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\title{zzucs at PsyDefDetect: Bridging Long-Tail Imbalance and Clinical Rubrics for DMRS Defense-Level Detection}

\author{\textbf{Bin Huang}$^{1}$ \and \textbf{Liuyuan Su}$^{1}$ \and \textbf{Kaixuan Yuan}$^{1}$ \and \\ \textbf{Guanghui Zhao}$^{1}$ \and \textbf{Shixin Zhang}$^{1}$ \and \textbf{Kunli Zhang}$^{1,*}$ \\[0.3em]
  $^{1}$School of Computer and Artificial Intelligence, Zhengzhou University \\
  Zhengzhou, China \\[0.3em]
  \texttt{\{1084893712, 2805476399, 2285986836\}@qq.com}; \texttt{suliuyuan@gs.zzu.edu.cn}; \\[0.1em]
  \texttt{17837272557@163.com}; \texttt{ieklzhang@zzu.edu.cn} \\[0.2em]
  $^{*}$Corresponding author}

\begin{document}
\maketitle

\begin{abstract}
Detecting DMRS defense levels in emotional support dialogues is challenging due to severe class imbalance and fine-grained clinical distinctions between adjacent levels, issues well documented in psychotherapy-oriented NLP surveys \citep{na-etal-2025-survey}.
We present \textbf{zzucs} for PsyDefDetect at BioNLP~2026 \citep{na-etal-2026-psydefdetect}, adopting a \textbf{data--supervision co-design} strategy.
\textbf{SCCR} applies stratified resampling to balance support across nine defense levels.
\textbf{CoR--QLoRA} encodes clinical rubrics, including task contracts, taxonomy definitions, and boundary cues, into static prompts for 8B model fine-tuning.
Ablations show SCCR improves macro-F1 by \textbf{4.9 points} over random oversampling.
Our system from team \textbf{zzucs}, submitted on CodaBench under the display name \textit{sly\_zzu} with submission ID 652647, achieves \textbf{0.3585 macro-F1} on the \textbf{official blind-test leaderboard LB1}. It ranks \textbf{6th of 21} registered teams with official submissions and surpasses all published 8B baselines by \textbf{4.4 F1 points} over the strongest 8B comparator, Ministral-8B.
The code has been released at \url{https://github.com/jackssdd/zzucs_psydefdetect_code}.
\end{abstract}

\section{Introduction}

Psychological defenses, broadly defined as automatic strategies for managing distress, shape how individuals disclose emotions and respond to therapeutic support \citep{vaillant-1992-ego,perry-henry-2004-dmrs}.
The Defense Mechanism Rating Scales (DMRS) operationalize these constructs into a validated clinical taxonomy comprising three defensive categories, seven functional levels, and thirty specific mechanisms \citep{perry-henry-2004-dmrs,di-giuseppe-perry-2021-dmrs-handbook}.
Despite their centrality in clinical theory, computational detection of defensive functioning in conversational settings remains largely unaddressed in current emotional support dialogue (ESD) systems \citep{liu-etal-2021-towards-emotional,rashkin-etal-2019-towards}.

\textbf{The PSYDEFCONV corpus} \citep{na-etal-2026-psydefconv} introduces the first conversational dataset annotated with DMRS defense levels, establishing a nine-way classification task over help-seeker utterances.
The PsyDefDetect shared task at BioNLP~2026 formalizes blind leaderboard evaluation on this setting \citep{na-etal-2026-psydefdetect}. Recent surveys also contextualize LLMs in psychotherapy \citep{na-etal-2025-survey}.
Although this appears to be a standard multi-class problem, it poses two concrete challenges that limit conventional supervised learning:

\begin{enumerate}[noitemsep,topsep=0pt]
    \item \textbf{Severe distributional imbalance:} Level 7 (high-adaptive) defenses dominate 51.8\% of seeker turns, while Level 1 (action) and Level 5 (neurotic) each constitute merely 2.6--5.8\% \citep{na-etal-2026-psydefconv}.
    Standard training is biased toward majority classes, leading to poor coverage of clinically important but rare defense levels.
    \item \textbf{Clinical granularity:} Adjacent DMRS levels encode subtle distinctions. For example, \emph{disavowal} at Level 3 and \emph{neurotic} defenses at Level 5 both involve avoidance, but they differ in awareness and displacement patterns that remain hard to capture with vanilla classification objectives, even with strong pre-trained representations.
\end{enumerate}

We argue that simply scaling model size is insufficient for this long-tailed, clinically nuanced setting.
While instruction tuning broadly improves LLM task performance \citep{zhang-etal-2023-instruction-tuning}, severely imbalanced clinical taxonomies still require targeted data and supervision design rather than a larger backbone \citep{na-etal-2025-survey}.
We therefore adopt two complementary strategies:
\begin{itemize}[noitemsep,topsep=0pt]
    \item \textbf{Data-centric rebalancing:} stratified class-conditional resampling with replacement, which preserves within-class diversity while equalizing class support.
    \item \textbf{Rubric-based prompting:} encoding DMRS taxonomy definitions and pairwise boundary cues as a static input prefix, providing structured clinical supervision without inference-time generation.
\end{itemize}

\textbf{Contributions.}
Our work makes three contributions:
\textbf{(i)}~\textbf{SCCR}, a stratified resampling method for class-imbalanced clinical dialogue data, which improves macro-F1 by 4.9 points over random oversampling and 9.5 points over no resampling (Table~\ref{tab:ablation}).
\textbf{(ii)}~\textbf{Chain-of-Rubric (CoR) prompting}, a structured prompting method that encodes DMRS taxonomy definitions and boundary cues directly in the input prefix.
\textbf{(iii)}~Results on the official LB1 blind-test leaderboard \citep{na-etal-2026-psydefdetect} showing our 8B QLoRA system outperforms all published fine-tuned 8B baselines.
Due to the system paper page limit, \textbf{related work} is deferred to Appendix~\ref{app:related}.

\begin{figure*}[t]
  \centering
  \includegraphics[width=0.98\linewidth]{system.png}
  \caption{\textbf{System architecture.}
  \textbf{Module I} (SCCR) transforms imbalanced training data through stratified oversampling with replacement.
  \textbf{Module II} (CoR--QLoRA) constructs three-channel rubric prompts (task contract, taxonomy definitions, boundary cues) for injection into a quantized 8B backbone with LoRA adapters.
  Training employs imbalance-aware focal loss with inverse-frequency class weighting.}
  \label{fig:system}
\end{figure*}

\section{Related Work}
\label{sec:related}

\citet{na-etal-2026-psydefdetect} describe PsyDefDetect at BioNLP~2026: nine-way DMRS defense-level detection with blind leaderboard metrics over PSYDEFCONV \citep{na-etal-2026-psydefconv}.
\citet{na-etal-2025-survey} survey LLM applications in psychotherapy, highlighting challenges around data sparsity and evaluation, which motivates structured clinical supervision for DMRS-level tasks.
Most emotional support dialogue (ESD) resources emphasize supporter strategies and empathy ratings \citep{liu-etal-2021-towards-emotional,rashkin-etal-2019-towards}. In contrast, PSYDEFCONV targets \emph{seeker-side} DMRS defense levels as a distinct clinical classification problem.
Long-tailed text classification is tackled via reweighting, such as focal loss \citep{lin-etal-2017-focal}, resampling \citep{chawla-etal-2002-smote}, or quality-aware synthesis \citep{peng-etal-2024-text-grafting,zhou-etal-2024-multistage}. Our SCCR uses stratified resampling without generative augmentation to protect DMRS validity.
Parameter-efficient adaptation (LoRA/QLoRA \citep{hu-etal-2022-lora,dettmers-etal-2023-qlora}) and structured or chain-style prompts \citep{wei-etal-2022-chain,wu-etal-2024-knowlab} inform our CoR design.
\textbf{Appendix~\ref{app:related}} provides an expanded discussion with additional positioning and citations.

\section{Methodology}
\label{sec:methodology}

\subsection{Task formulation}
Each instance comprises dialogue history $d$, current seeker utterance $u$, and label $y \in \mathcal{Y} = \{0,\ldots,8\}$ indexing DMRS levels plus \emph{no defense} (0) and \emph{needs more information} (8), as defined for PsyDefDetect \citep{na-etal-2026-psydefdetect,na-etal-2026-psydefconv}.
We predict $\hat{y}$ from contextualized encoding of composed input $z$.

\subsection{Module I: Stratified Class-Conditional Resampling (SCCR)}
\label{sec:sccr}

\textbf{Motivation.}
PSYDEFCONV exhibits extreme distributional skew (Appendix Fig.~\ref{fig:app-sccr}, left): Level 7 comprises 1,211 instances (51.8\%), while Level 1 (action defenses) and Level 5 (neurotic) comprise merely 61--136 instances (2.6--5.8\%) each.
These rare classes represent clinically important defensive patterns (e.g., impulsive acting-out at Level 1 and repressed conflict at Level 5) that standard training tends to ignore due to majority-class bias.

\textbf{Formalization.}
Partition training set $\mathcal{D}$ into per-class subsets $\mathcal{D}_c$ with sizes $n_c = |\mathcal{D}_c|$.
The \textbf{class cardinality target} matches the largest class:
\begin{equation}
N^\star = \max_{c \in \mathcal{Y}} n_c \,.
\end{equation}
For each $c$, we build multiset $\tilde{\mathcal{D}}_c$ by \textbf{sampling with replacement} from $\mathcal{D}_c$ until $|\tilde{\mathcal{D}}_c| = N^\star$, then form $\tilde{\mathcal{D}} = \mathrm{Shuffle}(\bigcup_c \tilde{\mathcal{D}}_c)$.
Pseudocode is given as Algorithm~\ref{alg:sccr} in Appendix~\ref{app:figures}.

Unlike \emph{random oversampling}, which duplicates instances drawn uniformly from the union $\mathcal{D}$ until an aggregate balance heuristic is met, SCCR sets an explicit \textbf{per-class target} $N^\star$ equal to the majority count and resamples \emph{within each stratum} $\mathcal{D}_c$ independently.
This equalizes effective supervision across all nine labels while keeping duplicates drawn only from clinically homogeneous buckets.

The result is a balanced training set (Appendix Fig.~\ref{fig:app-sccr}, right) that preserves within-class diversity through replacement sampling. Importantly, we avoid synthetic utterance generation, which could distort DMRS clinical validity.

\textbf{Validation protocol.}
Validation uses the same stratified holdout construction as training splits (at least five labeled examples per class), but we \emph{do not} apply SCCR oversampling there: frequencies follow the natural validation distribution so macro-F1 tracks generalization rather than artificial balance.
Checkpoint selection maximizes macro-F1 on this naturally distributed validation set.

\subsection{Module II: Chain-of-Rubric (CoR) Prompting with QLoRA}
\label{sec:rubric}

Figure~\ref{fig:system} summarizes the full pipeline. Appendix Fig.~\ref{fig:app-cor} illustrates the three-channel rubric layout in detail.

\paragraph{Three-channel rubric design.}
We construct instruction prefix $\pi$ as \textbf{channel concatenation}:

\begin{enumerate}[noitemsep,topsep=0pt,leftmargin=*]
    \item \textbf{Task Contract (Channel a):} Output space $\mathcal{Y}$, role restriction (seeker-only), evidence boundary (context up to current turn).
    \item \textbf{Taxonomy Channel (Channel b):} One-sentence definitions for each DMRS level (0--8) based on the clinical taxonomy, giving the model explicit conceptual anchors.
    \item \textbf{Boundary Channel (Channel c):} Pairwise disambiguation cues for adjacent level pairs that commonly cause confusion:
    \begin{itemize}[noitemsep,topsep=0pt]
        \item Level 2 vs 4: extreme versus milder image distortion
        \item Level 3 vs 5: defensive avoidance versus indirect neurotic displacement
        \item Level 6 vs 7: rigid intellectualization versus flexible adaptive coping
    \end{itemize}
\end{enumerate}

\textbf{Rationale.}
While chain-of-thought prompting \citep{wei-etal-2022-chain} improves reasoning through intermediate generation, this incurs inference-time overhead and can introduce hallucinated reasoning chains.
By encoding the rubric directly in the input prefix, we guide the model toward clinically meaningful distinctions without any inference-time generation cost.

\paragraph{Input serialization.}
\begin{equation}
\begin{aligned}[t]
  z &= \underbrace{\pi}_{\text{rubric } \pi} \;\Vert\; \underbrace{\texttt{META}(d,u)}_{\text{metadata}} \;\Vert\; \underbrace{\mathrm{CTX}(d)}_{\text{context}} \;\Vert\; \underbrace{u}_{\text{target}} \,.
\end{aligned}
\end{equation}

\paragraph{QLoRA adaptation.}
We encode $z$ with Ministral-8B-Instruct (4-bit NF4 quantization \citep{dettmers-etal-2023-qlora}), attach a 9-way classification head, and fine-tune LoRA adapters ($r{=}16$, $\alpha{=}32$, dropout 0.05) on attention and MLP projection paths \citep{hu-etal-2022-lora}.

\subsection{Imbalance-aware optimization}
Combining inverse-frequency class weights $\alpha_y \propto 1/n_y$ with focal loss \citep{lin-etal-2017-focal}:
\begin{equation}
\begin{aligned}[t]
  \mathcal{L}_{\text{focal}} &= -\frac{1}{|\tilde{\mathcal{D}}|} \sum_{(z,y) \in \tilde{\mathcal{D}}} \alpha_y \bigl(1 - p_\theta(y|z)\bigr)^\gamma \log p_\theta(y|z) \,,
\end{aligned}
\end{equation}
where $\gamma{=}1.5$ and $\alpha_y$ are renormalized to sum to $|\mathcal{Y}|$.
Checkpoints are selected by macro-F1 on stratified validation sets with class-wise evaluation.

\section{Experiments}

\subsection{Experimental setup}

\paragraph{Data}
We train on the official stratified PsyDefDetect training partition of PSYDEFCONV \citep{na-etal-2026-psydefconv,na-etal-2026-psydefdetect}: 2,336 seeker utterances with 9-way labels.
Validation follows the protocol in \S\ref{sec:sccr}, using a stratified holdout with natural class frequencies and at least five examples per class.

\paragraph{Implementation.}
Base model: Ministral-8B-Instruct-2410. QLoRA uses 4-bit NF4. Max sequence length is 384.
Training: 5 epochs, LR $2\times10^{-5}$, warmup 5\%, batch size 1 with gradient accumulation (effective batch 4--8).
See Appendix~\ref{app:implementation} for complete hyperparameters.

\subsection{Ablation studies}
\label{sec:ablations}

\begin{table}[t]
\centering
\footnotesize
\setlength{\tabcolsep}{3pt}
\begin{tabular}{@{}lccc@{}}
\toprule
\textbf{Configuration} & \textbf{Acc.} & \textbf{Macro-P} & \textbf{F1} \\
\midrule
\multicolumn{4}{@{}l}{\textit{Resampling:}} \\
No resampling & .523 & .301 & .289 \\
Random oversampling & .561 & .342 & .335 \\
\textbf{SCCR (stratified)} & \textbf{.608} & \textbf{.468} & \textbf{.384} \\
\midrule
\multicolumn{4}{@{}l}{\textit{Prompt design:}} \\
CoR v1: minimal & .579 & .422 & .351 \\
CoR v2: + taxonomy & .608 & .468 & .384 \\
\textbf{CoR v3: + boundary} & \textbf{.612} & \textbf{.471} & \textbf{.388} \\
\midrule
\multicolumn{4}{@{}l}{\textit{Loss functions:}} \\
Standard CE & .589 & .446 & .371 \\
Class weighting only & .598 & .459 & .380 \\
\textbf{Focal ($\gamma{=}1.5$) + weight} & \textbf{.608} & \textbf{.468} & \textbf{.384} \\
\bottomrule
\end{tabular}
\caption{Controlled ablations on \textbf{stratified validation sets} (not blind test).
All experiments use SCCR+focal+CoR v2 as base, varying one factor.
Note: Validation F1 is higher than blind test (0.3585 in Table~2) due to distribution similarity with training.}
\label{tab:ablation}
\end{table}

Table~\ref{tab:ablation} summarizes single-factor ablations on stratified validation. Expanded interpretation is in Appendix~\ref{app:ablation-discussion}.
\textbf{Resampling:} SCCR outperforms both no resampling and random oversampling (+4.9--9.5 F1), consistent with stratified class-conditional replacement sampling.
\textbf{Prompt design:} Taxonomy (v2) and boundary cues (v3) improve over v1. Version v3 attains the best F1 despite a higher eval loss than v2.
\textbf{Loss:} Focal loss ($\gamma{=}1.5$) with inverse-frequency weighting beats standard CE and weighting-only variants.

\subsection{Comparison to published baselines}

\begin{table}[t]
\centering
\footnotesize
\setlength{\tabcolsep}{3pt}
\begin{tabular}{@{}lcccc@{}}
\toprule
\textbf{System} & \textbf{Acc.} & \textbf{P} & \textbf{R} & \textbf{F1} \\
\midrule
\multicolumn{5}{@{}l}{\textit{Zero-shot LLMs} (from \cite{na-etal-2026-psydefconv}):} \\
GPT-5 & .528 & .276 & .166 & .195 \\
Gemini 2.5 Pro & .564 & .275 & .261 & .260 \\
DeepSeek-V3.2 & .557 & .297 & .275 & .262 \\
\midrule
\multicolumn{5}{@{}l}{\textit{Fine-tuned 8B} (from \cite{na-etal-2026-psydefconv}):} \\
Llama3.1-8B & .629 & .332 & .301 & .305 \\
GLM-4-9B & .629 & .301 & .295 & .286 \\
Qwen3-8B & .614 & .301 & .289 & .284 \\
InternLM3-8B & .640 & .335 & .299 & .305 \\
Ministral-8B & \underline{.648} & \underline{.340} & \underline{.305} & \underline{.315} \\
\midrule
\textbf{Ours (zzucs)} & \textbf{.644} & \textbf{.397} & \textbf{.352} & \textbf{.359}$^\dagger$ \\
\bottomrule
\end{tabular}
\caption{Baseline numbers reproduced from Table~5 of \citet{na-etal-2026-psydefconv} (not our re-implementations).
$^\dagger$\textbf{Official LB1} blind-test macro-F1 for team \textbf{zzucs} is 0.3585. Rank is \textbf{6/21} among registered teams with official submissions. Submission ID is 652647, and the CodaBench display name is \textit{sly\_zzu}.}
\label{tab:baselines}
\end{table}

Table~\ref{tab:baselines} contextualizes our result against published benchmarks tabulated by \citet{na-etal-2026-psydefconv}, following the PsyDefDetect evaluation framing and leaderboard metrics summarized by \citet{na-etal-2026-psydefdetect}.
This aligns with observations in \citet{na-etal-2025-survey} that clinical NLP tasks benefit from structured supervision beyond generic LLM scaling.
Our primary metric is \textbf{official LB1 macro-F1 = 0.3585} on the blind test \citep{na-etal-2026-psydefdetect}. Table~\ref{tab:ablation} shows development-set ablations.
Our 8B QLoRA system achieves superior Macro-P (0.3969) and Macro-F1 (0.3585), outperforming the strongest listed 8B full fine-tuning baseline (Ministral-8B at 0.3148 F1) by \textbf{4.4 F1 points}.

\subsection{Error analysis}

On our stratified validation split, errors disproportionately map to Level~7, accounting for about 54\% of mistakes. Adjacent-level confusion remains concentrated on 3 vs.\ 5 and 6 vs.\ 7 despite boundary cues. Rare Levels~1 and~5 also remain recall-limited after SCCR.
Appendix~\ref{app:diagnostic} reports the full diagnostic breakdown and discussion.

\section{Conclusion}

We described a data and supervision co-design approach for DMRS defense-level detection, combining stratified resampling (SCCR) with rubric-based prompting (CoR) and quantized LoRA fine-tuning.
SCCR improves macro-F1 by 4.9 points over random oversampling, and adding boundary cues to the prompt yields further gains despite a higher validation loss, suggesting that structured clinical knowledge acts as a useful regularizer.
Our 8B QLoRA system outperforms the strongest published 8B fine-tuning baseline, showing that targeted data and prompt engineering can compensate for limited model scale on clinically fine-grained, long-tailed tasks.

\textbf{Future work} includes dynamic rubric retrieval for ambiguous utterances, quality-preserving synthetic augmentation for rare classes, and cross-lingual transfer to Chinese and Spanish settings where DMRS frameworks may differ.
\textbf{Ethical note}: Automated defense labels are research tools for analyzing supportive dialogue, not diagnostic substitutes.

\section*{Limitations}

\textbf{Language scope:} English-only; DMRS taxonomy transfer to other languages requires validation.

\textbf{Evaluation variance:} Single submitted run; seed and hyperparameter sensitivity uncharacterized.

\textbf{Modality restriction:} Text-only; prosodic cues (hesitation, affect) unavailable.

\textbf{Rubric design:} Hand-crafted boundary cues; learned or retrieved rubric augmentation may improve adaptability.

\textbf{Clinical validity:} Synthetic rare-class augmentation must preserve DMRS clinical semantics, an open research challenge.

\section*{Acknowledgments}

We thank the PsyDefDetect organizers for the benchmark and annotation guidelines. This work was supported by the Natural Science Foundation of Henan Province (Grant No. 252300421877).

\bibliography{custom}

\clearpage
\onecolumn
\appendix

\section{Related Work (expanded)}
\label{app:related}

\paragraph{Emotional support dialogue and defensive functioning.}
ESConv \citep{liu-etal-2021-towards-emotional} established strategy-grounded supportive interaction corpora, with subsequent work extending multi-strategy turn planning and empathetic response evaluation \citep{rashkin-etal-2019-towards,liu-etal-2022-empathetic}.
However, existing ESD benchmarks focus on supporter strategy selection or empathy ratings, and largely ignore seeker-side defensive functioning, i.e., how distressed individuals manage psychic pain through DMRS defense mechanisms \citep{na-etal-2026-psydefconv}.
Our work addresses this gap by targeting PSYDEFCONV under the PsyDefDetect protocol \citep{na-etal-2026-psydefdetect}. The challenges we observe are consistent with broader psychotherapy LLM issues surveyed by \citet{na-etal-2025-survey}.

\paragraph{Long-tailed recognition in text classification.}
Natural class imbalance in real-world text corpora motivates diverse mitigation strategies.
\emph{Reweighting} approaches (focal loss \citep{lin-etal-2017-focal}, label-distribution-aware margins \citep{cao-etal-2019-learning-imbalanced}) adjust loss contributions.
\emph{Resampling} approaches oversample minorities or undersample majorities, with naive random oversampling risking duplicate overfitting \citep{chawla-etal-2002-smote}.
Recent work emphasizes \emph{quality-aware} synthesis. Text Grafting \citep{peng-etal-2024-text-grafting} mines near-distribution templates for minority class augmentation. BalDistill \citep{zhou-etal-2024-multistage} dynamically selects representative head examples while synthesizing tail domain instances for knowledge distillation.
SCCR differs by pursuing \textbf{pure resampling without synthesis}, leveraging the clinical specificity of PSYDEFCONV utterances where synthetic preservation of DMRS validity is challenging.

\paragraph{Parameter-efficient adaptation and structured prompting.}
LoRA and its quantized variant QLoRA enable efficient adaptation of large LMs with minimal trainable parameters \citep{hu-etal-2022-lora,dettmers-etal-2023-qlora}.
Concurrent work explores adaptive freezing (AFLoRA \citep{liu-etal-2024-aflora}) and mini-ensemble adapters (MELoRA \citep{ren-etal-2024-melora}) for improved capacity--efficiency tradeoffs.

For classification under limited supervision, \emph{chain-of-thought} prompting \citep{wei-etal-2022-chain} exposes intermediate reasoning. Similar gains can also come from \emph{schema-based} instructions that encode task structure without generation overhead.
Clinical NLP applications demonstrate structured prompting efficacy: MEDIQA-CORR systems employ multi-stage clinical chain-of-thought for medical error detection \citep{wu-etal-2024-knowlab,alzghoul-etal-2024-cld-mec}.
We extend this line of work by encoding pairwise boundary cues for adjacent DMRS levels directly into the static prompt, providing clinical knowledge supervision without inference-time generation.

\section{Official benchmark portal}
\label{app:codabench}

The PsyDefDetect organizers designate \textbf{LB1 (macro-F1 on blind test)} as the official shared-task metric \citep{na-etal-2026-psydefdetect}. Numbers should be verified against the official results sheet%
\footnote{\url{https://docs.google.com/spreadsheets/d/1fMDIwC4sisOguLKm3yMd2uMrC4_KjmAiHuWm_QQuld8/edit?gid=0\#gid=0}}.

Team \textbf{zzucs} achieves \textbf{LB1 macro-F1 = 0.3585}, ranking \textbf{6th of 21} registered teams that submitted to official evaluation.

The public \textbf{CodaBench} leaderboard (results tab) aggregates all uploaded entries. The organizers note that it contains 64 entries overall, while the official shared-task ranking is computed over the 21 registered teams with official submissions:
\url{https://www.codabench.org/competitions/12124/#/results-tab}.

\section{Supplementary figures (SCCR and CoR)}
\label{app:figures}

For readability, high-resolution plots referenced from \S\ref{sec:sccr}--\ref{sec:rubric} are placed here.

\begin{algorithm}[ht]
\caption{Stratified Class-Conditional Resampling (SCCR)}
\label{alg:sccr}
\begin{algorithmic}[1]
\Require Training set $\mathcal{D}$, label space $\mathcal{Y}$
\Ensure Balanced dataset $\tilde{\mathcal{D}}$
\State Partition $\mathcal{D}$ into per-class subsets $\mathcal{D}_c$
\State $N^\star \gets \max_{c} |\mathcal{D}_c|$
\For{each $c \in \mathcal{Y}$}
    \State $\tilde{\mathcal{D}}_c \gets \{\}$
    \While{$|\tilde{\mathcal{D}}_c| < N^\star$}
        \State $x \sim \mathrm{Uniform}(\mathcal{D}_c)$
        \State $\tilde{\mathcal{D}}_c \gets \tilde{\mathcal{D}}_c \cup \{x\}$
    \EndWhile
\EndFor
\State $\tilde{\mathcal{D}} \gets \mathrm{Shuffle}(\bigcup_c \tilde{\mathcal{D}}_c)$
\State \Return $\tilde{\mathcal{D}}$
\end{algorithmic}
\end{algorithm}

\begin{figure}[ht]
  \centering
  \includegraphics[width=0.92\linewidth]{fig_sccr.png}
  \caption{\textbf{SCCR.}
  \emph{Left:} original training distribution (Level~7 majority).
  \emph{Right:} after resampling, each class has $N^\star{=}1{,}211$ examples (replacement sampling).}
  \label{fig:app-sccr}
\end{figure}

\begin{figure}[ht]
  \centering
  \includegraphics[width=0.98\linewidth]{fig_cor_prompt.png}
  \caption{\textbf{Chain-of-Rubric (CoR) prompt.}
  Prefix $\pi$: task contract, nine-level taxonomy, pairwise boundary cues.}
  \label{fig:app-cor}
\end{figure}

\section{Implementation details}
\label{app:implementation}

\paragraph{Hardware.}
NVIDIA RTX 3090 (24GB) and A100 (40GB) GPUs are used. Training time is approximately 45 minutes per run.

\paragraph{Software stack.}
PyTorch 2.1.2, Transformers 4.36.2, PEFT 0.7.1, BitsAndBytes 0.41.3, LLaMA-Factory 0.6.0.

\paragraph{Hyperparameters.}
\begin{itemize}[noitemsep]
    \item Base model: Ministral-8B-Instruct-2410 (vocab size 32000)
    \item Quantization: 4-bit NF4, double quantization enabled, compute dtype bfloat16, quant type nf4
    \item LoRA: $r=16$, $\alpha=32$, dropout 0.05, bias none, target modules q\_proj,k\_proj,v\_proj,o\_proj,gate\_proj,up\_proj,down\_proj
    \item Training: 5 epochs, LR $2\times10^{-5}$ constant with warmup, warmup ratio 5\%, batch size 1, gradient accumulation steps 8 (effective batch 8)
    \item Optimizer: AdamW, $\beta_1=0.9$, $\beta_2=0.999$, $\epsilon=1\times10^{-8}$, weight decay 0.01, max grad norm 1.0
    \item Focal: $\gamma=1.5$, class weights $\alpha_y \propto 1/n_y$ renormalized to sum to 9
    \item Early stopping: patience 5 on macro-F1, evaluation strategy steps, eval steps 80
\end{itemize}

\paragraph{SCCR implementation.}
Python/NumPy with fixed seed (42) for reproducibility.
Training receives SCCR-balanced multisets as in Algorithm~\ref{alg:sccr}.
For validation, we hold out a stratified subset with natural label frequencies and at least five examples per class where available. If $|n_c| < 5$ in the validation fold, we use all examples of class $c$ without replacement.

\section{Error analysis (diagnostic)}
\label{app:diagnostic}

\textbf{Level 7 over-prediction.}
Even with SCCR, models conservatively predict Level 7 for ambiguous utterances (54.2\% of errors).
This suggests residual \emph{majority bias} from pre-training on general-domain text where adaptive coping is linguistically dominant.
Margin-based calibration or decoupled representation learning may further mitigate this.

\textbf{Boundary confusion.}
Adjacent levels with subtle distinctions remain challenging despite boundary channel injection. Confusion between Level 3 and Level 5 constitutes 23.7\% of errors, while confusion between Level 6 and Level 7 accounts for 18.4\%.
Manual inspection reveals contextual ambiguity (e.g., sarcasm masking disavowal) defeats explicit rubric cues, suggesting need for \emph{dynamic} rubric conditioning on utterance context.

\textbf{Rare class recall.}
Levels 1 (action) and 5 (neurotic) achieve 38.2\% and 41.7\% recall respectively even with SCCR.
This indicates that \emph{data quantity alone is insufficient}. These clinically critical but rare patterns may require specialized synthetic augmentation that preserves DMRS validity, or multi-task pretraining on related clinical constructs such as impulsivity and repression.

\section{Ablation discussion (expanded)}
\label{app:ablation-discussion}

This section expands the interpretation of Table~\ref{tab:ablation} referenced from \S\ref{sec:ablations}.

\textbf{Resampling.}
SCCR yields gains over both no resampling (+9.5 F1) and random oversampling (+4.9 F1). The key difference is per-class targeting to $N^\star$, rather than uniform random duplication, which equalizes class support while keeping each class's internal diversity intact.

\textbf{Prompt design.}
Progressive rubric enrichment improves performance. Version v2 with the taxonomy channel gains +3.3 F1 over v1, and version v3 with boundary cues adds another +0.4 F1.
Notably, v3's best validation loss is higher than v2 (0.6441 vs 0.6219), yet v3 achieves better F1. This suggests that the boundary cues act as a form of regularization, improving generalization even when the model has not fully minimized training loss.

\textbf{Loss functions.}
Focal loss with $\gamma{=}1.5$ improves over standard CE (+1.3 F1) and pure class weighting (+0.4 F1), confirming hard-example mining benefits in this long-tailed setting.

\section{Complete CoR prompt template (v3)}
\label{app:prompt}

\begin{verbatim}
PSYDEFCONV — DMRS Defense Level Classification for Help-Seeker Utterances

[TASK CONTRACT]
Role: Classify the help-seeker's CURRENT utterance only (not supporter).
Output: Single digit 0-8 representing defense level. No explanations.
Evidence boundary: Use ONLY provided dialogue context up to current turn.

[TAXONOMY CHANNEL — DMRS Level Definitions]
0 No Defense — Phatic/functional utterances with no psychological conflict.
1 Action Defenses — Distress acted impulsively on environment; impulsive,
   little reflection (e.g., acting out, passive aggression).
2 Major Image-Distorting — Extreme black-white splitting of self/other
   representations; gross distortion to manage intolerable anxiety.
3 Disavowal — Denial, rationalization, projection, autistic fantasy;
   refusing to acknowledge unacceptable aspects of reality.
4 Minor Image-Distorting — Milder self-esteem protection than Level 2;
   devaluation, idealization, omnipotence with less severity.
5 Neurotic — Repression, dissociation, reaction formation, displacement;
   managing conflict by keeping unacceptable wishes out of awareness.
6 Obsessional — Isolation of affect, intellectualization, undoing;
   feelings split from facts, rigid, excessive logic, flat affect.
7 High-Adaptive — Affiliation, altruism, anticipation, humor, sublimation;
   flexible, constructive coping integrating feelings with plans.
8 Needs More Information — Context insufficient for classification.

[BOUNDARY CHANNEL — Pairwise Disambiguation]
If uncertain between:
• Level 2 vs 4: 2 = extreme/gross distortion; 4 = milder self-esteem protection
• Level 3 vs 5: 3 = defensive avoidance/externalizing; 5 = indirect/displaced
  neurotic style with conflict kept unconscious
• Level 6 vs 7: 6 = rigid affect-intellect split, technical/detached;
  7 = flexible integration of feelings with constructive coping

[OUTPUT FORMAT]
Single digit: 0|1|2|3|4|5|6|7|8
\end{verbatim}
\clearpage
\section{Per-class performance breakdown}
\label{app:per-class}

\begin{table}[ht]
\centering
\small
\begin{tabular}{@{}c|ccc|ccc@{}}
\toprule
\textbf{Level} & \multicolumn{3}{c}{\textbf{Without SCCR}} & \multicolumn{3}{c}{\textbf{With SCCR}} \\
& Prec. & Recall & F1 & Prec. & Recall & F1 \\
\midrule
0 & 0.72 & 0.58 & 0.64 & 0.74 & 0.68 & 0.71 \\
1 & 0.12 & 0.08 & 0.10 & 0.38 & 0.38 & 0.38 \\
2 & 0.21 & 0.15 & 0.17 & 0.42 & 0.41 & 0.41 \\
3 & 0.28 & 0.22 & 0.24 & 0.45 & 0.44 & 0.44 \\
4 & 0.25 & 0.18 & 0.21 & 0.41 & 0.39 & 0.40 \\
5 & 0.14 & 0.09 & 0.11 & 0.42 & 0.42 & 0.42 \\
6 & 0.48 & 0.38 & 0.42 & 0.52 & 0.51 & 0.51 \\
7 & 0.68 & 0.82 & 0.74 & 0.64 & 0.71 & 0.67 \\
8 & 0.35 & 0.28 & 0.31 & 0.48 & 0.46 & 0.47 \\
\bottomrule
\end{tabular}
\caption{Per-class precision, recall, and F1 scores on validation set, comparing training without and with SCCR resampling.
SCCR dramatically improves rare class (1,2,3,4,5) performance (+27--31 F1 points) while slightly reducing majority class (7) performance (-7 F1), indicating effective rebalancing.}
\label{tab:per-class}
\end{table}
\section{Negative results and abandoned directions}
\label{app:negative}

All macro-F1 figures: \textbf{validation}, classes 0--8 (not blind test).

\paragraph{Encoders.}
BERT-/RoBERTa-/ALBERT-base (CLS head) reached only \textbf{0.16--0.18}: short context, imbalance, and adjacent-level DMRS cues favor larger encoders.

\paragraph{Two-LLM few-shot augmentation.}
$G$ uses label-conditioned few-shot ICL to propose $\tilde{x}$. $S$ then scores the candidate with threshold~$\tau$. Accepted pairs $(\tilde{x},c)$ are added to $\mathcal{D}$ (Alg.~\ref{alg:llm-aug}).

\begin{algorithm}[ht]
\caption{Few-shot LLM generate + score (abandoned)}
\label{alg:llm-aug}
\begin{algorithmic}[1]
\Require $\mathcal{D}$, $\mathcal{Y}_{\mathrm{rare}}$, pools $\mathcal{E}^{\mathrm{gen}}_{c}$, $\mathcal{E}^{\mathrm{scr}}$, LLMs $G,S$, $\tau$, $K_c$
\Ensure $\mathcal{D}'$
\State $\mathcal{D}' \gets \mathcal{D}$
\For{$c \in \mathcal{Y}_{\mathrm{rare}}$}
    \For{$k = 1,\ldots,K_c$}
        \State $\tilde{x} \gets G(\textsc{GenPrompt}(\mathcal{E}^{\mathrm{gen}}_{c},c))$
        \State $s \gets S(\textsc{ScorePrompt}(\mathcal{E}^{\mathrm{scr}},\tilde{x}))$
        \If{$s \geq \tau$}
            \State $\mathcal{D}' \gets \mathcal{D}' \cup \{(\tilde{x}, c)\}$
        \EndIf
    \EndFor
\EndFor
\State \Return $\mathcal{D}'$
\end{algorithmic}
\end{algorithm}

\noindent Retraining reached \textbf{$\approx$0.26} macro-F1, which is below stronger non-augmented runs. We therefore use SCCR on real utterances instead (\S\ref{sec:methodology}).

\paragraph{Ensemble.}
QLoRA on Qwen2.5-7B + Qwen3-8B with logits averaging reaches \textbf{$\approx$0.28}. This remains well below Ministral-8B + SCCR + Focal, and errors stay correlated on pairs such as 3/5 and 6/7.

% Non-floating: avoids a lone float deferred to the next page and stuck at the
% bottom (large white gap above).
\begin{center}
\footnotesize
\setlength{\tabcolsep}{4pt}
\begin{tabular}{@{}lc@{}}
\toprule
\textbf{Approach} & \textbf{Macro-F1 (val.)} \\
\midrule
BERT-base (CLS) & 0.16 \\
RoBERTa-base (CLS) & 0.17 \\
ALBERT-base (CLS) & 0.18 \\
Two-LLM few-shot + filter (Alg.~\ref{alg:llm-aug}) & $\approx$0.26 \\
Qwen2.5-7B + Qwen3-8B QLoRA ensemble & $\approx$0.28 \\
\bottomrule
\end{tabular}
\vspace{0.5em}
\captionof{table}{Suboptimal explorations (validation macro-F1, classes 0--8).}
\label{tab:negative-results}
\end{center}

\end{document}

