{"as_of":"2026-08-08T18:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c98e1af4391b8dac952643a73b5728392113671147ade6405d6068eef6575990","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T18:28:33.174280Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2512.05254/citation-record","integrity":"/paper/2512.05254/integrity","json":"/paper/2512.05254/citation-record.json","paper":"/paper/2512.05254"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:28:31.089792Z","title":"Deep learning with differential privacy","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.089792Z"},"links":{"citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:1ede16fab19abf315052a3cc6247a79255c2728477a861b7de9d737723c61fc8","observation_id":"4b3fee4e-ef15-4afa-91e5-6a8ed203dda9","resolution":{"observed_at":"2026-08-03T18:28:31.089792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03703","last_updated":"2020-08-09T10:12:28Z","snapshot_observed_at":"2026-08-05T13:16:54.633232Z","submitted_at":"2020-08-09T10:12:28Z","title":"What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03703","snapshot_observed_at":"2026-08-03T18:28:31.768107Z","title":"Ryan Giordano, Will Stephenson, Runjing Liu, Michael I","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.768107Z"},"links":{"cited_paper":"/paper/2008.03703","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:6c386afd3e8db7f4183a46c3181867b8c688fef22b4cefc01181158c10a7cff9","observation_id":"7305edd2-54f1-4a42-9cab-c644badcfdbe","resolution":{"observed_at":"2026-08-03T18:28:31.768107Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1806.00550","last_updated":"2020-02-07T17:18:46Z","snapshot_observed_at":"2026-08-05T09:08:36.872141Z","submitted_at":"2018-06-01T21:48:44Z","title":"A Swiss Army Infinitesimal Jackknife","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.00550","snapshot_observed_at":"2026-08-03T18:28:31.938971Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.938971Z"},"links":{"cited_paper":"/paper/1806.00550","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:d2d8a427b3134013822dad584e79cc37a5c3bc610060366a35013c7947fe8d81","observation_id":"52757022-dcf5-4f7b-a2b9-a84d011d2908","resolution":{"observed_at":"2026-08-03T18:28:31.938971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03296","last_updated":"2023-08-07T04:47:42Z","snapshot_observed_at":"2026-07-06T16:03:14.694457Z","submitted_at":"2023-08-07T04:47:42Z","title":"Studying Large Language Model Generalization with Influence Functions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03296","snapshot_observed_at":"2026-08-03T18:28:32.058194Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.058194Z"},"links":{"cited_paper":"/paper/2308.03296","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:7f1d38dc220903063a9714496cf12c903d4fb1ad564b58ae446aa6b1b43dc13f","observation_id":"1a25b215-16af-485a-a89d-ae9596289537","resolution":{"observed_at":"2026-08-03T18:28:32.058194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.04730","last_updated":"2020-12-29T22:40:43Z","snapshot_observed_at":"2026-08-03T04:46:56.967070Z","submitted_at":"2017-03-14T21:07:01Z","title":"Understanding Black-box Predictions via Influence Functions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.04730","snapshot_observed_at":"2026-08-03T18:28:32.268921Z","title":"Alex Krizhevsky, Geoffrey Hinton, et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.268921Z"},"links":{"cited_paper":"/paper/1703.04730","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:7ca6e43d93012db5e4b02aaabd0636f22e12cc36c59ea66dae36f8f2b19c5982","observation_id":"0a748625-897f-4672-8df3-7e4b3380310d","resolution":{"observed_at":"2026-08-03T18:28:32.268921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08484","last_updated":"2020-11-14T18:47:35Z","snapshot_observed_at":"2026-07-06T08:58:39.542748Z","submitted_at":"2020-02-19T22:40:32Z","title":"Estimating Training Data Influence by Tracing Gradient Descent","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08484","snapshot_observed_at":"2026-08-03T18:28:32.507165Z","title":"P Rajpurkar","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.507165Z"},"links":{"cited_paper":"/paper/2002.08484","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:16872cc35bffc4d4b258f7db42dce507884bff85345aeebec9a951f0cfd05a71","observation_id":"446f04a7-9d6d-434d-93ff-edcac189c2ed","resolution":{"observed_at":"2026-08-03T18:28:32.507165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03941","last_updated":"2024-06-05T01:14:19Z","snapshot_observed_at":"2026-07-06T15:51:43.130122Z","submitted_at":"2023-07-08T09:28:50Z","title":"Right to be Forgotten in the Era of Large Language Models: Implications, Challenges, and Solutions","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03941","snapshot_observed_at":"2026-08-03T18:28:32.926699Z","title":"URLhttps://doi.org/10","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.926699Z"},"links":{"cited_paper":"/paper/2307.03941","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:fcb8a05e40052d6dd4a491d92dd1669622be577fd00570b0bea0c3c806469ae8","observation_id":"cdd20980-fec3-4ebc-aa54-e52fd9bf09b4","resolution":{"observed_at":"2026-08-03T18:28:32.926699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.10654","last_updated":"2020-02-04T17:58:24Z","snapshot_observed_at":"2026-08-05T03:49:23.935968Z","submitted_at":"2019-08-28T11:40:51Z","title":"CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.10654","snapshot_observed_at":"2026-08-03T18:28:33.063415Z","title":null,"venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:33.063415Z"},"links":{"cited_paper":"/paper/1908.10654","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:ac30ec50e2e9d445d85251d83c18d29b27c81f1c3065f6f781c2e685c8097b18","observation_id":"b029dc7b-9d30-41b1-9dcc-9f5ea8de8577","resolution":{"observed_at":"2026-08-03T18:28:33.063415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:28:33.174280Z","title":"epochs\" and the learning rate of","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":128,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:33.174280Z"},"links":{"citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:1a1e50e6aef4ebe909b8abfc319d8824e3958ffce1f42b7529cd35d78b23e636","observation_id":"12df5b54-2ade-43c7-a9e7-f83d6fbe297d","resolution":{"observed_at":"2026-08-03T18:28:33.174280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:28:32.173941Z","title":"URLhttps: //www.tandfonline.com/doi/abs/10.1080/01621459.1974.10482962","venue":null,"work_id":null,"year":1974},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":1974,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.173941Z"},"links":{"citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:419e6a1edc1de1b4445b331f2a18128f5091cdb3cfd5c09fd5af4c56e776b8cc","observation_id":"5c380d18-0156-42cb-8579-ed65883efdcf","resolution":{"observed_at":"2026-08-03T18:28:32.173941Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:28:31.561149Z","title":"doi: 10.1561/0400000042","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.561149Z"},"links":{"citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:90791603bef8ad0e9f3c2fb4eb94921b933b39dc19939b4fbec2d9f321ae111d","observation_id":"bec5cbbb-6055-4e7e-ab44-c4a2ecb80628","resolution":{"observed_at":"2026-08-03T18:28:31.561149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-03T18:28:31.418282Z","title":"Jacob Devlin","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.418282Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:4bd511785a6256c57a5bcf4718d494766b436ad5e6df561ad74ff190e766072c","observation_id":"23b47b72-c6e5-415c-962f-eacec570cd13","resolution":{"observed_at":"2026-08-03T18:28:31.418282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T18:28:31.136601Z","title":"URLhttp: //dx.doi.org/10.1145/2976749.2978318","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.136601Z"},"links":{"citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:9383f2f896a2aa936f07f26bac510f779903a12dcec704d326db7a2e2caf3822","observation_id":"6eb60072-0966-4029-8160-f64f24685f30","resolution":{"observed_at":"2026-08-03T18:28:31.136601Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1610.05820","last_updated":"2017-03-31T22:17:07Z","snapshot_observed_at":"2026-07-06T05:15:08.120372Z","submitted_at":"2016-10-18T22:38:33Z","title":"Membership Inference Attacks against Machine Learning Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.05820","snapshot_observed_at":"2026-08-03T18:28:32.659749Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.659749Z"},"links":{"cited_paper":"/paper/1610.05820","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:71cf1a11e9f70c4fe448a423b0d025892469b7bc7689ab3b0f2b4bf7f02b53a4","observation_id":"be2211e6-435c-42df-acc3-f05a982c2ee5","resolution":{"observed_at":"2026-08-03T18:28:32.659749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.03817","last_updated":"2020-12-15T05:39:28Z","snapshot_observed_at":"2026-07-06T08:42:58.275367Z","submitted_at":"2019-12-09T02:16:53Z","title":"Machine Unlearning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.03817","snapshot_observed_at":"2026-08-03T18:28:31.230309Z","title":"Tamara Broderick, Ryan Giordano, and Rachael Meager","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.230309Z"},"links":{"cited_paper":"/paper/1912.03817","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:7412705a1ecc44bfeb0caba1016e5e03e42b6f7f22a3e8467bbd2452e38010eb","observation_id":"1f3a8acc-3023-498f-9f53-83d366028570","resolution":{"observed_at":"2026-08-03T18:28:31.230309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.05271","last_updated":"2021-01-10T07:19:48Z","snapshot_observed_at":"2026-08-07T14:02:44.326094Z","submitted_at":"2019-06-12T17:53:25Z","title":"Does Learning Require Memorization? A Short Tale about a Long Tail","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.05271","snapshot_observed_at":"2026-08-03T18:28:31.665232Z","title":"Vitaly Feldman and Chiyuan Zhang","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.665232Z"},"links":{"cited_paper":"/paper/1906.05271","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:76a872fda7056348cbd571048db0d83dca115aa4d4ac9a5ffd3a14cbbdd23feb","observation_id":"bc8b8263-8b9e-4f95-a9e4-f34468f99547","resolution":{"observed_at":"2026-08-03T18:28:31.665232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.14999","last_updated":"2023-07-19T17:09:07Z","snapshot_observed_at":"2026-08-08T05:07:41.461460Z","submitted_at":"2020-11-30T17:05:48Z","title":"An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference?","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.14999","snapshot_observed_at":"2026-08-03T18:28:31.326947Z","title":"Yinzhi Cao and Junfeng Yang","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:31.326947Z"},"links":{"cited_paper":"/paper/2011.14999","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:8bd1a28bbf99a0364e3a016ac2d9995da66df69afc99888b3fd7fd6f229a4ca0","observation_id":"88957db3-c618-4656-ae67-1148a55eb222","resolution":{"observed_at":"2026-08-03T18:28:31.326947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09073","last_updated":"2024-06-13T12:58:00Z","snapshot_observed_at":"2026-08-03T19:43:59.941345Z","submitted_at":"2024-06-13T12:58:00Z","title":"Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.09073","snapshot_observed_at":"2026-08-03T18:28:32.783687Z","title":"Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.783687Z"},"links":{"cited_paper":"/paper/2406.09073","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:665969b62fc17e7ede234b56e9a7393e24f6a9a88e3f12b7dc3a3b047205227f","observation_id":"613ad083-3961-419f-8842-ef4b696394e4","resolution":{"observed_at":"2026-08-03T18:28:32.783687Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.16614","last_updated":"2025-01-28T01:19:07Z","snapshot_observed_at":"2026-07-06T20:27:09.296190Z","submitted_at":"2025-01-28T01:19:07Z","title":"FUNU: Boosting Machine Unlearning Efficiency by Filtering Unnecessary Unlearning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.16614","snapshot_observed_at":"2026-08-03T18:28:32.408652Z","title":"Ken Ziyu Liu","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T18:28:32.408652Z"},"links":{"cited_paper":"/paper/2501.16614","citing_paper":"/paper/2512.05254"},"observation_digest":"sha256:1311d13d68012bbd83607be0c2fe44a51c653c0d6cd4b92e8e08c30ca4d0eb4d","observation_id":"d7f163fe-f2a6-4d42-88b5-8549dce692d3","resolution":{"observed_at":"2026-08-03T18:28:32.408652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.05254","last_updated":"2026-07-29T23:30:29Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T03:39:47.622792Z","submitted_at":"2025-12-04T21:10:31Z","title":"When unlearning is free: leveraging low influence points to reduce computational costs"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":19},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"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."}