{"as_of":"2026-08-09T16:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:513160c93c842e680fc52b4427831ddb33dcea8a75c0c6de5fa3e384ddff1ff1","coverage":[{"denominator":52,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":52,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T13:15:26.302918Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-05-13T18:53:48.881039Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-13T18:58:08.963428Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"cited_work":{"arxiv_id":"2510.01038","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2510.01038","snapshot_observed_at":"2026-07-07T03:17:12.509592Z","title":"Activation-deactivation: A general framework for robust post-hoc explainable ai","venue":null,"work_id":"db075644-77db-4d8b-8d10-8f10f64b3352","year":2025},"citing_paper":{"arxiv_id":"2604.02937","last_updated":"2026-04-03T10:08:53Z","snapshot_observed_at":"2026-08-02T18:04:55.530371Z","submitted_at":"2026-04-03T10:08:53Z","title":"If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-13T18:53:48.881039Z"},"links":{"cited_paper":"/paper/2510.01038","citing_paper":"/paper/2604.02937"},"observation_digest":"sha256:a4a26af2ca6a35037b9855e566a8429e8f1bf122cea75eceebbe35c1c512dd08","observation_id":"4eb501fc-c64a-48a4-8d51-5f9a0658df74","resolution":{"observed_at":"2026-07-07T03:17:12.509592Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2510.01038/citation-record","integrity":"/paper/2510.01038/integrity","json":"/paper/2510.01038/citation-record.json","paper":"/paper/2510.01038"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T13:15:23.257728Z","title":"On pixel-wise ex- planations for non-linear classifier decisions by layer-wise relevance propa- gation.PLoS ONE, 10(7), 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.257728Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:cadb047b5365bd5618fc92d963855860ed77336c29f1dd680746168549cdcbc6","observation_id":"7f2b35e8-f20a-4e72-8836-6748c03a463c","resolution":{"observed_at":"2026-08-04T13:15:23.257728Z","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":"2026-08-04T13:15:23.313227Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.313227Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:9e2e3f7990eaefd5290a02496ae7e4a9c15b23e926a5284ce8959c393b36692b","observation_id":"0fab1713-8324-49fd-9615-ac231c3e9be3","resolution":{"observed_at":"2026-08-04T13:15:23.313227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14567","last_updated":"2025-03-18T10:49:15Z","snapshot_observed_at":"2026-08-07T16:55:15.707762Z","submitted_at":"2025-03-18T10:49:15Z","title":"SpecReX: Explainable AI for Raman Spectroscopy","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14567","snapshot_observed_at":"2026-08-04T13:15:23.387202Z","title":"Specrex: Explainable AI for Raman spectroscopy.arXiv preprint arXiv:2503.14567, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.387202Z"},"links":{"cited_paper":"/paper/2503.14567","citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:330f32b8c215a895618d7c99e6b4d4c1c32a7a748e4f5ee6d9da6f66ec2b4b36","observation_id":"e713d5d1-ae3f-4315-8212-40fcee603199","resolution":{"observed_at":"2026-08-04T13:15:23.387202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.05818","last_updated":"2020-09-12T16:06:58Z","snapshot_observed_at":"2026-07-06T09:55:03.492247Z","submitted_at":"2020-09-12T16:06:58Z","title":"MeLIME: Meaningful Local Explanation for Machine Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.05818","snapshot_observed_at":"2026-08-04T13:15:23.453473Z","title":"Melime: Meaningful local explanation for machine learning models","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.453473Z"},"links":{"cited_paper":"/paper/2009.05818","citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:93b3472d98d46f6d7d7de513bc3c11a0fd2b829dabdc4f2d72d46e3fd4b57eb6","observation_id":"2f37dc30-e209-401c-b9cf-1ba66c9c60b1","resolution":{"observed_at":"2026-08-04T13:15:23.453473Z","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":"2026-08-04T13:15:23.498581Z","title":null,"venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.498581Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:ca38c1b65d78bc2f8535e8ef6e138b70430c2761988032c19dd76cfeac12d771","observation_id":"b4f6189c-5934-4b54-b25f-68ecef73889c","resolution":{"observed_at":"2026-08-04T13:15:23.498581Z","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":"2026-08-04T13:15:23.575145Z","title":"Grad-cam++: Generalized gradient-based visual expla- nations for deep convolutional networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.575145Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:9726ce461d29af7f7e4b922e8ddaa38f1c6bfb068545f6dfa14b3b1760a9bf22","observation_id":"3dab95a3-5ced-4dd4-9c28-beaed3f10ce8","resolution":{"observed_at":"2026-08-04T13:15:23.575145Z","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":"2026-08-04T13:15:23.657881Z","title":null,"venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.657881Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:ef99e7b67931423fef494515178b4387bc07baf348b6ed6544c80e71c7b05b91","observation_id":"85b465c2-195a-48e9-9f57-b371d99eec57","resolution":{"observed_at":"2026-08-04T13:15:23.657881Z","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":"2026-08-04T13:15:23.766342Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.766342Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:84eef2301168693da10943dba2a95d78c74507b531bb7e70981f8d1be1489c3e","observation_id":"cb8b53dd-c7ee-4afd-bbbf-a5cdc2216f7b","resolution":{"observed_at":"2026-08-04T13:15:23.766342Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.08875","last_updated":"2026-07-02T09:20:40Z","snapshot_observed_at":"2026-08-09T12:51:39.366147Z","submitted_at":"2024-11-13T18:52:42Z","title":"Causal Explanations for Image Classifiers","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.08875","snapshot_observed_at":"2026-08-04T13:15:23.830181Z","title":"Causal explanations for image classifiers.arXiv preprint arXiv:2411.08875, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.830181Z"},"links":{"cited_paper":"/paper/2411.08875","citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:5a5f0520623e6683955d51cbfb955b67c238c091e50a1fe3b227ea5c71134d71","observation_id":"eb9cbff6-6516-4542-8237-de17dfe0d4c1","resolution":{"observed_at":"2026-08-04T13:15:23.830181Z","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":"2026-08-04T13:15:23.900745Z","title":"Ima- genet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.900745Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:0cecf282069b1b3fb60285cb0c35a25d991efd871add613082f24d41b290234f","observation_id":"00c84f40-e9f3-42c1-9c56-59abe8afecb7","resolution":{"observed_at":"2026-08-04T13:15:23.900745Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.07983","last_updated":"2019-09-25T16:12:12Z","snapshot_observed_at":"2026-08-06T14:21:10.149429Z","submitted_at":"2019-06-19T09:13:23Z","title":"Explanations can be manipulated and geometry is to blame","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.07983","snapshot_observed_at":"2026-08-04T13:15:23.975544Z","title":"Anders, Mar- cel Ackermann, Klaus-Robert M¨ uller, and Pan Kessel","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:23.975544Z"},"links":{"cited_paper":"/paper/1906.07983","citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:e2af1e739937c14045dbaa3e1721dddc948f3a51b73b8f548d19d10b32f473ce","observation_id":"3a70f8c3-feef-4523-a94a-7aee668c3a2a","resolution":{"observed_at":"2026-08-04T13:15:23.975544Z","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":"2026-08-04T13:15:24.062926Z","title":"Williams, John Winn, and Andrew Zisserman","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.062926Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:1d5ed4b1ccfa408cff1f079e060f92b5b1cbd9e98d84f3d14e930c59fd70f990","observation_id":"f4eb86de-0071-4c3b-a4b7-178ba8a9a01a","resolution":{"observed_at":"2026-08-04T13:15:24.062926Z","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":"2026-08-04T13:15:24.145809Z","title":"MIT Press, 1988","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.145809Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:875df449da7b0de0d267e399758ed071442262d8242b86e260a9ca46926392e4","observation_id":"a85655cd-206e-4031-996b-318ed81145fb","resolution":{"observed_at":"2026-08-04T13:15:24.145809Z","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":"2026-08-04T13:15:24.207544Z","title":"Glymour and F","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.207544Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:3f892e873b17c0ef3463375fb602caa3951cefb0570f420f86415a328cc502db","observation_id":"d675809c-fde3-4025-a0d3-16eb4547528d","resolution":{"observed_at":"2026-08-04T13:15:24.207544Z","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":"2026-08-04T13:15:24.271514Z","title":"Caltech 256, Apr 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.271514Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:1e418d0f325f086d4158b326cd8fe46fe173b07623029368a6587bae5c223910","observation_id":"6bdc5b12-4785-4fe1-b7ec-53c0a1294c64","resolution":{"observed_at":"2026-08-04T13:15:24.271514Z","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":"2026-08-04T13:15:24.331488Z","title":null,"venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.331488Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:b818bcce324a50657bf52dc04aa448e3a3d6287aff3b63ec37bab6e50affcc8b","observation_id":"40f0c8e8-c448-4b72-8518-0994b7499133","resolution":{"observed_at":"2026-08-04T13:15:24.331488Z","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":"2026-08-04T13:15:24.439922Z","title":null,"venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.439922Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:3010cbf346311cdc9d94b79329d479d7695b96a9b755c06e7edcd1a9f4e9d9fe","observation_id":"5322bef5-7c4b-4c8f-95db-2d1ae2cc6b0e","resolution":{"observed_at":"2026-08-04T13:15:24.439922Z","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":"2026-08-04T13:15:24.518045Z","title":"Halpern.Actual Causality","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.518045Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:d81649a8479a3c70c092c51061af973d2a9b8f408aba895e4c14706ea79ae8d3","observation_id":"6d666a09-dc3c-4f43-9a98-ff3ad08d748d","resolution":{"observed_at":"2026-08-04T13:15:24.518045Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.00582","last_updated":"2020-09-07T08:55:19Z","snapshot_observed_at":"2026-08-07T14:52:55.205411Z","submitted_at":"2020-08-02T23:05:02Z","title":"audioLIME: Listenable Explanations Using Source Separation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.00582","snapshot_observed_at":"2026-08-04T13:15:24.605395Z","title":"audi- olime: Listenable explanations using source separation.arXiv preprint arXiv:2008.00582, 2020","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.605395Z"},"links":{"cited_paper":"/paper/2008.00582","citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:131f41bec9bbaa082c1aa1cb974512d46983fb965c1ac3c5dda0ccb11c0d1d94","observation_id":"345b7abf-d86b-47ee-a6fe-8878c9250ee1","resolution":{"observed_at":"2026-08-04T13:15:24.605395Z","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":"2026-08-04T13:15:24.663291Z","title":"Deep residual learning for image recognition.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.663291Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:dd2588da08a9ca5b333f8d812532be0a57827c907895d942738c881eabe03ffd","observation_id":"1ff1aefc-ae31-4a86-a27f-8738fb9281d2","resolution":{"observed_at":"2026-08-04T13:15:24.663291Z","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":"2026-08-04T13:15:24.738200Z","title":"Free Press, 1965","venue":null,"work_id":null,"year":1965},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.738200Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:28513c69f90d2befcf73536b2f44ef7d5e40819c8d77f914c2a3867ec1d58885","observation_id":"5488e720-2a7c-4a5c-89e6-3a98b0dee3d7","resolution":{"observed_at":"2026-08-04T13:15:24.738200Z","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":"2026-08-04T13:15:24.826371Z","title":"Hitchcock","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.826371Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:cd131adb9840ecfa87d88a87dd510c9847b1e36a624d5775076789c5878083a4","observation_id":"1b209917-b75a-4d56-bd08-54368f1c0c0a","resolution":{"observed_at":"2026-08-04T13:15:24.826371Z","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":"2026-08-04T13:15:24.837254Z","title":"Hitchcock","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.837254Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:b5cfa0337c9e7ca775f9dcbfff186e9dea2da392f96e92ab7cdf934887abd588","observation_id":"39b2a69c-9c01-4e15-bfff-dba8b9b3a098","resolution":{"observed_at":"2026-08-04T13:15:24.837254Z","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":"2026-08-04T13:15:24.915162Z","title":"A benchmark for interpretability methods in deep neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:24.915162Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:1848f60b1b144561801e1e245e217164a00cc1d9b89667d0804fbea571543eac","observation_id":"ac732c08-5de5-4cdf-9d46-c177f3489782","resolution":{"observed_at":"2026-08-04T13:15:24.915162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.23509","last_updated":"2025-07-31T12:45:09Z","snapshot_observed_at":"2026-08-06T10:44:08.008548Z","submitted_at":"2025-07-31T12:45:09Z","title":"I Am Big, You Are Little; I Am Right, You Are Wrong","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.23509","snapshot_observed_at":"2026-08-04T13:15:25.033682Z","title":"I am big, you are little; i am right, you are wrong.arXiv preprint arXiv:2507.23509, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.033682Z"},"links":{"cited_paper":"/paper/2507.23509","citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:4165d04222bfcc1ede81beaf326d865f5172a795f057f2167ff2f70cb9c1c58d","observation_id":"3689b086-c49d-41e0-81fb-d4189297970e","resolution":{"observed_at":"2026-08-04T13:15:25.033682Z","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":"2026-08-04T13:15:25.180344Z","title":"Causal identification of sufficient, con- trastive and complete feature sets in image classification.arXiv preprint arXiv:2507.23497, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.180344Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:7c771015c900808d5d078e1e9667980f29ddb79fa89cc38feafa711cff18f88a","observation_id":"8761655b-c27f-4e45-b1a6-77502f8f461e","resolution":{"observed_at":"2026-08-04T13:15:25.180344Z","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":"2026-08-04T13:15:25.324759Z","title":"Lundberg and Su-In Lee","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.324759Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:29f4471498873a793b4dcd17b76e3214ac46dc7115b70853dc9a0afeb769488f","observation_id":"fc99263a-e245-4f3c-b888-ba44b7dc22fb","resolution":{"observed_at":"2026-08-04T13:15:25.324759Z","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":"2026-08-04T13:15:25.427904Z","title":"Segal time series clas- sification—stable explanations using a generative model and an adaptive weighting method for lime.Neural Networks, 176:106345, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.427904Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:fb53a5e10369c676d5c22980f32cc5f77d958e220d5b80d498f9ec0f3d473daa","observation_id":"b5a9457b-063e-497f-ba33-9b2c536ff139","resolution":{"observed_at":"2026-08-04T13:15:25.427904Z","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":"2026-08-04T13:15:25.544816Z","title":"Local interpretable model-agnostic explanations for music content analysis","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.544816Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:df212965246275c97b58c1935a9c0619f026292894d7f859eb0f7a232be7cbba","observation_id":"04b525a1-e45b-4f00-bca9-de886b24adb2","resolution":{"observed_at":"2026-08-04T13:15:25.544816Z","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":"2026-08-04T13:15:25.714394Z","title":"Morgan Kauf- mann, 1988","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.714394Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:4471652e22f43e57f602b3abcfb2a36fac4a404745cd52bfe698b35d79ca342a","observation_id":"b77212ef-15c2-4519-aa3e-0aa017f798b9","resolution":{"observed_at":"2026-08-04T13:15:25.714394Z","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":"2026-08-04T13:15:25.851255Z","title":"RISE: randomized input sam- pling for explanation of black-box models","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.851255Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:92d4695fb84550ce0a9b211c1077b49b28133ed4417490fe8c08b269dc0847ba","observation_id":"85ad1cc8-54bb-46e3-8313-7ad8008fd671","resolution":{"observed_at":"2026-08-04T13:15:25.851255Z","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":"2026-08-04T13:15:25.967276Z","title":"Designing network design spaces","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:25.967276Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:e727b1aa00a5254b74253a9bbcd0a4d2a0952a7d8771c0075012d01127da67c0","observation_id":"cf7389e2-02e9-4a30-9234-2c2456d13b71","resolution":{"observed_at":"2026-08-04T13:15:25.967276Z","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":"2026-08-04T13:15:26.075265Z","title":"Do imagenet classifiers generalize to imagenet? InInternational conference on machine learning, pages 5389–5400","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.075265Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:b1c83ab3e8d43a7fe4aaade32fea27b28a8b926941372feca338568d18919758","observation_id":"2b8ec4d4-8863-40b4-ab01-b70d71b39033","resolution":{"observed_at":"2026-08-04T13:15:26.075265Z","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":"2026-08-04T13:15:26.225345Z","title":"Why should I trust you?","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.225345Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:0727c2f6d1dac80c0a6c3379b27ee819fa83494accdcd1daccaf1d1175be0a03","observation_id":"913fe0ed-6d16-4430-b4fa-e2a186dda468","resolution":{"observed_at":"2026-08-04T13:15:26.225345Z","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":"2026-08-04T13:15:26.230434Z","title":"Anchors: high-precision model-agnostic explanations","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.230434Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:e39a64b3829a799f450a8d4a44a97e8895d493b676883336d4e953c188d8b679","observation_id":"851f0949-74f9-4a13-8a10-1f9142d94c5f","resolution":{"observed_at":"2026-08-04T13:15:26.230434Z","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":"2026-08-04T13:15:26.235487Z","title":"Salmon.Four Decades of Scientific Explanation","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.235487Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:77afff72b6ebe244b36956c6dbe6e0baded7b3c1741338ee1b86b26edd6d79e3","observation_id":"24cbab49-5e26-442c-954e-633929e5a4a1","resolution":{"observed_at":"2026-08-04T13:15:26.235487Z","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":"2026-08-04T13:15:26.240253Z","title":"Grad-cam: Visual explanations from deep networks via gradient-based localization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.240253Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:03f611489c6a1112bcb902e07dfa6b3a09a29de92c938dcae9c69bbd57633ff1","observation_id":"2e8efd02-9a82-4b99-9bc2-8d688a1fc5fa","resolution":{"observed_at":"2026-08-04T13:15:26.240253Z","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":"2026-08-04T13:15:26.244423Z","title":"Learning im- portant features through propagating activation differences","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.244423Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:eea39150f6dd8d24212461563fb68cdeddc72db8a6b861ce5e9a8d823e80d216","observation_id":"c92b6a40-63e9-4250-b88f-6acca32dc39f","resolution":{"observed_at":"2026-08-04T13:15:26.244423Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6034","last_updated":"2014-04-19T11:54:52Z","snapshot_observed_at":"2026-07-06T03:31:30.452356Z","submitted_at":"2013-12-20T16:45:54Z","title":"Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6034","snapshot_observed_at":"2026-08-04T13:15:26.249077Z","title":"Deep inside con- volutional networks: Visualising image classification models and saliency maps.arXiv preprint arXiv:1312.6034, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.249077Z"},"links":{"cited_paper":"/paper/1312.6034","citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:99c9ca229b08b6e527745453c59cf8d47eed31996aa57d88e8a49076c10a2491","observation_id":"a331fae7-7851-4b44-812c-ab71d196c15f","resolution":{"observed_at":"2026-08-04T13:15:26.249077Z","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":"2026-08-04T13:15:26.253433Z","title":"Limesegment: Meaningful, realistic time series explanations","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.253433Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:1dc7205d101cfa23897dba1572721727cd4323343de4c91b969a723afa0e5378","observation_id":"45c61852-cc8f-4320-9149-3db42ed41c5d","resolution":{"observed_at":"2026-08-04T13:15:26.253433Z","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":"2026-08-04T13:15:26.257423Z","title":"Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.257423Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:76bf3c9ac9f7018a528c80f72a1b8dab8deb20087c29c01ee8b489ffb939323b","observation_id":"b8cda41c-d9c3-4e60-bac1-c63c36b41cc5","resolution":{"observed_at":"2026-08-04T13:15:26.257423Z","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":"2026-08-04T13:15:26.261672Z","title":"Riedmiller","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.261672Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:b171ac0c5cf25ba26c8691bf376024f0378d5d3c2856982521f04132f8bd5dd7","observation_id":"17d99c2d-7d13-4c8b-9b09-b0327aa4e86f","resolution":{"observed_at":"2026-08-04T13:15:26.261672Z","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":"2026-08-04T13:15:26.265867Z","title":"Axiomatic attribution for deep networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.265867Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:a72f95a267fe40b1f48b35d5d904fcf39100c338fbcacf44072e11e3387f1daf","observation_id":"1c7d7155-502b-4df8-96c5-d440233fd133","resolution":{"observed_at":"2026-08-04T13:15:26.265867Z","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":"2026-08-04T13:15:26.270025Z","title":"Efficientnetv2: Smaller models and faster training","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.270025Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:f37089a4fe20e59567e3eb6b5b5d7ec2698f3cf95173eafbe75c9d5d0b5c51e6","observation_id":"3a3fd05a-4071-435d-9cc6-f0d6d0a981a8","resolution":{"observed_at":"2026-08-04T13:15:26.270025Z","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":"2026-08-04T13:15:26.273920Z","title":"When can you trust your explanations? a robustness analysis on feature importances.arXiv preprint arXiv:2406.14349, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.273920Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:9c8ac4d81a05648e452a814ed6895efbfda7ffe738c279588530e1b439da3bac","observation_id":"1635a3ef-310f-461a-97e9-cbcc1b80bf69","resolution":{"observed_at":"2026-08-04T13:15:26.273920Z","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":"2026-08-04T13:15:26.278210Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.278210Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:0f27dddc5fab4eac4208bc75e46ce44eac0440a34d20996f39acbdab57dfb04d","observation_id":"62ccb53e-2b28-44df-affc-6679a78f9a8a","resolution":{"observed_at":"2026-08-04T13:15:26.278210Z","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":"2026-08-04T13:15:26.282197Z","title":"Woodward.Making Things Happen: A Theory of Causal Explanation","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.282197Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:aaae8b99e7ad69846d8161c72e2f5dac64d25e300c1388675288a16802ae4ddc","observation_id":"b1b668f4-49e3-4d12-823a-a4c9e14106ee","resolution":{"observed_at":"2026-08-04T13:15:26.282197Z","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":"2026-08-04T13:15:26.286589Z","title":"Ml-loo: Detecting adversarial examples with feature attribution","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.286589Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:187f05168124b974a4889c564a693a37c84c6c3e1385774402ca450d81c799e2","observation_id":"fbc9bd41-b26e-4c05-8f4a-f1f222ca9af5","resolution":{"observed_at":"2026-08-04T13:15:26.286589Z","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":"2026-08-04T13:15:26.290617Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.290617Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:f6d4b4275d479c7299068a34bcba2d049346e521adaaf904907f288ebea03216","observation_id":"9ad394a0-a539-4c1d-9d2f-3af15e94dd11","resolution":{"observed_at":"2026-08-04T13:15:26.290617Z","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":"2026-08-04T13:15:26.294721Z","title":"Visualizing and understanding con- volutional networks","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.294721Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:c6e84a7072c061a3a9d14586eb0de6ba3a59749162e84905074417df22483c2b","observation_id":"9c47b678-8789-4266-bf7b-d11a761258ed","resolution":{"observed_at":"2026-08-04T13:15:26.294721Z","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":"2026-08-04T13:15:26.298571Z","title":"Baylime: Bayesian local interpretable model-agnostic explanations","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.298571Z"},"links":{"citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:d4fb637e0b2f6fdcad217067354c6101a92f445bd76c0110433721345c8b8095","observation_id":"50a5c7fe-5cdc-4d7d-ab71-a90deef75562","resolution":{"observed_at":"2026-08-04T13:15:26.298571Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02970","last_updated":"2025-03-06T20:06:16Z","snapshot_observed_at":"2026-07-06T19:27:20.458797Z","submitted_at":"2024-10-03T20:23:06Z","title":"F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02970","snapshot_observed_at":"2026-08-04T13:15:26.302918Z","title":"F-fidelity: A robust framework for faith- fulness evaluation of explainable ai.arXiv preprint arXiv:2410.02970, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:26.302918Z"},"links":{"cited_paper":"/paper/2410.02970","citing_paper":"/paper/2510.01038"},"observation_digest":"sha256:24ef52573b7c291b0bcd2f7f786a5c4bc63fd31d772b30e8df110c32a903eaff","observation_id":"a056bfe2-d747-411c-af0d-3f7b13ee385f","resolution":{"observed_at":"2026-08-04T13:15:26.302918Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2510.01038","last_updated":"2026-07-04T14:49:01Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-09T03:33:18.650614Z","submitted_at":"2025-10-01T15:42:58Z","title":"Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI"},"reference_resolution":{"displayed":52,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":51,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":52},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2510.01038."}