{"as_of":"2026-08-18T19:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dd3c2795c2f10aea64b3941359fec5e29bb9059c7f405eb9cb046f0ae9508982","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:38:04.010049Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2504.20256/citation-record","integrity":"/paper/2504.20256/integrity","json":"/paper/2504.20256/citation-record.json","paper":"/paper/2504.20256"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:38:04.651063Z","title":"Bongard and H","venue":null,"work_id":"b27436ab-4bdb-4223-8385-8f93e697d41b","year":2007},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.828353Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:be4317d9bbd3c0b76b835cb5c9c7f801b2147467daab60edd3d29911ebd98dac","observation_id":"93bf5358-70e3-498f-8bac-6149125f4450","resolution":{"observed_at":"2026-08-16T05:38:04.655076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:03.833087Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.833087Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:d6233aedaae28011be2441b235c6d01864629a81d201c9019e32d538ed284159","observation_id":"1e50bd3a-aa59-4b40-b2fd-775259c9c42c","resolution":{"observed_at":"2026-08-16T05:38:03.833087Z","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":"2026-08-16T05:38:04.629360Z","title":"Berry and S","venue":null,"work_id":"b86f7d8e-0759-4b34-8059-a4a8430635e3","year":2023},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.837694Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:0e353dad15c1269ac4721c2b19d17602308f33123c02763a024d8c7a1481f82a","observation_id":"4d16e52b-0583-4bca-8973-13bfe51e7a0d","resolution":{"observed_at":"2026-08-16T05:38:04.633958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.616077Z","title":null,"venue":null,"work_id":"6d3023c6-2cb5-4592-92f8-499547e71f3f","year":2023},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.842480Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:d2342ce41241cec352cb3906f36e5da90f531f9788a3c4877efbdd049dd0b038","observation_id":"2219e28a-08d5-4d87-9ba0-b92505c57809","resolution":{"observed_at":"2026-08-16T05:38:04.620152Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.603222Z","title":"Tran and R","venue":null,"work_id":"38273edd-8b41-4014-a09e-35bdf94edc2a","year":2017},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.847273Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:928929f724f196b9ea80564c842b468fd56f1c43879c1f44e84b9c1c1db79ecf","observation_id":"33d1940f-36d1-4982-874b-3a9ad9be4b7b","resolution":{"observed_at":"2026-08-16T05:38:04.607413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.590102Z","title":null,"venue":null,"work_id":"7c84ddd7-6aa6-448d-8b7c-5dc3e5437078","year":2017},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.851342Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:bf742987ae78c2973d4776420de2fb51d8b8170b97975123c1c6dc17fc3e974e","observation_id":"59d45963-34d8-41cd-b2e8-fdb363dc31c9","resolution":{"observed_at":"2026-08-16T05:38:04.594430Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.577086Z","title":"Boninsegna, F","venue":null,"work_id":"ba8dc63d-8086-4547-97cb-4c9430f4e64a","year":2018},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.855853Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:0c4a08dad0ce0fc9fea780142fc7bab8e7a78284c6c5d7c25ad8ce9e21d2c200","observation_id":"7497e7cc-7282-4654-b451-4d5a0ba5ad93","resolution":{"observed_at":"2026-08-16T05:38:04.581195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.563857Z","title":null,"venue":null,"work_id":"1a96e4fb-9371-40ce-b2c5-8896134c64d1","year":2021},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.860074Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:7c794d0b3403f525a01e43ba2922e0509a437ae421f1288fe3adb8e8745987d2","observation_id":"c65e1301-5d87-4d5a-b2a1-94706ec410e1","resolution":{"observed_at":"2026-08-16T05:38:04.568124Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:03.863874Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.863874Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:c0f4120ae8a3a20415f2ef64c552756098715cb122f2b37d1269f201a1a1eff1","observation_id":"0d8fb0d1-7037-4b9c-adec-dd8df7615241","resolution":{"observed_at":"2026-08-16T05:38:03.863874Z","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":"2026-08-16T05:38:04.543273Z","title":null,"venue":null,"work_id":"30628e56-e4cf-4d70-8a2d-f7cd177bb93e","year":2021},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.867871Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:056519e4f4978bea1c44ff6f08876c7a06c0a1d9cfb6c7c59feffcbf150ff556","observation_id":"d583f99e-308e-4103-996c-5df4d5c7df55","resolution":{"observed_at":"2026-08-16T05:38:04.547710Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:03.872204Z","title":"Cuomo, V","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.872204Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:ce6971bfb19e550aa5f6bf4bda259e7b5c3cf4d7fa9f225da5b12770dd151cd4","observation_id":"786a37ea-5b0c-4c11-97ba-befceb607e33","resolution":{"observed_at":"2026-08-16T05:38:03.872204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.14688","last_updated":"2023-12-22T13:43:57Z","snapshot_observed_at":"2026-08-16T14:32:13.688261Z","submitted_at":"2023-12-22T13:43:57Z","title":"A Mathematical Guide to Operator Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.14688","snapshot_observed_at":"2026-08-16T05:38:03.876371Z","title":"Boull´ e and A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.876371Z"},"links":{"cited_paper":"/paper/2312.14688","citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:7cfbd7c152e8069090596266189befad41fc64bd1e88c68fd87ea0800d47009f","observation_id":"16f8a1c7-29e4-4c21-b3f8-9db063ab9c85","resolution":{"observed_at":"2026-08-16T05:38:03.876371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:38:03.880912Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.880912Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:e8be011a3af4af4bb8644c148b6491be045a92c6c54f33326152684301c9f449","observation_id":"1f916bb9-b78d-480c-b604-07b65f25dd01","resolution":{"observed_at":"2026-08-16T05:38:03.880912Z","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":"2026-08-16T05:38:04.514068Z","title":null,"venue":null,"work_id":"7966503d-f693-42d7-8293-bbef8633de0b","year":2023},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.884928Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:35126a8e71662450d37030c0083bd39b5b36274c317c81b5ceb457ae83d38d42","observation_id":"879d5d81-a150-4540-bc98-7c4bbb8c09f3","resolution":{"observed_at":"2026-08-16T05:38:04.518135Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.08895","last_updated":"2021-05-17T03:12:33Z","snapshot_observed_at":"2026-08-14T20:53:04.124337Z","submitted_at":"2020-10-18T00:34:21Z","title":"Fourier Neural Operator for Parametric Partial Differential Equations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.08895","snapshot_observed_at":"2026-08-16T05:38:03.888843Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.888843Z"},"links":{"cited_paper":"/paper/2010.08895","citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:eca089179c22caff3d0343456b36bc90d6a3b41bb9f19cef486bdb1427759cad","observation_id":"9fac5901-daed-4523-a482-be79642ab427","resolution":{"observed_at":"2026-08-16T05:38:03.888843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:38:03.893088Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.893088Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:7c7deff1b68ca61f2002671375a27dc009feb1fba4f7b03633ee25489e4d762a","observation_id":"f9e421b6-4c54-4d5c-856a-25cc530949b7","resolution":{"observed_at":"2026-08-16T05:38:03.893088Z","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":"2026-08-16T05:38:04.494490Z","title":null,"venue":null,"work_id":"521a6547-4de3-4261-bb37-59be4c399411","year":2023},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.897475Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:331e7ecb2060bcc903d8fa5ddf3791fe2f1f08bc674cebc67859afd93717140d","observation_id":"23dd7b67-94f6-4236-8eab-a9d88a4fedc6","resolution":{"observed_at":"2026-08-16T05:38:04.498610Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.11609","last_updated":"2024-09-17T23:53:34Z","snapshot_observed_at":"2026-08-16T13:17:40.412687Z","submitted_at":"2024-09-17T23:53:34Z","title":"Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.11609","snapshot_observed_at":"2026-08-16T05:38:03.901346Z","title":"Jollie, J","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.901346Z"},"links":{"cited_paper":"/paper/2409.11609","citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:fb1202be176fda18a47ea29088b55b816865590de1328894f6cf3bf4b45e840f","observation_id":"622b5cf2-e2c9-44a5-8001-c7c22b203853","resolution":{"observed_at":"2026-08-16T05:38:03.901346Z","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":"2026-08-16T05:38:04.481096Z","title":null,"venue":null,"work_id":"701143de-b27a-405a-904d-cae644f5eb77","year":2024},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.905419Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:4cd40ff24f6e0445e23397e053a549bf2ec1583c0528798fe40cac41dd9de607","observation_id":"1628db81-7423-4c5b-a16b-d1d33a7e812a","resolution":{"observed_at":"2026-08-16T05:38:04.485298Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.677267Z","title":"Finally we will compare these four algorithms in the following section","venue":null,"work_id":"a9cbc32a-be72-4ee1-a824-ecbc0ff73312","year":null},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.817387Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:b4611135711c93ea72ce5046576a20e15e21cb7f2a64cd692df58650f3c604dc","observation_id":"c4167f94-3ab9-4780-8cba-3da48dc73b17","resolution":{"observed_at":"2026-08-16T05:38:04.682014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.468066Z","title":"Fasel, J","venue":null,"work_id":"0554fecc-2733-413c-815b-a79139bbee32","year":2022},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.913829Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:8e2440f3180e662b1ca6bafbb817a6e7db580fe3b5776848d7464eda641daa87","observation_id":"d5c96ab4-e536-4d28-8ff7-4041cd4aa240","resolution":{"observed_at":"2026-08-16T05:38:04.472237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:03.909783Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.909783Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:5f596b4e506affef68e55ded03b3cca04962fa95cb87a155185fc11df8bdb417","observation_id":"f11b3e2a-bfbe-4f71-8899-fe2801178c57","resolution":{"observed_at":"2026-08-16T05:38:03.909783Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:38:03.921672Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.921672Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:36865285f5fb2af8718567ff0b5a0caee290b529e67dd90b643ae0f84fde9f6f","observation_id":"96a7e11d-277b-4411-992e-a715aa84e0cd","resolution":{"observed_at":"2026-08-16T05:38:03.921672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:38:03.917644Z","title":"Schaeffer and S","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.917644Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:ef0dbfd49ff04f60f7e961c99c88662c36dc38f750c7ca174c209c01c4f737c4","observation_id":"2474f83a-47ca-4168-bc44-819aa8b17d33","resolution":{"observed_at":"2026-08-16T05:38:03.917644Z","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":"2026-08-16T05:38:04.425828Z","title":"Schaeffer, G","venue":null,"work_id":"df8afe9b-4c1f-40d2-a55a-a683495e05e2","year":2018},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.929603Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:815ed045c074dc71868e9420de8ea2cf83acd515ecb906df99ed1d8ecfb3a6d1","observation_id":"4f0d5423-5c24-495a-adba-2b396eaba0f3","resolution":{"observed_at":"2026-08-16T05:38:04.429903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.438209Z","title":null,"venue":null,"work_id":"7e9679ff-516b-4ed1-8483-7cada39fc62e","year":2017},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.925609Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:2727add9e89a9525419224c711d29c3501ff9cafa080ee653d38b8e0944e6476","observation_id":"07b034ad-53c0-479c-b809-55f121931555","resolution":{"observed_at":"2026-08-16T05:38:04.442088Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:03.937977Z","title":"Zhang and H","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.937977Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:ab82130565b36c505d2e7920b3c79e2784b00535c6dde9b223ab290d310bb900","observation_id":"75bed9c8-b65c-433f-acca-e7d6a454087d","resolution":{"observed_at":"2026-08-16T05:38:03.937977Z","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":"2026-08-16T05:38:04.413115Z","title":"Fasel, E","venue":null,"work_id":"6396508e-dc6e-41b5-a69d-01211f0aff4f","year":2021},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.933773Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:327b100449c972f3917109350634f3faa9ea023b20517704a8a10d958ea08b58","observation_id":"2b426a23-db15-4ec9-ad42-08397401dc3e","resolution":{"observed_at":"2026-08-16T05:38:04.417462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.400544Z","title":null,"venue":null,"work_id":"e72899d8-2b94-4c17-ae31-36e2495e790e","year":2024},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.947408Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:f060768865b132d421237ee71ff7fe2bb4d3903bd3902f89d7ca39360d2589b8","observation_id":"99cca217-87f4-4c6a-8ca5-d457a4a92b44","resolution":{"observed_at":"2026-08-16T05:38:04.404687Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.16528","last_updated":"2025-08-11T16:23:44Z","snapshot_observed_at":"2026-08-17T07:14:08.250800Z","submitted_at":"2024-10-21T21:36:17Z","title":"ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.16528","snapshot_observed_at":"2026-08-16T05:38:03.942345Z","title":"Viknesh, Y","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.942345Z"},"links":{"cited_paper":"/paper/2410.16528","citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:0e1f2d597920e58094bedc888551cec1f5da71e357c5553084a21ea50f0528c1","observation_id":"5b774c7e-2646-4296-afb1-e6312e6f8aaa","resolution":{"observed_at":"2026-08-16T05:38:03.942345Z","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":"2026-08-16T05:38:04.387706Z","title":"Mars Gao and J","venue":null,"work_id":"9571aa9c-ee06-4aad-a5c5-a64eaf2ce088","year":2024},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.955789Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:e99c8cb55b8a2ce6d2b8ba83560d9ffc14b5b810707382c72fd90d043fd2bcb8","observation_id":"6a546058-62eb-48c6-8a99-3ac72f2eff19","resolution":{"observed_at":"2026-08-16T05:38:04.391861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.08062","last_updated":"2025-06-25T10:45:10Z","snapshot_observed_at":"2026-08-18T04:14:24.594733Z","submitted_at":"2024-08-15T10:03:30Z","title":"BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.08062","snapshot_observed_at":"2026-08-16T05:38:03.951600Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.951600Z"},"links":{"cited_paper":"/paper/2408.08062","citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:ba01c4e7dbe0e9e9cf91e438cee8dbfed644fc26731504f480893d352b4e6d90","observation_id":"a8338b9e-d71c-46c7-a15a-e4493ef77abb","resolution":{"observed_at":"2026-08-16T05:38:03.951600Z","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":"2026-08-16T05:38:04.375130Z","title":null,"venue":null,"work_id":"8e1e30b3-efd6-40bc-bcb7-a6cce067bbc8","year":2020},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.964556Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:224937502e07fc364c4bdc2bbcc88a0bd10a1bd358d7db3f119cc03333ea1c16","observation_id":"f9c50807-6ff3-404d-80ee-3d6ed13c22fb","resolution":{"observed_at":"2026-08-16T05:38:04.379197Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:03.960571Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.960571Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:8e56b23c4020f95c76a2e8c2730715d1fd3038ddac8c3260b5b84ad4bf821bd0","observation_id":"0c31c4f1-db77-4a63-9cbe-0b296dd51b30","resolution":{"observed_at":"2026-08-16T05:38:03.960571Z","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":"2026-08-16T05:38:04.347811Z","title":null,"venue":null,"work_id":"d089f5c9-1809-4d3a-a357-327ed41bc33c","year":2017},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.972932Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:368cac0f216cdcc6e2f99c11f4ededf852c58704a1e76ea190c11d32579b2f23","observation_id":"ff9b086d-af8f-49e4-b8f8-09eb96725305","resolution":{"observed_at":"2026-08-16T05:38:04.351839Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.361195Z","title":"Chartrand, Numerical differentiation of noisy, non- smooth data, International Scholarly Research Notices 2011, 164564 (2011)","venue":null,"work_id":"f2750def-17c7-4012-90e2-df7d741cde53","year":2011},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.969027Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:315f59af03aaa6060ed7199fec90718795a321b60173f75f4c335f420b602f9c","observation_id":"61a8b0ff-3ee7-4e5b-805b-b495be1645c3","resolution":{"observed_at":"2026-08-16T05:38:04.366408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.334600Z","title":"Van Breugel, J","venue":null,"work_id":"9667c1d5-0b6a-47dc-847c-4156f0168357","year":2020},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.981818Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:9ac27e6e0b424bd995e7c719e0206f3833cbca6aec774e76108eb557f710d7c0","observation_id":"8bdc9f35-a2de-4e49-9455-a5b4f74bfe4b","resolution":{"observed_at":"2026-08-16T05:38:04.338541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:03.977398Z","title":"van Breugel, J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.977398Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:3b2eaec8cba4e30cb3d7637e77e41184099e35c683ac47d5ee3b4ce71dbaf239","observation_id":"2383cc88-723b-488e-a546-433b36e9dd00","resolution":{"observed_at":"2026-08-16T05:38:03.977398Z","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":"2026-08-16T05:38:04.306842Z","title":"Geman and D","venue":null,"work_id":"72f6a9d4-76e6-4d1c-90cc-5860d9661991","year":1984},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.989865Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:e04a96ea900be74932ab989a9472f7fbc60b0e24241bad53c82fdfb7ad788b93","observation_id":"1f626c97-4431-47b2-8a14-8e66b688f631","resolution":{"observed_at":"2026-08-16T05:38:04.311170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.320736Z","title":"Kay, The risk of bias in denoising methods: Examples from neuroimaging, PLoS One 17, e0270895 (2022)","venue":null,"work_id":"e4e6f98b-a1d2-4e1b-a153-2dfa76aa0f06","year":2022},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.986000Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:5f190cc326cefc6a5fec6e8fc86d2b09073778e50c97aca1eea05fb799d0f9b3","observation_id":"deace1a4-f31c-4940-99ee-4b1a8e80093b","resolution":{"observed_at":"2026-08-16T05:38:04.325319Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.278173Z","title":"Blumensath and M","venue":null,"work_id":"704a8048-89c3-4277-a47f-86198021521a","year":2009},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.997353Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:99eeed180d3b85fc89fcb84ec560308b68d9844ae0f0ff2f8c211a2caa835409","observation_id":"bce46ec9-667f-4ea6-aa5d-c0c3aa3503a1","resolution":{"observed_at":"2026-08-16T05:38:04.282359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.291400Z","title":"Foucart, Hard thresholding pursuit: an algorithm for compressive sensing, SIAM Journal on numerical analysis 49, 2543 (2011)","venue":null,"work_id":"e3a2951d-dcd9-49b3-8a35-7cc55857e47a","year":2011},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.993674Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:621769ae02aaca830c6b41c09f097924ab3e86f4839aab5413c623658bbd5540","observation_id":"a19cac9c-9060-42b6-a8ee-1ba9dee0080f","resolution":{"observed_at":"2026-08-16T05:38:04.295874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.05266","last_updated":"2023-01-29T08:19:43Z","snapshot_observed_at":"2026-08-16T17:50:19.393339Z","submitted_at":"2021-10-11T13:39:41Z","title":"Chaos as an interpretable benchmark for forecasting and data-driven modelling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.05266","snapshot_observed_at":"2026-08-16T05:38:04.005383Z","title":"Gilpin, Chaos as an interpretable benchmark for forecasting and data-driven modelling, arXiv preprint arXiv:2110.05266 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:04.005383Z"},"links":{"cited_paper":"/paper/2110.05266","citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:817e20b11f64309712eaaad2bb0464bd94c872958bf71568159f9ecb505f7ea2","observation_id":"b10ae8a6-ee49-456f-b900-31e046dc3f3e","resolution":{"observed_at":"2026-08-16T05:38:04.005383Z","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":"2026-08-16T05:38:04.264353Z","title":"Lorenz, Deterministic nonperiodic flow, Journal of At- mospheric Sciences 20 (1963)","venue":null,"work_id":"65377f4b-5668-44b6-aeba-c52cc75bf77c","year":1963},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:04.001380Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:d5f924cd387cc5be5bd3b4ba2db9956e2bbb06b4d691219d22bb41ebb0503f32","observation_id":"1d1dfbf0-bc59-49f3-ac07-681821ec61f5","resolution":{"observed_at":"2026-08-16T05:38:04.269437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.249361Z","title":null,"venue":null,"work_id":"1f0ce34d-e583-47ca-af14-5ab894206505","year":1996},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:04.010049Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:c607f102a8accddf9207580dc7fa585aac31f9f7f7fc101c22cd43d201af00dc","observation_id":"0cbb90db-a1c4-4022-8937-be179024cf9e","resolution":{"observed_at":"2026-08-16T05:38:04.255080Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+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":"2026-08-16T05:38:04.663853Z","title":"for i = 1,..., 6 near α = π 5","venue":null,"work_id":"8601a51c-d78e-4a2d-b2c6-5b841d3ba4d2","year":null},"citing_paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-16T05:38:03.822888Z"},"links":{"citing_paper":"/paper/2504.20256"},"observation_digest":"sha256:ddc5f127d58f8e0c6bda2c3420f8160d72655e220f05ffcb2251e450f3c3bd35","observation_id":"a8333032-e22c-4fb5-a9f7-8ae5fbda3c77","resolution":{"observed_at":"2026-08-16T05:38:04.667936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.20256","last_updated":"2025-04-28T21:01:07Z","latest_version":1,"primary_category":"math.OC","snapshot_observed_at":"2026-08-18T13:35:46.891071Z","submitted_at":"2025-04-28T21:01:07Z","title":"Optimizing Hard Thresholding for Sparse Model Discovery"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":46},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2504.20256."}