Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Puliyanmakkal, Jiran Kurian; Rohini, V. Title A name given to the resource Towards a Human-Like AGI Architecture: GeneralIntelligence Framework (GIF) Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Computer Science;Volume;21;Issue;7;pp.1637-1650 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3844/jcssp.2025.1637.1650" target="_blank" rel="noreferrer noopener">https://doi.org/10.3844/jcssp.2025.1637.1650</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012831361?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012831361?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Puliyanmakkal J.K., Department of Computer Science, Christ (Deemed to be University), Bangalore, India; Rohini V., Department of Computer Science, Christ (Deemed to be University), Bangalore, India Description An account of the resource Artificial Intelligence (AI) has achieved significant breakthroughsbut remains limited by its specialization and inability to generalize acrossdomains, unlike human cognition. Current models such as Large LanguageModels (LLMs) and Multimodal Large Language Models (MLLMs) excel atspecific tasks but struggle with real-time adaptability and cross-domaingeneralization. This paper introduces the General Intelligence Framework(GIF), an approach designed to bridge this gap by mimicking human-likecognitive processes. By integrating Deep Learning (DL), Spiking NeuralNetworks (SNNs), and neuromorphic hardware, the framework fosters Real-Time Learning (RTL) and adaptability. The proposed framework holdspotential for industries like robotics, healthcare, education, astronomy,defense, autonomous systems, etc., where flexible, adaptive AI is critical.We hypothesize that the framework will enable AI systems to handleunforeseen inputs and tasks without requiring extensive retraining,representing a step toward achieving Artificial General Intelligence (AGI). 2025 Jiran Kurian Puliyanmakkal and Rohini V. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Subject The topic of the resource Artificial General Intelligence (AGI); General IntelligenceFramework (GIF); NeuromorphicComputing; Real-Time Learning (RTL); Spiking Neural Networks (SNNs) Publisher An entity responsible for making the resource available Science Publications Relation A related resource ISSN: 15493636; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online