mlsanityData to 7 Oct 2026

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

arXiv 2608.08883 ↗First posted 9 Aug 2026cs.AIcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

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.

Key facts. Cited by 1 paper in the map (data through 7 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.

Builds on

16 of 28 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups25 Jul
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 14

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 3,961
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  5. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 800
  6. 2025Humanity's Last Examcited by 467
  7. 2023Cognitive Architectures for Language Agentscited by 422
  8. 2024Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inferencecited by 14
  9. 2025The Science of Evaluating Foundation Modelscited by 5
  10. 2025Better Prompt Compression Without Multi-Layer Perceptronscited by 4
  11. 2025Latent Adaptive Planner for Dynamic Manipulationcited by 4
  12. 2025The Evolution of Reranking Models in Information Retrieval: From Heuristic Methods to Large Language Modelscited by 3
2 more
  1. 2025Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmologycited by 2
  2. 2025Large language models in materials science and the need for open-source approachescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Researchcited by 0

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  5. 2025RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning
  6. 2025On The Reproducibility Limitations of RAG Systems
  7. 2025A Socratic RAG Approach to Connect Natural Language Queries on Research Topics with Knowledge Organization Systems
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