Major Breakthrough in AI Field: Manus Model Sets Innovation Record in GAIA Test

Recently, the field of artificial intelligence has seen significant advancements. The Manus model has achieved breakthrough results in the GAIA benchmark tests, outperforming other large language models in the same class. This achievement means that Manus is capable of independently handling complex tasks, such as multinational business negotiations, involving multiple aspects like contract analysis, strategy formulation, and proposal generation.

Compared to traditional systems, Manus has advantages in its dynamic goal decomposition ability, cross-modal reasoning capability, and memory-augmented learning ability. It can decompose complex tasks into hundreds of executable subtasks, handle various types of data, and continuously improve decision-making efficiency and reduce error rates through reinforcement learning.

However, Manus's progress has also sparked discussions within the industry about the development path of AI: will the future be dominated by Artificial General Intelligence (AGI), or will it be collaboratively led by Multi-Agent Systems (MAS)? This debate essentially reflects the balance between efficiency and safety in the development of AI.

As AI systems become increasingly intelligent, their potential risks also increase. For example, in medical scenarios, AI needs to access sensitive patient data; in financial negotiations, there may be undisclosed corporate information involved. Furthermore, AI may also have issues such as algorithmic bias and security vulnerabilities.

To address these challenges, the industry is exploring various security solutions:

1. Zero Trust Security Model: Emphasizes strict authentication and authorization for every access request.

2. Decentralized Identity (DID): Achieving identity recognition without centralized registration.

3. Fully Homomorphic Encryption (FHE): Allows computation on encrypted data, protecting privacy.

FHE technology shows great potential in addressing security issues in the AI era. It can protect user information at the data level, implement encrypted model training at the algorithm level, and adopt threshold encryption at the collaborative level to prevent data leaks.

As AI technology increasingly approaches human intelligence levels, establishing a robust defense system becomes ever more important. FHE not only addresses current issues but also lays the foundation for security in the future era of strong AI. On the path to AGI, FHE has become an indispensable technological support.