Deepseek has released a new paper and model, ostensibly for Optical Character Recognition (OCR), but its core innovation has far greater implications. The research introduces a groundbreaking concept called “context optimal compression,” which uses vision as a powerful compression algorithm for text. This new approach could fundamentally change how AI systems handle memory and long-context processing.
The Core Idea: Compressing Text into Images
Large Language Models (LLMs) struggle to efficiently process extremely long documents or conversation histories, as each word typically corresponds to about one token. Deepseek’s breakthrough is to store text in images, allowing a small number of vision tokens to represent a much larger volume of text. Their model can use just 100 vision tokens to reconstruct 1,000 text tokens with 97% accuracy—a 10x compression ratio with near-perfect fidelity. This isn’t just about reading documents; it’s about creating a new form of memory for LLMs.
How It Works: The Deep Encoder
The key to this achievement is a novel two-stage “deep encoder” that processes high-resolution images efficiently. Instead of tokenizing an image patch-by-patch in a way that creates too many tokens, Deepseek’s method is more sophisticated:
- Stage 1: A small SAM model identifies details at high resolution, which are then compressed 16x using a CNN.
- Stage 2: A CLIP model uses global attention to understand the relationships within this compressed information.
This allows the model to represent a document that would traditionally require 6,000 text tokens with fewer than 800 vision tokens, often with improved performance.
Significant Conclusions and Takeaways
While demonstrated on OCR tasks, this research is a proof-of-concept for a much bigger idea. Imagine an AI that could take millions of tokens of conversation history, render older parts as compressed images, and still access that information within its context window. This could enable models with effective context windows of 10 to 20 million tokens, solving one of the biggest challenges in AI today. The paper presents a new paradigm for AI memory, showcasing Deepseek’s innovative approach to pushing the boundaries of what’s possible beyond just following industry trends.
Mentoring question
This research solved a major text-based problem (long context) by applying a solution from a different domain (computer vision). What is a persistent challenge in your own field that you could re-evaluate by applying principles from a completely unrelated area?
Source: https://youtube.com/watch?v=YEZHU4LSUfU&si=KC-RiJ-7sb5oNjhc
Leave a Reply