Researchers introduced MAServeBench, a benchmark for evaluating multi-agent LLM serving systems under hierarchical collaboration workloads, addressing how interdependent model and tool operations become ready at different times and consume evolving context rather than behaving like independent requests. The benchmark targets the serving layer of LLM-based multi-agent systems, where specialized agents coordinate to solve complex tasks.
Large language model (LLM)-based multi-agent systems (MAS) coordinate specialized agents to solve complex tasks, turning each user request into interdependent model and tool operations. Unlike independent requests, these operations become ready at different times and consume evolving context as agen
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