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

arXiv: https://arxiv.org/abs/2608.08883 · HTML: https://mlsanity.com/paper/2608.08883/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 16 of 16)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Evaluating Very Long-Term Conversational Memory of LLM Agents](https://mlsanity.com/paper/2402.17753/) (2024)
- [Humanity's Last Exam](https://mlsanity.com/paper/2501.14249/) (2025)
- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference](https://mlsanity.com/paper/2402.04647/) (2024)
- [The Science of Evaluating Foundation Models](https://mlsanity.com/paper/2502.09670/) (2025)
- [Better Prompt Compression Without Multi-Layer Perceptrons](https://mlsanity.com/paper/2501.06730/) (2025)
- [Latent Adaptive Planner for Dynamic Manipulation](https://mlsanity.com/paper/2505.03077/) (2025)
- [The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Models](https://mlsanity.com/paper/2512.16236/) (2025)
- [AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups](https://mlsanity.com/paper/2508.05648/) (2025)
- [Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology](https://mlsanity.com/paper/2510.22527/) (2025)
- [Large language models in materials science and the need for open-source approaches](https://mlsanity.com/paper/2511.10673/) (2025)

## Built on by (most cited first, 1 of 1)

- [AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research](https://mlsanity.com/paper/2609.16519/) (2026)

## Abstract (arXiv)

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.