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/README.md:
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1 |
2 | # Awesome AI Scientist Papers
3 |
4 |
5 | [](https://github.com/sindresorhus/awesome)
6 | 
7 | 
8 | 
9 | [](https://github.com/universea/Awesome-AI-Scientist-Papers/blob/main/LICENSE)
10 |
11 | Welcome to the **Awesome AI Scientist Papers** repository! This project aims to curate a collection of important papers to the field of AI/Robot Scientist.
12 |
13 |
14 | 
15 |
16 | Scaling Laws in Scientific Discovery with AI Scientists and Robot Scientists.
17 |
18 |
19 | ## Table of Contents
20 |
21 | - [Papers](#papers)
22 | - [Survey](#survey)
23 | - [News](#news)
24 | - [Benchmark](#benchmark)
25 | - [Cite](#cite)
26 |
27 |
28 | ## Papers
29 |
30 | - [Scaling Laws in Scientific Discovery with AI and Robot Scientists](https://arxiv.org/abs/2503.22444), Pengsong Zhang, Heng Zhang et al., arXiv, 2025
31 |
32 | - [Towards Data-Centric Automatic R&D](https://arxiv.org/abs/2404.11276), Haotian Chen et al., arXiv, 2024
33 |
34 | - [Mlr-copilot: Autonomous machine learning research based on large language models agents](https://arxiv.org/pdf/2408.14033), Ruochen Li et al., arXiv, 2024
35 |
36 | - [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://www.arxiv.org/abs/2408.06292), Chris Lu et al., arXiv, 2024
37 |
38 | - [Autonomous Generalist Scientist: Towards and Beyond Human-Level Scientific Research with Agentic and Embodied AI and Robots](http://dx.doi.org/10.13140/RG.2.2.35148.01923), Pengsong Zhang, Heng Zhang et al., ResearchGate, 2024
39 |
40 | - [ChatGPT as Research Scientist: Probing GPT’s capabilities as a Research Librarian, Research Ethicist, Data Generator, and Data Predictor](https://doi.org/10.1073/pnas.2404328121), Steven A. Lehr et al., PNAS, 2024
41 |
42 |
43 |
44 | Autonomous Literature Review
45 |
46 | - [SurveyX: Academic Survey Automation via Large Language Models](https://arxiv.org/abs/2502.14776), Xun Liang et al., arXiv, 2025
47 |
48 | - [PaSa: An LLM Agent for Comprehensive Academic Paper Search](https://arxiv.org/abs/2501.10120), Yichen He et al., arXiv, 2025
49 |
50 | - [SCILITLLM: HOW TO ADAPT LLMS FOR SCIENTIFIC LITERATURE UNDERSTANDING](https://arxiv.org/pdf/2408.15545), Sihang Li et al., arXiv, 2024
51 |
52 | - [AutoSurvey: Large Language Models Can Automatically Write Surveys](https://arxiv.org/pdf/2406.10252), Wenjin Yao et al., arXiv, 2024
53 |
54 | - [LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesis](https://arxiv.org/abs/2409.18812), Hamed Babaei Giglou et al., arXiv, 2024
55 |
56 | - [PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge](https://doi.org/10.1093/nar/gkae235), Chih-Hsuan Wei et al., Nucleic Acids Research, 2024
57 |
58 | - [PubMed and beyond: biomedical literature search in the age of artificial intelligence](https://doi.org/10.1016/j.ebiom.2024.104988), Qiao Jin et al., eBioMedicine, 2024
59 |
60 |
61 |
62 |
63 | Proposal, Idea Generation
64 |
65 | - [AgentRxiv: Towards Collaborative Autonomous Research](https://arxiv.org/abs/2503.18102), Samuel Schmidgall et al., arXiv, 2025
66 |
67 | - [Agent Laboratory: Using LLM Agents as Research Assistants](https://arxiv.org/abs/2503.18102), Samuel Schmidgall et al., arXiv, 2025
68 |
69 | - [An empirical investigation of the impact of ChatGPT on creativity](https://doi.org/10.1038/s41562-024-01953-1), Byung Cheol Lee et al., Nature Human Behaviour, 2024
70 |
71 | - [Nova: An Iterative Planning and Search Approach to Enhance Novelty and Diversity of LLM Generated Ideas](https://arxiv.org/abs/2410.14255), Xiang Hu et al., arXiv, 2024
72 |
73 | - [Two Heads Are Better Than One: A Multi-Agent System Has the Potential to Improve Scientific Idea Generation](https://arxiv.org/pdf/2410.09403), Haoyang Su et al., arXiv, 2024
74 |
75 | - [Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers](https://arxiv.org/abs/2409.04109), Chenglei Si et al., arXiv, 2024
76 |
77 | - [ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models](https://doi.org/10.48550/arXiv.2404.07738), Jinheon Baek et al., arXiv, 2024
78 |
79 | - [Forecasting high-impact research topics via machine learning on evolving knowledge graphs](https://arxiv.org/abs/2402.08640), Xuemei Gu et al., arXiv, 2024
80 |
81 | - [Large Language Models are Zero Shot Hypothesis Proposers](https://arxiv.org/abs/2311.05965), Biqing Qi et al., arXiv, 2023
82 |
83 | - [SciMON: Scientific Inspiration Machines Optimized for Novelty](https://arxiv.org/abs/2305.14259), Qingyun Wang et al., arXiv, 2023
84 |
85 |
86 |
87 |
88 |
89 | Virtual, Digital, Agent, Experimentation
90 |
91 | - [The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies](https://www.nature.com/articles/s41586-025-09442-9), Kyle Swanson et al., Nature, 2025
92 |
93 | - [aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists](https://arxiv.org/abs/2508.15126), Pengsong Zhang et al., arXiv, 2025
94 |
95 | - [AI Mathematician: Towards Fully Automated Frontier Mathematical Research](https://arxiv.org/abs/2505.22451), Yuanhang Liu et al., arXiv, 2025
96 |
97 | - [NovelSeek: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification](https://arxiv.org/abs/2505.16938), Bo Zhang et al., arXiv, 2025
98 |
99 | - [Large language models for scientific discovery in molecular property prediction](https://www.nature.com/articles/s42256-025-00994-z), Yizhen Zheng et al., Nature Machine Intelligence, 2025
100 |
101 | - [Towards an AI co-scientist](https://storage.googleapis.com/coscientist_paper/ai_coscientist.pdf), Juraj Gottweis et al., Google, 2025
102 |
103 | - [Agent Laboratory: Using LLM Agents as Research Assistants](https://arxiv.org/abs/2501.04227), Samuel Schmidgall et al., arXiv, 2025
104 |
105 | - [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://www.arxiv.org/abs/2408.06292), Chris Lu et al., arXiv, 2024
106 |
107 | - [SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning](https://www.arxiv.org/abs/2409.05556), Alireza Ghafarollahi et al., arXiv, 2024
108 |
109 | - [Empowering Biomedical Discovery with AI Agents](https://arxiv.org/abs/2404.02831), Shanghua Gao et al., arXiv, 2024
110 |
111 | - [Intelligent software for laboratory automation](https://doi.org/10.1016/j.tibtech.2004.07.010), Ken E. Whelan et al., Trends in Biotechnology, 2004
112 |
113 |
114 |
115 |
116 |
117 | Physical, Robot, Experimentation
118 |
119 | - [Augmenting large language models with chemistry tools](https://www.nature.com/articles/s42256-024-00832-8), Andres M. Bran et al., Nature Machine Intelligence, 2024
120 |
121 | - [ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterization](https://arxiv.org/abs/2401.06949), Kourosh Darvish et al., Matter, 2024
122 |
123 | - [MatPilot: an LLM-enabled AI Materials Scientist under the Framework of Human-Machine Collaboration](https://arxiv.org/abs/2411.08063), Ziqi Ni et al., arXiv, 2024
124 |
125 | - [Autonomous mobile robots for exploratory synthetic chemistry](https://www.nature.com/articles/s41586-024-08173-7), Tianwei Dai et al., Nature, 2024
126 |
127 | - [A multi-agent-driven robotic AI chemist enabling autonomous chemical research on demand](https://doi.org/10.26434/chemrxiv-2024-w953h-v2), Tao Song et al., Chemrxiv, 2024
128 |
129 | - [Autonomous chemical research with large language models](https://www.nature.com/articles/s41586-023-06792-0), Daniil A. Boiko et al., Nature, 2023
130 |
131 | - [An ontology for a Robot Scientist](https://doi.org/10.1093/bioinformatics/btl207), Larisa N. Soldatova et al., Bioinformatics, 2006
132 |
133 |
134 |
135 |
136 |
137 | Manuscript
138 |
139 | - [CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context](https://arxiv.org/abs/2302.07302), Joseph Chee Chang et al., arXiv, 2023
140 |
141 | - [Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks](https://arxiv.org/abs/2304.02623), Zejiang Shen et al., arXiv, 2023
142 |
143 |
144 |
145 |
146 |
147 | Peer Review
148 |
149 | - [Automated scholarly paper review: Concepts, technologies, and challenges](https://doi.org/10.1016/j.inffus.2023.101830), Jialiang Lin et al., Information Fusion, 2023
150 |
151 | - [Automated Peer Reviewing in Paper SEA: Standardization, Evaluation, and Analysis](https://doi.org/10.48550/arXiv.2407.12857), Jianxiang Yu et al., arXiv, 2024
152 |
153 | - [MARG: Multi-Agent Review Generation for Scientific Papers](https://arxiv.org/abs/2401.04259), Mike D'Arcy et al., arXiv, 2024
154 |
155 |
156 |
157 |
158 | ## Survey
159 |
160 | - [Synergy of robotics and microfluidics for intelligent micro-and nanomanipulation](https://doi.org/10.1063/5.0275644), Mengmeng Xi, Pengsong Zhang et al., Biomicrofluidics, 2025
161 |
162 | - [Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials](https://arxiv.org/abs/2409.04481), Yizhen Zheng et al., arXiv, 2024
163 |
164 | - [Towards Scientific Discovery with Generative AI: Progress, Opportunities, and Challenges](https://arxiv.org/abs/2412.11427), Chandan K Reddy et al., arXiv, 2024
165 |
166 | - [Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science](https://arxiv.org/abs/2412.09628), Yutong Xie et al., arXiv, 2024
167 |
168 | - [Paradigm shifts from data-intensive science to robot scientists](https://doi.org/10.1016/j.scib.2024.09.029), Xin Li et al., Science Bulletin, 2024
169 |
170 | - [A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery](https://arxiv.org/abs/2406.10833), Yu Zhang et al., arXiv, 2024
171 |
172 | - [Artificial Intelligence, Scientific Discovery, and Product Innovation](https://doi.org/10.1186%2F1759-4499-2-1), Toner-Rodgers Aidan, aidantr.github.io, 2024
173 |
174 | - [AI for Science: AI enabled scientific facility transforms fundamental research](https://bulletinofcas.researchcommons.org/journal/vol39/iss1/8/), Xiaokang Yang, Bulletin of Chinese Academy of Sciences, 2024
175 |
176 | - [Towards robot scientists for autonomous scientific discovery](https://doi.org/10.1186%2F1759-4499-2-1), Andrew Sparkes et al., Automated experimentation, 2010
177 |
178 | ## News
179 |
180 | - [Accelerating Discovery in Natural Science Laboratories with AI and Robotics: Perspectives and Challenges from the 2024 IEEE ICRA Workshop, Yokohama, Japan](https://arxiv.org/abs/2501.06847), Andrew I. Cooper et al., IEEE ICRA Workshop, 2024
181 |
182 | - [A new golden age of discovery](https://storage.googleapis.com/deepmind-media/DeepMind.com/Assets/Docs/a-new-golden-age-of-discovery_nov-2024.pdf), Conor Griffin et al., Google DeepMind, 2024
183 |
184 | - [Transforming science labs into automated factories of discovery](https://doi.org/10.1126/scirobotics.adm6991), Angelos Angelopoulos, Science Robotics, 2024
185 |
186 | - [Researchers built an ‘AI Scientist’ — what can it do?](https://doi.org/10.1038/d41586-024-02842-3), Davide Castelvecchi et al., Nature, 2024
187 |
188 | - [How to Enter the Chen Institute & Science Prize for AI Accelerated Research](https://www.science.org/content/page/how-enter-chen-institute-science-prize-ai-accelerated-research), ChenSciencePrize@aaas.org et al., Science, 2024
189 |
190 | - [Advancing scientific discovery with the aid of robotics](https://www.science.org/doi/10.1126/scirobotics.adt3842), Amos Matsiko, Science Robotics, 2024
191 |
192 |
193 | ## Benchmark
194 | - [CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark](https://arxiv.org/pdf/2409.11363v1), Zachary S. Siegel et al., arXiv, 2024
195 |
196 | - [CiteBench: A benchmark for Scientific Citation Text Generation](https://arxiv.org/abs/2212.09577), Martin Funkquist et al., Conference on Empirical Methods in Natural Language Processing, 2022
197 |
198 | ## Timeline
199 |
200 |
201 | 
202 |
203 | This timeline illustrates key milestones and future predictions in the development of autonomous AI Scientist and Robot Scientist.
204 |
205 |
206 |
207 | ## Discussions
208 |
209 | We love connecting with the community to discuss AI and Robot Scientist research, share insights, and collaborate on groundbreaking ideas! Join us on the following platforms:
210 |
211 | - **Discord**: Dive into our Discord server to engage in real-time discussions with researchers and enthusiasts worldwide.
212 | [Join Discord](https://discord.gg/6ADEMFsT)
213 |
214 | - **Slack**: Connect with us on Slack for professional conversations, paper discussions, and networking with the AI research community.
215 | [Join Slack](https://join.slack.com/t/awesome-ai-scientist/shared_invite/zt-35r3m9jeq-yT48jva9uqfIDDE5GCjoBQ)
216 |
217 | - **WeChat Group**: Join our WeChat group researchers to discuss papers, share updates, and collaborate.
218 | Scan the QR code or contact an admin (pengsong dot zhang@mail.utoronto.ca) to join:
219 |
220 |
221 | 
222 |
223 | Wechat group.
224 |
225 |
226 | We’re excited to hear your thoughts and contributions in our community!
227 |
228 | ## Contributing
229 |
230 | We welcome contributions to make this repository a richer resource for the AI and Robot Scientist community! To contribute:
231 |
232 | 1. **Add Papers**: Suggest new papers by submitting a pull request with the paper’s title, authors, link, and publication details in the appropriate section.
233 | 2. **Enhance Content**: Propose improvements to categories, summaries, or timelines.
234 | 3. **Report Issues**: Open an issue for typos, broken links, or suggestions.
235 |
236 | ## Cite
237 | ```
238 | bibtex
239 | @article{zhang2025scaling,
240 | title={Scaling Laws in Scientific Discovery with AI and Robot Scientists},
241 | author={Zhang, Pengsong and Zhang, Heng and Xu, Huazhe and Xu, Renjun and Wang, Zhenting and Wang, Cong and Garg, Animesh and Li, Zhibin and Ajoudani, Arash and Liu, Xinyu},
242 | journal={arXiv preprint arXiv:2503.22444},
243 | year={2025}
244 | }
245 |
246 | @article{zhangautonomous,
247 | title={Autonomous Generalist Scientist: Towards and Beyond Human-Level Scientific Research with Agentic and Embodied AI and Robots},
248 | author={Zhang, Pengsong and Zhang, Heng and Xu, Huazhe and Xu, Renjun and Wang, Zhenting and Wang, Cong and Garg, Animesh and Li, Zhibin and Liu, Xinyu and Ajoudani, Arash},
249 | journal={ResearchGate preprint RG.2.2.35148.01923},
250 | year={2024}
251 | }
252 | ```
253 | ## License
254 | MIT
255 |
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