InternAgent: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification

TL;DR

InternAgent is a versatile multi-agent framework that automates scientific research processes, enabling rapid hypothesis testing and verification across various fields with minimal human intervention.

Contribution

It introduces a scalable, interactive, and efficient closed-loop system for autonomous scientific research, integrating human feedback and multi-agent collaboration.

Findings

Demonstrated versatility across 12 scientific tasks.

Achieved significant performance improvements in key scientific problems.

Reduced time costs compared to traditional human research efforts.

Abstract

Artificial Intelligence (AI) is accelerating the transformation of scientific research paradigms, not only enhancing research efficiency but also driving innovation. We introduce InternAgent, a unified closed-loop multi-agent framework to conduct Autonomous Scientific Research (ASR) across various scientific research fields, enabling researchers to tackle complicated problems in these fields with unprecedented speed and precision. InternAgent highlights three key advantages: 1) Scalability: InternAgent has demonstrated its versatility across 12 scientific research tasks, capable of generating innovative ideas to enhance the performance of baseline code. 2) Interactivity: InternAgent provides an interface for human expert feedback and multi-agent interaction in automated end-to-end processes, allowing for the seamless integration of domain expert knowledge. 3) Efficiency: InternAgent has achieved promising performance gains in several scientific fields with significantly less time cost compared to human efforts. For instance, in reaction yield prediction, it increased from 27.6% to 35.4% in just 12 hours; in enhancer activity prediction, accuracy rose from 0.65 to 0.79 with only 4 hours of processing; and in 2D semantic segmentation, precision advanced from 78.8% to 81.0% in a mere 30 hours.