arXiv:2606.23720v1 Announce Type: cross Abstract: We present an integrated framework that unifies runtime verification and model-based diagnosis within the stream specification language LOLA. By encoding system descriptions, component health states, and observations into a single stream-based formalism, the approach enables continuous, online fault localization directly alongside fault detection, without requiring separate toolchains. The framework supports both time-invariant and transient faults, and naturally accommodates nondeterministic observations.
The increasing complexity and safety demands of AI-driven and autonomous systems necessitate more robust methods for real-time verification and fault diagnosis, making such unified frameworks critical.
This framework offers a significant advancement in ensuring the reliability and safety of AI systems by integrating fault detection and localization, which is crucial for deployment in critical applications.
The ability to continuously verify and diagnose faults in AI systems without separate toolchains streamlines development and deployment, making autonomous systems more trustworthy and easier to maintain.
- · AI software developers
- · Autonomous systems integrators
- · Critical infrastructure sectors
- · Safety-critical AI applications
- · Traditional, siloed verification tool vendors
Improved reliability and faster iteration cycles for complex AI and autonomous systems.
Accelerated adoption of AI in industries with high safety and diagnostic standards, like automotive and aerospace.
Potential for new regulatory frameworks to mandate integrated runtime verification and diagnosis for autonomous AI.
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Read at arXiv cs.AI