Emergence of Stable Low-Dimensional Regimes in Extreme Adaptive Causal Fields

This paper presents a large-scale computational study of adaptive causal fields operating under extreme conditions of dimensionality, coupling, plasticity, and noise. The system was initialized with high dimensional stability selection and dense nonlocal interactions, without predefined units, architectures, or task objectives. Stability emerged through intrinsic feedback and global selection rather than local optimization. Despite an initial dimensional selection of 8192, the system converged to a coherent regime with only 62 active interaction sites, indicating a dramatic reduction in effective dimensionality. The converged state exhibited high global stability, zero topology variance, moderate complexity, and sustained causal integration without pathological amplification or fragmentation. These results demonstrate that highly adaptive systems can self-organize into stable, low-dimensional regimes even under extreme dynamical stress. Stability arises not through suppression of interaction, but through reorganization of causal structure. The findings contribute to a broader understanding of resilience, regulation, and emergent order in complex adaptive systems.