What is the impact of modality dropout on the alignment accuracy of large multimodal models versus unimodal la

Abstract

Abstract: Reasoning lies at the heart of intelligence, shaping the ability to make decisions, draw conclusions, and generalize across domains. In artificial intelligence, as systems increasingly operate in open, uncertain, and multimodal environments, reasoning becomes essential for enabling robust and adaptive behavior. Large Multimodal Reasoning Models (LMRMs) have emerged as a promising paradigm, integrating modalities such as text, images, audio, and video to support complex reasoning capabilities and aiming to achieve comprehensive perception, precise understanding, and deep reasoning. As research

Research Question

What is the impact of modality dropout on the alignment accuracy of large multimodal models versus unimodal language models in zero-shot reasoning tasks?

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