This paper investigates a collaborative approach using large language models and multi-agent systems to enhance common-sense reasoning and knowledge synthesis.
: Artificial General Intelligence (AGI) aims to create machine intelligence rivaling human cognitive abilities across all domains. Current approaches struggle with common-sense reasoning, cross-domain knowledge transfer, and consistent multi-step task performance. This paper investigates a collaborative framework using AI agents built on Large Language Models (LLMs) to achieve AGI. By harnessing LLMs’ strengths in language processing, reasoning, and knowledge synthesis, we propose a multi-agent system where specialized agents work together to exhibit emergent general intelligence. We analyze LLM technologies, multi-agent coordination, and existing implementations like Google’s SayCan, OpenAI’s multi-agent systems, ChatDev, and Devin. Our findings suggest that collaborative LLM-based agent systems are the most promising path to AGI, despite challenges in embodied cognition, safety alignment, and true understanding versus pattern matching. We outline a roadmap for future research and key open questions in developing AGI through collaborative architectures.
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Mahesh Basavaraj (2025) studied this question.
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