Are We There Yet? Exploring the Capabilities of MLLMs in Assistive AI Applications
This study evaluates Multimodal Large Language Models (MLLMs) on assistive AI tasks including currency recognition, scene text QA, and multilingual reading. The authors built NetraLink, a system using a head-mounted GoPro to collect real-world egocentric data, and created a benchmark. Findings reveal strengths and limitations of current MLLMs for vision-language assistive technologies.
[2606.25084] Are We There Yet? Exploring the Capabilities of MLLMs in Assistive AI Applications
[Submitted on 23 Jun 2026]
Title:Are We There Yet? Exploring the Capabilities of MLLMs in Assistive AI Applications
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Abstract:Multimodal Large Language Models (MLLMs) have redefined visual understanding by combining vision encoders with large-scale language models. This unified architecture enables strong performance on tasks like image captioning, visual question answering, and multimodal dialogue, often in zero- and few-shot settings. Their general-purpose capabilities and flexible interfaces make MLLMs a promising foundation for real-world vision-language applications.
Assistive AI aims to help users interact with their environments through natural language. These scenarios demand robust visual recognition, contextual reasoning, and multilingual comprehension-capabilities that MLLMs are believed to offer. However, their effectiveness in assistive settings remains to be fully understood.
In this work, we explore whether MLLMs can support Assistive AI by evaluating state-of-the-art models on real-world tasks: recognizing everyday objects like currency, answering questions based on scene text, and reading visually presented content across multiple languages. To this end, we developed a system, NetraLink, using a head-mounted GoPro to capture real-world egocentric data, and collected a benchmark covering these assistive scenarios. Our findings provide a comprehensive diagnostic of current MLLMs, highlighting their strengths and limitations in enabling assistive technologies grounded in visual perception and language interaction.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.25084 [cs.CV]
(or arXiv:2606.25084v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.25084
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1145/3774521.3774575
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From: Shayon Dasgupta [view email] [v1] Tue, 23 Jun 2026 18:44:59 UTC (21,220 KB)
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