{"as_of":"2026-08-23T21:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:885adb5b9cc45e8a247d3d514e66c9fe3f404b553468ea931cc9d27c9ba01f87","coverage":[{"denominator":44,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":44,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T21:16:13.170736Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.18906/citation-record","integrity":"/paper/2606.18906/integrity","json":"/paper/2606.18906/citation-record.json","paper":"/paper/2606.18906"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Stable flow: Vital layers for training-free image editing","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:e51a93bfe30209a729b08d0bdd6e7a97efa37d175e813be0706909ad5749a3e2","observation_id":"16c6bca1-5dac-4241-9fd5-7b4c651d3eba","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Ledits++: Limitless image editing using text-to-image models.2024 IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 8861– 8870, 2023","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:8950e61414e94002a67bb3b89ef80013bdeb78162f98c5437edd3c499aa9e6d2","observation_id":"2b143c24-d3c1-4a25-aa87-57f7b9e02e19","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Masactrl: Tuning-free mutual self- attention control for consistent image synthesis and editing","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:f3976f71fb27924764d7c9b5cabaf958c85d0af4ffd68537b453b09d225cc95b","observation_id":"06239af8-6f27-4ef4-a978-16d0b20aaf41","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Lomoe: Localized multi-object editing via multi-diffusion","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:6694f49f45bc9713e5f46436f6d4bad010847c706843b7bd81762662136e0e6a","observation_id":"07258591-2ed8-44ff-8351-6a8f21c65751","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Attend-and-excite: Attention-based se- mantic guidance for text-to-image diffusion models.ACM Transactions on Graphics (TOG), 42:1 – 10, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:e47f17512cba429cce3fab19337f105e23409914efd6cfbb675883cd6620dac7","observation_id":"97d45712-c31e-4ad6-92bf-ce0473d11d6b","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Training- free layout control with cross-attention guidance.2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 5331–5341, 2023","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:33017d7afc80d48c9f92fd40f149de25d20b1a9064569ac52841caa3dca3ea0a","observation_id":"5b93386c-22ac-47de-ae87-0bb95589762d","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.06558","last_updated":"2024-11-15T14:38:32Z","snapshot_observed_at":"2026-08-20T09:11:36.349143Z","submitted_at":"2024-11-10T18:45:41Z","title":"Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement","version":2},"cited_work":{"arxiv_id":"2411.06558","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2411.06558","snapshot_observed_at":"2026-07-04T00:19:13.837374Z","title":"arXiv preprint arXiv:2411.06558 (2024)","venue":null,"work_id":"f9d700cb-bb3f-4fa2-b8ba-ea4a616649bc","year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2411.06558","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:91a07ea43cc4b9904692d53ffe56b85085709cf9793d4871324bda826430d58d","observation_id":"11e06250-7f8b-407f-b924-00113e7e8bfc","resolution":{"observed_at":"2026-07-04T00:19:13.838737Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.11427","last_updated":"2022-10-20T17:16:37Z","snapshot_observed_at":"2026-08-16T16:22:28.831035Z","submitted_at":"2022-10-20T17:16:37Z","title":"DiffEdit: Diffusion-based semantic image editing with mask guidance","version":1},"cited_work":{"arxiv_id":"2210.11427","doi":"10.48550/arxiv.2210.11427","metadata_source":"pith","pith_arxiv_id":"2210.11427","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Diffedit: Diffusion-based semantic image editing with mask guidance","venue":"cs.CV","work_id":"e0e59c16-b6da-4ec1-9bf5-488374e2cde5","year":2022},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2210.11427","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:73a3feba51ac095799d842d84e23578f8c3eb47a7f080c48b77407b486587a94","observation_id":"2670fe9c-6848-4502-9210-53e721f7a637","resolution":{"observed_at":"2026-07-04T00:19:13.824790Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Be yourself: Bounded attention for multi-subject text-to-image generation.In European Conference on Com- puter Vision (ECCV), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:ac0a4918b2e53b3cc82d243a8c2548d58c21b2c9a2f3ed0a08acf83ca6448ce3","observation_id":"ec33acf0-d838-4021-9bdc-11c93d7681f9","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Layeredit: Disentangled multi-object editing via conflict- aware multi-layer learning.The 40th Annual AAAI Confer- ence on Artificial Intelligence, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:b4ebcd5ef4d1b68ce9b0178a6e279d69aa5dd05113a1783994fa0f2290ee5a47","observation_id":"aee8442f-a95f-42e7-8454-28916fc5b5e0","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:158781084f01cddde518fcfff5ec70a33004f8687656a9a1053c055bd8962e27","observation_id":"942016ec-a28e-45de-afc3-9fee9c44d338","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.01626","last_updated":"2022-08-02T17:55:41Z","snapshot_observed_at":"2026-08-17T00:44:11.103098Z","submitted_at":"2022-08-02T17:55:41Z","title":"Prompt-to-Prompt Image Editing with Cross Attention Control","version":1},"cited_work":{"arxiv_id":"2208.01626","doi":"10.48500/arxiv.2208.01626","metadata_source":"pith","pith_arxiv_id":"2208.01626","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Prompt-to-Prompt Image Editing with Cross Attention Control","venue":"cs.CV","work_id":"196f7eef-d65a-47e4-b815-9a188f6aedcf","year":2022},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2208.01626","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:3bb01d45d52e5a83ad213db0ec7eda51201f89ed4e3988048c67fcda80668696","observation_id":"464e181e-dc17-4df7-9521-24b295871501","resolution":{"observed_at":"2026-07-04T00:19:13.817733Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Delta de- noising score","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:7700fb634eb9e5cdb88dc01198c5bd7dc930b099f1a63c57bf5fae5d35012e48","observation_id":"3d2f580c-72a1-437f-91d1-9485e2a61fb8","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:459c244b58b1784410f840e4cd45358e35bca02dea4998969b55f554e1c8fa72","observation_id":"f4e4bd06-dc6d-499a-9400-c7e2b0a4b6e5","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Paralleledits: Efficient multi- aspect text-driven image editing with attention grouping.Ad- vances in Neural Information Processing Systems 37, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:5acd3cc3d60a21b8aed129cf210f21aca3ba8300f0703f576d197c8d4666dc47","observation_id":"4844667d-6341-4f18-a84a-893de8724387","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"An edit friendly ddpm noise space: Inversion and manipulations","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:52a28e6f42e108d62219a6ab5a542e7003b816c04d0cd51accc88618377d7138","observation_id":"34ff08ce-1f1d-4a24-8b11-967eb7bd089c","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Bounded editing: Multi-object image manipulation with region- specific control.Proceedings of the 40th ACM/SIGAPP Sym- posium on Applied Computing, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:13108d3e41f714ac121621391197cf10f9025105e8a48d8c7a5b8cba02b1581f","observation_id":"a2ab82ab-ab52-43eb-910b-b70fa5f83356","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Flexiedit: Frequency-aware latent refinement for enhanced non-rigid editing.In European Conference on Computer Vision (ECCV), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:4a6eb3864d6edfdd651b7f814c7d69e59d51f620e2b1b1f8bf2c1152282fe8de","observation_id":"ac30a8b0-91c9-421a-917c-6d189cb31d24","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.08629","last_updated":"2025-07-22T12:07:56Z","snapshot_observed_at":"2026-08-20T13:09:17.892252Z","submitted_at":"2024-12-11T18:50:29Z","title":"FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models","version":2},"cited_work":{"arxiv_id":"2412.08629","doi":"10.48550/arxiv.2412.08629","metadata_source":"pith","pith_arxiv_id":"2412.08629","snapshot_observed_at":"2026-07-10T06:15:00.866473Z","title":"Flowedit: Inversion-free text-based editing using pre-trained flow models","venue":"cs.CV","work_id":"5b1b53d2-265b-4bab-a263-7ecdeb3bcd75","year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2412.08629","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:c8b8574604653c681fadf084db2e1e9904a2c4eaa62914ab54e319dadcdc64df","observation_id":"457c0be6-1ed3-456b-85f9-a095c669c764","resolution":{"observed_at":"2026-07-04T00:19:13.827410Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15742","last_updated":"2025-06-24T05:31:03Z","snapshot_observed_at":"2026-08-14T01:48:52.921086Z","submitted_at":"2025-06-17T20:18:23Z","title":"FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space","version":2},"cited_work":{"arxiv_id":"2506.15742","doi":"10.48550/arxiv.2506.15742","metadata_source":"pith","pith_arxiv_id":"2506.15742","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space","venue":"cs.GR","work_id":"5dfe19d5-3541-4803-8fe9-3c8b9e29b281","year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2506.15742","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:60295e0d0f966cf906c6378a01dd7a16233d84bb3d13fb29a15f3835588b7b09","observation_id":"2dcde4a5-0b0c-40ad-9481-6c784c0eef4a","resolution":{"observed_at":"2026-07-04T00:19:13.833618Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-05-25T00:53:20.581238+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T00:53:20.581238+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.10864","last_updated":"2024-07-14T16:20:19Z","snapshot_observed_at":"2026-08-17T07:40:20.856015Z","submitted_at":"2023-07-20T13:33:28Z","title":"Divide & Bind Your Attention for Improved Generative Semantic Nursing","version":3},"cited_work":{"arxiv_id":"2307.10864","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.10864","snapshot_observed_at":"2026-07-04T00:19:13.843471Z","title":"arXiv preprint arXiv:2307.10864 (2023) 4","venue":null,"work_id":"7e54b8ed-5dac-4d4d-a542-1d85f45cc63e","year":2023},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2307.10864","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:ef7209fc05963620822ac856c2e86af26f170f58c15e74e746edc21d28fa6551","observation_id":"3c42d4e5-c98d-4023-ac5b-99fe1c9390b7","resolution":{"observed_at":"2026-07-04T00:19:13.844965Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Moedit: On learning quantity perception for multi- object image editing.2025 IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 2683– 2693, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:d24db4cf60a9bd606e2978809daf79ddd4a07f2ab81eaa3b940e5d698174ddbf","observation_id":"1674cfc6-3d64-407c-af28-831fb7b4c0f4","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:3173af236d5e292d959c898118308a50a85dfa134203f3ad5ac223e729f2d4cb","observation_id":"8b09ec97-b56f-4182-aebf-afa110cc501f","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Sdedit: Guided image synthesis and editing with stochastic differential equa- tions","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:7e800116507e852fa63db0334935cae9b81174dedea8a70a30b95bfaae93e4d0","observation_id":"6754e6cf-0d15-4a16-85b7-b21ee377d394","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Null-text inversion for editing real im- ages using guided diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:dda36d51341a3cfe95277b7a76de990fc9b63b4f7defba180454f418ee68ead5","observation_id":"9080cd43-fa1c-41f5-a002-638fdebe4725","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Contrastive denoising score for text-guided latent diffusion image editing.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 9192–9201, 2023","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:b733cbc7c391c97c3fe76b1474cb0e0f076774af32e85719e6ffba341234a4dc","observation_id":"b89ef234-651f-4710-a5d2-c1a97e1e7565","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Cross-attention head position patterns can align with human visual concepts in text-to-image gener- ative models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:6373ee0185d869b3b6e85092974a0ae38227e90823cfcac62841c826c4a9c513","observation_id":"7314afed-4875-4dc7-883c-cc8734e11d8f","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Peebles and Saining Xie","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:996910328b061f3b8d223467c730d39d3705c411552d6a3c723fe681ee2260f5","observation_id":"3ab95261-ccf5-4b58-8876-2c1b4af90c06","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","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":"2026-08-14T22:54:08.184266Z","submitted_at":"2023-07-04T23:04:57Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","version":1},"cited_work":{"arxiv_id":"2307.01952","doi":"10.48500/arxiv.2307.01952","metadata_source":"pith","pith_arxiv_id":"2307.01952","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","venue":"cs.CV","work_id":"8034c587-fba6-4941-87ba-c98f2ac962cb","year":2023},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2307.01952","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:317ab5d0296ebdf8e4284a4840181f07b33231fc9a412ffd9dd0aa580df2568c","observation_id":"a2039f51-ad34-4cf6-8d4d-49d85c0d2c2d","resolution":{"observed_at":"2026-07-04T00:19:13.847368Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Self-cross diffu- sion guidance for text-to-image synthesis of similar subjects","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:98745e3ea569e0becb9ed02b42bcde0e55d997ecb6512fa9cbb7f9e6088cc466","observation_id":"9f3d871f-92b9-4c5d-a44e-fb2537796e07","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:a1848b35826afdf6f2b65890b4804ad4684e30634098747b68eb6fc07ebc270a","observation_id":"95ee18fe-851a-4396-bd47-7e24fd884f2a","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Fds: Frequency-aware denois- ing score for text-guided latent diffusion image editing","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:81c2156250a5f9f83aaa38e74d5c6f01e69df9fb666075814c7fb9b04b1c9665","observation_id":"d4a6eef5-7588-474c-9717-257bfa7eb781","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:14a04cf90f8b44d43cc76e8de49976f868d3b678a6ed5c4dede3eef8cb5572e5","observation_id":"745a8ce8-4e95-43d2-9e0e-7a997fa97f5c","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.11487","last_updated":"2022-05-23T17:42:53Z","snapshot_observed_at":"2026-08-17T05:51:02.087480Z","submitted_at":"2022-05-23T17:42:53Z","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","version":1},"cited_work":{"arxiv_id":"2205.11487","doi":"10.48550/arxiv.2205.11487","metadata_source":"pith","pith_arxiv_id":"2205.11487","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","venue":"cs.CV","work_id":"af16442b-a46f-469d-8818-c37b53a504c7","year":2022},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2205.11487","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:1f9d9109bb76235a7ef1cab89d6f4bda96f0800451ae8e92a9ae66f569a64619","observation_id":"18e041d9-1539-40bf-b226-876f0287bd20","resolution":{"observed_at":"2026-07-04T00:19:13.839796Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-14T13:50:02.677+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T13:50:02.677+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.07519","last_updated":"2025-08-11T00:40:12Z","snapshot_observed_at":"2026-08-12T01:50:08.903662Z","submitted_at":"2025-08-11T00:40:12Z","title":"Exploring Multimodal Diffusion Transformers for Enhanced Prompt-based Image Editing","version":1},"cited_work":{"arxiv_id":"2508.07519","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.07519","snapshot_observed_at":"2026-07-04T00:19:13.840980Z","title":"Exploring multimodal diffusion trans- formers for enhanced prompt-based image editing.ArXiv, abs/2508.07519, 2025","venue":null,"work_id":"6b4fc47c-da9e-4c7a-ad87-4c9bce8b5111","year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2508.07519","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:e13e19aab70d8d9d49691a50eb5293b87966bb1c4374954b48f3b68cd391eca7","observation_id":"1eca3212-d3f5-47d8-82a5-6eb21b98ca1b","resolution":{"observed_at":"2026-07-04T00:19:13.842408Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-08-11T15:38:14.931716Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":"2010.02502","doi":"10.48550/arxiv.2010.02502","metadata_source":"pith","pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Denoising Diffusion Implicit Models","venue":"cs.LG","work_id":"8fa2128b-d18c-405c-ac92-0e669cf89ac0","year":2020},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:58fbf8e57a406bb0b27b6db6d57d62f9c838c892d9f1dabd1d8096b14f6dbf93","observation_id":"f78142ee-d449-4165-a219-927bc518b650","resolution":{"observed_at":"2026-07-04T00:19:13.832292Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-07-12T13:49:41.147667+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-12T13:49:41.147667+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Lin, and Ferhan Ture","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:5b2001c366a1e414eb30d027fa176d1c2bba9c433db5bd39873b8048568bec1f","observation_id":"a951d1c0-8cf9-475d-8a02-a13728c00748","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Diffuse, attend, and segment: Unsupervised zero-shot segmentation using stable diffusion","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:493d9b5a11b4b3d9a55b13c6cf087f5fcef1325dff9e884b7cbe1e9beeb7cd3a","observation_id":"2649cd71-d4e5-4ad7-ad96-fbb7b831c843","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Plug-and-play diffusion features for text-driven image-to-image translation","venue":null,"work_id":null,"year":1921},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:3409f931bb5222a9e722a291cc1591ef8120fb8ccfe7ce3df3de58c91ab4c7e0","observation_id":"449d92f8-53a0-42bc-bf9d-4b1346f9843e","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:30c8fd31fddd5581ed69eea9aa6a5fcb3b217231c367fd31cdabcd4deeb32ba3","observation_id":"f990c516-374b-4803-bb24-7cd8f002f34c","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.12149","last_updated":"2024-03-18T08:36:02Z","snapshot_observed_at":"2026-08-16T14:50:58.598537Z","submitted_at":"2023-10-18T17:59:02Z","title":"Object-aware Inversion and Reassembly for Image Editing","version":2},"cited_work":{"arxiv_id":"2310.12149","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2310.12149","snapshot_observed_at":"2026-07-04T00:19:13.835961Z","title":"Object-aware inver- sion and reassembly for image editing","venue":null,"work_id":"29547e34-5447-47e5-a3c6-351bd052706c","year":2023},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2310.12149","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:30791d4b4b042c4a832f8d05f3937f44f25cfabe1aaf3f25882f65c5d99230e1","observation_id":"2f99d9d1-985e-4ed0-bda3-5856248da9c7","resolution":{"observed_at":"2026-07-04T00:19:13.837425Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Splitflow: Flow decomposition for inversion-free text-to- image editing.In Conference on Neural Information Pro- cessing Systems (NeurIPS), 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:d59e41f777a0803df360bf45d850fc5d23bb74ddce50c2aafdc948b809743975","observation_id":"bade26b0-5bd3-452d-995c-a16a84e37f33","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T21:16:13.170736Z","title":"Efros, Eli Shecht- man, and Oliver Wang","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:abc5ff451e9c810b082a6d0aa37e61ec99cfbdf7ff8f51281f6c865cc8c9265a","observation_id":"cf403afa-fc09-440d-93e1-99686dfe2982","resolution":{"observed_at":"2026-06-26T21:16:13.170736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.05101","last_updated":"2025-05-12T01:42:39Z","snapshot_observed_at":"2026-08-16T17:19:36.499159Z","submitted_at":"2025-05-08T10:01:14Z","title":"MDE-Edit: Masked Dual-Editing for Multi-Object Image Editing via Diffusion Models","version":2},"cited_work":{"arxiv_id":"2505.05101","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.05101","snapshot_observed_at":"2026-07-04T00:19:13.833379Z","title":"Mde- edit: Masked dual-editing for multi-object image editing via diffusion models.ArXiv, abs/2505.05101, 2025","venue":null,"work_id":"78ebed79-2f05-434f-a5ba-a17643f8ae67","year":2025},"citing_paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:13.170736Z"},"links":{"cited_paper":"/paper/2505.05101","citing_paper":"/paper/2606.18906"},"observation_digest":"sha256:a3044300d208e24f949d239815fd2c744536831334dd5ebbfdedf02ce73cb9c2","observation_id":"9126997b-766f-448e-b193-7a5a963ebd01","resolution":{"observed_at":"2026-07-04T00:19:13.834809Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.18906","last_updated":"2026-06-17T10:32:14Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T21:21:41.637013Z","submitted_at":"2026-06-17T10:32:14Z","title":"BindEdit: Taming Attention Leakage for Precise Multi-Object Image Editing"},"reference_resolution":{"displayed":44,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":32,"verified_exact":8,"verified_fuzzy":0},"total_outbound_references":44},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2606.18906."}