{"as_of":"2026-08-08T21:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:474091553b094dbae2855cbeaad0c3d158d7f87d99a851bfc78329961fc96303","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:59:22.574838Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-21T17:50:26.343235Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"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":"2026-08-07T05:59:22.574838Z","title":"An automatic finite-sample robustness metric: when can dropping a little data make a big difference? arXiv preprint arXiv:2011.14999 , 2020","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2506.06656","last_updated":"2025-09-09T20:12:30Z","snapshot_observed_at":"2026-08-08T09:29:53.887401Z","submitted_at":"2025-06-07T04:19:21Z","title":"Rescaled Influence Functions: Accurate Data Attribution in High Dimension","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T05:59:22.574838Z"},"links":{"cited_paper":"/paper/2011.14999","citing_paper":"/paper/2506.06656"},"observation_digest":"sha256:9e65ed9e38ea42c4d0e1122481310dfd947761eaff90cb7b51f4587d8098344e","observation_id":"287614ea-072b-47ae-a63f-bfced31cfe55","resolution":{"observed_at":"2026-08-07T05:59:22.574838Z","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":"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":"2011.14999","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2011.14999","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An automatic finite-sample robustness metric: when can dropping a little data make a big difference?arXiv preprint arXiv:2011.14999","venue":null,"work_id":"6b33bb31-1c77-45c9-8c34-2fa7b0416022","year":2011},"citing_paper":{"arxiv_id":"2512.12572","last_updated":"2026-05-15T21:16:17Z","snapshot_observed_at":"2026-08-06T17:12:59.276823Z","submitted_at":"2025-12-14T06:33:52Z","title":"On the Accuracy of Newton Step and Influence Function Data Attributions","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-21T17:47:21.976868Z"},"links":{"cited_paper":"/paper/2011.14999","citing_paper":"/paper/2512.12572"},"observation_digest":"sha256:755dc52c026ef3809361031d9e7cacad6e1ff86fd337687e3a5cdfb6f066458f","observation_id":"e87687fc-03a6-4e31-bd1c-4f26654ae548","resolution":{"observed_at":"2026-05-21T17:50:26.346329Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"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":"2011.14999","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2011.14999","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An automatic finite-sample robustness metric: when can dropping a little data make a big difference?arXiv preprint arXiv:2011.14999","venue":null,"work_id":"6b33bb31-1c77-45c9-8c34-2fa7b0416022","year":2011},"citing_paper":{"arxiv_id":"2605.01579","last_updated":"2026-05-02T19:04:08Z","snapshot_observed_at":"2026-07-06T23:14:47.444386Z","submitted_at":"2026-05-02T19:04:08Z","title":"Minimum Specification Perturbation: Robustness as Distance-to-Falsification in Causal Inference","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-09T18:01:15.063810Z"},"links":{"cited_paper":"/paper/2011.14999","citing_paper":"/paper/2605.01579"},"observation_digest":"sha256:0e64a65940898cf976fb57472e00ce853b34d290b0f3c56354fc74e0fb4e03d3","observation_id":"392c8038-2d1e-4673-95ad-db42bc405829","resolution":{"observed_at":"2026-05-11T16:16:10.692714Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"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":"2011.14999","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2011.14999","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An automatic finite-sample robustness metric: when can dropping a little data make a big difference?arXiv preprint arXiv:2011.14999","venue":null,"work_id":"6b33bb31-1c77-45c9-8c34-2fa7b0416022","year":2011},"citing_paper":{"arxiv_id":"2605.04317","last_updated":"2026-05-16T16:42:17Z","snapshot_observed_at":"2026-07-06T23:17:09.246351Z","submitted_at":"2026-05-05T21:36:45Z","title":"The Threshold Breakdown Point","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-08T16:52:35.205640Z"},"links":{"cited_paper":"/paper/2011.14999","citing_paper":"/paper/2605.04317"},"observation_digest":"sha256:5254c52ee8fbb869cc840a15ac93900710e4ac54459db626f1c26746b0298d60","observation_id":"78fcf15a-a3e3-4e0f-975c-396806067a6b","resolution":{"observed_at":"2026-05-11T17:56:08.694936Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"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":"2011.14999","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2011.14999","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"An automatic finite-sample robustness metric: when can dropping a little data make a big difference?arXiv preprint arXiv:2011.14999","venue":null,"work_id":"6b33bb31-1c77-45c9-8c34-2fa7b0416022","year":2011},"citing_paper":{"arxiv_id":"2605.04317","last_updated":"2026-05-16T16:42:17Z","snapshot_observed_at":"2026-07-06T23:17:09.246351Z","submitted_at":"2026-05-05T21:36:45Z","title":"The Threshold Breakdown Point","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-20T23:52:05.271119Z"},"links":{"cited_paper":"/paper/2011.14999","citing_paper":"/paper/2605.04317"},"observation_digest":"sha256:4e43456f5087f6b8dcdb0b2f738301429231367ef8f84a078f975595dec16e33","observation_id":"db1b81c5-beac-47eb-b8f7-fe4a11dc02d7","resolution":{"observed_at":"2026-05-20T23:53:51.551701Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"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"}],"state":"measured"}},{"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":"2026-07-13T04:24:43.867063Z","title":"arXiv preprint arXiv:2011.14999 , year=","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.09250","last_updated":"2026-07-10T10:01:11Z","snapshot_observed_at":"2026-08-08T02:18:28.425569Z","submitted_at":"2026-07-10T10:01:11Z","title":"Influence Diagnostics in High-dimensional M-estimation: Precise Asymptotics","version":1},"reference_index":127,"source":"arxiv_source","source_observed_at":"2026-07-13T04:24:43.867063Z"},"links":{"cited_paper":"/paper/2011.14999","citing_paper":"/paper/2607.09250"},"observation_digest":"sha256:2abe850cd4e37ce5c58a958e50561d82c9d0a0c5b2936b0596bf83523352f761","observation_id":"4fbf892f-d80b-4f02-a246-82cea64e9aae","resolution":{"observed_at":"2026-07-13T04:24:43.867063Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2011.14999/citation-record","integrity":"/paper/2011.14999/integrity","json":"/paper/2011.14999/citation-record.json","paper":"/paper/2011.14999"},"outbound":[],"paper":{"arxiv_id":"2011.14999","last_updated":"2023-07-19T17:09:07Z","latest_version":5,"primary_category":"stat.ME","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?"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2011.14999."}