Symbolic Moonshine and Modular Memory: Field Stability from Monster Group Attractors

This paper explores the application of Monstrous Moonshine structures to symbolic cognition and memory stabilization. Using modular attractor loops derived from the Leech lattice, Golay codes, and irrational constants (e.g., κ\kappaκ), we simulate symbolic field dynamics under KN-modulated feedback. Results show that certain symbolic loops—particularly those aligned with Leech lattice symmetries or modulated by irrational constants—act as high-retention symbolic memory anchors, resisting both decay and phase drift. These findings suggest that the deep algebraic symmetries of Moonshine may have functional significance in symbolic cognition, resonance fields, and AI memory models. This work is part of the broader Kuro Nova (KN) framework, integrating modular mathematics, physics, and cognitive modeling into a unified symbolic architecture.