arXiv:2605.26720v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong empirical gains as self-evolving agents for CUDA kernel generation, driven by feedback-conditioned planning across generations. However, how planning decisions attribute and combine heterogeneous feedback signals remains opaque. Standard end-to-end ablations fail to resolve this question, as iterative planning amplifies early perturbations and conflates feedback effects with trajectory-dependent drift. We introduce \texttt{CUDAnalyst}, a unified analysis layer for controlled, generation-level attribu
The rapid advancement of LLMs as agents necessitates deeper understanding and control over their decision-making, particularly in complex tasks like code generation.
Improving the interpretability and reliability of self-evolving LLM agents for CUDA kernel generation accelerates AI development and reduces dependencies on manual optimization.
The introduction of a unified analysis layer provides a tool to better understand and optimize how LLMs generate high-performance code, potentially leading to more efficient AI hardware utilization.
- · AI developers
- · GPU manufacturers (NVIDIA)
- · Cloud providers
- · High-performance computing
- · Manual CUDA optimization specialists
More efficient and autonomous AI model deployment and optimization become possible.
This could lead to a faster pace of innovation in AI hardware as software efficiency improves.
Increased compute efficiency might alleviate some pressure on energy consumption for AI workloads, but also enable larger models.
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Read at arXiv cs.AI