Tessa: Tensor-Based Probabilistic Model Checking for Markov Chains
A new tool called Tessa applies tensor computations to probabilistic model checking of Markov chains, specifically for step-bounded reachability probabilities. The approach, detailed in a paper on arXiv, reexamines the verification problem by encoding state-transition matrices as dense tensors, enabling the use of optimized compiler toolchains for hardware accelerators. The authors prove the soundness of this methodology and demonstrate massive speedups over state-of-the-art methods on selected benchmarks. The paper is categorized under Computer Science > Logic in Computer Science and was submitted to arXiv with the identifier 2608.00374.
Key facts
- Tessa is a tool for probabilistic model checking of Markov chains.
- It focuses on step-bounded reachability probabilities.
- The approach uses dense tensors instead of explicit or symbolic representations.
- It leverages off-the-shelf compiler toolchains for hardware accelerators.
- The soundness of the mapping to tensor computations is proven.
- Empirical evaluation shows massive speedups over state-of-the-art methods.
- The paper is available on arXiv with ID 2608.00374.
- The paper is categorized under Computer Science > Logic in Computer Science.
Entities
Institutions
- arXiv