6 papers
CEDAR: Agent-Orchestrated Tree Search for Goal-Directed Optimization of Complex Systems
Yingtao Tian
Complex systems, core objects of study in artificial life, model diverse phenomena through nonlinear, feedback-driven interactions that produce emergent behavior, with applications…
Shachi: A Modular, Controllable Framework for LLM-Based Agent-Based Modeling of Emergent Collective Behavior
So Kuroki, Yingtao Tian, Kou Misaki +3
How collective behaviors emerge from the interactions of individual LLM-driven agents is a central question in artificial life, yet controlled study of these emergent dynamics has…
Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents
Yingtao Tian
LLM agents in markets present algorithmic collusion risks. While prior work shows LLM agents reach supracompetitive prices through tacit coordination, existing research focuses on…
Discovering Novel LLM Experts via Task-Capability Coevolution
Andrew Dai, Boris Meinardus, Ciaran Regan +2
Frontier model developers aim to train models continually to possess emergent, diverse capabilities. To extend capabilities, the current pre-training and post-training paradigm req…
Evolution of Collective AI Beyond Individual Optimization
Ryosuke Takata, Yujin Tang, Yingtao Tian +3
This study investigates collective behaviors that emerge from a group of homogeneous individuals optimized for a specific capability. We created a group of simple, identical neural…
Position: Leverage Foundational Models for Black-Box Optimization
Xingyou Song, Yingtao Tian, Robert Tjarko Lange +3
Undeniably, Large Language Models (LLMs) have stirred an extraordinary wave of innovation in the machine learning research domain, resulting in substantial impact across diverse fi…