Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis
Quick summary
arXiv:2509.21629v4 Announce Type: replace-cross Abstract: 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 framework for LLM-based invariant synthesis with soundness guarantees and state-of-the-art performance. Unlike prior work that treats LLM outputs as noisy symbolic material requiring substantial post-processing, Quokka adopts a simpler algorithm design that dire
Key takeaways
- arXiv:2509.21629v4 Announce Type: replace-cross Abstract: 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 framework for LLM-based invariant synthesis with soundness guarantees and state-of-the-art performance.
Why it matters
“Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

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