# Fast Falsification of Hybrid Systems using Probabilistically Adaptive Input

arXiv: https://arxiv.org/abs/1812.04159 · HTML: https://mlsanity.com/paper/1812.04159/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2018.

## Built on by (most cited first, 4 of 4)

- [MDPFuzz: Testing Models Solving Markov Decision Processes](https://mlsanity.com/paper/2112.02807/) (2021)
- [Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees](https://mlsanity.com/paper/2110.10729/) (2021)
- [DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality](https://mlsanity.com/paper/2508.11514/) (2025)
- [Data-Driven Falsification of Cyber-Physical Systems](https://mlsanity.com/paper/2505.03863/) (2025)

## Abstract (arXiv)

We present an algorithm that quickly finds falsifying inputs for hybrid systems, i.e., inputs that steer the system towards violation of a given temporal logic requirement. Our method is based on a probabilistically directed search of an increasingly fine grained spatial and temporal discretization of the input space. A key feature is that it adapts to the difficulty of a problem at hand, specifically to the local complexity of each input segment, as needed for falsification. In experiments with standard benchmarks, our approach consistently outperforms existing techniques by a significant margin. In recognition of the way it works and to distinguish it from previous work, we describe our method as a "Las Vegas tree search".