# γ(3,4) `Attention' in Cognitive Agents: Ontology-Free Knowledge Representations With Promise Theoretic Semantics

arXiv: https://arxiv.org/abs/2512.19084 · HTML: https://mlsanity.com/paper/2512.19084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 22 Dec 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 7 of 7)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [Spacetimes with Semantics](https://mlsanity.com/paper/1411.5563/) (2014)
- [A Promise Theory Perspective on the Role of Intent in Group Dynamics](https://mlsanity.com/paper/2402.00598/) (2024)
- [Fairness in Augmented Graph Learning: A Survey](https://mlsanity.com/paper/2504.21296/) (2025)
- [Agent Semantics, Semantic Spacetime, and Graphical Reasoning](https://mlsanity.com/paper/2506.07756/) (2025)
- [On The Role of Intentionality in Knowledge Representation: Analyzing Scene Context for Cognitive Agents with a Tiny Language Model](https://mlsanity.com/paper/2507.10000/) (2025)

## Abstract (arXiv)

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 γ(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.