We introduce clever, the first curated benchmark for evaluating the generation of specifications and formally verified code in lean The benchmark comprises of 161 programming problems Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness One common approach is training models to refuse unsafe queries, but this strategy can be vulnerable to clever prompts, often referred to as jailbreak attacks, which can trick the ai into providing harmful responses Our method, stair (safety alignment with introspective reasoning), guides models to think more carefully before responding. While, as we mentioned earlier, there can be thorny “clever hans” issues about humans prompting llms, an automated verifier mechanically backprompting the llm doesn’t suffer from these
A fundamental limitation of current ai agents is their inability to learn complex skills on the fly at test time, often behaving like “clever but clueless interns” in novel environments This severely limits their practical utility Many intellectual endeavors require mathematical problem solving, but this skill remains beyond the capabilities of computers To measure this ability in machine learning models, we introduce math, a new dataset of 12,500 challenging competition mathematics problems
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