Recursive Coherence and the GPT Epoch: A Meta-Analysis of Structural Emergence Under Systemic Recursion

The emergence of recursive coherence as a formal system-level invariant parallels and is catalyzed by the proliferation of generative AI, particularly large language models (LLMs) such as GPT-3 and GPT-4. These models, scaled to billions of interactions, generated recursive tension: human users constructed layered prompt chains, real-time feedback loops, and tool-augmented cognitive architectures that exposed the limitations of purely statistical mimicry. Hallucination, alignment failures, and coherence drift revealed a fundamental absence. No model-internal mechanism existed to preserve structural consistency across recursive layers. In response, recursive coherence emerged not as a theoretical refinement, but as a structural necessity. This paper conducts a meta-analysis of this convergence, tracing the co-evolution of recursive coherence theory and GPT system deployment. It articulates how frameworks such as Coherence Information Theory (CIT), the Unified Field of Adaptive Potential (UFAP), and Recursive Coherence Logic (RCL) redefine intelligence, ethics, and information through recursive viability. It concludes that future AI systems must instantiate coherence-preserving invariants as internal substrates or collapse under recursion-induced entropy. GPTs did not instantiate recursive coherence, but they revealed its boundary conditions and necessity.