{"as_of":"2026-08-17T18:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32e12ed2a8ec487ba79113d818ea9614087df0bc0924ec4dedb020a02bef1ca7","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:31:51.330427Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2507.02969/citation-record","integrity":"/paper/2507.02969/integrity","json":"/paper/2507.02969/citation-record.json","paper":"/paper/2507.02969"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.10511","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:31:53.519275Z","title":"Cyber-security and reinforcement learning — A brief survey","venue":null,"work_id":"5d8ee3a8-a7a5-4e0c-859c-82fe859f4a53","year":2022},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.209263Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:050cc43ee0f1385ef0c1c6ac2ad42b1137ce657da9d6c74611ebbc3d09598e3e","observation_id":"b916c989-a1ad-40ea-bb76-404ef00cfa2b","resolution":{"observed_at":"2026-08-06T21:31:53.607008Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2016.78567","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:31:53.235811Z","title":"Automated penetration testing based on a threat model","venue":null,"work_id":"69e5c916-47a7-43d6-bc3d-763cb1a3474e","year":2016},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.215147Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:ddc38d51a6cb5b382104c928946e3188a550842f880d996f799e9f2bf4147376","observation_id":"1a976ebd-3014-43ef-b89d-9b7ace11c9d7","resolution":{"observed_at":"2026-08-06T21:31:53.315486Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T21:31:51.220039Z","title":"Emergence of Scaling in Random Networks","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.220039Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:351448c4090b5489462272e156e510c20e33601f293845478d70562ff3915637","observation_id":"b50cbf14-f74e-40a0-bd34-f55833b567e8","resolution":{"observed_at":"2026-08-06T21:31:51.220039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.13478","last_updated":"2021-05-02T16:16:03Z","snapshot_observed_at":"2026-08-12T23:06:05.148534Z","submitted_at":"2021-04-27T21:09:51Z","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.13478","snapshot_observed_at":"2026-08-06T21:31:51.228923Z","title":"Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.228923Z"},"links":{"cited_paper":"/paper/2104.13478","citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:d8921a8d7b514626ce5c77f17be46ebc9c661b31a01ecc8c33bb9f40eb4ed122","observation_id":"946ff248-9171-4296-a375-fc08f3b04a29","resolution":{"observed_at":"2026-08-06T21:31:51.228923Z","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":"2026-08-06T21:31:51.233866Z","title":"CYBERSHIELD : A Competitive Simulation Environment for Training AI in Cybersecurity","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.233866Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:27104f25ff06687e15e16135cbc17e61a5f87e0bd665b21584370ac2696e1b17","observation_id":"60d25505-05de-4ce5-ba4f-f063bcbd2134","resolution":{"observed_at":"2026-08-06T21:31:51.233866Z","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":"10.5220/0006197105590566","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Adversarial Reinforcement Learning in a Cyber Security Simulation","venue":null,"work_id":"c6f8c476-a468-45c5-beb1-b03bf668a1b1","year":2017},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.238479Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:fd401eb689ba80d3ad996b034e69866391690b22056760cb2e02e509d02bf676","observation_id":"bd00d303-045b-4d93-a907-38cd5cd365ad","resolution":{"observed_at":"2026-08-06T21:31:51.530619Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.16324","last_updated":"2024-10-18T11:04:07Z","snapshot_observed_at":"2026-08-16T13:08:04.569437Z","submitted_at":"2024-10-18T11:04:07Z","title":"CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.16324","snapshot_observed_at":"2026-08-06T21:31:51.243605Z","title":"CybORG ++: An Enhanced Gym for the Development of Autonomous Cyber Agents , October 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.243605Z"},"links":{"cited_paper":"/paper/2410.16324","citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:19e65fff4b43152c720a84849d427dd93e70dfd65725b72057710aa35c3f5d26","observation_id":"dfdec8b9-e949-4d1c-8a6a-b1fc78732014","resolution":{"observed_at":"2026-08-06T21:31:51.243605Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07124","last_updated":"2022-08-04T18:27:57Z","snapshot_observed_at":"2026-08-16T22:04:28.910104Z","submitted_at":"2021-08-16T14:45:50Z","title":"Using Cyber Terrain in Reinforcement Learning for Penetration Testing","version":2},"cited_work":{"arxiv_id":"2108.07124","doi":null,"metadata_source":"pith","pith_arxiv_id":"2108.07124","snapshot_observed_at":"2026-08-06T21:31:52.894360Z","title":"Using Cyber Terrain in Reinforcement Learning for Penetration Testing","venue":"cs.LG","work_id":"3507ccc6-0bfb-4b33-b6db-1da4886c1b6f","year":2021},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.248996Z"},"links":{"cited_paper":"/paper/2108.07124","citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:715a865749190fe59750bbb0c5c5216df655b5567a8dc0abc24307a8118b07bd","observation_id":"cb3db847-1225-45f8-b26a-d630b8b239c9","resolution":{"observed_at":"2026-08-06T21:31:52.981788Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.86115","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:31:52.773614Z","title":"Ghanem and Thomas M","venue":null,"work_id":"82790d99-7cf5-4122-8b20-c3fe08606651","year":2018},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.253538Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:fc58976087a90c6ea0d2e0dfe01876746a78da9d4383f70ff444034f757e1c6b","observation_id":"93b09098-bfa1-4f1b-9b9d-d23088c23361","resolution":{"observed_at":"2026-08-06T21:31:52.814174Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/info11010006","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Ghanem and Thomas M","venue":"Information","work_id":"49ddb91a-6cd5-441a-96a4-4eb493cdc57e","year":2020},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.257924Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:c42f8497d4d71b98419ca813413f36f2ddea31159b74883ef794401b70bc8d5f","observation_id":"9dc4a3bf-7dec-40e9-b5a8-5d34482c9165","resolution":{"observed_at":"2026-08-06T21:31:51.500561Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.05905","last_updated":"2019-01-29T12:10:47Z","snapshot_observed_at":"2026-08-17T07:51:41.384508Z","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":"2026-08-06T21:31:51.262249Z","title":"Soft Actor - Critic Algorithms and Applications , January 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.262249Z"},"links":{"cited_paper":"/paper/1812.05905","citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:52e16909708d387e53bb4dd3f1c76fc51d72f98a98f8f8537b667cd226c47cc5","observation_id":"a90084e8-b6b8-4eb9-be1d-05072f956d2b","resolution":{"observed_at":"2026-08-06T21:31:51.262249Z","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":"2026-08-06T21:31:51.267267Z","title":"Automated Penetration Testing Using Deep Reinforcement Learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.267267Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:5e9ce52aa72346b976965df38a162263a4ee6ec5bfaecd55ec4d2733caabcab6","observation_id":"769a2449-985a-441a-8f31-9d48c72c4f96","resolution":{"observed_at":"2026-08-06T21:31:51.267267Z","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":"2026-08-06T21:31:54.146183Z","title":"Prokopczyk, Yusra Al-Khazraji, Marek Matuszak, Mário Pinto, Edgar Marques, and Evridiki Ntagiou","venue":null,"work_id":"0b19dac2-d267-4427-89d2-a08a03e87df4","year":2023},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.271438Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:351d9af1181240c1743b881490977abb66a7269da684d7d5068bb35152ec2f7a","observation_id":"5d731883-4939-4a8d-b240-fa2e638de76f","resolution":{"observed_at":"2026-08-06T21:31:54.226859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.5602","last_updated":"2013-12-19T16:00:08Z","snapshot_observed_at":"2026-08-17T17:19:33.060917Z","submitted_at":"2013-12-19T16:00:08Z","title":"Playing Atari with Deep Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.5602","snapshot_observed_at":"2026-08-06T21:31:51.275967Z","title":"Playing Atari with Deep Reinforcement Learning , December 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.275967Z"},"links":{"cited_paper":"/paper/1312.5602","citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:df8812fe88754a19db2b583b8e83ec8d8639ddefc040f00b1eed380d27d2f0c7","observation_id":"9edae91f-20e1-42af-b198-2bef61896971","resolution":{"observed_at":"2026-08-06T21:31:51.275967Z","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":"2026-08-06T21:31:51.281568Z","title":"PenGym : Realistic training environment for reinforcement learning pentesting agents","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.281568Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:32aa0ae4bd12a50acfce702f9eaea50606e3694813e2fecdad92ab7b24c27340","observation_id":"48358497-3f10-44ae-bd30-3d3d220c8014","resolution":{"observed_at":"2026-08-06T21:31:51.281568Z","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":"2026-08-06T21:31:51.285956Z","title":"Multiobjective Tree - Structured Parzen Estimator","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.285956Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:90b8cf58d0bda3b8f33995a72515323e79dd35e807509f293e52b8357bc28c36","observation_id":"a6d613fc-d824-4faf-913f-978fa8b8321b","resolution":{"observed_at":"2026-08-06T21:31:51.285956Z","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":"2024.61113","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:31:52.283821Z","title":"An AI - Based Approach for Automating Penetration Testing","venue":null,"work_id":"7491de17-7d7d-422f-965e-bf7751cf9a6f","year":2024},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.290903Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:fcbdc3b4a61de150b172d5cb718c65b98d38a7204c657f01a74142d91c337adf","observation_id":"3c791f46-1a5a-4a9c-b22c-8875137b69e6","resolution":{"observed_at":"2026-08-06T21:31:52.413680Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T21:31:51.295345Z","title":"Bahaa-Eldin, and Zt Fayed","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.295345Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:0cdfb1af26235215c45c1fd0197f150835ce715e574d76e73ef2adae14737b69","observation_id":"c198d644-a8a7-45a7-a1af-93250f8625ca","resolution":{"observed_at":"2026-08-06T21:31:51.295345Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.06347","last_updated":"2017-08-28T09:20:06Z","snapshot_observed_at":"2026-08-15T20:26:32.102285Z","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":"2026-08-06T21:31:51.299782Z","title":"Proximal Policy Optimization Algorithms , August 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.299782Z"},"links":{"cited_paper":"/paper/1707.06347","citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:44e4bb961c38fb4a5eca356aee44adf6e9999deb8dfdf8df32afc4b389c2e584","observation_id":"cf9cc994-bb72-416b-9731-21b131cb2ce7","resolution":{"observed_at":"2026-08-06T21:31:51.299782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1905.05965","last_updated":"2019-05-15T06:18:14Z","snapshot_observed_at":"2026-08-14T16:32:07.314845Z","submitted_at":"2019-05-15T06:18:14Z","title":"Autonomous Penetration Testing using Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"1905.05965","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.05965","snapshot_observed_at":"2026-08-06T21:31:51.985700Z","title":"Autonomous Penetration Testing using Reinforcement Learning","venue":"cs.CR","work_id":"a7a50987-3e4a-4729-b131-2eff44d227db","year":2019},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.307799Z"},"links":{"cited_paper":"/paper/1905.05965","citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:6b1cac97fe625720b9881681b93f50436a63fffca2519c3db3ba5fb54b684020","observation_id":"98bc48af-4cb4-42e1-aa2a-2798c6e59b98","resolution":{"observed_at":"2026-08-06T21:31:52.052781Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-06T21:31:53.914681Z","title":"POMDP + Information - Decay : Incorporating Defender 's Behaviour in Autonomous Penetration Testing","venue":null,"work_id":"57a7a876-2f60-4c08-bef2-a914ffc8c18f","year":2020},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.312909Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:ef247f21bfbc931fd7ce24153a2852f30a19e6debaea5f812e6a7ee2db65a2eb","observation_id":"b0ead1a1-27f0-4494-af25-ee204abbeda7","resolution":{"observed_at":"2026-08-06T21:31:54.036382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.30419","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T21:31:51.797503Z","title":"Hameed, and Min Xu","venue":null,"work_id":"43bc3972-6891-4882-bb5e-fce88891985b","year":2020},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.317404Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:a4b1eac848d1019ebb99ef8579f79079b006e869f61bed2d7981f464e8d06c0e","observation_id":"0b71a4f9-6eb2-4e88-8dc1-0f1f4c656ff0","resolution":{"observed_at":"2026-08-06T21:31:51.883280Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.01815","last_updated":"2017-12-05T18:45:38Z","snapshot_observed_at":"2026-08-14T20:06:37.819179Z","submitted_at":"2017-12-05T18:45:38Z","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.01815","snapshot_observed_at":"2026-08-06T21:31:51.321684Z","title":"Mastering Chess and Shogi by Self - Play with a General Reinforcement Learning Algorithm , December 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.321684Z"},"links":{"cited_paper":"/paper/1712.01815","citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:ea959df418589bbe18d3e6fd4da706839fe3605caf14ebfa3f7b68ac02ee41fd","observation_id":"deed1063-266c-4cc0-a404-41faaf89ca6f","resolution":{"observed_at":"2026-08-06T21:31:51.321684Z","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":"2026-08-06T21:31:51.326203Z","title":"Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.326203Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:3e7249aa050c50f6357b8b8cdc3bcd34800d7b6de73cc60fdb476f2bb1de8374","observation_id":"dc0a2392-002f-4140-9c66-963613e04a08","resolution":{"observed_at":"2026-08-06T21:31:51.326203Z","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":"2026-08-06T21:31:53.698994Z","title":"Sutton and Andrew G","venue":null,"work_id":"d96becb6-2916-41a7-9e7f-f12a1bedb899","year":2018},"citing_paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T21:31:51.330427Z"},"links":{"citing_paper":"/paper/2507.02969"},"observation_digest":"sha256:b42eefd446a671bab5b59b62e4aefcb2ebc6c75ed1ed2ff7afae89497e5b8171","observation_id":"50f84214-c31c-4fd1-a2b2-5b4991db34a9","resolution":{"observed_at":"2026-08-06T21:31:53.829629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.02969","last_updated":"2025-06-30T15:06:17Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-16T18:27:23.350861Z","submitted_at":"2025-06-30T15:06:17Z","title":"Reinforcement Learning for Automated Cybersecurity Penetration Testing"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":13,"verified_exact":5,"verified_fuzzy":3},"total_outbound_references":25},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.02969."}