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cs.DC2026
Latency-Aware Orchestration for Multi-Agent LLM Workflows on Heterogeneous GPUs
Jinghao Wang, Yifeng Zhang, Xiao Zhou +7
Concurrent multi-agent workflows expose future dependencies and serving-state requirements while running on heterogeneous GPU pools with time-varying load, model residency, and res…
cs.DC2026
CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Disaggregation
Zhuoren Ye, Tianyu Wo, Dinghao Xue +4
Emerging LLM services increasingly host many sparse MoE models, yet most models receive sparse requests and remain cold. This creates a GPU memory problem: model weights are stable…
cs.DC2026
Maestro: Workload-Aware Cross-Cluster Scheduling for LLM-Based Multi-Agent Systems
Jinghao Wang, Xiao Zhou, Xiaoyang Sun +6
Large Language Model based Multi-Agent Systems (LLM-MAS) have emerged as a powerful paradigm for tackling complex tasks by breaking them into collaborative workflows of specialized…