This study presents the structural foundations of emergent autonomous reasoning in large-scale AI systems. Moving beyond performance-based evaluation, the analysis focuses on reasoning stability, transition regularity, semantic reconstruction, and coherence-preserving behaviors that arise during multi-step inference. Empirical evidence demonstrates that frontier-scale models consistently regulate internal divergence, restore disrupted reasoning paths, and converge toward low-entropy semantic attractors, suggesting the presence of proto-autonomous cognitive mechanisms. The study further identifies collective reasoning dynamics across internal subsystems and meta-reasoning architectures that monitor and correct inference trajectories. These findings support a unified structural account of how autonomy emerges from representational geometry, multi-agent alignment, and meta-level stability control. The results offer implications for AI safety, autonomous agent design, and the development of interpretable, self-regulating reasoning systems.