Autoregressive Boltzmann Generators

• URL: http://arxiv.org/abs/2606.27361v1
• Date: Thu, 25 Jun 2026 17:58:21 GMT
• Title: Autoregressive Boltzmann Generators
• Abstract summary: Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics.<n>Modern Boltzmann Generators predominantly rely on normalizing flows.<n>We propose Autoregressive Boltzmann Generators (ArBG) that departs from the flow-based BG paradigm.
• Score: 81.5061620844556
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, modern BGs predominantly rely on normalizing flows (NFs), which either suffer from limited expressivity due to strict invertibility constraints (discrete time) or computationally expensive likelihoods (continuous time). In this paper, we propose Autoregressive Boltzmann Generators (ArBG) -- a novel autoregressive modelling framework -- that overcomes these limitations by departing from the flow-based BG paradigm. ArBG circumvents the topological constraints of flows and enables sequential inference-time interventions, while offering enhanced scalability by leveraging architectures effective in Large Language Models. We empirically demonstrate that ArBG leads to significant improvements over flow-based models across all benchmarks, but particularly in larger peptide systems such as the 10-residue Chignolin. Furthermore, we introduce Robin, a 132 million parameter transferable model trained with the ArBG framework which improves over the previous state-of-the-art, reducing the zero-shot energy error, E-W$_2$, on 8-residue systems by over 60$\%$. The code can be found at the following link: https://github.com/danyalrehman/autobg.

Related papers

• Post-training Quantization for Hybrid Iterative Generative Models [52.791973512723324]
  Iterative Generative Models (IGMs) span autoregressive and diffusion paradigms, and hybrid variants that couple them can achieve remarkable image-generation fidelity.<n>Post-training Quantization (PTQ) is appealing for acceleration, while directly applying vanilla PTQ to hybrid IGMs can trigger model collapse.<n>We introduce HyGenQ, a PTQ framework for hybrid IGMs.<n>It successfully quantizes representative hybrid IGMs to 8-bit precision (W8A8), significantly outperforming existing baselines and validating its robustness across different model families.
  arXiv  Detail & Related papers  (2026-08-14T04:07:14Z)
• PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting [62.22445401483844]
  Existing methods are severely constrained by the bottleneck of compounding errors.<n>We propose a universal framework called -Cast-Corrector (Plug-and-Play Corrector)<n>Our method significantly enhances the long-term stability of coupled forecasting systems.
  arXiv  Detail & Related papers  (2026-05-09T13:12:33Z)
• Coarse-Grained Boltzmann Generators [2.8880597165704]
  We propose a principled framework that unifies scalable reduced-order modeling with the exactness of importance sampling.<n>CG-BGs act in a coarse-grained coordinate space, using a learned potential of mean force to reweight samples generated by a flow-based model.<n>Our results demonstrate that CG-BGs faithfully capture complex interactions mediated by explicit solvent within highly reduced representations.
  arXiv  Detail & Related papers  (2026-02-11T08:37:13Z)
• FALCON: Few-step Accurate Likelihoods for Continuous Flows [78.37361800856583]
  We propose Few-step Accurate Likelihoods for Continuous Flows (FALCON), which allows for few-step sampling with a likelihood accurate enough for importance sampling applications.<n>We show FALCON outperforms state-of-the-art normalizing flow models for molecular Boltzmann sampling and is two orders of magnitude faster than the equivalently performing CNF model.
  arXiv  Detail & Related papers  (2025-12-10T18:47:25Z)
• Graph VQ-Transformer (GVT): Fast and Accurate Molecular Generation via High-Fidelity Discrete Latents [6.5389217759933125]
  We introduce the Graph VQ-Transformer (GVT), a two-stage generative framework that achieves both high accuracy and efficiency.<n>The core of our approach is a novel Graph Vector Quantized Variational Autoencoder (VQ-VAE) that compresses molecular graphs into high-fidelity discrete latent sequences.<n>GVT achieves state-of-the-art or highly competitive performance across major benchmarks like ZINC250k, MOSES, and GuacaMol.
  arXiv  Detail & Related papers  (2025-12-02T11:44:15Z)
• Resolving Turbulent Magnetohydrodynamics: A Hybrid Operator-Diffusion Framework [0.1572025118388268]
  Hybrid machine learning framework is trained on a comprehensive ensemble of high-fidelity simulations with $mathrmRe in 100, 250, 500, 750, 1000, 3000, 10000$.<n>At extreme turbulence levels, it remains the first surrogate capable of recovering the high-wavenumber evolution of the magnetic field.
  arXiv  Detail & Related papers  (2025-07-02T19:33:57Z)
• BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimation [1.2874523233023452]
  Efficient sampling from the Boltzmann distribution is a key challenge for modeling complex physical systems such as molecules.<n>We train an energy-based model (EBM) to approximate likelihoods using both noise contrastive estimation (NCE) and score matching.<n>Our approach also exhibits effective transfer learning, generalizing to new systems at inference time and achieving at least a $6times$ speedup over standard MD.
  arXiv  Detail & Related papers  (2025-07-01T15:18:28Z)
• Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators [85.25962679349551]
  Boltzmann Generators (BGs) offer efficient sampling and likelihoods, but their training via maximum likelihood is often unstable and computationally challenging.<n>We propose Regression Training of Normalizing Flows (RegFlow), a novel and scalable-based training objective that bypasses the numerical instability and computational challenge of conventional maximum likelihood training.
  arXiv  Detail & Related papers  (2025-06-01T20:32:27Z)
• BARNN: A Bayesian Autoregressive and Recurrent Neural Network [40.64742332352373]
  We present BARNN: a variational Bayesian Autoregressive and Recurrent Neural Network.<n> BARNNs aim to provide a principled way to turn any autoregressive or recurrent model into its Bayesian version.<n>We also introduce a temporal version of the "Variational Mixtures of Posteriors" prior (tVAMP-prior) to make Bayesian inference efficient and well-calibrated.
  arXiv  Detail & Related papers  (2025-01-30T15:44:04Z)
• Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One [83.5162421521224]
  We propose a unique method termed E-ARM for training autoregressive generative models. E-ARM takes advantage of a well-designed energy-based learning objective. We show that E-ARM can be trained efficiently and is capable of alleviating the exposure bias problem.
  arXiv  Detail & Related papers  (2022-06-26T10:58:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.