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Paper Citation Record · LEDGER

MACS: Measurement-Aware Consistency Sampling for Inverse Problems

As of 12 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2510.02208.

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

pith.paper-citation-record.v1
2510.02208 v3

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T12:45:35.621990Z

measured 62 of 62 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

62 of 62 outbound references displayed

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Outbound references

Observation 4d041cb6-8884-44b7-8e67-f4d499b36b7b · outbound

This paper cites Solving inverse problems in medical imaging with score-based generative models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Solving inverse problems in medical imaging with score-based generative models,

Reference 1

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Observation cf419e5e-1ed6-4b31-8790-3ae6d7aec7b2 · outbound

This paper cites Compressed sensing us- ing generative models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Compressed sensing us- ing generative models,

Reference 2

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Observation 00d364ca-5328-4382-9030-2e6db7a58ad8 · outbound

This paper cites An overview of full-waveform inversion in exploration geophysics,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems An overview of full-waveform inversion in exploration geophysics,

Reference 3

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Observation 58daeb8f-7059-45bd-8bae-99ba047a86ef · outbound

This paper cites Video diffusion models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Video diffusion models,

Reference 4

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Observation 70125acc-13f2-4370-906d-4888c8e25427 · outbound

This paper cites Diffir2vr-zero: Zero-shot video restoration with diffusion- based image restoration models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Diffir2vr-zero: Zero-shot video restoration with diffusion- based image restoration models,

Reference 5

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Observation 4b68755e-055d-410f-b07c-4724b9ebffc7 · outbound

This paper cites Warped diffusion: Solving video inverse problems with image diffusion models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Warped diffusion: Solving video inverse problems with image diffusion models,

Reference 6

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Observation 80ee487e-a207-43d7-b9ae-1362392c0a10 · outbound

This paper cites Vision-XL: High definition video inverse problem solver using latent image diffusion models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Vision-XL: High definition video inverse problem solver using latent image diffusion models,

Reference 7

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Observation e4513e1a-5957-4e47-bc94-7dee6ebb48ea · outbound

This paper cites Solving video inverse problems using image diffusion models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Solving video inverse problems using image diffusion models,

Reference 8

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Observation c51b9d18-ccb3-45aa-bf57-5d4b3ee71816 · outbound

This paper cites Solving audio inverse prob- lems with a diffusion model,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Solving audio inverse prob- lems with a diffusion model,

Reference 9

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Observation 4da73747-dd53-4c6e-a422-856d67874031 · outbound

This paper cites Image denoising by sparse 3-D transform-domain collaborative filtering,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Image denoising by sparse 3-D transform-domain collaborative filtering,

Reference 10

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Observation 0b207c89-af45-4469-af3c-3cce1f4afdfb · outbound

This paper cites Image denoising via sparse and redundant representations over learned dictionaries,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Image denoising via sparse and redundant representations over learned dictionaries,

Reference 11

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Observation 700ba56d-2d2b-4a1c-a210-cb0d1c714803 · outbound

This paper cites Weighted nuclear norm minimization with application to image denoising,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Weighted nuclear norm minimization with application to image denoising,

Reference 12

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Observation df348bac-dd1d-4be3-a826-fe4b97c67725 · outbound

This paper cites Image denoising: The deep learning revolution and beyond—a survey paper,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Image denoising: The deep learning revolution and beyond—a survey paper,

Reference 13

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Observation 83af8e08-88fe-43da-b6ac-35c853d9130a · outbound

This paper cites A residual dense U-Net neural network for image denoising,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems A residual dense U-Net neural network for image denoising,

Reference 14

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Observation 1aed3f5b-ee8c-445f-9193-85ddeee0c6e3 · outbound

This paper cites Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,

Reference 15

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Observation 582280c2-535a-471d-9d82-5b9dde12d400 · outbound

This paper cites High perceptual quality image denoising with a posterior sampling cGAN,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems High perceptual quality image denoising with a posterior sampling cGAN,

Reference 16

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Observation d9bd0a07-e0c9-4525-b9a8-176e16c71c44 · outbound

This paper cites Denoising diffusion probabilistic models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Denoising diffusion probabilistic models,

Reference 17

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Observation 90632289-bb29-4291-963c-44f3d61822a4 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Generative modeling by estimating gradients of the data distribution,

Reference 18

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Observation a8d7c224-aaa7-4afa-85fc-2fbc68f56bd6 · outbound

This paper cites Diffusion models beat GANs on image synthesis,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Diffusion models beat GANs on image synthesis,

Reference 19

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Observation 8096a61e-9a4e-41ad-9583-2e97bd4a013f · outbound

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

MACS: Measurement-Aware Consistency Sampling for Inverse Problems High- resolution image synthesis with latent diffusion models,

Reference 20

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Observation 32ec346c-f3c9-437f-8a55-b92099630035 · outbound

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

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Score-Based Generative Modeling through Stochastic Differential Equations

Reference 21

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Observation 5352242f-d0b0-4843-be24-d32526f828e8 · outbound

This paper cites Denoising diffusion restoration models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Denoising diffusion restoration models,

Reference 22

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Observation 2573e1d2-8d6f-4f79-8c2e-d994ee874fd0 · outbound

This paper cites Deep back-projection networks for super-resolution,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Deep back-projection networks for super-resolution,

Reference 23

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Observation 251aa141-579d-4b4e-b444-56b262f67203 · outbound

This paper cites Image super-resolution via iterative refinement,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Image super-resolution via iterative refinement,

Reference 24

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Observation 7f19704b-90c8-4bd0-b6b6-07d4115759ee · outbound

This paper cites Semantic image inpainting with deep generative models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Semantic image inpainting with deep generative models,

Reference 25

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Observation 380be501-cd85-4e1e-b144-fa8791fd0208 · outbound

This paper cites RePaint: Inpainting using denoising diffusion probabilistic mod- els,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems RePaint: Inpainting using denoising diffusion probabilistic mod- els,

Reference 26

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Observation 890c1c34-b8c1-4173-bbed-41e45222f420 · outbound

This paper cites DeblurGAN-v2: De- blurring (orders-of-magnitude) faster and better,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems DeblurGAN-v2: De- blurring (orders-of-magnitude) faster and better,

Reference 27

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Observation 96ece306-0d2f-479e-b2f2-28e5ad252513 · outbound

This paper cites Palette: Image-to-image diffusion models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Palette: Image-to-image diffusion models,

Reference 28

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Observation a137b472-14c9-49fe-a42e-1adb76b79682 · outbound

This paper cites Solving inverse problems with latent diffusion models via hard data consistency,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Solving inverse problems with latent diffusion models via hard data consistency,

Reference 29

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Observation d826c344-1186-4367-a48a-ff916ac9a92c · outbound

This paper cites SILO: Solving inverse problems with latent operators,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems SILO: Solving inverse problems with latent operators,

Reference 30

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Observation 0b0bff03-c082-4178-aae4-6567fdb3b85a · outbound

This paper cites Improving diffusion models for inverse problems using manifold constraints,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Improving diffusion models for inverse problems using manifold constraints,

Reference 31

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Observation 700d3292-29cb-44d2-81fd-27c1bddf304e · outbound

This paper cites Diffusion posterior sampling for general noisy inverse problems,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Diffusion posterior sampling for general noisy inverse problems,

Reference 32

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Observation b74440b3-8c55-4266-927b-858d5893d7e2 · outbound

This paper cites Decomposed diffusion sampler for accelerating large-scale inverse problems,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Decomposed diffusion sampler for accelerating large-scale inverse problems,

Reference 33

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Observation 967eac2e-801e-4c2c-8a96-8526c9fa9938 · outbound

This paper cites Solving 3D inverse problems using pre-trained 2D diffusion models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Solving 3D inverse problems using pre-trained 2D diffusion models,

Reference 34

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Observation 36165551-15dc-4de4-a4b2-9a56537db377 · outbound

This paper cites SNIPS: Solving noisy inverse problems stochastically,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems SNIPS: Solving noisy inverse problems stochastically,

Reference 35

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Observation c0d9812d-4492-4d2b-9f16-70ebcf268125 · outbound

This paper cites Pseudoinverse-guided diffusion models for inverse problems,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Pseudoinverse-guided diffusion models for inverse problems,

Reference 36

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Observation 8f77b5dc-1d8e-415f-93c4-bd6922f41895 · outbound

This paper cites Zero-shot image restoration using de- noising diffusion null-space model,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Zero-shot image restoration using de- noising diffusion null-space model,

Reference 37

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Observation e0d56845-35b0-40ee-b51c-36379bc495fb · outbound

This paper cites ILVR: Condition- ing method for denoising diffusion probabilistic models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems ILVR: Condition- ing method for denoising diffusion probabilistic models,

Reference 38

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source=pdf_text observed=2026-08-04T12:45:32.512834Z digest=sha256:91e0f03c4bb5e9219e6e65159f5b785f46bc0661582dc19b6b6c98899a9b4044

Observation 99ab9ed2-57a3-45db-81cc-c2b054b42287 · outbound

This paper cites Direct diffusion bridge using data consistency for inverse problems,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Direct diffusion bridge using data consistency for inverse problems,

Reference 39

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source=pdf_text observed=2026-08-04T12:45:32.625436Z digest=sha256:d1e5a70c250e954f432be6b65c82360a3b11b2f420771a4809bbc4fe4224044b

Observation 576c4af1-1cfc-456d-aec6-e4cbd7512cef · outbound

This paper cites Denoising diffusion implicit models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Denoising diffusion implicit models,

Reference 40

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source=pdf_text observed=2026-08-04T12:45:32.753362Z digest=sha256:966890de43bfd9280bf42ae75176e5fafb14dd7ff7ccd52adf0b89834a920e18

Observation ac07c505-851c-4fb3-9ec0-bff93e5fa95d · outbound

This paper cites Consistency models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Consistency models,

Reference 41

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source=pdf_text observed=2026-08-04T12:45:32.863711Z digest=sha256:1d3bd5bcde05ea25b6f253ef1f0d7087aa1fbd826ee753e68bb1b09e74f1e2f9

Observation 277e6b13-bde6-4c47-abd4-4d5ff8d918a3 · outbound

This paper cites Multistep Consistency Models.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Multistep Consistency Models

Reference 42

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source=pdf_text observed=2026-08-04T12:45:32.918748Z digest=sha256:6c5ce5cc170380deaf3eca592a631599aafefb589db4b008709114d6b88e9575

Observation badf8d1f-d322-49c5-9a25-37389a9efc59 · outbound

This paper cites Consistency Models Made Easy.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Consistency Models Made Easy

Reference 43

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source=pdf_text observed=2026-08-04T12:45:32.996772Z digest=sha256:b2eae128f329436f2e0d58525ad7039682e8cd372925cc50dfe1a28eda22a7f1

Observation 997cb4d9-0a7b-4636-a87f-50c11c39ff7e · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 44

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source=pdf_text observed=2026-08-04T12:45:33.063084Z digest=sha256:2427e6f0b7c8c5e2facf0f40cce72de07895c73cda6da2a8a3d2c71e3db5b340

Observation a78783a9-d69a-4f9e-b07e-dbc5ee3eecd7 · outbound

This paper cites LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 45

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source=pdf_text observed=2026-08-04T12:45:33.132743Z digest=sha256:96cef3971321924b00c1c5282ac13df5e6de3f242ff6dca03fe6b936143b4596

Observation 6ee58323-581c-45af-9ec3-b8e7fae30cbc · outbound

This paper cites GANs trained by a two time-scale update rule converge to a local Nash equilibrium,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems GANs trained by a two time-scale update rule converge to a local Nash equilibrium,

Reference 46

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source=pdf_text observed=2026-08-04T12:45:33.190357Z digest=sha256:8294e97ceccc07b6ba887ffaa1ccba3d58c4652a44dca98ccba73e85a6a7f324

Observation cdc4b85a-0f07-43cf-a7b4-e6cd4539567c · outbound

This paper cites Demysti- fying MMD GANs,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Demysti- fying MMD GANs,

Reference 47

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source=pdf_text observed=2026-08-04T12:45:33.247650Z digest=sha256:f41c29af387f899a2995a1bf46c7134b269e7bbf97940375e0277c59218f982d

Observation de7ad89f-2b70-4307-874f-a4e9085fb7b8 · outbound

This paper cites Image quality assessment: From error visibility to structural similarity,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Image quality assessment: From error visibility to structural similarity,

Reference 48

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source=pdf_text observed=2026-08-04T12:45:33.370699Z digest=sha256:432ffb6ca36636f36f7c25ed63b27344f9b8a4d2d63bf1d530ed0b68268545db

Observation 491de452-d948-4855-a9f8-42d280e82462 · outbound

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

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference

Reference 49

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source=pdf_text observed=2026-08-04T12:45:33.491406Z digest=sha256:6ba10f791b3e485f982a13b0f72326c037a61aff6aa21dd185a102ba8708250a

Observation 70178b41-0932-4fc8-973b-cf4be4ceba3d · outbound

This paper cites InvFusion: Bridging supervised and zero-shot diffusion for inverse problems,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems InvFusion: Bridging supervised and zero-shot diffusion for inverse problems,

Reference 50

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source=pdf_text observed=2026-08-04T12:45:33.673148Z digest=sha256:9a4b947030b6bf586c0b802b31bdce1ef00486b69959d53cffa391ea23d8868f

Observation 382a8acb-1878-463f-b9f7-1783228ae6ae · outbound

This paper cites Loss-guided diffusion models for plug-and-play con- trollable generation,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Loss-guided diffusion models for plug-and-play con- trollable generation,

Reference 51

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source=pdf_text observed=2026-08-04T12:45:33.787310Z digest=sha256:63abf44446ac4e2639c6abcc7f036896e540b3089ce4a664316ef4ebe2c07632

Observation 587a762b-3e65-4c9c-ba5f-436effcdc338 · outbound

This paper cites DEFT: Efficient fine-tuning of diffusion models by learning the generalized h-transform,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems DEFT: Efficient fine-tuning of diffusion models by learning the generalized h-transform,

Reference 52

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source=pdf_text observed=2026-08-04T12:45:33.977543Z digest=sha256:2a610887c64473d5f221236ce22d6b84730760ceb8539720e14f154a10a1f262

Observation 4fe0d0a2-6ea0-4320-9045-63c0896039be · outbound

This paper cites A Survey on Diffusion Models for Inverse Problems.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems A Survey on Diffusion Models for Inverse Problems

Reference 53

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source=pdf_text observed=2026-08-04T12:45:34.100304Z digest=sha256:619f9413f19e1ffd67b92c25f4a38917aeb290c06c5b2464dd7f6671e94f8d9c

Observation 241b84e3-7e4d-439f-9091-34cf4837da09 · outbound

This paper cites CoSIGN: Few-step guidance of consistency model to solve general inverse problems,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems CoSIGN: Few-step guidance of consistency model to solve general inverse problems,

Reference 54

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source=pdf_text observed=2026-08-04T12:45:34.229581Z digest=sha256:bd916fe313e6147eb34e58d88bfa97998394ec506fac6d6055f1b01b364fa403

Observation b21215a3-ea4e-4c6c-8c0c-722d9f4e24ea · outbound

This paper cites Con- sistency models for scalable and fast simulation-based inference,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Con- sistency models for scalable and fast simulation-based inference,

Reference 55

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source=pdf_text observed=2026-08-04T12:45:34.378909Z digest=sha256:09b878cda143fbab4ec566ab595c5e4e7cc2b6171ba1f98f61490173c3c98f4b

Observation 8c98bb71-1683-4bce-bfe7-5be445227e26 · outbound

This paper cites LATINO-PRO: Latent consistency inverse solver with prompt opti- mization,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems LATINO-PRO: Latent consistency inverse solver with prompt opti- mization,

Reference 56

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source=pdf_text observed=2026-08-04T12:45:34.571188Z digest=sha256:0b2bacb747b77dfd7f6937c8d5d54bf1d4613791c9016075573ae825920f0033

Observation 3639c59b-d1a1-4549-849d-4e7c00147521 · outbound

This paper cites Zero-shot image restoration using few-step guidance of consistency models (and beyond),.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Zero-shot image restoration using few-step guidance of consistency models (and beyond),

Reference 57

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source=pdf_text observed=2026-08-04T12:45:34.739599Z digest=sha256:358729e35e44333dade21c3270635647a6137134e23bd82cebc310ed66c25b40

Observation 4aa33a5a-180d-4b9e-9ab1-0ba9a5fe4d98 · outbound

This paper cites DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps,

Reference 58

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source=pdf_text observed=2026-08-04T12:45:34.881721Z digest=sha256:d2994229e8305d49347fd503d7f64d3d96988c24284fe37f9991aa7056000e26

Observation 409ef1b6-a87e-4ca9-a260-3c57b49e1b53 · outbound

This paper cites Elucidating the design space of diffusion-based generative models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Elucidating the design space of diffusion-based generative models,

Reference 59

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source=pdf_text observed=2026-08-04T12:45:35.100462Z digest=sha256:b3acee1c0e08192ea47da99a8e3c37fd0ccff745bcc1ae18c968605f57d15b3b

Observation e7c229ee-b155-455e-be64-a73000c0738c · outbound

This paper cites The perception-distortion tradeoff,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems The perception-distortion tradeoff,

Reference 60

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source=pdf_text observed=2026-08-04T12:45:35.283748Z digest=sha256:ac1d6d90e5531b0527ac27da12f330dd78da4a5d8695cec3c4a63a6ce23b0a3e

Observation 59d1beca-3201-41c3-81ec-593af024a7c2 · outbound

This paper cites A theory of the distortion- perception tradeoff in wasserstein space,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems A theory of the distortion- perception tradeoff in wasserstein space,

Reference 61

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source=pdf_text observed=2026-08-04T12:45:35.497482Z digest=sha256:98f2da404ca6b15d34aa055f6635ea043e1bfc83f55c76617fbeb59c509e3efa

Observation f2783b0c-91dd-4a23-83ad-ba137b8b589c · outbound

This paper cites Looks too good to be true: An information-theoretic analysis of hallucinations in generative restoration models,.

MACS: Measurement-Aware Consistency Sampling for Inverse Problems Looks too good to be true: An information-theoretic analysis of hallucinations in generative restoration models,

Reference 62

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source=pdf_text observed=2026-08-04T12:45:35.621990Z digest=sha256:4ef716638daa99c91da534cd040ec2676ab01aa29c9dcf0828fcf0dc50ffde2d

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