{"as_of":"2026-08-18T17:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cbd567976eb275b7679ce701df397f7954f6d18f79449de6d6ae69a69eaeff04","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:52:19.140298Z","state":"measured"},{"denominator":20,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":20,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2504.19625/citation-record","integrity":"/paper/2504.19625/integrity","json":"/paper/2504.19625/citation-record.json","paper":"/paper/2504.19625"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:52:19.527367Z","title":"Retargetable compiler case studies","venue":null,"work_id":"4dbb1955-696d-409a-ba95-eb486965ad7d","year":2001},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.033140Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:0acec791b70a849881111d23e7d2f7deba07c7a1ddd8aa660d811392dd0dfc47","observation_id":"13cd5ea2-3b18-4a68-b3b6-d2a272aaf7ae","resolution":{"observed_at":"2026-08-16T05:52:19.532409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.11054","last_updated":"2020-03-01T00:38:46Z","snapshot_observed_at":"2026-08-15T17:18:59.163433Z","submitted_at":"2020-02-25T17:24:50Z","title":"MLIR: A Compiler Infrastructure for the End of Moore's Law","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.11054","snapshot_observed_at":"2026-08-16T05:52:19.039407Z","title":"Mlir: A compiler infrastructure for the end of moore’s law","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.039407Z"},"links":{"cited_paper":"/paper/2002.11054","citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:9f34ee4568c8d585ae1fc63b44ba06fb2ed7413857c211d9d4fb5f749f54db2c","observation_id":"d64f8c6c-ffdc-4bae-97e0-a4b31fa4c1ed","resolution":{"observed_at":"2026-08-16T05:52:19.039407Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.08272","last_updated":"2020-10-01T01:15:28Z","snapshot_observed_at":"2026-08-10T22:39:45.878362Z","submitted_at":"2020-08-19T05:28:08Z","title":"Compiling ONNX Neural Network Models Using MLIR","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.08272","snapshot_observed_at":"2026-08-16T05:52:19.045608Z","title":"Compiling onnx neural network models using mlir","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.045608Z"},"links":{"cited_paper":"/paper/2008.08272","citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:256df09c5ea36af627e9778ca86a7f665163cac537f909ec901c3f708fa77de9","observation_id":"e898757b-3198-4b31-8a82-f111159ab967","resolution":{"observed_at":"2026-08-16T05:52:19.045608Z","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":"2026-08-16T05:52:19.510431Z","title":"Aiwarek: Compiling pytorch model for ai processor usi ng mlir framework","venue":null,"work_id":"5172a1a3-8117-4369-85c1-51d3bde3f0d5","year":2022},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.053053Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:8da824e94054b24da83f80b0ac2a947dc5af086714d3b87e5d367ca51318c6a3","observation_id":"b16b2f18-3409-4ead-9678-f83e4fd71295","resolution":{"observed_at":"2026-08-16T05:52:19.515510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.493980Z","title":"Mlir: Scaling compiler infrastructure for domain specific computation","venue":null,"work_id":"3fe2186a-03c0-4efd-87d3-dbbcfb63d3ee","year":2021},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.061657Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:d1140d5b3bfbb36cc0d0a4ec218730084dd812ef7f80e09e7dca0abe67a91454","observation_id":"ae433534-50bc-41b6-82f4-02ccd4a8e0e3","resolution":{"observed_at":"2026-08-16T05:52:19.499169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.477198Z","title":null,"venue":null,"work_id":"25ee8c20-a9ba-4450-8eba-33564889ed2e","year":2020},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.067672Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:d72232cbbf23ca941c6caddad69e6be07e44cc5b432a445d842a7c489996d328","observation_id":"0f5526a6-6563-4e07-9503-07c473e9cf4d","resolution":{"observed_at":"2026-08-16T05:52:19.482948Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.461417Z","title":"A review of safe reinforcement learning: Methods, theory an d applications, 2024","venue":null,"work_id":"bf2a9cdf-073a-48a0-b8e0-97e086116c57","year":2024},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.074173Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:f7fa3f3094abfc461ec2cbb99e4002e9518c9205cb058710df6ab94f788b396c","observation_id":"5b88585a-b984-45ae-af30-7c2d75e674a0","resolution":{"observed_at":"2026-08-16T05:52:19.466342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.440073Z","title":"Openai gym, 2016","venue":null,"work_id":"0c1529ea-ff6b-4c18-a06f-14c63571ba88","year":2016},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.079324Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:550183df442c03f67ee0e0e9ba800b8d5029a2d69e170f8afb2b1f4214917ead","observation_id":"2da5bab9-9357-48fc-8fec-177db1675e83","resolution":{"observed_at":"2026-08-16T05:52:19.450094Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2026-08-16T05:52:19.084839Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.084839Z"},"links":{"cited_paper":"/paper/2407.17032","citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:e468c5a621be7b7b722d30c9ecb5173576107619bb75fe43c265d36089430a5d","observation_id":"c00f7750-0430-4c21-ad1b-784daf7394a5","resolution":{"observed_at":"2026-08-16T05:52:19.084839Z","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":"2026-08-16T05:52:19.422420Z","title":"Pgx: Hardware-accelerated parallel game si mulators for reinforcement learning","venue":null,"work_id":"de233eac-8800-445c-af2d-bb64bea561b5","year":2023},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.090232Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:2ca8ad9aab06d9f7377b1b865949f7f34cb31b678215c5fbeeaf9fd420346a6c","observation_id":"68e8df57-7a85-4f3a-a9f8-c1f52112bb84","resolution":{"observed_at":"2026-08-16T05:52:19.427556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1908.09453","last_updated":"2020-09-26T11:49:05Z","snapshot_observed_at":"2026-08-16T12:59:48.696588Z","submitted_at":"2019-08-26T03:31:35Z","title":"OpenSpiel: A Framework for Reinforcement Learning in Games","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.09453","snapshot_observed_at":"2026-08-16T05:52:19.095236Z","title":"OpenSpiel: A framework for reinforcement learning in games","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.095236Z"},"links":{"cited_paper":"/paper/1908.09453","citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:660e691824071b78b1a38bfdbe2be0a9fb00287a225cc6166897b48ca99e50bf","observation_id":"3ca857cc-52d7-4104-83b7-a8615cbac22b","resolution":{"observed_at":"2026-08-16T05:52:19.095236Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.08265","last_updated":"2020-02-21T18:05:30Z","snapshot_observed_at":"2026-08-13T20:33:12.542690Z","submitted_at":"2019-11-19T13:58:52Z","title":"Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.08265","snapshot_observed_at":"2026-08-16T05:52:19.100671Z","title":"Lillicrap, and David Silver","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.100671Z"},"links":{"cited_paper":"/paper/1911.08265","citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:2fba362122f0559068e5a3e85ce98ac9f665fc54a1e1012b4a5ad13f174d58da","observation_id":"b33276b5-d26c-4739-9b83-47624e6b42ca","resolution":{"observed_at":"2026-08-16T05:52:19.100671Z","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":"2026-08-16T05:52:19.403870Z","title":"Muzero general: Open rei mplementation of muzero","venue":null,"work_id":"a8bf8b6e-86ab-4a8d-b6f8-2beab9f7bd76","year":2019},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.106195Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:f44f35b3dff52b3cf5f292470aaf1b9727937541f0db27efab58d8fba66f9a68","observation_id":"d6f2fb3b-d118-4676-bcb0-4c08796bcf22","resolution":{"observed_at":"2026-08-16T05:52:19.409794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.384671Z","title":"Mastering atari games with limited data","venue":null,"work_id":"5df54a7f-6018-4aab-9c45-28b43798664b","year":2021},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.110797Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:05d412fb6c7a6ed18a186edebfd220017e6e837b633f8ac8ed58e7946debbe98","observation_id":"00972493-bedf-42d5-b4b4-64950c7e6a62","resolution":{"observed_at":"2026-08-16T05:52:19.391003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.365998Z","title":"Dijkstra","venue":null,"work_id":"0f3e8b58-2e09-49c6-a896-72bbead954a7","year":1968},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.115993Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:ab66d3f05a4a44fb4cff8f9b9e31c6ad5d1f3af186d022d3af67d8768a79b02f","observation_id":"2a1ddf41-ec57-4331-9b19-b0d4f338c51b","resolution":{"observed_at":"2026-08-16T05:52:19.371213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.346980Z","title":"The Art of Computer Programming, 3rd edition","venue":null,"work_id":"96e9ca42-192e-4e01-88a4-499b57d68e58","year":1997},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.120773Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:50235d91b69f33da594ca6ed02ba44cd7e75edd446277eaefeecc8b3d680476e","observation_id":"bc485c1b-fac0-498d-8485-380a0122da90","resolution":{"observed_at":"2026-08-16T05:52:19.352270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.328258Z","title":"Hopcroft, Rajeev Motwani, and Jeffrey D","venue":null,"work_id":"cb42ed68-deb0-41f8-b315-cd320fb67fd4","year":2006},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.125711Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:317f4b3de788f0ab051713601ab168dd7ca48833b5aecb8107cb9a9bdcb208ca","observation_id":"424a0a67-5708-4549-943e-cc1bb502c5ca","resolution":{"observed_at":"2026-08-16T05:52:19.334852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.310055Z","title":"Hanabi learning environment","venue":null,"work_id":"d156d813-5977-48be-aad3-05c19d3bec10","year":2019},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.130505Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:c27d1b34c4f6f82e9b8f26b92c76b13817e152d023c9517b66c22520f378a531","observation_id":"f8d7563f-dd2e-4264-8871-36959da1b070","resolution":{"observed_at":"2026-08-16T05:52:19.315161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.292624Z","title":"google-benchmark","venue":null,"work_id":"c4d2b8c6-b2cc-4aa8-88a6-1302821e388c","year":null},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.135141Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:2f1d00886a937eff34ae2fa67b764fb454d1451320db72da7829cccca0da8e4e","observation_id":"fc5baf2d-c947-4007-93ed-21a3c4e6a44d","resolution":{"observed_at":"2026-08-16T05:52:19.297703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:52:19.272889Z","title":"Warhammer 40.000, 10th edition","venue":null,"work_id":"c7fb50df-d04d-4e82-be1e-8c3ca92e9b53","year":null},"citing_paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T05:52:19.140298Z"},"links":{"citing_paper":"/paper/2504.19625"},"observation_digest":"sha256:e677c2d4d831c98237f3339910f83bc3afd0aa0b4ffd4e296048f66a86037da4","observation_id":"8b240f89-7095-42d7-b17f-17728c36c1b6","resolution":{"observed_at":"2026-08-16T05:52:19.279682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.19625","last_updated":"2025-04-28T09:34:34Z","latest_version":1,"primary_category":"cs.PL","snapshot_observed_at":"2026-08-17T17:09:42.006608Z","submitted_at":"2025-04-28T09:34:34Z","title":"Rulebook: bringing co-routines to reinforcement learning environments"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":0,"verified_fuzzy":14},"total_outbound_references":20},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2504.19625."}