AINeutralarXiv – CS AI · May 276/10
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Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation
Researchers introduce CUDAnalyst, a new analysis framework that reveals how large language models make planning decisions when generating CUDA kernels by decomposing feedback signals. The study demonstrates that explicit planning helps only when feedback is well-aligned and that effective planning emerges from structured multi-feedback interactions, with findings showing robustness across different models and workloads.