{"as_of":"2026-08-08T17:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:296bb17a36921fa0dd4182883501a648bc01510c990a02806cc6db3d5465d72d","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-31T08:17:05.889959Z","state":"measured"},{"denominator":12,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":12,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2607.24672/citation-record","integrity":"/paper/2607.24672/integrity","json":"/paper/2607.24672/citation-record.json","paper":"/paper/2607.24672"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-31T08:17:05.838638Z","title":"Kuznietsov, B","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.838638Z"},"links":{"citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:97f785e6db5e9146e8c8a40d8f4e78b3a3aa96e23cac7ff9640f5a4de9b94299","observation_id":"d33e0815-5d51-47e9-b3f7-1284e23ada1e","resolution":{"observed_at":"2026-07-31T08:17:05.838638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.00265","last_updated":"2025-01-13T00:29:56Z","snapshot_observed_at":"2026-07-06T19:08:31.009006Z","submitted_at":"2024-08-30T21:42:17Z","title":"Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.00265","snapshot_observed_at":"2026-07-31T08:17:05.843957Z","title":"Mersha, K","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.843957Z"},"links":{"cited_paper":"/paper/2409.00265","citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:902d13e075b4cfe9491d8228afc0e47d45f13e64451b7c84e12321678c35a242","observation_id":"e6277218-133d-435f-849b-a88df2e6215c","resolution":{"observed_at":"2026-07-31T08:17:05.843957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.06665","last_updated":"2025-08-29T04:07:26Z","snapshot_observed_at":"2026-07-06T14:17:30.922581Z","submitted_at":"2022-11-12T13:52:06Z","title":"A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.06665","snapshot_observed_at":"2026-07-31T08:17:05.849294Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.849294Z"},"links":{"cited_paper":"/paper/2211.06665","citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:4b7cafc808fff9ce5c2a16412edba80eee4e64b8305cc7892ddfbb142318a3b6","observation_id":"4064c662-9f03-4679-adca-bbeb248fd5e1","resolution":{"observed_at":"2026-07-31T08:17:05.849294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.06247","last_updated":"2020-05-13T10:52:49Z","snapshot_observed_at":"2026-08-07T18:18:09.378548Z","submitted_at":"2020-05-13T10:52:49Z","title":"Explainable Reinforcement Learning: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.06247","snapshot_observed_at":"2026-07-31T08:17:05.854195Z","title":"Puiutta and E","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.854195Z"},"links":{"cited_paper":"/paper/2005.06247","citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:6e0595da9cc2c20a9475c01baa492ce5f67b9759f4d54c0d0dab95499a52aa48","observation_id":"32396cd1-c004-4f69-8b80-b24b62085755","resolution":{"observed_at":"2026-07-31T08:17:05.854195Z","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":"2026-07-31T08:17:05.859289Z","title":"Atakishiyev, M","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.859289Z"},"links":{"citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:233ad877d0820d28d018b0c93f340be3d23aab4feaa94b73b0c2ac4e7878fff9","observation_id":"02e59558-1edf-4fe1-b40a-7f8cb65df504","resolution":{"observed_at":"2026-07-31T08:17:05.859289Z","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":"2026-07-31T08:17:05.863625Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.863625Z"},"links":{"citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:13f9d73e1855b51113089b95deec32abe3de4696d2740277a1def014f012df30","observation_id":"5fdcb96b-b39c-4931-82ef-069e5512354e","resolution":{"observed_at":"2026-07-31T08:17:05.863625Z","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":"2026-07-31T08:17:05.868977Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.868977Z"},"links":{"citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:a6684a92b8ed5d18a5b4b9100ea5fa04a639c78410c377b0c427b3e4a082324f","observation_id":"25502c35-4f50-423a-91da-20783383ef28","resolution":{"observed_at":"2026-07-31T08:17:05.868977Z","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":"2026-07-31T08:17:05.873152Z","title":"Tabrez and B","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.873152Z"},"links":{"citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:0ea5bb8d18e3830755487057a87d515b9b477700a7761b0038583a51c3b8d676","observation_id":"14c875b5-b2e3-4bc9-81c3-7066402e90a5","resolution":{"observed_at":"2026-07-31T08:17:05.873152Z","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":"2026-07-31T08:17:05.877411Z","title":"Fujimoto, H","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.877411Z"},"links":{"citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:8227ae9810017ad6f811ef31b72c46b1dc4a57cef59135d348402e3e1da1e6d2","observation_id":"1750245e-5098-4bd6-a7e0-d67282110165","resolution":{"observed_at":"2026-07-31T08:17:05.877411Z","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":"2026-07-31T08:17:05.881561Z","title":"Huang et al., ”CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms,”Journal of Machine Learning Research, vol","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.881561Z"},"links":{"citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:ea30f2f610034a861d1d540b1832c631345918a075abf275eeab00e2d091535e","observation_id":"defeab48-dcfd-4e9c-adf3-93244043a297","resolution":{"observed_at":"2026-07-31T08:17:05.881561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11632","last_updated":"2024-08-21T14:04:00Z","snapshot_observed_at":"2026-07-06T19:04:00.888268Z","submitted_at":"2024-08-21T14:04:00Z","title":"Optimizing Interpretable Decision Tree Policies for Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.11632","snapshot_observed_at":"2026-07-31T08:17:05.885673Z","title":"V os and S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.885673Z"},"links":{"cited_paper":"/paper/2408.11632","citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:fe317f10fabeeea0c80a7ee3a99c0f4b991f6a265a84ff3811fe0d3fda19d0dd","observation_id":"d44322c7-57fc-4867-b6bf-544eadecab10","resolution":{"observed_at":"2026-07-31T08:17:05.885673Z","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":"2026-07-31T08:17:05.889959Z","title":"Bastani, Y","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-31T08:17:05.889959Z"},"links":{"citing_paper":"/paper/2607.24672"},"observation_digest":"sha256:eed128d56c578abdce78d9d1c1c2f12555a0f18bd40a13cfa347336eadd9d754","observation_id":"f86364d9-67e4-45d5-9113-c83014acb8c9","resolution":{"observed_at":"2026-07-31T08:17:05.889959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.24672","last_updated":"2026-07-27T17:14:42Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T05:23:36.589882Z","submitted_at":"2026-07-27T17:14:42Z","title":"Explainable Reinforcement Learning via Physics-Aware Policy Distillation"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":12},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2607.24672."}