{"as_of":"2026-08-10T06:19:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6fcbdc4f73a3b5a4d479431de45cebe966cffb0e1c620024cb5b88aef1da0d68","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":35,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T13:00:05.386028Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":2,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2108.01073","last_updated":"2022-01-05T00:07:35Z","snapshot_observed_at":"2026-08-08T06:35:17.078531Z","submitted_at":"2021-08-02T17:59:47Z","title":"SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-12T22:30:44.744368Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2108.01073"},"observation_digest":"sha256:9837ec5c18ba563775dedecff37370ace6f3d679912a961230d4af01b3340643","observation_id":"bf4e8a6a-3034-4fa9-911c-eaa5febb614b","resolution":{"observed_at":"2026-05-12T22:30:44.779257Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2208.01618","last_updated":"2022-08-02T17:50:36Z","snapshot_observed_at":"2026-08-02T23:40:32.342515Z","submitted_at":"2022-08-02T17:50:36Z","title":"An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-11T18:08:55.311069Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2208.01618"},"observation_digest":"sha256:96c9c96d8243d767af17e73a0676999cdbbba9dc239d5583028465e45784dbe2","observation_id":"33ebe451-4c94-4fc0-9337-66e7fed17fb5","resolution":{"observed_at":"2026-05-11T18:08:55.377497Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2209.03003","last_updated":"2022-09-07T08:59:55Z","snapshot_observed_at":"2026-07-06T13:49:40.974495Z","submitted_at":"2022-09-07T08:59:55Z","title":"Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T13:17:51.511535Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2209.03003"},"observation_digest":"sha256:4e41e5d2cf3bc94e6ec8b2f6223d2bb89114ded1eb40a0c35f7cd3b291c4ff95","observation_id":"8217305e-ba07-443a-941c-021bc977f63b","resolution":{"observed_at":"2026-05-10T13:17:51.558144Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2308.08089","last_updated":"2023-08-16T01:43:41Z","snapshot_observed_at":"2026-07-06T16:06:38.424057Z","submitted_at":"2023-08-16T01:43:41Z","title":"DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory","version":1},"reference_index":151,"source":"arxiv_source","source_observed_at":"2026-05-20T13:03:57.828598Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2308.08089"},"observation_digest":"sha256:f26f4969d5fad513d39a67e9ed637922378d027fd5caaecc24e5eb7b312b82aa","observation_id":"8a828b35-4ae3-4c38-a12a-fcd934bfb09e","resolution":{"observed_at":"2026-05-20T13:03:58.028964Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2411.19182","last_updated":"2026-05-07T04:53:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-11-28T14:35:25Z","title":"SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-23T08:37:13.529654Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2411.19182"},"observation_digest":"sha256:8de93d1c519f13f3c0ed924354e4cf4a24c26a4a1b8f7a02d615b9ca03a67f3b","observation_id":"7311f6a6-45ac-4721-b413-1fea986e3b9a","resolution":{"observed_at":"2026-05-23T08:37:44.792920Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2412.08079","last_updated":"2026-04-07T04:07:30Z","snapshot_observed_at":"2026-07-06T20:05:01.312121Z","submitted_at":"2024-12-11T03:52:17Z","title":"Regional climate risk assessment from climate models using probabilistic machine learning","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-23T07:26:44.351983Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2412.08079"},"observation_digest":"sha256:86418e4fa7aad477e518de186becf942fd2a814ee80bcc9d5855819fe7f67f9f","observation_id":"f19588a5-2c9f-420b-ac58-10c2855d2afc","resolution":{"observed_at":"2026-05-23T07:27:42.638182Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-09T13:00:05.386028Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.02225","last_updated":"2025-02-04T11:04:36Z","snapshot_observed_at":"2026-08-09T12:53:11.800664Z","submitted_at":"2025-02-04T11:04:36Z","title":"Exploring the latent space of diffusion models directly through singular value decomposition","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T13:00:05.386028Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2502.02225"},"observation_digest":"sha256:18d8ddeeba629a27b53e983b1806f7b01acf0468fc8b8c4a2f2103a10a5b9081","observation_id":"f5482d9c-3bfb-40f7-84c5-6449d9deb452","resolution":{"observed_at":"2026-08-09T13:00:05.386028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-08T22:03:29.958564Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04646","last_updated":"2026-06-01T08:18:28Z","snapshot_observed_at":"2026-08-08T21:56:34.854735Z","submitted_at":"2025-02-07T04:09:03Z","title":"Efficient Weighted Sampling via Score-based Generative Models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T22:03:29.958564Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2502.04646"},"observation_digest":"sha256:1e1805227dcbddf4cceed3b6047a464becc6b06f7e70145f22b4797377696898","observation_id":"8124e34b-0a99-4125-a77c-3016671b6387","resolution":{"observed_at":"2026-08-08T22:03:29.958564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-07T20:31:55.636688Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.09793","last_updated":"2025-02-13T22:03:34Z","snapshot_observed_at":"2026-08-07T20:25:04.748122Z","submitted_at":"2025-02-13T22:03:34Z","title":"Noise Controlled CT Super-Resolution with Conditional Diffusion Model","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T20:31:55.636688Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2502.09793"},"observation_digest":"sha256:9123ea5235e84a7798d6ec73a227c5248a39f22c3b84baffae2730d5a8572982","observation_id":"a8c69be4-bcc0-48f3-8b82-804ed914538c","resolution":{"observed_at":"2026-08-07T20:31:55.636688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-07T15:16:37.555651Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.15777","last_updated":"2025-05-21T17:28:14Z","snapshot_observed_at":"2026-08-07T15:09:42.719706Z","submitted_at":"2025-05-21T17:28:14Z","title":"Projection-Based Correction for Enhancing Deep Inverse Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T15:16:37.555651Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2505.15777"},"observation_digest":"sha256:ae47297d8a899d8599d0a70dc7da41e0131a1efc6840782a2272cd12b5565c2e","observation_id":"d1cf9d12-7c1a-4e91-8a5f-1751990963ae","resolution":{"observed_at":"2026-08-07T15:16:37.555651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2505.17353","last_updated":"2026-05-13T22:09:17Z","snapshot_observed_at":"2026-08-01T21:58:33.424389Z","submitted_at":"2025-05-23T00:12:20Z","title":"Dual Ascent Diffusion for Inverse Problems","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-19T14:26:14.468161Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2505.17353"},"observation_digest":"sha256:faefc399f0c2acf641541cc82c6bd8e26f54ff09e48010a8f97e6b6426a7ef72","observation_id":"bb37f283-0c5d-436d-b003-c327b135e057","resolution":{"observed_at":"2026-05-19T14:27:24.118081Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-07T14:32:13.104272Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.18674","last_updated":"2025-05-27T03:47:50Z","snapshot_observed_at":"2026-08-07T14:25:42.980592Z","submitted_at":"2025-05-24T12:32:53Z","title":"Restoring Real-World Images with an Internal Detail Enhancement Diffusion Model","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:32:13.104272Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2505.18674"},"observation_digest":"sha256:3df16347e69d3903fd9653335f870ccb293a4872112587f1eee36be31ead2d17","observation_id":"36e7a21a-aff4-4b80-ac1d-01000fa4cd4f","resolution":{"observed_at":"2026-08-07T14:32:13.104272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-07T10:54:49.808516Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.03979","last_updated":"2025-06-05T04:27:46Z","snapshot_observed_at":"2026-08-08T12:18:48.758557Z","submitted_at":"2025-06-04T14:09:25Z","title":"Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T10:54:49.808516Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2506.03979"},"observation_digest":"sha256:2a72df4cf7267e0d3b726ffece529e4b5d2393eaa9b1b7029d543c733a9a022a","observation_id":"74ed7f42-1c97-44f6-b1b0-bbffc27658bb","resolution":{"observed_at":"2026-08-07T10:54:49.808516Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-06T21:52:23.340274Z","title":"arXiv preprint arXiv:2108.02938 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.23184","last_updated":"2025-06-29T11:02:45Z","snapshot_observed_at":"2026-08-09T05:30:23.870174Z","submitted_at":"2025-06-29T11:02:45Z","title":"Score-based Diffusion Model for Unpaired Virtual Histology Staining","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T21:52:23.340274Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2506.23184"},"observation_digest":"sha256:e700a9c92e7c9a3f6e4c62bafdf051b64dfa473b6ba4e0f3910d4466fac01e75","observation_id":"e33f17e9-3c58-41a4-9b07-dac67130bbeb","resolution":{"observed_at":"2026-08-06T21:52:23.340274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-06T21:45:48.693232Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.23461","last_updated":"2025-06-30T01:45:33Z","snapshot_observed_at":"2026-08-06T21:39:24.125352Z","submitted_at":"2025-06-30T01:45:33Z","title":"Time-variant Image Inpainting via Interactive Distribution Transition Estimation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T21:45:48.693232Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2506.23461"},"observation_digest":"sha256:1b09ae3404f5033af1d257a93beb2f52f856da83aa133d094987cceb9f6035c3","observation_id":"84e6d2bf-636a-494e-b33e-c58f4ebae6ab","resolution":{"observed_at":"2026-08-06T21:45:48.693232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-04T19:11:18.947801Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.09427","last_updated":"2025-09-11T13:10:22Z","snapshot_observed_at":"2026-08-09T07:41:28.893291Z","submitted_at":"2025-09-11T13:10:22Z","title":"FS-Diff: Semantic guidance and clarity-aware simultaneous multimodal image fusion and super-resolution","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-04T19:11:18.947801Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2509.09427"},"observation_digest":"sha256:95cd1250966f1ce9e2fd3cf46b95ec91d644a87dc71abef55948abe2a81a9e03","observation_id":"9d015630-0407-41bd-b559-815ae6d01e45","resolution":{"observed_at":"2026-08-04T19:11:18.947801Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-03T20:15:26.070930Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models.arXiv preprint arXiv:2108.02938, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2511.20651","last_updated":"2026-07-21T00:23:00Z","snapshot_observed_at":"2026-08-04T02:39:13.955398Z","submitted_at":"2025-11-25T18:59:55Z","title":"RubricRL: Simple Generalizable Rewards for Text-to-Image Generation","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T20:15:26.070930Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2511.20651"},"observation_digest":"sha256:161f546ff2ad61d5e544443e6d565f89e9fa760a00af1bc3678c39ec787c169b","observation_id":"87220f07-0de0-4025-8c82-a6f3c7bd6da1","resolution":{"observed_at":"2026-08-03T20:15:26.070930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-03T19:13:45.432972Z","title":"arXiv preprint arXiv:2108.02938 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.01572","last_updated":"2026-05-26T14:45:24Z","snapshot_observed_at":"2026-08-03T19:13:41.583927Z","submitted_at":"2025-12-01T11:46:14Z","title":"Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T19:13:45.432972Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2512.01572"},"observation_digest":"sha256:bfd009da48ac1f01f662f97c57702cd1350542a9d6f35e4c981ed046a15df8e4","observation_id":"99499028-131d-4618-9397-a369bf0465b7","resolution":{"observed_at":"2026-08-03T19:13:45.432972Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2601.05127","last_updated":"2026-04-23T07:24:19Z","snapshot_observed_at":"2026-07-06T22:41:10.836235Z","submitted_at":"2026-01-08T17:17:47Z","title":"LooseRoPE: Content-aware Attention Manipulation for Semantic Harmonization","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-16T16:06:20.797660Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2601.05127"},"observation_digest":"sha256:fb34d6bda23eef82a94ed5e8abce7bf4f64f7a41ccf4f863c247d72f99d72902","observation_id":"f506409d-84fa-4b02-8ac6-e6a17f64e2cb","resolution":{"observed_at":"2026-05-16T16:08:04.447208Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2602.07715","last_updated":"2026-05-16T18:42:41Z","snapshot_observed_at":"2026-07-06T22:45:00.816348Z","submitted_at":"2026-02-07T21:44:52Z","title":"Analyzing and Guiding Zero-Shot Posterior Sampling in Diffusion Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-21T13:27:45.428602Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2602.07715"},"observation_digest":"sha256:c3f04de89084de74e88b97f4eda322b589036fd14b8bfa5d869737626ec38900","observation_id":"1ba143a8-f52e-4435-88ac-9ab20fadefb1","resolution":{"observed_at":"2026-05-21T13:30:12.662141Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-07-14T22:29:39.282608Z","title":"arXiv preprint arXiv:2108.02938 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.12050","last_updated":"2026-06-29T07:07:05Z","snapshot_observed_at":"2026-07-30T07:37:03.327915Z","submitted_at":"2026-03-12T15:24:00Z","title":"Translationese as a Rational Response to Translation Task Difficulty","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T22:29:39.282608Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2603.12050"},"observation_digest":"sha256:fdf1129c7659ac07f90800966d226f24eba0838a64b0c373123c73589e58fe8c","observation_id":"4bf047c5-aa42-4de6-8c90-57bd34b6869b","resolution":{"observed_at":"2026-07-14T22:29:39.282608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2603.21045","last_updated":"2026-04-14T14:42:00Z","snapshot_observed_at":"2026-08-04T22:36:33.634104Z","submitted_at":"2026-03-22T03:52:38Z","title":"LPNSR: Optimal Noise-Guided Diffusion Image Super-Resolution Via Learnable Noise Prediction","version":5},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-15T07:29:04.811039Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2603.21045"},"observation_digest":"sha256:1709db45bb62e9dfb75d95aedb4c1ba8f2aa9cae67e3e5999366bd247832669b","observation_id":"64f9ae34-0640-442f-b34d-d8f84cece39a","resolution":{"observed_at":"2026-05-15T07:29:50.902707Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2604.06881","last_updated":"2026-05-29T16:13:59Z","snapshot_observed_at":"2026-07-13T08:47:32.874526Z","submitted_at":"2026-04-08T09:39:49Z","title":"MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T18:36:38.674342Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2604.06881"},"observation_digest":"sha256:8e7fe5d15f8567f098d8d09aada41b7631cf48d2409b03f8e102d0c4f51e0ecd","observation_id":"0add42f0-ee3b-4512-b6d7-fe700e42b2b0","resolution":{"observed_at":"2026-05-11T00:20:51.362786Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-07-13T08:47:34.631129Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2604.06881","last_updated":"2026-05-29T16:13:59Z","snapshot_observed_at":"2026-07-13T08:47:32.874526Z","submitted_at":"2026-04-08T09:39:49Z","title":"MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T08:47:34.631129Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2604.06881"},"observation_digest":"sha256:a37acf44ddfdb921bc814c305fcca34fac3770270fbb309e478b9d5669656d35","observation_id":"5d7e0140-cd51-482b-b807-26abcddfc893","resolution":{"observed_at":"2026-07-13T08:47:34.631129Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2604.12575","last_updated":"2026-04-14T10:55:43Z","snapshot_observed_at":"2026-07-06T23:00:46.620106Z","submitted_at":"2026-04-14T10:55:43Z","title":"StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T14:56:12.713405Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2604.12575"},"observation_digest":"sha256:82d051bc2f59c9ac007b7821203f82b39be285e4080a52485553d582da655251","observation_id":"8c534548-3139-4e2d-add2-b9fd657c0cae","resolution":{"observed_at":"2026-05-11T11:26:01.770304Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2604.13028","last_updated":"2026-04-14T17:58:07Z","snapshot_observed_at":"2026-07-06T23:01:05.634599Z","submitted_at":"2026-04-14T17:58:07Z","title":"Conflated Inverse Modeling to Generate Diverse and Temperature-Change Inducing Urban Vegetation Patterns","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T15:49:15.377492Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2604.13028"},"observation_digest":"sha256:207aa3ca2039e34cee150a64a4633a4be61db3e8c3e2143151f6c96f55f0448d","observation_id":"b1583950-a4dc-4e5a-a67b-7ee9317e41ea","resolution":{"observed_at":"2026-05-11T09:50:58.868171Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2604.13581","last_updated":"2026-04-15T07:41:52Z","snapshot_observed_at":"2026-08-04T04:04:04.143303Z","submitted_at":"2026-04-15T07:41:52Z","title":"SocialMirror: Reconstructing 3D Human Interaction Behaviors from Monocular Videos with Semantic and Geometric Guidance","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T13:40:26.545640Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2604.13581"},"observation_digest":"sha256:5ff7b4984e3ed8136c08bf0af058ada7230267566feb0eaa27d47b2f36d03e3c","observation_id":"be0fbf85-80de-42db-94b7-cb5e6a3e6b51","resolution":{"observed_at":"2026-05-10T13:45:28.721750Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2604.16558","last_updated":"2026-04-17T08:39:56Z","snapshot_observed_at":"2026-07-06T23:03:52.631613Z","submitted_at":"2026-04-17T08:39:56Z","title":"Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T08:14:04.769153Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2604.16558"},"observation_digest":"sha256:72c84f5a45d1c42d04d127a19dc3e8b5630e3292f7fb949507f379b9ff56bda9","observation_id":"d42c2dd0-2273-42b7-8afd-191f8546577d","resolution":{"observed_at":"2026-05-10T08:17:37.574958Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2604.17986","last_updated":"2026-04-20T09:08:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-20T09:08:13Z","title":"Latent Fourier Transform","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T03:45:07.892234Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2604.17986"},"observation_digest":"sha256:bd7d73f73705a0a124f1bef465433f73312af86bacc687f717ead376aca75fcf","observation_id":"b7b41484-3d1a-4300-9793-60b814846bc8","resolution":{"observed_at":"2026-05-11T12:21:06.918370Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2604.21315","last_updated":"2026-04-23T06:14:40Z","snapshot_observed_at":"2026-07-06T23:07:56.487654Z","submitted_at":"2026-04-23T06:14:40Z","title":"TopoStyle: Supporting Iterative Design with Generative AI for 2.5D Topology Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-09T21:26:27.447547Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2604.21315"},"observation_digest":"sha256:1d6ae84f7609768419c27c688e23e3b108a358572fc6e17a3470e09ea11981ca","observation_id":"fd5f235c-edb6-4976-9099-6bed68641bb1","resolution":{"observed_at":"2026-05-11T14:36:07.262263Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2605.05387","last_updated":"2026-05-06T19:19:54Z","snapshot_observed_at":"2026-07-06T23:18:02.987518Z","submitted_at":"2026-05-06T19:19:54Z","title":"Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-08T17:45:33.056954Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2605.05387"},"observation_digest":"sha256:a5eb8baab5fb0adcf60e9bc6feaa8a13929169243b6057e2d343750fe56be3ce","observation_id":"14da1d29-5717-46fe-9829-8f897ea81703","resolution":{"observed_at":"2026-05-11T17:16:08.114997Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2605.28900","last_updated":"2026-05-27T15:11:09Z","snapshot_observed_at":"2026-08-09T18:33:58.141182Z","submitted_at":"2026-05-27T15:11:09Z","title":"Spectral Guidance for Flexible and Efficient Control of Diffusion Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T14:37:36.517249Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2605.28900"},"observation_digest":"sha256:2875c893e40fecdc1f8a5d94f2a980f85ed7e186febfbb31a0002151563e6b61","observation_id":"c2f900ba-c555-45e7-9dc7-6fd799b8a298","resolution":{"observed_at":"2026-06-29T14:43:30.764348Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":"2108.02938","doi":"10.48550/arxiv.2108.02938","metadata_source":"arxiv_reference","pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ilvr: Conditioning method for denoising diffusion probabilistic models","venue":"arXiv (Cornell University)","work_id":"cfb1b920-fb4e-4617-b335-ddfe3d6f7f84","year":2021},"citing_paper":{"arxiv_id":"2606.06813","last_updated":"2026-06-05T01:27:44Z","snapshot_observed_at":"2026-08-06T01:41:43.591563Z","submitted_at":"2026-06-05T01:27:44Z","title":"Breaking the Lock-in: Diversifying Text-to-Image Generation via Representation Modulation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-06-27T22:59:39.440344Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2606.06813"},"observation_digest":"sha256:9b06d3303e0728edabab5d0bb769588dd25cb5f9488efc54e6ae9b7e551f7e37","observation_id":"e15bc0a2-4cbf-4496-9584-0d5167e0fada","resolution":{"observed_at":"2026-07-02T16:07:08.980658Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-01T12:53:10.023120Z","title":"arXiv preprint arXiv:2108.02938 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.19333","last_updated":"2026-07-21T17:53:36Z","snapshot_observed_at":"2026-08-10T00:27:59.635411Z","submitted_at":"2026-07-21T17:53:36Z","title":"Provable diffusion-based posterior sampling for linear inverse problems via DDIM","version":1},"reference_index":253,"source":"arxiv_source","source_observed_at":"2026-08-01T12:53:10.023120Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2607.19333"},"observation_digest":"sha256:37d1a83df6886ba06fcdac1b3d9ffd7b6e0ebea20874139f2ea2d98404a10b86","observation_id":"049328a0-e0f9-4604-8985-a070a0bc8cfe","resolution":{"observed_at":"2026-08-01T12:53:10.023120Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.02938","snapshot_observed_at":"2026-08-05T11:39:46.637200Z","title":"arXiv preprint arXiv:2108.02938 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.03822","last_updated":"2026-08-04T15:30:35Z","snapshot_observed_at":"2026-08-09T09:01:10.438964Z","submitted_at":"2026-08-04T15:30:35Z","title":"FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-05T11:39:46.637200Z"},"links":{"cited_paper":"/paper/2108.02938","citing_paper":"/paper/2608.03822"},"observation_digest":"sha256:14b26fc0470a9b8e196ccded28b483f338453623a6783eed554a5b83c8c5159e","observation_id":"dfd33291-660c-44df-9a2f-5b3727e7d880","resolution":{"observed_at":"2026-08-05T11:39:46.637200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2108.02938/citation-record","integrity":"/paper/2108.02938/integrity","json":"/paper/2108.02938/citation-record.json","paper":"/paper/2108.02938"},"outbound":[],"paper":{"arxiv_id":"2108.02938","last_updated":"2021-09-15T04:03:34Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T11:36:03.810011Z","submitted_at":"2021-08-06T04:43:13Z","title":"ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 35 inbound Pith citation observations for arXiv:2108.02938."}