@inproceedings{dong-etal-2026-identifying,
title = "Identifying and Transferring Reasoning-Critical Neurons: Improving {LLM} Inference Reliability via Activation Steering",
author = "Dong, Fangan and
Yan, Zuming and
Ge, Xuri and
Xu, Zhiwei and
Zhang, Mengqi and
Chen, Xuanang and
He, Ben and
Xin, Xin and
Chen, Zhumin and
Zhou, Ying",
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.1453/",
pages = "33179--33198",
ISBN = "979-8-89176-459-0",
abstract = "Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often requires post-training or computationally expensive sampling strategies, limiting their practical efficiency. In this work, we first show that a small subset of neurons in LLMs exhibits strong predictive correlations with reasoning correctness. Based on this observation, we propose AdaRAS (Adaptive Reasoning Activation Steering), a lightweight test-time framework that improves reasoning reliability by selectively intervening on neuron activations. AdaRAS identifies Reasoning-Critical Neurons (RCNs) via a polarity-aware mean-difference criterion and adaptively steers their activations during inference, enhancing incorrect reasoning traces while avoiding degradation on already-correct cases. Experiments on 10 mathematics and coding benchmarks demonstrate consistent improvements, including over 13{\%} gains on AIME-24 and AIME-25. Moreover, AdaRAS exhibits strong transferability across datasets and scalability to stronger models, outperforming post-training methods without additional training or sampling cost."
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<abstract>Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often requires post-training or computationally expensive sampling strategies, limiting their practical efficiency. In this work, we first show that a small subset of neurons in LLMs exhibits strong predictive correlations with reasoning correctness. Based on this observation, we propose AdaRAS (Adaptive Reasoning Activation Steering), a lightweight test-time framework that improves reasoning reliability by selectively intervening on neuron activations. AdaRAS identifies Reasoning-Critical Neurons (RCNs) via a polarity-aware mean-difference criterion and adaptively steers their activations during inference, enhancing incorrect reasoning traces while avoiding degradation on already-correct cases. Experiments on 10 mathematics and coding benchmarks demonstrate consistent improvements, including over 13% gains on AIME-24 and AIME-25. Moreover, AdaRAS exhibits strong transferability across datasets and scalability to stronger models, outperforming post-training methods without additional training or sampling cost.</abstract>
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%0 Conference Proceedings
%T Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steering
%A Dong, Fangan
%A Yan, Zuming
%A Ge, Xuri
%A Xu, Zhiwei
%A Zhang, Mengqi
%A Chen, Xuanang
%A He, Ben
%A Xin, Xin
%A Chen, Zhumin
%A Zhou, Ying
%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 dong-etal-2026-identifying
%X Despite the strong reasoning capabilities of recent large language models (LLMs), achieving reliable performance on challenging tasks often requires post-training or computationally expensive sampling strategies, limiting their practical efficiency. In this work, we first show that a small subset of neurons in LLMs exhibits strong predictive correlations with reasoning correctness. Based on this observation, we propose AdaRAS (Adaptive Reasoning Activation Steering), a lightweight test-time framework that improves reasoning reliability by selectively intervening on neuron activations. AdaRAS identifies Reasoning-Critical Neurons (RCNs) via a polarity-aware mean-difference criterion and adaptively steers their activations during inference, enhancing incorrect reasoning traces while avoiding degradation on already-correct cases. Experiments on 10 mathematics and coding benchmarks demonstrate consistent improvements, including over 13% gains on AIME-24 and AIME-25. Moreover, AdaRAS exhibits strong transferability across datasets and scalability to stronger models, outperforming post-training methods without additional training or sampling cost.
%U https://aclanthology.org/2026.emnlp-main.1453/
%P 33179-33198
Markdown (Informal)
[Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steering](https://aclanthology.org/2026.emnlp-main.1453/) (Dong et al., EMNLP 2026)
ACL
- Fangan Dong, Zuming Yan, Xuri Ge, Zhiwei Xu, Mengqi Zhang, Xuanang Chen, Ben He, Xin Xin, Zhumin Chen, and Ying Zhou. 2026. Identifying and Transferring Reasoning-Critical Neurons: Improving LLM Inference Reliability via Activation Steering. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 33179–33198, Budapest, Hungary. Association for Computational Linguistics.