1 citations · 1 across the 8 of their papers we have counts for
9 papers
Alignment Is Local: A Paired Diagnostic for GUI Agents under User-Side Persuasion
Haoxin An, Yunpeng Song, Zihao Bai +6
Trustworthy deployment of GUI agents in ubiquitous computing settings requires alignment that survives dynamic interaction and precise threat conditions, not just single-turn refus…
Autoregressive Diffusion World Models for Off-Policy Evaluation of LLM Agents
Kaixuan Liu, Guojun Xiong, Weinan Zhang +1
Evaluating large language model (LLM) agents in multi-turn interactive environments is expensive and risky, as it requires online environment interaction. We propose ADWM (Autoregr…
Social Networks of LLM Agents
Kaixuan Liu, Guojun Xiong, Weinan Zhang +1
Large language model (LLM) agents are increasingly deployed in interacting populations, raising the question of what such populations come to believe collectively. Whether a popula…
Health Facility Location in Ethiopia: Leveraging LLMs to Integrate Expert Knowledge into Algorithmic Planning
Yohai Trabelsi, Guojun Xiong, Fentabil Getnet +2
Ethiopia's Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, require careful prioritiza…
VORTEX: Aligning Task Utility and Human Preferences through LLM-Guided Reward Shaping
Guojun Xiong, Milind Tambe
In social impact optimization, AI decision systems often rely on solvers that optimize well-calibrated mathematical objectives. However, these solvers cannot directly accommodate e…
Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
Lingkai Kong, Haichuan Wang, Tonghan Wang +2
Incorporating pre-collected offline data can substantially improve the sample efficiency of reinforcement learning (RL), but its benefits can break down when the transition dynamic…
Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents
Mauricio Tec, Guojun Xiong, Haichuan Wang +2
Deep Reinforcement Learning (RL) is remarkably effective in addressing sequential resource allocation problems in domains such as healthcare, public policy, and resource management…
Finite-Horizon Single-Pull Restless Bandits: An Efficient Index Policy For Scarce Resource Allocation
Guojun Xiong, Haichuan Wang, Yuqi Pan +4
Restless multi-armed bandits (RMABs) have been highly successful in optimizing sequential resource allocation across many domains. However, in many practical settings with highly s…
Optimizing Vital Sign Monitoring in Resource-Constrained Maternal Care: An RL-Based Restless Bandit Approach
Niclas Boehmer, Yunfan Zhao, Guojun Xiong +5
Maternal mortality remains a significant global public health challenge. One promising approach to reducing maternal deaths occurring during facility-based childbirth is through ea…