Why AI Agents Need Million-Token Context — Thomas Wolf & Olive Song, MiniMax

תקציר מקורי באנגליתAn intern designed the sparse-attention architecture behind MiniMax M3. That detail comes after Olive Song explains the larger problem the team was trying to solve: short context windows aren’t enough when agents must work across long conversations, tool responses and complex environments. M3 combines a functional one-million-token context window with coding, agentic and multimodal capabilities, allowing it to understand text, images and video within the same model. Thomas Wolf and Olive Song unpack how MiniMax made that context window efficient, why the company trained M3 as multimodal from its very first step and what happens when that training goes wrong. They also discuss MiniMax’s open research culture, products used by hundreds of millions of people, the role of community feedback in