{"as_of":"2026-08-16T23:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ff73daf336526b741ddae645d97a7ad861b24edc37fb941444a576cbfea286f4","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":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":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:05:21.432391Z","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":"2026-07-03T14:58:33.273324Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2110.04652","last_updated":"2022-01-06T04:33:56Z","snapshot_observed_at":"2026-08-16T17:50:36.690049Z","submitted_at":"2021-10-09T22:04:34Z","title":"Representation Learning for Online and Offline RL in Low-rank MDPs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04652","snapshot_observed_at":"2026-08-15T21:05:21.432391Z","title":"Representation learning for online and offline rl in low-rank mdps","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.10861","last_updated":"2025-05-16T05:03:39Z","snapshot_observed_at":"2026-08-16T19:41:19.534817Z","submitted_at":"2025-05-16T05:03:39Z","title":"Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLM","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-15T21:05:21.432391Z"},"links":{"cited_paper":"/paper/2110.04652","citing_paper":"/paper/2505.10861"},"observation_digest":"sha256:f261dccf85d5e607d2ee4ad6f3e95302451e596fbb5d73ef44e0c0fbd2403b4b","observation_id":"6dcd2c35-d5d6-4a16-ad07-9515d53786e3","resolution":{"observed_at":"2026-08-15T21:05:21.432391Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04652","last_updated":"2022-01-06T04:33:56Z","snapshot_observed_at":"2026-08-16T17:50:36.690049Z","submitted_at":"2021-10-09T22:04:34Z","title":"Representation Learning for Online and Offline RL in Low-rank MDPs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04652","snapshot_observed_at":"2026-08-15T20:21:29.732840Z","title":"Representation learning for online and offline rl in low-rank mdps","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.13768","last_updated":"2025-06-27T21:05:50Z","snapshot_observed_at":"2026-08-15T20:08:46.735508Z","submitted_at":"2025-05-19T22:58:54Z","title":"Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis","version":3},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-15T20:21:29.732840Z"},"links":{"cited_paper":"/paper/2110.04652","citing_paper":"/paper/2505.13768"},"observation_digest":"sha256:0a0ad4aaa1b20900badfdf91b96ea2bcfffa59bd3a0dad67e86ea995ecb46c35","observation_id":"3cac87ee-b04c-4e77-b05d-d5f45757dcd9","resolution":{"observed_at":"2026-08-15T20:21:29.732840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04652","last_updated":"2022-01-06T04:33:56Z","snapshot_observed_at":"2026-08-16T17:50:36.690049Z","submitted_at":"2021-10-09T22:04:34Z","title":"Representation Learning for Online and Offline RL in Low-rank MDPs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04652","snapshot_observed_at":"2026-08-07T11:40:58.356295Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.01919","last_updated":"2025-06-02T17:39:31Z","snapshot_observed_at":"2026-08-16T02:05:22.144481Z","submitted_at":"2025-06-02T17:39:31Z","title":"Transformers as Multi-task Learners: Decoupling Features in Hidden Markov Models","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T11:40:58.356295Z"},"links":{"cited_paper":"/paper/2110.04652","citing_paper":"/paper/2506.01919"},"observation_digest":"sha256:546bc16a90c4b73026cd5871151f5290aba2ade32a24fd1de6df4e1ade13fa9c","observation_id":"8ec3ccfb-8c15-4498-bf95-836c4064540a","resolution":{"observed_at":"2026-08-07T11:40:58.356295Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04652","last_updated":"2022-01-06T04:33:56Z","snapshot_observed_at":"2026-08-16T17:50:36.690049Z","submitted_at":"2021-10-09T22:04:34Z","title":"Representation Learning for Online and Offline RL in Low-rank MDPs","version":3},"cited_work":{"arxiv_id":"2110.04652","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.04652","snapshot_observed_at":"2026-07-03T14:58:33.273324Z","title":"arXiv preprint arXiv:2110.04652 , year=","venue":null,"work_id":"ba10b80c-8cae-4680-a164-f7928de300d0","year":2021},"citing_paper":{"arxiv_id":"2605.01242","last_updated":"2026-05-02T04:46:54Z","snapshot_observed_at":"2026-08-10T22:34:08.334502Z","submitted_at":"2026-05-02T04:46:54Z","title":"Breaking the Computational Barrier: Provably Efficient Actor-Critic for Low-Rank MDPs","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-09T15:12:54.575483Z"},"links":{"cited_paper":"/paper/2110.04652","citing_paper":"/paper/2605.01242"},"observation_digest":"sha256:09217fd33d02d372f29ca95b71dbbb021975f8051b94d5b541bdccbde30ec48d","observation_id":"26fca4be-6254-4baf-b5e6-0b61569f8e44","resolution":{"observed_at":"2026-05-11T16:46:05.450866Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04652","last_updated":"2022-01-06T04:33:56Z","snapshot_observed_at":"2026-08-16T17:50:36.690049Z","submitted_at":"2021-10-09T22:04:34Z","title":"Representation Learning for Online and Offline RL in Low-rank MDPs","version":3},"cited_work":{"arxiv_id":"2110.04652","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2110.04652","snapshot_observed_at":"2026-07-03T14:58:33.273324Z","title":"arXiv preprint arXiv:2110.04652 , year=","venue":null,"work_id":"ba10b80c-8cae-4680-a164-f7928de300d0","year":2021},"citing_paper":{"arxiv_id":"2606.12890","last_updated":"2026-06-11T04:33:03Z","snapshot_observed_at":"2026-08-15T19:14:48.188465Z","submitted_at":"2026-06-11T04:33:03Z","title":"Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-27T06:49:02.060472Z"},"links":{"cited_paper":"/paper/2110.04652","citing_paper":"/paper/2606.12890"},"observation_digest":"sha256:2d95e2fdb03ba419bdfebb651c889d0931cb567d7402e996f406ea8ae2a4173b","observation_id":"daac241d-8084-42c3-a087-d55f75084383","resolution":{"observed_at":"2026-07-03T14:58:33.274884Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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/2110.04652/citation-record","integrity":"/paper/2110.04652/integrity","json":"/paper/2110.04652/citation-record.json","paper":"/paper/2110.04652"},"outbound":[],"paper":{"arxiv_id":"2110.04652","last_updated":"2022-01-06T04:33:56Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T17:50:36.690049Z","submitted_at":"2021-10-09T22:04:34Z","title":"Representation Learning for Online and Offline RL in Low-rank MDPs"},"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 5 inbound Pith citation observations for arXiv:2110.04652."}