提出自组织概念网络框架，让智能体自主生成认知单位。
An Axiomatic Approach to General Intelligence: SANC(E3) -- Self-organizing Active Network of Concepts with Energy E3
- 基于能量极小化机制，让认知单元在竞争中自发形成
- 实现感知、想象、预测与行动的统一处理流程
- 适合研究通用智能与认知架构的学者参考
通用智能必须将经验重组为内部结构，以在有限资源下实现预测与行动。现有系统隐含预设基础表征单元（如词元、子词、像素或预定义传感器通道），从而回避了表征单元如何产生并稳定的问题。本文提出SANC(E3)，一个公理化框架，其中表征单元并非先验给定，而是作为有限激活容量下竞争选择、重构与压缩的稳定结果出现，受显式能量泛函E3最小化的支配。SANC(E3)明确区分系统词元（如{此处, 此时, 我}和感官源）与通过共现事件自组织形成的词元。五个核心公理形式化了有限容量、共现关联、基于相似性的竞争、基于置信度的稳定化，以及重构-压缩-更新之间的权衡。关键特征是伪内存映射的I/O机制，使内部回放的格式塔通过与外部输入相同的公理路径处理。因此，感知、想象、预测、规划与行动统一于单一表征与能量过程。由公理推导出十二个命题，表明类别形成、层次组织、无监督学习及高层认知活动均可视为在E3极小化下的格式塔完成实例。
原文摘要 · Abstract (English)
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.
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