{"as_of":"2026-08-06T19:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b03ec56148df7c7efc8e7bb74abb8a820c88decba7edef07c4b37eab6a5d67a6","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-24T06:35:22.947009Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":18,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2204.06644","last_updated":"2022-04-16T22:06:50Z","snapshot_observed_at":"2026-08-05T13:32:49.675098Z","submitted_at":"2022-04-13T21:39:15Z","title":"METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals","version":2},"cited_work":{"arxiv_id":"2204.06644","doi":"10.48550/arxiv.2204.06644","metadata_source":"arxiv_reference","pith_arxiv_id":"2204.06644","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"05020873-2062-49f7-ac96-af667525847a","year":2022},"citing_paper":{"arxiv_id":"2310.17591","last_updated":"2023-10-26T17:13:07Z","snapshot_observed_at":"2026-07-06T16:39:04.459332Z","submitted_at":"2023-10-26T17:13:07Z","title":"Lil-Bevo: Explorations of Strategies for Training Language Models in More Humanlike Ways","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-24T06:35:22.947009Z"},"links":{"cited_paper":"/paper/2204.06644","citing_paper":"/paper/2310.17591"},"observation_digest":"sha256:008ef24361ae1f38a65ab60b84c2611eade0418030b041c285e6113fdf518ec2","observation_id":"0d227d5a-5188-43ba-a745-d0380534be26","resolution":{"observed_at":"2026-05-24T06:36:01.774883Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2204.06644/citation-record","integrity":"/paper/2204.06644/integrity","json":"/paper/2204.06644/citation-record.json","paper":"/paper/2204.06644"},"outbound":[],"paper":{"arxiv_id":"2204.06644","last_updated":"2022-04-16T22:06:50Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T13:32:49.675098Z","submitted_at":"2022-04-13T21:39:15Z","title":"METRO: Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals"},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2204.06644."}