Paper Citation Record · LEDGER

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems

As of 10 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2606.26592.

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

pith.paper-citation-record.v1
2606.26592 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T02:42:32.734600Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

13 of 13 outbound references displayed

verified exact5
verified fuzzy0
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 03f0a03e-07b7-4266-a5b2-40c30cd61dda · outbound

This paper cites Score-based generative modeling through stochastic differential equations.ICLR, 2021.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Score-based generative modeling through stochastic differential equations.ICLR, 2021

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-26T02:42:32.734600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e54b9cb-f26b-48e6-911c-29f0bba245bd · outbound

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

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems High-resolution image synthesis with latent diffusion models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T02:42:32.734600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95dc065b-16c0-4944-906e-e885f861ae2d · outbound

This paper cites Diffusion posterior sampling for general noisy inverse problems.ICLR, 2023.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Diffusion posterior sampling for general noisy inverse problems.ICLR, 2023

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T02:42:32.734600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4983535-200f-43ae-9375-2847e0b77c78 · outbound

This paper cites Surgin: Surrogate-guided generative inversion for subsurface multiphase flow with quantified uncertainty.arXiv preprint arXiv:2509.13189, 2025.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Surgin: Surrogate-guided generative inversion for subsurface multiphase flow with quantified uncertainty.arXiv preprint arXiv:2509.13189, 2025

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:39:58.494276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67d5ebfb-f0af-4812-8ae3-360bd590ea83 · outbound

This paper cites Denoising diffusion probabilistic models.NeurIPS, 2020.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Denoising diffusion probabilistic models.NeurIPS, 2020

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-26T02:42:32.734600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fdf0eda-a3fa-4438-b5b8-c7f35695ec7a · outbound

This paper cites Diffusion Posterior Sampling for General Noisy Inverse Problems.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Diffusion Posterior Sampling for General Noisy Inverse Problems

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-04T14:39:58.502916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 32d6cc96-0f19-43b9-afdd-ace9d376c997 · outbound

This paper cites Denoising diffusion restoration models.Advances in neural information processing systems, 35:23593–23606, 2022.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Denoising diffusion restoration models.Advances in neural information processing systems, 35:23593–23606, 2022

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T02:42:32.734600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf035b86-03d9-4610-b830-5fa53293ff7f · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Taming transformers for high-resolution image synthesis

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T02:42:32.734600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2ef9884-6491-4927-a992-ef5cd2301754 · outbound

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

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Zero-shot text-to-image generation

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-26T02:42:32.734600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf230107-83f6-473e-92fb-adabd9213783 · outbound

This paper cites VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-04T14:39:58.505853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fe062dfb-467f-4550-90a5-a9171d0377b6 · outbound

This paper cites Denoising Diffusion Implicit Models.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Denoising Diffusion Implicit Models

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-04T14:39:58.496776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d12cb9b4-2bcf-498d-b468-761514fa1827 · outbound

This paper cites Jacobian-Aware Posterior Sampling for Inverse Problems.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Jacobian-Aware Posterior Sampling for Inverse Problems

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-07-04T14:39:58.500205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8deee0c8-6fa3-4953-81f2-3f24d1df1206 · outbound

This paper cites Diffusion state-guided projected gradient for inverse problems.

Latent Diffusion Posterior Sampling with Surrogate Likelihood Guidance for PDE Inverse Problems Diffusion state-guided projected gradient for inverse problems

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-26T02:42:32.734600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.