arXiv:2605.26494v1 Announce Type: cross Abstract: We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The flagship M2 contains 229.9B total parameters with only 9.8B activated per token. Designed end-to-end for agentic deployment, the M2 series rests on three components: (i) agent-driven data pipelines producing large-scale, verifiable trajectories across agentic coding and agentic cowork, each grounded in an executable workspace and an artifact-aligned reward; (ii) Forg
The release of the MiniMax-M2 series reflects the ongoing research push towards more efficient and capable large language models, specifically tailored for sophisticated autonomous applications.
This development suggests a significant leap in AI model architecture, optimizing performance with fewer activated parameters, which could accelerate the practical deployment and scalability of AI agents.
The focus on 'mini activations' marks a shift from purely increasing total parameter count to achieving high intelligence with optimized compute per token, facilitating more efficient real-world agentic deployment.
- · AI developers
- · Agentic AI platforms
- · Cloud computing providers (hosting efficient models)
- · Enterprises adopting AI automation
- · Inefficient large language models
- · Companies without strong AI integration strategies
- · Compute-intensive AI architectures
More powerful and efficient AI agents become available for various applications, reducing the cost of complex AI tasks.
The widespread adoption of these agentic systems begins to automate increasingly sophisticated white-collar workflows, leading to significant productivity gains but also workforce disruption.
The enhanced capabilities of these autonomous agents could accelerate breakthroughs in scientific discovery and accelerate the development of even more advanced AI systems, creating a feedback loop of innovation.
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Read at arXiv cs.LG