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Paper Citation Record · LEDGER

Entity-based Reinforcement Learning for Autonomous Cyber Defence

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.17647.

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

pith.paper-citation-record.v1
2410.17647 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:44:17.696777Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T15:51:44.451994Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation eb798a59-5070-442c-a80e-a9b1d4c8b54d · inbound

Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems cites this paper.

Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems Entity-based Reinforcement Learning for Autonomous Cyber Defence

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-12T20:44:17.696777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T20:44:17.696777Z digest=sha256:38cc95388a390864000618901ca7e804b892ab694f845d61207873e17eec53fd

Observation d1c4347b-c5dc-4ee3-97fb-4b2407c1f608 · inbound

Self-Adaptive Multi-Agent LLM-Based Security Pattern Selection for IoT Systems cites this paper.

Self-Adaptive Multi-Agent LLM-Based Security Pattern Selection for IoT Systems Entity-based Reinforcement Learning for Autonomous Cyber Defence

Reference 59

Resolution
verified exact
arxiv_id, observed 2026-05-11T15:51:44.460147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-09T19:04:48.453055Z digest=sha256:639c0e137c7606c66d9a2ce53ad24ea7324ef03d403bff34abb6d85c7967593d