Bounded Reflexive Amplification Improves Functional Performance in Adaptive Dynamical Systems

Reflexive systems, characterized by internal self-evaluation and feedback, are prone to instability when reflexive amplification is unconstrained, and to premature rigidity when feedback is overly suppressed. Prior work introduced the Reflexive Dynamical Systems (RDS-Core) framework, establishing bounded reflexive amplification as a sufficient condition for stable yet dynamically active internal coherence. This work extends the RDS framework from structural stability to functional performance. Using a minimal reflexive optimizer as a controlled testbed, we evaluate whether smoothly bounded reflexive amplification improves behavior in non-convex optimization tasks. Across benchmark functions and dimensionalities, we compare unconstrained reflexivity, rigid capacity constraints, and smooth capacity constraints implemented via leaky saturation. The results show that smoothly bounded reflexive amplification yields superior stability–performance trade-offs by preventing runaway internal feedback while preserving adaptive variability. In higher-dimensional settings, bounded reflexivity consistently outperforms both unconstrained and rigidly constrained regimes, providing empirical support for bounded reflexive amplification as a functional design principle for adaptive reflexive systems. This document is intended as a functional validation of the RDS-Core theoretical framework and should be read as complementary to the core theory, not as a general optimization proposal. --- Additional materials (source code and experimental data) are provided for reproducibility purposes only. They are not intended as optimized implementations, but as minimal artifacts supporting the reported results.