{"as_of":"2026-08-09T18:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:224c15922de681203d1f411fd06702b1017b2fdd0cbabcfc8abe86299666b117","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":"2026-08-09T06:31:02.800959+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":"2026-07-11T05:02:16.134830Z","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":"2026-05-19T14:17:23.946322Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.07931","last_updated":"2022-10-14T16:30:23Z","snapshot_observed_at":"2026-08-05T15:15:37.504544Z","submitted_at":"2022-10-14T16:30:23Z","title":"Sequential Learning Of Neural Networks for Prequential MDL","version":1},"cited_work":{"arxiv_id":"2210.07931","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2210.07931","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sequential learning of neural networks for prequential mdl","venue":null,"work_id":"6b82ba79-4134-46bd-8228-420323f976f3","year":2022},"citing_paper":{"arxiv_id":"2505.17469","last_updated":"2026-05-13T10:49:50Z","snapshot_observed_at":"2026-08-07T19:05:17.091264Z","submitted_at":"2025-05-23T04:50:33Z","title":"Efficient compression of neural networks and datasets","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-19T14:15:10.275764Z"},"links":{"cited_paper":"/paper/2210.07931","citing_paper":"/paper/2505.17469"},"observation_digest":"sha256:68948819bf13192f6992e96db2be957e07e2a06d38d159ac5cd1199f7f38c6cd","observation_id":"f124b534-821f-4c1b-821b-244e1135b301","resolution":{"observed_at":"2026-05-19T14:17:23.948698Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.07931","last_updated":"2022-10-14T16:30:23Z","snapshot_observed_at":"2026-08-05T15:15:37.504544Z","submitted_at":"2022-10-14T16:30:23Z","title":"Sequential Learning Of Neural Networks for Prequential MDL","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.07931","snapshot_observed_at":"2026-07-11T05:02:16.134830Z","title":"arXiv preprint arXiv:2210.07931 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.05609","last_updated":"2026-07-06T20:11:53Z","snapshot_observed_at":"2026-08-08T12:56:50.633065Z","submitted_at":"2026-07-06T20:11:53Z","title":"To Retain or to Adapt? Generalizing Continual Learning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-07-11T05:02:16.134830Z"},"links":{"cited_paper":"/paper/2210.07931","citing_paper":"/paper/2607.05609"},"observation_digest":"sha256:9be76ae3cd6080a54f58871ad2590a378446973b34849b8401d70e80ffd11ee9","observation_id":"05e3e472-1e4a-4bfb-9368-1bb98f7b543f","resolution":{"observed_at":"2026-07-11T05:02:16.134830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2210.07931/citation-record","integrity":"/paper/2210.07931/integrity","json":"/paper/2210.07931/citation-record.json","paper":"/paper/2210.07931"},"outbound":[],"paper":{"arxiv_id":"2210.07931","last_updated":"2022-10-14T16:30:23Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-05T15:15:37.504544Z","submitted_at":"2022-10-14T16:30:23Z","title":"Sequential Learning Of Neural Networks for Prequential MDL"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.07931."}