{"as_of":"2026-08-20T01:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8acba40b372e16594bcf0ec6418018635810858a01b5621ae08605df37a10897","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T14:54:23.215863Z","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":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/2512.18921/citation-record","integrity":"/paper/2512.18921/integrity","json":"/paper/2512.18921/citation-record.json","paper":"/paper/2512.18921"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.19756","last_updated":"2025-02-09T21:09:09Z","snapshot_observed_at":"2026-08-15T02:33:31.807561Z","submitted_at":"2024-04-30T17:58:29Z","title":"KAN: Kolmogorov-Arnold Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.19756","snapshot_observed_at":"2026-08-03T14:54:22.527769Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.527769Z"},"links":{"cited_paper":"/paper/2404.19756","citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:0c83e19f4c02cb6d703766f4e98bd8e28c1df942da42d9d706d35a615f82edff","observation_id":"c8f1a774-ea4a-4f73-96e0-b60d8d7d263e","resolution":{"observed_at":"2026-08-03T14:54:22.527769Z","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":"2026-08-03T14:54:22.594762Z","title":"Igelnik and N","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.594762Z"},"links":{"citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:047af8c149b545e1cd341046b10d07d84ed89bdc2717080d7e1e58901c7f2d10","observation_id":"26b57cc4-9e96-40a3-9431-6efd37576e68","resolution":{"observed_at":"2026-08-03T14:54:22.594762Z","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":"2026-08-03T14:54:22.684736Z","title":"Poluektov and A","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.684736Z"},"links":{"citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:d7add7feb3ec44374e10b0177658624660695ef220f40f0e527de2b5fbd4dacd","observation_id":"6d7fc37f-826d-4b52-bcad-1cfab608177c","resolution":{"observed_at":"2026-08-03T14:54:22.684736Z","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":"2026-08-03T14:54:22.754211Z","title":"Montanelli and H","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.754211Z"},"links":{"citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:baf82d7ef64edc9c989aeb281eeb4c0d92011a66e94b611a98a6d210dc8e6aa8","observation_id":"e3355190-c3e7-423f-8360-0f6205e14f97","resolution":{"observed_at":"2026-08-03T14:54:22.754211Z","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":"2026-08-03T14:54:22.770871Z","title":"Polar and M","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.770871Z"},"links":{"citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:c7a2b05bbe5db5784ee5b9a3a2a979c7979af3d3cc26f8cda690db6337e0cba8","observation_id":"13eb678d-e6b6-4083-a6e0-71f6346d973f","resolution":{"observed_at":"2026-08-03T14:54:22.770871Z","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":"2026-08-03T14:54:22.817302Z","title":"Kolmogorov–arnold networks are radial basis function networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.817302Z"},"links":{"citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:c3312d41f323cda5d155504366b5e31084d2cf195ed8f59fb20b6c65e3ef080c","observation_id":"703235d4-85c8-425b-b6a5-d923ca00f989","resolution":{"observed_at":"2026-08-03T14:54:22.817302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.20100","last_updated":"2024-11-08T19:02:09Z","snapshot_observed_at":"2026-08-19T06:28:34.839162Z","submitted_at":"2024-07-29T15:28:26Z","title":"F-KANs: Federated Kolmogorov-Arnold Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.20100","snapshot_observed_at":"2026-08-03T14:54:22.874434Z","title":"Vaca-Rubio, Luis Blanco, Roberto Pereira, Marius Caus, and Abdullah Aydeger","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.874434Z"},"links":{"cited_paper":"/paper/2407.20100","citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:56a843ef9aa20a29b029b0008bb5d91eaf30c1d29aabac9be1064cfbc47994f8","observation_id":"0f68fca9-3727-4a2a-a215-8110718a56d0","resolution":{"observed_at":"2026-08-03T14:54:22.874434Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08961","last_updated":"2024-10-11T16:30:04Z","snapshot_observed_at":"2026-08-16T13:10:15.735847Z","submitted_at":"2024-10-11T16:30:04Z","title":"Evaluating Federated Kolmogorov-Arnold Networks on Non-IID Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08961","snapshot_observed_at":"2026-08-03T14:54:22.907611Z","title":"Evaluating federated kolmogorov-arnold networks on non-iid data.arXiv preprint, abs/2410.08961, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.907611Z"},"links":{"cited_paper":"/paper/2410.08961","citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:f49e1807ef1316ff4d0957fa40267834777f5b0a598868d0b42885d605e56f41","observation_id":"eccc15c4-573c-4e22-92f4-4e1d8efb6ec0","resolution":{"observed_at":"2026-08-03T14:54:22.907611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07629","last_updated":"2025-05-12T14:56:27Z","snapshot_observed_at":"2026-08-18T11:23:33.012625Z","submitted_at":"2025-05-12T14:56:27Z","title":"Enhancing Federated Learning with Kolmogorov-Arnold Networks: A Comparative Study Across Diverse Aggregation Strategies","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.07629","snapshot_observed_at":"2026-08-03T14:54:22.946411Z","title":"Enhancing federated learning with kolmogorov- arnold networks: A comparative study across diverse aggregation strategies.arXiv preprint, abs/2505.07629, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:22.946411Z"},"links":{"cited_paper":"/paper/2505.07629","citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:5f5990161cf33c8015bef0d905365af9f694ef259cf896aea63d2c22dbebf917","observation_id":"e2a3548b-b03a-4ccf-8d62-38860bc9460d","resolution":{"observed_at":"2026-08-03T14:54:22.946411Z","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":"2026-08-03T14:54:23.000463Z","title":"A unified benchmark of federated learning with kolmogorov-arnold networks for medical imaging.arXiv preprint, abs/2504.19639, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:23.000463Z"},"links":{"citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:aea003946af1037d933f66698caacbf64425adfa41bf79a5b04993db4adbfaf7","observation_id":"7a016525-f24b-4873-9e24-0894afde7cc2","resolution":{"observed_at":"2026-08-03T14:54:23.000463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.12850","last_updated":"2026-06-16T20:05:33Z","snapshot_observed_at":"2026-08-18T16:52:11.673069Z","submitted_at":"2025-12-14T21:29:10Z","title":"KANEL\\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.12850","snapshot_observed_at":"2026-08-03T14:54:23.071828Z","title":"KANELE: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation.arXiv preprint arXiv:2512.12850, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:23.071828Z"},"links":{"cited_paper":"/paper/2512.12850","citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:2aa69b72940c1f4f5ac573498a9b8ebf581e0c40e01a683b056d4992b6135cdf","observation_id":"e70dd4e0-7158-47f2-92be-e018faa52b47","resolution":{"observed_at":"2026-08-03T14:54:23.071828Z","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":"2026-08-03T14:54:23.144735Z","title":null,"venue":null,"work_id":null,"year":1979},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:23.144735Z"},"links":{"citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:c202769943cd30cc2c412de37c4eccc351cac883af16a7eb570bff3086014a69","observation_id":"070fc9e7-0a25-41ab-b46e-918693245885","resolution":{"observed_at":"2026-08-03T14:54:23.144735Z","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":"2026-08-03T14:54:23.215863Z","title":null,"venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation","version":5},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T14:54:23.215863Z"},"links":{"citing_paper":"/paper/2512.18921"},"observation_digest":"sha256:b8c9f90c1a773a7fad4297627cf1bb6df24a04a7a6e97e6c31c511289470e99c","observation_id":"e9a270f0-b774-4e7d-a07b-b1d0d30969f2","resolution":{"observed_at":"2026-08-03T14:54:23.215863Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.18921","last_updated":"2026-07-30T00:51:38Z","latest_version":5,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T15:41:09.094142Z","submitted_at":"2025-12-21T23:41:34Z","title":"Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":13},"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 20 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2512.18921."}