Sitzendorf Reidling

The subject of sitzendorf reidling encompasses a wide range of important elements. 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. 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. Submissions | OpenReview. Promoting openness in scientific communication and the peer-review process STAIR: Improving Safety Alignment with Introspective Reasoning.

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 providing harmful responses. Our method, STAIR (SafeTy Alignment with Introspective Reasoning), guides models to think more carefully before responding. 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. Evaluating the Robustness of Neural Networks: An Extreme Value.... Furthermore, 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.

La RoSA: Enhancing LLM Efficiency via Layerwise Rotated Sparse.... We use a clever technique that involves rotating the data within each layer of the model, making it easier to identify and keep only the most important parts for processing. From another angle, this ensures that the model remains fast and efficient without losing much accuracy. Moreover, 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 contrastive learning framework compatible with augmentation strategies of arbitrary complexity for various mainstream CL backbone models. Alias-Free Mamba Neural Operator | OpenReview. To counteract the dilemma, we propose a mamba neural operator with O (N) computational complexity, namely MambaNO. Functionally, MambaNO achieves a clever balance between global integration, facilitated by state space model of Mamba that scans the entire function, and local integration, engaged with an alias-free architecture.

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.