arXiv (All)
open access
Proving LLM-Generated GPU Kernels Correct and Safe: Floating-Point Verification in Lean and VerCors
Modern statistical model development begins with a high-level specification in an automatic differentiation framework such as PyTorch, JAX, or TensorFlow, which is then lowered to a target backend by a compiler or implemented manually by a research engineer. In this paper, we study whether frontier
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- How Much of a Real Workload Can LLM-Generated GPU Kernels Actually Reach?#1035028
- How Much of a Real Workload Can LLM-Generated GPU Kernels Actually Reach?#1006828
- M2K: Making the Model-Kernel Interface Explicit for Reliable CUDA Kernel Verification#793612
- M2K: Making the Model-Kernel Interface Explicit for Reliable CUDA Kernel Verification#789608
- Correct but Slow: An Empirical Study of the GPU Kernel Evaluation Gap in Modern Domain-Specific Languages#343555
Record · ID 1389494
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive