If you've worked with LLM Agents, you've felt this pain: the same model can feel brilliant in one framework and useless in another. An agent might crush a tool-use leaderboard but fail spectacularly at a simple, real-world task. This gap between benchmark performance and practical usability is one of the biggest challenges in the field.

When we designed M2, we knew we had to tackle this problem head-on. This led us to two core, and sometimes conflicting, objectives:

So, who do we align with? The answer is both. We align with benchmarks to build skill, but we must ultimately align with the user by ensuring those skills work everywhere.

While the methods for acing benchmarks are a deep topic for another day, I want to focus on that second, trickier objective: How do we train an agent for the wild?

Early in the project, we hit a frustrating wall. Agent performance was inconsistent, and we struggled to diagnose why. After many discussions, especially with Professor @Junxian He and @Wenhu Chen, we arrived at our first major conclusion: Agents require Interleaved Thinking.

This means that an agent's internal monologue—its "thinking"—can and should happen at any point during a task, not just once at the beginning like a standard reasoning model. This design is critical for two reasons:

Pro Tip for M2 Users: Because M2 relies on Interleaved Thinking, its context is its memory. For best performance, you must retain the full session history, including the thinking steps. We've noticed that much of the community feedback about performance gaps stems from accidentally discarding this vital context, which is a common practice with simpler reasoning models.

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