  Augmented reality (AR) requires the seamless integration of visual, auditory,
and linguistic channels for optimized human-computer interaction. While
auditory and visual inputs facilitate real-time and contextual user guidance,
the potential of large language models (LLMs) in this landscape remains largely
untapped. Our study introduces an innovative method harnessing LLMs to
assimilate information from visual, auditory, and contextual modalities.
Focusing on the unique challenge of task performance quantification in AR, we
utilize egocentric video, speech, and context analysis. The integration of LLMs
facilitates enhanced state estimation, marking a step towards more adaptive AR
systems. Code, dataset, and demo will be available at
https://github.com/nguyennm1024/misar.
