{"as_of":"2026-08-22T07:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:745ebf369c789a2a1b8ae791c44ddca1ae896f65b40ba4bc60d7d50bd30c6b2a","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:42:36.613897Z","state":"measured"},{"denominator":24,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":24,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.10786/citation-record","integrity":"/paper/2412.10786/integrity","json":"/paper/2412.10786/citation-record.json","paper":"/paper/2412.10786"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.876873Z","title":"Perception prioritized training of diffusion models","venue":null,"work_id":"c6cf967c-92df-45de-80c6-99851c39d7c9","year":2022},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.522129Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:4b5d253719419bfca9ebab4f1662db016e834e5ebedb7c87151d8bfe088850d6","observation_id":"07b7dfc2-2471-4cfb-b2f6-192420941499","resolution":{"observed_at":"2026-08-11T15:42:36.880756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.526768Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.526768Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:f94b8ff7a4966c29fc294b08f402e419669c3374e9ac1148525f14bfdae4ede7","observation_id":"39b0e0bd-ead0-452f-9a6f-d42b287cd96b","resolution":{"observed_at":"2026-08-11T15:42:36.526768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.859806Z","title":"Diffusion models beat gans on image synthesis","venue":null,"work_id":"867a204d-9d42-4783-af43-368f31cdfb50","year":2021},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.530793Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:2b653b2a6420ba7338859437f7591fe7e225910f07067f9708a6299db56cca1e","observation_id":"8e731039-9e70-4ddc-9d34-25befac9647d","resolution":{"observed_at":"2026-08-11T15:42:36.863501Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.848829Z","title":"Generative adversarial networks","venue":null,"work_id":"f8e97494-06bb-4a50-bc61-085ca2090d75","year":2020},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.535059Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:9d3cc48af11f2f93aa2bd939e544c236b2ecb7b9f624268dae914a8ba3fa2540","observation_id":"55b0ea29-4eef-42da-a68b-a1bf1dcbc65b","resolution":{"observed_at":"2026-08-11T15:42:36.852481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.539017Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilib- rium","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.539017Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:59c62dde46e90285ab011769f3c4c8319b6a23bf8c003914a29912d016454755","observation_id":"4f1bc5d9-7ce0-495f-a7c5-f4d0c2d50981","resolution":{"observed_at":"2026-08-11T15:42:36.539017Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.831020Z","title":"Denoising diffu- sion probabilistic models","venue":null,"work_id":"c2ce9446-598e-49a7-b3ff-92ea000c0686","year":2020},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.543706Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:c7a37434a6dad65bc970a65c5e46e89b0e566bc6418f3cd8f872d0b8c22f0f4f","observation_id":"49335c49-10a1-4179-810d-fe800bc0dfe0","resolution":{"observed_at":"2026-08-11T15:42:36.835563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.14080","last_updated":"2021-05-28T19:48:51Z","snapshot_observed_at":"2026-08-18T23:30:00.831277Z","submitted_at":"2021-05-28T19:48:51Z","title":"Gotta Go Fast When Generating Data with Score-Based Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.14080","snapshot_observed_at":"2026-08-11T15:42:36.548210Z","title":"Gotta go fast when generating data with score-based models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.548210Z"},"links":{"cited_paper":"/paper/2105.14080","citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:10baca2e628dac7ecb516cae88c40081cdd27dc72648d026f3b201810449d8e7","observation_id":"45fd4b90-5abf-47a3-87f6-5867aea62c67","resolution":{"observed_at":"2026-08-11T15:42:36.548210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.819304Z","title":"Elucidating the design space of diffusion-based generative models","venue":null,"work_id":"9ebcec78-279a-4d23-b8b2-d69beb15845c","year":2022},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.552349Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:f7577a6d2920ebbca2b3f94c9ef20577c1f8b65121844800963ae0c556308898","observation_id":"85b7e5b6-46a9-485d-adb0-0fe690f1f8b6","resolution":{"observed_at":"2026-08-11T15:42:36.823457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.807905Z","title":"Training generative adver- sarial networks with limited data","venue":null,"work_id":"2850e2cf-9903-4f10-9245-16c30daf8d0e","year":2020},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.556113Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:b33f0231d635be8f17e799eac6dc73ef33d77b29b54be78e8212fed6d76d571c","observation_id":"cd71cce0-2ef4-46a4-ae68-f257067f9cdb","resolution":{"observed_at":"2026-08-11T15:42:36.811994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.796081Z","title":"Variational diffusion models","venue":null,"work_id":"84125dd9-b011-4c69-b6a0-4b1d8db306ba","year":2021},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.559941Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:4ef7d968be20b36b8a79c89bf5d329628bc2559888986c323f8c87c931aadb36","observation_id":"189c13df-1b3d-4150-87db-f6ae246ef510","resolution":{"observed_at":"2026-08-11T15:42:36.800431Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-11T15:42:36.563605Z","title":"Auto-encoding varia- tional bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.563605Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:b482abd1606b4ee0c3b32c7263bf61ac8470c72178a2c892457fa62fe382ad01","observation_id":"f970f627-41d0-4aea-a91d-260af1b2da74","resolution":{"observed_at":"2026-08-11T15:42:36.563605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.567692Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.567692Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:74129e03bbca4baefb4e788657c137fad126c12c8ce81a1aab8231967fb620e9","observation_id":"46a1c041-26a7-4142-8f30-ea4ab3bb4d3a","resolution":{"observed_at":"2026-08-11T15:42:36.567692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.571552Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.571552Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:8827f2a0a5c277d0f37e46be4985a87be5cd73bf6b4429c341619782c30d77be","observation_id":"81e464a4-400a-43a1-8f34-b60a4589aa66","resolution":{"observed_at":"2026-08-11T15:42:36.571552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.575369Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.575369Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:1f808625a28f5f5b0a4909d77659791869a8bdf6f7514dcce69d8a34052745b8","observation_id":"c94c0917-7794-44d3-bd18-2749e3090409","resolution":{"observed_at":"2026-08-11T15:42:36.575369Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.766002Z","title":"U- net: Convolutional networks for biomedical image segmen- tation","venue":null,"work_id":"5a2c1f56-9b75-4be3-8b03-58615e90d655","year":2015},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.579036Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:6f6705404b78dcd8e7372fdbbf0e01defb4b23a7401ea8c8757f2fb21cbb420b","observation_id":"072ce836-2cd1-44cd-8101-7784b69f84f7","resolution":{"observed_at":"2026-08-11T15:42:36.769655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.754304Z","title":"Stylegan- xl: Scaling stylegan to large diverse datasets","venue":null,"work_id":"937cbbfe-867c-4c79-9d92-e9138036e1f9","year":2022},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.582684Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:dc0e22bc5bce25893039e8dea61ae77c35d9de258b7a5c11c91933d2720d9a96","observation_id":"50a1cf41-5e80-4d2a-8b4b-cc080759d113","resolution":{"observed_at":"2026-08-11T15:42:36.758740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.742469Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":"821a9d93-a493-40cd-aa7e-e82ac075196f","year":2015},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.586163Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:9d7d57e5a537b3918858a892dfc18c716df639c72c55b0942c44a856581df952","observation_id":"516f407c-3e3d-46b4-91a0-422a4f446b32","resolution":{"observed_at":"2026-08-11T15:42:36.746303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-11T15:42:36.589743Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.589743Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:15df0b9455a771af88a5cbb1b64248b6148391e2f7b5927af62d99185164a449","observation_id":"a193e91f-ec91-4673-9da0-8782a341570a","resolution":{"observed_at":"2026-08-11T15:42:36.589743Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.731227Z","title":"Maximum likelihood training of score-based diffusion mod- els","venue":null,"work_id":"1f757ae6-7e56-4cb2-8985-d393483cfe88","year":2021},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.593818Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:e6eeb3c21e632ec1cdcc23330f5b5c11fce928edac0374103ddc92cc113a70c0","observation_id":"a4730ade-50ea-491a-8c99-1c55ebdb644e","resolution":{"observed_at":"2026-08-11T15:42:36.735028Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.719595Z","title":"Generative modeling by esti- mating gradients of the data distribution","venue":null,"work_id":"77242941-6723-4d17-b9a6-f5e33263910b","year":2019},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.597737Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:c49bc67578873a566f570ad5531614398399fe9744fc6dc7cfbd70806b3e36a0","observation_id":"63d9b155-a56a-48b1-9239-a11e24ff15c1","resolution":{"observed_at":"2026-08-11T15:42:36.723578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-11T15:42:36.601662Z","title":"Score-based generative modeling through stochastic differential equa- tions","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.601662Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:57cb8b6c48c5f9f3dd3503660614a93730462478cfdb97c97281aadae5421538","observation_id":"419d72cf-c4fa-482b-9172-09429d9b6aa4","resolution":{"observed_at":"2026-08-11T15:42:36.601662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.707184Z","title":"Neural discrete representation learning","venue":null,"work_id":"95cffddc-c1b7-47b1-92c1-9e139f131752","year":2017},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.606109Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:8697c7440ab408ec79160056f60c39158a98151522132f845bc8645aad6fc2ca","observation_id":"6d19a007-cabe-4242-92b8-9ca72f890b45","resolution":{"observed_at":"2026-08-11T15:42:36.711640Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.07015","last_updated":"2024-06-28T17:14:13Z","snapshot_observed_at":"2026-08-16T15:33:46.445264Z","submitted_at":"2023-05-11T17:55:25Z","title":"Exploiting Diffusion Prior for Real-World Image Super-Resolution","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.07015","snapshot_observed_at":"2026-08-11T15:42:36.609933Z","title":"Exploiting diffusion prior for real-world image super-resolution","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.609933Z"},"links":{"cited_paper":"/paper/2305.07015","citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:b9a912141a7201feea048c03c898a847b73afc944a94e9997c69975cbd50203d","observation_id":"8066111a-bb5b-4603-8eb5-5414cc5b8703","resolution":{"observed_at":"2026-08-11T15:42:36.609933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T15:42:36.693281Z","title":"Learning fast samplers for diffusion models by differentiating through sample quality","venue":null,"work_id":"3b8a7add-6aa9-4e7d-a2ec-a80a2c81aea7","year":2021},"citing_paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T15:42:36.613897Z"},"links":{"citing_paper":"/paper/2412.10786"},"observation_digest":"sha256:af02b7f08dac37a502685582605835a270c6d1b8826e498dc4c2e544409ee73b","observation_id":"480af9d8-291b-4579-a4a9-cd0f06c330ed","resolution":{"observed_at":"2026-08-11T15:42:36.699192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.10786","last_updated":"2024-12-14T10:47:52Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-20T04:22:16.770128Z","submitted_at":"2024-12-14T10:47:52Z","title":"Optimizing Few-Step Sampler for Diffusion Probabilistic Model"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":14},"total_outbound_references":24},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2412.10786."}