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Paper Citation Record · LEDGER

Context-weighted Discrete Flow Matching

As of 12 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.21427.

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

pith.paper-citation-record.v1
2607.21427 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T07:34:07.768666Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

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  • verified fuzzy0
  • unresolved36
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 3ee5572a-0423-4bb7-abee-c6b6e8933fc8 · outbound

This paper cites an unresolved cited work.

Context-weighted Discrete Flow Matching Unresolved cited work

Reference 1

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source=pdf_text observed=2026-08-01T07:34:05.231007Z digest=sha256:479c536c75be1c6ae8d18828fa7f2460665b2aee0ba431cd4364e57ccae52336

Observation 748856b5-23e3-4043-9bf0-a21daf8b3c51 · outbound

This paper cites Openwebtext corpus.

Context-weighted Discrete Flow Matching Openwebtext corpus

Reference 2

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source=pdf_text observed=2026-08-01T07:34:05.325187Z digest=sha256:a6fb1ae21497a6db75db369403fe636617ed2f6ea4497051dc8a0e4d4bc1cad0

Observation 67a72afe-2c6f-43e7-94fb-5e384dc0a5c2 · outbound

This paper cites Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models.

Context-weighted Discrete Flow Matching Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

Reference 3

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source=pdf_text observed=2026-08-01T07:34:05.468795Z digest=sha256:4f5466324600dc3e3e538157714abfefe8fd708bd73d538bde0fa1df122ece9a

Observation a4d3d6b9-297c-44b7-a463-7da8a6fad5d4 · outbound

This paper cites A continuous time framework for discrete denoising models.Advances in Neural Information Processing Systems, 35:28266–28279, 2022.

Context-weighted Discrete Flow Matching A continuous time framework for discrete denoising models.Advances in Neural Information Processing Systems, 35:28266–28279, 2022

Reference 4

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source=pdf_text observed=2026-08-01T07:34:05.590848Z digest=sha256:e51478a69351bce3ace1484df56085ce4d779f1674ac44e108d728052004e6d4

Observation fadf521d-6287-40c0-b11d-3bdfc2ad849e · outbound

This paper cites Vector quantized diffusion model for text-to-image synthesis.

Context-weighted Discrete Flow Matching Vector quantized diffusion model for text-to-image synthesis

Reference 5

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source=pdf_text observed=2026-08-01T07:34:05.716081Z digest=sha256:84af93ae8067cfd47a8c7717f468732de3e6c3b48c84a114dd02ddf9c6dd4feb

Observation ad626098-c659-41cf-a07b-7ce662c24412 · outbound

This paper cites Digress: Discrete denoising diffusion for graph generation.

Context-weighted Discrete Flow Matching Digress: Discrete denoising diffusion for graph generation

Reference 6

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source=pdf_text observed=2026-08-01T07:34:05.863718Z digest=sha256:289d9fbe554c713d4419c3eda23a7b4b29b102dc0cb60c9167143cac1175e3fa

Observation 4ac8c2f5-8a58-4556-b733-b570dc9db32a · outbound

This paper cites Wyckoffdiff–a generative diffusion model for crystal symmetry.

Context-weighted Discrete Flow Matching Wyckoffdiff–a generative diffusion model for crystal symmetry

Reference 7

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source=pdf_text observed=2026-08-01T07:34:05.941090Z digest=sha256:ecd46f76fa07b82680d0a141dbf64eccbfbce55cc831de092005106a00a77542

Observation 1ed397d4-6172-4f6b-af01-302c34925896 · outbound

This paper cites Simple and effective masked diffusion language models.

Context-weighted Discrete Flow Matching Simple and effective masked diffusion language models

Reference 8

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source=pdf_text observed=2026-08-01T07:34:06.043368Z digest=sha256:f206447f4befa235817d8c51b7b178aa67aed1dada6080f5f0abfab70c0e2638

Observation ccf5143d-5833-428a-b655-4f6946f9e05a · outbound

This paper cites an unresolved cited work.

Context-weighted Discrete Flow Matching Unresolved cited work

Reference 9

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source=pdf_text observed=2026-08-01T07:34:06.111436Z digest=sha256:cc73f2a093286480d262631dcf52108be7558da1215fd4a1ee6fddcdab7f5012

Observation 8a605555-be10-4cf6-a219-fa15cae79dcb · outbound

This paper cites Kakade, and Sitan Chen.

Context-weighted Discrete Flow Matching Kakade, and Sitan Chen

Reference 10

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source=pdf_text observed=2026-08-01T07:34:06.233959Z digest=sha256:0e1bccf4318fac5400028bafa8c952555030b233eca5cba7744588d30db6fbb3

Observation 5adf0050-c66a-47db-a2b4-86ba5027b978 · outbound

This paper cites Halton scheduler for masked generative image transformer.

Context-weighted Discrete Flow Matching Halton scheduler for masked generative image transformer

Reference 11

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source=pdf_text observed=2026-08-01T07:34:06.292252Z digest=sha256:b23737534e3ed6abedc1db5569fbaf6f9eebfbb36ee112dedb4664ead0b12e56

Observation d2499422-02d1-44cb-9f5e-b2809416334e · outbound

This paper cites Set Block Decoding is a Language Model Inference Accelerator.

Context-weighted Discrete Flow Matching Set Block Decoding is a Language Model Inference Accelerator

Reference 12

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source=pdf_text observed=2026-08-01T07:34:06.349721Z digest=sha256:5d567683c35016beb3a0e5c61c16388ea09046768ab4c2e12ce1f777f76ed6e6

Observation de56c7d0-fcb6-4cc5-8cff-3bdf4536d9cd · outbound

This paper cites Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking.

Context-weighted Discrete Flow Matching Accelerated Sampling from Masked Diffusion Models via Entropy Bounded Unmasking

Reference 13

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source=pdf_text observed=2026-08-01T07:34:06.395458Z digest=sha256:a414578c3dcc1723484a24c5d6d1562be781f80411f45b9baea8f2440e0645b0

Observation 982af3d9-faac-4548-ac11-94a8507bc251 · outbound

This paper cites Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014.

Context-weighted Discrete Flow Matching Quantum chemistry structures and properties of 134 kilo molecules.Scientific data, 1(1):1–7, 2014

Reference 14

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source=pdf_text observed=2026-08-01T07:34:06.442021Z digest=sha256:4b61133d0b80d4a4a71153849d23b7806da0d8cee29327a84c8e9d175aa639ff

Observation 8536dacb-3e48-4d09-926a-793d44f82dbd · outbound

This paper cites Neighboring Autoregressive Modeling for Efficient Visual Generation.

Context-weighted Discrete Flow Matching Neighboring Autoregressive Modeling for Efficient Visual Generation

Reference 15

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source=pdf_text observed=2026-08-01T07:34:06.485748Z digest=sha256:7b9c8b12a84e6cd073377baa436a571768d38c661dfdd6be0ab397faebb6b28b

Observation 93c4ea35-bb67-44cd-a084-0de7b12734e0 · outbound

This paper cites Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference.

Context-weighted Discrete Flow Matching Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference

Reference 16

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source=pdf_text observed=2026-08-01T07:34:06.525371Z digest=sha256:3d597ef09c1464fa3256cbaf5854336821f084132ffe3371d6e595685b1d86a1

Observation d297ea58-3ea2-4090-a71c-37b096cb7523 · outbound

This paper cites Large language diffusion models.

Context-weighted Discrete Flow Matching Large language diffusion models

Reference 17

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source=pdf_text observed=2026-08-01T07:34:06.567490Z digest=sha256:5ddf98c4b4a36f1b50d65ed67c20d8ab68b1734fd583752cb1f46edd8c5b8ab6

Observation d1695ced-8d87-499b-b9fe-7b8aef7a784f · outbound

This paper cites LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models.

Context-weighted Discrete Flow Matching LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models

Reference 18

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source=pdf_text observed=2026-08-01T07:34:06.629959Z digest=sha256:80836385bc906d02aaa8adc9f5b0005d280c0a74ac1edbcd5186f00b9a0e2ae2

Observation 12042f15-0c8d-4fea-8550-dee32b7df065 · outbound

This paper cites LLaDA2.0: Scaling Up Diffusion Language Models to 100B.

Context-weighted Discrete Flow Matching LLaDA2.0: Scaling Up Diffusion Language Models to 100B

Reference 19

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source=pdf_text observed=2026-08-01T07:34:06.672977Z digest=sha256:393603d3c840a4d2bd1be42d9bdd71ee878c8b41800d5610323ad8d99a8a43fe

Observation fc879568-5d8e-4f74-ab9f-54fc9b0c2787 · outbound

This paper cites Llada2.1: Speeding up text diffusion via token editing, 2026.

Context-weighted Discrete Flow Matching Llada2.1: Speeding up text diffusion via token editing, 2026

Reference 20

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source=pdf_text observed=2026-08-01T07:34:06.723657Z digest=sha256:76f755022b7ce7511090124310a6637c1855193b16070e2487cc68a0bd2362ce

Observation 0216581a-e70e-4b8c-8a70-798cc48985a0 · outbound

This paper cites Autoregressive image generation with randomized parallel decoding.arXiv preprint arXiv:2503.10568, 2025.

Context-weighted Discrete Flow Matching Autoregressive image generation with randomized parallel decoding.arXiv preprint arXiv:2503.10568, 2025

Reference 21

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source=pdf_text observed=2026-08-01T07:34:06.757280Z digest=sha256:f0e9aa8e3f00346479330ac0f8abfec3b1a402593ec4f394fcf389e722b5da32

Observation f42da7b6-39a0-4acd-a74c-55103c058c21 · outbound

This paper cites A reparameterized discrete diffusion model for text generation.

Context-weighted Discrete Flow Matching A reparameterized discrete diffusion model for text generation

Reference 22

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source=pdf_text observed=2026-08-01T07:34:06.830645Z digest=sha256:26f9266bbc3bd1a39c6876dcf798a9eca4f9aba8897aac8f7d186a7d7fbcd783

Observation 1ce0084b-4b2c-4394-85dc-9194ae78a09a · outbound

This paper cites Flow matching with general discrete paths: A kinetic-optimal perspective.

Context-weighted Discrete Flow Matching Flow matching with general discrete paths: A kinetic-optimal perspective

Reference 23

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source=pdf_text observed=2026-08-01T07:34:06.875669Z digest=sha256:68b95e3e38a50b3d00a8d5b5ccc82f70fad246cbb15629a67fb2011e3cae49b3

Observation ac871d22-5d90-4cbe-8605-2430a15ab2cb · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Context-weighted Discrete Flow Matching Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 24

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source=pdf_text observed=2026-08-01T07:34:06.915662Z digest=sha256:d6dd1dc8ded802c79acff75297780c6403456a21f752818bfb53c13444f4f7d0

Observation 75f8c5b5-1234-40d3-8ac6-5436027268d5 · outbound

This paper cites Mauve: Measuring the gap between neural text and human text using divergence frontiers.Advances in Neural Information Processing Systems, 34:4816–4828, 2021.

Context-weighted Discrete Flow Matching Mauve: Measuring the gap between neural text and human text using divergence frontiers.Advances in Neural Information Processing Systems, 34:4816–4828, 2021

Reference 25

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source=pdf_text observed=2026-08-01T07:34:06.966199Z digest=sha256:5e9ace541d6cfc1d6ab060983e7e31aaa182d07bf869983c1769a01bc6acfa5f

Observation c00ad4e7-865c-4729-9999-50f3a6dae0a8 · outbound

This paper cites Decoupled weight decay regularization.

Context-weighted Discrete Flow Matching Decoupled weight decay regularization

Reference 26

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source=pdf_text observed=2026-08-01T07:34:07.006312Z digest=sha256:aafcb8b810bdf6a6e44c893f3704fd8261362d332e5d2b79d7de235ce927f532

Observation 23da8976-dd8f-4230-90ca-ae300e750bd5 · outbound

This paper cites Discrete diffusion modeling by estimating the ratios of the data distribution.

Context-weighted Discrete Flow Matching Discrete diffusion modeling by estimating the ratios of the data distribution

Reference 27

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source=pdf_text observed=2026-08-01T07:34:07.047727Z digest=sha256:c946147a5019efefeaae7b19839e5366e3c4c1e9dc794c2c8161c536a7be9746

Observation 49e718ef-c207-4f56-90d3-f4afb2fa9993 · outbound

This paper cites Pointer sentinel mixture models, 2016.

Context-weighted Discrete Flow Matching Pointer sentinel mixture models, 2016

Reference 28

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source=pdf_text observed=2026-08-01T07:34:07.167900Z digest=sha256:51a6f275b4e5fa423fd7c505edae6d110e664f5f889a50cceb4dbed97f589a64

Observation d37dc7de-4f82-430e-a38c-6a9dff25e132 · outbound

This paper cites an unresolved cited work.

Context-weighted Discrete Flow Matching Unresolved cited work

Reference 29

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source=pdf_text observed=2026-08-01T07:34:07.127870Z digest=sha256:5d4f3a53f9f7e383980be68ded729183ac858d97dc3f31bb067499fb2882132c

Observation 954eb64c-5619-464d-a668-a3540db392e7 · outbound

This paper cites Fineweb-edu: the finest collection of educational content, 2024.

Context-weighted Discrete Flow Matching Fineweb-edu: the finest collection of educational content, 2024

Reference 30

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source=pdf_text observed=2026-08-01T07:34:07.294941Z digest=sha256:6e14b42f8a1b17e2774f1e62e04a233773816ba54e61fcf44b658114e6a0a611

Observation e6f7cac6-9850-43a5-8e26-3bcd28357eae · outbound

This paper cites The lambada dataset: Word prediction requiring a broad discourse context.

Context-weighted Discrete Flow Matching The lambada dataset: Word prediction requiring a broad discourse context

Reference 31

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source=pdf_text observed=2026-08-01T07:34:07.225108Z digest=sha256:7d39228776bb19fd053fafac0495e0d68892ffd385775f2812869baec488f299

Observation add619c8-b71e-40d5-9ae0-ff1f6990bd9b · outbound

This paper cites The corresponding coordinate-wise velocity is then given by ui t(xi, z|x 0, x1) = ˙κt 1−κ t δ(xi, xi 1)−δ(x i, z).

Context-weighted Discrete Flow Matching The corresponding coordinate-wise velocity is then given by ui t(xi, z|x 0, x1) = ˙κt 1−κ t δ(xi, xi 1)−δ(x i, z)

Reference 33

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source=pdf_text observed=2026-08-01T07:34:07.400152Z digest=sha256:2b11e1e4112227d19b5ff8b07310d53acbcbc2ed19ff7a34b0e7f2d7c042b752

Observation 827e3c0e-d6e9-4725-94b4-9003646fd3ba · outbound

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Context-weighted Discrete Flow Matching Unresolved cited work

Reference 34

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source=pdf_text observed=2026-08-01T07:34:07.520765Z digest=sha256:b4da78535edb3d33b0468df820a2d66ff6a77419b5f678f8e2072484dba22279

Observation ecc385b1-fa48-4c4b-9994-66d648411134 · outbound

This paper cites an unresolved cited work.

Context-weighted Discrete Flow Matching Unresolved cited work

Reference 35

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Observation 47bae0c0-b79c-45bd-8989-163ae281bcea · outbound

This paper cites Thus the normalization condition preserves the distribution of the number of unmasked tokens.

Context-weighted Discrete Flow Matching Thus the normalization condition preserves the distribution of the number of unmasked tokens

Reference 36

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source=pdf_text observed=2026-08-01T07:34:07.768666Z digest=sha256:f4f93a97762e86dd7303f2db2294748d9c643aede2d92601a232352b20f64623

Observation 5ee54558-c0a4-433a-a7bd-6fa3c38f8eee · outbound

This paper cites Flow Matching Guide and Code.

Context-weighted Discrete Flow Matching Flow Matching Guide and Code

Reference 2024

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source=pdf_text observed=2026-08-01T07:34:06.157980Z digest=sha256:ed4918e1fb838eea248dd1c71f32b2f6316ac45fd373256deaf391058ab311c1

Pith citing papers

No inbound Pith citation observations are available.