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Paper Citation Record · LEDGER

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence

As of 23 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2411.17585.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.17585 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:02:40.889516Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:03:18.519635Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-09T21:03:18.690332Z

Reference resolution

33 of 33 outbound references displayed

  • verified exact4
  • verified fuzzy7
  • unresolved20
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

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Outbound references

Observation 01af10ca-db47-4a3d-9422-510d4794a23b · outbound

This paper cites Automated Cyber Defence: A Review.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Automated Cyber Defence: A Review

Reference 1

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source=pdf_text observed=2026-08-12T12:02:40.769760Z digest=sha256:e2185d7811154fd73b2ea4a842cfbb184ff688eeaad16299c76420346af4c865

Observation cf817c07-1578-4321-bb8b-e658c2ba2714 · outbound

This paper cites Kott et al.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Kott et al

Reference 2

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source=pdf_text observed=2026-08-12T12:02:40.774482Z digest=sha256:c292e16bfc659f03d11fcc0d81e03a71681852f9bb3f90ba8eedeece2b9091ca

Observation 892e682b-a2b9-40f3-ae95-2238592d501b · outbound

This paper cites Quantitative Measurement of Cyber Resilience: Modeling and Experimentation.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Quantitative Measurement of Cyber Resilience: Modeling and Experimentation

Reference 3

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local_arxiv, observed 2026-08-12T12:02:41.364315Z

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source=pdf_text observed=2026-08-12T12:02:40.777911Z digest=sha256:05695e91590aee7a61a25512b9aa70c5e4b05bf03e0744fc53919fa527fc9d64

Observation 95d8713c-43bf-4122-8004-a6243170fa87 · outbound

This paper cites CybORG: A Gym for the Development of Autonomous Cyber Agents.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence CybORG: A Gym for the Development of Autonomous Cyber Agents

Reference 4

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source=pdf_text observed=2026-08-12T12:02:40.781736Z digest=sha256:3957abba4ab94a80927c7ded357f381f9e340b5e9aaefe050267d5b72178fcb4

Observation eb214e28-f38d-45e6-a61f-c64fd81844aa · outbound

This paper cites Bates, V.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Bates, V

Reference 5

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source=pdf_text observed=2026-08-12T12:02:40.785269Z digest=sha256:af10878e2919d350e0367f073bd7844219265898bfc7ee3b1d8042940388bdaf

Observation 4a71a4ce-bcb0-4a97-9437-6d8bfb60b7e5 · outbound

This paper cites Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies

Reference 6

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source=pdf_text observed=2026-08-12T12:02:40.789669Z digest=sha256:180a7f72dda2a7e581b4ad439767784d4ce2090e350d986ecb00758e213bfe20

Observation 04a436e4-309e-4d26-8610-7c0bf82aeac3 · outbound

This paper cites an unresolved cited work.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Unresolved cited work

Reference 7

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source=pdf_text observed=2026-08-12T12:02:40.793862Z digest=sha256:2adf404c7b1d2edede517511595ea2440bfc5408a968c918cc4e2a3eb5dbb2d2

Observation 763c1254-3fa7-49bb-b5bc-db2148bc13f0 · outbound

This paper cites Abels, D.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Abels, D

Reference 8

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source=pdf_text observed=2026-08-12T12:02:40.798081Z digest=sha256:7762cc82e56b6a5c5d987ecb35a2236f01381ec44f0d2d391677acab480402a8

Observation b773dd45-7931-41f0-9316-8f811958fd54 · outbound

This paper cites Liu and X.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Liu and X

Reference 9

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source=pdf_text observed=2026-08-12T12:02:40.801326Z digest=sha256:1493ddad1e725a20aaeae45614dfee492a4cfbf7d700686df9e0291e338a625a

Observation 3264163e-14d8-4c9c-afbe-97810a7b881f · outbound

This paper cites Van Moffaert, M.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Van Moffaert, M

Reference 10

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source=pdf_text observed=2026-08-12T12:02:40.805289Z digest=sha256:d161b82e1c9f58c753446cc84a0fed63f1d835a4836f09e4988c87aaa37e6148

Observation 256efb16-5e6c-409a-8765-76e10f4cb953 · outbound

This paper cites an unresolved cited work.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Unresolved cited work

Reference 11

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source=pdf_text observed=2026-08-12T12:02:40.808500Z digest=sha256:c27e490991d419cd3099c9b63ad1ef532a030926756f51dc89d1b5dc1579af35

Observation b06a6f86-f1bf-4fe4-ac8d-9bfc492824e6 · outbound

This paper cites Van Moffaert and A.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Van Moffaert and A

Reference 12

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source=pdf_text observed=2026-08-12T12:02:40.816044Z digest=sha256:96d115cd56c82a1e946b8f6e69a92ff3582a51eeabe4d2320d76f932fd2f2465

Observation 12bf7401-296a-4991-b541-9284f1fc115e · outbound

This paper cites Pareto Conditioned Networks.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Pareto Conditioned Networks

Reference 13

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source=pdf_text observed=2026-08-12T12:02:40.819657Z digest=sha256:db4f6018c8660e1c2847440fc6808cc854d8e25f62a95cfd3746f47b2d2a9b7f

Observation 63bef60f-fe8e-48a0-a249-9dda6b632686 · outbound

This paper cites A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

Reference 14

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source=pdf_text observed=2026-08-12T12:02:40.822995Z digest=sha256:25ff9157f962ede3ef50932b6ba74995f40cc5eba5852b9b0e655572e04ae5f9

Observation 9bde770b-5c1d-4a85-90ab-535729f61e4a · outbound

This paper cites Skalse, L.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Skalse, L

Reference 15

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T12:02:40.826949Z digest=sha256:4e6dddf1812c4c00fb3cf7d5078fbebcc404fff5b8ff0aa4b2ee11e81b4d5579

Observation a7b524bd-18ef-47b5-a4da-80b45e61412f · outbound

This paper cites Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation

Reference 16

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source=pdf_text observed=2026-08-12T12:02:40.834038Z digest=sha256:64c0d869c40ac1a96cabfb989c01b74718a82c10e147b5cfe043184da4364b5e

Observation 16ecd45c-50ba-44d0-a281-515f813cbec7 · outbound

This paper cites Schrittwieser et al., ‘Mastering Atari, Go, chess and shogi by planning with a learned model’, Nature, vol.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Schrittwieser et al., ‘Mastering Atari, Go, chess and shogi by planning with a learned model’, Nature, vol

Reference 17

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source=pdf_text observed=2026-08-12T12:02:40.838110Z digest=sha256:11054d8385282df49421537176f2eb16e53778b88bc47b37f27e701f05470556

Observation 9d7476cf-83af-4048-aa22-249218d4307a · outbound

This paper cites Proximal Policy Optimization Algorithms.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Proximal Policy Optimization Algorithms

Reference 18

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source=pdf_text observed=2026-08-12T12:02:40.841532Z digest=sha256:260505a4e7b3c1740a4a5913fb08b70704397eced1196d40872692f02e6edd74

Observation 94ef4ee5-0439-4c26-8dab-1d6763ecea40 · outbound

This paper cites An Optimistic Perspective on Offline Reinforcement Learning.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence An Optimistic Perspective on Offline Reinforcement Learning

Reference 19

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Observation feebd25d-a516-48be-a6d5-2df793e20504 · outbound

This paper cites Decision Transformer: Reinforcement Learning via Sequence Modeling.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Decision Transformer: Reinforcement Learning via Sequence Modeling

Reference 20

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source=pdf_text observed=2026-08-12T12:02:40.849358Z digest=sha256:f36d6e6e9141520f981b70cb900bd4b6944b9789a3e11be56c1f76c30aeaeeed

Observation 2cba7dfe-1203-4e7b-b013-e613b43c7ed3 · outbound

This paper cites The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Models

Reference 21

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source=pdf_text observed=2026-08-12T12:02:40.853751Z digest=sha256:5ef60d427d914f229c036a10fb18b30d5bc25c65a2532a0e5d19a7ac19202bb1

Observation 1227aaeb-7e2e-44dc-a4bd-642ecb8ceaa4 · outbound

This paper cites Toward Diverse Text Generation with Inverse Reinforcement Learning.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Toward Diverse Text Generation with Inverse Reinforcement Learning

Reference 22

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T12:02:40.857184Z digest=sha256:62885442f9550be2e9caf418bf8b637d0d593e6e1104eae190882b0d15ced0b1

Observation 651534b6-3705-4498-89f3-30cf016bb184 · outbound

This paper cites Foley, K.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Foley, K

Reference 23

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source=pdf_text observed=2026-08-12T12:02:40.861136Z digest=sha256:a3fdac917f456db63d5f568e6060f5378a1c648fb39de5a3c85dca34d1c343c9

Observation 11c87942-2cec-4280-8348-1e38a379147c · outbound

This paper cites Inroads into Autonomous Network Defence using Explained Reinforcement Learning.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Inroads into Autonomous Network Defence using Explained Reinforcement Learning

Reference 24

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source=pdf_text observed=2026-08-12T12:02:40.864353Z digest=sha256:6b25d9ef92c8ae2d69603c41dc0e090aca60b91313bf11a92bc76f50bfbf2a35

Observation ddac07b7-de97-4c5d-bf04-f3891b21a152 · outbound

This paper cites Acuto and S.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Acuto and S

Reference 25

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T12:02:40.867851Z digest=sha256:e97e838288341c739017108a466b7783820201a057441ef9b6d5ddb1b56d0066

Observation 1070fc85-6b6e-411e-a42a-ad6303a5add4 · outbound

This paper cites Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach

Reference 26

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source=pdf_text observed=2026-08-12T12:02:40.871217Z digest=sha256:22494efa11a6aab8d8fd43ab8ceea463177d12b5e0995f260dd04f3af259620e

Observation 7fa06854-ffcd-481b-846c-e2cf00d82606 · outbound

This paper cites an unresolved cited work.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Unresolved cited work

Reference 27

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source=pdf_text observed=2026-08-12T12:02:40.874744Z digest=sha256:3810b48a5797baeac1f199d11927dc8110601abb9cd41ceb80acda948482ecd9

Observation f4021c3a-54ad-4c1d-aca4-f2b21361128e · outbound

This paper cites an unresolved cited work.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Unresolved cited work

Reference 28

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source=pdf_text observed=2026-08-12T12:02:40.878606Z digest=sha256:4d13569c2c2c48555f759861de7a087ef7711810dbbda4d5dc2b7dc93501f4ea

Observation 37de4480-68c5-44a0-8ce1-07f5e5704f72 · outbound

This paper cites The Fairness-Accuracy Pareto Front.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence The Fairness-Accuracy Pareto Front

Reference 29

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source=pdf_text observed=2026-08-12T12:02:40.882555Z digest=sha256:552b63d88b3c5d1873d022859d3c666294970d802bfe1971139c527f3cdd1f07

Observation 665db456-95e9-4dce-a217-8390a05d83f6 · outbound

This paper cites You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments

Reference 30

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source=pdf_text observed=2026-08-12T12:02:40.885992Z digest=sha256:07441ef2e6de496657f778d23b291c88149a719f85fd73f3836dab9cb0a30d5a

Observation 090b6887-ca8f-4a52-af20-a8e3c96b99a3 · outbound

This paper cites gamma": 0.99.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence gamma": 0.99

Reference 31

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source=pdf_text observed=2026-08-12T12:02:40.889516Z digest=sha256:d33da8be45b1051c261687d51f22060f9da66e9c21eda102f4fe5a38af918f3b

Observation ee2db359-9aac-4b5a-bcef-6a4019ef8d7e · outbound

This paper cites Available: https://proceedings.mlr.press/v119/xu20h.html.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Available: https://proceedings.mlr.press/v119/xu20h.html

Reference 2024

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T12:02:40.811588Z digest=sha256:43ba69ccd32e9f9406ab3f38d48b3718cc609ed0b4bdf03502ba037791feee51

Observation f51862d9-a0ab-4142-9979-4bf77223f7da · outbound

This paper cites an unresolved cited work.

Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence Unresolved cited work

Reference 3436

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source=pdf_text observed=2026-08-12T12:02:40.830768Z digest=sha256:fcc290a7c2732f5e272eafc032a8178a0f627d3fc97d683be51b0e91b6726033

Pith citing papers

Observation c09de5ba-2b2f-410e-87be-7013c6316f46 · inbound

An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents cites this paper.

An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents Multi-Objective Reinforcement Learning for Automated Resilient Cyber Defence

Reference 30

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local_arxiv, observed 2026-08-09T21:03:18.697595Z

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source=arxiv_source observed=2026-08-09T21:03:18.519635Z digest=sha256:d8812de9719794088c392435bea8d74d46e59b65a2a659d92b965e750c463de1