Compressing Sequences in the Latent Embedding Space: $K$-Token Merging for Large Language Models

2026-04-16 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

K-Token Merging is a novel latent-space compression framework designed to reduce the computational and memory costs associated with processing long prompts in Large Language Models (LLMs). Unlike existing token-space compression methods, this approach merges contiguous blocks of K token embeddings into a single embedding using a lightweight encoder. The resulting compressed sequence is then processed by a LoRA-adapted LLM, while the generation phase continues to use the original vocabulary. Experimental evaluations across tasks like structural reasoning (Textualized Tree), sentiment classification (Amazon Reviews), and code editing (CommitPackFT) demonstrate that K-Token Merging achieves up to 75% input length reduction with minimal performance degradation, positioning it on the Pareto frontier for performance versus compression efficiency.

Key takeaway

For AI Engineers optimizing LLM inference costs for long contexts, K-Token Merging offers a promising strategy to significantly reduce input sequence length by up to 75% with minimal performance impact. You should consider integrating this latent-space compression technique, especially for applications involving extensive textual data like code or detailed reviews, to improve throughput and memory efficiency on existing hardware.

Key insights

K-Token Merging compresses LLM inputs in the latent embedding space for efficiency.

Principles

• Latent-space compression improves LLM efficiency.
• Merging contiguous token blocks reduces sequence length.

Method

Merge K contiguous token embeddings into one via a lightweight encoder, process with a LoRA-adapted LLM, and generate in the original vocabulary.

In practice

• Achieve 75% input length reduction.
• Apply to structural reasoning or code editing.

Topics

• K-Token Merging
• Latent Embedding Space
• Large Language Models
• Token Compression
• Self-Attention

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.