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cs.CL2026
CUDA-Harness: Harnessing Agentic CUDA Kernel Generation and Optimization from Natural Language
Qi Fan, An Zou, Yehan Ma
Developing high-performance CUDA kernels demands specialized knowledge in algorithm implementation, correctness validation, and hardware-aware parallel optimization, creating a sub…
cs.CL2026
River-LLM: Large Language Model Seamless Exit Based on KV Share
Yingtao Shen, An Zou
Large Language Models (LLMs) have demonstrated exceptional performance across diverse domains but are increasingly constrained by high inference latency. Early Exit has emerged as…
cs.LG2026
CUDABench: Benchmarking LLMs for Text-to-CUDA Generation
Jiace Zhu, Wentao Chen, Qi Fan +6
Recent studies have demonstrated the potential of Large Language Models (LLMs) in generating GPU Kernels. Current benchmarks focus on the translation of high-level languages into C…