One Is Not Enough: How People Use Multiple AI Models in Everyday Life
This addresses the problem of multi-MLLM orchestration for everyday users, which is an incremental step in HCI research.
The study investigated how people use multiple multimodal large language models (MLLMs) simultaneously, finding that users create shifting hierarchies and personalized switching patterns based on task needs to manage coordination challenges like inconsistent behaviors and separate histories.
People increasingly use multiple Multimodal Large Language Models (MLLMs) concurrently, selecting each based on its perceived strengths. This cross-platform practice creates coordination challenges: adapting prompts to different interfaces, calibrating trust against inconsistent behaviors, and navigating separate conversation histories. Prior HCI research focused on single-agent interactions, leaving multi-MLLM orchestration underexplored. Through a diary study and semi-structured interviews (N=10), we examine how individuals organize work across competing AI systems. Our findings reveal that users construct primary and secondary hierarchies among models that shift over usage context. They also develop personalized switching patterns triggered by task aggregation to adjust effort and latency, and output credibility. These insights inform future tool design opportunities, supporting users to coordinate multi-MLLM workflows.