Paper
TagaVLM: Topology-Aware Global Action Reasoning for Vision-Language Navigation
Authors
Jiaxing Liu, Zexi Zhang, Xiaoyan Li, Boyue Wang, Yongli Hu, Baocai Yin
Abstract
Vision-Language Navigation (VLN) presents a unique challenge for Large Vision-Language Models (VLMs) due to their inherent architectural mismatch: VLMs are primarily pretrained on static, disembodied vision-language tasks, which fundamentally clash with the dynamic, embodied, and spatially-structured nature of navigation. Existing large-model-based methods often resort to converting rich visual and spatial information into text, forcing models to implicitly infer complex visual-topological relationships or limiting their global action capabilities. To bridge this gap, we propose TagaVLM (Topology-Aware Global Action reasoning), an end-to-end framework that explicitly injects topological structures into the VLM backbone. To introduce topological edge information, Spatial Topology Aware Residual Attention (STAR-Att) directly integrates it into the VLM's self-attention mechanism, enabling intrinsic spatial reasoning while preserving pretrained knowledge. To enhance topological node information, an Interleaved Navigation Prompt strengthens node-level visual-text alignment. Finally, with the embedded topological graph, the model is capable of global action reasoning, allowing for robust path correction. On the R2R benchmark, TagaVLM achieves state-of-the-art performance among large-model-based methods, with a Success Rate (SR) of 51.09% and SPL of 47.18 in unseen environments, outperforming prior work by 3.39% in SR and 9.08 in SPL. This demonstrates that, for embodied spatial reasoning, targeted enhancements on smaller open-source VLMs can be more effective than brute-force model scaling. The code will be released upon publication.Project page: https://apex-bjut.github.io/Taga-VLM
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.02972v1</id>\n <title>TagaVLM: Topology-Aware Global Action Reasoning for Vision-Language Navigation</title>\n <updated>2026-03-03T13:28:07Z</updated>\n <link href='https://arxiv.org/abs/2603.02972v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.02972v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Vision-Language Navigation (VLN) presents a unique challenge for Large Vision-Language Models (VLMs) due to their inherent architectural mismatch: VLMs are primarily pretrained on static, disembodied vision-language tasks, which fundamentally clash with the dynamic, embodied, and spatially-structured nature of navigation. Existing large-model-based methods often resort to converting rich visual and spatial information into text, forcing models to implicitly infer complex visual-topological relationships or limiting their global action capabilities. To bridge this gap, we propose TagaVLM (Topology-Aware Global Action reasoning), an end-to-end framework that explicitly injects topological structures into the VLM backbone. To introduce topological edge information, Spatial Topology Aware Residual Attention (STAR-Att) directly integrates it into the VLM's self-attention mechanism, enabling intrinsic spatial reasoning while preserving pretrained knowledge. To enhance topological node information, an Interleaved Navigation Prompt strengthens node-level visual-text alignment. Finally, with the embedded topological graph, the model is capable of global action reasoning, allowing for robust path correction. On the R2R benchmark, TagaVLM achieves state-of-the-art performance among large-model-based methods, with a Success Rate (SR) of 51.09% and SPL of 47.18 in unseen environments, outperforming prior work by 3.39% in SR and 9.08 in SPL. This demonstrates that, for embodied spatial reasoning, targeted enhancements on smaller open-source VLMs can be more effective than brute-force model scaling. The code will be released upon publication.Project page: https://apex-bjut.github.io/Taga-VLM</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <published>2026-03-03T13:28:07Z</published>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Jiaxing Liu</name>\n </author>\n <author>\n <name>Zexi Zhang</name>\n </author>\n <author>\n <name>Xiaoyan Li</name>\n </author>\n <author>\n <name>Boyue Wang</name>\n </author>\n <author>\n <name>Yongli Hu</name>\n </author>\n <author>\n <name>Baocai Yin</name>\n </author>\n </entry>"
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