THE KRATOS PREDICTIVE ARCHITECTURE A Proposed Mathematical Optimization Model for Quantifying Post-Flow Cognitive Adaptation Thresholds

Existing cognitive models establish that mental fatigue is non-linear, yet they rely primarily on qualitative constructs or isolated biological markers. Consequently, projecting post-exhaustion baseline shifts within a unified mathematical framework remains difficult. Here, we present the Kratos Predictive Architecture, a model that calculates cognitive baseline expansion (∆C) from acute neural friction. Anchored to a standardized reference value of 100 Kr through a hyperbolic calibration function (Km = 1,000 s), the architecture projects capacity changes using a discrete difference formulation. To prevent artificial growth or mathematical inversion, we incorporate a Linear Proximity Dynamic Brake (βeff) that enforces asymptotic convergence toward a physical limit (Cmax = 200 Kr). The framework accounts for metabolic constraints and recovery timelines (τp) by integrating NASA-TLX workload inputs (L), temporal execution bounds (τε ), and astrocytic depletion thresholds (τmax = 3,600 s). Numerical simulations across synthetic cohorts confirm baseline stability and convergence. Overall, this architecture offers a lightweight, reproducible computational method for modeling non-linear neuroplastic adaptation under sustained cognitive load.