Circuit Discovery

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 29

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Are We Recovering Mechanisms? Objective-Level Recovery Gaps in Mechanistic Interpretability
Oct 1, 2026Chuqin Geng, Li Zhang, Haolin Ye +3Circuit Discovery Mechanistic Interpretability
University of Toronto · McGill University
Attention as a Routing Graph: Live Circuit Extraction from a Single Forward Pass
Sep 21, 2026Ash Manvi, Samreena TajreenCircuit Discovery Attention Mechanisms
Aquin Labs
Circuit-Diff: Factual Edit-based Intervention Method for Localizing Knowledge in Attribution Graphs
Sep 20, 2026Edward G. Friedman, Xiangchen SongCircuit Discovery Mechanistic Interpretability
School of Computer Science Machine Learning Department Carnegie Mellon University Pittsburgh, PA 15213
Sparse Weight and Edge Circuit Discovery in Transformer-based Acoustic Models
Sep 11, 2026Jiankun Wei, Ewan Dunbar, Gerald PennTransformer Interpretability Circuit Discovery
Department of Computer Science University of Toronto Toronto, Canada
Analog-DB: An Agent-First Analog Integrated Circuit Database, From Blocks to Systems
Sep 1, 2026Danial Noori Zadeh, Mohamed B. ElamienCircuit Discovery Electronic Design Automation
The authors are with the Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
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
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
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
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
FactorLibrary: From Polynomials to Circuits via Recursive Subgoals
Jun 24, 2026Rohan Pandey, Michael Ruofan Zeng, Weikun K. Zhang +5Circuit Discovery Reinforcement Learning
University of Washington, Seattle, WA, USA.
Targeted Recovery of Weight-Space Mechanisms From Neural Networks
Jun 19, 2026Antoine Vigouroux, Lee SharkeyTransformer Interpretability Circuit Discovery
1MATS · 2Goodfire
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
Circuit Tracing in Autoregressive Protein Language Models
Jun 14, 2026Darin Tsui, William Deinzer, Daniel Saeedi +1Circuit Discovery Protein Fitness Prediction
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atltanta, GA.
Closure-Validated Circuit Discovery in Attention Heads: Co-activation Proposes, Ablation Disposes
Jun 8, 2026Yongzhong XuTransformer Interpretability Circuit Discovery
Many Circuits, One Mechanism: Input Variation and Evaluation Granularity in Circuit Discovery
Jun 4, 2026Alireza Bayat Makou, Jingcheng Niu, Subhabrata Dutta +1Circuit Discovery Mechanistic Interpretability
UKP Lab, Technical University of Darmstadt · National Research Center for Applied Cybersecurity ATHENE
MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability
May 25, 2026Barsat KhadkaTransformer Interpretability Circuit Discovery
School of Computing Sciences and Engineering The University of Southern Mississippi
CSP-Atlas: Concept-Specific Neural Circuits in a Sparse Python Transformer
May 23, 2026Piotr WilamTransformer Interpretability Circuit Discovery
University College London
Spectral Probe-Circuits: A Three-Step Recipe for Identifying Attention-Head Circuits in Pretrained Transformers
May 22, 2026Yongzhong XuTransformer Interpretability Circuit Discovery
From Circuit Evidence to Mechanistic Theory: An Inductive Logic Approach
May 20, 2026Nura Aljaafari, Danilo S. Carvalho, Andre FreitasCircuit Discovery Mechanistic Interpretability
Department of Computer Science, University of Manchester, United Kingdom · CRUK National Biomarker Centre, University of Manchester, United Kingdom · Idiap Research Institute, Switzerland
Transformers Linearly Represent Highly Structured World Models
May 13, 2026Roman Kniazev, Nathanaël FijalkowCircuit Discovery World Models
LaBRI, CNRS · University of Bordeaux · France
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
Carnegie Mellon University · Work done while at Carnegie Mellon University. · Northeastern University +1
Can Current Agents Close the Discovery-to-Application Gap? A Case Study in Minecraft
Apr 27, 2026Zhou Ziheng, Huacong Tang, Jinyuan Zhang +9AI Agents for Scientific Discovery Circuit Discovery
University of California, Los Angeles · Peking University · Amazon
Prune, Interpret, Evaluate: A Cross-Layer Transcoder-Native Framework for Efficient Circuit Discovery via Feature Attribution
Apr 18, 2026Qinhao Chen, Linyang He, Nima MesgaraniLLM Pruning Feature Attribution
Columbia University
Beyond Transfer Accuracy: Mechanism-Guided Controlled Adaptation for Low-Resource Languages
Jan 13, 2026Khumaisa Nur'aini, Ayu Purwarianti, Alham Fikri Aji +1Selective Fine-Tuning Circuit Discovery
Monash University Indonesia · MBZUAI · Institute Teknologi Bandung +1

Oct 1, 2026, cs.LGJ/K move · Enter open · S save

Are We Recovering Mechanisms? Objective-Level Recovery Gaps in Mechanistic Interpretability

Chuqin Geng, Li Zhang, Haolin Ye, Mark Zhang+2

University of Toronto · McGill University

Circuit Discovery Mechanistic Interpretability Explainability Evaluation

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Mechanistic interpretability aims to recover the internal computations responsible for model behavior. Progress in automated circuit discovery is often framed as a search problem: better attribution or optimization should identify better mechanisms. This assumes that the evaluation objective can recognize a better circuit once it is found. We show that intervention-defined faithfulness can instead prefer an equally sized circuit that reproduces the model's behavior less well, creating an objective-level recovery gap. Across four human-reference tasks and InterpBench, we compare validation faithfulness with behavior on held-out prompts under fixed ordinary resampling. The behavioral criterion is agreement with the intact model, including its mistakes, except on Greater-Than, where we use semantic accuracy. Controlled reference edits reveal misranking without any discovery algorithm, and outputs of EAP, EAP-IG, ACDC, and Edge-SP exhibit the same failure. Under resampling, KL misranks 9.4%-41.2% of candidate pairs across these methods on the human-reference tasks. We investigate context distortion as an explanation: replacing excluded signals changes the inputs on which retained components operate. Restoring selected signals from the recipient's intact-model execution repairs 96 of 100 persistent KL misrankings from the discovery pool on both validation and held-out prompts. The circuits and their original behavioral scores remain unchanged. These findings show why better discovery alone is insufficient when its objective rewards the wrong candidate.

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