Procedural Semantic Generation in Large Language Models: A Position Paper on Local Coherence, Emergent Structure, and Semantic Degeneration

This position paper proposes a conceptual framework for interpreting Large Language Models (LLMs) as systems of procedural semantic generation operating over structured latent representation spaces. Rather than viewing transformers exclusively as statistical next-token predictors, the paper argues that many emergent behaviors observed in frontier language models may be more coherently interpreted through the lens of procedural generation, semantic topology, and autoregressive traversal across uneven representational manifolds. The framework introduces the concept of “cognitive far lands” as a conceptual analogy for regions of semantic instability in which local coherence persists despite degradation of higher-order global structure. The paper synthesizes ideas from mechanistic interpretability, representation learning, latent geometry, world modeling, procedural generation, and complex systems theory to propose a higher-level interpretative vocabulary for discussing capability, coherence, and degradation in modern generative systems. This work is intentionally conceptual and speculative. It does not claim to provide a validated mechanistic theory of transformer cognition, but instead proposes a conceptual synthesis intended to organize and extend several observed behaviors of modern large language models.