{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T20:04:14Z","timestamp":1792008254973,"version":"4.4.0"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:00:00Z","timestamp":1771027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Generalization to unseen environments remains a fundamental challenge in Vision-Language Navigation. To tackle this issue, we propose a novel framework that leverages world knowledge embedded within Multimodal Large Language Models. We introduce Collaborative Agents in Visual-Language Navigation (CA-VLN), a framework based on a dual-agent architecture. This architecture comprises a Knowledge Agent, which infuses the action prediction process with semantic context and commonsense reasoning, and a Hierarchical History Agent, which constructs a detailed episodic memory to enable long-horizon planning. The collaboration between these agents facilitates a dynamic interplay between high-level semantic understanding and grounded episodic experience. Extensive experiments on the R2R, REVERIE and SOON datasets demonstrate that our model achieves state-of-the-art performance, significantly improving generalization and navigation success in previously unobserved environments.<\/jats:p>","DOI":"10.3390\/s26041254","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:38:39Z","timestamp":1771231119000},"page":"1254","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CA-VLN: Collaborative Agents in MLLM-Powered Visual-Language Navigation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4911-8584","authenticated-orcid":false,"given":"Ruolin","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaobin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5160-2138","authenticated-orcid":false,"given":"Zixing","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3506-635X","authenticated-orcid":false,"given":"Jing","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Communication University of China, Beijing 100024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100088, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,14]]},"reference":[{"key":"ref_1","unstructured":"Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., and Chebotar, Y. 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