{"as_of":"2026-08-08T13:50:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:dbae1e3aa313eb7054ebeee2d9b100363323b3b5d1e57499bbdb43e42c906812","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:58:50.714926Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.16725/citation-record","integrity":"/paper/2505.16725/integrity","json":"/paper/2505.16725/citation-record.json","paper":"/paper/2505.16725"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:55.968992Z","title":"Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","venue":null,"work_id":"0348356a-ecb8-4626-88d6-923adab9f647","year":2019},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:44.681994Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:0c51f43d886ffddcea59964cbfe6e8b07f7aef623c3b02dfb4685306146adf84","observation_id":"e746c292-416e-4eab-8ee8-19d3de6e7d1f","resolution":{"observed_at":"2026-08-07T14:58:56.062619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.21428/e4baedd9.e39b392d","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:51.117153Z","title":"From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen-AI","venue":null,"work_id":"43f28703-a316-45d6-9381-b302ed4f7da4","year":2024},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:44.788184Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:c9ffcb159f8667de7c9fb270bfd6209f906772fe0cdbdc9a5904a881627b66fb","observation_id":"91a466b8-6226-4d1b-9e66-51ef2751072c","resolution":{"observed_at":"2026-08-07T14:58:51.272992Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:44.894355Z","title":"Curriculum learning, in: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, Montreal Quebec Canada","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:44.894355Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:a87520a71b8de4a557307e27171b83612205be321bbd32b43631b74e28d5c7c7","observation_id":"6cff4fb2-f43a-4c80-9b68-1057e20d777e","resolution":{"observed_at":"2026-08-07T14:58:44.894355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.07543","last_updated":"2018-07-23T18:26:40Z","snapshot_observed_at":"2026-08-04T02:57:12.964401Z","submitted_at":"2018-07-19T17:17:23Z","title":"Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.07543","snapshot_observed_at":"2026-08-07T14:58:44.997063Z","title":"Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:44.997063Z"},"links":{"cited_paper":"/paper/1807.07543","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:fbbb06049151aa5e1763435ab3ea8a10fcbee1626f9965667ed14c6bbb69865d","observation_id":"1d6e2964-c68b-4428-80c0-dcd886796c2e","resolution":{"observed_at":"2026-08-07T14:58:44.997063Z","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":"10.1115/detc2016-60091","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:50.900018Z","title":null,"venue":null,"work_id":"f741ee4a-e0e4-41f7-a3c9-b86620f9148c","year":2016},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:45.106783Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:aac40ba2c33dcbc288155d726d3aafa02900be6d064fc01c1c233e284a929417","observation_id":"8fa287fd-3d6f-477f-a606-356c74194382","resolution":{"observed_at":"2026-08-07T14:58:50.959704Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:45.228094Z","title":"PaDGAN: Learning to Generate High-Quality Novel Designs","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:45.228094Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:75cc6eb529326a745fde917d97a7a8140f80b18fda5877f5f0ea9a79ecdf15a4","observation_id":"926bfae7-dc71-437c-919e-9ccdce788b10","resolution":{"observed_at":"2026-08-07T14:58:45.228094Z","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":"2026-08-07T14:58:45.379622Z","title":"Mo-padgan: Reparameterizing engineering designs for augmented multi-objective optimization","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:45.379622Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:e9c075f5bb13554f2a23b601a2b69b04a4b1aed6fc94e88d3d2d1a208d8e4e49","observation_id":"03bc651a-1c5a-48eb-b6eb-cf6c095b1c91","resolution":{"observed_at":"2026-08-07T14:58:45.379622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.09445","last_updated":"2022-10-26T10:48:06Z","snapshot_observed_at":"2026-07-06T12:49:14.981089Z","submitted_at":"2022-03-17T17:05:14Z","title":"Image Super-Resolution With Deep Variational Autoencoders","version":2},"cited_work":{"arxiv_id":"2203.09445","doi":null,"metadata_source":"pith","pith_arxiv_id":"2203.09445","snapshot_observed_at":"2026-08-07T14:58:53.479058Z","title":"Image Super-Resolution With Deep Variational Autoencoders","venue":"cs.CV","work_id":"6c61311a-6f3a-4da8-a0b2-e7d66dcf5568","year":2022},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:45.512273Z"},"links":{"cited_paper":"/paper/2203.09445","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:5e3b24e94104c95a7bbc84822d76fdacdc4fea5ce47a7df33fcada4db4c4f7f4","observation_id":"9c6f061b-4abd-4900-8968-29194f2251dd","resolution":{"observed_at":"2026-08-07T14:58:53.552617Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.05301","last_updated":"2021-03-21T11:42:08Z","snapshot_observed_at":"2026-08-05T02:26:30.716446Z","submitted_at":"2020-06-09T14:40:00Z","title":"VAEs in the Presence of Missing Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.05301","snapshot_observed_at":"2026-08-07T14:58:45.682886Z","title":"Vaes in the presence of missing data","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:45.682886Z"},"links":{"cited_paper":"/paper/2006.05301","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:7836561eb5580e90924fc8995ea9618001b8d3db307890f6f748234a45ff513b","observation_id":"f8daba1b-1b8b-4b13-8ede-b726b23fb83d","resolution":{"observed_at":"2026-08-07T14:58:45.682886Z","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":"2026-08-07T14:58:55.703141Z","title":"Diffusionmodelsbeatgansonimagesynthesis,in:Proceedingsofthe35thInternationalConferenceonNeural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA","venue":null,"work_id":"eb54df9c-e28d-4f0f-aa3a-337ccd8f05f6","year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:45.773929Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:78f0bcdcb50a4d3aaa652d57ac46f29d8675ccf9b4b842d416296c9f4af235da","observation_id":"f1f96085-13e2-477c-9b08-aa1476b6ba5d","resolution":{"observed_at":"2026-08-07T14:58:55.871040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03206","last_updated":"2024-03-05T18:45:39Z","snapshot_observed_at":"2026-07-06T17:40:01.975792Z","submitted_at":"2024-03-05T18:45:39Z","title":"Scaling Rectified Flow Transformers for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03206","snapshot_observed_at":"2026-08-07T14:58:45.859696Z","title":"Scaling rectified flow transformers for high-resolution image synthesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:45.859696Z"},"links":{"cited_paper":"/paper/2403.03206","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:ae9bc6d547ec53e024e288ab1e857968d4fdd57267b97d18bab0d65d3b46cc7b","observation_id":"8d371918-fdc8-4777-b342-8c6f95cb040e","resolution":{"observed_at":"2026-08-07T14:58:45.859696Z","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":"2026-08-07T14:58:55.470703Z","title":"Plantldm: A latent diffusion model for visual synthesis of plant images","venue":null,"work_id":"23e70f66-bfd4-4a81-be94-32e25bec059c","year":2022},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:45.967691Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:c4129c80661159fe9cf8c9f478e88dc91a4929cd303be0c20ca91e298399c42e","observation_id":"4425c055-7320-404d-907b-c76f8cb639c9","resolution":{"observed_at":"2026-08-07T14:58:55.560168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08718","last_updated":"2022-03-23T19:47:21Z","snapshot_observed_at":"2026-07-06T11:01:02.207193Z","submitted_at":"2021-04-18T05:00:29Z","title":"CLIPScore: A Reference-free Evaluation Metric for Image Captioning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08718","snapshot_observed_at":"2026-08-07T14:58:46.125311Z","title":"Clipscore: A reference-free evaluation metric for image captioning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:46.125311Z"},"links":{"cited_paper":"/paper/2104.08718","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:e3177db6b78784436758d5b18072d45d8bd5d294a9451524ff76f07f52a91e81","observation_id":"b2556855-8810-4637-9188-0857d57b743c","resolution":{"observed_at":"2026-08-07T14:58:46.125311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11239","last_updated":"2020-12-16T21:15:05Z","snapshot_observed_at":"2026-07-06T09:30:47.469703Z","submitted_at":"2020-06-19T17:24:44Z","title":"Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11239","snapshot_observed_at":"2026-08-07T14:58:46.280901Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:46.280901Z"},"links":{"cited_paper":"/paper/2006.11239","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:02602d2616fbcb0e8a1cb8fbb80c643af6e2e949f2354de695d304c3f80296e3","observation_id":"a058129e-e757-4a52-a1a4-50c2f9fb59c7","resolution":{"observed_at":"2026-08-07T14:58:46.280901Z","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":"2026-08-07T14:58:55.228490Z","title":"Dvm-car: A large-scale automotive dataset for visual marketing research and applications, in: Proceedings of IEEE International Conference on Big Data, pp","venue":null,"work_id":"15b2bd06-2204-4295-82e9-c16d134725b1","year":2022},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:46.407653Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:5815dbbf863fddc0edcb46dd36172d8839fabda6dbcee6ebca0f325cef421f1e","observation_id":"642d2d9f-57ba-4939-8d6c-bbda7c61a611","resolution":{"observed_at":"2026-08-07T14:58:55.382709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:54.974767Z","title":"Variational autoencoder with arbitrary conditioning, in: International Conference on Learning Representations","venue":null,"work_id":"0e9eb768-b687-4881-b1a2-df2188e67645","year":2019},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:46.517546Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:723c29a06051b3f726ca210c8b50c65c8ea6ea438438d75be15463b250581409","observation_id":"78f16d0f-ba8b-4f0a-a226-8a3666bf8868","resolution":{"observed_at":"2026-08-07T14:58:55.097720Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.12423","last_updated":"2021-10-18T10:52:33Z","snapshot_observed_at":"2026-08-04T23:46:27.521977Z","submitted_at":"2021-06-23T14:20:01Z","title":"Alias-Free Generative Adversarial Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.12423","snapshot_observed_at":"2026-08-07T14:58:46.619292Z","title":"Alias-free generative adversarial networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:46.619292Z"},"links":{"cited_paper":"/paper/2106.12423","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:5c71b263813f551f67684b14ee3aecb2bced999fe9562f5ade495fea83712dfe","observation_id":"229bba62-ad94-4f3c-a5c5-d22b79ce98b0","resolution":{"observed_at":"2026-08-07T14:58:46.619292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1912.04958","last_updated":"2020-03-23T17:21:07Z","snapshot_observed_at":"2026-07-06T08:43:31.085150Z","submitted_at":"2019-12-03T11:44:01Z","title":"Analyzing and Improving the Image Quality of StyleGAN","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1912.04958","snapshot_observed_at":"2026-08-07T14:58:46.750525Z","title":"Analyzing and improving the image quality of stylegan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:46.750525Z"},"links":{"cited_paper":"/paper/1912.04958","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:6da1950f8331b3a03fb6aa7b23c739b640527fe9c97361e9750c570387a698bd","observation_id":"a49c1f88-55f2-459c-b69f-3ee2fd4a2b98","resolution":{"observed_at":"2026-08-07T14:58:46.750525Z","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":"2026-08-07T14:58:46.830550Z","title":"Auto-Encoding Variational Bayes, in: ICLR 2014, arXiv.arXiv:1312.6114","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:46.830550Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:94b2c929ddb2356d44971715894d18fbb5d3c4ca83c438943ceadb3f369986d1","observation_id":"387a9380-3504-4796-8872-733b8f5d0aa6","resolution":{"observed_at":"2026-08-07T14:58:46.830550Z","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":"2026-08-07T14:58:46.971553Z","title":"An introduction to variational autoencoders","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:46.971553Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:74d2df4fe59a012de700f4c00c5d20406eea66abd6f7843d9bb3dc284ddbfba5","observation_id":"2b5e9c0f-d009-497c-be7b-8057c432888d","resolution":{"observed_at":"2026-08-07T14:58:46.971553Z","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":"2026-08-07T14:58:54.705643Z","title":"Flux.https://github.com/black-forest-labs/flux","venue":null,"work_id":"ca8e6d3f-4b9e-4395-b33d-b80e2b297836","year":2023},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:47.110795Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:005f78865b7f4e0c5e1035415faa679cbc7da7a917441417c538ca45899ef160","observation_id":"f1f4e7f8-4223-4b14-aed9-4111f7fcfbb6","resolution":{"observed_at":"2026-08-07T14:58:54.817985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11941","last_updated":"2020-06-21T23:47:32Z","snapshot_observed_at":"2026-07-06T09:31:11.484402Z","submitted_at":"2020-06-21T23:47:32Z","title":"VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11941","snapshot_observed_at":"2026-08-07T14:58:47.220219Z","title":"Vaem: a deep generative model for heterogeneous mixed type data, in: 34th Conference on Neural Information Processing Systems (NeurIPS 2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:47.220219Z"},"links":{"cited_paper":"/paper/2006.11941","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:41e6ef6077d42dba16b7a27689246c61b7c72a35cb8563b59b9d10a27af13736","observation_id":"b7185c67-3836-4d6c-84cd-243c4529115b","resolution":{"observed_at":"2026-08-07T14:58:47.220219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.11104","last_updated":"2025-05-21T10:55:54Z","snapshot_observed_at":"2026-07-06T18:46:43.829115Z","submitted_at":"2024-07-15T14:28:50Z","title":"Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception","version":2},"cited_work":{"arxiv_id":"2407.11104","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.11104","snapshot_observed_at":"2026-08-07T14:58:53.207109Z","title":"Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception","venue":"cs.LG","work_id":"634af20a-7c84-4b58-9c2a-0e52ac96fcbf","year":2024},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:47.334539Z"},"links":{"cited_paper":"/paper/2407.11104","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:9e6be1d844a56b954e9f94fce78eaebcb16ea103020080643cd51c6231904448","observation_id":"a5f0459f-d5a9-480f-8c0e-dff71486d16e","resolution":{"observed_at":"2026-08-07T14:58:53.304089Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.17045","last_updated":"2025-05-22T14:21:39Z","snapshot_observed_at":"2026-07-06T19:21:56.712367Z","submitted_at":"2024-09-25T15:57:59Z","title":"GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design","version":2},"cited_work":{"arxiv_id":"2409.17045","doi":null,"metadata_source":"pith","pith_arxiv_id":"2409.17045","snapshot_observed_at":"2026-08-07T14:58:52.946966Z","title":"GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design","venue":"cs.CV","work_id":"93ef6fd4-de0a-4720-b100-0d374c806705","year":2024},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:47.445897Z"},"links":{"cited_paper":"/paper/2409.17045","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:6170cf420e064f8529ad81ff97892497cfcdcadc4766675054e52e01b5454fc1","observation_id":"3a3c0e6a-d523-4977-ae39-5ce5378d3b2b","resolution":{"observed_at":"2026-08-07T14:58:53.085899Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10592","last_updated":"2024-07-15T10:15:58Z","snapshot_observed_at":"2026-08-06T03:36:11.761274Z","submitted_at":"2024-07-15T10:15:58Z","title":"InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture","version":1},"cited_work":{"arxiv_id":"2407.10592","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.10592","snapshot_observed_at":"2026-08-07T14:58:52.709413Z","title":"InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture","venue":"cs.CV","work_id":"4ad8cf7e-c691-45cd-96b1-b172a34c0950","year":2024},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:47.544091Z"},"links":{"cited_paper":"/paper/2407.10592","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:13c76a6ca05ebe163d008897d36b144602c795c5f72e512b353ca3ef02b089b6","observation_id":"8810cb6c-aeca-4cd0-bde8-f463da28f2e2","resolution":{"observed_at":"2026-08-07T14:58:52.802384Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:47.678146Z","title":"Handling incomplete heterogeneous data using vaes","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:47.678146Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:30c5f67fc1d9383d3199bea2adfc61ec0065a5632eff89ca3aacf28de3636371","observation_id":"16389661-c646-41c9-b82e-426188c09c34","resolution":{"observed_at":"2026-08-07T14:58:47.678146Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.03620","last_updated":"2021-06-07T13:45:12Z","snapshot_observed_at":"2026-07-06T11:16:43.669732Z","submitted_at":"2021-06-07T13:45:12Z","title":"PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design","version":1},"cited_work":{"arxiv_id":"2106.03620","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.03620","snapshot_observed_at":"2026-08-07T14:58:52.423815Z","title":"PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design","venue":"cs.LG","work_id":"0a70ef9b-0309-4f69-b0dd-e0ba580cee13","year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:47.795703Z"},"links":{"cited_paper":"/paper/2106.03620","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:c2de94a58e0c4dfaccc933296c5f30d13f7014c682fc27722d244f4edcca9fa0","observation_id":"647f3f90-deb6-4752-b969-8cde2e91193d","resolution":{"observed_at":"2026-08-07T14:58:52.591664Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06242","last_updated":"2021-03-10T18:22:35Z","snapshot_observed_at":"2026-07-06T10:48:42.637397Z","submitted_at":"2021-03-10T18:22:35Z","title":"CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis","version":1},"cited_work":{"arxiv_id":"2103.06242","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.06242","snapshot_observed_at":"2026-08-07T14:58:52.177845Z","title":"CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis","venue":"cs.LG","work_id":"91d2897e-2092-45a5-968e-4ac7c69b8b1b","year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:47.894761Z"},"links":{"cited_paper":"/paper/2103.06242","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:c6953104bb1b6de107521189d480477006c6036946f72d214bfc814ed0c52ce5","observation_id":"b74e434b-364a-4c6e-b177-9a2341c914f5","resolution":{"observed_at":"2026-08-07T14:58:52.253816Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1606.01583","last_updated":"2016-10-22T01:07:38Z","snapshot_observed_at":"2026-07-06T04:58:53.251426Z","submitted_at":"2016-06-05T23:42:19Z","title":"Semi-Supervised Learning with Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.01583","snapshot_observed_at":"2026-08-07T14:58:48.015421Z","title":"Semi-supervised learning with generative adversarial networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:48.015421Z"},"links":{"cited_paper":"/paper/1606.01583","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:af121368c7660caa0fef87c4219b700e99f75b51003dae9788128f5855459ed7","observation_id":"08934fbd-c742-4feb-a860-fdb79a8a9018","resolution":{"observed_at":"2026-08-07T14:58:48.015421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05328","last_updated":"2016-06-18T15:44:24Z","snapshot_observed_at":"2026-07-06T05:00:17.202185Z","submitted_at":"2016-06-16T19:40:56Z","title":"Conditional Image Generation with PixelCNN Decoders","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.05328","snapshot_observed_at":"2026-08-07T14:58:48.153714Z","title":"Conditionalimagegenerationwithpixelcnn decoders","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:48.153714Z"},"links":{"cited_paper":"/paper/1606.05328","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:dd900e6252ed8d2dd0cf1207777bc9ee0818cc13bd778fb87fb8448d6ea90ac0","observation_id":"954fa6ff-d585-4acf-beaa-3468cda08df9","resolution":{"observed_at":"2026-08-07T14:58:48.153714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.09748","last_updated":"2023-03-02T09:06:55Z","snapshot_observed_at":"2026-07-06T14:32:37.317828Z","submitted_at":"2022-12-19T18:59:58Z","title":"Scalable Diffusion Models with Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.09748","snapshot_observed_at":"2026-08-07T14:58:48.299848Z","title":"Scalable Diffusion Models with Transformers","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:48.299848Z"},"links":{"cited_paper":"/paper/2212.09748","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:87011c854f8c9cd35fffa3511a0650691ac09c717aaa375944509a42d5ca90a3","observation_id":"eb2b8fd4-1569-4e1b-b9ed-11daa343e422","resolution":{"observed_at":"2026-08-07T14:58:48.299848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12668","last_updated":"2024-12-09T18:54:36Z","snapshot_observed_at":"2026-08-05T14:56:31.433204Z","submitted_at":"2023-11-21T15:20:48Z","title":"From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12668","snapshot_observed_at":"2026-08-07T14:58:48.437632Z","title":"From Concept to Manufacturing: Evaluating Vision-Language Models for Engineering Design.arXiv:2311.12668","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:48.437632Z"},"links":{"cited_paper":"/paper/2311.12668","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:1027b19d9eed9f51d8995846bdb18eacebeaf824b0993f9e21fe54340518baa1","observation_id":"2fb8fe57-7e32-4b21-9bff-38d14533593c","resolution":{"observed_at":"2026-08-07T14:58:48.437632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07204","last_updated":"2023-10-11T05:32:29Z","snapshot_observed_at":"2026-08-04T02:08:00.313126Z","submitted_at":"2023-10-11T05:32:29Z","title":"State of the Art on Diffusion Models for Visual Computing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07204","snapshot_observed_at":"2026-08-07T14:58:48.569304Z","title":"State of the Art on Diffusion Models for Visual Computing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:48.569304Z"},"links":{"cited_paper":"/paper/2310.07204","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:caa720be679731b334fecd0962e8536a2d5b3648fa947087b79e7dd1f473aede","observation_id":"ceffeb42-9e3b-4fcf-b8a7-3a64085280d7","resolution":{"observed_at":"2026-08-07T14:58:48.569304Z","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":"2026-08-07T14:58:48.721817Z","title":"SDXL:ImprovingLatentDiffusion Models for High-Resolution Image Synthesis.arXiv:2307.01952","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:48.721817Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:3df8a1ce87c9425bd5d12863f715f2c816daba61e8eac02fbe1f1a06f773856d","observation_id":"7ef98347-8e22-4420-95a9-9798f083a0ef","resolution":{"observed_at":"2026-08-07T14:58:48.721817Z","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":"2026-08-07T14:58:54.441260Z","title":"Understanding Deep Learning","venue":null,"work_id":"8cf1ec93-b277-4586-b1b8-f43bb8b422cd","year":2023},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:48.858718Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:59881a386fc1fb62b9ea41aa070cb654d381748bda2810fb945a3ea70620097d","observation_id":"e55394ae-d076-474f-b822-063369ca34df","resolution":{"observed_at":"2026-08-07T14:58:54.604615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.05844","last_updated":"2021-08-17T21:30:38Z","snapshot_observed_at":"2026-08-03T16:54:45.908628Z","submitted_at":"2021-03-10T03:12:32Z","title":"BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks","version":3},"cited_work":{"arxiv_id":"2103.05844","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.05844","snapshot_observed_at":"2026-08-07T14:58:51.890350Z","title":"BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks","venue":"cs.LG","work_id":"f9f24cd2-8ddb-47da-bd32-9609415461a7","year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:48.976594Z"},"links":{"cited_paper":"/paper/2103.05844","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:414f07036ea246d4d6d9b5ef65515962dc906bf0efb2b688667719b12b1811d0","observation_id":"57bcaaa3-76b2-4597-afec-c28ffdd1e83f","resolution":{"observed_at":"2026-08-07T14:58:52.030763Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2112.10752","last_updated":"2022-04-13T11:38:44Z","snapshot_observed_at":"2026-07-06T12:20:47.369918Z","submitted_at":"2021-12-20T18:55:25Z","title":"High-Resolution Image Synthesis with Latent Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.10752","snapshot_observed_at":"2026-08-07T14:58:49.111985Z","title":"High-Resolution Image Synthesis with Latent Diffusion Models, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, arXiv.arXiv:2112.10752","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:49.111985Z"},"links":{"cited_paper":"/paper/2112.10752","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:a44f099cd5f6e14917f7c5f88362da6b9e6568e2c63222108273baa85373b5a3","observation_id":"7a4ab1d7-f089-4bb5-906d-0ee89cd1d3ab","resolution":{"observed_at":"2026-08-07T14:58:49.111985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1505.04597","last_updated":"2015-05-18T11:28:37Z","snapshot_observed_at":"2026-08-06T11:05:16.105361Z","submitted_at":"2015-05-18T11:28:37Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1505.04597","snapshot_observed_at":"2026-08-07T14:58:49.239746Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:49.239746Z"},"links":{"cited_paper":"/paper/1505.04597","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:171f8fc50558c36502793664dc17baf73cf4df86fda71adc345efb6cc9ae877d","observation_id":"55e06e56-1f6a-4080-802e-42087d184236","resolution":{"observed_at":"2026-08-07T14:58:49.239746Z","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":"2026-08-07T14:58:49.372709Z","title":"Palette: Image-to-image diffusion models, in: ACM SIGGRAPH 2022 Conference Proceedings, Association for Computing Machinery, New York, NY, USA","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:49.372709Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:085cd26021da4d04a6953006b0dfc4a7866c892d185c0bba99d29468dc5158bd","observation_id":"674020f2-9468-4f44-abfe-dcebc84caa49","resolution":{"observed_at":"2026-08-07T14:58:49.372709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.08402","last_updated":"2022-10-16T00:08:18Z","snapshot_observed_at":"2026-07-29T21:51:47.064287Z","submitted_at":"2022-10-16T00:08:18Z","title":"LAION-5B: An open large-scale dataset for training next generation image-text models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.08402","snapshot_observed_at":"2026-08-07T14:58:49.484697Z","title":"Laion-5b: An open large-scale dataset for training next generation image-text models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:49.484697Z"},"links":{"cited_paper":"/paper/2210.08402","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:c1a3a86409795b12a64cf59ba4c47df8abd6906d4a1d8cc9092440e879165a25","observation_id":"9aa01a32-a47b-4061-a067-6fd28f68d857","resolution":{"observed_at":"2026-08-07T14:58:49.484697Z","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":"2026-08-07T14:58:54.233233Z","title":null,"venue":null,"work_id":"9415acf6-cbef-4cd1-b3af-fec750737604","year":2015},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:49.620608Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:c15a790682a5b7eebdbaf7cb549e6c8e343b14f6ce44e79da93c7993fae453e2","observation_id":"ce768d48-b570-4775-99d7-69bee83cfdbc","resolution":{"observed_at":"2026-08-07T14:58:54.330908Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-07T14:58:49.769749Z","title":"Denoising diffusion implicit models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:49.769749Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:2538fea82d999ba4fcd8b905b38e97889bec9cd4ce5aee0ccc341987a8a3cc25","observation_id":"5d75d285-2496-4112-9219-6e57f6108036","resolution":{"observed_at":"2026-08-07T14:58:49.769749Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06390","last_updated":"2016-04-30T21:23:46Z","snapshot_observed_at":"2026-07-06T04:37:08.670695Z","submitted_at":"2015-11-19T21:26:58Z","title":"Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06390","snapshot_observed_at":"2026-08-07T14:58:49.911618Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:49.911618Z"},"links":{"cited_paper":"/paper/1511.06390","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:ca8e64426868f33c2bb2be8880012ac8c006010a7c2dc052458930d47c077bbd","observation_id":"42b861df-ee9e-47a5-9148-7c5e1476874b","resolution":{"observed_at":"2026-08-07T14:58:49.911618Z","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":"2026-08-07T14:58:54.007231Z","title":"Attention is all you need, in: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R","venue":null,"work_id":"44e382a7-68d3-4538-9ba0-eccd91849163","year":2017},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:50.057892Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:b49a3e47127c2596cac5161d54d6e1564459c94f2aa82bbf8d2defc75665bd60","observation_id":"76ee9450-3868-4b54-aff9-3278e34c9d47","resolution":{"observed_at":"2026-08-07T14:58:54.096905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:53.839050Z","title":"Diffusers: State-of-the-art diffusion models","venue":null,"work_id":"984427ca-6fac-4651-9a3b-e2997ec63062","year":2024},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:50.235760Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:48be8fc71daed82cb67cffb7dc9455595ef99ab8771827c69d3e8f1bf9e73eab","observation_id":"6b77fe0f-645a-42f8-a65f-1937380b7ad1","resolution":{"observed_at":"2026-08-07T14:58:53.925928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:58:50.341677Z","title":"Image quality assessment: from error visibility to structural similarity","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:50.341677Z"},"links":{"citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:e523c560837135fcf7a8a3846f16824d0e24c6ff4d8ad5b58674d9476aa042bb","observation_id":"3d6eab05-c699-4c94-a719-fb5b8e2110ed","resolution":{"observed_at":"2026-08-07T14:58:50.341677Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09901","last_updated":"2021-06-18T03:50:40Z","snapshot_observed_at":"2026-07-06T11:20:35.920607Z","submitted_at":"2021-06-18T03:50:40Z","title":"Generating various airfoil shapes with required lift coefficient using conditional variational autoencoders","version":1},"cited_work":{"arxiv_id":"2106.09901","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.09901","snapshot_observed_at":"2026-08-07T14:58:51.446961Z","title":"Generating various airfoil shapes with required lift coefficient using conditional variational autoencoders","venue":"cs.CE","work_id":"40c037ea-e2ce-4eea-9bc7-a30b7cc6fc65","year":2021},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:50.460425Z"},"links":{"cited_paper":"/paper/2106.09901","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:fc5f8ed67467fff79658b1ebbf72ef0509c101806a219f56660e2417a63b641a","observation_id":"adb76bd6-ae58-466f-8613-5d05b810be94","resolution":{"observed_at":"2026-08-07T14:58:51.563334Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.03924","last_updated":"2018-04-10T19:25:07Z","snapshot_observed_at":"2026-08-08T03:05:32.889595Z","submitted_at":"2018-01-11T18:54:17Z","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.03924","snapshot_observed_at":"2026-08-07T14:58:50.610200Z","title":"The unreasonable effectiveness of deep features as a perceptual metric","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:50.610200Z"},"links":{"cited_paper":"/paper/1801.03924","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:1e12b0e47b2296e5df0016e1e3827271f07ff82a25e4821f4db798526b120d79","observation_id":"756bb27e-36b0-4cd8-92e1-5753177c1d9d","resolution":{"observed_at":"2026-08-07T14:58:50.610200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.07964","last_updated":"2019-04-16T20:26:53Z","snapshot_observed_at":"2026-08-02T08:39:14.674659Z","submitted_at":"2019-04-16T20:26:53Z","title":"3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.07964","snapshot_observed_at":"2026-08-07T14:58:50.714926Z","title":"3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders.arXiv:1904.07964","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:50.714926Z"},"links":{"cited_paper":"/paper/1904.07964","citing_paper":"/paper/2505.16725"},"observation_digest":"sha256:a08b2f7011c56bc7511c682c23874124c14f8f5d00cc44abbf41e5b4594f4484","observation_id":"35dfb33a-f03e-48e3-a609-486a229010bc","resolution":{"observed_at":"2026-08-07T14:58:50.714926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.16725","last_updated":"2025-05-22T14:33:03Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T14:54:15.553828Z","submitted_at":"2025-05-22T14:33:03Z","title":"Masked Conditioning for Deep Generative Models"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":29,"verified_exact":10,"verified_fuzzy":9},"total_outbound_references":49},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2505.16725."}