AutoKD: Autonomous Knowledge Discovery

2026-09-06

AI Summary

本文提出AutoKD，一种多代理框架，通过协调六个LLM代理进行自主知识发现，利用持久洞察图积累和指导后续研究，解决数据丰富领域科学发现受限于人类带宽的问题。

Abstract

Scientific discovery in data-rich domains is currently constrained by human bandwidth: the growth in the volume and complexity of real-world data far outpaces the rate at which researchers can read, reason, and synthesize. Recent LLM-based multi-agent systems have begun to automate portions of the research cycle, but they target hypothesis generation in settings where validation cannot itself be automated, and each run is one-shot, with no mechanism for findings to accumulate or steer subsequent inquiry. This paper introduces AutoKD, a multi-agent framework for autonomous knowledge discovery that is both computational and cumulative, allowing validated findings to persist and inform subsequent inquiry. Six coordinated LLM agents collaborate in an open-ended discovery loop, where accepted findings are stored in a persistent insight graph that serves as both long-term memory and an exploration-steering mechanism. We evaluate AutoKD on three diverse datasets from two perspectives: Open-ended Quality against published findings, and Conditioned Quality via literature-derived queries. Across both evaluation perspectives, AutoKD covers known findings and surfaces substantive discoveries that complement human-driven research. Our code is available at https://github.com/GeQinwen/AutoKD.

Problem

• Scientific discovery
• data-rich domains
• human bandwidth
• LLM-based multi-agent systems
• hypothesis generation

Innovation

• multi-agent framework
• autonomous knowledge discovery
• persistent insight graph
• cumulative findings