标题： 关于上下文-内容不确定性原理 显示英文标题

标题： On Context-Content Uncertainty Principle

摘要： 上下文-内容不确定性原理（CCUP）提出，在不确定性下的推理受上下文和内容之间的熵不对称性所支配：高熵的上下文必须通过与低熵、结构化内容的对齐来解释。 在本文中，我们开发了一个分层的计算框架，该框架从此基本的不对称性中推导出操作原则。 在基础层，CCUP将推理形式化为方向性熵最小化，建立一个有利于内容优先结构的变分梯度。 在此基础上，我们确定了四个层次的操作原则：(\textbf{L1}) \emph{核心 推理约束}，包括结构优先于特定性、非对称推理流、循环一致性自举和条件压缩，所有这些都被证明是相互可约的；(\textbf{L2}) \emph{资源分配 原则}，例如精度加权注意力、非对称学习率和基于吸引子的记忆编码；(\textbf{L3}) \emph{时间自举 动力学}，它们通过结构引导的课程组织随时间的学习；以及(\textbf{L4}) \emph{空间分层组合}，它们将这些机制整合到自我组织的记忆、推理和规划循环中。 我们提出形式等价定理，原理之间的依赖格，以及展示CCUP对齐推理效率提升的计算模拟。 这项工作为理解大脑和机器如何通过递归结构特异性对齐来最小化不确定性提供了统一的理论基础。 大脑不仅仅是一个推理机器。 它是一个循环一致的熵梯度解决者，通过路径依赖、内容种子模拟来对齐结构和特异性。 显示英文摘要

摘要： The Context-Content Uncertainty Principle (CCUP) proposes that inference under uncertainty is governed by an entropy asymmetry between context and content: high-entropy contexts must be interpreted through alignment with low-entropy, structured content. In this paper, we develop a layered computational framework that derives operational principles from this foundational asymmetry. At the base level, CCUP formalizes inference as directional entropy minimization, establishing a variational gradient that favors content-first structuring. Building upon this, we identify four hierarchical layers of operational principles: (\textbf{L1}) \emph{Core Inference Constraints}, including structure-before-specificity, asymmetric inference flow, cycle-consistent bootstrapping, and conditional compression, all shown to be mutually reducible; (\textbf{L2}) \emph{Resource Allocation Principles}, such as precision-weighted attention, asymmetric learning rates, and attractor-based memory encoding; (\textbf{L3}) \emph{Temporal Bootstrapping Dynamics}, which organize learning over time via structure-guided curricula; and (\textbf{L4}) \emph{Spatial Hierarchical Composition}, which integrates these mechanisms into self-organizing cycles of memory, inference, and planning. We present formal equivalence theorems, a dependency lattice among principles, and computational simulations demonstrating the efficiency gains of CCUP-aligned inference. This work provides a unified theoretical foundation for understanding how brains and machines minimize uncertainty through recursive structure-specificity alignment. The brain is not just an inference machine. It is a cycle-consistent entropy gradient resolver, aligning structure and specificity via path-dependent, content-seeded simulation.