AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
Summary: arXiv:2604.02478v1 Announce Type: new
Abstract: Deep learning models excel at detecting anomaly patterns in normal data. However, they do not provide a direct solution for anomaly classification and scalability across diverse control systems, frequently failing to distinguish genuine faults from nuisance faults caused by noise or the control system’s large transient response. Consequently, because algorithmic fault validation remains unscalable, full Verification and Validation (V&V) operations are still managed by Human-in-the-Loop (HITL) analysis, resulting in an unsustainable manual workload. To automate this essential oversight, we propose Agent-Integrated Verification and Validation (AIVV), a hybrid framework that deploys Large Language Models (LLMs) as a deliberative outer loop. Because rigorous system verification strictly depends on accurate validation, AIVV escalates mathematically flagged anomalies to a role-specialized LLM council. The council agents perform collaborative validation by semantically validating nuisance and true failures based on natural-language (NL) requirements to secure a high-fidelity system-verification baseline. Building on this foundation, the council then performs system verification by assessing post-fault responses against NL operational tolerances, ultimately generating actionable V&V artifacts, such as gain-tuning proposals. Experiments on a time-series simulator for Unmanned Underwater Vehicles (UUVs) demonstrate that AIVV successfully digitizes the HITL V&V process, overcoming the limitations of rule-based fault classification and offering a scalable blueprint for LLM-mediated oversight in time-series data domains.
Introduction
The rapid advancement of autonomous systems necessitates robust mechanisms for ensuring their reliability and safety. Traditional methods for Verification and Validation (V&V) often rely heavily on Human-in-the-Loop (HITL) processes, which can be inefficient and prone to human error. As such, there is a pressing need for innovative frameworks that can streamline V&V operations while enhancing accuracy.
Challenges in Current V&V Practices
- Anomaly Detection Limitations: Deep learning models are adept at identifying anomalies but struggle with precise classification, particularly in complex systems.
- Nuisance Faults vs. Genuine Faults: Distinguishing between genuine faults and nuisance faults remains a significant challenge, often attributed to noise and transient responses.
- Scalability Issues: As systems become more intricate, the scalability of current V&V methodologies becomes increasingly unmanageable, necessitating a shift towards automated solutions.
The AIVV Framework
The Agent-Integrated Verification and Validation (AIVV) framework offers a novel approach to addressing these challenges. By integrating Large Language Models (LLMs) into the V&V process, AIVV creates a hybrid system that enhances both efficiency and accuracy.
Key Features of AIVV
- Deliberative Outer Loop: LLMs serve as a deliberative outer loop, enabling more sophisticated decision-making processes in anomaly validation.
- Collaborative Validation: A council of specialized LLM agents collaborates to semantically validate anomalies, ensuring a high-fidelity verification baseline.
- Actionable Artifacts: The system not only identifies issues but also generates actionable V&V artifacts, such as gain-tuning proposals, to enhance system performance.
Case Study: Unmanned Underwater Vehicles
Experiments conducted using a time-series simulator for Unmanned Underwater Vehicles (UUVs) validate the effectiveness of the AIVV framework. The results demonstrate that AIVV can successfully digitize the HITL V&V process, providing a scalable and efficient blueprint for future applications in time-series data domains.
Conclusion
The introduction of the AIVV framework marks a significant advancement in the field of autonomous systems. By leveraging the capabilities of LLMs for V&V operations, AIVV not only addresses existing challenges but also sets the stage for more reliable and trustworthy autonomous systems moving forward.