The PsyDefDetect shared task focuses on classifying nine psychological defense mechanisms in multi-turn dialogues, a problem complicated by severe label imbalance and the implicit, context-dependent nature of defenses. In this work, we investigate several approaches for dialogue-level defense detection, including supervised baselines and large language model (LLM)-based pipelines. Our primary system is a retrieval-augmented LLM framework with hierarchical prediction and lightweight heuristics for decision calibration. Experiments on the PSYDEFCONV dataset show that LLM-based methods improve overall performance compared to supervised baselines, but still struggle with fine-grained distinctions, especially for minority labels. These findings highlight the challenges of modeling implicit psychological constructs in dialogue.

Timely detection of depression symptoms is essential for early intervention, and the continuous stream of user-generated content on social media provides an ideal source for this purpose. To address this challenge, we propose HOPE, a Hybrid Optimized Parallel Encoding framework that combines supervised symptom relevance signals with unsupervised intrinsic semantic clustering. This parallel design enables robust symptom detection under limited labeled data and introduces a distinctive semantic-similarity perspective with automatic class-anchor adjustment. We also propose an optimized hybrid semantic fusion mechanism to combine supervised and unsupervised scores through a learnable module. We evaluate our system on multiple benchmark datasets and surpass previous approaches, demonstrating its effectiveness in detecting fine-grained symptoms and early warning of mental health risk. Source code is available at https://github.com/candleMind/hope.