VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via
  Travel Video Itinerary Reconstruction
Authors: name: Hao Wang, name: Eiki Murata, name: Lingfang Zhang, name: Ayako Sato, name: So Fukuda, name: Ziqi Yin, name: Wentao Hu, name: Keisuke Nakao, name: Yusuke Nakamura, name: Sebastian Zwirner, name: Yi-Chia Chen, name: Hiroyuki Otomo, name: Hiroki Ouchi, name: Daisuke Kawahara

Recent advances in multimodal large language models (MLLMs) have
significantly enhanced video understanding capabilities, opening new
possibilities for practical applications. Yet current video benchmarks focus
largely on indoor scenes or short-range outdoor activities, leaving the
challenges associated with long-distance travel largely unexplored. Mastering
extended geospatial-temporal trajectories is critical for next-generation
MLLMs, underpinning real-world tasks such as embodied-AI planning and
navigation. To bridge this gap, we present VIR-Bench, a novel benchmark
consisting of 200 travel videos that frames itinerary reconstruction as a
challenging task designed to evaluate and push forward MLLMs'
geospatial-temporal intelligence. Experimental results reveal that
state-of-the-art MLLMs, including proprietary ones, struggle to achieve high
scores, underscoring the difficulty of handling videos that span extended
spatial and temporal scales. Moreover, we conduct an in-depth case study in
which we develop a prototype travel-planning agent that leverages the insights
gained from VIR-Bench. The agent's markedly improved itinerary recommendations
verify that our evaluation protocol not only benchmarks models effectively but
also translates into concrete performance gains in user-facing applications.