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Paper Citation Record · LEDGER

SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization

As of 15 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 2026-08-15T06:32:42.880941+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:a8f4c5a18bf402dc09083abebd0e4a1493f2f33c9136a9ef7129fea1da23a3bb

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=sha256:9460f022a7fdf5e17154882ef1454967d14dcbcb0f5469874ccbf7992bce0842

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=sha256:6bfc5020111925e670aba3e59360700139866c8cb1d33fec2f0fb1ca8d723eae

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=sha256:9ce1d030a2f335d2bef1d58f179219887569656fd76805cb56469a1a48f2882a

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:b9af9c850104acb8d479202c307c3680affaa25cdcc206d0839e47d0ff7ab941

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=sha256:1551d651e5ef2763e1c671567220d99355c75ee020bdf554d9ee6a62311edba4

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=sha256:8c8a22739d5ed875a8ff3252a1a6b0aa131bd256ddc21adfbbac555f39b186c6

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:d63894190e88fe976ee2745800b782ae4dde1ab20842b28ed4fdcddbb7dcd159

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:a3be524e14f5829a34a1ade2ba8e97ff8e855c9593ce006d9b82fe134f27bef5

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=sha256:33f792859af01900f42ef5c8116d90c043e1ee770aec51c399854a7801670ccb

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:bcc23bcbfcee3f7ea17f118adcfb49cffecfe4a0d232075bf5db9f81e61b7b6a

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=sha256:24107ccb86de4fcb13149bcee28cdbf7c28723a79a9f267a2fd5ede2a0fdf34d

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=sha256:9ba98e8d3208880c144b0b00eacc8d9347ff353f36fc17904d9972f43c5c3830

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:bf810c3f605867b1fb878b918699aa3ec9cd1239d8dde4c1a7bbde98dc5ae1ab

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=sha256:2d933e86096c6196642da9075f8a2994baa9ad290a4710d192e1f498822acfd5

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=sha256:2099c9b2a9fd9b0c65c2908164f42142bd36f5279ef9f56eefd2f30da9e18b87

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:e1bfa949446f1014fe084b605b0278cdb70b6093d1b755693d90c5a1c1d3aef1

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=sha256:05fa704f2e23465ebd7bbaed0e538210647c50407b8f32746efd9742d0f8cd48

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=sha256:386d5c3555bfa876902888ebb719418e92eab2c8c1a616cd1c17e0b60b59f34f

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=sha256:27be655858bb4473cce23a3cbc13c2d33d01f0976e42b74e639b8ca4e2a983e6

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=sha256:0af2d0c51943dd09096eb52abf067f413cd17647e456a9da1e094cffa60f0c4d

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=sha256:29ee02db6641afdd42640a715c6e24ebc22b32a4f6f466e5d6d14d5fb10db1fc

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:d07b557c9a56bf0997d2b7857edf1a535d33862835bff3004dc2db16f27b94a8

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=sha256:1c6f39a4e7fc8ba8dba9708f7ba37214e91123a6532e51315765db2802a44b44

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-25T04:18:45.403718Z digest=sha256:f464b3ac0caa5b6185b4d2f8fe16bc190ea7c5de9e3d44d20811fad7c76a3b8c