Google’s New AI Just Broke Math… (Invented Its Own Algorithms)

Google DeepMind’s AlphaEvolve just broke long-standing mathematical records by evolving algorithms that improved several difficult Ramsey theory problems. Moonshot AI also revealed a new transformer concept called Attention Residuals that lets models focus on earlier layers instead of blending everything equally. And researchers from Zhipu AI and Tsinghua University released GLM-OCR, a compact model built to read complex documents with tables and formulas.

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🧠 What You’ll See
AlphaEvolve evolves algorithms to push forward difficult Ramsey theory math records
SOURCE: https://arxiv.org/abs/2603.09172

Moonshot AI introduces Attention Residuals architecture for transformer models
SOURCE: https://github.com/MoonshotAI/Attention-Residuals

GLM-OCR compact model reads complex documents with tables and formulas
SOURCE: https://blog.gopenai.com/glm-ocr-the-lightweight-ai-model-transforming-document-understanding-092990c167d0

OpenViking organizes AI agent memory using a hierarchical file system
SOURCE: https://github.com/volcengine/openviking

IBM Granite 4.0 1B Speech multilingual speech recognition model
SOURCE: https://huggingface.co/ibm-granite/granite-4.0-1b-speech

🚨 Why It Matters

AI progress is happening across several layers at once. AlphaEvolve helps advance difficult mathematics, Attention Residuals explores transformer improvements, GLM-OCR shows powerful document AI in a small model, OpenViking rethinks agent memory, and Granite Speech focuses on efficient multilingual speech systems.

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