Samarth Raina | Searcharxiv

arXiv2026

MENTIS: What Belief Changes Under Alignment? Measuring Multi-Scale Latent Torsion in Language Models

Preference alignment has substantially improved the observable behavior of large language models, yet it remains unclear what alignment changes internally. Aligned systems still fail under jailbreaks, prompt injection, and retrieval-time corruption, suggesting behavior-level evaluation alone is incomplete. Post-training should leave measurable traces in internal computation. We ask: when an instruction-tuned (IT) model becomes a preference-aligned (PA) model, what geometric structure changes, where do those changes concentrate, and how selectively do they vary across concepts, prompts, and model families? We introduce MENTIS, a geometry-first framework for measuring alignment-induced internal reorganization in paired checkpoints. MENTIS compares IT and PA models using a primary layerwise covariance-based torsion norm (T1), a secondary spectral torsion diagnostic (T2), and an Energy-Radiance-Activation measure (ERA) for depth localization. Across four 7-8B model pairs on LITMUS, our study reveals that alignment-induced change is selective rather than uniform: normative concepts exhibit larger torsion shifts than factual concepts on average; torsion is negatively correlated with contextual entropy; and peak effects localize to architecture-specific mid-to-late layers. The same pattern appears across word-level, prompt-level, and model-level analyses. These results suggest preference alignment leaves structured, depth-localized geometric signatures in internal computation beyond what behavior-level evaluation alone can reveal.

cs.CL↗

arXiv2025

D-STEER - Preference Alignment Techniques Learn to Behave, not to Believe -- Beneath the Surface, DPO as Steering Vector Perturbation in Activation Space

Direct Preference Optimization (DPO) has become a standard recipe for aligning large language models, yet it is still unclear what kind of change it actually induces inside the network. This paper argues that DPO does not rewrite a models internal beliefs; instead, it acts as a low rank steering mechanism that nudges activations along a small number of preference directions. Using a simple derivation, we show that the DPO gradient depends only on the difference between the logit embeddings of preferred and dispreferred completions, implying a first order shift in the final hidden representation rather than a deep restructuring of semantics. We then extract an empirical steering vector from a DPO tuned model and demonstrate that adding this vector to base activations reproduces most of the aligned behavior, while subtracting it nearly restores the original model. Finally, spectral analyses reveal rank-one dominance and entropy collapse in upper layers, indicating that alignment is funneled through a narrow subspace. Taken together, these results support a behavioral illusion view of DPO: it teaches models how to act aligned, not what to believe.

cs.LG↗