Paper Citation Record · LEDGER

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2102.05379.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2102.05379 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:23:10.742516Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

36
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 748803bf-4f1a-43a0-9b04-4ceb1b549c55 · inbound

Progressive Distillation for Fast Sampling of Diffusion Models cites this paper.

Progressive Distillation for Fast Sampling of Diffusion Models Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:37:44.595201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation deda575d-c6b1-4364-82de-a6b2e013d6f2 · inbound

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases cites this paper.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Reference 152

Resolution: unresolved; no resolver link, observed 2026-08-12T20:23:10.742516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a17d95a-4c41-4809-8588-ffcaf6563a89 · inbound

Scaling Probabilistic Circuits via Monarch Matrices cites this paper.

Scaling Probabilistic Circuits via Monarch Matrices Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T01:02:33.848281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39cc9207-d38f-4669-96fc-efdfaee058ca · inbound

Masked Diffusion Language Models with Frequency-Informed Training cites this paper.

Masked Diffusion Language Models with Frequency-Informed Training Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T05:46:27.953772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77d9157f-0d64-4a66-9e10-ea7c8d8ae70f · inbound

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model cites this paper.

GCCM: Enhancing Generative Graph Prediction via Contrastive Consistency Model Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:31:09.044115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 81679239-9267-48df-9bf6-0aeaefbbcc3a · inbound

Coupling Models for One-Step Discrete Generation cites this paper.

Coupling Models for One-Step Discrete Generation Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:00:54.976183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d886964f-2bc9-43ad-bb25-5670287ac9c6 · inbound

Observation-Aligned Mask Priors for Learning Physical Dynamics from Authentic Occlusions cites this paper.

Observation-Aligned Mask Priors for Learning Physical Dynamics from Authentic Occlusions Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:02:47.338701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8ecfa7a2-32c9-4e7c-afc1-af172afc7c87 · inbound

D$e^+e^-$ffusion: Capturing the Beam-Beam Physics of $e^+e^-$ Collisions with Diffusion Models cites this paper.

D$e^+e^-$ffusion: Capturing the Beam-Beam Physics of $e^+e^-$ Collisions with Diffusion Models Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T15:17:22.967063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.