Paper Citation Record · LEDGER

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction

As of 14 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2412.18390.

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

pith.paper-citation-record.v1
2412.18390 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:48:45.979499Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:25:40.135064Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T16:25:40.399899Z

Reference resolution

46 of 46 outbound references displayed

verified exact0
verified fuzzy11
unresolved35
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 722cb4c8-08e5-4881-ad8c-4090cf3caf33 · outbound

This paper cites GPT-4 Technical Report.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction GPT-4 Technical Report

Reference 1

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Unavailable: canonical work link unavailable.

Observation 0051ada9-b5f4-4294-be85-9e4918928198 · outbound

This paper cites Wasserstein generative adversarial networks.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Wasserstein generative adversarial networks

Reference 2

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Observation 86d1f114-ce9c-40aa-a6af-a1154d6a7d19 · outbound

This paper cites Layer Normalization.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Layer Normalization

Reference 3

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Observation b6e73c41-6aff-44ba-8e82-df75752a0728 · outbound

This paper cites Large Scale GAN Training for High Fidelity Natural Image Synthesis.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.707999Z

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Observation 378d4ee7-671f-4f25-a215-8b27a89c145a · outbound

This paper cites Lan- guage models are few-shot learners.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Lan- guage models are few-shot learners

Reference 5

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Observation 8cc39c52-6eed-4111-a392-482e8a486ec9 · outbound

This paper cites Maskgit: Masked generative image transformer.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Maskgit: Masked generative image transformer

Reference 6

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Observation c589549b-a237-4222-a084-3b6d8e306388 · outbound

This paper cites Muse: Text- to-image generation via masked generative transformers.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Muse: Text- to-image generation via masked generative transformers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:48:47.052361Z

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Observation 1bad2e19-0ae0-4d88-be79-16c90c578e5d · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.735680Z

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Observation c6092bb8-3400-457e-a88f-741cee9d3c85 · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 9

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Observation f015189b-0f8c-4978-8dfd-198d17ae2df0 · outbound

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

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Imagenet: A large-scale hierarchical image database

Reference 10

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Observation 7c1c22ad-9b6d-4566-b562-23e7f956e7af · outbound

This paper cites Diffusion models beat gans on image synthesis.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Diffusion models beat gans on image synthesis

Reference 11

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Observation 20e0f847-490e-40e7-a2c1-139c4b857da9 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Taming transformers for high-resolution image synthesis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:48:46.990175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation cd7ce5d8-2f1b-4571-bcde-d281171bc33a · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:48:46.968589Z

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Observation 0e801873-8235-4d80-93db-5fd35b08f2d6 · outbound

This paper cites Generative adversarial networks.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Generative adversarial networks

Reference 14

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Source-reported events for the cited work

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Observation d561c5f3-f1f4-411f-807d-65a6a5279ef3 · outbound

This paper cites Classifier-Free Diffusion Guidance.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Classifier-Free Diffusion Guidance

Reference 15

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Observation 6190c2a1-f6b8-46e5-833c-b384f0f0f78a · outbound

This paper cites Denoising dif- fusion probabilistic models.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Denoising dif- fusion probabilistic models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.782913Z

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Observation 7852b83d-a091-4f21-a868-0adea39dced5 · outbound

This paper cites Cascaded diffu- sion models for high fidelity image generation.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Cascaded diffu- sion models for high fidelity image generation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.788362Z

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Observation 34418e59-7af4-4308-a6a3-733ddc0baf27 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction LoRA: Low-Rank Adaptation of Large Language Models

Reference 18

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Source-reported events for the cited work

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Observation 5d16114c-6d5f-450e-b075-ce41c76d7785 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Categorical Reparameterization with Gumbel-Softmax

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.798935Z

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Observation a1100389-5259-446e-b38f-b1d7788bb203 · outbound

This paper cites Mistral 7B.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Mistral 7B

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.804220Z

Source-reported events for the cited work

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Observation 41f1b96b-5cbe-4b82-abbc-bfe3a5925788 · outbound

This paper cites Scal- ing up gans for text-to-image synthesis.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Scal- ing up gans for text-to-image synthesis

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:48:46.892531Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6a34175d-ce99-46d0-acea-5804ee967288 · outbound

This paper cites Auto-Encoding Variational Bayes.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Auto-Encoding Variational Bayes

Reference 22

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Source-reported events for the cited work

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Observation 10922be0-508b-4719-82d8-b9c025e05c2d · outbound

This paper cites Autoregressive image generation using residual quantization.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Autoregressive image generation using residual quantization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:48:46.871006Z

Source-reported events for the cited work

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Observation 05f5288e-57da-422c-9a2d-f8cf783dbe15 · outbound

This paper cites Mage: Masked generative encoder to unify representation learning and image synthe- sis.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Mage: Masked generative encoder to unify representation learning and image synthe- sis

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.826177Z

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Observation 2515c74c-3652-4b7d-9020-1d9abbd5761b · outbound

This paper cites Autoregressive Image Generation without Vector Quantization.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Autoregressive Image Generation without Vector Quantization

Reference 25

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Observation 6dcfa02a-9c58-4cff-87ee-019ac778908a · outbound

This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 26

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Observation 56f4dabd-7f2e-4581-8c60-b9a09be7c49f · outbound

This paper cites Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 27

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Source-reported events for the cited work

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Observation 0358fa6c-064a-4263-8eca-d429fe24bd3f · outbound

This paper cites Improved denoising diffusion probabilistic models.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Improved denoising diffusion probabilistic models

Reference 28

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Observation 31df711d-e4c4-4143-b673-d5d4d65f067b · outbound

This paper cites Scalable diffusion models with transformers.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Scalable diffusion models with transformers

Reference 29

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Observation fd141b8d-7657-4c27-99c0-109a38cbb2d8 · outbound

This paper cites Gener- ating diverse high-fidelity images with vq-vae-2.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Gener- ating diverse high-fidelity images with vq-vae-2

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:48:46.772881Z

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Observation 1abd6e00-c8ac-44c4-9d64-171d70c5b263 · outbound

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

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction High-resolution image synthesis with latent diffusion models

Reference 31

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Observation 7692a849-64ed-497c-8f26-ac2c39c01857 · outbound

This paper cites U- net: Convolutional networks for biomedical image segmen- tation.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction U- net: Convolutional networks for biomedical image segmen- tation

Reference 32

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Observation 718aec17-6ee2-46ba-9ec3-9d646ffe508a · outbound

This paper cites Scaling stylegan to large diverse datasets.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Scaling stylegan to large diverse datasets

Reference 33

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Observation e3db9284-1bbc-4ef6-9a7f-80498ba33561 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 34

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Source-reported events for the cited work

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Observation 9c96cf27-d256-423a-9420-6aa8ff90533a · outbound

This paper cites Denoising Diffusion Implicit Models.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Denoising Diffusion Implicit Models

Reference 35

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Source-reported events for the cited work

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Observation 1551edd7-6b2c-453d-a4b6-3c84d9a2485d · outbound

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

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Score-Based Generative Modeling through Stochastic Differential Equations

Reference 36

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Source-reported events for the cited work

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Observation 2c869670-b221-4a1e-a8a5-b6af0d6fe8fa · outbound

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RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Consistency Models

Reference 37

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Source-reported events for the cited work

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Observation 82064972-24f2-4b34-a2ff-b99f6f879b88 · outbound

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

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:48:46.665731Z

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Observation 09b1bea4-d5dc-491b-a2c3-33a159d5184d · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction LLaMA: Open and Efficient Foundation Language Models

Reference 39

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Source-reported events for the cited work

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Observation 0c048497-28f0-4f65-a460-93372eaddd8f · outbound

This paper cites GIVT: Generative Infinite-Vocabulary Transformers.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction GIVT: Generative Infinite-Vocabulary Transformers

Reference 40

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Observation ca9595bf-26f4-462f-bee6-e2833202f32a · outbound

This paper cites Neural discrete representation learning.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Neural discrete representation learning

Reference 41

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Source-reported events for the cited work

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Observation dbc50f6a-fc48-42e0-9eaf-042c19723c1b · outbound

This paper cites Show-o: One Single Transformer to Unify Multimodal Understanding and Generation.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Reference 42

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Source-reported events for the cited work

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Observation de863985-40f2-46b3-88bb-a8410c455741 · outbound

This paper cites Vector-quantized Image Modeling with Improved VQGAN.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Vector-quantized Image Modeling with Improved VQGAN

Reference 43

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Observation 7fde8846-8926-4f46-9932-8d22847d47d1 · outbound

This paper cites Magvit: Masked generative video transformer.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Magvit: Masked generative video transformer

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.955868Z

Source-reported events for the cited work

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Observation 24e759d5-6358-46ce-b0a3-cde42e3495f9 · outbound

This paper cites Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation.

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.971857Z

Source-reported events for the cited work

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Observation d7ba31bc-5a1a-47a7-af88-504624f25c78 · outbound

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

RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T04:48:45.979499Z

Source-reported events for the cited work

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Pith citing papers

Observation 152ac97f-5a7e-4748-bdea-23033e0b5b12 · inbound

Compressed Image Generation with Denoising Diffusion Codebook Models cites this paper.

Compressed Image Generation with Denoising Diffusion Codebook Models RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction

Reference 16

Resolution: malformed identifier; local_arxiv, observed 2026-08-09T16:25:40.406196Z

Source-reported events for the cited work

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