# Interpretable Early Failure Detection via Machine Learning and Trace Checking-based Monitoring

arXiv: https://arxiv.org/abs/2508.17786 · HTML: https://mlsanity.com/paper/2508.17786/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Impossibility Theorems for Feature Attribution](https://mlsanity.com/paper/2212.11870/) (2022)
- [RTAMT: Online Robustness Monitors from STL](https://mlsanity.com/paper/2005.11827/) (2020)
- [The Complexity of Learning Linear Temporal Formulas from Examples](https://mlsanity.com/paper/2102.00876/) (2021)
- [LTL learning on GPUs](https://mlsanity.com/paper/2402.12373/) (2024)
- [Two Birds with One Network: Unifying Failure Event Prediction and Time-to-failure Modeling](https://mlsanity.com/paper/1812.07142/) (2018)
- [RTAMT -- Runtime Robustness Monitors with Application to CPS and Robotics](https://mlsanity.com/paper/2501.18608/) (2025)

## Abstract (arXiv)

Monitoring is a runtime verification technique that allows one to check whether an ongoing computation of a system (partial trace) satisfies a given formula. It does not need a complete model of the system, but it typically requires the construction of a deterministic automaton doubly exponential in the size of the formula (in the worst case), which limits its practicality. In this paper, we show that, when considering finite, discrete traces, monitoring of pure past (co)safety fragments of Signal Temporal Logic (STL) can be reduced to trace checking, that is, evaluation of a formula over a trace, that can be performed in time polynomial in the size of the formula and the length of the trace. By exploiting such a result, we develop a GPU-accelerated framework for interpretable early failure detection based on vectorized trace checking, that employs genetic programming to learn temporal properties from historical trace data. The framework shows a 2-10% net improvement in key performance metrics compared to the state-of-the-art methods.