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It requires full formal specs and proofs Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness Histopathology, foundation models, batch effects, clever hans effect, robustness, generalization abstract
Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis. This severely limits their practical utility 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
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. 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
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