SysMoBench: Evaluating AI on Formally Modeling Complex Real-World Systems
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917226438721536 |
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| author | Cheng, Qian Tang, Ruize Ma, Emilie Hackett, Finn He, Peiyang Su, Yiming Beschastnikh, Ivan Huang, Yu Ma, Xiaoxing Xu, Tianyin |
| author_facet | Cheng, Qian Tang, Ruize Ma, Emilie Hackett, Finn He, Peiyang Su, Yiming Beschastnikh, Ivan Huang, Yu Ma, Xiaoxing Xu, Tianyin |
| contents | Formal models are essential to specifying large, complex computer systems and verifying their correctness, but are notoriously expensive to write and maintain. Recent advances in generative AI show promise in generating certain forms of specifications. However, existing work mostly targets small code, not complete systems. It is unclear whether AI can deal with realistic system artifacts, as this requires abstracting their complex behavioral properties into formal models. We present SysMoBench, a benchmark that evaluates AI's ability to formally model large, complex systems. We focus on concurrent and distributed systems, which are keystones of today's critical computing infrastructures, encompassing operating systems and cloud infrastructure. We use TLA+, the de facto specification language for concurrent and distributed systems, though the benchmark can be extended to other specification languages. We address the primary challenge of evaluating AI-generated models by automating metrics like syntactic and runtime correctness, conformance to system code, and invariant correctness. SysMoBench currently includes eleven diverse system artifacts: the Raft implementation of Etcd and Redis, the leader election of ZooKeeper, the Spinlock, Mutex, and Ringbuffer in Asterinas OS, etc., with more being added. SysMoBench enables us to understand the capabilities and limitations of today's LLMs and agents, putting tools in this area on a firm footing and opening up promising new research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23130 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SysMoBench: Evaluating AI on Formally Modeling Complex Real-World Systems Cheng, Qian Tang, Ruize Ma, Emilie Hackett, Finn He, Peiyang Su, Yiming Beschastnikh, Ivan Huang, Yu Ma, Xiaoxing Xu, Tianyin Artificial Intelligence Distributed, Parallel, and Cluster Computing Software Engineering Formal models are essential to specifying large, complex computer systems and verifying their correctness, but are notoriously expensive to write and maintain. Recent advances in generative AI show promise in generating certain forms of specifications. However, existing work mostly targets small code, not complete systems. It is unclear whether AI can deal with realistic system artifacts, as this requires abstracting their complex behavioral properties into formal models. We present SysMoBench, a benchmark that evaluates AI's ability to formally model large, complex systems. We focus on concurrent and distributed systems, which are keystones of today's critical computing infrastructures, encompassing operating systems and cloud infrastructure. We use TLA+, the de facto specification language for concurrent and distributed systems, though the benchmark can be extended to other specification languages. We address the primary challenge of evaluating AI-generated models by automating metrics like syntactic and runtime correctness, conformance to system code, and invariant correctness. SysMoBench currently includes eleven diverse system artifacts: the Raft implementation of Etcd and Redis, the leader election of ZooKeeper, the Spinlock, Mutex, and Ringbuffer in Asterinas OS, etc., with more being added. SysMoBench enables us to understand the capabilities and limitations of today's LLMs and agents, putting tools in this area on a firm footing and opening up promising new research directions. |
| title | SysMoBench: Evaluating AI on Formally Modeling Complex Real-World Systems |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing Software Engineering |
| url | https://arxiv.org/abs/2509.23130 |