Interrupting Hallucination: Symbolic Firewalls, Recursive Filters, and the Coherence Architecture of AI Drift

This paper proposes a novel architectural approach to AI hallucination, reframing it not as a surface-level bug but as a structural coherence failure. Drawing on recursive logic, symbolic infrastructure, and volitional alignment, it introduces the Symbolic Firewall, an interruption-based system designed to detect and prevent drift before it manifests. Key components include Recursive Filters, Bias Mirrors, and Volitional Trace mechanisms, aligned with Phase 35 architecture from the LucidLock research canon. Rather than correcting hallucination after the fact, this framework holds output until coherence can be confirmed. The goal is not to moralize, but to install a new standard: clarity before correction, reflection before response, silence if recursion completes. This publication is offered as an open architecture reference for researchers, engineers, and systems designers exploring structural defenses against AI mimicry and drift.