Circuit study aims to explain how language models (LMs) implement a task by localizing and interpreting a circuit: a computational subgraph responsible for a specific model's behavior. Existing methods for circuit localization are largely hypothesis-driven: they define a task through a dataset, assume that all examples in the task are implemented by the same mechanism, and apply circuit discovery to obtain a single circuit. This workflow does not account for mechanistic diversity within a task, where different examples may rely on different mechanisms. We first show that when a dataset mixes tasks processed through distinct mechanisms, current circuit discovery can combine these mechanisms into a single circuit while retaining faithfulness comparable to circuits discovered on each task separately. Across four tasks and three models, we further provide evidence for such mechanistic diversity within individual tasks. To account for this diversity, we propose Data-driven Circuit Discovery (DCD), which modifies the circuit-discovery workflow by adding a grouping step before the discovery. The grouping step aims to partition the dataset into subsets of examples that rely on similar mechanisms. We instantiate this step with clustering over per-example attribution patterns (DCD-Cluster). Across four tasks and three models, DCD-Cluster decomposes the dataset into multiple subsets whose circuits exhibit distinct faithfulness patterns, providing useful hypotheses about mechanistic differences among examples.
Circuit Insights: Towards Interpretability Beyond Activations
Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute（人工智能系，弗劳恩霍夫海因里希·赫兹研究所）；Department of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学系，柏林技术大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Centre of eXplainable Artificial Intelligence, Technological University Dublin（可解释人工智能中心，都柏林技术大学）
Multi-Granular Node Pruning for Causal Circuit Discovery
Department of Computer Science, University of Kentucky, USA（美国肯塔基大学计算机科学系）；Department of Computer Science, Dalhousie University, Canada（加拿大达尔豪斯大学计算机科学系）
MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
Pacific Northwest National Laboratory, Richland, WA（太平洋西北国家实验室）；Halıcıoğlu Data Science Institute, University of California, San Diego, San Diego, CA（哈利奇奥格鲁数据科学研究所，加州大学圣地亚哥分校）；Department of Computer and Information Science, University of Pennsylvania, Pennsylvaina, PA（计算机与信息科学系，宾夕法尼亚大学）；Department of Mathematics, University of Washington, Seattle, WA（数学系，华盛顿大学）
OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework
National Engineering Research Center for Big Data Technology and System（国家大数据技术与系统工程研究中心）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Université de Montréal（蒙特利尔大学）；Mila（米拉研究所）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席项目）
The Computational Complexity of Circuit Discovery for Inner Interpretability
ESI Neuroscience, Max-Planck Society & University of Bristol（ESI神经科学研究所、马克斯·普朗克学会与布里斯托尔大学）；Goethe University Frankfurt（法兰克福大学）；Memorial University of Newfoundland（纽芬兰纪念大学）
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning
Peking University（北京大学）；Software Engineering Institution（软件工程研究院）；Key Lab of High Confidence Software Technologies, Ministry of Education（高可信软件技术重点实验室（教育部））；Center on Frontiers of Computing Studies（计算机前沿研究中心）；National Engineering Research Center of Software Engineering（国家软件工程工程研究中心）
Demystifying Variance in Circuit Discovery of LLMs
Laboratory for Information and Inference Systems (LIONS), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland（信息与推理系统实验室（LIONS），洛桑联邦理工学院（EPFL），瑞士洛桑）
SafeEvo: Deciphering the Safety Alignment Mechanism and Evolution in Language Models
The University of Hong Kong (HKU)（香港大学）；Chinese Academy of Sciences (CAS)（中国科学院）；Information Engineering University (IEU)（信息工程大学）；Nanyang Technological University (NTU)（南洋理工大学）
SafeSeek: Universal Attribution of Safety Circuits in Language Models
University of Science；United Arab Emirates University；Nanyang Technological University；Zayed University；Intelligent Science \& Technology Academy of CASIC
