Paper Citation Record · LEDGER

COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2410.07959.

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

pith.paper-citation-record.v1
2410.07959 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:06:54.930734Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T09:57:00.691632Z

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 0efd86a3-2cc2-4eef-ab17-b0890489515e · inbound

Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation cites this paper.

Can We Trust AI Benchmarks? An Interdisciplinary Review of Current Issues in AI Evaluation COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-08T15:06:54.930734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18968b68-ef8f-4c59-8ec1-69467b5a8c12 · inbound

A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards cites this paper.

A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 232

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:46:36.978285Z

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 d12764bc-068e-4d6b-b48e-4ce9e13ea709 · inbound

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation cites this paper.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:44:31.174824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ca1663f-e8b9-4ce7-9873-268c1456183e · inbound

Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments cites this paper.

Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T21:45:01.720588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 232edc08-05fc-4859-ae8c-82d5204efa6b · inbound

AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models cites this paper.

AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T12:36:22.390278Z

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 7bc503b2-076e-41f3-ab3b-72eaf90a0254 · inbound

Swiss-Bench 003: Evaluating LLM Reliability and Adversarial Security for Swiss Regulatory Contexts cites this paper.

Swiss-Bench 003: Evaluating LLM Reliability and Adversarial Security for Swiss Regulatory Contexts COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:35:48.818939Z

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 81fd9744-4afa-4e10-bd14-8bb553e7aa52 · inbound

Meta-Benchmarks for Financial-Services LLM Evaluation cites this paper.

Meta-Benchmarks for Financial-Services LLM Evaluation COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-03T14:18:22.699375Z

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 4d7027e0-d598-417e-b515-4306f895bdd8 · inbound

Reverse Engineering Compliance: A Dual-Graph Verification Framework for Auditing Legacy IT Security Concepts cites this paper.

Reverse Engineering Compliance: A Dual-Graph Verification Framework for Auditing Legacy IT Security Concepts COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-10T09:57:00.693221Z

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 4e9ca873-9a3b-4e68-ba1c-8295710a7627 · inbound

Do Generative AI Assistants Respect robots.txt? Tracing Web Access Beyond Visible Answers cites this paper.

Do Generative AI Assistants Respect robots.txt? Tracing Web Access Beyond Visible Answers COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T02:07:30.687637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.