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DeepSeek has just released DeepSeek Math V2, a next-generation AI reasoning model that outperforms Gemini 3 DeepThink in structured logic, proof generation, and self-verification. Using a powerful student–teacher–supervisor loop, this model reaches Olympiad-level math proof accuracy, near-perfect Putnam scores, and dramatically reduces hallucinations through self-checking reasoning. At the same time, Tencent has launched HunyuanOCR, a 1-billion-parameter specialist AI model that beats massive vision-language models with tens of billions of parameters. HunyuanOCR excels at reading documents, receipts, tables, long pages, and multilingual layouts, outperforming larger models on benchmarks like OmniDocBench, OCRBench, and real-world document understanding tasks. These two releases reveal a major shift in artificial intelligence. Instead of relying only on massive all-in-one models, the future is moving toward small, expert AI systems that deliver higher accuracy, lower latency, and real-world reliability. In this video, we break down: How DeepSeek Math V2 builds and verifies proofs Why self-verification eliminates hallucinations How it surpasses Gemini 3 DeepThink in reasoning How Tencent HunyuanOCR preserves document layout Why a 1B parameter model beats giant VLMs What this means for the future of AI 🔗 Sources: DeepSeek Math V2 GitHub & Model Card Tencent HunyuanOCR Technical Report (arXiv) Public benchmark evaluations and discussions If you’re interested in AI reasoning, math AI, OCR, multimodal models, and the future of artificial intelligence, this video is a must-watch.