{"as_of":"2026-08-08T18:11:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e83b59e907248295db2aa8e137fc3badefd9b0b6fe06b2487d2d1d9b1cf5d2c1","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":"2026-08-08T06:32:00.761636+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":"2026-08-01T17:56:14.031838Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.04942","last_updated":"2020-08-27T15:49:08Z","snapshot_observed_at":"2026-07-06T08:14:16.969474Z","submitted_at":"2019-08-14T03:40:04Z","title":"Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.04942","snapshot_observed_at":"2026-08-01T17:56:14.031838Z","title":"arXiv preprint arXiv:1908.04942 , year=","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2607.17461","last_updated":"2026-07-20T01:22:06Z","snapshot_observed_at":"2026-08-08T05:25:22.463786Z","submitted_at":"2026-07-20T01:22:06Z","title":"HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T17:56:14.031838Z"},"links":{"cited_paper":"/paper/1908.04942","citing_paper":"/paper/2607.17461"},"observation_digest":"sha256:d2b7653b8bf3faaa14ca8faefebd4f7c92facb4bb8d7db76a3a255842954679f","observation_id":"77ee2d6e-f77a-487a-9908-351658bc9446","resolution":{"observed_at":"2026-08-01T17:56:14.031838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.04942","last_updated":"2020-08-27T15:49:08Z","snapshot_observed_at":"2026-07-06T08:14:16.969474Z","submitted_at":"2019-08-14T03:40:04Z","title":"Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.04942","snapshot_observed_at":"2026-08-01T14:56:36.796643Z","title":"arXiv preprint arXiv:1908.04942 , year=","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2607.18609","last_updated":"2026-07-21T01:07:40Z","snapshot_observed_at":"2026-08-03T01:50:59.439849Z","submitted_at":"2026-07-21T01:07:40Z","title":"Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T14:56:36.796643Z"},"links":{"cited_paper":"/paper/1908.04942","citing_paper":"/paper/2607.18609"},"observation_digest":"sha256:b57d7b0fc2f748a40eb161b5313a1c5edd6c4e3e5a7653c96e023783c4f0ff75","observation_id":"d9b6dd96-78d7-4140-9725-e3353d2463c6","resolution":{"observed_at":"2026-08-01T14:56:36.796643Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1908.04942/citation-record","integrity":"/paper/1908.04942/integrity","json":"/paper/1908.04942/citation-record.json","paper":"/paper/1908.04942"},"outbound":[],"paper":{"arxiv_id":"1908.04942","last_updated":"2020-08-27T15:49:08Z","latest_version":4,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T08:14:16.969474Z","submitted_at":"2019-08-14T03:40:04Z","title":"Reinforcement Learning Based Graph-to-Sequence Model for Natural Question Generation"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1908.04942."}