Semantic Caching: How Stochastic Fixation Freezes LLM Reconstruction Errors

This article introduces stochastic fixation as a mechanism by which semantic caching, deployed at the application layer of Large Language Model systems, freezes a single stochastic realization of a non-deterministic model and re-serves it to all users whose queries fall within a vector proximity radius. What the mainstream engineering literature describes as neutral infrastructure optimization is shown to determine what reconstruction of an organization gets served to thousands of users, based on a single throw of the dice at the moment of the first cache miss. The article situates stochastic fixation as a strict subcase of delivery-layer fixation, and extends the Signal Inference Optimization (SIO) framework to a fourth horizon: delivery. It introduces the methodological distinction between the native machine thesaurus (extracted through direct model interrogation) and the delivered machine thesaurus (extracted through the deployed application, including cache, retrieval, routing, prompt templates, and orchestration layers). Five consequences for reconstruction fidelity are analyzed: temporal lottery effects, variance elimination in the wrong direction, a breach in the three-thesaurus diagnostic, scaling to consumer answer engines and sanctioned enterprise assistants, and the decoupling of response freshness from reality. The cumulative effect is characterized as a silent erosion of informational quality at the scale of user populations. The article establishes the underlying mechanism; follow-up work will examine platform-level and organizational-scale consequences.