📄 中文摘要
适应性系统通常在多个情境中运行，同时由于内存、表示或物理资源的限制而重用固定的内部状态空间。这种单一状态的重用在自然和人工智能中普遍存在，但其基本的表征后果仍然不够清晰。研究表明，情境性并不是量子力学的特性，而是经典概率表示中单一状态重用的必然结果。将情境建模为作用于共享内部状态的干预，证明了任何重现情境结果统计的经典模型都必须承担不可减少的信息论成本：对情境的依赖不能仅通过内部状态来调节。
基于单一状态表示的情境性：适应性智能的信息论原则
📄 English Summary
Contextuality from Single-State Representations: An Information-Theoretic Principle for Adaptive Intelligence
Adaptive systems often operate across multiple contexts while reusing a fixed internal state space due to constraints on memory, representation, or physical resources. This single-state reuse is prevalent in both natural and artificial intelligence, yet its fundamental representational implications remain poorly understood. The research demonstrates that contextuality is not merely a feature of quantum mechanics but an inevitable outcome of single-state reuse in classical probabilistic representations. By modeling contexts as interventions acting on a shared internal state, it is proven that any classical model reproducing contextual outcome statistics must incur an irreducible information-theoretic cost: dependence on context cannot be mediated solely through the internal state.