{"as_of":"2026-08-12T15:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6420c76b8c808ab71371854bfa9f91feb7288e55fb908244967c6bfd46f55871","coverage":[{"denominator":122,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T17:33:18.101255Z","state":"measured"},{"denominator":101,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":101,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T07:11:28.600100Z","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":"2026-07-02T07:06:44.193357Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"cited_work":{"arxiv_id":"2412.09656","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.09656","snapshot_observed_at":"2026-07-02T07:06:44.193357Z","title":"From noise to nuance: Advances in deep generative image models.arXiv:2412.09656, 2024","venue":null,"work_id":"004412c0-6058-48ff-8911-772cafef337c","year":2024},"citing_paper":{"arxiv_id":"2606.11243","last_updated":"2026-06-03T09:11:28Z","snapshot_observed_at":"2026-08-08T13:48:59.651448Z","submitted_at":"2026-06-03T09:11:28Z","title":"ProHiFlo: Hierarchical Flow Matching with Functional Guidance for De Novo Protein Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T07:11:28.600100Z"},"links":{"cited_paper":"/paper/2412.09656","citing_paper":"/paper/2606.11243"},"observation_digest":"sha256:c2467745a6fb7225b67f0a29d2a0fb552fcddb5551a85c14a17baf0f7f8eecc4","observation_id":"c8b06f37-e68a-402a-b558-f39cf1bc7ec5","resolution":{"observed_at":"2026-07-02T07:06:44.196596Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.09656/citation-record","integrity":"/paper/2412.09656/integrity","json":"/paper/2412.09656/citation-record.json","paper":"/paper/2412.09656"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T17:33:17.531111Z","title":"Generative adversar ial net- works,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.531111Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:f5043923682f5574549a60f5f6721845ed7b4d37480ccccd112ae102ed29f7b6","observation_id":"03ec1d70-57d1-464f-96f0-7f7ca3e93816","resolution":{"observed_at":"2026-08-11T17:33:17.531111Z","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":"2026-08-11T17:33:17.538183Z","title":"Unsupervised repr esentation learning with deep convolutional generative adversarial n etworks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.538183Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:8618c2d3995510e39353f22e7b2f7e562dbac5fa57e6415ac4ee5166027b08ae","observation_id":"6ecf419c-948a-4e1e-98d9-2fee62c5766a","resolution":{"observed_at":"2026-08-11T17:33:17.538183Z","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":"2026-08-11T17:33:17.543409Z","title":"Denoising diffusion proba bilistic mod- els,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.543409Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:e49f27d1c8b02606cc51be586ce101859194a743a2027f8f4fbae69b054152ec","observation_id":"b32ae695-72c9-4ceb-ac39-7b12cb7b1235","resolution":{"observed_at":"2026-08-11T17:33:17.543409Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.13456","last_updated":"2021-02-10T18:17:04Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-11-26T19:39:10Z","title":"Score-Based Generative Modeling through Stochastic Differential Equations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.13456","snapshot_observed_at":"2026-08-11T17:33:17.550962Z","title":"Score-based generative modeling through sto chastic differential equations,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.550962Z"},"links":{"cited_paper":"/paper/2011.13456","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:0c19e10b83730ee7b1f41865cb662c9b16f45a33225ebd91dc7c2e3128d84823","observation_id":"350194f3-cf51-41f8-bee6-3bd67a3eae6a","resolution":{"observed_at":"2026-08-11T17:33:17.550962Z","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":"2026-08-11T17:33:17.556983Z","title":"Diffusion models beat gans on image synthesis,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.556983Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:6bc075890ecb1f02abacb253c598ba443311e9693e01cb3ad7390be14f7732d0","observation_id":"d6e47659-dccb-4ab2-ae9e-a1c7f35e0b00","resolution":{"observed_at":"2026-08-11T17:33:17.556983Z","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":"2026-08-11T17:33:17.562573Z","title":"Imagenet: A large-scale hierarchical image database,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.562573Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:459085452db5e5f8d29e4be221d2895e5b0799d3d2cc1d27d661d2db847069c6","observation_id":"d97cad55-9bae-4922-b650-c81ce70d7853","resolution":{"observed_at":"2026-08-11T17:33:17.562573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.02114","last_updated":"2021-11-03T10:16:39Z","snapshot_observed_at":"2026-08-02T08:12:49.547570Z","submitted_at":"2021-11-03T10:16:39Z","title":"LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.02114","snapshot_observed_at":"2026-08-11T17:33:17.569511Z","title":"Laion- 400m: Open dataset of clip-filtered 400 million image-text pairs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.569511Z"},"links":{"cited_paper":"/paper/2111.02114","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:ff956c55f38cea646df11be7b55ba147c35ac21106e28fce7e5c2cd54863da7a","observation_id":"54dc071f-1802-441e-b167-99d998bd1ace","resolution":{"observed_at":"2026-08-11T17:33:17.569511Z","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":"2026-08-11T17:33:17.575086Z","title":"In-datacenter performance analysis of a tensor processing unit,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.575086Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:f0fcfd79a951c2b8d74bff9bd2f1f88a62b643676770d76a2ca1d17183b937a6","observation_id":"9f784a18-50a6-4af7-a29e-a19230c8d3a5","resolution":{"observed_at":"2026-08-11T17:33:17.575086Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-11T17:33:17.581072Z","title":"Scaling l aws for neural language models,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.581072Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:0da1ff81acbd1fd95994908c156f37b049819c746b2199c8fddf03e8056d500e","observation_id":"cf17e39f-b0ad-425d-89d8-a0b053d18df1","resolution":{"observed_at":"2026-08-11T17:33:17.581072Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.08053","last_updated":"2020-03-13T23:45:18Z","snapshot_observed_at":"2026-08-12T10:50:46.357243Z","submitted_at":"2019-09-17T19:42:54Z","title":"Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.08053","snapshot_observed_at":"2026-08-11T17:33:17.586239Z","title":"Megatron-lm: Training multi-billion parameter lan guage models using model parallelism,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.586239Z"},"links":{"cited_paper":"/paper/1909.08053","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:f874a1b8b71ee6772e9b2907533a127509ecf373016d4b382b79d0332a5a34a9","observation_id":"ba41cd94-c9ac-4885-a5b1-ff8b1f4118cd","resolution":{"observed_at":"2026-08-11T17:33:17.586239Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.11446","last_updated":"2022-01-21T18:39:38Z","snapshot_observed_at":"2026-08-09T14:52:01.161814Z","submitted_at":"2021-12-08T19:41:47Z","title":"Scaling Language Models: Methods, Analysis & Insights from Training Gopher","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.11446","snapshot_observed_at":"2026-08-11T17:33:17.592113Z","title":"Scaling language models: Methods, analysis & insights from training gopher,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.592113Z"},"links":{"cited_paper":"/paper/2112.11446","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:98dd4f3c13a33b9370b995752cf0559f3659a754a1af9e304de7a4c0f26a1aa1","observation_id":"34dddb2c-d668-45be-b2b4-1a38eeb78a4c","resolution":{"observed_at":"2026-08-11T17:33:17.592113Z","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":"2026-08-11T17:33:17.598345Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.598345Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:55f140b8b9285be1651ae91983c1fc46cac3805a32339a39e32d12e9d7694350","observation_id":"6f7b9738-90c4-4027-8037-f4d49b1ee4f3","resolution":{"observed_at":"2026-08-11T17:33:17.598345Z","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":"2026-08-11T17:33:17.602739Z","title":"H ierarchi- cal text-conditional image generation with clip latents,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.602739Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:5f02ab3fa4962251a0782b7c62fa53c34dbc42015903c542787b0b1272f7c791","observation_id":"15f9479e-168e-4425-8754-6824f8fb7473","resolution":{"observed_at":"2026-08-11T17:33:17.602739Z","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":"2026-08-11T17:33:17.611283Z","title":"Photorealistic text-to-image diffusion models with dee p lan- guage understanding,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.611283Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:4a2ad1c459c6952a104345d4e10113ca1e6166c2a71907b21fd0f2e05a850ebd","observation_id":"793f6563-6aef-42b0-892a-d37679c98a62","resolution":{"observed_at":"2026-08-11T17:33:17.611283Z","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":"2026-08-11T17:33:17.617570Z","title":"Zero-shot text-to-image generation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.617570Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:9be25d26a2a8cd2171564e4f15456148a53dd28392cbcd55ffeef1d0e85c1317","observation_id":"22af2acf-65ce-4c15-99a0-127c2fb4651c","resolution":{"observed_at":"2026-08-11T17:33:17.617570Z","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":"2026-08-11T17:33:17.624244Z","title":"High-resolution image synthesis with latent diffusion mo dels,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.624244Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:7805412f262badb5b9748bc4b8238b192cc4c992e82ed54e35e1c1e317d958db","observation_id":"98aecbca-cec6-429f-8a71-34f8b0b3286b","resolution":{"observed_at":"2026-08-11T17:33:17.624244Z","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":"2026-08-11T17:33:17.630526Z","title":"Improved denoising diffu sion prob- abilistic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.630526Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:537b12d6c0cf7a8c234164f31a57917dc27c561224f3a32751819690453f7b48","observation_id":"363e5d2a-c608-4136-bfe3-f8534eab9257","resolution":{"observed_at":"2026-08-11T17:33:17.630526Z","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":"2026-08-11T17:33:17.643352Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.643352Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:f9d328839ddf9ed8bfd3915bac97f4561e7bbd065bb327d13e9162e4f2cedaf4","observation_id":"f95f2d35-0bdf-4856-ad37-1b886cadec1b","resolution":{"observed_at":"2026-08-11T17:33:17.643352Z","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":"2026-08-11T17:33:17.650381Z","title":"Alias-free generative adversarial networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.650381Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:86cfd6a38de9c11d9eb68758e21435fddc25f93d3e3fac632a97582cbfad97a3","observation_id":"75df1e2b-fe07-4360-937b-6e4adc6f87a9","resolution":{"observed_at":"2026-08-11T17:33:17.650381Z","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":"2026-08-11T17:33:17.656935Z","title":"Surveying the mllm landscape: A meta- review of current surveys,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.656935Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:dc4d21e0fe8bb3fc8f2cba7454f5d6303ad6c601e5c7d34fef9ef722b99726a1","observation_id":"b0549762-57bc-4ccd-b715-50a1e3cf171a","resolution":{"observed_at":"2026-08-11T17:33:17.656935Z","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":"2026-08-11T17:33:17.664941Z","title":"A survey of accelerator architectures for deep neural networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.664941Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:fcbcc37072b29c038606336c517754d29b39b373f2a4d5bec5a2b8c3890dde2c","observation_id":"48b53f74-2c07-4d31-8473-2276ecb769d0","resolution":{"observed_at":"2026-08-11T17:33:17.664941Z","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":"2026-08-11T17:33:17.673897Z","title":"The age of generative ai and ai-generated everything,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.673897Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:e3e3c329f2e1aa798c21186d1f7e3c2aa4441880bf20c250e56c4558766c4ee3","observation_id":"f88e0862-e044-4d91-ae77-ac36b1ac7ca1","resolution":{"observed_at":"2026-08-11T17:33:17.673897Z","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":"2026-08-11T17:33:17.678041Z","title":"Llms and diffusion models in ui/ux: Advancing human-computer interaction and design,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.678041Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:80ec37f1e7c3fb93021198e5b2a1eaa9ebf60694cca8ebd082cda3fa1e97db66","observation_id":"55a608e4-f0fe-4a7c-90ba-604a52ff7713","resolution":{"observed_at":"2026-08-11T17:33:17.678041Z","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":"2026-08-11T17:33:17.681897Z","title":"Lightweight g enerative adversarial networks for text-guided image manipulation,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.681897Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:a965f79b596766b2187753e247c4ab549b2a67a094b48746195fbf94978804a5","observation_id":"8152914a-dbdb-4463-8268-f4f6b990581f","resolution":{"observed_at":"2026-08-11T17:33:17.681897Z","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":"2026-08-11T17:33:17.687123Z","title":"Dpm-sol ver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.687123Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:b0709a315fecff5c416e78216a6e07c5c10200a2e9d10be9488ca9026abea801","observation_id":"90665ae6-368f-43e4-87f2-a0fd130476aa","resolution":{"observed_at":"2026-08-11T17:33:17.687123Z","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":"2026-08-11T17:33:17.692841Z","title":"The emergence of deepfake technology: A review,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.692841Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:5d8d4b51c03cc871bfcab9c93b93d9f784ddfeba3239d01979bd8980837f755b","observation_id":"ca38cf5b-2761-4b59-984a-ec4eb4af5457","resolution":{"observed_at":"2026-08-11T17:33:17.692841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18866","last_updated":"2024-10-24T15:51:04Z","snapshot_observed_at":"2026-08-11T00:49:49.198321Z","submitted_at":"2024-10-24T15:51:04Z","title":"The Cat and Mouse Game: The Ongoing Arms Race Between Diffusion Models and Detection Methods","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18866","snapshot_observed_at":"2026-08-11T17:33:17.700304Z","title":"The cat and mouse game: The ongoing arms race between diffusion models and det ection methods,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.700304Z"},"links":{"cited_paper":"/paper/2410.18866","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:2b51e10cfb8e2d80cae1cbb457f48c9dd153ccbef62c9122357665a6887ec1d7","observation_id":"2e45fd73-8ad3-4c73-aa07-9e701d14165b","resolution":{"observed_at":"2026-08-11T17:33:17.700304Z","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":"2026-08-11T17:33:17.708026Z","title":"Easily a ccessible text-to-image generation amplifies demographic stereotyp es at large scale,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.708026Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:45d35c52d3ae84234cfe9d4e019569bc793a4d333fc1fc0305633b1794f89a20","observation_id":"668a8317-7d42-4dac-93f6-7daa4bea292e","resolution":{"observed_at":"2026-08-11T17:33:17.708026Z","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":"2026-08-11T17:33:17.712610Z","title":"The creativity of text-to-image gen eration,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.712610Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:5344615100043ec7e81f6823254bb0a0f06407ac78b0c301060f01b2a8e6066f","observation_id":"acd9c581-a342-4f2f-b2ec-26efe490dcb5","resolution":{"observed_at":"2026-08-11T17:33:17.712610Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.15236","last_updated":"2026-05-27T16:38:46Z","snapshot_observed_at":"2026-07-06T19:36:32.581033Z","submitted_at":"2024-10-20T00:00:56Z","title":"Jailbreaking and Mitigation of Vulnerabilities in Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.15236","snapshot_observed_at":"2026-08-11T17:33:17.718300Z","title":"Jailbreaking and mitigation of vuln erabilities in large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.718300Z"},"links":{"cited_paper":"/paper/2410.15236","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:171e3dbb708a418a45fcc6682c34c02561829b10cb41ba9e387d246e1e5f81dc","observation_id":"3449ed24-1a1d-4918-bd8e-5efdef0e8b67","resolution":{"observed_at":"2026-08-11T17:33:17.718300Z","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":"2026-08-11T17:33:17.725607Z","title":"Scalable diffusion models with t ransformers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.725607Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:68348310a420c8654981b993b88270de1911d8b5868c166c85420eee4e9cdf2f","observation_id":"df1392d2-eff6-42ad-be34-c9185b195d53","resolution":{"observed_at":"2026-08-11T17:33:17.725607Z","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":"2026-08-11T17:33:17.731410Z","title":"Sd-dit: Unleashing the power of self-supervised discrimi nation in diffusion transformer,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.731410Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:c2c167f3b0e0506e6588dad5a93298b55fd96a8146f096c362838ed6661acdf8","observation_id":"82ffd210-7670-4fd8-9ad7-e9c3dbbfaf74","resolution":{"observed_at":"2026-08-11T17:33:17.731410Z","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":"2026-08-11T17:33:17.738513Z","title":"Scaling autoregres- sive models for content-rich text-to-image generation,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.738513Z"},"links":{"cited_paper":"/paper/2206.10789","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:9a244ebd9ba95d8d01583213c94254247c1f7dc7900495e189198ac5f05926ef","observation_id":"fcc8ce85-3d14-4468-b209-ac515c51044f","resolution":{"observed_at":"2026-08-11T17:33:17.738513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00704","last_updated":"2023-01-02T14:43:38Z","snapshot_observed_at":"2026-08-03T00:33:47.397969Z","submitted_at":"2023-01-02T14:43:38Z","title":"Muse: Text-To-Image Generation via Masked Generative Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00704","snapshot_observed_at":"2026-08-11T17:33:17.743533Z","title":"Muse: Text-to-image generation via masked generative transform ers,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.743533Z"},"links":{"cited_paper":"/paper/2301.00704","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:6d3f1fcfa9c9f2aa0f644312525c9c83912874fda8a5d352d65f146d43f87b4a","observation_id":"c1eaec2f-7f3c-415d-96e0-6dd4ecb7a48e","resolution":{"observed_at":"2026-08-11T17:33:17.743533Z","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":"2026-08-11T17:33:17.747637Z","title":"Cogview: Mastering text-to-image generation via transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.747637Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:ed204888bb1a11387b26185670634ca1bbdf04cc5bb2d02830c6c63a935a0e50","observation_id":"b7f8fb75-4b2c-45e1-81f8-b79dc6ba681d","resolution":{"observed_at":"2026-08-11T17:33:17.747637Z","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":"2026-08-11T17:33:17.753740Z","title":"Cogview2: Faste r and better text-to-image generation via hierarchical transfo rmers,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.753740Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:82eee8bcf601b7d1b81f531affed62338e0d47906e32f14895e49b105f4fe84f","observation_id":"c874e78f-9a16-4416-a840-aa07f0089395","resolution":{"observed_at":"2026-08-11T17:33:17.753740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05121","last_updated":"2024-03-08T07:32:50Z","snapshot_observed_at":"2026-08-12T04:39:19.769813Z","submitted_at":"2024-03-08T07:32:50Z","title":"CogView3: Finer and Faster Text-to-Image Generation via Relay Diffusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05121","snapshot_observed_at":"2026-08-11T17:33:17.758804Z","title":"Cogview3: Finer and faster text-to-im age generation via relay diffusion,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.758804Z"},"links":{"cited_paper":"/paper/2403.05121","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:257e24960e88b7a30942e38a430ad6f85a446fef287b36f611ba802bdaa9fca9","observation_id":"d04c625c-b45c-402b-b027-90853af58386","resolution":{"observed_at":"2026-08-11T17:33:17.758804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-11T17:33:17.763564Z","title":"Auto-encoding variational bayes,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.763564Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:66f000057185f98a4a1b905930bd6bed4c48fa9cd9698fe33d5e056c3e69be80","observation_id":"326f6d8a-1913-4cfc-a438-aac360fca71f","resolution":{"observed_at":"2026-08-11T17:33:17.763564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-11T17:33:17.768976Z","title":"Sdxl: Improving latent diffusion models for high-resolution image synthesis,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.768976Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:c0ffbabd0f190de9e1f82ebbf911ad9c6d1bb61466761b8f7c56550b76c4feaf","observation_id":"788b102c-a41d-43f4-96c8-ac90f9e6ce57","resolution":{"observed_at":"2026-08-11T17:33:17.768976Z","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":"2026-08-11T17:33:17.774647Z","title":"Adve rsarial diffusion distillation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.774647Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:d4578c34eb8b67943b94287d9111a4b9e84d076e1d5b5ca27341314319c093b5","observation_id":"4e0016da-fe15-4963-8fb5-09c07e6ee8a6","resolution":{"observed_at":"2026-08-11T17:33:17.774647Z","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":"2026-08-11T17:33:17.779619Z","title":"Scaling rectified flow transformers for high-resolution image synthesis,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.779619Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:bd621beab8b17ec5ea79fab66d6251afd8fe32246d069c2c640d784428746b7e","observation_id":"fbfd2977-c62a-42b6-b25e-4621e27cd2a9","resolution":{"observed_at":"2026-08-11T17:33:17.779619Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12015","last_updated":"2024-03-18T17:51:43Z","snapshot_observed_at":"2026-08-12T08:48:47.927103Z","submitted_at":"2024-03-18T17:51:43Z","title":"Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12015","snapshot_observed_at":"2026-08-11T17:33:17.784570Z","title":"Fast high-resolution image synthesis with latent ad versarial diffusion distillation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.784570Z"},"links":{"cited_paper":"/paper/2403.12015","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:ff8739268a58f559b28ff53fdd752afe4f886789428bcd11f18da9a0a6cb6858","observation_id":"5e685d04-7fa7-4230-93f0-aab37c24fa0d","resolution":{"observed_at":"2026-08-11T17:33:17.784570Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.00512","last_updated":"2022-06-07T09:17:35Z","snapshot_observed_at":"2026-08-12T01:14:44.484432Z","submitted_at":"2022-02-01T16:07:25Z","title":"Progressive Distillation for Fast Sampling of Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.00512","snapshot_observed_at":"2026-08-11T17:33:17.790242Z","title":"Progressive distillation for fa st sampling of diffusion models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.790242Z"},"links":{"cited_paper":"/paper/2202.00512","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:7acfcfc2e32de959b3448ce33aacd8ad34b1a2cef941c44b20c5491a6785f828","observation_id":"7b4cb2c6-f9be-4477-881d-a73698127376","resolution":{"observed_at":"2026-08-11T17:33:17.790242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13929","last_updated":"2024-03-02T09:09:32Z","snapshot_observed_at":"2026-08-12T13:08:43.602176Z","submitted_at":"2024-02-21T16:51:05Z","title":"SDXL-Lightning: Progressive Adversarial Diffusion Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13929","snapshot_observed_at":"2026-08-11T17:33:17.795277Z","title":"Sdxl-lightning: Progress ive adversarial diffusion distillation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.795277Z"},"links":{"cited_paper":"/paper/2402.13929","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:f1aceef2d76e16bbd951ef26792c1f964921d0b49cc46c671f8651bf6c9eb406","observation_id":"a39c38aa-5624-41d7-b1b4-a40b67bafac0","resolution":{"observed_at":"2026-08-11T17:33:17.795277Z","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":"2026-08-11T17:33:17.799288Z","title":"Google research imagen 2 update","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.799288Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:1174095ee87ec44e8b3b2c7852c4d9a7975755f9362db05950024c31891178ac","observation_id":"9d8dae51-e4d5-4346-aca5-dece3fe850e8","resolution":{"observed_at":"2026-08-11T17:33:17.799288Z","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":"2026-08-11T17:33:17.802895Z","title":"Google deepmind imagen 3 update","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.802895Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:a5400721e496402dd82826842f33a624fe76cb7686420fca3c5e655f212f6bb7","observation_id":"3662f6cc-efec-4ac8-9a19-8690ea1e5feb","resolution":{"observed_at":"2026-08-11T17:33:17.802895Z","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":"2026-08-11T17:33:17.807448Z","title":"From word vectors to multimodal embeddings: Techniques, applications, and future directions for large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.807448Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:0f0b7f1dd13e39d3416314f49bb113d4d712286e2720a4210777bba74f463180","observation_id":"ad2e3fa4-515b-4d51-ab64-b68a00467390","resolution":{"observed_at":"2026-08-11T17:33:17.807448Z","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":"2026-08-11T17:33:17.814091Z","title":"Dall-e 3: Ai that can create images from text wi th improved prompt following","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.814091Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:b6e4d0d4e90e17e1a45b0346bdb886bad1ce14af5358b59c526cf65d43269bdd","observation_id":"4e4343b1-98ed-44a4-a703-e1a01c3ff410","resolution":{"observed_at":"2026-08-11T17:33:17.814091Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.01469","last_updated":"2023-05-31T06:17:10Z","snapshot_observed_at":"2026-07-06T14:58:02.852672Z","submitted_at":"2023-03-02T18:30:16Z","title":"Consistency Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.01469","snapshot_observed_at":"2026-08-11T17:33:17.819318Z","title":"Consis tency models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.819318Z"},"links":{"cited_paper":"/paper/2303.01469","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:684c9de0db845d1082814a458a7a4cb470c7b72acdadaba2f7016937c19ea9f4","observation_id":"1cbd6894-fed2-46eb-af13-b02088beed88","resolution":{"observed_at":"2026-08-11T17:33:17.819318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14548","last_updated":"2024-10-10T21:59:49Z","snapshot_observed_at":"2026-08-09T07:18:02.667044Z","submitted_at":"2024-06-20T17:56:02Z","title":"Consistency Models Made Easy","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14548","snapshot_observed_at":"2026-08-11T17:33:17.824606Z","title":"Consi stency models made easy,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.824606Z"},"links":{"cited_paper":"/paper/2406.14548","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:a6a02d2656cc885e227a15792407e5a9d84dddb35ba22e7bbdc6149cd5bde938","observation_id":"bf3e2309-f07a-49c4-bf6f-102b56d9566b","resolution":{"observed_at":"2026-08-11T17:33:17.824606Z","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":"2026-08-11T17:33:17.831199Z","title":"Q-diffusion: Quantizing diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.831199Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:6da493a6b01f7c05230283c5f2569c673dc77768e1fd59159b31ef54f60393a6","observation_id":"58fdcfb7-dd50-4b5d-9c57-065fe7f3971f","resolution":{"observed_at":"2026-08-11T17:33:17.831199Z","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":"2026-08-11T17:33:17.836193Z","title":"Post-traini ng quantiza- tion on diffusion models,","venue":null,"work_id":null,"year":1972},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.836193Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:5384b9c522c7527a7d59ec06707d2a16ea922aa3fc051af386f73a8d1e6fd94c","observation_id":"f44c5024-862f-4c6e-bd66-a76fd2f4ac2d","resolution":{"observed_at":"2026-08-11T17:33:17.836193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03270","last_updated":"2024-04-13T07:33:57Z","snapshot_observed_at":"2026-08-09T06:00:18.288366Z","submitted_at":"2023-10-05T02:51:53Z","title":"EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03270","snapshot_observed_at":"2026-08-11T17:33:17.841802Z","title":"Efficientdm: Efficient quantization-aware fine-tuning of low-bit diffusion model s,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.841802Z"},"links":{"cited_paper":"/paper/2310.03270","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:82389c06e9d6830e92b409c3e775a644903d70d94b70c00fe3ae56c548d194e9","observation_id":"2147da06-2200-4587-b9c5-f32ce9b0ce0b","resolution":{"observed_at":"2026-08-11T17:33:17.841802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.11062","last_updated":"2025-05-19T06:20:01Z","snapshot_observed_at":"2026-08-08T01:47:51.652242Z","submitted_at":"2024-07-10T17:53:30Z","title":"EfficientQAT: Efficient Quantization-Aware Training for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.11062","snapshot_observed_at":"2026-08-11T17:33:17.847402Z","title":"Efficientqat: Efficient quantization-aware traini ng for large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.847402Z"},"links":{"cited_paper":"/paper/2407.11062","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:9f3f90a56fd56350b06ad79911e4bfe59f3a6c92a878482c0f661e0c366159e3","observation_id":"8eebff0e-a4a0-40e7-a482-3dbb84834c62","resolution":{"observed_at":"2026-08-11T17:33:17.847402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.03740","last_updated":"2018-02-15T20:04:02Z","snapshot_observed_at":"2026-08-12T13:00:33.339808Z","submitted_at":"2017-10-10T17:42:04Z","title":"Mixed Precision Training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.03740","snapshot_observed_at":"2026-08-11T17:33:17.851945Z","title":"Mixed precision training,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.851945Z"},"links":{"cited_paper":"/paper/1710.03740","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:b94abf90b8de3e80cd20be5a70f83d2591b8827f623e791545fc5fbd27de2342","observation_id":"6c3b349c-8a4d-4974-866f-c1ff439e6819","resolution":{"observed_at":"2026-08-11T17:33:17.851945Z","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":"2026-08-11T17:33:17.858258Z","title":"Adaptive quantization for deep neural network,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.858258Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:0d2c99ae32604c9c13d05a6c68a83426de05030d35915ce0eadce37e8d7997ca","observation_id":"0427c50a-d949-4379-97ae-a8b3034a1f1b","resolution":{"observed_at":"2026-08-11T17:33:17.858258Z","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":"2026-08-11T17:33:17.863165Z","title":"Parameter-efficie nt transfer learning for nlp,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.863165Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:4baabb97f43e5873ce1bcaf4394e1b1126cfc27d103738f705993548622b8d0a","observation_id":"d09ebafe-cba8-4dbe-9cae-cdc0dd4bcfb2","resolution":{"observed_at":"2026-08-11T17:33:17.863165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09685","last_updated":"2021-10-16T18:40:34Z","snapshot_observed_at":"2026-08-11T08:20:29.798517Z","submitted_at":"2021-06-17T17:37:18Z","title":"LoRA: Low-Rank Adaptation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09685","snapshot_observed_at":"2026-08-11T17:33:17.869307Z","title":"Lora: Low-rank adaptation of large language mo dels,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.869307Z"},"links":{"cited_paper":"/paper/2106.09685","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:466368e0cfb3837973b2fc28e26fe260e608ffa97d79ef6a09e66503a269b90c","observation_id":"293fb183-7be1-4c57-92f1-71a6acd6f8bc","resolution":{"observed_at":"2026-08-11T17:33:17.869307Z","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":"2026-08-11T17:33:17.877833Z","title":"Qlora: Efficient finetuning of quantized llms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.877833Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:daa752ab86deb622794d5b016b805ae1db1eab127ba7bd3879bce646cbc83073","observation_id":"fc4a88f0-56d9-4586-90d8-956bf707606c","resolution":{"observed_at":"2026-08-11T17:33:17.877833Z","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":"2026-08-11T17:33:17.883989Z","title":"Diffstyler: Diffusion-based localized image s tyle transfer,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.883989Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:e546d9d40dfe959ce9c5f1d2f1c565ef35057a0c49af0822834b1a337a7b3ac8","observation_id":"ca478f4a-b2d5-40e5-9408-b3630c690c42","resolution":{"observed_at":"2026-08-11T17:33:17.883989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.05556","last_updated":"2023-11-09T18:04:15Z","snapshot_observed_at":"2026-07-06T16:45:20.005195Z","submitted_at":"2023-11-09T18:04:15Z","title":"LCM-LoRA: A Universal Stable-Diffusion Acceleration Module","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.05556","snapshot_observed_at":"2026-08-11T17:33:17.889679Z","title":"Lcm-lora: A universal stable-diffusion acceleration module,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.889679Z"},"links":{"cited_paper":"/paper/2311.05556","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:2876b41d417e2826b73ed8eb2c895812015d578c99fe7183d76e051b28443bd1","observation_id":"04c68576-721c-43c2-b64d-e7a56823de9f","resolution":{"observed_at":"2026-08-11T17:33:17.889679Z","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":"2026-08-11T17:33:17.896989Z","title":"T2i- adapter: Learning adapters to dig out more controllable abi lity for text- to-image diffusion models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.896989Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:1e357978a3315f316024e9d936d504c5c416fd48c4daba1372a7b79d0cda45bc","observation_id":"2978468b-673e-4b52-b2a5-8fd60824503f","resolution":{"observed_at":"2026-08-11T17:33:17.896989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.06721","last_updated":"2023-08-13T08:34:51Z","snapshot_observed_at":"2026-07-06T16:05:39.158819Z","submitted_at":"2023-08-13T08:34:51Z","title":"IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.06721","snapshot_observed_at":"2026-08-11T17:33:17.901466Z","title":"Ip-adapter: Text compatible image prompt adapter for text-to-image diffusi on models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.901466Z"},"links":{"cited_paper":"/paper/2308.06721","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:7d0812f6e4fdc08a19d13f46ee48da193bdbc3f1e5beaf12018b01871e7a5f2a","observation_id":"2af8e51d-338a-4525-9655-7c1ad98ccf7d","resolution":{"observed_at":"2026-08-11T17:33:17.901466Z","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":"2026-08-11T17:33:17.906027Z","title":"Optimizing prompts f or text-to-image generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.906027Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:d59bc3f34bd42a1bff0d91661cf9f58915d1f3f8e91ce56d5c31cfe7c1f20190","observation_id":"53e2f0bb-d333-41db-82e8-7bfb1ef6901c","resolution":{"observed_at":"2026-08-11T17:33:17.906027Z","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":"2026-08-11T17:33:17.909750Z","title":"Sparsity-and hybrid ity-inspired visual parameter-efficient fine-tuning for medical diagnos is,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.909750Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:0f9884ca6f77954c904926611db076b39e61e29be781eb7d367076b624614b85","observation_id":"066ec47d-8e97-4acf-bf14-4b5894d0dc28","resolution":{"observed_at":"2026-08-11T17:33:17.909750Z","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":"2026-08-11T17:33:17.915133Z","title":"Zero: M emory optimizations toward training trillion parameter models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.915133Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:2d44fdd8247a9f2546e889b7d68934b64fcc3e7f49742afc52a8722042728411","observation_id":"2fae8c87-b4f0-46ad-b981-48d072ca126f","resolution":{"observed_at":"2026-08-11T17:33:17.915133Z","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":"2026-08-11T17:33:17.924168Z","title":"Efficient large-scale language model training on gpu clus ters using megatron-lm,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.924168Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:6c1e9c04062ec05c30656419da4cd00f6e9cb777dba20afefbd20df495ab099e","observation_id":"21cbe4f9-f7f2-4005-b835-441f02feee26","resolution":{"observed_at":"2026-08-11T17:33:17.924168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.01738","last_updated":"2024-11-04T01:40:38Z","snapshot_observed_at":"2026-08-06T20:23:19.678716Z","submitted_at":"2024-11-04T01:40:38Z","title":"xDiT: an Inference Engine for Diffusion Transformers (DiTs) with Massive Parallelism","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.01738","snapshot_observed_at":"2026-08-11T17:33:17.929634Z","title":"xdit: an infer ence engine for diffusion transformers (dits) with massive para llelism,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.929634Z"},"links":{"cited_paper":"/paper/2411.01738","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:cd0dcd71664652236e6be3e0649cc6cf306c2db71b6dcae82279ce98e83bad6e","observation_id":"b1caf7f2-bdb7-447a-8e3d-940e75636eeb","resolution":{"observed_at":"2026-08-11T17:33:17.929634Z","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":"2026-08-11T17:33:17.933788Z","title":"Deep generative model for imag e inpainting with local binary pattern learning and spatial a ttention,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.933788Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:834b394d6a2f40c8b7c294b7e92809792faa07ba4ede96d9b99cf1ecffef83dd","observation_id":"be0a2d13-e0eb-40cd-bb7d-a5f06ad55e03","resolution":{"observed_at":"2026-08-11T17:33:17.933788Z","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":"2026-08-11T17:33:17.938019Z","title":"Gen erative image inpainting with contextual attention,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.938019Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:5d4b4f2a99507e0efcfbcbd269c138652e95ea3110bcd6cc8fab8c4f8459264d","observation_id":"ecc82349-c306-4fd0-8f04-66441d2dfacd","resolution":{"observed_at":"2026-08-11T17:33:17.938019Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.12035","last_updated":"2023-04-24T12:19:06Z","snapshot_observed_at":"2026-08-02T18:12:09.775388Z","submitted_at":"2023-04-24T12:19:06Z","title":"GRIG: Few-Shot Generative Residual Image Inpainting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.12035","snapshot_observed_at":"2026-08-11T17:33:17.942904Z","title":"Grig: Few-shot generative residual image inpaint ing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.942904Z"},"links":{"cited_paper":"/paper/2304.12035","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:6722c5a7a93e6b7a8f506eb9a22829ed9bf73b0e7369e337beb2e9fad37e2611","observation_id":"73254ffd-cd26-4a63-aeab-3b2a94d33bad","resolution":{"observed_at":"2026-08-11T17:33:17.942904Z","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":"2026-08-11T17:33:20.003854Z","title":"Latentpaint : Image inpainting in latent space with diffusion models,","venue":null,"work_id":"77cd5363-6acd-49fe-a686-f7dd97670546","year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.948798Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:0709cb8435fbdc112a033d155c4b25949b68a313604bf5568dc4bb7cb862a13a","observation_id":"d7b0bd61-6ee9-445d-bebc-432d4aa7c034","resolution":{"observed_at":"2026-08-11T17:33:20.010105Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.984086Z","title":"Repaint: Inpainting using denoising diffusio n probabilis- tic models,","venue":null,"work_id":"6d63af41-d2d7-4968-a447-d889bb56616d","year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.953839Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:a583cc1960f385695c160f8e6db4e566c05d9c435bb446792ce026921dd049cf","observation_id":"fcbaa0a9-1f01-400b-bddc-34d1690f6c15","resolution":{"observed_at":"2026-08-11T17:33:19.990915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.965139Z","title":"Gradpaint: Gradi ent-guided inpainting with diffusion models,","venue":null,"work_id":"7e3e6a19-57e2-41fd-83f2-7325341cace5","year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.958784Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:982c142ec8203e0858a6ae2c75fb5ef9273fbea391c691facd7a6677ae03e556","observation_id":"94b398ab-9fe8-49ab-94c1-a221fdfcf59f","resolution":{"observed_at":"2026-08-11T17:33:19.970650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.948624Z","title":"Inout: Diverse image outpainting via gan inversio n,","venue":null,"work_id":"264310fa-19ad-42b3-a7de-c9359ec8b9da","year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.965294Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:ae519b5e5f232abfa76acade2a7ac9ee0e99b74dd25cf14a44afc0293dbb171f","observation_id":"65b5d348-be7f-40dc-ab1d-482fae254faa","resolution":{"observed_at":"2026-08-11T17:33:19.954920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.931092Z","title":"Painting outside as inside: Edge guided image outpainting via bidirec- tional rearrangement with progressive step learning,","venue":null,"work_id":"b3cbb152-f646-4964-9929-d243603060a4","year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.974223Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:019508c838e886172672890b70ccc9589c05e3656d00799c90cfc2f7cb4041b7","observation_id":"5d92696b-e5fe-4dda-ae83-dabbe423a5f7","resolution":{"observed_at":"2026-08-11T17:33:19.937063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.912423Z","title":"Towards reliable image outpainting: Learning structure-aware multimodal fusion with depth guidance,","venue":null,"work_id":"937d50c5-40a3-4ede-b50a-953c1714f220","year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.980137Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:811e62e5cb232c798c48495e2e53b7c112503ccaa6f05a0584740658094f743d","observation_id":"32f24867-aaec-46aa-ab66-084c64fef47d","resolution":{"observed_at":"2026-08-11T17:33:19.918684Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.896682Z","title":"Em ergent correspondence from image diffusion,","venue":null,"work_id":"596233c7-30ab-4f21-9e0f-6dfa471c75c4","year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.985307Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:9fc2fe58adc9ed7de8bf1dc907ea5c91f4f37e1416690d40a4a26741fa379758","observation_id":"d78f7174-db61-49fa-967d-6cc2d362ad6c","resolution":{"observed_at":"2026-08-11T17:33:19.902011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.877291Z","title":"Zero-1-to-3: Zero-shot one image to 3d object ,","venue":null,"work_id":"5c78a8dc-f2ed-42a9-843f-6a4b1f36f6ac","year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.990237Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:0146ece8a1d00d78461db7af0bdf99f06187bb1dee72db1bc09c3eda393e424d","observation_id":"d28f42f2-dbf9-43e2-b827-324163efc19d","resolution":{"observed_at":"2026-08-11T17:33:19.883337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.857881Z","title":"Wonder3d: Single image to 3d using cross-domain diffusion,","venue":null,"work_id":"e76e5936-8a54-4761-b0ed-4f3f1ddc3990","year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:17.995405Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:254cca48c7f3aa44bcb7b6d67bf8760daf7a69c73374a89a872357323ea21b32","observation_id":"2c2f0805-2dbe-4e13-982c-683465ee12eb","resolution":{"observed_at":"2026-08-11T17:33:19.862786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.840432Z","title":"Make-it-3d: High-fidelity 3d creation from a single image w ith diffu- sion prior,","venue":null,"work_id":"3c6ce3d5-f66f-471b-acbc-9286d506b892","year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.001069Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:68ecc297e1fc5f60afb4e92c2eae56e1272b4abb5743c43c9b00e915c83d71b3","observation_id":"ac786a30-8546-4ac1-b9e2-be4b3ad9f5fa","resolution":{"observed_at":"2026-08-11T17:33:19.846515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.817800Z","title":"Adding conditional c ontrol to text-to-image diffusion models,","venue":null,"work_id":"fd532210-0a00-411b-9904-0246bb18bf79","year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.006447Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:6c59e856bb0c11358df0cd3a2b950997726555f460e7c10b4faf2a3c3b077607","observation_id":"c60e56bb-7bf1-4104-ab32-8e125ce0b80c","resolution":{"observed_at":"2026-08-11T17:33:19.823009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.06573","last_updated":"2024-08-12T14:52:49Z","snapshot_observed_at":"2026-07-06T16:59:56.350207Z","submitted_at":"2023-12-11T17:58:06Z","title":"ControlNet-XS: Rethinking the Control of Text-to-Image Diffusion Models as Feedback-Control Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.06573","snapshot_observed_at":"2026-08-11T17:33:18.011212Z","title":"Controlnet- xs: Designing an efficient and effective architecture for controlling tex t-to-image diffusion models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.011212Z"},"links":{"cited_paper":"/paper/2312.06573","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:ac7bdb9de532e9525bbb35bd77988eea34e9309f030a3321d1bd220609debf95","observation_id":"d2af0bef-a05e-44f3-bb3c-13dcc3f33860","resolution":{"observed_at":"2026-08-11T17:33:18.011212Z","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":"2026-08-11T17:33:19.796646Z","title":"Uni-controlnet: All-in-one control to text-to-ima ge diffusion models,","venue":null,"work_id":"9b048682-d4fc-4bf9-8b5f-8c9fd6c10984","year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.018515Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:84f42822273592102f78cef11c4fa1a38d7ee85f33bd7a65ef18835ac8a97beb","observation_id":"944dfbb4-73b6-4f92-9b4b-da18fc0d23a2","resolution":{"observed_at":"2026-08-11T17:33:19.803159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.00983","last_updated":"2023-06-01T17:59:51Z","snapshot_observed_at":"2026-07-06T15:36:45.014921Z","submitted_at":"2023-06-01T17:59:51Z","title":"StyleDrop: Text-to-Image Generation in Any Style","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.00983","snapshot_observed_at":"2026-08-11T17:33:18.023945Z","title":"Styledrop: Text-to-image generation in any style,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.023945Z"},"links":{"cited_paper":"/paper/2306.00983","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:3b50c810940ef22aeee00c972382f3837f40e6838f83d335709d24a18e361502","observation_id":"309b61eb-7439-4968-8e0e-a7abc9033f95","resolution":{"observed_at":"2026-08-11T17:33:18.023945Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.09728","last_updated":"2023-04-20T04:17:31Z","snapshot_observed_at":"2026-08-12T09:50:49.124598Z","submitted_at":"2023-04-19T15:15:36Z","title":"Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate","version":2},"cited_work":{"arxiv_id":"2304.09728","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.09728","snapshot_observed_at":"2026-08-11T17:33:18.796048Z","title":"Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate","venue":"cs.CV","work_id":"d12d432e-ccf1-4fab-a024-d2955ce786bc","year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.028766Z"},"links":{"cited_paper":"/paper/2304.09728","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:e851d06df7365692d9e67d30d91075986aea3c78604cc52deefeacf14c5efd9b","observation_id":"6543d28d-ead1-4476-b7ca-d369949ed463","resolution":{"observed_at":"2026-08-11T17:33:18.801676Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.782736Z","title":"Clipstyler: Image style transfer w ith a single text condition,","venue":null,"work_id":"deef06cd-47f2-4b44-8a7d-1cbbc87514e1","year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.033573Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:d4eb4091deea5e7461b5914cce198f4a2aa1c70168c010c9374562db4e0c5a1b","observation_id":"5f1d7eca-9602-417e-9ad9-7fe36d5b6862","resolution":{"observed_at":"2026-08-11T17:33:19.787253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01414","last_updated":"2025-02-23T13:45:34Z","snapshot_observed_at":"2026-08-11T21:51:00.732318Z","submitted_at":"2024-07-01T16:05:18Z","title":"StyleShot: A Snapshot on Any Style","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01414","snapshot_observed_at":"2026-08-11T17:33:18.038844Z","title":"Styleshot: A snapshot on any style,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.038844Z"},"links":{"cited_paper":"/paper/2407.01414","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:409f77ed5e5bd17a252fef46e3c7c86dae086f84eddb86bf6abbe68f7e2648ae","observation_id":"13a4748c-9da6-44d0-a62e-52b0a829dc7b","resolution":{"observed_at":"2026-08-11T17:33:18.038844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.09762","last_updated":"2023-02-20T05:00:20Z","snapshot_observed_at":"2026-08-10T05:16:18.279057Z","submitted_at":"2023-02-20T05:00:20Z","title":"Metropolis Theorem and Its Applications in Single Image Detail Enhancement","version":1},"cited_work":{"arxiv_id":"2302.09762","doi":null,"metadata_source":"pith","pith_arxiv_id":"2302.09762","snapshot_observed_at":"2026-08-11T17:33:18.746295Z","title":"Metropolis Theorem and Its Applications in Single Image Detail Enhancement","venue":"cs.CV","work_id":"3f228cd9-6086-4916-8139-96d252aa9b1f","year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.044077Z"},"links":{"cited_paper":"/paper/2302.09762","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:f7884483ac1c7e88e92fee171e6eba3949eb9a1c5d9cfd7a078f140eb1a796fd","observation_id":"5aa9a9bd-8c63-4537-b4d7-0a378e6cf55f","resolution":{"observed_at":"2026-08-11T17:33:18.752170Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.770034Z","title":"Multi-scale image decomposition using a l ocal statistical edge model,","venue":null,"work_id":"e7b66d5b-1d23-43ac-9078-b13501baaac7","year":2021},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.049458Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:af595ec6eabaa424cf3d08a303e1a4d43cab61cc5e16b77cfe59b94005b8c3df","observation_id":"8e556311-82c4-457d-bc36-0c4ea4503e56","resolution":{"observed_at":"2026-08-11T17:33:19.773939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14132","last_updated":"2024-04-22T12:33:18Z","snapshot_observed_at":"2026-08-12T14:06:02.422861Z","submitted_at":"2024-04-22T12:33:18Z","title":"CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14132","snapshot_observed_at":"2026-08-11T17:33:18.055157Z","title":"Crnet: A detail-preserving network for unified i mage restoration and enhancement task,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.055157Z"},"links":{"cited_paper":"/paper/2404.14132","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:a6222b0176b0369e339342b354ae4f5ffdb6e7d089741387ea05655b794f630f","observation_id":"007aecf8-167e-4ace-a39a-55820318a1a4","resolution":{"observed_at":"2026-08-11T17:33:18.055157Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13281","last_updated":"2024-12-22T11:06:33Z","snapshot_observed_at":"2026-07-06T18:33:28.511600Z","submitted_at":"2024-06-19T07:21:31Z","title":"ECAFormer: Low-light Image Enhancement using Cross Attention","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.13281","snapshot_observed_at":"2026-08-11T17:33:18.060291Z","title":"Ecaformer: Low-light image enhancement using cross attention,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.060291Z"},"links":{"cited_paper":"/paper/2406.13281","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:11ed0c5d5f493e243ae61b1dbe529c504c3407a37859b7cb8f737047820e5356","observation_id":"66777fd2-f9ff-4ad1-8183-17ad40b08357","resolution":{"observed_at":"2026-08-11T17:33:18.060291Z","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":"2026-08-11T17:33:19.756966Z","title":"Gans trained by a two time-scale update rule converge to a lo cal nash equilibrium,","venue":null,"work_id":"6a8cb7c2-1fc9-406b-93d3-d7b6f5821ab5","year":2017},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.065434Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:6281f2fec21c208dc31be66792161d3832a647d4148d13482f92a7fea0fb3497","observation_id":"3e7a62bf-f393-48a5-9201-fc1b355bda7f","resolution":{"observed_at":"2026-08-11T17:33:19.761780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.740939Z","title":"On aliased resizing and surprising subtleties in gan evaluation,","venue":null,"work_id":"e65bb8ee-78bf-4142-988a-eea49614fdef","year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.069679Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:8166c803090f2b07505bea56efb5650cd6b38ef298f0ca6b5d3c5de30812c83e","observation_id":"200c23a7-122f-442b-b88c-4bf65caf11ed","resolution":{"observed_at":"2026-08-11T17:33:19.747731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.01401","last_updated":"2021-01-14T05:36:59Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2018-01-04T15:25:26Z","title":"Demystifying MMD GANs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.01401","snapshot_observed_at":"2026-08-11T17:33:18.074132Z","title":"Demysti- fying mmd gans,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.074132Z"},"links":{"cited_paper":"/paper/1801.01401","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:e36201b004b7e37c549b0156efe00e22bc6e82120d4621f01fa6e58d120693c1","observation_id":"898b6efc-d526-4469-a506-02e95e928f85","resolution":{"observed_at":"2026-08-11T17:33:18.074132Z","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":"2026-08-11T17:33:19.720062Z","title":"The unreasonable effectiveness of deep features as a perceptua l metric,","venue":null,"work_id":"28961006-a35a-43e4-95c8-a03b0846e578","year":2018},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.077966Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:22d0b15e351d0eee5f235787f330433d33d1764e4994a9803a24024ba69b45ff","observation_id":"3fa6d0fe-cac9-41c0-8afb-7289a171feeb","resolution":{"observed_at":"2026-08-11T17:33:19.725412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T17:33:19.705274Z","title":"Imagereward: Learning and evaluating human preferences f or text- to-image generation,","venue":null,"work_id":"45909164-5139-425a-b875-557252b4bb57","year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.081769Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:3339f7bcff42bbdb7cb15774f2963f2589d005e04ced23980d668166bb3aa04a","observation_id":"dc4875b2-12cf-406c-94b4-4cf1015ae173","resolution":{"observed_at":"2026-08-11T17:33:19.710306Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.15098","last_updated":"2025-08-06T05:19:30Z","snapshot_observed_at":"2026-08-12T07:03:24.625260Z","submitted_at":"2024-08-27T14:30:36Z","title":"CLIP-AGIQA: Boosting the Performance of AI-Generated Image Quality Assessment with CLIP","version":3},"cited_work":{"arxiv_id":"2408.15098","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.15098","snapshot_observed_at":"2026-08-11T17:33:18.639442Z","title":"CLIP-AGIQA: Boosting the Performance of AI-Generated Image Quality Assessment with CLIP","venue":"cs.CV","work_id":"2bae280a-9cf4-485c-915a-dca77ba40879","year":2024},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.089271Z"},"links":{"cited_paper":"/paper/2408.15098","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:fb6da75b5ea96302373c77f10bfbb2ad7a44e150260bdef458947e7b320b5a58","observation_id":"59026ac4-f31a-4c37-b235-88a3e276b62b","resolution":{"observed_at":"2026-08-11T17:33:18.647530Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.12112","last_updated":"2022-11-22T09:27:53Z","snapshot_observed_at":"2026-07-06T14:21:38.060588Z","submitted_at":"2022-11-22T09:27:53Z","title":"Human Evaluation of Text-to-Image Models on a Multi-Task Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.12112","snapshot_observed_at":"2026-08-11T17:33:18.094338Z","title":"Human evaluation of text-to-image models on a multi-task benchma rk,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.094338Z"},"links":{"cited_paper":"/paper/2211.12112","citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:4bdea28f336a3044fa2da585d421e1a5cfa5c139ef8fbda5b4ac47e90454e879","observation_id":"6b45f769-a6ce-4d1b-8f12-91128d61fe69","resolution":{"observed_at":"2026-08-11T17:33:18.094338Z","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":"2026-08-11T17:33:19.689224Z","title":"Hum an-ai co- creation: evaluating the impact of large-scale text-to-im age generative models on the creative process,","venue":null,"work_id":"af0ae5ad-3cd1-48c8-8088-82f11bad0e73","year":2023},"citing_paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-11T17:33:18.101255Z"},"links":{"citing_paper":"/paper/2412.09656"},"observation_digest":"sha256:c091ed66d070b2e9c06e5cc48492cc62c8a9fc12f21e7e26a6a37018d834c097","observation_id":"60b8a62b-4e35-4141-97af-bbedb72cd9d0","resolution":{"observed_at":"2026-08-11T17:33:19.693968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.09656","last_updated":"2024-12-12T02:09:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T08:43:46.523046Z","submitted_at":"2024-12-12T02:09:04Z","title":"From Noise to Nuance: Advances in Deep Generative Image Models"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":78,"verified_exact":3,"verified_fuzzy":19},"total_outbound_references":122},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 100 of 122 outbound references and 1 inbound Pith citation observation for arXiv:2412.09656."}