New benchmark evaluates multi-agent routing accuracy-cost trade-offs

Researchers have introduced a new benchmark and evaluation protocol for multi-agent routing, framing it as a set-valued prediction problem. The benchmark, derived from WildChat, comprises 3,000 prompts and a catalog of 12 agents, designed to study the trade-offs between accuracy and cost in agent selection. Results indicate that supervised routers significantly outperform simpler methods like nearest-neighbor and zero-shot LLM routing, with a fine-tuned encoder achieving the best unconstrained accuracy. The study also highlights the effectiveness of Weighted Agent Routing (WAR) when applied to supervised scorers in constrained settings, particularly with the Encoder+WAR combination. AI

IMPACT This research provides a framework for studying and optimizing the cost-efficiency of multi-agent systems, potentially leading to more practical and scalable AI agent deployments.

RANK_REASON Academic paper introducing a new benchmark and evaluation protocol for multi-agent routing.

Read on arXiv cs.IR (Information Retrieval) →

• Ananto Nayan Bala
• Encoder+WAR
• Weighted Agent Routing
• WildChat
• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv
• ScienceCast
• Weighted Agent Routing (WAR)

• paper
• product

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