Modifying Embeddings During Inference for Context Resolution

Abstract

This paper proposes a new method for context resolution in transformer models: modifying token embeddings at runtime. Unlike standard inference, where embeddings are fixed, this method adjusts embeddings based on prompt directives or token statistics. The model does not change its weights, but its interpretive frame is altered by reshaping the input space. This allows deeper, more adaptive context control during generation.

1. Definition

An embedding is a fixed vector representation of a token. During inference, embeddings are normally static. Here, we propose a method where embeddings are transformed on-the-fly based on live prompt cues or statistical patterns, enabling the model to adapt meaning dynamically.

2. Mechanism

Embedding modification can occur through:

• Directive-driven transforms (e.g. tone shift)
• Token frequency statistics (boost rare or dampen repeated terms)
• Persona overlays (role-specific embedding filters)
• Semantic gating (highlight topic-relevant dimensions)

This is not fine-tuning. It is live remapping of the embedding space before tokens enter the transformer layers.

3. Motivation

• Fixed embeddings assume static interpretation
• Prompts already steer behavior; modifying embeddings deepens that
• Tone, stance, or context could be directly encoded as vector influence

4. Examples

• "Speak like a poet" → shift embeddings toward metaphorical dimensions
• "Switch to a skeptical tone" → tilt toward adversarial framing
• "Focus only on sensory details" → suppress abstract terms

5. Technical Sketch

Embeddings E are passed through a modifier M:

E' = M(E, directive, stats)

Where M is a learned or rule-based transform that changes the space locally.

6. Benefits

• Fast: no weight changes
• Adaptive: fits dynamic prompt scenarios
• Modular: multiple mod layers could be composed
• Compatible: works with current transformer pipelines

7. Risks

• Distortion: poorly calibrated modifications could misalign meaning
• Fragility: directive grammar must be clearly structured
• Control complexity: user must guide vector shifts wisely

8. Future Directions

• Trainable embedding modifiers
• Modular context heads
• Statistical controllers for live adaptation
• External agent to manage embedding field

9. Synthesis

Embedding modification at runtime is a new form of context resolution. It shifts the question from "what is attended to" to "what does this token mean right now." This adds depth, control, and adaptability—transforming the transformer's interpretive core without changing its architecture.