MiniMax M2, a new AI model, has demonstrated impressive capabilities in complex agent tasks, yet it highlights the challenge of aligning agent performance with both benchmarks and real-world applications. The model's development focused on overcoming the disparity between benchmark success and practical usability by adopting "Interleaved Thinking," which allows for dynamic internal processes throughout a task. This approach enhances the model's ability to maintain focus on long tasks and adapt to unpredictable changes, ensuring robust generalization across diverse environments. The team discovered that agent generalization must address perturbations in various aspects of an agent's operational space, not just tool adaptation. By constructing a comprehensive data pipeline for full-trajectory generalization, M2 has shown promising results in internal tests, exceeding expectations even in unfamiliar frameworks. The developers invite the community to explore M2 and contribute to further advancements, emphasizing the model's potential for future research and development.