{"as_of":"2026-08-14T07:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:919411fb363c8550ec5f38c5d8fab4f6d0b40d1b4aae25ee6129682f14836643","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":"2026-08-14T06:32:32.682623+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":"2026-08-11T20:21:02.418038Z","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":"2026-08-11T18:12:11.972033Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.01854","last_updated":"2022-01-04T06:04:42Z","snapshot_observed_at":"2026-07-06T11:15:36.275682Z","submitted_at":"2021-06-03T13:57:32Z","title":"Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.01854","snapshot_observed_at":"2026-08-11T20:21:02.418038Z","title":"Taghibakhshi, S","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.05852","last_updated":"2024-12-08T08:21:35Z","snapshot_observed_at":"2026-08-12T12:42:01.728215Z","submitted_at":"2024-12-08T08:21:35Z","title":"Evolving Algebraic Multigrid Methods Using Grammar-Guided Genetic Programming","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:02.418038Z"},"links":{"cited_paper":"/paper/2106.01854","citing_paper":"/paper/2412.05852"},"observation_digest":"sha256:93266f0ecbdc3ab25f7ebe4a604865150889316f79d0696be1cbe8143b4a0612","observation_id":"f2275710-aa32-47e1-8c54-e082ed30af4a","resolution":{"observed_at":"2026-08-11T20:21:02.418038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.01854","last_updated":"2022-01-04T06:04:42Z","snapshot_observed_at":"2026-07-06T11:15:36.275682Z","submitted_at":"2021-06-03T13:57:32Z","title":"Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"2106.01854","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.01854","snapshot_observed_at":"2026-08-11T18:12:11.972033Z","title":"Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning","venue":"cs.LG","work_id":"eb4b121d-0aac-4e3d-8dfe-a9cd03e06057","year":2021},"citing_paper":{"arxiv_id":"2412.08186","last_updated":"2024-12-11T08:24:38Z","snapshot_observed_at":"2026-08-12T04:04:32.395737Z","submitted_at":"2024-12-11T08:24:38Z","title":"Towards Automated Algebraic Multigrid Preconditioner Design Using Genetic Programming for Large-Scale Laser Beam Welding Simulations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T18:12:11.800089Z"},"links":{"cited_paper":"/paper/2106.01854","citing_paper":"/paper/2412.08186"},"observation_digest":"sha256:e83d49b5ffa3fc37fd353942a12ae46e10e5bcd3ac9e45e8e438d1000885f9c4","observation_id":"f00c254a-0955-406f-8b06-54960978249a","resolution":{"observed_at":"2026-08-11T18:12:11.978134Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2106.01854/citation-record","integrity":"/paper/2106.01854/integrity","json":"/paper/2106.01854/citation-record.json","paper":"/paper/2106.01854"},"outbound":[],"paper":{"arxiv_id":"2106.01854","last_updated":"2022-01-04T06:04:42Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T11:15:36.275682Z","submitted_at":"2021-06-03T13:57:32Z","title":"Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2106.01854."}