This paper investigates the effectiveness of topic-matched contrast baselines in multi-directional refusal abliteration for instruction-tuned language models. The research focuses on extracting refusal-mediating directions from the residual stream activation space of the Qwen 3.5 2B model using per-category matched prompt pairs, Self-Organizing Map extraction, and Singular Value Decomposition orthogonalization. The key finding reveals that while unmatched contrast successfully achieves complete refusal elimination across tested weight levels and layers, topic-matched contrast produces no functional refusal directions. This work highlights the importance of contrast baseline construction as a critical methodological consideration rather than an implementation detail in model abliteration research.