{"as_of":"2026-08-11T13:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:74bdc74ce1e012645174db12d536b83c6bf77e2aaeb4c78c866c9eb91045438e","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:10:40.811000Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.00328/citation-record","integrity":"/paper/2506.00328/integrity","json":"/paper/2506.00328/citation-record.json","paper":"/paper/2506.00328"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:38.405931Z","title":"MIT Press, Cambridge, MA (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:38.405931Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:970e7de12f250573c534468f8d15bc83e7b1640fa798b39805424407af945d23","observation_id":"efbf18df-8cec-4cf1-9df5-636682a13c77","resolution":{"observed_at":"2026-08-07T12:10:38.405931Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:43.030914Z","title":"Journal of Artificial Intelligence Research 4, 237–285 (1996)","venue":null,"work_id":"df557b5b-8f3a-42cc-89d7-315f8325eea5","year":1996},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:38.478585Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:b68b581f250f8279f48f88c3ede80e7386ff12fa8d2c171c19bad2361dd954bc","observation_id":"edba8a00-1700-4c07-bf5f-cb5ddb4cdc75","resolution":{"observed_at":"2026-08-07T12:10:43.077385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:38.559210Z","title":"Nature 518, 529–533 (2015) https://doi.org/10.1038/nature14236","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:38.559210Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:fceec51580427bfd7a2518ee743b79a27e113bd2593f6f3b6e9b2bb56e0b9ef6","observation_id":"749402d5-443d-419e-84c3-c87b5562e028","resolution":{"observed_at":"2026-08-07T12:10:38.559210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-07T12:10:38.675197Z","title":"In: arXiv Preprint arXiv:1707.06347 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:38.675197Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:836df170ae5c1ad6fc2f63e5ff88bd8ab3a482fbf72d5e4eca5bad1bec5915b5","observation_id":"52d62da9-c86a-405c-8ff8-66828a12cd08","resolution":{"observed_at":"2026-08-07T12:10:38.675197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01290","last_updated":"2018-08-08T21:27:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-01-04T09:50:50Z","title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01290","snapshot_observed_at":"2026-08-07T12:10:38.758020Z","title":"In: Pro- ceedings of the 35th International Conference on Machine Learning (ICML) (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:38.758020Z"},"links":{"cited_paper":"/paper/1801.01290","citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:8be6475a057a44a50f761d3e5db9405cf7624941973f85cac6dccf260db49b5f","observation_id":"2170f388-8180-4f00-b136-87abc7062a63","resolution":{"observed_at":"2026-08-07T12:10:38.758020Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:38.824946Z","title":"Nature Machine Intelligence 1(5), 206–215 (2019) https://doi.org/10.1038/s42256-019-0048-x","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:38.824946Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:ae1df583d04458d5d10cd095b053e1de1bd574455acf31d1703016e2a8240aa5","observation_id":"fbe62795-7f6a-4d54-bf90-6805b59ae99f","resolution":{"observed_at":"2026-08-07T12:10:38.824946Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1702.08608","last_updated":"2017-03-02T19:32:10Z","snapshot_observed_at":"2026-08-07T08:20:39.814613Z","submitted_at":"2017-02-28T02:19:20Z","title":"Towards A Rigorous Science of Interpretable Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.08608","snapshot_observed_at":"2026-08-07T12:10:38.914231Z","title":"arXiv preprint arXiv:1702.08608 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:38.914231Z"},"links":{"cited_paper":"/paper/1702.08608","citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:7c164cb2a3d54defc7444b3e476a6535d91e98285a9b9da1a057741a395505c1","observation_id":"af5924b4-c7ff-422e-9ea4-06346d597401","resolution":{"observed_at":"2026-08-07T12:10:38.914231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.06567","last_updated":"2018-04-20T18:38:34Z","snapshot_observed_at":"2026-08-03T09:28:27.095641Z","submitted_at":"2017-12-18T18:22:05Z","title":"Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.06567","snapshot_observed_at":"2026-08-07T12:10:39.075705Z","title":"In: arXiv Preprint arXiv:1712.06567 (2017)","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:39.075705Z"},"links":{"cited_paper":"/paper/1712.06567","citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:36d3f9f342d7d35e3000441b00277e864ad6313585e07139352d0622c4aa0f94","observation_id":"05e07bc4-7265-403a-bbf4-ba066a6511d2","resolution":{"observed_at":"2026-08-07T12:10:39.075705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:39.201807Z","title":"MIT Press, Cambridge, MA (1992)","venue":null,"work_id":null,"year":1992},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:39.201807Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:fc007c00d6035aa053e5a583803a9f375711e38e89d2b6571bdd78e592be4cda","observation_id":"e0f0e048-95ec-4954-acba-0dc251bf68ad","resolution":{"observed_at":"2026-08-07T12:10:39.201807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1155/2009/736398","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Journal of Artificial Evolution and Applications2009, 1–25 (2009) https://doi.org/10.1155/2009/736398","venue":"Journal of Artificial Evolution and Applications","work_id":"0a162497-740a-435c-b80d-b651a0f5fdb1","year":2009},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:39.368111Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:178cb7a406c14f26ed9b90979b2da3665335df049ddce7662ecd07cfdf72f165","observation_id":"a54355d6-2085-4fab-b2bf-f15a912301aa","resolution":{"observed_at":"2026-08-07T12:10:41.615443Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:42.819622Z","title":"MIT Press, Cambridge, MA (1998)","venue":null,"work_id":"ed21ec92-007d-4484-8399-c6cdbc477183","year":1998},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:39.612946Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:f0d4080c0378c7f75952c263ccbd506010f15987a56e9eb812a8c356352d2236","observation_id":"2bdfe6db-8285-4c44-b07a-13e270f36397","resolution":{"observed_at":"2026-08-07T12:10:42.922888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:39.727958Z","title":"Frontiers in Robotics and AI 3, 40 (2016) https: //doi.org/10.3389/frobt.2016.00040","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:39.727958Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:dd9ff7a4e177db1af96af37060e77f014ae971611731d5b87e9b35cc89232540","observation_id":"e08bd402-8e1b-4133-8b09-e65970f7025f","resolution":{"observed_at":"2026-08-07T12:10:39.727958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:39.822131Z","title":"IEEE Access 8, 177437–177449 (2020) https://doi.org/10.1109/ACCESS.2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:39.822131Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:02360e722961147a155944b23b6e7714d36ca494be1d6afb65eab137f83264ed","observation_id":"24c29530-af97-4dd7-9d8a-e43b5fbc9695","resolution":{"observed_at":"2026-08-07T12:10:39.822131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:39.903130Z","title":"Nature 521(7553), 503–507 (2015) https://doi.org/10.1038/nature14422","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:39.903130Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:a838cada6cd76a90aef7aa909bc08473453fb2dfe315d30723b03e06b2b05581","observation_id":"89598e6e-c528-43ff-b9ce-2e287963818f","resolution":{"observed_at":"2026-08-07T12:10:39.903130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"5776.35775","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:42.392156Z","title":"In: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, pp","venue":null,"work_id":"132d2a4d-0b36-4850-a60c-36e0edb7ab9f","year":2023},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:39.972629Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:5fde198eba107b25bc42ffab07fb7545096ac24a679c3db1d3c8b85be43f2188","observation_id":"6b597130-5c23-47d0-a867-dfc5df7a8f09","resolution":{"observed_at":"2026-08-07T12:10:42.471386Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:40.040657Z","title":"In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.040657Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:26d6405efa204eacd8ccb6c6e62ff48e828a74949dc944713d9bc78c10e8de54","observation_id":"20f4c998-c3ae-4f9c-9c50-4ead1ce6f454","resolution":{"observed_at":"2026-08-07T12:10:40.040657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s40747-020-00156-6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:41.383731Z","title":"Complex & Intelligent Systems 6(3), 545–557 (2020) https://doi.org/10.1007/s40747-020-00156-6 21","venue":null,"work_id":"cb547a60-e3ab-4c9e-8e15-88f848763dd5","year":2020},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.142410Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:944186ca6387e6119e175de8871a9db4e8ce1a74e405736acc0773d34663d2cb","observation_id":"40fbbbe8-fa82-4704-9cf7-246f8d33f1e9","resolution":{"observed_at":"2026-08-07T12:10:41.430273Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.02477","last_updated":"2019-04-10T09:09:46Z","snapshot_observed_at":"2026-07-06T06:32:12.791177Z","submitted_at":"2018-04-06T22:17:18Z","title":"Programmatically Interpretable Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1804.02477","snapshot_observed_at":"2026-08-07T12:10:40.237902Z","title":"In: Proceedings of the 35th International Conference on Machine Learning, pp","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.237902Z"},"links":{"cited_paper":"/paper/1804.02477","citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:4752a72cb72a2c3a4bf820a858a477b0daada3110f783e3886477045edf81b4d","observation_id":"85e96d88-dea6-4d59-a2b1-967616435cbb","resolution":{"observed_at":"2026-08-07T12:10:40.237902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.24963/ijcai.2021/335","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), pp","venue":null,"work_id":"d08d2829-5681-4eee-a70a-89c02e544287","year":2021},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.330885Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:46dc8f5e32aecfa6b961ed97878abe75718bae059a81411bc35cc6b56450637c","observation_id":"67fc2e38-4f89-4445-b024-c351571f54a5","resolution":{"observed_at":"2026-08-07T12:10:41.260999Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:40.408152Z","title":"In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.408152Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:8bf34d4c94fd9cad7dd2377f8d4fd1de202c1a8a14ad99d7bb96c96499a62f6f","observation_id":"501645e6-b117-4150-a505-99fa02033ae8","resolution":{"observed_at":"2026-08-07T12:10:40.408152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:40.495025Z","title":"In: Proceedings of the Genetic and Evolutionary Computation Conference, pp","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.495025Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:824eb6d79f69cc7761f445818e0d7b20d67c8b464ad3a7572a6fe50dac5a13b3","observation_id":"7807d629-4f38-4ba5-bbd5-1aeab4c02ed3","resolution":{"observed_at":"2026-08-07T12:10:40.495025Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.09571","last_updated":"2022-07-08T10:30:50Z","snapshot_observed_at":"2026-08-05T03:19:57.389048Z","submitted_at":"2021-01-23T19:44:44Z","title":"BF++: a language for general-purpose program synthesis","version":6},"cited_work":{"arxiv_id":"2101.09571","doi":null,"metadata_source":"pith","pith_arxiv_id":"2101.09571","snapshot_observed_at":"2026-08-07T12:10:41.979111Z","title":"BF++: a language for general-purpose program synthesis","venue":"cs.AI","work_id":"fef7e39d-1e11-4c60-90b0-f4b72019bf51","year":2021},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.617125Z"},"links":{"cited_paper":"/paper/2101.09571","citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:b4641f092b64b2f192996b2ac19809b0438062aa5cf83438f7ae022f74cdec4b","observation_id":"1f6b6022-e23f-468a-b7b0-d00182ba67c9","resolution":{"observed_at":"2026-08-07T12:10:42.036144Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2011.2022","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:10:41.811377Z","title":"In: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp","venue":null,"work_id":"d58c0213-0a54-4231-91d1-d67435ba093a","year":2022},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.712812Z"},"links":{"citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:6fa7e40a63a6a5cbe3e8f8450a00cd39bcb4ba4cd8127e180f1f6d2dec9d46e5","observation_id":"30977525-e822-4e76-94a6-f096d42428b2","resolution":{"observed_at":"2026-08-07T12:10:41.880138Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11299","last_updated":"2023-04-20T17:55:47Z","snapshot_observed_at":"2026-08-09T15:30:34.398264Z","submitted_at":"2021-06-21T17:56:07Z","title":"Boundary Graph Neural Networks for 3D Simulations","version":7},"cited_work":{"arxiv_id":"2106.11299","doi":"10.48550/arxiv.2106.11299","metadata_source":"pith","pith_arxiv_id":"2106.11299","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Boundary Graph Neural Networks for 3D Simulations","venue":"cs.LG","work_id":"b013d10a-97e5-4a5f-81ce-62b19c3f2d4c","year":2021},"citing_paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:10:40.811000Z"},"links":{"cited_paper":"/paper/2106.11299","citing_paper":"/paper/2506.00328"},"observation_digest":"sha256:a2946dac082b6c19ede0f9363358204ada01368c01683dc4085a400369e21371","observation_id":"e1a39574-dcae-4d36-ab31-ef1efc0fc565","resolution":{"observed_at":"2026-08-07T12:10:41.068311Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.00328","last_updated":"2025-06-11T06:03:44Z","latest_version":3,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-10T09:33:40.916057Z","submitted_at":"2025-05-31T00:47:24Z","title":"BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":15,"verified_exact":5,"verified_fuzzy":2},"total_outbound_references":24},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2506.00328."}