Epoch-Governed Intelligence and Personal Context

Epoch-Governed Intelligence and Personal Context presents a foundational architectural framework for artificial intelligence systems that addresses persistent failures such as temporal hallucination, narrative drift, confidence-based authority, and the absence of internal auditability. Rather than attributing these failures to ethics or alignment alone, this work argues that they arise from missing structural boundaries in contemporary AI architectures—specifically, the lack of explicit governance over time, authority, learning, and interpretive context. The paper introduces Epochs as sealed, bounded semantic containers with explicitly defined authority classes. Authority is determined by design-time governance rather than temporal recency, preventing self-legitimizing learning and revisionist memory. Training-time (constitutional) epochs define the rules of knowing and audit invariants, while runtime (operational) epochs allow bounded post-deployment learning without authority escalation. A continuous audit and constrained auto-repair mechanism enforces structural invariants without performing cognitive correction. In parallel, the framework formalizes Personal Context as a necessary interpretive layer—a bounded reference frame that defines what the system knows, how it knows it, and where its authority ends—without introducing personality, ego, or selfhood. This work does not propose new learning algorithms or benchmark improvements. It offers a constitutional architecture for accountable intelligence, synthesizing prior analyses of temporal authority, bounded time, and PCS-governed reasoning into a unified design. The framework is intended for researchers in AI architecture, systems theory, and philosophy of technology concerned with long-term coherence, auditability, and epistemic integrity.