{"as_of":"2026-08-18T12:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ba728c5e13d65f68e7650c79a70cb7e0f7416ad67d44cefcfd30175211eaf5dd","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":"2026-08-15T23:17:16.474473Z","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":"2026-08-15T16:43:36.983559Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2111.04147","last_updated":"2021-11-21T22:37:29Z","snapshot_observed_at":"2026-08-18T08:41:32.184189Z","submitted_at":"2021-11-07T18:51:02Z","title":"Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.04147","snapshot_observed_at":"2026-08-15T23:17:16.474473Z","title":"Learning finite linear temporal logic specifications with a specialized neural operator","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.05106","last_updated":"2025-05-08T10:10:00Z","snapshot_observed_at":"2026-08-15T23:10:31.223875Z","submitted_at":"2025-05-08T10:10:00Z","title":"A Neuro-Symbolic Framework for Sequence Classification with Relational and Temporal Knowledge","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T23:17:16.474473Z"},"links":{"cited_paper":"/paper/2111.04147","citing_paper":"/paper/2505.05106"},"observation_digest":"sha256:80333c56fe154ad2812d16b5b313da93b1b8e2ba5384e94976e26241322d01f5","observation_id":"27a3f7ce-ef2a-442c-aab7-ca4e0c16324d","resolution":{"observed_at":"2026-08-15T23:17:16.474473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.04147","last_updated":"2021-11-21T22:37:29Z","snapshot_observed_at":"2026-08-18T08:41:32.184189Z","submitted_at":"2021-11-07T18:51:02Z","title":"Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator","version":2},"cited_work":{"arxiv_id":"2111.04147","doi":null,"metadata_source":"pith","pith_arxiv_id":"2111.04147","snapshot_observed_at":"2026-08-15T16:43:36.983559Z","title":"Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator","venue":"cs.AI","work_id":"0aec96a5-5b45-4296-ad31-abce7faa7284","year":2021},"citing_paper":{"arxiv_id":"2509.02491","last_updated":"2025-09-02T16:40:38Z","snapshot_observed_at":"2026-08-18T07:20:01.209828Z","submitted_at":"2025-09-02T16:40:38Z","title":"RNN Generalization to Omega-Regular Languages","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T16:43:36.456067Z"},"links":{"cited_paper":"/paper/2111.04147","citing_paper":"/paper/2509.02491"},"observation_digest":"sha256:c74d429209ed12c9ec91aa82624a519f30aecbb05e0c47eebb574e5f85d8ac05","observation_id":"10e5a74f-5edd-464f-a945-213f7eb85b8c","resolution":{"observed_at":"2026-08-15T16:43:37.085202Z","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/2111.04147/citation-record","integrity":"/paper/2111.04147/integrity","json":"/paper/2111.04147/citation-record.json","paper":"/paper/2111.04147"},"outbound":[],"paper":{"arxiv_id":"2111.04147","last_updated":"2021-11-21T22:37:29Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-18T08:41:32.184189Z","submitted_at":"2021-11-07T18:51:02Z","title":"Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator"},"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:2111.04147."}