{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:42:49Z","timestamp":1773801769431,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Recent advances in multimodal large language models (MLLMs) have significantly enhanced video understanding capabilities, opening new possibilities for practical applications.\nYet current video benchmarks focus largely on indoor scenes or short-range outdoor activities, leaving the challenges associated with long-distance travel largely unexplored.\nMastering extended geospatial-temporal trajectories is critical for next-generation MLLMs, underpinning real-world tasks such as embodied-AI planning and navigation.\nTo 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.\nExperimental 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.\nMoreover, we conduct an in-depth case study in which we develop a prototype travel-planning agent that leverages the insights gained from VIR-Bench.\nThe agent\u2019s 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.<\/jats:p>","DOI":"10.1609\/aaai.v40i12.37938","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:57:56Z","timestamp":1773791876000},"page":"9747-9756","source":"Crossref","is-referenced-by-count":0,"title":["VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstruction"],"prefix":"10.1609","volume":"40","author":[{"given":"Hao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Eiki","family":"Murata","sequence":"additional","affiliation":[]},{"given":"Lingfang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ayako","family":"Sato","sequence":"additional","affiliation":[]},{"given":"So","family":"Fukuda","sequence":"additional","affiliation":[]},{"given":"Ziqi","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Wentao","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Keisuke","family":"Nakao","sequence":"additional","affiliation":[]},{"given":"Yusuke","family":"Nakamura","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Zwirner","sequence":"additional","affiliation":[]},{"given":"Yi-Chia","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Hiroyuki","family":"Otomo","sequence":"additional","affiliation":[]},{"given":"Hiroki","family":"Ouchi","sequence":"additional","affiliation":[]},{"given":"Daisuke","family":"Kawahara","sequence":"additional","affiliation":[]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37938\/41900","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37938\/41900","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:57:56Z","timestamp":1773791876000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i12.37938","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}