@misc{nakajima2026activegraph,
  title         = {The Log is the Agent: Event-Sourced Reactive Graphs for Auditable, Forkable Agentic Systems},
  author        = {Nakajima, Yohei},
  year          = {2026},
  eprint        = {2605.21997},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2605.21997},
  note          = {Independent co-invention of the event-sourced log + deterministic replay architecture that JAMES T5 also implements; RAB measures this architectural class.}
}

@misc{wang2026evidencetrace,
  title         = {From Agent Traces to Trust: Evidence Tracing and Execution Provenance in {LLM} Agents},
  author        = {Wang, Yiqi and others},
  year          = {2026},
  eprint        = {2606.04990},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2606.04990},
  note          = {Survey of evidence tracing and execution provenance in LLM agents; its ``open challenges'' list explicitly names ``realistic execution-trace benchmarks'' alongside unified trace schemas, claim-level / semantic provenance, provenance-aware safety mechanisms, recovery-oriented evaluation, and privacy-aware audit infrastructure. RAB is a response to the execution-trace-benchmark item.}
}

@misc{aiact2024,
  title        = {Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act)},
  author       = {{European Parliament and Council}},
  year         = {2024},
  howpublished = {Official Journal of the European Union, L series},
  url          = {https://eur-lex.europa.eu/eli/reg/2024/1689/oj},
  note         = {Articles 10, 12, 19 anchor the RAB metrics. Effective 2026-08-02 per Article 113.}
}

@misc{w3cprov2013,
  title        = {{PROV-Overview}: An Overview of the {PROV} Family of Documents},
  author       = {Groth, Paul and Moreau, Luc},
  year         = {2013},
  howpublished = {W3C Working Group Note},
  url          = {https://www.w3.org/TR/prov-overview/},
  note         = {{\tt wasDerivedFrom} relation anchors RAB's PC metric.}
}

@misc{nist2023airmf,
  title        = {{Artificial Intelligence Risk Management Framework} (AI RMF 1.0)},
  author       = {{National Institute of Standards and Technology}},
  year         = {2023},
  howpublished = {NIST AI 100-1},
  url          = {https://doi.org/10.6028/NIST.AI.100-1}
}

@inproceedings{staufer2025auditcards,
  title     = {Audit Cards: Contextualizing {AI} Evaluations},
  author    = {Staufer, Leon and others},
  year      = {2025},
  eprint    = {2504.13839},
  archivePrefix = {arXiv},
  url       = {https://arxiv.org/abs/2504.13839}
}

@misc{casper2024blackbox,
  title         = {Black-Box Access is Insufficient for Rigorous AI Audits},
  author        = {Casper, Stephen and others},
  year          = {2024},
  eprint        = {2401.14446},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2401.14446}
}

@misc{airegbench2025,
  title         = {{AIReg-Bench}: Benchmarking Language Models That Assess AI Regulation Compliance},
  author        = {{AIReg-Bench authors}},
  year          = {2025},
  eprint        = {2510.01474},
  archivePrefix = {arXiv},
  url           = {https://arxiv.org/abs/2510.01474}
}

@misc{aiactevalbench2026,
  title         = {{AI Act Evaluation Benchmark}: An Open, Transparent, and Reproducible Evaluation Dataset for NLP and RAG Systems},
  author        = {{AI Act Evaluation Benchmark authors}},
  year          = {2026},
  eprint        = {2603.09435},
  archivePrefix = {arXiv},
  url           = {https://arxiv.org/abs/2603.09435}
}

@misc{lewis2020rag,
  title         = {Retrieval-Augmented Generation for Knowledge-Intensive {NLP} Tasks},
  author        = {Lewis, Patrick and others},
  year          = {2020},
  eprint        = {2005.11401},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2005.11401}
}

@misc{seo2026jamesv043,
  title        = {{PROJECT JAMES} v0.4.3 — {RAB} v0.1.1 (Replayable-Audit Benchmark) + Cycle $\gamma$ multi-hop arc closure},
  author       = {Seo, Jiwon},
  year         = {2026},
  howpublished = {Software, Zenodo},
  doi          = {10.5281/zenodo.20625533},
  url          = {https://doi.org/10.5281/zenodo.20625533},
  note         = {Source: \url{https://github.com/Hashevolution/James-RAG-Evol} (release tag {\tt v0.4.3}).}
}

@misc{eventsourcing,
  title        = {Event Sourcing},
  author       = {Fowler, Martin},
  year         = {2005},
  howpublished = {Online essay},
  url          = {https://martinfowler.com/eaaDev/EventSourcing.html},
  note         = {Background terminology used throughout the spec.}
}

@misc{khatchadourian2026dfah,
  title         = {Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness ({DFAH}) for Tool-Using {LLM} Agents},
  author        = {Khatchadourian, Raffi and others},
  year          = {2026},
  eprint        = {2601.15322},
  archivePrefix = {arXiv},
  primaryClass  = {cs.AI},
  url           = {https://arxiv.org/abs/2601.15322},
  note          = {DFAH: measures behavioral determinism (decision determinism / trajectory determinism: same input \(\to\) same output reproducibility) across 4{,}700+ agentic runs on three financial benchmarks (compliance triage, portfolio constraints, DataOps exceptions). Key finding: decision determinism and task accuracy are not detectably correlated. Adjacent axis to RAB: DFAH scores what the agent outputs; RAB scores the audit-log artifact the agent emits. The two are independently failable.}
}

@misc{guldimann2024complai,
  title         = {{COMPL-AI} Framework: A Technical Interpretation and {LLM} Benchmarking Suite for the {EU} Artificial Intelligence Act},
  author        = {Guldimann, Philipp and others},
  year          = {2024},
  eprint        = {2410.07959},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CY},
  url           = {https://arxiv.org/abs/2410.07959},
  note          = {ETH Z\"{u}rich + INSAIT + LatticeFlow collaboration. First technical interpretation of the EU AI Act translated into measurable requirements at the MODEL level (robustness, safety, diversity, fairness). RAB targets the SYSTEM-LEVEL record-keeping obligations of Articles 10/12/19 that COMPL-AI does not score; the two are complementary tracks of a single Act.}
}

@misc{otel2024genaisemconv,
  title        = {{OpenTelemetry GenAI Semantic Conventions}},
  author       = {{OpenTelemetry GenAI Working Group}},
  year         = {2024},
  howpublished = {Semantic-convention specification},
  url          = {https://opentelemetry.io/docs/specs/semconv/gen-ai/},
  note         = {Industry de-facto trace schema for LLM-based systems. Provides the
                   span vocabulary that an RAB Baseline-1 adapter maps onto Spec §1's
                   canonical event types --- the bolt-on tracing comparison point named
                   in Spec §5.}
}

