{"as_of":"2026-08-15T05:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:689cc34fa39c1b6c191dc8afe7b523e5a7d36dd305b022c0b0ca2ceef7b666d4","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:27:46.673171Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2507.05584/citation-record","integrity":"/paper/2507.05584/integrity","json":"/paper/2507.05584/citation-record.json","paper":"/paper/2507.05584"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.864529Z","title":"Journal of Computational Physics378, 686–707 (2019) 11 Fig","venue":null,"work_id":"0e6cdee9-d461-4d28-8ef3-af83aaca8825","year":2019},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.610631Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:4916b4bcd567fa8e1cf0d82c60247a88f42cc737c886698a38cdd1cae68f2ec5","observation_id":"a0b74df1-4e97-464c-916a-61d0885016e0","resolution":{"observed_at":"2026-08-06T19:27:46.867524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.614158Z","title":"Nature Reviews Physics3, 422–440 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.614158Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:6a0d9da4223cac2b63cd468b15926a956904a28ae3f2e6127f21a12da6aa7977","observation_id":"3ed3a2a5-7507-4af2-bbad-c7bd89662ae1","resolution":{"observed_at":"2026-08-06T19:27:46.614158Z","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":"2026-08-06T19:27:46.849821Z","title":"Acta Mechanica Sinica 38(11), 1725–1737 (2022)","venue":null,"work_id":"c4372ac3-33fc-49af-b17a-eacaf6b5127d","year":2022},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.617306Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:c1ce3a84dd09e20dfd63ee603971be83d8d805a7999f9971668349b238fc9dbe","observation_id":"9600901e-df25-4c5a-b9f8-6aa1b8fee57f","resolution":{"observed_at":"2026-08-06T19:27:46.853106Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.841024Z","title":"Journal of Computational Physics404, 109136 (2020)","venue":null,"work_id":"9a2bb17a-dcdf-4a65-a610-fa2472ec3b02","year":2020},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.620400Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:f26cd99162a79906ae5b58d06a4217275ec8d9541f990a69dcff333fcc171e82","observation_id":"982d973b-9a85-424c-b70d-2eaee14a9293","resolution":{"observed_at":"2026-08-06T19:27:46.843891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.833130Z","title":"Science367(6481), 1026– 1030 (2020)","venue":null,"work_id":"6ab886c8-6b6d-48a7-8180-ecb1386575b6","year":2020},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.623592Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:e957a8f04c1ee094abe962fecf444ed81153a59289a1cdec9db06384de402517","observation_id":"930f36dc-f722-4612-8abf-39c0578bafee","resolution":{"observed_at":"2026-08-06T19:27:46.835974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.824604Z","title":"In: International Conference on Learning Representations (2021)","venue":null,"work_id":"54efbbf6-d89e-41ba-ac4a-f1932f744dec","year":2021},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.627232Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:a31790fb3908e73a980da8e85639bee580db48dcb4bc3fb8136add2e2e0a8b04","observation_id":"dbd2b8ec-87cf-4ca7-98a3-d6ff7224c97e","resolution":{"observed_at":"2026-08-06T19:27:46.827401Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.816673Z","title":"In: International Conference on Learning Representations (2023)","venue":null,"work_id":"9a97c165-4074-496e-902a-abdca367ab42","year":2023},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.629855Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:8f7a9070d871e95c6fa69157a8a5b1e5ae3c50e8fce7a7c615be5363631bb8fa","observation_id":"bd1c353b-4b5e-4e80-8eb9-4bf21a13a155","resolution":{"observed_at":"2026-08-06T19:27:46.819507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.808628Z","title":"Nature Machine Intelligence3(3), 218–229 (2021)","venue":null,"work_id":"bfbc66fd-cdf5-462f-9a95-7c74d9a4e0c8","year":2021},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.632869Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:780322a99d4c2c88debf1614d4f1674266885224fee76d8b89628818f44f00f4","observation_id":"d6101c12-b348-480d-9d26-3dad458bf345","resolution":{"observed_at":"2026-08-06T19:27:46.811527Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.03485","last_updated":"2020-03-07T01:56:20Z","snapshot_observed_at":"2026-08-03T02:26:02.483819Z","submitted_at":"2020-03-07T01:56:20Z","title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.03485","snapshot_observed_at":"2026-08-06T19:27:46.635789Z","title":"arXiv preprint arXiv:2003.03485 (2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.635789Z"},"links":{"cited_paper":"/paper/2003.03485","citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:36a41a2367d6c9de3d39fae562343e6190ee80f715f0282437f381f1ec271f9d","observation_id":"8628f2c4-4b2a-4d55-ba9c-bcc5db9f97c9","resolution":{"observed_at":"2026-08-06T19:27:46.635789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.08481","last_updated":"2024-05-02T17:19:54Z","snapshot_observed_at":"2026-08-13T18:26:42.988315Z","submitted_at":"2021-08-19T03:56:49Z","title":"Neural Operator: Learning Maps Between Function Spaces","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.08481","snapshot_observed_at":"2026-08-06T19:27:46.639151Z","title":"arXiv preprint arXiv:2108.08481 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.639151Z"},"links":{"cited_paper":"/paper/2108.08481","citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:e29f5d5ed094fbff9abd551407789dd789e59f3ece59c5712c3cdff041c15724","observation_id":"ec2973c2-c63c-4f23-9b75-8f9b6b9383ce","resolution":{"observed_at":"2026-08-06T19:27:46.639151Z","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":"2026-08-06T19:27:46.799854Z","title":"In: International Conference on Learning Representations (2022)","venue":null,"work_id":"68ac71a8-da8e-404d-b94e-8ce8f89dce5c","year":2022},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.642662Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:db2c555627cd489c357e437b6b67ab238146e379488dfdcb6eaa0a111673aae3","observation_id":"57736654-9431-48a0-976e-79023fcfe43e","resolution":{"observed_at":"2026-08-06T19:27:46.802738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.11214","last_updated":"2022-02-22T22:19:35Z","snapshot_observed_at":"2026-08-14T08:28:25.306803Z","submitted_at":"2022-02-22T22:19:35Z","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.11214","snapshot_observed_at":"2026-08-06T19:27:46.645731Z","title":"arXiv preprint arXiv:2202.11214 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.645731Z"},"links":{"cited_paper":"/paper/2202.11214","citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:ca054910b910137af29687dcfd0f99a3dba8b8c4b46779b9a298f13d6f339dec","observation_id":"73b56ca5-8456-4b8a-906b-296f5ba7ac21","resolution":{"observed_at":"2026-08-06T19:27:46.645731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.03794","last_updated":"2023-07-29T07:58:37Z","snapshot_observed_at":"2026-08-13T17:38:44.513228Z","submitted_at":"2021-11-06T03:41:34Z","title":"Physics-Informed Neural Operator for Learning Partial Differential Equations","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.03794","snapshot_observed_at":"2026-08-06T19:27:46.648801Z","title":"arXiv preprint arXiv:2111.03794 (2022) 13","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.648801Z"},"links":{"cited_paper":"/paper/2111.03794","citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:636a261b6c8d4d2ff53321716e7c0010e6ef7d1f06838b1898a9b25f0a8c1553","observation_id":"275ecb16-bc20-45fe-8a49-1d485e4883ac","resolution":{"observed_at":"2026-08-06T19:27:46.648801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02441","last_updated":"2022-11-20T00:07:06Z","snapshot_observed_at":"2026-08-13T14:12:26.578774Z","submitted_at":"2022-10-05T17:59:45Z","title":"Ask Me Anything: A simple strategy for prompting language models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02441","snapshot_observed_at":"2026-08-06T19:27:46.651835Z","title":"arXiv preprint arXiv:2210.02441 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.651835Z"},"links":{"cited_paper":"/paper/2210.02441","citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:894c8757e92677bb6f4a85cfef72705334de2480dca27e78499ced5f981888d1","observation_id":"a7e47833-38d1-4aa7-9982-e4fe12197f44","resolution":{"observed_at":"2026-08-06T19:27:46.651835Z","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":"2026-08-06T19:27:46.791910Z","title":"In: Advances in Neural Information Processing Systems, pp","venue":null,"work_id":"d0ebd04c-3b00-4212-9d51-5ff7d339f587","year":2020},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.654915Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:51bfa179074eb804d7ccd13696dc08446b1cd40045cacf45fc7837bc4e39469e","observation_id":"46343a7c-3bfd-464a-b04f-cdc73aa48ccd","resolution":{"observed_at":"2026-08-06T19:27:46.794610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.783504Z","title":"In: Advances in Neural Information Processing Systems (2020)","venue":null,"work_id":"9e06b3cc-1b62-468a-af04-7676ff7ccaf9","year":2020},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.658446Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:d5453f3b977b02420538480c9da3f892efcc29c82500311a20459f7e6213bb7f","observation_id":"16cd8631-b842-4dd7-8ba9-0d9d8b4787a0","resolution":{"observed_at":"2026-08-06T19:27:46.786580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.02763","last_updated":"2023-02-06T13:22:33Z","snapshot_observed_at":"2026-08-13T12:50:15.138605Z","submitted_at":"2023-02-06T13:22:33Z","title":"The pairing symmetry in quasi-one-dimensional superconductor Rb2Mo3As3","version":1},"cited_work":{"arxiv_id":"2302.02763","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.02763","snapshot_observed_at":"2026-08-06T19:27:46.715602Z","title":"The pairing symmetry in quasi-one-dimensional superconductor Rb2Mo3As3","venue":"cond-mat.supr-con","work_id":"12f1a17c-aebf-483c-8770-3a637dde528d","year":2023},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.661065Z"},"links":{"cited_paper":"/paper/2302.02763","citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:9a5fbe838142c661c7090fa1a7ba74bf3e3ba363cfb4c3eaea0b29c5b7d0791d","observation_id":"c98e4aa7-4bd9-43da-be68-c4160b65dcb3","resolution":{"observed_at":"2026-08-06T19:27:46.718954Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07596","last_updated":"2023-04-15T17:03:21Z","snapshot_observed_at":"2026-08-13T12:02:23.557578Z","submitted_at":"2023-04-15T17:03:21Z","title":"Acoustic Beamforming for Object-relative Distance Estimation and Control in Unmanned Air Vehicles using Propulsion System Noise","version":1},"cited_work":{"arxiv_id":"2304.07596","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.07596","snapshot_observed_at":"2026-08-06T19:27:46.701329Z","title":"Acoustic Beamforming for Object-relative Distance Estimation and Control in Unmanned Air Vehicles using Propulsion System Noise","venue":"cs.RO","work_id":"c0912d49-7869-4070-8ed9-603aa7b2c866","year":2023},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.664006Z"},"links":{"cited_paper":"/paper/2304.07596","citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:664e3c8d25ca382112bee0d57863dccd8b5a88785398269ecd9cc329fb94d4eb","observation_id":"3e5be7e5-c89f-4652-b7ba-26e419b73a23","resolution":{"observed_at":"2026-08-06T19:27:46.706334Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.11871","last_updated":"2023-05-01T04:42:27Z","snapshot_observed_at":"2026-08-15T00:40:04.569070Z","submitted_at":"2022-06-05T18:38:42Z","title":"Offline RL for Natural Language Generation with Implicit Language Q Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.11871","snapshot_observed_at":"2026-08-06T19:27:46.666843Z","title":"arXiv preprint arXiv:2206.11871 (2022)","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.666843Z"},"links":{"cited_paper":"/paper/2206.11871","citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:0e1b08db0a884f7f3f68e846fc7b9c5b52e2f567ecfce116fad14a1597faa402","observation_id":"39dfec57-eb54-4149-9c3a-0761c4223173","resolution":{"observed_at":"2026-08-06T19:27:46.666843Z","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":"2026-08-06T19:27:46.775458Z","title":"In: International Conference on Learning Representations (2023)","venue":null,"work_id":"1b127b7e-b32a-4655-82da-6ac3e55d2bc4","year":2023},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.670216Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:03e75aaa276e8fded3e5a6192630c6fb34508406dd219641168c1e796c942f0d","observation_id":"53c9a3c1-9112-42b5-aef4-c943f083f4b2","resolution":{"observed_at":"2026-08-06T19:27:46.778285Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:27:46.763826Z","title":"In: Advances in Neural Information Processing Systems, pp","venue":null,"work_id":"62d3b262-a47a-4d92-a5ba-d8f139bd0806","year":2017},"citing_paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:27:46.673171Z"},"links":{"citing_paper":"/paper/2507.05584"},"observation_digest":"sha256:a4de4d772d0d0000c669bf8e45970f74465cbafaf7820e29166abba5cec5285b","observation_id":"8718293a-4a3c-4024-b8be-be0695239bb0","resolution":{"observed_at":"2026-08-06T19:27:46.766810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.05584","last_updated":"2025-07-08T01:43:33Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T18:36:01.999651Z","submitted_at":"2025-07-08T01:43:33Z","title":"The Fourier Spectral Transformer Networks For Efficient and Generalizable Nonlinear PDEs Prediction"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":21},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2507.05584."}