{"as_of":"2026-08-23T22:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:41420dd8b7618acecf553db89dc514d7387facf155d9aa3a03672a3b879c1f25","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":"2026-08-23T06:30:58.430688+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":"2026-08-14T12:58:19.090162Z","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":"2026-05-17T18:00:50.551127Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1902.01718","last_updated":"2019-09-18T01:31:39Z","snapshot_observed_at":"2026-08-14T17:20:47.062325Z","submitted_at":"2019-02-05T14:50:48Z","title":"End-to-End Open-Domain Question Answering with BERTserini","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01718","snapshot_observed_at":"2026-08-14T12:58:19.090162Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.06121","last_updated":"2020-06-19T20:24:05Z","snapshot_observed_at":"2026-08-17T16:36:13.486028Z","submitted_at":"2019-08-16T18:19:59Z","title":"CFO: A Framework for Building Production NLP Systems","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-14T12:58:19.090162Z"},"links":{"cited_paper":"/paper/1902.01718","citing_paper":"/paper/1908.06121"},"observation_digest":"sha256:f50bd80399c975fe2e4300c26a95a545a03e6dbc946c51347945126aa4b0a170","observation_id":"ccc9ae13-7056-44cf-b67c-427209cf66b0","resolution":{"observed_at":"2026-08-14T12:58:19.090162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01718","last_updated":"2019-09-18T01:31:39Z","snapshot_observed_at":"2026-08-14T17:20:47.062325Z","submitted_at":"2019-02-05T14:50:48Z","title":"End-to-End Open-Domain Question Answering with BERTserini","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01718","snapshot_observed_at":"2026-08-14T12:37:54.094300Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.06780","last_updated":"2019-08-19T13:14:02Z","snapshot_observed_at":"2026-08-17T19:27:42.524030Z","submitted_at":"2019-08-19T13:14:02Z","title":"A Study of BERT for Non-Factoid Question-Answering under Passage Length Constraints","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-14T12:37:54.094300Z"},"links":{"cited_paper":"/paper/1902.01718","citing_paper":"/paper/1908.06780"},"observation_digest":"sha256:f40603a7417e3aca49f4a1f91bceb621417eaf65ca19424f272690c77a6f44d1","observation_id":"5d46329f-b95a-4cdf-bbe2-95d5473dddd4","resolution":{"observed_at":"2026-08-14T12:37:54.094300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01718","last_updated":"2019-09-18T01:31:39Z","snapshot_observed_at":"2026-08-14T17:20:47.062325Z","submitted_at":"2019-02-05T14:50:48Z","title":"End-to-End Open-Domain Question Answering with BERTserini","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01718","snapshot_observed_at":"2026-08-14T11:50:52.542423Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.08167","last_updated":"2019-10-02T02:28:53Z","snapshot_observed_at":"2026-08-20T12:30:28.749372Z","submitted_at":"2019-08-22T02:00:53Z","title":"Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-14T11:50:52.542423Z"},"links":{"cited_paper":"/paper/1902.01718","citing_paper":"/paper/1908.08167"},"observation_digest":"sha256:ea5c5112b99882c20ada516e3f9d72c1268ebb762c5af5f6ae45ec62ce9c05f4","observation_id":"afcd2900-3c70-445f-9067-a42b62e9e2b2","resolution":{"observed_at":"2026-08-14T11:50:52.542423Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01718","last_updated":"2019-09-18T01:31:39Z","snapshot_observed_at":"2026-08-14T17:20:47.062325Z","submitted_at":"2019-02-05T14:50:48Z","title":"End-to-End Open-Domain Question Answering with BERTserini","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01718","snapshot_observed_at":"2026-08-14T11:02:19.910334Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.09940","last_updated":"2019-08-29T00:21:32Z","snapshot_observed_at":"2026-08-17T14:24:48.708341Z","submitted_at":"2019-08-26T22:15:55Z","title":"Don't paraphrase, detect! Rapid and Effective Data Collection for Semantic Parsing","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-14T11:02:19.910334Z"},"links":{"cited_paper":"/paper/1902.01718","citing_paper":"/paper/1908.09940"},"observation_digest":"sha256:91d747d4a3effca6f899594ed323dd988a672992450a76c5dd0919bb48715d98","observation_id":"73362987-ce1e-4398-a042-de5fbd55bbf9","resolution":{"observed_at":"2026-08-14T11:02:19.910334Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01718","last_updated":"2019-09-18T01:31:39Z","snapshot_observed_at":"2026-08-14T17:20:47.062325Z","submitted_at":"2019-02-05T14:50:48Z","title":"End-to-End Open-Domain Question Answering with BERTserini","version":2},"cited_work":{"arxiv_id":"1902.01718","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.01718","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"End-to-end open-domain question answering with bertserini","venue":null,"work_id":"9f14bb21-3975-41bc-8207-1d4ac9fb4f0d","year":1902},"citing_paper":{"arxiv_id":"2306.14048","last_updated":"2023-12-18T19:10:00Z","snapshot_observed_at":"2026-08-20T21:36:51.212852Z","submitted_at":"2023-06-24T20:11:14Z","title":"H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models","version":3},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-05-17T18:00:50.053377Z"},"links":{"cited_paper":"/paper/1902.01718","citing_paper":"/paper/2306.14048"},"observation_digest":"sha256:3a9d7ab32ea82322395fa63579cbd8be2c8b74e988b45e0db51f41d4be559855","observation_id":"e76e8d3a-f596-4325-90de-809355d75a96","resolution":{"observed_at":"2026-05-17T18:00:50.553208Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01718","last_updated":"2019-09-18T01:31:39Z","snapshot_observed_at":"2026-08-14T17:20:47.062325Z","submitted_at":"2019-02-05T14:50:48Z","title":"End-to-End Open-Domain Question Answering with BERTserini","version":2},"cited_work":{"arxiv_id":"1902.01718","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1902.01718","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"End-to-end open-domain question answering with bertserini","venue":null,"work_id":"9f14bb21-3975-41bc-8207-1d4ac9fb4f0d","year":1902},"citing_paper":{"arxiv_id":"2403.14608","last_updated":"2024-09-16T02:54:50Z","snapshot_observed_at":"2026-08-04T09:07:42.158421Z","submitted_at":"2024-03-21T17:55:50Z","title":"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey","version":7},"reference_index":169,"source":"pdf_text","source_observed_at":"2026-05-13T11:32:36.738536Z"},"links":{"cited_paper":"/paper/1902.01718","citing_paper":"/paper/2403.14608"},"observation_digest":"sha256:92290971546df8393122a943d919ae8e9f8c649536e1794df2d20fd0faa6a8df","observation_id":"14e9b474-af90-4672-9f78-bf03265fa12a","resolution":{"observed_at":"2026-05-13T11:32:36.881095Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.01718","last_updated":"2019-09-18T01:31:39Z","snapshot_observed_at":"2026-08-14T17:20:47.062325Z","submitted_at":"2019-02-05T14:50:48Z","title":"End-to-End Open-Domain Question Answering with BERTserini","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.01718","snapshot_observed_at":"2026-08-09T17:36:28.856204Z","title":null,"venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2502.00847","last_updated":"2025-02-02T16:40:21Z","snapshot_observed_at":"2026-08-17T16:48:59.004476Z","submitted_at":"2025-02-02T16:40:21Z","title":"SecPE: Secure Prompt Ensembling for Private and Robust Large Language Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T17:36:28.856204Z"},"links":{"cited_paper":"/paper/1902.01718","citing_paper":"/paper/2502.00847"},"observation_digest":"sha256:e3ac3cccb76c115e20ea0348e2cd3ef17a1e43c3d2b9c68b786f8b71a226d688","observation_id":"a3d7da0b-86ae-4976-b4f6-b369a51bca17","resolution":{"observed_at":"2026-08-09T17:36:28.856204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1902.01718/citation-record","integrity":"/paper/1902.01718/integrity","json":"/paper/1902.01718/citation-record.json","paper":"/paper/1902.01718"},"outbound":[],"paper":{"arxiv_id":"1902.01718","last_updated":"2019-09-18T01:31:39Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-14T17:20:47.062325Z","submitted_at":"2019-02-05T14:50:48Z","title":"End-to-End Open-Domain Question Answering with BERTserini"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1902.01718."}