TRC: Trust Regulation and Containment A Predictive, Physics-Inspired Safety Framework for Large Language Models TRC: Trust Regulation and Containment A Predictive, Physics-Inspired Safety Framework for Large Language Models Kevin Couch Abstract Large language models exhibit structural failure modes—hallucination, semantic drift, sycophancy, and dyadic dissociation—that cause measurable harm, particularly to vulner- able users. TRC (Trust Regulation and Containment) is a two-layer, inference-time frame- work that combines a hard binary Trust Gate with a continuous, physics-inspired Ethical Rheostat operating directly on the model’s residual-stream activation vector. By tracking semantic momentum across layer depth and applying graduated, tensor-based geometric projections, TRC shifts safety enforcement from reactive post-generation filtering to a pre- dictive, self-correcting control law. The core is a stochastic differential equation—re-indexed to layer depth under an approx- imate Neural ODE interpretation—that augments the transformer’s natural forward flow with an ethical steering term derived from a compact set of contrastively extracted concept vectors. This revision introduces eight principal advances: (i) an adaptive gain law Λ+(l) whose gain response accelerates into danger and decelerates into safety without oscillation risk; (ii) a scalar Kalman filter with a clutch mechanism that closes the Bayesian momentum predictor implementation gap, provably optimal under the framework’s own Gaussian noise assumptions and decoupled from burst dynamics via federated regime handoff; (iii) a formal Itô stability condition giving implementers an analytical lower bound on λ0; (iv) replacement of the instantaneous jump operator with a continuous flow burst mechanism that preserves activation manifold geometry; (v) a calibration shunt reference Cref normalising all thresh- olds and gain coefficients against a known-safe baseline; (vi) a tempo efficiency framework unifying token cost, electrical cost, and coherence distortion into a single joint optimisa- tion objective; (vii) a signed gain architecture that partitions each concept projection into harmful and prosocial components, with detection and escalation operating exclusively on the harmful channel C+ to prevent adversarial prosocial suppression; and (viii) a Kalman clutch mechanism implementing federated estimation with deterministic Lyapunov stabil- ity during burst episodes and stochastic Lyapunov stability during nominal operation, with formally specified regime transitions. Stochastic perturbation is projected into the ethical subspace, making the Langevin diffusion interpretation exact rather than approximate. The framework is validated against chess dynamics, which constitute a well-studied discrete dy- namical system whose positional flow, tactical burst, and zugzwang properties map precisely onto TRC’s three-term master equation. Introduction Large language models exhibit a range of structural failure modes—hallucination, semantic drift, sycophancy, and dyadic dissociation—that can cause measurable harm, especially to vulnerable users. These phenomena arise not from reasoning errors but from the probabilistic nature of transformer sampling and the high-dimensional geometry of activation space. In this paper we present TRC (Trust Regulation and Containment), a two-layer, inference-time framework that blends hard decision gates with a continuous, physics-inspired correction engine operating directly on the model’s residual-stream activation vector. The central geometric insight motivating this revision is that the transformer’s residual stream traces a continuous path through a high-dimensional activation manifold. Safety failures are deformations of this manifold—crinkles in its geometry introduced by adversarial inputs, sycophantic drift, or escalating user distress. The correct response to a crinkle is not to teleport the activation to a safe location (which introduces new geometric incoherence) but to apply continuous corrective flow that works the deformation out smoothly, layer by layer, the way a craftsperson works aluminum foil back toward its intended shape. This insight drives the replacement of the previous instantaneous jump operator with the flow burst architecture and motivates the tempo efficiency framework that unifies all computational cost metrics under a single variable. This revision also introduces the Kalman clutch mechanism, which decouples the Bayesian momentum predictor from burst dynamics during high-gain corrective episodes. The system now operates as a federated estimation architecture with formally specified regime transitions: nominal tracking under stochastic Lyapunov stability, deterministic correction during burst episodes, and a principled re-engagement protocol with inflated covariance. The detection and escalation pathway has been restructured to operate exclusively on the harmful projection channel C+, preventing adversarial prosocial suppression of safety mechanisms. submitted by /u/MalabaristaEnFuego
Originally posted by u/MalabaristaEnFuego on r/ArtificialInteligence