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Looping and depth-growing, two distinct methods for improving LLM reasoning, are actually two sides of the same iterative computation coin, and can be combined for even better results.
Object-centric representations win at compositional generalization when data is scarce, diverse, or compute-constrained, challenging the supremacy of dense representations in visually rich settings.
