{"as_of":"2026-08-15T11:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e42cbc0c4a03951db1473bff8634c8406ddd0bd47f2355972f7e6c5506c49c8a","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T23:26:00.426163Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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/2607.15459/citation-record","integrity":"/paper/2607.15459/integrity","json":"/paper/2607.15459/citation-record.json","paper":"/paper/2607.15459"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T23:25:54.982174Z","title":"Courville, and Marc G","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:54.982174Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:98ae0ccd430138c05549b768ff4591e03487096816bc538f94fa6b1ca3f67baa","observation_id":"673cee85-80f7-4a88-b352-6c1e9bfedeed","resolution":{"observed_at":"2026-08-01T23:25:54.982174Z","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":"2026-08-01T23:25:55.106059Z","title":"Verifiable reinforcement learning via policy extraction","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.106059Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:dcfe7121bf4024817356af75c3a85b77889571cc0251221c2fb1e1087edf43e7","observation_id":"a7b05676-bcbe-4c98-9bb5-6dbb77e7cf37","resolution":{"observed_at":"2026-08-01T23:25:55.106059Z","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":"2026-08-01T23:25:55.245270Z","title":null,"venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.245270Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:4908384c7587ed56207afbad3ed30966e53660b6cd227fc58961a66c5de28111","observation_id":"2707512f-c7ef-46b5-8dfc-9e406906bf92","resolution":{"observed_at":"2026-08-01T23:25:55.245270Z","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":"2026-08-01T23:25:55.353921Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.353921Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:be6a62775b70b828afb7985a9d4b652ed5d326988fb75560bd3a8a1e4e7be5c0","observation_id":"765d49d4-965f-4599-bb91-ea207323c05f","resolution":{"observed_at":"2026-08-01T23:25:55.353921Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.06009","last_updated":"2021-06-10T19:06:28Z","snapshot_observed_at":"2026-08-15T04:14:08.788403Z","submitted_at":"2021-06-10T19:06:28Z","title":"Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.06009","snapshot_observed_at":"2026-08-01T23:25:55.420922Z","title":"Jonker, and Ann Nowé","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.420922Z"},"links":{"cited_paper":"/paper/2106.06009","citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:5beeabd190815a6947354f1081a042f7bc3927dfbcc5d3a7ac631136fa8359be","observation_id":"b47f40c2-c64f-43e2-ba65-233b89229d60","resolution":{"observed_at":"2026-08-01T23:25:55.420922Z","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":"2026-08-01T23:25:55.528673Z","title":"Interpretable and explainable logical policies via neurally guided symbolic abstraction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.528673Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:e80f6e4b0b0a79ecaadff5dc0a6ebc9c322f03a0b21cbaff330c00d7322c95a5","observation_id":"385b5f86-d786-44f6-9cf6-3e387c0d8fc0","resolution":{"observed_at":"2026-08-01T23:25:55.528673Z","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":"2026-08-01T23:25:55.614617Z","title":"Interpretable concept bottlenecks to align reinforcement learning agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.614617Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:574110c00758b2fe7a05abb7ddaea027b824c24489ebcf722c9b4f7b416b4a65","observation_id":"5f7eb31e-747e-46af-ab63-5e6df266063d","resolution":{"observed_at":"2026-08-01T23:25:55.614617Z","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":"2026-08-13T03:10:58.738032Z","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":"2026-08-01T23:25:55.700229Z","title":"Towards a rigorous science of interpretable machine learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.700229Z"},"links":{"cited_paper":"/paper/1702.08608","citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:0f40b05e7d2df67c104d539733dd24863c50dfbfe3be0d7437ed5bccfc020acb","observation_id":"82596e3a-40c9-40d9-bb8a-04ef28024737","resolution":{"observed_at":"2026-08-01T23:25:55.700229Z","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":"2026-08-01T23:25:55.973042Z","title":"Garrido-Merchán and Cristina Puente","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:55.973042Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:e33b8e410084492ca257fa26eabf3604980439341953b361b23a7318a226edf0","observation_id":"05a5cfea-37b4-4bb7-bf0f-5928d70af529","resolution":{"observed_at":"2026-08-01T23:25:55.973042Z","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":"2026-08-01T23:25:56.132507Z","title":"Neural logic reinforcement learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:56.132507Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:369931de3635307c2ef19fafae6d0228f00b4f9511d4819dd98a4bd9305e9186","observation_id":"d232cd2e-986a-4f16-97f0-2587814730b4","resolution":{"observed_at":"2026-08-01T23:25:56.132507Z","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":"2026-08-01T23:25:56.227910Z","title":"Kakade and John Langford","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:56.227910Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:5a454ce1c39a5f3feb6e778d6b43fa9f0749302a3445a59f965f3ec2368f4ce9","observation_id":"d4072afc-0920-4780-9147-ad9493566de3","resolution":{"observed_at":"2026-08-01T23:25:56.227910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14956","last_updated":"2024-05-23T18:07:38Z","snapshot_observed_at":"2026-08-12T23:59:28.062726Z","submitted_at":"2024-05-23T18:07:38Z","title":"Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14956","snapshot_observed_at":"2026-08-01T23:25:56.343456Z","title":"Interpretable and editable programmatic tree policies for reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:56.343456Z"},"links":{"cited_paper":"/paper/2405.14956","citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:b10429a15042cdbc1d7425f39fd74f121d2baf81febe5893c9806a15b6cdd753","observation_id":"e8e864a3-c648-421f-bc60-c0a6106f6ec5","resolution":{"observed_at":"2026-08-01T23:25:56.343456Z","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":"2026-08-01T23:25:56.442238Z","title":"Learning finite state representations of recurrent policy networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:56.442238Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:63f4450ccfea8a6e9637f9623e1e62f6e5b25451b9ce0be9cc49575ab9544c8a","observation_id":"2c949210-8412-4aa4-8402-e3bd0dabe98a","resolution":{"observed_at":"2026-08-01T23:25:56.442238Z","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":"2026-08-01T23:25:56.542613Z","title":"Petersen, Sookyung Kim, Cl \\' a udio P","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:56.542613Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:79b8a913c25ef9b4bce76ecfe5083bee753c1c20dfea218fae080114ddf54842","observation_id":"4e66db30-8122-4a47-b3c1-7f90ae472ff5","resolution":{"observed_at":"2026-08-01T23:25:56.542613Z","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":"2026-08-01T23:25:56.679106Z","title":"Toward interpretable deep reinforcement learning with linear model U - T rees","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:56.679106Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:2fac48f45b307f99954252a3a8ffd6f82b9d967828cf66f7f9b6e6ada02f9586","observation_id":"ad88d6ec-33b0-417c-a8ea-7897a2dc695c","resolution":{"observed_at":"2026-08-01T23:25:56.679106Z","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":"2026-08-01T23:25:57.076450Z","title":"Gordon, and Drew Bagnell","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.076450Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:6d52082f031eb251f54c2d97a8bc0f5c5567dc55701c7947bf89b96141c9a1e1","observation_id":"49117ef8-d842-49ce-9b88-501c09c8d641","resolution":{"observed_at":"2026-08-01T23:25:57.076450Z","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":"2026-08-01T23:25:57.175985Z","title":"Proximal policy optimization algorithms","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.175985Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:60d150dc4545990f1bc47f7d41a495e3b6688df284b6966dce39cc41eec7a977","observation_id":"e5fdd9d6-cb2f-4ed7-a79e-0be6c70361e5","resolution":{"observed_at":"2026-08-01T23:25:57.175985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06107","last_updated":"2024-06-10T08:46:49Z","snapshot_observed_at":"2026-08-12T23:46:39.486185Z","submitted_at":"2024-06-10T08:46:49Z","title":"EXPIL: Explanatory Predicate Invention for Learning in Games","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06107","snapshot_observed_at":"2026-08-01T23:25:57.278460Z","title":"EXPIL : Explanatory predicate invention for learning in games","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.278460Z"},"links":{"cited_paper":"/paper/2406.06107","citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:d436bda5c3c2323ee29b3f2f72729301533875e6478d008581a73ec992d5bbe0","observation_id":"073e7dd7-579a-4b48-a778-21fb228342e4","resolution":{"observed_at":"2026-08-01T23:25:57.278460Z","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":"2026-08-01T23:25:57.346561Z","title":"B lend RL : A framework for merging symbolic and neural policy learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.346561Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:0e46458eaa32fdf0f203e95ffc2f566bbad3415a3b3d8a8f89815e1c060e9496","observation_id":"1fa479bb-5f13-4de0-86ff-caa12b054a41","resolution":{"observed_at":"2026-08-01T23:25:57.346561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17032","last_updated":"2025-11-02T13:42:19Z","snapshot_observed_at":"2026-08-13T22:24:37.672685Z","submitted_at":"2024-07-24T06:35:05Z","title":"Gymnasium: A Standard Interface for Reinforcement Learning Environments","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.17032","snapshot_observed_at":"2026-08-01T23:25:57.400121Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.400121Z"},"links":{"cited_paper":"/paper/2407.17032","citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:a0edfeccbe6f0d968f9099f0b3712e7e553f1b75ca1e78bca66855654ffd016d","observation_id":"b4785357-4054-4f39-b40a-7edf56e9a0a2","resolution":{"observed_at":"2026-08-01T23:25:57.400121Z","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":"2026-08-01T23:25:57.448602Z","title":"Programmatically interpretable reinforcement learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.448602Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:49d6fd32a1cdb609eee497a34cc5017d18a9e393208d09d4766a521eb1c99f57","observation_id":"2e62338d-4c5e-4122-b828-dbf9bc99bf19","resolution":{"observed_at":"2026-08-01T23:25:57.448602Z","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":"2026-08-01T23:25:57.554458Z","title":"Imitation-projected programmatic reinforcement learning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.554458Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:201779fce5036fc0d226b888d8cfab03d491c2b5daa088c603480474261d4ac9","observation_id":"03412034-877d-4252-83bd-e25057355f8c","resolution":{"observed_at":"2026-08-01T23:25:57.554458Z","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":"2026-08-01T23:25:57.773262Z","title":"Kakade and John Langford , editor =","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.773262Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:5d161379166cd1b5ed4baf6e29e295ed4bb2db62b785afb91e3c5ade8d24ccb3","observation_id":"06a8322c-dab3-4e91-a0e3-982ec7f80bd3","resolution":{"observed_at":"2026-08-01T23:25:57.773262Z","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":"2026-08-01T23:25:57.866516Z","title":"Verifiable Reinforcement Learning via Policy Extraction , booktitle =","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.866516Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:177fb7e08f62b41ed27125a1d4128cebe616ea370a381487d374453dab6bba45","observation_id":"383bf49f-201c-4b57-8057-1dd60b1974b1","resolution":{"observed_at":"2026-08-01T23:25:57.866516Z","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":"2026-08-01T23:25:57.977354Z","title":"Programmatically Interpretable Reinforcement Learning , booktitle =","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:57.977354Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:e4f4f46c15b48ddd2c5cc53ed42f385484372385b315c2efd9e315ec4beeb997","observation_id":"a396459d-8081-49c3-87f9-956e830ecebb","resolution":{"observed_at":"2026-08-01T23:25:57.977354Z","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":"2026-08-01T23:25:58.093001Z","title":"Imitation-Projected Programmatic Reinforcement Learning , booktitle =","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:58.093001Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:93b802a5b594247367444133a638348d28e2e593512097681dd2f4e6b2fc7fef","observation_id":"26049bc2-80cf-4a72-a1a5-863ed9f95268","resolution":{"observed_at":"2026-08-01T23:25:58.093001Z","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":"2026-08-01T23:25:58.202466Z","title":"Neural Logic Reinforcement Learning , booktitle =","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:58.202466Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:a3224db6d784f65470cbdcf2ed4113c401a100b8febbe6f720953bb4ed915f9b","observation_id":"60073971-b706-47eb-ae24-dfaaa1cfce64","resolution":{"observed_at":"2026-08-01T23:25:58.202466Z","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":"2026-08-01T23:25:58.312244Z","title":"Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction , booktitle =","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:58.312244Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:6cda5a0041c54680e90365730af20d5224890123ff20967ea38378007488bd51","observation_id":"30b036a1-7b60-41ea-af5a-9cb578e0f3ee","resolution":{"observed_at":"2026-08-01T23:25:58.312244Z","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":"2026-08-01T23:25:58.466159Z","title":"The Thirteenth International Conference on Learning Representations,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:58.466159Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:1b355e6feb542eecd3427b7505ff2950fcb47a412def54b47e73342d9bda0b40","observation_id":"30263fdd-78a2-4050-806d-7cc19c8be0c8","resolution":{"observed_at":"2026-08-01T23:25:58.466159Z","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":"2026-08-01T23:25:58.612636Z","title":"Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents , booktitle =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:58.612636Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:548e0484a2d4a45aee4eb103d7e1197a0dbb20a96553721215ed36cb93b0ae7e","observation_id":"4e2a84e7-3495-445e-99f2-4ca2ab645dfa","resolution":{"observed_at":"2026-08-01T23:25:58.612636Z","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":"2026-08-01T23:25:58.736051Z","title":"Petersen and Sookyung Kim and Cl","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:58.736051Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:58996d57e6ab46155249ad4afcd976b782e6ee5b74232a1ce2f6e4e88141fc59","observation_id":"f0a0faef-5e95-4c2a-a53f-73be27f44917","resolution":{"observed_at":"2026-08-01T23:25:58.736051Z","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":"2026-08-01T23:25:58.871100Z","title":"2024 , url =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:58.871100Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:ec742eac14f325a9b141ad14c6fccb85efa75d487e524e6e640ebe54c012400b","observation_id":"a487b52a-1981-46b8-87fb-c6041d315f29","resolution":{"observed_at":"2026-08-01T23:25:58.871100Z","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":"2026-08-01T23:25:59.021678Z","title":"Courville and Marc G","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.021678Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:4cb4e31d104e0ef544d548d456b5fc192962f20e13e783cdb23832849b0c8abd","observation_id":"92fa4c3a-5121-48fd-b1c1-150f659a341b","resolution":{"observed_at":"2026-08-01T23:25:59.021678Z","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/bf00117105","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ross Quinlan , title =","venue":"Machine Learning","work_id":"35c0a928-5335-4acf-bfe8-77d9c639b7d0","year":1990},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.146726Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:12846a5f05b392a5a37c8f47137b75602e23178def54ac8f6695a9c6b5b959da","observation_id":"10e9eb86-925f-4427-864b-516c423e32c8","resolution":{"observed_at":"2026-08-01T23:28:29.335729Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1023/a:1007694015589","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Machine Learning","work_id":"ff859c9a-9c83-42c7-a72c-368dde717f7d","year":2001},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.242904Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:4a775b011331189e1efbabceda5541c946c8a243dc6a700276dc2800e3513fc4","observation_id":"e569c93a-c744-4e75-89c1-dcca5f3993bf","resolution":{"observed_at":"2026-08-01T23:28:29.588148Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-01T23:25:59.336648Z","title":"A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , booktitle =","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.336648Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:dab309f071d78f71495d3fb6c4b33c82e2e9fd7bf6925252cfe50b965d352162","observation_id":"71785031-4105-4c7a-8ccf-4b4982a39658","resolution":{"observed_at":"2026-08-01T23:25:59.336648Z","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":"2026-08-01T23:25:59.500851Z","title":"7th International Conference on Learning Representations,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.500851Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:19910e1203d9efae0eb0a2d3006f5ed2df05d2697f12b251153c7ddd1e0481f7","observation_id":"a65ce775-2b2f-4f8b-a0cb-a054e0d7cdb6","resolution":{"observed_at":"2026-08-01T23:25:59.500851Z","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":"2026-08-01T23:25:59.637679Z","title":"Theory Pract","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.637679Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:10438f1600a1d2868f28307f75c58472b8db12d06b3e01e65c2de0fbdd3d61b9","observation_id":"7ef71411-43df-436e-a273-8dc8eabdfc9a","resolution":{"observed_at":"2026-08-01T23:25:59.637679Z","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":"10.1007/978-3-030-10928-8","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Toward Interpretable Deep Reinforcement Learning with Linear Model","venue":"Lecture notes in computer science","work_id":"141fe012-39cd-49ef-9f9b-c7c17a12164a","year":2018},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.717096Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:9be2a7c56c2a6a5e5305613f06ea9098aba6b69bb2ed6dac2a2c05ae6221a9e3","observation_id":"2c60be33-99dc-4d24-a92d-eb7d38c57c3e","resolution":{"observed_at":"2026-08-01T23:28:28.724163Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-01T23:25:59.796264Z","title":null,"venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.796264Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:9543cc3cdde563117f806e1c13776375c8977db080b0363a8206aa455578404f","observation_id":"cb821429-8fb6-40bf-8a13-4724f3304aa3","resolution":{"observed_at":"2026-08-01T23:25:59.796264Z","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":"2026-08-01T23:25:59.861800Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.861800Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:39c721ab50cc66a56efd0fc51ef54fa2e3d4067bc111d596f5f96e2426a82eea","observation_id":"5fe88b83-3c0b-4e53-838f-894facf9ba18","resolution":{"observed_at":"2026-08-01T23:25:59.861800Z","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":"2026-08-01T23:25:59.922798Z","title":"2017 , eprint =","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.922798Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:a0aca84712e5a9091ba57cb8ac49cb180d3bc5b62ddbfc74a3cf37462d1d3a6b","observation_id":"a8796f42-b7f1-4b49-a341-97c538fc5af1","resolution":{"observed_at":"2026-08-01T23:25:59.922798Z","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":"2026-08-01T23:25:59.991915Z","title":"Jonker and Ann Nowé , title =","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-01T23:25:59.991915Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:5141694f87a277f4a7bbf3707d9ae58e3ccfc46626b3898040a55274a252e22d","observation_id":"c66bcd36-7546-44ec-92c3-5a1144398f71","resolution":{"observed_at":"2026-08-01T23:25:59.991915Z","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":"2026-08-01T23:26:00.082561Z","title":"2024 , eprint =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T23:26:00.082561Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:d89fe140d35e851aa4a197b336215d16a3da164769e2b2c6feaec4a307e83de8","observation_id":"8617782b-4dae-44c6-9d5e-c2cfa4bd624c","resolution":{"observed_at":"2026-08-01T23:26:00.082561Z","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":"2026-08-01T23:26:00.174154Z","title":"2024 , eprint =","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-01T23:26:00.174154Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:4ff1516c240ed045763375a525e22faf04d91d730b9130fb523a3228e713279d","observation_id":"5030e046-d50a-4226-b4fd-50afcf72c8b7","resolution":{"observed_at":"2026-08-01T23:26:00.174154Z","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":"2026-08-01T23:26:00.256065Z","title":"Garrido-Merchán and Cristina Puente , title =","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-01T23:26:00.256065Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:1b539c750a3d77ce9d7d80033a6740d090ff1e4a3349254e3c8472f0820855a7","observation_id":"ecd95ef5-6dda-443e-84fb-78b1801785df","resolution":{"observed_at":"2026-08-01T23:26:00.256065Z","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":"2026-08-01T23:26:00.337919Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-01T23:26:00.337919Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:8426e4ce1ae82e7c6bee0679e8ec72c4f7f6c9a0bb327e4520a9838d34844457","observation_id":"e16022cf-4706-497d-bcf3-42dd60b54ffb","resolution":{"observed_at":"2026-08-01T23:26:00.337919Z","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":"2026-08-01T23:26:00.426163Z","title":"2017 , eprint =","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems","version":2},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-01T23:26:00.426163Z"},"links":{"citing_paper":"/paper/2607.15459"},"observation_digest":"sha256:6c09710c4c7077f7835f96ab7711ba63abf63b987c53d0640d71c51d77177e4e","observation_id":"6bb04b70-941a-4afc-b0d4-464a09c4174f","resolution":{"observed_at":"2026-08-01T23:26:00.426163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.15459","last_updated":"2026-07-20T09:36:34Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-11T10:58:01.456052Z","submitted_at":"2026-07-16T21:10:27Z","title":"From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":45,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":48},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2607.15459."}