An LLM agent with Rocq backend automatically builds a verified RISC-V RV32I interpreter (1859 lines Rocq, 2848 lines extracted C++) that passes 265 tests and 12-hour fuzzing, while a Dafny backend fails.
Cobblestone: A Divide-and-Conquer Approach for Automating Formal Verification
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abstract
Formal verification using proof assistants, such as Coq, is an effective way of improving software quality, but requires significant effort and expertise. Machine learning can automatically synthesize proofs, but such tools are able to prove only a fraction of desired software properties. We introduce Cobblestone, a divide-and-conquer approach for proof synthesis. Cobblestone uses a large language model (LLM) to generate potential proofs, uses those proofs to break the problem into simpler parts, automatically identifies which of those parts were successfully proven, and iterates on the remaining parts to build a correct proof that is guaranteed to be sound, despite the reliance on unsound LLMs. We evaluate Cobblestone on four benchmarks of open-source Coq projects, controlling for training data leakage. Fully automatically, Cobblestone outperforms state-of-the-art non-LLM tools, and proves many theorems that other LLM-based tools cannot, and on many benchmarks, outperforms them. Each Cobblestone run costs only $1.25 and takes 14.7 minutes, on average. Cobblestone can also be used with external input, from a user or another tool, providing a proof structure or relevant lemmas. Evaluated with such an oracle, Cobblestone proves up to 58% of theorems. Overall, our research shows that tools can make use of partial progress and external input to more effectively automate formal verification.
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A Lean library called Palamedes uses synthesis rules from generator semantics and catamorphism-anamorphism rewriting to automatically produce correct constrained random generators.
AutoSOUP automates component-level memory-safety verification by generating Safety-Oriented Unit Proofs via three techniques and a hybrid LLM-plus-program-synthesis architecture called LLM-As-Function-Call.
PROMISE reframes automated proof generation as stateful search over structural embeddings of proof states, outperforming prior LLM-based systems by up to 26 points on the seL4 benchmark.
A pattern-guided tactic search method improves automated proof synthesis success rates by an average of 8.05% and achieves a 20% increase on previously unproven theorems.
ReCent-Prover achieves a 22.58% relative improvement over prior state-of-the-art in proved theorems on the CoqStoq benchmark by using reasoning-centric techniques under a fixed LLM invocation budget.
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