Understanding how neural networks arrive at their predictions is essential for debugging, auditing, and deployment. Mechanistic interpretability pursues this goal by identifying circuits--minimal subnetworks responsible for specific behaviors. However, existing circuit discovery methods are brittle: circuits depend strongly on the chosen concept dataset and often fail to transfer out-of-distribution, raising doubts whether they capture the concept or merely dataset-specific artifacts. We introduce Certified Circuits, which provide provable stability guarantees for circuit discovery. Our framework wraps any black-box discovery algorithm with randomized data subsampling to certify that inclusion decisions over circuit components--neurons or edges of the model graph, depending on the base algorithm--are invariant to bounded edit-distance perturbations of the concept dataset. Unstable components are abstained from, yielding circuits that are more compact and more accurate. We validate across three architectures (ResNet, ViT, GPT-2) on vision (ImageNet and four OOD datasets) and language (IOI, IOI-Hard, Greater-Than) tasks. Certified circuits achieve up to 56% higher accuracy and up to 80% fewer components, and remain reliable where baselines degrade. Certified Circuits puts circuit discovery on formal ground by producing mechanistic explanations that are provably stable and better aligned with the target concept. Code: https://github.com/AlaaAnani/certified-circuits.
From Circuit Evidence to Mechanistic Theory: An Inductive Logic Approach
Department of Computer Science, University of Manchester（曼彻斯特大学计算机科学系）；Idiap Research Institute（Idiap研究机构）；CRUK National Biomarker Centre, University of Manchester（曼彻斯特大学癌症研究联盟国家生物标志物中心）
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）
Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference
Department of Computer Science, University of Manchester, UK（英国曼彻斯特大学计算机科学系）；Idiap Research Institute, Switzerland（瑞士Idiap研究所）；School of Computer Science, University of Sheffield, UK（英国谢菲尔德大学计算机科学学院）；National Biomarker Centre, CRUK-MI, University of Manchester, UK（英国曼彻斯特大学国家生物标志物中心）
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）
Weight Patching: Toward Source-Level Mechanistic Localization in LLMs
Key Laboratory of Ministry of Education for Brain Inspired Intelligent Perception and Cognition, University of Science and Technology of China（教育部脑科学与智能感知认知重点实验室，中国科学技术大学）
