12 citations · 25 across the 5 of their papers we have counts for
5 papers
ContextEvolve: Multi-Agent Context Compression for Systems Code Optimization
Hongyuan Su, Yu Zheng, Yong Li
Large language models are transforming systems research by automating the discovery of performance-critical algorithms for computer systems. Despite plausible codes generated by LL…
Skrull: Towards Efficient Long Context Fine-tuning through Dynamic Data Scheduling
Hongtao Xu, Wenting Shen, Yuanxin Wei +6
Long-context supervised fine-tuning (Long-SFT) plays a vital role in enhancing the performance of large language models (LLMs) on long-context tasks. To smoothly adapt LLMs to long…
Wan: Open and Advanced Large-Scale Video Generative Models
Team Wan, Ang Wang, Baole Ai +58
This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transfo…
Qwen2.5-1M Technical Report
An Yang, Bowen Yu, Chengyuan Li +25
We introduce Qwen2.5-1M, a series of models that extend the context length to 1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series have significantly enha…
GLOP: Learning Global Partition and Local Construction for Solving Large-scale Routing Problems in Real-time
Haoran Ye, Jiarui Wang, Helan Liang +3
The recent end-to-end neural solvers have shown promise for small-scale routing problems but suffered from limited real-time scaling-up performance. This paper proposes GLOP (Globa…