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cs.HC2026
Evaluating Visual Prompts with Eye-Tracking Data for MLLM-Based Human Activity Recognition
Jae Young Choi, Seon Gyeom Kim, Hyungjun Yoon +7
Large Language Models (LLMs) have emerged as foundation models for IoT applications such as human activity recognition (HAR). However, directly applying high-frequency and multi-di…
cs.HC2026
One Is Not Enough: How People Use Multiple AI Models in Everyday Life
Seunghwa Pyo, Donggun Lee, Jungwoo Rhee +2
People increasingly use multiple Multimodal Large Language Models (MLLMs) concurrently, selecting each based on its perceived strengths. This cross-platform practice creates coordi…