Paper Citation Record · LEDGER

Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.15010.

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

pith.paper-citation-record.v1
2502.15010 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:08:57.451004Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T09:55:40.171531Z

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 a6c2f15d-1476-443a-90d6-5cd8eb1b4817 · inbound

A Lightweight Method to Disrupt Memorized Sequences in LLM cites this paper.

A Lightweight Method to Disrupt Memorized Sequences in LLM Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-08T20:08:57.451004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6457de61-2b20-4211-8dc6-4b2eb21ea5ba · inbound

R-TOFU: Unlearning in Large Reasoning Models cites this paper.

R-TOFU: Unlearning in Large Reasoning Models Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:09.741457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64f102bb-1eb5-475e-a75f-db5c91b6f8b6 · inbound

Model Unlearning via Sparse Autoencoder Subspace Guided Projections cites this paper.

Model Unlearning via Sparse Autoencoder Subspace Guided Projections Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T12:28:04.135791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cbec085-34bb-468b-82df-2648e995cb9d · inbound

SoK: Machine Unlearning for Large Language Models cites this paper.

SoK: Machine Unlearning for Large Language Models Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T04:58:50.021380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44522f87-7492-49a7-aabf-461afa0c0394 · inbound

Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model cites this paper.

Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T03:45:30.751087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 496c2697-17d4-4639-8bf9-1a1d59873315 · inbound

Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law cites this paper.

Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law Obliviate: Efficient Unmemorization for Protecting Intellectual Property in Large Language Models

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-07-01T09:55:40.173531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.