Presents a general framework for generator matching on projected image spaces from latent Markov processes, generalizing static latent results to dynamic conditional processes.
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TRACE creates valid conformal prediction sets for complex generative models by scoring outputs via averaged denoising or velocity errors along stochastic transport paths instead of likelihoods.
Spherical flows on S^{d-1} with vMF noise reduce the continuity equation to a scalar ODE in cosine similarity, yielding posterior-weighted marginal velocity and score that enable ODE and predictor-corrector sampling for categorical sequences, with the posterior trained by cross-entropy and empirical
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Latent Process Generator Matching
Presents a general framework for generator matching on projected image spaces from latent Markov processes, generalizing static latent results to dynamic conditional processes.