{"as_of":"2026-08-08T09:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9d3efe01e9ffc21e5fc0861c683e7a0e03862586653e7f8a16a8938ea71a16c5","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:31:06.243141Z","state":"measured"},{"denominator":76,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":76,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T06:17:07.660975Z","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":"2026-06-29T14:43:31.568829Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"cited_work":{"arxiv_id":"2507.05441","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.05441","snapshot_observed_at":"2026-06-29T14:43:31.568829Z","title":null,"venue":null,"work_id":"25e37754-c2c8-424e-b09c-eac9693918ec","year":2025},"citing_paper":{"arxiv_id":"2605.30650","last_updated":"2026-05-28T23:10:04Z","snapshot_observed_at":"2026-07-06T23:39:53.132531Z","submitted_at":"2026-05-28T23:10:04Z","title":"When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-06-29T06:17:07.660975Z"},"links":{"cited_paper":"/paper/2507.05441","citing_paper":"/paper/2605.30650"},"observation_digest":"sha256:0e7745a6f87da54a6418b73d1c2da9f69b19f14429b650d00e03618d0bcf67b7","observation_id":"53478191-7b29-4514-b2fa-3dc4472cf9bc","resolution":{"observed_at":"2026-06-29T14:43:31.570242Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.05441/citation-record","integrity":"/paper/2507.05441/integrity","json":"/paper/2507.05441/citation-record.json","paper":"/paper/2507.05441"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"doc/6658106","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:07.839555Z","title":"Cornell Research Report On Enron 1998 | PDF | Enron | Discounted Cash Flow","venue":null,"work_id":"8c75ef3a-864f-4f3b-9ae8-551e2f7dca43","year":1998},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T19:30:59.334749Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:1c59967046501f13a9a065ec150bbea56e239a5ca79a17c913ea432d6830bc7a","observation_id":"e9096f2b-8838-4c8f-b95c-36c606a0ffb5","resolution":{"observed_at":"2026-08-06T19:31:07.846295Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6261.1968","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:07.770210Z","title":null,"venue":null,"work_id":"396475fe-8f9a-4071-96aa-d5dddd019a6a","year":1968},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T19:30:59.423336Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:fb71f4844afcbc0b8d6dc3fd61ba364c36298e21a2f34363842b1ec0a62b6e74","observation_id":"e4e40aea-bdbc-432d-8e98-4ced77a8a778","resolution":{"observed_at":"2026-08-06T19:31:07.779233Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:30:59.520411Z","title":"Real Attackers Don’t Compute Gradients","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T19:30:59.520411Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:8aeca2573750419a189921511fa0668295e198d1f9ecf1a3ebce564df70d4197","observation_id":"825a0b58-b81b-4770-9be3-a2666a94ef43","resolution":{"observed_at":"2026-08-06T19:30:59.520411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.00420","last_updated":"2018-07-31T00:09:56Z","snapshot_observed_at":"2026-07-06T06:21:22.353187Z","submitted_at":"2018-02-01T18:20:05Z","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.00420","snapshot_observed_at":"2026-08-06T19:30:59.640346Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T19:30:59.640346Z"},"links":{"cited_paper":"/paper/1802.00420","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:83331e4b98957149d351145c24485b1fe411cf19b16a3379114c47c6aee34f24","observation_id":"5d14e9ec-d4fe-4db3-991a-e2e48a3672d5","resolution":{"observed_at":"2026-08-06T19:30:59.640346Z","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":"10.1111/1475-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.562709Z","title":"JULIA YU, and JIE ZHANG","venue":null,"work_id":"32573bf7-c2f3-4540-aeb4-e94d0e687785","year":2020},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T19:30:59.785959Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:6c931508344c95afc7fc3c83b0bb09207f48bc723d538d7d89a43ea5221c6c42","observation_id":"f910eea0-df6b-4d35-b315-350b3194e918","resolution":{"observed_at":"2026-08-06T19:31:06.566232Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:08.097047Z","title":null,"venue":null,"work_id":"3463583d-6ca8-4d4d-8e7e-a143e145332e","year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T19:30:59.847805Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:98e04d69fc72b261e4a303f86475b33025d1f38772480a632f80b436dde54156","observation_id":"be742298-bc93-47a6-8a5f-6b352fe40ab4","resolution":{"observed_at":"2026-08-06T19:31:08.101525Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:08.086496Z","title":null,"venue":null,"work_id":"f7424d34-19d3-4239-9da3-6a74381a78aa","year":1997},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.003341Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:48abc015fb554e98f0500a0bd741df78d23a501a0738ae51fa188ff1997fcb15","observation_id":"085c2488-7941-4280-9c25-bbecf6fa26ae","resolution":{"observed_at":"2026-08-06T19:31:08.089988Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2469/faj.v55.n5.2296","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.551088Z","title":null,"venue":null,"work_id":"fface1f9-c0b7-4fe7-8c96-3ee513ba9010","year":1999},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.073903Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:bfbbccb1f11410dc1287e0e15a10e7575fe108d49312815cc1104c3e21ace3fd","observation_id":"850cfabb-02ed-43cc-a944-ece2e4f85406","resolution":{"observed_at":"2026-08-06T19:31:06.554581Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2139/ssrn.1998387","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.539051Z","title":"Beneish, Charles M","venue":null,"work_id":"92cdeead-aa70-4a43-a9a4-cf39055e6c5c","year":2012},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.154219Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:cfa2a6770b048ec6d2c0ba255283fefc91e14d1fc6dda74385c8926b4fc42f88","observation_id":"61802f14-427c-4b87-bff9-746929d1d2bb","resolution":{"observed_at":"2026-08-06T19:31:06.543314Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2139/ssrn.1006840","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.526825Z","title":"Beneish and Craig Nichols","venue":null,"work_id":"e960f599-874a-4d91-8ef0-6d7b577c9756","year":2007},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.242060Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:446319414c6e1f99b64ef77d4bdba31924f18b632548f9d84105f9c20c529cf6","observation_id":"ab841c77-c9b8-4edd-b7a6-39654b75edf2","resolution":{"observed_at":"2026-08-06T19:31:06.530266Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2139/ssrn.1134818","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.515633Z","title":"Beneish and Craig Nichols","venue":null,"work_id":"05457cdf-6cb8-4d55-95e7-e79c8ae74adc","year":2009},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.284457Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:febdd199e7a1514c082305d605b297b96ef60857fd86dd6ffb3ff100a7209f2a","observation_id":"44506709-ebf6-4115-9758-b124e5ec8683","resolution":{"observed_at":"2026-08-06T19:31:06.519412Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1109/tkde.2013.57","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.504207Z","title":null,"venue":null,"work_id":"0be614af-92a1-4ee3-9aec-b3889ae4a78a","year":2014},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.351533Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:f9515eb2eba0248fdbc819792a7fd8fd168ee3750048596c9db22fd523c86ff7","observation_id":"f62b44f9-b74f-4d19-93ff-5e5e6d8c4cb0","resolution":{"observed_at":"2026-08-06T19:31:06.508019Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:00.426752Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.426752Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:dbf5ae1b2417456ce967be5fd43fe77b0b0a234da580333a01641ede583f636b","observation_id":"3d4aa4fa-2aa0-44c7-90db-78acb3f41b08","resolution":{"observed_at":"2026-08-06T19:31:00.426752Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.03141","last_updated":"2018-07-19T08:27:23Z","snapshot_observed_at":"2026-08-07T04:51:03.940607Z","submitted_at":"2017-12-08T15:59:41Z","title":"Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.03141","snapshot_observed_at":"2026-08-06T19:31:00.518171Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.518171Z"},"links":{"cited_paper":"/paper/1712.03141","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:59eca978f9d1a74abe2b11133612ebe54ed1d9c8c58ae4fd07431d80bae9d89c","observation_id":"e4313f00-7bc3-4533-81e3-d54ad1036cec","resolution":{"observed_at":"2026-08-06T19:31:00.518171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2105.15183","last_updated":"2022-10-12T09:51:43Z","snapshot_observed_at":"2026-07-06T11:14:31.306862Z","submitted_at":"2021-05-31T17:45:58Z","title":"Efficient and Modular Implicit Differentiation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.15183","snapshot_observed_at":"2026-08-06T19:31:00.595705Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.595705Z"},"links":{"cited_paper":"/paper/2105.15183","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:124858b63335f2117e0254be5928b061437affff5e6c015329ac7b5342bb96e6","observation_id":"e8d61b8d-e3f7-4f99-b0df-0a16fda74e13","resolution":{"observed_at":"2026-08-06T19:31:00.595705Z","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":"2026-08-06T19:31:08.073507Z","title":null,"venue":null,"work_id":"5f7e1e2f-0174-408a-a0af-dfd99f3197c6","year":2018},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.709222Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:9b2571a1e16eb4b43545fbf9ec4b5d3194460da7868e1fedab7f4703b361ed7b","observation_id":"be697586-481d-42bb-91a7-beb6b9f23a72","resolution":{"observed_at":"2026-08-06T19:31:08.078602Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.15023","last_updated":"2021-06-28T23:19:02Z","snapshot_observed_at":"2026-07-06T11:24:00.099154Z","submitted_at":"2021-06-28T23:19:02Z","title":"Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.15023","snapshot_observed_at":"2026-08-06T19:31:00.800605Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.800605Z"},"links":{"cited_paper":"/paper/2106.15023","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:c5fcea46e56bd8cd456c098e70dab9f4aeea432334b72ff73debd78c283f194a","observation_id":"e9561ec4-4516-4264-ad64-9aac44697868","resolution":{"observed_at":"2026-08-06T19:31:00.800605Z","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":"2026-08-06T19:31:08.061082Z","title":null,"venue":null,"work_id":"cde46762-4724-46b6-a646-475bd84f5dfe","year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.879829Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:736f122853f9fa7af914cf2be83b7da3f6088ca034f749aab4d304be3d3bd7f5","observation_id":"a22daa2c-7488-48e6-abc1-1e760b09b9d2","resolution":{"observed_at":"2026-08-06T19:31:08.065769Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.07805","last_updated":"2021-06-15T17:45:26Z","snapshot_observed_at":"2026-08-06T17:18:50.002621Z","submitted_at":"2020-12-14T18:39:09Z","title":"Extracting Training Data from Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.07805","snapshot_observed_at":"2026-08-06T19:31:00.983161Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:00.983161Z"},"links":{"cited_paper":"/paper/2012.07805","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:4e2f247d4097a93ff4a2ce5cb610d2a7f1ce466bd55b9a402687edd9e330d11b","observation_id":"e941e346-f608-4269-aa6c-193a4c956cbc","resolution":{"observed_at":"2026-08-06T19:31:00.983161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.08232","last_updated":"2019-07-16T17:05:32Z","snapshot_observed_at":"2026-08-06T23:45:16.145644Z","submitted_at":"2018-02-22T18:42:41Z","title":"The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.08232","snapshot_observed_at":"2026-08-06T19:31:01.119521Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:01.119521Z"},"links":{"cited_paper":"/paper/1802.08232","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:e1b354eecc1a90f29cd5e53d7a126c0972fd7a9ab678918988b589b1c4298eb5","observation_id":"9d09bd1a-9fe9-4d40-9df8-1757428f7079","resolution":{"observed_at":"2026-08-06T19:31:01.119521Z","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":"2026-08-06T19:31:01.216373Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:01.216373Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:08b65fcdb98f64f8b192602a056ff2cda433fe9df1a93fdf8d09b3e9fa51ee1b","observation_id":"f99b7233-089a-4a4d-b0fc-a56d863457d8","resolution":{"observed_at":"2026-08-06T19:31:01.216373Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.01879","last_updated":"2025-03-06T11:05:33Z","snapshot_observed_at":"2026-07-06T17:24:37.090243Z","submitted_at":"2024-02-02T20:08:11Z","title":"$\\sigma$-zero: Gradient-based Optimization of $\\ell_0$-norm Adversarial Examples","version":3},"cited_work":{"arxiv_id":"2402.01879","doi":"10.48550/arxiv.2402.01879","metadata_source":"pith","pith_arxiv_id":"2402.01879","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"$\\sigma$-zero: Gradient-based Optimization of $\\ell_0$-norm Adversarial Examples","venue":"cs.LG","work_id":"50ae0718-69a7-4385-ba74-a68b47ab505d","year":2024},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:01.290307Z"},"links":{"cited_paper":"/paper/2402.01879","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:dba5daa0a0120a4b4e5db414344d55658b7a945617acb59750dad439d8e4f0a3","observation_id":"53f3a780-0b14-417b-b88a-af525ea93256","resolution":{"observed_at":"2026-08-06T19:31:06.471850Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16359","last_updated":"2025-05-29T05:54:54Z","snapshot_observed_at":"2026-08-04T10:31:21.024053Z","submitted_at":"2024-12-20T21:43:52Z","title":"Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context","version":3},"cited_work":{"arxiv_id":"2412.16359","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.16359","snapshot_observed_at":"2026-08-06T19:31:07.402925Z","title":"Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context","venue":"cs.CL","work_id":"f4743648-b64b-46be-88d1-a1de01c15839","year":2024},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:01.310794Z"},"links":{"cited_paper":"/paper/2412.16359","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:c057a3ef68ce10b75c610d3779eecc8f577c52718acf79d583a65aff55165da9","observation_id":"dc0f6c11-7bf5-4c72-a118-20f132d7d14a","resolution":{"observed_at":"2026-08-06T19:31:07.407612Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2308/accr.2002.77.s-1.35","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.448453Z","title":"Dechow and Ilia D","venue":null,"work_id":"66fd92fd-49c2-4331-91e8-f68abc2e47e7","year":2002},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:01.400845Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:27b0a313479bbe8afe36b1bcd465ef3a44bacdd5204cb8de4fdfbcbd94fb2ba7","observation_id":"cdf34f98-3832-456f-bcc7-cf6191f419f4","resolution":{"observed_at":"2026-08-06T19:31:06.452250Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:08.048518Z","title":"Dechow, Richard G","venue":null,"work_id":"d0a7d891-7f6d-49d1-a13a-589783b4fc0f","year":1995},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:01.511390Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:034b01834423eaa9e81a550ab5ed7543f2018e16f22dfd4813fbd9bd03d0bccd","observation_id":"4fa73389-8a55-46d8-a19c-9d3f417ee210","resolution":{"observed_at":"2026-08-06T19:31:08.053245Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/0165-4101(94)90008-6","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.437952Z","title":"DeFond and James Jiambalvo","venue":null,"work_id":"4d94a707-6bfc-4fe5-b75b-2fb8b9722d25","year":1994},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:01.762122Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:6cc9a364179f560881f94030af8387fe77ea1bf65d990af99307e21bc4ca996d","observation_id":"6d99efda-0f0a-4027-b72b-a418cff154b7","resolution":{"observed_at":"2026-08-06T19:31:06.441025Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:08.037046Z","title":null,"venue":null,"work_id":"7b966cc0-bcbb-4bb3-b64c-1ad2fbbd6974","year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:01.911615Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:4f9b2a7c0598cf687780076f531ef73d434fb0831e8fddc732221a4c0af1b472","observation_id":"395b6d73-11a9-41a1-84dd-197f6d877f2f","resolution":{"observed_at":"2026-08-06T19:31:08.040592Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:08.012095Z","title":null,"venue":null,"work_id":"9de1e825-38e3-486e-aac1-258a6f4d2da6","year":2008},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:02.272970Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:743079f8baf10e7517856bc56768440856ba288f6fe9a50453891decb7d6d9eb","observation_id":"15fad7d4-6b00-4a9b-bd01-0c829216708a","resolution":{"observed_at":"2026-08-06T19:31:08.016372Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.08177","last_updated":"2023-10-12T10:03:25Z","snapshot_observed_at":"2026-07-06T16:31:48.246401Z","submitted_at":"2023-10-12T10:03:25Z","title":"Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization","version":1},"cited_work":{"arxiv_id":"2310.08177","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.08177","snapshot_observed_at":"2026-08-06T19:31:07.385755Z","title":"Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization","venue":"cs.LG","work_id":"28479577-c18d-4288-8b85-5fb763dc5773","year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:02.453175Z"},"links":{"cited_paper":"/paper/2310.08177","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:85c0d09851b45408e0039d9e0e24802fa4bab70a93020dda8f6644f938d7105f","observation_id":"94ffdbf6-e959-4a7c-b9cf-c2ceeece5a3e","resolution":{"observed_at":"2026-08-06T19:31:07.389779Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:02.616791Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:02.616791Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:a58324d7a9f72ed309e9e4842bb240d55a3fe27fa6f14ef216b489e92772f94c","observation_id":"76ea5417-9bad-48cd-ac94-c177b19e15db","resolution":{"observed_at":"2026-08-06T19:31:02.616791Z","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":"2026-08-06T19:31:08.000142Z","title":"Carlin, Hal S","venue":null,"work_id":"3efcf372-32db-4c19-ba5b-2f7c1f442b7f","year":2013},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:02.767846Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:92316ddd27e45c0686ea6001c2ae0335aa94370c2b602e516b1adf3e0ab40dbb","observation_id":"3059d2e9-f2a4-44e2-bc67-03557bc90fe8","resolution":{"observed_at":"2026-08-06T19:31:08.004014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:07.987593Z","title":null,"venue":null,"work_id":"2e0b5412-7032-4033-9c1a-43101fd672e2","year":2021},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:02.962580Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:022b4a725c1b662c86cdd0b30ec356ef8ab7c926bcdd12251e126750e2e015d5","observation_id":"a271ae19-8494-4944-944a-2af9741f14d4","resolution":{"observed_at":"2026-08-06T19:31:07.992229Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/0165-4101(85)90029-1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.417826Z","title":null,"venue":null,"work_id":"986c8dcb-bb27-4bac-8f15-6e1a820dc8ba","year":1985},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:03.093222Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:afbe286acf2c3f9fb5141611ffab1e04d14963886d95353e0b6ce3dab706a5f7","observation_id":"935f5de7-09b3-456c-8204-70e8bab24b80","resolution":{"observed_at":"2026-08-06T19:31:06.421733Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1111/1475-679x.00041","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.406511Z","title":null,"venue":null,"work_id":"c520de55-cfba-42df-967d-4f93a051df98","year":2002},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:03.347831Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:fac2da363672a6c8ad9b55cbf8aaa4647090f60dba1efc983b92488717c76b92","observation_id":"8f8e4aeb-d6f0-4bd8-8a0a-e1889e19d812","resolution":{"observed_at":"2026-08-06T19:31:06.410260Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.eswa.2012.02.096","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.393744Z","title":null,"venue":null,"work_id":"183d6a14-06ea-46ae-a19f-996c2777684f","year":2012},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:03.524358Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:395fbfa78f73e1de7bbd770ffedc471dd4e8894568f8e2a955845f95ee84a3ee","observation_id":"bdd6490d-414f-4444-94c1-9847c0ef558f","resolution":{"observed_at":"2026-08-06T19:31:06.398275Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:07.974643Z","title":null,"venue":null,"work_id":"16e2d530-6e0f-4ef2-8481-65f7c2d10123","year":1991},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:03.670899Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:98478ae7a0aa2418a7d206b63e42a559735b78ccaaac26ec02fe3ec624d6fc60","observation_id":"5fe652c0-b971-4940-80be-9ffa5f98b779","resolution":{"observed_at":"2026-08-06T19:31:07.979465Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:03.842345Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:03.842345Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:1c9a0e1600c11819f55b963a0df5a6c50a1e6012e87bd7dd06b8c839d1151f54","observation_id":"5f570b33-80f2-4bed-8951-805aa35a43b9","resolution":{"observed_at":"2026-08-06T19:31:03.842345Z","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":"2026-08-06T19:31:04.027142Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.027142Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:04726883376d7ba647a3ca75e71405aa49a195343c75f633e385becabf53e025","observation_id":"64da8f70-95e7-4784-849c-e2e7058d41ad","resolution":{"observed_at":"2026-08-06T19:31:04.027142Z","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":"2026-08-06T19:31:04.194819Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.194819Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:2a4b4dab555acac5d7317917ab32353015e50d35050fe22c7e82b7840314d4e0","observation_id":"00dea18c-b95f-4c2d-bf27-73b36b400f7a","resolution":{"observed_at":"2026-08-06T19:31:04.194819Z","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":"2026-08-06T19:31:07.962858Z","title":null,"venue":null,"work_id":"1ed510da-dd66-4d67-b563-7cab357fe99d","year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.316288Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:a68363e74f55f01d048f9504f87b0c70801aad9a7271698e350fc6f08cb6e4d0","observation_id":"e8ff27a6-2f44-4ce6-b7eb-358dfadcedbc","resolution":{"observed_at":"2026-08-06T19:31:07.966537Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.33028","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:07.135753Z","title":null,"venue":null,"work_id":"e16a3aaa-c869-4ac5-b014-11b8ff751239","year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.476569Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:fe664cbe96071b399e938afea340f51e38687564238cad031deb59986b8f6dc7","observation_id":"98ff2931-c464-45e8-9e42-414870fd4b3a","resolution":{"observed_at":"2026-08-06T19:31:07.141067Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.00450","last_updated":"2020-12-01T12:51:53Z","snapshot_observed_at":"2026-07-06T10:19:28.469761Z","submitted_at":"2020-12-01T12:51:53Z","title":"Inverse spectral problem for a third-order differential operator with non-local potential","version":1},"cited_work":{"arxiv_id":"2012.00450","doi":null,"metadata_source":"pith","pith_arxiv_id":"2012.00450","snapshot_observed_at":"2026-08-06T19:31:07.059451Z","title":"Inverse spectral problem for a third-order differential operator with non-local potential","venue":"math.FA","work_id":"7b7f1dee-2d91-4ba2-ad42-7a8a81f9e2a9","year":2020},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.514402Z"},"links":{"cited_paper":"/paper/2012.00450","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:dd57809ba855184b3092cee6f073d62fd44d2d7a3a02e5f1053fe6e1f4eb77e3","observation_id":"5d864e0f-29a3-435e-998b-d221dabf6c3c","resolution":{"observed_at":"2026-08-06T19:31:07.064909Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:04.637779Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.637779Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:349b60200280eb403f8196b472abc9231efc12a0242fad746da852a2998bed2a","observation_id":"c078ce24-f77b-419d-a0a1-ba37a8bc6d09","resolution":{"observed_at":"2026-08-06T19:31:04.637779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.07005","last_updated":"2022-02-14T19:49:05Z","snapshot_observed_at":"2026-07-06T12:37:43.513161Z","submitted_at":"2022-02-14T19:49:05Z","title":"Continuously Generalized Ordinal Regression for Linear and Deep Models","version":1},"cited_work":{"arxiv_id":"2202.07005","doi":null,"metadata_source":"pith","pith_arxiv_id":"2202.07005","snapshot_observed_at":"2026-08-06T19:31:07.040631Z","title":"Continuously Generalized Ordinal Regression for Linear and Deep Models","venue":"cs.LG","work_id":"f7ddbd9d-5a2a-431d-af61-0baae2eaa124","year":2022},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.745977Z"},"links":{"cited_paper":"/paper/2202.07005","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:fdaa7ce418b7c045baa310d59d65d7d2c8ad7a40ae96dcf2bda11eef86281424","observation_id":"26bccd26-5735-4c8f-8338-8693aa95f86c","resolution":{"observed_at":"2026-08-06T19:31:07.046135Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1108/jfc-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.381898Z","title":null,"venue":null,"work_id":"c613001b-6e85-491f-ab6e-26ef5838660c","year":2020},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.842518Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:6a25e0bf703f9fb884b69365f5967724115eac44d266e65cd4040297b4312b24","observation_id":"873b2e41-7e2d-45be-9e8c-699db88eb195","resolution":{"observed_at":"2026-08-06T19:31:06.385856Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.10217","last_updated":"2018-07-25T02:33:41Z","snapshot_observed_at":"2026-07-06T06:25:45.299602Z","submitted_at":"2018-02-28T00:26:44Z","title":"Investigating Human Priors for Playing Video Games","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.10217","snapshot_observed_at":"2026-08-06T19:31:04.999970Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.999970Z"},"links":{"cited_paper":"/paper/1802.10217","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:9a11d8328262889d8b65a93d8f528ddc2f6409516d8b6eb735d645cce1789a47","observation_id":"47afb885-8bbf-4ff7-b813-5264ba0b7f67","resolution":{"observed_at":"2026-08-06T19:31:04.999970Z","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":"2026-08-06T19:31:07.951163Z","title":null,"venue":null,"work_id":"d32a90ce-9564-4aa2-9e68-440337956c6c","year":2024},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.077230Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:dbaa30aca7b46f2dd13e377f28dbc968d9e7c439535d27d15dcfae20a8f724b4","observation_id":"9951ae34-0244-4f9a-9bb1-780f509aa6ab","resolution":{"observed_at":"2026-08-06T19:31:07.955026Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.00224","last_updated":"2020-05-30T09:00:59Z","snapshot_observed_at":"2026-07-06T09:24:36.278462Z","submitted_at":"2020-05-30T09:00:59Z","title":"Casimir functions of free nilpotent Lie groups of steps three and four","version":1},"cited_work":{"arxiv_id":"2006.00224","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.00224","snapshot_observed_at":"2026-08-06T19:31:07.010104Z","title":"Casimir functions of free nilpotent Lie groups of steps three and four","venue":"math.DG","work_id":"2e211354-40ba-42d5-96ec-b3c8bb5632ab","year":2020},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.154024Z"},"links":{"cited_paper":"/paper/2006.00224","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:57531f84fac60b5591b3572694c8a053a3193f3403a4892a9c6ae92cd6bc076d","observation_id":"886adfa7-2243-4a1b-ae24-726c57c308d4","resolution":{"observed_at":"2026-08-06T19:31:07.014473Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.heliyon.2023.e13649","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.369619Z","title":null,"venue":null,"work_id":"56685a58-47d0-4f7d-9e04-c450e702c00c","year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.193744Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:1cf537be9ccfde5d78ba642e06b9ea3717d0bebef9e88227729785fcdd46edb8","observation_id":"a6530f3e-be2d-4997-af69-248375199950","resolution":{"observed_at":"2026-08-06T19:31:06.373390Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:05.274500Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.274500Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:02b4aead93f255797b18338dbbcd1e34156e618da8db1ca02f9b02040c12b884","observation_id":"6b67c388-7958-4256-997f-d93f2f6ab2b1","resolution":{"observed_at":"2026-08-06T19:31:05.274500Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.02885","last_updated":"2018-12-07T02:50:20Z","snapshot_observed_at":"2026-08-04T11:27:55.694274Z","submitted_at":"2018-12-07T02:50:20Z","title":"Adversarial Attacks, Regression, and Numerical Stability Regularization","version":1},"cited_work":{"arxiv_id":"1812.02885","doi":"10.48550/arxiv.1812.02885","metadata_source":"pith","pith_arxiv_id":"1812.02885","snapshot_observed_at":"2026-08-07T06:16:28.064256Z","title":"Adversarial Attacks, Regression, and Numerical Stability Regularization","venue":"cs.LG","work_id":"cf9da45a-c0fa-4f6f-8528-9d3dce471718","year":2018},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.447214Z"},"links":{"cited_paper":"/paper/1812.02885","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:25bd83f63547e5b846fc09dbff1fdb7b918e2bca0d3e754cdc4f6162bb4c76e4","observation_id":"1fefbb73-598c-4858-b186-dc1b56991533","resolution":{"observed_at":"2026-08-06T19:31:06.361150Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:05.602297Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.602297Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:a5af7146529bb75c3085b9c7ff78190a789a66972f0e40baa2cb1a4d4c53f028","observation_id":"ab4cc8ee-3e6e-4ca6-b680-3dbec7ad224a","resolution":{"observed_at":"2026-08-06T19:31:05.602297Z","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":"10.2307/2672906","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.340898Z","title":"Piotroski","venue":null,"work_id":"d663b3df-c669-4d1d-9e84-b00236c6b7e3","year":2000},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.831130Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:f250513db51e89fa6cc663d577752c51aac54a0bdacf63018f86016b6b14aa47","observation_id":"ce199876-f60b-41b9-a65d-b1de6ea272ba","resolution":{"observed_at":"2026-08-06T19:31:06.344164Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:05.962715Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.962715Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:2d76d8085d5e6060f521080976f268dbae8299718f0337da0cd139328afe2e5b","observation_id":"d1b08ec4-d1d4-4f30-a6e5-41aee001c48e","resolution":{"observed_at":"2026-08-06T19:31:05.962715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.09951","last_updated":"2023-06-16T16:32:27Z","snapshot_observed_at":"2026-07-06T15:43:31.881201Z","submitted_at":"2023-06-16T16:32:27Z","title":"You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks","version":1},"cited_work":{"arxiv_id":"2306.09951","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.09951","snapshot_observed_at":"2026-08-06T19:31:06.839486Z","title":"You Don't Need Robust Machine Learning to Manage Adversarial Attack Risks","venue":"cs.LG","work_id":"4cfb071f-3967-4857-8e8d-f7eb9784c59f","year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.088539Z"},"links":{"cited_paper":"/paper/2306.09951","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:3b584fbf5c96474cacfd33e461fa5c4d369177c41f345c08360df1582cc928a0","observation_id":"75b8b85d-5eb8-43e2-9d5d-5a24d7335ea7","resolution":{"observed_at":"2026-08-06T19:31:06.843698Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:07.917547Z","title":null,"venue":null,"work_id":"a298060b-7a2c-4f17-9111-afc9167a11e9","year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.148773Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:b090bd3df56e27303bdd723f3d0f34c7489d0e18f3382d5870b4824f59749ff6","observation_id":"56a9de09-995c-4461-bb39-7f89cd771770","resolution":{"observed_at":"2026-08-06T19:31:07.921688Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1911.04636","last_updated":"2019-11-12T02:17:01Z","snapshot_observed_at":"2026-08-01T17:05:12.088163Z","submitted_at":"2019-11-12T02:17:01Z","title":"Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory","version":1},"cited_work":{"arxiv_id":"1911.04636","doi":null,"metadata_source":"pith","pith_arxiv_id":"1911.04636","snapshot_observed_at":"2026-08-06T19:31:06.820429Z","title":"Robust Design of Deep Neural Networks against Adversarial Attacks based on Lyapunov Theory","venue":"cs.LG","work_id":"4ec3c00f-369e-4d22-8217-6af8720e5bd6","year":2019},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.193039Z"},"links":{"cited_paper":"/paper/1911.04636","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:7375532c3804603ffcf032e398c33d48247d645ce2bcf1001dcaf91888503b7b","observation_id":"930e781b-d0db-4221-a0f9-9e4aa67940d3","resolution":{"observed_at":"2026-08-06T19:31:06.826095Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:07.904517Z","title":"Martínez-Romero, and Teresa Mariño-Garrido","venue":null,"work_id":"c7b3a42f-46fa-4849-92ef-2840b2341d37","year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.197279Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:373c061318d13111ba2ae63cea4118aaa0982c4d50cd4bf5400539dcd9638ca2","observation_id":"d65a66c7-86cc-4ae7-bed1-18d02120abfb","resolution":{"observed_at":"2026-08-06T19:31:07.909899Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:06.205103Z","title":"Ribeiro and Thomas B","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.205103Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:3e66920b01e8ec87375fded7551c5db9bfe8be10a00cda2f69297f99fff298e8","observation_id":"e64ddd08-5258-4d2c-9682-f4fd8e02ee9d","resolution":{"observed_at":"2026-08-06T19:31:06.205103Z","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":"10.1506/8evn-9krb-","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.315456Z","title":null,"venue":null,"work_id":"2b22f1f0-c2d0-4b7d-be93-870d11687332","year":2003},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.209379Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:21499b99793c5439c688b139495457cb21a679bffd76d0fa74c738c8c0da9fe6","observation_id":"30afbc3c-d266-4c9f-b735-4a854033005d","resolution":{"observed_at":"2026-08-06T19:31:06.318953Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:06.213095Z","title":null,"venue":null,"work_id":null,"year":1984},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.213095Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:26ac3102104ae7533f640ae7c70170aa0bb01933e2adcae995adb9c480221741","observation_id":"5b364842-742a-4952-8a81-3d3204e56f95","resolution":{"observed_at":"2026-08-06T19:31:06.213095Z","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":"10.1007/978-3-030-73057-4_6","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T00:03:56.115653Z","title":null,"venue":"Studies in computational intelligence","work_id":"9b5248ad-b4dc-4431-8edb-55141f51e371","year":2021},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.216331Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:2ae406ed1c466dc4dac7a2d04b360933eb0bc319771bcec33d693aa36f6d0a9a","observation_id":"9d0aad24-f119-4f1c-93f2-55e14e13c116","resolution":{"observed_at":"2026-08-06T19:31:06.308606Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:07.892106Z","title":null,"venue":null,"work_id":"29319733-b2fe-4843-bed0-d693e034b55d","year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.219792Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:cafd4e353a1ac8dfe80dd9ec13c520f8a11806b67610dd4200ef5871d3daefe4","observation_id":"cb87ab62-4e6e-46bd-96c9-dc487b7f8c22","resolution":{"observed_at":"2026-08-06T19:31:07.896171Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ejfb.2017.10.001","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.327294Z","title":"European Journal of Family Business 7, 1 (Jan 2017), 41–53","venue":null,"work_id":"b652dc2a-87df-406b-b923-f613cc810abf","year":2017},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.201346Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:9b63bec9f943d4b2fc48f20f3d7b48419480bab1897600d0f394947aef2d181f","observation_id":"847ebbb1-37b5-403d-a465-f486a47e8cb2","resolution":{"observed_at":"2026-08-06T19:31:06.332049Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1108/02686900210424321","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.294813Z","title":null,"venue":null,"work_id":"c9600024-823b-416e-9bb5-c12b059d38aa","year":2002},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.225798Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:3716e5fd2125ac5671cae1d94933ff7ef21a0ea927b73310f340d2a4b234207a","observation_id":"42917526-1a21-49a0-9c77-ecf9a7dc3c44","resolution":{"observed_at":"2026-08-06T19:31:06.298344Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1108/eb043395","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.283872Z","title":null,"venue":null,"work_id":"b16aa551-160a-433a-be70-d567633c307a","year":2004},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.228925Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:bca9ef9dfcad5df5ce38df7913fc7264517ccdfcf4d6392bd0a47b1c66040716","observation_id":"21aefdbc-a188-4089-85ad-e79e2df9424f","resolution":{"observed_at":"2026-08-06T19:31:06.287620Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:07.868137Z","title":null,"venue":null,"work_id":"b7929b73-7371-45fe-9ac4-17de70ed5761","year":2023},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.232820Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:0675533612df7a33d6610b765110207246d4bd784439c9f9c15b83a10d88cbe2","observation_id":"8aa4b244-ad12-404f-acab-25fb5c57b92c","resolution":{"observed_at":"2026-08-06T19:31:07.871890Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:07.854413Z","title":null,"venue":null,"work_id":"15bf5886-f6b5-4a80-9070-1b3a0ea79760","year":2024},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.236592Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:4bace8a80dae0341ec4b7b59ca3e3d43121540dff15ea9a89ce98b655ef94b8b","observation_id":"f1f07793-af1d-4025-9e0e-2f333588db1f","resolution":{"observed_at":"2026-08-06T19:31:07.858472Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1609/aaai.v38i15.29574","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:31:06.271010Z","title":null,"venue":null,"work_id":"eb78b6ba-6861-4780-aa1b-63d3f85c654b","year":2024},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.239881Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:645708277423a4a56ad397347b4ac4fb40c498ed26ade3858a11cf4c9b8dce9f","observation_id":"84eed8c6-632a-422c-b82d-6f446fe98bb6","resolution":{"observed_at":"2026-08-06T19:31:06.275424Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:07.879457Z","title":"Simko, J.S","venue":null,"work_id":"14240ca3-4980-4bcb-9b52-d9d2fb633fb2","year":2020},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.222877Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:61fb4d5ed2e1926d2b496d248ebf3ac414364c5b4c88bce79209453e73b102a8","observation_id":"8147b814-2223-4c36-a6fc-11b3cd0b614a","resolution":{"observed_at":"2026-08-06T19:31:07.884195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:06.243141Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.243141Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:fdaa07deb5010bc09a8bc1be5b8a358cdb41c57540ee23948553998741894d3d","observation_id":"850f4ce6-60d8-4b11-a58c-dd26cce808b0","resolution":{"observed_at":"2026-08-06T19:31:06.243141Z","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":"2026-08-06T19:31:06.002449Z","title":"InProceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:06.002449Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:476a53344970a71b1648f76403734b605fa46f62331c7fbf5cf91a213c9b1f3d","observation_id":"fb21736d-be0a-4647-9238-e1d3067d34da","resolution":{"observed_at":"2026-08-06T19:31:06.002449Z","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":"2026-08-06T19:31:05.726841Z","title":"In 2020 IEEE Symposium on Security and Privacy (SP)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:05.726841Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:e801a9aae07c01c2d01b3c40cefd4c02ac66a7d0e552fc66405f2791e0c16a15","observation_id":"f31576b8-1d28-4d5b-bc6c-f7fbedc64df0","resolution":{"observed_at":"2026-08-06T19:31:05.726841Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.10876","last_updated":"2024-08-20T14:05:25Z","snapshot_observed_at":"2026-08-07T19:34:34.485073Z","submitted_at":"2024-08-20T14:05:25Z","title":"More Options for Prelabor Rupture of Membranes, A Bayesian Analysis","version":1},"cited_work":{"arxiv_id":"2408.10876","doi":null,"metadata_source":"pith","pith_arxiv_id":"2408.10876","snapshot_observed_at":"2026-08-06T19:31:07.152961Z","title":"More Options for Prelabor Rupture of Membranes, A Bayesian Analysis","venue":"stat.AP","work_id":"033371d1-74e4-4b7e-aaf0-37bc91807ba1","year":2024},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:04.430687Z"},"links":{"cited_paper":"/paper/2408.10876","citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:2eefb3e91fbba56a061c1cf671041c12f7757539ea1a610c8bb711ce82073af2","observation_id":"7d311886-5fb3-45d3-bce4-16b338617a6f","resolution":{"observed_at":"2026-08-06T19:31:07.158098Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-06T19:31:08.024590Z","title":"In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)","venue":null,"work_id":"2fba23e2-f39b-4b4c-b46f-c11a4b23a6e1","year":null},"citing_paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-06T19:31:02.035829Z"},"links":{"citing_paper":"/paper/2507.05441"},"observation_digest":"sha256:6cb6bba0805dac783f29f8e24198cbd23c351de33774dfd595198946f3045c1f","observation_id":"1ff10704-be11-4a29-bc3f-46d7ac598a5b","resolution":{"observed_at":"2026-08-06T19:31:08.028993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.05441","last_updated":"2025-07-07T19:45:46Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-06T19:23:56.565594Z","submitted_at":"2025-07-07T19:45:46Z","title":"Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":4,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":36,"verified_exact":26,"verified_fuzzy":5},"total_outbound_references":75},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 1 inbound Pith citation observation for arXiv:2507.05441."}