Context Resolution by Activating and Deactivating Embeddings via Prompt Directive

Abstract

This paper explains how prompt directives help a language model shift its current context by activating or deactivating parts of the embedding space. Instead of deleting memory or fetching data, the model reweights which meanings dominate, changing its current interpretive frame. This supports precise tone control, focus, and role shifts at runtime.

1. Definition

The model adjusts what it pays attention to by changing which embeddings are active. Prompt cues activate or deactivate certain interpretations.

2. Mechanism

Prompt directives early in the sequence influence all later tokens. Attention shifts, latent vectors reshape, and certain traits or tones emerge.

3. Examples

"Explain like I'm five"

"Return to legal tone"

"Switch to critical view"

4. Embedding Effects

Vector rotation

Tone shift

Trait emergence

5. Memory Comparison

Memory stores past data. Embedding control shifts current interpretation. Memory asks "What happened?" Embedding asks "What matters now?"

6. Applications

Role changes

Layered writing

Audience adjustment

7. Limits

Training dependency

Fuzzy deactivation

Drift risk

8. Future

Embedding heatmaps

Prompt tags

Directive patterns

9. Synthesis

Embedding activation/deactivation gives models lightweight, precise context control using only prompt structure.