FM-Agent is the first framework to automate compositional Hoare reasoning for large systems by having LLMs derive natural-language function specs from caller intent and then generate tests that found 522 new bugs in systems up to 143k lines of code.
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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.
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FM-Agent: Scaling Formal Methods to Large Systems via LLM-Based Hoare-Style Reasoning
FM-Agent is the first framework to automate compositional Hoare reasoning for large systems by having LLMs derive natural-language function specs from caller intent and then generate tests that found 522 new bugs in systems up to 143k lines of code.
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PROMISE: Proof Automation as Structural Imitation of Human Reasoning
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.