Paper Citation Record · LEDGER

Attribute-to-Delete: Machine Unlearning via Datamodel Matching

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.23232.

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

pith.paper-citation-record.v1
2410.23232 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:16:49.577284Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:52:49.571280Z

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 7bec49bc-847d-47c0-9a71-6e933ee8b6aa · inbound

Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning cites this paper.

Investigating the Feasibility of Mitigating Potential Copyright Infringement via Large Language Model Unlearning Attribute-to-Delete: Machine Unlearning via Datamodel Matching

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T14:16:49.577284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d3a309f-b016-485f-9c5f-2b0ea04d20c4 · inbound

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design cites this paper.

Invisible Watermarks, Visible Gains: Steering Machine Unlearning with Bi-Level Watermarking Design Attribute-to-Delete: Machine Unlearning via Datamodel Matching

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.982023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17b339ba-6e5c-4ce2-8980-950bf9b5bd94 · inbound

De-attribute to Forget for LLM Unlearning cites this paper.

De-attribute to Forget for LLM Unlearning Attribute-to-Delete: Machine Unlearning via Datamodel Matching

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:52:49.572616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a6353a4a-64fd-484a-b2bd-24b6e1cd7e2e · inbound

De-attribute to Forget for LLM Unlearning cites this paper.

De-attribute to Forget for LLM Unlearning Attribute-to-Delete: Machine Unlearning via Datamodel Matching

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T15:34:59.671830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f69c239-662a-491b-8cbd-68dcdaf360b4 · inbound

OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets cites this paper.

OriginBlame: Record- and Token-Level Data Provenance for AI Training Datasets Attribute-to-Delete: Machine Unlearning via Datamodel Matching

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T13:42:10.163234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.