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  • Clever: A Curated Benchmark for Formally Verified Code Generation
    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 both formal speci-fication generation and implementation synthesis from natural language, requiring formal correctness proofs for both
  • On the Planning Abilities of Large Language Models : A Critical . . .
    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 tested this setup on a subset of the failed instances in the one-shot natural language prompt configuration using GPT-4, given its larger context window
  • LLaVA-OneVision: Easy Visual Task Transfer | OpenReview
    We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series Our
  • Forum - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • CLEVER: A Curated Benchmark for Formally Verified Code Generation
    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 strong testbed for synthesis and formal reasoning
  • Counterfactual Debiasing for Fact Verification
    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- ence stage In CLEVER, the claim-evidence fusion model and the claim-only model are independently trained to capture the corresponding information
  • EvoTest: Evolutionary Test-Time Learning for Self-Improving Agentic . . .
    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 severely limits their practical utility To systematically measure and drive progress on this challenge, we first introduce the Jericho Test-Time Learning (J-TTL) benchmark J-TTL is a new evaluation
  • Evaluating the Robustness of Neural Networks: An Extreme Value. . .
    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 is computationally feasible for large neural networks
  • VGR: Visual Grounded Reasoning - OpenReview
    The dynamic visual memory replay mechanism is a clever way to enhance the model's ability to focus on relevant image regions during reasoning, which is crucial for fine-grained visual-linguistic tasks The experiments are well-executed and comprehensive, covering a wide range of visual reasoning benchmarks and a series of ablation studies
  • Efficient Edge Inference by Selective Query | OpenReview
    Especially, the routing module is trained via proxy supervision (using oracle), which is a clever way to incorporate all modules in the training Experiments are carefully designed and performed More importantly, actual MCU systems are used for the evaluation “Hybrid accuracy” seems to be a good metric for these two-stage inference systems





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