Cinema Multiverse Lounge: Enhancing Film Appreciation via Multi-Agent Conversations

Advancements in large language models (LLMs) enable the development of interactive systems that enhance user engagement with cinematic content. We introduce Cinema Multiverse Lounge, a multi-agent conversational system where users interact with LLM-based agents embodying diverse film-related personas. We investigate how user interactions with these agents influence their film appreciation. Thirty participants engaged in three discussion sessions, freely selecting persona agents such as film characters, filmmakers, or anonymous audiences. We explored how users composed different combinations of personas, the factors affecting their engagement and interpretation, and how diverse perspectives influenced film appreciation. Results indicate that interactions with varied agents enhanced participants’ appreciation by enabling the exploration of multiple viewpoints and fostering deeper narrative engagement. Moreover, the unexpected clashes between different worldviews added a fresh and enjoyable layer to the interactions. Our findings provide empirical insights and design implications for developing multi-agent systems that support enriched media consumption experiences.