---
title: "Who cites LLM and Simulation as Bilevel — 24 citing papers"
description: "24 papers of the Elamur Research library cite «LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific…"
canonical: "https://research.elamur.ai/free/en/papers/ma-2024-llm-simulation-bilevel-optimizers-new/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ma-2024-llm-simulation-bilevel-optimizers-new/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LLM and Simulation as Bilevel Optimizers: A New…](https://md.research.elamur.ai/free/en/papers/ma-2024-llm-simulation-bilevel-optimizers-new/) / cited by

# Who cites «LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery»

24 papers of our library cite it (of the 149,439 whose bibliographies we read) · 9 with a page here, the most cited first · [← back to «LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery»](https://md.research.elamur.ai/free/en/papers/ma-2024-llm-simulation-bilevel-optimizers-new/)

1. [**A Review of Large Language Models and Autonomous Agents in Chemistry arXiv:2407.01603**Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/)
2. [**Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models arXiv:2501.09686**Fengli Xu, Qianyue Hao, Zefang Zong, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-towards-large-reasoning-models-survey/)
3. [**LLM-SR: Scientific Equation Discovery via Programming with Large Language Models arXiv:2404.18400**Parshin Shojaee, Kazem Meidani, Shashank Gupta, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/shojaee-2024-llm-sr-scientific-equation/)
4. [**LLM4SR: A Survey on Large Language Models for Scientific Research arXiv:2501.04306**Ziming Luo, Zonglin Yang, Zexin Xu, et al.*2025 · 39 citations*](https://md.research.elamur.ai/free/en/papers/luo-2025-llm4sr-survey-large-language/)
5. [**Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents arXiv:2503.24047**Shuo Ren, Can Xie, Pu Jian, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/ren-2025-scientific-intelligence-survey-llm/)
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. [**LLM-SRBench: A New Benchmark for Scientific Equation Discovery with Large Language Models arXiv:2504.10415**Parshin Shojaee, Ngoc-Hieu Nguyen, Kazem Meidani, et al.*2025 · 18 citations*](https://md.research.elamur.ai/free/en/papers/shojaee-2025-llm-srbench-new-benchmark/)
8. **[Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design](https://md.research.elamur.ai/free/en/papers/yao-2026-instructional-agents-reducing-teaching-faculty/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Huaiyuan Yao, Wanpeng Xu, Justin Turnau, et al.*2026 · 4 citations*
9. **[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*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/ma-2024-llm-simulation-bilevel-optimizers-new/) — LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery