{"as_of":"2026-08-09T18:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:151017a645e5090cb3fb1819be7b31e361278331b0a5b8f964af161a92eb5174","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T18:46:59.707637Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2606.09923/citation-record","integrity":"/paper/2606.09923/integrity","json":"/paper/2606.09923/citation-record.json","paper":"/paper/2606.09923"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2107.07511","last_updated":"2022-12-07T05:08:01Z","snapshot_observed_at":"2026-07-06T11:29:34.579530Z","submitted_at":"2021-07-15T17:59:50Z","title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","version":6},"cited_work":{"arxiv_id":"2107.07511","doi":"10.48550/arxiv.2107.07511","metadata_source":"pith","pith_arxiv_id":"2107.07511","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","venue":"cs.LG","work_id":"7721c193-e1ff-4cac-a0b6-ced585d69d3d","year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2107.07511","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:920db1e6885f327a9cce062205c5b71f482bda6096400fbf0e83b6c165d32c9c","observation_id":"4b516ce5-e01e-420f-8f35-d108e2a27cb4","resolution":{"observed_at":"2026-07-02T22:37:25.904412Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-05-21T14:50:51.253719+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-21T14:50:51.253719+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T18:46:59.707637Z","title":"Uncertainty sets for image classifiers using conformal prediction","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:fd0e42e96e84192cfce2240f1553e892811139e52ea64c7079dd6fd9ffc3dc28","observation_id":"b0cc59c0-9b1b-428d-bb74-087a5f9b63e8","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.06470","last_updated":"2021-07-18T16:17:28Z","snapshot_observed_at":"2026-08-08T06:24:54.305156Z","submitted_at":"2020-02-15T23:28:19Z","title":"Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning","version":4},"cited_work":{"arxiv_id":"2002.06470","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.06470","snapshot_observed_at":"2026-07-02T22:37:25.920402Z","title":"https://arxiv.org/ abs/2002.06470","venue":null,"work_id":"265689ed-0861-49a2-b037-381a76f357e7","year":2002},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2002.06470","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:80c012e4fe693cafce9c4b7a4d9f3911c7fcafca4b9f9e258441a1f0d895c35e","observation_id":"dd27abc0-9b89-478f-aa4a-7fdddac9a4cd","resolution":{"observed_at":"2026-07-02T22:37:25.922041Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T18:46:59.707637Z","title":"Machine learning for fluid mechanics","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:263f7778bb408b4cd20b81c73b05043e07b58954c19216e9cb391695907eb1a7","observation_id":"a5859ffb-c9df-443a-bd22-0f7a0968943e","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Conformal prediction: A unified review of theory and new challenges","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:44fac28de586c5d6207b01d19a97d704eab3832fd2c4e51ead6bc6f3f10faed7","observation_id":"ee8a11f0-174d-4d13-8e6d-a62044059c48","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:7eef3f90212d1dbf345b3dec9dd9c71c74406b9d9608b21e1cda5e053b3d10a3","observation_id":"4336d0b5-174d-45be-99a1-321b57087042","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.00170","last_updated":"2021-10-28T17:04:20Z","snapshot_observed_at":"2026-08-09T16:22:42.853136Z","submitted_at":"2021-06-01T01:37:32Z","title":"Adaptive Conformal Inference Under Distribution Shift","version":3},"cited_work":{"arxiv_id":"2106.00170","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.00170","snapshot_observed_at":"2026-07-03T10:37:56.488995Z","title":"Adaptive Conformal Inference Under Distribution Shift , year =","venue":null,"work_id":"f86750e9-49bc-42a1-a59e-542b7f9319b8","year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2106.00170","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:89fc1b37519b2b35f5e5c1cdede2155ad3e01606e8a0af21c00465fb05d5d9fc","observation_id":"4339448a-4f72-4eca-bc2a-2fa342601a1b","resolution":{"observed_at":"2026-07-02T22:37:25.925744Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T18:46:59.707637Z","title":"Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:1c445bc67ef75d6e0588edbac715c1e6e91c417e4f832ad3282c6784ba6433ec","observation_id":"f40789e7-9a85-42ad-8801-845cca998613","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Physics-informed machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:731305c66ebdace55e1758357a9a1fbdee5f6cb7c815e26fa3bd992d09dcd7a0","observation_id":"d58f7e98-1ae5-492d-8924-917a0a09cfa5","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Building high accuracy emulators for scientific simulations with deep neural operators","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:e66d9560e90a8f95bd21cae62c2bebe516322edf811d0af642eacf7c1fe1001d","observation_id":"e4acd7f9-5f98-4556-8925-03a32e18e4a9","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"What uncertainties do we need in bayesian deep learning for computer vision? In Advances in Neural Information Processing Systems (NeurIPS), 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:e10f8aae1e7171bee97934200c504d11ddd2b4828e14ec8df58daee30f7da3b8","observation_id":"aeea1359-5838-42e5-8e7a-274a558a4bf9","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Neural operator: Learning maps between function spaces with applications to pdes","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:f531d8e6bc9df8e2ac3aacbc7d647295e6d8b601246d770c5c259f30a6565fd3","observation_id":"18f1d3e0-7b53-446f-9d0b-41cf8fa6f0f5","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Simple and scalable predictive uncertainty estimation using deep ensembles","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:867a7256901fbc921b1630045e2aa5422e90e45623120dd99787b2b5337bf8c6","observation_id":"a8ddb448-5452-4ded-8e62-a060f49ccf59","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Distribution-free predictive inference for regression","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:91760eef71fbb5d9f87587255b7f36e2d7213c6e8e9999d497d8dae7402189a4","observation_id":"f284141e-bd4a-4f0d-b012-f06c1ff9b0cc","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Neural operator: Graph kernel network for partial differential equations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:60069df3621d2db40e9240601856998994f47b0d52a0c512ddee32dbdfa3f14f","observation_id":"602f2bb5-bbc7-43ea-a65c-3395bb7b2249","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Fourier neural operator for parametric partial differential equations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:fb5a37a1278e624ea92c8e21f89aaa00d8e5d3333e1acb2c4dd92cd763e528a1","observation_id":"8e5e4fac-ce1e-4d21-966f-bc389f4f7ce9","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.03089","last_updated":"2023-01-11T13:54:20Z","snapshot_observed_at":"2026-07-06T13:18:09.946150Z","submitted_at":"2022-06-07T08:13:01Z","title":"Transmission in strained graphene subjected to laser and magnetic fields","version":2},"cited_work":{"arxiv_id":"2206.03089","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.03089","snapshot_observed_at":"2026-07-02T22:37:25.927006Z","title":"Fourier neural operator with learned deformations for parametric partial differential equations","venue":null,"work_id":"a11735a6-41a3-4460-8f98-633fa8f5534b","year":2022},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2206.03089","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:80ff0bf55a98e14840eed76caa1de3bf0c58d24f7c2d0c69cb0d93aa91de7150","observation_id":"a3b5f8d3-2d7a-4009-b3ac-d35c0e9e1b6a","resolution":{"observed_at":"2026-07-02T22:37:25.928623Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.08088","last_updated":"2022-06-26T13:51:41Z","snapshot_observed_at":"2026-08-02T23:51:52.292486Z","submitted_at":"2022-02-16T14:21:28Z","title":"Latent Outlier Exposure for Anomaly Detection with Contaminated Data","version":3},"cited_work":{"arxiv_id":"2202.08088","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.08088","snapshot_observed_at":"2026-07-02T22:37:25.905560Z","title":"Multiscale transformers: Hierarchical attention for scalable vision and beyond","venue":null,"work_id":"a551abee-6bf1-4ecd-bc0b-aa1a1e8edc80","year":2022},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2202.08088","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:2abf140059092e380ff7dc4ff906d6d4d17836df021893b84c6eaee17874c5f5","observation_id":"6f6b0cc2-6db8-401f-9946-6c56e49aedb2","resolution":{"observed_at":"2026-07-02T22:37:25.907185Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.06022","last_updated":"2024-03-04T06:11:51Z","snapshot_observed_at":"2026-07-06T15:40:50.807376Z","submitted_at":"2023-06-09T16:41:34Z","title":"Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing","version":2},"cited_work":{"arxiv_id":"2306.06022","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.06022","snapshot_observed_at":"2026-07-02T22:37:25.921072Z","title":"Geometry-informed neural operator for large-scale 3d pdes","venue":null,"work_id":"778489c3-5e31-410f-b7f4-8bdfb5a571c2","year":2023},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2306.06022","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:3c70cf3ca63ebe094b687074e9505f499552fd3a6e7d4dc6fa9fc77fda0d3a37","observation_id":"c95d89c8-5c96-4895-972a-9ffcf73b01f4","resolution":{"observed_at":"2026-07-02T22:37:25.922623Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T18:46:59.707637Z","title":"Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:ed1c9ce0142783aaf8e1d5e63d5d9f6a48e4d97042f7ea92fa9b276e9409a3bd","observation_id":"91101285-bac5-475b-832a-2e6325b7d21c","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06578","last_updated":"2022-04-13T18:00:32Z","snapshot_observed_at":"2026-07-06T13:00:08.475336Z","submitted_at":"2022-04-13T18:00:32Z","title":"Long-term evolution of multimass rotating star clusters","version":1},"cited_work":{"arxiv_id":"2204.06578","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2204.06578","snapshot_observed_at":"2026-07-02T22:37:25.929740Z","title":"Estimating the uncertainty of neural network predictions using conformal prediction","venue":null,"work_id":"02c128aa-bf19-4d9b-a809-4d9c72386eb3","year":2022},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2204.06578","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:794ed23d6bc6ac102a3c916391391ea9b7a35c9ed0adc84ac26f01f3e8c783c1","observation_id":"8e8a2083-97a6-4348-bc08-97bca9fa98a6","resolution":{"observed_at":"2026-07-02T22:37:25.931268Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05601","last_updated":"2023-06-09T00:23:51Z","snapshot_observed_at":"2026-08-02T07:47:22.268124Z","submitted_at":"2023-06-09T00:23:51Z","title":"Topologically protected vortex transport via chiral-symmetric disclination","version":1},"cited_work":{"arxiv_id":"2306.05601","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2306.05601","snapshot_observed_at":"2026-07-02T22:37:25.899291Z","title":"Inducing normalizing flows for neural operators","venue":null,"work_id":"a2587c75-6325-46f0-9c3c-9a5c238c0f87","year":2023},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2306.05601","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:72d9270f28ed6a0fae7ce5376b135b8b6277a65ac2c328d64a23e3e6bcbc1181","observation_id":"20273fd9-1a4f-4060-9d03-8d904499dcf2","resolution":{"observed_at":"2026-07-02T22:37:25.901238Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T18:46:59.707637Z","title":"Nvidia modulus: A framework for building, training, and fine-tuning deep learning models using physics-based data","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:920b457a5be422f3e32fb2e6cf28a919cf753eec85757401deae4ad3bae30fc6","observation_id":"f410a36d-f82d-4f63-9e25-4e0386012d56","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Inductive confidence machines for regression","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:7fd2f68f3b696c110c81b400535dc8801267f8f13c374ea731e91e6e37b5451a","observation_id":"aab50d2a-652a-4e94-9650-c8fbb0a2461e","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.11214","last_updated":"2022-02-22T22:19:35Z","snapshot_observed_at":"2026-08-02T21:09:07.860947Z","submitted_at":"2022-02-22T22:19:35Z","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","version":1},"cited_work":{"arxiv_id":"2202.11214","doi":"10.48550/arxiv.2202.11214","metadata_source":"pith","pith_arxiv_id":"2202.11214","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","venue":"physics.ao-ph","work_id":"f9242607-7911-49ac-bf05-992acb925f65","year":2022},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2202.11214","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:d8cfbd2861d8c894c8b80a506bfe591c13870ec77a9789fcf554f8ccb84ab158","observation_id":"f5e19801-4eb3-48e8-858b-74762753b475","resolution":{"observed_at":"2026-07-02T22:37:25.919181Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T18:46:59.707637Z","title":"Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:fc1c8969f3adb5ea9687d8df2baa677d3c295aec3f91eae76490429f1e1a5706","observation_id":"99d7c6fe-0a52-43fc-8372-a3b6073191c8","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.08792","last_updated":"2021-11-02T11:12:26Z","snapshot_observed_at":"2026-07-06T09:39:02.518657Z","submitted_at":"2020-07-17T07:32:24Z","title":"Uncertainty Quantification and Deep Ensembles","version":4},"cited_work":{"arxiv_id":"2007.08792","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.08792","snapshot_observed_at":"2026-07-02T22:37:25.918215Z","title":"Uncertainty quantification and deep ensembles","venue":null,"work_id":"3fb0115c-7c27-4e54-a593-e6b420f1cf70","year":2007},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2007.08792","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:0f2db3ae7a0b5cb99a791023a8d9dd0c93e180ad78e453376d37626cc03609e2","observation_id":"7b2a01db-8bd7-4164-97cc-b793032a3cfc","resolution":{"observed_at":"2026-07-02T22:37:25.919732Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T18:46:59.707637Z","title":"Conformalized quantile regression","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:864a47aa13ec34e53e6f72d31104e5959647f0437075cdace6c1b596a44b3f4d","observation_id":"a3f94879-96b0-42db-aa29-49a163f7f7c1","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"With malice toward none: Assessing uncertainty via equalized coverage","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:0bd678d8db5a15b656437d15f39a8cb6ecd21aad1c14a15483cb6977a236ed73","observation_id":"82e9449e-c694-4ea0-b5f0-abd59fd719b7","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"A comparison of some conformal quantile regression methods","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:f3b651455a5a38357323ac2d7a02f92b83713c56c0d6776db0fb30e5385b5160","observation_id":"9a9aaf76-d3d3-4a1b-b40a-7d5d778fd42d","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"A tutorial on conformal prediction","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:eedbef3dbf559c87955a5e45187faafd7f94f95c26f0c90901e7dbb0ab8fdee3","observation_id":"b039da47-c3e6-4984-890a-ee53cc180fdc","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Learned simulators for turbulence","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:bfe22eb3a1fbe0ea8686f26039efb5fb30c8418f75321be8221d46ffc382017e","observation_id":"f4514e7f-d701-45ef-b2a3-75f1473a5b7a","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01256","last_updated":"2022-11-14T07:58:14Z","snapshot_observed_at":"2026-08-06T20:49:58.177355Z","submitted_at":"2022-06-02T19:13:03Z","title":"PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images","version":3},"cited_work":{"arxiv_id":"2206.01256","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.01256","snapshot_observed_at":"2026-07-02T22:37:25.923217Z","title":"Petrv2: A unified framework for 3d perception from multi-camera images","venue":null,"work_id":"92937331-a663-421a-b8bd-8c988247bb9c","year":2022},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"cited_paper":"/paper/2206.01256","citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:72643d6fdcac58242155e7016c31787ce47e89d85991ec40e7cadd9a40164590","observation_id":"d09d309a-32a6-45e9-869a-9391ab384bda","resolution":{"observed_at":"2026-07-02T22:37:25.924649Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-06-27T18:46:59.707637Z","title":"Algorithmic Learning in a Random World","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:2a53195805a38ea98438893ec6de07ff5de3e10fe0d2c801e7c70a75f755445f","observation_id":"da1322bc-9fd4-4f4e-9a29-54650b77c2a6","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","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":"2026-06-27T18:46:59.707637Z","title":"Conformal prediction interval for dynamic time-series","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-06-27T18:46:59.707637Z"},"links":{"citing_paper":"/paper/2606.09923"},"observation_digest":"sha256:87d07c6fdc20c828a77bfd896fb75a62c1cda00df4c1cd817eaef279c2dad6ed","observation_id":"50afcd27-d552-4362-9668-17e3ea69a4b5","resolution":{"observed_at":"2026-06-27T18:46:59.707637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.09923","last_updated":"2026-06-07T08:52:38Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T08:15:52.369732Z","submitted_at":"2026-06-07T08:52:38Z","title":"Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":24,"verified_exact":10,"verified_fuzzy":0},"total_outbound_references":35},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2606.09923."}