{"as_of":"2026-08-18T01:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2eb61c5a1c2c81564a672af875fd788d09e9ee35be0ea8e67bd78e715ced6506","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":"2026-08-17T06:30:58.91139+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":"2026-08-07T04:54:02.731207Z","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":"2026-05-10T12:55:24.132930Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2209.15634","last_updated":"2022-09-30T17:59:16Z","snapshot_observed_at":"2026-08-16T16:27:45.960193Z","submitted_at":"2022-09-30T17:59:16Z","title":"A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15634","snapshot_observed_at":"2026-08-07T04:54:02.731207Z","title":"J., Yuan, A., Gu, Q., and Jordan, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.09940","last_updated":"2025-06-11T17:06:57Z","snapshot_observed_at":"2026-08-15T02:45:08.342678Z","submitted_at":"2025-06-11T17:06:57Z","title":"The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T04:54:02.731207Z"},"links":{"cited_paper":"/paper/2209.15634","citing_paper":"/paper/2506.09940"},"observation_digest":"sha256:9eccee1e59f34b4f9e0262a235074133d5d8df158666394df93bead8461ca632","observation_id":"ce103053-a1c5-4338-9e01-e38b1ffa5044","resolution":{"observed_at":"2026-08-07T04:54:02.731207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15634","last_updated":"2022-09-30T17:59:16Z","snapshot_observed_at":"2026-08-16T16:27:45.960193Z","submitted_at":"2022-09-30T17:59:16Z","title":"A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2209.15634","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2209.15634","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"A general framework for sample-efficient function approximation in reinforcement learning.arXiv preprint arXiv:2209.15634","venue":null,"work_id":"b8c53387-9549-4b44-83b6-05b06f97d828","year":null},"citing_paper":{"arxiv_id":"2604.13966","last_updated":"2026-04-15T15:17:40Z","snapshot_observed_at":"2026-08-14T05:26:22.799457Z","submitted_at":"2026-04-15T15:17:40Z","title":"Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T12:55:12.531822Z"},"links":{"cited_paper":"/paper/2209.15634","citing_paper":"/paper/2604.13966"},"observation_digest":"sha256:308413eadb874dd019212f29fdaa5b7d12c01457f1e2b757384c3fc8805e28c2","observation_id":"b8d695b5-c96e-4e3b-818a-5995305f991b","resolution":{"observed_at":"2026-05-10T12:55:24.135226Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2209.15634/citation-record","integrity":"/paper/2209.15634/integrity","json":"/paper/2209.15634/citation-record.json","paper":"/paper/2209.15634"},"outbound":[],"paper":{"arxiv_id":"2209.15634","last_updated":"2022-09-30T17:59:16Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T16:27:45.960193Z","submitted_at":"2022-09-30T17:59:16Z","title":"A General Framework for Sample-Efficient Function Approximation in Reinforcement Learning"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2209.15634."}