# Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Models

arXiv: https://arxiv.org/abs/2506.23692 · HTML: https://mlsanity.com/paper/2506.23692/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 30 Jun 2025.

## Builds on (most cited first, 2 of 2)

- [Breaking the Curse of Dimensionality with Convex Neural Networks](https://mlsanity.com/paper/1412.8690/) (2014)
- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)

## Built on by (most cited first, 2 of 2)

- [AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery](https://mlsanity.com/paper/2605.23204/) (2026)
- [AI-for-Science Low-code Platform with Bayesian Adversarial Multi-Agent Framework](https://mlsanity.com/paper/2603.03233/) (2026)

## Abstract (arXiv)

While AI for Science (AI4S) serves as an analytical tool in the current research paradigm, it doesn't solve its core inefficiency. We propose "Agent for Science" (Agent4S)-the use of LLM-driven agents to automate the entire research workflow-as the true Fifth Scientific Paradigm. This paper introduces a five-level classification for Agent4S, outlining a clear roadmap from simple task automation to fully autonomous, collaborative "AI Scientists." This framework defines the next revolutionary step in scientific discovery.