MiniMax M2.7: Early Echoes of Self-Evolution With 100,000 OpenClaw Cluster

As the AI landscape shifts from 'Model-Centric' experimentation to 'System-Centric' deployment, the true value of Large Language Models lies in their ability to act as autonomous agents. In this session, we explore how Minimax is bridging this gap, with industry-leading context windows for complex reasoning, cost-effective high-concurrency inference, and native multimodal capabilities. Through concrete case studies, we demonstrate how Minimax transforms static models into dynamic, task-solving systems, and how to leverage open standards and Minimax's architecture to build scalable, reliable, and sovereign AI solutions.