{"as_of":"2026-08-22T03:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:13dcb8b06ab23eeeeceb4bb0630f3b0b6116e442d4fb4db923890b7e883e4a81","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":"2026-08-21T06:32:19.484+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":"2026-08-12T12:01:21.896651Z","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":"2026-07-04T05:39:40.852625Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.01178","last_updated":"2023-07-03T17:44:22Z","snapshot_observed_at":"2026-08-16T15:19:04.692060Z","submitted_at":"2023-07-03T17:44:22Z","title":"Learning Mixtures of Gaussians Using the DDPM Objective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01178","snapshot_observed_at":"2026-08-12T12:01:21.896651Z","title":"James B Simon, Madeline Dickens, Dhruva Karkada, and Michael Deweese","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.896651Z"},"links":{"cited_paper":"/paper/2307.01178","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:dd4b3279f18548fab03140281c65f95b81f764d92eb6f01b26c8e8d5f7d4a0ac","observation_id":"9f8128d3-f482-4eea-a7e1-71308d33c80c","resolution":{"observed_at":"2026-08-12T12:01:21.896651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01178","last_updated":"2023-07-03T17:44:22Z","snapshot_observed_at":"2026-08-16T15:19:04.692060Z","submitted_at":"2023-07-03T17:44:22Z","title":"Learning Mixtures of Gaussians Using the DDPM Objective","version":1},"cited_work":{"arxiv_id":"2307.01178","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01178","snapshot_observed_at":"2026-07-04T05:39:40.852625Z","title":"& Klivans, A.Learning Mixtures of Gaussians Using the DDPM Objective2023","venue":null,"work_id":"08e36b13-ab8b-48d5-8417-d1d5e1633def","year":null},"citing_paper":{"arxiv_id":"2603.12901","last_updated":"2026-06-10T13:28:42Z","snapshot_observed_at":"2026-08-16T02:30:04.422518Z","submitted_at":"2026-03-13T11:07:01Z","title":"A theory of learning data statistics in diffusion models, from easy to hard","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-21T11:33:01.990748Z"},"links":{"cited_paper":"/paper/2307.01178","citing_paper":"/paper/2603.12901"},"observation_digest":"sha256:78cf19ff3810cdb0aeca50be692e535ffe7069acb795e77031314f13e6ff4fb8","observation_id":"80fd25a0-2f09-4f6e-aec7-f3b7f20cf36c","resolution":{"observed_at":"2026-05-21T11:34:08.940230Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01178","last_updated":"2023-07-03T17:44:22Z","snapshot_observed_at":"2026-08-16T15:19:04.692060Z","submitted_at":"2023-07-03T17:44:22Z","title":"Learning Mixtures of Gaussians Using the DDPM Objective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01178","snapshot_observed_at":"2026-07-14T22:02:58.403878Z","title":"& Klivans, A.Learning Mixtures of Gaussians Using the DDPM Objective2023","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2603.12901","last_updated":"2026-06-10T13:28:42Z","snapshot_observed_at":"2026-08-16T02:30:04.422518Z","submitted_at":"2026-03-13T11:07:01Z","title":"A theory of learning data statistics in diffusion models, from easy to hard","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T22:02:58.403878Z"},"links":{"cited_paper":"/paper/2307.01178","citing_paper":"/paper/2603.12901"},"observation_digest":"sha256:3f0fa89f5d6fb2715b784337e3970ca1b5e8070906ae4d2e633e1e5118059962","observation_id":"6ca74120-f4e5-467a-a5e3-3b3c71c3d985","resolution":{"observed_at":"2026-07-14T22:02:58.403878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01178","last_updated":"2023-07-03T17:44:22Z","snapshot_observed_at":"2026-08-16T15:19:04.692060Z","submitted_at":"2023-07-03T17:44:22Z","title":"Learning Mixtures of Gaussians Using the DDPM Objective","version":1},"cited_work":{"arxiv_id":"2307.01178","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01178","snapshot_observed_at":"2026-07-04T05:39:40.852625Z","title":"& Klivans, A.Learning Mixtures of Gaussians Using the DDPM Objective2023","venue":null,"work_id":"08e36b13-ab8b-48d5-8417-d1d5e1633def","year":null},"citing_paper":{"arxiv_id":"2606.20299","last_updated":"2026-07-01T14:03:37Z","snapshot_observed_at":"2026-08-15T21:10:11.569207Z","submitted_at":"2026-06-18T14:35:53Z","title":"Statistical Properties of Training & Generalization","version":1},"reference_index":265,"source":"arxiv_source","source_observed_at":"2026-06-26T15:35:51.654392Z"},"links":{"cited_paper":"/paper/2307.01178","citing_paper":"/paper/2606.20299"},"observation_digest":"sha256:cc46999457aedbb36603290564173773b07ca2f7a54d5a1e3986770a9b1abd21","observation_id":"a61d6efe-f98a-4683-9654-e9bb6702a551","resolution":{"observed_at":"2026-07-04T05:39:40.854241Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01178","last_updated":"2023-07-03T17:44:22Z","snapshot_observed_at":"2026-08-16T15:19:04.692060Z","submitted_at":"2023-07-03T17:44:22Z","title":"Learning Mixtures of Gaussians Using the DDPM Objective","version":1},"cited_work":{"arxiv_id":"2307.01178","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.01178","snapshot_observed_at":"2026-07-04T05:39:40.852625Z","title":"& Klivans, A.Learning Mixtures of Gaussians Using the DDPM Objective2023","venue":null,"work_id":"08e36b13-ab8b-48d5-8417-d1d5e1633def","year":null},"citing_paper":{"arxiv_id":"2606.20299","last_updated":"2026-07-01T14:03:37Z","snapshot_observed_at":"2026-08-15T21:10:11.569207Z","submitted_at":"2026-06-18T14:35:53Z","title":"Statistical Properties of Training & Generalization","version":2},"reference_index":265,"source":"arxiv_source","source_observed_at":"2026-07-02T21:51:13.457071Z"},"links":{"cited_paper":"/paper/2307.01178","citing_paper":"/paper/2606.20299"},"observation_digest":"sha256:ac8d5fb897061305d37b46a7a95ee83a1a85677b7aaab60e37aa62df1ac3b2f3","observation_id":"823f5f6d-8173-493d-a75c-acb6fdd2429e","resolution":{"observed_at":"2026-07-02T21:57:25.611609Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2307.01178/citation-record","integrity":"/paper/2307.01178/integrity","json":"/paper/2307.01178/citation-record.json","paper":"/paper/2307.01178"},"outbound":[],"paper":{"arxiv_id":"2307.01178","last_updated":"2023-07-03T17:44:22Z","latest_version":1,"primary_category":"cs.DS","snapshot_observed_at":"2026-08-16T15:19:04.692060Z","submitted_at":"2023-07-03T17:44:22Z","title":"Learning Mixtures of Gaussians Using the DDPM Objective"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.01178."}