mlsanityData to 14 Oct 2026

Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impaired

arXiv 2508.03651 ↗First posted 5 Aug 2025cs.HC cs.AIcited by 2

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

Abstract (arXiv)

Recent advancements in large multimodal models have provided blind or visually impaired (BVI) individuals with new capabilities to interpret and engage with the real world through interactive systems that utilize live video feeds. However, the potential benefits and challenges of such capabilities to support diverse real-world assistive tasks remain unclear. In this paper, we present findings from an exploratory study with eight BVI participants. Participants used ChatGPT's Advanced Voice with Video, a state-of-the-art live video AI released in late 2024, in various real-world scenarios, from locating objects to recognizing visual landmarks, across unfamiliar indoor and outdoor environments. Our findings indicate that current live video AI effectively provides guidance and answers for static visual scenes but falls short in delivering essential live descriptions required in dynamic situations. Despite inaccuracies in spatial and distance information, participants leveraged the provided visual information to supplement their mobility strategies. Although the system was perceived as human-like due to high-quality voice interactions, assumptions about users' visual abilities, hallucinations, generic responses, and a tendency towards sycophancy led to confusion, distrust, and potential risks for BVI users. Based on the results, we discuss implications for assistive video AI agents, including incorporating additional sensing capabilities for real-world use, determining appropriate intervention timing beyond turn-taking interactions, and addressing ecological and safety concerns.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 23 of its 94 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

23 of 94 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Beyond Visual Perception: Insights from Smartphone Interaction of Visually Impaired Users with Large Multimodal Models22 Feb

Other references · 22

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  2. 2024A Survey on the Memory Mechanism of Large Language Model based Agentscited by 634
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  9. 2024WorldScribe: Towards Context-Aware Live Visual Descriptionscited by 27
  10. 2024VIAssist: Adapting Multi-modal Large Language Models for Users with Visual Impairmentscited by 25
  11. 2022Context Matters for Image Descriptions for Accessibility: Challenges for Referenceless Evaluation Metricscited by 18
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10 more
  1. 2024Enhancing the Travel Experience for People with Visual Impairments through Multimodal Interaction: NaviGPT, A Real-Time AI-Driven Mobile Navigation Systemcited by 13
  2. 2025Towards Understanding the Use of MLLM-Enabled Applications for Visual Interpretation by Blind and Low Vision Peoplecited by 11
  3. 2020Hand-Priming in Object Localization for Assistive Egocentric Visioncited by 8
  4. 2025WanderGuide: Indoor Map-less Robotic Guide for Exploration by Blind Peoplecited by 8
  5. 2018Conversations for Vision: Remote Sighted Assistants Helping People with Visual Impairmentscited by 6
  6. 2022Blind Users Accessing Their Training Images in Teachable Object Recognizerscited by 6
  7. 2024EditScribe: Non-Visual Image Editing with Natural Language Verification Loopscited by 6
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  10. 2025Memory Reviver: Supporting Photo-Collection Reminiscence for People with Visual Impairment via a Proactive Chatbotcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025"I Can See Forever!": Evaluating Real-time VideoLLMs for Assisting Individuals with Visual Impairments
  2. 2026VIABench: A Comprehensive Video Benchmark Collected from Blind Individuals for Visual Impairment Assistance
  3. 2024Emerging Practices for Large Multimodal Model (LMM) Assistance for People with Visual Impairments: Implications for Design
  4. 2025Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users
  5. 2025EgoBlind: Towards Egocentric Visual Assistance for the Blind
  6. 2026Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Player
  7. 2026How Multimodal Large Language Models Support Access to Visual Information: A Diary Study With Blind and Low Vision People
  8. 2025NaviSense: A Multimodal Assistive Mobile application for Object Retrieval by Persons with Visual Impairment