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Paper Citation Record · LEDGER

Threats, Attacks, and Defenses in Machine Unlearning: A Survey

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.13682.

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

pith.paper-citation-record.v1
2403.13682 v5

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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:47:15.201431Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:29:50.458109Z

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 153ff993-0b35-4b9e-a6a0-c6be10f7c39e · inbound

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities cites this paper.

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities Threats, Attacks, and Defenses in Machine Unlearning: A Survey

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-09T14:47:15.201431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T14:47:15.201431Z digest=sha256:cd8b1c0c171e4cc78162857b1886b943e295077b3a3461862c30722e1be5ae33

Observation 1352092c-0a8a-43b8-958b-d6f12e85e2f0 · inbound

Leveraging Per-Instance Privacy for Machine Unlearning cites this paper.

Leveraging Per-Instance Privacy for Machine Unlearning Threats, Attacks, and Defenses in Machine Unlearning: A Survey

Reference 11

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:29:50.494008Z

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.

source=pdf_text observed=2026-08-07T14:29:49.360552Z digest=sha256:375cd272d3daaaa52bfea9fc974936b2b84c025ad898497ddcb53c3a77ec3ccf