Asinoid, developed by Asilab, represents a brain-inspired approach to artificial superintelligence (ASI) that fundamentally differs from traditional large language models (LLMs) by emulating human-like cognitive processes. Although specific technical details about Asinoid's architecture are not publicly disclosed, Asilab's claims regarding its brain-like structure, continuous learning, and autonomous reasoning provide a foundation for theorizing how it might integrate various components such as neural networks, attention layers, asynchronous state machinery, and symbolic reasoning to achieve general learning.

Neural networks are likely the backbone of Asinoid's ability to process and learn from diverse data, mimicking the human brain's neural structure. Asilab suggests that Asinoid has specialized regions for functions like language, memory, and planning, indicating a modular neural architecture rather than the monolithic transformer models used in LLMs like GPT-4 or Claude. This brain-inspired design likely involves a collection of specialized neural network subnets tailored to specific cognitive tasks. For instance, a language subnet might resemble a transformer for natural language processing, while a planning subnet could utilize recurrent neural networks for sequential decision-making or, more powerfully, GNNs. GNNs are particularly well-suited for tasks requiring relational reasoning and understanding complex interdependencies, such as representing planning states as nodes and actions as edges or modeling the relationships between entities in a dynamic environment. Furthermore, GNNs could form the basis of subnets dedicated to understanding structured data or social dynamics within Asinoid's Reality Host environments by learning representations of entities and their connections. These subnets could be loosely coupled, allowing for task-specific learning while maintaining global coordination, thereby enabling Asinoid to handle multimodal inputs.