Agentic Discovery: Closing the Loop with Cooperative Agents
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915555604168704 |
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| author | Pauloski, J. Gregory Chard, Kyle Foster, Ian T. |
| author_facet | Pauloski, J. Gregory Chard, Kyle Foster, Ian T. |
| contents | As data-driven methods, artificial intelligence (AI), and automated workflows accelerate scientific tasks, we see the rate of discovery increasingly limited by human decision-making tasks such as setting objectives, generating hypotheses, and designing experiments. We postulate that cooperative agents are needed to augment the role of humans and enable autonomous discovery. Realizing such agents will require progress in both AI and infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_13081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic Discovery: Closing the Loop with Cooperative Agents Pauloski, J. Gregory Chard, Kyle Foster, Ian T. Multiagent Systems Artificial Intelligence As data-driven methods, artificial intelligence (AI), and automated workflows accelerate scientific tasks, we see the rate of discovery increasingly limited by human decision-making tasks such as setting objectives, generating hypotheses, and designing experiments. We postulate that cooperative agents are needed to augment the role of humans and enable autonomous discovery. Realizing such agents will require progress in both AI and infrastructure. |
| title | Agentic Discovery: Closing the Loop with Cooperative Agents |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2510.13081 |