arXiv Notify me• Jun 25, 2026paper

Autoregressive Boltzmann Generators

Yoshua Bengio(guest)

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Executive Summary

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Limitations of Normalizing Flows in Molecular Simulation The Autoregressive Paradigm Shift for Physical Systems

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Concerns Raised

The potential training complexity and computational cost for large-scale ArBG models like Robin are not discussed.
The framework's generalizability to molecular systems significantly different from the training data remains an open question.

Opportunities Identified

Accelerating molecular dynamics simulations for drug discovery and materials science.
Applying autoregressive architectures to other complex generative modeling tasks in the physical sciences.
Development of larger, transferable foundational models for a wide range of molecular systems.

Key Themes

Research Findings8

The Autoregressive Boltzmann Generators (ArBG) framework is a novel approach that overcomes the limitations of expressivity and computational cost found in flow-based Boltzmann Generators.

The Autoregressive Boltzmann Generators (ArBG) framework provides significant performance improvements over flow-based models across all tested benchmarks, especially in larger peptide systems like the 10-residue Chignolin.

Robin, a 132 million parameter model trained using the Autoregressive Boltzmann Generators (ArBG) framework, reduces the zero-shot energy error (E-W2) on 8-residue systems by over 60% compared to the previous state-of-the-art.

Modern Boltzmann Generators that rely on normalizing flows are limited in expressivity due to strict invertibility constraints in discrete time models.

Modern Boltzmann Generators using normalizing flows have computationally expensive likelihoods in continuous time models.

The Autoregressive Boltzmann Generators (ArBG) framework circumvents the topological constraints inherent in normalizing flows.

The Autoregressive Boltzmann Generators (ArBG) framework enables sequential interventions at inference time.

The Autoregressive Boltzmann Generators (ArBG) framework achieves enhanced scalability by using architectures proven effective in Large Language Models.

Topics

Autoregressive Boltzmann Generators (ArBG)Normalizing Flows Molecular Dynamics Statistical Physics Thermodynamic Equilibrium Generative Models Large Language Models (LLMs)Computational Chemistry Peptide Systems Chignolin Importance Sampling Machine Learning Artificial Intelligence State-of-the-Art (SOTA)Yoshua Bengio

Processed Jul 28, 2026Daily intelligence brief → yt-dlp + mlx-whisper + Gemini

Autoregressive Boltzmann Generators

Continue your research

Concerns Raised

Opportunities Identified

Key Themes

Limitations of Normalizing Flows in Molecular Simulation

The Autoregressive Paradigm Shift for Physical Systems

Achieving State-of-the-Art Performance and Scalability

Enabling New Capabilities in Molecular Modeling

Research Findings8

Topics