{"as_of":"2026-08-09T18:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:54fdafd9b08ae1a6bf759d4a850537cc09d7b594e550ad3bdb298c555d483e38","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:54:28.533204Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":28,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2007.00753","last_updated":"2020-07-03T20:10:20Z","snapshot_observed_at":"2026-08-03T19:21:43.393777Z","submitted_at":"2020-07-01T21:00:32Z","title":"Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.00753","snapshot_observed_at":"2026-08-07T14:54:28.533204Z","title":"Opportunities and challenges in deep learning adversarial robustness: A survey","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2505.17254","last_updated":"2025-05-22T20:05:20Z","snapshot_observed_at":"2026-08-07T14:47:51.224231Z","submitted_at":"2025-05-22T20:05:20Z","title":"Approach to Finding a Robust Deep Learning Model","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:54:28.533204Z"},"links":{"cited_paper":"/paper/2007.00753","citing_paper":"/paper/2505.17254"},"observation_digest":"sha256:35a1f7ecf31b5b347ef2ab5f0cddd63b8749ecb4f4b3974ac8cfdae71152ddff","observation_id":"4158fb71-3318-408e-8a41-24726b59f4e6","resolution":{"observed_at":"2026-08-07T14:54:28.533204Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.00753","last_updated":"2020-07-03T20:10:20Z","snapshot_observed_at":"2026-08-03T19:21:43.393777Z","submitted_at":"2020-07-01T21:00:32Z","title":"Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.00753","snapshot_observed_at":"2026-08-05T22:21:08.176845Z","title":"arXiv preprint arXiv:2007.00753 (2020)","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2508.10038","last_updated":"2025-08-10T09:19:29Z","snapshot_observed_at":"2026-08-07T17:57:14.105500Z","submitted_at":"2025-08-10T09:19:29Z","title":"Certifiably robust malware detectors by design","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T22:21:08.176845Z"},"links":{"cited_paper":"/paper/2007.00753","citing_paper":"/paper/2508.10038"},"observation_digest":"sha256:ffbe7a8fcd0abf31301500db87a69aa0c94e65452819bcdb653cf2f731804e5f","observation_id":"20a3a510-f11c-409c-ad17-00ee8da2c1ff","resolution":{"observed_at":"2026-08-05T22:21:08.176845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.00753","last_updated":"2020-07-03T20:10:20Z","snapshot_observed_at":"2026-08-03T19:21:43.393777Z","submitted_at":"2020-07-01T21:00:32Z","title":"Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey","version":2},"cited_work":{"arxiv_id":"2007.00753","doi":"10.48550/arxiv.2007.00753","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.00753","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Oppor- tunities and challenges in deep learning adversarial ro- bustness: A survey","venue":"arXiv (Cornell University)","work_id":"22207258-eef5-4ffe-95da-65a039a69bfe","year":2007},"citing_paper":{"arxiv_id":"2604.19018","last_updated":"2026-04-21T03:09:46Z","snapshot_observed_at":"2026-07-06T23:05:44.499005Z","submitted_at":"2026-04-21T03:09:46Z","title":"Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-10T02:31:07.932802Z"},"links":{"cited_paper":"/paper/2007.00753","citing_paper":"/paper/2604.19018"},"observation_digest":"sha256:130a44d185b347ac2b2e77fc6c0980f7058414356afd5b9203d85ce913972a7f","observation_id":"f0c9225a-6a6b-4ee2-9761-d7f8986c44c3","resolution":{"observed_at":"2026-05-10T02:32:49.445446Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.00753","last_updated":"2020-07-03T20:10:20Z","snapshot_observed_at":"2026-08-03T19:21:43.393777Z","submitted_at":"2020-07-01T21:00:32Z","title":"Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey","version":2},"cited_work":{"arxiv_id":"2007.00753","doi":"10.48550/arxiv.2007.00753","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.00753","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Oppor- tunities and challenges in deep learning adversarial ro- bustness: A survey","venue":"arXiv (Cornell University)","work_id":"22207258-eef5-4ffe-95da-65a039a69bfe","year":2007},"citing_paper":{"arxiv_id":"2604.20704","last_updated":"2026-04-22T15:46:11Z","snapshot_observed_at":"2026-07-06T23:07:25.185196Z","submitted_at":"2026-04-22T15:46:11Z","title":"Auto-ART: Structured Literature Synthesis and Automated Adversarial Robustness Testing","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T00:39:43.196010Z"},"links":{"cited_paper":"/paper/2007.00753","citing_paper":"/paper/2604.20704"},"observation_digest":"sha256:d016f790a838cc69ce8baaac9c8a0b6c9a6566f343cf7248058a037b08a25b72","observation_id":"f75f80a8-ffa6-4fa8-8a45-2a5ad9e231ae","resolution":{"observed_at":"2026-05-10T00:39:48.102540Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.00753","last_updated":"2020-07-03T20:10:20Z","snapshot_observed_at":"2026-08-03T19:21:43.393777Z","submitted_at":"2020-07-01T21:00:32Z","title":"Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey","version":2},"cited_work":{"arxiv_id":"2007.00753","doi":"10.48550/arxiv.2007.00753","metadata_source":"arxiv_reference","pith_arxiv_id":"2007.00753","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Oppor- tunities and challenges in deep learning adversarial ro- bustness: A survey","venue":"arXiv (Cornell University)","work_id":"22207258-eef5-4ffe-95da-65a039a69bfe","year":2007},"citing_paper":{"arxiv_id":"2605.02109","last_updated":"2026-05-04T00:08:05Z","snapshot_observed_at":"2026-08-06T07:07:16.244314Z","submitted_at":"2026-05-04T00:08:05Z","title":"Detecting Adversarial Data via Provable Adversarial Noise Amplification","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-09T17:08:02.038221Z"},"links":{"cited_paper":"/paper/2007.00753","citing_paper":"/paper/2605.02109"},"observation_digest":"sha256:f9fa09365c830f639b1e822fd4a945a48d12e92fca9c06d3ae44c838d844ad39","observation_id":"90b1b9da-c1da-40fe-9589-6fabc45fe651","resolution":{"observed_at":"2026-05-11T16:26:06.102783Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2007.00753/citation-record","integrity":"/paper/2007.00753/integrity","json":"/paper/2007.00753/citation-record.json","paper":"/paper/2007.00753"},"outbound":[],"paper":{"arxiv_id":"2007.00753","last_updated":"2020-07-03T20:10:20Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-03T19:21:43.393777Z","submitted_at":"2020-07-01T21:00:32Z","title":"Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2007.00753."}