2 papers
cs.LG2026
Towards Automated Kernel Generation in the Era of LLMs
Yang Yu, Peiyu Zang, Chi Hsu Tsai +11
The performance of modern AI systems is fundamentally constrained by the quality of their underlying GPU kernels, which translate high-level algorithmic semantics into low-level ha…
cs.AI2025
MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination
Ao Jia, Haiming Wu, Guohui Yao +3
Large language models (LLMs) are prone to three types of hallucination: Input-Conflicting, Context-Conflicting and Fact-Conflicting hallucinations. The purpose of this study is to…