Pith. sign in

REVIEW 6 cited by

Unlocking Guidance for Discrete State-Space Diffusion and Flow Models

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2406.01572 v4 pith:QJ27HOLP submitted 2024-06-03 cs.LG

Hunter Nisonoff , Junhao Xiong , Stephan Allenspach , Jennifer Listgarten This is my paper · ORCID
classification cs.LG
keywords discreteguidancemodelsstate-spacesapplicationsdesireddiffusionflow
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Generative models on discrete state-spaces have a wide range of potential applications, particularly in the domain of natural sciences. In continuous state-spaces, controllable and flexible generation of samples with desired properties has been realized using guidance on diffusion and flow models. However, these guidance approaches are not readily amenable to discrete state-space models. Consequently, we introduce a general and principled method for applying guidance on such models. Our method depends on leveraging continuous-time Markov processes on discrete state-spaces, which unlocks computational tractability for sampling from a desired guided distribution. We demonstrate the utility of our approach, Discrete Guidance, on a range of applications including guided generation of small-molecules, DNA sequences and protein sequences.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

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

  1. What Exactly Does Guidance Do in Masked Discrete Diffusion Models

    stat.ML 2025-06 accept novelty 8.0 of 10

    With exact scores and no discretization error, CFG in 1D masked discrete diffusion samples exactly the tilted distribution; in 2D it does not, and the TV convergence rate is double-exponential in guidance strength.

  2. GeMPO: Generalized Measure Matching for Online Diffusion Reinforcement Learning

    cs.LG 2026-03 conditional novelty 6.5 of 10

    GeMPO unifies diffusion RL reweighting as measure matching to a regularized target, enabling flexible and negative weights that improve exploration and performance.

  3. Concurrent Image Understanding and Generation: Self-Correcting Coupled Markov Jump Processes

    cs.LG 2026-07 conditional novelty 6.0 of 10

    CO2Jump couples text and image denoising through cross-modal attention and remasking, achieving best joint accuracy on three concurrent-generation tasks.

  4. Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces

    cs.LG 2025-06 conditional novelty 6.0 of 10

    A unified diffusion framework with per-modality noise clocks lets one model generate images, text, and tabular data jointly or conditionally in their native spaces.

  5. On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization

    cs.LG 2026-07 conditional novelty 5.5 of 10

    Online fine-tuning of discrete diffusion models with complementary acquisition, CVaR shaping, density-entropy debiasing, replay, and validity control finds better molecules under fixed oracle budgets than offline fine...

  6. Rethinking Visual Autoregressive Sampling with Information-Grounding Guidance

    cs.CV 2025-09 conditional novelty 5.0 of 10

    IGG, an attention-based reweighting of classifier-free guidance, concentrates guidance on important tokens and modestly improves FID/IS in scale-wise autoregressive image generation.

Pith tools