{"as_of":"2026-08-11T14:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:05ef2a5884416108f4d0fa36176352b7e66bbe430f75ce26fc16293cf2e55581","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:55:14.216935Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T14:29:54.919032Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-08-07T13:55:14.216935Z","title":"Eagle: Large-scale learning of turbulent fluid dynamics with mesh transformers.arXiv preprint arXiv:2302.10803,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21572","last_updated":"2025-05-27T07:18:08Z","snapshot_observed_at":"2026-08-10T15:48:05.813260Z","submitted_at":"2025-05-27T07:18:08Z","title":"Thickness-aware E(3)-Equivariant 3D Mesh Neural Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:55:14.216935Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2505.21572"},"observation_digest":"sha256:d0a535474a1567a5b6db450274330c9aa353ce23545ae1cc71bff705f09a3b7e","observation_id":"c4529a0d-fe5c-4bba-b795-aeb200704ebc","resolution":{"observed_at":"2026-08-07T13:55:14.216935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-08-07T04:25:21.046730Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.10778","last_updated":"2025-06-12T14:53:36Z","snapshot_observed_at":"2026-08-10T01:56:30.052101Z","submitted_at":"2025-06-12T14:53:36Z","title":"SlotPi: Physics-informed Object-centric Reasoning Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T04:25:21.046730Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2506.10778"},"observation_digest":"sha256:e6690002ce161e818ef73ec2557cb0fff24e2f7d36f2e99f2b37ee9c7532d4bc","observation_id":"95edfe14-a420-4ece-a4a4-a7ca4e6e36f5","resolution":{"observed_at":"2026-08-07T04:25:21.046730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-08-04T17:48:34.950332Z","title":"Eagle: Large-scale learning of turbulent fluid dynamics with mesh transformers.arXiv preprint arXiv:2302.10803,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.10659","last_updated":"2025-09-12T19:38:38Z","snapshot_observed_at":"2026-08-07T20:08:35.955158Z","submitted_at":"2025-09-12T19:38:38Z","title":"M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations","version":1},"reference_index":1993,"source":"pdf_text","source_observed_at":"2026-08-04T17:48:34.950332Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2509.10659"},"observation_digest":"sha256:50b82c1550bf3cb78279e499790a9a949e680c429a84c86608c91d14464927c5","observation_id":"08e77182-826e-4212-a115-5ec82d7e7a2a","resolution":{"observed_at":"2026-08-04T17:48:34.950332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-08-03T05:22:11.610984Z","title":"Eagle: Large-scale learning of turbulent fluid dynamics with mesh transformers.arXiv preprint arXiv:2302.10803,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.02788","last_updated":"2026-06-08T20:08:07Z","snapshot_observed_at":"2026-08-03T05:22:09.531006Z","submitted_at":"2026-02-02T20:45:07Z","title":"Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T05:22:11.610984Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2602.02788"},"observation_digest":"sha256:88e1374214b8ab5018e60a385193f67a1900b31fde4bfb423e94a7aa43e8efc0","observation_id":"b18b5ca1-f824-4efb-8df0-7c80d657cbe5","resolution":{"observed_at":"2026-08-03T05:22:11.610984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":"2302.10803","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-07-04T14:29:54.919032Z","title":"arXiv preprint arXiv:2302.10803 , year=","venue":null,"work_id":"c4829078-1389-4287-968b-b5e8954cf829","year":2023},"citing_paper":{"arxiv_id":"2604.19355","last_updated":"2026-05-27T09:37:48Z","snapshot_observed_at":"2026-07-06T23:06:02.635464Z","submitted_at":"2026-04-21T11:36:09Z","title":"LASER: Learning Active Sensing for Continuum Field Reconstruction","version":1},"reference_index":84,"source":"arxiv_source","source_observed_at":"2026-05-10T03:35:23.240677Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2604.19355"},"observation_digest":"sha256:e3c3fae3326b12121bccb51c07d17b9be39403589269364b36bd0e72df95ff95","observation_id":"69f05ee8-256d-41a9-bdfb-2c5414a78a95","resolution":{"observed_at":"2026-05-11T12:31:02.497399Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":"2302.10803","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-07-04T14:29:54.919032Z","title":"arXiv preprint arXiv:2302.10803 , year=","venue":null,"work_id":"c4829078-1389-4287-968b-b5e8954cf829","year":2023},"citing_paper":{"arxiv_id":"2605.07687","last_updated":"2026-05-08T12:55:58Z","snapshot_observed_at":"2026-08-11T13:06:56.319363Z","submitted_at":"2026-05-08T12:55:58Z","title":"PhySPRING: Structure-Preserving Reduction of Physics-Informed Twins via GNN","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-11T03:20:31.317356Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2605.07687"},"observation_digest":"sha256:aa068c638c99399ad29204d3bedbaf424bc12c704da635163c9936aaf1b85cc6","observation_id":"86ea8e98-9803-48ac-84ed-f4fa7d6e42c1","resolution":{"observed_at":"2026-05-11T03:20:55.096001Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":"2302.10803","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-07-04T14:29:54.919032Z","title":"arXiv preprint arXiv:2302.10803 , year=","venue":null,"work_id":"c4829078-1389-4287-968b-b5e8954cf829","year":2023},"citing_paper":{"arxiv_id":"2605.08935","last_updated":"2026-06-02T09:05:25Z","snapshot_observed_at":"2026-08-11T13:45:18.257356Z","submitted_at":"2026-05-09T13:12:33Z","title":"PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting","version":1},"reference_index":134,"source":"arxiv_source","source_observed_at":"2026-05-12T03:27:14.464987Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2605.08935"},"observation_digest":"sha256:aa03475636c11daa4cb8e65a02df3c62ab8ca0ea99a3356843d5a684bf25319a","observation_id":"082677ec-aef9-40b0-82b3-0d3fb1161140","resolution":{"observed_at":"2026-05-12T07:21:26.179614Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":"2302.10803","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-07-04T14:29:54.919032Z","title":"arXiv preprint arXiv:2302.10803 , year=","venue":null,"work_id":"c4829078-1389-4287-968b-b5e8954cf829","year":2023},"citing_paper":{"arxiv_id":"2605.08935","last_updated":"2026-06-02T09:05:25Z","snapshot_observed_at":"2026-08-11T13:45:18.257356Z","submitted_at":"2026-05-09T13:12:33Z","title":"PnP-Corrector: A Universal Correction Framework for Coupled Spatiotemporal Forecasting","version":2},"reference_index":134,"source":"arxiv_source","source_observed_at":"2026-05-14T21:21:32.256476Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2605.08935"},"observation_digest":"sha256:3b9ef32fbb3bbef6aeb726ec57aff74a8d8a99fcfc4b07b6ebdeed045f29a84e","observation_id":"d134944a-3008-4217-9834-cfc7a51b0dae","resolution":{"observed_at":"2026-05-14T21:22:59.161065Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":"2302.10803","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-07-04T14:29:54.919032Z","title":"arXiv preprint arXiv:2302.10803 , year=","venue":null,"work_id":"c4829078-1389-4287-968b-b5e8954cf829","year":2023},"citing_paper":{"arxiv_id":"2606.01172","last_updated":"2026-07-13T05:09:46Z","snapshot_observed_at":"2026-08-09T15:34:14.080948Z","submitted_at":"2026-05-31T11:27:48Z","title":"Revisiting Neural Processes via Fourier Transform and Volterra Series","version":2},"reference_index":87,"source":"arxiv_source","source_observed_at":"2026-06-28T17:11:05.372933Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2606.01172"},"observation_digest":"sha256:2c16702eaba55f5255f035cd0257037ddce57b40f78dfad87510a7147435f062","observation_id":"a99d944a-ddb3-43ba-9651-f6627248c4e9","resolution":{"observed_at":"2026-06-28T17:12:24.420767Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers","version":2},"cited_work":{"arxiv_id":"2302.10803","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2302.10803","snapshot_observed_at":"2026-07-04T14:29:54.919032Z","title":"arXiv preprint arXiv:2302.10803 , year=","venue":null,"work_id":"c4829078-1389-4287-968b-b5e8954cf829","year":2023},"citing_paper":{"arxiv_id":"2606.26660","last_updated":"2026-06-25T06:47:39Z","snapshot_observed_at":"2026-07-07T00:00:56.276365Z","submitted_at":"2026-06-25T06:47:39Z","title":"Generating Special Triangulations with Transformers","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-26T03:27:36.821748Z"},"links":{"cited_paper":"/paper/2302.10803","citing_paper":"/paper/2606.26660"},"observation_digest":"sha256:2d713b1c7467231aaa1cd61ba9f65313d69020e0005cf246c8db6230b4003e46","observation_id":"d06f1a37-2c70-4bce-af10-8b631e1754c9","resolution":{"observed_at":"2026-07-04T14:29:54.921007Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2302.10803/citation-record","integrity":"/paper/2302.10803/integrity","json":"/paper/2302.10803/citation-record.json","paper":"/paper/2302.10803"},"outbound":[],"paper":{"arxiv_id":"2302.10803","last_updated":"2023-03-17T08:38:35Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T10:41:30.407731Z","submitted_at":"2023-02-16T12:59:08Z","title":"Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2302.10803."}