{"as_of":"2026-08-08T02:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:03ac950eb8c237ecad07add5d314908a6b930dd2ddea424bfd55437d4c6f93e2","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:10:40.440922Z","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":"2026-07-10T06:15:00.866473Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.00397","last_updated":"2022-03-01T12:46:28Z","snapshot_observed_at":"2026-08-05T00:05:21.423392Z","submitted_at":"2022-03-01T12:46:28Z","title":"A Theory of Abstraction in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.00397","snapshot_observed_at":"2026-08-07T14:10:40.440922Z","title":"A theory of abstraction in reinforcement learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.19837","last_updated":"2025-05-26T11:19:43Z","snapshot_observed_at":"2026-08-07T14:03:11.036063Z","submitted_at":"2025-05-26T11:19:43Z","title":"Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:10:40.440922Z"},"links":{"cited_paper":"/paper/2203.00397","citing_paper":"/paper/2505.19837"},"observation_digest":"sha256:933ee881ddce06b10417a5ec958f518f7f2fcfb214ae9ac7c7a98ed77572173f","observation_id":"62eff5e4-c4ae-4840-b3da-ab2cd23329e9","resolution":{"observed_at":"2026-08-07T14:10:40.440922Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.00397","last_updated":"2022-03-01T12:46:28Z","snapshot_observed_at":"2026-08-05T00:05:21.423392Z","submitted_at":"2022-03-01T12:46:28Z","title":"A Theory of Abstraction in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.00397","snapshot_observed_at":"2026-08-06T23:34:30.715934Z","title":"A theory of abstraction in reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.17518","last_updated":"2025-06-20T23:47:04Z","snapshot_observed_at":"2026-08-07T13:30:27.787034Z","submitted_at":"2025-06-20T23:47:04Z","title":"A Survey of State Representation Learning for Deep Reinforcement Learning","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T23:34:30.715934Z"},"links":{"cited_paper":"/paper/2203.00397","citing_paper":"/paper/2506.17518"},"observation_digest":"sha256:d7afc9e6c2b2593e13a14bc86c52a23ec08b7e3d6bf703f9301cc5331ce5d84a","observation_id":"fc0284af-a214-4ae6-bd9a-17278f658440","resolution":{"observed_at":"2026-08-06T23:34:30.715934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.00397","last_updated":"2022-03-01T12:46:28Z","snapshot_observed_at":"2026-08-05T00:05:21.423392Z","submitted_at":"2022-03-01T12:46:28Z","title":"A Theory of Abstraction in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2203.00397","doi":"10.48550/arxiv.2203.00397","metadata_source":"arxiv_reference","pith_arxiv_id":"2203.00397","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A Theory of Abstraction in Reinforcement Learning","venue":null,"work_id":"dd05bc7c-9f7c-4c75-8f1e-d814250d3692","year":2022},"citing_paper":{"arxiv_id":"2604.15289","last_updated":"2026-04-16T17:53:16Z","snapshot_observed_at":"2026-07-06T23:02:53.677687Z","submitted_at":"2026-04-16T17:53:16Z","title":"Abstract Sim2Real through Approximate Information States","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T10:39:57.845600Z"},"links":{"cited_paper":"/paper/2203.00397","citing_paper":"/paper/2604.15289"},"observation_digest":"sha256:d768a562b4f212a725ad79700d0d28a1a8d35984a0fc3871db23c4dabf6a8f45","observation_id":"4ef74acd-2049-400b-8cae-8d86af2074c3","resolution":{"observed_at":"2026-05-10T10:44:38.109266Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2203.00397","last_updated":"2022-03-01T12:46:28Z","snapshot_observed_at":"2026-08-05T00:05:21.423392Z","submitted_at":"2022-03-01T12:46:28Z","title":"A Theory of Abstraction in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2203.00397","doi":"10.48550/arxiv.2203.00397","metadata_source":"arxiv_reference","pith_arxiv_id":"2203.00397","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"A Theory of Abstraction in Reinforcement Learning","venue":null,"work_id":"dd05bc7c-9f7c-4c75-8f1e-d814250d3692","year":2022},"citing_paper":{"arxiv_id":"2607.00034","last_updated":"2026-06-24T11:49:30Z","snapshot_observed_at":"2026-08-05T11:11:17.135472Z","submitted_at":"2026-06-24T11:49:30Z","title":"Bayesian updates from coalgebraic determinisation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-07-02T21:20:58.414608Z"},"links":{"cited_paper":"/paper/2203.00397","citing_paper":"/paper/2607.00034"},"observation_digest":"sha256:6d3ed7a4451a0af9b0b356889e18b60b33b405cd1d95886240a6c55703762f6d","observation_id":"e5e2c5bd-969e-4bf1-b31f-1775f4316913","resolution":{"observed_at":"2026-07-02T21:27:23.844352Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2203.00397/citation-record","integrity":"/paper/2203.00397/integrity","json":"/paper/2203.00397/citation-record.json","paper":"/paper/2203.00397"},"outbound":[],"paper":{"arxiv_id":"2203.00397","last_updated":"2022-03-01T12:46:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T00:05:21.423392Z","submitted_at":"2022-03-01T12:46:28Z","title":"A Theory of Abstraction in Reinforcement 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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2203.00397."}