Paper Citation Record · LEDGER

When unlearning is free: leveraging low influence points to reduce computational costs

As of 8 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2512.05254.

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

pith.paper-citation-record.v1
2512.05254 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T18:28:33.174280Z

measured 19 of 19 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy0
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4b3fee4e-ef15-4afa-91e5-6a8ed203dda9 · outbound

This paper cites Deep learning with differential privacy.

When unlearning is free: leveraging low influence points to reduce computational costs Deep learning with differential privacy

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.089792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7305edd2-54f1-4a42-9cab-c644badcfdbe · outbound

This paper cites What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation.

When unlearning is free: leveraging low influence points to reduce computational costs What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.768107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52757022-dcf5-4f7b-a2b9-a84d011d2908 · outbound

This paper cites A Swiss Army Infinitesimal Jackknife.

When unlearning is free: leveraging low influence points to reduce computational costs A Swiss Army Infinitesimal Jackknife

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.938971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a25b215-16af-485a-a89d-ae9596289537 · outbound

This paper cites Studying Large Language Model Generalization with Influence Functions.

When unlearning is free: leveraging low influence points to reduce computational costs Studying Large Language Model Generalization with Influence Functions

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:32.058194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a748625-897f-4672-8df3-7e4b3380310d · outbound

This paper cites Understanding Black-box Predictions via Influence Functions.

When unlearning is free: leveraging low influence points to reduce computational costs Understanding Black-box Predictions via Influence Functions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:32.268921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 446f04a7-9d6d-434d-93ff-edcac189c2ed · outbound

This paper cites Estimating Training Data Influence by Tracing Gradient Descent.

When unlearning is free: leveraging low influence points to reduce computational costs Estimating Training Data Influence by Tracing Gradient Descent

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:32.507165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdd20980-fec3-4ebc-aa54-e52fd9bf09b4 · outbound

This paper cites Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions.

When unlearning is free: leveraging low influence points to reduce computational costs Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:32.926699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b029dc7b-9d30-41b1-9dcc-9f5ea8de8577 · outbound

This paper cites CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing.

When unlearning is free: leveraging low influence points to reduce computational costs CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:33.063415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12df5b54-2ade-43c7-a9e7-f83d6fbe297d · outbound

This paper cites epochs" and the learning rate of.

When unlearning is free: leveraging low influence points to reduce computational costs epochs" and the learning rate of

Reference 128

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:33.174280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c380d18-0156-42cb-8579-ed65883efdcf · outbound

This paper cites URLhttps: //www.tandfonline.com/doi/abs/10.1080/01621459.1974.10482962.

When unlearning is free: leveraging low influence points to reduce computational costs URLhttps: //www.tandfonline.com/doi/abs/10.1080/01621459.1974.10482962

Reference 1974

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:32.173941Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bec5cbbb-6055-4e7e-ab44-c4a2ecb80628 · outbound

This paper cites doi: 10.1561/0400000042.

When unlearning is free: leveraging low influence points to reduce computational costs doi: 10.1561/0400000042

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.561149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23b47b72-c6e5-415c-962f-eacec570cd13 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

When unlearning is free: leveraging low influence points to reduce computational costs BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.418282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6eb60072-0966-4029-8160-f64f24685f30 · outbound

This paper cites URLhttp: //dx.doi.org/10.1145/2976749.2978318.

When unlearning is free: leveraging low influence points to reduce computational costs URLhttp: //dx.doi.org/10.1145/2976749.2978318

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.136601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be2211e6-435c-42df-acc3-f05a982c2ee5 · outbound

This paper cites Membership Inference Attacks against Machine Learning Models.

When unlearning is free: leveraging low influence points to reduce computational costs Membership Inference Attacks against Machine Learning Models

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:32.659749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f3a8acc-3023-498f-9f53-83d366028570 · outbound

This paper cites Machine Unlearning.

When unlearning is free: leveraging low influence points to reduce computational costs Machine Unlearning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.230309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc8b8263-8b9e-4f95-a9e4-f34468f99547 · outbound

This paper cites Does Learning Require Memorization? A Short Tale about a Long Tail.

When unlearning is free: leveraging low influence points to reduce computational costs Does Learning Require Memorization? A Short Tale about a Long Tail

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.665232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88957db3-c618-4656-ae67-1148a55eb222 · outbound

This paper cites An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?.

When unlearning is free: leveraging low influence points to reduce computational costs An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:31.326947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 613ad083-3961-419f-8842-ef4b696394e4 · outbound

This paper cites Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition.

When unlearning is free: leveraging low influence points to reduce computational costs Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:32.783687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7f163fe-f2a6-4d42-88b5-8549dce692d3 · outbound

This paper cites FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning.

When unlearning is free: leveraging low influence points to reduce computational costs FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T18:28:32.408652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.