Empowering Reliable Visual-Centric Instruction Following in MLLMs

• URL: http://arxiv.org/abs/2601.03198v1
• Date: Tue, 06 Jan 2026 17:23:33 GMT
• Title: Empowering Reliable Visual-Centric Instruction Following in MLLMs
• Authors: Weilei He, Feng Ju, Zhiyuan Fan, Rui Min, Minhao Cheng, Yi R. Fung,
• Abstract summary: Existing benchmarks for evaluating MLLMs' instruction-following capability primarily focus on verbal instructions.<n>We introduce VC-IFEval, a new benchmark accompanied by a systematically constructed dataset.<n>Our benchmark systematically incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment.
• Score: 30.078761482873745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user-specified intentions. Nevertheless, existing benchmarks for evaluating MLLMs' instruction-following capability primarily focus on verbal instructions in the textual modality. These limitations hinder a thorough analysis of instruction-following capabilities, as they overlook the implicit constraints embedded in the semantically rich visual modality. To address this gap, we introduce VC-IFEval, a new benchmark accompanied by a systematically constructed dataset that evaluates MLLMs' instruction-following ability under multimodal settings. Our benchmark systematically incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions. Furthermore, by fine-tuning MLLMs on our dataset, we achieve substantial gains in visual instruction-following accuracy and adherence. Through extensive evaluation across representative MLLMs, we provide new insights into the strengths and limitations of current models.

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