Fractal–Holographic Memory for Symbolic Intelligence: Compression, Robust Recall, and Interference Resistance

This paper introduces a novel fractal–holographic memory system designed for symbolic intelligence. The system unifies compression and recall within a single representational substrate, enabling robust memory retrieval under aggressive compression, structural perturbation, and long-horizon interference. Through a series of controlled simulations, the paper evaluates five core capability domains: intrinsic compression behavior, robustness to translation and occlusion, semantic preservation under large displacement, retrieval latency scaling with memory growth, and resistance to interference from structurally similar distractors. Results demonstrate smooth degradation rather than threshold-based failure across all tested regimes, indicating that semantic structure is preserved even when surface-level detail is lost. Unlike embedding-based or database-style memory architectures, the proposed system encodes symbolic structure holographically and resolves recall through constrained reconstruction rather than similarity maximization. The work is presented as a capability benchmark, not an application study, and makes no claims regarding conversational agents or deployment scenarios. The memory architecture is proprietary to RAIT Enterprises and is currently integrated into an active symbolic AI system under evaluation. While implementation code is not publicly released, all experimental protocols and evaluation metrics are documented to support independent reproduction. Keywords fractal memory holographic memory symbolic intelligence memory compression robust recall interference resistance semantic reconstruction abstraction and reasoning ARC benchmark symbolic AI structural invariance memory scaling