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It requires full formal specs and proofs We propose clever (contrastive learning via equivariant representation), a novel equivariant contrastive learning framework compatible with augmentation strategies of arbitrary complexity for various mainstream cl backbone models. 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 In this paper, we revisit the roles of augmentation strategies and equivariance in improving cl's efficacy 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 This severely limits their practical utility Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis
Yet, the lack of annotated data and the impact of batch effects, e.g., systematic technical data differences across hospitals, hamper model robustness and generalization
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