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  1. CLEVER: A Curated Benchmark for Formally Verified Code Generation

    2025年7月8日 · TL;DR: We introduce CLEVER, a hand-curated benchmark for verified code generation in Lean. It requires full formal specs and proofs. No few-shot method solves all stages, making it a …

  2. 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; it evaluates …

  3. Submissions | OpenReview

    2025年1月22日 · Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers Lorenzo Pacchiardi, Marko Tesic, Lucy G Cheke, Jose Hernandez-Orallo 27 Sept 2024 …

  4. Evaluating the Robustness of Neural Networks: An Extreme Value...

    2018年2月15日 · Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness. The proposed CLEVER score is attack-agnostic and …

  5. STAIR: Improving Safety Alignment with Introspective Reasoning

    2025年5月1日 · 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 …

  6. EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic ...

    2025年9月16日 · 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 …

  7. Contrastive Learning Via Equivariant Representation - OpenReview

    2024年9月25日 · 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 …

  8. 579 In this paper, we have proposed a novel counter- factual framework CLEVER for debiasing fact- checking models. Unlike existing works, CLEVER is augmentation-free and mitigates biases on infer- …

  9. Dual-Model Defense: Safeguarding Diffusion Models from Membership …

    2024年9月27日 · Membership inference and memorization is a key challenge with diffusion models. Mitigating such vulnerabilities is hence an important topic. The idea of using an ensemble of model is …

  10. La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse...

    2025年5月1日 · 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. This ensures that …