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cs.CV2026
Investigating Length Bias and Robustness in LVLMs for Multiple-Choice Question Answering
Md. Atabuzzaman, Hani Alomari, Chris Thomas
Large Vision-Language Models (LVLMs) demonstrate strong performance on multiple-choice question answering (MCQA), yet their reliability under biased conditions remains underexplore…
cs.CV2026
IntroConformal: Conformal Factuality Guarantees for Large Vision-Language Models via Introspective Signals
Md. Atabuzzaman, Christian Alexander, Chris Thomas
Large Vision-Language Models (LVLMs) have achieved strong multimodal performance, yet ensuring the factual correctness of generated content remains challenging. Existing methods th…
cs.CV2026
Reliability Challenges in Diffusion Vision-Language Models
Md. Atabuzzaman, Chris Thomas
Diffusion-based Large Vision-Language Models (dLVLMs) have recently emerged as a compelling alternative to autoregressive (AR) LVLMs, offering advantages in parallel decoding, bidi…