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Paper Citation Record · LEDGER

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation

As of 6 August 2026, this Paper Citation Record lists 100 of 212 outbound references and 0 inbound Pith citation observations for arXiv:2607.13431.

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

pith.paper-citation-record.v1
2607.13431 v1

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measured 100 of 212 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-02T05:17:37.697168Z

measured 100 of 100 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

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100 of 212 outbound references displayed

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Outbound references

Observation c0851ba2-ccb4-4cbb-a955-609241c9dbd6 · outbound

This paper cites Structuring The Future: Diffusion LLM Speculative Decoding via Calibrated Draft Graphs.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Structuring The Future: Diffusion LLM Speculative Decoding via Calibrated Draft Graphs

Reference 1

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Observation f4e80de6-ee40-44c5-9195-99d936edacb3 · outbound

This paper cites Multi- conditioned graph diffusion for neural architecture search.Trans.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Multi- conditioned graph diffusion for neural architecture search.Trans

Reference 3

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Observation 8eeceaa1-36d7-42df-a73f-5c7ea191d469 · outbound

This paper cites Forget BIT, It is All about TOKEN: Towards Semantic Information Theory for LLMs.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Forget BIT, It is All about TOKEN: Towards Semantic Information Theory for LLMs

Reference 6

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Observation f613642c-7893-4fe7-8bb2-c20ac51afccb · outbound

This paper cites Enabling approximate joint sampling in diffusion lms.ArXiv preprint, abs/2509.22738,.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Enabling approximate joint sampling in diffusion lms.ArXiv preprint, abs/2509.22738,

Reference 7

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Observation e9c2f428-83aa-4079-aad2-e54579273865 · outbound

This paper cites Learning to parallel: Accelerating diffusion large language models via learnable parallel decoding, 2025a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Learning to parallel: Accelerating diffusion large language models via learnable parallel decoding, 2025a

Reference 8

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Observation 87b8f282-77c9-4994-96e0-125971b78c9e · outbound

This paper cites Simple and critical iterative denoising: A recasting of discrete diffusion in graph generatio.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Simple and critical iterative denoising: A recasting of discrete diffusion in graph generatio

Reference 10

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Observation c3158f0f-0c35-4f03-ae9a-2f25705d5fd9 · outbound

This paper cites Discrete Graph Auto-Encoder.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Discrete Graph Auto-Encoder

Reference 11

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Observation ccfca371-8fc9-4db1-9f9d-142d93ca243a · outbound

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Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Unresolved cited work

Reference 12

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Observation 175d1b65-03ed-4463-93d3-7b9e7dbeef98 · outbound

This paper cites ScanDL: A diffusion model for generating synthetic scanpaths on texts.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation ScanDL: A diffusion model for generating synthetic scanpaths on texts

Reference 13

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Observation d6ebe78e-3873-43f5-95be-0f1b5bffcdd1 · outbound

This paper cites Breckon, and Chris G.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Breckon, and Chris G

Reference 14

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Observation d745f52d-d994-4407-9582-7a90bec8eb76 · outbound

This paper cites Preference-Based Alignment of Discrete Diffusion Models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Preference-Based Alignment of Discrete Diffusion Models

Reference 15

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Observation d9dcf831-99c4-4920-98c0-848dcc667a5e · outbound

This paper cites Jaakkola.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Jaakkola

Reference 17

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Observation 36be6434-6669-466b-97d7-5d8e08aff6cc · outbound

This paper cites Self-speculative masked diffusions.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Self-speculative masked diffusions

Reference 18

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Observation 86b3b660-1029-4079-a5d6-6d85579940ec · outbound

This paper cites Generating directed graphs with dual attention and asymmetric encoding.ArXiv preprint, abs/2506.16404,.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Generating directed graphs with dual attention and asymmetric encoding.ArXiv preprint, abs/2506.16404,

Reference 19

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Observation cb31b453-c571-4beb-9006-d9bb0edaaf14 · outbound

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Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Unresolved cited work

Reference 20

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Observation b3098fca-ba68-46c6-b122-f19895d22ea0 · outbound

This paper cites Freeman, Michael Rubinstein, Yuanzhen Li, and Dilip Krishnan.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Freeman, Michael Rubinstein, Yuanzhen Li, and Dilip Krishnan

Reference 21

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Observation 8b1992ae-e30c-4829-8aea-447823f19226 · outbound

This paper cites Dtom: Decider-guided dynamic token merging for accelerating diffusion mllms.ArXiv preprint, abs/2511.12280,.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Dtom: Decider-guided dynamic token merging for accelerating diffusion mllms.ArXiv preprint, abs/2511.12280,

Reference 22

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Observation 4fcb1aad-3ba3-4e1a-b729-ab52ea5257cd · outbound

This paper cites Krishnan.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Krishnan

Reference 23

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Observation fb38532a-da63-4fc2-ad1b-f42eb85a0cc6 · outbound

This paper cites Aligning visual foundation encoders to tokenizers for diffusion models.ArXiv preprint, abs/2509.25162, 2025a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Aligning visual foundation encoders to tokenizers for diffusion models.ArXiv preprint, abs/2509.25162, 2025a

Reference 24

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Observation 3b267c34-a996-4352-9a20-ef6505b2b8bd · outbound

This paper cites Controllable conversation generation with conversation structures via diffusion models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Controllable conversation generation with conversation structures via diffusion models

Reference 26

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Observation 0fbc067a-6486-4465-b68c-cd4fee2c9eef · outbound

This paper cites A cheaper and better diffusion language model with soft-masked noise.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation A cheaper and better diffusion language model with soft-masked noise

Reference 27

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Observation 9a60d4dd-9534-44c6-88cf-ce74d5914457 · outbound

This paper cites Sdar-vl: Stable and efficient block-wise diffusion for vision-language understanding.ArXiv preprint, abs/2512.14068, 2025a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Sdar-vl: Stable and efficient block-wise diffusion for vision-language understanding.ArXiv preprint, abs/2512.14068, 2025a

Reference 29

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Observation 93758f68-8410-421b-9f9d-ec87a2962ab7 · outbound

This paper cites M2d2m: Multi-motion generation from text with discrete diffusion models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation M2d2m: Multi-motion generation from text with discrete diffusion models

Reference 30

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Observation 11be11f9-fe27-4c66-b52b-2c5ef596b46a · outbound

This paper cites Speculative diffusion decoding: Accelerating language generation through diffusion.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Speculative diffusion decoding: Accelerating language generation through diffusion

Reference 31

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Observation 90c15a08-5bab-4da6-bd31-0cf1811d7d7c · outbound

This paper cites Split gibbs discrete diffusion posterior sampling.ArXiv preprint, abs/2503.01161,.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Split gibbs discrete diffusion posterior sampling.ArXiv preprint, abs/2503.01161,

Reference 32

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Observation 2ac9e466-3242-4d6b-be4d-4012a0de0cc0 · outbound

This paper cites Gaus, Toby P.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Gaus, Toby P

Reference 33

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Observation c0c61636-e4ab-4a06-808e-921822e59ef5 · outbound

This paper cites ViLaD: A Large Vision Language Diffusion Framework for End-to-End Autonomous Driving.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation ViLaD: A Large Vision Language Diffusion Framework for End-to-End Autonomous Driving

Reference 34

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Observation 326619fb-c028-4682-a809-e8ab2bd20bf2 · outbound

This paper cites DiffER: Categorical Diffusion for Chemical Retrosynthesis.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation DiffER: Categorical Diffusion for Chemical Retrosynthesis

Reference 35

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Observation b8512c24-68a8-40c5-8839-41cd5e83899e · outbound

This paper cites Periodic materials generation using text-guided joint diffusion model.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Periodic materials generation using text-guided joint diffusion model

Reference 36

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Observation a681e60a-1003-49c1-8bd5-7de7113930c4 · outbound

This paper cites Discrete diffusion language model for efficient text summarization.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Discrete diffusion language model for efficient text summarization

Reference 37

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Observation eaf47460-f954-494c-aa60-ae0c8f640f3c · outbound

This paper cites Alexandre Défossez, Jade Copet, Gabriel Synnaeve, and Yossi Adi.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Alexandre Défossez, Jade Copet, Gabriel Synnaeve, and Yossi Adi

Reference 38

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Observation e249cd17-2620-430a-8dd8-bf41af7edce0 · outbound

This paper cites Uniform discrete diffusion with metric path for video generation.ArXiv preprint, abs/2510.24717,.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Uniform discrete diffusion with metric path for video generation.ArXiv preprint, abs/2510.24717,

Reference 39

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Observation c6fdf842-01c9-4952-943a-1437bc829bf0 · outbound

This paper cites Llada-medv: Exploring large language diffusion models for biomedical image understanding.ArXiv preprint, abs/2508.01617, 2025a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Llada-medv: Exploring large language diffusion models for biomedical image understanding.ArXiv preprint, abs/2508.01617, 2025a

Reference 40

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Observation 16301bc2-bbac-4515-8b49-9f9b80a6f5c8 · outbound

This paper cites Planning with diffusion models for target-oriented dialogue systems.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Planning with diffusion models for target-oriented dialogue systems

Reference 41

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Observation e9a438b3-224e-4dac-965f-31d8fe43669b · outbound

This paper cites Vector quantized diffusion model based speech bandwidth extension.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Vector quantized diffusion model based speech bandwidth extension

Reference 42

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Observation 65adc0e2-ffa0-43b1-a350-b52440e666f1 · outbound

This paper cites Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Bigger Isn't Always Memorizing: Early Stopping Overparameterized Diffusion Models

Reference 43

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Observation 275c27ae-7aac-4d04-b39d-b0f93b2c97fd · outbound

This paper cites Masked diffusion captioning for visual feature learning.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Masked diffusion captioning for visual feature learning

Reference 44

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Observation eb3fc785-a25f-4e4a-a91c-83c0cedd376f · outbound

This paper cites Learnable sampler distillation for discrete diffusion models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Learnable sampler distillation for discrete diffusion models

Reference 45

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source=pdf_text observed=2026-08-02T05:17:30.119369Z digest=sha256:7f7d0e8b48c74cb44c1fc6d0dc0a143a558bbf0a29336c977ccca9bc9d6734ae

Observation cdd5ea2c-9233-4ae9-997d-703bb19f12be · outbound

This paper cites DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints

Reference 46

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source=pdf_text observed=2026-08-02T05:17:30.319128Z digest=sha256:c03851d26635fa3ce03ac124b231e71d017010da3cca51ad58ec101806ed4bbe

Observation 4755cf5a-b8df-48d5-bc3c-1ef78a1ab9b8 · outbound

This paper cites FoldToken: Learning Protein Language via Vector Quantization and Beyond.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation FoldToken: Learning Protein Language via Vector Quantization and Beyond

Reference 47

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source=pdf_text observed=2026-08-02T05:17:30.392773Z digest=sha256:5f86db87b66448afbcc63f8fe9a6ea49aa596d893aedc1ce67aaf5406391eddc

Observation 1cd5413d-6d7b-42a5-a741-ba4b75bffc43 · outbound

This paper cites an unresolved cited work.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Unresolved cited work

Reference 48

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Observation 1edec23e-f446-4d4d-81be-3fae8cc3a777 · outbound

This paper cites Mask-predict: Parallel decoding of conditional masked language models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Mask-predict: Parallel decoding of conditional masked language models

Reference 49

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source=pdf_text observed=2026-08-02T05:17:30.514006Z digest=sha256:de3c47b8bb3cd87e498d5ae878e8a33c3067ea89ae3ea941d43c860e2caa1e7e

Observation fe508446-3fe9-4735-bfc9-14e1eddad06e · outbound

This paper cites an unresolved cited work.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Unresolved cited work

Reference 50

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source=pdf_text observed=2026-08-02T05:17:30.572570Z digest=sha256:6f38db13730f9bd1927d3f5716dbb467f70e0dfc8d1731b14a2da0018137e874

Observation c00fa05f-b078-4b39-8d9c-d1f7e0be03be · outbound

This paper cites Text-guidedmoleculegenerationwithdiffusionlanguage model.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Text-guidedmoleculegenerationwithdiffusionlanguage model

Reference 51

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source=pdf_text observed=2026-08-02T05:17:30.645276Z digest=sha256:94e7da90ead366c4c77dcbeb43e3ae6ea13b7418d1f612932b3e882366f554f5

Observation adb2c22b-069d-410f-81a3-0e1ded022e21 · outbound

This paper cites DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation DiffuCoder: Understanding and Improving Masked Diffusion Models for Code Generation

Reference 52

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source=pdf_text observed=2026-08-02T05:17:30.794886Z digest=sha256:3ba2128a9035181eed256c26dbc3b9547f910f27e7bcf1b80e1f59ea85ea9541

Observation 51f28e20-606a-4a89-b5eb-07646c20718b · outbound

This paper cites Frey, Tim G.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Frey, Tim G

Reference 53

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source=pdf_text observed=2026-08-02T05:17:30.955613Z digest=sha256:d91e18406742e7276b4e769cea35ec35b3d74e32ddace927e2638d080152e0cb

Observation 61e4ed69-321d-436b-ad36-8d822fd51f8d · outbound

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

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Vector quantized diffusion model for text-to-image synthesis

Reference 54

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source=pdf_text observed=2026-08-02T05:17:30.992600Z digest=sha256:8ac13fcf104b01a428fe074661eda1b27d65a9f8c52b389541fb26920a1f75f9

Observation d34ccce4-8127-4066-9df5-f82ff3f0a781 · outbound

This paper cites Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, and Stefano Ermon.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Powers, Weili Nie, Tomas Geffner, Karsten Kreis, Jure Leskovec, Arash Vahdat, and Stefano Ermon

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source=pdf_text observed=2026-08-02T05:17:31.033664Z digest=sha256:b531343c71b4c8a37b27325abf835513ff7013a0ca611736a8aa932e4a4434be

Observation 44e29ee7-e190-4370-b6cf-8d361f0cb055 · outbound

This paper cites Hashimoto.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Hashimoto

Reference 56

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source=pdf_text observed=2026-08-02T05:17:31.185077Z digest=sha256:21b12c1dc5ab8f67205d04428ffa17529c175082be3a0cd0d1dd1d6c4a3da6b4

Observation dca79464-4a53-482a-92cb-18d9c66dea75 · outbound

This paper cites Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding

Reference 57

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source=pdf_text observed=2026-08-02T05:17:31.309322Z digest=sha256:56442ac3687d499033aab26a0eebba8e7e0c500aa46a648553f205c48a881f07

Observation b0b8b430-6035-40ea-ae03-36d4dafbc1c4 · outbound

This paper cites Training-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Training-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Models

Reference 58

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source=pdf_text observed=2026-08-02T05:17:31.413207Z digest=sha256:aa32ef94c0d1bc0a612488e49211d6b6b9d78d568e29b7bf3af235cdac4bc771

Observation dd12318d-f770-4515-a490-95ecb1ebee97 · outbound

This paper cites Reward-weighted sampling: Enhancing non-autoregressive characteristics in masked diffusion llms.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Reward-weighted sampling: Enhancing non-autoregressive characteristics in masked diffusion llms

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source=pdf_text observed=2026-08-02T05:17:31.476330Z digest=sha256:eea1a409ad78573043c4bfc7564509e46335b7419c1cf24213e5cc7591f08623

Observation c4107daf-f858-4406-b072-2b081ff2b3e9 · outbound

This paper cites Diffusion Models for Graphs Benefit From Discrete State Spaces.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Diffusion Models for Graphs Benefit From Discrete State Spaces

Reference 60

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source=pdf_text observed=2026-08-02T05:17:31.527969Z digest=sha256:b7aae5483b487c08092b269fd20592e98c89923e3b8d499d254b7e321cb91793

Observation 2f7e9333-41b9-47e7-ac25-46db179b0508 · outbound

This paper cites Discrete diffusion trajectory alignment via stepwise decomposition.ArXiv preprint, abs/2507.04832, 2025a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Discrete diffusion trajectory alignment via stepwise decomposition.ArXiv preprint, abs/2507.04832, 2025a

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source=pdf_text observed=2026-08-02T05:17:31.619395Z digest=sha256:d85247579c6827d57bde30542cb5c2fd13303e66044d2dff9a94c16e091769ea

Observation dd84ffa1-1739-4a6e-bf15-12b026128db5 · outbound

This paper cites Discovering mathemat- ical equations with diffusion language model.ArXiv preprint, abs/2509.13136, 2025c.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Discovering mathemat- ical equations with diffusion language model.ArXiv preprint, abs/2509.13136, 2025c

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source=pdf_text observed=2026-08-02T05:17:31.740525Z digest=sha256:9d0ea2e9aa8b8101272105b6f706ae8f2b6b7faf0a4c7c979e8fd40f5fa73c44

Observation 1c0a6f7e-da2e-43e3-a819-d1e469396ae1 · outbound

This paper cites Efficient perplexity bound and ratio matching in discrete diffusion language models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Efficient perplexity bound and ratio matching in discrete diffusion language models

Reference 63

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source=pdf_text observed=2026-08-02T05:17:31.903972Z digest=sha256:a9daaecf56d225ef634d61b54638b607cf318e5e1f537cb3f08980b3e4f8d8ca

Observation 9791d7d0-0fe2-4cd9-8b74-847560e3546d · outbound

This paper cites Distillation of discrete diffusion through dimensional correlations.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Distillation of discrete diffusion through dimensional correlations

Reference 64

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source=pdf_text observed=2026-08-02T05:17:31.984545Z digest=sha256:9ba9ccafda6875364112e3d75b485b3185defa3fd2260f8cc5ca229bd21653b8

Observation f032fa3a-b237-41e7-afa4-727a8990fb14 · outbound

This paper cites Ultrallada: Scaling the context length to 128k for diffusion large language models.ArXiv preprint, abs/2510.10481, 2025a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Ultrallada: Scaling the context length to 128k for diffusion large language models.ArXiv preprint, abs/2510.10481, 2025a

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source=pdf_text observed=2026-08-02T05:17:32.053256Z digest=sha256:5ee5a2b1b25305052da44a54753d81f6b907ac21b59efad2a09e33c9a9ffb7e2

Observation 1a18c404-a64c-4c47-aefa-deff5ee1ae08 · outbound

This paper cites What Exactly Does Guidance Do in Masked Discrete Diffusion Models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation What Exactly Does Guidance Do in Masked Discrete Diffusion Models

Reference 66

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source=pdf_text observed=2026-08-02T05:17:32.147264Z digest=sha256:09266f2cd89086beeeaa6cfca157c21eae3391d06c49f9c5c70f8d99595d05ac

Observation 6dee0289-2409-4978-991c-1ebdac0547e2 · outbound

This paper cites Shao, Chaofan Gan, Shijie Li, Zuxuan Wu, and Weiyao Lin.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Shao, Chaofan Gan, Shijie Li, Zuxuan Wu, and Weiyao Lin

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source=pdf_text observed=2026-08-02T05:17:32.223467Z digest=sha256:f98eab32ae1987703ab8a3cd14f987ea350319bafe418bc5fc2f3c439f8844c2

Observation 3977ba91-dc7d-4e6c-96f9-703f04d46f9b · outbound

This paper cites Unsu- pervised training of diffusion models for feasible solution generation in neural combinatorial optimization, 2024a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Unsu- pervised training of diffusion models for feasible solution generation in neural combinatorial optimization, 2024a

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source=pdf_text observed=2026-08-02T05:17:32.335001Z digest=sha256:3109c215e3d37c21a070c79146de306aba82f2e3f7833f08ff01a5dae97c4d0f

Observation 0d6277e3-ec59-4d2c-9e3d-96c8f3832f46 · outbound

This paper cites Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans

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source=pdf_text observed=2026-08-02T05:17:32.337517Z digest=sha256:667394f0a7c310e8acc40d937999cdbafa45073c3b478b514236e3a940bae0ef

Observation 54801633-1881-4a7f-9773-b194df6c0d39 · outbound

This paper cites Bad: Bidirectional auto-regressive diffusion for text-to-motion generation.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Bad: Bidirectional auto-regressive diffusion for text-to-motion generation

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source=pdf_text observed=2026-08-02T05:17:32.417635Z digest=sha256:387badc68f726b2e2f1aa02c11aff7c90f83a0e62d1964979c39607a18377ccf

Observation 3d500c39-58b1-462d-b037-0abe148ab9e8 · outbound

This paper cites Suganthan.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Suganthan

Reference 72

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source=pdf_text observed=2026-08-02T05:17:32.566758Z digest=sha256:2b155d1fea89b9842137bf967f0061ff727b3ddef8ca66f09139a5a11f9989ff

Observation 58ab6e8a-5c87-4a4a-af61-12325b36ef57 · outbound

This paper cites Suganthan.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Suganthan

Reference 73

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source=pdf_text observed=2026-08-02T05:17:32.703434Z digest=sha256:65f6ea4a37ea206e91fde1f1acaef85d35599ff47064cf4b6518b1f953665c57

Observation b4080fd9-9a41-4d7a-8518-a083b1514b5d · outbound

This paper cites Mixed Diffusion for 3D Indoor Scene Synthesis.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Mixed Diffusion for 3D Indoor Scene Synthesis

Reference 74

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source=pdf_text observed=2026-08-02T05:17:32.790034Z digest=sha256:84bcd475955a87aef3aa54e5d76aa750989ce2da0540e159afaf0761dedf64a5

Observation fe4732c1-14e2-49d9-9c74-9c9e3356ee51 · outbound

This paper cites Abdelfattah, Jae sun Seo, Zhiru Zhang, and Udit Gupta.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Abdelfattah, Jae sun Seo, Zhiru Zhang, and Udit Gupta

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source=pdf_text observed=2026-08-02T05:17:32.987822Z digest=sha256:8f9035b86edc69b39fa3e64e6a0cb4f84a88bac98084fe957b04642665d9788e

Observation 36f7b735-a471-4ebe-bc6b-b120983e1f66 · outbound

This paper cites Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Tuning the Implicit Regularizer of Masked Diffusion Language Models: Enhancing Generalization via Insights from $k$-Parity

Reference 76

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source=pdf_text observed=2026-08-02T05:17:33.121605Z digest=sha256:c9eaa19fba188cd4dc5c96ab15e872dfb24b5dcdd1eb1eb4fe45375f7e6897c0

Observation e8b5d56e-5972-413b-a5b5-935dd9522279 · outbound

This paper cites an unresolved cited work.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Unresolved cited work

Reference 77

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source=pdf_text observed=2026-08-02T05:17:33.291621Z digest=sha256:0703c9186d637def69bcfddbaf3ec34629025d11c79bdacff881c2f598ea209d

Observation 7047bd65-9896-4799-93ed-754cd26dbf24 · outbound

This paper cites DiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation DiffGED: Computing Graph Edit Distance via Diffusion-based Graph Matching

Reference 78

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source=pdf_text observed=2026-08-02T05:17:33.452765Z digest=sha256:400419630c4a6ebc793cf31fdd1cb1c8cf931be5d38c258bdb3fc6ae28e41ab4

Observation 3dada1d2-545f-4eaa-8ce5-cdef7e3a6169 · outbound

This paper cites Reinforcing the diffusion chain of lateral thought with diffusion language models.ArXiv preprint, abs/2505.10446, 2025b.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Reinforcing the diffusion chain of lateral thought with diffusion language models.ArXiv preprint, abs/2505.10446, 2025b

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source=pdf_text observed=2026-08-02T05:17:33.603400Z digest=sha256:24b7429e9a6b6143c6367e2bd1673f26ef113853478e3a5513e6b4caec813929

Observation 413a5a95-60a1-45ff-b844-989515dcdedf · outbound

This paper cites Layoutdm: Discrete diffusion model for controllable layout generation.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Layoutdm: Discrete diffusion model for controllable layout generation

Reference 80

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source=pdf_text observed=2026-08-02T05:17:33.737724Z digest=sha256:ccdc47a1cb68e9fbe1e90a9729f5ff942eda0020e915be60547c0f1f93fdb871

Observation da49e11a-7c3a-4105-89f9-571a4fcb3665 · outbound

This paper cites Cheng, Guy Van den Broeck, Aditya Grover, Suvinay Subramanian, and Michael Carbin.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Cheng, Guy Van den Broeck, Aditya Grover, Suvinay Subramanian, and Michael Carbin

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source=pdf_text observed=2026-08-02T05:17:33.899597Z digest=sha256:ef1dc204a9e93f52858f1f222e9d007750192a0a972985182f2e00ff5266a126

Observation f191be1c-7bfc-44bf-a76a-ea44a8da6451 · outbound

This paper cites Layout-corrector: Alleviating layout sticking phenomenon in discrete diffusion model.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Layout-corrector: Alleviating layout sticking phenomenon in discrete diffusion model

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source=pdf_text observed=2026-08-02T05:17:34.071137Z digest=sha256:a0f330103999a07af4a74fcfd862f79d442c035d72242c0c356647bccf882db5

Observation 8f622027-1e51-43c6-90fc-d4f4ba497d6d · outbound

This paper cites Learning Unmasking Policies for Diffusion Language Models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Learning Unmasking Policies for Diffusion Language Models

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source=pdf_text observed=2026-08-02T05:17:34.231807Z digest=sha256:04a6f7ea116784202ac787bdf64edf63c57524bf073fab23ed55c04f05628061

Observation e9f04c9e-fc28-44e6-ac9d-974b8658c91d · outbound

This paper cites A2d: Any-order, any-step safety alignment for diffusion language models.ArXiv preprint, abs/2509.23286,.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation A2d: Any-order, any-step safety alignment for diffusion language models.ArXiv preprint, abs/2509.23286,

Reference 84

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source=pdf_text observed=2026-08-02T05:17:34.424703Z digest=sha256:15a994e987409f2ffed23ad1443c8790ba64ea3d4e2ecbab0582756652bfc601

Observation 4ed0f45c-2051-416d-8d38-521fcb97569f · outbound

This paper cites From denoising to refining: A corrective framework for vision-language diffusion model.ArXiv preprint, abs/2510.19871,.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation From denoising to refining: A corrective framework for vision-language diffusion model.ArXiv preprint, abs/2510.19871,

Reference 85

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source=pdf_text observed=2026-08-02T05:17:34.568617Z digest=sha256:557cd4762b8299e6bfe4c6892a85e1c57ded970809bbc3a4a4d83732e9fe2915

Observation fefb9a68-4acd-45a4-b24c-9767a6ca6e2f · outbound

This paper cites Bringing Stability to Diffusion: Decomposing and Reducing Variance of Training Masked Diffusion Models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Bringing Stability to Diffusion: Decomposing and Reducing Variance of Training Masked Diffusion Models

Reference 86

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source=pdf_text observed=2026-08-02T05:17:34.633934Z digest=sha256:ce04a8da743c771108f15334772becddf9a0e7cd1ea31e308f1935dc9de20c89

Observation 7faa59ca-2500-4b85-b922-4d7257105a20 · outbound

This paper cites Diffusion language models are provably optimal parallel samplers.ArXiv preprint, abs/2512.25014, 2025a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Diffusion language models are provably optimal parallel samplers.ArXiv preprint, abs/2512.25014, 2025a

Reference 87

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source=pdf_text observed=2026-08-02T05:17:34.696736Z digest=sha256:ccd7df50df7325297ccbc803aabf6a317c7c77d8a716d43ba5c3d417c31e4607

Observation 93123214-8b33-4119-9a52-898107a127ed · outbound

This paper cites Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall

Reference 88

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source=pdf_text observed=2026-08-02T05:17:34.759121Z digest=sha256:c172f891e79af6bc3328abe192c32c1d2ca60efca267e27832f73e0eed6fc372

Observation 154ee8ab-a2b8-46c8-898a-c7dfcebf3179 · outbound

This paper cites ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs

Reference 89

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source=pdf_text observed=2026-08-02T05:17:34.851055Z digest=sha256:41a18ed941d11879400703196f213c1ea8d4a8afab3a6e8af6fee206f963d9a9

Observation 0a0073fb-6228-486e-9331-4da1c3af14d2 · outbound

This paper cites Discriminator guidance for autoregressive diffusion models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Discriminator guidance for autoregressive diffusion models

Reference 90

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source=pdf_text observed=2026-08-02T05:17:35.060025Z digest=sha256:1e1f12d59492f80d4ea8ee8c73bbe76113fd0a4ed4a6c306d8c2f47e26dcc969

Observation 252ee7aa-104a-456c-9149-b02516be042b · outbound

This paper cites Andersson, Abhijith S.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Andersson, Abhijith S

Reference 91

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source=pdf_text observed=2026-08-02T05:17:35.205271Z digest=sha256:d32b3780246912c830acf77187fd0dbede6a5685795e59a103ece0af55427199

Observation 0215874d-dff8-469a-b948-618b95dbbba3 · outbound

This paper cites Training-Free Guidance for Discrete Diffusion Models for Molecular Generation.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Training-Free Guidance for Discrete Diffusion Models for Molecular Generation

Reference 92

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source=pdf_text observed=2026-08-02T05:17:35.360077Z digest=sha256:e6e1be9e902cb1387a9ade9c7fc125e2734d3d87aabff30d6f9bb80d2e3cac3f

Observation 8a646e21-2273-4970-a29f-13db29b93f22 · outbound

This paper cites Mercury: Ultra-Fast Language Models Based on Diffusion.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Mercury: Ultra-Fast Language Models Based on Diffusion

Reference 93

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source=pdf_text observed=2026-08-02T05:17:35.518221Z digest=sha256:160494fe21dea05cf86eadb230ccc5e1fbffd89487cd21f59ace1079533b190b

Observation 25b2acd8-f78c-4f8c-9efb-42b5f4fa7116 · outbound

This paper cites Fréchet Audio Distance: A Reference-Free Metric for Evaluating Music Enhancement Algorithms.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Fréchet Audio Distance: A Reference-Free Metric for Evaluating Music Enhancement Algorithms

Reference 94

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source=pdf_text observed=2026-08-02T05:17:35.670451Z digest=sha256:8a1725ecf9cd47ee17ab05ec507e6a7d9f5ba7d32e2f0c2026a95604d0903dcb

Observation 407d6d58-a3fc-4888-adaa-2f862b4868e3 · outbound

This paper cites Rainbow padding: Mitigating early termination in instruction-tuned diffusion llms.ArXiv preprint, abs/2510.03680, 2025a.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Rainbow padding: Mitigating early termination in instruction-tuned diffusion llms.ArXiv preprint, abs/2510.03680, 2025a

Reference 95

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source=pdf_text observed=2026-08-02T05:17:35.844263Z digest=sha256:7d360e187e31bd2031162066de3e9d1b832b3e6a0c5ccdc6d2049b1bf591b844

Observation 9a59f7b7-ffc9-4f3e-a52d-16f0278ffc08 · outbound

This paper cites an unresolved cited work.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Unresolved cited work

Reference 96

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source=pdf_text observed=2026-08-02T05:17:35.980632Z digest=sha256:ea9f5f904cbbfcd795715b9a3d930a80e6d8a56e45c82ae938e350fde163c531

Observation a7942f14-9df7-4ad9-954a-d3e882cc9cea · outbound

This paper cites Conditional [mask] discrete diffusionlanguagemodel.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Conditional [mask] discrete diffusionlanguagemodel

Reference 97

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source=pdf_text observed=2026-08-02T05:17:36.129090Z digest=sha256:65eac8a543ae132a27dc841cdcba79278b167b3a92d8c0ac160f508d7df1cf03

Observation 58721067-5fd4-42a4-bd3f-34e21cfafc5f · outbound

This paper cites Victoria, and Guorui Zhou.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Victoria, and Guorui Zhou

Reference 98

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source=pdf_text observed=2026-08-02T05:17:36.296704Z digest=sha256:3b33acdbf4d67feeba7e562c7bc129e2f79f7fdac67020e4669d4841ba602aa8

Observation cf47a84f-b6d8-43df-87b9-fc2d7b5a459a · outbound

This paper cites Aditya Prakash, and Chao Zhang.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Aditya Prakash, and Chao Zhang

Reference 99

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source=pdf_text observed=2026-08-02T05:17:36.425541Z digest=sha256:9fa01c0b24f7af5120e8e874a7641789b7ba4ac7badf647e40579aa807ee7a19

Observation 85daa80d-a464-4209-a85e-b68a55331050 · outbound

This paper cites Fairwire: Fair graph generation.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Fairwire: Fair graph generation

Reference 100

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source=pdf_text observed=2026-08-02T05:17:36.621406Z digest=sha256:0923e431e1eff374ba238a0b61fa6c3f1244d39bb7d7ab133687b524545e6749

Observation f56ee9da-5872-4c73-abe4-f1eb068252a5 · outbound

This paper cites Tabddpm: Modelling tabular data with diffusion models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Tabddpm: Modelling tabular data with diffusion models

Reference 101

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source=pdf_text observed=2026-08-02T05:17:36.750230Z digest=sha256:2fe5ed22cd6418366b9040fbbc843253dec8ee02ec6efe6a6744c888b9e911ef

Observation 066f80c3-31c5-405d-882a-6dd010226c33 · outbound

This paper cites Feedback guidance of diffusion models.ArXiv preprint, abs/2506.06085,.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Feedback guidance of diffusion models.ArXiv preprint, abs/2506.06085,

Reference 102

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source=pdf_text observed=2026-08-02T05:17:36.883140Z digest=sha256:9738a0d26643a3120e00cbb388dfa5ea7a4adabb75520df16732b18bf0f9adcb

Observation aeee64a9-8342-4fc7-bfca-7bdd77685fa7 · outbound

This paper cites Categorical Schr\"odinger Bridge Matching.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Categorical Schr\"odinger Bridge Matching

Reference 103

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source=pdf_text observed=2026-08-02T05:17:37.019763Z digest=sha256:6bcb4e73059c9c3b7dc9196b85c4e007c20426805c2d0d5f379764bb82a24963

Observation 589c4360-b8ca-4b1a-b40d-0c90ceef907c · outbound

This paper cites Lad: Lora-adapted diffusion.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Lad: Lora-adapted diffusion

Reference 104

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source=pdf_text observed=2026-08-02T05:17:37.152519Z digest=sha256:77c91523b9a6d5b0c5e1e4f283773afefb787c9ec79118be1fcdf0262063bc60

Observation 560d5cee-3394-4688-a127-c3c9311cbbef · outbound

This paper cites EdiText: Controllable coarse-to-fine text editing with diffusion language models.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation EdiText: Controllable coarse-to-fine text editing with diffusion language models

Reference 105

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source=pdf_text observed=2026-08-02T05:17:37.280041Z digest=sha256:04793ee5e5a0d285c8d045dfdb145dce58109d2cc553b7374b8f34dd3e89286b

Observation f06f7996-5b9f-4cb8-b0a1-d45285778812 · outbound

This paper cites Fragfm: Efficient fragment-based molecular generation via discrete flow matching.ArXiv preprint, abs/2502.15805, 2025b.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Fragfm: Efficient fragment-based molecular generation via discrete flow matching.ArXiv preprint, abs/2502.15805, 2025b

Reference 106

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source=pdf_text observed=2026-08-02T05:17:37.395813Z digest=sha256:559dde136fb1162254dc5440c64197e9d4cd3906b886c1f44b27671d0b311aa4

Observation 3dfcfebd-b66c-4856-a707-b40c4d3f47c8 · outbound

This paper cites DLT: conditioned layout generation with joint discrete-continuous diffusion layout transformer.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation DLT: conditioned layout generation with joint discrete-continuous diffusion layout transformer

Reference 107

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source=pdf_text observed=2026-08-02T05:17:37.561961Z digest=sha256:7db85d640e6e1f618ffcbf17bb93b22e82b63016f930be1d079d340a27ac7680

Observation 6b68693e-3e63-44ec-8795-dc49056e74ab · outbound

This paper cites Improved masked image generation with token-critic.

Discrete Diffusion Models: A Unified Framework from Tokenization to Generation Improved masked image generation with token-critic

Reference 108

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source=pdf_text observed=2026-08-02T05:17:37.697168Z digest=sha256:0d64aff8bab9286eafd84b4fc49611db6f83c488df4391b7dd62d1394f0df020

Pith citing papers

No inbound Pith citation observations are available.