3 citations · 3 across the 16 of their papers we have counts for
5 papers · 1 filter
Optimus: Elastic Decoding for Efficient Diffusion LLM Serving
Chiyue Wei, Cong Guo, Bowen Duan +7
Large language model (LLM) serving is fundamentally limited by inefficient hardware utilization. Autoregressive (AR) decoding underutilizes GPUs due to its strictly sequential exec…
EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture
Bowen Duan, Cong Guo, Chiyue Wei +8
Large Language Models (LLMs) have achieved impressive performance across diverse domains but remain inefficient during the autoregressive decoding phase. Unlike the prefill stage,…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching
Yuzhe Fu, Hancheng Ye, Cong Guo +7
Point-based Neural Networks (PNNs) have become a key approach for point cloud processing. However, a core operation in these models, Farthest Point Sampling (FPS), often introduces…
MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems
Yifei Wang, Hancheng Ye, Yechen Xu +8
Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators. Agentic workloads induce a temporal shi…