15 citations · 15 across the 5 of their papers we have counts for
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cs.AI2026
Engineering Simplicity: Simple Mechanism Interfaces Steer LLM Agents
Kehang Zhu, Anand Shah, David Parkes
Can interaction formats and textual scaffolds help large language model (LLM) agents make better decisions, and do better decisions come with better explanations? We study these qu…
cs.AI2025
Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining
Crystal Qian, Kehang Zhu, John Horton +4
Markets increasingly accommodate large language models (LLMs) as autonomous decision-making agents. As this transition occurs, it becomes critical to evaluate how these agents beha…
cs.AI2025
LLM-Powered Preference Elicitation in Combinatorial Assignment
Ermis Soumalias, Yanchen Jiang, Kehang Zhu +3
We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in combinatorial assignment. While traditional PE methods rely…