{"as_of":"2026-08-08T06:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:709338cc8c1b900cc330d2f3f2fe7ec35c88d8c0b1ad59b14e2edcbf9d44bf50","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-21T07:57:49.746594Z","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":"2026-05-21T07:59:50.157490Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2112.07628","last_updated":"2023-11-24T00:38:52Z","snapshot_observed_at":"2026-07-06T12:18:42.245356Z","submitted_at":"2021-12-14T18:13:36Z","title":"Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time","version":2},"cited_work":{"arxiv_id":"2112.07628","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.07628","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2112.07628 , year=","venue":null,"work_id":"5629bf2d-a557-4cef-a46e-8f93d9b4b1af","year":null},"citing_paper":{"arxiv_id":"2605.10933","last_updated":"2026-05-20T17:26:14Z","snapshot_observed_at":"2026-08-03T07:36:40.918472Z","submitted_at":"2026-05-11T17:58:28Z","title":"DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices","version":1},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-12T03:36:12.915133Z"},"links":{"cited_paper":"/paper/2112.07628","citing_paper":"/paper/2605.10933"},"observation_digest":"sha256:c45e8616a1da1f2dee448f98a2b30124335da1e72be16d68ab0bacbb26ca75e2","observation_id":"600ee460-7bea-4467-9cb9-3721c9685905","resolution":{"observed_at":"2026-05-12T03:36:19.909326Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.07628","last_updated":"2023-11-24T00:38:52Z","snapshot_observed_at":"2026-07-06T12:18:42.245356Z","submitted_at":"2021-12-14T18:13:36Z","title":"Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time","version":2},"cited_work":{"arxiv_id":"2112.07628","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.07628","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2112.07628 , year=","venue":null,"work_id":"5629bf2d-a557-4cef-a46e-8f93d9b4b1af","year":null},"citing_paper":{"arxiv_id":"2605.10933","last_updated":"2026-05-20T17:26:14Z","snapshot_observed_at":"2026-08-03T07:36:40.918472Z","submitted_at":"2026-05-11T17:58:28Z","title":"DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices","version":2},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-13T07:29:14.545746Z"},"links":{"cited_paper":"/paper/2112.07628","citing_paper":"/paper/2605.10933"},"observation_digest":"sha256:b7b579c68c2ba57959087615927e1759d27da20c7aea91812cd412b10e800ddb","observation_id":"b80f10e9-e338-4b2e-a14f-7e44169eb978","resolution":{"observed_at":"2026-05-13T07:32:30.456800Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.07628","last_updated":"2023-11-24T00:38:52Z","snapshot_observed_at":"2026-07-06T12:18:42.245356Z","submitted_at":"2021-12-14T18:13:36Z","title":"Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time","version":2},"cited_work":{"arxiv_id":"2112.07628","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2112.07628","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2112.07628 , year=","venue":null,"work_id":"5629bf2d-a557-4cef-a46e-8f93d9b4b1af","year":null},"citing_paper":{"arxiv_id":"2605.10933","last_updated":"2026-05-20T17:26:14Z","snapshot_observed_at":"2026-08-03T07:36:40.918472Z","submitted_at":"2026-05-11T17:58:28Z","title":"DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices","version":3},"reference_index":82,"source":"arxiv_source","source_observed_at":"2026-05-21T07:57:49.746594Z"},"links":{"cited_paper":"/paper/2112.07628","citing_paper":"/paper/2605.10933"},"observation_digest":"sha256:780ad309e14edb7ba2bfacf2c2bd30eb147a6d222482d80d3558941f8ca09eee","observation_id":"9fcf0709-5a2d-438c-ae53-8a03e1c62e36","resolution":{"observed_at":"2026-05-21T07:59:50.159507Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2112.07628/citation-record","integrity":"/paper/2112.07628/integrity","json":"/paper/2112.07628/citation-record.json","paper":"/paper/2112.07628"},"outbound":[],"paper":{"arxiv_id":"2112.07628","last_updated":"2023-11-24T00:38:52Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T12:18:42.245356Z","submitted_at":"2021-12-14T18:13:36Z","title":"Training Multi-Layer Over-Parametrized Neural Network in Subquadratic Time"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2112.07628."}