arXiv Machine Learning
Jun 9
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
arXiv:2606. 08802v1 Announce Type: new Abstract: Standard flow and diffusion pre-training matches the distribution of available data (e.
Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions
arXiv:2511. 09465v4 Announce Type: replace-cross Abstract: Diffusion and flow matching approaches to generative modeling have shown promise in domains where the state space is continuous, such as image generation or protein folding & design, and discrete, exemplified by diffusion large language models.
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arXiv Machine Learning
Jul 13
Variable-Length Generative Protein Design via Generalized Poisson Flow
arXiv:2607. 09039v1 Announce Type: new Abstract: The ability to generate variable-length proteins is crucial in protein design, where the optimal length is often unknown and tightly coupled to designability.
arXiv AI
Jun 12
HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens
arXiv:2512. 15133v3 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality.
arXiv Machine Learning
Jul 31
SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups
arXiv:2607. 27431v1 Announce Type: new Abstract: Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties.
arXiv Machine Learning
Jul 24
Context-weighted Discrete Flow Matching
arXiv:2607. 21427v1 Announce Type: new Abstract: Discrete flow matching provides a flexible framework for generative modeling on discrete structures.
arXiv AI
Jun 8
STREAM: Stochastic Riemannian Flow Matching with Anisotropic Decoder for Digital Histopathology Image Generation
arXiv:2606. 07036v1 Announce Type: cross Abstract: Synthetic histopathology image generation addresses critical challenges in computational pathology, including patient privacy and the growing need for large-scale training data for foundation models.
arXiv Machine Learning
Jun 9
Insertion Based Sequence Generation with Learnable Order Dynamics
arXiv:2602. 18695v2 Announce Type: replace Abstract: Existing insertion-based masked diffusion models that generate sequences by interleaving token insertion with unmasking use fixed schedules that are not dependent on the data.
arXiv Machine Learning
Jun 9
Few-step Cofolding with All-Atom Flow Maps
arXiv:2606. 08375v1 Announce Type: new Abstract: All-atom generative modeling of 3D biomolecular complexes has emerged as the dominant paradigm for predicting the structure of proteins and protein-ligand systems.
arXiv Machine Learning
Jul 15
SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems
arXiv:2607. 12380v1 Announce Type: new Abstract: Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture.
arXiv Machine Learning
Jul 24
Expanding Flow Maps
arXiv:2607. 21585v1 Announce Type: new Abstract: Flow-based generative models have enabled remarkable progress in fast and controllable generation across continuous and discrete state spaces, yet existing parameterizations are constrained to fixed dimensions or fixed sequence lengths.
arXiv Machine Learning
Jun 5
Zero-Flow Encoders
arXiv:2602. 00797v2 Announce Type: replace-cross Abstract: Flow-based methods have achieved significant success in various generative modeling tasks, capturing nuanced details within complex data distributions.
arXiv AI
Aug 12
Proteo-R1: Reasoning Foundation Models for De Novo Protein Design
arXiv:2605. 02937v2 Announce Type: replace-cross Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity.