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.