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Bayesian Flow Networks

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arxiv 2308.07037 v6 pith:BC5TGPQW submitted 2023-08-14 cs.LG cs.AI

Alex Graves , Rupesh Kumar Srivastava , Timothy Atkinson , Faustino Gomez This is my paper · ORCID
classification cs.LGcs.AI
keywords discretedatabayesianmodellingnetworkbfnsdiffusiondistributions
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This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution. Starting from a simple prior and iteratively updating the two distributions yields a generative procedure similar to the reverse process of diffusion models; however it is conceptually simpler in that no forward process is required. Discrete and continuous-time loss functions are derived for continuous, discretised and discrete data, along with sample generation procedures. Notably, the network inputs for discrete data lie on the probability simplex, and are therefore natively differentiable, paving the way for gradient-based sample guidance and few-step generation in discrete domains such as language modelling. The loss function directly optimises data compression and places no restrictions on the network architecture. In our experiments BFNs achieve competitive log-likelihoods for image modelling on dynamically binarized MNIST and CIFAR-10, and outperform all known discrete diffusion models on the text8 character-level language modelling task.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models

    cs.CL 2025-09 conditional novelty 6.0 of 10

    TraceRL aligns the RL objective with the DLM's actual step-by-step decoding, producing TraDo-4B/8B models that beat autoregressive baselines on math reasoning.

  2. Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows

    cs.LG 2025-07 conditional novelty 6.0 of 10

    TarFlowLM models language in a continuous latent space with transformer-based autoregressive normalizing flows, using mixture-CDF and Rosenblatt couplings, and reports competitive NELBO on TEXT8 and OpenWebText.

  3. Discrete Markov Bridge

    cs.LG 2025-05 conditional novelty 6.0 of 10

    Discrete Markov Bridge learns the forward rate matrix and the reverse score in a continuous-time Markov chain, achieving BPC 1.38 on Text8 and FID 11.63 on CIFAR-10.

  4. Steering Protein Family Design through Profile Bayesian Flow

    q-bio.BM 2025-02 conditional novelty 6.0 of 10

    ProfileBFN adapts Bayesian flow networks to accept protein-family profiles, enabling diverse, novel, and apparently functional family protein generation from single-sequence training.

  5. Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Parallel Trajectory Tempering with reservoirs and adaptive steps makes equilibrium maximum-likelihood training of EBMs practical and often better than PCD and deep generators on multimodal scientific data.

  6. Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration

    cs.LG 2025-08 reject novelty 5.0 of 10

    Gradient guidance inside Bayesian Flow Network updates generates 3D drug candidates with stronger predicted docking scores, better retrosynthesis feasibility, and improved kinase selectivity than diffusion baselines.

  7. AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model

    q-bio.BM 2025-07 conditional novelty 5.0 of 10

    AMix-1, a 1.7B-parameter Bayesian Flow Network protein model conditioned on MSA profiles and refined by an evolutionary test-time scaling loop, produced AmeR variants with up to 50x wild-type activity in wet-lab tests.

  8. MolPIF: A Parameter Interpolation Flow Model for Molecule Generation

    cs.LG 2025-07 conditional novelty 4.0 of 10

    MolPIF generates 3D ligands by interpolating the parameters of Gaussian coordinate and Dirichlet atom-type distributions, reporting stronger docking scores and geometric fidelity than prior flow and diffusion models o...

  9. A Survey on Latent Reasoning

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A survey that organizes latent reasoning methods into vertical recurrence, horizontal recurrence, and infinite-depth diffusion, arguing that silent reasoning can beat explicit chain-of-thought.

  10. Revisiting Sampling Strategies for Molecular Generation

    physics.chem-ph 2025-06 conditional novelty 4.0 of 10

    A maximally stochastic reverse sampler (StoMax) improves molecule stability and validity across DDPM- and BFN-based generators, at a cost in diversity.

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