{"as_of":"2026-08-05T14:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:64f4d1632f36d293859c8824c9ec43e37ff738074f0bef8bb34017e71bf69eea","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T04:58:23.066578Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+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/2606.27978/citation-record","integrity":"/paper/2606.27978/integrity","json":"/paper/2606.27978/citation-record.json","paper":"/paper/2606.27978"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T04:58:23.066578Z","title":"Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:3e56c22c6262811bfeb195ff88b30c1e382a4b523238258b8a8defbb03284d7b","observation_id":"90d23192-46b7-4356-86ff-c485e5ed208a","resolution":{"observed_at":"2026-06-29T04:58:23.066578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.07963","last_updated":"2025-04-10T17:59:56Z","snapshot_observed_at":"2026-08-04T06:26:36.498042Z","submitted_at":"2025-04-10T17:59:56Z","title":"PixelFlow: Pixel-Space Generative Models with Flow","version":1},"cited_work":{"arxiv_id":"2504.07963","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.07963","snapshot_observed_at":"2026-07-04T20:50:11.498748Z","title":"arXiv preprint arXiv:2504.07963 (2025)","venue":null,"work_id":"1beb1a44-0887-4a87-93c8-5dca148563bd","year":2025},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2504.07963","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:adb0e55c215c9da34d7d95247b622b6a79bf922bef45955a2c77f381cb38513b","observation_id":"cad54187-286f-419d-93e8-553daec3db32","resolution":{"observed_at":"2026-06-29T19:03:52.176135Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:bdb2f287b306c5f0c1d016d3dda111bcc2d817f14992724d7e6d407c998d4364","observation_id":"fc363d8c-9c35-4b26-8117-143339b2f1dd","resolution":{"observed_at":"2026-06-29T19:03:52.192512Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2511.13720","last_updated":"2026-01-07T05:36:57Z","snapshot_observed_at":"2026-07-06T22:36:08.495952Z","submitted_at":"2025-11-17T18:59:57Z","title":"Back to Basics: Let Denoising Generative Models Denoise","version":2},"cited_work":{"arxiv_id":"2511.13720","doi":"10.48550/arxiv.2511.13720","metadata_source":"pith","pith_arxiv_id":"2511.13720","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Back to Basics: Let Denoising Generative Models Denoise","venue":"cs.CV","work_id":"37973de8-a5e6-4d92-897b-a98fa9f7f2f3","year":2025},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2511.13720","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:71ed12ba7552ecb0a2e9477bdca6384fb35d6bf7583ab7e52a64ac9e8b9223a2","observation_id":"04a4d078-1c43-4674-a6f9-125cc3d5c7b5","resolution":{"observed_at":"2026-06-29T19:03:52.200152Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.17437","last_updated":"2025-02-25T14:28:34Z","snapshot_observed_at":"2026-07-06T20:41:51.646955Z","submitted_at":"2025-02-24T18:59:56Z","title":"Fractal Generative Models","version":2},"cited_work":{"arxiv_id":"2502.17437","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.17437","snapshot_observed_at":"2026-06-30T19:35:00.986893Z","title":"Fractal generative models","venue":null,"work_id":"807c7376-ba8b-4926-90d3-c7316349b40b","year":2025},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2502.17437","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:51b618d1fd2a22e55e7712dfd68d711a34e58e04a1c306faa277add0b8e01ae8","observation_id":"8215ed3b-f68c-4847-82c4-f3e1ccb927ae","resolution":{"observed_at":"2026-06-29T19:03:52.190127Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2209.03003","doi":"10.48550/arxiv.2209.03003","metadata_source":"pith","pith_arxiv_id":"2209.03003","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","venue":"cs.LG","work_id":"a1989e1b-d66d-4533-be3a-fb9c5fd62290","year":2022},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2209.03003","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:f8db5c98df4d6caba46e9bbe49199f9173d0d1e5e7eab47d2cd58f7e83854aad","observation_id":"b579ae4e-7eee-4560-bec8-3f8c4228a7bf","resolution":{"observed_at":"2026-06-29T19:03:52.197484Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-11T22:49:29.244251+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T22:49:29.244251+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":"1711.05101","doi":"10.1137/1.9781611972825.47","metadata_source":"pith","pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Decoupled Weight Decay Regularization","venue":"cs.LG","work_id":"07ef7360-d385-4033-83f7-8384a6325204","year":2017},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:df0a8887533d6c6b256efbcabf946eeb327ccbb975313eff67677dc3c006766f","observation_id":"cc74f7bf-aca8-458c-965e-0f26f548eeab","resolution":{"observed_at":"2026-06-29T19:03:52.192818Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2503.16278","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-29T19:03:52.183043Z","title":"Uni- 3dar: Unified 3d generation and understanding via autoregression on compressed spatial tokens","venue":null,"work_id":"598d4151-b16f-48f1-a2a1-41775842cb78","year":null},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:19e469b581d73e6cfca9da3b221fd92c7c1947cdfb3094cf78e392860dd1ac8b","observation_id":"d28ee7cd-d6a3-410d-863d-c6992944e5c8","resolution":{"observed_at":"2026-06-29T19:03:52.187105Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.08551","last_updated":"2025-05-27T05:07:56Z","snapshot_observed_at":"2026-08-05T05:08:56.833881Z","submitted_at":"2024-07-11T14:36:53Z","title":"Autoregressive Speech Synthesis without Vector Quantization","version":2},"cited_work":{"arxiv_id":"2407.08551","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.08551","snapshot_observed_at":"2026-06-29T19:03:52.201267Z","title":"Autoregressive speech synthesis without vector quantization","venue":null,"work_id":"575d89b8-5e74-44f6-819c-38e6e0c52a9f","year":2024},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2407.08551","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:6e9e66ccf3d8896af151d2c63bdc5979fda1be3013887c72d78c18f67dadbe85","observation_id":"c13c7250-02ea-497c-8330-040c204792c9","resolution":{"observed_at":"2026-06-29T19:03:52.202816Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05202","last_updated":"2020-02-12T19:57:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-02-12T19:57:13Z","title":"GLU Variants Improve Transformer","version":1},"cited_work":{"arxiv_id":"2002.05202","doi":"10.48550/arxiv.2002.05202","metadata_source":"pith","pith_arxiv_id":"2002.05202","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"GLU Variants Improve Transformer","venue":"cs.LG","work_id":"17d0763c-1016-41ab-a478-478e890765eb","year":2020},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2002.05202","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:d3552d1c0523e849b8d52ee37cfd6fba868a6cda4521a64af3e244fd9dce42ca","observation_id":"17538e1e-d20e-427b-abe3-291f7a6ad7d2","resolution":{"observed_at":"2026-06-29T19:03:52.178604Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-07-13T15:50:07.002485+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-13T15:50:07.002485+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06525","last_updated":"2024-06-10T17:59:52Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-10T17:59:52Z","title":"Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation","version":1},"cited_work":{"arxiv_id":"2406.06525","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.06525","snapshot_observed_at":"2026-07-10T11:37:03.266757Z","title":"Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation","venue":"cs.CV","work_id":"41efe203-9377-4c63-b1d6-e499cd6e46f6","year":2024},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2406.06525","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:7754d298e0bb91b86eb635e8869e535a6fc11498665d5a0278f28f992089ca41","observation_id":"682b1f2b-d92b-4f9d-8fc6-496217efd2fd","resolution":{"observed_at":"2026-06-29T19:03:52.195038Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.13211","last_updated":"2025-05-19T14:58:50Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-19T14:58:50Z","title":"MAGI-1: Autoregressive Video Generation at Scale","version":1},"cited_work":{"arxiv_id":"2505.13211","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.13211","snapshot_observed_at":"2026-07-10T01:46:41.063877Z","title":"MAGI-1: Autoregressive Video Generation at Scale","venue":"cs.CV","work_id":"25e8bd3d-e51c-43ae-8126-4ea6ecdb3321","year":2025},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2505.13211","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:35cfa4fb256322b778603bfc09db9bf0faffc6b7f47a03ddf426ad96509c045d","observation_id":"872c762f-f2b1-4f41-a771-c244ed291497","resolution":{"observed_at":"2026-06-29T19:03:52.169519Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19722","last_updated":"2025-05-19T15:26:21Z","snapshot_observed_at":"2026-08-02T07:10:00.001810Z","submitted_at":"2024-11-29T14:14:59Z","title":"JetFormer: An Autoregressive Generative Model of Raw Images and Text","version":2},"cited_work":{"arxiv_id":"2411.19722","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.19722","snapshot_observed_at":"2026-07-04T20:50:11.355561Z","title":"Jetformer: An autoregres- sive generative model of raw images and text","venue":null,"work_id":"9c4b50b2-965d-4159-85d4-b77b8d91ce72","year":2024},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2411.19722","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:881fb1b9945c3fc9dc30a791197b47150f5e4f4ecebecf2bd2b5e684b8397d23","observation_id":"8639a8e8-bfdf-495c-9d24-7ac1aa8a5726","resolution":{"observed_at":"2026-06-29T19:03:52.163282Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.23268","last_updated":"2025-08-04T02:46:11Z","snapshot_observed_at":"2026-08-02T00:59:17.582417Z","submitted_at":"2025-07-31T06:07:20Z","title":"PixNerd: Pixel Neural Field Diffusion","version":2},"cited_work":{"arxiv_id":"2507.23268","doi":"10.48550/arxiv.2507.23268","metadata_source":"arxiv_reference","pith_arxiv_id":"2507.23268","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Pixnerd: Pixel neural field diffusion","venue":"ArXiv.org","work_id":"74b9a257-28df-4dea-85a1-68d091aba6b8","year":2025},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2507.23268","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:a4c883a6af18714bd111294018ec09c504ee3e2121f81d91272c96e669b212a1","observation_id":"c598a02c-4799-443c-80ee-43fe9f1c2e98","resolution":{"observed_at":"2026-06-29T19:03:52.166667Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04627","last_updated":"2022-06-05T01:57:58Z","snapshot_observed_at":"2026-07-06T11:56:10.689708Z","submitted_at":"2021-10-09T18:36:00Z","title":"Vector-quantized Image Modeling with Improved VQGAN","version":3},"cited_work":{"arxiv_id":"2110.04627","doi":null,"metadata_source":"pith","pith_arxiv_id":"2110.04627","snapshot_observed_at":"2026-07-04T09:49:44.490632Z","title":"Vector-quantized Image Modeling with Improved VQGAN","venue":"cs.CV","work_id":"8ba56539-4766-42a4-868c-f9bef1281034","year":2021},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2110.04627","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:818523066851f89b5effc36f69aa0d688394bcb4a042bbffdac2624f07fe99c1","observation_id":"a1d09ca0-451c-4d3e-bbac-1d6837c7e98f","resolution":{"observed_at":"2026-06-29T19:03:52.178392Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.06940","last_updated":"2025-06-18T04:35:42Z","snapshot_observed_at":"2026-07-06T19:30:26.233621Z","submitted_at":"2024-10-09T14:34:53Z","title":"Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think","version":4},"cited_work":{"arxiv_id":"2410.06940","doi":"10.48550/arxiv.2410.06940","metadata_source":"pith","pith_arxiv_id":"2410.06940","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think","venue":"cs.CV","work_id":"1aff8ef8-079b-4afe-9e6a-148e6fd08e6a","year":2024},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2410.06940","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:c37815f607f7b384441c8a67c6c79ae72646188e6ac514d009165e9d3f554687","observation_id":"c6ce3766-0037-4599-8b9f-158aa42e238b","resolution":{"observed_at":"2026-06-29T19:03:52.195462Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"state":"measured"}},{"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":"2510.11690","doi":null,"metadata_source":"pith","pith_arxiv_id":"2510.11690","snapshot_observed_at":"2026-07-10T18:47:31.703880Z","title":"Diffusion Transformers with Representation Autoencoders","venue":"cs.CV","work_id":"c1a2d4de-4439-4005-8c56-e0124e4ed5fa","year":2025},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"cited_paper":"/paper/2510.11690","citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:91e54adb9267d59d06c187b2dd3aa69973b35b67ef2df620452aa02ca9ebcb93","observation_id":"5a2b551f-2059-4eaf-ba69-dc0f1817285e","resolution":{"observed_at":"2026-06-29T19:03:52.157950Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+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":"2026-06-29T04:58:23.066578Z","title":"Class conditioning is injected through K=16 learnable prefix tokens, obtained from an embedding table indexed by the class label and prepended to the patch sequence","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:4044a63027753b0de6a9e646d457c2f0e9093f4ae4ca2ca9e44a22e2e1264a28","observation_id":"0810648f-63cf-4e90-94d1-c8ebb19c4938","resolution":{"observed_at":"2026-06-29T04:58:23.066578Z","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":"2026-06-29T04:58:23.066578Z","title":"With probabilitypsample, a training example is selected for masking; within selected examples, each token is replaced by a learned mask embedding with probability ptoken","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-29T04:58:23.066578Z"},"links":{"citing_paper":"/paper/2606.27978"},"observation_digest":"sha256:af113f308a744965b79e447995bef1bac2ba0cbefe5c728f94bf7f9af7d7f0e6","observation_id":"6804b3cd-568a-4b83-bfd0-e87d1d3cf173","resolution":{"observed_at":"2026-06-29T04:58:23.066578Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.27978","last_updated":"2026-06-26T11:27:39Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-03T23:56:14.294002Z","submitted_at":"2026-06-26T11:27:39Z","title":"Parallel Rollout Approximation for Pixel-Space Autoregressive Image Generation"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":3,"verified_exact":15,"verified_fuzzy":0},"total_outbound_references":19},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 5 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2606.27978."}