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[2602.21442] MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
Summary
The paper introduces MINAR, a toolbox for mechanistic interpretability in neural algorithmic reasoning, enhancing understanding of GNNs' circuit formation and task performance.
Why It Matters
This research is significant as it bridges the gap between neural algorithmic reasoning and mechanistic interpretability, providing insights into how GNNs can emulate classical algorithms. Understanding these mechanisms can lead to improved model design and performance in AI applications.
Key Takeaways
- MINAR adapts attribution patching methods for GNNs.
- The study reveals how GNNs form and prune circuits during training.
- GNNs can reuse circuit components for related tasks, enhancing efficiency.
- Two case studies demonstrate MINAR's effectiveness in circuit discovery.
- The research contributes to the field of mechanistic interpretability in AI.
Computer Science > Machine Learning arXiv:2602.21442 (cs) [Submitted on 24 Feb 2026] Title:MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning Authors:Jesse He, Helen Jenne, Max Vargas, Davis Brown, Gal Mishne, Yusu Wang, Henry Kvinge View a PDF of the paper titled MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning, by Jesse He and 6 other authors View PDF HTML (experimental) Abstract:The recent field of neural algorithmic reasoning (NAR) studies the ability of graph neural networks (GNNs) to emulate classical algorithms like Bellman-Ford, a phenomenon known as algorithmic alignment. At the same time, recent advances in large language models (LLMs) have spawned the study of mechanistic interpretability, which aims to identify granular model components like circuits that perform specific computations. In this work, we introduce Mechanistic Interpretability for Neural Algorithmic Reasoning (MINAR), an efficient circuit discovery toolbox that adapts attribution patching methods from mechanistic interpretability to the GNN setting. We show through two case studies that MINAR recovers faithful neuron-level circuits from GNNs trained on algorithmic tasks. Our study sheds new light on the process of circuit formation and pruning during training, as well as giving new insight into how GNNs trained to perform multiple tasks in parallel reuse circuit components for related tasks. Our code is available at this https URL. Subjects: Machine Learning (...
