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NeurIPS2025顶会

SpecMAS: A Multi-Agent System for Self-Verifying System Generation via Formal Model Checking

Rishabh Agrawal, Kaushik T. Ranade, Aja Khanal, Kalyan S. Basu, Apurva Narayan

2025年份
1被引次数

摘要

We present SpecMAS, a novel multi-agent system that autonomously constructs and formally verifies executable system models from natural language specifications. Given a Standard Operating Procedure (SOP) describing a target system, SpecMAS parses the specification, identifies relevant operational modes, variables, transitions, and properties, and generates a formal model in NuSMV code syntax, an industry-standard symbolic model checker. A dedicated reasoning agent extracts both explicit and implicit properties from the SOP, and verification is performed via temporal logic model checking. If any properties fail to verify, an autonomous debugging agent analyzes counterexamples and iteratively corrects the model until all properties are satisfied. This closed-loop system design guarantees provable correctness by construction and advances the state of the art in automated, inter-pretable, and deployable verification pipelines. We demonstrate the generality, correctness, and practical feasibility of SpecMAS across a set of representative case studies and propose a new benchmark dataset for the evaluation and comparison of model checking performance. We release our code and benchmark dataset github.com/Idsl-group/SpecMAS.

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