DeepSeek Math V2: Learning by Proving, Not Just Solving

 

How an Honest AI and a Tiny Specialist Bot Are Outsmarting the Giants

In the world of artificial intelligence, the dominant narrative often revolves around size. Headlines constantly emphasize the race to build ever-larger models, with success measured in billions or even trillions of parameters. But the latest breakthroughs suggest that sheer scale may not be the only path to progress. Instead, smarter, more focused approaches are proving to be game-changers.

Two recent AI innovations—a math AI from DeepSeek and an OCR model from Tencent—challenge the assumption that bigger is always better. They highlight a fundamental question for the industry: Is the future of AI about size, or about intelligence and specialization?

 

DeepSeek Math V2: Learning by Proving, Not Just Solving

DeepSeek Math V2 stands out not only because it can outperform some of the largest AI models like Google's Gemini Deepthink, but because of how it achieves this. Traditional AI systems are trained to produce correct answers, but they often struggle to explain their reasoning. In mathematics, this is critical—an AI might guess the right number without truly understanding the problem.

DeepSeek tackles this with self-verifiable reasoning, a training framework where the AI must prove its work and critique itself. The system consists of three roles:

1. The Student: Generates mathematical proofs and grades its own work, identifying potential flaws.
2. The Teacher (Examiner): Evaluates the student’s reasoning, focusing not just on the answer but on the logic behind it. It grades the proof, highlights missing steps, and points out mistakes.
3. The Supervisor: Reviews the teacher’s feedback to ensure accuracy and prevent hallucinations.

Interestingly, the student is rewarded for honesty—if it makes a mistake but correctly identifies it, it receives positive reinforcement. This encourages human-like reasoning and reflection.

The results are remarkable: DeepSeek Math V2 scores 118/120 on the 2024 Putnam test and nearly 99% on the basic IMO proof benchmark. By continuously generating new training data and improving itself, this closed-loop system reduces the need for human graders and establishes a path toward fully verifiable AI reasoning.

In essence, DeepSeek shows that for AI to handle real mathematics, it needs to reason and verify, not just compute.

 

Tencent Hunyuan OCR: Small But Mighty

While DeepSeek focuses on reasoning, Tencent demonstrates the power of specialized, efficient AI. Its Hunyuan OCR, a 1-billion parameter model, is outperforming giants like Quen 3 VL (23.5B) and Gemini 2.5 Pro in complex Optical Character Recognition tasks.

Tencent’s approach replaces traditional multi-step OCR pipelines with a single end-to-end model. Two key innovations enable this:

1. Preserving Image Structure: The model processes images in their original resolution and aspect ratio, maintaining spatial information critical for complex layouts such as receipts, tables, or multi-column documents.
2. Four-Dimensional Text Understanding: Using a technique called "XD ropey," the AI captures text sequences across height, width, and even time for video, allowing accurate parsing of intricate formats.

Additionally, reinforcement learning penalizes the model for generating broken or misformatted outputs, ensuring reliability. The performance is impressive:

• 94.1 on OmniDoc, a challenging document benchmark
• State-of-the-art results on DOC ML in 14 languages
• First place at ICDAR 2025 DIMP

Hunyuan OCR demonstrates that compact, specialist AI can surpass larger general-purpose models in real-world applications.

 

Two Philosophies, One Question

These breakthroughs illustrate two distinct paths for AI development:

• DeepSeek Math V2: Emphasizes deep, verifiable reasoning and self-correction.
• Tencent Hunyuan OCR: Shows the power of streamlined, highly specialized models that outperform larger systems on targeted tasks.

The question now is critical: Will the future of AI be dominated by small, specialized models, or by giant, all-in-one systems?

As AI continues to evolve, these examples suggest that intelligence and focus may matter more than sheer size—and that sometimes, less really can be more.