Experience-Driven Structural Organization in a Multi-Layer Computational System Based on Rotational Stability

This work introduces a computational framework in which system organization emerges from accumulated experience rather than optimal decision computation. The proposed architecture consists of three layers: experience generation, experience processing, and decision closure. Experience is quantitatively defined through a rotational metric derived from node dynamics and reflection parameters. Nodes operate on error-corrected representations, ensuring structural consistency between input and output states. Simulation results demonstrate that structural selection appears before decision formation. Decisions close only after completion of experiential accumulation, indicating that internal system time is governed by sample completion rather than physical time. The results support the hypothesis that measurable experiential dynamics can organize computational structures independently of optimal decision policies.