{"as_of":"2026-08-09T14:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7c60458b44b2f17dbb2e95778a97320c9ebbb7ce48690d17459d75013103a0bd","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-11T01:54:07.071406Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.06610/citation-record","integrity":"/paper/2607.06610/integrity","json":"/paper/2607.06610/citation-record.json","paper":"/paper/2607.06610"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T01:57:58.018949Z","title":"Portfolio selection.Handbook of finance, 2:3–13, 2008","venue":null,"work_id":"ce3105de-2869-4689-854d-23b3d959c0ef","year":2008},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:8d3799198339969f33a68cf60e768cf07c44681251e9e1e2581c50a85bd42c49","observation_id":"6fef8a6d-5f09-49d2-8a1f-33729cb7a110","resolution":{"observed_at":"2026-07-11T01:57:58.046411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.281076Z","title":"The capital asset pricing model: Theory and evidence.Journal of economic perspectives, 18(3):25–46, 2004","venue":null,"work_id":"9e9e698b-9e3e-4e40-90b6-5cc21ba663f7","year":2004},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:4107f7bcfd903739e85f1f0c0166f63e8a47b8b026d0956246ef4f24aa2b275d","observation_id":"73b505a0-e09b-4743-8f7e-b7aa2413076c","resolution":{"observed_at":"2026-07-11T01:57:58.302860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.334923Z","title":"Value at risk.Financial analysts journal, 56(2):47–67, 2000","venue":null,"work_id":"42e30b79-b32c-418b-b414-197486f4c6e2","year":2000},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:0b403b1715d77124399be296b883cf12ed035872a7d1ce38a6781b9564f1c76d","observation_id":"90ff1172-9f9f-4aa8-94e6-b54b2015f3f9","resolution":{"observed_at":"2026-07-11T01:57:58.361919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.227872Z","title":"Conditional value-at-risk for general loss distributions.Journal of banking & finance, 26(7):1443–1471, 2002","venue":null,"work_id":"17ba16f0-aa3c-4eea-b650-b79ec68d7a52","year":2002},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:026edeecd66c81e03bd1aada8c33fc06398c0266cf7cf89e0fcbbf967ab9d311","observation_id":"747c25ad-d13e-4f70-8112-d27b90c1d2de","resolution":{"observed_at":"2026-07-11T01:57:58.252381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.201365Z","title":"A comparison of risk measures for portfolio optimization with cardinality constraints.Expert Systems with Applications, 228:120412, 2023","venue":null,"work_id":"69a190cf-635d-4897-b036-ef7e383c77a8","year":2023},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:446db73c5778509f22c867662161814dc003c6cc9c0426e122931626acaccc18","observation_id":"52ad6274-9ef4-412a-9c26-634472a90ca7","resolution":{"observed_at":"2026-07-11T01:57:58.223840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.366864Z","title":"A simulation comparison of risk measures for portfolio optimization","venue":null,"work_id":"3f5fe054-4b13-48c3-ae97-e153d7c1f986","year":2018},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:05b75712f7b0d077f3093276f18d956917f8a096ae593e401507f7ce89dde4ee","observation_id":"d8362fe3-f528-4872-ba36-6a59e3621423","resolution":{"observed_at":"2026-07-11T01:57:58.391882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.172469Z","title":"Portfolio optimisation problem: A taxonomic review of solution methodologies.IEEE Access, PP:1–1, 01 2023","venue":null,"work_id":"8abed70d-7320-4ac0-a6ee-8e160dedb6ab","year":2023},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:cac8a046460c82eb0d79e1285d95eee913f11d8b095e12eccb86f3310296c1a6","observation_id":"eea1ba46-9f80-421a-8d1a-4a3c44b78948","resolution":{"observed_at":"2026-07-11T01:57:58.197831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.306809Z","title":"Fifty years of portfolio optimization","venue":null,"work_id":"dba91ebe-0b31-4d47-b901-95a5016338e1","year":2024},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:08202de9c8d75bd89bc1ec1856d797426796c0e843687dba2b02b10c684d8ab9","observation_id":"d2c8d091-f231-47cf-8633-5ad32d71e79c","resolution":{"observed_at":"2026-07-11T01:57:58.331257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.423425Z","title":"A survey of swarm intelligence for portfolio optimization: Algorithms and applications.Swarm and evolutionary computation, 39:36–52, 2018","venue":null,"work_id":"6bef5cd5-30c1-4522-b8b3-98a8c518590f","year":2018},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:f3c5120437eff94b8a53ab7708af27451f60c296c75141d3deae2fa9c7131d38","observation_id":"c58161ec-9bd5-4228-ad68-a3fb3bbbabbc","resolution":{"observed_at":"2026-07-11T01:57:58.449793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.020685Z","title":"MIT press Cambridge","venue":null,"work_id":"bea6e816-e3fb-4d6c-a7ce-228cb17721d1","year":2016},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:5deb174c341e7819cab4239a35389fa42d0cbf6b444f14df22a283f2c8de45f9","observation_id":"dca64961-781c-468a-b33d-241110f47951","resolution":{"observed_at":"2026-07-11T01:57:58.048131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.238695Z","title":"Deep learning with long short-term memory networks for financial market predictions.European journal of operational research, 270(2):654–669, 2018","venue":null,"work_id":"b7b3fe4b-b4c0-422e-b705-e124dae58cd8","year":2018},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:17585ee51e1c74464196e15ce699e976d21e450f2e514b25d659436d43af034a","observation_id":"2d0f005a-e327-45ae-ad77-cb8b8cc1c2bc","resolution":{"observed_at":"2026-07-11T01:57:58.266342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.089259Z","title":"Prediction based mean-value- at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets","venue":null,"work_id":"5825e3ad-92af-4f6b-a3cf-b03c23f94c55","year":2023},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:1f617054e0ba9d469568fa26a1b5e99e3caee24ef8f2489d07d6f72245bb4603","observation_id":"83e71d1a-2ca9-4d04-9574-ee4bb65236c1","resolution":{"observed_at":"2026-07-11T01:57:58.137770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.142792Z","title":"Deep reinforcement learning: A brief survey.IEEE signal processing magazine, 34(6):26–38, 2017","venue":null,"work_id":"b0d03504-631b-4436-823a-c4b7f2a7ff45","year":2017},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:840c48dd0dbc0dfa9a9715be673b3c4eaf58d028ab33e25338cd8228aec0846a","observation_id":"eaee1156-8bf6-499f-b0a8-86b4b55275d6","resolution":{"observed_at":"2026-07-11T01:57:58.168721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.256215Z","title":"Risk-adjusted deep reinforcement learning for portfolio optimization: A multi-reward approach.International Journal of Computational Intelligence Systems, 18(1):126, 2025","venue":null,"work_id":"1511b6ee-8a04-4f9f-852c-85bcbbb48337","year":2025},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:50af61bf146737af61e2169b0979651117f8fb1c9d36be2206b262255a424ca1","observation_id":"d9fc6084-e3b2-4bef-aa2b-8af2fa816ee8","resolution":{"observed_at":"2026-07-11T01:57:58.277770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:58.395883Z","title":"Empirical asset pricing via machine learning.The Review of Financial Studies, 33(5):2223–2273","venue":null,"work_id":"b351bc77-e673-460c-97cb-b66251e9ace3","year":2020},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:3213ab6d9dbff98decc14cc95184eb11c732dbd322f58113afcdce3df15923c1","observation_id":"8611501e-fad4-466d-ab7f-0b9fedc29e39","resolution":{"observed_at":"2026-07-11T01:57:58.419641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.820648Z","title":"A cvar-constrained safe reinforcement learning framework with action repair for practical portfolio optimization.IEEE Transactions on Artificial Intelligence, 2026","venue":null,"work_id":"916bd055-8033-4d6c-bf13-461e36835ad6","year":2026},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:441618bfbe167be603196cb9a7d742ed84965aad39ad80844537d02ef6132a00","observation_id":"2bb07ad9-c0ce-458b-bdba-e3f81d188769","resolution":{"observed_at":"2026-07-11T01:57:57.850225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.886586Z","title":"Portfolio selection.The Journal of Finance, 7(1):77–91","venue":null,"work_id":"bff4591d-4be4-449f-9738-477f0bd4d9f9","year":1952},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:9dbf12c4878522c4ff3fb71589add95d7ccb5a1b2881f418468a27765d45eddb","observation_id":"227476ac-2c66-4350-9c25-1e034b66732b","resolution":{"observed_at":"2026-07-11T01:57:57.915729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.786461Z","title":"60 years of portfolio optimization: Practical challenges and current trends.European Journal of Operational Research, 234(2):356–371, 2014","venue":null,"work_id":"5edcb61d-bb00-4ddd-b09c-f0b6093567af","year":2014},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:b5f558244ac5906476455aab2f782cbc9c5b90866f90b95844d6565457a87924","observation_id":"4626211b-7d9e-4c61-8a97-9fe43ec79348","resolution":{"observed_at":"2026-07-11T01:57:57.816273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.854063Z","title":"Multi-objective heuristic algorithms for practical portfolio optimization and rebalancing with transaction cost.Applied Soft Computing, 67:865–894, 2018","venue":null,"work_id":"32148e8d-ba68-468f-8f84-f929fc400f95","year":2018},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:1cc124da4ecfacd59d4898d465182e359293b823ec2f814e657406fc3f2af6da","observation_id":"45a8cc91-292d-4e12-9a90-3ce2f49db1dc","resolution":{"observed_at":"2026-07-11T01:57:57.882683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.921698Z","title":"Continuous-time optimal investment with portfolio constraints: a reinforcement learning approach.European Journal of Operational Research, 2025","venue":null,"work_id":"ef31b80c-ea34-45f6-9751-7bb4fb8da11a","year":2025},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:5f714fdda3b1349ef4258a3b5dcc9ef197c9539e266f176d15fe8c365617ac05","observation_id":"7bdf0bcb-d128-4993-9fd6-b3cd193252b7","resolution":{"observed_at":"2026-07-11T01:57:57.951127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.04373","last_updated":"2019-12-15T05:13:52Z","snapshot_observed_at":"2026-08-09T02:41:02.654540Z","submitted_at":"2019-07-09T19:18:34Z","title":"Capturing Financial markets to apply Deep Reinforcement Learning","version":3},"cited_work":{"arxiv_id":"1907.04373","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.04373","snapshot_observed_at":"2026-07-11T01:57:51.829607Z","title":"Capturing Financial markets to apply Deep Reinforcement Learning","venue":"q-fin.CP","work_id":"3c940002-f111-4cac-91c0-3ebba504a94f","year":2019},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"cited_paper":"/paper/1907.04373","citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:a2fbb212794eb6f44bade556e96314fa3a1d048ff445a8e65be399683730c8d7","observation_id":"94a2bd98-b76b-4d9a-8147-1d474145a756","resolution":{"observed_at":"2026-07-11T01:57:51.852174Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.754108Z","title":"Application of deep reinforcement learning in stock trading strategies and stock forecasting.Computing, 2019","venue":null,"work_id":"f64b1f4a-4875-4bc9-a287-a5c3dc67fa04","year":2019},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:bf395de4cb10e64c5b5bd995fd3fd9fd16945964b0976d60d77d20cf94602980","observation_id":"fafb787f-f589-4cbf-897b-4eb466b41b7c","resolution":{"observed_at":"2026-07-11T01:57:57.782088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.655908Z","title":"Application of deep q-network in portfolio management","venue":null,"work_id":"09a2e53f-7154-47f6-9587-1e4c44523c27","year":2020},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:200d810b2de725d1450a47295956f4e7a2a4fd7ca7c9bd9f29b87aa43e53af67","observation_id":"56342f4a-c4c8-4bd2-b6b9-29a174bae8fc","resolution":{"observed_at":"2026-07-11T01:57:57.685289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.689295Z","title":"A framework of hierarchical deep q-network for portfolio management","venue":null,"work_id":"18181f4f-b0ea-4496-a576-e7038945f7ac","year":2021},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:be58ff6822043c00778f5c36a68283926e4b53a409a348c30022cfbb37cf8378","observation_id":"d04670c1-c408-4764-950d-eb7958b25815","resolution":{"observed_at":"2026-07-11T01:57:57.716947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.721334Z","title":"Deep reinforcement learning for portfolio selection.Global Finance Journal, 62:101016, 2024","venue":null,"work_id":"94f13992-f81d-4cf5-ba1a-c9b93bff1152","year":2024},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:6cb9251be9518ee496b2db287118ebb5406e56b1b78de175b475e5ac16d5c0e4","observation_id":"f1653bea-f29d-41d2-8043-72e48d0f211f","resolution":{"observed_at":"2026-07-11T01:57:57.749378Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.954199Z","title":"Predictive multi- period multi-objective portfolio optimization based on higher order moments: Deep learning approach.Computers & industrial engineering, 183:109450, 2023","venue":null,"work_id":"3bacf206-b243-41b8-a64d-dfce4d6df43f","year":2023},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:77bf33c27204614255c7a5933a280e363809b3c7424f88d13ff4b24ea8d9f472","observation_id":"3ec6dcb1-4469-4209-985f-04edbee08198","resolution":{"observed_at":"2026-07-11T01:57:57.980440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07916","last_updated":"2024-02-27T14:08:31Z","snapshot_observed_at":"2026-08-09T07:47:40.426502Z","submitted_at":"2024-02-27T14:08:31Z","title":"Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization","version":1},"cited_work":{"arxiv_id":"2403.07916","doi":null,"metadata_source":"pith","pith_arxiv_id":"2403.07916","snapshot_observed_at":"2026-07-11T01:57:51.912552Z","title":"Advancing Investment Frontiers: Industry-grade Deep Reinforcement Learning for Portfolio Optimization","venue":"cs.AI","work_id":"ca62382c-21e5-4745-a11f-c53577d95309","year":2024},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"cited_paper":"/paper/2403.07916","citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:0508da5beedf79c115d5623c51da15ea84aa175623ef761663db0b2b09f38e54","observation_id":"8c959c6a-72fe-4ff1-8308-2eba54633dfc","resolution":{"observed_at":"2026-07-11T01:57:51.936841Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.627158Z","title":"Reinforcement learning for deep portfolio optimization.Electronic Research Archive, 32(9):5176, 2024","venue":null,"work_id":"4122e1f4-61dc-4586-be8c-b6b2ae698b0a","year":2024},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:b69a86eb160d7039564236cd3caf5cceb153ab5862df2f43f6426040942e09d3","observation_id":"b40dc6cd-c932-4b48-9d9d-d0c0801695a3","resolution":{"observed_at":"2026-07-11T01:57:57.655548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.528975Z","title":"Deep reinforcement learning for stock portfolio optimization by connecting with modern portfolio theory.Expert Systems with Applications, 218:119556, 2023","venue":null,"work_id":"b88cbb86-67b2-4554-b4a5-83786fc78e8c","year":2023},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:595f40484f256b968ca2c5085b90a01808ffc13184d2832eff5565188da11b9c","observation_id":"cb324411-4016-47e6-86e9-b041afd0d301","resolution":{"observed_at":"2026-07-11T01:57:57.557748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.08740","last_updated":"2019-01-25T04:55:02Z","snapshot_observed_at":"2026-07-06T07:28:57.563261Z","submitted_at":"2019-01-25T04:55:02Z","title":"Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization","version":1},"cited_work":{"arxiv_id":"1901.08740","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.08740","snapshot_observed_at":"2026-07-11T01:57:51.883707Z","title":"Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization","venue":"cs.LG","work_id":"2bd78e6b-1f97-4589-b8fa-a40597a8d892","year":2019},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"cited_paper":"/paper/1901.08740","citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:45e0cd6f994cf78dfa025cb5557f3bb4bbbe4c6a1aa01fb681b8a7436459ff54","observation_id":"68663e34-dfbd-4326-b8fe-5592b9955272","resolution":{"observed_at":"2026-07-11T01:57:51.906438Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.595086Z","title":"Bi-objective reliability based optimization: an application to investment analysis.Annals of Operations Research, 333(1):47–78, 2024","venue":null,"work_id":"7fc6ead4-417a-4e16-acc0-d37d4a047e47","year":2024},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:f438e83551f22f77b28ea00aa4d59ac9a9f1d2c8e0f33e27d15d8d8a6183ab86","observation_id":"bbfd6e3c-397c-4446-842c-e4d6d0d0d4ce","resolution":{"observed_at":"2026-07-11T01:57:57.622581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.562919Z","title":"Reliability-based design optimization: a state-of-the-art review of its methodologies, applications, and challenges.Structural and Multidisciplinary Optimization, 67(9):168, 2024","venue":null,"work_id":"295f0906-43e1-4ff0-bec0-484eb4bccf56","year":2024},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:40c3b7620dbd16d48bec681eeec695a1374e85391c8205860d3acd5f8109322f","observation_id":"9963f3a7-0f1a-41e2-b535-6f46cae67b49","resolution":{"observed_at":"2026-07-11T01:57:57.589625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.986970Z","title":"Reliability in portfolio optimization using uncertain estimates.Sankhya B, 85(Suppl 1):199–233, 2023","venue":null,"work_id":"470b6bca-8c51-4591-8044-671316181d58","year":2023},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:ad9dca0134dd8274f50703993e927fcd7bcac07e0a599e9ee1c036cfbd4efaa1","observation_id":"c67ecfa3-d807-400b-82b9-6c94532c7ae4","resolution":{"observed_at":"2026-07-11T01:57:58.015827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.593403Z","title":"Multi-objective possibilistic model for portfolio selection with transaction cost.Journal of computational and applied mathematics, 228(1):188–196, 2009","venue":null,"work_id":"abd29a6b-b106-4122-80ca-5414389211e4","year":2009},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:d1fdd0426a62cc98b762ec6aa01d19ccbe5e2ce9c74e12aea11117245efea7e9","observation_id":"a40db653-1ec0-4c5e-834b-1537f2fcbdef","resolution":{"observed_at":"2026-07-11T01:57:57.623498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-07-11T01:57:57.724602Z","title":"Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem.Physica A: Statistical Mechanics and its Applications, 429:125–139, 2015","venue":null,"work_id":"fa3c40c4-2d27-41c0-a468-900ba79f73d0","year":2015},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:bf149dcf76a1c44b019acdffa99cc7b7198a2e0d83a6f00cd6f0ca3666da1e78","observation_id":"1670fce5-053a-4147-a746-889945d5fb97","resolution":{"observed_at":"2026-07-11T01:57:57.754159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":"1707.06347","doi":"10.1016/j.artint.2010.12.005","metadata_source":"pith","pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Proximal Policy Optimization Algorithms","venue":"cs.LG","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","year":2017},"citing_paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-11T01:54:07.071406Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2607.06610"},"observation_digest":"sha256:e8e0c78521ed39123c9c43ea6529473a51e33b423e300be8b693eab014c29357","observation_id":"8c226b0c-155f-4302-ba5c-999b3c558bed","resolution":{"observed_at":"2026-07-11T01:57:51.906054Z","resolver_source":"local_arxiv","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2607.06610","last_updated":"2026-07-07T06:24:32Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T10:38:37.877063Z","submitted_at":"2026-07-07T06:24:32Z","title":"Deep Reinforcement Learning for Reliability Based Bi-Objective Portfolio Optimization"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":3,"verified_fuzzy":32},"total_outbound_references":36},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.06610."}