Formalizes design space for human-LLM collaborative planning along mode, scope, and level axes; evaluates AMBIPOM prototype via user study and benchmark revealing hybrid workflows and trade-offs.
In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Ivan Habernal, Peter Schulam, and Jörg Tiede- mann (Eds.)
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How to Steer Your Multi-Agent System: Human-LLM Collaborative Planning
Formalizes design space for human-LLM collaborative planning along mode, scope, and level axes; evaluates AMBIPOM prototype via user study and benchmark revealing hybrid workflows and trade-offs.