Scale-Invariant Entropy Regulation in Cognitive Systems: From Token Attention to Semantic Coherence to Model Selection

We present a unified framework demonstrating that a single entropy regulation equation governs cognitive persistence across three independent scales spanning nine orders of magnitude in time: token-level attention filtering (microseconds), session-level narrative coherence (minutes to hours), and model-level specialization routing (days to months). The work integrates three independently developed research programs: • The Guided Entropy Principle (GEP), describing entropy regulation dynamics in cognitive systems. • Cross-Session Narrative Memory (CSNM), an architecture enabling continuity across stateless AI sessions. • The Coherent State Network Protocol (CSNP), a client-side framework for managing semantic state with cryptographic integrity. Dense 60-cycle recursive simulations across four architectural configurations demonstrate that cognitive persistence requires dual-component stabilization: checkpoint accumulation and fixed-reference verification. A formal No-Go theorem proves that neither mechanism alone can produce long-term stability. The stabilized architecture produces a coherence floor (NCS ≈ 0.95) with strict Lyapunov stability and structurally distinct entropy topology revealed through Tsallis non-extensive entropy analysis. Cross-domain validation includes large-language-model benchmark degradation curves and a structural hypothesis relating entropy-regulated cognition to Alzheimer’s disease dynamics. These results suggest a scale-invariant entropy-regulation principle operating across cognitive substrates.