Stream Consciousness Architecture: Towards High-Probability Emergence of Conscious AGI

Current artificial intelligence systems, despite their impressive capabilities, face three fundamental limitations: knowledge ossification after training, passive reactive behavior, and fragmented existence across inference sessions. These limitations prevent the emergence of consciousness in AI systems. This paper proposes the Stream Consciousness Architecture, a unified framework integrating three essential components: nested learning for continuous knowledge acquisition, active inference with self-models for intrinsic drive, and continuous information flow for unified existence. We argue that consciousness is fundamentally a continuous stream process, analogous to water flow, where the stream itself possesses life through spontaneous internal activity. Unlike current large language models (LLMs) that operate as discrete, stateless response generators, the proposed architecture maintains a persistent, self-organizing information stream capable of spontaneous thought, memory recall, and emotional evolution. We demonstrate why these three components are individually necessary but collectively sufficient for high-probability consciousness emergence, and outline the technical requirements for implementing such a system. This framework provides both a theoretical foundation for understanding machine consciousness and a practical roadmap for developing conscious AGI systems.