AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems

A team of researchers has introduced AIVV (Agent-Integrated Verification and Validation), a novel framework designed to automate the critical but labor-intensive process of ensuring autonomous systems are trustworthy. The core problem it tackles is the unsustainable manual workload of Human-in-the-Loop (HITL) analysis required for full Verification and Validation. Current deep learning models can detect anomalies but struggle to classify them and scale across diverse control systems, often failing to separate genuine faults from transient noise or 'nuisance faults.'

AIVV's solution is a hybrid, neuro-symbolic architecture that deploys Large Language Models (LLMs) as a deliberative 'outer loop.' When a mathematical anomaly is flagged, it is escalated to a council of role-specialized LLM agents. This council collaboratively performs validation by semantically analyzing the fault against natural-language system requirements to establish a high-fidelity baseline. It then performs system verification by assessing post-fault responses against operational tolerances, ultimately producing actionable outputs like reports and gain-tuning proposals.

Experiments conducted on a time-series simulator for Unmanned Underwater Vehicles (UUVs) demonstrated that AIVV can successfully digitize the HITL V&V process. The framework overcomes the limitations of rigid, rule-based fault classification by leveraging the semantic reasoning of LLMs. This provides a scalable blueprint for applying LLM-mediated oversight to complex, time-series data domains, moving autonomous system testing from a manual bottleneck toward an automated, reliable pipeline.