---
title: "Who cites Oncel Tuzel — 326 papers of our library"
description: "326 papers of the Elamur Research library cite the works of Oncel Tuzel, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/oncel-tuzel/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/oncel-tuzel/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Oncel Tuzel](https://md.research.elamur.ai/free/en/authors/oncel-tuzel/) / cited by

# Who cites Oncel Tuzel

332 citations of their works from 326 papers of our library (of the 149,439 whose bibliographies we read) · 48 with a page here, the most cited first · [← Oncel Tuzel](https://md.research.elamur.ai/free/en/authors/oncel-tuzel/)

1. [**LIMO: Less is More for Reasoning arXiv:2502.03387**Yixin Ye, Zhen Huang, Yang Xiao, et al.*2025 · 209 citations*](https://md.research.elamur.ai/free/en/papers/ye-2025-limo-less-more-reasoning/)
2. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations*
3. **[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*
4. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
5. **[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*
6. **[MathArena: Evaluating LLMs on Uncontaminated Math Competitions](https://md.research.elamur.ai/free/en/papers/balunovic-2025-matharena-evaluating-llms-uncontaminated-math/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Mislav Balunović, Jasper Dekoninck, Ivo Petrov, et al.*2025 · 99 citations*
7. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
8. **[Generalizing Verifiable Instruction Following](https://md.research.elamur.ai/free/en/papers/pyatkin-2025-generalizing-verifiable-instruction-following/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Valentina Pyatkin, Saumya Malik, Victoria Graf, et al.*2025 · 59 citations*
9. **[BIG-Bench Extra Hard](https://md.research.elamur.ai/free/en/papers/kazemi-2025-big-bench-extra-hard/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Bahare Fatemi, Hritik Bansal, et al.*2025 · 58 citations*
10. **[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*
11. **[Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions](https://md.research.elamur.ai/free/en/papers/ranaldi-2025-improving-chain-thought-reasoning-quasi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leonardo Ranaldi, Marco Valentino, Andre Freitas*2025 · 42 citations*
12. **[SemEval-2026 Task 11: Disentangling Content and Formal Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/valentino-2026-semeval-2026-task-11-disentangling/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Marco Valentino, Leonardo Ranaldi, Giulia Pucci, et al.*2026 · 22 citations*
13. **[Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens](https://md.research.elamur.ai/free/en/papers/zhao-2026-is-chain-thought-reasoning-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengshuai Zhao, Zhen Tan, Pingchuan Ma, et al.*2026 · 21 citations*
14. [**GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity? arXiv:2502.05252**Yang Zhou, Hongyi Liu, Zhuoming Chen, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2025-gsm-infinite-llms-behave-over/)
15. **[CoMAT: Chain of Mathematically Annotated Thought Improves Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/leang-2025-comat-chain-mathematically-annotated-thought/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joshua Ong Jun Leang, Aryo Pradipta Gema, Shay B. Cohen*2025 · 14 citations*
16. **[Semantic-Aware Logical Reasoning via a Semiotic Framework](https://md.research.elamur.ai/free/en/papers/zhang-2026-semantic-aware-logical-reasoning-semiotic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yunyao Zhang, Xinglang Zhang, Junxi Sheng, et al.*2026 · 13 citations*
17. **[FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2026-finchain-symbolic-benchmark-verifiable-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhuohan Xie, Daniil Orel, Rushil Thareja, et al.*2026 · 10 citations*
18. **[Arabic Synonym BERT-based Adversarial Examples for Text Classification](https://md.research.elamur.ai/free/en/papers/alshahrani-2024-arabic-synonym-bert-based-adversarial/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Norah Alshahrani, Saied Alshahrani, Esma Wali, et al.*2024 · 10 citations*
19. [**Guiding Reasoning in Small Language Models with LLM Assistance arXiv:2504.09923**Yujin Kim, Euiin Yi, Minu Kim, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/kim-2025-guiding-reasoning-small-language-models/)
20. **[Theorem Prover as a Judge for Synthetic Data Generation](https://md.research.elamur.ai/free/en/papers/leang-2025-theorem-prover-as-judge-synthetic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joshua Ong Jun Leang, Giwon Hong, Wenda Li, et al.*2025 · 9 citations*
21. **[EquiBench: Benchmarking Large Language Models' Reasoning about Program Semantics via Equivalence Checking](https://md.research.elamur.ai/free/en/papers/wei-2025-equibench-benchmarking-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Anjiang Wei, Jiannan Cao, Ran Li, et al.*2025 · 8 citations*
22. **[Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning](https://md.research.elamur.ai/free/en/papers/zhang-2026-beyond-random-sampling-efficient-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yang Zhang, Amr Mohamed, Hadi Abdine, et al.*2026 · 7 citations*
23. **[R2-MultiOmnia: Leading Multilingual Multimodal Reasoning via Self-Training](https://md.research.elamur.ai/free/en/papers/ranaldi-2025-r2-multiomnia-leading-multilingual-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leonardo Ranaldi, Federico Ranaldi, Giulia Pucci*2025 · 6 citations*
24. **[InductionBench: LLMs Fail in the Simplest Complexity Class](https://md.research.elamur.ai/free/en/papers/hua-2025-inductionbench-llms-fail-simplest-complexity/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenyue Hua, Tyler Wong, Fei Sun, et al.*2025 · 5 citations*
25. **[Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?](https://md.research.elamur.ai/free/en/papers/yan-2025-recitation-over-reasoning-how-cutting/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Kai Yan, Yufei Xu, Zhengyin Du, et al.*2025 · 5 citations*
26. **[What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/jia-2026-what-makes-good-curriculum-disentangling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaning Jia, Chunhui Zhang, Xingjian Diao, et al.*2026 · 4 citations*
27. **[Barriers to Discrete Reasoning with Transformers: A Survey Across Depth, Exactness, and Bandwidth](https://md.research.elamur.ai/free/en/papers/yuan-2026-barriers-discrete-reasoning-transformers-survey/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Michelle Yuan, Weiyi Sun, Amir H. Rezaeian, et al.*2026 · 3 citations*
28. **[EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving](https://md.research.elamur.ai/free/en/papers/zhou-2026-engibench-benchmark-evaluating-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiyuan Zhou, Xinlei Wang, Yirui He, et al.*2026 · 3 citations*
29. **[MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation](https://md.research.elamur.ai/free/en/papers/singh-2026-mt-osc-path-llms-that/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jyotika Singh, Fang Tu, Miguel Ballesteros, et al.*2026 · 3 citations*
30. **[TabularMath: Understanding Math Reasoning over Tables with Large Language Models](https://md.research.elamur.ai/free/en/papers/tian-2026-tabularmath-understanding-math-reasoning-over/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shi-Yu Tian, Zhi Zhou, Wei Dong, et al.*2026 · 3 citations*
31. **[One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement](https://md.research.elamur.ai/free/en/papers/zhou-2026-one-refiner-unlock-them-all/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixiao Zhou, Dongzhou Cheng, Zhiliang wu, et al.*2026 · 2 citations*
32. **[OpenExempt: A Diagnostic Benchmark for Legal Reasoning and a Framework for Creating Custom Benchmarks on Demand](https://md.research.elamur.ai/free/en/papers/servantez-2026-openexempt-diagnostic-benchmark-legal-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sergio Servantez, Sarah B. Lawsky, Rajiv Jain, et al.*2026 · 2 citations*
33. **[Quantifying the Impact of Structured Output Format on Large Language Models through Causal Inference](https://md.research.elamur.ai/free/en/papers/yuan-2026-quantifying-impact-structured-output-format/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Han Yuan, Yue Zhao, Li Zhang, et al.*2026 · 2 citations*
34. **[When Do Large Language Models Exhibit Unsolicited Deception?](https://md.research.elamur.ai/free/en/papers/taylor-2026-when-do-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Samuel M. Taylor, Benjamin K. Bergen*2026 · 1 citation*
35. **[CLARO: Controlled Attribute-Driven Reasoning Optimization for Efficient Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/schlesinger-2026-claro-controlled-attribute-driven-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Oded Schlesinger, Young Kyung Kim, J. Matias Di Martino, et al.*2026*
36. **[Consistency of LLMs to Comparative Statements in Mathematical Reasoning Tasks](https://md.research.elamur.ai/free/en/papers/san-2026-consistency-llms-comparative-statements-mathematical/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Aidan W. San, Daniel Juyoung Son, Xiaodong Liu, et al.*2026*
37. **[Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/shen-2026-does-rlvr-extend-reasoning-boundaries/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minghe Shen, Zhuo Zhi, Chonghan Liu, et al.*2026*
38. **[EduMARS: Can Vision-Language Models Grade Like Teachers? Benchmarking Multimodal, Rubric-Based Assessment on Chinese K-12 Answers](https://md.research.elamur.ai/free/en/papers/zhao-2026-edumars-can-vision-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuan Zhao, Jiashun Chen, Wanting xu, et al.*2026*
39. **[From Implicit to Explicit: Token-Efficient Logical Supervision for Mathematical Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-implicit-explicit-token-efficient-logical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shaojie Wang, Liang Zhang*2026*
40. **[GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training](https://md.research.elamur.ai/free/en/papers/xie-2026-gala-geometric-data-selection-strategic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhongwei Xie, Ruihao Liao, Zimo Wang, et al.*2026*
41. **[GSM-Noise: Exploring and Enhancing Large Language Models' Reasoning under Noisy Inputs](https://md.research.elamur.ai/free/en/papers/zhang-2026-gsm-noise-exploring-enhancing-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhengxin Zhang, Chengyu Huang, Xufu Liu, et al.*2026*
42. **[Multi-lingual Functional Evaluation for Large Language Models](https://md.research.elamur.ai/free/en/papers/ojewale-2026-multi-lingual-functional-evaluation-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Victor Ojewale, Inioluwa Deborah Raji, Suresh Venkatasubramanian*2026*
43. **[Program-of-Thought Reveals LLM Abstraction Ceilings](https://md.research.elamur.ai/free/en/papers/zhou-2026-program-thought-reveals-llm-abstraction/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mike Zhou, Fenil Bardoliya, Vivek Gupta, et al.*2026*
44. **[PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data](https://md.research.elamur.ai/free/en/papers/xiong-2026-puzzleclone-dsl-powered-framework-synthesizing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Xiong, Yanwei Huang, Rongjunchen Zhang, et al.*2026*
45. **[Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue](https://md.research.elamur.ai/free/en/papers/utting-2026-surprisal-minimisation-over-goal-directed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Thomas P. Utting, Mario Giulianelli, Arabella Sinclair*2026*
46. **[TARo: Token-level Adaptive Routing for LLM Test-time Alignment](https://md.research.elamur.ai/free/en/papers/rai-2026-taro-token-level-adaptive-routing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Arushi Rai, Qiang Zhang, Hanqing Zeng, et al.*2026*
47. **[Thinking in Schemas: Robust Syllogistic Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/ranaldi-2026-thinking-schemas-robust-syllogistic-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Federico Ranaldi, Leonardo Ranaldi, Fabio Massimo Zanzotto, et al.*2026*
48. **[When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors](https://md.research.elamur.ai/free/en/papers/yang-2026-when-llms-read-tables-carelessly/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuqing Yang, Qi Zhu, Zhen Han, et al.*2026*

278 more citing papers of our library have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/authors/oncel-tuzel/) — Oncel Tuzel