用极简模型分析场景中隐含意图，低成本识别内容与背景
On The Role of Intentionality in Knowledge Representation: Analyzing Scene Context for Cognitive Agents with a Tiny Language Model
- 通过时空一致性检测多尺度异常，区分有意内容与环境背景
- 无需训练或复杂推理，计算开销极低，适合基础智能体
- 适用于认知代理在有限记忆下识别潜在意图，具生物启发性
自塞尔关于意向性哲学探讨以来，科技领域对意图的实际意义关注甚少。本文基于承诺理论的语义时空模型，提出一种极简语言模型方法，仅凭过程一致性即可在不理解具体语言的前提下，从文本中识别主题与概念。任何智能体均可通过检测多尺度异常并评估其形成过程，粗略判断数据中隐含的‘意向性’程度。利用尺度分离，可将内容划分为‘有意’部分与‘环境背景’，以时空一致性为衡量标准。该方法实现成本极低，无需大规模概率批处理或训练，适用于计算资源受限的基础智能体。概念形成能力则取决于智能体的记忆容量。
原文摘要 · Abstract (English)
Since Searle's work deconstructing intent and intentionality in the realm of philosophy, the practical meaning of intent has received little attention in science and technology. Intentionality and context are both central to the scope of Promise Theory's model of Semantic Spacetime, used as an effective Tiny Language Model. One can identify themes and concepts from a text, on a low level (without knowledge of the specific language) by using process coherence as a guide. Any agent process can assess superficially a degree of latent `intentionality' in data by looking for anomalous multi-scale anomalies and assessing the work done to form them. Scale separation can be used to sort parts into `intended' content and `ambient context', using the spacetime coherence as a measure. This offers an elementary but pragmatic interpretation of latent intentionality for very low computational cost, and without reference to extensive training or reasoning capabilities. The process is well within the reach of basic organisms as it does not require large scale artificial probabilistic batch processing. The level of concept formation depends, however, on the memory capacity of the agent.
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