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cs.LG2026
MobileKernelBench: Can LLMs Write Efficient Kernels for Mobile Devices?
Xingze Zou, Jing Wang, Yuhua Zheng +8
Large language models (LLMs) have demonstrated remarkable capabilities in code generation, yet their potential for generating kernels specifically for mobile devices remains largel…
cs.LG2026
DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels
Haolei Bai, Lingcheng Kong, Xueyi Chen +3
Diffusion large language models (dLLMs) have emerged as a compelling alternative to autoregressive (AR) LLMs, owing to their capacity for parallel token generation. This paradigm i…
cs.LG2025
ConCuR: Conciseness Makes State-of-the-Art Kernel Generation
Lingcheng Kong, Jiateng Wei, Hanzhang Shen +1
GPU kernel generation by LLMs has recently experienced rapid development, leveraging test-time scaling and reinforcement learning techniques. However, a key challenge for kernel ge…