Towards Language Model Guided TLA+ Proof Automation (arxiv.org)

Researchers have introduced a novel approach to automate formal theorem proving in TLA+, a rigorous framework for system specifications. Their method utilizes large language models (LLMs) to assist in decomposing complex proof obligations into simpler, manageable sub-claims, addressing the intricate hierarchical structure unique to TLA+. By constraining LLMs to generate normalized claim decompositions instead of complete proofs, the risk of syntax errors is significantly minimized. This innovation allows for a more streamlined proof construction process, reducing the level of expertise and effort traditionally required. This development is particularly significant for the AI/ML community as it bridges the gap between advanced AI techniques and formal methods, opening new doors for automated verification in system design. The researchers also established a benchmark suite of 119 theorems derived from established mathematical sources and inductive proofs of distributed protocols, demonstrating that their approach consistently outperforms baseline methods. This advancement not only enhances the utility of LLMs in formal verification but also has implications for increasing the reliability and efficiency of system design processes in various AI applications.