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Paper Citation Record · LEDGER

Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.08254.

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

pith.paper-citation-record.v1
2403.08254 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 2026-08-15T06:32:42.880941+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:54:59.661423Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T21:54:59.850026Z

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 846e25bc-4712-4c77-bf47-83dbac3704d8 · inbound

MUBox: A Critical Evaluation Framework of Deep Machine Unlearning cites this paper.

MUBox: A Critical Evaluation Framework of Deep Machine Unlearning Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

Reference 51

Resolution
verified exact
local_arxiv, observed 2026-08-15T21:54:59.855297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T21:54:59.661423Z digest=sha256:ae6c63b1532f4aeebfbbcab091fc4665c1ea62a0ba9ac338af63160a67395592

Observation 32c9a414-781f-4eca-8f81-7d0f9a5324b0 · inbound

DECAF: De-Clustering for Adaptive Representational Unlearning cites this paper.

DECAF: De-Clustering for Adaptive Representational Unlearning Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects

Reference 32

Resolution
unresolved
no resolver link, observed 2026-07-31T23:33:51.678747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T23:33:51.678747Z digest=sha256:dc4ad7f94624f7e3f09e47a62d5e4db82a79bdfed5bae7befc586855f72155b0