{"as_of":"2026-08-16T07:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6efca39dc19a5764f4d42efe55de39b9520fea47cc08d55ff5cee121d5831b46","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T12:38:42.442531Z","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":"2026-08-11T18:16:15.004306Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.15599","last_updated":"2025-02-07T17:29:48Z","snapshot_observed_at":"2026-08-15T21:53:19.079862Z","submitted_at":"2024-06-21T18:57:38Z","title":"Pareto-Optimal Learning from Preferences with Hidden Context","version":2},"cited_work":{"arxiv_id":"2406.15599","doi":"10.48550/arxiv.2406.15599","metadata_source":"pith","pith_arxiv_id":"2406.15599","snapshot_observed_at":"2026-08-11T18:16:15.004306Z","title":"Pareto-Optimal Learning from Preferences with Hidden Context","venue":"cs.LG","work_id":"ca1fbf42-7d89-42d6-8e27-8aec11100ec3","year":2024},"citing_paper":{"arxiv_id":"2412.13998","last_updated":"2024-12-18T16:14:59Z","snapshot_observed_at":"2026-08-12T06:32:44.431104Z","submitted_at":"2024-12-18T16:14:59Z","title":"Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T12:38:42.442531Z"},"links":{"cited_paper":"/paper/2406.15599","citing_paper":"/paper/2412.13998"},"observation_digest":"sha256:a29d156bc07eaddd1b62afd7ef7894301e11133e4d52254e640dfa17c6bd9fa6","observation_id":"a26b12ce-55ee-4cc0-ac18-8e93e5019c61","resolution":{"observed_at":"2026-08-11T12:38:43.254158Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.15599/citation-record","integrity":"/paper/2406.15599/integrity","json":"/paper/2406.15599/citation-record.json","paper":"/paper/2406.15599"},"outbound":[],"paper":{"arxiv_id":"2406.15599","last_updated":"2025-02-07T17:29:48Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T21:53:19.079862Z","submitted_at":"2024-06-21T18:57:38Z","title":"Pareto-Optimal Learning from Preferences with Hidden Context"},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2406.15599."}