#G42 #MBZUAI #K2 Think V2

Overview of K2 Think V2

The Mohammed bin Zayed University of Artificial Intelligence (MBZUAI) has recently introduced its latest innovation, K2 Think V2, which represents a monumental leap in AI reasoning systems tailored for the UAE. Developed in collaboration with G42 and Cerebras Systems, this cutting-edge system boasts 70 billion parameters, signifying the robust capabilities of the new K2-V2 foundational model, specifically built to support K2 Think.

Major Breakthrough in AI Sovereignty

The launch of K2 Think V2 is a pivotal achievement in the quest for technological sovereignty in the UAE. While previous iterations like K2 Think were accessible and open to the public, K2 Think V2 is the first system to provide complete end-to-end transparency from pre-training data curation to post-training evaluations. This thorough openness results in a reasoning system that is not only more powerful but also significantly more transparent, aligning closely with the UAE's ambitions to lead in AI technology.

Key Distinctions of K2 Think V2

K2 Think V2 marks a turning point in open reasoning systems as it replaces traditional overlaid models with a deeply integrated foundational approach. By inheriting the long-context capabilities and complete training transparency of K2-V2, it operates as a fully sovereign system, ensuring that each phase of its reasoning process is open, verifiable, and reproducible. This feature enhances both scientific credibility and national sovereignty in AI.

The K2 Think V2 model is adept at solving complex challenges step-by-step, employing extensive reasoning chains across various domains including mathematics, science, coding, logic, and simulation. In comparative evaluations against other open-source reasoning systems, K2 Think V2 exhibits remarkable performance on complex reasoning tests such as AIME2025, GPQA-Diamond, HMMT, and IFBench.

Essential Features of K2 Think V2

1. Foundation Model Architecture:
K2 Think V2 is bolstered by the latest foundational model devised by MBZUAI's Foundation Model Institute. Designed from the ground up for reasoning and long-context processing, it enhances the effectiveness of the reasoning system.

2. Enhanced Long-Term Memory:
The model’s capability for long-context reasoning enables it to engage in multi-step thought processes across a significantly larger information corpus, paving the way for deeper intellectual engagement.

3. Independently Verified Data Pipeline:
Training relies solely on curated datasets meticulously selected and disseminated by the Foundation Model Institute, such as the Guru dataset. This ensures fair and reliable assessments, devoid of downstream references that might skew results.

4. 360-Degree Transparency:
From pre-training data to