CEDAR: Agent-Orchestrated Tree Search for Goal-Directed Optimization of Complex Systems

2026-08-07

AI Summary

This work addresses the challenge of goal-directed design in complex systems, whose emergent behaviors—arising from nonlinear feedback—are notoriously difficult to predict, while existing modeling approaches remain cumbersome and inefficient. To overcome these limitations, the authors propose a novel framework that deeply integrates large language models (LLMs) with Monte Carlo Tree Search (MCTS). In this approach, the LLM serves dual roles: as a structural editor implementing mutation operators and as a behavioral evaluator approximating the fitness function. Operating within a constrained search space composed of executable Python subsets and domain-specific primitives, the method enables efficient and interpretable evolution of system architectures. It substantially reduces manual intervention, automatically discovers structures that fulfill specified behavioral objectives, and uncovers causal mechanisms linking system structure to emergent dynamics, thereby advancing the application of complex systems across diverse domains.

Abstract

Complex systems, core objects of study in artificial life, model diverse phenomena through nonlinear, feedback-driven interactions that produce emergent behavior, with applications from population dynamics and biology to economic policy and strategic decision-making. Yet the difficulty of predicting how feedback structure gives rise to emergent behavior, a central open problem in artificial life, makes goal-directed design exceptionally challenging. In established practice, system structures are written in specialized modeling languages such as DYNAMO or STELLA, compounding the challenge with labor-intensive workflows that limit adoption and hinder timely decision-making. To address these challenges, we introduce CEDAR, an autonomous method that uses Large Language Model (LLM) agents to discover complex systems satisfying user-specified behavioral goals. Our key innovation is an LLM-driven Monte Carlo Tree Search (MCTS) deeply coupled with complex systems: at each iteration, an LLM Judge evaluates emergent behavior against specified goals and an LLM Editor proposes improved variants, with the Judge acting as a fitness function and the Editor as a variation operator, akin to a generate-and-evaluate loop in evolutionary computation. We represent complex systems as a restricted, runnable subset of Python with domain-specific primitives, letting LLMs modify system dynamics directly. CEDAR formalizes this as an MCTS variant with an LLM-parameterized transition kernel and value function, enabling goal-directed discovery of complex system behaviors while preserving solution diversity, and its LLM-based interpretability reveals how structural changes drive emergent behavior. CEDAR reduces human effort while enabling capabilities difficult to achieve with existing approaches, facilitating broader adoption of complex systems across domains.

Problem

• complex systems
• goal-directed optimization
• emergent behavior
• feedback structure
• system design

Innovation

• LLM-agent
• Monte Carlo Tree Search
• complex systems
• goal-directed optimization
• emergent behavior