The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning
🤖 AI Summary
针对AI工具如何融入日常科研的问题，提出包含五级整合分类、开源代理框架及案例研究的实用方法，以增强而非替代研究者。
Application Category
📝 Abstract
AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday research practice. This paper is a practical guide to AI-assisted research in mathematics and machine learning: We discuss how researchers can use modern AI systems productively, where these systems help most, and what kinds of guardrails are needed to use them responsibly. It is organized into three parts: (I) a five-level taxonomy of AI integration, (II) an open-source framework that, through a set of methodological rules formulated as agent prompts, turns CLI coding agents (e.g., Claude Code, Codex CLI, OpenCode) into autonomous research assistants, and (III) case studies from deep learning and mathematics. The framework runs inside a sandboxed container, works with any frontier LLM through existing CLI agents, is simple enough to install and use within minutes, and scales from personal-laptop prototyping to multi-node, multi-GPU experimentation across compute clusters. In practice, our longest autonomous session ran for over 20 hours, dispatching independent experiments across multiple nodes without human intervention. We stress that our framework is not intended to replace the researcher in the loop, but to augment them. Our code is publicly available at https://github.com/ZIB-IOL/The-Agentic-Researcher.
Problem
Research questions and friction points this paper is trying to address.
AI-assisted research
research practice
mathematics
machine learning
AI integration
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-assisted research
agentic framework
CLI coding agents
sandboxed autonomous experimentation
LLM integration
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