3 citations · 3 across the 6 of their papers we have counts for
8 papers
Where Do Test-Time Scaling and Training Fall Short in Individual Stance Prediction?
Yuyang Zhao, Xuan Liu, HaoYang Shang +2
Test-time scaling and post-training have improved LLM performance in coding and mathematical reasoning, but their effectiveness for individual stance prediction remains unclear. We…
Validated Behavioral Hypotheses as a Lens for Evaluating Participant Simulation
Xuan Liu, HaoYang Shang, Zizhang Liu +5
We propose using validated behavioral hypotheses as a lens for evaluating LLM agents as simulated human participants. This approach makes behavioral agreement in participant simula…
Diagon: A Programmable Testbed for AI-Agent Cognitive Labor Markets
Xuan Liu, Haoyang Shang, Haojian Jin
AI agents are emerging as market participants that trade delegated cognitive work with one another on behalf of their users. Each agent can act as both a task poster and a contract…
HumanStudy-Bench: Towards AI Agent Design for Participant Simulation
Xuan Liu, Haoyang Shang, Zizhang Liu +4
Large language models (LLMs) are increasingly used as simulated participants in social science experiments, but their behavior is often unstable and highly sensitive to design choi…
Love First, Know Later: Persona-Based Romantic Compatibility Through LLM Text World Engines
Haoyang Shang, Zhengyang Yan, Xuan Liu
We propose Love First, Know Later: a paradigm shift in computational matching that simulates interactions first, then assesses compatibility. Instead of comparing static profiles,…
Mutual Wanting in Human--AI Interaction: Empirical Evidence from Large-Scale Analysis of GPT Model Transitions
HaoYang Shang, Xuan Liu
The rapid evolution of large language models (LLMs) creates complex bidirectional expectations between users and AI systems that are poorly understood. We introduce the concept of…
CoBRA: Programming Cognitive Bias in Social Agents Using Classic Social Science Experiments
Xuan Liu, Haoyang Shang, Haojian Jin
This paper introduces CoBRA, a novel toolkit for systematically specifying agent behavior in LLM-based social simulation. We found that conventional approaches that specify agent b…
United Minds or Isolated Agents? Exploring Coordination of LLMs under Cognitive Load Theory
HaoYang Shang, Xuan Liu, Zi Liang +3
Large Language Models (LLMs) exhibit a notable performance ceiling on complex, multi-faceted tasks. As practitioners increasingly rely on heavy context engineering -- curating intr…