{"as_of":"2026-08-08T05:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:38a390fdd7f852c667fbe463c7cba09d212c0e6f2b7a705f6a719be479885c74","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:06:32.571772Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T15:27:30.789591Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1707.05922","last_updated":"2017-07-19T02:29:49Z","snapshot_observed_at":"2026-08-06T01:41:02.838863Z","submitted_at":"2017-07-19T02:29:49Z","title":"Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.05922","snapshot_observed_at":"2026-08-07T14:06:32.571772Z","title":"Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshmi- narayanan","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.20047","last_updated":"2025-05-26T14:34:04Z","snapshot_observed_at":"2026-08-07T23:31:54.437843Z","submitted_at":"2025-05-26T14:34:04Z","title":"Grammars of Formal Uncertainty: When to Trust LLMs in Automated Reasoning Tasks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:06:32.571772Z"},"links":{"cited_paper":"/paper/1707.05922","citing_paper":"/paper/2505.20047"},"observation_digest":"sha256:63c1011bfe2a0479a4e1da53834d7bf1a28b0d1c7c0751b941054bd1594d18eb","observation_id":"e3b164d0-649a-4c78-b2b4-debe8abd9ef4","resolution":{"observed_at":"2026-08-07T14:06:32.571772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.05922","last_updated":"2017-07-19T02:29:49Z","snapshot_observed_at":"2026-08-06T01:41:02.838863Z","submitted_at":"2017-07-19T02:29:49Z","title":"Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes","version":1},"cited_work":{"arxiv_id":"1707.05922","doi":null,"metadata_source":"pith","pith_arxiv_id":"1707.05922","snapshot_observed_at":"2026-08-06T15:27:30.789591Z","title":"Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes","venue":"stat.ML","work_id":"0a265826-f5a9-4888-b401-0ae8aea01040","year":2017},"citing_paper":{"arxiv_id":"2507.15987","last_updated":"2025-07-21T18:28:21Z","snapshot_observed_at":"2026-08-06T15:18:28.011587Z","submitted_at":"2025-07-21T18:28:21Z","title":"Semantic-Aware Gaussian Process Calibration with Structured Layerwise Kernels for Deep Neural Networks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T15:27:30.611339Z"},"links":{"cited_paper":"/paper/1707.05922","citing_paper":"/paper/2507.15987"},"observation_digest":"sha256:6764f89b4fb5d8333abfacfb4a9844891b46dc57c47811fbef0d75eb4ee64e45","observation_id":"f5dc54b4-0225-4e52-8327-ec689a82d327","resolution":{"observed_at":"2026-08-06T15:27:30.792694Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1707.05922/citation-record","integrity":"/paper/1707.05922/integrity","json":"/paper/1707.05922/citation-record.json","paper":"/paper/1707.05922"},"outbound":[],"paper":{"arxiv_id":"1707.05922","last_updated":"2017-07-19T02:29:49Z","latest_version":1,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-06T01:41:02.838863Z","submitted_at":"2017-07-19T02:29:49Z","title":"Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1707.05922."}