{"as_of":"2026-08-18T20:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eaad3507e34db88b42a8db67779b0b325fd23199c76398135cf063a1f1e83730","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:08:33.686370Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-15T18:16:14.067578Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.06356","last_updated":"2022-06-13T17:52:38Z","snapshot_observed_at":"2026-08-18T18:04:19.927039Z","submitted_at":"2022-06-13T17:52:38Z","title":"Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.06356","snapshot_observed_at":"2026-08-16T11:08:33.686370Z","title":"Modern Distributed Data - Parallel Large - Scale Pre -training Strategies For NLP models, June 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.18580","last_updated":"2025-04-23T05:11:21Z","snapshot_observed_at":"2026-08-17T18:28:37.066736Z","submitted_at":"2025-04-23T05:11:21Z","title":"Parameter-Efficient Checkpoint Merging via Metrics-Weighted Averaging","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-16T11:08:33.686370Z"},"links":{"cited_paper":"/paper/2206.06356","citing_paper":"/paper/2504.18580"},"observation_digest":"sha256:a4829dbf1a8937b5824e09a885d83cee0c2f2fcf4473770a0e18ef5199bcb7ff","observation_id":"45fa7540-30dd-4905-a29e-110b67cda58b","resolution":{"observed_at":"2026-08-16T11:08:33.686370Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.06356","last_updated":"2022-06-13T17:52:38Z","snapshot_observed_at":"2026-08-18T18:04:19.927039Z","submitted_at":"2022-06-13T17:52:38Z","title":"Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models","version":1},"cited_work":{"arxiv_id":"2206.06356","doi":"10.48550/arxiv.2206.06356","metadata_source":"pith","pith_arxiv_id":"2206.06356","snapshot_observed_at":"2026-08-15T18:16:14.067578Z","title":"Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models","venue":"cs.DC","work_id":"44fdb6e6-88c3-4cd4-961d-2c70a6f44b04","year":2022},"citing_paper":{"arxiv_id":"2608.03855","last_updated":"2026-08-06T12:23:11Z","snapshot_observed_at":"2026-08-18T18:01:10.046841Z","submitted_at":"2026-08-04T15:57:38Z","title":"Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T14:50:56.187201Z"},"links":{"cited_paper":"/paper/2206.06356","citing_paper":"/paper/2608.03855"},"observation_digest":"sha256:151cbf9d59f719cb35ff4f8eba65d1bf510b1ac867a7c93f75ff5dd08b79c794","observation_id":"8b4ec38d-0618-4713-ad7f-e26e52eea656","resolution":{"observed_at":"2026-08-15T14:50:57.550867Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2206.06356/citation-record","integrity":"/paper/2206.06356/integrity","json":"/paper/2206.06356/citation-record.json","paper":"/paper/2206.06356"},"outbound":[],"paper":{"arxiv_id":"2206.06356","last_updated":"2022-06-13T17:52:38Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-18T18:04:19.927039Z","submitted_at":"2022-06-13T17:52:38Z","title":"Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2206.06356."}