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CUDA-Harness is a framework that uses agentic techniques to generate and optimize CUDA kernels from natural language descriptions, addressing challenges in Text2CUDA by connecting high-level semantics with low-level implementation and verification.
This paper introduces CUDAnalyst, a tool for analyzing how individual feedback signals influence planning decisions in self-evolving LLM agents for CUDA kernel generation, using trajectory freezing and selective feedback injection to enable controlled attribution.