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PerSyn (Personalized data Synthesis), a novel synthesis strategy that operates under a new ``Route then Generate''paradigm to create data tailored to each student model, enabling it to learn more effectively, is proposed.
Findings show that compression can alter the scores behind unchanged answers, but do not establish a reliable benefit from adjusting precision for each question, and whether choosing different groups for each question offers benefits beyond shuffled controls is tested.
Whether vision language models can decide when to answer immediately and, when more evidence is needed, which experiment to perform is studied to reveal limitations in physical reasoning that conventional answer accuracy can overlook.
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