{"as_of":"2026-08-08T05:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:74ca5833373d27be91ebc0b5d3908d61e50710d78ff2a2dc8bd7882db1c99043","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:45:30.197000Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":448,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-07T13:45:30.197000Z","title":"Sanchez-Gonzalez , author J","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2505.21125","last_updated":"2025-06-11T07:06:08Z","snapshot_observed_at":"2026-08-07T13:32:40.059946Z","submitted_at":"2025-05-27T12:44:17Z","title":"Dynamical Data for More Efficient and Generalizable Learning: A Case Study in Disordered Elastic Networks","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T13:45:30.197000Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2505.21125"},"observation_digest":"sha256:62f060e9f602392230a28ddf34fbd8e42b9ad580181e572e5d00c9e7d29347b8","observation_id":"318a4edd-dd70-4231-a710-c9ab3471051d","resolution":{"observed_at":"2026-08-07T13:45:30.197000Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-06T19:20:44.506156Z","title":"LearningtoSimulateComplex Physics with Graph Networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05983","last_updated":"2025-07-08T13:40:16Z","snapshot_observed_at":"2026-08-06T19:11:28.821832Z","submitted_at":"2025-07-08T13:40:16Z","title":"Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T19:20:44.506156Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2507.05983"},"observation_digest":"sha256:96a3ecde22dcaa2be4477f3d043dee6f8853015bd1d79f5e75bcdf99217584d2","observation_id":"0b240076-3767-4425-b50f-7fdd72910282","resolution":{"observed_at":"2026-08-06T19:20:44.506156Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-06T16:31:00.356393Z","title":"Sanchez-Gonzalez, J","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.13459","last_updated":"2025-07-17T18:09:19Z","snapshot_observed_at":"2026-08-06T16:21:39.817346Z","submitted_at":"2025-07-17T18:09:19Z","title":"Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:31:00.356393Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2507.13459"},"observation_digest":"sha256:34b25ea473bc31a0307db055fbb64c9a11eb06b3f435f6c6faae829dd2c03c69","observation_id":"a671fdaf-0abf-4e7a-9392-dab7037b262f","resolution":{"observed_at":"2026-08-06T16:31:00.356393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":"2002.09405","doi":"10.48550/arxiv.2002.09405","metadata_source":"arxiv_reference","pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning to simulate complex physics with graph networks.arXiv preprint arXiv:2002.09405","venue":"arXiv (Cornell University)","work_id":"c91048ad-8d99-4b58-998e-ef0d2169b600","year":2020},"citing_paper":{"arxiv_id":"2510.04233","last_updated":"2026-05-09T23:49:06Z","snapshot_observed_at":"2026-08-06T05:01:20.188942Z","submitted_at":"2025-10-05T14:48:26Z","title":"PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-18T09:57:31.774637Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2510.04233"},"observation_digest":"sha256:d4ae544bdd41834e83211b9a328d352c334934d0841ab8daf49837caeef64a8b","observation_id":"b70fd527-87bd-4c62-8aa7-a2132ce7acc5","resolution":{"observed_at":"2026-05-18T10:01:13.623855Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":"2002.09405","doi":"10.48550/arxiv.2002.09405","metadata_source":"arxiv_reference","pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning to simulate complex physics with graph networks.arXiv preprint arXiv:2002.09405","venue":"arXiv (Cornell University)","work_id":"c91048ad-8d99-4b58-998e-ef0d2169b600","year":2020},"citing_paper":{"arxiv_id":"2605.25909","last_updated":"2026-05-25T14:46:04Z","snapshot_observed_at":"2026-08-02T05:45:51.113368Z","submitted_at":"2026-05-25T14:46:04Z","title":"R5DGS: Semantic-Aware 4D Gaussian Splatting with Rigid Body Constraints for Efficient Dynamic Scene Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T22:43:47.031006Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2605.25909"},"observation_digest":"sha256:f79e7b2898208ab1cb30e5ea895bac30af0a83855ae04e44b1aafcc13be3722e","observation_id":"7a97983b-36ba-4f43-b32a-0731443b9aad","resolution":{"observed_at":"2026-06-29T22:44:01.261236Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":"2002.09405","doi":"10.48550/arxiv.2002.09405","metadata_source":"arxiv_reference","pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning to simulate complex physics with graph networks.arXiv preprint arXiv:2002.09405","venue":"arXiv (Cornell University)","work_id":"c91048ad-8d99-4b58-998e-ef0d2169b600","year":2020},"citing_paper":{"arxiv_id":"2605.30429","last_updated":"2026-05-28T18:00:13Z","snapshot_observed_at":"2026-08-06T07:50:42.154413Z","submitted_at":"2026-05-28T18:00:13Z","title":"Attention-based optimizer for symmetry finding","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-29T06:35:55.732968Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2605.30429"},"observation_digest":"sha256:51c2fa0b0184e8129f54495f01a078c327bb37daa24c63a91b2c7366434a88cf","observation_id":"fa86bcfd-2f7b-419a-855d-08d05d6c0d81","resolution":{"observed_at":"2026-06-29T14:33:31.331279Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":"2002.09405","doi":"10.48550/arxiv.2002.09405","metadata_source":"arxiv_reference","pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning to simulate complex physics with graph networks.arXiv preprint arXiv:2002.09405","venue":"arXiv (Cornell University)","work_id":"c91048ad-8d99-4b58-998e-ef0d2169b600","year":2020},"citing_paper":{"arxiv_id":"2605.30542","last_updated":"2026-05-28T20:18:22Z","snapshot_observed_at":"2026-07-06T23:39:48.531480Z","submitted_at":"2026-05-28T20:18:22Z","title":"Physically Viable World Models: A Case for Query-Conditioned Embodied AI","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-29T06:55:57.801162Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2605.30542"},"observation_digest":"sha256:c76aaf99c2c01055274b30090b99f4b40c61ff5cbd4ee3b92255efba85f88e57","observation_id":"c68c420f-1c54-453e-af6e-c2f2edb5cc1a","resolution":{"observed_at":"2026-06-29T09:13:16.544337Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":"2002.09405","doi":"10.48550/arxiv.2002.09405","metadata_source":"arxiv_reference","pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Learning to simulate complex physics with graph networks.arXiv preprint arXiv:2002.09405","venue":"arXiv (Cornell University)","work_id":"c91048ad-8d99-4b58-998e-ef0d2169b600","year":2020},"citing_paper":{"arxiv_id":"2606.23251","last_updated":"2026-06-22T12:33:55Z","snapshot_observed_at":"2026-07-06T23:58:02.040198Z","submitted_at":"2026-06-22T12:33:55Z","title":"Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T06:10:09.472925Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2606.23251"},"observation_digest":"sha256:cee084ea6ce004b5337fcfb982738c54bbbcf9210ad87de02b5bac8dda7cf3ec","observation_id":"08d04187-0289-4195-86d0-1393b4e64dd2","resolution":{"observed_at":"2026-06-26T06:19:03.844593Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-01T10:13:39.803771Z","title":"URL https://arxiv.org/abs/2002.09405","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.20321","last_updated":"2026-07-23T06:22:08Z","snapshot_observed_at":"2026-08-07T18:16:59.476229Z","submitted_at":"2026-07-22T16:07:09Z","title":"Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T10:13:39.803771Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2607.20321"},"observation_digest":"sha256:246ca9775b8dc9663930873805cadd1828ec57c66fcf1ced823695b1d1222231","observation_id":"74429b51-0385-46fd-afb7-21f7376d1204","resolution":{"observed_at":"2026-08-01T10:13:39.803771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-06T00:30:47.327874Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2608.01164","last_updated":"2026-08-02T11:36:08Z","snapshot_observed_at":"2026-08-07T13:56:50.153309Z","submitted_at":"2026-08-02T11:36:08Z","title":"Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulation","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-06T00:30:47.327874Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2608.01164"},"observation_digest":"sha256:1321ebd12a811c17918ff430fd4c16a9cfd056890085e6c3de1c4216d739b16a","observation_id":"4103668e-4860-438a-b0a8-0cddca48fbd9","resolution":{"observed_at":"2026-08-06T00:30:47.327874Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.09405","snapshot_observed_at":"2026-08-05T19:30:33.780115Z","title":"Proceedings of the 37th International Conference on Machine Learning , year =","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2608.03413","last_updated":"2026-08-04T10:06:15Z","snapshot_observed_at":"2026-08-07T23:11:52.178450Z","submitted_at":"2026-08-04T10:06:15Z","title":"Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems","version":1},"reference_index":121,"source":"arxiv_source","source_observed_at":"2026-08-05T19:30:33.780115Z"},"links":{"cited_paper":"/paper/2002.09405","citing_paper":"/paper/2608.03413"},"observation_digest":"sha256:cab1e504f9a14763e851113dd674f6c1f9928b76a31d8ed246bf6cbf420bd131","observation_id":"f3b6fc2e-69e1-42f2-8715-c95bb0e63be2","resolution":{"observed_at":"2026-08-05T19:30:33.780115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2002.09405/citation-record","integrity":"/paper/2002.09405/integrity","json":"/paper/2002.09405/citation-record.json","paper":"/paper/2002.09405"},"outbound":[],"paper":{"arxiv_id":"2002.09405","last_updated":"2020-09-14T16:52:10Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T22:13:29.609057Z","submitted_at":"2020-02-21T16:44:28Z","title":"Learning to Simulate Complex Physics with Graph Networks"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2002.09405."}