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  1. 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 …

  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. 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 …

  4. Submissions | OpenReview

    Jan 22, 2025 · Promoting openness in scientific communication and the peer-review process

  5. 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 …

  6. Contrastive Learning Via Equivariant Representation | OpenReview

    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 …

  7. 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- …

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

    Sep 16, 2025 · 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 …

  9. KnowTrace: Explicit Knowledge Tracing for Structured...

    Sep 13, 2024 · TL;DR: We introduce a structured RAG paradigm (KnowTrace) that seamlessly integrates knowledge structuring and multi-step reasoning for improved MHQA performance.

  10. Do Histopathological Foundation Models Eliminate Batch Effects? A ...

    Oct 11, 2024 · Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis. Yet, the lack of annotated data …