mlsanityData to 7 Oct 2026

Mechanistic Interpretability for Neural Networks: Circuits, Sparse Features and Symbolic Reasoning

arXiv 2607.07316 ↗First posted 8 Jul 2026cs.LGcited by 1

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

This article offers a comprehensive overview of mechanistic interpretability, an emerging field that seeks to reverse-engineer the internal algorithms of modern neural networks. While traditional explainable AI methods often stop at surface-level input-output correlations, this approach directly addresses the opaque "black box" nature of machine learning models, which is essential for ensuring safety and auditability in high-stakes deployments. The paper provides a detailed examination of Transformer circuit analysis, exploring how internal components like the residual stream, attention mechanisms, and induction heads drive complex tasks and in-context learning. It subsequently tackles the core challenge of superposition and polysemanticity, demonstrating how tools like Sparse Autoencoders (SAEs) and transcoders can decompose tangled network activations into distinct, human-interpretable features. Furthermore, the paper explores methods for actively controlling and modifying model behavior through steering vectors and causal interventions. Finally, it connects these mechanistic insights with neurosymbolic AI frameworks designed to translate neural representations into explicit, executable logical rules.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 42 of its 58 references that are in the map. First posted on arXiv 8 Jul 2026.

Builds on

42 of 58 references.

References · 42

  1. 2016Understanding intermediate layers using linear classifier probescited by 1,279
  2. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 smallcited by 1,130
  3. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,009
  4. 2022In-context Learning and Induction Headscited by 930
  5. 2022Toy Models of Superpositioncited by 928
  6. 2023Steering Language Models With Activation Engineeringcited by 824
  7. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  8. 2024Scaling and evaluating sparse autoencoderscited by 530
  9. 2024Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelscited by 412
  10. 2024Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2cited by 381
  11. 2023Finding Neurons in a Haystack: Case Studies with Sparse Probingcited by 328
  12. 2024Transcoders Find Interpretable LLM Feature Circuitscited by 186
30 more
  1. 2025Open Problems in Mechanistic Interpretabilitycited by 173
  2. 2024Improving Dictionary Learning with Gated Sparse Autoencoderscited by 171
  3. 2024BatchTopK Sparse Autoencoderscited by 126
  4. 2024What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formationcited by 86
  5. 2024Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Controlcited by 83
  6. 2025A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Modelscited by 73
  7. 2024A Mechanistic Analysis of a Transformer Trained on a Symbolic Multi-Step Reasoning Taskcited by 49
  8. 2025Interpretable Steering of Large Language Models with Feature Guided Activation Additionscited by 32
  9. 2024Efficient Automated Circuit Discovery in Transformers using Contextual Decompositioncited by 23
  10. 2025Representation Engineering for Large-Language Models: Survey and Research Challengescited by 18
  11. 2022FOLD-SE: An Efficient Rule-based Machine Learning Algorithm with Scalable Explainabilitycited by 15
  12. 2026Sparse Autoencoders Reveal Interpretable and Steerable Features in VLA Modelscited by 14
  13. 2019Visualizing Attention in Transformer-Based Language Representation Modelscited by 8
  14. 2025Small Vectors, Big Effects: A Mechanistic Study of RL-Induced Reasoning via Steering Vectorscited by 8
  15. 2025Reinforcement Learning Fine-Tuning Enhances Activation Intensity and Diversity in the Internal Circuitry of LLMscited by 8
  16. 2026Mechanistic Interpretability for Large Language Model Alignment: Progress, Challenges, and Future Directionscited by 8
  17. 2025Model Whisper: Steering Vectors Unlock Large Language Models' Potential in Test-timecited by 7
  18. 2025Entropy-Lens: Uncovering Decision Strategies in LLMscited by 6
  19. 2025Unboxing the Black Box: Mechanistic Interpretability for Algorithmic Understanding of Neural Networkscited by 6
  20. 2024Investigating the Indirect Object Identification circuit in Mambacited by 5
  21. 2025Are explainable AI (XAI) evaluation strategies aligned? Comparing subjective, objective, and mathematical evaluation measures using saliency mapscited by 4
  22. 2025Circuit Insights: Towards Interpretability Beyond Activationscited by 4
  23. 2025Toward a Flexible Framework for Linear Representation Hypothesis Using Maximum Likelihood Estimationcited by 3
  24. 2025Mechanistic Interpretability with Sparse Autoencoder Neural Operatorscited by 3
  25. 2025PAHQ: Accelerating Automated Circuit Discovery through Mixed-Precision Inference Optimizationcited by 3
  26. 2025On the geometry and topology of representations: the manifolds of modular additioncited by 3
  27. 2023NeSyFOLD: Neurosymbolic Framework for Interpretable Image Classificationcited by 2
  28. 2026Improving Sparse Autoencoder with Dynamic Attentioncited by 2
  29. 2025Emergence of Minimal Circuits for Indirect Object Identification in Attention-Only Transformerscited by 1
  30. 2026DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignmentcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

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  1. 2025Combining Causal Models for More Accurate Abstractions of Neural Networks
  2. 2026MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
  3. 2025Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning
  4. 2026MechRL: Reinforcement Learning Agents Perform Circuit Discovery for Mechanistic Interpretability
  5. 2024Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures
  6. 2025Neural Probabilistic Circuits: Enabling Compositional and Interpretable Predictions through Logical Reasoning
  7. 2026Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era
  8. 2026Can Language Model Agents be Helpful Circuit Explainers in Mechanistic Interpretability?