{"as_of":"2026-08-08T23:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:eadc7fa97901d7bc7e1adcaace61f58568a292d432206af440d054c84cb6c453","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":"2026-08-04T11:08:05.138583Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2510.06928/citation-record","integrity":"/paper/2510.06928/integrity","json":"/paper/2510.06928/citation-record.json","paper":"/paper/2510.06928"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T11:07:57.678343Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:57.678343Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:3ef45034e9e854938b7c1fcd758e7b4a5b1809611f546a4a8b2451bd36f3d35d","observation_id":"049e532f-322e-46a0-9149-4b3ca1dcc867","resolution":{"observed_at":"2026-08-04T11:07:57.678343Z","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":"2026-08-04T11:07:57.835177Z","title":"Generative adversarial networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:57.835177Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:e48e2b98ed4ee1c45eadfb070231ec7f0aacb172730107029fd2857c0295388c","observation_id":"817fc176-9f49-44dd-8b3b-cb063ec2393f","resolution":{"observed_at":"2026-08-04T11:07:57.835177Z","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":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-10T17:59:52Z","title":"Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06525","snapshot_observed_at":"2026-08-04T11:07:58.021332Z","title":"Autoregressive model beats diffusion: Llama for scalable image generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:58.021332Z"},"links":{"cited_paper":"/paper/2406.06525","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:6157b8b02c9bec32ae351fa52ec0ae0e0084977b0a564ea3022cd8e24f956776","observation_id":"fc6d9cc3-8c20-474c-a676-7d08bceea912","resolution":{"observed_at":"2026-08-04T11:07:58.021332Z","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":"2026-08-04T11:07:58.213585Z","title":"Visual autoregressive modeling: Scalable image generation via next-scale prediction,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:58.213585Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:06c9a43049ca31b47d9f2ae2c6a74f85c82454ffafb827b987c0a7e460400332","observation_id":"2e6ea523-e037-4a4f-aff3-18cdda40f4bb","resolution":{"observed_at":"2026-08-04T11:07:58.213585Z","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":"2026-08-04T11:07:58.380273Z","title":"Maskgit: Masked generative image transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:58.380273Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:0b4fd6c87dd1022fecfacad62717870aa92784c378099d64f01dec1a7f945135","observation_id":"381eadbb-4f8c-4b4c-82b0-ccaa6ede6780","resolution":{"observed_at":"2026-08-04T11:07:58.380273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.08261","last_updated":"2025-03-13T15:09:10Z","snapshot_observed_at":"2026-07-06T19:31:23.072307Z","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":"2026-08-04T11:07:58.472324Z","title":"Meissonic: Revitalizing masked generative transformers for efficient high-resolution text-to-image synthesis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:58.472324Z"},"links":{"cited_paper":"/paper/2410.08261","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:ebadfb7b2f75d44c049f97e68ae17ec1a873ff441bce8f2a4e1b7d9655333724","observation_id":"5a085bde-fa9e-46be-82c3-6fa89a314576","resolution":{"observed_at":"2026-08-04T11:07:58.472324Z","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":"2026-08-04T11:07:58.563464Z","title":"Language models are unsupervised multitask learners,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:58.563464Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:58fb3f8dc600f84fc0d773a3c6c7d18c4ba9dca762ecb06360935d287f86c837","observation_id":"c0d69211-7e13-4d99-8f49-3c55d52e8b1f","resolution":{"observed_at":"2026-08-04T11:07:58.563464Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-04T11:07:58.657610Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:58.657610Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:6a617882d7cb9e81ab5044662095f1c7e7d863894a3a94ba983766c944093bdb","observation_id":"141fef8d-5b33-4383-83b1-5affbc034a69","resolution":{"observed_at":"2026-08-04T11:07:58.657610Z","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":"2026-08-04T11:07:58.804101Z","title":"Improving autoregressive visual generation with cluster- oriented token prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:58.804101Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:c3570c2ec3d017f07c662102ed82a7761a5312ab182b496a50560e161e13f522","observation_id":"73bd5896-2478-4386-a3d0-946df4c9c1c6","resolution":{"observed_at":"2026-08-04T11:07:58.804101Z","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":"2026-08-04T11:07:59.011255Z","title":"Neural discrete representation learning,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.011255Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:0a1f5edd5d4ce7c20f97f94946fbb3002c478df79a892216ac55f3f1827e0d46","observation_id":"74c07eca-867c-4cd3-a99d-5d2cacdbc4e2","resolution":{"observed_at":"2026-08-04T11:07:59.011255Z","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":"2026-08-04T11:07:59.175824Z","title":"Taming transformers for high-resolution image synthesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.175824Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:19fb248feb419570db8c0d434998b985c39258fd459c74a675aa8b473d1a0138","observation_id":"4d902f05-6f1a-413c-964a-abaab4025f38","resolution":{"observed_at":"2026-08-04T11:07:59.175824Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04627","snapshot_observed_at":"2026-08-04T11:07:59.306875Z","title":"Vector-quantized image modeling with improved vqgan,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.306875Z"},"links":{"cited_paper":"/paper/2110.04627","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:a528f71fe71d245e6c400c1f04d22961916b3c3fe6461325d0736d29c295c06f","observation_id":"c59bdd39-6e8e-4d3b-b20b-5de37ae60c6b","resolution":{"observed_at":"2026-08-04T11:07:59.306875Z","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":"2026-08-04T11:07:59.410546Z","title":"Autoregressive image generation using residual quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.410546Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:42344bd2e3cf7cf227d8d941db47aff2979a38f57bbfc29324c72e4cefba969b","observation_id":"d7de1890-d585-4e75-bf32-b7df17ec1747","resolution":{"observed_at":"2026-08-04T11:07:59.410546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.16681","last_updated":"2024-11-27T17:04:36Z","snapshot_observed_at":"2026-07-06T19:56:43.637339Z","submitted_at":"2024-11-25T18:59:53Z","title":"Factorized Visual Tokenization and Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.16681","snapshot_observed_at":"2026-08-04T11:07:59.517713Z","title":"Factorized visual tokenization and generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.517713Z"},"links":{"cited_paper":"/paper/2411.16681","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:a33a9256a7278ac15af29df3dad1088faf36acc27f38a40420137125a390454c","observation_id":"2400ffc2-4e56-4b21-8087-6ff3ed36557e","resolution":{"observed_at":"2026-08-04T11:07:59.517713Z","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":"2026-08-04T11:07:59.619389Z","title":"Unitok: A unified tokenizer for visual generation and understanding,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.619389Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:48f556c5eb13c7b2f49978d962edf5d17fc83f935924e9d3280b5f26064f2798","observation_id":"8bc022df-8025-437d-83e4-13865dcab879","resolution":{"observed_at":"2026-08-04T11:07:59.619389Z","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":"2026-08-04T11:07:59.708123Z","title":"Tokenflow: Unified image tokenizer for multimodal understanding and generation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.708123Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:4ac6ea01540fa103bebb56d4b97879b5dad306f5960c824b96aaf9e5aa60139a","observation_id":"fcf17e71-158a-43e9-9532-e21e9ff67449","resolution":{"observed_at":"2026-08-04T11:07:59.708123Z","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":"2026-08-04T11:07:59.788744Z","title":"Magvit: Masked generative video transformer,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.788744Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:0f93ea7fa6f61d19b0fc9bcdf861fdff1d7015e4b8ad928d47f081ea63f13641","observation_id":"dbf25c43-b95b-49e3-9629-48b7949e0184","resolution":{"observed_at":"2026-08-04T11:07:59.788744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.04410","last_updated":"2025-02-09T08:59:19Z","snapshot_observed_at":"2026-08-06T03:43:29.067278Z","submitted_at":"2024-09-06T17:14:53Z","title":"Open-MAGVIT2: An Open-Source Project Toward Democratizing Auto-regressive Visual Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.04410","snapshot_observed_at":"2026-08-04T11:07:59.892644Z","title":"Open- magvit2: An open-source project toward democratizing auto- regressive visual generation,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.892644Z"},"links":{"cited_paper":"/paper/2409.04410","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:bf3117f2db73fa68ea53f615c9e5e5868e887417d340fb68c5f5abbdb1edfdc7","observation_id":"e4be6a38-5275-463f-bdea-3801fea22e07","resolution":{"observed_at":"2026-08-04T11:07:59.892644Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14324","last_updated":"2026-04-20T17:55:12Z","snapshot_observed_at":"2026-08-02T12:34:37.994590Z","submitted_at":"2025-03-18T14:56:46Z","title":"DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14324","snapshot_observed_at":"2026-08-04T11:07:59.996610Z","title":"Dualtoken: Towards unifying visual understanding and generation with dual visual vocabularies,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T11:07:59.996610Z"},"links":{"cited_paper":"/paper/2503.14324","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:df3ec282b8be8e6759fc1130961e50ab8f3179909e1547a31fcf6ab84ca26867","observation_id":"1cd94d0a-20d1-43da-9808-d94a8aaace40","resolution":{"observed_at":"2026-08-04T11:07:59.996610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1411.1784","last_updated":"2014-11-06T22:33:22Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-11-06T22:33:22Z","title":"Conditional Generative Adversarial Nets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.1784","snapshot_observed_at":"2026-08-04T11:08:00.147719Z","title":"Conditional generative adversarial nets,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.147719Z"},"links":{"cited_paper":"/paper/1411.1784","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:7888594f402166e40dfea2fae713d092b1a6f20e38c674389c098d9ffa2c3961","observation_id":"d2b1ef64-4928-4254-bae3-e9df120c4756","resolution":{"observed_at":"2026-08-04T11:08:00.147719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06434","last_updated":"2016-01-07T23:09:39Z","snapshot_observed_at":"2026-08-04T04:34:27.861448Z","submitted_at":"2015-11-19T22:50:32Z","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06434","snapshot_observed_at":"2026-08-04T11:08:00.220632Z","title":"Unsupervised represen- tation learning with deep convolutional generative adversarial networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.220632Z"},"links":{"cited_paper":"/paper/1511.06434","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:a836b54c2ee218def00edc098f8c771938efe308dda51d68fda0b19ffc3880ab","observation_id":"a99e9f13-352a-4056-a186-8e662886d8eb","resolution":{"observed_at":"2026-08-04T11:08:00.220632Z","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":"2026-08-04T11:08:00.290489Z","title":"Improved techniques for training gans,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.290489Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:fc97c96615c330a6d5c2c0d97d07b7f0220b020b5aa3829948e6ecb92ea04ad5","observation_id":"b4357031-547e-4ee6-aa1d-c2bc99a4a801","resolution":{"observed_at":"2026-08-04T11:08:00.290489Z","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":"2026-08-04T11:08:00.363392Z","title":"Image-to-image translation with conditional adversarial networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.363392Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:db609bd62f1af34741c8164439564bd5a20235567f29f616eb7e96ae5816da4a","observation_id":"c3006ce8-63a8-41ee-8724-1a73aebd4b21","resolution":{"observed_at":"2026-08-04T11:08:00.363392Z","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":"2026-08-04T11:08:00.462818Z","title":"Unpaired image-to- image translation using cycle-consistent adversarial networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.462818Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:2514083c56709a99e07cc7db874ee771fbba77be54f0c2f623cd298e7d6c1bbb","observation_id":"092b3bfd-0ff3-45e4-b28e-40d641a9dde5","resolution":{"observed_at":"2026-08-04T11:08:00.462818Z","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":"2026-08-04T11:08:00.560643Z","title":"A style-based generator archi- tecture for generative adversarial networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.560643Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:b2e6d917a2d5eaa30e69e3997dbdb2b75e70d5e79295815842d0a84ea3bc367f","observation_id":"8c98fdfb-c6b6-48e2-bdb7-499590fe307a","resolution":{"observed_at":"2026-08-04T11:08:00.560643Z","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":"2026-08-04T11:08:00.653459Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.653459Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:27ddf27ad35f68e8fa0a43752a91f23dff933f68d8189f69f012c75f9260ecda","observation_id":"6c2103d3-d86c-4730-ab43-f127b5b51517","resolution":{"observed_at":"2026-08-04T11:08:00.653459Z","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":"2026-08-04T11:08:00.743523Z","title":"Improved denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.743523Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:2ec777f42a322e92b93e05bbc26c03fc998e3630d77d166d92fd8461150cc220","observation_id":"d37b2c90-e42a-4876-8925-beecaa522797","resolution":{"observed_at":"2026-08-04T11:08:00.743523Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-04T11:08:00.896222Z","title":"Denoising diffusion implicit models,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:00.896222Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:57052d5fdb19ace626633b1f407c795979497b2cf495de471d9bae49e46580b5","observation_id":"69cc972d-34fe-4598-9dd2-29c50472995a","resolution":{"observed_at":"2026-08-04T11:08:00.896222Z","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":"2026-08-04T11:08:01.006082Z","title":"Diffusion models beat gans on image synthesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:01.006082Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:b720b209258582551777b335cef41a5e4a05b357afc64fdb73b0d89094f819d7","observation_id":"e0ab263b-f96f-4207-b143-523d59cbbf59","resolution":{"observed_at":"2026-08-04T11:08:01.006082Z","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":"2026-08-04T11:08:01.092735Z","title":"Photorealistic text-to-image diffusion models with deep language understanding,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:01.092735Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:2396c9d99ff8a1364d5eb7d205b66e4e6be567a40a052e1bc8133f0d113209d1","observation_id":"8059375d-323a-4a5b-a22a-22e20179b6eb","resolution":{"observed_at":"2026-08-04T11:08:01.092735Z","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":"2026-08-04T11:08:01.271129Z","title":"High-resolution image synthesis with latent diffusion models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:01.271129Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:e290cb1fff0084c67e3d29386a1d13e79a90cc587e43c143ab46763eea7789c3","observation_id":"e0addd0a-fb11-453f-8e64-068b52f13a65","resolution":{"observed_at":"2026-08-04T11:08:01.271129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-04T11:08:01.525490Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:01.525490Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:32ac6e3eaa327415610a235ff4c450ce98ee1722f99581a98cc433ac253c69e0","observation_id":"446dc884-0c04-4bea-9fb6-69cfff0f61f9","resolution":{"observed_at":"2026-08-04T11:08:01.525490Z","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":"2026-08-04T11:08:01.775661Z","title":"Zero-shot text-to-image generation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:01.775661Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:9194c12110629f2b19d85e86ddf14c44f56a90931e9959c627e59d5c65f43367","observation_id":"201511b3-627a-4413-a899-39a702aa962b","resolution":{"observed_at":"2026-08-04T11:08:01.775661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.10789","last_updated":"2022-06-22T01:11:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-06-22T01:11:29Z","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.10789","snapshot_observed_at":"2026-08-04T11:08:01.939270Z","title":"Scaling autoregressive models for content-rich text-to-image generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:01.939270Z"},"links":{"cited_paper":"/paper/2206.10789","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:99203f9abe11385a308e90956d7aa1db50804e878a8dd8d83aec90b53fdff236","observation_id":"1c0875f4-5e9b-44e3-b2cb-6dfe9f159174","resolution":{"observed_at":"2026-08-04T11:08:01.939270Z","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":"2026-08-04T11:08:02.036688Z","title":"Llama: Open and efficient foundation language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:02.036688Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:0c59e8eb05599075f966d3374f09516e8f66ec1d0c1e0132e882fcccebbfcc94","observation_id":"3af00f49-1437-462d-b75b-f8ec10a8c087","resolution":{"observed_at":"2026-08-04T11:08:02.036688Z","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":"2026-08-04T11:08:02.207818Z","title":"Maskgit: Masked generative image transformer,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:02.207818Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:cdd6113c5b14836ab1f02b81af089b83305bf49c47cfa4d46f6ad945f65f49c3","observation_id":"137b03ea-6317-4dde-8497-24c62edf34f4","resolution":{"observed_at":"2026-08-04T11:08:02.207818Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00704","last_updated":"2023-01-02T14:43:38Z","snapshot_observed_at":"2026-08-03T00:33:47.397969Z","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":"2026-08-04T11:08:02.386459Z","title":"Muse: Text-to-image generation via masked generative trans- formers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:02.386459Z"},"links":{"cited_paper":"/paper/2301.00704","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:eb37ebd6d999d5a7af1361170689a7641740801cd4c0cb5f44cc6da563cc99b3","observation_id":"5b9524c7-760c-40a4-b39b-46c5de90dd0f","resolution":{"observed_at":"2026-08-04T11:08:02.386459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05737","last_updated":"2024-03-29T17:44:41Z","snapshot_observed_at":"2026-08-02T18:23:02.746177Z","submitted_at":"2023-10-09T14:10:29Z","title":"Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05737","snapshot_observed_at":"2026-08-04T11:08:02.602491Z","title":"Language model beats diffusion–tokenizer is key to visual generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:02.602491Z"},"links":{"cited_paper":"/paper/2310.05737","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:9a1bff51fd6f6e1c517691e52b15a64a722969684f01a01e4342932b4db03a6e","observation_id":"ed0b44ed-8e16-4de4-b403-31258fdda2e3","resolution":{"observed_at":"2026-08-04T11:08:02.602491Z","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":"2026-08-04T11:08:02.749548Z","title":"Taming transformers for high-resolution image synthesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:02.749548Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:ce2b363be79857dad981836cc7a3a8f1d4aaa9eccad3c8d78de85a8ac7acf2f8","observation_id":"84a294b4-809a-4dc0-8c1a-ffc9b8121f82","resolution":{"observed_at":"2026-08-04T11:08:02.749548Z","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":"2026-08-04T11:08:02.908070Z","title":"The unreasonable effectiveness of deep features as a perceptual metric,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:02.908070Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:c29d2ab9cacca0202bdd23c4fbcc3d115eb81a2ea240ea1433cf408096cf59bd","observation_id":"19a32505-eb1f-4aee-9915-60d2abf49c0a","resolution":{"observed_at":"2026-08-04T11:08:02.908070Z","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":"2026-08-04T11:08:03.061359Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:03.061359Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:537fb12771019c58035f5da515fb2e1348877defb5cf4e0904b43e49deab11ec","observation_id":"ced8aee8-81e9-4dae-988f-d6370fe23285","resolution":{"observed_at":"2026-08-04T11:08:03.061359Z","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":"2026-08-04T11:08:03.191412Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:03.191412Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:6528676465832f1bcd2c6effd08a6633283040df07349f0ff3b198ef20c05249","observation_id":"c20957e1-e251-468c-a243-c0cb5cfe5472","resolution":{"observed_at":"2026-08-04T11:08:03.191412Z","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":"2026-08-04T11:08:03.327397Z","title":"Improved techniques for training gans,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:03.327397Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:1f3bcba6dfcabdcad233fac855d6b246c56898dc7aecdc8c3928388ae83cb952","observation_id":"25664f64-3df2-4c82-8da6-723bdf38c6d7","resolution":{"observed_at":"2026-08-04T11:08:03.327397Z","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":"2026-08-04T11:08:03.465430Z","title":"Improved precision and recall metric for assessing generative models,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:03.465430Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:6960c3d9b0baa013415d487391a47427b200bbaed36282169f2f4e11d811e821","observation_id":"c19836a4-bdd3-402a-8527-fdecc91f943a","resolution":{"observed_at":"2026-08-04T11:08:03.465430Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.11096","last_updated":"2019-02-25T21:32:06Z","snapshot_observed_at":"2026-07-06T07:04:57.275371Z","submitted_at":"2018-09-28T15:38:49Z","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.11096","snapshot_observed_at":"2026-08-04T11:08:03.625805Z","title":"Large scale gan training for high fidelity natural image synthesis,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:03.625805Z"},"links":{"cited_paper":"/paper/1809.11096","citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:2b3c0493a0922c4397b12a00dc0fc8b7720d317423c3dd40743d423191b9dcd6","observation_id":"1a3f05f8-1d4a-4865-bf52-35b27eb85039","resolution":{"observed_at":"2026-08-04T11:08:03.625805Z","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":"2026-08-04T11:08:03.743292Z","title":"Scaling up gans for text-to-image synthesis,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:03.743292Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:2e3c695d853e6f5c6ff9b42189a9f7b2342ffe29ebaf3a8f423bdba613b0f639","observation_id":"cbb8200f-8e06-4c83-8a57-35b14393a5fa","resolution":{"observed_at":"2026-08-04T11:08:03.743292Z","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":"2026-08-04T11:08:03.844318Z","title":"Stylegan-xl: Scaling stylegan to large diverse datasets,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:03.844318Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:dd06df446f9182805db7ba581ea1c07c9da63d2acade99d4f10530aa9db2744f","observation_id":"0b47daef-fd94-4808-b910-d215bafb59ea","resolution":{"observed_at":"2026-08-04T11:08:03.844318Z","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":"2026-08-04T11:08:03.942201Z","title":"Diffusion models beat gans on image synthesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:03.942201Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:3bb763c5e8e6c6568d354015a07c1873869acb441becbc3efce6600f157c28c3","observation_id":"8dafaa0f-355e-488e-addd-7fd7fe875638","resolution":{"observed_at":"2026-08-04T11:08:03.942201Z","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":"2026-08-04T11:08:04.039939Z","title":"Cascaded diffusion models for high fidelity image generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:04.039939Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:aa19e388382c7929cc2527fcfa319f27197c39f5600c926a09ab22601f1c1d03","observation_id":"5b12bb73-fe92-415b-976c-136c7bd1e58d","resolution":{"observed_at":"2026-08-04T11:08:04.039939Z","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":"2026-08-04T11:08:04.135330Z","title":"High-resolution image synthesis with latent diffusion models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:04.135330Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:a2e20c4f8064f652234377234a699a8c4209b1bcbb2be3d686382e929b9f81af","observation_id":"519013a7-8363-4b70-ac48-d88bddf7a157","resolution":{"observed_at":"2026-08-04T11:08:04.135330Z","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":"2026-08-04T11:08:04.265110Z","title":"Scalable diffusion models with transform- ers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:04.265110Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:7af5b214072b790f5dbe7cac1b91794e0fcb07268b17c2380f25f92982cebdeb","observation_id":"0f77ae0a-6dde-4096-9966-1c126c8eb854","resolution":{"observed_at":"2026-08-04T11:08:04.265110Z","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":"2026-08-04T11:08:04.359012Z","title":"Vector-quantized image modeling with improved vqgan,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:04.359012Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:a07dcd5b9541f8cc94cce3921ab3052b6bf2024b582099875c0a9f77c90de758","observation_id":"1a434572-3206-4f60-8afb-ff5335379d42","resolution":{"observed_at":"2026-08-04T11:08:04.359012Z","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":"2026-08-04T11:08:04.515888Z","title":"Autoregressive image generation using residual quantization,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:04.515888Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:da9eff1d4478f5f3f9aea59287927140b014c4ba48a228b56ea960a64f15e355","observation_id":"d36aaa9c-ee82-427a-ba0f-7ee6494dbf8e","resolution":{"observed_at":"2026-08-04T11:08:04.515888Z","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":"2026-08-04T11:08:04.674653Z","title":"Min- ing top-k frequent itemsets through progressive sampling,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:04.674653Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:659cf54849a6a4f6c7f64c0a27c30471cc3886ec97ed8698c1f75bae394f8395","observation_id":"80e58c32-d972-4274-bc27-275e71a2ebe5","resolution":{"observed_at":"2026-08-04T11:08:04.674653Z","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":"2026-08-04T11:08:04.839950Z","title":"The curious case of neural text degeneration,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:04.839950Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:296188336af645a6745953a20c7e11accedce1837285713a29353e96f118ac0b","observation_id":"6b0eb96c-f1e1-4e55-8433-38883a9593d8","resolution":{"observed_at":"2026-08-04T11:08:04.839950Z","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":"2026-08-04T11:08:04.975467Z","title":"A learning algorithm for boltzmann machines,","venue":null,"work_id":null,"year":1985},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:04.975467Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:525780bbd23752761e5941e4d024403c92a6ba1ca563aa5b6fba942cd2d7945b","observation_id":"c69697e7-4a6a-494f-80a9-e67315609030","resolution":{"observed_at":"2026-08-04T11:08:04.975467Z","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":"2026-08-04T11:08:05.138583Z","title":"Synthetic literature: Writing science fiction in a co-creative process,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-04T11:08:05.138583Z"},"links":{"citing_paper":"/paper/2510.06928"},"observation_digest":"sha256:8bff2dfd1a0f8aa49ec9833a7daf6ba1431a0f2115864a96f5a66e105bab2ca3","observation_id":"bcc94a5e-70b7-41c4-b80b-063ef30bb31f","resolution":{"observed_at":"2026-08-04T11:08:05.138583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2510.06928","last_updated":"2026-05-27T05:34:49Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-08T18:27:28.960401Z","submitted_at":"2025-10-08T12:08:21Z","title":"IAR2: Improving Autoregressive Visual Generation with Semantic-Detail Associated Token Prediction"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":57,"verified_exact":0,"verified_fuzzy":0},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2510.06928."}