{"as_of":"2026-08-09T21:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:369269039092383f9e7e460a74fd8d9de11325f1cb36fa532990d4c1642f7859","coverage":[{"denominator":79,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":79,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:25:44.520732Z","state":"measured"},{"denominator":98,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":98,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":19,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T12:10:08.475625Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T13:39:51.249678Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2506.15564","last_updated":"2025-09-22T01:24:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-18T15:39:15Z","title":"Show-o2: Improved Native Unified Multimodal Models","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-12T18:51:15.428692Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2506.15564"},"observation_digest":"sha256:28f509be944a38c13e4a230344982c51b28cc66a4b0ba1f5ac2fa055d13381d5","observation_id":"b1f74876-055f-44f3-90c4-18203316a66c","resolution":{"observed_at":"2026-05-12T18:51:16.085575Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-08-06T12:10:08.475625Z","title":"Oneig-bench: Omni-dimensional nuanced evaluation for image generation.arXiv preprint arxiv:2506.07977, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22058","last_updated":"2025-07-29T17:59:04Z","snapshot_observed_at":"2026-08-06T15:57:37.748671Z","submitted_at":"2025-07-29T17:59:04Z","title":"X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-06T12:10:08.475625Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2507.22058"},"observation_digest":"sha256:dee70f79117ebf097b80692ffe44c75ef0cb033398628df7798c1db5e234b8c7","observation_id":"fba26abe-b8c3-4469-9e75-f0cae9f0748d","resolution":{"observed_at":"2026-08-06T12:10:08.475625Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2508.02324","last_updated":"2025-08-04T11:49:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-08-04T11:49:20Z","title":"Qwen-Image Technical Report","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T14:29:06.883874Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2508.02324"},"observation_digest":"sha256:3704bf66b2a4cd54db6e82fdafe8bdd682877918cf760f44536d26866fb995d9","observation_id":"22f6b097-4e7f-41ab-9e57-d280bed86530","resolution":{"observed_at":"2026-05-10T14:29:06.936121Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-08-04T22:55:44.864389Z","title":"Jiuhai Chen, Zhiyang Xu, Xichen Pan, Yushi Hu, Can Qin, Tom Goldstein, Lifu Huang, Tianyi Zhou, Saining Xie, Silvio Savarese, et al","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.06945","last_updated":"2025-09-09T10:50:30Z","snapshot_observed_at":"2026-08-07T20:36:07.154043Z","submitted_at":"2025-09-08T17:56:23Z","title":"Interleaving Reasoning for Better Text-to-Image Generation","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-04T22:55:44.864389Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2509.06945"},"observation_digest":"sha256:024dad50129e5a964461ca647b42931de4e88cf3be48c8247ebf1441e843471c","observation_id":"c1c9beee-3b0d-41a7-af2f-2b5562734f09","resolution":{"observed_at":"2026-08-04T22:55:44.864389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2510.26583","last_updated":"2025-10-30T15:11:16Z","snapshot_observed_at":"2026-08-07T15:13:13.622286Z","submitted_at":"2025-10-30T15:11:16Z","title":"Emu3.5: Native Multimodal Models are World Learners","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-18T01:12:13.426640Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2510.26583"},"observation_digest":"sha256:731884525616283ab2a28cab2ecffa6415147e3d4707f06be0266bcfce557bb2","observation_id":"7074b797-ab45-45af-9c31-e98a5a625764","resolution":{"observed_at":"2026-05-18T01:12:13.675245Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2511.22699","last_updated":"2026-07-06T06:19:03Z","snapshot_observed_at":"2026-08-03T19:47:26.577384Z","submitted_at":"2025-11-27T18:52:07Z","title":"Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-11T14:08:36.801359Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2511.22699"},"observation_digest":"sha256:faf1df2114884ce3bc8f815309c9c72dce6f229efcab715574559a924b72923c","observation_id":"6a23d3a8-3df1-4f94-b84b-6199dfdfac30","resolution":{"observed_at":"2026-05-11T14:08:37.325689Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-08-03T19:47:30.934626Z","title":"Oneig-bench: Omni-dimensional nuanced evaluation for image generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2511.22699","last_updated":"2026-07-06T06:19:03Z","snapshot_observed_at":"2026-08-03T19:47:26.577384Z","submitted_at":"2025-11-27T18:52:07Z","title":"Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer","version":5},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T19:47:30.934626Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2511.22699"},"observation_digest":"sha256:6fd8a18ebe451f43fe4f139e466b764ab0105b8776a1d38495d1861cb1af6b7e","observation_id":"245f5d62-2469-4ceb-8d6f-5b2ee348c88f","resolution":{"observed_at":"2026-08-03T19:47:30.934626Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2604.12163","last_updated":"2026-04-14T00:43:23Z","snapshot_observed_at":"2026-08-02T18:33:05.096448Z","submitted_at":"2026-04-14T00:43:23Z","title":"Nucleus-Image: Sparse MoE for Image Generation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T15:30:46.994872Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2604.12163"},"observation_digest":"sha256:8135a27013b80d3e573794bf3369060c4e34f26bffd3cd4454f38fb545327d31","observation_id":"568bf610-c062-4a32-9e82-cbf03f3e8202","resolution":{"observed_at":"2026-05-11T10:26:00.452725Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2604.22302","last_updated":"2026-04-24T07:33:52Z","snapshot_observed_at":"2026-07-30T08:04:44.230327Z","submitted_at":"2026-04-24T07:33:52Z","title":"Knowledge Visualization: A Benchmark and Method for Knowledge-Intensive Text-to-Image Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-08T12:31:52.893480Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2604.22302"},"observation_digest":"sha256:27202a6a73d5bca534cbe5775de2c9f6a43d147d6e836f6fa8c6b1956a11f4ae","observation_id":"804c0179-ebd8-4a8c-a3e3-ba96c5e1152f","resolution":{"observed_at":"2026-05-11T19:06:12.389791Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2605.12500","last_updated":"2026-05-12T17:59:58Z","snapshot_observed_at":"2026-07-06T23:24:13.851504Z","submitted_at":"2026-05-12T17:59:58Z","title":"SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-13T05:12:37.339084Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2605.12500"},"observation_digest":"sha256:f9358689af776db0488aaff2bbeeac8e9f1250739bde50ec660455684014d48a","observation_id":"71d5c1ad-1931-4f39-988a-69d7aa632b8f","resolution":{"observed_at":"2026-05-13T05:17:18.581960Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2605.19839","last_updated":"2026-06-04T12:21:01Z","snapshot_observed_at":"2026-07-06T23:30:30.141040Z","submitted_at":"2026-05-19T13:35:11Z","title":"When Preference Labels Fall Short: Aligning Diffusion Models from Real Data","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-20T06:13:01.821585Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2605.19839"},"observation_digest":"sha256:5c89da626ba221eb790e13566abe085a1466f5e7549718310151cad95dd884a9","observation_id":"9f0355ce-c8f9-46f5-bd35-77f9a17a7aba","resolution":{"observed_at":"2026-05-20T06:13:05.219203Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2605.19839","last_updated":"2026-06-04T12:21:01Z","snapshot_observed_at":"2026-07-06T23:30:30.141040Z","submitted_at":"2026-05-19T13:35:11Z","title":"When Preference Labels Fall Short: Aligning Diffusion Models from Real Data","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T18:12:11.972394Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2605.19839"},"observation_digest":"sha256:dc6512d819d85ea678ec208ecfb390fddf01d3bd605ccb2f223457c338c893b0","observation_id":"5b6d85ca-6a43-4c61-b1b2-4ede8d1828a1","resolution":{"observed_at":"2026-06-30T18:14:59.858980Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2606.12575","last_updated":"2026-06-10T18:24:50Z","snapshot_observed_at":"2026-07-06T23:51:27.152983Z","submitted_at":"2026-06-10T18:24:50Z","title":"High-Fidelity Two-Step Image Generation via Teacher-Aligned End-to-End Distillation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-27T09:54:36.000245Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2606.12575"},"observation_digest":"sha256:33520adb2fc1b3ce7c87bc0d0f6150bb1eeccda4e52414fdd7cd3f909a17e7d3","observation_id":"cb589d4d-a51a-483f-837b-433042cf561f","resolution":{"observed_at":"2026-07-03T10:37:56.949559Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2606.13289","last_updated":"2026-06-11T12:46:07Z","snapshot_observed_at":"2026-08-02T10:42:01.559662Z","submitted_at":"2026-06-11T12:46:07Z","title":"HYDRA-X: Native Unified Multimodal Models with Holistic Visual Tokenizers","version":1},"reference_index":212,"source":"arxiv_source","source_observed_at":"2026-06-27T07:01:07.362430Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2606.13289"},"observation_digest":"sha256:824c7c5972238272e8bd85bc1240cac5f1208ec586a4e438c9510dbb67354030","observation_id":"80b153a3-d307-46a6-a802-f7767d3dddfc","resolution":{"observed_at":"2026-07-03T14:28:29.699718Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2606.20100","last_updated":"2026-06-18T11:20:05Z","snapshot_observed_at":"2026-08-05T19:44:52.217038Z","submitted_at":"2026-06-18T11:20:05Z","title":"WeGenBench: A Multidimensional Diagnostic Benchmark towards Text-to-Image Model Optimization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T17:54:09.656061Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2606.20100"},"observation_digest":"sha256:c349735fe5be12bfc0253f8818ca7927584948b18be0c33013aa0dc3beb28d33","observation_id":"d73c5fff-b468-4059-aea2-1df7d5fd5d0f","resolution":{"observed_at":"2026-07-04T03:39:29.338045Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2606.27089","last_updated":"2026-06-25T14:26:27Z","snapshot_observed_at":"2026-08-05T17:24:00.220087Z","submitted_at":"2026-06-25T14:26:27Z","title":"TMP: Tree-structured Mixed-policy Pruning for Large-scale Image Generation and Editing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T04:59:12.294502Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2606.27089"},"observation_digest":"sha256:172ac557beb324d6700b0fa92ec770fc5d67b2804cca5bbd7c45ae042d0c5af4","observation_id":"37bfc01f-92ce-4066-bd89-395025e787b6","resolution":{"observed_at":"2026-07-04T13:39:51.252294Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":"2506.07977","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-07-04T13:39:51.249678Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image generation","venue":null,"work_id":"1b87af32-6bef-4951-a841-9ea611693ca6","year":2025},"citing_paper":{"arxiv_id":"2607.01709","last_updated":"2026-07-02T05:00:40Z","snapshot_observed_at":"2026-07-07T00:07:13.555571Z","submitted_at":"2026-07-02T05:00:40Z","title":"COMFYCLAW: Self-Evolving Skill Harnesses for Image Generation Workflows","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-03T14:22:25.280140Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2607.01709"},"observation_digest":"sha256:f5ebd59894b3f36592604c9d1bf47ef88a51b0137c86c25ea5d82218033c47de","observation_id":"3700a9bf-6ff2-4cf6-8ab3-46ed487984da","resolution":{"observed_at":"2026-07-03T14:28:31.278327Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-08-02T06:13:51.220875Z","title":"Oneig-bench: Omni-dimensional nuanced evaluation for image generation, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.13125","last_updated":"2026-07-18T17:28:17Z","snapshot_observed_at":"2026-08-03T17:18:33.842709Z","submitted_at":"2026-07-14T17:52:05Z","title":"Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T06:13:51.220875Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2607.13125"},"observation_digest":"sha256:0a7e9f90c854dbcc2317020aafa2b3da791a09c7895f8c3d3ca85519b589f6e9","observation_id":"3bf47c79-a902-40c7-92ea-b1dd3384aabc","resolution":{"observed_at":"2026-08-02T06:13:51.220875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07977","snapshot_observed_at":"2026-08-01T00:57:36.484539Z","title":"Oneig-bench: Omni- dimensional nuanced evaluation for image gener- ation, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.26004","last_updated":"2026-07-28T17:20:00Z","snapshot_observed_at":"2026-08-06T23:03:28.359761Z","submitted_at":"2026-07-28T17:20:00Z","title":"Parallel Decoding Distillation for Fast Image and Video Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T00:57:36.484539Z"},"links":{"cited_paper":"/paper/2506.07977","citing_paper":"/paper/2607.26004"},"observation_digest":"sha256:ed8803811a5a0a5dbafd651b38076e76a9716945691cef65539bc74bbdd360d2","observation_id":"5f638d58-3ce3-4a7f-9114-9e0ae05d6ef8","resolution":{"observed_at":"2026-08-01T00:57:36.484539Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.07977/citation-record","integrity":"/paper/2506.07977/integrity","json":"/paper/2506.07977/citation-record.json","paper":"/paper/2506.07977"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:43.986102Z","title":"Vqa: Visual question answering","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:43.986102Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:6b2ee58e19ddf3947b76fa6b5784a1b32804ade88b03b8c2400b250d119d8e81","observation_id":"0224aeab-852f-406a-89d3-108203ddf297","resolution":{"observed_at":"2026-08-07T05:25:43.986102Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.12966","last_updated":"2023-10-13T02:41:28Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-08-24T17:59:17Z","title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.12966","snapshot_observed_at":"2026-08-07T05:25:43.991862Z","title":"Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:43.991862Z"},"links":{"cited_paper":"/paper/2308.12966","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:0cab594bddd2826c7a6c94a423a0dc07c9f73ce20b0c9a22cd7ef6390484f0d4","observation_id":"7e1ba31c-aae5-4556-b7b4-c9aabfa73a0d","resolution":{"observed_at":"2026-08-07T05:25:43.991862Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-08-07T05:25:44.003618Z","title":"Qwen2.5-vl technical report","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.003618Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:ebdf74eca5c8c8b347f4b02c6f4b2eec4c0b15aeff47c7fbbee5c58eb4e20dc1","observation_id":"a46aea39-57c0-4e85-bce0-3fd07e1b15ce","resolution":{"observed_at":"2026-08-07T05:25:44.003618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.772921Z","title":"Hrs-bench: Holistic, reliable and scalable benchmark for text-to-image models","venue":null,"work_id":"755c3ad5-7937-46d3-94c7-ad448ad0662b","year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.009084Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:b9e598dc6944efd9ccc6c24114613fe383fc60acfb746420fad69d0ebd4d2e7b","observation_id":"63ed52be-1d1e-4d98-9d6a-e6bdc28dfd8f","resolution":{"observed_at":"2026-08-07T05:25:45.778509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.756075Z","title":"The official api of flux-1.dev","venue":null,"work_id":"fb542dba-1bde-46d6-a95c-9572186a7acd","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.014278Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:3d95479ba05054a696ad260408d57c0a435ae82a5cee8f174fa8ddcd2f992743","observation_id":"bd62b77d-ddf4-4c43-a4ee-adfa7808a266","resolution":{"observed_at":"2026-08-07T05:25:45.761832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.739915Z","title":"Attend-and-excite: Attention- based semantic guidance for text-to-image diffusion models","venue":null,"work_id":"365bd9e5-07ba-4f55-afb9-9aece8354ed5","year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.019581Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:41107c16d5a0c09422b3b9c842b668c81efca654bcf5f1ce99878d5341c6d6d8","observation_id":"67ba8ebf-cff5-4887-bf2d-d277df26f5af","resolution":{"observed_at":"2026-08-07T05:25:45.744916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09568","last_updated":"2025-05-14T17:11:07Z","snapshot_observed_at":"2026-07-06T21:23:57.084147Z","submitted_at":"2025-05-14T17:11:07Z","title":"BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09568","snapshot_observed_at":"2026-08-07T05:25:44.024265Z","title":"Blip3-o: A family of fully open unified multimodal models-architecture, training and dataset","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.024265Z"},"links":{"cited_paper":"/paper/2505.09568","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:2d8639a6a98b765ba2fa35f2e45ea1f7ff852165ec97d99be391b68d72d8514c","observation_id":"75a3d805-a6d0-4caa-9a43-22a23b0c37a1","resolution":{"observed_at":"2026-08-07T05:25:44.024265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.723892Z","title":"Pixart-sigma: Weak-to-strong training of diffusion transformer for 4k text-to-image generation","venue":null,"work_id":"2429d3a2-77d2-4589-be7b-04d4e8b90af1","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.029143Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:ecada2af8759c6eae312093a61ed23ac45a6c3a9c03762eb93ebcb9d4b3074ab","observation_id":"3debd87b-aef4-4233-b21b-43f9fdf8d9bf","resolution":{"observed_at":"2026-08-07T05:25:45.728845Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00426","last_updated":"2023-12-29T16:42:08Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-30T16:18:00Z","title":"PixArt-$\\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00426","snapshot_observed_at":"2026-08-07T05:25:44.033958Z","title":"Pixart-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.033958Z"},"links":{"cited_paper":"/paper/2310.00426","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:cbf8c44939f97d5f6044d3bf0dc0168d64ece6884338cd09ef1a4b1fa49a083b","observation_id":"b09b4e0d-f642-4469-8b74-b38645309a4c","resolution":{"observed_at":"2026-08-07T05:25:44.033958Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.07155","last_updated":"2019-06-17T17:58:12Z","snapshot_observed_at":"2026-08-03T13:01:27.142233Z","submitted_at":"2019-06-17T17:58:12Z","title":"MMDetection: Open MMLab Detection Toolbox and Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.07155","snapshot_observed_at":"2026-08-07T05:25:44.040123Z","title":"Mmdetection: Open mmlab detection toolbox and benchmark","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.040123Z"},"links":{"cited_paper":"/paper/1906.07155","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:758d0401d16a0c978cb4ac0293f72797db933c30edeafdf6041d0a81bbf51150","observation_id":"28c36f47-fe3c-40e4-ad65-3c1f65b77c9a","resolution":{"observed_at":"2026-08-07T05:25:44.040123Z","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":"2026-08-07T05:25:44.045275Z","title":"Generative pretraining from pixels","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.045275Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:75674a42de4406de4aa335964d4684efb81b80357bc45a44037d03c32addb2b8","observation_id":"a21fb488-8122-4e43-a190-1cae4cb794f3","resolution":{"observed_at":"2026-08-07T05:25:44.045275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.17811","last_updated":"2025-01-29T18:00:19Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-29T18:00:19Z","title":"Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.17811","snapshot_observed_at":"2026-08-07T05:25:44.050469Z","title":"Janus-pro: Unified multimodal understanding and generation with data and model scaling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.050469Z"},"links":{"cited_paper":"/paper/2501.17811","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:3f1f05f58a6131631203ad2463a43df0d5b135265da6cb2162eae25493db5c07","observation_id":"dc1bd828-3e8e-4271-a2fa-ab5d84a8dd93","resolution":{"observed_at":"2026-08-07T05:25:44.050469Z","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":"2026-08-07T05:25:44.055680Z","title":"Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.055680Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:6fdbc38aace51ba1988e1893f435e4c499bbdf4a51623b1a8fbf5fc529163b82","observation_id":"deced6d7-3dd1-4d2c-b650-b344aa5d0619","resolution":{"observed_at":"2026-08-07T05:25:44.055680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10764","last_updated":"2021-12-20T18:59:59Z","snapshot_observed_at":"2026-08-06T00:03:37.713050Z","submitted_at":"2021-12-20T18:59:59Z","title":"Mask2Former for Video Instance Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10764","snapshot_observed_at":"2026-08-07T05:25:44.060493Z","title":"Mask2former for video instance segmentation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.060493Z"},"links":{"cited_paper":"/paper/2112.10764","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:3205744ed48e5b4b9c22295c49aef830c4f5355c6314d8a2cab730891db56a53","observation_id":"10f86999-c4bc-44b4-bc1c-5005dc1bc195","resolution":{"observed_at":"2026-08-07T05:25:44.060493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.686331Z","title":"Davidsonian scene graph: Improving reliability in fine-grained evaluation for text-to-image generation","venue":null,"work_id":"a90fd9bc-470c-4c50-8e33-f8e0f5912894","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.065918Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:e470f9426ffb27d90cacc7f27f85bc30bea36fa1a7eeae23be8564d453031efe","observation_id":"45f95dfa-7c79-490a-8eb0-3d73ba18dab3","resolution":{"observed_at":"2026-08-07T05:25:45.691231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.669865Z","title":null,"venue":null,"work_id":"41da0b70-baa1-4980-b9db-40f167e924f7","year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.070702Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:2f22064d30109949ea85ad97f28391683657bac57d39a92f48084b522f5ffade","observation_id":"ff38fb48-5b64-448b-99fa-9dc7ca5a8b31","resolution":{"observed_at":"2026-08-07T05:25:45.675436Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.14683","last_updated":"2025-07-27T11:45:16Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-20T17:59:30Z","title":"Emerging Properties in Unified Multimodal Pretraining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.14683","snapshot_observed_at":"2026-08-07T05:25:44.076301Z","title":"Emerging properties in unified multimodal pretraining","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.076301Z"},"links":{"cited_paper":"/paper/2505.14683","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:d92aad71296827d8135ed717c8c36ec9fe861a8442681e72afa13e800fc21e1a","observation_id":"1c16c095-5132-41f6-9044-b4f46a23346a","resolution":{"observed_at":"2026-08-07T05:25:44.076301Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.652799Z","title":"Scaling rectified flow transformers for high-resolution image synthesis","venue":null,"work_id":"bcc37aef-1603-4438-9b45-acbee522a2d7","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.081486Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:e7ebb415a738dc48752ddb6726448ee19e559bf770007a01bac0c39628f53ed3","observation_id":"3a8a880e-3c23-47b6-8cc8-8dae56163a41","resolution":{"observed_at":"2026-08-07T05:25:45.658723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.05032","last_updated":"2023-02-28T23:46:24Z","snapshot_observed_at":"2026-08-09T14:37:46.826091Z","submitted_at":"2022-12-09T18:30:24Z","title":"Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.05032","snapshot_observed_at":"2026-08-07T05:25:44.086087Z","title":"Training-free structured diffusion guidance for compositional text-to-image synthesis","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.086087Z"},"links":{"cited_paper":"/paper/2212.05032","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:c82687fd7120b552c1ce7c5013e54b44bb26dcc8c55b75446614abee348c0dc4","observation_id":"ec9d9ce4-a2f7-453e-9788-e540916e9461","resolution":{"observed_at":"2026-08-07T05:25:44.086087Z","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":"2026-08-07T05:25:44.090847Z","title":"Dreamsim: Learning new dimensions of human visual similarity using synthetic data, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.090847Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:7fb13a985ae3ea22575f23d4c85001cf343bf160250132c702d7119d4830189d","observation_id":"0ceee4f6-5acf-4756-b317-1156f2595266","resolution":{"observed_at":"2026-08-07T05:25:44.090847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07546","last_updated":"2024-08-12T19:33:52Z","snapshot_observed_at":"2026-08-09T08:24:08.295414Z","submitted_at":"2024-06-11T17:59:48Z","title":"Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07546","snapshot_observed_at":"2026-08-07T05:25:44.095229Z","title":"Commonsense-t2i challenge: Can text-to-image generation models understand commonsense? arXiv preprint arXiv:2406.07546, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.095229Z"},"links":{"cited_paper":"/paper/2406.07546","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:d47ef1db75d8bf327d558062726a73621eca471bd0b2951d358d51c12f10e56b","observation_id":"16ad961e-f0ce-402b-b9f1-d84bc5b2ff81","resolution":{"observed_at":"2026-08-07T05:25:44.095229Z","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":"2026-08-07T05:25:44.100097Z","title":"Distilling diversity and control in diffusion models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.100097Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:78c34182c03749508de1c00aaf49e9c1c5513f1112dd29111963d06ebb0de3d8","observation_id":"1c5ff251-2783-4b48-93f1-ccda884f8dfa","resolution":{"observed_at":"2026-08-07T05:25:44.100097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11346","last_updated":"2025-06-28T11:46:35Z","snapshot_observed_at":"2026-07-06T21:09:50.780345Z","submitted_at":"2025-04-15T16:19:07Z","title":"Seedream 3.0 Technical Report","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11346","snapshot_observed_at":"2026-08-07T05:25:44.105493Z","title":"Seedream 3.0 technical report","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.105493Z"},"links":{"cited_paper":"/paper/2504.11346","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:157b5318e811ce1cf989ab9d7a43be337285993dc97e59cf569cae2a7fedd068","observation_id":"6520c209-2447-4565-9cc8-3748ff9e87cb","resolution":{"observed_at":"2026-08-07T05:25:44.105493Z","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":"2026-08-07T05:25:44.110467Z","title":"Geneval: An object-focused framework for evaluating text-to-image alignment","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.110467Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:c5db89d0979d198efa9e3c592436c1df151f051ecfcd558f94653da95233932e","observation_id":"a6949646-a872-48bc-bbf7-356dfb6921e2","resolution":{"observed_at":"2026-08-07T05:25:44.110467Z","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":"2026-08-07T05:25:44.116471Z","title":"Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.116471Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:3b36433f8cbe4e632d9ae99977f2659e70df110d289e9bb5a2afa4f1b39aff53","observation_id":"44db4c0d-9c4d-4f6d-a8d8-b228283e5424","resolution":{"observed_at":"2026-08-07T05:25:44.116471Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.605251Z","title":"Evalmuse-40k: A reliable and fine-grained benchmark with comprehensive human annotations for text-to-image generation model evaluation, 2024","venue":null,"work_id":"f9d9b9db-7db9-4332-a1bd-9810cac84b3a","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.123351Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:788d7ea93f3f74288f36be4124366f828fe93110c2b01059b4b53dd3d76187f2","observation_id":"03c0aa6c-bbed-40ca-b3ef-e4919b535d6f","resolution":{"observed_at":"2026-08-07T05:25:45.610302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:44.128309Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium.Advances in neural information processing systems, 30, 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.128309Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:af16bdfe57f25ea5752f886b1cc8845c444dab6a3d59abdc1fa93ac80d779d5a","observation_id":"2ba7f8f4-54af-43f7-91aa-f3bca75f2a50","resolution":{"observed_at":"2026-08-07T05:25:44.128309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.578285Z","title":"Hidream-i1","venue":null,"work_id":"157b9660-85d3-4789-94ef-2acfa42bca80","year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.132974Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:9a568d2fd31211f80ef11e6fe26aaa80483093165392629ef276f3f617675150","observation_id":"b315326f-0cf2-4edc-b4eb-601c0cc26228","resolution":{"observed_at":"2026-08-07T05:25:45.583510Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:44.137448Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.137448Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:7628d47545939fc8c6aec4b8b2ff2354214622daf9d59efd084197c401ac4e70","observation_id":"6ed22e4c-05e0-4a7c-bc7b-0d01489df1d4","resolution":{"observed_at":"2026-08-07T05:25:44.137448Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05135","last_updated":"2024-03-08T08:08:10Z","snapshot_observed_at":"2026-08-09T16:24:26.560430Z","submitted_at":"2024-03-08T08:08:10Z","title":"ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05135","snapshot_observed_at":"2026-08-07T05:25:44.141991Z","title":"Ella: Equip diffusion models with llm for enhanced semantic alignment","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.141991Z"},"links":{"cited_paper":"/paper/2403.05135","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:306791821f7f14c0915ecac7208ef872f0514d4016a2b4a817ac83908bebb331","observation_id":"4f3f05dd-3ad5-4fee-89e4-bd8d256c09ae","resolution":{"observed_at":"2026-08-07T05:25:44.141991Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.551267Z","title":"Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering","venue":null,"work_id":"90f4d15f-034f-4710-8b9a-e611a7b857c7","year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.146947Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:6b176c3123ef6676f2d7cf645189989f06e1c796464894c968e778da03962dec","observation_id":"b67fc08d-238f-4761-b46a-ccc65e0ad39f","resolution":{"observed_at":"2026-08-07T05:25:45.556705Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.535045Z","title":"T2i-compbench++: An enhanced and comprehensive benchmark for compositional text-to-image generation","venue":null,"work_id":"789cc318-aeb7-47e0-9816-837d530e4034","year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.151303Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:c788483c1d32c35ea003193b26da6def9ee4e981d312d42e75a637661b4d7c8e","observation_id":"f5732f7a-a022-4639-9bc7-71935afc4ef1","resolution":{"observed_at":"2026-08-07T05:25:45.540308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:44.156341Z","title":"T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.156341Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:c2c21f5485270d576f7b4a1f0d1d8670027fbf457f4a91a34946ced4f6aa232b","observation_id":"03576435-53b7-4798-811a-2fcc531e2899","resolution":{"observed_at":"2026-08-07T05:25:44.156341Z","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":"2026-08-07T05:25:44.161137Z","title":"Llm2clip: Powerful language model unlock richer visual representation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.161137Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:d58e20cf030fd4a8775d8b52a96796599edfcc6503c9d670a32bd328b95c24a2","observation_id":"0695f3ae-478a-4700-a002-8ef519ae4d8a","resolution":{"observed_at":"2026-08-07T05:25:44.161137Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.505815Z","title":"Imagen 3, 2024","venue":null,"work_id":"bfcbdd3c-6867-4cd3-a9ed-c1c521be1390","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.165584Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:b2cf034c9ff3447eee29676d88ec59e03023eb9f4f8d27a015489c9476c9f513","observation_id":"3a60a612-5238-4cb7-b067-5a1c93532628","resolution":{"observed_at":"2026-08-07T05:25:45.511400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:44.170115Z","title":"Pick-a- pic: An open dataset of user preferences for text-to-image generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.170115Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:426eb353ca0af0d992d7a8533cb18709206e635ac94862919dbebf5769806dbb","observation_id":"acd379d9-af82-4da0-adee-6decab8d9562","resolution":{"observed_at":"2026-08-07T05:25:44.170115Z","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":"2026-08-07T05:25:44.174864Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.174864Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:91056800666f25e20a12b995c71a898a8bdebc9aee134b973a603938d0174949","observation_id":"0e2bb515-390b-44e1-89c3-c43d159f40e7","resolution":{"observed_at":"2026-08-07T05:25:44.174864Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13743","last_updated":"2024-11-03T20:22:32Z","snapshot_observed_at":"2026-07-30T19:48:58.731788Z","submitted_at":"2024-06-19T18:00:07Z","title":"GenAI-Bench: Evaluating and Improving Compositional Text-to-Visual Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.13743","snapshot_observed_at":"2026-08-07T05:25:44.179662Z","title":"Genai-bench: Evaluating and improving compositional text-to-visual generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.179662Z"},"links":{"cited_paper":"/paper/2406.13743","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:0e0e9bcfae461361a072c1095bf67fcfb6895497df774bfcb764c6368c77c2de","observation_id":"57a1ef34-babb-4bcd-8a90-6077c31f9dce","resolution":{"observed_at":"2026-08-07T05:25:44.179662Z","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":"2026-08-07T05:25:44.184414Z","title":"Playground v2.5: Three insights towards enhancing aesthetic quality in text-to-image generation, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.184414Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:60ca2e1487fd95da7aa7299f72137e3167bb1c87c16789cf33eb9f649f89a757","observation_id":"af7f3d7c-83b6-460d-8f3b-eb2989350fb5","resolution":{"observed_at":"2026-08-07T05:25:44.184414Z","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":"2026-08-07T05:25:44.188963Z","title":"Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.188963Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:7260ed515c37aecbbc0203236ecc24ff0c295351212b42c7eb464a9b19511fd4","observation_id":"93446f06-d01f-42b5-addf-57f1fd37a952","resolution":{"observed_at":"2026-08-07T05:25:44.188963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.451842Z","title":"Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers","venue":null,"work_id":"7632fa1a-9643-4d19-87af-7bb2cb415b2e","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.324766Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:e304bed7a296aa64458daefb8afaec0b27112e8851a6f779b05afa1fe762e759","observation_id":"b1d644db-517c-43f0-82e0-07ae67a7ccf6","resolution":{"observed_at":"2026-08-07T05:25:45.456874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07265","last_updated":"2026-06-02T17:11:50Z","snapshot_observed_at":"2026-08-07T17:17:00.060047Z","submitted_at":"2025-03-10T12:47:53Z","title":"WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.07265","snapshot_observed_at":"2026-08-07T05:25:44.329662Z","title":"Wise: A world knowledge-informed semantic evaluation for text-to-image generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.329662Z"},"links":{"cited_paper":"/paper/2503.07265","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:5c4a1decceb7e0b4b0efd3af178dc3884c555e0a618fa364e93fc272ab2acc22","observation_id":"c19071cf-049e-4ce7-b263-d227424ead75","resolution":{"observed_at":"2026-08-07T05:25:44.329662Z","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":"2026-08-07T05:25:44.334614Z","title":"Gpt-4o system card","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.334614Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:fbee44919304085157443346ce0314e68ab49734565d1aaf83b5191a194e3c5e","observation_id":"207e4c3a-93a8-4f0c-940d-efe9d6d35553","resolution":{"observed_at":"2026-08-07T05:25:44.334614Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.427048Z","title":"Introducing 4o image generation","venue":null,"work_id":"a7f508b9-e4d8-4236-8393-dfe3b8477479","year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.339063Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:808e06dd428068734a54c9ca8761aac938839694bbade772fad35dd751f11772","observation_id":"1524515d-0ee5-492d-9439-a6f628046347","resolution":{"observed_at":"2026-08-07T05:25:45.432238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.01952","last_updated":"2023-07-04T23:04:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-08-07T05:25:44.344113Z","title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.344113Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:17fab97d5e4287820e122758d836b175a98deaa125d68b51b7a237476fc6ab49","observation_id":"220f40d5-9c80-4ceb-8cc6-64d61211148c","resolution":{"observed_at":"2026-08-07T05:25:44.344113Z","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":"2026-08-07T05:25:44.348906Z","title":"Lumina-image 2.0: A unified and efficient image generative framework, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.348906Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:301d7287badfafb0033f028fc54b506bb25e3318153e62a3d76b592691ad4134","observation_id":"e7405897-fa72-4c77-9977-948a928a367e","resolution":{"observed_at":"2026-08-07T05:25:44.348906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.16074","last_updated":"2025-05-18T14:13:34Z","snapshot_observed_at":"2026-08-07T16:00:09.967849Z","submitted_at":"2025-04-22T17:53:29Z","title":"PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.16074","snapshot_observed_at":"2026-08-07T05:25:44.353897Z","title":"Phybench: Holistic evaluation of physical perception and reasoning in large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.353897Z"},"links":{"cited_paper":"/paper/2504.16074","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:efefeb7313f696f12e2c4759ab24fa39e91d7b627d30600e20af15e2ffc5a908","observation_id":"ef9be859-608e-4677-a58a-2d2f2b39bf22","resolution":{"observed_at":"2026-08-07T05:25:44.353897Z","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":"2026-08-07T05:25:44.364376Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.364376Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:cd33adaf5ddc775ac7da64a7c031bd0f6e348ae4a452d0fe7c108349937ea2aa","observation_id":"42a7b0b3-c958-493a-9286-6edac2e725a4","resolution":{"observed_at":"2026-08-07T05:25:44.364376Z","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":"2026-08-07T05:25:44.368924Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.368924Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:b5f9d648b50946505e41ba2166b6ed6e1cc4fcefb8689d752df12549aca4ccce","observation_id":"9e90daba-94ee-4897-9e8c-ce0b37685468","resolution":{"observed_at":"2026-08-07T05:25:44.368924Z","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":"2026-08-07T05:25:44.374272Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.374272Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:515bf58bf11b36ea1e8e003112fe245f83042aa111d6da1cd28d4177c7661388","observation_id":"e62e35a5-d6ab-4e8b-aff7-21cf83a24ce2","resolution":{"observed_at":"2026-08-07T05:25:44.374272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-07T05:25:44.378716Z","title":"Hierarchical text-conditional image generation with clip latents","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.378716Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:2aebdb16ceffeec15ae4e7ff3089fbfe7a8a7edfa536f1ac5cdfbfd96240963d","observation_id":"5dd96539-18fb-4c72-a978-da805d2c4f69","resolution":{"observed_at":"2026-08-07T05:25:44.378716Z","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":"2026-08-07T05:25:44.383658Z","title":"Zero-shot text-to-image generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.383658Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:80da021c070e5cbcbaa92ddba53412183c99504ff6dde612d4b69877e3aeb7be","observation_id":"6e033c79-ef6c-44eb-9dc0-8bff93d04ee8","resolution":{"observed_at":"2026-08-07T05:25:44.383658Z","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":"2026-08-07T05:25:44.388060Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.388060Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:6be854777e1ca00d9630484f5fa81bb82ab5bcd5ee659aa4dec109f60dd82894","observation_id":"783e479a-eb5f-4b52-869c-2383b41277e5","resolution":{"observed_at":"2026-08-07T05:25:44.388060Z","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":"2026-08-07T05:25:44.394044Z","title":"Photorealistic text-to- image diffusion models with deep language understanding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.394044Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:1746d9f6665a491eba66839cd69b3a08aa6538c335ca1f4a1d64c26bd6661f25","observation_id":"c5000dc2-bdef-4a2a-8c4a-742e1dde46f6","resolution":{"observed_at":"2026-08-07T05:25:44.394044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.342294Z","title":"Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis","venue":null,"work_id":"f24f9a61-cebd-4c76-8976-9fb86d0c27ed","year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.399509Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:acaf42a81bb40c137e4a1f33db574f0b72bd4d9bfe25ac2df47d38934a845902","observation_id":"373711cc-d37d-488d-b2b6-cde41f1e96d0","resolution":{"observed_at":"2026-08-07T05:25:45.347074Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01292","last_updated":"2024-04-01T17:58:30Z","snapshot_observed_at":"2026-07-06T17:54:07.815827Z","submitted_at":"2024-04-01T17:58:30Z","title":"Measuring Style Similarity in Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01292","snapshot_observed_at":"2026-08-07T05:25:44.404354Z","title":"Measuring style similarity in diffusion models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.404354Z"},"links":{"cited_paper":"/paper/2404.01292","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:553f07555fe71fbc52f92ed8fb6b5434d79798bddc4931ae3e9aeefd86c9fefd","observation_id":"a4cec638-0a26-49a3-8f32-3db73838c31b","resolution":{"observed_at":"2026-08-07T05:25:44.404354Z","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":"2026-08-07T05:25:44.409667Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.409667Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:ef20453772e3fd61c2a0d6ed3ac1738aa7f79fdd38a18696d6ede471019ae4f8","observation_id":"52fe9ff2-7635-435d-b8ab-18940e5915b3","resolution":{"observed_at":"2026-08-07T05:25:44.409667Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.325173Z","title":"stable-diffusion-3.5-large","venue":null,"work_id":"13fc529e-6996-44b3-badd-eefac90bef73","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.415559Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:714f129727bd35c63fc21aa8d8cd2bf36675a0b15fb416e08ab29dbf1b72d0c5","observation_id":"d0bc6807-5c52-4eb1-90c8-ab840810a3b6","resolution":{"observed_at":"2026-08-07T05:25:45.330279Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06525","snapshot_observed_at":"2026-08-07T05:25:44.420597Z","title":"Autore- gressive model beats diffusion: Llama for scalable image generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.420597Z"},"links":{"cited_paper":"/paper/2406.06525","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:f70b2da4e79f43b46e94ee22e2d83cbd6e2e321d5e1553554daaf45864f75c60","observation_id":"8ea5534e-b4bb-429d-a619-65ec5d00e7de","resolution":{"observed_at":"2026-08-07T05:25:44.420597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.308634Z","title":"Evalalign: Evaluating text-to-image models through precision alignment of multimodal large models with supervised fine-tuning to human annotations","venue":null,"work_id":"a36b60da-8ae7-4b08-b219-96946dbe0281","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.425146Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:9005684ae94bbc1bb19bae2898a9c028c91e667022ea5f0b04947369b7ccd82c","observation_id":"f73d692f-6367-479b-b28d-b770f2fcec2d","resolution":{"observed_at":"2026-08-07T05:25:45.314177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.09818","last_updated":"2025-03-21T05:54:00Z","snapshot_observed_at":"2026-08-09T20:05:31.409634Z","submitted_at":"2024-05-16T05:23:41Z","title":"Chameleon: Mixed-Modal Early-Fusion Foundation Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.09818","snapshot_observed_at":"2026-08-07T05:25:44.429923Z","title":"Chameleon: Mixed-modal early-fusion foundation models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.429923Z"},"links":{"cited_paper":"/paper/2405.09818","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:d8aabfeacf1fbff2d13abc6e4a7bbcb69d64dfcb69466a4e067e7a652bf3ccaa","observation_id":"181de5af-0728-47ca-8398-898db9ce1af6","resolution":{"observed_at":"2026-08-07T05:25:44.429923Z","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":"2026-08-07T05:25:44.434498Z","title":"Kolors2.0","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.434498Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:8bcca8237ff0f4a88245f1447a0769ad3cca01519240a471879b48a98d06af84","observation_id":"05a32a37-fbc0-45c4-a9b6-85cd2b48eb85","resolution":{"observed_at":"2026-08-07T05:25:44.434498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.282817Z","title":"How to create sota image generation with text recrafts ml team insights","venue":null,"work_id":"7be83d10-2048-4413-b33e-275f46360f28","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.438808Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:428ff2e5562f43c13b6d157c00733cfa8fd9fa1332006d89dd5e679f62e86fd5","observation_id":"448a2f39-c47c-42f5-b174-deb4fb53bd21","resolution":{"observed_at":"2026-08-07T05:25:45.287831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.267017Z","title":"Recraft v3","venue":null,"work_id":"4f9d01d7-b5b0-47a9-a18a-3d2761edd143","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.443618Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:eba225e8212dc20ee0145a183afd3fa53041d29ec9144a2fc234529de933ec9d","observation_id":"c2ae66e5-18e1-4242-9ba0-a7b9ba6c4eea","resolution":{"observed_at":"2026-08-07T05:25:45.271877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12191","last_updated":"2024-10-03T15:54:49Z","snapshot_observed_at":"2026-08-06T05:35:29.109022Z","submitted_at":"2024-09-18T17:59:32Z","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12191","snapshot_observed_at":"2026-08-07T05:25:44.448457Z","title":"Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.448457Z"},"links":{"cited_paper":"/paper/2409.12191","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:e092651781329fa954434c34081d65a36d8fcc1ed964dc60b2713acf43f740cf","observation_id":"13106869-5090-48b4-bf5b-7f15e51e5d4e","resolution":{"observed_at":"2026-08-07T05:25:44.448457Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.18869","last_updated":"2024-09-27T16:06:11Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-27T16:06:11Z","title":"Emu3: Next-Token Prediction is All You Need","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.18869","snapshot_observed_at":"2026-08-07T05:25:44.453240Z","title":"Emu3: Next-token prediction is all you need","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.453240Z"},"links":{"cited_paper":"/paper/2409.18869","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:d7375c802f96645e32641e53fc08835b20f1d781165804b5015e0e1a529be936","observation_id":"8c9e52a7-9376-499a-9888-4cc69efb9600","resolution":{"observed_at":"2026-08-07T05:25:44.453240Z","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":"2026-08-07T05:25:44.458981Z","title":"Image quality assessment: from error visibility to structural similarity","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.458981Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:df03758fa9b9946f6627d8048aa011f4563b8a7670e32be92d493219ecca4106","observation_id":"2eeba09a-a1c8-43b2-8348-8fa434d0148a","resolution":{"observed_at":"2026-08-07T05:25:44.458981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.16820","last_updated":"2025-03-17T15:53:14Z","snapshot_observed_at":"2026-07-06T18:05:42.862609Z","submitted_at":"2024-04-25T17:58:43Z","title":"Revisiting Text-to-Image Evaluation with Gecko: On Metrics, Prompts, and Human Ratings","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.16820","snapshot_observed_at":"2026-08-07T05:25:44.463994Z","title":"Revisiting text-to-image evaluation with gecko: On metrics, prompts, and human ratings","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.463994Z"},"links":{"cited_paper":"/paper/2404.16820","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:6c9552a4bbceebae06756d89e80dd3db829bc1dae3d3d878e51f9efb1c8d0d6c","observation_id":"ac1cb8d5-3e7a-40dc-a870-2c8bc2c35341","resolution":{"observed_at":"2026-08-07T05:25:44.463994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.18871","last_updated":"2026-04-21T17:32:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-23T17:38:54Z","title":"OmniGen2: Towards Instruction-Aligned Multimodal Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.18871","snapshot_observed_at":"2026-08-07T05:25:44.468825Z","title":"Omnigen2: Exploration to advanced multimodal generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.468825Z"},"links":{"cited_paper":"/paper/2506.18871","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:9e13643461166db32d6b5dc23096ce5c951c9135b3cd6d390cfd104f4bf66056","observation_id":"c67b9732-02e1-478b-8c54-be1a6568717f","resolution":{"observed_at":"2026-08-07T05:25:44.468825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09341","last_updated":"2023-09-25T08:19:23Z","snapshot_observed_at":"2026-07-06T15:43:07.989730Z","submitted_at":"2023-06-15T17:59:31Z","title":"Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.09341","snapshot_observed_at":"2026-08-07T05:25:44.474579Z","title":"Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis.arXiv preprint arXiv:2306.09341, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.474579Z"},"links":{"cited_paper":"/paper/2306.09341","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:7da27b4074e2369fb1e9032a312c4263c5bc2fa49e638a2a82808e44ec11312e","observation_id":"170abaf6-347f-4310-9c00-3d5f7aa819f9","resolution":{"observed_at":"2026-08-07T05:25:44.474579Z","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":"2026-08-07T05:25:44.479994Z","title":"Sana 1.5: Efficient scaling of training-time and inference-time compute in linear diffusion transformer, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.479994Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:d91d3ab8040f3d99e0008d428828de28fe52924a55f4fe0964efd8b79c1dc883","observation_id":"8f301db4-0be5-4268-8054-69fe550fc666","resolution":{"observed_at":"2026-08-07T05:25:44.479994Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12528","last_updated":"2025-09-08T02:42:57Z","snapshot_observed_at":"2026-07-06T19:04:43.716629Z","submitted_at":"2024-08-22T16:32:32Z","title":"Show-o: One Single Transformer to Unify Multimodal Understanding and Generation","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12528","snapshot_observed_at":"2026-08-07T05:25:44.485018Z","title":"Show-o: One single transformer to unify multimodal understanding and generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.485018Z"},"links":{"cited_paper":"/paper/2408.12528","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:14d6564d107a90477680986587d681f716c333ae41505072c8f719812e4843f8","observation_id":"614d90cd-6d56-4b5d-9109-217b96512529","resolution":{"observed_at":"2026-08-07T05:25:44.485018Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15564","last_updated":"2025-09-22T01:24:29Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-06-18T15:39:15Z","title":"Show-o2: Improved Native Unified Multimodal Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.15564","snapshot_observed_at":"2026-08-07T05:25:44.490660Z","title":"Show-o2: Improved native unified multimodal models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.490660Z"},"links":{"cited_paper":"/paper/2506.15564","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:51e20509cf9262a04db15fd69d803cfc6fc220aff53d02d55fbc2733bb0fd288","observation_id":"b659cb1e-ddd8-4f3f-9172-17de88b0f0ab","resolution":{"observed_at":"2026-08-07T05:25:44.490660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16766","last_updated":"2024-09-04T10:42:41Z","snapshot_observed_at":"2026-08-07T09:10:29.603862Z","submitted_at":"2024-08-29T17:59:30Z","title":"CSGO: Content-Style Composition in Text-to-Image Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16766","snapshot_observed_at":"2026-08-07T05:25:44.495435Z","title":"Csgo: Content-style composition in text-to-image generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.495435Z"},"links":{"cited_paper":"/paper/2408.16766","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:ccf52fa09de28a21b37ab6b70126856a63c63084e014dfc69ee1ec0d2b37cf41","observation_id":"739d742f-a702-4a60-95fa-849647ae7b45","resolution":{"observed_at":"2026-08-07T05:25:44.495435Z","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":"2026-08-07T05:25:44.500274Z","title":"Scaling autoregressive models for content-rich text-to-image generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.500274Z"},"links":{"cited_paper":"/paper/2206.10789","citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:8dd025dc7137e8e7a6a0a49d111eb6de4317999fbfc37fc7c2eb72db74e670de","observation_id":"25a8ef6f-7c96-4537-acab-f6ba0e316079","resolution":{"observed_at":"2026-08-07T05:25:44.500274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.230940Z","title":"Cogview4","venue":null,"work_id":"489c7997-526c-4c4e-87ee-db890c5a4aca","year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.505964Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:31174b11d0718ff1b558c8e10a44ba2203fbe0dac9cf8935001bdd28aeef592a","observation_id":"01b65e69-e555-45a6-b796-a459c967543b","resolution":{"observed_at":"2026-08-07T05:25:45.235804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:44.510477Z","title":"Sigmoid loss for language image pre-training","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.510477Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:952270ea55b227d7076d5f65514f5834aee943eac05da43760e205dd8cef1d76","observation_id":"5119290e-d55a-449d-b210-6d98d9551256","resolution":{"observed_at":"2026-08-07T05:25:44.510477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.203731Z","title":"Worldgenbench: A world-knowledge-integrated benchmark for reasoning-driven text-to-image generation, 2025","venue":null,"work_id":"dae1386a-dcae-4fb7-bf8a-53e944c46cbd","year":2025},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.515840Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:b856043bad93c96003bfb32f1348ec05c7ebb22661da370cb379c18745b9641e","observation_id":"cd798b1e-c5ac-4aa6-9482-7b19ef2ffb2f","resolution":{"observed_at":"2026-08-07T05:25:45.209610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:25:45.186097Z","title":"K & R\" is the abbreviation for","venue":null,"work_id":"5bae991b-f273-4505-affd-18206442c447","year":2024},"citing_paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation","version":3},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T05:25:44.520732Z"},"links":{"citing_paper":"/paper/2506.07977"},"observation_digest":"sha256:84b91dae13f05eca57d2eaed825f5c021c84ccc1362ff384ae1094be03bc1da5","observation_id":"eac599bc-6709-4c91-a204-1626b965f6c9","resolution":{"observed_at":"2026-08-07T05:25:45.192133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.07977","last_updated":"2025-06-26T15:47:09Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T14:38:22.529752Z","submitted_at":"2025-06-09T17:50:21Z","title":"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation"},"reference_resolution":{"displayed":79,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":58,"verified_exact":0,"verified_fuzzy":21},"total_outbound_references":79},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 79 of 79 outbound references and 19 inbound Pith citation observations for arXiv:2506.07977."}