% Overview of the results
We evaluated both steering approaches across six models. We had to leave out the larger models (Qwen 30B and Gemma-3 27B) due to computational constraints. The full steering configurations with the optimal hyperparameters can be found in Appendix \ref{appendix:as_grid_search_results} (Table \ref{tab:steering_grid_results}). Table \ref{tab:as_results} in Appendix~\ref{app:main_steering_results} reports the performance of each model and approach on the test set.

\paragraph{Limited Effectiveness.}
The activation steering experiments did not yield much improvement over the baseline and generally fall short compared to the SFT approach. As shown in Table~\ref{tab:as_results}, Gemma 4B, the best-performing steered model, achieved an accuracy of $75.83\%$ using approach 1. It also got the highest combined score of $18.26$ using approach 2. However, this is only a negligible improvement over its baseline accuracy of $75.42\%$. Similarly, LLaMA 8B achieved $65.00\%$ accuracy with approach 1, which is identical to its baseline. The performance of several other models degraded compared to their baselines. Approach 2 for Qwen 3 4B did improve slightly on its baseline with an accuracy of $67.92\%$ (an increase of 1.67 percentage points) however, approach 1 resulted in exactly the same accuracy as the baseline. This strongly indicates that our activation steering approach is not able to mitigate the content effect bias.

\paragraph{Approach comparison.}
Both approaches yielded very similar results, which suggests that both methods impact the models reasoning process in comparable ways. While \citet{valentino2025mitigating} demonstrated that activation steering can mitigate content effect bias, our steering approaches may fail to cleanly isolate the validity or content effect bias resulting in these limited results.

\paragraph{Error analysis.} The per-quadrant accuracies (Table~\ref{tab:as_quadrant} in Appendix~\ref{appendix:steering_quadrants}) show that steering does not improve logical reasoning, but instead we see different types of failures. First, we observe a bias shift where models collapse towards a single class. Gemma 1B achieves near-perfect accuracy on valid quadrants (VP: $100.0\%$ VI: $98.5\%$) but collapses on invalid quadrants (IP: $9.5\%$, II: $19.3\%$), indicating the model defaults to "valid" regardless of logical structure. Llama 8B exhibits a milder version of this pattern. Interestingly, Qwen 4B shows the opposite pattern, with strong invalid quadrant performance (IP: $84.1\%$ II: $94.7\%$) but poor valid quadrant accuracy (VP: $59.3\%$, VI: $30.3\%$). Llama 3B displays a similar shift, with VI dropping to $31.8\%$ under approach 1 while II rises to $82.5\%$. This suggests the steering vector steered towards a certain class instead of enhancing reasoning. Across all models the content effect bias remains persistent. The performance on congruent quadrants (VP, II) is consistently better than on incongruent quadrants (VI, IP). This confirms that the steering interventions failed to decouple logical processing from semantic plausibility. 