{"as_of":"2026-08-18T14:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:db5d88a764dcc98b0f5736b99e74a2e9bd6c1c01e842840ef9cda911afc2b728","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":"2026-08-14T13:50:28.908069Z","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":"2026-08-14T10:13:40.649058Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1711.04956","last_updated":"2018-10-05T21:28:11Z","snapshot_observed_at":"2026-08-15T04:40:53.612743Z","submitted_at":"2017-11-14T05:47:08Z","title":"Classical Structured Prediction Losses for Sequence to Sequence Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.04956","snapshot_observed_at":"2026-08-14T13:50:28.908069Z","title":"arXiv preprint arXiv:1711.04956","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.04319","last_updated":"2019-09-26T23:57:44Z","snapshot_observed_at":"2026-08-16T12:52:32.574265Z","submitted_at":"2019-08-12T18:09:04Z","title":"Neural Text Generation with Unlikelihood Training","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-14T13:50:28.908069Z"},"links":{"cited_paper":"/paper/1711.04956","citing_paper":"/paper/1908.04319"},"observation_digest":"sha256:09c59fee6aa57274d787a17a917cfb4bc6a9a01539c316a16c665af695a569ad","observation_id":"277f5d67-988d-452d-aeef-dd4c329c6ce4","resolution":{"observed_at":"2026-08-14T13:50:28.908069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.04956","last_updated":"2018-10-05T21:28:11Z","snapshot_observed_at":"2026-08-15T04:40:53.612743Z","submitted_at":"2017-11-14T05:47:08Z","title":"Classical Structured Prediction Losses for Sequence to Sequence Learning","version":5},"cited_work":{"arxiv_id":"1711.04956","doi":null,"metadata_source":"pith","pith_arxiv_id":"1711.04956","snapshot_observed_at":"2026-08-14T10:13:40.649058Z","title":"Classical Structured Prediction Losses for Sequence to Sequence Learning","venue":"cs.CL","work_id":"add3e17d-932a-4702-addb-a3355b779a15","year":2017},"citing_paper":{"arxiv_id":"1908.11775","last_updated":"2019-11-11T21:51:11Z","snapshot_observed_at":"2026-08-17T15:16:33.375231Z","submitted_at":"2019-08-30T15:05:02Z","title":"Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel","version":4},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-14T10:13:40.470815Z"},"links":{"cited_paper":"/paper/1711.04956","citing_paper":"/paper/1908.11775"},"observation_digest":"sha256:7b8bab4b0fcdf137f6a2a539ea9d0c89d8723b26822aec2029f2b2b5b4dfb1c9","observation_id":"42b1b597-5f3c-43de-a497-e67a545a1957","resolution":{"observed_at":"2026-08-14T10:13:40.655090Z","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/1711.04956/citation-record","integrity":"/paper/1711.04956/integrity","json":"/paper/1711.04956/citation-record.json","paper":"/paper/1711.04956"},"outbound":[],"paper":{"arxiv_id":"1711.04956","last_updated":"2018-10-05T21:28:11Z","latest_version":5,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-15T04:40:53.612743Z","submitted_at":"2017-11-14T05:47:08Z","title":"Classical Structured Prediction Losses for Sequence to Sequence 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":"2026-08-18T06:34:40.430872+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:1711.04956."}