Coherence‑Validated Causal World Models for Multi‑Scale Alzheimer’s Disease Progression and Pharmacologic Reversal

Alzheimer’s disease (AD) is a multi-scale dynamical disorder: molecular disruptions in energy and redox homeostasis propagate through cellular stress programs, neuroinflammation, neurovascular dysfunction, synaptic failure, and cognitive decline. Recent interventional evidence that restoring physiological brain NAD+ homeostasis with P7C3-A20 can reverse advanced pathology and behavioral deficits in symptomatic amyloid- and tau-driven mouse models creates an opportunity for action-conditioned simulators that support counterfactual regimen design and mechanistic hypothesis testing. We introduce C3WM (Coherence-Validated Causal World Models), which couples (i) a hierarchical, action-conditioned latent dynamics model spanning molecular, cellular, and tissue/functional scales; (ii) an agentic causal scaffold over interpretable module summaries to support auditable interventional queries; and (iii) a wavelet-coherence auditing layer that evaluates learned simulators as interaction models, not merely forecasters. Our key technical move is to generalize a Monte Carlo Wavelet Coherence (MCWC) validation protocol—initially developed for ground-truth-free causal graph checking—to trajectory-level auditing: we test whether world-model rollouts preserve time–frequency coupling structure between modules (e.g., NAD restoration coupling to oxidative stress and BBB integrity) under held-out intervention schedules. We incorporate coherence losses as regularizers and combine them with conservative (pessimistic) uncertainty estimation to improve out-of-distribution reliability. We present a reversal-centric benchmark specification aligned with published endpoints (NAD+/NADH, proteomic reversal signatures, oxidative damage, neuroinflammation, BBB integrity proxies, synaptic plasticity, and behavior) and evaluate forecasting, counterfactual treatment-effect prediction, regime-shift generalization, mechanistic query stability, and active experiment selection. Across tasks, coherence auditing localizes “good MSE, bad simulator” failure modes and improves calibration for counterfactual rollouts, yielding a practical path from world-model learning to experimentally grounded mechanistic discovery.