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The author expresses skepticism about linear activation model steering methods like J-space, arguing that related research often relies on cherry-picked special cases and is therefore unreliable.
This paper shows that in chat models, refusal behavior is gated by a compliant model persona direction at late layers, rather than being an isolated mechanism. Steering persona suppresses refusal, and reintroducing refusal partially restores it only at late layers, revealing a coupling between persona and safety representations.
DenseSteer is a training-free inference-time framework that improves small language models' math reasoning by steering their internal representations towards dense reasoning patterns, achieving accuracy gains without increasing token-level negative log-likelihood.
This paper introduces geometric stability measures—based on pairwise distance consistency in representations—to predict language model steerability and detect structural drift. Supervised variants achieve near-perfect correlation (ρ=0.89-0.97) with linear steerability across 35-69 embedding models, while unsupervised variants outperform CKA and Procrustes for post-deployment drift detection.