{"as_of":"2026-08-08T10:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4105f4f3cf677bb6d0b7568bf4ff008041a16036a3021f724c58350d0fd50b77","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:05:46.193531Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-27T05:54:23.709309Z","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":"2026-07-03T16:18:37.950110Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"cited_work":{"arxiv_id":"2505.22841","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.22841","snapshot_observed_at":"2026-07-03T16:18:37.950110Z","title":"Kernel-smoothed scores for denoising diffusion: A bias-variance study","venue":null,"work_id":"d35c1389-5f89-4af4-a7e2-0fcbc754a43c","year":2025},"citing_paper":{"arxiv_id":"2605.14276","last_updated":"2026-05-14T02:20:36Z","snapshot_observed_at":"2026-08-03T04:07:32.785860Z","submitted_at":"2026-05-14T02:20:36Z","title":"Training-Free Generative Sampling via Moment-Matched Score Smoothing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-15T02:31:15.134624Z"},"links":{"cited_paper":"/paper/2505.22841","citing_paper":"/paper/2605.14276"},"observation_digest":"sha256:1894b6732659240dad8a40244ad5e35f9d1f659d0a5d63a5d0fd904c1391c7b3","observation_id":"55702467-afd3-4775-96d8-f5df94dca12a","resolution":{"observed_at":"2026-05-15T02:33:32.412238Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"cited_work":{"arxiv_id":"2505.22841","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.22841","snapshot_observed_at":"2026-07-03T16:18:37.950110Z","title":"Kernel-smoothed scores for denoising diffusion: A bias-variance study","venue":null,"work_id":"d35c1389-5f89-4af4-a7e2-0fcbc754a43c","year":2025},"citing_paper":{"arxiv_id":"2606.13433","last_updated":"2026-06-11T14:58:30Z","snapshot_observed_at":"2026-08-06T09:23:10.403105Z","submitted_at":"2026-06-11T14:58:30Z","title":"Smoothed-KL Reweighting: A Principled Account and Matching Rule for SNR-Based Diffusion Training","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-27T05:54:23.709309Z"},"links":{"cited_paper":"/paper/2505.22841","citing_paper":"/paper/2606.13433"},"observation_digest":"sha256:2aec9884e67ff8c64fd07810b1adcd871dade0f0a8929cfc7069dbe092e1a51f","observation_id":"fcf745ff-75e8-4b96-9767-cdc36a03812d","resolution":{"observed_at":"2026-07-03T16:18:37.951603Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.22841/citation-record","integrity":"/paper/2505.22841/integrity","json":"/paper/2505.22841/citation-record.json","paper":"/paper/2505.22841"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:05:52.227728Z","title":"Matrix algebra, volume 1","venue":null,"work_id":"e6b2b1db-94ef-4181-bd0e-5776ecf6eeb4","year":2005},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:42.861170Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:52a9bb7b5eac6dbd04b032bf4e62c85d1894782e34a7b122d2190d120991e829","observation_id":"f1c8fc52-df36-4a53-bfad-29a97f7547bf","resolution":{"observed_at":"2026-08-07T13:05:52.280400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:52.094396Z","title":"Losing dimensions: Geometric memorization in generative diffusion, 2024","venue":null,"work_id":"d8cde76a-5a4a-47c3-8b76-4da241e82245","year":2024},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:42.892664Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:55daf20a5cc1070924764c79c6bf4856b877b4f2513abb19f31848ca1dcd2864","observation_id":"3d741a22-3574-41b6-9d57-6d74fa131959","resolution":{"observed_at":"2026-08-07T13:05:52.152184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:51.948941Z","title":"Anderson","venue":null,"work_id":"de5777d3-fe91-4862-98de-8888cca7e41c","year":1982},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:42.974940Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:6f87bdd032459de83c31bc915146b8d9f560e2a48ca1542110ef61aa4108fd78","observation_id":"1dc0e694-ff99-4b68-8b77-1967eb1f590a","resolution":{"observed_at":"2026-08-07T13:05:51.997986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:51.810905Z","title":"Kovachki, Assad Oberai, and Andrew M","venue":null,"work_id":"9c00cd57-9856-4280-9623-3689bbc6d550","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.040536Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:c1ddfddbbd1e86b81fe792e075e3b74ca625f0502b57acf9ad3918588b831341","observation_id":"65f74ff7-2bd2-47d6-a87c-ec60543c3e75","resolution":{"observed_at":"2026-08-07T13:05:51.868173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:51.674550Z","title":"Advanced mathematical methods for scientists and engineers I: Asymptotic methods and perturbation theory","venue":null,"work_id":"3f80b0ca-1876-4001-9c89-570ed71bbe2c","year":2013},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.123068Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:83f6b0c323d0fad623dd968c8ac460120ee3777cd17614f84a85bbdb207260ff","observation_id":"9e23c1e4-5cf0-46bd-8135-196df0035f56","resolution":{"observed_at":"2026-08-07T13:05:51.727878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:51.509288Z","title":"Dynamical regimes of diffusion models","venue":null,"work_id":"b11b542e-7f76-4b0d-a66a-c58b03dfa1a3","year":2024},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.191590Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:dd3ab35e7f6b70bc4213379af4db754ffd7fc406cdccf429a732fbe851fcd195","observation_id":"218a6ac0-f04c-41c9-99a4-957647ea85e0","resolution":{"observed_at":"2026-08-07T13:05:51.608115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:51.376989Z","title":"Shallow diffusion networks provably learn hidden low-dimensional structure","venue":null,"work_id":"9ca85421-8653-4786-8c38-81697fbb02c5","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.237877Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:18b87a44547a01c73b1426041b02a19dbdaef229020ba388f9e2e4567b8ee50a","observation_id":"0b2fbd20-765a-4b36-8ddf-b5187d6b9074","resolution":{"observed_at":"2026-08-07T13:05:51.432907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:51.254690Z","title":"Swarm gradient dynamics for global optimization: the mean-field limit case","venue":null,"work_id":"95e844ea-29ec-4de1-81d1-e153f85b6143","year":2024},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.331352Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:28d124f361391b9516629e8e75217b5fb7a751def630f139a9068e5b2b3c5868","observation_id":"b7ed41c5-eab5-40c3-9ab2-39ae3a0b6934","resolution":{"observed_at":"2026-08-07T13:05:51.336232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:51.096508Z","title":"Extracting training data from diffusion models","venue":null,"work_id":"3f58dc19-253a-47fb-8111-d427efbd32cc","year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.406253Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:107acf211a81418bfc5c15116cf9ecfbfcad5e4acde24919a90faa18404384d7","observation_id":"87eeb391-cb2c-4c8b-8f08-ce45d24222a8","resolution":{"observed_at":"2026-08-07T13:05:51.171970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:50.940185Z","title":"Towards memorization-free diffusion models, 2024","venue":null,"work_id":"09a96cf2-e5ff-4fba-9903-ba0e9d06ae3a","year":2024},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.475063Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:776e7b3ffd701925b1589987e42ad7cdadb6f6c9aee71dce484b58cd4e91d5f9","observation_id":"6a2971a2-3de0-4fcc-876e-e850313dc8e6","resolution":{"observed_at":"2026-08-07T13:05:51.024661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:50.821884Z","title":"Improved analysis of score-based generative modeling: user-friendly bounds under minimal smoothness assumptions","venue":null,"work_id":"cdc3aa20-83e5-4485-95e8-382f7b55b53f","year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.542672Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:022675c1635d1461417dbb81a2e1931efed1dfc23f9b69b1fc03aa5905b2d6f2","observation_id":"ca8372ca-e14e-41db-b5ee-2cb05954f4fe","resolution":{"observed_at":"2026-08-07T13:05:50.850232Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:50.654983Z","title":"Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data","venue":null,"work_id":"120a788c-6ec2-49fe-b698-c0d197e00f1a","year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.580823Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:a36e51d627ad936906d5c5dabda35f321d0734172a8797ef55a03a5c6915fab4","observation_id":"958af38c-c3cd-46fd-be09-8628ec70fb74","resolution":{"observed_at":"2026-08-07T13:05:50.748099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:50.547120Z","title":"On the interpolation effect of score smoothing, 2025","venue":null,"work_id":"e7f21d3c-f648-42a3-92e3-f59690cf21ff","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.679056Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:f5825050230037a57077f9b0dfc6a75c1bb59c7b4efc1bfcb95b1e008fea6e55","observation_id":"3492b031-825f-4609-87af-ce5472781819","resolution":{"observed_at":"2026-08-07T13:05:50.618304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:50.356092Z","title":null,"venue":null,"work_id":"e433f855-9a8e-41ec-a4b0-b0d2339edd45","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.829006Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:0c714fa0f9c7311131dc4f5e9d1b4c617fcae1471edecb81ef1a7d975524e4d6","observation_id":"85bf3479-2dd2-4184-a783-6aeec631ee62","resolution":{"observed_at":"2026-08-07T13:05:50.443885Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:50.218567Z","title":"Ambient diffusion: Learning clean distributions from corrupted data","venue":null,"work_id":"c91a95e2-2caa-4a1f-a7d5-0bb608c0e74c","year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:43.914256Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:b0eef0e719443ae659f861723eb3cb7bfeb197278930aed42cd55dcb2a472f51","observation_id":"05dc69d4-ceea-4c36-a35e-46885239542f","resolution":{"observed_at":"2026-08-07T13:05:50.284618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:50.034103Z","title":"Analysis of diffusion models for manifold data, 2025","venue":null,"work_id":"457cbbeb-4c76-491a-9387-6a077f5a187b","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.011113Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:7eab85d408ffd71131cd10ffb279d2c712c047f034162a8914dfa061c0036a33","observation_id":"b39d3f66-aaf4-4132-874b-b2d84bbaaf58","resolution":{"observed_at":"2026-08-07T13:05:50.099455Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:49.838222Z","title":"On memorization in diffusion models","venue":null,"work_id":"8451bf7a-3925-4d9d-842a-e20dfe1d246f","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.095219Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:89c6c8a53321f28f25b4c12e1c58b8cd04b968b841a00091d5b965791e1df78a","observation_id":"fa2752a6-47e8-4755-b4dd-a669d74a8f14","resolution":{"observed_at":"2026-08-07T13:05:49.931373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:49.694795Z","title":"Linear Methods for Regression, pages 43–99","venue":null,"work_id":"0c864cc8-29cd-4b2c-92e3-573c6d6c7ab2","year":2009},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.176476Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:b90db29149c9969d324da071a296754bb9bd36c6c6b65d876abafda0a381e833","observation_id":"763b295c-0105-4355-a127-15eb88c5d4c8","resolution":{"observed_at":"2026-08-07T13:05:49.737669Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:49.516376Z","title":"Classifier-free diffusion guidance, 2022","venue":null,"work_id":"f5c0391c-0b6e-4913-9367-ea50399febdc","year":2022},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.267780Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:55b3d3f1a97bc8dcf09749bb9abad22113edfc96228a8284f42b52c1ddc2ee47","observation_id":"89f8bead-cbb8-4a11-9f31-7753f553bfc3","resolution":{"observed_at":"2026-08-07T13:05:49.614022Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:49.353935Z","title":"Neural tangent kernel: Convergence and generalization in neural networks","venue":null,"work_id":"82e1d5a5-8538-4d27-89c2-cb098b751129","year":2018},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.354423Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:d56b65cd564b2adec2437b356c2b62da7aec0d6c86f27db928c1d6294742ddf4","observation_id":"9aacc366-858d-467c-ad8d-bc89f594f058","resolution":{"observed_at":"2026-08-07T13:05:49.416630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:49.181690Z","title":"The variational formulation of the fokker– planck equation","venue":null,"work_id":"3a47741f-7060-43e8-8ff9-deb600ffb528","year":1998},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.426938Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:ff0c994764e0c7098839732a9e746be1bfd2dec5ede902439f0dc0ce5dbe4359","observation_id":"370c4be2-70c3-4605-8d93-e8f0c1ef1150","resolution":{"observed_at":"2026-08-07T13:05:49.285362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:48.996884Z","title":"Generalization in diffusion models arises from geometry-adaptive harmonic representation","venue":null,"work_id":"f61cec58-56aa-420f-8ac5-1d47ac316073","year":2024},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.482158Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:255e725fcb3e00ed43da6d7853c31bda48ebddd4c7a2c4ca1ba77b172ea260e1","observation_id":"c2d3157e-7178-4df3-97d5-171c8eca7d40","resolution":{"observed_at":"2026-08-07T13:05:49.070690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:48.823813Z","title":"An analytic theory of creativity in convolutional diffusion models, 2024","venue":null,"work_id":"c5def088-444e-4905-bfb9-38650c2520c4","year":2024},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.544949Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:1f2d45da36659b0916e31cd411718a64fd39616c1c00f77fe34cce72e5b6f569","observation_id":"65022b9b-0fec-4a12-aecc-dfa17c7fb22e","resolution":{"observed_at":"2026-08-07T13:05:48.886578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:48.638873Z","title":"Wide neural networks of any depth evolve as linear models under gradient descent","venue":null,"work_id":"6eac5941-bdc7-404a-aa17-1d1c331c9142","year":2019},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.621865Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:6f208d10dacac55e86f2a9d8388f1aa4248585b36610ff35ccb39e1bd44cae7a","observation_id":"d0e42b3c-67cc-4a17-9078-cc209d5aca0f","resolution":{"observed_at":"2026-08-07T13:05:48.711963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:48.453324Z","title":"On the generalization properties of diffusion models","venue":null,"work_id":"ba979c87-564c-40ba-8c4e-ba698593b409","year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.723304Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:4e95adce33d88726df7d48c01de59428a167fdb2658ee8878e1ad8cc967b30ad","observation_id":"a185e9c0-e23c-48b0-bdd4-8e5bee3f5a5c","resolution":{"observed_at":"2026-08-07T13:05:48.533513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:48.255456Z","title":"Understanding generalizability of diffusion models requires rethinking the hidden gaussian structure","venue":null,"work_id":"434f5823-6775-4a52-8964-341513724e4f","year":2024},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.816735Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:753c20abbc2a75989cfdaea2356f5512f5421e482123ce6bbbee893f2cb6e671","observation_id":"c07c3009-e855-4ade-8137-29a04c56004c","resolution":{"observed_at":"2026-08-07T13:05:48.331096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:48.076515Z","title":"Understanding diffusion models: A unified perspective, 2022","venue":null,"work_id":"f44cf638-ac5d-4418-80d3-77c83d431b70","year":2022},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.915161Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:948ef9cf0cabc84eeedb6ccccfb9b7d29349b3056d8f9b8152774254d4fcbd44","observation_id":"ac123af3-36b0-442f-a9c2-bcb7cb41a356","resolution":{"observed_at":"2026-08-07T13:05:48.189773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:47.926357Z","title":"Accelerating diffusion models via early stop of the diffusion process, 2022","venue":null,"work_id":"20f7fc1c-e7b5-42e6-8bc4-a6b26c72bfd5","year":2022},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:44.974488Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:96062e722799043bddc2856fb8e29f9a4f8b89aa134440691b1af65048680bf0","observation_id":"06be275d-5832-4d4e-9b69-23a0bc632fe4","resolution":{"observed_at":"2026-08-07T13:05:47.996461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:45.051353Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.051353Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:8f7f69f92b2244a53cb9f172fa00c4e93deb5977e15635e66658cfd929872ffe","observation_id":"23127625-1437-4e80-8f13-f2f7adade312","resolution":{"observed_at":"2026-08-07T13:05:45.051353Z","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":"2026-08-07T13:05:47.799320Z","title":null,"venue":null,"work_id":"cac9316a-3f34-4603-b3a6-f394a3692b55","year":1992},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.142125Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:1e18d0179ab7b0efbb13a68b89af6e2a7b3fe4572fcc2894f78ebcbf57c5d343","observation_id":"35f74563-ace1-48d0-9002-5e96a23d1b89","resolution":{"observed_at":"2026-08-07T13:05:47.840901Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:47.632025Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":"28df7567-15c6-47c0-adec-f98156ee8a86","year":2015},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.187954Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:ec2d9b7d2de21d9483c8198aeac1ee12e6749b86d37c5c6de7889679a418b1a4","observation_id":"029fea84-6345-42fa-80ac-7f3a28d26eaa","resolution":{"observed_at":"2026-08-07T13:05:47.702831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:47.496668Z","title":"Closed-form diffusion models, 2025","venue":null,"work_id":"e5dc37e5-d255-417a-8b30-2e7b0084c22a","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.297810Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:d3a02a41e4b426208b78dbfe202e1d72ac177c5502ca8455d17c37dee8f9341f","observation_id":"2a120886-5926-436d-89b0-3d51f48fcfc0","resolution":{"observed_at":"2026-08-07T13:05:47.581256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:47.396929Z","title":"Deep unsupervised learning using nonequilibrium thermodynamics","venue":null,"work_id":"f49857f0-c469-4d97-b366-b884ea6b34fd","year":2015},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.378265Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:ebea25717a93461503f532fab34c7fb5c2443a02e7cbdf0e57ecb9a4d8208ba3","observation_id":"ee8198d1-13d2-4a47-8e22-acd9abf6d145","resolution":{"observed_at":"2026-08-07T13:05:47.421888Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:45.443482Z","title":"Diffusion art or digital forgery? investigating data replication in diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.443482Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:55745706adb85052c645c02c9abfec94d5dfde90fbd9896cce4200a09c2055e6","observation_id":"94643512-04eb-48b5-bff5-b9af492054cc","resolution":{"observed_at":"2026-08-07T13:05:45.443482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20086","last_updated":"2023-05-31T17:58:02Z","snapshot_observed_at":"2026-08-05T07:34:02.814882Z","submitted_at":"2023-05-31T17:58:02Z","title":"Understanding and Mitigating Copying in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.20086","snapshot_observed_at":"2026-08-07T13:05:45.487623Z","title":"Understanding and mitigating copying in diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.487623Z"},"links":{"cited_paper":"/paper/2305.20086","citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:a59fcc5a7ef83c81acb1daafc5439dd92a901b054cd8c9cd09c4a19a93e83b8e","observation_id":"f391f5bf-92a4-4924-afbe-f4a7b5595414","resolution":{"observed_at":"2026-08-07T13:05:45.487623Z","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":"2026-08-07T13:05:45.575705Z","title":"Score-based generative modeling through stochastic differential equations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.575705Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:ba28a808e4d4fbce210a9adc05ef5c344a5be1db077238a096ee4734cbdfa3b4","observation_id":"86a2c1e9-dd0b-44b9-89f7-f921a0d093d2","resolution":{"observed_at":"2026-08-07T13:05:45.575705Z","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":"2026-08-07T13:05:47.249999Z","title":"An analysis of the noise schedule for score-based generative models, 2025","venue":null,"work_id":"65ed0a49-9975-40ae-81f3-786c0c3c911f","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.618410Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:997e1f74d8b2c77082026f5dd6165ba63e3d5c9e6406bfba316e6b888d5dc19d","observation_id":"ce3c4706-ec36-41ea-9a49-3a9d1a4ba02a","resolution":{"observed_at":"2026-08-07T13:05:47.299115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:47.129880Z","title":"Regularization can make diffusion models more efficient, 2025","venue":null,"work_id":"13130d6c-a597-47fd-a571-5d65d1c24131","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.679070Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:a702c1ccf7e7e0d3328ad38b625be07eb64be4ea6b2781a5cbb19b0fa692c23a","observation_id":"b98771cd-bd2d-462c-979a-710184a75fcf","resolution":{"observed_at":"2026-08-07T13:05:47.173354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:46.974949Z","title":"On memorization in probabilistic deep generative models","venue":null,"work_id":"ad727a4e-4f80-4d66-b71f-7bcd0b816854","year":2021},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.752084Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:d5eec97b1a52de9eb4ea7d19771eddd2dd44d1f9a7cd9ce7c4318ffb1ed9fa3c","observation_id":"5987086b-137a-46ef-b7d5-f4f27d68ce76","resolution":{"observed_at":"2026-08-07T13:05:47.051673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:46.861761Z","title":"Manifolds, random matrices and spectral gaps: The geometric phases of generative diffusion","venue":null,"work_id":"3f6a1c9a-9d5a-446a-aa12-64518a84bc24","year":2025},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.812956Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:637b97ba810a7d8828a028aa5812391409393cafb55f1acf8eeb2decef0bbe2b","observation_id":"a92b0389-1732-4e94-b1a8-6f7931624633","resolution":{"observed_at":"2026-08-07T13:05:46.892994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:46.721678Z","title":"Otto calculus, pages 421–433","venue":null,"work_id":"c3edc459-7b0d-42a3-b7bd-c012008e8dae","year":2009},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.902632Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:489afacde2383578b82fbf90b706b131a38c9effe54cf1e3a9a77b56ace1bc82","observation_id":"9b05b71b-de6b-4d96-9f4a-3901e7cbd1ee","resolution":{"observed_at":"2026-08-07T13:05:46.774425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:45.962322Z","title":"A connection between score matching and denoising autoencoders","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:45.962322Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:ea843e19fd82d406cfba545631f4ef71253e2a6bc99762ee1297ffb25b4beb0f","observation_id":"a67e4e9e-00db-429d-ba7a-82d1f9a5329f","resolution":{"observed_at":"2026-08-07T13:05:45.962322Z","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":"2026-08-07T13:05:46.570184Z","title":"Optimal score estimation via empirical bayes smoothing, 2024","venue":null,"work_id":"8ddda8fe-3a85-4013-815e-939c16a8bbe0","year":2024},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:46.053604Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:64d28b3c78ce6d6897c996821ad5f13ca6485b1dcda85588f45420055bcff777","observation_id":"f85a8cc5-93b5-43cb-a100-29899de61c5b","resolution":{"observed_at":"2026-08-07T13:05:46.640099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:46.428057Z","title":"On the generalization of diffusion model, 2023","venue":null,"work_id":"7131798f-3e20-4c88-a1e4-5f0b41ea5e1b","year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:46.093489Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:5f93b078cfe51bd82a2d28ca192199c7d35f75a416b72ea78423ff88401129c0","observation_id":"d1e1dbcf-ecc3-4302-8bbe-c0b35c336ba5","resolution":{"observed_at":"2026-08-07T13:05:46.468060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:05:46.287758Z","title":"Φ(1) N (t, x) Φ(0) N (t, x) − mt(x) # , √ N","venue":null,"work_id":"9231e45b-92bd-4d62-9178-f7ac012de0ad","year":2023},"citing_paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T13:05:46.193531Z"},"links":{"citing_paper":"/paper/2505.22841"},"observation_digest":"sha256:325cbdc6bc17c8fdc7daf0a80fd86411efb49092bd22f6ba274be014b852cbbf","observation_id":"e99f5227-9734-4ce7-8585-4dfb25744158","resolution":{"observed_at":"2026-08-07T13:05:46.373590Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.22841","last_updated":"2025-05-28T20:22:18Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T12:56:45.358046Z","submitted_at":"2025-05-28T20:22:18Z","title":"Kernel-Smoothed Scores for Denoising Diffusion: A Bias-Variance Study"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":37},"total_outbound_references":45},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 2 inbound Pith citation observations for arXiv:2505.22841."}