3 papers
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.SD2026
TQCodec: Towards neural audio codec for high-fidelity music streaming
Lixing He, Zhouxuan Chen, Mingshuai Liu +6
We propose TQCodec, a neural audio codec designed for high-bitrate, high-fidelity music streaming. Unlike existing neural codecs that primarily target ultra-low bitrates (<= 16kbps…
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…