Article

Sparse Autoencoders as an Interpretable Interface for LLM Development and Control

2026-07-03

Abstract excerpt

The increasing capability and opacity of large language models (LLMs) necessitate robust tools for understanding and steering their internal computations. Sparse autoencoders (SAEs) have recently emerged as a promising bridge between the high-dimensional, distributed representations of neural networks and human-interpretable concepts. This paper explores the hypothesis that SAEs can serve as a fundamental interpre...

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Literature Corpus work: 5d779e77-026a-5923-8d0a-e6781e0323b3
DOI: 10.14293/pr2199.004035.v1

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