4 citations · 4 across the 10 of their papers we have counts for
Showing cs.AIShow all
3 papers · 1 filter
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.AI2026
LLM Active Alignment: A Nash Equilibrium Perspective
Tonghan Wang, Yuqi Pan, Xinyi Yang +3
We develop a game-theoretic framework for predicting and steering the behavior of populations of large language models (LLMs) through Nash equilibrium (NE) analysis. To avoid the i…
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…