Executive Summary
The research paper Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents examines how next-generation AI models are evolving from language tools into autonomous scientific collaborators capable of hypothesis generation, experimentation, and discovery. By integrating reasoning engines, long-term memory, and specialized scientific tools, these agents can perform end-to-end research tasks that once required teams of experts. For business leaders, this signals a coming transformation in R&D productivity, where AI systems not only accelerate innovation but also ensure greater accuracy, reproducibility, and cross-domain insight. The rise of “scientific intelligence” positions AI as a force multiplier for scientific advancement and a catalyst for entire new industries in healthcare, materials, energy, and beyond.
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Key point: This paper establishes a roadmap for building autonomous, AI-powered scientific agents that can reason, experiment, and validate discoveries, marking a foundational step toward fully automated, reproducible, and ethically governed scientific research.
Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents
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Institutions:
Multimodal Artificial Intelligence Systems, Institute of Automation, University of Chinese Academy of Science, Wuhan AI Research
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