面向社会化人工智能的科学发现新范式

中文摘要 AI 辅助

科学发现通过知识组织的连续变革不断推进：观察与实验奠定了科学的经验基础，理论实现了从特定现象推导出普遍原理，计算将探究延伸至无法直接观察的系统，而数据密集型方法则开辟了模式与预测的新空间。当前科学面临新的前沿，核心挑战不再仅仅是产生更多信息，而是将不断扩展的知识、推理与证据组织成连贯的发现过程。本文提出了BLAZE（即连接文献、智能体与零间隙实验）这一社会化科学智能范式。BLAZE不将AI视为孤立研究任务的助手，而是作为科学发现的组织基础设施，它将持久知识、集体推理、经验验证与人类判断连接在连续的研究生命周期中，将碎片化活动转化为可累积的探究、批判与修正过程。BLAZE的核心前提是，科学智能并非仅由计算产生，而是源于知识、假说、实验与集体验证之间的持续互动。通过将人类与机器组织在共享的科学过程中，BLAZE使发现更具可追溯性、可重复性与累积性，同时保留人类的创造力、判断力与责任。社会化科学智能可能为科学的下一个时代提供基础，其目的并非取代人类发现，而是扩展集体科学探究的规模、深度与连续性。

英文摘要

Scientific discovery has advanced through successive transformations in the organization of knowledge. Observation and experimentation established the empirical foundations of science. Theory made it possible to derive general principles from particular phenomena. Computation extended inquiry into systems beyond direct observation, while data-intensive methods opened new spaces of pattern and prediction. Science now confronts a different frontier. The central challenge is no longer simply to produce more information, but to organize expanding knowledge, reasoning, and evidence into a coherent process of discovery. Here, we introduce Bridging Literature, Agents, and Zero-gap Experimentation (BLAZE), a paradigm of socialized scientific intelligence. BLAZE conceives AI not as an assistant for isolated research tasks, but as an organizational infrastructure for scientific discovery. It connects persistent knowledge, collective reasoning, empirical validation, and human judgment within a continuous research lifecycle, transforming fragmented activities into a cumulative process of inquiry, criticism, and revision. The central premise of BLAZE is that scientific intelligence does not arise from computation alone. It emerges from the sustained interaction among knowledge, hypotheses, experiments, and collective verification. By organizing humans and machines within a shared scientific process, BLAZE makes discovery more traceable, reproducible, and cumulative while preserving human creativity, judgment, and responsibility. Socialized scientific intelligence may provide a foundation for the next era of science. Its purpose is not to replace human discovery, but to extend the scale, depth, and continuity of collective scientific inquiry.

发表机构

• Baize Research（白泽研究院）
• Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）
• Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology（昆明理工大学云南省人工智能重点实验室）
• School of Automation, Southeast University（东南大学自动化学院）
• School of Artificial Intelligence, Tianjin University（天津大学人工智能学院）
• School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机学院）
• SeetaCloud Technology（思特云科技）
• GPUhub Pte. Ltd.（GPUhub私人有限公司）

机构由 AI 辅助整理，请以论文原文为准。