{"as_of":"2026-08-07T21:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c337419a7ef56cdf87ee7c654c399fc564681df907dec4fa75f6b7c3d313b889","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T17:05:14.409671Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T02:41:55.266846Z","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":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.11842","snapshot_observed_at":"2026-08-03T02:41:55.266846Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2602.10172","last_updated":"2026-06-08T04:06:41Z","snapshot_observed_at":"2026-08-06T15:02:38.491421Z","submitted_at":"2026-02-10T15:47:18Z","title":"Cosmo3DFlow: Wavelet Flow Matching for Spatial-to-Spectral Compression in Reconstructing the Early Universe","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T02:41:55.266846Z"},"links":{"cited_paper":"/paper/2507.11842","citing_paper":"/paper/2602.10172"},"observation_digest":"sha256:de58385457d2ee9d371159ec551a0c6eab51663fcab0913ea1dddd89109ea44f","observation_id":"d58e4073-94a9-4567-a324-f000d1d3eec3","resolution":{"observed_at":"2026-08-03T02:41:55.266846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"cited_work":{"arxiv_id":"2507.11842","doi":"10.48550/arxiv.2507.11842","metadata_source":"arxiv_reference","pith_arxiv_id":"2507.11842","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kannan, T","venue":"ArXiv.org","work_id":"d256726d-30dc-41a2-9f38-1584ac8c25f4","year":2025},"citing_paper":{"arxiv_id":"2605.23114","last_updated":"2026-05-22T00:20:49Z","snapshot_observed_at":"2026-07-06T23:33:24.215365Z","submitted_at":"2026-05-22T00:20:49Z","title":"Increasing the Precision of Surrogate Models for Weak Lensing Mass Maps with Flow Matching","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-25T03:57:18.799003Z"},"links":{"cited_paper":"/paper/2507.11842","citing_paper":"/paper/2605.23114"},"observation_digest":"sha256:5c810f8333d8f7cf79d6d862dad0b63852b6f8dfc8199dc22b91f627e0d2b798","observation_id":"d0f52779-ea1f-44bd-bc73-23bcef1d6f93","resolution":{"observed_at":"2026-05-25T04:00:19.186134Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"cited_work":{"arxiv_id":"2507.11842","doi":"10.48550/arxiv.2507.11842","metadata_source":"arxiv_reference","pith_arxiv_id":"2507.11842","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kannan, T","venue":"ArXiv.org","work_id":"d256726d-30dc-41a2-9f38-1584ac8c25f4","year":2025},"citing_paper":{"arxiv_id":"2606.10023","last_updated":"2026-06-08T18:08:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-08T18:08:00Z","title":"Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-06-27T15:22:40.822607Z"},"links":{"cited_paper":"/paper/2507.11842","citing_paper":"/paper/2606.10023"},"observation_digest":"sha256:ff917c8cf1b1d435aa9ee6be2985fbb3741c886cf20367f4b441d0e5f2d44c74","observation_id":"9588ac08-a813-4af9-97bb-7852630118c4","resolution":{"observed_at":"2026-06-27T19:11:10.735884Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.11842/citation-record","integrity":"/paper/2507.11842/integrity","json":"/paper/2507.11842/citation-record.json","paper":"/paper/2507.11842"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08797","last_updated":"2025-10-09T00:43:44Z","snapshot_observed_at":"2026-07-06T15:03:52.079498Z","submitted_at":"2023-03-15T17:43:42Z","title":"Stochastic Interpolants: A Unifying Framework for Flows and Diffusions","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08797","snapshot_observed_at":"2026-08-06T17:05:12.837091Z","title":"arXiv:2303.08797 [cs.LG] https://arxiv.org/ abs/2303.08797 Sambatra Andrianomena and Sultan Hassan","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:12.837091Z"},"links":{"cited_paper":"/paper/2303.08797","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:2b500d604f416dc74af600d43b47403c42504022b057beedae46743a0dd55bfa","observation_id":"12f3bc74-8ad1-4036-94f8-73bc57327793","resolution":{"observed_at":"2026-08-06T17:05:12.837091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00799","last_updated":"2023-11-01T19:33:52Z","snapshot_observed_at":"2026-07-06T16:41:51.334062Z","submitted_at":"2023-11-01T19:33:52Z","title":"Latent space representations of cosmological fields","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00799","snapshot_observed_at":"2026-08-06T17:05:12.842353Z","title":"arXiv:2311.00799 [astro-ph.CO] https://arxiv.org/abs/2311.00799 Johannes Ball´ e, Valero Laparra, and Eero P Simoncelli","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:12.842353Z"},"links":{"cited_paper":"/paper/2311.00799","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:29369366d96803b44a9d108feae062d4d23cd2c075cce4c2834fe14ef1add9ec","observation_id":"6e283e73-a263-485d-94b5-f8045e1cc6fd","resolution":{"observed_at":"2026-08-06T17:05:12.842353Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:05:14.770480Z","title":"Advances in neural information processing systems 31 (2018)","venue":null,"work_id":"269403ee-f2e9-40e1-837f-6f50e4406777","year":2018},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.148717Z"},"links":{"citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:34008833ea8d8a39509cf16a99164d6bef77292b45f3ffd9386ad4eb267032ca","observation_id":"0e184abf-4cd4-456a-942e-e5394897cc15","resolution":{"observed_at":"2026-08-06T17:05:14.775239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17901","last_updated":"2023-11-29T18:53:34Z","snapshot_observed_at":"2026-07-06T16:54:33.882767Z","submitted_at":"2023-11-29T18:53:34Z","title":"SODA: Bottleneck Diffusion Models for Representation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17901","snapshot_observed_at":"2026-08-06T17:05:13.234629Z","title":"arXiv:2311.17901 [cs.CV] https: //arxiv.org/abs/2311.17901 Diederik P Kingma and Max Welling","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.234629Z"},"links":{"cited_paper":"/paper/2311.17901","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:f66e3eed7b74126a957918313bc8763dad335c8d42a927457d88f82c7cf5c94d","observation_id":"c09c42e4-98d8-407c-8d49-467beebf58a5","resolution":{"observed_at":"2026-08-06T17:05:13.234629Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02747","last_updated":"2023-02-08T15:46:05Z","snapshot_observed_at":"2026-08-02T18:24:58.914589Z","submitted_at":"2022-10-06T08:32:20Z","title":"Flow Matching for Generative Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02747","snapshot_observed_at":"2026-08-06T17:05:13.431842Z","title":"arXiv:2210.02747 [cs.LG] https://arxiv.org/abs/2210","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.431842Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:a8b27facea772a1af90b12d65a15fe4f3f92fdb41c8c24faf429cb4c79ae437e","observation_id":"7d97044e-32d5-44cf-8c10-154b51b2dbd5","resolution":{"observed_at":"2026-08-06T17:05:13.431842Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02096","last_updated":"2023-04-04T19:45:04Z","snapshot_observed_at":"2026-07-06T15:12:18.426501Z","submitted_at":"2023-04-04T19:45:04Z","title":"The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02096","snapshot_observed_at":"2026-08-06T17:05:13.683830Z","title":"arXiv:2304.02096 [astro-ph.CO] https://arxiv.org/abs/2304.02096 Olaf Ronneberger, Philipp Fischer, and Thomas Brox","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.683830Z"},"links":{"cited_paper":"/paper/2304.02096","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:4a98ebe0bd5eb9f1cdd6ce90f7fdaff5addfbb7818a1616ab8c38363f08e373a","observation_id":"41393bd0-2aa8-446b-a9c2-fd56306c79d5","resolution":{"observed_at":"2026-08-06T17:05:13.683830Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.09747","last_updated":"2021-09-20T18:00:01Z","snapshot_observed_at":"2026-07-06T11:49:38.497583Z","submitted_at":"2021-09-20T18:00:01Z","title":"Multifield Cosmology with Artificial Intelligence","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.09747","snapshot_observed_at":"2026-08-06T17:05:14.113855Z","title":"arXiv preprint arXiv:2109.09747 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:14.113855Z"},"links":{"cited_paper":"/paper/2109.09747","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:a680f85203c3b8c94052b82982bf8981d59baf22260c9e81cba8eebde89d9d13","observation_id":"7f5e220b-94d6-44c9-a963-d3d11a386f72","resolution":{"observed_at":"2026-08-06T17:05:14.113855Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:05:14.743314Z","title":"Advances in Neural Information Processing Systems 36 (2023), 64971–64995","venue":null,"work_id":"c1cc6c20-2685-4a7d-b4d0-6c4c8ca15629","year":2023},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:14.190974Z"},"links":{"citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:b0e4553af5765af58c45ff75e1c5efc5a2959b0e1cc35ce9d0ad21a260e3bd5c","observation_id":"7c9b6b2d-5883-4e29-be53-42dee2947621","resolution":{"observed_at":"2026-08-06T17:05:14.747386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11430","last_updated":"2024-01-21T08:35:25Z","snapshot_observed_at":"2026-07-06T17:18:24.209069Z","submitted_at":"2024-01-21T08:35:25Z","title":"Exploring Diffusion Time-steps for Unsupervised Representation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.11430","snapshot_observed_at":"2026-08-06T17:05:14.291391Z","title":"arXiv preprint arXiv:2401.11430 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:14.291391Z"},"links":{"cited_paper":"/paper/2401.11430","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:5f4eca95638240d7760137310e525fba9014894c5fde010792f368f58cb29131","observation_id":"04cc12ab-9bb3-445b-8ea5-4128bcafbb3d","resolution":{"observed_at":"2026-08-06T17:05:14.291391Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:05:14.726915Z","title":"The decoder mirrors the encoder in architecture, and is composed of 4 upsampling convolutions each followed by an inverse GDN layer","venue":null,"work_id":"792219da-d396-4348-8fe3-dd191f7156e7","year":2019},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:14.409671Z"},"links":{"citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:f55eaaf083633d525ba3213c6c5fdd9ee2246c7c118f7c6f7c625ee996159770","observation_id":"ae79a379-bd8d-42c0-9262-e47e0be31e32","resolution":{"observed_at":"2026-08-06T17:05:14.733193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T17:05:14.756493Z","title":"In Medical image computing and computer-assisted intervention– MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18","venue":null,"work_id":"afa1cdc0-1298-4a65-b84a-9416526c1625","year":2015},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.782602Z"},"links":{"citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:1888457f39437ba30a7450c677c9aa83bb698a0f4244ecee8718270ae098cafb","observation_id":"c8a39434-92d2-490a-a88b-39b3cc7ec6ae","resolution":{"observed_at":"2026-08-06T17:05:14.761118Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01704","last_updated":"2017-03-03T14:53:13Z","snapshot_observed_at":"2026-08-06T14:10:12.161882Z","submitted_at":"2016-11-05T21:39:53Z","title":"End-to-end Optimized Image Compression","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01704","snapshot_observed_at":"2026-08-06T17:05:12.907724Z","title":"arXiv preprint arXiv:1611.01704 (2016)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:12.907724Z"},"links":{"cited_paper":"/paper/1611.01704","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:1100a0b872d5d7fdc1f5d506d70b9e3720b1dac5933b3eaccddaf3d59e33bc6d","observation_id":"8aa129cd-e6a4-4788-80a9-c293eecc02a0","resolution":{"observed_at":"2026-08-06T17:05:12.907724Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.01436","last_updated":"2018-05-01T05:30:29Z","snapshot_observed_at":"2026-08-02T09:34:56.255411Z","submitted_at":"2018-02-01T00:42:29Z","title":"Variational image compression with a scale hyperprior","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.01436","snapshot_observed_at":"2026-08-06T17:05:13.040965Z","title":"arXiv preprint arXiv:1802.01436 (2018)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.040965Z"},"links":{"cited_paper":"/paper/1802.01436","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:dc3c518517b29cdaa7281c0afc4550357e588bad7a15f74cdc45c4b7837936ed","observation_id":"be6c071f-3089-45a8-b68b-24875da04c21","resolution":{"observed_at":"2026-08-06T17:05:13.040965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-06T17:05:13.610697Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.610697Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:0cef5daaf88b8282aaad7d7193f1b6c7d9096519823eb7907f1f0beaabcdd706","observation_id":"592a8d2b-3f37-4270-9619-63b77b668dbb","resolution":{"observed_at":"2026-08-06T17:05:13.610697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-06T17:05:13.896984Z","title":"arXiv preprint arXiv:2010.02502 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.896984Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:3db9cf1c2e5a373a5b0d6ac5e6a199dc875ae215da4eae623893793906618ef7","observation_id":"76d033ff-b7b5-44a8-aa65-a40d35372922","resolution":{"observed_at":"2026-08-06T17:05:13.896984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-06T17:05:14.010962Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:14.010962Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:0d420d625bfb3c9340f0289f531430628e979cf6a430e50b6c5329812e9a6ec4","observation_id":"94549db5-9494-4c1b-85ae-b08cbe9d7ad4","resolution":{"observed_at":"2026-08-06T17:05:14.010962Z","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":"2026-07-06T02:11:23.670680Z","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":"2026-08-06T17:05:13.340050Z","title":"arXiv:1312.6114 [stat.ML] https://arxiv.org/abs/1312.6114 Yaron Lipman, Ricky T","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.340050Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:c061251d4f10898f1db273609f9fe0bfdea2964dbfe5a4d0eda000fd84946671","observation_id":"cfd07257-d07d-4ebc-9de1-c63cd986f414","resolution":{"observed_at":"2026-08-06T17:05:13.340050Z","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":"2026-08-06T17:05:12.800316Z","title":"2023), 7459–7481","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:12.800316Z"},"links":{"citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:e3fef40a8a6d5177390ecdec81ed929743e52752e5ecb5a5e802ac39ab6aa2f9","observation_id":"b1e5122c-20dc-4875-9ebe-7ddafda9f86d","resolution":{"observed_at":"2026-08-06T17:05:12.800316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.06264","last_updated":"2024-12-09T07:22:38Z","snapshot_observed_at":"2026-08-02T09:54:14.339169Z","submitted_at":"2024-12-09T07:22:38Z","title":"Flow Matching Guide and Code","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.06264","snapshot_observed_at":"2026-08-06T17:05:13.544526Z","title":"arXiv:2412.06264 [cs.LG] https://arxiv.org/abs/2412.06264 7 Ilya Loshchilov and Frank Hutter","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T17:05:13.544526Z"},"links":{"cited_paper":"/paper/2412.06264","citing_paper":"/paper/2507.11842"},"observation_digest":"sha256:050341ff14ed0f495e38614effcd83feeacdc1d7b69d49f6b9ce862fb783c155","observation_id":"9b1a9476-797f-4f87-98b6-97bc2c77b386","resolution":{"observed_at":"2026-08-06T17:05:13.544526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.11842","last_updated":"2025-07-16T02:15:31Z","latest_version":1,"primary_category":"astro-ph.CO","snapshot_observed_at":"2026-08-07T10:37:09.298489Z","submitted_at":"2025-07-16T02:15:31Z","title":"CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":4},"total_outbound_references":19},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 3 inbound Pith citation observations for arXiv:2507.11842."}