@inproceedings{yin-etal-2026-scalable,
title = "Scalable Circuit Learning for Interpreting Large Language Models",
author = "Yin, Naiyu and
Wei, Dennis and
Gao, Tian and
Dhurandhar, Amit and
Natesan Ramamurthy, Karthikeyan and
Yu, Yue",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1812/",
pages = "41296--41315",
ISBN = "979-8-89176-459-0",
abstract = "A prominent research direction in mechanistic interpretability is learning sparse circuits over LLM components to reveal how they jointly produce model behavior. However, raw neurons are polysemantic, making learned circuits hard to interpret. Sparse autoencoder (SAE) features alleviate this, but their high dimensionality makes existing intervention-based circuit-learning methods computationally prohibitive. We propose CircuitLasso, a scalable circuit-learning approach based on sparse linear regression. CircuitLasso recovers circuits whose structural accuracy matches that of state-of-the-art intervention-based methods on the benchmark data, at a fraction of the computational cost. For interpretability, CircuitLasso efficiently uncovers relationships among SAE features, showing how human-interpretable semantic features propagate through the model and influence its predictions. Finally, we validate the utility of our learned circuits by leveraging their insights to achieve comparable performance at substantially lower cost on a domain-generalization task."
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%0 Conference Proceedings
%T Scalable Circuit Learning for Interpreting Large Language Models
%A Yin, Naiyu
%A Wei, Dennis
%A Gao, Tian
%A Dhurandhar, Amit
%A Natesan Ramamurthy, Karthikeyan
%A Yu, Yue
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yin-etal-2026-scalable
%X A prominent research direction in mechanistic interpretability is learning sparse circuits over LLM components to reveal how they jointly produce model behavior. However, raw neurons are polysemantic, making learned circuits hard to interpret. Sparse autoencoder (SAE) features alleviate this, but their high dimensionality makes existing intervention-based circuit-learning methods computationally prohibitive. We propose CircuitLasso, a scalable circuit-learning approach based on sparse linear regression. CircuitLasso recovers circuits whose structural accuracy matches that of state-of-the-art intervention-based methods on the benchmark data, at a fraction of the computational cost. For interpretability, CircuitLasso efficiently uncovers relationships among SAE features, showing how human-interpretable semantic features propagate through the model and influence its predictions. Finally, we validate the utility of our learned circuits by leveraging their insights to achieve comparable performance at substantially lower cost on a domain-generalization task.
%U https://aclanthology.org/2026.emnlp-main.1812/
%P 41296-41315
Markdown (Informal)
[Scalable Circuit Learning for Interpreting Large Language Models](https://aclanthology.org/2026.emnlp-main.1812/) (Yin et al., EMNLP 2026)
ACL
- Naiyu Yin, Dennis Wei, Tian Gao, Amit Dhurandhar, Karthikeyan Natesan Ramamurthy, and Yue Yu. 2026. Scalable Circuit Learning for Interpreting Large Language Models. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 41296–41315, Budapest, Hungary. Association for Computational Linguistics.