Paper Citation Record · LEDGER

Avoiding Copyright Infringement via Large Language Model Unlearning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.10952.

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

pith.paper-citation-record.v1
2406.10952 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:16:49.543910Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T21:55:50.774629Z

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 7ece3b17-342b-46fc-b9da-6011b1c09021 · inbound

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities cites this paper.

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:16:04.851650Z

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 5d3db833-7952-449c-a37b-a26b2226f640 · inbound

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions cites this paper.

AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 185

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:55:50.777348Z

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 ae8db0dc-0a89-4801-9e6e-f8ceda3deb72 · 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 Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T14:16:49.543910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca05832e-3197-4170-8ffb-9cdac1e532ae · inbound

Multi-Objective Large Language Model Unlearning cites this paper.

Multi-Objective Large Language Model Unlearning Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T23:27:37.686292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10cc6fa2-5b71-4b89-ac83-393321c6b194 · inbound

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster cites this paper.

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T06:14:42.789722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbb42844-dbeb-4489-a2c6-00a9947ee442 · 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 Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-05T21:01:26.744729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62b21b24-5e48-4ba4-8b09-8614352f4b3a · inbound

Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning cites this paper.

Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T20:24:21.178419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb8b1db9-5d81-4db1-85bb-e9e3e1fb1437 · inbound

LU-500: A Logo Benchmark for Concept Unlearning cites this paper.

LU-500: A Logo Benchmark for Concept Unlearning Avoiding Copyright Infringement via Large Language Model Unlearning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T23:06:19.305299Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.