{"as_of":"2026-08-10T18:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2939fc3149ae34964946588fa34027b438aff0bf804693063bc9ba99986bc151","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:26:09.569617Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.21848/citation-record","integrity":"/paper/2505.21848/integrity","json":"/paper/2505.21848/citation-record.json","paper":"/paper/2505.21848"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-07T13:26:04.759240Z","title":"GPT-4 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:04.759240Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:05d3b6631e711709b198e12f5bc89cdeca89866af553b6c29872b2fecac120d9","observation_id":"761a0c53-34ef-40e4-a4c7-61a99729d7e7","resolution":{"observed_at":"2026-08-07T13:26:04.759240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:14.968962Z","title":"Extracting training data from diffu- sion models","venue":null,"work_id":"f0cec08c-c46b-4d66-aae4-1f246b849181","year":2023},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:04.824101Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:70e2717a61d542a38b52010a9339c093a9c2ac9cdba7de5d0fe58d95d0fa953f","observation_id":"e5e64a7d-ff12-43ae-9612-69518edb119f","resolution":{"observed_at":"2026-08-07T13:26:15.016806Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21665","last_updated":"2025-04-25T03:20:52Z","snapshot_observed_at":"2026-07-06T19:41:15.267950Z","submitted_at":"2024-10-29T02:16:01Z","title":"Exploring Local Memorization in Diffusion Models via Bright Ending Attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21665","snapshot_observed_at":"2026-08-07T13:26:04.866637Z","title":"Exploring local memorization in diffusion models via bright ending attention","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:04.866637Z"},"links":{"cited_paper":"/paper/2410.21665","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:a4d6cc6aecbd42edc219e6446ed25e53dba101147dab522e715461ecc104a014","observation_id":"a9d3123b-d74d-48c3-b23a-20c9e87b3183","resolution":{"observed_at":"2026-08-07T13:26:04.866637Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:14.788779Z","title":"Towards memorization-free diffusion models","venue":null,"work_id":"03ab8cdc-04d5-4221-8e40-06e07827fb40","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:04.945354Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:b9ea57045e847dfa6a7ad397992b9efdbea405adaf406ef8ae623c3bac981ea0","observation_id":"7264fcba-cdcf-4e2e-bee9-c1bd33a5770c","resolution":{"observed_at":"2026-08-07T13:26:14.871356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.10555","last_updated":"2020-03-23T21:17:42Z","snapshot_observed_at":"2026-08-06T14:37:14.514749Z","submitted_at":"2020-03-23T21:17:42Z","title":"ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.10555","snapshot_observed_at":"2026-08-07T13:26:04.995050Z","title":"ELECTRA: Pre-training text encoders as discriminators rather than generators","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:04.995050Z"},"links":{"cited_paper":"/paper/2003.10555","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:938c2d88e335d7637e76c7dc480625c12c0100862ea6f74e48924792eb303c2d","observation_id":"3b310a8d-023f-4e9e-b88c-ef9747117450","resolution":{"observed_at":"2026-08-07T13:26:04.995050Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:14.625058Z","title":"Diffusion models beat GANs on image synthesis","venue":null,"work_id":"227d3f5b-9f71-46b5-9b40-0b70ba2c75cf","year":2021},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.052080Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:dd1b022bf9e4b2fa7c1a55a0cc1a23f5153660249ce6f99ac66088d1f1659287","observation_id":"33b8e1f5-36ca-4f65-bb1f-9547eeeb5d48","resolution":{"observed_at":"2026-08-07T13:26:14.708574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:14.409767Z","title":"On the inherent regulariza- tion effects of noise injection during training","venue":null,"work_id":"d42c5851-2dd6-4e0e-9998-1469a7f59fe6","year":2021},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.098898Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:3e9345d9f670caf69aa81857d4af0735a67a6e721a5c0e03e722222138f98c61","observation_id":"6623414f-75c2-4e62-b91d-b6ebadc06015","resolution":{"observed_at":"2026-08-07T13:26:14.514225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:05.175525Z","title":"Generative adversarial networks","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.175525Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:369ae976cef3af295561ecd7eb1df2982b2428f4ed75a8167b18c8f9e321478b","observation_id":"fbd8452d-0b49-45d6-909c-8cecbc3c7f93","resolution":{"observed_at":"2026-08-07T13:26:05.175525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02664","last_updated":"2025-02-20T06:17:25Z","snapshot_observed_at":"2026-08-05T08:58:57.183527Z","submitted_at":"2023-10-04T09:04:20Z","title":"On Memorization in Diffusion Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02664","snapshot_observed_at":"2026-08-07T13:26:05.238193Z","title":"On memorization in diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.238193Z"},"links":{"cited_paper":"/paper/2310.02664","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:5e63cbb8c6801985ec592b2fd6e28e5b3dd632a318ac41c1a67ec2d611ad6042","observation_id":"852b3feb-c1df-4bb7-825a-c03e91374bf1","resolution":{"observed_at":"2026-08-07T13:26:05.238193Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:14.204084Z","title":"Finding NeMo: Localizing neurons responsible for memorization in diffu- sion models","venue":null,"work_id":"8de24e57-7f75-4172-9a90-d9a7e2d6287f","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.307600Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:5ec0a7acf8c1350edb024d7ce969cdff782bcadf91f693af62bd4b9285d8cd1f","observation_id":"4442f910-edc3-4b23-94f8-0eced1df83fa","resolution":{"observed_at":"2026-08-07T13:26:14.286829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:05.427660Z","title":"Denoising dif- fusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.427660Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:6febec17e836afc98b85b5324c230ef392e2a9e77bbff715477a671b30af2346","observation_id":"ac07fae5-7d12-43a9-9c1b-867dd36b8b16","resolution":{"observed_at":"2026-08-07T13:26:05.427660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:13.997786Z","title":"An introduction to variational autoencoders","venue":null,"work_id":"ee6ff8c4-8e38-4f5c-bddf-793c8e1e99c9","year":2019},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.507707Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:213b5ddafb76614c72a309a06402bce3e4df6bee2f084366c9f26de4f9e0cd69","observation_id":"39410939-c8c4-4110-b76c-6b0c56f5c448","resolution":{"observed_at":"2026-08-07T13:26:14.082907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.02692","last_updated":"2021-06-24T11:31:55Z","snapshot_observed_at":"2026-08-10T12:39:34.148774Z","submitted_at":"2021-05-06T14:12:26Z","title":"Learning to Perturb Word Embeddings for Out-of-distribution QA","version":3},"cited_work":{"arxiv_id":"2105.02692","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.02692","snapshot_observed_at":"2026-08-07T13:26:10.494971Z","title":"Learning to Perturb Word Embeddings for Out-of-distribution QA","venue":"cs.CL","work_id":"59a4ce41-cb8d-45b4-bc78-36e0344ef9b3","year":2021},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.590048Z"},"links":{"cited_paper":"/paper/2105.02692","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:f22c14a19b80f73e5c218cf9acb3efe6e954c9e40025856ffaebfebc051d470b","observation_id":"08071358-c136-4118-918b-b4b498dd3698","resolution":{"observed_at":"2026-08-07T13:26:10.565999Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:13.836577Z","title":"Mitigate replication and copying in diffusion mod- els with generalized caption and dual fusion enhancement","venue":null,"work_id":"d618be4b-2887-46d2-b5f4-2d928d033bc5","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.699219Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:6c7e8ea083ba1b9271162fd44f29f70d810d6a120f1b162fdc42c08363f9b085","observation_id":"8c771320-e440-4ef5-9792-1d2dd404a58e","resolution":{"observed_at":"2026-08-07T13:26:13.917759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.01118","last_updated":"2024-12-02T04:41:30Z","snapshot_observed_at":"2026-07-06T19:59:56.438747Z","submitted_at":"2024-12-02T04:41:30Z","title":"LoyalDiffusion: A Diffusion Model Guarding Against Data Replication","version":1},"cited_work":{"arxiv_id":"2412.01118","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.01118","snapshot_observed_at":"2026-08-07T13:26:10.289003Z","title":"LoyalDiffusion: A Diffusion Model Guarding Against Data Replication","venue":"cs.CV","work_id":"071e0735-8a19-4a75-90ba-4584d9665615","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.740848Z"},"links":{"cited_paper":"/paper/2412.01118","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:04621725cc5b29d05c3da19a70b271ac3e8e07c5c758411e5cec4b599e368362","observation_id":"8b84adb6-66e6-4b45-8888-63927fdf6077","resolution":{"observed_at":"2026-08-07T13:26:10.406749Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:13.663645Z","title":"Are GANs created equal? A large-scale study","venue":null,"work_id":"6722d822-545d-4c92-9093-9a0203adc7b8","year":2018},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.810758Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:96f82c64d706f33040a67091a57a7b3f6ff97edfb6e0efdf1e3cf010fd09fb07","observation_id":"14eaab51-6911-4d9f-a476-ac9d66618601","resolution":{"observed_at":"2026-08-07T13:26:13.788866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:13.460719Z","title":"Enhancing DreamBooth with LoRA for generating unlimited characters with Stable Dif- fusion","venue":null,"work_id":"19d24cec-cf46-49d6-abba-8c0cfe0efa0f","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.867247Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:f08c28e34ce0929dee685d9555a8dd4aa10aa2a9aa9c3fe6face32c8af778c00","observation_id":"c9b655f0-a86a-4867-861f-72214a75f697","resolution":{"observed_at":"2026-08-07T13:26:13.557917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:13.247245Z","title":"A self-supervised descriptor for image copy detection","venue":null,"work_id":"316f867d-505d-496e-a708-4f380362e3c9","year":2022},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.899381Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:ab49380f20417eec99dff7036fd5ec5c19eb3aff403161a621fd83a29e32da8b","observation_id":"1c57f15e-6199-42e8-9215-3263304de062","resolution":{"observed_at":"2026-08-07T13:26:13.372725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:13.125937Z","title":"Learning transferable visual models from natural language supervi- sion","venue":null,"work_id":"05bf722b-5a10-4861-85ff-0bb0a6aaeac9","year":2021},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:05.969966Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:967061c393a771b5e6d6b9613c167833fa1be2beb1cce6838ad8f25488efe2a1","observation_id":"4b6deddd-8e59-4ed6-a513-5313eb165eee","resolution":{"observed_at":"2026-08-07T13:26:13.152408Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:06.045871Z","title":"Zero-shot text-to-image generation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.045871Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:9989d12b1ea66a308825163e3da2384212495e0ebc6a8a2299b66c1557f9dd99","observation_id":"f6d62584-30ba-4731-86a7-fd34bdd03346","resolution":{"observed_at":"2026-08-07T13:26:06.045871Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:12.955595Z","title":"Unveiling and mitigating mem- orization in text-to-image diffusion models through cross at- tention","venue":null,"work_id":"b2db4645-1f8a-42f7-a81d-229faa952916","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.097339Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:bcf4d614867655290a72146e0a19ad1498b18cdde1939cb175b46d7190897dde","observation_id":"9fceadd2-fe27-4a32-a02c-9be7d469da71","resolution":{"observed_at":"2026-08-07T13:26:13.045332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:12.707080Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":"17b76c12-2511-4271-b62d-5af0ff1fd5c1","year":2022},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.188552Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:4f88bb66bc717052320c92a60016fe26effac9286b990fd2c6aa4473d24cea55","observation_id":"46db90c4-eb2f-4587-af1f-74ee3a8450ee","resolution":{"observed_at":"2026-08-07T13:26:12.817834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:12.561831Z","title":"U- Net: Convolutional networks for biomedical image segmen- tation","venue":null,"work_id":"184c4cf9-da3f-4a16-bca8-2aa22daff95f","year":2015},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.320767Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:0c29e636e69ecd2b600242cb9a9e65451dd2e64196bdb8055890764c901e4d40","observation_id":"5f0d1f3e-4d0b-40d6-b42a-8dcd01d89d78","resolution":{"observed_at":"2026-08-07T13:26:12.627034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:06.386324Z","title":"Photorealistic text-to-image diffusion models with deep language understanding","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.386324Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:de75c4fc5e597582f11eb860ee9c00b920b33a440ec98b27bc3b958efc1c8496","observation_id":"599dab5a-7a1f-40e2-895e-e7f0b1814d74","resolution":{"observed_at":"2026-08-07T13:26:06.386324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:12.292560Z","title":"Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models","venue":null,"work_id":"9b509db0-2ea9-432d-a786-996824d590f9","year":2023},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.486663Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:f6e7abfb9449a9a6b33e6491e92cf3e2b62fbf565e78033f8fc05cecfd082c6b","observation_id":"567930b9-1d68-4e7a-9e56-4385b0e1709d","resolution":{"observed_at":"2026-08-07T13:26:12.410737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:12.063370Z","title":"LAION-5B: An open large-scale dataset for train- ing next generation image-text models","venue":null,"work_id":"d5c0701f-0e0a-4dce-9cee-db64bd010b6b","year":2022},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.558213Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:3470c729321c7ec4c9f043172d1492738268a2f2155e6f9ac5dec399a0acdaf5","observation_id":"38404cab-097b-44f5-8e78-f0e0656d0244","resolution":{"observed_at":"2026-08-07T13:26:12.155278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:11.863894Z","title":"Diffusion art or digital forgery? Investigating data replication in diffusion models","venue":null,"work_id":"ffd91a5d-0a74-4508-a8da-ae1d477f8a10","year":2023},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.645051Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:b3431b7974bd3e6ba3d5fabb2f4b81083794acb11e9d2a1e692cbc27c7fe4945","observation_id":"8cab64d3-a69e-4c86-8b13-269b754d6662","resolution":{"observed_at":"2026-08-07T13:26:11.909731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:11.625859Z","title":"Understanding and mitigating copying in diffusion models","venue":null,"work_id":"90d17300-1163-4dc2-91d8-83939796ae68","year":2023},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.790991Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:1089171a43640ff70cfb56290dd0831520583e34e630ccc60c37451267cd7ca2","observation_id":"be1d0a5f-ca93-4a2d-b62f-687893797314","resolution":{"observed_at":"2026-08-07T13:26:11.746969Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-07T13:26:06.891309Z","title":"LLaMA: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.891309Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:c061adf4e035b6632800e1078293c5f8a4b0e29df15da1c2eef8ac240e3c9159","observation_id":"b409235e-9eb1-460b-829f-e62bcf6b64fe","resolution":{"observed_at":"2026-08-07T13:26:06.891309Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:11.412175Z","title":"Lexical density and register differentiation","venue":null,"work_id":"8d230fa3-01c7-4f34-8923-396c6086ecbf","year":1971},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:06.983320Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:21f3ad992cd173977ba27670675a2bc52c32d4c8186c38d1a5e1d9aca4c11820","observation_id":"413470f1-02b7-4ede-a2a7-1093cc5ccf6d","resolution":{"observed_at":"2026-08-07T13:26:11.519522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.12733","last_updated":"2023-03-17T17:39:06Z","snapshot_observed_at":"2026-08-10T16:53:48.258211Z","submitted_at":"2023-03-17T17:39:06Z","title":"On the De-duplication of LAION-2B","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.12733","snapshot_observed_at":"2026-08-07T13:26:07.125999Z","title":"On the de-duplication of LAION-2B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:07.125999Z"},"links":{"cited_paper":"/paper/2303.12733","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:7dec8aa461630e85b55c9e97a979feed66f960cdcf3be2846d11c481a513005a","observation_id":"d839a434-63c4-40d9-a0bb-158f40c9ec1e","resolution":{"observed_at":"2026-08-07T13:26:07.125999Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:11.180404Z","title":"De- tecting, explaining, and mitigating memorization in diffusion models","venue":null,"work_id":"7072d793-3bb5-4281-bc43-9ee1993fb7a5","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:07.297168Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:8dfdee1bc0977b15bdd632f2d4a72be923afc11edfb003a502e5329f0a6c30cc","observation_id":"92569f82-d648-4a55-831b-58af7b5df925","resolution":{"observed_at":"2026-08-07T13:26:11.281506Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08099","last_updated":"2023-06-06T03:01:43Z","snapshot_observed_at":"2026-08-10T06:27:03.110710Z","submitted_at":"2022-11-15T12:33:31Z","title":"A Universal Discriminator for Zero-Shot Generalization","version":2},"cited_work":{"arxiv_id":"2211.08099","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.08099","snapshot_observed_at":"2026-08-07T13:26:10.119415Z","title":"A Universal Discriminator for Zero-Shot Generalization","venue":"cs.CL","work_id":"c816e7bf-29e8-4f6d-b2cd-10a248e38a19","year":2022},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:07.510193Z"},"links":{"cited_paper":"/paper/2211.08099","citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:9089bb49e89e2fb9350631692047b13a8dfa0ff60704181ababc898c553cae05","observation_id":"6911a96d-ba42-4a1a-9cbe-d3469b86625a","resolution":{"observed_at":"2026-08-07T13:26:10.169450Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:11.021862Z","title":"Infusion: Preventing customized text-to-image diffusion from overfitting","venue":null,"work_id":"0b07e839-0105-4d41-9f71-1c32600cb762","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:07.965105Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:66c01b01ca7d0fb04f5762994a6b4e589973c7a1181773c8c5d707d37df847ef","observation_id":"04aa0093-6d13-4d35-a698-61be745e622f","resolution":{"observed_at":"2026-08-07T13:26:11.103060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:10.831574Z","title":"Forget-Me-Not: Learning to for- get in text-to-image diffusion models","venue":null,"work_id":"6311bdba-bdbc-46bc-bb9d-e3037d51374f","year":2024},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:08.570716Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:8c1d0d228ef500cdd958bdc73667b7eb999fae5108b2a856bcbaf4f6a69f3a60","observation_id":"ee20f7fb-9666-427a-b8ad-ecd1a241a84a","resolution":{"observed_at":"2026-08-07T13:26:10.912001Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:26:10.699350Z","title":"For the inference process, we generate sam- ples using S = 50 steps, uniformly spacing across the full diffusion process","venue":null,"work_id":"12a51a6b-5750-41f2-ad8a-7bd057b390fc","year":null},"citing_paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings","version":1},"reference_index":256,"source":"pdf_text","source_observed_at":"2026-08-07T13:26:09.569617Z"},"links":{"citing_paper":"/paper/2505.21848"},"observation_digest":"sha256:c8298ec477786c5439df570aa5f638fd1841c69b9a8e946b1f20b3b4eee38248","observation_id":"d22c370d-605d-4389-be10-44fd4cb9c223","resolution":{"observed_at":"2026-08-07T13:26:10.768749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.21848","last_updated":"2025-05-28T00:29:20Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T10:53:53.633413Z","submitted_at":"2025-05-28T00:29:20Z","title":"FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":3,"verified_fuzzy":23},"total_outbound_references":36},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2505.21848."}