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Paper Citation Record · LEDGER

Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 56 inbound Pith citation observations for arXiv:2208.04202.

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

pith.paper-citation-record.v1
2208.04202 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 56 of 56 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 56 of 56 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:04:05.989800Z

measured 1 of 1 external citation measurements

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

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

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

81
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4d9efefd-c65a-4b78-936b-063e063e018d · inbound

DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models cites this paper.

DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 1

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arxiv_id, observed 2026-05-20T06:50:38.286324Z

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.

source=pdf_text observed=2026-05-20T06:50:38.258692Z digest=sha256:23be01a9a2339510657097c9788467188cb374651551649457a96a362c19b8ef

Observation 275bf520-d88b-4b78-97d2-be77dc0e2150 · inbound

Continuous diffusion for categorical data cites this paper.

Continuous diffusion for categorical data Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 11

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arxiv_id, observed 2026-05-18T03:30:22.141666Z

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

source=arxiv_source observed=2026-05-18T03:30:22.025578Z digest=sha256:9144a316aaa3275cfe048ecf6d1502a0fb20202e18f9df8f432c4fc8d47c6f24

Observation e05b2d76-9360-4da1-9578-745506e24951 · inbound

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution cites this paper.

Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 75

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arxiv_id, observed 2026-05-16T08:12:31.388876Z

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

source=arxiv_source observed=2026-05-16T08:12:30.984870Z digest=sha256:404f8bab3ca4f0d0c71780aeabf1f578a519be04fd4ee0db00bce47f20dd91a4

Observation 4cc56589-a6fe-4994-867c-175570242cef · inbound

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution cites this paper.

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 1

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arxiv_id, observed 2026-05-13T02:59:23.870157Z

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.

source=pdf_text observed=2026-05-13T02:59:23.837269Z digest=sha256:f93438d591e211976201e8ce0aaa58a1e25f8dabf4f27d15ac119d8ffe8b44ff

Observation c1ee6285-147a-4f49-84da-611dec9aaf1c · inbound

HouseTune: Two-Stage Floorplan Generation with LLM Assistance cites this paper.

HouseTune: Two-Stage Floorplan Generation with LLM Assistance Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 9

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T17:51:01.432420Z digest=sha256:3ac51c24dfd99bd4456e2de32bda40afdee3de0899adfc0db5778fdce4526bfe

Observation ccef0c1a-a0f9-49c8-ab34-d512a345abe4 · inbound

DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model cites this paper.

DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 7

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source=pdf_text observed=2026-08-12T12:24:55.219635Z digest=sha256:3026a8148cfd15b07384bbef7a47ded3cd95872c592554fdb8e84527b7e8ca2d

Observation 2bb28a40-ae9a-4eb5-93b1-5c7126b9263f · inbound

Panoptic Diffusion Models: co-generation of images and segmentation maps cites this paper.

Panoptic Diffusion Models: co-generation of images and segmentation maps Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 10

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source=arxiv_source observed=2026-08-11T23:02:16.120211Z digest=sha256:aba11af2fe8ae240468746cda05983c3b309a6e113d61cdc6a830a7f111b215e

Observation 4d548e13-3cf5-4307-aa7d-a1ed5689ff5b · inbound

Factorized Video Autoencoders for Efficient Generative Modelling cites this paper.

Factorized Video Autoencoders for Efficient Generative Modelling Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 8

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source=pdf_text observed=2026-08-11T21:30:03.640968Z digest=sha256:367783aa93314f7299f8243ffcdb0b0d370245058a7f8c4b0d9123a5a7e58fe9

Observation 01636d89-6222-4275-844b-968f3e177d34 · inbound

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks cites this paper.

GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 63

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source=pdf_text observed=2026-08-11T18:06:11.483467Z digest=sha256:78b4dd4365a2d22e3d1dfde559d9d16e00f18aa23d69b9ac2c220f65ae0c8dd5

Observation 2f56a1d2-0998-4c99-9fb4-a4bf73db0424 · inbound

Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution cites this paper.

Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 13

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source=pdf_text observed=2026-08-11T11:38:24.400914Z digest=sha256:b4157d75b547c28da1ef6ed596c037e4a131467226553464f5bd2592dd8e5246

Observation 7203fae7-e095-4923-9c80-a057ab80e1cd · inbound

Dual Diffusion for Unified Image Generation and Understanding cites this paper.

Dual Diffusion for Unified Image Generation and Understanding Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 12

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source=pdf_text observed=2026-08-10T23:02:07.591748Z digest=sha256:e8100802e91f56047bb8555d0124a2e872de1b65a671d028a9987e6a42478a3d

Observation 0706661f-e94e-4081-b000-0bc171da4569 · inbound

Large Language Models to Diffusion Finetuning cites this paper.

Large Language Models to Diffusion Finetuning Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 2018

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:01:55.464466Z digest=sha256:72251af6bd4ce6e41d8848c7161de08b18c6da83ca7b8993fbed311e88934640

Observation 1fde2634-03fa-48db-a9c3-a763dbb9345b · inbound

Efficient Diffusion Models: A Survey cites this paper.

Efficient Diffusion Models: A Survey Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 2018

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no resolver link, observed 2026-08-09T16:13:35.767609Z

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source=pdf_text observed=2026-08-09T16:13:35.767609Z digest=sha256:86ee3f3297309f9c826b35810e983130fcee75ffddda4202df29eb0842f47fc8

Observation 626451f0-b75b-447e-ab29-2be2722aec21 · inbound

Theoretical Benefit and Limitation of Diffusion Language Model cites this paper.

Theoretical Benefit and Limitation of Diffusion Language Model Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 11

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source=arxiv_source observed=2026-08-07T20:56:22.739266Z digest=sha256:ba18d1a8ca567acb55b8a6d0b2d8121c463d404b4e188d17ff67c55b7f04c2ba

Observation 0f74e20c-a10a-4793-86b0-2e8a4632ff79 · inbound

Large Language Diffusion Models cites this paper.

Large Language Diffusion Models Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 46

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arxiv_id, observed 2026-05-11T01:42:55.193501Z

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.

source=pdf_text observed=2026-05-11T01:42:54.279353Z digest=sha256:6a558fc22c31d21a679e162ebb19715e072e5b5c57d95806c282a4a87e80be7c

Observation 1234a14f-1c07-489c-9fe8-d74f0af1f105 · inbound

Learning Genomic Structure from $k$-mers cites this paper.

Learning Genomic Structure from $k$-mers Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 19

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no resolver link, observed 2026-08-07T15:00:49.575504Z

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source=pdf_text observed=2026-08-07T15:00:49.575504Z digest=sha256:42426a27fa144e4a5f62b43f4dfb9883cc7b8ed4ffb47bc498e0baf9d89d0437

Observation 826ada18-bbca-486e-abb0-90a06845a8e1 · inbound

LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning cites this paper.

LLaDA-V: Large Language Diffusion Models with Visual Instruction Tuning Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 89

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arxiv_id, observed 2026-05-17T03:46:06.312461Z

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.

source=pdf_text observed=2026-05-17T03:46:06.074416Z digest=sha256:d7072ec9cd89b233a3107d40cdf35872ac8cb9dd47d8063724c6c9c25d6f61f0

Observation 08f27d32-77cd-48c5-a5f4-41694f585fd7 · inbound

Applications of Modular Co-Design for De Novo 3D Molecule Generation cites this paper.

Applications of Modular Co-Design for De Novo 3D Molecule Generation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 11

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source=arxiv_source observed=2026-08-07T14:36:00.148687Z digest=sha256:6f8940e8b7403d24d9dc881dbc611ad2a66eab628fcefea1180775f150cfcfe5

Observation f67d8e02-5f2f-4421-b3d8-f1da4c42f35f · inbound

A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly cites this paper.

A Graph Completion Method that Jointly Predicts Geometry and Topology Enables Effective Molecule Assembly Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 3

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source=arxiv_source observed=2026-08-07T13:26:22.495580Z digest=sha256:52c55e8d67300c231e83f2a1e5ad3e4a8c2745565c25f949b3c80c8ff8bc5961

Observation 894480e6-58d3-4343-abf7-f22f00fc0640 · inbound

TrajFlow: Multi-modal Motion Prediction via Flow Matching cites this paper.

TrajFlow: Multi-modal Motion Prediction via Flow Matching Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 47

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:14:34.769719Z digest=sha256:d84c34edfbe47e4ea04d84b0e14e0b935c7f8c68088a628acfaec192bfea9c9d

Observation 71dbcc7d-98ba-4a84-ab08-013cdf62394e · inbound

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective cites this paper.

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 5

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source=pdf_text observed=2026-08-06T23:48:39.299295Z digest=sha256:a1c120936bbfc5a5492f723716077f3f681277dab1e6737beb949937cef7ac04

Observation 9a498f54-4f5d-4d30-8cd3-f0fc88d4f0a8 · inbound

Exact Conditional Score-Guided Generative Modeling for Amortized Inference in Uncertainty Quantification cites this paper.

Exact Conditional Score-Guided Generative Modeling for Amortized Inference in Uncertainty Quantification Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 6

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source=pdf_text observed=2026-08-15T19:04:05.989800Z digest=sha256:18c616121d646626cf7e169621b4f7e1f33e5a56bfc337a5d3c6a5f61a93646d

Observation f5bb0fb7-9040-432b-a5e2-5a0633b079f1 · inbound

A Survey on Latent Reasoning cites this paper.

A Survey on Latent Reasoning Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 15

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source=arxiv_source observed=2026-08-06T19:14:23.025747Z digest=sha256:f54dd3a2dd28572a6bf85f1a77d9bd5952f56b08e85a9bc8bd1b00ffbf5cefc8

Observation b0fa07ab-34ea-4f8c-9c14-c19f0cbf81bc · inbound

The Philosophy and Physics of Duality cites this paper.

The Philosophy and Physics of Duality Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 6

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:32:07.423687Z digest=sha256:982f1173e4e72ed9f1acedfa6b4c44cf5e84fea422ac309d9000903846d1fcd7

Observation 6490236e-944b-464b-91a5-183df7e29e0f · inbound

FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation cites this paper.

FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 55

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source=arxiv_source observed=2026-08-15T17:25:08.397904Z digest=sha256:ce3a874726006f5a6e59b63a899e3cef39814334b00737252a541588466422bb

Observation 0a2f1641-0347-45f8-ad61-af1924a68ba4 · inbound

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion cites this paper.

SBGD: Improving Graph Diffusion Generative Model via Stochastic Block Diffusion Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 6

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source=arxiv_source observed=2026-08-05T18:43:42.404439Z digest=sha256:8c686ad021dd074b3338d73568ba4cbf20f6ea32180f5eedc313b404897a38b1

Observation 317525b0-4abe-417c-b9e9-60624d32d1cd · inbound

Multi-domain Distribution Learning for De Novo Drug Design cites this paper.

Multi-domain Distribution Learning for De Novo Drug Design Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 2023

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source=pdf_text observed=2026-08-15T17:05:01.331351Z digest=sha256:6d55b9e9b713dcef31164f02b95241e65eebbbc1734c4cf9a656b2af3ccf13b8

Observation d000b2f0-3e47-4c7b-bc31-a64b339ac0cb · inbound

LLaDA-VLA: Vision Language Diffusion Action Models cites this paper.

LLaDA-VLA: Vision Language Diffusion Action Models Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 8

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source=pdf_text observed=2026-08-04T22:55:29.831085Z digest=sha256:d2f2b31807766e06413c62b29675254e985c50b72d017e586c12a70e43e95997

Observation ff3fae01-91d5-48b5-bb51-d7b75d33a7d5 · inbound

Inpainting-Guided Policy Optimization for Diffusion Large Language Models cites this paper.

Inpainting-Guided Policy Optimization for Diffusion Large Language Models Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 11

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source=arxiv_source observed=2026-08-04T17:57:46.949498Z digest=sha256:4231fcf3b9de76cb6716268fc1d9aa39fa7a7e078a8eceb8d50396c24ba40911

Observation 29a287a3-f63b-4ccc-a1ca-5a5551d04db2 · inbound

CANDI: Hybrid Discrete-Continuous Diffusion Models cites this paper.

CANDI: Hybrid Discrete-Continuous Diffusion Models Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 7

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source=arxiv_source observed=2026-08-04T08:09:17.423679Z digest=sha256:444852de732402023a59d1e40a38270f8867d4af5e2db187f221eaed69ad1b3c

Observation 87231de0-7fae-44ac-a936-de7cb778ff45 · inbound

End-to-End Training for Autoregressive Video Diffusion via Self-Resampling cites this paper.

End-to-End Training for Autoregressive Video Diffusion via Self-Resampling Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 13

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source=pdf_text observed=2026-08-03T15:46:06.476387Z digest=sha256:e3b74f2d4e1f30efd3e28c1021e1799e57f2564a34a09f94915e0f08f9f788cd

Observation 56a78ade-ea5e-4f3d-910c-f63fc6d310b2 · inbound

Protein Autoregressive Modeling via Multiscale Structure Generation cites this paper.

Protein Autoregressive Modeling via Multiscale Structure Generation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 9

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arxiv_id, observed 2026-05-21T13:24:11.284361Z

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.

source=pdf_text observed=2026-05-21T13:21:53.668727Z digest=sha256:5f702a3b790f96b8291dee99d0940883299df4266807acdf09cc3164dc77084c

Observation f6b759b9-c42e-433e-9c00-acdf3e212cee · inbound

Flow Map Language Models: One-step Language Modeling via Continuous Denoising cites this paper.

Flow Map Language Models: One-step Language Modeling via Continuous Denoising Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 38

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arxiv_id, observed 2026-05-15T21:10:19.210172Z

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

source=pdf_text observed=2026-05-15T21:01:42.916835Z digest=sha256:e6342deb829ec32c99fd9fbc3edae663efbf02a43cae6f867d840d25dfbc6298

Observation 10ed26c8-0c81-4146-aa79-1a981a207ce6 · inbound

Flow Map Language Models: One-step Language Modeling via Continuous Denoising cites this paper.

Flow Map Language Models: One-step Language Modeling via Continuous Denoising Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 38

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arxiv_id, observed 2026-05-21T12:24:10.592170Z

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

source=pdf_text observed=2026-05-21T12:23:49.031978Z digest=sha256:046c8cadd54df09141fb80b1fe9579b76e322953303f83feb9bec714dbb01b19

Observation 8447635e-cde6-4c44-baf8-70e5f7a3f626 · inbound

Gumbel Distillation for Parallel Text Generation cites this paper.

Gumbel Distillation for Parallel Text Generation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 10

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source=arxiv_source observed=2026-08-02T17:49:49.320322Z digest=sha256:d24b0764a59e46ba263282e28fd7c1ff0992a164a4f4ecc1e6f5ae79f02db93f

Observation df0c4b38-2ced-4a63-b0a6-3dfa393a130f · inbound

LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling cites this paper.

LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 4

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verified exact
arxiv_id, observed 2026-05-11T08:50:59.518122Z

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.

source=pdf_text observed=2026-05-10T16:26:53.241071Z digest=sha256:9a62701248499875898b5843ae1adbb2a5682f4ab78bde22195034da63889fc1

Observation 0366da38-5726-4ea7-b703-b8adcf55016e · inbound

Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training cites this paper.

Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 201

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:46:03.894954Z

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.

source=arxiv_source observed=2026-05-10T03:04:54.146481Z digest=sha256:0176a6f159b85cfc919b5cd0bd16a9f243c3f4989eb3213af8abfb20e85168aa

Observation b894e5b5-e5e6-4279-a827-5ce0bc51ffc2 · inbound

$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction cites this paper.

$R^2$-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reduction Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:46:25.492440Z

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.

source=pdf_text observed=2026-05-10T02:56:32.367404Z digest=sha256:439bda81e3ffb209df436d3c9bc616db79f72cfae8e1d5b1f5c109f27704b76d

Observation d7f9282e-d4d0-40ce-8b0e-45634b4f3935 · inbound

Simple Self-Conditioning Adaptation for Masked Diffusion Models cites this paper.

Simple Self-Conditioning Adaptation for Masked Diffusion Models Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:36:33.506227Z

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.

source=pdf_text observed=2026-05-07T16:38:22.306140Z digest=sha256:1d68ead4ac048e74684681609382c2e2a9c05449e65ac2c0f06eb39e0d8a2cdc

Observation d169ac96-4801-4859-b905-17da94772aa2 · inbound

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

Coupling Models for One-Step Discrete Generation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-05-11T03:00:55.037893Z

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.

source=arxiv_source observed=2026-05-11T02:58:10.909499Z digest=sha256:1a4d848be03a11b2bec5b96b9464fed15450809e1372eef249d3a61ef2d5ec4f

Observation a6f7751b-7a81-46ff-8f8e-b794203483a4 · inbound

BitLM: Unlocking Multi-Token Language Generation with Bitwise Continuous Diffusion cites this paper.

BitLM: Unlocking Multi-Token Language Generation with Bitwise Continuous Diffusion Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:57:05.326607Z

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.

source=pdf_text observed=2026-05-13T01:54:23.273159Z digest=sha256:57aad533bfcab9bb884dced02ca5e86f4d2a8f804701f5b03f60be0166a6c8ec

Observation 52880562-8001-4b16-a0ac-55fe3b9a1aeb · inbound

Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention cites this paper.

Efficient Long-Context Modeling in Diffusion Language Models via Block Approximate Sparse Attention Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-20T05:53:04.641918Z

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.

source=pdf_text observed=2026-05-20T05:52:14.089334Z digest=sha256:008d440ec23d425e008fb5308ba35488c19a7b772620cd0f895e98fa3463a948

Observation ad738634-47ed-40c5-bb17-eaa30fb77b53 · inbound

BlockBatch: Multi-Scale Consensus Decoding for Efficient Diffusion Language Model Inference cites this paper.

BlockBatch: Multi-Scale Consensus Decoding for Efficient Diffusion Language Model Inference Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T08:43:15.918439Z

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.

source=pdf_text observed=2026-06-29T08:34:28.624577Z digest=sha256:6f0edaafa4e35866456abcf44690c974255c9279a7b6b8535a6577524f982a70

Observation 40db3e8f-c4e9-4176-b485-96109f14b143 · inbound

Variational Learning for Insertion-based Generation cites this paper.

Variational Learning for Insertion-based Generation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 43

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T22:26:17.757727Z

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.

source=arxiv_source observed=2026-06-28T15:23:32.821620Z digest=sha256:02181995870d20bf8bc42b607ee884796b275e007de7830c1af1f5f8f5cdc3e3

Observation 412f5a60-2fef-4489-9304-0db36894b833 · inbound

Property-Informed Diffusion-Based Text-to-Microstructure Generation cites this paper.

Property-Informed Diffusion-Based Text-to-Microstructure Generation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:17:25.133229Z

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.

source=pdf_text observed=2026-06-27T19:48:27.318590Z digest=sha256:27d6ff42025c64ae6d4028d8f0cebe11bc182d88855282eec9073e402ba2a30c

Observation 6204e9d3-684d-4c1c-b0f9-8b16a90f8abe · inbound

FlowBender: Feedback-Aware Training for Self-Correcting Conditional Flows cites this paper.

FlowBender: Feedback-Aware Training for Self-Correcting Conditional Flows Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:19:30.004069Z

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.

source=pdf_text observed=2026-06-26T18:17:25.479543Z digest=sha256:63029a888c0a89c627dba8d4467894f281b1d5409ebfe02cb4a997c005d31fcb

Observation 83133e6f-d6ef-4f69-87a4-222e6cc6bf80 · inbound

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation cites this paper.

Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:59:38.353919Z

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.

source=pdf_text observed=2026-06-26T14:56:52.316735Z digest=sha256:c11b6925d6210469c2f8332befcd6fcc441b8886225488b59afd4cdbdd6bfb82

Observation f7c5e9a6-9437-4450-a707-8c3d40b85988 · inbound

Modular Diffusion Models for Structured Visual Recognition cites this paper.

Modular Diffusion Models for Structured Visual Recognition Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:09:43.116042Z

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.

source=pdf_text observed=2026-06-26T10:29:04.711627Z digest=sha256:60f8f630e885fae7cf91e2d4be07bcdb0c57d8607072d692740de0d059a3cd46

Observation 0810526b-92e4-4f41-8266-134504019441 · inbound

Masked Language Flow Models cites this paper.

Masked Language Flow Models Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:06:02.664543Z

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.

source=arxiv_source observed=2026-06-29T01:17:56.122002Z digest=sha256:90239f5c9b28a60c7a53bc16a854deff34e3d859456e6dc7eabcd55542d0a755

Observation 50910bb2-1866-49a9-bc9c-98a5df05dc81 · inbound

Flow Reasoning Models: Scaling Reasoning Through Iterative Self-Refinement cites this paper.

Flow Reasoning Models: Scaling Reasoning Through Iterative Self-Refinement Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T08:04:28.463260Z

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.

source=pdf_text observed=2026-06-30T07:55:28.254309Z digest=sha256:dbf79f1c827f9687914879ca1eb14e0e38429ce0e80947f54b80da174c39d5be

Observation 29aedbae-512d-4782-8138-bdeb81e720ba · inbound

Low Perplexity is Repetition: A One-Dimensional Self-Conditioning Attractor in Continuous Diffusion LMs cites this paper.

Low Perplexity is Repetition: A One-Dimensional Self-Conditioning Attractor in Continuous Diffusion LMs Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-07-02T13:26:58.650265Z

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.

source=pdf_text observed=2026-07-02T13:17:01.144370Z digest=sha256:c43c39ceb90e160387e60262168b5fcae8cfae677e6e1be6dd9825619acdb8ca

Observation e5696531-b9ad-497e-82f8-c796d190024f · inbound

Self-conditioned Flow Map Language Models via Fixed-point Flows cites this paper.

Self-conditioned Flow Map Language Models via Fixed-point Flows Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-02T13:06:58.542798Z

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.

source=pdf_text observed=2026-07-02T13:01:21.252611Z digest=sha256:d441b3f2ccf56b1c97c69f0b3fe0b20956ea43c7b76122a2d18db5c64c9c8204

Observation 6ddd1c37-fdec-48d3-9f2b-59d7cf6cc01b · inbound

Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding cites this paper.

Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 48

Resolution
metadata mismatch
arxiv_id, observed 2026-07-03T17:38:43.585386Z

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.

source=arxiv_source observed=2026-07-03T17:30:39.458521Z digest=sha256:d13969c7d46c09b3849028a4b06d21f13b329d45804c642122d5aa2e3cd43bf2

Observation 300f6da9-df23-436e-b842-0e851817e251 · inbound

Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery cites this paper.

Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 5

Resolution
metadata mismatch
local_arxiv, observed 2026-07-09T22:56:37.659585Z

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.

source=pdf_text observed=2026-07-09T22:53:25.139723Z digest=sha256:279c93d2a9fe93555f84c3f177fe9aa028a05a727341269d3731a09e752210c5

Observation b5df5b26-3242-4dc1-b488-b105e9f40b5a · inbound

LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models cites this paper.

LLaDA MoE v2: Scaling Mixture-of-Experts Diffusion Language Models Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-15T14:54:53.036796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T14:54:53.036796Z digest=sha256:ada2b12bf9d9bd803a11d2fc58bd10bd91f5113b261716ba778bded501c111cc

Observation b4c5b4e5-c694-4aa0-9e09-508f322afe8f · inbound

Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution cites this paper.

Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-06T22:57:59.754749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T22:57:59.754749Z digest=sha256:ddda402d1eda38bb73b597295b8e0ab594e59b7903ca87e61fafa41fe6dce0c3