{"as_of":"2026-08-15T18:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:34c77f2b8f228524c583c9ea89b59c6e76e9ea8d419a9c38725ceb4ac717db9d","coverage":[{"denominator":92,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":92,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T16:55:41.844803Z","state":"measured"},{"denominator":92,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":92,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2608.05103/citation-record","integrity":"/paper/2608.05103/integrity","json":"/paper/2608.05103/citation-record.json","paper":"/paper/2608.05103"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T16:55:41.475636Z","title":"Toward unification of the multiscale modeling of the atmosphere.Atmospheric Chemistry and Physics, 11(8):3731–3742, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.475636Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:d09129117352c44f615c35c0fa495ba61375b671214504a0b793a4850864c7ac","observation_id":"bf93e8ac-b02a-4cc6-a206-5c776a7ff692","resolution":{"observed_at":"2026-08-08T16:55:41.475636Z","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":"2026-08-08T16:55:41.479904Z","title":"Springer Science & Business Media, 2003","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.479904Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:9e702bdb0a499247b6245e4834c4be61de0f72580f1bf44fc1f8a81543b5fb0e","observation_id":"8896efd4-8bb9-4b3a-9fc4-c263054fc8c0","resolution":{"observed_at":"2026-08-08T16:55:41.479904Z","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":"2026-08-08T16:55:41.484420Z","title":"Multiscale cloud system modeling.Reviews of Geophysics, 47(4), 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.484420Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:bdafa0845d10e46b1d916ee1244b9398ceb714056f00c25c019da730659ca950","observation_id":"08efbea0-fe80-4c2a-9884-ee3c826ed306","resolution":{"observed_at":"2026-08-08T16:55:41.484420Z","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":"2026-08-08T16:55:41.488394Z","title":"Satellite data assimilation in numerical weather prediction: An overview","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.488394Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:7f197571c220e181ff477d85479cecb325b43351a2b0ed175023693023109ed1","observation_id":"a735467b-5850-46b2-8834-3b60eab71c87","resolution":{"observed_at":"2026-08-08T16:55:41.488394Z","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":"2026-08-08T16:55:41.492521Z","title":"Observing-system experiments in the ecmwf 4d-var data assimilation system","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.492521Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:a002e7411c0a99c0417a030b14ddc776f6ca377c3681c844cfa69b0fa77a486f","observation_id":"9c3da922-e061-4c84-9760-022d06c245d3","resolution":{"observed_at":"2026-08-08T16:55:41.492521Z","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":"2026-08-08T16:55:41.496612Z","title":"A bayesian tutorial for data assimilation.Physica D: Nonlinear Phenomena, 230(1-2):1–16, 2007","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.496612Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:375cb7b4e2e46a06b420d048d7aa435ce8e33b8b504ef7c5247991c9283605dc","observation_id":"60615260-c0ea-474d-bc26-6067536953fc","resolution":{"observed_at":"2026-08-08T16:55:41.496612Z","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":"2026-08-08T16:55:41.500840Z","title":"Evaluating data assimilation algorithms.Monthly weather review, 140(11):3757–3782, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.500840Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:7022c3e39540043e4aad79bec5c551bfb00a302adf8e319ecc7dce5f1a4742b0","observation_id":"041508e1-2aa6-421a-8727-bf64f5c0b039","resolution":{"observed_at":"2026-08-08T16:55:41.500840Z","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":"2026-08-08T16:55:41.504893Z","title":"The era5 global reanalysis.Quarterly journal of the royal meteorological society, 146(730):1999–2049, 2020","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.504893Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:914ba42e9fdb30f8c42d081367ac25f7519a3698f6b5f626fb2eae0ecd2fb3fe","observation_id":"f548b00c-ed39-493e-8437-b31f62aeeace","resolution":{"observed_at":"2026-08-08T16:55:41.504893Z","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":"2026-08-08T16:55:41.509001Z","title":"World Scientific, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.509001Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:6718092bb175d3e3e8587c66aae0008589476f1292fac2be4938cb9d9c30450f","observation_id":"83c2273f-12f1-4231-b2cc-6add1309c5f0","resolution":{"observed_at":"2026-08-08T16:55:41.509001Z","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":"2026-08-08T16:55:41.513009Z","title":"Data assimilation and its applications.Proceedings of the National Academy of Sciences, 97(21):11143–11144, 2000","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.513009Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:814a4778f820cfc53205c4598e4a3d063ff498106aa0e9fc9ff7d838ad81586b","observation_id":"c81e8e4f-ac12-4644-927e-ab74cd68625f","resolution":{"observed_at":"2026-08-08T16:55:41.513009Z","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":"2026-08-08T16:55:41.517247Z","title":"Data assimilation methods in the earth sciences.Advances in water resources, 31(11):1411–1418, 2008","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.517247Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:7c3239ac288fa29a49dd7a9c2941a8de2567d2ab893e06d9f6ac82512d1d9c19","observation_id":"e83d361a-462c-4fe4-97d9-e8073115433d","resolution":{"observed_at":"2026-08-08T16:55:41.517247Z","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":"2026-08-08T16:55:41.521191Z","title":"Learning deep generative models.Annual Review of Statistics and Its Application, 2(1):361–385, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.521191Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:18b9b86042a08d56ad31561175d146d2a8f15ec5a87c7d5a9d0b2e2844d62db9","observation_id":"121eb791-6936-4c6c-8f00-5382438f0022","resolution":{"observed_at":"2026-08-08T16:55:41.521191Z","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":"2026-08-08T16:55:41.524891Z","title":"Generative learning for forecasting the dynamics of high-dimensional complex systems.Nature Communications, 15(1):8904, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.524891Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:583637443b8ccba07171c7322542bccea88ae88f1072079236b3771463da39e4","observation_id":"6a9accd2-9bb2-4990-94cc-f8527bb5966b","resolution":{"observed_at":"2026-08-08T16:55:41.524891Z","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":"2026-08-08T16:55:41.528609Z","title":"Probabilistic weather forecasting with machine learning.Nature, 637(8044):84–90, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.528609Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:5b3c90540ed901a4e59e30657eea1e00cfd550d688efe759b8fab5bdbc16291c","observation_id":"8d295466-0cf9-43db-af34-e9b2ed5b0c45","resolution":{"observed_at":"2026-08-08T16:55:41.528609Z","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":"2026-08-08T16:55:41.532005Z","title":"Maximum likelihood training of score-based diffusion models.Advances in neural information processing systems, 34:1415–1428, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.532005Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:09621f03e8449b6b32ce235e790174fb70c04dea45fea21e00dbd1fbac462423","observation_id":"164f4c37-90b2-4c34-9de3-f8c662d1f836","resolution":{"observed_at":"2026-08-08T16:55:41.532005Z","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":"2026-08-08T16:55:41.535462Z","title":"Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456, 2020","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.535462Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:b18291cc22e1c923607dcb83d8a845d387530b4d5de4be3f067c5fb74732a4f4","observation_id":"4200731f-f78e-49e7-805e-3bc71eca00cb","resolution":{"observed_at":"2026-08-08T16:55:41.535462Z","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":"2026-08-08T16:55:41.539420Z","title":"Elucidating the design space of diffusion-based generative models.Advances in neural information processing systems, 35:26565–26577, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.539420Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:b927b1ec3a548f5134b3366bdb54caa568c76ad974a34446803170924d29e43b","observation_id":"30d0e2ef-21c4-487a-9842-90411a3147ea","resolution":{"observed_at":"2026-08-08T16:55:41.539420Z","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":"2026-08-08T16:55:41.542925Z","title":"Flow matching for generative modeling.arXiv preprint arXiv:2210.02747, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.542925Z"},"links":{"cited_paper":"/paper/2210.02747","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:1ccb5432cbd4d6411187e626435a0cc87a944872ee88716d4d3f42b6d49db8f4","observation_id":"c9918ba3-ce81-4c39-b114-8bb717be06c4","resolution":{"observed_at":"2026-08-08T16:55:41.542925Z","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":"2026-08-08T16:55:41.547502Z","title":"Scaling rectified flow transformers for high-resolution image synthesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.547502Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:e4cc583174679f313b43fe8d31905cb13882ec40ca501c000bf93634aff64fe1","observation_id":"67d907b2-97ef-4ab9-89ac-cd2a2431e800","resolution":{"observed_at":"2026-08-08T16:55:41.547502Z","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":"2026-08-08T16:55:41.550912Z","title":"Diffusion models as plug-and-play priors.Advances in Neural Information Processing Systems, 35:14715–14728, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.550912Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:f8221f2d7da8a0641fdced4d665c8d01fdd8ab8bcc5292a60c42ca654f1875a6","observation_id":"377f2a29-399a-4fe6-9e9a-ab7b7ed5e2bd","resolution":{"observed_at":"2026-08-08T16:55:41.550912Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.14687","last_updated":"2024-05-20T04:23:45Z","snapshot_observed_at":"2026-08-14T03:13:28.801183Z","submitted_at":"2022-09-29T11:12:27Z","title":"Diffusion Posterior Sampling for General Noisy Inverse Problems","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.14687","snapshot_observed_at":"2026-08-08T16:55:41.554475Z","title":"Diffusion posterior sampling for general noisy inverse problems.arXiv preprint arXiv:2209.14687, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.554475Z"},"links":{"cited_paper":"/paper/2209.14687","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:2de3125dc999e4604f47bc16551816e3d4646fda8ffc026dd1bac4cdfd43caea","observation_id":"250f8aa9-edd0-463f-bf4d-e7e463f16b10","resolution":{"observed_at":"2026-08-08T16:55:41.554475Z","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":"2026-08-08T16:55:42.667605Z","title":"Multimodal atmospheric super-resolution with deep generative models.Machine Learning: Earth, 2(1):015001, 2026","venue":null,"work_id":"a56cd97b-3f5d-40ae-890d-163bf9d4b365","year":2026},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.558760Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:d86bc0fede7a4ff77abc77d56b33c26a4ba2a9512bb663a67db75b3973cf6c9d","observation_id":"df1ca2eb-22f2-47c9-bd04-c81c52c3124f","resolution":{"observed_at":"2026-08-08T16:55:42.674736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T16:55:41.562159Z","title":"Simplifying, stabilizing and scaling continuous-time consistency models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.562159Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:99c4095d2db20436e03889b6861a67e7613caf06cfbcc6021f933c77e0ce282e","observation_id":"b427cf66-3290-4667-9a7b-ac27349ad65c","resolution":{"observed_at":"2026-08-08T16:55:41.562159Z","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":"2026-08-08T16:55:41.565964Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.565964Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:f4b88b1fb0ac4f736896eb499de730b6f439f62dc763a15e22e5dc035a5ce1ed","observation_id":"61572849-b544-455d-9ad8-101ff87f2c95","resolution":{"observed_at":"2026-08-08T16:55:41.565964Z","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":"2026-08-08T16:55:41.569655Z","title":"Overview of the integrated global radiosonde archive.Journal of Climate, 19(1):53–68, 2006","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.569655Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:2f5c43d52ed009971af06b3db36a6e02e0e1794a6a6cd1072708d01da49d68d5","observation_id":"972be2bb-3377-47d2-8fc2-0a64735da082","resolution":{"observed_at":"2026-08-08T16:55:41.569655Z","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":"2026-08-08T16:55:41.573850Z","title":"The integrated surface database: Recent developments and partnerships","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.573850Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:54c9e49c30f3814ef8979a5c073ec2b3fd0df49961afce07fc51283848ad2d41","observation_id":"6b1dc16e-fb85-48e4-bcd0-a87b6ae23d5a","resolution":{"observed_at":"2026-08-08T16:55:41.573850Z","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":"2026-08-08T16:55:41.578108Z","title":"Icoads release 3.0: a major update to the historical marine climate record.International Journal of Climatology, 37(5):2211–2232, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.578108Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:58753c526be214f77e6a5f09f78bed22f8e6768350ffec6ef1ed6790f0aea0a0","observation_id":"5dbf0c90-f049-4396-907f-18537a142959","resolution":{"observed_at":"2026-08-08T16:55:41.578108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15687","last_updated":"2024-12-20T09:06:14Z","snapshot_observed_at":"2026-08-12T22:51:59.533658Z","submitted_at":"2024-12-20T09:06:14Z","title":"GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15687","snapshot_observed_at":"2026-08-08T16:55:41.581941Z","title":"Graphdop: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations.arXiv preprint arXiv:2412.15687, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.581941Z"},"links":{"cited_paper":"/paper/2412.15687","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:04a9b0afed590163c5d8473588f25962d8ecad7a7a988b794714dd7019aca5e5","observation_id":"f89ac30c-63f0-4ca0-be5d-a1c82df0a8c4","resolution":{"observed_at":"2026-08-08T16:55:41.581941Z","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":"2026-08-08T16:55:41.586401Z","title":"Super-resolution reconstruction of turbulent flows with machine learning.Journal of Fluid Mechanics, 870:106–120, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.586401Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:715eb69a9d4ea10d12a050bedf0492cdf34985909b2e6c925538086a3ec5302a","observation_id":"3d20d404-ea66-4ab4-bbd9-ce923de6c063","resolution":{"observed_at":"2026-08-08T16:55:41.586401Z","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":"2026-08-08T16:55:41.590427Z","title":"Super-resolution and denoising of 4d-flow mri using physics-informed deep neural nets","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.590427Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:4ff64a0d87f28b1cf68ce75af642b4ed5dfd6e9696ed2e598c55e1ee590dab21","observation_id":"c8d0d469-d0ba-4551-b372-c0fda2ee2a06","resolution":{"observed_at":"2026-08-08T16:55:41.590427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.17319","last_updated":"2022-11-07T14:16:11Z","snapshot_observed_at":"2026-08-15T16:33:34.514887Z","submitted_at":"2022-10-31T13:36:18Z","title":"Physics-Informed CNNs for Super-Resolution of Sparse Observations on Dynamical Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.17319","snapshot_observed_at":"2026-08-08T16:55:41.593998Z","title":"Physics-informed cnns for super-resolution of sparse observa- tions on dynamical systems.arXiv preprint arXiv:2210.17319, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.593998Z"},"links":{"cited_paper":"/paper/2210.17319","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:3914ec0193947029d02eb119fd859c09ae7e457029112cf838e813f0283fa1d5","observation_id":"8c88c211-3a87-47fc-ac15-ed90901d8b6e","resolution":{"observed_at":"2026-08-08T16:55:41.593998Z","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":"2026-08-08T16:55:41.598046Z","title":"Super-resolution analysis via machine learning: a survey for fluid flows.Theoretical and Computational Fluid Dynamics, 37(4):421–444, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.598046Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:a27983c8353b2a58674eb7cd3ff0cdba3743bae7b73851b99adff243a7e394b6","observation_id":"72496276-8301-4ce9-82ba-76b8716ee8b1","resolution":{"observed_at":"2026-08-08T16:55:41.598046Z","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":"2026-08-08T16:55:41.601478Z","title":"Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels.Physics of Fluids, 33(7), 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.601478Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:f904071a59f6fd8e0d0eb9712cfde6ebd94a8093be842a4e7e8d1d9930433491","observation_id":"08b75738-6db9-4c96-ae9f-490b043ba723","resolution":{"observed_at":"2026-08-08T16:55:41.601478Z","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":"2026-08-08T16:55:41.604911Z","title":"Physr: Physics-informed deep super-resolution for spatiotemporal data.Journal of Computational Physics, 492:112438, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.604911Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:eca12bbc6fb1c2f93a249cdd420a286a01f4ac7eccc78424635fb19add9d9422","observation_id":"f69037f5-ef84-46b7-9dbd-480b53d8b272","resolution":{"observed_at":"2026-08-08T16:55:41.604911Z","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":"2026-08-08T16:55:41.608713Z","title":"Single-snapshot machine learning for super-resolution of turbulence.Journal of Fluid Mechanics, 1001:A32, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.608713Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:a4ebf6e336fd0041e8f509eac355f3b4731c9386d946f41a6e1129d4c1f01dd9","observation_id":"14241a51-e774-4a86-88dd-c2aa7a4f171e","resolution":{"observed_at":"2026-08-08T16:55:41.608713Z","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":"2026-08-08T16:55:41.612465Z","title":"Mesh-based super-resolution of fluid flows with multiscale graph neural networks","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.612465Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:4dc89897a8ef6bc61790af81c576fba0d1e0d363df51be6915b1988337a131b0","observation_id":"3724e6ad-7aaa-4ea3-a824-edf19c554a89","resolution":{"observed_at":"2026-08-08T16:55:41.612465Z","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":"2026-08-08T16:55:41.616285Z","title":"Global field reconstruction from sparse sensors with voronoi tessellation-assisted deep learning.Nature Machine Intelligence, 3(11):945–951, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.616285Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:ade44b05f8100d5477c8a607fd07b90c485141e06fb67f08fe7e6ecfb700a03e","observation_id":"453eae81-5e17-4f0d-9815-8044a0c26b79","resolution":{"observed_at":"2026-08-08T16:55:41.616285Z","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":"2026-08-08T16:55:41.620000Z","title":"Probabilistic neural networks for fluid flow surrogate modeling and data recovery.Physical Review Fluids, 5(10):104401, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.620000Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:fcea293f9a63f576a1998142e08c22cc4ea500431a36b24b1af1dcaf7b83f54d","observation_id":"f076c575-3ecc-44dd-873f-61fa069a6c22","resolution":{"observed_at":"2026-08-08T16:55:41.620000Z","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":"2026-08-08T16:55:41.623862Z","title":"Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles.Physica D: Nonlinear Phenomena, 454:133852, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.623862Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:556ab1c2a34150c7bd0595693b1fcf0d13cf4d565a1697cc5c5f00a428740e27","observation_id":"39690cb6-ae27-448f-867c-7d71de5fb7ff","resolution":{"observed_at":"2026-08-08T16:55:41.623862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.05407","last_updated":"2019-02-25T14:18:11Z","snapshot_observed_at":"2026-08-14T19:36:09.254749Z","submitted_at":"2018-03-14T17:09:27Z","title":"Averaging Weights Leads to Wider Optima and Better Generalization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.05407","snapshot_observed_at":"2026-08-08T16:55:41.627612Z","title":"Averaging weights leads to wider optima and better generalization.arXiv preprint arXiv:1803.05407, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.627612Z"},"links":{"cited_paper":"/paper/1803.05407","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:46273b55ad1f64e92329a7af890e31e0fc4c4ffc3ffdbcaeb41628f3be9ed469","observation_id":"0ed67838-a923-490f-9218-88d19baad811","resolution":{"observed_at":"2026-08-08T16:55:41.627612Z","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":"2026-08-08T16:55:41.631730Z","title":"Assessments of epistemic uncertainty using gaussian stochastic weight averaging for fluid-flow regression.Physica D: Nonlinear Phenomena, 440:133454, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.631730Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:acadab2513b64f9754e2c5400e09c49276a2ead0b3b23d41f00056828a01bb6b","observation_id":"856365de-bfc2-4c21-995b-049b2139b94c","resolution":{"observed_at":"2026-08-08T16:55:41.631730Z","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":"2026-08-08T16:55:41.635405Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.635405Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:6b227ce3527d3c6f4b9a00f93ad319e98a2e5e1cdf6744c47f10ebf4148d18c2","observation_id":"4a94ce85-4f45-4344-8072-8549c7c79c0d","resolution":{"observed_at":"2026-08-08T16:55:41.635405Z","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":"2026-08-08T16:55:41.639630Z","title":"Denoising diffusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.639630Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:e46b004aa7207ceffe6b13b8935398fb590b789b6a4df2457e1b7bcf3e74bdf1","observation_id":"fcebf132-3b4a-48ce-8001-47cf61d4ce6c","resolution":{"observed_at":"2026-08-08T16:55:41.639630Z","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":"2026-08-08T16:55:42.534979Z","title":"Generative modeling by estimating gradients of the data distribution.Advances in neural information processing systems, 32, 2019","venue":null,"work_id":"449576ec-3cfb-47b7-b5ea-72af48fe0198","year":2019},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.643708Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:9962382d2ef0ba4f30e40e921248d7dfa92c7e4bbcfffd16d15f6d94932a2839","observation_id":"86d4d739-1610-47e5-94a2-308e01cf70ba","resolution":{"observed_at":"2026-08-08T16:55:42.540241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T16:55:41.647368Z","title":"Estimation of non-normalized statistical models by score matching.Journal of Machine Learning Research, 6(4), 2005","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.647368Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:902d45a5b3441e46febe09f17e9dd3362820c2ff2180f5e6208c45e769a1aacd","observation_id":"dff1cc9a-9eb1-4f61-a73c-ec86773b4f41","resolution":{"observed_at":"2026-08-08T16:55:41.647368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-08T16:55:41.650885Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow.arXiv preprint arXiv:2209.03003, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.650885Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:a5e65865b18ce2f84534eeca8ccbcf3df50e65641a0c93c6a866634cf5a5f81b","observation_id":"a159a39d-02f0-4119-af94-47804a9db569","resolution":{"observed_at":"2026-08-08T16:55:41.650885Z","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":"2026-08-08T16:55:41.654744Z","title":"Stochastic interpolants: A unifying framework for flows and diffusions.Journal of Machine Learning Research, 26(209):1–80, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.654744Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:0a50f8f270b0b80e5fac096d18a74ae6f538b0dc1669a0528229add5ff5ae862","observation_id":"40f35f20-7503-46be-b9f8-b2a3e996e457","resolution":{"observed_at":"2026-08-08T16:55:41.654744Z","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":"2026-08-08T16:55:41.658300Z","title":"Pseudoinverse-guided diffusion models for inverse problems","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.658300Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:0a9a554b0e240ad805ab211268b88d60f84dad355c37e1df7b13452ebe617c46","observation_id":"a77ddcc1-1055-48fd-83e0-7ea8a9348d14","resolution":{"observed_at":"2026-08-08T16:55:41.658300Z","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":"2026-08-08T16:55:41.661871Z","title":"Denoising diffusion restoration models.Advances in neural information processing systems, 35:23593–23606, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.661871Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:2c928a0081bbba0a76b0011b5651245a7dc34a76600ed9770ea98d5eca30d387","observation_id":"6a66658e-da84-446c-bde6-185dca19767d","resolution":{"observed_at":"2026-08-08T16:55:41.661871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.00490","last_updated":"2022-12-07T13:29:20Z","snapshot_observed_at":"2026-08-14T17:33:28.551351Z","submitted_at":"2022-12-01T13:33:47Z","title":"Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.00490","snapshot_observed_at":"2026-08-08T16:55:41.665559Z","title":"Zero-shot image restoration using denoising diffusion null-space model","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.665559Z"},"links":{"cited_paper":"/paper/2212.00490","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:e002ec2874defa943872881ae2ecab038cbf52a5c058ebafb06c0557031bc843","observation_id":"b08d37e0-e47e-43cd-9538-bd252f3b211f","resolution":{"observed_at":"2026-08-08T16:55:41.665559Z","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":"2026-08-08T16:55:41.669919Z","title":"A variational perspective on solving inverse problems with diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.669919Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:b65928a73b33417b2b2ac519d2fd583d8dd0558b894bb8034b496b4f6e7a3ac9","observation_id":"bff239b7-3148-44e7-a1f1-c1717a665ee4","resolution":{"observed_at":"2026-08-08T16:55:41.669919Z","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":"2026-08-08T16:55:41.673595Z","title":"Denoising diffusion models for plug-and-play image restoration","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.673595Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:ced71efdd7a82c9133b42e70f28a0d4de8c738acb480cd4379c0b1f42916dc16","observation_id":"cb28cb6c-e84b-4d6c-8588-095d4d19c972","resolution":{"observed_at":"2026-08-08T16:55:41.673595Z","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":"2026-08-08T16:55:41.677351Z","title":"Principled probabilistic imaging using diffusion models as plug-and-play priors.Advances in Neural Information Processing Systems, 37:118389–118427, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.677351Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:eaf4a80a9ebc81d4d1f5f4c4d4b790f3187f6e1cac91f519610d911b2f0f780e","observation_id":"04b84567-501b-4d3c-b868-2cbe292749a3","resolution":{"observed_at":"2026-08-08T16:55:41.677351Z","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":"2026-08-08T16:55:41.681085Z","title":"Improving diffusion inverse problem solving with decoupled noise annealing","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.681085Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:b114ea689c5b5d59f46abb56265b46a5a1d4fa980c3e251e423245392a7ab286","observation_id":"5cfd2699-37b3-458e-9bee-cda3672a6a0a","resolution":{"observed_at":"2026-08-08T16:55:41.681085Z","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":"2026-08-08T16:55:41.684684Z","title":"Solving linear inverse problems provably via posterior sampling with latent diffusion models.Advances in Neural Information Processing Systems, 36:49960–49990, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.684684Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:62ffc477554493bd88d088e9312d4c700a9cdffa282ef1a5eb21bdd1c9fec471","observation_id":"fbe87361-2648-4d93-99a5-9a09379813a3","resolution":{"observed_at":"2026-08-08T16:55:41.684684Z","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":"2026-08-08T16:55:41.688141Z","title":"Solving inverse problems with latent diffusion models via hard data consistency","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.688141Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:19b9b74e18347765daa79c37503d43d043d001b2675e11199c69a93d28f2d7a7","observation_id":"fda8e822-565a-4876-868f-f64747b48083","resolution":{"observed_at":"2026-08-08T16:55:41.688141Z","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":"2026-08-08T16:55:41.692181Z","title":"Inversebench: Benchmarking plug-and-play diffusion priors for inverse problems in physical sciences","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.692181Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:b8ad9d479a833ff41c83eff6a01ac9096568ea692d15f8f1fc9838e12b7dddad","observation_id":"514f45db-b2e2-4fe4-81c2-a0fb5bac8c49","resolution":{"observed_at":"2026-08-08T16:55:41.692181Z","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":"2026-08-08T16:55:41.695908Z","title":"Residual corrective diffusion modeling for km-scale atmospheric downscaling.Communications Earth & Environment, 6(1):124, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.695908Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:7e43ede4b77077c0f40c9443fcf93bccdbe1e2a13ab7e1e15e3978d6f56d4f1c","observation_id":"8945d08d-180c-4240-9a48-9ee6c726b8a0","resolution":{"observed_at":"2026-08-08T16:55:41.695908Z","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":"2026-08-08T16:55:41.699910Z","title":"Precipitation downscaling with spatiotemporal video diffusion.Advances in Neural Information Processing Systems, 37:56374–56400, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.699910Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:c2c7be790284df2c05ddb42fa1ab9dcf92b22a28f3076cd073f0766b6ebe2042","observation_id":"db3b32b2-a86f-4838-a264-33f83005cb66","resolution":{"observed_at":"2026-08-08T16:55:41.699910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17752","last_updated":"2024-04-27T01:49:14Z","snapshot_observed_at":"2026-08-13T00:20:31.915037Z","submitted_at":"2024-04-27T01:49:14Z","title":"Generative Diffusion-based Downscaling for Climate","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17752","snapshot_observed_at":"2026-08-08T16:55:41.703664Z","title":"Generative diffusion-based downscaling for climate.arXiv preprint arXiv:2404.17752, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.703664Z"},"links":{"cited_paper":"/paper/2404.17752","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:adf4e5df82a7c73d27b41a5655dfd56699a46a8fba50576bdcfd493e621c4d95","observation_id":"681cf1fb-2f96-4099-85dd-1fc3b566ad3a","resolution":{"observed_at":"2026-08-08T16:55:41.703664Z","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":"2026-08-08T16:55:41.707540Z","title":"Generative emulation of weather forecast ensembles with diffusion models.Science Advances, 10(13):eadk4489, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.707540Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:0f665d3f292c12cdf8f3e8398630e3185d1ed9773db7b997ea6893b35b86044e","observation_id":"0e3dbdb7-93c6-4c3d-a03b-14bae98f76d9","resolution":{"observed_at":"2026-08-08T16:55:41.707540Z","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":"2026-08-08T16:55:41.711080Z","title":"Continuous ensemble weather forecasting with diffusion models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.711080Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:3867cba1f27a8c43931b6565905d0a74855efe4bcc5c039c004a57c09d401d4e","observation_id":"ae89baed-2658-4ee1-b848-b1bbf102ac8e","resolution":{"observed_at":"2026-08-08T16:55:41.711080Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05932","last_updated":"2024-06-10T12:22:59Z","snapshot_observed_at":"2026-08-13T21:04:03.271467Z","submitted_at":"2024-01-11T14:11:12Z","title":"DiffDA: a Diffusion Model for Weather-scale Data Assimilation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05932","snapshot_observed_at":"2026-08-08T16:55:41.714528Z","title":"Diffda: a diffusion model for weather-scale data assimilation.arXiv preprint arXiv:2401.05932, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.714528Z"},"links":{"cited_paper":"/paper/2401.05932","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:23cee7fa0c1306f9d545fcdd15a1f81865418ee38df5408aa27685ccb99f1557","observation_id":"8b505ccc-40dd-4a0e-b9a1-b200083d49a4","resolution":{"observed_at":"2026-08-08T16:55:41.714528Z","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":"2026-08-08T16:55:42.417806Z","title":"Generative data assimilation of sparse weather station observations at kilometer scales.Journal of Advances in Modeling Earth Systems, 17(10):e2024MS004505, 2025","venue":null,"work_id":"91070869-709d-4565-89c1-a52bbffb32e7","year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.718379Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:e147d604567541bba3222745f5d86d3d4fe971bba2ecf787268e2de03d075c82","observation_id":"e57f5959-6ccb-45f3-8ab6-837991c62a33","resolution":{"observed_at":"2026-08-08T16:55:42.422351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.03038","last_updated":"2025-03-04T22:36:29Z","snapshot_observed_at":"2026-08-10T17:12:01.580095Z","submitted_at":"2025-03-04T22:36:29Z","title":"Generative assimilation and prediction for weather and climate","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.03038","snapshot_observed_at":"2026-08-08T16:55:41.722214Z","title":"Generative assimilation and prediction for weather and climate.arXiv preprint arXiv:2503.03038, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.722214Z"},"links":{"cited_paper":"/paper/2503.03038","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:738b91c1d6dfb57a3471b6d56a483d1906865aa28e3725cf77326b3b7509ca51","observation_id":"a34c0438-68bb-41cc-b332-20a85b28788f","resolution":{"observed_at":"2026-08-08T16:55:41.722214Z","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":"2026-08-08T16:55:41.726207Z","title":"Deep generative data assimilation in multimodal setting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.726207Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:e1a9d288c4f1f6493d1672062f1e6ae63ab715ffd41a2c9452edc8daaa61f739","observation_id":"cc4c2ed9-4274-40e6-827d-b882d8f22fec","resolution":{"observed_at":"2026-08-08T16:55:41.726207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.12882","last_updated":"2025-05-19T09:10:55Z","snapshot_observed_at":"2026-08-14T01:33:48.778845Z","submitted_at":"2025-05-19T09:10:55Z","title":"PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.12882","snapshot_observed_at":"2026-08-08T16:55:41.729854Z","title":"Phyda: Physics-guided diffusion models for data assimilation in atmospheric systems.arXiv preprint arXiv:2505.12882, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.729854Z"},"links":{"cited_paper":"/paper/2505.12882","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:935431b33de8cd5698d66ba35503f631eea19c1b3450378fef69c1f61b701a69","observation_id":"82565dcb-faf1-4636-978a-eae3fcdf0585","resolution":{"observed_at":"2026-08-08T16:55:41.729854Z","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":"2026-08-08T16:55:41.733701Z","title":"A score-based filter for nonlinear data assimilation.Journal of Computational Physics, 514:113207, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.733701Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:8df6962baddd84951a65a646446cea3bda8324a295296395339f4aef7ac90b7b","observation_id":"3d2f7248-73ff-40a5-aaf5-5e1ccc4dddc9","resolution":{"observed_at":"2026-08-08T16:55:41.733701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12419","last_updated":"2025-01-20T20:51:36Z","snapshot_observed_at":"2026-08-10T17:52:50.600079Z","submitted_at":"2025-01-20T20:51:36Z","title":"Ensemble score filter with image inpainting for data assimilation in tracking surface quasi-geostrophic dynamics with partial observations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12419","snapshot_observed_at":"2026-08-08T16:55:41.737309Z","title":"Ensemble score filter with image inpainting for data assimilation in tracking surface quasi-geostrophic dynamics with partial observations.arXiv preprint arXiv:2501.12419, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.737309Z"},"links":{"cited_paper":"/paper/2501.12419","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:0056307178bc8e22431ab7d23d4ff221aac3c47688fff2e1495be8af87d4fe5d","observation_id":"3728612c-5f40-47a0-a0b0-f3e40cd4c202","resolution":{"observed_at":"2026-08-08T16:55:41.737309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.07549","last_updated":"2025-06-10T01:43:21Z","snapshot_observed_at":"2026-08-15T11:34:27.376514Z","submitted_at":"2025-04-10T08:24:26Z","title":"STeP: A Framework for Solving Scientific Video Inverse Problems with Spatiotemporal Diffusion Priors","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.07549","snapshot_observed_at":"2026-08-08T16:55:41.741360Z","title":"Step: A framework for solving scientific video inverse problems with spatiotemporal diffusion priors.arXiv preprint arXiv:2504.07549, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.741360Z"},"links":{"cited_paper":"/paper/2504.07549","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:92f824d01d0fd137f141ba04bcb02c27c553e15c0d97ea0de87589b15b90d4ee","observation_id":"15e4ddd8-cb34-4159-80c8-79278c0d5d79","resolution":{"observed_at":"2026-08-08T16:55:41.741360Z","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":"2026-08-08T16:55:41.745123Z","title":"Appa: Bending weather dynamics with latent diffusion models for global data assimilation.arXiv preprint arXiv:2504.18720, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.745123Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:c42efa724f6d4a84aa377fc4526b5b755fc22632f59bbf110f92c24219fcf4d2","observation_id":"d3c37e3a-c726-47b6-8c73-50b465a1dfd1","resolution":{"observed_at":"2026-08-08T16:55:41.745123Z","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":"2026-08-08T16:55:41.748975Z","title":"Tweedie’s formula and selection bias.Journal of the American Statistical Association, 106(496):1602–1614, 2011","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.748975Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:24719825054ad4bb89e1678e70467822d600cc133009919aa8fef0b9ba3dd458","observation_id":"738a5eb3-9e02-4a61-8137-3a88214fedef","resolution":{"observed_at":"2026-08-08T16:55:41.748975Z","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":"2026-08-08T16:55:41.755617Z","title":"Analyzing and improving the training dynamics of diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.755617Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:ad3e29afcbc1e727ea03b59c70b5291fe60bcba11e6341a6c4ff6990edd33545","observation_id":"ce93c12d-16b5-43dd-be08-02a2616ad457","resolution":{"observed_at":"2026-08-08T16:55:41.755617Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12598","last_updated":"2022-07-26T01:42:07Z","snapshot_observed_at":"2026-08-14T06:37:15.299690Z","submitted_at":"2022-07-26T01:42:07Z","title":"Classifier-Free Diffusion Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12598","snapshot_observed_at":"2026-08-08T16:55:41.761965Z","title":"Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.761965Z"},"links":{"cited_paper":"/paper/2207.12598","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:5a75f6084dc5c1763d03e7c1ef20cf16f04b3c8d4f1d94e57ae45a1fc162274d","observation_id":"e89413cc-c997-4b1c-baac-f573267b6934","resolution":{"observed_at":"2026-08-08T16:55:41.761965Z","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":"2026-08-08T16:55:41.770295Z","title":"Score-based data assimilation.Advances in Neural Information Processing Systems, 36:40521–40541, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.770295Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:a924bbcf19c09d934804b24d2e2a5e26b0d7c6cf2f1a25379a6ab0754d3a4bb2","observation_id":"d4e38459-33c7-458d-a6be-c0121ec7da58","resolution":{"observed_at":"2026-08-08T16:55:41.770295Z","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":"2026-08-08T16:55:41.774399Z","title":"Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models.Machine Intelligence Research, 22(4):730–751, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.774399Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:71a1a1e259cde8a277583ab5b620737add56014ed43bdf1ba2a410be02b74765","observation_id":"a680224d-631e-4f32-8293-4dc504f08c6e","resolution":{"observed_at":"2026-08-08T16:55:41.774399Z","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":"2026-08-08T16:55:41.778386Z","title":"Traversing distortion-perception tradeoff using a single score-based generative model","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.778386Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:a4536daf4a92f573a60de9b3b8bf8d4751c9854c1564bc804ba3b17d52bedcb0","observation_id":"5c9720bd-e54e-4b62-907c-4fd09db4995a","resolution":{"observed_at":"2026-08-08T16:55:41.778386Z","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":"2026-08-08T16:55:41.782776Z","title":"Adaptive guidance: Training-free acceleration of conditional diffusion models","venue":null,"work_id":null,"year":1962},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.782776Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:cc14f7e60a3d1802f85b6a0866017827d645b71d7c87cd053a60e091a90359bc","observation_id":"a3eb7990-daa1-4fe0-8012-36f43979c999","resolution":{"observed_at":"2026-08-08T16:55:41.782776Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03201","last_updated":"2024-07-04T08:58:36Z","snapshot_observed_at":"2026-08-14T02:29:12.811337Z","submitted_at":"2024-02-05T17:12:21Z","title":"Guidance with Spherical Gaussian Constraint for Conditional Diffusion","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03201","snapshot_observed_at":"2026-08-08T16:55:41.786331Z","title":"Guidance with spherical gaussian constraint for conditional diffusion.arXiv preprint arXiv:2402.03201, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.786331Z"},"links":{"cited_paper":"/paper/2402.03201","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:04bc6d2190cbc0dbb13780b9082789529ef367e51cb974d19d1733ae245a8f5a","observation_id":"8973028b-bcb8-406e-950b-e9244e487c2e","resolution":{"observed_at":"2026-08-08T16:55:41.786331Z","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":"2026-08-08T16:55:41.790147Z","title":"A global three-dimensional multivariate statistical interpolation scheme.Monthly Weather Review, 109(4):701–721, 1981","venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.790147Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:b557ba9550479e055891f11ad4e22e2c9e5ee6cd4022704932d0624c2eaa1c3d","observation_id":"1aabc8c8-9063-47e1-aa69-a2cfb069cd91","resolution":{"observed_at":"2026-08-08T16:55:41.790147Z","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":"2026-08-08T16:55:41.794345Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.794345Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:58ae0150c9c0c4046d93e74c39f100ba9fe3f335a412b208063aab3761734013","observation_id":"f249553b-2049-4760-8d57-4f1739444858","resolution":{"observed_at":"2026-08-08T16:55:41.794345Z","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":"2026-08-08T16:55:41.798445Z","title":"Weatherbench: a benchmark data set for data-driven weather forecasting.Journal of Advances in Modeling Earth Systems, 12(11):e2020MS002203, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.798445Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:72803b4a8965dbea2fbbdb1afbbb170a148db0d08f21496d2e815bf1cb5f181d","observation_id":"04b90684-0cc9-4806-9d67-cf1f94cbc6b4","resolution":{"observed_at":"2026-08-08T16:55:41.798445Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-08T16:55:41.803322Z","title":"Adam: A method for stochastic optimization.arXiv preprint arXiv:1412.6980, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.803322Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:f963178e72ae51f10aedf4977a2f8238c12cb151907acbfd629813cb2ed6fbfd","observation_id":"f8b617fc-79ef-459d-b60c-1de2de1239bc","resolution":{"observed_at":"2026-08-08T16:55:41.803322Z","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":"2026-08-14T20:13:52.872565Z","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":"2026-08-08T16:55:41.807799Z","title":"Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.807799Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:84282131d6cc941d5340df2251cca8666e83e4838dc845373b3e7e4a739b999d","observation_id":"8f72a9c6-0da2-4440-950b-8f7210c39136","resolution":{"observed_at":"2026-08-08T16:55:41.807799Z","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":"2026-08-08T16:55:42.314758Z","title":"A fixed-lag kalman smoother for retrospective data assimilation.Monthly Weather Review, 122(12):2838–2867, 1994","venue":null,"work_id":"f95e5f82-d115-467b-bf27-2e07597d7dda","year":1994},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.812159Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:7c753d6694f4ca6f12a2d65c765b826c1da89a8f42871aefca9cfa141f87dbe7","observation_id":"14e26a5b-8768-40b9-9743-f5cde3dd51bd","resolution":{"observed_at":"2026-08-08T16:55:42.319016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T16:55:41.816785Z","title":"End-to-end data-driven weather prediction","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.816785Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:4ad5dfdc943066922471beaa383d56b96fcf3881086d7eb2d4426302b4f883c8","observation_id":"6dff6c20-20d1-49f1-966d-0e3a542d4484","resolution":{"observed_at":"2026-08-08T16:55:41.816785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.17636","last_updated":"2026-05-07T21:44:32Z","snapshot_observed_at":"2026-08-14T13:05:08.947939Z","submitted_at":"2026-01-25T00:07:26Z","title":"HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.17636","snapshot_observed_at":"2026-08-08T16:55:41.820537Z","title":"Healda: Highlighting the importance of initial errors in end-to-end ai weather forecasts.arXiv preprint arXiv:2601.17636, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.820537Z"},"links":{"cited_paper":"/paper/2601.17636","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:9aec1fcb5f060dd1c2f10eeda89640a0f09f80842dd538e6fa16dd140e4e9ab0","observation_id":"d4652a8a-4c47-4940-999a-a580d0c77c17","resolution":{"observed_at":"2026-08-08T16:55:41.820537Z","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":"2026-08-08T16:55:41.825943Z","title":"Applying guidance in a limited interval improves sample and distribution quality in diffusion models.Advances in Neural Information Processing Systems, 37:122458–122483, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.825943Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:bfca3e3fc29157cb5f843bb5f6efd02fc187cbb941beaf2fb805f3eb6a76a344","observation_id":"1320ce64-3037-4e79-9b32-a295849c2516","resolution":{"observed_at":"2026-08-08T16:55:41.825943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.01073","last_updated":"2022-01-05T00:07:35Z","snapshot_observed_at":"2026-08-14T03:40:56.071989Z","submitted_at":"2021-08-02T17:59:47Z","title":"SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.01073","snapshot_observed_at":"2026-08-08T16:55:41.831622Z","title":"Sdedit: Guided image synthesis and editing with stochastic differential equations.arXiv preprint arXiv:2108.01073, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.831622Z"},"links":{"cited_paper":"/paper/2108.01073","citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:6a7d9017b7d2f8bdb571b872e0f27a57e96db2a59dd6d598c539e50710e72235","observation_id":"6d1b6ccd-627b-4308-8b77-f903273f4d05","resolution":{"observed_at":"2026-08-08T16:55:41.831622Z","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":"2026-08-08T16:55:41.836541Z","title":"Restart sampling for improving generative processes.Advances in Neural Information Processing Systems, 36:76806–76838, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.836541Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:78662148749c60a43a6d19316c13e43d9dc9491e931e40df8d214fbd7ae18872","observation_id":"708ed70d-3e99-4e8f-abce-bc0e03bbddf6","resolution":{"observed_at":"2026-08-08T16:55:41.836541Z","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":"2026-08-08T16:55:41.840799Z","title":"The effect of thinning and superobservations in a simple one-dimensional data analysis with mischaracterized error.Monthly Weather Review, 146(4):1181–1195, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.840799Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:964582537562067caa6d45622f19bf84994176aab7ebfece54b679a6a72e9614","observation_id":"ee4bd202-b5a7-422e-a23d-7ca61da62dbb","resolution":{"observed_at":"2026-08-08T16:55:41.840799Z","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":"2026-08-08T16:55:42.267444Z","title":"Learning skillful medium-range global weather forecasting.Science, 382(6677):1416–1421, 2023","venue":null,"work_id":"82008d46-8953-47c7-81d3-50cb06412b94","year":2023},"citing_paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-08T16:55:41.844803Z"},"links":{"citing_paper":"/paper/2608.05103"},"observation_digest":"sha256:600e727defd6121c67fe95de24d6e62ba2e239b5ee9d44af6c36441b1dc908c7","observation_id":"ecfb4fc2-2a59-4472-b68a-de778df33a01","resolution":{"observed_at":"2026-08-08T16:55:42.273683Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.05103","last_updated":"2026-08-06T02:05:51Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T11:04:28.386365Z","submitted_at":"2026-08-05T17:42:29Z","title":"Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching"},"reference_resolution":{"displayed":92,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":87,"verified_exact":0,"verified_fuzzy":4},"total_outbound_references":92},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 92 of 92 outbound references and 0 inbound Pith citation observations for arXiv:2608.05103."}