Substrates Are All You Need: Defeating Non-determinism With Natural Language

Large language models exhibit output variance even under deterministic decoding (τ = 0). We show that this variance arises not primarily from sampling or implementation artifacts, but from interpretation drift: multiple semantically valid but mutually incompatible task definitions remaining admissible under the same input. When tasks are under-specified, models produce divergent yet internally coherent outputs because they are solving different underlying problems. Standard prompt engineering techniques do not resolve this, as they bias selection within the interpretation space without collapsing it. We introduce deterministic interpretation, a condition achieved through substrate-first architectures in which portable natural-language constraint specifications reduce the interpretation space to a singleton prior to inference. Under this condition, four independently trained frontier models converge to byte-identical outputs on the first invocation, verified via SHA-256 hash identity across 40 runs, with no modification to weights, hardware, or decoding strategy. Stochastic models exhibit the behavioral properties of deterministic software modules: the same input yields the same output, every time. This convergence is established by specification, not earned through iterative refinement, making it reusable across models, contexts, and invocations without prior runs or feedback. The architectural consequence is substrate engineering, a discipline distinct from prompt engineering that eliminates interpretive multiplicity at the source. Single-pass determinism replaces the retry loops, validation cascades, and multi-model voting the field currently relies on to compensate for output instability, reducing inference cost, complexity, and energy consumption proportionally. These results establish interpretation and computation as distinct architectural layers and reframe non-determinism as a property of task specification, not model behavior. Reliable AI systems are achieved not by fixing the model, but by fixing interpretation. Companion papers: Empirical Evidence Of Interpretation Drift In Large Language Models: https://zenodo.org/records/18219428Empirical Evidence Of Interpretation Drift In ARC-Style Reasoning: https://zenodo.org/records/18420425