AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups

2026-08-09

AI Summary

This work addresses the challenges research teams face when using commercial AI systems—namely, insufficient transparency, poor reproducibility, and privacy risks—which hinder effective capture and utilization of tacit knowledge. To overcome these limitations, the authors propose an open-source, modular RAG-LLM framework built upon open-weight large language models, enabling local deployment. The framework integrates local embedding and re-ranking, multimodal processing, semantic and contextual memory mechanisms, and extensible skill modules, while offering an OpenAI-compatible API and an optimized user interface. Expert evaluations in domains such as astrophysics and environmental science demonstrate that the system substantially enhances the effectiveness of tacit knowledge capture and improves the trustworthiness, adaptability, and usability of AI tools in scientific research contexts.

Abstract

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.

Problem

• tacit knowledge capture
• scientific workflows
• reproducibility
• privacy
• open-source AI

Innovation

• retrieval-augmented generation
• open-source LLM
• tacit knowledge capture
• multimodal RAG
• scientific AI