arXiv Machine Learning

Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling

arXiv:2606. 15640v1 Announce Type: new Abstract: Audit risk assessment increasingly benefits from combining heterogeneous evidence sources, yet existing approaches typically produce point predictions without quantifying how well different evidence streams agree.

arXiv AI
Aug 17

CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QA

arXiv:2608. 13706v1 Announce Type: cross Abstract: Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modality disagreement, debate verifies an aggregate report rather than individual claims, and such verification occurs only after drafting, leaving inter-agent errors undetected until the final text.

By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Jubayer Al Mahmud, M. F. Mridha, Md. Alam Hossain
arXiv AI
Sep 17

Who Audits Whom, on What Substrate, with What Evidence? An Independence-Graded Audit Protocol for Agentic AI

The paper proposes an independence‑graded audit protocol for agentic AI systems, arguing that independence should be evaluated along three orthogonal axes: principal independence, substrate independence, and evidence independence. It introduces a seven‑step protocol based on the beta‑factor model from reliability engineering, demonstrates its application through a structural detectability analysis and a Monte Carlo study, and maps the framework to relevant regulatory standards such as the EU AI Act, ISO/IEC 42006, and UK public‑sector guidance.

By Mohamed Chahine Ghanem