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arXiv AI By Seunghwa Pyo, Donggun Lee, Jungwoo Rhee, Soobin Park, Youn-kyung Lim

One Is Not Enough: How People Use Multiple AI Models in Everyday Life

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The article reports that people increasingly use multiple multimodal large language models (MLLMs) at the same time, choosing each model for its perceived strengths. This practice creates coordination challenges such as adapting prompts, calibrating trust, and managing separate conversation histories. A diary study and interviews with ten participants show that users build primary and secondary hierarchies among models, develop switching patterns based on task aggregation, and adjust effort, latency, and output credibility to manage multi‑MLLM workflows.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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