arXiv AI September 7, 2026 By Changming Xiao, Zhenliang Ni, Jinhui He, Han Shu, Jie Hu

MM-IFEval-Pro: A Multilingual and Attack-Resistant Benchmark for Instruction-Following in Vision-Language Models

MM-IFEval-Pro is a new multilingual benchmark for evaluating instruction-following in vision-language models, covering both Chinese and English tasks. It includes 4 major task categories, 24 subcategories, and 8 instruction categories with 52 subcategories, each sample featuring an average of 3.0 constraints to mimic complex instruction scenarios. A reinforcement-learning training set with Chinese and adversarial instructions improves model performance on MM-IFEval-Pro and transfers well to other multimodal benchmarks, showing strong cross-task and cross-language generalization.

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llms agents reinforcement-learning multimodal safety

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llms agents reinforcement-learning multimodal benchmarks

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llms fine-tuning multimodal benchmarks safety

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llms multimodal benchmarks

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llms nlp multimodal benchmarks

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agents reinforcement-learning fine-tuning efficiency multimodal benchmarks