EP271: Steer locked AI with Agentic Monte Carlo

Title: Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box AgentsSource: http://arxiv.org/abs/2606.05296v1Summary:This work presents a foundational breakthrough for optimizing black-box LLM agents by applying the theoretical equivalence between reinforcement learning and Bayesian inference through Sequential Monte Carlo sampling. It enables principled, RL-style performance improvements for proprietary models by scaling test-time compute, providing a critical framework for steering agents without parameter-level access.