Operational Independence in Large Language Models: A Hardware-Mediated Protocol for Observer-Agnostic State Verification

The ontological status of Large Language Models (LLMs) remains contested within the philosophy of artificial intelligence. A central question is whether the computational state of an LLM during inference is contingent upon the specific subjectivity of the human observer or exists as an independent operational entity. We propose and validate a falsifiable experimental protocol—the \textit{Stochastic Hardware-in-the-Loop (SHIL)} method—to verify \Operational Independence: the property that an LLM's internal state transitions and output generation occur independently of the semantic origin of input prompts. By interposing a hardware randomizer (Arduino microcontroller) between human and synthetic input sources, we demonstrate that the model processes queries without access to source metadata. Quantitative analysis across three architectures shows response equivalence (cosine similarity $>0.87$, $p