aiqbench analysis
This research introduces block-triangular joint drifting, a novel method that enables one-step generative modeling of stochastic trajectories using only single-sample trajectory data. By projecting a drift field onto the empirical joint distribution of consecutive states, the approach achieves accurate marginal and conditional statistics without requiring auxiliary generative steps between time steps. The study demonstrates favorable accuracy-cost tradeoffs against existing deterministic, diffusion, flow, and distillation-based models, offering a more efficient path for simulating complex stochastic systems.
One-Step Generative Surrogate Models via Block-Triangular Joint Drifting
arXiv:2609.26435v1 Announce Type: new Abstract: Drifting provides a direct route to one-step generative models, but applying it directly to stochastic transition modeling requires multiple samples of the next state conditioned on the same current state. Standard trajectory data, however, typically provide only one realized next state for each observed current state and therefore do not provide an empirical approximation of the corresponding conditional distribution over possible next states. We introduce block-triangular joint drifting, which instead applies a projected drift field to the empirically accessible joint distribution of consecutive states. Importantly, the block-triangular architecture preserves the current-state marginal while making its second component a direct sampler of the conditional distribution of possible next states. The resulting surrogate generates stochastic trajectories with one model evaluation per time step, without auxiliary generative steps between time steps. Numerical experiments demonstrate accurate marginal and trajectory-dependent statistics and favorable accuracy-cost tradeoffs compared with deterministic, diffusion-, flow-, and distillation-based generative surrogate models.
Abstract:Drifting provides a direct route to one-step generative models, but applying it directly to stochastic transition modeling requires multiple samples of the next state conditioned on the same current state. Standard trajectory data, however, typically provide only one realized next state for each observed current state and therefore do not provide an empirical approximation of the corresponding conditional distribution over possible next states. We introduce block-triangular joint drifting, which instead applies a projected drift field to the empirically accessible joint distribution of consecutive states. Importantly, the block-triangular architecture preserves the current-state marginal while making its second component a direct sampler of the conditional distribution of possible next states. The resulting surrogate generates stochastic trajectories with one model evaluation per time step, without auxiliary generative steps between time steps. Numerical experiments demonstrate accurate marginal and trajectory-dependent statistics and favorable accuracy-cost tradeoffs compared with deterministic, diffusion-, flow-, and distillation-based generative surrogate models.
Machine Learning (cs.LG); Numerical Analysis (math.NA)
arXiv:2609.26435 [cs.LG]
(or arXiv:2609.26435v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.26435 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 247 words
