{"as_of":"2026-08-12T04:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7ffc8887b9104b6b565ae248285e04b49f9a51978d770bcb9b7ec94a920c7f72","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T15:21:56.103111Z","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":"2026-07-04T21:10:09.189597Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.21968","last_updated":"2025-07-14T04:16:07Z","snapshot_observed_at":"2026-08-10T19:38:28.339817Z","submitted_at":"2025-03-27T20:32:49Z","title":"GLM Inference with AI-Generated Synthetic Data Using Misspecified Linear Regression","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.21968","snapshot_observed_at":"2026-08-04T15:21:56.103111Z","title":"Keret and A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.20345","last_updated":"2026-06-04T08:42:19Z","snapshot_observed_at":"2026-08-07T10:11:37.775717Z","submitted_at":"2025-09-24T17:37:14Z","title":"General Synthetic-Powered Inference","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-04T15:21:56.103111Z"},"links":{"cited_paper":"/paper/2503.21968","citing_paper":"/paper/2509.20345"},"observation_digest":"sha256:ce1737b3f7fb1522b2bb0399e287732adccd4b0ada85dd296a6c5920cd7a83fa","observation_id":"a3f48304-eb05-4411-88e9-ca84d9f86595","resolution":{"observed_at":"2026-08-04T15:21:56.103111Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21968","last_updated":"2025-07-14T04:16:07Z","snapshot_observed_at":"2026-08-10T19:38:28.339817Z","submitted_at":"2025-03-27T20:32:49Z","title":"GLM Inference with AI-Generated Synthetic Data Using Misspecified Linear Regression","version":2},"cited_work":{"arxiv_id":"2503.21968","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.21968","snapshot_observed_at":"2026-07-04T21:10:09.189597Z","title":"Keret and A","venue":null,"work_id":"5b4efcc1-4a76-43ad-9d5f-8a293db0cfd7","year":2025},"citing_paper":{"arxiv_id":"2605.05076","last_updated":"2026-05-24T22:21:17Z","snapshot_observed_at":"2026-08-11T18:23:56.230059Z","submitted_at":"2026-05-06T16:11:09Z","title":"High-Dimensional Statistics: Reflections on Progress and Open Problems","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-08T15:35:08.202464Z"},"links":{"cited_paper":"/paper/2503.21968","citing_paper":"/paper/2605.05076"},"observation_digest":"sha256:89304784253b3d824661873b8ac215a6c342788b7e65b731a160323d1e190609","observation_id":"fbc1a147-c355-4e78-b98f-855c76168e50","resolution":{"observed_at":"2026-05-11T18:36:06.694920Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21968","last_updated":"2025-07-14T04:16:07Z","snapshot_observed_at":"2026-08-10T19:38:28.339817Z","submitted_at":"2025-03-27T20:32:49Z","title":"GLM Inference with AI-Generated Synthetic Data Using Misspecified Linear Regression","version":2},"cited_work":{"arxiv_id":"2503.21968","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.21968","snapshot_observed_at":"2026-07-04T21:10:09.189597Z","title":"Keret and A","venue":null,"work_id":"5b4efcc1-4a76-43ad-9d5f-8a293db0cfd7","year":2025},"citing_paper":{"arxiv_id":"2605.05076","last_updated":"2026-05-24T22:21:17Z","snapshot_observed_at":"2026-08-11T18:23:56.230059Z","submitted_at":"2026-05-06T16:11:09Z","title":"High-Dimensional Statistics: Reflections on Progress and Open Problems","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-30T23:25:55.375541Z"},"links":{"cited_paper":"/paper/2503.21968","citing_paper":"/paper/2605.05076"},"observation_digest":"sha256:aa63351d3202804f855c517fc41548ec09307567b4f48b91b3ccc1a56d4a45fe","observation_id":"5053560d-c836-47c0-82d2-ef9743351f9b","resolution":{"observed_at":"2026-07-01T13:15:45.954016Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21968","last_updated":"2025-07-14T04:16:07Z","snapshot_observed_at":"2026-08-10T19:38:28.339817Z","submitted_at":"2025-03-27T20:32:49Z","title":"GLM Inference with AI-Generated Synthetic Data Using Misspecified Linear Regression","version":2},"cited_work":{"arxiv_id":"2503.21968","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.21968","snapshot_observed_at":"2026-07-04T21:10:09.189597Z","title":"Keret and A","venue":null,"work_id":"5b4efcc1-4a76-43ad-9d5f-8a293db0cfd7","year":2025},"citing_paper":{"arxiv_id":"2606.13629","last_updated":"2026-06-11T17:41:09Z","snapshot_observed_at":"2026-08-03T00:11:01.728067Z","submitted_at":"2026-06-11T17:41:09Z","title":"Valid Inference with Synthetic Data via Task Exchangeability","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-27T05:39:58.964043Z"},"links":{"cited_paper":"/paper/2503.21968","citing_paper":"/paper/2606.13629"},"observation_digest":"sha256:eb75f382174bb6a6aaeb20ddf123cf9ee845314ef9b73a4a751d3d2bdfcafdb0","observation_id":"903585e6-ac6c-4831-a8c1-05242c550ef7","resolution":{"observed_at":"2026-07-03T16:28:39.060469Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21968","last_updated":"2025-07-14T04:16:07Z","snapshot_observed_at":"2026-08-10T19:38:28.339817Z","submitted_at":"2025-03-27T20:32:49Z","title":"GLM Inference with AI-Generated Synthetic Data Using Misspecified Linear Regression","version":2},"cited_work":{"arxiv_id":"2503.21968","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.21968","snapshot_observed_at":"2026-07-04T21:10:09.189597Z","title":"Keret and A","venue":null,"work_id":"5b4efcc1-4a76-43ad-9d5f-8a293db0cfd7","year":2025},"citing_paper":{"arxiv_id":"2606.26053","last_updated":"2026-06-24T17:30:36Z","snapshot_observed_at":"2026-07-31T00:25:42.818814Z","submitted_at":"2026-06-24T17:30:36Z","title":"When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-06-25T19:03:59.234023Z"},"links":{"cited_paper":"/paper/2503.21968","citing_paper":"/paper/2606.26053"},"observation_digest":"sha256:3a1354d82e557b80c1934ad6514de778034af57a99ea181c5d8e9cba8d0dc65e","observation_id":"ba47d74e-6fb2-4df5-9e73-9979a82b8c31","resolution":{"observed_at":"2026-07-04T21:10:09.192278Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2503.21968/citation-record","integrity":"/paper/2503.21968/integrity","json":"/paper/2503.21968/citation-record.json","paper":"/paper/2503.21968"},"outbound":[],"paper":{"arxiv_id":"2503.21968","last_updated":"2025-07-14T04:16:07Z","latest_version":2,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-10T19:38:28.339817Z","submitted_at":"2025-03-27T20:32:49Z","title":"GLM Inference with AI-Generated Synthetic Data Using Misspecified Linear Regression"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2503.21968."}