{"as_of":"2026-08-08T18:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4bdc33f478177a97de2ebc20af44d5fe462a0caab493200b06d87ba7a0fc1041","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T14:53:03.638057Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2512.19311/citation-record","integrity":"/paper/2512.19311/integrity","json":"/paper/2512.19311/citation-record.json","paper":"/paper/2512.19311"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T14:52:57.797999Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"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":1,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:57.797999Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:7085f30a3146b10d29b8489332ccd0307406ee040bce4636582cfd8c1ac57554","observation_id":"054b02e0-e30c-4fdf-babe-0a326e1383bf","resolution":{"observed_at":"2026-08-03T14:52:57.797999Z","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":"2026-08-03T14:52:57.906990Z","title":"Diffusion models beat gans on image synthesis.Neural Information Process- ing Systems (NeurIPS), 2021","venue":null,"work_id":null,"year":2021},"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":2,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:57.906990Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:637704cea6f3e0571a1436b27fe437bc440991f38106a7244e567ddb77b443f5","observation_id":"95da6d68-e639-45df-8e5b-6fc63c848c9a","resolution":{"observed_at":"2026-08-03T14:52:57.906990Z","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":"2026-08-03T14:52:57.995582Z","title":"Scaling recti- fied flow transformers for high-resolution image synthesis","venue":null,"work_id":null,"year":null},"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":3,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:57.995582Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:8e6c75ee33ddf8a89f1cbc2385e5d53832410747a8ddf015d1e5cc836b17938f","observation_id":"7311d9da-5631-4bbc-ac95-97a2a6c3e409","resolution":{"observed_at":"2026-08-03T14:52:57.995582Z","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":"2026-08-03T14:52:58.152994Z","title":"(b) A bird on theleftof a clock","venue":null,"work_id":null,"year":null},"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":4,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:58.152994Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:6bf0c3c3a92db212232c1902b472a601f8c5aa3f5718debc2296f6f970276703","observation_id":"1117cca9-a32a-4799-aabb-cc1eb0239d33","resolution":{"observed_at":"2026-08-03T14:52:58.152994Z","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":"2026-08-03T14:52:58.318602Z","title":"One step diffusion via shortcut models","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":5,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:58.318602Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:f19f009d22ee2215ddd53e2dd5ec9519e3f7033c393840c8be3f8c3940c4c6af","observation_id":"94ed2cfa-1ecb-40ba-bee6-d50cc5eed9d0","resolution":{"observed_at":"2026-08-03T14:52:58.318602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.14389","last_updated":"2024-02-21T15:45:20Z","snapshot_observed_at":"2026-07-06T15:07:52.629778Z","submitted_at":"2023-03-25T07:47:21Z","title":"MDTv2: Masked Diffusion Transformer is a Strong Image Synthesizer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.14389","snapshot_observed_at":"2026-08-03T14:52:58.486535Z","title":"Mdtv2: Masked diffusion transformer is a strong image synthesizer.arXiv preprint arXiv:2303.14389,","venue":null,"work_id":null,"year":null},"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":6,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:58.486535Z"},"links":{"cited_paper":"/paper/2303.14389","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:8a0cdf99f0603404118e6c228ee737f90c4805fc7ff6204bc467a3f8d30b3ca9","observation_id":"03f3bb6d-8818-4b00-a448-c7feabaf1779","resolution":{"observed_at":"2026-08-03T14:52:58.486535Z","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":"2026-08-03T14:52:58.646140Z","title":"Diffit: Diffusion vision transformers for im- age generation","venue":null,"work_id":null,"year":2024},"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":7,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:58.646140Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:6c485f67a9e571caa1ddaebf50c50efab4e7eeeee39e93e301c73aa12b6bbf7d","observation_id":"40dd653d-960a-49dd-9657-cdd6dd1ba8e6","resolution":{"observed_at":"2026-08-03T14:52:58.646140Z","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":"2026-08-03T14:52:58.814280Z","title":"Classifier-free diffusion guidance","venue":null,"work_id":null,"year":2021},"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":8,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:58.814280Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:bfd1ca8b5825be647fca9eafa30e91f7f69ff53f3abbe189dbefdcaa4fd67b0d","observation_id":"c142b181-71ab-448f-86b7-2806ea2ed3fe","resolution":{"observed_at":"2026-08-03T14:52:58.814280Z","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":"2026-08-03T14:52:58.917521Z","title":"Denoising dif- fusion probabilistic models.Neural Information Processing Systems (NeurIPS), 2020","venue":null,"work_id":null,"year":2020},"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":9,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:58.917521Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:05a3288d5994e5b5caa4d1541d04e9d1a6b0251db7a6e4a26f7ed62de72b8382","observation_id":"77ff61d5-e0ea-4db3-973f-2728734e643b","resolution":{"observed_at":"2026-08-03T14:52:58.917521Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.19324","last_updated":"2025-03-22T19:42:20Z","snapshot_observed_at":"2026-07-06T19:39:33.077694Z","submitted_at":"2024-10-25T06:20:06Z","title":"Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.19324","snapshot_observed_at":"2026-08-03T14:52:59.010988Z","title":"Simpler diffusion (sid2): 1.5 fid on imagenet512 with pixel-space diffusion","venue":null,"work_id":null,"year":2024},"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":10,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.010988Z"},"links":{"cited_paper":"/paper/2410.19324","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:8e8cd8e8e549cf1373a3db5d75a5583d8978742604ff6b496979d80711ef3c13","observation_id":"6bc11d43-b9ee-40b5-96c8-78c5ce78bb4b","resolution":{"observed_at":"2026-08-03T14:52:59.010988Z","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":"2026-08-03T14:52:59.156954Z","title":"Self forcing: Bridging the train-test gap in autoregressive video diffusion.Neural Information Process- ing Systems (NeurIPS), 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":11,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.156954Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:3589693c926c50abf90a45e571aace9787a57309ddae5f2d11c98fb854099c54","observation_id":"131b5368-b01d-410e-ac1e-ef42764eb9aa","resolution":{"observed_at":"2026-08-03T14:52:59.156954Z","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":"2026-08-03T14:52:59.284840Z","title":"Fleet, and Ting Chen","venue":null,"work_id":null,"year":2023},"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":12,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.284840Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:5332f6d7a85f7b4572c2b57cb6fd3dd5668f30c27b1661b93d3fd78495e8c50b","observation_id":"3bcbf32c-aa23-4665-bf29-09ad4cdb08ac","resolution":{"observed_at":"2026-08-03T14:52:59.284840Z","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":"2026-08-03T14:52:59.402186Z","title":"No other representation component is needed: Diffusion transformers can provide representation guidance by themselves.arXiv preprint arXiv:2505.02831,","venue":null,"work_id":null,"year":null},"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":13,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.402186Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:27492d2d6cf336594cd5c8526c7eca59400983ed9a004e4b4e4761930f881deb","observation_id":"9e4cd830-168f-4fa3-85b8-3bf5806630c3","resolution":{"observed_at":"2026-08-03T14:52:59.402186Z","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":"2026-08-03T14:52:59.502823Z","title":"Elucidating the design space of diffusion-based generative models.Neural Information Processing Systems (NeurIPS),","venue":null,"work_id":null,"year":null},"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":14,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.502823Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:ef81c5f5f17c8bfee371ce9559e44ba474a347d0ef813e0101a9d665eab9c939","observation_id":"19d84541-e66c-41a5-b3b5-ecc3668337ee","resolution":{"observed_at":"2026-08-03T14:52:59.502823Z","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":"2026-08-03T14:52:59.667458Z","title":"Guiding a diffusion model with a bad version of itself","venue":null,"work_id":null,"year":2024},"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":15,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.667458Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:93f918ed70558fac1501db749b47aa6f9f3e10b2feefca6f706a2618262fbf23","observation_id":"df5cbe89-4a2e-49d5-9aa2-b15955ead42a","resolution":{"observed_at":"2026-08-03T14:52:59.667458Z","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":"2026-08-03T14:52:59.743097Z","title":"Analyzing and improving the training dynamics of diffusion models","venue":null,"work_id":null,"year":2024},"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":16,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.743097Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:c08b434c849298dc73bb82f86e54b542447db6dd9bb09a688470ff4c794bc412","observation_id":"c5651406-6942-4aa5-929f-e9a5cfa46acd","resolution":{"observed_at":"2026-08-03T14:52:59.743097Z","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":"2026-08-03T14:52:59.816393Z","title":"Variational diffusion models.Neural Information Pro- cessing Systems (NeurIPS), 2021","venue":null,"work_id":null,"year":2021},"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":17,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.816393Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:f4008350936073531b61c91ce971cea9da3e1b41ea4180396b172969d234f6d3","observation_id":"700508d5-de36-4744-8790-a4f349751898","resolution":{"observed_at":"2026-08-03T14:52:59.816393Z","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":"2026-08-03T14:52:59.946442Z","title":"Boosting generative image modeling via joint image-feature synthe- sis.arXiv preprint arXiv:2504.16064, 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":18,"source":"pdf_text","source_observed_at":"2026-08-03T14:52:59.946442Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:a36e6e232690679f821b6d1acc0fa7a90a151b4e66d3bc14e5b435eff544892c","observation_id":"fffb5023-c552-4a63-9cf1-e4a82e777f49","resolution":{"observed_at":"2026-08-03T14:52:59.946442Z","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":"2026-08-03T14:53:00.043326Z","title":"Repa-e: Unlocking vae for end-to-end tuning of latent diffusion transformers.IEEE International Conference on Computer Vision (ICCV), 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":19,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.043326Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:ff634fa3315e4bf1dbe3ecc095fb355d42f060ce3fef937ba11471c2ff291ba6","observation_id":"a279787d-bff8-4ca3-91a4-217fe899d0fc","resolution":{"observed_at":"2026-08-03T14:53:00.043326Z","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":"2026-08-03T14:53:00.162499Z","title":"Alleviating exposure bias in diffusion mod- els through sampling with shifted time steps.International Conference on Learning Representations (ICLR), 2024","venue":null,"work_id":null,"year":2024},"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":20,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.162499Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:976a8a45d44902d8cc39b7c3e64db7e02300a2e66d6c268783bda5096fcfe1ed","observation_id":"9c147807-becd-47aa-b097-11a7c20d32de","resolution":{"observed_at":"2026-08-03T14:53:00.162499Z","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":"2026-08-03T14:53:00.230702Z","title":"Autoregressive image generation without vec- tor quantization.Neural Information Processing Systems (NeurIPS), 2024","venue":null,"work_id":null,"year":2024},"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":21,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.230702Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:4eec09d37d0798d9d1b06475583865a834428ce3c3c46dbcb94ec7f4ddcd2a31","observation_id":"660126c7-b402-4101-9778-2b5d6a745732","resolution":{"observed_at":"2026-08-03T14:53:00.230702Z","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":"2026-08-03T14:53:00.320917Z","title":"On error propa- gation of diffusion models","venue":null,"work_id":null,"year":2024},"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":22,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.320917Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:bf5a9c32fe353f0d73a278e62d1acaba5e303b1f353636150028c29edc58c6f1","observation_id":"83b41f4f-de9e-4488-99fb-f1819ef5c665","resolution":{"observed_at":"2026-08-03T14:53:00.320917Z","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":"2026-08-03T14:53:00.434580Z","title":"Flow matching for generative modeling","venue":null,"work_id":null,"year":2022},"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":23,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.434580Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:586fb762dbefa1bb9c9864539375cda6b04b71942e04e8ebfaead9a3a91f4fa4","observation_id":"ef504f1b-567a-4e51-b1d6-27347819b290","resolution":{"observed_at":"2026-08-03T14:53:00.434580Z","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":"2026-08-03T14:53:00.536575Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":null,"year":2023},"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":24,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.536575Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:8b0e1fc15f9380238e0c5272b125cd334d748fef8a3520ab66160e67662b8e9b","observation_id":"716e41c1-8c12-40a7-9f1b-991d51ee6152","resolution":{"observed_at":"2026-08-03T14:53:00.536575Z","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":"2026-08-03T14:53:00.625872Z","title":"Instaflow: One step is enough for high-quality diffusion- based text-to-image generation","venue":null,"work_id":null,"year":2023},"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":25,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.625872Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:2d1dd70462081c372b683f31856d5f7ae848e3bb01ffbcad7fd09254fed40162","observation_id":"e31ea876-6eec-4552-bea1-9d3b7f4569bb","resolution":{"observed_at":"2026-08-03T14:53:00.625872Z","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":"2026-08-03T14:53:00.699118Z","title":"Sit: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers","venue":null,"work_id":null,"year":2024},"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":26,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.699118Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:b48455c58cdfef7ec212cdfb7c21982da5747592ff534be36a175a82ef51d561","observation_id":"09055d7d-5379-4810-aa31-8c551a235e7c","resolution":{"observed_at":"2026-08-03T14:53:00.699118Z","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":"2026-08-03T14:53:00.761152Z","title":"Input perturbation reduces exposure bias in diffusion models","venue":null,"work_id":null,"year":2023},"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":27,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.761152Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:3772c7cd13518e45936fb0148a083b1425038d0d24fb9f581ec013e926aa5f61","observation_id":"24ce087c-71d3-4237-8e3b-2e971ff298aa","resolution":{"observed_at":"2026-08-03T14:53:00.761152Z","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":"2026-08-03T14:53:00.863495Z","title":"Elucidating the exposure bias in diffusion models.International Conference on Learning Representa- tions (ICLR), 2024","venue":null,"work_id":null,"year":2024},"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":28,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.863495Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:2a616d928ce8f2026c7465f59e231f35bceb0d476c7581276bc9dc4c1b3b1992","observation_id":"3f911833-0bb6-4e75-b5d0-c31de5e02c7e","resolution":{"observed_at":"2026-08-03T14:53:00.863495Z","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":"2026-08-03T14:53:00.939423Z","title":"Dinov2: Learning robust visual features without supervision.Transactions on Machine Learning Research (TMLR), 2024","venue":null,"work_id":null,"year":2024},"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":29,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:00.939423Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:5620ab191a74779034846b227533f5080ac8cf6b7289d542739a98fb383013a8","observation_id":"fcbd7c2c-95e2-4437-b82a-ee0c55359c85","resolution":{"observed_at":"2026-08-03T14:53:00.939423Z","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":"2026-08-03T14:53:01.045872Z","title":"Normalizing flows for probabilistic modeling and inference","venue":null,"work_id":null,"year":2021},"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":30,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.045872Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:175866de313f12cd7937c9bfc6064afb26507e836a741102da326cb95f6ac08c","observation_id":"1aef12bb-921c-428b-9a35-a875aa54fdc2","resolution":{"observed_at":"2026-08-03T14:53:01.045872Z","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":"2026-08-03T14:53:01.202734Z","title":"Scalable diffusion mod- els with transformers","venue":null,"work_id":null,"year":2023},"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":31,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.202734Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:b7fab38209eef7dad92b18c77cf3a070858c3fbda64cd875422d84e494bff740","observation_id":"955458cf-4534-4bfa-ac4b-7502282b53c1","resolution":{"observed_at":"2026-08-03T14:53:01.202734Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06732","last_updated":"2016-05-06T21:18:46Z","snapshot_observed_at":"2026-07-31T03:58:50.695173Z","submitted_at":"2015-11-20T19:25:54Z","title":"Sequence Level Training with Recurrent Neural Networks","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06732","snapshot_observed_at":"2026-08-03T14:53:01.338778Z","title":"Sequence level training with recurrent neural networks.arXiv preprint arXiv:1511.06732, 2015","venue":null,"work_id":null,"year":2015},"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":32,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.338778Z"},"links":{"cited_paper":"/paper/1511.06732","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:25b0c612ccef98d175b18199e3a6544385d86cdc2a8d1976122b42eaf9faf9ad","observation_id":"b212e091-ae3e-448b-9fde-d95ece30a7c3","resolution":{"observed_at":"2026-08-03T14:53:01.338778Z","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":"2026-08-03T14:53:01.436267Z","title":"Multi-step denoising scheduled sampling: Towards alleviating exposure bias for diffusion models","venue":null,"work_id":null,"year":2024},"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":33,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.436267Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:3275552f8e4ae866e9e5c688e3dccc59afafe03a129b7f3de0fec7dfbba7be86","observation_id":"b1f8f997-2cdb-4538-80e7-e510a0f226dc","resolution":{"observed_at":"2026-08-03T14:53:01.436267Z","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":"2026-08-03T14:53:01.533970Z","title":"High-resolution image syn- thesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"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":34,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.533970Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:cc15811d7e1917bf14d28e18ba548790a2b93ea1ded4eebb9088bded2b581a9f","observation_id":"c4143a4f-a418-4773-81aa-ef4b008547a4","resolution":{"observed_at":"2026-08-03T14:53:01.533970Z","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":"2026-08-03T14:53:01.633576Z","title":"Photorealistic text-to-image diffusion models with deep language understanding.Neural Information Processing Sys- tems (NeurIPS), 2022","venue":null,"work_id":null,"year":2022},"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":35,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.633576Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:b465d18c399665d9c3b86ca280da6d55ff4c26198ba023797f7e8db529aff680","observation_id":"4a3e9c36-cac1-4220-b07f-f5381671ec75","resolution":{"observed_at":"2026-08-03T14:53:01.633576Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.00292","last_updated":"2019-11-07T06:55:36Z","snapshot_observed_at":"2026-08-06T13:05:16.615295Z","submitted_at":"2019-10-01T10:28:32Z","title":"Generalization in Generation: A closer look at Exposure Bias","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.00292","snapshot_observed_at":"2026-08-03T14:53:01.760287Z","title":"Generalization in generation: A closer look at exposure bias.arXiv preprint arXiv:1910.00292, 2019","venue":null,"work_id":null,"year":1910},"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":36,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.760287Z"},"links":{"cited_paper":"/paper/1910.00292","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:4314fad5b9fe826403df0deecf75895250dbeb0d8137886afa615582d8cd26b6","observation_id":"12255dfb-43f2-4967-bb70-4e5719909501","resolution":{"observed_at":"2026-08-03T14:53:01.760287Z","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":"2026-08-03T14:53:01.879773Z","title":"Denois- ing diffusion implicit models","venue":null,"work_id":null,"year":null},"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":37,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.879773Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:023c7a109c5e6d49d13d5e04ec7a4521e2a01c51844af6d5729050578f403f96","observation_id":"6153ac22-d340-49a4-8061-63ec0dbd570f","resolution":{"observed_at":"2026-08-03T14:53:01.879773Z","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":"2026-08-03T14:53:01.956785Z","title":"Selective underfitting in dif- fusion models.arXiv preprint arXiv:2510.01378, 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":38,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:01.956785Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:bb4166e4396ed5625b669a1b6103a2769ab474124d10c1a355b519d5886aedb4","observation_id":"c66255d5-cca7-4364-9f6d-0ab165784669","resolution":{"observed_at":"2026-08-03T14:53:01.956785Z","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":"2026-08-03T14:53:02.079729Z","title":"Generative modeling by esti- mating gradients of the data distribution.Neural Information Processing Systems (NeurIPS), 2019","venue":null,"work_id":null,"year":2019},"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":39,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.079729Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:6349b88aa28988abe75d5e8e71d0718493d650aef62f47dd8b1b106075a068ab","observation_id":"2ae699d3-a8a1-49ec-a4df-b394300cfea6","resolution":{"observed_at":"2026-08-03T14:53:02.079729Z","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":"2026-08-03T14:53:02.211011Z","title":"Score-based generative modeling through stochastic differential equa- tions","venue":null,"work_id":null,"year":2021},"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":40,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.211011Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:bc8c8bfaecce1f1ad53f1f715239c0df235efb7f5d412bbcae7ca407427bb671","observation_id":"a16d27d1-05fa-4375-8dea-3c924d1ddc87","resolution":{"observed_at":"2026-08-03T14:53:02.211011Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.05350","last_updated":"2025-06-05T17:59:58Z","snapshot_observed_at":"2026-08-07T10:18:48.536276Z","submitted_at":"2025-06-05T17:59:58Z","title":"Contrastive Flow Matching","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.05350","snapshot_observed_at":"2026-08-03T14:53:02.306664Z","title":"Contrastive flow match- ing.arXiv preprint arXiv:2506.05350, 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":41,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.306664Z"},"links":{"cited_paper":"/paper/2506.05350","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:ed92ec87068976b9edce3beef2ef767d68c99ec09eb245e146659f3db4b24e3a","observation_id":"302f2c90-00ee-453c-b387-e9460fa09c3e","resolution":{"observed_at":"2026-08-03T14:53:02.306664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.20388","last_updated":"2025-03-20T18:15:30Z","snapshot_observed_at":"2026-08-07T17:41:55.935183Z","submitted_at":"2025-02-27T18:59:08Z","title":"Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.20388","snapshot_observed_at":"2026-08-03T14:53:02.400983Z","title":"Beyond next-token: Next-x pre- diction for autoregressive visual generation.arXiv preprint arXiv:2502.20388, 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":42,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.400983Z"},"links":{"cited_paper":"/paper/2502.20388","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:e51511d906dbddbae3f14913fe57501b3198ad4527d903f8474435e50167d3a8","observation_id":"e833e703-4dcf-4062-b34a-e733a7d27cbe","resolution":{"observed_at":"2026-08-03T14:53:02.400983Z","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":"2026-08-03T14:53:02.471817Z","title":"Visual autoregressive modeling: Scalable image gen- eration via next-scale prediction.Neural Information Pro- cessing Systems (NeurIPS), 2024","venue":null,"work_id":null,"year":2024},"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":43,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.471817Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:1853e32b2961be12351a3fe2434786e5dab0a39b4fdcb44a715fc23885935537","observation_id":"50456e34-b845-4ed7-8cfd-e47f58e528a1","resolution":{"observed_at":"2026-08-03T14:53:02.471817Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.09027","last_updated":"2025-07-24T15:55:00Z","snapshot_observed_at":"2026-08-08T15:14:26.502885Z","submitted_at":"2025-06-10T17:53:29Z","title":"Diffuse and Disperse: Image Generation with Representation Regularization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.09027","snapshot_observed_at":"2026-08-03T14:53:02.592588Z","title":"Diffuse and disperse: Im- age generation with representation regularization.arXiv preprint arXiv:2506.09027, 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":44,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.592588Z"},"links":{"cited_paper":"/paper/2506.09027","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:90b4dc9d28a31a8c8f69a7218316d005f058d86193c4f0e33ca250992fc4bf4f","observation_id":"0a8703df-618b-46e1-b0e7-4774e77ea036","resolution":{"observed_at":"2026-08-03T14:53:02.592588Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.23268","last_updated":"2025-08-04T02:46:11Z","snapshot_observed_at":"2026-08-07T21:03:40.851564Z","submitted_at":"2025-07-31T06:07:20Z","title":"PixNerd: Pixel Neural Field Diffusion","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.23268","snapshot_observed_at":"2026-08-03T14:53:02.682342Z","title":"Pixnerd: Pixel neural field diffusion.arXiv preprint arXiv:2507.23268, 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":45,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.682342Z"},"links":{"cited_paper":"/paper/2507.23268","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:7e4aa695854a0e9cb62a178529bacbf559481da633bfb6900ab8a966016f0494","observation_id":"fd0bc562-bfdb-460a-a208-93b8e7f8c989","resolution":{"observed_at":"2026-08-03T14:53:02.682342Z","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T14:53:02.960817Z","title":"Representation entanglement for genera- tion: Training diffusion transformers is much easier than you think.Neural Information Processing Systems (NeurIPS),","venue":null,"work_id":null,"year":null},"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":47,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:02.960817Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:021b7a171c8490ff7325589f1fc45a351a07669314d208e22d2a4f3a349ce87a","observation_id":"177dec46-05d1-4387-a133-ff6f14aaea92","resolution":{"observed_at":"2026-08-03T14:53:02.960817Z","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":"2026-08-03T14:53:03.082609Z","title":"Poisson flow generative models.Neural Information Pro- cessing Systems (NeurIPS), 2022","venue":null,"work_id":null,"year":2022},"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":48,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:03.082609Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:97c078dbee49c71f1d84a4037eb61426e27c70f063ec4b52657d1120957662e9","observation_id":"14f8eb6e-8f55-42c8-9e5f-da6f697ef476","resolution":{"observed_at":"2026-08-03T14:53:03.082609Z","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":"2026-08-03T14:53:03.162256Z","title":"Reconstruc- tion vs","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":49,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:03.162256Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:ea9da252ee738419b390191c89176854aebf45d6e614c9b4bc47db8f6884cfbb","observation_id":"43c84aef-a425-43c2-a5ed-5c92d0a1e2e0","resolution":{"observed_at":"2026-08-03T14:53:03.162256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05737","last_updated":"2024-03-29T17:44:41Z","snapshot_observed_at":"2026-08-02T18:23:02.746177Z","submitted_at":"2023-10-09T14:10:29Z","title":"Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05737","snapshot_observed_at":"2026-08-03T14:53:03.206913Z","title":"Language model beats diffusion–tokenizer is key to visual generation.arXiv preprint arXiv:2310.05737, 2023","venue":null,"work_id":null,"year":2023},"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":50,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:03.206913Z"},"links":{"cited_paper":"/paper/2310.05737","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:29978ad77d32c53d08dd77d40907b2f0e6c4cfaa63ba6f3473292db1f80480d2","observation_id":"ed5a70c3-3676-43e2-a4be-1426cfbdd2fd","resolution":{"observed_at":"2026-08-03T14:53:03.206913Z","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":"2026-08-03T14:53:03.293847Z","title":"An image is worth 32 tokens for reconstruction and generation.Neural Infor- mation Processing Systems (NeurIPS), 2024","venue":null,"work_id":null,"year":2024},"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":51,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:03.293847Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:d6216230e16838327ca0e2b6995de50eab1b1961c2468f14ebc1f2b9975781c7","observation_id":"ad39361e-f380-4755-89c0-c1441d6057e4","resolution":{"observed_at":"2026-08-03T14:53:03.293847Z","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":"2026-08-03T14:53:03.385926Z","title":"Representation alignment for generation: Training diffusion transformers is easier than you think","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":52,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:03.385926Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:29a3a6100bf0450cdf8ff68ceed727ed201f1be567c5b41e0d57a5a9a39d0e97","observation_id":"1bbce601-00f8-4b19-a7fe-3b621dfe4252","resolution":{"observed_at":"2026-08-03T14:53:03.385926Z","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":"2026-08-03T14:53:03.462485Z","title":"Manifold con- 10 straint reduces exposure bias in accelerated diffusion sam- pling.International Conference on Learning Representa- tions (ICLR), 2024","venue":null,"work_id":null,"year":2024},"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":53,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:03.462485Z"},"links":{"citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:c7c846415566ca98db180244b9691d21b2bf07f02aac3f1e337ac675c92a8e7d","observation_id":"019868c8-4e9e-4331-a87b-c08e288eb0c4","resolution":{"observed_at":"2026-08-03T14:53:03.462485Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.11690","last_updated":"2025-10-13T17:51:39Z","snapshot_observed_at":"2026-07-06T22:32:32.632779Z","submitted_at":"2025-10-13T17:51:39Z","title":"Diffusion Transformers with Representation Autoencoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.11690","snapshot_observed_at":"2026-08-03T14:53:03.554233Z","title":"Diffusion transformers with representation autoen- coders","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":54,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:03.554233Z"},"links":{"cited_paper":"/paper/2510.11690","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:955f0b298671faa9f92b67470db4d599faa5d1160ab3998c27c2a89a68f156a1","observation_id":"fcb44d0d-3662-4efa-967b-95818b79c88c","resolution":{"observed_at":"2026-08-03T14:53:03.554233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09305","last_updated":"2024-03-05T01:10:18Z","snapshot_observed_at":"2026-08-03T18:21:25.175322Z","submitted_at":"2023-06-15T17:38:48Z","title":"Fast Training of Diffusion Models with Masked Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.09305","snapshot_observed_at":"2026-08-03T14:53:03.638057Z","title":"Sulphur-crested cockatoo","venue":null,"work_id":null,"year":2023},"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":55,"source":"pdf_text","source_observed_at":"2026-08-03T14:53:03.638057Z"},"links":{"cited_paper":"/paper/2306.09305","citing_paper":"/paper/2512.19311"},"observation_digest":"sha256:3e1ebf30f82ed5c0c085309dfef13563ed6b0976f7d986d75b182d30dbcc1de5","observation_id":"f37e1941-f099-465d-af4b-0c9760651f68","resolution":{"observed_at":"2026-08-03T14:53:03.638057Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2512.19311","last_updated":"2026-07-12T14:26:35Z","latest_version":2,"primary_category":"cs.CV","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"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":54,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":55},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2512.19311."}