Math Images to Text Boost Accuracy | Generated by AI
Question: Is the core issue that current large models train mathematical reasoning primarily on text, and that converting math images to text would significantly improve accuracy?
Answer:
Yes, you are essentially correct. Let me break down the reasoning systematically.
1. Text-Dominant Math Reasoning Training
Current frontier models (GPT-4, Gemini, Claude, DeepSeek, etc.) overwhelmingly train mathematical reasoning on text-based data:
- Math corpora: arXiv papers, textbooks, StackExchange — all primarily LaTeX/text
- RLHF and post-training pipelines (like GRPO, PPO for math) use text-based benchmarks: MATH, GSM8K, AIME, AMC — all text
- Even “multimodal” math benchmarks like MathVista or We-Math are relatively small compared to the massive text math corpus
So the model’s mathematical reasoning engine is fundamentally text/token based.
2. Image Understanding Is a Separate Competency
Vision-language models (VLMs) bolt on visual understanding via:
- A vision encoder (e.g., ViT)
- A projection layer mapping image tokens → language space
But this projection is lossy — especially for:
- Fine-grained mathematical symbols (∑, ∫, ∂, matrices)
- Geometric diagrams with precise measurements
- Handwritten equations
The model “sees” an approximation of the math, not the exact structure.
3. Google’s Multimodal Advantage
You are right that Google is notably stronger here. Gemini was trained natively multimodal from the start — not vision bolted onto a language model. This means image and text tokens are processed more uniformly. But even Gemini’s post-training math reasoning (reinforcement learning phase) likely still leans heavily on text-format math problems.
4. Image → Text Conversion Greatly Helps
This is a well-known practical trick:
| Approach | Accuracy |
|---|---|
| Raw image fed to VLM | Lower — vision encoder loses detail |
| OCR / image → LaTeX first, then reason | Much higher |
| Native text math problem | Highest |
Tools like Mathpix (image → LaTeX) exist precisely because of this gap. Once math is in text/LaTeX form, the LLM’s strong text-based reasoning takes over cleanly.
5. The MathGen Paper’s Core Point
The paper likely exposes this exact gap from the generation side — T2I models generate images that look mathematically correct but are actually wrong, because:
- The model learned visual patterns of math, not mathematical semantics
- Generating a correct equation image requires understanding the math, not just the visual style
- This is the mirror image of your point: text → image degrades accuracy, just as image → text improves it
Summary
Your intuition is spot-on:
Math reasoning lives in text space. Multimodal math is hard because image ↔ text conversion is imperfect. Converting math images to text (LaTeX/OCR) before reasoning is the most pragmatic fix today. True native multimodal math understanding remains an open research problem.
The fundamental bottleneck is that mathematical semantics are symbolic and discrete, while image representations are continuous and approximate — these two worlds don’t naturally align well yet.