{"as_of":"2026-08-18T12:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:19bd9985fb593a848bfc58dc1ba18bb90a056637d338c71ad587cd58120e855c","coverage":[{"denominator":18,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":18,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T07:56:55.290390Z","state":"measured"},{"denominator":18,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":18,"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/2601.18783/citation-record","integrity":"/paper/2601.18783/integrity","json":"/paper/2601.18783/citation-record.json","paper":"/paper/2601.18783"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T07:56:55.214003Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.214003Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:6edfa971000d38fab38d5c3b5854cca295ea937a968d704977d1893ed6d3043b","observation_id":"1add2569-99eb-4ffb-8cba-3a4cf6f37e1b","resolution":{"observed_at":"2026-08-03T07:56:55.214003Z","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":"2026-08-03T07:56:55.218220Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.218220Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:0fae69594f500df6779c77175b127968b239077648ad77de74f93f51cdc6fbeb","observation_id":"07a28ac5-4ed7-4a4a-9d80-ece139a8db9b","resolution":{"observed_at":"2026-08-03T07:56:55.218220Z","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":"2026-08-03T07:56:55.222320Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.222320Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:534a081931d30f6433df57474342676868631414531f1291815c9f4d51ed0369","observation_id":"abd9e47a-80d9-43d9-83f2-8c374f3f73cd","resolution":{"observed_at":"2026-08-03T07:56:55.222320Z","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":"2026-08-03T07:56:55.226581Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.226581Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:7b5f800b027ff8f2cc242e6830a81599259d717ab705aee85ae077913dbf5004","observation_id":"82f709cf-9dec-4d6d-b933-c269b36b5d17","resolution":{"observed_at":"2026-08-03T07:56:55.226581Z","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":"2026-08-03T07:56:55.230767Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.230767Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:8e900a4203e6ca2f0f30e2f8d922f9d403ad998293310dff01823f3d1ef4a3ba","observation_id":"1191eead-bfe3-4750-ad62-71d0e439293c","resolution":{"observed_at":"2026-08-03T07:56:55.230767Z","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":"2026-08-03T07:56:55.234787Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.234787Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:a53e0cc40af82bce9465a04a910771907f45ea84174f5dbe261c0aa774aa6c3f","observation_id":"5d8ba4a9-2d3a-4702-8cb4-c5fb70f343b3","resolution":{"observed_at":"2026-08-03T07:56:55.234787Z","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":"2026-08-03T07:56:55.239265Z","title":null,"venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.239265Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:3cd8bb340daf9c97cdc90e5a026a2303b1c8db35da0201dd6af7080d033a282d","observation_id":"8b20f34e-abd9-4bbe-bcaf-40b928851c61","resolution":{"observed_at":"2026-08-03T07:56:55.239265Z","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":"2026-08-03T07:56:55.243416Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.243416Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:d3ff85e395f11e73e58afd3ceb850a300f132edb337425c62a1d963f11e4a561","observation_id":"232138ab-ac64-4e0f-bac4-eff148607a73","resolution":{"observed_at":"2026-08-03T07:56:55.243416Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-03T07:56:55.259094Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.259094Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:bcd0fe3b46ddc4ae762a439588546988417323e6f98b06e464e2c10ce3aaebf1","observation_id":"08c067f1-f767-43eb-86f5-a355ed2db52c","resolution":{"observed_at":"2026-08-03T07:56:55.259094Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-03T07:56:55.263120Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.263120Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:d058307d773dc58f4ca09a4aef8fe0a65b432a5103a5f514c4c4273d817ef8ce","observation_id":"6edccf33-44cd-46ff-86d6-3fa198c20941","resolution":{"observed_at":"2026-08-03T07:56:55.263120Z","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":"2026-08-03T07:56:55.267009Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.267009Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:4dc3472c266398578f67fe613096add6d32cdd78f04334b2a4b50c890e418691","observation_id":"5bcf68fb-d89f-479a-bd87-b67ef689b689","resolution":{"observed_at":"2026-08-03T07:56:55.267009Z","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":"2026-08-03T07:56:55.271050Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.271050Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:255ccee8d23a5d13827a643d33bdfb21608192e6ded69bbf1f1e6d2a2f358840","observation_id":"60688ca6-3f6f-4d62-a5e2-dcced66759d2","resolution":{"observed_at":"2026-08-03T07:56:55.271050Z","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":"2026-08-03T07:56:55.274682Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.274682Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:ea2dbed92b5e53ba74adb32053b9db4a96a2863c7e7a881e8c5dd6490b178455","observation_id":"16bf639b-e8cf-4fc8-b73e-e1ecc947d55b","resolution":{"observed_at":"2026-08-03T07:56:55.274682Z","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":"2026-08-03T07:56:55.278331Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.278331Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:dc59fca28416a2ec136cf49e33279ae9892f273056ac5b10485b29655fb9398e","observation_id":"278953d1-cdaa-4c93-82e2-f567cef02276","resolution":{"observed_at":"2026-08-03T07:56:55.278331Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-03T07:56:55.282425Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.282425Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:811d398ef6d09c4ab0543d114f592fae2a1a77140c6cd29cb1cfdf2077846593","observation_id":"23070c99-800e-41f7-8bcd-c70595ab74df","resolution":{"observed_at":"2026-08-03T07:56:55.282425Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-03T07:56:55.286382Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.286382Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:1f29acde182978464503f70fe7c77d429c8eced016de991d24d39f0ef3e41ea8","observation_id":"1d6a9c58-ba12-40a7-b65b-6afd5c03c174","resolution":{"observed_at":"2026-08-03T07:56:55.286382Z","resolver_source":null,"status":"parse_uncertain"},"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":"2026-08-03T07:56:55.290390Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.290390Z"},"links":{"citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:d1e293485877c41fc6f951a67e0fa8779d184f6c55d5880f205a6de27c51d558","observation_id":"a07e5146-9e2e-4ce9-87ca-2d534f618048","resolution":{"observed_at":"2026-08-03T07:56:55.290390Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.05223","last_updated":"2025-07-18T15:16:10Z","snapshot_observed_at":"2026-08-15T23:07:34.867087Z","submitted_at":"2025-05-08T13:16:37Z","title":"Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.05223","snapshot_observed_at":"2026-08-03T07:56:55.207988Z","title":"[Brewittet al., 2021 ] Cillian Brewitt, Balint Gyevnar, Samuel Garcin, and Stefano V Albrecht","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-03T07:56:55.207988Z"},"links":{"cited_paper":"/paper/2505.05223","citing_paper":"/paper/2601.18783"},"observation_digest":"sha256:a0eaed37b3c7671170c809bb1a4aee04628e36a14880c27463f6a618e99655c9","observation_id":"1900112c-9271-406c-be81-4b597eb4bb36","resolution":{"observed_at":"2026-08-03T07:56:55.207988Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.18783","last_updated":"2026-06-01T08:59:51Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T08:49:25.326410Z","submitted_at":"2026-01-26T18:50:21Z","title":"Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic"},"reference_resolution":{"displayed":18,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":5,"unresolved":12,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":18},"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 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2601.18783."}