Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis

Program verification relies on loop invariants, yet automatically discovering strong invariants remains a long-standing challenge. We investigate whether large language models (LLMs) can accelerate program verification by generating useful loop invariants. We introduce Quokka, a first-order and effective framework for LLM-based invariant synthesis that provides sound evaluation while achieving state-of-the-art speedup results. Unlike prior work that designs complex, highly customized algorithms, Quokka employs a simple and principled verification procedure. We construct a benchmark of 866 instances and evaluate 9 state-of-the-art LLMs across multiple model families. Our results show that Quokka consistently outperforms all prior LLM-based verifiers: achieving speedups of at least 1.2x on 81 instances compared to 39 instances for the previous best approach. We further demonstrate that supervised fine-tuning and Best-of-N sampling can yield measurable improvements in accelerating verification.