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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.08979 |
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| _version_ | 1866929755397292032 |
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| author | Gridach, Mourad Nanavati, Jay Abidine, Khaldoun Zine El Mendes, Lenon Mack, Christina |
| author_facet | Gridach, Mourad Nanavati, Jay Abidine, Khaldoun Zine El Mendes, Lenon Mack, Christina |
| contents | The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agentic AI for scientific discovery, categorizing existing systems and tools, and highlighting recent progress across fields such as chemistry, biology, and materials science. We discuss key evaluation metrics, implementation frameworks, and commonly used datasets to offer a detailed understanding of the current state of the field. Finally, we address critical challenges, such as literature review automation, system reliability, and ethical concerns, while outlining future research directions that emphasize human-AI collaboration and enhanced system calibration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions Gridach, Mourad Nanavati, Jay Abidine, Khaldoun Zine El Mendes, Lenon Mack, Christina Computation and Language The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agentic AI for scientific discovery, categorizing existing systems and tools, and highlighting recent progress across fields such as chemistry, biology, and materials science. We discuss key evaluation metrics, implementation frameworks, and commonly used datasets to offer a detailed understanding of the current state of the field. Finally, we address critical challenges, such as literature review automation, system reliability, and ethical concerns, while outlining future research directions that emphasize human-AI collaboration and enhanced system calibration. |
| title | Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.08979 |