--- title: "Who cites From Decoding to — 49 citing papers" description: "49 papers of the Elamur Research library cite «From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models»…" canonical: "https://research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [From Decoding to Meta-Generation: Inference-time…](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/) / cited by # Who cites «From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models» 49 papers of our library cite it (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · [← back to «From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models»](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/) 1. **[s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 608 citations* 2. **[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* 3. [**L1: Controlling How Long A Reasoning Model Thinks With Reinforcement Learning arXiv:2503.04697**Pranjal Aggarwal, Sean Welleck*2025 · 125 citations*](https://md.research.elamur.ai/free/en/papers/aggarwal-2025-l1-controlling-long-reasoning-model/) 4. [**MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark arXiv:2409.02813**Xiang Yue, Tianyu Zheng, Yuansheng Ni, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-mmmu-pro-more-robust-multi/) 5. [**Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models arXiv:2408.00724**Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.*2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-inference-scaling-laws-empirical-analysis/) 6. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/) 7. **[TTRL: Test-Time Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zuo-2025-ttrl-test-time-reinforcement-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxin Zuo, Kaiyan Zhang, Li Sheng, et al.*2025 · 76 citations* 8. **[A Thorough Examination of Decoding Methods in the Era of LLMs](https://md.research.elamur.ai/free/en/papers/shi-2024-thorough-examination-decoding-methods-era/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chufan Shi, Haoran Yang, Deng Cai, et al.*2024 · 68 citations* 9. [**Deep Think with Confidence arXiv:2508.15260**Yichao Fu, Xuewei Wang, Yuandong Tian, et al.*2025 · 61 citations*](https://md.research.elamur.ai/free/en/papers/fu-2025-deep-think-confidence/) 10. [**AI Agents That Matter arXiv:2407.01502**Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, et al.*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/kapoor-2024-ai-agents-that-matter/) 11. **[PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving](https://md.research.elamur.ai/free/en/papers/parmar-2025-plangen-multi-agent-framework-generating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mihir Parmar, Xin Liu, Palash Goyal, et al.*2025 · 16 citations* 12. **[Examining False Positives under Inference Scaling for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2025-examining-false-positives-under-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yu Wang, Nan Yang, Liang Wang, et al.*2025 · 9 citations* 13. **[GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question Answering](https://md.research.elamur.ai/free/en/papers/luo-2026-gcot-decoding-unlocking-deep-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guanran Luo, Wentao Qiu, Zhongquan Jian, et al.*2026 · 6 citations* 14. **[G2: Guided Generation for Enhanced Output Diversity in LLMs](https://md.research.elamur.ai/free/en/papers/ruan-2025-g2-guided-generation-enhanced-output/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhiwen Ruan, Yixia Li, Yefeng Liu, et al.*2025 · 6 citations* 15. **[When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/khairi-2025-when-life-gives-you-samples/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ammar Khairi, Daniel D'souza, Ye Shen, et al.*2025 · 6 citations* 16. **[Sense and Sensitivity: “Reasoning” Models are More Robust, but can Diverge from Human Consensus in a Legal Interpretation Task](https://md.research.elamur.ai/free/en/papers/petersen-2026-sense-sensitivity-reasoning-models-are/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Dawson Petersen, Abhishek Purushothama, Nathan Schneider*2026 · 1 citation* 17. **[Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier](https://md.research.elamur.ai/free/en/papers/zhong-2026-solve-detect-verify-inference-time/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianyuan Zhong, Zeju Li, Zhijian Xu, et al.*2026 · 1 citation* 18. **[GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models](https://md.research.elamur.ai/free/en/papers/ruan-2026-gift-guided-fine-tuning-transfer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiwen Ruan, Yichao Du, Jianjie Zheng, et al.*2026* 31 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/) — From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models