{"as_of":"2026-08-10T08:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1025f32084faf8b86054f098e431f80fb9d0de5d1b730a59db0cc36f501cfe53","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":32,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T17:40:55.064751Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T22:27:25.965269Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2501.11568","last_updated":"2026-04-10T10:01:42Z","snapshot_observed_at":"2026-07-06T20:23:27.060903Z","submitted_at":"2025-01-20T16:18:40Z","title":"Graph Defense Diffusion Model","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-23T04:50:28.600618Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2501.11568"},"observation_digest":"sha256:c129c6057609e0b8a206e5b7ec70bbd56621a9368dcf9362e479f3c37552a1ec","observation_id":"e5993b06-7c4c-45af-9c06-d99c5b848108","resolution":{"observed_at":"2026-05-23T04:52:34.224746Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-09T17:40:55.064751Z","title":"Diffusion models for adversarial purifi- cation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2502.00834","last_updated":"2025-02-02T16:15:10Z","snapshot_observed_at":"2026-08-10T07:48:10.403933Z","submitted_at":"2025-02-02T16:15:10Z","title":"Boosting Adversarial Robustness and Generalization with Structural Prior","version":1},"reference_index":2016,"source":"pdf_text","source_observed_at":"2026-08-09T17:40:55.064751Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2502.00834"},"observation_digest":"sha256:5112b16c3b9709206867ab70555d591756108233c4930f1a32b01c9f9e584ce4","observation_id":"ce9161d4-f897-4e0c-ab53-98338762bc93","resolution":{"observed_at":"2026-08-09T17:40:55.064751Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-09T00:55:56.256910Z","title":"Diffusion models for adversarial purification","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.03758","last_updated":"2025-05-30T09:44:35Z","snapshot_observed_at":"2026-08-09T08:50:15.482582Z","submitted_at":"2025-02-06T03:43:34Z","title":"Improving Adversarial Robustness via Phase and Amplitude-aware Prompting","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T00:55:56.256910Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2502.03758"},"observation_digest":"sha256:8568ddf1afe19d22a00c2e1621e85ea2720bd167fbeefd56dd8dbb8775e3a0aa","observation_id":"c4d3c428-8df6-4787-a645-f2c5f417750e","resolution":{"observed_at":"2026-08-09T00:55:56.256910Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2503.06223","last_updated":"2026-08-06T06:47:13Z","snapshot_observed_at":"2026-08-09T23:09:10.814238Z","submitted_at":"2025-03-08T13:51:40Z","title":"RedDiffuser: Auditing Multimodal Safety Failures in Vision-Language Models via Reinforced Diffusion","version":5},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-23T00:13:08.603115Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2503.06223"},"observation_digest":"sha256:1d9bb4fe99345c80e96914ba7aa1034f79395d8397f3e6b5acefbcec6be835f9","observation_id":"3b0a971d-21fb-446c-be7c-b5ccdb3a9b26","resolution":{"observed_at":"2026-05-23T00:15:14.886294Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-07T15:08:58.305777Z","title":"Diffusion models for adversarial purification.arXiv preprint arXiv:2205.07460,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16166","last_updated":"2025-05-22T03:11:35Z","snapshot_observed_at":"2026-08-10T06:50:26.007636Z","submitted_at":"2025-05-22T03:11:35Z","title":"TRAIL: Transferable Robust Adversarial Images via Latent diffusion","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T15:08:58.305777Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2505.16166"},"observation_digest":"sha256:a5a425ef1646488c7f0974c49493932968712b0225f80669ff13b971f6d0b7d6","observation_id":"f816d41b-8613-423a-a657-24322fa6a08f","resolution":{"observed_at":"2026-08-07T15:08:58.305777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-07T15:07:56.509341Z","title":"Diffusion models for adversarial purification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16318","last_updated":"2025-05-22T07:21:04Z","snapshot_observed_at":"2026-08-09T12:23:19.491196Z","submitted_at":"2025-05-22T07:21:04Z","title":"SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T15:07:56.509341Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2505.16318"},"observation_digest":"sha256:e1fcdf7bb134f131874b112fcdbccc8976f783ffff7cf0bf179845e302543ed9","observation_id":"dbc64a64-c0f2-493f-be44-3ad245c44d6f","resolution":{"observed_at":"2026-08-07T15:07:56.509341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-07T12:12:08.650495Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.00325","last_updated":"2025-05-31T00:37:28Z","snapshot_observed_at":"2026-08-09T01:58:35.331124Z","submitted_at":"2025-05-31T00:37:28Z","title":"Towards Effective and Efficient Adversarial Defense with Diffusion Models for Robust Visual Tracking","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:08.650495Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2506.00325"},"observation_digest":"sha256:a9e534ec8e929c15c9bc755b9ee8d7e85f9ac4706d297b13090e156ebf826382","observation_id":"59559d1c-6f9f-4628-998e-f1a5393731c3","resolution":{"observed_at":"2026-08-07T12:12:08.650495Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-07T11:44:16.664363Z","title":"Diffusion models for adversarial purification.arXiv preprint arXiv:2205.07460,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.01591","last_updated":"2025-06-02T12:26:46Z","snapshot_observed_at":"2026-08-08T09:22:03.343433Z","submitted_at":"2025-06-02T12:26:46Z","title":"Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:44:16.664363Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2506.01591"},"observation_digest":"sha256:ecda1608c9b3eca8ca9b87faa5495caf58c90b71ef1c5bc36df1f952497b0964","observation_id":"9ee559b8-3d80-4510-b798-bb89eb1da984","resolution":{"observed_at":"2026-08-07T11:44:16.664363Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-07T10:59:19.095716Z","title":"Diffusion models for adversarial purification.arXiv preprint arXiv:2205.07460, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.03933","last_updated":"2026-06-10T10:07:38Z","snapshot_observed_at":"2026-08-07T16:57:10.610923Z","submitted_at":"2025-06-04T13:26:33Z","title":"Diffusion-based Cumulative Adversarial Purification for Vision Language Models","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T10:59:19.095716Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2506.03933"},"observation_digest":"sha256:25eeaef6627c35d425d0c84a72f152015d7e758a78b3516616e705085a99b815","observation_id":"3984e668-6a61-4cf5-b8ac-d5d2636b1064","resolution":{"observed_at":"2026-08-07T10:59:19.095716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-07T05:28:50.789545Z","title":"Diffusion models for adversarial purification","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.07841","last_updated":"2025-06-09T15:07:16Z","snapshot_observed_at":"2026-08-07T05:21:45.775948Z","submitted_at":"2025-06-09T15:07:16Z","title":"Diffusion models under low-noise regime","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:28:50.789545Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2506.07841"},"observation_digest":"sha256:753d613b7a47d17ee19567fce35fb5e1a994dc9cff57823a71336ad7ea615013","observation_id":"31831e22-33dd-4576-aa7e-05c41a487748","resolution":{"observed_at":"2026-08-07T05:28:50.789545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-06T16:24:05.886229Z","title":"Diffusion models for adversarial purification","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.13753","last_updated":"2025-07-18T08:59:02Z","snapshot_observed_at":"2026-08-09T23:30:14.905124Z","submitted_at":"2025-07-18T08:59:02Z","title":"Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:24:05.886229Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2507.13753"},"observation_digest":"sha256:ef93f558f30d0752d19cc7052cd84aab3084d6fb63da6616d49a1596355220bc","observation_id":"6d940d20-b81a-42e8-97de-901cd1f70086","resolution":{"observed_at":"2026-08-06T16:24:05.886229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-05T14:40:54.510736Z","title":"Diffusion models for adversarial purification.arXiv preprint arXiv:2205.07460, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.21072","last_updated":"2025-08-28T17:59:59Z","snapshot_observed_at":"2026-08-09T09:53:10.272428Z","submitted_at":"2025-08-28T17:59:59Z","title":"First-Place Solution to NeurIPS 2024 Invisible Watermark Removal Challenge","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T14:40:54.510736Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2508.21072"},"observation_digest":"sha256:49e6c2b5ceae5f6727515d8164f757f9cdef98cd48bd2cfa263b2a05984b2ec4","observation_id":"6a589f5b-b5a9-4d48-9015-06542f1e4e4b","resolution":{"observed_at":"2026-08-05T14:40:54.510736Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-04T10:42:00.374901Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.09288","last_updated":"2026-06-01T12:51:35Z","snapshot_observed_at":"2026-08-07T23:03:48.822663Z","submitted_at":"2025-10-10T11:28:30Z","title":"A unifying Bayesian framework for adversarial robustness","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-04T10:42:00.374901Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2510.09288"},"observation_digest":"sha256:01afae2a2ae60b40efddd8b325a5a240c76c4816514590a6f1a2e58cd9665770","observation_id":"85a3d6ef-1f22-417f-9aa3-9412a6c12bfd","resolution":{"observed_at":"2026-08-04T10:42:00.374901Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-04T09:44:17.356075Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.14025","last_updated":"2026-05-31T12:35:23Z","snapshot_observed_at":"2026-08-09T09:53:04.681319Z","submitted_at":"2025-10-15T19:05:59Z","title":"NAPPure: Adversarial Purification for Robust Image Classification under Non-Additive Perturbations","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T09:44:17.356075Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2510.14025"},"observation_digest":"sha256:8d749e0e4d405cf33acf1a08234750dcd9333ed5e4606cd22557109e482733a8","observation_id":"6bfb2f58-da90-4733-8934-6a6707af65b5","resolution":{"observed_at":"2026-08-04T09:44:17.356075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2601.21692","last_updated":"2026-05-22T01:46:45Z","snapshot_observed_at":"2026-08-02T10:02:20.704165Z","submitted_at":"2026-01-29T13:26:29Z","title":"TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T07:33:15.865358Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2601.21692"},"observation_digest":"sha256:3d180df5fe753c79ba9360d1dbe86f63dd0b7080585f7480c6ffc403b67870f7","observation_id":"fd64c5ed-a2fa-453e-85c7-14b2f3b3408f","resolution":{"observed_at":"2026-05-25T07:35:28.647735Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-03T04:23:54.479153Z","title":"Diffusion models for adversarial purification.arXiv preprint arXiv:2205.07460,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.05175","last_updated":"2026-06-07T16:42:07Z","snapshot_observed_at":"2026-08-09T23:15:45.203141Z","submitted_at":"2026-02-05T01:07:27Z","title":"Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T04:23:54.479153Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2602.05175"},"observation_digest":"sha256:c5c62e86a09aeba7fba7ca262ee98fe8bb4f71cac97ee53e9a4a82c5cc8846e0","observation_id":"2c001f9a-07cc-4d13-a13e-4155448f6715","resolution":{"observed_at":"2026-08-03T04:23:54.479153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2604.06662","last_updated":"2026-04-08T04:28:56Z","snapshot_observed_at":"2026-07-06T22:55:06.424752Z","submitted_at":"2026-04-08T04:28:56Z","title":"Towards Robust Content Watermarking Against Removal and Forgery Attacks","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-10T18:08:01.503806Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2604.06662"},"observation_digest":"sha256:ba2e8f61c6438392a63af4c207d9fb5f35abd7fd21a9ccc771236daf459cd2a3","observation_id":"af87611d-17cf-468f-8c2c-cf83d2f47146","resolution":{"observed_at":"2026-05-11T05:26:01.004856Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2605.01758","last_updated":"2026-05-14T12:12:16Z","snapshot_observed_at":"2026-07-06T23:14:57.459903Z","submitted_at":"2026-05-03T07:38:42Z","title":"Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T15:39:32.595169Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2605.01758"},"observation_digest":"sha256:cf1385e067c748e81db23282bb16364e42de7b262ec0af78a7c8f8485edc1565","observation_id":"f840480f-20fc-44fe-b05f-f549848ee6f8","resolution":{"observed_at":"2026-05-11T10:06:02.810154Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2605.01758","last_updated":"2026-05-14T12:12:16Z","snapshot_observed_at":"2026-07-06T23:14:57.459903Z","submitted_at":"2026-05-03T07:38:42Z","title":"Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-11T01:46:54.430546Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2605.01758"},"observation_digest":"sha256:4d0f391fdf529b174dc2a847c26c5629dcd2358aa164809029babb65885c4e73","observation_id":"9085e489-4ba0-49db-9488-7b9d36d52821","resolution":{"observed_at":"2026-05-11T04:20:58.082754Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2605.01758","last_updated":"2026-05-14T12:12:16Z","snapshot_observed_at":"2026-07-06T23:14:57.459903Z","submitted_at":"2026-05-03T07:38:42Z","title":"Catching the Infection Before It Spreads: Foresight-Guided Defense in Multi-Agent Systems","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-15T07:11:16.568353Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2605.01758"},"observation_digest":"sha256:4da61a5650a5facfc4e91398a7d73ea3a65c5e8aa2209b330b3ef7e62b443443","observation_id":"e1eb393a-f47d-453e-86ea-d5486878cd97","resolution":{"observed_at":"2026-05-15T07:15:11.958728Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2605.06357","last_updated":"2026-05-07T14:35:04Z","snapshot_observed_at":"2026-08-08T14:20:01.894187Z","submitted_at":"2026-05-07T14:35:04Z","title":"Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-08T12:53:11.185208Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2605.06357"},"observation_digest":"sha256:47cf4eb6507cac69199dc39b23ced9201f650b5bab52e7d6c9f5034a57797c17","observation_id":"63804e53-6d6d-4731-a8a6-1cbdba489607","resolution":{"observed_at":"2026-05-11T19:01:18.466349Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2605.09319","last_updated":"2026-05-10T04:32:57Z","snapshot_observed_at":"2026-08-07T01:41:16.929802Z","submitted_at":"2026-05-10T04:32:57Z","title":"PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-12T04:11:23.288357Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2605.09319"},"observation_digest":"sha256:29e897dc4b7efad2ddb9852a0f2d4ad31b39f2bbcf8c9008afa60bb55c50ace4","observation_id":"1c1265dc-91e2-452f-b676-c236f587ead3","resolution":{"observed_at":"2026-05-12T06:31:28.260562Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2605.15249","last_updated":"2026-05-14T12:45:36Z","snapshot_observed_at":"2026-08-05T01:18:05.152409Z","submitted_at":"2026-05-14T12:45:36Z","title":"Enabling Adversarial Robustness in AI Models through Kubeflow MLOps","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-19T16:29:27.765783Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2605.15249"},"observation_digest":"sha256:9116949df64a170db89caf8f7179c8c03fd25e847aef9f9539e618f3374b8371","observation_id":"b368d867-ff40-4a3e-9b4b-5fa27809b8a3","resolution":{"observed_at":"2026-05-19T16:32:39.441301Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2605.16720","last_updated":"2026-05-16T00:07:49Z","snapshot_observed_at":"2026-08-02T15:13:54.844191Z","submitted_at":"2026-05-16T00:07:49Z","title":"Compositional Adversarial Training for Robust Visual Watermarking","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-19T21:53:59.355980Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2605.16720"},"observation_digest":"sha256:9b5516754eb89d3d4eb55e734ec831b73ab8629ee398d7ca6f36c79e2f338ece","observation_id":"5337a6a6-99c1-49b0-98cb-60bdea2c4135","resolution":{"observed_at":"2026-05-19T21:57:48.563773Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2606.03713","last_updated":"2026-06-02T14:34:48Z","snapshot_observed_at":"2026-08-01T18:41:34.888563Z","submitted_at":"2026-06-02T14:34:48Z","title":"Investigating Adversarial Robustness of Multi-modal Large Language Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-28T11:11:34.152223Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2606.03713"},"observation_digest":"sha256:d5af2ecdb8a65f00a6b30d914c18ff5773c82d1c088b4ab03535823c7680d1c3","observation_id":"62b20559-e6de-4fc6-8943-83353099da30","resolution":{"observed_at":"2026-07-02T02:06:27.625796Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2606.03730","last_updated":"2026-06-04T14:10:51Z","snapshot_observed_at":"2026-08-03T22:55:12.534950Z","submitted_at":"2026-06-02T14:49:04Z","title":"Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T11:04:30.654255Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2606.03730"},"observation_digest":"sha256:95d43364d6661660cbb7194d40cf6a6f7ae24bd14d069e307edb21f5cf157ed7","observation_id":"8e0c1325-3b5e-43ad-bc75-b981fbd7338c","resolution":{"observed_at":"2026-07-02T02:16:26.817544Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":"2205.07460","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-02T22:27:25.965269Z","title":"Diffusion models for adversarial purifi- cation.arXiv preprint arXiv:2205.07460","venue":null,"work_id":"4ea851d8-0e2f-4c1e-a45a-ec7fbacc5113","year":2022},"citing_paper":{"arxiv_id":"2606.08745","last_updated":"2026-06-07T17:32:24Z","snapshot_observed_at":"2026-08-08T23:14:00.410081Z","submitted_at":"2026-06-07T17:32:24Z","title":"Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-27T18:53:59.156013Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2606.08745"},"observation_digest":"sha256:0099d349639bc1238fbe315f931f65e7cb513066bf4b6b09b000633c4647f31b","observation_id":"53738550-1b26-49fd-8da0-debf960e2fb3","resolution":{"observed_at":"2026-07-02T22:27:25.966902Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-07-14T15:45:08.348933Z","title":"arXiv preprint arXiv:2205.07460 (2022) 16 S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.09787","last_updated":"2026-07-08T14:35:05Z","snapshot_observed_at":"2026-08-06T22:46:40.052800Z","submitted_at":"2026-07-08T14:35:05Z","title":"Adversarially Guided Diffusion for LiDAR Range Image Synthesis","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T15:45:08.348933Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2607.09787"},"observation_digest":"sha256:636b20cdf00458f7e400056f206ac65e711610cc997e4544729844dde5947ea4","observation_id":"70c486cd-ef35-4a6a-b01e-c2b9a4831f71","resolution":{"observed_at":"2026-07-14T15:45:08.348933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-03T16:57:27.557413Z","title":"Diffusion models for adversarial purification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.28936","last_updated":"2026-07-31T01:33:53Z","snapshot_observed_at":"2026-08-07T08:38:15.536042Z","submitted_at":"2026-07-31T01:33:53Z","title":"DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T16:57:27.557413Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2607.28936"},"observation_digest":"sha256:d3452507a8b033922eaa6eb3eb4ecf5fae5b0f710791e323ecb8729dc60f536b","observation_id":"b12f1e3e-a643-4e9a-a49d-b140b7c4775f","resolution":{"observed_at":"2026-08-03T16:57:27.557413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-06T00:41:28.937593Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.01028","last_updated":"2026-08-02T06:06:28Z","snapshot_observed_at":"2026-08-07T17:25:43.376608Z","submitted_at":"2026-08-02T06:06:28Z","title":"VLAGuard: A Framework for Evaluating and Mitigating Physical Attention Hijacking in Vision-Language-Action Robots within Wireless Sensor Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T00:41:28.937593Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2608.01028"},"observation_digest":"sha256:ec85d9476535c3af6ab3cad6ee4b1466e2e25b407155a4226a91288ee44e3320","observation_id":"59773805-4db5-49ce-b21f-c37e2440c9d5","resolution":{"observed_at":"2026-08-06T00:41:28.937593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-05T23:06:14.738049Z","title":"Diffusion models for adversarial purification,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.03231","last_updated":"2026-08-04T07:03:05Z","snapshot_observed_at":"2026-08-09T09:53:14.297976Z","submitted_at":"2026-08-04T07:03:05Z","title":"Structure-Aware Robust Fine-Tuning: Defending Vision-Language-Action Robots Against Physical Attention Hijacking","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T23:06:14.738049Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2608.03231"},"observation_digest":"sha256:0afefaa5bf63fd0f3c54f48932cd6b84c02a806b48215c71a2646420eb4f7a7e","observation_id":"6c8ae63e-61ba-4009-9244-e7d4e2947e81","resolution":{"observed_at":"2026-08-05T23:06:14.738049Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.07460","snapshot_observed_at":"2026-08-05T21:17:17.271571Z","title":"arXiv preprint arXiv:2205.07460 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.03316","last_updated":"2026-08-04T08:23:57Z","snapshot_observed_at":"2026-08-10T01:22:50.457070Z","submitted_at":"2026-08-04T08:23:57Z","title":"Any-OPD: Heterogeneous On-Policy Distillation for Flow-Matching Models via Representation-Space Bridging","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-05T21:17:17.271571Z"},"links":{"cited_paper":"/paper/2205.07460","citing_paper":"/paper/2608.03316"},"observation_digest":"sha256:14d2c64ac3d6c8184b178a292eac6cfa93f568853c4682f3e9f28d4bd635045b","observation_id":"f1f0362c-90a0-4182-b3b6-ef05524ec83c","resolution":{"observed_at":"2026-08-05T21:17:17.271571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2205.07460/citation-record","integrity":"/paper/2205.07460/integrity","json":"/paper/2205.07460/citation-record.json","paper":"/paper/2205.07460"},"outbound":[],"paper":{"arxiv_id":"2205.07460","last_updated":"2022-05-16T06:03:00Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T18:06:26.992880Z","submitted_at":"2022-05-16T06:03:00Z","title":"Diffusion Models for Adversarial Purification"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+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 0 of 0 outbound references and 32 inbound Pith citation observations for arXiv:2205.07460."}