Bayesian control for coding agents

A new approach to orchestration in coding agents using Bayesian control has been proposed, where a Bayesian controller dynamically manages tool-use decisions based on a belief over candidate correctness. This method was evaluated across six LLM generators and nine coding benchmarks, demonstrating superior performance in scenarios where verification is costly and critics provide informative yet imperfect feedback. The belief state produced by this controller offers an interpretable correctness score that surpasses traditional metrics like token probability and raw tool success, enhancing uncertainty quantification for practitioners in AI development.