{"as_of":"2026-08-10T00:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32571e294671f19becf1ad32a8b954c2ebe4cd46e96f66b36c047f6760f2a33e","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T18:01:08.741295Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2608.05217/citation-record","integrity":"/paper/2608.05217/integrity","json":"/paper/2608.05217/citation-record.json","paper":"/paper/2608.05217"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.656393Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.656393Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:0cba9a6bf2fba9980499a6fb5b129aa549a9ade3afcb512f362d0685c5b652a8","observation_id":"ff69dcf7-80c7-45de-bf14-e1cd6e847ae4","resolution":{"observed_at":"2026-08-08T18:01:08.656393Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-08T18:01:08.661813Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.661813Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:124371b7b5d000964e7fa1bcd2e9040c832d89d0b6e5caf61cd00aef249d318f","observation_id":"1b0348b2-4590-4c60-889a-e9a068d5d3ad","resolution":{"observed_at":"2026-08-08T18:01:08.661813Z","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":"2026-08-08T18:01:09.021596Z","title":"Adaptive token sampling for efficient vision transformers,","venue":null,"work_id":"e6b1c003-d6c7-4076-a3b8-265918d9ee49","year":2022},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.666748Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:b22ceeed6bfc4cff501d1966e3f96d4ddb161fe651109c03a8afc44798610e55","observation_id":"4a126c37-9745-45a3-855f-761ccef9df17","resolution":{"observed_at":"2026-08-08T18:01:09.026593Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:09.006525Z","title":"Adavit: Adaptive vision transformers for efficient image recognition,","venue":null,"work_id":"0667ad9c-3b00-46b4-b5d2-5afc9dd8ba14","year":2022},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.671394Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:f09f942058db807e75791208e73dec21f8c8e607f12d33ac89f185b8c1436e82","observation_id":"253d830a-c7aa-4707-98bf-c74f36917ea2","resolution":{"observed_at":"2026-08-08T18:01:09.011157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.991216Z","title":"A-vit: Adaptive tokens for efficient vision transformer,","venue":null,"work_id":"2752067d-ae07-4414-a04b-1c1ac94a500b","year":2022},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.676253Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:22ffe9acbd48b0b9bd2d479d882c7956bf2f967102388d8d412b4a1157b6f86f","observation_id":"fa69c770-cbfd-4e19-90af-022f4b020fbe","resolution":{"observed_at":"2026-08-08T18:01:08.996059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.976170Z","title":"Slowformer: Adversarial attack on compute and energy consumption of efficient vision trans- formers,","venue":null,"work_id":"4369f395-5888-490b-a1db-9b2b3ae100b0","year":2024},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.680882Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:aec24d5a1b1cb636bf378fda179d9b6ddc61298068afe113a28503f5dd2f29f9","observation_id":"be007f23-af32-4cac-9aea-abee6facebbc","resolution":{"observed_at":"2026-08-08T18:01:08.981122Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.685909Z","title":"Curse of dimensionality in adversarial examples,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.685909Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:4a3deaff3c05d48d544ccd090e6448d910f06c07c305808ea05b6594d56ee200","observation_id":"6cd438c0-1da3-4d0f-858b-cc4b08b20a0b","resolution":{"observed_at":"2026-08-08T18:01:08.685909Z","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":"2026-08-08T18:01:08.950071Z","title":"Robustness against ad- versarial attacks using dimensionality,","venue":null,"work_id":"3cbea689-a9d8-4ee9-bd40-cbae2fb0c536","year":2021},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.690325Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:6a4f382458f2664cda863630b820fc4c1d489984c1c63a7f312e01730a9c9fdf","observation_id":"1cd822e5-8503-41ee-b965-8f50893c8ae0","resolution":{"observed_at":"2026-08-08T18:01:08.955239Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.930819Z","title":"Robust perception for au- tonomous vehicles using dimensionality reduction,","venue":null,"work_id":"ca4c1d69-9817-4eaa-8660-fea80850542f","year":2022},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.694500Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:660b5fc5997d6114f4549cf851c27c38b3b68523019e86ceb8d39960747158b0","observation_id":"4923bc6c-30f8-43de-a65b-c0df56243a9f","resolution":{"observed_at":"2026-08-08T18:01:08.936243Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.914478Z","title":"Oddr: Out- lier detection & dimension reduction based defense against adversarial patches,","venue":null,"work_id":"f7a9ee16-16d2-4de6-8091-92925e0fcdba","year":2025},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.698790Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:8153395acad309fb46d734d885e9e94116ff9a07a4dd0b104cb66a36a2ef368f","observation_id":"53340cc6-04ce-475d-ab28-7de5ed49519c","resolution":{"observed_at":"2026-08-08T18:01:08.919147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06249","last_updated":"2024-02-09T08:52:47Z","snapshot_observed_at":"2026-07-06T17:27:49.646799Z","submitted_at":"2024-02-09T08:52:47Z","title":"Anomaly Unveiled: Securing Image Classification against Adversarial Patch Attacks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06249","snapshot_observed_at":"2026-08-08T18:01:08.703033Z","title":"Anomaly unveiled: Se- curing image classification against adversarial patch attacks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.703033Z"},"links":{"cited_paper":"/paper/2402.06249","citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:48257d1e8de4b044b1520d3c6966e681009e5d8942a50edae16c13bb77d49f07","observation_id":"963c1c42-c7f1-487b-bd71-14d340ee1fa0","resolution":{"observed_at":"2026-08-08T18:01:08.703033Z","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":"2026-08-08T18:01:08.899126Z","title":"Ilfo: Adversarial attack on adap- tive neural networks,","venue":null,"work_id":"f1fba5fd-6af6-4a57-8c9f-6a92ed303087","year":2020},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.707676Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:a7f817e7943bb662008043ed16cc30385e78bcbeec84155f5eed56facd381bf5","observation_id":"e19befb6-2272-460b-8d4f-3b8e53884848","resolution":{"observed_at":"2026-08-08T18:01:08.903895Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.884047Z","title":"Skipnet: Learning dynamic routing in convolutional networks,","venue":null,"work_id":"877514c9-9ef6-4c99-841d-7966912fda15","year":2018},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.712223Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:e5b9cb80badb732eb6c9483aba23f5803ab7786e4aed00762ee340721fd1e574","observation_id":"4b3913a9-fa08-4f87-821a-515f337f5fe7","resolution":{"observed_at":"2026-08-08T18:01:08.888879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.868872Z","title":"Spatially adaptive computation time for residual networks,","venue":null,"work_id":"c9123d4e-aa81-4093-8edc-d54e75c5a107","year":2017},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.718085Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:7ac2d8a86914907ad8a46fc638a2d7725db365c17d9b73957951d11b35700078","observation_id":"9a1e8679-abf2-4c22-87ad-7e43f716fa74","resolution":{"observed_at":"2026-08-08T18:01:08.874159Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T18:01:08.853917Z","title":"Gradauto: Energy- oriented attack on dynamic neural networks,","venue":null,"work_id":"f494d9be-40f8-43f1-8aca-fae4512b63d1","year":2022},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.722553Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:0183bcd77a5554537fd79d5c71e7a8d7f237a9d534fd85d8f7e57c810d6ac22c","observation_id":"cf81dea2-2e5e-4840-b89c-ee0810594203","resolution":{"observed_at":"2026-08-08T18:01:08.858717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02432","last_updated":"2021-02-25T22:38:35Z","snapshot_observed_at":"2026-08-04T03:00:18.680368Z","submitted_at":"2020-10-06T02:06:52Z","title":"A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02432","snapshot_observed_at":"2026-08-08T18:01:08.726844Z","title":"A panda? no, it’s a sloth: Slowdown attacks on adaptive multi-exit neural network inference,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.726844Z"},"links":{"cited_paper":"/paper/2010.02432","citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:2574e338b5d421d3efb22d6cbd9019bdc96334d5129fe31af429cc30e3050828","observation_id":"28a4cd23-0cf9-4772-be78-06a3e0444a21","resolution":{"observed_at":"2026-08-08T18:01:08.726844Z","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":"2026-08-08T18:01:08.731926Z","title":"Transslowdown: Efficiency attacks on neural machine translation systems,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.731926Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:703471da1f358fefb9b3c63319d3ac74bc06d5b620804a38be93e8f8e9deb887","observation_id":"02e7a2d5-1c37-4c2b-9979-33bcf89d5383","resolution":{"observed_at":"2026-08-08T18:01:08.731926Z","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":"2026-08-08T18:01:08.736691Z","title":"Nicgslowdown: Evaluating the efficiency robustness of neural image caption generation models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.736691Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:388e31d18e0d734032f039f2264609a684befae5d13d17a7e09f79b8e8a98b65","observation_id":"55e22095-3bd0-4916-a661-3f8db7599013","resolution":{"observed_at":"2026-08-08T18:01:08.736691Z","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":"2026-08-08T18:01:08.817033Z","title":"Desparsify: Adversarial attack against token sparsification mechanisms,","venue":null,"work_id":"ac06f165-9522-4015-a375-6f38633e1497","year":2024},"citing_paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T18:01:08.741295Z"},"links":{"citing_paper":"/paper/2608.05217"},"observation_digest":"sha256:51c5ee311e675b75bf4de1adc41b2f922cb11c170d16162ce3611eb35309861f","observation_id":"61933ffd-3506-4606-9f6b-3d637b9c81d7","resolution":{"observed_at":"2026-08-08T18:01:08.823540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2608.05217","last_updated":"2026-08-05T10:29:06Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-09T23:10:19.046332Z","submitted_at":"2026-08-05T10:29:06Z","title":"A Survey of Adversarial Efficiency Degradation for Vision Transformer by Exploiting Input-adaptive Optimization"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":19},"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 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2608.05217."}