{"as_of":"2026-08-21T06:37:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0ef25e53eff50180039e3b8f0860194595218f856bcb75b1f9ba716151f5446d","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T12:01:21.926689Z","state":"measured"},{"denominator":42,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":42,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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":"2026-08-07T05:28:50.212651Z","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":"2026-05-21T10:54:07.982528Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.17807","snapshot_observed_at":"2026-08-07T05:28:50.212651Z","title":"From memorization to generalization: a theoretical framework for diffusion- based generative models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07841","last_updated":"2025-06-09T15:07:16Z","snapshot_observed_at":"2026-08-16T02:48:16.692836Z","submitted_at":"2025-06-09T15:07:16Z","title":"Diffusion models under low-noise regime","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:50.212651Z"},"links":{"cited_paper":"/paper/2411.17807","citing_paper":"/paper/2506.07841"},"observation_digest":"sha256:480c5817f08ee211d2a053dbb18951cdd2aae295c71086a5f72bf60b516106a9","observation_id":"e140d5ea-1b00-44e8-900d-4ee73d66c4cd","resolution":{"observed_at":"2026-08-07T05:28:50.212651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"cited_work":{"arxiv_id":"2411.17807","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.17807","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"9f4da320-6e10-43b4-a0d0-dc27875fcd85","year":2025},"citing_paper":{"arxiv_id":"2603.13419","last_updated":"2026-05-20T10:08:19Z","snapshot_observed_at":"2026-08-15T16:47:23.439569Z","submitted_at":"2026-03-12T21:02:17Z","title":"Diffusion Models Memorize in Training -- and Generalize in Inference","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-21T10:52:31.849094Z"},"links":{"cited_paper":"/paper/2411.17807","citing_paper":"/paper/2603.13419"},"observation_digest":"sha256:006a1751da3d021444516b5a05ffeec9a399ac194669c58c9dbf26fa46f32d09","observation_id":"5529318b-ff46-4fc0-8118-4bab545363d5","resolution":{"observed_at":"2026-05-21T10:54:07.984585Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.17807/citation-record","integrity":"/paper/2411.17807/integrity","json":"/paper/2411.17807/citation-record.json","paper":"/paper/2411.17807"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2102.06701","last_updated":"2024-04-29T00:55:09Z","snapshot_observed_at":"2026-08-16T18:45:54.659137Z","submitted_at":"2021-02-12T18:57:46Z","title":"Explaining Neural Scaling Laws","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.06701","snapshot_observed_at":"2026-08-12T12:01:21.761099Z","title":"Explaining neural scaling laws","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.761099Z"},"links":{"cited_paper":"/paper/2102.06701","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:002819615a0c64094129c1dcec47261f39170bd74762df25de4c2de258f7ca26","observation_id":"a6b7ad3d-957b-4ecd-8f9e-3880f57b6f96","resolution":{"observed_at":"2026-08-12T12:01:21.761099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.03686","last_updated":"2024-03-06T00:41:30Z","snapshot_observed_at":"2026-08-16T15:10:13.061639Z","submitted_at":"2023-08-07T16:01:14Z","title":"Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.03686","snapshot_observed_at":"2026-08-12T12:01:21.765898Z","title":"Blake Bordelon and Cengiz Pehlevan","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.765898Z"},"links":{"cited_paper":"/paper/2308.03686","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:90fb17ca9886fd7745e5529165c483842288720e95aadb83f9f9e4169e424a15","observation_id":"f08e24af-568b-47a2-8fe4-912215dcc377","resolution":{"observed_at":"2026-08-12T12:01:21.765898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.09000","last_updated":"2024-08-23T17:21:35Z","snapshot_observed_at":"2026-08-20T20:01:39.152895Z","submitted_at":"2024-08-16T20:00:55Z","title":"Classifier-Free Guidance is a Predictor-Corrector","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.09000","snapshot_observed_at":"2026-08-12T12:01:21.775052Z","title":"Abdulkadir Canatar, Blake Bordelon, and Cengiz Pehlevan","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.775052Z"},"links":{"cited_paper":"/paper/2408.09000","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:d9af5197f111e78d3698935ae2c56a9883feb4661627d36ab36185afe07d06bf","observation_id":"441db321-fd75-4e46-b96a-cfebdfcee63d","resolution":{"observed_at":"2026-08-12T12:01:21.775052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.10972","last_updated":"2023-05-21T07:07:55Z","snapshot_observed_at":"2026-08-17T13:04:22.753084Z","submitted_at":"2023-01-26T07:37:22Z","title":"On the Importance of Noise Scheduling for Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.10972","snapshot_observed_at":"2026-08-12T12:01:21.784199Z","title":"Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, and Jianfeng Lu","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.784199Z"},"links":{"cited_paper":"/paper/2301.10972","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:4acfc91a8cb2abc1ea1978df37a5f78da00446af0f8a5fbeef7bcbbc6bd8560c","observation_id":"e5a387af-de49-4c5b-b64f-a7e83a5002dd","resolution":{"observed_at":"2026-08-12T12:01:21.784199Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.13074","last_updated":"2024-09-19T20:16:33Z","snapshot_observed_at":"2026-08-16T13:17:03.736803Z","submitted_at":"2024-09-19T20:16:33Z","title":"What does guidance do? A fine-grained analysis in a simple setting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.13074","snapshot_observed_at":"2026-08-12T12:01:21.788813Z","title":"St´ ephane d’Ascoli, Levent Sagun, and Giulio Biroli","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.788813Z"},"links":{"cited_paper":"/paper/2409.13074","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:660a97dbc5ab191a1b07c3ac608a396dee79da7f9ec5861f98d6f15b8c09bf12","observation_id":"0a7d84c3-5081-4df2-95b6-f6f5dcf2598b","resolution":{"observed_at":"2026-08-12T12:01:21.788813Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03223","last_updated":"2023-12-13T22:10:45Z","snapshot_observed_at":"2026-08-16T15:18:08.751927Z","submitted_at":"2023-07-06T18:00:01Z","title":"Neural Network Field Theories: Non-Gaussianity, Actions, and Locality","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03223","snapshot_observed_at":"2026-08-12T12:01:21.793079Z","title":"Prafulla Dhariwal and Alex Nichol","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.793079Z"},"links":{"cited_paper":"/paper/2307.03223","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:1edbb0aedef6103f205004c57210c86277cdd964c704a4746db5720c2eb75c56","observation_id":"6921097b-ae96-44bc-b45d-f888d38eabcf","resolution":{"observed_at":"2026-08-12T12:01:21.793079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.05233","last_updated":"2021-06-01T17:49:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-05-11T17:50:24Z","title":"Diffusion Models Beat GANs on Image Synthesis","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.05233","snapshot_observed_at":"2026-08-12T12:01:21.797391Z","title":"Oussama Dhifallah and Yue M Lu","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.797391Z"},"links":{"cited_paper":"/paper/2105.05233","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:b04a3f799808df9a079e7a2c4f54dc73bccce7637861d9a4c7dbf464d70d5dd0","observation_id":"2877ec30-bf06-4ca4-b08c-1a78fef767c7","resolution":{"observed_at":"2026-08-12T12:01:21.797391Z","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":"2026-08-12T12:01:22.460297Z","title":"Universality laws for high-dimensional learning with random features","venue":null,"work_id":"52dc88a1-1262-47ae-abfc-6e1dc15be1b5","year":1932},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.817415Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:ef3e501df6024359172d9fd94f42ac095ea417397d863e8748909c53f12ee957","observation_id":"958dd0e5-6c77-43bc-8157-a0e4a5f67ee4","resolution":{"observed_at":"2026-08-12T12:01:22.464310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.11972","last_updated":"2023-06-14T03:32:57Z","snapshot_observed_at":"2026-08-16T16:06:32.038168Z","submitted_at":"2022-12-22T18:55:45Z","title":"Scalable Adaptive Computation for Iterative Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.11972","snapshot_observed_at":"2026-08-12T12:01:21.821238Z","title":"Arthur Jacot, Franck Gabriel, and Clement Hongler","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.821238Z"},"links":{"cited_paper":"/paper/2212.11972","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:98c17f89800b0b7475c773c2a73ecca4337f858aff3e1798a1ec962e8168408c","observation_id":"3411f095-a1ae-481e-9bc8-900c297df070","resolution":{"observed_at":"2026-08-12T12:01:21.821238Z","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":"2026-08-12T12:01:22.447291Z","title":"Arthur Jacot, Franck Gabriel, and Cl´ ement Hongler","venue":null,"work_id":"2770ac02-6761-492c-96f1-66996a394b38","year":2018},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.825231Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:9d1fe55acce451a68024a825db2a3f078998c5455e0d379b55d815e9603f5e05","observation_id":"89047bd0-5d31-4e21-9ca1-69e44610585c","resolution":{"observed_at":"2026-08-12T12:01:22.451505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.07572","last_updated":"2020-02-10T08:39:09Z","snapshot_observed_at":"2026-08-14T19:01:37.246770Z","submitted_at":"2018-06-20T06:35:46Z","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.07572","snapshot_observed_at":"2026-08-12T12:01:21.829061Z","title":"Zahra Kadkhodaie and Eero Peter Simoncelli","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.829061Z"},"links":{"cited_paper":"/paper/1806.07572","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:0c1b2fabb3aab07fa4f4d72aa29d5956d63b72caf5f03a864bf47e51ec9be0d1","observation_id":"bc04e6c6-ed4f-4766-b453-07e208b14e19","resolution":{"observed_at":"2026-08-12T12:01:21.829061Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02557","last_updated":"2024-04-12T15:48:47Z","snapshot_observed_at":"2026-08-21T00:12:00.474038Z","submitted_at":"2023-10-04T03:30:32Z","title":"Generalization in diffusion models arises from geometry-adaptive harmonic representations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02557","snapshot_observed_at":"2026-08-12T12:01:21.833413Z","title":"Zahra Kadkhodaie, Florentin Guth, Eero P Simoncelli, and St´ ephane Mallat","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.833413Z"},"links":{"cited_paper":"/paper/2310.02557","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:f5a4ddf54f0f757e2a3cfadc079b200b6855441392c08f3e44d0c2f688fa59a3","observation_id":"fdb82886-b5c1-47cc-afed-2c2dffe5cea8","resolution":{"observed_at":"2026-08-12T12:01:21.833413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.20292","last_updated":"2025-06-05T05:09:27Z","snapshot_observed_at":"2026-08-16T08:43:12.375693Z","submitted_at":"2024-12-28T22:33:29Z","title":"An analytic theory of creativity in convolutional diffusion models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.20292","snapshot_observed_at":"2026-08-12T12:01:21.837544Z","title":"An analytic theory of creativity in convolutional diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.837544Z"},"links":{"cited_paper":"/paper/2412.20292","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:c1b8c0477d235d70841d231a518ce90aa782718fdfc6b87846aa23eee0f0c8b2","observation_id":"e669ea83-e84a-45e8-a3a5-a36e8e2404cc","resolution":{"observed_at":"2026-08-12T12:01:21.837544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.00364","last_updated":"2022-10-11T13:20:30Z","snapshot_observed_at":"2026-07-06T13:16:16.926712Z","submitted_at":"2022-06-01T10:03:24Z","title":"Elucidating the Design Space of Diffusion-Based Generative Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.00364","snapshot_observed_at":"2026-08-12T12:01:21.841836Z","title":"Anders Krogh and John A Hertz","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.841836Z"},"links":{"cited_paper":"/paper/2206.00364","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:fef2443e86140ce1d06f9b4cbc4dc9748ace9e390e30bab88637aa09133c7cf6","observation_id":"4c897229-2de3-4e14-b3c1-e38dde8a0b9b","resolution":{"observed_at":"2026-08-12T12:01:21.841836Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.06227","last_updated":"2023-05-03T17:51:05Z","snapshot_observed_at":"2026-08-16T16:53:23.623559Z","submitted_at":"2022-06-13T14:57:35Z","title":"Convergence for score-based generative modeling with polynomial complexity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.06227","snapshot_observed_at":"2026-08-12T12:01:21.846281Z","title":"Jaehoon Lee, Lechao Xiao, Samuel Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, and Jeffrey Pennington","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.846281Z"},"links":{"cited_paper":"/paper/2206.06227","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:7d7c902105277fe127b60484e4fb15ab94486334ff36f2d6b3f3e52cb3ab2075","observation_id":"70251dff-64fe-4db8-b68f-9a7d4b866ce2","resolution":{"observed_at":"2026-08-12T12:01:21.846281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.16859","last_updated":"2022-10-30T15:13:18Z","snapshot_observed_at":"2026-08-17T11:15:48.593128Z","submitted_at":"2022-10-30T15:13:18Z","title":"A Solvable Model of Neural Scaling Laws","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.16859","snapshot_observed_at":"2026-08-12T12:01:21.856440Z","title":"A solvable model of neural scaling laws","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.856440Z"},"links":{"cited_paper":"/paper/2210.16859","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:4122cae846004470b5696144a3c80581b3632427538088c3e217b34a1ea30ee0","observation_id":"d39f3736-b790-422d-8ea4-6ea9458ad96e","resolution":{"observed_at":"2026-08-12T12:01:21.856440Z","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":"2026-08-12T12:01:21.861312Z","title":"doi: 10.1073/pnas.1806579115","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.861312Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:c07f311dfc8811fda809cd925879acd43536e90a8d55f899d5ddf5e39286b434","observation_id":"f4b8d289-35a0-4a99-bc01-9a7d9cd5b533","resolution":{"observed_at":"2026-08-12T12:01:21.861312Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.07242","last_updated":"2019-12-16T08:28:26Z","snapshot_observed_at":"2026-07-06T08:44:41.235996Z","submitted_at":"2019-12-16T08:28:26Z","title":"More Data Can Hurt for Linear Regression: Sample-wise Double Descent","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.07242","snapshot_observed_at":"2026-08-12T12:01:21.865366Z","title":"More data can hurt for linear regression: Sample-wise double descent","venue":null,"work_id":null,"year":1912},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.865366Z"},"links":{"cited_paper":"/paper/1912.07242","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:eac6be6589eb01d8eca709dd2faac05f927fc33fd5edca2d10471906f0935010","observation_id":"abd1c615-f8fe-4487-86a9-233687768668","resolution":{"observed_at":"2026-08-12T12:01:21.865366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.09672","last_updated":"2021-02-18T23:44:17Z","snapshot_observed_at":"2026-08-17T08:18:36.930250Z","submitted_at":"2021-02-18T23:44:17Z","title":"Improved Denoising Diffusion Probabilistic Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.09672","snapshot_observed_at":"2026-08-12T12:01:21.870039Z","title":"Maya Okawa, Ekdeep Singh Lubana, Robert P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.870039Z"},"links":{"cited_paper":"/paper/2102.09672","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:3f111dc42d0d36991c918b78f4563055cc7a41bb8f3b86b788478bd283cb1639","observation_id":"38bf5865-2f47-4bd7-a087-745671341fb7","resolution":{"observed_at":"2026-08-12T12:01:21.870039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-08-15T12:50:58.405488Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-12T12:01:21.874175Z","title":"Daniel A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.874175Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:3bd1d69c0e1fc1114309af4c37a96929ce562df57b1dc5da24d0f45829e36321","observation_id":"e7a7f54e-a9fe-4147-a1b4-d6159c8585b2","resolution":{"observed_at":"2026-08-12T12:01:21.874175Z","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":"2026-08-12T12:01:21.877801Z","title":"doi: 10.1017/9781009023405","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.877801Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:f1e4d285f5d4df9a067f00d3eea0ae75aaa814a03deede759e84042f3edb019a","observation_id":"93da91c2-3440-4a14-bc17-84ca3b415937","resolution":{"observed_at":"2026-08-12T12:01:21.877801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10752","last_updated":"2022-04-13T11:38:44Z","snapshot_observed_at":"2026-07-06T12:20:47.369918Z","submitted_at":"2021-12-20T18:55:25Z","title":"High-Resolution Image Synthesis with Latent Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10752","snapshot_observed_at":"2026-08-12T12:01:21.882696Z","title":"org/abs/2112.10752","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.882696Z"},"links":{"cited_paper":"/paper/2112.10752","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:9646ca3d7f94335ff12fc26a2b68fce720c594c1fe87ada51ceba666c11dd118","observation_id":"584237e3-3d2b-425b-bdd5-c0088001139a","resolution":{"observed_at":"2026-08-12T12:01:21.882696Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11487","last_updated":"2022-05-23T17:42:53Z","snapshot_observed_at":"2026-08-17T05:51:02.087480Z","submitted_at":"2022-05-23T17:42:53Z","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.11487","snapshot_observed_at":"2026-08-12T12:01:21.892184Z","title":"Kulin Shah, Sitan Chen, and Adam Klivans","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.892184Z"},"links":{"cited_paper":"/paper/2205.11487","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:5befac9fe6a41da62c29f72a21e5551ad23e6a463b2b44110d2bf60e5b517dae","observation_id":"2dce61e3-99eb-4716-8166-c042494fd4de","resolution":{"observed_at":"2026-08-12T12:01:21.892184Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01178","last_updated":"2023-07-03T17:44:22Z","snapshot_observed_at":"2026-08-16T15:19:04.692060Z","submitted_at":"2023-07-03T17:44:22Z","title":"Learning Mixtures of Gaussians Using the DDPM Objective","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01178","snapshot_observed_at":"2026-08-12T12:01:21.896651Z","title":"James B Simon, Madeline Dickens, Dhruva Karkada, and Michael Deweese","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.896651Z"},"links":{"cited_paper":"/paper/2307.01178","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:dd4b3279f18548fab03140281c65f95b81f764d92eb6f01b26c8e8d5f7d4a0ac","observation_id":"9f8128d3-f482-4eea-a7e1-71308d33c80c","resolution":{"observed_at":"2026-08-12T12:01:21.896651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.03585","last_updated":"2015-11-18T21:50:51Z","snapshot_observed_at":"2026-08-21T03:32:27.484213Z","submitted_at":"2015-03-12T04:51:37Z","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","version":8},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.03585","snapshot_observed_at":"2026-08-12T12:01:21.901133Z","title":"org/abs/1503.03585","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.901133Z"},"links":{"cited_paper":"/paper/1503.03585","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:58b8a778c272d750de2d27ebca780ea2c5dc4b3798b3f89a674a1fe45622ae3f","observation_id":"65ee2a81-0243-4332-8f43-6e4ef8159318","resolution":{"observed_at":"2026-08-12T12:01:21.901133Z","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":"2026-08-12T12:01:22.421098Z","title":"Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole","venue":null,"work_id":"5feac730-9a1c-47c1-86dd-e3957286c644","year":2020},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.906077Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:4437a87e14ed656679fcd71ddb1d4e84a07c03c9e7f78ecea3fe8118daef44fc","observation_id":"570cf89b-ea6b-4efa-b5de-082b04677b02","resolution":{"observed_at":"2026-08-12T12:01:22.425345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01639","last_updated":"2024-03-03T23:15:48Z","snapshot_observed_at":"2026-08-16T14:13:17.400541Z","submitted_at":"2024-03-03T23:15:48Z","title":"Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01639","snapshot_observed_at":"2026-08-12T12:01:21.915161Z","title":"Greg Yang and Edward J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.915161Z"},"links":{"cited_paper":"/paper/2403.01639","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:99e8a592822e1c07998eaf1bd7bc1251f3bb76966f38703ff2d039d9d584034b","observation_id":"c7f83dee-a7a3-4ef0-9dbf-32cc867360c8","resolution":{"observed_at":"2026-08-12T12:01:21.915161Z","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":"2026-08-12T12:01:22.405899Z","title":"TaeHo Yoon, Joo Young Choi, Sehyun Kwon, and Ernest K","venue":null,"work_id":"bf6fa50d-6768-401f-a7ce-937ccdcfaac8","year":2023},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.919033Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:0214cd79b6bca084ff0efd8247bc3f2efbc4b88c15ef309e8287e9905602af6b","observation_id":"f3b9c1dd-87ac-492b-813d-3099cb0d50f5","resolution":{"observed_at":"2026-08-12T12:01:22.411871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05264","last_updated":"2026-06-09T14:55:52Z","snapshot_observed_at":"2026-08-17T11:00:29.277455Z","submitted_at":"2023-10-08T19:02:46Z","title":"The Emergence of Reproducibility and Generalizability in Diffusion Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05264","snapshot_observed_at":"2026-08-12T12:01:21.922740Z","title":"The emergence of reproducibility and generalizability in diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.922740Z"},"links":{"cited_paper":"/paper/2310.05264","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:a283062f5ced23f91f31182d508db23224598439e87db6bea943fc9beeb217bf","observation_id":"9e2ea0d9-3927-488c-b382-50c9221a37fc","resolution":{"observed_at":"2026-08-12T12:01:21.922740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.07579","last_updated":"2021-10-14T17:41:12Z","snapshot_observed_at":"2026-08-17T01:48:24.750582Z","submitted_at":"2021-10-14T17:41:12Z","title":"Diffusion Normalizing Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.07579","snapshot_observed_at":"2026-08-12T12:01:21.926689Z","title":"org/abs/2110.07579","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.926689Z"},"links":{"cited_paper":"/paper/2110.07579","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:15a95c4ffe3753b9f75a4369aad898039a0201aa3584b71be1d58751dee35cb1","observation_id":"297cd266-ff9a-4aef-9de0-42f920a7229b","resolution":{"observed_at":"2026-08-12T12:01:21.926689Z","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":"2026-08-12T12:01:21.910839Z","title":"22 A solvable generative model with a linear, one-step denoiser Yuchen Wu, Minshuo Chen, Zihao Li, Mengdi Wang, and Yuting Wei","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.910839Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:302dc1bfa0fe1e9d18bd3243c1485232d090296f425976560c737a3d6ed65798","observation_id":"cfb3e229-9056-42c8-8905-7824f0b7fe2b","resolution":{"observed_at":"2026-08-12T12:01:21.910839Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1505.04597","last_updated":"2015-05-18T11:28:37Z","snapshot_observed_at":"2026-08-15T21:30:31.645090Z","submitted_at":"2015-05-18T11:28:37Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.04597","snapshot_observed_at":"2026-08-12T12:01:21.887660Z","title":"Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.887660Z"},"links":{"cited_paper":"/paper/1505.04597","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:3d87aaa972e33077c3c1f78c08039056e06a0d14d26ea358d60ec248a7459a92","observation_id":"e3b566e8-7413-4d00-af13-5b2a9914e16f","resolution":{"observed_at":"2026-08-12T12:01:21.887660Z","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":"2026-08-12T12:01:21.802019Z","title":"URL https://doi.org/10.3150/14-BEJ609","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.802019Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:29cecf1b7a5ab391855f34fa6a3a3d0bff18eec7ff6a780cc5e3b8b4c30a0d1f","observation_id":"a4d9c465-295e-4787-8378-6c61d003d132","resolution":{"observed_at":"2026-08-12T12:01:21.802019Z","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":"2026-08-12T12:01:21.806183Z","title":"URL https://doi.org/10.1214/17-AOS1549","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.806183Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:2051ad873898a8d60587f4d1d14673c5e3ad84df994dee28cb2b94f547d31df6","observation_id":"93d8d3fd-35c7-4025-ace1-bf8eb5788528","resolution":{"observed_at":"2026-08-12T12:01:21.806183Z","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":"2026-08-12T12:01:22.434679Z","title":"Marvin Li and Sitan Chen","venue":null,"work_id":"ba1ee316-2df0-4809-930b-61af38f58ffb","year":2019},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.851614Z"},"links":{"citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:d9450fd5033a7db0939d116c8f32423b3973a490b48c98d4a7b096b1ee39ad12","observation_id":"0410298f-f478-4c63-a0ef-d89f03a8f1a6","resolution":{"observed_at":"2026-08-12T12:01:22.438430Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12089","last_updated":"2025-06-04T11:03:45Z","snapshot_observed_at":"2026-08-16T12:57:36.618017Z","submitted_at":"2025-02-17T18:06:33Z","title":"How Compositional Generalization and Creativity Improve as Diffusion Models are Trained","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12089","snapshot_observed_at":"2026-08-12T12:01:21.809967Z","title":"mlr.press/v119/d-ascoli20a.html","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.809967Z"},"links":{"cited_paper":"/paper/2502.12089","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:6bdaa5639cf7039cd6db4868b4c34cc5193ab51f5ca756f18338bf5c8877ea9a","observation_id":"83cde080-2aac-467e-9949-adf6f0afefc1","resolution":{"observed_at":"2026-08-12T12:01:21.809967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.02561","last_updated":"2021-02-25T18:40:10Z","snapshot_observed_at":"2026-08-16T13:51:36.607630Z","submitted_at":"2020-02-07T00:03:40Z","title":"Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.02561","snapshot_observed_at":"2026-08-12T12:01:21.770518Z","title":"Arwen Bradley and Preetum Nakkiran","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.770518Z"},"links":{"cited_paper":"/paper/2002.02561","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:efd0d4f64ba3fbf96c53995551861c919e9f91fd73eb3e57692de5726208a4e1","observation_id":"92174a88-317b-47b6-8ec5-4a47599c5aa2","resolution":{"observed_at":"2026-08-12T12:01:21.770518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.12598","last_updated":"2022-07-26T01:42:07Z","snapshot_observed_at":"2026-08-14T06:37:15.299690Z","submitted_at":"2022-07-26T01:42:07Z","title":"Classifier-Free Diffusion Guidance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.12598","snapshot_observed_at":"2026-08-12T12:01:21.813722Z","title":"Jonathan Ho, Ajay Jain, and Pieter Abbeel","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.813722Z"},"links":{"cited_paper":"/paper/2207.12598","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:d3ece58baa1ee83c5a5934868f53d60f7ba84104f5ad64e9bd7885822eaae434","observation_id":"8930e371-0787-40ce-b1cd-f83e1a157426","resolution":{"observed_at":"2026-08-12T12:01:21.813722Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01916","last_updated":"2023-02-02T10:34:04Z","snapshot_observed_at":"2026-08-16T16:18:49.641036Z","submitted_at":"2022-11-03T15:51:00Z","title":"Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.01916","snapshot_observed_at":"2026-08-12T12:01:21.779618Z","title":"Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions, 2023a","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.779618Z"},"links":{"cited_paper":"/paper/2211.01916","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:995eb47c69ffa4a6fcd11d99eb380f61d29d5254b4922eec965b7451f98a8d1b","observation_id":"25dc32df-5a1c-4397-864f-a7f0878401d8","resolution":{"observed_at":"2026-08-12T12:01:21.779618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00592","last_updated":"2025-06-30T15:11:57Z","snapshot_observed_at":"2026-08-17T18:15:23.741383Z","submitted_at":"2024-05-01T15:59:00Z","title":"Scaling and renormalization in high-dimensional regression","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.00592","snapshot_observed_at":"2026-08-12T12:01:21.756132Z","title":"Francis Bach","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser","version":3},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-12T12:01:21.756132Z"},"links":{"cited_paper":"/paper/2405.00592","citing_paper":"/paper/2411.17807"},"observation_digest":"sha256:89ae8f8da4fdf919b532cc459f0a13f99d4908c5854d77bd241d9dbb42adf1ee","observation_id":"590ce95a-ccec-4605-b282-4e94fcb2b5ac","resolution":{"observed_at":"2026-08-12T12:01:21.756132Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.17807","last_updated":"2025-08-06T18:39:59Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-20T06:08:19.286046Z","submitted_at":"2024-11-26T19:00:01Z","title":"A solvable generative model with a linear, one-step denoiser"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":40},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2411.17807."}