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This paper formalizes the numerical divergence between training and inference kernels in modern AI post-training pipelines, proposing a kernel contract specification and a chain of Lipschitz-style bounds to mitigate off-policy bias, slice-level regressions, and reproducibility issues.
The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and operations tailored for machine learning, supporting configurable width, precision, signedness, and infinities, along with extensive rounding modes including stochastic rounding and a novel scale-invariant approximation measure called kappa-approximation.