Deep Research of Deep Research: From Transformer to Agent, From AI to AI for Science
Summary: arXiv:2603.28361v1 Announce Type: new
Abstract: With the advancement of large language models (LLMs) in their knowledge base and reasoning capabilities, their interactive modalities have evolved from pure text to multimodality and further to agentic tool use. Consequently, their applications have broadened from question answering to AI assistants and now to general-purpose agents.
Deep research (DR) represents a prototypical vertical application for general-purpose agents, which serves as an ideal approach for intelligent information processing and assisting humans in discovering and solving problems. The ultimate goal is to reach or even surpass the level of top human scientists. This paper provides a deep research of deep research.
Key Insights and Definitions
We articulate a clear and precise definition of deep research and unify perspectives from industry’s deep research and academia’s AI for Science (AI4S) within a developmental framework. The study positions large language models and Stable Diffusion as the twin pillars of generative AI.
Roadmap from Transformer to Agents
In our analysis, we lay out a roadmap that evolves from the Transformer architecture to sophisticated agents capable of performing complex tasks. This transition marks a significant milestone in the development of AI technologies.
AI for Science (AI4S) Progress
We examine the progress of AI4S across various disciplines, identifying the predominant paradigms of human-AI interaction and prevailing system architectures. This progress is crucial as it reflects the integration of AI in scientific research and innovation.
Challenges and Research Issues
Despite the advancements, we discuss the major challenges and fundamental research issues that remain in the field. Addressing these challenges is essential for the continued evolution of AI technologies and their applications in science.
Mutual Growth: AI and Science
- AI supports scientific innovation: AI technologies enhance the capabilities of researchers and scientists, enabling them to explore new frontiers in various fields.
- Science contributes to AI growth: Scientific discoveries and methodologies can lead to advancements in AI, creating a symbiotic relationship between the two domains.
- Bridging Communities: We hope this paper can help bridge the gap between the AI and AI4S communities, fostering collaboration and mutual understanding.
Conclusion
In conclusion, the evolution of AI technologies from simple language models to complex agents marks a significant turning point in how we approach research and problem-solving. By examining deep research within a unified framework, we aim to contribute to the ongoing dialogue between AI and scientific communities, ultimately enhancing both fields.