{"as_of":"2026-08-23T17:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:82e096a57dd5bedd304a529d953f6159e2e96ca0132a9f2ed4d32be87f37b50f","coverage":[{"denominator":7,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T18:02:29.830545Z","state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:44:10.378820Z","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":"2026-05-11T16:21:06.597102Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"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":"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":"2508.15283","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.15283","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ad- versarial attacks against neural ranking models via in-context learning","venue":null,"work_id":"94871521-1277-4795-a4e1-e3e3d9c120fd","year":2025},"citing_paper":{"arxiv_id":"2604.12138","last_updated":"2026-07-08T18:30:50Z","snapshot_observed_at":"2026-08-17T10:10:53.645855Z","submitted_at":"2026-04-13T23:39:39Z","title":"Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T15:00:05.173476Z"},"links":{"cited_paper":"/paper/2508.15283","citing_paper":"/paper/2604.12138"},"observation_digest":"sha256:4536a7f37f294e6c6f9abe1f6a54973c8cf988a2d99394f87e817d9aa536e784","observation_id":"978cd852-ba3e-4c4b-9ee8-d8357915b91a","resolution":{"observed_at":"2026-05-11T11:21:03.535087Z","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":"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":"2508.15283","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.15283","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Ad- versarial attacks against neural ranking models via in-context learning","venue":null,"work_id":"94871521-1277-4795-a4e1-e3e3d9c120fd","year":2025},"citing_paper":{"arxiv_id":"2605.01591","last_updated":"2026-05-02T19:26:34Z","snapshot_observed_at":"2026-08-15T05:43:56.796973Z","submitted_at":"2026-05-02T19:26:34Z","title":"Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-09T17:37:56.757376Z"},"links":{"cited_paper":"/paper/2508.15283","citing_paper":"/paper/2605.01591"},"observation_digest":"sha256:f5b71b1c33b54a6285569bcea4868a72518207510366843aa8cddd87e40e80aa","observation_id":"4a9f2311-0d48-40e1-9429-56c99c878e2f","resolution":{"observed_at":"2026-05-11T16:21:06.600579Z","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/2508.15283/citation-record","integrity":"/paper/2508.15283/integrity","json":"/paper/2508.15283/citation-record.json","paper":"/paper/2508.15283"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:02:31.317116Z","title":null,"venue":null,"work_id":"9295372e-3419-4ed2-bd6f-5636c26ab8aa","year":2026},"citing_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},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T18:02:29.188462Z"},"links":{"citing_paper":"/paper/2508.15283"},"observation_digest":"sha256:296259dbca3b77c76231968423b1f7e3a7e2c4cd461980e67c3b4bea738a66cc","observation_id":"a33fde8a-b4ae-402c-974b-93467d4dc657","resolution":{"observed_at":"2026-08-05T18:02:31.436062Z","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":"2026-08-05T18:02:29.830545Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_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},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T18:02:29.830545Z"},"links":{"citing_paper":"/paper/2508.15283"},"observation_digest":"sha256:610a7214e497c7df88a921d55c229a05ae6295d002e93840ef1cfe59ecc76667","observation_id":"ac26cfd4-c1e5-494a-a359-79e23266b613","resolution":{"observed_at":"2026-08-05T18:02:29.830545Z","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":"2026-08-05T18:02:30.760587Z","title":null,"venue":null,"work_id":"10c045c4-2f94-407f-9257-2d0d3d194b24","year":2003},"citing_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},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T18:02:29.509200Z"},"links":{"citing_paper":"/paper/2508.15283"},"observation_digest":"sha256:ad8dcc15c16dfbf8d06c3381d688ab9cfeac94d55b7921e020d8aeec4ffaf01b","observation_id":"3a18375f-b8aa-4505-a06e-c486bf8b76e3","resolution":{"observed_at":"2026-08-05T18:02:30.900216Z","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":"2026-08-05T18:02:30.392838Z","title":null,"venue":null,"work_id":"993c8a39-b7ae-4e85-9a16-a49d3dff83da","year":2020},"citing_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},"reference_index":1986,"source":"pdf_text","source_observed_at":"2026-08-05T18:02:29.593996Z"},"links":{"citing_paper":"/paper/2508.15283"},"observation_digest":"sha256:06f520473d80278da416726a4cee813c200b3e1373a0eb8eb43856a27fd79cd5","observation_id":"9263cabd-6856-43cf-8eda-7a62e62dd635","resolution":{"observed_at":"2026-08-05T18:02:30.557469Z","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":"2026-08-05T18:02:30.152044Z","title":"Then they are rotated and resam- pled onto a grid centered at the location of the transient and oriented along the same direction of the slit when the spectrum is taken","venue":null,"work_id":"50b36592-3a3f-4426-bec7-53b828e73fbb","year":2024},"citing_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},"reference_index":2010,"source":"pdf_text","source_observed_at":"2026-08-05T18:02:29.696940Z"},"links":{"citing_paper":"/paper/2508.15283"},"observation_digest":"sha256:55548517272b6b7b469d22692520f667fe2aa8eb300251c52d3fa52e204bd8bb","observation_id":"cf37ee91-8c39-4004-b6fa-5ab737f5f121","resolution":{"observed_at":"2026-08-05T18:02:30.239021Z","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":"2026-08-05T18:02:31.079097Z","title":"Milligan et al","venue":null,"work_id":"e066564d-cc8c-477e-b0a7-54b5236cdd1d","year":2025},"citing_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},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-05T18:02:29.265376Z"},"links":{"citing_paper":"/paper/2508.15283"},"observation_digest":"sha256:66445a12c952b484c2c349cb95e7ef3529b11389ad1c4f2f4811b956b5e8616a","observation_id":"f885ef32-8fdd-491e-9505-6db15037fb06","resolution":{"observed_at":"2026-08-05T18:02:31.188290Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:02:29.381491Z","title":"Dessart & D","venue":null,"work_id":null,"year":2020},"citing_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},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-05T18:02:29.381491Z"},"links":{"citing_paper":"/paper/2508.15283"},"observation_digest":"sha256:00009941898c1686e67329d0ead9d094eb724df1867d3e874a2cd30aba76217c","observation_id":"64e28298-03ef-429f-803e-35c302260365","resolution":{"observed_at":"2026-08-05T18:02:29.381491Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.15283","last_updated":"2025-08-21T06:19:00Z","latest_version":1,"primary_category":"cs.IR","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"},"reference_resolution":{"displayed":7,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":7},"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 7 of 7 outbound references and 3 inbound Pith citation observations for arXiv:2508.15283."}