Vision language model

From Scratch to SOTA: Training a 3B State-Space Vision Model — Krishna Prasad Srinivasan, Sarvam

From Scratch to SOTA: Training a 3B State-Space Vision Model — Krishna Prasad Srinivasan, Sarvam

Sarvam introduces Vision India, a 3B parameter Vision Language Model achieving state-of-the-art in document AI for English and 22 Indian languages. It leverages a State Space Model (SSM) backbone for efficiency with long documents and a novel block-level OCR approach. A four-stage training curriculum, including advanced RL with verifiable rewards, and a robust data engine for low-resource languages underpin its superior performance, enabling the digitization of millions of pages for critical sectors.

🔬 The Limits of AI in Science - Why We Need Self-Driving Labs — Joseph Krause, Radical AI

🔬 The Limits of AI in Science - Why We Need Self-Driving Labs — Joseph Krause, Radical AI

Joseph Krause, CEO of Radical AI, details how his company uses Self-Driving Labs (SDLs) and AI scientists to overcome the experimental bottleneck in materials science. By automating the full loop of hypothesis generation, synthesis, characterization, and testing, Radical AI is accelerating the discovery of novel alloys for aerospace, defense, and semiconductor applications, achieving 10x the pace of traditional methods. Krause explains why materials science is uniquely challenging for AI, how human intuition trains the AI, and why experimental data, not models, forms the core competitive advantage in this rapidly evolving, geopolitically significant field.