Paper Citation Record · LEDGER

Scaling Properties of Diffusion Models for Perceptual Tasks

As of 23 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 5 inbound Pith citation observations for arXiv:2411.08034.

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

pith.paper-citation-record.v1
2411.08034 v3

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T22:02:11.432360Z

measured 64 of 64 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:50:29.998263Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T14:31:40.570211Z

Reference resolution

59 of 59 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3a9fcf9b-0ab8-4444-af4b-fe0a81de63fa · outbound

This paper cites SegDiff: Image Segmentation with Diffusion Probabilistic Models.

Scaling Properties of Diffusion Models for Perceptual Tasks SegDiff: Image Segmentation with Diffusion Probabilistic Models

Reference 1

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Source-reported events for the cited work

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Observation 08691dcc-daa6-49ed-bbc2-dda80b41b7f6 · outbound

This paper cites Label-Efficient Semantic Segmentation with Diffusion Models.

Scaling Properties of Diffusion Models for Perceptual Tasks Label-Efficient Semantic Segmentation with Diffusion Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T22:02:10.969006Z

Source-reported events for the cited work

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Observation dcf12a00-c034-4999-9169-14ba82117c15 · outbound

This paper cites Denoising pretraining for semantic segmentation.

Scaling Properties of Diffusion Models for Perceptual Tasks Denoising pretraining for semantic segmentation

Reference 3

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Observation 49a2736b-01a0-4c07-9fce-86ac1e7c455e · outbound

This paper cites InstructPix2Pix: Learning to Follow Image Editing Instructions.

Scaling Properties of Diffusion Models for Perceptual Tasks InstructPix2Pix: Learning to Follow Image Editing Instructions

Reference 4

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Source-reported events for the cited work

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Observation 70032374-149c-4eac-aa8b-582eb6c52b81 · outbound

This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.

Scaling Properties of Diffusion Models for Perceptual Tasks Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Reference 5

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Observation dc1d8333-0fb5-4004-a18f-914cc9ca19fe · outbound

This paper cites Muse: Text-To-Image Generation via Masked Generative Transformers.

Scaling Properties of Diffusion Models for Perceptual Tasks Muse: Text-To-Image Generation via Masked Generative Transformers

Reference 6

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Source-reported events for the cited work

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Observation c0ead95b-587f-4edf-bba7-84906f52a805 · outbound

This paper cites A generalist framework for panoptic segmentation of images and videos.

Scaling Properties of Diffusion Models for Perceptual Tasks A generalist framework for panoptic segmentation of images and videos

Reference 7

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Observation 435a4e4e-f61f-4096-ab9f-356a658b283c · outbound

This paper cites DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation.

Scaling Properties of Diffusion Models for Perceptual Tasks DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation

Reference 8

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Source-reported events for the cited work

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Observation dd52db63-07d4-4426-b15e-3772697d5997 · outbound

This paper cites Swag: Storytelling with action guidance.

Scaling Properties of Diffusion Models for Perceptual Tasks Swag: Storytelling with action guidance

Reference 9

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Observation c80cc919-b96b-48f0-96ec-acc43697bcb2 · outbound

This paper cites Scaling Diffusion Transformers to 16 Billion Parameters.

Scaling Properties of Diffusion Models for Perceptual Tasks Scaling Diffusion Transformers to 16 Billion Parameters

Reference 10

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Observation 62b632c6-fc1c-4c05-9424-7d20894c549b · outbound

This paper cites Scaling diffusion transformers to 16 billion parameters.

Scaling Properties of Diffusion Models for Perceptual Tasks Scaling diffusion transformers to 16 billion parameters

Reference 11

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Observation f3f77ccb-36c7-4114-8523-00e0a75fd58a · outbound

This paper cites FlowNet: Learning Optical Flow with Convolutional Networks.

Scaling Properties of Diffusion Models for Perceptual Tasks FlowNet: Learning Optical Flow with Convolutional Networks

Reference 12

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Source-reported events for the cited work

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Observation 652ae7da-bd96-4092-af7b-5fed7ee57b9a · outbound

This paper cites GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image.

Scaling Properties of Diffusion Models for Perceptual Tasks GeoWizard: Unleashing the Diffusion Priors for 3D Geometry Estimation from a Single Image

Reference 13

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Source-reported events for the cited work

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Observation ca4d43d4-85b5-451d-bbd4-b9541a5759ac · outbound

This paper cites Generative adversarial nets.

Scaling Properties of Diffusion Models for Perceptual Tasks Generative adversarial nets

Reference 14

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Observation 10ca0a3e-07ce-4afd-8c3d-c9379c4ea6d4 · outbound

This paper cites Diffusioninst: Diffusion model for instance segmentation.

Scaling Properties of Diffusion Models for Perceptual Tasks Diffusioninst: Diffusion model for instance segmentation

Reference 15

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Source-reported events for the cited work

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Observation d84e2cab-3149-4424-9d49-809beb821231 · outbound

This paper cites MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis.

Scaling Properties of Diffusion Models for Perceptual Tasks MARS: Mixture of Auto-Regressive Models for Fine-grained Text-to-image Synthesis

Reference 16

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Source-reported events for the cited work

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Observation 5b0007c5-ea98-48f9-b046-003638c8ee89 · outbound

This paper cites Denoising diffusion probabilistic models.

Scaling Properties of Diffusion Models for Perceptual Tasks Denoising diffusion probabilistic models

Reference 17

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Observation 0a383a76-cc84-40b3-901d-c2281fbec339 · outbound

This paper cites Argmax flows and multinomial diffusion: Learning categorical distributions.

Scaling Properties of Diffusion Models for Perceptual Tasks Argmax flows and multinomial diffusion: Learning categorical distributions

Reference 18

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Observation 1a3658f5-3df9-4cca-85a8-a243e82e7fde · outbound

This paper cites Zero-shot text-guided object generation with dream fields.

Scaling Properties of Diffusion Models for Perceptual Tasks Zero-shot text-guided object generation with dream fields

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:02:12.837153Z

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Observation 09a95fbf-aedc-445e-a967-fc6320ad7dfd · outbound

This paper cites Ddp: Diffusion model for dense visual prediction.

Scaling Properties of Diffusion Models for Perceptual Tasks Ddp: Diffusion model for dense visual prediction

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:02:12.815167Z

Source-reported events for the cited work

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Observation 5d7d3b7a-196e-426a-ad73-b4ddedca2757 · outbound

This paper cites Repurposing diffusion-based image generators for monocular depth estimation.

Scaling Properties of Diffusion Models for Perceptual Tasks Repurposing diffusion-based image generators for monocular depth estimation

Reference 21

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Observation e6ea3261-97fd-48a2-94b2-a05251c07cac · outbound

This paper cites Auto-Encoding Variational Bayes.

Scaling Properties of Diffusion Models for Perceptual Tasks Auto-Encoding Variational Bayes

Reference 22

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Observation 3d06de83-56b7-488d-9320-718b273cccbc · outbound

This paper cites Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints.

Scaling Properties of Diffusion Models for Perceptual Tasks Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints

Reference 24

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Observation 093e33da-254c-4f2e-a9ea-a71889a0e662 · outbound

This paper cites On the scalability of diffusion-based text-to-image generation.

Scaling Properties of Diffusion Models for Perceptual Tasks On the scalability of diffusion-based text-to-image generation

Reference 25

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Source-reported events for the cited work

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Observation d70b1771-8f05-484f-858d-30d902a7a76a · outbound

This paper cites Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation.

Scaling Properties of Diffusion Models for Perceptual Tasks Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation

Reference 26

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Observation 1d0a1b4c-9ea3-4c02-ab5c-5f2dd2155f0a · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object.

Scaling Properties of Diffusion Models for Perceptual Tasks Zero-1-to-3: Zero-shot one image to 3d object

Reference 27

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Observation fd276a89-13f1-4e8a-8baf-11f37ddf09aa · outbound

This paper cites StyleGAN Encoder-Based Attack for Block Scrambled Face Images.

Scaling Properties of Diffusion Models for Perceptual Tasks StyleGAN Encoder-Based Attack for Block Scrambled Face Images

Reference 28

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Observation 35c5927a-0b1a-46e9-bd6d-f1f2332b61cb · outbound

This paper cites Flowdiffuser: Advancing optical flow estimation with diffusion models.

Scaling Properties of Diffusion Models for Perceptual Tasks Flowdiffuser: Advancing optical flow estimation with diffusion models

Reference 29

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Source-reported events for the cited work

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Observation 5a69619f-3dee-44f9-a8ac-2167687a78af · outbound

This paper cites Improved Denoising Diffusion Probabilistic Models.

Scaling Properties of Diffusion Models for Perceptual Tasks Improved Denoising Diffusion Probabilistic Models

Reference 30

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Observation b4c93dc3-f60c-4bf3-b16f-771973243457 · outbound

This paper cites Learning to reason with llms.

Scaling Properties of Diffusion Models for Perceptual Tasks Learning to reason with llms

Reference 31

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Observation 6da9359f-9c5a-4c4b-a0c0-c2516ad7d397 · outbound

This paper cites pix2gestalt: Amodal segmentation by synthesizing wholes.

Scaling Properties of Diffusion Models for Perceptual Tasks pix2gestalt: Amodal segmentation by synthesizing wholes

Reference 32

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Observation 07e33b04-6bf4-4806-939c-534f75a17310 · outbound

This paper cites Scalable diffusion models with transformers.

Scaling Properties of Diffusion Models for Perceptual Tasks Scalable diffusion models with transformers

Reference 33

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Observation a9acd574-3371-4902-a418-284d06c176d6 · outbound

This paper cites DreamFusion: Text-to-3D using 2D Diffusion.

Scaling Properties of Diffusion Models for Perceptual Tasks DreamFusion: Text-to-3D using 2D Diffusion

Reference 34

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Observation f829052d-9aee-47e6-bd98-870374e5f380 · outbound

This paper cites Variational inference with normalizing flows.

Scaling Properties of Diffusion Models for Perceptual Tasks Variational inference with normalizing flows

Reference 35

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Observation b29583d0-bd0a-4cbd-ba45-d5ea8974b457 · outbound

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

Scaling Properties of Diffusion Models for Perceptual Tasks High-resolution image synthesis with latent diffusion models

Reference 36

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Observation fb955c42-4f32-4ea2-98ec-d662042368ad · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Scaling Properties of Diffusion Models for Perceptual Tasks ImageNet Large Scale Visual Recognition Challenge

Reference 37

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Observation 5ca9f93d-d359-4318-959f-2f04588d2ca7 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Scaling Properties of Diffusion Models for Perceptual Tasks Photorealistic text-to-image diffusion models with deep language understanding

Reference 38

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Observation 7201d5ed-4643-4786-9d67-7e576f465818 · outbound

This paper cites Monocular Depth Estimation using Diffusion Models.

Scaling Properties of Diffusion Models for Perceptual Tasks Monocular Depth Estimation using Diffusion Models

Reference 39

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Observation 12d21be3-8e26-4a96-bd40-baf3971dfea2 · outbound

This paper cites The surprising effectiveness of diffusion models for optical flow and monocular depth estimation.

Scaling Properties of Diffusion Models for Perceptual Tasks The surprising effectiveness of diffusion models for optical flow and monocular depth estimation

Reference 40

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Observation 0bf58258-f477-4836-bf33-196d85b22726 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Scaling Properties of Diffusion Models for Perceptual Tasks Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 41

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Observation 30364e32-d307-44d6-930d-37d376ed501c · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

Scaling Properties of Diffusion Models for Perceptual Tasks Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 42

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Observation 668891c8-ac15-4767-aa40-a623b06f523e · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Scaling Properties of Diffusion Models for Perceptual Tasks Deep unsupervised learning using nonequilibrium thermodynamics

Reference 43

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Observation 3e9b56b1-6372-42b6-af20-91df50de1b14 · outbound

This paper cites Denoising diffusion implicit models.

Scaling Properties of Diffusion Models for Perceptual Tasks Denoising diffusion implicit models

Reference 44

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Source-reported events for the cited work

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Observation 3c5b9128-8e04-4274-974f-67b428656cb9 · outbound

This paper cites Consistency Models.

Scaling Properties of Diffusion Models for Perceptual Tasks Consistency Models

Reference 45

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Observation 4750175a-84ab-48c8-aaa1-f094c6567017 · outbound

This paper cites EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing.

Scaling Properties of Diffusion Models for Perceptual Tasks EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T22:02:11.323966Z

Source-reported events for the cited work

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Observation 607e5e9b-962e-46f6-af17-2aaa416804c8 · outbound

This paper cites Semantic diffusion network for semantic segmentation.

Scaling Properties of Diffusion Models for Perceptual Tasks Semantic diffusion network for semantic segmentation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:02:12.558504Z

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Observation b40cfc3a-2057-4383-b8fa-95eaf1d4f8db · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Scaling Properties of Diffusion Models for Perceptual Tasks Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 48

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Source-reported events for the cited work

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Observation 99cb7438-5131-4c71-b3ef-80195e1d42d1 · outbound

This paper cites Pixel recurrent neural networks.

Scaling Properties of Diffusion Models for Perceptual Tasks Pixel recurrent neural networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:02:12.536370Z

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Observation f1da8509-5078-4660-bfe9-43155b428bfe · outbound

This paper cites Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation.

Scaling Properties of Diffusion Models for Perceptual Tasks Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:02:12.516369Z

Source-reported events for the cited work

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Observation 7cdf42af-3c41-462f-afbe-b23a374e6e79 · outbound

This paper cites Novel View Synthesis with Diffusion Models.

Scaling Properties of Diffusion Models for Perceptual Tasks Novel View Synthesis with Diffusion Models

Reference 51

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Source-reported events for the cited work

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Observation 0c3e6e51-dd0c-42cf-b509-0128b74f8c55 · outbound

This paper cites Deepflow: Large displacement optical flow with deep matching.

Scaling Properties of Diffusion Models for Perceptual Tasks Deepflow: Large displacement optical flow with deep matching

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:02:12.496174Z

Source-reported events for the cited work

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Observation 1b83eb03-e90e-4a55-9761-4693cac45fc4 · outbound

This paper cites Diffusion models for implicit image segmentation ensembles.

Scaling Properties of Diffusion Models for Perceptual Tasks Diffusion models for implicit image segmentation ensembles

Reference 53

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Source-reported events for the cited work

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Observation bad35701-1b32-4cbb-8e58-33746a41a021 · outbound

This paper cites Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer.

Scaling Properties of Diffusion Models for Perceptual Tasks Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T22:02:11.382422Z

Source-reported events for the cited work

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Observation aa49a38c-b6d2-4f51-94e5-66ac96c28f84 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

Scaling Properties of Diffusion Models for Perceptual Tasks Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 55

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Source-reported events for the cited work

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Observation 494ed852-ecd8-4d43-9c63-947f3416d3a1 · outbound

This paper cites Unleashing text-to-image diffusion models for visual perception.

Scaling Properties of Diffusion Models for Perceptual Tasks Unleashing text-to-image diffusion models for visual perception

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T22:02:12.462023Z

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Observation 325b7b5d-a573-4e99-936a-282b658335cb · outbound

This paper cites write newline.

Scaling Properties of Diffusion Models for Perceptual Tasks write newline

Reference 57

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Source-reported events for the cited work

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Observation b0e850ec-2d2c-4184-b52e-82a345f2f3e6 · outbound

This paper cites @esa (Ref.

Scaling Properties of Diffusion Models for Perceptual Tasks @esa (Ref

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-12T22:02:11.416620Z

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Unavailable: canonical work link unavailable.

Observation 008dc0bf-fdfd-4396-b905-a1472cb9af67 · outbound

This paper cites an unresolved cited work.

Scaling Properties of Diffusion Models for Perceptual Tasks Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T22:02:11.425725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af11e845-9a8a-4112-8314-2b1173db15ba · outbound

This paper cites 1bl ` u| 8.

Scaling Properties of Diffusion Models for Perceptual Tasks 1bl ` u| 8

Reference 60

Resolution: verified exact; raw_fallback, observed 2026-08-12T22:02:11.747920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 4bcd7db4-3eca-4331-9add-4844bfe52a34 · inbound

Exploring Representation-Aligned Latent Space for Better Generation cites this paper.

Exploring Representation-Aligned Latent Space for Better Generation Scaling Properties of Diffusion Models for Perceptual Tasks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T19:22:41.707119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 166d2c90-6e8e-4ee5-a84a-bcb8a8e6eabc · inbound

gen2seg: Generative Models Enable Generalizable Instance Segmentation cites this paper.

gen2seg: Generative Models Enable Generalizable Instance Segmentation Scaling Properties of Diffusion Models for Perceptual Tasks

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:31:40.573171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4cfc5efb-48f3-449b-8c94-83085e319eb2 · inbound

LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion cites this paper.

LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion Scaling Properties of Diffusion Models for Perceptual Tasks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T20:26:10.651525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f0d569b-3af3-4b5d-ad76-926b708c4bce · inbound

Perfect diffusion is $\mathsf{TC}^0$ -- Bad diffusion is Turing-complete cites this paper.

Perfect diffusion is $\mathsf{TC}^0$ -- Bad diffusion is Turing-complete Scaling Properties of Diffusion Models for Perceptual Tasks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:29.998263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0463b1c6-fa76-4b69-a177-e173c6016dd3 · inbound

The Serial Scaling Hypothesis cites this paper.

The Serial Scaling Hypothesis Scaling Properties of Diffusion Models for Perceptual Tasks

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:12:02.489954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.