mlsanityData to 14 Oct 2026

Interpreting Transformers Through Attention Head Intervention

arXiv 2601.04398 ↗First posted 7 Jan 2026cs.CLcited by 1

On the map: Mechanistic interpretability → Circuits and mechanistic analysis

Abstract (arXiv)

Neural networks are growing more capable on their own, but we do not understand their neural mechanisms. Understanding these mechanisms' decision-making processes, or mechanistic interpretability, enables (1) accountability and control in high-stakes domains, (2) the study of digital brains and the emergence of cognition, and (3) discovery of new knowledge when AI systems outperform humans. This paper traces how attention head intervention emerged as a key method for causal interpretability of transformers. The evolution from visualization to intervention represents a paradigm shift from observing correlations to causally validating mechanistic hypotheses through direct intervention. Head intervention studies revealed robust empirical findings while also highlighting limitations that complicate interpretation. Recent work demonstrates that mechanistic understanding now enables targeted control of model behaviour, successfully suppressing toxic outputs and manipulating semantic content through selective attention head intervention, validating the practical utility of interpretability research for AI safety.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Circuits and mechanistic analysis, one of 13 directions in Mechanistic interpretability. Builds on 15 of its 22 references that are in the map. First posted on arXiv 7 Jan 2026.

Builds on

15 of 22 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Open Problems in Mechanistic Interpretability27 Jan
  2. 2022Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small1 Nov

Other references · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 1,003
  3. 2022Toy Models of Superpositioncited by 971
  4. 2019Attention is not Explanationcited by 929
  5. 2019Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Prunedcited by 856
  6. 2019Are Sixteen Heads Really Better than One?cited by 816
  7. 2020Quantifying Attention Flow in Transformerscited by 775
  8. 2019Attention is not not Explanationcited by 636
  9. 2020Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?cited by 599
  10. 2019Is Attention Interpretable?cited by 389
  11. 2019A Multiscale Visualization of Attention in the Transformer Modelcited by 292
  12. 2021The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanationscited by 87
1 more
  1. 2025Head Pursuit: Probing Attention Specialization in Multimodal Transformerscited by 17

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurementcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024The Cognitive Revolution in Interpretability: From Explaining Behavior to Interpreting Representations and Algorithms
  2. 2024Mechanistic Interpretability for AI Safety -- A Review
  3. 2025Mechanistic Interpretability of Fine-Tuned Vision Transformers on Distorted Images: Decoding Attention Head Behavior for Transparent and Trustworthy AI
  4. 2025Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers
  5. 2026Mechanistic Interpretability for Neural Networks: Circuits, Sparse Features and Symbolic Reasoning
  6. 2026Generative Interpretability via Scalable Neuro-Symbolic Models
  7. 2026Causally Grounded Mechanistic Interpretability for LLMs with Faithful Natural-Language Explanations
  8. 2025Addressing divergent representations from causal interventions on neural networks