AI RESEARCH
Theoretical Analysis of Engression and Reverse Markov Engression
arXiv CS.LG
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ArXi:2606.01002v1 Announce Type: cross Engression is a recently proposed and effective framework for conditional distribution learning. Its multi-step Reverse Marko extension further improves generative flexibility by decomposing complex conditional sampling into sequential reverse transitions. Despite their strong empirical performance, rigorous finite-sample statistical guarantees for these methods remain unavailable.