GPUs increasingly accelerate database systems, but query-specific peak performance still often relies on hand-written kernels. Existing LLM kernel benchmarks focus on machine learning operators, leaving irregular, heterogeneous, data-movement-heavy database-style operators untested. We introduce **DataKernelBench**, which translates SQL into validated PyTorch **TorchPlan** programs and evaluates LLMs that optimize either the core tensor-bounded snippet or the full query in CUDA or Triton through execution-guided repair. Across ten proprietary and open-weight models on **TPC-H SF10** with an H100 GPU, the strongest full-query CUDA configuration achieves **2.11× speedup over ‘torch.compile‘** at full pass rate. We find that higher-performing implementations commonly use kernel fusion and execution-strategy changes, stronger models benefit most from full-query specialization, and workload context matters more than hardware context. To handle data larger than GPU memory, we extend TorchPlan with Dask-cuDF for on-demand partition loading on **TPC-H SF100** with four H100 GPUs, achieving **2.54×** speedup.

**Project page:** https://kerneldf.github.io/datakernelbench

Large language models often generate plausible yet unreliable answers, making uncertainty quantification essential for detecting potential hallucinations. Prior work has shown that the information needed to detect hallucinations and quantify a model’s confidence in its generations may already be present in its internal states, but how to extract this information effectively remains an open problem. We introduce Semantic Coverage, a white-box, representation-level spectral uncertainty estimator inspired by coverage in reinforcement learning. Given multiple sampled generations for a prompt, Semantic Coverage quantifies uncertainty by measuring how broadly their internal representations span the model’s latent semantic space. Across three open-weights language models and four short-form question-answering benchmarks, Semantic Coverage achieves the strongest overall performance among the evaluated methods and consistently outperforms prior representation-level methods in quantifying LLM uncertainty.