We present our system for SemEval-2026 Task 6 (CLARITY: Unmasking Political Question Evasions), which addresses political evasion detection in English question-answer pairs from U.S. presidential interviews.We compare two paradigms: (1) parameter-efficient fine-tuning of Qwen3 models (4B–32B) using QLoRA with tiered upsampling and weighted cross-entropy loss to address severe class imbalance, and (2) structured Chain-of-Thought (CoT) prompting with reasoning-capable API models, including DeepSeek-V3.2 and Grok-4-Fast.Our best system uses Grok-4-Fast with extended reasoning and few-shot hierarchical CoT prompting, achieving Macro F1 scores of 0.5147 on Subtask 2 (9-class evasion) and 0.7979 on Subtask 1 (3-class clarity). On the official leaderboard, it ranks 8/33 on Subtask 2 and 13/41 on Subtask 1. Ablation results show that hierarchical label presentation provides a useful reasoning scaffold and that extended reasoning helps models handle subtle pragmatic distinctions, although the strongest prompt variants are not statistically distinguishable in Macro F1.

We present our approach to SemEval 2026 Task 4: Narrative Story Similarity and Narrative Representation Learning. Our solution uses contrastive learning with fine-tuned sentence transformers to capture narrative similarity across abstract themes, course of action, and outcomes. We develop two pipelines: (Track A) a single-view method that encodes full narratives with smart layer freezing to reduce overfitting, and (Track B) a multi-view method that models theme, plot, and outcome with view-specific projection heads and self-supervised alignment. Both pipelines build on sentence-transformers models and are trained with contrastive loss on synthetic data. The code is available at the following GitHub repository: https://github.com/dinhthienan33/SemEval2026-Task4-ttda704.