LLMs don't hallucinate – they hit a structural boundary (RCC theory) (www.effacermonexistence.com)

Recent insights into the behavior of Large Language Models (LLMs) have led to the introduction of the Recursive Collapse Constraints (RCC) theory, which posits that issues like hallucination and reasoning drift are inherent structural limitations rather than mere engineering failures. RCC outlines four axioms that describe the geometric boundaries faced by embedded inference systems, emphasizing that these models cannot achieve global stability or visibility due to their fundamentally local means of operation. This perspective reframes common issues with LLMs, suggesting that scaling and alignment efforts, while potentially improving local performance, are unlikely to eliminate these structural challenges. The significance of RCC lies in its potential to redirect research towards more effective methodologies. Instead of treating hallucination and drift as problems to be fixed, RCC encourages the design of architectures that work within these limits. This shift allows for the development of targeted models, optimized scaling practices, and more effective planning strategies by better understanding the boundaries within which LLMs operate. Ultimately, RCC redefines the expectations for AI systems by acknowledging the geometric realities of inference processes and suggesting that future advancements will require navigating these constraints rather than circumventing them.