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Scaling can lead to compositional generalization

arXiv — cs.LG•Monday, October 27, 2025 at 4:00:00 AM
NeutralArtificial Intelligence
Recent research explores whether large-scale neural networks can effectively capture discrete, compositional task structures, despite their inherent continuous nature. While these models demonstrate impressive capabilities, they still encounter frequent failures that challenge their compositionality. Understanding the conditions under which these networks can generalize compositional tasks is crucial for advancing AI technology and improving model reliability.
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