Large Language Models as Artificial Probability Fields: Failure Modes, Physical Limits, and State-Driven Correction

This preprint proposes a physics-grounded interpretation of large language models (LLMs) as artificial probability fields rather than symbolic reasoning systems or conversational agents. It reframes common failure modes — including hallucination, instruction drift, context collapse, and brittle reasoning — as predictable consequences of uncontrolled energy injection, entropy amplification, and lack of state stabilization within high-dimensional probabilistic dynamics. The paper introduces a state-driven interaction framework that separates cognitive state from linguistic context, treating intent, constraints, and typed state as explicit boundary conditions on the probability field. It argues for slow, gated updates and constraint-first orchestration as necessary mechanisms for stabilizing model behavior and improving reliability, rather than continued scaling of prompt length or retrieval volume. This work is intended as a position paper establishing a conceptual and architectural foundation for future systems that treat LLMs as dynamic physical systems rather than programmable text engines. It is released as a preprint for open technical discussion and early feedback. Author: Timothy Wesley Stone Affiliation: Independent Researcher