{"as_of":"2026-08-10T23:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:efd69732b2317a792e5183baca7ea430c44747245ee52b1f01a08f2ee03231df","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":"2026-08-10T06:31:04.303077+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":"2026-08-03T08:02:20.623495Z","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":"2026-07-03T10:37:57.213882Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.07578","last_updated":"2025-05-21T17:09:44Z","snapshot_observed_at":"2026-08-07T17:16:00.128826Z","submitted_at":"2025-03-10T17:44:46Z","title":"Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation","version":2},"cited_work":{"arxiv_id":"2503.07578","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.07578","snapshot_observed_at":"2026-07-03T10:37:57.213882Z","title":"Denoising score distillation: From noisy diffusion pretraining to one-step high-quality generation.arXiv preprint arXiv:2503.07578","venue":null,"work_id":"594a9fc0-6225-447e-b065-d514f65a238b","year":2025},"citing_paper":{"arxiv_id":"2512.10857","last_updated":"2026-05-13T02:55:53Z","snapshot_observed_at":"2026-07-06T22:38:45.907386Z","submitted_at":"2025-12-11T17:53:38Z","title":"Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-16T23:01:37.752828Z"},"links":{"cited_paper":"/paper/2503.07578","citing_paper":"/paper/2512.10857"},"observation_digest":"sha256:be2167e07680d5d0440be51d7a8658fe4bb51cf10224f1ef76c51613a2d56359","observation_id":"cf4604d4-56e4-4fc1-95ec-d54a03a53636","resolution":{"observed_at":"2026-05-16T23:03:39.123129Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07578","last_updated":"2025-05-21T17:09:44Z","snapshot_observed_at":"2026-08-07T17:16:00.128826Z","submitted_at":"2025-03-10T17:44:46Z","title":"Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.07578","snapshot_observed_at":"2026-08-03T08:02:20.623495Z","title":"Denoising score distillation: From noisy diffusion pretraining to one-step high-quality generation.arXiv preprint arXiv:2503.07578, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.18728","last_updated":"2026-05-27T18:01:34Z","snapshot_observed_at":"2026-08-09T20:11:36.580153Z","submitted_at":"2026-01-26T17:51:52Z","title":"Riemannian AmbientFlow: Towards Simultaneous Manifold Learning and Generative Modeling from Corrupted Data","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T08:02:20.623495Z"},"links":{"cited_paper":"/paper/2503.07578","citing_paper":"/paper/2601.18728"},"observation_digest":"sha256:3df35c02e6717ebe560f4cd9e8608bb36f2ab3114ea4bb4b4b3b1e541b0af86a","observation_id":"4dc7683a-e9d6-4d5a-941f-585004b7f05b","resolution":{"observed_at":"2026-08-03T08:02:20.623495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07578","last_updated":"2025-05-21T17:09:44Z","snapshot_observed_at":"2026-08-07T17:16:00.128826Z","submitted_at":"2025-03-10T17:44:46Z","title":"Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation","version":2},"cited_work":{"arxiv_id":"2503.07578","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.07578","snapshot_observed_at":"2026-07-03T10:37:57.213882Z","title":"Denoising score distillation: From noisy diffusion pretraining to one-step high-quality generation.arXiv preprint arXiv:2503.07578","venue":null,"work_id":"594a9fc0-6225-447e-b065-d514f65a238b","year":2025},"citing_paper":{"arxiv_id":"2606.12365","last_updated":"2026-06-10T17:34:12Z","snapshot_observed_at":"2026-08-02T09:09:52.074746Z","submitted_at":"2026-06-10T17:34:12Z","title":"Ambient Diffusion Policy: Imitation Learning from Suboptimal Data in Robotics","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-27T09:52:38.167538Z"},"links":{"cited_paper":"/paper/2503.07578","citing_paper":"/paper/2606.12365"},"observation_digest":"sha256:bd9265f982a18252b9b3bb4c50d0d032196e117c348ee62fb5159566c73c41e4","observation_id":"ff1c019d-338f-4124-9aa5-0674005d1602","resolution":{"observed_at":"2026-07-03T10:37:57.215601Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2503.07578/citation-record","integrity":"/paper/2503.07578/integrity","json":"/paper/2503.07578/citation-record.json","paper":"/paper/2503.07578"},"outbound":[],"paper":{"arxiv_id":"2503.07578","last_updated":"2025-05-21T17:09:44Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T17:16:00.128826Z","submitted_at":"2025-03-10T17:44:46Z","title":"Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2503.07578."}