Towards Language Model Guided TLA+ Proof Automation

Abstract Formal theorem proving with $\texttt {TLA}^{+}$ TLA + provides rigorous guarantees for system specifications, but constructing proofs requires substantial expertise and effort. While large language models have shown promise in automating proofs for tactic-based theorem provers like Lean, applying these approaches directly to $\texttt {TLA}^{+}$ TLA + faces significant challenges due to the hierarchical proof structure of the $\texttt {TLA}^{+}$ TLA + proof system. We present a prompt-based approach that leverages LLMs to guide hierarchical decomposition of complex proof obligations into simpler sub-claims, while relying on symbolic provers for verification. Our key insight is to constrain LLMs to generate normalized claim decompositions rather than complete proofs, significantly reducing syntax errors. We also introduce a benchmark suite of 119 theorems adapted from (1) established mathematical collections and (2) inductive proofs of distributed protocols. Our approach consistently outperforms baseline methods across the benchmark suite.