TinyScientist：一个用于构建研究智能体的交互式、可扩展且可控的框架
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents
AI总结：
TinyScientist 提出一个交互式、可扩展且可控的框架，识别自动研究核心组件，提供开源代码、网页演示和 Python 包，以降低构建研究智能体的复杂性。
摘要：
利用大型语言模型（LLMs）进行自动研究正迅速变得重要，推动了涉及多智能体系统、规划、工具使用、代码执行以及人机交互的日益复杂工作流的发展，以加速研究进程。然而，随着越来越多的研究人员和开发者开始使用并基于这些工具和平台进行构建，扩展和维护此类智能体工作流的复杂性和难度已成为一项重大挑战，尤其是在算法和架构不断进步的背景下。为了解决这一日益增长的复杂性，TinyScientist 识别了自动研究工作流的基本组成部分，并提出了一种交互式、可扩展且可控的框架，该框架易于适配新工具并支持迭代式增长。我们提供了一个开源代码库、一个交互式网页演示以及一个 PyPI Python 包，使最先进的自动研究流水线能够广泛地被每位研究人员和开发者使用。
英文摘要：
Automatic research with Large Language Models (LLMs) is rapidly gaining importance, driving the development of increasingly complex workflows involving multi-agent systems, planning, tool usage, code execution, and human-agent interaction to accelerate research processes. However, as more researchers and developers begin to use and build upon these tools and platforms, the complexity and difficulty of extending and maintaining such agentic workflows have become a significant challenge, particularly as algorithms and architectures continue to advance. To address this growing complexity, TinyScientist identifies the essential components of the automatic research workflow and proposes an interactive, extensible, and controllable framework that easily adapts to new tools and supports iterative growth. We provide an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
