---
title: "Who cites Mechanistic Interpretability for AI Safety — A Review"
description: "79 papers of the Elamur Research library cite «Mechanistic Interpretability for AI Safety — A Review», found in their bibliographies; the most…"
canonical: "https://research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Mechanistic Interpretability for AI Safety — A…](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/) / cited by

# Who cites «Mechanistic Interpretability for AI Safety — A Review»

79 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «Mechanistic Interpretability for AI Safety — A Review»](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/)

1. **[Multi-Step Reasoning with Large Language Models, a Survey](https://md.research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Aske Plaat, Annie Wong, Suzan Verberne, et al.*2024 · 106 citations*
2. [**A Primer on the Inner Workings of Transformer-based Language Models arXiv:2405.00208**Javier Ferrando, Gabriele Sarti, Arianna Bisazza, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/ferrando-2024-primer-inner-workings-transformer-based/)
3. [**Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation arXiv:2503.11926**Bowen Baker, Joost Huizinga, Leo Gao, et al.*2025 · 63 citations*](https://md.research.elamur.ai/free/en/papers/baker-2025-monitoring-reasoning-models-misbehavior-risks/)
4. [**Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey arXiv:2404.01869**Philipp Mondorf, Barbara Plank*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/)
5. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
6. **[Beyond Prompt Engineering: Robust Behavior Control in LLMs via Steering Target Atoms](https://md.research.elamur.ai/free/en/papers/wang-2025-beyond-prompt-engineering-robust-behavior/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ziwen Xu, Shengyu Mao, et al.*2025 · 14 citations*
7. **[Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race](https://md.research.elamur.ai/free/en/papers/sun-2025-aligned-but-blind-alignment-increases/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lihao Sun, Chengzhi Mao, Valentin Hofmann, et al.*2025 · 10 citations*
8. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
9. **[Thinking beyond the anthropomorphic paradigm benefits LLM research](https://md.research.elamur.ai/free/en/papers/ibrahim-2026-thinking-beyond-anthropomorphic-paradigm-benefits/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lujain Ibrahim, Myra Cheng*2026 · 8 citations*
10. **[The Quest for the Right Mediator: Surveying Mechanistic Interpretability for NLP Through the Lens of Causal Mediation Analysis](https://md.research.elamur.ai/free/en/papers/mueller-2026-quest-right-mediator-surveying-mechanistic/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Aaron Mueller, Jannik Brinkmann, Millicent Li, et al.*2026 · 7 citations*
11. **[All for One: LLMs Solve Mental Math at the Last Token With Information Transferred From Other Tokens](https://md.research.elamur.ai/free/en/papers/mamidanna-2025-all-one-llms-solve-mental/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Siddarth Mamidanna, Daking Rai, Ziyu Yao, et al.*2025 · 6 citations*
12. **[The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It](https://md.research.elamur.ai/free/en/papers/bertolazzi-2025-validation-gap-mechanistic-analysis-how/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Leonardo Bertolazzi, Philipp Mondorf, Barbara Plank, et al.*2025 · 6 citations*
13. [**Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models arXiv:2605.11887**Boyi Deng, Xu Wang, Yaoning Wang, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/deng-2026-qwen-scope-turning-sparse/)
14. **[Aligned Probing: Relating Toxic Behavior and Model Internals](https://md.research.elamur.ai/free/en/papers/waldis-2026-aligned-probing-relating-toxic-behavior/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Andreas Waldis, Vagrant Gautam, Anne Lauscher, et al.*2026 · 1 citation*
15. **[A Unified View on Emotion Representation in Large Language Models](https://md.research.elamur.ai/free/en/papers/maheswaran-2026-unified-view-emotion-representation-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Aishwarya Maheswaran, Maunendra Sankar Desarkar*2026*
16. **[How Do LLMs Generate Contrastive Sentiments? A Mechanistic Perspective](https://md.research.elamur.ai/free/en/papers/nguyen-2026-how-do-llms-generate-contrastive/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Van Bach Nguyen, Jörg Schlötterer, Christin Seifert*2026*
17. **[Mapping the Circumplex of Affect: Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning](https://md.research.elamur.ai/free/en/papers/yamauchi-2026-mapping-circumplex-affect-geometric-analysis/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yusuke Yamauchi, Akiko Aizawa*2026*
18. **[Mechanistic Interpretability Meets Cognitive Linguistics: Modelling Locative Image Schemas in the Circuit Framework](https://md.research.elamur.ai/free/en/papers/proietti-2026-mechanistic-interpretability-meets-cognitive-linguistics/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Mattia Proietti, Afra Alishahi, Grzegorz Chrupała, et al.*2026*
19. **[Soft Head Selection for Injecting ICL-Derived Task Embeddings](https://md.research.elamur.ai/free/en/papers/park-2026-soft-head-selection-injecting-icl/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jungwon Park, Jimyeong Kim, Changin Choi, et al.*2026*

60 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/) — Mechanistic Interpretability for AI Safety — A Review
