Abstract
The semantics and dynamics of `attention' are closely related to promise theoretic notions developed for autonomous agents and can thus easily be written down in promise framework. In this way one may establish a bridge between vectorized Machine Learning and Knowledge Graph representations without relying on language models implicitly. Our expectations for knowledge presume a degree of statistical stability, i.e. average invariance under repeated observation, or `trust' in the data. Both learning networks and knowledge graph representations can meaningfully coexist to preserve different aspects of data. While vectorized data are useful for probabilistic estimation, graphs preserve the intentionality of the source even under data fractionation. Using a Semantic Spacetime $\gamma(3,4)$ graph, one avoids complex ontologies in favour of classification of features by their roles in semantic processes. The latter favours an approach to reasoning under conditions of uncertainty. Appropriate attention to causal boundary conditions may lead to orders of magnitude compression of data required for such context determination, as required in the contexts of autonomous robotics, defence deployments, and ad hoc emergency services.
Abstract (translated)
`注意力'的语义和动态与为自主代理开发的承诺理论概念密切相关，因此可以很容易地在承诺框架中描述。通过这种方式，人们可以在不依赖语言模型的情况下，在向量化的机器学习表示和知识图表示之间建立一座桥梁。我们对知识的期望假设数据具有一定程度的统计稳定性，即重复观察下的平均不变性或“信任”。无论是学习网络还是知识图表示都能有意义地共存以保存数据的不同方面：虽然向量化数据对于概率估计很有用，但在数据分割的情况下，图形仍然可以保留源的意图。使用语义时空 $\gamma(3,4)$ 图，人们可以通过按其在语义过程中的角色分类特征来避免复杂的本体论，这有利于不确定条件下的推理方法。适当关注因果边界条件可能会导致大量数据压缩，以确定此类上下文所需的数据量，这对于自主机器人、防御部署和临时紧急服务等应用至关重要。
URL
https://arxiv.org/abs/2512.19084
