A learned local rule (Weight Transformer) iteratively self-organizes task-network weights from local graph neighborhoods and generalizes across unseen MLP, CNN, and ResNet architectures up to ~2M parameters.
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2026 2representative citing papers
A uniform-temporal-step tokenization for symbolic music improves generation quality, efficiency, and long-range coherence over event-based alternatives.
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Architecture Generalization with MetaNCA
A learned local rule (Weight Transformer) iteratively self-organizes task-network weights from local graph neighborhoods and generalizes across unseen MLP, CNN, and ResNet architectures up to ~2M parameters.
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BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps
A uniform-temporal-step tokenization for symbolic music improves generation quality, efficiency, and long-range coherence over event-based alternatives.