UTS at PsyDefDetect: Multi-Agent Councils and Absence-Based Reasoning for Defense Mechanism Classification
Dima Galat, Marian-Andrei Rizoiu

TL;DR
This paper presents a multi-agent system for classifying psychological defense mechanisms in dialogues, emphasizing the importance of absence cues and achieving competitive results without fine-tuning.
Contribution
Introduces a novel multi-agent council architecture and absence-based prompt rules for defense mechanism classification, improving performance and interpretability.
Findings
Achieved second place with F1 0.406 among 64 teams.
Absence-based prompt rules significantly improved classification accuracy (+11.4pp F1).
A targeted override ensemble increased F1 by 2.4pp using structured multi-agent strategies.
Abstract
This paper describes our system for classifying psychological defense mechanisms in emotional support dialogues using the Defense Mechanism Rating Scales (DMRS), placing second (F1 0.406) among 64 teams. A central insight is that defense mechanisms are defined by what is absent: missing affect, blocked cognition, denied reality. We encode this as an affect-cognition integration spectrum in prompt-level clinical rules, which account for the largest single gain (+11.4pp F1). Our architecture is a multi-phase deliberative council of Gemini 2.5 agents where class-specific advocates rate evidence strength rather than voting, achieving F1 0.382 with no fine-tuning - a top-5 result on its own. We find, however, that the council is confidently wrong about minority classes: 59-80% of stable minority predictions are incorrect, driven by a systematic "L7 attractor" in which emotional content…
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