{"as_of":"2026-08-20T09:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4c48180b1a2f4f4eb07c1031fca60abb109c3e490a085153ba968d7ca92ce73c","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":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T22:48:32.814848Z","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":"2026-07-09T22:56:37.735104Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-08-08T22:48:32.814848Z","title":null,"venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2502.04477","last_updated":"2025-06-13T13:25:59Z","snapshot_observed_at":"2026-08-10T19:09:01.614318Z","submitted_at":"2025-02-06T20:01:59Z","title":"Near-Optimal Sample Complexity for MDPs via Anchoring","version":2},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-08T22:48:32.814848Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2502.04477"},"observation_digest":"sha256:99471f5b84bb2bd1a7922b7115aedc1596bdd3fdabf14641ee62d336aee51431","observation_id":"a7e16fd6-1b54-4c91-a3da-ba2c5e5aec17","resolution":{"observed_at":"2026-08-08T22:48:32.814848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2504.18743","last_updated":"2026-04-05T13:54:13Z","snapshot_observed_at":"2026-08-17T11:51:06.893835Z","submitted_at":"2025-04-25T23:41:14Z","title":"From Set Convergence to Pointwise Convergence: Finite-Time Guarantees for Average-Reward Q-Learning with Adaptive Stepsizes","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-22T17:34:49.191496Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2504.18743"},"observation_digest":"sha256:798bf51a6f2b70fd5d68308bd94845a4be4d06aee69e85adf4eb5bf85bb24a62","observation_id":"db9b0175-46a3-4991-819e-a16e72750e78","resolution":{"observed_at":"2026-05-22T17:35:00.870887Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-08-06T16:15:15.902771Z","title":"arXiv preprint arXiv:1906.04697","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.14444","last_updated":"2025-07-19T02:42:41Z","snapshot_observed_at":"2026-08-14T15:14:45.544908Z","submitted_at":"2025-07-19T02:42:41Z","title":"Statistical and Algorithmic Foundations of Reinforcement Learning","version":1},"reference_index":119,"source":"pdf_text","source_observed_at":"2026-08-06T16:15:15.902771Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2507.14444"},"observation_digest":"sha256:25baa2587871f40b0840684df3322ef88a85e9fab3c717fa3f58f2cdd85325b1","observation_id":"4a183801-060a-4eee-905a-2a31da41e93c","resolution":{"observed_at":"2026-08-06T16:15:15.902771Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2510.16132","last_updated":"2026-04-03T19:00:32Z","snapshot_observed_at":"2026-08-14T08:50:23.111350Z","submitted_at":"2025-10-17T18:19:00Z","title":"A Minimal-Assumption Analysis of Q-Learning with Time-Varying Policies","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-18T05:47:47.782246Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2510.16132"},"observation_digest":"sha256:a43aaaa93205aef1ed3b96baf3a77b7f811af6ce6ebbfa20ce94b3f44983b01a","observation_id":"e442f492-2650-42c9-ba2e-7ec42922dc85","resolution":{"observed_at":"2026-05-18T05:50:57.149874Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2604.07323","last_updated":"2026-04-08T17:37:15Z","snapshot_observed_at":"2026-08-17T14:30:18.165842Z","submitted_at":"2026-04-08T17:37:15Z","title":"Gaussian Approximation for Asynchronous Q-learning","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T17:11:48.816106Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2604.07323"},"observation_digest":"sha256:25d434f86bdbc62fae2a154d74fccbd0f61c67923e21b87364de73feb965bcd1","observation_id":"d976f237-ad61-48dd-99a0-9f29ce3c1dce","resolution":{"observed_at":"2026-05-11T07:25:59.876792Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2605.13639","last_updated":"2026-05-13T15:04:59Z","snapshot_observed_at":"2026-08-17T11:21:11.252674Z","submitted_at":"2026-05-13T15:04:59Z","title":"Achieving $\\epsilon^{-2}$ Sample Complexity for Single-Loop Actor-Critic under Minimal Assumptions","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-14T19:28:32.795407Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2605.13639"},"observation_digest":"sha256:1da5e55197f8dc810229d751ce6c3c11965db1f26524bc4a9ead6be66e22e2eb","observation_id":"55c8a0bd-c112-4d6b-882e-435756089d32","resolution":{"observed_at":"2026-05-14T19:29:23.748333Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2605.17678","last_updated":"2026-05-17T22:23:25Z","snapshot_observed_at":"2026-08-15T02:18:50.081946Z","submitted_at":"2026-05-17T22:23:25Z","title":"On Gaussian approximation for entropy-regularized Q-learning with function approximation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-19T22:11:31.021067Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2605.17678"},"observation_digest":"sha256:17c8675ba742b63ea1c6676d42967b78fdab25b334076d5e4dbded439614f985","observation_id":"0ccccb12-8fc1-4a95-8d3b-35cbb733e050","resolution":{"observed_at":"2026-05-19T22:12:50.614532Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2606.05110","last_updated":"2026-06-03T17:11:42Z","snapshot_observed_at":"2026-08-16T21:14:09.184899Z","submitted_at":"2026-06-03T17:11:42Z","title":"Randomization for Faster Exact Optimization of Discounted Markov Decision Processes","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T03:30:29.891537Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2606.05110"},"observation_digest":"sha256:0f626a0709b1af470d1ea00acec7327c31e03aa6c6ae394a4891dd382f2e4517","observation_id":"10b65a33-d25c-4af0-8a5f-a075caa5b6f8","resolution":{"observed_at":"2026-07-02T11:26:55.007464Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2606.22579","last_updated":"2026-06-21T16:29:25Z","snapshot_observed_at":"2026-08-02T07:11:59.062207Z","submitted_at":"2026-06-21T16:29:25Z","title":"Stationary Robust Mean-Field Games under Model Mismatches","version":1},"reference_index":280,"source":"arxiv_source","source_observed_at":"2026-06-26T10:50:40.841967Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2606.22579"},"observation_digest":"sha256:3ba8df2c1b4e5849fe8f35d559e16f37c999b21f61e35e73f5f562d1fe4aca8f","observation_id":"aa90a590-bddc-475e-9c15-7f8b4377e926","resolution":{"observed_at":"2026-07-04T08:49:42.736437Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2606.25170","last_updated":"2026-06-23T21:02:18Z","snapshot_observed_at":"2026-08-19T13:39:21.700460Z","submitted_at":"2026-06-23T21:02:18Z","title":"Minimax PAC Bounds for Learning in Exogenous Contextual MDPs","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-25T21:39:39.968655Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2606.25170"},"observation_digest":"sha256:6b3b5e7b2bc989896a2b2dc34822c045617aa1d34ad71343f1bdce28ece03d99","observation_id":"ed66b643-142d-45c6-8141-7f23ca46de9a","resolution":{"observed_at":"2026-07-04T19:10:05.254216Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal","version":2},"cited_work":{"arxiv_id":"1906.04697","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.04697","snapshot_observed_at":"2026-07-09T22:56:37.735104Z","title":"Variance-reduced q-learning is minimax optimal","venue":"cs.LG","work_id":"60497a6e-266a-42d8-9dd6-acc8d3171cc4","year":2019},"citing_paper":{"arxiv_id":"2607.06935","last_updated":"2026-07-08T02:57:22Z","snapshot_observed_at":"2026-08-20T04:14:44.604875Z","submitted_at":"2026-07-08T02:57:22Z","title":"Mathematical methods of reinforcement learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-09T22:47:51.676289Z"},"links":{"cited_paper":"/paper/1906.04697","citing_paper":"/paper/2607.06935"},"observation_digest":"sha256:2f17b09af5c7c3bd9199c5f691dbe74080db6b9d01399f2995acf301fd33bf5e","observation_id":"9f6c1b55-1e44-4b51-b1d4-082933adde0a","resolution":{"observed_at":"2026-07-09T22:56:37.736273Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1906.04697/citation-record","integrity":"/paper/1906.04697/integrity","json":"/paper/1906.04697/citation-record.json","paper":"/paper/1906.04697"},"outbound":[],"paper":{"arxiv_id":"1906.04697","last_updated":"2019-08-08T17:33:58Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T13:38:47.841787Z","submitted_at":"2019-06-11T16:58:54Z","title":"Variance-reduced $Q$-learning is minimax optimal"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:1906.04697."}