Paper Citation Record · LEDGER

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models

As of 7 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 3 inbound Pith citation observations for arXiv:2506.01337.

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

pith.paper-citation-record.v1
2506.01337 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:51:35.556235Z

measured 58 of 58 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T22:28:35.981853Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:16:48.029764Z

Reference resolution

55 of 55 outbound references displayed

verified exact0
verified fuzzy5
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation edcc13ea-09ce-41d1-a1f9-60a7934a4be3 · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:31.828130Z

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Observation 7899e537-3c9d-4685-9983-c25d0d5b5c19 · outbound

This paper cites Weak-to-Strong Diffusion with Reflection.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Weak-to-Strong Diffusion with Reflection

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:31.890351Z

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Observation c4e9dff3-c1a4-4b23-8517-32c007fe5213 · outbound

This paper cites Attend-and-excite: Attention- based semantic guidance for text-to-image diffusion models.ACM transactions on Graphics (TOG), 42(4):1–10, 2023.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Attend-and-excite: Attention- based semantic guidance for text-to-image diffusion models.ACM transactions on Graphics (TOG), 42(4):1–10, 2023

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:31.955004Z

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Observation 9cb01901-5060-4d24-8f44-133993e455f3 · outbound

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

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.004357Z

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Observation 746dbf60-39cd-465e-8e21-343a84678303 · outbound

This paper cites Generative pretraining from pixels.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Generative pretraining from pixels

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.064060Z

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Observation 0d37f516-4750-4ca1-9301-967f6e209491 · outbound

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

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models On the Importance of Noise Scheduling for Diffusion Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.141743Z

Source-reported events for the cited work

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Observation dbd467b8-9e43-40f0-af7a-d79274d17fc3 · outbound

This paper cites Bert: Pre-training of deep bidi- rectional transformers for language understanding.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Bert: Pre-training of deep bidi- rectional transformers for language understanding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.223900Z

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Observation 31eec31b-dcb2-40f9-b501-890642730bf0 · outbound

This paper cites Diffusion models beat gans on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Diffusion models beat gans on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.294293Z

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Observation ee9f5e3c-1c92-4579-b745-186dcc8083c5 · outbound

This paper cites Geneval: An object-focused framework for evaluating text-to-image alignment.Advances in Neural Information Processing Systems, 36:52132–52152, 2023.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Geneval: An object-focused framework for evaluating text-to-image alignment.Advances in Neural Information Processing Systems, 36:52132–52152, 2023

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.377687Z

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Observation 31a41766-875e-40db-a7a4-c377edb79bde · outbound

This paper cites Initno: Boosting text-to- image diffusion models via initial noise optimization.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Initno: Boosting text-to- image diffusion models via initial noise optimization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:51:36.816818Z

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Observation 86c8965f-f345-46df-99a4-054f7ab2d2aa · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Dream to Control: Learning Behaviors by Latent Imagination

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.506555Z

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Observation de2465a8-5340-4c14-aab3-5b1623fcdcb5 · outbound

This paper cites CLIPScore: A Reference-free Evaluation Metric for Image Captioning.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.596199Z

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Observation 280cbccb-f85e-4e22-b6f0-1a9d0d3d8664 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.656657Z

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Observation 3efd6211-0e85-45a9-a167-cde52ee2a6b1 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.739267Z

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Observation c145103f-367a-496e-bd06-28f6b0b16bae · outbound

This paper cites Classifier-free diffusion guidance.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Classifier-free diffusion guidance

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.815142Z

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Observation 4f575e5e-4ae6-4589-8be4-e5f4f813126f · outbound

This paper cites Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.896108Z

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Observation c12de654-cdc7-4643-bd39-dab06b9e635b · outbound

This paper cites Auto-encoding variational bayes, 2013.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Auto-encoding variational bayes, 2013

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:32.974766Z

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Observation bbee80aa-e0c9-43ad-8ab6-61b799cd7faa · outbound

This paper cites Pick-a- pic: An open dataset of user preferences for text-to-image generation.Advances in Neural Information Processing Systems, 36:36652–36663, 2023.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Pick-a- pic: An open dataset of user preferences for text-to-image generation.Advances in Neural Information Processing Systems, 36:36652–36663, 2023

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:51:36.645590Z

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 4dff0b0f-03ae-444c-b6cf-bf358fb79019 · outbound

This paper cites Autoregressive image generation without vector quantization.Advances in Neural Information Processing Systems, 37:56424–56445, 2024.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Autoregressive image generation without vector quantization.Advances in Neural Information Processing Systems, 37:56424–56445, 2024

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.130560Z

Source-reported events for the cited work

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Observation 90fc9349-56ef-4074-ae6e-9e5335767cba · outbound

This paper cites Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.213194Z

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Observation 4a9805c2-387e-4d3b-8016-87eeb5e7b2f0 · outbound

This paper cites Microsoft coco: Common objects in context.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Microsoft coco: Common objects in context

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.285744Z

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Observation 1073ed53-87c6-4753-bb03-f865c4fa43be · outbound

This paper cites Flow Matching for Generative Modeling.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Flow Matching for Generative Modeling

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.333704Z

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Observation 12a1dfae-7c2c-4259-b519-7fadb68d4b20 · outbound

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

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.381494Z

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Observation e8c45042-9274-4cd7-a17c-5345050cf3f2 · outbound

This paper cites Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.464926Z

Source-reported events for the cited work

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Observation 64decbae-9572-4a90-a5ee-8eb848bc6a7e · outbound

This paper cites GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.526140Z

Source-reported events for the cited work

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Observation 9c83f28d-1aeb-4d6c-a098-1d3f76347c50 · outbound

This paper cites Improved denoising diffusion probabilistic models.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Improved denoising diffusion probabilistic models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.585921Z

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Observation 050249dd-0a00-4cf5-a3ed-51e68ff0a188 · outbound

This paper cites Image transformer.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Image transformer

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.772917Z

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Observation b552eee0-9027-4f3b-a005-0c491aa0d5c6 · outbound

This paper cites Scalable diffusion models with transformers.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Scalable diffusion models with transformers

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.847752Z

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Observation bb115050-3b65-44a2-89ab-9636b946fbf3 · outbound

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

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:33.920499Z

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Observation 9296336b-28e2-4671-8c8a-32d1b531b716 · outbound

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

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Improving language understanding by generative pre-training

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.003343Z

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Observation 785bd10d-7576-4dcd-b30c-135aa9eb8ea0 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Direct preference optimization: Your language model is secretly a reward model.Advances in Neural Information Processing Systems, 36:53728–53741, 2023

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.043568Z

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Observation 437b609a-898c-445b-8030-55b05322b013 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.122267Z

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Observation 81e7e57c-30cd-40be-a81c-85ddcf9e4d6b · outbound

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

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Zero-shot text-to-image generation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.185660Z

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Observation 8f3f915e-7f0f-4b08-815c-3904b7bc447f · outbound

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

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Generating diverse high-fidelity images with vq-vae-2

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.266660Z

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Observation b999cc47-82aa-4315-bea4-bb088edc51e0 · outbound

This paper cites Variational inference with normalizing flows.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Variational inference with normalizing flows

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.344496Z

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Observation 6a2350b8-efac-4801-b64b-e019f283852a · outbound

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

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models High-resolution image synthesis with latent diffusion models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.434480Z

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Observation a8939d9c-5597-4ae0-83d8-d52ba9adbd79 · outbound

This paper cites Photorealistic text-to- image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Photorealistic text-to- image diffusion models with deep language understanding.Advances in neural information processing systems, 35:36479–36494, 2022

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.599910Z

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Observation eb11e8b2-f6c2-4b53-a710-9c4d21ab7eac · outbound

This paper cites Adversarial diffusion distillation.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Adversarial diffusion distillation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:51:36.335413Z

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Observation cd69f0c5-f914-41d5-8bd1-2cb44547ecd8 · outbound

This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Laion-5b: An open large-scale dataset for training next generation image-text models.Advances in neural information processing systems, 35:25278–25294, 2022

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.737445Z

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Observation 4bec739b-c5d2-4973-a64d-95dfef3d8656 · outbound

This paper cites Denoising Diffusion Implicit Models.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Denoising Diffusion Implicit Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.794514Z

Source-reported events for the cited work

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Observation a56329c9-6f67-4af4-ba3e-e345d0ff1f34 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.856510Z

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Observation 7c718799-23f8-4d0d-9b5f-4d98c2de8432 · outbound

This paper cites Sequence to sequence learning with neural networks.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Sequence to sequence learning with neural networks

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.896513Z

Source-reported events for the cited work

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Observation 9b26c422-a26a-4b42-ae74-5d1dfb8ed421 · outbound

This paper cites MIT press Cambridge, 1998.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models MIT press Cambridge, 1998

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.930206Z

Source-reported events for the cited work

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Observation 2c02d3e1-bd82-45e2-aeb7-5b66e1f54b2d · outbound

This paper cites Conditional image generation with pixelcnn decoders.Advances in neural information processing systems, 29, 2016.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Conditional image generation with pixelcnn decoders.Advances in neural information processing systems, 29, 2016

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:34.972824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1843bf28-6c53-4aa8-89ac-bd021c463773 · outbound

This paper cites Pixel recurrent neural networks.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Pixel recurrent neural networks

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:35.027074Z

Source-reported events for the cited work

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Observation 3fe91d91-e348-4df7-a645-d28ce64152a6 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:35.078789Z

Source-reported events for the cited work

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Observation 24241b77-8ab8-4096-b6b1-05846126c70a · outbound

This paper cites A learning algorithm for continually running fully recurrent neural networks.Neural computation, 1(2):270–280, 1989.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models A learning algorithm for continually running fully recurrent neural networks.Neural computation, 1(2):270–280, 1989

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:35.160016Z

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Observation eafafbd4-9457-4e35-88e4-eb9e2db15f30 · outbound

This paper cites Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:35.223086Z

Source-reported events for the cited work

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Observation 29afe5ee-0f0a-434c-ab57-6d72d7a2a30f · outbound

This paper cites Imagereward: Learning and evaluating human preferences for text-to-image generation.Advances in Neural Information Processing Systems, 36:15903–15935, 2023.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Imagereward: Learning and evaluating human preferences for text-to-image generation.Advances in Neural Information Processing Systems, 36:15903–15935, 2023

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:35.295604Z

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Observation 3b315967-0c14-4afe-b9d5-44e469e659e3 · outbound

This paper cites Good seed makes a good crop: Discovering secret seeds in text-to-image diffusion models.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Good seed makes a good crop: Discovering secret seeds in text-to-image diffusion models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T11:51:35.355999Z

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Observation fec2dca6-a528-4282-9a88-5dac34530d0a · outbound

This paper cites Learn- ing multi-dimensional human preference for text-to-image generation.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Learn- ing multi-dimensional human preference for text-to-image generation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:51:36.146230Z

Source-reported events for the cited work

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Observation 45dd44b7-c359-49d2-a3db-b932b0f05125 · outbound

This paper cites Golden Noise for Diffusion Models: A Learning Framework.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Golden Noise for Diffusion Models: A Learning Framework

Reference 53

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Observation be0e67a5-1c1f-4020-a38c-3524d9412ae2 · outbound

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NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:51:36.113267Z

Source-reported events for the cited work

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Observation 0e83dfbd-0143-4e5f-a8f1-ae5a98e428ec · outbound

This paper cites an unresolved cited work.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:51:36.073874Z

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 c51ca203-0008-4e7f-b0ec-d031602be9de · outbound

This paper cites Initial State Manipulation.

NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models Initial State Manipulation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:51:36.025708Z

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.

Pith citing papers

Observation f9ea0861-f79f-4efc-b9b6-519e6f4d1653 · inbound

Na\"ive PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation cites this paper.

Na\"ive PAINE: Lightweight Text-to-Image Generation Improvement with Prompt Evaluation NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-14T22:28:35.981853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9cbbaaa-77f1-4275-9dfc-2e436e12ec6c · inbound

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling cites this paper.

LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T01:45:51.163286Z

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 e104c5ca-4df4-4c13-b6b4-5ba01c98853d · inbound

Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So? cites this paper.

Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So? NoiseAR: AutoRegressing Initial Noise Prior for Diffusion Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:16:48.031164Z

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.