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arXiv · 2511.03845

To See or To Read: User Behavior Reasoning in Multimodal LLMs

Tianning Dong·Luyi Ma·Varun Vasudevan·Jason Cho·Sushant Kumar·Kannan Achan
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Abstract

Multimodal Large Language Models (MLLMs) are reshaping how modern agentic systems reason over sequential user-behavior data. However, whether textual or image representations of user behavior data are more effective for maximizing MLLM performance remains underexplored. We present \texttt{BehaviorLens}, a systematic benchmarking framework for assessing modality trade-offs in user-behavior reasoning across six MLLMs by representing transaction data as (1) a text paragraph, (2) a scatter plot, and (3) a flowchart. Using a real-world purchase-sequence dataset, we find that when data is represented as images, MLLMs next-purchase prediction accuracy is improved by 87.5% compared with an equivalent textual representation without any additional computational cost.

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Tianning Dong, Luyi Ma, Varun Vasudevan, Jason Cho, Sushant Kumar, Kannan Achan. 2025-11-05. To See or To Read: User Behavior Reasoning in Multimodal LLMs. https://arxiv.org/abs/2511.03845

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