---
title: "Who cites Alice in Wonderland: Simple — 28 citing papers"
description: "28 papers of the Elamur Research library cite «Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in…"
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---
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# Who cites «Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models»

28 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models»](https://md.research.elamur.ai/free/en/papers/nezhurina-2024-alice-wonderland-simple-tasks-showing/)

1. [**GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models arXiv:2410.05229**Iman Mirzadeh, Keivan Alizadeh, Hooman Shahrokhi, et al.*2024 · 273 citations*](https://md.research.elamur.ai/free/en/papers/mirzadeh-2024-gsm-symbolic-understanding-limitations-mathematical/)
2. [**OpenThoughts: Data Recipes for Reasoning Models arXiv:2506.04178**Etash Guha, Ryan Marten, Sedrick Keh, et al.*2025 · 122 citations*](https://md.research.elamur.ai/free/en/papers/guha-2025-openthoughts-data-recipes-reasoning-models/)
3. **[The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://md.research.elamur.ai/free/en/papers/shojaee-2025-illusion-thinking-understanding-strengths-limitations/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Parshin Shojaee, Iman Mirzadeh, Keivan Alizadeh, et al.*2025 · 104 citations*
4. **[On Memorization of Large Language Models in Logical Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2025-memorization-large-language-models-logical/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Chulin Xie, Yangsibo Huang, Chiyuan Zhang, et al.*2025 · 45 citations*
5. [**DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models arXiv:2411.00836**Chengke Zou, Xingang Guo, Rui Yang, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/zou-2024-dynamath-dynamic-visual-benchmark-evaluating/)
6. [**A Sober Look at Progress in Language Model Reasoning: Pitfalls and Paths to Reproducibility arXiv:2504.07086**Andreas Hochlehnert, Hardik Bhatnagar, Vishaal Udandarao, et al.*2025 · 36 citations*](https://md.research.elamur.ai/free/en/papers/hochlehnert-2025-sober-look-progress-language-model/)
7. **[The Role of Deductive and Inductive Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/cai-2025-role-deductive-inductive-reasoning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chengkun Cai, Xu Zhao, Haoliang Liu, et al.*2025 · 17 citations*
8. **[Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/singh-2025-exposing-achilles-heel-evaluating-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joykirat Singh, Akshay Nambi, Vibhav Vineet*2025 · 8 citations*
9. **[Construction Grammar Evidence for How LLMs Use Context-Directed Extrapolation to Solve Tasks](https://md.research.elamur.ai/free/en/papers/madabushi-2025-construction-grammar-evidence-how-llms/) [International Workshop on Construction Grammars and NLP](https://md.research.elamur.ai/free/en/venues/cxgsnlp/)**Harish Tayyar Madabushi, Claire Bonial*2025 · 1 citation*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/nezhurina-2024-alice-wonderland-simple-tasks-showing/) — Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models