}\) is defined as follows:
\begin{equation}
\delta_{x,y} =
\begin{cases} 
1, & \text{x is later than y} \\
-1, & \text{x is earlier than y} \\
0, & \text{UNCERTAIN}
\end{cases}
\end{equation}

Based on the candidate anchor information, main change cues, and coarse-grained stage labels from the global overview, we compute an anchor score for each image:
\begin{equation}
S_{\text{early}}(x) = A_{\text{early}}(x) - P_{\text{axis}}(x) + B_{\text{early}}(x)
\end{equation}
\begin{equation}
S_{\text{late}}(x) = A_{\text{late}}(x) + P_{\text{axis}}(x) + B_{\text{late}}(x)
\end{equation}

Here, $A_{\text{early}}(x)$ and $A_{\text{late}}(x)$ denote the early and late candidate confidences given in the global overview stage, respectively; $P_{\text{axis}}(x)$ denotes the relative progression value of the main change cues for the image; and $B_{\text{early}}(x)$ and $B_{\text{late}}(x)$ respectively denote the stage reward terms derived from the coarse-grained stage labels.

Based on anchor scores and marginal differences, the model adaptively selects a mode \(m \in \{\text{dual}, \text{single}, \text{none}\}\). In dual mode, early and late anchors fix the start and end, with intermediate images sorted by axis progression. In single mode, one anchor fixes one end, and the remaining images are sorted according to their progression estimates. In none mode, the model estimates relative progression directly. After obtaining the initial sequence \(Y^{(0)} = [y_1, y_2, \ldots, y_n]\), local corrections using pairwise scores and cached comparisons, followed by lightweight global verification, yield the final sequence \(Y\) and its explanation.

\subsubsection{EvidenceFlow-R: Rule-Guided Ordering Implementation}
Unlike EvidenceFlow-M, EvidenceFlow-R generates ordering results through stage consistency correction, group-level position priors, continuous progression scoring, and directed pairwise comparisons. Specifically, we first refine the coarse-grained stage label \(z_i\) obtained from the local evidence stage by combining the local evidence \(e_i\) and the global overview information \(G\), yielding a corrected stage value \(\tilde{z}_i\). On this basis, the corrected stage information, the aggregated local evidence scores, and the group-level position priors are jointly mapped into a rule-flow ordering score:
\begin{equation}
S_R(x_i) = \alpha \tilde{z}_i + \beta E_i + \gamma P_i
\end{equation}

Here, \(E_i\) denotes the aggregated local evidence score, and \(P_i\) denotes the group-level position prior. The coefficients \(\alpha\), \(\beta\), and \(\gamma\) denote the corresponding weights. By sorting \(S_R(x_i)\) in ascending order, an initial sequence \(Y^{(0)} = [y_1, y_2, \dots, y_n]\) can be obtained. For image pairs with close ordering scores, the framework further performs directed pairwise comparisons for local refinement. If necessary, lightweight global verification is then applied to further correct adjacent errors and produce the final sequence \(Y\) together with its natural language explanation.
% Figure 2
\begin{figure*}[t]
    \centering
    \includegraphics[width=0.95\textwidth]{paper/img/combined_image .png}
    \caption{Case study of easy and hard scenarios in CPR emergency response.}
    \label{fig:case_study}
\end{figure*}
\section{Experimental Setup}
Experiments are conducted on the ClinSkill QA dataset (200 samples, each containing 4--6 keyframes). The base model Qwen2.5-VL-7B-Instruct is used in a zero-shot setting with 4-bit quantization and a temperature of 0 on a single RTX4090. All experiments are conducted without task-specific training. Both variants share the same backbone and evidence extraction setting, differing only in the final ordering strategy. Following the official evaluation protocol, we report the overall Score, Task Acc (exact sequence match), Pair Micro (pairwise order accuracy), and BERT F1 \citep{zhang2020bertscore}, which measures semantic similarity between the generated explanation and the reference.

\section{Experimental Results}
\subsection{Main Experimental Results}
We compare EvidenceFlow-R and EvidenceFlow-M under the same base model and official evaluation protocol. The results are shown in Table~\ref{tab:main_results}.
\begin{table}[!ht]
    \centering
    \footnotesize
    \setlength{\tabcolsep}{3pt}
    \begin{tabular}{@{}lcccc@{}}
        \toprule
        Variant & Score & Task Acc & Pair Micro & BERT F1 \\
        \midrule
        EvidenceFlow-R & 30.44 & 0.10 & 0.55 & 0.57 \\
        EvidenceFlow-M & 28.71 & 0.03 & 0.51 & 0.74 \\
        \bottomrule
    \end{tabular}
    \caption{Main results on the ClinSkill QA test set.}
    \label{tab:main_results}
\end{table}

As shown in Table~\ref{tab:main_results}, EvidenceFlow-R outperforms EvidenceFlow-M on Score, Task Acc, and Pair Micro, indicating more stable sequence recovery, while EvidenceFlow-M achieves higher BERT F1, reflecting better semantic similarity in explanation generation.

\subsection{Comparison with Public Submission Results}
Table~\ref{tab:public_results} compares our method with several public submissions from the same test phase.
\begin{table}[!ht]
    \centering
    \footnotesize
    \setlength{\tabcolsep}{3pt}
    \begin{tabular}{@{}lcccc@{}}
        \toprule
        Method & Score & Task Acc & Pair Micro & BERT F1 \\
        \midrule
        baovy & 71.43 & 0.63 & 0.86 & 0.79 \\
        qppprun & 56.73 & 0.47 & 0.79 & 0.55 \\
        VerbaNexAI Lab & 37.96 & 0.17 & 0.60 & 0.71 \\
        EvidenceFlow-R & 30.44 & 0.10 & 0.55 & 0.57 \\
        EvidenceFlow-M & 28.71 & 0.03 & 0.51 & 0.74 \\
        \bottomrule
    \end{tabular}
    \caption{Comparison of public submissions from the same test phase.}
    \label{tab:public_results}
\end{table}
Compared with public submissions, our frame-
work appears more competitive in explanation qual-
ity than in ordering accuracy. This suggests that
the model can extract useful local cues, but still
struggles to organize them into a consistent global
temporal structure. Together with the internal com-
parison in Table 1, these results indicate that stable
cross-image cue integration remains the main bot-
tleneck of the task.

\subsection{Error Analysis}
Figure~\ref{fig:case_study} shows that (a) is an easy case correctly predicted by Qwen2.5-VL-7B-Instruct; (b) is a hard case with ground truth C→A→B→D, but the model predicts C→B→A→D, swapping frames A and B. This error arises from high visual similarity during bag-valve mask ventilation, where subtle hand and mask changes are hard to capture, revealing insufficient fine-grained temporal discrimination as the main bottleneck.

\section{Conclusion}
We propose EvidenceFlow, a structured zero-shot framework for clinical keyframe ordering. The results show that the rule-guided variant yields better ordering performance, whereas the model-led variant produces better explanations, revealing a trade-off between ordering stability and rationale quality. This work provides an interpretable zero-shot baseline for multi-image temporal reasoning in clinical scenarios.
\section*{Limitations}
The 7B model is limited in fine-grained cross-image temporal integration, especially on hard samples. Our analysis lacks ablations and large-scale verification, and the framework depends on prompt design and handcrafted constraints.

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