{"as_of":"2026-08-10T02:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a08cb226b81e54014c3e19a9979072fe0ebfd4f7afcd2224afe777c9c4cfb84f","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T11:46:23.205545Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2502.05215/citation-record","integrity":"/paper/2502.05215/integrity","json":"/paper/2502.05215/citation-record.json","paper":"/paper/2502.05215"},"outbound":[{"citation":{"cited_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},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.01618","snapshot_observed_at":"2026-08-09T11:46:23.088415Z","title":"Broken arrows","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.088415Z"},"links":{"cited_paper":"/paper/2208.01618","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:aed2dc7ffa848ff549645721e6b8b1b2c7a7ece3e4902e521342c7ccef96391d","observation_id":"17da1c9a-18c3-4e11-bfa1-38d071b070e8","resolution":{"observed_at":"2026-08-09T11:46:23.088415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-08-09T19:52:33.533277Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-08-09T11:46:23.097403Z","title":"Long short-term memory.Neural Computation MIT-Press, 1997","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.097403Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:f521f2fdb209b7a65ad4f4ca945e7d3287a9b75907ecac6a292d1059ef45934c","observation_id":"b6fbdde1-239e-4e38-a130-071eb18838a0","resolution":{"observed_at":"2026-08-09T11:46:23.097403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.04088","last_updated":"2024-01-08T18:47:34Z","snapshot_observed_at":"2026-08-08T06:16:25.839566Z","submitted_at":"2024-01-08T18:47:34Z","title":"Mixtral of Experts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.04088","snapshot_observed_at":"2026-08-09T11:46:23.106421Z","title":"Spoofing countermeasure based on analysis of linear prediction error","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.106421Z"},"links":{"cited_paper":"/paper/2401.04088","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:9f40f268bff2f958ac2eb64b7d1e340e2c76f46630d85acc81bb06c59347777c","observation_id":"6c31919c-bde5-486b-9e53-1caf687483e8","resolution":{"observed_at":"2026-08-09T11:46:23.106421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.19361","last_updated":"2024-06-24T14:48:29Z","snapshot_observed_at":"2026-08-02T14:37:19.434140Z","submitted_at":"2024-02-29T17:12:39Z","title":"Watermark Stealing in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.19361","snapshot_observed_at":"2026-08-09T11:46:23.110929Z","title":"Ipcert:Provablyrobustintellectual property protection for machine learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.110929Z"},"links":{"cited_paper":"/paper/2402.19361","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:3d576e5a92bb32208741a7b80aaa48c5046e9ab8a8b9d8bca8e92a9bc2bb0788","observation_id":"69cc228f-e7a1-4d2f-9750-ea66f8df7daf","resolution":{"observed_at":"2026-08-09T11:46:23.110929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-09T11:46:23.115672Z","title":"Scaling laws for neural language models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.115672Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:f60d7b4b813b8ad37c75d877132b6decabea05b6aef5ff7e76e08f6f315865c0","observation_id":"7b6c77fb-1536-4e81-8c84-d30d1c5cd30c","resolution":{"observed_at":"2026-08-09T11:46:23.115672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.15352","last_updated":"2023-03-05T09:14:16Z","snapshot_observed_at":"2026-08-07T16:54:38.689558Z","submitted_at":"2022-09-30T10:17:05Z","title":"AudioGen: Textually Guided Audio Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.15352","snapshot_observed_at":"2026-08-09T11:46:23.120195Z","title":"Audiogen: Textually guided audio generation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.120195Z"},"links":{"cited_paper":"/paper/2209.15352","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:b0e3f794af8c323e1e9a452b98e2a52dbe953cd64b9d98aa764313f9cd2d54b5","observation_id":"ea6fdf99-c86f-4979-9a02-e9f0147347e9","resolution":{"observed_at":"2026-08-09T11:46:23.120195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.11384","last_updated":"2021-06-21T19:37:06Z","snapshot_observed_at":"2026-07-06T11:21:30.713389Z","submitted_at":"2021-06-21T19:37:06Z","title":"Membership Inference on Word Embedding and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.11384","snapshot_observed_at":"2026-08-09T11:46:23.129851Z","title":"Distortion ag- nostic deep watermarking","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.129851Z"},"links":{"cited_paper":"/paper/2106.11384","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:d633f43f8a6a97cad8f62e269c481b7860b307d0802695c570b982816a2144b2","observation_id":"43324ef5-5fdc-4982-8528-a408de310342","resolution":{"observed_at":"2026-08-09T11:46:23.129851Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-07-06T18:55:11.576666Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-09T11:46:23.134318Z","title":"The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.134318Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:f4b4e14a455fcf21e3bcc730026f1d1183389c5bc0c938181247346a2c240f3d","observation_id":"15bd14ff-4a5d-4766-8bd4-c86d4fecee78","resolution":{"observed_at":"2026-08-09T11:46:23.134318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09794","last_updated":"2022-11-17T18:58:14Z","snapshot_observed_at":"2026-08-04T20:45:04.370492Z","submitted_at":"2022-11-17T18:58:14Z","title":"Null-text Inversion for Editing Real Images using Guided Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.09794","snapshot_observed_at":"2026-08-09T11:46:23.138935Z","title":"Null-text inversion for editing real images using guided diffusion models.arXiv preprint arXiv:2211.09794, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.138935Z"},"links":{"cited_paper":"/paper/2211.09794","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:fbce637422be2e1b532fbe27e7c1970ae4999250f958f859e746394efa4283ee","observation_id":"80372540-ad80-455d-9481-446f38e02808","resolution":{"observed_at":"2026-08-09T11:46:23.138935Z","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":"2026-08-09T11:46:23.927755Z","title":"Robust image watermarking in the spatial domain.Signal processing, 1998","venue":null,"work_id":"df1cf9d2-e26e-4133-b3f8-6f6bf137caa4","year":1998},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.144239Z"},"links":{"citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:2b40001b34b742cfb5af57c186c21e9041f706fa6b1614a16f41f159a52cc33b","observation_id":"91d4dc7d-9225-4bdb-823d-779aebc5b0d5","resolution":{"observed_at":"2026-08-09T11:46:23.931904Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2405.10051","last_updated":"2024-10-26T05:11:11Z","snapshot_observed_at":"2026-08-07T12:46:43.264007Z","submitted_at":"2024-05-16T12:40:01Z","title":"MarkLLM: An Open-Source Toolkit for LLM Watermarking","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.10051","snapshot_observed_at":"2026-08-09T11:46:23.148479Z","title":"ChatGPT: Optimizing language models for dialogue., 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.148479Z"},"links":{"cited_paper":"/paper/2405.10051","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:08a045f25bfb4f684a555e708d7b5fb28d7f65868e7a585d580677767d1d31be","observation_id":"f4c5e5fd-2ed4-40b3-99f2-8d84993ab1e1","resolution":{"observed_at":"2026-08-09T11:46:23.148479Z","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":"2026-08-09T11:46:23.913436Z","title":"Dct-based watermark recovering without resorting to the uncorrupted original image","venue":null,"work_id":"3869dd81-6f50-4b0e-8ce9-5226e853936a","year":1997},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.152898Z"},"links":{"citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:559b06eb4a7a56dd308ce59cdd1d935857eac24e9efc552409b2be6cfd6bab45","observation_id":"24d63e47-4748-4b49-925a-c9b3616f7e47","resolution":{"observed_at":"2026-08-09T11:46:23.917836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2401.16820","last_updated":"2025-01-28T03:21:30Z","snapshot_observed_at":"2026-08-09T00:56:12.716565Z","submitted_at":"2024-01-30T08:46:48Z","title":"Provably Robust Multi-bit Watermarking for AI-generated Text","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.16820","snapshot_observed_at":"2026-08-09T11:46:23.157014Z","title":"Estimatingtrainingdata influence by tracing gradient descent.Advances in Neural Information Processing Systems, 33:19920–19930, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.157014Z"},"links":{"cited_paper":"/paper/2401.16820","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:ca29d66e1c3103bfc8a24d0371b33e8d3fa1d7cc9a7ddc4de2153bd787353f7d","observation_id":"bdd8fde9-6f77-45a3-a1a8-a24357632ad0","resolution":{"observed_at":"2026-08-09T11:46:23.157014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06125","last_updated":"2022-04-13T01:10:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-13T01:10:33Z","title":"Hierarchical Text-Conditional Image Generation with CLIP Latents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06125","snapshot_observed_at":"2026-08-09T11:46:23.161628Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.161628Z"},"links":{"cited_paper":"/paper/2204.06125","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:a68769823ee84646253e22681ac557f6a24f91ad70baffb545cffa1360cf5963","observation_id":"e70fc1e1-303f-4a96-9b3f-2e8e6bb61cf4","resolution":{"observed_at":"2026-08-09T11:46:23.161628Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1508.07909","last_updated":"2016-06-10T14:45:08Z","snapshot_observed_at":"2026-07-06T04:28:16.222296Z","submitted_at":"2015-08-31T16:37:31Z","title":"Neural Machine Translation of Rare Words with Subword Units","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.07909","snapshot_observed_at":"2026-08-09T11:46:23.166231Z","title":"Laion- 5b: An open large-scale dataset for training next generation image-text models.Advances in Neural Information Processing Systems, 35:25278–25294, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.166231Z"},"links":{"cited_paper":"/paper/1508.07909","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:b241c7074ee67c70f429a4dc9794c3bde1530fa4532a6d7101cbdbeb9c891632","observation_id":"a7a52313-6d62-4cc4-9c18-d1b341771f40","resolution":{"observed_at":"2026-08-09T11:46:23.166231Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.09864","last_updated":"2023-11-08T13:36:32Z","snapshot_observed_at":"2026-07-06T11:01:58.137141Z","submitted_at":"2021-04-20T09:54:06Z","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.09864","snapshot_observed_at":"2026-08-09T11:46:23.170935Z","title":"Ai model gpt-3 (dis) informs us better than humans.Science Advances, 9(26):eadh1850, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.170935Z"},"links":{"cited_paper":"/paper/2104.09864","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:cabe6e8e90daae3ff34d1ddf9027de67e435aea2b256f4bf3ff0ddceb5169532","observation_id":"908b72a9-d1da-4e88-a172-bb08a85933b6","resolution":{"observed_at":"2026-08-09T11:46:23.170935Z","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":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T11:46:23.504928Z","title":"Snr-constrained heuristics for optimizing the scaling parameter of robust audio watermarking.IEEE Trans","venue":null,"work_id":"7e6d79ae-7203-4b06-ac5f-3b931cfc6584","year":2018},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.175048Z"},"links":{"citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:5bfe44c942bca3edb027ef47a739aad3697dfbbd6211b7316b7e36b62fe97307","observation_id":"e9e764aa-33fb-4f6c-99c5-9c761be60717","resolution":{"observed_at":"2026-08-09T11:46:23.509585Z","resolver_source":"arxiv_id_nonexistent","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":"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":"2026-08-09T11:46:23.179300Z","title":"Optimal probabilistic fingerprint codes","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.179300Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:c2175c7a88cfdffec72820d33e46773445c538195f3f56e9827b6498cf86da5e","observation_id":"85da5622-e2f4-48bc-8013-25534c4e67a7","resolution":{"observed_at":"2026-08-09T11:46:23.179300Z","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":"2026-08-09T11:46:23.899076Z","title":"Lightfieldmessagingwithdeepphotographicsteganography","venue":null,"work_id":"707055ed-d482-4e30-aaa8-7d784b1e9953","year":2019},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.196985Z"},"links":{"citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:cecb22b63c734a4140bd6acb4724e1d8d2d3d96ae71fee4bde263b98fe04b80d","observation_id":"42745c84-4f9a-4081-98e2-6ba54818cf0f","resolution":{"observed_at":"2026-08-09T11:46:23.903515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2210.05559","last_updated":"2022-12-07T04:42:12Z","snapshot_observed_at":"2026-08-05T10:49:55.158488Z","submitted_at":"2022-10-11T15:53:52Z","title":"Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.05559","snapshot_observed_at":"2026-08-09T11:46:23.201046Z","title":"Unifying diffusion models’ latent space, with appli- cations to cyclediffusion and guidance.arXiv preprint arXiv:2210.05559, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.201046Z"},"links":{"cited_paper":"/paper/2210.05559","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:3962ff18239c4abf346f560daab69c2bb3821a094c34ac76f9be69482f929908","observation_id":"8c89fa09-e44d-4aac-baef-3d9051e49870","resolution":{"observed_at":"2026-08-09T11:46:23.201046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04627","last_updated":"2022-06-05T01:57:58Z","snapshot_observed_at":"2026-07-06T11:56:10.689708Z","submitted_at":"2021-10-09T18:36:00Z","title":"Vector-quantized Image Modeling with Improved VQGAN","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04627","snapshot_observed_at":"2026-08-09T11:46:23.205545Z","title":"Large batch optimization for deep learning: Training bert in 76 minutes","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.205545Z"},"links":{"cited_paper":"/paper/2110.04627","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:bfeb01397416b68e1859ed93d08ebf65b6b6b937a654365225e2489e44f8e6fe","observation_id":"53944e93-4520-4cfd-9871-f1e153b6021a","resolution":{"observed_at":"2026-08-09T11:46:23.205545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.08440","last_updated":"2022-04-11T09:23:43Z","snapshot_observed_at":"2026-07-06T12:09:00.789234Z","submitted_at":"2021-11-15T12:32:20Z","title":"On the Importance of Difficulty Calibration in Membership Inference Attacks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.08440","snapshot_observed_at":"2026-08-09T11:46:23.192542Z","title":"On the importance of difficulty calibration in membership inference attacks.arXiv preprint arXiv:2111.08440, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":1993,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.192542Z"},"links":{"cited_paper":"/paper/2111.08440","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:f322f44153e73b43fc5459f76fac8363da935db25d67916c41cd7ca24a0e5a37","observation_id":"1d60a02f-a961-4453-a433-5e76ee8022b1","resolution":{"observed_at":"2026-08-09T11:46:23.192542Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.18226","last_updated":"2025-03-11T08:08:05Z","snapshot_observed_at":"2026-07-06T15:34:51.593721Z","submitted_at":"2023-05-26T11:07:25Z","title":"HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.18226","snapshot_observed_at":"2026-08-09T11:46:23.183741Z","title":"Howkgpt: Investigating the detection of chatgpt-generated university student homework through context-aware perplexity analysis.arXiv preprint arXiv:2305.18226, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":1994,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.183741Z"},"links":{"cited_paper":"/paper/2305.18226","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:c825042fa8dcdd136f65125bc9178c45a25b6339ccf1dc8ecc5e2ebdc3938c72","observation_id":"10688d71-1672-4556-8c46-c3c322f0810c","resolution":{"observed_at":"2026-08-09T11:46:23.183741Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.09636","last_updated":"2023-05-16T17:41:25Z","snapshot_observed_at":"2026-08-06T19:45:49.830925Z","submitted_at":"2023-05-16T17:41:25Z","title":"SoundStorm: Efficient Parallel Audio Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.09636","snapshot_observed_at":"2026-08-09T11:46:23.072517Z","title":"Audiolm: A language modeling approach to audio generation.IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31:2523–2533, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":1996,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.072517Z"},"links":{"cited_paper":"/paper/2305.09636","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:c091286be213d5b470bb8c02e738deab6c7f71216c31d7d51216004818ab85e2","observation_id":"23c907a3-1547-40a4-a428-d01fa510d1ef","resolution":{"observed_at":"2026-08-09T11:46:23.072517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.03300","last_updated":"2021-01-12T18:57:11Z","snapshot_observed_at":"2026-08-09T10:28:06.906299Z","submitted_at":"2020-09-07T17:59:25Z","title":"Measuring Massive Multitask Language Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.03300","snapshot_observed_at":"2026-08-09T11:46:23.093021Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":1997,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.093021Z"},"links":{"cited_paper":"/paper/2009.03300","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:2b99e6a5d59d88ac06d1e629eeb6d819a40503c978c39c79eae166c9e0c0d353","observation_id":"fdf8cc5d-6164-4670-9a03-168e14d9e85d","resolution":{"observed_at":"2026-08-09T11:46:23.093021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.05770","last_updated":"2025-06-26T05:07:48Z","snapshot_observed_at":"2026-07-06T14:16:52.598523Z","submitted_at":"2022-11-10T18:55:48Z","title":"Efficient Image Generation with Variadic Attention Heads","version":3},"cited_work":{"arxiv_id":"2211.05770","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.05770","snapshot_observed_at":"2026-08-09T11:46:23.292760Z","title":"Efficient Image Generation with Variadic Attention Heads","venue":"cs.CV","work_id":"4a57aad6-8f25-4186-9e64-240113ad1362","year":2022},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.187963Z"},"links":{"cited_paper":"/paper/2211.05770","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:8212a75f444475617878dc329ecb4815a87e7b1756e0807c6aa2aa92c1229231","observation_id":"46aeac13-7e49-4029-add8-f691bb580e80","resolution":{"observed_at":"2026-08-09T11:46:23.299718Z","resolver_source":"local_arxiv","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":"2403.15690","last_updated":"2024-03-23T02:44:20Z","snapshot_observed_at":"2026-07-06T17:49:24.075614Z","submitted_at":"2024-03-23T02:44:20Z","title":"EAGLE: A Domain Generalization Framework for AI-generated Text Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.15690","snapshot_observed_at":"2026-08-09T11:46:23.067675Z","title":"Multidimensional binary search trees used for associative searching.Com- munications of the ACM, 18(9):509–517, 1975","venue":null,"work_id":null,"year":1975},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.067675Z"},"links":{"cited_paper":"/paper/2403.15690","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:fdd3f52d9e514370be2f5edc78fd0e9b643ace385249050917b90a7cbc508249","observation_id":"7a3ed29e-7ebd-477d-bbc8-11f4ccf548e8","resolution":{"observed_at":"2026-08-09T11:46:23.067675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15060","last_updated":"2024-07-03T15:09:52Z","snapshot_observed_at":"2026-08-01T15:14:35.069049Z","submitted_at":"2023-05-24T11:49:52Z","title":"Who Wrote this Code? Watermarking for Code Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15060","snapshot_observed_at":"2026-08-09T11:46:23.125023Z","title":"Who wrote this code? watermarking for code generation.arXiv preprint arXiv:2305.15060, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":2015,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.125023Z"},"links":{"cited_paper":"/paper/2305.15060","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:206c3c61f0ebf79fc3c376993f38052a1c6c5eb5b52002a9ca0be6f950a35a7a","observation_id":"354f022b-6d08-42c9-badf-07a5187e1013","resolution":{"observed_at":"2026-08-09T11:46:23.125023Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07145","last_updated":"2024-05-12T03:04:48Z","snapshot_observed_at":"2026-07-06T18:13:04.026351Z","submitted_at":"2024-05-12T03:04:48Z","title":"Stable Signature is Unstable: Removing Image Watermark from Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07145","snapshot_observed_at":"2026-08-09T11:46:23.101769Z","title":"Stable signature is unstable: Removing image watermark from diffusion models.arXiv preprint arXiv:2405.07145, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.101769Z"},"links":{"cited_paper":"/paper/2405.07145","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:ffc752fe851c19f81301a2f2027cde6b368e3c81cfba344e2677d7e8a6acecc5","observation_id":"afd6a256-8f37-482b-9e3a-fab4d8938168","resolution":{"observed_at":"2026-08-09T11:46:23.101769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08337","last_updated":"2024-06-12T15:42:52Z","snapshot_observed_at":"2026-08-10T00:36:02.168571Z","submitted_at":"2024-06-12T15:42:52Z","title":"WMAdapter: Adding WaterMark Control to Latent Diffusion Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08337","snapshot_observed_at":"2026-08-09T11:46:23.077744Z","title":"Generative pretraining from pixels","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.077744Z"},"links":{"cited_paper":"/paper/2406.08337","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:d2c3df5838bb008ccae54c5392024a73dd50d86a177c75d873f51bfa5cef3ffe","observation_id":"6214a7f4-1f51-436b-a9e0-ccdf9edab361","resolution":{"observed_at":"2026-08-09T11:46:23.077744Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09672","last_updated":"2022-02-21T10:12:06Z","snapshot_observed_at":"2026-08-04T15:44:52.552034Z","submitted_at":"2021-06-17T17:23:59Z","title":"The 2021 Image Similarity Dataset and Challenge","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09672","snapshot_observed_at":"2026-08-09T11:46:23.082966Z","title":"The 2021 image similarity dataset and challenge.arXiv preprint arXiv:2106.09672, 2021a","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.082966Z"},"links":{"cited_paper":"/paper/2106.09672","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:4b1a1c70221ca01548585ba6bf60500cc0119211c3b4cdf45d4f23e220e96798","observation_id":"8a970900-4a3f-4af4-b1e8-96d6986b1fea","resolution":{"observed_at":"2026-08-09T11:46:23.082966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01324","last_updated":"2023-03-14T00:22:14Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-11-02T17:43:04Z","title":"eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.01324","snapshot_observed_at":"2026-08-09T11:46:23.056519Z","title":"21 Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, et al","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.056519Z"},"links":{"cited_paper":"/paper/2211.01324","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:dce4100b531ed434c2cf58acd7dff59a1322293912935b884b1083131ad6fb34","observation_id":"25f25f0e-8ac1-44d4-949e-6aa22a1d3f8c","resolution":{"observed_at":"2026-08-09T11:46:23.056519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.03029","last_updated":"2020-11-05T18:40:50Z","snapshot_observed_at":"2026-08-09T03:55:25.994986Z","submitted_at":"2020-11-05T18:40:50Z","title":"CompressAI: a PyTorch library and evaluation platform for end-to-end compression research","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.03029","snapshot_observed_at":"2026-08-09T11:46:23.062311Z","title":"break our steganographic system","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-09T11:46:23.062311Z"},"links":{"cited_paper":"/paper/2011.03029","citing_paper":"/paper/2502.05215"},"observation_digest":"sha256:e761275ae8e45c02ba4f5caeac17e6af20fa727dc110713cc5c25dfedd41f6ea","observation_id":"1ff48bd7-75c7-47e9-abd5-5dc7d3e3703c","resolution":{"observed_at":"2026-08-09T11:46:23.062311Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.05215","last_updated":"2025-02-04T18:49:50Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-09T11:39:50.709602Z","submitted_at":"2025-02-04T18:49:50Z","title":"Watermarking across Modalities for Content Tracing and Generative AI"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":28,"verified_exact":1,"verified_fuzzy":3},"total_outbound_references":33},"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 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2502.05215."}