The Best Technical Route to ASI is D-MAS Rather Than AGI

This paper proposes the Domain-Specific Modular Agent System (D-MAS) as the optimal technical roadmap toward Artificial Superintelligence (ASI), providing a critical re-evaluation of current mainstream pathways focused on Artificial General Intelligence (AGI). Existing AI models built on Large Language Models (LLMs) suffer from homogenisation, excessive focus on summarizing mainstream viewpoints, and wordy yet non-committal answers. True technological innovation relies on accurate and rigorous domain-specific knowledge, an area where general-purpose AI systems frequently fail to establish trust. Developed independently by Dr. Hung-Chih Lu, D-MAS applies the engineering principles of Multi-Chip Integrated Silicon Photonics (MCI-SiP) to AI agent architecture. Just as Wavelength Division Multiplexing (WDM) allows distinct optical signals to transmit through a single fiber without mutual interference, D-MAS trains agents independently and allocates resources dynamically to achieve ASI-level capability within specific domains. D-MAS offers three key advantages, including zero weight contamination, overcoming data ceilings associated with Scaling Law, and efficient modular iteration. Demonstrating its execution through a four-layer architecture applied to chip design workflows, this study establishes that specialized modular collaboration provides a reliable and precise technical paradigm for future technological advancements.