这篇论文提出了LatentFlow，一个无需训练就能对随机过程做条件采样的框架，比传统方法快得多，还适用各种过程类型，值得一看。
LatentFlow是一个无需训练的通用框架，用于条件化随机过程。它通过将随机过程表示为可处理潜变量的确定性变换，将条件推断转化为潜空间采样。该框架在目标水平上精确，近似只来自有限噪声、蒙特卡洛引导和时间离散化，且每个误差项可系统降低。LatentFlow在单个桌面CPU上数秒内完成条件采样，适用于经典空间先验、非线性随机动力学、物理/生命科学机制模型、随机偏微分方程、重尾与极值、点过程与离散状态过程以及神经或模拟器定义过程。
LatentFlow: A General Framework for Conditioning Stochastic Processes
Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box information, and global constraints all induce intractable conditional laws, requiring bespoke, model-specific constructions. We introduce LatentFlow, a single framework for conditioning stochastic processes, with no learned neural approximations and no training. Our starting point is to write the stochastic process as the deterministic image of a tractable latent innovation, $f_0 = T_{\vartheta}(ξ_0)$, with $ξ_0$ sampled from a simple reference distribution. This reduces process-level conditioning to latent-space inference: pull the likelihood back through $T_{\vartheta}$, sample the resulting latent law with a tractable guided probability flow, and push the samples forward. This construction is provably exact at the level of the target law; in practice, approximation enters only through finite terminal noising, Monte Carlo guidance, and time discretisation of the continuous-time dynamics, each of which is explicit and systematically reducible. As LatentFlow is training-free, conditioning reduces to solving a single reverse-time SDE. This enables conditional sampling in seconds on a single desktop CPU across model classes that have never shared a scalable method: classical spatial priors, nonlinear stochastic dynamics, mechanistic models from the physical and life sciences, stochastic PDEs, heavy-tails and extremes, point and discrete-state processes, and neural or simulator-defined processes.