2 citations · 2 across the 3 of their papers we have counts for
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cs.NI2026
Multi-stage Flow Scheduling for LLM Serving
Yijun Sun, Xudong Liao, Songrun Xie +5
Meeting stringent Time-To-First-Token (TTFT) requirements is crucial for LLM applications. To improve efficiency, modern LLM serving systems adopt disaggregated architectures with…
cs.NI2025
Analyzing Communication Predictability in LLM Training
Wenxue Li, Xiangzhou Liu, Yuxuan Li +9
Effective communication is essential in distributed training, with predictability being one of its most significant characteristics. However, existing studies primarily focus on ex…
cs.NI2024★ 2 cited
Towards Fair and Efficient Learning-based Congestion Control
Xudong Liao, Han Tian, Chaoliang Zeng +2
Recent years have witnessed a plethora of learning-based solutions for congestion control (CC) that demonstrate better performance over traditional TCP schemes. However, they fail…