{"as_of":"2026-08-10T08:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f848d0b8e0491044e0e08a455a2391bd8d840b9288b7ef3f23891f115e235dee","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":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":8,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T16:44:22.855657Z","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":"2026-07-02T00:16:23.976549Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks","version":2},"cited_work":{"arxiv_id":"2406.11045","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.11045","snapshot_observed_at":"2026-07-02T00:16:23.976549Z","title":"Kolmogorov arnold informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on kolmogorov arnold networks, 2024 c","venue":null,"work_id":"00570a51-14eb-40b4-8378-da4e730ee0de","year":2024},"citing_paper":{"arxiv_id":"2410.01990","last_updated":"2026-05-14T18:26:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-02T19:53:14Z","title":"Deep Learning Alternatives of the Kolmogorov Superposition Theorem","version":3},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-23T19:58:00.914732Z"},"links":{"cited_paper":"/paper/2406.11045","citing_paper":"/paper/2410.01990"},"observation_digest":"sha256:6a02913999fd124f3a2a84425f493bd403b430770fdd3dc7c1c103861b58effa","observation_id":"85eb5eb0-9366-454d-b97c-7411885b3cd5","resolution":{"observed_at":"2026-05-23T19:58:23.517358Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks","version":2},"cited_work":{"arxiv_id":"2406.11045","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.11045","snapshot_observed_at":"2026-07-02T00:16:23.976549Z","title":"Kolmogorov arnold informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on kolmogorov arnold networks, 2024 c","venue":null,"work_id":"00570a51-14eb-40b4-8378-da4e730ee0de","year":2024},"citing_paper":{"arxiv_id":"2410.04096","last_updated":"2026-05-27T13:27:42Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-05T09:33:39Z","title":"Sinc Kolmogorov-Arnold network and its application for solving PDEs with singularities","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-23T19:42:53.470132Z"},"links":{"cited_paper":"/paper/2406.11045","citing_paper":"/paper/2410.04096"},"observation_digest":"sha256:7e60dd78e7a220d1252aa37783ff97bdc0d8ddf15af84bb39bb1a9f7f514d428","observation_id":"fe250b7d-c521-40b7-bbc0-02f7b1f3eaba","resolution":{"observed_at":"2026-05-23T19:43:22.893495Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks","version":2},"cited_work":{"arxiv_id":"2406.11045","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.11045","snapshot_observed_at":"2026-07-02T00:16:23.976549Z","title":"Kolmogorov arnold informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on kolmogorov arnold networks, 2024 c","venue":null,"work_id":"00570a51-14eb-40b4-8378-da4e730ee0de","year":2024},"citing_paper":{"arxiv_id":"2410.04435","last_updated":"2026-05-13T08:15:37Z","snapshot_observed_at":"2026-07-06T19:28:35.110264Z","submitted_at":"2024-10-06T10:11:57Z","title":"QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-23T20:14:02.080356Z"},"links":{"cited_paper":"/paper/2406.11045","citing_paper":"/paper/2410.04435"},"observation_digest":"sha256:42b0502bbd39cfb2e2d84b1c6e77ee1041c4a075812a8b575c73f286ce2e8e4f","observation_id":"881a4ae9-fce9-415a-af56-ca5f758d3b44","resolution":{"observed_at":"2026-05-23T20:15:48.274014Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11045","snapshot_observed_at":"2026-08-08T16:44:22.855657Z","title":"Shiyu Wang, Haixu Wu, Xiaoming Shi, Tengge Hu, Huakun Luo, Lintao Ma, James Y","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.06910","last_updated":"2025-02-26T09:04:40Z","snapshot_observed_at":"2026-08-10T00:18:07.087470Z","submitted_at":"2025-02-10T03:51:26Z","title":"TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T16:44:22.855657Z"},"links":{"cited_paper":"/paper/2406.11045","citing_paper":"/paper/2502.06910"},"observation_digest":"sha256:1bee8960960cfdad09683ae9a4721c55391024ae07c868a09af233983638e420","observation_id":"eec619d3-c492-4980-b738-13dc1e4896f9","resolution":{"observed_at":"2026-08-08T16:44:22.855657Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11045","snapshot_observed_at":"2026-08-06T13:58:32.412078Z","title":"arXiv preprint arXiv:2406.11045 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.19888","last_updated":"2025-07-26T09:36:18Z","snapshot_observed_at":"2026-08-09T06:46:06.385009Z","submitted_at":"2025-07-26T09:36:18Z","title":"Multi-Resolution Training-Enhanced Kolmogorov-Arnold Networks for Multi-Scale PDE Problems","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T13:58:32.412078Z"},"links":{"cited_paper":"/paper/2406.11045","citing_paper":"/paper/2507.19888"},"observation_digest":"sha256:8b6faf235c29d0d603cb72c3567f05c60022f10cc77894c707d18c9d18fb7e39","observation_id":"32a989f9-0a01-4139-beaf-abf72d98f397","resolution":{"observed_at":"2026-08-06T13:58:32.412078Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks","version":2},"cited_work":{"arxiv_id":"2406.11045","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.11045","snapshot_observed_at":"2026-07-02T00:16:23.976549Z","title":"Kolmogorov arnold informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on kolmogorov arnold networks, 2024 c","venue":null,"work_id":"00570a51-14eb-40b4-8378-da4e730ee0de","year":2024},"citing_paper":{"arxiv_id":"2605.06740","last_updated":"2026-05-07T14:24:53Z","snapshot_observed_at":"2026-08-02T10:02:24.244371Z","submitted_at":"2026-05-07T14:24:53Z","title":"Geometric Kolmogorov--Arnold Network (GeoKAN)","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-11T01:19:01.262852Z"},"links":{"cited_paper":"/paper/2406.11045","citing_paper":"/paper/2605.06740"},"observation_digest":"sha256:4948b84d3ad1d31a13b81ec28b6c3abf35b503fc369eb9917c6d92cc029333cd","observation_id":"767ef72b-bc14-424f-9b0b-fdba7f1916c8","resolution":{"observed_at":"2026-05-11T04:30:56.003534Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks","version":2},"cited_work":{"arxiv_id":"2406.11045","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.11045","snapshot_observed_at":"2026-07-02T00:16:23.976549Z","title":"Kolmogorov arnold informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on kolmogorov arnold networks, 2024 c","venue":null,"work_id":"00570a51-14eb-40b4-8378-da4e730ee0de","year":2024},"citing_paper":{"arxiv_id":"2605.29688","last_updated":"2026-05-28T09:51:54Z","snapshot_observed_at":"2026-08-09T13:52:23.598593Z","submitted_at":"2026-05-28T09:51:54Z","title":"A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-29T09:02:09.817046Z"},"links":{"cited_paper":"/paper/2406.11045","citing_paper":"/paper/2605.29688"},"observation_digest":"sha256:682efb31bc245080a99e54b4be04091de93e75ff8c1cb1ac8957c87542febfd6","observation_id":"b891ec09-add5-4654-b49a-31f16477a277","resolution":{"observed_at":"2026-06-29T09:03:15.601237Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks","version":2},"cited_work":{"arxiv_id":"2406.11045","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.11045","snapshot_observed_at":"2026-07-02T00:16:23.976549Z","title":"Kolmogorov arnold informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on kolmogorov arnold networks, 2024 c","venue":null,"work_id":"00570a51-14eb-40b4-8378-da4e730ee0de","year":2024},"citing_paper":{"arxiv_id":"2606.02335","last_updated":"2026-06-01T14:44:48Z","snapshot_observed_at":"2026-08-06T09:18:04.025655Z","submitted_at":"2026-06-01T14:44:48Z","title":"Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T13:33:19.259276Z"},"links":{"cited_paper":"/paper/2406.11045","citing_paper":"/paper/2606.02335"},"observation_digest":"sha256:038f907271c974d68d2de9313dbf5d275573b1002eca87e06e1125b4837c064d","observation_id":"ba475d4a-2e60-466f-8dbe-3df8ce10df1c","resolution":{"observed_at":"2026-07-02T00:16:23.978910Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.11045/citation-record","integrity":"/paper/2406.11045/integrity","json":"/paper/2406.11045/citation-record.json","paper":"/paper/2406.11045"},"outbound":[],"paper":{"arxiv_id":"2406.11045","last_updated":"2024-08-04T05:12:25Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T07:30:26.112943Z","submitted_at":"2024-06-16T19:07:06Z","title":"Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.11045."}