{"as_of":"2026-08-19T19:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aefe466f7be0c6d1077a65612b714dd6a66dd1b82c4ce56ae69a95fbeedfa925","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":6,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":6,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":6,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":6,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:12:32.926123Z","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":"2026-05-19T10:52:15.270991Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.02439","last_updated":"2024-02-22T00:05:12Z","snapshot_observed_at":"2026-08-16T14:21:43.978709Z","submitted_at":"2024-02-04T10:30:23Z","title":"DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02439","snapshot_observed_at":"2026-08-12T18:44:20.204521Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.11327","last_updated":"2024-11-18T06:44:14Z","snapshot_observed_at":"2026-08-14T23:35:11.827947Z","submitted_at":"2024-11-18T06:44:14Z","title":"Enhancing Decision Transformer with Diffusion-Based Trajectory Branch Generation","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-12T18:44:20.204521Z"},"links":{"cited_paper":"/paper/2402.02439","citing_paper":"/paper/2411.11327"},"observation_digest":"sha256:cb1cb001643d0a47b0e050e0376d25f4e526bae39d12d4e92dd7f0b509da05dc","observation_id":"484eb15e-169a-4353-8c98-92fd0a4783fb","resolution":{"observed_at":"2026-08-12T18:44:20.204521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02439","last_updated":"2024-02-22T00:05:12Z","snapshot_observed_at":"2026-08-16T14:21:43.978709Z","submitted_at":"2024-02-04T10:30:23Z","title":"DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02439","snapshot_observed_at":"2026-08-15T22:12:32.926123Z","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":"2026-08-18T14:45:46.910267Z","submitted_at":"2025-05-12T17:50:10Z","title":"Improving Trajectory Stitching with Flow Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T22:12:32.926123Z"},"links":{"cited_paper":"/paper/2402.02439","citing_paper":"/paper/2505.07802"},"observation_digest":"sha256:6c14d2dff343e535d407bbae069a1d53b43aff4b84b36d53592622045bd0d1cf","observation_id":"be772153-61d4-4e6a-b952-0f36c580fa84","resolution":{"observed_at":"2026-08-15T22:12:32.926123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02439","last_updated":"2024-02-22T00:05:12Z","snapshot_observed_at":"2026-08-16T14:21:43.978709Z","submitted_at":"2024-02-04T10:30:23Z","title":"DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02439","snapshot_observed_at":"2026-08-07T14:20:38.939358Z","title":"Diffstitch: Boosting offline reinforcement learning with diffusion-based trajectory stitching","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20350","last_updated":"2025-05-26T03:42:20Z","snapshot_observed_at":"2026-08-12T00:42:47.737693Z","submitted_at":"2025-05-26T03:42:20Z","title":"Decision Flow Policy Optimization","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T14:20:38.939358Z"},"links":{"cited_paper":"/paper/2402.02439","citing_paper":"/paper/2505.20350"},"observation_digest":"sha256:162f7d2bfe7e76e5fd2745523c5c151eefaccc9334d9585fb72111cfbcd6f10b","observation_id":"4c22e361-d8f6-477d-9dbc-b58ce2e550ac","resolution":{"observed_at":"2026-08-07T14:20:38.939358Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02439","last_updated":"2024-02-22T00:05:12Z","snapshot_observed_at":"2026-08-16T14:21:43.978709Z","submitted_at":"2024-02-04T10:30:23Z","title":"DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching","version":2},"cited_work":{"arxiv_id":"2402.02439","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02439","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Diffstitch: Boosting offline reinforcement learning with diffusion-based trajectory stitching","venue":null,"work_id":"3db8f1ac-3bfd-4d01-b111-e04bdbce0a89","year":2024},"citing_paper":{"arxiv_id":"2506.05762","last_updated":"2026-05-14T17:01:38Z","snapshot_observed_at":"2026-08-15T00:07:14.719526Z","submitted_at":"2025-06-06T05:41:33Z","title":"BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning","version":5},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-19T10:48:28.980868Z"},"links":{"cited_paper":"/paper/2402.02439","citing_paper":"/paper/2506.05762"},"observation_digest":"sha256:6b0ac6446c1e0d53fb57f91f535501742e9ba7967e3771d7c0f18f7c6ced6f34","observation_id":"0e7c37aa-249c-4739-bd17-aa0709625456","resolution":{"observed_at":"2026-05-19T10:52:15.272603Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02439","last_updated":"2024-02-22T00:05:12Z","snapshot_observed_at":"2026-08-16T14:21:43.978709Z","submitted_at":"2024-02-04T10:30:23Z","title":"DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02439","snapshot_observed_at":"2026-08-03T13:13:45.241242Z","title":"Diffstitch: Boost- ing offline reinforcement learning with diffusion-based trajectory stitching.arXiv preprint arXiv:2402.02439, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.00126","last_updated":"2026-07-17T22:35:37Z","snapshot_observed_at":"2026-08-19T17:57:45.523058Z","submitted_at":"2025-12-31T22:03:19Z","title":"Compositional Diffusion with Guided Search for Long-Horizon Planning","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T13:13:45.241242Z"},"links":{"cited_paper":"/paper/2402.02439","citing_paper":"/paper/2601.00126"},"observation_digest":"sha256:987e7afde07e4083508f189d636d479e124359752732dc3f529075b6ff7ec7e2","observation_id":"7bc94c89-01de-423a-8a44-3998a190c1eb","resolution":{"observed_at":"2026-08-03T13:13:45.241242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02439","last_updated":"2024-02-22T00:05:12Z","snapshot_observed_at":"2026-08-16T14:21:43.978709Z","submitted_at":"2024-02-04T10:30:23Z","title":"DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching","version":2},"cited_work":{"arxiv_id":"2402.02439","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.02439","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Diffstitch: Boosting offline reinforcement learning with diffusion-based trajectory stitching","venue":null,"work_id":"3db8f1ac-3bfd-4d01-b111-e04bdbce0a89","year":2024},"citing_paper":{"arxiv_id":"2605.13054","last_updated":"2026-05-13T06:23:51Z","snapshot_observed_at":"2026-08-12T22:58:57.118093Z","submitted_at":"2026-05-13T06:23:51Z","title":"Bridging Domain Gaps with Target-Aligned Generation for Offline Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-14T20:12:19.948073Z"},"links":{"cited_paper":"/paper/2402.02439","citing_paper":"/paper/2605.13054"},"observation_digest":"sha256:85ac4863c4127c92ca6c2121c0be199218fb5621a1af31d44f2a2203946f88c7","observation_id":"4709d47b-ef65-42e4-8547-370b66fbcdee","resolution":{"observed_at":"2026-05-14T20:12:54.876711Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.02439/citation-record","integrity":"/paper/2402.02439/integrity","json":"/paper/2402.02439/citation-record.json","paper":"/paper/2402.02439"},"outbound":[],"paper":{"arxiv_id":"2402.02439","last_updated":"2024-02-22T00:05:12Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T14:21:43.978709Z","submitted_at":"2024-02-04T10:30:23Z","title":"DiffStitch: Boosting Offline Reinforcement Learning with Diffusion-based Trajectory Stitching"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.02439."}