arXiv Machine Learning
Jun 9
Graph-GRPO: Training Graph Flow Models with Reinforcement Learning
arXiv:2603. 10395v2 Announce Type: replace Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery.
H-GLow: Accelerating Graph Generation via Hazard-Guided Discrete Flow Matching
H-Glow is a hazard‑guided framework that accelerates graph generation by decomposing Continuous‑Time Markov chain generators into hazards and conditional jump distributions. It plans graph edits and dynamically adjusts sampling time based on graph activity, allowing pretrained graph DFM models to produce higher‑quality graphs with fewer steps. The authors also propose a new graph DFM model that directly predicts hazards and conditional jumps, whose training objective cleanly splits into hazard matching and weighted jump prediction, and demonstrate its effectiveness on molecular and synthetic graph benchmarks.
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arXiv Machine Learning
Jun 24
A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching
arXiv:2606. 24140v1 Announce Type: new Abstract: Discrete flow matching (DFM) provides a principled framework for generative modeling on discrete state spaces via continuous-time Markov chain dynamics.
arXiv Machine Learning
Oct 15
SNACK: A Sequential Notation Framework for Probabilistic Graph Generation
arXiv:2610.19781v1 Announce Type: cross Abstract: We introduce SNACK, a string representation for probabilistic graph generation that enables sequence models to generate and model graphs with discret...
arXiv Machine Learning
Jun 4
FLAGG: Flexible Autoregressive Graph Generation
arXiv:2606. 05067v1 Announce Type: new Abstract: The Deep Graph Generation's panorama spans two extremes: one-shot and sequential models.
arXiv Machine Learning
Oct 27
Trajectory Matching: Discrete Flow Matching with Structured Actions
arXiv:2610.32602v1 Announce Type: new Abstract: The natural units of structured discrete generation are often not independent single-component edits but richer actions: attaching a fragment to a mole...
arXiv Machine Learning
Oct 9
Few-Step Generation via Data-Space Iteration
The paper introduces data‑space iteration, a few‑step generation framework that eliminates the need for a fixed probability‑flow discretization. By refining predictions directly in data space with a shared generator, each iteration is trained to produce the best possible sample within its capacity. Experiments on class‑conditional ImageNet 256×256 show that data‑space iteration outperforms standard discretization baselines and matches or improves upon schedule‑searched variants without schedule‑specific training.
arXiv AI
Jul 14
Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm
arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.
arXiv AI
Jul 2
Flow-Map GRPO: Reinforcement Learning for Few-Step Flow-Map Generators via Anchored Stochastic Composition
arXiv:2607. 00535v1 Announce Type: cross Abstract: Few-step flow-map generators, such as consistency models and MeanFlow, accelerate sampling by directly learning long-range transport maps between noise and data.
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 AI
Jun 2
Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation
arXiv:2603. 23398v3 Announce Type: replace-cross Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and materials design.
Hugging Face Trending Papers
Jun 23
A Time-Reparameterized Cumulative Intensity Extrapolation Sampler for Discrete Flow Matching
Discrete flow matching (DFM) provides a principled framework for generative modeling on discrete state spaces via continuous-time Markov chain dynamics. In practice, sampling for DFM commonly employs discretizations such as $τ$-leaping, yet efficient sampling methods under a limited number of function evaluations (NFE) remain less studied.
Hugging Face Trending Papers
Oct 5
Constrained Goal-directed Planar Graph Generation with Grammar-based Reinforcement Learning
The paper introduces a dataset‑free approach to generate planar graph embeddings that meet strict structural and geometric constraints. By integrating parametric graph grammars with safe reinforcement learning, the authors formulate the task as a constrained Markov decision process and use action projection to keep actions within safe sets. Their method consistently outperforms classical and deep generative baselines on a new benchmark suite for constrained, goal‑directed planar graph generation.