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Researchers identify 'Lazy Attention' problem in multimodal AI training, boost reasoning by 7%
A new paper from arXiv identifies a critical flaw in how multimodal large reasoning models initialize training: they fail to properly attend to visual tokens, a phenomenon researchers call Lazy Attention Localization. The team proposes AVAR, a framework that corrects this through visual-anchored data synthesis and attention-guided objectives, achieving 7% average improvements across seven multimodal reasoning benchmarks when applied to Qwen2.5-VL-7B.