We identify 310 emotion-exclusive sparse autoencoder features in Gemma 3 1B-IT with psychologically valid geometry, construct emotion-specific echo vectors, and demonstrate two distinct effects: the echo alone steepens the threat-safety gradient, and combined with knowledge, it transforms decision quality.

Does the somatic-marker mechanism survive at scale? Across Gemma 3 4B, 12B, and 27B with Gemma Scope 2 sparse autoencoders, the echo's discrimination of threat from safety replicates at every scale. In a multi-turn decision task the echo shifts choices toward safety — and the effect grows with model size. Most strikingly, decision quality decouples from output coherence: as the generated text degrades, the choice keeps improving. Valence outlives syntax — pointing to a deployment recipe of feeling during deliberation and generating without it.