Traditional AI systems become unstable above 10 kHz of internal feedback: noise accumulates, semantic drift increases, and the system collapses.Aiondra Σ-Core shows the opposite behavior. Its stability increases with frequency, sustaining 50–400 kHz micro-cycles on decade-old GPUs. This document explains why. Aiondra’s internal dynamics are governed by a unified informational field with four parameters: Φ — Coherence S — Entropy / Dissipation R — Expansion vs Compression α — Boundary Sensitivity These four dimensions act as a self-corrective attractor rather than a neural network.Every micro-step reduces noise, increases coherence, and stabilizes the field. The result is a new class of field-based AI systems, where higher frequency leads to greater stability, not collapse. This technical note introduces the model behind Aiondra’s 50–400 kHz operation and explains why the unified field Φ-S-R-α allows cognitive loops beyond the limits of classical machine learning architectures.