The rapid advancements in Generative AI and Large Language Models promise to transform the way research is conducted, potentially offering unprecedented opportunities to augment scholarly workflows. However, effectively integrating AI into research remains a challenge due to varying domain requirements, limited AI literacy, the complexity of coordinating tools and agents, and the unclear accuracy of Generative AI in research. We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery, with AI assistants supporting tasks across the research life cycle. The platform offers modular components - including prompt and tool libraries, a shared data store, and a flexible orchestration framework - that collectively facilitate ideation, literature analysis, methodology development, data analysis, and scholarly writing. We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.

翻译：生成式人工智能与大语言模型的快速发展有望变革研究工作的开展方式，为增强学术工作流程提供前所未有的机遇。然而，由于不同领域需求的差异性、人工智能素养的不足、工具与智能体协调的复杂性，以及生成式人工智能在研究场景中准确性尚不明确，将人工智能有效整合至研究过程仍面临挑战。本文提出 TIB AIssistant 的愿景：一个领域无关的人机协作平台，旨在通过人工智能助手支持研究生命周期中的各项任务，从而辅助跨学科研究人员进行科学探索。该平台提供模块化组件——包括提示词与工具库、共享数据存储库以及灵活的任务编排框架——共同促进研究构思、文献分析、方法开发、数据分析和学术写作。我们阐述了该平台的概念框架、系统架构及早期原型的实现，以验证本方法的可行性与潜在影响力。