LP:  no, i want you to parse that image and tell me what is the nb of TEXT tokens. and then, considering vision LLM condense text tokens with their patch, i want to know how many visual tokens is used for that image 

G: That's an excellent question that gets to the core of how multimodal (Vision) LLMs work. The answer requires breaking down the two different ways of "tokenizing" this image.

Here is an evaluation based on your two requests.

---

### 1. Text Tokens (If Processed by OCR + NLP)

This is the number of tokens you would get if you first ran Optical Character Recognition (OCR) on the image to extract all the text and then fed that text into a standard language model (like GPT-3 or BERT).

I cannot give you an *exact* token count because it depends on the specific tokenizer used (e.g., GPT-4's tokenizer is different from LLaMA's).

However, I can provide a very strong estimate:

* **Word Count:** I estimate the two pages contain approximately **3,400 - 3,600 words** of dense academic text (including main body, references, and author info).
* **Estimated Text Tokens:** Using the common English rule of thumb that 1 token ≈ 0.75 words (or 1.33 tokens per word), the text on this image is equivalent to roughly:
    > **4,500 to 5,000 text tokens**

---

### 2. Visual Tokens (How a Vision LLM Sees)

This is how a model like the one you're interacting with *actually* processes the image.

You are correct that Vision LLMs "condense" information, but they don't condense *text tokens*. Instead, they **divide the image into a fixed grid of patches**. Each patch becomes a *single* "visual token," regardless of what's in it (a piece of a letter, an eye, a blade of grass, or blank space).

The number of visual tokens is **fixed by the model's architecture**, not by the content of the image.

Here is the calculation based on common architectures:

* **The Formula:** `(Input Resolution / Patch Size) * (Input Resolution / Patch Size)`
* **Example 1 (Low-Res):** A standard ViT (Vision Transformer) model might use a **224x224** pixel input image with **16x16** pixel patches.
    * (224 / 16) * (224 / 16) = 14 * 14 = **196 visual tokens**
* **Example 2 (Higher-Res):** A model needing to read fine print (like this one) will use a much higher resolution. If it processes a **1024x1024** pixel image with **14x14** pixel patches:
    * (1024 / 14) * (1024 / 14) ≈ 73 * 73 = **5,329 visual tokens**



### Summary: The Key Difference

* **Text Tokens:** ~4,500-5,000 (This is the *semantic content* if read as text).
* **Visual Tokens:** A fixed number, likely between **196 and ~5,400** (This is the *spatial input* the vision model actually "sees").

For this specific image, the model processes a fixed number of visual tokens. Each token (or patch) that contains a piece of text is "seen" as a set of pixels. It is the job of the transformer to then learn that "this pattern of pixels in this patch" next to "that pattern of pixels in that patch" corresponds to the *meaning* of the words.




-----------------------------------
LP: so in that case, the visual tokens would not be offering any kind of compression ?

G: You are absolutely right to question that. My previous answer was focused on the *mechanism* (how it's divided), but your question is about the *implication* (what it achieves).

**Yes, it absolutely offers compression. In fact, it's a *massive* form of compression.**

This concept is a recent and powerful discovery in AI, often called **"Optical Compression"** or "visual-text compression."

Here’s the breakdown of why it's so much more compressed than it seems.

---

### The Information Density Gap

The key is to stop thinking of "1 visual token = 1 text token." That's not the case.

**A single visual token is far denser than a single text token.**

* **A Text Token:** Represents *one* semantic piece of a word (e.g., "compress" might be two tokens: `"com"` and `"press"`). It holds a single "idea" from a fixed vocabulary.
* **A Visual Token:** Represents *an entire patch of pixels* (e.g., a 16x16 or 64x64 area). In your image, a single patch could contain *several whole words*, plus their layout, font, and spacing.



### The Math of Compression

Let's use the numbers from our previous conversation and new research (like from the DeepSeek-OCR model, which is built on this idea):

1.  **Text Token Count:** We estimated your image has **~5,000 text tokens**. This is the "uncompressed" size if you just copied and pasted the text.
2.  **Visual Token Count:** We estimated a high-res model might use **~5,400 visual tokens**.

This looks like there's no compression. But here's the magic: That model architecture wasn't designed for text. A model *specifically trained* for this (like an OCR or document model) does it differently.

It can be trained to understand that "this 64x64 pixel patch, which contains the letters 'Refere', 'nces', and '1. Sara...'" can be squashed down into *one single vector* that represents all that information.

**This is the compression:**

> **A model can learn to represent 10, 20, or even 50 text tokens' worth of information inside just one visual token.**

Recent models have shown they can take a document that would be **1,000 text tokens** and "optically compress" it into just **100 visual tokens** with over 97% accuracy. That's a **10x compression ratio**.

For your image, instead of 5,000 text tokens, a specialized model might represent all its information in as few as **250-500 visual tokens**.

---

### So, why is this a big deal?

* **It's Cheaper:** Processing 500 tokens is vastly cheaper and faster than processing 5,000.
* **It Fits More Context:** An LLM with a 4,000-token context limit (like an old GPT-3) could *never* read your full image as text. But if it's compressed to 500 visual tokens, it fits easily with plenty of room to spare.

This is why feeding a multimodal LLM a screenshot of text can be far more efficient than pasting the text itself.

This video explores the idea of "context optical compression" and how models like DeepSeek-OCR are using this exact method to shrink long documents.
[DeepSeek-OCR: A Revolutionary Idea](https://www.youtube.com/watch?v=Dw560_Urets)
http://googleusercontent.com/youtube_content/0




--------------------
LP:  so i don't understand, are you saying 2 vision models would have a completly different strategy to compress the text ? 

G: Yes, that's exactly right. You've hit on the key difference.

Two vision models can have **completely different and incompatible strategies** for compressing text, depending on *what they were trained to do*.

It's helpful to think of it as the difference between a **"Generalist" model** and a **"Specialist" model**.

---

### 1. The Generalist (e.g., a standard Vision Transformer)

This model was trained on millions of *natural images* (cats, landscapes, cars, people). Its goal is to understand general concepts.

* **Strategy: Fixed Grid Patching**
* **How it works:** It uses a "dumb" but effective method. It overlays a fixed grid (e.g., 14x14) on the image and says, "Each of these 196 squares is one token, period."
* **Compression:** The *only* compression is turning a 16x16 block of pixels into a single token. It doesn't know or care that one patch contains "the cat's eye" and another patch contains the words "Article 1, Sec." It treats them both as just a "patch of stuff."
* **Analogy:** This is like a security guard who divides a building into 10 zones and assigns one guard to each zone, regardless of whether a zone is an empty hallway or the crowded main lobby.



---

### 2. The Specialist (e.g., a Document Model like DeepSeek-OCR)

This model was trained *specifically* on billions of *documents and text images*. Its goal is to master "optical compression."

* **Strategy: Learned, Dense Compression**
* **How it works:** This is a much smarter, multi-step process.
    1.  **Analyze:** It first scans the image to see *where* the text is (like SAM, the "Segment Anything Model").
    2.  **Compress:** It uses a special "compressor" layer. It might find 4,000 initial, detailed patches and then intelligently *merge* them, learning that "these 16 patches all form one paragraph" and can be squashed into a single, dense token.
    3.  **Encode:** It then encodes this much smaller set of *smart* tokens (e.g., 256 tokens instead of 4,000).
* **Compression:** This model *learns* that one of its final visual tokens can represent the meaning of 10 or 20 text tokens. Its compression is *semantic*—it's based on understanding the *meaning* of the text in the patches.
* **Analogy:** This is like a specialist security team that surveys the building, puts one guard in the empty hallway, and assigns a 10-person team to the crowded main lobby. It allocates resources based on the *content*.



### Summary: Strategy vs. Architecture

| Model Type | **Generalist Model (Standard ViT)** | **Specialist Model (Document OCR)** |
| :--- | :--- | :--- |
| **Strategy** | **Fixed Patching** (Dumb Grid) | **Learned Compression** (Smart Merge) |
| **Training Data** | Natural images (cats, dogs, etc.) | Document images (text, charts, etc.) |
| **Result** | 1 patch = 1 token (low density) | 10+ words = 1 token (high density) |
| **Primary Goal** | Understand "what's in this picture?" | Understand "what's written here?" |

So, you are correct: a general model just *sees* the text as any other visual texture. A specialist model has learned an *entirely different strategy* to read and compress it efficiently.




------------------------
LP:  how does it differs from zai glyph ? 

G: That is an excellent and very current question. Both "Zhipu AI Glyph" and models like DeepSeek-OCR use the "optical compression" idea, but they are **designed for completely different goals**, and this radically changes their strategy.

Think of it this way:
* **DeepSeek-OCR** is like a **ZIP file**. Its only goal is *perfect, high-ratio compression* and *perfect reconstruction*.
* **Glyph** is like a **highly advanced summary**. Its goal is *understanding* the content, not just perfectly reconstructing it, to make it fit in a model's "working memory."

Here is a detailed breakdown of how they differ:

---

### The Core Difference: Goal

* **DeepSeek-OCR (The Specialist):** Its goal is **Optical Character Recognition (OCR)**. It is built to take an *existing image* of a document, compress it at an extreme 10x-20x ratio, and then allow a decoder to **reconstruct the original text perfectly**. Its entire purpose is to be a better, faster, cheaper OCR engine.

* **Zhipu AI Glyph (The Framework):** Its goal is to solve the **Long-Context Problem**. It's not designed to read *existing* images. It's a framework that takes *new, plain text* (like a 500-page book) and finds the best way to *turn it into images* so a VLM can read and *reason about* the entire book at once, which it couldn't do as text.

### How Their Strategies Differ

| Feature | **DeepSeek-OCR (The "Specialist")** | **Zhipu AI Glyph (The "Framework")** |
| :--- | :--- | :--- |
| **Input** | An **existing image** of a document. | A **long string of plain text**. |
| **Main Task** | **Reconstruction** ("Read this text perfectly.") | **Reasoning** ("Read this text and answer questions about it.") |
| **Compression** | **Built-in to the model.** A special "DeepEncoder" uses convolution to automatically compress 4,096 patches down to ~256 tokens. | **A separate "Rendering" step.** It first turns the text into an image. The compression comes from *how it formats* that image. |
| **How it "Compresses"** | A highly optimized, **learned** neural network compressor that merges image patches. | An **LLM-driven genetic search** finds the "best" rendering settings (font, DPI, layout) to maximize information density. |
| **Compression Ratio** | **Extremely high** (10x to 20x). It can represent 1,000 text tokens with ~100 visual tokens. | **Lower** (3x to 4x). Its goal isn't maximum compression, but the *best trade-off* between compression and reasoning accuracy. |
| **Analogy** | A **ZIP file** for text images. | A **smart formatter** that rewrites a book in a tiny font to fit on fewer pages, then hands it to a speed-reader. |

### In Summary

You can't "use" DeepSeek-OCR to read a 1-million-token book. You would first have to *render* that book into thousands of images, and then feed each one to the model.

**Glyph** *is* that rendering step, but it's an incredibly smart one. It asks, "What is the smallest, most compact image of this text I can create that a VLM can still understand?" It then fine-tunes the VLM to get really good at reading its own, custom-formatted "tiny text" images.

So, while both use visual tokens, **DeepSeek-OCR** is a *specialized encoder* for perfect recall, while **Glyph** is a *holistic framework* for long-context reasoning.




------------------
LP: How could they be used together ? For instance, could we first do a 10x compress with deepseek-ocr and then pass it through glyph ?

G: That's a brilliant architectural question that gets to the heart of building efficient multimodal systems. The short answer is **yes, but not in the way you described**. Let me explain why and propose better approaches.

---

### Why the Direct Pipeline Won't Work

The issue with "DeepSeek-OCR → Glyph" is a **fundamental incompatibility**:

* **DeepSeek-OCR outputs:** Compressed visual tokens (vectors in latent space)
* **Glyph inputs:** Plain text strings

DeepSeek-OCR doesn't give you text back—it gives you dense visual embeddings. You'd need a decoder to turn those embeddings back into text, which defeats the compression purpose.

---

### Three Better Hybrid Approaches

#### **Approach 1: Parallel Processing (Best for Quality)**

Use both systems in parallel for different strengths:

```
Long Document (1M tokens)
    ├── Split into chunks
    ├── Route by content type:
    │   ├── Dense text → Glyph rendering → VLM
    │   └── Images/tables → DeepSeek-OCR → VLM
    └── Merge results
```

**Why this works:** Each system handles what it's optimized for. Glyph excels at reasoning over structured text, while DeepSeek-OCR excels at extracting information from existing document images.

#### **Approach 2: Sequential with Reconstruction (Best for Extreme Compression)**

```
Text → Glyph Render → DeepSeek-OCR Compress → Store
Later: Retrieve → DeepSeek-OCR Decode → VLM Reasoning
```

**The key insight:** Use Glyph's smart rendering to create the *optimal image format* for DeepSeek-OCR's compression. Glyph finds the best font/layout/density, then DeepSeek-OCR compresses that optimized image.

**Compression potential:** 
- Glyph: 1M tokens → 250K visual tokens (4x)
- DeepSeek-OCR: 250K visual tokens → 25K compressed tokens (10x)
- **Combined: 40x compression ratio**

#### **Approach 3: Learned Hybrid Architecture (Most Promising)**

Train a new model that combines both strategies:

```
Input Text → Smart Renderer (Glyph-inspired) → Learned Compressor (DeepSeek-inspired) → Reasoning VLM
```

This would learn to:
1. **Render** text optimally for compression (like Glyph)
2. **Compress** those rendered images semantically (like DeepSeek-OCR)
3. **Reason** directly from compressed representations

---

### Real-World Implementation Strategy

For your AbstractCore project, I'd recommend **Approach 1** initially:

1. **Content Router:** Analyze input to determine optimal processing path
2. **Glyph Pipeline:** For reasoning-heavy tasks over long text
3. **DeepSeek-OCR Pipeline:** For extracting/compressing existing document images
4. **Unified Interface:** Present both results through a single API

This gives you the benefits of both systems while maintaining architectural clarity and avoiding the complexity of hybrid training.

The key insight is that these aren't competing technologies—they're complementary tools for different aspects of the long-context problem.