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Paper Citation Record · LEDGER

Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.19732.

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

pith.paper-citation-record.v1
2409.19732 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:15:29.660775Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T09:23:16.088230Z

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 49f52eaf-8a0a-4f67-903a-ca18f278c2f8 · inbound

Targeted Forgetting of Image Subgroups in CLIP Models cites this paper.

Targeted Forgetting of Image Subgroups in CLIP Models Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-07T11:15:29.660775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:15:29.660775Z digest=sha256:1112d9cc7c9edcb325f9330c97771cffc2ed04e344f371419e813735ada1e832

Observation 6391c40c-a1ce-4535-a091-f9a6a8ca7638 · inbound

A Full-Pipeline Framework for Evaluating Membership Inference Attacks in Machine Learning cites this paper.

A Full-Pipeline Framework for Evaluating Membership Inference Attacks in Machine Learning Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-06-29T09:23:16.089662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-29T09:20:39.053125Z digest=sha256:4681234a31833677eede9561bdd26ca2984ae0f6c575780f28b1b53925f58118