Abstract
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.
Abstract (translated)
人工智能（AI）的快速发展在化学工程领域展示了巨大的潜力，但现有的AI系统在跨学科协作和解决未知问题方面仍存在局限。为了解决这些问题，我们提出了“Cyber Academia-Chemical Engineering”(CA-ChemE)系统，这是一个能够通过多代理协作实现自我驱动研究进化的数字城镇，从而促进新兴科学发现。该系统结合了特定领域的知识库、知识增强技术和合作代理，成功构建了一个智能生态系统，能够在专业深度推理和高效的跨学科协作方面发挥作用。 我们的研究表明，在所有七个专家代理中，知识库支持的增强机制使对话质量评分平均提高了10-15%，从根本上确保技术判断建立在可验证的科学证据基础上。然而，我们观察到一个关键瓶颈在于跨领域合作效率低下，从而引入了一种具备本体工程能力的合作代理（CA）。CA的介入为远离其专业知识领域的专家对组带来了8.5%的改进，而仅为邻近领域专家对组带来的改进则只有0.8%，两者之间的差异达到了10.6倍。这揭示了“由于知识库差距而导致跨学科合作效率降低”的现象。 本研究展示了精心设计的多代理架构如何为化学工程领域的自主科学发现提供一条可行路径。
URL
https://arxiv.org/abs/2510.01293
