This evaluation examined whether Digital Neutron remains active as a runtime stabilizer when applied through an API-mediated inference pathway using Mistral-7B-Instruct-v0.2. Regime-specific anchors were calibrated across short, mid, and long prompt classes, saved externally, loaded at runtime, and applied through forward hooks during autoregressive generation. No retraining was required. The notebook results show a consistent pattern. Generation begins with a strong early perturbation, especially near initialization, followed by rapid damping in the deeper sampled layers. Aggregate instability falls sharply after startup and then persists as a low-amplitude oscillatory correction signal rather than collapsing into a static flat line. Mean scale remains tightly bounded, suggesting that control is being achieved through relatively constrained modulation rather than blunt suppression.
Royce Priem (Fri,) studied this question.