The Limits of Attention Vectors#
In model architectures, attention is represented mathematically as a vector—a weight matrix that determines which tokens influence the next token prediction. While this feature is powerful, it is too narrow. In complex environments, attention is not merely a model parameter; it is a governance problem.
What a system pays attention to dictates what enters deliberation, what remains invisible, and where processing resources are spent.
Attention as a Resource#
When an AI system is deployed in a workflow, its attention must be structured. If a system is overwhelmed by noisy logs, irrelevant inputs, or low-priority queries, its raw capability is wasted.
Governing attention means:
- Filtering inputs: Establishing boundaries on what data the system is allowed to process.
- Prioritizing signals: Distinguishing high-priority evidence from background noise.
- Structuring prompts: Bounding the system's focus so it remains aligned with user intents.
Designing Bounded Focus#
To build effective systems, we must govern attention at the architectural level. We must design systems that can deliberately ignore noise, protect their focus from distraction, and allocate computational resources where they matter most.