{"as_of":"2026-08-16T20:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:207503b9bb70a562760e4d078ef50b51aaec754916e16e534a711b59942f7ec4","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":"2026-08-16T06:30:59.297886+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":"2026-08-15T22:12:32.934772Z","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":"2026-05-11T17:11:18.894929Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.16907","last_updated":"2024-11-07T02:24:18Z","snapshot_observed_at":"2026-08-16T15:16:29.397785Z","submitted_at":"2024-05-27T07:55:45Z","title":"GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.16907","snapshot_observed_at":"2026-08-15T22:12:32.934772Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07802","last_updated":"2025-06-03T16:45:05Z","snapshot_observed_at":"2026-08-15T22:05:33.602229Z","submitted_at":"2025-05-12T17:50:10Z","title":"Improving Trajectory Stitching with Flow Models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T22:12:32.934772Z"},"links":{"cited_paper":"/paper/2405.16907","citing_paper":"/paper/2505.07802"},"observation_digest":"sha256:576bdbd01d3601e0f8ce5bd30740b6ec85248f0799b25ebde2fc881bc5fe0ba7","observation_id":"b18e1dc7-1bbc-4348-811d-f624f2f353d0","resolution":{"observed_at":"2026-08-15T22:12:32.934772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16907","last_updated":"2024-11-07T02:24:18Z","snapshot_observed_at":"2026-08-16T15:16:29.397785Z","submitted_at":"2024-05-27T07:55:45Z","title":"GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement Learning","version":5},"cited_work":{"arxiv_id":"2405.16907","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2405.16907","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gta: Generative trajectory augmentation with guidance for offline reinforcement learning","venue":null,"work_id":"4ceb5471-d0b0-4d8c-8415-61740a1d45a0","year":2024},"citing_paper":{"arxiv_id":"2605.03075","last_updated":"2026-05-04T18:44:19Z","snapshot_observed_at":"2026-08-13T16:38:45.654668Z","submitted_at":"2026-05-04T18:44:19Z","title":"Refining Compositional Diffusion for Reliable Long-Horizon Planning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-08T17:47:58.141126Z"},"links":{"cited_paper":"/paper/2405.16907","citing_paper":"/paper/2605.03075"},"observation_digest":"sha256:f5a1dafb1dbf0670406aefc77910eb7818bbb01c454f4d36eb156954b3f68084","observation_id":"327975c3-f33f-4973-a6f4-3c7f95656201","resolution":{"observed_at":"2026-05-11T17:11:18.897403Z","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"}}],"links":{"evidence":"/evidence","html":"/paper/2405.16907/citation-record","integrity":"/paper/2405.16907/integrity","json":"/paper/2405.16907/citation-record.json","paper":"/paper/2405.16907"},"outbound":[],"paper":{"arxiv_id":"2405.16907","last_updated":"2024-11-07T02:24:18Z","latest_version":5,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-16T15:16:29.397785Z","submitted_at":"2024-05-27T07:55:45Z","title":"GTA: Generative Trajectory Augmentation with Guidance for Offline Reinforcement 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":"2026-08-16T06:30:59.297886+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 2 inbound Pith citation observations for arXiv:2405.16907."}