Invariant-Preserving Value Structures for AI Alignment: A Bayesian Monitoring Framework for Decoherence Detection in Recursive Systems

1.1 AbstractCurrent approaches to AI alignment, particularly Reinforcement Learning from Human Feedback(RLHF), operate primarily at the behavioural level, rewarding outputs without monitoring theinternal representational dynamics that generate them. This surface-level control creates vul-nerability to semantic drift, reward hacking, and value decoherence—failures that emerge fromunmonitored transformations in the system’s internal state space. We propose an architecture forinvariant-preserving value structures that embeds alignment constraints as structural preservationconditions rather than post-hoc rules. The framework introduces a six-stage monitored processingpipeline analogous to an Engine Control Unit (ECU), with Bayesian inference tracking the posteriorprobability of value decoherence at each transformation stage. Key innovations include: (1) aformal specification language for value invariants as constraints on admissible transformations; (2)Bayesian monitoring of semantic compression and expansion using operationalised signals (branchinstability, prototype-based category drift, invariant residuals); (3) state-dependent hazard modelling2for pre-breach trajectory detection; (4) continuous control gains and a meta-monitor for fault-tolerantoversight; and (5) ecological homeostasis through bonded communication that supports invariantpluralism. The framework addresses a fundamental gap in alignment research: the absence of internalmonitoring systems capable of detecting value drift during the reasoning process itself, not merely atoutput. We present formal definitions, a worked example with operationalised signals, an empiricalvalidation plan, and discuss implications for recursive self-improvement and AGI safety.