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Paper Citation Record · LEDGER

The Path To Autonomous Cyber Defense

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.10788.

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

pith.paper-citation-record.v1
2404.10788 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:56:30.650166Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T08:15:33.915688Z

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 b65e0d08-fa42-40a2-a85e-26282f48ff3b · inbound

Agentic AI and the Cyber Arms Race cites this paper.

Agentic AI and the Cyber Arms Race The Path To Autonomous Cyber Defense

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-08T14:56:30.650166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:56:30.650166Z digest=sha256:6e96967c8d710581dfa854b051ce058fda880c1dfd7a673969875406bf781306

Observation 98fbf916-86dd-4dfa-8cb7-a4cbd774fa92 · inbound

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications cites this paper.

Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications The Path To Autonomous Cyber Defense

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T14:10:39.779777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:10:39.779777Z digest=sha256:c3fe937a9d9da8b70ccef586fc8be1d7639153fa36685d9b20e017144b64d6d9

Observation 1e639d4a-d4ca-4827-8aa7-4a9f740c975a · inbound

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation cites this paper.

FATHOMS-RAG: A Framework for the Assessment of Thinking and Observation in Multimodal Systems that use Retrieval Augmented Generation The Path To Autonomous Cyber Defense

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-25T08:15:33.918465Z

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.

source=pdf_text observed=2026-05-25T08:13:05.328746Z digest=sha256:b66c8ccbc2fafc6ee458fbf6bec87e85460a00b92a32f5f96b7a1790ec3a4f69