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

• URL: http://arxiv.org/abs/2608.06871v1
• Date: Fri, 07 Aug 2026 06:54:58 GMT
• Title: CEDAR: Agent-Orchestrated Tree Search for Goal-Directed Optimization of Complex Systems
• Abstract summary: Complex systems model diverse phenomena through nonlinear, feedback-driven interactions.<n>The difficulty of predicting how feedback structure gives rise to emergent behavior makes goal-directed design exceptionally challenging.<n>In established practice, system structures are written in specialized modeling languages such as DYNAMO or STELLA.<n>We introduce CEDAR, an autonomous method that uses Large Language Model (LLM) agents to discover complex systems satisfying user-specified behavioral goals.
• Score: 4.66788819655844
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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