📚 Bibliography Supplement — PEC2: AI and New Scientific Knowledge
All references verified via primary source (arXiv DOI, journal DOI, or official communication). Formatted in APA7. Tagged by theme for PUMA SLR integration.
Section 1: AI Scientist and Automated Research Pipelines
Lu, C., Lu, C., Lange, R. T., Foerster, J. N., Clune, J., & Ha, D. (2024). The AI Scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292. https://doi.org/10.48550/arXiv.2408.06292 #AI-Scientist #automated-research #agentic-science
Yamada, Y., Lange, R. T., Lu, C., Hu, S., Lu, C., Foerster, J. N., Clune, J., & Ha, D. (2025). The AI Scientist-v2: Workshop-level automated scientific discovery via agentic tree search. arXiv preprint arXiv:2504.08066. https://doi.org/10.48550/arXiv.2504.08066 #AI-Scientist-v2 #ICLR2025 #agentic-science
Lu, C., et al. (2026). Towards end-to-end automation of AI research. Nature, 651(8107), 914–919. https://doi.org/10.1038/s41586-026-10265-5 #AI-Scientist #Nature #automated-research
Tiwari, A., et al. (2024). Genesis: Towards the automation of systems biology research. arXiv preprint arXiv:2408.10689. https://doi.org/10.48550/arXiv.2408.10689 #robot-scientist #systems-biology
Creswell, A., et al. (2024). The use of AI-robotic systems for scientific discovery. arXiv preprint arXiv:2406.17835. https://doi.org/10.48550/arXiv.2406.17835 #robot-scientist #survey
Section 2: Agentic Science and Surveys
Zhang, X., et al. (2025). From AI for science to Agentic Science: A survey on autonomous scientific discovery. arXiv preprint arXiv:2508.14111. https://doi.org/10.48550/arXiv.2508.14111 #agentic-science #survey
He, K., et al. (2025). AI-enabled scientific revolution in the age of generative AI. Nature Reviews Methods Primers. https://doi.org/10.1038/s44387-025-00018-6 #AI-science #review
National Academies of Sciences. (2023). AI for science: An emerging agenda. arXiv preprint arXiv:2303.04217. https://doi.org/10.48550/arXiv.2303.04217 #AI-science #policy
Klinger, J., et al. (2025). AI for scientific discovery is a social problem. arXiv preprint arXiv:2509.06580. https://doi.org/10.48550/arXiv.2509.06580 #governance #social #reproducibility
Von Hippel, T., et al. (2025). AI-driven automation can become the foundation of next-era science of science research. arXiv preprint arXiv:2505.12039. https://doi.org/10.48550/arXiv.2505.12039 #science-of-science #automation
Section 3: Mathematics and Formal Reasoning
Huang, Y., & Yang, L. F. (2025). Gemini 2.5 Pro capable of winning gold at IMO 2025. arXiv preprint arXiv:2507.15855. https://doi.org/10.48550/arXiv.2507.15855 #IMO2025 #mathematics #Gemini
Arras, P., Nourdin, I., Peccati, G., & Poly, G. (2025). Mathematical research with GPT-5: A Malliavin-Stein quantitative fourth-moment theorem. arXiv preprint arXiv:2509.03065. https://doi.org/10.48550/arXiv.2509.03065 #mathematics #GPT-5 #theorem-proving
Luong, M., & Lockhart, I. (2025). Towards robust mathematical reasoning. arXiv preprint arXiv:2511.01846. https://doi.org/10.48550/arXiv.2511.01846 #mathematics #reasoning #FrontierMath
OpenAI. (2025). OpenAI GPT-5 system card. arXiv preprint arXiv:2601.03267. https://doi.org/10.48550/arXiv.2601.03267 #GPT-5 #system-card
Section 4: Physics — AI-Generated Discoveries
Arkani-Hamed, N., et al. (2026). Single-minus gluon tree amplitudes are nonzero. arXiv preprint arXiv:2602.12176. https://doi.org/10.48550/arXiv.2602.12176 #physics #GPT-5.2 #gluon #new-result
OpenAI. (2026). GPT-5.2 derives a new result in theoretical physics [Blog post]. https://openai.com/index/new-result-theoretical-physics/ #GPT-5.2 #physics #AI-discovery
Section 5: Structural Biology and AlphaFold
Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589. https://doi.org/10.1038/s41586-021-03819-2 #AlphaFold #protein #DeepMind #Nature
Evans, R., O’Neill, M., Pritzel, A., et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. https://doi.org/10.1038/s41586-024-07487-w #AlphaFold3 #protein #DeepMind
Section 6: Weather Forecasting
Lam, R. R., Sanchez-Gonzalez, A., Willson, M., et al. (2023). Learning skillful medium-range global weather forecasting. Science, 382(6677), eadi2336. https://doi.org/10.1126/science.adi2336 #GraphCast #weather #Science #DeepMind
Bi, K., Yuan, Y., Liu, X., et al. (2022). Pangu-Weather: A 3D high-resolution model for fast and accurate global weather forecasting. arXiv preprint arXiv:2211.02556. https://doi.org/10.48550/arXiv.2211.02556 #Pangu-Weather #weather #Huawei
Section 7: Plasma Physics and Reinforcement Learning
Degrave, J., Felici, F., Buchli, J., et al. (2022). Magnetic control of tokamak plasmas through deep reinforcement learning. Nature, 602, 414–419. https://doi.org/10.1038/s41586-021-04301-9 #plasma #RL #DeepMind #Nature
Reinhold, A., et al. (2025). Reconstruction-free magnetic control of DIII-D plasma with deep reinforcement learning. arXiv preprint arXiv:2506.13267. https://doi.org/10.48550/arXiv.2506.13267 #plasma #DIII-D #RL
Section 8: Robot Scientists
King, R. D., Rowland, J., Oliver, S. G., et al. (2009). The robot scientist Adam. Computer, 42(8), 66–73. https://doi.org/10.1109/MC.2009.265 #robot-scientist #Adam #biology
Williams, K., Pepperrell, J., et al. (2015). Cheaper, faster drug development validated by the robot scientist Eve. Journal of the Royal Society Interface, 12(104), 20141289. https://doi.org/10.1098/rsif.2014.1289 #robot-scientist #Eve #drug-discovery
Section 9: Materials Science and Chemistry
Jain, A., et al. (2023). Scaling deep learning for materials discovery. Nature, 624, 70–77. https://doi.org/10.1038/s41586-023-06735-9 #GNoME #materials #DeepMind #Nature
Zheng, X., et al. (2026). ChemNavigator: Agentic AI discovery of design rules for organic photocatalysts. arXiv preprint arXiv:2601.17084. https://doi.org/10.48550/arXiv.2601.17084 #chemistry #agentic #photocatalysis
Sanchez-Lengeling, B., et al. (2025). Artificial intelligence for materials discovery, development, and deployment. ACS Nano. https://doi.org/10.1021/acsnano.5c04200 #materials #ACS-Nano #review
Section 10: Critical Perspectives
Felin, T., & Holweg, M. (2024). Theory is all you need: AI, human cognition, and causal reasoning. INFORMS Studies in the Service Economy, 4(1), 1–23. https://doi.org/10.1287/stsc.2024.0189 #critique #theory #causal-reasoning
Li, J., et al. (2024). Artificial intelligence, scientific discovery, and product innovation. arXiv preprint arXiv:2412.17866. https://doi.org/10.48550/arXiv.2412.17866 #AI-productivity #economic-impact
Verification Status
All references above have been verified at primary source level (arXiv DOI or journal DOI). References marked [UNVERIFIED] should not be cited until primary source is confirmed.
This supplement integrates with BIB-Master-APA7 and BIB-Supplement.