{"as_of":"2026-08-23T16:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:035024302e3723cc5a79a11db82cf531c62116717d3344c47ba1643468c02553","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-02T11:59:04.103002Z","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":"2026-07-02T12:06:55.347413Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1909.11591","last_updated":"2019-11-22T12:57:35Z","snapshot_observed_at":"2026-08-15T13:59:45.313847Z","submitted_at":"2019-09-23T18:10:00Z","title":"Modular Deep Reinforcement Learning with Temporal Logic Specifications","version":2},"cited_work":{"arxiv_id":"1909.11591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.11591","snapshot_observed_at":"2026-07-02T12:06:55.347413Z","title":"Z., Hasanbeig, M., Abate, A., and Kroening, D","venue":null,"work_id":"77541537-cde8-463d-8cbf-a826be76129c","year":1909},"citing_paper":{"arxiv_id":"2605.24740","last_updated":"2026-05-23T21:29:15Z","snapshot_observed_at":"2026-08-10T17:49:25.865777Z","submitted_at":"2026-05-23T21:29:15Z","title":"Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T14:15:45.030536Z"},"links":{"cited_paper":"/paper/1909.11591","citing_paper":"/paper/2605.24740"},"observation_digest":"sha256:d4ef1782de43b6902d58e5b513f93cdee551dc557b7ffa6573eab932e9cb77e2","observation_id":"e1a92c9d-f4d2-41d6-ae61-2b33d703ebf1","resolution":{"observed_at":"2026-06-30T14:24:45.270482Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11591","last_updated":"2019-11-22T12:57:35Z","snapshot_observed_at":"2026-08-15T13:59:45.313847Z","submitted_at":"2019-09-23T18:10:00Z","title":"Modular Deep Reinforcement Learning with Temporal Logic Specifications","version":2},"cited_work":{"arxiv_id":"1909.11591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.11591","snapshot_observed_at":"2026-07-02T12:06:55.347413Z","title":"Z., Hasanbeig, M., Abate, A., and Kroening, D","venue":null,"work_id":"77541537-cde8-463d-8cbf-a826be76129c","year":1909},"citing_paper":{"arxiv_id":"2606.00838","last_updated":"2026-05-30T18:26:59Z","snapshot_observed_at":"2026-08-13T02:13:39.159943Z","submitted_at":"2026-05-30T18:26:59Z","title":"Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-28T18:25:37.397393Z"},"links":{"cited_paper":"/paper/1909.11591","citing_paper":"/paper/2606.00838"},"observation_digest":"sha256:5809958663e8b3f4f7b842d022c4d310afb95e89569f703d8bc0b29cb4d2e639","observation_id":"b1b7785d-ec9d-409b-bd18-ea28c61cfdcb","resolution":{"observed_at":"2026-06-28T20:42:37.712320Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11591","last_updated":"2019-11-22T12:57:35Z","snapshot_observed_at":"2026-08-15T13:59:45.313847Z","submitted_at":"2019-09-23T18:10:00Z","title":"Modular Deep Reinforcement Learning with Temporal Logic Specifications","version":2},"cited_work":{"arxiv_id":"1909.11591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.11591","snapshot_observed_at":"2026-07-02T12:06:55.347413Z","title":"Z., Hasanbeig, M., Abate, A., and Kroening, D","venue":null,"work_id":"77541537-cde8-463d-8cbf-a826be76129c","year":1909},"citing_paper":{"arxiv_id":"2607.00442","last_updated":"2026-07-01T04:57:39Z","snapshot_observed_at":"2026-08-13T07:25:57.864655Z","submitted_at":"2026-07-01T04:57:39Z","title":"Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-02T11:59:04.103002Z"},"links":{"cited_paper":"/paper/1909.11591","citing_paper":"/paper/2607.00442"},"observation_digest":"sha256:5f832bbb99b6529a7468d52c1bc40011bc3d12307e7b661a9c242eaa8f332c4c","observation_id":"ba3bcda3-a67c-4a59-9c78-6c54d952e569","resolution":{"observed_at":"2026-07-02T12:06:55.348968Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1909.11591/citation-record","integrity":"/paper/1909.11591/integrity","json":"/paper/1909.11591/citation-record.json","paper":"/paper/1909.11591"},"outbound":[],"paper":{"arxiv_id":"1909.11591","last_updated":"2019-11-22T12:57:35Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T13:59:45.313847Z","submitted_at":"2019-09-23T18:10:00Z","title":"Modular Deep Reinforcement Learning with Temporal Logic Specifications"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1909.11591."}