{"as_of":"2026-08-08T23:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bdefc0fd4ead7ac980766879e912860e5662467cbfa7a89e47ab3ab59aa39708","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":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":21,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T12:10:03.561554Z","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":"2026-07-10T06:15:00.866473Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-08T12:10:03.561554Z","title":"Kochmann","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.07636","last_updated":"2025-02-11T15:23:14Z","snapshot_observed_at":"2026-08-08T12:02:37.322789Z","submitted_at":"2025-02-11T15:23:14Z","title":"Consistency Training with Physical Constraints","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T12:10:03.561554Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2502.07636"},"observation_digest":"sha256:6aedb020288d24664c447c248cc657f30749d14280b657b32c6671a2e3b6470c","observation_id":"643f25fc-9644-486d-9997-356d6d83c53d","resolution":{"observed_at":"2026-08-08T12:10:03.561554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-07T12:37:00.117534Z","title":"Physics-informed di ffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.24145","last_updated":"2025-05-30T02:37:05Z","snapshot_observed_at":"2026-08-07T12:30:27.644172Z","submitted_at":"2025-05-30T02:37:05Z","title":"Autoregressive regularized score-based diffusion models for multi-scenarios fluid flow prediction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T12:37:00.117534Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2505.24145"},"observation_digest":"sha256:b7bcb320e4cedfcc25ac9bec0ddae1e598da1a9be75bf6eed048de9995f770fe","observation_id":"a02a4fb4-f6a2-44f0-b129-476d745f122c","resolution":{"observed_at":"2026-08-07T12:37:00.117534Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-06T19:16:30.861193Z","title":"Physics-informed diffusion models.arXiv preprint arXiv:2403.14404, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.06133","last_updated":"2025-07-24T19:54:06Z","snapshot_observed_at":"2026-08-06T19:07:49.020007Z","submitted_at":"2025-07-08T16:18:18Z","title":"Bridging Sequential Deep Operator Network and Video Diffusion: Residual Refinement of Spatio-Temporal PDE Solutions","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T19:16:30.861193Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2507.06133"},"observation_digest":"sha256:11421e2f25ca0efead847bd7ea77ea70a969fc4e8d84e1bcb6aae34092bc6bdb","observation_id":"14f63233-fa66-47ff-83be-0672d7943ddd","resolution":{"observed_at":"2026-08-06T19:16:30.861193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2507.15753","last_updated":"2026-04-24T13:01:43Z","snapshot_observed_at":"2026-08-05T13:24:42.047640Z","submitted_at":"2025-07-21T16:09:26Z","title":"Algebraic Language Models for Inverse Design of Metamaterials via Diffusion Transformers","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-19T04:18:32.034089Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2507.15753"},"observation_digest":"sha256:f73077000c58332e532c34ba07fc0fdbfe408f45e91125d22be0b4dafed3cbec","observation_id":"cb973b14-e781-4e68-bf62-0bdd3972b5d1","resolution":{"observed_at":"2026-05-19T04:22:02.869048Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-05T19:56:10.901979Z","title":"arXiv preprint arXiv:2403.14404 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.11528","last_updated":"2025-08-15T15:13:32Z","snapshot_observed_at":"2026-08-08T17:08:37.926758Z","submitted_at":"2025-08-15T15:13:32Z","title":"Physics-Informed Diffusion Models for Unsupervised Anomaly Detection in Multivariate Time Series","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T19:56:10.901979Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2508.11528"},"observation_digest":"sha256:441237db12fa1ff8a6184f42a60955a032475eb41512c1119d94998387eb5c5b","observation_id":"62421ddc-4f76-4b69-9ab7-ca6dbbbd1ba7","resolution":{"observed_at":"2026-08-05T19:56:10.901979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-05T19:32:58.703447Z","title":"& Kochmann, D","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.16640","last_updated":"2025-08-17T23:26:47Z","snapshot_observed_at":"2026-08-08T03:46:33.720829Z","submitted_at":"2025-08-17T23:26:47Z","title":"Generative Latent Diffusion Model for Inverse Modeling and Uncertainty Analysis in Geological Carbon Sequestration","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T19:32:58.703447Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2508.16640"},"observation_digest":"sha256:3b95abb5fdc6d59e6b6d625f2980596cc7646cbc3ad72e5fc381f01b4adcbf6a","observation_id":"d003936c-1567-4254-849f-ea46dab1e7ac","resolution":{"observed_at":"2026-08-05T19:32:58.703447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-05T13:48:48.531678Z","title":"Physics-Informed Diffusion Models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.02607","last_updated":"2025-08-30T00:30:42Z","snapshot_observed_at":"2026-08-05T13:48:39.146247Z","submitted_at":"2025-08-30T00:30:42Z","title":"Towards Digital Twins for Optimal Radioembolization","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-05T13:48:48.531678Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2509.02607"},"observation_digest":"sha256:a46f6b13eeaebdebeaa471b8a9c3d81030348535ebb6b6a7a60045a84b8e7d8a","observation_id":"4e422015-608d-40e2-a619-d98f7f416c2e","resolution":{"observed_at":"2026-08-05T13:48:48.531678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2509.18611","last_updated":"2026-04-20T01:36:57Z","snapshot_observed_at":"2026-07-06T22:30:31.933240Z","submitted_at":"2025-09-23T04:00:41Z","title":"Flow marching for a generative PDE foundation model","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-18T13:48:14.532529Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2509.18611"},"observation_digest":"sha256:c3de740e94f30b9e988c403253963b9302d8715a0fc4897059213332740a7d32","observation_id":"b5db45a3-c998-42b7-84fa-8a8bbe6cdd4c","resolution":{"observed_at":"2026-05-18T13:51:25.655084Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2512.19469","last_updated":"2026-04-07T15:00:29Z","snapshot_observed_at":"2026-08-01T00:30:05.756393Z","submitted_at":"2025-12-22T15:23:19Z","title":"GLUE: Coordinating Pre-Trained Generative Models for System-Level Design","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-16T20:25:49.836532Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2512.19469"},"observation_digest":"sha256:4af6bad96d667a5c6fe34d0aec9989a7cad857316390d225e6cc76e5cd753359","observation_id":"8f7680f2-feff-499d-9cba-ede80edd9bbe","resolution":{"observed_at":"2026-05-16T20:28:24.088082Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-03T02:51:04.612190Z","title":"M.Physics-informed diffusion models.arXiv:2403.14404 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2602.09708","last_updated":"2026-06-02T11:20:40Z","snapshot_observed_at":"2026-08-08T01:43:45.324104Z","submitted_at":"2026-02-10T12:11:07Z","title":"Physics-informed diffusion models in spectral space","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T02:51:04.612190Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2602.09708"},"observation_digest":"sha256:952c56bb939265820f46107ce0de78f5afa92b89868ba0f7276620037a1b81cb","observation_id":"2c17f2e2-0582-471e-8a91-889cc6a81f6b","resolution":{"observed_at":"2026-08-03T02:51:04.612190Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2603.28932","last_updated":"2026-04-02T19:12:34Z","snapshot_observed_at":"2026-07-06T22:51:09.377351Z","submitted_at":"2026-03-30T19:11:13Z","title":"A Unified Multiscale Auxiliary PINN Framework for Generalized Phonon Transport","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-14T00:12:22.784507Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2603.28932"},"observation_digest":"sha256:343c04b58954ea2f18a716790e23276c903fb287ee06a0b82fb7fa0260c01f53","observation_id":"fe143022-6fcc-493d-bfb3-60152d3256e0","resolution":{"observed_at":"2026-05-14T00:13:28.947551Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2604.03409","last_updated":"2026-05-12T00:29:55Z","snapshot_observed_at":"2026-07-31T11:34:00.467003Z","submitted_at":"2026-04-03T19:12:01Z","title":"Generative AI for material design: A mechanics perspective from burgers to matter","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-13T18:14:13.241978Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2604.03409"},"observation_digest":"sha256:c3ede69b26d0e1915300563a88b89a986018f80c17c72d9b1ab7bf5c3d5e0584","observation_id":"530656f0-faf1-4cf3-93ec-af05fc7a59b7","resolution":{"observed_at":"2026-05-13T18:18:06.084609Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2604.09039","last_updated":"2026-04-10T06:58:42Z","snapshot_observed_at":"2026-08-03T00:44:35.825994Z","submitted_at":"2026-04-10T06:58:42Z","title":"Diffusion Inpainting MIMO-OFDM Channels with Limited Noisy Observations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T17:34:45.311617Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2604.09039"},"observation_digest":"sha256:f4362c41ae1428404dd1a4fe046944e526bffe2d5505b6049ef7f40ce6e0526a","observation_id":"3efb9422-d4ef-4608-9514-3b2c49310bc9","resolution":{"observed_at":"2026-05-11T06:36:01.444352Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2605.12754","last_updated":"2026-05-12T21:07:13Z","snapshot_observed_at":"2026-08-06T22:47:40.719313Z","submitted_at":"2026-05-12T21:07:13Z","title":"Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-14T21:16:07.851540Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2605.12754"},"observation_digest":"sha256:225edd2226caf360067084895c16ec0e7cf2536b8bb1ce5c91567c7a90d837e9","observation_id":"32544ba7-eefb-47e3-9888-422da39cf318","resolution":{"observed_at":"2026-05-14T21:17:59.109893Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2605.16818","last_updated":"2026-05-16T05:23:49Z","snapshot_observed_at":"2026-07-06T23:27:52.850836Z","submitted_at":"2026-05-16T05:23:49Z","title":"Observation-Aligned Mask Priors for Learning Physical Dynamics from Authentic Occlusions","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-19T20:59:55.644530Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2605.16818"},"observation_digest":"sha256:535c5c6d80f048e438f727c5122fdbe4d64397e7c3d95a389a3b905edf332308","observation_id":"e4f0974a-b133-4b70-a39c-204e991aa245","resolution":{"observed_at":"2026-05-19T21:02:47.308384Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2605.20780","last_updated":"2026-05-20T06:22:44Z","snapshot_observed_at":"2026-07-06T23:31:18.520630Z","submitted_at":"2026-05-20T06:22:44Z","title":"Learning to Think in Physics: Breaking Shortcut Learning in Scientific Diffusion via Representation Alignment","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-21T06:24:04.891249Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2605.20780"},"observation_digest":"sha256:de2da3b53172715f581458d6aa6471d41ac16b45bd1f480ea94827b57a527655","observation_id":"d03a584a-a143-4dbb-a452-3628687b3b49","resolution":{"observed_at":"2026-05-21T06:24:41.307037Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2605.28368","last_updated":"2026-05-28T11:37:13Z","snapshot_observed_at":"2026-08-01T11:57:30.958731Z","submitted_at":"2026-05-27T12:04:15Z","title":"LEIA: Learned Environment for Interactive Architected Materials","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T13:37:38.479547Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2605.28368"},"observation_digest":"sha256:911566ec871d98e43038b3f459e90dcddd8c4d3d3d4ef0949572fe10086e4509","observation_id":"c2a89421-8b20-4880-a002-8b22cfb3d9c2","resolution":{"observed_at":"2026-06-29T13:43:29.068178Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":"2403.14404","doi":"10.48550/arxiv.2403.14404","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Chen, W., Jia, H., Lai, S., Wu, K., Xiao, H., Hu, L., and Yue, Y","venue":null,"work_id":"810a4c41-5857-41d5-bd9e-00032a70ab2c","year":2024},"citing_paper":{"arxiv_id":"2605.31321","last_updated":"2026-05-29T13:54:21Z","snapshot_observed_at":"2026-08-07T16:52:12.952570Z","submitted_at":"2026-05-29T13:54:21Z","title":"Surface Constraint Policy for Learning Surface-Constrained and Dynamically Feasible Robot Skills","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T21:53:36.465761Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2605.31321"},"observation_digest":"sha256:2645892d4ebe4c2649192e6ede78478f5f2bebd834f0fcdc43f7e34ef05729b4","observation_id":"145a7f61-c0ba-43ea-80ae-dbf2c31b380d","resolution":{"observed_at":"2026-07-01T19:56:11.197895Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-14T11:28:23.511747Z","title":"Kochmann","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10474","last_updated":"2026-07-11T20:53:04Z","snapshot_observed_at":"2026-08-08T22:09:54.543218Z","submitted_at":"2026-07-11T20:53:04Z","title":"Reinforcement Learning with Verifiable Physics: Post-training LLMs with Continuous Rewards","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-14T11:28:23.511747Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2607.10474"},"observation_digest":"sha256:0d0ae2737837706315d3a3a08586e2776c9bcd3b437acc05adaa521fe133a6a4","observation_id":"546f6ddc-d397-4231-b52c-47b2f65662d5","resolution":{"observed_at":"2026-07-14T11:28:23.511747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-08-02T02:18:47.410297Z","title":"Physics-informed diffusion models.arXiv preprint arXiv:2403.14404, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.14398","last_updated":"2026-07-15T22:25:59Z","snapshot_observed_at":"2026-08-08T11:30:57.852094Z","submitted_at":"2026-07-15T22:25:59Z","title":"Integration Matters: Rollout-Based Training for Constrained Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T02:18:47.410297Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2607.14398"},"observation_digest":"sha256:ca7eb1d59dc63d66e8aba8803b3adfd742d23397f2535d99e55507b8209733d9","observation_id":"46a02c33-683f-4f64-8210-0abb5fa78c2b","resolution":{"observed_at":"2026-08-02T02:18:47.410297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14404","snapshot_observed_at":"2026-07-31T02:22:20.407763Z","title":"Kochmann","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.25060","last_updated":"2026-07-27T20:42:39Z","snapshot_observed_at":"2026-08-07T12:34:30.427211Z","submitted_at":"2026-07-27T20:42:39Z","title":"Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-31T02:22:20.407763Z"},"links":{"cited_paper":"/paper/2403.14404","citing_paper":"/paper/2607.25060"},"observation_digest":"sha256:28bd7e83da64b0dcc82458b1d668c98ae584a71c794d02296a6a7dadb06d4d15","observation_id":"9c70487e-c666-4fe3-bd8a-d1ceaf346a40","resolution":{"observed_at":"2026-07-31T02:22:20.407763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2403.14404/citation-record","integrity":"/paper/2403.14404/integrity","json":"/paper/2403.14404/citation-record.json","paper":"/paper/2403.14404"},"outbound":[],"paper":{"arxiv_id":"2403.14404","last_updated":"2025-03-13T08:07:40Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T06:19:45.105925Z","submitted_at":"2024-03-21T13:52:55Z","title":"Physics-Informed Diffusion Models"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:2403.14404."}