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Dissecting Training-Free Uncertainty Estimation in Multimodal Large Language Models

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Multimodal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of multimodal tasks, yet understanding and quantifying their predictive uncertainty remains underexplored despite being central for safety critical applications. In this work, we present a systematic study of training-free uncertainty quantification strategies for MLLMs, categorizing existing approaches into three conceptual families: token-level methods, which operate directly in the text output space; verbalized methods, which elicit uncertainty estimates or abstention signals via natural langua

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