{"as_of":"2026-08-15T01:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c1f7d652df2b7316f559ddc910cfb541b48fab56c03696dd988a318887ca20e1","coverage":[{"denominator":57,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T19:23:16.909720Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T15:36:11.820004Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-15T15:31:11.142210Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.10781","snapshot_observed_at":"2026-08-11T15:36:11.820004Z","title":"Bag of design choices for inference of high-resolution masked generative transformer","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10891","last_updated":"2024-12-17T05:23:42Z","snapshot_observed_at":"2026-08-12T04:12:58.083448Z","submitted_at":"2024-12-14T16:42:41Z","title":"Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T15:36:11.820004Z"},"links":{"cited_paper":"/paper/2411.10781","citing_paper":"/paper/2412.10891"},"observation_digest":"sha256:38828378a4c05e9c9f0805e156a250580cab40e620b9969fd5b8f951c66065fc","observation_id":"facd7dd1-b936-4c9e-b537-2c1757926930","resolution":{"observed_at":"2026-08-11T15:36:11.820004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"cited_work":{"arxiv_id":"2411.10781","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.10781","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Available: https://arxiv.org/abs/2411.10781 JOURNAL OF LATEX CLASS FILES, VOL","venue":null,"work_id":"0f99ad84-8416-425b-88a9-3eb224649f9f","year":2026},"citing_paper":{"arxiv_id":"2603.06165","last_updated":"2026-04-25T08:55:22Z","snapshot_observed_at":"2026-08-11T23:16:53.593578Z","submitted_at":"2026-03-06T11:17:37Z","title":"Reflective Flow Sampling Enhancement","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-15T15:30:45.053146Z"},"links":{"cited_paper":"/paper/2411.10781","citing_paper":"/paper/2603.06165"},"observation_digest":"sha256:6219b89b00159e23630f63be7801c7b398f8c3e72833ca077930c7ccd02c4924","observation_id":"67ae4d6f-148e-4521-b18c-5743fe7fe898","resolution":{"observed_at":"2026-05-15T15:31:11.146040Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.10781/citation-record","integrity":"/paper/2411.10781/integrity","json":"/paper/2411.10781/citation-record.json","paper":"/paper/2411.10781"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-12T19:23:16.530122Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.530122Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:fc6242860c5339c1c7aca25c60f9cfed371d7b6a4c47c16466a8b09ab526376a","observation_id":"f8fe0f60-0207-4716-acf2-5fb0f2be6991","resolution":{"observed_at":"2026-08-12T19:23:16.530122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.360566Z","title":"Zigzag diffusion sampling: The path to success ls zigzag","venue":null,"work_id":"94d27958-993e-4f91-8762-26f982089250","year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.534340Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:d12fb5912a62cd7f2bd2a245cb639eae72eb099d44490fd374d7c22ee0b11880","observation_id":"7807e726-37ed-4bb9-9317-22757df2ef80","resolution":{"observed_at":"2026-08-12T19:23:18.505253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08261","last_updated":"2025-03-13T15:09:10Z","snapshot_observed_at":"2026-08-14T14:23:37.312213Z","submitted_at":"2024-10-10T17:59:17Z","title":"Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.08261","snapshot_observed_at":"2026-08-12T19:23:16.538021Z","title":"Meissonic: Revitalizing masked generative trans- formers for efficient high-resolution text-to-image synthesis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.538021Z"},"links":{"cited_paper":"/paper/2410.08261","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:f8f0c19dd6f1fd610f3b97b6b6ac2096dac8f0fa041ea291d2b11db0d764f738","observation_id":"25edc504-926a-446f-af51-64f28bedcf01","resolution":{"observed_at":"2026-08-12T19:23:16.538021Z","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":"2026-08-12T19:23:16.541917Z","title":"Token merging for fast sta- ble diffusion","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.541917Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:a469467c823831cf12a193d3fac0ad31643448cb60de8b8fe7a5f2b35b2cb224","observation_id":"f3d85b88-e98c-46fb-b2e9-345f0c8818c5","resolution":{"observed_at":"2026-08-12T19:23:16.541917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.311648Z","title":"Maskgit: Masked generative image transformer","venue":null,"work_id":"8d36377b-b98a-4a37-b184-a37b310db79c","year":2022},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.545692Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:d3f4fd4c0c83c69b1f4e7a8c26fa9d91febc79055f2a059bb2f33f3a14d5524d","observation_id":"3a0cc937-e97c-47b3-954f-c881011eee9a","resolution":{"observed_at":"2026-08-12T19:23:18.314899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00704","last_updated":"2023-01-02T14:43:38Z","snapshot_observed_at":"2026-08-13T13:10:50.058243Z","submitted_at":"2023-01-02T14:43:38Z","title":"Muse: Text-To-Image Generation via Masked Generative Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00704","snapshot_observed_at":"2026-08-12T19:23:16.549538Z","title":"Muse: Text-to-image generation via masked generative transform- ers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.549538Z"},"links":{"cited_paper":"/paper/2301.00704","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:30b127f181e4a881e6b970698b8d331324b03a7cc7678a4e1283651efd6eea41","observation_id":"d67cac3b-6c36-465c-bfae-26d1df098170","resolution":{"observed_at":"2026-08-12T19:23:16.549538Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.17343","last_updated":"2024-11-19T09:58:07Z","snapshot_observed_at":"2026-08-13T18:42:41.939802Z","submitted_at":"2024-06-25T07:57:27Z","title":"Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.17343","snapshot_observed_at":"2026-08-12T19:23:16.553899Z","title":"Q-dit: Ac- curate post-training quantization for diffusion transformers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.553899Z"},"links":{"cited_paper":"/paper/2406.17343","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:3f807de6776290d0c660d0a312972db9c48bb88331404f2de0819efea5f93a90","observation_id":"7a8cdf3f-2fd5-4f62-a0d1-ea8cba030f0b","resolution":{"observed_at":"2026-08-12T19:23:16.553899Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13863","last_updated":"2024-10-17T17:59:59Z","snapshot_observed_at":"2026-08-13T23:30:37.164749Z","submitted_at":"2024-10-17T17:59:59Z","title":"Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13863","snapshot_observed_at":"2026-08-12T19:23:16.557979Z","title":"Fluid: Scaling autoregressive text-to-image generative models with continuous tokens","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.557979Z"},"links":{"cited_paper":"/paper/2410.13863","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:2d24b1bec01eda0844d2b4180ff6d93610440eae6a04b01bfde422b20abfdc83","observation_id":"d2f17968-6e81-40bd-a313-b0ccdd9b841a","resolution":{"observed_at":"2026-08-12T19:23:16.557979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.302848Z","title":"Geneval: An object-focused framework for evaluating text- to-image alignment","venue":null,"work_id":"f5201a41-2d18-4a6b-a7c8-d83bd0378705","year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.561305Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:4baffed2de3171cb1c49c03364cfa4e2cd8b04f8458771d397b0064f4fdbd527","observation_id":"c4e4b540-d032-45b6-9a1a-9e6b7f883bad","resolution":{"observed_at":"2026-08-12T19:23:18.306107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.293948Z","title":"Clipscore: A reference-free evaluation met- ric for image captioning, 2022","venue":null,"work_id":"821d44cc-6aed-4a30-a175-f13d64d6e093","year":2022},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.564163Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:28c7ec5434a9ba4f82ac41d7b07e52a15183876694a3d31a8cdb86c5c80f61a9","observation_id":"70a10bcd-6919-48de-aa1b-87e1b4d226de","resolution":{"observed_at":"2026-08-12T19:23:18.297329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.286065Z","title":"Gans trained by a two time-scale update rule converge to a local nash equi- librium","venue":null,"work_id":"ca2dd4f5-e611-42b4-87ef-6ce1a6005482","year":2017},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.567744Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:193480eed0315146c56f7be8405eb5b63a01bc714bb45213287185b67af32e52","observation_id":"5274d7fa-7819-4421-8183-650976f42a71","resolution":{"observed_at":"2026-08-12T19:23:18.288542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.276812Z","title":"Classifier-free diffusion guidance","venue":null,"work_id":"d7d42df0-2b1b-4632-b55a-34bbab2ccbb0","year":2021},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.570884Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:983f21e7604b828e51c9f088f0c4656e3174af00ca7fdf0a4b07f5aa3a2b4cd8","observation_id":"987903f6-f21d-4aed-ac26-05c829bd23b1","resolution":{"observed_at":"2026-08-12T19:23:18.281331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.151409Z","title":"Denoising diffu- sion probabilistic models","venue":null,"work_id":"5543fa25-6f3f-480a-87a6-7b42508b8402","year":2020},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.573875Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:c3706b5e09f2d0ee22dffd6113144c456c12045fb8afb5e4e4bab38b19e474a7","observation_id":"a6987512-a5c5-4b19-a8e0-41624bde2b4d","resolution":{"observed_at":"2026-08-12T19:23:18.207615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.086618Z","title":"T2i-compbench: A comprehensive bench- mark for open-world compositional text-to-image genera- tion","venue":null,"work_id":"304f168f-0b05-41ee-be9c-1000e6465d3c","year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.576419Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:702113322a29a9fe26f34ef67ce283faacfcda08e5cc40027bd7ae106a1a26dd","observation_id":"977aa2bd-8d97-45c8-b399-8f9ff0c11c64","resolution":{"observed_at":"2026-08-12T19:23:18.095930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.077877Z","title":"Quantization and training of neural networks for efficient integer-arithmetic-only inference","venue":null,"work_id":"6022ca3e-c583-4326-8cec-663adf374c29","year":2018},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.579637Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:33292e14c0afe34ae6216a776996701c21558049b01c1f2eb03f4091ae073e53","observation_id":"18d2f4f9-76f7-4449-bf70-971ee6b5cf98","resolution":{"observed_at":"2026-08-12T19:23:18.080761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.067941Z","title":"Elucidating the design space of diffusion-based generative models","venue":null,"work_id":"15b48444-26af-4233-ae52-c36c2a5288ab","year":2022},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.583907Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:63b6d328be0144e749844958a80714779adb9d83cd8301fbea7d45b1f86a327a","observation_id":"39d840ca-c1cf-4af1-9c5b-514619772348","resolution":{"observed_at":"2026-08-12T19:23:18.071609Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-12T19:23:16.586603Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.586603Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:209c6a61ddb6b9759481a29c656144c3b2e975c90f8f762f5dbda82e6acd9328","observation_id":"961d8206-d2dd-4719-830c-9cc024a4c887","resolution":{"observed_at":"2026-08-12T19:23:16.586603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.058949Z","title":null,"venue":null,"work_id":"03e5d07b-0403-44b4-9195-584c4ebf5580","year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.590191Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:462c7abaea428a4a98a222ce83de25bb5b1e802a5ce5af5366aefcf715cb9536","observation_id":"c6edc341-95d7-4385-82d1-242e618cfb94","resolution":{"observed_at":"2026-08-12T19:23:18.061692Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.049543Z","title":"Laion-aesthetics","venue":null,"work_id":"60ded42b-d890-40c0-a5ea-36190de4b76e","year":2022},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.593840Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:3545f83fcc09af71ea349dfb492d77fb8bb8c72338762a7c4bf0219e15c79c4d","observation_id":"bddf4892-38e6-4ff6-a402-dc3c2becba7f","resolution":{"observed_at":"2026-08-12T19:23:18.052548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.12003","last_updated":"2023-05-25T11:33:13Z","snapshot_observed_at":"2026-08-13T12:56:12.738403Z","submitted_at":"2023-01-27T21:52:03Z","title":"Minimizing Trajectory Curvature of ODE-based Generative Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.12003","snapshot_observed_at":"2026-08-12T19:23:16.597807Z","title":"Minimizing trajectory curvature of ode-based generative models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.597807Z"},"links":{"cited_paper":"/paper/2301.12003","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:f83cb2979444dc0d6193945c7735c0e76aa1c84da78d49f2ac4f46ee2e363866","observation_id":"4c646a68-e3f4-4003-8536-fd7a2dfc973a","resolution":{"observed_at":"2026-08-12T19:23:16.597807Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.040775Z","title":"Microsoft coco: Common objects in context","venue":null,"work_id":"b12a3d35-bbcd-4765-b654-72bfcb721b05","year":2014},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.600989Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:50953356498088ea30193c8404d1cc592d946e27e49db368fa9c713fb2b3d8d6","observation_id":"7c4c8868-d152-4b96-bcef-60e58c1ebea9","resolution":{"observed_at":"2026-08-12T19:23:18.043871Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:16.604268Z","title":"Alignment of dif- fusion models: Fundamentals, challenges, and future","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.604268Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:a61c58fac0fde34fe7d42c6223eca7d595e17a7eb44e47ba4a9e95d7cefbbe28","observation_id":"197d06cc-2898-40c1-ba2b-075218ff9edc","resolution":{"observed_at":"2026-08-12T19:23:16.604268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.02657","last_updated":"2025-04-24T16:16:27Z","snapshot_observed_at":"2026-08-14T09:17:25.015096Z","submitted_at":"2024-08-05T17:46:53Z","title":"Lumina-mGPT: Illuminate Flexible Photorealistic Text-to-Image Generation with Multimodal Generative Pretraining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.02657","snapshot_observed_at":"2026-08-12T19:23:16.608097Z","title":"Lumina-mgpt: Illuminate flexible photorealistic text-to-image generation with multimodal generative pretraining","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.608097Z"},"links":{"cited_paper":"/paper/2408.02657","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:326ba9b3d887a67987d9ba0f91c90e4d1db4f0caf9286ef92d1a2bb2b51969de","observation_id":"602fed6f-7d05-4df8-87ff-2c1d7997c77b","resolution":{"observed_at":"2026-08-12T19:23:16.608097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-12T19:23:16.611283Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.611283Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:cd5b956a0af6a765af71283376613b00abca816c4a286ed868f48806d0d8755a","observation_id":"7b283b00-220b-4556-b97d-b316529a5e27","resolution":{"observed_at":"2026-08-12T19:23:16.611283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01095","last_updated":"2025-05-19T07:56:56Z","snapshot_observed_at":"2026-08-13T05:49:42.133862Z","submitted_at":"2022-11-02T13:14:30Z","title":"DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.01095","snapshot_observed_at":"2026-08-12T19:23:16.614486Z","title":"Dpm-solver++: Fast solver for guided sam- pling of diffusion probabilistic models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.614486Z"},"links":{"cited_paper":"/paper/2211.01095","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:264b8c6bc3404e483098084b756a6da8405d8dec9906c22c48a76a19d69cf8dc","observation_id":"a4a22087-f91c-45fb-a4ab-a314ffe24389","resolution":{"observed_at":"2026-08-12T19:23:16.614486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:18.030801Z","title":"Dpm-solver: A fast ode solver for diffusion 9 probabilistic model sampling in around 10 steps","venue":null,"work_id":"7c1145ac-d901-47d9-8113-09055827eeca","year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.617487Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:8fb8b31da31275220d392921a167ab378923e75873761bde57e1321d08c1b310","observation_id":"b374eb31-ac84-4ffa-9149-07fa094f04a6","resolution":{"observed_at":"2026-08-12T19:23:18.034222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03142","last_updated":"2023-04-12T21:23:35Z","snapshot_observed_at":"2026-08-13T14:11:01.883329Z","submitted_at":"2022-10-06T18:03:56Z","title":"On Distillation of Guided Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03142","snapshot_observed_at":"2026-08-12T19:23:16.621198Z","title":"On distillation of guided diffusion models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.621198Z"},"links":{"cited_paper":"/paper/2210.03142","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:4720d66e98109584039f78e75f48bd30b1c744af34208d72420358f2a9840e5e","observation_id":"33acd21d-acb2-4839-ae6d-fccbdea1b0ee","resolution":{"observed_at":"2026-08-12T19:23:16.621198Z","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":"2026-08-12T19:23:16.624450Z","title":"Null-text inversion for editing real im- ages using guided diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.624450Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:83760d3aaab0fd6de4da943aa0492327f174a1a4d541c761481908b5f600f307","observation_id":"246b36c9-d98d-4141-83c1-baaeb197c950","resolution":{"observed_at":"2026-08-12T19:23:16.624450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.928469Z","title":"Learning to reason with llms, 2024","venue":null,"work_id":"02204608-ca9c-4077-b5db-ab2fbdd05732","year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.628844Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:41f4461843d50629e6feed219f71369f8d7b3b9dc569b5a835b2ca4d1b4d15b8","observation_id":"e05e6de0-ef87-4fd3-95d0-9fa71e7a55a0","resolution":{"observed_at":"2026-08-12T19:23:18.018436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:16.632383Z","title":"Scalable diffusion models with transformers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.632383Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:ac9abef4769e8971a86e9720be7996cca480f51a806281f99f14807fed805c28","observation_id":"06579d00-3f63-4200-91df-b52e668e7c82","resolution":{"observed_at":"2026-08-12T19:23:16.632383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.879503Z","title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis","venue":null,"work_id":"1bf70f9b-8d7b-4199-9d29-cf2a17b21f8d","year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.635241Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:d8553f5332337598e990a13e747a30f5c861d2811af4a8d636dd556ad65d767a","observation_id":"8139e639-dc7a-4f02-aba1-e58a29f0e2f6","resolution":{"observed_at":"2026-08-12T19:23:17.882555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14041","last_updated":"2024-07-27T14:22:56Z","snapshot_observed_at":"2026-08-12T23:18:58.374622Z","submitted_at":"2024-07-19T05:36:22Z","title":"Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14041","snapshot_observed_at":"2026-08-12T19:23:16.642183Z","title":"Not all noises are created equally: Diffusion noise selection and optimization","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.642183Z"},"links":{"cited_paper":"/paper/2407.14041","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:7a71454d28901376c5f2f91e25323da17fcfa7a0aec703733dd9cc5be9a40ae4","observation_id":"308f4ec8-c255-4f43-8584-2cb7cd9b283a","resolution":{"observed_at":"2026-08-12T19:23:16.642183Z","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":"2026-08-12T19:23:16.656197Z","title":"Learning transferable visual models from natural language supervision, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.656197Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:aa5ce57d7412c3025700df00120a6dc058fa26c57bb7e7250d2b2449a944bfca","observation_id":"1009d400-fd03-4bef-b85b-816e2270c5d1","resolution":{"observed_at":"2026-08-12T19:23:16.656197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.863684Z","title":"Progressive distillation for fast sampling of diffusion models","venue":null,"work_id":"22d1a08c-de18-4625-8a24-5ba7963c885a","year":2022},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.678783Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:e3c1e337db0f4ec7e87dca28106a1b4c2ef064d888a34a9b606f62a15df4c3c5","observation_id":"431549dd-e91d-4bb8-935e-8e6db94b2a05","resolution":{"observed_at":"2026-08-12T19:23:17.866626Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.854293Z","title":"Improved tech- niques for training gans","venue":null,"work_id":"42c6621e-b22d-4ba4-b90a-69055ee3805d","year":2016},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.681949Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:679ec1b7a13075865b1e0c4397574cde34101d24501ed43d4f3d92ec1c33e67f","observation_id":"0efb5d80-915c-4f01-84c1-6b0a7a481352","resolution":{"observed_at":"2026-08-12T19:23:17.857909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1508.07909","last_updated":"2016-06-10T14:45:08Z","snapshot_observed_at":"2026-08-13T18:41:35.355818Z","submitted_at":"2015-08-31T16:37:31Z","title":"Neural Machine Translation of Rare Words with Subword Units","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.07909","snapshot_observed_at":"2026-08-12T19:23:16.685482Z","title":"Neural machine translation of rare words with subword units","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.685482Z"},"links":{"cited_paper":"/paper/1508.07909","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:7bd3867ca042ea2e2908d3faa169431a54b456f3eb375b949a2d8f13ee7e6b74","observation_id":"891bde65-d934-42b4-bf11-a2c9fbfe7a79","resolution":{"observed_at":"2026-08-12T19:23:16.685482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10769","last_updated":"2024-12-13T06:36:28Z","snapshot_observed_at":"2026-08-13T11:39:41.842565Z","submitted_at":"2023-05-18T07:23:12Z","title":"Catch-Up Distillation: You Only Need to Train Once for Accelerating Sampling","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10769","snapshot_observed_at":"2026-08-12T19:23:16.688741Z","title":"Catch-up distillation: You only need to train once for accelerating sampling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.688741Z"},"links":{"cited_paper":"/paper/2305.10769","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:88437de8a6dcb4e4266d4cc57f039190251aad5010adc8d5f0b5a8ee3aca468b","observation_id":"c7818279-8292-4e42-a0dc-63b531a722db","resolution":{"observed_at":"2026-08-12T19:23:16.688741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.13416","last_updated":"2023-06-06T09:44:19Z","snapshot_observed_at":"2026-08-14T13:39:20.266923Z","submitted_at":"2023-04-26T09:55:12Z","title":"DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.13416","snapshot_observed_at":"2026-08-12T19:23:16.693067Z","title":"Diffuseexpand: Expanding dataset for 2d medical image segmentation using diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.693067Z"},"links":{"cited_paper":"/paper/2304.13416","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:326de05d80a1958e716cceb162cbe72dd48562ab253b56f6b1bf88973fe7b639","observation_id":"ddba156a-a57e-4e8c-9e16-c0aecded2ead","resolution":{"observed_at":"2026-08-12T19:23:16.693067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.725871Z","title":"Denois- ing diffusion implicit models","venue":null,"work_id":"3dc4d076-f03f-48f7-9276-513b64920cfc","year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.696968Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:3d740ce3951499d8b651ed2fc7926f934a581606cae0e6e1c2ac8d34b376d3ed","observation_id":"fa104296-19a9-4aef-8921-4fd833491005","resolution":{"observed_at":"2026-08-12T19:23:17.797163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.01469","last_updated":"2023-05-31T06:17:10Z","snapshot_observed_at":"2026-08-14T04:45:59.072029Z","submitted_at":"2023-03-02T18:30:16Z","title":"Consistency Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.01469","snapshot_observed_at":"2026-08-12T19:23:16.700168Z","title":"Consistency models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.700168Z"},"links":{"cited_paper":"/paper/2303.01469","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:2b0fb850033b3de67c4e6f3f807c634087318f04a10b8c7e8f64e951c97fbe80","observation_id":"67e4542f-e009-4d8b-a838-13dcc5e8ce5c","resolution":{"observed_at":"2026-08-12T19:23:16.700168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.569680Z","title":"Score-based generative modeling through stochastic differential equa- tions","venue":null,"work_id":"264e5bb7-d781-435f-949d-8fe2e48b0495","year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.703925Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:9f2f2417cdbda3e213661a2cd373e27616b5392570b721cea57672231454dad6","observation_id":"b87fc8ea-e126-4fc4-b735-ff5b5aeb9cf7","resolution":{"observed_at":"2026-08-12T19:23:17.607014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.561437Z","title":"Introducing stable diffusion 3.5","venue":null,"work_id":"731ca991-babf-4fdd-812d-01293f32b57e","year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.707808Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:a9e32634c3193c4d2dc6d029c5fb1b7ade7796d0c093889ee62435bf4c3eacb4","observation_id":"dbaec182-03c4-49dd-a84f-ee7bd4dfc7c2","resolution":{"observed_at":"2026-08-12T19:23:17.563913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:16.710846Z","title":"Roformer: Enhanced transformer with rotary position embedding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.710846Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:356e0594333f83810a8bf035604b9853a1b328008f124e6759834c8da9d23cbb","observation_id":"9ae94db2-d98f-43ba-a1ef-a75e8ad95f3f","resolution":{"observed_at":"2026-08-12T19:23:16.710846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06525","last_updated":"2024-06-10T17:59:52Z","snapshot_observed_at":"2026-08-13T22:05:34.844117Z","submitted_at":"2024-06-10T17:59:52Z","title":"Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06525","snapshot_observed_at":"2026-08-12T19:23:16.713464Z","title":"Autoregressive model beats diffusion: Llama for scalable image generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.713464Z"},"links":{"cited_paper":"/paper/2406.06525","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:4a27658e5e2bdfeb57668c162a87b95628ffe2a3d79615b281a2b507a29fe455","observation_id":"584ee1d9-85f7-48bb-9cb0-15f443b14552","resolution":{"observed_at":"2026-08-12T19:23:16.713464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.08295","last_updated":"2024-04-16T12:52:47Z","snapshot_observed_at":"2026-08-03T03:29:01.959523Z","submitted_at":"2024-03-13T06:59:16Z","title":"Gemma: Open Models Based on Gemini Research and Technology","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08295","snapshot_observed_at":"2026-08-12T19:23:16.716736Z","title":"Gemma: Open models based on gemini research and tech- nology","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.716736Z"},"links":{"cited_paper":"/paper/2403.08295","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:5fc7a875096bd2c44b77434369d97b55523e973de187b1b45c5f927f425b9d05","observation_id":"4875331e-0a03-4f20-adfa-634433d0f733","resolution":{"observed_at":"2026-08-12T19:23:16.716736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.01699","last_updated":"2025-03-04T04:33:27Z","snapshot_observed_at":"2026-08-14T20:37:16.155305Z","submitted_at":"2024-10-02T16:05:27Z","title":"Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.01699","snapshot_observed_at":"2026-08-12T19:23:16.723843Z","title":"Accelerating auto- regressive text-to-image generation with training-free spec- ulative jacobi decoding","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.723843Z"},"links":{"cited_paper":"/paper/2410.01699","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:37e5003218943471c85a37dae975121b78e4a4daf41f828ddcba306480a27cd2","observation_id":"78dc088a-12dd-42ce-bb63-c27399977de9","resolution":{"observed_at":"2026-08-12T19:23:16.723843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-12T19:23:16.727170Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.727170Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:3ef72779f0d59534877118d5c45a5976c91e980536d081a0d3fe6598232dbcf5","observation_id":"74c73d58-cf63-400c-9610-f4e0b31b6218","resolution":{"observed_at":"2026-08-12T19:23:16.727170Z","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":"2026-08-12T19:23:16.749152Z","title":"Neural discrete representation learning","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.749152Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:d6c385e608bd9183a7998ee75fa3feff9183eba4dbf89b672ffb4fec3eaca56d","observation_id":"e3e5411a-e75d-47d1-b3e2-d4ebdf2b6a9f","resolution":{"observed_at":"2026-08-12T19:23:16.749152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.442979Z","title":"Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis, 2023","venue":null,"work_id":"be112965-fbf0-4f1f-9e5d-62be454f9223","year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.792527Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:5c9cd8abdf6d14257ea4384924b6bde86b245ddf723d43b450d18102d5f3a209","observation_id":"47177146-c07d-4fa0-b978-b4c05460e98e","resolution":{"observed_at":"2026-08-12T19:23:17.515992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09341","last_updated":"2023-09-25T08:19:23Z","snapshot_observed_at":"2026-08-14T02:46:25.655503Z","submitted_at":"2023-06-15T17:59:31Z","title":"Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.09341","snapshot_observed_at":"2026-08-12T19:23:16.843506Z","title":"Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.843506Z"},"links":{"cited_paper":"/paper/2306.09341","citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:4d9a5b060c30e7dc8310c9ce30650d1304357b88811cc2f5845b4a0badaeb4a9","observation_id":"4c833d4f-dbca-42ae-8623-aacf5d5cee7f","resolution":{"observed_at":"2026-08-12T19:23:16.843506Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.431690Z","title":"Imagere- ward: Learning and evaluating human preferences for text- to-image generation, 2023","venue":null,"work_id":"3c3ba7dc-9510-4928-b1bc-1d7a0686af57","year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.888896Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:a09268a5451e420a25234c7b15201901f01f14143b0c5e69ae75f220eb90ae22","observation_id":"04470fee-b319-44d8-a2dd-61b9d9246f40","resolution":{"observed_at":"2026-08-12T19:23:17.435522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.423152Z","title":"Fast sampling of dif- fusion models with exponential integrator","venue":null,"work_id":"63de1325-6bec-4817-9db3-25b1bac1316f","year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.893380Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:484982b59b107910f0c796844f178f2b6fe700a33cfc0afac929bdba44fa1781","observation_id":"d476a6ab-9b33-4713-b2dc-55f0ad8987ab","resolution":{"observed_at":"2026-08-12T19:23:17.425760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.380354Z","title":"Exploring self-attention for image recognition","venue":null,"work_id":"c0e6de5a-ce04-4987-a652-f1ffee4182b0","year":2020},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.896034Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:ddafaa557b866864971c9fa545903ed9e3fb04889a5c396bdff29e417791a9a2","observation_id":"664e5a1a-3d0d-4a26-a673-ca4dfb0e1428","resolution":{"observed_at":"2026-08-12T19:23:17.416375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.347136Z","title":"Dpm- solver-v3: Improved diffusion ode solver with empirical model statistics","venue":null,"work_id":"58ac44b5-5c3a-455e-a8d8-1a1ee8f51f64","year":2023},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.899003Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:a8567a9640bb65c532278506dffe942b83e8ed01c12d46f6d45f54ca57360330","observation_id":"ce95bff6-f0db-4e05-b98b-96b9c16df326","resolution":{"observed_at":"2026-08-12T19:23:17.351560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.331435Z","title":null,"venue":null,"work_id":"7170bd67-62fc-4fc8-89c7-3ac6cb5c8946","year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.903384Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:5d2ad58e1254a226027e8ba9ea956b331c70b7c845e2bcbcf949c636763fbfa9","observation_id":"41fc00f4-01e6-4286-8a74-08a07e9ffa3e","resolution":{"observed_at":"2026-08-12T19:23:17.339078Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:17.300216Z","title":null,"venue":null,"work_id":"907498c2-4b16-4ddc-aa37-e5e6a3e40880","year":null},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.906241Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:0210f02e9555f2ea0c7b022c46ab45da8feaa2107cf97c132eb7895b2d6e2770","observation_id":"d96a7b7c-4aa2-4d1f-9484-254c0c450cf1","resolution":{"observed_at":"2026-08-12T19:23:17.315558Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"0166.1017","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T19:23:16.979220Z","title":null,"venue":null,"work_id":"26212720-06ed-4cdf-8faf-643bd3a74273","year":1949},"citing_paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T19:23:16.909720Z"},"links":{"citing_paper":"/paper/2411.10781"},"observation_digest":"sha256:a97b1cc4cad96ad6612f40e22e20a2732f1bf7c9f607a74c03317204f7c51440","observation_id":"d4dfc0b0-a77d-459d-83da-da13d6066890","resolution":{"observed_at":"2026-08-12T19:23:17.011626Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.10781","last_updated":"2025-02-27T13:51:10Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T21:24:33.591460Z","submitted_at":"2024-11-16T11:51:33Z","title":"Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":0,"verified_fuzzy":25},"total_outbound_references":57},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 2 inbound Pith citation observations for arXiv:2411.10781."}