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Bohyung Han

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#machine learning Preprint Oct 2026

Are We Overthinking One-Step Generation?

Diffusion and flow matching are now dominant frameworks for generative modeling in continuous domains, but they typically require multiple network evaluations at inference. This has motivated extensive research on one-step generative modeling primarily built on diffusion and flow, though existing approaches often rely...

Woobin Park, Jaeyeon Kim, Yi-Lun Du et al. · 0 citations
#machine learning Preprint Sep 2026

ReLOBGen: Replayable Limit Order Book Message Generation

ReLOBGen achieves 100% replayability, improves market realism, particularly for top-of-book statistics and the relative prices of LOB messages, and provides a $2.7\text{-}3.6\times$ speedup per replayed message over the LOBS5 baseline.

Junoh Kang, Kiseop Lee, Bohyung Han · 0 citations

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