Language Model Circuits Are Sparse in the Neuron Basis

📰 ArXiv cs.AI

Language models use sparse neuron basis for computation, enabling interpretability through techniques like sparse autoencoders

advanced Published 12 Jun 2026

Action Steps

Apply sparse autoencoders to decompose neuron basis into interpretable units
Analyze the resulting sparse representation to understand model computation
Use techniques like Smolensky's work to align high-level concepts with individual neurons
Evaluate the interpretability of language models using sparse neuron basis
Compare the performance of sparse autoencoders with other interpretability techniques

Who Needs to Know This

ML researchers and engineers working on language model interpretability can benefit from this research, as it provides new insights into the neural network's computation process

Key Insight

💡 Language model circuits are sparse in the neuron basis, enabling interpretability through techniques like sparse autoencoders

Key Takeaways

Language models use sparse neuron basis for computation, enabling interpretability through techniques like sparse autoencoders

Full Article

Title: Language Model Circuits Are Sparse in the Neuron Basis

Abstract:
arXiv:2601.22594v2 Announce Type: replace-cross Abstract: The high-level concepts that a neural network uses to perform computation need not be aligned to individual neurons (Smolensky, 1986). Language model interpretability research has thus turned to techniques which decompose the neuron basis into more interpretable units of model computation, such as sparse autoencoders (SAEs). However, not all neuron-based representations are uninterpretable. For the first time, we empirically show that MLP

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