Quest of AI towards Specializable Generalist: From Reasoning to Scientific Discovery

The pursuit of high-efficiency Artificial General Intelligence (AGI) requires more than brute-force scaling of model size and data. While scaling remains a key driver of capability, equally important are scalable architectural and principles—designs that continue to work, improve, and remain controllable as we vary model scale, domains, and modalities. Central to our approach is the concept of the “Specialized Generalist” – a pathway that achieves deep expertise across multiple domains without sacrificing broad generalization capabilities. In this talk, we introduce the “Specialized Generalist” paradigm and our implementation of it, SAGE (Synergistic Architecture for Generalized Expertise), a three-layer architecture designed to balance specialization and generalization in a systematic way. We will describe how SAGE’s Base Model, Synergy Fusion, and Exploration-Evolution layers interact in practice, focusing on concrete mechanisms for coordinating domain-specific expertise with broad general reasoning. We will share empirical results and recent advances in large reasoning models, embodied AI, and scientific applications to further illustrate the approach. A central motivation is to support “AGI for Science” by building a stable plateau of capabilities that can reliably assist with complex scientific workflows rather than isolated demos. Finally, we will outline the safety and governance questions that arise when deploying Specialized Generalist systems in high-impact settings, and discuss what we have learned so far about monitoring, alignment, and operational safeguards.