AGI32

AGI32 is a fictional or hypothetical reference model used in discussions of artificial general intelligence. Unlike concrete deployments, AGI32 functions as a conceptual baseline for evaluating how a system might integrate perception, reasoning, learning, and action across diverse tasks. The name combines the general concept of AGI with a version-like suffix, suggesting a prototypical, scalable architecture, rather than a specific product.

In theoretical work, AGI32 is imagined as a modular cognitive architecture that coordinates subsystems for perception, memory, reasoning, planning, and agentic control. It emphasizes continual and meta-learning, multi-modal input, and the ability to generalize to novel domains. Safety and alignment components are assumed to be integral rather than add-ons, reflecting concerns about reliability and controllability in powerful general-purpose systems.

Implementation of AGI32 has never been standardized since it is not an official specification. Researchers reference the model to discuss design trade-offs, such as data efficiency, robustness, interpretability, and the integration of symbolic and subsymbolic reasoning. Proponents use AGI32-like concepts to frame experiments that test long-horizon planning, tool use, and adaptive problem solving across domains.

Critics argue that AGI32 is an overly abstract shorthand that may obscure practical challenges in real-world AGI development, including safety, governance, and socio-economic impacts. As a teaching tool, it remains useful for clarifying goals and limitations in conversations about general intelligence.

See also: artificial general intelligence, AI alignment, lifelong learning, meta-learning.