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Jul 8, 2025 · We introduce ${\\rm C{\\small LEVER}}$, a high-quality, manually curated benchmark of 161 problems for end-to-end verified code generation in Lean. Each problem cons`Promoting openness in scientific communication and the peer-review process*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 prob*While, as we mentioned earlier, there can be thorny âclever hansâ issues about humans prompting LLMs, an automated verifier mechanically backprompting the LLM doesnât suffer +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 specific/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, a.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
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Jul 8, 2025 · We introduce ${\\rm C{\\small LEVER}}$, a high-quality, manually curated benchmark of 161 problems for end-to-end verified code generation in Lean. Each problem cons`Promoting openness in scientific communication and the peer-review process*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 prob*While, as we mentioned earlier, there can be thorny âclever hansâ issues about humans prompting LLMs, an automated verifier mechanically backprompting the LLM doesnât suffer +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 specific/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, a.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