{"as_of":"2026-08-10T21:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c0c7bca7b9cbcc90750608e18084e60f4ef395b52a9ec439cad8cfeb9a69e4f2","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T13:32:34.833636Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2502.07839/citation-record","integrity":"/paper/2502.07839/integrity","json":"/paper/2502.07839/citation-record.json","paper":"/paper/2502.07839"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T13:32:35.089679Z","title":"APF-CPP: An artificial potential field based multi-robot online coverage path planning approach,","venue":null,"work_id":"459606fa-cb33-45dd-b4d3-1e6a2f552695","year":2024},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.764195Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:26c6da49b27056de6ab976ef5eb331733ef28dfa2745ec621cd2f3c06804f8c1","observation_id":"cf3c2972-21aa-4a23-a1b8-c6231f45ac2a","resolution":{"observed_at":"2026-08-08T13:32:35.095335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:32:35.071610Z","title":"MINER-RRT*: A hierarchical and fast trajectory planning framework in 3d cluttered environments,","venue":null,"work_id":"a051620e-b9d1-4115-924a-ce80221f5e73","year":2025},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.770107Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:f78c0efaaa7d0ad00bfcad58389af346d23283c49156d97caee76e12d02f6763","observation_id":"752b297c-ce9b-4d4a-9cd7-5e567db1c37b","resolution":{"observed_at":"2026-08-08T13:32:35.077413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:32:35.053020Z","title":"A comprehensive survey and tutorial on smart vehicles: Emerging technologies, security issues, and solutions using machine learning,","venue":null,"work_id":"0fa217de-28fb-46a5-a470-0a98194837c6","year":2024},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.775276Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:ea8113f20617553c340aabf3ff0e3e7b6f989d816f919eb0f4f098e4c57d92f3","observation_id":"f9188d0d-f634-4b2c-9893-e66975f53fec","resolution":{"observed_at":"2026-08-08T13:32:35.060469Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:32:35.037040Z","title":"Lasso-based detection and identification of actuator integrity attacks in remote control systems,","venue":null,"work_id":"5fd1fed4-cf3e-41d0-9ae9-30d13fedaa88","year":2023},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.780472Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:d636144b40872dc9ad263d352d75ba97f58184e75afb8520fec8fae4c947bd73","observation_id":"a862bf83-7961-4f0b-b433-a01b5502639c","resolution":{"observed_at":"2026-08-08T13:32:35.042249Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:32:35.020477Z","title":"Learning-based dos attack power allocation in multiprocess systems,","venue":null,"work_id":"9ed8fbf8-e3a6-4c00-9e29-a6c1c7de30a9","year":2022},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.786007Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:00dc04cdd372f6ab3a46f1a96701b78a194d44d493f118afb5a76e23f4469f29","observation_id":"4c6f8e9b-d23e-4fe1-b5fa-6a55e161fffb","resolution":{"observed_at":"2026-08-08T13:32:35.025927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:32:35.001196Z","title":"A secure robot learning framework for cyber attack scheduling and countermeasure,","venue":null,"work_id":"b9a05377-fcb0-40c8-8d2e-2d7a4e1726a0","year":2023},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.791531Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:3a0123cf3c644e01ca4f29e971eabd5f26c41bb301250196a069f5f6bdeade0a","observation_id":"08cb7a8e-56c9-4b79-8e0b-cef6ce29e29c","resolution":{"observed_at":"2026-08-08T13:32:35.007253Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:32:34.984326Z","title":"Optimal data injection attacks in cyber- physical systems,","venue":null,"work_id":"aea5d0f5-19ce-4ccb-8a58-0924a4aad899","year":2018},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.797360Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:53b530411ea4b345a951a5fc56499af4dac38bba0a58681eff2b83265c2ae764","observation_id":"82b89981-6050-4506-a27c-3ea50d230424","resolution":{"observed_at":"2026-08-08T13:32:34.989617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:32:34.967397Z","title":"A class of optimal switching mixed data injection attack in cyber-physical systems,","venue":null,"work_id":"17b58acd-07fe-4e22-804c-627d1f4c8a5f","year":2021},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.803830Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:28b2635c534dfda9d21c29096892e92d7e18c6d88d0dcd5146bf12a5e0215e96","observation_id":"5b5fcb52-e2d7-4cec-8a11-d93dcf5fec5e","resolution":{"observed_at":"2026-08-08T13:32:34.972707Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-08T13:32:34.950737Z","title":"Secure pose estimation for autonomous vehicles under cyber attacks,","venue":null,"work_id":"c5f9291c-8a88-4378-a5f9-36a5017c712d","year":2019},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.809263Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:9ac688a68b38ad2611e39d39aca180ec4c6b93e3caacf25e454b8c065d49bc0d","observation_id":"b9680db9-2317-4bcf-ae62-e094c3739aeb","resolution":{"observed_at":"2026-08-08T13:32:34.955949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.00707","last_updated":"2024-06-14T09:15:22Z","snapshot_observed_at":"2026-08-10T16:26:28.658474Z","submitted_at":"2024-06-02T11:15:03Z","title":"QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs","version":2},"cited_work":{"arxiv_id":"2406.00707","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.00707","snapshot_observed_at":"2026-08-08T13:32:34.911526Z","title":"QUADFormer: Learning-based Detection of Cyber Attacks in Quadrotor UAVs","venue":"cs.RO","work_id":"11aa9ca7-f7e2-430a-8007-e873c2561981","year":2024},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.814326Z"},"links":{"cited_paper":"/paper/2406.00707","citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:402176c8c39380f386d0147aa6a6ca89ee9e0eb07cfd6d091407f1ecc8626754","observation_id":"5f758e9c-3c59-4146-9661-9cbc0f3c2c75","resolution":{"observed_at":"2026-08-08T13:32:34.920859Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-07-20T02:32:33Z","title":"Proximal Policy Optimization Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.06347","snapshot_observed_at":"2026-08-08T13:32:34.820612Z","title":"Proximal policy optimization algorithms,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.820612Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:3497c02fa787955bcc6773bdc8e56266ce5340280212329bb3378f14a507c7df","observation_id":"7c1f55d0-4213-497b-9718-c3a874038169","resolution":{"observed_at":"2026-08-08T13:32:34.820612Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05905","last_updated":"2019-01-29T12:10:47Z","snapshot_observed_at":"2026-08-01T15:24:35.515954Z","submitted_at":"2018-12-13T04:44:29Z","title":"Soft Actor-Critic Algorithms and Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.05905","snapshot_observed_at":"2026-08-08T13:32:34.827682Z","title":"Soft actor-critic algorithms and applications,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.827682Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:7601290b87f1f78963f2d0c661b28912be9999eacedd8299401259e1c9510a93","observation_id":"83151a41-a9e7-4ef0-83b4-16c16938dc22","resolution":{"observed_at":"2026-08-08T13:32:34.827682Z","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":"2026-08-08T13:32:34.933968Z","title":"Simultaneous stabilization and tracking of nonholonomic mobile robots: A lyapunov-based ap- proach,","venue":null,"work_id":"31671f19-52f4-4f04-9b2b-eda4b8cd850a","year":2015},"citing_paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T13:32:34.833636Z"},"links":{"citing_paper":"/paper/2502.07839"},"observation_digest":"sha256:4a18ec5c1baa4a9e13c9c3d3c6fe344921387bcdfc28743b5db7f4d33003c23b","observation_id":"28beccdf-8fc7-4ed0-a6a2-d616988d0e57","resolution":{"observed_at":"2026-08-08T13:32:34.939363Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.07839","last_updated":"2025-02-11T03:01:05Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-10T04:34:30.388073Z","submitted_at":"2025-02-11T03:01:05Z","title":"Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement Learning"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":1,"verified_fuzzy":10},"total_outbound_references":13},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2502.07839."}