mlsanityData to 7 Oct 2026

Are Large Vision-Language Models Ready to Guide Blind and Low-Vision Individuals?

arXiv 2510.00766 ↗First posted 1 Oct 2025cs.CV cs.AIcited by 1

On the map: Multimodal LLMs / vision-language models → VLM benchmarks and visual reasoning

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 25 of its 76 references that are in the map. First posted on arXiv 1 Oct 2025.

Builds on

25 of 76 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024WalkVLM:Aid Visually Impaired People Walking by Vision Language Model30 Dec
  2. 2024Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models25 Sep
  3. 2023OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models2 Aug

Other references · 22

  1. 2023Visual Instruction Tuningcited by 7,792
  2. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
  3. 2024RewardBench: Evaluating Reward Models for Language Modelingcited by 480
  4. 2024Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Expertscited by 369
  5. 2024InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Modelcited by 326
  6. 2024VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generationcited by 148
  7. 2024LLaVA-Critic: Learning to Evaluate Multimodal Modelscited by 132
  8. 2024VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Modelscited by 85
  9. 2025InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Modelcited by 73
  10. 2024VIAssist: Adapting Multi-modal Large Language Models for Users with Visual Impairmentscited by 24
  11. 2024VIALM: A Survey and Benchmark of Visually Impaired Assistance with Large Modelscited by 23
  12. 2024G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4ocited by 23
10 more
  1. 2025Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluationcited by 23
  2. 2025Pareto Multi-Objective Alignment for Language Modelscited by 20
  3. 2024Positive-Augmented Contrastive Learning for Vision-and-Language Evaluation and Trainingcited by 16
  4. 2019Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detectioncited by 11
  5. 2025CHARM: Calibrating Reward Models With Chatbot Arena Scorescited by 7
  6. 2024MGDA Converges under Generalized Smoothness, Provablycited by 6
  7. 2024BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairmentscited by 5
  8. 2024ObjectFinder: An Open-Vocabulary Assistive System for Interactive Object Search by Blind Peoplecited by 5
  9. 2025EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-Tuningcited by 4
  10. 2025Sightation Counts: Leveraging Sighted User Feedback in Building a BLV-aligned Dataset of Diagram Descriptionscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Interpretable Debiasing of Vision-Language Models for Social Fairnesscited by 6

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  1. 2025How Blind and Low-Vision Individuals Prefer Large Vision-Language Model-Generated Scene Descriptions
  2. 2026Exploring the Use of VLMs for Navigation Assistance for People with Blindness and Low Vision
  3. 2025Towards Blind and Low-Vision Accessibility of Lightweight VLMs and Custom LLM-Evals
  4. 2026How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People
  5. 2025Guiding Multimodal Large Language Models with Blind and Low Vision People Visual Questions for Proactive Visual Interpretations
  6. 2024Vision-Language Models under Cultural and Inclusive Considerations
  7. 2025Not There Yet: Evaluating Vision Language Models in Simulating the Visual Perception of People with Low Vision
  8. 2025Towards Understanding the Use of MLLM-Enabled Applications for Visual Interpretation by Blind and Low Vision People