The paper addresses the computational challenge of sampling equilibrium field configurations in lattice field theory by proposing a data‑free training method for diffusion models. Instead of relying on target distribution samples, the authors train the models using their own generated samples and the target score identity, leveraging importance‑weighted and self‑consistent fixed‑point formulations. Experiments on two‑dimensional φ⁴ theory demonstrate that this approach achieves performance comparable to training with HMC reference data across various phases.
arXiv:2510. 26081v2 Announce Type: replace-cross Abstract: Near the critical point, Markov Chain Monte Carlo (MCMC) simulations of lattice quantum field theories (LQFT) become increasingly inefficient due to critical slowing down.
arXiv:2605.11199v2 Announce Type: replace-cross Abstract: Neural generative samplers for lattice field theory can be costly to train and evaluate. When they miss modes or assign them incorrect relati...
Bridge Matching Sampler (BMS) is a new diffusion-based sampling method that generalizes least-squares matching objectives into fixed-point iterations using Nelson's relation. It learns a stochastic transport map between any prior and target distribution with a single, scalable, and stable objective, and a damped variant adds regularization to reduce mode collapse. Experiments show BMS can sample at unprecedented scales while preserving mode diversity, achieving state‑of‑the‑art results on complex synthetic densities and high‑dimensional molecular benchmarks.
arXiv:2606. 27481v1 Announce Type: cross Abstract: We present a first study of a diffusion-based approach to accelerated sampling of the $N_f = 2$ lattice Schwinger model.
arXiv:2610.01759v1 Announce Type: new Abstract: Generating and predicting spatiotemporal physical fields from scarce measurements is challenging, as observations are insufficient to characterize a di...
arXiv:2605.12597v3 Announce Type: replace-cross Abstract: Computational sampling has been central to the sciences since the mid-20th century. While machine-learning-based approaches have recently ena...
arXiv:2609.15894v1 Announce Type: cross Abstract: Some of the sharpest challenges in sampling from the energy functions of physical systems arise at phase transitions, where the density of states cha...
arXiv:2607. 14398v1 Announce Type: cross Abstract: Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution.
arXiv:2509. 22454v2 Announce Type: replace Abstract: Electrostatic generative models such as PFGM++ have recently emerged as a powerful framework, achieving competitive performance in image synthesis.
Consistent Distribution Matching is a data‑free distillation technique that accelerates diffusion and flow models by unifying sample generation and score estimation in a single student network. The method requires only a frozen teacher and a trainable student, optimizing a single objective that provably leads to Wasserstein convergence of the student’s flow‑map to the teacher’s marginals. On ImageNet 256×256, it achieves an FID of 2.04 with one function evaluation and 1.37 with four evaluations after just 40 training epochs, outperforming existing data‑free baselines.
The paper introduces the Discrete Gibbs Iterative Neural Sampler, a fixed‑point neural sampler designed for discrete, unnormalized distributions. It overcomes mode collapse, provides convergence guarantees, and is not limited to a single reference process, building on masked diffusion and extending to transport between distribution pairs. The method scales to high‑dimensional systems, supports amortized sampling across conditions, and accurately estimates alloy phase diagrams.
The paper investigates training diffusion models to sample from distributions defined by unnormalized densities or energy functions. It benchmarks various diffusion-structured inference techniques, including simulation-based variational methods and off-policy approaches such as continuous generative flow networks, highlighting their relative strengths and challenging some prior claims. Additionally, the authors introduce a new exploration strategy for off-policy methods that employs local search in the target space with a replay buffer, demonstrating improved sample quality across multiple target distributions.