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cs.GT2026
Learning to Persuade a Biased Receiver
Yuqi Pan, Sadie Zhao, Milind Tambe +1
We study a repeated information design setting in which the receiver, who is also the decision-maker, updates beliefs in a systematically biased way. More specifically, a distorted…
cs.AI2026
Adaptive Multi-Round Allocation with Stochastic Arrivals
Yuqi Pan, Davin Choo, Haichuan Wang +3
We study a sequential resource allocation problem motivated by adaptive network recruitment, in which a limited budget of identical resources must be allocated over multiple rounds…
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…