CEDAR

Complex systems research models feedback-driven phenomena from population dynamics to economic policy, and its central open problem is that nobody can predict how feedback structure gives rise to emergent behavior, which makes goal-directed design very hard. CEDAR, from Sakana AI, attacks that with LLM agents running Monte Carlo Tree Search over the space of feedback structures rather than tuning parameters on a fixed one. Systems are represented as a restricted runnable subset of Python with domain-specific primitives so the models can edit dynamics directly, an LLM Judge scores emergent behavior against the stated goal as a fitness function, and an LLM Editor proposes variants as a variation operator. The formalization is an MCTS variant with an LLM-parameterized transition kernel and value function, which preserves solution diversity while searching, and the LLM-based interpretability makes it possible to read back how a structural change produced the behavior.