Paper Citation Record · LEDGER

Quantize-then-Rectify: Efficient VQ-VAE Training

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2507.10547.

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

pith.paper-citation-record.v1
2507.10547 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:34:57.412377Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T07:09:25.049534Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T05:27:39.713417Z

Reference resolution

45 of 45 outbound references displayed

verified exact1
verified fuzzy25
unresolved19
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6d13776c-15ee-4bca-b638-36f7d06c1d33 · outbound

This paper cites Cosmos World Foundation Model Platform for Physical AI.

Quantize-then-Rectify: Efficient VQ-VAE Training Cosmos World Foundation Model Platform for Physical AI

Reference 1

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Source-reported events for the cited work

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Observation bef4090c-818f-4beb-9fd2-2bf59bf1030e · outbound

This paper cites Sequential modeling enables scalable learning for large vision models.

Quantize-then-Rectify: Efficient VQ-VAE Training Sequential modeling enables scalable learning for large vision models

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.334245Z

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Observation d5d3bf88-dcd6-4b53-8c38-9171da923c6e · outbound

This paper cites Beit: Bert pre-training of image transformers.

Quantize-then-Rectify: Efficient VQ-VAE Training Beit: Bert pre-training of image transformers

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.318202Z

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Observation e4bf82d0-e1c5-4576-9ce8-cac96b3ff7d0 · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 4

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Observation fdd3f91e-c10f-4482-9e2d-bdc58a23cdee · outbound

This paper cites Language Models are Few-Shot Learners.

Quantize-then-Rectify: Efficient VQ-VAE Training Language Models are Few-Shot Learners

Reference 5

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Observation 0c05fa44-a9bf-4215-a599-476fda321c54 · outbound

This paper cites Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction.

Quantize-then-Rectify: Efficient VQ-VAE Training Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.302847Z

Source-reported events for the cited work

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Observation bb6bfa9e-147f-4c9f-834f-ba3f713f8066 · outbound

This paper cites Efficient-vqgan: Towards high-resolution image generation with efficient vision transformers.

Quantize-then-Rectify: Efficient VQ-VAE Training Efficient-vqgan: Towards high-resolution image generation with efficient vision transformers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.286375Z

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Observation 4f1bdfaa-8854-41a2-9b2d-cc9daf1031ae · outbound

This paper cites Maskgit: Masked generative image transformer.

Quantize-then-Rectify: Efficient VQ-VAE Training Maskgit: Masked generative image transformer

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.270033Z

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Observation 89a4f98d-1ae8-4717-a93a-01bdde1a3bd8 · outbound

This paper cites Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models.

Quantize-then-Rectify: Efficient VQ-VAE Training Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models

Reference 9

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Observation 1c03790f-a295-454c-9eb0-d9c59cbd7c44 · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Imagenet: A large-scale hierarchical image database

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.250244Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a9a9de34-9404-4e23-a976-c3ab8fd302dc · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Taming transformers for high-resolution image synthesis

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.233381Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4538924e-32da-42c5-840b-a19971c1011e · outbound

This paper cites Making llama see and draw with seed tokenizer.

Quantize-then-Rectify: Efficient VQ-VAE Training Making llama see and draw with seed tokenizer

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.216090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 67235580-562d-46fe-bf08-80cedea93729 · outbound

This paper cites Generative adversarial nets.

Quantize-then-Rectify: Efficient VQ-VAE Training Generative adversarial nets

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.199109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02754de5-52a4-46be-90d9-1955ec69ea5e · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Quantize-then-Rectify: Efficient VQ-VAE Training Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.184091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a7767200-c83a-4fdd-92e6-d50c86af8e40 · outbound

This paper cites Reducing the dimensionality of data with neural networks.

Quantize-then-Rectify: Efficient VQ-VAE Training Reducing the dimensionality of data with neural networks

Reference 15

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Observation 266b44db-84e4-4598-9684-44a76ff543c2 · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Straightening out the straight-through estimator: Overcoming optimization challenges in vector quantized networks

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.158896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b0ef9193-d2fc-4197-ad45-269bc5b15302 · outbound

This paper cites Image-to-image translation with conditional adversarial networks.

Quantize-then-Rectify: Efficient VQ-VAE Training Image-to-image translation with conditional adversarial networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.142003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fb512558-c19b-4771-a97b-d47ab80e48dc · outbound

This paper cites Unified language-vision pretraining in llm with dynamic discrete visual tokenization.

Quantize-then-Rectify: Efficient VQ-VAE Training Unified language-vision pretraining in llm with dynamic discrete visual tokenization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.124232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d6813e7a-4472-43bb-90ff-88fb8bce4b60 · outbound

This paper cites Perceptual losses for real-time style transfer and super-resolution.

Quantize-then-Rectify: Efficient VQ-VAE Training Perceptual losses for real-time style transfer and super-resolution

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.107432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 844c2563-09f3-4f63-9d4e-90da474ca2d9 · outbound

This paper cites Auto-Encoding Variational Bayes.

Quantize-then-Rectify: Efficient VQ-VAE Training Auto-Encoding Variational Bayes

Reference 20

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Source-reported events for the cited work

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Observation 9bc0ca49-2723-43bc-ba51-d1aca8a4a0ca · outbound

This paper cites Autoencoding beyond pixels using a learned similarity metric.

Quantize-then-Rectify: Efficient VQ-VAE Training Autoencoding beyond pixels using a learned similarity metric

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.091571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 376f562c-8ea0-4bef-98ae-f1cd8816b237 · outbound

This paper cites Autoregressive image generation using residual quantization.

Quantize-then-Rectify: Efficient VQ-VAE Training Autoregressive image generation using residual quantization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.073056Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7b9476ec-888c-4a38-bd6d-a99fe9aa7fd7 · outbound

This paper cites Imagefolder: Autoregressive image generation with folded tokens.

Quantize-then-Rectify: Efficient VQ-VAE Training Imagefolder: Autoregressive image generation with folded tokens

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.053548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 078135d7-761c-45ba-81ae-65a034f8b129 · outbound

This paper cites Coda: Repurposing continuous vaes for discrete tokenization.

Quantize-then-Rectify: Efficient VQ-VAE Training Coda: Repurposing continuous vaes for discrete tokenization

Reference 24

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Source-reported events for the cited work

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Observation 63fc74cb-16f1-4789-8ea4-f77b1d0a1193 · outbound

This paper cites Decoupled weight decay regularization.

Quantize-then-Rectify: Efficient VQ-VAE Training Decoupled weight decay regularization

Reference 25

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Source-reported events for the cited work

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Observation a476ca04-1e45-457b-9c42-0e3225327cbb · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Unitok: A unified tokenizer for visual generation and understanding

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:55.710416Z

Source-reported events for the cited work

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Observation 8db4ecb3-7768-4f44-af38-9e7cf65cb2db · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Finite Scalar Quantization: VQ-VAE Made Simple

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:55.810987Z

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Observation d8b04a58-ea4a-4c0b-a2ac-15afaad2195b · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Quantize-then-Rectify: Efficient VQ-VAE Training DINOv2: Learning Robust Visual Features without Supervision

Reference 28

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Observation 30328996-8a35-4d2f-94ea-dd0463b5e4a2 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Quantize-then-Rectify: Efficient VQ-VAE Training Pytorch: An imperative style, high-performance deep learning library

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.024236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 059f2c69-cc51-42d3-89bf-7843480f9fe9 · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Generating diverse high-fidelity images with vq-vae-2

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:58.004521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1d929ec7-fb04-44f3-92a7-92393d153cf4 · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training High-resolution image synthesis with latent diffusion models

Reference 31

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Source-reported events for the cited work

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Observation c6fa32a0-b339-4fa7-88a2-71040c987b3c · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:56.385577Z

Source-reported events for the cited work

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Observation 8693338f-dcd4-4928-a44f-d51fe3e0537f · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Visual autoregressive modeling: Scalable image generation via next-scale prediction

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.974098Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6fa439a7-cd9c-40d3-a1e1-aec5151407fe · outbound

This paper cites Neural discrete representation learning.

Quantize-then-Rectify: Efficient VQ-VAE Training Neural discrete representation learning

Reference 34

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Observation 6e6c8907-d525-4e33-89c3-0ebdcfa23c5e · outbound

This paper cites Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation.

Quantize-then-Rectify: Efficient VQ-VAE Training Bridging Continuous and Discrete Tokens for Autoregressive Visual Generation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:56.721604Z

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Observation a38ed33f-67da-4871-8be9-2ac9c12565b7 · outbound

This paper cites Image quality assessment: from error visibility to structural similarity.

Quantize-then-Rectify: Efficient VQ-VAE Training Image quality assessment: from error visibility to structural similarity

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.945992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1276ac86-5a7f-483a-bb71-01fe61f451b5 · outbound

This paper cites MaskBit: Embedding-free Image Generation via Bit Tokens.

Quantize-then-Rectify: Efficient VQ-VAE Training MaskBit: Embedding-free Image Generation via Bit Tokens

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:57.020631Z

Source-reported events for the cited work

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Observation 43104f53-51cc-47b3-8215-d2bb232bcba0 · outbound

This paper cites Vector-quantized image modeling with improved vqgan.

Quantize-then-Rectify: Efficient VQ-VAE Training Vector-quantized image modeling with improved vqgan

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:57.147209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e3e3bbc-72e6-47c9-9524-faaa152eb636 · outbound

This paper cites Language model beats diffusion-tokenizer is key to visual generation.

Quantize-then-Rectify: Efficient VQ-VAE Training Language model beats diffusion-tokenizer is key to visual generation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.918105Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7ebc17c2-5621-4ae2-8840-c6efd4dd35dd · outbound

This paper cites An image is worth 32 tokens for reconstruction and generation.

Quantize-then-Rectify: Efficient VQ-VAE Training An image is worth 32 tokens for reconstruction and generation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.901482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4ee414ec-de86-4feb-b144-a30196dd9f79 · outbound

This paper cites Preventing Local Pitfalls in Vector Quantization via Optimal Transport.

Quantize-then-Rectify: Efficient VQ-VAE Training Preventing Local Pitfalls in Vector Quantization via Optimal Transport

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:34:57.483082Z

Source-reported events for the cited work

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Observation 8b41b840-113d-4c7b-9eba-9564390f40f7 · outbound

This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Quantize-then-Rectify: Efficient VQ-VAE Training The unreasonable effectiveness of deep features as a perceptual metric

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.884191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0bd58538-844c-4411-bfc5-55ae55ad6689 · outbound

This paper cites Movq: Modulating quantized vectors for high-fidelity image generation.

Quantize-then-Rectify: Efficient VQ-VAE Training Movq: Modulating quantized vectors for high-fidelity image generation

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:34:57.865394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 750424a1-162d-4ad8-82f6-b5b90669c3c6 · outbound

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

Quantize-then-Rectify: Efficient VQ-VAE Training Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:57.408009Z

Source-reported events for the cited work

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Observation eac202ce-a54a-4c99-bf3a-278b245311c0 · outbound

This paper cites write newline.

Quantize-then-Rectify: Efficient VQ-VAE Training write newline

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T17:34:57.412377Z

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Pith citing papers

Observation e620f385-af54-4bb4-9596-395452360203 · inbound

ChannelTok: Efficient Flexible-Length Vision Tokenization cites this paper.

ChannelTok: Efficient Flexible-Length Vision Tokenization Quantize-then-Rectify: Efficient VQ-VAE Training

Reference 28

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Source-reported events for the cited work

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Observation d51873ce-da92-49df-911b-7e5cf6ec0189 · 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 Quantize-then-Rectify: Efficient VQ-VAE Training

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-03T05:27:39.714854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.