{"as_of":"2026-08-12T17:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:770f3b13eba493c59be48298b3fe9b7c201ea93bb5a97abbf5d6e3b84de1a712","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T13:20:06.479667Z","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":"2026-07-04T00:49:18.652717Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.01026","last_updated":"2023-09-27T22:04:41Z","snapshot_observed_at":"2026-08-12T12:51:41.445187Z","submitted_at":"2023-07-03T13:58:20Z","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01026","snapshot_observed_at":"2026-08-11T13:20:06.479667Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.13283","last_updated":"2024-12-17T19:27:24Z","snapshot_observed_at":"2026-08-11T13:14:28.842784Z","submitted_at":"2024-12-17T19:27:24Z","title":"Enhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approach","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T13:20:06.479667Z"},"links":{"cited_paper":"/paper/2307.01026","citing_paper":"/paper/2412.13283"},"observation_digest":"sha256:ffcf4055824daecfe44f1359c87b2d4263dea98df36737fc83559796bcf6b828","observation_id":"e4ef822a-8572-415a-98f5-2406c592dab5","resolution":{"observed_at":"2026-08-11T13:20:06.479667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01026","last_updated":"2023-09-27T22:04:41Z","snapshot_observed_at":"2026-08-12T12:51:41.445187Z","submitted_at":"2023-07-03T13:58:20Z","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01026","snapshot_observed_at":"2026-08-10T23:21:33.854214Z","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs , September 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.21046","last_updated":"2024-12-30T16:07:41Z","snapshot_observed_at":"2026-08-12T00:13:44.807677Z","submitted_at":"2024-12-30T16:07:41Z","title":"Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-10T23:21:33.854214Z"},"links":{"cited_paper":"/paper/2307.01026","citing_paper":"/paper/2412.21046"},"observation_digest":"sha256:b0714ad9847db5ac06e540685a5958ca2a29165fbf171e1271439c5295e5298d","observation_id":"c52038ec-5990-4332-951c-6b68500a9953","resolution":{"observed_at":"2026-08-10T23:21:33.854214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01026","last_updated":"2023-09-27T22:04:41Z","snapshot_observed_at":"2026-08-12T12:51:41.445187Z","submitted_at":"2023-07-03T13:58:20Z","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","version":2},"cited_work":{"arxiv_id":"2307.01026","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01026","snapshot_observed_at":"2026-07-04T00:49:18.652717Z","title":"Advances in Neural Information Processing Systems (NeurIPS) , year=","venue":null,"work_id":"d7259cc0-a265-4d10-9347-9ad22299e704","year":2023},"citing_paper":{"arxiv_id":"2605.28659","last_updated":"2026-05-27T15:57:40Z","snapshot_observed_at":"2026-08-08T01:56:11.069102Z","submitted_at":"2026-05-27T15:57:40Z","title":"Applications of temporal graph learning for predicting the dynamics of biological systems","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T13:56:45.020535Z"},"links":{"cited_paper":"/paper/2307.01026","citing_paper":"/paper/2605.28659"},"observation_digest":"sha256:7b5f0746861419d6d89bbd7f4822b031890304a0ec0c203b1e9e32693e9da44f","observation_id":"b7638cf4-05b3-4fa9-a866-16911869fa9e","resolution":{"observed_at":"2026-06-29T14:03:29.631519Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01026","last_updated":"2023-09-27T22:04:41Z","snapshot_observed_at":"2026-08-12T12:51:41.445187Z","submitted_at":"2023-07-03T13:58:20Z","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","version":2},"cited_work":{"arxiv_id":"2307.01026","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01026","snapshot_observed_at":"2026-07-04T00:49:18.652717Z","title":"Advances in Neural Information Processing Systems (NeurIPS) , year=","venue":null,"work_id":"d7259cc0-a265-4d10-9347-9ad22299e704","year":2023},"citing_paper":{"arxiv_id":"2606.19501","last_updated":"2026-06-29T18:36:47Z","snapshot_observed_at":"2026-08-12T06:45:51.592670Z","submitted_at":"2026-06-17T18:40:08Z","title":"DeXposure-Claw: An Agentic System for DeFi Risk Supervision","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-06-26T20:57:32.647600Z"},"links":{"cited_paper":"/paper/2307.01026","citing_paper":"/paper/2606.19501"},"observation_digest":"sha256:dd96998d9b1bf7fca32b852adbf8bd03ec53fddad6895597abad08c5afe49a4d","observation_id":"cfede8d1-10ed-4d91-adbf-956fc6515f00","resolution":{"observed_at":"2026-07-04T00:49:18.655180Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01026","last_updated":"2023-09-27T22:04:41Z","snapshot_observed_at":"2026-08-12T12:51:41.445187Z","submitted_at":"2023-07-03T13:58:20Z","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs","version":2},"cited_work":{"arxiv_id":"2307.01026","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01026","snapshot_observed_at":"2026-07-04T00:49:18.652717Z","title":"Advances in Neural Information Processing Systems (NeurIPS) , year=","venue":null,"work_id":"d7259cc0-a265-4d10-9347-9ad22299e704","year":2023},"citing_paper":{"arxiv_id":"2606.19501","last_updated":"2026-06-29T18:36:47Z","snapshot_observed_at":"2026-08-12T06:45:51.592670Z","submitted_at":"2026-06-17T18:40:08Z","title":"DeXposure-Claw: An Agentic System for DeFi Risk Supervision","version":2},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-07-01T07:31:47.518981Z"},"links":{"cited_paper":"/paper/2307.01026","citing_paper":"/paper/2606.19501"},"observation_digest":"sha256:0a27332f43a0ab150ee8c827d04751ec9e9bc34431ee551693d81ad840b6e8e0","observation_id":"cbf3a18c-5430-4580-9829-556240f85642","resolution":{"observed_at":"2026-07-01T07:35:28.890704Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2307.01026/citation-record","integrity":"/paper/2307.01026/integrity","json":"/paper/2307.01026/citation-record.json","paper":"/paper/2307.01026"},"outbound":[],"paper":{"arxiv_id":"2307.01026","last_updated":"2023-09-27T22:04:41Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T12:51:41.445187Z","submitted_at":"2023-07-03T13:58:20Z","title":"Temporal Graph Benchmark for Machine Learning on Temporal Graphs"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.01026."}