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This research paper analyzes the internal mechanics of Large Vision-Language Models (LVLMs) using information theory, revealing that attention mechanisms may be redundant while Feed-Forward Networks drive semantic innovation. The authors demonstrate that replacing learned attention weights with random values can yield comparable performance, suggesting current models 'get lost in attention'.