Circuits in Language Models

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15 papers in the last four weeks, up 200% on the four weeks before. 0.1% of all new papers.

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LLMs Can Annotate Attribution Graphs
Jul 28, 2026Ameen Patel, Max Zhang, Nathan HuLLM Interpretability LLM-Assisted Annotation
Stanford University
What, Where, and How: Disentangling the Roles of Task, Language, and Model in Code Model Representations
Jul 23, 2026Piotr WilamCircuit Discovery Language Model Introspection
University College London
Where Animacy Lives in Large Language Models: Tracing the Circuits of the Animacy Concept
Jul 23, 2026Samuele Punzo, Giovanni Cinà, Sandro PezzelleLLM Interpretability Circuits in Language Models
Graduate School of Informatics, University of Amsterdam · Department of Medical Informatics, Amsterdam University Medical Center · ILLC, University of Amsterdam
CircuitKIT : Circuit Discovery, Evaluation, and Application Toolkit for Mechanistic Interpretability
Jul 21, 2026Pratinav Seth, Hem Gosalia, Aditya Kasliwal +1Circuit Discovery Mechanistic Interpretability
Lexsi Labs
Circuit Claims Depend on What Is Extracted and How It Is Compared
Jul 21, 2026Yang Sheng, Jie FuTransformer Interpretability Circuit Discovery
1Fudan University · 2Shanghai Innovation Institute · 3IQuest Research
Are Arithmetic Heuristic Neurons Form-Invariant? A Mechanistic Analysis of Symbols, Text, and Code in LLMs
Jul 18, 2026Sharath Naganna, Tanvir Ahmed Sijan, Uddipta KalitaLLM Interpretability Circuits in Language Models
Indian Institute of Technology Bombay, Mumbai, India · Jahangirnagar University, Dhaka, Bangladesh · National Institute of Technology, Silchar, India
Induction in Both Directions: A Mechanistic Analysis of In-Context Learning in Masked Diffusion Language Models
Jul 17, 2026Andy Catruna, Emilian RadoiMasked Diffusion Models In-Context Learning
National University of Science and Technology POLITEHNICA Bucharest · Academy of Romanian Scientists
Transcoders for Investigating Deception in Language Models
Jul 16, 2026Darius Lim, Nathan Leow, Xin Wei ChiaDeception in Language Models Circuits in Language Models
Home Team Science & Technology Agency (HTX) Singapore
A Shared Subcircuit Lets LLMs Count Down Across Tasks
Jul 14, 2026Jacob Dunefsky, Wes Gurnee, Emmanuel AmeisenCircuit Discovery Circuits in Language Models
Yale University · Anthropic
Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
Jul 13, 2026Tiberiu Musat, Tiago Pimentel, Nicolas Zucchet +1In-Context Learning Circuits in Language Models
ETH Zurich · Stanford University
Belief-reality separation lives in routing over a shared value slot in language models
Jul 11, 2026Oliver Steele, Jiangtao Wen, Yuxing HanTheory of Mind LLM Interpretability
Tsinghua University · NYU Shanghai
One mechanism for many mental spaces: a shared router over a value slot in language models
Jul 11, 2026Oliver Steele, Jiangtao Wen, Yuxing HanLLM Interpretability Circuits in Language Models
Tsinghua University · NYU Shanghai
Mechanistic Interpretability for Neural Networks: Circuits, Sparse Features and Symbolic Reasoning
Jul 8, 2026Pranav Sawant, Jakub KrejčíTransformer Interpretability Mechanistic Interpretability
The University of Texas at Dallas 800 W Campbell Rd, Richardson, TX 75080, United States · VSB—Technical University of Ostrava 708 00, Ostrava, Czech Republic
Distributed Sparse Interventions in Language Models
Jul 8, 2026Maximilian S. Ernst, Lorenz Linhardt, Aaron Peikert +1Language Model Steering LLM Interpretability
Max Planck School of Cognition · Center for Lifespan Psychology Max Planck Institute for Human Development · Machine Learning Group Technische Universität Berlin +5
Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits
Jul 2, 2026Zhiren Gong, He Lu, Tiantong Wang +6Circuit Discovery Mechanistic Interpretability
College of Computing and Data Science, Nanyang Technological University, Singapore · Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?
Jun 23, 2026Ayan Antik Khan, Harsh Kohli, Yuekun Yao +2LLM Agent Evaluation Mechanistic Interpretability
George Mason University · The Ohio State University
First-Token Broadcasters: Mechanistic Origins of Language Identity and Distributed Robustness in Transformers
Jun 21, 2026Arjun Pillai, Christian Hoang, Anjelo Jann LarozaMultilingual Language Models LLM Interpretability
1Irvington High School · 2GenAI4E · 3Mapua University
Scalable Circuit Learning for Interpreting Large Language Models
Jun 15, 2026Naiyu Yin, Dennis Wei, Tian Gao +3Circuit Discovery LLM Interpretability
Lehigh University, Mathematics Department, Bethlehem, USA · IBM Research, Yorktown Heights, USA.
Demystifying Variance in Circuit Discovery of LLMs
Jun 15, 2026Frank Zhengqing Wu, Francesco Tonin, Volkan CevherPrompt Sensitivity Circuit Discovery
Laboratory for Information and Inference Systems (LIONS) École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland · Laboratory for Informationand Inference Systems (LIONS) École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Localizing Anchoring Pathways in Language Models
Jun 11, 2026Hillary N. Owusu, Sarah Wiegreffe, Naomi H. FeldmanNumerical Reasoning in Language Models LLM Interpretability
University of Maryland, College Park
When Attribution Patching Lies: Diagnosis and a Second-Order Correction
Jun 5, 2026Luyang Zhang, Jialu WangGradient-Based Attribution LLM Interpretability
1Carnegie Mellon University · University of California, Santa Cruz
Pattern Selectivity is Not Task-Causal Structure: A Cross-Architecture Mechanistic Study of Composed-Task Circuits in 1B-Class Language Models
Jun 3, 2026Yongzhong XuAttention Head Analysis Decoder-Only Language Models
Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy
May 25, 2026Xu Shen, Zhen Tan, Song Wang +4CoT Faithfulness LLM Auditing
1Jilin University · University of Central Florida · 3Arizona State University +2
CSP-Atlas: Concept-Specific Neural Circuits in a Sparse Python Transformer
May 23, 2026Piotr WilamTransformer Interpretability Circuit Discovery
University College London
Mechanistic origins of catastrophic forgetting: why RL preserves circuits better than SFT?
May 21, 2026Jeanmely Rojas Nunez, Viraj Sawant, Nathan Allen +4Supervised Fine-Tuning Mechanistic Interpretability
Algoverse AI Research · Independent
Language-Switching Triggers Take a Latent Detour Through Language Models
May 18, 2026Francis Kulumba, Wissam Antoun, Théo Lasnier +2LLM Backdoor Attacks Mechanistic Interpretability
1Inria Paris · 2Sorbonne Université
All Circuits Lead to Rome: Rethinking Functional Anisotropy in Circuit and Sheaf Discovery for LLMs
May 12, 2026Xi Chen, Mingyu Jin, Jingcheng Niu +7Circuit Discovery LLM Interpretability
University of Toronto · The Hong Kong University of Science and Technology (Guangzhou) · Rutgers University +2
Data-driven Circuit Discovery for Interpretability of Language Models
May 9, 2026Daking Rai, Mor Geva, Ziyu YaoCircuit Discovery LLM Interpretability
George Mason University · Tel Aviv University
Architecture, Not Scale: Circuit Localization in Large Language Models
May 9, 2026Sohan VenkateshTransformer Interpretability Grouped-Query Attention
Manipal Institute of Technology Bengaluru.
How Much Do Circuits Tell Us? Measuring the Consistency and Specificity of Language Model Circuits
May 8, 2026Michael Li, Nishant SubramaniCircuit Discovery Mechanistic Interpretability
Northeastern University · Carnegie Mellon University – Language Technologies Institute

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LLMs Can Annotate Attribution Graphs

Ameen Patel, Max Zhang, Nathan Hu

Stanford University

LLM Interpretability LLM-Assisted Annotation Circuits in Language Models

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Circuit tracing is an exciting technique for revealing the internal computation of language models, but it requires a time-intensive manual step of grouping individual features or MLP neurons into supernodes. We present a simple pipeline for automating this step: directly presenting feature descriptions to a language model that groups them into supernodes. Using automated interpretability metrics, we confirm that supernodes generated by our pipeline are as interpretable as those generated by human annotators. On a two-hop Capitals task, our pipeline recovers a supernode corresponding to the intermediate hop in 97 of 100 prompts. Finally, we present a simple proof of concept using our pipeline for open-ended exploration, where we automatically annotate 1000 attribution graphs from Wikipedia prompt completions and then use an LLM judge to flag interesting graphs worth human review. We hope this work demonstrates that even simple automation can produce meaningful attribution graph annotations, motivating further work on automated circuit tracing.

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