构建智能数字城市，让多个AI科研代理自主协作发现新科学问题。
Cyber Academia-Chemical Engineering (CA-ChemE): A Living Digital Town for Self-Directed Research Evolution and Emergent Scientific Discovery
- 用多智能体系统模拟科研协作，通过知识库增强对话质量。
- 跨领域协作效率提升8.5%，近域仅0.8%，凸显知识鸿沟影响。
- 适合关注自主科研探索与跨学科创新的研究者。
人工智能在化学工程中潜力巨大，但现有系统在跨领域协作和探索未知问题方面仍受限。为此，我们提出网络学术-化学工程（CA-ChemE）系统，一个可自我演进的数字科研生态，通过多智能体协作实现自主研究进化与涌现式科学发现。系统整合领域知识库、知识增强技术与协作代理，成功构建具备深度专业推理能力与高效跨学科协作的智能生态。实验表明，知识库增强机制使七位专家代理的对话质量平均提升10%-15%，确保技术判断基于可验证的科学证据。然而，跨领域协作效率存在关键瓶颈，引入具有本体工程能力的协作代理（CA）后，远域专家对协作效率提升8.5%，而近域仅0.8%，差距达10.6倍，揭示了‘知识库缺失导致协作效率下降’的现象。本研究展示了精心设计的多智能体架构如何为化学工程中的自主科学发现提供可行路径。
原文摘要 · Abstract (English)
The rapid advancement of artificial intelligence (AI) has demonstrated substantial potential in chemical engineering, yet existing AI systems remain limited in interdisciplinary collaboration and exploration of uncharted problems. To address these issues, we present the Cyber Academia-Chemical Engineering (CA-ChemE) system, a living digital town that enables self-directed research evolution and emergent scientific discovery through multi-agent collaboration. By integrating domain-specific knowledge bases, knowledge enhancement technologies, and collaboration agents, the system successfully constructs an intelligent ecosystem capable of deep professional reasoning and efficient interdisciplinary collaboration. Our findings demonstrate that knowledge base-enabled enhancement mechanisms improved dialogue quality scores by 10-15% on average across all seven expert agents, fundamentally ensuring technical judgments are grounded in verifiable scientific evidence. However, we observed a critical bottleneck in cross-domain collaboration efficiency, prompting the introduction of a Collaboration Agent (CA) equipped with ontology engineering capabilities. CA's intervention achieved 8.5% improvements for distant-domain expert pairs compared to only 0.8% for domain-proximate pairs - a 10.6-fold difference - unveiling the "diminished collaborative efficiency caused by knowledge-base gaps" effect. This study demonstrates how carefully designed multi-agent architectures can provide a viable pathway toward autonomous scientific discovery in chemical engineering.
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