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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. 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 …

  4. 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 …

  5. Weakly-Supervised Affordance Grounding Guided by Part-Level...

    Jan 22, 2025 · In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and …

  6. 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 …

  7. Submissions | OpenReview

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

  8. 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 …

  9. 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 …

  10. 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- …