arXiv: cs.AI preprintsInternational6 November 2026
Set-Valued Routing for Language-Model Agent Systems: A WildChat Benchmark and Cost-Aware Evaluation
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arXiv:2606.28925v3 Announce Type: replace-cross Abstract: Language-model agent systems must map natural-language requests to one or more specialized agents before planning or tool execution. This decision is often treated as top-1 selection, although compositional requests can require several capabilities and unnecessary dispatch raises cost. We formulate fixed-catalog routing as set-valued language understanding and construct a benchmark of 3,000 WildChat prompts over 12 capability-defined agents. Reproducible schema-guided reference sets are balanced by agent frequency and route cardinality.
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