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Paper Citation Record · LEDGER

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 1 inbound Pith citation observation for arXiv:2510.04961.

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

pith.paper-citation-record.v1
2510.04961 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:25:36.104805Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T04:18:45.403718Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:20:19.343984Z

Reference resolution

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved24
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation afd23de3-b2cc-4395-9129-b0f4404e7310 · outbound

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

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Cosmos World Foundation Model Platform for Physical AI

Reference 1

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source=pdf_text observed=2026-08-04T11:25:33.514720Z digest=sha256:da34e375e4c710727b1db61463ab971c892903678583519ddc20f7a67a3ff86d

Observation bf31865b-3481-4483-b7ff-9cedb76ac3ae · outbound

This paper cites Models directly trained at128×128.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Models directly trained at128×128

Reference 2

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source=pdf_text observed=2026-08-04T11:25:36.104805Z digest=sha256:495c340ab4162d999d3a70528645b947ab4107601ff13716ba4636e01e3ef245

Observation 71222710-f588-4aef-a23d-073e33cec4cc · outbound

This paper cites On the Importance of Noise Scheduling for Diffusion Models.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization On the Importance of Noise Scheduling for Diffusion Models

Reference 4

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source=pdf_text observed=2026-08-04T11:25:33.852695Z digest=sha256:b7be2f22d2a5f839fb379fbaee500b3489584cbb1799628fddafc24f1c43d879

Observation 991313a8-36de-40d4-8e9d-caa704b7b1c5 · outbound

This paper cites Mixture-of-transformers: A sparse and scalable architecture for multi-modal foundation models.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Mixture-of-transformers: A sparse and scalable architecture for multi-modal foundation models

Reference 8

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source=pdf_text observed=2026-08-04T11:25:34.320465Z digest=sha256:db09bb1909af26162f5d2d75c510aeb6ddfa101501bf01b43af922dcfe507ef3

Observation d9beb344-1cc4-48c0-81b3-3e989b95ed57 · outbound

This paper cites DGAE: Diffusion-guided autoencoder for efficient latent representation learning.arXiv preprint, 2506.09644,.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization DGAE: Diffusion-guided autoencoder for efficient latent representation learning.arXiv preprint, 2506.09644,

Reference 9

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source=pdf_text observed=2026-08-04T11:25:34.405950Z digest=sha256:6388a4f6f66f2c6299c108e567ac9cb13064fbf8f9ca4efabb3e623985d4a1e7

Observation 1a665255-829a-4519-992c-b7ddce89f401 · outbound

This paper cites Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

Reference 10

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source=pdf_text observed=2026-08-04T11:25:34.483894Z digest=sha256:e2e20e7f18f198bbc75be5362979da02621d457ab9c128e178d5124ad4fa48ef

Observation 6b362679-350b-45dc-a69e-b4c80fd4f4b8 · outbound

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

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Movie Gen: A Cast of Media Foundation Models

Reference 12

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source=pdf_text observed=2026-08-04T11:25:34.677649Z digest=sha256:927d12e8e3b8b527089036939624f62ff20034a155482a24b5799d60f94e78f1

Observation 5d542e41-4086-4bba-b548-3d1251fc1e4a · outbound

This paper cites DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization DiVAE: Photorealistic Images Synthesis with Denoising Diffusion Decoder

Reference 15

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source=pdf_text observed=2026-08-04T11:25:34.998830Z digest=sha256:5b0cf0601c25e37cbe7666b7b8434e98a9e3b05bbaacaffa2f8ad45a21d2c744

Observation c3d31ada-40d1-498a-aa91-1451e099ad77 · outbound

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

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 16

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source=pdf_text observed=2026-08-04T11:25:35.126850Z digest=sha256:35ed4459e1f246ac7bf6b5f48807b5c439bf12b4ba90d6db9093fcc30e4d3909

Observation d897b9ba-f9e8-45c0-924f-f3213b0b7702 · outbound

This paper cites Chameleon: Mixed-Modal Early-Fusion Foundation Models.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Chameleon: Mixed-Modal Early-Fusion Foundation Models

Reference 17

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source=pdf_text observed=2026-08-04T11:25:35.251399Z digest=sha256:d0e9635b3db6ffa883b94e8bc81a40d39b0abe46c5cbad8f185342746116dbc6

Observation 5c813ace-5135-4c5e-9136-b92ce70c1b66 · outbound

This paper cites Latent denoising makes good visual tokenizers.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Latent denoising makes good visual tokenizers

Reference 18

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source=pdf_text observed=2026-08-04T11:25:35.389368Z digest=sha256:8484b3fee894ae1b210a02087739fedd3ea6b25ff92f4feef9b13e7637528f8a

Observation 5d1ba256-33da-4e8c-80f3-800dab3f237a · outbound

This paper cites Randomized Autoregressive Visual Generation.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Randomized Autoregressive Visual Generation

Reference 19

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source=pdf_text observed=2026-08-04T11:25:35.474279Z digest=sha256:df322670783ccc188415400547d218a555a64cdf4f817b22a6f9e79e9520cf1f

Observation c7c18563-f116-4d62-a1c5-a2eab0d2727d · outbound

This paper cites Z x∈E−1(z) ∆(x,ˆy) # P(D g(z) =y)dydx(2) = min ˆy.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Z x∈E−1(z) ∆(x,ˆy) # P(D g(z) =y)dydx(2) = min ˆy

Reference 20

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source=pdf_text observed=2026-08-04T11:25:35.579729Z digest=sha256:04b2d8623f3189fcc960376f8a8549638a6584e403255e0f840981c530a8cffc

Observation e7ac77c7-ae75-473f-a99b-15c2c20f68a3 · outbound

This paper cites Impact of sampling steps on reconstruction.Quality of samples from diffusion models usually improves with a higher number of sampling steps.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Impact of sampling steps on reconstruction.Quality of samples from diffusion models usually improves with a higher number of sampling steps

Reference 22

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source=pdf_text observed=2026-08-04T11:25:35.830861Z digest=sha256:9d731b31f7d496acea2b55d1a741f863318d5de0fdf1836e24b20889bc2fcf09

Observation 94e3542a-068c-41ff-b080-80403a152ea8 · outbound

This paper cites We show in Table S3 that SSDDoutperforms the original decoders on reconstruction performance, despite being conditioned on features optimized for a different architecture.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization We show in Table S3 that SSDDoutperforms the original decoders on reconstruction performance, despite being conditioned on features optimized for a different architecture

Reference 23

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source=pdf_text observed=2026-08-04T11:25:35.988569Z digest=sha256:24bbd7682fb59a738ec70537d7e1eeec92b9749d382bbbc6e04c822d45b4b7cd

Observation 586244c7-883b-4b3c-843e-d4df2de85647 · outbound

This paper cites Classifier-free diffusion guidance.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Classifier-free diffusion guidance

Reference 2006

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source=pdf_text observed=2026-08-04T11:25:34.036258Z digest=sha256:5ca44ba712a1a36f7e5c9e2172eb4411ce2abc2b913f6806a8f1b53cfc361632

Observation f1afc006-9434-4351-b0d9-5f3f98801ddb · outbound

This paper cites We use the following loss coefficients: λLPIPS = 0 .5, λREPA = 0 .25, λKL = 10 −6.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization We use the following loss coefficients: λLPIPS = 0 .5, λREPA = 0 .25, λKL = 10 −6

Reference 2009

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source=pdf_text observed=2026-08-04T11:25:35.709669Z digest=sha256:b85ecd6c365d2cf6a61db7a1293e757026aeab9f877a6fa39a1dda93d232785e

Observation 55b8f0bb-8bca-4482-a8cd-ef7cee9acb91 · outbound

This paper cites GLU Variants Improve Transformer.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization GLU Variants Improve Transformer

Reference 2018

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source=pdf_text observed=2026-08-04T11:25:34.878371Z digest=sha256:b1c1864c2792d7bde082647e76a38fa72d01b4610453a7f43a1251dc59633688

Observation cd865685-340e-41a1-8847-db8730428d08 · outbound

This paper cites High-Fidelity Image Compression with Score-based Generative Models.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization High-Fidelity Image Compression with Score-based Generative Models

Reference 2020

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source=pdf_text observed=2026-08-04T11:25:34.201290Z digest=sha256:eaf3422946c98ec76008dc7c0d4c8f6f854c1b78d97b56cbc9d1f99e14dfd0fc

Observation f9683d53-1770-4b88-8d04-dfc80b613961 · outbound

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

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 2021

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source=pdf_text observed=2026-08-04T11:25:34.567462Z digest=sha256:afbb4e4497d0336f98c7f56aae54a91ae82cba1cf0c4e4e6423cd9a44d72cc5c

Observation d42dc319-f30f-40b2-9cb9-684040991e75 · outbound

This paper cites Flow to the mode: Mode-seeking diffusion autoencoders for state-of-the-art image tokenization.arXiv preprint, 2503.11056,.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Flow to the mode: Mode-seeking diffusion autoencoders for state-of-the-art image tokenization.arXiv preprint, 2503.11056,

Reference 2022

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source=pdf_text observed=2026-08-04T11:25:34.811180Z digest=sha256:dc4fda9640a357f769cac83da0247cc8b8b17877eeb68d252aeaadf821cf470a

Observation 918554f6-a36e-4b25-801b-a7567b8d98a5 · outbound

This paper cites Diffusion Autoencoders are Scalable Image Tokenizers.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Diffusion Autoencoders are Scalable Image Tokenizers

Reference 2023

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source=pdf_text observed=2026-08-04T11:25:33.951411Z digest=sha256:e0c59f6bfcc0935296c9d31bc3b431d32d234224df0db5976a345237ffaf19cb

Observation 807130a9-b35f-43ca-885e-fa91186cda13 · outbound

This paper cites an unresolved cited work.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Unresolved cited work

Reference 2024

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source=pdf_text observed=2026-08-04T11:25:33.726998Z digest=sha256:be12b4161732cccb057054bbba5e3ffb51e27d53fe0bd42cfb583f45eb62e108

Observation 63759571-4e80-45c6-87d2-dd4fae17b2a6 · outbound

This paper cites Imagen 3.arXiv preprint, 2408.07009,.

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization Imagen 3.arXiv preprint, 2408.07009,

Reference 2025

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source=pdf_text observed=2026-08-04T11:25:33.604375Z digest=sha256:d50c858775f5c4e53a37f9479e0b24b544dd38dfdc56e7b8576a25ea86fa55f4

Pith citing papers

Observation cdd01faf-e136-481a-a2c1-0ce84f24032e · inbound

PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion cites this paper.

PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

Reference 39

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arxiv_id, observed 2026-06-30T03:18:05.272041Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-25T04:18:45.403718Z digest=sha256:f918c3f31dafd500baf9f64c4c921ab2db7cf4a58543b8fdeadedd42732cdfd4