{"as_of":"2026-08-05T07:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1b35888948fa77e7af25c40efbba93498a29cc098bf8fbabfe5b749758c092cc","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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":"2026-06-30T22:53:19.163233Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":12,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2211.09423","last_updated":"2022-11-18T05:28:17Z","snapshot_observed_at":"2026-07-06T14:19:43.043908Z","submitted_at":"2022-11-17T09:17:42Z","title":"DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation","version":2},"cited_work":{"arxiv_id":"2211.09423","doi":"10.48550/arxiv.2211.09423","metadata_source":"arxiv_reference","pith_arxiv_id":"2211.09423","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"b2ea395f-e82b-4424-b74a-e89f514270e5","year":2022},"citing_paper":{"arxiv_id":"2605.09989","last_updated":"2026-06-03T20:48:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-11T05:06:12Z","title":"StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-12T03:59:25.546955Z"},"links":{"cited_paper":"/paper/2211.09423","citing_paper":"/paper/2605.09989"},"observation_digest":"sha256:7dd4a4b82bf6fda27cc0a958fbbbe13b647770c57c3571e05c095a9744881553","observation_id":"2bbe090e-a737-4d7e-a4be-8dfafac9185b","resolution":{"observed_at":"2026-05-12T04:01:20.251741Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09423","last_updated":"2022-11-18T05:28:17Z","snapshot_observed_at":"2026-07-06T14:19:43.043908Z","submitted_at":"2022-11-17T09:17:42Z","title":"DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation","version":2},"cited_work":{"arxiv_id":"2211.09423","doi":"10.48550/arxiv.2211.09423","metadata_source":"arxiv_reference","pith_arxiv_id":"2211.09423","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"b2ea395f-e82b-4424-b74a-e89f514270e5","year":2022},"citing_paper":{"arxiv_id":"2605.09989","last_updated":"2026-06-03T20:48:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-11T05:06:12Z","title":"StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T22:53:19.163233Z"},"links":{"cited_paper":"/paper/2211.09423","citing_paper":"/paper/2605.09989"},"observation_digest":"sha256:dbe1c0a6e4e651a5f4a6796e1a72a505d38c7954dc1655221ce835fb116fe1cf","observation_id":"a00bb43f-a510-4d66-bc7c-ed94d0cf758c","resolution":{"observed_at":"2026-06-30T22:55:06.356552Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09423","last_updated":"2022-11-18T05:28:17Z","snapshot_observed_at":"2026-07-06T14:19:43.043908Z","submitted_at":"2022-11-17T09:17:42Z","title":"DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation","version":2},"cited_work":{"arxiv_id":"2211.09423","doi":"10.48550/arxiv.2211.09423","metadata_source":"arxiv_reference","pith_arxiv_id":"2211.09423","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"b2ea395f-e82b-4424-b74a-e89f514270e5","year":2022},"citing_paper":{"arxiv_id":"2606.27475","last_updated":"2026-06-25T18:52:27Z","snapshot_observed_at":"2026-08-02T12:36:42.748876Z","submitted_at":"2026-06-25T18:52:27Z","title":"Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-29T01:57:04.293058Z"},"links":{"cited_paper":"/paper/2211.09423","citing_paper":"/paper/2606.27475"},"observation_digest":"sha256:81ccbff6812ea2960349c93871a291e09d84a431d83bfa68f7121237a7e65941","observation_id":"6cf09918-71b0-482b-a58c-e46c107d139c","resolution":{"observed_at":"2026-07-01T18:25:58.766966Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2211.09423/citation-record","integrity":"/paper/2211.09423/integrity","json":"/paper/2211.09423/citation-record.json","paper":"/paper/2211.09423"},"outbound":[],"paper":{"arxiv_id":"2211.09423","last_updated":"2022-11-18T05:28:17Z","latest_version":2,"primary_category":"cs.RO","snapshot_observed_at":"2026-07-06T14:19:43.043908Z","submitted_at":"2022-11-17T09:17:42Z","title":"DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation"},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2211.09423."}