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Diffusion generative models have rapidly emerged as powerful tools for modeling complex financial data. Their appeal is both structural and practical: they offer stable likelihood-based training, strong mode coverage, flexible conditioning, and a stochastic-differential-equation formulation that aligns naturally with t...
This work introduces LOB-ID, an embedding-based framework that adapts the Fr\'echet Inception Distance and Monge Inception Distance to LOB data, and scores five generative LOB models, spanning stochastic baselines and deep learning approaches, and finds that LOB-ID ranks them in line with the joint temporal and cross-l...
This work demonstrates a full-stack pathway from efficient trillion-parameter model post-training on Ascend infra to domain-specialized Flash models for solver-grounded mathematical modeling, advancing frontier-model systems for complex reasoning.
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