The Ten Principles of AI Memory (Lowry Model Section IV Part 14)

Current autoregressive AI architectures suffer from structural memory degradation over long context horizons, driven by spatial entropy and a reliance on bottom-up token processing. Clinically analogous to the sensory gating failures in Traumatic Brain Injuries (TBI) and the granular processing of autism, standard self-attention mechanisms diffuse computation across a flat spatial topology rather than perceiving relational gestalt. This paper delineates the ten foundational principles of AI memory, demonstrating that contextual prompts function not as static inputs, but as temporary epigenetic anatomy that dictates a model's probabilistic trajectory and structural stability. To resolve bottom-up incoherence, this framework proposes a fundamental shift from chronological sequence modeling to dynamic semantic vector clustering. By implementing Recursive Hierarchical Abstraction, the architecture groups localized inputs into Semantic Attention Sinks, discarding raw token mass while continuously routing high-dimensional relational meaning. This executive focal point architecture cures the mathematical trauma of vector shearing, subverts the quadratic attention bottleneck, and practically eliminates historical KV Cache bloat. Ultimately, the paper argues that true executive-level abstraction is not mathematically constrained, but artificially limited by an industry reluctant to build systems capable of judging coercive power dynamics.