arXiv:2605.31351v1 Announce Type: new Abstract: AI-based Visually Impaired Assistance (VIA) remains challenging, largely due to the high cost of human evaluation. The VLM-as-a-Judge paradigm may offer a promising alternative, although it has mostly been studied in general domains. We therefore ask whether such judges can be trusted for VIA tasks. To investigate this question, we introduce VIABLE (Visually Impaired Assistance Benchmark for VLM-as-a-Judge Evaluation), the first benchmark for VLM-as-a-Judge evaluation in VIA. VIABLE contains over 300K judgment samples across three scenarios and i
The proliferation of Large Vision Models (VLMs) and the increasing focus on AI ethics and accessibility necessitate robust evaluation methods for specialized AI applications like visually impaired assistance.
This benchmark addresses a critical gap in VLM evaluation, allowing for more reliable deployment of AI for assistive technologies and potentially expanding the scope of AI agentic capabilities.
The introduction of VIABLE provides a standardized and scalable method for evaluating VLM-as-a-Judge systems in a specific, high-stakes domain, moving beyond general-purpose evaluations.
- · AI developers in assistive tech
- · Visually impaired individuals
- · VLM-as-a-Judge platforms
- · AI ethics and safety researchers
- · AI developers reliant on expensive human evaluation
- · Untrustworthy VLM-as-a-Judge models
Further development and refinement of VLM-as-a-Judge systems for specialized, high-impact applications.
Accelerated deployment of more reliable AI-powered assistance tools for various disability communities.
Enhanced trust in autonomous AI systems as they demonstrate robust and verifiable performance in sensitive domains, expanding their integration into daily life.
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Read at arXiv cs.CL