# AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research

arXiv: https://arxiv.org/abs/2609.16519 · HTML: https://mlsanity.com/paper/2609.16519/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 24 of its 59 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 24 of 24)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [Language agents achieve superhuman synthesis of scientific knowledge](https://mlsanity.com/paper/2409.13740/) (2024)
- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [LLM4SR: A Survey on Large Language Models for Scientific Research](https://mlsanity.com/paper/2501.04306/) (2025)
- [AstroLLaMA: Towards Specialized Foundation Models in Astronomy](https://mlsanity.com/paper/2309.06126/) (2023)
- [Benchmarking LLM Faithfulness in RAG with Evolving Leaderboards](https://mlsanity.com/paper/2505.04847/) (2025)
- [A review of faithfulness metrics for hallucination assessment in Large Language Models](https://mlsanity.com/paper/2501.00269/) (2024)
- [GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning](https://mlsanity.com/paper/2410.00271/) (2024)
- [Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models](https://mlsanity.com/paper/2411.18440/) (2024)
- [Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation](https://mlsanity.com/paper/2411.18054/) (2024)
- [Elements of effective machine learning datasets in astronomy](https://mlsanity.com/paper/2211.14401/) (2022)
- [Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative Models](https://mlsanity.com/paper/2407.07229/) (2024)
- [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)
- [Improving Generalization and Uncertainty Quantification of Photometric Redshift Models](https://mlsanity.com/paper/2601.17222/) (2026)
- [Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction](https://mlsanity.com/paper/2601.00146/) (2026)
- [Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.10143/) (2026)
- [AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups](https://mlsanity.com/paper/2608.08883/) (2026)

## Abstract (arXiv)

Scientific research increasingly relies on large, heterogeneous data sources, motivating interest in retrieval-augmented generation (RAG) systems that provide natural language access to scientific knowledge and research workflows. Researchers are exploring the viability of these systems as natural language interfaces for document search and for generating analysis code and pipeline components. At the same time, concerns about data privacy and control over research infrastructure have motivated interest in open-weight models and open-source deployments hosted within research institutions. In astronomy, this development follows a long history of computational infrastructure development, from archival databases and Structured Query Language (SQL)-based systems to large language model (LLM)-assisted research tools. This paper presents a domain-expert evaluation of faithfulness for AquiLLM, an open-weight, offline RAG-LLM platform designed to support scientific research groups in the use and preservation of tacit and formal knowledge. We define faithfulness as the extent to which generated responses remain grounded in retrieved scientific context without unsupported claims or omissions. We report results from an astronomy case study evaluating AquiLLM across retrieval and scientific analysis tasks. AquiLLM performs most reliably on explicit retrieval-oriented questions grounded in the RAG collection, while faithfulness degrades for queries requiring synthesis or ambiguity resolution. These results highlight both the promise and limitations of open-weight RAG-LLM systems for scientific research and demonstrate the importance of domain-expert evaluation beyond standard benchmark leaderboards.