{"as_of":"2026-08-08T21:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:96cf9ad334188cc95e26a20efb3e1939dd6cf5af28a21cd37dc76c03fa33fbe2","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:01:36.208070Z","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":"2026-05-11T14:26:04.339772Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.09056","last_updated":"2024-09-09T20:54:39Z","snapshot_observed_at":"2026-07-06T17:29:56.393307Z","submitted_at":"2024-02-14T10:07:05Z","title":"Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09056","snapshot_observed_at":"2026-08-07T12:01:36.208070Z","title":"u rgens, Nis Meinert, Viktor Bengs, Eyke H \\","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00918","last_updated":"2025-06-01T09:13:27Z","snapshot_observed_at":"2026-08-07T11:52:31.292702Z","submitted_at":"2025-06-01T09:13:27Z","title":"Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T12:01:36.208070Z"},"links":{"cited_paper":"/paper/2402.09056","citing_paper":"/paper/2506.00918"},"observation_digest":"sha256:c70510bda771d9fdcb293e918da7a69e896515d02bb8cbe0c5d1054936c02421","observation_id":"c0c5ddd3-62b5-45e7-b474-817a7b2aa6db","resolution":{"observed_at":"2026-08-07T12:01:36.208070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09056","last_updated":"2024-09-09T20:54:39Z","snapshot_observed_at":"2026-07-06T17:29:56.393307Z","submitted_at":"2024-02-14T10:07:05Z","title":"Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?","version":3},"cited_work":{"arxiv_id":"2402.09056","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.09056","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mira Juergens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, and Willem Waegeman","venue":null,"work_id":"0f90d068-2c71-46b1-91c4-8dbedb9ceef8","year":2026},"citing_paper":{"arxiv_id":"2604.06032","last_updated":"2026-04-07T16:28:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-07T16:28:20Z","title":"Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T18:38:30.129473Z"},"links":{"cited_paper":"/paper/2402.09056","citing_paper":"/paper/2604.06032"},"observation_digest":"sha256:c63560187dd5ab23ef473fb2d5a3999fb86ab5ee089ffe6fc4bae6a5963444bf","observation_id":"12f139ef-044a-429a-b2c6-7b965c2437f7","resolution":{"observed_at":"2026-05-11T00:15:51.997586Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09056","last_updated":"2024-09-09T20:54:39Z","snapshot_observed_at":"2026-07-06T17:29:56.393307Z","submitted_at":"2024-02-14T10:07:05Z","title":"Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?","version":3},"cited_work":{"arxiv_id":"2402.09056","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.09056","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Mira Juergens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, and Willem Waegeman","venue":null,"work_id":"0f90d068-2c71-46b1-91c4-8dbedb9ceef8","year":2026},"citing_paper":{"arxiv_id":"2604.22110","last_updated":"2026-04-23T23:06:48Z","snapshot_observed_at":"2026-08-03T03:45:15.450142Z","submitted_at":"2026-04-23T23:06:48Z","title":"Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-09T21:48:15.641442Z"},"links":{"cited_paper":"/paper/2402.09056","citing_paper":"/paper/2604.22110"},"observation_digest":"sha256:b2abfdf45352b7c9548ce49abb0d1a81b3c9ac5dd32fda96f07ef43ccb49b9eb","observation_id":"72e3e072-986e-4c15-81ae-6b6436b030d6","resolution":{"observed_at":"2026-05-11T14:26:04.341693Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.09056/citation-record","integrity":"/paper/2402.09056/integrity","json":"/paper/2402.09056/citation-record.json","paper":"/paper/2402.09056"},"outbound":[],"paper":{"arxiv_id":"2402.09056","last_updated":"2024-09-09T20:54:39Z","latest_version":3,"primary_category":"cs.AI","snapshot_observed_at":"2026-07-06T17:29:56.393307Z","submitted_at":"2024-02-14T10:07:05Z","title":"Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2402.09056."}