{"as_of":"2026-08-09T08:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c97e9d723a66baaefae42a72388d0ce00a07821c1c0eb66f7612338e5ab93aea","coverage":[{"denominator":2,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T09:09:53.522461Z","state":"measured"},{"denominator":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-08-01T07:35:56.679877Z","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":"2026-05-20T23:49:15.006610Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.15353","last_updated":"2026-07-12T06:33:48Z","snapshot_observed_at":"2026-08-03T09:09:41.710946Z","submitted_at":"2026-01-21T04:58:49Z","title":"Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions","version":2},"cited_work":{"arxiv_id":"2601.15353","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.15353","snapshot_observed_at":"2026-07-14T02:20:22.498368Z","title":"Statistical reinforcement learning in the real world: A survey of challenges and future directions.arXiv preprint arXiv:2601.15353","venue":null,"work_id":"ec2bff23-0af6-4b48-a334-380e25f45e98","year":null},"citing_paper":{"arxiv_id":"2604.28005","last_updated":"2026-05-15T18:39:07Z","snapshot_observed_at":"2026-07-06T23:13:20.516759Z","submitted_at":"2026-04-30T15:27:34Z","title":"Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-07T07:00:32.206081Z"},"links":{"cited_paper":"/paper/2601.15353","citing_paper":"/paper/2604.28005"},"observation_digest":"sha256:703e6b297f8988982c1c707728bd679c96c0615cad379fe15dd2d0206c6e7feb","observation_id":"2c17b700-8c59-459f-b0ac-73ab4422e4b7","resolution":{"observed_at":"2026-07-14T02:20:22.498368Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.15353","last_updated":"2026-07-12T06:33:48Z","snapshot_observed_at":"2026-08-03T09:09:41.710946Z","submitted_at":"2026-01-21T04:58:49Z","title":"Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions","version":2},"cited_work":{"arxiv_id":"2601.15353","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.15353","snapshot_observed_at":"2026-07-14T02:20:22.498368Z","title":"Statistical reinforcement learning in the real world: A survey of challenges and future directions.arXiv preprint arXiv:2601.15353","venue":null,"work_id":"ec2bff23-0af6-4b48-a334-380e25f45e98","year":null},"citing_paper":{"arxiv_id":"2604.28005","last_updated":"2026-05-15T18:39:07Z","snapshot_observed_at":"2026-07-06T23:13:20.516759Z","submitted_at":"2026-04-30T15:27:34Z","title":"Kernelized Advantage Estimation: From Nonparametric Statistics to LLM Reasoning","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-20T23:47:53.282259Z"},"links":{"cited_paper":"/paper/2601.15353","citing_paper":"/paper/2604.28005"},"observation_digest":"sha256:109dd860c55cee9a4b280890bad5717c6e05391b149464fcd689178200f4f4fa","observation_id":"6d682d5f-2242-4a5e-9255-9b1b1406014f","resolution":{"observed_at":"2026-07-14T02:20:22.498368Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2601.15353","last_updated":"2026-07-12T06:33:48Z","snapshot_observed_at":"2026-08-03T09:09:41.710946Z","submitted_at":"2026-01-21T04:58:49Z","title":"Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.15353","snapshot_observed_at":"2026-08-01T07:35:56.679877Z","title":"arXiv preprint arXiv:2601.15353 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21403","last_updated":"2026-07-23T15:03:16Z","snapshot_observed_at":"2026-08-08T08:34:41.666642Z","submitted_at":"2026-07-23T15:03:16Z","title":"A Diffusion-Model Subpopulation Digital Twin for Mobile Health Deployment: A Case Study on the HeartSteps Intervention","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-01T07:35:56.679877Z"},"links":{"cited_paper":"/paper/2601.15353","citing_paper":"/paper/2607.21403"},"observation_digest":"sha256:e29c62c5be58bdd3979077dfc7fec66a09e3ae890c07f20d2d2ce038e1e885cc","observation_id":"348f570c-20ab-4a61-817b-31de6590a574","resolution":{"observed_at":"2026-08-01T07:35:56.679877Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2601.15353/citation-record","integrity":"/paper/2601.15353/integrity","json":"/paper/2601.15353/citation-record.json","paper":"/paper/2601.15353"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2008.02598","last_updated":"2021-02-12T08:17:23Z","snapshot_observed_at":"2026-08-03T00:24:16.718666Z","submitted_at":"2020-08-06T12:09:18Z","title":"Offline Meta Learning of Exploration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.02598","snapshot_observed_at":"2026-08-03T09:09:53.416330Z","title":"it felt more real","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.15353","last_updated":"2026-07-12T06:33:48Z","snapshot_observed_at":"2026-08-03T09:09:41.710946Z","submitted_at":"2026-01-21T04:58:49Z","title":"Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T09:09:53.416330Z"},"links":{"cited_paper":"/paper/2008.02598","citing_paper":"/paper/2601.15353"},"observation_digest":"sha256:3b48f9b2f45089ce410fdf1181c6dcd0e60ca12d167f57bd4f01224cd056d30d","observation_id":"38586ef8-4bfb-4e30-a06e-20bc3e6cd2ff","resolution":{"observed_at":"2026-08-03T09:09:53.416330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.06355","last_updated":"2022-12-13T03:38:57Z","snapshot_observed_at":"2026-08-06T02:47:43.502360Z","submitted_at":"2022-12-13T03:38:57Z","title":"A Review of Off-Policy Evaluation in Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.06355","snapshot_observed_at":"2026-08-03T09:09:53.522461Z","title":"doi:10.1609/AAAI.V39I28.35143","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.15353","last_updated":"2026-07-12T06:33:48Z","snapshot_observed_at":"2026-08-03T09:09:41.710946Z","submitted_at":"2026-01-21T04:58:49Z","title":"Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-03T09:09:53.522461Z"},"links":{"cited_paper":"/paper/2212.06355","citing_paper":"/paper/2601.15353"},"observation_digest":"sha256:1f354aee96900d1888c417e1bfd3031bfecba780c622af647a838a5efc23d41a","observation_id":"21904c4c-9910-44da-bd85-a2836baf8866","resolution":{"observed_at":"2026-08-03T09:09:53.522461Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.15353","last_updated":"2026-07-12T06:33:48Z","latest_version":2,"primary_category":"stat.AP","snapshot_observed_at":"2026-08-03T09:09:41.710946Z","submitted_at":"2026-01-21T04:58:49Z","title":"Reinforcement Learning in the Real World: A Survey of Statistical Challenges and Future Directions"},"reference_resolution":{"displayed":2,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":2},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 3 inbound Pith citation observations for arXiv:2601.15353."}