{"as_of":"2026-08-06T10:46:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4a44e12d402794f045f99be0f61e83f397c69b0fd4b4f08c459a7db7e5a95bd3","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":"2026-08-06T06:34:29.942622+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":"2026-07-01T06:50:59.743859Z","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":"2026-07-01T06:55:29.510936Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.05740","last_updated":"2023-04-11T21:03:47Z","snapshot_observed_at":"2026-08-05T21:26:08.146481Z","submitted_at":"2021-10-12T05:07:43Z","title":"Temporal Abstraction in Reinforcement Learning with the Successor Representation","version":3},"cited_work":{"arxiv_id":"2110.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.05740","snapshot_observed_at":"2026-07-01T06:55:29.510936Z","title":"Machado, André Barreto, Doina Precup, and Michael Bowling","venue":null,"work_id":"c15ae30e-1ecb-4964-acb0-68b9ccefe53d","year":2023},"citing_paper":{"arxiv_id":"2605.14323","last_updated":"2026-05-14T03:35:46Z","snapshot_observed_at":"2026-07-06T23:25:44.508166Z","submitted_at":"2026-05-14T03:35:46Z","title":"Dynamic Latent Routing","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-15T01:49:14.934586Z"},"links":{"cited_paper":"/paper/2110.05740","citing_paper":"/paper/2605.14323"},"observation_digest":"sha256:f4d5544f2a74b67de7c63ecfd129dbfff08a3136152792920a0646adc73e492a","observation_id":"ca652e6f-5741-46ed-8d79-22256bfed4a8","resolution":{"observed_at":"2026-05-15T01:49:38.178035Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.05740","last_updated":"2023-04-11T21:03:47Z","snapshot_observed_at":"2026-08-05T21:26:08.146481Z","submitted_at":"2021-10-12T05:07:43Z","title":"Temporal Abstraction in Reinforcement Learning with the Successor Representation","version":3},"cited_work":{"arxiv_id":"2110.05740","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.05740","snapshot_observed_at":"2026-07-01T06:55:29.510936Z","title":"Machado, André Barreto, Doina Precup, and Michael Bowling","venue":null,"work_id":"c15ae30e-1ecb-4964-acb0-68b9ccefe53d","year":2023},"citing_paper":{"arxiv_id":"2606.29980","last_updated":"2026-06-30T08:37:47Z","snapshot_observed_at":"2026-07-07T00:03:51.213896Z","submitted_at":"2026-06-29T08:55:24Z","title":"Exploration and Online Transfer with Behavioral Foundation Models","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-07-01T06:50:59.743859Z"},"links":{"cited_paper":"/paper/2110.05740","citing_paper":"/paper/2606.29980"},"observation_digest":"sha256:c8d007fc2d067cd2d8eb42a20d936f0a4271d11eac7b42f122ed92e0e4b9b4f2","observation_id":"dac7fbbc-6f19-41db-9efb-6bce0d2bafb4","resolution":{"observed_at":"2026-07-01T06:55:29.514716Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2110.05740/citation-record","integrity":"/paper/2110.05740/integrity","json":"/paper/2110.05740/citation-record.json","paper":"/paper/2110.05740"},"outbound":[],"paper":{"arxiv_id":"2110.05740","last_updated":"2023-04-11T21:03:47Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T21:26:08.146481Z","submitted_at":"2021-10-12T05:07:43Z","title":"Temporal Abstraction in Reinforcement Learning with the Successor Representation"},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.05740."}