Paper Citation Record · LEDGER

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization

As of 6 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 3 inbound Pith citation observations for arXiv:2509.10140.

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

pith.paper-citation-record.v1
2509.10140 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T18:12:48.666757Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

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

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T09:39:33.755572Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T05:27:39.704413Z

Reference resolution

59 of 59 outbound references displayed

verified exact0
verified fuzzy0
unresolved58
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ed32ebae-949d-41b8-96de-4a4fde58e9bf · outbound

This paper cites write newline.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization write newline

Reference 1

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Observation 2f67d640-cc1d-4278-bd5d-963198d7fc23 · outbound

This paper cites Large-dit-imagenet.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Large-dit-imagenet

Reference 2

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Observation 05bf05b2-3367-46a2-9331-c37f12e0ea8e · outbound

This paper cites Qwen Technical Report.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Qwen Technical Report

Reference 3

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Observation 677a2059-029f-463f-a91a-efd53ef894ea · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 4

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Observation 3c0ec79f-fd75-4c61-a6e5-5c069766c453 · outbound

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

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Large Scale GAN Training for High Fidelity Natural Image Synthesis

Reference 5

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Observation c62f0724-8fc5-49eb-8fb4-262218c28c2f · outbound

This paper cites Language models are few-shot learners.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Language models are few-shot learners

Reference 6

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Observation b61311a5-53c9-46dd-a42c-f7d93de3b223 · outbound

This paper cites an unresolved cited work.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Unresolved cited work

Reference 7

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Observation 5ba00256-c63d-4724-8936-6b986b4e7f54 · outbound

This paper cites Generative pretraining from pixels.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Generative pretraining from pixels

Reference 8

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Observation 2bcbc62a-7e29-48a3-aae3-3fad01fee5ca · outbound

This paper cites Image N et: A large-scale hierarchical image database.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Image N et: A large-scale hierarchical image database

Reference 9

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Observation 453fd2f5-6048-48f0-b1e3-2586b93e8411 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

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Observation 407e10c1-68bf-49e3-96ef-693007ceda19 · outbound

This paper cites Diffusion models beat gans on image synthesis.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Diffusion models beat gans on image synthesis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T18:12:45.263732Z

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Observation ffc0ae6b-53e7-41b9-b777-c28e31aea145 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

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Observation 4e27dcc3-8f38-44c9-bb76-c7d668eec08c · outbound

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

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Taming transformers for high-resolution image synthesis

Reference 13

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Observation 26f0ca7c-45e5-4344-8ae2-0e73958eb24a · outbound

This paper cites The Llama 3 Herd of Models.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization The Llama 3 Herd of Models

Reference 14

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Observation 7f3a140c-dd9a-4f8f-8a2d-5a2af208e6c6 · outbound

This paper cites Denoising diffusion probabilistic models.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Denoising diffusion probabilistic models

Reference 15

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Observation 86a6802d-e4c8-4228-883f-e46364dd7da0 · outbound

This paper cites Cascaded diffusion models for high fidelity image generation.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Cascaded diffusion models for high fidelity image generation

Reference 16

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Observation 7cfeb22c-d003-480a-b986-ebe89c637fb3 · outbound

This paper cites Straightening out the straight-through estimator: Overcoming optimization challenges in vector quantized networks.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Straightening out the straight-through estimator: Overcoming optimization challenges in vector quantized networks

Reference 17

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Observation fce15510-2ac7-4d24-84d5-83fbf74c88dd · outbound

This paper cites an unresolved cited work.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Unresolved cited work

Reference 18

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Observation 8e6af703-fd1f-43a2-95e6-6fb4ff1d15f6 · outbound

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

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Scaling up gans for text-to-image synthesis

Reference 19

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Observation 89d1cdd7-5c99-490f-9a07-6938d7ccee65 · outbound

This paper cites Auto-Encoding Variational Bayes.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Auto-Encoding Variational Bayes

Reference 20

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Observation be6717f7-c37b-4396-ad3e-ccfc206a61ec · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Adam: A Method for Stochastic Optimization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T18:12:45.997737Z

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Observation 0682fba3-6a86-4519-87bc-e44b1bc805b6 · outbound

This paper cites The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

Reference 22

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Observation ab52ab92-ebd7-4180-8e7c-7f4fdc56879b · outbound

This paper cites Laion-coco 600m.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Laion-coco 600m

Reference 23

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Source-reported events for the cited work

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Observation 7af1878d-7f8b-4266-9e2e-128f9716ea1c · outbound

This paper cites Robust training of vector quantized bottleneck models.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Robust training of vector quantized bottleneck models

Reference 24

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Observation fda13932-b51f-45b8-9c0f-b35896ab5190 · outbound

This paper cites Autoregressive image generation using residual quantization.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Autoregressive image generation using residual quantization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T18:12:46.238435Z

Source-reported events for the cited work

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Observation 0882d2e5-5fb7-4928-8e63-a45a1d1aaa23 · outbound

This paper cites Return of unconditional generation: A self-supervised representation generation method.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Return of unconditional generation: A self-supervised representation generation method

Reference 26

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Observation 446f4224-f419-4f9c-8afb-694c7fff1172 · outbound

This paper cites Autoregressive Image Generation without Vector Quantization.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Autoregressive Image Generation without Vector Quantization

Reference 27

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Observation d5e50374-a2b9-449f-87d5-ed9edc794b65 · outbound

This paper cites Flow Matching for Generative Modeling.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Flow Matching for Generative Modeling

Reference 28

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Observation c4f05df4-2edf-460d-a1ef-5bd97dc6147c · outbound

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

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation

Reference 29

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Observation 6b1eb43a-6fe1-494c-bde8-b554f9d75662 · outbound

This paper cites Unitok: A unified tokenizer for visual generation and understanding.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Unitok: A unified tokenizer for visual generation and understanding

Reference 30

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Observation 8528b6f5-21f6-4aa4-8087-3840642fe6f1 · outbound

This paper cites Finite Scalar Quantization: VQ-VAE Made Simple.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Finite Scalar Quantization: VQ-VAE Made Simple

Reference 31

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Unavailable: canonical work link unavailable.

Observation 5da56636-ee7b-4ba6-bb5d-87f26279110c · outbound

This paper cites RandAR: Decoder-only Autoregressive Visual Generation in Random Orders.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization RandAR: Decoder-only Autoregressive Visual Generation in Random Orders

Reference 32

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Observation 6ab251cf-4b09-455d-a0f6-4d5ea18bd7ac · outbound

This paper cites Scalable diffusion models with transformers.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Scalable diffusion models with transformers

Reference 33

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Observation 26e04914-b8fb-4a0f-9f15-669184b67ead · outbound

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

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Improving language understanding by generative pre-training

Reference 34

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Observation e160463f-953f-44da-ac50-eba76c7f0139 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Learning transferable visual models from natural language supervision

Reference 35

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Observation 449e2d80-d8cc-4972-ac8d-b13651eda255 · outbound

This paper cites Zero-shot text-to-image generation.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Zero-shot text-to-image generation

Reference 36

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

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Generating diverse high-fidelity images with vq-vae-2

Reference 37

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Observation df4d16d9-d29d-4875-b3a7-2a56a8aa15dd · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization High-resolution image synthesis with latent diffusion models

Reference 38

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization High-resolution image synthesis with latent diffusion models

Reference 39

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Observation a80b09fb-9c25-4eb7-8583-5ede8ae57cdf · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Stylegan-xl: Scaling stylegan to large diverse datasets

Reference 40

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Observation 84c704ae-008d-48e2-9e95-1752a04f3115 · outbound

This paper cites Scalable Image Tokenization with Index Backpropagation Quantization.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Scalable Image Tokenization with Index Backpropagation Quantization

Reference 41

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Observation 113d149d-b37f-4560-849c-6258a66f0ce6 · outbound

This paper cites Denoising Diffusion Implicit Models.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Denoising Diffusion Implicit Models

Reference 42

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Observation 8d511910-4ad0-401d-8630-a4616be7a80e · outbound

This paper cites Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 43

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Observation e72b48e1-f76a-42fb-86b3-dcad5db7bf7e · outbound

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

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 44

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Observation da219bac-86b1-44e2-935c-4ed412fce074 · outbound

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

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization LLaMA: Open and Efficient Foundation Language Models

Reference 45

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Observation 36edb0f2-756a-42d4-9789-a8e4a60d1746 · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Conditional image generation with pixelcnn decoders

Reference 46

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Observation 951b700a-5d79-4cf3-b4da-e752e5bb74a7 · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Neural discrete representation learning

Reference 47

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Observation a1f8e1f8-d226-4067-961c-e547f0999382 · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Vector-quantized image modeling with improved VQGAN

Reference 48

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Observation fea1c7ea-94a2-4540-8952-86e24a8280d6 · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Language model beats diffusion - tokenizer is key to visual generation

Reference 49

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Observation 282f7c6d-80b6-4c6c-9a70-54a0225dd3b0 · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization An Image is Worth 32 Tokens for Reconstruction and Generation

Reference 50

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Regularized vector quantization for tokenized image synthesis

Reference 51

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Efros, Eli Shechtman, and Oliver Wang

Reference 52

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Online clustered codebook

Reference 53

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Movq: Modulating quantized vectors for high-fidelity image generation

Reference 54

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Observation 8c1944f5-a9c2-48ea-8d90-fe0410a0f41e · outbound

This paper cites Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%.

Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T18:12:48.383882Z

Source-reported events for the cited work

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Addressing representation collapse in vector quantized models with one linear layer

Reference 56

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization @esa (Ref

Reference 57

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Observation 0ddec6ed-7caa-469d-aade-769c7adea287 · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Unresolved cited work

Reference 58

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Observation 5aba5afd-1bff-4c84-9716-e0cb5aa3753c · outbound

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Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization Left: Performance comparison showing generation (FID) and reconstruction (rFID) quality across different methods

Reference 59

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Pith citing papers

Observation fab95155-c004-4647-8004-a03e0967c8d0 · inbound

NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization cites this paper.

NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:27:39.705956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7c266761-1fdc-409d-8a94-9c3906cc0e2c · inbound

Nemotron-Labs-Diffusion-Image: Advancing Masked Discrete Diffusion for High-Resolution Image Synthesis cites this paper.

Nemotron-Labs-Diffusion-Image: Advancing Masked Discrete Diffusion for High-Resolution Image Synthesis Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T08:14:26.687079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 91aa0228-a3ca-4eb1-bc04-95ba5b51e8b6 · inbound

Nemotron-Labs-Diffusion-Image: Advancing Masked Discrete Diffusion for High-Resolution Image Synthesis cites this paper.

Nemotron-Labs-Diffusion-Image: Advancing Masked Discrete Diffusion for High-Resolution Image Synthesis Scalable Training for Vector-Quantized Networks with 100% Codebook Utilization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T09:39:33.755572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.