{"as_of":"2026-08-12T16:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:89c96c50c8b8cbbadb484637c3de2ea13d4553b8b08a340b4bff3962091b15a2","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T00:47:16.717162Z","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":"2026-07-04T16:19:57.617417Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.13171","last_updated":"2025-03-17T13:49:43Z","snapshot_observed_at":"2026-08-07T16:57:55.723801Z","submitted_at":"2025-03-17T13:49:43Z","title":"HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation Learning","version":1},"cited_work":{"arxiv_id":"2503.13171","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.13171","snapshot_observed_at":"2026-07-04T16:19:57.617417Z","title":"arXiv preprint arXiv:2503.13171 , year=","venue":null,"work_id":"bd4be9d1-32cc-4074-b14b-f53da04de798","year":2025},"citing_paper":{"arxiv_id":"2605.16813","last_updated":"2026-07-01T09:02:12Z","snapshot_observed_at":"2026-08-06T05:21:32.460815Z","submitted_at":"2026-05-16T05:04:10Z","title":"QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning","version":1},"reference_index":138,"source":"arxiv_source","source_observed_at":"2026-05-19T19:48:38.423387Z"},"links":{"cited_paper":"/paper/2503.13171","citing_paper":"/paper/2605.16813"},"observation_digest":"sha256:3de115db167f4d92a90f388f6fd1cc8e9dd403f3b6d434f42093caaad509c0d2","observation_id":"a9f879dc-a2b5-4bdc-97e7-e6821f6dadd6","resolution":{"observed_at":"2026-05-19T19:52:44.583444Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.13171","last_updated":"2025-03-17T13:49:43Z","snapshot_observed_at":"2026-08-07T16:57:55.723801Z","submitted_at":"2025-03-17T13:49:43Z","title":"HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation Learning","version":1},"cited_work":{"arxiv_id":"2503.13171","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.13171","snapshot_observed_at":"2026-07-04T16:19:57.617417Z","title":"arXiv preprint arXiv:2503.13171 , year=","venue":null,"work_id":"bd4be9d1-32cc-4074-b14b-f53da04de798","year":2025},"citing_paper":{"arxiv_id":"2606.24078","last_updated":"2026-06-23T02:47:11Z","snapshot_observed_at":"2026-08-09T11:58:13.721440Z","submitted_at":"2026-06-23T02:47:11Z","title":"MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T00:47:16.717162Z"},"links":{"cited_paper":"/paper/2503.13171","citing_paper":"/paper/2606.24078"},"observation_digest":"sha256:1b1e1dd1c5fedc1e57be47628cc909ce5fd8be3dd94062f1ddd78e4f9659985c","observation_id":"be621ef8-871a-4a62-a9ef-3856dabd7eb0","resolution":{"observed_at":"2026-07-04T16:19:57.619339Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2503.13171/citation-record","integrity":"/paper/2503.13171/integrity","json":"/paper/2503.13171/citation-record.json","paper":"/paper/2503.13171"},"outbound":[],"paper":{"arxiv_id":"2503.13171","last_updated":"2025-03-17T13:49:43Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-07T16:57:55.723801Z","submitted_at":"2025-03-17T13:49:43Z","title":"HybridGen: VLM-Guided Hybrid Planning for Scalable Data Generation of Imitation 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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.13171."}