{"as_of":"2026-08-09T16:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d1c323fafb0ae23c23035b00633beb571549f227a674ef9cda8d84010a47faa8","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":"2026-08-03T21:50:15.587059Z","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":"2026-05-12T16:06:20.794692Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1905.03813","last_updated":"2019-10-15T06:11:03Z","snapshot_observed_at":"2026-08-08T18:02:52.018013Z","submitted_at":"2019-05-09T18:47:38Z","title":"When Deep Learning Met Code Search","version":4},"cited_work":{"arxiv_id":"1905.03813","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1905.03813","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:1905.03813 (2019)","venue":null,"work_id":"d2707374-16a6-46ae-906d-fd13b8c35747","year":1905},"citing_paper":{"arxiv_id":"1909.09436","last_updated":"2020-06-08T09:09:28Z","snapshot_observed_at":"2026-08-06T10:54:14.530969Z","submitted_at":"2019-09-20T11:52:45Z","title":"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-12T16:06:20.777086Z"},"links":{"cited_paper":"/paper/1905.03813","citing_paper":"/paper/1909.09436"},"observation_digest":"sha256:c68b090fe7278a646cdb0acd9f606919910f849f686b838a92a5f9cb550fcecb","observation_id":"f813163b-b8dc-440d-ad5b-1423fffca25b","resolution":{"observed_at":"2026-05-12T16:06:20.799494Z","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":"1905.03813","last_updated":"2019-10-15T06:11:03Z","snapshot_observed_at":"2026-08-08T18:02:52.018013Z","submitted_at":"2019-05-09T18:47:38Z","title":"When Deep Learning Met Code Search","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.03813","snapshot_observed_at":"2026-08-03T21:50:15.587059Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2511.13663","last_updated":"2026-06-02T16:19:57Z","snapshot_observed_at":"2026-08-06T00:47:45.850094Z","submitted_at":"2025-11-17T18:16:36Z","title":"SAIL: Sound Abstract Interpreters with LLMs","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T21:50:15.587059Z"},"links":{"cited_paper":"/paper/1905.03813","citing_paper":"/paper/2511.13663"},"observation_digest":"sha256:c199369317348f206663fa608cd76729a4e355149a367af13de1e5e442acd0c3","observation_id":"047bb28d-6c3b-4e91-ab52-ecc33dc642e5","resolution":{"observed_at":"2026-08-03T21:50:15.587059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1905.03813/citation-record","integrity":"/paper/1905.03813/integrity","json":"/paper/1905.03813/citation-record.json","paper":"/paper/1905.03813"},"outbound":[],"paper":{"arxiv_id":"1905.03813","last_updated":"2019-10-15T06:11:03Z","latest_version":4,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-08T18:02:52.018013Z","submitted_at":"2019-05-09T18:47:38Z","title":"When Deep Learning Met Code Search"},"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:1905.03813."}