{"as_of":"2026-08-13T06:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fb4896fa6bf2c7bc239221b5988f45c0bc8367cec58bc519679d62ccdd4b7abf","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T11:49:50.643915Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T11:59:18.223000Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T08:19:43.723658Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"cited_work":{"arxiv_id":"2509.02267","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.02267","snapshot_observed_at":"2026-07-04T08:19:43.723658Z","title":"arXiv preprint arXiv:2509.02267 , year=","venue":null,"work_id":"a6c9673c-b8e0-4105-9d45-8d0a6e001110","year":null},"citing_paper":{"arxiv_id":"2606.21925","last_updated":"2026-06-20T07:47:41Z","snapshot_observed_at":"2026-07-06T23:56:49.977855Z","submitted_at":"2026-06-20T07:47:41Z","title":"PhiBE-Q-Learning: Bridging Off-Policy Reinforcement Learning and Continuous-Time Control","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-26T11:59:18.223000Z"},"links":{"cited_paper":"/paper/2509.02267","citing_paper":"/paper/2606.21925"},"observation_digest":"sha256:98cee3bc08216d42949c41f2c4dd628019b5344307b10b30fe0abcb05aed2adb","observation_id":"04c12d0c-ce18-4373-bfe1-5e6fa0c88265","resolution":{"observed_at":"2026-07-04T08:19:43.725204Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.02267/citation-record","integrity":"/paper/2509.02267/integrity","json":"/paper/2509.02267/citation-record.json","paper":"/paper/2509.02267"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T11:49:54.230579Z","title":null,"venue":null,"work_id":"c019a4ac-d5d8-41cf-908a-68a810da1039","year":2022},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:47.713103Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:6807fa42b3691b5fa4a4e36431f40b1e00dc5ec6a05228452b55de81b47cc2e2","observation_id":"8de1c57b-3262-4d0a-8c5d-a3e029c01386","resolution":{"observed_at":"2026-08-05T11:49:54.234615Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.216167Z","title":null,"venue":null,"work_id":"40a7958c-100b-4048-a4ba-31861ba1dac0","year":2021},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:47.809227Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:0024767fc9f754b84bb132851b1d66f9f3dcdab3326a71136ddb2231c45677b7","observation_id":"1acab505-c2d6-4c24-9707-c7032b2f1969","resolution":{"observed_at":"2026-08-05T11:49:54.220839Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.202387Z","title":null,"venue":null,"work_id":"2d565f42-34ca-48fa-9f46-3b5da43ce406","year":2015},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:47.883334Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:ca35eaef4f749ebee0ff59c0d1e3095e900eb48ca5b1a3f2cb5271b1c0500dee","observation_id":"61049cb5-c349-4d96-b8ea-bfea3fd5f651","resolution":{"observed_at":"2026-08-05T11:49:54.206246Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.188485Z","title":null,"venue":null,"work_id":"19c41220-1454-42ea-a019-97f7566ddc75","year":2010},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:47.987678Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:f4836e30666b21680097fcd9a06421fb70451eb492b67116b0a524cb3cbb47cc","observation_id":"b5fc825d-1655-4075-a14b-c036e1332f88","resolution":{"observed_at":"2026-08-05T11:49:54.192771Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.173893Z","title":null,"venue":null,"work_id":"c867d2ce-d0b0-48c6-ba6c-e4a69ef10063","year":2018},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.032092Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:cbe47a70a9399ef926dbf08054c8ba449e20b6417fbf47cab27f00d748673097","observation_id":"3a06667a-889f-4082-8431-77c11a8f0a16","resolution":{"observed_at":"2026-08-05T11:49:54.178403Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.158847Z","title":"Caccioli, I","venue":null,"work_id":"d92bd4c0-dc3a-4882-a1c0-db0d90451c02","year":2016},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.143412Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:48e7f5d925eb4fcc411e10e09dc368094aca8d9f9506abd8526c87c7d3eb3ec1","observation_id":"9ca79c8d-2601-4561-a4aa-dcac7b4a7540","resolution":{"observed_at":"2026-08-05T11:49:54.162875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.145091Z","title":"Chellathurai and T","venue":null,"work_id":"de7aaf20-b913-49f9-acb4-eff99a4ef4f7","year":2062},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.182071Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:f97114ff213f93e4b1d0f2f4266130af0cd5ce28a9286b124c0bda0688ef84f3","observation_id":"47e469fe-5e4c-4d9c-96ec-1147ac2b912a","resolution":{"observed_at":"2026-08-05T11:49:54.149236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.131531Z","title":null,"venue":null,"work_id":"0ecaac44-a4dd-4b44-88f3-6af48c8d3f66","year":2007},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.277306Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:e432d93a73e9e667fe47b08e247715c465fc416353b1d6888bfa060b71fefd91","observation_id":"b2afd5f1-3990-43a4-bea2-a45b9909912d","resolution":{"observed_at":"2026-08-05T11:49:54.135874Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.117202Z","title":null,"venue":null,"work_id":"f13fb340-6da8-463a-806e-27ebf4b33550","year":2010},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.350212Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:e2a7d7f6dd8b292cd2f137fa27a5df592c8221c7a7f916531dd35f6e5df62dc3","observation_id":"02ad0b2b-d9d5-48da-a6e8-98cbf4b756d8","resolution":{"observed_at":"2026-08-05T11:49:54.121765Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.102944Z","title":"Dai and Y","venue":null,"work_id":"54ba6ee2-19ff-4c58-8e9b-fd57db4be942","year":2008},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.453787Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:615ce66320ae67c18619afd1425309bb166b8e7156db798bb33477c9b09397c7","observation_id":"e7f514c9-6fe5-4ccf-aa63-99a83446b3ba","resolution":{"observed_at":"2026-08-05T11:49:54.107019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.087793Z","title":null,"venue":null,"work_id":"484c3c86-f63d-4d46-8ff7-4fce5bfb49c5","year":1990},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.531841Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:be850bedd28331ffe2cb55c38d8273c4c61fde4e7f01f4cfb0157731b555f8a4","observation_id":"093af381-de67-40d6-b8d3-14b3939ade86","resolution":{"observed_at":"2026-08-05T11:49:54.092261Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.074457Z","title":null,"venue":null,"work_id":"8aa38710-3fcf-4ffe-8eae-9c724d2ae20c","year":2020},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.619633Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:126758ffc7fb1608d46a44aef45905f46f25a1d54dfa2c17e3de1145c4215513","observation_id":"309d2411-1078-4b6d-9f8a-d91b544ba387","resolution":{"observed_at":"2026-08-05T11:49:54.078279Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.060788Z","title":"Feng, M.-W","venue":null,"work_id":"d9824b9e-ccc1-4209-a828-46b479c512b6","year":2014},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.707819Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:be069550024267326ce9fdb91a3c607f981ed88dc7e7aec56ec3bd0ffcc059cc","observation_id":"6529d4f9-58cc-4b54-a07b-d6749e6073c9","resolution":{"observed_at":"2026-08-05T11:49:54.064982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:54.046374Z","title":"Feng, M.-W","venue":null,"work_id":"53e2f95a-df95-4963-860c-1f5df2eb631d","year":2016},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.786484Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:39f4d39a032d71017c8bac8633e68ff22893de1ef5fe143f8fc1f8e69efec479","observation_id":"a00313b1-1a6d-4529-9f80-66cf7da7c194","resolution":{"observed_at":"2026-08-05T11:49:54.050426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:53.922275Z","title":null,"venue":null,"work_id":"6d69cc08-25d9-4b2d-b29b-e67aa7bf5ca9","year":2007},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.914868Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:899049b87805f4e4fa935d09fba3621a94c7849d7fc5c02cbb1a6dd55b4e2de3","observation_id":"a61ae73b-41c8-4317-be39-40b6237cc3a8","resolution":{"observed_at":"2026-08-05T11:49:54.036545Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:53.706024Z","title":"Funahashi","venue":null,"work_id":"1c2a5e02-10bb-4491-8d52-84cb050625fb","year":1989},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:48.993678Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:81f08287672efdbd37f2b8f6483d0adb87bbc2ef0fb9b8792b3f544531ba0441","observation_id":"ae790629-a77a-4ec4-86f9-5781fa653bb1","resolution":{"observed_at":"2026-08-05T11:49:53.828846Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:53.486945Z","title":"Gonz´ alez and G","venue":null,"work_id":"425d9c47-fea7-40a0-b2fb-a45c4c83d9d1","year":2011},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.072294Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:790af6f227a8a244fc6fcfa9bd0029da76cd3a31cf91cac4e911d6414e77f231","observation_id":"853d3a97-644a-495f-acb8-35a67a9d274c","resolution":{"observed_at":"2026-08-05T11:49:53.622953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:53.355134Z","title":null,"venue":null,"work_id":"b171b516-0688-463d-bfee-23621b9a5fa4","year":2017},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.138383Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:d281881c210a1be11f56555d59b407c89d952021cb6e6b5fbcf61393b9256cb8","observation_id":"e87517d1-e6ff-4a1c-9af3-2841ae3b6b81","resolution":{"observed_at":"2026-08-05T11:49:53.437093Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:53.234571Z","title":"Grohs, F","venue":null,"work_id":"b9883e4d-46e3-4a9b-8077-133b76cb817c","year":2023},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.178316Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:1ba274f36fe037d652025781b1c16bd7ec31c626c49c73453b3c648418aea947","observation_id":"ac653782-87ee-4645-91b2-9b8bc2346e12","resolution":{"observed_at":"2026-08-05T11:49:53.271365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:53.049028Z","title":"Ha and H","venue":null,"work_id":"66225a37-220d-4403-b28f-1fa39845e243","year":2020},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.255761Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:0a50d393ac08e8f32ccf639e205c9c349d66c685b2a63081a9ffef8da0581433","observation_id":"45964c72-9491-47af-90b6-17636d295cf0","resolution":{"observed_at":"2026-08-05T11:49:53.151096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:52.793267Z","title":"He and S","venue":null,"work_id":"b5453b71-e591-49c3-901b-77d0efb82641","year":2024},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.348381Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:078ff6ca24b87a76378a8fe3dcd77d855289cb0b9e20e487e73d105c666821f8","observation_id":"ad052299-0c2a-4c7a-b837-50aaf021ef90","resolution":{"observed_at":"2026-08-05T11:49:52.868900Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:52.610284Z","title":null,"venue":null,"work_id":"4f1d8c5a-72f1-4c9a-8589-caa5616b657e","year":1993},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.447999Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:99f8df08471c13789d22cce4e06cb44aa7a67b2ca89dd63f0f5efc74217e55ff","observation_id":"f5924618-cbe5-4195-8085-75b5262b4414","resolution":{"observed_at":"2026-08-05T11:49:52.748352Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:52.423406Z","title":null,"venue":null,"work_id":"51aa0b05-6c49-4eee-9cb3-08cebd4d5a79","year":1960},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.539746Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:c96ae31c174ee009419c92cf6d0d38e5cf2ed04bab7bc6bcf98d26b3f3bb70c4","observation_id":"821bcdd9-8b0f-491e-a701-81899f4af274","resolution":{"observed_at":"2026-08-05T11:49:52.545883Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:52.281328Z","title":null,"venue":null,"work_id":"c3597717-ea94-4426-8ecc-af7c940654f6","year":2017},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.584029Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:a304883660ad906d6403288dad1a333c7deb939cf9973ac460b909ea0f315b92","observation_id":"166a1250-4fe3-4b40-a81d-909ca3e6f2fb","resolution":{"observed_at":"2026-08-05T11:49:52.355347Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:52.183344Z","title":"Kerimkulov, D","venue":null,"work_id":"54bab62f-c582-41e9-b0d2-064b6982921a","year":2020},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.682648Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:975e0e430bcbb69f4d4baad24f44ffe26d8cebaee5c8b371115ac2921969c15a","observation_id":"7296f410-048d-4445-bac4-5a53fa38f647","resolution":{"observed_at":"2026-08-05T11:49:52.199677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T11:49:49.757086Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.757086Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:a0f6379e486d9caaf51d3185f69393157a94f75dc8c9408e6ffe980c9506e411","observation_id":"0543527d-6fae-4cc6-aba1-f9554410fef0","resolution":{"observed_at":"2026-08-05T11:49:49.757086Z","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":"2026-08-05T11:49:52.083872Z","title":null,"venue":null,"work_id":"55f73748-53d2-477d-8433-5ee95e0d912f","year":2005},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.845823Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:c741fe53c8f52f3ade2e2ef97f2bcf7e119dcb5bac6beee8b3573bc0a57d5212","observation_id":"f8e6b0f2-e3cd-48c9-a12c-48d53dde66b4","resolution":{"observed_at":"2026-08-05T11:49:52.150840Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:51.900062Z","title":null,"venue":null,"work_id":"ad0cbe91-c52d-4721-8705-cbde23306395","year":2021},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.873745Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:de9d9a5dbc8c6550bcfeca3dfa10aa55ac0ff3b42d104269dd543dae27aeddd8","observation_id":"5a4e60e8-91bc-4531-8cbd-fa09f7343940","resolution":{"observed_at":"2026-08-05T11:49:51.972111Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:51.755252Z","title":"Ly Vath, M","venue":null,"work_id":"e851b8d3-b071-4be9-b573-436d22ac32f9","year":2007},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:49.931836Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:a682c6a67984fd74afe9cf34f6aeaecaf016d0ed69fd49a027de3f49a650f7d1","observation_id":"bc613bf0-89b0-42ab-b935-bbcb5c9476b3","resolution":{"observed_at":"2026-08-05T11:49:51.813468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:51.624398Z","title":"Markowitz","venue":null,"work_id":"da43998f-a4fc-418f-b26c-76f86541ff73","year":1952},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.034115Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:26bb66bccf87063627e0981149ca934e83f92897bcba1d0305bf4c266f42cb9c","observation_id":"d52ad5f9-54cc-43f2-a733-efeae89052dc","resolution":{"observed_at":"2026-08-05T11:49:51.690230Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:51.514364Z","title":null,"venue":null,"work_id":"cf328912-f219-4aa5-92cb-c48d96f39175","year":2023},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.106625Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:2e749879ac10d7f28e21adc489969cac947505491ade153dc94a7158e6bc59ce","observation_id":"6e698d7d-70ef-42ba-8fce-6043814633c4","resolution":{"observed_at":"2026-08-05T11:49:51.555549Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:51.380435Z","title":null,"venue":null,"work_id":"fa3fdf07-5b06-45df-b660-d93488895c71","year":1971},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.192251Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:25dc89a9642e8cd3a5360efb21158e077a2d332c9168bc61ddaf987c247daeff","observation_id":"bac3ec95-ccdb-4594-bb93-80e7a3c23fe8","resolution":{"observed_at":"2026-08-05T11:49:51.438626Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:51.254387Z","title":null,"venue":null,"work_id":"9da47f4a-5ba0-4bbc-8f89-8cf90b414452","year":2012},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.284669Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:9772a27343a53bae6c20cb03c83e41ca96f452ca9940e79b7a540682ec2aafad","observation_id":"3f33938d-da8d-42e2-9656-257c7ed24373","resolution":{"observed_at":"2026-08-05T11:49:51.299291Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:51.155002Z","title":"Pasricha, S.-P","venue":null,"work_id":"6d587c13-bd6f-475e-b32c-7025377e6bc1","year":2022},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.338902Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:ce59ada9adfd3c2e82db4b67ba25ecf2629565a0a2004af800fa23627324e256","observation_id":"106c1a42-fd9f-4a3d-81d0-5956621d6fb4","resolution":{"observed_at":"2026-08-05T11:49:51.202945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:51.045075Z","title":"Patel and M","venue":null,"work_id":"b89558da-edd6-47ca-85ec-389b9657991c","year":1982},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.367721Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:f478278394ae294c5657e17a34a51ccb62f48aaf96baa52d3ad633b82c467d7b","observation_id":"ecc0c938-887c-43b1-8a57-a6449d35d5a6","resolution":{"observed_at":"2026-08-05T11:49:51.109222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:50.953300Z","title":null,"venue":null,"work_id":"6009d5e8-ac63-41ba-b9fb-f9e7a9b7590e","year":2008},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.445279Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:cceca53878e26542d547a71db7384a8dcd06863fbf3d1246abcdb6496ca51505","observation_id":"eac20679-221d-4afe-a1b4-d63400572dc0","resolution":{"observed_at":"2026-08-05T11:49:50.971265Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:50.854008Z","title":null,"venue":null,"work_id":"04d3a9ee-ff9d-4642-b124-543d73b7aa31","year":2022},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.522783Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:58330bcde05d061e11a3fb1f46246bb45117da135742f2ad8e44cf4ea43607a1","observation_id":"cb8fb4d8-293f-4659-9228-6ac29899517f","resolution":{"observed_at":"2026-08-05T11:49:50.901795Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-05T11:49:50.599819Z","title":"Raissi, P","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.599819Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:27e701262928816094c1db04e875f235ff563549770c5ec7ef45c09ed65759e9","observation_id":"f09d1e49-a8fb-4766-8c80-516763ac888f","resolution":{"observed_at":"2026-08-05T11:49:50.599819Z","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":"2026-08-05T11:49:50.741573Z","title":"W ANG and S","venue":null,"work_id":"6e565337-b94d-41af-a5ba-597fef461f40","year":2013},"citing_paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T11:49:50.643915Z"},"links":{"citing_paper":"/paper/2509.02267"},"observation_digest":"sha256:cd242e86607fe53db16687c054ec3851f45bfb925d8d61be84976ef979fd3add","observation_id":"18450a4b-f3be-4522-bb6f-445396a16b14","resolution":{"observed_at":"2026-08-05T11:49:50.799531Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.02267","last_updated":"2025-09-02T12:43:46Z","latest_version":1,"primary_category":"q-fin.MF","snapshot_observed_at":"2026-08-09T08:54:18.387770Z","submitted_at":"2025-09-02T12:43:46Z","title":"A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":23,"verified_exact":0,"verified_fuzzy":16},"total_outbound_references":39},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2509.02267."}