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Paper Citation Record · LEDGER

Gradient Guidance for Diffusion Models: An Optimization Perspective

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2404.14743.

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

pith.paper-citation-record.v1
2404.14743 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:49:42.701836Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:26:24.919909Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 93cfe7ca-3d47-453b-97f7-ccfd25d57703 · inbound

Nonlinear Assimilation via Score-based Sequential Langevin Sampling cites this paper.

Nonlinear Assimilation via Score-based Sequential Langevin Sampling Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:03:12.546742Z

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.

source=arxiv_source observed=2026-05-23T17:00:36.064749Z digest=sha256:20b02e0ac2dd317d537254ea2aef33501013f6e830b69c7d42e8a1cc7eb42731

Observation 2feb56d4-84a0-4303-a379-ecef849385d2 · inbound

Flow Matching Guide and Code cites this paper.

Flow Matching Guide and Code Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:28:14.062945Z

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.

source=arxiv_source observed=2026-05-12T10:28:14.014706Z digest=sha256:7cb1db5d959a36c4bc4ff3024c431b00699dfd40c58ad7d323884129096a07f3

Observation 21ad3f4a-8f13-4fb1-9598-168fdd07bd19 · inbound

MMaDA: Multimodal Large Diffusion Language Models cites this paper.

MMaDA: Multimodal Large Diffusion Language Models Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-05-15T14:50:59.868901Z

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.

source=pdf_text observed=2026-05-15T14:50:59.661153Z digest=sha256:5e68ec075e18d9e8328bf00e04070ea7c990812f1dacdc1594930c7e7f4be399

Observation 9f3a3545-7dcb-47eb-b18a-55120cd63b88 · inbound

Scaling Image and Video Generation via Test-Time Evolutionary Search cites this paper.

Scaling Image and Video Generation via Test-Time Evolutionary Search Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T14:49:42.701836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:49:42.701836Z digest=sha256:a233ee39771b4f947d4a0981809a495eae96442ebe84bb703644c3ad4590bac0

Observation f7b710fe-50d9-4110-91c3-906c1a2711f5 · inbound

Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation cites this paper.

Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generation Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T11:58:38.208407Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:58:38.208407Z digest=sha256:80f142d23e7b4278d1c78cb488b3271c481288d3f5e12380992132530a27c92c

Observation d24272f8-9c4a-4ed4-b193-3ac49db65569 · inbound

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach cites this paper.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 110

Resolution
unresolved
no resolver link, observed 2026-08-07T10:54:50.046204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T10:54:50.046204Z digest=sha256:84bf7f142e4d6253cf13c50f4acf8d567542f6e3a7f800b647044190f20bf447

Observation a97cb007-cd59-49c9-8682-85279d24b011 · inbound

Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models cites this paper.

Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T21:48:52.464757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:48:52.464757Z digest=sha256:7ddf1108aaecca21c356511d96a34a0850b19d79e2578883d7f91b176c278a40

Observation a5feea8d-7175-412c-8958-6aea03dbb2fa · inbound

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration cites this paper.

Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T14:25:28.120881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:25:28.120881Z digest=sha256:f21218401879ab241d39ed87321ae2f7d80a4cce58cc56d3089c116fd8ab1bcc

Observation f703322d-d52b-41d0-a2ee-4f2f6277696c · inbound

Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling cites this paper.

Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling Gradient Guidance for Diffusion Models: An Optimization Perspective

Reference 95

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T01:26:24.921563Z

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.

source=arxiv_source observed=2026-06-28T11:58:18.626259Z digest=sha256:1d0baf3d4cacbe62694d8e765605d93c20c669eea68cc0704662e7d2df8e7631