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  1. 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 …

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

    Jul 8, 2025 · 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 …

  3. The Clever Hans Mirage: A Comprehensive Survey on Spurious...

    Feb 21, 2026 · This survey on spurious correlations uses the Clever Hans metaphor to motivate the problem, formalizes a group-based setup g=(y,a) with core metrics (worst-group, average-group, bias …

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

    Sep 18, 2025 · This paper introduces CLEVER, a benchmark dataset designed to evaluate LLMs on formally verified code generation. It consists of 161 carefully crafted Lean specifications derived from …

  5. 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- …

  6. STAIR: Improving Safety Alignment with Introspective Reasoning

    May 1, 2025 · 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 …

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

    Feb 15, 2018 · 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 …

  8. 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. We …

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

    Sep 27, 2024 · 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. How Powerful are Graph Neural Networks? | OpenReview

    Dec 20, 2018 · An anonymous reader and Reviewer2 made a clever observation that our original GIN aggregation in Eq. (4.1) and Theorem 3a-Eqn.2) of the initial submission and cannot distinguish …