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This paper formulates multi-agent routing as set-valued prediction, introduces a WildChat-derived benchmark with 3,000 prompts over a 12-agent catalog, and evaluates methods including supervised classifiers and cost-aware routing to study accuracy-cost trade-offs.
This paper analyzes longitudinal conversational trajectories of Bing Copilot users and compares them with WildChat data, finding that individual user habits are sticky and that WildChat overrepresents power users, challenging static views of user-LLM interactions.