MiniMax M2: Interleaved Thinking for Robust Agent Generalization

AI Impact Summary

MiniMax M2's architecture centers on interleaved thinking, a design choice that allows the agent to continuously re-evaluate and adapt to external perturbations during complex tasks. This approach, driven by the recognition that benchmarks don't accurately reflect real-world agent performance, highlights a critical distinction between training for benchmarks and building truly generalizable agents. The team’s focus on maintaining full session history and robust data pipelines demonstrates a commitment to addressing the core challenge of agent adaptation to unpredictable environments.