Presents a WildChat-derived benchmark for multi-agent routing as set-valued prediction and reports that supervised methods outperform nearest-neighbor and zero-shot LLM baselines in both unconstrained accuracy and constrained cost settings.
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Multi-Agent Routing as Set-Valued Prediction: A WildChat Benchmark and Cost-Aware Evaluation
Presents a WildChat-derived benchmark for multi-agent routing as set-valued prediction and reports that supervised methods outperform nearest-neighbor and zero-shot LLM baselines in both unconstrained accuracy and constrained cost settings.