AI科学家闭环系统，自动提假设、做验证、出成果
InternAgent: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification
- 多智能体协同闭环，从假说到验证全流程自动化
- 12项任务表现提升，30小时完成2D分割精度突破
- 适合科研加速、自动化实验设计的团队或个人
人工智能正加速科学范式转型，不仅提升研究效率，更推动创新。我们提出InternAgent，一个统一的闭合回路多智能体框架，可在多个科学领域实现自主科学研究（ASR），使研究人员以前所未有的速度与精度应对复杂问题。InternAgent具备三大优势：1）可扩展性：在12个科学任务中展现通用性，能生成创新思路以提升基线代码性能；2）交互性：提供人机反馈接口与多智能体协作机制，实现领域专家知识无缝融入自动化流程；3）高效性：在多个领域显著降低耗时，例如反应产率预测从27.6%提升至35.4%仅用12小时；增强子活性预测准确率由0.65升至0.79，仅耗时4小时；2D语义分割精度从78.8%增至81.0%，仅用30小时。
原文摘要 · Abstract (English)
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.
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