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)
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