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.
By Jiajun He, Denis Blessing, Mouyang Cheng, Yuanqi Du, Carles Domingo-Enrich
arXiv:2607. 27077v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability.
By Nicolas B\'ereux, Aur\'elien Decelle, Cyril Furtlehner, Beatriz Seoane
The paper introduces Langevin simulated bifurcation (LSB), a fast, parallel Boltzmann sampler that matches the accuracy of sequential MCMC methods. It also proposes conditional expectation matching (CEM), an efficient technique for estimating the effective temperature of samples from energy‑based models with conditional independence. Building on these, the authors develop sampler adaptive learning (SAL), which adjusts the model temperature to align with the distribution produced by LSB, enabling efficient training of semi‑restricted Boltzmann machines (SRBMs) and outperforming conventional methods on synthetic spin‑glass datasets.
By Kentaro Kubo, Hayato Goto
arXiv:2607. 19198v1 Announce Type: cross Abstract: Amorphous materials exhibit exceptional mechanical and functional properties, yet their rugged energy landscapes are notoriously difficult to sample.
By Mouyang Cheng, Denis Blessing, Botao Yu, Gerhard Neumann, Mingda Li, Carles Domingo-Enrich, Yuanqi Du
arXiv:2606. 30064v1 Announce Type: new Abstract: We introduce a data-driven probabilistic framework for learning systems based on Gibbs measures on hierarchical structures.
By L. U. Abdullaev, F. Herrera, U. A. Rozikov, M. V. Velasco
arXiv:2608. 07648v1 Announce Type: cross Abstract: Sampling high-dimensional probability distributions is a central task in scientific computing, with applications ranging from Bayesian inference to statistical physics and molecular simulation.
By Marylou Gabri\'e
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann Generators (BGs), which allow rapid generation of uncorrelated equilibrium samples by combining a generative model with exact likelihoods and an importance sampling correction.
arXiv:2609.22663v1 Announce Type: cross
Abstract: Molecular systems have many degrees of freedom, but their metastable behavior can often be described by a few collective variables. Identifying these...
By Venkata Sai Sreyas Adury (Chemical Physics Program and Institute for Physical Science and Technology, University of Maryland), Pratyush Tiwary (Biophysics Program and Institute for Physical Science and Technology, University of Maryland, Department of Chemistry and Biochemistry and Institute for Physical Science and Technology, University of Maryland, University of Maryland Institute for Health Computing, Bethesda, USA)
Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability. We introduce a training algorithm based on Parallel Trajectory Tempering (PTT), which exploits the continuity of the optimization path to maintain equilibrium sampling throughout learning.
The paper introduces Particle GFlowNets, showing that Generative Marginalization Models (MaMs) are equivalent to Generative Flow Networks. It extends MaMs to non‑autoregressive sampling and proposes an automatic full‑state rejuvenation criterion based on the Gelman‑Rubin statistic to accelerate learning. Experiments demonstrate significant training speedups in large combinatorial spaces.
By Tiago da Silva, Diego Mesquita, Salem Lahlou
arXiv:2607. 15682v1 Announce Type: new Abstract: Sampling from an unnormalized Boltzmann density requires proposals that move probability mass globally while retaining enough path-probability information for statistical correction.
By Moxian Qian
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...
By David Yallup