---
title: "Who cites Senthooran Rajamanoharan — 178 citing papers"
description: "178 papers of the Elamur Research library cite the works of Senthooran Rajamanoharan, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/senthooran-rajamanoharan/cited-by/"
---
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# Who cites Senthooran Rajamanoharan

200 citations of their works from 178 papers of our library (of the 149,439 whose bibliographies we read) · 58 with a page here, the most cited first · [← Senthooran Rajamanoharan](https://md.research.elamur.ai/free/en/authors/senthooran-rajamanoharan/)

1. [**Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2 arXiv:2408.05147**Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, et al.*2024 · 115 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2024-gemma-scope-open-sparse-autoencoders/)
2. [**Mechanistic Interpretability for AI Safety — A Review arXiv:2404.14082**Leonard Bereska, Efstratios Gavves*2024 · 108 citations*](https://md.research.elamur.ai/free/en/papers/bereska-2024-mechanistic-interpretability-ai-safety-review/)
3. [**Reasoning Models Don't Always Say What They Think arXiv:2505.05410**Yanda Chen, Joe Benton, Ansh Radhakrishnan, et al.*2025 · 106 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-reasoning-models-don-t-always/)
4. [**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/)
5. [**Phi-4-reasoning Technical Report arXiv:2504.21318**Marah Abdin, Sahaj Agarwal, Ahmed Awadallah, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2025-phi-4-reasoning-technical-report/)
6. [**Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders arXiv:2410.20526**Zhengfu He, Wentao Shu, Xuyang Ge, et al.*2024 · 63 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-llama-scope-extracting-millions-features/)
7. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
8. **[Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://md.research.elamur.ai/free/en/papers/suzgun-2026-dynamic-cheatsheet-test-time-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, et al.*2026 · 37 citations*
9. [**Scaling Latent Reasoning via Looped Language Models arXiv:2510.25741**Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-scaling-latent-reasoning-looped-language/)
10. **[Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/deng-2025-unveiling-language-specific-features-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boyi Deng, Yu Wan, Baosong Yang, et al.*2025 · 18 citations*
11. **[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*
12. **[Enhancing Automated Interpretability with Output-Centric Feature Descriptions](https://md.research.elamur.ai/free/en/papers/arieh-2025-enhancing-automated-interpretability-output-centric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yoav Gur-Arieh, Roy Mayan, Chen Agassy, et al.*2025 · 13 citations*
13. **[Personalized Text Generation with Contrastive Activation Steering](https://md.research.elamur.ai/free/en/papers/zhang-2025-personalized-text-generation-contrastive-activation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinghao Zhang, Yuting Liu, Wenjie Wang, et al.*2025 · 13 citations*
14. **[SAEs Are Good for Steering – If You Select the Right Features](https://md.research.elamur.ai/free/en/papers/arad-2025-saes-are-good-steering-if/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dana Arad, Aaron Mueller, Yonatan Belinkov*2025 · 12 citations*
15. **[Feature Extraction and Steering for Enhanced Chain-of-Thought Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/li-2025-feature-extraction-steering-enhanced-chain/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zihao Li, Xu Wang, Yuzhe Yang, et al.*2025 · 11 citations*
16. **[Route Sparse Autoencoder to Interpret Large Language Models](https://md.research.elamur.ai/free/en/papers/shi-2025-route-sparse-autoencoder-interpret-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Shi, Sihang Li, Tao Liang, et al.*2025 · 11 citations*
17. **[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*
18. **[Model Unlearning via Sparse Autoencoder Subspace Guided Projections](https://md.research.elamur.ai/free/en/papers/wang-2025-model-unlearning-sparse-autoencoder-subspace/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xu Wang, Zihao Li, Benyou Wang, et al.*2025 · 8 citations*
19. **[Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis](https://md.research.elamur.ai/free/en/papers/boughorbel-2024-improving-language-models-trained-translated/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Sabri Boughorbel, Md Rizwan Parvez, Majd Hawasly*2024 · 8 citations*
20. **[A Comprehensive Evaluation of Multilingual Chain-of-Thought Reasoning: Performance, Consistency, and Faithfulness Across Languages](https://md.research.elamur.ai/free/en/papers/zhao-2026-comprehensive-evaluation-multilingual-chain-thought/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Raoyuan Zhao, Yihong Liu, Hinrich Schuetze, et al.*2026 · 7 citations*
21. **[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*
22. **[Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization](https://md.research.elamur.ai/free/en/papers/zaman-2026-is-chain-thought-really-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kerem Zaman, Shashank Srivastava*2026 · 5 citations*
23. **[KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality](https://md.research.elamur.ai/free/en/papers/ren-2026-knowrl-exploring-knowledgeable-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Baochang Ren, Shuofei Qiao, Ningyu Zhang, et al.*2026 · 5 citations*
24. **[Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful Post-hoc Rationalisation?](https://md.research.elamur.ai/free/en/papers/lim-2025-analysing-chain-thought-dynamics-active/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel Lewis-Lim, Xingwei Tan, Zhixue Zhao, et al.*2025 · 5 citations*
25. **[Denoising Concept Vectors with Sparse Autoencoders for Improved Language Model Steering](https://md.research.elamur.ai/free/en/papers/zhao-2026-denoising-concept-vectors-sparse-autoencoders/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haiyan Zhao, Xuansheng Wu, Fan Yang, et al.*2026 · 3 citations*
26. [**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/)
27. **[Steering Large Language Models for Machine Translation Personalization](https://md.research.elamur.ai/free/en/papers/scalena-2026-steering-large-language-models-machine/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Daniel Scalena, Gabriele Sarti, Arianna Bisazza, et al.*2026 · 3 citations*
28. **[AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor](https://md.research.elamur.ai/free/en/papers/yang-2026-automonitor-bench-evaluating-reliability-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shu Yang, Jingyu Hu, Tong Li, et al.*2026 · 2 citations*
29. **[C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/mittal-2026-c2-faith-benchmarking-llm-judges/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Avni Mittal, Rauno Arike*2026 · 2 citations*
30. **[LLM Reasoning as Trajectories: Step-Specific Representation Geometry and Correctness Signals](https://md.research.elamur.ai/free/en/papers/sun-2026-llm-reasoning-as-trajectories-step/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lihao Sun, Hang Dong, Bo Qiao, et al.*2026 · 2 citations*
31. **[SAE-FiRE: Enhancing Earnings Surprise Predictions Through Sparse Autoencoder Feature Selection](https://md.research.elamur.ai/free/en/papers/zhang-2026-sae-fire-enhancing-earnings-surprise/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Huopu Zhang, Yanguang Liu, Miao Zhang, et al.*2026 · 2 citations*
32. **[Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework](https://md.research.elamur.ai/free/en/papers/weng-2026-safe-sail-fine-grained-safety/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaqi Weng, Han Zheng, Hanyu Zhang, et al.*2026 · 2 citations*
33. **[Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals](https://md.research.elamur.ai/free/en/papers/havens-2025-investigating-capabilities-limitations-machine-learning/) [CHI Conference on Human Factors in Computing Systems](https://md.research.elamur.ai/free/en/venues/chi-conference-on-human-factors-in-computing-systems/)**Lucy Havens, Benjamin Bach, Melissa Terras, et al.*2025 · 2 citations*
34. **[A Comprehensive Evaluation of Chain-of-Thought Faithfulness in Persian Classification Tasks](https://md.research.elamur.ai/free/en/papers/yazdani-2026-comprehensive-evaluation-chain-thought-faithfulness/) [Workshop on Language Models for Low-Resource Languages](https://md.research.elamur.ai/free/en/venues/loreslm/)**Shakib Yazdani, Cristina España-Bonet, Eleftherios Avramidis, et al.*2026 · 1 citation*
35. **[Constructing Interpretable Features from Compositional Neuron Groups](https://md.research.elamur.ai/free/en/papers/shafran-2026-constructing-interpretable-features-compositional-neuron/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Or David Shafran, Atticus Geiger, Mor Geva*2026 · 1 citation*
36. **[Deciphering Cultural Representations in Large Language Models via Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/zou-2026-deciphering-cultural-representations-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenye Zou, Difan Jiao, Lijie Hu*2026 · 1 citation*
37. **[From Weights to Activations: Is Steering the Next Frontier of Adaptation?](https://md.research.elamur.ai/free/en/papers/ostermann-2026-weights-activations-is-steering-next/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Simon Ostermann, Daniil Gurgurov, Tanja Baeumel, et al.*2026 · 1 citation*
38. **[Mechanistic Interpretability Should Prioritize Feature Consistency in Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/song-2026-mechanistic-interpretability-should-prioritize-feature/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiangchen Song, Aashiq Muhamed, Yujia Zheng, et al.*2026 · 1 citation*
39. **[SPD-Faith Bench: Diagnosing and Improving Faithfulness in Chain-of-Thought for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/lv-2026-spd-faith-bench-diagnosing-improving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Weijiang Lv, Yaoxuan Feng, Xiaobo Xia, et al.*2026 · 1 citation*
40. **[AdaptiveK: Complexity-Driven Sparse Autoencoders for Interpretable Language Model Representations](https://md.research.elamur.ai/free/en/papers/yao-2026-adaptivek-complexity-driven-sparse-autoencoders/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yifei Yao, Hanrong Zhang, Mengnan Du*2026*
41. **[Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/yan-2026-beyond-cross-modal-alignment-measuring/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hanqi Yan, Xiangxiang Cui, Lu Yin, et al.*2026*
42. **[Difference in Task Performance on Sparse Speech Representations](https://md.research.elamur.ai/free/en/papers/peng-2026-difference-task-performance-sparse-speech/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenjie Peng, Chen Chen, Thomas Hain*2026*
43. **[Evaluating the Impact of SAE-based Language Steering on LLM Performance](https://md.research.elamur.ai/free/en/papers/zwirner-2026-evaluating-impact-sae-based-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sebastian Zwirner, Wentao Hu, Koshiro Aoki, et al.*2026*
44. **[From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models](https://md.research.elamur.ai/free/en/papers/shi-2026-insight-action-novel-framework-interpretability/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ling Shi, Xinwei Wu, Xiaohu Zhao, et al.*2026*
45. **[From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?](https://md.research.elamur.ai/free/en/papers/mueller-2026-isolation-entanglement-when-do-interpretability/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aaron Mueller, Andrew Lee, Shruti Joshi, et al.*2026*
46. **[From Latents to Labels: Zero-Shot Named Entity Recognition using Sparse Autoencoder Features](https://md.research.elamur.ai/free/en/papers/vuth-2026-latents-labels-zero-shot-named/) [Joint Conference on Lexical and Computational Semantics (*SEM)](https://md.research.elamur.ai/free/en/venues/starsem/)**Nakanyseth Vuth, Gilles Sérasset, Didier Schwab*2026*
47. **[How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities](https://md.research.elamur.ai/free/en/papers/xu-2026-how-controllable-are-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziwen Xu, Kewei Xu, Haoming Xu, et al.*2026*
48. **[Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation](https://md.research.elamur.ai/free/en/papers/wang-2026-interpretable-safety-alignment-sae-constructed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dianyun Wang, Qingsen Ma, Yuhu Shang, et al.*2026*
49. **[Knowledge Vector of Logical Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-knowledge-vector-logical-reasoning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zixuan Wang, Yuanyuan Lei*2026*
50. **[Measuring Mechanistic Independence: Can Bias Be Removed Without Erasing Demographics?](https://md.research.elamur.ai/free/en/papers/shan-2026-measuring-mechanistic-independence-can-bias/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zhengyang Shan, Aaron Mueller*2026*
51. **[Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/zheng-2026-model-directions-not-words-mechanistic/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Carolina Zheng, Nicolas Beltran-Velez, Sweta Karlekar, et al.*2026*
52. **[Multilingual Language Models Encode Script Over Linguistic Structure](https://md.research.elamur.ai/free/en/papers/verma-2026-multilingual-language-models-encode-script/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aastha A K Verma, Anwoy Chatterjee, Mehak Gupta, et al.*2026*
53. **[NSF-CoT: Neuro-Symbolic Formal Verification of Chain-of-Thought Faithfulness in Contextual Question Answering](https://md.research.elamur.ai/free/en/papers/pramanik-2026-nsf-cot-neuro-symbolic-formal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vishal Pramanik, Maisha Maliha, Nathaniel D. Bastian, et al.*2026*
54. **[Specialization through Collaboration: Understanding Expert Interaction in Mixture-of-Expert Large Language Models](https://md.research.elamur.ai/free/en/papers/tang-2026-specialization-through-collaboration-understanding-expert/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yuanbo Tang, Naifan Zhang, Yan Tang, et al.*2026*
55. **[Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation](https://md.research.elamur.ai/free/en/papers/mule-2026-teaching-language-models-forecast-research/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Srujan P Mule, Aniketh Garikaparthi, Manasi Patwardhan*2026*
56. **[Towards Reliable AI Fairness: Challenges in Steering Features within Bias-Implicated Neurons](https://md.research.elamur.ai/free/en/papers/munoz-2026-reliable-ai-fairness-challenges-steering/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ismael Garrido-Munoz, Arturo Montejo-Raez, Fernando Martínez-Santiago*2026*
57. **[Understanding Emergent Misalignment via Feature Superposition Geometry](https://md.research.elamur.ai/free/en/papers/minegishi-2026-understanding-emergent-misalignment-feature-superposition/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gouki Minegishi, Hiroki Furuta, Takeshi Kojima, et al.*2026*
58. **[Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story](https://md.research.elamur.ai/free/en/papers/vladislav-2026-unveiling-intrinsic-dimension-texts-academic/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Pedashenko Vladislav, Laida Kushnareva, Yana Khassan Nibal, et al.*2026*

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