Neural LoFi models deep learning as layer-wise spectral filtering that selects maximal low-degree correlations, yielding a tractable surrogate for hierarchical representation learning beyond the lazy regime.
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BISON learns bilevel policies over symbolic world models to generalize long-horizon robotic planning beyond VLA and end-to-end baselines while remaining efficient even at 10,000-object scale.
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Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
Neural LoFi models deep learning as layer-wise spectral filtering that selects maximal low-degree correlations, yielding a tractable surrogate for hierarchical representation learning beyond the lazy regime.
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Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning
BISON learns bilevel policies over symbolic world models to generalize long-horizon robotic planning beyond VLA and end-to-end baselines while remaining efficient even at 10,000-object scale.