3 citations · 5 across the 10 of their papers we have counts for
13 papers
AStockArena: Beyond Returns, Can We Entrust Real Capital to the Decisions of Financial Agents?
Zeyu Liu, Sing Kwong Lai, Xuanzhi Feng +4
Large language models (LLMs) are increasingly used in dynamic financial trading, yet real-time trading benchmarks mainly evaluate agents by cumulative returns and risk-adjusted per…
Exploring Causal Mechanisms with Generative Agent-Based Models
Xuan Liu, Haoyang Shang, Tanya Bhat +1
In this paper, we explore using generative agent-based models for a classical ABM application: testing how individual-level behavioral rules produce collective phenomena. We introd…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
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…
Mitigating Memorization Where It Happens
Xuanqi Zhang, Haoyang Shang, Xiaoxiao Li
Large language models (LLMs) can memorize and reproduce training sequences verbatim, raising privacy and copyright concerns. Existing mitigation methods apply interventions uniform…
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…
Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
Zi Liang, Zhiyao Wu, Haoyang Shang +5
Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in revealing core model properties an…
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…
Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View
Xuan Liu, Jie Zhang, Haoyang Shang +3
Large language models (LLMs) have been shown to face hallucination issues due to the data they trained on often containing human bias; whether this is reflected in the decision-mak…