---
title: "Who cites Beyond Accuracy: Evaluating the — 40 citing papers"
description: "40 papers of the Elamur Research library cite «Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A…"
canonical: "https://research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Beyond Accuracy: Evaluating the Reasoning…](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/) / cited by

# Who cites «Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey»

40 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey»](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/)

1. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
2. **[What is the Role of Small Models in the LLM Era: A Survey](https://md.research.elamur.ai/free/en/papers/chen-2024-role-small-models-llm-era/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Lihu Chen, Gael Varoquaux*2024 · 23 citations*
3. [**Rethinking Reflection in Pre-Training arXiv:2504.04022**Essential AI: Darsh J Shah, Peter Rushton, Somanshu Singla, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shah-2025-rethinking-reflection-pre-training/)
4. [**The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofs arXiv:2506.21621**Jasper Dekoninck, Ivo Petrov, Kristian Minchev, et al.*2025 · 11 citations*](https://md.research.elamur.ai/free/en/papers/dekoninck-2025-open-proof-corpus-large-scale/)
5. [**Disparities in LLM Reasoning Accuracy and Explanations: A Case Study on African American English arXiv:2503.04099**Runtao Zhou, Guangya Wan, Saadia Gabriel, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2025-disparities-llm-reasoning-accuracy-explanations/)
6. **[Garbage In, Reasoning Out? Why Benchmark Scores are Unreliable and What to Do About It](https://md.research.elamur.ai/free/en/papers/mousavi-2026-garbage-reasoning-out-why-benchmark/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seyed Mahed Mousavi, Edoardo Cecchinato, Lucia Horníková, et al.*2026 · 2 citations*
7. **[Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning](https://md.research.elamur.ai/free/en/papers/merenda-2026-can-llms-reason-like-doctors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Flavio Merenda, Jose Manuel Gomez-Perez, German Rigau*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/) — Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey