We applied the logit lens technique to all baseline models across the three architecture families. The smallest models (Gemma 3 1B, LlaMa 3.2 1B) showed no meaningful differentiation between quadrants. Among the remaining models, a consistent pattern was visible, in the later layers, trajectories grouped by plausibility rather than logical validity. The one exception was Gemma 3 27B, which was the only baseline model to achieve validity aligned grouping.

To illustrate how fine-tuning changes these internal dynamics, we compare the baseline Qwen 4B model and the fine-tuned Qwen 4B model (see Figure~\ref{fig:app_instruct_analysis} and Figure~\ref{fig:app_sft_analysis}).
In the baseline model, the quadrant comparison (Figure~\ref{fig:app_instruct_analysis} c ) shows that the model's intermediate predictions are shaped by plausibility. Valid plausible syllogisms are handled correctly, but for VI syllogisms the logit difference trajectories follow almost the same pattern as for the invalid cases, resulting in wrong answers. This effect is primarily caused by the content effect bias. 

After fine-tuning, this pattern reverses (Figure~\ref{fig:app_sft_analysis} c). The two valid quadrants now track together towards the correct label regardless of plausibility, as do the two invalid quadrants. This indicates that the model has learned to base its prediction on logical structure rather than content. Notably, this change is confined to the later layers. Early layer processing remains largely unchanged between the two models, suggesting that the fine-tuning does not alter how the model encodes the input or understands the language (which are usually parts of the early layers), but rather how it uses that information to arrive at a logical conclusion. This also means that logical reasoning is more dominant in later layers. 