{"ID":2862020,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.00766","arxiv_id":"2510.00766","title":"Are Large Vision-Language Models Ready to Guide Blind and Low-Vision Individuals?","abstract":"Large Vision-Language Models (LVLMs) demonstrate a promising direction for assisting individuals with blindness or low-vision (BLV). Yet, measuring their true utility in real-world scenarios is challenging because evaluating whether their descriptions are BLV-informative requires a fundamentally different approach from assessing standard scene descriptions. While the \"VLM-as-a-metric\" or \"LVLM-as-a-judge\" paradigm has emerged, existing evaluators still fall short of capturing the unique requirements of BLV-centric evaluation, lacking at least one of the following key properties: (1) High correlation with human judgments, (2) Long instruction understanding, (3) Score generation efficiency, and (4) Multi-dimensional assessment. To this end, we propose a unified framework to bridge the gap between automated evaluation and actual BLV needs. First, we conduct an in-depth user study with BLV participants to understand and quantify their navigational preferences, curating VL-GUIDEDATA, a large-scale BLV user-simulated preference dataset containing image-request-response-score pairs. We then leverage the dataset to develop an accessibility-aware evaluator, VL-GUIDE-S, which outperforms existing (L)VLM judges in both human alignment and inference efficiency. Notably, its effectiveness extends beyond a single domain, demonstrating strong performance across multiple fine-grained, BLV-critical dimensions. We hope our work lays as a foundation for automatic AI judges that advance safe, barrier-free navigation for BLV users.","short_abstract":"Large Vision-Language Models (LVLMs) demonstrate a promising direction for assisting individuals with blindness or low-vision (BLV). Yet, measuring their true utility in real-world scenarios is challenging because evaluating whether their descriptions are BLV-informative requires a fundamentally different approach from...","url_abs":"https://arxiv.org/abs/2510.00766","url_pdf":"https://arxiv.org/pdf/2510.00766v2","authors":"[\"Eunki Kim\",\"Na Min An\",\"Wan Ju Kang\",\"Sangryul Kim\",\"James Thorne\",\"Hyunjung Shim\"]","published":"2025-10-01T10:55:33Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}