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We introduce clever, the first curated benchmark for evaluating the generation of specifications and formally verified code in lean With a clever usage of the equivalence between reward models and the corresponding optimal policy, the algorithm features a simple objective that combines (i) a preference optimization loss that directly aligns the policy with human preference, and (ii) a supervised learning loss which explicitly imitates the policy with a baseline distribution. The benchmark comprises of 161 programming problems
It requires full formal specs and proofs Diffusion models have demonstrated remarkable capabilities in image synthesis, but their recently proven vulnerability to membership inference attacks (mias) poses a critical privacy concern Building on recent explainable ai techniques, this article highlights the pervasiveness of clever hans effects in unsupervised learning and the substantial risks associated with these effects in terms of the prediction accuracy on new data.
Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness
We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability.
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