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\newcommand{\task}{CLARITY - Unmasking Political Question Evasions}
\newcommand{\framework}{\textsc{CAMSR-CoT}} % Confidence-Aware Multi-Stage Reasoning + CoT

\title{TeleAI at SemEval-2026 Task 6: A Confidence-Aware Multi-Stage Reasoning Framework with Chain-of-Thought}

% \author{
%   TeleAI Team \\
%   China Telecom AI Research Institute \\
%   \texttt{clarity2026@teleai.com}
% }

\author{
 \textbf{Lingling Shi}\thanks{Equal contribution.}\textsuperscript{1},
 \textbf{Haoyu Jin}\footnotemark[1]\textsuperscript{1},
 \textbf{Shiquan Wang}\textsuperscript{1},\\
 \textbf{Ruiyu Fang}\textsuperscript{1},
 \textbf{Shuangyong Song}\textsuperscript{1},
 \textbf{Xuelong Li}\thanks{Corresponding author.}\textsuperscript{2}
\\
\\
\textsuperscript{1}Xingchen AGI Lab, China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd
\\
\textsuperscript{2}Institute of Artificial Intelligence (TeleAI), China Telecom
\\
 \small{
   \href{mailto:email@domain}{jinhaoyu@bupt.edu.cn, shill2,wangsq23,fangry,songshy@chinatelecom.cn, xuelong\_li@ieee.org}
 }
}


\begin{document}
\maketitle
\begin{abstract}
This paper describes our framework for SemEval-2026 Task~6 (\task{}), which focuses on classifying clarity and fine-grained evasion types in political question-answering dialogues.
We propose \framework{}, a \textbf{C}onfidence-\textbf{A}ware \textbf{M}ulti-\textbf{S}tage \textbf{R}easoning framework with \textbf{C}hain-\textbf{o}f-\textbf{T}hought that unifies the two subtasks through hierarchical label modeling. The framework adopts a confidence-based routing strategy: high-certainty cases are directly resolved, while ambiguous samples are routed to deeper Chain-of-Thought reasoning stages with boundary-aware few-shot exemplars to mitigate label confusion.
On the development set, our framework achieves Macro-F1 scores of 0.812 on SubTask~1 and 0.617 on SubTask~2. On the official hidden test set, it ranks 1st in both SubTask~1 (Macro-F1 = 0.89) and SubTask~2 (Macro-F1 = 0.68).

% This paper describes our framework for SemEval-2026 Task~6 (\task{}). The task involves classifying the clarity and evasion types of answers in political Q\&A, consisting of two subtasks: SubTask~1 for 3-way clarity classification and SubTask~2 for 9-way evasion type classification.
% We propose \framework{}: a confidence-aware multi-stage reasoning framework. The framework adopts a ``hierarchical progression'' strategy, utilizing confidence routing to diverse samples into reasoning paths of varying difficulty: high-certainty answers are quickly identified, while ambiguous, high-risk samples enter a deeper Chain-of-Thought reasoning stage. To address the confusion among fine-grained labels, we further introduce contrastive few-shot examples mined from confusion matrices to enhance the model's perception of decision boundaries.
% On the development set, our framework achieves a Macro-F1 of 0.812 on SubTask~1 and 0.617 on SubTask~2.
% On the official hidden evaluation set, our framework ranks 1st in both SubTask~1 (Macro-F1 = 0.89) and SubTask~2 (Macro-F1 = 0.68) (Table~\ref{tab:leaderboard}).
\end{abstract}

\section{Introduction}
\task{} (SemEval-2026 Task~6) \citep{thomas2026semeval2026task6clarity} focuses on classifying the clarity and evasion strategies of answers in English political interviews \citep{thomas2024ineversaidthat}. It includes two subtasks: SubTask~1 is a 3-way classification (Clear Reply / Ambivalent / Clear Non-Reply), and SubTask~2 is a fine-grained 9-way evasion classification. The test set adopts a multi-reference evaluation (each sample has three reference labels). Evasion and equivocation in political Q\&A are classic phenomena in discourse analysis \citep{bull1993microanalysis}, and automatically identifying evasion strategies is significant for improving transparency in political accountability.

We propose \framework{}: a three-stage prompting reasoning framework with confidence-based routing\footnote{Code available at: \url{https://github.com/ther7777/semeval-2026-task6-camsr-cot}}, built upon the AI Flow paradigm \citep{an2026aiflow}. Analysis of the training set confusion matrix suggests that the three Non-Reply classes (\emph{Declining} / \emph{Claims ignorance} / \emph{Clarification}) have relatively distinct linguistic signals but tend to produce high false positive rates. Meanwhile, the remaining 6 classes show considerable mutual confusion within the 9-class framework. Accordingly, we designed a ``divide-and-conquer'' strategy: Step~1 uses a lightweight Gate to identify high-confidence Non-Replies and output them directly; medium-to-low confidence suspected Non-Replies enter Step~2 for a 9-class correction review; the remaining samples enter Step~3 for fine-grained classification within the 6-class space. Each stage employs Chain-of-Thought (CoT) reasoning \citep{wei2022chain} with dynamically assembled prompts that include refined label definitions, confusion-pair guidelines, and contrastive few-shot examples mined from the training set confusion matrix.

Technically, \framework{} builds on in-context learning \citep{brown2020language} and Chain-of-Thought prompting \citep{wei2022chain}. Decomposing the 9-class decision into staged sub-problems reduces the model's tendency to rely on surface cues when labels overlap semantically, in line with the least-to-most decomposition principle \citep{zhou2023leasttomost}.
On the development set, \framework{} improves SubTask~2 performance by +0.127 (0.490$\rightarrow$0.617) compared to a single-step Chain-of-Thought (CoT) baseline. Ablation studies show that Gate confidence routing and Step~3 few-shot boundaries are the main contributors to the performance gain. The main bottleneck is the \emph{Dodging} class (Recall only 0.179), whose boundaries with \emph{General} and \emph{Deflection} are often ambiguous---human annotators show substantial disagreement on these classes.



\section{Background}

\subsection{Task Setup and Data Overview}
\label{sec:background-task}
Each sample consists of an interview question, an interview answer, and a simplified sub-question derived from the original question. The framework must classify the answer with respect to the sub-question.
There is a deterministic mapping between the two SubTask levels: \emph{Explicit} corresponds to Clear Reply; \emph{Declining to answer}, \emph{Claims ignorance}, and \emph{Clarification} correspond to Clear Non-Reply; the remaining 5 classes (\emph{Implicit}, \emph{Partial/half-answer}, \emph{General}, \emph{Deflection}, \emph{Dodging}) correspond to Ambivalent. Our framework does not predict SubTask~1 independently but derives it from SubTask~2 results via this mapping to ensure consistency. Throughout this paper, we refer to the three SubTask~2 labels corresponding to \emph{Clear Non-Reply} collectively as \emph{Non-Reply}.

The training set contains 3,448 single-label English samples; the development set contains 308 multi-reference samples (each annotated independently by 3 annotators), with a pronounced long-tail distribution (see Appendix Table~\ref{tab:label-dist}): minority classes such as \emph{Partial/half-answer} (2.3\%) and \emph{Clarification} (2.7\%) are extremely sparse, and there is a distribution shift in \emph{Dodging} (Train 20.5\%$\rightarrow$Dev 18.7\%) and \emph{General} (11.2\%$\rightarrow$18.6\%).

\subsection{Label Ambiguity and Confusion Patterns}
\label{sec:task-analysis}

Multi-reference annotations in the development set reveal the inherent ambiguity of the task \citep{plank2014linguistically}: only 40.6\% of samples have unanimous agreement among three annotators, 48.7\% have two different labels, and 10.7\% have three different labels.
\emph{General} appears in 4 of the top 5 confusion pairs (with General$\leftrightarrow$Implicit being the highest at 12.0\%), indicating that the boundary between "general answers" and other evasion types is blurred.
Furthermore, high disagreement rates in \emph{Explicit}$\leftrightarrow$\emph{General} (10.7\%) and \emph{Explicit}$\leftrightarrow$\emph{Implicit} (8.8\%) suggest that even on the fundamental judgment of "whether information was provided," human annotators disagree significantly. See Appendix Table~\ref{tab:annotator-confusion} for details.

\paragraph{Baseline Analysis.}
Zero-shot reasoning \citep{kojima2022large} with DeepSeek-V3 using the original label definitions \citep{thomas2024ineversaidthat} achieves only 0.421 SubTask~2 Macro-F1 (Table~\ref{tab:method-comparison}), with the model being over-sensitive to refusal cues (misclassifying \emph{Dodging} and \emph{General} as \emph{Declining to answer}) and significant \emph{Implicit}$\leftrightarrow$\emph{General} confusion.
These patterns motivate two design decisions: (1)~mining contrastive few-shot examples from the training set confusion matrix to teach boundary distinctions; (2)~staged label-space reduction---isolating the three Non-Reply classes first, then classifying within the reduced 6-class space.

\subsection{Related Work}
\label{sec:related-work}
Our work intersects three lines of research.
Equivocation typologies for political interviews \citep{bull1993microanalysis}, further refined by \citet{rasiah2010evasion}, inform the response clarity taxonomy underlying our task \citep{thomas2024ineversaidthat}.
Methodologically, our framework builds on Chain-of-Thought prompting \citep{wei2022chain} and its multi-step extensions---least-to-most decomposition \citep{zhou2023leasttomost}, Tree-of-Thought \citep{yao2023tree}, and Plan-and-Solve \citep{wang2023planandsolve}---which demonstrate that structured staged reasoning improves LLM performance across various complex tasks \citep{dong2024icl, shen2025videoseg, wu2025multislu, chen2025tool, lemur2024, chainoflure, cemt2026, mruie, tablereasoner, mathclg, llmsr_xllm25, tablereasoner_semeval}. Our confusion-matrix-driven example selection relates to active example selection strategies \citep{diao2023active}; automatic prompt optimization \citep{khattab2024dspy} offers a complementary direction we leave to future work.
Our multi-reference evaluation draws on work treating annotation disagreement as a legitimate signal rather than noise \citep{plank2014linguistically, uma2021learning}.

\begin{figure*}[t]
  \centering
  \includegraphics[width=\textwidth]{../picture/architect2.jpg}
  \caption{Three-stage routing pipeline of \framework{}.}
  \label{fig:pipeline}
\end{figure*}

\section{Framework Overview}

\subsection{Overall Pipeline}

Figure~\ref{fig:pipeline} illustrates the three-stage inference pipeline of \framework{}.
In Step~1 (the \emph{Gate}), we predict a label and a confidence level.
If the answer is a high-confidence Non-Reply, we output it directly; otherwise, we route it to Step~2 for review or Step~3 for subdivision.

Given an input sample $x$, let $g$ and $c$ denote the Gate's predicted label and confidence, respectively, and let $\mathcal{S}_{\text{NR}}$ be the set of Non-Reply classes. The routing logic for the SubTask~2 prediction $\hat{y}$ is:
\begin{equation}
\hat{y} =
\begin{cases}
  g & \text{if } g \in \mathcal{S}_{\text{NR}} \land c = \text{High} \\
  \operatorname{Step2}(x, g) & \text{if } g \in \mathcal{S}_{\text{NR}} \land c \neq \text{High} \\
  \operatorname{Step3}(x) & \text{if } g = \text{OTHER}
\end{cases}
\end{equation}

This design diverts low-ambiguity samples early and concentrates extra prompt budget on high-risk branches.
Final SubTask~1 labels are generated via the fixed mapping described in Section~\ref{sec:background-task}.
Each stage uses dynamically assembled prompts, the components of which are summarized in Table~\ref{tab:prompt-components}.

\paragraph{Routing Statistics and Inference Cost.}
On dev, 86.7\% of the samples enter the OTHER$\rightarrow$Step~3 branch, 7.5\% trigger Step~2 correction review, and only 5.8\% are direct exits belonging to high-confidence Non-Reply. This results in an average of 1.94 model calls per sample. See Appendix~\ref{sec:appendix-inference-cost} for detailed cost analysis. A complete end-to-end walkthrough of a development set sample is provided in Appendix~\ref{sec:appendix-running-example}.

\begin{table}[t]
  \centering
  \small
  \begin{tabular}{lccc}
    \toprule
    Component & Step 1 & Step 2 & Step 3 \\
    \midrule
    Anchors     & $\times$ & \checkmark (2) & \checkmark (6) \\
    Boundaries  & $\times$ & \checkmark (3) & \checkmark (10) \\
    Confusion Guide    & $\times$ & \checkmark & \checkmark \\
    Prior Analysis & $\times$ & \checkmark & $\times$ \\
    Confidence  & \checkmark & $\times$ & $\times$ \\
    CoT         & \checkmark & \checkmark & \checkmark \\
    \bottomrule
  \end{tabular}
  \caption{Usage of prompt components in each stage. Numbers in parentheses indicate the count of few-shot examples injected.}
  \label{tab:prompt-components}
\end{table}

\subsection{Step~1: Gate with Confidence (4-way)}
The Gate outputs only three Non-Reply classes (\emph{Declining/Claims/Clarification}) or \emph{OTHER}, along with a \emph{High/Medium/Low} confidence.
We adopt a conservative confidence criterion (full prompt in Appendix~\ref{appendix:prompts}) centered on a key-information override rule: if the answer provides any information requested by the sub-question, the Gate must output \emph{OTHER} regardless of refusal cues. The three confidence levels are defined as follows:
\begin{itemize}
  \item \emph{High}: the Non-Reply cue is explicit and unambiguous, the answer is short and consists mostly of the refusal/ignorance/clarification itself, and there is no possibility of the answer being \emph{Implicit} or \emph{Partial}.
  \item \emph{Medium}: the answer fits a Non-Reply definition, but the refusal or ignorance cue is embedded in a longer explanation, followed by pivoting phrases (e.g., ``but\ldots'', ``however\ldots''), or otherwise mixed with additional content that may warrant reclassification.
  \item \emph{Low}: the answer shows some Non-Reply features, but may arguably be providing partial or implicit information; the Gate flags it as a potential Non-Reply for downstream verification.
\end{itemize}
Only High-confidence Non-Replies are output directly; all other cases are routed to Step~2 to reduce false positives.

\subsection{Step~2: Non-Reply Correction Review (9-way)}
When the Gate predicts Non-Reply with Medium/Low confidence, Step~2 is triggered for a 9-class review. The prompt components are detailed in Table~\ref{tab:prompt-components}. The \emph{Prior Analysis} component unique to Step~2 summarizes the label and confidence determined by the Gate, providing prior context for correction review. Boundary counter-examples ($B{=}3$) are all mined from the training set zero-shot confusion matrix, focusing on covering high-frequency misclassification pairs such as \emph{Dodging}$\rightarrow$\emph{Declining}.

\subsection{Step~3: OTHER Branch Subdivision (6-way)}
When the Gate outputs OTHER, the sample may correspond to either Clear Reply or Ambivalent in SubTask~1; Step~3 classifies it within the 6-class set (excluding the three Non-Reply classes), from which the SubTask~1 label is derived via the mapping in Section~\ref{sec:background-task}. This reduced label space lowers overlap and improves discrimination accuracy. Prompt components are shown in Table~\ref{tab:prompt-components}: Step~3 injects 6 anchors (1 per class) and 10 boundaries (covering the most confusing counter-example pairs). Prior analysis is excluded to prevent Gate priors from influencing subdivision decisions.

\section{Experimental Setup}
\paragraph{Data and Metrics.}
Both subtasks are evaluated with Macro-F1 using the official scorer; SubTask~2 uses a multi-reference variant where a prediction is correct if it matches any annotator label (formal definitions in Appendix~\ref{sec:appendix-macro-f1}).
Anchors and boundaries are mined from zero-shot reasoning on the training set; main results are reported on the development set.

\paragraph{Model and Inference Configuration.}
Our main framework (\framework{}) and its ablation variants use DeepSeek-V3~\citep{deepseekv3} as the backbone, accessed through the DeepSeek API with temperature 0.0 and a maximum generation length of 1,800 tokens to accommodate full CoT reasoning traces.

\paragraph{Comparison Methods.}
Table~\ref{tab:method-comparison} compares three groups of methods: (1) \textbf{Prompting Methods} (DeepSeek-V3): including direct 9-class baseline (no CoT), single-step CoT baseline, and \framework{} (this paper); (2) \textbf{Alternative Backbone Models}: migrating this framework to Qwen3-235B~\citep{qwen2.5}, TeleChat3-36B-Thinking~\citep{liu2025trainingreporttelechat3moe, telechat1, telechat_acl, telechat2, teleflm, teleflm1t}, and Qwen2.5-7B~\citep{qwen2.5} to evaluate framework generality; (3) \textbf{Supervised Fine-Tuning} (Qwen2.5-7B)~\citep{ouyang2022training}: including direct SFT and knowledge distillation~\citep{hinton2015distilling} using \framework{} output as teacher.

\begin{table}[t]
  \centering
  \small
  \begin{tabular}{lcc}
    \toprule
    Method (dev) & \shortstack{SubTask~1\\F1} & \shortstack{SubTask~2\\F1} \\
    \midrule
    \multicolumn{3}{l}{\textit{Prompting Methods (DeepSeek-V3)}} \\
    \quad Direct 9-class (No CoT) & 0.662 & 0.421 \\
    \quad CoT Single-step 9-class & 0.710 & 0.490 \\
    \quad \textbf{\framework{} (Ours)} & \textbf{0.812} & \textbf{0.617} \\
    \midrule
    \multicolumn{3}{l}{\textit{Alternative Backbones (\textup{\framework{}} Pipeline)}} \\
    \quad Qwen2.5-7B-Instruct$^\dag$ & 0.481 & 0.288 \\
    \quad Qwen3-235B-A22B & 0.772 & 0.556 \\
    \quad TeleChat3-36B-Thinking & 0.763 & 0.561 \\
    \midrule
    \multicolumn{3}{l}{\textit{Supervised Fine-Tuning (Qwen2.5-7B)}} \\
    \quad Direct SFT & 0.587 & 0.495 \\
    \quad SFT + Distillation$^\ddag$ & 0.758 & 0.509 \\
    \bottomrule
  \end{tabular}
  \caption{Method comparison (dev). $^\dag$Uses simplified prompts due to context length limits. $^\ddag$Teacher is Qwen3-235B-A22B running the \framework{} pipeline. SubTask~2 uses official multi-reference scorer.}
  \label{tab:method-comparison}
\end{table}

\section{Results}
\label{sec:results}

\subsection{Main Results}
Table~\ref{tab:method-comparison} presents the comparison results on the development set.
Within the prompting methods group, \framework{} improves SubTask~2 performance by +0.127 (0.490$\rightarrow$0.617) over the single-step CoT baseline, suggesting that the multi-stage routing strategy yields substantial gains.
In the alternative backbone experiments, Qwen3-235B~\citep{qwen2.5} and TeleChat3-36B-Thinking~\citep{liu2025trainingreporttelechat3moe} achieve SubTask~2 F1 scores of 0.556 and 0.561 respectively using the same pipeline, indicating that the \framework{} framework has strong cross-model transferability, although performance remains lower than DeepSeek-V3 (0.617).
Qwen2.5-7B, due to its smaller model size, has limited effectiveness when running the pipeline directly (SubTask~2 F1 = 0.288).
In the supervised fine-tuning group, direct SFT achieves a SubTask~2 F1 of 0.495, comparable to the single-step CoT baseline (0.490). Knowledge distillation notably improves SubTask~1 (0.587$\rightarrow$0.758) with a modest SubTask~2 gain (0.509), suggesting that the teacher's CoT reasoning helps the student better capture coarse-grained clarity distinctions. Both SFT variants, however, remain below large-model prompting approaches. Training details are provided in Appendix~\ref{sec:appendix-sft}.

\paragraph{Fine-grained Performance Analysis.}
Per-class results (Appendix Table~\ref{tab:task2-perclass}) show that Gate routing achieves a Precision of 1.000 for all three Non-Reply classes. The main bottleneck is \emph{Dodging} (Recall = 0.179), whose boundaries with \emph{General} and \emph{Deflection} tend to be ambiguous, consistent with annotator disagreement patterns (Section~\ref{sec:task-analysis}).

\begin{table}[t]
  \centering
  \small
  \setlength{\tabcolsep}{3pt}
  \begin{tabular}{clc|lc}
    \toprule
    & \multicolumn{2}{c|}{\textbf{SubTask 1}} & \multicolumn{2}{c}{\textbf{SubTask 2}} \\
    Rank & Team & F1 & Team & F1 \\
    \midrule
    \textbf{1} & \textbf{TeleAI (Ours)} & \textbf{0.89} & \textbf{TeleAI (Ours)} & \textbf{0.68} \\
    2 & AsymVerify & 0.85 & ChulaNLP$^\dag$ & 0.61 \\
    3 & CSE-UOI & 0.85 & CLaC @ CLARITY & 0.59 \\
    4 & Rasende Rakete & 0.83 & Rasende Rakete & 0.59 \\
    5 & Evaluators & 0.83 & YNU-HPCC & 0.59 \\
    \bottomrule
  \end{tabular}
  \caption{Official hidden evaluation leaderboard (Top 5). Our framework ranks first in both tasks. $^\dag$The original Codabench username was \emph{moswisarut}, updated to the team name \emph{ChulaNLP}.}
  \label{tab:leaderboard}
\end{table}

\paragraph{Official Evaluation Results.}
On the official hidden evaluation set (Table~\ref{tab:leaderboard}; see Appendix~Table~\ref{tab:full-leaderboard} for full results), our framework (TeleAI) ranks 1st in both SubTask~1 (Macro-F1 = 0.89) and SubTask~2 (Macro-F1 = 0.68), leading the second place by 4 and 7 percentage points respectively.




\subsection{Ablation Study and Analysis}

\begin{table}[t]
  \centering
  \small
  \setlength{\tabcolsep}{3pt}
  \begin{tabular}{@{}lcc@{}}
    \toprule
    Variant (dev) & \shortstack{SubTask~1\\F1} & \shortstack{SubTask~2\\F1} \\
    \midrule
    CoT single-step 9-class (Baseline) & 0.710 & 0.490 \\
    + Gate routing (no confidence) & 0.761 & 0.465 \\
    + Confidence routing (Base Pipeline) & 0.811 & 0.503 \\
    + Refined label definitions & 0.800 & 0.520 \\
    + Step-3 few-shot $B{=}10$ (Ours) & \textbf{0.812} & \textbf{0.617} \\
    \bottomrule
  \end{tabular}
  \caption{Ablation study (Dev Macro-F1). Each row adds a component to the previous one. \emph{Base Pipeline} refers to the three-stage framework with basic definitions and no Step-3 few-shots.}
  \label{tab:ablation}
\end{table}
Table~\ref{tab:ablation} shows the contribution of each component to final performance.
Key findings: (1) Gate routing + confidence routing improved SubTask~1 from 0.710 to 0.811, mainly contributing to the reduction of Non-Reply false positives; (2) Switching to refined label definitions improved SubTask~2 (+0.017) but had a slight impact on SubTask~1 ($-$0.011); (3) Injecting few-shot boundaries in Step~3 brought the largest SubTask~2 improvement (+0.097) while maintaining SubTask~1 stability.

\paragraph{Impact of Confidence Routing.}
The Step~2 correction branch is triggered on only 23/308 dev samples, yet it has a disproportionate influence on Macro-F1: under the multi-reference scorer, an incorrect Non-Reply prediction incurs one FP and multiple FNs (one per reference label), heavily penalizing precision-sensitive classes.
By restricting High confidence to short, unambiguous refusals, we route borderline samples into Step~2, where boundary counter-examples help distinguish genuine refusals from strategic deflections. As a result, all three Non-Reply classes achieve a precision of 1.000 on dev (Table~\ref{tab:task2-perclass}).

\paragraph{Contribution of Definitions and Boundaries.}
From the base pipeline to the final framework, SubTask~2 Macro-F1 improved by +0.114. The largest per-class gains are in \emph{General} (+0.334) and \emph{Implicit} (+0.383)---notably the two classes with the highest annotator disagreement (Section~\ref{sec:task-analysis}). We attribute this to the refined definitions spelling out the key difference between these two classes (``inferable specific information'' vs.\ ``on-topic but vague''), combined with boundary examples that present direct \emph{General}$\leftrightarrow$\emph{Implicit} contrasts.

Additional ablation dimensions---boundary count sensitivity ($B{=}8{-}14$) and the isolated contribution of the confusion guide (+0.102 F1)---are reported in Appendix~\ref{sec:appendix-ablation-extra}. Detailed per-class ablation comparisons for SubTask~1 and SubTask~2 are provided in Appendix Tables~\ref{tab:appendix-task1} and~\ref{tab:appendix-task2}.

\paragraph{Summary of Key Drivers.}
From the ablation experiments, we identify three key performance drivers in decreasing order of impact:
(1)~confidence-aware Gate routing most significantly reduces Non-Reply false positives, yielding the largest SubTask~1 gain (+0.101 F1);
(2)~boundary-aware few-shot examples in Step~3 contribute the largest SubTask~2 gain (+0.097 F1), directly addressing confusion among fine-grained evasion types;
(3)~refined label definitions and confusion guides help resolve the persistent ambiguity between \emph{General} and \emph{Implicit} (+0.017 SubTask~2 F1).
All three components are complementary: removing any one degrades overall performance.

\subsection{Error Analysis}
Of 308 dev samples, 112 (36.4\%) are misclassified, with 47 on unanimously annotated samples.
Errors concentrate in the \emph{Dodging}--\emph{General}--\emph{Deflection} triangle (77.7\% of all errors); all 18 direct-exit samples are correct (0\% error), while Step~2 and Step~3 error rates are 47.8\% and 37.8\%.
Closer inspection of Step~2 errors reveals a systematic pattern: 9 of the 10 misclassified samples are over-corrected to \emph{Deflection}, with gold labels spanning \emph{General}~(7), \emph{Declining}~(3), \emph{Dodging}~(3), and \emph{Implicit}~(2).
Because Step~2 receives only borderline Non-Reply samples that the Gate flagged with medium or low confidence, these cases inherently sit at the semantic boundary between refusal and evasion; the 9-class review tends to over-correct toward \emph{Deflection}---the most frequent evasion type in the correction prompt---rather than preserving the original Non-Reply label or selecting other Ambivalent classes.

Two residual patterns dominate: (1) Evasion Triangle Blur: the model defaults to \emph{General} when answers stay on-topic but sidestep the sub-question, while annotators favor \emph{Dodging} or \emph{Deflection}; (2) Hedging Bias: hedging language biases the model toward \emph{Implicit} even when specific information is provided (13 cases crossing the Clear Reply boundary).

The \emph{Dodging} class is the primary bottleneck (Recall = 0.179, F1 = 0.289): we attribute this to both annotation ambiguity and model limitations.
On the annotation side, \emph{Dodging} has the lowest inter-annotator agreement among all nine classes, frequently confused with \emph{General} and \emph{Deflection} by human raters themselves (Section~\ref{sec:task-analysis}); on the model side, LLMs exhibit a tendency to interpret deliberately vague but on-topic responses as merely general rather than intentionally evasive, making \emph{Dodging} inherently difficult to capture without explicit intent modeling.

See Appendix~\ref{sec:appendix-error-analysis} for the confusion matrix, per-path statistics, and illustrative case studies.

\section{Conclusion}
% This paper proposes \framework{}: a multi-stage prompting reasoning framework with confidence routing. Through analysis of label ambiguity and model confusion patterns (Section~\ref{sec:task-analysis}), we designed a routing strategy and dynamic contrastive examples, achieving 1st place in both SubTask~1 and SubTask~2 in the SemEval-2026 Task~6 official evaluation.
% Ablation studies show that Gate confidence routing and few-shot boundaries are two key factors for performance improvement. Future work includes combining strategies like self-consistency~\citep{wang2023selfconsistency} to further improve robustness, automating boundary retrieval and adaptive selection, and distilling the reasoning process~\citep{hinton2015distilling} into smaller models to reduce deployment costs.
This paper proposes \framework{}: a multi-stage prompting framework with confidence routing. By analyzing label ambiguity and model confusion patterns (Section~\ref{sec:task-analysis}), we designed a routing strategy and dynamic contrastive examples, achieving 1st place in SemEval-2026 Task~6. Ablation studies confirm that Gate confidence routing and few-shot boundaries are key to performance.
We did not evaluate majority voting or alternative prompt templates for the Step~2 correction path in the current work. Future directions include exploring more robust correction mechanisms (e.g., self-consistency~\citep{wang2023selfconsistency} or multi-sample voting) for the Step~2 branch, automating boundary retrieval and adaptive example selection, and distilling the reasoning pipeline~\citep{hinton2015distilling} into smaller models to reduce deployment costs.
% \section*{Acknowledgments}
% % Thanks to anonymous reviewers for their valuable feedback.

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\clearpage
\appendix
\input{appendix_results_en}
\input{appendix_prompts_en}

\end{document}
