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Long video generation fails not just because of limited context length, but because of *how* that context is allocated – and ReCA's hierarchical approach shows a way to fix it.
Forget costly training or reward models: MATO unlocks personalized LLM alignment by optimizing objective weights *during* generation, offering unprecedented control and adaptability.
Despite advances in vision-language models, reasoning across sparse, multi-view observations remains surprisingly unsolved, with current models barely outperforming random guessing on a new benchmark.
