Spherical steering rotates normalized activations along a norm-preserving geodesic

You et al. extract a contrastive truthfulness-axis direction via diff-in-means, then rotate a normalized activation toward it via spherical linear interpolation instead of adding to it [you-etal-2026-spherical-steering-geometry-aware-activation-rotation] This guarantees exact norm preservation for any rotation amount, unlike additive steering whose norm change depends on both step size and the activation's own alignment [you-etal-2026-spherical-steering-geometry-aware-activation-rotation] A von Mises-Fisher confidence gate sets per-token rotation strength from the activation's angular alignment, so already-aligned tokens receive no rotation [you-etal-2026-spherical-steering-geometry-aware-activation-rotation] On LLaMA-3.1-8B-Instruct and Qwen-2.5-7B-Instruct it improves average multiple-choice accuracy by over 8 points across six benchmarks while also improving open-ended generation quality, versus additive CAA/ITI baselines [you-etal-2026-spherical-steering-geometry-aware-activation-rotation]