{"as_of":"2026-08-05T21:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0308ebba69a2c1b7d6604c8f8ede676535fed1992da8f3b3e4162e5a18233fc2","coverage":[{"denominator":64,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":64,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-09T18:00:55.330564Z","state":"measured"},{"denominator":64,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":64,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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/2605.00820/citation-record","integrity":"/paper/2605.00820/integrity","json":"/paper/2605.00820/citation-record.json","paper":"/paper/2605.00820"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-09T19:06:28.447413Z","title":"Journal of Computational Physics , volume =","venue":null,"work_id":"67e5c016-7788-4c71-993b-8f8ca836da1b","year":2019},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:46d51e457981bf3257e1f4b2ed288246a0ec02de6d2ef48a358f68346ac5df45","observation_id":"0db06398-b52a-45df-a612-71232a38be13","resolution":{"observed_at":"2026-05-25T23:46:22.072542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Learning nonlinear operators via","venue":null,"work_id":"8dd59758-e014-49db-b393-2405c9c2dc4f","year":2021},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:1c56950cfa64d8fd7be485c915c5e8611a26bd91c5b0d854c250e974dc7ec02e","observation_id":"5166ba2f-bbaa-4c55-a61a-09050313c558","resolution":{"observed_at":"2026-05-25T23:46:22.154940Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Neural Operator: Learning Maps Between Function Spaces with Applications to","venue":null,"work_id":"362be8f7-33b4-4405-8e32-b747680d7aa9","year":2023},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:cc1ee837e32d8e271f1ba32f973f2a1284a4bfb9ece764625f4e934e8da8be93","observation_id":"7462db16-b5dd-4632-9f7c-6c3200a1fb7c","resolution":{"observed_at":"2026-05-25T23:46:22.089440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"International Conference on Learning Representations (ICLR) , year =","venue":null,"work_id":"fbaf53b2-8222-4e2c-895f-467374ea6b56","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:171213185286cfa8c1afc8b0aee0b4a8d03aab2f20a33303b3abefbab58d20e2","observation_id":"414fbeda-0c07-4f42-b658-17fd8692f3a9","resolution":{"observed_at":"2026-05-25T23:46:22.085427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Convolutional Neural Operators for Robust and Accurate Learning of","venue":null,"work_id":"83bdfe11-5419-4d45-95bf-e2bf66edcaa5","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:09cd26cca9325e3bd4336a61fe944629f06b8ee0af67afa1eee6563b19746d85","observation_id":"9f78cdb7-332a-49c2-895e-4b69b89c9682","resolution":{"observed_at":"2026-05-25T23:46:22.101548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"ACM/IMS Journal of Data Science , volume =","venue":null,"work_id":"accb2750-65a6-4c1e-b394-5b81494b6787","year":2024},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:5736e06793b9e2e278f554aeb3edec20b2a211d43335469c72e598347cf4ccaa","observation_id":"b4308e0f-fe23-4210-bc32-fb9201574503","resolution":{"observed_at":"2026-05-25T23:46:22.008303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Poseidon: Efficient Foundation Models for","venue":null,"work_id":"24ebb4e9-7a55-464d-8635-1523fc3a61c1","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:d95a31cbcd3fc6c8a4cb9f00723abd820552d0b7fc5a70bfa5bee8effd9a5192","observation_id":"ea8699b1-d7d6-424c-bb4f-2668252621f1","resolution":{"observed_at":"2026-05-25T23:46:22.178080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Scientific Computing , volume =","venue":null,"work_id":"b1533b4d-91b8-4732-84af-98b199bbd052","year":2022},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:adaf0d41486e111cb0975151ecf8d043f71a6c855d3ccf831719ace98752ba22","observation_id":"54c81a60-1a44-47de-a2ae-b9414af915cc","resolution":{"observed_at":"2026-05-25T23:46:22.046112Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Scientific Computing , volume =","venue":null,"work_id":"5f4664f8-ec6d-4020-82b1-911a8c1c1aee","year":2023},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:870b8cb7b60385ed4a3e3ccdb204af01afb47b3ae80cedc450b638e4dfbfcf39","observation_id":"41a83b0b-d2d0-4915-8f3b-cc5158b284ee","resolution":{"observed_at":"2026-05-25T23:46:22.080826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Annual Review of Fluid Mechanics , volume =","venue":null,"work_id":"e0662e06-156b-4676-bc0b-df8cc4ec7918","year":2024},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:11c90ec45a2b8278c0eec83d4931f16b450149b50b6a451a090649b942d957cc","observation_id":"02070b91-d117-4e20-9e21-5b39ffd87bf2","resolution":{"observed_at":"2026-05-25T23:46:22.038267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"journal =","venue":null,"work_id":"4d647026-464d-4ef2-be7e-e2d1d549f787","year":2021},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:a6e6d09af8faedf5906a2633d9f241804714695618af9a92371b5cfa635be1b3","observation_id":"b5a85186-98c3-4758-ba3b-76de00112225","resolution":{"observed_at":"2026-05-25T23:46:22.059014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Journal of Computational Physics , volume =","venue":null,"work_id":"4978652e-0918-4d8c-b1c6-422e97b5dd85","year":2024},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:133a0855b4bf03722b9b7b64fa2d5eea693b196e2864bf6eb1954b7765713901","observation_id":"822fb134-1f76-40a2-b47c-79a22c9a039d","resolution":{"observed_at":"2026-05-25T23:46:22.042276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Annual Reviews in Control , volume =","venue":null,"work_id":"928d8071-260c-4216-a72d-9452d1b7f3b0","year":2021},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:cd79effe28064f28b1d47f8ce11028b3af6c0f449f047cac1eed2c3db0cc01d4","observation_id":"a12c2bb6-4c5f-4045-9c12-b8f2cf1b895e","resolution":{"observed_at":"2026-05-25T23:46:22.191556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Budi","venue":null,"work_id":"46fd23a1-25e3-4a4b-a472-83dc822abb68","year":2022},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:5ddf1e212af7fe154c73a24b8a9dab5e3fac1c34c8b2add929ab088349ce7317","observation_id":"891f7c73-89ce-4f21-ac63-ab57f31e6686","resolution":{"observed_at":"2026-05-25T23:46:22.195568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Azizzadenesheli, Kamyar , journal =","venue":null,"work_id":"668c3796-48af-472f-a791-9501058f13cb","year":2023},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:bc4517724752f3eda8b25189f1c80a4c086a3422359725173653415e1eb76264","observation_id":"4bba71bc-5230-44e4-8705-3fba5d3c1f22","resolution":{"observed_at":"2026-05-25T23:46:22.054574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the 41st International Conference on Machine Learning (ICML) , pages =","venue":null,"work_id":"58290f39-dc97-49f8-8cdd-68df33b71bb3","year":2024},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:b740b149da4d42a748c970f784a6fe079af471298275635c322c3f80fb66b672","observation_id":"9c4bdde7-d910-4c68-9356-e9bbfb1852ad","resolution":{"observed_at":"2026-05-25T23:46:22.158688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"2015 , volume =","venue":null,"work_id":"ab6d2521-0b21-45d0-b52b-adc55e4e33de","year":2015},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:0c0c50f144c7cd80e5c0a02716289976aea67e8c1cc7229b3edb6aa652395254","observation_id":"4f8eb02b-ebd0-4a23-8abf-5fccf782c590","resolution":{"observed_at":"2026-05-25T23:46:22.033971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"2025 , eprint=","venue":null,"work_id":"1df4bf02-bca1-446a-9fa0-7333a1330c63","year":2025},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:526d61cca93e0acfac43b2ae4b1d77c3e9ebe86ed8bedc2ff4f62cb1e92c69dd","observation_id":"ae49dc0c-06c7-4ce4-b90a-1d26861805dc","resolution":{"observed_at":"2026-05-25T23:46:22.182894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"2025 , eprint=","venue":null,"work_id":"dfcb7755-1598-4c33-9a86-050f5117c172","year":2025},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:42706ba019d76c0f92bb00a5e21cec10674aea9aa436c211b089f79c3e245733","observation_id":"ae418982-b371-4e75-970e-242147c19c05","resolution":{"observed_at":"2026-05-25T23:46:22.237189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Factorized","venue":null,"work_id":"1c9788b0-0468-4729-8b07-5b5965e3354d","year":2023},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:b053c884181a90bc1464ef07363f6ee1e2cc77723c23e51ddbeb85a0a9764b67","observation_id":"8c832794-e9a8-4d0d-b7da-67347f44a19e","resolution":{"observed_at":"2026-05-25T23:46:22.203876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Advances in Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks , year =","venue":null,"work_id":"24d77274-82c3-44fe-9d8f-2e942b974461","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:ceaaf79121469c8e4e1bec3e2b91303889a951b1e1f2847eec260b43fce2dd1f","observation_id":"9529990a-a459-40aa-950f-730aecb62e65","resolution":{"observed_at":"2026-05-25T23:46:22.150864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"38a26170-25fd-4511-8f8c-6f2ea94c8050","year":2024},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:a7323522a42c58a664211a8e340bcdbb3d2513dda8ac1f33ca1337de0bb26197","observation_id":"68c1c923-3d55-4f9a-a5e1-872e9226a720","resolution":{"observed_at":"2026-05-25T23:46:22.146718Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Fractal decomposition of exponential operators with applications to many-body theories and","venue":null,"work_id":"a96f62c3-fa43-4232-b45d-1a51cc1ca102","year":1990},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:0376c773d51f5eb0288e0bbc29e96efc810c3a058886493c8e33b4bb39becde8","observation_id":"e5a7da39-1afb-45c8-901d-9e1a1549e480","resolution":{"observed_at":"2026-05-25T23:46:22.207811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Su, Yuan and Tran, Minh C","venue":null,"work_id":"a35b2f52-eda3-4b1e-89d5-c0cc3844630d","year":2021},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:e16cc294164823759682e7e866682c6697f61a05777cada16123be04f0486aa6","observation_id":"96566c6e-1671-47ea-9419-85be7118e73e","resolution":{"observed_at":"2026-05-25T23:46:22.131603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Childs, Andrew M","venue":null,"work_id":"2c4042c0-c142-4620-a13c-13ec04cd03ce","year":2015},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:1575839449afdf189722ec16f049c80cdd05fe1d21bf8abd4f2aa8982493d54e","observation_id":"b10732bf-78ee-4bc9-9404-35b3debf8dc2","resolution":{"observed_at":"2026-05-25T23:46:22.123425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2602.00884","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-01T21:56:15.358028Z","title":"arXiv preprint arXiv:2602.00884 , year=","venue":null,"work_id":"408712db-7765-48ba-bec8-ca7c613c1b77","year":2026},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:73cea287491fce986dbd4192ecb81bd2ac784f566bce382f67cd92e46651bd6e","observation_id":"15058a47-bdb5-4d04-b569-02db67719acc","resolution":{"observed_at":"2026-05-11T16:16:10.766538Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"47d45f63-15cb-4ab8-80fd-17c3d00b8b88","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:7c037ef3f3197d6217840c9ff6916cb504afb1607e670a2897c2f123ca32a5b9","observation_id":"24a500c7-8cac-40f0-b937-e6b105080e4a","resolution":{"observed_at":"2026-05-25T23:46:22.211715Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12938","last_updated":"2025-07-21T18:33:22Z","snapshot_observed_at":"2026-07-06T17:47:13.814205Z","submitted_at":"2024-03-19T17:43:57Z","title":"Learning Neural Differential Algebraic Equations via Operator Splitting","version":3},"cited_work":{"arxiv_id":"2403.12938","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12938","snapshot_observed_at":"2026-07-03T04:17:37.094015Z","title":"arXiv preprint arXiv:2403.12938 , year=","venue":null,"work_id":"aa38e0fa-6442-4bc4-a119-747631c83ea9","year":2024},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"cited_paper":"/paper/2403.12938","citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:323f07e3a7c4303c8842bbfacbf929ff42423657c1b8574988b29140124a6d55","observation_id":"0082b654-4d8f-465a-bd37-499d0259abf3","resolution":{"observed_at":"2026-05-11T16:16:10.749227Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2602.23113","last_updated":"2026-04-02T22:53:09Z","snapshot_observed_at":"2026-07-06T22:47:11.228912Z","submitted_at":"2026-02-26T15:27:14Z","title":"Learning Physical Operators using Neural Operators","version":2},"cited_work":{"arxiv_id":"2602.23113","doi":"10.48550/arxiv.2602.23113","metadata_source":"pith","pith_arxiv_id":"2602.23113","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"Learning Physical Operators using Neural Operators","venue":"cs.LG","work_id":"7b45764b-2ef4-45fc-9f45-f1e179cfa19e","year":2026},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"cited_paper":"/paper/2602.23113","citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:0248da8c3fb89f45d32d550943f5dac2fcf6d8025ef140bd48bf7f96bcf4e0a6","observation_id":"8a1ca6ab-7b6e-47f7-bfe2-3995b14ea201","resolution":{"observed_at":"2026-05-11T16:16:10.780734Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.23889","last_updated":"2024-11-08T14:45:55Z","snapshot_observed_at":"2026-07-06T19:42:51.617598Z","submitted_at":"2024-10-31T12:51:40Z","title":"GEPS: Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning","version":2},"cited_work":{"arxiv_id":"2410.23889","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.23889","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"K., Benet, J","venue":null,"work_id":"c56c4e32-2d7b-40fd-a3b2-96e2f7ab736d","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"cited_paper":"/paper/2410.23889","citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:f4a9e0f482ae14fdcb95bdaeb32d054dbc3252344d139c56472a6ee97dedd03d","observation_id":"a0ed9469-320f-464f-a53d-bbc5022af951","resolution":{"observed_at":"2026-05-11T16:16:10.797836Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"69b41427-1479-4a06-8d81-f915e7857a44","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:945590bb42d53c3d69bedf8b113f6ef278aca081d37a8450a871931958f48395","observation_id":"d3df41f9-bd47-4b78-8119-1ec8da3d85a6","resolution":{"observed_at":"2026-05-25T23:46:22.025593Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Perdikaris, Paris and Turner, Richard E","venue":null,"work_id":"259634ef-e8a2-431f-9521-07418a903124","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:40cb7097eafcb29e0abb55c8db4a27dfbff9d6267178397e2ecd56cad0f42250","observation_id":"98d0685d-5efb-4961-a73b-ca9d33cdfdda","resolution":{"observed_at":"2026-05-25T23:46:22.200101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Blending Neural Operators and Relaxation Methods in","venue":null,"work_id":"d401230b-4496-485d-9f0c-65db014a8572","year":2024},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:616eba3adc3d8f946f8585f973d993d064192e201f5f56b1860e44848bd5d710","observation_id":"4201efad-6d65-4d53-8344-1f8fac2982d1","resolution":{"observed_at":"2026-05-25T23:46:22.127680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Machine Learning: Science and Technology , volume=","venue":null,"work_id":"3ef9d38e-85dd-4160-b498-4af98581b202","year":2026},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:570724970e5431d1079e3cb021d51785fef1bbada130a2619faef96f411706a8","observation_id":"5f269c23-be65-4f3d-b38c-2a50ce77cb2b","resolution":{"observed_at":"2026-05-25T23:46:22.215833Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"55th AIAA Aerospace Sciences Meeting , year=","venue":null,"work_id":"de319d1f-15cb-4b83-9d15-a9ca6042536f","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:86c98d9949143e57e2b1f8f6ee58dba572f0856877196f0a93e2096b46aac58a","observation_id":"453b6176-c0cd-483f-9159-468ee04c06d0","resolution":{"observed_at":"2026-05-25T23:46:22.139263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3202f3f5-6a4b-4673-b50b-2eb17aeff397","year":2018},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:0703d8ee6328efde79d6962f2d5b05708ad1889e8da980c4e724a59276cec7d4","observation_id":"8bcb3866-9b96-4366-8c45-5a278c38c3fb","resolution":{"observed_at":"2026-05-25T23:46:22.135492Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Fournier, John J","venue":null,"work_id":"15aa4916-6512-4e32-bed8-1cf1d35272d3","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:0030781242e08a7e8e2239c8b60e4c812a2b9ef2eed406f3056dd7aaf5c6f361","observation_id":"eeb62409-9df6-4c3d-b15d-b8547cd84eb4","resolution":{"observed_at":"2026-05-25T23:46:22.220431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"b0b5ab2e-9718-4b4f-b7c3-ea12c77e4efb","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:6a2c2ac63993a7e11ac60382555d49d5d9ce870bbf081a556c3080429bd9d7d6","observation_id":"794ec7b8-1233-4a1d-83eb-e74ae3e9c9ac","resolution":{"observed_at":"2026-05-25T23:46:22.016675Z","resolver_source":"raw_fallback","status":"parse_uncertain"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Acta Numerica , volume =","venue":null,"work_id":"33018899-53cc-4ca6-891d-b840877dca77","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:d7a9f05151c7e675f9ca59a0d1b390866d4c3fc68650d59544bb5dea2499da68","observation_id":"5411d911-d67f-41f9-9a13-9f6ea8c6098e","resolution":{"observed_at":"2026-05-25T23:46:22.105610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-06T18:22:49.198378Z","title":"Mathematics of Control, Signals and Systems , volume =","venue":null,"work_id":"e6efde33-e5a9-4f8e-a742-d7bc33f57b74","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:b2b5bbfb69d897c62385ba3344cdc071fef179b7c006ee8079b91be48a122989","observation_id":"928efccc-032c-404f-b2f4-4ff5cdfaecca","resolution":{"observed_at":"2026-05-25T23:46:22.109603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Kaper, T","venue":null,"work_id":"f6689a94-b2a5-40e2-90a9-44c8c1da7c13","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:dc76355f26221579cec361aa85073000763dc3b3b9dfc84c8b9518b809d0f355","observation_id":"460770f8-615b-425a-b5ac-67982b011501","resolution":{"observed_at":"2026-05-25T23:46:22.224486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"e14d5cb4-d119-427e-8ac4-8e79c65815c4","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:e670d03a188ecf941c35a46772128c6a6b848fae4a9d9746f1ea5272074c5267","observation_id":"15fe4928-5617-4b01-875d-39a06ab33ebe","resolution":{"observed_at":"2026-05-25T23:46:22.142896Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Neural Networks , volume =","venue":null,"work_id":"9f3caa93-5e69-48b4-89ef-120c3412d357","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:6aee2753587c6d5c00eeef2712748897535e93058ca91cedd496b18a72462dfb","observation_id":"c9e6f612-675f-4d43-b2f6-26519521797e","resolution":{"observed_at":"2026-05-25T23:46:22.187351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"BIT Numerical Mathematics , volume =","venue":null,"work_id":"93c8ccf2-a5ba-4ae0-abf6-d09967d52370","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:1c50e7c1f7af5c993a6c70ea57b5565359eb37e1ae23170529d6818bc7689d63","observation_id":"3cd3e754-7f72-4f95-9deb-1d0daafc01eb","resolution":{"observed_at":"2026-05-25T23:46:22.228663Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Quispel, G","venue":null,"work_id":"4957328a-24ef-4d13-92ed-3accb4920112","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:715073b94f80a939b5f53dff3657ae32e53ab4ee4fea4dd30ca819d844a924e9","observation_id":"e31099c6-c789-45ff-a0c4-705d9772c24a","resolution":{"observed_at":"2026-05-25T23:46:22.114831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Annals of Mathematics , volume =","venue":null,"work_id":"64dbd9e6-c3a4-47ac-ac52-5dcc203b1c8f","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:11787b0db2d5f7f9906f702a915dbb904346a6f3ffeb8ca6525ef43e2955ea9f","observation_id":"c190f065-35c9-4865-86c8-a19df9e7a15b","resolution":{"observed_at":"2026-05-25T23:46:22.068174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.03864","last_updated":"2017-09-07T23:28:48Z","snapshot_observed_at":"2026-07-06T05:33:17.404544Z","submitted_at":"2017-03-10T23:02:19Z","title":"Evolution Strategies as a Scalable Alternative to Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"1703.03864","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.03864","snapshot_observed_at":"2026-07-09T23:46:37.371902Z","title":"Evolution Strategies as a Scalable Alternative to Reinforcement Learning","venue":"stat.ML","work_id":"21902a38-3d82-4a5c-936c-b6932ab0fc03","year":2017},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"cited_paper":"/paper/1703.03864","citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:1db960b92ff273a1caaa8897a255ad7f2252ed8f5190212524b0154e9fda866a","observation_id":"0a2db300-9f77-4511-9196-6a3c123f929b","resolution":{"observed_at":"2026-05-11T16:16:10.738987Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"IMA Journal of Numerical Analysis , volume =","venue":null,"work_id":"46bf80b6-de61-4982-88a2-bc4a5b44b12e","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:573087dca8cf5cfb1be39f5b340db2b8de3b48084697dda410bf5d7df3af458f","observation_id":"d7090696-e859-4692-ad76-f6aefe8714f5","resolution":{"observed_at":"2026-05-25T23:46:22.012373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Journal of Mathematical Physics , volume =","venue":null,"work_id":"7ed767dc-ff30-49f0-b078-9678926779a1","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:ce38506d3a3f8975add8edaa37c0828b28ce34d7b587d6bd8f7b49805103a9e2","observation_id":"82490926-196f-4c62-9d4e-ec994e86097d","resolution":{"observed_at":"2026-05-25T23:46:22.021460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"SIAM Journal on Numerical Analysis , volume =","venue":null,"work_id":"e2b59718-e92d-4baa-92aa-6c151a082f07","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:a4e45200ac529f10b7336bd08c48f66007e52095bbb8013a80fb21702bfb52a6","observation_id":"a553e697-020c-4739-9aca-1a3eed1f7d74","resolution":{"observed_at":"2026-05-25T23:46:22.029609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Solving Ordinary Differential Equations","venue":null,"work_id":"b2af6330-5099-4308-bc4a-0153a62af19e","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:27fcb3a0f079aeaa242185f035ef206a52017b63c0325514a05081145804cc9e","observation_id":"1bba50c8-7244-410a-b14d-1626ea4081c4","resolution":{"observed_at":"2026-05-25T23:46:22.050629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"2017 , eprint=","venue":null,"work_id":"79c2f70f-2e73-4f09-9dbe-a2cb408bfd2a","year":2017},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:3101ab94871415d96ec111621d270fe865a2d5f22f5d87dd11fd5b13d60f848a","observation_id":"207164a6-b963-4a85-bcd4-14edc8965529","resolution":{"observed_at":"2026-05-25T23:46:22.064410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-07-09T04:05:56.531924Z","title":"SIAM journal on numerical analysis , volume=","venue":null,"work_id":"eae3465f-b820-43b6-9956-19e374eaff12","year":1968},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:05c04c503d57ccffc81a2aedd83b2279fd6a44242636dd09d3beddd63160247c","observation_id":"b352fddf-e7ac-4b65-a372-86cd45716a68","resolution":{"observed_at":"2026-05-25T23:46:21.999849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Splitting Methods in Communication, Imaging, Science, and Engineering , editor=","venue":null,"work_id":"8ec2ce69-0bb2-4492-ab60-3fb31f37fd77","year":2016},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:dd00fcd7ee1754da69bb2cf1eed221f8857f84dd4dc8bb1dcfc2fbe45f52de05","observation_id":"8bd2f2e6-e611-40fd-80d4-1268d3e88c8f","resolution":{"observed_at":"2026-05-25T23:46:22.003951Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"Proceedings of the American Mathematical Society , volume=","venue":null,"work_id":"0c55adcf-4e5e-47f8-b2bb-9b39406655bf","year":1959},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:ee76e3904b0359be021f671b0f5f895b49ad6e0d55b1105b5483e9c0fe2c834d","observation_id":"9979bfc6-9c2c-4156-9ed4-49a50fd603b9","resolution":{"observed_at":"2026-05-25T23:46:21.995553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"series =","venue":null,"work_id":"b04d3145-ec11-4077-b624-096e6313e4c6","year":1993},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:a696c31b831cc84fe63c88835f0a9ef5c87c35c008b3558d39f51d19c11eaefb","observation_id":"30bd0fac-d26e-4056-bbc0-fd4744a8d94d","resolution":{"observed_at":"2026-05-25T23:46:22.093346Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"series=","venue":null,"work_id":"a07f0ff5-e1de-4ae9-824d-a9ba56f31d9c","year":2015},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:41cf047fb1cc42965506da8120d303d98594d7a12e15ccf6b6a0a5b37b908e24","observation_id":"29f64411-0855-4171-bc86-2b72e87c7e13","resolution":{"observed_at":"2026-05-25T23:46:22.097492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"1983 , publisher =","venue":null,"work_id":"96b918ea-8a72-4678-afb2-dc66c36fbaa2","year":1983},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:60dc86d841d0d028e87046daa0ffaa67d580922f782165bcf6078c3b5b7738b9","observation_id":"85a24e9c-ecdc-4e68-b301-53b36eaebb3d","resolution":{"observed_at":"2026-05-25T23:46:21.986688Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Liggett, Thomas M","venue":null,"work_id":"95345767-ac9e-4da9-9364-3abbcafbbc6c","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:86fc5129aae551f419c061e029509b68fc9d47353deddef47ff5d61f324bc1ed","observation_id":"f54ed161-581c-434f-ad3d-72eda9cb3ab6","resolution":{"observed_at":"2026-05-25T23:46:22.076708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"2024 , publisher=","venue":null,"work_id":"52f4a158-af36-42e7-bd12-d42edfc70d87","year":2024},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:26fb44aca0a1b5b498d09ddbcb88b50b743adac312d379d5404a0854924c7bd4","observation_id":"94a8830c-38f3-4b3c-b9ea-f495d4271b34","resolution":{"observed_at":"2026-05-25T23:46:22.241705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"and Dean, Joseph P","venue":null,"work_id":"5716662d-b5e9-4da5-bc78-ce3383ec3aae","year":2023},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:dcf8d43b2dfc65796029cbf2c5dfcb3215e4e51d9473f3b85ae3607364ea1c3e","observation_id":"e46cc872-3cac-4eee-947a-558ccce5a6ad","resolution":{"observed_at":"2026-05-25T23:46:22.232774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"0f450f76-7923-4d96-b89e-fdfc44b55598","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:15d47ec37371cd0f0b30070dbd87fb889d3fc945923db9445c1a41dc82755a6c","observation_id":"0baa4460-2b2e-43bd-bf95-1b1e061ae457","resolution":{"observed_at":"2026-05-25T23:46:22.119023Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"bb9c8815-0fec-4be6-80b9-d2b38c6f461d","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:89d22b4e4837c44efd06326df899f44952a6eb61e358d7866ed5ca3c675472c5","observation_id":"da303aed-656f-4250-aba3-c253892d8e38","resolution":{"observed_at":"2026-05-25T23:46:21.982300Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-05T21:23:00.469572Z","title":"^ Depends on whether the fixed schedule suits the target regime","venue":null,"work_id":"3ad558b6-4478-4fb9-84ae-cbec45351e5e","year":null},"citing_paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-05-09T18:00:55.330564Z"},"links":{"citing_paper":"/paper/2605.00820"},"observation_digest":"sha256:e63c9bbb394653844ac9d8d81b8579eb5036270cdc4c12389491f1d327186a65","observation_id":"c751e6ac-eb48-4c67-bd52-11206fdf3392","resolution":{"observed_at":"2026-05-25T23:46:21.991571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.00820","last_updated":"2026-05-01T17:57:48Z","latest_version":1,"primary_category":"cs.CE","snapshot_observed_at":"2026-07-06T23:14:11.211164Z","submitted_at":"2026-05-01T17:57:48Z","title":"HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs"},"reference_resolution":{"displayed":64,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":1,"unresolved":7,"verified_exact":2,"verified_fuzzy":51},"total_outbound_references":64},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2605.00820."}