{"as_of":"2026-08-08T02:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:44ddf5155c6599c1905dd7085a6fab1983fc30264c05462c38d843376aa7dbeb","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":"2026-08-07T06:34:17.273281+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":"2026-08-07T14:20:01.086255Z","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":"2026-07-03T21:08:58.073496Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.12456","last_updated":"2025-03-03T23:57:58Z","snapshot_observed_at":"2026-08-05T11:35:47.037300Z","submitted_at":"2024-10-16T11:08:02Z","title":"Training Neural Samplers with Reverse Diffusive KL Divergence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12456","snapshot_observed_at":"2026-08-07T14:20:01.086255Z","title":"Training neural samplers with reverse diffusive kl divergence.arXiv preprint arXiv:2410.12456, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19431","last_updated":"2025-05-26T02:48:26Z","snapshot_observed_at":"2026-08-07T14:11:55.796750Z","submitted_at":"2025-05-26T02:48:26Z","title":"Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:20:01.086255Z"},"links":{"cited_paper":"/paper/2410.12456","citing_paper":"/paper/2505.19431"},"observation_digest":"sha256:442ae0410ffa2ae327dc9e9047f147f76aaaa73b0080ee742ce02a0de0b3c885","observation_id":"536e5f22-2691-420d-9cc8-16a93816b43f","resolution":{"observed_at":"2026-08-07T14:20:01.086255Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12456","last_updated":"2025-03-03T23:57:58Z","snapshot_observed_at":"2026-08-05T11:35:47.037300Z","submitted_at":"2024-10-16T11:08:02Z","title":"Training Neural Samplers with Reverse Diffusive KL Divergence","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.12456","snapshot_observed_at":"2026-08-05T19:28:36.165145Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.12518","last_updated":"2025-08-17T22:51:27Z","snapshot_observed_at":"2026-08-06T05:26:23.208699Z","submitted_at":"2025-08-17T22:51:27Z","title":"Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T19:28:36.165145Z"},"links":{"cited_paper":"/paper/2410.12456","citing_paper":"/paper/2508.12518"},"observation_digest":"sha256:4d2dbbec1cd8bf2d364f17e1953f09310dc6d2e928c3f2f489d1d0e0fafe08c9","observation_id":"b4cca44a-3edb-42c2-a480-b0d6a5594bb7","resolution":{"observed_at":"2026-08-05T19:28:36.165145Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12456","last_updated":"2025-03-03T23:57:58Z","snapshot_observed_at":"2026-08-05T11:35:47.037300Z","submitted_at":"2024-10-16T11:08:02Z","title":"Training Neural Samplers with Reverse Diffusive KL Divergence","version":2},"cited_work":{"arxiv_id":"2410.12456","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.12456","snapshot_observed_at":"2026-07-03T21:08:58.073496Z","title":"arXiv preprint arXiv:2410.12456 , year=","venue":null,"work_id":"f142331f-27c3-49c6-a113-ac7a167c1997","year":2024},"citing_paper":{"arxiv_id":"2509.03726","last_updated":"2026-04-21T00:07:07Z","snapshot_observed_at":"2026-07-06T22:23:10.158908Z","submitted_at":"2025-09-03T21:16:03Z","title":"Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-18T18:43:13.941495Z"},"links":{"cited_paper":"/paper/2410.12456","citing_paper":"/paper/2509.03726"},"observation_digest":"sha256:e0f0ee289befe8569d50ef80a96e15e81074c741df5a4f63c411c9d3639a21f4","observation_id":"1f188ec8-f354-4a15-a97d-8bb4c2d72567","resolution":{"observed_at":"2026-05-18T18:46:45.400878Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12456","last_updated":"2025-03-03T23:57:58Z","snapshot_observed_at":"2026-08-05T11:35:47.037300Z","submitted_at":"2024-10-16T11:08:02Z","title":"Training Neural Samplers with Reverse Diffusive KL Divergence","version":2},"cited_work":{"arxiv_id":"2410.12456","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.12456","snapshot_observed_at":"2026-07-03T21:08:58.073496Z","title":"arXiv preprint arXiv:2410.12456 , year=","venue":null,"work_id":"f142331f-27c3-49c6-a113-ac7a167c1997","year":2024},"citing_paper":{"arxiv_id":"2605.05710","last_updated":"2026-05-07T05:55:10Z","snapshot_observed_at":"2026-07-06T23:18:17.333660Z","submitted_at":"2026-05-07T05:55:10Z","title":"On the Blessing of Pre-training in Weak-to-Strong Generalization","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-08T14:59:19.883399Z"},"links":{"cited_paper":"/paper/2410.12456","citing_paper":"/paper/2605.05710"},"observation_digest":"sha256:69c5d1014e90578b993cadc381779287f78740f1bc553395d441b508f381a5ed","observation_id":"d3077ff9-6652-4186-b5ad-eca6e29a6506","resolution":{"observed_at":"2026-05-11T18:36:08.580791Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.12456","last_updated":"2025-03-03T23:57:58Z","snapshot_observed_at":"2026-08-05T11:35:47.037300Z","submitted_at":"2024-10-16T11:08:02Z","title":"Training Neural Samplers with Reverse Diffusive KL Divergence","version":2},"cited_work":{"arxiv_id":"2410.12456","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.12456","snapshot_observed_at":"2026-07-03T21:08:58.073496Z","title":"arXiv preprint arXiv:2410.12456 , year=","venue":null,"work_id":"f142331f-27c3-49c6-a113-ac7a167c1997","year":2024},"citing_paper":{"arxiv_id":"2606.18478","last_updated":"2026-06-23T08:04:55Z","snapshot_observed_at":"2026-08-05T20:33:40.212920Z","submitted_at":"2026-06-16T20:38:30Z","title":"Data-Forcing Distillation: Restoring Diversity and Fidelity in Few-Step Video Generation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-27T00:55:20.691480Z"},"links":{"cited_paper":"/paper/2410.12456","citing_paper":"/paper/2606.18478"},"observation_digest":"sha256:f919bc992dae07724a1ce544b3eed9dc5f219d94a626b66251ecdf828dd0fa5f","observation_id":"d217d265-fb7f-4805-9fbc-50510221aef3","resolution":{"observed_at":"2026-07-03T21:08:58.074990Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.12456/citation-record","integrity":"/paper/2410.12456/integrity","json":"/paper/2410.12456/citation-record.json","paper":"/paper/2410.12456"},"outbound":[],"paper":{"arxiv_id":"2410.12456","last_updated":"2025-03-03T23:57:58Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-05T11:35:47.037300Z","submitted_at":"2024-10-16T11:08:02Z","title":"Training Neural Samplers with Reverse Diffusive KL Divergence"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.12456."}