We introduce a new approach to systematically map features discovered by sparse autoencoder across consecutive layers of large language models, extending earlier work that examined inter-layer feature links. By using a data-free cosine similarity technique, we trace how specific features persist, transform, or first appear at each stage. This method yields granular flow graphs of feature evolution, enabling fine-grained interpretability and mechanistic insights into model computations. Crucially, we demonstrate how these cross-layer feature maps facilitate direct steering of model behavior by amplifying or suppressing chosen features, achieving targeted thematic control in text generation. Together, our findings highlight the utility of a causal, cross-layer interpretability framework that not only clarifies how features develop through forward passes but also provides new means for transparent manipulation of large language models.
Is Inference Mediated by Distinct Semantic Structures in LLMs? A Mechanistic Interpretation
University of Manchester（曼彻斯特大学）；University of Sheffield（谢菲尔德大学）；Idiap Research Institute（Idiap研究所）；CRUK National Biomarker Centre, University of Manchester（CRUK国家生物标志物中心，曼彻斯特大学）
Artificial entrepreneurial cognition: Locating and causally steering an opportunity recognition dial inside large language models (LLMs)
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学跨学科安全、可靠性与信任中心（SnT））；Amsterdam Business School, University of Amsterdam（阿姆斯特丹大学阿姆斯特丹商学院）；Stanford Graduate School of Business, Stanford University（斯坦福大学斯坦福商学院）
Enhancing Cross-task Transfer of Large Language Models via Activation Steering
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学耿丽人工智能学院）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）；Ant Group（蚂蚁集团）
CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models
National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, Huazhong University of Science and Technology（华中科技大学国家大数据技术与系统工程技术研究中心、服务计算技术与系统实验室、集群与网格计算实验室）；Singapore Management University（新加坡管理大学）
Style-Specific Neurons for Steering LLMs in Text Style Transfer
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Center for Information and Language Processing, LMU Munich（慕尼黑大学信息与语言处理中心）
LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering
Ludwig Maximilian University of Munich（慕尼黑大学）；Munich Research Center, Huawei Technologies（华为技术有限公司慕尼黑研发中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）
