arXiv preprint examining how power law distributions and asymmetric structures enable compositional reasoning in neural networks. The work explores fundamental principles underlying how AI models compose concepts and operations, potentially improving their ability to handle complex multi-step reasoning tasks.
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The Power of Power Law: Asymmetry Enables Compositional Reasoning
Power law asymmetry in neural network structures is fundamental to compositional reasoning, revealing why AI models can combine simple concepts into complex multi-step reasoning.
Tuesday, April 28, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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