As artificial agents become increasingly capable, what internal structure is *necessary* for an agent to act competently under uncertainty? Classical results show that optimal control can be *implemented* using belief states or world models, but not that such representations are required. We prove quantitative "selection theorems" showing that strong task performance (low *average-case regret*) forces world models, belief-like memory and -- under task mixtures -- persistent variables resembling core primitives associated with emotion, along with informational modularity under block-structured tasks. Our results cover stochastic policies, partial observability, and evaluation under task distributions, without assuming optimality, determinism, or access to an explicit model. Technically, we reduce predictive modeling to binary "betting" decisions and show that regret bounds limit probability mass on suboptimal bets, enforcing the predictive distinctions needed to separate high-margin outcomes. In fully observed settings, this yields approximate recovery of the interventional transition kernel; under partial observability, it implies necessity of predictive state and belief-like memory, addressing an open question in prior world-model recovery work.

翻译：摘要：随着人工智能主体能力日益增强，何种内部结构是主体在不确定性下胜任行动所*必需*的？经典结果表明，最优控制可通过信念状态或世界模型*实现*，但并未指出此类表征是必要的。我们证明了量化的"选择定理"：强任务表现（低*平均情形遗憾值*）迫使主体具备世界模型、类信念记忆，并在任务混合条件下迫使存在类似情感核心原语的持久变量，以及在分块结构任务下迫使信息模块化。我们的结果涵盖随机策略、部分可观测性及任务分布下的评估，不假设最优性、确定性或显式模型的存在。技术层面，我们将预测建模简化为二元"博弈"决策，并证明遗憾界限制了次优博弈上的概率质量，从而强制要求做出分离高收益结果所需的预测区分。在完全可观测设定中，这导致干预转移核的近似恢复；在部分可观测性下，它意味着预测状态与类信念记忆的必要性，从而解答了先前世界模型恢复研究中的一个开放问题。