---
title: "Who cites Nicolas Papernot — 313 papers of our library"
description: "313 papers of the Elamur Research library cite the works of Nicolas Papernot, found in their bibliographies; the most cited first."
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---
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# Who cites Nicolas Papernot

318 citations of their works from 313 papers of our library (of the 149,439 whose bibliographies we read) · 64 with a page here, the most cited first · [← Nicolas Papernot](https://md.research.elamur.ai/free/en/authors/nicolas-papernot/)

1. [**Llama 2: Open Foundation and Fine-Tuned Chat Models arXiv:2307.09288**Hugo Touvron, Louis Martin, Kevin Stone, et al.*2023 · 9,669 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-2-open-foundation-fine/)
2. [**A Simple Framework for Contrastive Learning of Visual Representations arXiv:2002.05709**Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.*2020 · 8,000 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/)
3. [**Unsupervised Data Augmentation for Consistency Training arXiv:1904.12848**Qizhe Xie, Zihang Dai, Eduard Hovy, et al.*2019 · 1,871 citations*](https://md.research.elamur.ai/free/en/papers/xie-2019-unsupervised-data-augmentation-consistency-training/)
4. **[MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification](https://md.research.elamur.ai/free/en/papers/chen-2020-mixtext-linguistically-informed-interpolation-hidden/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaao Chen, Zichao Yang, Diyi Yang*2020 · 417 citations*
5. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
6. **[HuatuoGPT, Towards Taming Language Model to Be a Doctor](https://md.research.elamur.ai/free/en/papers/zhang-2023-huatuogpt-taming-language-model-be/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hongbo Zhang, Junying Chen, Feng Jiang, et al.*2023 · 282 citations*
7. **[A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://md.research.elamur.ai/free/en/papers/wu-2025-survey-llm-generated-text-detection/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Junchao Wu, Shu Yang, Runzhe Zhan, et al.*2025 · 277 citations*
8. **[Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 262 citations*
9. **[An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiaao Chen, Derek Tam, Colin Raffel, et al.*2023 · 254 citations*
10. [**Reinforced Self-Training (ReST) for Language Modeling arXiv:2308.08998**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
11. [**Will we run out of data? Limits of LLM scaling based on human-generated data arXiv:2211.04325**Pablo Villalobos, Anson Ho, Jaime Sevilla, et al.*2022 · 198 citations*](https://md.research.elamur.ai/free/en/papers/villalobos-2022-will-run-out-data-limits/)
12. [**Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier arXiv:2412.04261**John Dang, Shivalika Singh, Daniel D'souza, et al.*2024 · 184 citations*](https://md.research.elamur.ai/free/en/papers/dang-2024-aya-expanse-combining-research-breakthroughs/)
13. [**Scaling Synthetic Data Creation with 1,000,000,000 Personas arXiv:2406.20094**Tao Ge, Xin Chan, Xiaoyang Wang, et al.*2024 · 171 citations*](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/)
14. [**Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks arXiv:2306.07899**Veniamin Veselovsky, Manoel Horta Ribeiro, Robert West*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/veselovsky-2023-artificial-artificial-artificial-intelligence-crowd/)
15. [**On the Reliability of Watermarks for Large Language Models arXiv:2306.04634**John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/kirchenbauer-2023-reliability-watermarks-large-language-models/)
16. [**Training Socially Aligned Language Models on Simulated Social Interactions arXiv:2305.16960**Ruibo Liu, Ruixin Yang, Chenyan Jia, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-training-socially-aligned-language-models/)
17. **[Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling](https://md.research.elamur.ai/free/en/papers/maini-2024-rephrasing-web-recipe-compute-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pratyush Maini, Skyler Seto, Richard Bai, et al.*2024 · 90 citations*
18. **[Alignment for Honesty](https://md.research.elamur.ai/free/en/papers/yang-2023-alignment-honesty/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.*2023 · 90 citations*
19. [**Mapping the Increasing Use of LLMs in Scientific Papers arXiv:2404.01268**Weixin Liang, Yaohui Zhang, Zhengxuan Wu, et al.*2024 · 89 citations*](https://md.research.elamur.ai/free/en/papers/liang-2024-mapping-increasing-use-llms/)
20. [**HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs arXiv:2412.18925**Junying Chen, Zhenyang Cai, Ke Ji, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-huatuogpt-o1-towards-medical-complex/)
21. [**A Survey of Large Language Models in Medicine: Progress, Application, and Challenge arXiv:2311.05112**Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/)
22. [**O1 Replication Journey: A Strategic Progress Report — Part 1 arXiv:2410.18982**Yiwei Qin, Xuefeng Li, Haoyang Zou, et al.*2024 · 86 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-o1-replication-journey-strategic-progress/)
23. **[GPTEval: A Survey on Assessments of ChatGPT and GPT-4](https://md.research.elamur.ai/free/en/papers/mao-2024-gpteval-survey-assessments-chatgpt-gpt/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Rui Mao, Guanyi Chen, Xulang Zhang, et al.*2024 · 75 citations*
24. [**RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning arXiv:2504.20073**Zihan Wang, Kangrui Wang, Qineng Wang, et al.*2025 · 71 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-ragen-understanding-self-evolution-llm/)
25. [**DARWIN Series: Domain Specific Large Language Models for Natural Science arXiv:2308.13565**Tong Xie, Yuwei Wan, Wei Huang, et al.*2023 · 55 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-darwin-series-domain-specific-large/)
26. [**Prompting Multilingual Large Language Models to Generate Code-Mixed Texts: The Case of South East Asian Languages arXiv:2303.13592**Zheng-Xin Yong, Ruochen Zhang, Jessica Zosa Forde, et al.*2023 · 48 citations*](https://md.research.elamur.ai/free/en/papers/yong-2023-prompting-multilingual-large-language-models/)
27. **[Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/guo-2025-do-large-language-models-have/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanzhu Guo, Simone Conia, Zelin Zhou, et al.*2025 · 25 citations*
28. [**MoRAL: MoE Augmented LoRA for LLMs' Lifelong Learning arXiv:2402.11260**Shu Yang; Muhammad Asif Ali; Cheng-Long Wang; Lijie Hu;, Di Wang*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-moral-moe-augmented-lora-llms/)
29. **[A Survey of Post-Training Scaling in Large Language Models](https://md.research.elamur.ai/free/en/papers/lai-2025-survey-post-training-scaling-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanyu Lai, Xiao Liu, Junjie Gao, et al.*2025 · 18 citations*
30. [**Preference Optimization for Reasoning with Pseudo Feedback arXiv:2411.16345**Fangkai Jiao, Geyang Guo, Xingxing Zhang, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/jiao-2024-preference-optimization-reasoning-pseudo-feedback/)
31. [**MiniPLM: Knowledge Distillation for Pre-Training Language Models arXiv:2410.17215**Yuxian Gu, Hao Zhou, Fandong Meng, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-miniplm-knowledge-distillation-pre-training/)
32. [**Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress arXiv:2408.14960**Ayomide Odumakinde, Daniel D'souza, Pat Verga, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/odumakinde-2024-multilingual-arbitrage-optimizing-data-pools/)
33. **[Superficial Self-Improved Reasoners Benefit from Model Merging](https://md.research.elamur.ai/free/en/papers/yuan-2025-superficial-self-improved-reasoners-benefit/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiangchi Yuan, Chunhui Zhang, Zheyuan Liu, et al.*2025 · 8 citations*
34. **[Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs](https://md.research.elamur.ai/free/en/papers/anshumann-2025-sparse-logit-sampling-accelerating-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anshumann, Mohd Abbas Zaidi, Akhil Kedia, et al.*2025 · 6 citations*
35. **[Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging](https://md.research.elamur.ai/free/en/papers/yavuz-2020-simple-data-augmentation-mask-token/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Semih Yavuz, Kazuma Hashimoto, Wenhao Liu, et al.*2020 · 5 citations*
36. **[Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation](https://md.research.elamur.ai/free/en/papers/burns-2026-aleph-alpha-germanweb-improving-german/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, et al.*2026 · 4 citations*
37. **[Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English](https://md.research.elamur.ai/free/en/papers/anderson-2025-model-misalignment-language-change-traces/) [Proceedings of the AAAI/ACM Conference on AI Ethics and Society](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-acm-conference-on-ai-ethics-and-society/)**Bryce Anderson, Riley Galpin, Tom S. Juzek*2025 · 4 citations*
38. **[MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis](https://md.research.elamur.ai/free/en/papers/yu-2026-mathagent-adversarial-evolution-constraint-graphs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zixiong Yu, Jun Rao, Guhan Chen, et al.*2026 · 3 citations*
39. **[Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption](https://md.research.elamur.ai/free/en/papers/liu-2026-position-llm-watermarking-should-align/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yepeng Liu, Xuandong Zhao, Dawn Song, et al.*2026 · 2 citations*
40. **[The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models](https://md.research.elamur.ai/free/en/papers/lohr-2026-hidden-bias-study-explicit-implicit/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Konrad Löhr, Shuzhou Yuan, Michael Färber*2026 · 2 citations*
41. **[Automatic Machine Translation Detection Using a Surrogate Multilingual Translation Model](https://md.research.elamur.ai/free/en/papers/romero-2026-automatic-machine-translation-detection-surrogate/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Cristian García-Romero, Miquel Esplà-Gomis, Felipe Sánchez-Martínez*2026 · 1 citation*
42. **[DemMA: Dementia Multi-Turn Dialogue Agent with Expert-Guided Reasoning and Action Simulation](https://md.research.elamur.ai/free/en/papers/song-2026-demma-dementia-multi-turn-dialogue/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yutong Song, Jiang Wu, Kazi Shaharair Sharif, et al.*2026 · 1 citation*
43. **[Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yu-2026-easy-samples-are-all-you/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiyin Yu, Bo Zhang, Qibin Hou, et al.*2026 · 1 citation*
44. **[Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives](https://md.research.elamur.ai/free/en/papers/xu-2026-modeling-annotator-disagreement-demographic-aware/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yinuo Xu, Veronica Derricks, Allison Earl, et al.*2026 · 1 citation*
45. **[Topic-Based Watermarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/nemecek-2026-topic-based-watermarks-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexander Nemecek, Yuzhou Jiang, Erman Ayday*2026 · 1 citation*
46. **[A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions](https://md.research.elamur.ai/free/en/papers/yu-2026-survey-reinforcement-learning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyin Yu, Yuchen Mou, Juncheng Yan, et al.*2026*
47. **[Data Pollination: An Emergent Ecological Process Driving AI Population Evolution](https://md.research.elamur.ai/free/en/papers/xie-2026-data-pollination-emergent-ecological-process/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shufang Xie, Qizhi Pei, Ang Lv, et al.*2026*
48. **[Diversity and Homogenisation in Generative AI Translation: A Comparative Study of English-Dutch Translation Across Domains](https://md.research.elamur.ai/free/en/papers/shterionov-2026-diversity-homogenisation-generative-ai-translation/) [Annual Conference of the European Association for Machine Translation (EAMT)](https://md.research.elamur.ai/free/en/venues/eamt/)**Dimitar Shterionov, Noa van Helleman, Eva Vanmassenhove*2026*
49. **[EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/yeom-2026-epicar-knowing-what-you-don/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jewon Yeom, Jaewon Sok, Seonghyeon Park, et al.*2026*
50. **[GCIG: GraphRAG-based Cross-document Instruction Generation for Boosting LLM Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-gcig-graphrag-based-cross-document/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoliang Xu, Huang Yuan, Junmei Wang, et al.*2026*
51. **[GhostWriter: Hidden AI-Generated Texts over Multiple Languages, Domains and Generators](https://md.research.elamur.ai/free/en/papers/schaaf-2026-ghostwriter-hidden-ai-generated-texts/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Manuel Schaaf, Kevin Bönisch, Alexander Mehler*2026*
52. **[Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions](https://md.research.elamur.ai/free/en/papers/zhao-2026-large-language-models-are-bad/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minda Zhao, Yilun Du, Mengyu Wang*2026*
53. **[Legal Considerations in the Use of Synthetic Data for AI Development and Finetuning: The Case of LLMs4EU](https://md.research.elamur.ai/free/en/papers/talmoudi-2026-legal-considerations-use-synthetic-data/) [Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy @ LREC](https://md.research.elamur.ai/free/en/venues/legal/)**Kossay Talmoudi, Khalid Choukri, Amélie Gourgeot, et al.*2026*
54. **[Losing our Tail, Again: (Un)Natural Selection & Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/vanmassenhove-2026-losing-our-tail-again-un/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eva Vanmassenhove*2026*
55. **[Lunar-Bench: Towards Evaluating Task-Oriented Reasoning of LLMs in Lunar Exploration Scenarios](https://md.research.elamur.ai/free/en/papers/xiao-2026-lunar-bench-evaluating-task-oriented/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xin-Yu Xiao, Ye Tian, Erwei Yin, et al.*2026*
56. **[Observations and Remedies for Large Language Model Bias in Self-Consuming Performative Loop](https://md.research.elamur.ai/free/en/papers/wang-2026-observations-remedies-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaxuan Wang, Zhongteng Cai, Yujia Bao, et al.*2026*
57. [**Repetition over Diversity: High-Signal Data Filtering for Sample-Efficient German Language Modeling arXiv:2604.28075**Ansar Aynetdinov, Patrick Haller, Alan Akbik*2026*](https://md.research.elamur.ai/free/en/papers/aynetdinov-2026-repetition-over-diversity-high-signal/)
58. **[Student Guides Teacher: Weak-to-Strong Inference via Spectral Orthogonal Exploration](https://md.research.elamur.ai/free/en/papers/wang-2026-student-guides-teacher-weak-strong/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dayu Wang, Jiaye Yang, Weikang Li, et al.*2026*
59. **[Synthetic Data Generation for Training Diversified Commonsense Reasoning Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-synthetic-data-generation-training-diversified/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianhui Zhang, Bei Peng, Danushka Bollegala*2026*
60. **[Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/schaffelder-2026-synthetic-eggs-many-baskets-impact/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Max Schaffelder, Albert Gatt*2026*
61. **[Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale](https://md.research.elamur.ai/free/en/papers/liu-2026-systematic-evaluation-quality-synthetic-clinical/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Jinghui Liu, Sarvesh Soni, Anthony Nguyen*2026*
62. **[Task-Aware LLM Routing with Multi-Level Task-Profile-Guided Data Synthesis for Cold-Start Scenarios](https://md.research.elamur.ai/free/en/papers/liu-2026-task-aware-llm-routing-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Liu, Bin Zou, Kecheng Chen, et al.*2026*
63. **[The Broken Telephone Changes Tone: Examining Nuanced Linguistic Cues in LLM Chains-of-Translation](https://md.research.elamur.ai/free/en/papers/nguyen-2026-broken-telephone-changes-tone-examining/) [Workshop on Multilinguality in the Era of Large Language Models](https://md.research.elamur.ai/free/en/venues/mellm/)**Quang Minh Nguyen, Maida Aizaz, Braahmi Padmakumar*2026*
64. **[The Generator-Eraser Paradox: Community Guidelines for Responsible LLM-Assisted Dialect Resource Creation](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-generator-eraser-paradox-community-guidelines/) [Workshop on Dialects in NLP –- A Resource Perspective](https://md.research.elamur.ai/free/en/venues/dialres/)**Wajdi Zaghouani*2026*

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