4 citations · 4 across the 8 of their papers we have counts for
8 papers
KernelGenBench: Can LLMs and Agents Write Efficient Kernels Across Operator Sources and Hardware Platforms?
Peiyu Zang, Jian Tao, Jialing Zhang +4
Modern AI systems depend on specialized accelerator kernels, whose development is complicated by increasingly diverse operators and hardware. LLMs and agentic systems promise to au…
HetCCL: Enabling Collective Communication For Mixed-Vendor Heterogeneous Clusters
Yuejie Wang, Tao Chang, Yuanyuan Zhao +10
Training Large Language Models (LLMs) on heterogeneous clusters presents significant challenges for collective communication, as hardware from multiple vendors introduces diverse n…
FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching
Zihao Zheng, Xingyue Zhou, Zhihao Mao +7
Vision-Language-Navigation (VLN) models exhibit excellent navigation accuracy but incur high computational overhead. Token caching has emerged as a promising training-free strategy…
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…
Infinity Instruct: Scaling Instruction Selection and Synthesis to Enhance Language Models
Jijie Li, Li Du, Hanyu Zhao +5
Large Language Models (LLMs) demonstrate strong performance in real-world applications, yet existing open-source instruction datasets often concentrate on narrow domains, such as m…
CCI4.0: A Bilingual Pretraining Dataset for Enhancing Reasoning in Large Language Models
Guang Liu, Liangdong Wang, Jijie Li +6
We introduce CCI4.0, a large-scale bilingual pre-training dataset engineered for superior data quality and diverse human-like reasoning trajectory. CCI4.0 occupies roughly TB…
Emu3: Next-Token Prediction is All You Need
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo +22
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diff…
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
Bo-Wen Zhang, Liangdong Wang, Ye Yuan +24
In recent years, with the rapid application of large language models across various fields, the scale of these models has gradually increased, and the resources required for their…