一般智能的公理方法：SANC(E3) -- 具有能量E3的自组织概念网络
An Axiomatic Approach to General Intelligence: SANC(E3) -- Self-organizing Active Network of Concepts with Energy E3
AI总结：
SANC(E3)通过公理框架实现一般智能，统一感知、想象、预测等过程，基于能量函数E3的最小化。
摘要：
一般智能必须将经验重新组织为内部结构，以在有限资源下实现预测和行动。现有系统隐含地假定了固定的基本单元——标记、子词、像素或预定义的传感器通道——从而绕过了代表单元本身如何产生和稳定的问题。本文提出了SANC(E3)，一个公理框架，在其中代表单元不是事先给定的，而是作为有限激活能力下竞争选择、重建和压缩的稳定结果出现，受显式最小化能量函数E3的约束。SANC(E3)在系统标记——如{这里，现在，我}和感官来源——与通过自我组织在同时事件中产生的标记之间划清了界限。五个核心公理规范了有限容量、从共现中关联、基于相似性的竞争、基于置信度的稳定化，以及重建-压缩-更新的权衡。一个关键特征是伪内存映射I/O机制，通过该机制，内部回放的完形被通过相同的公理路径处理，如同外部感官输入。因此，感知、想象、预测、规划和行动在单一的表示和能量过程中得到统一。从公理中推导出十二个命题，显示类别形成、分层组织、无监督学习和高级认知活动都可以被理解为E3最小化下的完形完成实例。
英文摘要：
General intelligence must reorganize experience into internal structures that enable prediction and action under finite resources. Existing systems implicitly presuppose fixed primitive units -- tokens, subwords, pixels, or predefined sensor channels -- thereby bypassing the question of how representational units themselves emerge and stabilize. This paper proposes SANC(E3), an axiomatic framework in which representational units are not given a priori but instead arise as stable outcomes of competitive selection, reconstruction, and compression under finite activation capacity, governed by the explicit minimization of an energy functional E3. SANC(E3) draws a principled distinction between system tokens -- structural anchors such as {here, now, I} and sensory sources -- and tokens that emerge through self-organization during co-occurring events. Five core axioms formalize finite capacity, association from co-occurrence, similarity-based competition, confidence-based stabilization, and the reconstruction-compression-update trade-off. A key feature is a pseudo-memory-mapped I/O mechanism, through which internally replayed Gestalts are processed via the same axiomatic pathway as external sensory input. As a result, perception, imagination, prediction, planning, and action are unified within a single representational and energetic process. From the axioms, twelve propositions are derived, showing that category formation, hierarchical organization, unsupervised learning, and high-level cognitive activities can all be understood as instances of Gestalt completion under E3 minimization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
