Discrete Beckmann Transport Models for One-Step Language Modeling and Reasoning

Discrete Beckmann Transport Models (DBTM) are introduced as a new type of discrete diffusion and flow model that can generate language in a single step by mapping any point to a fixed point on the simplex vertices. Unlike previous approaches, DBTM eliminates the need for a pretrained teacher model and time conditioning by minimizing a conservation equation directly from data. The model can be partially trained and iterated until convergence, and it can be extended to a partial‑context interpolant that refines outputs with additional function evaluations. Experiments on language modeling and reasoning tasks show that DBTM achieves higher quality and accuracy than existing discrete diffusion and continuous flow baselines.

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• llms
• agents
• diffusion