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Our analysis yields a novel robustness metric called clever, which is short for cross lipschitz extreme value for network robustness This ensures that the model remains fast and efficient without losing much accuracy. It requires full formal specs and proofs

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We introduce clever, the first curated benchmark for evaluating the generation of specifications and formally verified code in lean We use a clever technique that involves rotating the data within each layer of the model, making it easier to identify and keep only the most important parts for processing The benchmark comprises of 161 programming problems

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

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. Leaving the barn door open for clever hans 05 feb 2025) submitted to iclr 2025 readers

In this paper, we revisit the roles of augmentation strategies and equivariance in improving cl's efficacy 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. To counteract the dilemma, we propose a mamba neural operator with o (n) computational complexity, namely mambano

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