Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation 事件

Towards Feedback-to-Plan Decisions for Self-Evolving LLM Agents in CUDA Kernel Generation 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 am