{"as_of":"2026-08-20T14:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bdb7469125d73f7da0aa685c72194a1dea6f1af006942d8f1fd84b63f513b260","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T10:37:25.819654Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-16T12:16:17.039197Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2405.00392","last_updated":"2024-05-01T08:45:57Z","snapshot_observed_at":"2026-08-18T20:14:49.934443Z","submitted_at":"2024-05-01T08:45:57Z","title":"Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing","version":1},"cited_work":{"arxiv_id":"2405.00392","doi":"10.48550/arxiv.2405.00392","metadata_source":"pith","pith_arxiv_id":"2405.00392","snapshot_observed_at":"2026-08-16T12:16:17.039197Z","title":"Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing","venue":"cs.CR","work_id":"1c960865-dcaf-41bd-8652-b673616ed2ca","year":2024},"citing_paper":{"arxiv_id":"2504.17684","last_updated":"2025-04-24T15:54:56Z","snapshot_observed_at":"2026-08-19T18:52:02.782721Z","submitted_at":"2025-04-24T15:54:56Z","title":"Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T10:37:25.819654Z"},"links":{"cited_paper":"/paper/2405.00392","citing_paper":"/paper/2504.17684"},"observation_digest":"sha256:a9ba19f61b0eb159a74a18e69abfdf6e4b40792b84d934ee034497fd54657e51","observation_id":"a3479f44-41cf-42f6-b3d3-e028db9a0790","resolution":{"observed_at":"2026-08-16T10:37:26.073110Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.00392","last_updated":"2024-05-01T08:45:57Z","snapshot_observed_at":"2026-08-18T20:14:49.934443Z","submitted_at":"2024-05-01T08:45:57Z","title":"Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.00392","snapshot_observed_at":"2026-07-31T21:53:20.909079Z","title":"Certified adversarial robustness of machine learning-based malware detectors via (de)randomized smoothing,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.24177","last_updated":"2026-07-27T09:02:56Z","snapshot_observed_at":"2026-08-19T22:56:32.914202Z","submitted_at":"2026-07-27T09:02:56Z","title":"EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-31T21:53:20.909079Z"},"links":{"cited_paper":"/paper/2405.00392","citing_paper":"/paper/2607.24177"},"observation_digest":"sha256:873cb41dafcd7faa8f356b94d44bf329553d22389bca8b6ca49c832400232c6d","observation_id":"63e869bd-ed37-435d-9d72-89a9fd9b42aa","resolution":{"observed_at":"2026-07-31T21:53:20.909079Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2405.00392/citation-record","integrity":"/paper/2405.00392/integrity","json":"/paper/2405.00392/citation-record.json","paper":"/paper/2405.00392"},"outbound":[],"paper":{"arxiv_id":"2405.00392","last_updated":"2024-05-01T08:45:57Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-18T20:14:49.934443Z","submitted_at":"2024-05-01T08:45:57Z","title":"Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.00392."}