{"as_of":"2026-08-23T09:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f29fbbe58e762cefd7e0b17be524b9d87884cfdf5e5f15fb5c156b33b5bcc769","coverage":[{"denominator":93,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":93,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:44:10.652420Z","state":"measured"},{"denominator":95,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":95,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T05:29:41.946357Z","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":"2026-05-12T05:31:23.579211Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"cited_work":{"arxiv_id":"2509.03787","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.03787","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3b39c234-714f-49f4-b4b9-28c38e0c9a3f","year":2025},"citing_paper":{"arxiv_id":"2605.01302","last_updated":"2026-05-02T07:22:24Z","snapshot_observed_at":"2026-08-13T05:15:28.429527Z","submitted_at":"2026-05-02T07:22:24Z","title":"Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-09T15:04:40.429929Z"},"links":{"cited_paper":"/paper/2509.03787","citing_paper":"/paper/2605.01302"},"observation_digest":"sha256:6126782e069e8d6e9753465c5047ba57125de6f53c06793bb398d2a4b3fcbbf5","observation_id":"9f1ad7ee-8fca-429a-89e2-8c70be8b58f0","resolution":{"observed_at":"2026-05-11T16:46:11.573012Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"cited_work":{"arxiv_id":"2509.03787","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.03787","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3b39c234-714f-49f4-b4b9-28c38e0c9a3f","year":2025},"citing_paper":{"arxiv_id":"2605.10253","last_updated":"2026-05-11T09:22:53Z","snapshot_observed_at":"2026-08-15T04:38:17.323235Z","submitted_at":"2026-05-11T09:22:53Z","title":"Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-12T05:29:41.946357Z"},"links":{"cited_paper":"/paper/2509.03787","citing_paper":"/paper/2605.10253"},"observation_digest":"sha256:a9923a5f3a0113820fb136132f182ae3c7b0f064501ca8a006b2fd70f8986383","observation_id":"a30b22e0-4cfa-4eb4-98ff-74d712f2e562","resolution":{"observed_at":"2026-05-12T05:31:23.581877Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.03787/citation-record","integrity":"/paper/2509.03787/integrity","json":"/paper/2509.03787/citation-record.json","paper":"/paper/2509.03787"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.08905","last_updated":"2024-12-12T03:37:41Z","snapshot_observed_at":"2026-08-20T14:44:18.211453Z","submitted_at":"2024-12-12T03:37:41Z","title":"Phi-4 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.08905","snapshot_observed_at":"2026-08-05T10:44:10.359303Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.359303Z"},"links":{"cited_paper":"/paper/2412.08905","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:d03cdc51286bffddbb786c71bd3c7bfa95db6e6c5fdeaf28ebadd3b376f34635","observation_id":"9d1efffb-72a7-4f3a-a84e-ad9311ab36a0","resolution":{"observed_at":"2026-08-05T10:44:10.359303Z","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":"2026-08-05T10:44:10.364024Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.364024Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:35df90f2803b9965bd10267d1ebfb3c03ce6a491759cd5e5c028c091616326d3","observation_id":"d37dfe9d-3a38-4539-9b28-c2901e114761","resolution":{"observed_at":"2026-08-05T10:44:10.364024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22284","last_updated":"2024-10-29T17:36:59Z","snapshot_observed_at":"2026-08-16T13:04:52.009331Z","submitted_at":"2024-10-29T17:36:59Z","title":"Embedding-based classifiers can detect prompt injection attacks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.22284","snapshot_observed_at":"2026-08-05T10:44:10.367998Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.367998Z"},"links":{"cited_paper":"/paper/2410.22284","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:9e5b45387d6fda1887035e790a2cc679a0a2cba9909e40cddf8d581cd5adf7e9","observation_id":"b8102041-b5ed-486b-a1a9-b473682b79f0","resolution":{"observed_at":"2026-08-05T10:44:10.367998Z","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":"2026-08-05T10:44:10.371919Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.371919Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:810bb8f5223fd9b79195146ebbaecc512ca9b354b7f8ac2bdc994b97373e41b6","observation_id":"1329cf1f-9917-486b-a118-5ac62721170f","resolution":{"observed_at":"2026-08-05T10:44:10.371919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16382","last_updated":"2025-02-26T23:46:31Z","snapshot_observed_at":"2026-08-19T11:15:45.447444Z","submitted_at":"2024-12-20T22:36:19Z","title":"EMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16382","snapshot_observed_at":"2026-08-05T10:44:10.375281Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.375281Z"},"links":{"cited_paper":"/paper/2412.16382","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:7a0b66d29e6e641bcd20068b96d5aeb0b44ef247cd7b760522d4404427c3bfbf","observation_id":"c53b43f2-503b-4582-802a-406e7eb5cd2d","resolution":{"observed_at":"2026-08-05T10:44:10.375281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.15283","last_updated":"2025-08-21T06:19:00Z","snapshot_observed_at":"2026-08-10T20:08:00.889567Z","submitted_at":"2025-08-21T06:19:00Z","title":"Adversarial Attacks against Neural Ranking Models via In-Context Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.15283","snapshot_observed_at":"2026-08-05T10:44:10.378820Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.378820Z"},"links":{"cited_paper":"/paper/2508.15283","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:e066e79d3f2231a3499ce475c3cf5bd86c1ab6b25709d4975d2998ea3b3bb8fa","observation_id":"08cfc278-7da6-4c6b-9e7c-187095a36d41","resolution":{"observed_at":"2026-08-05T10:44:10.378820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05030","last_updated":"2025-07-29T09:44:14Z","snapshot_observed_at":"2026-08-20T07:01:28.184093Z","submitted_at":"2024-03-08T04:22:48Z","title":"Defending Against Unforeseen Failure Modes with Latent Adversarial Training","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05030","snapshot_observed_at":"2026-08-05T10:44:10.382722Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.382722Z"},"links":{"cited_paper":"/paper/2403.05030","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:9b9d25ecadd406a345663599232a77904462e12cf804f48086eb349a8b72710c","observation_id":"6fc69d1a-04dc-4d07-9c5b-2233f71cc917","resolution":{"observed_at":"2026-08-05T10:44:10.382722Z","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":"2026-08-05T10:44:10.386226Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.386226Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:a1ada72f9bc6de727e84dd2796048596eacaae60eb7aecad46043866b3d06c4a","observation_id":"c3ac7339-7a0a-4473-a297-c00baf8f4199","resolution":{"observed_at":"2026-08-05T10:44:10.386226Z","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":"2026-08-05T10:44:10.389177Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.389177Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:50324c7d24364b40b00522ba248b16070c9b317f6e04b21fc28e20902d668118","observation_id":"bcc23ca0-9379-480b-a6e2-06d423e697ce","resolution":{"observed_at":"2026-08-05T10:44:10.389177Z","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":"2026-08-05T10:44:10.392496Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.392496Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:fc2bd64f1d9773f8a4e13dd6559820d9416f3875f674a0833b65f322083c109c","observation_id":"940262c2-4ed1-49da-8b0f-3e9611bde491","resolution":{"observed_at":"2026-08-05T10:44:10.392496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.07974","last_updated":"2025-08-25T16:49:10Z","snapshot_observed_at":"2026-08-16T18:37:44.319794Z","submitted_at":"2025-07-10T17:51:05Z","title":"Defending Against Prompt Injection With a Few DefensiveTokens","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.07974","snapshot_observed_at":"2026-08-05T10:44:10.395605Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.395605Z"},"links":{"cited_paper":"/paper/2507.07974","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:f273d0d3cf225f2af4eb06c2839867bee1925cf308339c62ebdd9d33d9357498","observation_id":"3759cf91-e76f-435b-a7b1-a60fb611481f","resolution":{"observed_at":"2026-08-05T10:44:10.395605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.05451","last_updated":"2025-07-03T05:45:41Z","snapshot_observed_at":"2026-08-16T13:11:45.940997Z","submitted_at":"2024-10-07T19:34:35Z","title":"SecAlign: Defending Against Prompt Injection with Preference Optimization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.05451","snapshot_observed_at":"2026-08-05T10:44:10.399166Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.399166Z"},"links":{"cited_paper":"/paper/2410.05451","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:3eaaef58480a8b928e6e850b0bc77e3e2dca64241ec3f80d0b692d69563db202","observation_id":"16634dc5-cecf-4163-a0d7-286a5723c790","resolution":{"observed_at":"2026-08-05T10:44:10.399166Z","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":"2026-08-05T10:44:10.402649Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.402649Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:e5ae9ab0f8e4ae07cc1dee44a07238f1fe52b4b12118c8dd5326ad02817a9043","observation_id":"a353e47c-88e4-438b-a794-eca3ab8aaa75","resolution":{"observed_at":"2026-08-05T10:44:10.402649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.01860","last_updated":"2023-05-03T02:09:29Z","snapshot_observed_at":"2026-08-16T15:36:05.642012Z","submitted_at":"2023-05-03T02:09:29Z","title":"Towards Imperceptible Document Manipulations against Neural Ranking Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.01860","snapshot_observed_at":"2026-08-05T10:44:10.405623Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.405623Z"},"links":{"cited_paper":"/paper/2305.01860","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:5753edd6a5f041301c3acdeffcc8783b4c7c554b8918ef268da351ea2add467a","observation_id":"d0f4a70f-bfa6-4f3c-8aef-140e65fcb8a6","resolution":{"observed_at":"2026-08-05T10:44:10.405623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13401","last_updated":"2024-07-07T05:03:53Z","snapshot_observed_at":"2026-08-16T13:50:43.977893Z","submitted_at":"2024-05-22T07:21:32Z","title":"TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13401","snapshot_observed_at":"2026-08-05T10:44:10.408846Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.408846Z"},"links":{"cited_paper":"/paper/2405.13401","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:45c5a06ce2d3e6e35f01767e3e0fa5eac42378946430dc2e7e9ea8c9e81d3f25","observation_id":"580c21fc-3a13-4587-a63b-4e0d02514d3f","resolution":{"observed_at":"2026-08-05T10:44:10.408846Z","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":"2026-08-05T10:44:10.412031Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.412031Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:2d344033ac4290becdad3c96132594e6f0aba7dade299629e53a9d5b11f734ef","observation_id":"4c93c43e-f461-4543-bdc9-b525bc71f5e1","resolution":{"observed_at":"2026-08-05T10:44:10.412031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.13948","last_updated":"2024-10-22T14:07:57Z","snapshot_observed_at":"2026-08-19T01:07:39.255482Z","submitted_at":"2024-04-22T07:49:36Z","title":"Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.13948","snapshot_observed_at":"2026-08-05T10:44:10.414941Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.414941Z"},"links":{"cited_paper":"/paper/2404.13948","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:1447a0b8f57b60308bc2813b678c59167298c11b94ca083d211f1145161e06e8","observation_id":"9e56de89-bc26-4953-bb1d-b8788837cc3e","resolution":{"observed_at":"2026-08-05T10:44:10.414941Z","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":"2026-08-05T10:44:10.418454Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.418454Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:039d38a039ccf0ec37f193a40660c5c539f2636930a6bede74419479dacac828","observation_id":"2115510b-3d10-462c-a1bc-319ed78ac1e6","resolution":{"observed_at":"2026-08-05T10:44:10.418454Z","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":"2026-08-05T10:44:10.421250Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.421250Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:2a1719a5a61c94c0765d6489519444c31d1dc96997df7091e6b6e067f3fae053","observation_id":"98a3d487-10ad-48cd-94a4-f344f522e655","resolution":{"observed_at":"2026-08-05T10:44:10.421250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2102.07662","last_updated":"2021-02-15T16:47:00Z","snapshot_observed_at":"2026-08-16T18:45:27.659994Z","submitted_at":"2021-02-15T16:47:00Z","title":"Overview of the TREC 2020 deep learning track","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.07662","snapshot_observed_at":"2026-08-05T10:44:10.424204Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.424204Z"},"links":{"cited_paper":"/paper/2102.07662","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:207036eccae7d0b41931183fdd08a474a466c4989e90ee0aeed7457f1818a415","observation_id":"dcd5842f-a502-4ce1-9166-c459cd7ffec8","resolution":{"observed_at":"2026-08-05T10:44:10.424204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.10865","last_updated":"2025-07-10T20:48:22Z","snapshot_observed_at":"2026-08-20T00:11:29.542804Z","submitted_at":"2025-07-10T20:48:22Z","title":"Overview of the TREC 2022 deep learning track","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.10865","snapshot_observed_at":"2026-08-05T10:44:10.427316Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.427316Z"},"links":{"cited_paper":"/paper/2507.10865","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:a766ca83abe6423d508feac45015dbcb528d92315daaa6dba57a349c658c5992","observation_id":"9b5b9f45-1b6a-4521-b304-062cf82abe40","resolution":{"observed_at":"2026-08-05T10:44:10.427316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.07820","last_updated":"2020-03-18T16:56:56Z","snapshot_observed_at":"2026-08-20T07:34:53.904375Z","submitted_at":"2020-03-17T17:12:36Z","title":"Overview of the TREC 2019 deep learning track","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.07820","snapshot_observed_at":"2026-08-05T10:44:10.430472Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.430472Z"},"links":{"cited_paper":"/paper/2003.07820","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:d5429fb33214923f725dc0e6481a617d3d4fe6d5bb50f89c22c5b30482550a8f","observation_id":"f706c97a-a66b-47c4-b4f2-04a3d76be986","resolution":{"observed_at":"2026-08-05T10:44:10.430472Z","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":"2026-08-05T10:44:10.433584Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.433584Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:9150b6b6fe90b38f9e2e926165c86798b119fe0e5319d0db985af4e5900ba8aa","observation_id":"d16b04cc-a5a5-4766-8482-c37ed4068048","resolution":{"observed_at":"2026-08-05T10:44:10.433584Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.08715","last_updated":"2023-10-25T07:30:51Z","snapshot_observed_at":"2026-08-19T11:42:57.272541Z","submitted_at":"2023-07-16T01:07:15Z","title":"MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.08715","snapshot_observed_at":"2026-08-05T10:44:10.439996Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.439996Z"},"links":{"cited_paper":"/paper/2307.08715","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:a2242a76705e04ae82addb03896b7ff5aba5bd352e77b037f82917cbe24f57b9","observation_id":"4a909c0b-6654-4cbe-aebf-7d55ade6bc19","resolution":{"observed_at":"2026-08-05T10:44:10.439996Z","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":"2026-08-05T10:44:10.443903Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.443903Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:ca67f2032df8e17324e4ccfadb0f906aae6aedc8496c0ed3944b91b3a3c4fe0b","observation_id":"cbf42087-3eba-4f8f-945e-c3451f8e8aab","resolution":{"observed_at":"2026-08-05T10:44:10.443903Z","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":"2026-08-05T10:44:10.447074Z","title":null,"venue":null,"work_id":null,"year":1981},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.447074Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:e4727b9e561651374287d76738449aee593c8bfdfb24275c229a68d9b107cf74","observation_id":"8dfd1971-30db-4e6e-a3eb-8a55af771ad1","resolution":{"observed_at":"2026-08-05T10:44:10.447074Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.18141","last_updated":"2025-03-10T09:49:31Z","snapshot_observed_at":"2026-08-19T18:57:50.841080Z","submitted_at":"2024-10-22T11:23:11Z","title":"SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.18141","snapshot_observed_at":"2026-08-05T10:44:10.450344Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.450344Z"},"links":{"cited_paper":"/paper/2410.18141","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:513424f7eedd62cd9764c3351a4510bd89b45092771f916db2dd2ace89406268","observation_id":"5fd553e3-9ff8-4322-ad58-4de3cbf20816","resolution":{"observed_at":"2026-08-05T10:44:10.450344Z","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":"2026-08-05T10:44:10.453742Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.453742Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:33bf9cba4681fe0feed52984e1d72c1c757bbb7bae596b2834e808530ddb1da9","observation_id":"ce32d79c-49f7-41c1-abc4-da246228da5c","resolution":{"observed_at":"2026-08-05T10:44:10.453742Z","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":"2026-08-05T10:44:10.457255Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.457255Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:98ea0fa4771726215bd38fd05d5f564291dfb50b64812cafe2949261d9f667a2","observation_id":"42240a80-6f34-44bf-9969-37700e675af6","resolution":{"observed_at":"2026-08-05T10:44:10.457255Z","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":"2026-08-05T10:44:10.460771Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.460771Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:67b224956b1ed10515fb59096425869cbff6c003606ebff5258cce1f91da2c2b","observation_id":"87c2b80e-6d60-4b64-91f9-7d678a33db03","resolution":{"observed_at":"2026-08-05T10:44:10.460771Z","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":"2026-08-05T10:44:11.586111Z","title":null,"venue":null,"work_id":"8e0e29d6-778d-4fc1-b0af-84e645ffd64e","year":2020},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.463930Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:6fc787c3dbe69237a0b54012fa498dd70893e9e3a1a9fd8107a1ef1dd1bd40a3","observation_id":"2db050e8-977c-4237-992e-339c101137e9","resolution":{"observed_at":"2026-08-05T10:44:11.589531Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.575278Z","title":null,"venue":null,"work_id":"487a2d30-b848-45ac-8500-895a511017d9","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.467142Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:04e8339409bb4639d13e9d11a5117b666f0bac32b6d3f1677f8aca87725d3933","observation_id":"d33025a0-f49b-447d-9f96-7776db8f6ce7","resolution":{"observed_at":"2026-08-05T10:44:11.578632Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:10.470919Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.470919Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:a633e9872277c627c8d8c18406bb0f7e46ec26e9725251b4e067a2549b19ee63","observation_id":"ea0dc7f7-484d-4214-a895-9832b3ce4261","resolution":{"observed_at":"2026-08-05T10:44:10.470919Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.01282","last_updated":"2021-02-03T09:18:34Z","snapshot_observed_at":"2026-08-18T00:05:48.019000Z","submitted_at":"2020-07-02T17:44:57Z","title":"Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.01282","snapshot_observed_at":"2026-08-05T10:44:10.473963Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.473963Z"},"links":{"cited_paper":"/paper/2007.01282","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:c102ff872cf73bf10ad14ed28f92c479dcb25e142e4200702a60aa532acd362b","observation_id":"8bd35032-50cb-4f06-be7f-e6b0ee11a1bc","resolution":{"observed_at":"2026-08-05T10:44:10.473963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2208.03299","last_updated":"2022-11-16T16:38:18Z","snapshot_observed_at":"2026-08-13T00:16:02.335397Z","submitted_at":"2022-08-05T17:39:22Z","title":"Atlas: Few-shot Learning with Retrieval Augmented Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2208.03299","snapshot_observed_at":"2026-08-05T10:44:10.477250Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.477250Z"},"links":{"cited_paper":"/paper/2208.03299","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:28ea22aedf0eb44b97dd788b0064139f073a065523d439cbb1931d7d6bb92fec","observation_id":"bc08841a-91f8-476c-83a7-301b2c4a51a5","resolution":{"observed_at":"2026-08-05T10:44:10.477250Z","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":"2026-08-05T10:44:11.554613Z","title":null,"venue":null,"work_id":"257b13e4-6113-454b-b94b-8737ba862360","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.481079Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:bb021ce1bbc74a5cacba5e8cae9da4f90baccce64ea79438bea1f88f561a8c4c","observation_id":"4675c99e-32c3-4f47-a029-dfb4a86981df","resolution":{"observed_at":"2026-08-05T10:44:11.558385Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.00614","last_updated":"2023-09-04T17:47:36Z","snapshot_observed_at":"2026-07-06T16:13:23.343694Z","submitted_at":"2023-09-01T17:59:44Z","title":"Baseline Defenses for Adversarial Attacks Against Aligned Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.00614","snapshot_observed_at":"2026-08-05T10:44:10.484302Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.484302Z"},"links":{"cited_paper":"/paper/2309.00614","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:81dd78f03689fd3c10dd735514e0fa103c2b4f61683d9566736114fa67c41d1f","observation_id":"230ccece-c89d-4d08-b91c-854e504c7f21","resolution":{"observed_at":"2026-08-05T10:44:10.484302Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.02705","last_updated":"2025-02-04T19:47:09Z","snapshot_observed_at":"2026-08-19T18:59:35.297879Z","submitted_at":"2023-09-06T04:37:20Z","title":"Certifying LLM Safety against Adversarial Prompting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.02705","snapshot_observed_at":"2026-08-05T10:44:10.487753Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.487753Z"},"links":{"cited_paper":"/paper/2309.02705","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:6c502a51321080e3ec5285433c0e819fe4badabccd721b8224d7ccc3cadb15b2","observation_id":"72fec629-b593-4885-80d1-c63beb3e8e61","resolution":{"observed_at":"2026-08-05T10:44:10.487753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13461","last_updated":"2019-10-29T18:01:00Z","snapshot_observed_at":"2026-07-06T08:33:12.534026Z","submitted_at":"2019-10-29T18:01:00Z","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13461","snapshot_observed_at":"2026-08-05T10:44:10.490972Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.490972Z"},"links":{"cited_paper":"/paper/1910.13461","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:1a603e95f96cf5ee8d507e8b2d09afe640a45fd203b75c655ffec3358e222c37","observation_id":"c92daef5-3137-41fd-8d5b-caebea42b2eb","resolution":{"observed_at":"2026-08-05T10:44:10.490972Z","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":"2026-08-05T10:44:10.494288Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.494288Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:32c2a3bbd05d22eac6f49af35e5360457b0d964d36efb6ca31927b2b9e6947da","observation_id":"e9793daa-44e2-4e6b-a95c-6a641531d260","resolution":{"observed_at":"2026-08-05T10:44:10.494288Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.05197","last_updated":"2023-11-01T05:14:01Z","snapshot_observed_at":"2026-08-22T05:25:02.232378Z","submitted_at":"2023-04-11T13:05:04Z","title":"Multi-step Jailbreaking Privacy Attacks on ChatGPT","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.05197","snapshot_observed_at":"2026-08-05T10:44:10.497463Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.497463Z"},"links":{"cited_paper":"/paper/2304.05197","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:a5ab69e3eefa12b4aa2b650bfdad9334319b301736893e89c6f23bc1a64812ec","observation_id":"1fc3e1a8-895c-4e0f-8215-22f8b45590cc","resolution":{"observed_at":"2026-08-05T10:44:10.497463Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.07391","last_updated":"2025-01-13T15:07:55Z","snapshot_observed_at":"2026-08-21T18:47:23.734594Z","submitted_at":"2025-01-13T15:07:55Z","title":"Enhancing Retrieval-Augmented Generation: A Study of Best Practices","version":1},"cited_work":{"arxiv_id":"2501.07391","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.07391","snapshot_observed_at":"2026-08-05T10:44:10.910461Z","title":"Enhancing Retrieval-Augmented Generation: A Study of Best Practices","venue":"cs.CL","work_id":"11f8e48a-7550-427d-9b6c-7222dda97b70","year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.500599Z"},"links":{"cited_paper":"/paper/2501.07391","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:fdb227380a43ddb8189c36b1c44b8a9c21506e5967792b6cd1ccb0b9043f7b8a","observation_id":"cf576f7a-dc1f-4263-8d4c-101f0cc7d63d","resolution":{"observed_at":"2026-08-05T10:44:10.914017Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.534007Z","title":null,"venue":null,"work_id":"9fa330d5-9e72-4fc6-9e63-15f3154d4870","year":2022},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.503656Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:0ea80b9d00d432d556526398af2e4004c6a78bad7d7c35ee0ad0137e3455ecd5","observation_id":"9069fb3f-58ce-42e0-9d44-8f4697d2b82f","resolution":{"observed_at":"2026-08-05T10:44:11.537389Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.03172","last_updated":"2023-11-20T23:09:34Z","snapshot_observed_at":"2026-07-06T15:51:12.179086Z","submitted_at":"2023-07-06T17:54:11Z","title":"Lost in the Middle: How Language Models Use Long Contexts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.03172","snapshot_observed_at":"2026-08-05T10:44:10.506488Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.506488Z"},"links":{"cited_paper":"/paper/2307.03172","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:95f05d0f131dd25a4e3e965d9ed03a9a6e17f577629510404d7fa02915f4ed14","observation_id":"c1002836-7593-4bb0-b6af-0b5e0a4121cc","resolution":{"observed_at":"2026-08-05T10:44:10.506488Z","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":"2026-08-05T10:44:11.523317Z","title":null,"venue":null,"work_id":"d7da85a9-4921-4084-b7b7-3dde08fb32a6","year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.509976Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:27728ca13d4fb604125e1ab889dde0bb997e9e31dbd662cc83b6dc38bb366536","observation_id":"0bd13822-500e-4d02-a5cd-b7ee5e5d5191","resolution":{"observed_at":"2026-08-05T10:44:11.526608Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.513201Z","title":null,"venue":null,"work_id":"4defb9aa-053b-4778-869a-e5ca9dcb96b8","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.513064Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:a7993910481ad65b6d17bb0d72afb2e4c918c1e6a4aab27ff11863d7df416ee5","observation_id":"6bc8acb2-3211-44c0-8c1b-92f974eb6ffb","resolution":{"observed_at":"2026-08-05T10:44:11.516673Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.503040Z","title":null,"venue":null,"work_id":"fdc5c89d-7535-4fea-a674-bdae094be903","year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.516054Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:2ccf9415ba1c5ae423a528ec6af8160abf124743269cc2d751e71548cc86d95e","observation_id":"35825980-6647-4960-9afa-97561e0994ac","resolution":{"observed_at":"2026-08-05T10:44:11.506312Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.492768Z","title":null,"venue":null,"work_id":"5dce6b17-b37c-4684-a1b0-f2d3851d782e","year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.519360Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:f712b4ea1fed670f96c309bd7121e989de8af1b059f6ae9dcb1bd15562545c23","observation_id":"4908ab8e-6fb9-4ac8-8aca-bd79dfef8171","resolution":{"observed_at":"2026-08-05T10:44:11.496070Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14493","last_updated":"2024-06-28T12:04:53Z","snapshot_observed_at":"2026-08-16T15:30:27.697182Z","submitted_at":"2023-05-23T19:45:45Z","title":"Do prompt positions really matter?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14493","snapshot_observed_at":"2026-08-05T10:44:10.522442Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.522442Z"},"links":{"cited_paper":"/paper/2305.14493","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:63183c8c07dab89cf2c0c05a1158191575f702e80f3ebdef6ee8f7bd155e2fb8","observation_id":"43cdc183-f5e9-43d6-ae02-6ce047b53266","resolution":{"observed_at":"2026-08-05T10:44:10.522442Z","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":"2026-08-05T10:44:11.482673Z","title":null,"venue":null,"work_id":"f0666c59-ef6b-4de5-84cd-ae4fe77540c7","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.525255Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:66a5b6755b906ecfddfda52e00037be2400132f6fbd30ab5e744cae2a4f0789f","observation_id":"13b73a45-c2cd-4165-8e0d-731471f6e80d","resolution":{"observed_at":"2026-08-05T10:44:11.485931Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:10.527830Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.527830Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:1de5bf7564d5dd3c1cd49ab6e527abcfc4a34c23c5e86838bcad9a54615601c8","observation_id":"56274bdb-6b54-47a3-9135-3abe54e1288e","resolution":{"observed_at":"2026-08-05T10:44:10.527830Z","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":"2026-08-05T10:44:11.453788Z","title":null,"venue":null,"work_id":"397889be-7aef-4c2e-a6e9-1d43f0f4a487","year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.534018Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:66f57b3fb7e07b81a181883aa55fa688f2563912094eb8b2fd21f78b5715efa9","observation_id":"f2d940ad-8f4f-45c8-a03d-6bbe5a66d677","resolution":{"observed_at":"2026-08-05T10:44:11.457223Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:10.536691Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.536691Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:adaf219640b102c177a34651719cf996da18af444b433746129aca9534029894","observation_id":"29259915-7009-4761-95cb-e68bb7ea88de","resolution":{"observed_at":"2026-08-05T10:44:10.536691Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2003.06713","last_updated":"2020-03-14T22:29:50Z","snapshot_observed_at":"2026-08-18T20:12:18.878856Z","submitted_at":"2020-03-14T22:29:50Z","title":"Document Ranking with a Pretrained Sequence-to-Sequence Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2003.06713","snapshot_observed_at":"2026-08-05T10:44:10.539618Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.539618Z"},"links":{"cited_paper":"/paper/2003.06713","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:39f846e5baf828a6da8cd586bd0db5b9b172898d34a8c3d1efa3a2e2b85e9998","observation_id":"08a32764-1db1-437f-a151-8f30f5c942ea","resolution":{"observed_at":"2026-08-05T10:44:10.539618Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.02832","last_updated":"2022-05-05T17:59:31Z","snapshot_observed_at":"2026-08-16T17:02:17.696795Z","submitted_at":"2022-05-05T17:59:31Z","title":"Entity Cloze By Date: What LMs Know About Unseen Entities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.02832","snapshot_observed_at":"2026-08-05T10:44:10.542462Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.542462Z"},"links":{"cited_paper":"/paper/2205.02832","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:9b6541c0c4bf2d5981d10cbe1bdc296f4348a2b480f28c024458430d9fda8bd9","observation_id":"c67ec331-c07f-4f3c-a7bc-b2187990be3c","resolution":{"observed_at":"2026-08-05T10:44:10.542462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.01990","last_updated":"2025-01-27T20:37:59Z","snapshot_observed_at":"2026-08-18T21:55:38.909137Z","submitted_at":"2023-08-03T19:03:18Z","title":"From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application?","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.01990","snapshot_observed_at":"2026-08-05T10:44:10.545760Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.545760Z"},"links":{"cited_paper":"/paper/2308.01990","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:b8215c161910124adae651b27802302f3bd742c9d6811af605f6a21ea411a071","observation_id":"f601853c-768d-47c4-9cfc-4debd33fa37c","resolution":{"observed_at":"2026-08-05T10:44:10.545760Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.06956","last_updated":"2025-07-09T15:39:17Z","snapshot_observed_at":"2026-08-18T10:58:20.807113Z","submitted_at":"2025-07-09T15:39:17Z","title":"Investigating the Robustness of Retrieval-Augmented Generation at the Query Level","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.06956","snapshot_observed_at":"2026-08-05T10:44:10.548768Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.548768Z"},"links":{"cited_paper":"/paper/2507.06956","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:1d3fc9e8bf4ac85f89f20872c453fad459b7e09c0270ab43a8fd90d2594d5b31","observation_id":"d59be122-e9c8-4cef-814f-b209fd44a5f0","resolution":{"observed_at":"2026-08-05T10:44:10.548768Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09527","last_updated":"2022-11-17T13:43:20Z","snapshot_observed_at":"2026-08-18T00:11:55.170013Z","submitted_at":"2022-11-17T13:43:20Z","title":"Ignore Previous Prompt: Attack Techniques For Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.09527","snapshot_observed_at":"2026-08-05T10:44:10.551784Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.551784Z"},"links":{"cited_paper":"/paper/2211.09527","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:0273d7bafc56466b3bc752a703ebe2fcb5d484e30add1a9ae2fbf3386b714fe9","observation_id":"b32b16f5-2f37-4314-a8e4-a7645bf8912d","resolution":{"observed_at":"2026-08-05T10:44:10.551784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07308","last_updated":"2024-05-02T14:28:39Z","snapshot_observed_at":"2026-08-20T05:22:16.665208Z","submitted_at":"2023-08-14T17:54:10Z","title":"LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07308","snapshot_observed_at":"2026-08-05T10:44:10.554735Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.554735Z"},"links":{"cited_paper":"/paper/2308.07308","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:c258e74f2d0ef947d14c985b11b79c3d048d532cd31b604dcee7661504b1d587","observation_id":"4364f9d1-2877-4a4c-9788-806fc557f7b2","resolution":{"observed_at":"2026-08-05T10:44:10.554735Z","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":"2026-08-05T10:44:11.435609Z","title":null,"venue":null,"work_id":"de0cb859-7d6a-47cb-91d7-d00b97c0bdf7","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.558983Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:8e45dbcb337ea0b3b916233d2c49a14ace64a1e13dcf436737ea9b423b9e9fd1","observation_id":"409ddb97-3e07-43e0-861a-de40d038f05b","resolution":{"observed_at":"2026-08-05T10:44:11.438589Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.425047Z","title":null,"venue":null,"work_id":"a8cbc728-6fb7-4ea6-9b51-ebcd9bc7017a","year":2017},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.561970Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:8a09ad44506db25b02bc53f642aadc000151a3737342970daf6a3a2164ff4698","observation_id":"1969eef6-59cb-4a85-af68-201ff2cf3879","resolution":{"observed_at":"2026-08-05T10:44:11.428892Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.414141Z","title":null,"venue":null,"work_id":"59ed8cad-e84b-47b5-bb67-7c5c2e876064","year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.564673Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:6930df7597b7e4e06c5b31b67155516e59526d1f234e6cc5a7658d0c752906f8","observation_id":"b0289010-f263-4507-ab76-66de5934b18f","resolution":{"observed_at":"2026-08-05T10:44:11.417272Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.15833","last_updated":"2024-02-24T15:00:58Z","snapshot_observed_at":"2026-08-16T14:15:40.635933Z","submitted_at":"2024-02-24T15:00:58Z","title":"Prompt Perturbation Consistency Learning for Robust Language Models","version":1},"cited_work":{"arxiv_id":"2402.15833","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.15833","snapshot_observed_at":"2026-08-05T10:44:10.811821Z","title":"Prompt Perturbation Consistency Learning for Robust Language Models","venue":"cs.CL","work_id":"50c7f2f8-9a90-4b4b-9b92-bf7ccfcc5c46","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.567619Z"},"links":{"cited_paper":"/paper/2402.15833","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:0524a0586718014cb5ede8eca8d01121e5ad11a5196741ef11d2c771946f0e3c","observation_id":"81cb42fd-32db-4390-a9e8-8454e905e854","resolution":{"observed_at":"2026-08-05T10:44:10.817393Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.403645Z","title":null,"venue":null,"work_id":"471f017e-f05c-40d1-b0f5-4aec8f6f45f1","year":null},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.571158Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:5da064d6d71a2970c5e31680e73660878d79109b62003e080993731bcc4ba91a","observation_id":"81892dd7-ba3f-47a4-901c-54779842b347","resolution":{"observed_at":"2026-08-05T10:44:11.406919Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.381848Z","title":null,"venue":null,"work_id":"2979cf3b-527c-4198-b77d-073a8a798391","year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.577344Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:769abdda57969f18ce4d14449dd7808dc5e1ddfd34e75c475140c7007dd67fd8","observation_id":"7a23eb1c-0502-414f-9294-9075e306505d","resolution":{"observed_at":"2026-08-05T10:44:11.385379Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03684","last_updated":"2024-06-11T19:02:52Z","snapshot_observed_at":"2026-08-17T15:25:03.596568Z","submitted_at":"2023-10-05T17:01:53Z","title":"SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03684","snapshot_observed_at":"2026-08-05T10:44:10.580434Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.580434Z"},"links":{"cited_paper":"/paper/2310.03684","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:e5c0790a07e9899c654ae6bf347ada1b7674eddea74890d08853cebc82600814","observation_id":"581ffc31-b7da-4a44-9e1a-939b2134d496","resolution":{"observed_at":"2026-08-05T10:44:10.580434Z","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":"2026-08-05T10:44:11.393145Z","title":null,"venue":null,"work_id":"a02982ef-44ac-48b9-a79f-bd4d1414cced","year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.574353Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:22c8cb94b47c719b9a780f6eceaca08868ebe4942aaeec19b304babff37f9ee1","observation_id":"ef7f8013-f8f3-4afa-8906-71479ce23a53","resolution":{"observed_at":"2026-08-05T10:44:11.396487Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.370923Z","title":"do anything now","venue":null,"work_id":"7e44b486-a8d4-4f51-a356-04d03c47ebf1","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.586817Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:4d1e7f5dcf662c7e024dcf5894c7552a4d1b46024e1c8604bc900a3b95867b69","observation_id":"c64504aa-1161-4f7a-bd6f-c116b8dbeabc","resolution":{"observed_at":"2026-08-05T10:44:11.374391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.358434Z","title":null,"venue":null,"work_id":"c793b26d-cdae-440e-afb9-0baccf059ec9","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.589573Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:74a15e1b696d7d16125b5336073ec52637d8dedce73e714ab6d46f798d609c30","observation_id":"fb91bcd2-468d-4001-ba8d-da88f264442a","resolution":{"observed_at":"2026-08-05T10:44:11.362247Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.18837","last_updated":"2025-01-31T01:09:32Z","snapshot_observed_at":"2026-08-08T23:58:18.293467Z","submitted_at":"2025-01-31T01:09:32Z","title":"Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.18837","snapshot_observed_at":"2026-08-05T10:44:10.583699Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.583699Z"},"links":{"cited_paper":"/paper/2501.18837","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:25ec785ebb92aa9df586ce25f9433e240638a105098f5e97eea5009cb3804570","observation_id":"6077579c-8a79-4f05-bd11-b9290276d170","resolution":{"observed_at":"2026-08-05T10:44:10.583699Z","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":"2026-08-05T10:44:10.595505Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.595505Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:a36abcafd904342ed4afcdd3bfdca13fa1c5ea66b7412b7441335329d5b0391b","observation_id":"4dc9a289-4b86-4749-bce7-aefb53115db7","resolution":{"observed_at":"2026-08-05T10:44:10.595505Z","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":"2026-08-05T10:44:11.340717Z","title":null,"venue":null,"work_id":"9092f221-af92-4e6b-9337-403c4dc1031f","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.598284Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:146cb0278509a023fff19754e1651e48ff5ba73888d92f8b5002048400f974fd","observation_id":"6be4a335-3a1b-4f6d-a855-12ed15ebb2b6","resolution":{"observed_at":"2026-08-05T10:44:11.344046Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.15219","last_updated":"2025-07-21T03:41:44Z","snapshot_observed_at":"2026-08-06T15:35:31.690602Z","submitted_at":"2025-07-21T03:41:44Z","title":"PromptArmor: Simple yet Effective Prompt Injection Defenses","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.15219","snapshot_observed_at":"2026-08-05T10:44:10.592328Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.592328Z"},"links":{"cited_paper":"/paper/2507.15219","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:5fd9212c30f4e1879fa6cd9a5426fd993471c44a0ecfce3f1461d2b1dc5d61d9","observation_id":"c46614cc-2f71-42d5-94f1-28156b4a7284","resolution":{"observed_at":"2026-08-05T10:44:10.592328Z","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":"2026-08-05T10:44:11.330419Z","title":null,"venue":null,"work_id":"d53e28cd-1a8d-4455-a7a5-4736d834f937","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.604112Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:51f0b1a8691751e231a1fbaf5e88e945c365e89c6f92f5fbd7f199b1ada70184","observation_id":"ed2657a2-91f2-49f8-8d80-8c802c3cd594","resolution":{"observed_at":"2026-08-05T10:44:11.333686Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.320167Z","title":null,"venue":null,"work_id":"5cd80258-4dfd-4ebb-a282-221eacfdcdbd","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.606900Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:c85dc3b41f528bf4beb4b762bab471a805de05388a6fe7ea9c93aa40ed117b3b","observation_id":"cb59fae0-327d-4f7b-8a18-53892e51b031","resolution":{"observed_at":"2026-08-05T10:44:11.323499Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.15042","last_updated":"2025-07-20T16:48:20Z","snapshot_observed_at":"2026-08-22T11:19:32.753835Z","submitted_at":"2025-07-20T16:48:20Z","title":"DeRAG: Black-box Adversarial Attacks on Multiple Retrieval-Augmented Generation Applications via Prompt Injection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.15042","snapshot_observed_at":"2026-08-05T10:44:10.601057Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.601057Z"},"links":{"cited_paper":"/paper/2507.15042","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:00c8c6b49494f4a0be79bd9d9e77b09b9cf7760aa9ac777bb8ee78bef696122c","observation_id":"349d7963-e035-454b-a5f8-dcefb9cfe494","resolution":{"observed_at":"2026-08-05T10:44:10.601057Z","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":"2026-08-05T10:44:11.298432Z","title":null,"venue":null,"work_id":"7604aa19-c483-4e3e-b88f-314bbc67b68a","year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.612427Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:c01163f4dd97fb2b7a21ba27de403b97bc7ac1ee68595d798d62ca3ca028798e","observation_id":"ae6401b3-b785-4a24-823b-0e7c613df139","resolution":{"observed_at":"2026-08-05T10:44:11.301691Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.287938Z","title":null,"venue":null,"work_id":"f0df09b6-8ef0-4d11-91b8-f0943683a4c2","year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.615312Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:9c12fb8301a4ee95aa23748fc0a110e2d6d78b88abc0911159741784db9e7626","observation_id":"1899c61f-1715-441c-8035-66c8c6aad87c","resolution":{"observed_at":"2026-08-05T10:44:11.291131Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:11.309562Z","title":null,"venue":null,"work_id":"8cd36f07-1dc4-4893-a519-e03b88c91c7c","year":2022},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.609728Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:5f0f7bd9391923c524935d39c7fb3f17dbf906fe61b1e722b5983d55de5e4aeb","observation_id":"cd1baf12-17c8-4159-9966-cc94cf58e100","resolution":{"observed_at":"2026-08-05T10:44:11.313177Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00083","last_updated":"2024-06-06T13:38:42Z","snapshot_observed_at":"2026-08-18T22:35:56.156622Z","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":"2026-08-05T10:44:10.621289Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.621289Z"},"links":{"cited_paper":"/paper/2406.00083","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:b632b11b6ac482415ba8c49cb03ad1cc62670dd4beda9c4ba05b0077937b3c95","observation_id":"0698889b-9b55-4363-ba93-fe97c98b0fb0","resolution":{"observed_at":"2026-08-05T10:44:10.621289Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13031","last_updated":"2024-07-23T22:56:13Z","snapshot_observed_at":"2026-08-20T02:52:47.433585Z","submitted_at":"2024-03-19T07:25:02Z","title":"RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13031","snapshot_observed_at":"2026-08-05T10:44:10.624358Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.624358Z"},"links":{"cited_paper":"/paper/2403.13031","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:afe32eb5e4572f137d187c6b98532523c2792d101e187d5c6b871739d5c879d0","observation_id":"89484cf8-d788-442d-9c63-6211efac8682","resolution":{"observed_at":"2026-08-05T10:44:10.624358Z","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":"2026-08-05T10:44:11.277691Z","title":null,"venue":null,"work_id":"ea718ded-17dd-40e0-bcf0-48cc57bdd1f6","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.618498Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:42a7935dcabef30d6c7175cf008977369b4e67c4510a030479fc1c9d18128598","observation_id":"8bd4de9c-c249-4e66-b36e-93e75ab4f2c2","resolution":{"observed_at":"2026-08-05T10:44:11.281380Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13534","last_updated":"2023-05-22T23:14:28Z","snapshot_observed_at":"2026-08-20T13:09:49.499190Z","submitted_at":"2023-05-22T23:14:28Z","title":"How Language Model Hallucinations Can Snowball","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13534","snapshot_observed_at":"2026-08-05T10:44:10.630613Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.630613Z"},"links":{"cited_paper":"/paper/2305.13534","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:db24faea5bbc8bd3ddb2ea679de9d41fe9fe0223b5886ca4795717b06e5c1784","observation_id":"5c342607-bb58-4b62-86ba-353bf506b0c8","resolution":{"observed_at":"2026-08-05T10:44:10.630613Z","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":"2026-08-05T10:44:11.267260Z","title":null,"venue":null,"work_id":"3b22c518-2d1c-4eb6-8069-d9e669f5accd","year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.633700Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:7a6c2f3ccbf67c9682204965622f5e725eda89c864f6fb25376d16ac77de7d3b","observation_id":"067d602c-2047-4754-b5c2-0dd1b6c7e1dd","resolution":{"observed_at":"2026-08-05T10:44:11.270624Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.04783","last_updated":"2024-11-14T18:14:00Z","snapshot_observed_at":"2026-08-21T21:03:30.042059Z","submitted_at":"2024-03-02T16:52:22Z","title":"AutoDefense: Multi-Agent LLM Defense against Jailbreak Attacks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.04783","snapshot_observed_at":"2026-08-05T10:44:10.627459Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.627459Z"},"links":{"cited_paper":"/paper/2403.04783","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:5feac846918b5ce971f35430cbb83b258db128d411a0071db877eea6b45ba6cc","observation_id":"6a78fe27-3580-4bdf-95e6-6f4d72bcf790","resolution":{"observed_at":"2026-08-05T10:44:10.627459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.22832","last_updated":"2024-10-30T09:15:51Z","snapshot_observed_at":"2026-08-16T13:04:38.165779Z","submitted_at":"2024-10-30T09:15:51Z","title":"HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.22832","snapshot_observed_at":"2026-08-05T10:44:10.643154Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.643154Z"},"links":{"cited_paper":"/paper/2410.22832","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:fa13ecbfa63cfef2f514ccee71a5daedc78db1c836966332942b089045205c46","observation_id":"87f7df2a-d8fa-490f-a266-324501ec82be","resolution":{"observed_at":"2026-08-05T10:44:10.643154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.05987","last_updated":"2023-02-18T18:27:49Z","snapshot_observed_at":"2026-08-21T17:16:00.737786Z","submitted_at":"2022-07-13T06:47:51Z","title":"DocPrompting: Generating Code by Retrieving the Docs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.05987","snapshot_observed_at":"2026-08-05T10:44:10.646252Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.646252Z"},"links":{"cited_paper":"/paper/2207.05987","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:bbde919380bc5812704cbd841aefadeaa57d1d49f5f4bec2c275773d9c02ca77","observation_id":"c485faf8-3f0e-4424-896b-4cfdfd3c3ca4","resolution":{"observed_at":"2026-08-05T10:44:10.646252Z","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":"2026-08-05T10:44:11.257134Z","title":null,"venue":null,"work_id":"8e11c91d-6428-4c95-8718-de706b9b34d1","year":null},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.636645Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:c21752cbf7cbf468023448b17a2932edd76a1c50a2d6a703c644a76fdaaa08ab","observation_id":"d30f2417-4c60-4566-8af2-766b8ab4bc69","resolution":{"observed_at":"2026-08-05T10:44:11.260224Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.07557","last_updated":"2025-02-11T13:50:50Z","snapshot_observed_at":"2026-08-19T00:16:08.532848Z","submitted_at":"2025-02-11T13:50:50Z","title":"JBShield: Defending Large Language Models from Jailbreak Attacks through Activated Concept Analysis and Manipulation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.07557","snapshot_observed_at":"2026-08-05T10:44:10.639804Z","title":"arXiv preprint arXiv:2502.07557 (2025)","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.639804Z"},"links":{"cited_paper":"/paper/2502.07557","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:da3a8888ba88b427d79c9e27a3bfce97cbbd07568da0545884a46ff7ce7e9284","observation_id":"fda83275-8882-44aa-b99b-3e65f871a595","resolution":{"observed_at":"2026-08-05T10:44:10.639804Z","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":"2026-08-12T09:06:50.363435Z","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":"2026-08-05T10:44:10.649233Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.649233Z"},"links":{"cited_paper":"/paper/2307.15043","citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:0fc1fa69785eebc4ac289eee917b00c7ff5ee35f1b3092076dc46c7ca7e93d40","observation_id":"0e9b4736-8bc7-48de-a651-d088b13a28c2","resolution":{"observed_at":"2026-08-05T10:44:10.649233Z","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":"2026-08-05T10:44:11.245787Z","title":null,"venue":null,"work_id":"48379334-4d02-42a3-b1ab-717600b5d92e","year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.652420Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:bafa2720dfc4793ebf377437b75097340a5304af090b6867d2a253732e702fef","observation_id":"903329cd-298d-4b0b-ad1d-8d2abdbd9464","resolution":{"observed_at":"2026-08-05T10:44:11.249126Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+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":"2026-08-05T10:44:10.436793Z","title":"In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.436793Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:e4b1ba64c84c89cbec111d2aba67dba44f6f6f97222f3e09df5ebc1b08f61f8e","observation_id":"bb9e0039-7807-47cb-a936-43b03388ae7f","resolution":{"observed_at":"2026-08-05T10:44:10.436793Z","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":"2026-08-05T10:44:11.464513Z","title":"ACM Transactions on Intelligent Systems and Technology 16, 5 (2025), 1–72","venue":null,"work_id":"5a8cf976-f1e0-4146-9ebe-bf6cf53a63f5","year":2025},"citing_paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-05T10:44:10.531004Z"},"links":{"citing_paper":"/paper/2509.03787"},"observation_digest":"sha256:84672c3a239f2bcbf80c47a302104cbcda3fa8442a03a514e4d5412a31fa7365","observation_id":"7d419938-5213-4c46-9de1-f0e065058555","resolution":{"observed_at":"2026-08-05T10:44:11.468193Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.03787","last_updated":"2025-09-04T00:45:58Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-22T09:09:46.965070Z","submitted_at":"2025-09-04T00:45:58Z","title":"Evaluating the Robustness of Retrieval-Augmented Generation to Adversarial Evidence in the Health Domain"},"reference_resolution":{"displayed":93,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":89,"verified_exact":2,"verified_fuzzy":2},"total_outbound_references":93},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 93 of 93 outbound references and 2 inbound Pith citation observations for arXiv:2509.03787."}