VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via
  Travel Video Itinerary Reconstruction
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.
68d3903b0e215259d193b3ad
Hao Wang
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2025-09-24T13:47:21.213Z
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Hao Wang
user
conan1024hao
68d3903b0e215259d193b3ae
Eiki Murata
admin_assigned
2025-09-24T15:32:28.453Z
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Eiki Murata
user
Eiki
68d3903b0e215259d193b3af
Lingfang Zhang
admin_assigned
2025-09-24T15:32:35.274Z
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Lingfang Zhang
user
mamezlf
68d3903b0e215259d193b3b0
Ayako Sato
68d3903b0e215259d193b3b1
So Fukuda
68d3903b0e215259d193b3b2
Ziqi Yin
admin_assigned
2025-09-24T15:32:46.087Z
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Ziqi Yin
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Zikky
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Wentao Hu
admin_assigned
2025-09-24T15:32:52.936Z
6583ea04a76333af9f59f8f6
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Wentao Hu
user
wentaohu
68d3903b0e215259d193b3b4
Keisuke Nakao
68d3903b0e215259d193b3b5
Yusuke Nakamura
68d3903b0e215259d193b3b6
Sebastian Zwirner
admin_assigned
2025-09-24T15:33:30.658Z
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Sebastian Zwirner
user
SebastianZw
68d3903b0e215259d193b3b7
Yi-Chia Chen
68d3903b0e215259d193b3b8
Hiroyuki Otomo
68d3903b0e215259d193b3b9
Hiroki Ouchi
admin_assigned
2025-09-24T15:33:14.758Z
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Hiroki Ouchi
user
hiroki-o
68d3903b0e215259d193b3ba
Daisuke Kawahara
VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via
  Travel Video Itinerary Reconstruction
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.
VIR-Bench, a new benchmark for travel videos, evaluates and enhances MLLMs' geospatial-temporal intelligence, improving itinerary recommendations in real-world applications.
multimodal large language models MLLMs video understanding video benchmarks indoor scenes outdoor activities long-distance travel geospatial-temporal trajectories embodied-AI planning navigation itinerary reconstruction geospatial-temporal intelligence travel-planning agent itinerary recommendations