# The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence

arXiv: https://arxiv.org/abs/2307.07522 · HTML: https://mlsanity.com/paper/2307.07522/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 89 references that are in the map.
- First posted on arXiv 9 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Machine learning for molecular simulation](https://mlsanity.com/paper/1911.02792/) (2019)
- [Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI](https://mlsanity.com/paper/1909.02309/) (2019)
- [Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution](https://mlsanity.com/paper/2210.08340/) (2022)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Structure Learning in Graphical Modeling](https://mlsanity.com/paper/1606.02359/) (2016)
- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [A Survey of Deep Learning for Scientific Discovery](https://mlsanity.com/paper/2003.11755/) (2020)
- [AI for Science: An Emerging Agenda](https://mlsanity.com/paper/2303.04217/) (2023)
- [Emergence and algorithmic information dynamics of systems and observers](https://mlsanity.com/paper/2105.14707/) (2021)

## Built on by (most cited first, 12 of 12)

- [Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models](https://mlsanity.com/paper/2501.05382/) (2025)
- [Scientific Hypothesis Generation by a Large Language Model: Laboratory Validation in Breast Cancer Treatment](https://mlsanity.com/paper/2405.12258/) (2024)
- [Oil & Water? Diffusion of AI Within and Across Scientific Fields](https://mlsanity.com/paper/2405.15828/) (2024)
- [Scaling Laws in Scientific Discovery with AI and Robot Scientists](https://mlsanity.com/paper/2503.22444/) (2025)
- [Science Is Falling Behind the Frontier: Foundation Model Adoption Across Half a Million Papers](https://mlsanity.com/paper/2511.21739/) (2025)
- [Decoding complexity: how machine learning is redefining scientific discovery](https://mlsanity.com/paper/2405.04161/) (2024)
- [Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI](https://mlsanity.com/paper/2506.19613/) (2025)
- [Speculative Exploration on the Concept of Artificial Agents Conducting Autonomous Research](https://mlsanity.com/paper/2312.03497/) (2023)
- [The Use of AI-Robotic Systems for Scientific Discovery](https://mlsanity.com/paper/2406.17835/) (2024)
- [Active Inference AI Systems for Scientific Discovery](https://mlsanity.com/paper/2506.21329/) (2025)
- [Artificially intelligent agents in the social and behavioral sciences: A history and outlook](https://mlsanity.com/paper/2510.05743/) (2025)
- [The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning](https://mlsanity.com/paper/2608.26116/) (2026)

## Abstract (arXiv)

Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contribution to technology can come from multiple approaches that require access to large training data sets and clear performance evaluation criteria, ranging from pattern recognition and classification to generative models. Yet, AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Generative AI, in general, and Large Language Models in particular, may represent an opportunity to augment and accelerate the scientific discovery of fundamental deep science with quantitative models. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery, including self-driven hypothesis generation and open-ended autonomous exploration of the hypothesis space. Integrating AI-driven automation into the practice of science would mitigate current problems, including the replication of findings, systematic production of data, and ultimately democratisation of the scientific process. Realising these possibilities requires a vision for augmented AI coupled with a diversity of AI approaches able to deal with fundamental aspects of causality analysis and model discovery while enabling unbiased search across the space of putative explanations. These advances hold the promise to unleash AI's potential for searching and discovering the fundamental structure of our world beyond what human scientists have been able to achieve. Such a vision would push the boundaries of new fundamental science rather than automatize current workflows and instead open doors for technological innovation to tackle some of the greatest challenges facing humanity today.