Explore/agent app/Improving Collaborative Storytelling with a Multi-Agent Framework Based on Large Language Models
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Arturo Valdivia, Paolo Burelli/Improving Collaborative Storytelling with a Multi-Agent Framework Based on Large Language ModelsUnknown

The topic of Co-creation, i.e., AI agents interacting with humans to generate outputs (e.g., art), has gained significant attention recently. However, most studies focus on adult-human interactions in a digital setting. This paper explores a novel ludic co-creation scenario involving children and Large Language Models (LLMs) interacting through a physical board game to create written stories. Our goal is to develop a multi-agent framework capable of producing high-quality narratives suitable for young players. At the core of our approach is an iterative Writer-Editor process in which one LLM generates stories while another evaluates them and provides feedback for refinement. Through a simulation study involving multiple LLMs, we show that this iterative interaction consistently improves the perceived quality of generated stories across successive loops. The results indicate that a small number of refinement steps may be sufficient to achieve high-quality outputs in interactive storytelling systems.

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2026-07-262026-08-27
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✓ Best for

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  • Learning agentic patterns

◎ Strengths

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55
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