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Collaborative Artificial Intelligence Using Local Models and Task Chunking: An Economical Framework for Collective Knowledge Production

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Abstract Recent discussions of collaborative artificial intelligence frequently assume the use of multiple large language models operating simultaneously. While effective, such approaches introduce significant computational and financial costs that limit practical deployment. This paper proposes an alternative architecture based on a single locally hosted foundation model operating through multip...
The argument successfully challenges the prevailing narrative that multi-agent systems inherently require expensive model ensembles, reframing the challenge from computational power to organizational structure. The focus on local deployment and task chunking addresses a significant practical barrier to AI adoption, introducing a compelling economic and architectural alternative. However, the claim that "collaboration emerges not from multiple models but from multiple intellectual roles enacted b...