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

Yingtao Tian

Keywords: Complex Systems, Monte Carlo Tree Search (MCTS), Large Language Models (LLMs), LLM-guided Optimization, Program-space optimization, Long-horizon dynamics

TL;DR: CEDAR uses LLM-guided MCTS to edit complex system models for natural-language goals, combining an Editor and Judge for strong, interpretable results.

Abstract:

Complex systems modeling analyzes nonlinear, feedback-driven phenomena from population dynamics to economic policy, supporting decisions with significant societal impact.
In established practice, models are often authored in specialized system-dynamics languages (e.g., DYNAMO, STELLA) that specify the models' structure.
However, building and refining such models requires extensive manual effort due to (1) the opaque relationship between the structure and emergent behavior and (2) the labor-intensive workflows imposed by these languages.
These barriers limit adoption and hinder effective decision-making.
To address these challenges, we introduce \method (\methodlong), an autonomous method that uses LLM (Large Language Model) agents to discover and improve complex systems that satisfy user-specified goals.
Our key innovation is an LLM-driven MCTS (Monte Carlo Tree Search) process deeply coupled with complex system} at each iteration, an LLM Judge evaluates performance against goals and an LLM Editor proposes improved system variants.
We represent systems using a restricted, runnable subset of Python with domain-specific primitives, enabling LLMs to meaningfully and modify system dynamics directly.
CEDAR is theoretically designed with formalization in mind, and empirically enables automatic optimization of vague goals, thereby reducing human effort while achieving capabilities beyond existing approaches.
The unified design handles diverse systems across domains, constructing complex systems that would otherwise require extensive manual fitting.
Moreover, by using LLMs to interpret systems, \method makes system design transparent and accessible, facilitating broader adoption of complex systems modeling.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 24272