{"as_of":"2026-08-21T00:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5bd76ea828f04544a838b20f81ebf70e7d14555df24939369698e5945c7c9dc0","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T13:10:30.261122Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.20996/citation-record","integrity":"/paper/2507.20996/integrity","json":"/paper/2507.20996/citation-record.json","paper":"/paper/2507.20996"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T13:10:26.989787Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:26.989787Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:affa60bb6411468253da0188aa6669096c8f83233659d143163741ab46114b4c","observation_id":"0c92335d-6fe3-493f-9abc-c369c5998128","resolution":{"observed_at":"2026-08-06T13:10:26.989787Z","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":"2026-08-06T13:10:33.870986Z","title":"Fast r-cnn,","venue":null,"work_id":"c28351fa-1c3b-43f5-a263-a821a45a9cad","year":2015},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.108831Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:8bed8073f26f3a6363f908e8f182d95da7595ac53e8ee86d7eb67cb16fa19afe","observation_id":"bf2e6e6b-de12-47ed-94c4-8caf8f18ab30","resolution":{"observed_at":"2026-08-06T13:10:33.948368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:33.735314Z","title":"Deep speech 2: End-to-end speech recognition in english and mandarin,","venue":null,"work_id":"d681c14f-cdd9-4607-9f92-91e6457a8780","year":2016},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.220101Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:d494b228e71e070b8f283d1214ca845b3f46538fdd89a2bb290d5b9bbbefee67","observation_id":"d661a137-daa8-4084-a363-4147d2b85507","resolution":{"observed_at":"2026-08-06T13:10:33.797037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-08-15T16:41:15.505782Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-06T13:10:27.325111Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.325111Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:205941680497f39d33b4c0758cffe61e6f2832609957119b0ab67176fa7d1b90","observation_id":"cbcf82d5-b74e-4f54-a3c5-20c03b6aa174","resolution":{"observed_at":"2026-08-06T13:10:27.325111Z","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":"2026-08-06T13:10:33.635811Z","title":"Explaining and harnessing adversarial examples. proceedings of the 3rd international conference on learning representations, iclr 2015,","venue":null,"work_id":"3820723c-006c-4879-87f5-17ed93e03b23","year":2015},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.401822Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:c6576cd44df1d98037fdf1e2028bfd411be84e04952a1af25d858614417172b6","observation_id":"999014eb-b2fc-4f64-923f-bb0e75458b9c","resolution":{"observed_at":"2026-08-06T13:10:33.681070Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1711.00117","last_updated":"2018-01-25T19:04:48Z","snapshot_observed_at":"2026-08-20T19:38:40.948450Z","submitted_at":"2017-10-31T21:22:16Z","title":"Countering Adversarial Images using Input Transformations","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.00117","snapshot_observed_at":"2026-08-06T13:10:27.509845Z","title":"Counter- ing adversarial images using input transformations,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.509845Z"},"links":{"cited_paper":"/paper/1711.00117","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:6f49b94b2fc33d19205c1d6514e0edb371652f04066dcf6bf472c349e85660fc","observation_id":"623ee1bc-a9f4-418d-bd82-661d9ac5c17b","resolution":{"observed_at":"2026-08-06T13:10:27.509845Z","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":"2026-08-06T13:10:27.626930Z","title":"Defense against adversarial attacks using high-level representation guided denoiser,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.626930Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:1ba51129f168c66657d37081e15480cbeaa36f319a7c7a176b765c8a6c060411","observation_id":"9bb82667-e3ec-4a70-b4f0-3345bd10a13d","resolution":{"observed_at":"2026-08-06T13:10:27.626930Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.02613","last_updated":"2018-03-14T07:24:10Z","snapshot_observed_at":"2026-08-14T19:56:59.076369Z","submitted_at":"2018-01-08T18:54:40Z","title":"Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.02613","snapshot_observed_at":"2026-08-06T13:10:27.763207Z","title":"Characterizing adversar- ial subspaces using local intrinsic dimensionality,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.763207Z"},"links":{"cited_paper":"/paper/1801.02613","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:78cc35f58945712e8778a8654c233a6ae19ea15941ef0e02080c8100f4ce771a","observation_id":"453766f8-fe24-4fa3-ba2b-8fdad49695e1","resolution":{"observed_at":"2026-08-06T13:10:27.763207Z","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":"2026-08-06T13:10:33.512550Z","title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks,","venue":null,"work_id":"9f075c7d-90a7-4162-aff2-2306b5f6212b","year":2018},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.832014Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:4963d30fedfedf8a7e26e1f2d8dc8c795133827a9e29fd1bdab7405167ecad70","observation_id":"ccab8a8c-06ab-486d-bc64-5aba3c7cdeb8","resolution":{"observed_at":"2026-08-06T13:10:33.563855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:27.910058Z","title":"Certified adversarial robustness via randomized smoothing,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.910058Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:ac389300fd7ffdc90612bd36c0974e489b9c5133f915f852b8171a3c59fb356d","observation_id":"08857bde-cc42-4a76-b957-338c026cc50d","resolution":{"observed_at":"2026-08-06T13:10:27.910058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.06083","last_updated":"2019-09-04T18:53:10Z","snapshot_observed_at":"2026-08-07T14:27:46.872660Z","submitted_at":"2017-06-19T17:53:11Z","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.06083","snapshot_observed_at":"2026-08-06T13:10:27.978658Z","title":"Towards deep learning models resistant to adversarial attacks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:27.978658Z"},"links":{"cited_paper":"/paper/1706.06083","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:ac077247cbb27903fcaa602feac895b713c6d21279d8698df465e73ae542d80d","observation_id":"d6304759-769d-4bd3-9a58-727bb2d604b1","resolution":{"observed_at":"2026-08-06T13:10:27.978658Z","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":"2026-08-06T13:10:33.382979Z","title":"Adversarial robustness through local linearization,","venue":null,"work_id":"3bf9d2cf-e7fa-4c2d-9eb0-f4a84a7c3b6c","year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.034236Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:5ee085210e656dc88c70744ae7ab27d86c5392ae60b7d9e8067b26c76467aa17","observation_id":"5ef3df00-2073-48f3-88c3-d5dca361b35a","resolution":{"observed_at":"2026-08-06T13:10:33.443320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:33.201806Z","title":"Theoretically principled trade-off between robustness and accuracy,","venue":null,"work_id":"df14271b-c421-4ba1-ac4e-09ac9a6d596a","year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.108993Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:d6e9197b5bfbdcfdc9758067469bcb6cedb34b0d4a9c5a5857b6a1442c01a9a6","observation_id":"d504dab1-7ac2-4c1d-af52-f13fbf0c49e9","resolution":{"observed_at":"2026-08-06T13:10:33.268539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:33.039225Z","title":"Improving adversarial robustness requires revisiting misclassified examples,","venue":null,"work_id":"4bb87f38-82ca-4ed9-ac26-c06360b943ac","year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.196695Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:05155ec0572d36f44aef7343f18380fc5f7e58d78a0123829648261f1765e9e6","observation_id":"1e720ea3-b9d2-44cb-9333-e1b26e3a7cf7","resolution":{"observed_at":"2026-08-06T13:10:33.126630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:32.894549Z","title":"Magnet: a two-pronged defense against adver- sarial examples,","venue":null,"work_id":"a6403734-5e60-43e5-8b3a-40f3a766ebcc","year":2017},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.271077Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:a114937fee2ea8b5d02f84f5bcaa61233fe84a58aa4067f7f9063babda2e5444","observation_id":"38d36101-7ed7-493c-8d69-f6883a897079","resolution":{"observed_at":"2026-08-06T13:10:32.954485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:32.759779Z","title":"Image super- resolution as a defense against adversarial attacks,","venue":null,"work_id":"b46ccd2b-e690-4543-aebe-1500684831ef","year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.376230Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:bd277b3145f809041007efa0f4fd4024b14cdf9beaf5045fc3c81b2351ad9f0b","observation_id":"9527b2f0-3468-4fd0-983f-4a9a6802a1b8","resolution":{"observed_at":"2026-08-06T13:10:32.833981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.02900","last_updated":"2017-05-08T14:55:32Z","snapshot_observed_at":"2026-08-19T19:48:27.949861Z","submitted_at":"2017-05-08T14:55:32Z","title":"Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.02900","snapshot_observed_at":"2026-08-06T13:10:28.434157Z","title":"Keeping the bad guys out: Protecting and vaccinating deep learning with jpeg compression,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.434157Z"},"links":{"cited_paper":"/paper/1705.02900","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:db1947a95515bedc7fdd69407bc339eb21ab4bfebdd10ccb808544b11656fe0f","observation_id":"7a381a2c-26d8-4f63-b5bd-4c9755d5f4b4","resolution":{"observed_at":"2026-08-06T13:10:28.434157Z","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":"2026-08-06T13:10:32.651543Z","title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples,","venue":null,"work_id":"a96a8802-c8e0-45d4-96dd-8e9420d2bc7e","year":2018},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.532361Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:9157f8fc2ca052c6b22d82cc8c0d10ba0be97fc294fd8aa915a3a14a75eb3ef3","observation_id":"b885fb60-a895-49ce-92d7-8bc9d0c82628","resolution":{"observed_at":"2026-08-06T13:10:32.695336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:32.524678Z","title":"Adversarially robust distillation,","venue":null,"work_id":"b9e6deb3-0d13-4fdc-8833-8f7d0ad77964","year":2020},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.616876Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:8653b1b13e4b5f8c34760cce2ddd4fdf9440b7298ab05af3a39b901ad244ad06","observation_id":"38bada16-cdd9-4a04-ac46-d30730b4b2e3","resolution":{"observed_at":"2026-08-06T13:10:32.586587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.08307","last_updated":"2022-01-16T07:27:47Z","snapshot_observed_at":"2026-08-16T18:39:44.802831Z","submitted_at":"2021-03-11T03:44:16Z","title":"Improving Adversarial Robustness via Channel-wise Activation Suppressing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.08307","snapshot_observed_at":"2026-08-06T13:10:28.693152Z","title":"Improving adversarial robustness via channel-wise activation suppressing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.693152Z"},"links":{"cited_paper":"/paper/2103.08307","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:9cba1736aca85cc5762a04004fae50a8966ca2b77f6ea24b3c760b39277c29fb","observation_id":"f0391d41-a0ac-4b12-88a7-5e02b437ae8b","resolution":{"observed_at":"2026-08-06T13:10:28.693152Z","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":"2026-08-06T13:10:32.406015Z","title":"Robust overfitting may be mitigated by properly learned smoothening,","venue":null,"work_id":"830ec9d3-ccb1-4bef-9628-f6fb2a7a99bc","year":2020},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.792092Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:eb318667958cb08ed2c2a0831d5a962d009c6ad50bc8360eee1b2bd1f9cf03a3","observation_id":"3869761c-2186-40d2-9a3e-a1a96c4438ab","resolution":{"observed_at":"2026-08-06T13:10:32.467984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.04928","last_updated":"2022-03-10T13:06:23Z","snapshot_observed_at":"2026-08-16T18:18:28.196391Z","submitted_at":"2021-06-09T09:22:39Z","title":"Reliable Adversarial Distillation with Unreliable Teachers","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.04928","snapshot_observed_at":"2026-08-06T13:10:28.903046Z","title":"Reliable adversarial distillation with unreliable teachers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:28.903046Z"},"links":{"cited_paper":"/paper/2106.04928","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:1d5aae47186c172b32c349ea0148028d1efac6561f96406787c72fd08b86f1a0","observation_id":"5e11760f-e306-4de7-95cd-a803360e463d","resolution":{"observed_at":"2026-08-06T13:10:28.903046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.07204","last_updated":"2020-04-26T22:20:25Z","snapshot_observed_at":"2026-08-14T20:59:26.461775Z","submitted_at":"2017-05-19T21:56:43Z","title":"Ensemble Adversarial Training: Attacks and Defenses","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.07204","snapshot_observed_at":"2026-08-06T13:10:29.006476Z","title":"Ensemble adversarial training: Attacks and defenses,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.006476Z"},"links":{"cited_paper":"/paper/1705.07204","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:fccab08de098f58005b5fe233e2f1fce1ca21a724d12ae132c1b886519f4bae9","observation_id":"af883479-0476-4a8a-96a6-4bc37fb667cc","resolution":{"observed_at":"2026-08-06T13:10:29.006476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.00387","last_updated":"2020-05-30T23:23:56Z","snapshot_observed_at":"2026-07-30T14:35:53.038242Z","submitted_at":"2020-05-30T23:23:56Z","title":"Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training","version":1},"cited_work":{"arxiv_id":"2006.00387","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.00387","snapshot_observed_at":"2026-08-06T13:10:30.457184Z","title":"Exploring Model Robustness with Adaptive Networks and Improved Adversarial Training","venue":"cs.LG","work_id":"af7ce92b-f9cc-4396-8125-a29ee4cb7e8e","year":2020},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.115776Z"},"links":{"cited_paper":"/paper/2006.00387","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:be2d38c6a6615837d48223bb5279b1377c8a3aa09c6b3d43ba1b29e9659e1f5e","observation_id":"bc6cc512-ecfb-479b-9b5c-727120f94286","resolution":{"observed_at":"2026-08-06T13:10:30.558547Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:32.258920Z","title":"Adversarial attacks and de- fences competition,","venue":null,"work_id":"18684612-42ee-494f-b0c6-7a8a5358c1b6","year":2018},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.183055Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:39b6eca5b312f9107be9ca1c981b33007c9b41601b1e76b23e7ed9e9511d202f","observation_id":"2d1e3443-c13c-449e-9552-d9b7128e02cf","resolution":{"observed_at":"2026-08-06T13:10:32.320460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:32.110221Z","title":"Deepfool: a simple and accurate method to fool deep neural networks,","venue":null,"work_id":"15b18f6d-567d-4ec0-afd9-ac81b529aaef","year":2016},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.313752Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:86f668188178e18c43e305e04a2a156fad0729c71279a1c4e5ad584f3f25741a","observation_id":"3d87186d-8705-4e5d-825f-695dc7f3f6bc","resolution":{"observed_at":"2026-08-06T13:10:32.166639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:31.942417Z","title":"Towards evaluating the robustness of neural networks,","venue":null,"work_id":"9dab9162-73a1-450e-95b8-2ef17660dfed","year":2017},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.391023Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:c47aa6087f637ebdbb6a43600a284724cc86bcc676f8a865fff4c96224bacc0c","observation_id":"c815da73-eeff-4321-8811-db39667a48b9","resolution":{"observed_at":"2026-08-06T13:10:32.026162Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:31.778362Z","title":"Adversarial risk and the dangers of evaluating against weak attacks,","venue":null,"work_id":"4b27525a-30da-4115-b9e5-4526be7a454d","year":2018},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.493600Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:109c383dbad8726b19d7f4b4dabebb72110be6ddf429d8b5114f5d252d27415f","observation_id":"167a151f-8e46-4507-a41f-0bdd58b2e12b","resolution":{"observed_at":"2026-08-06T13:10:31.861658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:31.585790Z","title":"Do wider neural networks really help adversarial robustness?","venue":null,"work_id":"9b233623-4370-4a71-91ca-7d53615d85ce","year":2021},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.591397Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:7d7bf8a352ed40b6e7163c389057c3988d68f938ef808d77ad1304ee4cfacea5","observation_id":"3a7f6dc8-f0d8-440e-8a43-0b90bfe2c782","resolution":{"observed_at":"2026-08-06T13:10:31.673190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:31.467873Z","title":"Unlabeled data improves adversarial robustness,","venue":null,"work_id":"005f4ebd-fd64-4bd6-b369-fc8e00edc295","year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.674502Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:7eff9c09c33cbc703209a35d7ac7c3e51d1812343c6a6aa92f26146302d3c5c7","observation_id":"bb69cc41-54ed-46b7-8b4e-21dac503f42d","resolution":{"observed_at":"2026-08-06T13:10:31.509629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.00740","last_updated":"2019-03-15T08:37:29Z","snapshot_observed_at":"2026-08-14T18:21:07.441911Z","submitted_at":"2018-10-01T14:52:08Z","title":"Improving the Generalization of Adversarial Training with Domain Adaptation","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.00740","snapshot_observed_at":"2026-08-06T13:10:29.727067Z","title":"Improving the general- ization of adversarial training with domain adaptation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.727067Z"},"links":{"cited_paper":"/paper/1810.00740","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:c57b462fcdcf7607e96b829a59a6eddaea251d659c321bb8841c7ab054d41bb3","observation_id":"bb448af5-4187-40ea-a56b-25e0eb276faa","resolution":{"observed_at":"2026-08-06T13:10:29.727067Z","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":"2026-08-06T13:10:31.308997Z","title":"Adversarial weight perturbation helps robust generalization,","venue":null,"work_id":"8771b7b1-ce74-40cf-b1de-ff5ac01f964f","year":2020},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.826098Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:edaf8b16ddcc6c6d698d93a0192b69c56afd013c88fb5f869855dcd57331f9c9","observation_id":"c763cb56-c891-4c5a-90c6-97ff675d0995","resolution":{"observed_at":"2026-08-06T13:10:31.390059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.03593","last_updated":"2021-03-30T08:08:12Z","snapshot_observed_at":"2026-08-16T19:15:08.480301Z","submitted_at":"2020-10-07T18:19:09Z","title":"Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.03593","snapshot_observed_at":"2026-08-06T13:10:29.932746Z","title":"Uncovering the limits of adversarial training against norm-bounded adversarial examples,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:29.932746Z"},"links":{"cited_paper":"/paper/2010.03593","citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:76dcb1721aeead81197ce16ea8824c2f2a9a8521a167f00dade235a91f5ca2ad","observation_id":"11a12567-367b-4e34-9323-ad3a729219dd","resolution":{"observed_at":"2026-08-06T13:10:29.932746Z","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":"2026-08-06T13:10:31.150826Z","title":"Adversarial training for free!","venue":null,"work_id":"5e4c93c7-ab3a-4024-a22d-42f24757ee02","year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:30.048552Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:e13e9714e821ad03f5cefa8cc58d3c5e145dd1c193c3d7569dfa6a8cf16d7cfa","observation_id":"3da9de8b-19a1-47e8-9e06-0533a3ac1818","resolution":{"observed_at":"2026-08-06T13:10:31.227864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:30.976772Z","title":"A kernelized manifold mapping to diminish the effect of adversarial perturbations,","venue":null,"work_id":"c18dfcc1-cd08-4dac-93aa-53714a08866b","year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:30.147397Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:8df5fc84e2c4e424af334854b2b98d8ba714499229aa8f0dad1d9cae50914176","observation_id":"bb1a9e98-d18f-426c-9489-cf98a08f4cfa","resolution":{"observed_at":"2026-08-06T13:10:31.074601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-06T13:10:30.779508Z","title":"Improving adversarial ro- bustness via promoting ensemble diversity,","venue":null,"work_id":"71b033f1-269f-477a-b0bd-fed14d3aa0f6","year":2019},"citing_paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T13:10:30.261122Z"},"links":{"citing_paper":"/paper/2507.20996"},"observation_digest":"sha256:aaef1805ffe3c84058879373092ccf3a02e3f366d93aee02586606468bca2182","observation_id":"79726bbf-b7b0-4751-8a03-1906ca464abb","resolution":{"observed_at":"2026-08-06T13:10:30.887758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.20996","last_updated":"2025-07-28T17:08:40Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-19T19:48:56.704140Z","submitted_at":"2025-07-28T17:08:40Z","title":"Improving Adversarial Robustness Through Adaptive Learning-Driven Multi-Teacher Knowledge Distillation"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":1,"verified_fuzzy":22},"total_outbound_references":36},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2507.20996."}