{"as_of":"2026-08-19T05:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6ee5a397926652b46287a0549095c466d4d24b9c287785692d6794c808cc5dd9","coverage":[{"denominator":62,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":62,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T15:51:50.481045Z","state":"measured"},{"denominator":62,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":62,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.02073/citation-record","integrity":"/paper/2608.02073/integrity","json":"/paper/2608.02073/citation-record.json","paper":"/paper/2608.02073"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:50:32.470771Z","title":"Bidirectional relation between cma evolution strategies and natural evolution strategies","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T15:50:32.470771Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:dba314d061839b3b6d196253fa51e93cf997d30428be9096060d5411ba41b8ad","observation_id":"0c6e728e-e529-4f69-a035-72ff673f8ccd","resolution":{"observed_at":"2026-08-04T15:50:32.470771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:15.933440Z","title":"Springer, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:15.933440Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:a759681c139d118b44af35b3060d38819e19cfd109707e69e906b394118a466c","observation_id":"30e0bb24-31f8-42b3-b944-81bacd5eef93","resolution":{"observed_at":"2026-08-04T15:51:15.933440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:38.135117Z","title":"Robust optimization for unconstrained simulation- based problems.Operations Research, 58(1):161–178, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:38.135117Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:98a1a887ad5bf4e820788f7bc41f969828c43f7f6dff6e4d4114365d0b0bbd20","observation_id":"cec26107-e0ad-47da-947d-45655e36acac","resolution":{"observed_at":"2026-08-04T15:51:38.135117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:44.608680Z","title":"Actuator noise in recombinant evolution strategies on general quadratic fitness models","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:44.608680Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:835808554c054768193bd5e3deca6f3c406c2b94776ed70226eb8bef10624aa1","observation_id":"f676c444-4250-443d-a0f3-21df9f31dc7a","resolution":{"observed_at":"2026-08-04T15:51:44.608680Z","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":"10.1016/j.cma.2007.03.003","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Robust optimization – a comprehensive survey.Computer Methods in Applied Mechanics and Engineering, 196(33):3190–3218, 2007","venue":"Computer Methods in Applied Mechanics and Engineering","work_id":"222cf795-041b-480f-8c92-07dd8577eaef","year":2007},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:46.357983Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:24c5bbb8c49578d0c102a46dccdec8fdb088534185a0ace73a7bf9b5bc652809","observation_id":"fc4b8099-d71f-4d43-89cb-0d6c4a9aa720","resolution":{"observed_at":"2026-08-04T15:54:09.316285Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:48.941373Z","title":"Conditional Expectation and Unbiased Sequential Estimation.The Annals of Mathematical Statistics, 18(1):105 – 110, 1947","venue":null,"work_id":null,"year":1947},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:48.941373Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:58a161190d9318c2e5a7f3b64d4f4a945300a0f65a30b31db458a9d37f78452d","observation_id":"c37aaba6-b810-42e0-9848-8a9d600afbaf","resolution":{"observed_at":"2026-08-04T15:51:48.941373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.055910Z","title":"Springer, 2005","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.055910Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:16265658b20ad400dda524d88ebdecbd7ddc2f0eae7620ffe2d6fdd0e53e42b7","observation_id":"e9d6b8d9-4b47-4e4d-a9b7-53da8346a3ce","resolution":{"observed_at":"2026-08-04T15:51:50.055910Z","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":"10.1115/1.2826915","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Journal of Mechanical Design","work_id":"af4b8397-4de5-46d7-b2e1-a75a5d3e77bf","year":1996},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.061885Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:c04c95f1df06a4ea1c82a2b95c2dfea3e97725e3c59a42bc982d86f2c0631805","observation_id":"2d11b44d-b479-4263-9cb3-4c21594f894f","resolution":{"observed_at":"2026-08-04T15:54:09.193087Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.069154Z","title":"Stanley, and Jeff Clune","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.069154Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:9205497c57cb4752d36c1348508558a01d5d0489f0a9a25d0efa3be0dd4f9655","observation_id":"59440c32-746a-4644-b949-c08f2f99f843","resolution":{"observed_at":"2026-08-04T15:51:50.069154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.075930Z","title":"Duchi, Michael I","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.075930Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:9ed59240595beccdd59ef9ec666cb2fdecba34cae6f8fb678f67ec7d183bd90f","observation_id":"e491c49a-159f-4992-b326-2275e3ca8bad","resolution":{"observed_at":"2026-08-04T15:51:50.075930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.088721Z","title":"Flaxman, Adam Tauman Kalai, and H","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.088721Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:382d6bab534e93f47af4896110e9ed901496bfdd4b3aa486a30a12d106be29db","observation_id":"3f2040fd-db90-4123-b119-491785ad901a","resolution":{"observed_at":"2026-08-04T15:51:50.088721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.093890Z","title":"Noisy-input entropy search for efficient robust bayesian optimization","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.093890Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:89ab5f1fe4560fc57c21caa30b8135b6718701707347f02443724ab337aacaa9","observation_id":"e89b9ceb-6810-4bea-8573-ffac96009f6a","resolution":{"observed_at":"2026-08-04T15:51:50.093890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.099139Z","title":"Exponential natural evolution strategies","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.099139Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:568ba20753f6ddde9abb11142a2985457e9493af8de3e4519df70b2d6e34f8ff","observation_id":"a0aea0db-0f1d-458a-b791-83a7aca010be","resolution":{"observed_at":"2026-08-04T15:51:50.099139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.103291Z","title":"Understanding the difficulty of training deep feedforward neural networks","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.103291Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:3780c91701354c2f9324be487a7449b7274986255db48a79d5c4bf70ab84b935","observation_id":"11369877-daeb-4979-891b-5b90e6ec404b","resolution":{"observed_at":"2026-08-04T15:51:50.103291Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.109414Z","title":"Variance reduction techniques for gradient estimates in reinforcement learning","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.109414Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:07d4be33eaddd990ece87129e13d9d7ffdc007aac8c9d03374db9e0f0fc25137","observation_id":"595bd657-a197-4845-b466-06fd3a2e3177","resolution":{"observed_at":"2026-08-04T15:51:50.109414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.116014Z","title":"Hansen, A","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.116014Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:8519e098ad1507209d7ec1ed59f481d7a863ced59aac40dac317cefed534adb2","observation_id":"36f640ea-ab51-4309-8d85-03651cefdc50","resolution":{"observed_at":"2026-08-04T15:51:50.116014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.123423Z","title":"The CMA evolution strategy: A tutorial, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.123423Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:e44acb77751063942fa5a26d9b16a3d308791cedfdc2f95f2c82af0032dfd3fd","observation_id":"70a3868b-f88d-4da6-9371-96bc893d255d","resolution":{"observed_at":"2026-08-04T15:51:50.123423Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.252937Z","title":"Completely derandomized self-adaptation in evolution strategies","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.252937Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:76998e80ccda8a41fa5a8b9cf624fd71d75ce5a830f2565c8b8a393e2a31e8c8","observation_id":"43878c2e-be48-4999-b34f-d0e1f0e4354a","resolution":{"observed_at":"2026-08-04T15:51:50.252937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.259705Z","title":"Comparing results of 31 algorithms from the black-box optimization benchmarking bbob-2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.259705Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:32d8353c127c2e2b1e037684a39283c6b3c9fd1e53107be7444f79a7c96fee16","observation_id":"d11d8306-4c0f-469c-a250-80b170a91609","resolution":{"observed_at":"2026-08-04T15:51:50.259705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.270854Z","title":"A largest empty hypersphere metaheuristic for robust op- timisation with implementation uncertainty.Computers & Operations Research, 103:64–80, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.270854Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:7c6e2551a99e4448fba852fbaa826428495ebfb27af8d66f3542226662f07183","observation_id":"fd20647e-4b56-475f-ae88-20d5f8ac918f","resolution":{"observed_at":"2026-08-04T15:51:50.270854Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.282893Z","title":"Particle swarm metaheuristics for robust optimisa- tion with implementation uncertainty.Computers & Operations Research, 122:104998, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.282893Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:6a329dcfa93caccf0beb4f510f875954c6f9d364052b27d0266925b9a22b81e9","observation_id":"72255c0d-a00c-456e-92ef-91ce612b3837","resolution":{"observed_at":"2026-08-04T15:51:50.282893Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.265958Z","title":"ISBN 9781450300735","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.265958Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:c60152d883544f58fb54819e1538b5ecdb4155eda52158588249841ad58ee6e7","observation_id":"fbe32f9f-9b58-4a2a-9652-60c502f274c0","resolution":{"observed_at":"2026-08-04T15:51:50.265958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.298835Z","title":"Jordan and R.A","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.298835Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:4cfa7acbbb01dc0bce15fbd09b43d0d9f68bf24854824c34001ecbd759927e99","observation_id":"36670715-f428-4e20-96b6-8f1121c10bd5","resolution":{"observed_at":"2026-08-04T15:51:50.298835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.310651Z","title":"Kakade, Shai Shalev-Shwartz, and Ambuj Tewari","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.310651Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:9519355a2b5ebb66ecaefc667e296183708a458d2d15a47ca6c5cc358d19c7ce","observation_id":"e251c0d4-c098-47c0-9b44-2a6c0d630cae","resolution":{"observed_at":"2026-08-04T15:51:50.310651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-04T15:51:50.314820Z","title":"Kingma and Max Welling","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.314820Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:1879aa30d8476ed9c463e83fdc95f03f1aff49fc9147aa3e48139a54eb57f84c","observation_id":"a14a539c-e788-47f9-b689-fc7f005879a7","resolution":{"observed_at":"2026-08-04T15:51:50.314820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.287785Z","title":"URLhttps://www.sciencedirect.com/science/ article/pii/S0305054820301155","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.287785Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:fa38768933667614d365863ecda945a3e0875c7a0f0d8e26a9b729980668e843","observation_id":"b3ea3362-68a5-4ab5-902b-816b5f7a0dc8","resolution":{"observed_at":"2026-08-04T15:51:50.287785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.292141Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.292141Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:ad180d90e68e55f36911e45cfaa712bd7b050ed7997b63d0ec64b1f5ae281eeb","observation_id":"44a870db-6847-4ccc-948f-75593ed8aca4","resolution":{"observed_at":"2026-08-04T15:51:50.292141Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.332758Z","title":"Springer, 1998","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.332758Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:66636c41e178f20e84c3b6f3df2cb3474fbdf9a46e5a25fec39a942aee3c7977","observation_id":"25b5b486-c2c9-4dee-a714-961c2329f7ae","resolution":{"observed_at":"2026-08-04T15:51:50.332758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.337705Z","title":"Rao-blackwellized stochas- tic gradients for discrete distributions","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.337705Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:1a56b938ac60b93b0984d0a3620573d47363183e8a9abad6fcfc93f68f3ad0e7","observation_id":"a21b915a-bbac-4b57-a1d8-f4e331d30980","resolution":{"observed_at":"2026-08-04T15:51:50.337705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.343600Z","title":"Simple random search of static linear policies is com- petitive for reinforcement learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.343600Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:292ced2a95b13836438edc3f86e8981a4d4c127fc1348126acea2e1fc2728372","observation_id":"73b1ffe5-365a-43a1-90b3-b9241838a07c","resolution":{"observed_at":"2026-08-04T15:51:50.343600Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.350017Z","title":"Monte carlo gradient estimation in machine learning.Journal of Machine Learning Research, 21(132):1–62, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.350017Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:c853d0743e67fd28d19c755eba3b000ca98e8e7b7b7b30df813b90c682db43b3","observation_id":"e9f752cf-b650-45cf-b75c-95609547082e","resolution":{"observed_at":"2026-08-04T15:51:50.350017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.321280Z","title":"Lam, Thang D Bui, George Deligiannidis, and Yee Whye Teh","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.321280Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:dd51378cca3c5c403b66992d0b8e85d080291ee2b8342486d1568dc14afbf802","observation_id":"b9830d6e-d491-4498-b438-870312f0bfa5","resolution":{"observed_at":"2026-08-04T15:51:50.321280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.328097Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.328097Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:c5b8dbd1b140b28aec0441e348dd13dcfc5f4a2cd7d4fec75092d03a46cd4e0b","observation_id":"8d293b1b-a0e1-4a02-8c3e-f369cb394195","resolution":{"observed_at":"2026-08-04T15:51:50.328097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.364631Z","title":"Kobayashi","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.364631Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:99f112e3db02af0a3c5a23d150686eaddd7fc076cd2bdaf67718e2f963fe7337","observation_id":"afd42ebc-3196-46ff-abc3-1e9ca23d096c","resolution":{"observed_at":"2026-08-04T15:51:50.364631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.370115Z","title":"Random gradient-free minimization of convex functions.Foundations of Computational Mathematics, 17(2):527–566, Apr 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.370115Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:150ad0ed4221f6f46886212e3614cc228ab7942fb4671a950be9a651ae88ec31","observation_id":"b10b75eb-5568-4ba2-9ff2-2fc90253b8b7","resolution":{"observed_at":"2026-08-04T15:51:50.370115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.375045Z","title":"Bayesian optimisation under uncertain inputs","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.375045Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:1d9b32da21939e1cefc225eca224c97f46b570aa564d0079be4801c98c863f08","observation_id":"6ad0d8cb-5041-4849-9e7f-be6f5cbe43a6","resolution":{"observed_at":"2026-08-04T15:51:50.375045Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.379754Z","title":"Information-geometric optimization algo- rithms: A unifying picture via invariance principles.Journal of Machine Learning Research, 18(18):1–65, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.379754Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:bf70c9b9d34faf4fbdba7761f930d01086c1326bb2dc95bf08f0111a62f7ff88","observation_id":"683b81a5-1e27-4866-95f9-2405840074f5","resolution":{"observed_at":"2026-08-04T15:51:50.379754Z","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":"10.1103/physrevresearch.4.013069","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kobayashi","venue":"Physical Review Research","work_id":"e26b64c4-7c74-4f72-95de-a15b54506ac8","year":2022},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.354830Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:20457f998383719bad03b02269823dc0144048c9c679514ed6b4e2b4de8c319e","observation_id":"08921454-7d18-48fd-9b1c-2477a9855d86","resolution":{"observed_at":"2026-08-04T15:54:08.898075Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09493","last_updated":"2024-09-02T16:19:25Z","snapshot_observed_at":"2026-08-16T13:25:43.645392Z","submitted_at":"2024-08-18T14:16:55Z","title":"Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09493","snapshot_observed_at":"2026-08-04T15:51:50.359259Z","title":"Kobayashi","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.359259Z"},"links":{"cited_paper":"/paper/2408.09493","citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:6731e7cf307918e8eaba27f3221150d4ec9c03e5e07197658d8c64edd9fccc26","observation_id":"7ee49148-e960-4007-9cb2-be3c5eb372c1","resolution":{"observed_at":"2026-08-04T15:51:50.359259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.396049Z","title":"Information and the accuracy attainable in the estimation of statistical parameters","venue":null,"work_id":null,"year":1945},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.396049Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:e5cdefdb6deaad88cfac3a9223bdc8e277a40451e1820697d948125f852ddc7b","observation_id":"480d45d7-8e90-4c5b-99e9-b710fc7359b3","resolution":{"observed_at":"2026-08-04T15:51:50.396049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.401374Z","title":"Eigen.Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution","venue":null,"work_id":null,"year":1973},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.401374Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:e0025352951f86f049c54fb2998e9507469e199b69a7933816da38ee8bdfc347","observation_id":"2af2ea4a-0d48-40f1-bfa6-951bba2390cb","resolution":{"observed_at":"2026-08-04T15:51:50.401374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.405934Z","title":"Stochastic backpropagation and approximate in- ference in deep generative models","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.405934Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:d6742fddcd2b79e304a6a41f1ed5c85f1ac438218d19139849032ca7d7639296","observation_id":"c71d09c9-ce73-457a-b72d-d7a8d858a68c","resolution":{"observed_at":"2026-08-04T15:51:50.405934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.411229Z","title":"Sahinidis","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.411229Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:209c252e9b139d781dac290f33a2bcac02845b16c14462d64278ae4ad2041afa","observation_id":"abf4f4f2-77c3-47ec-9053-c1b7c4d40fe6","resolution":{"observed_at":"2026-08-04T15:51:50.411229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.386075Z","title":"Performative prediction","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.386075Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:2f3f10b7eef133ae770dc9cf416d07498af0f3b7b118d2a70bf7afeb324a0cb4","observation_id":"30aeec87-1657-481f-a792-b0f5b6029f90","resolution":{"observed_at":"2026-08-04T15:51:50.386075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.390848Z","title":"Black Box Variational Inference","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.390848Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:8a8f53c0c7bf31592e7453bc5b97953b1265892c1083ac70e077b39514b253b9","observation_id":"a413814b-19c8-48ee-bfc2-1fd07f4d4003","resolution":{"observed_at":"2026-08-04T15:51:50.390848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.426487Z","title":"Gradient estimation using stochastic com- putation graphs","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.426487Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:aac76e47b04f3ca8cbadcaefe3dc8ffe540cb6853c18b33cb0aa4d2c1b41a00f","observation_id":"763d75b4-2311-43c1-b03e-09781949059e","resolution":{"observed_at":"2026-08-04T15:51:50.426487Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.432332Z","title":"An optimal algorithm for bandit and zero-order convex optimization with two-point feedback","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.432332Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:4e3fbe18718667fb09d957beaac0f0f48998752030646a85859cb566a65d0af9","observation_id":"6b390e8d-49d8-4299-8bfd-3b55023e82d1","resolution":{"observed_at":"2026-08-04T15:51:50.432332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.437860Z","title":"MIT press Cambridge, 1998","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.437860Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:4e54f0414564bf53242cd744a75b6da3469f539a56709b634fffe1318eff2e03","observation_id":"3612da90-3bec-4a34-8ce7-dd097a70c197","resolution":{"observed_at":"2026-08-04T15:51:50.437860Z","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":"10.1007/978-1-4684-1472-1_5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Taguchi and M","venue":null,"work_id":"9d0a99d3-bb0c-4b2c-8941-91e1fd112190","year":1989},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.444153Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:798edcbeebf7259afeb255d4663e18f62a12137c8364c963f376ce3b55793c76","observation_id":"bf975510-b721-4146-8115-a85afdf3834b","resolution":{"observed_at":"2026-08-04T15:54:08.547875Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1214/13-ba858","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Bayesian Analysis","work_id":"1f347abd-be47-4a7f-b72b-7b44b7bf5075","year":2013},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.416251Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:45f8650651af19147e300b54f712cd910367c5717c0c4ffe0cffe51e8602f5a5","observation_id":"f0c2fb5f-8835-4f24-a349-cfede50e8586","resolution":{"observed_at":"2026-08-04T15:54:08.727420Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.03864","last_updated":"2017-09-07T23:28:48Z","snapshot_observed_at":"2026-08-14T21:12:24.197125Z","submitted_at":"2017-03-10T23:02:19Z","title":"Evolution Strategies as a Scalable Alternative to Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.03864","snapshot_observed_at":"2026-08-04T15:51:50.421494Z","title":"Evolution strategies as a scalable alternative to reinforcement learning.arXiv preprint arXiv:1703.03864, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.421494Z"},"links":{"cited_paper":"/paper/1703.03864","citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:30356490b8d6021d4405c9267888637c498018997b4ebc68af1e6ff043a4cca5","observation_id":"a75c7191-0786-498d-a04c-07c695a63168","resolution":{"observed_at":"2026-08-04T15:51:50.421494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.458712Z","title":"Natural evolution strategies.Journal of Machine Learning Research, 15(27):949–980, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.458712Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:200fa0b0e83752b2eb506ab9e3688cce33aa72dee90c798360386dc9fff73bb4","observation_id":"6da80348-7380-4aaa-ad80-9ed32a46840e","resolution":{"observed_at":"2026-08-04T15:51:50.458712Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.463166Z","title":"Variance reduction for policy gradient with action-dependent factorized baselines","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.463166Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:5cca900a00199b7d5eb14a02aeb30eb1d91fd2fddd4ee144a48de872de33f7d0","observation_id":"5e34b6ca-26ba-4c40-9bb4-a3c9edb0575d","resolution":{"observed_at":"2026-08-04T15:51:50.463166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.468366Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.468366Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:7b406fc824986e2694c536c75a260ed36f23c6e7c83f483d05ebbb4e1bfc902c","observation_id":"50c0d257-0df6-47b4-9a47-21d817425546","resolution":{"observed_at":"2026-08-04T15:51:50.468366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.12228","last_updated":"2025-08-17T04:01:51Z","snapshot_observed_at":"2026-08-05T19:36:20.997940Z","submitted_at":"2025-08-17T04:01:51Z","title":"Can a One-Point Feedback Zeroth-order Algorithm Achieve Linear Dimension Dependent Sample Complexity?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.12228","snapshot_observed_at":"2026-08-04T15:51:50.474133Z","title":"Can a one-point feedback zeroth-order algorithm achieve linear dimension dependent sample complexity?, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.474133Z"},"links":{"cited_paper":"/paper/2508.12228","citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:0cdf7cccf31f2f2a914b075e0a10f8312e34272f85536776ddfa3adb658a3012","observation_id":"bbd62bd5-5b01-4967-a45c-38dc0efc79b8","resolution":{"observed_at":"2026-08-04T15:51:50.474133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.17032","last_updated":"2025-11-02T13:42:19Z","snapshot_observed_at":"2026-08-13T22:24:37.672685Z","submitted_at":"2024-07-24T06:35:05Z","title":"Gymnasium: A Standard Interface for Reinforcement Learning Environments","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.17032","snapshot_observed_at":"2026-08-04T15:51:50.448727Z","title":"Gymnasium: A standard interface for reinforcement learning environments.arXiv preprint arXiv:2407.17032, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.448727Z"},"links":{"cited_paper":"/paper/2407.17032","citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:204a36df11605d30ef9e07d81b5d06208e43520e575ccc2f3b6bda86d94cb6d9","observation_id":"327101e2-b828-4514-90ab-5865aed37f39","resolution":{"observed_at":"2026-08-04T15:51:50.448727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.453745Z","title":null,"venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.453745Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:97900e21170ae58fafb9d973f5a9c00795a4fac41392b4168a43de102dfdcda2","observation_id":"73f6f442-c063-4e38-9bac-12476455feb2","resolution":{"observed_at":"2026-08-04T15:51:50.453745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.481045Z","title":"∂ ∂ψ g FW # =E PF(θ|x)","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.481045Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:add9574abdf87f06cd2ddf79583ff6c2a323fb814b4e6fb38cf9364fd8f6a02d","observation_id":"bfc09788-036d-467f-b72b-2532726b55ce","resolution":{"observed_at":"2026-08-04T15:51:50.481045Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.cor.2018.10.013","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"URLhttps://www.sciencedirect.com/science/ article/pii/S0305054818302727","venue":"Computers & Operations Research","work_id":"e250408a-ad48-4c99-abf0-88ebf661d4dd","year":2018},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":548,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.277554Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:4f8b077086ac632957c36087c76fb557aab55e4e2f503f81c63cacc0c8be4880","observation_id":"9029de18-bdf1-4e85-ae98-cd45b9809827","resolution":{"observed_at":"2026-08-04T15:54:09.037167Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.305557Z","title":null,"venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":1993,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.305557Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:c06f44a58869961aac8a1803fb22c667b707e9769f5ae2d6ec217b285c03cb08","observation_id":"e14ea159-fbfd-4097-9150-22c1d8c0bbe6","resolution":{"observed_at":"2026-08-04T15:51:50.305557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:50:50.701998Z","title":"ISBN 978-3-642-15844-5","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-04T15:50:50.701998Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:6f3699b4e2c77d70a4bc8305ed5abd0accce33ffd290845c5699c3f4fd1b884f","observation_id":"2ff045a8-c3c8-4015-be09-af5ed536ad4f","resolution":{"observed_at":"2026-08-04T15:50:50.701998Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T15:51:50.084594Z","title":"doi: 10.1109/TIT.2015.2409256","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-04T15:51:50.084594Z"},"links":{"citing_paper":"/paper/2608.02073"},"observation_digest":"sha256:d430f5d7d03d5c87ab4b814a14e5d292a08b6903812ab34c94ae599ebbc9106f","observation_id":"eea16352-f13e-45d8-9633-f82bf596f821","resolution":{"observed_at":"2026-08-04T15:51:50.084594Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2608.02073","last_updated":"2026-08-03T11:21:30Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-13T20:07:23.234749Z","submitted_at":"2026-08-03T11:21:30Z","title":"Accelerating Evolutionary Strategy via Rao-Blackwellizing Realization of Uncertain Input"},"reference_resolution":{"displayed":62,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":55,"verified_exact":6,"verified_fuzzy":0},"total_outbound_references":62},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2608.02073."}