{"as_of":"2026-08-10T06:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6e3ed02151ad77512bf6470b15031965322a46a73d471e0fa8696e25b473274b","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":"2026-08-09T06:31:02.800959+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":"2026-08-06T22:18:39.117624Z","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":"2026-05-18T00:02:25.436461Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.10445","last_updated":"2023-03-02T09:28:25Z","snapshot_observed_at":"2026-07-06T14:20:26.566667Z","submitted_at":"2022-11-18T14:59:42Z","title":"Building a Subspace of Policies for Scalable Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.10445","snapshot_observed_at":"2026-08-06T22:18:39.117624Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.21899","last_updated":"2025-06-27T04:36:05Z","snapshot_observed_at":"2026-08-09T12:29:02.937883Z","submitted_at":"2025-06-27T04:36:05Z","title":"Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review","version":1},"reference_index":4309,"source":"pdf_text","source_observed_at":"2026-08-06T22:18:39.117624Z"},"links":{"cited_paper":"/paper/2211.10445","citing_paper":"/paper/2506.21899"},"observation_digest":"sha256:66861530eaa77361ebe2b12d69b069a16f1b51f7b745f124a707c44ee0c6ed1d","observation_id":"e58bb50c-5338-4da9-a3e7-54726ced6629","resolution":{"observed_at":"2026-08-06T22:18:39.117624Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.10445","last_updated":"2023-03-02T09:28:25Z","snapshot_observed_at":"2026-07-06T14:20:26.566667Z","submitted_at":"2022-11-18T14:59:42Z","title":"Building a Subspace of Policies for Scalable Continual Learning","version":3},"cited_work":{"arxiv_id":"2211.10445","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.10445","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Building a subspace of policies for scalable continual learning","venue":null,"work_id":"e4c52c26-5bde-40f7-aa6f-f19a75018335","year":2022},"citing_paper":{"arxiv_id":"2509.08827","last_updated":"2025-10-09T17:08:52Z","snapshot_observed_at":"2026-08-06T15:38:05.011922Z","submitted_at":"2025-09-10T17:59:43Z","title":"A Survey of Reinforcement Learning for Large Reasoning Models","version":3},"reference_index":157,"source":"arxiv_source","source_observed_at":"2026-05-18T00:02:24.352947Z"},"links":{"cited_paper":"/paper/2211.10445","citing_paper":"/paper/2509.08827"},"observation_digest":"sha256:750f5b5bbe9a4daa6e1b30b8a68a342573929af2da243ab1155e7e9eb12eb536","observation_id":"955eaa68-913c-4aec-a5bf-4394259d6f59","resolution":{"observed_at":"2026-05-18T00:02:25.438764Z","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":"2211.10445","last_updated":"2023-03-02T09:28:25Z","snapshot_observed_at":"2026-07-06T14:20:26.566667Z","submitted_at":"2022-11-18T14:59:42Z","title":"Building a Subspace of Policies for Scalable Continual Learning","version":3},"cited_work":{"arxiv_id":"2211.10445","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.10445","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Building a subspace of policies for scalable continual learning","venue":null,"work_id":"e4c52c26-5bde-40f7-aa6f-f19a75018335","year":2022},"citing_paper":{"arxiv_id":"2603.23964","last_updated":"2026-04-13T08:15:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-03-25T05:56:54Z","title":"From Pixels to Digital Agents: An Empirical Study on the Taxonomy and Technological Trends of Reinforcement Learning Environments","version":2},"reference_index":199,"source":"pdf_text","source_observed_at":"2026-05-15T01:20:03.181903Z"},"links":{"cited_paper":"/paper/2211.10445","citing_paper":"/paper/2603.23964"},"observation_digest":"sha256:96214aea0120d0aaa0f5a4b25f611a051e7c2662c5ae9f4227917629a093a9a6","observation_id":"f9e54f64-4e99-402e-bfd0-45e05fbcf66c","resolution":{"observed_at":"2026-05-15T01:23:27.259408Z","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":"2211.10445","last_updated":"2023-03-02T09:28:25Z","snapshot_observed_at":"2026-07-06T14:20:26.566667Z","submitted_at":"2022-11-18T14:59:42Z","title":"Building a Subspace of Policies for Scalable Continual Learning","version":3},"cited_work":{"arxiv_id":"2211.10445","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2211.10445","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Building a subspace of policies for scalable continual learning","venue":null,"work_id":"e4c52c26-5bde-40f7-aa6f-f19a75018335","year":2022},"citing_paper":{"arxiv_id":"2604.15414","last_updated":"2026-06-09T00:58:37Z","snapshot_observed_at":"2026-07-12T19:46:39.068858Z","submitted_at":"2026-04-16T17:06:54Z","title":"Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T11:01:17.325738Z"},"links":{"cited_paper":"/paper/2211.10445","citing_paper":"/paper/2604.15414"},"observation_digest":"sha256:6e7ea4002c8ddf95dda301a045a504f06d97f3e7134cf102cfea75f44dca54d7","observation_id":"d09e928a-6342-441d-9fc4-180b22d8f06d","resolution":{"observed_at":"2026-05-10T11:05:08.931329Z","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":"2211.10445","last_updated":"2023-03-02T09:28:25Z","snapshot_observed_at":"2026-07-06T14:20:26.566667Z","submitted_at":"2022-11-18T14:59:42Z","title":"Building a Subspace of Policies for Scalable Continual Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.10445","snapshot_observed_at":"2026-07-12T19:46:39.624903Z","title":"Building a subspace of policies for scalable continual learning.arXiv preprint arXiv:2211.10445,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2604.15414","last_updated":"2026-06-09T00:58:37Z","snapshot_observed_at":"2026-07-12T19:46:39.068858Z","submitted_at":"2026-04-16T17:06:54Z","title":"Beyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-12T19:46:39.624903Z"},"links":{"cited_paper":"/paper/2211.10445","citing_paper":"/paper/2604.15414"},"observation_digest":"sha256:d345229a5a48e7fef1f5763749626341bc8f927a894f178a891777e2cf8d19d8","observation_id":"dff30a91-3157-46fd-b1ad-307b142c05e2","resolution":{"observed_at":"2026-07-12T19:46:39.624903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2211.10445/citation-record","integrity":"/paper/2211.10445/integrity","json":"/paper/2211.10445/citation-record.json","paper":"/paper/2211.10445"},"outbound":[],"paper":{"arxiv_id":"2211.10445","last_updated":"2023-03-02T09:28:25Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T14:20:26.566667Z","submitted_at":"2022-11-18T14:59:42Z","title":"Building a Subspace of Policies for Scalable Continual 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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2211.10445."}