AI RESEARCH
AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents
arXiv CS.LG
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ArXi:2605.16819v1 Announce Type: cross GPU kernel optimization is increasingly critical for efficient deep learning systems, but writing high-performance kernels still requires substantial low-level expertise. Recent AI coding agents can iteratively read code, invoke compilers and profilers, and refine implementations, yet existing kernel benchmarks evaluate single LLM calls rather than full agent workflows, and none include both kernel-to-kernel optimization and unseen-configuration generalization testing.