Governed Context & Memory (GCM): Context Configuration as Constitutional Surface, Semantic Hashing as Enforcement, and Explicit Ignorance as First-Class Success in Generational AI

Governed Context & Memory (GCM) proposes a constitutional architecture for long-horizon AI systems in which context configuration, memory, and epistemic boundaries are structurally governed rather than implicitly inferred. Contemporary AI systems rely on finite context windows and probabilistic reconstruction to simulate continuity. Over extended interaction, this leads to semantic drift, fabricated recall, and erosion of invariant commitments. GCM defines the Semantic Thrash Threshold as the phase transition at which declared context and memory commitments cease to exert binding authority over outputs due to hidden mutation or unconstrained inference. To address this instability, GCM introduces three core mechanisms: Context Configuration as Constitutional SurfaceContext (identity, lexicon, invariants, authority rules) is treated as a versioned, typed, and hash-bound layer that governs execution rather than being embedded implicitly in prompts. Semantic Hashing as EnforcementMemory artifacts are canonicalized under declared semantic lenses and hashed as commitments to equivalence classes of meaning. Drift becomes mechanically detectable. Amendments are explicit rather than silent. Explicit Ignorance as First-Class SuccessGCM formalizes the epistemic orderingUnknown > Speculation > Fabrication,recasting “I DON’T KNOW” from failure mode to structural integrity signal. Refusal under uncertainty preserves invariants and prevents hallucinated continuity. The framework is model-agnostic and applies to generational AI systems, long-lived conversational agents, and embodied platforms. By separating execution authority from context authority and externalizing memory with hash-bound invariants, GCM provides a foundation for durable identity, auditable continuity, and trust-preserving refusal in generational AI.