Conversation Resolution as a Method of Model Reduction

Abstract

This paper describes how conversation resolution can function as a lightweight method of model reduction. Instead of reducing parameters or changing architecture, the model narrows its active interpretive field by focusing on the current conversation. By resolving scope, tone, and task via prompt, the model operates with fewer competing meanings, creating the effect of a smaller, purpose-tuned system.

1. Definition

Model reduction means limiting model behavior. Here, it is done by narrowing context scope, not weights or size.

2. Mechanism

Directives reduce attention spread. "Ignore above" or "Focus only on step two" shrink the active semantic range.

3. Examples

- "You are only the planner"

- "Summarize this part"

- "Forget previous section"

4. Benefits

- Less noise

- Sharper focus

- Simpler outputs

5. Comparison

Compression reduces parameters. Resolution reduces scope. Both reduce load—but in different ways.

6. Integration

Works well with prompt control, embedding activation, and thread markers.

7. Limits

- Depends on user clarity

- Not enforced memory wiping

- Vulnerable to drift

8. Future

- Runtime scope visualizers

- Scoped KV segments

- Reusable resolution patterns

9. Synthesis

Conversation resolution is a prompt-based runtime control method that reduces operational complexity without altering the model.