{"as_of":"2026-08-08T03:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1f110f06e8c838fe7d7cd34e4dd1fd04babcc55a8d77d8c2d7fb613d9997bb90","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:42:08.232124Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2507.18202/citation-record","integrity":"/paper/2507.18202/integrity","json":"/paper/2507.18202/citation-record.json","paper":"/paper/2507.18202"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:09.061656Z","title":null,"venue":null,"work_id":"6a53442b-5d03-4eac-8669-59e9983d4818","year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:07.999550Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:44ffda297a543b0c4b9a45738cef451c10378550c90dfe532380dc33dd159466","observation_id":"061e9e6b-0cfb-443c-b6f4-da90e162e514","resolution":{"observed_at":"2026-08-06T14:42:09.066159Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.004367Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.004367Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:d420001e700a32656bc9df5a78b54e2a4836f71ad478b05ed93b5b17ffea9467","observation_id":"54707790-5cd3-45ca-8e16-707da5fc6080","resolution":{"observed_at":"2026-08-06T14:42:08.004367Z","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":"2026-08-06T14:42:09.037601Z","title":null,"venue":null,"work_id":"f9fae53a-28f2-4408-a0dd-656f4825debb","year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.009037Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:0317b2518ec875f7fde5360b61c596ea0014f4faba8711606aa5e2c919ef94c5","observation_id":"054197df-2196-45ff-937a-d75eef6bbae8","resolution":{"observed_at":"2026-08-06T14:42:09.042668Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.013831Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.013831Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:f7c5f5f4e5af508beede3654b9b4097075f45574b8227d11dc3fb9629681e851","observation_id":"7edce108-614e-417a-9dbc-6a25418bc1c7","resolution":{"observed_at":"2026-08-06T14:42:08.013831Z","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":"2026-08-06T14:42:09.013354Z","title":null,"venue":null,"work_id":"90ad2fb8-6431-4167-ac18-6ef7a4f4e70e","year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.018398Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:ef746238f4538b3eb2c41ccd37564da2d67275e70e66d506221d0d04c68edaba","observation_id":"db407138-7be8-4fb4-aee6-0405f47af325","resolution":{"observed_at":"2026-08-06T14:42:09.017992Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.022937Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.022937Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:f834bcee5af4d6c2a64152125d576ab992aeda54a40e6eb6c2efc75a8196bb4a","observation_id":"e47dde4e-2e6f-4036-8553-07542a4b3821","resolution":{"observed_at":"2026-08-06T14:42:08.022937Z","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":"2026-08-06T14:42:08.027820Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.027820Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:29080dcfba1ea6c6b9d5dc44cf744f5cee0a94441c4cd0392568ff48fb065dab","observation_id":"cfd800ae-d0f9-49bf-9411-c64e48d81520","resolution":{"observed_at":"2026-08-06T14:42:08.027820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.032688Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.032688Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:4f3e248ba5d58348e7ec36e7a191b30597667f9c114830c3c77c9e2d2e5edafa","observation_id":"0a8846a9-0316-4d0f-9ec2-a6103bd0786e","resolution":{"observed_at":"2026-08-06T14:42:08.032688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-08-06T14:42:08.036993Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.036993Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:b01530dbf9c5c2e2184515e6e015f0e485a0e2000e6faaf794cb266d89e74895","observation_id":"66966222-a3ac-4217-aa5b-1b7cb742d8c5","resolution":{"observed_at":"2026-08-06T14:42:08.036993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.041719Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.041719Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:94bfbc2094c842f54599a09370c3ada66f47cc3d2939a31b47182c3e41c79218","observation_id":"77917687-ce43-4167-8f44-3229fbedc9a2","resolution":{"observed_at":"2026-08-06T14:42:08.041719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.05232","last_updated":"2024-11-19T12:42:45Z","snapshot_observed_at":"2026-07-06T16:45:07.733095Z","submitted_at":"2023-11-09T09:25:37Z","title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.05232","snapshot_observed_at":"2026-08-06T14:42:08.045898Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.045898Z"},"links":{"cited_paper":"/paper/2311.05232","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:749ae0943d5fbf2186ac9dbeaf0c9f84aeaa88b516facc9155b1f74863209207","observation_id":"2d718f99-e4b9-49ab-a142-a7bde5391efc","resolution":{"observed_at":"2026-08-06T14:42:08.045898Z","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":"2026-08-06T14:42:08.979263Z","title":"Poly-encoders: Architectures and pre-training strategies for fast and accurate multi-sentence scoring","venue":null,"work_id":"2a08699c-0101-4eb0-8165-809e8db0be5f","year":null},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.051062Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:19d7373944b8dbe13057864f4f513bd462ffa96c0975d33988c26b3a7f0c8358","observation_id":"7fde3748-76ea-4df8-9f6b-9d913e8af87a","resolution":{"observed_at":"2026-08-06T14:42:08.984919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.964329Z","title":"Unsupervised dense information retrieval with contrastive learning","venue":null,"work_id":"3c550187-aebf-4c7c-a061-0054e529a487","year":null},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.055603Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:7c218f8884e04d1d60199c7e32009d737bc600a61b9e2089fa9eb0054168865f","observation_id":"ea8e5989-e7aa-4063-9d92-7588d51b3a77","resolution":{"observed_at":"2026-08-06T14:42:08.968929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.060034Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.060034Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:e6f84adc9db04fb39655515d48285b4940e93838a387d7e7010dfeca08f52d09","observation_id":"538766c3-0005-4021-abf6-9d8103c1c30f","resolution":{"observed_at":"2026-08-06T14:42:08.060034Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.064460Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.064460Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:1488fc90d6b2f1c50f26e0e6f0d976d43b52c0c8458136c6224224354147e529","observation_id":"6b0f6a3e-767e-4a05-9808-f68ac9c019f0","resolution":{"observed_at":"2026-08-06T14:42:08.064460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.068634Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.068634Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:ce06d5535465aad4db162fbb1af924a6e072f5db00cebd7bc4f46494b244e7b1","observation_id":"eef6daa7-87db-4fef-afb0-84a5dd27c722","resolution":{"observed_at":"2026-08-06T14:42:08.068634Z","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":"2026-08-06T14:42:08.940353Z","title":null,"venue":null,"work_id":"1b52a384-503f-4908-8a1b-dd9316c88d76","year":2020},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.072963Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:213f2370e515a65d2d3e612a1164f1bb8ac59b277d2e7b419bc650da2ae73285","observation_id":"23dc701e-022f-41b0-8384-f42ca8022063","resolution":{"observed_at":"2026-08-06T14:42:08.945365Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.077127Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.077127Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:22eebc7161734f5b2e0f5e7327d707cd398601196388c4827f56c37948972580","observation_id":"cb5c1216-a79b-4cf5-bfe2-af12b31cec1f","resolution":{"observed_at":"2026-08-06T14:42:08.077127Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.081414Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.081414Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:b531d749ec94eb711439cdfb1906d85b67fbcf411dcfc4d1108e436f74481bd0","observation_id":"2dab6782-910d-4ae0-b6ac-a18a7050ad6a","resolution":{"observed_at":"2026-08-06T14:42:08.081414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.086210Z","title":"Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.086210Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:ca64f1e26c2b53100388b32be2b4d85ef2bab998de77bb691d0b16077d1f2995","observation_id":"97d231cf-d76c-472c-b6a7-91df476ea2a4","resolution":{"observed_at":"2026-08-06T14:42:08.086210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.091545Z","title":"u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt \\","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.091545Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:b1e056636385372d99c342231a64f4165d903f2979316838462876ab5bb2ede2","observation_id":"6939cccc-c973-4c3e-892b-aac1d3f2fde5","resolution":{"observed_at":"2026-08-06T14:42:08.091545Z","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":"2026-08-06T14:42:08.906919Z","title":null,"venue":null,"work_id":"aa697898-c9e0-4a11-a1bb-6c1daffe8089","year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.095972Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:e33775fd88e309dd0fe67f2ff614223593231d974ed289efbbad220689c3dada","observation_id":"1334a91a-b866-4079-b675-1385fcc0e1fa","resolution":{"observed_at":"2026-08-06T14:42:08.911275Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.02777","last_updated":"2024-01-30T07:02:30Z","snapshot_observed_at":"2026-08-02T18:12:51.901453Z","submitted_at":"2024-01-05T12:26:46Z","title":"From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.02777","snapshot_observed_at":"2026-08-06T14:42:08.101243Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.101243Z"},"links":{"cited_paper":"/paper/2401.02777","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:55226c3df2823dede900916c7658119044f235f72dc870af6bea75abcd314585","observation_id":"2303844d-d03d-4d4b-a1d9-22cc4a392e07","resolution":{"observed_at":"2026-08-06T14:42:08.101243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-06T14:42:08.105462Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.105462Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:cc19c7eda154e90403718a8c083760b4bd9ff524a8b3fa6a76b37cc2ecc1ddd5","observation_id":"cb1fd4a6-3c8a-40e0-97b9-8cfb274af0e6","resolution":{"observed_at":"2026-08-06T14:42:08.105462Z","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":"2026-08-06T14:42:08.892503Z","title":"A language agent for autonomous driving","venue":null,"work_id":"462b163a-2008-4071-ad00-6c4841dc0bea","year":null},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.109844Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:284a6f17136ca000abd9536f1779c4bf396f0e79cf78719c58c7ec9c14fd5efe","observation_id":"228a1b63-e721-4cb2-b2e8-75e0e315d1ac","resolution":{"observed_at":"2026-08-06T14:42:08.897173Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.877329Z","title":null,"venue":null,"work_id":"c16163d1-32ae-40d3-b054-fd78152983b8","year":2022},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.114256Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:e3da22316261979477b105fc396bcdb3e05d66429c512c20a73edfb7f0591d1a","observation_id":"041cde6a-f447-4859-8a24-fefa4ad1e66e","resolution":{"observed_at":"2026-08-06T14:42:08.882278Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.862181Z","title":"Ms marco: A human generated machine reading comprehension dataset","venue":null,"work_id":"c2bfa1f9-75a4-4b10-b8fd-70593417a107","year":null},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.118683Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:92206c2670a728a8fc77ea4d897f32f2a0d552daad5d3266fba54ef9430656a8","observation_id":"2eeaa05e-cff9-4d7d-8b71-592692987cb9","resolution":{"observed_at":"2026-08-06T14:42:08.866842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.04085","last_updated":"2020-04-14T14:57:40Z","snapshot_observed_at":"2026-08-02T11:18:37.014004Z","submitted_at":"2019-01-13T23:27:58Z","title":"Passage Re-ranking with BERT","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.04085","snapshot_observed_at":"2026-08-06T14:42:08.124859Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.124859Z"},"links":{"cited_paper":"/paper/1901.04085","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:4bf3210ed751b7a0f1a04bbbac5eb5a89809636a98cdaa6e0bd7e8ac2a4f2ffb","observation_id":"5f76994a-07dc-45ce-8cbc-6046fcbc5ffd","resolution":{"observed_at":"2026-08-06T14:42:08.124859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.129519Z","title":"Language models are unsupervised multitask learners","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.129519Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:2a6dd618b8ec318c018d986801e17c79ae1cde827024a612b6b771d4e858f7b0","observation_id":"62c2bde4-9325-4e74-bc55-a1a89baa7a02","resolution":{"observed_at":"2026-08-06T14:42:08.129519Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.134263Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.134263Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:1a31c5171e6568c0c42a49578a52598e788880e149bd522128daba97fea7ec23","observation_id":"e640a48c-36a3-4c1b-88a4-9b2d441b8ce8","resolution":{"observed_at":"2026-08-06T14:42:08.134263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05530","last_updated":"2024-12-16T17:39:39Z","snapshot_observed_at":"2026-07-06T17:41:42.995949Z","submitted_at":"2024-03-08T18:54:20Z","title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05530","snapshot_observed_at":"2026-08-06T14:42:08.138719Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.138719Z"},"links":{"cited_paper":"/paper/2403.05530","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:0eaa477e21b8abe09116ba39012fd71a06a70f4eac494b3ac6e54a511f2c2cf7","observation_id":"aa0d27c1-fb5e-4325-ad2c-1e1aeef56f9d","resolution":{"observed_at":"2026-08-06T14:42:08.138719Z","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":"2026-08-06T14:42:08.829474Z","title":null,"venue":null,"work_id":"b8c44f8a-ef1c-48ef-b798-db20dbd09811","year":2022},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.143106Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:998cf665606eca551994c6d1c5ed591b81abb6fa38f1debc5110842d3c6ac4cd","observation_id":"97749d64-2648-4ea1-ab12-1a6e26bdebd3","resolution":{"observed_at":"2026-08-06T14:42:08.834038Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.814969Z","title":null,"venue":null,"work_id":"62ca8e8d-5e3a-4bf4-8cfb-2ab6cc06eae8","year":2023},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.147327Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:161fa2323f90b227e07aaabb1eec699b4ce516200a001e3d1ebdc57ed136aa5d","observation_id":"76208578-c0fc-4201-a6c6-03d1215f64c8","resolution":{"observed_at":"2026-08-06T14:42:08.819619Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.151659Z","title":"do anything now","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.151659Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:23eafb7a74da2d871717ba31d38956ad92ef080d18fd65a64aab94616332f7f1","observation_id":"4c246378-1043-4cf7-84e6-810aa31a8b71","resolution":{"observed_at":"2026-08-06T14:42:08.151659Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.156030Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.156030Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:4d048eb0e116061d399573dccb09d1e4449124353998befb3c61f94312ed3b5d","observation_id":"b68f2aa6-cbb1-44b9-a29a-1a26b10a9689","resolution":{"observed_at":"2026-08-06T14:42:08.156030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.08295","last_updated":"2024-04-16T12:52:47Z","snapshot_observed_at":"2026-08-03T03:29:01.959523Z","submitted_at":"2024-03-13T06:59:16Z","title":"Gemma: Open Models Based on Gemini Research and Technology","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.08295","snapshot_observed_at":"2026-08-06T14:42:08.160712Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.160712Z"},"links":{"cited_paper":"/paper/2403.08295","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:cd186162668950f66d26de9935763626fabdf8f99c089c88a60e75153880e30a","observation_id":"5539a2b9-5e77-43bd-a901-86ba58ea62e9","resolution":{"observed_at":"2026-08-06T14:42:08.160712Z","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":"2026-08-06T14:42:08.781045Z","title":"Beir: A heterogeneous benchmark for zero-shot evaluation of information retrieval models","venue":null,"work_id":"fcad4c60-27b4-4969-88c3-86aec7456d7a","year":null},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.165472Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:8fae0a77b195e3512b1f4130e642d1c2d224058544f76cea4beb55fbaf3d2434","observation_id":"333dfac3-3e72-4197-8c66-0b0f1057b557","resolution":{"observed_at":"2026-08-06T14:42:08.786858Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-06T14:42:08.170383Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.170383Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:88fc80cf0ecc47e8d33c355b6e0cd11b8c5b96a07accc6b434b11e624703e7f0","observation_id":"b4649bc9-41c6-47ed-995b-8858f34936a7","resolution":{"observed_at":"2026-08-06T14:42:08.170383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.174851Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.174851Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:6dc88ac9e648402f011c9fb926a92bd5cdba454eed3eee85c6b85b7b1ee515e3","observation_id":"bbdf690e-d6c9-4d9e-8b4f-02ae3b46fa3f","resolution":{"observed_at":"2026-08-06T14:42:08.174851Z","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":"2026-08-06T14:42:08.765819Z","title":null,"venue":null,"work_id":"b3954cae-b728-4ab5-9317-4e2031eba29b","year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.179119Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:ff7e531c5cb6f977f50e60f00b567b36f9d928e1979f911cfe93a45d04bca90a","observation_id":"b0ed3078-e1cf-48e8-a0e3-949ea19c36b5","resolution":{"observed_at":"2026-08-06T14:42:08.770323Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.751856Z","title":null,"venue":null,"work_id":"e9683bfd-8ab6-428e-bb66-e07dcf9f10b6","year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.185025Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:7ffa786e1bae7e4a4332d974d602964ff43b749b77562529110e949f19b1285d","observation_id":"f3398431-35f7-4cb4-8236-379b39c71325","resolution":{"observed_at":"2026-08-06T14:42:08.756307Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00083","last_updated":"2024-06-06T13:38:42Z","snapshot_observed_at":"2026-08-06T22:05:18.163179Z","submitted_at":"2024-06-03T02:25:33Z","title":"BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.00083","snapshot_observed_at":"2026-08-06T14:42:08.191718Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.191718Z"},"links":{"cited_paper":"/paper/2406.00083","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:53eb4380a7f85488b8f49df9519d1b116ab7040215f45275d75487f4c093e46f","observation_id":"da8ed986-5354-434a-80d0-a496ca3b8b19","resolution":{"observed_at":"2026-08-06T14:42:08.191718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.196142Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.196142Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:378770172fb415f7f96c41ded657279029445d0e6963c16ee605605091d5fe81","observation_id":"067f4f68-e03d-487f-b79d-9fedc48ca25a","resolution":{"observed_at":"2026-08-06T14:42:08.196142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02163","last_updated":"2025-03-06T00:58:55Z","snapshot_observed_at":"2026-08-07T02:18:22.354749Z","submitted_at":"2024-10-03T03:06:42Z","title":"Adversarial Decoding: Generating Readable Documents for Adversarial Objectives","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02163","snapshot_observed_at":"2026-08-06T14:42:08.200756Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.200756Z"},"links":{"cited_paper":"/paper/2410.02163","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:bba9607c769c6ed5508dbfcc0d646b43c30e7fd7f6ad41a021cfd7b4c5d0fe59","observation_id":"6f47ebb5-822e-485d-9953-4cdd4e61fb59","resolution":{"observed_at":"2026-08-06T14:42:08.200756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.205188Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.205188Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:516e1a3f98f4d27eca9193bc50d9f9dbde0c1a7e9e6dfcc5f1e6e2b0f97cf730","observation_id":"4af58b14-04a1-4bfc-90c7-29a326e64862","resolution":{"observed_at":"2026-08-06T14:42:08.205188Z","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":"2026-08-06T14:42:08.727050Z","title":null,"venue":null,"work_id":"36f0e7ca-cc9e-4530-a43d-43e282dbe45e","year":2023},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.209473Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:759cafdbdc520b7a0e3f432924964ab3ac7dcea5811b0a161a2454c599e141c4","observation_id":"953c2fa5-4de2-48f3-a7e2-881df1906e67","resolution":{"observed_at":"2026-08-06T14:42:08.732657Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.00879","last_updated":"2025-05-22T22:00:19Z","snapshot_observed_at":"2026-07-06T20:15:31.955797Z","submitted_at":"2025-01-01T15:57:34Z","title":"TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.00879","snapshot_observed_at":"2026-08-06T14:42:08.213731Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.213731Z"},"links":{"cited_paper":"/paper/2501.00879","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:a008dc7de69959481bee3e23c5e367221260e9a2b8bbb958c0c956074affca67","observation_id":"7c118fca-841a-4df3-b003-6efa317bae5a","resolution":{"observed_at":"2026-08-06T14:42:08.213731Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15043","last_updated":"2023-12-20T20:48:57Z","snapshot_observed_at":"2026-07-06T15:59:23.019044Z","submitted_at":"2023-07-27T17:49:12Z","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15043","snapshot_observed_at":"2026-08-06T14:42:08.218547Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.218547Z"},"links":{"cited_paper":"/paper/2307.15043","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:182bda53effe91d52c979ada8760cceada969bfa3a99114c5d6f8b266b89e470","observation_id":"39d40f62-47b1-4f12-a3ce-39c1cd4c782c","resolution":{"observed_at":"2026-08-06T14:42:08.218547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07867","last_updated":"2024-08-13T01:55:06Z","snapshot_observed_at":"2026-07-06T17:29:05.185768Z","submitted_at":"2024-02-12T18:28:36Z","title":"PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07867","snapshot_observed_at":"2026-08-06T14:42:08.223022Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.223022Z"},"links":{"cited_paper":"/paper/2402.07867","citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:68b6f464ed4f6522070528088fc268144a2fc5ee54af46f469a55e6e9115e78d","observation_id":"68edff08-d516-4b51-998d-c33ea54feda0","resolution":{"observed_at":"2026-08-06T14:42:08.223022Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.227599Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.227599Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:05ff5212de05fb5fad4826bbb3be417c8f524cdfd5254bcaf19d598f34e5ce9e","observation_id":"e3ad181f-0b44-4d71-b7cc-ce5ca33510e4","resolution":{"observed_at":"2026-08-06T14:42:08.227599Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T14:42:08.232124Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-06T14:42:08.232124Z"},"links":{"citing_paper":"/paper/2507.18202"},"observation_digest":"sha256:f07b29e324739597bacf1fb10f5c0190a04c89b9211eb83f410bcef4625fb319","observation_id":"9c1c2eff-41ab-44bf-9ea0-3c7e6ecdcaed","resolution":{"observed_at":"2026-08-06T14:42:08.232124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.18202","last_updated":"2025-07-24T08:58:41Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T04:03:26.028673Z","submitted_at":"2025-07-24T08:58:41Z","title":"Safeguarding RAG Pipelines with GMTP: A Gradient-based Masked Token Probability Method for Poisoned Document Detection"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":46,"verified_exact":0,"verified_fuzzy":5},"total_outbound_references":51},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2507.18202."}