{"as_of":"2026-08-10T11:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bf13d01d0b9819fe7da8274de91f7a612cd97030081194f1bb5f95a5083c3d8b","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":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T11:05:12.883348Z","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":"2026-06-29T22:04:00.547273Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-08-06T11:05:12.883348Z","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.00037","last_updated":"2025-07-31T01:24:01Z","snapshot_observed_at":"2026-08-06T11:05:06.034184Z","submitted_at":"2025-07-31T01:24:01Z","title":"Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T11:05:12.883348Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2508.00037"},"observation_digest":"sha256:133b50e3116fb6c2be0f5466983cec1d186cd8d9d1b94015d27c1d05bfa28ad5","observation_id":"3219deb4-fbed-4acd-bba5-0a2573f90135","resolution":{"observed_at":"2026-08-06T11:05:12.883348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-08-04T12:39:06.532399Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.03381","last_updated":"2026-06-05T07:39:08Z","snapshot_observed_at":"2026-08-09T23:15:13.472108Z","submitted_at":"2025-10-03T15:26:56Z","title":"Proxy Reconstruction Pre-training for Ramp Flow Prediction at Highway Interchanges","version":3},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-04T12:39:06.532399Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2510.03381"},"observation_digest":"sha256:b58e6aea8f427dee7a5b31d162c819ca41ba7f905d18a71330ccaef7bf9c1114","observation_id":"835afc26-9730-4367-972a-20ceb7b75f77","resolution":{"observed_at":"2026-08-04T12:39:06.532399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":"2206.09112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-06-29T22:04:00.547273Z","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","venue":null,"work_id":"6c5f7791-864d-4c46-a077-8dc8a53a3e33","year":2022},"citing_paper":{"arxiv_id":"2603.05301","last_updated":"2026-05-09T10:12:15Z","snapshot_observed_at":"2026-07-06T22:47:59.530407Z","submitted_at":"2026-03-05T15:42:03Z","title":"Uniform Inductive Spatio-Temporal Kriging","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-15T16:28:01.222142Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2603.05301"},"observation_digest":"sha256:cd5c729bff17f9b57271e1f83dfbd0fdf8b9e8fb83b22ffd496443a60a7bd795","observation_id":"201d456e-b45b-4fbf-8aa1-b8b968e99ec3","resolution":{"observed_at":"2026-05-15T16:30:09.739766Z","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":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-07-15T14:41:18.515733Z","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.05310","last_updated":"2026-06-25T07:03:22Z","snapshot_observed_at":"2026-08-08T13:12:16.880009Z","submitted_at":"2026-03-05T15:51:09Z","title":"Latent-Mark: An Audio Watermark Robust to Neural Codec Compression","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-15T14:41:18.515733Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2603.05310"},"observation_digest":"sha256:c1f10d5ef1907be38b72f204dee0bdac1876225edbc110d9ca4164e0d26cd69f","observation_id":"f076bc54-7411-4780-ba4e-c1360d6fa2f2","resolution":{"observed_at":"2026-07-15T14:41:18.515733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":"2206.09112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-06-29T22:04:00.547273Z","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","venue":null,"work_id":"6c5f7791-864d-4c46-a077-8dc8a53a3e33","year":2022},"citing_paper":{"arxiv_id":"2605.05854","last_updated":"2026-05-07T08:25:43Z","snapshot_observed_at":"2026-08-09T00:53:51.383566Z","submitted_at":"2026-05-07T08:25:43Z","title":"AirQualityBench: A Realistic Evaluation Benchmark for Global Air Quality Forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-08T11:16:49.338637Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2605.05854"},"observation_digest":"sha256:9a4754a559448ae6180317bc837015fb9eefc96c38114c8b98102dcec21e88bc","observation_id":"3ce63ccb-dde2-4e6c-9311-34938ffce602","resolution":{"observed_at":"2026-05-11T19:41:09.151795Z","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":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":"2206.09112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-06-29T22:04:00.547273Z","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","venue":null,"work_id":"6c5f7791-864d-4c46-a077-8dc8a53a3e33","year":2022},"citing_paper":{"arxiv_id":"2605.09208","last_updated":"2026-05-09T22:55:52Z","snapshot_observed_at":"2026-07-06T23:21:21.560069Z","submitted_at":"2026-05-09T22:55:52Z","title":"TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-12T02:03:09.116351Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2605.09208"},"observation_digest":"sha256:55917dc88b265480f7448f089b3f531e72b1894dc919a29f809c75ab8c575b4e","observation_id":"68ff3f6c-2706-41dd-b80f-e5031bf650ef","resolution":{"observed_at":"2026-05-12T02:06:15.284885Z","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":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":"2206.09112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-06-29T22:04:00.547273Z","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","venue":null,"work_id":"6c5f7791-864d-4c46-a077-8dc8a53a3e33","year":2022},"citing_paper":{"arxiv_id":"2605.25543","last_updated":"2026-05-25T08:00:02Z","snapshot_observed_at":"2026-08-09T23:15:06.749509Z","submitted_at":"2026-05-25T08:00:02Z","title":"ADMFormer: An Adaptive-Decomposition Transformer with Time-Varying Masked Spatial Attention for Traffic Forecasting","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T21:58:37.536550Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2605.25543"},"observation_digest":"sha256:1e3c377ee0bdf61c211d3f285d8d63ce9d90851a78059a3be5fc6c44529262ac","observation_id":"c9a61593-3f87-4063-bf9f-4c92e7b9c4d2","resolution":{"observed_at":"2026-06-29T22:04:00.458810Z","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":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":"2206.09112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-06-29T22:04:00.547273Z","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","venue":null,"work_id":"6c5f7791-864d-4c46-a077-8dc8a53a3e33","year":2022},"citing_paper":{"arxiv_id":"2605.25554","last_updated":"2026-05-25T08:10:16Z","snapshot_observed_at":"2026-07-06T23:35:31.372756Z","submitted_at":"2026-05-25T08:10:16Z","title":"PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T21:55:30.343816Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2605.25554"},"observation_digest":"sha256:7af6a23e4c2e1de93f4add4e00e0b73e8deca69e2af409fa1d26ae2923e54dc1","observation_id":"0c129e82-8bf1-4135-bd98-9fe2f024ff45","resolution":{"observed_at":"2026-06-29T22:04:00.548679Z","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":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":"2206.09112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-06-29T22:04:00.547273Z","title":"Decoupled dynamic spatial-temporal graph neural network for traffic forecasting","venue":null,"work_id":"6c5f7791-864d-4c46-a077-8dc8a53a3e33","year":2022},"citing_paper":{"arxiv_id":"2605.27884","last_updated":"2026-05-27T03:07:53Z","snapshot_observed_at":"2026-08-09T23:15:12.318251Z","submitted_at":"2026-05-27T03:07:53Z","title":"A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T13:55:31.399633Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2605.27884"},"observation_digest":"sha256:b7a6e74e098d44d92b68c121f926269f3298802255d0b683d364f8185d510700","observation_id":"ca8a9135-2b1f-4343-8385-834d54d03662","resolution":{"observed_at":"2026-06-29T14:03:29.702686Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2206.09112","last_updated":"2022-09-05T01:56:09Z","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.09112","snapshot_observed_at":"2026-07-15T05:55:25.279392Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.12462","last_updated":"2026-07-29T09:32:51Z","snapshot_observed_at":"2026-08-06T18:28:11.420100Z","submitted_at":"2026-07-14T07:44:01Z","title":"Do We Really Need Adaptive Global Spatial Attention for Traffic Forecasting?","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-15T05:55:25.279392Z"},"links":{"cited_paper":"/paper/2206.09112","citing_paper":"/paper/2607.12462"},"observation_digest":"sha256:0c03f30cb2487995b1fa8cd9f1defd0717cd5a0bd8714fe2326a5b395bd651ba","observation_id":"bbb3ed58-104b-4d65-9a35-d06e80e23b83","resolution":{"observed_at":"2026-07-15T05:55:25.279392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2206.09112/citation-record","integrity":"/paper/2206.09112/integrity","json":"/paper/2206.09112/citation-record.json","paper":"/paper/2206.09112"},"outbound":[],"paper":{"arxiv_id":"2206.09112","last_updated":"2022-09-05T01:56:09Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T11:21:47.320755Z","submitted_at":"2022-06-18T04:14:38Z","title":"Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting"},"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 10 inbound Pith citation observations for arXiv:2206.09112."}