bayesian-control

This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control in nonlinear dynamical systems, using a spiking neural network model demonstrated on a benchmark problem.

This paper formulates orchestration of coding agents as cost-sensitive sequential hypothesis testing using a Bayesian controller that dynamically decides when to gather evidence, refine, verify, or stop. Experiments across six generators and nine benchmarks show Bayesian control is most valuable when verification is costly and critics are informative but imperfect.