Hello HN! I'm sharing a technical preprint on AGI architecture that moves away from the simple scaling paradigm.
Our key thesis: The hallucination problem in LLMs is not a bug in training, but a feature of their current architecture. Both the human brain and LLMs are Generative Engines with a built-in priority: "Coherence > Historical Truth."
Architectural Implication: The path to true AGI requires not a larger model, but one capable of Metacognition (see section 4). We need an architecture that can monitor and regulate its own uncertainty (not to be confused with simple entropy), rather than thoughtlessly generating the most probable sequence.
What are your general thoughts on this direction for AGI?
jakcwi•2mo ago
Our key thesis: The hallucination problem in LLMs is not a bug in training, but a feature of their current architecture. Both the human brain and LLMs are Generative Engines with a built-in priority: "Coherence > Historical Truth."
Architectural Implication: The path to true AGI requires not a larger model, but one capable of Metacognition (see section 4). We need an architecture that can monitor and regulate its own uncertainty (not to be confused with simple entropy), rather than thoughtlessly generating the most probable sequence.
What are your general thoughts on this direction for AGI?