{"as_of":"2026-08-14T06:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:740b056fd306ab85df25150448a9077860d4a2eb2361244a6e74339c9a054878","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:54:50.447194Z","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":"2026-07-01T14:15:47.194426Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.13394","last_updated":"2025-09-06T19:33:03Z","snapshot_observed_at":"2026-08-12T13:09:33.164348Z","submitted_at":"2025-02-19T03:09:18Z","title":"Flow-based generative models as iterative algorithms in probability space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13394","snapshot_observed_at":"2026-08-07T10:54:50.447194Z","title":"Flow-based generative models as iterative algorithms in proba- bility space","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.03979","last_updated":"2025-06-05T04:27:46Z","snapshot_observed_at":"2026-08-13T04:29:31.484159Z","submitted_at":"2025-06-04T14:09:25Z","title":"Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach","version":2},"reference_index":226,"source":"pdf_text","source_observed_at":"2026-08-07T10:54:50.447194Z"},"links":{"cited_paper":"/paper/2502.13394","citing_paper":"/paper/2506.03979"},"observation_digest":"sha256:3e2499bdb4e834d176e4b6d92e230ef02311e1fa3aea7cff17d7a1f4edb6e747","observation_id":"1a7ef1cc-85ca-4b79-986c-18f8d88bb609","resolution":{"observed_at":"2026-08-07T10:54:50.447194Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13394","last_updated":"2025-09-06T19:33:03Z","snapshot_observed_at":"2026-08-12T13:09:33.164348Z","submitted_at":"2025-02-19T03:09:18Z","title":"Flow-based generative models as iterative algorithms in probability space","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13394","snapshot_observed_at":"2026-08-07T00:49:49.371726Z","title":"Xie and X","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.13061","last_updated":"2025-08-14T01:01:23Z","snapshot_observed_at":"2026-08-13T05:10:56.361935Z","submitted_at":"2025-06-16T03:09:25Z","title":"Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models","version":3},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T00:49:49.371726Z"},"links":{"cited_paper":"/paper/2502.13394","citing_paper":"/paper/2506.13061"},"observation_digest":"sha256:981de7387b1d7681a4313320710e39e38711743f0ef5e2766238087b47658b9b","observation_id":"439d6922-5e2e-4467-abfe-1129a68d8577","resolution":{"observed_at":"2026-08-07T00:49:49.371726Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13394","last_updated":"2025-09-06T19:33:03Z","snapshot_observed_at":"2026-08-12T13:09:33.164348Z","submitted_at":"2025-02-19T03:09:18Z","title":"Flow-based generative models as iterative algorithms in probability space","version":2},"cited_work":{"arxiv_id":"2502.13394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.13394","snapshot_observed_at":"2026-07-01T14:15:47.194426Z","title":"Flow-based generative models as iterative algorithms in probability space","venue":null,"work_id":"1e0d152e-08d0-49e6-a100-9a550db8600c","year":2025},"citing_paper":{"arxiv_id":"2605.11755","last_updated":"2026-05-26T21:22:24Z","snapshot_observed_at":"2026-08-13T18:09:11.125525Z","submitted_at":"2026-05-12T08:29:44Z","title":"One-Step Generative Modeling via Wasserstein Gradient Flows","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-13T07:45:39.888214Z"},"links":{"cited_paper":"/paper/2502.13394","citing_paper":"/paper/2605.11755"},"observation_digest":"sha256:53aa6ab5b141dd5297d2ff9f914da15f4922b28bdb3da0c86cba7f5e6da58815","observation_id":"f17c4be7-eccb-4f54-847b-83296e6c6f70","resolution":{"observed_at":"2026-05-13T07:47:33.112764Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13394","last_updated":"2025-09-06T19:33:03Z","snapshot_observed_at":"2026-08-12T13:09:33.164348Z","submitted_at":"2025-02-19T03:09:18Z","title":"Flow-based generative models as iterative algorithms in probability space","version":2},"cited_work":{"arxiv_id":"2502.13394","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.13394","snapshot_observed_at":"2026-07-01T14:15:47.194426Z","title":"Flow-based generative models as iterative algorithms in probability space","venue":null,"work_id":"1e0d152e-08d0-49e6-a100-9a550db8600c","year":2025},"citing_paper":{"arxiv_id":"2605.11755","last_updated":"2026-05-26T21:22:24Z","snapshot_observed_at":"2026-08-13T18:09:11.125525Z","submitted_at":"2026-05-12T08:29:44Z","title":"One-Step Generative Modeling via Wasserstein Gradient Flows","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-06-30T22:09:22.474650Z"},"links":{"cited_paper":"/paper/2502.13394","citing_paper":"/paper/2605.11755"},"observation_digest":"sha256:695600890dba6b25a39cd3ff698bc87647d6844b5cb42800f06370c1b3cfe9d0","observation_id":"f7d0f421-5237-4878-8395-97465fe54c26","resolution":{"observed_at":"2026-07-01T14:15:47.195907Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.13394/citation-record","integrity":"/paper/2502.13394/integrity","json":"/paper/2502.13394/citation-record.json","paper":"/paper/2502.13394"},"outbound":[],"paper":{"arxiv_id":"2502.13394","last_updated":"2025-09-06T19:33:03Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T13:09:33.164348Z","submitted_at":"2025-02-19T03:09:18Z","title":"Flow-based generative models as iterative algorithms in probability space"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2502.13394."}