用图模型构建可扩展的语义时空，解决知识路径中的信息泄露问题。
Agent Semantics, Semantic Spacetime, and Graphical Reasoning
- 基于有限γ(3,4)表示定义闭合操作集，支持任意复杂度语义建模。
- 发现局部图中吸收态普遍存在，导致信息泄漏，本质与除零错误相关。
- 揭示边界信息如何通过意图介入，为知识系统提供可预测性保障。
本文介绍语义时空图模型的部分形式化特征，用于有向知识表征与过程建模。定义了有限γ(3,4)表示，形成可扩展至任意语义复杂度的闭合操作集。语义时空公设在图路径上实现最小约束下的可预测性。任何局部图中吸收态的普遍出现意味着图过程存在信息泄漏，该问题与除零错误密切相关，暗示闭包丧失，需人工注入补救信息。语义时空模型（及其源自承诺理论）有助于阐明吸收态与边界信息的关联，说明意图如何在此处进入系统。
原文摘要 · Abstract (English)
Some formal aspects of the Semantic Spacetime graph model are presented, with reference to its use for directed knowledge representations and process modelling. A finite $γ(3,4)$ representation is defined to form a closed set of operations that can scale to any degree of semantic complexity. The Semantic Spacetime postulates bring predictability with minimal constraints to pathways in graphs. The ubiquitous appearance of absorbing states in any partial graph means that a graph process leaks information. The issue is closely associated with the issue of division by zero, which signals a loss of closure and the need for manual injection of remedial information. The Semantic Spacetime model (and its Promise Theory) origins help to clarify how such absorbing states are associated with boundary information where intentionality can enter.
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