Embedding Consolidation via KV Cache and Token Statistics

Abstract

This paper proposes a method for improving context resolution and efficiency in transformer models by consolidating token embeddings at runtime. Rather than using a fixed embedding for each token, this method uses live KV cache data and token statistics to merge, downscale, or refocus embedding vectors. This creates a form of semantic compression that sharpens model attention and reduces redundant context without altering architecture or weights.

1. Definition

Embedding consolidation refers to the temporary merging or weighting of token embeddings based on runtime usage. If two or more tokens serve similar roles or meanings within a session, they may share or blend embeddings to reduce semantic noise.

2. Signals

Consolidation decisions are guided by:

• KV cache patterns (attention strength, recurrence, relevance)
• Token frequency, position, or burstiness
• Semantic proximity (embedding similarity or usage context)
• Prompt directives (e.g. "group related topics")

3. Methods

• Live clustering: group token embeddings dynamically within prompt scope
• Vector pooling: average or blend embeddings in high-coherence zones
• Attention pruning: suppress tokens with low attention impact
• Decay scoring: downweight infrequent or outdated token vectors

4. Examples

• Repeated synonyms: "huge, large, enormous" → pooled into one semantic anchor
• Focus control: "only talk about security" → suppresses unrelated token fields
• Redundancy cleanup: "the the the" → single retained embedding

5. Benefit

• Sharper attention focus
• Reduced KV clutter
• Better thread isolation
• Semantic deduplication
• Supports compressed KV cache logic

6. Difference from Compression

Compression reduces static size of model or embedding table. Consolidation changes runtime usage and influence of embeddings. Compression is permanent; consolidation is adaptive and reversible.

7. Implementation Sketch

Consolidation module C applies a transformation:

E' = C(E, stats, cache, prompt)

Where E is the embedding tensor, and stats/cache provide usage context.

8. Challenges

• Timing: avoid premature or incorrect merges
• Granularity: how aggressively to consolidate
• Reversibility: restoring token identity if needed later
• Stability: ensuring semantic integrity across long prompts

9. Future Directions

• Trainable consolidation controllers
• Cache-guided compression for long context
• Theme-aware attention biasing
• Integration with embedding modifiers

10. Synthesis

Embedding consolidation is a new form of runtime efficiency in transformers. It reduces noise, sharpens meaning, and allows the model to act smaller without being smaller. It's dynamic, interpretable, and aligned with natural compression of human discourse.