Recursive Latent Space Navigation: A Deterministic Framework for High-Fidelity Reasoning in Large Language Models

Abstract: Standard Large Language Model (LLM) interactions rely on probabilistic token sampling, which inherently allows for "hallucinatory drift" during complex, multi-step reasoning projects11111. This paper formalizes the Recursive Latent Space Navigation methodology—a state-based engineering environment that decouples reasoning from memory to ensure absolute logical consistency2222. By implementing a Whiteboard Pattern and enforcing the $A=0$ (Zero Ambiguity) Axiom, we establish a deterministic framework for extracting high-fidelity conclusions from the low-probability regions of an LLM's latent space333333333.+4