{"as_of":"2026-08-19T23:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a6356508529c4cd47b14cce051a691a201e598b72e95c1952a7b6349aa761771","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":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":13,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:07:02.542974Z","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":58,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-11T20:54:26.658593Z","title":"Nvidia flare: Federated learning from simulation to real-world,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.05183","last_updated":"2024-12-06T17:04:09Z","snapshot_observed_at":"2026-08-18T11:59:57.968575Z","submitted_at":"2024-12-06T17:04:09Z","title":"Privacy Drift: Evolving Privacy Concerns in Incremental Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T20:54:26.658593Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2412.05183"},"observation_digest":"sha256:15373796ea1b9e69833b852267f8932255b99652ceec3866466e96a33d7962fe","observation_id":"712d9831-118f-473c-acb6-5c3b6f053b70","resolution":{"observed_at":"2026-08-11T20:54:26.658593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-11T13:24:42.837784Z","title":"URLhttps://arxiv","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.13163","last_updated":"2024-12-18T21:26:14Z","snapshot_observed_at":"2026-08-15T18:55:12.401108Z","submitted_at":"2024-12-17T18:42:21Z","title":"C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T13:24:42.837784Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2412.13163"},"observation_digest":"sha256:56cbeb1e511b01d9d0ad951d9fcaa917c7ab3ff15dd3fcc9b8a4a924c519d8a4","observation_id":"babf39e9-021e-4ede-b121-625188733540","resolution":{"observed_at":"2026-08-11T13:24:42.837784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-10T21:42:32.088147Z","title":"Patel, K","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.04443","last_updated":"2025-02-24T08:50:58Z","snapshot_observed_at":"2026-08-16T04:59:38.031266Z","submitted_at":"2025-01-08T11:52:43Z","title":"Revisiting LocalSGD and SCAFFOLD: Improved Rates and Missing Analysis","version":3},"reference_index":4157,"source":"pdf_text","source_observed_at":"2026-08-10T21:42:32.088147Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2501.04443"},"observation_digest":"sha256:2dad2b84c352a5c3f0a3bcd27992bfd067bb6c47de5dde05a4e6af07336fe5b0","observation_id":"4194786f-af0a-428c-bc5c-7616115cc84c","resolution":{"observed_at":"2026-08-10T21:42:32.088147Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-15T22:07:02.542974Z","title":"Nvidia flare: Federated learning from simulation to real-world,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.08085","last_updated":"2025-05-12T21:40:35Z","snapshot_observed_at":"2026-08-18T02:12:00.234992Z","submitted_at":"2025-05-12T21:40:35Z","title":"A Federated Random Forest Solution for Secure Distributed Machine Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T22:07:02.542974Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2505.08085"},"observation_digest":"sha256:3dec4a476c98b297bf4d429bdc6518c687d7052c00dd0d3b900bd590d98d35c0","observation_id":"8d60c5fc-d42a-430f-8e63-4d45b2166216","resolution":{"observed_at":"2026-08-15T22:07:02.542974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-15T21:53:23.265672Z","title":"Nvidiaflare:Federated learning from simulation to real-world","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.08646","last_updated":"2025-05-13T15:04:55Z","snapshot_observed_at":"2026-08-18T10:35:24.679323Z","submitted_at":"2025-05-13T15:04:55Z","title":"Modular Federated Learning: A Meta-Framework Perspective","version":1},"reference_index":224,"source":"pdf_text","source_observed_at":"2026-08-15T21:53:23.265672Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2505.08646"},"observation_digest":"sha256:c3bc785711f6d276e6edb8ceb9149b8f14b11b58c0b1935c72e2f69f50a34a74","observation_id":"159c3a30-e963-4810-8fce-1ef9c30efb90","resolution":{"observed_at":"2026-08-15T21:53:23.265672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-07T12:42:31.594274Z","title":"Nvidia flare: Federated learning from simulation to real-world,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.23710","last_updated":"2025-05-29T17:45:02Z","snapshot_observed_at":"2026-08-18T22:43:30.940825Z","submitted_at":"2025-05-29T17:45:02Z","title":"From Connectivity to Autonomy: The Dawn of Self-Evolving Communication Systems","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:42:31.594274Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2505.23710"},"observation_digest":"sha256:ed5ec829c0917ea842db363c26191be89b2f1602589b6c0d0e5f68bd1b7a3abf","observation_id":"2449db9e-25bf-40c1-b09b-dcf0ef52cb1e","resolution":{"observed_at":"2026-08-07T12:42:31.594274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-07T05:38:39.414041Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07605","last_updated":"2025-07-13T17:21:43Z","snapshot_observed_at":"2026-08-14T02:10:27.315544Z","submitted_at":"2025-06-09T10:06:03Z","title":"TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T05:38:39.414041Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2506.07605"},"observation_digest":"sha256:26332edb6dc6c03351ab67f8ede37c3a5ea26769b9d0bf8ed1a27c6d4b19a546","observation_id":"86837bfc-f495-43ce-9759-08711a0b878f","resolution":{"observed_at":"2026-08-07T05:38:39.414041Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-06T01:02:28.515340Z","title":null,"venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2508.04100","last_updated":"2025-08-06T05:42:41Z","snapshot_observed_at":"2026-08-19T22:48:35.763639Z","submitted_at":"2025-08-06T05:42:41Z","title":"SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-06T01:02:28.515340Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2508.04100"},"observation_digest":"sha256:e825e3c0b5b7cf8deb5fb14c3477d72b6d93ffb7e3710eaef8bc3c105f19ac35","observation_id":"7b446025-0d94-49ab-8778-0a26587e85a3","resolution":{"observed_at":"2026-08-06T01:02:28.515340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":"2210.13291","doi":"10.48550/arxiv.2210.13291","metadata_source":"arxiv_reference","pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"26 Somasundaram E, Taylor Z, Alves V V ., Qiu L, Fortson BL, Mahalingam N et al","venue":"arXiv (Cornell University)","work_id":"ee65983d-4551-4fce-9e7c-3de903b9499b","year":2022},"citing_paper":{"arxiv_id":"2512.16455","last_updated":"2026-06-27T22:47:04Z","snapshot_observed_at":"2026-08-11T08:10:46.251699Z","submitted_at":"2025-12-18T12:20:31Z","title":"AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research","version":3},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-16T21:31:35.045436Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2512.16455"},"observation_digest":"sha256:7ec247f1a6eb3a319bee2c7460257bf3f42593c4d76c60d0f9ec522d20b3e501","observation_id":"f463972b-d646-4c59-8871-de7428634fca","resolution":{"observed_at":"2026-05-16T21:33:33.339894Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-03T15:33:25.890801Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.16455","last_updated":"2026-06-27T22:47:04Z","snapshot_observed_at":"2026-08-11T08:10:46.251699Z","submitted_at":"2025-12-18T12:20:31Z","title":"AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research","version":4},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-03T15:33:25.890801Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2512.16455"},"observation_digest":"sha256:d1b38d91919d5fc907004fa8f5790b672921e0c376f2655505c841ce3088b571","observation_id":"bd15dccc-be44-487c-9ba5-ce730515dae8","resolution":{"observed_at":"2026-08-03T15:33:25.890801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":"2210.13291","doi":"10.48550/arxiv.2210.13291","metadata_source":"arxiv_reference","pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"26 Somasundaram E, Taylor Z, Alves V V ., Qiu L, Fortson BL, Mahalingam N et al","venue":"arXiv (Cornell University)","work_id":"ee65983d-4551-4fce-9e7c-3de903b9499b","year":2022},"citing_paper":{"arxiv_id":"2605.06820","last_updated":"2026-05-07T18:21:43Z","snapshot_observed_at":"2026-08-11T12:24:05.006164Z","submitted_at":"2026-05-07T18:21:43Z","title":"Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-11T01:01:18.837912Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2605.06820"},"observation_digest":"sha256:0ff29669018f179db6735d053e6d149b1fea8d24ad8e41cf3def2eff1d336125","observation_id":"38416e5b-9268-4b63-8d52-c95b8134e5b6","resolution":{"observed_at":"2026-05-11T01:05:50.961082Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":"2210.13291","doi":"10.48550/arxiv.2210.13291","metadata_source":"arxiv_reference","pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"26 Somasundaram E, Taylor Z, Alves V V ., Qiu L, Fortson BL, Mahalingam N et al","venue":"arXiv (Cornell University)","work_id":"ee65983d-4551-4fce-9e7c-3de903b9499b","year":2022},"citing_paper":{"arxiv_id":"2605.21103","last_updated":"2026-05-20T12:34:24Z","snapshot_observed_at":"2026-08-07T16:37:21.688135Z","submitted_at":"2026-05-20T12:34:24Z","title":"A Typed Tensor Language for Federated Learning","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-21T06:33:24.400786Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2605.21103"},"observation_digest":"sha256:4ba37cc50fed400efa1dd812724b72c9a684990f464f56b2e6650898b07cdaf9","observation_id":"e3542640-b500-484c-9afe-f75f956e6e8b","resolution":{"observed_at":"2026-05-21T06:34:00.659284Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","version":3},"cited_work":{"arxiv_id":"2210.13291","doi":"10.48550/arxiv.2210.13291","metadata_source":"arxiv_reference","pith_arxiv_id":"2210.13291","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"26 Somasundaram E, Taylor Z, Alves V V ., Qiu L, Fortson BL, Mahalingam N et al","venue":"arXiv (Cornell University)","work_id":"ee65983d-4551-4fce-9e7c-3de903b9499b","year":2022},"citing_paper":{"arxiv_id":"2607.01366","last_updated":"2026-07-01T18:28:09Z","snapshot_observed_at":"2026-08-17T07:41:47.543173Z","submitted_at":"2026-07-01T18:28:09Z","title":"Auto-FL-Research: Agentic Search for Federated Learning Algorithms","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-03T20:33:30.815266Z"},"links":{"cited_paper":"/paper/2210.13291","citing_paper":"/paper/2607.01366"},"observation_digest":"sha256:c4599e572acf1afa2d4f2ce709b41981a8b1bc50ccd1e4f24e4eb18248e98853","observation_id":"0d5bac93-0801-42ca-b93f-d01fd8a42980","resolution":{"observed_at":"2026-07-03T20:38:55.128423Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2210.13291/citation-record","integrity":"/paper/2210.13291/integrity","json":"/paper/2210.13291/citation-record.json","paper":"/paper/2210.13291"},"outbound":[],"paper":{"arxiv_id":"2210.13291","last_updated":"2023-04-28T22:35:18Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T14:04:48.705601Z","submitted_at":"2022-10-24T14:30:50Z","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2210.13291."}