{"as_of":"2026-08-09T18:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0fa7db5461f07c08fb46820eb0a0ad635036bf47f11b87b45444de42d8b18682","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T19:39:40.115786Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T12:49:52.753358Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.15335","last_updated":"2022-03-20T19:19:16Z","snapshot_observed_at":"2026-08-08T02:03:17.133731Z","submitted_at":"2021-10-28T17:47:31Z","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2110.15335","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.15335","snapshot_observed_at":"2026-07-04T12:49:52.753358Z","title":"Ruppert (1988), Efficient estimations from a slowly convergent Robbins-Monro pro- cess, Technical report, Cornell University","venue":null,"work_id":"4bb97ffa-674e-4445-a3db-c8a72c6e2476","year":2023},"citing_paper":{"arxiv_id":"2306.10430","last_updated":"2024-12-23T17:56:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-06-17T21:47:19Z","title":"Variational Sequential Optimal Experimental Design using Reinforcement Learning","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-24T08:21:09.386442Z"},"links":{"cited_paper":"/paper/2110.15335","citing_paper":"/paper/2306.10430"},"observation_digest":"sha256:4fd1133bf8122c3b76c64e18b68fb0abead19ff595b46ee00ad525b687bf1c1a","observation_id":"98ae7a5f-f14f-483a-a690-b9fe93fd5f44","resolution":{"observed_at":"2026-05-24T08:24:11.187412Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2110.15335","last_updated":"2022-03-20T19:19:16Z","snapshot_observed_at":"2026-08-08T02:03:17.133731Z","submitted_at":"2021-10-28T17:47:31Z","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2110.15335","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.15335","snapshot_observed_at":"2026-07-04T12:49:52.753358Z","title":"Ruppert (1988), Efficient estimations from a slowly convergent Robbins-Monro pro- cess, Technical report, Cornell University","venue":null,"work_id":"4bb97ffa-674e-4445-a3db-c8a72c6e2476","year":2023},"citing_paper":{"arxiv_id":"2407.16212","last_updated":"2026-04-29T20:39:22Z","snapshot_observed_at":"2026-08-07T16:55:12.999376Z","submitted_at":"2024-07-23T06:33:37Z","title":"Optimal experimental design: Formulations and computations","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-23T23:12:32.418707Z"},"links":{"cited_paper":"/paper/2110.15335","citing_paper":"/paper/2407.16212"},"observation_digest":"sha256:4fd4110512d6e9ca293c3fba1b1e389d17a0d9648856d88b9af1613637bcad3a","observation_id":"9d7dab90-d802-4981-b8b5-3dfbfa3b0c82","resolution":{"observed_at":"2026-05-23T23:13:36.341374Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2110.15335","last_updated":"2022-03-20T19:19:16Z","snapshot_observed_at":"2026-08-08T02:03:17.133731Z","submitted_at":"2021-10-28T17:47:31Z","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.15335","snapshot_observed_at":"2026-08-08T19:39:40.115786Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05372","last_updated":"2025-08-10T02:11:17Z","snapshot_observed_at":"2026-08-09T17:55:57.721412Z","submitted_at":"2025-02-07T22:54:20Z","title":"Active Learning of Model Discrepancy with Bayesian Experimental Design","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T19:39:40.115786Z"},"links":{"cited_paper":"/paper/2110.15335","citing_paper":"/paper/2502.05372"},"observation_digest":"sha256:3f0b416a9c65c38475ec7919088d1d96e449e739f6d7093df135a05070beb5e7","observation_id":"870bc0b3-26c2-4679-81a6-97958f1d1376","resolution":{"observed_at":"2026-08-08T19:39:40.115786Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.15335","last_updated":"2022-03-20T19:19:16Z","snapshot_observed_at":"2026-08-08T02:03:17.133731Z","submitted_at":"2021-10-28T17:47:31Z","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2110.15335","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.15335","snapshot_observed_at":"2026-07-04T12:49:52.753358Z","title":"Ruppert (1988), Efficient estimations from a slowly convergent Robbins-Monro pro- cess, Technical report, Cornell University","venue":null,"work_id":"4bb97ffa-674e-4445-a3db-c8a72c6e2476","year":2023},"citing_paper":{"arxiv_id":"2604.25193","last_updated":"2026-06-06T10:20:30Z","snapshot_observed_at":"2026-07-06T23:11:07.254460Z","submitted_at":"2026-04-28T04:02:17Z","title":"Adaptive Sensing beyond Non-Adaptive Information Limits: End-to-End Co-Design of Geometry, Policy, and Inference","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-07T15:52:51.892677Z"},"links":{"cited_paper":"/paper/2110.15335","citing_paper":"/paper/2604.25193"},"observation_digest":"sha256:6c09c20ce459be69f730eceedc71dc9554a34d8079dd4bdfad988cdabbafc86b","observation_id":"74fca89e-4991-4ff1-b45d-d5cc4ccb8311","resolution":{"observed_at":"2026-05-12T00:01:16.832365Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2110.15335","last_updated":"2022-03-20T19:19:16Z","snapshot_observed_at":"2026-08-08T02:03:17.133731Z","submitted_at":"2021-10-28T17:47:31Z","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2110.15335","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.15335","snapshot_observed_at":"2026-07-04T12:49:52.753358Z","title":"Ruppert (1988), Efficient estimations from a slowly convergent Robbins-Monro pro- cess, Technical report, Cornell University","venue":null,"work_id":"4bb97ffa-674e-4445-a3db-c8a72c6e2476","year":2023},"citing_paper":{"arxiv_id":"2606.23662","last_updated":"2026-06-22T17:48:07Z","snapshot_observed_at":"2026-08-07T06:51:40.268203Z","submitted_at":"2026-06-22T17:48:07Z","title":"Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-06-26T05:52:40.175977Z"},"links":{"cited_paper":"/paper/2110.15335","citing_paper":"/paper/2606.23662"},"observation_digest":"sha256:b947b8b08c1671f942144b91f7bbadec3d4d3d6b3a62470d57c15ba6975181b3","observation_id":"f35fc61c-24e1-4fb2-9dbf-1c7a1dbc1117","resolution":{"observed_at":"2026-07-04T12:49:52.754567Z","resolver_source":"arxiv_id","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"}}],"links":{"evidence":"/evidence","html":"/paper/2110.15335/citation-record","integrity":"/paper/2110.15335/integrity","json":"/paper/2110.15335/citation-record.json","paper":"/paper/2110.15335"},"outbound":[],"paper":{"arxiv_id":"2110.15335","last_updated":"2022-03-20T19:19:16Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T02:03:17.133731Z","submitted_at":"2021-10-28T17:47:31Z","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2110.15335."}