arXiv 2510.012932025-10-01cs.AIcs.LG

Cyber Academia-Chemical Engineering (CA-ChemE)：面向自主研究演化与涌现式科学发现的活态数字小镇

Cyber Academia-Chemical Engineering (CA-ChemE): A Living Digital Town for Self-Directed Research Evolution and Emergent Scientific Discovery

State Key Laboratory of Heavy Oil Processing, College of Carbon Neutrality Future Technology, China University of Petroleum (Beijing), Beijing 102249, China（石油加工国家重点实验室，碳中和未来技术学院，中国石油大学（北京），北京102249）

Zekun Jiang, Chunming Xu, Tianhang Zhou

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AI总结：

针对现有AI在化学工程领域跨学科协作与未知探索的局限，提出多智能体协作的CA-ChemE活态数字小镇系统，通过知识库增强与具备本体工程能力的协作智能体，提升科研协作效率，为自主科学发现提供可行路径。

摘要：

人工智能（AI）的快速发展已在化学工程领域展现出巨大潜力，但现有AI系统在跨学科协作、未知问题探索方面仍存在局限。为解决这些问题，我们提出Cyber Academia-Chemical Engineering（CA-ChemE）系统——一个活态数字小镇，通过多智能体协作实现自主研究演化与涌现式科学发现。系统整合领域专属知识库、知识增强技术与协作智能体，成功构建了具备深度专业推理能力与高效跨学科协作能力的智能生态系统。研究结果表明，基于知识库的增强机制使7个专家智能体的对话质量评分平均提升10-15%，从根本上确保技术判断以可验证的科学证据为基础。但我们发现跨领域协作效率存在关键瓶颈，因此引入了具备本体工程能力的协作智能体（Collaboration Agent, CA）。CA介入后，远领域专家对的协作效率提升8.5%，而近领域专家对仅提升0.8%，差距达10.6倍，揭示了“知识库差距导致协作效率递减”效应。本研究表明，精心设计的多智能体架构可为化学工程领域的自主科学发现提供可行路径。

英文摘要：

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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