Where Paths Split: Localized, Calibrated Control of Moral Reasoning in Large Language Models
Year: 2026
Type: conference-paper
Abstract: Large language models often display heterogeneous moral preferences across settings.We study inference-time steering toward a desired ethical framework while preserving general competence.We present Convergent-Divergent Routing, which traces and edits minimal branch points inside transformer blocks where ethicalframework-related pathways first converge and then diverge.Gating non-target branches at these loci blocks the downstream propagation while leaving upstream computations intact.We find that this intervention alone increases targeted ethical-framework reasoning.To achieve fine-grained control, we adapt Common Spatial Patterns to the residual stream and extract, for each branch-point layer, a pair of directions that discriminate between utilitarian and deontological frameworks.We then introduce Dual Logit Calibration, a closed-form, minimum-ℓ 2 -norm update that moves the residual within this two-dimensional subspace so the resulting directional projections align with userspecified preference weights.Experiments on real-life moral dilemmas show that our method reliably achieves preference calibration and largely preserves general capabilities, outperforming recent baselines while providing an interpretable mechanism.
Authors: Chenchen Yuan, Zheyu Zhang, Gjergji Kasneci
Institution: Technical University of Munich
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166802215