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 indepen
MAServeBench evaluates LLM serving and hardware by modeling multi-agent collaboration as a configurable workload dimension with reproducible dependency traces.
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 agents coordinate and exchange results. This creates changing patterns of concurrency, computation, and memory demand that can expose different bottlenecks in LLM serving hardware. Yet benchmarking these effects requires capturing the collaboration rules that generate serving workloads, beyond the requests observed during execution. We present MAServeBench, a benchmark that treats multi-agent…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
MAServeBench: Benchmarking Multi-Agent LLM Serving Systems with Hierarchical Collaboration Workloads was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 7 checks on 2 different days. Its best position was #25 of 30, reached on Nov 3, 15:28 UTC. It dropped off the list after Nov 4, 03:28 UTC.
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Not at the moment. It was last on the list on Nov 4, 03:28 UTC, after 2 days of trending.
#25 on Hugging Face · Daily papers, on Nov 3, 15:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (7 checks) since.