Paper Citation Record · LEDGER

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

As of 20 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 26 inbound Pith citation observations for arXiv:2501.01423.

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

pith.paper-citation-record.v1
2501.01423 v3

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:34:14.574576Z

measured 75 of 75 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 26 of 26 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:18:29.808278Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

49 of 49 outbound references displayed

verified exact0
verified fuzzy16
unresolved33
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation c41bd1e8-92fa-46e7-87d7-3f764bcd57ee · outbound

This paper cites Intriguing properties of quantization at scale.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Intriguing properties of quantization at scale

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.809331Z

Source-reported events for the cited work

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Observation b0bb0ab4-9a2a-445d-8027-d34b3039f120 · outbound

This paper cites Maskgit: Masked generative image transformer.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Maskgit: Masked generative image transformer

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.782413Z

Source-reported events for the cited work

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Observation c487a767-bff0-415b-8b20-5beade26d296 · outbound

This paper cites PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 3

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Observation 59428732-c49a-4d97-9419-cd2d1a2f45ab · outbound

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

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models

Reference 4

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Observation f19520ce-a364-4178-8c8b-80e88f52c4c7 · outbound

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

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:34:14.190258Z

Source-reported events for the cited work

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Observation 534ab6e7-38f2-4671-88be-55bc5dc645ea · outbound

This paper cites Tam- ing transformers for high-resolution image synthesis.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Tam- ing transformers for high-resolution image synthesis

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.756701Z

Source-reported events for the cited work

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Observation 50e2518c-6808-4b72-96aa-aa838e9c5421 · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.734829Z

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Observation 7c3eb9c9-7771-4ff1-8cb0-d6026385ca26 · outbound

This paper cites AuraFlow-v0.3.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models AuraFlow-v0.3

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.712518Z

Source-reported events for the cited work

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Observation 196ea4ce-b745-4db4-aa07-cd6e38aeb50e · outbound

This paper cites Eva: Exploring the limits of masked visual represen- tation learning at scale.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Eva: Exploring the limits of masked visual represen- tation learning at scale

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.686962Z

Source-reported events for the cited work

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Observation 7f2264ac-3a17-4ecb-93cb-f017f9639c8d · outbound

This paper cites Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers

Reference 10

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Observation d4388221-9608-4beb-8e69-c08780d6140f · outbound

This paper cites Masked diffusion transformer is a strong image synthesizer.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Masked diffusion transformer is a strong image synthesizer

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.653624Z

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Observation 32dccecb-b654-4c3a-b469-3a005fd34c6c · outbound

This paper cites MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer

Reference 12

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Source-reported events for the cited work

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Observation 8aced042-e162-4570-886e-9dab325db929 · outbound

This paper cites Rethinking the objectives of vector- quantized tokenizers for image synthesis.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Rethinking the objectives of vector- quantized tokenizers for image synthesis

Reference 13

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Source-reported events for the cited work

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Observation 8a29ce9c-c8a6-43e0-9693-478012c7f450 · outbound

This paper cites Photorealistic video generation with diffusion models.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Photorealistic video generation with diffusion models

Reference 14

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Source-reported events for the cited work

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Observation 7d498831-8890-4f40-9234-6a2dc279cc36 · outbound

This paper cites Deep residual learning for image recognition.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Deep residual learning for image recognition

Reference 15

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Observation b2f37bb4-dd87-4b5b-9462-fbc11a7ae01f · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Masked Autoencoders Are Scalable Vision Learners

Reference 16

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Observation 68afa573-f410-483a-9082-4d3a37abde0d · outbound

This paper cites Computational Tradeoffs in Image Synthesis: Diffusion, Masked-Token, and Next-Token Prediction.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Computational Tradeoffs in Image Synthesis: Diffusion, Masked-Token, and Next-Token Prediction

Reference 17

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Observation d5e605f3-b0bb-4c97-b5b5-b93ffe38e63a · outbound

This paper cites Auto-Encoding Variational Bayes.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Auto-Encoding Variational Bayes

Reference 18

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Observation fb921112-5afa-458c-90cb-71e755e6bfd2 · outbound

This paper cites Segment anything.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Segment anything

Reference 19

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Observation 1bf7951b-5ee2-44a3-9639-bfd6adf49b98 · outbound

This paper cites Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models

Reference 20

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Observation 707f38c8-8699-4771-8bec-2654e5dd6772 · outbound

This paper cites Frontier ai lab.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Frontier ai lab

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.553070Z

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Observation cf16ce34-04be-4325-8f9c-e7f29e7c954e · outbound

This paper cites Autoregressive Image Generation without Vector Quantization.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Autoregressive Image Generation without Vector Quantization

Reference 22

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Observation 8a02f3cc-7f46-4de5-8ec3-04ece55250d7 · outbound

This paper cites Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Playground v3: Improving Text-to-Image Alignment with Deep-Fusion Large Language Models

Reference 23

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Observation ebeb49fc-5601-4df4-a3f0-44c911f68f81 · outbound

This paper cites Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 24

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Observation 82cf0fa4-3c63-4371-a065-1744c49eedbb · outbound

This paper cites A convnet for the 2020s.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models A convnet for the 2020s

Reference 25

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Observation 05178896-7e5b-4316-b1e1-82ef3d10c4f5 · outbound

This paper cites Vdt: General- purpose video diffusion transformers via mask model- ing.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Vdt: General- purpose video diffusion transformers via mask model- ing

Reference 26

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Observation 8645ad78-5e7a-4491-9f5f-5f26b1bbdc6c · outbound

This paper cites SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

Reference 27

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Observation a60ad96f-6e53-4bc5-aba1-d6e2a1311238 · outbound

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Unresolved cited work

Reference 28

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Observation 4ee1ad68-e2eb-4272-b2ba-838b9e702974 · outbound

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Unresolved cited work

Reference 29

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Observation 79cc3ce1-42de-451e-ba4b-71f4cbb20112 · outbound

This paper cites Scalable diffusion mod- els with transformers.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Scalable diffusion mod- els with transformers

Reference 30

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Observation 66cad7b7-ad8b-4296-9074-a0f149d19859 · outbound

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

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 31

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Movie Gen: A Cast of Media Foundation Models

Reference 32

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Observation 5b317737-3ebb-47cc-9fe8-75ce50ee0362 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Learning transferable visual models from natural language supervi- sion

Reference 33

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Observation 144e4ed2-f5d7-4b75-8d9c-e98e4385a247 · outbound

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 34

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Observation a67485b8-7327-4107-9708-a6a76dfe0464 · outbound

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models GLU Variants Improve Transformer

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Roformer: Enhanced transformer with rotary position embedding

Reference 36

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Reference 37

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Observation f71c0b10-61aa-498c-ac39-63d75cd3b9b4 · outbound

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Torch Compile Tutorial

Reference 38

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Observation f47b1b30-6003-4a8f-a8a0-7fdab5631a8e · outbound

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction

Reference 39

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Neural discrete representation learning

Reference 40

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Observation 7ca0e218-fb64-464c-b5d5-8d58d84132e0 · outbound

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Visualizing data using t-sne

Reference 41

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformers

Reference 42

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Observation 1758c7c4-738c-4b0f-8e77-346c463fd748 · outbound

This paper cites FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification

Reference 43

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Observation 708918a2-c3db-4d23-b28d-954a94aebfeb · outbound

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

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation

Reference 44

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This paper cites Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Reference 45

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Observation 65ff6dea-ac0f-462d-8adf-c98e816aa607 · outbound

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Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Root mean square layer normalization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:34:15.291138Z

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Observation 2f5d5819-a497-423e-bfa0-39fc45d5d85f · outbound

This paper cites Fast Training of Diffusion Models with Masked Transformers.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Fast Training of Diffusion Models with Masked Transformers

Reference 47

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This paper cites iBOT: Image BERT Pre-Training with Online Tokenizer.

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 48

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Observation ea50a6f4-551d-4743-8670-bc21ae9a44b0 · outbound

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

Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%

Reference 49

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Pith citing papers

Observation 1d1a5b03-d8d8-4f92-abcd-419691160227 · inbound

DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving cites this paper.

DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 49

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Observation 9d4c211b-ad04-46bc-8849-5514d4617327 · inbound

VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension cites this paper.

VersatileMotion: A Unified Framework for Motion Synthesis and Comprehension Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 91

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GMem: A Modular Approach for Ultra-Efficient Generative Models cites this paper.

GMem: A Modular Approach for Ultra-Efficient Generative Models Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 25

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Observation b55997f5-f1f5-4024-82d7-7b2d4262b30b · inbound

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation cites this paper.

Learnings from Scaling Visual Tokenizers for Reconstruction and Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-10T19:48:08.230701Z

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Observation 4975f68d-d73b-4453-ba8e-dd1a490ff62e · inbound

Masked Autoencoders Are Effective Tokenizers for Diffusion Models cites this paper.

Masked Autoencoders Are Effective Tokenizers for Diffusion Models Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-09T04:47:30.447243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 239dd823-bff6-4a1b-a735-2adfe6e23457 · inbound

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? cites this paper.

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T21:50:24.578485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4fe71f5-3d36-41bf-90d8-e695bf085cad · inbound

Efficient Diffusion Models: A Survey cites this paper.

Efficient Diffusion Models: A Survey Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-09T16:13:37.294468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18bc2b63-b362-4392-bff1-31668749423e · inbound

Unified Continuous Generative Models cites this paper.

Unified Continuous Generative Models Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T22:25:31.172575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b553b981-26be-4d8a-92ae-a673c15ab3ab · inbound

VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models cites this paper.

VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:09.922611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5053abe2-6e75-491c-bbf8-404f1115cdb3 · inbound

MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation cites this paper.

MagiCodec: Simple Masked Gaussian-Injected Codec for High-Fidelity Reconstruction and Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T12:12:30.011254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f8b32c0-e3f5-4db2-8a04-60aeb491a8b6 · inbound

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis cites this paper.

Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T20:49:42.947470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dc8fd61-c9cd-4a30-b492-7dd3f91223b9 · inbound

PixNerd: Pixel Neural Field Diffusion cites this paper.

PixNerd: Pixel Neural Field Diffusion Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T10:59:53.473492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba4b9476-f5ee-4732-9ffa-0a6cfb21a065 · inbound

DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation cites this paper.

DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:49:08.186107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 766379dd-9c88-43ea-bf5c-b359e439c54a · inbound

PixelGen: Improving Pixel Diffusion with Perceptual Supervision cites this paper.

PixelGen: Improving Pixel Diffusion with Perceptual Supervision Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:57:33.214832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a97c9a2d-426e-4489-90e2-409f0158ede8 · inbound

End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer cites this paper.

End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T15:36:05.934365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e8760d58-206f-46e1-9f21-fde921f97276 · inbound

ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters cites this paper.

ViTok-v2: Scaling Native Resolution Auto-Encoders to 5 Billion Parameters Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:51:06.420122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6a17a132-7be3-4ce7-9ed3-287697a09b46 · inbound

CaloArt: Large-Patch x-Prediction Diffusion Transformers for High-Granularity Calorimeter Shower Generation cites this paper.

CaloArt: Large-Patch x-Prediction Diffusion Transformers for High-Granularity Calorimeter Shower Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-13T04:42:15.855442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8a755f98-52cd-4f10-b3af-b26d162f56ac · inbound

Improved Baselines with Representation Autoencoders cites this paper.

Improved Baselines with Representation Autoencoders Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:43:15.302178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a22dab3a-321b-4198-a409-b5db793c0d81 · inbound

GPIC: A Giant Permissive Image Corpus for Visual Generation cites this paper.

GPIC: A Giant Permissive Image Corpus for Visual Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-29T07:43:14.150899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c776846f-e51d-4cea-ae1b-44c91f7bf19f · inbound

DiffusionBench: On Holistic Evaluation of Diffusion Transformers cites this paper.

DiffusionBench: On Holistic Evaluation of Diffusion Transformers Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T16:59:58.168322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7dff96bd-e47a-4b6e-8eb9-4a93157db383 · inbound

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation cites this paper.

MIMFlow: Integrating Masked Image Modeling with Normalizing Flows for End-to-End Image Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:50:11.495947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72b3a6a7-16c3-409f-bd17-21bebb113e0b · inbound

Flow Matching in Feature Space for Stochastic World Modeling cites this paper.

Flow Matching in Feature Space for Stochastic World Modeling Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T09:34:33.790768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 71c09cae-d359-44a1-92fa-f9069c1b6241 · inbound

OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation cites this paper.

OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-30T10:34:57.644671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1e7537f-ad84-4ebd-ba0f-abe4e25c184d · inbound

OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation cites this paper.

OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T01:50:56.920896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4fefa1c-fa19-437b-bb23-f9a0a1dc7f6e · inbound

KVAE: Family of Tokenizers for Multimodal Generative Models cites this paper.

KVAE: Family of Tokenizers for Multimodal Generative Models Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 113

Resolution: unresolved; no resolver link, observed 2026-08-07T23:24:49.398739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 299aafc6-be85-449a-aa3a-071e93332035 · inbound

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling cites this paper.

XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-16T00:18:29.808278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.