The paper introduces compositional interpretability as a category-theoretic framework that casts mechanistic explanations as commuting syntactic-semantic mappings optimized under faithfulness and complexity constraints derived from minimum description length.
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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
HNNs recover known sparse hierarchies on synthetic tasks and match or exceed dense DNNs on real datasets while using orders of magnitude fewer parameters and showing lower hyperparameter sensitivity.
Derives approximation rates and excess risk bounds for Frobenius norm-constrained DNNs learning sparse compositional functions on DAGs, applicable to multi-index models and binary trees while avoiding the curse of dimensionality.
citing papers explorer
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From Mechanistic to Compositional Interpretability
The paper introduces compositional interpretability as a category-theoretic framework that casts mechanistic explanations as commuting syntactic-semantic mappings optimized under faithfulness and complexity constraints derived from minimum description length.
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Compositional Sparsity as an Inductive Bias for Neural Architecture Design
HNNs recover known sparse hierarchies on synthetic tasks and match or exceed dense DNNs on real datasets while using orders of magnitude fewer parameters and showing lower hyperparameter sensitivity.
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Learning Sparse Compositional Functions with Norm-Constrained Neural Networks
Derives approximation rates and excess risk bounds for Frobenius norm-constrained DNNs learning sparse compositional functions on DAGs, applicable to multi-index models and binary trees while avoiding the curse of dimensionality.