发表机构
Southern Oregon University; University of California, Los Angeles（南俄勒冈大学; 加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AquiLLM是一款采用开放权重模型的开源模块化RAG-LLM框架，经领域专家反馈优化了架构与功能，可支持研究群体捕获隐性知识，助力AI贴合科研实践。
AI 中文摘要
检索增强生成（RAG）和大语言模型（LLM）的最新进展使研究人员能将AI整合到科研工作流中，但使用专有商业AI系统引发了透明度、可复现性和隐私方面的担忧，而这些对科研实践至关重要。为此，AquiLLM作为一款开源模块化RAG-LLM框架，采用开放权重模型开发，旨在支持研究群体捕获隐性知识。本研究对AquiLLM进行了一系列架构改进和功能增强，包括本地嵌入与重排序、多模态能力、兼容OpenAI的推理接口、用户界面优化、语义与情景记忆能力，以及技能支持。这些改进是通过与天体物理学家、环境研究人员等领域专家讨论后确定的，代表着向更贴合科研实践的AI系统迈出的一步。
英文摘要
Recent advances in retrieval-augmented generation (RAG) and large language models (LLMs) enable researchers to integrate AI into scientific workflows. However, using proprietary commercial AI systems raises concerns about transparency, reproducibility and privacy, which are essential for scientific practices. To this end, AquiLLM was developed as an open-source modular RAG-LLM framework using open-weight models, designed to support research groups in capturing tacit knowledge. In this work, we present a series of architectural improvements and feature enhancements to AquiLLM, including local embedding and reranking, multimodal capabilities, OpenAI-compatible inference interfaces, user interface improvements, semantic and episodic memory capabilities, and skills support. These enhancements were informed by discussions with domain experts, including astrophysicists and environmental researchers, and represent a step toward AI systems more closely aligned with scientific research practices.
CommentsAccepted for publication in the NGEN-AI 2026 proceedings
