SysMoBench Introduced to Gauge AI-Generated Formal Models for Complex Distributed Systems

Global: SysMoBench Benchmark Launches to Assess AI-Generated Formal Specifications

Researchers have unveiled SysMoBench, a new benchmark aimed at evaluating large language models’ ability to automatically generate formal specifications for sizable, concurrent and distributed computing systems. The benchmark, which currently incorporates eleven diverse system artifacts—including Raft implementations of Etcd and Redis, ZooKeeper leader election, and synchronization primitives from the Asterinas operating system—automates assessment criteria such as syntactic validity, runtime correctness, code conformance, and invariant preservation.

Motivation and Background

Formal models are essential for verifying the correctness of large-scale software, yet authoring and maintaining them is notoriously resource‑intensive. Recent advances in generative AI suggest a potential shortcut, but prior studies have focused on small code snippets rather than full‑scale system components. SysMoBench seeks to fill this gap by providing a realistic testbed that reflects the complexity of modern infrastructure.

Benchmark Design

The suite adopts TLA+ as its primary specification language, reflecting its status as the de facto standard for modeling concurrent and distributed behavior. Although TLA+ is the default, the framework is designed to accommodate additional specification languages as the field evolves.

Included System Artifacts

Among the eleven artifacts are the Raft consensus algorithm implementations used in Etcd and Redis, the leader election mechanism of ZooKeeper, and low‑level synchronization constructs such as spinlocks, mutexes, and ring buffers from the Asterinas OS. These selections represent a cross‑section of critical infrastructure components.

Automated Evaluation Metrics

SysMoBench automates several quantitative metrics: (1) syntactic correctness of the generated TLA+ code, (2) runtime correctness verified through model checking, (3) alignment with the original system code, and (4) validation of key invariants that capture intended system properties.

Implications for AI-Assisted Formal Modeling

By providing a systematic way to measure AI performance on realistic system specifications, the benchmark offers researchers insight into both the capabilities and current limitations of large language models and autonomous agents in this domain. The results are expected to guide future tool development and research directions.

Future Directions

The authors plan to expand SysMoBench with additional artifacts and to support alternative specification languages, thereby broadening its applicability across various sectors of computing infrastructure.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.