{"as_of":"2026-08-19T22:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:53ca80caa605cd444dbf862139b54fc5a298d36d132d7250541242237f93641d","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":"2026-08-19T06:32:44.657259+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":"2026-08-07T11:40:58.184550Z","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":"2026-05-20T22:23:48.215700Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.10649","last_updated":"2024-07-03T15:23:42Z","snapshot_observed_at":"2026-08-18T18:27:14.300762Z","submitted_at":"2023-10-16T17:59:54Z","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.10649","snapshot_observed_at":"2026-08-07T11:40:58.184550Z","title":"A computational framework for solving Wasserstein Lagrangian flows.arXiv preprint arXiv:2310.10649, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.01904","last_updated":"2025-06-02T17:25:03Z","snapshot_observed_at":"2026-08-16T13:05:09.545873Z","submitted_at":"2025-06-02T17:25:03Z","title":"Machine-Learned Sampling of Conditioned Path Measures","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:40:58.184550Z"},"links":{"cited_paper":"/paper/2310.10649","citing_paper":"/paper/2506.01904"},"observation_digest":"sha256:b3cd632a43ef1764730e0f231db535beb7acba55037119b93a2c696ad16d497d","observation_id":"823245e7-073f-4793-846e-072164e64d1d","resolution":{"observed_at":"2026-08-07T11:40:58.184550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10649","last_updated":"2024-07-03T15:23:42Z","snapshot_observed_at":"2026-08-18T18:27:14.300762Z","submitted_at":"2023-10-16T17:59:54Z","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows","version":3},"cited_work":{"arxiv_id":"2310.10649","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.10649","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2310.10649 , year=","venue":null,"work_id":"6bcb921c-f660-4afb-861f-cf5036ec3d7d","year":2023},"citing_paper":{"arxiv_id":"2605.07319","last_updated":"2026-05-08T06:28:23Z","snapshot_observed_at":"2026-08-17T04:29:35.944740Z","submitted_at":"2026-05-08T06:28:23Z","title":"Generative Modeling with Flux Matching","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-11T01:28:59.133443Z"},"links":{"cited_paper":"/paper/2310.10649","citing_paper":"/paper/2605.07319"},"observation_digest":"sha256:4bbf23a978450c765e2500f49fb246f56ed9a5fd2ae26abf2b83108ea032ef24","observation_id":"224f3d13-d0b8-4991-9f14-92c19d475995","resolution":{"observed_at":"2026-05-11T01:45:52.166687Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10649","last_updated":"2024-07-03T15:23:42Z","snapshot_observed_at":"2026-08-18T18:27:14.300762Z","submitted_at":"2023-10-16T17:59:54Z","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows","version":3},"cited_work":{"arxiv_id":"2310.10649","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.10649","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2310.10649 , year=","venue":null,"work_id":"6bcb921c-f660-4afb-861f-cf5036ec3d7d","year":2023},"citing_paper":{"arxiv_id":"2605.08550","last_updated":"2026-05-16T02:31:48Z","snapshot_observed_at":"2026-08-12T12:26:02.413866Z","submitted_at":"2026-05-08T23:21:17Z","title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","version":1},"reference_index":159,"source":"arxiv_source","source_observed_at":"2026-05-12T01:20:12.816671Z"},"links":{"cited_paper":"/paper/2310.10649","citing_paper":"/paper/2605.08550"},"observation_digest":"sha256:566b6965cb8ecf5ddd7dad603b629658094ad1790d01d653ff517d46a5c17b90","observation_id":"ba5bbfbe-0ca9-40cc-b701-f0f4c039c8e4","resolution":{"observed_at":"2026-05-12T08:06:26.856069Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10649","last_updated":"2024-07-03T15:23:42Z","snapshot_observed_at":"2026-08-18T18:27:14.300762Z","submitted_at":"2023-10-16T17:59:54Z","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows","version":3},"cited_work":{"arxiv_id":"2310.10649","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.10649","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2310.10649 , year=","venue":null,"work_id":"6bcb921c-f660-4afb-861f-cf5036ec3d7d","year":2023},"citing_paper":{"arxiv_id":"2605.08550","last_updated":"2026-05-16T02:31:48Z","snapshot_observed_at":"2026-08-12T12:26:02.413866Z","submitted_at":"2026-05-08T23:21:17Z","title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","version":2},"reference_index":159,"source":"arxiv_source","source_observed_at":"2026-05-14T20:49:26.890463Z"},"links":{"cited_paper":"/paper/2310.10649","citing_paper":"/paper/2605.08550"},"observation_digest":"sha256:5f7a8882ddc6b7e6426c3d2161ef19d9fbe28133344a1efc78f2b2f56928b4b1","observation_id":"239eabde-51b0-4ceb-96bb-0220a95193e8","resolution":{"observed_at":"2026-05-14T20:52:59.169432Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.10649","last_updated":"2024-07-03T15:23:42Z","snapshot_observed_at":"2026-08-18T18:27:14.300762Z","submitted_at":"2023-10-16T17:59:54Z","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows","version":3},"cited_work":{"arxiv_id":"2310.10649","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.10649","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"arXiv preprint arXiv:2310.10649 , year=","venue":null,"work_id":"6bcb921c-f660-4afb-861f-cf5036ec3d7d","year":2023},"citing_paper":{"arxiv_id":"2605.08550","last_updated":"2026-05-16T02:31:48Z","snapshot_observed_at":"2026-08-12T12:26:02.413866Z","submitted_at":"2026-05-08T23:21:17Z","title":"A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots","version":3},"reference_index":159,"source":"arxiv_source","source_observed_at":"2026-05-20T22:20:03.150386Z"},"links":{"cited_paper":"/paper/2310.10649","citing_paper":"/paper/2605.08550"},"observation_digest":"sha256:06c2d9eb583d51391dd0f863e0d69709cdb7d49f1eb71ac155b4d037bbf7568a","observation_id":"5e128c48-4718-4769-aaf0-d48a99b4704f","resolution":{"observed_at":"2026-05-20T22:23:48.218777Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.10649/citation-record","integrity":"/paper/2310.10649/integrity","json":"/paper/2310.10649/citation-record.json","paper":"/paper/2310.10649"},"outbound":[],"paper":{"arxiv_id":"2310.10649","last_updated":"2024-07-03T15:23:42Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-18T18:27:14.300762Z","submitted_at":"2023-10-16T17:59:54Z","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2310.10649."}