---
title: "Who cites Maxwell Nye — page 3 — 1,310 papers of our library"
description: "1,310 papers of the Elamur Research library cite the works of Maxwell Nye, found in their bibliographies · page 3 of 5 · 201–300; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/3/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/3/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Maxwell Nye](https://md.research.elamur.ai/free/en/authors/maxwell-nye/) / [cited by](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/) / page 3

# Who cites Maxwell Nye

1,354 citations of their works from 1,310 papers of our library (of the 149,439 whose bibliographies we read) · 471 with a page here, the most cited first · page 3 of 5 · 201–300 · [← Maxwell Nye](https://md.research.elamur.ai/free/en/authors/maxwell-nye/)

[← prev](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/4/) [5](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/5/) [next →](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/4/)

201. [**Getting the most out of your tokenizer for pre-training and domain adaptation arXiv:2402.01035**Gautier Dagan, Gabriel Synnaeve, Baptiste Roziere*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/dagan-2024-getting-most-out-tokenizer-pre/)
202. **[MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures](https://md.research.elamur.ai/free/en/papers/ni-2024-mixeval-deriving-wisdom-crowd-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jinjie Ni, Fuzhao Xue, Xiang Yue, et al.*2024 · 47 citations*
203. **[Execution-based Code Generation using Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/shojaee-2023-execution-based-code-generation-using/) [Transactions on Machine Learning Research](https://md.research.elamur.ai/free/en/venues/transactions-on-machine-learning-research/)**Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, et al.*2023 · 47 citations*
204. [**RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation arXiv:2403.05313**Zihao Wang, Anji Liu, Haowei Lin, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-rat-retrieval-augmented-thoughts-elicit/)
205. [**Stable LM 2 1.6B Technical Report arXiv:2402.17834**Marco Bellagente, Jonathan Tow, Dakota Mahan, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/bellagente-2024-stable-lm-2-1-6b/)
206. [**Towards AI-Assisted Synthesis of Verified Dafny Methods arXiv:2402.00247**Md Rakib Hossain Misu, Cristina V. Lopes, Iris Ma, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/misu-2024-ai-assisted-synthesis-verified-dafny/)
207. **[CoCoMIC: Code Completion by Jointly Modeling In-file and Cross-file Context](https://md.research.elamur.ai/free/en/papers/ding-2024-cocomic-code-completion-jointly-modeling/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yangruibo Ding, Zijian Wang, Wasi Ahmad, et al.*2024 · 45 citations*
208. [**GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements arXiv:2402.10963**Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.*2024 · 45 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/)
209. [**Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization arXiv:2411.10442**Weiyun Wang, Zhe Chen, Wenhai Wang, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-enhancing-reasoning-ability-multimodal-large/)
210. [**HyperCLOVA X Technical Report arXiv:2404.01954**Kang Min Yoo, Jaegeun Han, Sookyo In, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/yoo-2024-hyperclova-x-technical-report/)
211. [**MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series arXiv:2405.19327**Ge Zhang, Scott Qu, Jiaheng Liu, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-map-neo-highly-capable-transparent/)
212. [**xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval arXiv:2303.03004**Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/khan-2023-xcodeeval-large-scale-multilingual-multitask/)
213. [**Let's reward step by step: Step-Level reward model as the Navigators for Reasoning arXiv:2310.10080**Qianli Ma, Haotian Zhou, Tingkai Liu, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-let-s-reward-step-step/)
214. [**PanGu-{\Sigma}: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing arXiv:2303.10845**Xiaozhe Ren, Pingyi Zhou, Xinfan Meng, et al.*2023 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ren-2023-pangu-sigma-trillion-parameter/)
215. [**Leveraging Automated Unit Tests for Unsupervised Code Translation arXiv:2110.06773**Baptiste Roziere, Jie M. Zhang, Francois Charton, et al.*2022 · 40 citations*](https://md.research.elamur.ai/free/en/papers/roziere-2022-leveraging-automated-unit-tests-unsupervised/)
216. [**A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems arXiv:2504.09037**Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ke-2025-survey-frontiers-llm-reasoning-inference/)
217. [**NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model arXiv:2508.14444**NVIDIA: Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/basant-2025-nvidia-nemotron-nano-2-accurate/)
218. **[Fault-Aware Neural Code Rankers](https://md.research.elamur.ai/free/en/papers/inala-2022-fault-aware-neural-code-rankers/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeevana Priya Inala, Chenglong Wang, Mei Yang, et al.*2022 · 39 citations*
219. [**Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions arXiv:2205.14318**Ansong Ni, Jeevana Priya Inala, Chenglong Wang, et al.*2022 · 39 citations*](https://md.research.elamur.ai/free/en/papers/ni-2022-learning-math-reasoning-self-sampled/)
220. [**Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models arXiv:2504.03624**NVIDIA: Aaron Blakeman, Aarti Basant, Abhinav Khattar, et al.*2025 · 38 citations*](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-h-family-accurate-efficient/)
221. **[Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpus](https://md.research.elamur.ai/free/en/papers/lee-2024-reasoning-abilities-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Seungpil Lee, Woochang Sim, Donghyeon Shin, et al.*2024 · 38 citations*
222. **[BioCoder: A Benchmark for Bioinformatics Code Generation with Large Language Models](https://md.research.elamur.ai/free/en/papers/tang-2023-biocoder-benchmark-bioinformatics-code-generation/) [Bioinformatics](https://md.research.elamur.ai/free/en/venues/bioinformatics/)**Xiangru Tang, Bill Qian, Rick Gao, et al.*2023 · 38 citations*
223. **[Code Execution with Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/liu-2023-code-execution-pre-trained-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenxiao Liu, Shuai Lu, Weizhu Chen, et al.*2023 · 38 citations*
224. **[AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling](https://md.research.elamur.ai/free/en/papers/liu-2025-acemath-advancing-frontier-math-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihan Liu, Yang Chen, Mohammad Shoeybi, et al.*2025 · 37 citations*
225. [**CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings arXiv:2501.01257**Shanghaoran Quan, Jiaxi Yang, Bowen Yu, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/quan-2025-codeelo-benchmarking-competition-level-code/)
226. [**MiMo: Unlocking the Reasoning Potential of Language Model — From Pretraining to Posttraining arXiv:2505.07608**LLM-Core Xiaomi, Bingquan Xia, Bowen Shen, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/xiaomi-2025-mimo-unlocking-reasoning-potential-language/)
227. **[SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2024-sciinstruct-self-reflective-instruction-annotated/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Dan Zhang, Ziniu Hu, Sining Zhoubian, et al.*2024 · 37 citations*
228. **[KodCode: A Diverse, Challenging, and Verifiable Synthetic Dataset for Coding](https://md.research.elamur.ai/free/en/papers/xu-2025-kodcode-diverse-challenging-verifiable-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhangchen Xu, Yang Liu, Yueqin Yin, et al.*2025 · 36 citations*
229. **[Measuring AI Ability to Complete Long Software Tasks](https://md.research.elamur.ai/free/en/papers/kwa-2025-measuring-ai-ability-complete/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Thomas Kwa, Ben West, Joel Becker, et al.*2025 · 36 citations*
230. [**The Surprising Effectiveness of Test-Time Training for Few-Shot Learning arXiv:2411.07279**Ekin Akyürek, Mehul Damani, Adam Zweiger, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2024-surprising-effectiveness-test-time-training/)
231. **[MasRouter: Learning to Route LLMs for Multi-Agent Systems](https://md.research.elamur.ai/free/en/papers/yue-2025-masrouter-learning-route-llms-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanwei Yue, Guibin Zhang, Boyang Liu, et al.*2025 · 35 citations*
232. [**Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap arXiv:2402.19450**Saurabh Srivastava, Annarose M B, Anto P, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2024-functional-benchmarks-robust-evaluation-reasoning/)
233. [**Passport: Improving Automated Formal Verification Using Identifiers arXiv:2204.10370 (ACM Transactions on Programming Languages and Systems (TOPLAS), 45(2):12:1-12:30, June 2023)**Alex Sanchez-Stern, Emily First, Timothy Zhou, et al.*2022 · 35 citations*](https://md.research.elamur.ai/free/en/papers/stern-2022-passport-improving-automated-formal-verification/)
234. [**OpenCodeReasoning: Advancing Data Distillation for Competitive Coding arXiv:2504.01943**Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, et al.*2025 · 33 citations*](https://md.research.elamur.ai/free/en/papers/ahmad-2025-opencodereasoning-advancing-data-distillation-competitive/)
235. [**ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code arXiv:2311.09835**Xiangru Tang, Yuliang Liu, Zefan Cai, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-ml-bench-evaluating-large-language/)
236. **[ACECODER: Acing Coder RL via Automated Test-Case Synthesis](https://md.research.elamur.ai/free/en/papers/zeng-2025-acecoder-acing-coder-rl-automated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huaye Zeng, Dongfu Jiang, Haozhe Wang, et al.*2025 · 32 citations*
237. **[Evaluating Language Models as Synthetic Data Generators](https://md.research.elamur.ai/free/en/papers/kim-2025-evaluating-language-models-as-synthetic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Seungone Kim, Juyoung Suk, Xiang Yue, et al.*2025 · 32 citations*
238. **[SelfCodeAlign: Self-Alignment for Code Generation](https://md.research.elamur.ai/free/en/papers/wei-2024-selfcodealign-self-alignment-code-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiang Wei, Federico Cassano, Jiawei Liu, et al.*2024 · 32 citations*
239. **[StepCoder: Improving Code Generation with Reinforcement Learning from Compiler Feedback](https://md.research.elamur.ai/free/en/papers/dou-2024-stepcoder-improving-code-generation-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shihan Dou, Yan Liu, Haoxiang Jia, et al.*2024 · 32 citations*
240. [**LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations arXiv:2305.18354**Yudong Xu, Wenhao Li, Pashootan Vaezipoor, et al.*2023 · 32 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-llms-abstraction-reasoning-corpus-successes/)
241. [**ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages arXiv:2212.06742**Yekun Chai, Shuohuan Wang, Chao Pang, et al.*2022 · 32 citations*](https://md.research.elamur.ai/free/en/papers/chai-2022-ernie-code-english-centric/)
242. [**How to Evaluate Reward Models for RLHF arXiv:2410.14872**Evan Frick, Tianle Li, Connor Chen, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/frick-2024-evaluate-reward-models-rlhf/)
243. [**SpreadsheetCoder: Formula Prediction from Semi-structured Context arXiv:2106.15339**Xinyun Chen, Petros Maniatis, Rishabh Singh, et al.*2021 · 31 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-spreadsheetcoder-formula-prediction-semi-structured/)
244. [**Language Models as Science Tutors arXiv:2402.11111**Alexis Chevalier, Jiayi Geng, Alexander Wettig, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/chevalier-2024-language-models-science-tutors/)
245. [**LLM Augmented LLMs: Expanding Capabilities through Composition arXiv:2401.02412**Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2024-llm-augmented-llms-expanding-capabilities/)
246. [**Nemotron-4 15B Technical Report arXiv:2402.16819**Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/parmar-2024-nemotron-4-15b-technical-report/)
247. [**NExT: Teaching Large Language Models to Reason about Code Execution arXiv:2404.14662**Ansong Ni, Miltiadis Allamanis, Arman Cohan, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/ni-2024-next-teaching-large-language-models/)
248. [**InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models arXiv:2503.06692**Yuchen Yan, Yongliang Shen, Yang Liu, et al.*2025 · 29 citations*](https://md.research.elamur.ai/free/en/papers/yan-2025-inftythink-breaking-length-limits-long/)
249. **[Towards Effective and Efficient Continual Pre-training of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jie Chen, Zhipeng Chen, Jiapeng Wang, et al.*2025 · 29 citations*
250. [**Log Probabilities Are a Reliable Estimate of Semantic Plausibility in Base and Instruction-Tuned Language Models arXiv:2403.14859**Carina Kauf, Emmanuele Chersoni, Alessandro Lenci, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/kauf-2024-log-probabilities-reliable-estimate-semantic/)
251. [**Length Generalization in Arithmetic Transformers arXiv:2306.15400**Samy Jelassi, Stéphane d'Ascoli, Carles Domingo-Enrich, et al.*2023 · 29 citations*](https://md.research.elamur.ai/free/en/papers/jelassi-2023-length-generalization-arithmetic-transformers/)
252. **[AutoVerus: Automated Proof Generation for Rust Code](https://md.research.elamur.ai/free/en/papers/yang-2024-autoverus-automated-proof-generation-rust/) [Proceedings of the ACM on Programming Languages](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-acm-on-programming-languages/)**Chenyuan Yang, Xuheng Li, Md Rakib Hossain Misu, et al.*2024 · 28 citations*
253. [**Can Language Models Solve Olympiad Programming? arXiv:2404.10952**Quan Shi, Michael Tang, Karthik Narasimhan, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/shi-2024-language-models-solve-olympiad-programming/)
254. [**JetMoE: Reaching Llama2 Performance with 0.1M Dollars arXiv:2404.07413**Yikang Shen, Zhen Guo, Tianle Cai, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/shen-2024-jetmoe-reaching-llama2-performance-0/)
255. **[AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents](https://md.research.elamur.ai/free/en/papers/xu-2025-androidlab-training-systematic-benchmarking-android/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Xu, Xiao Liu, Xueqiao Sun, et al.*2025 · 27 citations*
256. **[L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models](https://md.research.elamur.ai/free/en/papers/ni-2024-l2ceval-evaluating-language-code-generation/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ansong Ni, Pengcheng Yin, Yilun Zhao, et al.*2024 · 27 citations*
257. [**Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling arXiv:2408.16737**Hritik Bansal, Arian Hosseini, Rishabh Agarwal, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/bansal-2024-smaller-weaker-yet-better-training/)
258. [**SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering? arXiv:2502.12115**Samuel Miserendino, Michele Wang, Tejal Patwardhan, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/miserendino-2025-swe-lancer-frontier-llms-earn/)
259. [**Seed-Coder: Let the Code Model Curate Data for Itself arXiv:2506.03524**ByteDance Seed, Yuyu Zhang, Jing Su, et al.*2025 · 25 citations*](https://md.research.elamur.ai/free/en/papers/bytedance-seed-2025-seed-coder-let-code/)
260. **[Soft Thinking: Unlocking the Reasoning Potential of LLMs in Continuous Concept Space](https://md.research.elamur.ai/free/en/papers/zhang-2025-soft-thinking-unlocking-reasoning-potential/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhen Zhang, Xuehai He, Weixiang Yan, et al.*2025 · 25 citations*
261. [**Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent arXiv:2411.02265**Xingwu Sun, Yanfeng Chen, Yiqing Huang, et al.*2024 · 25 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-hunyuan-large-open-source/)
262. [**Baichuan-M1: Pushing the Medical Capability of Large Language Models arXiv:2502.12671**Bingning Wang, Haizhou Zhao, Huozhi Zhou, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-baichuan-m1-pushing-medical/)
263. [**Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning arXiv:2504.17192**Minju Seo, Jinheon Baek, Seongyun Lee, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/seo-2025-paper2code-automating-code-generation/)
264. [**Pretraining on the Test Set Is All You Need arXiv:2309.08632**Rylan Schaeffer*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-pretraining-test-set-all-need/)
265. [**SciReplicate-Bench: Benchmarking LLMs in Agent-driven Algorithmic Reproduction from Research Papers arXiv:2504.00255**Yanzheng Xiang, Hanqi Yan, Shuyin Ouyang, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/xiang-2025-scireplicate-bench-benchmarking-llms/)
266. **[DDK: Distilling Domain Knowledge for Efficient Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2024-ddk-distilling-domain-knowledge-efficient/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaheng Liu, Chenchen Zhang, Jinyang Guo, et al.*2024 · 23 citations*
267. **[Symbolic Regression with a Learned Concept Library](https://md.research.elamur.ai/free/en/papers/grayeli-2024-symbolic-regression-learned-concept-library/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, et al.*2024 · 23 citations*
268. [**MiniCPM4: Ultra-Efficient LLMs on End Devices arXiv:2506.07900**MiniCPM Team, Chaojun Xiao, Yuxuan Li, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/minicpm-team-2025-minicpm4-ultra-efficient-llms/)
269. [**Scaling Latent Reasoning via Looped Language Models arXiv:2510.25741**Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-scaling-latent-reasoning-looped-language/)
270. **[Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling](https://md.research.elamur.ai/free/en/papers/luo-2025-turning-trash-into-treasure-accelerating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xianzhen Luo, Yixuan Wang, Qingfu Zhu, et al.*2025 · 22 citations*
271. [**Measuring The Impact Of Programming Language Distribution arXiv:2302.01973**Gabriel Orlanski, Kefan Xiao, Xavier Garcia, et al.*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/orlanski-2023-measuring-impact-programming-language-distribution/)
272. **[Communicating Natural Programs to Humans and Machines](https://md.research.elamur.ai/free/en/papers/acquaviva-2021-communicating-natural-programs-humans-machines/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Samuel Acquaviva, Yewen Pu, Marta Kryven, et al.*2021 · 22 citations*
273. **[A Survey on Evaluation of LLM-based Agents](https://md.research.elamur.ai/free/en/papers/yehudai-2026-survey-evaluation-llm-based-agents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Asaf Yehudai, Lilach Eden, Alan Li, et al.*2026 · 21 citations*
274. **[CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2025-codetree-agent-guided-tree-search/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jierui Li, Hung Le, Yingbo Zhou, et al.*2025 · 21 citations*
275. [**The Limits of Inference Scaling Through Resampling arXiv:2411.17501**Benedikt Stroebl, Sayash Kapoor, Arvind Narayanan*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/stroebl-2024-limits-inference-scaling-through-resampling/)
276. **[Learning to Reason and Memorize with Self-Notes](https://md.research.elamur.ai/free/en/papers/lanchantin-2023-learning-reason-memorize-self-notes/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jack Lanchantin, Shubham Toshniwal, Jason Weston, et al.*2023 · 21 citations*
277. **[Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment](https://md.research.elamur.ai/free/en/papers/oosterlinck-2025-anchored-preference-optimization-contrastive-revisions/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Karel D'Oosterlinck, Winnie Xu, Chris Develder, et al.*2025 · 20 citations*
278. **[CodeDPO: Aligning Code Models with Self Generated and Verified Source Code](https://md.research.elamur.ai/free/en/papers/zhang-2025-codedpo-aligning-code-models-self/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kechi Zhang, Ge Li, Yihong Dong, et al.*2025 · 20 citations*
279. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
280. **[Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs](https://md.research.elamur.ai/free/en/papers/yang-2025-code-think-think-code-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dayu Yang, Tianyang Liu, Daoan Zhang, et al.*2025 · 19 citations*
281. **[Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation](https://md.research.elamur.ai/free/en/papers/tang-2025-synthesizing-post-training-data-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuo Tang, Xianghe Pang, Zexi Liu, et al.*2025 · 19 citations*
282. [**ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers arXiv:2305.14591**Kexun Zhang, Danqing Wang, Jingtao Xia, et al.*2023 · 19 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-algo-synthesizing-algorithmic-programs-llm/)
283. [**Densing Law of LLMs arXiv:2412.04315**Chaojun Xiao, Jie Cai, Weilin Zhao, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2024-densing-law-llms/)
284. [**Evaluating statistical language models as pragmatic reasoners arXiv:2305.01020**Benjamin Lipkin, Lionel Wong, Gabriel Grand, et al.*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lipkin-2023-evaluating-statistical-language-models-pragmatic/)
285. [**Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning arXiv:2312.05230**Zhiting Hu, Tianmin Shu*2023 · 18 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-language-models-agent-models-world/)
286. [**DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively arXiv:2509.26603**Yixuan Weng, Minjun Zhu, Qiujie Xie, et al.*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/weng-2025-deepscientist-advancing-frontier-pushing/)
287. **[LLM The Genius Paradox: A Linguistic and Math Expert's Struggle with Simple Word-based Counting Problems](https://md.research.elamur.ai/free/en/papers/xu-2025-llm-genius-paradox-linguistic-math/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nan Xu, Xuezhe Ma*2025 · 17 citations*
288. **[Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-let-expert-stick-his/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zihan Wang, Deli Chen, Damai Dai, et al.*2024 · 17 citations*
289. **[NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Queries](https://md.research.elamur.ai/free/en/papers/zhang-2024-naturalcodebench-examining-coding-performance-mismatch/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shudan Zhang, Hanlin Zhao, Xiao Liu, et al.*2024 · 17 citations*
290. **[CodeContests+: High-Quality Test Case Generation for Competitive Programming](https://md.research.elamur.ai/free/en/papers/wang-2025-codecontests-high-quality-test-case/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihan Wang, Siyao Liu, Yang Sun, et al.*2025 · 16 citations*
291. **[RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation](https://md.research.elamur.ai/free/en/papers/li-2025-rethinkmcts-refining-erroneous-thoughts-monte/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Qingyao Li, Wei Xia, Xinyi Dai, et al.*2025 · 16 citations*
292. **[Discursive Socratic Questioning: Evaluating the Faithfulness of Language Models' Understanding of Discourse Relations](https://md.research.elamur.ai/free/en/papers/miao-2024-discursive-socratic-questioning-evaluating-faithfulness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yisong Miao, Hongfu Liu, Wenqiang Lei, et al.*2024 · 16 citations*
293. [**Meta Large Language Model Compiler: Foundation Models of Compiler Optimization arXiv:2407.02524**Chris Cummins, Volker Seeker, Dejan Grubisic, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/cummins-2024-meta-large-language-model-compiler/)
294. [**Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance arXiv:2507.22448**Jingwei Zuo, Maksim Velikanov, Ilyas Chahed, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/zuo-2025-falcon-h1-family-hybrid-head/)
295. **[Language Models Encode the Value of Numbers Linearly](https://md.research.elamur.ai/free/en/papers/zhu-2025-language-models-encode-value-numbers/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Fangwei Zhu, Damai Dai, Zhifang Sui*2025 · 15 citations*
296. [**Rewriting Pre-Training Data Boosts LLM Performance in Math and Code arXiv:2505.02881**Kazuki Fujii, Yukito Tajima, Sakae Mizuki, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/fujii-2025-rewriting-pre-training-data-boosts/)
297. [**DafnyBench: A Benchmark for Formal Software Verification arXiv:2406.08467**Chloe Loughridge, Qinyi Sun, Seth Ahrenbach, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/loughridge-2024-dafnybench-benchmark-formal-software-verification/)
298. [**Data-driven Discovery with Large Generative Models arXiv:2402.13610**Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/majumder-2024-data-driven-discovery-large-generative/)
299. **[From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging](https://md.research.elamur.ai/free/en/papers/shi-2024-code-correctness-closing-last-mile/) [IEEE/ACM International Conference on Software Engineering (ICSE)](https://md.research.elamur.ai/free/en/venues/ieee-acm-international-conference-on-software-engineering-icse/)**Yuling Shi, Songsong Wang, Chengcheng Wan, et al.*2024 · 15 citations*
300. [**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/)

[← prev](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/2/) **3** [4](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/4/) [5](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/5/) [next →](https://md.research.elamur.ai/free/en/authors/maxwell-nye/cited-by/page/4/)

- [← back to](https://md.research.elamur.ai/free/en/authors/maxwell-nye/) — Maxwell Nye