To gain insight into how fine-tuning affects internal model dynamics, we applied the logit lens technique to all baseline models and the fine-tuned Qwen 4B model. Across baseline models, intermediate predictions group by plausibility rather than logical validity in the later layers, directly reflecting the content effect bias. The one exception was Gemma 3 27B, which was the only baseline model to achieve validity-aligned grouping. Since Qwen 3 30B, a comparably sized model, still exhibits plausibility-driven trajectories, this appears to reflect model-family-specific properties rather than scale alone. After fine-tuning, this pattern reverses: the two valid quadrants track together toward the correct label regardless of plausibility, as do the two invalid quadrants, indicating a shift from content-driven to logic-driven reasoning. Full results and visualizations are provided in Appendix~\ref{app:logit_lens}.
