Multi-Agent Routing as Set-Valued Prediction: A WildChat Benchmark and Cost-Aware Evaluation
arXiv:2606.28925v1 Announce Type: new Abstract: Tool and agent routing from natural-language prompts is naturally a set-valued prediction problem: a single query may require multiple agents, while over-selection increases execution cost. The benchmark introduced here is derived from WildChat and contains 3,000 prompts over a fixed 12-agent catalog, with AI-assisted heuristic labels under a fixed schema and controlled rebalancing for multi-label evaluation. The evaluation protocol combines set-level metrics (Precision, Recall, F1, Jaccard, and Exact Match), latency, an execution-oriented capabi
The proliferation of AI models and tools necessitates more sophisticated methods for managing and orchestrating them, particularly as applications move towards complex multi-agent systems.
This benchmark addresses a critical bottleneck in the development of robust AI agents, providing a standardized way to evaluate performance and cost-effectiveness in multi-agent routing.
The introduction of WildChat as a set-valued prediction benchmark and its cost-aware evaluation protocol standardizes how multi-agent routing systems are assessed, leading to more efficient and reliable AI agent deployments.
- · AI agents developers
- · Companies adopting multi-agent systems
- · AI research institutions
- · Tool developers
- · Inefficient AI routing solutions
- · Companies reliant on single-agent architectures
- · Organizations with high AI execution costs
Improved benchmarks will accelerate the development of more capable and cost-efficient multi-agent AI systems.
The widespread adoption of these systems will enable further automation of complex workflows currently requiring human oversight.
This could lead to a significant restructuring of service industries as AI agents gain the ability to autonomously manage multi-step processes with optimized resource allocation.
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