To operate reliably under changing conditions, complex systems require feedback on how effectively they use resources, not just whether objectives are met. Current AI systems process vast information to produce sophisticated predictions, yet predictions can appear successful while the underlying interaction with the environment degrades. What is missing is a principled measure of how much of the total information a system deploys is actually shared between its observations, actions, and outcomes. We prove this shared fraction, which we term bipredictability, P, is intrinsic to any interaction, derivable from first principles, and strictly bounded: P can reach unity in quantum systems, P equal to, or smaller than 0.5 in classical systems, and lower once agency (action selection) is introduced. We confirm these bounds in a physical system (double pendulum), reinforcement learning agents, and multi turn LLM conversations. These results distinguish agency from intelligence: agency is the capacity to act on predictions, whereas intelligence additionally requires learning from interaction, self-monitoring of its learning effectiveness, and adapting the scope of observations, actions, and outcomes to restore effective learning. By this definition, current AI systems achieve agency but not intelligence. Inspired by thalamocortical regulation in biological systems, we demonstrate a feedback architecture that monitors P in real time, establishing a prerequisite for adaptive, resilient AI.

翻译：为了在变化条件下可靠运行，复杂系统需要关于其资源使用效率的反馈，而不仅仅是目标是否达成。当前的人工智能系统处理海量信息以产生复杂的预测，然而预测可能看似成功，而其与环境的底层交互却在退化。缺失的是一个原则性的度量，用于衡量系统部署的总信息中，究竟有多少实际共享于其观测、行动与结果之间。我们证明这一共享比例（我们称之为双可预测性P）内在于任何交互，可从第一性原理推导，且具有严格界限：在量子系统中P可达1，在经典系统中P等于或小于0.5，而一旦引入代理（行动选择）则进一步降低。我们在物理系统（双摆）、强化学习智能体及多轮LLM对话中验证了这些界限。这些结果区分了代理与智能：代理是基于预测行动的能力，而智能则额外要求从交互中学习、自我监控其学习效率，并调整观测、行动与结果的范围以恢复有效学习。根据此定义，当前人工智能系统实现了代理但尚未达到智能。受生物系统中丘脑皮质调节机制的启发，我们展示了一种实时监测P的反馈架构，为构建自适应、强韧的人工智能奠定了先决条件。