Emergent Stability in Maximal Adaptive Causal-Field Systems Under Extreme Coupling and Plasticity

This work investigates the stability properties of a maximal adaptive causal-field system subjected to extreme dimensionality, dense nonlocal coupling, feedback amplification, plasticity, entropy-driven adaptation, and stochastic perturbation. The system is initialized in an 8192-dimensional candidate space and evolved under stability-driven selection without domain-specific tuning or unit-level modeling. Despite the aggressive configuration, the system consistently converges to a low-dimensional stable attractor manifold within milliseconds of runtime. The resulting emergent regime exhibits bounded causal density, high self-similarity, and controlled complexity, demonstrating robust coherence under conditions that typically destabilize large adaptive models. No claim is made regarding biological realism or domain-specific interpretation. Instead, the results highlight a universality class of adaptive systems in which stability acts as the dominant organizing principle, enabling scalable modeling of complex behavior without reliance on brute-force simulation. These findings provide a foundation for future work on interpretable, large-scale adaptive dynamics across multiple domains.