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These findings establish correct-solution retention as a complementary objective of recursive self-improvement (RSI) and show that generation-time intervention can improve the solution repertoire retained by subsequent students.
The inference performance of Multimodal Large Language Models (MLLMs) typically relies on a massive number of visual block tokens, leading to substantial computational overhead. However, existing training-free compression methods often sacrifice semantic relevance, disrupt spatial coherence, or require extensive archit...
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