Paper Citation Record · LEDGER

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models

As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 5 inbound Pith citation observations for arXiv:2504.17789.

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

pith.paper-citation-record.v1
2504.17789 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:35:43.681356Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:16:36.507549Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T05:38:05.420263Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy7
unresolved34
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ca73cd6-1b25-4f5e-9ae8-cc6468ef83ad · outbound

This paper cites CM3: A Causal Masked Multimodal Model of the Internet.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models CM3: A Causal Masked Multimodal Model of the Internet

Reference 1

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Source-reported events for the cited work

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Observation c149b152-6393-4563-bbc2-688bd0b34785 · outbound

This paper cites The prompts are from GenEval Ghosh et al.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models The prompts are from GenEval Ghosh et al

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:44.070231Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cc870101-39ca-4938-8ed4-af6bc5e9af86 · outbound

This paper cites LongNet: Scaling Transformers to 1,000,000,000 Tokens.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models LongNet: Scaling Transformers to 1,000,000,000 Tokens

Reference 4

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Observation c10a3c53-c37b-4281-bed4-18596091c2f5 · outbound

This paper cites The Llama 3 Herd of Models.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models The Llama 3 Herd of Models

Reference 5

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Observation 9aa4903c-6460-4750-8b37-1d9ae703600e · outbound

This paper cites Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens

Reference 6

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Observation 8900b32c-204d-4d5e-8640-e23bed42f665 · outbound

This paper cites Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:43.547967Z

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Observation 9f978dbc-ff12-40ca-bfd1-fd04a7b45d3c · outbound

This paper cites Better & Faster Large Language Models via Multi-token Prediction.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Better & Faster Large Language Models via Multi-token Prediction

Reference 8

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Observation 6623d2ad-790a-4ddb-9bd3-f5785bd26d7d · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T10:35:43.555452Z

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Observation f3cd3569-b761-44e8-b2d6-dede0611d1d4 · outbound

This paper cites ElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content Separation.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models ElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content Separation

Reference 10

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Observation 54348ea7-d209-443f-a712-16d66901f4ce · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 11

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Observation 333e5d38-ab10-44e6-8445-b990bf06ea4a · outbound

This paper cites However, as training progresses, the model consistently generates visual tokens up to the <|end_of_image|> token, resulting in complete images.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models However, as training progresses, the model consistently generates visual tokens up to the <|end_of_image|> token, resulting in complete images

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:44.119612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9b2c457f-ebf4-44f0-8d14-8898f957bb99 · outbound

This paper cites GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation

Reference 13

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Observation 83e69c32-228e-4c25-9b0c-6a8ff7285e45 · outbound

This paper cites Flow Matching for Generative Modeling.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Flow Matching for Generative Modeling

Reference 15

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Observation 2eb2c979-3db1-4656-a2c5-e564d5db7e1a · outbound

This paper cites Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation

Reference 17

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Observation be21d35b-382a-4deb-8548-5611c7f3fb69 · outbound

This paper cites STAR: Scale-wise Text-conditioned AutoRegressive image generation.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models STAR: Scale-wise Text-conditioned AutoRegressive image generation

Reference 18

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Observation 9450c0e9-bcc2-4e36-9386-beeb7c1f5f27 · outbound

This paper cites Language Models are Few-Shot Learners.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Language Models are Few-Shot Learners

Reference 19

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Observation 6ef786a7-24b5-4600-a3db-3490cf4a0bc4 · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models RWKV: Reinventing RNNs for the Transformer Era

Reference 21

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Observation 5472723f-208f-4d0a-972b-078bc884c873 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 22

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Observation 9e11f2f2-2c61-4866-83f9-421a4f324ce6 · outbound

This paper cites Movie Gen: A Cast of Media Foundation Models.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Movie Gen: A Cast of Media Foundation Models

Reference 23

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Observation 754f8313-bc20-4da7-8671-ad2d9808c311 · outbound

This paper cites FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free Scale Fusion.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models FreeScale: Unleashing the Resolution of Diffusion Models via Tuning-Free Scale Fusion

Reference 24

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Observation ada7ff7a-37da-4599-98c5-1c7e6ebdfadb · outbound

This paper cites UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks

Reference 26

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Observation dd8a5f0f-1858-401b-9caf-5ff1e0f2437e · outbound

This paper cites Progressive Distillation for Fast Sampling of Diffusion Models.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Progressive Distillation for Fast Sampling of Diffusion Models

Reference 27

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Observation caeb0836-18b2-41f7-bced-7d317f10d1e7 · outbound

This paper cites Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 29

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Observation 3279a8ab-c99b-4ee0-8c9c-b3b63e51e0c3 · outbound

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

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models LLaMA: Open and Efficient Foundation Language Models

Reference 30

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Observation 298a6227-b9ba-4455-b56e-cc376be95a1f · outbound

This paper cites Analysis of Classifier-Free Guidance Weight Schedulers.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Analysis of Classifier-Free Guidance Weight Schedulers

Reference 31

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Observation 63902960-c22b-4e0f-a904-e5558fa8c2fb · outbound

This paper cites SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 32

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Observation 3a345d76-c8ed-497a-ad95-5881eff7fdb4 · outbound

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

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 33

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Observation b28cc293-e3e1-4135-acde-bd4b37de9d76 · outbound

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

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Reference 34

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Observation b7c7f62a-eb59-47f3-8052-640d298de74e · outbound

This paper cites We provide detailed implementations in Sec.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models We provide detailed implementations in Sec

Reference 35

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Observation acb00189-344d-437c-8fe8-fc4dd81ff1c6 · outbound

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Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Unresolved cited work

Reference 39

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Observation bfe3e18d-2f05-4554-a287-ec0dcc526a1f · outbound

This paper cites All images were generated with a shuffle window size of 2, half-linear CFG-scheduler with a scale of 7.5, as stated previously.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models All images were generated with a shuffle window size of 2, half-linear CFG-scheduler with a scale of 7.5, as stated previously

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:44.081907Z

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Observation 0becff87-95b6-4245-9b9e-2785210f7f91 · outbound

This paper cites The prompts are from our internal evaluation prompts.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models The prompts are from our internal evaluation prompts

Reference 42

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Observation 1ec5193d-c66c-48e0-8a2b-17358305aef0 · outbound

This paper cites The 16× VQGAN model is taken from the previous LlamaGen T2I checkpoint, while the8×VQGAN is derived from our internal checkpoint.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models The 16× VQGAN model is taken from the previous LlamaGen T2I checkpoint, while the8×VQGAN is derived from our internal checkpoint

Reference 512

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:44.104320Z

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Observation ff7c0184-246c-4eef-bec4-fa1698096961 · outbound

This paper cites Evaluating Text-to-Visual Generation with Image-to-Text Generation.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Evaluating Text-to-Visual Generation with Image-to-Text Generation

Reference 2014

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Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Denoising Diffusion Implicit Models

Reference 2016

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Observation d042b77f-7b4b-4596-8352-793ef75badd4 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Classifier-Free Diffusion Guidance

Reference 2017

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Observation e5aae8a8-58c6-44a4-8bbc-33d2e0ba33c7 · outbound

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Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Transfer between Modalities with MetaQueries

Reference 2020

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Observation 58a264b8-fb4a-4f2c-9bb9-f7e10f675ba8 · outbound

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Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 2021

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This paper cites Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining

Reference 2022

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Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 2023

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Observation ffde7b45-3d3e-48e4-ac22-297dcb7e982f · outbound

This paper cites Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 2024

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Observation 19982be7-677c-48c2-916f-4fa7e113ade7 · outbound

This paper cites To investigate the cause of unstable training, we analyze the training process in detail.

Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models To investigate the cause of unstable training, we analyze the training process in detail

Reference 2048

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:35:44.130810Z

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Pith citing papers

Observation ddfc14a4-8183-44ba-926e-89e36f2c7c4d · inbound

BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset cites this paper.

BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models

Reference 21

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Observation f83de64b-06dd-478d-bd12-388f9ba8b9ae · inbound

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation cites this paper.

VTBench: Evaluating Visual Tokenizers for Autoregressive Image Generation Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models

Reference 28

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Observation 2d6a1d00-a672-4445-9d96-00aea3c77fb2 · inbound

Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations cites this paper.

Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models

Reference 44

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Observation 67bd2455-c0da-4b8b-81aa-20e1d767cd27 · inbound

CASCADE: Context-Aware Relaxation for Speculative Image Decoding cites this paper.

CASCADE: Context-Aware Relaxation for Speculative Image Decoding Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:55:54.975543Z

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Observation f1b0cdb9-be2a-4455-98ab-cd845258c5ec · inbound

Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models cites this paper.

Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models Token-Shuffle: Towards High-Resolution Image Generation with Autoregressive Models

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:38:05.424225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.