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cs.CV2026
Towards Responsible Multimodal Medical Reasoning via Context-Aligned Vision-Language Models
Sumra Khan, Sagar Chhabriya, Aizan Zafar +5
Medical vision-language models (VLMs) show strong performance on radiology tasks but often produce fluent yet weakly grounded conclusions due to over-reliance on a dominant modalit…
cs.CV2025
Bias in the Picture: Benchmarking VLMs with Social-Cue News Images and LLM-as-Judge Assessment
Aravind Narayanan, Vahid Reza Khazaie, Shaina Raza
Large vision-language models (VLMs) can jointly interpret images and text, but they are also prone to absorbing and reproducing harmful social stereotypes when visual cues such as…
cs.CV2025
LinguaMark: Do Multimodal Models Speak Fairly? A Benchmark-Based Evaluation
Ananya Raval, Aravind Narayanan, Vahid Reza Khazaie +1
Large Multimodal Models (LMMs) are typically trained on vast corpora of image-text data but are often limited in linguistic coverage, leading to biased and unfair outputs across la…