Paper Citation Record · LEDGER

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models

As of 10 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2505.20789.

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

pith.paper-citation-record.v1
2505.20789 v3

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:54:40.519274Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact1
verified fuzzy4
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation edfad58b-96e7-41d9-93b8-cfbd7360beff · outbound

This paper cites (20) Based on Lemma A.1 and Assumptions 4.1 and 4.2, we present the proof of Theorem 4.4 as follows.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models (20) Based on Lemma A.1 and Assumptions 4.1 and 4.2, we present the proof of Theorem 4.4 as follows

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:54:41.369764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8eced0c2-72f7-4d6b-a0b6-2a87557338f7 · outbound

This paper cites Progressive Growing of GANs for Improved Quality, Stability, and Variation.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Progressive Growing of GANs for Improved Quality, Stability, and Variation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:40.014632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bfae31c-2693-4c6e-ad1b-420afe5be06f · outbound

This paper cites w/” denotes methods that utilize sparse deviations, and “w/o.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models w/” denotes methods that utilize sparse deviations, and “w/o

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:54:41.069981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a90b1b00-ae2d-48ab-834e-71eb3cf724f8 · outbound

This paper cites Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Consistency Model is an Effective Posterior Sample Approximation for Diffusion Inverse Solvers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:40.173877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6073c547-3608-4e24-95a4-27512707164c · outbound

This paper cites Improving diffusion-based inverse algorithms under few-step con- straint via learnable linear extrapolation.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Improving diffusion-based inverse algorithms under few-step con- straint via learnable linear extrapolation

Reference 8

Resolution: verified exact; raw_fallback, observed 2026-08-07T13:54:40.828252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 931c81d7-7d8e-4d14-9170-0e6e51041d34 · outbound

This paper cites Proof of Theorem 4.4 First, we present the following basic concentration inequality for the Gaussian measurement matrix.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Proof of Theorem 4.4 First, we present the following basic concentration inequality for the Gaussian measurement matrix

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:54:41.486956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a5122d7-f7bf-40ae-94cc-9879d564937b · outbound

This paper cites (23) Moreover, since g1 is L1-Lipschitz continuous, if M is a (δ/L1)-net of X2 + Bn 1 (r), we have that g1(M ) is a δ-net of g1 X2 + Bn 1 (r).

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models (23) Moreover, since g1 is L1-Lipschitz continuous, if M is a (δ/L1)-net of X2 + Bn 1 (r), we have that g1(M ) is a δ-net of g1 X2 + Bn 1 (r)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:54:41.223832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0a80a344-a195-4cc9-9383-ee04aa23ca60 · outbound

This paper cites Low-Memory Neural Network Training: A Technical Report.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Low-Memory Neural Network Training: A Technical Report

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:40.107433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 046466d1-bb26-4a63-b8aa-caece6dc7d4d · outbound

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

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:40.232620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60a352cc-3a65-4b6d-9453-18e22a973729 · outbound

This paper cites Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Variational Bayesian Imaging with an Efficient Surrogate Score-based Prior

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:39.941722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2586ddd5-b636-4e0c-85e8-a4ef1e097c08 · outbound

This paper cites DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:40.075414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83c82a94-01dd-4689-b5a7-010318908f37 · outbound

This paper cites Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:39.875188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.