Paper Citation Record · LEDGER

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers

As of 8 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2506.22706.

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

pith.paper-citation-record.v1
2506.22706 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:05:11.207927Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T04:54:18.327034Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T04:57:04.544044Z

Reference resolution

27 of 27 outbound references displayed

verified exact1
verified fuzzy12
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 862c9737-04af-4a3c-9fab-e5c4382f1900 · outbound

This paper cites write newline.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:08.889000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17415263-7722-4575-bb38-61b58fb6fece · outbound

This paper cites Ae-ot: A new generative model based on extended semi-discrete optimal transport.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Ae-ot: A new generative model based on extended semi-discrete optimal transport

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:13.725949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dd370022-9348-4f60-afe3-ac090c1e4596 · outbound

This paper cites Ae-ot-gan: Training gans from data specific latent distribution.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Ae-ot-gan: Training gans from data specific latent distribution

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:13.620019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5326984e-9ff2-4dbf-b05c-3cb09a1c1768 · outbound

This paper cites an unresolved cited work.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:05:13.518690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 78a0536b-803c-4910-aad6-51963923cd31 · outbound

This paper cites Graph optimal transport for cross-domain alignment.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Graph optimal transport for cross-domain alignment

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:09.174066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 96a974b6-e6c2-4219-861f-5d0f932c8ec0 · outbound

This paper cites Prospective Artificial Intelligence Approaches for Active Cyber Defence.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Prospective Artificial Intelligence Approaches for Active Cyber Defence

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:05:11.494176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5a71cb84-f721-497b-8be4-210034b309de · outbound

This paper cites On the evolution of random graphs.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers On the evolution of random graphs

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:13.414266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 118b5cd6-e08c-4bd2-98a8-38ba36b3cd1b · outbound

This paper cites and Stadler, R.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers and Stadler, R

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:13.284098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation d00dd392-b865-465f-8bed-cb6fda893b1f · outbound

This paper cites Graph Convolutional Reinforcement Learning.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Graph Convolutional Reinforcement Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:09.547643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5405339-c12a-4500-9b59-a289016a1a63 · outbound

This paper cites On Autonomous Agents in a Cyber Defence Environment.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers On Autonomous Agents in a Cyber Defence Environment

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:09.643939Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f22bf344-8fc8-4efc-b27f-c35e7e028318 · outbound

This paper cites Variational Graph Auto-Encoders.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Variational Graph Auto-Encoders

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:09.787623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27879020-9e1f-4bdd-b570-4a21ad9096b1 · outbound

This paper cites an unresolved cited work.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:05:13.165912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 06a6d39d-51be-420f-983f-cf06ad6b1015 · outbound

This paper cites an unresolved cited work.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:05:13.088443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 49cf17c5-d670-47ef-9881-049cf0552420 · outbound

This paper cites and Kolter, J.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers and Kolter, J

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:12.948030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1c195201-eece-428c-8fc9-1978ebbd06d9 · outbound

This paper cites and Johnson, P.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers and Johnson, P

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:12.835082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c670904b-24bd-41b0-8f4a-d3db3b9c8c58 · outbound

This paper cites Machine learning for autonomous cyber defense.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Machine learning for autonomous cyber defense

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:12.721767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 73593902-45b1-4994-a514-95d5ebb0b27a · outbound

This paper cites Optimal transport for applied mathematicians.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Optimal transport for applied mathematicians

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:12.581373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2fb6124d-7ca5-448a-973f-f86c582002d3 · outbound

This paper cites Proximal Policy Optimization Algorithms.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Proximal Policy Optimization Algorithms

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:10.431660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52be52d7-1201-40f2-937e-feef95b79579 · outbound

This paper cites Optimal transport on discrete domains.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Optimal transport on discrete domains

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:12.367943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e57c6ea7-18ef-4fef-b936-3ad1db780a76 · outbound

This paper cites an unresolved cited work.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Unresolved cited work

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:10.590507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51e0c61a-4784-4761-8e65-cea61d2e3658 · outbound

This paper cites B., Silva, V.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers B., Silva, V

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:12.173016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1a8c2258-b086-43b8-8364-f6f9ce0f3cd6 · outbound

This paper cites Introduction to optimal transport.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Introduction to optimal transport

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:11.970687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 556ec7ad-97ff-40f7-9fee-5d6d5de3ac1a · outbound

This paper cites Attention is all you need.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Attention is all you need

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:10.867375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef09164d-de75-4602-9730-63da3e13df30 · outbound

This paper cites Optimal Transport for structured data with application on graphs.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Optimal Transport for structured data with application on graphs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:10.932266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed0ba7e8-7dd3-40d2-ae97-d187fc481157 · outbound

This paper cites an unresolved cited work.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:05:11.809258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8946f02e-5fcb-4b63-945e-19f9f7b151fe · outbound

This paper cites Z., and Li, L.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Z., and Li, L

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:05:11.647205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 77d575b2-a9a5-405c-a8f6-c5e2125dad3f · outbound

This paper cites Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34: 0 28877--28888, 2021.

General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers Do transformers really perform badly for graph representation? Advances in neural information processing systems, 34: 0 28877--28888, 2021

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:05:11.207927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation bbbebb24-b578-40df-9d5e-1064bec66911 · inbound

Adaptive Network Security Policies via Belief Aggregation and Rollout cites this paper.

Adaptive Network Security Policies via Belief Aggregation and Rollout General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-19T04:57:04.547057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.