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Standard DRL collapses in volatile environments because it mistakes irreducible noise for a lack of data, but RE-SAC fixes this by explicitly separating these uncertainties.
Transformers have a hidden symmetry: depth-wise residuals are secretly doing the same thing as sequence-wise sliding window attention, unlocking new architectural insights.
Today's best multimodal models can only solve half of compositional visual tool-use tasks, revealing a critical gap in their ability to plan and execute complex, multi-step visual reasoning.
