5 citations · 5 across the 5 of their papers we have counts for
7 papers
Revisiting Parameter Server in LLM Post-Training
Xinyi Wan, Penghui Qi, Guangxing Huang +3
Modern data parallel (DP) training favors collective communication over parameter servers (PS) for its simplicity and efficiency under balanced workloads. However, the balanced wor…
Reaching Agreement Among Reasoning LLM Agents
Chaoyi Ruan, Yiliang Wang, Ziji Shi +1
Multi-agent systems have extended the capability of agentic AI. Instead of single inference passes, multiple agents perform collective reasoning to derive high quality answers. How…
Cortex: Achieving Low-Latency, Cost-Efficient Remote Data Access For LLM via Semantic-Aware Knowledge Caching
Chaoyi Ruan, Chao Bi, Kaiwen Zheng +3
Large Language Model (LLM) agents tackle data-intensive tasks such as deep research and code generation. However, their effectiveness depends on frequent interactions with knowledg…
DynaServe: Unified and Elastic Execution for Dynamic Disaggregated LLM Serving
Chaoyi Ruan, Yinhe Chen, Dongqi Tian +4
LLM inference must meet strict latency SLOs (e.g., 100 ms P99 time-between-tokens) while maximizing goodput. Yet, real-world variability in prompt and response lengths skews comput…
PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models
Chenzhuo Zhao, Ziqian Liu, Xinda Wang +2
Prompt optimization is a practical and widely applicable alternative to fine tuning for improving large language model performance. Yet many existing methods evaluate candidate pro…
Fanar: An Arabic-Centric Multimodal Generative AI Platform
Fanar Team, Ummar Abbas, Mohammad Shahmeer Ahmad +39
We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star an…