{"as_of":"2026-08-09T19:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bcfb88a4d54a085f762a39741b50c8eb1e797baa1c6367f372f3d7bf681520f2","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T00:56:24.164688Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-09T00:56:24.164688Z","title":"URL https://doi.org/10.48550/arXiv.2404.00551","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03795","last_updated":"2025-02-06T05:50:21Z","snapshot_observed_at":"2026-08-09T12:18:32.149182Z","submitted_at":"2025-02-06T05:50:21Z","title":"Distribution learning via neural differential equations: minimal energy regularization and approximation theory","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T00:56:24.164688Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2502.03795"},"observation_digest":"sha256:8d8296c639d1c8bf0151231ce63d5550b3b4b186d19821c231795d74c23b6152","observation_id":"0fb56a2c-638b-47c3-b5b2-809b98d1244b","resolution":{"observed_at":"2026-08-09T00:56:24.164688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-09T00:56:24.160700Z","title":"Y uan Gao, Jian Huang, Y uling Jiao, and Shurong Zheng","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.03795","last_updated":"2025-02-06T05:50:21Z","snapshot_observed_at":"2026-08-09T12:18:32.149182Z","submitted_at":"2025-02-06T05:50:21Z","title":"Distribution learning via neural differential equations: minimal energy regularization and approximation theory","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-09T00:56:24.160700Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2502.03795"},"observation_digest":"sha256:facf9e801082cbe2c46be2ffe9513ebaf1e21c56e0f40bd7db57a78e1853aada","observation_id":"3dae8e99-7908-4d8b-a877-4181574450d0","resolution":{"observed_at":"2026-08-09T00:56:24.160700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-05T20:56:33.499799Z","title":"Convergence of continuous normalizing flows for learning probability distributions","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.10944","last_updated":"2025-08-13T13:35:56Z","snapshot_observed_at":"2026-08-08T06:56:16.360636Z","submitted_at":"2025-08-13T13:35:56Z","title":"Non-asymptotic convergence bound of conditional diffusion models","version":1},"reference_index":68,"source":"arxiv_source","source_observed_at":"2026-08-05T20:56:33.499799Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2508.10944"},"observation_digest":"sha256:c771e20522628531910e211e789c8ec4a8a2f12cb2ac214b9177251ddec31e9a","observation_id":"cf3bee2c-0e53-4cea-9a84-77d00e847132","resolution":{"observed_at":"2026-08-05T20:56:33.499799Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":"2404.00551","doi":"10.48550/arxiv.2404.00551","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Convergence of continuous normalizing flows for learning probability distributions","venue":"arXiv (Cornell University)","work_id":"a1425d5a-e87f-4a79-898a-623e7f645834","year":2024},"citing_paper":{"arxiv_id":"2604.06065","last_updated":"2026-04-07T16:46:40Z","snapshot_observed_at":"2026-07-06T22:54:40.349479Z","submitted_at":"2026-04-07T16:46:40Z","title":"Lipschitz regularity in Flow Matching and Diffusion Models: sharp sampling rates and functional inequalities","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T18:15:58.587798Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2604.06065"},"observation_digest":"sha256:a1aa296189947600e500ec42e3c61193732f9ae0b2c34bcc82c65b34e21699f4","observation_id":"3a6a4cb7-77eb-4ad8-97c4-68c2d6cd2743","resolution":{"observed_at":"2026-05-10T20:30:47.473061Z","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":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":"2404.00551","doi":"10.48550/arxiv.2404.00551","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Convergence of continuous normalizing flows for learning probability distributions","venue":"arXiv (Cornell University)","work_id":"a1425d5a-e87f-4a79-898a-623e7f645834","year":2024},"citing_paper":{"arxiv_id":"2605.05890","last_updated":"2026-05-07T09:02:44Z","snapshot_observed_at":"2026-08-09T00:24:34.614252Z","submitted_at":"2026-05-07T09:02:44Z","title":"RepFlow: Representation Enhanced Flow Matching for Causal Effect Estimation","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-09T15:24:59.445688Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2605.05890"},"observation_digest":"sha256:65ff4ca0b5e89067f06e5a0b6f064fca4cb1714f20654ab62943841e345c982f","observation_id":"0a63b6b0-fdda-4f1f-bb25-647a6c5e5017","resolution":{"observed_at":"2026-05-11T16:41:14.855566Z","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":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":"2404.00551","doi":"10.48550/arxiv.2404.00551","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Convergence of continuous normalizing flows for learning probability distributions","venue":"arXiv (Cornell University)","work_id":"a1425d5a-e87f-4a79-898a-623e7f645834","year":2024},"citing_paper":{"arxiv_id":"2605.09291","last_updated":"2026-05-10T03:36:49Z","snapshot_observed_at":"2026-07-06T23:21:26.017420Z","submitted_at":"2026-05-10T03:36:49Z","title":"dFlowGRPO: Rate-Aware Policy Optimization for Discrete Flow Models","version":1},"reference_index":97,"source":"arxiv_source","source_observed_at":"2026-05-12T03:52:05.779559Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2605.09291"},"observation_digest":"sha256:b1a874ac2fa053ffd13bc7642dac5d31215708c883ab1bcb64deb6b958001a7c","observation_id":"95fcbade-640b-4095-aa61-9c03a8792f99","resolution":{"observed_at":"2026-05-12T06:51:29.657464Z","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":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":"2404.00551","doi":"10.48550/arxiv.2404.00551","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Convergence of continuous normalizing flows for learning probability distributions","venue":"arXiv (Cornell University)","work_id":"a1425d5a-e87f-4a79-898a-623e7f645834","year":2024},"citing_paper":{"arxiv_id":"2605.13063","last_updated":"2026-05-13T06:34:08Z","snapshot_observed_at":"2026-08-03T09:52:45.402828Z","submitted_at":"2026-05-13T06:34:08Z","title":"Ergodic Trajectory Design by Learned Pushforward Maps: Provable Coverage via Conditional Flow Matching","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-14T20:08:15.501474Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2605.13063"},"observation_digest":"sha256:2a7f0996fb28e21683e0d279ab6e5be6c36123f493b89ddcb3d48b102f5b5144","observation_id":"003a3f11-9ecd-457c-b465-87af59edb463","resolution":{"observed_at":"2026-05-14T20:09:26.345755Z","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":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":"2404.00551","doi":"10.48550/arxiv.2404.00551","metadata_source":"arxiv_reference","pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Convergence of continuous normalizing flows for learning probability distributions","venue":"arXiv (Cornell University)","work_id":"a1425d5a-e87f-4a79-898a-623e7f645834","year":2024},"citing_paper":{"arxiv_id":"2606.29105","last_updated":"2026-06-27T23:05:34Z","snapshot_observed_at":"2026-08-05T12:03:01.442002Z","submitted_at":"2026-06-27T23:05:34Z","title":"Panel Flow Matching: A Generative Approach to Learning Distributions of Longitudinal Data","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-06-30T08:20:03.575151Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2606.29105"},"observation_digest":"sha256:c51f18f811cca310fed878624d478f2119eb2852274bdcf5a200807b314ab850","observation_id":"767f599b-1f5c-4ddb-89a5-59f5c7054d21","resolution":{"observed_at":"2026-06-30T08:24:26.496061Z","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":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-08-01T20:13:45.161874Z","title":"arXiv preprint arXiv:2404.00551 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16725","last_updated":"2026-07-18T09:21:53Z","snapshot_observed_at":"2026-08-09T12:18:36.381441Z","submitted_at":"2026-07-18T09:21:53Z","title":"Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-01T20:13:45.161874Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2607.16725"},"observation_digest":"sha256:f2e97c99dbdadeb827b8f7df3e682953b0b2addcb995a542bc758409f8d4cbd8","observation_id":"41059bd0-ef53-4b3f-ad56-b39d70f01169","resolution":{"observed_at":"2026-08-01T20:13:45.161874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00551","last_updated":"2024-03-31T03:39:04Z","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00551","snapshot_observed_at":"2026-07-31T18:14:56.787787Z","title":"arXiv preprint arXiv:2404.00551 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.24324","last_updated":"2026-07-27T12:03:48Z","snapshot_observed_at":"2026-08-06T02:57:07.979643Z","submitted_at":"2026-07-27T12:03:48Z","title":"Diffusion Bootstrap for High-Dimensional Linear Models","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-07-31T18:14:56.787787Z"},"links":{"cited_paper":"/paper/2404.00551","citing_paper":"/paper/2607.24324"},"observation_digest":"sha256:2b67d9d5f2b58531d5accf8c5c02260c7ece662845daacdb3f7ed41e39cf2187","observation_id":"28585768-8f44-498c-a8d1-eff692d7b4d0","resolution":{"observed_at":"2026-07-31T18:14:56.787787Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2404.00551/citation-record","integrity":"/paper/2404.00551/integrity","json":"/paper/2404.00551/citation-record.json","paper":"/paper/2404.00551"},"outbound":[],"paper":{"arxiv_id":"2404.00551","last_updated":"2024-03-31T03:39:04Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-09T12:18:08.291760Z","submitted_at":"2024-03-31T03:39:04Z","title":"Convergence of Continuous Normalizing Flows for Learning Probability Distributions"},"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 10 inbound Pith citation observations for arXiv:2404.00551."}