--- title: "Who cites MLGym: A New Framework and — 46 citing papers" description: "46 papers of the Elamur Research library cite «MLGym: A New Framework and Benchmark for Advancing AI Research Agents», found in…" canonical: "https://research.elamur.ai/free/en/papers/nathani-2025-mlgym-new-framework-benchmark/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/nathani-2025-mlgym-new-framework-benchmark/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [MLGym: A New Framework and Benchmark for…](https://md.research.elamur.ai/free/en/papers/nathani-2025-mlgym-new-framework-benchmark/) / cited by # Who cites «MLGym: A New Framework and Benchmark for Advancing AI Research Agents» 46 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← back to «MLGym: A New Framework and Benchmark for Advancing AI Research Agents»](https://md.research.elamur.ai/free/en/papers/nathani-2025-mlgym-new-framework-benchmark/) 1. [**AgentRxiv: Towards Collaborative Autonomous Research arXiv:2503.18102**Samuel Schmidgall, Michael Moor*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/schmidgall-2025-agentrxiv-collaborative-autonomous-research/) 2. **[From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery](https://md.research.elamur.ai/free/en/papers/zheng-2025-automation-autonomy-survey-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianshi Zheng, Zheye Deng, Hong Ting Tsang, et al.*2025 · 39 citations* 3. **[Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://md.research.elamur.ai/free/en/papers/eger-2025-transforming-science-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Steffen Eger, Yong Cao, Jennifer D'Souza, et al.*2025 · 25 citations* 4. [**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/) 5. **[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* 6. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations* 7. [**EXP-Bench: Can AI Conduct AI Research Experiments? arXiv:2505.24785**Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu, et al.*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/kon-2025-exp-bench-ai-conduct/) 8. **[ResearchCodeBench: Benchmarking LLMs on Implementing Novel Machine Learning Research Code](https://md.research.elamur.ai/free/en/papers/hua-2025-researchcodebench-benchmarking-llms-implementing/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tianyu Hua; Harper Hua; Violet Xiang; Benjamin Klieger; Sang T. Truong; Weixin Liang; Fan-Yun Sun;, Nick Haber*2025 · 16 citations* 9. [**Sparks of Science: Hypothesis Generation Using Structured Paper Data arXiv:2504.12976**Charles O'Neill, Tirthankar Ghosal, Roberta R\uaileanu, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/o-neill-2025-sparks-science-hypothesis-generation-using/) 10. **[MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?](https://md.research.elamur.ai/free/en/papers/zhang-2025-mlrc-bench-language-agents/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yunxiang Zhang, Muhammad Khalifa, Shitanshu Bhushan, et al.*2025 · 13 citations* 11. [**InnovatorBench: Evaluating Agents' Ability to Conduct Innovative LLM Research arXiv:2510.27598**Yunze Wu, Dayuan Fu, Weiye Si, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/wu-2025-innovatorbench-evaluating-agents-ability-conduct/) 12. [**Training AI Co-Scientists Using Rubric Rewards arXiv:2512.23707**Shashwat Goel, Rishi Hazra, Dulhan Jayalath, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/goel-2025-training-ai-co-scientists/) 13. [**AI for Auto-Research: Roadmap & User Guide arXiv:2605.18661**Lingdong Kong, Xian Sun, Wei Chow, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/kong-2026-ai-auto-research-roadmap-user/) 14. [**BoxingGym: Benchmarking Progress in Automated Experimental Design and Model Discovery arXiv:2501.01540**Kanishk Gandhi, Michael Y. Li, Lyle Goodyear, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/gandhi-2025-boxinggym-benchmarking-progress-automated/) 15. **[The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements](https://md.research.elamur.ai/free/en/papers/zhao-2025-automated-llm-speedrunning-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Bingchen Zhao, Despoina Magka, Minqi Jiang, et al.*2025 · 4 citations* 16. **[From Charts to Code: A Hierarchical Benchmark for Multimodal Models](https://md.research.elamur.ai/free/en/papers/tang-2026-charts-code-hierarchical-benchmark-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, et al.*2026 · 3 citations* 17. **[SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science](https://md.research.elamur.ai/free/en/papers/seo-2026-spio-ensemble-selective-strategies-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wonduk Seo, Juhyeon Lee, Yanjun Shao, et al.*2026 · 3 citations* 18. **[Can We Predict Before Executing Machine Learning Agents?](https://md.research.elamur.ai/free/en/papers/zheng-2026-can-we-predict-before-executing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingsheng Zheng, Jintian Zhang, Yujie Luo, et al.*2026 · 2 citations* 19. [**Training AI Scientists to Replicate Research arXiv:2608.13331**Damon Falck, Samer Sabri, Anja Surina, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/falck-2026-training-ai-scientists-replicate/) 20. **[What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations](https://md.research.elamur.ai/free/en/papers/luo-2026-what-makes-ai-research-replicable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yujie Luo, Zhuoyun Yu, Xuehai Wang, et al.*2026* 21. **[WildSci: Advancing Scientific Reasoning from In-the-Wild Literature](https://md.research.elamur.ai/free/en/papers/liu-2026-wildsci-advancing-scientific-reasoning-wild/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tengxiao Liu, Deepak Nathani, Zekun Li, et al.*2026* 25 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/nathani-2025-mlgym-new-framework-benchmark/) — MLGym: A New Framework and Benchmark for Advancing AI Research Agents