{"as_of":"2026-08-21T17:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:39542c949cfa0d35626f24320a0405e43dde8f2cc455dcc7705ed7bb08909637","coverage":[{"denominator":75,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":75,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:21:48.268251Z","state":"measured"},{"denominator":75,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":75,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2505.08814/citation-record","integrity":"/paper/2505.08814/integrity","json":"/paper/2505.08814/citation-record.json","paper":"/paper/2505.08814"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:21:49.600514Z","title":"Black-box testing of deep neural networks through test case diversity 2023.IEEE Transactions on Software Engineering49, 5, 3182–3204","venue":null,"work_id":"45b567ff-f8c8-4a6e-8f0e-0af6a415c1ce","year":2023},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.964825Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:8ee004fff4310288b189c0e73915d432e569d960c93e734998e905c976ad444e","observation_id":"779f7b25-2dc9-4ed7-a0f8-efbd3870b092","resolution":{"observed_at":"2026-08-15T22:21:49.604791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.587029Z","title":"A systematic review on code clone detection 2019.IEEE access7, 86121–86144","venue":null,"work_id":"4602ea46-074e-4184-aa90-3b87f5666b44","year":2019},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.969753Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:b9da210534756712db13009986f5718574e6f500e6cac167ec2d0f0e6ed5eef7","observation_id":"7af1c660-694a-4225-a24b-f739be492315","resolution":{"observed_at":"2026-08-15T22:21:49.591729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.573637Z","title":"The non-fungible token (NFT) market and its relationship with Bitcoin and Ethereum 2022.FinTech1, 3, 216–224","venue":null,"work_id":"529a1202-e058-4551-a7f6-c75f2145b4ff","year":2022},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.973832Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:973baacd2bd36e71080885b4ca9d10c2091f6f151cb2ee28469e042d9c0e8b6e","observation_id":"e95b8ce8-74b6-4390-9bee-e7b8bec3fe60","resolution":{"observed_at":"2026-08-15T22:21:49.578468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.560485Z","title":"Non-fungible token (NFT) markets on the Ethereum blockchain: Temporal development, cointegration and interrelations 2023.Economics of Innovation and New Technology32, 8, 1216–1234","venue":null,"work_id":"9aec3aac-0071-47f4-908b-0699383040a7","year":2023},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.978142Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:209d5f51563ad3a3793155a57428d2cc1c51115009bbf6a53fa915c213d926f3","observation_id":"d0abd5e0-98a4-420a-81cc-9ac5eedb6f9c","resolution":{"observed_at":"2026-08-15T22:21:49.564919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-15T22:21:47.982137Z","title":"Longformer: The long- document transformer 2020.arXiv preprint arXiv:2004.05150","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.982137Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:bf23bdca1e51982abd5f1ab3127aac4187162d723a8c96d92a95fdd175d63694","observation_id":"7bc3ff12-37c6-4fcb-9cc4-28d364aabc4f","resolution":{"observed_at":"2026-08-15T22:21:47.982137Z","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":"2026-08-15T22:21:49.546612Z","title":"Formal verification of smart contracts: Short paper 2016","venue":null,"work_id":"9255738b-7fed-4460-bbcf-90e8a6574595","year":2016},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.986153Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:eea532f900dc98a02f2a35fbeb46e05461ba1fa5325b8ede6a6d391974424812","observation_id":"a86197a0-35ad-4446-aaf2-adb14397807e","resolution":{"observed_at":"2026-08-15T22:21:49.551102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.533135Z","title":"Enriching word vectors with subword information 2017.Transactions of the association for computational linguistics5, 135–146","venue":null,"work_id":"81ab1082-d85f-4468-baea-ca90c169f53e","year":2017},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.990395Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:c15d75efca3d9c8356864789a0ee6d56bbd65a5856413df68e571786abd01a3e","observation_id":"515bb792-57e7-49b5-b73d-946a4ec7e529","resolution":{"observed_at":"2026-08-15T22:21:49.537590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:47.993992Z","title":"Enhancing smart contract vulnerability detection in dapps leveraging fine-tuned llm 2025.arXiv preprint arXiv:2504.05006","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.993992Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:ad94dacc05ec29fcdb5a12ce8c760822c23435ad989404bbdf6d4e54598b713d","observation_id":"ff12a60b-9a34-4208-a9a5-3d8176b56402","resolution":{"observed_at":"2026-08-15T22:21:47.993992Z","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":"2026-08-15T22:21:47.997546Z","title":"SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode 2025.arXiv preprint arXiv:2504.05002","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:47.997546Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:d2c8080f2faf71b24a78c5664766ca89105a43f0e7c9fe4622afb700a723b432","observation_id":"90156d0b-db59-4a12-9ea9-472872e8e6e2","resolution":{"observed_at":"2026-08-15T22:21:47.997546Z","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":"2026-08-15T22:21:49.519999Z","title":"Deepinspect: A black-box trojan detection and mitigation framework for deep neural networks","venue":null,"work_id":"75be2f27-aeee-43af-9211-f5552aea8ca1","year":null},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.001007Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:e2c3b6624ea2b67075482e9708b764ec24005a457d94b563d664809dcc15696f","observation_id":"abb32e58-173e-4730-92d6-aef5fae4a03b","resolution":{"observed_at":"2026-08-15T22:21:49.524784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.14794","last_updated":"2022-11-19T12:45:21Z","snapshot_observed_at":"2026-08-12T04:58:34.201421Z","submitted_at":"2020-09-30T17:09:09Z","title":"Rethinking Attention with Performers","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.14794","snapshot_observed_at":"2026-08-15T22:21:48.008736Z","title":"Rethinking attention with performers 2020.arXiv preprint arXiv:2009.14794","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.008736Z"},"links":{"cited_paper":"/paper/2009.14794","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:0789c56a6a5e5aa25fac62e1db3a55bec6c4dbe9e21a8041e9b8ab0e73a55e26","observation_id":"ece985cb-6c30-4538-9a90-f7572fdd05de","resolution":{"observed_at":"2026-08-15T22:21:48.008736Z","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":"2026-08-15T22:21:49.493378Z","title":"A survey on smart contract vulnerabilities: Data sources, detection and repair 2023.Information and Software Technology159, 107221","venue":null,"work_id":"677e585b-1663-499a-a484-443d631701a0","year":2023},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.012479Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:0f85e532c8457398d81815fa0cebbd9affc8771744bf3e30602622128f511b93","observation_id":"6ceeee74-7d55-402a-8e57-0e11f73746a8","resolution":{"observed_at":"2026-08-15T22:21:49.498154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.480461Z","title":"SmartBugs: A Framework to Analyze Solidity Smart Contracts 2020","venue":null,"work_id":"15460529-87e0-4586-8294-a33ce5515488","year":2020},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.016067Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:d95f2cac0809c28af533199944208317489b1b4347f3cb4d262e444fa01f1b78","observation_id":"047574ca-edaa-42b8-8b21-12575df3daae","resolution":{"observed_at":"2026-08-15T22:21:49.484835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.467286Z","title":"Checking Smart Contracts with Structural Code Embedding 2020.IEEE Transactions on Software Engineering","venue":null,"work_id":"a4ac51c4-3bbf-4d3b-934c-51730bcc0171","year":2020},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.019574Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:942b3cbd7e654c32047b469f8432321cd69e01dab69469cfd5700f5928974aef","observation_id":"c6712440-d26f-4d31-997e-cf198984bb6e","resolution":{"observed_at":"2026-08-15T22:21:49.471683Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.454030Z","title":"How effective are smart contract analysis tools? evaluating smart contract static analysis tools using bug injection","venue":null,"work_id":"1fe2ebff-8e00-4640-8ed3-6ccc8f0e34e2","year":null},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.023206Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:dbb52f8d99f1c0f8994a43761dc42d9f480d33e72bad24f1441d42894a391a7a","observation_id":"66bf6453-5199-489c-b814-5ec93c5fda7e","resolution":{"observed_at":"2026-08-15T22:21:49.458578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.427387Z","title":"Achecker: Statically de- tecting smart contract access control vulnerabilities 2023","venue":null,"work_id":"7c162d5a-3932-4347-afd4-8bb0d379f3e7","year":2023},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.031165Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:a96edbc9070e8ec165dfc6b142a8209d30cf799787d9771ad7048a40e0fa4584","observation_id":"d27a58cc-90e7-4607-b69b-59d07b53af66","resolution":{"observed_at":"2026-08-15T22:21:49.431721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-19T07:17:20.004918Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-15T22:21:48.036251Z","title":"Explaining and harnessing adversarial examples 2014.arXiv preprint arXiv:1412.6572","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.036251Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:6c3963799b8200017fb5834006f4181a8ecd28b47485e189a949b5f5f0f79700","observation_id":"90970d09-deb7-4865-a2dd-7ec8ece82910","resolution":{"observed_at":"2026-08-15T22:21:48.036251Z","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":"2026-08-15T22:21:49.413590Z","title":"Deep residual learning for image recognition 2016","venue":null,"work_id":"c38ef184-53df-4a0e-b98f-18c601dc4de9","year":2016},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.041140Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:6167c4c51d190e740c8120ce9ad7b25035de707f6dea33f10fea9905a6b5f43c","observation_id":"30ad9389-de26-4f3c-8807-cc150a028dd3","resolution":{"observed_at":"2026-08-15T22:21:49.417962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.399729Z","title":"Characterizing code clones in the ethereum smart contract ecosystem 2020","venue":null,"work_id":"ff570de2-f812-4045-8330-b3430d81ccfc","year":2020},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.045214Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:e9db2ce1b3f0a7cf267617fd53215f589c186bf681f74436d59cf47619da4a23","observation_id":"42b6ccf0-5739-4a53-bf18-f5b57635ed0d","resolution":{"observed_at":"2026-08-15T22:21:49.404153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.385651Z","title":"Hunting vulnerable smart contracts via graph embedding based bytecode matching 2021.IEEE Transactions on Information Forensics and Security16, 2144–2156","venue":null,"work_id":"8ca3f4c3-62da-4f8a-ae31-8b78cfcd9d13","year":2021},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.050150Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:e59337652e9e3403cb5e7184c756d59043bc8d506577bd10a37717a68a61c8ed","observation_id":"44f78c0d-727a-4e29-bd14-ce6af6f1fc6f","resolution":{"observed_at":"2026-08-15T22:21:49.390477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.371842Z","title":"Characterizing the Solana NFT ecosys- tem 2024","venue":null,"work_id":"f35a99ba-fa1c-4a36-87b5-536334da7a3b","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.054357Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:ff2924c414773d20790e99984afebd36d70f6ba7191ba9ecb2c7342a87c8f905","observation_id":"645e08b4-bdbc-4a90-b3e3-f2cb062b411a","resolution":{"observed_at":"2026-08-15T22:21:49.376342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.358149Z","title":"Neural network models and deep learning 2019.Current Biology29, 7, R231–R236","venue":null,"work_id":"21b1c96f-d357-44ab-ac53-0dc0413f8c44","year":2019},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.058556Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:a7aed226299f514d613f3bf55375f284dfb1a1040a8f5a08a6d0948518780138","observation_id":"c3b41481-7da9-4380-84ce-5adf6d89c7e7","resolution":{"observed_at":"2026-08-15T22:21:49.362713Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.344331Z","title":"Gradient-based learning applied to document recognition 1998.Proc","venue":null,"work_id":"5924247b-994d-43f2-9139-8947e1349436","year":1998},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.062464Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:8218e2f54ad2cb4f585c915a72e786ba8104aadd3234d118f1b4e3bbc4e61e3e","observation_id":"23319913-5759-4276-b633-2166cd226981","resolution":{"observed_at":"2026-08-15T22:21:49.349036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.330416Z","title":"Cobra: interaction-aware bytecode-level vulnerability detector for smart contracts 2024","venue":null,"work_id":"df50ff9b-052f-46d1-8b87-48015512a775","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.066746Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:4efbddabacb250ac417a8cff4c18b4eb1f9665e65e2b8befa1be6dbc9e7c6e39","observation_id":"db3d6681-0399-4533-bdf2-dd56a8befa7f","resolution":{"observed_at":"2026-08-15T22:21:49.335042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.316981Z","title":"Detecting Malicious Accounts in Web3 through Transaction Graph 2024","venue":null,"work_id":"cfa67a9b-48ac-4430-b3b6-30a904f84615","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.070339Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:dc08d02d364e5ec38f6901a8c4e4f3b650359cb6bd70b8c73250d84c03cce18c","observation_id":"777e739a-b11e-4297-858d-295083565cfa","resolution":{"observed_at":"2026-08-15T22:21:49.321515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.303415Z","title":"Hybrid analysis of smart contracts and malicious behaviors in ethereum 2021","venue":null,"work_id":"f3b25978-98b2-4dc0-837b-23560eef9d6b","year":2021},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.074253Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:2e33e18f32fe18ea4b21aa7b768825c2d3b370eaac18a24103140392d52b36bf","observation_id":"a33809ef-f3b8-41e0-a14e-3f4b13d4c687","resolution":{"observed_at":"2026-08-15T22:21:49.307762Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.290116Z","title":"CLUE: towards discovering locked cryptocurrencies in ethereum 2021","venue":null,"work_id":"c86282f3-bdb3-48cb-9da2-63697c980a01","year":2021},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.077979Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:43d4f7cb367764f6004057de609754d250a964ec4d57a1f8590cf38a86afd477","observation_id":"bad44214-a40b-4c59-9081-909fdf57dd49","resolution":{"observed_at":"2026-08-15T22:21:49.294240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.277498Z","title":null,"venue":null,"work_id":"986676e7-773d-45d2-b879-3f1e1862c409","year":2017},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.081570Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:d977df02a8eac03d3bb8595ad3ec2baef48d52ddee61316d79d5068bfab49ee0","observation_id":"68637299-4a27-497a-b905-a1d644ebe2da","resolution":{"observed_at":"2026-08-15T22:21:49.281585Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.263909Z","title":"ModelDiff: Testing-based DNN similarity comparison for model reuse detection 2021","venue":null,"work_id":"59c53938-c57e-41d2-8cb2-dd81b9c9cd86","year":2021},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.085028Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:97a5444d7ea3e59c13791ac427324a95b49e87de0e2ca053c30d74a6e8208afd","observation_id":"e108a882-74b0-424e-b1f4-548c72d9239d","resolution":{"observed_at":"2026-08-15T22:21:49.268851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.251181Z","title":"StateGuard: Detecting State Derailment Defects in Decentralized Exchange Smart Contract 2024","venue":null,"work_id":"2fb22a53-4f1b-4da5-95e4-e144fd7db0db","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.088536Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:5e284212d44d0d95748f34fd4b0a050dcde9ac1a9da5145e22ca01587855c971","observation_id":"24969b26-ae8c-475b-aa6d-6b945b808498","resolution":{"observed_at":"2026-08-15T22:21:49.255615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.092074Z","title":"SCALM: Detecting Bad Practices in Smart Contracts Through LLMs 2025.arXiv preprint arXiv:2502.04347","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.092074Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:1d588aa8c7fd88c429b1c583e618b1990e678e899acfafe5d08c962af55e073e","observation_id":"46677bf5-def2-41ce-ae9e-dac3dc832689","resolution":{"observed_at":"2026-08-15T22:21:48.092074Z","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":"2026-08-15T22:21:49.236999Z","title":"On identity, transaction, and smart contract privacy on permissioned and per- missionless blockchain: A comprehensive survey 2024.Comput","venue":null,"work_id":"31fac3c0-d736-4e55-90f8-e7ebe46fff74","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.095658Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:be756ffe3405589fa0e653e569aa5589525b7f9a3702f10d6cba621f37e5e865","observation_id":"f06ba1dd-dc42-46f6-8f88-9b2fe153db4f","resolution":{"observed_at":"2026-08-15T22:21:49.242328Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.22156","last_updated":"2025-03-28T05:21:30Z","snapshot_observed_at":"2026-08-20T14:07:26.411180Z","submitted_at":"2025-03-28T05:21:30Z","title":"SoK: Security Analysis of Blockchain-based Cryptocurrency","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.22156","snapshot_observed_at":"2026-08-15T22:21:48.099395Z","title":"SoK: Security Analysis of Blockchain-based Cryptocur- rency 2025.arXiv preprint arXiv:2503.22156","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.099395Z"},"links":{"cited_paper":"/paper/2503.22156","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:2300ed1f80683dc1a6f2477e766fbbbdedf37126efc49e3c3b5af68828f61adf","observation_id":"f149bc6f-48b9-4412-ba95-c4c795968b8e","resolution":{"observed_at":"2026-08-15T22:21:48.099395Z","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":"2026-08-15T22:21:49.223238Z","title":"GasTrace: Detecting Sand- wich Attack Malicious Accounts in Ethereum 2024","venue":null,"work_id":"3d5b5ce5-86fe-4fd2-b48f-84e4cca1a224","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.103446Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:02d8cbfe3f694c6016661dcd0657aefaac471f6780af5113b7cd8b9528ac8a02","observation_id":"761d4f15-dee7-4e95-86c7-896d83940e29","resolution":{"observed_at":"2026-08-15T22:21:49.227647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.209436Z","title":"Deepgauge: Multi-granularity testing criteria for deep learning systems 2018","venue":null,"work_id":"360350f0-5b52-4984-b7aa-98e85c39eaa5","year":2018},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.107129Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:b6af135f72b4785e87734a9d8657e85e4ce7abf89c488dbba1680a27bb39a161","observation_id":"8b956032-4a44-42e8-a84c-f9c36b541b92","resolution":{"observed_at":"2026-08-15T22:21:49.213924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.16073","last_updated":"2024-09-14T07:18:14Z","snapshot_observed_at":"2026-08-16T14:06:59.619782Z","submitted_at":"2024-03-24T09:26:53Z","title":"Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.16073","snapshot_observed_at":"2026-08-15T22:21:48.111072Z","title":"Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications 2024.arXiv preprint arXiv:2403.16073","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.111072Z"},"links":{"cited_paper":"/paper/2403.16073","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:cd0e34e0a90f87b2613f819c118b502b0bd34599f3e75ea4a29daf4b12b615d2","observation_id":"117d6354-3116-425b-8ac9-c2ad2efec4ba","resolution":{"observed_at":"2026-08-15T22:21:48.111072Z","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":"2026-08-15T22:21:49.195505Z","title":null,"venue":null,"work_id":"276f6d4d-c0ab-4233-82a3-cd2e6c39b77b","year":2018},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.115125Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:8de539e1fbd5cd19f4c8922cef37a755261be324beca3ff8d8b02eccff09aa92","observation_id":"78cf3a7a-33c1-42ae-abc3-78cd41c3a599","resolution":{"observed_at":"2026-08-15T22:21:49.199971Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.04863","last_updated":"2025-03-13T07:05:15Z","snapshot_observed_at":"2026-08-19T18:56:26.836820Z","submitted_at":"2024-02-07T13:58:26Z","title":"SCLA: Automated Smart Contract Summarization via LLMs and Control Flow Prompt","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.04863","snapshot_observed_at":"2026-08-15T22:21:48.118747Z","title":"SCLA: Automated Smart Contract Summarization via LLMs and Semantic Augmentation 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.118747Z"},"links":{"cited_paper":"/paper/2402.04863","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:880f88ee399b41823f6ac2c2b3bec67428e7cca191df7715c0a05eed0d98dba2","observation_id":"f886d3c5-b33e-47a9-b8bb-cde20def4343","resolution":{"observed_at":"2026-08-15T22:21:48.118747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3781","last_updated":"2013-09-07T00:30:40Z","snapshot_observed_at":"2026-07-06T03:04:11.148340Z","submitted_at":"2013-01-16T18:24:43Z","title":"Efficient Estimation of Word Representations in Vector Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3781","snapshot_observed_at":"2026-08-15T22:21:48.122827Z","title":"Efficient estimation of word representations in vector space 2013.arXiv preprint arXiv:1301.3781","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.122827Z"},"links":{"cited_paper":"/paper/1301.3781","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:ef19f797a75ccd0f5dbe4bbf4e0b6a72182ae27fa8cef32cd80defc73cde6327","observation_id":"17b61bfd-a8c2-4f93-a36c-5c909aab09ec","resolution":{"observed_at":"2026-08-15T22:21:48.122827Z","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":"2026-08-15T22:21:49.182114Z","title":"Mapping the NFT revolution: market trends, trade networks, and visual features 2021.Scientific reports11, 1, 20902","venue":null,"work_id":"df2dc894-54e3-4c5d-85f8-38831bc13b98","year":2021},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.126741Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:60e0a8af9c7d092e774bb7b10cdcd28abb0e75bf63422b413e1ad50d33a4302b","observation_id":"9700f2e8-3f03-46cb-b589-ef0ea1d65299","resolution":{"observed_at":"2026-08-15T22:21:49.186680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.168567Z","title":"Understanding source code evolution using abstract syntax tree matching 2005","venue":null,"work_id":"9393bf94-0daa-4403-9c95-4f411f031650","year":2005},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.130876Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:31c509982dc102d6bc18ec9209767eef90222965b5b6b123cabe2460a58c6adf","observation_id":"084bfe09-39d0-423d-bf4c-c6cdc08eb35c","resolution":{"observed_at":"2026-08-15T22:21:49.173210Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.15327","last_updated":"2021-04-10T01:44:17Z","snapshot_observed_at":"2026-08-19T18:56:24.045444Z","submitted_at":"2020-10-29T02:57:21Z","title":"Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.15327","snapshot_observed_at":"2026-08-15T22:21:48.134721Z","title":"Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth 2020.arXiv preprint arXiv:2010.15327","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.134721Z"},"links":{"cited_paper":"/paper/2010.15327","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:14b471498cafb5de528a219c49e0bba8ab82922ed3cd40ca9ad1f80be962b1d3","observation_id":"de660cbf-aff0-4374-94e7-58fe4a8af29f","resolution":{"observed_at":"2026-08-15T22:21:48.134721Z","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":"2026-08-15T22:21:49.154009Z","title":"Unveiling wash trading in popular NFT markets 2024","venue":null,"work_id":"56521cd1-c905-4ccd-b93c-fcde4772d68b","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.139141Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:9dbb9744c5558ebb53802dfe7b45843db3549acb022b7e95d10328afcb02a938","observation_id":"11d2a465-c2f8-46c9-b27b-16e873dc9425","resolution":{"observed_at":"2026-08-15T22:21:49.159555Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.140199Z","title":"Enhancing Ethereum smart-contracts static analysis by computing a precise Control-Flow Graph of Ethereum bytecode 2023.Journal of Systems and Software200, 111653","venue":null,"work_id":"dc08e4b1-d971-4319-968f-924558aeb042","year":2023},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.143381Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:365890c97168e97cceda3c864094b3c10f5334733067a11dff51ae5098735317","observation_id":"69ed1b8c-0948-4c8c-8a4a-3962d95d7e00","resolution":{"observed_at":"2026-08-15T22:21:49.145161Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.126428Z","title":"Deepxplore: Automated whitebox testing of deep learning systems 2017","venue":null,"work_id":"8af12767-f727-4ad2-b0d5-5fe9bacb3bfc","year":2017},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.147123Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:568b0c96e42efbd9224f9f8a7d0401a8aa5f90877c7b1cbb22031a655655626d","observation_id":"89df1304-d9ca-4262-be81-7fb105d8bb3e","resolution":{"observed_at":"2026-08-15T22:21:49.131414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2209.05872","last_updated":"2022-09-13T10:35:42Z","snapshot_observed_at":"2026-08-17T13:18:41.108104Z","submitted_at":"2022-09-13T10:35:42Z","title":"Smart Contract Vulnerability Detection Technique: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.05872","snapshot_observed_at":"2026-08-15T22:21:48.151178Z","title":"Smart contract vulnerability detection technique: A survey 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.151178Z"},"links":{"cited_paper":"/paper/2209.05872","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:9f34315555801e4af0ff5eb860127de28833aa0f8c965826ac5a1b6dd017076b","observation_id":"e0e357b7-163c-460d-875a-f1f659ce189e","resolution":{"observed_at":"2026-08-15T22:21:48.151178Z","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":"2026-08-15T22:21:49.112209Z","title":"Sourcerercc: Scaling code clone detection to big-code 2016","venue":null,"work_id":"fe37f5c0-daca-47f3-9452-92fffcf80c19","year":2016},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.155517Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:3fec997387f9c51272346f0483bbde41b8700b9294b393c50d0064367455c606","observation_id":"b88a5d30-14b2-445f-8fe2-73887776e0dc","resolution":{"observed_at":"2026-08-15T22:21:49.116491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.099379Z","title":"An empirical study on test case prioritization metrics for deep neural networks 2021","venue":null,"work_id":"5f37a9a0-ff65-4ea1-b1c3-5d4ebeada98f","year":2021},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.159597Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:a8ba4a297584d2ab329dad3a352530abdd4fb28f38517b832c76fb427f95c2ca","observation_id":"cd0175ed-49c5-4ce7-bb44-ba5d6582e1fd","resolution":{"observed_at":"2026-08-15T22:21:49.103634Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-17T19:17:06.411141Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-15T22:21:48.163591Z","title":"Very deep convolutional networks for large-scale image recognition 2014.arXiv preprint arXiv:1409.1556","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.163591Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:dc9bcd69d403d892cbde2d66eb2b422d939e9d2a6539d4b42456de2918c78ebc","observation_id":"00f9ccac-6531-4966-bc14-99a906e24de2","resolution":{"observed_at":"2026-08-15T22:21:48.163591Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08796","last_updated":"2020-10-05T15:26:10Z","snapshot_observed_at":"2026-08-19T18:56:01.706645Z","submitted_at":"2020-06-15T22:07:54Z","title":"Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08796","snapshot_observed_at":"2026-08-15T22:21:48.168027Z","title":"Fast graph attention networks using effective resistance based graph sparsification 2020.arXiv preprint arXiv:2006.08796","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.168027Z"},"links":{"cited_paper":"/paper/2006.08796","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:12ee5455181855aa1a92c8af236257dfdd517bdd9af7844601e2dffbf13318da","observation_id":"fd322528-0ec1-4afe-ae06-375d46c4f27b","resolution":{"observed_at":"2026-08-15T22:21:48.168027Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1803.04792","last_updated":"2019-04-15T16:49:14Z","snapshot_observed_at":"2026-08-17T17:09:33.205199Z","submitted_at":"2018-03-10T23:19:13Z","title":"Testing Deep Neural Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.04792","snapshot_observed_at":"2026-08-15T22:21:48.172376Z","title":"Testing deep neural networks 2018.arXiv preprint arXiv:1803.04792","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.172376Z"},"links":{"cited_paper":"/paper/1803.04792","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:4b357bab50ca6863c14a67a0012eab39f13c60db2eff058f855f48852de08b2d","observation_id":"ccd6c7c5-1c37-4779-920a-0a316c0c148f","resolution":{"observed_at":"2026-08-15T22:21:48.172376Z","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":"2026-08-15T22:21:49.085671Z","title":"DeepConcolic: Testing and debugging deep neural networks","venue":null,"work_id":"ab4057b0-88e3-4e9c-86ea-569ffc328082","year":null},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.176790Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:9502a8e9e28dbbffe7fa3510efa407d0a54e7a59b438edf6b6023306c7296d71","observation_id":"00b9abd4-3e34-4eca-8946-c857ad6d001e","resolution":{"observed_at":"2026-08-15T22:21:49.090645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.060074Z","title":"Structural test coverage criteria for deep neural networks 2019.ACM Transactions on Embedded Computing Systems (TECS)18, 5s, 1–23","venue":null,"work_id":"6be77b76-d3db-4dce-95bb-e4efd4fb9d8a","year":2019},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.186178Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:3926a0d1217adbd3a2738a75b6a165c22011df041e20b7ebe7328b97c506db1d","observation_id":"52d24046-695d-4b03-bf4b-5d66d6903d59","resolution":{"observed_at":"2026-08-15T22:21:49.064317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.046974Z","title":"Smart contracts: building blocks for digital markets 1996.EXTROPY: The Journal of Transhumanist Thought,(16)18, 2, 28","venue":null,"work_id":"fcd43b89-0cf6-4adc-a7be-6825275d7da5","year":1996},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.191105Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:0c454feb5cb159f739fdbed474a72970a2b39706302c84ddc4d2962d0d0337b4","observation_id":"48b405bf-04f9-4163-b10e-e6413d6abef4","resolution":{"observed_at":"2026-08-15T22:21:49.051616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.072605Z","title":null,"venue":null,"work_id":"662c6484-3c5c-4fc4-94d5-bf108239f9f7","year":null},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.181078Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:9c78cdb794839229431f3e0c27673b341207e19df5284f90307e5676755c7381","observation_id":"60819138-89bf-4283-b65e-2dabc0ff071d","resolution":{"observed_at":"2026-08-15T22:21:49.076814Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.019593Z","title":"Smartcheck: Static analysis of ethereum smart contracts 2018","venue":null,"work_id":"a65d2599-bc7d-48a5-b1fd-71be49c2e28e","year":2018},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.201408Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:3caf6e9a6b1183c897696b0f195c4d38d549b8984df67bd377299909a5f5cb16","observation_id":"7093676e-1537-48fe-ad96-eeafb899eebb","resolution":{"observed_at":"2026-08-15T22:21:49.024033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.006005Z","title":"A survey of smart contract formal specification and verification 2021.ACM Computing Surveys (CSUR)54, 7, 1–38","venue":null,"work_id":"164bff4d-cafb-4c71-9674-b6d6ad2a8af6","year":2021},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.205776Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:d6b95723bf375c3418a546ee53b1cd3c281ff59a08062695af2e98bc4bc3d74a","observation_id":"704dafcb-9260-4507-80e3-cb4acc3a0ab2","resolution":{"observed_at":"2026-08-15T22:21:49.010480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.033046Z","title":"Ethereum Smart Contract Representation Learning for Robust Bytecode-Level Similarity Detection","venue":null,"work_id":"aa3adc7f-8288-4b47-ac96-932dcaddb3e6","year":2022},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.197034Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:009d49ed84173e839a8cffc826a8d155c4181fb11b64b4ac558d45fca5471fbf","observation_id":"295b8c69-a528-4be2-9bd5-f5dd6772df3b","resolution":{"observed_at":"2026-08-15T22:21:49.037355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.07447","last_updated":"2021-10-25T01:23:45Z","snapshot_observed_at":"2026-08-18T05:39:37.851009Z","submitted_at":"2021-05-16T14:50:26Z","title":"Non-Fungible Token (NFT): Overview, Evaluation, Opportunities and Challenges","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.07447","snapshot_observed_at":"2026-08-15T22:21:48.214103Z","title":"Non-fungible token (NFT): Overview, evaluation, opportunities and challenges 2021.arXiv preprint arXiv:2105.07447","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.214103Z"},"links":{"cited_paper":"/paper/2105.07447","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:9bae514a00c37bf8ea3cf76bd51a9d6c9ecba2c8b45b7df63a36998341b43d3b","observation_id":"62e09d44-e8fe-4e99-9979-72706f2811af","resolution":{"observed_at":"2026-08-15T22:21:48.214103Z","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":"2026-08-15T22:21:48.218925Z","title":"Smart contracts in the real world: A statistical exploration of external data dependencies 2024.arXiv preprint arXiv:2406.13253","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.218925Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:61b1cad6fe3bff803fc6dc7e9c7109b63c11c64d856f8554d96eeb8d402b5d87","observation_id":"696314b4-e204-4e24-8ab2-91e80a751b88","resolution":{"observed_at":"2026-08-15T22:21:48.218925Z","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":"2026-08-15T22:21:48.992770Z","title":"Securify: Practical security analysis of smart con- tracts 2018","venue":null,"work_id":"9a8a7959-f19d-4c98-957c-d6ee007a1229","year":2018},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.209961Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:976df594438fd4398f8286df5ed0a6012c607be599e35c5492e6baa1192f5a5d","observation_id":"19ed1513-447a-4498-8f6b-037858e4ce26","resolution":{"observed_at":"2026-08-15T22:21:48.996928Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.966401Z","title":"WakeMint: Detecting Sleep- minting Vulnerabilities in NFT Smart Contracts 2025","venue":null,"work_id":"241a6ebe-2282-4d0a-87c0-43238489fca0","year":2025},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.229426Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:4bcf1686048e951ddcea916af9b3659b33aacb31644aff5d5cd82299b414ecb6","observation_id":"d2fab646-e1f2-4d00-a376-fb5575318b3b","resolution":{"observed_at":"2026-08-15T22:21:48.970682Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.952520Z","title":"Npc: Neuron path coverage via characterizing decision logic of deep neural 9 Conference’17, July 2017, Washington, DC, USA Wenkai and Xiaoqi, et al","venue":null,"work_id":"cf072b97-a16b-4848-9c9b-979bc0d2f54d","year":2017},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.233009Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:99469e67ad42ae31490233db9479d3071a875309e965917c8f2eb639d2c6259a","observation_id":"e5906fdc-050e-4a66-98c8-9795a8f4b87a","resolution":{"observed_at":"2026-08-15T22:21:48.956998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.979726Z","title":"Deep learning code fragments for code clone detection 2016","venue":null,"work_id":"9fb42b85-01a7-408c-8983-6eb9272695d9","year":2016},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.225478Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:2350de4ad3a3466e23943e9681a4a70cd85905a16fce889540cba743fc91d6d0","observation_id":"bf077917-42e6-4ca4-89ab-85e5d31e9dbc","resolution":{"observed_at":"2026-08-15T22:21:48.983976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.920923Z","title":"Correlations between deep neural network model coverage criteria and model quality 2020","venue":null,"work_id":"56feebda-6d01-4289-9118-c6b7f2c63d20","year":2020},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.241055Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:79414e7c7ee682909871da6277326908b597b1ef0fdef33ba9ac697fc519fbf6","observation_id":"8b39072a-ce50-4b64-a73a-da2018ed060f","resolution":{"observed_at":"2026-08-15T22:21:48.926003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.906433Z","title":"Un- cover the premeditated attacks: Detecting exploitable reentrancy vulnerabilities by identifying attacker contracts 2024","venue":null,"work_id":"26a6dd7e-d6bd-4294-9161-0b012d0276b1","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.244868Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:b7256110d99375ca4b106161142d4b92eeae563a4cafb110bbbf1b6e27afd4f4","observation_id":"b166b666-2689-48a5-af5a-b340c20e3f56","resolution":{"observed_at":"2026-08-15T22:21:48.911241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.936577Z","title":"Deephunter: a coverage-guided fuzz testing framework for deep neural networks 2019","venue":null,"work_id":"54d633b1-3026-4ade-a3d4-d51627516acc","year":2019},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.236743Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:4eb6925c6d514cdb11df07b1445de52549aaf74f544a4f92bc8bceb5b1c9954b","observation_id":"23d15dd3-69dc-4abf-99e1-6da61f326250","resolution":{"observed_at":"2026-08-15T22:21:48.942647Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.18225","last_updated":"2024-12-24T07:15:48Z","snapshot_observed_at":"2026-08-20T07:15:14.467253Z","submitted_at":"2024-12-24T07:15:48Z","title":"Combining GPT and Code-Based Similarity Checking for Effective Smart Contract Vulnerability Detection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.18225","snapshot_observed_at":"2026-08-15T22:21:48.252508Z","title":"Combining GPT and Code-Based Similarity Checking for Effective Smart Contract Vulnerability Detection 2024.arXiv preprint arXiv:2412.18225","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.252508Z"},"links":{"cited_paper":"/paper/2412.18225","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:664c8b712bb675809d8c9323cd4ff3892d153d449ecbb32028987f1f9697c11d","observation_id":"16e71f73-c67e-4fbe-8b12-4c2d03fd0cb8","resolution":{"observed_at":"2026-08-15T22:21:48.252508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.06838","last_updated":"2025-07-21T05:24:59Z","snapshot_observed_at":"2026-08-19T18:57:28.833497Z","submitted_at":"2024-03-11T15:59:59Z","title":"ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.06838","snapshot_observed_at":"2026-08-15T22:21:48.256637Z","title":"Acfix: Guiding llms with mined common rbac practices for context- aware repair of access control vulnerabilities in smart contracts 2024.arXiv preprint arXiv:2403.06838","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.256637Z"},"links":{"cited_paper":"/paper/2403.06838","citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:fa909ce8b53be5fd0d59cd9adcb58754a29bbea7232700caaa46894ff0d2c3fa","observation_id":"3c48dd57-0413-4df2-b5ec-986d3d5d8da6","resolution":{"observed_at":"2026-08-15T22:21:48.256637Z","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":"2026-08-15T22:21:48.891069Z","title":"Revisiting neuron coverage for dnn testing: A layer-wise and distribution-aware criterion 2023","venue":null,"work_id":"e9b997b7-3d84-4bb9-8797-5d688bd3868f","year":2023},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.248868Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:193d030d8fa9b99afdd2a894912e262e1fadc4abb83382c61a9b0bb120b8bd04","observation_id":"7fe1c075-9ec6-4463-bc11-16be7d0adb8e","resolution":{"observed_at":"2026-08-15T22:21:48.896421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.856890Z","title":"Byte- code similarity detection of smart contract across optimization options and compiler versions based on triplet network 2022.Electronics11, 4, 597","venue":null,"work_id":"e4f664ad-ed1f-44ec-b223-854ac752f304","year":2022},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.264759Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:5442414d66882bcfd0af032a394a9359a925657318188966ad058c066d422779","observation_id":"65e45761-78c7-428c-ad9a-ee3bb756090a","resolution":{"observed_at":"2026-08-15T22:21:48.864477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:48.268251Z","title":"Malicious Code Detection in Smart Contracts via Opcode Vectorization 2025.arXiv preprint arXiv:2504.12720","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.268251Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:1d8bae2cbda9da54f411d2094df9ab4be351a191308a5c37fee7b26d76e26a6e","observation_id":"245fa38c-1a71-4d7d-8065-ebcc0fe73ba4","resolution":{"observed_at":"2026-08-15T22:21:48.268251Z","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":"2026-08-15T22:21:48.875328Z","title":"PrettySmart: Detecting Permission Re-delegation Vulnerability for To- ken Behaviors in Smart Contracts 2024","venue":null,"work_id":"ad01e2ab-cf64-477b-9c13-4c9ac5fe5f2c","year":2024},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.260791Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:7645b8679edfd9bb1d51639ae43181a67401a9d44c2d10356cac1421d35f99d0","observation_id":"1b4ef592-81c5-4853-b7a2-2fcf823e93e0","resolution":{"observed_at":"2026-08-15T22:21:48.880679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.507051Z","title":null,"venue":null,"work_id":"d13b9b5b-87de-4ee2-84a8-3681486be65b","year":null},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.004719Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:dde3d1dea2c79840d627375c97b7bc6d21d595d5d402e7cf3678cecd7dbd6382","observation_id":"5aa1626c-6fb0-4274-8e77-447dd0f55338","resolution":{"observed_at":"2026-08-15T22:21:49.511354Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T22:21:49.440564Z","title":null,"venue":null,"work_id":"c43dfa3c-9785-4b4a-a33f-8c2089d005d1","year":null},"citing_paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test","version":3},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T22:21:48.027027Z"},"links":{"citing_paper":"/paper/2505.08814"},"observation_digest":"sha256:8d88c96d94dbbc08105b0f4cabc7354a5ccbfbe9e9cf5bde079e203bdd400397","observation_id":"b0e46ce5-8100-4c29-b2b4-f7860b55ccba","resolution":{"observed_at":"2026-08-15T22:21:49.444900Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.08814","last_updated":"2026-07-03T12:16:58Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T22:12:20.025808Z","submitted_at":"2025-05-12T08:25:55Z","title":"Towards Understanding Deep Learning Model in Image Recognition via Coverage Test"},"reference_resolution":{"displayed":75,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":25,"verified_exact":0,"verified_fuzzy":50},"total_outbound_references":75},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 0 inbound Pith citation observations for arXiv:2505.08814."}