September 25, 2025Open Access

How Multimodal LLMs Solve Image Tasks: A Lens on Visual Grounding, Task Reasoning, and Answer Decoding

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The probing framework shows MLLMs effectively perform visual grounding, semantic reasoning, and output formatting across layers.
Linear classifiers successfully predict visual categories from token embeddings, showcasing the model's internal dynamics.
Systematic evaluation across prompt variations indicates the consistency of layer functions in MLLMs despite architectural changes.
Findings emphasize the stable stage-wise structure of MLLMs while adapting to different visual tokenization methods.

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Analysis reveals systematic processing of visual and textual inputs in multimodal LLMs, suggesting improvements in optical reasoning.

Yu et al. (2025) studied this question.

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