Paper Citation Record · LEDGER

Data Unlearning in Diffusion Models

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2503.01034.

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

pith.paper-citation-record.v1
2503.01034 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T21:27:18.851174Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:42:49.472109Z

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 87eacfca-431d-41d5-9ccc-5b6f04900163 · inbound

Machine Unlearning: A Comprehensive Survey cites this paper.

Machine Unlearning: A Comprehensive Survey Data Unlearning in Diffusion Models

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:13:42.667066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a7e02d56-52e3-45fb-a5e4-80d4342c1c1c · inbound

You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models cites this paper.

You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models Data Unlearning in Diffusion Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T21:27:18.851174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e0ca18e-5640-4b46-9efe-f3e88c0af789 · inbound

Is your algorithm unlearning or untraining? cites this paper.

Is your algorithm unlearning or untraining? Data Unlearning in Diffusion Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:30:59.078083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 00685df2-75cf-4196-bc67-adb31659f9da · inbound

Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints cites this paper.

Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints Data Unlearning in Diffusion Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:42:49.473533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.