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Paper Citation Record · LEDGER

Generative Distribution Distillation

As of 9 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 2 inbound Pith citation observations for arXiv:2507.14503.

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

pith.paper-citation-record.v1
2507.14503 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:10:22.241118Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T13:10:37.285667Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T20:03:56.729210Z

Reference resolution

59 of 59 outbound references displayed

  • verified exact0
  • verified fuzzy23
  • unresolved35
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

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Outbound references

Observation 00289b22-9a01-4fce-9e55-b243a350392b · outbound

This paper cites write newline.

Generative Distribution Distillation write newline

Reference 1

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Observation e75a3e5a-5a75-4bf8-8afe-1fe0368fbfc5 · outbound

This paper cites GPT-4 Technical Report.

Generative Distribution Distillation GPT-4 Technical Report

Reference 2

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source=arxiv_source observed=2026-08-06T16:10:22.008628Z digest=sha256:2d5c7d92d81517ab52f8f302a4308994ebe4b774ee20d899979224afe5619db2

Observation 92232be3-ae4b-4912-9060-de10d2f25e78 · outbound

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

Generative Distribution Distillation Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

Reference 3

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Observation 6c15bad7-8834-4eba-a88b-9381ba73faee · outbound

This paper cites Language models are few-shot learners.

Generative Distribution Distillation Language models are few-shot learners

Reference 4

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source=arxiv_source observed=2026-08-06T16:10:22.017288Z digest=sha256:af633fd2b586525aa01f3fadefa52ee1bd5a5b86fde07b195da6aa65d1198f8d

Observation bea7b8f3-bfb6-4c2f-abdd-884232b29721 · outbound

This paper cites Learning imbalanced datasets with label-distribution-aware margin loss.

Generative Distribution Distillation Learning imbalanced datasets with label-distribution-aware margin loss

Reference 5

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source=arxiv_source observed=2026-08-06T16:10:22.021410Z digest=sha256:32f78f884d04f581e8e6bd07a6801356b1eb8d9d98b7b4749b4922cd4f187ae0

Observation c9a3e461-a7c8-4daf-89e3-101fc97fb431 · outbound

This paper cites Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts.

Generative Distribution Distillation Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts

Reference 6

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.025224Z digest=sha256:9a9312c0d8ed90855ee28d7aee31daf4326657b8eb21f71b342acab1b3b85eb0

Observation 38b89597-0092-4e95-9ad1-3aa43af21e20 · outbound

This paper cites Distilling knowledge via knowledge review.

Generative Distribution Distillation Distilling knowledge via knowledge review

Reference 7

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source=arxiv_source observed=2026-08-06T16:10:22.029135Z digest=sha256:5b2a0b064675799b129e3865677121739f58db12171b6a2d0cccf294111614ce

Observation 2c559846-f796-41e5-a87a-a98f8120ed8e · outbound

This paper cites On the efficacy of knowledge distillation.

Generative Distribution Distillation On the efficacy of knowledge distillation

Reference 8

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source=arxiv_source observed=2026-08-06T16:10:22.033377Z digest=sha256:f721737f32037a9485f4e96fe6a78b698498e697763aff1ccda07e4338a8afc9

Observation a3e3816c-b831-4764-a3fd-997e1cf27707 · outbound

This paper cites Parametric contrastive learning.

Generative Distribution Distillation Parametric contrastive learning

Reference 9

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source=arxiv_source observed=2026-08-06T16:10:22.037614Z digest=sha256:341c134e431609283c199e0952bb7dee1c153464ca9a0efc0365ff1e186e3a7f

Observation af72f74d-c777-4fef-84f7-313a4d224f59 · outbound

This paper cites Reslt: Residual learning for long-tailed recognition.

Generative Distribution Distillation Reslt: Residual learning for long-tailed recognition

Reference 10

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source=arxiv_source observed=2026-08-06T16:10:22.042361Z digest=sha256:a2f5184dbff5a197ed1a89bba4e9dabd097d1ade21990cabb0d48b8e4ad62302

Observation cb1e7e5c-3c98-4810-9a00-17c5e96b9d17 · outbound

This paper cites Generalized parametric contrastive learning.

Generative Distribution Distillation Generalized parametric contrastive learning

Reference 11

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source=arxiv_source observed=2026-08-06T16:10:22.046863Z digest=sha256:dcd388d2831e1a34f0c6031d2ab1367a2c0eea094ed85365042e2ef5123dfb68

Observation bbed58e5-729f-4d75-a157-d726204359a4 · outbound

This paper cites Decoupled kullback-leibler divergence loss.

Generative Distribution Distillation Decoupled kullback-leibler divergence loss

Reference 12

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source=arxiv_source observed=2026-08-06T16:10:22.050403Z digest=sha256:ea0a36e43bef7d6fa4946a7c2332f745652a553946d05fd806b649c2bfd7cebc

Observation 7068f846-0d6e-4587-8529-0f0a2b191f41 · outbound

This paper cites Classes are not equal: An empirical study on image recognition fairness.

Generative Distribution Distillation Classes are not equal: An empirical study on image recognition fairness

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.054032Z digest=sha256:3ea4908d1bcbf2fee27f3fe5dd168a4f9bb14feec60b76dda460afe4677cab43

Observation a3aa7c05-a9a2-46cd-980e-5f719d207598 · outbound

This paper cites Generalized Kullback-Leibler Divergence Loss.

Generative Distribution Distillation Generalized Kullback-Leibler Divergence Loss

Reference 14

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source=arxiv_source observed=2026-08-06T16:10:22.057881Z digest=sha256:f0eb5a0a3b4c464331edf9a3178e4d6cf8e5dc316fe9bbe4da886518cb1121b4

Observation c036e058-5be9-49ed-be49-4d3ff8d0a571 · outbound

This paper cites Class-balanced loss based on effective number of samples.

Generative Distribution Distillation Class-balanced loss based on effective number of samples

Reference 15

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source=arxiv_source observed=2026-08-06T16:10:22.061740Z digest=sha256:99053758f653d82e924841dc199f7b15de2300dc06b0ac60483aa94734e1c87f

Observation e2c9e902-8dd8-4fa0-9d59-7507c1334178 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Generative Distribution Distillation Imagenet: A large-scale hierarchical image database

Reference 16

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source=arxiv_source observed=2026-08-06T16:10:22.065193Z digest=sha256:6b92138fc71cfedd6ced6728758cc4c8b01e9d977ee01cff9fd05edeefff6c96

Observation 3e050806-5d68-47c6-a051-591c3ae8a5e5 · outbound

This paper cites Unified Autoregressive Visual Generation and Understanding with Continuous Tokens.

Generative Distribution Distillation Unified Autoregressive Visual Generation and Understanding with Continuous Tokens

Reference 17

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source=arxiv_source observed=2026-08-06T16:10:22.068784Z digest=sha256:099d82f6bdaaf2f255c4c5b0075ebb5cb8bbe964f888fe7305a730e3629921b4

Observation 7ce6a014-960d-44e6-8e67-699c1f7bd170 · outbound

This paper cites Born again neural networks.

Generative Distribution Distillation Born again neural networks

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.072865Z digest=sha256:74a53565f345352053c8ec184ca830e7f8287766c7342e4e30e2e5c941b0c69a

Observation 1a0263c8-1df2-4af6-8423-cc57ee6ad496 · outbound

This paper cites Discrete flow matching.

Generative Distribution Distillation Discrete flow matching

Reference 19

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source=arxiv_source observed=2026-08-06T16:10:22.077223Z digest=sha256:a7570d48dbc1fd42093c1dd82cadffd452f1bf7e6c642c98dc880e95cb52d6a3

Observation 006f0252-7cd0-4ee8-8d52-78adc277af6e · outbound

This paper cites Mean Flows for One-step Generative Modeling.

Generative Distribution Distillation Mean Flows for One-step Generative Modeling

Reference 20

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Observation 88a44338-3450-4ef8-ae08-fdebcd383331 · outbound

This paper cites VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale.

Generative Distribution Distillation VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale

Reference 21

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Observation 19a85b92-d408-4e48-8e06-8e1cc22b1b26 · outbound

This paper cites A comprehensive overhaul of feature distillation.

Generative Distribution Distillation A comprehensive overhaul of feature distillation

Reference 22

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.090370Z digest=sha256:160f642413ee5c35946205890bbd52c19b1258be0ce402f3ca2100f472a693ab

Observation 8188e776-6479-4440-b267-84713b8ea9d3 · outbound

This paper cites Distilling the knowledge in a neural network.

Generative Distribution Distillation Distilling the knowledge in a neural network

Reference 23

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source=arxiv_source observed=2026-08-06T16:10:22.094240Z digest=sha256:848485ec15e1c217042eae4f017fbdb0a547f0b3425ce326d27b8a09ff189fc0

Observation fd7a3f07-4624-483c-b074-1f4fa2968962 · outbound

This paper cites Denoising diffusion probabilistic models.

Generative Distribution Distillation Denoising diffusion probabilistic models

Reference 24

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source=arxiv_source observed=2026-08-06T16:10:22.097806Z digest=sha256:8f6e88dbd0b34997734f603aea8c4363286c7fbd28cdf0c19e19dd15f8fc446d

Observation ec8ea7dd-be06-4e09-85f6-b5763bd811ba · outbound

This paper cites Knowledge distillation from a stronger teacher.

Generative Distribution Distillation Knowledge distillation from a stronger teacher

Reference 25

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.101195Z digest=sha256:6c9fd728ea4a903065643a954cb34ec715e8c45e5c2005dfdf8eb3e5fcf6e039

Observation a9a4a2a2-52d5-48d4-98e3-bf6f768257e8 · outbound

This paper cites Knowledge diffusion for distillation.

Generative Distribution Distillation Knowledge diffusion for distillation

Reference 26

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source=arxiv_source observed=2026-08-06T16:10:22.104694Z digest=sha256:68bbcd22226114cbf51bff8ef68bdbf5ea8b2a663b9e37c7798de3ecfa602c02

Observation 0ec7153e-f242-4d93-9ef7-0c14dc41a800 · outbound

This paper cites Decoupling Representation and Classifier for Long-Tailed Recognition.

Generative Distribution Distillation Decoupling Representation and Classifier for Long-Tailed Recognition

Reference 27

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source=arxiv_source observed=2026-08-06T16:10:22.108808Z digest=sha256:ecf4367c3ec90c27b49601d807d9d84d4f8fcb91174502e50077c66c8ae5b73c

Observation 1b041e38-2dcc-45e6-869c-9681aea10e5a · outbound

This paper cites Auto-encoding variational bayes, 2013.

Generative Distribution Distillation Auto-encoding variational bayes, 2013

Reference 28

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source=arxiv_source observed=2026-08-06T16:10:22.113263Z digest=sha256:8f9c30083f344fa50f9c7be64a26731c00a699b8adfafe54019df863c6c5d507

Observation 214f586e-9b62-407b-a563-84a44a445fdb · outbound

This paper cites Learning multiple layers of features from tiny images.

Generative Distribution Distillation Learning multiple layers of features from tiny images

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9fa548b9-554e-4523-9622-1f5ab9e43d61 · outbound

This paper cites Learning multiple layers of features from tiny images.

Generative Distribution Distillation Learning multiple layers of features from tiny images

Reference 30

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source=arxiv_source observed=2026-08-06T16:10:22.122073Z digest=sha256:5b910764b88061f0e7eb28cbbd2087d9592431f6422dbb897bc4b78b8847da5f

Observation 7f5dce79-1515-414e-8f72-053a2cfb79bb · outbound

This paper cites Autoregressive image generation without vector quantization.

Generative Distribution Distillation Autoregressive image generation without vector quantization

Reference 31

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raw_fallback, observed 2026-08-06T16:10:22.715411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.126506Z digest=sha256:1c1f6969cf7cb4978c39aac29838c9b88896874b73e836fe03157bdce89759ba

Observation 1c8be4b6-91e9-4ad8-a6ce-16fcf63514da · outbound

This paper cites Flow Matching for Generative Modeling.

Generative Distribution Distillation Flow Matching for Generative Modeling

Reference 32

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.130843Z digest=sha256:5e8af906ad41d79a2d76299a4d525b627bccae9f3e1ddfba8adc491e855fe663

Observation f6a1a124-cc38-44e4-b5ef-24bde7a1e720 · outbound

This paper cites Visual instruction tuning.

Generative Distribution Distillation Visual instruction tuning

Reference 33

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source=arxiv_source observed=2026-08-06T16:10:22.135732Z digest=sha256:51fbdc1bfe85ecda0fd0dab241d17c965e329bdd48fc947e23af75aaab84feaa

Observation f241c782-b1e0-425b-bf30-ad6221574c1d · outbound

This paper cites Large-scale long-tailed recognition in an open world.

Generative Distribution Distillation Large-scale long-tailed recognition in an open world

Reference 34

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.140331Z digest=sha256:a834a22ab169921a3f5ab5df4dccf459355c856232a3670afda03fda8cf0ae2f

Observation b416ad32-b37e-4f61-9367-693f00d2a378 · outbound

This paper cites Wasserstein distance rivals kullback-leibler divergence for knowledge distillation.

Generative Distribution Distillation Wasserstein distance rivals kullback-leibler divergence for knowledge distillation

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.144802Z digest=sha256:11ce67e5bf7378864995ed5f83e2a6d7e4b900d8dffd9ea6a7fc38cdea747490

Observation 793a43ec-dedc-4571-a3ef-88b76e5c22c8 · outbound

This paper cites Long-tail learning via logit adjustment.

Generative Distribution Distillation Long-tail learning via logit adjustment

Reference 36

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source=arxiv_source observed=2026-08-06T16:10:22.148947Z digest=sha256:02127777302051e88cd9023c923e81b05d983512d8074a915dac694c79c8e92b

Observation 8edff2ae-a6be-4d4b-a334-11eec92b15c6 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Generative Distribution Distillation Improved denoising diffusion probabilistic models

Reference 37

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source=arxiv_source observed=2026-08-06T16:10:22.153518Z digest=sha256:4627f8bdaa6e34fa1fc9c1c71b2aa965f1a64e6ff5c694ebf44050eb11238fc1

Observation 11687084-e6de-4d79-90c5-a0d3e5ac1e2d · outbound

This paper cites Training language models to follow instructions with human feedback.

Generative Distribution Distillation Training language models to follow instructions with human feedback

Reference 38

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source=arxiv_source observed=2026-08-06T16:10:22.157695Z digest=sha256:94b2442ae5041e107c7453f8b65e4fd9a013205e74b85056cfb2f29c5c9d3dc8

Observation dbabca6d-069b-4a48-8611-ec504db73496 · outbound

This paper cites Relational knowledge distillation.

Generative Distribution Distillation Relational knowledge distillation

Reference 39

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raw_fallback, observed 2026-08-06T16:10:22.652884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.161480Z digest=sha256:13c0e1e5893244b532801a587b2e59d14a85c63ec90d51489ac88fbad59c8b76

Observation c6efe5a0-ae41-44b0-a7d5-3ccb31c3d847 · outbound

This paper cites Improving language understanding by generative pre-training.

Generative Distribution Distillation Improving language understanding by generative pre-training

Reference 40

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source=arxiv_source observed=2026-08-06T16:10:22.165154Z digest=sha256:8faef3e8b04a943d2f720d4b7b5f053ed32f7b41a432a20288beb25df8af4aad

Observation 002596e8-6f15-40dd-a7c5-8e65454d629a · outbound

This paper cites Language models are unsupervised multitask learners.

Generative Distribution Distillation Language models are unsupervised multitask learners

Reference 41

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source=arxiv_source observed=2026-08-06T16:10:22.168579Z digest=sha256:927f6cdc852716392ad0dc7f752006caaf3492ee00ca9846139dece92f2f56fa

Observation a16ff439-282b-4042-af76-68bda21fd743 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Generative Distribution Distillation High-resolution image synthesis with latent diffusion models

Reference 42

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no resolver link, observed 2026-08-06T16:10:22.173215Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.173215Z digest=sha256:fbf341be343901be568e0413880cd736c35ee9b2f0ef37998dfbba53c1983531

Observation a8fd183a-1581-4d05-afed-8bf040627c11 · outbound

This paper cites Fitnets: Hints for thin deep nets.

Generative Distribution Distillation Fitnets: Hints for thin deep nets

Reference 43

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source=arxiv_source observed=2026-08-06T16:10:22.177835Z digest=sha256:b747d46af69eacfb41dafb9ec8754b813f886fece223c85b7e4226077d7b4cfd

Observation fd61dea6-788b-42bf-8dfc-c3e38ab2d0cf · outbound

This paper cites Image N et large scale visual recognition challenge.

Generative Distribution Distillation Image N et large scale visual recognition challenge

Reference 44

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.181677Z digest=sha256:a57a9bf0dd092087231368b6c0af9c375bacd64e6113c54059744b924d658dfc

Observation c39baa72-671a-4e44-b30b-6d37219c993b · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Generative Distribution Distillation Deep unsupervised learning using nonequilibrium thermodynamics

Reference 45

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no resolver link, observed 2026-08-06T16:10:22.185332Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.185332Z digest=sha256:b6ff1b8bfd76e7dba7392e5be8732dbfae248500b24fdf0cd4239a421a228d77

Observation 2da85518-a297-445b-a8b5-47aeeb926d64 · outbound

This paper cites Denoising Diffusion Implicit Models.

Generative Distribution Distillation Denoising Diffusion Implicit Models

Reference 46

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.188899Z digest=sha256:29f6d668d82afae3fdaa5e490993e404791f5de2d114f4f9509b4f5759fd082e

Observation 62f5f4f6-24be-449f-ac5c-12d0bc37da7f · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

Generative Distribution Distillation Generative modeling by estimating gradients of the data distribution

Reference 47

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verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.583872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.192795Z digest=sha256:5c80b7382d06f1b3b8c8e72b2668c7bbbf218c19fedbd5f8d988bd419ac9dff9

Observation 180062ff-4614-4e6f-83ac-0597f6a140fc · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Generative Distribution Distillation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 48

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source=arxiv_source observed=2026-08-06T16:10:22.196299Z digest=sha256:c68641d0f6b28dd0b6c792f60d20d9bf006a60eff1705939c4307693e89b31c4

Observation 5c7edff9-837a-4e9f-9bbc-efce08b68baf · outbound

This paper cites Visual autoregressive modeling: Scalable image generation via next-scale prediction.

Generative Distribution Distillation Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 49

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verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.569615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.200377Z digest=sha256:19e5ac62a814b3528d1a6d21d3ee0dd7f3fb632414a97b33e0846b1db17dafbb

Observation fcd01fc7-515d-42a8-be76-441b8e4910e6 · outbound

This paper cites Contrastive representation distillation.

Generative Distribution Distillation Contrastive representation distillation

Reference 50

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.204036Z digest=sha256:bde4f1c2275a93ac9bad08870b31731d21437191da98c83d9c64fe69155cce3c

Observation c0d19935-d655-4c3f-8ad9-56f0e8c967df · outbound

This paper cites VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation.

Generative Distribution Distillation VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

Reference 51

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-06T16:10:22.207798Z digest=sha256:1c3d08ec12fb7c5f99906d6d5e46ad7768b37bccdc0c8127250394472cd59612

Observation 7caf39e7-b05f-461c-8c5f-f2a924b64ee5 · outbound

This paper cites MMaDA: Multimodal Large Diffusion Language Models.

Generative Distribution Distillation MMaDA: Multimodal Large Diffusion Language Models

Reference 52

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source=arxiv_source observed=2026-08-06T16:10:22.212561Z digest=sha256:69a1531fd4970aa77a15af47de79603d96d155321bcfc76a7abc43121a5738f6

Observation 2ce37a5f-7b02-4b0d-b799-7080718b3840 · outbound

This paper cites Deep mutual learning.

Generative Distribution Distillation Deep mutual learning

Reference 53

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verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.539452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.217003Z digest=sha256:485db3c81ec0f44262be4bcc11ad4ad5b406d663d2bcf622ee721b24582d9f1f

Observation f683dcb1-7554-4b74-85c2-5a14e5498bee · outbound

This paper cites Decoupled knowledge distillation.

Generative Distribution Distillation Decoupled knowledge distillation

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:10:22.526901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T16:10:22.221371Z digest=sha256:ed44a6c23ef493911cb75c32d178e6cc4e1993dbe38ab55a09b401be50e86126

Observation 95675ddd-1f5c-4957-bbde-9da3c5876992 · outbound

This paper cites Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model.

Generative Distribution Distillation Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 55

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source=arxiv_source observed=2026-08-06T16:10:22.225433Z digest=sha256:09e1dc0f7f2451971fd8c17f40204602683f9b23254a667d9066055d773a4fb9

Observation 5ecdfa42-a0b2-4026-a4b2-bef8e0b43399 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Generative Distribution Distillation MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 56

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source=arxiv_source observed=2026-08-06T16:10:22.229058Z digest=sha256:ccd0b98d30143d2f55f73d4bfc5c01bb60756ee1e3033a9a6a06609ff9e08f3e

Observation ff4f5492-4641-485b-b1bc-4151b36a044e · outbound

This paper cites @esa (Ref.

Generative Distribution Distillation @esa (Ref

Reference 57

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:10:22.232728Z digest=sha256:196ea41b1a74cbf5661156250df344e4329b11b4f2fa7cbf1525d9c78423b01e

Observation 37ee81bf-89d0-44b2-91a6-804f2d669a6b · outbound

This paper cites an unresolved cited work.

Generative Distribution Distillation Unresolved cited work

Reference 58

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source=arxiv_source observed=2026-08-06T16:10:22.236971Z digest=sha256:ba93e5094122fdaf17c9ae161491dddb4eb8f3efbb72e4e40bfac39dc6fd3c58

Observation 0081f099-869b-4096-bdc2-e6650e38824c · outbound

This paper cites A naive GenDD baseline encounters two major challenges: the curse of high-dimensional optimization and the lack of semantic supervision from labels.

Generative Distribution Distillation A naive GenDD baseline encounters two major challenges: the curse of high-dimensional optimization and the lack of semantic supervision from labels

Reference 59

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malformed identifier
no resolver link, observed 2026-08-06T16:10:22.241118Z

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source=arxiv_source observed=2026-08-06T16:10:22.241118Z digest=sha256:a1c1459af3f0b0163079419a0ecc2bb8bf966a217d93ed80c0bce7813440d546

Pith citing papers

Observation 43f35ce3-9858-4652-941e-00e68dc4085e · inbound

Class-frequency Guided Noise Schedule for Diffusion Models cites this paper.

Class-frequency Guided Noise Schedule for Diffusion Models Generative Distribution Distillation

Reference 29

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arxiv_id, observed 2026-06-29T20:03:56.730958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T04:35:43.680865Z digest=sha256:fa68d9c4f7428e6772f50f3e8be3f36c82d5f11634cc6e2914c822c65202b111

Observation a25eee4c-821e-4e41-ac0d-395d08dabaec · inbound

Visual Token Compression Enhances Robustness of MLLMs cites this paper.

Visual Token Compression Enhances Robustness of MLLMs Generative Distribution Distillation

Reference 18

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source=pdf_text observed=2026-08-01T13:10:37.285667Z digest=sha256:18905e2aedf3cf5073c44fa3238220c809c988c8fec6a28ff24bbfbff394be83