SAGI: A New Paradigm for Reconstructing General Artificial Intelligence from First Principles

SAGI is an open-source project that proposes to redesign the general artificial intelligence architecture from first principles. Through a distributed collaborative society composed of independent cognitive units, it systematically solves the fundamental dilemmas of current large models, such as hallucinations, context window limitations, high computational costs, and fragile value alignment, achieving endogenous task-driven capability, native embodied adaptation, and hierarchical value security.

Current large language models centered on Transformer perform well in many tasks, but they are essentially probabilistic generation systems based on statistical pattern matching. They have insurmountable limitations such as hallucinations, context window constraints, high computational costs, and fragile alignment with human values. Against this background, the SAGI project proposes the proposition of redesigning the architecture from first principles.

The core innovation of SAGI lies in the shift in architectural philosophy: building a distributed collaborative society composed of independent cognitive units. Each independent cognitive unit has complete perception, reasoning, decision-making, and execution capabilities, with advantages including modular interpretability, elastic fault tolerance, and parallel scalability. The distributed collaborative society forms a dynamic network through protocols, drawing on swarm intelligence to emerge collective intelligence.

Endogenous task-driven: Proactively perceive the environment, independently set goals and plan paths to adapt to dynamic scenarios. Native embodied adaptation: Consider embodied needs from the initial design stage, directly interact with physical devices, suitable for scenarios like robotics. Hierarchical value security: Unit-level built-in constraints, collaboration-level security protocols, and society-level value alignment, with multi-layer protection for greater robustness.

Compared to large models with hundreds of billions of parameters, SAGI is lightweight, with advantages including low-threshold deployment (runs on ordinary hardware, lowering the entry barrier for startup teams), flexible customization (modular combination to adapt to different applications), and continuous evolution (online learning expansion without full retraining).

SAGI uses the MIT license, and the authors waive all rights except the right of attribution. It aims to promote the democratization of general AI technology, allowing more people to participate and benefit, and highlighting the value of technology sharing in commercial competition.

SAGI represents a technical exploration different from mainstream large models, providing a more sustainable, secure, and inclusive path. Although it is in the early stage and needs community improvement, the open-source spirit drives technological progress, which is worthy of attention from AI practitioners.