Large Language Models as Falsifiers for Cyber-Physical Systems

Problem

The paper addresses a significant gap in the capability of falsifying specifications in cyber-physical systems (CPS) using traditional black-box search algorithms. Existing methods often struggle with the complexity and variability inherent in CPS, necessitating more effective approaches. This work is particularly relevant as it explores the application of large language models (LLMs) in this domain, a topic that has not been extensively covered in the literature. The authors present their findings in a preprint format, indicating that the work is unreviewed.

Method

The core technical contribution is the introduction of the LLM-Falsifier, an architecture that leverages large language models to perform falsification tasks. The algorithm focuses on minimizing the robustness degree of Signal Temporal Logic (STL) specifications, which are crucial for defining the behavior of CPS. The LLM-Falsifier utilizes data from the ARCH-COMP falsification benchmarks, which provide a standardized set of specifications for evaluating falsification methods. Notably, the training compute required for the model is not specified in the paper. Additionally, the approach incorporates natural-language input/output names, output trajectories, and critical-time witnesses, enhancing its interpretability and usability in practical applications.

Results

The LLM-Falsifier demonstrates superior performance compared to existing falsification tools, achieving better results on 14 out of 21 specifications. The benchmarks include comparisons against surrogate-based methods, Bayesian optimization techniques, and traditional search-based testing approaches. This performance indicates a significant advancement in the capability of falsification tools for CPS, showcasing the potential of LLMs in this area.

Limitations

The authors do not report any limitations in their work, which may suggest a need for further exploration of potential weaknesses or areas for improvement. However, the absence of specified training compute could be a concern for reproducibility and practical deployment. Additionally, the reliance on specific benchmarks may limit the generalizability of the results to other types of CPS or specifications not covered in the ARCH-COMP dataset.

Why it matters

The implications of this work are substantial for downstream research and applications in CPS. By demonstrating that LLMs can effectively serve as falsifiers, the study opens avenues for integrating advanced AI techniques into the verification and validation processes of CPS. This could lead to more robust and reliable systems, ultimately enhancing safety and performance in critical applications such as autonomous vehicles, industrial automation, and smart infrastructure.