Paper Citation Record · LEDGER

Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.16257.

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

pith.paper-citation-record.v1
2406.16257 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:01:33.203487Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:19:13.812709Z

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 b7a8716a-f427-4034-83d1-ab562330eafa · inbound

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

System-Aware Unlearning Algorithms: Use Lesser, Forget Faster Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T06:14:42.256207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 103e6e45-fb35-4abe-a527-3646daba9f93 · inbound

UCD: Unlearning in LLMs via Contrastive Decoding cites this paper.

UCD: Unlearning in LLMs via Contrastive Decoding Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T04:22:59.819768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0f69f10-61a1-44ad-9f5c-9bbf55b8b101 · inbound

BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap cites this paper.

BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:33.203487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a9b28ad-500f-447c-92b3-7401a64d5bac · inbound

Revisiting the Past: Data Unlearning with Model State History cites this paper.

Revisiting the Past: Data Unlearning with Model State History Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-19T08:13:01.630624Z

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.

Observation 47453586-a83c-4628-9fce-dab7447d229b · inbound

The Measure of Deception: An Analysis of Data Forging in Machine Unlearning cites this paper.

The Measure of Deception: An Analysis of Data Forging in Machine Unlearning Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:42:47.372475Z

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.

Observation b6b4ed1e-7d93-473d-b528-fea30e2e3e76 · inbound

Machine Unlearning for the XGBoost Model with Network Intrusion Datasets cites this paper.

Machine Unlearning for the XGBoost Model with Network Intrusion Datasets Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:19:13.814168Z

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.

Observation 58265f02-b8eb-4688-bc97-fc0d5241e4bd · inbound

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

DECAF: De-Clustering for Adaptive Representational Unlearning Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

Reference 119

Resolution: unresolved; no resolver link, observed 2026-07-31T23:34:00.184735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.