{"as_of":"2026-08-12T04:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:66041e63fb1f45580c6633d4f892cd22cc7892bbaa91d963d534e5314ac6c6ae","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":41,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T04:33:41.276333Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T18:47:31.564501Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-06T10:59:53.563286Z","title":"Decoupled diffusion transformer.arXiv preprint arXiv:2504.05741, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.23268","last_updated":"2025-08-04T02:46:11Z","snapshot_observed_at":"2026-08-09T02:04:57.127931Z","submitted_at":"2025-07-31T06:07:20Z","title":"PixNerd: Pixel Neural Field Diffusion","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T10:59:53.563286Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2507.23268"},"observation_digest":"sha256:f37e80136ce298d81b3f2d85619c7deae81294b635dde98f88c66d183a8ce3f2","observation_id":"3a0155e0-346b-4061-9868-78ff42a6fa32","resolution":{"observed_at":"2026-08-06T10:59:53.563286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2511.13720","last_updated":"2026-01-07T05:36:57Z","snapshot_observed_at":"2026-07-06T22:36:08.495952Z","submitted_at":"2025-11-17T18:59:57Z","title":"Back to Basics: Let Denoising Generative Models Denoise","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-11T22:16:26.996599Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2511.13720"},"observation_digest":"sha256:eb1d0fc0c9174710fd5ccf33146dedc31a44c821e0ae5da5ac627da6bc1780f4","observation_id":"440cd4c7-a2ea-4bdb-b2f6-a256a24765df","resolution":{"observed_at":"2026-05-11T22:16:27.732383Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2511.19365","last_updated":"2026-04-08T04:08:23Z","snapshot_observed_at":"2026-08-11T04:23:34.279866Z","submitted_at":"2025-11-24T17:59:06Z","title":"DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-17T05:47:24.669763Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2511.19365"},"observation_digest":"sha256:72cf7b07c278cb82efc7223dff731f5db5e3c54c8a8680956827073e74a7264c","observation_id":"c86d4aa6-9687-4ab3-bc72-5a3ac11905f6","resolution":{"observed_at":"2026-05-17T05:49:08.231701Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2512.02012","last_updated":"2026-05-09T17:02:52Z","snapshot_observed_at":"2026-08-11T02:50:30.630796Z","submitted_at":"2025-12-01T18:59:49Z","title":"Improved Mean Flows: On the Challenges of Fastforward Generative Models","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-17T02:17:34.544725Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2512.02012"},"observation_digest":"sha256:9874232a496fbd5423a31a0a24920f62eea8f442b58ce1c5efc397669fa1af7f","observation_id":"a75863b1-908e-4920-9fb2-a692a6eedc7a","resolution":{"observed_at":"2026-05-17T02:18:52.480793Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-03T15:34:59.622377Z","title":"DDT: Decoupled diffusion transformer.arXiv preprint arXiv:2504.05741, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.16636","last_updated":"2026-07-18T11:24:13Z","snapshot_observed_at":"2026-08-07T17:53:52.987639Z","submitted_at":"2025-12-18T15:10:42Z","title":"REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-03T15:34:59.622377Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2512.16636"},"observation_digest":"sha256:ded96f8f8bdb25664de1411b2c06e9c9d1029afd98a27351f468ec9a5bffe788","observation_id":"0fe80813-3054-47a8-9d57-4acccafd8e1d","resolution":{"observed_at":"2026-08-03T15:34:59.622377Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-03T14:53:02.795014Z","title":"Ddt: Decoupled diffusion transformer.arXiv preprint arXiv:2504.05741, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2512.19311","last_updated":"2026-07-12T14:26:35Z","snapshot_observed_at":"2026-08-07T18:54:26.140556Z","submitted_at":"2025-12-22T12:00:12Z","title":"MixFlow Training: Alleviating Exposure Bias with Slowed Interpolation Mixture","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.795014Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:d11a03d442eb244444c49f23078134338b93b7cd93f301a15065820fe9aeb77d","observation_id":"9b0589bc-3d60-475f-873f-c07e22c1978e","resolution":{"observed_at":"2026-08-03T14:53:02.795014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-03T11:04:33.265929Z","title":"Ddt: Decoupled diffusion transformer.arXiv preprint arXiv:2504.05741, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.07773","last_updated":"2026-07-10T09:39:56Z","snapshot_observed_at":"2026-08-11T23:05:47.775025Z","submitted_at":"2026-01-12T17:52:11Z","title":"Self-transcendence: Is External Feature Guidance Indispensable for Accelerating Diffusion Transformer Training?","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-03T11:04:33.265929Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2601.07773"},"observation_digest":"sha256:0206fe3a04fba6a5d7c1bcaaf021bbdc26f7fd14680b97dec0a5c79d2e70d0d6","observation_id":"848296e8-9d39-4e26-870f-229af9ce8183","resolution":{"observed_at":"2026-08-03T11:04:33.265929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-03T10:35:06.609343Z","title":"DDT: Decoupled diffusion transformer.arXiv preprint arXiv:2504.05741, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2601.09881","last_updated":"2026-07-09T22:49:12Z","snapshot_observed_at":"2026-08-09T17:44:11.904091Z","submitted_at":"2026-01-14T21:30:03Z","title":"Transition Matching Distillation for Fast Video Generation","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-03T10:35:06.609343Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2601.09881"},"observation_digest":"sha256:5090e4cc035f98af587bd3109caae3477bfd684556c7883945274c40c1a1a2dc","observation_id":"6b5f55b0-c005-4baa-9918-562bb9e20303","resolution":{"observed_at":"2026-08-03T10:35:06.609343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-15T12:07:05.640150Z","title":"Ddt: Decoupled diffusion transformer.arXiv preprint arXiv:2504.05741, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.09241","last_updated":"2026-06-26T11:58:42Z","snapshot_observed_at":"2026-08-10T02:35:54.013091Z","submitted_at":"2026-03-10T06:16:23Z","title":"RAE-NWM: Navigation World Model in Dense Visual Representation Space","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-15T12:07:05.640150Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2603.09241"},"observation_digest":"sha256:30d59dba802e24510351bef00419277e02aff9073ac885bd17bb211565970c5a","observation_id":"21a7ad84-fe7f-4f79-a37f-b15e90f6066d","resolution":{"observed_at":"2026-07-15T12:07:05.640150Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2604.11331","last_updated":"2026-04-13T11:32:36Z","snapshot_observed_at":"2026-08-11T14:54:11.537553Z","submitted_at":"2026-04-13T11:32:36Z","title":"Any 3D Scene is Worth 1K Tokens: 3D-Grounded Representation for Scene Generation at Scale","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-10T16:30:33.113292Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2604.11331"},"observation_digest":"sha256:6d45b8b5a5ce2ab38571d00a87c623c8764d52164c85743c3d9701544eb80297","observation_id":"27133aff-4dcb-4ae4-9e71-3a24947fcd0d","resolution":{"observed_at":"2026-05-11T08:45:59.505998Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2604.11521","last_updated":"2026-04-13T14:23:31Z","snapshot_observed_at":"2026-08-11T14:21:05.404965Z","submitted_at":"2026-04-13T14:23:31Z","title":"Continuous Adversarial Flow Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-10T15:25:53.420119Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2604.11521"},"observation_digest":"sha256:e957a9d9061185f2d4d91de01e2cb8db43147a36f5ade5c27e75fedd7b09a922","observation_id":"959da0d1-7478-43a7-885f-f189d94f46bc","resolution":{"observed_at":"2026-05-11T10:31:05.077271Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2604.12273","last_updated":"2026-04-14T04:36:02Z","snapshot_observed_at":"2026-07-06T23:00:32.607699Z","submitted_at":"2026-04-14T04:36:02Z","title":"SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-10T15:10:36.321169Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2604.12273"},"observation_digest":"sha256:15ff54c5943e654c74fa80b20eb24d2f0ccbf73765cf3b2157299f7dfca222d3","observation_id":"33407334-7d95-4121-9383-2500b0f15ef9","resolution":{"observed_at":"2026-05-11T11:06:01.021002Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2604.16503","last_updated":"2026-05-19T02:31:57Z","snapshot_observed_at":"2026-08-11T04:21:05.545043Z","submitted_at":"2026-04-14T15:09:39Z","title":"Motif-Video 2B: Technical Report","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T15:50:04.001693Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2604.16503"},"observation_digest":"sha256:ba2f93418d7ccc1e8684ff4a444ac5bfd4ebcde2decaf4a6cf654c3f311bad3c","observation_id":"86f15ace-5617-4ee6-baf9-716917e7237d","resolution":{"observed_at":"2026-05-11T09:50:58.012424Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2604.16503","last_updated":"2026-05-19T02:31:57Z","snapshot_observed_at":"2026-08-11T04:21:05.545043Z","submitted_at":"2026-04-14T15:09:39Z","title":"Motif-Video 2B: Technical Report","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-21T00:12:23.096146Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2604.16503"},"observation_digest":"sha256:1178b824b1fb4022a6b7cb831536072ab9f990a1b66f04bd07f746e8657721c8","observation_id":"bb029d7b-00ca-42ee-9c75-aa9816059fd2","resolution":{"observed_at":"2026-05-21T00:13:52.996851Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2604.17492","last_updated":"2026-04-19T15:29:15Z","snapshot_observed_at":"2026-08-11T06:58:11.510404Z","submitted_at":"2026-04-19T15:29:15Z","title":"Coevolving Representations in Joint Image-Feature Diffusion","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T06:30:52.371482Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2604.17492"},"observation_digest":"sha256:4f84ede9b5895def362daf1d68e2a6110f0134e026eababae17d4f68acb16736","observation_id":"01914b4b-89f6-4e8d-8322-b922d791f4a5","resolution":{"observed_at":"2026-05-10T06:31:30.462061Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.06388","last_updated":"2026-05-07T15:05:26Z","snapshot_observed_at":"2026-08-02T19:03:40.916172Z","submitted_at":"2026-05-07T15:05:26Z","title":"Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-08T13:16:27.868477Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.06388"},"observation_digest":"sha256:9767430387e79dd6989ec92f5b49ee00cc296bf7bbcf92796ce3cb9176bb5515","observation_id":"8d747a25-e0f3-44a0-ba4b-273a5dc3b641","resolution":{"observed_at":"2026-05-11T18:56:07.049761Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.07915","last_updated":"2026-05-08T15:52:51Z","snapshot_observed_at":"2026-08-11T15:39:40.726292Z","submitted_at":"2026-05-08T15:52:51Z","title":"What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-05-11T01:57:24.033068Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.07915"},"observation_digest":"sha256:9b2e6a8958b1762203488fb7e0619eba9be3aa6aa705394126dc9326801b92f1","observation_id":"be609edd-f900-426e-9c32-7387713fee54","resolution":{"observed_at":"2026-05-11T04:05:57.214693Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.15741","last_updated":"2026-06-03T01:59:39Z","snapshot_observed_at":"2026-07-06T23:27:01.213106Z","submitted_at":"2026-05-15T08:51:55Z","title":"HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-20T19:08:26.689023Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.15741"},"observation_digest":"sha256:db1edcbd866f24752dffe2f29dc5236f08f787012db12fd4c8cab86f8f35b808","observation_id":"6cb2d1ee-d595-4925-9431-26bfb7d5d724","resolution":{"observed_at":"2026-05-20T19:08:53.990742Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.15741","last_updated":"2026-06-03T01:59:39Z","snapshot_observed_at":"2026-07-06T23:27:01.213106Z","submitted_at":"2026-05-15T08:51:55Z","title":"HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-30T19:33:53.876614Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.15741"},"observation_digest":"sha256:086f692f30120506b53c89bf7a3cfad4b151181ec790fe0a30bd6e5b0c5a05de","observation_id":"ef2ec53b-3517-4573-a09c-3283e7f8bd66","resolution":{"observed_at":"2026-06-30T19:35:00.979978Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.16949","last_updated":"2026-05-16T12:01:04Z","snapshot_observed_at":"2026-08-01T18:23:13.593122Z","submitted_at":"2026-05-16T12:01:04Z","title":"Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-19T20:49:25.902880Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.16949"},"observation_digest":"sha256:fd701df9114ef1d0a4b5841983ab59ac7b2c58612dd0dc3241671f70d3cf4a5a","observation_id":"97913379-8c74-4b4d-a0f0-f0d43d9f7429","resolution":{"observed_at":"2026-05-19T20:52:46.182074Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.17759","last_updated":"2026-05-18T02:25:07Z","snapshot_observed_at":"2026-08-01T22:43:12.616139Z","submitted_at":"2026-05-18T02:25:07Z","title":"FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-20T12:43:58.746650Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.17759"},"observation_digest":"sha256:f739dc4b2f9ea382e776b4be163ef17b2a46a9819f568c34750e6e5b09fbd59e","observation_id":"139bf28f-2230-4fb0-aa9d-1deab630159c","resolution":{"observed_at":"2026-05-20T12:48:17.645042Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.18267","last_updated":"2026-07-24T06:29:36Z","snapshot_observed_at":"2026-08-02T13:49:17.490757Z","submitted_at":"2026-05-18T12:03:41Z","title":"SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-20T11:59:54.139888Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.18267"},"observation_digest":"sha256:cc88e53f0781ea0386cf9a1f59f9aae21277799cfc3080dbd95139183543a1e1","observation_id":"5c0a33c1-04cf-4649-801e-b45d9ef76acb","resolution":{"observed_at":"2026-05-20T12:03:15.328477Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.18267","last_updated":"2026-07-24T06:29:36Z","snapshot_observed_at":"2026-08-02T13:49:17.490757Z","submitted_at":"2026-05-18T12:03:41Z","title":"SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-30T18:39:40.667006Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.18267"},"observation_digest":"sha256:ef08e4d54c56c4f4edb3637cbd880ce0da2b55a7cd37710e37cbc9f9abdd576a","observation_id":"6798de54-c56d-4681-89bb-8a442494830e","resolution":{"observed_at":"2026-06-30T18:45:00.696366Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-02T13:49:20.291891Z","title":"Decoupled diffusion transformer,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.18267","last_updated":"2026-07-24T06:29:36Z","snapshot_observed_at":"2026-08-02T13:49:17.490757Z","submitted_at":"2026-05-18T12:03:41Z","title":"SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-02T13:49:20.291891Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.18267"},"observation_digest":"sha256:ebd2b30cfa2055fc6ea515ad309c243a1e738fe6876dd95d170b56c91124dfa5","observation_id":"d9f29842-50b2-47f6-ae1d-6d79f23b4d1c","resolution":{"observed_at":"2026-08-02T13:49:20.291891Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.18390","last_updated":"2026-05-18T13:38:43Z","snapshot_observed_at":"2026-07-06T23:29:14.916114Z","submitted_at":"2026-05-18T13:38:43Z","title":"Vision Foundation Models as Generalist Tokenizers for Image Generation","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-05-20T11:01:24.738195Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.18390"},"observation_digest":"sha256:da2e9f0b5b1f8ac2ff8c05d0208b171a023d35bff34371bdb5b17c4126f408db","observation_id":"9687e62e-6039-45ed-8208-77a743a3694c","resolution":{"observed_at":"2026-05-20T11:03:13.550688Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.26230","last_updated":"2026-05-25T18:01:05Z","snapshot_observed_at":"2026-08-08T08:15:58.432143Z","submitted_at":"2026-05-25T18:01:05Z","title":"Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-06-29T23:08:52.333329Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.26230"},"observation_digest":"sha256:249426221c9cfdae34e94fcaeb2bca7b3e9fed5359a2f59bb362beee534e0032","observation_id":"053973c3-6baa-4bb4-be59-d43dc7352f58","resolution":{"observed_at":"2026-06-29T23:14:01.813525Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2605.26449","last_updated":"2026-05-26T02:01:11Z","snapshot_observed_at":"2026-08-03T00:29:49.339089Z","submitted_at":"2026-05-26T02:01:11Z","title":"Cross-scale Aligned Supervision for Training GANs","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-29T18:57:36.810793Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2605.26449"},"observation_digest":"sha256:1b350e2ddd16b145ce0ec29641fce9e93790776b6953c67d1bb6faf32912280f","observation_id":"37f01b46-e6db-42b5-8f8b-d5a288380718","resolution":{"observed_at":"2026-06-29T19:03:51.339124Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2606.00094","last_updated":"2026-07-01T12:56:35Z","snapshot_observed_at":"2026-07-06T23:40:51.363776Z","submitted_at":"2026-05-25T08:43:14Z","title":"Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-06-29T22:44:39.440271Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2606.00094"},"observation_digest":"sha256:1fbad8ce16aa62654cd540ba670f1d9ee47e73f6a1a8e26bb0227482c5ed7122","observation_id":"9d187021-3d88-4a88-838b-df22a1b74f05","resolution":{"observed_at":"2026-06-29T22:54:01.504676Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2606.18765","last_updated":"2026-06-17T07:25:24Z","snapshot_observed_at":"2026-08-10T03:58:42.274539Z","submitted_at":"2026-06-17T07:25:24Z","title":"SpectralDiT: Timestep-Conditioned Spectral Residual Correction for Flow-Matching DiTs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:52.234386Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2606.18765"},"observation_digest":"sha256:cbab19841f66a7f1f598080a5f29fa1d19fcdb78a6eb3bb47db6e4dc617b7b66","observation_id":"90e76e4e-9f84-47d1-98dc-fe01b0aa6d1a","resolution":{"observed_at":"2026-07-04T00:19:13.753179Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2606.27760","last_updated":"2026-06-26T06:39:06Z","snapshot_observed_at":"2026-07-31T06:11:06.764017Z","submitted_at":"2026-06-26T06:39:06Z","title":"PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-29T04:31:57.169935Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2606.27760"},"observation_digest":"sha256:0c84ddb7f3e724dedbc46db295ce6d609e4030cfe9ad832ca478c18def10124b","observation_id":"4dc72874-666d-4520-a303-5642e8fa66ac","resolution":{"observed_at":"2026-06-29T20:03:57.149300Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2607.02508","last_updated":"2026-07-02T17:59:25Z","snapshot_observed_at":"2026-07-07T00:07:56.573640Z","submitted_at":"2026-07-02T17:59:25Z","title":"From SRA to Self-Flow: Data Augmentation or Self-Supervision?","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-03T14:36:59.833888Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2607.02508"},"observation_digest":"sha256:8c1aeefce1ab2663176322e92862e3a96679957d0aa7ff4e30d6ef859f876250","observation_id":"0d31a359-55ad-4fdd-b717-028de6710273","resolution":{"observed_at":"2026-07-03T14:38:28.540450Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-11T20:11:31.576642Z","title":"arXiv preprint arXiv:2504.05741 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04311","last_updated":"2026-07-07T14:30:21Z","snapshot_observed_at":"2026-08-11T18:31:58.276527Z","submitted_at":"2026-07-05T13:54:33Z","title":"Aura: Consistent Multi-Subject Video Generation via VLM-Grounded Semantic Alignment","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-07-11T20:11:31.576642Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2607.04311"},"observation_digest":"sha256:990b33e8d6c6c69a301b90c6aa81b07e40535100c2a9c117ec3a9daee1a72c51","observation_id":"2d8fe449-ca46-4028-95d1-75aaba40373d","resolution":{"observed_at":"2026-07-11T20:11:31.576642Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2607.08375","last_updated":"2026-07-09T11:49:57Z","snapshot_observed_at":"2026-08-01T14:34:54.273695Z","submitted_at":"2026-07-09T11:49:57Z","title":"WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-10T08:30:29.351159Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2607.08375"},"observation_digest":"sha256:827b8b57b88d159d7186e7a82eb45dcf0bb09d77c428eab2839cd1583665d54c","observation_id":"c547a0e9-d1d9-410d-84a8-8ab35862a8e2","resolution":{"observed_at":"2026-07-10T08:36:59.807676Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":"2504.05741","doi":null,"metadata_source":"pith","pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-10T18:47:31.564501Z","title":"Ddt: Decoupled diffusion transformer","venue":"cs.CV","work_id":"8625279b-e85e-4c87-ba22-16d4d9e1f709","year":2025},"citing_paper":{"arxiv_id":"2607.08436","last_updated":"2026-07-08T16:11:37Z","snapshot_observed_at":"2026-08-02T08:56:31.321169Z","submitted_at":"2026-07-08T16:11:37Z","title":"EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-07-10T18:39:34.356696Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2607.08436"},"observation_digest":"sha256:ed868e9049851bad8fb0973b5f1b7c16f0aeae8872fbbc8671bba035df084837","observation_id":"40a9b4da-4b54-48e9-a1c7-9a0d6451b285","resolution":{"observed_at":"2026-07-10T18:47:31.565745Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-07-13T05:10:26.667731Z","title":"arXiv preprint arXiv:2504.05741 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.09134","last_updated":"2026-07-10T06:44:04Z","snapshot_observed_at":"2026-08-07T15:40:45.553118Z","submitted_at":"2026-07-10T06:44:04Z","title":"ReGen: Hierarchical Multi-Prompt Representation Generation for Efficient Waveform Diffusion Models","version":1},"reference_index":110,"source":"arxiv_source","source_observed_at":"2026-07-13T05:10:26.667731Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2607.09134"},"observation_digest":"sha256:d41c28f3f7e8a27702effec8e62c87177279e59ce27e83f9bdbb0a3f9240d73a","observation_id":"f59ccfe4-f506-4510-8748-ed81ab0c2826","resolution":{"observed_at":"2026-07-13T05:10:26.667731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-01T11:10:13.846053Z","title":"DDT: decoupled diffusion transformer,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.19986","last_updated":"2026-07-22T10:19:43Z","snapshot_observed_at":"2026-08-08T13:29:52.628835Z","submitted_at":"2026-07-22T10:19:43Z","title":"STEREOFLOW: Progressive Stereo Matching with StereoDiT and Transition Flow Matching","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T11:10:13.846053Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2607.19986"},"observation_digest":"sha256:7aa6adb379b9b255e35461da836ae0109da612334203a9cbf65bc882747733cf","observation_id":"36b6f0a4-186c-4195-a818-f643495d8100","resolution":{"observed_at":"2026-08-01T11:10:13.846053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-03T00:34:35.551196Z","title":"DDT: Decoupled diffusion transformer","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.28760","last_updated":"2026-07-30T18:26:19Z","snapshot_observed_at":"2026-08-10T00:00:29.055278Z","submitted_at":"2026-07-30T18:26:19Z","title":"WaiT for the Signal: Simple Frequency-Aware Flow-Matching","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-03T00:34:35.551196Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2607.28760"},"observation_digest":"sha256:58caf70bc57494be839ac067cb953ec412232ce5b3995f9b85e68c6eb86eb72b","observation_id":"18ada578-e8b1-420b-94c0-c98c6a61b253","resolution":{"observed_at":"2026-08-03T00:34:35.551196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-06T00:24:06.326744Z","title":"arXiv preprint arXiv:2504.05741 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.01306","last_updated":"2026-08-02T15:20:55Z","snapshot_observed_at":"2026-08-09T23:24:20.476591Z","submitted_at":"2026-08-02T15:20:55Z","title":"SPAE: Spectrally Guided Autoencoder for Pretrained Visual Latents","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T00:24:06.326744Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2608.01306"},"observation_digest":"sha256:be77ea3bb11aa2b4a6abd056b5049d9399851fca97aa0f6ac7754060b836908d","observation_id":"a6a0413b-84eb-4a87-bb71-6642c6e5235f","resolution":{"observed_at":"2026-08-06T00:24:06.326744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-04T06:04:05.948176Z","title":"arXiv preprint arXiv:2504.05741 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2608.02502","last_updated":"2026-08-03T17:04:43Z","snapshot_observed_at":"2026-08-08T01:53:43.616263Z","submitted_at":"2026-08-03T17:04:43Z","title":"CMuon: Accelerating and Stabilizing Diffusion Transformer Training via Chunked Momentum Orthogonalization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T06:04:05.948176Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2608.02502"},"observation_digest":"sha256:5ec9b1bbf281b9843a7367cae53cfd70d2c5abe2fcaab037d23aec0cc8737015","observation_id":"c6c644a1-7741-4299-a77e-8e56d351235e","resolution":{"observed_at":"2026-08-04T06:04:05.948176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-07T23:08:32.772386Z","title":"arXiv preprint arXiv:2504.05741 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05811","last_updated":"2026-08-07T08:39:46Z","snapshot_observed_at":"2026-08-12T04:09:33.517527Z","submitted_at":"2026-08-06T09:44:00Z","title":"Energy-Guided Flow Matching","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T23:08:32.772386Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2608.05811"},"observation_digest":"sha256:62f939aff238f69fbaada6037c6e49f78b42bc85699847af4d65dac54043debd","observation_id":"82a0c968-adb9-4729-be9e-995dc190e2ff","resolution":{"observed_at":"2026-08-07T23:08:32.772386Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.05741","snapshot_observed_at":"2026-08-10T04:33:41.276333Z","title":"arXiv preprint arXiv:2504.05741 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.05811","last_updated":"2026-08-07T08:39:46Z","snapshot_observed_at":"2026-08-12T04:09:33.517527Z","submitted_at":"2026-08-06T09:44:00Z","title":"Energy-Guided Flow Matching","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-10T04:33:41.276333Z"},"links":{"cited_paper":"/paper/2504.05741","citing_paper":"/paper/2608.05811"},"observation_digest":"sha256:7965aadff6ab880450f880898e1f721c85655aab77f798efda5887b402746b79","observation_id":"8199a181-8465-4401-9a03-26938c3c3f4a","resolution":{"observed_at":"2026-08-10T04:33:41.276333Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.05741/citation-record","integrity":"/paper/2504.05741/integrity","json":"/paper/2504.05741/citation-record.json","paper":"/paper/2504.05741"},"outbound":[],"paper":{"arxiv_id":"2504.05741","last_updated":"2025-04-09T04:23:38Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T10:06:06.251834Z","submitted_at":"2025-04-08T07:17:45Z","title":"DDT: Decoupled Diffusion Transformer"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 inbound Pith citation observations for arXiv:2504.05741."}