{"as_of":"2026-08-14T08:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6f43e7ac205c58514d28a7f1783765abd6d972dded9db00ea754e98082ea4c0a","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T11:54:20.947768Z","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":"2026-05-21T14:20:13.829626Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.17487","last_updated":"2024-04-26T15:43:06Z","snapshot_observed_at":"2026-08-13T00:20:46.580118Z","submitted_at":"2024-04-26T15:43:06Z","title":"Conformal Prediction with Learned Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17487","snapshot_observed_at":"2026-08-09T11:54:20.947768Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02561","last_updated":"2025-02-04T18:37:10Z","snapshot_observed_at":"2026-08-10T07:43:20.648312Z","submitted_at":"2025-02-04T18:37:10Z","title":"Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-09T11:54:20.947768Z"},"links":{"cited_paper":"/paper/2404.17487","citing_paper":"/paper/2502.02561"},"observation_digest":"sha256:a36f27952600892721cc23b46cb7cd5de2883b55d4b9959c1bbd3499e75d5883","observation_id":"9c17deb7-c8c3-4403-a740-708e313f2f24","resolution":{"observed_at":"2026-08-09T11:54:20.947768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17487","last_updated":"2024-04-26T15:43:06Z","snapshot_observed_at":"2026-08-13T00:20:46.580118Z","submitted_at":"2024-04-26T15:43:06Z","title":"Conformal Prediction with Learned Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17487","snapshot_observed_at":"2026-08-07T04:27:30.435680Z","title":"Conformal prediction with learned features,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12095","last_updated":"2025-06-12T11:39:21Z","snapshot_observed_at":"2026-08-13T22:40:10.968675Z","submitted_at":"2025-06-12T11:39:21Z","title":"DoublyAware: Dual Planning and Policy Awareness for Temporal Difference Learning in Humanoid Locomotion","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T04:27:30.435680Z"},"links":{"cited_paper":"/paper/2404.17487","citing_paper":"/paper/2506.12095"},"observation_digest":"sha256:a43db2b6fc399092d98f33782270f90567a462b112af78547da1c3425efe722f","observation_id":"614f192f-7e1f-4661-83c0-acde746bf96c","resolution":{"observed_at":"2026-08-07T04:27:30.435680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17487","last_updated":"2024-04-26T15:43:06Z","snapshot_observed_at":"2026-08-13T00:20:46.580118Z","submitted_at":"2024-04-26T15:43:06Z","title":"Conformal Prediction with Learned Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17487","snapshot_observed_at":"2026-08-05T12:44:34.793076Z","title":"arXiv preprint arXiv:2404.17487 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.01338","last_updated":"2025-09-01T10:19:00Z","snapshot_observed_at":"2026-08-10T09:19:56.585379Z","submitted_at":"2025-09-01T10:19:00Z","title":"Conformal Predictive Monitoring for Multi-Modal Scenarios","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T12:44:34.793076Z"},"links":{"cited_paper":"/paper/2404.17487","citing_paper":"/paper/2509.01338"},"observation_digest":"sha256:2c50531081b2c89a6a3e5a725b67fea88569237225a60241b7a76116a4302dde","observation_id":"cf70c365-c660-444c-9ac9-f16e3c06ed25","resolution":{"observed_at":"2026-08-05T12:44:34.793076Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17487","last_updated":"2024-04-26T15:43:06Z","snapshot_observed_at":"2026-08-13T00:20:46.580118Z","submitted_at":"2024-04-26T15:43:06Z","title":"Conformal Prediction with Learned Features","version":1},"cited_work":{"arxiv_id":"2404.17487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.17487","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Confor- mal prediction with learned features.arXiv preprint arXiv:2404.17487","venue":null,"work_id":"40668680-75ee-4ac6-b92a-748786515921","year":null},"citing_paper":{"arxiv_id":"2602.01733","last_updated":"2026-07-17T08:08:16Z","snapshot_observed_at":"2026-08-12T18:07:29.591148Z","submitted_at":"2026-02-02T07:18:35Z","title":"Improving Backward Conformal Prediction via Non-Conformity Score Transformation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-21T14:15:26.433846Z"},"links":{"cited_paper":"/paper/2404.17487","citing_paper":"/paper/2602.01733"},"observation_digest":"sha256:4337bffa428ba038ddf01d9e7a5a549e485c141bf020ce26369dc8078cd218db","observation_id":"69d866a7-3669-432e-8867-d59b4f11a183","resolution":{"observed_at":"2026-05-21T14:20:13.831901Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17487","last_updated":"2024-04-26T15:43:06Z","snapshot_observed_at":"2026-08-13T00:20:46.580118Z","submitted_at":"2024-04-26T15:43:06Z","title":"Conformal Prediction with Learned Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17487","snapshot_observed_at":"2026-08-03T05:39:30.429023Z","title":"Confor- mal prediction with learned features.arXiv preprint arXiv:2404.17487,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.01733","last_updated":"2026-07-17T08:08:16Z","snapshot_observed_at":"2026-08-12T18:07:29.591148Z","submitted_at":"2026-02-02T07:18:35Z","title":"Improving Backward Conformal Prediction via Non-Conformity Score Transformation","version":3},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-03T05:39:30.429023Z"},"links":{"cited_paper":"/paper/2404.17487","citing_paper":"/paper/2602.01733"},"observation_digest":"sha256:ad5e28d6811a2d8aeb92915302a694f8e6f4f93226b7061e9875b75860e90730","observation_id":"7e61d6cd-5f2c-44ce-9944-50bd70c9f081","resolution":{"observed_at":"2026-08-03T05:39:30.429023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17487","last_updated":"2024-04-26T15:43:06Z","snapshot_observed_at":"2026-08-13T00:20:46.580118Z","submitted_at":"2024-04-26T15:43:06Z","title":"Conformal Prediction with Learned Features","version":1},"cited_work":{"arxiv_id":"2404.17487","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2404.17487","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Confor- mal prediction with learned features.arXiv preprint arXiv:2404.17487","venue":null,"work_id":"40668680-75ee-4ac6-b92a-748786515921","year":null},"citing_paper":{"arxiv_id":"2605.06479","last_updated":"2026-05-07T16:03:24Z","snapshot_observed_at":"2026-08-12T14:47:34.218748Z","submitted_at":"2026-05-07T16:03:24Z","title":"Risk-Controlled Post-Processing of Decision Policies","version":1},"reference_index":139,"source":"arxiv_source","source_observed_at":"2026-05-08T04:49:25.540705Z"},"links":{"cited_paper":"/paper/2404.17487","citing_paper":"/paper/2605.06479"},"observation_digest":"sha256:c30e26c6a005c67b7e6a41e781d8e45719e68989d76696e4d2fb12e2660932c0","observation_id":"ed352d43-4a84-4e00-99d9-4b3634c47500","resolution":{"observed_at":"2026-05-11T21:36:17.076302Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.17487","last_updated":"2024-04-26T15:43:06Z","snapshot_observed_at":"2026-08-13T00:20:46.580118Z","submitted_at":"2024-04-26T15:43:06Z","title":"Conformal Prediction with Learned Features","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.17487","snapshot_observed_at":"2026-07-30T18:06:39.359709Z","title":"arXiv preprint arXiv:2404.17487 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.26887","last_updated":"2026-07-29T13:16:27Z","snapshot_observed_at":"2026-08-01T23:37:39.979128Z","submitted_at":"2026-07-29T13:16:27Z","title":"Conformalized Rate-Adaptive Sensing","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-07-30T18:06:39.359709Z"},"links":{"cited_paper":"/paper/2404.17487","citing_paper":"/paper/2607.26887"},"observation_digest":"sha256:9d242e59f9178a5154080cd8b522d8327fb671eea066e9b892965cd44a63d097","observation_id":"268931fe-a21b-4e6c-8c45-002407b05aec","resolution":{"observed_at":"2026-07-30T18:06:39.359709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2404.17487/citation-record","integrity":"/paper/2404.17487/integrity","json":"/paper/2404.17487/citation-record.json","paper":"/paper/2404.17487"},"outbound":[],"paper":{"arxiv_id":"2404.17487","last_updated":"2024-04-26T15:43:06Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T00:20:46.580118Z","submitted_at":"2024-04-26T15:43:06Z","title":"Conformal Prediction with Learned Features"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2404.17487."}